From 67abe3c24d0f911a06ff7490da5e2ec36f9a06a5 Mon Sep 17 00:00:00 2001 From: Cheng Chang Date: Sun, 10 Nov 2024 12:35:42 -0500 Subject: [PATCH 1/2] Training session 0004 --- .../log20241110-122512-AE29379C0F36C79E.log | 69 + .../log20241110-123408-5E9F0D032444FA5F.log | 69 + training/gatekeepersgf/stdout.txt | 138 + training/logs/outshuffle.txt | 67 + .../add_kata_selfplay_meta_to_npzs.py | 73 + .../scripts/dated/20241110-122511/board.py | 1436 ++ .../dated/20241110-122511/clean_checkpoint.py | 34 + .../scripts/dated/20241110-122511/data.py | 163 + .../data_processing_pytorch.py | 238 + .../scripts/dated/20241110-122511/diff.txt | 47 + .../dated/20241110-122511/diffstaged.txt | 19665 ++++++++++++++++ .../dated/20241110-122511/edit_checkpoint.py | 58 + training/scripts/dated/20241110-122511/elo.py | 855 + .../export_model_for_selfplay.sh | 115 + .../20241110-122511/export_model_pytorch.py | 461 + .../scripts/dated/20241110-122511/features.py | 340 + .../dated/20241110-122511/forward_model.py | 146 + .../dated/20241110-122511/gamestate.py | 314 + .../dated/20241110-122511/gatekeeper.cfg | 112 + .../dated/20241110-122511/genboard_common.py | 129 + .../dated/20241110-122511/genboard_run.py | 203 + .../dated/20241110-122511/genboard_train.py | 492 + .../dated/20241110-122511/genclumpyposes.py | 705 + .../dated/20241110-122511/genrandomposes.py | 163 + .../dated/20241110-122511/humanslnet_gui.py | 634 + .../20241110-122511/humanslnet_server.py | 81 + .../scripts/dated/20241110-122511/katago.exe | Bin 0 -> 4685824 bytes .../dated/20241110-122511/load_model.py | 96 + .../dated/20241110-122511/metrics_logging.py | 55 + .../dated/20241110-122511/metrics_pytorch.py | 847 + .../20241110-122511/migrate_double_v1.py | 90 + .../migrate_optimistic_policy.py | 73 + .../20241110-122511/migrate_softplus_fix.py | 64 + .../migrate_squared_softplus.py | 54 + .../dated/20241110-122511/model_pytorch.py | 1921 ++ .../dated/20241110-122511/modelconfigs.py | 1525 ++ .../scripts/dated/20241110-122511/play.py | 648 + .../query_analysis_engine_example.py | 136 + .../save_model_for_export_manual.py | 162 + .../dated/20241110-122511/selfplay.cfg | 185 + .../dated/20241110-122511/sgfmetadata.py | 186 + .../scripts/dated/20241110-122511/shuffle.py | 814 + .../scripts/dated/20241110-122511/shuffle.sh | 122 + .../shuffle_and_export_loop.sh | 62 + .../dated/20241110-122511/shuffle_loop.sh | 60 + .../summarize_old_selfplay_files.py | 194 + .../dated/20241110-122511/summarize_sgfs.py | 128 + .../dated/20241110-122511/synchronous_loop.sh | 103 + .../scripts/dated/20241110-122511/test.py | 279 + .../scripts/dated/20241110-122511/train.py | 1368 ++ .../scripts/dated/20241110-122511/train.sh | 87 + .../dated/20241110-122511/upload_model.py | 178 + .../dated/20241110-122511/upload_poses.py | 219 + .../scripts/dated/20241110-122511/version.txt | 5 + .../add_kata_selfplay_meta_to_npzs.py | 73 + .../scripts/dated/20241110-123407/board.py | 1436 ++ .../dated/20241110-123407/clean_checkpoint.py | 34 + .../scripts/dated/20241110-123407/data.py | 163 + .../data_processing_pytorch.py | 238 + .../scripts/dated/20241110-123407/diff.txt | 61 + .../dated/20241110-123407/diffstaged.txt | 19665 ++++++++++++++++ .../dated/20241110-123407/edit_checkpoint.py | 58 + training/scripts/dated/20241110-123407/elo.py | 855 + .../export_model_for_selfplay.sh | 115 + .../20241110-123407/export_model_pytorch.py | 461 + .../scripts/dated/20241110-123407/features.py | 340 + .../dated/20241110-123407/forward_model.py | 146 + .../dated/20241110-123407/gamestate.py | 314 + .../dated/20241110-123407/gatekeeper.cfg | 112 + .../dated/20241110-123407/genboard_common.py | 129 + .../dated/20241110-123407/genboard_run.py | 203 + .../dated/20241110-123407/genboard_train.py | 492 + .../dated/20241110-123407/genclumpyposes.py | 705 + .../dated/20241110-123407/genrandomposes.py | 163 + .../dated/20241110-123407/humanslnet_gui.py | 634 + 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| 32 + .../shuffleddata/20241110-121745/train.json | 1 + .../20241110-121745/train/data0.json | 1 + .../20241110-121745/train/data0.npz | Bin 0 -> 2596890 bytes .../shuffleddata/20241110-123012/outtrain.txt | 33 + .../shuffleddata/20241110-123012/train.json | 1 + .../20241110-123012/train/data0.json | 1 + .../20241110-123012/train/data0.npz | Bin 0 -> 3001185 bytes 123 files changed, 74072 insertions(+) create mode 100644 training/gatekeepersgf/log20241110-122512-AE29379C0F36C79E.log create mode 100644 training/gatekeepersgf/log20241110-123408-5E9F0D032444FA5F.log create mode 100644 training/scripts/dated/20241110-122511/add_kata_selfplay_meta_to_npzs.py create mode 100644 training/scripts/dated/20241110-122511/board.py create mode 100644 training/scripts/dated/20241110-122511/clean_checkpoint.py create mode 100644 training/scripts/dated/20241110-122511/data.py create mode 100644 training/scripts/dated/20241110-122511/data_processing_pytorch.py create mode 100644 training/scripts/dated/20241110-122511/diff.txt create mode 100644 training/scripts/dated/20241110-122511/diffstaged.txt create mode 100644 training/scripts/dated/20241110-122511/edit_checkpoint.py create mode 100644 training/scripts/dated/20241110-122511/elo.py create mode 100644 training/scripts/dated/20241110-122511/export_model_for_selfplay.sh create mode 100644 training/scripts/dated/20241110-122511/export_model_pytorch.py create mode 100644 training/scripts/dated/20241110-122511/features.py create mode 100644 training/scripts/dated/20241110-122511/forward_model.py create mode 100644 training/scripts/dated/20241110-122511/gamestate.py create mode 100644 training/scripts/dated/20241110-122511/gatekeeper.cfg create mode 100644 training/scripts/dated/20241110-122511/genboard_common.py create mode 100644 training/scripts/dated/20241110-122511/genboard_run.py create mode 100644 training/scripts/dated/20241110-122511/genboard_train.py create mode 100644 training/scripts/dated/20241110-122511/genclumpyposes.py create mode 100644 training/scripts/dated/20241110-122511/genrandomposes.py create mode 100644 training/scripts/dated/20241110-122511/humanslnet_gui.py create mode 100644 training/scripts/dated/20241110-122511/humanslnet_server.py create mode 100644 training/scripts/dated/20241110-122511/katago.exe create mode 100644 training/scripts/dated/20241110-122511/load_model.py create mode 100644 training/scripts/dated/20241110-122511/metrics_logging.py create mode 100644 training/scripts/dated/20241110-122511/metrics_pytorch.py create mode 100644 training/scripts/dated/20241110-122511/migrate_double_v1.py create mode 100644 training/scripts/dated/20241110-122511/migrate_optimistic_policy.py create mode 100644 training/scripts/dated/20241110-122511/migrate_softplus_fix.py create mode 100644 training/scripts/dated/20241110-122511/migrate_squared_softplus.py create mode 100644 training/scripts/dated/20241110-122511/model_pytorch.py create mode 100644 training/scripts/dated/20241110-122511/modelconfigs.py create mode 100644 training/scripts/dated/20241110-122511/play.py create mode 100644 training/scripts/dated/20241110-122511/query_analysis_engine_example.py create mode 100644 training/scripts/dated/20241110-122511/save_model_for_export_manual.py create mode 100644 training/scripts/dated/20241110-122511/selfplay.cfg create mode 100644 training/scripts/dated/20241110-122511/sgfmetadata.py create mode 100644 training/scripts/dated/20241110-122511/shuffle.py create mode 100644 training/scripts/dated/20241110-122511/shuffle.sh create mode 100644 training/scripts/dated/20241110-122511/shuffle_and_export_loop.sh create mode 100644 training/scripts/dated/20241110-122511/shuffle_loop.sh create mode 100644 training/scripts/dated/20241110-122511/summarize_old_selfplay_files.py create mode 100644 training/scripts/dated/20241110-122511/summarize_sgfs.py create mode 100644 training/scripts/dated/20241110-122511/synchronous_loop.sh create mode 100644 training/scripts/dated/20241110-122511/test.py create mode 100644 training/scripts/dated/20241110-122511/train.py create mode 100644 training/scripts/dated/20241110-122511/train.sh create mode 100644 training/scripts/dated/20241110-122511/upload_model.py create mode 100644 training/scripts/dated/20241110-122511/upload_poses.py create mode 100644 training/scripts/dated/20241110-122511/version.txt create mode 100644 training/scripts/dated/20241110-123407/add_kata_selfplay_meta_to_npzs.py create mode 100644 training/scripts/dated/20241110-123407/board.py create mode 100644 training/scripts/dated/20241110-123407/clean_checkpoint.py create mode 100644 training/scripts/dated/20241110-123407/data.py create mode 100644 training/scripts/dated/20241110-123407/data_processing_pytorch.py create mode 100644 training/scripts/dated/20241110-123407/diff.txt create mode 100644 training/scripts/dated/20241110-123407/diffstaged.txt create mode 100644 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training/scripts/dated/20241110-123407/train.sh create mode 100644 training/scripts/dated/20241110-123407/upload_model.py create mode 100644 training/scripts/dated/20241110-123407/upload_poses.py create mode 100644 training/scripts/dated/20241110-123407/version.txt create mode 100644 training/selfplay/kata1-b18c384nbt-s9908371456-d4296205232.bin.gz/selfplay-AE001DCEEADF7AB1.cfg create mode 100644 training/selfplay/kata1-b18c384nbt-s9908371456-d4296205232.bin.gz/selfplay-FDE1CB731B80A9AA.cfg create mode 100644 training/selfplay/kata1-b18c384nbt-s9908371456-d4296205232.bin.gz/sgfs/4DD0EBC7BF08F2FA.sgfs create mode 100644 training/selfplay/kata1-b18c384nbt-s9908371456-d4296205232.bin.gz/sgfs/73935E5B0135F8FD.sgfs create mode 100644 training/selfplay/kata1-b18c384nbt-s9908371456-d4296205232.bin.gz/tdata/BB8D1D4E83F730DE.npz create mode 100644 training/selfplay/kata1-b18c384nbt-s9908371456-d4296205232.bin.gz/tdata/F6CD6AF355B27725.npz create mode 100644 training/selfplay/log20241110-122512-71720487F87C862B.log create mode 100644 training/selfplay/log20241110-123408-158367354CDDA823.log create mode 100644 training/shuffleddata/20241110-121745/outtrain.txt create mode 100644 training/shuffleddata/20241110-121745/train.json create mode 100644 training/shuffleddata/20241110-121745/train/data0.json create mode 100644 training/shuffleddata/20241110-121745/train/data0.npz create mode 100644 training/shuffleddata/20241110-123012/outtrain.txt create mode 100644 training/shuffleddata/20241110-123012/train.json create mode 100644 training/shuffleddata/20241110-123012/train/data0.json create mode 100644 training/shuffleddata/20241110-123012/train/data0.npz diff --git a/training/gatekeepersgf/log20241110-122512-AE29379C0F36C79E.log b/training/gatekeepersgf/log20241110-122512-AE29379C0F36C79E.log new file mode 100644 index 0000000..20c42b7 --- /dev/null +++ b/training/gatekeepersgf/log20241110-122512-AE29379C0F36C79E.log @@ -0,0 +1,69 @@ +2024-11-10 12:25:12-0500: Running with following config: +allowRectangleProb = 0.50 +allowResignation = true +bSizeRelProbs = 1,1,8 +bSizes = 7,8,9 +chosenMovePrune = 1 +chosenMoveSubtract = 0 +chosenMoveTemperature = 0.2 +chosenMoveTemperatureEarly = 0.5 +chosenMoveTemperatureHalflife = 19 +cpuctExploration = 1.1 +cpuctExplorationLog = 0.0 +cudaUseFP16 = auto +cudaUseNHWC = auto +drawEquivalentWinsForWhite = 0.5 +dynamicScoreCenterScale = 0.50 +dynamicScoreCenterZeroWeight = 0.25 +dynamicScoreUtilityFactor = 0.25 +fpuReductionMax = 0.2 +handicapCompensateKomiProb = 1.0 +handicapProb = 0.0 +hasButtons = false,false,true +koRules = SIMPLE,POSITIONAL,SITUATIONAL +komiAuto = True +lcbStdevs = 5.0 +logGamesEvery = 10 +logMoves = false +logSearchInfo = false +logToStdout = true +maxMovesPerGame = 1600 +maxVisits = 150 +minVisitPropForLCB = 0.15 +multiStoneSuicideLegals = false,true +nnCacheSizePowerOfTwo = 21 +nnMaxBatchSize = 128 +nnMutexPoolSizePowerOfTwo = 15 +nnRandomize = true +noResultUtilityForWhite = 0.0 +numGameThreads = 128 +numGamesPerGating = 200 +numNNServerThreadsPerModel = 1 +numSearchThreads = 1 +numVirtualLossesPerThread = 1 +policyOptimism = 1.0 +resignConsecTurns = 5 +resignThreshold = -0.90 +rootEndingBonusPoints = 0.5 +rootFpuReductionMax = 0.1 +rootPolicyOptimism = 0.0 +rootPruneUselessMoves = true +scoringRules = AREA,TERRITORY +staticScoreUtilityFactor = 0.00 +subtreeValueBiasFactor = 0.35 +subtreeValueBiasWeightExponent = 0.8 +taxRules = NONE,NONE,SEKI,SEKI,ALL +uncertaintyCoeff = 0.25 +uncertaintyExponent = 1.0 +useGraphSearch = true +useLcbForSelection = true +useNonBuggyLcb = true +useUncertainty = true +valueWeightExponent = 0.5 +winLossUtilityFactor = 1.0 + +2024-11-10 12:25:12-0500: Gatekeeper Engine starting... +2024-11-10 12:25:12-0500: Git revision: 7939e2c3eb43df04e08de8c166889c6d5e937bfa +2024-11-10 12:25:12-0500: Required candidate win prop: 0.5 +2024-11-10 12:25:12-0500: Loaded all config stuff, watching for new neural nets in C:/Users/chang/OneDrive/Documents/GitHub/Kata_speed/training/modelstobetested/ +2024-11-10 12:25:12-0500: All cleaned up, quitting diff --git a/training/gatekeepersgf/log20241110-123408-5E9F0D032444FA5F.log b/training/gatekeepersgf/log20241110-123408-5E9F0D032444FA5F.log new file mode 100644 index 0000000..6ac6506 --- /dev/null +++ b/training/gatekeepersgf/log20241110-123408-5E9F0D032444FA5F.log @@ -0,0 +1,69 @@ +2024-11-10 12:34:08-0500: Running with following config: +allowRectangleProb = 0.50 +allowResignation = true +bSizeRelProbs = 1,1,8 +bSizes = 7,8,9 +chosenMovePrune = 1 +chosenMoveSubtract = 0 +chosenMoveTemperature = 0.2 +chosenMoveTemperatureEarly = 0.5 +chosenMoveTemperatureHalflife = 19 +cpuctExploration = 1.1 +cpuctExplorationLog = 0.0 +cudaUseFP16 = auto +cudaUseNHWC = auto +drawEquivalentWinsForWhite = 0.5 +dynamicScoreCenterScale = 0.50 +dynamicScoreCenterZeroWeight = 0.25 +dynamicScoreUtilityFactor = 0.25 +fpuReductionMax = 0.2 +handicapCompensateKomiProb = 1.0 +handicapProb = 0.0 +hasButtons = false,false,true +koRules = SIMPLE,POSITIONAL,SITUATIONAL +komiAuto = True +lcbStdevs = 5.0 +logGamesEvery = 10 +logMoves = false +logSearchInfo = false +logToStdout = true +maxMovesPerGame = 1600 +maxVisits = 150 +minVisitPropForLCB = 0.15 +multiStoneSuicideLegals = false,true +nnCacheSizePowerOfTwo = 21 +nnMaxBatchSize = 128 +nnMutexPoolSizePowerOfTwo = 15 +nnRandomize = true +noResultUtilityForWhite = 0.0 +numGameThreads = 128 +numGamesPerGating = 200 +numNNServerThreadsPerModel = 1 +numSearchThreads = 1 +numVirtualLossesPerThread = 1 +policyOptimism = 1.0 +resignConsecTurns = 5 +resignThreshold = -0.90 +rootEndingBonusPoints = 0.5 +rootFpuReductionMax = 0.1 +rootPolicyOptimism = 0.0 +rootPruneUselessMoves = true +scoringRules = AREA,TERRITORY +staticScoreUtilityFactor = 0.00 +subtreeValueBiasFactor = 0.35 +subtreeValueBiasWeightExponent = 0.8 +taxRules = NONE,NONE,SEKI,SEKI,ALL +uncertaintyCoeff = 0.25 +uncertaintyExponent = 1.0 +useGraphSearch = true +useLcbForSelection = true +useNonBuggyLcb = true +useUncertainty = true +valueWeightExponent = 0.5 +winLossUtilityFactor = 1.0 + +2024-11-10 12:34:08-0500: Gatekeeper Engine starting... +2024-11-10 12:34:08-0500: Git revision: 7939e2c3eb43df04e08de8c166889c6d5e937bfa +2024-11-10 12:34:08-0500: Required candidate win prop: 0.5 +2024-11-10 12:34:08-0500: Loaded all config stuff, watching for new neural nets in C:/Users/chang/OneDrive/Documents/GitHub/Kata_speed/training/modelstobetested/ +2024-11-10 12:34:08-0500: All cleaned up, quitting diff --git a/training/gatekeepersgf/stdout.txt b/training/gatekeepersgf/stdout.txt index e9c8d68..8578d05 100644 --- a/training/gatekeepersgf/stdout.txt +++ b/training/gatekeepersgf/stdout.txt @@ -513,3 +513,141 @@ winLossUtilityFactor = 1.0 2024-11-10 12:12:46-0500: Required candidate win prop: 0.5 2024-11-10 12:12:46-0500: Loaded all config stuff, watching for new neural nets in C:/Users/chang/OneDrive/Documents/GitHub/Kata_speed/training/modelstobetested/ 2024-11-10 12:12:46-0500: All cleaned up, quitting +2024-11-10 12:25:12-0500: Running with following config: +allowRectangleProb = 0.50 +allowResignation = true +bSizeRelProbs = 1,1,8 +bSizes = 7,8,9 +chosenMovePrune = 1 +chosenMoveSubtract = 0 +chosenMoveTemperature = 0.2 +chosenMoveTemperatureEarly = 0.5 +chosenMoveTemperatureHalflife = 19 +cpuctExploration = 1.1 +cpuctExplorationLog = 0.0 +cudaUseFP16 = auto +cudaUseNHWC = auto +drawEquivalentWinsForWhite = 0.5 +dynamicScoreCenterScale = 0.50 +dynamicScoreCenterZeroWeight = 0.25 +dynamicScoreUtilityFactor = 0.25 +fpuReductionMax = 0.2 +handicapCompensateKomiProb = 1.0 +handicapProb = 0.0 +hasButtons = false,false,true +koRules = SIMPLE,POSITIONAL,SITUATIONAL +komiAuto = True +lcbStdevs = 5.0 +logGamesEvery = 10 +logMoves = false +logSearchInfo = false +logToStdout = true +maxMovesPerGame = 1600 +maxVisits = 150 +minVisitPropForLCB = 0.15 +multiStoneSuicideLegals = false,true +nnCacheSizePowerOfTwo = 21 +nnMaxBatchSize = 128 +nnMutexPoolSizePowerOfTwo = 15 +nnRandomize = true +noResultUtilityForWhite = 0.0 +numGameThreads = 128 +numGamesPerGating = 200 +numNNServerThreadsPerModel = 1 +numSearchThreads = 1 +numVirtualLossesPerThread = 1 +policyOptimism = 1.0 +resignConsecTurns = 5 +resignThreshold = -0.90 +rootEndingBonusPoints = 0.5 +rootFpuReductionMax = 0.1 +rootPolicyOptimism = 0.0 +rootPruneUselessMoves = true +scoringRules = AREA,TERRITORY +staticScoreUtilityFactor = 0.00 +subtreeValueBiasFactor = 0.35 +subtreeValueBiasWeightExponent = 0.8 +taxRules = NONE,NONE,SEKI,SEKI,ALL +uncertaintyCoeff = 0.25 +uncertaintyExponent = 1.0 +useGraphSearch = true +useLcbForSelection = true +useNonBuggyLcb = true +useUncertainty = true +valueWeightExponent = 0.5 +winLossUtilityFactor = 1.0 + +2024-11-10 12:25:12-0500: Gatekeeper Engine starting... +2024-11-10 12:25:12-0500: Git revision: 7939e2c3eb43df04e08de8c166889c6d5e937bfa +2024-11-10 12:25:12-0500: Required candidate win prop: 0.5 +2024-11-10 12:25:12-0500: Loaded all config stuff, watching for new neural nets in C:/Users/chang/OneDrive/Documents/GitHub/Kata_speed/training/modelstobetested/ +2024-11-10 12:25:12-0500: All cleaned up, quitting +2024-11-10 12:34:08-0500: Running with following config: +allowRectangleProb = 0.50 +allowResignation = true +bSizeRelProbs = 1,1,8 +bSizes = 7,8,9 +chosenMovePrune = 1 +chosenMoveSubtract = 0 +chosenMoveTemperature = 0.2 +chosenMoveTemperatureEarly = 0.5 +chosenMoveTemperatureHalflife = 19 +cpuctExploration = 1.1 +cpuctExplorationLog = 0.0 +cudaUseFP16 = auto +cudaUseNHWC = auto +drawEquivalentWinsForWhite = 0.5 +dynamicScoreCenterScale = 0.50 +dynamicScoreCenterZeroWeight = 0.25 +dynamicScoreUtilityFactor = 0.25 +fpuReductionMax = 0.2 +handicapCompensateKomiProb = 1.0 +handicapProb = 0.0 +hasButtons = false,false,true +koRules = SIMPLE,POSITIONAL,SITUATIONAL +komiAuto = True +lcbStdevs = 5.0 +logGamesEvery = 10 +logMoves = false +logSearchInfo = false +logToStdout = true +maxMovesPerGame = 1600 +maxVisits = 150 +minVisitPropForLCB = 0.15 +multiStoneSuicideLegals = false,true +nnCacheSizePowerOfTwo = 21 +nnMaxBatchSize = 128 +nnMutexPoolSizePowerOfTwo = 15 +nnRandomize = true +noResultUtilityForWhite = 0.0 +numGameThreads = 128 +numGamesPerGating = 200 +numNNServerThreadsPerModel = 1 +numSearchThreads = 1 +numVirtualLossesPerThread = 1 +policyOptimism = 1.0 +resignConsecTurns = 5 +resignThreshold = -0.90 +rootEndingBonusPoints = 0.5 +rootFpuReductionMax = 0.1 +rootPolicyOptimism = 0.0 +rootPruneUselessMoves = true +scoringRules = AREA,TERRITORY +staticScoreUtilityFactor = 0.00 +subtreeValueBiasFactor = 0.35 +subtreeValueBiasWeightExponent = 0.8 +taxRules = NONE,NONE,SEKI,SEKI,ALL +uncertaintyCoeff = 0.25 +uncertaintyExponent = 1.0 +useGraphSearch = true +useLcbForSelection = true +useNonBuggyLcb = true +useUncertainty = true +valueWeightExponent = 0.5 +winLossUtilityFactor = 1.0 + +2024-11-10 12:34:08-0500: Gatekeeper Engine starting... +2024-11-10 12:34:08-0500: Git revision: 7939e2c3eb43df04e08de8c166889c6d5e937bfa +2024-11-10 12:34:08-0500: Required candidate win prop: 0.5 +2024-11-10 12:34:08-0500: Loaded all config stuff, watching for new neural nets in C:/Users/chang/OneDrive/Documents/GitHub/Kata_speed/training/modelstobetested/ +2024-11-10 12:34:08-0500: All cleaned up, quitting diff --git a/training/logs/outshuffle.txt b/training/logs/outshuffle.txt index 94607a4..36486c9 100644 --- a/training/logs/outshuffle.txt +++ b/training/logs/outshuffle.txt @@ -39,3 +39,70 @@ Finished: Computing rows for unsummarized files in 0.31481337547302246 seconds Beginning: Processing found files Finished: Processing found files in 0.0 seconds Not enough rows, only 8800 (fewer than 100000) +Beginning shuffle at 2024-11-10 12:17:45 +Beginning: Finding files +Finished: Finding files in 0.0009992122650146484 seconds +Total number of files: 4 +Total number of files with unknown row count: 4 +Excluded count: 0 +Excluded count due to looking like temp file: 0 +Excluded count due to cmdline excludes file: 0 +GC collect +Beginning: Sorting +Finished: Sorting in 0.0 seconds +Beginning: Computing rows for unsummarized files +Finished: Computing rows for unsummarized files in 0.34592175483703613 seconds +Beginning: Processing found files +Finished: Processing found files in 0.0 seconds +Total rows found: 9962 (9962 usable) +Desired num rows: 3857 / 9962 +Beginning: Computing desired rows +Using: C:/Users/chang/OneDrive/Documents/GitHub/Kata_speed/training/selfplay/kata1-b18c384nbt-s9908371456-d4296205232.bin.gz\tdata\BB8D1D4E83F730DE.npz (8800-8800) (1162/3857 desired rows) +Using: C:/Users/chang/OneDrive/Documents/GitHub/Kata_speed/training/selfplay/kata1-b18c384nbt-s9908371456-d4296205232.bin.gz\tdata\9C008A7DD1B87C8D.npz (1677-1677) (8285/3857 desired rows) +Finished: Computing desired rows in 0.0 seconds +Finally, using: (1677-9962) (8285/3857 desired rows) +GC collect +Writing 1 output files with 8285 kept / 3857 desired rows +Due to only_include_md5, filtering down to 2/2 files +Grouping 2 input files into 1 sharding groups +Beginning: Sharding +Finished: Sharding in 0.8459525108337402 seconds +Beginning: Merging +Finished: Merging in 0.6311211585998535 seconds +Number of rows by output file: +[('C:/Users/chang/OneDrive/Documents/GitHub/Kata_speed/training/shuffleddata/20241110-121745/train\\data0.npz', 8192)] +Cleaning up tmp dir: C:/Users/chang/OneDrive/Documents/GitHub/Kata_speed/training/shufflescratch/train\tmp.shuf0 +Beginning shuffle at 2024-11-10 12:30:13 +Beginning: Finding files +Finished: Finding files in 0.0 seconds +Total number of files: 5 +Total number of files with unknown row count: 5 +Excluded count: 0 +Excluded count due to looking like temp file: 0 +Excluded count due to cmdline excludes file: 0 +GC collect +Beginning: Sorting +Finished: Sorting in 0.0 seconds +Beginning: Computing rows for unsummarized files +Finished: Computing rows for unsummarized files in 0.3347489833831787 seconds +Beginning: Processing found files +Finished: Processing found files in 0.0 seconds +Total rows found: 11273 (11273 usable) +Desired num rows: 4347 / 11273 +Beginning: Computing desired rows +Using: C:/Users/chang/OneDrive/Documents/GitHub/Kata_speed/training/selfplay/kata1-b18c384nbt-s9908371456-d4296205232.bin.gz\tdata\F6CD6AF355B27725.npz (9962-9962) (1311/4347 desired rows) +Using: C:/Users/chang/OneDrive/Documents/GitHub/Kata_speed/training/selfplay/kata1-b18c384nbt-s9908371456-d4296205232.bin.gz\tdata\BB8D1D4E83F730DE.npz (8800-8800) (2473/4347 desired rows) +Using: C:/Users/chang/OneDrive/Documents/GitHub/Kata_speed/training/selfplay/kata1-b18c384nbt-s9908371456-d4296205232.bin.gz\tdata\9C008A7DD1B87C8D.npz (1677-1677) (9596/4347 desired rows) +Finished: Computing desired rows in 0.0 seconds +Finally, using: (1677-11273) (9596/4347 desired rows) +GC collect +Writing 1 output files with 9596 kept / 4347 desired rows +Due to only_include_md5, filtering down to 3/3 files +Grouping 3 input files into 1 sharding groups +Beginning: Sharding +Finished: Sharding in 0.9192070960998535 seconds +Beginning: Merging +Finished: Merging in 0.7125730514526367 seconds +Number of rows by output file: +[('C:/Users/chang/OneDrive/Documents/GitHub/Kata_speed/training/shuffleddata/20241110-123012/train\\data0.npz', 9472)] +Cleaning up tmp dir: C:/Users/chang/OneDrive/Documents/GitHub/Kata_speed/training/shufflescratch/train\tmp.shuf0 diff --git a/training/scripts/dated/20241110-122511/add_kata_selfplay_meta_to_npzs.py b/training/scripts/dated/20241110-122511/add_kata_selfplay_meta_to_npzs.py new file mode 100644 index 0000000..8e48045 --- /dev/null +++ b/training/scripts/dated/20241110-122511/add_kata_selfplay_meta_to_npzs.py @@ -0,0 +1,73 @@ +import os +import sys +import random +import numpy as np + +from sgfmetadata import SGFMetadata + +expected_keys = [ + "binaryInputNCHWPacked", + "globalInputNC", + "policyTargetsNCMove", + "globalTargetsNC", + "scoreDistrN", + "valueTargetsNCHW", +] + +def process_npz_files(in_dir, out_dir): + rand = random.Random() + for filename in os.listdir(in_dir): + if filename.endswith(".npz"): + print("Processing " + filename, flush=True) + with np.load(os.path.join(in_dir, filename)) as npz: + assert(set(npz.keys()) == set(expected_keys)) + binaryInputNCHWPacked = npz["binaryInputNCHWPacked"] + globalInputNC = npz["globalInputNC"] + policyTargetsNCMove = npz["policyTargetsNCMove"] + globalTargetsNC = npz["globalTargetsNC"] + scoreDistrN = npz["scoreDistrN"] + valueTargetsNCHW = npz["valueTargetsNCHW"] + + pos_len = valueTargetsNCHW.shape[2] + + binaryInputNCHW = np.unpackbits(binaryInputNCHWPacked,axis=2) + assert len(binaryInputNCHW.shape) == 3 + assert binaryInputNCHW.shape[2] == ((pos_len * pos_len + 7) // 8) * 8 + binaryInputNCHW = binaryInputNCHW[:,:,:pos_len*pos_len] + binaryInputNCHW = np.reshape(binaryInputNCHW, ( + binaryInputNCHW.shape[0], binaryInputNCHW.shape[1], pos_len, pos_len + )).astype(np.float32) + + metadataInputNC = np.zeros((binaryInputNCHW.shape[0],SGFMetadata.METADATA_INPUT_NUM_CHANNELS), dtype=np.float32) + for i in range(binaryInputNCHW.shape[0]): + board_area = int(np.sum(binaryInputNCHW[i,0])) + sgfmeta = SGFMetadata.get_katago_selfplay_metadata(rand=rand) + # nextPlayer doesn't matter in selfplay because it only is used to swap pla/opp metadata + # and in this case both sides are identically katago + metadataInputNC[i] = sgfmeta.get_metadata_row(nextPlayer=Board.WHITE,boardArea=board_area) + + np.savez_compressed( + os.path.join(out_dir, filename), + binaryInputNCHWPacked = binaryInputNCHWPacked, + globalInputNC = globalInputNC, + policyTargetsNCMove = policyTargetsNCMove, + globalTargetsNC = globalTargetsNC, + scoreDistrN = scoreDistrN, + valueTargetsNCHW = valueTargetsNCHW, + metadataInputNC = metadataInputNC, + ) + print("Done") + +if __name__ == "__main__": + if len(sys.argv) != 3: + print("Usage: python add_kata_selfplay_meta_to_npzs.py ") + sys.exit(1) + + in_dir = sys.argv[1] + out_dir = sys.argv[2] + + if os.path.exists(out_dir): + raise Exception(out_dir + " already exists") + os.mkdir(out_dir) + + process_npz_files(in_dir, out_dir) diff --git a/training/scripts/dated/20241110-122511/board.py b/training/scripts/dated/20241110-122511/board.py new file mode 100644 index 0000000..daa4902 --- /dev/null +++ b/training/scripts/dated/20241110-122511/board.py @@ -0,0 +1,1436 @@ +import random +import numpy as np + +class IllegalMoveError(ValueError): + pass + +Pos = int +Loc = int +Player = int + +#Implements legal moves without superko +class Board: + EMPTY = 0 + BLACK = 1 + WHITE = 2 + WALL = 3 + + ZOBRIST_STONE = [[],[],[],[]] + ZOBRIST_PLA = [] + + ZOBRIST_RAND = random.Random() + ZOBRIST_RAND.seed(123987456) + + PASS_LOC = 0 + + for i in range((19+1)*(19+2)+1): + ZOBRIST_STONE[BLACK].append(ZOBRIST_RAND.getrandbits(64)) + ZOBRIST_STONE[WHITE].append(ZOBRIST_RAND.getrandbits(64)) + for i in range(4): + ZOBRIST_PLA.append(ZOBRIST_RAND.getrandbits(64)) + + def __init__(self,size,copy_other=None): + if size < 2 or size > 39: + raise ValueError("Invalid board size: " + str(size)) + self.size = size + self.arrsize = (size+1)*(size+2)+1 + self.dy = size+1 + self.adj = [-self.dy,-1,1,self.dy] + self.diag = [-self.dy-1,-self.dy+1,self.dy-1,self.dy+1] + + if copy_other is not None: + self.pla = copy_other.pla + self.board = np.copy(copy_other.board) + self.group_head = np.copy(copy_other.group_head) + self.group_stone_count = np.copy(copy_other.group_stone_count) + self.group_liberty_count = np.copy(copy_other.group_liberty_count) + self.group_next = np.copy(copy_other.group_next) + self.group_prev = np.copy(copy_other.group_prev) + self.zobrist = copy_other.zobrist + self.simple_ko_point = copy_other.simple_ko_point + self.num_captures_made = copy_other.num_captures_made.copy() + self.num_non_pass_moves_made = copy_other.num_non_pass_moves_made.copy() + else: + self.pla = Board.BLACK + self.board = np.zeros(shape=(self.arrsize), dtype=np.int8) + self.group_head = np.zeros(shape=(self.arrsize), dtype=np.int16) + self.group_stone_count = np.zeros(shape=(self.arrsize), dtype=np.int16) + self.group_liberty_count = np.zeros(shape=(self.arrsize), dtype=np.int16) + self.group_next = np.zeros(shape=(self.arrsize), dtype=np.int16) + self.group_prev = np.zeros(shape=(self.arrsize), dtype=np.int16) + self.zobrist = 0 + self.simple_ko_point = None + self.num_captures_made = {Board.BLACK: 0, Board.WHITE: 0} + self.num_non_pass_moves_made = {Board.BLACK: 0, Board.WHITE: 0} + + for i in range(-1,size+1): + self.board[self.loc(i,-1)] = Board.WALL + self.board[self.loc(i,size)] = Board.WALL + self.board[self.loc(-1,i)] = Board.WALL + self.board[self.loc(size,i)] = Board.WALL + + #More-easily catch errors + self.group_head[0] = -1 + self.group_next[0] = -1 + self.group_prev[0] = -1 + + def copy(self): + return Board(self.size,copy_other=self) + + @staticmethod + def get_opp(pla): + return 3-pla + @staticmethod + def loc_static(x,y,size): + return (x+1) + (size+1)*(y+1) + + def loc(self,x,y): + return (x+1) + self.dy*(y+1) + def loc_x(self,loc): + return (loc % self.dy)-1 + def loc_y(self,loc): + return (loc // self.dy)-1 + + def is_adjacent(self,loc1,loc2): + return loc1 == loc2 + self.adj[0] or loc1 == loc2 + self.adj[1] or loc1 == loc2 + self.adj[2] or loc1 == loc2 + self.adj[3] + + def pos_zobrist(self): + return self.zobrist + def sit_zobrist(self): + return self.zobrist ^ Board.ZOBRIST_PLA[self.pla] + + def num_liberties(self,loc): + if self.board[loc] == Board.EMPTY or self.board[loc] == Board.WALL: + return 0 + return self.group_liberty_count[self.group_head[loc]] + + def is_simple_eye(self,pla,loc): + adj0 = loc + self.adj[0] + adj1 = loc + self.adj[1] + adj2 = loc + self.adj[2] + adj3 = loc + self.adj[3] + + if (self.board[adj0] != pla and self.board[adj0] != Board.WALL) or \ + (self.board[adj1] != pla and self.board[adj1] != Board.WALL) or \ + (self.board[adj2] != pla and self.board[adj2] != Board.WALL) or \ + (self.board[adj3] != pla and self.board[adj3] != Board.WALL): + return False + + opp = Board.get_opp(pla) + opp_corners = 0 + diag0 = loc + self.diag[0] + diag1 = loc + self.diag[1] + diag2 = loc + self.diag[2] + diag3 = loc + self.diag[3] + if self.board[diag0] == opp: + opp_corners += 1 + if self.board[diag1] == opp: + opp_corners += 1 + if self.board[diag2] == opp: + opp_corners += 1 + if self.board[diag3] == opp: + opp_corners += 1 + + if opp_corners >= 2: + return False + if opp_corners <= 0: + return True + + against_wall = ( + self.board[adj0] == Board.WALL or \ + self.board[adj1] == Board.WALL or \ + self.board[adj2] == Board.WALL or \ + self.board[adj3] == Board.WALL + ) + + if against_wall: + return False + return True + + + def would_be_legal(self,pla,loc): + if pla != Board.BLACK and pla != Board.WHITE: + return False + if loc == Board.PASS_LOC: + return True + if not self.is_on_board(loc): + return False + if self.board[loc] != Board.EMPTY: + return False + if self.would_be_single_stone_suicide(pla,loc): + return False + if loc == self.simple_ko_point: + return False + return True + + def would_be_suicide(self,pla,loc): + adj0 = loc + self.adj[0] + adj1 = loc + self.adj[1] + adj2 = loc + self.adj[2] + adj3 = loc + self.adj[3] + + opp = Board.get_opp(pla) + + #If empty or capture, then not suicide + if self.board[adj0] == Board.EMPTY or (self.board[adj0] == opp and self.group_liberty_count[self.group_head[adj0]] == 1) or \ + self.board[adj1] == Board.EMPTY or (self.board[adj1] == opp and self.group_liberty_count[self.group_head[adj1]] == 1) or \ + self.board[adj2] == Board.EMPTY or (self.board[adj2] == opp and self.group_liberty_count[self.group_head[adj2]] == 1) or \ + self.board[adj3] == Board.EMPTY or (self.board[adj3] == opp and self.group_liberty_count[self.group_head[adj3]] == 1): + return False + #If connects to own stone with enough liberties, then not suicide + if self.board[adj0] == pla and self.group_liberty_count[self.group_head[adj0]] > 1 or \ + self.board[adj1] == pla and self.group_liberty_count[self.group_head[adj1]] > 1 or \ + self.board[adj2] == pla and self.group_liberty_count[self.group_head[adj2]] > 1 or \ + self.board[adj3] == pla and self.group_liberty_count[self.group_head[adj3]] > 1: + return False + return True + + def would_be_single_stone_suicide(self,pla,loc): + adj0 = loc + self.adj[0] + adj1 = loc + self.adj[1] + adj2 = loc + self.adj[2] + adj3 = loc + self.adj[3] + + opp = Board.get_opp(pla) + + #If empty or capture, then not suicide + if self.board[adj0] == Board.EMPTY or (self.board[adj0] == opp and self.group_liberty_count[self.group_head[adj0]] == 1) or \ + self.board[adj1] == Board.EMPTY or (self.board[adj1] == opp and self.group_liberty_count[self.group_head[adj1]] == 1) or \ + self.board[adj2] == Board.EMPTY or (self.board[adj2] == opp and self.group_liberty_count[self.group_head[adj2]] == 1) or \ + self.board[adj3] == Board.EMPTY or (self.board[adj3] == opp and self.group_liberty_count[self.group_head[adj3]] == 1): + return False + #If connects to own stone, then not single stone suicide + if self.board[adj0] == pla or \ + self.board[adj1] == pla or \ + self.board[adj2] == pla or \ + self.board[adj3] == pla: + return False + return True + + #Returns the number of liberties a new stone placed here would have, or maxLibs if it would be >= maxLibs. + def get_liberties_after_play(self,pla,loc,maxLibs): + opp = Board.get_opp(pla) + libs = [] + capturedGroupHeads = [] + + #First, count immediate liberties and groups that would be captured + for i in range(4): + adj = loc + self.adj[i] + if self.board[adj] == Board.EMPTY: + libs.append(adj) + if len(libs) >= maxLibs: + return maxLibs + + elif self.board[adj] == opp and self.num_liberties(adj) == 1: + libs.append(adj) + if len(libs) >= maxLibs: + return maxLibs + + head = self.group_head[adj] + if head not in capturedGroupHeads: + capturedGroupHeads.append(head) + + def wouldBeEmpty(possibleLib): + if self.board[possibleLib] == Board.EMPTY: + return True + elif self.board[possibleLib] == opp: + return (self.group_head[possibleLib] in capturedGroupHeads) + return False + + #Next, walk through all stones of all surrounding groups we would connect with and count liberties, avoiding overlap. + connectingGroupHeads = [] + for i in range(4): + adj = loc + self.adj[i] + if self.board[adj] == pla: + head = self.group_head[adj] + if head not in connectingGroupHeads: + connectingGroupHeads.append(head) + + cur = adj + while True: + for k in range(4): + possibleLib = cur + self.adj[k] + if possibleLib != loc and wouldBeEmpty(possibleLib) and possibleLib not in libs: + libs.append(possibleLib) + if len(libs) >= maxLibs: + return maxLibs + + cur = self.group_next[cur] + if cur == adj: + break + + return len(libs) + + + def to_string(self): + def get_piece(x,y): + loc = self.loc(x,y) + if self.board[loc] == Board.BLACK: + return 'X ' + elif self.board[loc] == Board.WHITE: + return 'O ' + elif (x == 3 or x == self.size/2 or x == self.size-1-3) and (y == 3 or y == self.size/2 or y == self.size-1-3): + return '* ' + else: + return '. ' + + return "\n".join("".join(get_piece(x,y) for x in range(self.size)) for y in range(self.size)) + + def to_liberty_string(self): + def get_piece(x,y): + loc = self.loc(x,y) + if self.board[loc] == Board.BLACK or self.board[loc] == Board.WHITE: + libs = self.group_liberty_count[self.group_head[loc]] + if libs <= 9: + return str(libs) + ' ' + else: + return '@ ' + elif (x == 3 or x == self.size/2 or x == self.size-1-3) and (y == 3 or y == self.size/2 or y == self.size-1-3): + return '* ' + else: + return '. ' + + return "\n".join("".join(get_piece(x,y) for x in range(self.size)) for y in range(self.size)) + + def set_pla(self,pla): + self.pla = pla + def is_on_board(self,loc): + return loc >= 0 and loc < self.arrsize and self.board[loc] != Board.WALL + + #Set a given location with error checking. Suicide setting allowed. + def set_stone(self,pla,loc): + if pla != Board.EMPTY and pla != Board.BLACK and pla != Board.WHITE: + raise IllegalMoveError("Invalid pla for board.set") + if not self.is_on_board(loc): + raise IllegalMoveError("Invalid loc for board.set") + + if self.board[loc] == pla: + pass + elif self.board[loc] == Board.EMPTY: + self.add_unsafe(pla,loc) + elif pla == Board.EMPTY: + self.remove_single_stone_unsafe(loc) + else: + self.remove_single_stone_unsafe(loc) + self.add_unsafe(pla,loc) + + #Clear any ko restrictions + self.simple_ko_point = None + + + #Play a stone at the given location, with non-superko legality checking and updating the pla and simple ko point + #Single stone suicide is disallowed but suicide is allowed, to support rule sets and sgfs that have suicide + def play(self,pla,loc): + if pla != Board.BLACK and pla != Board.WHITE: + raise IllegalMoveError("Invalid pla for board.play") + + if loc != Board.PASS_LOC: + if not self.is_on_board(loc): + raise IllegalMoveError("Invalid loc for board.set") + if self.board[loc] != Board.EMPTY: + raise IllegalMoveError("Location is nonempty") + if self.would_be_single_stone_suicide(pla,loc): + raise IllegalMoveError("Move would be illegal single stone suicide") + if loc == self.simple_ko_point: + raise IllegalMoveError("Move would be illegal simple ko recapture") + + self.playUnsafe(pla,loc) + + def playUnsafe(self,pla,loc): + if loc == Board.PASS_LOC: + self.simple_ko_point = None + self.pla = Board.get_opp(pla) + else: + self.add_unsafe(pla,loc) + self.pla = Board.get_opp(pla) + + def playRecordedUnsafe(self,pla,loc): + capDirs = [] + opp = Board.get_opp(pla) + old_simple_ko_point = self.simple_ko_point + for i in range(4): + adj = loc + self.adj[i] + if self.board[adj] == opp and self.group_liberty_count[self.group_head[adj]] == 1: + capDirs.append(i) + old_num_captures_made = self.num_captures_made.copy() + old_num_non_pass_moves_made = self.num_non_pass_moves_made.copy() + + self.playUnsafe(pla,loc) + + #Suicide + selfCap = False + if self.board[loc] == Board.EMPTY: + selfCap = True + return (pla,loc,old_simple_ko_point,capDirs,selfCap,old_num_captures_made,old_num_non_pass_moves_made) + + def undo(self,record): + (pla,loc,simple_ko_point,capDirs,selfCap,old_num_captures_made,old_num_non_pass_moves_made) = record + opp = Board.get_opp(pla) + + self.simple_ko_point = simple_ko_point + self.pla = pla + self.num_captures_made = old_num_captures_made.copy() + self.num_non_pass_moves_made = old_num_non_pass_moves_made.copy() + + if loc == Board.PASS_LOC: + return + + #Re-fill stones in all captured directions + for capdir in capDirs: + adj = loc + self.adj[capdir] + if self.board[adj] == Board.EMPTY: + self.floodFillStones(opp,adj) + + if selfCap: + self.floodFillStones(pla,loc) + + #Delete the stone played here. + self.zobrist ^= Board.ZOBRIST_STONE[pla][loc] + self.board[loc] = Board.EMPTY + + #Zero out stuff in preparation for rebuilding + head = self.group_head[loc] + stone_count = self.group_stone_count[head] + self.group_stone_count[head] = 0 + self.group_liberty_count[head] = 0 + + #Uneat enemy liberties + self.changeSurroundingLiberties(loc,Board.get_opp(pla),+1) + + #If this was not a single stone, we need to recompute the chain from scratch + if stone_count > 1: + #Run through the whole chain and make their heads point to nothing + cur = loc + while True: + self.group_head[cur] = Board.PASS_LOC + cur = self.group_next[cur] + if cur == loc: + break + + #Rebuild each chain adjacent now + for i in range(4): + adj = loc + self.adj[i] + if self.board[adj] == pla and self.group_head[adj] == Board.PASS_LOC: + self.rebuildChain(pla,adj) + + self.group_head[loc] = 0 + self.group_next[loc] = 0 + self.group_prev[loc] = 0 + + + #Add a chain of the given player to the given region of empty space, floodfilling it. + #Assumes that this region does not border any chains of the desired color already + def floodFillStones(self,pla,loc): + head = loc + self.group_liberty_count[head] = 0 + self.group_stone_count[head] = 0 + + #Add a chain with links front <-> ... <-> head <-> head with all head pointers towards head + front = self.floodFillStonesHelper(head, head, head, pla) + + #Now, we make head point to front, and that completes the circle! + self.group_next[head] = front + self.group_prev[front] = head + + #Floodfill a chain of the given color into this region of empty spaces + #Make the specified loc the head for all the chains and updates the chainData of head with the number of stones. + #Does NOT connect the stones into a circular list. Rather, it produces an linear linked list with the tail pointing + #to tailTarget, and returns the head of the list. The tail is guaranteed to be loc. + def floodFillStonesHelper(self, head, tailTarget, loc, pla): + self.board[loc] = pla + self.zobrist ^= Board.ZOBRIST_STONE[pla][loc] + + self.group_head[loc] = head + self.group_stone_count[head] += 1 + self.group_next[loc] = tailTarget + self.group_prev[tailTarget] = loc + + #Eat enemy liberties + self.changeSurroundingLiberties(loc,Board.get_opp(pla),-1) + + #Recursively add stones around us. + nextTailTarget = loc + for i in range(4): + adj = loc + self.adj[i] + if self.board[adj] == Board.EMPTY: + nextTailTarget = self.floodFillStonesHelper(head,nextTailTarget,adj,pla) + return nextTailTarget + + #Floods through a chain of the specified player already on the board + #rebuilding its links and counting its liberties as we go. + #Requires that all their heads point towards + #some invalid location, such as PASS_LOC or a location not of color. + #The head of the chain will be loc. + def rebuildChain(self,pla,loc): + head = loc + self.group_liberty_count[head] = 0 + self.group_stone_count[head] = 0 + + #Rebuild chain with links front <-> ... <-> head <-> head with all head pointers towards head + front = self.rebuildChainHelper(head, head, head, pla) + + #Now, we make head point to front, and that completes the circle! + self.group_next[head] = front + self.group_prev[front] = head + + + #Does same thing as addChain, but floods through a chain of the specified color already on the board + #rebuilding its links and also counts its liberties as we go. Requires that all their heads point towards + #some invalid location, such as NULL_LOC or a location not of color. + def rebuildChainHelper(self, head, tailTarget, loc, pla): + #Count new liberties + for dloc in self.adj: + if self.board[loc+dloc] == Board.EMPTY and not self.is_group_adjacent(head,loc+dloc): + self.group_liberty_count[head] += 1 + + #Add stone here to the chain by setting its head + self.group_head[loc] = head + self.group_stone_count[head] += 1 + self.group_next[loc] = tailTarget + self.group_prev[tailTarget] = loc + + #Recursively add stones around us. + nextTailTarget = loc + for i in range(4): + adj = loc + self.adj[i] + if self.board[adj] == pla and self.group_head[adj] != head: + nextTailTarget = self.rebuildChainHelper(head,nextTailTarget,adj,pla) + return nextTailTarget + + + #Add a stone, assumes that the location is empty without checking + def add_unsafe(self,pla,loc): + opp = Board.get_opp(pla) + + #Put the stone down + self.board[loc] = pla + self.zobrist ^= Board.ZOBRIST_STONE[pla][loc] + + #Initialize the group for that stone + self.group_head[loc] = loc + self.group_stone_count[loc] = 1 + liberties = 0 + for dloc in self.adj: + if self.board[loc+dloc] == Board.EMPTY: + liberties += 1 + self.group_liberty_count[loc] = liberties + self.group_next[loc] = loc + self.group_prev[loc] = loc + + #Fill surrounding liberties of all adjacent groups + #Carefully avoid doublecounting + adj0 = loc + self.adj[0] + adj1 = loc + self.adj[1] + adj2 = loc + self.adj[2] + adj3 = loc + self.adj[3] + if self.board[adj0] == Board.BLACK or self.board[adj0] == Board.WHITE: + self.group_liberty_count[self.group_head[adj0]] -= 1 + if self.board[adj1] == Board.BLACK or self.board[adj1] == Board.WHITE: + if self.group_head[adj1] != self.group_head[adj0]: + self.group_liberty_count[self.group_head[adj1]] -= 1 + if self.board[adj2] == Board.BLACK or self.board[adj2] == Board.WHITE: + if self.group_head[adj2] != self.group_head[adj0] and \ + self.group_head[adj2] != self.group_head[adj1]: + self.group_liberty_count[self.group_head[adj2]] -= 1 + if self.board[adj3] == Board.BLACK or self.board[adj3] == Board.WHITE: + if self.group_head[adj3] != self.group_head[adj0] and \ + self.group_head[adj3] != self.group_head[adj1] and \ + self.group_head[adj3] != self.group_head[adj2]: + self.group_liberty_count[self.group_head[adj3]] -= 1 + + #Merge groups + if self.board[adj0] == pla: + self.merge_unsafe(loc,adj0) + if self.board[adj1] == pla: + self.merge_unsafe(loc,adj1) + if self.board[adj2] == pla: + self.merge_unsafe(loc,adj2) + if self.board[adj3] == pla: + self.merge_unsafe(loc,adj3) + + #Resolve captures + opp_stones_captured = 0 + caploc = 0 + if self.board[adj0] == opp and self.group_liberty_count[self.group_head[adj0]] == 0: + opp_stones_captured += self.group_stone_count[self.group_head[adj0]] + caploc = adj0 + self.remove_unsafe(adj0) + if self.board[adj1] == opp and self.group_liberty_count[self.group_head[adj1]] == 0: + opp_stones_captured += self.group_stone_count[self.group_head[adj1]] + caploc = adj1 + self.remove_unsafe(adj1) + if self.board[adj2] == opp and self.group_liberty_count[self.group_head[adj2]] == 0: + opp_stones_captured += self.group_stone_count[self.group_head[adj2]] + caploc = adj2 + self.remove_unsafe(adj2) + if self.board[adj3] == opp and self.group_liberty_count[self.group_head[adj3]] == 0: + opp_stones_captured += self.group_stone_count[self.group_head[adj3]] + caploc = adj3 + self.remove_unsafe(adj3) + + #Suicide + pla_stones_captured = 0 + if self.group_liberty_count[self.group_head[loc]] == 0: + pla_stones_captured += self.group_stone_count[self.group_head[loc]] + self.remove_unsafe(loc) + + self.num_captures_made[pla] += pla_stones_captured + self.num_captures_made[opp] += opp_stones_captured + self.num_non_pass_moves_made[pla] += 1 + + #Update ko point for legality checking + if opp_stones_captured == 1 and \ + self.group_stone_count[self.group_head[loc]] == 1 and \ + self.group_liberty_count[self.group_head[loc]] == 1: + self.simple_ko_point = caploc + else: + self.simple_ko_point = None + + #Apply the specified delta to the liberties of all adjacent groups of the specified color + def changeSurroundingLiberties(self,loc,pla,delta): + #Carefully avoid doublecounting + adj0 = loc + self.adj[0] + adj1 = loc + self.adj[1] + adj2 = loc + self.adj[2] + adj3 = loc + self.adj[3] + if self.board[adj0] == pla: + self.group_liberty_count[self.group_head[adj0]] += delta + if self.board[adj1] == pla: + if self.group_head[adj1] != self.group_head[adj0]: + self.group_liberty_count[self.group_head[adj1]] += delta + if self.board[adj2] == pla: + if self.group_head[adj2] != self.group_head[adj0] and \ + self.group_head[adj2] != self.group_head[adj1]: + self.group_liberty_count[self.group_head[adj2]] += delta + if self.board[adj3] == pla: + if self.group_head[adj3] != self.group_head[adj0] and \ + self.group_head[adj3] != self.group_head[adj1] and \ + self.group_head[adj3] != self.group_head[adj2]: + self.group_liberty_count[self.group_head[adj3]] += delta + + def countImmediateLiberties(self,loc): + adj0 = loc + self.adj[0] + adj1 = loc + self.adj[1] + adj2 = loc + self.adj[2] + adj3 = loc + self.adj[3] + count = 0 + if self.board[adj0] == Board.EMPTY: + count += 1 + if self.board[adj1] == Board.EMPTY: + count += 1 + if self.board[adj2] == Board.EMPTY: + count += 1 + if self.board[adj3] == Board.EMPTY: + count += 1 + return count + + def is_group_adjacent(self,head,loc): + return ( + self.group_head[loc+self.adj[0]] == head or \ + self.group_head[loc+self.adj[1]] == head or \ + self.group_head[loc+self.adj[2]] == head or \ + self.group_head[loc+self.adj[3]] == head + ) + + #Helper, merge two groups assuming they're owned by the same player and adjacent + def merge_unsafe(self,loc0,loc1): + if self.group_stone_count[self.group_head[loc0]] >= self.group_stone_count[self.group_head[loc1]]: + parent = loc0 + child = loc1 + else: + child = loc0 + parent = loc1 + + phead = self.group_head[parent] + chead = self.group_head[child] + if phead == chead: + return + + #Walk the child group assigning the new head and simultaneously counting liberties + new_stone_count = self.group_stone_count[phead] + self.group_stone_count[chead] + new_liberties = self.group_liberty_count[phead] + loc = child + while True: + adj0 = loc + self.adj[0] + adj1 = loc + self.adj[1] + adj2 = loc + self.adj[2] + adj3 = loc + self.adj[3] + + #Any adjacent empty space is a new liberty as long as it isn't adjacent to the parent + if self.board[adj0] == Board.EMPTY and not self.is_group_adjacent(phead,adj0): + new_liberties += 1 + if self.board[adj1] == Board.EMPTY and not self.is_group_adjacent(phead,adj1): + new_liberties += 1 + if self.board[adj2] == Board.EMPTY and not self.is_group_adjacent(phead,adj2): + new_liberties += 1 + if self.board[adj3] == Board.EMPTY and not self.is_group_adjacent(phead,adj3): + new_liberties += 1 + + #Now assign the new parent head to take over the child (this also + #prevents double-counting liberties) + self.group_head[loc] = phead + + #Advance around the linked list + loc = self.group_next[loc] + if loc == child: + break + + #Zero out the old head + self.group_stone_count[chead] = 0 + self.group_liberty_count[chead] = 0 + + #Update the new head + self.group_stone_count[phead] = new_stone_count + self.group_liberty_count[phead] = new_liberties + + #Combine the linked lists + plast = self.group_prev[phead] + clast = self.group_prev[chead] + self.group_next[clast] = phead + self.group_next[plast] = chead + self.group_prev[chead] = plast + self.group_prev[phead] = clast + + #Remove all stones in a group + def remove_unsafe(self,group): + head = self.group_head[group] + pla = self.board[group] + opp = Board.get_opp(pla) + + #Walk all the stones in the group and delete them + loc = group + while True: + #Add a liberty to all surrounding opposing groups, taking care to avoid double counting + adj0 = loc + self.adj[0] + adj1 = loc + self.adj[1] + adj2 = loc + self.adj[2] + adj3 = loc + self.adj[3] + if self.board[adj0] == opp: + self.group_liberty_count[self.group_head[adj0]] += 1 + if self.board[adj1] == opp: + if self.group_head[adj1] != self.group_head[adj0]: + self.group_liberty_count[self.group_head[adj1]] += 1 + if self.board[adj2] == opp: + if self.group_head[adj2] != self.group_head[adj0] and \ + self.group_head[adj2] != self.group_head[adj1]: + self.group_liberty_count[self.group_head[adj2]] += 1 + if self.board[adj3] == opp: + if self.group_head[adj3] != self.group_head[adj0] and \ + self.group_head[adj3] != self.group_head[adj1] and \ + self.group_head[adj3] != self.group_head[adj2]: + self.group_liberty_count[self.group_head[adj3]] += 1 + + next_loc = self.group_next[loc] + + #Zero out all the stuff + self.board[loc] = Board.EMPTY + self.zobrist ^= Board.ZOBRIST_STONE[opp][loc] + self.group_head[loc] = 0 + self.group_next[loc] = 0 + self.group_prev[loc] = 0 + + #Advance around the linked list + loc = next_loc + if loc == group: + break + + #Zero out the head + self.group_stone_count[head] = 0 + self.group_liberty_count[head] = 0 + + #Remove a single stone + def remove_single_stone_unsafe(self,rloc): + pla = self.board[rloc] + + #Record all the stones in the group + stones = [] + loc = rloc + while True: + stones.append(loc) + loc = self.group_next[loc] + if loc == rloc: + break + + #Remove them all + self.remove_unsafe(rloc) + + #Then add them back one by one + for loc in stones: + if loc != rloc: + self.add_unsafe(pla,loc) + + + #Helper, find liberties of group at loc. Fills in buf. + def findLiberties(self, loc, buf): + cur = loc + while True: + for i in range(4): + lib = cur + self.adj[i] + if self.board[lib] == Board.EMPTY: + if lib not in buf: + buf.append(lib) + + cur = self.group_next[cur] + if cur == loc: + break + + #Helper, find captures that gain liberties for the group at loc. Fills in buf + def findLibertyGainingCaptures(self, loc, buf): + pla = self.board[loc] + opp = Board.get_opp(pla) + + #For performance, avoid checking for captures on any chain twice + chainHeadsChecked = [] + + cur = loc + while True: + for i in range(4): + adj = cur + self.adj[i] + if self.board[adj] == opp: + head = self.group_head[adj] + + if self.group_liberty_count[head] == 1: + if head not in chainHeadsChecked: + #Capturing moves are precisely the liberties of the groups around us with 1 liberty. + self.findLiberties(adj, buf) + chainHeadsChecked.append(head) + + cur = self.group_next[cur] + if cur == loc: + break + + #Helper, does the group at loc have at least one opponent group adjacent to it in atari? + def hasLibertyGainingCaptures(self, loc): + pla = self.board[loc] + opp = Board.get_opp(pla) + + cur = loc + while True: + for i in range(4): + adj = cur + self.adj[i] + if self.board[adj] == opp: + head = self.group_head[adj] + if self.group_liberty_count[head] == 1: + return True + + cur = self.group_next[cur] + if cur == loc: + break + + return False + + def wouldBeKoCapture(self, loc, pla): + if self.board[loc] != Board.EMPTY: + return False + #Check that surounding points are are all opponent owned and exactly one of them is capturable + opp = Board.get_opp(pla) + oppCapturableLoc = None + for i in range(4): + adj = loc + self.adj[i] + if self.board[adj] != Board.WALL and self.board[adj] != opp: + return False + if self.board[adj] == opp and self.group_liberty_count[self.group_head[adj]] == 1: + if oppCapturableLoc is not None: + return False + oppCapturableLoc = adj + + if oppCapturableLoc is None: + return False + + #Check that the capturable loc has exactly one stone + if self.group_stone_count[self.group_head[oppCapturableLoc]] != 1: + return False + return True + + def countHeuristicConnectionLiberties(self,loc,pla): + adj0 = loc + self.adj[0] + adj1 = loc + self.adj[1] + adj2 = loc + self.adj[2] + adj3 = loc + self.adj[3] + count = 0.0 + if self.board[adj0] == pla: + count += max(0.0,self.group_liberty_count[self.group_head[adj0]]-1.5) + if self.board[adj1] == pla: + count += max(0.0,self.group_liberty_count[self.group_head[adj1]]-1.5) + if self.board[adj2] == pla: + count += max(0.0,self.group_liberty_count[self.group_head[adj2]]-1.5) + if self.board[adj3] == pla: + count += max(0.0,self.group_liberty_count[self.group_head[adj3]]-1.5) + return count + + def searchIsLadderCapturedAttackerFirst2Libs(self,loc): + if not self.is_on_board(loc): + return [] + if self.board[loc] != Board.BLACK and self.board[loc] != Board.WHITE: + return [] + if self.group_liberty_count[self.group_head[loc]] != 2: + return [] + + #Make it so that pla is always the defender + pla = self.board[loc] + opp = Board.get_opp(pla) + + moves = [] + self.findLiberties(loc,moves) + assert(len(moves) == 2) + + move0 = moves[0] + move1 = moves[1] + move0Works = False + move1Works = False + + if self.would_be_legal(opp,move0): + record = self.playRecordedUnsafe(opp,move0) + move0Works = self.searchIsLadderCaptured(loc,True) + self.undo(record) + if self.would_be_legal(opp,move1): + record = self.playRecordedUnsafe(opp,move1) + move1Works = self.searchIsLadderCaptured(loc,True) + self.undo(record) + + workingMoves = [] + if move0Works: + workingMoves.append(move0) + if move1Works: + workingMoves.append(move1) + + return workingMoves + + + def searchIsLadderCaptured(self,loc,defenderFirst): + if not self.is_on_board(loc): + return False + if self.board[loc] != Board.BLACK and self.board[loc] != Board.WHITE: + return False + + if self.group_liberty_count[self.group_head[loc]] > 2 or (defenderFirst and self.group_liberty_count[self.group_head[loc]] > 1): + return False + + #Make it so that pla is always the defender + pla = self.board[loc] + opp = Board.get_opp(pla) + + arrSize = self.size * self.size * 2 #A bit bigger due to paranoia about recaptures making the sequence longer. + + #Stack for the search. These are lists of possible moves to search at each level of the stack + moveLists = [[] for i in range(arrSize)] + moveListCur = [0 for i in range(arrSize)] #Current move list idx searched, equal to -1 if list has not been generated. + records = [None for i in range(arrSize)] #Records so that we can undo moves as we search back up. + stackIdx = 0 + + moveLists[0] = [] + moveListCur[0] = -1 + + returnValue = False + returnedFromDeeper = False + + #Clear the ko loc for the defender at the root node - assume all kos work for the defender + saved_simple_ko_point = self.simple_ko_point + if defenderFirst: + self.simple_ko_point = None + + # debug = True + # if debug: + # print("SEARCHING " + str(self.loc_x(loc)) + " " + str(self.loc_y(loc))) + + while True: + # if debug: + # print(str(stackIdx) + " " + str(moveListCur[stackIdx]) + "/" + str(len(moveLists[stackIdx])) + " " + str(returnValue) + " " + str(returnedFromDeeper)) + + #Returned from the root - so that's the answer + if stackIdx <= -1: + assert(stackIdx == -1) + self.simple_ko_point = saved_simple_ko_point + return returnValue + + isDefender = (defenderFirst and (stackIdx % 2) == 0) or (not defenderFirst and (stackIdx % 2) == 1) + + #We just entered this level? + if moveListCur[stackIdx] == -1: + libs = self.group_liberty_count[self.group_head[loc]] + + #Base cases. + #If we are the attacker and the group has only 1 liberty, we already win. + if not isDefender and libs <= 1: + returnValue = True + returnedFromDeeper = True + stackIdx -= 1 + continue + + #If we are the attacker and the group has 3 liberties, we already lose. + if not isDefender and libs >= 3: + returnValue = False + returnedFromDeeper = True + stackIdx -= 1 + continue + + #If we are the defender and the group has 2 liberties, we already win. + if isDefender and libs >= 2: + returnValue = False + returnedFromDeeper = True + stackIdx -= 1 + continue + + #If we are the defender and the attacker left a simple ko point, assume we already win + #because we don't want to say yes on ladders that depend on kos + #This should also hopefully prevent any possible infinite loops - I don't know of any infinite loop + #that would come up in a continuous atari sequence that doesn't ever leave a simple ko point. + if isDefender and self.simple_ko_point is not None: + returnValue = False + returnedFromDeeper = True + stackIdx -= 1 + continue + + #Otherwise we need to keep searching. + #Generate the move list. Attacker and defender generate moves on the group's liberties, but only the defender + #generates moves on surrounding capturable opposing groups. + if isDefender: + moveLists[stackIdx] = [] + self.findLibertyGainingCaptures(loc,moveLists[stackIdx]) + self.findLiberties(loc,moveLists[stackIdx]) + else: + moveLists[stackIdx] = [] + self.findLiberties(loc,moveLists[stackIdx]) + assert(len(moveLists[stackIdx]) == 2) + + #Early quitouts if the liberties are not adjacent + #(so that filling one doesn't fill an immediate liberty of the other) + move0 = moveLists[stackIdx][0] + move1 = moveLists[stackIdx][1] + + libs0 = self.countImmediateLiberties(move0) + libs1 = self.countImmediateLiberties(move1) + + #If we are the attacker and we're in a double-ko death situation, then assume we win. + #Both defender liberties must be ko mouths, connecting either ko mouth must not increase the defender's + #liberties, and none of the attacker's surrounding stones can currently be in atari. + #This is not complete - there are situations where the defender's connections increase liberties, or where + #the attacker has stones in atari, but where the defender is still in inescapable atari even if they have + #a large finite number of ko threats. But it's better than nothing. + if libs0 == 0 and libs1 == 0 and self.wouldBeKoCapture(move0,opp) and self.wouldBeKoCapture(move1,opp) : + if self.get_liberties_after_play(pla,move0,3) <= 2 and self.get_liberties_after_play(pla,move1,3) <= 2: + if self.hasLibertyGainingCaptures(loc): + returnValue = True + returnedFromDeeper = True + stackIdx -= 1 + continue + + if not self.is_adjacent(move0,move1): + #We lose automatically if both escapes get the defender too many libs + if libs0 >= 3 and libs1 >= 3: + returnValue = False + returnedFromDeeper = True + stackIdx -= 1 + continue + #Move 1 is not possible, so shrink the list + elif libs0 >= 3: + moveLists[stackIdx] = [move0] + #Move 0 is not possible, so shrink the list + elif libs1 >= 3: + moveLists[stackIdx] = [move1] + + #Order the two moves based on a simple heuristic - for each neighboring group with any liberties + #count that the opponent could connect to, count liberties - 1.5. + if len(moveLists[stackIdx]) > 1: + libs0 += self.countHeuristicConnectionLiberties(move0,pla) + libs1 += self.countHeuristicConnectionLiberties(move1,pla) + if libs1 > libs0: + moveLists[stackIdx][0] = move1 + moveLists[stackIdx][1] = move0 + + #And indicate to begin search on the first move generated. + moveListCur[stackIdx] = 0 + + #Else, we returned from a deeper level (or the same level, via illegal move) + else: + assert(moveListCur[stackIdx] >= 0) + assert(moveListCur[stackIdx] < len(moveLists[stackIdx])) + #If we returned from deeper we need to undo the move we made + if returnedFromDeeper: + self.undo(records[stackIdx]) + + #Defender has a move that is not ladder captured? + if isDefender and not returnValue: + #Return! (returnValue is still false, as desired) + returnedFromDeeper = True + stackIdx -= 1 + continue + + #Attacker has a move that does ladder capture? + if not isDefender and returnValue: + #Return! (returnValue is still true, as desired) + returnedFromDeeper = True + stackIdx -= 1 + continue + + #Move on to the next move to search + moveListCur[stackIdx] += 1 + + #If there is no next move to search, then we lose. + if moveListCur[stackIdx] >= len(moveLists[stackIdx]): + #For a defender, that means a ladder capture. + #For an attacker, that means no ladder capture found. + returnValue = isDefender + returnedFromDeeper = True + stackIdx -= 1 + continue + + + #Otherwise we do have an next move to search. Grab it. + move = moveLists[stackIdx][moveListCur[stackIdx]] + p = (pla if isDefender else opp) + + # if debug: + # print("play " + str(self.loc_x(move)) + " " + str(self.loc_y(move)) + " " + str(p)) + # print(self.to_string()) + + #Illegal move - treat it the same as a failed move, but don't return up a level so that we + #loop again and just try the next move. + if not self.would_be_legal(p,move): + returnValue = isDefender + returnedFromDeeper = False + #if(print) cout << "illegal " << endl; + continue + + #Play and record the move! + records[stackIdx] = self.playRecordedUnsafe(p,move) + + #And recurse to the next level + stackIdx += 1 + moveListCur[stackIdx] = -1 + moveLists[stackIdx] = [] + + + def calculateArea(self, result, nonPassAliveStones, safeBigTerritories, unsafeBigTerritories, isMultiStoneSuicideLegal): + for i in range(self.arrsize): + result[i] = Board.EMPTY + self.calculateAreaForPla(Board.BLACK,safeBigTerritories,unsafeBigTerritories,isMultiStoneSuicideLegal,result) + self.calculateAreaForPla(Board.WHITE,safeBigTerritories,unsafeBigTerritories,isMultiStoneSuicideLegal,result) + + if nonPassAliveStones: + for y in range(self.size): + for x in range(self.size): + loc = self.loc(x,y) + if result[loc] == Board.EMPTY: + result[loc] = self.board[loc] + + def calculateNonDameTouchingArea(self, result, keepTerritories, keepStones, isMultiStoneSuicideLegal): + #First, just compute basic area. + basicArea = [Board.EMPTY for i in range(self.arrsize)] + for i in range(self.arrsize): + result[i] = Board.EMPTY + self.calculateAreaForPla(Board.BLACK,True,True,isMultiStoneSuicideLegal,basicArea) + self.calculateAreaForPla(Board.WHITE,True,True,isMultiStoneSuicideLegal,basicArea) + + for y in range(self.size): + for x in range(self.size): + loc = self.loc(x,y) + if basicArea[loc] == Board.EMPTY: + basicArea[loc] = self.board[loc] + + self.calculateNonDameTouchingAreaHelper(basicArea,result) + + if keepTerritories: + for y in range(self.size): + for x in range(self.size): + loc = self.loc(x,y) + if basicArea[loc] != Board.EMPTY and basicArea[loc] != self.board[loc]: + result[loc] = basicArea[loc] + + if keepStones: + for y in range(self.size): + for x in range(self.size): + loc = self.loc(x,y) + if basicArea[loc] != Board.EMPTY and basicArea[loc] == self.board[loc]: + result[loc] = basicArea[loc] + + + def calculateAreaForPla(self, pla, safeBigTerritories, unsafeBigTerritories, isMultiStoneSuicideLegal, result): + opp = self.get_opp(pla) + #First compute all empty-or-opp regions + + #For each loc, if it's empty or opp, the head of the region + regionHeadByLoc = [Board.PASS_LOC for i in range(self.arrsize)] + #For each loc, if it's empty or opp, the next empty or opp belonging to the same region + nextEmptyOrOpp = [Board.PASS_LOC for i in range(self.arrsize)] + #Does this border a pla group that has been marked as not pass alive? + bordersNonPassAlivePlaByHead = [False for i in range(self.arrsize)] + + #A list for each region head, indicating which pla group heads the region is vital for. + #A region is vital for a pla group if all its spaces are adjacent to that pla group. + #All lists are concatenated together, the most we can have is bounded by (MAX_LEN * MAX_LEN+1) / 2 + #independent regions, each one vital for at most 4 pla groups, add some extra just in case. + maxRegions = (self.size * self.size + 1)//2 + 1 + vitalForPlaHeadsListsMaxLen = maxRegions * 4 + vitalForPlaHeadsLists = [-1 for i in range(vitalForPlaHeadsListsMaxLen)] + vitalForPlaHeadsListsTotal = 0 + + #A list of region heads + numRegions = 0 + regionHeads = [-1 for i in range(maxRegions)] + #Start indices and list lengths in vitalForPlaHeadsLists + vitalStart = [-1 for i in range(maxRegions)] + vitalLen = [-1 for i in range(maxRegions)] + #For each region, are there 0, 1, or 2+ spaces of that region not bordering any pla? + numInternalSpacesMax2 = [-1 for i in range(maxRegions)] + containsOpp = [False for i in range(maxRegions)] + + def isAdjacentToPlaHead(loc,plaHead): + for i in range(4): + adj = loc + self.adj[i] + if self.board[adj] == pla and self.group_head[adj] == plaHead: + return True + return False + + #Recursively trace maximal non-pla regions of the board and record their properties and join them into a + #linked list through nextEmptyOrOpp. + #Takes as input the location serving as the head, the tip node of the linked list so far, the next loc, and the + #numeric index of the region + #Returns the loc serving as the current tip node ("tailTarget") of the linked list. + def buildRegion(head, tailTarget, loc, regionIdx): + #Already traced this location, skip + if regionHeadByLoc[loc] != Board.PASS_LOC: + return tailTarget + regionHeadByLoc[loc] = head + + #First, filter out any pla heads it turns out we're not vital for because we're not adjacent to them + #In the case where suicide is allowed, we only do this filtering on intersections that are actually empty + if isMultiStoneSuicideLegal or self.board[loc] == Board.EMPTY: + vStart = vitalStart[regionIdx] + oldVLen = vitalLen[regionIdx] + newVLen = 0 + for i in range(oldVLen): + if isAdjacentToPlaHead(loc,vitalForPlaHeadsLists[vStart+i]): + vitalForPlaHeadsLists[vStart+newVLen] = vitalForPlaHeadsLists[vStart+i] + newVLen += 1 + vitalLen[regionIdx] = newVLen + + #Determine if this point is internal, unless we already have many internal points + if numInternalSpacesMax2[regionIdx] < 2: + isInternal = True + for i in range(4): + adj = loc + self.adj[i] + if self.board[adj] == pla: + isInternal = False + break + if isInternal: + numInternalSpacesMax2[regionIdx] += 1 + + if self.board[loc] == opp: + containsOpp[regionIdx] = True + + #Next, recurse everywhere + nextEmptyOrOpp[loc] = tailTarget + nextTailTarget = loc + for i in range(4): + adj = loc + self.adj[i] + if self.board[adj] == Board.EMPTY or self.board[adj] == opp: + nextTailTarget = buildRegion(head,nextTailTarget,adj,regionIdx) + + return nextTailTarget + + atLeastOnePla = False + for y in range(self.size): + for x in range(self.size): + loc = self.loc(x,y) + + if regionHeadByLoc[loc] != Board.PASS_LOC: + continue + if self.board[loc] != Board.EMPTY: + atLeastOnePla |= (self.board[loc] == pla) + continue + + regionIdx = numRegions + numRegions += 1 + assert(numRegions <= maxRegions) + + #Initialize region metadata + head = loc + regionHeads[regionIdx] = head + vitalStart[regionIdx] = vitalForPlaHeadsListsTotal + vitalLen[regionIdx] = 0 + numInternalSpacesMax2[regionIdx] = 0 + containsOpp[regionIdx] = False + + #Fill in all adjacent pla heads as vital, which will get filtered during buildRegion + vStart = vitalStart[regionIdx] + assert(vStart + 4 <= vitalForPlaHeadsListsMaxLen) + initialVLen = 0 + for i in range(4): + adj = loc + self.adj[i] + if self.board[adj] == pla: + plaHead = self.group_head[adj] + alreadyPresent = False + for j in range(initialVLen): + if vitalForPlaHeadsLists[vStart+j] == plaHead: + alreadyPresent = True + break + + if not alreadyPresent: + vitalForPlaHeadsLists[vStart+initialVLen] = plaHead + initialVLen += 1 + + vitalLen[regionIdx] = initialVLen + + tailTarget = buildRegion(head,head,loc,regionIdx) + nextEmptyOrOpp[head] = tailTarget + + vitalForPlaHeadsListsTotal += vitalLen[regionIdx] + + #Also accumulate all player heads + numPlaHeads = 0 + allPlaHeads = [] + + #Accumulate with duplicates + for y in range(self.size): + for x in range(self.size): + loc = self.loc(x,y) + if self.board[loc] == pla: + allPlaHeads.append(self.group_head[loc]) + numPlaHeads += 1 + + #Filter duplicates + allPlaHeads = list(set(allPlaHeads)) + numPlaHeads = len(allPlaHeads) + + plaHasBeenKilled = [False for i in range(numPlaHeads)] + + #Now, we can begin the benson iteration + vitalCountByPlaHead = [0 for i in range(self.arrsize)] + while(True): + #Zero out vital liberties by head + for i in range(numPlaHeads): + vitalCountByPlaHead[allPlaHeads[i]] = 0 + + #Walk all regions that are still bordered only by pass-alive stuff and accumulate a vital liberty to each pla it is vital for. + for i in range(numRegions): + head = regionHeads[i] + if bordersNonPassAlivePlaByHead[head]: + continue + + vStart = vitalStart[i] + vLen = vitalLen[i] + for j in range(vLen): + plaHead = vitalForPlaHeadsLists[vStart+j] + vitalCountByPlaHead[plaHead] += 1 + + #Walk all player heads and kill them if they haven't accumulated at least 2 vital liberties + killedAnything = False + for i in range(numPlaHeads): + #Already killed - skip + if plaHasBeenKilled[i]: + continue + + plaHead = allPlaHeads[i] + if vitalCountByPlaHead[plaHead] < 2: + plaHasBeenKilled[i] = True + killedAnything = True + #Walk the pla chain to update bordering regions + cur = plaHead + while(True): + for j in range(4): + adj = cur + self.adj[j] + if self.board[adj] == Board.EMPTY or self.board[adj] == opp: + bordersNonPassAlivePlaByHead[regionHeadByLoc[adj]] = True + + cur = self.group_next[cur] + + if cur == plaHead: + break + + if not killedAnything: + break + + #Mark result with pass-alive groups + for i in range(numPlaHeads): + if not plaHasBeenKilled[i]: + plaHead = allPlaHeads[i] + cur = plaHead + while(True): + result[cur] = pla + cur = self.group_next[cur] + if cur == plaHead: + break + + #Mark result with territory + for i in range(numRegions): + head = regionHeads[i] + shouldMark = numInternalSpacesMax2[i] <= 1 and atLeastOnePla and not bordersNonPassAlivePlaByHead[head] + shouldMark = shouldMark or (safeBigTerritories and atLeastOnePla and not containsOpp[i] and not bordersNonPassAlivePlaByHead[head]) + shouldMark = shouldMark or (unsafeBigTerritories and atLeastOnePla and not containsOpp[i]) + + if shouldMark: + cur = head + while(True): + result[cur] = pla + cur = nextEmptyOrOpp[cur] + if cur == head: + break + + def calculateNonDameTouchingAreaHelper(self, basicArea, result): + queue = [Board.PASS_LOC for i in range(self.arrsize)] + + #Iterate through all the regions that players own via area scoring and mark + #all the ones that are touching dame + isDameTouching = [False for i in range(self.arrsize)] + + queueHead = 0 + queueTail = 0 + + ADJ0 = self.adj[0] + ADJ1 = self.adj[1] + ADJ2 = self.adj[2] + ADJ3 = self.adj[3] + + for y in range(self.size): + for x in range(self.size): + loc = self.loc(x,y) + if basicArea[loc] != Board.EMPTY and not isDameTouching[loc]: + #Touches dame? + if((self.board[loc+ADJ0] == Board.EMPTY and basicArea[loc+ADJ0] == Board.EMPTY) or + (self.board[loc+ADJ1] == Board.EMPTY and basicArea[loc+ADJ1] == Board.EMPTY) or + (self.board[loc+ADJ2] == Board.EMPTY and basicArea[loc+ADJ2] == Board.EMPTY) or + (self.board[loc+ADJ3] == Board.EMPTY and basicArea[loc+ADJ3] == Board.EMPTY)): + + pla = basicArea[loc] + isDameTouching[loc] = True + queue[queueTail] = loc + queueTail += 1 + while queueHead != queueTail: + #Pop next location off queue + nextLoc = queue[queueHead] + queueHead += 1 + + #Look all around it, floodfill + for j in range(4): + adj = nextLoc + self.adj[j] + if basicArea[adj] == pla and not isDameTouching[adj]: + isDameTouching[adj] = True + queue[queueTail] = adj + queueTail += 1 + + queueHead = 0 + queueTail = 0 + + #Now, walk through and copy all non-dame-touching basic areas into the result counting + #how many there are. + for y in range(self.size): + for x in range(self.size): + loc = self.loc(x,y) + if basicArea[loc] != Board.EMPTY and not isDameTouching[loc] and result[loc] != basicArea[loc]: + pla = basicArea[loc] + result[loc] = basicArea[loc] + queue[queueTail] = loc + queueTail += 1 + while queueHead != queueTail: + #Pop next location off queue + nextLoc = queue[queueHead] + queueHead += 1 + + #Look all around it, floodfill + for j in range(4): + adj = nextLoc + self.adj[j] + if basicArea[adj] == pla and result[adj] != basicArea[adj]: + result[adj] = basicArea[adj] + queue[queueTail] = adj + queueTail += 1 diff --git a/training/scripts/dated/20241110-122511/clean_checkpoint.py b/training/scripts/dated/20241110-122511/clean_checkpoint.py new file mode 100644 index 0000000..e5e1802 --- /dev/null +++ b/training/scripts/dated/20241110-122511/clean_checkpoint.py @@ -0,0 +1,34 @@ +#!/usr/bin/python3 +import sys +import os +import argparse +import math +import torch +import json + +description = """ +Utility for cleaning torch checkpoints for release. +""" + +parser = argparse.ArgumentParser(description=description) +parser.add_argument('-checkpoint', help='Checkpoint file path', required=True) +parser.add_argument('-output', help='Output checkpoint file path', required=True) +args = vars(parser.parse_args()) + +checkpoint_path = args["checkpoint"] +output_path = args["output"] + +data = torch.load(checkpoint_path,map_location="cpu") + +if "optimizer" in data: + del data["optimizer"] +del data["running_metrics"] +del data["metrics"] +del data["train_state"]["old_train_data_dirs"] +del data["train_state"]["data_files_used"] + +if "last_val_metrics" in data: + del data["last_val_metrics"] + +torch.save(data, output_path) +print(f"Cleaned {checkpoint_path} -> {output_path} for release") diff --git a/training/scripts/dated/20241110-122511/data.py b/training/scripts/dated/20241110-122511/data.py new file mode 100644 index 0000000..cb530b5 --- /dev/null +++ b/training/scripts/dated/20241110-122511/data.py @@ -0,0 +1,163 @@ + +from sgfmill import sgf as Sgf +from sgfmill import sgf_properties as Sgf_properties + +from board import Board + +class Metadata: + def __init__(self, size, bname, wname, brank, wrank, komi, handicap): + self.size = size + self.bname = bname + self.wname = wname + self.brank = brank + self.wrank = wrank + self.komi = komi + self.handicap = handicap + +#Returns (metadata, list of setup stones, list of move stones) +#Setup and move stones are both pairs of (pla,loc) +def load_sgf_moves_exn(path): + sgf_file = open(path,"rb") + contents = sgf_file.read() + sgf_file.close() + + game = Sgf.Sgf_game.from_bytes(contents) + size = game.get_size() + + root = game.get_root() + ab, aw, ae = root.get_setup_stones() + setup = [] + if ab or aw: + for (row,col) in ab: + loc = Board.loc_static(col,size-1-row,size) + setup.append((Board.BLACK,loc)) + for (row,col) in aw: + loc = Board.loc_static(col,size-1-row,size) + setup.append((Board.WHITE,loc)) + + color,raw = root.get_raw_move() + if color is not None: + raise Exception("Found both setup stones and normal moves in root node") + + #Walk down the leftmost branch and assume that this is the game + moves = [] + prev_pla = None + seen_white_moves = False + node = root + while node: + node = node[0] + if node.has_setup_stones(): + raise Exception("Found setup stones after the root node") + + color,raw = node.get_raw_move() + if color is None: + raise Exception("Found node without move color") + + if color == 'b': + pla = Board.BLACK + elif color == 'w': + pla = Board.WHITE + else: + raise Exception("Invalid move color: " + color) + + rc = Sgf_properties.interpret_go_point(raw, size) + if rc is None: #pass + loc = Board.PASS_LOC + else: + (row,col) = rc + loc = Board.loc_static(col,size-1-row,size) + + #Forbid consecutive moves by the same player, unless the previous player was black and we've seen no white moves yet (handicap setup) + if pla == prev_pla and not (prev_pla == Board.BLACK and not seen_white_moves): + raise Exception("Multiple moves in a row by same player") + moves.append((pla,loc)) + + prev_pla = pla + if pla == Board.WHITE: + seen_white_moves = True + + #If there are multiple black moves in a row at the start, assume they are more handicap stones + first_white_move_idx = 0 + while first_white_move_idx < len(moves) and moves[first_white_move_idx][0] == Board.BLACK: + first_white_move_idx += 1 + if first_white_move_idx >= 2: + setup.extend((pla,loc) for (pla,loc) in moves[:first_white_move_idx] if loc is not None) + moves = moves[first_white_move_idx:] + + bname = root.get("PB") + wname = root.get("PW") + brank = (root.get("BR") if root.has_property("BR") else None) + wrank = (root.get("WR") if root.has_property("WR") else None) + komi = (root.get("KM") if root.has_property("KM") else None) + rulesstr = (root.get("RU") if root.has_property("RU") else None) + handicap = (root.get("HA") if root.has_property("HA") else None) + + rules = None + if rulesstr is not None: + if rulesstr.lower() == "japanese" or rulesstr.lower() == "jp": + rules = { + "koRule": "KO_SIMPLE", + "scoringRule": "SCORING_TERRITORY", + "multiStoneSuicideLegal": False, + "encorePhase": 0, + "passWouldEndPhase": False, + "whiteKomi": komi + } + elif rulesstr.lower() == "chinese": + rules = { + "koRule": "KO_SIMPLE", + "scoringRule": "SCORING_AREA", + "multiStoneSuicideLegal": False, + "encorePhase": 0, + "passWouldEndPhase": False, + "whiteKomi": komi + } + elif rulesstr.startswith("ko"): + rules = {} + origrulesstr = rulesstr + rulesstr = rulesstr[2:] + if rulesstr.startswith("SIMPLE"): + rules["koRule"] = "KO_SIMPLE" + rulesstr = rulesstr[6:] + elif rulesstr.startswith("POSITIONAL"): + rules["koRule"] = "KO_POSITIONAL" + rulesstr = rulesstr[10:] + elif rulesstr.startswith("SITUATIONAL"): + rules["koRule"] = "KO_SITUATIONAL" + rulesstr = rulesstr[11:] + elif rulesstr.startswith("SPIGHT"): + rules["koRule"] = "KO_SPIGHT" + rulesstr = rulesstr[6:] + else: + raise Exception("Could not parse rules: " + origrulesstr) + + if rulesstr.startswith("score"): + rulesstr = rulesstr[5:] + else: + raise Exception("Could not parse rules: " + origrulesstr) + + if rulesstr.startswith("AREA"): + rules["scoringRule"] = "SCORING_AREA" + rulesstr = rulesstr[4:] + elif rulesstr.startswith("TERRITORY"): + rules["scoringRule"] = "SCORING_TERRITORY" + rulesstr = rulesstr[9:] + else: + raise Exception("Could not parse rules: " + origrulesstr) + + if rulesstr.startswith("sui"): + rulesstr = rulesstr[3:] + else: + raise Exception("Could not parse rules: " + origrulesstr) + + if rulesstr.startswith("false"): + rules["multiStoneSuicideLegal"] = False + rulesstr = rulesstr[5:] + elif rulesstr.startswith("true"): + rules["multiStoneSuicideLegal"] = True + rulesstr = rulesstr[4:] + else: + raise Exception("Could not parse rules: " + origrulesstr) + + metadata = Metadata(size, bname, wname, brank, wrank, komi, handicap) + return metadata, setup, moves, rules diff --git a/training/scripts/dated/20241110-122511/data_processing_pytorch.py b/training/scripts/dated/20241110-122511/data_processing_pytorch.py new file mode 100644 index 0000000..55fcc58 --- /dev/null +++ b/training/scripts/dated/20241110-122511/data_processing_pytorch.py @@ -0,0 +1,238 @@ +import logging +import os + +import numpy as np + +import torch +import torch.nn.functional + +import modelconfigs + +def read_npz_training_data( + npz_files, + batch_size: int, + world_size: int, + rank: int, + pos_len: int, + device, + randomize_symmetries: bool, + include_meta: bool, + model_config: modelconfigs.ModelConfig, +): + rand = np.random.default_rng(seed=list(os.urandom(12))) + num_bin_features = modelconfigs.get_num_bin_input_features(model_config) + num_global_features = modelconfigs.get_num_global_input_features(model_config) + (h_base,h_builder) = build_history_matrices(model_config, device) + + for npz_file in npz_files: + with np.load(npz_file) as npz: + binaryInputNCHWPacked = npz["binaryInputNCHWPacked"] + globalInputNC = npz["globalInputNC"] + policyTargetsNCMove = npz["policyTargetsNCMove"].astype(np.float32) + globalTargetsNC = npz["globalTargetsNC"] + scoreDistrN = npz["scoreDistrN"].astype(np.float32) + valueTargetsNCHW = npz["valueTargetsNCHW"].astype(np.float32) + if include_meta: + metadataInputNC = npz["metadataInputNC"].astype(np.float32) + del npz + + binaryInputNCHW = np.unpackbits(binaryInputNCHWPacked,axis=2) + assert len(binaryInputNCHW.shape) == 3 + assert binaryInputNCHW.shape[2] == ((pos_len * pos_len + 7) // 8) * 8 + binaryInputNCHW = binaryInputNCHW[:,:,:pos_len*pos_len] + binaryInputNCHW = np.reshape(binaryInputNCHW, ( + binaryInputNCHW.shape[0], binaryInputNCHW.shape[1], pos_len, pos_len + )).astype(np.float32) + + assert binaryInputNCHW.shape[1] == num_bin_features + assert globalInputNC.shape[1] == num_global_features + + num_samples = binaryInputNCHW.shape[0] + # Just discard stuff that doesn't divide evenly + num_whole_steps = num_samples // (batch_size * world_size) + + logging.info(f"Beginning {npz_file} with {num_whole_steps * world_size} usable batches, my rank is {rank}") + for n in range(num_whole_steps): + start = (n * world_size + rank) * batch_size + end = start + batch_size + + batch_binaryInputNCHW = torch.from_numpy(binaryInputNCHW[start:end]).to(device) + batch_globalInputNC = torch.from_numpy(globalInputNC[start:end]).to(device) + batch_policyTargetsNCMove = torch.from_numpy(policyTargetsNCMove[start:end]).to(device) + batch_globalTargetsNC = torch.from_numpy(globalTargetsNC[start:end]).to(device) + batch_scoreDistrN = torch.from_numpy(scoreDistrN[start:end]).to(device) + batch_valueTargetsNCHW = torch.from_numpy(valueTargetsNCHW[start:end]).to(device) + if include_meta: + batch_metadataInputNC = torch.from_numpy(metadataInputNC[start:end]).to(device) + + (batch_binaryInputNCHW, batch_globalInputNC) = apply_history_matrices( + model_config, batch_binaryInputNCHW, batch_globalInputNC, batch_globalTargetsNC, h_base, h_builder + ) + + if randomize_symmetries: + symm = int(rand.integers(0,8)) + batch_binaryInputNCHW = apply_symmetry(batch_binaryInputNCHW, symm) + batch_policyTargetsNCMove = apply_symmetry_policy(batch_policyTargetsNCMove, symm, pos_len) + batch_valueTargetsNCHW = apply_symmetry(batch_valueTargetsNCHW, symm) + batch_binaryInputNCHW = batch_binaryInputNCHW.contiguous() + batch_policyTargetsNCMove = batch_policyTargetsNCMove.contiguous() + batch_valueTargetsNCHW = batch_valueTargetsNCHW.contiguous() + + if include_meta: + batch = dict( + binaryInputNCHW = batch_binaryInputNCHW, + globalInputNC = batch_globalInputNC, + policyTargetsNCMove = batch_policyTargetsNCMove, + globalTargetsNC = batch_globalTargetsNC, + scoreDistrN = batch_scoreDistrN, + valueTargetsNCHW = batch_valueTargetsNCHW, + metadataInputNC = batch_metadataInputNC, + ) + else: + batch = dict( + binaryInputNCHW = batch_binaryInputNCHW, + globalInputNC = batch_globalInputNC, + policyTargetsNCMove = batch_policyTargetsNCMove, + globalTargetsNC = batch_globalTargetsNC, + scoreDistrN = batch_scoreDistrN, + valueTargetsNCHW = batch_valueTargetsNCHW, + ) + yield batch + + +def apply_symmetry_policy(tensor, symm, pos_len): + """Same as apply_symmetry but also handles the pass index""" + batch_size = tensor.shape[0] + channels = tensor.shape[1] + tensor_without_pass = tensor[:,:,:-1].view((batch_size, channels, pos_len, pos_len)) + tensor_transformed = apply_symmetry(tensor_without_pass, symm) + return torch.cat(( + tensor_transformed.reshape(batch_size, channels, pos_len*pos_len), + tensor[:,:,-1:] + ), dim=2) + +def apply_symmetry(tensor, symm): + """ + Apply a symmetry operation to the given tensor. + + Args: + tensor (torch.Tensor): Tensor to be rotated. (..., W, W) + symm (int): + 0, 1, 2, 3: Rotation by symm * pi / 2 radians. + 4, 5, 6, 7: Mirror symmetry on top of rotation. + """ + assert tensor.shape[-1] == tensor.shape[-2] + + if symm == 0: + return tensor + if symm == 1: + return tensor.transpose(-2, -1).flip(-2) + if symm == 2: + return tensor.flip(-1).flip(-2) + if symm == 3: + return tensor.transpose(-2, -1).flip(-1) + if symm == 4: + return tensor.transpose(-2, -1) + if symm == 5: + return tensor.flip(-1) + if symm == 6: + return tensor.transpose(-2, -1).flip(-1).flip(-2) + if symm == 7: + return tensor.flip(-2) + + +def build_history_matrices(model_config: modelconfigs.ModelConfig, device): + num_bin_features = modelconfigs.get_num_bin_input_features(model_config) + assert num_bin_features == 22, "Currently this code is hardcoded for this many features" + + h_base = torch.diag( + torch.tensor( + [ + 1.0, # 0 + 1.0, # 1 + 1.0, # 2 + 1.0, # 3 + 1.0, # 4 + 1.0, # 5 + 1.0, # 6 + 1.0, # 7 + 1.0, # 8 + 0.0, # 9 Location of move 1 turn ago + 0.0, # 10 Location of move 2 turns ago + 0.0, # 11 Location of move 3 turns ago + 0.0, # 12 Location of move 4 turns ago + 0.0, # 13 Location of move 5 turns ago + 1.0, # 14 Ladder-threatened stone + 0.0, # 15 Ladder-threatened stone, 1 turn ago + 0.0, # 16 Ladder-threatened stone, 2 turns ago + 1.0, # 17 + 1.0, # 18 + 1.0, # 19 + 1.0, # 20 + 1.0, # 21 + ], + device=device, + requires_grad=False, + ) + ) + # Because we have ladder features that express past states rather than past diffs, + # the most natural encoding when we have no history is that they were always the + # same, rather than that they were all zero. So rather than zeroing them we have no + # history, we add entries in the matrix to copy them over. + # By default, without history, the ladder features 15 and 16 just copy over from 14. + h_base[14, 15] = 1.0 + h_base[14, 16] = 1.0 + + h0 = torch.zeros(num_bin_features, num_bin_features, device=device, requires_grad=False) + # When have the prev move, we enable feature 9 and 15 + h0[9, 9] = 1.0 # Enable 9 -> 9 + h0[14, 15] = -1.0 # Stop copying 14 -> 15 + h0[14, 16] = -1.0 # Stop copying 14 -> 16 + h0[15, 15] = 1.0 # Enable 15 -> 15 + h0[15, 16] = 1.0 # Start copying 15 -> 16 + + h1 = torch.zeros(num_bin_features, num_bin_features, device=device, requires_grad=False) + # When have the prevprev move, we enable feature 10 and 16 + h1[10, 10] = 1.0 # Enable 10 -> 10 + h1[15, 16] = -1.0 # Stop copying 15 -> 16 + h1[16, 16] = 1.0 # Enable 16 -> 16 + + h2 = torch.zeros(num_bin_features, num_bin_features, device=device, requires_grad=False) + h2[11, 11] = 1.0 + + h3 = torch.zeros(num_bin_features, num_bin_features, device=device, requires_grad=False) + h3[12, 12] = 1.0 + + h4 = torch.zeros(num_bin_features, num_bin_features, device=device, requires_grad=False) + h4[13, 13] = 1.0 + + # (1, n_bin, n_bin) + h_base = h_base.reshape((1, num_bin_features, num_bin_features)) + # (5, n_bin, n_bin) + h_builder = torch.stack((h0, h1, h2, h3, h4), dim=0) + + return (h_base, h_builder) + + +def apply_history_matrices(model_config, batch_binaryInputNCHW, batch_globalInputNC, batch_globalTargetsNC, h_base, h_builder): + num_global_features = modelconfigs.get_num_global_input_features(model_config) + # include_history = batch_globalTargetsNC[:,36:41] + should_stop_history = torch.rand_like(batch_globalTargetsNC[:,36:41]) >= 0.98 + include_history = (torch.cumsum(should_stop_history,axis=1,dtype=torch.float32) <= 0.1).to(torch.float32) + + # include_history: (N, 5) + # bi * ijk -> bjk, (N, 5) * (5, n_bin, n_bin) -> (N, n_bin, n_bin) + h_matrix = h_base + torch.einsum("bi,ijk->bjk", include_history, h_builder) + + + # batch_binaryInputNCHW: (N, n_bin_in, 19, 19) + # h_matrix: (N, n_bin_in, n_bin_out) + # Result: (N, n_bin_out, 19, 19) + batch_binaryInputNCHW = torch.einsum("bijk,bil->bljk", batch_binaryInputNCHW, h_matrix) + + # First 5 global input features exactly correspond to include_history, pointwise multiply to + # enable/disable them + batch_globalInputNC = batch_globalInputNC * torch.nn.functional.pad( + include_history, ((0, num_global_features - include_history.shape[1])), value=1.0 + ) + return batch_binaryInputNCHW, batch_globalInputNC diff --git a/training/scripts/dated/20241110-122511/diff.txt b/training/scripts/dated/20241110-122511/diff.txt new file mode 100644 index 0000000..a187cbd --- /dev/null +++ b/training/scripts/dated/20241110-122511/diff.txt @@ -0,0 +1,47 @@ +diff --git a/python/selfplay/shuffle.sh b/python/selfplay/shuffle.sh +index 0ac0124b..14c3b080 100755 +--- a/python/selfplay/shuffle.sh ++++ b/python/selfplay/shuffle.sh +@@ -1,8 +1,8 @@ + #!/bin/bash -eu + set -o pipefail + { +-#Shuffles and copies selfplay training from selfplay/ to shuffleddata/current/ +-#Should be run periodically. ++# Shuffles and copies selfplay training from selfplay/ to shuffleddata/current/ ++# Should be run periodically. + + if [[ $# -lt 4 ]] + then +@@ -96,10 +96,10 @@ else + fi + #set +x + +-#Just in case, give a little time for nfs ++# Just in case, give a little time for nfs + sleep 10 + +-#rm if it already exists ++# rm if it already exists + rm -f "$BASEDIR"/shuffleddata/current_tmp + + ln -s $OUTDIR "$BASEDIR"/shuffleddata/current_tmp +@@ -107,14 +107,14 @@ mv -Tf "$BASEDIR"/shuffleddata/current_tmp "$BASEDIR"/shuffleddata/current + + # CLEANUP --------------------------------------------------------------- + +-#Among shuffled dirs older than 2 hours, remove all but the most recent 5 of them. +-#This should be VERY conservative and allow plenty of time for the training to switch +-#to newer ones as they get generated. ++# Among shuffled dirs older than 2 hours, remove all but the most recent 5 of them. ++# This should be VERY conservative and allow plenty of time for the training to switch ++# to newer ones as they get generated. + echo "Cleaning up any old dirs" + find "$BASEDIR"/shuffleddata/ -mindepth 1 -maxdepth 1 -type d -mmin +120 | sort | head -n -5 | xargs --no-run-if-empty rm -r + + echo "Finished shuffle at" $(date "+%Y-%m-%d %H:%M:%S") +-#Make a little space between shuffles ++# Make a little space between shuffles + echo "" + echo "" + diff --git a/training/scripts/dated/20241110-122511/diffstaged.txt b/training/scripts/dated/20241110-122511/diffstaged.txt new file mode 100644 index 0000000..10d775f --- /dev/null +++ b/training/scripts/dated/20241110-122511/diffstaged.txt @@ -0,0 +1,19665 @@ +diff --git a/cpp/KataGoData/opencltuning/tune11_gpuNVIDIAGeForceRTX4060LaptopGPU_x19_y19_c320_mv10.txt b/cpp/KataGoData/opencltuning/tune11_gpuNVIDIAGeForceRTX4060LaptopGPU_x19_y19_c320_mv10.txt +new file mode 100644 +index 00000000..5b66d67d +--- /dev/null ++++ b/cpp/KataGoData/opencltuning/tune11_gpuNVIDIAGeForceRTX4060LaptopGPU_x19_y19_c320_mv10.txt +@@ -0,0 +1,33 @@ ++VERSION=11 ++#canUseFP16Storage ++1 ++#canUseFP16Compute ++0 ++#canUseFP16TensorCores ++1 ++#canUseFP16TensorCoresFor1x1 ++0 ++#shouldUseFP16Storage ++1 ++#shouldUseFP16Compute ++0 ++#shouldUseFP16TensorCores ++1 ++#shouldUseFP16TensorCoresFor1x1 ++0 ++#xGemmDirect ++WGD=32 MDIMCD=8 NDIMCD=8 MDIMAD=16 NDIMBD=16 KWID=2 VWMD=2 VWND=2 PADA=1 PADB=1 ++#xGemm ++MWG=64 NWG=64 KWG=16 MDIMC=16 NDIMC=8 MDIMA=16 NDIMB=8 KWI=2 VWM=4 VWN=4 STRM=0 STRN=0 SA=1 SB=1 ++#xGemm16 ++MWG=64 NWG=64 KWG=16 MDIMC=16 NDIMC=8 MDIMA=16 NDIMB=8 KWI=2 VWM=4 VWN=4 STRM=0 STRN=0 SA=1 SB=1 ++#hGemmWmma ++MWG=64 NWG=64 KWG=16 MWAVE=32 NWAVE=8 MWARP=32 NWARP=8 VWM=2 VWN=2 SA=0 SB=0 ++#hGemmWmmaNCHW ++MWG=16 NWG=16 KWG=16 MWAVE=16 NWAVE=16 MWARP=16 NWARP=16 VWM=1 VWN=2 SB=0 ++#conv3x3 ++INTILE_XSIZE=6 INTILE_YSIZE=6 OUTTILE_XSIZE=4 OUTTILE_YSIZE=4 transLocalSize0=128 transLocalSize1=1 untransLocalSize0=8 untransLocalSize1=4 untransLocalSize2=2 ++#conv5x5 ++INTILE_XSIZE=6 INTILE_YSIZE=6 OUTTILE_XSIZE=2 OUTTILE_YSIZE=2 transLocalSize0=128 transLocalSize1=1 untransLocalSize0=8 untransLocalSize1=4 untransLocalSize2=2 ++#gPool ++XYSTRIDE=16 CHANNELSTRIDE=2 BATCHSTRIDE=2 +diff --git a/cpp/KataGoData/opencltuning/tune11_gpuNVIDIAGeForceRTX4060LaptopGPU_x19_y19_c384_mv14.txt b/cpp/KataGoData/opencltuning/tune11_gpuNVIDIAGeForceRTX4060LaptopGPU_x19_y19_c384_mv14.txt +new file mode 100644 +index 00000000..71421834 +--- /dev/null ++++ b/cpp/KataGoData/opencltuning/tune11_gpuNVIDIAGeForceRTX4060LaptopGPU_x19_y19_c384_mv14.txt +@@ -0,0 +1,33 @@ ++VERSION=11 ++#canUseFP16Storage ++1 ++#canUseFP16Compute ++0 ++#canUseFP16TensorCores ++1 ++#canUseFP16TensorCoresFor1x1 ++0 ++#shouldUseFP16Storage ++1 ++#shouldUseFP16Compute ++0 ++#shouldUseFP16TensorCores ++1 ++#shouldUseFP16TensorCoresFor1x1 ++0 ++#xGemmDirect ++WGD=32 MDIMCD=8 NDIMCD=8 MDIMAD=8 NDIMBD=8 KWID=8 VWMD=2 VWND=4 PADA=1 PADB=1 ++#xGemm ++MWG=64 NWG=64 KWG=16 MDIMC=16 NDIMC=16 MDIMA=16 NDIMB=16 KWI=2 VWM=4 VWN=4 STRM=0 STRN=0 SA=1 SB=1 ++#xGemm16 ++MWG=64 NWG=64 KWG=16 MDIMC=16 NDIMC=16 MDIMA=16 NDIMB=16 KWI=2 VWM=4 VWN=4 STRM=0 STRN=0 SA=1 SB=1 ++#hGemmWmma ++MWG=64 NWG=64 KWG=64 MWAVE=16 NWAVE=16 MWARP=16 NWARP=16 VWM=2 VWN=2 SA=0 SB=0 ++#hGemmWmmaNCHW ++MWG=16 NWG=16 KWG=16 MWAVE=16 NWAVE=16 MWARP=16 NWARP=16 VWM=1 VWN=2 SB=0 ++#conv3x3 ++INTILE_XSIZE=6 INTILE_YSIZE=6 OUTTILE_XSIZE=4 OUTTILE_YSIZE=4 transLocalSize0=64 transLocalSize1=1 untransLocalSize0=16 untransLocalSize1=2 untransLocalSize2=2 ++#conv5x5 ++INTILE_XSIZE=6 INTILE_YSIZE=6 OUTTILE_XSIZE=2 OUTTILE_YSIZE=2 transLocalSize0=64 transLocalSize1=1 untransLocalSize0=16 untransLocalSize1=2 untransLocalSize2=2 ++#gPool ++XYSTRIDE=32 CHANNELSTRIDE=1 BATCHSTRIDE=2 +diff --git a/cpp/README.txt b/cpp/README.txt +new file mode 100644 +index 00000000..fb82db4b +--- /dev/null ++++ b/cpp/README.txt +@@ -0,0 +1,118 @@ ++KataGo v1.15.1 ++https://github.com/lightvector/KataGo ++ ++For neural nets from the latest run, download from here: ++https://katagotraining.org/ ++For nets from earlier runs, see: ++https://katagoarchive.org/ ++ ++For the human SL net (b18c384nbt-humanv0.bin.gz), it was released with: ++https://github.com/lightvector/KataGo/releases/tag/v1.15.0 ++ ++For differences between this version and older versions, see releases page at ++https://github.com/lightvector/KataGo/releases/ ++ ++----------------------------------------------------- ++USAGE: ++----------------------------------------------------- ++KataGo is just an engine and does not have its own graphical interface. So generally you will want to use KataGo along with a GUI or analysis program. ++(https://github.com/lightvector/KataGo#guis) ++ ++FIRST: Run a command like this to make sure KataGo is working, with the neural net file you downloaded. On OpenCL, it will also tune for your GPU. ++ ++./katago.exe benchmark # if you have default_gtp.cfg and default_model.bin.gz ++./katago.exe benchmark -model .bin.gz # if you have default_gtp.cfg ++./katago.exe benchmark -model .bin.gz -config gtp_custom.cfg # use this .bin.gz neural net and this .cfg file ++ ++It will tell you a good number of threads. Edit your .cfg file and set "numSearchThreads" to that many to get best performance. ++ ++OR: Run this command to have KataGo generate a custom gtp config for you based on answering some questions: ++ ++./katago.exe genconfig -model .bin.gz -output gtp_custom.cfg ++ ++NEXT: A command like this will run KataGo's engine. This is the command to give to your [GUI or analysis program](#guis) so that it can run KataGo. ++ ++./katago.exe gtp # if you have default_gtp.cfg and default_model.bin.gz ++./katago.exe gtp -model .bin.gz # if you have default_gtp.cfg ++./katago.exe gtp -model .bin.gz -config gtp_custom.cfg # use this .bin.gz neural net and this .cfg file ++ ++You may need to specify different paths when entering KataGo's command for a GUI program, e.g.: ++ ++path/to/katago.exe gtp -model path/to/.bin.gz ++path/to/katago.exe gtp -model path/to/.bin.gz -config path/to/gtp_custom.cfg ++ ++KataGo should be able to work with any GUI program that supports GTP, as well as any analysis program that supports Leela Zero's `lz-analyze` command, such as Lizzie (https://github.com/featurecat/lizzie) or Sabaki (https://sabaki.yichuanshen.de/). ++ ++NOTE for Windows users: ++If you encounter errors due to a missing "msvcp140.dll" or "msvcp140_1.dll" or "msvcp140_2.dll" or "vcruntime140.dll" or similar, you need to download and install the Microsoft Visual C++ Redistributable, here: ++https://www.microsoft.com/en-us/download/details.aspx?id=48145 ++If this is for a 64-bit Windows version of KataGo, these dll files have already been included for you, otherwise you will need to install them yourself. On a 64-bit Windows version, there is a rare chance that you may need to delete them if you already have it installed yourself separately and the pre-included files are actually causing problems running KataGo. ++ ++----------------------------------------------------- ++HUMAN-STYLE PLAY AND ANALYSIS: ++----------------------------------------------------- ++ ++You can also have KataGo imitate human play if you download the human SL model b18c384nbt-humanv0.bin.gz from https://github.com/lightvector/KataGo/releases/tag/v1.15.0, and run a command like the following, providing both a model and the human SL model: ++ ++./katago.exe gtp -model .bin.gz -human-model b18c384nbt-humanv0.bin.gz -config gtp_human5k_example.cfg ++ ++The gtp_human5k_example.cfg configures KataGo to imitate 5-kyu-level players. You can change it to imitate other ranks too, as well as to do many more things, including making KataGo play in a human style but still at a strong level or analyze in interesting ways. Read the config file itself for documentation on some of these possibilities! ++ ++And see also this guide to using the human SL model, which is written from the perspective of the JSON-based analysis engine mentioned below, but is also applicable to gtp as well. ++https://github.com/lightvector/KataGo/blob/master/docs/Analysis_Engine.md#human-sl-analysis-guide ++ ++----------------------------------------------------- ++OTHER THINGS YOU CAN DO: ++----------------------------------------------------- ++ ++Run a JSON-based analysis engine (https://github.com/lightvector/KataGo/blob/master/docs/Analysis_Engine.md) that can do efficient batched evaluations for a backend Go service: ++ ++./katago.exe analysis -model .gz -config .cfg ++ ++Run a high-performance match engine that will play a pool of bots against each other sharing the same GPU batches and CPUs with each other: ++ ++./katago.exe match -config .cfg -log-file match.log -sgf-output-dir ++ ++Force OpenCL tuner to re-tune: ++ ++./katago.exe tuner -config .cfg ++ ++Print version: ++ ++./katago.exe version ++ ++ ++----------------------------------------------------- ++OPENCL VS CUDA VS EIGEN: ++----------------------------------------------------- ++Explanation of the various versions available at https://github.com/lightvector/KataGo/releases ++ ++OpenCL - Use this if you have a modern GPU and want the easiest setup. ++ ++TensorRT, CUDA11 - Try one of these if you have a top-end NVIDIA GPU, are willing to do some more technical setup work, and care about getting every bit of performance. It MIGHT OR MIGHT NOT be faster than OpenCL! You have to test it on your specific GPU. TensorRT requires CUDA11 as well, and is likely to be the fastest. ++ ++Eigen AVX2 - Use this if you have no good GPU, but you have an Intel or AMD CPU from the last several years. This is a pure CPU version of KataGo. ++ ++Eigen - Use this if you don't have a GPU or your GPU is too old to work, and your CPU turns out not to support AVX2 or FMA. This is a pure CPU version for KataGo for ++This is the pure CPU version of KataGo, with no special instructions, which should hopefully run just about anywhere. ++ ++ ++----------------------------------------------------- ++TUNING FOR PERFORMANCE: ++----------------------------------------------------- ++You will very likely want to tune some of the parameters in `default_gtp.cfg` for your system for good performance, including the number of threads, fp16 usage, NN cache size, pondering settings, and so on. You can also adjust things like KataGo's resign threshold or utility function. Most of the relevant parameters should be be reasonably well documented directly inline in that config. ++ ++There are other a few notes about usage and performance at : https://github.com/lightvector/KataGo ++ ++----------------------------------------------------- ++TROUBLESHOOTING: ++----------------------------------------------------- ++Some common issues are described here: ++https://github.com/lightvector/KataGo#common-causes-of-errors ++ ++Or, feel free to hop into the Leela Zero discord chat, which has become a general chatroom for a variety of computer Go hobbyists and users, and which you can often find people willing to help. ++https://discord.gg/fhDHgfk ++ ++ ++ ++ +diff --git a/cpp/analysis_example.cfg b/cpp/analysis_example.cfg +new file mode 100644 +index 00000000..ca648321 +--- /dev/null ++++ b/cpp/analysis_example.cfg +@@ -0,0 +1,402 @@ ++# Config for KataGo C++ Analysis engine, i.e. "./katago.exe analysis" ++ ++# Example config for C++ (non-python) analysis engine ++ ++# SEE NOTES ABOUT PERFORMANCE AND MEMORY USAGE IN gtp_example.cfg ++# SEE NOTES ABOUT numSearchThreads AND OTHER IMPORTANT PARAMS BELOW! ++ ++# Logs------------------------------------------------------------------------------------ ++ ++# Where to output log? ++logDir = analysis_logs # Each run of KataGo will log to a separate file in this dir ++# logDirDated = analysis_logs # Use this instead of logDir to also write separate dated subdirs ++# logFile = analysis.log # Use this instead of logDir to just specify a single file directly ++# logToStderr = true # Echo everything output to log file to stderr as well ++# logAllRequests = false # Log all input lines received to the analysis engine. ++# logAllResponses = false # Log all lines output to stdout from the analysis engine. ++# logErrorsAndWarnings = true # Log all lines output to stdout from the analysis engine that are errors and warnings ++# logSearchInfo = false # Log debug info for every search performed ++ ++# Report a warning whenever a query contains a field that is unused. Helps to guard against ++# typos when writing code that queries the analysis engine. ++# warnUnusedFields = true ++ ++# Analysis------------------------------------------------------------------------------------ ++ ++# Controls the number of moves after the first move in a variation. ++# analysisPVLen = 15 ++ ++# Report winrates for analysis as (BLACK|WHITE|SIDETOMOVE). ++reportAnalysisWinratesAs = BLACK ++ ++# Larger values will make KataGo explore the top move(s) less deeply and accurately, ++# but explore and give evaluations to a greater variety of moves. ++# An extreme value like 1 will distribute many playouts across every move on the board, even very bad moves. ++# NOTE: defaults to 0.04, under the presumption that the analysis engine will be used mainly for analysis. ++# If you are intending to use the analysis engine to also play games and you want to maximize playing strength, ++# set this to 0.0 either in this config or in the overrides. ++# wideRootNoise = 0.04 ++ ++# Try to limit the effect of possible bad or bogus move sequences in the ++# history leading to this position from affecting KataGo's move predictions. ++# ignorePreRootHistory = true ++ ++# Bot behavior--------------------------------------------------------------------------------------- ++ ++# Handicap ------------- ++ ++# Assume that if black makes many moves in a row right at the start of the game, then the game is a handicap game. ++# This is necessary on some servers and for some GUIs and also when initializing from many SGF files, which may ++# set up a handicap game using repeated GTP "play" commands for black rather than GTP "place_free_handicap" commands. ++# However, it may also lead to incorrect understanding of komi if whiteHandicapBonus is used and a server does NOT ++# have such a practice. ++# Defaults to true! Uncomment and set to false to disable this behavior. ++# assumeMultipleStartingBlackMovesAreHandicap = true ++ ++# Passing and cleanup ------------- ++ ++# Make the bot never assume that its pass will end the game, even if passing would end and "win" under Tromp-Taylor rules. ++# Usually this is a good idea when using it for analysis or playing on servers where scoring may be implemented non-tromp-taylorly. ++# Defaults to true! Uncomment and set to false to disable this. ++# conservativePass = true ++ ++# When using territory scoring, self-play games continue beyond two passes with special cleanup ++# rules that may be confusing for human players. This option prevents the special cleanup phases from being ++# reachable when using the bot for GTP play. ++# Defaults to true! Uncomment and set to false if you want KataGo to be able to enter special cleanup. ++# For example, if you are testing it against itself, or against another bot that has precisely implemented the rules ++# documented at https://lightvector.github.io/KataGo/rules.html ++# preventCleanupPhase = true ++ ++# Search limits----------------------------------------------------------------------------------- ++ ++# By default, if NOT specified in an individual request, limit maximum number of root visits per search to this much ++maxVisits = 500 ++# If provided, cap search time at this many seconds ++# maxTime = 60 ++ ++# Search threads, batching, GPUs-------------------------------------------------------------------------- ++ ++# Try a configuration like this if you only expect the engine to be handling a few queries at a time and you want ++# individual queries to return more quickly, and are okay with the results being a bit lower-quality and the overall ++# peak throughput on queries to be lower. ++numAnalysisThreads = 2 ++numSearchThreadsPerAnalysisThread = 16 ++ ++# Try a configuration like this if you expect to be sending large numbers of queries at a time, and want to maximize ++# total throughput and also the evaluation quality of all the queries and you never care about the response latency ++# of the individual queries, only the throughput as a whole. ++# numAnalysisThreads = 32 ++# numSearchThreadsPerAnalysisThread = 1 ++ ++# You will want to increase one or both numbers if you have a powerful GPU, and possibly decrease one or both if you ++# have a very weak GPU, and play with the balance between them depending on your use case. ++# Read the explanation below to understand how to set these parameters: ++ ++# EXPLANATION: ++# numAnalysisThreads: the number of POSITIONS to be able to search in parallel. ++# numSearchThreadsPerAnalysisThread: the number of threads to use in the tree search for EACH position. ++# (older analysis configs might just have 'numSearchThreads', this is an alias for 'numSearchThreadsPerAnalysisThread') ++ ++# Therefore, the total number of search threads that may be active at a given time could be as large as the product: ++# numAnalysisThreads * numSearchThreadsPerAnalysisThread ++ ++# Searching more positions in parallel is more efficient since the different threads aren't conflicting with each ++# other on the same MCTS search tree. Using multiple threads on the same search will both make things slower ++# and weaken the search (holding playouts fixed) due to out of date statistics on nodes and suboptimal exploration, ++# although the cost is minor for only 2,4,8 threads. ++ ++# So unlike in GTP, which only ever searches one position at a time and where therefore you might as well make ++# numSearchThreads as large as possible, in the analysis engine you often want you often want to keep numSearchThreads small, ++# and instead parallelize across positions, so you can reduce conflict between threads and improve the overall throughput ++# and strength of the search. ++ ++# But obviously you only get the benefit of parallelization across positions when you actually have lots of positions ++# that you are querying at once! For example, setting numAnalysisThreads = 8 is useless if you only ever send one or two ++# queries at a time! ++ ++# Therefore: ++# * If you plan to use the analysis engine only for batch processing large numbers of positions, ++# it's preferable to numSearchThreadsPerAnalysisThread to only a small number (e.g. 1,2,4) and use a higher numAnalysisThreads. ++# * But if you sometimes plan to query the analysis engine for single positions, or otherwise in smaller quantities ++# than -num-analysis-threads, or if you plan to be user-interactive such that the response time on some individual ++# analysis requests is important to keep low, then set numSearchThreadsPerAnalysisThread to a larger number and use ++# a lower numAnalysisThreads. That way, individual searches complete faster due to having more threads on each one. ++ ++# For 19x19 boards, weaker GPUs probably want a TOTAL number of threads (numAnalysisThreads * numSearchThreadsPerAnalysisThread) ++# between 4 and 32. Mid-tier GPUs probably between 16 and 64. Strong GPUs probably between 32 and 256. ++# But there's no substitute for experimenting and seeing what's best for your hardware and your usage case. ++ ++# Keep in mind that the number of threads you want does NOT necessarily have much to do with how many cores you have on your ++# system. The optimal may easily exceed the number of cores! GPU batching is (usually) the dominant consideration. ++ ++# ------------- ++ ++# nnMaxBatchSize is the max number of positions to send to a single GPU at once. Generally, it should be the case that: ++# (number of GPUs you will use * nnMaxBatchSize) >= (numSearchThreads * num-analysis-threads) ++# That way, when each threads tries to request a GPU eval, your batch size summed across GPUs is large enough to handle them ++# all at once. However, it can be sensible to set this a little smaller if you are limited on GPU memory, ++# too large a number may fail if the GPU doesn't have enough memory. ++nnMaxBatchSize = 64 ++ ++# Uncomment and set these smaller if you are going to use the analysis engine EXCLUSIVELY for smaller boards (or plan to ++# run multiple instances, with some instances only handling smaller boards). It should improve performance. ++# It may also mean you can use more threads profitably. ++# maxBoardXSizeForNNBuffer = 19 ++# maxBoardYSizeForNNBuffer = 19 ++ ++# Uncomment and set this to true if you are going to use the analysis engine EXCLUSIVELY for exactly the board size ++# specified by maxBoardXSizeForNNBuffer and maxBoardYSizeForNNBuffer. It may slightly improve performance on some GPUs. ++# requireMaxBoardSize = true ++ ++# TO USE MULTIPLE GPUS: ++# Uncomment and set this to the number of GPUs you have and/or would like to use... ++# AND if it is more than 1, uncomment the appropriate CUDA or OpenCL section below. ++# numNNServerThreadsPerModel = 1 ++ ++# Other General GPU Settings------------------------------------------------------------------------------- ++ ++# Cache up to 2 ** this many neural net evaluations in case of transpositions in the tree. ++nnCacheSizePowerOfTwo = 23 ++# Size of mutex pool for nnCache is 2 ** this ++nnMutexPoolSizePowerOfTwo = 17 ++# Randomize board orientation when running neural net evals? ++nnRandomize = true ++ ++ ++# TENSORRT GPU settings-------------------------------------- ++# These only apply when using the TENSORRT version of KataGo. ++ ++# IF USING ONE GPU: optionally uncomment and change this if the GPU you want to use turns out to be not device 0 ++# trtDeviceToUse = 0 ++ ++# IF USING TWO GPUS: Uncomment these two lines (AND set numNNServerThreadsPerModel above): ++# trtDeviceToUseThread0 = 0 # change this if the first GPU you want to use turns out to be not device 0 ++# trtDeviceToUseThread1 = 1 # change this if the second GPU you want to use turns out to be not device 1 ++ ++# IF USING THREE GPUS: Uncomment these three lines (AND set numNNServerThreadsPerModel above): ++# trtDeviceToUseThread0 = 0 # change this if the first GPU you want to use turns out to be not device 0 ++# trtDeviceToUseThread1 = 1 # change this if the second GPU you want to use turns out to be not device 1 ++# trtDeviceToUseThread2 = 2 # change this if the third GPU you want to use turns out to be not device 2 ++ ++# You can probably guess the pattern if you have four, five, etc. GPUs. ++ ++ ++# CUDA-specific GPU settings-------------------------------------- ++# These only apply when using the CUDA version of KataGo. ++ ++# IF USING ONE GPU: optionally uncomment and change this if the GPU you want to use turns out to be not device 0 ++# cudaDeviceToUse = 0 ++ ++# IF USING TWO GPUS: Uncomment these two lines (AND set numNNServerThreadsPerModel above): ++# cudaDeviceToUseThread0 = 0 # change this if the first GPU you want to use turns out to be not device 0 ++# cudaDeviceToUseThread1 = 1 # change this if the second GPU you want to use turns out to be not device 1 ++ ++# IF USING THREE GPUS: Uncomment these three lines (AND set numNNServerThreadsPerModel above): ++# cudaDeviceToUseThread0 = 0 # change this if the first GPU you want to use turns out to be not device 0 ++# cudaDeviceToUseThread1 = 1 # change this if the second GPU you want to use turns out to be not device 1 ++# cudaDeviceToUseThread2 = 2 # change this if the third GPU you want to use turns out to be not device 2 ++ ++# You can probably guess the pattern if you have four, five, etc. GPUs. ++ ++# KataGo will automatically use FP16 or not based on the compute capability of your NVIDIA GPU. If you ++# want to try to force a particular behavior though you can uncomment these lines and change them ++# to "true" or "false". E.g. it's using FP16 but on your card that's giving an error, or it's not using ++# FP16 but you think it should. ++# cudaUseFP16 = auto ++# cudaUseNHWC = auto ++ ++ ++# OpenCL-specific GPU settings-------------------------------------- ++# These only apply when using the OpenCL version of KataGo. ++ ++# Uncomment to tune OpenCL for every board size separately, rather than only the largest possible size ++# openclReTunePerBoardSize = true ++ ++# IF USING ONE GPU: optionally uncomment and change this if the best device to use is guessed incorrectly. ++# The default behavior tries to guess the 'best' GPU or device on your system to use, usually it will be a good guess. ++# openclDeviceToUse = 0 ++ ++# IF USING TWO GPUS: Uncomment these two lines and replace X and Y with the device ids of the devices you want to use. ++# It might NOT be 0 and 1, some computers will have many OpenCL devices. You can see what the devices are when ++# KataGo starts up - it should print or log all the devices it finds. ++# (AND also set numNNServerThreadsPerModel above) ++# openclDeviceToUseThread0 = X ++# openclDeviceToUseThread1 = Y ++ ++# IF USING THREE GPUS: Uncomment these three lines and replace X and Y and Z with the device ids of the devices you want to use. ++# It might NOT be 0 and 1 and 2, some computers will have many OpenCL devices. You can see what the devices are when ++# KataGo starts up - it should print or log all the devices it finds. ++# (AND also set numNNServerThreadsPerModel above) ++# openclDeviceToUseThread0 = X ++# openclDeviceToUseThread1 = Y ++# openclDeviceToUseThread2 = Z ++ ++# You can probably guess the pattern if you have four, five, etc. GPUs. ++ ++# KataGo will automatically use FP16 or not based on testing your GPU during tuning. If you ++# want to try to force a particular behavior though you can uncomment this lines and change it ++# to "true" or "false". This is a fairly blunt setting - more detailed settings are testable ++# by rerunning the tuner with various arguments. ++# openclUseFP16 = auto ++ ++ ++# Eigen-specific settings-------------------------------------- ++# These only apply when using the Eigen (pure CPU) version of KataGo. ++ ++# This is the number of CPU threads for evaluating the neural net on the Eigen backend. ++# It defaults to min(numAnalysisThreads * numSearchThreadsPerAnalysisThread, numCPUCores). ++# numEigenThreadsPerModel = X ++ ++ ++# Misc Behavior -------------------- ++ ++# If the board is symmetric, search only one copy of each equivalent move. Attempts to also account for ko/superko, will not theoretically perfect for superko. ++# Uncomment and set to false to disable this. ++# rootSymmetryPruning = true ++ ++# Uncomment and set to true to make KataGo avoid a particular joseki that some KataGo nets misevaluate, ++# and also to improve opening diversity versus some particular other bots that like to play it all the time. ++# avoidMYTDaggerHack = false ++ ++# Have KataGo mildly prefer to avoid playing the same joseki in every corner of the board. ++# Uncomment to set to a specific value. A small value like 0.005 should produce already a noticeable behavior change. ++# avoidRepeatedPatternUtility = 0.0 ++ ++# Enable some hacks that mitigate rare instances when passing messes up deeper searches. ++# enablePassingHacks = true ++ ++# Root move selection and biases------------------------------------------------------------------------------ ++# Uncomment and edit any of the below values to change them from their default. ++# Not all of these parameters are applicable to analysis, some are only used for actual play ++ ++# Temperature for the early game, randomize between chosen moves with this temperature ++# chosenMoveTemperatureEarly = 0.5 ++# Decay temperature for the early game by 0.5 every this many moves, scaled with board size. ++# chosenMoveTemperatureHalflife = 19 ++# At the end of search after the early game, randomize between chosen moves with this temperature ++# chosenMoveTemperature = 0.10 ++# Subtract this many visits from each move prior to applying chosenMoveTemperature ++# (unless all moves have too few visits) to downweight unlikely moves ++# chosenMoveSubtract = 0 ++# The same as chosenMoveSubtract but only prunes moves that fall below the threshold, does not affect moves above ++# chosenMovePrune = 1 ++ ++# Number of symmetries to sample (WITHOUT replacement) and average at the root ++# rootNumSymmetriesToSample = 1 ++ ++# Using LCB for move selection? ++# useLcbForSelection = true ++# How many stdevs a move needs to be better than another for LCB selection ++# lcbStdevs = 5.0 ++# Only use LCB override when a move has this proportion of visits as the top move ++# minVisitPropForLCB = 0.15 ++ ++# Internal params------------------------------------------------------------------------------ ++# Uncomment and edit any of the below values to change them from their default. ++ ++# Scales the utility of winning/losing ++# winLossUtilityFactor = 1.0 ++# Scales the utility for trying to maximize score ++# staticScoreUtilityFactor = 0.10 ++# dynamicScoreUtilityFactor = 0.30 ++# Adjust dynamic score center this proportion of the way towards zero, capped at a reasonable amount. ++# dynamicScoreCenterZeroWeight = 0.20 ++# dynamicScoreCenterScale = 0.75 ++# The utility of getting a "no result" due to triple ko or other long cycle in non-superko rulesets (-1 to 1) ++# noResultUtilityForWhite = 0.0 ++# The number of wins that a draw counts as, for white. (0 to 1) ++# drawEquivalentWinsForWhite = 0.5 ++ ++# Exploration constant for mcts ++# cpuctExploration = 1.0 ++# cpuctExplorationLog = 0.45 ++ ++# Parameters that control exploring more in volatile positions, exploring less in stable positions. ++# cpuctUtilityStdevPrior = 0.40 ++# cpuctUtilityStdevPriorWeight = 2.0 ++# cpuctUtilityStdevScale = 0.85 ++ ++# FPU reduction constant for mcts ++# fpuReductionMax = 0.2 ++# rootFpuReductionMax = 0.1 ++# fpuParentWeightByVisitedPolicy = true ++ ++# Parameters that control weighting of evals based on the net's own self-reported uncertainty. ++# useUncertainty = true ++# uncertaintyExponent = 1.0 ++# uncertaintyCoeff = 0.25 ++ ++# Explore using optimistic policy ++# rootPolicyOptimism = 0.2 ++# policyOptimism = 1.0 ++ ++# Amount to apply a downweighting of children with very bad values relative to good ones ++# valueWeightExponent = 0.25 ++ ++# Slight incentive for the bot to behave human-like with regard to passing at the end, filling the dame, ++# not wasting time playing in its own territory, etc, and not play moves that are equivalent in terms of ++# points but a bit more unfriendly to humans. ++# rootEndingBonusPoints = 0.5 ++ ++# Make the bot prune useless moves that are just prolonging the game to avoid losing yet ++# rootPruneUselessMoves = true ++ ++# Apply bias correction based on local pattern keys ++# subtreeValueBiasFactor = 0.45 ++# subtreeValueBiasWeightExponent = 0.85 ++ ++# Use graph search rather than tree search - identify and share search for transpositions. ++# useGraphSearch = true ++ ++# How much to shard the node table for search synchronization ++# nodeTableShardsPowerOfTwo = 16 ++# How many virtual losses to add when a thread descends through a node ++# numVirtualLossesPerThread = 1 ++ ++# Improve the quality of evals under heavy multithreading ++# useNoisePruning = true ++ ++ ++# Avoid SGF Patterns ------------------------------------------------------------------------------ ++# The parameters in this section provide a powerful way to customize KataGo to avoid moves that follow specific patterns ++# based on a set of provided SGF files loaded upon startup. Uncomment them to use this feature. ++# Additionally, if the SGF file contains the string %SKIP% in a comment on a move, that move will be ignored for this purpose. ++ ++# Load sgf files from this directory when the engine is started (ONLY on startup, will not reload unless engine is restarted) ++# avoidSgfPatternDirs = path/to/directory/with/sgfs/ ++# You can also surround the file path in double quotes if the file path contains trailing spaces or hash signs. ++# Within double quotes, backslashes are escape characters. ++# avoidSgfPatternDirs = "path/to/directory/with/sgfs/" ++ ++# Penalize this much utility per matching move. ++# Set this negative if you instead want to make KataGo favor the SGF patterns instead of penalizing it! ++# This number does not need to be large, even 0.001 will make a difference. Too-large values may lead to bad play. ++# avoidSgfPatternUtility = 0.001 ++ ++# Optional - load only the newest this many files ++# avoidSgfPatternMaxFiles = 20 ++ ++# Optional - Penalty is multiplied by this per each older SGF file, so that old sgf files matter less than newer ones. ++# avoidSgfPatternLambda = 0.90 ++ ++# Optional - pay attention only to moves that were made by players with this name. ++# For example you can set it to the name that your bot's past games will show up as in the SGF, so that the bot will only avoid repeating ++# moves that itself made in past games, not the moves that its opponents made. ++# avoidSgfPatternAllowedNames = my-ogs-bot-name1,my-ogs-bot-name2 ++ ++# Optional - Ignore any moves in SGF files that occurred before this turn number. ++# avoidSgfPatternMinTurnNumber = 0 ++ ++# For more avoid patterns: ++# You can also specify a second set of parameters, and a third, fourth, etc by numbering 2,3,4,... ++# avoidSgf2PatternDirs = ... ++# avoidSgf2PatternUtility = ... ++# avoidSgf2PatternMaxFiles = ... ++# avoidSgf2PatternLambda = ... ++# avoidSgf2PatternAllowedNames = ... ++# avoidSgf2PatternMinTurnNumber = ... ++ ++ ++ ++ +diff --git a/cpp/b28.cfg b/cpp/b28.cfg +new file mode 100644 +index 00000000..302e8a40 +--- /dev/null ++++ b/cpp/b28.cfg +@@ -0,0 +1,721 @@ ++# Configuration for KataGo C++ GTP engine ++ ++# Run the program using: `./katago.exe gtp` ++ ++# In this example config, when a parameter is given as a commented out value, ++# that value also is the default value, unless described otherwise. You can ++# uncomment it (remove the pound sign) and change it if you want. ++ ++# =========================================================================== ++# Running on an online server or in a real tournament or match ++# =========================================================================== ++# If you plan to run online or in a tournament, read through the "Rules" ++# section below for proper handling of komi, handicaps, end-of-game cleanup, ++# and other details. ++ ++# =========================================================================== ++# Notes about performance and memory usage ++# =========================================================================== ++# Important: For good performance, you will very likely want to tune the ++# "numSearchThreads" parameter in the Search limits section below! Run ++# "./katago benchmark" to test KataGo and to suggest a reasonable value ++# of this parameter. ++ ++# For multi-GPU systems, read "OpenCL GPU settings" or "CUDA GPU settings". ++# ++# When using OpenCL, verify that KataGo picks the correct device! Some systems ++# may have both an Intel CPU OpenCL and GPU OpenCL. If # KataGo picks the wrong ++# one, correct this by specifying "openclGpuToUse". ++# ++# Consider adjusting "maxVisits", "ponderingEnabled", "resignThreshold", and ++# other parameters depending on your intended usage. ++ ++# =========================================================================== ++# Command-line usage ++# =========================================================================== ++# All of the below values may be set or overridden via command-line arguments: ++# ++# -override-config KEY=VALUE,KEY=VALUE,... ++ ++# =========================================================================== ++# Logs and files ++# =========================================================================== ++# This section defines where and what logging information is produced. ++ ++# Each run of KataGo will log to a separate file in this dir. ++# This is the default. ++logDir = gtp_logs ++# Uncomment and specify this instead of logDir to write separate dated subdirs ++# logDirDated = gtp_logs ++# Uncomment and specify this instead of logDir to log to only a single file ++# logFile = gtp.log ++ ++# Logging options ++logAllGTPCommunication = true ++logSearchInfo = true ++logSearchInfoForChosenMove = false ++logToStderr = true ++ ++# KataGo will display some info to stderr on GTP startup ++# Uncomment the next line and set it to false to suppress that and remain silent ++# startupPrintMessageToStderr = true ++ ++# Write information to stderr, for use in things like malkovich chat to OGS. ++ ogsChatToStderr = true ++ ++# Uncomment and set this to a directory to override where openCLTuner files ++# and other cached data is written. By default it saves into a subdir of the ++# current directory on windows, and a subdir of ~/.katago on Linux. ++# homeDataDir = PATH_TO_DIRECTORY ++ ++# =========================================================================== ++# Analysis ++# =========================================================================== ++# This section configures analysis settings. ++# ++# The maximum number of moves after the first move displayed in variations ++# from analysis commands like kata-analyze or lz-analyze. ++# analysisPVLen = 15 ++ ++# Report winrates for chat and analysis as (BLACK|WHITE|SIDETOMOVE). ++# Most GUIs and analysis tools will expect SIDETOMOVE. ++# reportAnalysisWinratesAs = SIDETOMOVE ++ ++# Extra noise for wider exploration. Large values will force KataGo to ++# analyze a greater variety of moves than it normally would. ++# An extreme value like 1 distributes playouts across every move on the board, ++# even very bad moves. ++# Affects analysis only, does not affect play. ++# analysisWideRootNoise = 0.04 ++ ++# Try to limit the effect of possible bad or bogus move sequences in the ++# history leading to this position from affecting KataGo's move predictions. ++# analysisIgnorePreRootHistory = true ++ ++# =========================================================================== ++# Rules ++# =========================================================================== ++# This section configures the scoring and playing rules. Rules can also be ++# changed mid-run by issuing custom GTP commands. ++# ++# See https://lightvector.github.io/KataGo/rules.html for rules details. ++# ++# See https://github.com/lightvector/KataGo/blob/master/docs/GTP_Extensions.md ++# for GTP commands. ++ ++# Specify the rules as a string. ++# Some legal values include: ++# chinese, japanese, korean, aga, chinese-ogs, new-zealand, stone-scoring, ++# ancient-territory, bga, aga-button ++# ++# For some human rulesets that require complex adjudication in tricky cases ++# (e.g. japanese, korean) KataGo may not precisely match the ruleset in such ++# cases but will do its best. ++rules = tromp-taylor ++ ++# By default, the "rules" parameter is used, but if you comment it out and ++# uncomment one option in each of the sections below, you can specify an ++# arbitrary combination of individual rules. ++ ++# koRule = SIMPLE # Simple ko rules (triple ko = no result) ++# koRule = POSITIONAL # Positional superko ++# koRule = SITUATIONAL # Situational superko ++ ++# scoringRule = AREA # Area scoring ++# scoringRule = TERRITORY # Territory scoring (special computer-friendly territory rules) ++ ++# taxRule = NONE # All surrounded empty points are scored ++# taxRule = SEKI # Eyes in seki do NOT count as points ++# taxRule = ALL # All groups are taxed up to 2 points for the two eyes needed to live ++ ++# Is multiple-stone suicide legal? (Single-stone suicide is always illegal). ++# multiStoneSuicideLegal = false ++# multiStoneSuicideLegal = true ++ ++# "Button go" - the first pass when area scoring awards 0.5 points and does ++# not count for ending the game. ++# Allows area scoring rulesets that have far simpler rules to achieve the same ++# final scoring precision and reward for precise play as territory scoring. ++# hasButton = false ++# hasButton = true ++ ++# Is this a human ruleset where it's okay to pass before having physically ++# captured and removed all dead stones? ++# friendlyPassOk = false ++# friendlyPassOk = true ++ ++# How handicap stones in handicap games are compensated ++# whiteHandicapBonus = 0 # White gets no compensation for black's handicap stones (Tromp-taylor, NZ, JP) ++# whiteHandicapBonus = N-1 # White gets N-1 points for black's N handicap stones (AGA) ++# whiteHandicapBonus = N # White gets N points for black's N handicap stones (Chinese) ++ ++# ------------------------------ ++# Other rules hacks ++# ------------------------------ ++# Uncomment and change to adjust what board size KataGo uses upon startup ++# by default when GTP doesn't specify. ++# defaultBoardSize = 19 ++ ++# By default, Katago will use the komi that the GUI or GTP controller tries to set. ++# Uncomment and set this to have KataGo ignore the controller and always use this komi. ++# ignoreGTPAndForceKomi = 7 ++ ++# =========================================================================== ++# Bot behavior ++# =========================================================================== ++ ++# ------------------------------ ++# Resignation ++# ------------------------------ ++ ++# Resignation occurs if for at least resignConsecTurns in a row, the ++# winLossUtility (on a [-1,1] scale) is below resignThreshold. ++allowResignation = true ++resignThreshold = -0.90 ++resignConsecTurns = 3 ++ ++# By default, KataGo may resign games that it is confidently losing even if they ++# are very close in score. Uncomment and set this to avoid resigning games ++# if the estimated difference is points is less than or equal to this. ++# resignMinScoreDifference = 10 ++ ++# ------------------------------ ++# Handicap ++# ------------------------------ ++# Assume that if black makes many moves in a row right at the start of the ++# game, then the game is a handicap game. This is necessary on some servers ++# and for some GUIs and also when initializing from many SGF files, which may ++# set up a handicap game using repeated GTP "play" commands for black rather ++# than GTP "place_free_handicap" commands; however, it may also lead to ++# incorrect understanding of komi if whiteHandicapBonus is used and a server ++# does not have such a practice. Uncomment and set to false to disable. ++# assumeMultipleStartingBlackMovesAreHandicap = true ++ ++# Makes katago dynamically adjust in handicap or altered-komi games to assume ++# based on those game settings that it must be stronger or weaker than the ++# opponent and to play accordingly. Greatly improves handicap strength by ++# biasing winrates and scores to favor appropriate safe/aggressive play. ++# Does NOT affect analysis (lz-analyze, kata-analyze, used by programs like ++# Lizzie) so analysis remains unbiased. Uncomment and set this to 0 to disable ++# this and make KataGo play the same always. ++# dynamicPlayoutDoublingAdvantageCapPerOppLead = 0.045 ++ ++# Instead of "dynamicPlayoutDoublingAdvantageCapPerOppLead", you can comment ++# that out and uncomment and set "playoutDoublingAdvantage" to a fixed value ++# from -3.0 to 3.0 that will not change dynamically. ++# ALSO affects analysis tools (lz-analyze, kata-analyze, used by e.g. Lizzie). ++# Negative makes KataGo behave as if it is much weaker than the opponent. ++# Positive makes KataGo behave as if it is much stronger than the opponent. ++# KataGo will adjust to favor safe/aggressive play as appropriate based on ++# the combination of who is ahead and how much stronger/weaker it thinks it is, ++# and report winrates and scores taking the strength difference into account. ++# ++# If this and "dynamicPlayoutDoublingAdvantageCapPerOppLead" are both set ++# then dynamic will be used for all games and this fixed value will be used ++# for analysis tools. ++# playoutDoublingAdvantage = 0.0 ++ ++# Uncomment one of these when using "playoutDoublingAdvantage" to enforce ++# that it will only apply when KataGo plays as the specified color and will be ++# negated when playing as the opposite color. ++# playoutDoublingAdvantagePla = BLACK ++# playoutDoublingAdvantagePla = WHITE ++ ++# ------------------------------ ++# Passing and cleanup ++# ------------------------------ ++# Make the bot never assume that its pass will end the game, even if passing ++# would end and "win" under Tromp-Taylor rules. Usually this is a good idea ++# when using it for analysis or playing on servers where scoring may be ++# implemented non-tromp-taylorly. Uncomment and set to false to disable. ++# conservativePass = true ++ ++# When using territory scoring, self-play games continue beyond two passes ++# with special cleanup rules that may be confusing for human players. This ++# option prevents the special cleanup phases from being reachable when using ++# the bot for GTP play. Uncomment and set to false to enable entering special ++# cleanup. For example, if you are testing it against itself, or against ++# another bot that has precisely implemented the rules documented at ++# https://lightvector.github.io/KataGo/rules.html ++# preventCleanupPhase = true ++ ++# ------------------------------ ++# Miscellaneous behavior ++# ------------------------------ ++# If the board is symmetric, search only one copy of each equivalent move. ++# Attempts to also account for ko/superko, will not theoretically perfect for ++# superko. Uncomment and set to false to disable. ++# rootSymmetryPruning = true ++ ++# Uncomment and set to true to avoid a particular joseki that some networks ++# misevaluate, and also to improve opening diversity versus some particular ++# other bots that like to play it all the time. ++# avoidMYTDaggerHack = false ++ ++# Prefer to avoid playing the same joseki in every corner of the board. ++# Uncomment to set to a specific value. See "Avoid SGF patterns" section. ++# By default: 0 (even games), 0.005 (handicap games) ++# avoidRepeatedPatternUtility = 0.0 ++ ++# Experimental logic to fight against mirror Go even with unfavorable komi. ++# Uncomment to set to a specific value to use for both playing and analysis. ++# By default: true when playing via GTP, but false when analyzing. ++# antiMirror = true ++ ++# Enable some hacks that mitigate rare instances when passing messes up deeper searches. ++# enablePassingHacks = true ++ ++# Uncomment and set this to true to prevent bad or bogus move sequences ++# in the history leading to this position from affecting KataGo's move choices. ++# Same as analysisIgnorePreRootHistory (see above) but applies to actual play. ++# You can enable this if KataGo is being asked to play from positions that it did not ++# choose the moves to reach. ++# ignorePreRootHistory = false ++ ++# =========================================================================== ++# Search limits ++# =========================================================================== ++ ++# Terminology: ++# "Playouts" is the number of new playouts of search performed each turn. ++# "Visits" is the same as "Playouts" but also counts search performed on ++# previous turns that is still applicable to this turn. ++# "Time" is the time in seconds. ++ ++# For example, if KataGo searched 200 nodes on the previous turn, and then ++# after the opponent's reply, 50 nodes of its search tree was still valid, ++# then a visit limit of 200 would allow KataGo to search 150 new nodes ++# (for a final tree size of 200 nodes), whereas a playout limit of of 200 ++# would allow KataGo to search 200 nodes (for a final tree size of 250 nodes). ++ ++# Additionally, KataGo may also move before than the limit in order to ++# obey time controls (e.g. byo-yomi, etc) if the GTP controller has ++# told KataGo that the game has is being played with a given time control. ++ ++# Limits for search on the current turn. ++# If commented out or unspecified, the default is to have no limit. ++# maxVisits = 30000 ++# maxPlayouts = 300 ++maxTime = 1.0 ++ ++# Ponder on the opponent's turn? ++ponderingEnabled = true ++ ++# Limits for search when pondering on the opponent's turn. ++# If commented out or unspecified, the default is to have no limit. ++# Limiting the maximum time is recommended so that KataGo won't burn CPU/GPU ++# forever and/or run out of RAM if left unattended while pondering is enabled. ++# maxVisitsPondering = 5000 ++# maxPlayoutsPondering = 3000 ++maxTimePondering = 120.0 ++ ++ ++# ------------------------------ ++# Other search limits and behavior ++# ------------------------------ ++ ++# Approx number of seconds to buffer for lag for GTP time controls - will ++# move a bit faster assuming there is this much lag per move. ++lagBuffer = 1.0 ++ ++# YOU PROBABLY WANT TO TUNE THIS PARAMETER! ++# The number of threads to use when searching. On powerful GPUs the optimal ++# threads may be much higher than the number of CPU cores you have because ++# many threads are needed to feed efficient large batches to the GPU. ++# ++# Run "./katago benchmark" to tune this parameter and test the effect ++# of changes to any of other parameters. ++numSearchThreads = 10 ++ ++# Play a little faster if the opponent is passing, for human-friendliness. ++# Comment these out to disable them, such as if running a controlled match ++# where you are testing KataGo with fixed compute per move vs other bots. ++searchFactorAfterOnePass = 0.50 ++searchFactorAfterTwoPass = 0.25 ++ ++# Play a little faster if super-winning, for human-friendliness. ++# Comment these out to disable them, such as if running a controlled match ++# where you are testing KataGo with fixed compute per move vs other bots. ++searchFactorWhenWinning = 0.40 ++searchFactorWhenWinningThreshold = 0.95 ++ ++# =========================================================================== ++# GPU settings ++# =========================================================================== ++# This section configures GPU settings. ++# ++# Maximum number of positions to send to a single GPU at once. The default ++# value is roughly equal to numSearchThreads, but can be specified manually ++# if running out of memory, or using multiple GPUs that expect to share work. ++# nnMaxBatchSize = ++ ++# Controls the neural network cache size, which is the primary RAM/memory use. ++# KataGo will cache up to (2 ** nnCacheSizePowerOfTwo) many neural net ++# evaluations in case of transpositions in the tree. ++# Increase this to improve performance for searches with tens of thousands ++# of visits or more. Decrease this to limit memory usage. ++# If you're happy to do some math - each neural net entry takes roughly ++# 1.5KB, except when using whole-board ownership/territory ++# visualizations, where each entry will take roughly 3KB. The number of ++# entries is (2 ** nnCacheSizePowerOfTwo). (E.g. 2 ** 18 = 262144.) ++# You can compute roughly how much memory the cache will use based on this. ++# nnCacheSizePowerOfTwo = 20 ++ ++# Size of mutex pool for nnCache is (2 ** this). ++# nnMutexPoolSizePowerOfTwo = 16 ++ ++# Randomize board orientation when running neural net evals? Uncomment and ++# set to false to disable. ++# nnRandomize = true ++ ++# If provided, force usage of a specific seed for nnRandomize. ++# The default is to use a randomly generated seed. ++# nnRandSeed = abcdefg ++ ++# Uncomment and set to true to force GTP to use the maximum board size for ++# internal buffers for the neural net. This will make KataGo slower when ++# evaluating small boards, but will avoid a lengthy initialization time on every ++# change of board size due to having to re-size the neural net buffers on the GPU. ++# This can be useful for example, for OGS's persistent bot mode that uses a single ++# bot instance to handle multiple games and may thrash between different board sizes ++# if there are concurrent games of multiple sizes. ++# gtpForceMaxNNSize = false ++ ++# ------------------------------ ++# Multiple GPUs ++# ------------------------------ ++# Set this to the number of GPUs to use or that are available. ++# IMPORTANT: If more than 1, also uncomment the appropriate TensorRT ++# or CUDA or OpenCL section. ++# numNNServerThreadsPerModel = 1 ++ ++# ------------------------------ ++# TENSORRT GPU settings ++# ------------------------------ ++# These only apply when using the TENSORRT version of KataGo. ++ ++# For one GPU: optionally uncomment this option and change if the GPU to ++# use is not device 0. ++# trtDeviceToUse = 0 ++ ++# For two GPUs: Uncomment these options, AND set numNNServerThreadsPerModel above. ++# Also, change their values if the devices you want to use are not 0 and 1. ++# trtDeviceToUseThread0 = 0 ++# trtDeviceToUseThread1 = 1 ++ ++# For three GPUs: Uncomment these options, AND set numNNServerThreadsPerModel above. ++# Also, change their values if the devices you want to use are not 0 and 1 and 2. ++# trtDeviceToUseThread0 = 0 ++# trtDeviceToUseThread1 = 1 ++# trtDeviceToUseThread2 = 2 ++ ++# The pattern continues for additional GPUs. ++ ++# ------------------------------ ++# CUDA GPU settings ++# ------------------------------ ++# These only apply when using the CUDA version of KataGo. ++ ++# For one GPU: optionally uncomment and change this if the GPU you want to ++# use is not device 0 ++# cudaDeviceToUse = 0 ++ ++# For two GPUs: Uncomment these options, AND set numNNServerThreadsPerModel above. ++# Also, change their values if the devices you want to use are not 0 and 1. ++# cudaDeviceToUseThread0 = 0 ++# cudaDeviceToUseThread1 = 1 ++ ++# For three GPUs: Uncomment these options, AND set numNNServerThreadsPerModel above. ++# Also, change their values if the devices you want to use are not 0 and 1 and 2. ++# cudaDeviceToUseThread0 = 0 ++# cudaDeviceToUseThread1 = 1 ++# cudaDeviceToUseThread2 = 2 ++ ++# The pattern continues for additional GPUs. ++ ++# KataGo will automatically use FP16 or not based on the compute capability ++# of your NVIDIA GPU. If you want to try to force a particular behavior ++# you can uncomment these lines and change them to "true" or "false". ++# cudaUseFP16 = auto ++# cudaUseNHWC = auto ++ ++# ------------------------------ ++# OpenCL GPU settings ++# ------------------------------ ++# These only apply when using the OpenCL version of KataGo. ++ ++# Uncomment and set to true to tune OpenCL for every board size separately, ++# rather than only the largest possible size. ++# openclReTunePerBoardSize = false ++ ++# For one GPU: optionally uncomment and change this if the best device to use is guessed incorrectly. ++# The default behavior tries to guess the 'best' GPU or device on your system to use, usually it will be a good guess. ++# openclDeviceToUse = 0 ++ ++# For two GPUs: Uncomment these two lines and replace X and Y with the device ids of the devices you want to use. ++# It might NOT be 0 and 1, some computers will have many OpenCL devices. You can see what the devices are when ++# KataGo starts up - it should print or log all the devices it finds. ++# (AND also set numNNServerThreadsPerModel above) ++# openclDeviceToUseThread0 = X ++# openclDeviceToUseThread1 = Y ++ ++# For three GPUs: Uncomment these three lines and replace X and Y and Z with the device ids of the devices you want to use. ++# It might NOT be 0 and 1 and 2, some computers will have many OpenCL devices. You can see what the devices are when ++# KataGo starts up - it should print or log all the devices it finds. ++# (AND also set numNNServerThreadsPerModel above) ++# openclDeviceToUseThread0 = X ++# openclDeviceToUseThread1 = Y ++# openclDeviceToUseThread2 = Z ++ ++# The pattern continues for additional GPUs. ++ ++# KataGo will automatically use FP16 or not based on testing your GPU during ++# tuning. If you want to try to force a particular behavior though you can ++# uncomment this option and change it to "true" or "false". This is a fairly ++# blunt setting - more detailed settings are testable by rerunning the tuner ++# with various arguments (./katago tuner). ++# openclUseFP16 = auto ++ ++# ------------------------------ ++# Eigen-specific settings ++# ------------------------------ ++# These only apply when using the Eigen (pure CPU) version of KataGo. ++ ++# Number of CPU threads for evaluating the neural net on the Eigen backend. ++# ++# Default: numSearchThreads ++# numEigenThreadsPerModel = X ++ ++# =========================================================================== ++# Root move selection and biases ++# =========================================================================== ++# Uncomment and edit any of the below values to change them from their default. ++ ++# If provided, force usage of a specific seed for various random things in ++# the search. The default is to use a random seed. ++# searchRandSeed = hijklmn ++ ++# Temperature for the early game, randomize between chosen moves with ++# this temperature ++# chosenMoveTemperatureEarly = 0.5 ++ ++# Decay temperature for the early game by 0.5 every this many moves, ++# scaled with board size. ++# chosenMoveTemperatureHalflife = 19 ++ ++# At the end of search after the early game, randomize between chosen ++# moves with this temperature ++# chosenMoveTemperature = 0.10 ++ ++# Subtract this many visits from each move prior to applying ++# chosenMoveTemperature (unless all moves have too few visits) to downweight ++# unlikely moves ++# chosenMoveSubtract = 0 ++ ++# The same as chosenMoveSubtract but only prunes moves that fall below ++# the threshold. This setting does not affect chosenMoveSubtract. ++# chosenMovePrune = 1 ++ ++# Number of symmetries to sample (without replacement) and average at the root ++# rootNumSymmetriesToSample = 1 ++ ++# Using LCB for move selection? ++# useLcbForSelection = true ++ ++# How many stdevs a move needs to be better than another for LCB selection ++# lcbStdevs = 5.0 ++ ++# Only use LCB override when a move has this proportion of visits as the ++# top move. ++# minVisitPropForLCB = 0.15 ++ ++# =========================================================================== ++# Internal params ++# =========================================================================== ++# Uncomment and edit any of the below values to change them from their default. ++ ++# Scales the utility of winning/losing ++# winLossUtilityFactor = 1.0 ++ ++# Scales the utility for trying to maximize score ++# staticScoreUtilityFactor = 0.10 ++# dynamicScoreUtilityFactor = 0.30 ++ ++# Adjust dynamic score center this proportion of the way towards zero, ++# capped at a reasonable amount. ++# dynamicScoreCenterZeroWeight = 0.20 ++# dynamicScoreCenterScale = 0.75 ++ ++# The utility of getting a "no result" due to triple ko or other long cycle ++# in non-superko rulesets (-1 to 1) ++# noResultUtilityForWhite = 0.0 ++ ++# The number of wins that a draw counts as, for white. (0 to 1) ++# drawEquivalentWinsForWhite = 0.5 ++ ++# Exploration constant for mcts ++# cpuctExploration = 1.0 ++# cpuctExplorationLog = 0.45 ++ ++# Parameters that control exploring more in volatile positions, exploring ++# less in stable positions. ++# cpuctUtilityStdevPrior = 0.40 ++# cpuctUtilityStdevPriorWeight = 2.0 ++# cpuctUtilityStdevScale = 0.85 ++ ++# FPU reduction constant for mcts ++# fpuReductionMax = 0.2 ++# rootFpuReductionMax = 0.1 ++# fpuParentWeightByVisitedPolicy = true ++ ++# Parameters that control weighting of evals based on the net's own ++# self-reported uncertainty. ++# useUncertainty = true ++# uncertaintyExponent = 1.0 ++# uncertaintyCoeff = 0.25 ++ ++# Explore using optimistic policy ++# rootPolicyOptimism = 0.2 ++# policyOptimism = 1.0 ++ ++# Amount to apply a downweighting of children with very bad values relative ++# to good ones. ++# valueWeightExponent = 0.25 ++ ++# Slight incentive for the bot to behave human-like with regard to passing at ++# the end, filling the dame, not wasting time playing in its own territory, ++# etc., and not play moves that are equivalent in terms of points but a bit ++# more unfriendly to humans. ++# rootEndingBonusPoints = 0.5 ++ ++# Make the bot prune useless moves that are just prolonging the game to ++# avoid losing yet. ++# rootPruneUselessMoves = true ++ ++# Apply bias correction based on local pattern keys ++# subtreeValueBiasFactor = 0.45 ++# subtreeValueBiasWeightExponent = 0.85 ++ ++# Use graph search rather than tree search - identify and share search for ++# transpositions. ++# useGraphSearch = true ++ ++# How much to shard the node table for search synchronization ++# nodeTableShardsPowerOfTwo = 16 ++ ++# How many virtual losses to add when a thread descends through a node ++# numVirtualLossesPerThread = 1 ++ ++# Improve the quality of evals under heavy multithreading ++# useNoisePruning = true ++ ++# =========================================================================== ++# Automatic avoid patterns ++# =========================================================================== ++# The parameters in this section provide a way to bias away from moves that match ++# patterns that this instance of KataGo has played in previous games, by auto-saving ++# moves to a directory and then auto-loading and biasing against them each new game. ++# Uncomment them to use them. When using this feature, all parameters must be specified. ++ ++# Different board sizes are tracked separately, but all board sizes share the same ++# parameters by default. Every parameter *except* for autoAvoidRepeatDir and ++# autoAvoidRepeatSaveChunkSize can be overridden per board size, ++# e.g. "autoAvoidRepeatMinTurnNumber13x13". You must ALSO specify the ++# defaults even if you have specified board-size-specific values. ++ ++# Directory to auto-save moves KataGo plays, to avoid them in future games. ++# You can create a new empty directory and put its path here. ++# If you run parallel instances of KataGo, use different directories if you ++# want them not to share their biases, use the same directory if you want ++# all of them to bias away from past moves that any of them have played. ++# KataGo will also automatically DELETE old data in this directory, so it is ++# recommended that if you do share the same directory between parallel instances, ++# that they all use the same settings that affect data saving/deletion. ++# autoAvoidRepeatDir = PATH_TO_NEW_DIRECTORY ++ ++# Penalize this much utility per matching move. ++# Values that are too large may lead to bad play. The value of 0.004 is fairly large ++# and might be large enough to result in some early weird/bad moves when trying ++# to avoid past games' moves if enough games begin the same way. You can experiment with it. ++# autoAvoidRepeatUtility = 0.004 ++ ++# Per each new move saved, exponentially decay prior saved moves by this factor. ++# This way, the bias against moves from many games ago is gradually phased out. ++# For example, 0.9995 = 1 - 1/2000, so would be roughly 2000 prior moves worth of ++# penalty weight remembered, in steady state. Depending on what turn number range ++# you are saving, this might equate to a different number of games For example ++# saving the first 50 moves per game would make this roughly 2000 / 50 = 40 games ++# worth of memory. ++# autoAvoidRepeatLambda = 0.9995 ++ ++# Affects data saving/deletion. ++# When the number of saved moves exceeds this, outright delete them to avoid too many ++# files and disk space building up. Also may affect the speed of saving/loading on start ++# of each game if this is set large and a lot of data builds up. ++# autoAvoidRepeatMaxPoses = 10000 ++ ++# Affects data saving/deletion. ++# Only save data for moves within this turn number range of those games. ++# E.g. setting autoAvoidRepeatMinTurnNumber to a number like 4 or 5 would tend to make ++# KataGo not develop a bias against the initial 3-4 and 4-4 corner moves in almost every game. ++# autoAvoidRepeatMinTurnNumber = 0 ++# autoAvoidRepeatMaxTurnNumber = 50 ++ ++# Affects data saving/deletion. ++# Within a single run of a program, wait to accumulate this many samples ++# (possibly across multiple clear_boards/games) before saving the data. ++# Can help to avoid writing too many small files to disk, especially when GTP is used ++# in a way that clears the board very frequently (e.g. gtp2ogs pooled manager). ++# autoAvoidRepeatSaveChunkSize = 200 ++ ++# =========================================================================== ++# Avoid SGF patterns ++# =========================================================================== ++# The parameters in this section provide a way to avoid moves that follow ++# specific patterns based on a set of SGF files loaded upon startup. ++# This is basically the same as the above "Automatic avoid patterns" section ++# above except you supply your own SGF files to avoid moves from. ++# Uncomment them to use this feature. Additionally, if the SGF file ++# contains the string %SKIP% in a comment on a move, that move will be ++# ignored for this purpose. ++ ++# Load SGF files from this directory when the engine is started ++# (only on startup, will not reload unless engine is restarted) ++# avoidSgfPatternDirs = path/to/directory/with/sgfs/ ++# You can also surround the file path in double quotes if the file path contains trailing spaces or hash signs. ++# Within double quotes, backslashes are escape characters. ++# avoidSgfPatternDirs = "path/to/directory/with/sgfs/" ++ ++# Penalize this much utility per matching move. ++# Set this negative if you instead want to favor SGF patterns instead of ++# penalizing them. This number does not need to be large, even 0.001 will ++# make a difference. Values that are too large may lead to bad play. ++# avoidSgfPatternUtility = 0.001 ++ ++# Optional - load only the newest this many files ++# avoidSgfPatternMaxFiles = 20 ++ ++# Optional - Penalty is multiplied by this per each older SGF file, so that ++# old SGF files matter less than newer ones. ++# avoidSgfPatternLambda = 0.90 ++ ++# Optional - pay attention only to moves made by players with this name. ++# For example, set it to the name that your bot's past games will show up ++# as in the SGF, so that the bot will only avoid repeating moves that itself ++# made in past games, not the moves that its opponents made. ++# avoidSgfPatternAllowedNames = my-ogs-bot-name1,my-ogs-bot-name2 ++ ++# Optional - Ignore moves in SGF files that occurred before this turn number. ++# avoidSgfPatternMinTurnNumber = 0 ++ ++# For more avoid patterns: ++# You can also specify a second set of parameters, and a third, fourth, ++# etc. by numbering 2,3,4,... ++# ++# avoidSgf2PatternDirs = ... ++# avoidSgf2PatternUtility = ... ++# avoidSgf2PatternMaxFiles = ... ++# avoidSgf2PatternLambda = ... ++# avoidSgf2PatternAllowedNames = ... ++# avoidSgf2PatternMinTurnNumber = ... ++ +diff --git a/cpp/cacert.pem b/cpp/cacert.pem +new file mode 100644 +index 00000000..f9bd706b +--- /dev/null ++++ b/cpp/cacert.pem +@@ -0,0 +1,3447 @@ ++## ++## Bundle of CA Root Certificates ++## ++## Certificate data from Mozilla as of: Wed Jul 22 03:12:14 2020 GMT ++## ++## This is a bundle of X.509 certificates of public Certificate Authorities ++## (CA). These were automatically extracted from Mozilla's root certificates ++## file (certdata.txt). This file can be found in the mozilla source tree: ++## https://hg.mozilla.org/releases/mozilla-release/raw-file/default/security/nss/lib/ckfw/builtins/certdata.txt ++## ++## It contains the certificates in PEM format and therefore ++## can be directly used with curl / libcurl / php_curl, or with ++## an Apache+mod_ssl webserver for SSL client authentication. ++## Just configure this file as the SSLCACertificateFile. ++## ++## Conversion done with mk-ca-bundle.pl version 1.28. ++## SHA256: cc6408bd4be7fbfb8699bdb40ccb7f6de5780d681d87785ea362646e4dad5e8e ++## ++ ++ ++GlobalSign Root CA ++================== ++-----BEGIN CERTIFICATE----- ++MIIDdTCCAl2gAwIBAgILBAAAAAABFUtaw5QwDQYJKoZIhvcNAQEFBQAwVzELMAkGA1UEBhMCQkUx ++GTAXBgNVBAoTEEdsb2JhbFNpZ24gbnYtc2ExEDAOBgNVBAsTB1Jvb3QgQ0ExGzAZBgNVBAMTEkds ++b2JhbFNpZ24gUm9vdCBDQTAeFw05ODA5MDExMjAwMDBaFw0yODAxMjgxMjAwMDBaMFcxCzAJBgNV ++BAYTAkJFMRkwFwYDVQQKExBHbG9iYWxTaWduIG52LXNhMRAwDgYDVQQLEwdSb290IENBMRswGQYD 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CERTIFICATE----- ++ ++Comodo AAA Services root ++======================== ++-----BEGIN CERTIFICATE----- ++MIIEMjCCAxqgAwIBAgIBATANBgkqhkiG9w0BAQUFADB7MQswCQYDVQQGEwJHQjEbMBkGA1UECAwS ++R3JlYXRlciBNYW5jaGVzdGVyMRAwDgYDVQQHDAdTYWxmb3JkMRowGAYDVQQKDBFDb21vZG8gQ0Eg ++TGltaXRlZDEhMB8GA1UEAwwYQUFBIENlcnRpZmljYXRlIFNlcnZpY2VzMB4XDTA0MDEwMTAwMDAw ++MFoXDTI4MTIzMTIzNTk1OVowezELMAkGA1UEBhMCR0IxGzAZBgNVBAgMEkdyZWF0ZXIgTWFuY2hl ++c3RlcjEQMA4GA1UEBwwHU2FsZm9yZDEaMBgGA1UECgwRQ29tb2RvIENBIExpbWl0ZWQxITAfBgNV ++BAMMGEFBQSBDZXJ0aWZpY2F0ZSBTZXJ2aWNlczCCASIwDQYJKoZIhvcNAQEBBQADggEPADCCAQoC ++ggEBAL5AnfRu4ep2hxxNRUSOvkbIgwadwSr+GB+O5AL686tdUIoWMQuaBtDFcCLNSS1UY8y2bmhG ++C1Pqy0wkwLxyTurxFa70VJoSCsN6sjNg4tqJVfMiWPPe3M/vg4aijJRPn2jymJBGhCfHdr/jzDUs ++i14HZGWCwEiwqJH5YZ92IFCokcdmtet4YgNW8IoaE+oxox6gmf049vYnMlhvB/VruPsUK6+3qszW ++Y19zjNoFmag4qMsXeDZRrOme9Hg6jc8P2ULimAyrL58OAd7vn5lJ8S3frHRNG5i1R8XlKdH5kBjH ++Ypy+g8cmez6KJcfA3Z3mNWgQIJ2P2N7Sw4ScDV7oL8kCAwEAAaOBwDCBvTAdBgNVHQ4EFgQUoBEK 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true # In non-compensated handicap and fork games, vary komi + + # Match----------------------------------------------------------------------------------- + +-numGameThreads = 128 ++numGameThreads = 30 + maxMovesPerGame = 1600 + + # Rules------------------------------------------------------------------------------------ +diff --git a/cpp/contribute_example.cfg b/cpp/contribute_example.cfg +new file mode 100644 +index 00000000..a6c28531 +--- /dev/null ++++ b/cpp/contribute_example.cfg +@@ -0,0 +1,125 @@ ++ ++# Be careful not to share any config files where you have entered your password. ++ ++serverUrl = https://katagotraining.org/ ++username = Fill in your username here ++password = Fill in your password here ++# If your password has hash signs or trailing spaces, you can also surround the password with double quotes, like this. ++# Within double quotes, backslashes are escape characters. ++# password = "Fill in your password here" ++ ++# How many games should KataGo play at once? For good GPUs, putting too small a number here will be *very* inefficient. ++# For absolute-top-tier GPUs, try numbers like 32, 40, 64, etc. ++# For modern good middle-tier GPUs, you can try numbers like 8 or 16. ++# For very old or weak GPUs, or if you want KataGo to put less load on your system, try 4. ++maxSimultaneousGames = Fill in a number here ++ ++# Set to true if you want one of KataGo's games to be streamed to a text file, so you can watch. ++# Follow in a separate shell with a command like the following: ++# Linux: "tail -f watchgame.txt" ++# Windows Powershell: "Get-Content .\watchgame.txt -Tail 50 -Wait" ++watchOngoingGameInFile = false ++watchOngoingGameInFileName = watchgame.txt ++ ++# If you use an https proxy to connect to the internet, set these to the host and port of your proxy. ++# proxyHost = HOSTNAME ++# proxyPort = 1234 ++ ++# Special-purpose options. Most users should not need to adjust these. ++# onlyPlayRatingMatches = false ++# maxRatingMatches = 1 ++# disablePredownloadLoop = false ++# warnTaskUnusedKeys = false ++ ++# KataGo will only use one GPU by default. You can edit the below if you have multiple GPUs and want to use them all. ++ ++ ++# TO USE MULTIPLE GPUS: ++# Set this to the number of GPUs you have and/or would like to use. ++# **AND** if it is more than 1, uncomment the appropriate CUDA or OpenCL section below. ++# numNNServerThreadsPerModel = 1 ++ ++ ++# TENSORRT GPU settings-------------------------------------- ++# These only apply when using the TENSORRT version of KataGo. ++ ++# IF USING ONE GPU: optionally uncomment and change this if the GPU you want to use turns out to be not device 0 ++# trtDeviceToUse = 0 ++ ++# IF USING TWO GPUS: Uncomment these two lines (AND set numNNServerThreadsPerModel above): ++# trtDeviceToUseThread0 = 0 # change this if the first GPU you want to use turns out to be not device 0 ++# trtDeviceToUseThread1 = 1 # change this if the second GPU you want to use turns out to be not device 1 ++ ++# IF USING THREE GPUS: Uncomment these three lines (AND set numNNServerThreadsPerModel above): ++# trtDeviceToUseThread0 = 0 # change this if the first GPU you want to use turns out to be not device 0 ++# trtDeviceToUseThread1 = 1 # change this if the second GPU you want to use turns out to be not device 1 ++# trtDeviceToUseThread2 = 2 # change this if the third GPU you want to use turns out to be not device 2 ++ ++# You can probably guess the pattern if you have four, five, etc. GPUs. ++ ++ ++# CUDA GPU settings-------------------------------------- ++# These only apply when using the CUDA version of KataGo. ++ ++# IF USING ONE GPU: optionally uncomment and change this if the GPU you want to use turns out to be not device 0 ++# cudaDeviceToUse = 0 ++ ++# IF USING TWO GPUS: Uncomment these two lines (AND set numNNServerThreadsPerModel above): ++# cudaDeviceToUseThread0 = 0 # change this if the first GPU you want to use turns out to be not device 0 ++# cudaDeviceToUseThread1 = 1 # change this if the second GPU you want to use turns out to be not device 1 ++ ++# IF USING THREE GPUS: Uncomment these three lines (AND set numNNServerThreadsPerModel above): ++# cudaDeviceToUseThread0 = 0 # change this if the first GPU you want to use turns out to be not device 0 ++# cudaDeviceToUseThread1 = 1 # change this if the second GPU you want to use turns out to be not device 1 ++# cudaDeviceToUseThread2 = 2 # change this if the third GPU you want to use turns out to be not device 2 ++ ++# You can probably guess the pattern if you have four, five, etc. GPUs. ++ ++# KataGo will automatically use FP16 or not based on the compute capability of your NVIDIA GPU. If you ++# want to try to force a particular behavior though you can uncomment these lines and change them ++# to "true" or "false". E.g. it's using FP16 but on your card that's giving an error, or it's not using ++# FP16 but you think it should. ++# cudaUseFP16 = auto ++# cudaUseNHWC = auto ++ ++ ++# OpenCL GPU settings-------------------------------------- ++# These only apply when using the OpenCL version of KataGo. ++ ++# Uncomment to tune OpenCL for every board size separately, rather than only the largest possible size ++# openclReTunePerBoardSize = true ++ ++# IF USING ONE GPU: optionally uncomment and change this if the best device to use is guessed incorrectly. ++# The default behavior tries to guess the 'best' GPU or device on your system to use, usually it will be a good guess. ++# openclDeviceToUse = 0 ++ ++# IF USING TWO GPUS: Uncomment these two lines and replace X and Y with the device ids of the devices you want to use. ++# It might NOT be 0 and 1, some computers will have many OpenCL devices. You can see what the devices are when ++# KataGo starts up - it should print or log all the devices it finds. ++# (AND also set numNNServerThreadsPerModel above) ++# openclDeviceToUseThread0 = X ++# openclDeviceToUseThread1 = Y ++ ++# IF USING THREE GPUS: Uncomment these three lines and replace X and Y and Z with the device ids of the devices you want to use. ++# It might NOT be 0 and 1 and 2, some computers will have many OpenCL devices. You can see what the devices are when ++# KataGo starts up - it should print or log all the devices it finds. ++# (AND also set numNNServerThreadsPerModel above) ++# openclDeviceToUseThread0 = X ++# openclDeviceToUseThread1 = Y ++# openclDeviceToUseThread2 = Z ++ ++# You can probably guess the pattern if you have four, five, etc. GPUs. ++ ++# KataGo will automatically use FP16 or not based on testing your GPU during tuning. If you ++# want to try to force a particular behavior though you can uncomment this lines and change it ++# to "true" or "false". This is a fairly blunt setting - more detailed settings are testable ++# by rerunning the tuner with various arguments. ++# openclUseFP16 = auto ++ ++ ++# Eigen-specific settings-------------------------------------- ++# These only apply when using the Eigen (pure CPU) version of KataGo. ++ ++# This is the number of CPU threads for evaluating the neural net on the Eigen backend. ++# It defaults to numSearchThreads. ++# numEigenThreadsPerModel = X +diff --git a/cpp/default_gtp.cfg b/cpp/default_gtp.cfg +new file mode 100644 +index 00000000..8bf19d85 +--- /dev/null ++++ b/cpp/default_gtp.cfg +@@ -0,0 +1,721 @@ ++# Configuration for KataGo C++ GTP engine ++ ++# Run the program using: `./katago.exe gtp` ++ ++# In this example config, when a parameter is given as a commented out value, ++# that value also is the default value, unless described otherwise. You can ++# uncomment it (remove the pound sign) and change it if you want. ++ ++# =========================================================================== ++# Running on an online server or in a real tournament or match ++# =========================================================================== ++# If you plan to run online or in a tournament, read through the "Rules" ++# section below for proper handling of komi, handicaps, end-of-game cleanup, ++# and other details. ++ ++# =========================================================================== ++# Notes about performance and memory usage ++# =========================================================================== ++# Important: For good performance, you will very likely want to tune the ++# "numSearchThreads" parameter in the Search limits section below! Run ++# "./katago benchmark" to test KataGo and to suggest a reasonable value ++# of this parameter. ++ ++# For multi-GPU systems, read "OpenCL GPU settings" or "CUDA GPU settings". ++# ++# When using OpenCL, verify that KataGo picks the correct device! Some systems ++# may have both an Intel CPU OpenCL and GPU OpenCL. If # KataGo picks the wrong ++# one, correct this by specifying "openclGpuToUse". ++# ++# Consider adjusting "maxVisits", "ponderingEnabled", "resignThreshold", and ++# other parameters depending on your intended usage. ++ ++# =========================================================================== ++# Command-line usage ++# =========================================================================== ++# All of the below values may be set or overridden via command-line arguments: ++# ++# -override-config KEY=VALUE,KEY=VALUE,... ++ ++# =========================================================================== ++# Logs and files ++# =========================================================================== ++# This section defines where and what logging information is produced. ++ ++# Each run of KataGo will log to a separate file in this dir. ++# This is the default. ++logDir = gtp_logs ++# Uncomment and specify this instead of logDir to write separate dated subdirs ++# logDirDated = gtp_logs ++# Uncomment and specify this instead of logDir to log to only a single file ++# logFile = gtp.log ++ ++# Logging options ++logAllGTPCommunication = true ++logSearchInfo = true ++logSearchInfoForChosenMove = false ++logToStderr = false ++ ++# KataGo will display some info to stderr on GTP startup ++# Uncomment the next line and set it to false to suppress that and remain silent ++# startupPrintMessageToStderr = true ++ ++# Write information to stderr, for use in things like malkovich chat to OGS. ++ ogsChatToStderr = true ++ ++# Uncomment and set this to a directory to override where openCLTuner files ++# and other cached data is written. By default it saves into a subdir of the ++# current directory on windows, and a subdir of ~/.katago on Linux. ++# homeDataDir = PATH_TO_DIRECTORY ++ ++# =========================================================================== ++# Analysis ++# =========================================================================== ++# This section configures analysis settings. ++# ++# The maximum number of moves after the first move displayed in variations ++# from analysis commands like kata-analyze or lz-analyze. ++# analysisPVLen = 15 ++ ++# Report winrates for chat and analysis as (BLACK|WHITE|SIDETOMOVE). ++# Most GUIs and analysis tools will expect SIDETOMOVE. ++# reportAnalysisWinratesAs = SIDETOMOVE ++ ++# Extra noise for wider exploration. Large values will force KataGo to ++# analyze a greater variety of moves than it normally would. ++# An extreme value like 1 distributes playouts across every move on the board, ++# even very bad moves. ++# Affects analysis only, does not affect play. ++# analysisWideRootNoise = 0.04 ++ ++# Try to limit the effect of possible bad or bogus move sequences in the ++# history leading to this position from affecting KataGo's move predictions. ++# analysisIgnorePreRootHistory = true ++ ++# =========================================================================== ++# Rules ++# =========================================================================== ++# This section configures the scoring and playing rules. Rules can also be ++# changed mid-run by issuing custom GTP commands. ++# ++# See https://lightvector.github.io/KataGo/rules.html for rules details. ++# ++# See https://github.com/lightvector/KataGo/blob/master/docs/GTP_Extensions.md ++# for GTP commands. ++ ++# Specify the rules as a string. ++# Some legal values include: ++# chinese, japanese, korean, aga, chinese-ogs, new-zealand, stone-scoring, ++# ancient-territory, bga, aga-button ++# ++# For some human rulesets that require complex adjudication in tricky cases ++# (e.g. japanese, korean) KataGo may not precisely match the ruleset in such ++# cases but will do its best. ++rules = tromp-taylor ++ ++# By default, the "rules" parameter is used, but if you comment it out and ++# uncomment one option in each of the sections below, you can specify an ++# arbitrary combination of individual rules. ++ ++# koRule = SIMPLE # Simple ko rules (triple ko = no result) ++# koRule = POSITIONAL # Positional superko ++# koRule = SITUATIONAL # Situational superko ++ ++# scoringRule = AREA # Area scoring ++# scoringRule = TERRITORY # Territory scoring (special computer-friendly territory rules) ++ ++# taxRule = NONE # All surrounded empty points are scored ++# taxRule = SEKI # Eyes in seki do NOT count as points ++# taxRule = ALL # All groups are taxed up to 2 points for the two eyes needed to live ++ ++# Is multiple-stone suicide legal? (Single-stone suicide is always illegal). ++# multiStoneSuicideLegal = false ++# multiStoneSuicideLegal = true ++ ++# "Button go" - the first pass when area scoring awards 0.5 points and does ++# not count for ending the game. ++# Allows area scoring rulesets that have far simpler rules to achieve the same ++# final scoring precision and reward for precise play as territory scoring. ++# hasButton = false ++# hasButton = true ++ ++# Is this a human ruleset where it's okay to pass before having physically ++# captured and removed all dead stones? ++# friendlyPassOk = false ++# friendlyPassOk = true ++ ++# How handicap stones in handicap games are compensated ++# whiteHandicapBonus = 0 # White gets no compensation for black's handicap stones (Tromp-taylor, NZ, JP) ++# whiteHandicapBonus = N-1 # White gets N-1 points for black's N handicap stones (AGA) ++# whiteHandicapBonus = N # White gets N points for black's N handicap stones (Chinese) ++ ++# ------------------------------ ++# Other rules hacks ++# ------------------------------ ++# Uncomment and change to adjust what board size KataGo uses upon startup ++# by default when GTP doesn't specify. ++# defaultBoardSize = 19 ++ ++# By default, Katago will use the komi that the GUI or GTP controller tries to set. ++# Uncomment and set this to have KataGo ignore the controller and always use this komi. ++# ignoreGTPAndForceKomi = 7 ++ ++# =========================================================================== ++# Bot behavior ++# =========================================================================== ++ ++# ------------------------------ ++# Resignation ++# ------------------------------ ++ ++# Resignation occurs if for at least resignConsecTurns in a row, the ++# winLossUtility (on a [-1,1] scale) is below resignThreshold. ++allowResignation = true ++resignThreshold = -0.90 ++resignConsecTurns = 3 ++ ++# By default, KataGo may resign games that it is confidently losing even if they ++# are very close in score. Uncomment and set this to avoid resigning games ++# if the estimated difference is points is less than or equal to this. ++# resignMinScoreDifference = 10 ++ ++# ------------------------------ ++# Handicap ++# ------------------------------ ++# Assume that if black makes many moves in a row right at the start of the ++# game, then the game is a handicap game. This is necessary on some servers ++# and for some GUIs and also when initializing from many SGF files, which may ++# set up a handicap game using repeated GTP "play" commands for black rather ++# than GTP "place_free_handicap" commands; however, it may also lead to ++# incorrect understanding of komi if whiteHandicapBonus is used and a server ++# does not have such a practice. Uncomment and set to false to disable. ++# assumeMultipleStartingBlackMovesAreHandicap = true ++ ++# Makes katago dynamically adjust in handicap or altered-komi games to assume ++# based on those game settings that it must be stronger or weaker than the ++# opponent and to play accordingly. Greatly improves handicap strength by ++# biasing winrates and scores to favor appropriate safe/aggressive play. ++# Does NOT affect analysis (lz-analyze, kata-analyze, used by programs like ++# Lizzie) so analysis remains unbiased. Uncomment and set this to 0 to disable ++# this and make KataGo play the same always. ++# dynamicPlayoutDoublingAdvantageCapPerOppLead = 0.045 ++ ++# Instead of "dynamicPlayoutDoublingAdvantageCapPerOppLead", you can comment ++# that out and uncomment and set "playoutDoublingAdvantage" to a fixed value ++# from -3.0 to 3.0 that will not change dynamically. ++# ALSO affects analysis tools (lz-analyze, kata-analyze, used by e.g. Lizzie). ++# Negative makes KataGo behave as if it is much weaker than the opponent. ++# Positive makes KataGo behave as if it is much stronger than the opponent. ++# KataGo will adjust to favor safe/aggressive play as appropriate based on ++# the combination of who is ahead and how much stronger/weaker it thinks it is, ++# and report winrates and scores taking the strength difference into account. ++# ++# If this and "dynamicPlayoutDoublingAdvantageCapPerOppLead" are both set ++# then dynamic will be used for all games and this fixed value will be used ++# for analysis tools. ++# playoutDoublingAdvantage = 0.0 ++ ++# Uncomment one of these when using "playoutDoublingAdvantage" to enforce ++# that it will only apply when KataGo plays as the specified color and will be ++# negated when playing as the opposite color. ++# playoutDoublingAdvantagePla = BLACK ++# playoutDoublingAdvantagePla = WHITE ++ ++# ------------------------------ ++# Passing and cleanup ++# ------------------------------ ++# Make the bot never assume that its pass will end the game, even if passing ++# would end and "win" under Tromp-Taylor rules. Usually this is a good idea ++# when using it for analysis or playing on servers where scoring may be ++# implemented non-tromp-taylorly. Uncomment and set to false to disable. ++# conservativePass = true ++ ++# When using territory scoring, self-play games continue beyond two passes ++# with special cleanup rules that may be confusing for human players. This ++# option prevents the special cleanup phases from being reachable when using ++# the bot for GTP play. Uncomment and set to false to enable entering special ++# cleanup. For example, if you are testing it against itself, or against ++# another bot that has precisely implemented the rules documented at ++# https://lightvector.github.io/KataGo/rules.html ++# preventCleanupPhase = true ++ ++# ------------------------------ ++# Miscellaneous behavior ++# ------------------------------ ++# If the board is symmetric, search only one copy of each equivalent move. ++# Attempts to also account for ko/superko, will not theoretically perfect for ++# superko. Uncomment and set to false to disable. ++# rootSymmetryPruning = true ++ ++# Uncomment and set to true to avoid a particular joseki that some networks ++# misevaluate, and also to improve opening diversity versus some particular ++# other bots that like to play it all the time. ++# avoidMYTDaggerHack = false ++ ++# Prefer to avoid playing the same joseki in every corner of the board. ++# Uncomment to set to a specific value. See "Avoid SGF patterns" section. ++# By default: 0 (even games), 0.005 (handicap games) ++# avoidRepeatedPatternUtility = 0.0 ++ ++# Experimental logic to fight against mirror Go even with unfavorable komi. ++# Uncomment to set to a specific value to use for both playing and analysis. ++# By default: true when playing via GTP, but false when analyzing. ++# antiMirror = true ++ ++# Enable some hacks that mitigate rare instances when passing messes up deeper searches. ++# enablePassingHacks = true ++ ++# Uncomment and set this to true to prevent bad or bogus move sequences ++# in the history leading to this position from affecting KataGo's move choices. ++# Same as analysisIgnorePreRootHistory (see above) but applies to actual play. ++# You can enable this if KataGo is being asked to play from positions that it did not ++# choose the moves to reach. ++# ignorePreRootHistory = false ++ ++# =========================================================================== ++# Search limits ++# =========================================================================== ++ ++# Terminology: ++# "Playouts" is the number of new playouts of search performed each turn. ++# "Visits" is the same as "Playouts" but also counts search performed on ++# previous turns that is still applicable to this turn. ++# "Time" is the time in seconds. ++ ++# For example, if KataGo searched 200 nodes on the previous turn, and then ++# after the opponent's reply, 50 nodes of its search tree was still valid, ++# then a visit limit of 200 would allow KataGo to search 150 new nodes ++# (for a final tree size of 200 nodes), whereas a playout limit of of 200 ++# would allow KataGo to search 200 nodes (for a final tree size of 250 nodes). ++ ++# Additionally, KataGo may also move before than the limit in order to ++# obey time controls (e.g. byo-yomi, etc) if the GTP controller has ++# told KataGo that the game has is being played with a given time control. ++ ++# Limits for search on the current turn. ++# If commented out or unspecified, the default is to have no limit. ++# maxVisits = 30000 ++# maxPlayouts = 300 ++maxTime = 1.0 ++ ++# Ponder on the opponent's turn? ++ponderingEnabled = true ++ ++# Limits for search when pondering on the opponent's turn. ++# If commented out or unspecified, the default is to have no limit. ++# Limiting the maximum time is recommended so that KataGo won't burn CPU/GPU ++# forever and/or run out of RAM if left unattended while pondering is enabled. ++# maxVisitsPondering = 5000 ++# maxPlayoutsPondering = 3000 ++maxTimePondering = 120.0 ++ ++ ++# ------------------------------ ++# Other search limits and behavior ++# ------------------------------ ++ ++# Approx number of seconds to buffer for lag for GTP time controls - will ++# move a bit faster assuming there is this much lag per move. ++lagBuffer = 1.0 ++ ++# YOU PROBABLY WANT TO TUNE THIS PARAMETER! ++# The number of threads to use when searching. On powerful GPUs the optimal ++# threads may be much higher than the number of CPU cores you have because ++# many threads are needed to feed efficient large batches to the GPU. ++# ++# Run "./katago benchmark" to tune this parameter and test the effect ++# of changes to any of other parameters. ++numSearchThreads = 40 ++ ++# Play a little faster if the opponent is passing, for human-friendliness. ++# Comment these out to disable them, such as if running a controlled match ++# where you are testing KataGo with fixed compute per move vs other bots. ++searchFactorAfterOnePass = 0.50 ++searchFactorAfterTwoPass = 0.25 ++ ++# Play a little faster if super-winning, for human-friendliness. ++# Comment these out to disable them, such as if running a controlled match ++# where you are testing KataGo with fixed compute per move vs other bots. ++searchFactorWhenWinning = 0.40 ++searchFactorWhenWinningThreshold = 0.95 ++ ++# =========================================================================== ++# GPU settings ++# =========================================================================== ++# This section configures GPU settings. ++# ++# Maximum number of positions to send to a single GPU at once. The default ++# value is roughly equal to numSearchThreads, but can be specified manually ++# if running out of memory, or using multiple GPUs that expect to share work. ++# nnMaxBatchSize = ++ ++# Controls the neural network cache size, which is the primary RAM/memory use. ++# KataGo will cache up to (2 ** nnCacheSizePowerOfTwo) many neural net ++# evaluations in case of transpositions in the tree. ++# Increase this to improve performance for searches with tens of thousands ++# of visits or more. Decrease this to limit memory usage. ++# If you're happy to do some math - each neural net entry takes roughly ++# 1.5KB, except when using whole-board ownership/territory ++# visualizations, where each entry will take roughly 3KB. The number of ++# entries is (2 ** nnCacheSizePowerOfTwo). (E.g. 2 ** 18 = 262144.) ++# You can compute roughly how much memory the cache will use based on this. ++# nnCacheSizePowerOfTwo = 20 ++ ++# Size of mutex pool for nnCache is (2 ** this). ++# nnMutexPoolSizePowerOfTwo = 16 ++ ++# Randomize board orientation when running neural net evals? Uncomment and ++# set to false to disable. ++# nnRandomize = true ++ ++# If provided, force usage of a specific seed for nnRandomize. ++# The default is to use a randomly generated seed. ++# nnRandSeed = abcdefg ++ ++# Uncomment and set to true to force GTP to use the maximum board size for ++# internal buffers for the neural net. This will make KataGo slower when ++# evaluating small boards, but will avoid a lengthy initialization time on every ++# change of board size due to having to re-size the neural net buffers on the GPU. ++# This can be useful for example, for OGS's persistent bot mode that uses a single ++# bot instance to handle multiple games and may thrash between different board sizes ++# if there are concurrent games of multiple sizes. ++# gtpForceMaxNNSize = false ++ ++# ------------------------------ ++# Multiple GPUs ++# ------------------------------ ++# Set this to the number of GPUs to use or that are available. ++# IMPORTANT: If more than 1, also uncomment the appropriate TensorRT ++# or CUDA or OpenCL section. ++# numNNServerThreadsPerModel = 1 ++ ++# ------------------------------ ++# TENSORRT GPU settings ++# ------------------------------ ++# These only apply when using the TENSORRT version of KataGo. ++ ++# For one GPU: optionally uncomment this option and change if the GPU to ++# use is not device 0. ++# trtDeviceToUse = 0 ++ ++# For two GPUs: Uncomment these options, AND set numNNServerThreadsPerModel above. ++# Also, change their values if the devices you want to use are not 0 and 1. ++# trtDeviceToUseThread0 = 0 ++# trtDeviceToUseThread1 = 1 ++ ++# For three GPUs: Uncomment these options, AND set numNNServerThreadsPerModel above. ++# Also, change their values if the devices you want to use are not 0 and 1 and 2. ++# trtDeviceToUseThread0 = 0 ++# trtDeviceToUseThread1 = 1 ++# trtDeviceToUseThread2 = 2 ++ ++# The pattern continues for additional GPUs. ++ ++# ------------------------------ ++# CUDA GPU settings ++# ------------------------------ ++# These only apply when using the CUDA version of KataGo. ++ ++# For one GPU: optionally uncomment and change this if the GPU you want to ++# use is not device 0 ++# cudaDeviceToUse = 0 ++ ++# For two GPUs: Uncomment these options, AND set numNNServerThreadsPerModel above. ++# Also, change their values if the devices you want to use are not 0 and 1. ++# cudaDeviceToUseThread0 = 0 ++# cudaDeviceToUseThread1 = 1 ++ ++# For three GPUs: Uncomment these options, AND set numNNServerThreadsPerModel above. ++# Also, change their values if the devices you want to use are not 0 and 1 and 2. ++# cudaDeviceToUseThread0 = 0 ++# cudaDeviceToUseThread1 = 1 ++# cudaDeviceToUseThread2 = 2 ++ ++# The pattern continues for additional GPUs. ++ ++# KataGo will automatically use FP16 or not based on the compute capability ++# of your NVIDIA GPU. If you want to try to force a particular behavior ++# you can uncomment these lines and change them to "true" or "false". ++# cudaUseFP16 = auto ++# cudaUseNHWC = auto ++ ++# ------------------------------ ++# OpenCL GPU settings ++# ------------------------------ ++# These only apply when using the OpenCL version of KataGo. ++ ++# Uncomment and set to true to tune OpenCL for every board size separately, ++# rather than only the largest possible size. ++# openclReTunePerBoardSize = false ++ ++# For one GPU: optionally uncomment and change this if the best device to use is guessed incorrectly. ++# The default behavior tries to guess the 'best' GPU or device on your system to use, usually it will be a good guess. ++# openclDeviceToUse = 0 ++ ++# For two GPUs: Uncomment these two lines and replace X and Y with the device ids of the devices you want to use. ++# It might NOT be 0 and 1, some computers will have many OpenCL devices. You can see what the devices are when ++# KataGo starts up - it should print or log all the devices it finds. ++# (AND also set numNNServerThreadsPerModel above) ++# openclDeviceToUseThread0 = X ++# openclDeviceToUseThread1 = Y ++ ++# For three GPUs: Uncomment these three lines and replace X and Y and Z with the device ids of the devices you want to use. ++# It might NOT be 0 and 1 and 2, some computers will have many OpenCL devices. You can see what the devices are when ++# KataGo starts up - it should print or log all the devices it finds. ++# (AND also set numNNServerThreadsPerModel above) ++# openclDeviceToUseThread0 = X ++# openclDeviceToUseThread1 = Y ++# openclDeviceToUseThread2 = Z ++ ++# The pattern continues for additional GPUs. ++ ++# KataGo will automatically use FP16 or not based on testing your GPU during ++# tuning. If you want to try to force a particular behavior though you can ++# uncomment this option and change it to "true" or "false". This is a fairly ++# blunt setting - more detailed settings are testable by rerunning the tuner ++# with various arguments (./katago tuner). ++# openclUseFP16 = auto ++ ++# ------------------------------ ++# Eigen-specific settings ++# ------------------------------ ++# These only apply when using the Eigen (pure CPU) version of KataGo. ++ ++# Number of CPU threads for evaluating the neural net on the Eigen backend. ++# ++# Default: numSearchThreads ++# numEigenThreadsPerModel = X ++ ++# =========================================================================== ++# Root move selection and biases ++# =========================================================================== ++# Uncomment and edit any of the below values to change them from their default. ++ ++# If provided, force usage of a specific seed for various random things in ++# the search. The default is to use a random seed. ++# searchRandSeed = hijklmn ++ ++# Temperature for the early game, randomize between chosen moves with ++# this temperature ++# chosenMoveTemperatureEarly = 0.5 ++ ++# Decay temperature for the early game by 0.5 every this many moves, ++# scaled with board size. ++# chosenMoveTemperatureHalflife = 19 ++ ++# At the end of search after the early game, randomize between chosen ++# moves with this temperature ++# chosenMoveTemperature = 0.10 ++ ++# Subtract this many visits from each move prior to applying ++# chosenMoveTemperature (unless all moves have too few visits) to downweight ++# unlikely moves ++# chosenMoveSubtract = 0 ++ ++# The same as chosenMoveSubtract but only prunes moves that fall below ++# the threshold. This setting does not affect chosenMoveSubtract. ++# chosenMovePrune = 1 ++ ++# Number of symmetries to sample (without replacement) and average at the root ++# rootNumSymmetriesToSample = 1 ++ ++# Using LCB for move selection? ++# useLcbForSelection = true ++ ++# How many stdevs a move needs to be better than another for LCB selection ++# lcbStdevs = 5.0 ++ ++# Only use LCB override when a move has this proportion of visits as the ++# top move. ++# minVisitPropForLCB = 0.15 ++ ++# =========================================================================== ++# Internal params ++# =========================================================================== ++# Uncomment and edit any of the below values to change them from their default. ++ ++# Scales the utility of winning/losing ++# winLossUtilityFactor = 1.0 ++ ++# Scales the utility for trying to maximize score ++# staticScoreUtilityFactor = 0.10 ++# dynamicScoreUtilityFactor = 0.30 ++ ++# Adjust dynamic score center this proportion of the way towards zero, ++# capped at a reasonable amount. ++# dynamicScoreCenterZeroWeight = 0.20 ++# dynamicScoreCenterScale = 0.75 ++ ++# The utility of getting a "no result" due to triple ko or other long cycle ++# in non-superko rulesets (-1 to 1) ++# noResultUtilityForWhite = 0.0 ++ ++# The number of wins that a draw counts as, for white. (0 to 1) ++# drawEquivalentWinsForWhite = 0.5 ++ ++# Exploration constant for mcts ++# cpuctExploration = 1.0 ++# cpuctExplorationLog = 0.45 ++ ++# Parameters that control exploring more in volatile positions, exploring ++# less in stable positions. ++# cpuctUtilityStdevPrior = 0.40 ++# cpuctUtilityStdevPriorWeight = 2.0 ++# cpuctUtilityStdevScale = 0.85 ++ ++# FPU reduction constant for mcts ++# fpuReductionMax = 0.2 ++# rootFpuReductionMax = 0.1 ++# fpuParentWeightByVisitedPolicy = true ++ ++# Parameters that control weighting of evals based on the net's own ++# self-reported uncertainty. ++# useUncertainty = true ++# uncertaintyExponent = 1.0 ++# uncertaintyCoeff = 0.25 ++ ++# Explore using optimistic policy ++# rootPolicyOptimism = 0.2 ++# policyOptimism = 1.0 ++ ++# Amount to apply a downweighting of children with very bad values relative ++# to good ones. ++# valueWeightExponent = 0.25 ++ ++# Slight incentive for the bot to behave human-like with regard to passing at ++# the end, filling the dame, not wasting time playing in its own territory, ++# etc., and not play moves that are equivalent in terms of points but a bit ++# more unfriendly to humans. ++# rootEndingBonusPoints = 0.5 ++ ++# Make the bot prune useless moves that are just prolonging the game to ++# avoid losing yet. ++# rootPruneUselessMoves = true ++ ++# Apply bias correction based on local pattern keys ++# subtreeValueBiasFactor = 0.45 ++# subtreeValueBiasWeightExponent = 0.85 ++ ++# Use graph search rather than tree search - identify and share search for ++# transpositions. ++# useGraphSearch = true ++ ++# How much to shard the node table for search synchronization ++# nodeTableShardsPowerOfTwo = 16 ++ ++# How many virtual losses to add when a thread descends through a node ++# numVirtualLossesPerThread = 1 ++ ++# Improve the quality of evals under heavy multithreading ++# useNoisePruning = true ++ ++# =========================================================================== ++# Automatic avoid patterns ++# =========================================================================== ++# The parameters in this section provide a way to bias away from moves that match ++# patterns that this instance of KataGo has played in previous games, by auto-saving ++# moves to a directory and then auto-loading and biasing against them each new game. ++# Uncomment them to use them. When using this feature, all parameters must be specified. ++ ++# Different board sizes are tracked separately, but all board sizes share the same ++# parameters by default. Every parameter *except* for autoAvoidRepeatDir and ++# autoAvoidRepeatSaveChunkSize can be overridden per board size, ++# e.g. "autoAvoidRepeatMinTurnNumber13x13". You must ALSO specify the ++# defaults even if you have specified board-size-specific values. ++ ++# Directory to auto-save moves KataGo plays, to avoid them in future games. ++# You can create a new empty directory and put its path here. ++# If you run parallel instances of KataGo, use different directories if you ++# want them not to share their biases, use the same directory if you want ++# all of them to bias away from past moves that any of them have played. ++# KataGo will also automatically DELETE old data in this directory, so it is ++# recommended that if you do share the same directory between parallel instances, ++# that they all use the same settings that affect data saving/deletion. ++# autoAvoidRepeatDir = PATH_TO_NEW_DIRECTORY ++ ++# Penalize this much utility per matching move. ++# Values that are too large may lead to bad play. The value of 0.004 is fairly large ++# and might be large enough to result in some early weird/bad moves when trying ++# to avoid past games' moves if enough games begin the same way. You can experiment with it. ++# autoAvoidRepeatUtility = 0.004 ++ ++# Per each new move saved, exponentially decay prior saved moves by this factor. ++# This way, the bias against moves from many games ago is gradually phased out. ++# For example, 0.9995 = 1 - 1/2000, so would be roughly 2000 prior moves worth of ++# penalty weight remembered, in steady state. Depending on what turn number range ++# you are saving, this might equate to a different number of games For example ++# saving the first 50 moves per game would make this roughly 2000 / 50 = 40 games ++# worth of memory. ++# autoAvoidRepeatLambda = 0.9995 ++ ++# Affects data saving/deletion. ++# When the number of saved moves exceeds this, outright delete them to avoid too many ++# files and disk space building up. Also may affect the speed of saving/loading on start ++# of each game if this is set large and a lot of data builds up. ++# autoAvoidRepeatMaxPoses = 10000 ++ ++# Affects data saving/deletion. ++# Only save data for moves within this turn number range of those games. ++# E.g. setting autoAvoidRepeatMinTurnNumber to a number like 4 or 5 would tend to make ++# KataGo not develop a bias against the initial 3-4 and 4-4 corner moves in almost every game. ++# autoAvoidRepeatMinTurnNumber = 0 ++# autoAvoidRepeatMaxTurnNumber = 50 ++ ++# Affects data saving/deletion. ++# Within a single run of a program, wait to accumulate this many samples ++# (possibly across multiple clear_boards/games) before saving the data. ++# Can help to avoid writing too many small files to disk, especially when GTP is used ++# in a way that clears the board very frequently (e.g. gtp2ogs pooled manager). ++# autoAvoidRepeatSaveChunkSize = 200 ++ ++# =========================================================================== ++# Avoid SGF patterns ++# =========================================================================== ++# The parameters in this section provide a way to avoid moves that follow ++# specific patterns based on a set of SGF files loaded upon startup. ++# This is basically the same as the above "Automatic avoid patterns" section ++# above except you supply your own SGF files to avoid moves from. ++# Uncomment them to use this feature. Additionally, if the SGF file ++# contains the string %SKIP% in a comment on a move, that move will be ++# ignored for this purpose. ++ ++# Load SGF files from this directory when the engine is started ++# (only on startup, will not reload unless engine is restarted) ++# avoidSgfPatternDirs = path/to/directory/with/sgfs/ ++# You can also surround the file path in double quotes if the file path contains trailing spaces or hash signs. ++# Within double quotes, backslashes are escape characters. ++# avoidSgfPatternDirs = "path/to/directory/with/sgfs/" ++ ++# Penalize this much utility per matching move. ++# Set this negative if you instead want to favor SGF patterns instead of ++# penalizing them. This number does not need to be large, even 0.001 will ++# make a difference. Values that are too large may lead to bad play. ++# avoidSgfPatternUtility = 0.001 ++ ++# Optional - load only the newest this many files ++# avoidSgfPatternMaxFiles = 20 ++ ++# Optional - Penalty is multiplied by this per each older SGF file, so that ++# old SGF files matter less than newer ones. ++# avoidSgfPatternLambda = 0.90 ++ ++# Optional - pay attention only to moves made by players with this name. ++# For example, set it to the name that your bot's past games will show up ++# as in the SGF, so that the bot will only avoid repeating moves that itself ++# made in past games, not the moves that its opponents made. ++# avoidSgfPatternAllowedNames = my-ogs-bot-name1,my-ogs-bot-name2 ++ ++# Optional - Ignore moves in SGF files that occurred before this turn number. ++# avoidSgfPatternMinTurnNumber = 0 ++ ++# For more avoid patterns: ++# You can also specify a second set of parameters, and a third, fourth, ++# etc. by numbering 2,3,4,... ++# ++# avoidSgf2PatternDirs = ... ++# avoidSgf2PatternUtility = ... ++# avoidSgf2PatternMaxFiles = ... ++# avoidSgf2PatternLambda = ... ++# avoidSgf2PatternAllowedNames = ... ++# avoidSgf2PatternMinTurnNumber = ... ++ +diff --git a/cpp/doudoubot.cfg b/cpp/doudoubot.cfg +new file mode 100644 +index 00000000..bf81254f +--- /dev/null ++++ b/cpp/doudoubot.cfg +@@ -0,0 +1,721 @@ ++# Configuration for KataGo C++ GTP engine ++ ++# Run the program using: `./katago.exe gtp` ++ ++# In this example config, when a parameter is given as a commented out value, ++# that value also is the default value, unless described otherwise. You can ++# uncomment it (remove the pound sign) and change it if you want. ++ ++# =========================================================================== ++# Running on an online server or in a real tournament or match ++# =========================================================================== ++# If you plan to run online or in a tournament, read through the "Rules" ++# section below for proper handling of komi, handicaps, end-of-game cleanup, ++# and other details. ++ ++# =========================================================================== ++# Notes about performance and memory usage ++# =========================================================================== ++# Important: For good performance, you will very likely want to tune the ++# "numSearchThreads" parameter in the Search limits section below! Run ++# "./katago benchmark" to test KataGo and to suggest a reasonable value ++# of this parameter. ++ ++# For multi-GPU systems, read "OpenCL GPU settings" or "CUDA GPU settings". ++# ++# When using OpenCL, verify that KataGo picks the correct device! Some systems ++# may have both an Intel CPU OpenCL and GPU OpenCL. If # KataGo picks the wrong ++# one, correct this by specifying "openclGpuToUse". ++# ++# Consider adjusting "maxVisits", "ponderingEnabled", "resignThreshold", and ++# other parameters depending on your intended usage. ++ ++# =========================================================================== ++# Command-line usage ++# =========================================================================== ++# All of the below values may be set or overridden via command-line arguments: ++# ++# -override-config KEY=VALUE,KEY=VALUE,... ++ ++# =========================================================================== ++# Logs and files ++# =========================================================================== ++# This section defines where and what logging information is produced. ++ ++# Each run of KataGo will log to a separate file in this dir. ++# This is the default. ++logDir = gtp_logs ++# Uncomment and specify this instead of logDir to write separate dated subdirs ++# logDirDated = gtp_logs ++# Uncomment and specify this instead of logDir to log to only a single file ++# logFile = gtp.log ++ ++# Logging options ++logAllGTPCommunication = true ++logSearchInfo = true ++logSearchInfoForChosenMove = false ++logToStderr = false ++ ++# KataGo will display some info to stderr on GTP startup ++# Uncomment the next line and set it to false to suppress that and remain silent ++# startupPrintMessageToStderr = true ++ ++# Write information to stderr, for use in things like malkovich chat to OGS. ++ ogsChatToStderr = true ++ ++# Uncomment and set this to a directory to override where openCLTuner files ++# and other cached data is written. By default it saves into a subdir of the ++# current directory on windows, and a subdir of ~/.katago on Linux. ++# homeDataDir = PATH_TO_DIRECTORY ++ ++# =========================================================================== ++# Analysis ++# =========================================================================== ++# This section configures analysis settings. ++# ++# The maximum number of moves after the first move displayed in variations ++# from analysis commands like kata-analyze or lz-analyze. ++# analysisPVLen = 15 ++ ++# Report winrates for chat and analysis as (BLACK|WHITE|SIDETOMOVE). ++# Most GUIs and analysis tools will expect SIDETOMOVE. ++# reportAnalysisWinratesAs = SIDETOMOVE ++ ++# Extra noise for wider exploration. Large values will force KataGo to ++# analyze a greater variety of moves than it normally would. ++# An extreme value like 1 distributes playouts across every move on the board, ++# even very bad moves. ++# Affects analysis only, does not affect play. ++# analysisWideRootNoise = 0.04 ++ ++# Try to limit the effect of possible bad or bogus move sequences in the ++# history leading to this position from affecting KataGo's move predictions. ++# analysisIgnorePreRootHistory = true ++ ++# =========================================================================== ++# Rules ++# =========================================================================== ++# This section configures the scoring and playing rules. Rules can also be ++# changed mid-run by issuing custom GTP commands. ++# ++# See https://lightvector.github.io/KataGo/rules.html for rules details. ++# ++# See https://github.com/lightvector/KataGo/blob/master/docs/GTP_Extensions.md ++# for GTP commands. ++ ++# Specify the rules as a string. ++# Some legal values include: ++# chinese, japanese, korean, aga, chinese-ogs, new-zealand, stone-scoring, ++# ancient-territory, bga, aga-button ++# ++# For some human rulesets that require complex adjudication in tricky cases ++# (e.g. japanese, korean) KataGo may not precisely match the ruleset in such ++# cases but will do its best. ++rules = tromp-taylor ++ ++# By default, the "rules" parameter is used, but if you comment it out and ++# uncomment one option in each of the sections below, you can specify an ++# arbitrary combination of individual rules. ++ ++# koRule = SIMPLE # Simple ko rules (triple ko = no result) ++# koRule = POSITIONAL # Positional superko ++# koRule = SITUATIONAL # Situational superko ++ ++# scoringRule = AREA # Area scoring ++# scoringRule = TERRITORY # Territory scoring (special computer-friendly territory rules) ++ ++# taxRule = NONE # All surrounded empty points are scored ++# taxRule = SEKI # Eyes in seki do NOT count as points ++# taxRule = ALL # All groups are taxed up to 2 points for the two eyes needed to live ++ ++# Is multiple-stone suicide legal? (Single-stone suicide is always illegal). ++# multiStoneSuicideLegal = false ++# multiStoneSuicideLegal = true ++ ++# "Button go" - the first pass when area scoring awards 0.5 points and does ++# not count for ending the game. ++# Allows area scoring rulesets that have far simpler rules to achieve the same ++# final scoring precision and reward for precise play as territory scoring. ++# hasButton = false ++# hasButton = true ++ ++# Is this a human ruleset where it's okay to pass before having physically ++# captured and removed all dead stones? ++# friendlyPassOk = false ++# friendlyPassOk = true ++ ++# How handicap stones in handicap games are compensated ++# whiteHandicapBonus = 0 # White gets no compensation for black's handicap stones (Tromp-taylor, NZ, JP) ++# whiteHandicapBonus = N-1 # White gets N-1 points for black's N handicap stones (AGA) ++# whiteHandicapBonus = N # White gets N points for black's N handicap stones (Chinese) ++ ++# ------------------------------ ++# Other rules hacks ++# ------------------------------ ++# Uncomment and change to adjust what board size KataGo uses upon startup ++# by default when GTP doesn't specify. ++# defaultBoardSize = 19 ++ ++# By default, Katago will use the komi that the GUI or GTP controller tries to set. ++# Uncomment and set this to have KataGo ignore the controller and always use this komi. ++# ignoreGTPAndForceKomi = 7 ++ ++# =========================================================================== ++# Bot behavior ++# =========================================================================== ++ ++# ------------------------------ ++# Resignation ++# ------------------------------ ++ ++# Resignation occurs if for at least resignConsecTurns in a row, the ++# winLossUtility (on a [-1,1] scale) is below resignThreshold. ++allowResignation = true ++resignThreshold = -0.90 ++resignConsecTurns = 3 ++ ++# By default, KataGo may resign games that it is confidently losing even if they ++# are very close in score. Uncomment and set this to avoid resigning games ++# if the estimated difference is points is less than or equal to this. ++# resignMinScoreDifference = 10 ++ ++# ------------------------------ ++# Handicap ++# ------------------------------ ++# Assume that if black makes many moves in a row right at the start of the ++# game, then the game is a handicap game. This is necessary on some servers ++# and for some GUIs and also when initializing from many SGF files, which may ++# set up a handicap game using repeated GTP "play" commands for black rather ++# than GTP "place_free_handicap" commands; however, it may also lead to ++# incorrect understanding of komi if whiteHandicapBonus is used and a server ++# does not have such a practice. Uncomment and set to false to disable. ++# assumeMultipleStartingBlackMovesAreHandicap = true ++ ++# Makes katago dynamically adjust in handicap or altered-komi games to assume ++# based on those game settings that it must be stronger or weaker than the ++# opponent and to play accordingly. Greatly improves handicap strength by ++# biasing winrates and scores to favor appropriate safe/aggressive play. ++# Does NOT affect analysis (lz-analyze, kata-analyze, used by programs like ++# Lizzie) so analysis remains unbiased. Uncomment and set this to 0 to disable ++# this and make KataGo play the same always. ++# dynamicPlayoutDoublingAdvantageCapPerOppLead = 0.045 ++ ++# Instead of "dynamicPlayoutDoublingAdvantageCapPerOppLead", you can comment ++# that out and uncomment and set "playoutDoublingAdvantage" to a fixed value ++# from -3.0 to 3.0 that will not change dynamically. ++# ALSO affects analysis tools (lz-analyze, kata-analyze, used by e.g. Lizzie). ++# Negative makes KataGo behave as if it is much weaker than the opponent. ++# Positive makes KataGo behave as if it is much stronger than the opponent. ++# KataGo will adjust to favor safe/aggressive play as appropriate based on ++# the combination of who is ahead and how much stronger/weaker it thinks it is, ++# and report winrates and scores taking the strength difference into account. ++# ++# If this and "dynamicPlayoutDoublingAdvantageCapPerOppLead" are both set ++# then dynamic will be used for all games and this fixed value will be used ++# for analysis tools. ++# playoutDoublingAdvantage = 0.0 ++ ++# Uncomment one of these when using "playoutDoublingAdvantage" to enforce ++# that it will only apply when KataGo plays as the specified color and will be ++# negated when playing as the opposite color. ++# playoutDoublingAdvantagePla = BLACK ++# playoutDoublingAdvantagePla = WHITE ++ ++# ------------------------------ ++# Passing and cleanup ++# ------------------------------ ++# Make the bot never assume that its pass will end the game, even if passing ++# would end and "win" under Tromp-Taylor rules. Usually this is a good idea ++# when using it for analysis or playing on servers where scoring may be ++# implemented non-tromp-taylorly. Uncomment and set to false to disable. ++# conservativePass = true ++ ++# When using territory scoring, self-play games continue beyond two passes ++# with special cleanup rules that may be confusing for human players. This ++# option prevents the special cleanup phases from being reachable when using ++# the bot for GTP play. Uncomment and set to false to enable entering special ++# cleanup. For example, if you are testing it against itself, or against ++# another bot that has precisely implemented the rules documented at ++# https://lightvector.github.io/KataGo/rules.html ++# preventCleanupPhase = true ++ ++# ------------------------------ ++# Miscellaneous behavior ++# ------------------------------ ++# If the board is symmetric, search only one copy of each equivalent move. ++# Attempts to also account for ko/superko, will not theoretically perfect for ++# superko. Uncomment and set to false to disable. ++# rootSymmetryPruning = true ++ ++# Uncomment and set to true to avoid a particular joseki that some networks ++# misevaluate, and also to improve opening diversity versus some particular ++# other bots that like to play it all the time. ++# avoidMYTDaggerHack = false ++ ++# Prefer to avoid playing the same joseki in every corner of the board. ++# Uncomment to set to a specific value. See "Avoid SGF patterns" section. ++# By default: 0 (even games), 0.005 (handicap games) ++# avoidRepeatedPatternUtility = 0.0 ++ ++# Experimental logic to fight against mirror Go even with unfavorable komi. ++# Uncomment to set to a specific value to use for both playing and analysis. ++# By default: true when playing via GTP, but false when analyzing. ++# antiMirror = true ++ ++# Enable some hacks that mitigate rare instances when passing messes up deeper searches. ++# enablePassingHacks = true ++ ++# Uncomment and set this to true to prevent bad or bogus move sequences ++# in the history leading to this position from affecting KataGo's move choices. ++# Same as analysisIgnorePreRootHistory (see above) but applies to actual play. ++# You can enable this if KataGo is being asked to play from positions that it did not ++# choose the moves to reach. ++# ignorePreRootHistory = false ++ ++# =========================================================================== ++# Search limits ++# =========================================================================== ++ ++# Terminology: ++# "Playouts" is the number of new playouts of search performed each turn. ++# "Visits" is the same as "Playouts" but also counts search performed on ++# previous turns that is still applicable to this turn. ++# "Time" is the time in seconds. ++ ++# For example, if KataGo searched 200 nodes on the previous turn, and then ++# after the opponent's reply, 50 nodes of its search tree was still valid, ++# then a visit limit of 200 would allow KataGo to search 150 new nodes ++# (for a final tree size of 200 nodes), whereas a playout limit of of 200 ++# would allow KataGo to search 200 nodes (for a final tree size of 250 nodes). ++ ++# Additionally, KataGo may also move before than the limit in order to ++# obey time controls (e.g. byo-yomi, etc) if the GTP controller has ++# told KataGo that the game has is being played with a given time control. ++ ++# Limits for search on the current turn. ++# If commented out or unspecified, the default is to have no limit. ++# maxVisits = 500 ++# maxPlayouts = 300 ++ maxTime = 0.5 ++ ++# Ponder on the opponent's turn? ++ponderingEnabled = true ++ ++# Limits for search when pondering on the opponent's turn. ++# If commented out or unspecified, the default is to have no limit. ++# Limiting the maximum time is recommended so that KataGo won't burn CPU/GPU ++# forever and/or run out of RAM if left unattended while pondering is enabled. ++# maxVisitsPondering = 5000 ++# maxPlayoutsPondering = 3000 ++maxTimePondering = 120.0 ++ ++ ++# ------------------------------ ++# Other search limits and behavior ++# ------------------------------ ++ ++# Approx number of seconds to buffer for lag for GTP time controls - will ++# move a bit faster assuming there is this much lag per move. ++lagBuffer = 1.0 ++ ++# YOU PROBABLY WANT TO TUNE THIS PARAMETER! ++# The number of threads to use when searching. On powerful GPUs the optimal ++# threads may be much higher than the number of CPU cores you have because ++# many threads are needed to feed efficient large batches to the GPU. ++# ++# Run "./katago benchmark" to tune this parameter and test the effect ++# of changes to any of other parameters. ++numSearchThreads = 16 ++ ++# Play a little faster if the opponent is passing, for human-friendliness. ++# Comment these out to disable them, such as if running a controlled match ++# where you are testing KataGo with fixed compute per move vs other bots. ++searchFactorAfterOnePass = 0.50 ++searchFactorAfterTwoPass = 0.25 ++ ++# Play a little faster if super-winning, for human-friendliness. ++# Comment these out to disable them, such as if running a controlled match ++# where you are testing KataGo with fixed compute per move vs other bots. ++searchFactorWhenWinning = 0.40 ++searchFactorWhenWinningThreshold = 0.95 ++ ++# =========================================================================== ++# GPU settings ++# =========================================================================== ++# This section configures GPU settings. ++# ++# Maximum number of positions to send to a single GPU at once. The default ++# value is roughly equal to numSearchThreads, but can be specified manually ++# if running out of memory, or using multiple GPUs that expect to share work. ++# nnMaxBatchSize = ++ ++# Controls the neural network cache size, which is the primary RAM/memory use. ++# KataGo will cache up to (2 ** nnCacheSizePowerOfTwo) many neural net ++# evaluations in case of transpositions in the tree. ++# Increase this to improve performance for searches with tens of thousands ++# of visits or more. Decrease this to limit memory usage. ++# If you're happy to do some math - each neural net entry takes roughly ++# 1.5KB, except when using whole-board ownership/territory ++# visualizations, where each entry will take roughly 3KB. The number of ++# entries is (2 ** nnCacheSizePowerOfTwo). (E.g. 2 ** 18 = 262144.) ++# You can compute roughly how much memory the cache will use based on this. ++# nnCacheSizePowerOfTwo = 20 ++ ++# Size of mutex pool for nnCache is (2 ** this). ++# nnMutexPoolSizePowerOfTwo = 16 ++ ++# Randomize board orientation when running neural net evals? Uncomment and ++# set to false to disable. ++# nnRandomize = true ++ ++# If provided, force usage of a specific seed for nnRandomize. ++# The default is to use a randomly generated seed. ++# nnRandSeed = abcdefg ++ ++# Uncomment and set to true to force GTP to use the maximum board size for ++# internal buffers for the neural net. This will make KataGo slower when ++# evaluating small boards, but will avoid a lengthy initialization time on every ++# change of board size due to having to re-size the neural net buffers on the GPU. ++# This can be useful for example, for OGS's persistent bot mode that uses a single ++# bot instance to handle multiple games and may thrash between different board sizes ++# if there are concurrent games of multiple sizes. ++# gtpForceMaxNNSize = false ++ ++# ------------------------------ ++# Multiple GPUs ++# ------------------------------ ++# Set this to the number of GPUs to use or that are available. ++# IMPORTANT: If more than 1, also uncomment the appropriate TensorRT ++# or CUDA or OpenCL section. ++# numNNServerThreadsPerModel = 1 ++ ++# ------------------------------ ++# TENSORRT GPU settings ++# ------------------------------ ++# These only apply when using the TENSORRT version of KataGo. ++ ++# For one GPU: optionally uncomment this option and change if the GPU to ++# use is not device 0. ++# trtDeviceToUse = 0 ++ ++# For two GPUs: Uncomment these options, AND set numNNServerThreadsPerModel above. ++# Also, change their values if the devices you want to use are not 0 and 1. ++# trtDeviceToUseThread0 = 0 ++# trtDeviceToUseThread1 = 1 ++ ++# For three GPUs: Uncomment these options, AND set numNNServerThreadsPerModel above. ++# Also, change their values if the devices you want to use are not 0 and 1 and 2. ++# trtDeviceToUseThread0 = 0 ++# trtDeviceToUseThread1 = 1 ++# trtDeviceToUseThread2 = 2 ++ ++# The pattern continues for additional GPUs. ++ ++# ------------------------------ ++# CUDA GPU settings ++# ------------------------------ ++# These only apply when using the CUDA version of KataGo. ++ ++# For one GPU: optionally uncomment and change this if the GPU you want to ++# use is not device 0 ++# cudaDeviceToUse = 0 ++ ++# For two GPUs: Uncomment these options, AND set numNNServerThreadsPerModel above. ++# Also, change their values if the devices you want to use are not 0 and 1. ++# cudaDeviceToUseThread0 = 0 ++# cudaDeviceToUseThread1 = 1 ++ ++# For three GPUs: Uncomment these options, AND set numNNServerThreadsPerModel above. ++# Also, change their values if the devices you want to use are not 0 and 1 and 2. ++# cudaDeviceToUseThread0 = 0 ++# cudaDeviceToUseThread1 = 1 ++# cudaDeviceToUseThread2 = 2 ++ ++# The pattern continues for additional GPUs. ++ ++# KataGo will automatically use FP16 or not based on the compute capability ++# of your NVIDIA GPU. If you want to try to force a particular behavior ++# you can uncomment these lines and change them to "true" or "false". ++# cudaUseFP16 = auto ++# cudaUseNHWC = auto ++ ++# ------------------------------ ++# OpenCL GPU settings ++# ------------------------------ ++# These only apply when using the OpenCL version of KataGo. ++ ++# Uncomment and set to true to tune OpenCL for every board size separately, ++# rather than only the largest possible size. ++# openclReTunePerBoardSize = false ++ ++# For one GPU: optionally uncomment and change this if the best device to use is guessed incorrectly. ++# The default behavior tries to guess the 'best' GPU or device on your system to use, usually it will be a good guess. ++# openclDeviceToUse = 0 ++ ++# For two GPUs: Uncomment these two lines and replace X and Y with the device ids of the devices you want to use. ++# It might NOT be 0 and 1, some computers will have many OpenCL devices. You can see what the devices are when ++# KataGo starts up - it should print or log all the devices it finds. ++# (AND also set numNNServerThreadsPerModel above) ++# openclDeviceToUseThread0 = X ++# openclDeviceToUseThread1 = Y ++ ++# For three GPUs: Uncomment these three lines and replace X and Y and Z with the device ids of the devices you want to use. ++# It might NOT be 0 and 1 and 2, some computers will have many OpenCL devices. You can see what the devices are when ++# KataGo starts up - it should print or log all the devices it finds. ++# (AND also set numNNServerThreadsPerModel above) ++# openclDeviceToUseThread0 = X ++# openclDeviceToUseThread1 = Y ++# openclDeviceToUseThread2 = Z ++ ++# The pattern continues for additional GPUs. ++ ++# KataGo will automatically use FP16 or not based on testing your GPU during ++# tuning. If you want to try to force a particular behavior though you can ++# uncomment this option and change it to "true" or "false". This is a fairly ++# blunt setting - more detailed settings are testable by rerunning the tuner ++# with various arguments (./katago tuner). ++# openclUseFP16 = auto ++ ++# ------------------------------ ++# Eigen-specific settings ++# ------------------------------ ++# These only apply when using the Eigen (pure CPU) version of KataGo. ++ ++# Number of CPU threads for evaluating the neural net on the Eigen backend. ++# ++# Default: numSearchThreads ++# numEigenThreadsPerModel = X ++ ++# =========================================================================== ++# Root move selection and biases ++# =========================================================================== ++# Uncomment and edit any of the below values to change them from their default. ++ ++# If provided, force usage of a specific seed for various random things in ++# the search. The default is to use a random seed. ++# searchRandSeed = hijklmn ++ ++# Temperature for the early game, randomize between chosen moves with ++# this temperature ++# chosenMoveTemperatureEarly = 0.5 ++ ++# Decay temperature for the early game by 0.5 every this many moves, ++# scaled with board size. ++# chosenMoveTemperatureHalflife = 19 ++ ++# At the end of search after the early game, randomize between chosen ++# moves with this temperature ++# chosenMoveTemperature = 0.10 ++ ++# Subtract this many visits from each move prior to applying ++# chosenMoveTemperature (unless all moves have too few visits) to downweight ++# unlikely moves ++# chosenMoveSubtract = 0 ++ ++# The same as chosenMoveSubtract but only prunes moves that fall below ++# the threshold. This setting does not affect chosenMoveSubtract. ++# chosenMovePrune = 1 ++ ++# Number of symmetries to sample (without replacement) and average at the root ++# rootNumSymmetriesToSample = 1 ++ ++# Using LCB for move selection? ++# useLcbForSelection = true ++ ++# How many stdevs a move needs to be better than another for LCB selection ++# lcbStdevs = 5.0 ++ ++# Only use LCB override when a move has this proportion of visits as the ++# top move. ++# minVisitPropForLCB = 0.15 ++ ++# =========================================================================== ++# Internal params ++# =========================================================================== ++# Uncomment and edit any of the below values to change them from their default. ++ ++# Scales the utility of winning/losing ++# winLossUtilityFactor = 1.0 ++ ++# Scales the utility for trying to maximize score ++# staticScoreUtilityFactor = 0.10 ++# dynamicScoreUtilityFactor = 0.30 ++ ++# Adjust dynamic score center this proportion of the way towards zero, ++# capped at a reasonable amount. ++# dynamicScoreCenterZeroWeight = 0.20 ++# dynamicScoreCenterScale = 0.75 ++ ++# The utility of getting a "no result" due to triple ko or other long cycle ++# in non-superko rulesets (-1 to 1) ++# noResultUtilityForWhite = 0.0 ++ ++# The number of wins that a draw counts as, for white. (0 to 1) ++# drawEquivalentWinsForWhite = 0.5 ++ ++# Exploration constant for mcts ++# cpuctExploration = 1.0 ++# cpuctExplorationLog = 0.45 ++ ++# Parameters that control exploring more in volatile positions, exploring ++# less in stable positions. ++# cpuctUtilityStdevPrior = 0.40 ++# cpuctUtilityStdevPriorWeight = 2.0 ++# cpuctUtilityStdevScale = 0.85 ++ ++# FPU reduction constant for mcts ++# fpuReductionMax = 0.2 ++# rootFpuReductionMax = 0.1 ++# fpuParentWeightByVisitedPolicy = true ++ ++# Parameters that control weighting of evals based on the net's own ++# self-reported uncertainty. ++# useUncertainty = true ++# uncertaintyExponent = 1.0 ++# uncertaintyCoeff = 0.25 ++ ++# Explore using optimistic policy ++# rootPolicyOptimism = 0.2 ++# policyOptimism = 1.0 ++ ++# Amount to apply a downweighting of children with very bad values relative ++# to good ones. ++# valueWeightExponent = 0.25 ++ ++# Slight incentive for the bot to behave human-like with regard to passing at ++# the end, filling the dame, not wasting time playing in its own territory, ++# etc., and not play moves that are equivalent in terms of points but a bit ++# more unfriendly to humans. ++# rootEndingBonusPoints = 0.5 ++ ++# Make the bot prune useless moves that are just prolonging the game to ++# avoid losing yet. ++# rootPruneUselessMoves = true ++ ++# Apply bias correction based on local pattern keys ++# subtreeValueBiasFactor = 0.45 ++# subtreeValueBiasWeightExponent = 0.85 ++ ++# Use graph search rather than tree search - identify and share search for ++# transpositions. ++# useGraphSearch = true ++ ++# How much to shard the node table for search synchronization ++# nodeTableShardsPowerOfTwo = 16 ++ ++# How many virtual losses to add when a thread descends through a node ++# numVirtualLossesPerThread = 1 ++ ++# Improve the quality of evals under heavy multithreading ++# useNoisePruning = true ++ ++# =========================================================================== ++# Automatic avoid patterns ++# =========================================================================== ++# The parameters in this section provide a way to bias away from moves that match ++# patterns that this instance of KataGo has played in previous games, by auto-saving ++# moves to a directory and then auto-loading and biasing against them each new game. ++# Uncomment them to use them. When using this feature, all parameters must be specified. ++ ++# Different board sizes are tracked separately, but all board sizes share the same ++# parameters by default. Every parameter *except* for autoAvoidRepeatDir and ++# autoAvoidRepeatSaveChunkSize can be overridden per board size, ++# e.g. "autoAvoidRepeatMinTurnNumber13x13". You must ALSO specify the ++# defaults even if you have specified board-size-specific values. ++ ++# Directory to auto-save moves KataGo plays, to avoid them in future games. ++# You can create a new empty directory and put its path here. ++# If you run parallel instances of KataGo, use different directories if you ++# want them not to share their biases, use the same directory if you want ++# all of them to bias away from past moves that any of them have played. ++# KataGo will also automatically DELETE old data in this directory, so it is ++# recommended that if you do share the same directory between parallel instances, ++# that they all use the same settings that affect data saving/deletion. ++# autoAvoidRepeatDir = PATH_TO_NEW_DIRECTORY ++ ++# Penalize this much utility per matching move. ++# Values that are too large may lead to bad play. The value of 0.004 is fairly large ++# and might be large enough to result in some early weird/bad moves when trying ++# to avoid past games' moves if enough games begin the same way. You can experiment with it. ++# autoAvoidRepeatUtility = 0.004 ++ ++# Per each new move saved, exponentially decay prior saved moves by this factor. ++# This way, the bias against moves from many games ago is gradually phased out. ++# For example, 0.9995 = 1 - 1/2000, so would be roughly 2000 prior moves worth of ++# penalty weight remembered, in steady state. Depending on what turn number range ++# you are saving, this might equate to a different number of games For example ++# saving the first 50 moves per game would make this roughly 2000 / 50 = 40 games ++# worth of memory. ++# autoAvoidRepeatLambda = 0.9995 ++ ++# Affects data saving/deletion. ++# When the number of saved moves exceeds this, outright delete them to avoid too many ++# files and disk space building up. Also may affect the speed of saving/loading on start ++# of each game if this is set large and a lot of data builds up. ++# autoAvoidRepeatMaxPoses = 10000 ++ ++# Affects data saving/deletion. ++# Only save data for moves within this turn number range of those games. ++# E.g. setting autoAvoidRepeatMinTurnNumber to a number like 4 or 5 would tend to make ++# KataGo not develop a bias against the initial 3-4 and 4-4 corner moves in almost every game. ++# autoAvoidRepeatMinTurnNumber = 0 ++# autoAvoidRepeatMaxTurnNumber = 50 ++ ++# Affects data saving/deletion. ++# Within a single run of a program, wait to accumulate this many samples ++# (possibly across multiple clear_boards/games) before saving the data. ++# Can help to avoid writing too many small files to disk, especially when GTP is used ++# in a way that clears the board very frequently (e.g. gtp2ogs pooled manager). ++# autoAvoidRepeatSaveChunkSize = 200 ++ ++# =========================================================================== ++# Avoid SGF patterns ++# =========================================================================== ++# The parameters in this section provide a way to avoid moves that follow ++# specific patterns based on a set of SGF files loaded upon startup. ++# This is basically the same as the above "Automatic avoid patterns" section ++# above except you supply your own SGF files to avoid moves from. ++# Uncomment them to use this feature. Additionally, if the SGF file ++# contains the string %SKIP% in a comment on a move, that move will be ++# ignored for this purpose. ++ ++# Load SGF files from this directory when the engine is started ++# (only on startup, will not reload unless engine is restarted) ++# avoidSgfPatternDirs = path/to/directory/with/sgfs/ ++# You can also surround the file path in double quotes if the file path contains trailing spaces or hash signs. ++# Within double quotes, backslashes are escape characters. ++# avoidSgfPatternDirs = "path/to/directory/with/sgfs/" ++ ++# Penalize this much utility per matching move. ++# Set this negative if you instead want to favor SGF patterns instead of ++# penalizing them. This number does not need to be large, even 0.001 will ++# make a difference. Values that are too large may lead to bad play. ++# avoidSgfPatternUtility = 0.001 ++ ++# Optional - load only the newest this many files ++# avoidSgfPatternMaxFiles = 20 ++ ++# Optional - Penalty is multiplied by this per each older SGF file, so that ++# old SGF files matter less than newer ones. ++# avoidSgfPatternLambda = 0.90 ++ ++# Optional - pay attention only to moves made by players with this name. ++# For example, set it to the name that your bot's past games will show up ++# as in the SGF, so that the bot will only avoid repeating moves that itself ++# made in past games, not the moves that its opponents made. ++# avoidSgfPatternAllowedNames = my-ogs-bot-name1,my-ogs-bot-name2 ++ ++# Optional - Ignore moves in SGF files that occurred before this turn number. ++# avoidSgfPatternMinTurnNumber = 0 ++ ++# For more avoid patterns: ++# You can also specify a second set of parameters, and a third, fourth, ++# etc. by numbering 2,3,4,... ++# ++# avoidSgf2PatternDirs = ... ++# avoidSgf2PatternUtility = ... ++# avoidSgf2PatternMaxFiles = ... ++# avoidSgf2PatternLambda = ... ++# avoidSgf2PatternAllowedNames = ... ++# avoidSgf2PatternMinTurnNumber = ... ++ +diff --git a/cpp/golaxy_9d.cfg b/cpp/golaxy_9d.cfg +new file mode 100644 +index 00000000..31e66efd +--- /dev/null ++++ b/cpp/golaxy_9d.cfg +@@ -0,0 +1,713 @@ ++# Configuration for KataGo C++ GTP engine ++ ++# Run the program using: `./katago.exe gtp` ++ ++# In this example config, when a parameter is given as a commented out value, ++# that value also is the default value, unless described otherwise. You can ++# uncomment it (remove the pound sign) and change it if you want. ++ ++# =========================================================================== ++# Running on an online server or in a real tournament or match ++# =========================================================================== ++# If you plan to run online or in a tournament, read through the "Rules" ++# section below for proper handling of komi, handicaps, end-of-game cleanup, ++# and other details. ++ ++# =========================================================================== ++# Notes about performance and memory usage ++# =========================================================================== ++# Important: For good performance, you will very likely want to tune the ++# "numSearchThreads" parameter in the Search limits section below! Run ++# "./katago benchmark" to test KataGo and to suggest a reasonable value ++# of this parameter. ++ ++# For multi-GPU systems, read "OpenCL GPU settings" or "CUDA GPU settings". ++# ++# When using OpenCL, verify that KataGo picks the correct device! Some systems ++# may have both an Intel CPU OpenCL and GPU OpenCL. If # KataGo picks the wrong ++# one, correct this by specifying "openclGpuToUse". ++# ++# Consider adjusting "maxVisits", "ponderingEnabled", "resignThreshold", and ++# other parameters depending on your intended usage. ++ ++# =========================================================================== ++# Command-line usage ++# =========================================================================== ++# All of the below values may be set or overridden via command-line arguments: ++# ++# -override-config KEY=VALUE,KEY=VALUE,... ++ ++# =========================================================================== ++# Logs and files ++# =========================================================================== ++# This section defines where and what logging information is produced. ++ ++# Each run of KataGo will log to a separate file in this dir. ++# This is the default. ++logDir = gtp_logs ++# Uncomment and specify this instead of logDir to write separate dated subdirs ++# logDirDated = gtp_logs ++# Uncomment and specify this instead of logDir to log to only a single file ++# logFile = gtp.log ++ ++# Logging options ++logAllGTPCommunication = true ++logSearchInfo = true ++logSearchInfoForChosenMove = false ++logToStderr = true ++ ++# KataGo will display some info to stderr on GTP startup ++# Uncomment the next line and set it to false to suppress that and remain silent ++# startupPrintMessageToStderr = true ++ ++# Write information to stderr, for use in things like malkovich chat to OGS. ++ ogsChatToStderr = true ++ ++# Uncomment and set this to a directory to override where openCLTuner files ++# and other cached data is written. By default it saves into a subdir of the ++# current directory on windows, and a subdir of ~/.katago on Linux. ++# homeDataDir = PATH_TO_DIRECTORY ++ ++# =========================================================================== ++# Analysis ++# =========================================================================== ++# This section configures analysis settings. ++# ++# The maximum number of moves after the first move displayed in variations ++# from analysis commands like kata-analyze or lz-analyze. ++# analysisPVLen = 15 ++ ++# Report winrates for chat and analysis as (BLACK|WHITE|SIDETOMOVE). ++# Most GUIs and analysis tools will expect SIDETOMOVE. ++# reportAnalysisWinratesAs = SIDETOMOVE ++ ++# Extra noise for wider exploration. Large values will force KataGo to ++# analyze a greater variety of moves than it normally would. ++# An extreme value like 1 distributes playouts across every move on the board, ++# even very bad moves. ++# Affects analysis only, does not affect play. ++# analysisWideRootNoise = 0.04 ++ ++# Try to limit the effect of possible bad or bogus move sequences in the ++# history leading to this position from affecting KataGo's move predictions. ++# analysisIgnorePreRootHistory = true ++ ++# =========================================================================== ++# Rules ++# =========================================================================== ++# This section configures the scoring and playing rules. Rules can also be ++# changed mid-run by issuing custom GTP commands. ++# ++# See https://lightvector.github.io/KataGo/rules.html for rules details. ++# ++# See https://github.com/lightvector/KataGo/blob/master/docs/GTP_Extensions.md ++# for GTP commands. ++ ++# Specify the rules as a string. ++# Some legal values include: ++# chinese, japanese, korean, aga, chinese-ogs, new-zealand, stone-scoring, ++# ancient-territory, bga, aga-button ++# ++# For some human rulesets that require complex adjudication in tricky cases ++# (e.g. japanese, korean) KataGo may not precisely match the ruleset in such ++# cases but will do its best. ++# rules = tromp-taylor ++ ++# By default, the "rules" parameter is used, but if you comment it out and ++# uncomment one option in each of the sections below, you can specify an ++# arbitrary combination of individual rules. ++ ++# Ko Rule Settings ++koRule = POSITIONAL # Positional superko ++ ++# Scoring Rule Settings ++scoringRule = AREA # Area scoring ++ ++# Tax Rule Settings ++taxRule = NONE # All surrounded empty points are scored ++ ++# Is multiple-stone suicide legal? (Single-stone suicide is always illegal). ++multiStoneSuicideLegal = false ++ ++# "Button go" - the first pass when area scoring awards 0.5 points and does ++# not count for ending the game. ++hasButton = false ++ ++# Is this a human ruleset where it's okay to pass before having physically ++# captured and removed all dead stones? ++friendlyPassOk = false ++ ++# How handicap stones in handicap games are compensated ++whiteHandicapBonus = 0 # White gets no compensation for black's handicap stones (Tromp-taylor, NZ, JP) ++ ++ ++# ------------------------------ ++# Other rules hacks ++# ------------------------------ ++# Uncomment and change to adjust what board size KataGo uses upon startup ++# by default when GTP doesn't specify. ++# defaultBoardSize = 19 ++ ++# By default, Katago will use the komi that the GUI or GTP controller tries to set. ++# Uncomment and set this to have KataGo ignore the controller and always use this komi. ++# ignoreGTPAndForceKomi = 7 ++ ++# =========================================================================== ++# Bot behavior ++# =========================================================================== ++ ++# ------------------------------ ++# Resignation ++# ------------------------------ ++ ++# Resignation occurs if for at least resignConsecTurns in a row, the ++# winLossUtility (on a [-1,1] scale) is below resignThreshold. ++allowResignation = true ++resignThreshold = -0.90 ++resignConsecTurns = 3 ++ ++# By default, KataGo may resign games that it is confidently losing even if they ++# are very close in score. Uncomment and set this to avoid resigning games ++# if the estimated difference is points is less than or equal to this. ++# resignMinScoreDifference = 10 ++ ++# ------------------------------ ++# Handicap ++# ------------------------------ ++# Assume that if black makes many moves in a row right at the start of the ++# game, then the game is a handicap game. This is necessary on some servers ++# and for some GUIs and also when initializing from many SGF files, which may ++# set up a handicap game using repeated GTP "play" commands for black rather ++# than GTP "place_free_handicap" commands; however, it may also lead to ++# incorrect understanding of komi if whiteHandicapBonus is used and a server ++# does not have such a practice. Uncomment and set to false to disable. ++# assumeMultipleStartingBlackMovesAreHandicap = true ++ ++# Makes katago dynamically adjust in handicap or altered-komi games to assume ++# based on those game settings that it must be stronger or weaker than the ++# opponent and to play accordingly. Greatly improves handicap strength by ++# biasing winrates and scores to favor appropriate safe/aggressive play. ++# Does NOT affect analysis (lz-analyze, kata-analyze, used by programs like ++# Lizzie) so analysis remains unbiased. Uncomment and set this to 0 to disable ++# this and make KataGo play the same always. ++# dynamicPlayoutDoublingAdvantageCapPerOppLead = 0.045 ++ ++# Instead of "dynamicPlayoutDoublingAdvantageCapPerOppLead", you can comment ++# that out and uncomment and set "playoutDoublingAdvantage" to a fixed value ++# from -3.0 to 3.0 that will not change dynamically. ++# ALSO affects analysis tools (lz-analyze, kata-analyze, used by e.g. Lizzie). ++# Negative makes KataGo behave as if it is much weaker than the opponent. ++# Positive makes KataGo behave as if it is much stronger than the opponent. ++# KataGo will adjust to favor safe/aggressive play as appropriate based on ++# the combination of who is ahead and how much stronger/weaker it thinks it is, ++# and report winrates and scores taking the strength difference into account. ++# ++# If this and "dynamicPlayoutDoublingAdvantageCapPerOppLead" are both set ++# then dynamic will be used for all games and this fixed value will be used ++# for analysis tools. ++# playoutDoublingAdvantage = 0.0 ++ ++# Uncomment one of these when using "playoutDoublingAdvantage" to enforce ++# that it will only apply when KataGo plays as the specified color and will be ++# negated when playing as the opposite color. ++# playoutDoublingAdvantagePla = BLACK ++# playoutDoublingAdvantagePla = WHITE ++ ++# ------------------------------ ++# Passing and cleanup ++# ------------------------------ ++# Make the bot never assume that its pass will end the game, even if passing ++# would end and "win" under Tromp-Taylor rules. Usually this is a good idea ++# when using it for analysis or playing on servers where scoring may be ++# implemented non-tromp-taylorly. Uncomment and set to false to disable. ++# conservativePass = true ++ ++# When using territory scoring, self-play games continue beyond two passes ++# with special cleanup rules that may be confusing for human players. This ++# option prevents the special cleanup phases from being reachable when using ++# the bot for GTP play. Uncomment and set to false to enable entering special ++# cleanup. For example, if you are testing it against itself, or against ++# another bot that has precisely implemented the rules documented at ++# https://lightvector.github.io/KataGo/rules.html ++# preventCleanupPhase = true ++ ++# ------------------------------ ++# Miscellaneous behavior ++# ------------------------------ ++# If the board is symmetric, search only one copy of each equivalent move. ++# Attempts to also account for ko/superko, will not theoretically perfect for ++# superko. Uncomment and set to false to disable. ++# rootSymmetryPruning = true ++ ++# Uncomment and set to true to avoid a particular joseki that some networks ++# misevaluate, and also to improve opening diversity versus some particular ++# other bots that like to play it all the time. ++# avoidMYTDaggerHack = false ++ ++# Prefer to avoid playing the same joseki in every corner of the board. ++# Uncomment to set to a specific value. See "Avoid SGF patterns" section. ++# By default: 0 (even games), 0.005 (handicap games) ++# avoidRepeatedPatternUtility = 0.0 ++ ++# Experimental logic to fight against mirror Go even with unfavorable komi. ++# Uncomment to set to a specific value to use for both playing and analysis. ++# By default: true when playing via GTP, but false when analyzing. ++# antiMirror = true ++ ++# Enable some hacks that mitigate rare instances when passing messes up deeper searches. ++# enablePassingHacks = true ++ ++# Uncomment and set this to true to prevent bad or bogus move sequences ++# in the history leading to this position from affecting KataGo's move choices. ++# Same as analysisIgnorePreRootHistory (see above) but applies to actual play. ++# You can enable this if KataGo is being asked to play from positions that it did not ++# choose the moves to reach. ++# ignorePreRootHistory = false ++ ++# =========================================================================== ++# Search limits ++# =========================================================================== ++ ++# Terminology: ++# "Playouts" is the number of new playouts of search performed each turn. ++# "Visits" is the same as "Playouts" but also counts search performed on ++# previous turns that is still applicable to this turn. ++# "Time" is the time in seconds. ++ ++# For example, if KataGo searched 200 nodes on the previous turn, and then ++# after the opponent's reply, 50 nodes of its search tree was still valid, ++# then a visit limit of 200 would allow KataGo to search 150 new nodes ++# (for a final tree size of 200 nodes), whereas a playout limit of of 200 ++# would allow KataGo to search 200 nodes (for a final tree size of 250 nodes). ++ ++# Additionally, KataGo may also move before than the limit in order to ++# obey time controls (e.g. byo-yomi, etc) if the GTP controller has ++# told KataGo that the game has is being played with a given time control. ++ ++# Limits for search on the current turn. ++# If commented out or unspecified, the default is to have no limit. ++# maxVisits = 50000 ++# maxPlayouts = 300 ++ maxTime = 1.0 ++ ++# Ponder on the opponent's turn? ++ponderingEnabled = true ++ ++# Limits for search when pondering on the opponent's turn. ++# If commented out or unspecified, the default is to have no limit. ++# Limiting the maximum time is recommended so that KataGo won't burn CPU/GPU ++# forever and/or run out of RAM if left unattended while pondering is enabled. ++# maxVisitsPondering = 5000 ++# maxPlayoutsPondering = 3000 ++maxTimePondering = 120.0 ++ ++ ++# ------------------------------ ++# Other search limits and behavior ++# ------------------------------ ++ ++# Approx number of seconds to buffer for lag for GTP time controls - will ++# move a bit faster assuming there is this much lag per move. ++lagBuffer = 1.0 ++ ++# YOU PROBABLY WANT TO TUNE THIS PARAMETER! ++# The number of threads to use when searching. On powerful GPUs the optimal ++# threads may be much higher than the number of CPU cores you have because ++# many threads are needed to feed efficient large batches to the GPU. ++# ++# Run "./katago benchmark" to tune this parameter and test the effect ++# of changes to any of other parameters. ++numSearchThreads = 40 ++ ++# Play a little faster if the opponent is passing, for human-friendliness. ++# Comment these out to disable them, such as if running a controlled match ++# where you are testing KataGo with fixed compute per move vs other bots. ++searchFactorAfterOnePass = 0.50 ++searchFactorAfterTwoPass = 0.25 ++ ++# Play a little faster if super-winning, for human-friendliness. ++# Comment these out to disable them, such as if running a controlled match ++# where you are testing KataGo with fixed compute per move vs other bots. ++searchFactorWhenWinning = 0.40 ++searchFactorWhenWinningThreshold = 0.95 ++ ++# =========================================================================== ++# GPU settings ++# =========================================================================== ++# This section configures GPU settings. ++# ++# Maximum number of positions to send to a single GPU at once. The default ++# value is roughly equal to numSearchThreads, but can be specified manually ++# if running out of memory, or using multiple GPUs that expect to share work. ++# nnMaxBatchSize = ++ ++# Controls the neural network cache size, which is the primary RAM/memory use. ++# KataGo will cache up to (2 ** nnCacheSizePowerOfTwo) many neural net ++# evaluations in case of transpositions in the tree. ++# Increase this to improve performance for searches with tens of thousands ++# of visits or more. Decrease this to limit memory usage. ++# If you're happy to do some math - each neural net entry takes roughly ++# 1.5KB, except when using whole-board ownership/territory ++# visualizations, where each entry will take roughly 3KB. The number of ++# entries is (2 ** nnCacheSizePowerOfTwo). (E.g. 2 ** 18 = 262144.) ++# You can compute roughly how much memory the cache will use based on this. ++# nnCacheSizePowerOfTwo = 20 ++ ++# Size of mutex pool for nnCache is (2 ** this). ++# nnMutexPoolSizePowerOfTwo = 16 ++ ++# Randomize board orientation when running neural net evals? Uncomment and ++# set to false to disable. ++# nnRandomize = true ++ ++# If provided, force usage of a specific seed for nnRandomize. ++# The default is to use a randomly generated seed. ++# nnRandSeed = abcdefg ++ ++# Uncomment and set to true to force GTP to use the maximum board size for ++# internal buffers for the neural net. This will make KataGo slower when ++# evaluating small boards, but will avoid a lengthy initialization time on every ++# change of board size due to having to re-size the neural net buffers on the GPU. ++# This can be useful for example, for OGS's persistent bot mode that uses a single ++# bot instance to handle multiple games and may thrash between different board sizes ++# if there are concurrent games of multiple sizes. ++# gtpForceMaxNNSize = false ++ ++# ------------------------------ ++# Multiple GPUs ++# ------------------------------ ++# Set this to the number of GPUs to use or that are available. ++# IMPORTANT: If more than 1, also uncomment the appropriate TensorRT ++# or CUDA or OpenCL section. ++# numNNServerThreadsPerModel = 1 ++ ++# ------------------------------ ++# TENSORRT GPU settings ++# ------------------------------ ++# These only apply when using the TENSORRT version of KataGo. ++ ++# For one GPU: optionally uncomment this option and change if the GPU to ++# use is not device 0. ++# trtDeviceToUse = 0 ++ ++# For two GPUs: Uncomment these options, AND set numNNServerThreadsPerModel above. ++# Also, change their values if the devices you want to use are not 0 and 1. ++# trtDeviceToUseThread0 = 0 ++# trtDeviceToUseThread1 = 1 ++ ++# For three GPUs: Uncomment these options, AND set numNNServerThreadsPerModel above. ++# Also, change their values if the devices you want to use are not 0 and 1 and 2. ++# trtDeviceToUseThread0 = 0 ++# trtDeviceToUseThread1 = 1 ++# trtDeviceToUseThread2 = 2 ++ ++# The pattern continues for additional GPUs. ++ ++# ------------------------------ ++# CUDA GPU settings ++# ------------------------------ ++# These only apply when using the CUDA version of KataGo. ++ ++# For one GPU: optionally uncomment and change this if the GPU you want to ++# use is not device 0 ++# cudaDeviceToUse = 0 ++ ++# For two GPUs: Uncomment these options, AND set numNNServerThreadsPerModel above. ++# Also, change their values if the devices you want to use are not 0 and 1. ++# cudaDeviceToUseThread0 = 0 ++# cudaDeviceToUseThread1 = 1 ++ ++# For three GPUs: Uncomment these options, AND set numNNServerThreadsPerModel above. ++# Also, change their values if the devices you want to use are not 0 and 1 and 2. ++# cudaDeviceToUseThread0 = 0 ++# cudaDeviceToUseThread1 = 1 ++# cudaDeviceToUseThread2 = 2 ++ ++# The pattern continues for additional GPUs. ++ ++# KataGo will automatically use FP16 or not based on the compute capability ++# of your NVIDIA GPU. If you want to try to force a particular behavior ++# you can uncomment these lines and change them to "true" or "false". ++# cudaUseFP16 = auto ++# cudaUseNHWC = auto ++ ++# ------------------------------ ++# OpenCL GPU settings ++# ------------------------------ ++# These only apply when using the OpenCL version of KataGo. ++ ++# Uncomment and set to true to tune OpenCL for every board size separately, ++# rather than only the largest possible size. ++# openclReTunePerBoardSize = false ++ ++# For one GPU: optionally uncomment and change this if the best device to use is guessed incorrectly. ++# The default behavior tries to guess the 'best' GPU or device on your system to use, usually it will be a good guess. ++# openclDeviceToUse = 0 ++ ++# For two GPUs: Uncomment these two lines and replace X and Y with the device ids of the devices you want to use. ++# It might NOT be 0 and 1, some computers will have many OpenCL devices. You can see what the devices are when ++# KataGo starts up - it should print or log all the devices it finds. ++# (AND also set numNNServerThreadsPerModel above) ++# openclDeviceToUseThread0 = X ++# openclDeviceToUseThread1 = Y ++ ++# For three GPUs: Uncomment these three lines and replace X and Y and Z with the device ids of the devices you want to use. ++# It might NOT be 0 and 1 and 2, some computers will have many OpenCL devices. You can see what the devices are when ++# KataGo starts up - it should print or log all the devices it finds. ++# (AND also set numNNServerThreadsPerModel above) ++# openclDeviceToUseThread0 = X ++# openclDeviceToUseThread1 = Y ++# openclDeviceToUseThread2 = Z ++ ++# The pattern continues for additional GPUs. ++ ++# KataGo will automatically use FP16 or not based on testing your GPU during ++# tuning. If you want to try to force a particular behavior though you can ++# uncomment this option and change it to "true" or "false". This is a fairly ++# blunt setting - more detailed settings are testable by rerunning the tuner ++# with various arguments (./katago tuner). ++# openclUseFP16 = auto ++ ++# ------------------------------ ++# Eigen-specific settings ++# ------------------------------ ++# These only apply when using the Eigen (pure CPU) version of KataGo. ++ ++# Number of CPU threads for evaluating the neural net on the Eigen backend. ++# ++# Default: numSearchThreads ++# numEigenThreadsPerModel = X ++ ++# =========================================================================== ++# Root move selection and biases ++# =========================================================================== ++# Uncomment and edit any of the below values to change them from their default. ++ ++# If provided, force usage of a specific seed for various random things in ++# the search. The default is to use a random seed. ++# searchRandSeed = hijklmn ++ ++# Temperature for the early game, randomize between chosen moves with ++# this temperature ++# chosenMoveTemperatureEarly = 0.5 ++ ++# Decay temperature for the early game by 0.5 every this many moves, ++# scaled with board size. ++# chosenMoveTemperatureHalflife = 19 ++ ++# At the end of search after the early game, randomize between chosen ++# moves with this temperature ++# chosenMoveTemperature = 0.10 ++ ++# Subtract this many visits from each move prior to applying ++# chosenMoveTemperature (unless all moves have too few visits) to downweight ++# unlikely moves ++# chosenMoveSubtract = 0 ++ ++# The same as chosenMoveSubtract but only prunes moves that fall below ++# the threshold. This setting does not affect chosenMoveSubtract. ++# chosenMovePrune = 1 ++ ++# Number of symmetries to sample (without replacement) and average at the root ++# rootNumSymmetriesToSample = 1 ++ ++# Using LCB for move selection? ++# useLcbForSelection = true ++ ++# How many stdevs a move needs to be better than another for LCB selection ++# lcbStdevs = 5.0 ++ ++# Only use LCB override when a move has this proportion of visits as the ++# top move. ++# minVisitPropForLCB = 0.15 ++ ++# =========================================================================== ++# Internal params ++# =========================================================================== ++# Uncomment and edit any of the below values to change them from their default. ++ ++# Scales the utility of winning/losing ++# winLossUtilityFactor = 1.0 ++ ++# Scales the utility for trying to maximize score ++# staticScoreUtilityFactor = 0.10 ++# dynamicScoreUtilityFactor = 0.30 ++ ++# Adjust dynamic score center this proportion of the way towards zero, ++# capped at a reasonable amount. ++# dynamicScoreCenterZeroWeight = 0.20 ++# dynamicScoreCenterScale = 0.75 ++ ++# The utility of getting a "no result" due to triple ko or other long cycle ++# in non-superko rulesets (-1 to 1) ++# noResultUtilityForWhite = 0.0 ++ ++# The number of wins that a draw counts as, for white. (0 to 1) ++# drawEquivalentWinsForWhite = 0.5 ++ ++# Exploration constant for mcts ++# cpuctExploration = 1.0 ++# cpuctExplorationLog = 0.45 ++ ++# Parameters that control exploring more in volatile positions, exploring ++# less in stable positions. ++# cpuctUtilityStdevPrior = 0.40 ++# cpuctUtilityStdevPriorWeight = 2.0 ++# cpuctUtilityStdevScale = 0.85 ++ ++# FPU reduction constant for mcts ++# fpuReductionMax = 0.2 ++# rootFpuReductionMax = 0.1 ++# fpuParentWeightByVisitedPolicy = true ++ ++# Parameters that control weighting of evals based on the net's own ++# self-reported uncertainty. ++# useUncertainty = true ++# uncertaintyExponent = 1.0 ++# uncertaintyCoeff = 0.25 ++ ++# Explore using optimistic policy ++# rootPolicyOptimism = 0.2 ++# policyOptimism = 1.0 ++ ++# Amount to apply a downweighting of children with very bad values relative ++# to good ones. ++# valueWeightExponent = 0.25 ++ ++# Slight incentive for the bot to behave human-like with regard to passing at ++# the end, filling the dame, not wasting time playing in its own territory, ++# etc., and not play moves that are equivalent in terms of points but a bit ++# more unfriendly to humans. ++# rootEndingBonusPoints = 0.5 ++ ++# Make the bot prune useless moves that are just prolonging the game to ++# avoid losing yet. ++# rootPruneUselessMoves = true ++ ++# Apply bias correction based on local pattern keys ++# subtreeValueBiasFactor = 0.45 ++# subtreeValueBiasWeightExponent = 0.85 ++ ++# Use graph search rather than tree search - identify and share search for ++# transpositions. ++# useGraphSearch = true ++ ++# How much to shard the node table for search synchronization ++# nodeTableShardsPowerOfTwo = 16 ++ ++# How many virtual losses to add when a thread descends through a node ++# numVirtualLossesPerThread = 1 ++ ++# Improve the quality of evals under heavy multithreading ++# useNoisePruning = true ++ ++# =========================================================================== ++# Automatic avoid patterns ++# =========================================================================== ++# The parameters in this section provide a way to bias away from moves that match ++# patterns that this instance of KataGo has played in previous games, by auto-saving ++# moves to a directory and then auto-loading and biasing against them each new game. ++# Uncomment them to use them. When using this feature, all parameters must be specified. ++ ++# Different board sizes are tracked separately, but all board sizes share the same ++# parameters by default. Every parameter *except* for autoAvoidRepeatDir and ++# autoAvoidRepeatSaveChunkSize can be overridden per board size, ++# e.g. "autoAvoidRepeatMinTurnNumber13x13". You must ALSO specify the ++# defaults even if you have specified board-size-specific values. ++ ++# Directory to auto-save moves KataGo plays, to avoid them in future games. ++# You can create a new empty directory and put its path here. ++# If you run parallel instances of KataGo, use different directories if you ++# want them not to share their biases, use the same directory if you want ++# all of them to bias away from past moves that any of them have played. ++# KataGo will also automatically DELETE old data in this directory, so it is ++# recommended that if you do share the same directory between parallel instances, ++# that they all use the same settings that affect data saving/deletion. ++# autoAvoidRepeatDir = PATH_TO_NEW_DIRECTORY ++ ++# Penalize this much utility per matching move. ++# Values that are too large may lead to bad play. The value of 0.004 is fairly large ++# and might be large enough to result in some early weird/bad moves when trying ++# to avoid past games' moves if enough games begin the same way. You can experiment with it. ++# autoAvoidRepeatUtility = 0.004 ++ ++# Per each new move saved, exponentially decay prior saved moves by this factor. ++# This way, the bias against moves from many games ago is gradually phased out. ++# For example, 0.9995 = 1 - 1/2000, so would be roughly 2000 prior moves worth of ++# penalty weight remembered, in steady state. Depending on what turn number range ++# you are saving, this might equate to a different number of games For example ++# saving the first 50 moves per game would make this roughly 2000 / 50 = 40 games ++# worth of memory. ++# autoAvoidRepeatLambda = 0.9995 ++ ++# Affects data saving/deletion. ++# When the number of saved moves exceeds this, outright delete them to avoid too many ++# files and disk space building up. Also may affect the speed of saving/loading on start ++# of each game if this is set large and a lot of data builds up. ++# autoAvoidRepeatMaxPoses = 10000 ++ ++# Affects data saving/deletion. ++# Only save data for moves within this turn number range of those games. ++# E.g. setting autoAvoidRepeatMinTurnNumber to a number like 4 or 5 would tend to make ++# KataGo not develop a bias against the initial 3-4 and 4-4 corner moves in almost every game. ++# autoAvoidRepeatMinTurnNumber = 0 ++# autoAvoidRepeatMaxTurnNumber = 50 ++ ++# Affects data saving/deletion. ++# Within a single run of a program, wait to accumulate this many samples ++# (possibly across multiple clear_boards/games) before saving the data. ++# Can help to avoid writing too many small files to disk, especially when GTP is used ++# in a way that clears the board very frequently (e.g. gtp2ogs pooled manager). ++# autoAvoidRepeatSaveChunkSize = 200 ++ ++# =========================================================================== ++# Avoid SGF patterns ++# =========================================================================== ++# The parameters in this section provide a way to avoid moves that follow ++# specific patterns based on a set of SGF files loaded upon startup. ++# This is basically the same as the above "Automatic avoid patterns" section ++# above except you supply your own SGF files to avoid moves from. ++# Uncomment them to use this feature. Additionally, if the SGF file ++# contains the string %SKIP% in a comment on a move, that move will be ++# ignored for this purpose. ++ ++# Load SGF files from this directory when the engine is started ++# (only on startup, will not reload unless engine is restarted) ++# avoidSgfPatternDirs = path/to/directory/with/sgfs/ ++# You can also surround the file path in double quotes if the file path contains trailing spaces or hash signs. ++# Within double quotes, backslashes are escape characters. ++# avoidSgfPatternDirs = "path/to/directory/with/sgfs/" ++ ++# Penalize this much utility per matching move. ++# Set this negative if you instead want to favor SGF patterns instead of ++# penalizing them. This number does not need to be large, even 0.001 will ++# make a difference. Values that are too large may lead to bad play. ++# avoidSgfPatternUtility = 0.001 ++ ++# Optional - load only the newest this many files ++# avoidSgfPatternMaxFiles = 20 ++ ++# Optional - Penalty is multiplied by this per each older SGF file, so that ++# old SGF files matter less than newer ones. ++# avoidSgfPatternLambda = 0.90 ++ ++# Optional - pay attention only to moves made by players with this name. ++# For example, set it to the name that your bot's past games will show up ++# as in the SGF, so that the bot will only avoid repeating moves that itself ++# made in past games, not the moves that its opponents made. ++# avoidSgfPatternAllowedNames = my-ogs-bot-name1,my-ogs-bot-name2 ++ ++# Optional - Ignore moves in SGF files that occurred before this turn number. ++# avoidSgfPatternMinTurnNumber = 0 ++ ++# For more avoid patterns: ++# You can also specify a second set of parameters, and a third, fourth, ++# etc. by numbering 2,3,4,... ++# ++# avoidSgf2PatternDirs = ... ++# avoidSgf2PatternUtility = ... ++# avoidSgf2PatternMaxFiles = ... ++# avoidSgf2PatternLambda = ... ++# avoidSgf2PatternAllowedNames = ... ++# avoidSgf2PatternMinTurnNumber = ... ++ +diff --git a/cpp/gtp_human5k_example.cfg b/cpp/gtp_human5k_example.cfg +new file mode 100644 +index 00000000..9098f5ab +--- /dev/null ++++ b/cpp/gtp_human5k_example.cfg +@@ -0,0 +1,189 @@ ++ ++# This is an example config for configuring KataGo to attempt to imitate a weaker human player. ++# Running with this config requires giving a human SL model b18c384nbt-humanv0.bin.gz ++# on the command line such as: ++# ./katago gtp -config gtp_human5k_example.cfg -model your_favorite_normal_model_for_katago.bin.gz -human-model b18c384nbt-humanv0.bin.gz ++# You can obtain the human model at https://github.com/lightvector/KataGo/releases/tag/v1.15.0 ++ ++# Below, the most important parts of the config for human-like play are commented. ++# See the original gtp_example for comments on other parameters. ++ ++# For another useful guide on human-style analysis, see here: ++# https://github.com/lightvector/KataGo/blob/master/docs/Analysis_Engine.md#human-sl-analysis-guide ++ ++# It is ALSO possible to pass in simply '-model b18c384nbt-humanv0.bin.gz' and NOT ++# pass in -human-model, i.e. use the human model as if it were KataGo's normal neural net. ++# If you do that, you need to use a config more like the normal gtp_example.cfg, not this config! ++# Keep in mind that if using the model normally, or if using -human-model but also altering ++# parameters below to blend in some of KataGo's search, KataGo's play might NOT be very human-like, ++# or might be human-like but play at a strength very different than the humanSLProfile. ++# You can experiment, some of the comments below hopefully will help illustrate things too. ++ ++logDir = gtp_logs ++logAllGTPCommunication = true ++logSearchInfo = true ++logSearchInfoForChosenMove = false ++logToStderr = false ++ ++# Use these rules by default, but a GUI or GTP controller might override this. ++rules = japanese ++ ++# When using -human-model, we only resign when far behind since a weaker player ++# might continue to fight much longer than a strong bot normally would. ++allowResignation = true ++resignThreshold = -0.99 ++resignConsecTurns = 20 ++resignMinScoreDifference = 40 ++resignMinMovesPerBoardArea = 0.4 ++ ++# Note: unless you change other parameters too, by default increasing visits won't do much. ++# If humanSLChosenMoveProp = 1.0 AND humanSLChosenMovePiklLambda is a large number, ++# then KataGo's normal search is ignored except for possibly choosing whether to pass/resign, ++# so more visits will have no effect on play. Still, having some visits is good for ++# ensuring good pass/resign judgment. ++maxVisits = 40 ++numSearchThreads = 1 ++lagBuffer = 1.0 ++ ++# Rough scale in seconds to randomly delay moving, so as not to respond instantly. ++# Some moves will delay longer, some moves will delay a little less. ++delayMoveScale = 2.0 ++delayMoveMax = 10.0 ++ ++# =========================================================================== ++# HUMAN SL PARAMETERS ++# =========================================================================== ++ ++# The most important parameter for human-like play configuration! ++# Choose the "profile" of players that the human SL model will imitate. ++# Available options are: ++# preaz_{RANK from 20k to 9d} - imitate player of given rank, before AlphaZero opening style became popular ++# rank_{RANK from 20k to 9d} - imitate player of given rank, after human openings changed due to AlphaZero. ++# preaz_{BRANK}_{WRANK} or rank_{BRANK}_{WRANK} - same, but imitate how black with the rank BR and white ++# with the rank WR would play against each other, knowing that the other player is stronger/weaker than them. ++# Warning: for rank differences > 9 ranks, or drastically mis-matched to the handicap used in the game, ++# this may be out of distribution due to lack of training data and the model might not behave well! Experiment with care. ++# proyear_{YEAR from 1800 to 2023} - imitate historical pros or insei from given year. ++humanSLProfile = preaz_5k ++ ++# The probability that we should play a HUMAN-like move, rather than playing KataGo's move. ++# Applies BEFORE temperature. ++humanSLChosenMoveProp = 1.0 ++ ++# If true, ignore the human SL model's choice of when to pass, and still use KataGo to determine that. ++# The human SL model, in theory, is not guaranteed to be reliable at when to pass for all profiles, ++# since e.g. some historical games omit passes. ++humanSLChosenMoveIgnorePass = true ++ ++# By default humanSLChosenMovePiklLambda is a large number which effectively disables it. ++# Setting it to a smaller number will "suppress" human-like moves that KataGo disapproves of. ++# In particular, if set to, for example, 0.4 when KataGo judges a human SL move to lose 0.4 utility, ++# it will substantially suppress the chance of playing that move (in particular, by a factor of exp(1)). ++# Less-bad moves will also be suppressed, but not by as much, e.g. a move losing 0.2 would get lowered ++# by a factor of exp(0.5). ++# As configured lower down, utilities by default range from -1.0 (loss) to +1.0 (win), plus up to +/- 0.3 for score. ++# WARNING: ONLY moves that KataGo actually searches will get suppressed! If a move is so bad that KataGo ++# rejects it without searching it, it will NOT get suppressed. ++# Therefore, to use humanSLChosenMovePiklLambda, it is STRONGLY recommended that you also use something ++# like humanSLRootExploreProbWeightless to ensure most human moves including bad moves get searched, ++# and ALSO use at least hundreds and ideally thousands of maxVisits, to ensure enough visits. ++humanSLChosenMovePiklLambda = 100000000 ++ ++# These parameters tell KataGo to use the human SL policy for exploration during search. ++# Each of these specifies the probability that KataGo will perform PUCT using the Human SL policy to ++# explore different moves, rather than using KataGo's normal policy, after a certain minimal number of visits. ++# "Root": applies only at the root of the search ++# "Pla": applies during non-root nodes of the search where it is katago's turn. ++# "Opp": applies during non-root nodes of the search where it is the opponent's turn. ++# "Weightless": search the move to evaluate it, but do NOT allow this visit to affect the parent's average utility. ++# "Weightful": search the move to evaluate it, and DO allow this visit to affect the parent's average utility. ++# For example, humanSLRootExploreProbWeightless = 0.5 would tell KataGo at the root of the search to spend ++# 50% of its visits to judge different possible human moves, but NOT to use those visits for determining the ++# value of the position (avoiding biasing the utility if some human SL moves are very bad). ++# If you don't understand these well, ask for help or look up some online explainers for MCTS (Monte-Carlo Tree Search). ++humanSLRootExploreProbWeightless = 0.0 ++humanSLRootExploreProbWeightful = 0.0 ++humanSLPlaExploreProbWeightless = 0.0 ++humanSLPlaExploreProbWeightful = 0.0 ++humanSLOppExploreProbWeightless = 0.0 ++humanSLOppExploreProbWeightful = 0.0 ++ ++# When using the human SL policy for exploration during search, use this cPUCT. ++# This only has an effect if at least one of humanSL{Root,Pla,Opp}ExploreProbWeight{less,ful} is nonzero. ++humanSLCpuctExploration = 0.50 ++ ++# Same as humanSLCpuctExploration, but NEVER diminshes its exploration no matter how many visits are used. ++# Normally, PUCT will sharpen with visits and spend a diminishing proportion of visits on moves with lower utility. ++# This is the coefficient for a term that does NOT diminish, i.e. if this is 0.2, then roughly moves within ++# 0.2 utility (about 10% winrate) of the best move will forever continue getting a decent fraction of visits, ++# smoothly falling off for greater utility differences. ++# Note that in combination with Weightful exploration above, if used for Opp exploration, this could be used ++# to model an opponent that will always have some chance to make small mistakes no matter how deep they search. ++# If further this was increased to a very large value, it would model an opponent that always played according ++# to the human SL raw policy. These might be interesting to experiment with for handicap play. ++humanSLCpuctPermanent = 0.2 ++ ++ ++# =========================================================================== ++# OTHER USEFUL PARAMETERS FOR HUMAN PLAY ADJUSTMENT ++# =========================================================================== ++ ++# Choosing temperature near 1, and restricting it to only affect moves already below 1% chance, ++# so that we sample close to the full range of human play. ++# You can also reduce the temperature to settings more like the plain gtp_example.cfg. ++# Then, rather than imitating a realistic human player, it will be more like imitating the ++# *majority vote* of players at that rank. For example it would avoid a lot of blunders ++# that players of that level would make, because even if players often blunder, the *majority vote* ++# of players would be much less likely to select any given blunder that an individual player would. ++chosenMoveTemperatureEarly = 0.85 ++chosenMoveTemperature = 0.70 ++chosenMoveTemperatureHalflife = 80 ++chosenMoveTemperatureOnlyBelowProb = 0.01 # temperature only starts to dampen moves below this ++chosenMoveSubtract = 0 ++chosenMovePrune = 0 ++ ++# Use a small NN cache to save memory since we're using very low visits anyways. You can increase ++# these back to more like the plain gtp_example.cfg if you are doing more extensive searches to ++# improve performance. ++nnCacheSizePowerOfTwo = 17 ++nnMutexPoolSizePowerOfTwo = 14 ++ ++# =========================================================================== ++# PARAMETERS CHANGED FROM DEFAULT TO MAKE SURE HUMAN SL USAGE WORKS WELL ++# =========================================================================== ++ ++# Make sure to take into account the recent moves in the game, don't ignore history. ++# This will produce the best imitation/prediction, since humans definitely do play differently based on where the ++# most recent moves in the game were, rather than coming fresh to the board position on every turn. ++ignorePreRootHistory = false ++analysisIgnorePreRootHistory = false ++ ++# Average 2 neural net samples at the root - ensures a bit smoother probabilities and results in ++# 8 * 7 / 2 = 28 possible policies instead of 8 possibilities. ++rootNumSymmetriesToSample = 2 ++# LCB improves strength for KataGo, but we disable it so it doesn't mess up move selection when blending human play. ++useLcbForSelection = false ++ ++# We disable dynamicScoreUtilityFactor - the human SL model can make score predictions that are a bit swingy, so ++# if we do want to do a search that blends human SL values in (TODO there isn't a way to do this anyways yet), using ++# static score utility might be a bit more stable. ++winLossUtilityFactor = 1.0 ++staticScoreUtilityFactor = 0.30 ++dynamicScoreUtilityFactor = 0.00 ++ ++# Uncertainty improves strength for KataGo normally, but messes with the weights of playouts in complicated ways, ++# so lets turn it off when doing human SL stuff. ++useUncertainty = false ++ ++# Subtree value bias improves strength for KataGo normally, but messes with the values of nodes in complicated ways, ++# so let's turn it off when doing human SL stuff. ++subtreeValueBiasFactor = 0.0 ++ ++# Noise pruning prunes out weight from moves that KataGo thinks are bad, but if we are doing human SL we might actively ++# want to be playing or exploring and weighting "bad" but human-like moves. So disable this. ++# Warning: when this is false, there is much less protection against the search severely misbehaving when you use too many threads. ++# Make sure not to set numSearchThreads to be too large - at a minimum, keep at least a 20x buffer between ++# the number of visits you use and the number of threads you use. ++# (as an aside, ideally, you want to have visits be a sufficient factor larger than threads EVEN when ++# useNoisePruning is true, this parameter just blunts the worst effects but doesn't entirely fix the badness). ++useNoisePruning = false +diff --git a/cpp/leela_zero_strong.cfg b/cpp/leela_zero_strong.cfg +new file mode 100644 +index 00000000..e1167b84 +--- /dev/null ++++ b/cpp/leela_zero_strong.cfg +@@ -0,0 +1,719 @@ ++# Configuration for KataGo C++ GTP engine ++ ++# Run the program using: `./katago.exe gtp` ++ ++# In this example config, when a parameter is given as a commented out value, ++# that value also is the default value, unless described otherwise. You can ++# uncomment it (remove the pound sign) and change it if you want. ++ ++# =========================================================================== ++# Running on an online server or in a real tournament or match ++# =========================================================================== ++# If you plan to run online or in a tournament, read through the "Rules" ++# section below for proper handling of komi, handicaps, end-of-game cleanup, ++# and other details. ++ ++# =========================================================================== ++# Notes about performance and memory usage ++# =========================================================================== ++# Important: For good performance, you will very likely want to tune the ++# "numSearchThreads" parameter in the Search limits section below! Run ++# "./katago benchmark" to test KataGo and to suggest a reasonable value ++# of this parameter. ++ ++# For multi-GPU systems, read "OpenCL GPU settings" or "CUDA GPU settings". ++# ++# When using OpenCL, verify that KataGo picks the correct device! Some systems ++# may have both an Intel CPU OpenCL and GPU OpenCL. If # KataGo picks the wrong ++# one, correct this by specifying "openclGpuToUse". ++# ++# Consider adjusting "maxVisits", "ponderingEnabled", "resignThreshold", and ++# other parameters depending on your intended usage. ++ ++# =========================================================================== ++# Command-line usage ++# =========================================================================== ++# All of the below values may be set or overridden via command-line arguments: ++# ++# -override-config KEY=VALUE,KEY=VALUE,... ++ ++# =========================================================================== ++# Logs and files ++# =========================================================================== ++# This section defines where and what logging information is produced. ++ ++# Each run of KataGo will log to a separate file in this dir. ++# This is the default. ++logDir = gtp_logs ++# Uncomment and specify this instead of logDir to write separate dated subdirs ++# logDirDated = gtp_logs ++# Uncomment and specify this instead of logDir to log to only a single file ++# logFile = gtp.log ++ ++# Logging options ++logAllGTPCommunication = true ++logSearchInfo = true ++logSearchInfoForChosenMove = false ++logToStderr = true ++ ++# KataGo will display some info to stderr on GTP startup ++# Uncomment the next line and set it to false to suppress that and remain silent ++# startupPrintMessageToStderr = true ++ ++# Write information to stderr, for use in things like malkovich chat to OGS. ++ ogsChatToStderr = true ++ ++# Uncomment and set this to a directory to override where openCLTuner files ++# and other cached data is written. By default it saves into a subdir of the ++# current directory on windows, and a subdir of ~/.katago on Linux. ++# homeDataDir = PATH_TO_DIRECTORY ++ ++# =========================================================================== ++# Analysis ++# =========================================================================== ++# This section configures analysis settings. ++# ++# The maximum number of moves after the first move displayed in variations ++# from analysis commands like kata-analyze or lz-analyze. ++# analysisPVLen = 15 ++ ++# Report winrates for chat and analysis as (BLACK|WHITE|SIDETOMOVE). ++# Most GUIs and analysis tools will expect SIDETOMOVE. ++# reportAnalysisWinratesAs = SIDETOMOVE ++ ++# Extra noise for wider exploration. Large values will force KataGo to ++# analyze a greater variety of moves than it normally would. ++# An extreme value like 1 distributes playouts across every move on the board, ++# even very bad moves. ++# Affects analysis only, does not affect play. ++# analysisWideRootNoise = 0.04 ++ ++# Try to limit the effect of possible bad or bogus move sequences in the ++# history leading to this position from affecting KataGo's move predictions. ++# analysisIgnorePreRootHistory = true ++ ++# =========================================================================== ++# Rules ++# =========================================================================== ++# This section configures the scoring and playing rules. Rules can also be ++# changed mid-run by issuing custom GTP commands. ++# ++# See https://lightvector.github.io/KataGo/rules.html for rules details. ++# ++# See https://github.com/lightvector/KataGo/blob/master/docs/GTP_Extensions.md ++# for GTP commands. ++ ++# Specify the rules as a string. ++# Some legal values include: ++# chinese, japanese, korean, aga, chinese-ogs, new-zealand, stone-scoring, ++# ancient-territory, bga, aga-button ++# ++# For some human rulesets that require complex adjudication in tricky cases ++# (e.g. japanese, korean) KataGo may not precisely match the ruleset in such ++# cases but will do its best. ++# rules = tromp-taylor ++ ++# By default, the "rules" parameter is used, but if you comment it out and ++# uncomment one option in each of the sections below, you can specify an ++# arbitrary combination of individual rules. ++ ++koRule = SIMPLE # Simple ko rules (triple ko = no result) ++# koRule = POSITIONAL # Positional superko ++# koRule = SITUATIONAL # Situational superko ++ ++scoringRule = AREA # Area scoring ++# scoringRule = TERRITORY # Territory scoring (special computer-friendly territory rules) ++ ++taxRule = NONE # All surrounded empty points are scored ++# taxRule = SEKI # Eyes in seki do NOT count as points ++# taxRule = ALL # All groups are taxed up to 2 points for the two eyes needed to live ++ ++# Is multiple-stone suicide legal? (Single-stone suicide is always illegal). ++multiStoneSuicideLegal = true ++# multiStoneSuicideLegal = false ++ ++# "Button go" - the first pass when area scoring awards 0.5 points and does ++# not count for ending the game. ++hasButton = false ++# hasButton = true ++ ++# Is this a human ruleset where it's okay to pass before having physically ++# captured and removed all dead stones? ++friendlyPassOk = true ++# friendlyPassOk = false ++ ++# How handicap stones in handicap games are compensated ++whiteHandicapBonus = 0 # White gets no compensation for black's handicap stones (Tromp-taylor, NZ, JP) ++# whiteHandicapBonus = N-1 # White gets N-1 points for black's N handicap stones (AGA) ++# whiteHandicapBonus = N # White gets N points for black's N handicap stones (Chinese) ++ ++# ------------------------------ ++# Other rules hacks ++# ------------------------------ ++# Uncomment and change to adjust what board size KataGo uses upon startup ++# by default when GTP doesn't specify. ++# defaultBoardSize = 19 ++ ++# By default, Katago will use the komi that the GUI or GTP controller tries to set. ++# Uncomment and set this to have KataGo ignore the controller and always use this komi. ++# ignoreGTPAndForceKomi = 7 ++ ++# =========================================================================== ++# Bot behavior ++# =========================================================================== ++ ++# ------------------------------ ++# Resignation ++# ------------------------------ ++ ++# Resignation occurs if for at least resignConsecTurns in a row, the ++# winLossUtility (on a [-1,1] scale) is below resignThreshold. ++allowResignation = true ++resignThreshold = -0.90 ++resignConsecTurns = 3 ++ ++# By default, KataGo may resign games that it is confidently losing even if they ++# are very close in score. Uncomment and set this to avoid resigning games ++# if the estimated difference is points is less than or equal to this. ++# resignMinScoreDifference = 10 ++ ++# ------------------------------ ++# Handicap ++# ------------------------------ ++# Assume that if black makes many moves in a row right at the start of the ++# game, then the game is a handicap game. This is necessary on some servers ++# and for some GUIs and also when initializing from many SGF files, which may ++# set up a handicap game using repeated GTP "play" commands for black rather ++# than GTP "place_free_handicap" commands; however, it may also lead to ++# incorrect understanding of komi if whiteHandicapBonus is used and a server ++# does not have such a practice. Uncomment and set to false to disable. ++# assumeMultipleStartingBlackMovesAreHandicap = true ++ ++# Makes katago dynamically adjust in handicap or altered-komi games to assume ++# based on those game settings that it must be stronger or weaker than the ++# opponent and to play accordingly. Greatly improves handicap strength by ++# biasing winrates and scores to favor appropriate safe/aggressive play. ++# Does NOT affect analysis (lz-analyze, kata-analyze, used by programs like ++# Lizzie) so analysis remains unbiased. Uncomment and set this to 0 to disable ++# this and make KataGo play the same always. ++# dynamicPlayoutDoublingAdvantageCapPerOppLead = 0.045 ++ ++# Instead of "dynamicPlayoutDoublingAdvantageCapPerOppLead", you can comment ++# that out and uncomment and set "playoutDoublingAdvantage" to a fixed value ++# from -3.0 to 3.0 that will not change dynamically. ++# ALSO affects analysis tools (lz-analyze, kata-analyze, used by e.g. Lizzie). ++# Negative makes KataGo behave as if it is much weaker than the opponent. ++# Positive makes KataGo behave as if it is much stronger than the opponent. ++# KataGo will adjust to favor safe/aggressive play as appropriate based on ++# the combination of who is ahead and how much stronger/weaker it thinks it is, ++# and report winrates and scores taking the strength difference into account. ++# ++# If this and "dynamicPlayoutDoublingAdvantageCapPerOppLead" are both set ++# then dynamic will be used for all games and this fixed value will be used ++# for analysis tools. ++# playoutDoublingAdvantage = 0.0 ++ ++# Uncomment one of these when using "playoutDoublingAdvantage" to enforce ++# that it will only apply when KataGo plays as the specified color and will be ++# negated when playing as the opposite color. ++# playoutDoublingAdvantagePla = BLACK ++# playoutDoublingAdvantagePla = WHITE ++ ++# ------------------------------ ++# Passing and cleanup ++# ------------------------------ ++# Make the bot never assume that its pass will end the game, even if passing ++# would end and "win" under Tromp-Taylor rules. Usually this is a good idea ++# when using it for analysis or playing on servers where scoring may be ++# implemented non-tromp-taylorly. Uncomment and set to false to disable. ++# conservativePass = true ++ ++# When using territory scoring, self-play games continue beyond two passes ++# with special cleanup rules that may be confusing for human players. This ++# option prevents the special cleanup phases from being reachable when using ++# the bot for GTP play. Uncomment and set to false to enable entering special ++# cleanup. For example, if you are testing it against itself, or against ++# another bot that has precisely implemented the rules documented at ++# https://lightvector.github.io/KataGo/rules.html ++# preventCleanupPhase = true ++ ++# ------------------------------ ++# Miscellaneous behavior ++# ------------------------------ ++# If the board is symmetric, search only one copy of each equivalent move. ++# Attempts to also account for ko/superko, will not theoretically perfect for ++# superko. Uncomment and set to false to disable. ++# rootSymmetryPruning = true ++ ++# Uncomment and set to true to avoid a particular joseki that some networks ++# misevaluate, and also to improve opening diversity versus some particular ++# other bots that like to play it all the time. ++# avoidMYTDaggerHack = false ++ ++# Prefer to avoid playing the same joseki in every corner of the board. ++# Uncomment to set to a specific value. See "Avoid SGF patterns" section. ++# By default: 0 (even games), 0.005 (handicap games) ++# avoidRepeatedPatternUtility = 0.0 ++ ++# Experimental logic to fight against mirror Go even with unfavorable komi. ++# Uncomment to set to a specific value to use for both playing and analysis. ++# By default: true when playing via GTP, but false when analyzing. ++# antiMirror = true ++ ++# Enable some hacks that mitigate rare instances when passing messes up deeper searches. ++# enablePassingHacks = true ++ ++# Uncomment and set this to true to prevent bad or bogus move sequences ++# in the history leading to this position from affecting KataGo's move choices. ++# Same as analysisIgnorePreRootHistory (see above) but applies to actual play. ++# You can enable this if KataGo is being asked to play from positions that it did not ++# choose the moves to reach. ++# ignorePreRootHistory = false ++ ++# =========================================================================== ++# Search limits ++# =========================================================================== ++ ++# Terminology: ++# "Playouts" is the number of new playouts of search performed each turn. ++# "Visits" is the same as "Playouts" but also counts search performed on ++# previous turns that is still applicable to this turn. ++# "Time" is the time in seconds. ++ ++# For example, if KataGo searched 200 nodes on the previous turn, and then ++# after the opponent's reply, 50 nodes of its search tree was still valid, ++# then a visit limit of 200 would allow KataGo to search 150 new nodes ++# (for a final tree size of 200 nodes), whereas a playout limit of of 200 ++# would allow KataGo to search 200 nodes (for a final tree size of 250 nodes). ++ ++# Additionally, KataGo may also move before than the limit in order to ++# obey time controls (e.g. byo-yomi, etc) if the GTP controller has ++# told KataGo that the game has is being played with a given time control. ++ ++# Limits for search on the current turn. ++# If commented out or unspecified, the default is to have no limit. ++# maxVisits = 30000 ++# maxPlayouts = 300 ++maxTime = 1.0 ++ ++# Ponder on the opponent's turn? ++ponderingEnabled = true ++ ++# Limits for search when pondering on the opponent's turn. ++# If commented out or unspecified, the default is to have no limit. ++# Limiting the maximum time is recommended so that KataGo won't burn CPU/GPU ++# forever and/or run out of RAM if left unattended while pondering is enabled. ++# maxVisitsPondering = 5000 ++# maxPlayoutsPondering = 3000 ++maxTimePondering = 120.0 ++ ++ ++# ------------------------------ ++# Other search limits and behavior ++# ------------------------------ ++ ++# Approx number of seconds to buffer for lag for GTP time controls - will ++# move a bit faster assuming there is this much lag per move. ++lagBuffer = 1.0 ++ ++# YOU PROBABLY WANT TO TUNE THIS PARAMETER! ++# The number of threads to use when searching. On powerful GPUs the optimal ++# threads may be much higher than the number of CPU cores you have because ++# many threads are needed to feed efficient large batches to the GPU. ++# ++# Run "./katago benchmark" to tune this parameter and test the effect ++# of changes to any of other parameters. ++numSearchThreads = 40 ++ ++# Play a little faster if the opponent is passing, for human-friendliness. ++# Comment these out to disable them, such as if running a controlled match ++# where you are testing KataGo with fixed compute per move vs other bots. ++searchFactorAfterOnePass = 0.50 ++searchFactorAfterTwoPass = 0.25 ++ ++# Play a little faster if super-winning, for human-friendliness. ++# Comment these out to disable them, such as if running a controlled match ++# where you are testing KataGo with fixed compute per move vs other bots. ++searchFactorWhenWinning = 0.40 ++searchFactorWhenWinningThreshold = 0.95 ++ ++# =========================================================================== ++# GPU settings ++# =========================================================================== ++# This section configures GPU settings. ++# ++# Maximum number of positions to send to a single GPU at once. The default ++# value is roughly equal to numSearchThreads, but can be specified manually ++# if running out of memory, or using multiple GPUs that expect to share work. ++# nnMaxBatchSize = ++ ++# Controls the neural network cache size, which is the primary RAM/memory use. ++# KataGo will cache up to (2 ** nnCacheSizePowerOfTwo) many neural net ++# evaluations in case of transpositions in the tree. ++# Increase this to improve performance for searches with tens of thousands ++# of visits or more. Decrease this to limit memory usage. ++# If you're happy to do some math - each neural net entry takes roughly ++# 1.5KB, except when using whole-board ownership/territory ++# visualizations, where each entry will take roughly 3KB. The number of ++# entries is (2 ** nnCacheSizePowerOfTwo). (E.g. 2 ** 18 = 262144.) ++# You can compute roughly how much memory the cache will use based on this. ++# nnCacheSizePowerOfTwo = 20 ++ ++# Size of mutex pool for nnCache is (2 ** this). ++# nnMutexPoolSizePowerOfTwo = 16 ++ ++# Randomize board orientation when running neural net evals? Uncomment and ++# set to false to disable. ++# nnRandomize = true ++ ++# If provided, force usage of a specific seed for nnRandomize. ++# The default is to use a randomly generated seed. ++# nnRandSeed = abcdefg ++ ++# Uncomment and set to true to force GTP to use the maximum board size for ++# internal buffers for the neural net. This will make KataGo slower when ++# evaluating small boards, but will avoid a lengthy initialization time on every ++# change of board size due to having to re-size the neural net buffers on the GPU. ++# This can be useful for example, for OGS's persistent bot mode that uses a single ++# bot instance to handle multiple games and may thrash between different board sizes ++# if there are concurrent games of multiple sizes. ++# gtpForceMaxNNSize = false ++ ++# ------------------------------ ++# Multiple GPUs ++# ------------------------------ ++# Set this to the number of GPUs to use or that are available. ++# IMPORTANT: If more than 1, also uncomment the appropriate TensorRT ++# or CUDA or OpenCL section. ++# numNNServerThreadsPerModel = 1 ++ ++# ------------------------------ ++# TENSORRT GPU settings ++# ------------------------------ ++# These only apply when using the TENSORRT version of KataGo. ++ ++# For one GPU: optionally uncomment this option and change if the GPU to ++# use is not device 0. ++# trtDeviceToUse = 0 ++ ++# For two GPUs: Uncomment these options, AND set numNNServerThreadsPerModel above. ++# Also, change their values if the devices you want to use are not 0 and 1. ++# trtDeviceToUseThread0 = 0 ++# trtDeviceToUseThread1 = 1 ++ ++# For three GPUs: Uncomment these options, AND set numNNServerThreadsPerModel above. ++# Also, change their values if the devices you want to use are not 0 and 1 and 2. ++# trtDeviceToUseThread0 = 0 ++# trtDeviceToUseThread1 = 1 ++# trtDeviceToUseThread2 = 2 ++ ++# The pattern continues for additional GPUs. ++ ++# ------------------------------ ++# CUDA GPU settings ++# ------------------------------ ++# These only apply when using the CUDA version of KataGo. ++ ++# For one GPU: optionally uncomment and change this if the GPU you want to ++# use is not device 0 ++# cudaDeviceToUse = 0 ++ ++# For two GPUs: Uncomment these options, AND set numNNServerThreadsPerModel above. ++# Also, change their values if the devices you want to use are not 0 and 1. ++# cudaDeviceToUseThread0 = 0 ++# cudaDeviceToUseThread1 = 1 ++ ++# For three GPUs: Uncomment these options, AND set numNNServerThreadsPerModel above. ++# Also, change their values if the devices you want to use are not 0 and 1 and 2. ++# cudaDeviceToUseThread0 = 0 ++# cudaDeviceToUseThread1 = 1 ++# cudaDeviceToUseThread2 = 2 ++ ++# The pattern continues for additional GPUs. ++ ++# KataGo will automatically use FP16 or not based on the compute capability ++# of your NVIDIA GPU. If you want to try to force a particular behavior ++# you can uncomment these lines and change them to "true" or "false". ++# cudaUseFP16 = auto ++# cudaUseNHWC = auto ++ ++# ------------------------------ ++# OpenCL GPU settings ++# ------------------------------ ++# These only apply when using the OpenCL version of KataGo. ++ ++# Uncomment and set to true to tune OpenCL for every board size separately, ++# rather than only the largest possible size. ++# openclReTunePerBoardSize = false ++ ++# For one GPU: optionally uncomment and change this if the best device to use is guessed incorrectly. ++# The default behavior tries to guess the 'best' GPU or device on your system to use, usually it will be a good guess. ++# openclDeviceToUse = 0 ++ ++# For two GPUs: Uncomment these two lines and replace X and Y with the device ids of the devices you want to use. ++# It might NOT be 0 and 1, some computers will have many OpenCL devices. You can see what the devices are when ++# KataGo starts up - it should print or log all the devices it finds. ++# (AND also set numNNServerThreadsPerModel above) ++# openclDeviceToUseThread0 = X ++# openclDeviceToUseThread1 = Y ++ ++# For three GPUs: Uncomment these three lines and replace X and Y and Z with the device ids of the devices you want to use. ++# It might NOT be 0 and 1 and 2, some computers will have many OpenCL devices. You can see what the devices are when ++# KataGo starts up - it should print or log all the devices it finds. ++# (AND also set numNNServerThreadsPerModel above) ++# openclDeviceToUseThread0 = X ++# openclDeviceToUseThread1 = Y ++# openclDeviceToUseThread2 = Z ++ ++# The pattern continues for additional GPUs. ++ ++# KataGo will automatically use FP16 or not based on testing your GPU during ++# tuning. If you want to try to force a particular behavior though you can ++# uncomment this option and change it to "true" or "false". This is a fairly ++# blunt setting - more detailed settings are testable by rerunning the tuner ++# with various arguments (./katago tuner). ++# openclUseFP16 = auto ++ ++# ------------------------------ ++# Eigen-specific settings ++# ------------------------------ ++# These only apply when using the Eigen (pure CPU) version of KataGo. ++ ++# Number of CPU threads for evaluating the neural net on the Eigen backend. ++# ++# Default: numSearchThreads ++# numEigenThreadsPerModel = X ++ ++# =========================================================================== ++# Root move selection and biases ++# =========================================================================== ++# Uncomment and edit any of the below values to change them from their default. ++ ++# If provided, force usage of a specific seed for various random things in ++# the search. The default is to use a random seed. ++# searchRandSeed = hijklmn ++ ++# Temperature for the early game, randomize between chosen moves with ++# this temperature ++# chosenMoveTemperatureEarly = 0.5 ++ ++# Decay temperature for the early game by 0.5 every this many moves, ++# scaled with board size. ++# chosenMoveTemperatureHalflife = 19 ++ ++# At the end of search after the early game, randomize between chosen ++# moves with this temperature ++# chosenMoveTemperature = 0.10 ++ ++# Subtract this many visits from each move prior to applying ++# chosenMoveTemperature (unless all moves have too few visits) to downweight ++# unlikely moves ++# chosenMoveSubtract = 0 ++ ++# The same as chosenMoveSubtract but only prunes moves that fall below ++# the threshold. This setting does not affect chosenMoveSubtract. ++# chosenMovePrune = 1 ++ ++# Number of symmetries to sample (without replacement) and average at the root ++# rootNumSymmetriesToSample = 1 ++ ++# Using LCB for move selection? ++# useLcbForSelection = true ++ ++# How many stdevs a move needs to be better than another for LCB selection ++# lcbStdevs = 5.0 ++ ++# Only use LCB override when a move has this proportion of visits as the ++# top move. ++# minVisitPropForLCB = 0.15 ++ ++# =========================================================================== ++# Internal params ++# =========================================================================== ++# Uncomment and edit any of the below values to change them from their default. ++ ++# Scales the utility of winning/losing ++# winLossUtilityFactor = 1.0 ++ ++# Scales the utility for trying to maximize score ++# staticScoreUtilityFactor = 0.10 ++# dynamicScoreUtilityFactor = 0.30 ++ ++# Adjust dynamic score center this proportion of the way towards zero, ++# capped at a reasonable amount. ++# dynamicScoreCenterZeroWeight = 0.20 ++# dynamicScoreCenterScale = 0.75 ++ ++# The utility of getting a "no result" due to triple ko or other long cycle ++# in non-superko rulesets (-1 to 1) ++# noResultUtilityForWhite = 0.0 ++ ++# The number of wins that a draw counts as, for white. (0 to 1) ++# drawEquivalentWinsForWhite = 0.5 ++ ++# Exploration constant for mcts ++# cpuctExploration = 1.0 ++# cpuctExplorationLog = 0.45 ++ ++# Parameters that control exploring more in volatile positions, exploring ++# less in stable positions. ++# cpuctUtilityStdevPrior = 0.40 ++# cpuctUtilityStdevPriorWeight = 2.0 ++# cpuctUtilityStdevScale = 0.85 ++ ++# FPU reduction constant for mcts ++# fpuReductionMax = 0.2 ++# rootFpuReductionMax = 0.1 ++# fpuParentWeightByVisitedPolicy = true ++ ++# Parameters that control weighting of evals based on the net's own ++# self-reported uncertainty. ++# useUncertainty = true ++# uncertaintyExponent = 1.0 ++# uncertaintyCoeff = 0.25 ++ ++# Explore using optimistic policy ++# rootPolicyOptimism = 0.2 ++# policyOptimism = 1.0 ++ ++# Amount to apply a downweighting of children with very bad values relative ++# to good ones. ++# valueWeightExponent = 0.25 ++ ++# Slight incentive for the bot to behave human-like with regard to passing at ++# the end, filling the dame, not wasting time playing in its own territory, ++# etc., and not play moves that are equivalent in terms of points but a bit ++# more unfriendly to humans. ++# rootEndingBonusPoints = 0.5 ++ ++# Make the bot prune useless moves that are just prolonging the game to ++# avoid losing yet. ++# rootPruneUselessMoves = true ++ ++# Apply bias correction based on local pattern keys ++# subtreeValueBiasFactor = 0.45 ++# subtreeValueBiasWeightExponent = 0.85 ++ ++# Use graph search rather than tree search - identify and share search for ++# transpositions. ++# useGraphSearch = true ++ ++# How much to shard the node table for search synchronization ++# nodeTableShardsPowerOfTwo = 16 ++ ++# How many virtual losses to add when a thread descends through a node ++# numVirtualLossesPerThread = 1 ++ ++# Improve the quality of evals under heavy multithreading ++# useNoisePruning = true ++ ++# =========================================================================== ++# Automatic avoid patterns ++# =========================================================================== ++# The parameters in this section provide a way to bias away from moves that match ++# patterns that this instance of KataGo has played in previous games, by auto-saving ++# moves to a directory and then auto-loading and biasing against them each new game. ++# Uncomment them to use them. When using this feature, all parameters must be specified. ++ ++# Different board sizes are tracked separately, but all board sizes share the same ++# parameters by default. Every parameter *except* for autoAvoidRepeatDir and ++# autoAvoidRepeatSaveChunkSize can be overridden per board size, ++# e.g. "autoAvoidRepeatMinTurnNumber13x13". You must ALSO specify the ++# defaults even if you have specified board-size-specific values. ++ ++# Directory to auto-save moves KataGo plays, to avoid them in future games. ++# You can create a new empty directory and put its path here. ++# If you run parallel instances of KataGo, use different directories if you ++# want them not to share their biases, use the same directory if you want ++# all of them to bias away from past moves that any of them have played. ++# KataGo will also automatically DELETE old data in this directory, so it is ++# recommended that if you do share the same directory between parallel instances, ++# that they all use the same settings that affect data saving/deletion. ++# autoAvoidRepeatDir = PATH_TO_NEW_DIRECTORY ++ ++# Penalize this much utility per matching move. ++# Values that are too large may lead to bad play. The value of 0.004 is fairly large ++# and might be large enough to result in some early weird/bad moves when trying ++# to avoid past games' moves if enough games begin the same way. You can experiment with it. ++# autoAvoidRepeatUtility = 0.004 ++ ++# Per each new move saved, exponentially decay prior saved moves by this factor. ++# This way, the bias against moves from many games ago is gradually phased out. ++# For example, 0.9995 = 1 - 1/2000, so would be roughly 2000 prior moves worth of ++# penalty weight remembered, in steady state. Depending on what turn number range ++# you are saving, this might equate to a different number of games For example ++# saving the first 50 moves per game would make this roughly 2000 / 50 = 40 games ++# worth of memory. ++# autoAvoidRepeatLambda = 0.9995 ++ ++# Affects data saving/deletion. ++# When the number of saved moves exceeds this, outright delete them to avoid too many ++# files and disk space building up. Also may affect the speed of saving/loading on start ++# of each game if this is set large and a lot of data builds up. ++# autoAvoidRepeatMaxPoses = 10000 ++ ++# Affects data saving/deletion. ++# Only save data for moves within this turn number range of those games. ++# E.g. setting autoAvoidRepeatMinTurnNumber to a number like 4 or 5 would tend to make ++# KataGo not develop a bias against the initial 3-4 and 4-4 corner moves in almost every game. ++# autoAvoidRepeatMinTurnNumber = 0 ++# autoAvoidRepeatMaxTurnNumber = 50 ++ ++# Affects data saving/deletion. ++# Within a single run of a program, wait to accumulate this many samples ++# (possibly across multiple clear_boards/games) before saving the data. ++# Can help to avoid writing too many small files to disk, especially when GTP is used ++# in a way that clears the board very frequently (e.g. gtp2ogs pooled manager). ++# autoAvoidRepeatSaveChunkSize = 200 ++ ++# =========================================================================== ++# Avoid SGF patterns ++# =========================================================================== ++# The parameters in this section provide a way to avoid moves that follow ++# specific patterns based on a set of SGF files loaded upon startup. ++# This is basically the same as the above "Automatic avoid patterns" section ++# above except you supply your own SGF files to avoid moves from. ++# Uncomment them to use this feature. Additionally, if the SGF file ++# contains the string %SKIP% in a comment on a move, that move will be ++# ignored for this purpose. ++ ++# Load SGF files from this directory when the engine is started ++# (only on startup, will not reload unless engine is restarted) ++# avoidSgfPatternDirs = path/to/directory/with/sgfs/ ++# You can also surround the file path in double quotes if the file path contains trailing spaces or hash signs. ++# Within double quotes, backslashes are escape characters. ++# avoidSgfPatternDirs = "path/to/directory/with/sgfs/" ++ ++# Penalize this much utility per matching move. ++# Set this negative if you instead want to favor SGF patterns instead of ++# penalizing them. This number does not need to be large, even 0.001 will ++# make a difference. Values that are too large may lead to bad play. ++# avoidSgfPatternUtility = 0.001 ++ ++# Optional - load only the newest this many files ++# avoidSgfPatternMaxFiles = 20 ++ ++# Optional - Penalty is multiplied by this per each older SGF file, so that ++# old SGF files matter less than newer ones. ++# avoidSgfPatternLambda = 0.90 ++ ++# Optional - pay attention only to moves made by players with this name. ++# For example, set it to the name that your bot's past games will show up ++# as in the SGF, so that the bot will only avoid repeating moves that itself ++# made in past games, not the moves that its opponents made. ++# avoidSgfPatternAllowedNames = my-ogs-bot-name1,my-ogs-bot-name2 ++ ++# Optional - Ignore moves in SGF files that occurred before this turn number. ++# avoidSgfPatternMinTurnNumber = 0 ++ ++# For more avoid patterns: ++# You can also specify a second set of parameters, and a third, fourth, ++# etc. by numbering 2,3,4,... ++# ++# avoidSgf2PatternDirs = ... ++# avoidSgf2PatternUtility = ... ++# avoidSgf2PatternMaxFiles = ... ++# avoidSgf2PatternLambda = ... ++# avoidSgf2PatternAllowedNames = ... ++# avoidSgf2PatternMinTurnNumber = ... ++ +diff --git a/cpp/libcrypto-3-x64.dll b/cpp/libcrypto-3-x64.dll +new file mode 100644 +index 00000000..cac7f4ec +Binary files /dev/null and b/cpp/libcrypto-3-x64.dll differ +diff --git a/cpp/libssl-3-x64.dll b/cpp/libssl-3-x64.dll +new file mode 100644 +index 00000000..8ad0908e +Binary files /dev/null and b/cpp/libssl-3-x64.dll differ +diff --git a/cpp/libz.dll b/cpp/libz.dll +new file mode 100644 +index 00000000..930a0421 +Binary files /dev/null and b/cpp/libz.dll differ +diff --git a/cpp/libzip.dll b/cpp/libzip.dll +new file mode 100644 +index 00000000..167e69f7 +Binary files /dev/null and b/cpp/libzip.dll differ +diff --git a/cpp/lizgoban.cfg b/cpp/lizgoban.cfg +new file mode 100644 +index 00000000..53f4832b +--- /dev/null ++++ b/cpp/lizgoban.cfg +@@ -0,0 +1,721 @@ ++# Configuration for KataGo C++ GTP engine ++ ++# Run the program using: `./katago.exe gtp` ++ ++# In this example config, when a parameter is given as a commented out value, ++# that value also is the default value, unless described otherwise. You can ++# uncomment it (remove the pound sign) and change it if you want. ++ ++# =========================================================================== ++# Running on an online server or in a real tournament or match ++# =========================================================================== ++# If you plan to run online or in a tournament, read through the "Rules" ++# section below for proper handling of komi, handicaps, end-of-game cleanup, ++# and other details. ++ ++# =========================================================================== ++# Notes about performance and memory usage ++# =========================================================================== ++# Important: For good performance, you will very likely want to tune the ++# "numSearchThreads" parameter in the Search limits section below! Run ++# "./katago benchmark" to test KataGo and to suggest a reasonable value ++# of this parameter. ++ ++# For multi-GPU systems, read "OpenCL GPU settings" or "CUDA GPU settings". ++# ++# When using OpenCL, verify that KataGo picks the correct device! Some systems ++# may have both an Intel CPU OpenCL and GPU OpenCL. If # KataGo picks the wrong ++# one, correct this by specifying "openclGpuToUse". ++# ++# Consider adjusting "maxVisits", "ponderingEnabled", "resignThreshold", and ++# other parameters depending on your intended usage. ++ ++# =========================================================================== ++# Command-line usage ++# =========================================================================== ++# All of the below values may be set or overridden via command-line arguments: ++# ++# -override-config KEY=VALUE,KEY=VALUE,... ++ ++# =========================================================================== ++# Logs and files ++# =========================================================================== ++# This section defines where and what logging information is produced. ++ ++# Each run of KataGo will log to a separate file in this dir. ++# This is the default. ++logDir = gtp_logs ++# Uncomment and specify this instead of logDir to write separate dated subdirs ++# logDirDated = gtp_logs ++# Uncomment and specify this instead of logDir to log to only a single file ++# logFile = gtp.log ++ ++# Logging options ++logAllGTPCommunication = true ++logSearchInfo = true ++logSearchInfoForChosenMove = false ++logToStderr = true ++ ++# KataGo will display some info to stderr on GTP startup ++# Uncomment the next line and set it to false to suppress that and remain silent ++# startupPrintMessageToStderr = true ++ ++# Write information to stderr, for use in things like malkovich chat to OGS. ++ ogsChatToStderr = true ++ ++# Uncomment and set this to a directory to override where openCLTuner files ++# and other cached data is written. By default it saves into a subdir of the ++# current directory on windows, and a subdir of ~/.katago on Linux. ++# homeDataDir = PATH_TO_DIRECTORY ++ ++# =========================================================================== ++# Analysis ++# =========================================================================== ++# This section configures analysis settings. ++# ++# The maximum number of moves after the first move displayed in variations ++# from analysis commands like kata-analyze or lz-analyze. ++# analysisPVLen = 15 ++ ++# Report winrates for chat and analysis as (BLACK|WHITE|SIDETOMOVE). ++# Most GUIs and analysis tools will expect SIDETOMOVE. ++# reportAnalysisWinratesAs = SIDETOMOVE ++ ++# Extra noise for wider exploration. Large values will force KataGo to ++# analyze a greater variety of moves than it normally would. ++# An extreme value like 1 distributes playouts across every move on the board, ++# even very bad moves. ++# Affects analysis only, does not affect play. ++# analysisWideRootNoise = 0.04 ++ ++# Try to limit the effect of possible bad or bogus move sequences in the ++# history leading to this position from affecting KataGo's move predictions. ++# analysisIgnorePreRootHistory = true ++ ++# =========================================================================== ++# Rules ++# =========================================================================== ++# This section configures the scoring and playing rules. Rules can also be ++# changed mid-run by issuing custom GTP commands. ++# ++# See https://lightvector.github.io/KataGo/rules.html for rules details. ++# ++# See https://github.com/lightvector/KataGo/blob/master/docs/GTP_Extensions.md ++# for GTP commands. ++ ++# Specify the rules as a string. ++# Some legal values include: ++# chinese, japanese, korean, aga, chinese-ogs, new-zealand, stone-scoring, ++# ancient-territory, bga, aga-button ++# ++# For some human rulesets that require complex adjudication in tricky cases ++# (e.g. japanese, korean) KataGo may not precisely match the ruleset in such ++# cases but will do its best. ++rules = tromp-taylor ++ ++# By default, the "rules" parameter is used, but if you comment it out and ++# uncomment one option in each of the sections below, you can specify an ++# arbitrary combination of individual rules. ++ ++# koRule = SIMPLE # Simple ko rules (triple ko = no result) ++# koRule = POSITIONAL # Positional superko ++# koRule = SITUATIONAL # Situational superko ++ ++# scoringRule = AREA # Area scoring ++# scoringRule = TERRITORY # Territory scoring (special computer-friendly territory rules) ++ ++# taxRule = NONE # All surrounded empty points are scored ++# taxRule = SEKI # Eyes in seki do NOT count as points ++# taxRule = ALL # All groups are taxed up to 2 points for the two eyes needed to live ++ ++# Is multiple-stone suicide legal? (Single-stone suicide is always illegal). ++# multiStoneSuicideLegal = false ++# multiStoneSuicideLegal = true ++ ++# "Button go" - the first pass when area scoring awards 0.5 points and does ++# not count for ending the game. ++# Allows area scoring rulesets that have far simpler rules to achieve the same ++# final scoring precision and reward for precise play as territory scoring. ++# hasButton = false ++# hasButton = true ++ ++# Is this a human ruleset where it's okay to pass before having physically ++# captured and removed all dead stones? ++# friendlyPassOk = false ++# friendlyPassOk = true ++ ++# How handicap stones in handicap games are compensated ++# whiteHandicapBonus = 0 # White gets no compensation for black's handicap stones (Tromp-taylor, NZ, JP) ++# whiteHandicapBonus = N-1 # White gets N-1 points for black's N handicap stones (AGA) ++# whiteHandicapBonus = N # White gets N points for black's N handicap stones (Chinese) ++ ++# ------------------------------ ++# Other rules hacks ++# ------------------------------ ++# Uncomment and change to adjust what board size KataGo uses upon startup ++# by default when GTP doesn't specify. ++# defaultBoardSize = 19 ++ ++# By default, Katago will use the komi that the GUI or GTP controller tries to set. ++# Uncomment and set this to have KataGo ignore the controller and always use this komi. ++# ignoreGTPAndForceKomi = 7 ++ ++# =========================================================================== ++# Bot behavior ++# =========================================================================== ++ ++# ------------------------------ ++# Resignation ++# ------------------------------ ++ ++# Resignation occurs if for at least resignConsecTurns in a row, the ++# winLossUtility (on a [-1,1] scale) is below resignThreshold. ++allowResignation = true ++resignThreshold = -0.90 ++resignConsecTurns = 3 ++ ++# By default, KataGo may resign games that it is confidently losing even if they ++# are very close in score. Uncomment and set this to avoid resigning games ++# if the estimated difference is points is less than or equal to this. ++# resignMinScoreDifference = 10 ++ ++# ------------------------------ ++# Handicap ++# ------------------------------ ++# Assume that if black makes many moves in a row right at the start of the ++# game, then the game is a handicap game. This is necessary on some servers ++# and for some GUIs and also when initializing from many SGF files, which may ++# set up a handicap game using repeated GTP "play" commands for black rather ++# than GTP "place_free_handicap" commands; however, it may also lead to ++# incorrect understanding of komi if whiteHandicapBonus is used and a server ++# does not have such a practice. Uncomment and set to false to disable. ++# assumeMultipleStartingBlackMovesAreHandicap = true ++ ++# Makes katago dynamically adjust in handicap or altered-komi games to assume ++# based on those game settings that it must be stronger or weaker than the ++# opponent and to play accordingly. Greatly improves handicap strength by ++# biasing winrates and scores to favor appropriate safe/aggressive play. ++# Does NOT affect analysis (lz-analyze, kata-analyze, used by programs like ++# Lizzie) so analysis remains unbiased. Uncomment and set this to 0 to disable ++# this and make KataGo play the same always. ++# dynamicPlayoutDoublingAdvantageCapPerOppLead = 0.045 ++ ++# Instead of "dynamicPlayoutDoublingAdvantageCapPerOppLead", you can comment ++# that out and uncomment and set "playoutDoublingAdvantage" to a fixed value ++# from -3.0 to 3.0 that will not change dynamically. ++# ALSO affects analysis tools (lz-analyze, kata-analyze, used by e.g. Lizzie). ++# Negative makes KataGo behave as if it is much weaker than the opponent. ++# Positive makes KataGo behave as if it is much stronger than the opponent. ++# KataGo will adjust to favor safe/aggressive play as appropriate based on ++# the combination of who is ahead and how much stronger/weaker it thinks it is, ++# and report winrates and scores taking the strength difference into account. ++# ++# If this and "dynamicPlayoutDoublingAdvantageCapPerOppLead" are both set ++# then dynamic will be used for all games and this fixed value will be used ++# for analysis tools. ++# playoutDoublingAdvantage = 0.0 ++ ++# Uncomment one of these when using "playoutDoublingAdvantage" to enforce ++# that it will only apply when KataGo plays as the specified color and will be ++# negated when playing as the opposite color. ++# playoutDoublingAdvantagePla = BLACK ++# playoutDoublingAdvantagePla = WHITE ++ ++# ------------------------------ ++# Passing and cleanup ++# ------------------------------ ++# Make the bot never assume that its pass will end the game, even if passing ++# would end and "win" under Tromp-Taylor rules. Usually this is a good idea ++# when using it for analysis or playing on servers where scoring may be ++# implemented non-tromp-taylorly. Uncomment and set to false to disable. ++# conservativePass = true ++ ++# When using territory scoring, self-play games continue beyond two passes ++# with special cleanup rules that may be confusing for human players. This ++# option prevents the special cleanup phases from being reachable when using ++# the bot for GTP play. Uncomment and set to false to enable entering special ++# cleanup. For example, if you are testing it against itself, or against ++# another bot that has precisely implemented the rules documented at ++# https://lightvector.github.io/KataGo/rules.html ++# preventCleanupPhase = true ++ ++# ------------------------------ ++# Miscellaneous behavior ++# ------------------------------ ++# If the board is symmetric, search only one copy of each equivalent move. ++# Attempts to also account for ko/superko, will not theoretically perfect for ++# superko. Uncomment and set to false to disable. ++# rootSymmetryPruning = true ++ ++# Uncomment and set to true to avoid a particular joseki that some networks ++# misevaluate, and also to improve opening diversity versus some particular ++# other bots that like to play it all the time. ++# avoidMYTDaggerHack = false ++ ++# Prefer to avoid playing the same joseki in every corner of the board. ++# Uncomment to set to a specific value. See "Avoid SGF patterns" section. ++# By default: 0 (even games), 0.005 (handicap games) ++# avoidRepeatedPatternUtility = 0.0 ++ ++# Experimental logic to fight against mirror Go even with unfavorable komi. ++# Uncomment to set to a specific value to use for both playing and analysis. ++# By default: true when playing via GTP, but false when analyzing. ++# antiMirror = true ++ ++# Enable some hacks that mitigate rare instances when passing messes up deeper searches. ++# enablePassingHacks = true ++ ++# Uncomment and set this to true to prevent bad or bogus move sequences ++# in the history leading to this position from affecting KataGo's move choices. ++# Same as analysisIgnorePreRootHistory (see above) but applies to actual play. ++# You can enable this if KataGo is being asked to play from positions that it did not ++# choose the moves to reach. ++# ignorePreRootHistory = false ++ ++# =========================================================================== ++# Search limits ++# =========================================================================== ++ ++# Terminology: ++# "Playouts" is the number of new playouts of search performed each turn. ++# "Visits" is the same as "Playouts" but also counts search performed on ++# previous turns that is still applicable to this turn. ++# "Time" is the time in seconds. ++ ++# For example, if KataGo searched 200 nodes on the previous turn, and then ++# after the opponent's reply, 50 nodes of its search tree was still valid, ++# then a visit limit of 200 would allow KataGo to search 150 new nodes ++# (for a final tree size of 200 nodes), whereas a playout limit of of 200 ++# would allow KataGo to search 200 nodes (for a final tree size of 250 nodes). ++ ++# Additionally, KataGo may also move before than the limit in order to ++# obey time controls (e.g. byo-yomi, etc) if the GTP controller has ++# told KataGo that the game has is being played with a given time control. ++ ++# Limits for search on the current turn. ++# If commented out or unspecified, the default is to have no limit. ++# maxVisits = 500 ++# maxPlayouts = 300 ++ maxTime = 0.5 ++ ++# Ponder on the opponent's turn? ++ponderingEnabled = true ++ ++# Limits for search when pondering on the opponent's turn. ++# If commented out or unspecified, the default is to have no limit. ++# Limiting the maximum time is recommended so that KataGo won't burn CPU/GPU ++# forever and/or run out of RAM if left unattended while pondering is enabled. ++# maxVisitsPondering = 5000 ++# maxPlayoutsPondering = 3000 ++maxTimePondering = 60.0 ++ ++ ++# ------------------------------ ++# Other search limits and behavior ++# ------------------------------ ++ ++# Approx number of seconds to buffer for lag for GTP time controls - will ++# move a bit faster assuming there is this much lag per move. ++lagBuffer = 1.0 ++ ++# YOU PROBABLY WANT TO TUNE THIS PARAMETER! ++# The number of threads to use when searching. On powerful GPUs the optimal ++# threads may be much higher than the number of CPU cores you have because ++# many threads are needed to feed efficient large batches to the GPU. ++# ++# Run "./katago benchmark" to tune this parameter and test the effect ++# of changes to any of other parameters. ++numSearchThreads = 16 ++ ++# Play a little faster if the opponent is passing, for human-friendliness. ++# Comment these out to disable them, such as if running a controlled match ++# where you are testing KataGo with fixed compute per move vs other bots. ++searchFactorAfterOnePass = 0.50 ++searchFactorAfterTwoPass = 0.25 ++ ++# Play a little faster if super-winning, for human-friendliness. ++# Comment these out to disable them, such as if running a controlled match ++# where you are testing KataGo with fixed compute per move vs other bots. ++searchFactorWhenWinning = 0.40 ++searchFactorWhenWinningThreshold = 0.95 ++ ++# =========================================================================== ++# GPU settings ++# =========================================================================== ++# This section configures GPU settings. ++# ++# Maximum number of positions to send to a single GPU at once. The default ++# value is roughly equal to numSearchThreads, but can be specified manually ++# if running out of memory, or using multiple GPUs that expect to share work. ++# nnMaxBatchSize = ++ ++# Controls the neural network cache size, which is the primary RAM/memory use. ++# KataGo will cache up to (2 ** nnCacheSizePowerOfTwo) many neural net ++# evaluations in case of transpositions in the tree. ++# Increase this to improve performance for searches with tens of thousands ++# of visits or more. Decrease this to limit memory usage. ++# If you're happy to do some math - each neural net entry takes roughly ++# 1.5KB, except when using whole-board ownership/territory ++# visualizations, where each entry will take roughly 3KB. The number of ++# entries is (2 ** nnCacheSizePowerOfTwo). (E.g. 2 ** 18 = 262144.) ++# You can compute roughly how much memory the cache will use based on this. ++# nnCacheSizePowerOfTwo = 20 ++ ++# Size of mutex pool for nnCache is (2 ** this). ++# nnMutexPoolSizePowerOfTwo = 16 ++ ++# Randomize board orientation when running neural net evals? Uncomment and ++# set to false to disable. ++# nnRandomize = true ++ ++# If provided, force usage of a specific seed for nnRandomize. ++# The default is to use a randomly generated seed. ++# nnRandSeed = abcdefg ++ ++# Uncomment and set to true to force GTP to use the maximum board size for ++# internal buffers for the neural net. This will make KataGo slower when ++# evaluating small boards, but will avoid a lengthy initialization time on every ++# change of board size due to having to re-size the neural net buffers on the GPU. ++# This can be useful for example, for OGS's persistent bot mode that uses a single ++# bot instance to handle multiple games and may thrash between different board sizes ++# if there are concurrent games of multiple sizes. ++# gtpForceMaxNNSize = false ++ ++# ------------------------------ ++# Multiple GPUs ++# ------------------------------ ++# Set this to the number of GPUs to use or that are available. ++# IMPORTANT: If more than 1, also uncomment the appropriate TensorRT ++# or CUDA or OpenCL section. ++# numNNServerThreadsPerModel = 1 ++ ++# ------------------------------ ++# TENSORRT GPU settings ++# ------------------------------ ++# These only apply when using the TENSORRT version of KataGo. ++ ++# For one GPU: optionally uncomment this option and change if the GPU to ++# use is not device 0. ++# trtDeviceToUse = 0 ++ ++# For two GPUs: Uncomment these options, AND set numNNServerThreadsPerModel above. ++# Also, change their values if the devices you want to use are not 0 and 1. ++# trtDeviceToUseThread0 = 0 ++# trtDeviceToUseThread1 = 1 ++ ++# For three GPUs: Uncomment these options, AND set numNNServerThreadsPerModel above. ++# Also, change their values if the devices you want to use are not 0 and 1 and 2. ++# trtDeviceToUseThread0 = 0 ++# trtDeviceToUseThread1 = 1 ++# trtDeviceToUseThread2 = 2 ++ ++# The pattern continues for additional GPUs. ++ ++# ------------------------------ ++# CUDA GPU settings ++# ------------------------------ ++# These only apply when using the CUDA version of KataGo. ++ ++# For one GPU: optionally uncomment and change this if the GPU you want to ++# use is not device 0 ++# cudaDeviceToUse = 0 ++ ++# For two GPUs: Uncomment these options, AND set numNNServerThreadsPerModel above. ++# Also, change their values if the devices you want to use are not 0 and 1. ++# cudaDeviceToUseThread0 = 0 ++# cudaDeviceToUseThread1 = 1 ++ ++# For three GPUs: Uncomment these options, AND set numNNServerThreadsPerModel above. ++# Also, change their values if the devices you want to use are not 0 and 1 and 2. ++# cudaDeviceToUseThread0 = 0 ++# cudaDeviceToUseThread1 = 1 ++# cudaDeviceToUseThread2 = 2 ++ ++# The pattern continues for additional GPUs. ++ ++# KataGo will automatically use FP16 or not based on the compute capability ++# of your NVIDIA GPU. If you want to try to force a particular behavior ++# you can uncomment these lines and change them to "true" or "false". ++# cudaUseFP16 = auto ++# cudaUseNHWC = auto ++ ++# ------------------------------ ++# OpenCL GPU settings ++# ------------------------------ ++# These only apply when using the OpenCL version of KataGo. ++ ++# Uncomment and set to true to tune OpenCL for every board size separately, ++# rather than only the largest possible size. ++# openclReTunePerBoardSize = false ++ ++# For one GPU: optionally uncomment and change this if the best device to use is guessed incorrectly. ++# The default behavior tries to guess the 'best' GPU or device on your system to use, usually it will be a good guess. ++# openclDeviceToUse = 0 ++ ++# For two GPUs: Uncomment these two lines and replace X and Y with the device ids of the devices you want to use. ++# It might NOT be 0 and 1, some computers will have many OpenCL devices. You can see what the devices are when ++# KataGo starts up - it should print or log all the devices it finds. ++# (AND also set numNNServerThreadsPerModel above) ++# openclDeviceToUseThread0 = X ++# openclDeviceToUseThread1 = Y ++ ++# For three GPUs: Uncomment these three lines and replace X and Y and Z with the device ids of the devices you want to use. ++# It might NOT be 0 and 1 and 2, some computers will have many OpenCL devices. You can see what the devices are when ++# KataGo starts up - it should print or log all the devices it finds. ++# (AND also set numNNServerThreadsPerModel above) ++# openclDeviceToUseThread0 = X ++# openclDeviceToUseThread1 = Y ++# openclDeviceToUseThread2 = Z ++ ++# The pattern continues for additional GPUs. ++ ++# KataGo will automatically use FP16 or not based on testing your GPU during ++# tuning. If you want to try to force a particular behavior though you can ++# uncomment this option and change it to "true" or "false". This is a fairly ++# blunt setting - more detailed settings are testable by rerunning the tuner ++# with various arguments (./katago tuner). ++# openclUseFP16 = auto ++ ++# ------------------------------ ++# Eigen-specific settings ++# ------------------------------ ++# These only apply when using the Eigen (pure CPU) version of KataGo. ++ ++# Number of CPU threads for evaluating the neural net on the Eigen backend. ++# ++# Default: numSearchThreads ++# numEigenThreadsPerModel = X ++ ++# =========================================================================== ++# Root move selection and biases ++# =========================================================================== ++# Uncomment and edit any of the below values to change them from their default. ++ ++# If provided, force usage of a specific seed for various random things in ++# the search. The default is to use a random seed. ++# searchRandSeed = hijklmn ++ ++# Temperature for the early game, randomize between chosen moves with ++# this temperature ++# chosenMoveTemperatureEarly = 0.5 ++ ++# Decay temperature for the early game by 0.5 every this many moves, ++# scaled with board size. ++# chosenMoveTemperatureHalflife = 19 ++ ++# At the end of search after the early game, randomize between chosen ++# moves with this temperature ++# chosenMoveTemperature = 0.10 ++ ++# Subtract this many visits from each move prior to applying ++# chosenMoveTemperature (unless all moves have too few visits) to downweight ++# unlikely moves ++# chosenMoveSubtract = 0 ++ ++# The same as chosenMoveSubtract but only prunes moves that fall below ++# the threshold. This setting does not affect chosenMoveSubtract. ++# chosenMovePrune = 1 ++ ++# Number of symmetries to sample (without replacement) and average at the root ++# rootNumSymmetriesToSample = 1 ++ ++# Using LCB for move selection? ++# useLcbForSelection = true ++ ++# How many stdevs a move needs to be better than another for LCB selection ++# lcbStdevs = 5.0 ++ ++# Only use LCB override when a move has this proportion of visits as the ++# top move. ++# minVisitPropForLCB = 0.15 ++ ++# =========================================================================== ++# Internal params ++# =========================================================================== ++# Uncomment and edit any of the below values to change them from their default. ++ ++# Scales the utility of winning/losing ++# winLossUtilityFactor = 1.0 ++ ++# Scales the utility for trying to maximize score ++# staticScoreUtilityFactor = 0.10 ++# dynamicScoreUtilityFactor = 0.30 ++ ++# Adjust dynamic score center this proportion of the way towards zero, ++# capped at a reasonable amount. ++# dynamicScoreCenterZeroWeight = 0.20 ++# dynamicScoreCenterScale = 0.75 ++ ++# The utility of getting a "no result" due to triple ko or other long cycle ++# in non-superko rulesets (-1 to 1) ++# noResultUtilityForWhite = 0.0 ++ ++# The number of wins that a draw counts as, for white. (0 to 1) ++# drawEquivalentWinsForWhite = 0.5 ++ ++# Exploration constant for mcts ++# cpuctExploration = 1.0 ++# cpuctExplorationLog = 0.45 ++ ++# Parameters that control exploring more in volatile positions, exploring ++# less in stable positions. ++# cpuctUtilityStdevPrior = 0.40 ++# cpuctUtilityStdevPriorWeight = 2.0 ++# cpuctUtilityStdevScale = 0.85 ++ ++# FPU reduction constant for mcts ++# fpuReductionMax = 0.2 ++# rootFpuReductionMax = 0.1 ++# fpuParentWeightByVisitedPolicy = true ++ ++# Parameters that control weighting of evals based on the net's own ++# self-reported uncertainty. ++# useUncertainty = true ++# uncertaintyExponent = 1.0 ++# uncertaintyCoeff = 0.25 ++ ++# Explore using optimistic policy ++# rootPolicyOptimism = 0.2 ++# policyOptimism = 1.0 ++ ++# Amount to apply a downweighting of children with very bad values relative ++# to good ones. ++# valueWeightExponent = 0.25 ++ ++# Slight incentive for the bot to behave human-like with regard to passing at ++# the end, filling the dame, not wasting time playing in its own territory, ++# etc., and not play moves that are equivalent in terms of points but a bit ++# more unfriendly to humans. ++# rootEndingBonusPoints = 0.5 ++ ++# Make the bot prune useless moves that are just prolonging the game to ++# avoid losing yet. ++# rootPruneUselessMoves = true ++ ++# Apply bias correction based on local pattern keys ++# subtreeValueBiasFactor = 0.45 ++# subtreeValueBiasWeightExponent = 0.85 ++ ++# Use graph search rather than tree search - identify and share search for ++# transpositions. ++# useGraphSearch = true ++ ++# How much to shard the node table for search synchronization ++# nodeTableShardsPowerOfTwo = 16 ++ ++# How many virtual losses to add when a thread descends through a node ++# numVirtualLossesPerThread = 1 ++ ++# Improve the quality of evals under heavy multithreading ++# useNoisePruning = true ++ ++# =========================================================================== ++# Automatic avoid patterns ++# =========================================================================== ++# The parameters in this section provide a way to bias away from moves that match ++# patterns that this instance of KataGo has played in previous games, by auto-saving ++# moves to a directory and then auto-loading and biasing against them each new game. ++# Uncomment them to use them. When using this feature, all parameters must be specified. ++ ++# Different board sizes are tracked separately, but all board sizes share the same ++# parameters by default. Every parameter *except* for autoAvoidRepeatDir and ++# autoAvoidRepeatSaveChunkSize can be overridden per board size, ++# e.g. "autoAvoidRepeatMinTurnNumber13x13". You must ALSO specify the ++# defaults even if you have specified board-size-specific values. ++ ++# Directory to auto-save moves KataGo plays, to avoid them in future games. ++# You can create a new empty directory and put its path here. ++# If you run parallel instances of KataGo, use different directories if you ++# want them not to share their biases, use the same directory if you want ++# all of them to bias away from past moves that any of them have played. ++# KataGo will also automatically DELETE old data in this directory, so it is ++# recommended that if you do share the same directory between parallel instances, ++# that they all use the same settings that affect data saving/deletion. ++# autoAvoidRepeatDir = PATH_TO_NEW_DIRECTORY ++ ++# Penalize this much utility per matching move. ++# Values that are too large may lead to bad play. The value of 0.004 is fairly large ++# and might be large enough to result in some early weird/bad moves when trying ++# to avoid past games' moves if enough games begin the same way. You can experiment with it. ++# autoAvoidRepeatUtility = 0.004 ++ ++# Per each new move saved, exponentially decay prior saved moves by this factor. ++# This way, the bias against moves from many games ago is gradually phased out. ++# For example, 0.9995 = 1 - 1/2000, so would be roughly 2000 prior moves worth of ++# penalty weight remembered, in steady state. Depending on what turn number range ++# you are saving, this might equate to a different number of games For example ++# saving the first 50 moves per game would make this roughly 2000 / 50 = 40 games ++# worth of memory. ++# autoAvoidRepeatLambda = 0.9995 ++ ++# Affects data saving/deletion. ++# When the number of saved moves exceeds this, outright delete them to avoid too many ++# files and disk space building up. Also may affect the speed of saving/loading on start ++# of each game if this is set large and a lot of data builds up. ++# autoAvoidRepeatMaxPoses = 10000 ++ ++# Affects data saving/deletion. ++# Only save data for moves within this turn number range of those games. ++# E.g. setting autoAvoidRepeatMinTurnNumber to a number like 4 or 5 would tend to make ++# KataGo not develop a bias against the initial 3-4 and 4-4 corner moves in almost every game. ++# autoAvoidRepeatMinTurnNumber = 0 ++# autoAvoidRepeatMaxTurnNumber = 50 ++ ++# Affects data saving/deletion. ++# Within a single run of a program, wait to accumulate this many samples ++# (possibly across multiple clear_boards/games) before saving the data. ++# Can help to avoid writing too many small files to disk, especially when GTP is used ++# in a way that clears the board very frequently (e.g. gtp2ogs pooled manager). ++# autoAvoidRepeatSaveChunkSize = 200 ++ ++# =========================================================================== ++# Avoid SGF patterns ++# =========================================================================== ++# The parameters in this section provide a way to avoid moves that follow ++# specific patterns based on a set of SGF files loaded upon startup. ++# This is basically the same as the above "Automatic avoid patterns" section ++# above except you supply your own SGF files to avoid moves from. ++# Uncomment them to use this feature. Additionally, if the SGF file ++# contains the string %SKIP% in a comment on a move, that move will be ++# ignored for this purpose. ++ ++# Load SGF files from this directory when the engine is started ++# (only on startup, will not reload unless engine is restarted) ++# avoidSgfPatternDirs = path/to/directory/with/sgfs/ ++# You can also surround the file path in double quotes if the file path contains trailing spaces or hash signs. ++# Within double quotes, backslashes are escape characters. ++# avoidSgfPatternDirs = "path/to/directory/with/sgfs/" ++ ++# Penalize this much utility per matching move. ++# Set this negative if you instead want to favor SGF patterns instead of ++# penalizing them. This number does not need to be large, even 0.001 will ++# make a difference. Values that are too large may lead to bad play. ++# avoidSgfPatternUtility = 0.001 ++ ++# Optional - load only the newest this many files ++# avoidSgfPatternMaxFiles = 20 ++ ++# Optional - Penalty is multiplied by this per each older SGF file, so that ++# old SGF files matter less than newer ones. ++# avoidSgfPatternLambda = 0.90 ++ ++# Optional - pay attention only to moves made by players with this name. ++# For example, set it to the name that your bot's past games will show up ++# as in the SGF, so that the bot will only avoid repeating moves that itself ++# made in past games, not the moves that its opponents made. ++# avoidSgfPatternAllowedNames = my-ogs-bot-name1,my-ogs-bot-name2 ++ ++# Optional - Ignore moves in SGF files that occurred before this turn number. ++# avoidSgfPatternMinTurnNumber = 0 ++ ++# For more avoid patterns: ++# You can also specify a second set of parameters, and a third, fourth, ++# etc. by numbering 2,3,4,... ++# ++# avoidSgf2PatternDirs = ... ++# avoidSgf2PatternUtility = ... ++# avoidSgf2PatternMaxFiles = ... ++# avoidSgf2PatternLambda = ... ++# avoidSgf2PatternAllowedNames = ... ++# avoidSgf2PatternMinTurnNumber = ... ++ +diff --git a/cpp/match_example.cfg b/cpp/match_example.cfg +new file mode 100644 +index 00000000..945ac44e +--- /dev/null ++++ b/cpp/match_example.cfg +@@ -0,0 +1,228 @@ ++# Example config for C++ match runner ++# This is an example template config for the "match" subcommand of KataGo. e.g: ++# ./katago match -config-file configs/match_example.cfg -log-file match.log -sgf-output-dir match_sgfs/ ++# ++# On a good GPU, the match subcommand enables testing of KataGo nets against each other ++# or a net against itself with different parameters vastly faster than anything ++# else, because multiple games can share GPU batching. ++# ++# Beware however of using this to test differing numbers of threads or test time-based search limits. ++# Because many games will be run simultaneously, they will compete from each other for compute power, ++# and although the total will run much faster than if you had run them one by one, you might also get ++# substantially different results not reflective of the strength of a configuration when run in a real ++# match setting, on a machine by itself. For fixed numbers of visits or playouts instead of fixed time, ++# and with numSearchThreads = 1, there should be no problem though, because then the compute time has ++# no influence on the result of the computation. ++# ++# See gtp config for descriptions of most of these params. ++# ++# For almost any parameter in this config that is related to a bot, rather than to the match as a whole ++# (so, visits, search parameters, model files, etc. but NOT the rules, max games, log info, etc) ++# you can specify them differentially between different bots by appending the index of the bot. ++# For example, if you were testing different numbers of visits, you could try: ++# ++# numBots = 3 ++# botName0 = lowVisits ++# botName1 = midVisits ++# botName2 = highVisits ++# ++# maxVisits0 = 100 ++# maxVisits1 = 300 ++# maxVisits2 = 1000 ++# ++# Or, if you were testing different neural nets, with different search configurations, you could do: ++# ++# nnModelFile0 = path/to/first/model/file.bin.gz ++# nnModelFile1 = path/to/second/model/file.bin.gz ++# ++# And specify different search parameters for them if you wanted: ++# cpuctExploration0 = 1.5 ++# cpuctExploration1 = 1.3 ++ ++# Logs------------------------------------------------------------------------------------ ++ ++logSearchInfo = false ++logMoves = false ++logGamesEvery = 50 ++logToStdout = true ++ ++# Bots------------------------------------------------------------------------------------- ++# For multiple bots, you can specify their names as botName0,botName1, etc. ++# If the bots are using different models, specify nnModelFile0, nnModelFile1, etc. ++ ++numBots=1 ++botName=FOO ++nnModelFile=PATH_TO_MODEL ++ ++# These bots will not play each other, but will still be opponents for other bots ++# secondaryBots = 0,1,3 ++# Only these bots will actually play games. Useful if you have a config file with many more ++# bots defined but you want to only selectively enable a few. ++# includeBots = 0,2,6 ++ ++# Specify extra pairings of bots to play. ++# extraPairs = 0-4,1-4 ++# Uncomment and set this to true if you don't want extraPairs to be mirrored by color. ++# (i.e. 0-4 should mean 0 plays black and 4 plays white only, rather than having two games with swapped colors). ++# extraPairsAreOneSidedBW = false ++ ++# Match----------------------------------------------------------------------------------- ++numGameThreads=8 # How many games to run in parallel at a time? ++ ++numGamesTotal=1000000 ++maxMovesPerGame=1200 ++ ++allowResignation = true ++resignThreshold = -0.95 ++resignConsecTurns = 6 ++ ++# Rules------------------------------------------------------------------------------------ ++# See https://lightvector.github.io/KataGo/rules.html for a description of the rules. ++ ++koRules = SIMPLE,POSITIONAL,SITUATIONAL ++scoringRules = AREA,TERRITORY ++taxRules = NONE,SEKI,ALL ++multiStoneSuicideLegals = false,true ++hasButtons = false,true ++ ++bSizes = 19,13,9 ++bSizeRelProbs = 90,5,5 ++ ++# If you want to specify exact distributions of boards, including rectangles, specify this instead ++# of bSizes. E.g. to randomize between a 7x5, a 9x12, and a 10x10 board with probabilities 25%, 25%, 50%: ++# bSizesXY=7-5,9-12,10-10 ++# bSizeRelProbs = 1,1,2 ++ ++komiAuto = True # Automatically adjust komi to what the neural nets think are fair ++# komiMean = 7.5 # Specify explicit komi ++# policyInitAreaProp = 0 ++# compensateAfterPolicyInitProb = 1.0 # Additionally make komi fair this often after the high-temperature moves. ++# policyInitAreaTemperature = 1 ++handicapProb = 0.0 ++handicapCompensateKomiProb = 1.0 ++# numExtraBlackFixed = 3 # When playing handicap games, always use exactly this many extra black moves ++ ++# Search limits----------------------------------------------------------------------------------- ++ ++maxVisits = 500 ++# maxPlayouts = 300 ++# maxTime = 60 ++ ++numSearchThreads = 1 ++ ++# GPU Settings------------------------------------------------------------------------------- ++ ++nnMaxBatchSize = 32 ++nnCacheSizePowerOfTwo = 21 ++nnMutexPoolSizePowerOfTwo = 17 ++nnRandomize = true ++ ++ ++# How many threads should there be to feed positions to the neural net? ++# Server threads are indexed 0,1,...(n-1) for the purposes of the below GPU settings arguments ++# that specify which threads should use which GPUs. ++# NOTE: This parameter is probably ONLY useful if you have multiple GPUs, since each GPU will need a thread. ++# If you're tuning single-GPU performance, use numSearchThreads instead. ++numNNServerThreadsPerModel = 1 ++ ++ ++# TENSORRT GPU settings-------------------------------------- ++# These only apply when using the TENSORRT version of KataGo. ++ ++# IF USING ONE GPU: optionally uncomment and change this if the GPU you want to use turns out to be not device 0 ++# trtDeviceToUse = 0 ++ ++# IF USING TWO GPUS: Uncomment these two lines (AND set numNNServerThreadsPerModel above): ++# trtDeviceToUseThread0 = 0 # change this if the first GPU you want to use turns out to be not device 0 ++# trtDeviceToUseThread1 = 1 # change this if the second GPU you want to use turns out to be not device 1 ++ ++# IF USING THREE GPUS: Uncomment these three lines (AND set numNNServerThreadsPerModel above): ++# trtDeviceToUseThread0 = 0 # change this if the first GPU you want to use turns out to be not device 0 ++# trtDeviceToUseThread1 = 1 # change this if the second GPU you want to use turns out to be not device 1 ++# trtDeviceToUseThread2 = 2 # change this if the third GPU you want to use turns out to be not device 2 ++ ++# You can probably guess the pattern if you have four, five, etc. GPUs. ++ ++ ++# CUDA GPU settings-------------------------------------- ++# For the below, "model" refers to a neural net, from the nnModelFile parameter(s) above. ++# cudaGpuToUse = 0 #use gpu 0 for all server threads (numNNServerThreadsPerModel) unless otherwise specified per-model or per-thread-per-model ++# cudaGpuToUseModel0 = 3 #use gpu 3 for model 0 for all threads unless otherwise specified per-thread for this model ++# cudaGpuToUseModel1 = 2 #use gpu 2 for model 1 for all threads unless otherwise specified per-thread for this model ++# cudaGpuToUseModel0Thread0 = 3 #use gpu 3 for model 0, server thread 0 ++# cudaGpuToUseModel0Thread1 = 2 #use gpu 2 for model 0, server thread 1 ++ ++# cudaUseFP16 = auto ++# cudaUseNHWC = auto ++ ++ ++# OpenCL GPU settings-------------------------------------- ++# These only apply when using OpenCL as the backend for inference. ++# (For GTP, we only ever have one model, when playing matches, we might have more than one, see match_example.cfg) ++ ++# Default behavior is just to always use gpu 0, you will want to uncomment and adjust one or more of these lines ++# to take advantage of a multi-gpu machine ++# openclGpuToUse = 0 #use gpu 0 for all server threads (numNNServerThreadsPerModel) unless otherwise specified per-model or per-thread-per-model ++# openclGpuToUseModel0 = 3 #use gpu 3 for model 0 for all threads unless otherwise specified per-thread for this model ++# openclGpuToUseModel1 = 2 #use gpu 2 for model 1 for all threads unless otherwise specified per-thread for this model ++# openclGpuToUseModel0Thread0 = 3 #use gpu 3 for model 0, server thread 0 ++# openclGpuToUseModel0Thread1 = 2 #use gpu 2 for model 0, server thread 1 ++ ++# Uncomment to tune OpenCL for every board size separately, rather than only the largest possible size ++# openclReTunePerBoardSize = true ++ ++# openclUseFP16 = auto ++ ++ ++# Eigen-specific settings-------------------------------------- ++# These only apply when using the Eigen (pure CPU) version of KataGo. ++ ++# This is the number of CPU threads for evaluating the neural net on the Eigen backend. ++# It defaults to numSearchThreads. ++# numEigenThreadsPerModel = X ++ ++ ++# Root move selection and biases------------------------------------------------------------------------------ ++# Uncomment and edit any of the below values to change them from their default. ++# Values in this section can be specified per-bot as well ++ ++chosenMoveTemperatureEarly = 0.60 ++# chosenMoveTemperatureHalflife = 19 ++chosenMoveTemperature = 0.20 ++# chosenMoveSubtract = 0 ++# chosenMovePrune = 1 ++ ++# rootNumSymmetriesToSample = 1 ++ ++# useLcbForSelection = true ++# lcbStdevs = 5.0 ++# minVisitPropForLCB = 0.15 ++ ++# Internal params------------------------------------------------------------------------------ ++# Uncomment and edit any of the below values to change them from their default. ++# Values in this section can be specified per-bot as well ++ ++# winLossUtilityFactor = 1.0 ++# staticScoreUtilityFactor = 0.10 ++# dynamicScoreUtilityFactor = 0.30 ++# dynamicScoreCenterZeroWeight = 0.20 ++# dynamicScoreCenterScale = 0.75 ++# noResultUtilityForWhite = 0.0 ++# drawEquivalentWinsForWhite = 0.5 ++ ++# cpuctExploration = 0.9 ++# cpuctExplorationLog = 0.4 ++# fpuReductionMax = 0.2 ++# rootFpuReductionMax = 0.1 ++# fpuParentWeightByVisitedPolicy = true ++# valueWeightExponent = 0.25 ++# rootEndingBonusPoints = 0.5 ++# rootPruneUselessMoves = true ++# subtreeValueBiasFactor = 0.45 ++# subtreeValueBiasWeightExponent = 0.85 ++# useGraphSearch = true ++# rootPolicyOptimism = 0.2 ++# policyOptimism = 1.0 ++ ++# nodeTableShardsPowerOfTwo = 16 ++# numVirtualLossesPerThread = 1 +diff --git a/cpp/msvcp140.dll b/cpp/msvcp140.dll +new file mode 100644 +index 00000000..f7c342d1 +Binary files /dev/null and b/cpp/msvcp140.dll differ +diff --git a/cpp/msvcp140_1.dll b/cpp/msvcp140_1.dll +new file mode 100644 +index 00000000..6f55c9d1 +Binary files /dev/null and b/cpp/msvcp140_1.dll differ +diff --git a/cpp/msvcp140_2.dll b/cpp/msvcp140_2.dll +new file mode 100644 +index 00000000..b127e0c8 +Binary files /dev/null and b/cpp/msvcp140_2.dll differ +diff --git a/cpp/msvcp140_atomic_wait.dll b/cpp/msvcp140_atomic_wait.dll +new file mode 100644 +index 00000000..b8fa2030 +Binary files /dev/null and b/cpp/msvcp140_atomic_wait.dll differ +diff --git a/cpp/msvcp140_codecvt_ids.dll b/cpp/msvcp140_codecvt_ids.dll +new file mode 100644 +index 00000000..7549b2b5 +Binary files /dev/null and b/cpp/msvcp140_codecvt_ids.dll differ +diff --git a/cpp/vcruntime140.dll b/cpp/vcruntime140.dll +new file mode 100644 +index 00000000..5d5b81dc +Binary files /dev/null and b/cpp/vcruntime140.dll differ +diff --git a/cpp/vcruntime140_1.dll b/cpp/vcruntime140_1.dll +new file mode 100644 +index 00000000..3a02196f +Binary files /dev/null and b/cpp/vcruntime140_1.dll differ +diff --git "a/cpp/\344\277\261\344\271\220\351\203\250AI.cfg" "b/cpp/\344\277\261\344\271\220\351\203\250AI.cfg" +new file mode 100644 +index 00000000..9b9287b0 +--- /dev/null ++++ "b/cpp/\344\277\261\344\271\220\351\203\250AI.cfg" +@@ -0,0 +1,721 @@ ++# Configuration for KataGo C++ GTP engine ++ ++# Run the program using: `./katago.exe gtp` ++ ++# In this example config, when a parameter is given as a commented out value, ++# that value also is the default value, unless described otherwise. You can ++# uncomment it (remove the pound sign) and change it if you want. ++ ++# =========================================================================== ++# Running on an online server or in a real tournament or match ++# =========================================================================== ++# If you plan to run online or in a tournament, read through the "Rules" ++# section below for proper handling of komi, handicaps, end-of-game cleanup, ++# and other details. ++ ++# =========================================================================== ++# Notes about performance and memory usage ++# =========================================================================== ++# Important: For good performance, you will very likely want to tune the ++# "numSearchThreads" parameter in the Search limits section below! Run ++# "./katago benchmark" to test KataGo and to suggest a reasonable value ++# of this parameter. ++ ++# For multi-GPU systems, read "OpenCL GPU settings" or "CUDA GPU settings". ++# ++# When using OpenCL, verify that KataGo picks the correct device! Some systems ++# may have both an Intel CPU OpenCL and GPU OpenCL. If # KataGo picks the wrong ++# one, correct this by specifying "openclGpuToUse". ++# ++# Consider adjusting "maxVisits", "ponderingEnabled", "resignThreshold", and ++# other parameters depending on your intended usage. ++ ++# =========================================================================== ++# Command-line usage ++# =========================================================================== ++# All of the below values may be set or overridden via command-line arguments: ++# ++# -override-config KEY=VALUE,KEY=VALUE,... ++ ++# =========================================================================== ++# Logs and files ++# =========================================================================== ++# This section defines where and what logging information is produced. ++ ++# Each run of KataGo will log to a separate file in this dir. ++# This is the default. ++logDir = gtp_logs ++# Uncomment and specify this instead of logDir to write separate dated subdirs ++# logDirDated = gtp_logs ++# Uncomment and specify this instead of logDir to log to only a single file ++# logFile = gtp.log ++ ++# Logging options ++logAllGTPCommunication = true ++logSearchInfo = true ++logSearchInfoForChosenMove = false ++logToStderr = false ++ ++# KataGo will display some info to stderr on GTP startup ++# Uncomment the next line and set it to false to suppress that and remain silent ++# startupPrintMessageToStderr = true ++ ++# Write information to stderr, for use in things like malkovich chat to OGS. ++ ogsChatToStderr = true ++ ++# Uncomment and set this to a directory to override where openCLTuner files ++# and other cached data is written. By default it saves into a subdir of the ++# current directory on windows, and a subdir of ~/.katago on Linux. ++# homeDataDir = PATH_TO_DIRECTORY ++ ++# =========================================================================== ++# Analysis ++# =========================================================================== ++# This section configures analysis settings. ++# ++# The maximum number of moves after the first move displayed in variations ++# from analysis commands like kata-analyze or lz-analyze. ++# analysisPVLen = 15 ++ ++# Report winrates for chat and analysis as (BLACK|WHITE|SIDETOMOVE). ++# Most GUIs and analysis tools will expect SIDETOMOVE. ++# reportAnalysisWinratesAs = SIDETOMOVE ++ ++# Extra noise for wider exploration. Large values will force KataGo to ++# analyze a greater variety of moves than it normally would. ++# An extreme value like 1 distributes playouts across every move on the board, ++# even very bad moves. ++# Affects analysis only, does not affect play. ++# analysisWideRootNoise = 0.04 ++ ++# Try to limit the effect of possible bad or bogus move sequences in the ++# history leading to this position from affecting KataGo's move predictions. ++# analysisIgnorePreRootHistory = true ++ ++# =========================================================================== ++# Rules ++# =========================================================================== ++# This section configures the scoring and playing rules. Rules can also be ++# changed mid-run by issuing custom GTP commands. ++# ++# See https://lightvector.github.io/KataGo/rules.html for rules details. ++# ++# See https://github.com/lightvector/KataGo/blob/master/docs/GTP_Extensions.md ++# for GTP commands. ++ ++# Specify the rules as a string. ++# Some legal values include: ++# chinese, japanese, korean, aga, chinese-ogs, new-zealand, stone-scoring, ++# ancient-territory, bga, aga-button ++# ++# For some human rulesets that require complex adjudication in tricky cases ++# (e.g. japanese, korean) KataGo may not precisely match the ruleset in such ++# cases but will do its best. ++rules = tromp-taylor ++ ++# By default, the "rules" parameter is used, but if you comment it out and ++# uncomment one option in each of the sections below, you can specify an ++# arbitrary combination of individual rules. ++ ++# koRule = SIMPLE # Simple ko rules (triple ko = no result) ++# koRule = POSITIONAL # Positional superko ++# koRule = SITUATIONAL # Situational superko ++ ++# scoringRule = AREA # Area scoring ++# scoringRule = TERRITORY # Territory scoring (special computer-friendly territory rules) ++ ++# taxRule = NONE # All surrounded empty points are scored ++# taxRule = SEKI # Eyes in seki do NOT count as points ++# taxRule = ALL # All groups are taxed up to 2 points for the two eyes needed to live ++ ++# Is multiple-stone suicide legal? (Single-stone suicide is always illegal). ++# multiStoneSuicideLegal = false ++# multiStoneSuicideLegal = true ++ ++# "Button go" - the first pass when area scoring awards 0.5 points and does ++# not count for ending the game. ++# Allows area scoring rulesets that have far simpler rules to achieve the same ++# final scoring precision and reward for precise play as territory scoring. ++# hasButton = false ++# hasButton = true ++ ++# Is this a human ruleset where it's okay to pass before having physically ++# captured and removed all dead stones? ++# friendlyPassOk = false ++# friendlyPassOk = true ++ ++# How handicap stones in handicap games are compensated ++# whiteHandicapBonus = 0 # White gets no compensation for black's handicap stones (Tromp-taylor, NZ, JP) ++# whiteHandicapBonus = N-1 # White gets N-1 points for black's N handicap stones (AGA) ++# whiteHandicapBonus = N # White gets N points for black's N handicap stones (Chinese) ++ ++# ------------------------------ ++# Other rules hacks ++# ------------------------------ ++# Uncomment and change to adjust what board size KataGo uses upon startup ++# by default when GTP doesn't specify. ++# defaultBoardSize = 19 ++ ++# By default, Katago will use the komi that the GUI or GTP controller tries to set. ++# Uncomment and set this to have KataGo ignore the controller and always use this komi. ++# ignoreGTPAndForceKomi = 7 ++ ++# =========================================================================== ++# Bot behavior ++# =========================================================================== ++ ++# ------------------------------ ++# Resignation ++# ------------------------------ ++ ++# Resignation occurs if for at least resignConsecTurns in a row, the ++# winLossUtility (on a [-1,1] scale) is below resignThreshold. ++allowResignation = true ++resignThreshold = -0.90 ++resignConsecTurns = 3 ++ ++# By default, KataGo may resign games that it is confidently losing even if they ++# are very close in score. Uncomment and set this to avoid resigning games ++# if the estimated difference is points is less than or equal to this. ++# resignMinScoreDifference = 10 ++ ++# ------------------------------ ++# Handicap ++# ------------------------------ ++# Assume that if black makes many moves in a row right at the start of the ++# game, then the game is a handicap game. This is necessary on some servers ++# and for some GUIs and also when initializing from many SGF files, which may ++# set up a handicap game using repeated GTP "play" commands for black rather ++# than GTP "place_free_handicap" commands; however, it may also lead to ++# incorrect understanding of komi if whiteHandicapBonus is used and a server ++# does not have such a practice. Uncomment and set to false to disable. ++# assumeMultipleStartingBlackMovesAreHandicap = true ++ ++# Makes katago dynamically adjust in handicap or altered-komi games to assume ++# based on those game settings that it must be stronger or weaker than the ++# opponent and to play accordingly. Greatly improves handicap strength by ++# biasing winrates and scores to favor appropriate safe/aggressive play. ++# Does NOT affect analysis (lz-analyze, kata-analyze, used by programs like ++# Lizzie) so analysis remains unbiased. Uncomment and set this to 0 to disable ++# this and make KataGo play the same always. ++# dynamicPlayoutDoublingAdvantageCapPerOppLead = 0.045 ++ ++# Instead of "dynamicPlayoutDoublingAdvantageCapPerOppLead", you can comment ++# that out and uncomment and set "playoutDoublingAdvantage" to a fixed value ++# from -3.0 to 3.0 that will not change dynamically. ++# ALSO affects analysis tools (lz-analyze, kata-analyze, used by e.g. Lizzie). ++# Negative makes KataGo behave as if it is much weaker than the opponent. ++# Positive makes KataGo behave as if it is much stronger than the opponent. ++# KataGo will adjust to favor safe/aggressive play as appropriate based on ++# the combination of who is ahead and how much stronger/weaker it thinks it is, ++# and report winrates and scores taking the strength difference into account. ++# ++# If this and "dynamicPlayoutDoublingAdvantageCapPerOppLead" are both set ++# then dynamic will be used for all games and this fixed value will be used ++# for analysis tools. ++# playoutDoublingAdvantage = 0.0 ++ ++# Uncomment one of these when using "playoutDoublingAdvantage" to enforce ++# that it will only apply when KataGo plays as the specified color and will be ++# negated when playing as the opposite color. ++# playoutDoublingAdvantagePla = BLACK ++# playoutDoublingAdvantagePla = WHITE ++ ++# ------------------------------ ++# Passing and cleanup ++# ------------------------------ ++# Make the bot never assume that its pass will end the game, even if passing ++# would end and "win" under Tromp-Taylor rules. Usually this is a good idea ++# when using it for analysis or playing on servers where scoring may be ++# implemented non-tromp-taylorly. Uncomment and set to false to disable. ++# conservativePass = true ++ ++# When using territory scoring, self-play games continue beyond two passes ++# with special cleanup rules that may be confusing for human players. This ++# option prevents the special cleanup phases from being reachable when using ++# the bot for GTP play. Uncomment and set to false to enable entering special ++# cleanup. For example, if you are testing it against itself, or against ++# another bot that has precisely implemented the rules documented at ++# https://lightvector.github.io/KataGo/rules.html ++# preventCleanupPhase = true ++ ++# ------------------------------ ++# Miscellaneous behavior ++# ------------------------------ ++# If the board is symmetric, search only one copy of each equivalent move. ++# Attempts to also account for ko/superko, will not theoretically perfect for ++# superko. Uncomment and set to false to disable. ++# rootSymmetryPruning = true ++ ++# Uncomment and set to true to avoid a particular joseki that some networks ++# misevaluate, and also to improve opening diversity versus some particular ++# other bots that like to play it all the time. ++# avoidMYTDaggerHack = false ++ ++# Prefer to avoid playing the same joseki in every corner of the board. ++# Uncomment to set to a specific value. See "Avoid SGF patterns" section. ++# By default: 0 (even games), 0.005 (handicap games) ++# avoidRepeatedPatternUtility = 0.0 ++ ++# Experimental logic to fight against mirror Go even with unfavorable komi. ++# Uncomment to set to a specific value to use for both playing and analysis. ++# By default: true when playing via GTP, but false when analyzing. ++# antiMirror = true ++ ++# Enable some hacks that mitigate rare instances when passing messes up deeper searches. ++# enablePassingHacks = true ++ ++# Uncomment and set this to true to prevent bad or bogus move sequences ++# in the history leading to this position from affecting KataGo's move choices. ++# Same as analysisIgnorePreRootHistory (see above) but applies to actual play. ++# You can enable this if KataGo is being asked to play from positions that it did not ++# choose the moves to reach. ++# ignorePreRootHistory = false ++ ++# =========================================================================== ++# Search limits ++# =========================================================================== ++ ++# Terminology: ++# "Playouts" is the number of new playouts of search performed each turn. ++# "Visits" is the same as "Playouts" but also counts search performed on ++# previous turns that is still applicable to this turn. ++# "Time" is the time in seconds. ++ ++# For example, if KataGo searched 200 nodes on the previous turn, and then ++# after the opponent's reply, 50 nodes of its search tree was still valid, ++# then a visit limit of 200 would allow KataGo to search 150 new nodes ++# (for a final tree size of 200 nodes), whereas a playout limit of of 200 ++# would allow KataGo to search 200 nodes (for a final tree size of 250 nodes). ++ ++# Additionally, KataGo may also move before than the limit in order to ++# obey time controls (e.g. byo-yomi, etc) if the GTP controller has ++# told KataGo that the game has is being played with a given time control. ++ ++# Limits for search on the current turn. ++# If commented out or unspecified, the default is to have no limit. ++# maxVisits = 500 ++# maxPlayouts = 300 ++ maxTime = 1.0 ++ ++# Ponder on the opponent's turn? ++ponderingEnabled = true ++ ++# Limits for search when pondering on the opponent's turn. ++# If commented out or unspecified, the default is to have no limit. ++# Limiting the maximum time is recommended so that KataGo won't burn CPU/GPU ++# forever and/or run out of RAM if left unattended while pondering is enabled. ++# maxVisitsPondering = 5000 ++# maxPlayoutsPondering = 3000 ++maxTimePondering = 120.0 ++ ++ ++# ------------------------------ ++# Other search limits and behavior ++# ------------------------------ ++ ++# Approx number of seconds to buffer for lag for GTP time controls - will ++# move a bit faster assuming there is this much lag per move. ++lagBuffer = 1.0 ++ ++# YOU PROBABLY WANT TO TUNE THIS PARAMETER! ++# The number of threads to use when searching. On powerful GPUs the optimal ++# threads may be much higher than the number of CPU cores you have because ++# many threads are needed to feed efficient large batches to the GPU. ++# ++# Run "./katago benchmark" to tune this parameter and test the effect ++# of changes to any of other parameters. ++numSearchThreads = 12 ++ ++# Play a little faster if the opponent is passing, for human-friendliness. ++# Comment these out to disable them, such as if running a controlled match ++# where you are testing KataGo with fixed compute per move vs other bots. ++searchFactorAfterOnePass = 0.50 ++searchFactorAfterTwoPass = 0.25 ++ ++# Play a little faster if super-winning, for human-friendliness. ++# Comment these out to disable them, such as if running a controlled match ++# where you are testing KataGo with fixed compute per move vs other bots. ++searchFactorWhenWinning = 0.40 ++searchFactorWhenWinningThreshold = 0.95 ++ ++# =========================================================================== ++# GPU settings ++# =========================================================================== ++# This section configures GPU settings. ++# ++# Maximum number of positions to send to a single GPU at once. The default ++# value is roughly equal to numSearchThreads, but can be specified manually ++# if running out of memory, or using multiple GPUs that expect to share work. ++# nnMaxBatchSize = ++ ++# Controls the neural network cache size, which is the primary RAM/memory use. ++# KataGo will cache up to (2 ** nnCacheSizePowerOfTwo) many neural net ++# evaluations in case of transpositions in the tree. ++# Increase this to improve performance for searches with tens of thousands ++# of visits or more. Decrease this to limit memory usage. ++# If you're happy to do some math - each neural net entry takes roughly ++# 1.5KB, except when using whole-board ownership/territory ++# visualizations, where each entry will take roughly 3KB. The number of ++# entries is (2 ** nnCacheSizePowerOfTwo). (E.g. 2 ** 18 = 262144.) ++# You can compute roughly how much memory the cache will use based on this. ++# nnCacheSizePowerOfTwo = 20 ++ ++# Size of mutex pool for nnCache is (2 ** this). ++# nnMutexPoolSizePowerOfTwo = 16 ++ ++# Randomize board orientation when running neural net evals? Uncomment and ++# set to false to disable. ++# nnRandomize = true ++ ++# If provided, force usage of a specific seed for nnRandomize. ++# The default is to use a randomly generated seed. ++# nnRandSeed = abcdefg ++ ++# Uncomment and set to true to force GTP to use the maximum board size for ++# internal buffers for the neural net. This will make KataGo slower when ++# evaluating small boards, but will avoid a lengthy initialization time on every ++# change of board size due to having to re-size the neural net buffers on the GPU. ++# This can be useful for example, for OGS's persistent bot mode that uses a single ++# bot instance to handle multiple games and may thrash between different board sizes ++# if there are concurrent games of multiple sizes. ++# gtpForceMaxNNSize = false ++ ++# ------------------------------ ++# Multiple GPUs ++# ------------------------------ ++# Set this to the number of GPUs to use or that are available. ++# IMPORTANT: If more than 1, also uncomment the appropriate TensorRT ++# or CUDA or OpenCL section. ++# numNNServerThreadsPerModel = 1 ++ ++# ------------------------------ ++# TENSORRT GPU settings ++# ------------------------------ ++# These only apply when using the TENSORRT version of KataGo. ++ ++# For one GPU: optionally uncomment this option and change if the GPU to ++# use is not device 0. ++# trtDeviceToUse = 0 ++ ++# For two GPUs: Uncomment these options, AND set numNNServerThreadsPerModel above. ++# Also, change their values if the devices you want to use are not 0 and 1. ++# trtDeviceToUseThread0 = 0 ++# trtDeviceToUseThread1 = 1 ++ ++# For three GPUs: Uncomment these options, AND set numNNServerThreadsPerModel above. ++# Also, change their values if the devices you want to use are not 0 and 1 and 2. ++# trtDeviceToUseThread0 = 0 ++# trtDeviceToUseThread1 = 1 ++# trtDeviceToUseThread2 = 2 ++ ++# The pattern continues for additional GPUs. ++ ++# ------------------------------ ++# CUDA GPU settings ++# ------------------------------ ++# These only apply when using the CUDA version of KataGo. ++ ++# For one GPU: optionally uncomment and change this if the GPU you want to ++# use is not device 0 ++# cudaDeviceToUse = 0 ++ ++# For two GPUs: Uncomment these options, AND set numNNServerThreadsPerModel above. ++# Also, change their values if the devices you want to use are not 0 and 1. ++# cudaDeviceToUseThread0 = 0 ++# cudaDeviceToUseThread1 = 1 ++ ++# For three GPUs: Uncomment these options, AND set numNNServerThreadsPerModel above. ++# Also, change their values if the devices you want to use are not 0 and 1 and 2. ++# cudaDeviceToUseThread0 = 0 ++# cudaDeviceToUseThread1 = 1 ++# cudaDeviceToUseThread2 = 2 ++ ++# The pattern continues for additional GPUs. ++ ++# KataGo will automatically use FP16 or not based on the compute capability ++# of your NVIDIA GPU. If you want to try to force a particular behavior ++# you can uncomment these lines and change them to "true" or "false". ++# cudaUseFP16 = auto ++# cudaUseNHWC = auto ++ ++# ------------------------------ ++# OpenCL GPU settings ++# ------------------------------ ++# These only apply when using the OpenCL version of KataGo. ++ ++# Uncomment and set to true to tune OpenCL for every board size separately, ++# rather than only the largest possible size. ++# openclReTunePerBoardSize = false ++ ++# For one GPU: optionally uncomment and change this if the best device to use is guessed incorrectly. ++# The default behavior tries to guess the 'best' GPU or device on your system to use, usually it will be a good guess. ++# openclDeviceToUse = 0 ++ ++# For two GPUs: Uncomment these two lines and replace X and Y with the device ids of the devices you want to use. ++# It might NOT be 0 and 1, some computers will have many OpenCL devices. You can see what the devices are when ++# KataGo starts up - it should print or log all the devices it finds. ++# (AND also set numNNServerThreadsPerModel above) ++# openclDeviceToUseThread0 = X ++# openclDeviceToUseThread1 = Y ++ ++# For three GPUs: Uncomment these three lines and replace X and Y and Z with the device ids of the devices you want to use. ++# It might NOT be 0 and 1 and 2, some computers will have many OpenCL devices. You can see what the devices are when ++# KataGo starts up - it should print or log all the devices it finds. ++# (AND also set numNNServerThreadsPerModel above) ++# openclDeviceToUseThread0 = X ++# openclDeviceToUseThread1 = Y ++# openclDeviceToUseThread2 = Z ++ ++# The pattern continues for additional GPUs. ++ ++# KataGo will automatically use FP16 or not based on testing your GPU during ++# tuning. If you want to try to force a particular behavior though you can ++# uncomment this option and change it to "true" or "false". This is a fairly ++# blunt setting - more detailed settings are testable by rerunning the tuner ++# with various arguments (./katago tuner). ++# openclUseFP16 = auto ++ ++# ------------------------------ ++# Eigen-specific settings ++# ------------------------------ ++# These only apply when using the Eigen (pure CPU) version of KataGo. ++ ++# Number of CPU threads for evaluating the neural net on the Eigen backend. ++# ++# Default: numSearchThreads ++# numEigenThreadsPerModel = X ++ ++# =========================================================================== ++# Root move selection and biases ++# =========================================================================== ++# Uncomment and edit any of the below values to change them from their default. ++ ++# If provided, force usage of a specific seed for various random things in ++# the search. The default is to use a random seed. ++# searchRandSeed = hijklmn ++ ++# Temperature for the early game, randomize between chosen moves with ++# this temperature ++# chosenMoveTemperatureEarly = 0.5 ++ ++# Decay temperature for the early game by 0.5 every this many moves, ++# scaled with board size. ++# chosenMoveTemperatureHalflife = 19 ++ ++# At the end of search after the early game, randomize between chosen ++# moves with this temperature ++# chosenMoveTemperature = 0.10 ++ ++# Subtract this many visits from each move prior to applying ++# chosenMoveTemperature (unless all moves have too few visits) to downweight ++# unlikely moves ++# chosenMoveSubtract = 0 ++ ++# The same as chosenMoveSubtract but only prunes moves that fall below ++# the threshold. This setting does not affect chosenMoveSubtract. ++# chosenMovePrune = 1 ++ ++# Number of symmetries to sample (without replacement) and average at the root ++# rootNumSymmetriesToSample = 1 ++ ++# Using LCB for move selection? ++# useLcbForSelection = true ++ ++# How many stdevs a move needs to be better than another for LCB selection ++# lcbStdevs = 5.0 ++ ++# Only use LCB override when a move has this proportion of visits as the ++# top move. ++# minVisitPropForLCB = 0.15 ++ ++# =========================================================================== ++# Internal params ++# =========================================================================== ++# Uncomment and edit any of the below values to change them from their default. ++ ++# Scales the utility of winning/losing ++# winLossUtilityFactor = 1.0 ++ ++# Scales the utility for trying to maximize score ++# staticScoreUtilityFactor = 0.10 ++# dynamicScoreUtilityFactor = 0.30 ++ ++# Adjust dynamic score center this proportion of the way towards zero, ++# capped at a reasonable amount. ++# dynamicScoreCenterZeroWeight = 0.20 ++# dynamicScoreCenterScale = 0.75 ++ ++# The utility of getting a "no result" due to triple ko or other long cycle ++# in non-superko rulesets (-1 to 1) ++# noResultUtilityForWhite = 0.0 ++ ++# The number of wins that a draw counts as, for white. (0 to 1) ++# drawEquivalentWinsForWhite = 0.5 ++ ++# Exploration constant for mcts ++# cpuctExploration = 1.0 ++# cpuctExplorationLog = 0.45 ++ ++# Parameters that control exploring more in volatile positions, exploring ++# less in stable positions. ++# cpuctUtilityStdevPrior = 0.40 ++# cpuctUtilityStdevPriorWeight = 2.0 ++# cpuctUtilityStdevScale = 0.85 ++ ++# FPU reduction constant for mcts ++# fpuReductionMax = 0.2 ++# rootFpuReductionMax = 0.1 ++# fpuParentWeightByVisitedPolicy = true ++ ++# Parameters that control weighting of evals based on the net's own ++# self-reported uncertainty. ++# useUncertainty = true ++# uncertaintyExponent = 1.0 ++# uncertaintyCoeff = 0.25 ++ ++# Explore using optimistic policy ++# rootPolicyOptimism = 0.2 ++# policyOptimism = 1.0 ++ ++# Amount to apply a downweighting of children with very bad values relative ++# to good ones. ++# valueWeightExponent = 0.25 ++ ++# Slight incentive for the bot to behave human-like with regard to passing at ++# the end, filling the dame, not wasting time playing in its own territory, ++# etc., and not play moves that are equivalent in terms of points but a bit ++# more unfriendly to humans. ++# rootEndingBonusPoints = 0.5 ++ ++# Make the bot prune useless moves that are just prolonging the game to ++# avoid losing yet. ++# rootPruneUselessMoves = true ++ ++# Apply bias correction based on local pattern keys ++# subtreeValueBiasFactor = 0.45 ++# subtreeValueBiasWeightExponent = 0.85 ++ ++# Use graph search rather than tree search - identify and share search for ++# transpositions. ++# useGraphSearch = true ++ ++# How much to shard the node table for search synchronization ++# nodeTableShardsPowerOfTwo = 16 ++ ++# How many virtual losses to add when a thread descends through a node ++# numVirtualLossesPerThread = 1 ++ ++# Improve the quality of evals under heavy multithreading ++# useNoisePruning = true ++ ++# =========================================================================== ++# Automatic avoid patterns ++# =========================================================================== ++# The parameters in this section provide a way to bias away from moves that match ++# patterns that this instance of KataGo has played in previous games, by auto-saving ++# moves to a directory and then auto-loading and biasing against them each new game. ++# Uncomment them to use them. When using this feature, all parameters must be specified. ++ ++# Different board sizes are tracked separately, but all board sizes share the same ++# parameters by default. Every parameter *except* for autoAvoidRepeatDir and ++# autoAvoidRepeatSaveChunkSize can be overridden per board size, ++# e.g. "autoAvoidRepeatMinTurnNumber13x13". You must ALSO specify the ++# defaults even if you have specified board-size-specific values. ++ ++# Directory to auto-save moves KataGo plays, to avoid them in future games. ++# You can create a new empty directory and put its path here. ++# If you run parallel instances of KataGo, use different directories if you ++# want them not to share their biases, use the same directory if you want ++# all of them to bias away from past moves that any of them have played. ++# KataGo will also automatically DELETE old data in this directory, so it is ++# recommended that if you do share the same directory between parallel instances, ++# that they all use the same settings that affect data saving/deletion. ++# autoAvoidRepeatDir = PATH_TO_NEW_DIRECTORY ++ ++# Penalize this much utility per matching move. ++# Values that are too large may lead to bad play. The value of 0.004 is fairly large ++# and might be large enough to result in some early weird/bad moves when trying ++# to avoid past games' moves if enough games begin the same way. You can experiment with it. ++# autoAvoidRepeatUtility = 0.004 ++ ++# Per each new move saved, exponentially decay prior saved moves by this factor. ++# This way, the bias against moves from many games ago is gradually phased out. ++# For example, 0.9995 = 1 - 1/2000, so would be roughly 2000 prior moves worth of ++# penalty weight remembered, in steady state. Depending on what turn number range ++# you are saving, this might equate to a different number of games For example ++# saving the first 50 moves per game would make this roughly 2000 / 50 = 40 games ++# worth of memory. ++# autoAvoidRepeatLambda = 0.9995 ++ ++# Affects data saving/deletion. ++# When the number of saved moves exceeds this, outright delete them to avoid too many ++# files and disk space building up. Also may affect the speed of saving/loading on start ++# of each game if this is set large and a lot of data builds up. ++# autoAvoidRepeatMaxPoses = 10000 ++ ++# Affects data saving/deletion. ++# Only save data for moves within this turn number range of those games. ++# E.g. setting autoAvoidRepeatMinTurnNumber to a number like 4 or 5 would tend to make ++# KataGo not develop a bias against the initial 3-4 and 4-4 corner moves in almost every game. ++# autoAvoidRepeatMinTurnNumber = 0 ++# autoAvoidRepeatMaxTurnNumber = 50 ++ ++# Affects data saving/deletion. ++# Within a single run of a program, wait to accumulate this many samples ++# (possibly across multiple clear_boards/games) before saving the data. ++# Can help to avoid writing too many small files to disk, especially when GTP is used ++# in a way that clears the board very frequently (e.g. gtp2ogs pooled manager). ++# autoAvoidRepeatSaveChunkSize = 200 ++ ++# =========================================================================== ++# Avoid SGF patterns ++# =========================================================================== ++# The parameters in this section provide a way to avoid moves that follow ++# specific patterns based on a set of SGF files loaded upon startup. ++# This is basically the same as the above "Automatic avoid patterns" section ++# above except you supply your own SGF files to avoid moves from. ++# Uncomment them to use this feature. Additionally, if the SGF file ++# contains the string %SKIP% in a comment on a move, that move will be ++# ignored for this purpose. ++ ++# Load SGF files from this directory when the engine is started ++# (only on startup, will not reload unless engine is restarted) ++# avoidSgfPatternDirs = path/to/directory/with/sgfs/ ++# You can also surround the file path in double quotes if the file path contains trailing spaces or hash signs. ++# Within double quotes, backslashes are escape characters. ++# avoidSgfPatternDirs = "path/to/directory/with/sgfs/" ++ ++# Penalize this much utility per matching move. ++# Set this negative if you instead want to favor SGF patterns instead of ++# penalizing them. This number does not need to be large, even 0.001 will ++# make a difference. Values that are too large may lead to bad play. ++# avoidSgfPatternUtility = 0.001 ++ ++# Optional - load only the newest this many files ++# avoidSgfPatternMaxFiles = 20 ++ ++# Optional - Penalty is multiplied by this per each older SGF file, so that ++# old SGF files matter less than newer ones. ++# avoidSgfPatternLambda = 0.90 ++ ++# Optional - pay attention only to moves made by players with this name. ++# For example, set it to the name that your bot's past games will show up ++# as in the SGF, so that the bot will only avoid repeating moves that itself ++# made in past games, not the moves that its opponents made. ++# avoidSgfPatternAllowedNames = my-ogs-bot-name1,my-ogs-bot-name2 ++ ++# Optional - Ignore moves in SGF files that occurred before this turn number. ++# avoidSgfPatternMinTurnNumber = 0 ++ ++# For more avoid patterns: ++# You can also specify a second set of parameters, and a third, fourth, ++# etc. by numbering 2,3,4,... ++# ++# avoidSgf2PatternDirs = ... ++# avoidSgf2PatternUtility = ... ++# avoidSgf2PatternMaxFiles = ... ++# avoidSgf2PatternLambda = ... ++# avoidSgf2PatternAllowedNames = ... ++# avoidSgf2PatternMinTurnNumber = ... ++ +diff --git a/python/selfplay/KataGoData/opencltuning/tune11_gpuNVIDIAGeForceRTX4060LaptopGPU_x19_y19_c320_mv10.txt b/python/selfplay/KataGoData/opencltuning/tune11_gpuNVIDIAGeForceRTX4060LaptopGPU_x19_y19_c320_mv10.txt +new file mode 100644 +index 00000000..5b66d67d +--- /dev/null ++++ b/python/selfplay/KataGoData/opencltuning/tune11_gpuNVIDIAGeForceRTX4060LaptopGPU_x19_y19_c320_mv10.txt +@@ -0,0 +1,33 @@ ++VERSION=11 ++#canUseFP16Storage ++1 ++#canUseFP16Compute ++0 ++#canUseFP16TensorCores ++1 ++#canUseFP16TensorCoresFor1x1 ++0 ++#shouldUseFP16Storage ++1 ++#shouldUseFP16Compute ++0 ++#shouldUseFP16TensorCores ++1 ++#shouldUseFP16TensorCoresFor1x1 ++0 ++#xGemmDirect ++WGD=32 MDIMCD=8 NDIMCD=8 MDIMAD=16 NDIMBD=16 KWID=2 VWMD=2 VWND=2 PADA=1 PADB=1 ++#xGemm ++MWG=64 NWG=64 KWG=16 MDIMC=16 NDIMC=8 MDIMA=16 NDIMB=8 KWI=2 VWM=4 VWN=4 STRM=0 STRN=0 SA=1 SB=1 ++#xGemm16 ++MWG=64 NWG=64 KWG=16 MDIMC=16 NDIMC=8 MDIMA=16 NDIMB=8 KWI=2 VWM=4 VWN=4 STRM=0 STRN=0 SA=1 SB=1 ++#hGemmWmma ++MWG=64 NWG=64 KWG=16 MWAVE=32 NWAVE=8 MWARP=32 NWARP=8 VWM=2 VWN=2 SA=0 SB=0 ++#hGemmWmmaNCHW ++MWG=16 NWG=16 KWG=16 MWAVE=16 NWAVE=16 MWARP=16 NWARP=16 VWM=1 VWN=2 SB=0 ++#conv3x3 ++INTILE_XSIZE=6 INTILE_YSIZE=6 OUTTILE_XSIZE=4 OUTTILE_YSIZE=4 transLocalSize0=128 transLocalSize1=1 untransLocalSize0=8 untransLocalSize1=4 untransLocalSize2=2 ++#conv5x5 ++INTILE_XSIZE=6 INTILE_YSIZE=6 OUTTILE_XSIZE=2 OUTTILE_YSIZE=2 transLocalSize0=128 transLocalSize1=1 untransLocalSize0=8 untransLocalSize1=4 untransLocalSize2=2 ++#gPool ++XYSTRIDE=16 CHANNELSTRIDE=2 BATCHSTRIDE=2 +diff --git a/python/selfplay/KataGoData/opencltuning/tune11_gpuNVIDIAGeForceRTX4060LaptopGPU_x19_y19_c384_mv14.txt b/python/selfplay/KataGoData/opencltuning/tune11_gpuNVIDIAGeForceRTX4060LaptopGPU_x19_y19_c384_mv14.txt +new file mode 100644 +index 00000000..71421834 +--- /dev/null ++++ b/python/selfplay/KataGoData/opencltuning/tune11_gpuNVIDIAGeForceRTX4060LaptopGPU_x19_y19_c384_mv14.txt +@@ -0,0 +1,33 @@ ++VERSION=11 ++#canUseFP16Storage ++1 ++#canUseFP16Compute ++0 ++#canUseFP16TensorCores ++1 ++#canUseFP16TensorCoresFor1x1 ++0 ++#shouldUseFP16Storage ++1 ++#shouldUseFP16Compute ++0 ++#shouldUseFP16TensorCores ++1 ++#shouldUseFP16TensorCoresFor1x1 ++0 ++#xGemmDirect ++WGD=32 MDIMCD=8 NDIMCD=8 MDIMAD=8 NDIMBD=8 KWID=8 VWMD=2 VWND=4 PADA=1 PADB=1 ++#xGemm ++MWG=64 NWG=64 KWG=16 MDIMC=16 NDIMC=16 MDIMA=16 NDIMB=16 KWI=2 VWM=4 VWN=4 STRM=0 STRN=0 SA=1 SB=1 ++#xGemm16 ++MWG=64 NWG=64 KWG=16 MDIMC=16 NDIMC=16 MDIMA=16 NDIMB=16 KWI=2 VWM=4 VWN=4 STRM=0 STRN=0 SA=1 SB=1 ++#hGemmWmma ++MWG=64 NWG=64 KWG=64 MWAVE=16 NWAVE=16 MWARP=16 NWARP=16 VWM=2 VWN=2 SA=0 SB=0 ++#hGemmWmmaNCHW ++MWG=16 NWG=16 KWG=16 MWAVE=16 NWAVE=16 MWARP=16 NWARP=16 VWM=1 VWN=2 SB=0 ++#conv3x3 ++INTILE_XSIZE=6 INTILE_YSIZE=6 OUTTILE_XSIZE=4 OUTTILE_YSIZE=4 transLocalSize0=64 transLocalSize1=1 untransLocalSize0=16 untransLocalSize1=2 untransLocalSize2=2 ++#conv5x5 ++INTILE_XSIZE=6 INTILE_YSIZE=6 OUTTILE_XSIZE=2 OUTTILE_YSIZE=2 transLocalSize0=64 transLocalSize1=1 untransLocalSize0=16 untransLocalSize1=2 untransLocalSize2=2 ++#gPool ++XYSTRIDE=32 CHANNELSTRIDE=1 BATCHSTRIDE=2 +diff --git a/python/selfplay/README.txt b/python/selfplay/README.txt +new file mode 100644 +index 00000000..fb82db4b +--- /dev/null ++++ b/python/selfplay/README.txt +@@ -0,0 +1,118 @@ ++KataGo v1.15.1 ++https://github.com/lightvector/KataGo ++ ++For neural nets from the latest run, download from here: ++https://katagotraining.org/ ++For nets from earlier runs, see: ++https://katagoarchive.org/ ++ ++For the human SL net (b18c384nbt-humanv0.bin.gz), it was released with: ++https://github.com/lightvector/KataGo/releases/tag/v1.15.0 ++ ++For differences between this version and older versions, see releases page at ++https://github.com/lightvector/KataGo/releases/ ++ ++----------------------------------------------------- ++USAGE: ++----------------------------------------------------- ++KataGo is just an engine and does not have its own graphical interface. So generally you will want to use KataGo along with a GUI or analysis program. ++(https://github.com/lightvector/KataGo#guis) ++ ++FIRST: Run a command like this to make sure KataGo is working, with the neural net file you downloaded. On OpenCL, it will also tune for your GPU. ++ ++./katago.exe benchmark # if you have default_gtp.cfg and default_model.bin.gz ++./katago.exe benchmark -model .bin.gz # if you have default_gtp.cfg ++./katago.exe benchmark -model .bin.gz -config gtp_custom.cfg # use this .bin.gz neural net and this .cfg file ++ ++It will tell you a good number of threads. Edit your .cfg file and set "numSearchThreads" to that many to get best performance. ++ ++OR: Run this command to have KataGo generate a custom gtp config for you based on answering some questions: ++ ++./katago.exe genconfig -model .bin.gz -output gtp_custom.cfg ++ ++NEXT: A command like this will run KataGo's engine. This is the command to give to your [GUI or analysis program](#guis) so that it can run KataGo. ++ ++./katago.exe gtp # if you have default_gtp.cfg and default_model.bin.gz ++./katago.exe gtp -model .bin.gz # if you have default_gtp.cfg ++./katago.exe gtp -model .bin.gz -config gtp_custom.cfg # use this .bin.gz neural net and this .cfg file ++ ++You may need to specify different paths when entering KataGo's command for a GUI program, e.g.: ++ ++path/to/katago.exe gtp -model path/to/.bin.gz ++path/to/katago.exe gtp -model path/to/.bin.gz -config path/to/gtp_custom.cfg ++ ++KataGo should be able to work with any GUI program that supports GTP, as well as any analysis program that supports Leela Zero's `lz-analyze` command, such as Lizzie (https://github.com/featurecat/lizzie) or Sabaki (https://sabaki.yichuanshen.de/). ++ ++NOTE for Windows users: ++If you encounter errors due to a missing "msvcp140.dll" or "msvcp140_1.dll" or "msvcp140_2.dll" or "vcruntime140.dll" or similar, you need to download and install the Microsoft Visual C++ Redistributable, here: ++https://www.microsoft.com/en-us/download/details.aspx?id=48145 ++If this is for a 64-bit Windows version of KataGo, these dll files have already been included for you, otherwise you will need to install them yourself. On a 64-bit Windows version, there is a rare chance that you may need to delete them if you already have it installed yourself separately and the pre-included files are actually causing problems running KataGo. ++ ++----------------------------------------------------- ++HUMAN-STYLE PLAY AND ANALYSIS: ++----------------------------------------------------- ++ ++You can also have KataGo imitate human play if you download the human SL model b18c384nbt-humanv0.bin.gz from https://github.com/lightvector/KataGo/releases/tag/v1.15.0, and run a command like the following, providing both a model and the human SL model: ++ ++./katago.exe gtp -model .bin.gz -human-model b18c384nbt-humanv0.bin.gz -config gtp_human5k_example.cfg ++ ++The gtp_human5k_example.cfg configures KataGo to imitate 5-kyu-level players. You can change it to imitate other ranks too, as well as to do many more things, including making KataGo play in a human style but still at a strong level or analyze in interesting ways. Read the config file itself for documentation on some of these possibilities! ++ ++And see also this guide to using the human SL model, which is written from the perspective of the JSON-based analysis engine mentioned below, but is also applicable to gtp as well. ++https://github.com/lightvector/KataGo/blob/master/docs/Analysis_Engine.md#human-sl-analysis-guide ++ ++----------------------------------------------------- ++OTHER THINGS YOU CAN DO: ++----------------------------------------------------- ++ ++Run a JSON-based analysis engine (https://github.com/lightvector/KataGo/blob/master/docs/Analysis_Engine.md) that can do efficient batched evaluations for a backend Go service: ++ ++./katago.exe analysis -model .gz -config .cfg ++ ++Run a high-performance match engine that will play a pool of bots against each other sharing the same GPU batches and CPUs with each other: ++ ++./katago.exe match -config .cfg -log-file match.log -sgf-output-dir ++ ++Force OpenCL tuner to re-tune: ++ ++./katago.exe tuner -config .cfg ++ ++Print version: ++ ++./katago.exe version ++ ++ ++----------------------------------------------------- ++OPENCL VS CUDA VS EIGEN: ++----------------------------------------------------- ++Explanation of the various versions available at https://github.com/lightvector/KataGo/releases ++ ++OpenCL - Use this if you have a modern GPU and want the easiest setup. ++ ++TensorRT, CUDA11 - Try one of these if you have a top-end NVIDIA GPU, are willing to do some more technical setup work, and care about getting every bit of performance. It MIGHT OR MIGHT NOT be faster than OpenCL! You have to test it on your specific GPU. TensorRT requires CUDA11 as well, and is likely to be the fastest. ++ ++Eigen AVX2 - Use this if you have no good GPU, but you have an Intel or AMD CPU from the last several years. This is a pure CPU version of KataGo. ++ ++Eigen - Use this if you don't have a GPU or your GPU is too old to work, and your CPU turns out not to support AVX2 or FMA. This is a pure CPU version for KataGo for ++This is the pure CPU version of KataGo, with no special instructions, which should hopefully run just about anywhere. ++ ++ ++----------------------------------------------------- ++TUNING FOR PERFORMANCE: ++----------------------------------------------------- ++You will very likely want to tune some of the parameters in `default_gtp.cfg` for your system for good performance, including the number of threads, fp16 usage, NN cache size, pondering settings, and so on. You can also adjust things like KataGo's resign threshold or utility function. Most of the relevant parameters should be be reasonably well documented directly inline in that config. ++ ++There are other a few notes about usage and performance at : https://github.com/lightvector/KataGo ++ ++----------------------------------------------------- ++TROUBLESHOOTING: ++----------------------------------------------------- ++Some common issues are described here: ++https://github.com/lightvector/KataGo#common-causes-of-errors ++ ++Or, feel free to hop into the Leela Zero discord chat, which has become a general chatroom for a variety of computer Go hobbyists and users, and which you can often find people willing to help. ++https://discord.gg/fhDHgfk ++ ++ ++ ++ +diff --git a/python/selfplay/analysis_example.cfg b/python/selfplay/analysis_example.cfg +new file mode 100644 +index 00000000..ca648321 +--- /dev/null ++++ b/python/selfplay/analysis_example.cfg +@@ -0,0 +1,402 @@ ++# Config for KataGo C++ Analysis engine, i.e. "./katago.exe analysis" ++ ++# Example config for C++ (non-python) analysis engine ++ ++# SEE NOTES ABOUT PERFORMANCE AND MEMORY USAGE IN gtp_example.cfg ++# SEE NOTES ABOUT numSearchThreads AND OTHER IMPORTANT PARAMS BELOW! ++ ++# Logs------------------------------------------------------------------------------------ ++ ++# Where to output log? ++logDir = analysis_logs # Each run of KataGo will log to a separate file in this dir ++# logDirDated = analysis_logs # Use this instead of logDir to also write separate dated subdirs ++# logFile = analysis.log # Use this instead of logDir to just specify a single file directly ++# logToStderr = true # Echo everything output to log file to stderr as well ++# logAllRequests = false # Log all input lines received to the analysis engine. ++# logAllResponses = false # Log all lines output to stdout from the analysis engine. ++# logErrorsAndWarnings = true # Log all lines output to stdout from the analysis engine that are errors and warnings ++# logSearchInfo = false # Log debug info for every search performed ++ ++# Report a warning whenever a query contains a field that is unused. Helps to guard against ++# typos when writing code that queries the analysis engine. ++# warnUnusedFields = true ++ ++# Analysis------------------------------------------------------------------------------------ ++ ++# Controls the number of moves after the first move in a variation. ++# analysisPVLen = 15 ++ ++# Report winrates for analysis as (BLACK|WHITE|SIDETOMOVE). ++reportAnalysisWinratesAs = BLACK ++ ++# Larger values will make KataGo explore the top move(s) less deeply and accurately, ++# but explore and give evaluations to a greater variety of moves. ++# An extreme value like 1 will distribute many playouts across every move on the board, even very bad moves. ++# NOTE: defaults to 0.04, under the presumption that the analysis engine will be used mainly for analysis. ++# If you are intending to use the analysis engine to also play games and you want to maximize playing strength, ++# set this to 0.0 either in this config or in the overrides. ++# wideRootNoise = 0.04 ++ ++# Try to limit the effect of possible bad or bogus move sequences in the ++# history leading to this position from affecting KataGo's move predictions. ++# ignorePreRootHistory = true ++ ++# Bot behavior--------------------------------------------------------------------------------------- ++ ++# Handicap ------------- ++ ++# Assume that if black makes many moves in a row right at the start of the game, then the game is a handicap game. ++# This is necessary on some servers and for some GUIs and also when initializing from many SGF files, which may ++# set up a handicap game using repeated GTP "play" commands for black rather than GTP "place_free_handicap" commands. ++# However, it may also lead to incorrect understanding of komi if whiteHandicapBonus is used and a server does NOT ++# have such a practice. ++# Defaults to true! Uncomment and set to false to disable this behavior. ++# assumeMultipleStartingBlackMovesAreHandicap = true ++ ++# Passing and cleanup ------------- ++ ++# Make the bot never assume that its pass will end the game, even if passing would end and "win" under Tromp-Taylor rules. ++# Usually this is a good idea when using it for analysis or playing on servers where scoring may be implemented non-tromp-taylorly. ++# Defaults to true! Uncomment and set to false to disable this. ++# conservativePass = true ++ ++# When using territory scoring, self-play games continue beyond two passes with special cleanup ++# rules that may be confusing for human players. This option prevents the special cleanup phases from being ++# reachable when using the bot for GTP play. ++# Defaults to true! Uncomment and set to false if you want KataGo to be able to enter special cleanup. ++# For example, if you are testing it against itself, or against another bot that has precisely implemented the rules ++# documented at https://lightvector.github.io/KataGo/rules.html ++# preventCleanupPhase = true ++ ++# Search limits----------------------------------------------------------------------------------- ++ ++# By default, if NOT specified in an individual request, limit maximum number of root visits per search to this much ++maxVisits = 500 ++# If provided, cap search time at this many seconds ++# maxTime = 60 ++ ++# Search threads, batching, GPUs-------------------------------------------------------------------------- ++ ++# Try a configuration like this if you only expect the engine to be handling a few queries at a time and you want ++# individual queries to return more quickly, and are okay with the results being a bit lower-quality and the overall ++# peak throughput on queries to be lower. ++numAnalysisThreads = 2 ++numSearchThreadsPerAnalysisThread = 16 ++ ++# Try a configuration like this if you expect to be sending large numbers of queries at a time, and want to maximize ++# total throughput and also the evaluation quality of all the queries and you never care about the response latency ++# of the individual queries, only the throughput as a whole. ++# numAnalysisThreads = 32 ++# numSearchThreadsPerAnalysisThread = 1 ++ ++# You will want to increase one or both numbers if you have a powerful GPU, and possibly decrease one or both if you ++# have a very weak GPU, and play with the balance between them depending on your use case. ++# Read the explanation below to understand how to set these parameters: ++ ++# EXPLANATION: ++# numAnalysisThreads: the number of POSITIONS to be able to search in parallel. ++# numSearchThreadsPerAnalysisThread: the number of threads to use in the tree search for EACH position. ++# (older analysis configs might just have 'numSearchThreads', this is an alias for 'numSearchThreadsPerAnalysisThread') ++ ++# Therefore, the total number of search threads that may be active at a given time could be as large as the product: ++# numAnalysisThreads * numSearchThreadsPerAnalysisThread ++ ++# Searching more positions in parallel is more efficient since the different threads aren't conflicting with each ++# other on the same MCTS search tree. Using multiple threads on the same search will both make things slower ++# and weaken the search (holding playouts fixed) due to out of date statistics on nodes and suboptimal exploration, ++# although the cost is minor for only 2,4,8 threads. ++ ++# So unlike in GTP, which only ever searches one position at a time and where therefore you might as well make ++# numSearchThreads as large as possible, in the analysis engine you often want you often want to keep numSearchThreads small, ++# and instead parallelize across positions, so you can reduce conflict between threads and improve the overall throughput ++# and strength of the search. ++ ++# But obviously you only get the benefit of parallelization across positions when you actually have lots of positions ++# that you are querying at once! For example, setting numAnalysisThreads = 8 is useless if you only ever send one or two ++# queries at a time! ++ ++# Therefore: ++# * If you plan to use the analysis engine only for batch processing large numbers of positions, ++# it's preferable to numSearchThreadsPerAnalysisThread to only a small number (e.g. 1,2,4) and use a higher numAnalysisThreads. ++# * But if you sometimes plan to query the analysis engine for single positions, or otherwise in smaller quantities ++# than -num-analysis-threads, or if you plan to be user-interactive such that the response time on some individual ++# analysis requests is important to keep low, then set numSearchThreadsPerAnalysisThread to a larger number and use ++# a lower numAnalysisThreads. That way, individual searches complete faster due to having more threads on each one. ++ ++# For 19x19 boards, weaker GPUs probably want a TOTAL number of threads (numAnalysisThreads * numSearchThreadsPerAnalysisThread) ++# between 4 and 32. Mid-tier GPUs probably between 16 and 64. Strong GPUs probably between 32 and 256. ++# But there's no substitute for experimenting and seeing what's best for your hardware and your usage case. ++ ++# Keep in mind that the number of threads you want does NOT necessarily have much to do with how many cores you have on your ++# system. The optimal may easily exceed the number of cores! GPU batching is (usually) the dominant consideration. ++ ++# ------------- ++ ++# nnMaxBatchSize is the max number of positions to send to a single GPU at once. Generally, it should be the case that: ++# (number of GPUs you will use * nnMaxBatchSize) >= (numSearchThreads * num-analysis-threads) ++# That way, when each threads tries to request a GPU eval, your batch size summed across GPUs is large enough to handle them ++# all at once. However, it can be sensible to set this a little smaller if you are limited on GPU memory, ++# too large a number may fail if the GPU doesn't have enough memory. ++nnMaxBatchSize = 64 ++ ++# Uncomment and set these smaller if you are going to use the analysis engine EXCLUSIVELY for smaller boards (or plan to ++# run multiple instances, with some instances only handling smaller boards). It should improve performance. ++# It may also mean you can use more threads profitably. ++# maxBoardXSizeForNNBuffer = 19 ++# maxBoardYSizeForNNBuffer = 19 ++ ++# Uncomment and set this to true if you are going to use the analysis engine EXCLUSIVELY for exactly the board size ++# specified by maxBoardXSizeForNNBuffer and maxBoardYSizeForNNBuffer. It may slightly improve performance on some GPUs. ++# requireMaxBoardSize = true ++ ++# TO USE MULTIPLE GPUS: ++# Uncomment and set this to the number of GPUs you have and/or would like to use... ++# AND if it is more than 1, uncomment the appropriate CUDA or OpenCL section below. ++# numNNServerThreadsPerModel = 1 ++ ++# Other General GPU Settings------------------------------------------------------------------------------- ++ ++# Cache up to 2 ** this many neural net evaluations in case of transpositions in the tree. ++nnCacheSizePowerOfTwo = 23 ++# Size of mutex pool for nnCache is 2 ** this ++nnMutexPoolSizePowerOfTwo = 17 ++# Randomize board orientation when running neural net evals? ++nnRandomize = true ++ ++ ++# TENSORRT GPU settings-------------------------------------- ++# These only apply when using the TENSORRT version of KataGo. ++ ++# IF USING ONE GPU: optionally uncomment and change this if the GPU you want to use turns out to be not device 0 ++# trtDeviceToUse = 0 ++ ++# IF USING TWO GPUS: Uncomment these two lines (AND set numNNServerThreadsPerModel above): ++# trtDeviceToUseThread0 = 0 # change this if the first GPU you want to use turns out to be not device 0 ++# trtDeviceToUseThread1 = 1 # change this if the second GPU you want to use turns out to be not device 1 ++ ++# IF USING THREE GPUS: Uncomment these three lines (AND set numNNServerThreadsPerModel above): ++# trtDeviceToUseThread0 = 0 # change this if the first GPU you want to use turns out to be not device 0 ++# trtDeviceToUseThread1 = 1 # change this if the second GPU you want to use turns out to be not device 1 ++# trtDeviceToUseThread2 = 2 # change this if the third GPU you want to use turns out to be not device 2 ++ ++# You can probably guess the pattern if you have four, five, etc. GPUs. ++ ++ ++# CUDA-specific GPU settings-------------------------------------- ++# These only apply when using the CUDA version of KataGo. ++ ++# IF USING ONE GPU: optionally uncomment and change this if the GPU you want to use turns out to be not device 0 ++# cudaDeviceToUse = 0 ++ ++# IF USING TWO GPUS: Uncomment these two lines (AND set numNNServerThreadsPerModel above): ++# cudaDeviceToUseThread0 = 0 # change this if the first GPU you want to use turns out to be not device 0 ++# cudaDeviceToUseThread1 = 1 # change this if the second GPU you want to use turns out to be not device 1 ++ ++# IF USING THREE GPUS: Uncomment these three lines (AND set numNNServerThreadsPerModel above): ++# cudaDeviceToUseThread0 = 0 # change this if the first GPU you want to use turns out to be not device 0 ++# cudaDeviceToUseThread1 = 1 # change this if the second GPU you want to use turns out to be not device 1 ++# cudaDeviceToUseThread2 = 2 # change this if the third GPU you want to use turns out to be not device 2 ++ ++# You can probably guess the pattern if you have four, five, etc. GPUs. ++ ++# KataGo will automatically use FP16 or not based on the compute capability of your NVIDIA GPU. If you ++# want to try to force a particular behavior though you can uncomment these lines and change them ++# to "true" or "false". E.g. it's using FP16 but on your card that's giving an error, or it's not using ++# FP16 but you think it should. ++# cudaUseFP16 = auto ++# cudaUseNHWC = auto ++ ++ ++# OpenCL-specific GPU settings-------------------------------------- ++# These only apply when using the OpenCL version of KataGo. ++ ++# Uncomment to tune OpenCL for every board size separately, rather than only the largest possible size ++# openclReTunePerBoardSize = true ++ ++# IF USING ONE GPU: optionally uncomment and change this if the best device to use is guessed incorrectly. ++# The default behavior tries to guess the 'best' GPU or device on your system to use, usually it will be a good guess. ++# openclDeviceToUse = 0 ++ ++# IF USING TWO GPUS: Uncomment these two lines and replace X and Y with the device ids of the devices you want to use. ++# It might NOT be 0 and 1, some computers will have many OpenCL devices. You can see what the devices are when ++# KataGo starts up - it should print or log all the devices it finds. ++# (AND also set numNNServerThreadsPerModel above) ++# openclDeviceToUseThread0 = X ++# openclDeviceToUseThread1 = Y ++ ++# IF USING THREE GPUS: Uncomment these three lines and replace X and Y and Z with the device ids of the devices you want to use. ++# It might NOT be 0 and 1 and 2, some computers will have many OpenCL devices. You can see what the devices are when ++# KataGo starts up - it should print or log all the devices it finds. ++# (AND also set numNNServerThreadsPerModel above) ++# openclDeviceToUseThread0 = X ++# openclDeviceToUseThread1 = Y ++# openclDeviceToUseThread2 = Z ++ ++# You can probably guess the pattern if you have four, five, etc. GPUs. ++ ++# KataGo will automatically use FP16 or not based on testing your GPU during tuning. If you ++# want to try to force a particular behavior though you can uncomment this lines and change it ++# to "true" or "false". This is a fairly blunt setting - more detailed settings are testable ++# by rerunning the tuner with various arguments. ++# openclUseFP16 = auto ++ ++ ++# Eigen-specific settings-------------------------------------- ++# These only apply when using the Eigen (pure CPU) version of KataGo. ++ ++# This is the number of CPU threads for evaluating the neural net on the Eigen backend. ++# It defaults to min(numAnalysisThreads * numSearchThreadsPerAnalysisThread, numCPUCores). ++# numEigenThreadsPerModel = X ++ ++ ++# Misc Behavior -------------------- ++ ++# If the board is symmetric, search only one copy of each equivalent move. Attempts to also account for ko/superko, will not theoretically perfect for superko. ++# Uncomment and set to false to disable this. ++# rootSymmetryPruning = true ++ ++# Uncomment and set to true to make KataGo avoid a particular joseki that some KataGo nets misevaluate, ++# and also to improve opening diversity versus some particular other bots that like to play it all the time. ++# avoidMYTDaggerHack = false ++ ++# Have KataGo mildly prefer to avoid playing the same joseki in every corner of the board. ++# Uncomment to set to a specific value. A small value like 0.005 should produce already a noticeable behavior change. ++# avoidRepeatedPatternUtility = 0.0 ++ ++# Enable some hacks that mitigate rare instances when passing messes up deeper searches. ++# enablePassingHacks = true ++ ++# Root move selection and biases------------------------------------------------------------------------------ ++# Uncomment and edit any of the below values to change them from their default. ++# Not all of these parameters are applicable to analysis, some are only used for actual play ++ ++# Temperature for the early game, randomize between chosen moves with this temperature ++# chosenMoveTemperatureEarly = 0.5 ++# Decay temperature for the early game by 0.5 every this many moves, scaled with board size. ++# chosenMoveTemperatureHalflife = 19 ++# At the end of search after the early game, randomize between chosen moves with this temperature ++# chosenMoveTemperature = 0.10 ++# Subtract this many visits from each move prior to applying chosenMoveTemperature ++# (unless all moves have too few visits) to downweight unlikely moves ++# chosenMoveSubtract = 0 ++# The same as chosenMoveSubtract but only prunes moves that fall below the threshold, does not affect moves above ++# chosenMovePrune = 1 ++ ++# Number of symmetries to sample (WITHOUT replacement) and average at the root ++# rootNumSymmetriesToSample = 1 ++ ++# Using LCB for move selection? ++# useLcbForSelection = true ++# How many stdevs a move needs to be better than another for LCB selection ++# lcbStdevs = 5.0 ++# Only use LCB override when a move has this proportion of visits as the top move ++# minVisitPropForLCB = 0.15 ++ ++# Internal params------------------------------------------------------------------------------ ++# Uncomment and edit any of the below values to change them from their default. ++ ++# Scales the utility of winning/losing ++# winLossUtilityFactor = 1.0 ++# Scales the utility for trying to maximize score ++# staticScoreUtilityFactor = 0.10 ++# dynamicScoreUtilityFactor = 0.30 ++# Adjust dynamic score center this proportion of the way towards zero, capped at a reasonable amount. ++# dynamicScoreCenterZeroWeight = 0.20 ++# dynamicScoreCenterScale = 0.75 ++# The utility of getting a "no result" due to triple ko or other long cycle in non-superko rulesets (-1 to 1) ++# noResultUtilityForWhite = 0.0 ++# The number of wins that a draw counts as, for white. (0 to 1) ++# drawEquivalentWinsForWhite = 0.5 ++ ++# Exploration constant for mcts ++# cpuctExploration = 1.0 ++# cpuctExplorationLog = 0.45 ++ ++# Parameters that control exploring more in volatile positions, exploring less in stable positions. ++# cpuctUtilityStdevPrior = 0.40 ++# cpuctUtilityStdevPriorWeight = 2.0 ++# cpuctUtilityStdevScale = 0.85 ++ ++# FPU reduction constant for mcts ++# fpuReductionMax = 0.2 ++# rootFpuReductionMax = 0.1 ++# fpuParentWeightByVisitedPolicy = true ++ ++# Parameters that control weighting of evals based on the net's own self-reported uncertainty. ++# useUncertainty = true ++# uncertaintyExponent = 1.0 ++# uncertaintyCoeff = 0.25 ++ ++# Explore using optimistic policy ++# rootPolicyOptimism = 0.2 ++# policyOptimism = 1.0 ++ ++# Amount to apply a downweighting of children with very bad values relative to good ones ++# valueWeightExponent = 0.25 ++ ++# Slight incentive for the bot to behave human-like with regard to passing at the end, filling the dame, ++# not wasting time playing in its own territory, etc, and not play moves that are equivalent in terms of ++# points but a bit more unfriendly to humans. ++# rootEndingBonusPoints = 0.5 ++ ++# Make the bot prune useless moves that are just prolonging the game to avoid losing yet ++# rootPruneUselessMoves = true ++ ++# Apply bias correction based on local pattern keys ++# subtreeValueBiasFactor = 0.45 ++# subtreeValueBiasWeightExponent = 0.85 ++ ++# Use graph search rather than tree search - identify and share search for transpositions. ++# useGraphSearch = true ++ ++# How much to shard the node table for search synchronization ++# nodeTableShardsPowerOfTwo = 16 ++# How many virtual losses to add when a thread descends through a node ++# numVirtualLossesPerThread = 1 ++ ++# Improve the quality of evals under heavy multithreading ++# useNoisePruning = true ++ ++ ++# Avoid SGF Patterns ------------------------------------------------------------------------------ ++# The parameters in this section provide a powerful way to customize KataGo to avoid moves that follow specific patterns ++# based on a set of provided SGF files loaded upon startup. Uncomment them to use this feature. ++# Additionally, if the SGF file contains the string %SKIP% in a comment on a move, that move will be ignored for this purpose. ++ ++# Load sgf files from this directory when the engine is started (ONLY on startup, will not reload unless engine is restarted) ++# avoidSgfPatternDirs = path/to/directory/with/sgfs/ ++# You can also surround the file path in double quotes if the file path contains trailing spaces or hash signs. ++# Within double quotes, backslashes are escape characters. ++# avoidSgfPatternDirs = "path/to/directory/with/sgfs/" ++ ++# Penalize this much utility per matching move. ++# Set this negative if you instead want to make KataGo favor the SGF patterns instead of penalizing it! ++# This number does not need to be large, even 0.001 will make a difference. Too-large values may lead to bad play. ++# avoidSgfPatternUtility = 0.001 ++ ++# Optional - load only the newest this many files ++# avoidSgfPatternMaxFiles = 20 ++ ++# Optional - Penalty is multiplied by this per each older SGF file, so that old sgf files matter less than newer ones. ++# avoidSgfPatternLambda = 0.90 ++ ++# Optional - pay attention only to moves that were made by players with this name. ++# For example you can set it to the name that your bot's past games will show up as in the SGF, so that the bot will only avoid repeating ++# moves that itself made in past games, not the moves that its opponents made. ++# avoidSgfPatternAllowedNames = my-ogs-bot-name1,my-ogs-bot-name2 ++ ++# Optional - Ignore any moves in SGF files that occurred before this turn number. ++# avoidSgfPatternMinTurnNumber = 0 ++ ++# For more avoid patterns: ++# You can also specify a second set of parameters, and a third, fourth, etc by numbering 2,3,4,... ++# avoidSgf2PatternDirs = ... ++# avoidSgf2PatternUtility = ... ++# avoidSgf2PatternMaxFiles = ... ++# avoidSgf2PatternLambda = ... ++# avoidSgf2PatternAllowedNames = ... ++# avoidSgf2PatternMinTurnNumber = ... ++ ++ ++ ++ +diff --git a/python/selfplay/b28.cfg b/python/selfplay/b28.cfg +new file mode 100644 +index 00000000..302e8a40 +--- /dev/null ++++ b/python/selfplay/b28.cfg +@@ -0,0 +1,721 @@ ++# Configuration for KataGo C++ GTP engine ++ ++# Run the program using: `./katago.exe gtp` ++ ++# In this example config, when a parameter is given as a commented out value, ++# that value also is the default value, unless described otherwise. You can ++# uncomment it (remove the pound sign) and change it if you want. ++ ++# =========================================================================== ++# Running on an online server or in a real tournament or match ++# =========================================================================== ++# If you plan to run online or in a tournament, read through the "Rules" ++# section below for proper handling of komi, handicaps, end-of-game cleanup, ++# and other details. ++ ++# =========================================================================== ++# Notes about performance and memory usage ++# =========================================================================== ++# Important: For good performance, you will very likely want to tune the ++# "numSearchThreads" parameter in the Search limits section below! Run ++# "./katago benchmark" to test KataGo and to suggest a reasonable value ++# of this parameter. ++ ++# For multi-GPU systems, read "OpenCL GPU settings" or "CUDA GPU settings". ++# ++# When using OpenCL, verify that KataGo picks the correct device! Some systems ++# may have both an Intel CPU OpenCL and GPU OpenCL. If # KataGo picks the wrong ++# one, correct this by specifying "openclGpuToUse". ++# ++# Consider adjusting "maxVisits", "ponderingEnabled", "resignThreshold", and ++# other parameters depending on your intended usage. ++ ++# =========================================================================== ++# Command-line usage ++# =========================================================================== ++# All of the below values may be set or overridden via command-line arguments: ++# ++# -override-config KEY=VALUE,KEY=VALUE,... ++ ++# =========================================================================== ++# Logs and files ++# =========================================================================== ++# This section defines where and what logging information is produced. ++ ++# Each run of KataGo will log to a separate file in this dir. ++# This is the default. ++logDir = gtp_logs ++# Uncomment and specify this instead of logDir to write separate dated subdirs ++# logDirDated = gtp_logs ++# Uncomment and specify this instead of logDir to log to only a single file ++# logFile = gtp.log ++ ++# Logging options ++logAllGTPCommunication = true ++logSearchInfo = true ++logSearchInfoForChosenMove = false ++logToStderr = true ++ ++# KataGo will display some info to stderr on GTP startup ++# Uncomment the next line and set it to false to suppress that and remain silent ++# startupPrintMessageToStderr = true ++ ++# Write information to stderr, for use in things like malkovich chat to OGS. ++ ogsChatToStderr = true ++ ++# Uncomment and set this to a directory to override where openCLTuner files ++# and other cached data is written. By default it saves into a subdir of the ++# current directory on windows, and a subdir of ~/.katago on Linux. ++# homeDataDir = PATH_TO_DIRECTORY ++ ++# =========================================================================== ++# Analysis ++# =========================================================================== ++# This section configures analysis settings. ++# ++# The maximum number of moves after the first move displayed in variations ++# from analysis commands like kata-analyze or lz-analyze. ++# analysisPVLen = 15 ++ ++# Report winrates for chat and analysis as (BLACK|WHITE|SIDETOMOVE). ++# Most GUIs and analysis tools will expect SIDETOMOVE. ++# reportAnalysisWinratesAs = SIDETOMOVE ++ ++# Extra noise for wider exploration. Large values will force KataGo to ++# analyze a greater variety of moves than it normally would. ++# An extreme value like 1 distributes playouts across every move on the board, ++# even very bad moves. ++# Affects analysis only, does not affect play. ++# analysisWideRootNoise = 0.04 ++ ++# Try to limit the effect of possible bad or bogus move sequences in the ++# history leading to this position from affecting KataGo's move predictions. ++# analysisIgnorePreRootHistory = true ++ ++# =========================================================================== ++# Rules ++# =========================================================================== ++# This section configures the scoring and playing rules. Rules can also be ++# changed mid-run by issuing custom GTP commands. ++# ++# See https://lightvector.github.io/KataGo/rules.html for rules details. ++# ++# See https://github.com/lightvector/KataGo/blob/master/docs/GTP_Extensions.md ++# for GTP commands. ++ ++# Specify the rules as a string. ++# Some legal values include: ++# chinese, japanese, korean, aga, chinese-ogs, new-zealand, stone-scoring, ++# ancient-territory, bga, aga-button ++# ++# For some human rulesets that require complex adjudication in tricky cases ++# (e.g. japanese, korean) KataGo may not precisely match the ruleset in such ++# cases but will do its best. ++rules = tromp-taylor ++ ++# By default, the "rules" parameter is used, but if you comment it out and ++# uncomment one option in each of the sections below, you can specify an ++# arbitrary combination of individual rules. ++ ++# koRule = SIMPLE # Simple ko rules (triple ko = no result) ++# koRule = POSITIONAL # Positional superko ++# koRule = SITUATIONAL # Situational superko ++ ++# scoringRule = AREA # Area scoring ++# scoringRule = TERRITORY # Territory scoring (special computer-friendly territory rules) ++ ++# taxRule = NONE # All surrounded empty points are scored ++# taxRule = SEKI # Eyes in seki do NOT count as points ++# taxRule = ALL # All groups are taxed up to 2 points for the two eyes needed to live ++ ++# Is multiple-stone suicide legal? (Single-stone suicide is always illegal). ++# multiStoneSuicideLegal = false ++# multiStoneSuicideLegal = true ++ ++# "Button go" - the first pass when area scoring awards 0.5 points and does ++# not count for ending the game. ++# Allows area scoring rulesets that have far simpler rules to achieve the same ++# final scoring precision and reward for precise play as territory scoring. ++# hasButton = false ++# hasButton = true ++ ++# Is this a human ruleset where it's okay to pass before having physically ++# captured and removed all dead stones? ++# friendlyPassOk = false ++# friendlyPassOk = true ++ ++# How handicap stones in handicap games are compensated ++# whiteHandicapBonus = 0 # White gets no compensation for black's handicap stones (Tromp-taylor, NZ, JP) ++# whiteHandicapBonus = N-1 # White gets N-1 points for black's N handicap stones (AGA) ++# whiteHandicapBonus = N # White gets N points for black's N handicap stones (Chinese) ++ ++# ------------------------------ ++# Other rules hacks ++# ------------------------------ ++# Uncomment and change to adjust what board size KataGo uses upon startup ++# by default when GTP doesn't specify. ++# defaultBoardSize = 19 ++ ++# By default, Katago will use the komi that the GUI or GTP controller tries to set. ++# Uncomment and set this to have KataGo ignore the controller and always use this komi. ++# ignoreGTPAndForceKomi = 7 ++ ++# =========================================================================== ++# Bot behavior ++# =========================================================================== ++ ++# ------------------------------ ++# Resignation ++# ------------------------------ ++ ++# Resignation occurs if for at least resignConsecTurns in a row, the ++# winLossUtility (on a [-1,1] scale) is below resignThreshold. ++allowResignation = true ++resignThreshold = -0.90 ++resignConsecTurns = 3 ++ ++# By default, KataGo may resign games that it is confidently losing even if they ++# are very close in score. Uncomment and set this to avoid resigning games ++# if the estimated difference is points is less than or equal to this. ++# resignMinScoreDifference = 10 ++ ++# ------------------------------ ++# Handicap ++# ------------------------------ ++# Assume that if black makes many moves in a row right at the start of the ++# game, then the game is a handicap game. This is necessary on some servers ++# and for some GUIs and also when initializing from many SGF files, which may ++# set up a handicap game using repeated GTP "play" commands for black rather ++# than GTP "place_free_handicap" commands; however, it may also lead to ++# incorrect understanding of komi if whiteHandicapBonus is used and a server ++# does not have such a practice. Uncomment and set to false to disable. ++# assumeMultipleStartingBlackMovesAreHandicap = true ++ ++# Makes katago dynamically adjust in handicap or altered-komi games to assume ++# based on those game settings that it must be stronger or weaker than the ++# opponent and to play accordingly. Greatly improves handicap strength by ++# biasing winrates and scores to favor appropriate safe/aggressive play. ++# Does NOT affect analysis (lz-analyze, kata-analyze, used by programs like ++# Lizzie) so analysis remains unbiased. Uncomment and set this to 0 to disable ++# this and make KataGo play the same always. ++# dynamicPlayoutDoublingAdvantageCapPerOppLead = 0.045 ++ ++# Instead of "dynamicPlayoutDoublingAdvantageCapPerOppLead", you can comment ++# that out and uncomment and set "playoutDoublingAdvantage" to a fixed value ++# from -3.0 to 3.0 that will not change dynamically. ++# ALSO affects analysis tools (lz-analyze, kata-analyze, used by e.g. Lizzie). ++# Negative makes KataGo behave as if it is much weaker than the opponent. ++# Positive makes KataGo behave as if it is much stronger than the opponent. ++# KataGo will adjust to favor safe/aggressive play as appropriate based on ++# the combination of who is ahead and how much stronger/weaker it thinks it is, ++# and report winrates and scores taking the strength difference into account. ++# ++# If this and "dynamicPlayoutDoublingAdvantageCapPerOppLead" are both set ++# then dynamic will be used for all games and this fixed value will be used ++# for analysis tools. ++# playoutDoublingAdvantage = 0.0 ++ ++# Uncomment one of these when using "playoutDoublingAdvantage" to enforce ++# that it will only apply when KataGo plays as the specified color and will be ++# negated when playing as the opposite color. ++# playoutDoublingAdvantagePla = BLACK ++# playoutDoublingAdvantagePla = WHITE ++ ++# ------------------------------ ++# Passing and cleanup ++# ------------------------------ ++# Make the bot never assume that its pass will end the game, even if passing ++# would end and "win" under Tromp-Taylor rules. Usually this is a good idea ++# when using it for analysis or playing on servers where scoring may be ++# implemented non-tromp-taylorly. Uncomment and set to false to disable. ++# conservativePass = true ++ ++# When using territory scoring, self-play games continue beyond two passes ++# with special cleanup rules that may be confusing for human players. This ++# option prevents the special cleanup phases from being reachable when using ++# the bot for GTP play. Uncomment and set to false to enable entering special ++# cleanup. For example, if you are testing it against itself, or against ++# another bot that has precisely implemented the rules documented at ++# https://lightvector.github.io/KataGo/rules.html ++# preventCleanupPhase = true ++ ++# ------------------------------ ++# Miscellaneous behavior ++# ------------------------------ ++# If the board is symmetric, search only one copy of each equivalent move. ++# Attempts to also account for ko/superko, will not theoretically perfect for ++# superko. Uncomment and set to false to disable. ++# rootSymmetryPruning = true ++ ++# Uncomment and set to true to avoid a particular joseki that some networks ++# misevaluate, and also to improve opening diversity versus some particular ++# other bots that like to play it all the time. ++# avoidMYTDaggerHack = false ++ ++# Prefer to avoid playing the same joseki in every corner of the board. ++# Uncomment to set to a specific value. See "Avoid SGF patterns" section. ++# By default: 0 (even games), 0.005 (handicap games) ++# avoidRepeatedPatternUtility = 0.0 ++ ++# Experimental logic to fight against mirror Go even with unfavorable komi. ++# Uncomment to set to a specific value to use for both playing and analysis. ++# By default: true when playing via GTP, but false when analyzing. ++# antiMirror = true ++ ++# Enable some hacks that mitigate rare instances when passing messes up deeper searches. ++# enablePassingHacks = true ++ ++# Uncomment and set this to true to prevent bad or bogus move sequences ++# in the history leading to this position from affecting KataGo's move choices. ++# Same as analysisIgnorePreRootHistory (see above) but applies to actual play. ++# You can enable this if KataGo is being asked to play from positions that it did not ++# choose the moves to reach. ++# ignorePreRootHistory = false ++ ++# =========================================================================== ++# Search limits ++# =========================================================================== ++ ++# Terminology: ++# "Playouts" is the number of new playouts of search performed each turn. ++# "Visits" is the same as "Playouts" but also counts search performed on ++# previous turns that is still applicable to this turn. ++# "Time" is the time in seconds. ++ ++# For example, if KataGo searched 200 nodes on the previous turn, and then ++# after the opponent's reply, 50 nodes of its search tree was still valid, ++# then a visit limit of 200 would allow KataGo to search 150 new nodes ++# (for a final tree size of 200 nodes), whereas a playout limit of of 200 ++# would allow KataGo to search 200 nodes (for a final tree size of 250 nodes). ++ ++# Additionally, KataGo may also move before than the limit in order to ++# obey time controls (e.g. byo-yomi, etc) if the GTP controller has ++# told KataGo that the game has is being played with a given time control. ++ ++# Limits for search on the current turn. ++# If commented out or unspecified, the default is to have no limit. ++# maxVisits = 30000 ++# maxPlayouts = 300 ++maxTime = 1.0 ++ ++# Ponder on the opponent's turn? ++ponderingEnabled = true ++ ++# Limits for search when pondering on the opponent's turn. ++# If commented out or unspecified, the default is to have no limit. ++# Limiting the maximum time is recommended so that KataGo won't burn CPU/GPU ++# forever and/or run out of RAM if left unattended while pondering is enabled. ++# maxVisitsPondering = 5000 ++# maxPlayoutsPondering = 3000 ++maxTimePondering = 120.0 ++ ++ ++# ------------------------------ ++# Other search limits and behavior ++# ------------------------------ ++ ++# Approx number of seconds to buffer for lag for GTP time controls - will ++# move a bit faster assuming there is this much lag per move. ++lagBuffer = 1.0 ++ ++# YOU PROBABLY WANT TO TUNE THIS PARAMETER! ++# The number of threads to use when searching. On powerful GPUs the optimal ++# threads may be much higher than the number of CPU cores you have because ++# many threads are needed to feed efficient large batches to the GPU. ++# ++# Run "./katago benchmark" to tune this parameter and test the effect ++# of changes to any of other parameters. ++numSearchThreads = 10 ++ ++# Play a little faster if the opponent is passing, for human-friendliness. ++# Comment these out to disable them, such as if running a controlled match ++# where you are testing KataGo with fixed compute per move vs other bots. ++searchFactorAfterOnePass = 0.50 ++searchFactorAfterTwoPass = 0.25 ++ ++# Play a little faster if super-winning, for human-friendliness. ++# Comment these out to disable them, such as if running a controlled match ++# where you are testing KataGo with fixed compute per move vs other bots. ++searchFactorWhenWinning = 0.40 ++searchFactorWhenWinningThreshold = 0.95 ++ ++# =========================================================================== ++# GPU settings ++# =========================================================================== ++# This section configures GPU settings. ++# ++# Maximum number of positions to send to a single GPU at once. The default ++# value is roughly equal to numSearchThreads, but can be specified manually ++# if running out of memory, or using multiple GPUs that expect to share work. ++# nnMaxBatchSize = ++ ++# Controls the neural network cache size, which is the primary RAM/memory use. ++# KataGo will cache up to (2 ** nnCacheSizePowerOfTwo) many neural net ++# evaluations in case of transpositions in the tree. ++# Increase this to improve performance for searches with tens of thousands ++# of visits or more. Decrease this to limit memory usage. ++# If you're happy to do some math - each neural net entry takes roughly ++# 1.5KB, except when using whole-board ownership/territory ++# visualizations, where each entry will take roughly 3KB. The number of ++# entries is (2 ** nnCacheSizePowerOfTwo). (E.g. 2 ** 18 = 262144.) ++# You can compute roughly how much memory the cache will use based on this. ++# nnCacheSizePowerOfTwo = 20 ++ ++# Size of mutex pool for nnCache is (2 ** this). ++# nnMutexPoolSizePowerOfTwo = 16 ++ ++# Randomize board orientation when running neural net evals? Uncomment and ++# set to false to disable. ++# nnRandomize = true ++ ++# If provided, force usage of a specific seed for nnRandomize. ++# The default is to use a randomly generated seed. ++# nnRandSeed = abcdefg ++ ++# Uncomment and set to true to force GTP to use the maximum board size for ++# internal buffers for the neural net. This will make KataGo slower when ++# evaluating small boards, but will avoid a lengthy initialization time on every ++# change of board size due to having to re-size the neural net buffers on the GPU. ++# This can be useful for example, for OGS's persistent bot mode that uses a single ++# bot instance to handle multiple games and may thrash between different board sizes ++# if there are concurrent games of multiple sizes. ++# gtpForceMaxNNSize = false ++ ++# ------------------------------ ++# Multiple GPUs ++# ------------------------------ ++# Set this to the number of GPUs to use or that are available. ++# IMPORTANT: If more than 1, also uncomment the appropriate TensorRT ++# or CUDA or OpenCL section. ++# numNNServerThreadsPerModel = 1 ++ ++# ------------------------------ ++# TENSORRT GPU settings ++# ------------------------------ ++# These only apply when using the TENSORRT version of KataGo. ++ ++# For one GPU: optionally uncomment this option and change if the GPU to ++# use is not device 0. ++# trtDeviceToUse = 0 ++ ++# For two GPUs: Uncomment these options, AND set numNNServerThreadsPerModel above. ++# Also, change their values if the devices you want to use are not 0 and 1. ++# trtDeviceToUseThread0 = 0 ++# trtDeviceToUseThread1 = 1 ++ ++# For three GPUs: Uncomment these options, AND set numNNServerThreadsPerModel above. ++# Also, change their values if the devices you want to use are not 0 and 1 and 2. ++# trtDeviceToUseThread0 = 0 ++# trtDeviceToUseThread1 = 1 ++# trtDeviceToUseThread2 = 2 ++ ++# The pattern continues for additional GPUs. ++ ++# ------------------------------ ++# CUDA GPU settings ++# ------------------------------ ++# These only apply when using the CUDA version of KataGo. ++ ++# For one GPU: optionally uncomment and change this if the GPU you want to ++# use is not device 0 ++# cudaDeviceToUse = 0 ++ ++# For two GPUs: Uncomment these options, AND set numNNServerThreadsPerModel above. ++# Also, change their values if the devices you want to use are not 0 and 1. ++# cudaDeviceToUseThread0 = 0 ++# cudaDeviceToUseThread1 = 1 ++ ++# For three GPUs: Uncomment these options, AND set numNNServerThreadsPerModel above. ++# Also, change their values if the devices you want to use are not 0 and 1 and 2. ++# cudaDeviceToUseThread0 = 0 ++# cudaDeviceToUseThread1 = 1 ++# cudaDeviceToUseThread2 = 2 ++ ++# The pattern continues for additional GPUs. ++ ++# KataGo will automatically use FP16 or not based on the compute capability ++# of your NVIDIA GPU. If you want to try to force a particular behavior ++# you can uncomment these lines and change them to "true" or "false". ++# cudaUseFP16 = auto ++# cudaUseNHWC = auto ++ ++# ------------------------------ ++# OpenCL GPU settings ++# ------------------------------ ++# These only apply when using the OpenCL version of KataGo. ++ ++# Uncomment and set to true to tune OpenCL for every board size separately, ++# rather than only the largest possible size. ++# openclReTunePerBoardSize = false ++ ++# For one GPU: optionally uncomment and change this if the best device to use is guessed incorrectly. ++# The default behavior tries to guess the 'best' GPU or device on your system to use, usually it will be a good guess. ++# openclDeviceToUse = 0 ++ ++# For two GPUs: Uncomment these two lines and replace X and Y with the device ids of the devices you want to use. ++# It might NOT be 0 and 1, some computers will have many OpenCL devices. You can see what the devices are when ++# KataGo starts up - it should print or log all the devices it finds. ++# (AND also set numNNServerThreadsPerModel above) ++# openclDeviceToUseThread0 = X ++# openclDeviceToUseThread1 = Y ++ ++# For three GPUs: Uncomment these three lines and replace X and Y and Z with the device ids of the devices you want to use. ++# It might NOT be 0 and 1 and 2, some computers will have many OpenCL devices. You can see what the devices are when ++# KataGo starts up - it should print or log all the devices it finds. ++# (AND also set numNNServerThreadsPerModel above) ++# openclDeviceToUseThread0 = X ++# openclDeviceToUseThread1 = Y ++# openclDeviceToUseThread2 = Z ++ ++# The pattern continues for additional GPUs. ++ ++# KataGo will automatically use FP16 or not based on testing your GPU during ++# tuning. If you want to try to force a particular behavior though you can ++# uncomment this option and change it to "true" or "false". This is a fairly ++# blunt setting - more detailed settings are testable by rerunning the tuner ++# with various arguments (./katago tuner). ++# openclUseFP16 = auto ++ ++# ------------------------------ ++# Eigen-specific settings ++# ------------------------------ ++# These only apply when using the Eigen (pure CPU) version of KataGo. ++ ++# Number of CPU threads for evaluating the neural net on the Eigen backend. ++# ++# Default: numSearchThreads ++# numEigenThreadsPerModel = X ++ ++# =========================================================================== ++# Root move selection and biases ++# =========================================================================== ++# Uncomment and edit any of the below values to change them from their default. ++ ++# If provided, force usage of a specific seed for various random things in ++# the search. The default is to use a random seed. ++# searchRandSeed = hijklmn ++ ++# Temperature for the early game, randomize between chosen moves with ++# this temperature ++# chosenMoveTemperatureEarly = 0.5 ++ ++# Decay temperature for the early game by 0.5 every this many moves, ++# scaled with board size. ++# chosenMoveTemperatureHalflife = 19 ++ ++# At the end of search after the early game, randomize between chosen ++# moves with this temperature ++# chosenMoveTemperature = 0.10 ++ ++# Subtract this many visits from each move prior to applying ++# chosenMoveTemperature (unless all moves have too few visits) to downweight ++# unlikely moves ++# chosenMoveSubtract = 0 ++ ++# The same as chosenMoveSubtract but only prunes moves that fall below ++# the threshold. This setting does not affect chosenMoveSubtract. ++# chosenMovePrune = 1 ++ ++# Number of symmetries to sample (without replacement) and average at the root ++# rootNumSymmetriesToSample = 1 ++ ++# Using LCB for move selection? ++# useLcbForSelection = true ++ ++# How many stdevs a move needs to be better than another for LCB selection ++# lcbStdevs = 5.0 ++ ++# Only use LCB override when a move has this proportion of visits as the ++# top move. ++# minVisitPropForLCB = 0.15 ++ ++# =========================================================================== ++# Internal params ++# =========================================================================== ++# Uncomment and edit any of the below values to change them from their default. ++ ++# Scales the utility of winning/losing ++# winLossUtilityFactor = 1.0 ++ ++# Scales the utility for trying to maximize score ++# staticScoreUtilityFactor = 0.10 ++# dynamicScoreUtilityFactor = 0.30 ++ ++# Adjust dynamic score center this proportion of the way towards zero, ++# capped at a reasonable amount. ++# dynamicScoreCenterZeroWeight = 0.20 ++# dynamicScoreCenterScale = 0.75 ++ ++# The utility of getting a "no result" due to triple ko or other long cycle ++# in non-superko rulesets (-1 to 1) ++# noResultUtilityForWhite = 0.0 ++ ++# The number of wins that a draw counts as, for white. (0 to 1) ++# drawEquivalentWinsForWhite = 0.5 ++ ++# Exploration constant for mcts ++# cpuctExploration = 1.0 ++# cpuctExplorationLog = 0.45 ++ ++# Parameters that control exploring more in volatile positions, exploring ++# less in stable positions. ++# cpuctUtilityStdevPrior = 0.40 ++# cpuctUtilityStdevPriorWeight = 2.0 ++# cpuctUtilityStdevScale = 0.85 ++ ++# FPU reduction constant for mcts ++# fpuReductionMax = 0.2 ++# rootFpuReductionMax = 0.1 ++# fpuParentWeightByVisitedPolicy = true ++ ++# Parameters that control weighting of evals based on the net's own ++# self-reported uncertainty. ++# useUncertainty = true ++# uncertaintyExponent = 1.0 ++# uncertaintyCoeff = 0.25 ++ ++# Explore using optimistic policy ++# rootPolicyOptimism = 0.2 ++# policyOptimism = 1.0 ++ ++# Amount to apply a downweighting of children with very bad values relative ++# to good ones. ++# valueWeightExponent = 0.25 ++ ++# Slight incentive for the bot to behave human-like with regard to passing at ++# the end, filling the dame, not wasting time playing in its own territory, ++# etc., and not play moves that are equivalent in terms of points but a bit ++# more unfriendly to humans. ++# rootEndingBonusPoints = 0.5 ++ ++# Make the bot prune useless moves that are just prolonging the game to ++# avoid losing yet. ++# rootPruneUselessMoves = true ++ ++# Apply bias correction based on local pattern keys ++# subtreeValueBiasFactor = 0.45 ++# subtreeValueBiasWeightExponent = 0.85 ++ ++# Use graph search rather than tree search - identify and share search for ++# transpositions. ++# useGraphSearch = true ++ ++# How much to shard the node table for search synchronization ++# nodeTableShardsPowerOfTwo = 16 ++ ++# How many virtual losses to add when a thread descends through a node ++# numVirtualLossesPerThread = 1 ++ ++# Improve the quality of evals under heavy multithreading ++# useNoisePruning = true ++ ++# =========================================================================== ++# Automatic avoid patterns ++# =========================================================================== ++# The parameters in this section provide a way to bias away from moves that match ++# patterns that this instance of KataGo has played in previous games, by auto-saving ++# moves to a directory and then auto-loading and biasing against them each new game. ++# Uncomment them to use them. When using this feature, all parameters must be specified. ++ ++# Different board sizes are tracked separately, but all board sizes share the same ++# parameters by default. Every parameter *except* for autoAvoidRepeatDir and ++# autoAvoidRepeatSaveChunkSize can be overridden per board size, ++# e.g. "autoAvoidRepeatMinTurnNumber13x13". You must ALSO specify the ++# defaults even if you have specified board-size-specific values. ++ ++# Directory to auto-save moves KataGo plays, to avoid them in future games. ++# You can create a new empty directory and put its path here. ++# If you run parallel instances of KataGo, use different directories if you ++# want them not to share their biases, use the same directory if you want ++# all of them to bias away from past moves that any of them have played. ++# KataGo will also automatically DELETE old data in this directory, so it is ++# recommended that if you do share the same directory between parallel instances, ++# that they all use the same settings that affect data saving/deletion. ++# autoAvoidRepeatDir = PATH_TO_NEW_DIRECTORY ++ ++# Penalize this much utility per matching move. ++# Values that are too large may lead to bad play. The value of 0.004 is fairly large ++# and might be large enough to result in some early weird/bad moves when trying ++# to avoid past games' moves if enough games begin the same way. You can experiment with it. ++# autoAvoidRepeatUtility = 0.004 ++ ++# Per each new move saved, exponentially decay prior saved moves by this factor. ++# This way, the bias against moves from many games ago is gradually phased out. ++# For example, 0.9995 = 1 - 1/2000, so would be roughly 2000 prior moves worth of ++# penalty weight remembered, in steady state. Depending on what turn number range ++# you are saving, this might equate to a different number of games For example ++# saving the first 50 moves per game would make this roughly 2000 / 50 = 40 games ++# worth of memory. ++# autoAvoidRepeatLambda = 0.9995 ++ ++# Affects data saving/deletion. ++# When the number of saved moves exceeds this, outright delete them to avoid too many ++# files and disk space building up. Also may affect the speed of saving/loading on start ++# of each game if this is set large and a lot of data builds up. ++# autoAvoidRepeatMaxPoses = 10000 ++ ++# Affects data saving/deletion. ++# Only save data for moves within this turn number range of those games. ++# E.g. setting autoAvoidRepeatMinTurnNumber to a number like 4 or 5 would tend to make ++# KataGo not develop a bias against the initial 3-4 and 4-4 corner moves in almost every game. ++# autoAvoidRepeatMinTurnNumber = 0 ++# autoAvoidRepeatMaxTurnNumber = 50 ++ ++# Affects data saving/deletion. ++# Within a single run of a program, wait to accumulate this many samples ++# (possibly across multiple clear_boards/games) before saving the data. ++# Can help to avoid writing too many small files to disk, especially when GTP is used ++# in a way that clears the board very frequently (e.g. gtp2ogs pooled manager). ++# autoAvoidRepeatSaveChunkSize = 200 ++ ++# =========================================================================== ++# Avoid SGF patterns ++# =========================================================================== ++# The parameters in this section provide a way to avoid moves that follow ++# specific patterns based on a set of SGF files loaded upon startup. ++# This is basically the same as the above "Automatic avoid patterns" section ++# above except you supply your own SGF files to avoid moves from. ++# Uncomment them to use this feature. Additionally, if the SGF file ++# contains the string %SKIP% in a comment on a move, that move will be ++# ignored for this purpose. ++ ++# Load SGF files from this directory when the engine is started ++# (only on startup, will not reload unless engine is restarted) ++# avoidSgfPatternDirs = path/to/directory/with/sgfs/ ++# You can also surround the file path in double quotes if the file path contains trailing spaces or hash signs. ++# Within double quotes, backslashes are escape characters. ++# avoidSgfPatternDirs = "path/to/directory/with/sgfs/" ++ ++# Penalize this much utility per matching move. ++# Set this negative if you instead want to favor SGF patterns instead of ++# penalizing them. This number does not need to be large, even 0.001 will ++# make a difference. Values that are too large may lead to bad play. ++# avoidSgfPatternUtility = 0.001 ++ ++# Optional - load only the newest this many files ++# avoidSgfPatternMaxFiles = 20 ++ ++# Optional - Penalty is multiplied by this per each older SGF file, so that ++# old SGF files matter less than newer ones. ++# avoidSgfPatternLambda = 0.90 ++ ++# Optional - pay attention only to moves made by players with this name. ++# For example, set it to the name that your bot's past games will show up ++# as in the SGF, so that the bot will only avoid repeating moves that itself ++# made in past games, not the moves that its opponents made. ++# avoidSgfPatternAllowedNames = my-ogs-bot-name1,my-ogs-bot-name2 ++ ++# Optional - Ignore moves in SGF files that occurred before this turn number. ++# avoidSgfPatternMinTurnNumber = 0 ++ ++# For more avoid patterns: ++# You can also specify a second set of parameters, and a third, fourth, ++# etc. by numbering 2,3,4,... ++# ++# avoidSgf2PatternDirs = ... ++# avoidSgf2PatternUtility = ... ++# avoidSgf2PatternMaxFiles = ... ++# avoidSgf2PatternLambda = ... ++# avoidSgf2PatternAllowedNames = ... ++# avoidSgf2PatternMinTurnNumber = ... ++ +diff --git a/python/selfplay/cacert.pem b/python/selfplay/cacert.pem +new file mode 100644 +index 00000000..f9bd706b +--- /dev/null ++++ b/python/selfplay/cacert.pem +@@ -0,0 +1,3447 @@ ++## ++## Bundle of CA Root Certificates ++## ++## Certificate data from Mozilla as of: Wed Jul 22 03:12:14 2020 GMT ++## ++## This is a bundle of X.509 certificates of public Certificate Authorities ++## (CA). These were automatically extracted from Mozilla's root certificates ++## file (certdata.txt). This file can be found in the mozilla source tree: ++## https://hg.mozilla.org/releases/mozilla-release/raw-file/default/security/nss/lib/ckfw/builtins/certdata.txt ++## ++## It contains the certificates in PEM format and therefore ++## can be directly used with curl / libcurl / php_curl, or with ++## an Apache+mod_ssl webserver for SSL client authentication. ++## Just configure this file as the SSLCACertificateFile. ++## ++## Conversion done with mk-ca-bundle.pl version 1.28. ++## SHA256: cc6408bd4be7fbfb8699bdb40ccb7f6de5780d681d87785ea362646e4dad5e8e ++## ++ ++ ++GlobalSign Root CA ++================== ++-----BEGIN CERTIFICATE----- ++MIIDdTCCAl2gAwIBAgILBAAAAAABFUtaw5QwDQYJKoZIhvcNAQEFBQAwVzELMAkGA1UEBhMCQkUx ++GTAXBgNVBAoTEEdsb2JhbFNpZ24gbnYtc2ExEDAOBgNVBAsTB1Jvb3QgQ0ExGzAZBgNVBAMTEkds ++b2JhbFNpZ24gUm9vdCBDQTAeFw05ODA5MDExMjAwMDBaFw0yODAxMjgxMjAwMDBaMFcxCzAJBgNV ++BAYTAkJFMRkwFwYDVQQKExBHbG9iYWxTaWduIG52LXNhMRAwDgYDVQQLEwdSb290IENBMRswGQYD ++VQQDExJHbG9iYWxTaWduIFJvb3QgQ0EwggEiMA0GCSqGSIb3DQEBAQUAA4IBDwAwggEKAoIBAQDa ++DuaZjc6j40+Kfvvxi4Mla+pIH/EqsLmVEQS98GPR4mdmzxzdzxtIK+6NiY6arymAZavpxy0Sy6sc ++THAHoT0KMM0VjU/43dSMUBUc71DuxC73/OlS8pF94G3VNTCOXkNz8kHp1Wrjsok6Vjk4bwY8iGlb ++Kk3Fp1S4bInMm/k8yuX9ifUSPJJ4ltbcdG6TRGHRjcdGsnUOhugZitVtbNV4FpWi6cgKOOvyJBNP ++c1STE4U6G7weNLWLBYy5d4ux2x8gkasJU26Qzns3dLlwR5EiUWMWea6xrkEmCMgZK9FGqkjWZCrX ++gzT/LCrBbBlDSgeF59N89iFo7+ryUp9/k5DPAgMBAAGjQjBAMA4GA1UdDwEB/wQEAwIBBjAPBgNV ++HRMBAf8EBTADAQH/MB0GA1UdDgQWBBRge2YaRQ2XyolQL30EzTSo//z9SzANBgkqhkiG9w0BAQUF ++AAOCAQEA1nPnfE920I2/7LqivjTFKDK1fPxsnCwrvQmeU79rXqoRSLblCKOzyj1hTdNGCbM+w6Dj ++Y1Ub8rrvrTnhQ7k4o+YviiY776BQVvnGCv04zcQLcFGUl5gE38NflNUVyRRBnMRddWQVDf9VMOyG ++j/8N7yy5Y0b2qvzfvGn9LhJIZJrglfCm7ymPAbEVtQwdpf5pLGkkeB6zpxxxYu7KyJesF12KwvhH ++hm4qxFYxldBniYUr+WymXUadDKqC5JlR3XC321Y9YeRq4VzW9v493kHMB65jUr9TU/Qr6cf9tveC ++X4XSQRjbgbMEHMUfpIBvFSDJ3gyICh3WZlXi/EjJKSZp4A== ++-----END CERTIFICATE----- ++ ++GlobalSign Root CA - R2 ++======================= ++-----BEGIN CERTIFICATE----- ++MIIDujCCAqKgAwIBAgILBAAAAAABD4Ym5g0wDQYJKoZIhvcNAQEFBQAwTDEgMB4GA1UECxMXR2xv ++YmFsU2lnbiBSb290IENBIC0gUjIxEzARBgNVBAoTCkdsb2JhbFNpZ24xEzARBgNVBAMTCkdsb2Jh ++bFNpZ24wHhcNMDYxMjE1MDgwMDAwWhcNMjExMjE1MDgwMDAwWjBMMSAwHgYDVQQLExdHbG9iYWxT ++aWduIFJvb3QgQ0EgLSBSMjETMBEGA1UEChMKR2xvYmFsU2lnbjETMBEGA1UEAxMKR2xvYmFsU2ln ++bjCCASIwDQYJKoZIhvcNAQEBBQADggEPADCCAQoCggEBAKbPJA6+Lm8omUVCxKs+IVSbC9N/hHD6 ++ErPLv4dfxn+G07IwXNb9rfF73OX4YJYJkhD10FPe+3t+c4isUoh7SqbKSaZeqKeMWhG8eoLrvozp ++s6yWJQeXSpkqBy+0Hne/ig+1AnwblrjFuTosvNYSuetZfeLQBoZfXklqtTleiDTsvHgMCJiEbKjN ++S7SgfQx5TfC4LcshytVsW33hoCmEofnTlEnLJGKRILzdC9XZzPnqJworc5HGnRusyMvo4KD0L5CL ++TfuwNhv2GXqF4G3yYROIXJ/gkwpRl4pazq+r1feqCapgvdzZX99yqWATXgAByUr6P6TqBwMhAo6C ++ygPCm48CAwEAAaOBnDCBmTAOBgNVHQ8BAf8EBAMCAQYwDwYDVR0TAQH/BAUwAwEB/zAdBgNVHQ4E ++FgQUm+IHV2ccHsBqBt5ZtJot39wZhi4wNgYDVR0fBC8wLTAroCmgJ4YlaHR0cDovL2NybC5nbG9i 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you have entered your password. ++ ++serverUrl = https://katagotraining.org/ ++username = Fill in your username here ++password = Fill in your password here ++# If your password has hash signs or trailing spaces, you can also surround the password with double quotes, like this. ++# Within double quotes, backslashes are escape characters. ++# password = "Fill in your password here" ++ ++# How many games should KataGo play at once? For good GPUs, putting too small a number here will be *very* inefficient. ++# For absolute-top-tier GPUs, try numbers like 32, 40, 64, etc. ++# For modern good middle-tier GPUs, you can try numbers like 8 or 16. ++# For very old or weak GPUs, or if you want KataGo to put less load on your system, try 4. ++maxSimultaneousGames = Fill in a number here ++ ++# Set to true if you want one of KataGo's games to be streamed to a text file, so you can watch. ++# Follow in a separate shell with a command like the following: ++# Linux: "tail -f watchgame.txt" ++# Windows Powershell: "Get-Content .\watchgame.txt -Tail 50 -Wait" ++watchOngoingGameInFile = false ++watchOngoingGameInFileName = watchgame.txt ++ ++# If you use an https proxy to connect to the internet, set these to the host and port of your proxy. ++# proxyHost = HOSTNAME ++# proxyPort = 1234 ++ ++# Special-purpose options. Most users should not need to adjust these. ++# onlyPlayRatingMatches = false ++# maxRatingMatches = 1 ++# disablePredownloadLoop = false ++# warnTaskUnusedKeys = false ++ ++# KataGo will only use one GPU by default. You can edit the below if you have multiple GPUs and want to use them all. ++ ++ ++# TO USE MULTIPLE GPUS: ++# Set this to the number of GPUs you have and/or would like to use. ++# **AND** if it is more than 1, uncomment the appropriate CUDA or OpenCL section below. ++# numNNServerThreadsPerModel = 1 ++ ++ ++# TENSORRT GPU settings-------------------------------------- ++# These only apply when using the TENSORRT version of KataGo. ++ ++# IF USING ONE GPU: optionally uncomment and change this if the GPU you want to use turns out to be not device 0 ++# trtDeviceToUse = 0 ++ ++# IF USING TWO GPUS: Uncomment these two lines (AND set numNNServerThreadsPerModel above): ++# trtDeviceToUseThread0 = 0 # change this if the first GPU you want to use turns out to be not device 0 ++# trtDeviceToUseThread1 = 1 # change this if the second GPU you want to use turns out to be not device 1 ++ ++# IF USING THREE GPUS: Uncomment these three lines (AND set numNNServerThreadsPerModel above): ++# trtDeviceToUseThread0 = 0 # change this if the first GPU you want to use turns out to be not device 0 ++# trtDeviceToUseThread1 = 1 # change this if the second GPU you want to use turns out to be not device 1 ++# trtDeviceToUseThread2 = 2 # change this if the third GPU you want to use turns out to be not device 2 ++ ++# You can probably guess the pattern if you have four, five, etc. GPUs. ++ ++ ++# CUDA GPU settings-------------------------------------- ++# These only apply when using the CUDA version of KataGo. ++ ++# IF USING ONE GPU: optionally uncomment and change this if the GPU you want to use turns out to be not device 0 ++# cudaDeviceToUse = 0 ++ ++# IF USING TWO GPUS: Uncomment these two lines (AND set numNNServerThreadsPerModel above): ++# cudaDeviceToUseThread0 = 0 # change this if the first GPU you want to use turns out to be not device 0 ++# cudaDeviceToUseThread1 = 1 # change this if the second GPU you want to use turns out to be not device 1 ++ ++# IF USING THREE GPUS: Uncomment these three lines (AND set numNNServerThreadsPerModel above): ++# cudaDeviceToUseThread0 = 0 # change this if the first GPU you want to use turns out to be not device 0 ++# cudaDeviceToUseThread1 = 1 # change this if the second GPU you want to use turns out to be not device 1 ++# cudaDeviceToUseThread2 = 2 # change this if the third GPU you want to use turns out to be not device 2 ++ ++# You can probably guess the pattern if you have four, five, etc. GPUs. ++ ++# KataGo will automatically use FP16 or not based on the compute capability of your NVIDIA GPU. If you ++# want to try to force a particular behavior though you can uncomment these lines and change them ++# to "true" or "false". E.g. it's using FP16 but on your card that's giving an error, or it's not using ++# FP16 but you think it should. ++# cudaUseFP16 = auto ++# cudaUseNHWC = auto ++ ++ ++# OpenCL GPU settings-------------------------------------- ++# These only apply when using the OpenCL version of KataGo. ++ ++# Uncomment to tune OpenCL for every board size separately, rather than only the largest possible size ++# openclReTunePerBoardSize = true ++ ++# IF USING ONE GPU: optionally uncomment and change this if the best device to use is guessed incorrectly. ++# The default behavior tries to guess the 'best' GPU or device on your system to use, usually it will be a good guess. ++# openclDeviceToUse = 0 ++ ++# IF USING TWO GPUS: Uncomment these two lines and replace X and Y with the device ids of the devices you want to use. ++# It might NOT be 0 and 1, some computers will have many OpenCL devices. You can see what the devices are when ++# KataGo starts up - it should print or log all the devices it finds. ++# (AND also set numNNServerThreadsPerModel above) ++# openclDeviceToUseThread0 = X ++# openclDeviceToUseThread1 = Y ++ ++# IF USING THREE GPUS: Uncomment these three lines and replace X and Y and Z with the device ids of the devices you want to use. ++# It might NOT be 0 and 1 and 2, some computers will have many OpenCL devices. You can see what the devices are when ++# KataGo starts up - it should print or log all the devices it finds. ++# (AND also set numNNServerThreadsPerModel above) ++# openclDeviceToUseThread0 = X ++# openclDeviceToUseThread1 = Y ++# openclDeviceToUseThread2 = Z ++ ++# You can probably guess the pattern if you have four, five, etc. GPUs. ++ ++# KataGo will automatically use FP16 or not based on testing your GPU during tuning. If you ++# want to try to force a particular behavior though you can uncomment this lines and change it ++# to "true" or "false". This is a fairly blunt setting - more detailed settings are testable ++# by rerunning the tuner with various arguments. ++# openclUseFP16 = auto ++ ++ ++# Eigen-specific settings-------------------------------------- ++# These only apply when using the Eigen (pure CPU) version of KataGo. ++ ++# This is the number of CPU threads for evaluating the neural net on the Eigen backend. ++# It defaults to numSearchThreads. ++# numEigenThreadsPerModel = X +diff --git a/python/selfplay/default_gtp.cfg b/python/selfplay/default_gtp.cfg +new file mode 100644 +index 00000000..8bf19d85 +--- /dev/null ++++ b/python/selfplay/default_gtp.cfg +@@ -0,0 +1,721 @@ ++# Configuration for KataGo C++ GTP engine ++ ++# Run the program using: `./katago.exe gtp` ++ ++# In this example config, when a parameter is given as a commented out value, ++# that value also is the default value, unless described otherwise. You can ++# uncomment it (remove the pound sign) and change it if you want. ++ ++# =========================================================================== ++# Running on an online server or in a real tournament or match ++# =========================================================================== ++# If you plan to run online or in a tournament, read through the "Rules" ++# section below for proper handling of komi, handicaps, end-of-game cleanup, ++# and other details. ++ ++# =========================================================================== ++# Notes about performance and memory usage ++# =========================================================================== ++# Important: For good performance, you will very likely want to tune the ++# "numSearchThreads" parameter in the Search limits section below! Run ++# "./katago benchmark" to test KataGo and to suggest a reasonable value ++# of this parameter. ++ ++# For multi-GPU systems, read "OpenCL GPU settings" or "CUDA GPU settings". ++# ++# When using OpenCL, verify that KataGo picks the correct device! Some systems ++# may have both an Intel CPU OpenCL and GPU OpenCL. If # KataGo picks the wrong ++# one, correct this by specifying "openclGpuToUse". ++# ++# Consider adjusting "maxVisits", "ponderingEnabled", "resignThreshold", and ++# other parameters depending on your intended usage. ++ ++# =========================================================================== ++# Command-line usage ++# =========================================================================== ++# All of the below values may be set or overridden via command-line arguments: ++# ++# -override-config KEY=VALUE,KEY=VALUE,... ++ ++# =========================================================================== ++# Logs and files ++# =========================================================================== ++# This section defines where and what logging information is produced. ++ ++# Each run of KataGo will log to a separate file in this dir. ++# This is the default. ++logDir = gtp_logs ++# Uncomment and specify this instead of logDir to write separate dated subdirs ++# logDirDated = gtp_logs ++# Uncomment and specify this instead of logDir to log to only a single file ++# logFile = gtp.log ++ ++# Logging options ++logAllGTPCommunication = true ++logSearchInfo = true ++logSearchInfoForChosenMove = false ++logToStderr = false ++ ++# KataGo will display some info to stderr on GTP startup ++# Uncomment the next line and set it to false to suppress that and remain silent ++# startupPrintMessageToStderr = true ++ ++# Write information to stderr, for use in things like malkovich chat to OGS. ++ ogsChatToStderr = true ++ ++# Uncomment and set this to a directory to override where openCLTuner files ++# and other cached data is written. By default it saves into a subdir of the ++# current directory on windows, and a subdir of ~/.katago on Linux. ++# homeDataDir = PATH_TO_DIRECTORY ++ ++# =========================================================================== ++# Analysis ++# =========================================================================== ++# This section configures analysis settings. ++# ++# The maximum number of moves after the first move displayed in variations ++# from analysis commands like kata-analyze or lz-analyze. ++# analysisPVLen = 15 ++ ++# Report winrates for chat and analysis as (BLACK|WHITE|SIDETOMOVE). ++# Most GUIs and analysis tools will expect SIDETOMOVE. ++# reportAnalysisWinratesAs = SIDETOMOVE ++ ++# Extra noise for wider exploration. Large values will force KataGo to ++# analyze a greater variety of moves than it normally would. ++# An extreme value like 1 distributes playouts across every move on the board, ++# even very bad moves. ++# Affects analysis only, does not affect play. ++# analysisWideRootNoise = 0.04 ++ ++# Try to limit the effect of possible bad or bogus move sequences in the ++# history leading to this position from affecting KataGo's move predictions. ++# analysisIgnorePreRootHistory = true ++ ++# =========================================================================== ++# Rules ++# =========================================================================== ++# This section configures the scoring and playing rules. Rules can also be ++# changed mid-run by issuing custom GTP commands. ++# ++# See https://lightvector.github.io/KataGo/rules.html for rules details. ++# ++# See https://github.com/lightvector/KataGo/blob/master/docs/GTP_Extensions.md ++# for GTP commands. ++ ++# Specify the rules as a string. ++# Some legal values include: ++# chinese, japanese, korean, aga, chinese-ogs, new-zealand, stone-scoring, ++# ancient-territory, bga, aga-button ++# ++# For some human rulesets that require complex adjudication in tricky cases ++# (e.g. japanese, korean) KataGo may not precisely match the ruleset in such ++# cases but will do its best. ++rules = tromp-taylor ++ ++# By default, the "rules" parameter is used, but if you comment it out and ++# uncomment one option in each of the sections below, you can specify an ++# arbitrary combination of individual rules. ++ ++# koRule = SIMPLE # Simple ko rules (triple ko = no result) ++# koRule = POSITIONAL # Positional superko ++# koRule = SITUATIONAL # Situational superko ++ ++# scoringRule = AREA # Area scoring ++# scoringRule = TERRITORY # Territory scoring (special computer-friendly territory rules) ++ ++# taxRule = NONE # All surrounded empty points are scored ++# taxRule = SEKI # Eyes in seki do NOT count as points ++# taxRule = ALL # All groups are taxed up to 2 points for the two eyes needed to live ++ ++# Is multiple-stone suicide legal? (Single-stone suicide is always illegal). ++# multiStoneSuicideLegal = false ++# multiStoneSuicideLegal = true ++ ++# "Button go" - the first pass when area scoring awards 0.5 points and does ++# not count for ending the game. ++# Allows area scoring rulesets that have far simpler rules to achieve the same ++# final scoring precision and reward for precise play as territory scoring. ++# hasButton = false ++# hasButton = true ++ ++# Is this a human ruleset where it's okay to pass before having physically ++# captured and removed all dead stones? ++# friendlyPassOk = false ++# friendlyPassOk = true ++ ++# How handicap stones in handicap games are compensated ++# whiteHandicapBonus = 0 # White gets no compensation for black's handicap stones (Tromp-taylor, NZ, JP) ++# whiteHandicapBonus = N-1 # White gets N-1 points for black's N handicap stones (AGA) ++# whiteHandicapBonus = N # White gets N points for black's N handicap stones (Chinese) ++ ++# ------------------------------ ++# Other rules hacks ++# ------------------------------ ++# Uncomment and change to adjust what board size KataGo uses upon startup ++# by default when GTP doesn't specify. ++# defaultBoardSize = 19 ++ ++# By default, Katago will use the komi that the GUI or GTP controller tries to set. ++# Uncomment and set this to have KataGo ignore the controller and always use this komi. ++# ignoreGTPAndForceKomi = 7 ++ ++# =========================================================================== ++# Bot behavior ++# =========================================================================== ++ ++# ------------------------------ ++# Resignation ++# ------------------------------ ++ ++# Resignation occurs if for at least resignConsecTurns in a row, the ++# winLossUtility (on a [-1,1] scale) is below resignThreshold. ++allowResignation = true ++resignThreshold = -0.90 ++resignConsecTurns = 3 ++ ++# By default, KataGo may resign games that it is confidently losing even if they ++# are very close in score. Uncomment and set this to avoid resigning games ++# if the estimated difference is points is less than or equal to this. ++# resignMinScoreDifference = 10 ++ ++# ------------------------------ ++# Handicap ++# ------------------------------ ++# Assume that if black makes many moves in a row right at the start of the ++# game, then the game is a handicap game. This is necessary on some servers ++# and for some GUIs and also when initializing from many SGF files, which may ++# set up a handicap game using repeated GTP "play" commands for black rather ++# than GTP "place_free_handicap" commands; however, it may also lead to ++# incorrect understanding of komi if whiteHandicapBonus is used and a server ++# does not have such a practice. Uncomment and set to false to disable. ++# assumeMultipleStartingBlackMovesAreHandicap = true ++ ++# Makes katago dynamically adjust in handicap or altered-komi games to assume ++# based on those game settings that it must be stronger or weaker than the ++# opponent and to play accordingly. Greatly improves handicap strength by ++# biasing winrates and scores to favor appropriate safe/aggressive play. ++# Does NOT affect analysis (lz-analyze, kata-analyze, used by programs like ++# Lizzie) so analysis remains unbiased. Uncomment and set this to 0 to disable ++# this and make KataGo play the same always. ++# dynamicPlayoutDoublingAdvantageCapPerOppLead = 0.045 ++ ++# Instead of "dynamicPlayoutDoublingAdvantageCapPerOppLead", you can comment ++# that out and uncomment and set "playoutDoublingAdvantage" to a fixed value ++# from -3.0 to 3.0 that will not change dynamically. ++# ALSO affects analysis tools (lz-analyze, kata-analyze, used by e.g. Lizzie). ++# Negative makes KataGo behave as if it is much weaker than the opponent. ++# Positive makes KataGo behave as if it is much stronger than the opponent. ++# KataGo will adjust to favor safe/aggressive play as appropriate based on ++# the combination of who is ahead and how much stronger/weaker it thinks it is, ++# and report winrates and scores taking the strength difference into account. ++# ++# If this and "dynamicPlayoutDoublingAdvantageCapPerOppLead" are both set ++# then dynamic will be used for all games and this fixed value will be used ++# for analysis tools. ++# playoutDoublingAdvantage = 0.0 ++ ++# Uncomment one of these when using "playoutDoublingAdvantage" to enforce ++# that it will only apply when KataGo plays as the specified color and will be ++# negated when playing as the opposite color. ++# playoutDoublingAdvantagePla = BLACK ++# playoutDoublingAdvantagePla = WHITE ++ ++# ------------------------------ ++# Passing and cleanup ++# ------------------------------ ++# Make the bot never assume that its pass will end the game, even if passing ++# would end and "win" under Tromp-Taylor rules. Usually this is a good idea ++# when using it for analysis or playing on servers where scoring may be ++# implemented non-tromp-taylorly. Uncomment and set to false to disable. ++# conservativePass = true ++ ++# When using territory scoring, self-play games continue beyond two passes ++# with special cleanup rules that may be confusing for human players. This ++# option prevents the special cleanup phases from being reachable when using ++# the bot for GTP play. Uncomment and set to false to enable entering special ++# cleanup. For example, if you are testing it against itself, or against ++# another bot that has precisely implemented the rules documented at ++# https://lightvector.github.io/KataGo/rules.html ++# preventCleanupPhase = true ++ ++# ------------------------------ ++# Miscellaneous behavior ++# ------------------------------ ++# If the board is symmetric, search only one copy of each equivalent move. ++# Attempts to also account for ko/superko, will not theoretically perfect for ++# superko. Uncomment and set to false to disable. ++# rootSymmetryPruning = true ++ ++# Uncomment and set to true to avoid a particular joseki that some networks ++# misevaluate, and also to improve opening diversity versus some particular ++# other bots that like to play it all the time. ++# avoidMYTDaggerHack = false ++ ++# Prefer to avoid playing the same joseki in every corner of the board. ++# Uncomment to set to a specific value. See "Avoid SGF patterns" section. ++# By default: 0 (even games), 0.005 (handicap games) ++# avoidRepeatedPatternUtility = 0.0 ++ ++# Experimental logic to fight against mirror Go even with unfavorable komi. ++# Uncomment to set to a specific value to use for both playing and analysis. ++# By default: true when playing via GTP, but false when analyzing. ++# antiMirror = true ++ ++# Enable some hacks that mitigate rare instances when passing messes up deeper searches. ++# enablePassingHacks = true ++ ++# Uncomment and set this to true to prevent bad or bogus move sequences ++# in the history leading to this position from affecting KataGo's move choices. ++# Same as analysisIgnorePreRootHistory (see above) but applies to actual play. ++# You can enable this if KataGo is being asked to play from positions that it did not ++# choose the moves to reach. ++# ignorePreRootHistory = false ++ ++# =========================================================================== ++# Search limits ++# =========================================================================== ++ ++# Terminology: ++# "Playouts" is the number of new playouts of search performed each turn. ++# "Visits" is the same as "Playouts" but also counts search performed on ++# previous turns that is still applicable to this turn. ++# "Time" is the time in seconds. ++ ++# For example, if KataGo searched 200 nodes on the previous turn, and then ++# after the opponent's reply, 50 nodes of its search tree was still valid, ++# then a visit limit of 200 would allow KataGo to search 150 new nodes ++# (for a final tree size of 200 nodes), whereas a playout limit of of 200 ++# would allow KataGo to search 200 nodes (for a final tree size of 250 nodes). ++ ++# Additionally, KataGo may also move before than the limit in order to ++# obey time controls (e.g. byo-yomi, etc) if the GTP controller has ++# told KataGo that the game has is being played with a given time control. ++ ++# Limits for search on the current turn. ++# If commented out or unspecified, the default is to have no limit. ++# maxVisits = 30000 ++# maxPlayouts = 300 ++maxTime = 1.0 ++ ++# Ponder on the opponent's turn? ++ponderingEnabled = true ++ ++# Limits for search when pondering on the opponent's turn. ++# If commented out or unspecified, the default is to have no limit. ++# Limiting the maximum time is recommended so that KataGo won't burn CPU/GPU ++# forever and/or run out of RAM if left unattended while pondering is enabled. ++# maxVisitsPondering = 5000 ++# maxPlayoutsPondering = 3000 ++maxTimePondering = 120.0 ++ ++ ++# ------------------------------ ++# Other search limits and behavior ++# ------------------------------ ++ ++# Approx number of seconds to buffer for lag for GTP time controls - will ++# move a bit faster assuming there is this much lag per move. ++lagBuffer = 1.0 ++ ++# YOU PROBABLY WANT TO TUNE THIS PARAMETER! ++# The number of threads to use when searching. On powerful GPUs the optimal ++# threads may be much higher than the number of CPU cores you have because ++# many threads are needed to feed efficient large batches to the GPU. ++# ++# Run "./katago benchmark" to tune this parameter and test the effect ++# of changes to any of other parameters. ++numSearchThreads = 40 ++ ++# Play a little faster if the opponent is passing, for human-friendliness. ++# Comment these out to disable them, such as if running a controlled match ++# where you are testing KataGo with fixed compute per move vs other bots. ++searchFactorAfterOnePass = 0.50 ++searchFactorAfterTwoPass = 0.25 ++ ++# Play a little faster if super-winning, for human-friendliness. ++# Comment these out to disable them, such as if running a controlled match ++# where you are testing KataGo with fixed compute per move vs other bots. ++searchFactorWhenWinning = 0.40 ++searchFactorWhenWinningThreshold = 0.95 ++ ++# =========================================================================== ++# GPU settings ++# =========================================================================== ++# This section configures GPU settings. ++# ++# Maximum number of positions to send to a single GPU at once. The default ++# value is roughly equal to numSearchThreads, but can be specified manually ++# if running out of memory, or using multiple GPUs that expect to share work. ++# nnMaxBatchSize = ++ ++# Controls the neural network cache size, which is the primary RAM/memory use. ++# KataGo will cache up to (2 ** nnCacheSizePowerOfTwo) many neural net ++# evaluations in case of transpositions in the tree. ++# Increase this to improve performance for searches with tens of thousands ++# of visits or more. Decrease this to limit memory usage. ++# If you're happy to do some math - each neural net entry takes roughly ++# 1.5KB, except when using whole-board ownership/territory ++# visualizations, where each entry will take roughly 3KB. The number of ++# entries is (2 ** nnCacheSizePowerOfTwo). (E.g. 2 ** 18 = 262144.) ++# You can compute roughly how much memory the cache will use based on this. ++# nnCacheSizePowerOfTwo = 20 ++ ++# Size of mutex pool for nnCache is (2 ** this). ++# nnMutexPoolSizePowerOfTwo = 16 ++ ++# Randomize board orientation when running neural net evals? Uncomment and ++# set to false to disable. ++# nnRandomize = true ++ ++# If provided, force usage of a specific seed for nnRandomize. ++# The default is to use a randomly generated seed. ++# nnRandSeed = abcdefg ++ ++# Uncomment and set to true to force GTP to use the maximum board size for ++# internal buffers for the neural net. This will make KataGo slower when ++# evaluating small boards, but will avoid a lengthy initialization time on every ++# change of board size due to having to re-size the neural net buffers on the GPU. ++# This can be useful for example, for OGS's persistent bot mode that uses a single ++# bot instance to handle multiple games and may thrash between different board sizes ++# if there are concurrent games of multiple sizes. ++# gtpForceMaxNNSize = false ++ ++# ------------------------------ ++# Multiple GPUs ++# ------------------------------ ++# Set this to the number of GPUs to use or that are available. ++# IMPORTANT: If more than 1, also uncomment the appropriate TensorRT ++# or CUDA or OpenCL section. ++# numNNServerThreadsPerModel = 1 ++ ++# ------------------------------ ++# TENSORRT GPU settings ++# ------------------------------ ++# These only apply when using the TENSORRT version of KataGo. ++ ++# For one GPU: optionally uncomment this option and change if the GPU to ++# use is not device 0. ++# trtDeviceToUse = 0 ++ ++# For two GPUs: Uncomment these options, AND set numNNServerThreadsPerModel above. ++# Also, change their values if the devices you want to use are not 0 and 1. ++# trtDeviceToUseThread0 = 0 ++# trtDeviceToUseThread1 = 1 ++ ++# For three GPUs: Uncomment these options, AND set numNNServerThreadsPerModel above. ++# Also, change their values if the devices you want to use are not 0 and 1 and 2. ++# trtDeviceToUseThread0 = 0 ++# trtDeviceToUseThread1 = 1 ++# trtDeviceToUseThread2 = 2 ++ ++# The pattern continues for additional GPUs. ++ ++# ------------------------------ ++# CUDA GPU settings ++# ------------------------------ ++# These only apply when using the CUDA version of KataGo. ++ ++# For one GPU: optionally uncomment and change this if the GPU you want to ++# use is not device 0 ++# cudaDeviceToUse = 0 ++ ++# For two GPUs: Uncomment these options, AND set numNNServerThreadsPerModel above. ++# Also, change their values if the devices you want to use are not 0 and 1. ++# cudaDeviceToUseThread0 = 0 ++# cudaDeviceToUseThread1 = 1 ++ ++# For three GPUs: Uncomment these options, AND set numNNServerThreadsPerModel above. ++# Also, change their values if the devices you want to use are not 0 and 1 and 2. ++# cudaDeviceToUseThread0 = 0 ++# cudaDeviceToUseThread1 = 1 ++# cudaDeviceToUseThread2 = 2 ++ ++# The pattern continues for additional GPUs. ++ ++# KataGo will automatically use FP16 or not based on the compute capability ++# of your NVIDIA GPU. If you want to try to force a particular behavior ++# you can uncomment these lines and change them to "true" or "false". ++# cudaUseFP16 = auto ++# cudaUseNHWC = auto ++ ++# ------------------------------ ++# OpenCL GPU settings ++# ------------------------------ ++# These only apply when using the OpenCL version of KataGo. ++ ++# Uncomment and set to true to tune OpenCL for every board size separately, ++# rather than only the largest possible size. ++# openclReTunePerBoardSize = false ++ ++# For one GPU: optionally uncomment and change this if the best device to use is guessed incorrectly. ++# The default behavior tries to guess the 'best' GPU or device on your system to use, usually it will be a good guess. ++# openclDeviceToUse = 0 ++ ++# For two GPUs: Uncomment these two lines and replace X and Y with the device ids of the devices you want to use. ++# It might NOT be 0 and 1, some computers will have many OpenCL devices. You can see what the devices are when ++# KataGo starts up - it should print or log all the devices it finds. ++# (AND also set numNNServerThreadsPerModel above) ++# openclDeviceToUseThread0 = X ++# openclDeviceToUseThread1 = Y ++ ++# For three GPUs: Uncomment these three lines and replace X and Y and Z with the device ids of the devices you want to use. ++# It might NOT be 0 and 1 and 2, some computers will have many OpenCL devices. You can see what the devices are when ++# KataGo starts up - it should print or log all the devices it finds. ++# (AND also set numNNServerThreadsPerModel above) ++# openclDeviceToUseThread0 = X ++# openclDeviceToUseThread1 = Y ++# openclDeviceToUseThread2 = Z ++ ++# The pattern continues for additional GPUs. ++ ++# KataGo will automatically use FP16 or not based on testing your GPU during ++# tuning. If you want to try to force a particular behavior though you can ++# uncomment this option and change it to "true" or "false". This is a fairly ++# blunt setting - more detailed settings are testable by rerunning the tuner ++# with various arguments (./katago tuner). ++# openclUseFP16 = auto ++ ++# ------------------------------ ++# Eigen-specific settings ++# ------------------------------ ++# These only apply when using the Eigen (pure CPU) version of KataGo. ++ ++# Number of CPU threads for evaluating the neural net on the Eigen backend. ++# ++# Default: numSearchThreads ++# numEigenThreadsPerModel = X ++ ++# =========================================================================== ++# Root move selection and biases ++# =========================================================================== ++# Uncomment and edit any of the below values to change them from their default. ++ ++# If provided, force usage of a specific seed for various random things in ++# the search. The default is to use a random seed. ++# searchRandSeed = hijklmn ++ ++# Temperature for the early game, randomize between chosen moves with ++# this temperature ++# chosenMoveTemperatureEarly = 0.5 ++ ++# Decay temperature for the early game by 0.5 every this many moves, ++# scaled with board size. ++# chosenMoveTemperatureHalflife = 19 ++ ++# At the end of search after the early game, randomize between chosen ++# moves with this temperature ++# chosenMoveTemperature = 0.10 ++ ++# Subtract this many visits from each move prior to applying ++# chosenMoveTemperature (unless all moves have too few visits) to downweight ++# unlikely moves ++# chosenMoveSubtract = 0 ++ ++# The same as chosenMoveSubtract but only prunes moves that fall below ++# the threshold. This setting does not affect chosenMoveSubtract. ++# chosenMovePrune = 1 ++ ++# Number of symmetries to sample (without replacement) and average at the root ++# rootNumSymmetriesToSample = 1 ++ ++# Using LCB for move selection? ++# useLcbForSelection = true ++ ++# How many stdevs a move needs to be better than another for LCB selection ++# lcbStdevs = 5.0 ++ ++# Only use LCB override when a move has this proportion of visits as the ++# top move. ++# minVisitPropForLCB = 0.15 ++ ++# =========================================================================== ++# Internal params ++# =========================================================================== ++# Uncomment and edit any of the below values to change them from their default. ++ ++# Scales the utility of winning/losing ++# winLossUtilityFactor = 1.0 ++ ++# Scales the utility for trying to maximize score ++# staticScoreUtilityFactor = 0.10 ++# dynamicScoreUtilityFactor = 0.30 ++ ++# Adjust dynamic score center this proportion of the way towards zero, ++# capped at a reasonable amount. ++# dynamicScoreCenterZeroWeight = 0.20 ++# dynamicScoreCenterScale = 0.75 ++ ++# The utility of getting a "no result" due to triple ko or other long cycle ++# in non-superko rulesets (-1 to 1) ++# noResultUtilityForWhite = 0.0 ++ ++# The number of wins that a draw counts as, for white. (0 to 1) ++# drawEquivalentWinsForWhite = 0.5 ++ ++# Exploration constant for mcts ++# cpuctExploration = 1.0 ++# cpuctExplorationLog = 0.45 ++ ++# Parameters that control exploring more in volatile positions, exploring ++# less in stable positions. ++# cpuctUtilityStdevPrior = 0.40 ++# cpuctUtilityStdevPriorWeight = 2.0 ++# cpuctUtilityStdevScale = 0.85 ++ ++# FPU reduction constant for mcts ++# fpuReductionMax = 0.2 ++# rootFpuReductionMax = 0.1 ++# fpuParentWeightByVisitedPolicy = true ++ ++# Parameters that control weighting of evals based on the net's own ++# self-reported uncertainty. ++# useUncertainty = true ++# uncertaintyExponent = 1.0 ++# uncertaintyCoeff = 0.25 ++ ++# Explore using optimistic policy ++# rootPolicyOptimism = 0.2 ++# policyOptimism = 1.0 ++ ++# Amount to apply a downweighting of children with very bad values relative ++# to good ones. ++# valueWeightExponent = 0.25 ++ ++# Slight incentive for the bot to behave human-like with regard to passing at ++# the end, filling the dame, not wasting time playing in its own territory, ++# etc., and not play moves that are equivalent in terms of points but a bit ++# more unfriendly to humans. ++# rootEndingBonusPoints = 0.5 ++ ++# Make the bot prune useless moves that are just prolonging the game to ++# avoid losing yet. ++# rootPruneUselessMoves = true ++ ++# Apply bias correction based on local pattern keys ++# subtreeValueBiasFactor = 0.45 ++# subtreeValueBiasWeightExponent = 0.85 ++ ++# Use graph search rather than tree search - identify and share search for ++# transpositions. ++# useGraphSearch = true ++ ++# How much to shard the node table for search synchronization ++# nodeTableShardsPowerOfTwo = 16 ++ ++# How many virtual losses to add when a thread descends through a node ++# numVirtualLossesPerThread = 1 ++ ++# Improve the quality of evals under heavy multithreading ++# useNoisePruning = true ++ ++# =========================================================================== ++# Automatic avoid patterns ++# =========================================================================== ++# The parameters in this section provide a way to bias away from moves that match ++# patterns that this instance of KataGo has played in previous games, by auto-saving ++# moves to a directory and then auto-loading and biasing against them each new game. ++# Uncomment them to use them. When using this feature, all parameters must be specified. ++ ++# Different board sizes are tracked separately, but all board sizes share the same ++# parameters by default. Every parameter *except* for autoAvoidRepeatDir and ++# autoAvoidRepeatSaveChunkSize can be overridden per board size, ++# e.g. "autoAvoidRepeatMinTurnNumber13x13". You must ALSO specify the ++# defaults even if you have specified board-size-specific values. ++ ++# Directory to auto-save moves KataGo plays, to avoid them in future games. ++# You can create a new empty directory and put its path here. ++# If you run parallel instances of KataGo, use different directories if you ++# want them not to share their biases, use the same directory if you want ++# all of them to bias away from past moves that any of them have played. ++# KataGo will also automatically DELETE old data in this directory, so it is ++# recommended that if you do share the same directory between parallel instances, ++# that they all use the same settings that affect data saving/deletion. ++# autoAvoidRepeatDir = PATH_TO_NEW_DIRECTORY ++ ++# Penalize this much utility per matching move. ++# Values that are too large may lead to bad play. The value of 0.004 is fairly large ++# and might be large enough to result in some early weird/bad moves when trying ++# to avoid past games' moves if enough games begin the same way. You can experiment with it. ++# autoAvoidRepeatUtility = 0.004 ++ ++# Per each new move saved, exponentially decay prior saved moves by this factor. ++# This way, the bias against moves from many games ago is gradually phased out. ++# For example, 0.9995 = 1 - 1/2000, so would be roughly 2000 prior moves worth of ++# penalty weight remembered, in steady state. Depending on what turn number range ++# you are saving, this might equate to a different number of games For example ++# saving the first 50 moves per game would make this roughly 2000 / 50 = 40 games ++# worth of memory. ++# autoAvoidRepeatLambda = 0.9995 ++ ++# Affects data saving/deletion. ++# When the number of saved moves exceeds this, outright delete them to avoid too many ++# files and disk space building up. Also may affect the speed of saving/loading on start ++# of each game if this is set large and a lot of data builds up. ++# autoAvoidRepeatMaxPoses = 10000 ++ ++# Affects data saving/deletion. ++# Only save data for moves within this turn number range of those games. ++# E.g. setting autoAvoidRepeatMinTurnNumber to a number like 4 or 5 would tend to make ++# KataGo not develop a bias against the initial 3-4 and 4-4 corner moves in almost every game. ++# autoAvoidRepeatMinTurnNumber = 0 ++# autoAvoidRepeatMaxTurnNumber = 50 ++ ++# Affects data saving/deletion. ++# Within a single run of a program, wait to accumulate this many samples ++# (possibly across multiple clear_boards/games) before saving the data. ++# Can help to avoid writing too many small files to disk, especially when GTP is used ++# in a way that clears the board very frequently (e.g. gtp2ogs pooled manager). ++# autoAvoidRepeatSaveChunkSize = 200 ++ ++# =========================================================================== ++# Avoid SGF patterns ++# =========================================================================== ++# The parameters in this section provide a way to avoid moves that follow ++# specific patterns based on a set of SGF files loaded upon startup. ++# This is basically the same as the above "Automatic avoid patterns" section ++# above except you supply your own SGF files to avoid moves from. ++# Uncomment them to use this feature. Additionally, if the SGF file ++# contains the string %SKIP% in a comment on a move, that move will be ++# ignored for this purpose. ++ ++# Load SGF files from this directory when the engine is started ++# (only on startup, will not reload unless engine is restarted) ++# avoidSgfPatternDirs = path/to/directory/with/sgfs/ ++# You can also surround the file path in double quotes if the file path contains trailing spaces or hash signs. ++# Within double quotes, backslashes are escape characters. ++# avoidSgfPatternDirs = "path/to/directory/with/sgfs/" ++ ++# Penalize this much utility per matching move. ++# Set this negative if you instead want to favor SGF patterns instead of ++# penalizing them. This number does not need to be large, even 0.001 will ++# make a difference. Values that are too large may lead to bad play. ++# avoidSgfPatternUtility = 0.001 ++ ++# Optional - load only the newest this many files ++# avoidSgfPatternMaxFiles = 20 ++ ++# Optional - Penalty is multiplied by this per each older SGF file, so that ++# old SGF files matter less than newer ones. ++# avoidSgfPatternLambda = 0.90 ++ ++# Optional - pay attention only to moves made by players with this name. ++# For example, set it to the name that your bot's past games will show up ++# as in the SGF, so that the bot will only avoid repeating moves that itself ++# made in past games, not the moves that its opponents made. ++# avoidSgfPatternAllowedNames = my-ogs-bot-name1,my-ogs-bot-name2 ++ ++# Optional - Ignore moves in SGF files that occurred before this turn number. ++# avoidSgfPatternMinTurnNumber = 0 ++ ++# For more avoid patterns: ++# You can also specify a second set of parameters, and a third, fourth, ++# etc. by numbering 2,3,4,... ++# ++# avoidSgf2PatternDirs = ... ++# avoidSgf2PatternUtility = ... ++# avoidSgf2PatternMaxFiles = ... ++# avoidSgf2PatternLambda = ... ++# avoidSgf2PatternAllowedNames = ... ++# avoidSgf2PatternMinTurnNumber = ... ++ +diff --git a/python/selfplay/doudoubot.cfg b/python/selfplay/doudoubot.cfg +new file mode 100644 +index 00000000..bf81254f +--- /dev/null ++++ b/python/selfplay/doudoubot.cfg +@@ -0,0 +1,721 @@ ++# Configuration for KataGo C++ GTP engine ++ ++# Run the program using: `./katago.exe gtp` ++ ++# In this example config, when a parameter is given as a commented out value, ++# that value also is the default value, unless described otherwise. You can ++# uncomment it (remove the pound sign) and change it if you want. ++ ++# =========================================================================== ++# Running on an online server or in a real tournament or match ++# =========================================================================== ++# If you plan to run online or in a tournament, read through the "Rules" ++# section below for proper handling of komi, handicaps, end-of-game cleanup, ++# and other details. ++ ++# =========================================================================== ++# Notes about performance and memory usage ++# =========================================================================== ++# Important: For good performance, you will very likely want to tune the ++# "numSearchThreads" parameter in the Search limits section below! Run ++# "./katago benchmark" to test KataGo and to suggest a reasonable value ++# of this parameter. ++ ++# For multi-GPU systems, read "OpenCL GPU settings" or "CUDA GPU settings". ++# ++# When using OpenCL, verify that KataGo picks the correct device! Some systems ++# may have both an Intel CPU OpenCL and GPU OpenCL. If # KataGo picks the wrong ++# one, correct this by specifying "openclGpuToUse". ++# ++# Consider adjusting "maxVisits", "ponderingEnabled", "resignThreshold", and ++# other parameters depending on your intended usage. ++ ++# =========================================================================== ++# Command-line usage ++# =========================================================================== ++# All of the below values may be set or overridden via command-line arguments: ++# ++# -override-config KEY=VALUE,KEY=VALUE,... ++ ++# =========================================================================== ++# Logs and files ++# =========================================================================== ++# This section defines where and what logging information is produced. ++ ++# Each run of KataGo will log to a separate file in this dir. ++# This is the default. ++logDir = gtp_logs ++# Uncomment and specify this instead of logDir to write separate dated subdirs ++# logDirDated = gtp_logs ++# Uncomment and specify this instead of logDir to log to only a single file ++# logFile = gtp.log ++ ++# Logging options ++logAllGTPCommunication = true ++logSearchInfo = true ++logSearchInfoForChosenMove = false ++logToStderr = false ++ ++# KataGo will display some info to stderr on GTP startup ++# Uncomment the next line and set it to false to suppress that and remain silent ++# startupPrintMessageToStderr = true ++ ++# Write information to stderr, for use in things like malkovich chat to OGS. ++ ogsChatToStderr = true ++ ++# Uncomment and set this to a directory to override where openCLTuner files ++# and other cached data is written. By default it saves into a subdir of the ++# current directory on windows, and a subdir of ~/.katago on Linux. ++# homeDataDir = PATH_TO_DIRECTORY ++ ++# =========================================================================== ++# Analysis ++# =========================================================================== ++# This section configures analysis settings. ++# ++# The maximum number of moves after the first move displayed in variations ++# from analysis commands like kata-analyze or lz-analyze. ++# analysisPVLen = 15 ++ ++# Report winrates for chat and analysis as (BLACK|WHITE|SIDETOMOVE). ++# Most GUIs and analysis tools will expect SIDETOMOVE. ++# reportAnalysisWinratesAs = SIDETOMOVE ++ ++# Extra noise for wider exploration. Large values will force KataGo to ++# analyze a greater variety of moves than it normally would. ++# An extreme value like 1 distributes playouts across every move on the board, ++# even very bad moves. ++# Affects analysis only, does not affect play. ++# analysisWideRootNoise = 0.04 ++ ++# Try to limit the effect of possible bad or bogus move sequences in the ++# history leading to this position from affecting KataGo's move predictions. ++# analysisIgnorePreRootHistory = true ++ ++# =========================================================================== ++# Rules ++# =========================================================================== ++# This section configures the scoring and playing rules. Rules can also be ++# changed mid-run by issuing custom GTP commands. ++# ++# See https://lightvector.github.io/KataGo/rules.html for rules details. ++# ++# See https://github.com/lightvector/KataGo/blob/master/docs/GTP_Extensions.md ++# for GTP commands. ++ ++# Specify the rules as a string. ++# Some legal values include: ++# chinese, japanese, korean, aga, chinese-ogs, new-zealand, stone-scoring, ++# ancient-territory, bga, aga-button ++# ++# For some human rulesets that require complex adjudication in tricky cases ++# (e.g. japanese, korean) KataGo may not precisely match the ruleset in such ++# cases but will do its best. ++rules = tromp-taylor ++ ++# By default, the "rules" parameter is used, but if you comment it out and ++# uncomment one option in each of the sections below, you can specify an ++# arbitrary combination of individual rules. ++ ++# koRule = SIMPLE # Simple ko rules (triple ko = no result) ++# koRule = POSITIONAL # Positional superko ++# koRule = SITUATIONAL # Situational superko ++ ++# scoringRule = AREA # Area scoring ++# scoringRule = TERRITORY # Territory scoring (special computer-friendly territory rules) ++ ++# taxRule = NONE # All surrounded empty points are scored ++# taxRule = SEKI # Eyes in seki do NOT count as points ++# taxRule = ALL # All groups are taxed up to 2 points for the two eyes needed to live ++ ++# Is multiple-stone suicide legal? (Single-stone suicide is always illegal). ++# multiStoneSuicideLegal = false ++# multiStoneSuicideLegal = true ++ ++# "Button go" - the first pass when area scoring awards 0.5 points and does ++# not count for ending the game. ++# Allows area scoring rulesets that have far simpler rules to achieve the same ++# final scoring precision and reward for precise play as territory scoring. ++# hasButton = false ++# hasButton = true ++ ++# Is this a human ruleset where it's okay to pass before having physically ++# captured and removed all dead stones? ++# friendlyPassOk = false ++# friendlyPassOk = true ++ ++# How handicap stones in handicap games are compensated ++# whiteHandicapBonus = 0 # White gets no compensation for black's handicap stones (Tromp-taylor, NZ, JP) ++# whiteHandicapBonus = N-1 # White gets N-1 points for black's N handicap stones (AGA) ++# whiteHandicapBonus = N # White gets N points for black's N handicap stones (Chinese) ++ ++# ------------------------------ ++# Other rules hacks ++# ------------------------------ ++# Uncomment and change to adjust what board size KataGo uses upon startup ++# by default when GTP doesn't specify. ++# defaultBoardSize = 19 ++ ++# By default, Katago will use the komi that the GUI or GTP controller tries to set. ++# Uncomment and set this to have KataGo ignore the controller and always use this komi. ++# ignoreGTPAndForceKomi = 7 ++ ++# =========================================================================== ++# Bot behavior ++# =========================================================================== ++ ++# ------------------------------ ++# Resignation ++# ------------------------------ ++ ++# Resignation occurs if for at least resignConsecTurns in a row, the ++# winLossUtility (on a [-1,1] scale) is below resignThreshold. ++allowResignation = true ++resignThreshold = -0.90 ++resignConsecTurns = 3 ++ ++# By default, KataGo may resign games that it is confidently losing even if they ++# are very close in score. Uncomment and set this to avoid resigning games ++# if the estimated difference is points is less than or equal to this. ++# resignMinScoreDifference = 10 ++ ++# ------------------------------ ++# Handicap ++# ------------------------------ ++# Assume that if black makes many moves in a row right at the start of the ++# game, then the game is a handicap game. This is necessary on some servers ++# and for some GUIs and also when initializing from many SGF files, which may ++# set up a handicap game using repeated GTP "play" commands for black rather ++# than GTP "place_free_handicap" commands; however, it may also lead to ++# incorrect understanding of komi if whiteHandicapBonus is used and a server ++# does not have such a practice. Uncomment and set to false to disable. ++# assumeMultipleStartingBlackMovesAreHandicap = true ++ ++# Makes katago dynamically adjust in handicap or altered-komi games to assume ++# based on those game settings that it must be stronger or weaker than the ++# opponent and to play accordingly. Greatly improves handicap strength by ++# biasing winrates and scores to favor appropriate safe/aggressive play. ++# Does NOT affect analysis (lz-analyze, kata-analyze, used by programs like ++# Lizzie) so analysis remains unbiased. Uncomment and set this to 0 to disable ++# this and make KataGo play the same always. ++# dynamicPlayoutDoublingAdvantageCapPerOppLead = 0.045 ++ ++# Instead of "dynamicPlayoutDoublingAdvantageCapPerOppLead", you can comment ++# that out and uncomment and set "playoutDoublingAdvantage" to a fixed value ++# from -3.0 to 3.0 that will not change dynamically. ++# ALSO affects analysis tools (lz-analyze, kata-analyze, used by e.g. Lizzie). ++# Negative makes KataGo behave as if it is much weaker than the opponent. ++# Positive makes KataGo behave as if it is much stronger than the opponent. ++# KataGo will adjust to favor safe/aggressive play as appropriate based on ++# the combination of who is ahead and how much stronger/weaker it thinks it is, ++# and report winrates and scores taking the strength difference into account. ++# ++# If this and "dynamicPlayoutDoublingAdvantageCapPerOppLead" are both set ++# then dynamic will be used for all games and this fixed value will be used ++# for analysis tools. ++# playoutDoublingAdvantage = 0.0 ++ ++# Uncomment one of these when using "playoutDoublingAdvantage" to enforce ++# that it will only apply when KataGo plays as the specified color and will be ++# negated when playing as the opposite color. ++# playoutDoublingAdvantagePla = BLACK ++# playoutDoublingAdvantagePla = WHITE ++ ++# ------------------------------ ++# Passing and cleanup ++# ------------------------------ ++# Make the bot never assume that its pass will end the game, even if passing ++# would end and "win" under Tromp-Taylor rules. Usually this is a good idea ++# when using it for analysis or playing on servers where scoring may be ++# implemented non-tromp-taylorly. Uncomment and set to false to disable. ++# conservativePass = true ++ ++# When using territory scoring, self-play games continue beyond two passes ++# with special cleanup rules that may be confusing for human players. This ++# option prevents the special cleanup phases from being reachable when using ++# the bot for GTP play. Uncomment and set to false to enable entering special ++# cleanup. For example, if you are testing it against itself, or against ++# another bot that has precisely implemented the rules documented at ++# https://lightvector.github.io/KataGo/rules.html ++# preventCleanupPhase = true ++ ++# ------------------------------ ++# Miscellaneous behavior ++# ------------------------------ ++# If the board is symmetric, search only one copy of each equivalent move. ++# Attempts to also account for ko/superko, will not theoretically perfect for ++# superko. Uncomment and set to false to disable. ++# rootSymmetryPruning = true ++ ++# Uncomment and set to true to avoid a particular joseki that some networks ++# misevaluate, and also to improve opening diversity versus some particular ++# other bots that like to play it all the time. ++# avoidMYTDaggerHack = false ++ ++# Prefer to avoid playing the same joseki in every corner of the board. ++# Uncomment to set to a specific value. See "Avoid SGF patterns" section. ++# By default: 0 (even games), 0.005 (handicap games) ++# avoidRepeatedPatternUtility = 0.0 ++ ++# Experimental logic to fight against mirror Go even with unfavorable komi. ++# Uncomment to set to a specific value to use for both playing and analysis. ++# By default: true when playing via GTP, but false when analyzing. ++# antiMirror = true ++ ++# Enable some hacks that mitigate rare instances when passing messes up deeper searches. ++# enablePassingHacks = true ++ ++# Uncomment and set this to true to prevent bad or bogus move sequences ++# in the history leading to this position from affecting KataGo's move choices. ++# Same as analysisIgnorePreRootHistory (see above) but applies to actual play. ++# You can enable this if KataGo is being asked to play from positions that it did not ++# choose the moves to reach. ++# ignorePreRootHistory = false ++ ++# =========================================================================== ++# Search limits ++# =========================================================================== ++ ++# Terminology: ++# "Playouts" is the number of new playouts of search performed each turn. ++# "Visits" is the same as "Playouts" but also counts search performed on ++# previous turns that is still applicable to this turn. ++# "Time" is the time in seconds. ++ ++# For example, if KataGo searched 200 nodes on the previous turn, and then ++# after the opponent's reply, 50 nodes of its search tree was still valid, ++# then a visit limit of 200 would allow KataGo to search 150 new nodes ++# (for a final tree size of 200 nodes), whereas a playout limit of of 200 ++# would allow KataGo to search 200 nodes (for a final tree size of 250 nodes). ++ ++# Additionally, KataGo may also move before than the limit in order to ++# obey time controls (e.g. byo-yomi, etc) if the GTP controller has ++# told KataGo that the game has is being played with a given time control. ++ ++# Limits for search on the current turn. ++# If commented out or unspecified, the default is to have no limit. ++# maxVisits = 500 ++# maxPlayouts = 300 ++ maxTime = 0.5 ++ ++# Ponder on the opponent's turn? ++ponderingEnabled = true ++ ++# Limits for search when pondering on the opponent's turn. ++# If commented out or unspecified, the default is to have no limit. ++# Limiting the maximum time is recommended so that KataGo won't burn CPU/GPU ++# forever and/or run out of RAM if left unattended while pondering is enabled. ++# maxVisitsPondering = 5000 ++# maxPlayoutsPondering = 3000 ++maxTimePondering = 120.0 ++ ++ ++# ------------------------------ ++# Other search limits and behavior ++# ------------------------------ ++ ++# Approx number of seconds to buffer for lag for GTP time controls - will ++# move a bit faster assuming there is this much lag per move. ++lagBuffer = 1.0 ++ ++# YOU PROBABLY WANT TO TUNE THIS PARAMETER! ++# The number of threads to use when searching. On powerful GPUs the optimal ++# threads may be much higher than the number of CPU cores you have because ++# many threads are needed to feed efficient large batches to the GPU. ++# ++# Run "./katago benchmark" to tune this parameter and test the effect ++# of changes to any of other parameters. ++numSearchThreads = 16 ++ ++# Play a little faster if the opponent is passing, for human-friendliness. ++# Comment these out to disable them, such as if running a controlled match ++# where you are testing KataGo with fixed compute per move vs other bots. ++searchFactorAfterOnePass = 0.50 ++searchFactorAfterTwoPass = 0.25 ++ ++# Play a little faster if super-winning, for human-friendliness. ++# Comment these out to disable them, such as if running a controlled match ++# where you are testing KataGo with fixed compute per move vs other bots. ++searchFactorWhenWinning = 0.40 ++searchFactorWhenWinningThreshold = 0.95 ++ ++# =========================================================================== ++# GPU settings ++# =========================================================================== ++# This section configures GPU settings. ++# ++# Maximum number of positions to send to a single GPU at once. The default ++# value is roughly equal to numSearchThreads, but can be specified manually ++# if running out of memory, or using multiple GPUs that expect to share work. ++# nnMaxBatchSize = ++ ++# Controls the neural network cache size, which is the primary RAM/memory use. ++# KataGo will cache up to (2 ** nnCacheSizePowerOfTwo) many neural net ++# evaluations in case of transpositions in the tree. ++# Increase this to improve performance for searches with tens of thousands ++# of visits or more. Decrease this to limit memory usage. ++# If you're happy to do some math - each neural net entry takes roughly ++# 1.5KB, except when using whole-board ownership/territory ++# visualizations, where each entry will take roughly 3KB. The number of ++# entries is (2 ** nnCacheSizePowerOfTwo). (E.g. 2 ** 18 = 262144.) ++# You can compute roughly how much memory the cache will use based on this. ++# nnCacheSizePowerOfTwo = 20 ++ ++# Size of mutex pool for nnCache is (2 ** this). ++# nnMutexPoolSizePowerOfTwo = 16 ++ ++# Randomize board orientation when running neural net evals? Uncomment and ++# set to false to disable. ++# nnRandomize = true ++ ++# If provided, force usage of a specific seed for nnRandomize. ++# The default is to use a randomly generated seed. ++# nnRandSeed = abcdefg ++ ++# Uncomment and set to true to force GTP to use the maximum board size for ++# internal buffers for the neural net. This will make KataGo slower when ++# evaluating small boards, but will avoid a lengthy initialization time on every ++# change of board size due to having to re-size the neural net buffers on the GPU. ++# This can be useful for example, for OGS's persistent bot mode that uses a single ++# bot instance to handle multiple games and may thrash between different board sizes ++# if there are concurrent games of multiple sizes. ++# gtpForceMaxNNSize = false ++ ++# ------------------------------ ++# Multiple GPUs ++# ------------------------------ ++# Set this to the number of GPUs to use or that are available. ++# IMPORTANT: If more than 1, also uncomment the appropriate TensorRT ++# or CUDA or OpenCL section. ++# numNNServerThreadsPerModel = 1 ++ ++# ------------------------------ ++# TENSORRT GPU settings ++# ------------------------------ ++# These only apply when using the TENSORRT version of KataGo. ++ ++# For one GPU: optionally uncomment this option and change if the GPU to ++# use is not device 0. ++# trtDeviceToUse = 0 ++ ++# For two GPUs: Uncomment these options, AND set numNNServerThreadsPerModel above. ++# Also, change their values if the devices you want to use are not 0 and 1. ++# trtDeviceToUseThread0 = 0 ++# trtDeviceToUseThread1 = 1 ++ ++# For three GPUs: Uncomment these options, AND set numNNServerThreadsPerModel above. ++# Also, change their values if the devices you want to use are not 0 and 1 and 2. ++# trtDeviceToUseThread0 = 0 ++# trtDeviceToUseThread1 = 1 ++# trtDeviceToUseThread2 = 2 ++ ++# The pattern continues for additional GPUs. ++ ++# ------------------------------ ++# CUDA GPU settings ++# ------------------------------ ++# These only apply when using the CUDA version of KataGo. ++ ++# For one GPU: optionally uncomment and change this if the GPU you want to ++# use is not device 0 ++# cudaDeviceToUse = 0 ++ ++# For two GPUs: Uncomment these options, AND set numNNServerThreadsPerModel above. ++# Also, change their values if the devices you want to use are not 0 and 1. ++# cudaDeviceToUseThread0 = 0 ++# cudaDeviceToUseThread1 = 1 ++ ++# For three GPUs: Uncomment these options, AND set numNNServerThreadsPerModel above. ++# Also, change their values if the devices you want to use are not 0 and 1 and 2. ++# cudaDeviceToUseThread0 = 0 ++# cudaDeviceToUseThread1 = 1 ++# cudaDeviceToUseThread2 = 2 ++ ++# The pattern continues for additional GPUs. ++ ++# KataGo will automatically use FP16 or not based on the compute capability ++# of your NVIDIA GPU. If you want to try to force a particular behavior ++# you can uncomment these lines and change them to "true" or "false". ++# cudaUseFP16 = auto ++# cudaUseNHWC = auto ++ ++# ------------------------------ ++# OpenCL GPU settings ++# ------------------------------ ++# These only apply when using the OpenCL version of KataGo. ++ ++# Uncomment and set to true to tune OpenCL for every board size separately, ++# rather than only the largest possible size. ++# openclReTunePerBoardSize = false ++ ++# For one GPU: optionally uncomment and change this if the best device to use is guessed incorrectly. ++# The default behavior tries to guess the 'best' GPU or device on your system to use, usually it will be a good guess. ++# openclDeviceToUse = 0 ++ ++# For two GPUs: Uncomment these two lines and replace X and Y with the device ids of the devices you want to use. ++# It might NOT be 0 and 1, some computers will have many OpenCL devices. You can see what the devices are when ++# KataGo starts up - it should print or log all the devices it finds. ++# (AND also set numNNServerThreadsPerModel above) ++# openclDeviceToUseThread0 = X ++# openclDeviceToUseThread1 = Y ++ ++# For three GPUs: Uncomment these three lines and replace X and Y and Z with the device ids of the devices you want to use. ++# It might NOT be 0 and 1 and 2, some computers will have many OpenCL devices. You can see what the devices are when ++# KataGo starts up - it should print or log all the devices it finds. ++# (AND also set numNNServerThreadsPerModel above) ++# openclDeviceToUseThread0 = X ++# openclDeviceToUseThread1 = Y ++# openclDeviceToUseThread2 = Z ++ ++# The pattern continues for additional GPUs. ++ ++# KataGo will automatically use FP16 or not based on testing your GPU during ++# tuning. If you want to try to force a particular behavior though you can ++# uncomment this option and change it to "true" or "false". This is a fairly ++# blunt setting - more detailed settings are testable by rerunning the tuner ++# with various arguments (./katago tuner). ++# openclUseFP16 = auto ++ ++# ------------------------------ ++# Eigen-specific settings ++# ------------------------------ ++# These only apply when using the Eigen (pure CPU) version of KataGo. ++ ++# Number of CPU threads for evaluating the neural net on the Eigen backend. ++# ++# Default: numSearchThreads ++# numEigenThreadsPerModel = X ++ ++# =========================================================================== ++# Root move selection and biases ++# =========================================================================== ++# Uncomment and edit any of the below values to change them from their default. ++ ++# If provided, force usage of a specific seed for various random things in ++# the search. The default is to use a random seed. ++# searchRandSeed = hijklmn ++ ++# Temperature for the early game, randomize between chosen moves with ++# this temperature ++# chosenMoveTemperatureEarly = 0.5 ++ ++# Decay temperature for the early game by 0.5 every this many moves, ++# scaled with board size. ++# chosenMoveTemperatureHalflife = 19 ++ ++# At the end of search after the early game, randomize between chosen ++# moves with this temperature ++# chosenMoveTemperature = 0.10 ++ ++# Subtract this many visits from each move prior to applying ++# chosenMoveTemperature (unless all moves have too few visits) to downweight ++# unlikely moves ++# chosenMoveSubtract = 0 ++ ++# The same as chosenMoveSubtract but only prunes moves that fall below ++# the threshold. This setting does not affect chosenMoveSubtract. ++# chosenMovePrune = 1 ++ ++# Number of symmetries to sample (without replacement) and average at the root ++# rootNumSymmetriesToSample = 1 ++ ++# Using LCB for move selection? ++# useLcbForSelection = true ++ ++# How many stdevs a move needs to be better than another for LCB selection ++# lcbStdevs = 5.0 ++ ++# Only use LCB override when a move has this proportion of visits as the ++# top move. ++# minVisitPropForLCB = 0.15 ++ ++# =========================================================================== ++# Internal params ++# =========================================================================== ++# Uncomment and edit any of the below values to change them from their default. ++ ++# Scales the utility of winning/losing ++# winLossUtilityFactor = 1.0 ++ ++# Scales the utility for trying to maximize score ++# staticScoreUtilityFactor = 0.10 ++# dynamicScoreUtilityFactor = 0.30 ++ ++# Adjust dynamic score center this proportion of the way towards zero, ++# capped at a reasonable amount. ++# dynamicScoreCenterZeroWeight = 0.20 ++# dynamicScoreCenterScale = 0.75 ++ ++# The utility of getting a "no result" due to triple ko or other long cycle ++# in non-superko rulesets (-1 to 1) ++# noResultUtilityForWhite = 0.0 ++ ++# The number of wins that a draw counts as, for white. (0 to 1) ++# drawEquivalentWinsForWhite = 0.5 ++ ++# Exploration constant for mcts ++# cpuctExploration = 1.0 ++# cpuctExplorationLog = 0.45 ++ ++# Parameters that control exploring more in volatile positions, exploring ++# less in stable positions. ++# cpuctUtilityStdevPrior = 0.40 ++# cpuctUtilityStdevPriorWeight = 2.0 ++# cpuctUtilityStdevScale = 0.85 ++ ++# FPU reduction constant for mcts ++# fpuReductionMax = 0.2 ++# rootFpuReductionMax = 0.1 ++# fpuParentWeightByVisitedPolicy = true ++ ++# Parameters that control weighting of evals based on the net's own ++# self-reported uncertainty. ++# useUncertainty = true ++# uncertaintyExponent = 1.0 ++# uncertaintyCoeff = 0.25 ++ ++# Explore using optimistic policy ++# rootPolicyOptimism = 0.2 ++# policyOptimism = 1.0 ++ ++# Amount to apply a downweighting of children with very bad values relative ++# to good ones. ++# valueWeightExponent = 0.25 ++ ++# Slight incentive for the bot to behave human-like with regard to passing at ++# the end, filling the dame, not wasting time playing in its own territory, ++# etc., and not play moves that are equivalent in terms of points but a bit ++# more unfriendly to humans. ++# rootEndingBonusPoints = 0.5 ++ ++# Make the bot prune useless moves that are just prolonging the game to ++# avoid losing yet. ++# rootPruneUselessMoves = true ++ ++# Apply bias correction based on local pattern keys ++# subtreeValueBiasFactor = 0.45 ++# subtreeValueBiasWeightExponent = 0.85 ++ ++# Use graph search rather than tree search - identify and share search for ++# transpositions. ++# useGraphSearch = true ++ ++# How much to shard the node table for search synchronization ++# nodeTableShardsPowerOfTwo = 16 ++ ++# How many virtual losses to add when a thread descends through a node ++# numVirtualLossesPerThread = 1 ++ ++# Improve the quality of evals under heavy multithreading ++# useNoisePruning = true ++ ++# =========================================================================== ++# Automatic avoid patterns ++# =========================================================================== ++# The parameters in this section provide a way to bias away from moves that match ++# patterns that this instance of KataGo has played in previous games, by auto-saving ++# moves to a directory and then auto-loading and biasing against them each new game. ++# Uncomment them to use them. When using this feature, all parameters must be specified. ++ ++# Different board sizes are tracked separately, but all board sizes share the same ++# parameters by default. Every parameter *except* for autoAvoidRepeatDir and ++# autoAvoidRepeatSaveChunkSize can be overridden per board size, ++# e.g. "autoAvoidRepeatMinTurnNumber13x13". You must ALSO specify the ++# defaults even if you have specified board-size-specific values. ++ ++# Directory to auto-save moves KataGo plays, to avoid them in future games. ++# You can create a new empty directory and put its path here. ++# If you run parallel instances of KataGo, use different directories if you ++# want them not to share their biases, use the same directory if you want ++# all of them to bias away from past moves that any of them have played. ++# KataGo will also automatically DELETE old data in this directory, so it is ++# recommended that if you do share the same directory between parallel instances, ++# that they all use the same settings that affect data saving/deletion. ++# autoAvoidRepeatDir = PATH_TO_NEW_DIRECTORY ++ ++# Penalize this much utility per matching move. ++# Values that are too large may lead to bad play. The value of 0.004 is fairly large ++# and might be large enough to result in some early weird/bad moves when trying ++# to avoid past games' moves if enough games begin the same way. You can experiment with it. ++# autoAvoidRepeatUtility = 0.004 ++ ++# Per each new move saved, exponentially decay prior saved moves by this factor. ++# This way, the bias against moves from many games ago is gradually phased out. ++# For example, 0.9995 = 1 - 1/2000, so would be roughly 2000 prior moves worth of ++# penalty weight remembered, in steady state. Depending on what turn number range ++# you are saving, this might equate to a different number of games For example ++# saving the first 50 moves per game would make this roughly 2000 / 50 = 40 games ++# worth of memory. ++# autoAvoidRepeatLambda = 0.9995 ++ ++# Affects data saving/deletion. ++# When the number of saved moves exceeds this, outright delete them to avoid too many ++# files and disk space building up. Also may affect the speed of saving/loading on start ++# of each game if this is set large and a lot of data builds up. ++# autoAvoidRepeatMaxPoses = 10000 ++ ++# Affects data saving/deletion. ++# Only save data for moves within this turn number range of those games. ++# E.g. setting autoAvoidRepeatMinTurnNumber to a number like 4 or 5 would tend to make ++# KataGo not develop a bias against the initial 3-4 and 4-4 corner moves in almost every game. ++# autoAvoidRepeatMinTurnNumber = 0 ++# autoAvoidRepeatMaxTurnNumber = 50 ++ ++# Affects data saving/deletion. ++# Within a single run of a program, wait to accumulate this many samples ++# (possibly across multiple clear_boards/games) before saving the data. ++# Can help to avoid writing too many small files to disk, especially when GTP is used ++# in a way that clears the board very frequently (e.g. gtp2ogs pooled manager). ++# autoAvoidRepeatSaveChunkSize = 200 ++ ++# =========================================================================== ++# Avoid SGF patterns ++# =========================================================================== ++# The parameters in this section provide a way to avoid moves that follow ++# specific patterns based on a set of SGF files loaded upon startup. ++# This is basically the same as the above "Automatic avoid patterns" section ++# above except you supply your own SGF files to avoid moves from. ++# Uncomment them to use this feature. Additionally, if the SGF file ++# contains the string %SKIP% in a comment on a move, that move will be ++# ignored for this purpose. ++ ++# Load SGF files from this directory when the engine is started ++# (only on startup, will not reload unless engine is restarted) ++# avoidSgfPatternDirs = path/to/directory/with/sgfs/ ++# You can also surround the file path in double quotes if the file path contains trailing spaces or hash signs. ++# Within double quotes, backslashes are escape characters. ++# avoidSgfPatternDirs = "path/to/directory/with/sgfs/" ++ ++# Penalize this much utility per matching move. ++# Set this negative if you instead want to favor SGF patterns instead of ++# penalizing them. This number does not need to be large, even 0.001 will ++# make a difference. Values that are too large may lead to bad play. ++# avoidSgfPatternUtility = 0.001 ++ ++# Optional - load only the newest this many files ++# avoidSgfPatternMaxFiles = 20 ++ ++# Optional - Penalty is multiplied by this per each older SGF file, so that ++# old SGF files matter less than newer ones. ++# avoidSgfPatternLambda = 0.90 ++ ++# Optional - pay attention only to moves made by players with this name. ++# For example, set it to the name that your bot's past games will show up ++# as in the SGF, so that the bot will only avoid repeating moves that itself ++# made in past games, not the moves that its opponents made. ++# avoidSgfPatternAllowedNames = my-ogs-bot-name1,my-ogs-bot-name2 ++ ++# Optional - Ignore moves in SGF files that occurred before this turn number. ++# avoidSgfPatternMinTurnNumber = 0 ++ ++# For more avoid patterns: ++# You can also specify a second set of parameters, and a third, fourth, ++# etc. by numbering 2,3,4,... ++# ++# avoidSgf2PatternDirs = ... ++# avoidSgf2PatternUtility = ... ++# avoidSgf2PatternMaxFiles = ... ++# avoidSgf2PatternLambda = ... ++# avoidSgf2PatternAllowedNames = ... ++# avoidSgf2PatternMinTurnNumber = ... ++ +diff --git a/python/selfplay/golaxy_9d.cfg b/python/selfplay/golaxy_9d.cfg +new file mode 100644 +index 00000000..31e66efd +--- /dev/null ++++ b/python/selfplay/golaxy_9d.cfg +@@ -0,0 +1,713 @@ ++# Configuration for KataGo C++ GTP engine ++ ++# Run the program using: `./katago.exe gtp` ++ ++# In this example config, when a parameter is given as a commented out value, ++# that value also is the default value, unless described otherwise. You can ++# uncomment it (remove the pound sign) and change it if you want. ++ ++# =========================================================================== ++# Running on an online server or in a real tournament or match ++# =========================================================================== ++# If you plan to run online or in a tournament, read through the "Rules" ++# section below for proper handling of komi, handicaps, end-of-game cleanup, ++# and other details. ++ ++# =========================================================================== ++# Notes about performance and memory usage ++# =========================================================================== ++# Important: For good performance, you will very likely want to tune the ++# "numSearchThreads" parameter in the Search limits section below! Run ++# "./katago benchmark" to test KataGo and to suggest a reasonable value ++# of this parameter. ++ ++# For multi-GPU systems, read "OpenCL GPU settings" or "CUDA GPU settings". ++# ++# When using OpenCL, verify that KataGo picks the correct device! Some systems ++# may have both an Intel CPU OpenCL and GPU OpenCL. If # KataGo picks the wrong ++# one, correct this by specifying "openclGpuToUse". ++# ++# Consider adjusting "maxVisits", "ponderingEnabled", "resignThreshold", and ++# other parameters depending on your intended usage. ++ ++# =========================================================================== ++# Command-line usage ++# =========================================================================== ++# All of the below values may be set or overridden via command-line arguments: ++# ++# -override-config KEY=VALUE,KEY=VALUE,... ++ ++# =========================================================================== ++# Logs and files ++# =========================================================================== ++# This section defines where and what logging information is produced. ++ ++# Each run of KataGo will log to a separate file in this dir. ++# This is the default. ++logDir = gtp_logs ++# Uncomment and specify this instead of logDir to write separate dated subdirs ++# logDirDated = gtp_logs ++# Uncomment and specify this instead of logDir to log to only a single file ++# logFile = gtp.log ++ ++# Logging options ++logAllGTPCommunication = true ++logSearchInfo = true ++logSearchInfoForChosenMove = false ++logToStderr = true ++ ++# KataGo will display some info to stderr on GTP startup ++# Uncomment the next line and set it to false to suppress that and remain silent ++# startupPrintMessageToStderr = true ++ ++# Write information to stderr, for use in things like malkovich chat to OGS. ++ ogsChatToStderr = true ++ ++# Uncomment and set this to a directory to override where openCLTuner files ++# and other cached data is written. By default it saves into a subdir of the ++# current directory on windows, and a subdir of ~/.katago on Linux. ++# homeDataDir = PATH_TO_DIRECTORY ++ ++# =========================================================================== ++# Analysis ++# =========================================================================== ++# This section configures analysis settings. ++# ++# The maximum number of moves after the first move displayed in variations ++# from analysis commands like kata-analyze or lz-analyze. ++# analysisPVLen = 15 ++ ++# Report winrates for chat and analysis as (BLACK|WHITE|SIDETOMOVE). ++# Most GUIs and analysis tools will expect SIDETOMOVE. ++# reportAnalysisWinratesAs = SIDETOMOVE ++ ++# Extra noise for wider exploration. Large values will force KataGo to ++# analyze a greater variety of moves than it normally would. ++# An extreme value like 1 distributes playouts across every move on the board, ++# even very bad moves. ++# Affects analysis only, does not affect play. ++# analysisWideRootNoise = 0.04 ++ ++# Try to limit the effect of possible bad or bogus move sequences in the ++# history leading to this position from affecting KataGo's move predictions. ++# analysisIgnorePreRootHistory = true ++ ++# =========================================================================== ++# Rules ++# =========================================================================== ++# This section configures the scoring and playing rules. Rules can also be ++# changed mid-run by issuing custom GTP commands. ++# ++# See https://lightvector.github.io/KataGo/rules.html for rules details. ++# ++# See https://github.com/lightvector/KataGo/blob/master/docs/GTP_Extensions.md ++# for GTP commands. ++ ++# Specify the rules as a string. ++# Some legal values include: ++# chinese, japanese, korean, aga, chinese-ogs, new-zealand, stone-scoring, ++# ancient-territory, bga, aga-button ++# ++# For some human rulesets that require complex adjudication in tricky cases ++# (e.g. japanese, korean) KataGo may not precisely match the ruleset in such ++# cases but will do its best. ++# rules = tromp-taylor ++ ++# By default, the "rules" parameter is used, but if you comment it out and ++# uncomment one option in each of the sections below, you can specify an ++# arbitrary combination of individual rules. ++ ++# Ko Rule Settings ++koRule = POSITIONAL # Positional superko ++ ++# Scoring Rule Settings ++scoringRule = AREA # Area scoring ++ ++# Tax Rule Settings ++taxRule = NONE # All surrounded empty points are scored ++ ++# Is multiple-stone suicide legal? (Single-stone suicide is always illegal). ++multiStoneSuicideLegal = false ++ ++# "Button go" - the first pass when area scoring awards 0.5 points and does ++# not count for ending the game. ++hasButton = false ++ ++# Is this a human ruleset where it's okay to pass before having physically ++# captured and removed all dead stones? ++friendlyPassOk = false ++ ++# How handicap stones in handicap games are compensated ++whiteHandicapBonus = 0 # White gets no compensation for black's handicap stones (Tromp-taylor, NZ, JP) ++ ++ ++# ------------------------------ ++# Other rules hacks ++# ------------------------------ ++# Uncomment and change to adjust what board size KataGo uses upon startup ++# by default when GTP doesn't specify. ++# defaultBoardSize = 19 ++ ++# By default, Katago will use the komi that the GUI or GTP controller tries to set. ++# Uncomment and set this to have KataGo ignore the controller and always use this komi. ++# ignoreGTPAndForceKomi = 7 ++ ++# =========================================================================== ++# Bot behavior ++# =========================================================================== ++ ++# ------------------------------ ++# Resignation ++# ------------------------------ ++ ++# Resignation occurs if for at least resignConsecTurns in a row, the ++# winLossUtility (on a [-1,1] scale) is below resignThreshold. ++allowResignation = true ++resignThreshold = -0.90 ++resignConsecTurns = 3 ++ ++# By default, KataGo may resign games that it is confidently losing even if they ++# are very close in score. Uncomment and set this to avoid resigning games ++# if the estimated difference is points is less than or equal to this. ++# resignMinScoreDifference = 10 ++ ++# ------------------------------ ++# Handicap ++# ------------------------------ ++# Assume that if black makes many moves in a row right at the start of the ++# game, then the game is a handicap game. This is necessary on some servers ++# and for some GUIs and also when initializing from many SGF files, which may ++# set up a handicap game using repeated GTP "play" commands for black rather ++# than GTP "place_free_handicap" commands; however, it may also lead to ++# incorrect understanding of komi if whiteHandicapBonus is used and a server ++# does not have such a practice. Uncomment and set to false to disable. ++# assumeMultipleStartingBlackMovesAreHandicap = true ++ ++# Makes katago dynamically adjust in handicap or altered-komi games to assume ++# based on those game settings that it must be stronger or weaker than the ++# opponent and to play accordingly. Greatly improves handicap strength by ++# biasing winrates and scores to favor appropriate safe/aggressive play. ++# Does NOT affect analysis (lz-analyze, kata-analyze, used by programs like ++# Lizzie) so analysis remains unbiased. Uncomment and set this to 0 to disable ++# this and make KataGo play the same always. ++# dynamicPlayoutDoublingAdvantageCapPerOppLead = 0.045 ++ ++# Instead of "dynamicPlayoutDoublingAdvantageCapPerOppLead", you can comment ++# that out and uncomment and set "playoutDoublingAdvantage" to a fixed value ++# from -3.0 to 3.0 that will not change dynamically. ++# ALSO affects analysis tools (lz-analyze, kata-analyze, used by e.g. Lizzie). ++# Negative makes KataGo behave as if it is much weaker than the opponent. ++# Positive makes KataGo behave as if it is much stronger than the opponent. ++# KataGo will adjust to favor safe/aggressive play as appropriate based on ++# the combination of who is ahead and how much stronger/weaker it thinks it is, ++# and report winrates and scores taking the strength difference into account. ++# ++# If this and "dynamicPlayoutDoublingAdvantageCapPerOppLead" are both set ++# then dynamic will be used for all games and this fixed value will be used ++# for analysis tools. ++# playoutDoublingAdvantage = 0.0 ++ ++# Uncomment one of these when using "playoutDoublingAdvantage" to enforce ++# that it will only apply when KataGo plays as the specified color and will be ++# negated when playing as the opposite color. ++# playoutDoublingAdvantagePla = BLACK ++# playoutDoublingAdvantagePla = WHITE ++ ++# ------------------------------ ++# Passing and cleanup ++# ------------------------------ ++# Make the bot never assume that its pass will end the game, even if passing ++# would end and "win" under Tromp-Taylor rules. Usually this is a good idea ++# when using it for analysis or playing on servers where scoring may be ++# implemented non-tromp-taylorly. Uncomment and set to false to disable. ++# conservativePass = true ++ ++# When using territory scoring, self-play games continue beyond two passes ++# with special cleanup rules that may be confusing for human players. This ++# option prevents the special cleanup phases from being reachable when using ++# the bot for GTP play. Uncomment and set to false to enable entering special ++# cleanup. For example, if you are testing it against itself, or against ++# another bot that has precisely implemented the rules documented at ++# https://lightvector.github.io/KataGo/rules.html ++# preventCleanupPhase = true ++ ++# ------------------------------ ++# Miscellaneous behavior ++# ------------------------------ ++# If the board is symmetric, search only one copy of each equivalent move. ++# Attempts to also account for ko/superko, will not theoretically perfect for ++# superko. Uncomment and set to false to disable. ++# rootSymmetryPruning = true ++ ++# Uncomment and set to true to avoid a particular joseki that some networks ++# misevaluate, and also to improve opening diversity versus some particular ++# other bots that like to play it all the time. ++# avoidMYTDaggerHack = false ++ ++# Prefer to avoid playing the same joseki in every corner of the board. ++# Uncomment to set to a specific value. See "Avoid SGF patterns" section. ++# By default: 0 (even games), 0.005 (handicap games) ++# avoidRepeatedPatternUtility = 0.0 ++ ++# Experimental logic to fight against mirror Go even with unfavorable komi. ++# Uncomment to set to a specific value to use for both playing and analysis. ++# By default: true when playing via GTP, but false when analyzing. ++# antiMirror = true ++ ++# Enable some hacks that mitigate rare instances when passing messes up deeper searches. ++# enablePassingHacks = true ++ ++# Uncomment and set this to true to prevent bad or bogus move sequences ++# in the history leading to this position from affecting KataGo's move choices. ++# Same as analysisIgnorePreRootHistory (see above) but applies to actual play. ++# You can enable this if KataGo is being asked to play from positions that it did not ++# choose the moves to reach. ++# ignorePreRootHistory = false ++ ++# =========================================================================== ++# Search limits ++# =========================================================================== ++ ++# Terminology: ++# "Playouts" is the number of new playouts of search performed each turn. ++# "Visits" is the same as "Playouts" but also counts search performed on ++# previous turns that is still applicable to this turn. ++# "Time" is the time in seconds. ++ ++# For example, if KataGo searched 200 nodes on the previous turn, and then ++# after the opponent's reply, 50 nodes of its search tree was still valid, ++# then a visit limit of 200 would allow KataGo to search 150 new nodes ++# (for a final tree size of 200 nodes), whereas a playout limit of of 200 ++# would allow KataGo to search 200 nodes (for a final tree size of 250 nodes). ++ ++# Additionally, KataGo may also move before than the limit in order to ++# obey time controls (e.g. byo-yomi, etc) if the GTP controller has ++# told KataGo that the game has is being played with a given time control. ++ ++# Limits for search on the current turn. ++# If commented out or unspecified, the default is to have no limit. ++# maxVisits = 50000 ++# maxPlayouts = 300 ++ maxTime = 1.0 ++ ++# Ponder on the opponent's turn? ++ponderingEnabled = true ++ ++# Limits for search when pondering on the opponent's turn. ++# If commented out or unspecified, the default is to have no limit. ++# Limiting the maximum time is recommended so that KataGo won't burn CPU/GPU ++# forever and/or run out of RAM if left unattended while pondering is enabled. ++# maxVisitsPondering = 5000 ++# maxPlayoutsPondering = 3000 ++maxTimePondering = 120.0 ++ ++ ++# ------------------------------ ++# Other search limits and behavior ++# ------------------------------ ++ ++# Approx number of seconds to buffer for lag for GTP time controls - will ++# move a bit faster assuming there is this much lag per move. ++lagBuffer = 1.0 ++ ++# YOU PROBABLY WANT TO TUNE THIS PARAMETER! ++# The number of threads to use when searching. On powerful GPUs the optimal ++# threads may be much higher than the number of CPU cores you have because ++# many threads are needed to feed efficient large batches to the GPU. ++# ++# Run "./katago benchmark" to tune this parameter and test the effect ++# of changes to any of other parameters. ++numSearchThreads = 40 ++ ++# Play a little faster if the opponent is passing, for human-friendliness. ++# Comment these out to disable them, such as if running a controlled match ++# where you are testing KataGo with fixed compute per move vs other bots. ++searchFactorAfterOnePass = 0.50 ++searchFactorAfterTwoPass = 0.25 ++ ++# Play a little faster if super-winning, for human-friendliness. ++# Comment these out to disable them, such as if running a controlled match ++# where you are testing KataGo with fixed compute per move vs other bots. ++searchFactorWhenWinning = 0.40 ++searchFactorWhenWinningThreshold = 0.95 ++ ++# =========================================================================== ++# GPU settings ++# =========================================================================== ++# This section configures GPU settings. ++# ++# Maximum number of positions to send to a single GPU at once. The default ++# value is roughly equal to numSearchThreads, but can be specified manually ++# if running out of memory, or using multiple GPUs that expect to share work. ++# nnMaxBatchSize = ++ ++# Controls the neural network cache size, which is the primary RAM/memory use. ++# KataGo will cache up to (2 ** nnCacheSizePowerOfTwo) many neural net ++# evaluations in case of transpositions in the tree. ++# Increase this to improve performance for searches with tens of thousands ++# of visits or more. Decrease this to limit memory usage. ++# If you're happy to do some math - each neural net entry takes roughly ++# 1.5KB, except when using whole-board ownership/territory ++# visualizations, where each entry will take roughly 3KB. The number of ++# entries is (2 ** nnCacheSizePowerOfTwo). (E.g. 2 ** 18 = 262144.) ++# You can compute roughly how much memory the cache will use based on this. ++# nnCacheSizePowerOfTwo = 20 ++ ++# Size of mutex pool for nnCache is (2 ** this). ++# nnMutexPoolSizePowerOfTwo = 16 ++ ++# Randomize board orientation when running neural net evals? Uncomment and ++# set to false to disable. ++# nnRandomize = true ++ ++# If provided, force usage of a specific seed for nnRandomize. ++# The default is to use a randomly generated seed. ++# nnRandSeed = abcdefg ++ ++# Uncomment and set to true to force GTP to use the maximum board size for ++# internal buffers for the neural net. This will make KataGo slower when ++# evaluating small boards, but will avoid a lengthy initialization time on every ++# change of board size due to having to re-size the neural net buffers on the GPU. ++# This can be useful for example, for OGS's persistent bot mode that uses a single ++# bot instance to handle multiple games and may thrash between different board sizes ++# if there are concurrent games of multiple sizes. ++# gtpForceMaxNNSize = false ++ ++# ------------------------------ ++# Multiple GPUs ++# ------------------------------ ++# Set this to the number of GPUs to use or that are available. ++# IMPORTANT: If more than 1, also uncomment the appropriate TensorRT ++# or CUDA or OpenCL section. ++# numNNServerThreadsPerModel = 1 ++ ++# ------------------------------ ++# TENSORRT GPU settings ++# ------------------------------ ++# These only apply when using the TENSORRT version of KataGo. ++ ++# For one GPU: optionally uncomment this option and change if the GPU to ++# use is not device 0. ++# trtDeviceToUse = 0 ++ ++# For two GPUs: Uncomment these options, AND set numNNServerThreadsPerModel above. ++# Also, change their values if the devices you want to use are not 0 and 1. ++# trtDeviceToUseThread0 = 0 ++# trtDeviceToUseThread1 = 1 ++ ++# For three GPUs: Uncomment these options, AND set numNNServerThreadsPerModel above. ++# Also, change their values if the devices you want to use are not 0 and 1 and 2. ++# trtDeviceToUseThread0 = 0 ++# trtDeviceToUseThread1 = 1 ++# trtDeviceToUseThread2 = 2 ++ ++# The pattern continues for additional GPUs. ++ ++# ------------------------------ ++# CUDA GPU settings ++# ------------------------------ ++# These only apply when using the CUDA version of KataGo. ++ ++# For one GPU: optionally uncomment and change this if the GPU you want to ++# use is not device 0 ++# cudaDeviceToUse = 0 ++ ++# For two GPUs: Uncomment these options, AND set numNNServerThreadsPerModel above. ++# Also, change their values if the devices you want to use are not 0 and 1. ++# cudaDeviceToUseThread0 = 0 ++# cudaDeviceToUseThread1 = 1 ++ ++# For three GPUs: Uncomment these options, AND set numNNServerThreadsPerModel above. ++# Also, change their values if the devices you want to use are not 0 and 1 and 2. ++# cudaDeviceToUseThread0 = 0 ++# cudaDeviceToUseThread1 = 1 ++# cudaDeviceToUseThread2 = 2 ++ ++# The pattern continues for additional GPUs. ++ ++# KataGo will automatically use FP16 or not based on the compute capability ++# of your NVIDIA GPU. If you want to try to force a particular behavior ++# you can uncomment these lines and change them to "true" or "false". ++# cudaUseFP16 = auto ++# cudaUseNHWC = auto ++ ++# ------------------------------ ++# OpenCL GPU settings ++# ------------------------------ ++# These only apply when using the OpenCL version of KataGo. ++ ++# Uncomment and set to true to tune OpenCL for every board size separately, ++# rather than only the largest possible size. ++# openclReTunePerBoardSize = false ++ ++# For one GPU: optionally uncomment and change this if the best device to use is guessed incorrectly. ++# The default behavior tries to guess the 'best' GPU or device on your system to use, usually it will be a good guess. ++# openclDeviceToUse = 0 ++ ++# For two GPUs: Uncomment these two lines and replace X and Y with the device ids of the devices you want to use. ++# It might NOT be 0 and 1, some computers will have many OpenCL devices. You can see what the devices are when ++# KataGo starts up - it should print or log all the devices it finds. ++# (AND also set numNNServerThreadsPerModel above) ++# openclDeviceToUseThread0 = X ++# openclDeviceToUseThread1 = Y ++ ++# For three GPUs: Uncomment these three lines and replace X and Y and Z with the device ids of the devices you want to use. ++# It might NOT be 0 and 1 and 2, some computers will have many OpenCL devices. You can see what the devices are when ++# KataGo starts up - it should print or log all the devices it finds. ++# (AND also set numNNServerThreadsPerModel above) ++# openclDeviceToUseThread0 = X ++# openclDeviceToUseThread1 = Y ++# openclDeviceToUseThread2 = Z ++ ++# The pattern continues for additional GPUs. ++ ++# KataGo will automatically use FP16 or not based on testing your GPU during ++# tuning. If you want to try to force a particular behavior though you can ++# uncomment this option and change it to "true" or "false". This is a fairly ++# blunt setting - more detailed settings are testable by rerunning the tuner ++# with various arguments (./katago tuner). ++# openclUseFP16 = auto ++ ++# ------------------------------ ++# Eigen-specific settings ++# ------------------------------ ++# These only apply when using the Eigen (pure CPU) version of KataGo. ++ ++# Number of CPU threads for evaluating the neural net on the Eigen backend. ++# ++# Default: numSearchThreads ++# numEigenThreadsPerModel = X ++ ++# =========================================================================== ++# Root move selection and biases ++# =========================================================================== ++# Uncomment and edit any of the below values to change them from their default. ++ ++# If provided, force usage of a specific seed for various random things in ++# the search. The default is to use a random seed. ++# searchRandSeed = hijklmn ++ ++# Temperature for the early game, randomize between chosen moves with ++# this temperature ++# chosenMoveTemperatureEarly = 0.5 ++ ++# Decay temperature for the early game by 0.5 every this many moves, ++# scaled with board size. ++# chosenMoveTemperatureHalflife = 19 ++ ++# At the end of search after the early game, randomize between chosen ++# moves with this temperature ++# chosenMoveTemperature = 0.10 ++ ++# Subtract this many visits from each move prior to applying ++# chosenMoveTemperature (unless all moves have too few visits) to downweight ++# unlikely moves ++# chosenMoveSubtract = 0 ++ ++# The same as chosenMoveSubtract but only prunes moves that fall below ++# the threshold. This setting does not affect chosenMoveSubtract. ++# chosenMovePrune = 1 ++ ++# Number of symmetries to sample (without replacement) and average at the root ++# rootNumSymmetriesToSample = 1 ++ ++# Using LCB for move selection? ++# useLcbForSelection = true ++ ++# How many stdevs a move needs to be better than another for LCB selection ++# lcbStdevs = 5.0 ++ ++# Only use LCB override when a move has this proportion of visits as the ++# top move. ++# minVisitPropForLCB = 0.15 ++ ++# =========================================================================== ++# Internal params ++# =========================================================================== ++# Uncomment and edit any of the below values to change them from their default. ++ ++# Scales the utility of winning/losing ++# winLossUtilityFactor = 1.0 ++ ++# Scales the utility for trying to maximize score ++# staticScoreUtilityFactor = 0.10 ++# dynamicScoreUtilityFactor = 0.30 ++ ++# Adjust dynamic score center this proportion of the way towards zero, ++# capped at a reasonable amount. ++# dynamicScoreCenterZeroWeight = 0.20 ++# dynamicScoreCenterScale = 0.75 ++ ++# The utility of getting a "no result" due to triple ko or other long cycle ++# in non-superko rulesets (-1 to 1) ++# noResultUtilityForWhite = 0.0 ++ ++# The number of wins that a draw counts as, for white. (0 to 1) ++# drawEquivalentWinsForWhite = 0.5 ++ ++# Exploration constant for mcts ++# cpuctExploration = 1.0 ++# cpuctExplorationLog = 0.45 ++ ++# Parameters that control exploring more in volatile positions, exploring ++# less in stable positions. ++# cpuctUtilityStdevPrior = 0.40 ++# cpuctUtilityStdevPriorWeight = 2.0 ++# cpuctUtilityStdevScale = 0.85 ++ ++# FPU reduction constant for mcts ++# fpuReductionMax = 0.2 ++# rootFpuReductionMax = 0.1 ++# fpuParentWeightByVisitedPolicy = true ++ ++# Parameters that control weighting of evals based on the net's own ++# self-reported uncertainty. ++# useUncertainty = true ++# uncertaintyExponent = 1.0 ++# uncertaintyCoeff = 0.25 ++ ++# Explore using optimistic policy ++# rootPolicyOptimism = 0.2 ++# policyOptimism = 1.0 ++ ++# Amount to apply a downweighting of children with very bad values relative ++# to good ones. ++# valueWeightExponent = 0.25 ++ ++# Slight incentive for the bot to behave human-like with regard to passing at ++# the end, filling the dame, not wasting time playing in its own territory, ++# etc., and not play moves that are equivalent in terms of points but a bit ++# more unfriendly to humans. ++# rootEndingBonusPoints = 0.5 ++ ++# Make the bot prune useless moves that are just prolonging the game to ++# avoid losing yet. ++# rootPruneUselessMoves = true ++ ++# Apply bias correction based on local pattern keys ++# subtreeValueBiasFactor = 0.45 ++# subtreeValueBiasWeightExponent = 0.85 ++ ++# Use graph search rather than tree search - identify and share search for ++# transpositions. ++# useGraphSearch = true ++ ++# How much to shard the node table for search synchronization ++# nodeTableShardsPowerOfTwo = 16 ++ ++# How many virtual losses to add when a thread descends through a node ++# numVirtualLossesPerThread = 1 ++ ++# Improve the quality of evals under heavy multithreading ++# useNoisePruning = true ++ ++# =========================================================================== ++# Automatic avoid patterns ++# =========================================================================== ++# The parameters in this section provide a way to bias away from moves that match ++# patterns that this instance of KataGo has played in previous games, by auto-saving ++# moves to a directory and then auto-loading and biasing against them each new game. ++# Uncomment them to use them. When using this feature, all parameters must be specified. ++ ++# Different board sizes are tracked separately, but all board sizes share the same ++# parameters by default. Every parameter *except* for autoAvoidRepeatDir and ++# autoAvoidRepeatSaveChunkSize can be overridden per board size, ++# e.g. "autoAvoidRepeatMinTurnNumber13x13". You must ALSO specify the ++# defaults even if you have specified board-size-specific values. ++ ++# Directory to auto-save moves KataGo plays, to avoid them in future games. ++# You can create a new empty directory and put its path here. ++# If you run parallel instances of KataGo, use different directories if you ++# want them not to share their biases, use the same directory if you want ++# all of them to bias away from past moves that any of them have played. ++# KataGo will also automatically DELETE old data in this directory, so it is ++# recommended that if you do share the same directory between parallel instances, ++# that they all use the same settings that affect data saving/deletion. ++# autoAvoidRepeatDir = PATH_TO_NEW_DIRECTORY ++ ++# Penalize this much utility per matching move. ++# Values that are too large may lead to bad play. The value of 0.004 is fairly large ++# and might be large enough to result in some early weird/bad moves when trying ++# to avoid past games' moves if enough games begin the same way. You can experiment with it. ++# autoAvoidRepeatUtility = 0.004 ++ ++# Per each new move saved, exponentially decay prior saved moves by this factor. ++# This way, the bias against moves from many games ago is gradually phased out. ++# For example, 0.9995 = 1 - 1/2000, so would be roughly 2000 prior moves worth of ++# penalty weight remembered, in steady state. Depending on what turn number range ++# you are saving, this might equate to a different number of games For example ++# saving the first 50 moves per game would make this roughly 2000 / 50 = 40 games ++# worth of memory. ++# autoAvoidRepeatLambda = 0.9995 ++ ++# Affects data saving/deletion. ++# When the number of saved moves exceeds this, outright delete them to avoid too many ++# files and disk space building up. Also may affect the speed of saving/loading on start ++# of each game if this is set large and a lot of data builds up. ++# autoAvoidRepeatMaxPoses = 10000 ++ ++# Affects data saving/deletion. ++# Only save data for moves within this turn number range of those games. ++# E.g. setting autoAvoidRepeatMinTurnNumber to a number like 4 or 5 would tend to make ++# KataGo not develop a bias against the initial 3-4 and 4-4 corner moves in almost every game. ++# autoAvoidRepeatMinTurnNumber = 0 ++# autoAvoidRepeatMaxTurnNumber = 50 ++ ++# Affects data saving/deletion. ++# Within a single run of a program, wait to accumulate this many samples ++# (possibly across multiple clear_boards/games) before saving the data. ++# Can help to avoid writing too many small files to disk, especially when GTP is used ++# in a way that clears the board very frequently (e.g. gtp2ogs pooled manager). ++# autoAvoidRepeatSaveChunkSize = 200 ++ ++# =========================================================================== ++# Avoid SGF patterns ++# =========================================================================== ++# The parameters in this section provide a way to avoid moves that follow ++# specific patterns based on a set of SGF files loaded upon startup. ++# This is basically the same as the above "Automatic avoid patterns" section ++# above except you supply your own SGF files to avoid moves from. ++# Uncomment them to use this feature. Additionally, if the SGF file ++# contains the string %SKIP% in a comment on a move, that move will be ++# ignored for this purpose. ++ ++# Load SGF files from this directory when the engine is started ++# (only on startup, will not reload unless engine is restarted) ++# avoidSgfPatternDirs = path/to/directory/with/sgfs/ ++# You can also surround the file path in double quotes if the file path contains trailing spaces or hash signs. ++# Within double quotes, backslashes are escape characters. ++# avoidSgfPatternDirs = "path/to/directory/with/sgfs/" ++ ++# Penalize this much utility per matching move. ++# Set this negative if you instead want to favor SGF patterns instead of ++# penalizing them. This number does not need to be large, even 0.001 will ++# make a difference. Values that are too large may lead to bad play. ++# avoidSgfPatternUtility = 0.001 ++ ++# Optional - load only the newest this many files ++# avoidSgfPatternMaxFiles = 20 ++ ++# Optional - Penalty is multiplied by this per each older SGF file, so that ++# old SGF files matter less than newer ones. ++# avoidSgfPatternLambda = 0.90 ++ ++# Optional - pay attention only to moves made by players with this name. ++# For example, set it to the name that your bot's past games will show up ++# as in the SGF, so that the bot will only avoid repeating moves that itself ++# made in past games, not the moves that its opponents made. ++# avoidSgfPatternAllowedNames = my-ogs-bot-name1,my-ogs-bot-name2 ++ ++# Optional - Ignore moves in SGF files that occurred before this turn number. ++# avoidSgfPatternMinTurnNumber = 0 ++ ++# For more avoid patterns: ++# You can also specify a second set of parameters, and a third, fourth, ++# etc. by numbering 2,3,4,... ++# ++# avoidSgf2PatternDirs = ... ++# avoidSgf2PatternUtility = ... ++# avoidSgf2PatternMaxFiles = ... ++# avoidSgf2PatternLambda = ... ++# avoidSgf2PatternAllowedNames = ... ++# avoidSgf2PatternMinTurnNumber = ... ++ +diff --git a/python/selfplay/gtp_human5k_example.cfg b/python/selfplay/gtp_human5k_example.cfg +new file mode 100644 +index 00000000..9098f5ab +--- /dev/null ++++ b/python/selfplay/gtp_human5k_example.cfg +@@ -0,0 +1,189 @@ ++ ++# This is an example config for configuring KataGo to attempt to imitate a weaker human player. ++# Running with this config requires giving a human SL model b18c384nbt-humanv0.bin.gz ++# on the command line such as: ++# ./katago gtp -config gtp_human5k_example.cfg -model your_favorite_normal_model_for_katago.bin.gz -human-model b18c384nbt-humanv0.bin.gz ++# You can obtain the human model at https://github.com/lightvector/KataGo/releases/tag/v1.15.0 ++ ++# Below, the most important parts of the config for human-like play are commented. ++# See the original gtp_example for comments on other parameters. ++ ++# For another useful guide on human-style analysis, see here: ++# https://github.com/lightvector/KataGo/blob/master/docs/Analysis_Engine.md#human-sl-analysis-guide ++ ++# It is ALSO possible to pass in simply '-model b18c384nbt-humanv0.bin.gz' and NOT ++# pass in -human-model, i.e. use the human model as if it were KataGo's normal neural net. ++# If you do that, you need to use a config more like the normal gtp_example.cfg, not this config! ++# Keep in mind that if using the model normally, or if using -human-model but also altering ++# parameters below to blend in some of KataGo's search, KataGo's play might NOT be very human-like, ++# or might be human-like but play at a strength very different than the humanSLProfile. ++# You can experiment, some of the comments below hopefully will help illustrate things too. ++ ++logDir = gtp_logs ++logAllGTPCommunication = true ++logSearchInfo = true ++logSearchInfoForChosenMove = false ++logToStderr = false ++ ++# Use these rules by default, but a GUI or GTP controller might override this. ++rules = japanese ++ ++# When using -human-model, we only resign when far behind since a weaker player ++# might continue to fight much longer than a strong bot normally would. ++allowResignation = true ++resignThreshold = -0.99 ++resignConsecTurns = 20 ++resignMinScoreDifference = 40 ++resignMinMovesPerBoardArea = 0.4 ++ ++# Note: unless you change other parameters too, by default increasing visits won't do much. ++# If humanSLChosenMoveProp = 1.0 AND humanSLChosenMovePiklLambda is a large number, ++# then KataGo's normal search is ignored except for possibly choosing whether to pass/resign, ++# so more visits will have no effect on play. Still, having some visits is good for ++# ensuring good pass/resign judgment. ++maxVisits = 40 ++numSearchThreads = 1 ++lagBuffer = 1.0 ++ ++# Rough scale in seconds to randomly delay moving, so as not to respond instantly. ++# Some moves will delay longer, some moves will delay a little less. ++delayMoveScale = 2.0 ++delayMoveMax = 10.0 ++ ++# =========================================================================== ++# HUMAN SL PARAMETERS ++# =========================================================================== ++ ++# The most important parameter for human-like play configuration! ++# Choose the "profile" of players that the human SL model will imitate. ++# Available options are: ++# preaz_{RANK from 20k to 9d} - imitate player of given rank, before AlphaZero opening style became popular ++# rank_{RANK from 20k to 9d} - imitate player of given rank, after human openings changed due to AlphaZero. ++# preaz_{BRANK}_{WRANK} or rank_{BRANK}_{WRANK} - same, but imitate how black with the rank BR and white ++# with the rank WR would play against each other, knowing that the other player is stronger/weaker than them. ++# Warning: for rank differences > 9 ranks, or drastically mis-matched to the handicap used in the game, ++# this may be out of distribution due to lack of training data and the model might not behave well! Experiment with care. ++# proyear_{YEAR from 1800 to 2023} - imitate historical pros or insei from given year. ++humanSLProfile = preaz_5k ++ ++# The probability that we should play a HUMAN-like move, rather than playing KataGo's move. ++# Applies BEFORE temperature. ++humanSLChosenMoveProp = 1.0 ++ ++# If true, ignore the human SL model's choice of when to pass, and still use KataGo to determine that. ++# The human SL model, in theory, is not guaranteed to be reliable at when to pass for all profiles, ++# since e.g. some historical games omit passes. ++humanSLChosenMoveIgnorePass = true ++ ++# By default humanSLChosenMovePiklLambda is a large number which effectively disables it. ++# Setting it to a smaller number will "suppress" human-like moves that KataGo disapproves of. ++# In particular, if set to, for example, 0.4 when KataGo judges a human SL move to lose 0.4 utility, ++# it will substantially suppress the chance of playing that move (in particular, by a factor of exp(1)). ++# Less-bad moves will also be suppressed, but not by as much, e.g. a move losing 0.2 would get lowered ++# by a factor of exp(0.5). ++# As configured lower down, utilities by default range from -1.0 (loss) to +1.0 (win), plus up to +/- 0.3 for score. ++# WARNING: ONLY moves that KataGo actually searches will get suppressed! If a move is so bad that KataGo ++# rejects it without searching it, it will NOT get suppressed. ++# Therefore, to use humanSLChosenMovePiklLambda, it is STRONGLY recommended that you also use something ++# like humanSLRootExploreProbWeightless to ensure most human moves including bad moves get searched, ++# and ALSO use at least hundreds and ideally thousands of maxVisits, to ensure enough visits. ++humanSLChosenMovePiklLambda = 100000000 ++ ++# These parameters tell KataGo to use the human SL policy for exploration during search. ++# Each of these specifies the probability that KataGo will perform PUCT using the Human SL policy to ++# explore different moves, rather than using KataGo's normal policy, after a certain minimal number of visits. ++# "Root": applies only at the root of the search ++# "Pla": applies during non-root nodes of the search where it is katago's turn. ++# "Opp": applies during non-root nodes of the search where it is the opponent's turn. ++# "Weightless": search the move to evaluate it, but do NOT allow this visit to affect the parent's average utility. ++# "Weightful": search the move to evaluate it, and DO allow this visit to affect the parent's average utility. ++# For example, humanSLRootExploreProbWeightless = 0.5 would tell KataGo at the root of the search to spend ++# 50% of its visits to judge different possible human moves, but NOT to use those visits for determining the ++# value of the position (avoiding biasing the utility if some human SL moves are very bad). ++# If you don't understand these well, ask for help or look up some online explainers for MCTS (Monte-Carlo Tree Search). ++humanSLRootExploreProbWeightless = 0.0 ++humanSLRootExploreProbWeightful = 0.0 ++humanSLPlaExploreProbWeightless = 0.0 ++humanSLPlaExploreProbWeightful = 0.0 ++humanSLOppExploreProbWeightless = 0.0 ++humanSLOppExploreProbWeightful = 0.0 ++ ++# When using the human SL policy for exploration during search, use this cPUCT. ++# This only has an effect if at least one of humanSL{Root,Pla,Opp}ExploreProbWeight{less,ful} is nonzero. ++humanSLCpuctExploration = 0.50 ++ ++# Same as humanSLCpuctExploration, but NEVER diminshes its exploration no matter how many visits are used. ++# Normally, PUCT will sharpen with visits and spend a diminishing proportion of visits on moves with lower utility. ++# This is the coefficient for a term that does NOT diminish, i.e. if this is 0.2, then roughly moves within ++# 0.2 utility (about 10% winrate) of the best move will forever continue getting a decent fraction of visits, ++# smoothly falling off for greater utility differences. ++# Note that in combination with Weightful exploration above, if used for Opp exploration, this could be used ++# to model an opponent that will always have some chance to make small mistakes no matter how deep they search. ++# If further this was increased to a very large value, it would model an opponent that always played according ++# to the human SL raw policy. These might be interesting to experiment with for handicap play. ++humanSLCpuctPermanent = 0.2 ++ ++ ++# =========================================================================== ++# OTHER USEFUL PARAMETERS FOR HUMAN PLAY ADJUSTMENT ++# =========================================================================== ++ ++# Choosing temperature near 1, and restricting it to only affect moves already below 1% chance, ++# so that we sample close to the full range of human play. ++# You can also reduce the temperature to settings more like the plain gtp_example.cfg. ++# Then, rather than imitating a realistic human player, it will be more like imitating the ++# *majority vote* of players at that rank. For example it would avoid a lot of blunders ++# that players of that level would make, because even if players often blunder, the *majority vote* ++# of players would be much less likely to select any given blunder that an individual player would. ++chosenMoveTemperatureEarly = 0.85 ++chosenMoveTemperature = 0.70 ++chosenMoveTemperatureHalflife = 80 ++chosenMoveTemperatureOnlyBelowProb = 0.01 # temperature only starts to dampen moves below this ++chosenMoveSubtract = 0 ++chosenMovePrune = 0 ++ ++# Use a small NN cache to save memory since we're using very low visits anyways. You can increase ++# these back to more like the plain gtp_example.cfg if you are doing more extensive searches to ++# improve performance. ++nnCacheSizePowerOfTwo = 17 ++nnMutexPoolSizePowerOfTwo = 14 ++ ++# =========================================================================== ++# PARAMETERS CHANGED FROM DEFAULT TO MAKE SURE HUMAN SL USAGE WORKS WELL ++# =========================================================================== ++ ++# Make sure to take into account the recent moves in the game, don't ignore history. ++# This will produce the best imitation/prediction, since humans definitely do play differently based on where the ++# most recent moves in the game were, rather than coming fresh to the board position on every turn. ++ignorePreRootHistory = false ++analysisIgnorePreRootHistory = false ++ ++# Average 2 neural net samples at the root - ensures a bit smoother probabilities and results in ++# 8 * 7 / 2 = 28 possible policies instead of 8 possibilities. ++rootNumSymmetriesToSample = 2 ++# LCB improves strength for KataGo, but we disable it so it doesn't mess up move selection when blending human play. ++useLcbForSelection = false ++ ++# We disable dynamicScoreUtilityFactor - the human SL model can make score predictions that are a bit swingy, so ++# if we do want to do a search that blends human SL values in (TODO there isn't a way to do this anyways yet), using ++# static score utility might be a bit more stable. ++winLossUtilityFactor = 1.0 ++staticScoreUtilityFactor = 0.30 ++dynamicScoreUtilityFactor = 0.00 ++ ++# Uncertainty improves strength for KataGo normally, but messes with the weights of playouts in complicated ways, ++# so lets turn it off when doing human SL stuff. ++useUncertainty = false ++ ++# Subtree value bias improves strength for KataGo normally, but messes with the values of nodes in complicated ways, ++# so let's turn it off when doing human SL stuff. ++subtreeValueBiasFactor = 0.0 ++ ++# Noise pruning prunes out weight from moves that KataGo thinks are bad, but if we are doing human SL we might actively ++# want to be playing or exploring and weighting "bad" but human-like moves. So disable this. ++# Warning: when this is false, there is much less protection against the search severely misbehaving when you use too many threads. ++# Make sure not to set numSearchThreads to be too large - at a minimum, keep at least a 20x buffer between ++# the number of visits you use and the number of threads you use. ++# (as an aside, ideally, you want to have visits be a sufficient factor larger than threads EVEN when ++# useNoisePruning is true, this parameter just blunts the worst effects but doesn't entirely fix the badness). ++useNoisePruning = false +diff --git a/python/selfplay/leela_zero_strong.cfg b/python/selfplay/leela_zero_strong.cfg +new file mode 100644 +index 00000000..e1167b84 +--- /dev/null ++++ b/python/selfplay/leela_zero_strong.cfg +@@ -0,0 +1,719 @@ ++# Configuration for KataGo C++ GTP engine ++ ++# Run the program using: `./katago.exe gtp` ++ ++# In this example config, when a parameter is given as a commented out value, ++# that value also is the default value, unless described otherwise. You can ++# uncomment it (remove the pound sign) and change it if you want. ++ ++# =========================================================================== ++# Running on an online server or in a real tournament or match ++# =========================================================================== ++# If you plan to run online or in a tournament, read through the "Rules" ++# section below for proper handling of komi, handicaps, end-of-game cleanup, ++# and other details. ++ ++# =========================================================================== ++# Notes about performance and memory usage ++# =========================================================================== ++# Important: For good performance, you will very likely want to tune the ++# "numSearchThreads" parameter in the Search limits section below! Run ++# "./katago benchmark" to test KataGo and to suggest a reasonable value ++# of this parameter. ++ ++# For multi-GPU systems, read "OpenCL GPU settings" or "CUDA GPU settings". ++# ++# When using OpenCL, verify that KataGo picks the correct device! Some systems ++# may have both an Intel CPU OpenCL and GPU OpenCL. If # KataGo picks the wrong ++# one, correct this by specifying "openclGpuToUse". ++# ++# Consider adjusting "maxVisits", "ponderingEnabled", "resignThreshold", and ++# other parameters depending on your intended usage. ++ ++# =========================================================================== ++# Command-line usage ++# =========================================================================== ++# All of the below values may be set or overridden via command-line arguments: ++# ++# -override-config KEY=VALUE,KEY=VALUE,... ++ ++# =========================================================================== ++# Logs and files ++# =========================================================================== ++# This section defines where and what logging information is produced. ++ ++# Each run of KataGo will log to a separate file in this dir. ++# This is the default. ++logDir = gtp_logs ++# Uncomment and specify this instead of logDir to write separate dated subdirs ++# logDirDated = gtp_logs ++# Uncomment and specify this instead of logDir to log to only a single file ++# logFile = gtp.log ++ ++# Logging options ++logAllGTPCommunication = true ++logSearchInfo = true ++logSearchInfoForChosenMove = false ++logToStderr = true ++ ++# KataGo will display some info to stderr on GTP startup ++# Uncomment the next line and set it to false to suppress that and remain silent ++# startupPrintMessageToStderr = true ++ ++# Write information to stderr, for use in things like malkovich chat to OGS. ++ ogsChatToStderr = true ++ ++# Uncomment and set this to a directory to override where openCLTuner files ++# and other cached data is written. By default it saves into a subdir of the ++# current directory on windows, and a subdir of ~/.katago on Linux. ++# homeDataDir = PATH_TO_DIRECTORY ++ ++# =========================================================================== ++# Analysis ++# =========================================================================== ++# This section configures analysis settings. ++# ++# The maximum number of moves after the first move displayed in variations ++# from analysis commands like kata-analyze or lz-analyze. ++# analysisPVLen = 15 ++ ++# Report winrates for chat and analysis as (BLACK|WHITE|SIDETOMOVE). ++# Most GUIs and analysis tools will expect SIDETOMOVE. ++# reportAnalysisWinratesAs = SIDETOMOVE ++ ++# Extra noise for wider exploration. Large values will force KataGo to ++# analyze a greater variety of moves than it normally would. ++# An extreme value like 1 distributes playouts across every move on the board, ++# even very bad moves. ++# Affects analysis only, does not affect play. ++# analysisWideRootNoise = 0.04 ++ ++# Try to limit the effect of possible bad or bogus move sequences in the ++# history leading to this position from affecting KataGo's move predictions. ++# analysisIgnorePreRootHistory = true ++ ++# =========================================================================== ++# Rules ++# =========================================================================== ++# This section configures the scoring and playing rules. Rules can also be ++# changed mid-run by issuing custom GTP commands. ++# ++# See https://lightvector.github.io/KataGo/rules.html for rules details. ++# ++# See https://github.com/lightvector/KataGo/blob/master/docs/GTP_Extensions.md ++# for GTP commands. ++ ++# Specify the rules as a string. ++# Some legal values include: ++# chinese, japanese, korean, aga, chinese-ogs, new-zealand, stone-scoring, ++# ancient-territory, bga, aga-button ++# ++# For some human rulesets that require complex adjudication in tricky cases ++# (e.g. japanese, korean) KataGo may not precisely match the ruleset in such ++# cases but will do its best. ++# rules = tromp-taylor ++ ++# By default, the "rules" parameter is used, but if you comment it out and ++# uncomment one option in each of the sections below, you can specify an ++# arbitrary combination of individual rules. ++ ++koRule = SIMPLE # Simple ko rules (triple ko = no result) ++# koRule = POSITIONAL # Positional superko ++# koRule = SITUATIONAL # Situational superko ++ ++scoringRule = AREA # Area scoring ++# scoringRule = TERRITORY # Territory scoring (special computer-friendly territory rules) ++ ++taxRule = NONE # All surrounded empty points are scored ++# taxRule = SEKI # Eyes in seki do NOT count as points ++# taxRule = ALL # All groups are taxed up to 2 points for the two eyes needed to live ++ ++# Is multiple-stone suicide legal? (Single-stone suicide is always illegal). ++multiStoneSuicideLegal = true ++# multiStoneSuicideLegal = false ++ ++# "Button go" - the first pass when area scoring awards 0.5 points and does ++# not count for ending the game. ++hasButton = false ++# hasButton = true ++ ++# Is this a human ruleset where it's okay to pass before having physically ++# captured and removed all dead stones? ++friendlyPassOk = true ++# friendlyPassOk = false ++ ++# How handicap stones in handicap games are compensated ++whiteHandicapBonus = 0 # White gets no compensation for black's handicap stones (Tromp-taylor, NZ, JP) ++# whiteHandicapBonus = N-1 # White gets N-1 points for black's N handicap stones (AGA) ++# whiteHandicapBonus = N # White gets N points for black's N handicap stones (Chinese) ++ ++# ------------------------------ ++# Other rules hacks ++# ------------------------------ ++# Uncomment and change to adjust what board size KataGo uses upon startup ++# by default when GTP doesn't specify. ++# defaultBoardSize = 19 ++ ++# By default, Katago will use the komi that the GUI or GTP controller tries to set. ++# Uncomment and set this to have KataGo ignore the controller and always use this komi. ++# ignoreGTPAndForceKomi = 7 ++ ++# =========================================================================== ++# Bot behavior ++# =========================================================================== ++ ++# ------------------------------ ++# Resignation ++# ------------------------------ ++ ++# Resignation occurs if for at least resignConsecTurns in a row, the ++# winLossUtility (on a [-1,1] scale) is below resignThreshold. ++allowResignation = true ++resignThreshold = -0.90 ++resignConsecTurns = 3 ++ ++# By default, KataGo may resign games that it is confidently losing even if they ++# are very close in score. Uncomment and set this to avoid resigning games ++# if the estimated difference is points is less than or equal to this. ++# resignMinScoreDifference = 10 ++ ++# ------------------------------ ++# Handicap ++# ------------------------------ ++# Assume that if black makes many moves in a row right at the start of the ++# game, then the game is a handicap game. This is necessary on some servers ++# and for some GUIs and also when initializing from many SGF files, which may ++# set up a handicap game using repeated GTP "play" commands for black rather ++# than GTP "place_free_handicap" commands; however, it may also lead to ++# incorrect understanding of komi if whiteHandicapBonus is used and a server ++# does not have such a practice. Uncomment and set to false to disable. ++# assumeMultipleStartingBlackMovesAreHandicap = true ++ ++# Makes katago dynamically adjust in handicap or altered-komi games to assume ++# based on those game settings that it must be stronger or weaker than the ++# opponent and to play accordingly. Greatly improves handicap strength by ++# biasing winrates and scores to favor appropriate safe/aggressive play. ++# Does NOT affect analysis (lz-analyze, kata-analyze, used by programs like ++# Lizzie) so analysis remains unbiased. Uncomment and set this to 0 to disable ++# this and make KataGo play the same always. ++# dynamicPlayoutDoublingAdvantageCapPerOppLead = 0.045 ++ ++# Instead of "dynamicPlayoutDoublingAdvantageCapPerOppLead", you can comment ++# that out and uncomment and set "playoutDoublingAdvantage" to a fixed value ++# from -3.0 to 3.0 that will not change dynamically. ++# ALSO affects analysis tools (lz-analyze, kata-analyze, used by e.g. Lizzie). ++# Negative makes KataGo behave as if it is much weaker than the opponent. ++# Positive makes KataGo behave as if it is much stronger than the opponent. ++# KataGo will adjust to favor safe/aggressive play as appropriate based on ++# the combination of who is ahead and how much stronger/weaker it thinks it is, ++# and report winrates and scores taking the strength difference into account. ++# ++# If this and "dynamicPlayoutDoublingAdvantageCapPerOppLead" are both set ++# then dynamic will be used for all games and this fixed value will be used ++# for analysis tools. ++# playoutDoublingAdvantage = 0.0 ++ ++# Uncomment one of these when using "playoutDoublingAdvantage" to enforce ++# that it will only apply when KataGo plays as the specified color and will be ++# negated when playing as the opposite color. ++# playoutDoublingAdvantagePla = BLACK ++# playoutDoublingAdvantagePla = WHITE ++ ++# ------------------------------ ++# Passing and cleanup ++# ------------------------------ ++# Make the bot never assume that its pass will end the game, even if passing ++# would end and "win" under Tromp-Taylor rules. Usually this is a good idea ++# when using it for analysis or playing on servers where scoring may be ++# implemented non-tromp-taylorly. Uncomment and set to false to disable. ++# conservativePass = true ++ ++# When using territory scoring, self-play games continue beyond two passes ++# with special cleanup rules that may be confusing for human players. This ++# option prevents the special cleanup phases from being reachable when using ++# the bot for GTP play. Uncomment and set to false to enable entering special ++# cleanup. For example, if you are testing it against itself, or against ++# another bot that has precisely implemented the rules documented at ++# https://lightvector.github.io/KataGo/rules.html ++# preventCleanupPhase = true ++ ++# ------------------------------ ++# Miscellaneous behavior ++# ------------------------------ ++# If the board is symmetric, search only one copy of each equivalent move. ++# Attempts to also account for ko/superko, will not theoretically perfect for ++# superko. Uncomment and set to false to disable. ++# rootSymmetryPruning = true ++ ++# Uncomment and set to true to avoid a particular joseki that some networks ++# misevaluate, and also to improve opening diversity versus some particular ++# other bots that like to play it all the time. ++# avoidMYTDaggerHack = false ++ ++# Prefer to avoid playing the same joseki in every corner of the board. ++# Uncomment to set to a specific value. See "Avoid SGF patterns" section. ++# By default: 0 (even games), 0.005 (handicap games) ++# avoidRepeatedPatternUtility = 0.0 ++ ++# Experimental logic to fight against mirror Go even with unfavorable komi. ++# Uncomment to set to a specific value to use for both playing and analysis. ++# By default: true when playing via GTP, but false when analyzing. ++# antiMirror = true ++ ++# Enable some hacks that mitigate rare instances when passing messes up deeper searches. ++# enablePassingHacks = true ++ ++# Uncomment and set this to true to prevent bad or bogus move sequences ++# in the history leading to this position from affecting KataGo's move choices. ++# Same as analysisIgnorePreRootHistory (see above) but applies to actual play. ++# You can enable this if KataGo is being asked to play from positions that it did not ++# choose the moves to reach. ++# ignorePreRootHistory = false ++ ++# =========================================================================== ++# Search limits ++# =========================================================================== ++ ++# Terminology: ++# "Playouts" is the number of new playouts of search performed each turn. ++# "Visits" is the same as "Playouts" but also counts search performed on ++# previous turns that is still applicable to this turn. ++# "Time" is the time in seconds. ++ ++# For example, if KataGo searched 200 nodes on the previous turn, and then ++# after the opponent's reply, 50 nodes of its search tree was still valid, ++# then a visit limit of 200 would allow KataGo to search 150 new nodes ++# (for a final tree size of 200 nodes), whereas a playout limit of of 200 ++# would allow KataGo to search 200 nodes (for a final tree size of 250 nodes). ++ ++# Additionally, KataGo may also move before than the limit in order to ++# obey time controls (e.g. byo-yomi, etc) if the GTP controller has ++# told KataGo that the game has is being played with a given time control. ++ ++# Limits for search on the current turn. ++# If commented out or unspecified, the default is to have no limit. ++# maxVisits = 30000 ++# maxPlayouts = 300 ++maxTime = 1.0 ++ ++# Ponder on the opponent's turn? ++ponderingEnabled = true ++ ++# Limits for search when pondering on the opponent's turn. ++# If commented out or unspecified, the default is to have no limit. ++# Limiting the maximum time is recommended so that KataGo won't burn CPU/GPU ++# forever and/or run out of RAM if left unattended while pondering is enabled. ++# maxVisitsPondering = 5000 ++# maxPlayoutsPondering = 3000 ++maxTimePondering = 120.0 ++ ++ ++# ------------------------------ ++# Other search limits and behavior ++# ------------------------------ ++ ++# Approx number of seconds to buffer for lag for GTP time controls - will ++# move a bit faster assuming there is this much lag per move. ++lagBuffer = 1.0 ++ ++# YOU PROBABLY WANT TO TUNE THIS PARAMETER! ++# The number of threads to use when searching. On powerful GPUs the optimal ++# threads may be much higher than the number of CPU cores you have because ++# many threads are needed to feed efficient large batches to the GPU. ++# ++# Run "./katago benchmark" to tune this parameter and test the effect ++# of changes to any of other parameters. ++numSearchThreads = 40 ++ ++# Play a little faster if the opponent is passing, for human-friendliness. ++# Comment these out to disable them, such as if running a controlled match ++# where you are testing KataGo with fixed compute per move vs other bots. ++searchFactorAfterOnePass = 0.50 ++searchFactorAfterTwoPass = 0.25 ++ ++# Play a little faster if super-winning, for human-friendliness. ++# Comment these out to disable them, such as if running a controlled match ++# where you are testing KataGo with fixed compute per move vs other bots. ++searchFactorWhenWinning = 0.40 ++searchFactorWhenWinningThreshold = 0.95 ++ ++# =========================================================================== ++# GPU settings ++# =========================================================================== ++# This section configures GPU settings. ++# ++# Maximum number of positions to send to a single GPU at once. The default ++# value is roughly equal to numSearchThreads, but can be specified manually ++# if running out of memory, or using multiple GPUs that expect to share work. ++# nnMaxBatchSize = ++ ++# Controls the neural network cache size, which is the primary RAM/memory use. ++# KataGo will cache up to (2 ** nnCacheSizePowerOfTwo) many neural net ++# evaluations in case of transpositions in the tree. ++# Increase this to improve performance for searches with tens of thousands ++# of visits or more. Decrease this to limit memory usage. ++# If you're happy to do some math - each neural net entry takes roughly ++# 1.5KB, except when using whole-board ownership/territory ++# visualizations, where each entry will take roughly 3KB. The number of ++# entries is (2 ** nnCacheSizePowerOfTwo). (E.g. 2 ** 18 = 262144.) ++# You can compute roughly how much memory the cache will use based on this. ++# nnCacheSizePowerOfTwo = 20 ++ ++# Size of mutex pool for nnCache is (2 ** this). ++# nnMutexPoolSizePowerOfTwo = 16 ++ ++# Randomize board orientation when running neural net evals? Uncomment and ++# set to false to disable. ++# nnRandomize = true ++ ++# If provided, force usage of a specific seed for nnRandomize. ++# The default is to use a randomly generated seed. ++# nnRandSeed = abcdefg ++ ++# Uncomment and set to true to force GTP to use the maximum board size for ++# internal buffers for the neural net. This will make KataGo slower when ++# evaluating small boards, but will avoid a lengthy initialization time on every ++# change of board size due to having to re-size the neural net buffers on the GPU. ++# This can be useful for example, for OGS's persistent bot mode that uses a single ++# bot instance to handle multiple games and may thrash between different board sizes ++# if there are concurrent games of multiple sizes. ++# gtpForceMaxNNSize = false ++ ++# ------------------------------ ++# Multiple GPUs ++# ------------------------------ ++# Set this to the number of GPUs to use or that are available. ++# IMPORTANT: If more than 1, also uncomment the appropriate TensorRT ++# or CUDA or OpenCL section. ++# numNNServerThreadsPerModel = 1 ++ ++# ------------------------------ ++# TENSORRT GPU settings ++# ------------------------------ ++# These only apply when using the TENSORRT version of KataGo. ++ ++# For one GPU: optionally uncomment this option and change if the GPU to ++# use is not device 0. ++# trtDeviceToUse = 0 ++ ++# For two GPUs: Uncomment these options, AND set numNNServerThreadsPerModel above. ++# Also, change their values if the devices you want to use are not 0 and 1. ++# trtDeviceToUseThread0 = 0 ++# trtDeviceToUseThread1 = 1 ++ ++# For three GPUs: Uncomment these options, AND set numNNServerThreadsPerModel above. ++# Also, change their values if the devices you want to use are not 0 and 1 and 2. ++# trtDeviceToUseThread0 = 0 ++# trtDeviceToUseThread1 = 1 ++# trtDeviceToUseThread2 = 2 ++ ++# The pattern continues for additional GPUs. ++ ++# ------------------------------ ++# CUDA GPU settings ++# ------------------------------ ++# These only apply when using the CUDA version of KataGo. ++ ++# For one GPU: optionally uncomment and change this if the GPU you want to ++# use is not device 0 ++# cudaDeviceToUse = 0 ++ ++# For two GPUs: Uncomment these options, AND set numNNServerThreadsPerModel above. ++# Also, change their values if the devices you want to use are not 0 and 1. ++# cudaDeviceToUseThread0 = 0 ++# cudaDeviceToUseThread1 = 1 ++ ++# For three GPUs: Uncomment these options, AND set numNNServerThreadsPerModel above. ++# Also, change their values if the devices you want to use are not 0 and 1 and 2. ++# cudaDeviceToUseThread0 = 0 ++# cudaDeviceToUseThread1 = 1 ++# cudaDeviceToUseThread2 = 2 ++ ++# The pattern continues for additional GPUs. ++ ++# KataGo will automatically use FP16 or not based on the compute capability ++# of your NVIDIA GPU. If you want to try to force a particular behavior ++# you can uncomment these lines and change them to "true" or "false". ++# cudaUseFP16 = auto ++# cudaUseNHWC = auto ++ ++# ------------------------------ ++# OpenCL GPU settings ++# ------------------------------ ++# These only apply when using the OpenCL version of KataGo. ++ ++# Uncomment and set to true to tune OpenCL for every board size separately, ++# rather than only the largest possible size. ++# openclReTunePerBoardSize = false ++ ++# For one GPU: optionally uncomment and change this if the best device to use is guessed incorrectly. ++# The default behavior tries to guess the 'best' GPU or device on your system to use, usually it will be a good guess. ++# openclDeviceToUse = 0 ++ ++# For two GPUs: Uncomment these two lines and replace X and Y with the device ids of the devices you want to use. ++# It might NOT be 0 and 1, some computers will have many OpenCL devices. You can see what the devices are when ++# KataGo starts up - it should print or log all the devices it finds. ++# (AND also set numNNServerThreadsPerModel above) ++# openclDeviceToUseThread0 = X ++# openclDeviceToUseThread1 = Y ++ ++# For three GPUs: Uncomment these three lines and replace X and Y and Z with the device ids of the devices you want to use. ++# It might NOT be 0 and 1 and 2, some computers will have many OpenCL devices. You can see what the devices are when ++# KataGo starts up - it should print or log all the devices it finds. ++# (AND also set numNNServerThreadsPerModel above) ++# openclDeviceToUseThread0 = X ++# openclDeviceToUseThread1 = Y ++# openclDeviceToUseThread2 = Z ++ ++# The pattern continues for additional GPUs. ++ ++# KataGo will automatically use FP16 or not based on testing your GPU during ++# tuning. If you want to try to force a particular behavior though you can ++# uncomment this option and change it to "true" or "false". This is a fairly ++# blunt setting - more detailed settings are testable by rerunning the tuner ++# with various arguments (./katago tuner). ++# openclUseFP16 = auto ++ ++# ------------------------------ ++# Eigen-specific settings ++# ------------------------------ ++# These only apply when using the Eigen (pure CPU) version of KataGo. ++ ++# Number of CPU threads for evaluating the neural net on the Eigen backend. ++# ++# Default: numSearchThreads ++# numEigenThreadsPerModel = X ++ ++# =========================================================================== ++# Root move selection and biases ++# =========================================================================== ++# Uncomment and edit any of the below values to change them from their default. ++ ++# If provided, force usage of a specific seed for various random things in ++# the search. The default is to use a random seed. ++# searchRandSeed = hijklmn ++ ++# Temperature for the early game, randomize between chosen moves with ++# this temperature ++# chosenMoveTemperatureEarly = 0.5 ++ ++# Decay temperature for the early game by 0.5 every this many moves, ++# scaled with board size. ++# chosenMoveTemperatureHalflife = 19 ++ ++# At the end of search after the early game, randomize between chosen ++# moves with this temperature ++# chosenMoveTemperature = 0.10 ++ ++# Subtract this many visits from each move prior to applying ++# chosenMoveTemperature (unless all moves have too few visits) to downweight ++# unlikely moves ++# chosenMoveSubtract = 0 ++ ++# The same as chosenMoveSubtract but only prunes moves that fall below ++# the threshold. This setting does not affect chosenMoveSubtract. ++# chosenMovePrune = 1 ++ ++# Number of symmetries to sample (without replacement) and average at the root ++# rootNumSymmetriesToSample = 1 ++ ++# Using LCB for move selection? ++# useLcbForSelection = true ++ ++# How many stdevs a move needs to be better than another for LCB selection ++# lcbStdevs = 5.0 ++ ++# Only use LCB override when a move has this proportion of visits as the ++# top move. ++# minVisitPropForLCB = 0.15 ++ ++# =========================================================================== ++# Internal params ++# =========================================================================== ++# Uncomment and edit any of the below values to change them from their default. ++ ++# Scales the utility of winning/losing ++# winLossUtilityFactor = 1.0 ++ ++# Scales the utility for trying to maximize score ++# staticScoreUtilityFactor = 0.10 ++# dynamicScoreUtilityFactor = 0.30 ++ ++# Adjust dynamic score center this proportion of the way towards zero, ++# capped at a reasonable amount. ++# dynamicScoreCenterZeroWeight = 0.20 ++# dynamicScoreCenterScale = 0.75 ++ ++# The utility of getting a "no result" due to triple ko or other long cycle ++# in non-superko rulesets (-1 to 1) ++# noResultUtilityForWhite = 0.0 ++ ++# The number of wins that a draw counts as, for white. (0 to 1) ++# drawEquivalentWinsForWhite = 0.5 ++ ++# Exploration constant for mcts ++# cpuctExploration = 1.0 ++# cpuctExplorationLog = 0.45 ++ ++# Parameters that control exploring more in volatile positions, exploring ++# less in stable positions. ++# cpuctUtilityStdevPrior = 0.40 ++# cpuctUtilityStdevPriorWeight = 2.0 ++# cpuctUtilityStdevScale = 0.85 ++ ++# FPU reduction constant for mcts ++# fpuReductionMax = 0.2 ++# rootFpuReductionMax = 0.1 ++# fpuParentWeightByVisitedPolicy = true ++ ++# Parameters that control weighting of evals based on the net's own ++# self-reported uncertainty. ++# useUncertainty = true ++# uncertaintyExponent = 1.0 ++# uncertaintyCoeff = 0.25 ++ ++# Explore using optimistic policy ++# rootPolicyOptimism = 0.2 ++# policyOptimism = 1.0 ++ ++# Amount to apply a downweighting of children with very bad values relative ++# to good ones. ++# valueWeightExponent = 0.25 ++ ++# Slight incentive for the bot to behave human-like with regard to passing at ++# the end, filling the dame, not wasting time playing in its own territory, ++# etc., and not play moves that are equivalent in terms of points but a bit ++# more unfriendly to humans. ++# rootEndingBonusPoints = 0.5 ++ ++# Make the bot prune useless moves that are just prolonging the game to ++# avoid losing yet. ++# rootPruneUselessMoves = true ++ ++# Apply bias correction based on local pattern keys ++# subtreeValueBiasFactor = 0.45 ++# subtreeValueBiasWeightExponent = 0.85 ++ ++# Use graph search rather than tree search - identify and share search for ++# transpositions. ++# useGraphSearch = true ++ ++# How much to shard the node table for search synchronization ++# nodeTableShardsPowerOfTwo = 16 ++ ++# How many virtual losses to add when a thread descends through a node ++# numVirtualLossesPerThread = 1 ++ ++# Improve the quality of evals under heavy multithreading ++# useNoisePruning = true ++ ++# =========================================================================== ++# Automatic avoid patterns ++# =========================================================================== ++# The parameters in this section provide a way to bias away from moves that match ++# patterns that this instance of KataGo has played in previous games, by auto-saving ++# moves to a directory and then auto-loading and biasing against them each new game. ++# Uncomment them to use them. When using this feature, all parameters must be specified. ++ ++# Different board sizes are tracked separately, but all board sizes share the same ++# parameters by default. Every parameter *except* for autoAvoidRepeatDir and ++# autoAvoidRepeatSaveChunkSize can be overridden per board size, ++# e.g. "autoAvoidRepeatMinTurnNumber13x13". You must ALSO specify the ++# defaults even if you have specified board-size-specific values. ++ ++# Directory to auto-save moves KataGo plays, to avoid them in future games. ++# You can create a new empty directory and put its path here. ++# If you run parallel instances of KataGo, use different directories if you ++# want them not to share their biases, use the same directory if you want ++# all of them to bias away from past moves that any of them have played. ++# KataGo will also automatically DELETE old data in this directory, so it is ++# recommended that if you do share the same directory between parallel instances, ++# that they all use the same settings that affect data saving/deletion. ++# autoAvoidRepeatDir = PATH_TO_NEW_DIRECTORY ++ ++# Penalize this much utility per matching move. ++# Values that are too large may lead to bad play. The value of 0.004 is fairly large ++# and might be large enough to result in some early weird/bad moves when trying ++# to avoid past games' moves if enough games begin the same way. You can experiment with it. ++# autoAvoidRepeatUtility = 0.004 ++ ++# Per each new move saved, exponentially decay prior saved moves by this factor. ++# This way, the bias against moves from many games ago is gradually phased out. ++# For example, 0.9995 = 1 - 1/2000, so would be roughly 2000 prior moves worth of ++# penalty weight remembered, in steady state. Depending on what turn number range ++# you are saving, this might equate to a different number of games For example ++# saving the first 50 moves per game would make this roughly 2000 / 50 = 40 games ++# worth of memory. ++# autoAvoidRepeatLambda = 0.9995 ++ ++# Affects data saving/deletion. ++# When the number of saved moves exceeds this, outright delete them to avoid too many ++# files and disk space building up. Also may affect the speed of saving/loading on start ++# of each game if this is set large and a lot of data builds up. ++# autoAvoidRepeatMaxPoses = 10000 ++ ++# Affects data saving/deletion. ++# Only save data for moves within this turn number range of those games. ++# E.g. setting autoAvoidRepeatMinTurnNumber to a number like 4 or 5 would tend to make ++# KataGo not develop a bias against the initial 3-4 and 4-4 corner moves in almost every game. ++# autoAvoidRepeatMinTurnNumber = 0 ++# autoAvoidRepeatMaxTurnNumber = 50 ++ ++# Affects data saving/deletion. ++# Within a single run of a program, wait to accumulate this many samples ++# (possibly across multiple clear_boards/games) before saving the data. ++# Can help to avoid writing too many small files to disk, especially when GTP is used ++# in a way that clears the board very frequently (e.g. gtp2ogs pooled manager). ++# autoAvoidRepeatSaveChunkSize = 200 ++ ++# =========================================================================== ++# Avoid SGF patterns ++# =========================================================================== ++# The parameters in this section provide a way to avoid moves that follow ++# specific patterns based on a set of SGF files loaded upon startup. ++# This is basically the same as the above "Automatic avoid patterns" section ++# above except you supply your own SGF files to avoid moves from. ++# Uncomment them to use this feature. Additionally, if the SGF file ++# contains the string %SKIP% in a comment on a move, that move will be ++# ignored for this purpose. ++ ++# Load SGF files from this directory when the engine is started ++# (only on startup, will not reload unless engine is restarted) ++# avoidSgfPatternDirs = path/to/directory/with/sgfs/ ++# You can also surround the file path in double quotes if the file path contains trailing spaces or hash signs. ++# Within double quotes, backslashes are escape characters. ++# avoidSgfPatternDirs = "path/to/directory/with/sgfs/" ++ ++# Penalize this much utility per matching move. ++# Set this negative if you instead want to favor SGF patterns instead of ++# penalizing them. This number does not need to be large, even 0.001 will ++# make a difference. Values that are too large may lead to bad play. ++# avoidSgfPatternUtility = 0.001 ++ ++# Optional - load only the newest this many files ++# avoidSgfPatternMaxFiles = 20 ++ ++# Optional - Penalty is multiplied by this per each older SGF file, so that ++# old SGF files matter less than newer ones. ++# avoidSgfPatternLambda = 0.90 ++ ++# Optional - pay attention only to moves made by players with this name. ++# For example, set it to the name that your bot's past games will show up ++# as in the SGF, so that the bot will only avoid repeating moves that itself ++# made in past games, not the moves that its opponents made. ++# avoidSgfPatternAllowedNames = my-ogs-bot-name1,my-ogs-bot-name2 ++ ++# Optional - Ignore moves in SGF files that occurred before this turn number. ++# avoidSgfPatternMinTurnNumber = 0 ++ ++# For more avoid patterns: ++# You can also specify a second set of parameters, and a third, fourth, ++# etc. by numbering 2,3,4,... ++# ++# avoidSgf2PatternDirs = ... ++# avoidSgf2PatternUtility = ... ++# avoidSgf2PatternMaxFiles = ... ++# avoidSgf2PatternLambda = ... ++# avoidSgf2PatternAllowedNames = ... ++# avoidSgf2PatternMinTurnNumber = ... ++ +diff --git a/python/selfplay/libcrypto-3-x64.dll b/python/selfplay/libcrypto-3-x64.dll +new file mode 100644 +index 00000000..cac7f4ec +Binary files /dev/null and b/python/selfplay/libcrypto-3-x64.dll differ +diff --git a/python/selfplay/libssl-3-x64.dll b/python/selfplay/libssl-3-x64.dll +new file mode 100644 +index 00000000..8ad0908e +Binary files /dev/null and b/python/selfplay/libssl-3-x64.dll differ +diff --git a/python/selfplay/libz.dll b/python/selfplay/libz.dll +new file mode 100644 +index 00000000..930a0421 +Binary files /dev/null and b/python/selfplay/libz.dll differ +diff --git a/python/selfplay/libzip.dll b/python/selfplay/libzip.dll +new file mode 100644 +index 00000000..167e69f7 +Binary files /dev/null and b/python/selfplay/libzip.dll differ +diff --git a/python/selfplay/lizgoban.cfg b/python/selfplay/lizgoban.cfg +new file mode 100644 +index 00000000..53f4832b +--- /dev/null ++++ b/python/selfplay/lizgoban.cfg +@@ -0,0 +1,721 @@ ++# Configuration for KataGo C++ GTP engine ++ ++# Run the program using: `./katago.exe gtp` ++ ++# In this example config, when a parameter is given as a commented out value, ++# that value also is the default value, unless described otherwise. You can ++# uncomment it (remove the pound sign) and change it if you want. ++ ++# =========================================================================== ++# Running on an online server or in a real tournament or match ++# =========================================================================== ++# If you plan to run online or in a tournament, read through the "Rules" ++# section below for proper handling of komi, handicaps, end-of-game cleanup, ++# and other details. ++ ++# =========================================================================== ++# Notes about performance and memory usage ++# =========================================================================== ++# Important: For good performance, you will very likely want to tune the ++# "numSearchThreads" parameter in the Search limits section below! Run ++# "./katago benchmark" to test KataGo and to suggest a reasonable value ++# of this parameter. ++ ++# For multi-GPU systems, read "OpenCL GPU settings" or "CUDA GPU settings". ++# ++# When using OpenCL, verify that KataGo picks the correct device! Some systems ++# may have both an Intel CPU OpenCL and GPU OpenCL. If # KataGo picks the wrong ++# one, correct this by specifying "openclGpuToUse". ++# ++# Consider adjusting "maxVisits", "ponderingEnabled", "resignThreshold", and ++# other parameters depending on your intended usage. ++ ++# =========================================================================== ++# Command-line usage ++# =========================================================================== ++# All of the below values may be set or overridden via command-line arguments: ++# ++# -override-config KEY=VALUE,KEY=VALUE,... ++ ++# =========================================================================== ++# Logs and files ++# =========================================================================== ++# This section defines where and what logging information is produced. ++ ++# Each run of KataGo will log to a separate file in this dir. ++# This is the default. ++logDir = gtp_logs ++# Uncomment and specify this instead of logDir to write separate dated subdirs ++# logDirDated = gtp_logs ++# Uncomment and specify this instead of logDir to log to only a single file ++# logFile = gtp.log ++ ++# Logging options ++logAllGTPCommunication = true ++logSearchInfo = true ++logSearchInfoForChosenMove = false ++logToStderr = true ++ ++# KataGo will display some info to stderr on GTP startup ++# Uncomment the next line and set it to false to suppress that and remain silent ++# startupPrintMessageToStderr = true ++ ++# Write information to stderr, for use in things like malkovich chat to OGS. ++ ogsChatToStderr = true ++ ++# Uncomment and set this to a directory to override where openCLTuner files ++# and other cached data is written. By default it saves into a subdir of the ++# current directory on windows, and a subdir of ~/.katago on Linux. ++# homeDataDir = PATH_TO_DIRECTORY ++ ++# =========================================================================== ++# Analysis ++# =========================================================================== ++# This section configures analysis settings. ++# ++# The maximum number of moves after the first move displayed in variations ++# from analysis commands like kata-analyze or lz-analyze. ++# analysisPVLen = 15 ++ ++# Report winrates for chat and analysis as (BLACK|WHITE|SIDETOMOVE). ++# Most GUIs and analysis tools will expect SIDETOMOVE. ++# reportAnalysisWinratesAs = SIDETOMOVE ++ ++# Extra noise for wider exploration. Large values will force KataGo to ++# analyze a greater variety of moves than it normally would. ++# An extreme value like 1 distributes playouts across every move on the board, ++# even very bad moves. ++# Affects analysis only, does not affect play. ++# analysisWideRootNoise = 0.04 ++ ++# Try to limit the effect of possible bad or bogus move sequences in the ++# history leading to this position from affecting KataGo's move predictions. ++# analysisIgnorePreRootHistory = true ++ ++# =========================================================================== ++# Rules ++# =========================================================================== ++# This section configures the scoring and playing rules. Rules can also be ++# changed mid-run by issuing custom GTP commands. ++# ++# See https://lightvector.github.io/KataGo/rules.html for rules details. ++# ++# See https://github.com/lightvector/KataGo/blob/master/docs/GTP_Extensions.md ++# for GTP commands. ++ ++# Specify the rules as a string. ++# Some legal values include: ++# chinese, japanese, korean, aga, chinese-ogs, new-zealand, stone-scoring, ++# ancient-territory, bga, aga-button ++# ++# For some human rulesets that require complex adjudication in tricky cases ++# (e.g. japanese, korean) KataGo may not precisely match the ruleset in such ++# cases but will do its best. ++rules = tromp-taylor ++ ++# By default, the "rules" parameter is used, but if you comment it out and ++# uncomment one option in each of the sections below, you can specify an ++# arbitrary combination of individual rules. ++ ++# koRule = SIMPLE # Simple ko rules (triple ko = no result) ++# koRule = POSITIONAL # Positional superko ++# koRule = SITUATIONAL # Situational superko ++ ++# scoringRule = AREA # Area scoring ++# scoringRule = TERRITORY # Territory scoring (special computer-friendly territory rules) ++ ++# taxRule = NONE # All surrounded empty points are scored ++# taxRule = SEKI # Eyes in seki do NOT count as points ++# taxRule = ALL # All groups are taxed up to 2 points for the two eyes needed to live ++ ++# Is multiple-stone suicide legal? (Single-stone suicide is always illegal). ++# multiStoneSuicideLegal = false ++# multiStoneSuicideLegal = true ++ ++# "Button go" - the first pass when area scoring awards 0.5 points and does ++# not count for ending the game. ++# Allows area scoring rulesets that have far simpler rules to achieve the same ++# final scoring precision and reward for precise play as territory scoring. ++# hasButton = false ++# hasButton = true ++ ++# Is this a human ruleset where it's okay to pass before having physically ++# captured and removed all dead stones? ++# friendlyPassOk = false ++# friendlyPassOk = true ++ ++# How handicap stones in handicap games are compensated ++# whiteHandicapBonus = 0 # White gets no compensation for black's handicap stones (Tromp-taylor, NZ, JP) ++# whiteHandicapBonus = N-1 # White gets N-1 points for black's N handicap stones (AGA) ++# whiteHandicapBonus = N # White gets N points for black's N handicap stones (Chinese) ++ ++# ------------------------------ ++# Other rules hacks ++# ------------------------------ ++# Uncomment and change to adjust what board size KataGo uses upon startup ++# by default when GTP doesn't specify. ++# defaultBoardSize = 19 ++ ++# By default, Katago will use the komi that the GUI or GTP controller tries to set. ++# Uncomment and set this to have KataGo ignore the controller and always use this komi. ++# ignoreGTPAndForceKomi = 7 ++ ++# =========================================================================== ++# Bot behavior ++# =========================================================================== ++ ++# ------------------------------ ++# Resignation ++# ------------------------------ ++ ++# Resignation occurs if for at least resignConsecTurns in a row, the ++# winLossUtility (on a [-1,1] scale) is below resignThreshold. ++allowResignation = true ++resignThreshold = -0.90 ++resignConsecTurns = 3 ++ ++# By default, KataGo may resign games that it is confidently losing even if they ++# are very close in score. Uncomment and set this to avoid resigning games ++# if the estimated difference is points is less than or equal to this. ++# resignMinScoreDifference = 10 ++ ++# ------------------------------ ++# Handicap ++# ------------------------------ ++# Assume that if black makes many moves in a row right at the start of the ++# game, then the game is a handicap game. This is necessary on some servers ++# and for some GUIs and also when initializing from many SGF files, which may ++# set up a handicap game using repeated GTP "play" commands for black rather ++# than GTP "place_free_handicap" commands; however, it may also lead to ++# incorrect understanding of komi if whiteHandicapBonus is used and a server ++# does not have such a practice. Uncomment and set to false to disable. ++# assumeMultipleStartingBlackMovesAreHandicap = true ++ ++# Makes katago dynamically adjust in handicap or altered-komi games to assume ++# based on those game settings that it must be stronger or weaker than the ++# opponent and to play accordingly. Greatly improves handicap strength by ++# biasing winrates and scores to favor appropriate safe/aggressive play. ++# Does NOT affect analysis (lz-analyze, kata-analyze, used by programs like ++# Lizzie) so analysis remains unbiased. Uncomment and set this to 0 to disable ++# this and make KataGo play the same always. ++# dynamicPlayoutDoublingAdvantageCapPerOppLead = 0.045 ++ ++# Instead of "dynamicPlayoutDoublingAdvantageCapPerOppLead", you can comment ++# that out and uncomment and set "playoutDoublingAdvantage" to a fixed value ++# from -3.0 to 3.0 that will not change dynamically. ++# ALSO affects analysis tools (lz-analyze, kata-analyze, used by e.g. Lizzie). ++# Negative makes KataGo behave as if it is much weaker than the opponent. ++# Positive makes KataGo behave as if it is much stronger than the opponent. ++# KataGo will adjust to favor safe/aggressive play as appropriate based on ++# the combination of who is ahead and how much stronger/weaker it thinks it is, ++# and report winrates and scores taking the strength difference into account. ++# ++# If this and "dynamicPlayoutDoublingAdvantageCapPerOppLead" are both set ++# then dynamic will be used for all games and this fixed value will be used ++# for analysis tools. ++# playoutDoublingAdvantage = 0.0 ++ ++# Uncomment one of these when using "playoutDoublingAdvantage" to enforce ++# that it will only apply when KataGo plays as the specified color and will be ++# negated when playing as the opposite color. ++# playoutDoublingAdvantagePla = BLACK ++# playoutDoublingAdvantagePla = WHITE ++ ++# ------------------------------ ++# Passing and cleanup ++# ------------------------------ ++# Make the bot never assume that its pass will end the game, even if passing ++# would end and "win" under Tromp-Taylor rules. Usually this is a good idea ++# when using it for analysis or playing on servers where scoring may be ++# implemented non-tromp-taylorly. Uncomment and set to false to disable. ++# conservativePass = true ++ ++# When using territory scoring, self-play games continue beyond two passes ++# with special cleanup rules that may be confusing for human players. This ++# option prevents the special cleanup phases from being reachable when using ++# the bot for GTP play. Uncomment and set to false to enable entering special ++# cleanup. For example, if you are testing it against itself, or against ++# another bot that has precisely implemented the rules documented at ++# https://lightvector.github.io/KataGo/rules.html ++# preventCleanupPhase = true ++ ++# ------------------------------ ++# Miscellaneous behavior ++# ------------------------------ ++# If the board is symmetric, search only one copy of each equivalent move. ++# Attempts to also account for ko/superko, will not theoretically perfect for ++# superko. Uncomment and set to false to disable. ++# rootSymmetryPruning = true ++ ++# Uncomment and set to true to avoid a particular joseki that some networks ++# misevaluate, and also to improve opening diversity versus some particular ++# other bots that like to play it all the time. ++# avoidMYTDaggerHack = false ++ ++# Prefer to avoid playing the same joseki in every corner of the board. ++# Uncomment to set to a specific value. See "Avoid SGF patterns" section. ++# By default: 0 (even games), 0.005 (handicap games) ++# avoidRepeatedPatternUtility = 0.0 ++ ++# Experimental logic to fight against mirror Go even with unfavorable komi. ++# Uncomment to set to a specific value to use for both playing and analysis. ++# By default: true when playing via GTP, but false when analyzing. ++# antiMirror = true ++ ++# Enable some hacks that mitigate rare instances when passing messes up deeper searches. ++# enablePassingHacks = true ++ ++# Uncomment and set this to true to prevent bad or bogus move sequences ++# in the history leading to this position from affecting KataGo's move choices. ++# Same as analysisIgnorePreRootHistory (see above) but applies to actual play. ++# You can enable this if KataGo is being asked to play from positions that it did not ++# choose the moves to reach. ++# ignorePreRootHistory = false ++ ++# =========================================================================== ++# Search limits ++# =========================================================================== ++ ++# Terminology: ++# "Playouts" is the number of new playouts of search performed each turn. ++# "Visits" is the same as "Playouts" but also counts search performed on ++# previous turns that is still applicable to this turn. ++# "Time" is the time in seconds. ++ ++# For example, if KataGo searched 200 nodes on the previous turn, and then ++# after the opponent's reply, 50 nodes of its search tree was still valid, ++# then a visit limit of 200 would allow KataGo to search 150 new nodes ++# (for a final tree size of 200 nodes), whereas a playout limit of of 200 ++# would allow KataGo to search 200 nodes (for a final tree size of 250 nodes). ++ ++# Additionally, KataGo may also move before than the limit in order to ++# obey time controls (e.g. byo-yomi, etc) if the GTP controller has ++# told KataGo that the game has is being played with a given time control. ++ ++# Limits for search on the current turn. ++# If commented out or unspecified, the default is to have no limit. ++# maxVisits = 500 ++# maxPlayouts = 300 ++ maxTime = 0.5 ++ ++# Ponder on the opponent's turn? ++ponderingEnabled = true ++ ++# Limits for search when pondering on the opponent's turn. ++# If commented out or unspecified, the default is to have no limit. ++# Limiting the maximum time is recommended so that KataGo won't burn CPU/GPU ++# forever and/or run out of RAM if left unattended while pondering is enabled. ++# maxVisitsPondering = 5000 ++# maxPlayoutsPondering = 3000 ++maxTimePondering = 60.0 ++ ++ ++# ------------------------------ ++# Other search limits and behavior ++# ------------------------------ ++ ++# Approx number of seconds to buffer for lag for GTP time controls - will ++# move a bit faster assuming there is this much lag per move. ++lagBuffer = 1.0 ++ ++# YOU PROBABLY WANT TO TUNE THIS PARAMETER! ++# The number of threads to use when searching. On powerful GPUs the optimal ++# threads may be much higher than the number of CPU cores you have because ++# many threads are needed to feed efficient large batches to the GPU. ++# ++# Run "./katago benchmark" to tune this parameter and test the effect ++# of changes to any of other parameters. ++numSearchThreads = 16 ++ ++# Play a little faster if the opponent is passing, for human-friendliness. ++# Comment these out to disable them, such as if running a controlled match ++# where you are testing KataGo with fixed compute per move vs other bots. ++searchFactorAfterOnePass = 0.50 ++searchFactorAfterTwoPass = 0.25 ++ ++# Play a little faster if super-winning, for human-friendliness. ++# Comment these out to disable them, such as if running a controlled match ++# where you are testing KataGo with fixed compute per move vs other bots. ++searchFactorWhenWinning = 0.40 ++searchFactorWhenWinningThreshold = 0.95 ++ ++# =========================================================================== ++# GPU settings ++# =========================================================================== ++# This section configures GPU settings. ++# ++# Maximum number of positions to send to a single GPU at once. The default ++# value is roughly equal to numSearchThreads, but can be specified manually ++# if running out of memory, or using multiple GPUs that expect to share work. ++# nnMaxBatchSize = ++ ++# Controls the neural network cache size, which is the primary RAM/memory use. ++# KataGo will cache up to (2 ** nnCacheSizePowerOfTwo) many neural net ++# evaluations in case of transpositions in the tree. ++# Increase this to improve performance for searches with tens of thousands ++# of visits or more. Decrease this to limit memory usage. ++# If you're happy to do some math - each neural net entry takes roughly ++# 1.5KB, except when using whole-board ownership/territory ++# visualizations, where each entry will take roughly 3KB. The number of ++# entries is (2 ** nnCacheSizePowerOfTwo). (E.g. 2 ** 18 = 262144.) ++# You can compute roughly how much memory the cache will use based on this. ++# nnCacheSizePowerOfTwo = 20 ++ ++# Size of mutex pool for nnCache is (2 ** this). ++# nnMutexPoolSizePowerOfTwo = 16 ++ ++# Randomize board orientation when running neural net evals? Uncomment and ++# set to false to disable. ++# nnRandomize = true ++ ++# If provided, force usage of a specific seed for nnRandomize. ++# The default is to use a randomly generated seed. ++# nnRandSeed = abcdefg ++ ++# Uncomment and set to true to force GTP to use the maximum board size for ++# internal buffers for the neural net. This will make KataGo slower when ++# evaluating small boards, but will avoid a lengthy initialization time on every ++# change of board size due to having to re-size the neural net buffers on the GPU. ++# This can be useful for example, for OGS's persistent bot mode that uses a single ++# bot instance to handle multiple games and may thrash between different board sizes ++# if there are concurrent games of multiple sizes. ++# gtpForceMaxNNSize = false ++ ++# ------------------------------ ++# Multiple GPUs ++# ------------------------------ ++# Set this to the number of GPUs to use or that are available. ++# IMPORTANT: If more than 1, also uncomment the appropriate TensorRT ++# or CUDA or OpenCL section. ++# numNNServerThreadsPerModel = 1 ++ ++# ------------------------------ ++# TENSORRT GPU settings ++# ------------------------------ ++# These only apply when using the TENSORRT version of KataGo. ++ ++# For one GPU: optionally uncomment this option and change if the GPU to ++# use is not device 0. ++# trtDeviceToUse = 0 ++ ++# For two GPUs: Uncomment these options, AND set numNNServerThreadsPerModel above. ++# Also, change their values if the devices you want to use are not 0 and 1. ++# trtDeviceToUseThread0 = 0 ++# trtDeviceToUseThread1 = 1 ++ ++# For three GPUs: Uncomment these options, AND set numNNServerThreadsPerModel above. ++# Also, change their values if the devices you want to use are not 0 and 1 and 2. ++# trtDeviceToUseThread0 = 0 ++# trtDeviceToUseThread1 = 1 ++# trtDeviceToUseThread2 = 2 ++ ++# The pattern continues for additional GPUs. ++ ++# ------------------------------ ++# CUDA GPU settings ++# ------------------------------ ++# These only apply when using the CUDA version of KataGo. ++ ++# For one GPU: optionally uncomment and change this if the GPU you want to ++# use is not device 0 ++# cudaDeviceToUse = 0 ++ ++# For two GPUs: Uncomment these options, AND set numNNServerThreadsPerModel above. ++# Also, change their values if the devices you want to use are not 0 and 1. ++# cudaDeviceToUseThread0 = 0 ++# cudaDeviceToUseThread1 = 1 ++ ++# For three GPUs: Uncomment these options, AND set numNNServerThreadsPerModel above. ++# Also, change their values if the devices you want to use are not 0 and 1 and 2. ++# cudaDeviceToUseThread0 = 0 ++# cudaDeviceToUseThread1 = 1 ++# cudaDeviceToUseThread2 = 2 ++ ++# The pattern continues for additional GPUs. ++ ++# KataGo will automatically use FP16 or not based on the compute capability ++# of your NVIDIA GPU. If you want to try to force a particular behavior ++# you can uncomment these lines and change them to "true" or "false". ++# cudaUseFP16 = auto ++# cudaUseNHWC = auto ++ ++# ------------------------------ ++# OpenCL GPU settings ++# ------------------------------ ++# These only apply when using the OpenCL version of KataGo. ++ ++# Uncomment and set to true to tune OpenCL for every board size separately, ++# rather than only the largest possible size. ++# openclReTunePerBoardSize = false ++ ++# For one GPU: optionally uncomment and change this if the best device to use is guessed incorrectly. ++# The default behavior tries to guess the 'best' GPU or device on your system to use, usually it will be a good guess. ++# openclDeviceToUse = 0 ++ ++# For two GPUs: Uncomment these two lines and replace X and Y with the device ids of the devices you want to use. ++# It might NOT be 0 and 1, some computers will have many OpenCL devices. You can see what the devices are when ++# KataGo starts up - it should print or log all the devices it finds. ++# (AND also set numNNServerThreadsPerModel above) ++# openclDeviceToUseThread0 = X ++# openclDeviceToUseThread1 = Y ++ ++# For three GPUs: Uncomment these three lines and replace X and Y and Z with the device ids of the devices you want to use. ++# It might NOT be 0 and 1 and 2, some computers will have many OpenCL devices. You can see what the devices are when ++# KataGo starts up - it should print or log all the devices it finds. ++# (AND also set numNNServerThreadsPerModel above) ++# openclDeviceToUseThread0 = X ++# openclDeviceToUseThread1 = Y ++# openclDeviceToUseThread2 = Z ++ ++# The pattern continues for additional GPUs. ++ ++# KataGo will automatically use FP16 or not based on testing your GPU during ++# tuning. If you want to try to force a particular behavior though you can ++# uncomment this option and change it to "true" or "false". This is a fairly ++# blunt setting - more detailed settings are testable by rerunning the tuner ++# with various arguments (./katago tuner). ++# openclUseFP16 = auto ++ ++# ------------------------------ ++# Eigen-specific settings ++# ------------------------------ ++# These only apply when using the Eigen (pure CPU) version of KataGo. ++ ++# Number of CPU threads for evaluating the neural net on the Eigen backend. ++# ++# Default: numSearchThreads ++# numEigenThreadsPerModel = X ++ ++# =========================================================================== ++# Root move selection and biases ++# =========================================================================== ++# Uncomment and edit any of the below values to change them from their default. ++ ++# If provided, force usage of a specific seed for various random things in ++# the search. The default is to use a random seed. ++# searchRandSeed = hijklmn ++ ++# Temperature for the early game, randomize between chosen moves with ++# this temperature ++# chosenMoveTemperatureEarly = 0.5 ++ ++# Decay temperature for the early game by 0.5 every this many moves, ++# scaled with board size. ++# chosenMoveTemperatureHalflife = 19 ++ ++# At the end of search after the early game, randomize between chosen ++# moves with this temperature ++# chosenMoveTemperature = 0.10 ++ ++# Subtract this many visits from each move prior to applying ++# chosenMoveTemperature (unless all moves have too few visits) to downweight ++# unlikely moves ++# chosenMoveSubtract = 0 ++ ++# The same as chosenMoveSubtract but only prunes moves that fall below ++# the threshold. This setting does not affect chosenMoveSubtract. ++# chosenMovePrune = 1 ++ ++# Number of symmetries to sample (without replacement) and average at the root ++# rootNumSymmetriesToSample = 1 ++ ++# Using LCB for move selection? ++# useLcbForSelection = true ++ ++# How many stdevs a move needs to be better than another for LCB selection ++# lcbStdevs = 5.0 ++ ++# Only use LCB override when a move has this proportion of visits as the ++# top move. ++# minVisitPropForLCB = 0.15 ++ ++# =========================================================================== ++# Internal params ++# =========================================================================== ++# Uncomment and edit any of the below values to change them from their default. ++ ++# Scales the utility of winning/losing ++# winLossUtilityFactor = 1.0 ++ ++# Scales the utility for trying to maximize score ++# staticScoreUtilityFactor = 0.10 ++# dynamicScoreUtilityFactor = 0.30 ++ ++# Adjust dynamic score center this proportion of the way towards zero, ++# capped at a reasonable amount. ++# dynamicScoreCenterZeroWeight = 0.20 ++# dynamicScoreCenterScale = 0.75 ++ ++# The utility of getting a "no result" due to triple ko or other long cycle ++# in non-superko rulesets (-1 to 1) ++# noResultUtilityForWhite = 0.0 ++ ++# The number of wins that a draw counts as, for white. (0 to 1) ++# drawEquivalentWinsForWhite = 0.5 ++ ++# Exploration constant for mcts ++# cpuctExploration = 1.0 ++# cpuctExplorationLog = 0.45 ++ ++# Parameters that control exploring more in volatile positions, exploring ++# less in stable positions. ++# cpuctUtilityStdevPrior = 0.40 ++# cpuctUtilityStdevPriorWeight = 2.0 ++# cpuctUtilityStdevScale = 0.85 ++ ++# FPU reduction constant for mcts ++# fpuReductionMax = 0.2 ++# rootFpuReductionMax = 0.1 ++# fpuParentWeightByVisitedPolicy = true ++ ++# Parameters that control weighting of evals based on the net's own ++# self-reported uncertainty. ++# useUncertainty = true ++# uncertaintyExponent = 1.0 ++# uncertaintyCoeff = 0.25 ++ ++# Explore using optimistic policy ++# rootPolicyOptimism = 0.2 ++# policyOptimism = 1.0 ++ ++# Amount to apply a downweighting of children with very bad values relative ++# to good ones. ++# valueWeightExponent = 0.25 ++ ++# Slight incentive for the bot to behave human-like with regard to passing at ++# the end, filling the dame, not wasting time playing in its own territory, ++# etc., and not play moves that are equivalent in terms of points but a bit ++# more unfriendly to humans. ++# rootEndingBonusPoints = 0.5 ++ ++# Make the bot prune useless moves that are just prolonging the game to ++# avoid losing yet. ++# rootPruneUselessMoves = true ++ ++# Apply bias correction based on local pattern keys ++# subtreeValueBiasFactor = 0.45 ++# subtreeValueBiasWeightExponent = 0.85 ++ ++# Use graph search rather than tree search - identify and share search for ++# transpositions. ++# useGraphSearch = true ++ ++# How much to shard the node table for search synchronization ++# nodeTableShardsPowerOfTwo = 16 ++ ++# How many virtual losses to add when a thread descends through a node ++# numVirtualLossesPerThread = 1 ++ ++# Improve the quality of evals under heavy multithreading ++# useNoisePruning = true ++ ++# =========================================================================== ++# Automatic avoid patterns ++# =========================================================================== ++# The parameters in this section provide a way to bias away from moves that match ++# patterns that this instance of KataGo has played in previous games, by auto-saving ++# moves to a directory and then auto-loading and biasing against them each new game. ++# Uncomment them to use them. When using this feature, all parameters must be specified. ++ ++# Different board sizes are tracked separately, but all board sizes share the same ++# parameters by default. Every parameter *except* for autoAvoidRepeatDir and ++# autoAvoidRepeatSaveChunkSize can be overridden per board size, ++# e.g. "autoAvoidRepeatMinTurnNumber13x13". You must ALSO specify the ++# defaults even if you have specified board-size-specific values. ++ ++# Directory to auto-save moves KataGo plays, to avoid them in future games. ++# You can create a new empty directory and put its path here. ++# If you run parallel instances of KataGo, use different directories if you ++# want them not to share their biases, use the same directory if you want ++# all of them to bias away from past moves that any of them have played. ++# KataGo will also automatically DELETE old data in this directory, so it is ++# recommended that if you do share the same directory between parallel instances, ++# that they all use the same settings that affect data saving/deletion. ++# autoAvoidRepeatDir = PATH_TO_NEW_DIRECTORY ++ ++# Penalize this much utility per matching move. ++# Values that are too large may lead to bad play. The value of 0.004 is fairly large ++# and might be large enough to result in some early weird/bad moves when trying ++# to avoid past games' moves if enough games begin the same way. You can experiment with it. ++# autoAvoidRepeatUtility = 0.004 ++ ++# Per each new move saved, exponentially decay prior saved moves by this factor. ++# This way, the bias against moves from many games ago is gradually phased out. ++# For example, 0.9995 = 1 - 1/2000, so would be roughly 2000 prior moves worth of ++# penalty weight remembered, in steady state. Depending on what turn number range ++# you are saving, this might equate to a different number of games For example ++# saving the first 50 moves per game would make this roughly 2000 / 50 = 40 games ++# worth of memory. ++# autoAvoidRepeatLambda = 0.9995 ++ ++# Affects data saving/deletion. ++# When the number of saved moves exceeds this, outright delete them to avoid too many ++# files and disk space building up. Also may affect the speed of saving/loading on start ++# of each game if this is set large and a lot of data builds up. ++# autoAvoidRepeatMaxPoses = 10000 ++ ++# Affects data saving/deletion. ++# Only save data for moves within this turn number range of those games. ++# E.g. setting autoAvoidRepeatMinTurnNumber to a number like 4 or 5 would tend to make ++# KataGo not develop a bias against the initial 3-4 and 4-4 corner moves in almost every game. ++# autoAvoidRepeatMinTurnNumber = 0 ++# autoAvoidRepeatMaxTurnNumber = 50 ++ ++# Affects data saving/deletion. ++# Within a single run of a program, wait to accumulate this many samples ++# (possibly across multiple clear_boards/games) before saving the data. ++# Can help to avoid writing too many small files to disk, especially when GTP is used ++# in a way that clears the board very frequently (e.g. gtp2ogs pooled manager). ++# autoAvoidRepeatSaveChunkSize = 200 ++ ++# =========================================================================== ++# Avoid SGF patterns ++# =========================================================================== ++# The parameters in this section provide a way to avoid moves that follow ++# specific patterns based on a set of SGF files loaded upon startup. ++# This is basically the same as the above "Automatic avoid patterns" section ++# above except you supply your own SGF files to avoid moves from. ++# Uncomment them to use this feature. Additionally, if the SGF file ++# contains the string %SKIP% in a comment on a move, that move will be ++# ignored for this purpose. ++ ++# Load SGF files from this directory when the engine is started ++# (only on startup, will not reload unless engine is restarted) ++# avoidSgfPatternDirs = path/to/directory/with/sgfs/ ++# You can also surround the file path in double quotes if the file path contains trailing spaces or hash signs. ++# Within double quotes, backslashes are escape characters. ++# avoidSgfPatternDirs = "path/to/directory/with/sgfs/" ++ ++# Penalize this much utility per matching move. ++# Set this negative if you instead want to favor SGF patterns instead of ++# penalizing them. This number does not need to be large, even 0.001 will ++# make a difference. Values that are too large may lead to bad play. ++# avoidSgfPatternUtility = 0.001 ++ ++# Optional - load only the newest this many files ++# avoidSgfPatternMaxFiles = 20 ++ ++# Optional - Penalty is multiplied by this per each older SGF file, so that ++# old SGF files matter less than newer ones. ++# avoidSgfPatternLambda = 0.90 ++ ++# Optional - pay attention only to moves made by players with this name. ++# For example, set it to the name that your bot's past games will show up ++# as in the SGF, so that the bot will only avoid repeating moves that itself ++# made in past games, not the moves that its opponents made. ++# avoidSgfPatternAllowedNames = my-ogs-bot-name1,my-ogs-bot-name2 ++ ++# Optional - Ignore moves in SGF files that occurred before this turn number. ++# avoidSgfPatternMinTurnNumber = 0 ++ ++# For more avoid patterns: ++# You can also specify a second set of parameters, and a third, fourth, ++# etc. by numbering 2,3,4,... ++# ++# avoidSgf2PatternDirs = ... ++# avoidSgf2PatternUtility = ... ++# avoidSgf2PatternMaxFiles = ... ++# avoidSgf2PatternLambda = ... ++# avoidSgf2PatternAllowedNames = ... ++# avoidSgf2PatternMinTurnNumber = ... ++ +diff --git a/python/selfplay/match_example.cfg b/python/selfplay/match_example.cfg +new file mode 100644 +index 00000000..945ac44e +--- /dev/null ++++ b/python/selfplay/match_example.cfg +@@ -0,0 +1,228 @@ ++# Example config for C++ match runner ++# This is an example template config for the "match" subcommand of KataGo. e.g: ++# ./katago match -config-file configs/match_example.cfg -log-file match.log -sgf-output-dir match_sgfs/ ++# ++# On a good GPU, the match subcommand enables testing of KataGo nets against each other ++# or a net against itself with different parameters vastly faster than anything ++# else, because multiple games can share GPU batching. ++# ++# Beware however of using this to test differing numbers of threads or test time-based search limits. ++# Because many games will be run simultaneously, they will compete from each other for compute power, ++# and although the total will run much faster than if you had run them one by one, you might also get ++# substantially different results not reflective of the strength of a configuration when run in a real ++# match setting, on a machine by itself. For fixed numbers of visits or playouts instead of fixed time, ++# and with numSearchThreads = 1, there should be no problem though, because then the compute time has ++# no influence on the result of the computation. ++# ++# See gtp config for descriptions of most of these params. ++# ++# For almost any parameter in this config that is related to a bot, rather than to the match as a whole ++# (so, visits, search parameters, model files, etc. but NOT the rules, max games, log info, etc) ++# you can specify them differentially between different bots by appending the index of the bot. ++# For example, if you were testing different numbers of visits, you could try: ++# ++# numBots = 3 ++# botName0 = lowVisits ++# botName1 = midVisits ++# botName2 = highVisits ++# ++# maxVisits0 = 100 ++# maxVisits1 = 300 ++# maxVisits2 = 1000 ++# ++# Or, if you were testing different neural nets, with different search configurations, you could do: ++# ++# nnModelFile0 = path/to/first/model/file.bin.gz ++# nnModelFile1 = path/to/second/model/file.bin.gz ++# ++# And specify different search parameters for them if you wanted: ++# cpuctExploration0 = 1.5 ++# cpuctExploration1 = 1.3 ++ ++# Logs------------------------------------------------------------------------------------ ++ ++logSearchInfo = false ++logMoves = false ++logGamesEvery = 50 ++logToStdout = true ++ ++# Bots------------------------------------------------------------------------------------- ++# For multiple bots, you can specify their names as botName0,botName1, etc. ++# If the bots are using different models, specify nnModelFile0, nnModelFile1, etc. ++ ++numBots=1 ++botName=FOO ++nnModelFile=PATH_TO_MODEL ++ ++# These bots will not play each other, but will still be opponents for other bots ++# secondaryBots = 0,1,3 ++# Only these bots will actually play games. Useful if you have a config file with many more ++# bots defined but you want to only selectively enable a few. ++# includeBots = 0,2,6 ++ ++# Specify extra pairings of bots to play. ++# extraPairs = 0-4,1-4 ++# Uncomment and set this to true if you don't want extraPairs to be mirrored by color. ++# (i.e. 0-4 should mean 0 plays black and 4 plays white only, rather than having two games with swapped colors). ++# extraPairsAreOneSidedBW = false ++ ++# Match----------------------------------------------------------------------------------- ++numGameThreads=8 # How many games to run in parallel at a time? ++ ++numGamesTotal=1000000 ++maxMovesPerGame=1200 ++ ++allowResignation = true ++resignThreshold = -0.95 ++resignConsecTurns = 6 ++ ++# Rules------------------------------------------------------------------------------------ ++# See https://lightvector.github.io/KataGo/rules.html for a description of the rules. ++ ++koRules = SIMPLE,POSITIONAL,SITUATIONAL ++scoringRules = AREA,TERRITORY ++taxRules = NONE,SEKI,ALL ++multiStoneSuicideLegals = false,true ++hasButtons = false,true ++ ++bSizes = 19,13,9 ++bSizeRelProbs = 90,5,5 ++ ++# If you want to specify exact distributions of boards, including rectangles, specify this instead ++# of bSizes. E.g. to randomize between a 7x5, a 9x12, and a 10x10 board with probabilities 25%, 25%, 50%: ++# bSizesXY=7-5,9-12,10-10 ++# bSizeRelProbs = 1,1,2 ++ ++komiAuto = True # Automatically adjust komi to what the neural nets think are fair ++# komiMean = 7.5 # Specify explicit komi ++# policyInitAreaProp = 0 ++# compensateAfterPolicyInitProb = 1.0 # Additionally make komi fair this often after the high-temperature moves. ++# policyInitAreaTemperature = 1 ++handicapProb = 0.0 ++handicapCompensateKomiProb = 1.0 ++# numExtraBlackFixed = 3 # When playing handicap games, always use exactly this many extra black moves ++ ++# Search limits----------------------------------------------------------------------------------- ++ ++maxVisits = 500 ++# maxPlayouts = 300 ++# maxTime = 60 ++ ++numSearchThreads = 1 ++ ++# GPU Settings------------------------------------------------------------------------------- ++ ++nnMaxBatchSize = 32 ++nnCacheSizePowerOfTwo = 21 ++nnMutexPoolSizePowerOfTwo = 17 ++nnRandomize = true ++ ++ ++# How many threads should there be to feed positions to the neural net? ++# Server threads are indexed 0,1,...(n-1) for the purposes of the below GPU settings arguments ++# that specify which threads should use which GPUs. ++# NOTE: This parameter is probably ONLY useful if you have multiple GPUs, since each GPU will need a thread. ++# If you're tuning single-GPU performance, use numSearchThreads instead. ++numNNServerThreadsPerModel = 1 ++ ++ ++# TENSORRT GPU settings-------------------------------------- ++# These only apply when using the TENSORRT version of KataGo. ++ ++# IF USING ONE GPU: optionally uncomment and change this if the GPU you want to use turns out to be not device 0 ++# trtDeviceToUse = 0 ++ ++# IF USING TWO GPUS: Uncomment these two lines (AND set numNNServerThreadsPerModel above): ++# trtDeviceToUseThread0 = 0 # change this if the first GPU you want to use turns out to be not device 0 ++# trtDeviceToUseThread1 = 1 # change this if the second GPU you want to use turns out to be not device 1 ++ ++# IF USING THREE GPUS: Uncomment these three lines (AND set numNNServerThreadsPerModel above): ++# trtDeviceToUseThread0 = 0 # change this if the first GPU you want to use turns out to be not device 0 ++# trtDeviceToUseThread1 = 1 # change this if the second GPU you want to use turns out to be not device 1 ++# trtDeviceToUseThread2 = 2 # change this if the third GPU you want to use turns out to be not device 2 ++ ++# You can probably guess the pattern if you have four, five, etc. GPUs. ++ ++ ++# CUDA GPU settings-------------------------------------- ++# For the below, "model" refers to a neural net, from the nnModelFile parameter(s) above. ++# cudaGpuToUse = 0 #use gpu 0 for all server threads (numNNServerThreadsPerModel) unless otherwise specified per-model or per-thread-per-model ++# cudaGpuToUseModel0 = 3 #use gpu 3 for model 0 for all threads unless otherwise specified per-thread for this model ++# cudaGpuToUseModel1 = 2 #use gpu 2 for model 1 for all threads unless otherwise specified per-thread for this model ++# cudaGpuToUseModel0Thread0 = 3 #use gpu 3 for model 0, server thread 0 ++# cudaGpuToUseModel0Thread1 = 2 #use gpu 2 for model 0, server thread 1 ++ ++# cudaUseFP16 = auto ++# cudaUseNHWC = auto ++ ++ ++# OpenCL GPU settings-------------------------------------- ++# These only apply when using OpenCL as the backend for inference. ++# (For GTP, we only ever have one model, when playing matches, we might have more than one, see match_example.cfg) ++ ++# Default behavior is just to always use gpu 0, you will want to uncomment and adjust one or more of these lines ++# to take advantage of a multi-gpu machine ++# openclGpuToUse = 0 #use gpu 0 for all server threads (numNNServerThreadsPerModel) unless otherwise specified per-model or per-thread-per-model ++# openclGpuToUseModel0 = 3 #use gpu 3 for model 0 for all threads unless otherwise specified per-thread for this model ++# openclGpuToUseModel1 = 2 #use gpu 2 for model 1 for all threads unless otherwise specified per-thread for this model ++# openclGpuToUseModel0Thread0 = 3 #use gpu 3 for model 0, server thread 0 ++# openclGpuToUseModel0Thread1 = 2 #use gpu 2 for model 0, server thread 1 ++ ++# Uncomment to tune OpenCL for every board size separately, rather than only the largest possible size ++# openclReTunePerBoardSize = true ++ ++# openclUseFP16 = auto ++ ++ ++# Eigen-specific settings-------------------------------------- ++# These only apply when using the Eigen (pure CPU) version of KataGo. ++ ++# This is the number of CPU threads for evaluating the neural net on the Eigen backend. ++# It defaults to numSearchThreads. ++# numEigenThreadsPerModel = X ++ ++ ++# Root move selection and biases------------------------------------------------------------------------------ ++# Uncomment and edit any of the below values to change them from their default. ++# Values in this section can be specified per-bot as well ++ ++chosenMoveTemperatureEarly = 0.60 ++# chosenMoveTemperatureHalflife = 19 ++chosenMoveTemperature = 0.20 ++# chosenMoveSubtract = 0 ++# chosenMovePrune = 1 ++ ++# rootNumSymmetriesToSample = 1 ++ ++# useLcbForSelection = true ++# lcbStdevs = 5.0 ++# minVisitPropForLCB = 0.15 ++ ++# Internal params------------------------------------------------------------------------------ ++# Uncomment and edit any of the below values to change them from their default. ++# Values in this section can be specified per-bot as well ++ ++# winLossUtilityFactor = 1.0 ++# staticScoreUtilityFactor = 0.10 ++# dynamicScoreUtilityFactor = 0.30 ++# dynamicScoreCenterZeroWeight = 0.20 ++# dynamicScoreCenterScale = 0.75 ++# noResultUtilityForWhite = 0.0 ++# drawEquivalentWinsForWhite = 0.5 ++ ++# cpuctExploration = 0.9 ++# cpuctExplorationLog = 0.4 ++# fpuReductionMax = 0.2 ++# rootFpuReductionMax = 0.1 ++# fpuParentWeightByVisitedPolicy = true ++# valueWeightExponent = 0.25 ++# rootEndingBonusPoints = 0.5 ++# rootPruneUselessMoves = true ++# subtreeValueBiasFactor = 0.45 ++# subtreeValueBiasWeightExponent = 0.85 ++# useGraphSearch = true ++# rootPolicyOptimism = 0.2 ++# policyOptimism = 1.0 ++ ++# nodeTableShardsPowerOfTwo = 16 ++# numVirtualLossesPerThread = 1 +diff --git a/python/selfplay/msvcp140.dll b/python/selfplay/msvcp140.dll +new file mode 100644 +index 00000000..f7c342d1 +Binary files /dev/null and b/python/selfplay/msvcp140.dll differ +diff --git a/python/selfplay/msvcp140_1.dll b/python/selfplay/msvcp140_1.dll +new file mode 100644 +index 00000000..6f55c9d1 +Binary files /dev/null and b/python/selfplay/msvcp140_1.dll differ +diff --git a/python/selfplay/msvcp140_2.dll b/python/selfplay/msvcp140_2.dll +new file mode 100644 +index 00000000..b127e0c8 +Binary files /dev/null and b/python/selfplay/msvcp140_2.dll differ +diff --git a/python/selfplay/msvcp140_atomic_wait.dll b/python/selfplay/msvcp140_atomic_wait.dll +new file mode 100644 +index 00000000..b8fa2030 +Binary files /dev/null and b/python/selfplay/msvcp140_atomic_wait.dll differ +diff --git a/python/selfplay/msvcp140_codecvt_ids.dll b/python/selfplay/msvcp140_codecvt_ids.dll +new file mode 100644 +index 00000000..7549b2b5 +Binary files /dev/null and b/python/selfplay/msvcp140_codecvt_ids.dll differ +diff --git a/python/selfplay/synchronous_loop.bat b/python/selfplay/synchronous_loop.bat +new file mode 100644 +index 00000000..a8e01370 +--- /dev/null ++++ b/python/selfplay/synchronous_loop.bat +@@ -0,0 +1,77 @@ ++@echo off ++setlocal EnableDelayedExpansion ++ ++set "-o" "pipefail" ++IF [[ "%#%" LSS "5" "]]" ( ++ echo "Usage: %~0 NAMEPREFIX BASEDIR TRAININGNAME MODELKIND USEGATING" ++ echo "Assumes katago is already built in the 'cpp' directory of the KataGo repo and the executable is present at cpp\katago." ++ echo "NAMEPREFIX string prefix for this training run, try to pick something globally unique. Will be displayed to users when KataGo loads the model." ++ echo "BASEDIR containing selfplay data and models and related directories" ++ echo "TRANINGNAME name to prefix models with, specific to this training daemon" ++ echo "MODELKIND what size model to train, like 'b10c128', see %CD%\..\modelconfigs.py" ++ echo "USEGATING = 1 to use gatekeeper, 0 to not use gatekeeper" ++ exit "0" ++) ++SET "NAMEPREFIX=%~1" ++shift ++SET "%~1BASEDIRRAW=%~1" ++shift ++SET "%~1%~1TRAININGNAME=%~1" ++shift ++SET "%~1%~1%~1MODELKIND=%~1" ++shift ++SET "%~1%~1%~1%~1USEGATING=%~1" ++shift ++SET _INTERPOLATION_0= ++FOR /f "delims=" %%a in ('realpath "$BASEDIRRAW"') DO (SET "_INTERPOLATION_0=!_INTERPOLATION_0! %%a") ++SET "%~1%~1%~1%~1%~1BASEDIR=!_INTERPOLATION_0:~1!" ++SET _INTERPOLATION_1= ++FOR /f "delims=" %%a in ('git rev-parse --show-toplevel') DO (SET "_INTERPOLATION_1=!_INTERPOLATION_1! %%a") ++SET _INTERPOLATION_2= ++FOR /f "delims=" %%a in ('realpath "$BASEDIRRAW"') DO (SET "_INTERPOLATION_2=!_INTERPOLATION_2! %%a") ++SET "%~1%~1%~1%~1%~1!_INTERPOLATION_2:~1!GITROOTDIR=!_INTERPOLATION_1:~1!" ++SET _INTERPOLATION_3= ++FOR /f "delims=" %%a in ('git rev-parse --show-toplevel') DO (SET "_INTERPOLATION_3=!_INTERPOLATION_3! %%a") ++SET _INTERPOLATION_4= ++FOR /f "delims=" %%a in ('realpath "$BASEDIRRAW"') DO (SET "_INTERPOLATION_4=!_INTERPOLATION_4! %%a") ++SET "%~1%~1%~1%~1%~1!_INTERPOLATION_4:~1!!_INTERPOLATION_3:~1!LOGSDIR="!BASEDIR!"\logs" ++SET _INTERPOLATION_5= ++FOR /f "delims=" %%a in ('git rev-parse --show-toplevel') DO (SET "_INTERPOLATION_5=!_INTERPOLATION_5! %%a") ++SET _INTERPOLATION_6= ++FOR /f "delims=" %%a in ('realpath "$BASEDIRRAW"') DO (SET "_INTERPOLATION_6=!_INTERPOLATION_6! %%a") ++SET "%~1%~1%~1%~1%~1!_INTERPOLATION_6:~1!!_INTERPOLATION_5:~1!!BASEDIR!SCRATCHDIR="!BASEDIR!"\shufflescratch" ++mkdir "-p" "!BASEDIR!" ++mkdir "-p" "!LOGSDIR!" ++mkdir "-p" "!SCRATCHDIR!" ++mkdir "-p" "!BASEDIR!"\selfplay ++mkdir "-p" "!BASEDIR!"\gatekeepersgf ++SET "!BASEDIR!NUM_GAMES_PER_CYCLE=500" ++SET "NUM_THREADS_FOR_SHUFFLING=8" ++SET "NUM_TRAIN_SAMPLES_PER_EPOCH=100000" ++SET "MAX_TRAIN_PER_DATA=8" ++SET "NUM_TRAIN_SAMPLES_PER_SWA=80000" ++SET "BATCHSIZE=128" ++SET "SHUFFLE_MINROWS=100000" ++SET "MAX_TRAIN_SAMPLES_PER_CYCLE=500000" ++SET "TAPER_WINDOW_SCALE=50000" ++SET "SHUFFLE_KEEPROWS=600000" ++SET "SELFPLAY_CONFIG="!GITROOTDIR!"\cpp\configs\training\selfplay1.cfg" ++SET "!GITROOTDIR!GATING_CONFIG="!GITROOTDIR!"\cpp\configs\training\gatekeeper1.cfg" ++SET _INTERPOLATION_7= ++FOR /f "delims=" %%a in ('date "+%Y%m%d-%H%M%S"') DO (SET "_INTERPOLATION_7=!_INTERPOLATION_7! %%a") ++SET "!GITROOTDIR!!GITROOTDIR!DATE_FOR_FILENAME=!_INTERPOLATION_7:~1!" ++SET _INTERPOLATION_8= ++FOR /f "delims=" %%a in ('date "+%Y%m%d-%H%M%S"') DO (SET "_INTERPOLATION_8=!_INTERPOLATION_8! %%a") ++SET "!GITROOTDIR!!GITROOTDIR!!_INTERPOLATION_8:~1!DATED_ARCHIVE=!BASEDIR!"/scripts/dated/"!DATE_FOR_FILENAME!" ++mkdir "-p" "!DATED_ARCHIVE!" ++COPY "!GITROOTDIR!"/python/*.py "!GITROOTDIR!"/python/selfplay/*.sh "!DATED_ARCHIVE!" ++COPY "!GITROOTDIR!"\cpp\katago "!DATED_ARCHIVE!" ++COPY "!SELFPLAY_CONFIG!" "!DATED_ARCHIVE!"\selfplay.cfg ++COPY "!GATING_CONFIG!" "!DATED_ARCHIVE!"\gatekeeper.cfg ++git "show" "--no-patch" "--no-color" REM UNKNOWN: {"type":"Redirect","op":{"text":">","type":"great"},"file":{"text":"\"$DATED_ARCHIVE\"/version.txt","expansion":[{"loc":{"start":1,"end":14},"parameter":"DATED_ARCHIVE","type":"ParameterExpansion"}],"type":"Word"}} ++git "diff" "--no-color" REM UNKNOWN: {"type":"Redirect","op":{"text":">","type":"great"},"file":{"text":"\"$DATED_ARCHIVE\"/diff.txt","expansion":[{"loc":{"start":1,"end":14},"parameter":"DATED_ARCHIVE","type":"ParameterExpansion"}],"type":"Word"}} ++git "diff" "--staged" "--no-color" REM UNKNOWN: {"type":"Redirect","op":{"text":">","type":"great"},"file":{"text":"\"$DATED_ARCHIVE\"/diffstaged.txt","expansion":[{"loc":{"start":1,"end":14},"parameter":"DATED_ARCHIVE","type":"ParameterExpansion"}],"type":"Word"}} ++cd "!DATED_ARCHIVE!" ++set "-x" ++REM UNKNOWN: {"type":"While","clause":{"type":"CompoundList","commands":[{"type":"Command","name":{"text":"true","type":"Word"}}]},"do":{"type":"CompoundList","commands":[{"type":"Command","name":{"text":"echo","type":"Word"},"suffix":[{"text":"Gatekeeper","type":"Word"}]},{"type":"Pipeline","commands":[{"type":"Command","name":{"text":"time","type":"Word"},"suffix":[{"text":"./katago","type":"Word"},{"text":"gatekeeper","type":"Word"},{"text":"-rejected-models-dir","type":"Word"},{"text":"\"$BASEDIR\"/rejectedmodels","expansion":[{"loc":{"start":1,"end":8},"parameter":"BASEDIR","type":"ParameterExpansion"}],"type":"Word"},{"text":"-accepted-models-dir","type":"Word"},{"text":"\"$BASEDIR\"/models/","expansion":[{"loc":{"start":1,"end":8},"parameter":"BASEDIR","type":"ParameterExpansion"}],"type":"Word"},{"text":"-sgf-output-dir","type":"Word"},{"text":"\"$BASEDIR\"/gatekeepersgf/","expansion":[{"loc":{"start":1,"end":8},"parameter":"BASEDIR","type":"ParameterExpansion"}],"type":"Word"},{"text":"-test-models-dir","type":"Word"},{"text":"\"$BASEDIR\"/modelstobetested/","expansion":[{"loc":{"start":1,"end":8},"parameter":"BASEDIR","type":"ParameterExpansion"}],"type":"Word"},{"text":"-config","type":"Word"},{"text":"\"$DATED_ARCHIVE\"/gatekeeper.cfg","expansion":[{"loc":{"start":1,"end":14},"parameter":"DATED_ARCHIVE","type":"ParameterExpansion"}],"type":"Word"},{"text":"-quit-if-no-nets-to-test","type":"Word"}]},{"type":"Command","name":{"text":"tee","type":"Word"},"suffix":[{"text":"-a","type":"Word"},{"text":"\"$BASEDIR\"/gatekeepersgf/stdout.txt","expansion":[{"loc":{"start":1,"end":8},"parameter":"BASEDIR","type":"ParameterExpansion"}],"type":"Word"}]}]},{"type":"Command","name":{"text":"echo","type":"Word"},"suffix":[{"text":"Selfplay","type":"Word"}]},{"type":"Pipeline","commands":[{"type":"Command","name":{"text":"time","type":"Word"},"suffix":[{"text":"./katago","type":"Word"},{"text":"selfplay","type":"Word"},{"text":"-max-games-total","type":"Word"},{"text":"\"$NUM_GAMES_PER_CYCLE\"","expansion":[{"loc":{"start":1,"end":20},"parameter":"NUM_GAMES_PER_CYCLE","type":"ParameterExpansion"}],"type":"Word"},{"text":"-output-dir","type":"Word"},{"text":"\"$BASEDIR\"/selfplay","expansion":[{"loc":{"start":1,"end":8},"parameter":"BASEDIR","type":"ParameterExpansion"}],"type":"Word"},{"text":"-models-dir","type":"Word"},{"text":"\"$BASEDIR\"/models","expansion":[{"loc":{"start":1,"end":8},"parameter":"BASEDIR","type":"ParameterExpansion"}],"type":"Word"},{"text":"-config","type":"Word"},{"text":"\"$DATED_ARCHIVE\"/selfplay.cfg","expansion":[{"loc":{"start":1,"end":14},"parameter":"DATED_ARCHIVE","type":"ParameterExpansion"}],"type":"Word"}]},{"type":"Command","name":{"text":"tee","type":"Word"},"suffix":[{"text":"-a","type":"Word"},{"text":"\"$BASEDIR\"/selfplay/stdout.txt","expansion":[{"loc":{"start":1,"end":8},"parameter":"BASEDIR","type":"ParameterExpansion"}],"type":"Word"}]}]},{"type":"Command","name":{"text":"echo","type":"Word"},"suffix":[{"text":"Shuffle","type":"Word"}]},{"type":"Subshell","list":{"type":"CompoundList","commands":[{"type":"Pipeline","commands":[{"type":"Command","name":{"text":"time","type":"Word"},"suffix":[{"text":"SKIP_VALIDATE=1","type":"Word"},{"text":"./shuffle.sh","type":"Word"},{"text":"\"$BASEDIR\"","expansion":[{"loc":{"start":1,"end":8},"parameter":"BASEDIR","type":"ParameterExpansion"}],"type":"Word"},{"text":"\"$SCRATCHDIR\"","expansion":[{"loc":{"start":1,"end":11},"parameter":"SCRATCHDIR","type":"ParameterExpansion"}],"type":"Word"},{"text":"\"$NUM_THREADS_FOR_SHUFFLING\"","expansion":[{"loc":{"start":1,"end":26},"parameter":"NUM_THREADS_FOR_SHUFFLING","type":"ParameterExpansion"}],"type":"Word"},{"text":"\"$BATCHSIZE\"","expansion":[{"loc":{"start":1,"end":10},"parameter":"BATCHSIZE","type":"ParameterExpansion"}],"type":"Word"},{"text":"-min-rows","type":"Word"},{"text":"\"$SHUFFLE_MINROWS\"","expansion":[{"loc":{"start":1,"end":16},"parameter":"SHUFFLE_MINROWS","type":"ParameterExpansion"}],"type":"Word"},{"text":"-keep-target-rows","type":"Word"},{"text":"\"$SHUFFLE_KEEPROWS\"","expansion":[{"loc":{"start":1,"end":17},"parameter":"SHUFFLE_KEEPROWS","type":"ParameterExpansion"}],"type":"Word"},{"text":"-taper-window-scale","type":"Word"},{"text":"\"$TAPER_WINDOW_SCALE\"","expansion":[{"loc":{"start":1,"end":19},"parameter":"TAPER_WINDOW_SCALE","type":"ParameterExpansion"}],"type":"Word"}]},{"type":"Command","name":{"text":"tee","type":"Word"},"suffix":[{"text":"-a","type":"Word"},{"text":"\"$BASEDIR\"/logs/outshuffle.txt","expansion":[{"loc":{"start":1,"end":8},"parameter":"BASEDIR","type":"ParameterExpansion"}],"type":"Word"}]}]}]}},{"type":"Command","name":{"text":"echo","type":"Word"},"suffix":[{"text":"Train","type":"Word"}]},{"type":"Command","name":{"text":"time","type":"Word"},"suffix":[{"text":"./train.sh","type":"Word"},{"text":"\"$BASEDIR\"","expansion":[{"loc":{"start":1,"end":8},"parameter":"BASEDIR","type":"ParameterExpansion"}],"type":"Word"},{"text":"\"$TRAININGNAME\"","expansion":[{"loc":{"start":1,"end":13},"parameter":"TRAININGNAME","type":"ParameterExpansion"}],"type":"Word"},{"text":"\"$MODELKIND\"","expansion":[{"loc":{"start":1,"end":10},"parameter":"MODELKIND","type":"ParameterExpansion"}],"type":"Word"},{"text":"\"$BATCHSIZE\"","expansion":[{"loc":{"start":1,"end":10},"parameter":"BATCHSIZE","type":"ParameterExpansion"}],"type":"Word"},{"text":"main","type":"Word"},{"text":"-samples-per-epoch","type":"Word"},{"text":"\"$NUM_TRAIN_SAMPLES_PER_EPOCH\"","expansion":[{"loc":{"start":1,"end":28},"parameter":"NUM_TRAIN_SAMPLES_PER_EPOCH","type":"ParameterExpansion"}],"type":"Word"},{"text":"-swa-period-samples","type":"Word"},{"text":"\"$NUM_TRAIN_SAMPLES_PER_SWA\"","expansion":[{"loc":{"start":1,"end":26},"parameter":"NUM_TRAIN_SAMPLES_PER_SWA","type":"ParameterExpansion"}],"type":"Word"},{"text":"-quit-if-no-data","type":"Word"},{"text":"-stop-when-train-bucket-limited","type":"Word"},{"text":"-no-repeat-files","type":"Word"},{"text":"-max-train-bucket-per-new-data","type":"Word"},{"text":"\"$MAX_TRAIN_PER_DATA\"","expansion":[{"loc":{"start":1,"end":19},"parameter":"MAX_TRAIN_PER_DATA","type":"ParameterExpansion"}],"type":"Word"},{"text":"-max-train-bucket-size","type":"Word"},{"text":"\"$MAX_TRAIN_SAMPLES_PER_CYCLE\"","expansion":[{"loc":{"start":1,"end":28},"parameter":"MAX_TRAIN_SAMPLES_PER_CYCLE","type":"ParameterExpansion"}],"type":"Word"}]},{"type":"Command","name":{"text":"echo","type":"Word"},"suffix":[{"text":"Export","type":"Word"}]},{"type":"Subshell","list":{"type":"CompoundList","commands":[{"type":"Pipeline","commands":[{"type":"Command","name":{"text":"time","type":"Word"},"suffix":[{"text":"./export_model_for_selfplay.sh","type":"Word"},{"text":"\"$NAMEPREFIX\"","expansion":[{"loc":{"start":1,"end":11},"parameter":"NAMEPREFIX","type":"ParameterExpansion"}],"type":"Word"},{"text":"\"$BASEDIR\"","expansion":[{"loc":{"start":1,"end":8},"parameter":"BASEDIR","type":"ParameterExpansion"}],"type":"Word"},{"text":"\"$USEGATING\"","expansion":[{"loc":{"start":1,"end":10},"parameter":"USEGATING","type":"ParameterExpansion"}],"type":"Word"}]},{"type":"Command","name":{"text":"tee","type":"Word"},"suffix":[{"text":"-a","type":"Word"},{"text":"\"$BASEDIR\"/logs/outexport.txt","expansion":[{"loc":{"start":1,"end":8},"parameter":"BASEDIR","type":"ParameterExpansion"}],"type":"Word"}]}]}]}}]}} ++exit "0" +\ No newline at end of file +diff --git a/python/selfplay/synchronous_loop.sh b/python/selfplay/synchronous_loop.sh +index 46cfbc3d..33f4be30 100755 +--- a/python/selfplay/synchronous_loop.sh ++++ b/python/selfplay/synchronous_loop.sh +@@ -12,7 +12,7 @@ set -o pipefail + if [[ $# -lt 5 ]] + then + echo "Usage: $0 NAMEPREFIX BASEDIR TRAININGNAME MODELKIND USEGATING" +- echo "Assumes katago is already built in the 'cpp' directory of the KataGo repo and the executable is present at cpp/katago." ++ echo "Assumes katago is already built in the 'cpp' directory of the KataGo repo and the executable is present at cpp/katago.exe." + echo "NAMEPREFIX string prefix for this training run, try to pick something globally unique. Will be displayed to users when KataGo loads the model." + echo "BASEDIR containing selfplay data and models and related directories" + echo "TRANINGNAME name to prefix models with, specific to this training daemon" +@@ -44,39 +44,27 @@ mkdir -p "$BASEDIR"/selfplay + mkdir -p "$BASEDIR"/gatekeepersgf + + # Parameters for the training run +-# NOTE: You may want to adjust the below numbers. +-# NOTE: You probably want to edit settings in cpp/configs/training/selfplay1.cfg +-# NOTE: You probably want to edit settings in cpp/configs/training/gatekeeper1.cfg +-# Such as what board sizes and rules, you want to learn, number of visits to use, etc. +- +-# Also, the parameters below are relatively small, and probably +-# good for less powerful hardware and tighter turnaround during very early training, but if +-# you have strong hardware or are later into a run you may want to reduce the overhead by scaling +-# these numbers up and doing more games and training per cycle, exporting models less frequently, etc. +- +-NUM_GAMES_PER_CYCLE=500 # Every cycle, play this many games ++NUM_GAMES_PER_CYCLE=90 # Every cycle, play this many games + NUM_THREADS_FOR_SHUFFLING=8 + NUM_TRAIN_SAMPLES_PER_EPOCH=100000 # Training will proceed in chunks of this many rows, subject to MAX_TRAIN_PER_DATA. + MAX_TRAIN_PER_DATA=8 # On average, train only this many times on each data row. Larger numbers may cause overfitting. + NUM_TRAIN_SAMPLES_PER_SWA=80000 # Stochastic weight averaging frequency. + BATCHSIZE=128 # For lower-end GPUs 64 or smaller may be needed to avoid running out of GPU memory. +-SHUFFLE_MINROWS=100000 # Require this many rows at the very start before beginning training. ++SHUFFLE_MINROWS=1 # Require this many rows at the very start before beginning training. + MAX_TRAIN_SAMPLES_PER_CYCLE=500000 # Each cycle will do at most this many training steps. + TAPER_WINDOW_SCALE=50000 # Parameter setting the scale at which the shuffler will make the training window grow sublinearly. + SHUFFLE_KEEPROWS=600000 # Needs to be larger than MAX_TRAIN_SAMPLES_PER_CYCLE, so the shuffler samples enough rows each cycle for the training to use. + + # Paths to the selfplay and gatekeeper configs that contain board sizes, rules, search parameters, etc. +-# See cpp/configs/training/README.md for some notes on other selfplay configs. +-SELFPLAY_CONFIG="$GITROOTDIR"/cpp/configs/training/selfplay1.cfg +-GATING_CONFIG="$GITROOTDIR"/cpp/configs/training/gatekeeper1.cfg ++SELFPLAY_CONFIG="$GITROOTDIR"/cpp/configs/training/selfplay1_maxsize9.cfg ++GATING_CONFIG="$GITROOTDIR"/cpp/configs/training/gatekeeper1_maxsize9.cfg + + # Copy all the relevant scripts and configs and the katago executable to a dated directory. +-# For archival and logging purposes - you can look back and see exactly the python code on a particular date + DATE_FOR_FILENAME=$(date "+%Y%m%d-%H%M%S") + DATED_ARCHIVE="$BASEDIR"/scripts/dated/"$DATE_FOR_FILENAME" + mkdir -p "$DATED_ARCHIVE" + cp "$GITROOTDIR"/python/*.py "$GITROOTDIR"/python/selfplay/*.sh "$DATED_ARCHIVE" +-cp "$GITROOTDIR"/cpp/katago "$DATED_ARCHIVE" ++cp "$GITROOTDIR"/cpp/katago.exe "$DATED_ARCHIVE"/katago.exe # Ensure the executable is copied + cp "$SELFPLAY_CONFIG" "$DATED_ARCHIVE"/selfplay.cfg + cp "$GATING_CONFIG" "$DATED_ARCHIVE"/gatekeeper.cfg + git show --no-patch --no-color > "$DATED_ARCHIVE"/version.txt +@@ -91,15 +79,13 @@ set -x + while true + do + echo "Gatekeeper" +- time ./katago gatekeeper -rejected-models-dir "$BASEDIR"/rejectedmodels -accepted-models-dir "$BASEDIR"/models/ -sgf-output-dir "$BASEDIR"/gatekeepersgf/ -test-models-dir "$BASEDIR"/modelstobetested/ -config "$DATED_ARCHIVE"/gatekeeper.cfg -quit-if-no-nets-to-test | tee -a "$BASEDIR"/gatekeepersgf/stdout.txt ++ time "C:\Users\chang\Downloads\katago-v1.15.1-opencl-windows-x64\katago.exe" gatekeeper -rejected-models-dir "$BASEDIR"/rejectedmodels -accepted-models-dir "$BASEDIR"/models/ -sgf-output-dir "$BASEDIR"/gatekeepersgf/ -test-models-dir "$BASEDIR"/modelstobetested/ -config "$DATED_ARCHIVE"/gatekeeper.cfg -quit-if-no-nets-to-test | tee -a "$BASEDIR"/gatekeepersgf/stdout.txt + + echo "Selfplay" +- time ./katago selfplay -max-games-total "$NUM_GAMES_PER_CYCLE" -output-dir "$BASEDIR"/selfplay -models-dir "$BASEDIR"/models -config "$DATED_ARCHIVE"/selfplay.cfg | tee -a "$BASEDIR"/selfplay/stdout.txt ++ time "C:\Users\chang\Downloads\katago-v1.15.1-opencl-windows-x64\katago.exe" selfplay -max-games-total "$NUM_GAMES_PER_CYCLE" -output-dir "$BASEDIR"/selfplay -models-dir "$BASEDIR"/models -config "$DATED_ARCHIVE"/selfplay.cfg | tee -a "$BASEDIR"/selfplay/stdout.txt + + echo "Shuffle" + ( +- # Skip validate since peeling off 5% of data is actually a bit too chunky and discrete when running at a small scale, and validation data +- # doesn't actually add much to debugging a fast-changing RL training. + time SKIP_VALIDATE=1 ./shuffle.sh "$BASEDIR" "$SCRATCHDIR" "$NUM_THREADS_FOR_SHUFFLING" "$BATCHSIZE" -min-rows "$SHUFFLE_MINROWS" -keep-target-rows "$SHUFFLE_KEEPROWS" -taper-window-scale "$TAPER_WINDOW_SCALE" | tee -a "$BASEDIR"/logs/outshuffle.txt + ) + +diff --git a/python/selfplay/vcruntime140.dll b/python/selfplay/vcruntime140.dll +new file mode 100644 +index 00000000..5d5b81dc +Binary files /dev/null and b/python/selfplay/vcruntime140.dll differ +diff --git a/python/selfplay/vcruntime140_1.dll b/python/selfplay/vcruntime140_1.dll +new file mode 100644 +index 00000000..3a02196f +Binary files /dev/null and b/python/selfplay/vcruntime140_1.dll differ +diff --git "a/python/selfplay/\344\277\261\344\271\220\351\203\250AI.cfg" "b/python/selfplay/\344\277\261\344\271\220\351\203\250AI.cfg" +new file mode 100644 +index 00000000..9b9287b0 +--- /dev/null ++++ "b/python/selfplay/\344\277\261\344\271\220\351\203\250AI.cfg" +@@ -0,0 +1,721 @@ ++# Configuration for KataGo C++ GTP engine ++ ++# Run the program using: `./katago.exe gtp` ++ ++# In this example config, when a parameter is given as a commented out value, ++# that value also is the default value, unless described otherwise. You can ++# uncomment it (remove the pound sign) and change it if you want. ++ ++# =========================================================================== ++# Running on an online server or in a real tournament or match ++# =========================================================================== ++# If you plan to run online or in a tournament, read through the "Rules" ++# section below for proper handling of komi, handicaps, end-of-game cleanup, ++# and other details. ++ ++# =========================================================================== ++# Notes about performance and memory usage ++# =========================================================================== ++# Important: For good performance, you will very likely want to tune the ++# "numSearchThreads" parameter in the Search limits section below! Run ++# "./katago benchmark" to test KataGo and to suggest a reasonable value ++# of this parameter. ++ ++# For multi-GPU systems, read "OpenCL GPU settings" or "CUDA GPU settings". ++# ++# When using OpenCL, verify that KataGo picks the correct device! Some systems ++# may have both an Intel CPU OpenCL and GPU OpenCL. If # KataGo picks the wrong ++# one, correct this by specifying "openclGpuToUse". ++# ++# Consider adjusting "maxVisits", "ponderingEnabled", "resignThreshold", and ++# other parameters depending on your intended usage. ++ ++# =========================================================================== ++# Command-line usage ++# =========================================================================== ++# All of the below values may be set or overridden via command-line arguments: ++# ++# -override-config KEY=VALUE,KEY=VALUE,... ++ ++# =========================================================================== ++# Logs and files ++# =========================================================================== ++# This section defines where and what logging information is produced. ++ ++# Each run of KataGo will log to a separate file in this dir. ++# This is the default. ++logDir = gtp_logs ++# Uncomment and specify this instead of logDir to write separate dated subdirs ++# logDirDated = gtp_logs ++# Uncomment and specify this instead of logDir to log to only a single file ++# logFile = gtp.log ++ ++# Logging options ++logAllGTPCommunication = true ++logSearchInfo = true ++logSearchInfoForChosenMove = false ++logToStderr = false ++ ++# KataGo will display some info to stderr on GTP startup ++# Uncomment the next line and set it to false to suppress that and remain silent ++# startupPrintMessageToStderr = true ++ ++# Write information to stderr, for use in things like malkovich chat to OGS. ++ ogsChatToStderr = true ++ ++# Uncomment and set this to a directory to override where openCLTuner files ++# and other cached data is written. By default it saves into a subdir of the ++# current directory on windows, and a subdir of ~/.katago on Linux. ++# homeDataDir = PATH_TO_DIRECTORY ++ ++# =========================================================================== ++# Analysis ++# =========================================================================== ++# This section configures analysis settings. ++# ++# The maximum number of moves after the first move displayed in variations ++# from analysis commands like kata-analyze or lz-analyze. ++# analysisPVLen = 15 ++ ++# Report winrates for chat and analysis as (BLACK|WHITE|SIDETOMOVE). ++# Most GUIs and analysis tools will expect SIDETOMOVE. ++# reportAnalysisWinratesAs = SIDETOMOVE ++ ++# Extra noise for wider exploration. Large values will force KataGo to ++# analyze a greater variety of moves than it normally would. ++# An extreme value like 1 distributes playouts across every move on the board, ++# even very bad moves. ++# Affects analysis only, does not affect play. ++# analysisWideRootNoise = 0.04 ++ ++# Try to limit the effect of possible bad or bogus move sequences in the ++# history leading to this position from affecting KataGo's move predictions. ++# analysisIgnorePreRootHistory = true ++ ++# =========================================================================== ++# Rules ++# =========================================================================== ++# This section configures the scoring and playing rules. Rules can also be ++# changed mid-run by issuing custom GTP commands. ++# ++# See https://lightvector.github.io/KataGo/rules.html for rules details. ++# ++# See https://github.com/lightvector/KataGo/blob/master/docs/GTP_Extensions.md ++# for GTP commands. ++ ++# Specify the rules as a string. ++# Some legal values include: ++# chinese, japanese, korean, aga, chinese-ogs, new-zealand, stone-scoring, ++# ancient-territory, bga, aga-button ++# ++# For some human rulesets that require complex adjudication in tricky cases ++# (e.g. japanese, korean) KataGo may not precisely match the ruleset in such ++# cases but will do its best. ++rules = tromp-taylor ++ ++# By default, the "rules" parameter is used, but if you comment it out and ++# uncomment one option in each of the sections below, you can specify an ++# arbitrary combination of individual rules. ++ ++# koRule = SIMPLE # Simple ko rules (triple ko = no result) ++# koRule = POSITIONAL # Positional superko ++# koRule = SITUATIONAL # Situational superko ++ ++# scoringRule = AREA # Area scoring ++# scoringRule = TERRITORY # Territory scoring (special computer-friendly territory rules) ++ ++# taxRule = NONE # All surrounded empty points are scored ++# taxRule = SEKI # Eyes in seki do NOT count as points ++# taxRule = ALL # All groups are taxed up to 2 points for the two eyes needed to live ++ ++# Is multiple-stone suicide legal? (Single-stone suicide is always illegal). ++# multiStoneSuicideLegal = false ++# multiStoneSuicideLegal = true ++ ++# "Button go" - the first pass when area scoring awards 0.5 points and does ++# not count for ending the game. ++# Allows area scoring rulesets that have far simpler rules to achieve the same ++# final scoring precision and reward for precise play as territory scoring. ++# hasButton = false ++# hasButton = true ++ ++# Is this a human ruleset where it's okay to pass before having physically ++# captured and removed all dead stones? ++# friendlyPassOk = false ++# friendlyPassOk = true ++ ++# How handicap stones in handicap games are compensated ++# whiteHandicapBonus = 0 # White gets no compensation for black's handicap stones (Tromp-taylor, NZ, JP) ++# whiteHandicapBonus = N-1 # White gets N-1 points for black's N handicap stones (AGA) ++# whiteHandicapBonus = N # White gets N points for black's N handicap stones (Chinese) ++ ++# ------------------------------ ++# Other rules hacks ++# ------------------------------ ++# Uncomment and change to adjust what board size KataGo uses upon startup ++# by default when GTP doesn't specify. ++# defaultBoardSize = 19 ++ ++# By default, Katago will use the komi that the GUI or GTP controller tries to set. ++# Uncomment and set this to have KataGo ignore the controller and always use this komi. ++# ignoreGTPAndForceKomi = 7 ++ ++# =========================================================================== ++# Bot behavior ++# =========================================================================== ++ ++# ------------------------------ ++# Resignation ++# ------------------------------ ++ ++# Resignation occurs if for at least resignConsecTurns in a row, the ++# winLossUtility (on a [-1,1] scale) is below resignThreshold. ++allowResignation = true ++resignThreshold = -0.90 ++resignConsecTurns = 3 ++ ++# By default, KataGo may resign games that it is confidently losing even if they ++# are very close in score. Uncomment and set this to avoid resigning games ++# if the estimated difference is points is less than or equal to this. ++# resignMinScoreDifference = 10 ++ ++# ------------------------------ ++# Handicap ++# ------------------------------ ++# Assume that if black makes many moves in a row right at the start of the ++# game, then the game is a handicap game. This is necessary on some servers ++# and for some GUIs and also when initializing from many SGF files, which may ++# set up a handicap game using repeated GTP "play" commands for black rather ++# than GTP "place_free_handicap" commands; however, it may also lead to ++# incorrect understanding of komi if whiteHandicapBonus is used and a server ++# does not have such a practice. Uncomment and set to false to disable. ++# assumeMultipleStartingBlackMovesAreHandicap = true ++ ++# Makes katago dynamically adjust in handicap or altered-komi games to assume ++# based on those game settings that it must be stronger or weaker than the ++# opponent and to play accordingly. Greatly improves handicap strength by ++# biasing winrates and scores to favor appropriate safe/aggressive play. ++# Does NOT affect analysis (lz-analyze, kata-analyze, used by programs like ++# Lizzie) so analysis remains unbiased. Uncomment and set this to 0 to disable ++# this and make KataGo play the same always. ++# dynamicPlayoutDoublingAdvantageCapPerOppLead = 0.045 ++ ++# Instead of "dynamicPlayoutDoublingAdvantageCapPerOppLead", you can comment ++# that out and uncomment and set "playoutDoublingAdvantage" to a fixed value ++# from -3.0 to 3.0 that will not change dynamically. ++# ALSO affects analysis tools (lz-analyze, kata-analyze, used by e.g. Lizzie). ++# Negative makes KataGo behave as if it is much weaker than the opponent. ++# Positive makes KataGo behave as if it is much stronger than the opponent. ++# KataGo will adjust to favor safe/aggressive play as appropriate based on ++# the combination of who is ahead and how much stronger/weaker it thinks it is, ++# and report winrates and scores taking the strength difference into account. ++# ++# If this and "dynamicPlayoutDoublingAdvantageCapPerOppLead" are both set ++# then dynamic will be used for all games and this fixed value will be used ++# for analysis tools. ++# playoutDoublingAdvantage = 0.0 ++ ++# Uncomment one of these when using "playoutDoublingAdvantage" to enforce ++# that it will only apply when KataGo plays as the specified color and will be ++# negated when playing as the opposite color. ++# playoutDoublingAdvantagePla = BLACK ++# playoutDoublingAdvantagePla = WHITE ++ ++# ------------------------------ ++# Passing and cleanup ++# ------------------------------ ++# Make the bot never assume that its pass will end the game, even if passing ++# would end and "win" under Tromp-Taylor rules. Usually this is a good idea ++# when using it for analysis or playing on servers where scoring may be ++# implemented non-tromp-taylorly. Uncomment and set to false to disable. ++# conservativePass = true ++ ++# When using territory scoring, self-play games continue beyond two passes ++# with special cleanup rules that may be confusing for human players. This ++# option prevents the special cleanup phases from being reachable when using ++# the bot for GTP play. Uncomment and set to false to enable entering special ++# cleanup. For example, if you are testing it against itself, or against ++# another bot that has precisely implemented the rules documented at ++# https://lightvector.github.io/KataGo/rules.html ++# preventCleanupPhase = true ++ ++# ------------------------------ ++# Miscellaneous behavior ++# ------------------------------ ++# If the board is symmetric, search only one copy of each equivalent move. ++# Attempts to also account for ko/superko, will not theoretically perfect for ++# superko. Uncomment and set to false to disable. ++# rootSymmetryPruning = true ++ ++# Uncomment and set to true to avoid a particular joseki that some networks ++# misevaluate, and also to improve opening diversity versus some particular ++# other bots that like to play it all the time. ++# avoidMYTDaggerHack = false ++ ++# Prefer to avoid playing the same joseki in every corner of the board. ++# Uncomment to set to a specific value. See "Avoid SGF patterns" section. ++# By default: 0 (even games), 0.005 (handicap games) ++# avoidRepeatedPatternUtility = 0.0 ++ ++# Experimental logic to fight against mirror Go even with unfavorable komi. ++# Uncomment to set to a specific value to use for both playing and analysis. ++# By default: true when playing via GTP, but false when analyzing. ++# antiMirror = true ++ ++# Enable some hacks that mitigate rare instances when passing messes up deeper searches. ++# enablePassingHacks = true ++ ++# Uncomment and set this to true to prevent bad or bogus move sequences ++# in the history leading to this position from affecting KataGo's move choices. ++# Same as analysisIgnorePreRootHistory (see above) but applies to actual play. ++# You can enable this if KataGo is being asked to play from positions that it did not ++# choose the moves to reach. ++# ignorePreRootHistory = false ++ ++# =========================================================================== ++# Search limits ++# =========================================================================== ++ ++# Terminology: ++# "Playouts" is the number of new playouts of search performed each turn. ++# "Visits" is the same as "Playouts" but also counts search performed on ++# previous turns that is still applicable to this turn. ++# "Time" is the time in seconds. ++ ++# For example, if KataGo searched 200 nodes on the previous turn, and then ++# after the opponent's reply, 50 nodes of its search tree was still valid, ++# then a visit limit of 200 would allow KataGo to search 150 new nodes ++# (for a final tree size of 200 nodes), whereas a playout limit of of 200 ++# would allow KataGo to search 200 nodes (for a final tree size of 250 nodes). ++ ++# Additionally, KataGo may also move before than the limit in order to ++# obey time controls (e.g. byo-yomi, etc) if the GTP controller has ++# told KataGo that the game has is being played with a given time control. ++ ++# Limits for search on the current turn. ++# If commented out or unspecified, the default is to have no limit. ++# maxVisits = 500 ++# maxPlayouts = 300 ++ maxTime = 1.0 ++ ++# Ponder on the opponent's turn? ++ponderingEnabled = true ++ ++# Limits for search when pondering on the opponent's turn. ++# If commented out or unspecified, the default is to have no limit. ++# Limiting the maximum time is recommended so that KataGo won't burn CPU/GPU ++# forever and/or run out of RAM if left unattended while pondering is enabled. ++# maxVisitsPondering = 5000 ++# maxPlayoutsPondering = 3000 ++maxTimePondering = 120.0 ++ ++ ++# ------------------------------ ++# Other search limits and behavior ++# ------------------------------ ++ ++# Approx number of seconds to buffer for lag for GTP time controls - will ++# move a bit faster assuming there is this much lag per move. ++lagBuffer = 1.0 ++ ++# YOU PROBABLY WANT TO TUNE THIS PARAMETER! ++# The number of threads to use when searching. On powerful GPUs the optimal ++# threads may be much higher than the number of CPU cores you have because ++# many threads are needed to feed efficient large batches to the GPU. ++# ++# Run "./katago benchmark" to tune this parameter and test the effect ++# of changes to any of other parameters. ++numSearchThreads = 12 ++ ++# Play a little faster if the opponent is passing, for human-friendliness. ++# Comment these out to disable them, such as if running a controlled match ++# where you are testing KataGo with fixed compute per move vs other bots. ++searchFactorAfterOnePass = 0.50 ++searchFactorAfterTwoPass = 0.25 ++ ++# Play a little faster if super-winning, for human-friendliness. ++# Comment these out to disable them, such as if running a controlled match ++# where you are testing KataGo with fixed compute per move vs other bots. ++searchFactorWhenWinning = 0.40 ++searchFactorWhenWinningThreshold = 0.95 ++ ++# =========================================================================== ++# GPU settings ++# =========================================================================== ++# This section configures GPU settings. ++# ++# Maximum number of positions to send to a single GPU at once. The default ++# value is roughly equal to numSearchThreads, but can be specified manually ++# if running out of memory, or using multiple GPUs that expect to share work. ++# nnMaxBatchSize = ++ ++# Controls the neural network cache size, which is the primary RAM/memory use. ++# KataGo will cache up to (2 ** nnCacheSizePowerOfTwo) many neural net ++# evaluations in case of transpositions in the tree. ++# Increase this to improve performance for searches with tens of thousands ++# of visits or more. Decrease this to limit memory usage. ++# If you're happy to do some math - each neural net entry takes roughly ++# 1.5KB, except when using whole-board ownership/territory ++# visualizations, where each entry will take roughly 3KB. The number of ++# entries is (2 ** nnCacheSizePowerOfTwo). (E.g. 2 ** 18 = 262144.) ++# You can compute roughly how much memory the cache will use based on this. ++# nnCacheSizePowerOfTwo = 20 ++ ++# Size of mutex pool for nnCache is (2 ** this). ++# nnMutexPoolSizePowerOfTwo = 16 ++ ++# Randomize board orientation when running neural net evals? Uncomment and ++# set to false to disable. ++# nnRandomize = true ++ ++# If provided, force usage of a specific seed for nnRandomize. ++# The default is to use a randomly generated seed. ++# nnRandSeed = abcdefg ++ ++# Uncomment and set to true to force GTP to use the maximum board size for ++# internal buffers for the neural net. This will make KataGo slower when ++# evaluating small boards, but will avoid a lengthy initialization time on every ++# change of board size due to having to re-size the neural net buffers on the GPU. ++# This can be useful for example, for OGS's persistent bot mode that uses a single ++# bot instance to handle multiple games and may thrash between different board sizes ++# if there are concurrent games of multiple sizes. ++# gtpForceMaxNNSize = false ++ ++# ------------------------------ ++# Multiple GPUs ++# ------------------------------ ++# Set this to the number of GPUs to use or that are available. ++# IMPORTANT: If more than 1, also uncomment the appropriate TensorRT ++# or CUDA or OpenCL section. ++# numNNServerThreadsPerModel = 1 ++ ++# ------------------------------ ++# TENSORRT GPU settings ++# ------------------------------ ++# These only apply when using the TENSORRT version of KataGo. ++ ++# For one GPU: optionally uncomment this option and change if the GPU to ++# use is not device 0. ++# trtDeviceToUse = 0 ++ ++# For two GPUs: Uncomment these options, AND set numNNServerThreadsPerModel above. ++# Also, change their values if the devices you want to use are not 0 and 1. ++# trtDeviceToUseThread0 = 0 ++# trtDeviceToUseThread1 = 1 ++ ++# For three GPUs: Uncomment these options, AND set numNNServerThreadsPerModel above. ++# Also, change their values if the devices you want to use are not 0 and 1 and 2. ++# trtDeviceToUseThread0 = 0 ++# trtDeviceToUseThread1 = 1 ++# trtDeviceToUseThread2 = 2 ++ ++# The pattern continues for additional GPUs. ++ ++# ------------------------------ ++# CUDA GPU settings ++# ------------------------------ ++# These only apply when using the CUDA version of KataGo. ++ ++# For one GPU: optionally uncomment and change this if the GPU you want to ++# use is not device 0 ++# cudaDeviceToUse = 0 ++ ++# For two GPUs: Uncomment these options, AND set numNNServerThreadsPerModel above. ++# Also, change their values if the devices you want to use are not 0 and 1. ++# cudaDeviceToUseThread0 = 0 ++# cudaDeviceToUseThread1 = 1 ++ ++# For three GPUs: Uncomment these options, AND set numNNServerThreadsPerModel above. ++# Also, change their values if the devices you want to use are not 0 and 1 and 2. ++# cudaDeviceToUseThread0 = 0 ++# cudaDeviceToUseThread1 = 1 ++# cudaDeviceToUseThread2 = 2 ++ ++# The pattern continues for additional GPUs. ++ ++# KataGo will automatically use FP16 or not based on the compute capability ++# of your NVIDIA GPU. If you want to try to force a particular behavior ++# you can uncomment these lines and change them to "true" or "false". ++# cudaUseFP16 = auto ++# cudaUseNHWC = auto ++ ++# ------------------------------ ++# OpenCL GPU settings ++# ------------------------------ ++# These only apply when using the OpenCL version of KataGo. ++ ++# Uncomment and set to true to tune OpenCL for every board size separately, ++# rather than only the largest possible size. ++# openclReTunePerBoardSize = false ++ ++# For one GPU: optionally uncomment and change this if the best device to use is guessed incorrectly. ++# The default behavior tries to guess the 'best' GPU or device on your system to use, usually it will be a good guess. ++# openclDeviceToUse = 0 ++ ++# For two GPUs: Uncomment these two lines and replace X and Y with the device ids of the devices you want to use. ++# It might NOT be 0 and 1, some computers will have many OpenCL devices. You can see what the devices are when ++# KataGo starts up - it should print or log all the devices it finds. ++# (AND also set numNNServerThreadsPerModel above) ++# openclDeviceToUseThread0 = X ++# openclDeviceToUseThread1 = Y ++ ++# For three GPUs: Uncomment these three lines and replace X and Y and Z with the device ids of the devices you want to use. ++# It might NOT be 0 and 1 and 2, some computers will have many OpenCL devices. You can see what the devices are when ++# KataGo starts up - it should print or log all the devices it finds. ++# (AND also set numNNServerThreadsPerModel above) ++# openclDeviceToUseThread0 = X ++# openclDeviceToUseThread1 = Y ++# openclDeviceToUseThread2 = Z ++ ++# The pattern continues for additional GPUs. ++ ++# KataGo will automatically use FP16 or not based on testing your GPU during ++# tuning. If you want to try to force a particular behavior though you can ++# uncomment this option and change it to "true" or "false". This is a fairly ++# blunt setting - more detailed settings are testable by rerunning the tuner ++# with various arguments (./katago tuner). ++# openclUseFP16 = auto ++ ++# ------------------------------ ++# Eigen-specific settings ++# ------------------------------ ++# These only apply when using the Eigen (pure CPU) version of KataGo. ++ ++# Number of CPU threads for evaluating the neural net on the Eigen backend. ++# ++# Default: numSearchThreads ++# numEigenThreadsPerModel = X ++ ++# =========================================================================== ++# Root move selection and biases ++# =========================================================================== ++# Uncomment and edit any of the below values to change them from their default. ++ ++# If provided, force usage of a specific seed for various random things in ++# the search. The default is to use a random seed. ++# searchRandSeed = hijklmn ++ ++# Temperature for the early game, randomize between chosen moves with ++# this temperature ++# chosenMoveTemperatureEarly = 0.5 ++ ++# Decay temperature for the early game by 0.5 every this many moves, ++# scaled with board size. ++# chosenMoveTemperatureHalflife = 19 ++ ++# At the end of search after the early game, randomize between chosen ++# moves with this temperature ++# chosenMoveTemperature = 0.10 ++ ++# Subtract this many visits from each move prior to applying ++# chosenMoveTemperature (unless all moves have too few visits) to downweight ++# unlikely moves ++# chosenMoveSubtract = 0 ++ ++# The same as chosenMoveSubtract but only prunes moves that fall below ++# the threshold. This setting does not affect chosenMoveSubtract. ++# chosenMovePrune = 1 ++ ++# Number of symmetries to sample (without replacement) and average at the root ++# rootNumSymmetriesToSample = 1 ++ ++# Using LCB for move selection? ++# useLcbForSelection = true ++ ++# How many stdevs a move needs to be better than another for LCB selection ++# lcbStdevs = 5.0 ++ ++# Only use LCB override when a move has this proportion of visits as the ++# top move. ++# minVisitPropForLCB = 0.15 ++ ++# =========================================================================== ++# Internal params ++# =========================================================================== ++# Uncomment and edit any of the below values to change them from their default. ++ ++# Scales the utility of winning/losing ++# winLossUtilityFactor = 1.0 ++ ++# Scales the utility for trying to maximize score ++# staticScoreUtilityFactor = 0.10 ++# dynamicScoreUtilityFactor = 0.30 ++ ++# Adjust dynamic score center this proportion of the way towards zero, ++# capped at a reasonable amount. ++# dynamicScoreCenterZeroWeight = 0.20 ++# dynamicScoreCenterScale = 0.75 ++ ++# The utility of getting a "no result" due to triple ko or other long cycle ++# in non-superko rulesets (-1 to 1) ++# noResultUtilityForWhite = 0.0 ++ ++# The number of wins that a draw counts as, for white. (0 to 1) ++# drawEquivalentWinsForWhite = 0.5 ++ ++# Exploration constant for mcts ++# cpuctExploration = 1.0 ++# cpuctExplorationLog = 0.45 ++ ++# Parameters that control exploring more in volatile positions, exploring ++# less in stable positions. ++# cpuctUtilityStdevPrior = 0.40 ++# cpuctUtilityStdevPriorWeight = 2.0 ++# cpuctUtilityStdevScale = 0.85 ++ ++# FPU reduction constant for mcts ++# fpuReductionMax = 0.2 ++# rootFpuReductionMax = 0.1 ++# fpuParentWeightByVisitedPolicy = true ++ ++# Parameters that control weighting of evals based on the net's own ++# self-reported uncertainty. ++# useUncertainty = true ++# uncertaintyExponent = 1.0 ++# uncertaintyCoeff = 0.25 ++ ++# Explore using optimistic policy ++# rootPolicyOptimism = 0.2 ++# policyOptimism = 1.0 ++ ++# Amount to apply a downweighting of children with very bad values relative ++# to good ones. ++# valueWeightExponent = 0.25 ++ ++# Slight incentive for the bot to behave human-like with regard to passing at ++# the end, filling the dame, not wasting time playing in its own territory, ++# etc., and not play moves that are equivalent in terms of points but a bit ++# more unfriendly to humans. ++# rootEndingBonusPoints = 0.5 ++ ++# Make the bot prune useless moves that are just prolonging the game to ++# avoid losing yet. ++# rootPruneUselessMoves = true ++ ++# Apply bias correction based on local pattern keys ++# subtreeValueBiasFactor = 0.45 ++# subtreeValueBiasWeightExponent = 0.85 ++ ++# Use graph search rather than tree search - identify and share search for ++# transpositions. ++# useGraphSearch = true ++ ++# How much to shard the node table for search synchronization ++# nodeTableShardsPowerOfTwo = 16 ++ ++# How many virtual losses to add when a thread descends through a node ++# numVirtualLossesPerThread = 1 ++ ++# Improve the quality of evals under heavy multithreading ++# useNoisePruning = true ++ ++# =========================================================================== ++# Automatic avoid patterns ++# =========================================================================== ++# The parameters in this section provide a way to bias away from moves that match ++# patterns that this instance of KataGo has played in previous games, by auto-saving ++# moves to a directory and then auto-loading and biasing against them each new game. ++# Uncomment them to use them. When using this feature, all parameters must be specified. ++ ++# Different board sizes are tracked separately, but all board sizes share the same ++# parameters by default. Every parameter *except* for autoAvoidRepeatDir and ++# autoAvoidRepeatSaveChunkSize can be overridden per board size, ++# e.g. "autoAvoidRepeatMinTurnNumber13x13". You must ALSO specify the ++# defaults even if you have specified board-size-specific values. ++ ++# Directory to auto-save moves KataGo plays, to avoid them in future games. ++# You can create a new empty directory and put its path here. ++# If you run parallel instances of KataGo, use different directories if you ++# want them not to share their biases, use the same directory if you want ++# all of them to bias away from past moves that any of them have played. ++# KataGo will also automatically DELETE old data in this directory, so it is ++# recommended that if you do share the same directory between parallel instances, ++# that they all use the same settings that affect data saving/deletion. ++# autoAvoidRepeatDir = PATH_TO_NEW_DIRECTORY ++ ++# Penalize this much utility per matching move. ++# Values that are too large may lead to bad play. The value of 0.004 is fairly large ++# and might be large enough to result in some early weird/bad moves when trying ++# to avoid past games' moves if enough games begin the same way. You can experiment with it. ++# autoAvoidRepeatUtility = 0.004 ++ ++# Per each new move saved, exponentially decay prior saved moves by this factor. ++# This way, the bias against moves from many games ago is gradually phased out. ++# For example, 0.9995 = 1 - 1/2000, so would be roughly 2000 prior moves worth of ++# penalty weight remembered, in steady state. Depending on what turn number range ++# you are saving, this might equate to a different number of games For example ++# saving the first 50 moves per game would make this roughly 2000 / 50 = 40 games ++# worth of memory. ++# autoAvoidRepeatLambda = 0.9995 ++ ++# Affects data saving/deletion. ++# When the number of saved moves exceeds this, outright delete them to avoid too many ++# files and disk space building up. Also may affect the speed of saving/loading on start ++# of each game if this is set large and a lot of data builds up. ++# autoAvoidRepeatMaxPoses = 10000 ++ ++# Affects data saving/deletion. ++# Only save data for moves within this turn number range of those games. ++# E.g. setting autoAvoidRepeatMinTurnNumber to a number like 4 or 5 would tend to make ++# KataGo not develop a bias against the initial 3-4 and 4-4 corner moves in almost every game. ++# autoAvoidRepeatMinTurnNumber = 0 ++# autoAvoidRepeatMaxTurnNumber = 50 ++ ++# Affects data saving/deletion. ++# Within a single run of a program, wait to accumulate this many samples ++# (possibly across multiple clear_boards/games) before saving the data. ++# Can help to avoid writing too many small files to disk, especially when GTP is used ++# in a way that clears the board very frequently (e.g. gtp2ogs pooled manager). ++# autoAvoidRepeatSaveChunkSize = 200 ++ ++# =========================================================================== ++# Avoid SGF patterns ++# =========================================================================== ++# The parameters in this section provide a way to avoid moves that follow ++# specific patterns based on a set of SGF files loaded upon startup. ++# This is basically the same as the above "Automatic avoid patterns" section ++# above except you supply your own SGF files to avoid moves from. ++# Uncomment them to use this feature. Additionally, if the SGF file ++# contains the string %SKIP% in a comment on a move, that move will be ++# ignored for this purpose. ++ ++# Load SGF files from this directory when the engine is started ++# (only on startup, will not reload unless engine is restarted) ++# avoidSgfPatternDirs = path/to/directory/with/sgfs/ ++# You can also surround the file path in double quotes if the file path contains trailing spaces or hash signs. ++# Within double quotes, backslashes are escape characters. ++# avoidSgfPatternDirs = "path/to/directory/with/sgfs/" ++ ++# Penalize this much utility per matching move. ++# Set this negative if you instead want to favor SGF patterns instead of ++# penalizing them. This number does not need to be large, even 0.001 will ++# make a difference. Values that are too large may lead to bad play. ++# avoidSgfPatternUtility = 0.001 ++ ++# Optional - load only the newest this many files ++# avoidSgfPatternMaxFiles = 20 ++ ++# Optional - Penalty is multiplied by this per each older SGF file, so that ++# old SGF files matter less than newer ones. ++# avoidSgfPatternLambda = 0.90 ++ ++# Optional - pay attention only to moves made by players with this name. ++# For example, set it to the name that your bot's past games will show up ++# as in the SGF, so that the bot will only avoid repeating moves that itself ++# made in past games, not the moves that its opponents made. ++# avoidSgfPatternAllowedNames = my-ogs-bot-name1,my-ogs-bot-name2 ++ ++# Optional - Ignore moves in SGF files that occurred before this turn number. ++# avoidSgfPatternMinTurnNumber = 0 ++ ++# For more avoid patterns: ++# You can also specify a second set of parameters, and a third, fourth, ++# etc. by numbering 2,3,4,... ++# ++# avoidSgf2PatternDirs = ... ++# avoidSgf2PatternUtility = ... ++# avoidSgf2PatternMaxFiles = ... ++# avoidSgf2PatternLambda = ... ++# avoidSgf2PatternAllowedNames = ... ++# avoidSgf2PatternMinTurnNumber = ... ++ diff --git a/training/scripts/dated/20241110-122511/edit_checkpoint.py b/training/scripts/dated/20241110-122511/edit_checkpoint.py new file mode 100644 index 0000000..62c4189 --- /dev/null +++ b/training/scripts/dated/20241110-122511/edit_checkpoint.py @@ -0,0 +1,58 @@ +#!/usr/bin/python3 +import sys +import os +import argparse +import math +import torch +import json + +description = """ +Utility for dumping or modifying torch checkpoint file contents +""" + +parser = argparse.ArgumentParser(description=description) +parser.add_argument('-checkpoint', help='Checkpoint file path', required=True) +parser.add_argument('-output-json-to', help='Output json contents to here, except for model parameters and optimizer state') +parser.add_argument('-overwrite-checkpoint-from-json', help='Use this json contents to overwrite the apprpriate fields of the state dict of the checkpoint') +args = vars(parser.parse_args()) + +checkpoint_path = args["checkpoint"] +output_json_to = args["output_json_to"] +overwrite_checkpoint_from_json = args["overwrite_checkpoint_from_json"] + +data = torch.load(checkpoint_path,map_location="cpu") + +if output_json_to is not None: + assert output_json_to.endswith(".json") + data_to_write = dict( + running_metrics = data["running_metrics"], + train_state = data["train_state"], + config = data["config"] if "config" in data else None, + ) + with open(output_json_to,"w") as f: + json.dump(data,f,indent=2,default=repr) + print(f"Dumped to {output_json_to}") + +elif overwrite_checkpoint_from_json: + with open(overwrite_checkpoint_from_json) as f: + data_to_use = json.load(f) + if "running_metrics" in data_to_use: + print("Overwriting running_metrics") + data["running_metrics"] = data_to_use["running_metrics"] + if "train_state" in data_to_use: + print("Overwriting train_state") + data["train_state"] = data_to_use["train_state"] + if "config" in data_to_use: + print("Overwriting config") + data["config"] = data_to_use["config"] + + torch.save(data, checkpoint_path) + print(f"Updated {checkpoint_path} with new fields") + +else: + data_to_write = dict( + running_metrics = data["running_metrics"], + train_state = data["train_state"], + config = data["config"] if "config" in data else None, + ) + print(json.dumps(data_to_write,indent=2,default=repr)) diff --git a/training/scripts/dated/20241110-122511/elo.py b/training/scripts/dated/20241110-122511/elo.py new file mode 100644 index 0000000..f54d1a4 --- /dev/null +++ b/training/scripts/dated/20241110-122511/elo.py @@ -0,0 +1,855 @@ +import copy +import math +import numpy as np +import os +import scipy.stats +import scipy.special +import itertools +from abc import ABC, abstractmethod +from typing import List, Dict, Tuple, Set, Sequence +from dataclasses import dataclass + +Player = str +PlayerIdx = int + +ELO_PER_STRENGTH = 400.0 * math.log10(math.exp(1.0)) +P1_ADVANTAGE_NAME = "P1Advantage" + +class EloInfo: + """Summarizes maximum likelihood Elos and uncertainties for a group of players.""" + + def __init__( + self, + players: List[Player], + elo: Dict[Player,float], + elo_stderr: Dict[Player,float], + elo_covariance: Dict[Tuple[Player,Player],float], + effective_game_count: Dict[Player,float], + ): + self.players = players + self.elo = elo + self.elo_stderr = elo_stderr + self.elo_covariance = elo_covariance + self.effective_game_count = effective_game_count + + self.players = sorted(self.players, key=(lambda player: -elo[player] if player != P1_ADVANTAGE_NAME else 1e50)) + + def get_players(self) -> List[Player]: + return self.players + + def get_elo(self, p1: Player) -> float: + """Returns the maximum likelihood Elo of p1""" + return self.elo[p1] + + def get_approx_elo_stderr(self, p1: Player) -> float: + """Returns an approximation of the standard error on the Elo of p1, ASSUMING all other players Elos are equal to their maximum likelihood value. + This approximation may underestimate if the amount of data is very small.""" + return self.elo_stderr[p1] + + def get_elo_difference(self, p1: Player, p2: Player) -> float: + """Returns the maximum likelhood difference in Elo between p1 and p2""" + return self.elo[p1] - self.elo[p2] + + def get_approx_elo_difference_stderr(self, p1: Player, p2: Player) -> float: + """Returns an approximation of the standard error on difference in Elo between p1 and p2. + This approximation may underestimate if the amount of data is very small.""" + return math.sqrt( + self.elo_covariance[(p1,p1)] - self.elo_covariance[(p1,p2)] - self.elo_covariance[(p2,p1)] + self.elo_covariance[(p2,p2)] + ) + + def get_approx_likelihood_of_superiority(self, p1: Player, p2: Player) -> float: + """Returns an approximation of the likelihood that elo(p1) - elo(p2) > 0, given the data. + This approximation may be overconfident or inaccurate if the amount of data is very small.""" + if p1 == p2: + return 0.5 + mean = self.get_elo_difference(p1,p2) + stderr = self.get_approx_elo_difference_stderr(p1,p2) + return scipy.stats.t.cdf(mean/stderr,df=self.effective_game_count[p1]-1) + + def get_log10_odds_surprise_max_likelihood(self, p1: Player, p2: Player, g1: float, total: int) -> float: + """Returns an indication of how surprised we would be for p1 to win g1 games out of total versus p2, given the maximum likeihood Elos. + Doesn't explicitly model draw prob, if g1 is fractional then assumes the fractional part randomly rounds to a full 1 or 0 + and averages the probabilities. + A value of 3.0 means that g1 is larger than we would expect given the players' estimated Elos, such that a result + that extreme or more has less than 1 : 10^3.0 odds of happening under the posterior. + A value of -3.0 means that g1 is smaller larger than we would expect given the players' estimated Elos, such that a result + that extreme or more has less than 1 : 10^3.0 odds of happening under the posterior. + """ + if total <= 0: + return 0 + mean = self.get_elo_difference(p1,p2) / ELO_PER_STRENGTH + + max_likelihood_winprob = 1.0 / (1.0 + math.exp(-mean)) + max_likelihood_g1 = max_likelihood_winprob * total + if max_likelihood_g1 < g1: + signflip = -1 + mean = -mean + g1 = total - g1 + else: + signflip = 1 + + winprob = 1.0 / (1.0 + math.exp(-mean)) + + g1_floor = int(math.floor(g1)) + g1_frac = g1 - g1_floor + if g1_frac != 0.0: + # Average endpoints for draws + logxf = scipy.stats.binom.logcdf(g1_floor,total,winprob) + logxp1 = scipy.stats.binom.logcdf(g1_floor+1,total,winprob) + log_prob = scipy.special.logsumexp([logxf+math.log(1.0-g1_frac),logxp1+math.log(g1_frac)]) + else: + log_prob = scipy.stats.binom.logcdf(g1,total,winprob) + + log_odds = log_prob - (math.log(-log_prob) if log_prob > -1e-10 else math.log(1.0 - math.exp(log_prob))) + return -signflip * max(0.0, -log_odds / math.log(10.0)) + + def get_approx_log10_odds_surprise_bayes(self, p1: Player, p2: Player, g1: float, total: int) -> float: + """Returns an indication of how surprised we would be for p1 to win g1 games out of total versus p2, given the posterior. + Computed via numeric integration over the pairwise posteriors. + Doesn't explicitly model draw prob, if g1 is fractional then assumes the fractional part randomly rounds to a full 1 or 0 + and averages the probabilities. + A value of 3.0 means that g1 is larger than we would expect given the players' estimated Elos, such that a result + that extreme or more has less than 1 : 10^3.0 odds of happening under the posterior. + A value of -3.0 means that g1 is smaller larger than we would expect given the players' estimated Elos, such that a result + that extreme or more has less than 1 : 10^3.0 odds of happening under the posterior. + """ + mean = self.get_elo_difference(p1,p2) / ELO_PER_STRENGTH + stderr = self.get_approx_elo_difference_stderr(p1,p2) / ELO_PER_STRENGTH + + max_likelihood_winprob = 1.0 / (1.0 + math.exp(-mean)) + max_likelihood_g1 = max_likelihood_winprob * total + if max_likelihood_g1 < g1: + signflip = -1 + mean = -mean + g1 = total - g1 + else: + signflip = 1 + + logw_list = [] + logwx_list = [] + for x in np.linspace(-15.0,15.0,num=1000,endpoint=False): + logw = scipy.stats.norm.logpdf(x) + winprob = 1.0 / (1.0 + math.exp(-(mean + x * stderr))) + # print("X", x) + # print("winprob", winprob) + # print("g1 total", g1, total, g1/total) + + g1_floor = int(math.floor(g1)) + g1_frac = g1 - g1_floor + if g1_frac != 0.0: + # Average endpoints for draws + logxf = scipy.stats.binom.logcdf(g1_floor,total,winprob) + logxp1 = scipy.stats.binom.logcdf(g1_floor+1,total,winprob) + log_prob = scipy.special.logsumexp([logxf+math.log(1.0-g1_frac),logxp1+math.log(g1_frac)]) + else: + logx = scipy.stats.binom.logcdf(g1,total,winprob) + + # print("logw logx", logw, logx) + logw_list.append(logw) + logwx_list.append(logw+logx) + + log_wsum = scipy.special.logsumexp(logw_list) + log_wxsum = scipy.special.logsumexp(logwx_list) + # print("log_wsum",log_wsum) + # print("log_wxsum",log_wxsum) + + log_prob = log_wxsum - log_wsum + log_odds = log_prob - (math.log(-log_prob) if log_prob > -1e-10 else math.log(1.0 - math.exp(log_prob))) + return -signflip * max(0.0, -log_odds / math.log(10.0)) + + def __str__(self) -> str: + lines = [] + for player in self.players: + lines.append(f"{player:20s}: {self.elo[player]:8.2f} +/- {self.elo_stderr[player]:5.2f}") + return "\n".join(lines) + + +class Likelihood: + """Summarizes the information in an observed datapoint about player Elos, or a prior about them. + + Represents that sum_{p in playercombo} Strength(p)*playercombo[p] + offset has likelihood function f raised to the power of weight. + If kind is SIGMOID_KIND, f is the sigmoid function. + If kind is GAUSSIAN_KIND, f is the pdf of a unit gaussian + + where strength is such that 1 unit of strength is e:1 odds of winning in a head-to-head game. + """ + + SIGMOID_KIND = 1 + GAUSSIAN_KIND = 2 + + def __init__( + self, + playercombo: Dict[Player,float], + offset: float, + weight: float, + gamecount: float, + kind: int, + ): + self.playercombo = playercombo + self.offset = offset + self.weight = weight + self.gamecount = gamecount + self.kind = kind + assert kind == Likelihood.SIGMOID_KIND or kind == Likelihood.GAUSSIAN_KIND, "invalid kind" + + def add_idxs(self, player_to_idx: Dict[Player,PlayerIdx]): + self.pidxcombo : List[Tuple[PlayerIdx,float]] = [(player_to_idx[player],coeff) for (player,coeff) in self.playercombo.items()] + + LOG_ONE_OVER_SQRT_TWO_PI = math.log(1.0 / math.sqrt(2.0 * math.pi)) + + def get_loglikelihood(self, strengths: np.array) -> float: + strength_total = self.offset + sum(strengths[pidx] * coeff for (pidx,coeff) in self.pidxcombo) + if self.kind == Likelihood.SIGMOID_KIND: + if strength_total < -40: + return strength_total + return -self.weight * math.log(1.0 + math.exp(-strength_total)) + else: + return self.weight * (Likelihood.LOG_ONE_OVER_SQRT_TWO_PI - 0.5 * strength_total * strength_total) + + def accum_dloglikelihood_dstrength(self, strengths: np.array, accum: np.array): + strength_total = self.offset + sum(strengths[pidx] * coeff for (pidx,coeff) in self.pidxcombo) + if self.kind == Likelihood.SIGMOID_KIND: + dloglikelihood_dstrength_total = self.weight / (1.0 + math.exp(strength_total)) + else: + dloglikelihood_dstrength_total = -self.weight * strength_total + for (pidx,coeff) in self.pidxcombo: + accum[pidx] += coeff * dloglikelihood_dstrength_total + + def accum_d2loglikelihood_dstrength2(self, strengths: np.array, accum: np.array): + strength_total = self.offset + sum(strengths[pidx] * coeff for (pidx,coeff) in self.pidxcombo) + if self.kind == Likelihood.SIGMOID_KIND: + denom = math.exp(-0.5 * strength_total) + math.exp(0.5 * strength_total) + d2loglikelihood_dstrength_total2 = -self.weight / (denom * denom) + else: + d2loglikelihood_dstrength_total2 = -self.weight + + for (pidx1,coeff1) in self.pidxcombo: + for (pidx2,coeff2) in self.pidxcombo: + accum[pidx1,pidx2] += coeff1 * coeff2 * d2loglikelihood_dstrength_total2 + + def accum_d2loglikelihood_dstrength2_scalepow(self, strengths: np.array, accum: np.array, scale: float, power: float): + strength_total = self.offset + sum(strengths[pidx] * coeff for (pidx,coeff) in self.pidxcombo) + if self.kind == Likelihood.SIGMOID_KIND: + denom = math.exp(-0.5 * strength_total) + math.exp(0.5 * strength_total) + d2loglikelihood_dstrength_total2 = -self.weight / (denom * denom) + else: + d2loglikelihood_dstrength_total2 = -self.weight + + for (pidx1,coeff1) in self.pidxcombo: + for (pidx2,coeff2) in self.pidxcombo: + x = coeff1 * coeff2 * d2loglikelihood_dstrength_total2 + accum[pidx1,pidx2] += scale * (x ** power) + + +def likelihood_of_games( + p1: Player, + p2: Player, + num_games: float, + p1_won_proportion: float, + include_first_player_advantage: bool, +) -> List[Likelihood]: + """Return a list of Likelihood objects representing the result of set of games between p1 and p2 + + These Likelihoods can accumulated with any other Likelihoods, and then all passed to compute_elos + to compute maximum likelihood Elos for all the players. + + NOTE: For performance reasons, you should try to minimize the number of these you create. If p1 and p2 played + a large number of games, don't call this function once per game. Instead, call it once for all the games + together (or twice, if you are using include_first_player_advantage=True, separately reporting the stats + that occured when each side was the first player). + + NOTE: If specifying include_first_player_advantage=True, make sure to add a make_single_player_prior on + P1_ADVANTAGE_NAME. + + Arguments: + p1: Name of the first player + p2: Name of the second player + num_games: The number of games played + p1_won_proportion: The proportion of games that p1 won among those games played, counting draws as 0.5. + include_first_player_advantage: If true, will also make the computation take into account that the first player + might have an advantage (or a disadvantage!) and it will try to estimate the amount of that advantage. + + Returns: + List of likelihood objects summarizing the information. + """ + + ret = [] + assert p1_won_proportion >= 0.0 and p1_won_proportion <= 1.0 + assert num_games >= 0.0 + assert p1 != p2 + + if num_games > 0.0: + if not include_first_player_advantage: + if p1_won_proportion > 0.0: + ret.append(Likelihood( + playercombo={p1: 1.0, p2: -1.0}, + offset=0.0, + weight=p1_won_proportion*num_games, + gamecount=p1_won_proportion*num_games, + kind=Likelihood.SIGMOID_KIND + )) + if p1_won_proportion < 1.0: + ret.append(Likelihood( + playercombo={p2: 1.0, p1: -1.0}, + offset=0.0, + weight=(1.0-p1_won_proportion)*num_games, + gamecount=(1.0-p1_won_proportion)*num_games, + kind=Likelihood.SIGMOID_KIND + )) + else: + if p1_won_proportion > 0.0: + ret.append(Likelihood( + playercombo={p1: 1.0, p2: -1.0, P1_ADVANTAGE_NAME: 1.0}, + offset=0.0, + weight=p1_won_proportion*num_games, + gamecount=p1_won_proportion*num_games, + kind=Likelihood.SIGMOID_KIND + )) + if p1_won_proportion < 1.0: + ret.append(Likelihood( + playercombo={p2: 1.0, p1: -1.0, P1_ADVANTAGE_NAME: -1.0}, + offset=0.0, + weight=(1.0-p1_won_proportion)*num_games, + gamecount=(1.0-p1_won_proportion)*num_games, + kind=Likelihood.SIGMOID_KIND + )) + + return ret + +def make_single_player_prior( + p1: Player, + num_games: float, + elo: float, +) -> List[Likelihood]: + """Return a list of Likelihood objects representing a Bayesian prior that p1 is the specified Elo. + + The strength of the prior that p1 is the specified Elo is as if p1 were observed to have played + num_games many games against a known player of that Elo and won half and lost half. + + Returns: + List of likelihood objects summarizing the information. + """ + ret = [] + assert num_games >= 0.0 + assert np.isfinite(elo) + if num_games > 0.0: + ret.append(Likelihood( + playercombo={p1: 1.0}, + offset=(-elo / ELO_PER_STRENGTH), + weight=0.5*num_games, + gamecount=0.5*num_games, + kind=Likelihood.SIGMOID_KIND + )) + ret.append(Likelihood( + playercombo={p1: -1.0}, + offset=(elo / ELO_PER_STRENGTH), + weight=0.5*num_games, + gamecount=0.5*num_games, + kind=Likelihood.SIGMOID_KIND + )) + return ret + + +def make_sequential_prior( + players: List[Player], + num_games: float, +) -> List[Likelihood]: + """Return a list of Likelihood objects representing a Bayesian prior that each player in the sequence is similar in strength to the previous. + + This can be used, for example, if there were a sequence of changes between different versions, such that each version on average + is expected to be more similar to its neighbors. + + The strength of the prior between each sequential pair of players is as if they were observed to have played + num_games many games against each other and won half and lost half. + + Returns: + List of likelihood objects summarizing the information. + """ + ret = [] + assert num_games >= 0.0 + assert len(set(players)) == len(players), "players must not contain any duplicates" + + if len(players) < 1: + return ret + + for i in range(len(players)-1): + ret.extend(likelihood_of_games( + p1=players[i], + p2=players[i+1], + num_games=num_games, + p1_won_proportion=0.5, + include_first_player_advantage=False, + )) + return ret + + +def make_center_elos_prior( + players: Sequence[Player], + elo: float, +) -> List[Likelihood]: + """Return a list of Likelihood objects representing a Bayesian prior that the mean of all player Elos is the specified Elo. + + This prior will have no effect on the relative Elos of the players, unless it fights with another that sets players to + specific Elos, such as make_single_player_prior. It can simply be used to center all the Elos of the players. + + Returns: + List of likelihood objects summarizing the information. + """ + ret = [] + assert np.isfinite(elo) + assert len(set(players)) == len(players), "players must not contain any duplicates" + playercombo = { player: 1.0 for player in players } + ret.append(Likelihood( + playercombo=playercombo, + offset=-len(players) * elo / ELO_PER_STRENGTH, + weight=0.001, + gamecount=0.0, + kind=Likelihood.GAUSSIAN_KIND + )) + return ret + + +def compute_elos( + data: List[Likelihood], + tolerance: float = 0.001, + max_iters: int = 1000, + verbose: bool = False, +) -> EloInfo: + """Compute maximum-likelihood Elo ratings given the data and any priors. + + NOTE: It is recommend that you specify some sort of prior, even if relatively weak, for numerical stability. + When you call this function, it is up to you to make sure that the data and priors you have provided result in all + all Elos of all players being "anchored". For example, this function may crash or fail or return bad values if: + + * There is a player with no data at all, and no prior for that player. + (since that would mean an Elo for that player cannot be defined). + + * There is a player who has only won and never lost, and there is no prior that restricts that player's rating. + (since then nothing could stop player's Elo from going to infinity). + + * All players *have* won and lost against other players, but there is nothing that anchors the Elo of the + population as a whole, such no player having a make_single_player_prior AND there being no make_center_elos_prior. + (since even if all players Elos are known relative to one another, the Elos to report are undefined - there is nothing + to say where the Elos should be centered or what value they should start from). + + Examples of things that are normally sufficient to anchor everyone: + If all players have won and lost against other players, adding a make_center_elos_prior is sufficient. + If all players have won and lost against other players, AND every player has transitively beat every other player and + transitively lost to every other player, then adding a make_single_player_prior to one player is sufficient. + Regardless of whether the players have won or lost, adding a make_single_player_prior to every player is sufficient. + Regardless of whether the players have won or lost, adding both a make_sequential_prior and a make_center_elos_prior is sufficient. + + NOTE: Even aside from ensuring numeric stability and anchoring as above, it is probably good practice to add some mild prior + beyond that anyways. If your players are sequential (e.g. a series of different nets), a weak make_sequential_prior could be good. + If you want to just put all players on equal footing unbiasedly, adding a weak make_single_player_prior to each player that its Elo + is 0 (or 1000, or whatever) is also good. + + And if you have actual prior beliefs about the players Elos, feel free to add those. + + Arguments: + data: A single list of all the likelihoods from all your games and priors. + tolerance: Stop soon after the Elos stop changing by more than this. + max_iters: Bail out if the optimization takes more than this many iterations. + verbose: Print out the iteration as it proceeds. + + Returns: + Elos. Yay! + """ + + players = [] + for d in data: + players.extend(d.playercombo.keys()) + players = list(set(players)) + players.sort() + player_to_idx = { player: i for (i,player) in enumerate(players) } + + data = [copy.copy(d) for d in data] + for d in data: + d.add_idxs(player_to_idx) + + num_players = len(players) + + def compute_loglikelihood(strengths: np.array) -> float: + total = 0.0 + for d in data: + total += d.get_loglikelihood(strengths) + return total + + # Gauss newton + def find_ascent_vector(strengths: np.array) -> np.array: + dloglikelihood_dstrength = np.zeros(num_players,dtype=np.float64) + d2loglikelihood_dstrength2 = np.zeros((num_players,num_players),dtype=np.float64) + + for d in data: + d.accum_dloglikelihood_dstrength(strengths, dloglikelihood_dstrength) + d.accum_d2loglikelihood_dstrength2(strengths, d2loglikelihood_dstrength2) + + ascent = -np.linalg.solve(d2loglikelihood_dstrength2,dloglikelihood_dstrength) + return ascent + + def line_search_ascend(strengths: np.array, cur_loglikelihood: float) -> Tuple[np.array,float]: + ascent = find_ascent_vector(strengths) + # Try up to this many times to find an improvement + for i in range(30): + new_strengths = strengths + ascent + new_loglikelihood = compute_loglikelihood(new_strengths) + if new_loglikelihood > cur_loglikelihood: + return (new_strengths, new_loglikelihood) + # Shrink ascent step and try again + ascent *= 0.6 + return (strengths,cur_loglikelihood) + + strengths = np.zeros(num_players,dtype=np.float64) + loglikelihood = compute_loglikelihood(strengths) + iters_since_big_change = 0 + last_elo_change = None + for i in range(max_iters): + if verbose: + print(f"Beginning iteration {i}, cur log likelihood {loglikelihood}, last elo change {last_elo_change}") + + (new_strengths, new_loglikelihood) = line_search_ascend(strengths, loglikelihood) + elodiff = (new_strengths - strengths) * ELO_PER_STRENGTH + last_elo_change = 0 if len(elodiff) <= 0 else np.max(np.abs(elodiff)) + + strengths = new_strengths + loglikelihood = new_loglikelihood + + iters_since_big_change += 1 + if np.any(elodiff > tolerance): + iters_since_big_change = 0 + + if iters_since_big_change > 3: + break + + + d2loglikelihood_dstrength2 = np.zeros((num_players,num_players),dtype=np.float64) + for d in data: + d.accum_d2loglikelihood_dstrength2(strengths, d2loglikelihood_dstrength2) + strength_precision = -d2loglikelihood_dstrength2 + elo_precision = strength_precision / (ELO_PER_STRENGTH * ELO_PER_STRENGTH) + elo_covariance = np.linalg.inv(elo_precision) + + sqrt_ess_numerator = np.zeros((num_players,num_players),dtype=np.float64) + ess_denominator = np.zeros((num_players,num_players),dtype=np.float64) + for d in data: + if d.gamecount > 0.0: + d.accum_d2loglikelihood_dstrength2_scalepow(strengths, sqrt_ess_numerator, scale = 1.0, power=1.0) + d.accum_d2loglikelihood_dstrength2_scalepow(strengths, ess_denominator, scale = 1.0 / d.gamecount, power=2.0) + + info = EloInfo( + players = players, + elo = { player: ELO_PER_STRENGTH * strengths[player_to_idx[player]] for player in players }, + elo_stderr = { player: math.sqrt(1.0 / elo_precision[player_to_idx[player],player_to_idx[player]]) for player in players }, + elo_covariance = { (p1,p2): elo_covariance[player_to_idx[p1],player_to_idx[p2]] for p1 in players for p2 in players }, + effective_game_count = { + player: (np.square(sqrt_ess_numerator[player_to_idx[player],player_to_idx[player]]) / + ess_denominator[player_to_idx[player],player_to_idx[player]]) + for player in players + }, + ) + return info + +def has_only_factors_of_2_and_3(n: int) -> bool: + while n > 1: + if n % 2 == 0: + n //= 2 + elif n % 3 == 0: + n //= 3 + else: + return False + return True + +@dataclass +class GameRecord: + player1: str + player2: str + win: int = 0 + loss: int = 0 + draw: int = 0 + +class GameResultSummary: + + def __init__( + self, + elo_prior_games: float, + estimate_first_player_advantage: bool, + ): + self.results = {} # dict of { (player1_name, player2_name) : GameRecord } + + self._all_game_files = set() + self._elo_prior_games = elo_prior_games # number of games for bayesian prior around Elo 0 + self._estimate_first_player_advantage = estimate_first_player_advantage + self._elo_info = None + self._game_count = 0 + + def add_games_from_file_or_dir(self, input_file_or_dir: str, recursive=False): + """Add games found in input_file_or_dir into the results. Repeated paths to the same file across multiple calls will be ignored.""" + new_files = self._add_files(input_file_or_dir, recursive) + + def add_game_record(self, record: GameRecord): + """Add game record to results.""" + if (record.player1, record.player2) not in self.results: + self.results[(record.player1, record.player2)] = GameRecord(player1=record.player1,player2=record.player2) + self.results[(record.player1, record.player2)].win += record.win + self.results[(record.player1, record.player2)].loss += record.loss + self.results[(record.player1, record.player2)].draw += record.draw + self._game_count += record.win + record.loss + record.draw + + def clear(self): + """Clear all data added.""" + self.results = {} + self._all_game_files = set() + self._elo_info = None + + def print_game_results(self): + """Print tables of wins and win percentage.""" + pla_names = set(itertools.chain(*(name_pair for name_pair in self.results.keys()))) + self._print_result_matrix(pla_names) + + def print_elos(self): + """Print game results and maximum likelihood posterior Elos.""" + elo_info = self._compute_elos_if_needed() + real_players = [player for player in elo_info.players if player != P1_ADVANTAGE_NAME] + self._print_result_matrix(real_players) + print("Elos (+/- one approx standard error):") + print(elo_info) + + print("Pairwise approx % likelihood of superiority of row over column:") + los_matrix = [] + for player in real_players: + los_row = [] + for player2 in real_players: + los = elo_info.get_approx_likelihood_of_superiority(player,player2) + los_row.append(f"{los*100:.2f}") + los_matrix.append(los_row) + self._print_matrix(real_players,los_matrix) + + surprise_matrix = [] + for pla1 in real_players: + row = [] + for pla2 in real_players: + if (pla1 == pla2): + row.append("-") + continue + else: + pla1_pla2 = self.results[(pla1, pla2)] if (pla1, pla2) in self.results else GameRecord(pla1,pla2) + pla2_pla1 = self.results[(pla2, pla1)] if (pla2, pla1) in self.results else GameRecord(pla2,pla1) + win = pla1_pla2.win + pla2_pla1.loss + 0.5 * (pla1_pla2.draw + pla2_pla1.draw) + total = pla1_pla2.win + pla2_pla1.win + pla1_pla2.loss + pla2_pla1.loss + pla1_pla2.draw + pla2_pla1.draw + surprise = elo_info.get_log10_odds_surprise_max_likelihood(pla1, pla2, win, total) + if total <= 0: + row.append("-") + else: + row.append(f"{surprise:.1f}") + surprise_matrix.append(row) + print("Log10odds surprise matrix given the maximum-likelihood Elos:") + print("E.g. +3.0 means a 1:1000 unexpected good performance by row vs column.") + print("E.g. -4.0 means a 1:10000 unexpected bad performance by row vs column.") + print("Extreme numbers may indicate rock/paper/scissors, or Elo is not a good model for the data.") + self._print_matrix(real_players,surprise_matrix) + + print(f"Used a prior of {self._elo_prior_games} games worth that each player is near Elo 0.") + + def get_elos(self) -> EloInfo: + return self._compute_elos_if_needed() + + def get_game_results(self) -> Dict: + """Return a dictionary of game results as { (player1_name, player2_name) : GameRecord } + + You can retrieve results by player's name like: + results[(player1_name, player2_name)].win + results[(player1_name, player2_name)].loss + results[(player1_name, player2_name)].draw + """ + return self.results + + # Functions that can be implemented by subclasses ----------------------------------------------------- + # You can override these methods if you want add_games_from_file_or_dir to work. + # Otherwise, you can just add game records yourself via add_game_record. + @abstractmethod + def is_game_file(self, input_file: str) -> bool: + """Returns true if this file or directory is one that should have game results in it somewhere""" + raise NotImplementedError() + + @abstractmethod + def get_game_records(self, input_file: str) -> List[GameRecord]: + """Return all game records contained in this file""" + raise NotImplementedError() + + # Private functions ------------------------------------------------------------------------------------ + + def _compute_elos_if_needed(self): + if self._elo_info is None: + self._elo_info = self._estimate_elo() + return self._elo_info + + def _add_files(self, input_file_or_dir, recursive): + print(f"Searching and adding files in {input_file_or_dir}, please wait...") + + if not os.path.exists(input_file_or_dir): + raise Exception(f"There is no file or directory with name: {input_file_or_dir}") + + files = [] + if os.path.isdir(input_file_or_dir): + if recursive: + for (dirpath, dirnames, filenames) in os.walk(input_file_or_dir): + files += [os.path.join(dirpath, file) for file in filenames if self.is_game_file(os.path.join(dirpath, file))] + else: + files = [os.path.join(input_file_or_dir, file) for file in os.listdir(input_file_or_dir) if self.is_game_file(os.path.join(input_file_or_dir, file))] + else: + if self.is_game_file(input_file_or_dir): + files.append(input_file_or_dir) + + # Remove duplicates + new_game_files = set(files) + new_game_files = new_game_files.difference(self._all_game_files) + self._all_game_files = self._all_game_files.union(new_game_files) + self._add_new_games_to_result_dict(new_game_files, input_file_or_dir) + + print(f"Added {len(new_game_files)} new game files from {input_file_or_dir}") + + def _add_new_games_to_result_dict(self, new_game_files, source): + idx = 0 + for game_file in new_game_files: + records = self.get_game_records(game_file) + idx += 1 + if idx % 10 == 0 and has_only_factors_of_2_and_3(idx // 10): + print(f"Added {idx}/{len(new_game_files)} game files for {source}") + + for record in records: + self.add_game_record(record) + + def _estimate_elo(self) -> EloInfo: + """Estimate and print elo values. This function must be called after adding all games""" + pla_names = set(itertools.chain(*(name_pair for name_pair in self.results.keys()))) + data = [] + for pla_first in pla_names: + for pla_second in pla_names: + if (pla_first == pla_second): + continue + else: + if (pla_first, pla_second) not in self.results: + continue + record = self.results[(pla_first, pla_second)] + total = record.win + record.loss + record.draw + assert total >= 0 + if total == 0: + continue + + win = record.win + 0.5 * record.draw + winrate = win / total + data.extend(likelihood_of_games( + pla_first, + pla_second, + total, + winrate, + include_first_player_advantage=self._estimate_first_player_advantage + )) + + for pla in pla_names: + data.extend(make_single_player_prior(pla, self._elo_prior_games,0)) + data.extend(make_center_elos_prior(list(pla_names),0)) # Add this in case user put elo_prior_games = 0 + if self._estimate_first_player_advantage: + data.extend(make_single_player_prior(P1_ADVANTAGE_NAME, (1.0 + self._elo_prior_games) * 2.0, 0)) + + info = compute_elos(data, verbose=True) + return info + + def _print_matrix(self,pla_names,results_matrix): + per_elt_space = 2 + for sublist in results_matrix: + for elt in sublist: + per_elt_space = max(per_elt_space, len(str(elt))) + per_elt_space += 2 + + per_name_space = 1 if len(pla_names) == 0 else max(len(name) for name in pla_names) + per_name_space += 1 + if per_name_space > per_elt_space: + per_elt_space += 1 + + row_format = f"{{:>{per_name_space}}}" + f"{{:>{per_elt_space}}}" * len(results_matrix) + print(row_format.format("", *[name[:per_elt_space-2] for name in pla_names])) + for name, row in zip(pla_names, results_matrix): + print(row_format.format(name, *row)) + + def _print_result_matrix(self, pla_names): + print(f"Total games: {self._game_count}") + print("Games by player:") + for pla1 in pla_names: + total = 0 + for pla2 in pla_names: + if (pla1 == pla2): + continue + else: + pla1_pla2 = self.results[(pla1, pla2)] if (pla1, pla2) in self.results else GameRecord(pla1,pla2) + pla2_pla1 = self.results[(pla2, pla1)] if (pla2, pla1) in self.results else GameRecord(pla2,pla1) + total += pla1_pla2.win + pla2_pla1.win + pla1_pla2.loss + pla2_pla1.loss + pla1_pla2.draw + pla2_pla1.draw + print(f"{pla1}: {total:.1f}") + + print("Wins by row player against column player:") + result_matrix = [] + for pla1 in pla_names: + row = [] + for pla2 in pla_names: + if (pla1 == pla2): + row.append("-") + continue + else: + pla1_pla2 = self.results[(pla1, pla2)] if (pla1, pla2) in self.results else GameRecord(pla1,pla2) + pla2_pla1 = self.results[(pla2, pla1)] if (pla2, pla1) in self.results else GameRecord(pla2,pla1) + win = pla1_pla2.win + pla2_pla1.loss + 0.5 * (pla1_pla2.draw + pla2_pla1.draw) + total = pla1_pla2.win + pla2_pla1.win + pla1_pla2.loss + pla2_pla1.loss + pla1_pla2.draw + pla2_pla1.draw + row.append(f"{win:.1f}/{total:.1f}") + result_matrix.append(row) + self._print_matrix(pla_names,result_matrix) + + print("Win% by row player against column player:") + result_matrix = [] + for pla1 in pla_names: + row = [] + for pla2 in pla_names: + if (pla1 == pla2): + row.append("-") + continue + else: + pla1_pla2 = self.results[(pla1, pla2)] if (pla1, pla2) in self.results else GameRecord(pla1,pla2) + pla2_pla1 = self.results[(pla2, pla1)] if (pla2, pla1) in self.results else GameRecord(pla2,pla1) + win = pla1_pla2.win + pla2_pla1.loss + 0.5 * (pla1_pla2.draw + pla2_pla1.draw) + total = pla1_pla2.win + pla2_pla1.win + pla1_pla2.loss + pla2_pla1.loss + pla1_pla2.draw + pla2_pla1.draw + if total <= 0: + row.append("-") + else: + row.append(f"{win/total*100.0:.1f}%") + result_matrix.append(row) + + self._print_matrix(pla_names,result_matrix) + +# Testing code +if __name__ == "__main__": + # Example 1 + # data = [] + # data.extend(likelihood_of_games("Alice","Bob", 18, 12/18, False)) + # data.extend(likelihood_of_games("Bob","Carol", 18, 12/18, False)) + # data.extend(likelihood_of_games("Carol","Dan", 36, 12/18, False)) + # data.extend(likelihood_of_games("Dan","Eve", 48, 40/48, False)) + # data.extend(make_center_elos_prior(["Alice","Bob","Carol","Dan","Eve"],0)) + # info = compute_elos(data,verbose=True) + + # for player in info.players: + # for player2 in info.players: + # print(info.get_approx_likelihood_of_superiority(player,player2),end=" ") + # print() + + # Example 2 + summary = GameResultSummary( + elo_prior_games=1.0, + estimate_first_player_advantage=False, + ) + summary.add_game_record(GameRecord("Alice","Bob",win=12,loss=6,draw=0)) + summary.add_game_record(GameRecord("Bob","Carol",win=12,loss=6,draw=0)) + summary.add_game_record(GameRecord("Carol","Dan",win=36,loss=18,draw=0)) + summary.add_game_record(GameRecord("Dan","Eve",win=48,loss=24,draw=0)) + summary.print_elos() + + + diff --git a/training/scripts/dated/20241110-122511/export_model_for_selfplay.sh b/training/scripts/dated/20241110-122511/export_model_for_selfplay.sh new file mode 100644 index 0000000..eac659b --- /dev/null +++ b/training/scripts/dated/20241110-122511/export_model_for_selfplay.sh @@ -0,0 +1,115 @@ +#!/bin/bash -eu +set -o pipefail +{ +#Takes any models in torchmodels_toexport/ and outputs a cuda-runnable model file to modelstobetested/ +#Takes any models in torchmodels_toexport_extra/ and outputs a cuda-runnable model file to models_extra/ +#Should be run periodically. + +if [[ $# -ne 3 ]] +then + echo "Usage: $0 NAMEPREFIX BASEDIR USEGATING" + echo "Currently expects to be run from within the 'python' directory of the KataGo repo, or otherwise in the same dir as export_model.py." + echo "NAMEPREFIX string prefix for this training run, try to pick something globally unique. Will be displayed to users when KataGo loads the model." + echo "BASEDIR containing selfplay data and models and related directories" + echo "USEGATING = 1 to use gatekeeper, 0 to not use gatekeeper and output directly to models/" + exit 0 +fi +NAMEPREFIX="$1" +shift +BASEDIR="$1" +shift +USEGATING="$1" +shift + +#------------------------------------------------------------------------------ + +mkdir -p "$BASEDIR"/torchmodels_toexport +mkdir -p "$BASEDIR"/torchmodels_toexport_extra +mkdir -p "$BASEDIR"/modelstobetested +mkdir -p "$BASEDIR"/models_extra +mkdir -p "$BASEDIR"/models + +function exportStuff() { + FROMDIR="$1" + TODIR="$2" + + #Sort by timestamp so that we process in order of oldest to newest if there are multiple + for FILEPATH in $(find "$BASEDIR"/"$FROMDIR"/ -mindepth 1 -maxdepth 1 -printf "%T@ %p\n" | sort -n | cut -d ' ' -f 2) + do + #Make sure to skip tmp directories that are transiently there by the training, + #they are probably in the process of being written + if [ ${FILEPATH: -4} == ".tmp" ] + then + echo "Skipping tmp file:" "$FILEPATH" + elif [ ${FILEPATH: -9} == ".exported" ] + then + echo "Skipping self tmp file:" "$FILEPATH" + else + echo "Found model to export:" "$FILEPATH" + NAME="$(basename "$FILEPATH")" + + SRC="$BASEDIR"/"$FROMDIR"/"$NAME" + TMPDST="$BASEDIR"/"$FROMDIR"/"$NAME".exported + TARGET="$BASEDIR"/"$TODIR"/"$NAME" + + if [ -d "$BASEDIR"/modelstobetested/"$NAME" ] || \ + [ -d "$BASEDIR"/rejectedmodels/"$NAME" ] || \ + [ -d "$BASEDIR"/models/"$NAME" ] || \ + [ -d "$BASEDIR"/models_extra/"$NAME" ] || \ + [ -d "$BASEDIR"/modelsuploaded/"$NAME" ] + then + echo "Model with same name aleady exists, so skipping:" "$SRC" + else + rm -rf "$TMPDST" + mkdir "$TMPDST" + + set -x + python3 ./export_model_pytorch.py \ + -checkpoint "$SRC"/model.ckpt \ + -export-dir "$TMPDST" \ + -model-name "$NAMEPREFIX""-""$NAME" \ + -filename-prefix model \ + -use-swa + + python3 ./clean_checkpoint.py \ + -checkpoint "$SRC"/model.ckpt \ + -output "$TMPDST"/model.ckpt + set +x + + rm -r "$SRC" + gzip "$TMPDST"/model.bin + + #Make a bunch of the directories that selfplay will need so that there isn't a race on the selfplay + #machines to concurrently make it, since sometimes concurrent making of the same directory can corrupt + #a filesystem + #Only when not gating. When gating, gatekeeper is responsible. + if [ "$USEGATING" -eq 0 ] + then + if [ "$TODIR" != "models_extra" ] + then + mkdir -p "$BASEDIR"/selfplay/"$NAME" + mkdir -p "$BASEDIR"/selfplay/"$NAME"/sgfs + mkdir -p "$BASEDIR"/selfplay/"$NAME"/tdata + fi + fi + + #Sleep a little to allow some tolerance on the filesystem + sleep 5 + + mv "$TMPDST" "$TARGET" + echo "Done exporting:" "$NAME" "to" "$TARGET" + fi + fi + done +} + +if [ "$USEGATING" -eq 0 ] +then + exportStuff "torchmodels_toexport" "models" +else + exportStuff "torchmodels_toexport" "modelstobetested" +fi +exportStuff "torchmodels_toexport_extra" "models_extra" + +exit 0 +} diff --git a/training/scripts/dated/20241110-122511/export_model_pytorch.py b/training/scripts/dated/20241110-122511/export_model_pytorch.py new file mode 100644 index 0000000..a2b23b4 --- /dev/null +++ b/training/scripts/dated/20241110-122511/export_model_pytorch.py @@ -0,0 +1,461 @@ +#!/usr/bin/python3 +import sys +import os +import argparse +import traceback +import random +import math +import time +import struct +import json +import datetime +import logging +import numpy as np +from collections import defaultdict +from typing import Dict, List + +import torch +import torch.nn +from torch.optim.swa_utils import AveragedModel + +import modelconfigs +from model_pytorch import Model, ResBlock, NestedBottleneckResBlock +from load_model import load_model + +#Command and args------------------------------------------------------------------- + +description = """ +Export neural net weights to file for KataGo engine. +""" + +parser = argparse.ArgumentParser(description=description) +parser.add_argument('-checkpoint', help='Checkpoint to test', required=True) +parser.add_argument('-export-dir', help='model file dir to save to', required=True) +parser.add_argument('-model-name', help='name to record in model file', required=True) +parser.add_argument('-filename-prefix', help='filename prefix to save to within dir', required=True) +parser.add_argument('-use-swa', help='Use SWA model', action="store_true", required=False) +parser.add_argument('-export-14-as-15', help='Export model version 14 as 15', action="store_true", required=False) +args = vars(parser.parse_args()) + + +def main(args): + checkpoint_file = args["checkpoint"] + export_dir = args["export_dir"] + model_name = args["model_name"] + filename_prefix = args["filename_prefix"] + use_swa = args["use_swa"] + export_14_as_15 = args["export_14_as_15"] + + os.makedirs(export_dir,exist_ok=True) + + logging.root.handlers = [] + logging.basicConfig( + level=logging.INFO, + format="%(message)s", + handlers=[ + logging.StreamHandler(stream=sys.stdout), + logging.FileHandler(export_dir + "/log.txt"), + ], + ) + np.set_printoptions(linewidth=150) + + logging.info(str(sys.argv)) + + # LOAD MODEL --------------------------------------------------------------------- + model, swa_model, other_state_dict = load_model(checkpoint_file, use_swa, device="cpu", verbose=True) + model_config = model.config + + # WRITING MODEL ---------------------------------------------------------------- + extension = ".bin" + mode = "wb" + f = open(export_dir + "/" + filename_prefix + extension, mode) + def writeln(s): + f.write((str(s)+"\n").encode(encoding="ascii",errors="backslashreplace")) + def writestr(s): + f.write(s.encode(encoding="ascii",errors="backslashreplace")) + + # Ignore what's in the config if less than 11 since a lot of testing models + # are on old version but actually have various new architectures. + version = max(model_config["version"],11) + true_version = version + # Hack to be able to export version 14 as version 15 + if version == 14 and export_14_as_15: + version = 15 + + writeln(model_name) + writeln(version) + writeln(modelconfigs.get_num_bin_input_features(model_config)) + writeln(modelconfigs.get_num_global_input_features(model_config)) + + if version <= 12: + assert model.td_score_multiplier == 20.0 + assert model.scoremean_multiplier == 20.0 + assert model.scorestdev_multiplier == 20.0 + assert model.lead_multiplier == 20.0 + assert model.variance_time_multiplier == 40.0 + assert model.shortterm_value_error_multiplier == 0.25 + assert model.shortterm_score_error_multiplier == 30.0 + else: + writeln(model.td_score_multiplier) + writeln(model.scoremean_multiplier) + writeln(model.scorestdev_multiplier) + writeln(model.lead_multiplier) + writeln(model.variance_time_multiplier) + writeln(model.shortterm_value_error_multiplier) + writeln(model.shortterm_score_error_multiplier) + + if version >= 15: + if model.metadata_encoder is not None: + writeln(model.metadata_encoder.meta_encoder_version) + else: + writeln(0) + + # Write some dummy placeholders for future features + writeln(0) + writeln(0) + writeln(0) + writeln(0) + writeln(0) + writeln(0) + writeln(0) + + + def write_weights(weights): + # Little endian + reshaped = np.reshape(weights.detach().numpy(),[-1]) + num_weights = len(reshaped) + writestr("@BIN@") + f.write(struct.pack(f'<{num_weights}f',*reshaped)) + writestr("\n") + + def write_conv_weight(name,convweight): + (out_channels, in_channels, diamy, diamx) = convweight.shape + dilation = 1 + writeln(name) + writeln(diamy) #y + writeln(diamx) #x + writeln(in_channels) + writeln(out_channels) + writeln(dilation) #y + writeln(dilation) #x + # Torch order is oc,ic,y,x + # Desired output order is y,x,ic,oc + write_weights(torch.permute(convweight,(2,3,1,0))) + + def write_conv(name,conv): + assert conv.bias is None + write_conv_weight(name, conv.weight) + + def write_bn(name,normmask): + writeln(name) + + writeln(normmask.c_in) + epsilon = 1e-20 + writeln(epsilon) + has_gamma_or_scale = normmask.scale is not None or normmask.gamma is not None + has_beta = True + writeln(1 if has_gamma_or_scale else 0) + writeln(1 if has_beta else 0) + + if hasattr(normmask,"running_mean") and normmask.running_mean is not None: + assert normmask.is_using_batchnorm + assert normmask.running_mean.shape == (normmask.c_in,) + write_weights(normmask.running_mean) + else: + assert not normmask.is_using_batchnorm + write_weights(torch.zeros(normmask.c_in, dtype=torch.float)) + + if hasattr(normmask,"running_std") and normmask.running_std is not None: + assert normmask.is_using_batchnorm + assert normmask.running_std.shape == (normmask.c_in,) + write_weights(torch.maximum(torch.tensor(1e-20), normmask.running_std * normmask.running_std - epsilon)) + else: + assert not normmask.is_using_batchnorm + write_weights((1.0-epsilon) * torch.ones(normmask.c_in, dtype=torch.float)) + + if normmask.scale is not None: + if normmask.gamma is not None: + assert normmask.gamma.shape == (1, normmask.c_in, 1, 1) + assert has_gamma_or_scale + write_weights(normmask.scale * normmask.gamma) + else: + assert has_gamma_or_scale + write_weights(normmask.scale * torch.ones(normmask.c_in, dtype=torch.float, device="cpu")) + else: + if normmask.gamma is not None: + assert normmask.gamma.shape == (1, normmask.c_in, 1, 1) + assert has_gamma_or_scale + write_weights(normmask.gamma) + else: + assert not has_gamma_or_scale + pass + + assert normmask.beta.shape == (1, normmask.c_in, 1, 1) + write_weights(normmask.beta) + + def write_biasmask(name,biasmask): + writeln(name) + + writeln(biasmask.c_in) + epsilon = 1e-20 + writeln(epsilon) + has_gamma_or_scale = biasmask.scale is not None + has_beta = True + writeln(1 if has_gamma_or_scale else 0) + writeln(1 if has_beta else 0) + + write_weights(torch.zeros(biasmask.c_in, dtype=torch.float)) + write_weights((1.0-epsilon) * torch.ones(biasmask.c_in, dtype=torch.float)) + + if biasmask.scale is not None: + write_weights(biasmask.scale * torch.ones(biasmask.c_in, dtype=torch.float, device="cpu")) + + assert biasmask.beta.shape == (1, biasmask.c_in, 1, 1) + write_weights(biasmask.beta) + + def write_activation(name, activation): + writeln(name) + if isinstance(activation,torch.nn.ReLU): + writeln("ACTIVATION_RELU") + elif isinstance(activation,torch.nn.Mish): + writeln("ACTIVATION_MISH") + elif isinstance(activation,torch.nn.Identity): + writeln("ACTIVATION_IDENTITY") + else: + assert False, f"Activation not supported for export: {activation}" + + + def write_matmul(name,linearweight): + writeln(name) + (out_channels,in_channels) = linearweight.shape + writeln(in_channels) + writeln(out_channels) + # Torch order is oc,ic + # Desired output order is ic,oc + write_weights(torch.permute(linearweight,(1,0))) + + def write_matbias(name,linearbias): + writeln(name) + (out_channels,) = linearbias.shape + writeln(out_channels) + write_weights(linearbias) + + def write_normactconv(name,normactconv): + if normactconv.c_gpool is None: + assert normactconv.convpool is None + if normactconv.conv1x1 is None: + write_bn(name+".norm", normactconv.norm) + write_activation(name+".act", normactconv.act) + write_conv(name+".conv", normactconv.conv) + else: + write_bn(name+".norm", normactconv.norm) + write_activation(name+".act", normactconv.act) + # Torch conv order is oc,ic,h,w + # We want to add the 1x1 conv to the center of the h,w + h,w = (normactconv.conv.weight.shape[2],normactconv.conv.weight.shape[3]) + assert h % 2 == 1, "Conv1x1 can't be merged with even-sized convolution kernel" + assert w % 2 == 1, "Conv1x1 can't be merged with even-sized convolution kernel" + combined_conv = normactconv.conv.weight.detach().clone() + combined_conv[:,:,h//2:h//2+1,w//2:w//2+1] += normactconv.conv1x1.weight + assert normactconv.conv.bias is None + assert normactconv.conv1x1.bias is None + write_conv_weight(name+".conv", combined_conv) + else: + assert normactconv.convpool is not None + assert normactconv.conv1x1 is None + write_bn(name+".norm", normactconv.norm) + write_activation(name+".act", normactconv.act) + write_conv(name+".convpool.conv1r", normactconv.convpool.conv1r) + write_conv(name+".convpool.conv1g", normactconv.convpool.conv1g) + write_bn(name+".convpool.normg", normactconv.convpool.normg) + write_activation(name+".convpool.actg", normactconv.convpool.actg) + write_matmul(name+".convpool.linear_g", normactconv.convpool.linear_g.weight) + assert normactconv.convpool.linear_g.bias is None + + def write_block(name,block): + if isinstance(block,ResBlock) and block.normactconv1.c_gpool is None: + assert block.normactconv2.c_gpool is None + writeln("ordinary_block") + writeln(name) + write_normactconv(name+".normactconv1", block.normactconv1) + write_normactconv(name+".normactconv2", block.normactconv2) + elif isinstance(block,ResBlock) and block.normactconv1.c_gpool is not None: + assert block.normactconv2.c_gpool is None + writeln("gpool_block") + writeln(name) + write_normactconv(name+".normactconv1", block.normactconv1) + write_normactconv(name+".normactconv2", block.normactconv2) + elif isinstance(block,NestedBottleneckResBlock): + writeln("nested_bottleneck_block") + writeln(name) + writeln(block.internal_length) + assert block.internal_length == len(block.blockstack) + write_normactconv(name+".normactconvp", block.normactconvp) + for i,subblock in enumerate(block.blockstack): + write_block(name+".blockstack."+str(i),subblock) + write_normactconv(name+".normactconvq", block.normactconvq) + else: + assert False, "This kind of block is not supported for export right now" + + def write_metadata_encoder(name,encoder): + writeln(name) + writeln(encoder.c_input) + # Torch order is oc,ic. Flatten feature mask into the first mul + write_matmul(name+".mul1", encoder.linear1.weight * encoder.feature_mask.reshape((1,-1))) + write_matbias(name+".bias1", encoder.linear1.bias) + write_activation(name+".act1", encoder.act1) + write_matmul(name+".mul2", encoder.linear2.weight) + write_matbias(name+".bias2", encoder.linear2.bias) + write_activation(name+".act2", encoder.act2) + write_matmul(name+".mul3", encoder.out_scale * encoder.linear_output_to_trunk.weight) + assert encoder.linear_output_to_trunk.bias is None + + def write_trunk(name,model): + writeln("trunk") + writeln(len(model.blocks)) + writeln(model.c_trunk) + writeln(model.c_mid) + writeln(model.c_mid-model.c_gpool) + writeln(model.c_gpool) + writeln(model.c_gpool) + if version >= 15: + # Write some dummy placeholders for future features + writeln(0) + writeln(0) + writeln(0) + writeln(0) + writeln(0) + writeln(0) + + write_conv("model.conv_spatial", model.conv_spatial) + write_matmul("model.linear_global", model.linear_global.weight) + assert model.linear_global.bias is None + if model.metadata_encoder is not None: + assert version >= 15 + write_metadata_encoder("model.sgf_metadata_encoder",model.metadata_encoder) + + for i,block in enumerate(model.blocks): + write_block("model.blocks."+str(i), block) + if model.trunk_normless: + write_biasmask("model.norm_trunkfinal", model.norm_trunkfinal) + else: + write_bn("model.norm_trunkfinal", model.norm_trunkfinal) + write_activation("model.act_trunkfinal", model.act_trunkfinal) + + def write_policy_head(name,policyhead): + writeln(name) + write_conv(name+".conv1p", policyhead.conv1p) + write_conv(name+".conv1g", policyhead.conv1g) + write_biasmask(name+".biasg", policyhead.biasg) + write_activation(name+".actg", policyhead.actg) + write_matmul(name+".linear_g", policyhead.linear_g.weight) + assert policyhead.linear_g.bias is None + write_biasmask(name+".bias2", policyhead.bias2) + write_activation(name+".act2", policyhead.act2) + + # Write the this-move prediction and the optimistic policy prediction + if version <= 11: + assert policyhead.conv2p.weight.shape[0] == 4 + write_conv_weight(name+".conv2p", torch.stack((policyhead.conv2p.weight[0],), dim=0)) + assert policyhead.linear_pass.weight.shape[0] == 4 + write_matmul(name+".linear_pass", torch.stack((policyhead.linear_pass.weight[0],), dim=0)) + assert policyhead.linear_pass.bias is None + elif version <= 14: + assert policyhead.conv2p.weight.shape[0] == 6 + write_conv_weight(name+".conv2p", torch.stack((policyhead.conv2p.weight[0], policyhead.conv2p.weight[5]), dim=0)) + assert policyhead.linear_pass.weight.shape[0] == 6 + write_matmul(name+".linear_pass", torch.stack((policyhead.linear_pass.weight[0], policyhead.linear_pass.weight[5]), dim=0)) + assert policyhead.linear_pass.bias is None + elif version == 15 and true_version == 14: + assert policyhead.conv2p.weight.shape[0] == 6 + write_conv_weight(name+".conv2p", torch.stack((policyhead.conv2p.weight[0], policyhead.conv2p.weight[5]), dim=0)) + assert policyhead.linear_pass.weight.shape[0] == 6 + linear_pass_stack = [policyhead.linear_pass.weight[0], policyhead.linear_pass.weight[5]] + c_p1 = int(policyhead.linear_g.weight.shape[0]) + for _ in range(c_p1-2): + linear_pass_stack.append(torch.zeros_like(linear_pass_stack[0])) + write_matmul(name+".linear_pass", torch.stack(linear_pass_stack, dim=0)) + assert policyhead.linear_pass.bias is None + write_matbias(name+".linear_pass_bias", torch.tensor([0.0]*c_p1,dtype=torch.float32,device="cpu")) + write_activation(name+".act_pass", torch.nn.Identity()) + write_matmul(name+".linear_pass2", torch.tensor([[1.0,0.0]+[0.0]*(c_p1-2),[0.0,1.0]+[0.0]*(c_p1-2)],dtype=torch.float32,device="cpu")) + else: + assert policyhead.conv2p.weight.shape[0] == 6 + write_conv_weight(name+".conv2p", torch.stack((policyhead.conv2p.weight[0], policyhead.conv2p.weight[5]), dim=0)) + write_matmul(name+".linear_pass", policyhead.linear_pass.weight) + write_matbias(name+".linear_pass_bias", policyhead.linear_pass.bias) + write_activation(name+".act_pass", policyhead.act_pass) + assert policyhead.linear_pass2.weight.shape[0] == 6 + write_matmul(name+".linear_pass2", torch.stack((policyhead.linear_pass2.weight[0], policyhead.linear_pass2.weight[5]), dim=0)) + assert policyhead.linear_pass2.bias is None + + assert policyhead.conv2p.bias is None + + + def write_value_head(name, valuehead): + writeln(name) + write_conv(name+".conv1", valuehead.conv1) + write_biasmask(name+".bias1", valuehead.bias1) + write_activation(name+".act1", valuehead.act1) + write_matmul(name+".linear2", valuehead.linear2.weight) + write_matbias(name+".bias2", valuehead.linear2.bias) + write_activation(name+".act2", valuehead.act2) + write_matmul(name+".linear_valuehead", valuehead.linear_valuehead.weight) + write_matbias(name+".bias_valuehead", valuehead.linear_valuehead.bias) + + # For now, only output the scoremean and scorestdev and lead and vtime channels + w = valuehead.linear_miscvaluehead.weight[0:4] + b = valuehead.linear_miscvaluehead.bias[0:4] + # Grab the shortterm channels + w2 = valuehead.linear_moremiscvaluehead.weight[0:2] + b2 = valuehead.linear_moremiscvaluehead.bias[0:2] + w = torch.cat((w,w2),dim=0) + b = torch.cat((b,b2),dim=0) + write_matmul(name+".linear_miscvaluehead", w) + write_matbias(name+".bias_miscvaluehead", b) + + write_conv(name+".conv_ownership",valuehead.conv_ownership) + + def write_model(model): + write_trunk("model",model) + write_policy_head("model.policy_head",model.policy_head) + write_value_head("model.value_head",model.value_head) + + if swa_model is not None: + logging.info("Writing SWA model") + write_model(swa_model) + else: + logging.info("Writing model") + write_model(model) + f.close() + + with open(os.path.join(export_dir,"metadata.json"),"w") as f: + train_state = other_state_dict["train_state"] + data = {} + if "global_step_samples" in train_state: + data["global_step_samples"] = train_state["global_step_samples"] + if "total_num_data_rows" in train_state: + data["total_num_data_rows"] = train_state["total_num_data_rows"] + if "running_metrics" in other_state_dict: + assert sorted(list(other_state_dict["running_metrics"].keys())) == ["sums", "weights"] + data["extra_stats"] = { + "sums": { key: value for (key,value) in other_state_dict["running_metrics"]["sums"].items() if "sopt" not in key and "lopt" not in key }, + "weights": { key: value for (key,value) in other_state_dict["running_metrics"]["weights"].items() if "sopt" not in key and "lopt" not in key }, + } + if "last_val_metrics" in other_state_dict and "sums" in other_state_dict["last_val_metrics"] and "weights" in other_state_dict["last_val_metrics"]: + data["extra_stats"]["last_val_metrics"] = { + "sums": { key: value for (key,value) in other_state_dict["last_val_metrics"]["sums"].items() if "sopt" not in key and "lopt" not in key }, + "weights": { key: value for (key,value) in other_state_dict["last_val_metrics"]["weights"].items() if "sopt" not in key and "lopt" not in key }, + } + json.dump(data,f) + + + logging.info("Exported at: ") + logging.info(str(datetime.datetime.utcnow()) + " UTC") + + sys.stdout.flush() + sys.stderr.flush() + + +if __name__ == "__main__": + main(args) diff --git a/training/scripts/dated/20241110-122511/features.py b/training/scripts/dated/20241110-122511/features.py new file mode 100644 index 0000000..1452e42 --- /dev/null +++ b/training/scripts/dated/20241110-122511/features.py @@ -0,0 +1,340 @@ +import math +import numpy as np +from typing import Callable, List + +from board import Board, Loc, Pos, Player +import modelconfigs + +class Features: + def __init__(self, config: modelconfigs.ModelConfig, pos_len: int): + self.config = config + self.pos_len = pos_len + self.version = modelconfigs.get_version(config) + self.pass_pos = self.pos_len * self.pos_len + self.bin_input_shape = [modelconfigs.get_num_bin_input_features(config), pos_len, pos_len] + self.global_input_shape = [modelconfigs.get_num_global_input_features(config)] + + def xy_to_tensor_pos(self,x,y): + return y * self.pos_len + x + def loc_to_tensor_pos(self,loc,board): + if loc == Board.PASS_LOC: + return self.pass_pos + return board.loc_y(loc) * self.pos_len + board.loc_x(loc) + + def tensor_pos_to_loc(self,pos,board): + if pos == self.pass_pos: + return None + pos_len = self.pos_len + bsize = board.size + assert(self.pos_len >= bsize) + x = pos % pos_len + y = pos // pos_len + if x < 0 or x >= bsize or y < 0 or y >= bsize: + return board.loc(-10,-10) #Return an illegal move since this is offboard + return board.loc(x,y) + + def sym_tensor_pos(self,pos,symmetry): + if pos == self.pass_pos: + return pos + pos_len = self.pos_len + x = pos % pos_len + y = pos // pos_len + if symmetry >= 4: + symmetry -= 4 + tmp = x + x = y + y = tmp + if symmetry >= 2: + symmetry -= 2 + x = pos_len-x-1 + if symmetry >= 1: + symmetry -= 1 + y = pos_len-y-1 + return y * pos_len + x + + #Calls f on each location that is part of an inescapable atari, or a group that can be put into inescapable atari + def iterLadders(self, board, f): + chainHeadsSolved = {} + copy = board.copy() + + bsize = board.size + assert(self.pos_len >= bsize) + + for y in range(bsize): + for x in range(bsize): + pos = self.xy_to_tensor_pos(x,y) + loc = board.loc(x,y) + stone = board.board[loc] + + if stone == Board.BLACK or stone == Board.WHITE: + libs = board.num_liberties(loc) + if libs == 1 or libs == 2: + head = board.group_head[loc] + if head in chainHeadsSolved: + laddered = chainHeadsSolved[head] + if laddered: + f(loc,pos,[]) + else: + #Perform search on copy so as not to mess up tracking of solved heads + if libs == 1: + workingMoves = [] + laddered = copy.searchIsLadderCaptured(loc,True) + else: + workingMoves = copy.searchIsLadderCapturedAttackerFirst2Libs(loc) + laddered = len(workingMoves) > 0 + + chainHeadsSolved[head] = laddered + if laddered: + f(loc,pos,workingMoves) + + + #Returns the new idx, which could be the same as idx if this isn't a good training row + def fill_row_features(self, board, pla, opp, boards, moves, move_idx, rules, bin_input_data, global_input_data, idx): + assert(self.version >= 10) + + bsize = board.size + assert(self.pos_len >= bsize) + assert(len(boards) > 0) + assert(board.zobrist == boards[move_idx].zobrist) + + for y in range(bsize): + for x in range(bsize): + pos = self.xy_to_tensor_pos(x,y) + bin_input_data[idx,pos,0] = 1.0 + loc = board.loc(x,y) + stone = board.board[loc] + if stone == pla: + bin_input_data[idx,pos,1] = 1.0 + elif stone == opp: + bin_input_data[idx,pos,2] = 1.0 + + if stone == pla or stone == opp: + libs = board.num_liberties(loc) + if libs == 1: + bin_input_data[idx,pos,3] = 1.0 + elif libs == 2: + bin_input_data[idx,pos,4] = 1.0 + elif libs == 3: + bin_input_data[idx,pos,5] = 1.0 + + #Python code does NOT handle superko + if board.simple_ko_point is not None: + pos = self.loc_to_tensor_pos(board.simple_ko_point,board) + bin_input_data[idx,pos,6] = 1.0 + #Python code does NOT handle ko-prohibited encore spots or anything relating to the encore + #so features 7 and 8 leave that blank + + if move_idx >= 1 and moves[move_idx-1][0] == opp: + prev1_loc = moves[move_idx-1][1] + if prev1_loc is not None and prev1_loc != Board.PASS_LOC: + pos = self.loc_to_tensor_pos(prev1_loc,board) + bin_input_data[idx,pos,9] = 1.0 + elif prev1_loc == Board.PASS_LOC: + global_input_data[idx,0] = 1.0 + + if move_idx >= 2 and moves[move_idx-2][0] == pla: + prev2_loc = moves[move_idx-2][1] + if prev2_loc is not None and prev2_loc != Board.PASS_LOC: + pos = self.loc_to_tensor_pos(prev2_loc,board) + bin_input_data[idx,pos,10] = 1.0 + elif prev2_loc == Board.PASS_LOC: + global_input_data[idx,1] = 1.0 + + if move_idx >= 3 and moves[move_idx-3][0] == opp: + prev3_loc = moves[move_idx-3][1] + if prev3_loc is not None and prev3_loc != Board.PASS_LOC: + pos = self.loc_to_tensor_pos(prev3_loc,board) + bin_input_data[idx,pos,11] = 1.0 + elif prev3_loc == Board.PASS_LOC: + global_input_data[idx,2] = 1.0 + + if move_idx >= 4 and moves[move_idx-4][0] == pla: + prev4_loc = moves[move_idx-4][1] + if prev4_loc is not None and prev4_loc != Board.PASS_LOC: + pos = self.loc_to_tensor_pos(prev4_loc,board) + bin_input_data[idx,pos,12] = 1.0 + elif prev4_loc == Board.PASS_LOC: + global_input_data[idx,3] = 1.0 + + if move_idx >= 5 and moves[move_idx-5][0] == opp: + prev5_loc = moves[move_idx-5][1] + if prev5_loc is not None and prev5_loc != Board.PASS_LOC: + pos = self.loc_to_tensor_pos(prev5_loc,board) + bin_input_data[idx,pos,13] = 1.0 + elif prev5_loc == Board.PASS_LOC: + global_input_data[idx,4] = 1.0 + + def addLadderFeature(loc,pos,workingMoves): + assert(board.board[loc] == Board.BLACK or board.board[loc] == Board.WHITE) + bin_input_data[idx,pos,14] = 1.0 + if board.board[loc] == opp and board.num_liberties(loc) > 1: + for workingMove in workingMoves: + workingPos = self.loc_to_tensor_pos(workingMove,board) + bin_input_data[idx,workingPos,17] = 1.0 + + self.iterLadders(board, addLadderFeature) + + if move_idx > 0: + prevBoard = boards[move_idx-1] + else: + prevBoard = board + def addPrevLadderFeature(loc,pos,workingMoves): + assert(prevBoard.board[loc] == Board.BLACK or prevBoard.board[loc] == Board.WHITE) + bin_input_data[idx,pos,15] = 1.0 + self.iterLadders(prevBoard, addPrevLadderFeature) + + if move_idx > 1: + prevPrevBoard = boards[move_idx-2] + else: + prevPrevBoard = prevBoard + def addPrevPrevLadderFeature(loc,pos,workingMoves): + assert(prevPrevBoard.board[loc] == Board.BLACK or prevPrevBoard.board[loc] == Board.WHITE) + bin_input_data[idx,pos,16] = 1.0 + self.iterLadders(prevPrevBoard, addPrevPrevLadderFeature) + + #Features 18,19 - area + area = [0 for i in range(board.arrsize)] + + if rules["scoringRule"] == "SCORING_AREA" and rules["taxRule"] == "TAX_NONE": + safeBigTerritories = True + nonPassAliveStones = True + unsafeBigTerritories = True + board.calculateArea(area,nonPassAliveStones,safeBigTerritories,unsafeBigTerritories,rules["multiStoneSuicideLegal"]) + else: + hasAreaFeature = False + keepTerritories = False + keepStones = False + if rules["scoringRule"] == "SCORING_AREA" and (rules["taxRule"] == "TAX_SEKI" or rules["taxRule"] == "TAX_ALL"): + hasAreaFeature = True + keepTerritories = False + keepStones = True + elif rules["scoringRule"] == "SCORING_TERRITORY" and rules["taxRule"] == "TAX_NONE": + if rules["encorePhase"] >= 2: + hasAreaFeature = True + keepTerritories = True + keepStones = False + elif rules["scoringRule"] == "SCORING_TERRITORY" and (rules["taxRule"] == "TAX_SEKI" or rules["taxRule"] == "TAX_ALL"): + if rules["encorePhase"] >= 2: + hasAreaFeature = True + keepTerritories = False + keepStones = False + else: + assert(False) + + if hasAreaFeature: + board.calculateNonDameTouchingArea( + area, + keepTerritories, + keepStones, + rules["multiStoneSuicideLegal"] + ) + + for y in range(bsize): + for x in range(bsize): + loc = board.loc(x,y) + pos = self.xy_to_tensor_pos(x,y) + + if area[loc] == pla: + bin_input_data[idx,pos,18] = 1.0 + elif area[loc] == opp: + bin_input_data[idx,pos,19] = 1.0 + + #Features 20,21 - second encore phase starting stones, we just set them to the current stones in pythong + #since we don't really have a jp rules impl + if rules["encorePhase"] >= 2: + for y in range(bsize): + for x in range(bsize): + pos = self.xy_to_tensor_pos(x,y) + loc = board.loc(x,y) + stone = board.board[loc] + if stone == pla: + bin_input_data[idx,pos,20] = 1.0 + elif stone == opp: + bin_input_data[idx,pos,21] = 1.0 + + bArea = board.size * board.size + whiteKomi = rules["whiteKomi"] + if rules["scoringRule"] == "SCORING_TERRITORY": + whiteSelfKomi = whiteKomi + board.num_non_pass_moves_made[Board.BLACK] - board.num_non_pass_moves_made[Board.WHITE] + selfKomi = (whiteSelfKomi if pla == Board.WHITE else -whiteSelfKomi) + else: + selfKomi = (whiteKomi if pla == Board.WHITE else -whiteKomi) + + if selfKomi > bArea+1: + selfKomi = bArea+1 + if selfKomi < -bArea-1: + selfKomi = -bArea-1 + global_input_data[idx,5] = selfKomi/20.0 + + if rules["koRule"] == "KO_SIMPLE": + pass + elif rules["koRule"] == "KO_POSITIONAL" or rules["koRule"] == "KO_SPIGHT": + global_input_data[idx,6] = 1.0 + global_input_data[idx,7] = 0.5 + elif rules["koRule"] == "KO_SITUATIONAL": + global_input_data[idx,6] = 1.0 + global_input_data[idx,7] = -0.5 + else: + assert(False) + + if rules["multiStoneSuicideLegal"]: + global_input_data[idx,8] = 1.0 + + if rules["scoringRule"] == "SCORING_AREA": + pass + elif rules["scoringRule"] == "SCORING_TERRITORY": + global_input_data[idx,9] = 1.0 + else: + assert(False) + + if rules["taxRule"] == "TAX_NONE": + pass + elif rules["taxRule"] == "TAX_SEKI": + global_input_data[idx,10] = 1.0 + elif rules["taxRule"] == "TAX_ALL": + global_input_data[idx,10] = 1.0 + global_input_data[idx,11] = 1.0 + else: + assert(False) + + if rules["encorePhase"] > 0: + global_input_data[idx,12] = 1.0 + if rules["encorePhase"] > 1: + global_input_data[idx,13] = 1.0 + passWouldEndPhase = rules["passWouldEndPhase"] + global_input_data[idx,14] = (1.0 if passWouldEndPhase else 0.0) + + global_input_data[idx,15] = 1.0 if rules["asymPowersOfTwo"] != 0 else 0.0 + global_input_data[idx,16] = rules["asymPowersOfTwo"] + + if "hasButton" in rules and rules["hasButton"] and Board.PASS_LOC not in [move[1] for move in moves]: + global_input_data[idx,17] = 1.0 + + if rules["scoringRule"] == "SCORING_AREA" or rules["encorePhase"] > 1: + boardAreaIsEven = (board.size % 2 == 0) + + drawableKomisAreEven = boardAreaIsEven + + if drawableKomisAreEven: + komiFloor = math.floor(selfKomi / 2.0) * 2.0 + else: + komiFloor = math.floor((selfKomi-1.0) / 2.0) * 2.0 + 1.0 + + delta = selfKomi - komiFloor + assert(delta >= -0.0001) + assert(delta <= 2.0001) + if delta < 0.0: + delta = 0.0 + if delta > 2.0: + delta = 2.0 + + if delta < 0.5: + wave = delta + elif delta < 1.5: + wave = 1.0-delta + else: + wave = delta-2.0 + + global_input_data[idx,18] = wave + + return idx+1 diff --git a/training/scripts/dated/20241110-122511/forward_model.py b/training/scripts/dated/20241110-122511/forward_model.py new file mode 100644 index 0000000..97a46df --- /dev/null +++ b/training/scripts/dated/20241110-122511/forward_model.py @@ -0,0 +1,146 @@ +#!/usr/bin/python3 +import sys +import os +import argparse +import traceback +import random +import math +import time +import logging +import json +import numpy as np +from collections import defaultdict +from typing import Dict, List + +import torch +import torch.nn +from torch.optim.swa_utils import AveragedModel + +import modelconfigs +from model_pytorch import Model +from metrics_pytorch import Metrics +import data_processing_pytorch +from load_model import load_model + +# HANDLE COMMAND AND ARGS ------------------------------------------------------------------- + +if __name__ == "__main__": + + description = """ + Test neural net on Go positions from npz files of batches from selfplay. + """ + + parser = argparse.ArgumentParser(description=description) + parser.add_argument('-npz', help='NPZ file to evaluate', required=True) + parser.add_argument('-checkpoint', help='Checkpoint to test', required=False) + parser.add_argument('-pos-len', help='Spatial length of expected training data', type=int, required=True) + parser.add_argument('-use-swa', help='Use SWA model', action="store_true", required=False) + parser.add_argument('-gpu-idx', help='GPU idx', type=int, required=False) + + args = vars(parser.parse_args()) + +def main(args): + npz_file = args["npz"] + checkpoint_file = args["checkpoint"] + pos_len = args["pos_len"] + use_swa = args["use_swa"] + gpu_idx = args["gpu_idx"] + + world_size = 1 + rank = 0 + + # SET UP LOGGING ------------------------------------------------------------- + + logging.root.handlers = [] + logging.basicConfig( + level=logging.INFO, + format="%(message)s", + handlers=[ + logging.StreamHandler(stream=sys.stdout) + ], + ) + np.set_printoptions(linewidth=150) + + # FIGURE OUT GPU ------------------------------------------------------------ + if gpu_idx is not None: + torch.cuda.set_device(gpu_idx) + logging.info("Using GPU device: " + torch.cuda.get_device_name()) + device = torch.device("cuda", gpu_idx) + elif torch.cuda.is_available(): + logging.info("Using GPU device: " + torch.cuda.get_device_name()) + device = torch.device("cuda") + else: + logging.warning("WARNING: No GPU, using CPU") + device = torch.device("cpu") + + # LOAD MODEL --------------------------------------------------------------------- + + model, swa_model, _ = load_model(checkpoint_file, use_swa, device=device, pos_len=pos_len, verbose=False) + model_config = model.config + + batch = np.load(npz_file) + + with torch.no_grad(): + model.eval() + if swa_model is not None: + swa_model.eval() + + if swa_model is not None: + model_outputs = swa_model(torch.tensor(batch["binaryInputNCHW"],device=device),torch.tensor(batch["globalInputNC"],device=device)) + else: + model_outputs = model(torch.tensor(batch["binaryInputNCHW"],device=device),torch.tensor(batch["globalInputNC"],device=device)) + + postprocessed = model.postprocess_output(model_outputs) + + results = tuple(tensor.cpu() for tensor in postprocessed[0]) + + ( + policy_logits, # N, num_policy_outputs, move + value_logits, # N, {win,loss,noresult} + td_value_logits, # N, {long, mid, short} {win,loss,noresult} + pred_td_score, # N, {long, mid, short} + ownership_pretanh, # N, 1, y, x + pred_scoring, # N, 1, y, x + futurepos_pretanh, # N, 2, y, x + seki_logits, # N, 4, y, x + pred_scoremean, # N + pred_scorestdev, # N + pred_lead, # N + pred_variance_time, # N + pred_shortterm_value_error, # N + pred_shortterm_score_error, # N + scorebelief_logits, # N, 2 * (self.pos_len*self.pos_len + EXTRA_SCORE_DISTR_RADIUS) + ) = results + + for batch_idx in range(batch["binaryInputNCHW"].shape[0]): + print(f"Batch idx {batch_idx}") + + policy = torch.softmax(policy_logits[batch_idx],dim=1).numpy() + print("Policy") + print(f"Pass {policy[0,pos_len*pos_len]}") + for y in range(pos_len): + print(" ".join(["%5.2f%%" % (100.0 * prob) for prob in policy[0,y*pos_len:(y+1)*pos_len]])) + if model_config["version"] >= 12: + print("LongOptPolicy") + print(f"Pass {policy[4,pos_len*pos_len]}") + for y in range(pos_len): + print(" ".join(["%5.2f%%" % (100.0 * prob) for prob in policy[4,y*pos_len:(y+1)*pos_len]])) + print("ShortOptPolicy") + print(f"Pass {policy[5,pos_len*pos_len]}") + for y in range(pos_len): + print(" ".join("%5.2f%%" % (100.0 * prob) for prob in policy[5,y*pos_len:(y+1)*pos_len])) + ownership = torch.tanh(ownership_pretanh[batch_idx,0]) + print("Ownership") + for y in range(pos_len): + print(" ".join("%6.2f" % (100.0 * prob) for prob in ownership[y])) + + print("Value " + " ".join("%5.2fc" % (100.0 * prob) for prob in torch.softmax(value_logits[batch_idx],dim=0).numpy())) + print("ScoreMean " + str(pred_scoremean[batch_idx].item())) + print("ScoreMeanSq " + str(pred_scorestdev[batch_idx].item() ** 2 + pred_scoremean[batch_idx].item() ** 2)) + print("Lead " + str(pred_lead[batch_idx].item())) + print("Vartime " + str(pred_variance_time[batch_idx].item())) + print("STWinLossError " + str(100.0 * math.sqrt(pred_shortterm_value_error[batch_idx].item())) + "c") + print("STScoreError " + str(math.sqrt(pred_shortterm_score_error[batch_idx].item()))) + +if __name__ == "__main__": + main(args) diff --git a/training/scripts/dated/20241110-122511/gamestate.py b/training/scripts/dated/20241110-122511/gamestate.py new file mode 100644 index 0000000..dbfec57 --- /dev/null +++ b/training/scripts/dated/20241110-122511/gamestate.py @@ -0,0 +1,314 @@ + +from typing import Dict, Any, List, Optional, TYPE_CHECKING +import math + +import numpy as np + +from board import Board +from features import Features +from sgfmetadata import SGFMetadata + +if TYPE_CHECKING: + from model_pytorch import Model + +class GameState: + RULES_TT = { + "koRule": "KO_POSITIONAL", + "scoringRule": "SCORING_AREA", + "taxRule": "TAX_NONE", + "multiStoneSuicideLegal": True, + "hasButton": False, + "encorePhase": 0, + "passWouldEndPhase": False, + "whiteKomi": 7.5, + "asymPowersOfTwo": 0.0, + } + RULES_CHINESE = { + "koRule": "KO_SIMPLE", + "scoringRule": "SCORING_AREA", + "taxRule": "TAX_NONE", + "multiStoneSuicideLegal": False, + "hasButton": False, + "encorePhase": 0, + "passWouldEndPhase": False, + "whiteKomi": 7.5, + "asymPowersOfTwo": 0.0, + } + RULES_JAPANESE = { + "koRule": "KO_SIMPLE", + "scoringRule": "SCORING_TERRITORY", + "taxRule": "TAX_SEKI", + "multiStoneSuicideLegal": False, + "hasButton": False, + "encorePhase": 0, + "passWouldEndPhase": False, + "whiteKomi": 6.5, + "asymPowersOfTwo": 0.0, + } + + + def __init__(self, board_size: int, rules: Dict[str,Any]): + self.board_size = board_size + self.board = Board(size=board_size) + self.moves = [] + self.boards = [self.board.copy()] + self.rules = rules.copy() + self.redo_stack = [] + + def play(self, pla, loc): + self.board.play(pla,loc) + self.moves.append((pla,loc)) + self.boards.append(self.board.copy()) + if len(self.redo_stack) > 0: + move, _ = self.redo_stack[-1] + if move == (pla,loc): + self.redo_stack.pop() + else: + self.redo_stack.clear() + + def can_undo(self) -> bool: + return len(self.moves) > 0 + + def undo(self): + assert self.can_undo() + move = self.moves.pop() + board = self.boards.pop() + self.board = self.boards[-1].copy() + self.redo_stack.append((move,board)) + + def can_redo(self) -> bool: + return len(self.redo_stack) > 0 + + def redo(self): + assert self.can_redo() + move, board = self.redo_stack.pop() + self.moves.append(move) + self.boards.append(board) + self.board = self.boards[-1].copy() + + def get_input_features(self, features: Features): + bin_input_data = np.zeros(shape=[1]+features.bin_input_shape, dtype=np.float32) + global_input_data = np.zeros(shape=[1]+features.global_input_shape, dtype=np.float32) + pos_len = features.pos_len + pla = self.board.pla + opp = Board.get_opp(pla) + move_idx = len(self.moves) + # fill_row_features assumes N(HW)C order but we actually use NCHW order in the model, so work with it and revert + bin_input_data = np.transpose(bin_input_data,axes=(0,2,3,1)) + bin_input_data = bin_input_data.reshape([1,pos_len*pos_len,-1]) + features.fill_row_features(self.board,pla,opp,self.boards,self.moves,move_idx,self.rules,bin_input_data,global_input_data,idx=0) + bin_input_data = bin_input_data.reshape([1,pos_len,pos_len,-1]) + bin_input_data = np.transpose(bin_input_data,axes=(0,3,1,2)) + return bin_input_data, global_input_data + + def get_model_outputs(self, model: "Model", sgfmeta: Optional[SGFMetadata] = None, extra_output_names: List[str] = []): + import torch + from model_pytorch import Model, ExtraOutputs + with torch.no_grad(): + model.eval() + features = Features(model.config, model.pos_len) + + bin_input_data, global_input_data = self.get_input_features(features) + # Currently we don't actually do any symmetries + # symmetry = 0 + # model_outputs = model(apply_symmetry(batch["binaryInputNCHW"],symmetry),batch["globalInputNC"]) + + input_meta = None + if sgfmeta is not None: + metarow = sgfmeta.get_metadata_row(nextPlayer=self.board.pla, boardArea=self.board.size*self.board.size) + input_meta = torch.tensor(metarow, dtype=torch.float32, device=model.device) + input_meta = input_meta.reshape([1,-1]) + + extra_outputs = ExtraOutputs(extra_output_names) + + model_outputs = model( + torch.tensor(bin_input_data, dtype=torch.float32, device=model.device), + torch.tensor(global_input_data, dtype=torch.float32, device=model.device), + input_meta = input_meta, + extra_outputs=extra_outputs, + ) + + available_extra_outputs = extra_outputs.available + + outputs = model.postprocess_output(model_outputs) + ( + policy_logits, # N, num_policy_outputs, move + value_logits, # N, {win,loss,noresult} + td_value_logits, # N, {long, mid, short} {win,loss,noresult} + pred_td_score, # N, {long, mid, short} + ownership_pretanh, # N, 1, y, x + pred_scoring, # N, 1, y, x + futurepos_pretanh, # N, 2, y, x + seki_logits, # N, 4, y, x + pred_scoremean, # N + pred_scorestdev, # N + pred_lead, # N + pred_variance_time, # N + pred_shortterm_value_error, # N + pred_shortterm_score_error, # N + scorebelief_logits, # N, 2 * (self.pos_len*self.pos_len + EXTRA_SCORE_DISTR_RADIUS) + ) = (x[0] for x in outputs[0]) # N = 0 + + policy0 = torch.nn.functional.softmax(policy_logits[0,:],dim=0).cpu().numpy() + policy1 = torch.nn.functional.softmax(policy_logits[1,:],dim=0).cpu().numpy() + value = torch.nn.functional.softmax(value_logits,dim=0).cpu().numpy() + td_value = torch.nn.functional.softmax(td_value_logits[0,:],dim=0).cpu().numpy() + td_value2 = torch.nn.functional.softmax(td_value_logits[1,:],dim=0).cpu().numpy() + td_value3 = torch.nn.functional.softmax(td_value_logits[2,:],dim=0).cpu().numpy() + scoremean = pred_scoremean.cpu().item() + td_score = pred_td_score.cpu().numpy() + scorestdev = pred_scorestdev.cpu().item() + lead = pred_lead.cpu().item() + vtime = pred_variance_time.cpu().item() + estv = math.sqrt(pred_shortterm_value_error.cpu().item()) + ests = math.sqrt(pred_shortterm_score_error.cpu().item()) + ownership = torch.tanh(ownership_pretanh).cpu().numpy() + scoring = pred_scoring.cpu().numpy() + futurepos = torch.tanh(futurepos_pretanh).cpu().numpy() + seki_probs = torch.nn.functional.softmax(seki_logits[0:3,:,:],dim=0).cpu().numpy() + seki = seki_probs[1] - seki_probs[2] + seki2 = torch.sigmoid(seki_logits[3,:,:]).cpu().numpy() + scorebelief = torch.nn.functional.softmax(scorebelief_logits,dim=0).cpu().numpy() + + board = self.board + + moves_and_probs0 = [] + for i in range(len(policy0)): + move = features.tensor_pos_to_loc(i,board) + if i == len(policy0)-1: + moves_and_probs0.append((Board.PASS_LOC,policy0[i])) + elif board.would_be_legal(board.pla,move): + moves_and_probs0.append((move,policy0[i])) + + moves_and_probs1 = [] + for i in range(len(policy1)): + move = features.tensor_pos_to_loc(i,board) + if i == len(policy1)-1: + moves_and_probs1.append((Board.PASS_LOC,policy1[i])) + elif board.would_be_legal(board.pla,move): + moves_and_probs1.append((move,policy1[i])) + + ownership_flat = ownership.reshape([features.pos_len * features.pos_len]) + ownership_by_loc = [] + board = self.board + for y in range(board.size): + for x in range(board.size): + loc = board.loc(x,y) + pos = features.loc_to_tensor_pos(loc,board) + if board.pla == Board.WHITE: + ownership_by_loc.append((loc,ownership_flat[pos])) + else: + ownership_by_loc.append((loc,-ownership_flat[pos])) + + scoring_flat = scoring.reshape([features.pos_len * features.pos_len]) + scoring_by_loc = [] + board = self.board + for y in range(board.size): + for x in range(board.size): + loc = board.loc(x,y) + pos = features.loc_to_tensor_pos(loc,board) + if board.pla == Board.WHITE: + scoring_by_loc.append((loc,scoring_flat[pos])) + else: + scoring_by_loc.append((loc,-scoring_flat[pos])) + + futurepos0_flat = futurepos[0,:,:].reshape([features.pos_len * features.pos_len]) + futurepos0_by_loc = [] + board = self.board + for y in range(board.size): + for x in range(board.size): + loc = board.loc(x,y) + pos = features.loc_to_tensor_pos(loc,board) + if board.pla == Board.WHITE: + futurepos0_by_loc.append((loc,futurepos0_flat[pos])) + else: + futurepos0_by_loc.append((loc,-futurepos0_flat[pos])) + + futurepos1_flat = futurepos[1,:,:].reshape([features.pos_len * features.pos_len]) + futurepos1_by_loc = [] + board = self.board + for y in range(board.size): + for x in range(board.size): + loc = board.loc(x,y) + pos = features.loc_to_tensor_pos(loc,board) + if board.pla == Board.WHITE: + futurepos1_by_loc.append((loc,futurepos1_flat[pos])) + else: + futurepos1_by_loc.append((loc,-futurepos1_flat[pos])) + + seki_flat = seki.reshape([features.pos_len * features.pos_len]) + seki_by_loc = [] + board = self.board + for y in range(board.size): + for x in range(board.size): + loc = board.loc(x,y) + pos = features.loc_to_tensor_pos(loc,board) + if board.pla == Board.WHITE: + seki_by_loc.append((loc,seki_flat[pos])) + else: + seki_by_loc.append((loc,-seki_flat[pos])) + + seki_flat2 = seki2.reshape([features.pos_len * features.pos_len]) + seki_by_loc2 = [] + board = self.board + for y in range(board.size): + for x in range(board.size): + loc = board.loc(x,y) + pos = features.loc_to_tensor_pos(loc,board) + seki_by_loc2.append((loc,seki_flat2[pos])) + + moves_and_probs = sorted(moves_and_probs0, key=lambda moveandprob: moveandprob[1], reverse=True) + # Generate a random number biased small and then find the appropriate move to make + # Interpolate from moving uniformly to choosing from the triangular distribution + alpha = 1 + beta = 1 + math.sqrt(max(0,len(self.moves)-20)) + r = np.random.beta(alpha,beta) + probsum = 0.0 + i = 0 + genmove_result = Board.PASS_LOC + while True: + (move,prob) = moves_and_probs[i] + probsum += prob + if i >= len(moves_and_probs)-1 or probsum > r: + genmove_result = move + break + i += 1 + + # Transpose attention so that both it and reverse attention are in n c (hw) format. + for name in list(extra_outputs.returned.keys()): + if name.endswith(".attention"): + extra_outputs.returned[name] = torch.transpose(extra_outputs.returned[name],1,2) + + return { + "policy0": policy0, + "policy1": policy1, + "moves_and_probs0": moves_and_probs0, + "moves_and_probs1": moves_and_probs1, + "value": value, + "td_value": td_value, + "td_value2": td_value2, + "td_value3": td_value3, + "scoremean": scoremean, + "td_score": td_score, + "scorestdev": scorestdev, + "lead": lead, + "vtime": vtime, + "estv": estv, + "ests": ests, + "ownership": ownership, + "ownership_by_loc": ownership_by_loc, + "scoring": scoring, + "scoring_by_loc": scoring_by_loc, + "futurepos": futurepos, + "futurepos0_by_loc": futurepos0_by_loc, + "futurepos1_by_loc": futurepos1_by_loc, + "seki": seki, + "seki_by_loc": seki_by_loc, + "seki2": seki2, + "seki_by_loc2": seki_by_loc2, + "scorebelief": scorebelief, + "genmove_result": genmove_result, + **{ name:activation[0].numpy() for name, activation in extra_outputs.returned.items() }, + "available_extra_outputs": available_extra_outputs, + } + diff --git a/training/scripts/dated/20241110-122511/gatekeeper.cfg b/training/scripts/dated/20241110-122511/gatekeeper.cfg new file mode 100644 index 0000000..6dbedc4 --- /dev/null +++ b/training/scripts/dated/20241110-122511/gatekeeper.cfg @@ -0,0 +1,112 @@ + +# Logs------------------------------------------------------------------------------------ + +logSearchInfo = false +logMoves = false +logGamesEvery = 10 +logToStdout = true + +# Fancy game selfplay settings-------------------------------------------------------------------- + +# startPosesProb = 0.0 # Play this proportion of games starting from SGF positions +# startPosesFromSgfDir = DIRECTORYPATH # Load SGFs from this dir +# startPosesLoadProb = 1.0 # Only load each position from each SGF with this chance (save memory) +# startPosesTurnWeightLambda = 0 # 0 = equal weight 0.01 = decrease probability by 1% per turn -0.01 = increase probability by 1% per turn. + +# Match----------------------------------------------------------------------------------- + +numGameThreads = 128 +maxMovesPerGame = 1600 +numGamesPerGating = 200 + +allowResignation = true +resignThreshold = -0.90 +resignConsecTurns = 5 + +# Disabled, since we're not using any root noise and such +# Could have a slight weirdness on rootEndingBonusPoints, but shouldn't be a big deal. +# clearBotBeforeSearch = true + +# Rules------------------------------------------------------------------------------------ + +koRules = SIMPLE,POSITIONAL,SITUATIONAL +scoringRules = AREA,TERRITORY +taxRules = NONE,NONE,SEKI,SEKI,ALL +multiStoneSuicideLegals = false,true +hasButtons = false,false,true + +bSizes = 7,8,9 +bSizeRelProbs = 1,1,8 +allowRectangleProb = 0.50 + +komiAuto = True # Automatically adjust komi to what the neural nets think are fair +#komiMean = 7.5 # Specify explicit komi +handicapProb = 0.0 # Probability of handicap game +handicapCompensateKomiProb = 1.0 # Probability of compensating komi to fair during handicap game +# numExtraBlackFixed = 3 # When playing handicap games, always use exactly this many extra black moves + +# Search limits----------------------------------------------------------------------------------- +maxVisits = 150 +numSearchThreads = 1 + +# GPU Settings------------------------------------------------------------------------------- + +nnMaxBatchSize = 128 +nnCacheSizePowerOfTwo = 21 +nnMutexPoolSizePowerOfTwo = 15 +numNNServerThreadsPerModel = 1 +nnRandomize = true + +# CUDA GPU settings-------------------------------------- +# cudaDeviceToUse = 0 #use device 0 for all server threads (numNNServerThreadsPerModel) unless otherwise specified per-model or per-thread-per-model +# cudaDeviceToUseModel0 = 3 #use device 3 for model 0 for all threads unless otherwise specified per-thread for this model +# cudaDeviceToUseModel1 = 2 #use device 2 for model 1 for all threads unless otherwise specified per-thread for this model +# cudaDeviceToUseModel0Thread0 = 3 #use device 3 for model 0, server thread 0 +# cudaDeviceToUseModel0Thread1 = 2 #use device 2 for model 0, server thread 1 + +cudaUseFP16 = auto +cudaUseNHWC = auto + +# Root move selection and biases------------------------------------------------------------------------------ + +chosenMoveTemperatureEarly = 0.5 +chosenMoveTemperatureHalflife = 19 +chosenMoveTemperature = 0.2 +chosenMoveSubtract = 0 +chosenMovePrune = 1 + +useLcbForSelection = true +lcbStdevs = 5.0 +minVisitPropForLCB = 0.15 + +# Internal params------------------------------------------------------------------------------ + +winLossUtilityFactor = 1.0 +staticScoreUtilityFactor = 0.00 +dynamicScoreUtilityFactor = 0.25 +dynamicScoreCenterZeroWeight = 0.25 +dynamicScoreCenterScale = 0.50 +noResultUtilityForWhite = 0.0 +drawEquivalentWinsForWhite = 0.5 + +rootEndingBonusPoints = 0.5 +rootPruneUselessMoves = true + +cpuctExploration = 1.1 +cpuctExplorationLog = 0.0 +fpuReductionMax = 0.2 +rootFpuReductionMax = 0.1 +valueWeightExponent = 0.5 +policyOptimism = 1.0 +rootPolicyOptimism = 0.0 + +subtreeValueBiasFactor = 0.35 +subtreeValueBiasWeightExponent = 0.8 +useNonBuggyLcb = true +useGraphSearch = true + +useUncertainty = true +uncertaintyExponent = 1.0 +uncertaintyCoeff = 0.25 + +numVirtualLossesPerThread = 1 diff --git a/training/scripts/dated/20241110-122511/genboard_common.py b/training/scripts/dated/20241110-122511/genboard_common.py new file mode 100644 index 0000000..7873988 --- /dev/null +++ b/training/scripts/dated/20241110-122511/genboard_common.py @@ -0,0 +1,129 @@ +import traceback +import json +import math +import torch +import torch.nn as nn +import torch.nn.functional as F + +class ResBlock(nn.Module): + def __init__(self,num_channels,scale_init): + super(ResBlock, self).__init__() + kernel_size = 3 + self.biasa = nn.Parameter(torch.zeros(num_channels,1,1)) + self.conva = nn.Conv2d(in_channels=num_channels, out_channels=num_channels, kernel_size=kernel_size, padding=1, bias=False) + torch.nn.init.normal_(self.conva.weight,std=math.sqrt(2.0 / num_channels / kernel_size / kernel_size)*scale_init) + self.biasb = nn.Parameter(torch.zeros(num_channels,1,1)) + self.scalb = nn.Parameter(torch.ones(num_channels,1,1)) + self.convb = nn.Conv2d(in_channels=num_channels, out_channels=num_channels, kernel_size=kernel_size, padding=1, bias=False) + torch.nn.init.zeros_(self.convb.weight) + + def forward(self, trunk): + x = F.relu(trunk+self.biasa) + x = self.conva(x) + x = F.relu(x*self.scalb+self.biasb) + x = self.convb(x) + return trunk+x + +class GPoolResBlock(nn.Module): + def __init__(self,num_channels,scale_init): + super(GPoolResBlock, self).__init__() + kernel_size = 3 + self.biasa = nn.Parameter(torch.zeros(num_channels,1,1)) + self.conva = nn.Conv2d(in_channels=num_channels, out_channels=num_channels, kernel_size=kernel_size, padding=1, bias=False) + torch.nn.init.normal_(self.conva.weight,std=math.sqrt(1.0 / num_channels / kernel_size / kernel_size)*scale_init) + self.convg = nn.Conv2d(in_channels=num_channels, out_channels=num_channels, kernel_size=kernel_size, padding=1, bias=False) + torch.nn.init.normal_(self.convg.weight,std=math.sqrt(1.0 / num_channels / kernel_size / kernel_size)*math.sqrt(scale_init)) + self.matg = nn.Parameter(torch.zeros(num_channels,num_channels)) + torch.nn.init.normal_(self.matg,std=math.sqrt(1.0 / num_channels)*math.sqrt(scale_init)) + self.biasb = nn.Parameter(torch.zeros(num_channels,1,1)) + self.scalb = nn.Parameter(torch.ones(num_channels,1,1)) + self.convb = nn.Conv2d(in_channels=num_channels, out_channels=num_channels, kernel_size=kernel_size, padding=1, bias=False) + torch.nn.init.zeros_(self.convb.weight) + + def forward(self, trunk): + x = F.relu(trunk+self.biasa) + x = self.conva(x) + g = self.convg(x) + gsize = g.size() + g = torch.sum(g,(2,3)) / (gsize[2] * gsize[3]) # nchw -> nc + g = torch.matmul(g,self.matg) + g = g.view(gsize[0],gsize[1],1,1) + x = x + g + x = F.relu(x*self.scalb+self.biasb) + x = self.convb(x) + return trunk+x + + +class Model(nn.Module): + def __init__(self, num_channels, num_blocks): + super(Model, self).__init__() + # Channel 0: Next inference point + # Channel 1: On-board + # Channel 2: Black + # Channel 3: White + # Channel 4: Unknown + # Channel 5: Turn number / 100 + # Channel 6: Noise stdev in turn number / 50 + # Channel 7: Source + + self.inference_channel = 0 + self.num_channels = num_channels + self.num_blocks = num_blocks + self.conv0 = nn.Conv2d(in_channels=8, out_channels=self.num_channels, kernel_size=3, padding=1, bias=False) + + self.blocks = nn.ModuleList([]) + self.fixup_scale_init = 1.0 / math.sqrt(self.num_blocks) + self.blocks.append(ResBlock(self.num_channels,self.fixup_scale_init)) + self.blocks.append(ResBlock(self.num_channels,self.fixup_scale_init)) + + next_is_gpool = True + for b in range(num_blocks-2): + if next_is_gpool: + self.blocks.append(GPoolResBlock(self.num_channels,self.fixup_scale_init)) + else: + self.blocks.append(ResBlock(self.num_channels,self.fixup_scale_init)) + next_is_gpool = not next_is_gpool + + assert(len(self.blocks) == self.num_blocks) + + self.endtrunk_bias_focus = nn.Parameter(torch.zeros(self.num_channels,1,1)) + self.endtrunk_bias_g = nn.Parameter(torch.zeros(self.num_channels,1,1)) + self.convg = nn.Conv2d(in_channels=self.num_channels, out_channels=self.num_channels, kernel_size=1, padding=0, bias=False) + + self.fc1 = nn.Linear(self.num_channels*2, self.num_channels) + self.fc2 = nn.Linear(self.num_channels,3) + self.convaux = nn.Conv2d(in_channels=self.num_channels, out_channels=3, kernel_size=1, padding=0, bias=True) + + def forward(self, inputs): + trunk = self.conv0(inputs) + for i in range(self.num_blocks): + trunk = self.blocks[i](trunk) + + head_focus = F.relu(trunk+self.endtrunk_bias_focus) + head_g = F.relu(trunk+self.endtrunk_bias_g) + aux = self.convaux(head_focus) + gsize = head_g.size() + + x = torch.sum(head_focus * inputs[:,self.inference_channel:self.inference_channel+1,:,:],(2,3)) + g = torch.sum(head_g,(2,3)) / (gsize[2] * gsize[3]) # nchw -> nc + + x = torch.cat((x,g),dim=1) + + x = F.relu(self.fc1(x)) + x = self.fc2(x) + return x,aux + + def save_to_file(self, filename): + state_dict = self.state_dict() + data = {} + data["num_channels"] = self.num_channels + data["num_blocks"] = self.num_blocks + data["state_dict"] = state_dict + torch.save(data, filename) + + @staticmethod + def load_from_file(filename): + data = torch.load(filename) + model = Model(data["num_channels"], data["num_blocks"]) + model.load_state_dict(data["state_dict"]) + return model diff --git a/training/scripts/dated/20241110-122511/genboard_run.py b/training/scripts/dated/20241110-122511/genboard_run.py new file mode 100644 index 0000000..6d57139 --- /dev/null +++ b/training/scripts/dated/20241110-122511/genboard_run.py @@ -0,0 +1,203 @@ +#!/usr/bin/python3 +import sys +import os +import argparse +import traceback +import logging +import json +import math +import random +import hashlib +import numpy as np +import torch +import torch.nn as nn +import torch.nn.functional as F +import torch.optim as optim +from copy import deepcopy + +import data +from board import Board, IllegalMoveError +from genboard_common import Model + +if __name__ == '__main__': + + description = """ + Generate completions of Go positions + """ + + parser = argparse.ArgumentParser(description=description) + parser.add_argument('-model', help='Model file to load', required=True) + parser.add_argument('-board', help='Board pattern using {.,*},X,O,? for empty, black, white, unknown', required=True) + parser.add_argument('-turn', help='Approx turn number to tell the net to generate for, [0,300]', required=True, type=float) + parser.add_argument('-turnstdev', help='Approx turn number randomness [0,100]', required=True, type=float) + parser.add_argument('-source', help='Tell the net to mimic positions from source, {-1,0,1}', required=True, type=int) + parser.add_argument('-verbose', help='Print various info and debug messages instead of only the board', required=False, action='store_true') + parser.add_argument('-n', help='How many batches to generate, default 1', required=False, type=int, default=1) + parser.add_argument('-batchsize', help='How many positions to generate, default 1', required=False, type=int, default=1) + args = vars(parser.parse_args()) + + modelfile = args["model"] + boardstr = args["board"] + turn = args["turn"] + turnstdev = args["turnstdev"] + source = args["source"] + verbose = args["verbose"] + numbatches = args["n"] + batchsize = args["batchsize"] + + if turn < 0 or turn > 300: + raise Exception("Turn must be in [0,300]") + if turnstdev < 0 or turnstdev > 100: + raise Exception("Turn must be in [0,100]") + if source != -1 and source != 0 and source != 1: + raise Exception("Source must be in {-1,0,1}") + if numbatches < 0: + raise Exception("Num batches must be nonnegative") + if batchsize < 1: + raise Exception("Batchsize must be positive") + + cpudevice = torch.device("cpu") + if torch.cuda.is_available(): + if verbose: + print("CUDA is available, using it",flush=True) + gpudevice = torch.device("cuda:0") + else: + gpudevice = cpudevice + model = Model.load_from_file(modelfile).to(gpudevice) + + size = 19 + boardbase = [["." for x in range(size)] for y in range(size)] + boardbase[3][3] = "," + boardbase[9][3] = "," + boardbase[15][3] = "," + boardbase[3][9] = "," + boardbase[9][9] = "," + boardbase[15][9] = "," + boardbase[3][15] = "," + boardbase[9][15] = "," + boardbase[15][15] = "," + + num_channels = 8 + inputsbase = torch.zeros((1,num_channels,size,size)) + + inference_point_channel = 0 + black_channel = 2 + white_channel = 3 + unknown_channel = 4 + + # Channel 1: On-board + inputsbase[:,1,:,:].fill_(1.0) + + def fail_if_idx_too_large(idx): + if idx >= size * size: + raise Exception("Provided board is larger than 19x19") + + idx = 0 + for c in boardstr: + y = idx // 19 + x = idx % 19 + if c == "." or c == "*" or c == ",": + fail_if_idx_too_large(idx) + elif c == "X" or c == "x" or c == "B" or c == "b": + fail_if_idx_too_large(idx) + boardbase[y][x] = "X" + inputsbase[0,black_channel,y,x] = 1.0 + elif c == "O" or c == "o" or c == "W" or c == "w": + fail_if_idx_too_large(idx) + boardbase[y][x] = "O" + inputsbase[0,white_channel,y,x] = 1.0 + elif c == "?": + fail_if_idx_too_large(idx) + inputsbase[0,unknown_channel,y,x] = 1.0 + else: + # Ignore this char, counteract the += 1 at the end + idx -= 1 + idx += 1 + + # Channel 5: Turn number / 100 + inputsbase[:,5,:,:].fill_(turn / 100.0) + # Channel 6: Noise stdev in turn number / 50 + inputsbase[:,6,:,:].fill_(turnstdev / 50.0) + # Channel 7: Source + inputsbase[:,7,:,:].fill_(float(source)) + + rand = random.Random(os.urandom(32) + hashlib.md5(boardstr.encode()).hexdigest().encode()) + + with torch.no_grad(): + + for i in range(numbatches): + + flipx = rand.random() < 0.5 + flipy = rand.random() < 0.5 + swapxy = rand.random() < 0.5 + + flipx2 = rand.random() < 0.5 + flipy2 = rand.random() < 0.5 + swapxy2 = rand.random() < 0.5 + + def query_model(inputs): + inputstransformed = inputs.detach().clone() + if flipx: + if flipy: + inputstransformed = torch.flip(inputstransformed,[2,3]) + else: + inputstransformed = torch.flip(inputstransformed,[2]) + else: + if flipx: + inputstransformed = torch.flip(inputstransformed,[3]) + else: + pass + if swapxy: + inputstransformed = torch.transpose(inputstransformed,2,3) + + preds, auxpreds = model(inputstransformed.to(gpudevice)) + preds = F.softmax(preds,dim=1) + assert(len(preds.size()) == 2) + assert(preds.size()[0] == batchsize) + assert(preds.size()[1] == 3) + choices = [] + for b in range(batchsize): + weights = [preds[b,0],preds[b,1],preds[b,2]] + choice = rand.choices([0,1,2],weights=weights)[0] + choices.append(choice) + return choices + + inputs = inputsbase.expand([batchsize,-1,-1,-1]).detach().clone() + boards = [ deepcopy(boardbase) for b in range(batchsize) ] + + for y in range(size): + for x in range(size): + sx = x + sy = y + if flipx2: + sx = size - sx - 1 + if flipy2: + sy = size - sy - 1 + if swapxy2: + tmp = sx + sx = sy + sy = tmp + + if inputs[0,unknown_channel,sy,sx] == 1.0: + for b in range(batchsize): + inputs[b,unknown_channel,sy,sx] = 0.0 + inputs[b,inference_point_channel,sy,sx] = 1.0 + choices = query_model(inputs) + for b in range(batchsize): + inputs[b,inference_point_channel,sy,sx] = 0.0 + + choice = choices[b] + if choice == 0: + pass + elif choice == 1: + inputs[b,black_channel,sy,sx] = 1.0 + boards[b][sy][sx] = "X" + elif choice == 2: + inputs[b,white_channel,sy,sx] = 1.0 + boards[b][sy][sx] = "O" + + for b in range(batchsize): + s = "\n".join([" ".join(row) for row in boards[b]]) + s += "\n" + print(s) + sys.stdout.flush() diff --git a/training/scripts/dated/20241110-122511/genboard_train.py b/training/scripts/dated/20241110-122511/genboard_train.py new file mode 100644 index 0000000..2799d2f --- /dev/null +++ b/training/scripts/dated/20241110-122511/genboard_train.py @@ -0,0 +1,492 @@ +#!/usr/bin/python3 +import sys +import os +import argparse +import traceback +import logging +import json +import math +import random +import hashlib +import numpy as np +import torch +import torch.nn as nn +import torch.nn.functional as F +import torch.optim as optim + +import data +from board import Board, IllegalMoveError +from genboard_common import Model + +class ShuffledDataset(torch.utils.data.IterableDataset): + def __init__(self, dataset, shuffle_buffer_size): + super().__init__() + self.dataset = dataset + self.shuffle_buffer_size = shuffle_buffer_size + + def __iter__(self): + worker_info = torch.utils.data.get_worker_info() + if worker_info is None: + rand = random.Random(os.urandom(32)) + else: + rand = random.Random(os.urandom(32)+ "#ShuffledDataset#".encode() + str(worker_info.id).encode()) + + shuffle_buffer = [] + try: + it = iter(self.dataset) + while len(shuffle_buffer) < self.shuffle_buffer_size: + item = next(it) + if isinstance(item, Exception): + yield item + else: + shuffle_buffer.append(item) + except StopIteration: + self.shuffle_buffer_size = len(shuffle_buffer) + + print("Initial shuffle buffer filled", flush=True) + rand.shuffle(shuffle_buffer) + try: + while True: + try: + item = next(it) + if isinstance(item, Exception): + yield item + else: + idx = rand.randint(0, self.shuffle_buffer_size-1) + old_item = shuffle_buffer[idx] + shuffle_buffer[idx] = item + yield old_item + except StopIteration: + break + while len(shuffle_buffer) > 0: + yield shuffle_buffer.pop() + except GeneratorExit: + pass + +def rand_triangular(rand,maxvalue): + r = (maxvalue+1) * (1.0 - math.sqrt(rand.random())) + r = int(math.floor(r)) + if r <= 0: + return 0 + if r >= maxvalue: + return maxvalue + return r + +def random_subinterval(rand,size): + # Anchor rectangles near the edge more often + if rand.random() < 0.5: + x0 = rand_triangular(rand,size)-1 + x1 = rand_triangular(rand,size)-1 + else: + x0 = rand.randint(0,size-1) + x1 = rand.randint(0,size-1) + + if rand.random() < 0.5: + x0 = size - x0 - 1 + x1 = size - x1 - 1 + + if x0 > x1: + return (x1,x0) + return (x0,x1) + + +class SgfDataset(torch.utils.data.IterableDataset): + def __init__(self, files, max_turn, break_prob_per_turn, sample_prob, endless): + self.files = files + self.max_turn = max_turn + self.break_prob_per_turn = break_prob_per_turn + self.sample_prob = sample_prob + self.endless = endless + + def __iter__(self): + worker_info = torch.utils.data.get_worker_info() + if worker_info is None: + rand = random.Random(os.urandom(32)) + else: + rand = random.Random(os.urandom(32)+ "#SgfDataset#".encode() + str(worker_info.id).encode()) + + files = self.files + cpudevice = torch.device("cpu") + + try: + while True: + rand.shuffle(files) + file_count = 0 + error_count = 0 + print("Iterator beginning reading of files %d / %d" % (file_count, len(files)), flush=True) + for filename in files: + try: + (metadata,setup,moves,rules) = data.load_sgf_moves_exn(filename) + except Exception as e: + error_count += 1 + continue + # Only even 19x19 games! + if metadata.size != 19 or len(setup) != 0 or (metadata.handicap is not None and metadata.handicap != 0): + continue + board = Board(size=metadata.size) + turn_number = 0 + for (pla,loc) in moves: + + if rand.random() < self.sample_prob: + inputs = torch.zeros((8,metadata.size,metadata.size),dtype=torch.float32,device=cpudevice) + result = torch.zeros((3,),dtype=torch.float32,device=cpudevice) + aux = torch.zeros((3,metadata.size,metadata.size),dtype=torch.float32,device=cpudevice) + + (alwaysknownxmin,alwaysknownxmax) = random_subinterval(rand,metadata.size) + (alwaysknownymin,alwaysknownymax) = random_subinterval(rand,metadata.size) + + if alwaysknownxmin <= 0 and alwaysknownxmax >= metadata.size-1 and alwaysknownymin <= 0 and alwaysknownymax >= metadata.size-1: + pass + else: + # Channel 1: On-board + inputs[1,:,:].fill_(1.0) + + num_always_known_poses = 0 + if alwaysknownxmax < 0 or alwaysknownxmin >= metadata.size or alwaysknownymax < 0 or alwaysknownymin >= metadata.size: + num_always_known_poses = 0 + else: + num_always_known_poses = ( + ( min(alwaysknownxmax, metadata.size-1) - max(alwaysknownxmin, 0) + 1) * + ( min(alwaysknownymax, metadata.size-1) - max(alwaysknownymin, 0) + 1) + ) + num_not_always_known_poses = metadata.size * metadata.size - num_always_known_poses + inferenceidx = rand.randint(0,num_not_always_known_poses-1) + + flipx = rand.random() < 0.5 + flipy = rand.random() < 0.5 + swapxy = rand.random() < 0.5 + + idx = 0 + for y in range(metadata.size): + for x in range(metadata.size): + pos = y * metadata.size + x + always_known = (x >= alwaysknownxmin and x <= alwaysknownxmax and y >= alwaysknownymin and y <= alwaysknownymax) + + sx = x + sy = y + if flipx: + sx = metadata.size - sx - 1 + if flipy: + sy = metadata.size - sy - 1 + if swapxy: + tmp = sx + sx = sy + sy = tmp + stone = board.board[board.loc(sx,sy)] + + # Channel 4: Unknown + if idx > inferenceidx and not always_known: + inputs[4,y,x] = 1.0 + # Channel 0: Next inference point + elif idx == inferenceidx and not always_known: + inputs[0,y,x] = 1.0 + result + if stone == Board.BLACK: + result[1] = 1.0 + elif stone == Board.WHITE: + result[2] = 1.0 + else: + result[0] = 1.0 + else: + # Channel 2: Black + if stone == Board.BLACK: + inputs[2,y,x] = 1.0 + # Channel 3: White + elif stone == Board.WHITE: + inputs[3,y,x] = 1.0 + + if stone == Board.BLACK: + aux[1,y,x] = 1.0 + elif stone == Board.WHITE: + aux[2,y,x] = 1.0 + else: + aux[0,y,x] = 1.0 + + if not always_known: + idx += 1 + + assert(idx == num_not_always_known_poses) + + if rand.random() < 0.3: + turn_noise_stdev = 0.0 + reported_turn = turn_number + else: + turn_noise_stdev = (rand.random() ** 2.0) * 100 + reported_turn = turn_number + rand.normalvariate(0.0,turn_noise_stdev) + + # Channel 5: Turn number / 100 + inputs[5,:,:].fill_(reported_turn / 100.0) + # Channel 6: Noise stdev in turn number / 50 + inputs[6,:,:].fill_(turn_noise_stdev / 50.0) + # Channel 7: Source + is_kgs = ("/kgs" in filename) or ("\\KGS" in filename) or ("/KGS" in filename) or ("\\KGS" in filename) + is_fox = ("/fox" in filename) or ("\\fox" in filename) or ("/FOX" in filename) or ("\\FOX" in filename) + if is_kgs: + inputs[7,:,:].fill_(1.0) + elif is_fox: + inputs[7,:,:].fill_(-1.0) + + if rand.random() < 0.5: + if rand.random() < 0.5: + inputs = torch.flip(inputs,[1,2]) + aux = torch.flip(aux,[1,2]) + else: + inputs = torch.flip(inputs,[1]) + aux = torch.flip(aux,[1]) + else: + if rand.random() < 0.5: + inputs = torch.flip(inputs,[2]) + aux = torch.flip(aux,[2]) + else: + pass + + if rand.random() < 0.5: + inputs = torch.transpose(inputs,1,2) + aux = torch.transpose(aux,1,2) + + yield (inputs,result,aux) + + try: + board.play(pla,loc) + except IllegalMoveError as e: + # On illegal move in the SGF, don't attempt to recover, just move on to new game + print("Illegal move, skipping file " + filename + ":" + str(e), flush=True) + break + turn_number += 1 + if turn_number > self.max_turn: + break + if rand.random() < self.break_prob_per_turn: + break + + file_count += 1 + if file_count % 200 == 0: + print("Read through file %d / %d (error count %d)" % (file_count, len(files), error_count), flush=True) + + if not self.endless: + break + + except GeneratorExit: + pass + except Exception as e: + print("EXCEPTION IN GENERATOR: " + str(e)) + traceback.print_exc() + print("---",flush=True) + yield e + + +def save_json(data,filename): + with open(filename,"w") as f: + json.dump(data,f) + f.flush() + os.fsync(f.fileno()) + +def load_json(filename): + with open(filename) as f: + data = json.load(f) + return data + + +if __name__ == '__main__': + + description = """ + Train net to predict Go positions one stone at a time + """ + + parser = argparse.ArgumentParser(description=description) + parser.add_argument('-traindir', help='Dir to write to for recording training results', required=True) + parser.add_argument('-datadirs', help='Directory with sgfs', required=True) + parser.add_argument('-testprop', help='Proportion of data for test', type=float, required=True) + parser.add_argument('-lr-scale', help='LR multiplier', type=float, required=False) + parser.add_argument('-channels', help='Channels', type=int, required=True) + parser.add_argument('-blocks', help='Blocks', type=int, required=True) + parser.add_argument('-grad-clip-scale', help='Gradient clip multiplier', type=float, required=False) + parser.add_argument('-num-data-workers', help='Number of processes for data loading', type=int, required=False) + args = vars(parser.parse_args()) + + traindir = args["traindir"] + datadirs = args["datadirs"] + testprop = args["testprop"] + lr_scale = args["lr_scale"] + num_channels = args["channels"] + num_blocks = args["blocks"] + grad_clip_scale = args["grad_clip_scale"] + num_data_workers = args["num_data_workers"] + logfilemode = "a" + + if lr_scale is None: + lr_scale = 1.0 + if grad_clip_scale is None: + grad_clip_scale = 1.0 + + if num_data_workers is None: + num_data_workers = 0 + + if not os.path.exists(traindir): + os.mkdir(traindir) + + bareformatter = logging.Formatter("%(asctime)s %(message)s") + fh = logging.FileHandler(os.path.join(traindir,"train.log"), mode=logfilemode) + fh.setFormatter(bareformatter) + stdouthandler = logging.StreamHandler(sys.stdout) + stdouthandler.setFormatter(bareformatter) + trainlogger = logging.getLogger("trainlogger") + trainlogger.setLevel(logging.INFO) + trainlogger.addHandler(fh) + trainlogger.addHandler(stdouthandler) + trainlogger.propagate=False + np.set_printoptions(linewidth=150) + def trainlog(s): + trainlogger.info(s) + sys.stdout.flush() + + shuffle_buffer_size = 100000 + + files_found = 0 + trainfiles = [] + testfiles = [] + for datadir in datadirs.split(","): + for parent, subdirs, files in os.walk(datadir): + for name in files: + if name.endswith(".sgf"): + files_found += 1 + if files_found % 10000 == 0: + trainlog("Found %d sgfs..." % files_found) + r = float.fromhex("0."+hashlib.md5(os.path.join(parent,name).encode()).hexdigest()[:16]) + if r < testprop: + testfiles.append(os.path.join(parent,name)) + else: + trainfiles.append(os.path.join(parent,name)) + + trainlog("Found %d training sgfs" % len(trainfiles)) + trainlog("Found %d testing sgfs" % len(testfiles)) + + max_turn = 300 + break_prob_per_turn = 0.01 + + traindataset = ShuffledDataset(SgfDataset(trainfiles,max_turn,break_prob_per_turn,sample_prob=0.5,endless=True),shuffle_buffer_size) + testdataset = SgfDataset(testfiles,max_turn,break_prob_per_turn,sample_prob=0.2,endless=True) + + batch_size = 128 + trainloader = torch.utils.data.DataLoader(traindataset, batch_size=batch_size, shuffle=False, num_workers=num_data_workers, drop_last=True) + testloader = torch.utils.data.DataLoader(testdataset, batch_size=batch_size, shuffle=False, num_workers=num_data_workers, drop_last=True) + + trainlog("Made data loaders") + + samples_per_epoch = 400000 + samples_per_test = 25600 + batches_per_epoch = samples_per_epoch // batch_size + batches_per_test = samples_per_test // batch_size + + def lossfunc(inputs, results, preds, aux, auxpreds): + assert(preds.size()[1] == 3) + assert(auxpreds.size()[1] == 3) + main_loss = -torch.sum(results * F.log_softmax(preds,dim=1)) + aux_loss = -torch.sum(aux * F.log_softmax(auxpreds,dim=1) * inputs[:,4:5,:,:] / torch.sum(inputs[:,1:2,:,:], dim=[2,3], keepdim=True)) * 0.3 + return main_loss, aux_loss + + cpudevice = torch.device("cpu") + if torch.cuda.is_available(): + trainlog("CUDA is available, using it") + gpudevice = torch.device("cuda:0") + else: + gpudevice = cpudevice + + modelpath = os.path.join(traindir,"model.data") + optimpath = os.path.join(traindir,"optim.data") + traindatapath = os.path.join(traindir,"traindata.json") + if os.path.exists(modelpath): + trainlog("Loading preexisting model!") + model = Model.load_from_file(modelpath).to(gpudevice) + if model.num_channels != num_channels: + raise Exception("Number of channels in model is %d but command line arg was %d" % (model.num_channels,num_channels)) + if model.num_blocks != num_blocks: + raise Exception("Number of blocks in model is %d but command line arg was %d" % (model.num_blocks,num_blocks)) + optimizer = optim.SGD(model.parameters(), lr=0.00001*lr_scale, momentum=0.9) + optimizer.load_state_dict(torch.load(optimpath)) + traindata = load_json(traindatapath) + else: + model = Model(num_channels=num_channels, num_blocks=num_blocks).to(gpudevice) + optimizer = optim.SGD(model.parameters(), lr=0.00001*lr_scale, momentum=0.9) + traindata = {"samples_so_far":0, "batches_so_far":0} + + trainlog("Saving!") + model.save_to_file(modelpath) + torch.save(optimizer.state_dict(), optimpath) + save_json(traindata,traindatapath) + + grad_clip_max = 400 * grad_clip_scale + #Loosen gradient clipping as we shift to smaller learning rates + grad_clip_max = grad_clip_max / math.sqrt(lr_scale) + + running_batch_count = 0 + running_main_loss = 0.0 + running_aux_loss = 0.0 + running_gnorm = 0.0 + running_ewms_exgnorm = 0.0 + print_every_batches = 100 + trainiter = iter(trainloader) + testiter = iter(testloader) + while True: + for i in range(batches_per_epoch): + inputs, results, auxs = next(trainiter) + inputs = inputs.to(gpudevice) + results = results.to(gpudevice) + auxs = auxs.to(gpudevice) + + optimizer.zero_grad() + + preds, auxpreds = model(inputs) + main_loss,aux_loss = lossfunc(inputs, results, preds, auxs, auxpreds) + loss = main_loss + aux_loss + loss.backward() + gnorm = torch.nn.utils.clip_grad_norm_(model.parameters(), grad_clip_max) + optimizer.step() + + traindata["samples_so_far"] += batch_size + traindata["batches_so_far"] += 1 + + running_batch_count += 1 + running_main_loss += main_loss.item() + running_aux_loss += aux_loss.item() + running_gnorm += gnorm + running_ewms_exgnorm += max(0.0, gnorm - grad_clip_max) + if running_batch_count >= print_every_batches: + trainlog("TRAIN samples: %d, batches: %d, main loss: %.5f, aux loss: %.5f, gnorm: %.2f, ewms_exgnorm: %.3g" % ( + traindata["samples_so_far"], + traindata["batches_so_far"], + running_main_loss / (running_batch_count * batch_size), + running_aux_loss / (running_batch_count * batch_size), + running_gnorm / (running_batch_count), + running_ewms_exgnorm / (running_batch_count), + )) + running_batch_count = 0 + running_main_loss = 0.0 + running_aux_loss = 0.0 + running_gnorm = 0.0 + running_ewms_exgnorm *= 0.5 + + trainlog("Saving!") + model.save_to_file(modelpath) + torch.save(optimizer.state_dict(), optimpath) + save_json(traindata,traindatapath) + + trainlog("Testing!") + test_samples = 0 + test_main_loss = 0.0 + test_aux_loss = 0.0 + with torch.no_grad(): + for i in range(batches_per_test): + inputs, results, auxs = next(testiter) + inputs = inputs.to(gpudevice) + results = results.to(gpudevice) + auxs = auxs.to(gpudevice) + + preds, auxpreds = model(inputs) + main_loss, aux_loss = lossfunc(inputs, results, preds, auxs, auxpreds) + test_samples += batch_size + test_main_loss += main_loss.item() + test_aux_loss += aux_loss.item() + trainlog("TEST samples %d, main loss: %.5f, aux loss %.5f" % (test_samples, test_main_loss / test_samples, test_aux_loss / test_samples)) + + +trainlog('Finished Training') diff --git a/training/scripts/dated/20241110-122511/genclumpyposes.py b/training/scripts/dated/20241110-122511/genclumpyposes.py new file mode 100644 index 0000000..c6aa590 --- /dev/null +++ b/training/scripts/dated/20241110-122511/genclumpyposes.py @@ -0,0 +1,705 @@ +import json +import random +import argparse +import os +import sgfmill +import math +import sys +from dataclasses import dataclass +from collections import defaultdict + +from PIL import Image, ImageDraw + +from board import Board + +# . stone or rest of board +# e empty +# s stone +# c critical +eyepatterns = [ + (50, [ + "s", + ]), + (20, [ + "e", + ]), + (10, [ + "ee", + ]), + (5, [ + "ece", + ]), + (5, [ + "ec", + ".e", + ]), + (1, [ + "ecce", + ]), + (1, [ + "ec.", + ".ce", + ]), + (1, [ + "ecc", + "..e", + ]), + (2, [ + "ee", + "ee", + ]), + (3, [ + "ee.", + "ece", + ]), + (1, [ + ".e.", + "ece", + ".e.", + ]), + (1, [ + "eccce", + ]), + (1, [ + "eccc", + "...e" + ]), + (1.5, [ + ".ee", + "ece", + ".e.", + ]), + (1, [ + "ece", + "ece", + ".e.", + ]), + (1, [ + ".ee", + "ccc", + "ee.", + ]), + (1, [ + "ece", + "ece", + ]), +] + +def randint_exponential(scale): + r = 0 + while r <= 0: + r = random.random() + return int(math.floor(-math.log(r) * scale)) + +@dataclass +class GroupInfo: + has_eye: bool = False + pla: int = 0 + size: int = 0 + +def gen(xsize,ysize): + stones = [ [ 0 for _ in range(xsize) ] for _ in range(ysize) ] + owned = [ [ 0 for _ in range(xsize) ] for _ in range(ysize) ] + kinds = [ [ '.' for _ in range(xsize) ] for _ in range(ysize) ] + patternids = [ [ 0 for _ in range(xsize) ] for _ in range(ysize) ] + + next_pattern_id = 1 + def place_pattern(pla): + nonlocal next_pattern_id + pattern = random.choices( + [pattern for (weight,pattern) in eyepatterns], + weights=[weight for (weight,pattern) in eyepatterns], + k=1 + )[0] + pattern = [ [ c for c in row ] for row in pattern ] + if random.random() >= 0.5: + pattern = list(zip(*pattern)) + if random.random() >= 0.5: + pattern = list(list(reversed(row)) for row in pattern) + if random.random() >= 0.5: + pattern = list(reversed(pattern)) + + pysize = len(pattern) + pxsize = len(pattern[0]) + + # Generate too large and clamp to border, to make borders more likely + yoffset = random.randint(-1,ysize-pysize+1) + xoffset = random.randint(-1,xsize-pxsize+1) + yoffset = min(yoffset,ysize-pysize) + xoffset = min(xoffset,xsize-pxsize) + yoffset = max(yoffset,0) + xoffset = max(xoffset,0) + + numcs = sum(kind == 'c' for row in pattern for kind in row) + for dy in range(pysize): + for dx in range(pxsize): + kind = pattern[dy][dx] + if kind == 'c': + cpla = 0 + if numcs == 1: + r = random.random() + if r < 0.7: + cpla = 3-pla + elif r < 0.9: + cpla = 0 + else: + cpla = pla + elif numcs == 2: + r = random.random() + if r < 0.3: + cpla = 3-pla + elif r < 0.95: + cpla = 0 + else: + cpla = pla + kinds[yoffset+dy][xoffset+dx] = 'e' + owned[yoffset+dy][xoffset+dx] = pla + stones[yoffset+dy][xoffset+dx] = cpla + patternids[yoffset+dy][xoffset+dx] = next_pattern_id + else: + kinds[yoffset+dy][xoffset+dx] = kind + owned[yoffset+dy][xoffset+dx] = pla + patternids[yoffset+dy][xoffset+dx] = next_pattern_id + + next_pattern_id += 1 + + num_b_patterns = 1 + num_w_patterns = 1 + if random.random() < 0.5: + place_pattern(1) + place_pattern(2) + else: + place_pattern(2) + place_pattern(1) + + num_extra_patterns = 0 + num_extra_patterns = randint_exponential(10) + randint_exponential(3) + for _ in range(num_extra_patterns): + if random.random() < num_b_patterns / (num_b_patterns + num_w_patterns): + num_w_patterns += 1 + place_pattern(2) + else: + num_b_patterns += 1 + place_pattern(1) + + def filleyewall(y,x,pla): + if owned[y][x] != 0 and owned[y][x] != pla: + stones[y][x] = random.randint(1,2) + owned[y][x] = 3 + kinds[y][x] = 'x' + else: + owned[y][x] = pla + stones[y][x] = pla + + # Fill in single stones + for y in range(ysize): + for x in range(xsize): + if kinds[y][x] == 's': + stones[y][x] = owned[y][x] + + # Fill in sides + for y in range(ysize): + for x in range(xsize-1): + if kinds[y][x] == 'e' and kinds[y][x+1] == '.': + filleyewall(y,x+1,owned[y][x]) + elif kinds[y][x+1] == 'e' and kinds[y][x] == '.': + filleyewall(y,x,owned[y][x+1]) + for x in range(xsize): + for y in range(ysize-1): + if kinds[y][x] == 'e' and kinds[y+1][x] == '.': + filleyewall(y+1,x,owned[y][x]) + elif kinds[y+1][x] == 'e' and kinds[y][x] == '.': + filleyewall(y,x,owned[y+1][x]) + + # Fill in corners + corneroppprob = random.random() * 0.24 + 0.18 + corneropps_by_patternid = defaultdict(list) + for y in range(ysize-1): + for x in range(xsize-1): + ecount = ( + int(kinds[y][x] == 'e') + + int(kinds[y][x+1] == 'e') + + int(kinds[y+1][x] == 'e') + + int(kinds[y+1][x+1] == 'e') + ) + ccount = ( + int(kinds[y][x] == 'c') + + int(kinds[y][x+1] == 'c') + + int(kinds[y+1][x] == 'c') + + int(kinds[y+1][x+1] == 'c') + ) + dotcount = ( + int(kinds[y][x] == '.') + + int(kinds[y][x+1] == '.') + + int(kinds[y+1][x] == '.') + + int(kinds[y+1][x+1] == '.') + ) + scount = ( + int(kinds[y][x] == 's') + + int(kinds[y][x+1] == 's') + + int(kinds[y+1][x] == 's') + + int(kinds[y+1][x+1] == 's') + ) + if ecount + ccount == 1 and dotcount + scount == 3: + if kinds[y][x] == 'e': + cornerx = x+1 + cornery = y+1 + cornerpla = owned[y][x] + patternid = patternids[y][x] + elif kinds[y+1][x] == 'e': + cornerx = x+1 + cornery = y + cornerpla = owned[y+1][x] + patternid = patternids[y+1][x] + elif kinds[y][x+1] == 'e': + cornerx = x + cornery = y+1 + cornerpla = owned[y][x+1] + patternid = patternids[y][x+1] + elif kinds[y+1][x+1] == 'e': + cornerx = x + cornery = y + cornerpla = owned[y+1][x+1] + patternid = patternids[y+1][x+1] + else: + assert False + + if random.random() < corneroppprob: + corneropps_by_patternid[patternid].append((cornerx,cornery)) + filleyewall(cornery,cornerx,3-cornerpla) + elif random.random() < 0.60: + pass + else: + filleyewall(cornery,cornerx,cornerpla) + + info_by_label = {} + owned_labels = [ [ 0 for _ in range(xsize) ] for _ in range(ysize) ] + def label_owned_region(label, pla, x, y): + if owned_labels[y][x] == 0 and owned[y][x] == pla: + if stones[y][x] == 0: + info_by_label[label].has_eye = True + owned_labels[y][x] = label + info_by_label[label].size += 1 + if x > 0: + label_owned_region(label, pla, x-1, y) + if x < xsize-1: + label_owned_region(label, pla, x+1, y) + if y > 0: + label_owned_region(label, pla, x, y-1) + if y < ysize-1: + label_owned_region(label, pla, x, y+1) + + next_owned_label = 1 + for y in range(ysize): + for x in range(xsize): + if owned_labels[y][x] == 0 and owned[y][x] != 0: + info_by_label[next_owned_label] = GroupInfo() + info_by_label[next_owned_label].pla = owned[y][x] + label_owned_region(next_owned_label, owned[y][x], x, y) + next_owned_label += 1 + + # print(corneropps_by_patternid) + + def merge(label1, label2): + assert info_by_label[label1].pla == info_by_label[label2].pla + info_by_label[label1].has_eye = (info_by_label[label1].has_eye or info_by_label[label2].has_eye) + info_by_label[label1].size = (info_by_label[label1].size + info_by_label[label2].size) + for y in range(ysize): + for x in range(xsize): + if owned_labels[y][x] == label2: + owned_labels[y][x] = label1 + del info_by_label[label2] + + num_merges = random.random() * 0.25 * len(info_by_label) + num_merges = int(num_merges) + (1 if random.random() < (num_merges - int(num_merges)) else 0) + for _ in range(num_merges): + keys = list(info_by_label.keys()) + key1 = random.choice(keys) + otherkeys = [key for key in keys if info_by_label[key].pla == info_by_label[key1].pla and key != key1] + if len(otherkeys) > 0: + key2 = random.choice(otherkeys) + merge(key1,key2) + + for corneropps in corneropps_by_patternid.values(): + if random.random() < 0.7: + for (x1,y1) in corneropps: + for (x2,y2) in corneropps: + label1 = owned_labels[y1][x1] + label2 = owned_labels[y2][x2] + if label1 != label2: + if info_by_label[label1].pla == info_by_label[label2].pla: + merge(label1,label2) + + def adj(x,y): + if x > 0: + yield (x-1,y) + if x < xsize-1: + yield (x+1,y) + if y > 0: + yield (x,y-1) + if y < ysize-1: + yield (x,y+1) + def diag_reachable(x,y): + if x > 0: + if y > 0: + if owned[y][x-1] == 0 or owned[y-1][x] == 0: + yield (x-1,y-1) + if y < ysize-1: + if owned[y][x-1] == 0 or owned[y+1][x] == 0: + yield (x-1,y+1) + if x < xsize-1: + if y > 0: + if owned[y][x+1] == 0 or owned[y-1][x] == 0: + yield (x+1,y-1) + if y < ysize-1: + if owned[y][x+1] == 0 or owned[y+1][x] == 0: + yield (x+1,y+1) + + # Is this a location with 2 adjacent opponents and 1 adjacent player that + # is NOT the given label + def is_crossroads(x,y,pla,label): + if owned[y][x] != 0: + return False + pcount = 0 + ocount = 0 + opp = 3-pla + for (ax,ay) in adj(x,y): + if owned[ay][ax] == pla and owned_labels[ay][ax] != label: + pcount += 1 + elif owned[ay][ax] == opp: + ocount += 1 + if pcount == 1 and ocount == 2: + return True + return False + + black_grow_prob = sum(random.random() for i in range(8)) / 8 + def grow(allow_diag_blocker): + pla = 1 if random.random() < black_grow_prob else 2 + growspots = [] + for y in range(ysize): + for x in range(xsize): + if owned[y][x] == 0: + adjacent_labels = [] + blocker_labels = [] + for (ax,ay) in adj(x,y): + if owned[ay][ax] == pla: + adjacent_labels.append(owned_labels[ay][ax]) + for (ax,ay) in diag_reachable(x,y): + if owned[ay][ax] == pla: + blocker_labels.append(owned_labels[ay][ax]) + + adjacent_labels = list(set(adjacent_labels)) + blocker_labels = list(set(blocker_labels).difference(set(adjacent_labels))) + if len(adjacent_labels) == 1 and (allow_diag_blocker or len(blocker_labels) == 0): + label = adjacent_labels[0] + has_crossroads = False + for (ax,ay) in adj(x,y): + if is_crossroads(ax,ay,pla,label): + has_crossroads = True + if not has_crossroads: + weight = 1.0 / math.sqrt(0.1 + info_by_label[label].size) + growspots.append(((x,y),label,weight)) + + # print(growspots) + if len(growspots) > 0: + ((x,y),label,weight) = random.choices(growspots,weights=(weight for (_,_,weight) in growspots))[0] + owned[y][x] = pla + owned_labels[y][x] = label + stones[y][x] = pla + info_by_label[label].size += 1 + + for i in range(700): + grow(allow_diag_blocker=False) + for i in range(100): + grow(allow_diag_blocker=True) + + def shrink(allow_far_shrink, allow_disconnect, replace_with_opp): + shrinkables = [] + for y in range(ysize): + for x in range(xsize): + if stones[y][x] != 0: + pla = stones[y][x] + opp = 3-pla + adjplalabels = [] + adjopplabels = [] + for (ax,ay) in adj(x,y): + if owned[ay][ax] == pla: + adjplalabels.append(owned_labels[ay][ax]) + if owned[ay][ax] == opp and (allow_far_shrink or stones[ay][ax] == opp): + adjopplabels.append(owned_labels[ay][ax]) + + adjplalabelset = set(adjplalabels) + adjopplabelset = set(adjopplabels) + if not (len(adjplalabelset) == 1 and len(adjopplabelset) == 1): + continue + if replace_with_opp and len(adjopplabels) != 1: + continue + + allow = True + if not allow_disconnect: + # Set temporarily + stones[y][x] = 0 + for (ax,ay) in adj(x,y): + if stones[ay][ax] == pla: + floodfill = [ [ 0 for _ in range(xsize) ] for _ in range(ysize) ] + queue = [(ax,ay)] + idx = 0 + while idx < len(queue): + fx, fy = queue[idx] + idx += 1 + if floodfill[fy][fx] == 0: + floodfill[fy][fx] = 1 + for (fx2,fy2) in adj(fx,fy): + if stones[fy2][fx2] == pla: + queue.append((fx2,fy2)) + break + disconnected = False + for (ax,ay) in adj(x,y): + if stones[ay][ax] == pla and floodfill[ay][ax] != 1: + disconnected = True + break + if disconnected: + allow = False + # Undo + stones[y][x] = pla + + if allow: + shrinkables.append((x,y,pla,adjopplabels[0])) + + if len(shrinkables) > 0: + (x,y,pla,adjopplabel) = random.choice(shrinkables) + if replace_with_opp: + stones[y][x] = 3-pla + owned_labels[y][x] = adjopplabel + owned[y][x] = 3-pla + else: + stones[y][x] = 0 + + num_replaces = randint_exponential(25) + for _ in range(num_replaces): + shrink(allow_far_shrink=False, allow_disconnect=False, replace_with_opp=True) + + shrinks = [] + num_shrinks = randint_exponential(22 + int(len(info_by_label) * 0.3)) + num_far_shrinks = randint_exponential(20) + for _ in range(num_shrinks): + shrinks.append("shrink") + for _ in range(num_far_shrinks): + shrinks.append("far_shrink") + random.shuffle(shrinks) + for s in shrinks: + if s == "shrink": + shrink(allow_far_shrink=False, allow_disconnect=False, replace_with_opp=False) + elif s == "far_shrink": + shrink(allow_far_shrink=True, allow_disconnect=False, replace_with_opp=False) + + num_disconnect_shrinks = randint_exponential(5) + for _ in range(num_disconnect_shrinks): + shrink(allow_far_shrink=True, allow_disconnect=True, replace_with_opp=False) + + # print("=========================================================") + # # print("\n".join("".join(row) for row in kinds)) + # # print("-----------------------------------------") + # print("\n".join("".join(str("x" if elt == 1 else "o" if elt == 2 else ".") for elt in row) for row in owned)) + # print("-----------------------------------------") + # print("\n".join("".join(str("0123456789abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ"[elt]) for elt in row) for row in owned_labels)) + # print(info_by_label) + # print("-----------------------------------------") + # print("\n".join("".join(str("x" if elt == 1 else "o" if elt == 2 else ".") for elt in row) for row in stones)) + + locs = [] + for y in range(ysize): + for x in range(xsize): + locs.append((x,y)) + + assert xsize == ysize + b = Board(xsize) + for (x,y) in locs: + loc = b.loc(x,y) + # Final slight omission to poke occasional eyes into huge solid stone areas. + if random.random() < 1.0 / 2000.0: + stones[y][x] = 0 + + pla = stones[y][x] + if b.would_be_legal(pla,loc): + b.play(pla,loc) + + num_diffs = 0 + for (x,y) in locs: + loc = b.loc(x,y) + if b.board[loc] != stones[y][x]: + num_diffs += 1 + + if num_diffs > 10: + return None + + return b + + +def save_image(board, xsize, ysize): + CELL_SIZE = 20 + STONE_RADIUS = 8 + + # Create blank board image + img = Image.new('RGB', (CELL_SIZE*ysize, CELL_SIZE*xsize), color=(160,82,45)) + + # Draw grid lines + draw = ImageDraw.Draw(img) + for x in range(xsize): + draw.line((CELL_SIZE*x, 0, CELL_SIZE*x, CELL_SIZE*ysize), fill=(0,0,0)) + for y in range(ysize): + draw.line((0, CELL_SIZE*y, CELL_SIZE*xsize, CELL_SIZE*y), fill=(0,0,0)) + + # Draw stones + for y in range(ysize): + for x in range(xsize): + if board[y][x] == 1: + draw.ellipse((CELL_SIZE*y+CELL_SIZE/2-STONE_RADIUS, CELL_SIZE*x+CELL_SIZE/2-STONE_RADIUS, CELL_SIZE*y+CELL_SIZE/2+STONE_RADIUS, CELL_SIZE*x+CELL_SIZE/2+STONE_RADIUS), fill=(0,0,0)) + elif board[y][x] == 2: + draw.ellipse((CELL_SIZE*y+CELL_SIZE/2-STONE_RADIUS, CELL_SIZE*x+CELL_SIZE/2-STONE_RADIUS, CELL_SIZE*y+CELL_SIZE/2+STONE_RADIUS, CELL_SIZE*x+CELL_SIZE/2+STONE_RADIUS), fill=(255,255,255)) + + # Save image + img.save('tmpclumpypos.png') + + +def gen_sgfs_debug(): + for i in range(100): + xsize = 19 + ysize = 19 + b = None + while b is None: + b = gen(xsize,ysize) + initial_pla = random.choice([1,2]) + + with open(f"tmp/clumpysgfs/{i}.sgf","w") as f: + f.write("(;FF[4]GM[1]SZ[19]HA[0]KM[6.5]") + whitepieces = [] + blackpieces = [] + for y in range(ysize): + for x in range(xsize): + loc = b.loc(x,y) + color = b.board[loc] + if color == 1: + xc = "abcdefghijklmnopqrstuvwxyz"[x] + yc = "abcdefghijklmnopqrstuvwxyz"[y] + blackpieces.append(f"[{xc}{yc}]") + elif color == 2: + xc = "abcdefghijklmnopqrstuvwxyz"[x] + yc = "abcdefghijklmnopqrstuvwxyz"[y] + whitepieces.append(f"[{xc}{yc}]") + if len(whitepieces) > 0: + f.write("AW"+"".join(whitepieces)) + if len(blackpieces) > 0: + f.write("AB"+"".join(blackpieces)) + f.write("PL[W]" if initial_pla == 2 else "") + f.write(")") + + print(i) + print(b.to_string()) + + # board = [ [ 0 for _ in range(xsize) ] for _ in range(ysize) ] + # for y in range(ysize): + # for x in range(xsize): + # loc = b.loc(x,y) + # board[y][x] = b.board[loc] + # save_image(board,xsize,ysize) + + +def maybe_generate_one_pos(out, training_weight, max_lopsidedness, soft_filter_large_group_scale): + xsize = 19 + ysize = 19 + board = None + while board is None: + board = gen(xsize,ysize) + + soft_keep_prob = 1.0 + visited_group_heads = set() + for y in range(ysize): + for x in range(xsize): + loc = board.loc(x,y) + if board.board[loc] == 1 or board.board[loc] == 2: + head = board.group_head[loc] + if head not in visited_group_heads: + visited_group_heads.add(head) + stonecount = board.group_stone_count[head] + if stonecount > 2 * soft_filter_large_group_scale: + soft_keep_prob = 0.0 + elif stonecount > soft_filter_large_group_scale: + excess = stonecount - soft_filter_large_group_scale + soft_keep_prob *= math.exp(-excess * 3 / soft_filter_large_group_scale) + + initial_pla = random.choice([1,2]) + + to_write = {} + to_write["board"] = "" + num_stones = 0 + num_black = 0 + num_white = 0 + for y in range(ysize): + for x in range(xsize): + loc = board.loc(x,y) + if board.board[loc] == 1: + to_write["board"] += "X" + num_stones += 1 + num_black += 1 + elif board.board[loc] == 2: + to_write["board"] += "O" + num_stones += 1 + num_white += 1 + else: + to_write["board"] += "." + to_write["board"] += "/" + + if abs(num_black-num_white) > max_lopsidedness: + return False + + # print(str(soft_keep_prob) + "\n" + to_write["board"].replace("/","\n")) + if soft_keep_prob < 0.05: + return False + if random.random() >= soft_keep_prob: + return False + + to_write["hintLoc"] = "null" + to_write["nextPla"] = ("B" if initial_pla == 1 else "W") + to_write["initialTurnNumber"] = num_stones + to_write["moveLocs"] = [] + to_write["movePlas"] = [] + to_write["weight"] = 1.0 + to_write["trainingWeight"] = training_weight + to_write["xSize"] = board.size + to_write["ySize"] = board.size + out.write(json.dumps(to_write) + "\n") + out.flush() + return True + +def main(num_to_generate, out_file, training_weight, max_lopsidedness, soft_filter_large_group_scale): + with open(out_file,"w") as out: + num_kept = 0 + while True: + suc = maybe_generate_one_pos(out, training_weight, max_lopsidedness, soft_filter_large_group_scale) + if suc: + num_kept += 1 + if num_kept >= num_to_generate: + break + if num_kept % 100 == 0: + print(f"Kept {num_kept} so far") + sys.stdout.flush() + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + parser.add_argument("-num-to-generate", required=True, type=int) + parser.add_argument("-out-file", required=True) + parser.add_argument("-training-weight", required=True, type=float) + parser.add_argument("-max-lopsidedness", required=True, type=int) + parser.add_argument("-soft-filter-large-group-scale", required=True, type=float) + args = vars(parser.parse_args()) + + main( + args["num_to_generate"], + args["out_file"], + args["training_weight"], + args["max_lopsidedness"], + args["soft_filter_large_group_scale"], + ) + + diff --git a/training/scripts/dated/20241110-122511/genrandomposes.py b/training/scripts/dated/20241110-122511/genrandomposes.py new file mode 100644 index 0000000..9c170a2 --- /dev/null +++ b/training/scripts/dated/20241110-122511/genrandomposes.py @@ -0,0 +1,163 @@ +#!/usr/bin/python + +import argparse +import os +import re +import json +import random +import math +import sys +from pathlib import Path +from typing import List + +import sgfmill +import sgfmill.sgf +import sgfmill.sgf_moves +from query_analysis_engine_example import KataGo, sgfmill_to_str +from board import Board + +def is_fair_enough_pos(board: Board, katago: KataGo, initial_player: int, score_rejection_pickiness: float) -> bool: + query = {} + query["id"] = "" + query["moves"] = [] + query["initialStones"] = [] + for y in range(board.size): + for x in range(board.size): + loc = board.loc(x,y) + pla = board.board[loc] + if pla != 0: + query["initialStones"].append((("B" if pla == 1 else "W"), sgfmill_to_str((y,x)))) + + query["rules"] = "Japanese" + query["initialPlayer"] = ("B" if initial_player == 1 else "W") + query["komi"] = 7.0 + query["boardXSize"] = board.size + query["boardYSize"] = board.size + query["maxVisits"] = 400 + + result = katago.query_raw(query) + winrate = result['rootInfo']['winrate'] + score = result['rootInfo']['scoreLead'] + + if abs(score) > 150: + return False + keep_prob = 1.0 / (1.0 + score_rejection_pickiness * abs(score)) + if random.random() < keep_prob: + # print(board.to_string()) + # print(query["initialPlayer"]) + print(winrate, score) + sys.stdout.flush() + return True + return False + +def randint_exponential(scale): + r = 0 + while r <= 0: + r = random.random() + return int(math.floor(-math.log(r) * scale)) + +def maybe_generate_one_pos(katago, score_rejection_pickiness, out): + board = Board(19) + both_plays = 1 + randint_exponential(5) + randint_exponential(5) + randint_exponential(12) + extra_b_plays = randint_exponential(1.5) + extra_w_plays = randint_exponential(1) + + plays = [] + for _ in range(both_plays+extra_b_plays): + plays.append(1) + for _ in range(both_plays+extra_w_plays): + plays.append(2) + random.shuffle(plays) + + if len(plays) > 140: + return False + + for pla in plays: + choices = [] + weights = [] + for y in range(board.size): + for x in range(board.size): + line = min(y+1,x+1,19-y,19-x) + if line <= 1: + relprob = 1 + elif line <= 2: + relprob = 4 + else: + relprob = 20 + choices.append((x,y)) + weights.append(relprob) + (x,y) = random.choices(choices,weights=weights,k=1)[0] + loc = board.loc(x,y) + if board.would_be_legal(pla,loc): + board.play(pla,loc) + + initial_pla = random.choice([1,2]) + if is_fair_enough_pos(board, katago, initial_pla, score_rejection_pickiness): + to_write = {} + to_write["board"] = "" + num_stones = 0 + for y in range(board.size): + for x in range(board.size): + loc = board.loc(x,y) + if board.board[loc] == 1: + to_write["board"] += "X" + num_stones += 1 + elif board.board[loc] == 2: + to_write["board"] += "O" + num_stones += 1 + else: + to_write["board"] += "." + to_write["board"] += "/" + to_write["hintLoc"] = "null" + to_write["nextPla"] = ("B" if initial_pla == 1 else "W") + to_write["initialTurnNumber"] = num_stones + to_write["moveLocs"] = [] + to_write["movePlas"] = [] + to_write["weight"] = 1.0 + to_write["xSize"] = board.size + to_write["ySize"] = board.size + out.write(json.dumps(to_write) + "\n") + out.flush() + return True + return False + +def main(katago_path, config_path, model_path, score_rejection_pickiness, num_to_generate, out_file): + with open(out_file,"w") as out: + + katago = KataGo( + katago_path, + config_path, + model_path, + additional_args=["-override-config","numSearchThreadsPerAnalysisThread=8,reportAnalysisWinratesAs=BLACK"] + ) + + num_kept = 0 + while True: + kept = maybe_generate_one_pos(katago, score_rejection_pickiness, out) + if kept: + num_kept += 1 + if num_kept >= num_to_generate: + break + if num_kept % 100 == 0: + print(f"Kept {num_kept} so far") + sys.stdout.flush() + katago.close() + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + parser.add_argument("-katago-path", required=True) + parser.add_argument("-config-path", required=True) + parser.add_argument("-model-path", required=True) + parser.add_argument("-score-rejection-pickiness", required=True, type=float) + parser.add_argument("-num-to-generate", required=True, type=int) + parser.add_argument("-out-file", required=True) + args = vars(parser.parse_args()) + + main( + args["katago_path"], + args["config_path"], + args["model_path"], + args["score_rejection_pickiness"], + args["num_to_generate"], + args["out_file"] + ) diff --git a/training/scripts/dated/20241110-122511/humanslnet_gui.py b/training/scripts/dated/20241110-122511/humanslnet_gui.py new file mode 100644 index 0000000..535e139 --- /dev/null +++ b/training/scripts/dated/20241110-122511/humanslnet_gui.py @@ -0,0 +1,634 @@ +import wx +import subprocess +import json +import sys +import os +from threading import Thread +import atexit +import datetime + +from gamestate import GameState +from board import Board +from sgfmetadata import SGFMetadata + +from sgfmill import sgf, sgf_moves + +import matplotlib.pyplot as plt +import matplotlib.colors as mcolors +import numpy as np + +def interpolateColor(points,x): + for i in range(len(points)): + x1,c1 = points[i] + if x < x1: + if i <= 0: + return c1 + x0,c0 = points[i-1] + interp = (x-x0)/(x1-x0) + return c0 + (c1-c0)*interp + return points[-1][1] + +POLICY_COLORS = [ + (0.00, np.array([100,0,0,15])), + (0.35, np.array([184,0,0,255])), + (0.50, np.array([220,0,0,255])), + (0.65, np.array([255,100,0,255])), + (0.85, np.array([205,220,60,255])), + (0.94, np.array([120,235,130,255])), + (1.00, np.array([100,255,245,255])), +] + +def policy_color(prob): + r,g,b,a = interpolateColor(POLICY_COLORS,prob**0.25) + return (round(r), round(g), round(b), round(a)) + +def load_sgf_game_state(file_path): + with open(file_path, 'rb') as f: + game = sgf.Sgf_game.from_bytes(f.read()) + + size = game.get_size() + if size < 9 or size > 19: + raise ValueError("Board size must be between 9 and 19 inclusive.") + + board, plays = sgf_moves.get_setup_and_moves(game) + + moves = [] + for y in range(size): + for x in range(size): + color = board.get(y, x) + if color is not None: + moves.append((x, size - 1 - y, (Board.BLACK if color == "b" else Board.WHITE))) + + for color, move in plays: + if move is not None: + y, x = move + moves.append((x, size - 1 - y, (Board.BLACK if color == "b" else Board.WHITE))) + + game_state = GameState(size, GameState.RULES_JAPANESE) + for (x,y,color) in moves: + game_state.play(color, game_state.board.loc(x,y)) + + return game_state + +class GoBoard(wx.Panel): + def __init__(self, parent, game_state, cell_size=30, margin=30): + super().__init__(parent) + self.game_state = game_state + self.board_size = game_state.board.size + self.cell_size = cell_size + self.margin = margin + + self.sgfmeta = SGFMetadata() + self.latest_model_response = None + + self.SetBackgroundStyle(wx.BG_STYLE_CUSTOM) + self.Bind(wx.EVT_PAINT, self.on_paint) + self.Bind(wx.EVT_LEFT_UP, self.on_click) + + def get_desired_size(self): + board_width = self.board_size * self.cell_size + 2 * self.margin + board_height = self.board_size * self.cell_size + self.cell_size + 2 * self.margin + return board_width, board_height + + def px_of_x(self, x): + return round(self.cell_size * x + self.margin + self.cell_size / 2) + def py_of_y(self, y): + return round(self.cell_size * y + self.margin + self.cell_size / 2) + + def x_of_px(self, px): + return round((px - self.margin - self.cell_size / 2) / self.cell_size) + def y_of_py(self, py): + return round((py - self.margin - self.cell_size / 2) / self.cell_size) + + + def on_paint(self, event): + dc = wx.AutoBufferedPaintDC(self) + gc = wx.GraphicsContext.Create(dc) + + gc.SetBrush(wx.Brush(wx.Colour(200, 150, 100))) + gc.SetPen(wx.TRANSPARENT_PEN) + gc.DrawRectangle(0, 0, self.GetSize().Width, self.GetSize().Height) + + gc.SetPen(wx.Pen(wx.BLACK, 1)) + + for i in range(self.board_size): + gc.StrokeLine( + self.px_of_x(0), + self.py_of_y(i), + self.px_of_x(self.board_size - 1), + self.py_of_y(i), + ) + gc.StrokeLine( + self.px_of_x(i), + self.py_of_y(0), + self.px_of_x(i), + self.py_of_y(self.board_size - 1), + ) + + for x in range(self.board_size): + for y in range(self.board_size): + loc = self.game_state.board.loc(x, y) + + if self.game_state.board.board[loc] == Board.BLACK: + gc.SetBrush(wx.Brush(wx.BLACK)) + gc.DrawEllipse(self.px_of_x(x) - (self.cell_size // 2 - 2), self.py_of_y(y) - (self.cell_size // 2 - 2), self.cell_size - 4, self.cell_size - 4) + elif self.game_state.board.board[loc] == Board.WHITE: + gc.SetBrush(wx.Brush(wx.WHITE)) + gc.DrawEllipse(self.px_of_x(x) - (self.cell_size // 2 - 2), self.py_of_y(y) - (self.cell_size // 2 - 2), self.cell_size - 4, self.cell_size - 4) + + gc.SetBrush(wx.Brush(wx.BLACK, wx.TRANSPARENT)) + for x in range(self.board_size): + for y in range(self.board_size): + loc = self.game_state.board.loc(x, y) + + if len(self.game_state.moves) > 0 and self.game_state.moves[-1][1] == loc: + if self.game_state.moves[-1][0] == Board.BLACK: + gc.SetPen(wx.Pen(wx.Colour(0, 120, 255), 2)) + else: + gc.SetPen(wx.Pen(wx.Colour(0, 50, 255), 2)) + gc.DrawEllipse(self.px_of_x(x) - (self.cell_size // 2 - 6), self.py_of_y(y) - (self.cell_size // 2 - 6), self.cell_size - 12, self.cell_size - 12) + + # Draw column labels + gc.SetFont(wx.Font(10, wx.FONTFAMILY_DEFAULT, wx.FONTSTYLE_NORMAL, wx.FONTWEIGHT_NORMAL), wx.BLACK) + for x in range(self.board_size): + col_label = "ABCDEFGHJKLMNOPQRSTUVWXYZ"[x] + text_width, text_height = gc.GetTextExtent(col_label) + text_x = self.px_of_x(x) - text_width // 2 + gc.DrawText(col_label, text_x, self.py_of_y(-0.8)-text_height//2) + gc.DrawText(col_label, text_x, self.py_of_y(self.board_size-0.2)-text_height//2) + + # Draw row labels + for y in range(self.board_size): + row_label = str(self.board_size - y) + text_width, text_height = gc.GetTextExtent(row_label) + text_y = self.py_of_y(y) - text_height // 2 + gc.DrawText(row_label, self.px_of_x(-0.8)-text_width//2, text_y) + gc.DrawText(row_label, self.px_of_x(self.board_size-0.2)-text_width//2, text_y) + + if self.latest_model_response is not None: + bigger_font = wx.Font(10, wx.FONTFAMILY_DEFAULT, wx.FONTSTYLE_NORMAL, wx.FONTWEIGHT_NORMAL) + small_font = wx.Font(8, wx.FONTFAMILY_DEFAULT, wx.FONTSTYLE_NORMAL, wx.FONTWEIGHT_NORMAL) + + value_prediction = self.latest_model_response["value"] + score_prediction = self.latest_model_response["lead"] + scorestdev_prediction = self.latest_model_response["scorestdev"] + sign = (1 if self.game_state.board.pla == Board.BLACK else -1) + + gc.SetBrush(wx.Brush(wx.BLACK)) + gc.SetPen(wx.Pen(wx.BLACK,1)) + gc.SetFont(bigger_font,wx.BLACK) + + black_wr = 0.5 + 0.5 * (value_prediction[0] - value_prediction[1]) * sign + label = "Raw NN Black Win%% %.1f" % (black_wr*100.0) + text_width, text_height = gc.GetTextExtent(label) + text_x = self.px_of_x(0) + text_y = self.py_of_y(self.board_size+0.6) - text_height // 2 + gc.DrawText(label, text_x, text_y) + + black_score = score_prediction * sign + label = "Raw NN Black ScoreMean %.1f +/- %.1f (2 stdv)" % (black_score, 2 * scorestdev_prediction) + text_width, text_height = gc.GetTextExtent(label) + text_x = self.px_of_x(8) + text_y = self.py_of_y(self.board_size+0.6) - text_height // 2 + gc.DrawText(label, text_x, text_y) + + moves_and_probs0 = dict(self.latest_model_response["moves_and_probs0"]) + # print(moves_and_probs0) + for y in range(self.board_size): + for x in range(self.board_size): + loc = self.game_state.board.loc(x, y) + max_prob = max(moves_and_probs0.values()) + if loc in moves_and_probs0: + prob = moves_and_probs0[loc] + r,g,b,a = policy_color(prob / max_prob ** 0.7) + # print(r,g,b,a,prob) + gc.SetBrush(wx.Brush(wx.Colour(r,g,b,alpha=round(a*0.9)))) + gc.SetPen(wx.Pen(wx.Colour(0,0,0,alpha=a),1)) + gc.SetFont(small_font,wx.Colour(0,0,0,alpha=a)) + gc.DrawRectangle( + self.px_of_x(x-0.45), + self.py_of_y(y-0.45), + self.px_of_x(x+0.45)-self.px_of_x(x-0.45), + self.py_of_y(y+0.45)-self.py_of_y(y-0.45), + ) + + label = "%.1f" % (prob*100.0) + text_width, text_height = gc.GetTextExtent(label) + text_x = self.px_of_x(x) - text_width // 2 + text_y = self.py_of_y(y) - text_height // 2 + gc.DrawText(label, text_x, text_y) + + + def on_click(self, event): + x = self.x_of_px(event.GetX()) + y = self.y_of_py(event.GetY()) + + if 0 <= x < self.board_size and 0 <= y < self.board_size: + loc = self.game_state.board.loc(x, y) + pla = self.game_state.board.pla + + if self.game_state.board.would_be_legal(pla,loc): + self.game_state.play(pla, loc) + + command = {"command": "play", "pla": pla, "loc": loc} + parent = self.GetParent().GetParent() + parent.send_command(command) + response = parent.receive_response() + if response != {"outputs": ""}: + parent.handle_error(f"Unexpected response from server: {response}") + + self.Refresh() + self.refresh_model() + + def set_sgfmeta(self, sgfmeta): + self.sgfmeta = sgfmeta + + def refresh_model(self): + sgfmeta = self.sgfmeta + command = {"command": "get_model_outputs", "sgfmeta": sgfmeta.to_dict()} + parent = self.GetParent().GetParent() + parent.send_command(command) + response = parent.receive_response() + if "outputs" not in response: + parent.handle_error(f"Unexpected response from server: {response}") + self.latest_model_response = response["outputs"] + self.Refresh() + + +class LabeledSlider(wx.Panel): + def __init__(self, parent, title, options, on_scroll_callback=None, start_option=None): + super().__init__(parent) + + self.options = options + self.on_scroll_callback = on_scroll_callback + self.title = title + self.is_extrapolation = False + + # Create the slider + start_idx = 0 if start_option is None else options.index(start_option) + self.slider = wx.Slider(self, value=start_idx, minValue=0, maxValue=len(options) - 1, style=wx.SL_HORIZONTAL | wx.SL_AUTOTICKS) + self.slider.SetTickFreq(1) # Set the tick frequency to 1 + self.slider.Bind(wx.EVT_SCROLL, self.on_slider_scroll) + + # Create the label to display the selected option + self.label = wx.StaticText(self, label = self.title + ": " + str(self.options[start_idx])) + + font_size = 12 + + font = self.label.GetFont() + font.SetPointSize(font_size) + self.label.SetFont(font) + font = self.slider.GetFont() + font.SetPointSize(font_size) + self.slider.SetFont(font) + + # Create a sizer to arrange the widgets + sizer = wx.BoxSizer(wx.VERTICAL) + sizer.Add(self.label, 0, wx.ALIGN_LEFT | wx.ALL, 10) + sizer.Add(self.slider, 0, wx.EXPAND | wx.ALL, 10) + self.SetSizer(sizer) + + def get_selected_index(self): + return self.slider.GetValue() + + def get_selected_option(self): + selected_index = self.get_selected_index() + return self.options[selected_index] + + def refresh_label(self): + option_index = self.slider.GetValue() + selected_option = self.options[option_index] + self.label.SetLabel(self.title + ": " + str(selected_option) + ("" if not self.is_extrapolation else " (No Training Data)")) + + def set_is_extrapolation(self, b): + if self.is_extrapolation != b: + self.is_extrapolation = b + self.refresh_label() + + def on_slider_scroll(self, event): + option_index = self.slider.GetValue() + selected_option = self.options[option_index] + self.refresh_label() + if self.on_scroll_callback: + self.on_scroll_callback(option_index, selected_option) + + +class SliderWindow(wx.Frame): + def __init__(self, parent): + super().__init__(parent, title="Sliders") + panel = wx.Panel(self) + panel_sizer = wx.BoxSizer(wx.VERTICAL) + panel_sizer.Add(400,0,0) + + self.source_slider = LabeledSlider(panel, title="Source", options=["KG","OGS","KGS","Fox","Tygem(Unused)","GoGoD","Go4Go"], + on_scroll_callback = (lambda idx, option: self.update_metadata()), + start_option="GoGoD", + ) + panel_sizer.Add(self.source_slider, 0, wx.EXPAND | wx.LEFT | wx.RIGHT | wx.BOTTOM, 10) + + self.rank_slider = LabeledSlider(panel, title="Rank", options=[ + "KG","9d","8d","7d","6d","5d","4d","3d","2d","1d","1k","2k","3k","4k","5k","6k","7k","8k","9k","10k","11k","12k","13k","14k","15k","16k","17k","18k","19k","20k" + ], + on_scroll_callback = (lambda idx, option: self.update_metadata()), + start_option="9d", + ) + panel_sizer.Add(self.rank_slider, 0, wx.EXPAND | wx.LEFT | wx.RIGHT | wx.BOTTOM, 10) + + self.date_slider = LabeledSlider(panel, title="Date", options=[ + 1800,1825,1850,1875,1900,1915,1930,1940,1950,1960,1970,1980,1985,1990,1995,2000,2005,2008,2010,2012,2013,2014,2015,2016,2017,2018,2019,2020,2021,2022,2023], + on_scroll_callback = (lambda idx, option: self.update_metadata()), + start_option=2020, + ) + panel_sizer.Add(self.date_slider, 0, wx.EXPAND | wx.LEFT | wx.RIGHT | wx.BOTTOM, 10) + + self.tc_slider = LabeledSlider(panel, title="TimeControl", options=["Blitz","Fast","Slow","Unknown"], + on_scroll_callback = (lambda idx, option: self.update_metadata()), + start_option="Unknown", + ) + panel_sizer.Add(self.tc_slider, 0, wx.EXPAND | wx.LEFT | wx.RIGHT | wx.BOTTOM, 10) + + + panel.SetSizer(panel_sizer) + sizer = wx.BoxSizer(wx.VERTICAL) + sizer.Add(panel, 1, wx.EXPAND) + self.SetSizerAndFit(sizer) + + self.Bind(wx.EVT_CLOSE, self.on_close) + + def on_close(self, event): + self.GetParent().server_process.terminate() + self.GetParent().Close() + + def update_metadata(self): + sgfmeta = SGFMetadata( + inverseBRank = self.rank_slider.get_selected_index(), + inverseWRank = self.rank_slider.get_selected_index(), + bIsHuman = self.rank_slider.get_selected_index() != 0, + wIsHuman = self.rank_slider.get_selected_index() != 0, + gameIsUnrated = False, + gameRatednessIsUnknown = self.source_slider.get_selected_option() == "KGS", + tcIsUnknown = self.tc_slider.get_selected_option() == "Unknown", + tcIsByoYomi = self.tc_slider.get_selected_option() != "Unknown", + mainTimeSeconds = [300,900,1800,0][self.tc_slider.get_selected_index()], + periodTimeSeconds = [10,15,30,0][self.tc_slider.get_selected_index()], + byoYomiPeriods = [5,5,5,0][self.tc_slider.get_selected_index()], + gameDate = datetime.date(self.date_slider.get_selected_option(),6,1), + source = self.source_slider.get_selected_index(), + ) + + source = self.source_slider.get_selected_option() + if source == "KG": + self.rank_slider.set_is_extrapolation(self.rank_slider.get_selected_index() != 0) + self.tc_slider.set_is_extrapolation(self.tc_slider.get_selected_option() == "Unknown") + self.date_slider.set_is_extrapolation(self.date_slider.get_selected_option() < 2022) + elif source == "OGS": + self.rank_slider.set_is_extrapolation(self.rank_slider.get_selected_index() == 0) + self.tc_slider.set_is_extrapolation(self.tc_slider.get_selected_option() == "Unknown") + self.date_slider.set_is_extrapolation(self.date_slider.get_selected_option() < 2007) + elif source == "KGS": + self.rank_slider.set_is_extrapolation(self.rank_slider.get_selected_index() == 0) + self.tc_slider.set_is_extrapolation(self.tc_slider.get_selected_option() == "Unknown") + self.date_slider.set_is_extrapolation(self.date_slider.get_selected_option() < 2016) + elif source == "Fox": + self.rank_slider.set_is_extrapolation(self.rank_slider.get_selected_index() == 0 or self.rank_slider.get_selected_index() >= 28) + self.tc_slider.set_is_extrapolation(self.tc_slider.get_selected_option() == "Unknown") + self.date_slider.set_is_extrapolation(self.date_slider.get_selected_option() < 2014 or self.date_slider.get_selected_option() > 2019) + elif source == "Tygem(Unused)": + self.rank_slider.set_is_extrapolation(True) + self.tc_slider.set_is_extrapolation(True) + self.date_slider.set_is_extrapolation(True) + elif source == "GoGoD": + self.rank_slider.set_is_extrapolation(self.rank_slider.get_selected_index() == 0 or self.rank_slider.get_selected_index() > 5) + self.tc_slider.set_is_extrapolation(self.tc_slider.get_selected_option() != "Unknown") + self.date_slider.set_is_extrapolation(self.date_slider.get_selected_option() > 2020) + elif source == "Go4Go": + self.rank_slider.set_is_extrapolation(self.rank_slider.get_selected_index() != 1) + self.tc_slider.set_is_extrapolation(self.tc_slider.get_selected_option() != "Unknown") + self.date_slider.set_is_extrapolation(self.date_slider.get_selected_option() < 2020) + + self.GetParent().board.set_sgfmeta(sgfmeta) + self.GetParent().board.refresh_model() + +class FileDropTarget(wx.FileDropTarget): + def __init__(self, window): + super().__init__() + self.window = window + + def OnDropFiles(self, x, y, sgf_files): + return self.window.on_drop_files(sgf_files) + +class GoClient(wx.Frame): + def __init__(self, server_command, game_state): + super().__init__(parent=None, title="HumanSLNetViz") + self.server_command = server_command + self.game_state = game_state + self.board_size = self.game_state.board_size + + self.SetDropTarget(FileDropTarget(self)) + + self.start_server() + self.init_ui() + + + def init_ui(self): + panel = wx.Panel(self) + self.board = GoBoard(panel, self.game_state) + sizer = wx.BoxSizer(wx.VERTICAL) + sizer.Add(self.board, 1, wx.EXPAND) + panel.SetSizer(sizer) + + self.Bind(wx.EVT_CHAR_HOOK, self.on_key_down) + + # Set the initial size of the window based on the board size + board_width, board_height = self.board.get_desired_size() + self.SetClientSize(board_width, board_height) + + screen_width, screen_height = wx.DisplaySize() + frame_width, frame_height = self.GetSize() + pos_x = (screen_width - frame_width) // 2 - 300 + pos_y = (screen_height - frame_height) // 2 + self.SetPosition((pos_x, pos_y)) + + self.slider_window = SliderWindow(self) + + frame_width, frame_height = self.slider_window.GetSize() + pos_x = (screen_width - frame_width) // 2 + 240 + pos_y = (screen_height - frame_height) // 2 + self.slider_window.SetPosition((pos_x, pos_y)) + + + def start_server(self): + print(f"Starting server with command: {self.server_command}") + self.server_process = subprocess.Popen( + self.server_command, + stdin=subprocess.PIPE, + stdout=subprocess.PIPE, + stderr=subprocess.PIPE, + universal_newlines=True, + ) + atexit.register(self.server_process.terminate) + + def print_stderr(): + while True: + line = self.server_process.stderr.readline() + if not line: + returncode = self.server_process.poll() + if returncode is not None: + return + print(line,end="") + + t = Thread(target=print_stderr) + t.daemon = True + t.start() + + self.init_server() + + def init_server(self): + command = {"command": "start", "board_size": self.board_size, "rules": GameState.RULES_JAPANESE} + self.send_command(command) + response = self.receive_response() + if response != {"outputs": ""}: + self.handle_error(f"Unexpected response from server: {response}") + + for (pla,loc) in self.game_state.moves: + command = {"command": "play", "pla": pla, "loc": loc} + self.send_command(command) + response = self.receive_response() + if response != {"outputs": ""}: + self.handle_error(f"Unexpected response from server: {response}") + + def send_command(self, command): + print(f"Sending: {json.dumps(command)}") + self.server_process.stdin.write(json.dumps(command) + "\n") + self.server_process.stdin.flush() + + def receive_response(self): + print(f"Waiting for response") + while True: + returncode = self.server_process.poll() + if returncode is not None: + raise OSError(f"Server terminated unexpectedly with {returncode=}") + response = self.server_process.stdout.readline().strip() + if response != "": + break + print(f"Got response (first 100 chars): {str(response[:100])}") + return json.loads(response) + + def handle_error(self, error_message): + print(f"Error: {error_message}") + self.server_process.terminate() + + sys.exit(1) + + def on_key_down(self, event): + key_code = event.GetKeyCode() + if (key_code == wx.WXK_LEFT or key_code == wx.WXK_BACK) and event.ShiftDown(): + self.undo(10) + elif key_code == wx.WXK_LEFT or key_code == wx.WXK_BACK: + self.undo() + elif key_code == wx.WXK_RIGHT and event.ShiftDown(): + self.redo(10) + elif key_code == wx.WXK_RIGHT: + self.redo() + elif key_code == wx.WXK_DOWN: + self.undo(len(self.game_state.moves)) + elif key_code == wx.WXK_UP: + self.redo(len(self.game_state.redo_stack)) + event.Skip() + + def undo(self, undo_count = 1): + is_refresh_needed = False + for i in range(undo_count): + if not self.game_state.can_undo(): + break + + is_refresh_needed = True + + self.game_state.undo() + + command = {"command": "undo"} + self.send_command(command) + response = self.receive_response() + if response != {"outputs": ""}: + self.handle_error(f"Unexpected response from server: {response}") + + if is_refresh_needed: + self.board.Refresh() + self.board.refresh_model() + + def redo(self, redo_count = 1): + is_refresh_needed = False + for i in range(redo_count): + if not self.game_state.can_redo(): + break + + is_refresh_needed = True + + self.game_state.redo() + + command = {"command": "redo"} + self.send_command(command) + response = self.receive_response() + if response != {"outputs": ""}: + self.handle_error(f"Unexpected response from server: {response}") + + if is_refresh_needed: + self.board.Refresh() + self.board.refresh_model() + + def on_drop_files(self, sgf_files): + if len(sgf_files) == 0: + return False + + sgf_file = sgf_files[0] + file_extension = os.path.splitext(sgf_file)[1] + if file_extension != ".sgf": + return False + + game_state = load_sgf_game_state(sgf_file) + + self.game_state = game_state + self.board_size = self.game_state.board_size + + self.board.game_state = game_state + self.board.board_size = self.board_size + + self.init_server() + + self.board.Refresh() + self.board.refresh_model() + + return True + + def on_close(self, event): + self.server_process.terminate() + event.Skip() + +def main(): + sgf_file = None + server_command = sys.argv[1:] + + if len(server_command) >= 2 and server_command[0] == "-sgf": + sgf_file = server_command[1] + server_command = server_command[2:] + + if not server_command: + print("Usage: python humanslnet_gui.py [-sgf FILENAME] ") + sys.exit(1) + + if sgf_file is not None: + game_state = load_sgf_game_state(sgf_file) + else: + game_state = GameState(19, GameState.RULES_JAPANESE) + + app = wx.App() + client = GoClient(server_command, game_state) + client.Bind(wx.EVT_CLOSE, client.on_close) + client.Show() + client.slider_window.Show() + client.slider_window.update_metadata() + + app.MainLoop() + +if __name__ == "__main__": + main() diff --git a/training/scripts/dated/20241110-122511/humanslnet_server.py b/training/scripts/dated/20241110-122511/humanslnet_server.py new file mode 100644 index 0000000..9d6e351 --- /dev/null +++ b/training/scripts/dated/20241110-122511/humanslnet_server.py @@ -0,0 +1,81 @@ +import sys +import json +import numpy as np +from load_model import load_model +from gamestate import GameState +from features import Features +from sgfmetadata import SGFMetadata +import argparse + +def numpy_array_encoder(obj): + if isinstance(obj, np.ndarray): + return obj.tolist() + elif isinstance(obj, np.float32): + return float(obj) + raise TypeError(f'Object of type {obj.__class__.__name__} is not JSON serializable') + +def main(): + parser = argparse.ArgumentParser() + parser.add_argument('-checkpoint', help='Checkpoint to test', required=True) + parser.add_argument('-use-swa', help='Use SWA model', action="store_true", required=False) + parser.add_argument('-device', help='Device to use, such as cpu or cuda:0', required=True) + args = parser.parse_args() + + model, swa_model, _ = load_model(args.checkpoint, use_swa=args.use_swa, device=args.device, pos_len=19, verbose=False) + if swa_model is not None: + model = swa_model + game_state = None + + def write(output): + sys.stdout.write(json.dumps(output,default=numpy_array_encoder) + "\n") + sys.stdout.flush() + + # DEBUGGING + # game_state = GameState(board_size=19, rules=GameState.RULES_JAPANESE) + # sgfmeta = SGFMetadata() + # outputs = game_state.get_model_outputs(model, sgfmeta=sgfmeta) + # write(dict(outputs=outputs)) + + for line in sys.stdin: + line = line.strip() + if not line: + continue + + data = json.loads(line) + + if data["command"] == "start": + board_size = data["board_size"] + rules = data["rules"] + game_state = GameState(board_size, rules) + write(dict(outputs="")) + + elif data["command"] == "play": + pla = data["pla"] + loc = data["loc"] + game_state.play(pla, loc) + write(dict(outputs="")) + + elif data["command"] == "undo": + game_state.undo() + write(dict(outputs="")) + + elif data["command"] == "redo": + game_state.redo() + write(dict(outputs="")) + + elif data["command"] == "get_model_outputs": + sgfmeta = SGFMetadata.of_dict(data["sgfmeta"]) + # features = Features(model.config, model.pos_len) + # foo = game_state.get_input_features(features) + outputs = game_state.get_model_outputs(model, sgfmeta=sgfmeta) + filtered_outputs = {} + for key in outputs: + if key in ["moves_and_probs0", "value", "lead", "scorestdev"]: + filtered_outputs[key] = outputs[key] + write(dict(outputs=filtered_outputs)) + + else: + raise ValueError(f"Unknown command: {data['command']}") + +if __name__ == "__main__": + main() diff --git a/training/scripts/dated/20241110-122511/katago.exe b/training/scripts/dated/20241110-122511/katago.exe new file mode 100644 index 0000000000000000000000000000000000000000..2ad6e330a7efb336105529a3ff1a4731e23c8e39 GIT binary patch literal 4685824 zcmd?SeSB2K^*_ELS%^T`4YCpxWYvh#AT9(o8q{6L!d=)%RMc2OqaZ~^O+x~)A_kL{ z-CowlYFpb%KlrJ(w)Fv@0{EB!CgDK{S`@?we7-TLpe+Pc_V<3Dxp#N-0AfGi|9*MB 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batch_size + self.world_size = world_size + self.pos_len = raw_model.pos_len + self.pos_area = raw_model.pos_len * raw_model.pos_len + self.policy_len = raw_model.pos_len * raw_model.pos_len + 1 + self.value_len = 3 + self.num_td_values = 3 + self.num_futurepos_values = 2 + self.num_seki_logits = 4 + self.scorebelief_len = 2 * (self.pos_len*self.pos_len + EXTRA_SCORE_DISTR_RADIUS) + + self.score_belief_offset_vector = raw_model.value_head.score_belief_offset_vector + self.moving_unowned_proportion_sum = 0.0 + self.moving_unowned_proportion_weight = 0.0 + + def state_dict(self): + return dict( + moving_unowned_proportion_sum = self.moving_unowned_proportion_sum, + moving_unowned_proportion_weight = self.moving_unowned_proportion_weight, + ) + def load_state_dict(self, state_dict: Dict[str,Any]): + if isinstance(state_dict["moving_unowned_proportion_sum"],torch.Tensor): + self.moving_unowned_proportion_sum = state_dict["moving_unowned_proportion_sum"].item() + else: + self.moving_unowned_proportion_sum = state_dict["moving_unowned_proportion_sum"] + self.moving_unowned_proportion_weight = state_dict["moving_unowned_proportion_weight"] + + def loss_policy_player_samplewise(self, pred_logits, target_probs, weight, global_weight): + assert pred_logits.shape == (self.n, self.policy_len) + assert target_probs.shape == (self.n, self.policy_len) + loss = cross_entropy(pred_logits, target_probs, dim=1) + return global_weight * weight * loss + + def loss_policy_opponent_samplewise(self, pred_logits, target_probs, weight, global_weight): + assert pred_logits.shape == (self.n, self.policy_len) + assert target_probs.shape == (self.n, self.policy_len) + loss = cross_entropy(pred_logits, target_probs, dim=1) + return 0.15 * global_weight * weight * loss + + + def loss_value_samplewise(self, pred_logits, target_probs, weight, global_weight): + assert pred_logits.shape == (self.n, self.value_len) + assert target_probs.shape == (self.n, self.value_len) + assert weight.shape == (self.n,) + loss = cross_entropy(pred_logits, target_probs, dim=1) + return 1.20 * global_weight * weight * loss + + def loss_td_value_samplewise(self, pred_logits, target_probs, weight, global_weight): + assert pred_logits.shape == (self.n, self.num_td_values, self.value_len) + assert target_probs.shape == (self.n, self.num_td_values, self.value_len) + assert weight.shape == (self.n,) + assert global_weight.shape == (self.n,) + loss = cross_entropy(pred_logits, target_probs, dim=2) - cross_entropy(torch.log(target_probs + 1.0e-30), target_probs, dim=2) + return 1.20 * global_weight.unsqueeze(1) * weight.unsqueeze(1) * loss + + def loss_td_score_samplewise(self, pred, target, weight, global_weight): + assert pred.shape == (self.n, self.num_td_values) + assert target.shape == (self.n, self.num_td_values) + loss = torch.sum(huber_loss(pred, target, delta = 12.0), dim=1) + return 0.0004 * global_weight * weight * loss + + + def loss_ownership_samplewise(self, pred_pretanh, target, weight, mask, mask_sum_hw, global_weight): + # This uses a formulation where each batch element cares about its average loss. + # In particular this means that ownership loss predictions on small boards "count more" per spot. + # Not unlike the way that policy and value loss are also equal-weighted by batch element. + assert pred_pretanh.shape == (self.n, 1, self.pos_len, self.pos_len) + assert target.shape == (self.n, self.pos_len, self.pos_len) + assert mask.shape == (self.n, self.pos_len, self.pos_len) + assert mask_sum_hw.shape == (self.n,) + pred_logits = torch.cat((pred_pretanh, -pred_pretanh), dim=1).view(self.n,2,self.pos_area) + target_probs = torch.stack(((1.0 + target) / 2.0, (1.0 - target) / 2.0), dim=1).view(self.n,2,self.pos_area) + loss = torch.sum(cross_entropy(pred_logits, target_probs, dim=1) * mask.view(self.n,self.pos_area), dim=1) / mask_sum_hw + return 1.5 * global_weight * weight * loss + + + def loss_scoring_samplewise(self, pred_scoring, target, weight, mask, mask_sum_hw, global_weight): + assert pred_scoring.shape == (self.n, 1, self.pos_len, self.pos_len) + assert target.shape == (self.n, self.pos_len, self.pos_len) + assert mask.shape == (self.n, self.pos_len, self.pos_len) + assert mask_sum_hw.shape == (self.n,) + + loss = torch.sum(torch.square(pred_scoring.squeeze(1) - target) * mask, dim=(1,2)) / mask_sum_hw + # Simple huberlike transform to reduce crazy values + loss = 4.0 * (torch.sqrt(loss * 0.5 + 1.0) - 1.0) + return global_weight * weight * loss + + + def loss_futurepos_samplewise(self, pred_pretanh, target, weight, mask, mask_sum_hw, global_weight): + # The futurepos targets extrapolate a fixed number of steps into the future independent + # of board size. So unlike the ownership above, generally a fixed number of spots are going to be + # "wrong" independent of board size, so we should just equal-weight the prediction per spot. + # However, on larger boards often the entropy of where the future moves will be should be greater + # and also in the event of capture, there may be large captures that don't occur on small boards, + # causing some scaling with board size. So, I dunno, let's compromise and scale by sqrt(boardarea). + # Also, the further out targets should be weighted a little less due to them being higher entropy + # due to simply being farther in the future, so multiply by [1,0.25]. + assert pred_pretanh.shape == (self.n, self.num_futurepos_values, self.pos_len, self.pos_len) + assert target.shape == (self.n, self.num_futurepos_values, self.pos_len, self.pos_len) + assert mask.shape == (self.n, self.pos_len, self.pos_len) + assert mask_sum_hw.shape == (self.n,) + loss = torch.square(torch.tanh(pred_pretanh) - target) * mask.unsqueeze(1) + loss = loss * constant_like([1.0,0.25], loss).view(1,2,1,1) + loss = torch.sum(loss, dim=(1, 2, 3)) / torch.sqrt(mask_sum_hw) + return 0.25 * global_weight * weight * loss + + + def loss_seki_samplewise(self, pred_logits, target, target_ownership, weight, mask, mask_sum_hw, global_weight, is_training, skip_moving_update): + assert self.num_seki_logits == 4 + assert pred_logits.shape == (self.n, self.num_seki_logits, self.pos_len, self.pos_len) + assert target.shape == (self.n, self.pos_len, self.pos_len) + assert target_ownership.shape == (self.n, self.pos_len, self.pos_len) + assert mask.shape == (self.n, self.pos_len, self.pos_len) + assert mask_sum_hw.shape == (self.n,) + + owned_target = torch.square(target_ownership) + unowned_target = 1.0 - owned_target + unowned_proportion = torch.sum(unowned_target * mask, dim=(1, 2)) / (1.0 + mask_sum_hw) + unowned_proportion = torch.mean(unowned_proportion * weight) + if is_training: + if not skip_moving_update: + self.moving_unowned_proportion_sum *= 0.998 + self.moving_unowned_proportion_weight *= 0.998 + self.moving_unowned_proportion_sum += unowned_proportion.item() + self.moving_unowned_proportion_weight += 1.0 + moving_unowned_proportion = self.moving_unowned_proportion_sum / self.moving_unowned_proportion_weight + seki_weight_scale = 8.0 * 0.005 / (0.005 + moving_unowned_proportion) + else: + seki_weight_scale = 7.0 + + # Loss for predicting the exact sign of seki points + sign_pred = pred_logits[:, 0:3, :, :] + sign_target = torch.stack( + ( + 1.0 - torch.square(target), + torch.nn.functional.relu(target), + torch.nn.functional.relu(-target), + ), + dim=1, + ) + loss_sign = torch.sum(cross_entropy(sign_pred, sign_target, dim=1) * mask, dim=(1, 2)) + + # Loss for generally predicting points that nobody will own + neutral_pred = torch.stack( + (pred_logits[:, 3, :, :], torch.zeros_like(target_ownership)), dim=1 + ) + neutral_target = torch.stack((unowned_target, owned_target), dim=1) + loss_neutral = torch.sum(cross_entropy(neutral_pred, neutral_target, dim=1) * mask, dim=(1, 2)) + + loss = loss_sign + 0.5 * loss_neutral + loss = loss / mask_sum_hw + return (global_weight * seki_weight_scale * weight * loss, seki_weight_scale) + + + def loss_scoremean_samplewise(self, pred, target, weight, global_weight): + # Huber will incentivize this to not actually converge to the mean, + #but rather something meanlike locally and something medianlike + # for very large possible losses. This seems... okay - it might actually + # be what users want. + assert pred.shape == (self.n,) + assert target.shape == (self.n,) + loss = huber_loss(pred, target, delta = 12.0) + return 0.0015 * global_weight * weight * loss + + + def loss_scorebelief_cdf_samplewise(self, pred_logits, target_probs, weight, global_weight): + assert pred_logits.shape == (self.n,self.scorebelief_len) + assert target_probs.shape == (self.n,self.scorebelief_len) + pred_cdf = torch.cumsum(torch.nn.functional.softmax(pred_logits, dim=1), dim=1) + target_cdf = torch.cumsum(target_probs, dim=1) + loss = torch.sum(torch.square(pred_cdf-target_cdf),axis=1) + return 0.020 * global_weight * weight * loss + + def loss_scorebelief_pdf_samplewise(self, pred_logits, target_probs, weight, global_weight): + assert pred_logits.shape == (self.n,self.scorebelief_len) + assert target_probs.shape == (self.n,self.scorebelief_len) + loss = cross_entropy(pred_logits, target_probs, dim=1) + return 0.020 * global_weight * weight * loss + + def loss_scorestdev_samplewise(self, pred, scorebelief_logits, global_weight): + assert pred.shape == (self.n,) + assert scorebelief_logits.shape == (self.n,self.scorebelief_len) + assert self.score_belief_offset_vector.shape == (self.scorebelief_len,) + scorebelief_probs = torch.nn.functional.softmax(scorebelief_logits, dim=1) + expected_score_from_belief = torch.sum(scorebelief_probs * self.score_belief_offset_vector.view(1,-1),dim=1,keepdim=True) + stdev_of_belief = torch.sqrt(0.001 + torch.sum( + scorebelief_probs * torch.square( + self.score_belief_offset_vector.view(1,-1) - expected_score_from_belief + ), + dim=1 + )) + loss = huber_loss(pred, stdev_of_belief, delta = 10.0) + return 0.001 * global_weight * loss + + def loss_lead_samplewise(self, pred, target, weight, global_weight): + # Huber will incentivize this to not actually converge to the mean, + #but rather something meanlike locally and something medianlike + # for very large possible losses. This seems... okay - it might actually + # be what users want. + assert pred.shape == (self.n,) + assert target.shape == (self.n,) + loss = huber_loss(pred, target, delta = 8.0) + return 0.0060 * global_weight * weight * loss + + def loss_variance_time_samplewise(self, pred, target, weight, global_weight): + assert pred.shape == (self.n,) + assert target.shape == (self.n,) + # Even if the training target is 0, add a tiny bit of irreducible error for regularizing the prediction. + loss = huber_loss(pred, target + 1.0e-5, delta = 50.0) + return 0.0003 * global_weight * weight * loss + + + def loss_shortterm_value_error_samplewise(self, pred, td_value_pred_logits, td_value_target_probs, weight, global_weight): + td_value_pred_probs = torch.softmax(td_value_pred_logits[:,2,:],axis=1) + predvalue = (td_value_pred_probs[:,0] - td_value_pred_probs[:,1]).detach() + realvalue = td_value_target_probs[:,2,0] - td_value_target_probs[:,2,1] + # Even if the training target is 0, add a tiny bit of irreducible error for regularizing the prediction, 0.01%. + sqerror = torch.square(predvalue-realvalue) + 1.0e-8 + loss = huber_loss(pred, sqerror, delta = 0.4) + return 2.0 * global_weight * weight * loss + + def loss_shortterm_score_error_samplewise(self, pred, td_score_pred, td_score_target, weight, global_weight): + predscore = td_score_pred[:,2].detach() + realscore = td_score_target[:,2] + # Even if the training target is 0, add a tiny bit of irreducible error for regularizing the prediction, one hundredth of a point. + sqerror = torch.square(predscore-realscore) + 1.0e-4 + loss = huber_loss(pred, sqerror, delta = 100.0) + return 0.00002 * global_weight * weight * loss + + def accuracy1(self, pred_logits, target_probs, weight, global_weight): + return torch.sum(global_weight * weight * (torch.argmax(pred_logits,dim=1) == torch.argmax(target_probs,dim=1))) + + def target_entropy(self, target_probs, weight, global_weight): + return torch.sum(global_weight * weight * -torch.sum(target_probs * torch.log(target_probs + 1e-30), dim=-1)) + + def square_value(self, value_logits, global_weight): + return torch.sum(global_weight * torch.square(torch.sum(torch.softmax(value_logits,dim=1) * constant_like([1,-1,0],global_weight), dim=1))) + + # Returns 0.5 times the sum of squared model weights, for each reg group of model weights + @staticmethod + def get_model_norms(raw_model): + reg_dict : Dict[str,List] = {} + raw_model.add_reg_dict(reg_dict) + + device = reg_dict["normal"][0].device + dtype = torch.float32 + + modelnorm_normal = torch.zeros([],device=device,dtype=dtype) + modelnorm_normal_gamma = torch.zeros([],device=device,dtype=dtype) + modelnorm_output = torch.zeros([],device=device,dtype=dtype) + modelnorm_noreg = torch.zeros([],device=device,dtype=dtype) + modelnorm_output_noreg = torch.zeros([],device=device,dtype=dtype) + for tensor in reg_dict["normal"]: + modelnorm_normal += torch.sum(tensor * tensor) + for tensor in reg_dict["normal_gamma"]: + modelnorm_normal_gamma += torch.sum(tensor * tensor) + for tensor in reg_dict["output"]: + modelnorm_output += torch.sum(tensor * tensor) + for tensor in reg_dict["noreg"]: + modelnorm_noreg += torch.sum(tensor * tensor) + for tensor in reg_dict["output_noreg"]: + modelnorm_output_noreg += torch.sum(tensor * tensor) + modelnorm_normal *= 0.5 + modelnorm_normal_gamma *= 0.5 + modelnorm_output *= 0.5 + modelnorm_noreg *= 0.5 + modelnorm_output_noreg *= 0.5 + return (modelnorm_normal, modelnorm_normal_gamma, modelnorm_output, modelnorm_noreg, modelnorm_output_noreg) + + def get_specific_norms_and_gradient_stats(self,raw_model): + with torch.no_grad(): + params = {} + for name, param in raw_model.named_parameters(): + params[name] = param + + stats = {} + def add_norm_and_grad_stats(name): + param = params[name] + if name.endswith(".weight"): + fanin = param.shape[1] + elif name.endswith(".gamma"): + fanin = 1 + elif name.endwith(".beta"): + fanin = 1 + else: + assert False, "unimplemented case to compute stats on parameter" + + # 1.0 means that the average squared magnitude of a parameter in this tensor is around where + # it would be at initialization, assuming it uses the activation that the model generally + # uses (e.g. relu or mish) + param_scale = torch.sqrt(torch.mean(torch.square(param))) / compute_gain(raw_model.activation) * math.sqrt(fanin) + stats[f"{name}.SCALE_batch"] = param_scale + + # How large is the gradient, on the same scale? + stats[f"{name}.GRADSC_batch"] = torch.sqrt(torch.mean(torch.square(param.grad))) / compute_gain(raw_model.activation) * math.sqrt(fanin) + + # And how large is the component of the gradient that is orthogonal to the overall magnitude of the parameters? + orthograd = param.grad - param * (torch.sum(param.grad * param) / (1e-20 + torch.sum(torch.square(param)))) + stats[f"{name}.OGRADSC_batch"] = torch.sqrt(torch.mean(torch.square(orthograd))) / compute_gain(raw_model.activation) * math.sqrt(fanin) + + add_norm_and_grad_stats("blocks.1.normactconvp.conv.weight") + add_norm_and_grad_stats("blocks.1.blockstack.0.normactconv1.conv.weight") + add_norm_and_grad_stats("blocks.1.blockstack.0.normactconv2.conv.weight") + add_norm_and_grad_stats("blocks.1.blockstack.1.normactconv2.norm.gamma") + add_norm_and_grad_stats("blocks.1.normactconvq.conv.weight") + add_norm_and_grad_stats("blocks.1.normactconvq.norm.gamma") + + add_norm_and_grad_stats("blocks.6.normactconvp.conv.weight") + add_norm_and_grad_stats("blocks.6.blockstack.0.normactconv1.conv.weight") + add_norm_and_grad_stats("blocks.6.blockstack.0.normactconv2.conv.weight") + add_norm_and_grad_stats("blocks.6.blockstack.1.normactconv2.norm.gamma") + add_norm_and_grad_stats("blocks.6.normactconvq.conv.weight") + add_norm_and_grad_stats("blocks.6.normactconvq.norm.gamma") + + add_norm_and_grad_stats("blocks.10.normactconvp.conv.weight") + add_norm_and_grad_stats("blocks.10.blockstack.0.normactconv1.conv.weight") + add_norm_and_grad_stats("blocks.10.blockstack.0.normactconv2.conv.weight") + add_norm_and_grad_stats("blocks.10.blockstack.1.normactconv2.norm.gamma") + add_norm_and_grad_stats("blocks.10.normactconvq.conv.weight") + add_norm_and_grad_stats("blocks.10.normactconvq.norm.gamma") + + add_norm_and_grad_stats("blocks.16.normactconvp.conv.weight") + add_norm_and_grad_stats("blocks.16.blockstack.0.normactconv1.conv.weight") + add_norm_and_grad_stats("blocks.16.blockstack.0.normactconv2.conv.weight") + add_norm_and_grad_stats("blocks.16.blockstack.1.normactconv2.norm.gamma") + add_norm_and_grad_stats("blocks.16.normactconvq.conv.weight") + add_norm_and_grad_stats("blocks.16.normactconvq.norm.gamma") + + add_norm_and_grad_stats("policy_head.conv1p.weight") + add_norm_and_grad_stats("value_head.conv1.weight") + add_norm_and_grad_stats("intermediate_policy_head.conv1p.weight") + add_norm_and_grad_stats("intermediate_value_head.conv1.weight") + + return stats + + def metrics_dict_batchwise( + self, + raw_model, + model_output_postprocessed_byheads, + extra_outputs, + batch, + is_training, + soft_policy_weight_scale, + disable_optimistic_policy, + meta_kata_only_soft_policy, + value_loss_scale, + td_value_loss_scales, + seki_loss_scale, + variance_time_loss_scale, + main_loss_scale, + intermediate_loss_scale, + ): + results = self.metrics_dict_batchwise_single_heads_output( + raw_model, + model_output_postprocessed_byheads[0], + batch, + is_training=is_training, + soft_policy_weight_scale=soft_policy_weight_scale, + disable_optimistic_policy=disable_optimistic_policy, + meta_kata_only_soft_policy=meta_kata_only_soft_policy, + value_loss_scale=value_loss_scale, + td_value_loss_scales=td_value_loss_scales, + seki_loss_scale=seki_loss_scale, + variance_time_loss_scale=variance_time_loss_scale, + is_intermediate=False, + ) + if main_loss_scale is not None: + results["loss_sum"] = main_loss_scale * results["loss_sum"] + + if raw_model.get_has_intermediate_head(): + assert len(model_output_postprocessed_byheads) > 1 + if raw_model.training: + assert intermediate_loss_scale is not None + else: + if intermediate_loss_scale is None: + intermediate_loss_scale = 1.0 + + if intermediate_loss_scale is not None: + iresults = self.metrics_dict_batchwise_single_heads_output( + raw_model, + model_output_postprocessed_byheads[1], + batch, + is_training=is_training, + soft_policy_weight_scale=soft_policy_weight_scale, + disable_optimistic_policy=disable_optimistic_policy, + meta_kata_only_soft_policy=meta_kata_only_soft_policy, + value_loss_scale=value_loss_scale, + td_value_loss_scales=td_value_loss_scales, + seki_loss_scale=seki_loss_scale, + variance_time_loss_scale=variance_time_loss_scale, + is_intermediate=True, + ) + for key,value in iresults.items(): + if key != "loss_sum": + results["I"+key] = value + results["loss_sum"] = results["loss_sum"] + intermediate_loss_scale * iresults["loss_sum"] + + return results + + def metrics_dict_batchwise_single_heads_output( + self, + raw_model, + model_output_postprocessed, + batch, + is_training, + soft_policy_weight_scale, + disable_optimistic_policy, + meta_kata_only_soft_policy, + value_loss_scale, + td_value_loss_scales, + seki_loss_scale, + variance_time_loss_scale, + is_intermediate, + ): + ( + policy_logits, + value_logits, + td_value_logits, + pred_td_score, + ownership_pretanh, + pred_scoring, + futurepos_pretanh, + seki_logits, + pred_scoremean, + pred_scorestdev, + pred_lead, + pred_variance_time, + pred_shortterm_value_error, + pred_shortterm_score_error, + scorebelief_logits, + ) = model_output_postprocessed + + input_binary_nchw = batch["binaryInputNCHW"] + input_global_nc = batch["globalInputNC"] + target_policy_ncmove = batch["policyTargetsNCMove"] + target_global_nc = batch["globalTargetsNC"] + score_distribution_ns = batch["scoreDistrN"] + target_value_nchw = batch["valueTargetsNCHW"] + + mask = input_binary_nchw[:, 0, :, :].contiguous() + mask_sum_hw = torch.sum(mask,dim=(1,2)) + + n = input_binary_nchw.shape[0] + h = input_binary_nchw.shape[2] + w = input_binary_nchw.shape[3] + + policymask = torch.cat((mask.view(n,h*w),mask.new_ones((n,1))),dim=1) + + target_policy_player = target_policy_ncmove[:, 0, :] + target_policy_player = target_policy_player / torch.sum(target_policy_player, dim=1, keepdim=True) + target_policy_opponent = target_policy_ncmove[:, 1, :] + target_policy_opponent = target_policy_opponent / torch.sum(target_policy_opponent, dim=1, keepdim=True) + target_policy_player_soft = (target_policy_player + 1e-7) * policymask + target_policy_player_soft = torch.pow(target_policy_player_soft, 0.25) + target_policy_player_soft /= torch.sum(target_policy_player_soft, dim=1, keepdim=True) + target_policy_opponent_soft = (target_policy_opponent + 1e-7) * policymask + target_policy_opponent_soft = torch.pow(target_policy_opponent_soft, 0.25) + target_policy_opponent_soft /= torch.sum(target_policy_opponent_soft, dim=1, keepdim=True) + + target_weight_policy_player = target_global_nc[:, 26] + target_weight_policy_opponent = target_global_nc[:, 28] + + target_value = target_global_nc[:, 0:3] + target_scoremean = target_global_nc[:, 3] + target_td_value = torch.stack( + (target_global_nc[:, 4:7], target_global_nc[:, 8:11], target_global_nc[:, 12:15]), dim=1 + ) + target_td_score = torch.cat( + (target_global_nc[:, 7:8], target_global_nc[:, 11:12], target_global_nc[:, 15:16]), dim=1 + ) + target_lead = target_global_nc[:, 21] + target_variance_time = target_global_nc[:, 22] + global_weight = target_global_nc[:, 25] + target_weight_ownership = target_global_nc[:, 27] + target_weight_lead = target_global_nc[:, 29] + target_weight_futurepos = target_global_nc[:, 33] + target_weight_scoring = target_global_nc[:, 34] + target_weight_value = 1.0 - target_global_nc[:, 35] + target_weight_td_value = 1.0 - target_global_nc[:, 24] + + target_score_distribution = score_distribution_ns / 100.0 + + target_ownership = target_value_nchw[:, 0, :, :] + target_seki = target_value_nchw[:, 1, :, :] + target_futurepos = target_value_nchw[:, 2:4, :, :] + target_scoring = target_value_nchw[:, 4, :, :] / 120.0 + + if raw_model.config["version"] <= 11: + assert raw_model.policy_head.num_policy_outputs == 4 + policy_opt_loss_scale = 1.000 + long_policy_opt_loss_scale = 0.0 + short_policy_opt_loss_scale = 0.0 + else: + assert raw_model.policy_head.num_policy_outputs == 6 + policy_opt_loss_scale = 0.930 + long_policy_opt_loss_scale = 0.100 + short_policy_opt_loss_scale = 0.200 + + loss_policy_player = self.loss_policy_player_samplewise( + policy_logits[:, 0, :], + target_policy_player, + target_weight_policy_player, + global_weight, + ).sum() + loss_policy_opponent = self.loss_policy_opponent_samplewise( + policy_logits[:, 1, :], + target_policy_opponent, + target_weight_policy_opponent, + global_weight, + ).sum() + + target_weight_policy_player_soft = target_weight_policy_player + target_weight_policy_opponent_soft = target_weight_policy_opponent + if meta_kata_only_soft_policy: + metadata_input_nc = batch["metadataInputNC"] + assert metadata_input_nc.shape[0] == target_weight_policy_player_soft.shape[0] + # 151 indicates source 0 = katago + target_weight_policy_player_soft = target_weight_policy_player_soft * metadata_input_nc[:,151] + target_weight_policy_opponent_soft = target_weight_policy_opponent_soft * metadata_input_nc[:,151] + + loss_policy_player_soft = self.loss_policy_player_samplewise( + policy_logits[:, 2, :], + target_policy_player_soft, + target_weight_policy_player_soft, + global_weight, + ).sum() + loss_policy_opponent_soft = self.loss_policy_opponent_samplewise( + policy_logits[:, 3, :], + target_policy_opponent_soft, + target_weight_policy_opponent_soft, + global_weight, + ).sum() + + if raw_model.config["version"] <= 11: + target_weight_longoptimistic_policy = torch.zeros_like(global_weight) + loss_longoptimistic_policy = torch.zeros_like(loss_policy_player) + elif disable_optimistic_policy: + target_weight_longoptimistic_policy = target_weight_policy_player * 0.5 + loss_longoptimistic_policy = self.loss_policy_player_samplewise( + policy_logits[:, 4, :], + target_policy_player, + target_weight_longoptimistic_policy, + global_weight, + ).sum() + else: + # Long-term optimistic policy. Weight policy by: + # Final game win squared (squaring discourages draws) + win_squared = torch.square( + target_global_nc[:, 0] # win (or draw, weighted by draw utility) + + 0.5 * target_global_nc[:, 2] # noresult + ) + # Or the score outcome of the game being around 1.5 sigma more than expected + # Add a small amount to the variance to avoid division by zero or overly small numbers + longterm_score_stdevs_excess = (target_global_nc[:, 3] - pred_scoremean.detach()) / torch.sqrt(torch.square(pred_scorestdev.detach()) + 0.25) + target_weight_longoptimistic_policy = torch.clamp( + win_squared + torch.sigmoid((longterm_score_stdevs_excess - 1.5) * 3.0), + min=0.0, + max=1.0, + ) + target_weight_longoptimistic_policy = ( + target_weight_longoptimistic_policy + * target_weight_policy_player # game has normal target + * target_weight_ownership # and also actually ended in full ownership and score, not a sidepos + ) + loss_longoptimistic_policy = self.loss_policy_player_samplewise( + policy_logits[:, 4, :], + target_policy_player, + target_weight_longoptimistic_policy, + global_weight, + ).sum() + + assert len(loss_longoptimistic_policy.shape) == 0 + assert len(target_weight_longoptimistic_policy.shape) == 1 + assert target_weight_longoptimistic_policy.shape[0] == n + target_weight_longoptimistic_policy_sum = (global_weight * target_weight_longoptimistic_policy).sum() + + if raw_model.config["version"] <= 11: + target_weight_shortoptimistic_policy = torch.zeros_like(global_weight) + loss_shortoptimistic_policy = torch.zeros_like(loss_policy_player) + elif disable_optimistic_policy: + target_weight_shortoptimistic_policy = target_weight_policy_player * 0.5 + loss_shortoptimistic_policy = self.loss_policy_player_samplewise( + policy_logits[:, 5, :], + target_policy_player, + target_weight_shortoptimistic_policy, + global_weight, + ).sum() + else: + # Short-term optimistic policy. Weight policy by: + # The shortterm value outcome being around 1.5 sigma more than expected + # Add a small amount to the variance to avoid division by zero or overly small numbers + shortterm_value_actual = target_global_nc[:, 12] - target_global_nc[:, 13] + shortterm_value_pred = torch.nn.functional.softmax(td_value_logits[:, 2, :].detach(), dim=1) + shortterm_value_pred = shortterm_value_pred[:, 0] - shortterm_value_pred[:, 1] + shortterm_value_stdevs_excess = (shortterm_value_actual - shortterm_value_pred) / torch.sqrt(pred_shortterm_value_error.detach() + 0.0001) + # Or the shortterm score outcome being around 1.5 sigma more than expected + # Add a small amount to the variance to avoid division by zero or overly small numbers + shortterm_score_stdevs_excess = (target_global_nc[:, 15] - pred_td_score[:,2].detach()) / torch.sqrt(pred_shortterm_score_error.detach() + 0.25) + target_weight_shortoptimistic_policy = torch.clamp( + torch.sigmoid((shortterm_value_stdevs_excess - 1.5) * 3.0) + torch.sigmoid((shortterm_score_stdevs_excess - 1.5) * 3.0), + min=0.0, + max=1.0, + ) + target_weight_shortoptimistic_policy = ( + target_weight_shortoptimistic_policy + * target_weight_policy_player # game has normal target + * target_weight_ownership # and also actually ended in full ownership and score, not a sidepos + ) + loss_shortoptimistic_policy = self.loss_policy_player_samplewise( + policy_logits[:, 5, :], + target_policy_player, + target_weight_shortoptimistic_policy, + global_weight, + ).sum() + + assert len(loss_shortoptimistic_policy.shape) == 0 + assert len(target_weight_shortoptimistic_policy.shape) == 1 + assert target_weight_shortoptimistic_policy.shape[0] == n + target_weight_shortoptimistic_policy_sum = (global_weight * target_weight_shortoptimistic_policy).sum() + + + loss_value = self.loss_value_samplewise( + value_logits, target_value, target_weight_value, global_weight + ).sum() + + loss_td_value_unsummed = self.loss_td_value_samplewise( + td_value_logits, target_td_value, target_weight_td_value, global_weight + ) + assert self.num_td_values == 3 + loss_td_value1 = loss_td_value_unsummed[:,0].sum() + loss_td_value2 = loss_td_value_unsummed[:,1].sum() + loss_td_value3 = loss_td_value_unsummed[:,2].sum() + + loss_td_score = self.loss_td_score_samplewise( + pred_td_score, target_td_score, target_weight_ownership, global_weight + ).sum() + + loss_ownership = self.loss_ownership_samplewise( + ownership_pretanh, + target_ownership, + target_weight_ownership, + mask, + mask_sum_hw, + global_weight, + ).sum() + loss_scoring = self.loss_scoring_samplewise( + pred_scoring, + target_scoring, + target_weight_scoring, + mask, + mask_sum_hw, + global_weight, + ).sum() + loss_futurepos = self.loss_futurepos_samplewise( + futurepos_pretanh, + target_futurepos, + target_weight_futurepos, + mask, + mask_sum_hw, + global_weight, + ).sum() + (loss_seki,seki_weight_scale) = self.loss_seki_samplewise( + seki_logits, + target_seki, + target_ownership, + target_weight_ownership, + mask, + mask_sum_hw, + global_weight, + is_training, + skip_moving_update=is_intermediate, + ) + loss_seki = loss_seki.sum() + seki_weight_scale = seki_weight_scale.sum() if not isinstance(seki_weight_scale,float) else seki_weight_scale + loss_scoremean = self.loss_scoremean_samplewise( + pred_scoremean, + target_scoremean, + target_weight_ownership, + global_weight, + ).sum() + loss_scorebelief_cdf = self.loss_scorebelief_cdf_samplewise( + scorebelief_logits, + target_score_distribution, + target_weight_ownership, + global_weight, + ).sum() + loss_scorebelief_pdf = self.loss_scorebelief_pdf_samplewise( + scorebelief_logits, + target_score_distribution, + target_weight_ownership, + global_weight, + ).sum() + loss_scorestdev = self.loss_scorestdev_samplewise( + pred_scorestdev, + scorebelief_logits, + global_weight, + ).sum() + loss_lead = self.loss_lead_samplewise( + pred_lead, + target_lead, + target_weight_lead, + global_weight, + ).sum() + loss_variance_time = self.loss_variance_time_samplewise( + pred_variance_time, + target_variance_time, + target_weight_ownership, + global_weight, + ).sum() + loss_shortterm_value_error = self.loss_shortterm_value_error_samplewise( + pred_shortterm_value_error, + td_value_logits, + target_td_value, + target_weight_ownership, + global_weight, + ).sum() + loss_shortterm_score_error = self.loss_shortterm_score_error_samplewise( + pred_shortterm_score_error, + pred_td_score, + target_td_score, + target_weight_ownership, + global_weight, + ).sum() + + loss_sum = ( + loss_policy_player * policy_opt_loss_scale + + loss_policy_opponent + + loss_policy_player_soft * soft_policy_weight_scale + + loss_policy_opponent_soft * soft_policy_weight_scale + + loss_longoptimistic_policy * long_policy_opt_loss_scale + + loss_shortoptimistic_policy * short_policy_opt_loss_scale + + loss_value * value_loss_scale + + loss_td_value1 * td_value_loss_scales[0] + + loss_td_value2 * td_value_loss_scales[1] + + loss_td_value3 * td_value_loss_scales[2] + + loss_td_score + + loss_ownership + + loss_scoring * 0.5 + + loss_futurepos + + loss_seki * seki_loss_scale + + loss_scoremean + + loss_scorebelief_cdf + + loss_scorebelief_pdf + + loss_scorestdev + + loss_lead + + loss_variance_time * variance_time_loss_scale + + loss_shortterm_value_error + + loss_shortterm_score_error + ) + + policy_acc1 = self.accuracy1( + policy_logits[:, 0, :], + target_policy_player, + target_weight_policy_player, + global_weight, + ) + square_value = self.square_value(value_logits, global_weight) + + results = { + "p0loss_sum": loss_policy_player, + "p1loss_sum": loss_policy_opponent, + "p0softloss_sum": loss_policy_player_soft, + "p1softloss_sum": loss_policy_opponent_soft, + "p0lopt_sum": loss_longoptimistic_policy, + "p0loptw_sum": target_weight_longoptimistic_policy_sum, + "p0sopt_sum": loss_shortoptimistic_policy, + "p0soptw_sum": target_weight_shortoptimistic_policy_sum, + "vloss_sum": loss_value, + "tdvloss1_sum": loss_td_value1, + "tdvloss2_sum": loss_td_value2, + "tdvloss3_sum": loss_td_value3, + "tdsloss_sum": loss_td_score, + "oloss_sum": loss_ownership, + "sloss_sum": loss_scoring, + "fploss_sum": loss_futurepos, + "skloss_sum": loss_seki, + "smloss_sum": loss_scoremean, + "sbcdfloss_sum": loss_scorebelief_cdf, + "sbpdfloss_sum": loss_scorebelief_pdf, + "sdregloss_sum": loss_scorestdev, + "leadloss_sum": loss_lead, + "vtimeloss_sum": loss_variance_time, + "evstloss_sum": loss_shortterm_value_error, + "esstloss_sum": loss_shortterm_score_error, + "loss_sum": loss_sum, + "pacc1_sum": policy_acc1, + "vsquare_sum": square_value, + } + + if is_intermediate: + return results + else: + weight = global_weight.sum() + nsamples = int(global_weight.shape[0]) + policy_target_entropy = self.target_entropy( + target_policy_player, + target_weight_policy_player, + global_weight, + ) + soft_policy_target_entropy = self.target_entropy( + target_policy_player_soft, + target_weight_policy_player, + global_weight, + ) + + (modelnorm_normal, modelnorm_normal_gamma, modelnorm_output, modelnorm_noreg, modelnorm_output_noreg) = self.get_model_norms(raw_model) + + extra_results = { + "wsum": weight * self.world_size, + "nsamp": nsamples * self.world_size, + "ptentr_sum": policy_target_entropy, + "ptsoftentr_sum": soft_policy_target_entropy, + "sekiweightscale_sum": seki_weight_scale * weight, + "norm_normal_batch": modelnorm_normal, + "norm_normal_gamma_batch": modelnorm_normal_gamma, + "norm_output_batch": modelnorm_output, + "norm_noreg_batch": modelnorm_noreg, + "norm_output_noreg_batch": modelnorm_output_noreg, + } + for key,value in extra_results.items(): + results[key] = value + return results diff --git a/training/scripts/dated/20241110-122511/migrate_double_v1.py b/training/scripts/dated/20241110-122511/migrate_double_v1.py new file mode 100644 index 0000000..1f6cf5a --- /dev/null +++ b/training/scripts/dated/20241110-122511/migrate_double_v1.py @@ -0,0 +1,90 @@ +#!/usr/bin/python3 +import sys +import os +import argparse +import math +import torch + +description = """ +Hacky script to double number of v1 channels. +Run on a torch checkpoint.ckpt file. Replace the old one with the new one after a backup. +""" + +parser = argparse.ArgumentParser(description=description) +parser.add_argument('-checkpoint', help='Checkpoint file path', required=True) +parser.add_argument('-output', help='Output new checkpoint to here', required=True) +args = vars(parser.parse_args()) + +checkpoint_path = args["checkpoint"] +output_path = args["output"] + +data = torch.load(checkpoint_path,map_location="cpu") + +def noisify(tensor): + scale = 0.02 * math.sqrt(torch.mean(torch.square(tensor)).item()) + return tensor * (0.9 + 0.15 * torch.rand(tensor.shape, device=tensor.device)) + scale * (2.0 * torch.rand(tensor.shape, device=tensor.device) - 1.0) + +# Torch input channel dimension is dim 1. +def double_linear_in_dim(name, tensor, scale): + ic = tensor.shape[1] + print(f"{name} tensor has {ic} input channels, doubling to {ic*2}") + tensor = torch.cat((noisify(tensor),noisify(tensor)), dim=1) * scale + return tensor + +# Torch output channel dimension is dim 0. +def double_linear_out_dim(name, tensor, scale): + oc = tensor.shape[0] + print(f"{name} tensor has {oc} output channels, doubling to {oc*2}") + tensor = torch.cat((noisify(tensor),noisify(tensor)), dim=0) * scale + return tensor + +def expand_in_dim_for(name, scale): + if f"module.{name}" in data["model"]: + data["model"][f"module.{name}"] = double_linear_in_dim(f"module.{name}", data["model"][f"module.{name}"], scale) + elif name in data["model"]: + data["model"][name] = double_linear_in_dim(name, data["model"][name], scale) + else: + assert False, f"{name} not found in saved model" + +def expand_out_dim_for(name, scale): + if f"module.{name}" in data["model"]: + data["model"][f"module.{name}"] = double_linear_out_dim(f"module.{name}", data["model"][f"module.{name}"], scale) + elif name in data["model"]: + data["model"][name] = double_linear_out_dim(name, data["model"][name], scale) + else: + assert False, f"{name} not found in saved model" + +expand_out_dim_for("value_head.conv1.weight", scale=1.0) +expand_in_dim_for("value_head.bias1.beta", scale=1.0) +expand_in_dim_for("value_head.linear2.weight", scale=math.sqrt(0.5)) +expand_in_dim_for("value_head.conv_ownership.weight", scale=math.sqrt(0.5)) +expand_in_dim_for("value_head.conv_scoring.weight", scale=math.sqrt(0.5)) +expand_in_dim_for("value_head.linear_s2.weight", scale=math.sqrt(0.5)) +expand_in_dim_for("value_head.linear_smix.weight", scale=math.sqrt(0.5)) + + +if any("intermediate_value_head" in key for key in data["model"].keys()): + expand_out_dim_for("intermediate_value_head.conv1.weight", scale=1.0) + expand_in_dim_for("intermediate_value_head.bias1.beta", scale=1.0) + expand_in_dim_for("intermediate_value_head.linear2.weight", scale=math.sqrt(0.5)) + expand_in_dim_for("intermediate_value_head.conv_ownership.weight", scale=math.sqrt(0.5)) + expand_in_dim_for("intermediate_value_head.conv_scoring.weight", scale=math.sqrt(0.5)) + expand_in_dim_for("intermediate_value_head.linear_s2.weight", scale=math.sqrt(0.5)) + expand_in_dim_for("intermediate_value_head.linear_smix.weight", scale=math.sqrt(0.5)) + + +old_v1_num_channels = data["config"]["v1_num_channels"] +print(f"Doubling v1_num_channels {old_v1_num_channels}") +data["config"]["v1_num_channels"] = old_v1_num_channels * 2 + + +if "optimizer" in data: + print("Deleting optimizer state") + del data["optimizer"] +if "swa_model" in data: + print("Deleting swa model state") + del data["swa_model"] + +print(f"Saving to {output_path}") +torch.save(data, output_path) +print("Done") diff --git a/training/scripts/dated/20241110-122511/migrate_optimistic_policy.py b/training/scripts/dated/20241110-122511/migrate_optimistic_policy.py new file mode 100644 index 0000000..290411f --- /dev/null +++ b/training/scripts/dated/20241110-122511/migrate_optimistic_policy.py @@ -0,0 +1,73 @@ +#!/usr/bin/python3 +import sys +import os +import argparse +import math +import torch + +description = """ +Hacky script to migrate v11 model to v12 with optimistic policy. +Run on a torch checkpoint.ckpt file. Replace the old one with the new one after a backup. +And then manually edit the model.config.json to also bump the json file version to 12. +""" + +parser = argparse.ArgumentParser(description=description) +parser.add_argument('-checkpoint', help='Checkpoint file path', required=True) +parser.add_argument('-output', help='Output new checkpoint to here', required=True) +args = vars(parser.parse_args()) + +checkpoint_path = args["checkpoint"] +output_path = args["output"] + +data = torch.load(checkpoint_path,map_location="cpu") +assert data["config"]["version"] == 11 + +def noisify(tensor): + scale = 0.01 * math.sqrt(torch.mean(torch.square(tensor)).item()) + return tensor * (0.95 + 0.05 * torch.rand(tensor.shape, device=tensor.device)) + scale * (2.0 * torch.rand(tensor.shape, device=tensor.device) - 1.0) + + +# Torch output channel dimension is dim 0. +def expand_policy_out_dim(name, tensor): + if tensor.shape[0] == 6: + print(f"{name} tensor already has 6 output channels, not modifying it") + return tensor + elif tensor.shape[0] == 5: + print(f"{name} tensor has 5 output channels, adding one more initialized from channel 0") + slice0 = tensor[0:1] + tensor = torch.cat((tensor,noisify(slice0)), dim=0) + return tensor + elif tensor.shape[0] == 4: + print(f"{name} tensor has 4 output channels, adding two more initialized from channel 0") + slice0 = tensor[0:1] + tensor = torch.cat((tensor,noisify(slice0),noisify(slice0)), dim=0) + return tensor + else: + assert False, f"Unexpected {name} tensor shape: {tensor.shape}" + +def expand_policy_out_dim_for(name): + if f"module.{name}" in data["model"]: + data["model"][f"module.{name}"] = expand_policy_out_dim(f"module.{name}", data["model"][f"module.{name}"]) + elif name in data["model"]: + data["model"][name] = expand_policy_out_dim(name, data["model"][name]) + else: + assert False, f"{name} not found in saved model" + +expand_policy_out_dim_for("policy_head.conv2p.weight") +expand_policy_out_dim_for("policy_head.linear_pass.weight") +if any("intermediate_policy_head" in key for key in data["model"].keys()): + expand_policy_out_dim_for("intermediate_policy_head.conv2p.weight") + expand_policy_out_dim_for("intermediate_policy_head.linear_pass.weight") + +if "optimizer" in data: + print("Deleting optimizer state") + del data["optimizer"] +if "swa_model" in data: + print("Deleting swa model state") + del data["swa_model"] +print("Setting version to 12") +data["config"]["version"] = 12 + +print(f"Saving to {output_path}") +torch.save(data, output_path) +print("Done, don't forget to edit the model.config.json to version 12 if applicable") diff --git a/training/scripts/dated/20241110-122511/migrate_softplus_fix.py b/training/scripts/dated/20241110-122511/migrate_softplus_fix.py new file mode 100644 index 0000000..e9639d3 --- /dev/null +++ b/training/scripts/dated/20241110-122511/migrate_softplus_fix.py @@ -0,0 +1,64 @@ +#!/usr/bin/python3 +import sys +import os +import argparse +import math +import torch + +description = """ +Hacky script to partially migrate v12 optimistic policy to v13 changing score error. +Run on a torch checkpoint.ckpt file. Replace the old one with the new one after a backup. +""" + +parser = argparse.ArgumentParser(description=description) +parser.add_argument('-checkpoint', help='Checkpoint file path', required=True) +parser.add_argument('-output', help='Output new checkpoint to here', required=True) +args = vars(parser.parse_args()) + +checkpoint_path = args["checkpoint"] +output_path = args["output"] + +data = torch.load(checkpoint_path,map_location="cpu") +assert data["config"]["version"] == 12 + +def shift_bias(name, channel_idx): + if f"module.{name}" in data["model"]: + data["model"][f"module.{name}"][channel_idx] -= math.log(150.0 / 30.0) + elif name in data["model"]: + data["model"][name][channel_idx] -= math.log(150.0 / 30.0) + else: + assert False, f"{name} not found in saved model" + if f"module.{name}" in data["swa_model"]: + data["swa_model"][f"module.{name}"][channel_idx] -= math.log(150.0 / 30.0) + elif name in data["swa_model"]: + data["swa_model"][name][channel_idx] -= math.log(150.0 / 30.0) + else: + assert False, f"{name} not found in swa model" + +shift_bias("value_head.linear_moremiscvaluehead.bias", channel_idx=1) +if any("intermediate_value_head" in key for key in data["model"].keys()): + shift_bias("intermediate_value_head.linear_moremiscvaluehead.bias", channel_idx=1) + +if "optimizer" in data: + print("Deleting optimizer state") + del data["optimizer"] +# if "swa_model" in data: +# print("Deleting swa model state") +# del data["swa_model"] +if "running_metrics" in data: + print("Resetting shortterm score error running metrics") + data["running_metrics"]["sums"]["esstloss_sum"] /= 100000.0 + data["running_metrics"]["weights"]["esstloss_sum"] /= 100000.0 + data["running_metrics"]["sums"]["Iesstloss_sum"] /= 100000.0 + data["running_metrics"]["weights"]["Iesstloss_sum"] /= 100000.0 + +print("Clearing export cycle counter to give time to reconverge") +assert "export_cycle_counter" in data["train_state"] +data["train_state"]["export_cycle_counter"] = 0 + +print("Setting version to 13") +data["config"]["version"] = 13 + +print(f"Saving to {output_path}") +torch.save(data, output_path) +print("Done") diff --git a/training/scripts/dated/20241110-122511/migrate_squared_softplus.py b/training/scripts/dated/20241110-122511/migrate_squared_softplus.py new file mode 100644 index 0000000..62cff9b --- /dev/null +++ b/training/scripts/dated/20241110-122511/migrate_squared_softplus.py @@ -0,0 +1,54 @@ +#!/usr/bin/python3 +import sys +import os +import argparse +import math +import torch + +description = """ +Hacky script to partially migrate v13 to v14 new squared softplus activation for error preds. +Run on a torch checkpoint.ckpt file. Replace the old one with the new one after a backup. +""" + +parser = argparse.ArgumentParser(description=description) +parser.add_argument('-checkpoint', help='Checkpoint file path', required=True) +parser.add_argument('-output', help='Output new checkpoint to here', required=True) +args = vars(parser.parse_args()) + +checkpoint_path = args["checkpoint"] +output_path = args["output"] + +data = torch.load(checkpoint_path,map_location="cpu") +assert data["config"]["version"] == 13 + +# No modification of any weight is needed since for values that aren't large positive values, +# due to the fact that softplus(x) ~= exp(x) and exp(0.5*x) ** 2 ~= exp(x), the existing +# weights of the model already should be a good starting point for the new activation. + +if "optimizer" in data: + print("Deleting optimizer state") + del data["optimizer"] +# if "swa_model" in data: +# print("Deleting swa model state") +# del data["swa_model"] +if "running_metrics" in data: + print("Resetting shortterm value and score error running metrics") + data["running_metrics"]["sums"]["evstloss_sum"] /= 100000.0 + data["running_metrics"]["weights"]["evstloss_sum"] /= 100000.0 + data["running_metrics"]["sums"]["Ievstloss_sum"] /= 100000.0 + data["running_metrics"]["weights"]["Ievstloss_sum"] /= 100000.0 + data["running_metrics"]["sums"]["esstloss_sum"] /= 100000.0 + data["running_metrics"]["weights"]["esstloss_sum"] /= 100000.0 + data["running_metrics"]["sums"]["Iesstloss_sum"] /= 100000.0 + data["running_metrics"]["weights"]["Iesstloss_sum"] /= 100000.0 + +print("Clearing export cycle counter to give time to reconverge") +assert "export_cycle_counter" in data["train_state"] +data["train_state"]["export_cycle_counter"] = 0 + +print("Setting version to 14") +data["config"]["version"] = 14 + +print(f"Saving to {output_path}") +torch.save(data, output_path) +print("Done") diff --git a/training/scripts/dated/20241110-122511/model_pytorch.py b/training/scripts/dated/20241110-122511/model_pytorch.py new file mode 100644 index 0000000..dcae5b4 --- /dev/null +++ b/training/scripts/dated/20241110-122511/model_pytorch.py @@ -0,0 +1,1921 @@ +import math +import numpy as np +import torch +import torch.nn +import torch.nn.functional +import torch.nn.init +import packaging +import packaging.version +from typing import List, Dict, Optional, Set + +import modelconfigs + +EXTRA_SCORE_DISTR_RADIUS = 60 + +class ExtraOutputs: + def __init__(self, requested: List[str]): + self.requested: Set[str] = set(requested) + self.available: List[str] = [] + self.returned: Dict[str,torch.Tensor] = {} + + def add_requested(self, requested: List[str]): + self.requested = self.requested.union(set(requested)) + + def report(self, name: str, value: torch.Tensor): + self.available.append(name) + if name in self.requested: + self.returned[name] = value.detach() + +def act(activation, inplace=False): + if activation == "relu": + return torch.nn.ReLU(inplace=inplace) + if activation == "elu": + return torch.nn.ELU(inplace=inplace) + if activation == "mish": + return torch.nn.Mish(inplace=inplace) + if activation == "gelu": + return torch.nn.GELU(inplace=inplace) + if activation == "hardswish": + if packaging.version.parse(torch.__version__) > packaging.version.parse("1.6.0"): + return torch.nn.Hardswish(inplace=inplace) + else: + return torch.nn.Hardswish() + if activation == "identity": + return torch.nn.Identity() + assert False, f"Unknown activation name: {activation}" + +def compute_gain(activation): + if activation == "relu" or activation == "hardswish": + gain = math.sqrt(2.0) + elif activation == "elu": + gain = math.sqrt(1.55052) + elif activation == "mish": + gain = math.sqrt(2.210277) + elif activation == "gelu": + gain = math.sqrt(2.351718) + elif activation == "identity": + gain = 1.0 + else: + assert False, f"Unknown activation name: {activation}" + return gain + +def init_weights(tensor, activation, scale, fan_tensor=None): + gain = compute_gain(activation) + + if fan_tensor is not None: + (fan_in, _) = torch.nn.init._calculate_fan_in_and_fan_out(fan_tensor) + else: + (fan_in, _) = torch.nn.init._calculate_fan_in_and_fan_out(tensor) + + target_std = scale * gain / math.sqrt(fan_in) + # Multiply slightly since we use truncated normal + std = target_std / 0.87962566103423978 + if std < 1e-10: + tensor.fill_(0.0) + else: + torch.nn.init.trunc_normal_(tensor, mean=0.0, std=std, a=-2.0*std, b=2.0*std) + +class SoftPlusWithGradientFloorFunction(torch.autograd.Function): + """ + Same as softplus, except on backward pass, we never let the gradient decrease below grad_floor. + Equivalent to having a dynamic learning rate depending on stop_grad(x) where x is the input. + If square, then also squares the result while halving the input, and still also keeping the same gradient. + """ + @staticmethod + def forward(ctx, x: torch.Tensor, grad_floor: float, square: bool): + ctx.save_for_backward(x) + ctx.grad_floor = grad_floor # grad_floor is not a tensor + if square: + return torch.square(torch.nn.functional.softplus(0.5 * x)) + else: + return torch.nn.functional.softplus(x) + + @staticmethod + def backward(ctx, grad_output: torch.Tensor): + (x,) = ctx.saved_tensors + grad_floor = ctx.grad_floor + grad_x = None + grad_grad_floor = None + grad_square = None + if ctx.needs_input_grad[0]: + grad_x = grad_output * (grad_floor + (1.0 - grad_floor) / (1.0 + torch.exp(-x))) + return grad_x, grad_grad_floor, grad_square + +class BiasMask(torch.nn.Module): + def __init__( + self, + c_in, + config: modelconfigs.ModelConfig, + is_after_batchnorm: bool = False, + ): + super(BiasMask, self).__init__() + self.c_in = c_in + self.beta = torch.nn.Parameter(torch.zeros(1, c_in, 1, 1)) + self.is_after_batchnorm = is_after_batchnorm + self.scale = None + + def set_scale(self, scale: Optional[float]): + self.scale = scale + + def add_reg_dict(self, reg_dict:Dict[str,List]): + if self.is_after_batchnorm: + reg_dict["output_noreg"].append(self.beta) + else: + reg_dict["noreg"].append(self.beta) + + def set_brenorm_params(self, renorm_avg_momentum: float, rmax: float, dmax: float): + pass + + def add_brenorm_clippage(self, upper_rclippage, lower_rclippage, dclippage): + pass + + def forward(self, x, mask, mask_sum: float): + """ + Parameters: + x: NCHW + mask: N1HW + mask_sum: scalar + + Returns: NCHW + """ + if self.scale is not None: + return (x * self.scale + self.beta) * mask + else: + return (x + self.beta) * mask + + +class NormMask(torch.nn.Module): + def __init__( + self, + c_in, + config: modelconfigs.ModelConfig, + fixup_use_gamma: bool, + force_use_gamma: bool = False, + is_last_batchnorm: bool = False, + ): + """Various kinds of normalization. + + bnorm - batch norm + brenorm - batch renorm + fixup - fixup initialization https://arxiv.org/abs/1901.09321 + fixscale - fixed scaling initialization. Normalization layers simply multiply a constant scalar according + to what batchnorm *would* do if all inputs were unit variance and all linear layers or convolutions + preserved variance. + fixbrenorm - fixed scaling normalization PLUS batch renorm. + fixscaleonenorm - fixed scaling normalization PLUS only have one batch norm layer in the entire net, at the end of the residual trunk. + """ + + super(NormMask, self).__init__() + self.norm_kind = config["norm_kind"] + self.epsilon = config["bnorm_epsilon"] + self.running_avg_momentum = config["bnorm_running_avg_momentum"] + self.fixup_use_gamma = fixup_use_gamma + self.is_last_batchnorm = is_last_batchnorm + self.use_gamma = ( + ("bnorm_use_gamma" in config and config["bnorm_use_gamma"]) or + ((self.norm_kind == "fixup" or self.norm_kind == "fixscale" or self.norm_kind == "fixscaleonenorm") and fixup_use_gamma) or + force_use_gamma + ) + self.c_in = c_in + + self.scale = None + self.gamma = None + if self.norm_kind == "bnorm" or (self.norm_kind == "fixscaleonenorm" and self.is_last_batchnorm): + self.is_using_batchnorm = True + if self.use_gamma: + self.gamma = torch.nn.Parameter(torch.ones(1, c_in, 1, 1)) + self.beta = torch.nn.Parameter(torch.zeros(1, c_in, 1, 1)) + self.register_buffer( + "running_mean", torch.zeros(c_in, dtype=torch.float) + ) + self.register_buffer( + "running_std", torch.ones(c_in, dtype=torch.float) + ) + elif self.norm_kind == "brenorm" or self.norm_kind == "fixbrenorm": + self.is_using_batchnorm = True + if self.use_gamma: + self.gamma = torch.nn.Parameter(torch.ones(1, c_in, 1, 1)) + self.beta = torch.nn.Parameter(torch.zeros(1, c_in, 1, 1)) + self.register_buffer( + "running_mean", torch.zeros(c_in, dtype=torch.float) + ) + self.register_buffer( + "running_std", torch.ones(c_in, dtype=torch.float) + ) + self.register_buffer( + "renorm_running_mean", torch.zeros(c_in, dtype=torch.float) + ) + self.register_buffer( + "renorm_running_std", torch.ones(c_in, dtype=torch.float) + ) + self.register_buffer( + "renorm_upper_rclippage", torch.zeros((), dtype=torch.float) + ) + self.register_buffer( + "renorm_lower_rclippage", torch.zeros((), dtype=torch.float) + ) + self.register_buffer( + "renorm_dclippage", torch.zeros((), dtype=torch.float) + ) + + elif self.norm_kind == "fixup" or self.norm_kind == "fixscale" or (self.norm_kind == "fixscaleonenorm" and not self.is_last_batchnorm): + self.is_using_batchnorm = False + self.beta = torch.nn.Parameter(torch.zeros(1, c_in, 1, 1)) + if self.use_gamma: + self.gamma = torch.nn.Parameter(torch.ones(1, c_in, 1, 1)) + else: + assert False, f"Unimplemented norm_kind: {self.norm_kind}" + + def set_scale(self, scale: Optional[float]): + self.scale = scale + + def add_reg_dict(self, reg_dict:Dict[str,List]): + if self.is_last_batchnorm: + if self.gamma is not None: + reg_dict["output"].append(self.gamma) + reg_dict["output_noreg"].append(self.beta) + else: + if self.gamma is not None: + reg_dict["normal_gamma"].append(self.gamma) + reg_dict["noreg"].append(self.beta) + + def set_brenorm_params(self, renorm_avg_momentum: float, rmax: float, dmax: float): + self.renorm_avg_momentum = renorm_avg_momentum + self.rmax = rmax + self.dmax = dmax + + def add_brenorm_clippage(self, upper_rclippage, lower_rclippage, dclippage): + upper_rclippage.append(self.renorm_upper_rclippage.cpu().item()) + lower_rclippage.append(self.renorm_lower_rclippage.cpu().item()) + dclippage.append(self.renorm_dclippage.cpu().item()) + + def _compute_bnorm_values(self, x, mask, mask_sum: float): + # This is the mean, computed only over exactly the areas of the mask, weighting each spot equally, + # even across different elements in the batch that might have different board sizes. + mean = torch.sum(x * mask, dim=(0,2,3),keepdim=True) / mask_sum + zeromean_x = x - mean + # Similarly, the variance computed exactly only over those spots + var = torch.sum(torch.square(zeromean_x * mask),dim=(0,2,3),keepdim=True) / mask_sum + std = torch.sqrt(var + self.epsilon) + return zeromean_x, mean, std + + def apply_gamma_beta_scale_mask(self, x, mask): + if self.scale is not None: + if self.gamma is not None: + return (x * (self.gamma * self.scale) + self.beta) * mask + else: + return (x * self.scale + self.beta) * mask + else: + if self.gamma is not None: + return (x * self.gamma + self.beta) * mask + else: + return (x + self.beta) * mask + + + def forward(self, x, mask, mask_sum: float): + """ + Parameters: + x: NCHW + mask: N1HW + mask_sum: scalar + + Returns: NCHW + """ + + if self.norm_kind == "bnorm" or (self.norm_kind == "fixscaleonenorm" and self.is_last_batchnorm): + assert x.shape[1] == self.c_in + if self.training: + zeromean_x, mean, std = self._compute_bnorm_values(x, mask, mask_sum) + + detached_mean = mean.view(self.c_in).detach() + detached_std = std.view(self.c_in).detach() + with torch.no_grad(): + self.running_mean += self.running_avg_momentum * (detached_mean - self.running_mean) + self.running_std += self.running_avg_momentum * (detached_std - self.running_std) + + return self.apply_gamma_beta_scale_mask(zeromean_x / std, mask) + else: + return self.apply_gamma_beta_scale_mask((x - self.running_mean.view(1,self.c_in,1,1)) / self.running_std.view(1,self.c_in,1,1), mask) + + elif self.norm_kind == "brenorm" or self.norm_kind == "fixbrenorm": + assert x.shape[1] == self.c_in + if self.training: + zeromean_x, mean, std = self._compute_bnorm_values(x, mask, mask_sum) + + detached_mean = mean.view(self.c_in).detach() + detached_std = std.view(self.c_in).detach() + with torch.no_grad(): + unclipped_r = detached_std / self.renorm_running_std + unclipped_d = (detached_mean - self.renorm_running_mean) / self.renorm_running_std + r = unclipped_r.clamp(1.0 / self.rmax, self.rmax) + d = unclipped_d.clamp(-self.dmax, self.dmax) + + self.renorm_running_mean += self.renorm_avg_momentum * (detached_mean - self.renorm_running_mean) + self.renorm_running_std += self.renorm_avg_momentum * (detached_std - self.renorm_running_std) + self.running_mean += self.running_avg_momentum * (detached_mean - self.running_mean) + self.running_std += self.running_avg_momentum * (detached_std - self.running_std) + + upper_rclippage = torch.mean(torch.nn.functional.relu(torch.log(unclipped_r / r))) + lower_rclippage = torch.mean(torch.nn.functional.relu(-torch.log(unclipped_r / r))) + dclippage = torch.mean(torch.abs(unclipped_d - d)) + self.renorm_upper_rclippage += 0.01 * (upper_rclippage - self.renorm_upper_rclippage) + self.renorm_lower_rclippage += 0.01 * (lower_rclippage - self.renorm_lower_rclippage) + self.renorm_dclippage += 0.01 * (dclippage - self.renorm_dclippage) + + if self.rmax > 1.00000001 or self.dmax > 0.00000001: + return self.apply_gamma_beta_scale_mask(zeromean_x / std * r.detach().view(1,self.c_in,1,1) + d.detach().view(1,self.c_in,1,1), mask) + else: + return self.apply_gamma_beta_scale_mask(zeromean_x / std, mask) + + else: + return self.apply_gamma_beta_scale_mask((x - self.running_mean.view(1,self.c_in,1,1)) / self.running_std.view(1,self.c_in,1,1), mask) + + elif self.norm_kind == "fixup" or self.norm_kind == "fixscale" or (self.norm_kind == "fixscaleonenorm" and not self.is_last_batchnorm): + return self.apply_gamma_beta_scale_mask(x, mask) + + else: + assert False + + +class KataGPool(torch.nn.Module): + def __init__(self): + super(KataGPool, self).__init__() + + def forward(self, x, mask, mask_sum_hw): + """ + Parameters: + x: NCHW + mask: N1HW + mask_sum_hw: N111 + + Returns: NC11 + """ + mask_sum_hw_sqrt_offset = torch.sqrt(mask_sum_hw) - 14.0 + + layer_mean = torch.sum(x, dim=(2, 3), keepdim=True, dtype=torch.float32) / mask_sum_hw + # All activation functions we use right now are always greater than -1.0, and map 0 -> 0. + # So off-board areas will equal 0, and then this max is mask-safe if we assign -1.0 to off-board areas. + (layer_max,_argmax) = torch.max((x+(mask-1.0)).view(x.shape[0],x.shape[1],-1).to(torch.float32), dim=2) + layer_max = layer_max.view(x.shape[0],x.shape[1],1,1) + + out_pool1 = layer_mean + out_pool2 = layer_mean * (mask_sum_hw_sqrt_offset / 10.0) + out_pool3 = layer_max + + out = torch.cat((out_pool1, out_pool2, out_pool3), dim=1) + return out + + +class KataValueHeadGPool(torch.nn.Module): + def __init__(self): + super(KataValueHeadGPool, self).__init__() + + def forward(self, x, mask, mask_sum_hw): + """ + Parameters: + x: NCHW + mask: N1HW + mask_sum_hw: N111 + + Returns: NC11 + """ + mask_sum_hw_sqrt_offset = torch.sqrt(mask_sum_hw) - 14.0 + + layer_mean = torch.sum(x, dim=(2, 3), keepdim=True, dtype=torch.float32) / mask_sum_hw + + out_pool1 = layer_mean + out_pool2 = layer_mean * (mask_sum_hw_sqrt_offset / 10.0) + out_pool3 = layer_mean * ((mask_sum_hw_sqrt_offset * mask_sum_hw_sqrt_offset) / 100.0 - 0.1) + + out = torch.cat((out_pool1, out_pool2, out_pool3), dim=1) + return out + +class KataConvAndGPool(torch.nn.Module): + def __init__(self, name, c_in, c_out, c_gpool, config, activation): + super(KataConvAndGPool, self).__init__() + self.name = name + self.norm_kind = config["norm_kind"] + self.activation = activation + self.conv1r = torch.nn.Conv2d(c_in, c_out, kernel_size=3, padding="same", bias=False) + self.conv1g = torch.nn.Conv2d(c_in, c_gpool, kernel_size=3, padding="same", bias=False) + self.normg = NormMask( + c_gpool, + config=config, + fixup_use_gamma=False, + ) + self.actg = act(self.activation, inplace=True) + self.gpool = KataGPool() + self.linear_g = torch.nn.Linear(3 * c_gpool, c_out, bias=False) + + def initialize(self, scale): + # Scaling so that variance on the r and g branches adds up to 1.0 + r_scale = 0.8 + g_scale = 0.6 + if self.norm_kind == "fixup" or self.norm_kind == "fixscale" or self.norm_kind == "fixbrenorm" or self.norm_kind == "fixscaleonenorm": + init_weights(self.conv1r.weight, self.activation, scale=scale * r_scale) + init_weights(self.conv1g.weight, self.activation, scale=math.sqrt(scale) * math.sqrt(g_scale)) + init_weights(self.linear_g.weight, self.activation, scale=math.sqrt(scale) * math.sqrt(g_scale)) + else: + init_weights(self.conv1r.weight, self.activation, scale=scale*r_scale) + init_weights(self.conv1g.weight, self.activation, scale=math.sqrt(scale) * 1.0) + init_weights(self.linear_g.weight, self.activation, scale=math.sqrt(scale) * g_scale) + + def add_reg_dict(self, reg_dict:Dict[str,List]): + reg_dict["normal"].append(self.conv1r.weight) + reg_dict["normal"].append(self.conv1g.weight) + self.normg.add_reg_dict(reg_dict) + reg_dict["normal"].append(self.linear_g.weight) + + def set_brenorm_params(self, renorm_avg_momentum: float, rmax: float, dmax: float): + self.normg.set_brenorm_params(renorm_avg_momentum, rmax, dmax) + + def add_brenorm_clippage(self, upper_rclippage, lower_rclippage, dclippage): + self.normg.add_brenorm_clippage(upper_rclippage, lower_rclippage, dclippage) + + + def forward(self, x, mask, mask_sum_hw, mask_sum:float, extra_outputs: Optional[ExtraOutputs]): + """ + Parameters: + x: NCHW + mask: N1HW + mask_sum_hw: N111 + mask_sum: scalar + + Returns: NCHW + """ + out = x + outr = self.conv1r(out) + outg = self.conv1g(out) + + outg = self.normg(outg, mask=mask, mask_sum=mask_sum) + outg = self.actg(outg) + outg = self.gpool(outg, mask=mask, mask_sum_hw=mask_sum_hw).squeeze(-1).squeeze(-1) + outg = self.linear_g(outg).unsqueeze(-1).unsqueeze(-1) + + out = outr + outg + return out + + +class KataConvAndAttentionPool(torch.nn.Module): + def __init__(self, name, c_in, c_out, c_gpool, config, activation): + super(KataConvAndAttentionPool, self).__init__() + self.name = name + self.norm_kind = config["norm_kind"] + self.c_gpool = c_gpool + self.c_apheads = config["num_attention_pool_heads"] + self.activation = activation + self.conv1r = torch.nn.Conv2d(c_in, c_out, kernel_size=3, padding="same", bias=False) + self.conv1g = torch.nn.Conv2d(c_in, c_gpool, kernel_size=3, padding="same", bias=False) + self.conv1k = torch.nn.Conv2d(c_in, c_gpool, kernel_size=1, padding="same", bias=False) + self.conv1q = torch.nn.Conv2d(c_in, c_gpool, kernel_size=1, padding="same", bias=False) + + assert c_gpool % self.c_apheads == 0, "Gpool channels must be divisible by num_attention_pool_heads" + + self.normg = NormMask( + c_gpool, + config=config, + fixup_use_gamma=False, + ) + self.actg = act(activation, inplace=True) + self.conv_mix = torch.nn.Conv2d(c_gpool*2, c_out, kernel_size=1, padding="same", bias=False) + + def initialize(self, scale): + # Scaling so that variance on the r and g branches adds up to 1.0 + r_scale = 0.8 + g_scale = 0.6 + if self.norm_kind == "fixup" or self.norm_kind == "fixscale" or self.norm_kind == "fixbrenorm" or self.norm_kind == "fixscaleonenorm": + init_weights(self.conv1r.weight, self.activation, scale=scale * r_scale) + init_weights(self.conv1g.weight, self.activation, scale=math.sqrt(scale) * math.sqrt(g_scale)) + init_weights(self.conv1k.weight, "identity", scale=math.sqrt(2.0)) + init_weights(self.conv1q.weight, "identity", scale=math.sqrt(2.0)) + init_weights(self.conv_mix.weight, self.activation, scale=math.sqrt(scale) * math.sqrt(g_scale)) + else: + init_weights(self.conv1r.weight, self.activation, scale=scale*r_scale) + init_weights(self.conv1g.weight, self.activation, scale=math.sqrt(scale) * 1.0) + init_weights(self.conv1k.weight, "identity", scale=math.sqrt(2.0)) + init_weights(self.conv1q.weight, "identity", scale=math.sqrt(2.0)) + init_weights(self.conv_mix.weight, self.activation, scale=math.sqrt(scale) * g_scale) + + def add_reg_dict(self, reg_dict:Dict[str,List]): + reg_dict["normal"].append(self.conv1r.weight) + reg_dict["normal"].append(self.conv1g.weight) + reg_dict["output"].append(self.conv1k.weight) + reg_dict["output"].append(self.conv1q.weight) + self.normg.add_reg_dict(reg_dict) + reg_dict["normal"].append(self.conv_mix.weight) + + def set_brenorm_params(self, renorm_avg_momentum: float, rmax: float, dmax: float): + self.normg.set_brenorm_params(renorm_avg_momentum, rmax, dmax) + + def add_brenorm_clippage(self, upper_rclippage, lower_rclippage, dclippage): + self.normg.add_brenorm_clippage(upper_rclippage, lower_rclippage, dclippage) + + def forward(self, x, mask, mask_sum_hw, mask_sum:float, extra_outputs: Optional[ExtraOutputs]): + """ + Parameters: + x: NCHW + mask: N1HW + mask_sum_hw: N111 + mask_sum: scalar + + Returns: NCHW + """ + n = x.shape[0] + h = x.shape[2] + w = x.shape[3] + + out = x + outr = self.conv1r(out) + outg = self.conv1g(out) + outk = self.conv1k(out).view(n*self.c_apheads, self.c_gpool//self.c_apheads, h*w) + outq = self.conv1q(out).view(n*self.c_apheads, self.c_gpool//self.c_apheads, h*w) + attention_logits = torch.bmm(torch.transpose(outk,1,2), outq) # n*heads, src h*w, dst h*w + attention_logits = attention_logits.view(n, self.c_apheads, h*w, h*w) + attention_logits = attention_logits - (1.0 - mask.view(n,1,h*w,1)) * 6000.0 + attention_logits = attention_logits.view(n * self.c_apheads, h*w, h*w) + attention = torch.nn.functional.softmax(attention_logits, dim=1) + attention_scale = 0.1 / torch.sqrt(torch.sum(torch.square(attention), dim=1, keepdim=True)) # n*heads, 1, h*w + + outg = self.normg(outg, mask=mask, mask_sum=mask_sum) + outg = self.actg(outg).view(n*self.c_apheads, self.c_gpool//self.c_apheads, h*w) + + out_pool1 = torch.bmm(outg, attention) + out_pool2 = out_pool1 * attention_scale + out_pool1 = out_pool1.view(n, self.c_gpool, h*w) + out_pool2 = out_pool2.view(n, self.c_gpool, h*w) + + outg = torch.cat((out_pool1, out_pool2), dim=1).view(n, 2 * self.c_gpool, h, w) * mask + outg = self.conv_mix(outg) + out = outr + outg + return out + + # def forward(self, x, mask, mask_sum_hw, mask_sum:float): + # """ + # Parameters: + # x: NCHW + # mask: N1HW + # mask_sum_hw: N111 + # mask_sum: scalar + + # Returns: NCHW + # """ + # n = x.shape[0] + # h = x.shape[2] + # w = x.shape[3] + + # out = x + # outr = self.conv1r(out) + # outg = self.conv1g(out) + # outk = self.conv1k(out) - (1.0 - mask) * 5000.0 + # outq = self.conv1q(out) + + # outk = outk.view(n*self.c_apheads, self.c_gpool//self.c_apheads, h*w) + # outq = outq.view(n*self.c_apheads, self.c_gpool//self.c_apheads, h*w) + + # mask_sum_hw_sqrt_offset = torch.sqrt(mask_sum_hw) - 14.0 + + # outg = self.normg(outg, mask=mask, mask_sum=mask_sum) + # outg = self.actg(outg).view(n*self.c_apheads, self.c_gpool//self.c_apheads, h*w) + # # Shen et al. Efficient Attention: Attention with Linear Complexities + # outg = torch.bmm(outg, torch.transpose(torch.nn.functional.softmax(outk,dim=2),1,2)) + # outg = torch.bmm(outg, torch.nn.functional.softmax(outq,dim=1)) + # outg = outg.view(n, self.c_gpool, h, w) * mask + # outg = torch.cat(( + # outg, + # outg * (mask_sum_hw_sqrt_offset / 10.0) + # ),dim=1) + + # outg = self.conv_mix(outg) + # out = outr + outg + # return out + + +class NormActConv(torch.nn.Module): + def __init__( + self, + name: str, + c_in: int, + c_out: int, + c_gpool: Optional[int], + config: modelconfigs.ModelConfig, + activation: str, + kernel_size: int, + fixup_use_gamma: bool, + ): + super(NormActConv, self).__init__() + self.name = name + self.c_in = c_in + self.c_out = c_out + self.c_gpool = c_gpool + self.norm = NormMask( + c_in, + config=config, + fixup_use_gamma=fixup_use_gamma, + ) + self.activation = activation + self.act = act(activation, inplace=True) + self.use_repvgg_init = kernel_size > 1 and "use_repvgg_init" in config and config["use_repvgg_init"] + + if c_gpool is not None: + if config["use_attention_pool"]: + self.convpool = KataConvAndAttentionPool(name=name+".convpool",c_in=c_in, c_out=c_out, c_gpool=c_gpool, config=config, activation=activation) + self.conv = None + else: + self.convpool = KataConvAndGPool(name=name+".convpool",c_in=c_in, c_out=c_out, c_gpool=c_gpool, config=config, activation=activation) + self.conv = None + else: + self.conv = torch.nn.Conv2d(c_in, c_out, kernel_size=kernel_size, padding="same", bias=False) + self.convpool = None + + self.conv1x1 = None + if self.conv is not None and kernel_size > 1 and "use_repvgg_linear" in config and config["use_repvgg_linear"]: + self.conv1x1 = torch.nn.Conv2d(c_in, c_out, kernel_size=1, padding="same", bias=False) + + def initialize(self, scale, norm_scale=None): + self.norm.set_scale(norm_scale) + if self.convpool is not None: + self.convpool.initialize(scale=scale) + else: + if self.conv1x1 is not None: + init_weights(self.conv1x1.weight, self.activation, scale=scale*0.6) + init_weights(self.conv.weight, self.activation, scale=scale*0.8) + else: + if self.use_repvgg_init: + init_weights(self.conv.weight, self.activation, scale=scale*0.8) + center_bonus = self.conv.weight.new_zeros((self.conv.weight.shape[0],self.conv.weight.shape[1]),requires_grad=False) + init_weights(center_bonus, self.activation, scale=scale*0.6) + self.conv.weight[:,:,1,1] += center_bonus + else: + init_weights(self.conv.weight, self.activation, scale=scale) + + + def add_reg_dict(self, reg_dict:Dict[str,List]): + self.norm.add_reg_dict(reg_dict) + if self.convpool is not None: + self.convpool.add_reg_dict(reg_dict) + else: + if self.conv1x1 is not None: + reg_dict["normal"].append(self.conv1x1.weight) + reg_dict["normal"].append(self.conv.weight) + + def set_brenorm_params(self, renorm_avg_momentum: float, rmax: float, dmax: float): + self.norm.set_brenorm_params(renorm_avg_momentum, rmax, dmax) + if self.convpool is not None: + self.convpool.set_brenorm_params(renorm_avg_momentum, rmax, dmax) + + def add_brenorm_clippage(self, upper_rclippage, lower_rclippage, dclippage): + self.norm.add_brenorm_clippage(upper_rclippage, lower_rclippage, dclippage) + if self.convpool is not None: + self.convpool.add_brenorm_clippage(upper_rclippage, lower_rclippage, dclippage) + + def forward(self, x, mask, mask_sum_hw, mask_sum: float, extra_outputs: Optional[ExtraOutputs]): + """ + Parameters: + x: NCHW + mask: N1HW + mask_sum_hw: N111 + mask_sum: scalar + + Returns: NCHW + """ + out = x + out = self.norm(out, mask=mask, mask_sum=mask_sum) + out = self.act(out) + # print("TENSOR AFTER NORMACT") + # print(out) + if self.convpool is not None: + out = self.convpool(out, mask=mask, mask_sum_hw=mask_sum_hw, mask_sum=mask_sum, extra_outputs=extra_outputs) + else: + if self.conv1x1 is not None: + out = self.conv(out) + self.conv1x1(out) + else: + out = self.conv(out) + if extra_outputs is not None: + extra_outputs.report(self.name+".out", out) + return out + + +class ResBlock(torch.nn.Module): + def __init__( + self, + name: str, + c_main: int, + c_mid: int, + c_gpool: Optional[int], + config: modelconfigs.ModelConfig, + activation: str, + ): + super(ResBlock, self).__init__() + self.name = name + self.norm_kind = config["norm_kind"] + self.normactconv1 = NormActConv( + name=name+".normactconv1", + c_in=c_main, + c_out=c_mid - (0 if c_gpool is None else c_gpool), + c_gpool=c_gpool, + config=config, + activation=activation, + kernel_size=3, + fixup_use_gamma=False, + ) + self.normactconv2 = NormActConv( + name=name+".normactconv2", + c_in=c_mid - (0 if c_gpool is None else c_gpool), + c_out=c_main, + c_gpool=None, + config=config, + activation=activation, + kernel_size=3, + fixup_use_gamma=True, + ) + + def initialize(self, fixup_scale): + if self.norm_kind == "fixup": + self.normactconv1.initialize(scale=fixup_scale) + self.normactconv2.initialize(scale=0.0) + elif self.norm_kind == "fixscale" or self.norm_kind == "fixbrenorm" or self.norm_kind == "fixscaleonenorm": + self.normactconv1.initialize(scale=1.0, norm_scale=fixup_scale) + self.normactconv2.initialize(scale=1.0) + else: + self.normactconv1.initialize(scale=1.0) + self.normactconv2.initialize(scale=1.0) + + def add_reg_dict(self, reg_dict:Dict[str,List]): + self.normactconv1.add_reg_dict(reg_dict) + self.normactconv2.add_reg_dict(reg_dict) + + def set_brenorm_params(self, renorm_avg_momentum: float, rmax: float, dmax: float): + self.normactconv1.set_brenorm_params(renorm_avg_momentum, rmax, dmax) + self.normactconv2.set_brenorm_params(renorm_avg_momentum, rmax, dmax) + + def add_brenorm_clippage(self, upper_rclippage, lower_rclippage, dclippage): + self.normactconv1.add_brenorm_clippage(upper_rclippage, lower_rclippage, dclippage) + self.normactconv2.add_brenorm_clippage(upper_rclippage, lower_rclippage, dclippage) + + def forward(self, x, mask, mask_sum_hw, mask_sum: float, extra_outputs: Optional[ExtraOutputs]): + """ + Parameters: + x: NCHW + mask: N1HW + mask_sum_hw: N111 + mask_sum: scalar + + Returns: NCHW + """ + out = x + out = self.normactconv1(out, mask=mask, mask_sum_hw=mask_sum_hw, mask_sum=mask_sum, extra_outputs=extra_outputs) + out = self.normactconv2(out, mask=mask, mask_sum_hw=mask_sum_hw, mask_sum=mask_sum, extra_outputs=extra_outputs) + result = x + out + if extra_outputs is not None: + extra_outputs.report(self.name+".out", result) + return result + + +class BottleneckResBlock(torch.nn.Module): + def __init__( + self, + name: str, + internal_length: int, + c_main: int, + c_mid: int, + c_gpool: Optional[int], + config: modelconfigs.ModelConfig, + activation: str, + ): + super(BottleneckResBlock, self).__init__() + self.name = name + self.norm_kind = config["norm_kind"] + self.internal_length = internal_length + assert internal_length >= 1 + + self.normactconvp = NormActConv( + name=name+".normactconvp", + c_in=c_main, + c_out=c_mid, + c_gpool=None, + config=config, + activation=activation, + kernel_size=1, + fixup_use_gamma=False, + ) + + self.normactconvstack = torch.nn.ModuleList() + self.normactconvstack.append(NormActConv( + name=name+".normactconvstack."+str(0), + c_in=c_mid, + c_out=c_mid - (0 if c_gpool is None else c_gpool), + c_gpool=c_gpool, + config=config, + activation=activation, + kernel_size=3, + fixup_use_gamma=False, + )) + for i in range(self.internal_length-1): + self.normactconvstack.append(NormActConv( + name=name+".normactconvstack."+str(i+1), + c_in=self.normactconvstack[-1].c_out, + c_out=c_mid, + c_gpool=None, + config=config, + activation=activation, + kernel_size=3, + fixup_use_gamma=False, + )) + + self.normactconvq = NormActConv( + name=name+".normactconvq", + c_in=self.normactconvstack[-1].c_out, + c_out=c_main, + c_gpool=None, + config=config, + activation=activation, + kernel_size=1, + fixup_use_gamma=True, + ) + + def initialize(self, fixup_scale): + if self.norm_kind == "fixup": + self.normactconvp.initialize(scale=math.pow(fixup_scale, 1.0 / (1.0 + self.internal_length))) + for i in range(self.internal_length): + self.normactconvstack[i].initialize(scale=math.pow(fixup_scale, 1.0 / (1.0 + self.internal_length))) + self.normactconvq.initialize(scale=0.0) + elif self.norm_kind == "fixscale" or self.norm_kind == "fixbrenorm" or self.norm_kind == "fixscaleonenorm": + self.normactconvp.initialize(scale=1.0, norm_scale=fixup_scale) + for i in range(self.internal_length): + self.normactconvstack[i].initialize(scale=1.0) + self.normactconvq.initialize(scale=1.0) + else: + self.normactconvp.initialize(scale=1.0) + for i in range(self.internal_length): + self.normactconvstack[i].initialize(scale=1.0) + self.normactconvq.initialize(scale=1.0) + + def add_reg_dict(self, reg_dict:Dict[str,List]): + self.normactconvp.add_reg_dict(reg_dict) + for i in range(self.internal_length): + self.normactconvstack[i].add_reg_dict(reg_dict) + self.normactconvq.add_reg_dict(reg_dict) + + def set_brenorm_params(self, renorm_avg_momentum: float, rmax: float, dmax: float): + self.normactconvp.set_brenorm_params(renorm_avg_momentum, rmax, dmax) + for i in range(self.internal_length): + self.normactconvstack[i].set_brenorm_params(renorm_avg_momentum, rmax, dmax) + self.normactconvq.set_brenorm_params(renorm_avg_momentum, rmax, dmax) + + def add_brenorm_clippage(self, upper_rclippage, lower_rclippage, dclippage): + self.normactconvp.add_brenorm_clippage(upper_rclippage, lower_rclippage, dclippage) + for i in range(self.internal_length): + self.normactconvstack[i].add_brenorm_clippage(upper_rclippage, lower_rclippage, dclippage) + self.normactconvq.add_brenorm_clippage(upper_rclippage, lower_rclippage, dclippage) + + def forward(self, x, mask, mask_sum_hw, mask_sum: float, extra_outputs: Optional[ExtraOutputs]): + """ + Parameters: + x: NCHW + mask: N1HW + mask_sum_hw: N111 + mask_sum: scalar + + Returns: NCHW + """ + out = x + out = self.normactconvp(out, mask=mask, mask_sum_hw=mask_sum_hw, mask_sum=mask_sum, extra_outputs=extra_outputs) + for i in range(self.internal_length): + out = self.normactconvstack[i](out, mask=mask, mask_sum_hw=mask_sum_hw, mask_sum=mask_sum, extra_outputs=extra_outputs) + out = self.normactconvq(out, mask=mask, mask_sum_hw=mask_sum_hw, mask_sum=mask_sum, extra_outputs=extra_outputs) + result = x + out + if extra_outputs is not None: + extra_outputs.report(self.name+".out", result) + return result + + +class NestedBottleneckResBlock(torch.nn.Module): + def __init__( + self, + name: str, + internal_length: int, + c_main: int, + c_mid: int, + c_gpool: Optional[int], + config: modelconfigs.ModelConfig, + activation: str, + ): + super(NestedBottleneckResBlock, self).__init__() + self.name = name + self.norm_kind = config["norm_kind"] + self.internal_length = internal_length + assert internal_length >= 1 + + self.normactconvp = NormActConv( + name=name+".normactconvp", + c_in=c_main, + c_out=c_mid, + c_gpool=None, + config=config, + activation=activation, + kernel_size=1, + fixup_use_gamma=False, + ) + + self.blockstack = torch.nn.ModuleList() + for i in range(self.internal_length): + self.blockstack.append(ResBlock( + name=name+".blockstack."+str(i), + c_main=c_mid, + c_mid=c_mid, + c_gpool=(c_gpool if i == 0 else None), + config=config, + activation=activation, + )) + + self.normactconvq = NormActConv( + name=name+".normactconvq", + c_in=c_mid, + c_out=c_main, + c_gpool=None, + config=config, + activation=activation, + kernel_size=1, + fixup_use_gamma=True, + ) + + def initialize(self, fixup_scale): + if self.norm_kind == "fixup": + self.normactconvp.initialize(scale=math.pow(fixup_scale, 1.0 / (1.0 + self.internal_length))) + for i in range(self.internal_length): + self.blockstack[i].initialize(fixup_scale=math.pow(fixup_scale, 1.0 / (1.0 + self.internal_length))) + self.normactconvq.initialize(scale=0.0) + elif self.norm_kind == "fixscale" or self.norm_kind == "fixbrenorm" or self.norm_kind == "fixscaleonenorm": + self.normactconvp.initialize(scale=1.0, norm_scale=fixup_scale) + for i in range(self.internal_length): + self.blockstack[i].initialize(fixup_scale=1.0 / math.sqrt(i+1.0)) + self.normactconvq.initialize(scale=1.0, norm_scale=1.0 / math.sqrt(self.internal_length+1.0)) + else: + self.normactconvp.initialize(scale=1.0) + for i in range(self.internal_length): + self.blockstack[i].initialize(fixup_scale=1.0) + self.normactconvq.initialize(scale=1.0) + + def add_reg_dict(self, reg_dict:Dict[str,List]): + self.normactconvp.add_reg_dict(reg_dict) + for i in range(self.internal_length): + self.blockstack[i].add_reg_dict(reg_dict) + self.normactconvq.add_reg_dict(reg_dict) + + def set_brenorm_params(self, renorm_avg_momentum: float, rmax: float, dmax: float): + self.normactconvp.set_brenorm_params(renorm_avg_momentum, rmax, dmax) + for i in range(self.internal_length): + self.blockstack[i].set_brenorm_params(renorm_avg_momentum, rmax, dmax) + self.normactconvq.set_brenorm_params(renorm_avg_momentum, rmax, dmax) + + def add_brenorm_clippage(self, upper_rclippage, lower_rclippage, dclippage): + self.normactconvp.add_brenorm_clippage(upper_rclippage, lower_rclippage, dclippage) + for i in range(self.internal_length): + self.blockstack[i].add_brenorm_clippage(upper_rclippage, lower_rclippage, dclippage) + self.normactconvq.add_brenorm_clippage(upper_rclippage, lower_rclippage, dclippage) + + def forward(self, x, mask, mask_sum_hw, mask_sum: float, extra_outputs: Optional[ExtraOutputs]): + """ + Parameters: + x: NCHW + mask: N1HW + mask_sum_hw: N111 + mask_sum: scalar + + Returns: NCHW + """ + out = x + out = self.normactconvp(out, mask=mask, mask_sum_hw=mask_sum_hw, mask_sum=mask_sum, extra_outputs=extra_outputs) + for i in range(self.internal_length): + out = self.blockstack[i](out, mask=mask, mask_sum_hw=mask_sum_hw, mask_sum=mask_sum, extra_outputs=extra_outputs) + out = self.normactconvq(out, mask=mask, mask_sum_hw=mask_sum_hw, mask_sum=mask_sum, extra_outputs=extra_outputs) + result = x + out + if extra_outputs is not None: + extra_outputs.report(self.name+".out", result) + return result + + + +class NestedNestedBottleneckResBlock(torch.nn.Module): + def __init__( + self, + name: str, + internal_length: int, + sub_internal_length: int, + c_main: int, + c_outermid: int, + c_mid: int, + c_gpool: Optional[int], + config: modelconfigs.ModelConfig, + activation: str, + ): + super(NestedNestedBottleneckResBlock, self).__init__() + self.name = name + self.norm_kind = config["norm_kind"] + self.internal_length = internal_length + assert internal_length >= 1 + + self.normactconvp = NormActConv( + name=name+".normactconvp", + c_in=c_main, + c_out=c_outermid, + c_gpool=None, + config=config, + activation=activation, + kernel_size=1, + fixup_use_gamma=False, + ) + + self.blockstack = torch.nn.ModuleList() + for i in range(self.internal_length): + self.blockstack.append(NestedBottleneckResBlock( + name=name+".blockstack."+str(i), + internal_length=sub_internal_length, + c_main=c_outermid, + c_mid=c_mid, + c_gpool=(c_gpool if i == 0 else None), + config=config, + activation=activation, + )) + + self.normactconvq = NormActConv( + name=name+".normactconvq", + c_in=c_outermid, + c_out=c_main, + c_gpool=None, + config=config, + activation=activation, + kernel_size=1, + fixup_use_gamma=True, + ) + + def initialize(self, fixup_scale): + if self.norm_kind == "fixup": + self.normactconvp.initialize(scale=math.pow(fixup_scale, 1.0 / (1.0 + self.internal_length))) + for i in range(self.internal_length): + self.blockstack[i].initialize(fixup_scale=math.pow(fixup_scale, 1.0 / (1.0 + self.internal_length))) + self.normactconvq.initialize(scale=0.0) + elif self.norm_kind == "fixscale" or self.norm_kind == "fixbrenorm" or self.norm_kind == "fixscaleonenorm": + self.normactconvp.initialize(scale=1.0, norm_scale=fixup_scale) + for i in range(self.internal_length): + self.blockstack[i].initialize(fixup_scale=1.0 / math.sqrt(i+1.0)) + self.normactconvq.initialize(scale=1.0, norm_scale=1.0 / math.sqrt(self.internal_length+1.0)) + else: + self.normactconvp.initialize(scale=1.0) + for i in range(self.internal_length): + self.blockstack[i].initialize(fixup_scale=1.0) + self.normactconvq.initialize(scale=1.0) + + def add_reg_dict(self, reg_dict:Dict[str,List]): + self.normactconvp.add_reg_dict(reg_dict) + for i in range(self.internal_length): + self.blockstack[i].add_reg_dict(reg_dict) + self.normactconvq.add_reg_dict(reg_dict) + + def set_brenorm_params(self, renorm_avg_momentum: float, rmax: float, dmax: float): + self.normactconvp.set_brenorm_params(renorm_avg_momentum, rmax, dmax) + for i in range(self.internal_length): + self.blockstack[i].set_brenorm_params(renorm_avg_momentum, rmax, dmax) + self.normactconvq.set_brenorm_params(renorm_avg_momentum, rmax, dmax) + + def add_brenorm_clippage(self, upper_rclippage, lower_rclippage, dclippage): + self.normactconvp.add_brenorm_clippage(upper_rclippage, lower_rclippage, dclippage) + for i in range(self.internal_length): + self.blockstack[i].add_brenorm_clippage(upper_rclippage, lower_rclippage, dclippage) + self.normactconvq.add_brenorm_clippage(upper_rclippage, lower_rclippage, dclippage) + + def forward(self, x, mask, mask_sum_hw, mask_sum: float, extra_outputs: Optional[ExtraOutputs]): + """ + Parameters: + x: NCHW + mask: N1HW + mask_sum_hw: N111 + mask_sum: scalar + + Returns: NCHW + """ + out = x + out = self.normactconvp(out, mask=mask, mask_sum_hw=mask_sum_hw, mask_sum=mask_sum, extra_outputs=extra_outputs) + for i in range(self.internal_length): + out = self.blockstack[i](out, mask=mask, mask_sum_hw=mask_sum_hw, mask_sum=mask_sum, extra_outputs=extra_outputs) + out = self.normactconvq(out, mask=mask, mask_sum_hw=mask_sum_hw, mask_sum=mask_sum, extra_outputs=extra_outputs) + result = x + out + if extra_outputs is not None: + extra_outputs.report(self.name+".out", result) + return result + + +class PolicyHead(torch.nn.Module): + def __init__(self, c_in, c_p1, c_g1, config, activation): + super(PolicyHead, self).__init__() + self.config = config + self.activation = activation + + if config["version"] <= 11: + self.num_policy_outputs = 4 + else: + self.num_policy_outputs = 6 + # Output 0: policy prediction + # Output 1: opponent reply policy prediction + # Output 2: soft policy prediction + # Output 3: soft opponent reply policy prediction + # Output 4: long-term-optimistic policy prediction + # Output 5: short-term-optimistic policy prediction + + self.conv1p = torch.nn.Conv2d(c_in, c_p1, kernel_size=1, padding="same", bias=False) + self.conv1g = torch.nn.Conv2d(c_in, c_g1, kernel_size=1, padding="same", bias=False) + + self.biasg = BiasMask( + c_g1, + config=config, + is_after_batchnorm=True, + ) + self.actg = act(self.activation) + self.gpool = KataGPool() + + self.linear_g = torch.nn.Linear(3 * c_g1, c_p1, bias=False) + if config["version"] <= 14: + self.linear_pass = torch.nn.Linear(3 * c_g1, self.num_policy_outputs, bias=False) + else: + self.linear_pass = torch.nn.Linear(3 * c_g1, c_p1, bias=True) + self.act_pass = act(self.activation) + self.linear_pass2 = torch.nn.Linear(c_p1, self.num_policy_outputs, bias=False) + + self.bias2 = BiasMask( + c_p1, + config=config, + is_after_batchnorm=True, + ) + self.act2 = act(activation) + self.conv2p = torch.nn.Conv2d(c_p1, self.num_policy_outputs, kernel_size=1, padding="same", bias=False) + + + def initialize(self): + # Scaling so that variance on the p and g branches adds up to 1.0 + p_scale = 0.8 + g_scale = 0.6 + bias_scale = 0.2 + # Extra scaling for outputs + scale_output = 0.3 + init_weights(self.conv1p.weight, self.activation, scale=p_scale) + init_weights(self.conv1g.weight, self.activation, scale=1.0) + init_weights(self.linear_g.weight, self.activation, scale=g_scale) + if self.config["version"] <= 14: + init_weights(self.linear_pass.weight, "identity", scale=scale_output) + else: + init_weights(self.linear_pass.weight, self.activation, scale=1.0) + init_weights(self.linear_pass.bias, self.activation, scale=bias_scale, fan_tensor=self.linear_pass.weight) + init_weights(self.linear_pass2.weight, "identity", scale=scale_output) + init_weights(self.conv2p.weight, "identity", scale=scale_output) + + def add_reg_dict(self, reg_dict:Dict[str,List]): + reg_dict["output"].append(self.conv1p.weight) + reg_dict["output"].append(self.conv1g.weight) + reg_dict["output"].append(self.linear_g.weight) + if self.config["version"] <= 14: + reg_dict["output"].append(self.linear_pass.weight) + else: + reg_dict["output"].append(self.linear_pass.weight) + reg_dict["output_noreg"].append(self.linear_pass.bias) + reg_dict["output"].append(self.linear_pass2.weight) + + reg_dict["output"].append(self.conv2p.weight) + self.biasg.add_reg_dict(reg_dict) + self.bias2.add_reg_dict(reg_dict) + + def set_brenorm_params(self, renorm_avg_momentum: float, rmax: float, dmax: float): + pass + + def add_brenorm_clippage(self, upper_rclippage, lower_rclippage, dclippage): + pass + + def forward(self, x, mask, mask_sum_hw, mask_sum:float, extra_outputs: Optional[ExtraOutputs]): + outp = self.conv1p(x) + outg = self.conv1g(x) + + outg = self.biasg(outg, mask=mask, mask_sum=mask_sum) + outg = self.actg(outg) + outg = self.gpool(outg, mask=mask, mask_sum_hw=mask_sum_hw).squeeze(-1).squeeze(-1) # NC + + if self.config["version"] <= 14: + outpass = self.linear_pass(outg) # NC + else: + outpass = self.linear_pass(outg) # NC + outpass = self.act_pass(outpass) # NC + outpass = self.linear_pass2(outpass) # NC + + outg = self.linear_g(outg).unsqueeze(-1).unsqueeze(-1) # NCHW + + outp = outp + outg + outp = self.bias2(outp, mask=mask, mask_sum=mask_sum) + outp = self.act2(outp) + outp = self.conv2p(outp) + outpolicy = outp + + # mask out parts outside the board by making them a huge neg number, so that they're 0 after softmax + outpolicy = outpolicy - (1.0 - mask) * 5000.0 + # NC(HW) concat with NC1 + return torch.cat((outpolicy.view(outpolicy.shape[0],outpolicy.shape[1],-1), outpass.unsqueeze(-1)),dim=2) + + +class ValueHead(torch.nn.Module): + def __init__(self, c_in, c_v1, c_v2, c_sv2, num_scorebeliefs, config, activation, pos_len): + super(ValueHead, self).__init__() + self.activation = activation + self.conv1 = torch.nn.Conv2d(c_in, c_v1, kernel_size=1, padding="same", bias=False) + self.bias1 = BiasMask( + c_v1, + config=config, + is_after_batchnorm=True, + ) + self.act1 = act(activation) + self.gpool = KataValueHeadGPool() + + self.linear2 = torch.nn.Linear(3 * c_v1, c_v2, bias=True) + self.act2 = act(activation) + + self.linear_valuehead = torch.nn.Linear(c_v2, 3, bias=True) + self.linear_miscvaluehead = torch.nn.Linear(c_v2, 10, bias=True) + self.linear_moremiscvaluehead = torch.nn.Linear(c_v2, 8, bias=True) + self.conv_ownership = torch.nn.Conv2d(c_v1, 1, kernel_size=1, padding="same", bias=False) + self.conv_scoring = torch.nn.Conv2d(c_v1, 1, kernel_size=1, padding="same", bias=False) + self.conv_futurepos = torch.nn.Conv2d(c_in, 2, kernel_size=1, padding="same", bias=False) + self.conv_seki = torch.nn.Conv2d(c_in, 4, kernel_size=1, padding="same", bias=False) + + self.pos_len = pos_len + self.scorebelief_mid = self.pos_len*self.pos_len + EXTRA_SCORE_DISTR_RADIUS + self.scorebelief_len = self.scorebelief_mid * 2 + self.num_scorebeliefs = num_scorebeliefs + self.c_sv2 = c_sv2 + + self.linear_s2 = torch.nn.Linear(3 * c_v1, c_sv2, bias=True) + self.linear_s2off = torch.nn.Linear(1, c_sv2, bias=False) + self.linear_s2par = torch.nn.Linear(1, c_sv2, bias=False) + self.linear_s3 = torch.nn.Linear(c_sv2, num_scorebeliefs, bias=True) + self.linear_smix = torch.nn.Linear(3 * c_v1, num_scorebeliefs, bias=True) + + self.register_buffer("score_belief_offset_vector", torch.tensor( + data=[(float(i-self.scorebelief_mid)+0.5) for i in range(self.scorebelief_len)], + dtype=torch.float32, + requires_grad=False, + ), persistent=False) + self.register_buffer("score_belief_offset_bias_vector", torch.tensor( + data=[0.05 * (float(i-self.scorebelief_mid)+0.5) for i in range(self.scorebelief_len)], + dtype=torch.float32, + requires_grad=False, + ), persistent=False) + self.register_buffer("score_belief_parity_vector", torch.tensor( + [0.5-float((i-self.scorebelief_mid) % 2) for i in range(self.scorebelief_len)], + dtype=torch.float32, + requires_grad=False, + ), persistent=False) + + + def initialize(self): + bias_scale = 0.2 + init_weights(self.conv1.weight, self.activation, scale=1.0) + init_weights(self.linear2.weight, self.activation, scale=1.0) + init_weights(self.linear2.bias, self.activation, scale=bias_scale, fan_tensor=self.linear2.weight) + + init_weights(self.linear_valuehead.weight, "identity", scale=1.0) + init_weights(self.linear_valuehead.bias, "identity", scale=bias_scale, fan_tensor=self.linear_valuehead.weight) + + init_weights(self.linear_miscvaluehead.weight, "identity", scale=1.0) + init_weights(self.linear_miscvaluehead.bias, "identity", scale=bias_scale, fan_tensor=self.linear_miscvaluehead.weight) + + init_weights(self.linear_moremiscvaluehead.weight, "identity", scale=1.0) + init_weights(self.linear_moremiscvaluehead.bias, "identity", scale=bias_scale, fan_tensor=self.linear_moremiscvaluehead.weight) + + aux_spatial_output_scale = 0.2 + init_weights(self.conv_ownership.weight, "identity", scale=aux_spatial_output_scale) + init_weights(self.conv_scoring.weight, "identity", scale=aux_spatial_output_scale) + init_weights(self.conv_futurepos.weight, "identity", scale=aux_spatial_output_scale) + init_weights(self.conv_seki.weight, "identity", scale=aux_spatial_output_scale) + + init_weights(self.linear_s2.weight, self.activation, scale=1.0) + init_weights(self.linear_s2.bias, self.activation, scale=1.0, fan_tensor=self.linear_s2.weight) + init_weights(self.linear_s2off.weight, self.activation, scale=1.0, fan_tensor=self.linear_s2.weight) + init_weights(self.linear_s2par.weight, self.activation, scale=1.0, fan_tensor=self.linear_s2.weight) + + scorebelief_output_scale = 0.5 + init_weights(self.linear_s3.weight, "identity", scale=scorebelief_output_scale) + init_weights(self.linear_s3.bias, "identity", scale=scorebelief_output_scale*bias_scale, fan_tensor=self.linear_s3.weight) + init_weights(self.linear_smix.weight, "identity", scale=1.0) + init_weights(self.linear_smix.bias, "identity", scale=bias_scale, fan_tensor=self.linear_smix.weight) + + def add_reg_dict(self, reg_dict:Dict[str,List]): + reg_dict["output"].append(self.conv1.weight) + reg_dict["output"].append(self.linear2.weight) + reg_dict["output_noreg"].append(self.linear2.bias) + reg_dict["output"].append(self.linear_valuehead.weight) + reg_dict["output_noreg"].append(self.linear_valuehead.bias) + reg_dict["output"].append(self.linear_miscvaluehead.weight) + reg_dict["output_noreg"].append(self.linear_miscvaluehead.bias) + reg_dict["output"].append(self.linear_moremiscvaluehead.weight) + reg_dict["output_noreg"].append(self.linear_moremiscvaluehead.bias) + reg_dict["output"].append(self.conv_ownership.weight) + reg_dict["output"].append(self.conv_scoring.weight) + reg_dict["output"].append(self.conv_futurepos.weight) + reg_dict["output"].append(self.conv_seki.weight) + reg_dict["output"].append(self.linear_s2.weight) + reg_dict["output_noreg"].append(self.linear_s2.bias) + reg_dict["output"].append(self.linear_s2off.weight) + reg_dict["output"].append(self.linear_s2par.weight) + reg_dict["output"].append(self.linear_s3.weight) + reg_dict["output_noreg"].append(self.linear_s3.bias) + reg_dict["output"].append(self.linear_smix.weight) + reg_dict["output_noreg"].append(self.linear_smix.bias) + self.bias1.add_reg_dict(reg_dict) + + def set_brenorm_params(self, renorm_avg_momentum: float, rmax: float, dmax: float): + pass + + def add_brenorm_clippage(self, upper_rclippage, lower_rclippage, dclippage): + pass + + def forward(self, x, mask, mask_sum_hw, mask_sum:float, input_global, extra_outputs: Optional[ExtraOutputs]): + outv1 = x + outv1 = self.conv1(outv1) + outv1 = self.bias1(outv1, mask=mask, mask_sum=mask_sum) + outv1 = self.act1(outv1) + + outpooled = self.gpool(outv1, mask=mask, mask_sum_hw=mask_sum_hw).squeeze(-1).squeeze(-1) + + outv2 = self.linear2(outpooled) + outv2 = self.act2(outv2) + + # Different subheads + out_value = self.linear_valuehead(outv2) + out_miscvalue = self.linear_miscvaluehead(outv2) + out_moremiscvalue = self.linear_moremiscvaluehead(outv2) + out_ownership = self.conv_ownership(outv1) * mask + out_scoring = self.conv_scoring(outv1) * mask + out_futurepos = self.conv_futurepos(x) * mask + out_seki = self.conv_seki(x) * mask + + # Score belief head + batch_size = x.shape[0] + outsv2 = ( + self.linear_s2(outpooled).view(batch_size,1,self.c_sv2) + + self.linear_s2off(self.score_belief_offset_bias_vector.view(1,self.scorebelief_len,1)) + + self.linear_s2par((self.score_belief_parity_vector.view(1,self.scorebelief_len) * input_global[:,-1:]).view(batch_size,self.scorebelief_len,1)) + ) # N,scorebelief_len,c_sv2 + + outsv2 = self.act2(outsv2) + outsv3 = self.linear_s3(outsv2) # N, scorebelief_len, num_scorebeliefs + + outsmix = self.linear_smix(outpooled) # N, num_scorebeliefs + outsmix_logweights = torch.nn.functional.log_softmax(outsmix, dim=1) + # For each of num_scorebeliefs, compute softmax to make it into probability distribution + out_scorebelief_logprobs = torch.nn.functional.log_softmax(outsv3, dim=1) + # Take the mixture distribution weighted by outsmix_weights + out_scorebelief_logprobs = torch.logsumexp(out_scorebelief_logprobs + outsmix_logweights.view(-1, 1, self.num_scorebeliefs), dim=2) + + return ( + out_value, + out_miscvalue, + out_moremiscvalue, + out_ownership, + out_scoring, + out_futurepos, + out_seki, + out_scorebelief_logprobs, + ) + +class MetadataEncoder(torch.nn.Module): + def __init__(self, config: modelconfigs.ModelConfig): + super(MetadataEncoder, self).__init__() + + self.config = config + self.activation = config["activation"] + self.meta_encoder_version = 1 if "meta_encoder_version" not in config["metadata_encoder"] else config["metadata_encoder"]["meta_encoder_version"] + + self.c_input = modelconfigs.get_num_meta_encoder_input_features(self.meta_encoder_version) + assert self.c_input == 192 + + self.c_internal = self.config["metadata_encoder"]["internal_num_channels"] + self.c_trunk = self.config["trunk_num_channels"] + self.out_scale = 0.5 + + self.register_buffer("feature_mask", torch.tensor( + # 86 is board area + data=[(0.0 if i == 86 else 1.0) for i in range(self.c_input)], + dtype=torch.float32, + requires_grad=False, + ), persistent=True) + + self.linear1 = torch.nn.Linear(self.c_input, self.c_internal, bias=True) + self.act1 = act(self.activation, inplace=True) + self.linear2 = torch.nn.Linear(self.c_internal, self.c_internal, bias=True) + self.act2 = act(self.activation, inplace=True) + self.linear_output_to_trunk = torch.nn.Linear(self.c_internal, self.c_trunk, bias=False) + + def initialize(self): + weight_scale = 0.8 + bias_scale = 0.2 + with torch.no_grad(): + init_weights(self.linear1.weight, self.activation, scale=weight_scale) + init_weights(self.linear1.bias, self.activation, scale=bias_scale, fan_tensor=self.linear1.weight) + init_weights(self.linear2.weight, self.activation, scale=weight_scale) + init_weights(self.linear2.bias, self.activation, scale=bias_scale, fan_tensor=self.linear2.weight) + init_weights(self.linear_output_to_trunk.weight, self.activation, scale=weight_scale) + + def add_reg_dict(self, reg_dict:Dict[str,List]): + reg_dict["output"].append(self.linear1.weight) + reg_dict["output_noreg"].append(self.linear1.bias) + reg_dict["output"].append(self.linear2.weight) + reg_dict["output_noreg"].append(self.linear2.bias) + reg_dict["normal"].append(self.linear_output_to_trunk.weight) + + def forward(self, input_meta, extra_outputs: Optional[ExtraOutputs]): + x = input_meta + x = x * self.feature_mask.reshape((1,-1)) + x = self.linear1(x) + x = self.act1(x) + x = self.linear2(x) + x = self.act2(x) + return self.out_scale * self.linear_output_to_trunk(x) + + +class Model(torch.nn.Module): + def __init__(self, config: modelconfigs.ModelConfig, pos_len: int): + super(Model, self).__init__() + + self.config = config + self.norm_kind = config["norm_kind"] + self.block_kind = config["block_kind"] + self.c_trunk = config["trunk_num_channels"] + self.c_mid = config["mid_num_channels"] + self.c_gpool = config["gpool_num_channels"] + self.c_outermid = config["outermid_num_channels"] if "outermid_num_channels" in config else self.c_mid + self.c_p1 = config["p1_num_channels"] + self.c_g1 = config["g1_num_channels"] + self.c_v1 = config["v1_num_channels"] + self.c_v2 = config["v2_size"] + self.c_sv2 = config["sbv2_num_channels"] + self.num_scorebeliefs = config["num_scorebeliefs"] + self.num_total_blocks = len(self.block_kind) + self.pos_len = pos_len + + if config["version"] <= 12: + self.td_score_multiplier = 20.0 + self.scoremean_multiplier = 20.0 + self.scorestdev_multiplier = 20.0 + self.lead_multiplier = 20.0 + self.variance_time_multiplier = 40.0 + self.shortterm_value_error_multiplier = 0.25 + self.shortterm_score_error_multiplier = 30.0 + else: + self.td_score_multiplier = 20.0 + self.scoremean_multiplier = 20.0 + self.scorestdev_multiplier = 20.0 + self.lead_multiplier = 20.0 + self.variance_time_multiplier = 40.0 + self.shortterm_value_error_multiplier = 0.25 + self.shortterm_score_error_multiplier = 150.0 + + self.trunk_normless = "trunk_normless" in config and config["trunk_normless"] + + if "has_intermediate_head" in config and config["has_intermediate_head"]: + self.has_intermediate_head = True + self.intermediate_head_blocks = config["intermediate_head_blocks"] + else: + self.has_intermediate_head = False + self.intermediate_head_blocks = 0 + + self.activation = "relu" if "activation" not in config else config["activation"] + + if config["initial_conv_1x1"]: + self.conv_spatial = torch.nn.Conv2d(22, self.c_trunk, kernel_size=1, padding="same", bias=False) + else: + self.conv_spatial = torch.nn.Conv2d(22, self.c_trunk, kernel_size=3, padding="same", bias=False) + self.linear_global = torch.nn.Linear(19, self.c_trunk, bias=False) + + if "metadata_encoder" in config and config["metadata_encoder"] is not None: + self.metadata_encoder = MetadataEncoder(config) + else: + self.metadata_encoder = None + + self.bin_input_shape = [22, pos_len, pos_len] + self.global_input_shape = [19] + + self.blocks = torch.nn.ModuleList() + for block_config in self.block_kind: + block_name = block_config[0] + block_kind = block_config[1] + use_gpool_this_block = False + if block_kind.endswith("gpool"): + use_gpool_this_block = True + block_kind = block_kind[:-5] + + if block_kind == "regular": + self.blocks.append(ResBlock( + name=block_name, + c_main=self.c_trunk, + c_mid=self.c_mid, + c_gpool=(self.c_gpool if use_gpool_this_block else None), + config=self.config, + activation=self.activation, + )) + elif block_kind == "bottle1" or block_kind == "bottle": + self.blocks.append(BottleneckResBlock( + name=block_name, + internal_length=1, + c_main=self.c_trunk, + c_mid=self.c_mid, + c_gpool=(self.c_gpool if use_gpool_this_block else None), + config=self.config, + activation=self.activation, + )) + elif block_kind == "bottle2": + self.blocks.append(BottleneckResBlock( + name=block_name, + internal_length=2, + c_main=self.c_trunk, + c_mid=self.c_mid, + c_gpool=(self.c_gpool if use_gpool_this_block else None), + config=self.config, + activation=self.activation, + )) + elif block_kind == "bottle3": + self.blocks.append(BottleneckResBlock( + name=block_name, + internal_length=3, + c_main=self.c_trunk, + c_mid=self.c_mid, + c_gpool=(self.c_gpool if use_gpool_this_block else None), + config=self.config, + activation=self.activation, + )) + elif block_kind == "bottlenest2": + self.blocks.append(NestedBottleneckResBlock( + name=block_name, + internal_length=2, + c_main=self.c_trunk, + c_mid=self.c_mid, + c_gpool=(self.c_gpool if use_gpool_this_block else None), + config=self.config, + activation=self.activation, + )) + elif block_kind == "bottlenest3": + self.blocks.append(NestedBottleneckResBlock( + name=block_name, + internal_length=3, + c_main=self.c_trunk, + c_mid=self.c_mid, + c_gpool=(self.c_gpool if use_gpool_this_block else None), + config=self.config, + activation=self.activation, + )) + elif block_kind == "bottlenest2bottlenest2": + self.blocks.append(NestedNestedBottleneckResBlock( + name=block_name, + internal_length=2, + sub_internal_length=2, + c_main=self.c_trunk, + c_outermid=self.c_outermid, + c_mid=self.c_mid, + c_gpool=(self.c_gpool if use_gpool_this_block else None), + config=self.config, + activation=self.activation, + )) + else: + assert False, f"Unknown block kind: {block_config[1]}" + + if self.trunk_normless: + self.norm_trunkfinal = BiasMask(self.c_trunk, self.config, is_after_batchnorm=True) + else: + self.norm_trunkfinal = NormMask(self.c_trunk, self.config, fixup_use_gamma=False, is_last_batchnorm=True) + self.act_trunkfinal = act(self.activation) + + self.policy_head = PolicyHead( + self.c_trunk, + self.c_p1, + self.c_g1, + self.config, + self.activation, + ) + self.value_head = ValueHead( + self.c_trunk, + self.c_v1, + self.c_v2, + self.c_sv2, + self.num_scorebeliefs, + self.config, + self.activation, + self.pos_len, + ) + if self.has_intermediate_head: + self.norm_intermediate_trunkfinal = NormMask(self.c_trunk, self.config, fixup_use_gamma=False, is_last_batchnorm=True) + self.act_intermediate_trunkfinal = act(self.activation) + self.intermediate_policy_head = PolicyHead( + self.c_trunk, + self.c_p1, + self.c_g1, + self.config, + self.activation, + ) + self.intermediate_value_head = ValueHead( + self.c_trunk, + self.c_v1, + self.c_v2, + self.c_sv2, + self.num_scorebeliefs, + self.config, + self.activation, + self.pos_len, + ) + + @property + def device(self): + return self.linear_global.weight.device + + def initialize(self): + with torch.no_grad(): + spatial_scale = 0.8 + global_scale = 0.6 + init_weights(self.conv_spatial.weight, self.activation, scale=spatial_scale) + init_weights(self.linear_global.weight, self.activation, scale=global_scale) + + if self.metadata_encoder is not None: + self.metadata_encoder.initialize() + + if self.norm_kind == "fixup": + fixup_scale = 1.0 / math.sqrt(self.num_total_blocks) + for block in self.blocks: + block.initialize(fixup_scale=fixup_scale) + elif self.norm_kind == "fixscale" or self.norm_kind == "fixbrenorm" or self.norm_kind == "fixscaleonenorm": + for i, block in enumerate(self.blocks): + block.initialize(fixup_scale=1.0 / math.sqrt(i+1.0)) + self.norm_trunkfinal.set_scale(1.0 / math.sqrt(self.num_total_blocks+1.0)) + else: + for block in self.blocks: + block.initialize(fixup_scale=1.0) + + self.policy_head.initialize() + self.value_head.initialize() + if self.has_intermediate_head: + self.intermediate_policy_head.initialize() + self.intermediate_value_head.initialize() + + def get_norm_kind(self) -> bool: + return self.norm_kind + + def get_has_intermediate_head(self) -> bool: + return self.has_intermediate_head + def get_has_metadata_encoder(self) -> bool: + return self.metadata_encoder is not None + + def add_reg_dict(self, reg_dict:Dict[str,List]): + reg_dict["normal"] = [] + reg_dict["normal_gamma"] = [] + reg_dict["output"] = [] + reg_dict["noreg"] = [] + reg_dict["output_noreg"] = [] + + reg_dict["normal"].append(self.conv_spatial.weight) + reg_dict["normal"].append(self.linear_global.weight) + if self.metadata_encoder is not None: + self.metadata_encoder.add_reg_dict(reg_dict) + for block in self.blocks: + block.add_reg_dict(reg_dict) + self.norm_trunkfinal.add_reg_dict(reg_dict) + self.policy_head.add_reg_dict(reg_dict) + self.value_head.add_reg_dict(reg_dict) + if self.has_intermediate_head: + self.norm_intermediate_trunkfinal.add_reg_dict(reg_dict) + self.intermediate_policy_head.add_reg_dict(reg_dict) + self.intermediate_value_head.add_reg_dict(reg_dict) + + + def set_brenorm_params(self, renorm_avg_momentum: float, rmax: float, dmax: float): + for block in self.blocks: + block.set_brenorm_params(renorm_avg_momentum, rmax, dmax) + self.norm_trunkfinal.set_brenorm_params(renorm_avg_momentum, rmax, dmax) + self.policy_head.set_brenorm_params(renorm_avg_momentum, rmax, dmax) + self.value_head.set_brenorm_params(renorm_avg_momentum, rmax, dmax) + if self.has_intermediate_head: + self.norm_intermediate_trunkfinal.set_brenorm_params(renorm_avg_momentum, rmax, dmax) + self.intermediate_policy_head.set_brenorm_params(renorm_avg_momentum, rmax, dmax) + self.intermediate_value_head.set_brenorm_params(renorm_avg_momentum, rmax, dmax) + + def add_brenorm_clippage(self, upper_rclippage, lower_rclippage, dclippage): + for block in self.blocks: + block.add_brenorm_clippage(upper_rclippage, lower_rclippage, dclippage) + self.norm_trunkfinal.add_brenorm_clippage(upper_rclippage, lower_rclippage, dclippage) + self.policy_head.add_brenorm_clippage(upper_rclippage, lower_rclippage, dclippage) + self.value_head.add_brenorm_clippage(upper_rclippage, lower_rclippage, dclippage) + if self.has_intermediate_head: + self.norm_intermediate_trunkfinal.add_brenorm_clippage(upper_rclippage, lower_rclippage, dclippage) + self.intermediate_policy_head.add_brenorm_clippage(upper_rclippage, lower_rclippage, dclippage) + self.intermediate_value_head.add_brenorm_clippage(upper_rclippage, lower_rclippage, dclippage) + + # Returns a tuple of tuples of outputs + # The outer tuple indexes different sets of heads, such as if the net also computes intermediate heads. + # 0 is the main output, 1 is intermediate. + # The inner tuple ranges over the outputs of a set of heads (policy, value, etc). + def forward( + self, + input_spatial, + input_global, + input_meta = None, + extra_outputs: Optional[ExtraOutputs] = None, + ): + # float_formatter = "{:.3f}".format + # np.set_printoptions(formatter={'float_kind':float_formatter}, threshold=1000000, linewidth=10000) + + mask = input_spatial[:, 0:1, :, :].contiguous() + mask_sum_hw = torch.sum(mask,dim=(2,3),keepdim=True) + mask_sum = torch.sum(mask) + + x_spatial = self.conv_spatial(input_spatial) + x_global = self.linear_global(input_global).unsqueeze(-1).unsqueeze(-1) + + out = x_spatial + x_global + + if self.metadata_encoder is not None: + assert input_meta is not None + x_meta = self.metadata_encoder.forward(input_meta,extra_outputs) + out = out + x_meta.unsqueeze(-1).unsqueeze(-1) + + # print("TENSOR BEFORE TRUNK") + # print(out) + + if self.has_intermediate_head: + count = 0 + for block in self.blocks[:self.intermediate_head_blocks]: + # print("TENSOR BEFORE BLOCK") + # print(count) + # print(out) + out = block(out, mask=mask, mask_sum_hw=mask_sum_hw, mask_sum=mask_sum, extra_outputs=extra_outputs) + count += 1 + + # print("INTERMEDIATE") + iout = out + iout = self.norm_intermediate_trunkfinal(iout, mask=mask, mask_sum=mask_sum) + iout = self.act_intermediate_trunkfinal(iout) + iout_policy = self.intermediate_policy_head(iout, mask=mask, mask_sum_hw=mask_sum_hw, mask_sum=mask_sum, extra_outputs=extra_outputs) + ( + iout_value, + iout_miscvalue, + iout_moremiscvalue, + iout_ownership, + iout_scoring, + iout_futurepos, + iout_seki, + iout_scorebelief_logprobs, + ) = self.intermediate_value_head(iout, mask=mask, mask_sum_hw=mask_sum_hw, mask_sum=mask_sum, input_global=input_global, extra_outputs=extra_outputs) + + for block in self.blocks[self.intermediate_head_blocks:]: + # print("TENSOR BEFORE BLOCK") + # print(count) + # print(out) + out = block(out, mask=mask, mask_sum_hw=mask_sum_hw, mask_sum=mask_sum, extra_outputs=extra_outputs) + count += 1 + + else: + count = 0 + for block in self.blocks: + # print("TENSOR BEFORE BLOCK") + # print(count) + # print(out) + out = block(out, mask=mask, mask_sum_hw=mask_sum_hw, mask_sum=mask_sum, extra_outputs=extra_outputs) + count += 1 + + out = self.norm_trunkfinal(out, mask=mask, mask_sum=mask_sum) + out = self.act_trunkfinal(out) + + if extra_outputs is not None: + extra_outputs.report("trunkfinal", out) + + # print("MAIN") + out_policy = self.policy_head(out, mask=mask, mask_sum_hw=mask_sum_hw, mask_sum=mask_sum, extra_outputs=extra_outputs) + ( + out_value, + out_miscvalue, + out_moremiscvalue, + out_ownership, + out_scoring, + out_futurepos, + out_seki, + out_scorebelief_logprobs, + ) = self.value_head(out, mask=mask, mask_sum_hw=mask_sum_hw, mask_sum=mask_sum, input_global=input_global, extra_outputs=extra_outputs) + + if self.has_intermediate_head: + return ( + ( + out_policy, + out_value, + out_miscvalue, + out_moremiscvalue, + out_ownership, + out_scoring, + out_futurepos, + out_seki, + out_scorebelief_logprobs, + ), + ( + iout_policy, + iout_value, + iout_miscvalue, + iout_moremiscvalue, + iout_ownership, + iout_scoring, + iout_futurepos, + iout_seki, + iout_scorebelief_logprobs, + ), + ) + else: + return (( + out_policy, + out_value, + out_miscvalue, + out_moremiscvalue, + out_ownership, + out_scoring, + out_futurepos, + out_seki, + out_scorebelief_logprobs, + ),) + + def float32ify_output(self, outputs_byheads): + return tuple(self.float32ify_single_heads_output(outputs) for outputs in outputs_byheads) + + def float32ify_single_heads_output(self, outputs): + ( + out_policy, + out_value, + out_miscvalue, + out_moremiscvalue, + out_ownership, + out_scoring, + out_futurepos, + out_seki, + out_scorebelief_logprobs, + ) = outputs + return ( + out_policy.to(torch.float32), + out_value.to(torch.float32), + out_miscvalue.to(torch.float32), + out_moremiscvalue.to(torch.float32), + out_ownership.to(torch.float32), + out_scoring.to(torch.float32), + out_futurepos.to(torch.float32), + out_seki.to(torch.float32), + out_scorebelief_logprobs.to(torch.float32), + ) + + def postprocess_output(self, outputs_byheads): + return tuple(self.postprocess_single_heads_output(outputs) for outputs in outputs_byheads) + + def postprocess_single_heads_output(self, outputs): + ( + out_policy, + out_value, + out_miscvalue, + out_moremiscvalue, + out_ownership, + out_scoring, + out_futurepos, + out_seki, + out_scorebelief_logprobs, + ) = outputs + + policy_logits = out_policy + value_logits = out_value + td_value_logits = torch.stack((out_miscvalue[:,4:7], out_miscvalue[:,7:10], out_moremiscvalue[:,2:5]), dim=1) + pred_td_score = out_moremiscvalue[:,5:8] * self.td_score_multiplier + ownership_pretanh = out_ownership + pred_scoring = out_scoring + futurepos_pretanh = out_futurepos + seki_logits = out_seki + pred_scoremean = out_miscvalue[:, 0] * self.scoremean_multiplier + pred_scorestdev = SoftPlusWithGradientFloorFunction.apply(out_miscvalue[:, 1], 0.05, False) * self.scorestdev_multiplier + pred_lead = out_miscvalue[:, 2] * self.lead_multiplier + pred_variance_time = SoftPlusWithGradientFloorFunction.apply(out_miscvalue[:, 3], 0.05, False) * self.variance_time_multiplier + if self.config["version"] < 14: + pred_shortterm_value_error = SoftPlusWithGradientFloorFunction.apply(out_moremiscvalue[:,0], 0.05, False) * self.shortterm_value_error_multiplier + pred_shortterm_score_error = SoftPlusWithGradientFloorFunction.apply(out_moremiscvalue[:,1], 0.05, False) * self.shortterm_score_error_multiplier + else: + pred_shortterm_value_error = SoftPlusWithGradientFloorFunction.apply(out_moremiscvalue[:,0], 0.05, True) * self.shortterm_value_error_multiplier + pred_shortterm_score_error = SoftPlusWithGradientFloorFunction.apply(out_moremiscvalue[:,1], 0.05, True) * self.shortterm_score_error_multiplier + scorebelief_logits = out_scorebelief_logprobs + + return ( + policy_logits, # N, num_policy_outputs, move + value_logits, # N, {win,loss,noresult} + td_value_logits, # N, {long, mid, short} {win,loss,noresult} + pred_td_score, # N, {long, mid, short} + ownership_pretanh, # N, 1, y, x + pred_scoring, # N, 1, y, x + futurepos_pretanh, # N, 2, y, x + seki_logits, # N, 4, y, x + pred_scoremean, # N + pred_scorestdev, # N + pred_lead, # N + pred_variance_time, # N + pred_shortterm_value_error, # N + pred_shortterm_score_error, # N + scorebelief_logits, # N, 2 * (self.pos_len*self.pos_len + EXTRA_SCORE_DISTR_RADIUS) + ) diff --git a/training/scripts/dated/20241110-122511/modelconfigs.py b/training/scripts/dated/20241110-122511/modelconfigs.py new file mode 100644 index 0000000..7bfe24a --- /dev/null +++ b/training/scripts/dated/20241110-122511/modelconfigs.py @@ -0,0 +1,1525 @@ +#!/usr/bin/python3 +""" +This file contains a bunch of configs for models of different sizes. +See the bottom of this file "base_config_of_name" for a dictionary of all the different +base model architectures, and which ones are recommended of each different model size. + +For each base model, additional configs are also pregenerated with different suffixes. + +For example, for b10c384nbt, we also have models like: +b10c384nbt-mish (use mish instead of relu) +b10c384nbt-bn-mish-rvgl (use batchnorm, mish, and repvgg-linear-style convolutions). + +KataGo's main models for the distributed training run currently find the following to work +well or best: "-fson-mish-rvgl-bnh" +* Use fixed activation scale initialization + one batch norm for the whole net +* Mish activation +* Repvgg-linear-style convolutions +* Batch norm output head + non-batch-norm output head, where the former drives optimization + but the latter is used for inference. +""" + +from typing import Dict, Any, Union + +ModelConfig = Dict[str,Any] + +# version = 0 # V1 features, with old head architecture using crelus (no longer supported) +# version = 1 # V1 features, with new head architecture, no crelus +# version = 2 # V2 features, no internal architecture change. +# version = 3 # V3 features, selfplay-planned features with lots of aux targets +# version = 4 # V3 features, but supporting belief stdev and dynamic scorevalue +# version = 5 # V4 features, slightly different pass-alive stones feature +# version = 6 # V5 features, most higher-level go features removed +# version = 7 # V6 features, more rules support +# version = 8 # V7 features, asym, lead, variance time +# version = 9 # V7 features, shortterm value error prediction, inference actually uses variance time, unsupported now +# version = 10 # V7 features, shortterm value error prediction done properly +# version = 11 # V7 features, New architectures! +# version = 12 # V7 features, Optimistic policy head +# version = 13 # V7 features, Adjusted scaling on shortterm score variance, and made C++ side read in scalings. +# version = 14 # V7 features, Squared softplus for error variance predictions +# version = 15 # V7 features, Extra nonlinearity for pass output + +def get_version(config: ModelConfig): + return config["version"] + +def get_num_bin_input_features(config: ModelConfig): + version = get_version(config) + if version == 10 or version == 11 or version == 12 or version == 13 or version == 14 or version == 15: + return 22 + else: + assert(False) + +def get_num_global_input_features(config: ModelConfig): + version = get_version(config) + if version == 10 or version == 11 or version == 12 or version == 13 or version == 14 or version == 15: + return 19 + else: + assert(False) + +def get_num_meta_encoder_input_features(config_or_meta_encoder_version: Union[ModelConfig,int]): + if isinstance(config_or_meta_encoder_version,int): + version = config_or_meta_encoder_version + else: + if "metadata_encoder" not in config: + version = 0 + elif "meta_encoder_version" not in config["metadata_encoder"]: + version = 1 + else: + version = config["metadata_encoder"]["meta_encoder_version"] + assert version == 1 + return 192 + +b1c6nbt = { + "version":15, + "norm_kind":"fixup", + "bnorm_epsilon": 1e-4, + "bnorm_running_avg_momentum": 0.001, + "initial_conv_1x1": False, + "trunk_num_channels":6, + "mid_num_channels":4, + "gpool_num_channels":4, + "use_attention_pool":False, + "num_attention_pool_heads":2, + "block_kind": [ + ["rconv1","bottlenest2"], + ], + "p1_num_channels":4, + "g1_num_channels":4, + "v1_num_channels":4, + "sbv2_num_channels":4, + "num_scorebeliefs":2, + "v2_size":6, +} + +b2c16 = { + "version":15, + "norm_kind":"fixup", + "bnorm_epsilon": 1e-4, + "bnorm_running_avg_momentum": 0.001, + "initial_conv_1x1": False, + "trunk_num_channels":16, + "mid_num_channels":16, + "gpool_num_channels":8, + "use_attention_pool":False, + "num_attention_pool_heads":2, + "block_kind": [ + ["rconv1","regular"], + ["rconv2","regulargpool"], + ], + "p1_num_channels":8, + "g1_num_channels":8, + "v1_num_channels":8, + "sbv2_num_channels":12, + "num_scorebeliefs":2, + "v2_size":12, +} + +b4c32 = { + "version":15, + "norm_kind":"fixup", + "bnorm_epsilon": 1e-4, + "bnorm_running_avg_momentum": 0.001, + "initial_conv_1x1": False, + "trunk_num_channels":32, + "mid_num_channels":32, + "gpool_num_channels":16, + "use_attention_pool":False, + "num_attention_pool_heads":2, + "block_kind": [ + ["rconv1","regular"], + ["rconv2","regular"], + ["rconv3","regulargpool"], + ["rconv4","regular"], + ], + "p1_num_channels":12, + "g1_num_channels":12, + "v1_num_channels":12, + "sbv2_num_channels":24, + "num_scorebeliefs":4, + "v2_size":24, +} + +b6c96 = { + "version":15, + "norm_kind":"fixup", + "bnorm_epsilon": 1e-4, + "bnorm_running_avg_momentum": 0.001, + "initial_conv_1x1": False, + "trunk_num_channels":96, + "mid_num_channels":96, + "gpool_num_channels":32, + "use_attention_pool":False, + "num_attention_pool_heads":4, + "block_kind": [ + ["rconv1","regular"], + ["rconv2","regular"], + ["rconv3","regulargpool"], + ["rconv4","regular"], + ["rconv5","regulargpool"], + ["rconv6","regular"], + ], + "p1_num_channels":32, + "g1_num_channels":32, + "v1_num_channels":32, + "sbv2_num_channels":48, + "num_scorebeliefs":4, + "v2_size":64, +} + +b10c128 = { + "version":15, + "norm_kind":"fixup", + "bnorm_epsilon": 1e-4, + "bnorm_running_avg_momentum": 0.001, + "initial_conv_1x1": False, + "trunk_num_channels":128, + "mid_num_channels":128, + "gpool_num_channels":32, + "use_attention_pool":False, + "num_attention_pool_heads":4, + "block_kind": [ + ["rconv1","regular"], + ["rconv2","regular"], + ["rconv3","regular"], + ["rconv4","regular"], + ["rconv5","regulargpool"], + ["rconv6","regular"], + ["rconv7","regular"], + ["rconv8","regulargpool"], + ["rconv9","regular"], + ["rconv10","regular"], + ], + "p1_num_channels":32, + "g1_num_channels":32, + "v1_num_channels":32, + "sbv2_num_channels":64, + "num_scorebeliefs":6, + "v2_size":80, +} + +b5c192nbt = { + "version":15, + "norm_kind":"fixup", + "bnorm_epsilon": 1e-4, + "bnorm_running_avg_momentum": 0.001, + "initial_conv_1x1": False, + "trunk_num_channels":192, + "mid_num_channels":96, + "gpool_num_channels":32, + "use_attention_pool":False, + "num_attention_pool_heads":4, + "block_kind": [ + ["rconv1","bottlenest2"], + ["rconv2","bottlenest2gpool"], + ["rconv3","bottlenest2"], + ["rconv4","bottlenest2gpool"], + ["rconv5","bottlenest2"], + ], + "p1_num_channels":32, + "g1_num_channels":32, + "v1_num_channels":32, + "sbv2_num_channels":64, + "num_scorebeliefs":6, + "v2_size":80, +} + +b15c192 = { + "version":15, + "norm_kind":"fixup", + "bnorm_epsilon": 1e-4, + "bnorm_running_avg_momentum": 0.001, + "initial_conv_1x1": False, + "trunk_num_channels":192, + "mid_num_channels":192, + "gpool_num_channels":64, + "use_attention_pool":False, + "num_attention_pool_heads":4, + "block_kind": [ + ["rconv1","regular"], + ["rconv2","regular"], + ["rconv3","regular"], + ["rconv4","regular"], + ["rconv5","regular"], + ["rconv6","regular"], + ["rconv7","regulargpool"], + ["rconv8","regular"], + ["rconv9","regular"], + ["rconv10","regular"], + ["rconv11","regular"], + ["rconv12","regulargpool"], + ["rconv13","regular"], + ["rconv14","regular"], + ["rconv15","regular"], + ], + "p1_num_channels":32, + "g1_num_channels":32, + "v1_num_channels":32, + "sbv2_num_channels":80, + "num_scorebeliefs":8, + "v2_size":96, +} + +b20c256 = { + "version":15, + "norm_kind":"fixup", + "bnorm_epsilon": 1e-4, + "bnorm_running_avg_momentum": 0.001, + "initial_conv_1x1": False, + "trunk_num_channels":256, + "mid_num_channels":256, + "gpool_num_channels":64, + "use_attention_pool":False, + "num_attention_pool_heads":4, + "block_kind": [ + ["rconv1","regular"], + ["rconv2","regular"], + ["rconv3","regular"], + ["rconv4","regular"], + ["rconv5","regular"], + ["rconv6","regular"], + ["rconv7","regulargpool"], + ["rconv8","regular"], + ["rconv9","regular"], + ["rconv10","regular"], + ["rconv11","regular"], + ["rconv12","regulargpool"], + ["rconv13","regular"], + ["rconv14","regular"], + ["rconv15","regular"], + ["rconv16","regular"], + ["rconv17","regulargpool"], + ["rconv18","regular"], + 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["rconv14","bottlenest2"], + ], + "p1_num_channels":48, + "g1_num_channels":48, + "v1_num_channels":96, + "sbv2_num_channels":112, + "num_scorebeliefs":8, + "v2_size":128, +} + +b40c384 = { + "version":15, + "norm_kind":"fixup", + "bnorm_epsilon": 1e-4, + "bnorm_running_avg_momentum": 0.001, + "initial_conv_1x1": False, + "trunk_num_channels":384, + "mid_num_channels":384, + "gpool_num_channels":128, + "use_attention_pool":False, + "num_attention_pool_heads":4, + "block_kind": [ + ["rconv1","regular"], + ["rconv2","regular"], + ["rconv3","regular"], + ["rconv4","regular"], + ["rconv5","regular"], + ["rconv6","regulargpool"], + ["rconv7","regular"], + ["rconv8","regular"], + ["rconv9","regular"], + ["rconv10","regular"], + ["rconv11","regulargpool"], + ["rconv12","regular"], + ["rconv13","regular"], + ["rconv14","regular"], + ["rconv15","regular"], + ["rconv16","regulargpool"], + ["rconv17","regular"], + ["rconv18","regular"], + ["rconv19","regular"], + ["rconv20","regular"], + ["rconv21","regulargpool"], + ["rconv22","regular"], + ["rconv23","regular"], + ["rconv24","regular"], + ["rconv25","regular"], + ["rconv26","regulargpool"], + ["rconv27","regular"], + ["rconv28","regular"], + ["rconv29","regular"], + ["rconv30","regular"], + ["rconv31","regulargpool"], + ["rconv32","regular"], + ["rconv33","regular"], + ["rconv34","regular"], + ["rconv35","regular"], + ["rconv36","regulargpool"], + ["rconv37","regular"], + ["rconv38","regular"], + ["rconv39","regular"], + ["rconv40","regular"], + ], + "p1_num_channels":64, + "g1_num_channels":64, + "v1_num_channels":96, + "sbv2_num_channels":128, + "num_scorebeliefs":8, + "v2_size":144, +} + + +b60c320 = { + "version":15, + "norm_kind":"fixup", + "bnorm_epsilon": 1e-4, + "bnorm_running_avg_momentum": 0.001, + "initial_conv_1x1": False, + "trunk_num_channels":320, + "mid_num_channels":320, + "gpool_num_channels":96, + "use_attention_pool":False, + "num_attention_pool_heads":4, + "block_kind": [ + ["rconv1","regular"], + ["rconv2","regular"], + ["rconv3","regular"], + ["rconv4","regular"], + ["rconv5","regular"], + ["rconv6","regulargpool"], + ["rconv7","regular"], + ["rconv8","regular"], + ["rconv9","regular"], + ["rconv10","regular"], + ["rconv11","regulargpool"], + ["rconv12","regular"], + ["rconv13","regular"], + ["rconv14","regular"], + ["rconv15","regular"], + ["rconv16","regulargpool"], + ["rconv17","regular"], + ["rconv18","regular"], + ["rconv19","regular"], + ["rconv20","regular"], + ["rconv21","regulargpool"], + ["rconv22","regular"], + ["rconv23","regular"], + ["rconv24","regular"], + ["rconv25","regular"], + ["rconv26","regulargpool"], + ["rconv27","regular"], + ["rconv28","regular"], + ["rconv29","regular"], + ["rconv30","regular"], + ["rconv31","regulargpool"], + ["rconv32","regular"], + ["rconv33","regular"], + ["rconv34","regular"], + ["rconv35","regular"], + ["rconv36","regulargpool"], + ["rconv37","regular"], + ["rconv38","regular"], + ["rconv39","regular"], + ["rconv40","regular"], + ["rconv41","regulargpool"], + ["rconv42","regular"], + ["rconv43","regular"], + ["rconv44","regular"], + ["rconv45","regular"], + ["rconv46","regulargpool"], + ["rconv47","regular"], + ["rconv48","regular"], + ["rconv49","regular"], + ["rconv50","regular"], + ["rconv51","regulargpool"], + ["rconv52","regular"], + ["rconv53","regular"], + ["rconv54","regular"], + ["rconv55","regular"], + ["rconv56","regulargpool"], + ["rconv57","regular"], + ["rconv58","regular"], + ["rconv59","regular"], + ["rconv60","regular"], + ], + "p1_num_channels":64, + "g1_num_channels":64, + "v1_num_channels":96, + "sbv2_num_channels":128, + "num_scorebeliefs":8, + "v2_size":144, +} + + +b41c384nbt = { + "version":15, + "norm_kind":"fixup", + "bnorm_epsilon": 1e-4, + "bnorm_running_avg_momentum": 0.001, + "initial_conv_1x1": False, + "trunk_num_channels":384, + "mid_num_channels":192, + "gpool_num_channels":64, + "use_attention_pool":False, + "num_attention_pool_heads":4, + "block_kind": [ + ["rconv1","bottlenest2"], + ["rconv2","bottlenest2"], + ["rconv3","bottlenest2gpool"], + ["rconv4","bottlenest2"], + ["rconv5","bottlenest2"], + ["rconv6","bottlenest2gpool"], + ["rconv7","bottlenest2"], + ["rconv8","bottlenest2"], + ["rconv9","bottlenest2gpool"], + ["rconv10","bottlenest2"], + ["rconv11","bottlenest2"], + ["rconv12","bottlenest2gpool"], + ["rconv13","bottlenest2"], + ["rconv14","bottlenest2"], + ["rconv15","bottlenest2gpool"], + ["rconv16","bottlenest2"], + ["rconv17","bottlenest2"], + ["rconv18","bottlenest2gpool"], + ["rconv19","bottlenest2"], + ["rconv20","bottlenest2"], + ["rconv21","bottlenest2gpool"], + ["rconv22","bottlenest2"], + ["rconv23","bottlenest2"], + ["rconv24","bottlenest2gpool"], + ["rconv25","bottlenest2"], + ["rconv26","bottlenest2"], + ["rconv27","bottlenest2gpool"], + ["rconv28","bottlenest2"], + ["rconv29","bottlenest2"], + ["rconv30","bottlenest2gpool"], + ["rconv31","bottlenest2"], + ["rconv32","bottlenest2"], + ["rconv33","bottlenest2gpool"], + ["rconv34","bottlenest2"], + ["rconv35","bottlenest2"], + ["rconv36","bottlenest2gpool"], + ["rconv37","bottlenest2"], + ["rconv38","bottlenest2"], + ["rconv39","bottlenest2gpool"], + ["rconv40","bottlenest2"], + ["rconv41","bottlenest2"], + ], + "p1_num_channels":64, + "g1_num_channels":64, + "v1_num_channels":96, + "sbv2_num_channels":128, + "num_scorebeliefs":8, + "v2_size":144, +} + +b32c448nbt = { + "version":15, + "norm_kind":"fixup", + "bnorm_epsilon": 1e-4, + "bnorm_running_avg_momentum": 0.001, + "initial_conv_1x1": False, + "trunk_num_channels":448, + "mid_num_channels":224, + "gpool_num_channels":64, + "use_attention_pool":False, + "num_attention_pool_heads":4, + "block_kind": [ + ["rconv1","bottlenest2"], + ["rconv2","bottlenest2"], + ["rconv3","bottlenest2gpool"], + ["rconv4","bottlenest2"], + ["rconv5","bottlenest2"], + ["rconv6","bottlenest2gpool"], + ["rconv7","bottlenest2"], + ["rconv8","bottlenest2"], + ["rconv9","bottlenest2gpool"], + ["rconv10","bottlenest2"], + ["rconv11","bottlenest2"], + ["rconv12","bottlenest2gpool"], + ["rconv13","bottlenest2"], + ["rconv14","bottlenest2"], + ["rconv15","bottlenest2gpool"], + ["rconv16","bottlenest2"], + ["rconv17","bottlenest2"], + ["rconv18","bottlenest2gpool"], + ["rconv19","bottlenest2"], + ["rconv20","bottlenest2"], + ["rconv21","bottlenest2gpool"], + ["rconv22","bottlenest2"], + ["rconv23","bottlenest2"], + ["rconv24","bottlenest2gpool"], + ["rconv25","bottlenest2"], + ["rconv26","bottlenest2"], + ["rconv27","bottlenest2gpool"], + ["rconv28","bottlenest2"], + ["rconv29","bottlenest2"], + ["rconv30","bottlenest2gpool"], + ["rconv31","bottlenest2"], + ["rconv32","bottlenest2"], + ], + "p1_num_channels":64, + "g1_num_channels":64, + "v1_num_channels":96, + "sbv2_num_channels":128, + "num_scorebeliefs":8, + "v2_size":144, +} + + +b28c512nbt = { + "version":15, + "norm_kind":"fixup", + "bnorm_epsilon": 1e-4, + "bnorm_running_avg_momentum": 0.001, + "initial_conv_1x1": False, + "trunk_num_channels":512, + "mid_num_channels":256, + "gpool_num_channels":64, + "use_attention_pool":False, + "num_attention_pool_heads":4, + "block_kind": [ + ["rconv1","bottlenest2"], + ["rconv2","bottlenest2"], + ["rconv3","bottlenest2gpool"], + ["rconv4","bottlenest2"], + ["rconv5","bottlenest2"], + ["rconv6","bottlenest2gpool"], + ["rconv7","bottlenest2"], + ["rconv8","bottlenest2"], + ["rconv9","bottlenest2gpool"], + ["rconv10","bottlenest2"], + ["rconv11","bottlenest2"], + ["rconv12","bottlenest2gpool"], + ["rconv13","bottlenest2"], + ["rconv14","bottlenest2"], + ["rconv15","bottlenest2gpool"], + ["rconv16","bottlenest2"], + ["rconv17","bottlenest2"], + ["rconv18","bottlenest2gpool"], + ["rconv19","bottlenest2"], + ["rconv20","bottlenest2"], + ["rconv21","bottlenest2gpool"], + ["rconv22","bottlenest2"], + ["rconv23","bottlenest2"], + ["rconv24","bottlenest2gpool"], + ["rconv25","bottlenest2"], + ["rconv26","bottlenest2"], + ["rconv27","bottlenest2gpool"], + ["rconv28","bottlenest2"], + ], + "p1_num_channels":64, + "g1_num_channels":64, + "v1_num_channels":128, + "sbv2_num_channels":128, + "num_scorebeliefs":8, + "v2_size":144, +} + +b20c640nbt = { + "version":15, + "norm_kind":"fixup", + "bnorm_epsilon": 1e-4, + "bnorm_running_avg_momentum": 0.001, + "initial_conv_1x1": False, + "trunk_num_channels":640, + "mid_num_channels":320, + "gpool_num_channels":96, + "use_attention_pool":False, + "num_attention_pool_heads":4, + "block_kind": [ + ["rconv1","bottlenest2"], + ["rconv2","bottlenest2"], + ["rconv3","bottlenest2gpool"], + ["rconv4","bottlenest2"], + ["rconv5","bottlenest2"], + ["rconv6","bottlenest2gpool"], + ["rconv7","bottlenest2"], + ["rconv8","bottlenest2"], + ["rconv9","bottlenest2gpool"], + ["rconv10","bottlenest2"], + ["rconv11","bottlenest2"], + ["rconv12","bottlenest2gpool"], + ["rconv13","bottlenest2"], + ["rconv14","bottlenest2"], + ["rconv15","bottlenest2gpool"], + ["rconv16","bottlenest2"], + ["rconv17","bottlenest2"], + ["rconv18","bottlenest2gpool"], + ["rconv19","bottlenest2"], + ["rconv20","bottlenest2"], + ], + "p1_num_channels":64, + "g1_num_channels":64, + "v1_num_channels":96, + "sbv2_num_channels":128, + "num_scorebeliefs":8, + "v2_size":144, +} + +sandbox = { + "version":15, + "norm_kind":"fixup", + "bnorm_epsilon": 1e-4, + "bnorm_running_avg_momentum": 0.001, + "initial_conv_1x1": False, + "trunk_num_channels":256, + "mid_num_channels":256, + "gpool_num_channels":64, + "use_attention_pool":False, + "num_attention_pool_heads":4, + "block_kind": [ + ["rconv1","regular"], + ["rconv2","regular"], + ["rconv3","regular"], + ["rconv4","regular"], + ["rconv5","regular"], + ["rconv6","regular"], + ["rconv7","regulargpool"], + ["rconv8","regular"], + ["rconv9","regular"], + ["rconv10","regular"], + ["rconv11","regular"], + ["rconv12","regulargpool"], + ["rconv13","regular"], + ["rconv14","regular"], + ["rconv15","regular"], + ["rconv16","regular"], + ["rconv17","regulargpool"], + ["rconv18","regular"], + ["rconv19","regular"], + ["rconv20","regular"], + ], + "p1_num_channels":48, + "g1_num_channels":48, + "v1_num_channels":96, + "sbv2_num_channels":96, + "num_scorebeliefs":8, + "v2_size":112, +} + + +base_config_of_name = { + # Micro-sized model configs + "b1c6nbt": b1c6nbt, + "b2c16": b2c16, + "b4c32": b4c32, + "b6c96": b6c96, + + # Small model configs, not too different in inference cost from b10c128 + "b10c128": b10c128, + "b5c192nbt": b5c192nbt, + + # Medium model configs, not too different in inference cost from b15c192 + "b15c192": b15c192, + + # Roughly AlphaZero-sized, not too different in inference cost from b20c256 + "b20c256": b20c256, + "b30c256bt": b30c256bt, + "b24c320bt": b24c320bt, + "b20c384bt": b20c384bt, + "b23c256lbt": b23c256lbt, + "b18c320lbt": b18c320lbt, + "b15c384lbt": b15c384lbt, + "b10c512lbt": b10c512lbt, + "b12c384llbt": b12c384llbt, + "b10c384nbt": b10c384nbt, # Recommended best config for this cost + "b10c480nb3t": b10c480nb3t, + "b7c384lnbt": b7c384lnbt, + "b5c512nnbt": b5c512nnbt, + "b20c384lbt": b20c384lbt, + + # Roughly AlphaGoZero-sized, not too different in inference cost from b40c256 + "b30c320": b30c320, + "b40c256": b40c256, + "b18c384nbt": b18c384nbt, # Recommended best config for this cost + "b14c448nbt": b14c448nbt, + + # Large model configs, not too different in inference cost from b60c320 + "b40c384": b40c384, + "b60c320": b60c320, + "b41c384nbt": b41c384nbt, + "b32c448nbt": b32c448nbt, + "b28c512nbt": b28c512nbt, # Recommended best config for this cost + "b20c640nbt": b20c640nbt, + + "sandbox": sandbox, +} + +config_of_name = {} +for name, base_config in base_config_of_name.items(): + config = base_config.copy() + config_of_name[name] = config + + +for name, base_config in list(config_of_name.items()): + # Fixup initialization + config = base_config.copy() + config["norm_kind"] = "fixup" + config_of_name[name+""] = config + + # Fixed scaling normalization + config = base_config.copy() + config["norm_kind"] = "fixscale" + config_of_name[name+"-fs"] = config + + # Batchnorm without gamma terms + config = base_config.copy() + config["norm_kind"] = "bnorm" + config_of_name[name+"-bn"] = config + + # Batchrenorm without gamma terms + config = base_config.copy() + config["norm_kind"] = "brenorm" + config_of_name[name+"-brn"] = config + + # Fixed scaling normalization + Batchrenorm without gamma terms + config = base_config.copy() + config["norm_kind"] = "fixbrenorm" + config_of_name[name+"-fbrn"] = config + + # Batchnorm with gamma terms + config = base_config.copy() + config["norm_kind"] = "bnorm" + config["bnorm_use_gamma"] = True + config_of_name[name+"-bng"] = config + + # Batchrenorm with gamma terms + config = base_config.copy() + config["norm_kind"] = "brenorm" + config["bnorm_use_gamma"] = True + config_of_name[name+"-brng"] = config + + # Fixed scaling normalization + Batchrenorm with gamma terms + config = base_config.copy() + config["norm_kind"] = "fixbrenorm" + config["bnorm_use_gamma"] = True + config_of_name[name+"-fbrng"] = config + + # Fixed scaling normalization + ONE batch norm layer in the entire net. + config = base_config.copy() + config["norm_kind"] = "fixscaleonenorm" + config["bnorm_use_gamma"] = True + config_of_name[name+"-fson"] = config + +for name, base_config in list(config_of_name.items()): + config = base_config.copy() + config["activation"] = "elu" + config_of_name[name+"-elu"] = config + + config = base_config.copy() + config["activation"] = "gelu" + config_of_name[name+"-gelu"] = config + + config = base_config.copy() + config["activation"] = "mish" + config_of_name[name+"-mish"] = config + +for name, base_config in list(config_of_name.items()): + config = base_config.copy() + config["use_attention_pool"] = True + config_of_name[name+"-ap"] = config + +for name, base_config in list(config_of_name.items()): + config = base_config.copy() + config["use_repvgg_init"] = True + config_of_name[name+"-rvgi"] = config + + config = base_config.copy() + config["use_repvgg_linear"] = True + config_of_name[name+"-rvgl"] = config + + config = base_config.copy() + config["use_repvgg_init"] = True + config["use_repvgg_learning_rate"] = True + config_of_name[name+"-rvglr"] = config + +for name, base_config in list(config_of_name.items()): + # Add intermediate heads, for use with self-distillation or embedding small net in big one. + config = base_config.copy() + config["has_intermediate_head"] = True + config["intermediate_head_blocks"] = len(config["block_kind"]) // 2 + config_of_name[name+"-ih"] = config + + # Add parallel heads that uses the final trunk batchnorm. + # The original normal heads disables the final trunk batchnorm + # This only makes sense for configs that use some form of batchnorm. + if "norm" in config["norm_kind"]: + config = base_config.copy() + config["has_intermediate_head"] = True + config["intermediate_head_blocks"] = len(config["block_kind"]) + config["trunk_normless"] = True + config_of_name[name+"-bnh"] = config + +for name, base_config in list(config_of_name.items()): + config = base_config.copy() + config["metadata_encoder"] = { + "meta_encoder_version": 1, + "internal_num_channels": config["trunk_num_channels"], + } + config_of_name[name+"-meta"] = config diff --git a/training/scripts/dated/20241110-122511/play.py b/training/scripts/dated/20241110-122511/play.py new file mode 100644 index 0000000..6121db5 --- /dev/null +++ b/training/scripts/dated/20241110-122511/play.py @@ -0,0 +1,648 @@ +#!/usr/bin/python3 +import sys +import os +import argparse +import traceback +import random +import math +import time +import re +import logging +import colorsys +import json +import numpy as np + +from board import Board +from features import Features +from gamestate import GameState + +from typing import Dict, Any, List + +import torch +import torch.nn + +import modelconfigs +from model_pytorch import Model, EXTRA_SCORE_DISTR_RADIUS, ExtraOutputs +from data_processing_pytorch import apply_symmetry +from load_model import load_model + +description = """ +Play go with a trained neural net! +Implements a basic GTP engine that uses the neural net directly to play moves. +""" + +parser = argparse.ArgumentParser(description=description) +parser.add_argument('-checkpoint', help='Checkpoint to test', required=False) +parser.add_argument('-use-swa', help='Use SWA model', action="store_true", required=False) +parser.add_argument('-device', help='Pytorch device, like cpu or cuda:0', required=False) + +args = vars(parser.parse_args()) + +checkpoint_file = args["checkpoint"] +use_swa = args["use_swa"] +device = args["device"] + +# Hardcoded max board size +pos_len = 19 + +# Model ---------------------------------------------------------------- + +logging.root.handlers = [] +logging.basicConfig( + level=logging.INFO, + format="%(message)s", + handlers=[ + logging.StreamHandler(stream=sys.stderr), + ], +) +np.set_printoptions(linewidth=150) +torch.set_printoptions(precision=7,sci_mode=False,linewidth=100000,edgeitems=1000,threshold=1000000) + +model, swa_model, _ = load_model(checkpoint_file, use_swa, device=device, pos_len=pos_len, verbose=True) +if swa_model is not None: + model = swa_model +model_config = model.config +model.eval() + +features = Features(model_config, pos_len) + + +# Moves ---------------------------------------------------------------- + +def get_input_feature(gs, feature_idx): + bin_input_data, global_input_data = gs.get_input_features(features) + locs_and_values = [] + for y in range(gs.board.size): + for x in range(gs.board.size): + loc = board.loc(x,y) + locs_and_values.append((loc,bin_input_data[0,feature_idx,y,x])) + return locs_and_values + +def get_pass_alive(gs): + board = gs.board + rules = gs.rules + pla = board.pla + opp = Board.get_opp(pla) + area = [-1 for i in range(board.arrsize)] + nonPassAliveStones = False + safeBigTerritories = True + unsafeBigTerritories = False + board.calculateArea(area,nonPassAliveStones,safeBigTerritories,unsafeBigTerritories,rules["multiStoneSuicideLegal"]) + + locs_and_values = [] + for y in range(board.size): + for x in range(board.size): + loc = board.loc(x,y) + locs_and_values.append((loc,area[loc])) + return locs_and_values + + +def get_gfx_commands_for_heatmap(locs_and_values, board, normalization_div, is_percent, value_and_score_from=None, hotcold=False): + gfx_commands = [] + divisor = 1.0 + if normalization_div == "max": + max_abs_value = max(abs(value) for (loc,value) in locs_and_values) + divisor = max(0.0000000001,max_abs_value) # avoid divide by zero + elif normalization_div is not None: + divisor = normalization_div + + # Caps value at 1.0, using an asymptotic curve + def loose_cap(x): + def transformed_softplus(x): + return -math.log(math.exp(-(x-1.0)*8.0)+1.0)/8.0+1.0 + base = transformed_softplus(0.0) + return (transformed_softplus(x) - base) / (1.0 - base) + + # Softly curves a value so that it ramps up faster than linear in that range + def soft_curve(x,x0,x1): + p = (x-x0)/(x1-x0) + def curve(p): + return math.sqrt(p+0.16)-0.4 + p = curve(p) / curve(1.0) + return x0 + p * (x1-x0) + + if hotcold: + for (loc,value) in locs_and_values: + if loc != Board.PASS_LOC: + value = value / divisor + + if value < 0: + value = -loose_cap(-value) + else: + value = loose_cap(value) + + interpoints = [ + (-1.00,(0,0,0)), + (-0.85,(15,0,50)), + (-0.60,(60,0,160)), + (-0.35,(0,0,255)), + (-0.15,(0,100,255)), + ( 0.00,(115,115,115)), + ( 0.15,(250,45,40)), + ( 0.25,(255,55,0)), + ( 0.60,(255,255,20)), + ( 0.85,(255,255,128)), + ( 1.00,(255,255,255)), + ] + + def lerp(p,y0,y1): + return y0 + p*(y1-y0) + + i = 0 + while i < len(interpoints): + if value <= interpoints[i][0]: + break + i += 1 + i -= 1 + + if i < 0: + (r,g,b) = interpoints[0][1] + if i >= len(interpoints)-1: + (r,g,b) = interpoints[len(interpoints)-1][1] + + p = (value - interpoints[i][0]) / (interpoints[i+1][0] - interpoints[i][0]) + + (r0,g0,b0) = interpoints[i][1] + (r1,g1,b1) = interpoints[i+1][1] + r = lerp(p,r0,r1) + g = lerp(p,g0,g1) + b = lerp(p,b0,b1) + + r = ("%02x" % int(r)) + g = ("%02x" % int(g)) + b = ("%02x" % int(b)) + gfx_commands.append("COLOR #%s%s%s %s" % (r,g,b,str_coord(loc,board))) + + else: + for (loc,value) in locs_and_values: + if loc != Board.PASS_LOC: + value = value / divisor + if value < 0: + value = -value + huestart = 0.50 + huestop = 0.86 + else: + huestart = -0.02 + huestop = 0.38 + + value = loose_cap(value) + + def lerp(p,x0,x1,y0,y1): + return y0 + (y1-y0) * (p-x0)/(x1-x0) + + if value <= 0.03: + hue = huestart + lightness = 0.00 + 0.50 * (value / 0.03) + saturation = value / 0.03 + (r,g,b) = colorsys.hls_to_rgb((hue+1)%1, lightness, saturation) + elif value <= 0.60: + hue = lerp(value,0.03,0.60,huestart,huestop) + val = 1.0 + saturation = 1.0 + (r,g,b) = colorsys.hsv_to_rgb((hue+1)%1, val, saturation) + else: + hue = huestop + lightness = lerp(value,0.60,1.00,0.5,0.95) + saturation = 1.0 + (r,g,b) = colorsys.hls_to_rgb((hue+1)%1, lightness, saturation) + + r = ("%02x" % int(r*255)) + g = ("%02x" % int(g*255)) + b = ("%02x" % int(b*255)) + gfx_commands.append("COLOR #%s%s%s %s" % (r,g,b,str_coord(loc,board))) + + locs_and_values = sorted(locs_and_values, key=lambda loc_and_value: loc_and_value[1]) + locs_and_values_rev = sorted(locs_and_values, key=lambda loc_and_value: loc_and_value[1], reverse=True) + texts = [] + texts_rev = [] + texts_value = [] + maxlen_per_side = 1000 + if len(locs_and_values) > 0 and locs_and_values[0][1] < 0: + maxlen_per_side = 500 + + for i in range(min(len(locs_and_values),maxlen_per_side)): + (loc,value) = locs_and_values[i] + if is_percent: + texts.append("%s %4.1f%%" % (str_coord(loc,board),value*100)) + else: + texts.append("%s %.3f" % (str_coord(loc,board),value)) + texts.reverse() + + for i in range(min(len(locs_and_values_rev),maxlen_per_side)): + (loc,value) = locs_and_values_rev[i] + if is_percent: + texts_rev.append("%s %4.1f%%" % (str_coord(loc,board),value*100)) + else: + texts_rev.append("%s %.3f" % (str_coord(loc,board),value)) + + if value_and_score_from is not None: + value = value_and_score_from["value"] + score = value_and_score_from["scoremean"] + lead = value_and_score_from["lead"] + vtime = value_and_score_from["vtime"] + texts_value.append("wv %.2fc nr %.2f%% ws %.1f wl %.1f vt %.1f" % ( + 100*(value[0]-value[1] if board.pla == Board.WHITE else value[1] - value[0]), + 100*value[2], + (score if board.pla == Board.WHITE else -score), + (lead if board.pla == Board.WHITE else -lead), + vtime + )) + + gfx_commands.append("TEXT " + ", ".join(texts_value + texts_rev + texts)) + return gfx_commands + +def print_scorebelief(gs,outputs): + board = gs.board + scorebelief = outputs["scorebelief"] + lead = outputs["lead"] + scoremean = outputs["scoremean"] + scorestdev = outputs["scorestdev"] + + scorebelief = list(scorebelief) + # Flip so that it's in perspective of the player playing + if board.pla != Board.WHITE: + scorebelief.reverse() + scoremean = -scoremean + lead = -lead + + scoredistrmid = pos_len * pos_len + EXTRA_SCORE_DISTR_RADIUS + ret = "" + ret += "TEXT " + ret += "ScoreBelief: \n" + for i in range(17,-1,-1): + ret += "TEXT " + ret += "%+6.1f" %(-(i*20+0.5)) + for j in range(20): + idx = scoredistrmid-(i*20+j)-1 + ret += " %4.0f" % (scorebelief[idx] * 10000) + ret += "\n" + for i in range(18): + ret += "TEXT " + ret += "%+6.1f" %((i*20+0.5)) + for j in range(20): + idx = scoredistrmid+(i*20+j) + ret += " %4.0f" % (scorebelief[idx] * 10000) + ret += "\n" + + beliefscore = 0 + beliefscoresq = 0 + beliefwin = 0 + belieftotal = 0 + for idx in range(scoredistrmid*2): + score = idx-scoredistrmid+0.5 + if score > 0: + beliefwin += scorebelief[idx] + else: + beliefwin -= scorebelief[idx] + belieftotal += scorebelief[idx] + beliefscore += score*scorebelief[idx] + beliefscoresq += score*score*scorebelief[idx] + + beliefscoremean = beliefscore/belieftotal + beliefscoremeansq = beliefscoresq/belieftotal + beliefscorevar = max(0,beliefscoremeansq-beliefscoremean*beliefscoremean) + beliefscorestdev = math.sqrt(beliefscorevar) + + ret += "TEXT BeliefWin: %.2fc\n" % (100*beliefwin/belieftotal) + ret += "TEXT BeliefScoreMean: %.2f\n" % (beliefscoremean) + ret += "TEXT BeliefScoreStdev: %.2f\n" % (beliefscorestdev) + ret += "TEXT Lead: %.3f\n" % (lead) + ret += "TEXT ScoreMean: %.3f\n" % (scoremean) + ret += "TEXT ScoreStdev: %.3f\n" % (scorestdev) + ret += "TEXT Value: %s\n" % str(["%.3f" % x for x in outputs["value"]]) + ret += "TEXT TDValue: %s\n" % str(["%.3f" % x for x in outputs["td_value"]]) + ret += "TEXT TDValue2: %s\n" % str(["%.3f" % x for x in outputs["td_value2"]]) + ret += "TEXT TDValue3: %s\n" % str(["%.3f" % x for x in outputs["td_value3"]]) + ret += "TEXT TDScore: %s\n" % str(["%.3f" % x for x in outputs["td_score"]]) + ret += "TEXT Estv: %s\n" % str(outputs["estv"]) + ret += "TEXT Ests: %s\n" % str(outputs["ests"]) + ret += "TEXT Vtime: %s\n" % str(outputs["vtime"]) + return ret + + +# Basic parsing -------------------------------------------------------- +colstr = 'ABCDEFGHJKLMNOPQRST' +def parse_coord(s,board): + if s == 'pass': + return Board.PASS_LOC + return board.loc(colstr.index(s[0].upper()), board.size - int(s[1:])) + +def str_coord(loc,board): + if loc == Board.PASS_LOC: + return 'pass' + x = board.loc_x(loc) + y = board.loc_y(loc) + return '%c%d' % (colstr[x], board.size - y) + + +# GTP Implementation ----------------------------------------------------- + +# Adapted from https://github.com/pasky/michi/blob/master/michi.py, which is distributed under MIT license +# https://opensource.org/licenses/MIT + +known_commands = [ + 'boardsize', + 'clear_board', + 'showboard', + 'komi', + 'play', + 'genmove', + 'quit', + 'name', + 'version', + 'known_command', + 'list_commands', + 'protocol_version', + 'gogui-analyze_commands', + 'setrule', + 'policy', + 'policy1', + 'logpolicy', + 'ownership', + 'scoring', + 'futurepos0', + 'futurepos1', + 'seki', + 'seki2', + 'scorebelief', + 'passalive', +] +known_analyze_commands = [ + 'gfx/Policy/policy', + 'gfx/Policy1/policy1', + 'gfx/LogPolicy/logpolicy', + 'gfx/Ownership/ownership', + 'gfx/Scoring/scoring', + 'gfx/FuturePos0/futurepos0', + 'gfx/FuturePos1/futurepos1', + 'gfx/Seki/seki', + 'gfx/Seki2/seki2', + 'gfx/ScoreBelief/scorebelief', + 'gfx/PassAlive/passalive', +] + +board_size = 19 +gs = GameState(board_size, GameState.RULES_TT) + +input_feature_command_lookup = dict() +def add_input_feature_visualizations(layer_name, feature_idx, normalization_div): + command_name = layer_name + command_name = command_name.replace("/",":") + known_commands.append(command_name) + known_analyze_commands.append("gfx/" + command_name + "/" + command_name) + input_feature_command_lookup[command_name] = (feature_idx,normalization_div) +for i in range(model.bin_input_shape[1]): + add_input_feature_visualizations("input-" + str(i),i, normalization_div=1) + +attention_feature_command_lookup = dict() +def add_attention_visualizations(extra_output_name, extra_output): + for c in range(extra_output.shape[0]): + command_name = extra_output_name + command_name = command_name.replace("/",":") + command_name += ":" + str(c) + known_commands.append(command_name) + known_analyze_commands.append("gfx/" + command_name + "/" + command_name) + attention_feature_command_lookup[command_name] = (extra_output_name, c) +with torch.no_grad(): + dummy_outputs = gs.get_model_outputs(model) + extra_attention_output_names = [name for name in dummy_outputs["available_extra_outputs"] if name.endswith(".attention") or name.endswith(".reverse_attention")] + dummy_outputs = gs.get_model_outputs(model) + for name in extra_attention_output_names: + add_attention_visualizations(name,dummy_outputs[name]) + del dummy_outputs + + +def get_board_matrix_str(matrix, scale, formatstr): + ret = "" + matrix = matrix.reshape([features.pos_len,features.pos_len]) + for y in range(features.pos_len): + for x in range(features.pos_len): + ret += formatstr % (scale * matrix[y,x]) + ret += " " + ret += "\n" + return ret + + +while True: + try: + line = input().strip() + except EOFError: + break + if line == '': + continue + command = [s.lower() for s in line.split()] + if re.match('\d+', command[0]): + cmdid = command[0] + command = command[1:] + else: + cmdid = '' + + ret = '' + if command[0] == "boardsize": + if int(command[1]) > features.pos_len: + print("Warning: Trying to set incompatible boardsize %s (!= %d)" % (command[1], N), file=sys.stderr) + ret = None + board_size = int(command[1]) + gs = GameState(board_size) + elif command[0] == "clear_board": + gs = GameState(board_size) + elif command[0] == "showboard": + ret = "\n" + gs.board.to_string().strip() + elif command[0] == "komi": + gs.rules["whiteKomi"] = float(command[1]) + elif command[0] == "play": + pla = (Board.BLACK if command[1] == "B" or command[1] == "b" else Board.WHITE) + loc = parse_coord(command[2],gs.board) + gs.play(pla,loc) + elif command[0] == "genmove": + outputs = gs.get_model_outputs(model) + loc = outputs["genmove_result"] + pla = gs.board.pla + + if len(command) > 1: + pla = (Board.BLACK if command[1] == "B" or command[1] == "b" else Board.WHITE) + gs.play(pla,loc) + ret = str_coord(loc,gs.board) + + elif command[0] == "name": + ret = 'KataGo Raw Neural Net Debug/Test Script' + elif command[0] == "version": + ret = '1.0' + elif command[0] == "list_commands": + ret = '\n'.join(known_commands) + elif command[0] == "known_command": + ret = 'true' if command[1] in known_commands else 'false' + elif command[0] == "gogui-analyze_commands": + ret = '\n'.join(known_analyze_commands) + elif command[0] == "setrule": + ret = "" + if command[1] == "korule": + gs.rules["koRule"] = command[2].upper() + elif command[1] == "scoringrule": + gs.rules["scoringRule"] = command[2].upper() + elif command[1] == "taxrule": + gs.rules["taxRule"] = command[2].upper() + elif command[1] == "multistonesuicidelegal": + gs.rules["multiStoneSuicideLegal"] = (command[2].lower() == "true") + elif command[1] == "hasbutton": + gs.rules["hasButton"] = (command[2].lower() == "true") + elif command[1] == "encorephase": + gs.rules["encorePhase"] = int(command[2]) + elif command[1] == "passwouldendphase": + gs.rules["passWouldEndPhase"] = (command[2].lower() == "true") + elif command[1] == "whitekomi" or command[1] == "komi": + gs.rules["whiteKomi"] = float(command[2]) + elif command[1] == "asym": + gs.rules["asymPowersOfTwo"] = float(command[2]) + else: + ret = "Unknown rules setting" + elif command[0] == "policy": + outputs = gs.get_model_outputs(model) + gfx_commands = get_gfx_commands_for_heatmap(outputs["moves_and_probs0"], gs.board, normalization_div=None, is_percent=True, value_and_score_from=outputs) + ret = "\n".join(gfx_commands) + elif command[0] == "policy1": + outputs = gs.get_model_outputs(model) + gfx_commands = get_gfx_commands_for_heatmap(outputs["moves_and_probs1"], gs.board, normalization_div=None, is_percent=True, value_and_score_from=outputs) + ret = "\n".join(gfx_commands) + elif command[0] == "logpolicy": + outputs = gs.get_model_outputs(model) + moves_and_logprobs = [(move,max(0.0,4.9+math.log10(prob))) for (move,prob) in outputs["moves_and_probs0"]] + gfx_commands = get_gfx_commands_for_heatmap(moves_and_logprobs, gs.board, normalization_div=6, is_percent=False, value_and_score_from=outputs) + ret = "\n".join(gfx_commands) + elif command[0] == "ownership": + outputs = gs.get_model_outputs(model) + gfx_commands = get_gfx_commands_for_heatmap(outputs["ownership_by_loc"], gs.board, normalization_div=None, is_percent=True, value_and_score_from=None, hotcold=True) + ret = "\n".join(gfx_commands) + elif command[0] == "scoring": + outputs = gs.get_model_outputs(model) + gfx_commands = get_gfx_commands_for_heatmap(outputs["scoring_by_loc"], gs.board, normalization_div=None, is_percent=True, value_and_score_from=None, hotcold=True) + ret = "\n".join(gfx_commands) + elif command[0] == "futurepos0": + outputs = gs.get_model_outputs(model) + gfx_commands = get_gfx_commands_for_heatmap(outputs["futurepos0_by_loc"], gs.board, normalization_div=None, is_percent=True, value_and_score_from=None, hotcold=True) + ret = "\n".join(gfx_commands) + elif command[0] == "futurepos1": + outputs = gs.get_model_outputs(model) + gfx_commands = get_gfx_commands_for_heatmap(outputs["futurepos1_by_loc"], gs.board, normalization_div=None, is_percent=True, value_and_score_from=None, hotcold=True) + ret = "\n".join(gfx_commands) + elif command[0] == "seki": + outputs = gs.get_model_outputs(model) + gfx_commands = get_gfx_commands_for_heatmap(outputs["seki_by_loc"], gs.board, normalization_div=None, is_percent=True, value_and_score_from=None) + ret = "\n".join(gfx_commands) + elif command[0] == "seki2": + outputs = gs.get_model_outputs(model) + gfx_commands = get_gfx_commands_for_heatmap(outputs["seki_by_loc2"], gs.board, normalization_div=None, is_percent=True, value_and_score_from=None) + ret = "\n".join(gfx_commands) + + elif command[0] == "policy_raw": + outputs = gs.get_model_outputs(model) + ret = "\n" + + policysum = 0.0 + for y in range(gs.board.size): + for x in range(gs.board.size): + loc = gs.board.loc(x,y) + pos = features.loc_to_tensor_pos(loc,gs.board) + gs.board.would_be_legal(gs.board.pla,loc) + policysum += outputs["policy0"][pos] + loc = Board.PASS_LOC + pos = features.loc_to_tensor_pos(loc,gs.board) + policysum += outputs["policy0"][pos] + + for y in range(gs.board.size): + for x in range(gs.board.size): + loc = gs.board.loc(x,y) + pos = features.loc_to_tensor_pos(loc,gs.board) + if gs.board.would_be_legal(gs.board.pla,loc): + ret += "%6.3f" % (100.0 * outputs["policy0"][pos] / policysum) + else: + ret += " - " + ret += " " + ret += "\n" + loc = Board.PASS_LOC + pos = features.loc_to_tensor_pos(loc,gs.board) + ret += "Pass: %6.3f" % (100.0 * outputs["policy0"][pos] / policysum) + + elif command[0] == "policy1_raw": + outputs = gs.get_model_outputs(model) + ret = "\n" + + for y in range(gs.board.size): + for x in range(gs.board.size): + loc = gs.board.loc(x,y) + pos = features.loc_to_tensor_pos(loc,gs.board) + if gs.board.would_be_legal(gs.board.pla,loc): + ret += "%6.3f" % (100.0 * outputs["policy1"][pos]) + else: + ret += " - " + ret += " " + ret += "\n" + loc = Board.PASS_LOC + pos = features.loc_to_tensor_pos(loc,gs.board) + ret += "Pass: %6.3f" % (100.0 * outputs["policy1"][pos]) + + elif command[0] == "ownership_raw": + outputs = gs.get_model_outputs(model) + ret = get_board_matrix_str(outputs["ownership"], 100.0, "%+7.3f") + elif command[0] == "scoring_raw": + outputs = gs.get_model_outputs(model) + ret = get_board_matrix_str(outputs["scoring"], 100.0, "%+7.3f") + elif command[0] == "futurepos0_raw": + outputs = gs.get_model_outputs(model) + ret = get_board_matrix_str(outputs["futurepos"][0], 100.0, "%+7.3f") + elif command[0] == "futurepos1_raw": + outputs = gs.get_model_outputs(model) + ret = get_board_matrix_str(outputs["futurepos"][1], 100.0, "%+7.3f") + elif command[0] == "seki_raw": + outputs = gs.get_model_outputs(model) + ret = get_board_matrix_str(outputs["seki"], 100.0, "%+7.3f") + elif command[0] == "seki2_raw": + outputs = gs.get_model_outputs(model) + ret = get_board_matrix_str(outputs["seki2"], 100.0, "%+7.3f") + + elif command[0] in input_feature_command_lookup: + (feature_idx,normalization_div) = input_feature_command_lookup[command[0]] + locs_and_values = get_input_feature(gs, feature_idx) + gfx_commands = get_gfx_commands_for_heatmap(locs_and_values, gs.board, normalization_div, is_percent=False) + ret = "\n".join(gfx_commands) + + elif command[0] in attention_feature_command_lookup: + (extra_output_name,channel_idx) = attention_feature_command_lookup[command[0]] + outputs = gs.get_model_outputs(model, extra_output_names=[extra_output_name]) + output = outputs[extra_output_name] # shape c, hw + output = output[channel_idx] + locs_and_values = [] + board = gs.board + for y in range(board.size): + for x in range(board.size): + loc = board.loc(x,y) + pos = features.loc_to_tensor_pos(loc,board) + locs_and_values.append((loc,output[pos])) + + normalization_div = "max" + gfx_commands = get_gfx_commands_for_heatmap(locs_and_values, gs.board, normalization_div, is_percent=True) + ret = "\n".join(gfx_commands) + + elif command[0] == "passalive": + locs_and_values = get_pass_alive(gs) + gfx_commands = get_gfx_commands_for_heatmap(locs_and_values, gs.board, normalization_div=None, is_percent=False) + ret = "\n".join(gfx_commands) + + elif command[0] == "scorebelief": + outputs = gs.get_model_outputs(model) + ret = print_scorebelief(gs,outputs) + + elif command[0] == "protocol_version": + ret = '2' + elif command[0] == "quit": + print('=%s \n\n' % (cmdid,), end='') + break + else: + print('Warning: Ignoring unknown command - %s' % (line,), file=sys.stderr) + ret = None + + if ret is not None: + print('=%s %s\n\n' % (cmdid, ret,), end='') + else: + print('?%s ???\n\n' % (cmdid,), end='') + sys.stdout.flush() diff --git a/training/scripts/dated/20241110-122511/query_analysis_engine_example.py b/training/scripts/dated/20241110-122511/query_analysis_engine_example.py new file mode 100644 index 0000000..2b803e9 --- /dev/null +++ b/training/scripts/dated/20241110-122511/query_analysis_engine_example.py @@ -0,0 +1,136 @@ +""" +This is a simple python program that demonstrates how to run KataGo's +analysis engine as a subprocess and send it a query. It queries the +result of playing the 4-4 point on an empty board and prints out +the json response. +""" + +import argparse +import json +import subprocess +import time +from threading import Thread +import sgfmill +import sgfmill.boards +import sgfmill.ascii_boards +from typing import Tuple, List, Optional, Union, Literal, Any, Dict + +Color = Union[Literal["b"],Literal["w"]] +Move = Union[None,Literal["pass"],Tuple[int,int]] + +def sgfmill_to_str(move: Move) -> str: + if move is None: + return "pass" + if move == "pass": + return "pass" + (y,x) = move + return "ABCDEFGHJKLMNOPQRSTUVWXYZ"[x] + str(y+1) + +class KataGo: + + def __init__(self, katago_path: str, config_path: str, model_path: str, additional_args: List[str] = []): + self.query_counter = 0 + katago = subprocess.Popen( + [katago_path, "analysis", "-config", config_path, "-model", model_path, *additional_args], + stdin=subprocess.PIPE, + stdout=subprocess.PIPE, + stderr=subprocess.PIPE, + ) + self.katago = katago + def printforever(): + while katago.poll() is None: + data = katago.stderr.readline() + time.sleep(0) + if data: + print("KataGo: ", data.decode(), end="") + data = katago.stderr.read() + if data: + print("KataGo: ", data.decode(), end="") + self.stderrthread = Thread(target=printforever) + self.stderrthread.start() + + def close(self): + self.katago.stdin.close() + + + def query(self, initial_board: sgfmill.boards.Board, moves: List[Tuple[Color,Move]], komi: float, max_visits=None): + query = {} + + query["id"] = str(self.query_counter) + self.query_counter += 1 + + query["moves"] = [(color,sgfmill_to_str(move)) for color, move in moves] + query["initialStones"] = [] + for y in range(initial_board.side): + for x in range(initial_board.side): + color = initial_board.get(y,x) + if color: + query["initialStones"].append((color,sgfmill_to_str((y,x)))) + query["rules"] = "Chinese" + query["komi"] = komi + query["boardXSize"] = initial_board.side + query["boardYSize"] = initial_board.side + query["includePolicy"] = True + if max_visits is not None: + query["maxVisits"] = max_visits + return self.query_raw(query) + + def query_raw(self, query: Dict[str,Any]): + self.katago.stdin.write((json.dumps(query) + "\n").encode()) + self.katago.stdin.flush() + + # print(json.dumps(query)) + + line = "" + while line == "": + if self.katago.poll(): + time.sleep(1) + raise Exception("Unexpected katago exit") + line = self.katago.stdout.readline() + line = line.decode().strip() + # print("Got: " + line) + response = json.loads(line) + + # print(response) + return response + +if __name__ == "__main__": + description = """ + Example script showing how to run KataGo analysis engine and query it from python. + """ + parser = argparse.ArgumentParser(description=description) + parser.add_argument( + "-katago-path", + help="Path to katago executable", + required=True, + ) + parser.add_argument( + "-config-path", + help="Path to KataGo analysis config (e.g. cpp/configs/analysis_example.cfg in KataGo repo)", + required=True, + ) + parser.add_argument( + "-model-path", + help="Path to neural network .bin.gz file", + required=True, + ) + args = vars(parser.parse_args()) + print(args) + + katago = KataGo(args["katago_path"], args["config_path"], args["model_path"]) + + board = sgfmill.boards.Board(19) + komi = 6.5 + moves = [("b",(3,3))] + + displayboard = board.copy() + for color, move in moves: + if move != "pass": + row,col = move + displayboard.play(row,col,color) + print(sgfmill.ascii_boards.render_board(displayboard)) + + print("Query result: ") + print(katago.query(board, moves, komi)) + + katago.close() diff --git a/training/scripts/dated/20241110-122511/save_model_for_export_manual.py b/training/scripts/dated/20241110-122511/save_model_for_export_manual.py new file mode 100644 index 0000000..b0d670a --- /dev/null +++ b/training/scripts/dated/20241110-122511/save_model_for_export_manual.py @@ -0,0 +1,162 @@ +#!/usr/bin/python3 +import sys +import os +import argparse +import traceback +import random +import math +import time +import logging +import contextlib +import json +import datetime +from datetime import timezone +import gc +import shutil +import glob +import numpy as np +import itertools +import copy +import atexit +from collections import defaultdict +from typing import Dict, List + +import torch +import torch.nn +import torch.optim +import torch.distributed +import torch.multiprocessing + +import modelconfigs +from model_pytorch import Model +from metrics_pytorch import Metrics +import load_model +import data_processing_pytorch + +# HANDLE COMMAND AND ARGS ------------------------------------------------------------------- + +if __name__ == "__main__": + + description = """ + Script for one-off taking a checkpoint training under train.py and making it save itself for one export, + without having to use train.py arguments where it would do so repeatedly periodically. + """ + + parser = argparse.ArgumentParser(description=description) + parser.add_argument('-traindir', help='Dir to write to for recording training results', required=True) + parser.add_argument('-exportdir', help='Directory to export models periodically', required=True) + parser.add_argument('-exportprefix', help='Prefix to append to names of models', required=True) + + args = vars(parser.parse_args()) + + +def make_dirs(args): + exportdir = args["exportdir"] + + if exportdir is not None and not os.path.exists(exportdir): + os.makedirs(exportdir) + + +def main(args): + traindir = args["traindir"] + exportdir = args["exportdir"] + exportprefix = args["exportprefix"] + + # SET UP LOGGING ------------------------------------------------------------- + + logging.root.handlers = [] + logging.basicConfig( + level=logging.INFO, + format="%(message)s", + handlers=[ + logging.FileHandler(os.path.join(traindir,f"save_model_for_export_manual.log"), mode="a"), + logging.StreamHandler() + ], + ) + np.set_printoptions(linewidth=150) + + logging.info(str(sys.argv)) + + # LOAD MODEL --------------------------------------------------------------------- + + def get_checkpoint_path(): + return os.path.join(traindir,"checkpoint.ckpt") + + def save(model_state_dict, swa_model_state_dict, optimizer_state_dict, metrics_obj_state_dict, running_metrics, train_state, last_val_metrics, path): + assert path is not None + + state_dict = {} + state_dict["model"] = model_state_dict + state_dict["optimizer"] = optimizer_state_dict + state_dict["metrics"] = metrics_obj_state_dict + state_dict["running_metrics"] = running_metrics + state_dict["train_state"] = train_state + state_dict["last_val_metrics"] = last_val_metrics + state_dict["config"] = model_config + + if swa_model_state_dict is not None: + state_dict["swa_model"] = swa_model_state_dict + + logging.info("Saving checkpoint: " + path) + torch.save(state_dict, path + ".tmp") + time.sleep(1) + os.replace(path + ".tmp", path) + + def load(): + path_to_load_from = get_checkpoint_path() + assert path_to_load_from is not None + + state_dict = torch.load(path_to_load_from, map_location="cpu") + model_config = state_dict["config"] + logging.info(str(model_config)) + + train_state = state_dict["train_state"] + model_state_dict = state_dict["model"] + swa_model_state_dict = None + if "swa_model" in state_dict: + swa_model_state_dict = state_dict["swa_model"] + + metrics_obj_state_dict = state_dict["metrics"] + running_metrics = state_dict["running_metrics"] + + optimizer_state_dict = state_dict["optimizer"] + last_val_metrics = state_dict["last_val_metrics"] + + return (model_config, model_state_dict, swa_model_state_dict, optimizer_state_dict, metrics_obj_state_dict, running_metrics, train_state, last_val_metrics) + + (model_config, model_state_dict, swa_model_state_dict, optimizer_state_dict, metrics_obj_state_dict, running_metrics, train_state, last_val_metrics) = load() + + assert "global_step_samples" in train_state + assert "total_num_data_rows" in train_state + + logging.info("=========================================================================") + logging.info("SAVING MODEL FOR EXPORT MANUAL") + logging.info("=========================================================================") + logging.info("Current time: " + str(datetime.datetime.now())) + logging.info("Global step: %d samples" % (train_state["global_step_samples"])) + logging.info("Currently up to data row " + str(train_state["total_num_data_rows"])) + logging.info(f"Training dir: {traindir}") + logging.info(f"Export dir: {exportdir}") + + # Export a model for testing, unless somehow it already exists + modelname = "%s-s%d-d%d" % ( + exportprefix, + train_state["global_step_samples"], + train_state["total_num_data_rows"], + ) + savepath = os.path.join(exportdir,modelname) + savepathtmp = os.path.join(exportdir,modelname+".tmp") + if os.path.exists(savepath): + logging.info("NOT saving model, already exists at: " + savepath) + else: + os.mkdir(savepathtmp) + logging.info("SAVING MODEL FOR EXPORT TO: " + savepath) + save(model_state_dict, swa_model_state_dict, optimizer_state_dict, metrics_obj_state_dict, running_metrics, train_state, last_val_metrics, path=os.path.join(savepathtmp,"model.ckpt")) + time.sleep(2) + os.rename(savepathtmp,savepath) + + + +if __name__ == "__main__": + make_dirs(args) + main(args) diff --git a/training/scripts/dated/20241110-122511/selfplay.cfg b/training/scripts/dated/20241110-122511/selfplay.cfg new file mode 100644 index 0000000..50bc598 --- /dev/null +++ b/training/scripts/dated/20241110-122511/selfplay.cfg @@ -0,0 +1,185 @@ + +# Logs------------------------------------------------------------------------------------ + +logSearchInfo = false +logMoves = false +logGamesEvery = 10 +logToStdout = true + +# Data writing----------------------------------------------------------------------------------- + +# Spatial size of tensors of data, must match -pos-len in python/train.py and be at least as large as the +# largest boardsize in the data. If you train only on smaller board sizes, you can decrease this and +# pass in a smaller -pos-len to python/train.py (e.g. modify "-pos-len 19" in selfplay/train.sh script), +# to train faster, although it may be awkward if you ever want to increase it again later since data with +# different values cannot be shuffled together. +dataBoardLen = 19 + +maxDataQueueSize = 2000 +maxRowsPerTrainFile = 10000 +firstFileRandMinProp = 0.15 + +# Fancy game selfplay settings-------------------------------------------------------------------- + +# These take dominance - if a game is forked for any of these reasons, that will be the next game played. +# For forked games, randomization of rules and "initGamesWithPolicy" is disabled, komi is made fair with prob "forkCompensateKomiProb". +earlyForkGameProb = 0.04 # Fork to try alternative opening variety with this probability +earlyForkGameExpectedMoveProp = 0.025 # Fork roughly within the first (boardArea * this) many moves +forkGameProb = 0.01 # Fork to try alternative crazy move anywhere in game with this probability if not early forking +forkGameMinChoices = 3 # Choose from the best of at least this many random choices +earlyForkGameMaxChoices = 12 # Choose from the best of at most this many random choices +forkGameMaxChoices = 36 # Choose from the best of at most this many random choices +# Otherwise, with this probability, learn a bit more about the differing evaluation of seki in different rulesets. +sekiForkHackProb = 0.02 + +# Otherwise, play some proportion of games starting from SGF positions, with randomized rules (ignoring the sgf rules) +# On SGF positions, high temperature policy init is allowed +# startPosesProb = 0.0 # Play this proportion of games starting from SGF positions +# startPosesFromSgfDir = DIRECTORYPATH # Load SGFs from this dir +# startPosesLoadProb = 1.0 # Only load each position from each SGF with this chance (save memory) +# startPosesTurnWeightLambda = 0 # 0 = equal weight 0.01 = decrease probability by 1% per turn -0.01 = increase probability by 1% per turn. +# startPosesPolicyInitAreaProp = 0.0 # Same as policyInitAreaProp but for SGF positions + +# Otherwise, play some proportion of games starting from hint positions (generated using "dataminesgfs" command), with randomized rules. +# On hint positions, "initGamesWithPolicy" does not apply. +# hintPosesProb = 0.0 +# hintPosesDir = DIRECTORYPATH + +# Otherwise we are playing a "normal" game, potentially with handicap stones, depending on "handicapProb", and +# potentially with komi randomization, depending on things like "komiStdev", and potentially with different +# board sizes, etc. + +# Most of the remaining parameters here below apply regardless of the initialization, although a few of them +# vary depending on handicap vs normal game, and some are explicitly disabled (e.g. initGamesWithPolicy on hint positions). + +initGamesWithPolicy = true # Play the first few moves of a game high-temperaturely from policy +policyInitAreaProp = 0.04 # The avg number of moves to play +compensateAfterPolicyInitProb = 0.2 # Additionally make komi fair this often after the high-temperature moves. +sidePositionProb = 0.020 # With this probability, train on refuting bad alternative moves. + +cheapSearchProb = 0.75 # Do cheap searches with this probaiblity +cheapSearchVisits = 100 # Number of visits for cheap search +cheapSearchTargetWeight = 0.0 # Training weight for cheap search + +reduceVisits = true # Reduce visits when one side is winning +reduceVisitsThreshold = 0.9 # How winning a side needs to be (winrate) +reduceVisitsThresholdLookback = 3 # How many consecutive turns needed to be that winning +reducedVisitsMin = 100 # Minimum number of visits (never reduce below this) +reducedVisitsWeight = 0.1 # Minimum training weight + +handicapAsymmetricPlayoutProb = 0.5 # In handicap games, play with unbalanced players with this probablity +normalAsymmetricPlayoutProb = 0.01 # In regular games, play with unbalanced players with this probability +maxAsymmetricRatio = 8.0 # Max ratio to unbalance visits between players +minAsymmetricCompensateKomiProb = 0.4 # Compensate komi with at least this probability for unbalanced players + +policySurpriseDataWeight = 0.5 # This proportion of training weight should be concentrated on surprising moves +valueSurpriseDataWeight = 0.1 # This proportion of training weight should be concentrated on surprising position results + +estimateLeadProb = 0.05 # Train lead, rather than just scoremean. Consumes a decent number of extra visits, can be quite slow using low visits to set too high. +switchNetsMidGame = true # When a new neural net is loaded, switch to it immediately instead of waiting for new game +fancyKomiVarying = true # In non-compensated handicap and fork games, vary komi to better learn komi and large score differences that would never happen in even games. + +# Match----------------------------------------------------------------------------------- + +numGameThreads = 30 +maxMovesPerGame = 1600 + +# Rules------------------------------------------------------------------------------------ + +koRules = SIMPLE,POSITIONAL,SITUATIONAL +scoringRules = AREA,TERRITORY +taxRules = NONE,NONE,SEKI,SEKI,ALL +multiStoneSuicideLegals = false,true +hasButtons = false,false,true + +bSizes = 7,8,9 +bSizeRelProbs = 1,1,8 +allowRectangleProb = 0.50 + +komiAuto = True # Automatically adjust komi to what the neural nets think are fair based on the empty board, but still apply komiStdev. +# komiMean = 7.0 # Specify explicit komi +komiStdev = 1.0 # Standard deviation of random variation to komi. +komiBigStdevProb = 0.06 # Probability of applying komiBigStdev +komiBigStdev = 12.0 # Standard deviation of random big variation to komi + +handicapProb = 0.10 # Probability of handicap game +handicapCompensateKomiProb = 0.50 # In handicap games, adjust komi to fair with this probability based on the handicap placement +forkCompensateKomiProb = 0.80 # For forks, adjust komi to fair with this probability based on the forked position +sgfCompensateKomiProb = 0.90 # For sgfs, adjust komi to fair with this probability based on the specific starting position + +drawRandRadius = 0.5 +noResultStdev = 0.166666666 + +# Search limits----------------------------------------------------------------------------------- + +maxVisits = 600 +numSearchThreads = 1 + +# GPU Settings------------------------------------------------------------------------------- + +nnMaxBatchSize = 128 +nnCacheSizePowerOfTwo = 21 +nnMutexPoolSizePowerOfTwo = 15 +numNNServerThreadsPerModel = 1 +nnRandomize = true + +# CUDA GPU settings-------------------------------------- +# cudaDeviceToUse = 0 #use device 0 for all server threads (numNNServerThreadsPerModel) unless otherwise specified per-model or per-thread-per-model +# cudaDeviceToUseModel0 = 3 #use device 3 for model 0 for all threads unless otherwise specified per-thread for this model +# cudaDeviceToUseModel1 = 2 #use device 2 for model 1 for all threads unless otherwise specified per-thread for this model +# cudaDeviceToUseModel0Thread0 = 3 #use device 3 for model 0, server thread 0 +# cudaDeviceToUseModel0Thread1 = 2 #use device 2 for model 0, server thread 1 + +cudaUseFP16 = auto +cudaUseNHWC = auto + +# Root move selection and biases------------------------------------------------------------------------------ + +chosenMoveTemperatureEarly = 0.75 +chosenMoveTemperatureHalflife = 19 +chosenMoveTemperature = 0.15 +chosenMoveSubtract = 0 +chosenMovePrune = 1 + +rootNoiseEnabled = true +rootDirichletNoiseTotalConcentration = 10.83 +rootDirichletNoiseWeight = 0.25 + +rootDesiredPerChildVisitsCoeff = 2 +rootNumSymmetriesToSample = 4 + +useLcbForSelection = true +lcbStdevs = 5.0 +minVisitPropForLCB = 0.15 + +# Internal params------------------------------------------------------------------------------ + +winLossUtilityFactor = 1.0 +staticScoreUtilityFactor = 0.00 +dynamicScoreUtilityFactor = 0.40 +dynamicScoreCenterZeroWeight = 0.25 +dynamicScoreCenterScale = 0.50 +noResultUtilityForWhite = 0.0 +drawEquivalentWinsForWhite = 0.5 + +rootEndingBonusPoints = 0.5 +rootPruneUselessMoves = true + +rootPolicyTemperatureEarly = 1.25 +rootPolicyTemperature = 1.1 + +cpuctExploration = 1.1 +cpuctExplorationLog = 0.0 +fpuReductionMax = 0.2 +rootFpuReductionMax = 0.0 + +numVirtualLossesPerThread = 1 + +# These parameters didn't exist historically during early KataGo runs +valueWeightExponent = 0.5 +subtreeValueBiasFactor = 0.30 +subtreeValueBiasWeightExponent = 0.8 +useNonBuggyLcb = true +useGraphSearch = true +fpuParentWeightByVisitedPolicy = true +fpuParentWeightByVisitedPolicyPow = 2.0 diff --git a/training/scripts/dated/20241110-122511/sgfmetadata.py b/training/scripts/dated/20241110-122511/sgfmetadata.py new file mode 100644 index 0000000..55d1b21 --- /dev/null +++ b/training/scripts/dated/20241110-122511/sgfmetadata.py @@ -0,0 +1,186 @@ +# See the equivalents in trainingwrite.h and trainingwrite.cpp in the cpp directory. +# Translated from the c++ implementation. + +from dataclasses import dataclass +from typing import ClassVar +import datetime +import math +import numpy as np +import random +from board import Board +import modelconfigs + +@dataclass +class SGFMetadata: + inverseBRank: int = 0 + inverseWRank: int = 0 + bIsUnranked: bool = False + wIsUnranked: bool = False + bRankIsUnknown: bool = False + wRankIsUnknown: bool = False + bIsHuman: bool = False + wIsHuman: bool = False + + gameIsUnrated: bool = False + gameRatednessIsUnknown: bool = False + + tcIsUnknown: bool = True + tcIsNone: bool = False + tcIsAbsolute: bool = False + tcIsSimple: bool = False + tcIsByoYomi: bool = False + tcIsCanadian: bool = False + tcIsFischer: bool = False + + mainTimeSeconds: float = 0.0 + periodTimeSeconds: float = 0.0 + byoYomiPeriods: int = 0 + canadianMoves: int = 0 + + gameDate: datetime.date = datetime.date(1970, 1, 1) + + source: int = 0 + + SOURCE_OGS: ClassVar[int] = 1 + SOURCE_KGS: ClassVar[int] = 2 + SOURCE_FOX: ClassVar[int] = 3 + SOURCE_TYGEM: ClassVar[int] = 4 + SOURCE_GOGOD: ClassVar[int] = 5 + SOURCE_GO4GO: ClassVar[int] = 6 + + METADATA_INPUT_NUM_CHANNELS: ClassVar[int] = 192 + + @classmethod + def of_dict(cls, data: dict): + data["gameDate"] = datetime.date.fromisoformat(data["gameDate"]) + return cls(**data) + + def to_dict(self): + data = self.__dict__.copy() + data["gameDate"] = data["gameDate"].isoformat() + return data + + @classmethod + def get_katago_selfplay_metadata(cls, rand: random.Random) -> "SGFMetadata": + return SGFMetadata( + inverseBRank = 0, + inverseWRank = 0, + bIsUnranked = False, + wIsUnranked = False, + bRankIsUnknown = False, + wRankIsUnknown = False, + bIsHuman = False, + wIsHuman = False, + + gameIsUnrated = False, + gameRatednessIsUnknown = False, + + tcIsUnknown = False, + tcIsNone = False, + tcIsAbsolute = False, + tcIsSimple = False, + tcIsByoYomi = True, + tcIsCanadian = False, + tcIsFischer = False, + + mainTimeSeconds = rand.choice([300,600,900,1200]), + periodTimeSeconds = rand.choice([10.0,15.0,30.0]), + byoYomiPeriods = 5, + canadianMoves = 0, + + gameDate = datetime.date(2022, 1, 1) + datetime.timedelta(days=rand.randint(0,722)), + source = 0, + ) + + + def get_metadata_row(self, nextPlayer, boardArea) -> np.ndarray: + if isinstance(nextPlayer,str): + if nextPlayer.lower() == "w": + nextPlayer = Board.WHITE + elif nextPlayer.lower() == "b": + nextPlayer = Board.BLACK + + # This class implements "version 1" of sgf metadata encoding, make sure channel dims match up. + meta_encoder_version = 1 + assert modelconfigs.get_num_meta_encoder_input_features(meta_encoder_version) == self.METADATA_INPUT_NUM_CHANNELS + + rowMetadata = np.zeros(self.METADATA_INPUT_NUM_CHANNELS, dtype=np.float32) + + plaIsHuman = self.wIsHuman if nextPlayer == Board.WHITE else self.bIsHuman + oppIsHuman = self.bIsHuman if nextPlayer == Board.WHITE else self.wIsHuman + rowMetadata[0] = 1.0 if plaIsHuman else 0.0 + rowMetadata[1] = 1.0 if oppIsHuman else 0.0 + + plaIsUnranked = self.wIsUnranked if nextPlayer == Board.WHITE else self.bIsUnranked + oppIsUnranked = self.bIsUnranked if nextPlayer == Board.WHITE else self.wIsUnranked + rowMetadata[2] = 1.0 if plaIsUnranked else 0.0 + rowMetadata[3] = 1.0 if oppIsUnranked else 0.0 + + plaRankIsUnknown = self.wRankIsUnknown if nextPlayer == Board.WHITE else self.bRankIsUnknown + oppRankIsUnknown = self.bRankIsUnknown if nextPlayer == Board.WHITE else self.wRankIsUnknown + rowMetadata[4] = 1.0 if plaRankIsUnknown else 0.0 + rowMetadata[5] = 1.0 if oppRankIsUnknown else 0.0 + + RANK_START_IDX = 6 + invPlaRank = self.inverseWRank if nextPlayer == Board.WHITE else self.inverseBRank + invOppRank = self.inverseBRank if nextPlayer == Board.WHITE else self.inverseWRank + RANK_LEN_PER_PLA = 34 + if not plaIsUnranked: + for i in range(min(invPlaRank, RANK_LEN_PER_PLA)): + rowMetadata[RANK_START_IDX + i] = 1.0 + if not oppIsUnranked: + for i in range(min(invOppRank, RANK_LEN_PER_PLA)): + rowMetadata[RANK_START_IDX + RANK_LEN_PER_PLA + i] = 1.0 + + assert RANK_START_IDX + 2 * RANK_LEN_PER_PLA == 74 + rowMetadata[74] = 0.5 if self.gameRatednessIsUnknown else 1.0 if self.gameIsUnrated else 0.0 + + rowMetadata[75] = 1.0 if self.tcIsUnknown else 0.0 + rowMetadata[76] = 1.0 if self.tcIsNone else 0.0 + rowMetadata[77] = 1.0 if self.tcIsAbsolute else 0.0 + rowMetadata[78] = 1.0 if self.tcIsSimple else 0.0 + rowMetadata[79] = 1.0 if self.tcIsByoYomi else 0.0 + rowMetadata[80] = 1.0 if self.tcIsCanadian else 0.0 + rowMetadata[81] = 1.0 if self.tcIsFischer else 0.0 + assert ( + rowMetadata[75] + + rowMetadata[76] + + rowMetadata[77] + + rowMetadata[78] + + rowMetadata[79] + + rowMetadata[80] + + rowMetadata[81] + == 1.0 + ) + + mainTimeSecondsCapped = min(max(self.mainTimeSeconds, 0.0), 3.0 * 86400) + periodTimeSecondsCapped = min(max(self.periodTimeSeconds, 0.0), 1.0 * 86400) + rowMetadata[82] = 0.4 * (math.log(mainTimeSecondsCapped + 60.0) - 6.5) + rowMetadata[83] = 0.3 * (math.log(periodTimeSecondsCapped + 1.0) - 3.0) + byoYomiPeriodsCapped = min(max(self.byoYomiPeriods, 0), 50) + canadianMovesCapped = min(max(self.canadianMoves, 0), 50) + rowMetadata[84] = 0.5 * (math.log(byoYomiPeriodsCapped + 2.0) - 1.5) + rowMetadata[85] = 0.25 * (math.log(canadianMovesCapped + 2.0) - 1.5) + + rowMetadata[86] = 0.5 * math.log(boardArea / 361.0) + + daysDifference = (self.gameDate - datetime.date(1970, 1, 1)).days + DATE_START_IDX = 87 + DATE_LEN = 32 + period = 7.0 + factor = pow(80000.0, 1.0 / (DATE_LEN - 1)) + twopi = 6.283185307179586476925 + for i in range(DATE_LEN): + numRevolutions = daysDifference / period + rowMetadata[DATE_START_IDX + i * 2 + 0] = math.cos(numRevolutions * twopi) + rowMetadata[DATE_START_IDX + i * 2 + 1] = math.sin(numRevolutions * twopi) + period *= factor + + assert DATE_START_IDX + 2 * DATE_LEN == 151 + + assert 0 <= self.source < 16 + rowMetadata[151 + self.source] = 1.0 + + assert 151 + 16 < self.METADATA_INPUT_NUM_CHANNELS + + return rowMetadata diff --git a/training/scripts/dated/20241110-122511/shuffle.py b/training/scripts/dated/20241110-122511/shuffle.py new file mode 100644 index 0000000..8a535d2 --- /dev/null +++ b/training/scripts/dated/20241110-122511/shuffle.py @@ -0,0 +1,814 @@ +#!/usr/bin/python3 +# Shuffles npz selfplay data for training, choosing a window size based on a power law. +# Run 'python shuffle.py --help' for details on how the window size is chosen and how to use this script. +import sys +import os +import argparse +import traceback +import math +import time +import logging +import zipfile +import shutil +import psutil +import json +import hashlib +import datetime +import gc + +import multiprocessing + +import numpy as np + +def assert_keys(npz,include_meta): + keys = [ + "binaryInputNCHWPacked", + "globalInputNC", + "policyTargetsNCMove", + "globalTargetsNC", + "scoreDistrN", + "valueTargetsNCHW", + ] + if include_meta: + keys.append("metadataInputNC") + assert(set(npz.keys()) == set(keys)) + +def is_temp_npz_like(filename): + return "_" in filename + +def joint_shuffle_take_first_n(n,arrs): + for arr in arrs: + assert(len(arr) == len(arrs[0])) + perm = np.random.permutation(len(arrs[0])) + perm = perm[:n] + shuffled_arrs = [] + for arr in arrs: + shuffled_arrs.append(arr[perm]) + return shuffled_arrs + +def memusage_mb(): + return psutil.Process(os.getpid()).memory_info().rss // 1048576 + +def shardify(input_idx, input_file_group, num_out_files, out_tmp_dirs, keep_prob, include_meta): + np.random.seed([int.from_bytes(os.urandom(4), byteorder='little') for i in range(4)]) + + assert(len(input_file_group) > 0) + num_files_not_found = 0 + + if len(input_file_group) == 1: + try: + with np.load(input_file_group[0]) as npz: + assert_keys(npz,include_meta) + ### + # WARNING - if adding anything here, also add it to joint_shuffle below! + ### + binaryInputNCHWPacked = npz["binaryInputNCHWPacked"] + globalInputNC = npz["globalInputNC"] + policyTargetsNCMove = npz["policyTargetsNCMove"] + globalTargetsNC = npz["globalTargetsNC"] + scoreDistrN = npz["scoreDistrN"] + valueTargetsNCHW = npz["valueTargetsNCHW"] + metadataInputNC = npz["metadataInputNC"] if include_meta else None + except FileNotFoundError: + num_files_not_found += 1 + print("WARNING: file not found by shardify: ", input_file_group[0]) + return num_files_not_found # Early quit since we don't know shapes + else: + binaryInputNCHWPackedList = [] + globalInputNCList = [] + policyTargetsNCMoveList = [] + globalTargetsNCList = [] + scoreDistrNList = [] + valueTargetsNCHWList = [] + metadataInputNCList = [] + + for input_file in input_file_group: + try: + with np.load(input_file) as npz: + assert_keys(npz,include_meta) + binaryInputNCHWPackedList.append(npz["binaryInputNCHWPacked"]) + globalInputNCList.append(npz["globalInputNC"]) + policyTargetsNCMoveList.append(npz["policyTargetsNCMove"]) + globalTargetsNCList.append(npz["globalTargetsNC"]) + scoreDistrNList.append(npz["scoreDistrN"]) + valueTargetsNCHWList.append(npz["valueTargetsNCHW"]) + metadataInputNCList.append(npz["metadataInputNC"] if include_meta else None) + except FileNotFoundError: + num_files_not_found += 1 + print("WARNING: file not found by shardify: ", input_file) + pass + if len(binaryInputNCHWPackedList) <= 0: + return num_files_not_found # Early quit since we don't know shapes + + binaryInputNCHWPacked = np.concatenate(binaryInputNCHWPackedList,axis=0) + globalInputNC = np.concatenate(globalInputNCList,axis=0) + policyTargetsNCMove = np.concatenate(policyTargetsNCMoveList,axis=0) + globalTargetsNC = np.concatenate(globalTargetsNCList,axis=0) + scoreDistrN = np.concatenate(scoreDistrNList,axis=0) + valueTargetsNCHW = np.concatenate(valueTargetsNCHWList,axis=0) + metadataInputNC = np.concatenate(metadataInputNCList,axis=0) if include_meta else None + + num_rows_to_keep = binaryInputNCHWPacked.shape[0] + assert(globalInputNC.shape[0] == num_rows_to_keep) + assert(policyTargetsNCMove.shape[0] == num_rows_to_keep) + assert(globalTargetsNC.shape[0] == num_rows_to_keep) + assert(scoreDistrN.shape[0] == num_rows_to_keep) + assert(valueTargetsNCHW.shape[0] == num_rows_to_keep) + assert(metadataInputNC.shape[0] == num_rows_to_keep if include_meta else True) + + if keep_prob < 1.0: + num_rows_to_keep = min(num_rows_to_keep,int(round(num_rows_to_keep * keep_prob))) + + if include_meta: + [binaryInputNCHWPacked,globalInputNC,policyTargetsNCMove,globalTargetsNC,scoreDistrN,valueTargetsNCHW,metadataInputNC] = ( + joint_shuffle_take_first_n( + num_rows_to_keep, + [binaryInputNCHWPacked,globalInputNC,policyTargetsNCMove,globalTargetsNC,scoreDistrN,valueTargetsNCHW,metadataInputNC] + ) + ) + else: + [binaryInputNCHWPacked,globalInputNC,policyTargetsNCMove,globalTargetsNC,scoreDistrN,valueTargetsNCHW] = ( + joint_shuffle_take_first_n( + num_rows_to_keep, + [binaryInputNCHWPacked,globalInputNC,policyTargetsNCMove,globalTargetsNC,scoreDistrN,valueTargetsNCHW] + ) + ) + + assert(binaryInputNCHWPacked.shape[0] == num_rows_to_keep) + assert(globalInputNC.shape[0] == num_rows_to_keep) + assert(policyTargetsNCMove.shape[0] == num_rows_to_keep) + assert(globalTargetsNC.shape[0] == num_rows_to_keep) + assert(scoreDistrN.shape[0] == num_rows_to_keep) + assert(valueTargetsNCHW.shape[0] == num_rows_to_keep) + assert(metadataInputNC.shape[0] == num_rows_to_keep if include_meta else True) + + rand_assts = np.random.randint(num_out_files,size=[num_rows_to_keep]) + counts = np.bincount(rand_assts,minlength=num_out_files) + countsums = np.cumsum(counts) + assert(countsums[len(countsums)-1] == num_rows_to_keep) + + # if input_idx % 29 == 0: + # print("%s: Shardify writing... (mem usage %dMB)" % (str(datetime.datetime.now()),memusage_mb()), flush=True) + + for out_idx in range(num_out_files): + start = countsums[out_idx]-counts[out_idx] + stop = countsums[out_idx] + if include_meta: + np.savez_compressed( + os.path.join(out_tmp_dirs[out_idx], str(input_idx) + ".npz"), + binaryInputNCHWPacked = binaryInputNCHWPacked[start:stop], + globalInputNC = globalInputNC[start:stop], + policyTargetsNCMove = policyTargetsNCMove[start:stop], + globalTargetsNC = globalTargetsNC[start:stop], + scoreDistrN = scoreDistrN[start:stop], + valueTargetsNCHW = valueTargetsNCHW[start:stop], + metadataInputNC = metadataInputNC[start:stop], + ) + else: + np.savez_compressed( + os.path.join(out_tmp_dirs[out_idx], str(input_idx) + ".npz"), + binaryInputNCHWPacked = binaryInputNCHWPacked[start:stop], + globalInputNC = globalInputNC[start:stop], + policyTargetsNCMove = policyTargetsNCMove[start:stop], + globalTargetsNC = globalTargetsNC[start:stop], + scoreDistrN = scoreDistrN[start:stop], + valueTargetsNCHW = valueTargetsNCHW[start:stop], + ) + return num_files_not_found + +def merge_shards(filename, num_shards_to_merge, out_tmp_dir, batch_size, ensure_batch_multiple, output_npz, include_meta): + np.random.seed([int.from_bytes(os.urandom(4), byteorder='little') for i in range(5)]) + + if output_npz: + record_writer = None + else: + assert False, "No longer supports outputting tensorflow data" + + binaryInputNCHWPackeds = [] + globalInputNCs = [] + policyTargetsNCMoves = [] + globalTargetsNCs = [] + scoreDistrNs = [] + valueTargetsNCHWs = [] + metadataInputNCs = [] + + for input_idx in range(num_shards_to_merge): + shard_filename = os.path.join(out_tmp_dir, str(input_idx) + ".npz") + try: + with np.load(shard_filename) as npz: + assert_keys(npz,include_meta) + + binaryInputNCHWPacked = npz["binaryInputNCHWPacked"] + globalInputNC = npz["globalInputNC"] + policyTargetsNCMove = npz["policyTargetsNCMove"] + globalTargetsNC = npz["globalTargetsNC"] + scoreDistrN = npz["scoreDistrN"] + valueTargetsNCHW = npz["valueTargetsNCHW"] + metadataInputNC = npz["metadataInputNC"] if include_meta else None + + binaryInputNCHWPackeds.append(binaryInputNCHWPacked) + globalInputNCs.append(globalInputNC) + policyTargetsNCMoves.append(policyTargetsNCMove) + globalTargetsNCs.append(globalTargetsNC) + scoreDistrNs.append(scoreDistrN) + valueTargetsNCHWs.append(valueTargetsNCHW) + metadataInputNCs.append(metadataInputNC) + except FileNotFoundError: + print("WARNING: Empty shard in merge_shards for shard :", input_idx, filename) + + if len(binaryInputNCHWPackeds) <= 0: + print("WARNING: empty merge file: ", filename) + return 0 + + ### + # WARNING - if adding anything here, also add it to joint_shuffle below! + ### + binaryInputNCHWPacked = np.concatenate(binaryInputNCHWPackeds) + globalInputNC = np.concatenate(globalInputNCs) + policyTargetsNCMove = np.concatenate(policyTargetsNCMoves) + globalTargetsNC = np.concatenate(globalTargetsNCs) + scoreDistrN = np.concatenate(scoreDistrNs) + valueTargetsNCHW = np.concatenate(valueTargetsNCHWs) + metadataInputNC = np.concatenate(metadataInputNCs) if include_meta else None + + num_rows = binaryInputNCHWPacked.shape[0] + assert(globalInputNC.shape[0] == num_rows) + assert(policyTargetsNCMove.shape[0] == num_rows) + assert(globalTargetsNC.shape[0] == num_rows) + assert(scoreDistrN.shape[0] == num_rows) + assert(valueTargetsNCHW.shape[0] == num_rows) + assert(metadataInputNC.shape[0] == num_rows if include_meta else True) + + if include_meta: + [binaryInputNCHWPacked,globalInputNC,policyTargetsNCMove,globalTargetsNC,scoreDistrN,valueTargetsNCHW,metadataInputNC] = ( + joint_shuffle_take_first_n( + num_rows, + [binaryInputNCHWPacked,globalInputNC,policyTargetsNCMove,globalTargetsNC,scoreDistrN,valueTargetsNCHW,metadataInputNC], + ) + ) + else: + [binaryInputNCHWPacked,globalInputNC,policyTargetsNCMove,globalTargetsNC,scoreDistrN,valueTargetsNCHW] = ( + joint_shuffle_take_first_n( + num_rows, + [binaryInputNCHWPacked,globalInputNC,policyTargetsNCMove,globalTargetsNC,scoreDistrN,valueTargetsNCHW], + ) + ) + + assert(binaryInputNCHWPacked.shape[0] == num_rows) + assert(globalInputNC.shape[0] == num_rows) + assert(policyTargetsNCMove.shape[0] == num_rows) + assert(globalTargetsNC.shape[0] == num_rows) + assert(scoreDistrN.shape[0] == num_rows) + assert(valueTargetsNCHW.shape[0] == num_rows) + assert(metadataInputNC.shape[0] == num_rows if include_meta else True) + + # print("%s: Merge writing... (mem usage %dMB)" % (str(datetime.datetime.now()),memusage_mb()), flush=True) + + # Just truncate and lose the batch at the end, it's fine + num_batches = (num_rows // (batch_size * ensure_batch_multiple)) * ensure_batch_multiple + if output_npz: + start = 0 + stop = num_batches*batch_size + if include_meta: + np.savez_compressed( + filename, + binaryInputNCHWPacked = binaryInputNCHWPacked[start:stop], + globalInputNC = globalInputNC[start:stop], + policyTargetsNCMove = policyTargetsNCMove[start:stop], + globalTargetsNC = globalTargetsNC[start:stop], + scoreDistrN = scoreDistrN[start:stop], + valueTargetsNCHW = valueTargetsNCHW[start:stop], + metadataInputNC = metadataInputNC[start:stop], + ) + else: + np.savez_compressed( + filename, + binaryInputNCHWPacked = binaryInputNCHWPacked[start:stop], + globalInputNC = globalInputNC[start:stop], + policyTargetsNCMove = policyTargetsNCMove[start:stop], + globalTargetsNC = globalTargetsNC[start:stop], + scoreDistrN = scoreDistrN[start:stop], + valueTargetsNCHW = valueTargetsNCHW[start:stop], + ) + else: + assert False, "No longer supports outputting tensorflow data" + + jsonfilename = os.path.splitext(filename)[0] + ".json" + with open(jsonfilename,"w") as f: + json.dump({"num_rows":num_rows,"num_batches":num_batches},f) + + if record_writer is not None: + record_writer.close() + return num_batches * batch_size + +def get_numpy_npz_headers(filename): + with zipfile.ZipFile(filename) as z: + wasbad = False + numrows = 0 + npzheaders = {} + for subfilename in z.namelist(): + npyfile = z.open(subfilename) + try: + version = np.lib.format.read_magic(npyfile) + except ValueError: + wasbad = True + print("WARNING: bad file, skipping it: %s (bad array %s)" % (filename,subfilename)) + else: + (shape, is_fortran, dtype) = np.lib.format._read_array_header(npyfile,version) + npzheaders[subfilename] = (shape, is_fortran, dtype) + if wasbad: + return None + return npzheaders + + +def compute_num_rows(filename): + try: + npheaders = get_numpy_npz_headers(filename) + except PermissionError: + print("WARNING: No permissions for reading file: ", filename) + return (filename,None) + except zipfile.BadZipFile: + print("WARNING: Bad zip file: ", filename) + return (filename,None) + if npheaders is None or len(npheaders) <= 0: + print("WARNING: bad npz headers for file: ", filename) + return (filename,None) + + if "binaryInputNCHWPacked" in npheaders: + (shape, is_fortran, dtype) = npheaders["binaryInputNCHWPacked"] + else: + (shape, is_fortran, dtype) = npheaders["binaryInputNCHWPacked.npy"] + num_rows = shape[0] + return (filename,num_rows) + + +class TimeStuff(object): + + def __init__(self,taskstr): + self.taskstr = taskstr + + def __enter__(self): + print("Beginning: %s" % self.taskstr, flush=True) + self.t0 = time.time() + + def __exit__(self, exception_type, exception_val, trace): + self.t1 = time.time() + print("Finished: %s in %s seconds" % (self.taskstr, str(self.t1 - self.t0)), flush=True) + return False + + +if __name__ == '__main__': + parser = argparse.ArgumentParser(add_help=False,formatter_class=argparse.RawTextHelpFormatter,description=""" + Shuffle data files! + + This shuffle script is designed for ongoing self-play training. It shuffles the most recent window of data among the data it's provided. It chooses the window size dynamically based on the total amount of data in the run so far, assuming that the directories provided contain all of the data for the run so far. If you don't actually have all of the data, e.g. you've archived or deleted the older data, or else want to compute the window size as if there were more data, use -add-to-data-rows. + + The window size is a power law based on the number of rows in the run N: + WINDOWSIZE(N) = (N^EXPONENT - MIN_ROWS^EXPONENT) / (EXPONENT * MIN_ROWS^(EXPONENT-1)) * INITIAL_WINDOW_PER_ROW + MIN_ROWS + + given arguments: + -taper-window-exponent EXPONENT \\ + -expand-window-per-row INITIAL_WINDOW_PER_ROW \\ + -min-rows MIN_ROWS (default 250k) + + This may look a bit complex, but basically it is simply the power law N^EXPONENT with shifting and scaling such that: + WINDOWSIZE(MIN_ROWS) = MIN_ROWS + (dWINDOWSIZE/dN)(MIN_ROWS) = INITIAL_WINDOW_PER_ROW + + Reasonable arguments similar to those used for KataGo's main runs would be + -taper-window-exponent 0.65 or 0.675 \\ + -expand-window-per-row 0.4 \\ + -min-rows 250000 (default) + + If you want to control the "scale" of the power law differently than the min rows, you can specify -taper-window-scale as well. + There is also a bit of a hack to cap the number of random rows (rows generated by random play without a neural net), since random row generation at the start of a run can be very fast due to not hitting the GPU, and overpopulate the run. + + Additionally, NOT all of the shuffled window is output, only a random shuffled 20M rows will be kept. Adjust this using -keep-target-rows. The intention is that this script will be repeatedly run as new data comes in, such that well before train.py would need more than 20M rows, the data would have been shuffled again and a new random 20M rows chosen. + + If you are NOT doing ongoing self-play training, but simply want to shuffle an entire dataset (not just a window of it) and want to output all of it (not just 20M of it) then you can use arguments like: + -taper-window-exponent 1.0 \\ + -expand-window-per-row 1.0 \\ + -keep-target-rows SOME_VERY_LARGE_NUMBER + + If you ARE doing ongoing self-play training, but want a fixed window size, then you can use arguments like: + -min-rows YOUR_DESIRED_SIZE \\ + -taper-window-exponent 1.0 \\ + -expand-window-per-row 0.0 + """) + parser.add_argument('dirs', metavar='DIR', nargs='+', help='Directories of training data files') + + required_args = parser.add_argument_group('required arguments') + optional_args = parser.add_argument_group('optional arguments') + optional_args.add_argument( + '-h', + '--help', + action='help', + default=argparse.SUPPRESS, + help='show this help message and exit' + ) + optional_args.add_argument('-min-rows', type=int, required=False, help='Minimum training rows to use, default 250k') + optional_args.add_argument('-max-rows', type=int, required=False, help='Maximum training rows to use, default unbounded') + optional_args.add_argument('-keep-target-rows', type=int, required=False, help='Target number of rows to actually keep in the final data set, default 20M') + required_args.add_argument('-expand-window-per-row', type=float, required=True, help='Beyond min rows, initially expand the window by this much every post-random data row') + required_args.add_argument('-taper-window-exponent', type=float, required=True, help='Make the window size asymtotically grow as this power of the data rows') + optional_args.add_argument('-taper-window-scale', type=float, required=False, help='The scale at which the power law applies, defaults to -min-rows') + optional_args.add_argument('-add-to-data-rows', type=float, required=False, help='Compute the window size as if the number of data rows were this much larger/smaller') + optional_args.add_argument('-add-to-window-size', type=float, required=False, help='DEPRECATED due to being misnamed name, use -add-to-data-rows') + optional_args.add_argument('-summary-file', required=False, help='Summary json file for directory contents') + required_args.add_argument('-out-dir', required=True, help='Dir to output training files') + required_args.add_argument('-out-tmp-dir', required=True, help='Dir to use as scratch space') + optional_args.add_argument('-approx-rows-per-out-file', type=int, required=False, default=70000, help='Number of rows per output file, default 70k') + required_args.add_argument('-num-processes', type=int, required=True, help='Number of multiprocessing processes for shuffling in parallel') + required_args.add_argument('-batch-size', type=int, required=True, help='Batch size to write training examples in') + optional_args.add_argument('-ensure-batch-multiple', type=int, required=False, help='Ensure each file is a multiple of this many batches') + optional_args.add_argument('-worker-group-size', type=int, required=False, default=80000, help='Internally, target having many rows per parallel sharding worker (doesnt affect merge)') + optional_args.add_argument('-exclude', required=False, help='Text file with npzs to ignore, one per line') + optional_args.add_argument('-exclude-prefix', required=False, help='Prefix to concat to lines in exclude to produce the full file path') + optional_args.add_argument('-exclude-basename', required=False, action="store_true", help='Consider an exclude to match if basename matches') + optional_args.add_argument('-only-include-md5-path-prop-lbound', type=float, required=False, help='Just before sharding, include only filepaths hashing to float >= this') + optional_args.add_argument('-only-include-md5-path-prop-ubound', type=float, required=False, help='Just before sharding, include only filepaths hashing to float < this') + optional_args.add_argument('-output-npz', action="store_true", required=False, help='Output results as npz files') + optional_args.add_argument('-include-meta', action="store_true", required=False, help='Include sgf metadata inputs') + + args = parser.parse_args() + dirs = args.dirs + min_rows = args.min_rows + max_rows = args.max_rows + keep_target_rows = args.keep_target_rows + expand_window_per_row = args.expand_window_per_row + taper_window_exponent = args.taper_window_exponent + taper_window_scale = args.taper_window_scale + add_to_data_rows = args.add_to_data_rows + if args.add_to_data_rows is not None and args.add_to_window_size is not None: + print("Cannot specify both -add-to-data-rows and -add-to-window-size. Please use only -add-to-data-rows, -add-to-window-size is deprecated") + if args.add_to_data_rows is None and args.add_to_window_size is not None: + print("WARNING: -add-to-window-size is deprecated due to being misnamed, use -add-to-data-rows") + add_to_data_rows = args.add_to_window_size + + summary_file = args.summary_file + out_dir = args.out_dir + out_tmp_dir = args.out_tmp_dir + approx_rows_per_out_file = args.approx_rows_per_out_file + num_processes = args.num_processes + batch_size = args.batch_size + ensure_batch_multiple = 1 + if args.ensure_batch_multiple is not None: + ensure_batch_multiple = args.ensure_batch_multiple + worker_group_size = args.worker_group_size + exclude = args.exclude + exclude_prefix = args.exclude_prefix + if exclude_prefix is None: + exclude_prefix = "" + exclude_basename = args.exclude_basename + only_include_md5_path_prop_lbound = args.only_include_md5_path_prop_lbound + only_include_md5_path_prop_ubound = args.only_include_md5_path_prop_ubound + output_npz = args.output_npz + include_meta = args.include_meta + + if min_rows is None: + print("NOTE: -min-rows was not specified, defaulting to requiring 250K rows before shuffling.") + min_rows = 250000 + if keep_target_rows is None: + print("NOTE: -keep-target-rows was not specified, defaulting to sampling a random 20M rows out of the computed window.") + print("If you intended to shuffle the whole dataset instead, pass in -keep-target-rows ") + keep_target_rows = 20000000 + if add_to_data_rows is None: + add_to_data_rows = 0 + + summary_data_by_dirpath = {} + if summary_file is not None: + with TimeStuff("Loading " + summary_file): + # Try a bunch of times, just to be robust to if the file is being swapped out in nfs + for i in range(10): + success = False + try: + with open(summary_file) as fp: + summary_data_by_dirpath = json.load(fp) + success = True + except OSError: + success = False + except ValueError: + success = False + if success: + break + time.sleep(1) + if not success: + raise RuntimeError("Could not load summary file") + + exclude_set = set() + if exclude is not None: + with TimeStuff("Loading " + exclude): + # Try a bunch of times, just to be robust to if the file is being swapped out in nfs + for i in range(10): + success = False + try: + with open(exclude,"r") as exclude_in: + excludes = exclude_in.readlines() + excludes = [x.strip() for x in excludes] + excludes = [x for x in excludes if len(x) > 0] + excludes = [exclude_prefix + x for x in excludes] + exclude_set = set(excludes) + success = True + except OSError: + success = False + except ValueError: + success = False + if success: + break + time.sleep(1) + if not success: + raise RuntimeError("Could not load summary file") + + # If excluding basenames, also add them to the set + if exclude_basename: + basenames = [os.path.basename(path) for path in exclude_set] + exclude_set.update(basenames) + + all_files = [] + files_with_unknown_num_rows = [] + excluded_count = 0 + excluded_due_to_excludes_count = 0 + tempfilelike_count = 0 + with TimeStuff("Finding files"): + for d in dirs: + for (path,dirnames,filenames) in os.walk(d, followlinks=True): + i = 0 + while i < len(dirnames): + dirname = dirnames[i] + summary_data = summary_data_by_dirpath.get(os.path.abspath(os.path.join(path, dirname))) + if summary_data is not None: + filename_mtime_num_rowss = summary_data["filename_mtime_num_rowss"] + del dirnames[i] + i -= 1 + for (filename,mtime,num_rows) in filename_mtime_num_rowss: + if is_temp_npz_like(filename): + # print("WARNING: file looks like a temp file, treating as exclude: ", os.path.join(path,dirname,filename)) + excluded_count += 1 + tempfilelike_count += 1 + continue + if exclude_basename and os.path.basename(filename) in exclude_set: + excluded_count += 1 + excluded_due_to_excludes_count += 1 + continue + filename = os.path.join(path,dirname,filename) + if not exclude_basename and filename in exclude_set: + excluded_count += 1 + excluded_due_to_excludes_count += 1 + continue + if num_rows is None: + print("WARNING: Skipping bad rowless file, treating as exclude: ", filename) + excluded_count += 1 + continue + all_files.append((filename,mtime,num_rows)) + i += 1 + + filtered_filenames = [] + for filename in filenames: + if not filename.endswith(".npz"): + continue + if is_temp_npz_like(filename): + # print("WARNING: file looks like a temp file, treating as exclude: ", os.path.join(path,filename)) + excluded_count += 1 + tempfilelike_count += 1 + continue + if exclude_basename and os.path.basename(filename) in exclude_set: + excluded_count += 1 + excluded_due_to_excludes_count += 1 + continue + filename = os.path.join(path,filename) + if not exclude_basename and filename in exclude_set: + excluded_count += 1 + excluded_due_to_excludes_count += 1 + continue + filtered_filenames.append(filename) + filenames = filtered_filenames + + files_with_unknown_num_rows.extend(filenames) + filenames = [(filename,os.path.getmtime(filename)) for filename in filenames] + all_files.extend(filenames) + print("Total number of files: %d" % len(all_files), flush=True) + print("Total number of files with unknown row count: %d" % len(files_with_unknown_num_rows), flush=True) + print("Excluded count: %d" % excluded_count, flush=True) + print("Excluded count due to looking like temp file: %d" % tempfilelike_count, flush=True) + print("Excluded count due to cmdline excludes file: %d" % excluded_due_to_excludes_count, flush=True) + + print("GC collect", flush=True) + del summary_data_by_dirpath + gc.collect() + + with TimeStuff("Sorting"): + all_files.sort(key=(lambda x: x[1]), reverse=False) + + # Wait a few seconds just in case to limit the chance of filesystem races, now that we know exactly + # the set of filenames we want + time.sleep(3) + + with TimeStuff("Computing rows for unsummarized files"): + with multiprocessing.Pool(num_processes) as pool: + results = pool.map(compute_num_rows,files_with_unknown_num_rows) + results = dict(results) + for i in range(len(all_files)): + info = all_files[i] + if len(info) < 3: + num_rows = results[info[0]] + all_files[i] = (info[0], info[1], num_rows) + + num_rows_total = 0 # Number of data rows + num_random_rows_capped = 0 # Number of random data rows, capped at min_rows - we never keep more than min_rows many data rows if they're from random. + num_postrandom_rows = 0 # Number of NON-random rows + + # How far offset do we start on the power-law window tail? E.g. what number of postrandom rows do we need before the window size grows by a factor + # of 2^(taper_window_exponent)? For now, we set it equal to the min rows + if taper_window_scale is not None: + window_taper_offset = taper_window_scale + else: + window_taper_offset = min_rows + + def num_usable_rows(): + global num_random_rows_capped + global num_postrandom_rows + return num_random_rows_capped + num_postrandom_rows + def num_desired_rows(): + # Every post-random row moves one row beyond window_taper_offset + power_law_x = num_usable_rows() - min_rows + window_taper_offset + add_to_data_rows + # Apply power law and correct for window_taper_offset so we're still anchored at 0 + unscaled_power_law = (power_law_x ** taper_window_exponent) - (window_taper_offset ** taper_window_exponent) + # Scale so that we have an initial derivative of 1 + scaled_power_law = unscaled_power_law / (taper_window_exponent * (window_taper_offset ** (taper_window_exponent-1))) + # Scale so that we have the desired initial slope, and add back the minimum random rows + return int(scaled_power_law * expand_window_per_row + min_rows) + + with TimeStuff("Processing found files"): + for (filename,mtime,num_rows) in all_files: + if num_rows is None: + print("WARNING: Skipping bad file: ", filename) + continue + if num_rows <= 0: + continue + num_rows_total += num_rows + if "random/tdata/" not in filename and "random\\tdata\\" not in filename: + num_postrandom_rows += num_rows + else: + num_random_rows_capped = min(num_random_rows_capped + num_rows, min_rows) + + if os.path.exists(out_dir): + raise Exception(out_dir + " already exists") + os.mkdir(out_dir) + + if num_rows_total <= 0: + print("No rows found") + sys.exit(0) + + # If we don't have enough rows, then quit out + if num_rows_total < min_rows: + print("Not enough rows, only %d (fewer than %d)" % (num_rows_total,min_rows)) + sys.exit(0) + + print("Total rows found: %d (%d usable)" % (num_rows_total,num_usable_rows()), flush=True) + + # Reverse so that recent files are first + all_files.reverse() + + # Now assemble only the files we need to hit our desired window size + desired_num_rows = num_desired_rows() + desired_num_rows = max(desired_num_rows,min_rows) + desired_num_rows = min(desired_num_rows,max_rows) if max_rows is not None else desired_num_rows + print("Desired num rows: %d / %d" % (desired_num_rows,num_rows_total), flush=True) + + desired_input_files = [] + min_start_row = num_rows_total + max_end_row = num_rows_total + num_rows_used = 0 + print_stride = 1 + len(all_files) // 80 + end_row = num_rows_total + with TimeStuff("Computing desired rows"): + for i in range(len(all_files)): + (filename,mtime,num_rows) = all_files[i] + + # This could happen if the .summary.json file is inaccurate after file deletions + # Actually we just handle that in shardify - and accept that it might make our window slightly not far back enough + # if not os.path.exists(filename): + # continue + + if num_rows is not None and num_rows > 0: + desired_input_files.append((filename,num_rows)) + start_row = end_row - num_rows + min_start_row = min(start_row, min_start_row) + num_rows_used += num_rows + end_row -= num_rows + else: + start_row = end_row + + if i % print_stride == 0 or num_rows_used >= desired_num_rows: + print("Using: %s (%d-%d) (%d/%d desired rows)" % (filename,start_row,end_row,num_rows_used,desired_num_rows), flush=True) + if num_rows_used >= desired_num_rows: + break + + print("Finally, using: (%d-%d) (%d/%d desired rows)" % (min_start_row,max_end_row,num_rows_used,desired_num_rows), flush=True) + + print("GC collect", flush=True) + del all_files + gc.collect() + + np.random.seed() + np.random.shuffle(desired_input_files) + + approx_rows_to_keep = num_rows_used + if keep_target_rows is not None: + approx_rows_to_keep = min(approx_rows_to_keep, keep_target_rows) + keep_prob = approx_rows_to_keep / num_rows_used + + num_out_files = int(round(approx_rows_to_keep / approx_rows_per_out_file)) + num_out_files = max(num_out_files,1) + + if output_npz: + out_files = [os.path.join(out_dir, "data%d.npz" % i) for i in range(num_out_files)] + else: + assert False, "No longer supports outputting tensorflow data" + + out_tmp_dirs = [os.path.join(out_tmp_dir, "tmp.shuf%d" % i) for i in range(num_out_files)] + print("Writing %d output files with %d kept / %d desired rows" % (num_out_files, approx_rows_to_keep, desired_num_rows), flush=True) + + def clean_tmp_dirs(): + for tmp_dir in out_tmp_dirs: + if os.path.exists(tmp_dir): + print("Cleaning up tmp dir: " + tmp_dir) + shutil.rmtree(tmp_dir) + + clean_tmp_dirs() + for tmp_dir in out_tmp_dirs: + os.mkdir(tmp_dir) + + num_rows_in_desired_files = 0 + if only_include_md5_path_prop_lbound is not None or only_include_md5_path_prop_ubound is not None: + new_desired_input_files = [] + for (input_file,num_rows_in_file) in desired_input_files: + input_file_base = os.path.basename(input_file) + hashfloat = int("0x"+hashlib.md5(str(input_file_base).encode('utf-8')).hexdigest()[:13],16) / 2 ** 52 + ok = True + if only_include_md5_path_prop_lbound is not None and hashfloat < only_include_md5_path_prop_lbound: + ok = False + if only_include_md5_path_prop_ubound is not None and hashfloat >= only_include_md5_path_prop_ubound: + ok = False + if ok: + new_desired_input_files.append((input_file,num_rows_in_file)) + num_rows_in_desired_files += num_rows_in_file + print("Due to only_include_md5, filtering down to %d/%d files" % (len(new_desired_input_files),len(desired_input_files))) + desired_input_files = new_desired_input_files + else: + for (input_file,num_rows_in_file) in desired_input_files: + num_rows_in_desired_files += num_rows_in_file + + if len(desired_input_files) <= 0: + print("No files after filtering for desired range") + sys.exit(0) + if num_rows_in_desired_files <= 0: + print("No rows in desired files") + sys.exit(0) + + # Clump files into sharding groups. More efficient if shuffling a ton of small npz files + # since we aren't doing separate tasks for every individual file but rather handling a bunch + # of files at once, and also makes chunkier shards on disk when it comes time to shuffle. + desired_input_file_groups = [] + group_size_so_far = 0 + group_so_far = [] + for (input_file,num_rows_in_file) in desired_input_files: + if num_rows_in_file <= 0: + continue + group_so_far.append(input_file) + group_size_so_far += num_rows_in_file + if group_size_so_far >= worker_group_size: + desired_input_file_groups.append(group_so_far) + group_so_far = [] + group_size_so_far = 0 + if group_size_so_far > 0: + desired_input_file_groups.append(group_so_far) + group_so_far = [] + group_size_so_far = 0 + print("Grouping %d input files into %d sharding groups" % (len(desired_input_files),len(desired_input_file_groups)),flush=True) + + with multiprocessing.Pool(num_processes) as pool: + with TimeStuff("Sharding"): + shard_results = pool.starmap(shardify, [ + (input_idx, desired_input_file_groups[input_idx], num_out_files, out_tmp_dirs, keep_prob, include_meta) + for input_idx in range(len(desired_input_file_groups)) + ]) + + with TimeStuff("Merging"): + num_shards_to_merge = len(desired_input_file_groups) + merge_results = pool.starmap(merge_shards, [ + (out_files[idx],num_shards_to_merge,out_tmp_dirs[idx],batch_size,ensure_batch_multiple,output_npz,include_meta) + for idx in range(len(out_files)) + ]) + print("Number of rows by output file:",flush=True) + print(list(zip(out_files,merge_results)),flush=True) + sys.stdout.flush() + + clean_tmp_dirs() + + dump_value = { + "range": (min_start_row, max_end_row) + } + + with open(out_dir + ".json", 'w') as f: + json.dump(dump_value, f) diff --git a/training/scripts/dated/20241110-122511/shuffle.sh b/training/scripts/dated/20241110-122511/shuffle.sh new file mode 100644 index 0000000..14c3b08 --- /dev/null +++ b/training/scripts/dated/20241110-122511/shuffle.sh @@ -0,0 +1,122 @@ +#!/bin/bash -eu +set -o pipefail +{ +# Shuffles and copies selfplay training from selfplay/ to shuffleddata/current/ +# Should be run periodically. + +if [[ $# -lt 4 ]] +then + echo "Usage: $0 BASEDIR TMPDIR NTHREADS BATCHSIZE" + echo "Currently expects to be run from within the 'python' directory of the KataGo repo, or otherwise in the same dir as shuffle.py." + echo "BASEDIR containing selfplay data and models and related directories" + echo "TMPDIR scratch space, ideally on fast local disk, unique to this loop" + echo "NTHREADS number of parallel threads/processes to use in shuffle" + echo "BATCHSIZE number of samples to concat together per batch for training" + exit 0 +fi +BASEDIR="$1" +shift +TMPDIR="$1" +shift +NTHREADS="$1" +shift +BATCHSIZE="$1" +shift + +#------------------------------------------------------------------------------ + +OUTDIR=$(date "+%Y%m%d-%H%M%S") +OUTDIRTRAIN=$OUTDIR/train +OUTDIRVAL=$OUTDIR/val + +mkdir -p "$BASEDIR"/shuffleddata/$OUTDIR +mkdir -p "$TMPDIR"/train +mkdir -p "$TMPDIR"/val + +echo "Beginning shuffle at" $(date "+%Y-%m-%d %H:%M:%S") + +if [[ -n "${SKIP_VALIDATE:-}" ]] +then + ( + time python3 ./shuffle.py \ + "$BASEDIR"/selfplay/ \ + -expand-window-per-row 0.4 \ + -taper-window-exponent 0.65 \ + -out-dir "$BASEDIR"/shuffleddata/$OUTDIRTRAIN \ + -out-tmp-dir "$TMPDIR"/train \ + -approx-rows-per-out-file 70000 \ + -num-processes "$NTHREADS" \ + -batch-size "$BATCHSIZE" \ + -only-include-md5-path-prop-lbound 0.00 \ + -only-include-md5-path-prop-ubound 1.00 \ + -output-npz \ + "$@" \ + 2>&1 | tee "$BASEDIR"/shuffleddata/$OUTDIR/outtrain.txt & + + wait + ) +else + # Randomly peels off 5% of files generated by selfplay as validation data + ( + time python3 ./shuffle.py \ + "$BASEDIR"/selfplay/ \ + -expand-window-per-row 0.4 \ + -taper-window-exponent 0.65 \ + -out-dir "$BASEDIR"/shuffleddata/$OUTDIRVAL \ + -out-tmp-dir "$TMPDIR"/val \ + -approx-rows-per-out-file 70000 \ + -num-processes "$NTHREADS" \ + -batch-size "$BATCHSIZE" \ + -only-include-md5-path-prop-lbound 0.95 \ + -only-include-md5-path-prop-ubound 1.00 \ + -output-npz \ + "$@" \ + 2>&1 | tee "$BASEDIR"/shuffleddata/$OUTDIR/outval.txt & + + wait + ) + ( + time python3 ./shuffle.py \ + "$BASEDIR"/selfplay/ \ + -expand-window-per-row 0.4 \ + -taper-window-exponent 0.65 \ + -out-dir "$BASEDIR"/shuffleddata/$OUTDIRTRAIN \ + -out-tmp-dir "$TMPDIR"/train \ + -approx-rows-per-out-file 70000 \ + -num-processes "$NTHREADS" \ + -batch-size "$BATCHSIZE" \ + -only-include-md5-path-prop-lbound 0.00 \ + -only-include-md5-path-prop-ubound 0.95 \ + -output-npz \ + "$@" \ + 2>&1 | tee "$BASEDIR"/shuffleddata/$OUTDIR/outtrain.txt & + + wait + ) +fi +#set +x + +# Just in case, give a little time for nfs +sleep 10 + +# rm if it already exists +rm -f "$BASEDIR"/shuffleddata/current_tmp + +ln -s $OUTDIR "$BASEDIR"/shuffleddata/current_tmp +mv -Tf "$BASEDIR"/shuffleddata/current_tmp "$BASEDIR"/shuffleddata/current + +# CLEANUP --------------------------------------------------------------- + +# Among shuffled dirs older than 2 hours, remove all but the most recent 5 of them. +# This should be VERY conservative and allow plenty of time for the training to switch +# to newer ones as they get generated. +echo "Cleaning up any old dirs" +find "$BASEDIR"/shuffleddata/ -mindepth 1 -maxdepth 1 -type d -mmin +120 | sort | head -n -5 | xargs --no-run-if-empty rm -r + +echo "Finished shuffle at" $(date "+%Y-%m-%d %H:%M:%S") +# Make a little space between shuffles +echo "" +echo "" + +exit 0 +} diff --git a/training/scripts/dated/20241110-122511/shuffle_and_export_loop.sh b/training/scripts/dated/20241110-122511/shuffle_and_export_loop.sh new file mode 100644 index 0000000..d9ae159 --- /dev/null +++ b/training/scripts/dated/20241110-122511/shuffle_and_export_loop.sh @@ -0,0 +1,62 @@ +#!/bin/bash -eu +set -o pipefail +{ +if [[ $# -lt 6 ]] +then + echo "Usage: $0 NAMEPREFIX BASEDIR TMPDIR NTHREADS BATCHSIZE USEGATING" + echo "NAMEPREFIX string prefix for this training run, try to pick something globally unique. Will be displayed to users when KataGo loads the model." + echo "BASEDIR containing selfplay data and models and related directories" + echo "TMPDIR scratch space, ideally on fast local disk, unique to this loop" + echo "NTHREADS number of parallel threads/processes to use in shuffle" + echo "BATCHSIZE number of samples to concat together per batch for training, must match training" + echo "USEGATING = 1 to use gatekeeper, 0 to not use gatekeeper" + exit 0 +fi +NAMEPREFIX="$1" +shift +BASEDIRRAW="$1" +shift +TMPDIRRAW="$1" +shift +NTHREADS="$1" +shift +BATCHSIZE="$1" +shift +USEGATING="$1" +shift + +GITROOTDIR="$(git rev-parse --show-toplevel)" + +basedir="$(realpath "$BASEDIRRAW")" +tmpdir="$(realpath "$TMPDIRRAW")" + +mkdir -p "$basedir"/scripts +mkdir -p "$basedir"/logs +cp "$GITROOTDIR"/python/*.py "$GITROOTDIR"/python/selfplay/*.sh "$basedir"/scripts + +# For archival and logging purposes - you can look back and see exactly the python code on a particular date +DATE_FOR_FILENAME=$(date "+%Y%m%d-%H%M%S") +DATED_ARCHIVE="$basedir"/scripts/dated/"$DATE_FOR_FILENAME" +mkdir -p "$DATED_ARCHIVE" +cp "$GITROOTDIR"/python/*.py "$GITROOTDIR"/python/selfplay/*.sh "$DATED_ARCHIVE" + +( + cd "$basedir"/scripts + while true + do + ./shuffle.sh "$basedir" "$tmpdir" "$NTHREADS" "$BATCHSIZE" "$@" + sleep 20 + done +) >> "$basedir"/logs/outshuffle.txt 2>&1 & disown + +( + cd "$basedir"/scripts + while true + do + ./export_model_for_selfplay.sh "$NAMEPREFIX" "$basedir" "$USEGATING" + sleep 10 + done +) >> "$basedir"/logs/outexport.txt 2>&1 & disown + +exit 0 +} diff --git a/training/scripts/dated/20241110-122511/shuffle_loop.sh b/training/scripts/dated/20241110-122511/shuffle_loop.sh new file mode 100644 index 0000000..6f75b0c --- /dev/null +++ b/training/scripts/dated/20241110-122511/shuffle_loop.sh @@ -0,0 +1,60 @@ +#!/bin/bash -eu +set -o pipefail +{ +if [[ $# -lt 4 ]] +then + echo "Usage: $0 BASEDIR TMPDIR NTHREADS BATCHSIZE" + echo "Currently expects to be run from within the 'python' directory of the KataGo repo, or otherwise in the same dir as export_model.py." + echo "BASEDIR containing selfplay data and models and related directories" + echo "TMPDIR scratch space, ideally on fast local disk, unique to this loop" + echo "NTHREADS number of parallel threads/processes to use in shuffle" + echo "BATCHSIZE number of samples to concat together per batch for training, must match training" + exit 0 +fi +BASEDIRRAW="$1" +shift +TMPDIRRAW="$1" +shift +NTHREADS="$1" +shift +BATCHSIZE="$1" +shift + +GITROOTDIR="$(git rev-parse --show-toplevel)" + +basedir="$(realpath "$BASEDIRRAW")" +tmpdir="$(realpath "$TMPDIRRAW")" + +mkdir -p "$basedir"/scripts +mkdir -p "$basedir"/logs +cp "$GITROOTDIR"/python/*.py "$GITROOTDIR"/python/selfplay/*.sh "$GITROOTDIR"/python/selfplay/distributed/*.sh "$basedir"/scripts + +# For archival and logging purposes - you can look back and see exactly the python code on a particular date +DATE_FOR_FILENAME=$(date "+%Y%m%d-%H%M%S") +DATED_ARCHIVE="$basedir"/scripts/dated/"$DATE_FOR_FILENAME" +mkdir -p "$DATED_ARCHIVE" +cp "$GITROOTDIR"/python/*.py "$DATED_ARCHIVE" +cp -r "$GITROOTDIR"/python/selfplay "$DATED_ARCHIVE" + + +( + cd "$basedir"/scripts + while true + do + rm -f "$basedir"/selfplay.summary.json.tmp + time python3 ./summarize_old_selfplay_files.py "$basedir"/selfplay/ \ + -old-summary-file-to-assume-correct "$basedir"/selfplay.summary.json \ + -new-summary-file "$basedir"/selfplay.summary.json.tmp + mv "$basedir"/selfplay.summary.json.tmp "$basedir"/selfplay.summary.json + sleep 10 + + for i in {1..10} + do + ./shuffle.sh "$basedir" "$tmpdir" "$NTHREADS" "$BATCHSIZE" -summary-file "$basedir"/selfplay.summary.json "$@" + sleep 600 + done + done +) >> "$basedir"/logs/outshuffle.txt 2>&1 & disown + +exit 0 +} diff --git a/training/scripts/dated/20241110-122511/summarize_old_selfplay_files.py b/training/scripts/dated/20241110-122511/summarize_old_selfplay_files.py new file mode 100644 index 0000000..712374b --- /dev/null +++ b/training/scripts/dated/20241110-122511/summarize_old_selfplay_files.py @@ -0,0 +1,194 @@ +#!/usr/bin/python3 +import sys +import os +import argparse +import traceback +import time +import logging +import zipfile +import json +import datetime +import dateutil.parser +import shutil + +import multiprocessing + +import numpy as np + +def get_numpy_npz_headers(filename): + with zipfile.ZipFile(filename) as z: + wasbad = False + numrows = 0 + npzheaders = {} + for subfilename in z.namelist(): + npyfile = z.open(subfilename) + try: + version = np.lib.format.read_magic(npyfile) + except ValueError: + wasbad = True + print("WARNING: bad file, skipping it: %s (bad array %s)" % (filename,subfilename)) + else: + (shape, is_fortran, dtype) = np.lib.format._read_array_header(npyfile,version) + npzheaders[subfilename] = (shape, is_fortran, dtype) + if wasbad: + return None + return npzheaders + +def is_temp_npz_like(filename): + return "_" in filename + +def summarize_dir(dirpath): + filenames = [filename for filename in os.listdir(dirpath) if filename.endswith('.npz')] + + num_rows_this_dir = 0 + filename_mtime_num_rowss = [] + for filename in filenames: + filepath = os.path.join(dirpath,filename) + mtime = os.path.getmtime(filepath) + + # Files that look like they are temp files should be recorded and warned + if is_temp_npz_like(filename): + print("WARNING: file looks like a temp file: ", filepath) + filename_mtime_num_rowss.append((filename,mtime,None)) + continue + + try: + npheaders = get_numpy_npz_headers(filepath) + except PermissionError: + print("WARNING: No permissions for reading file: ", filepath) + filename_mtime_num_rowss.append((filename,mtime,None)) + continue + except zipfile.BadZipFile: + print("WARNING: Bad zip file: ", filepath) + filename_mtime_num_rowss.append((filename,mtime,None)) + continue + + if npheaders is None or len(npheaders) <= 0: + print("WARNING: bad npz headers for file: ", filepath) + filename_mtime_num_rowss.append((filename,mtime,None)) + continue + + if "binaryInputNCHWPacked" in npheaders: + (shape, is_fortran, dtype) = npheaders["binaryInputNCHWPacked"] + else: + (shape, is_fortran, dtype) = npheaders["binaryInputNCHWPacked.npy"] + num_rows = shape[0] + num_rows_this_dir += num_rows + + filename_mtime_num_rowss.append((filename,mtime,num_rows)) + + print("Summarizing new dir with %d rows: %s" % (num_rows_this_dir,dirpath),flush=True) + return (dirpath, filename_mtime_num_rowss, num_rows_this_dir) + + +class TimeStuff(object): + + def __init__(self,taskstr): + self.taskstr = taskstr + + def __enter__(self): + print("Beginning: %s" % self.taskstr, flush=True) + self.t0 = time.time() + + def __exit__(self, exception_type, exception_val, trace): + self.t1 = time.time() + print("Finished: %s in %s seconds" % (self.taskstr, str(self.t1 - self.t0)), flush=True) + return True + + +if __name__ == '__main__': + parser = argparse.ArgumentParser(description='Shuffle data files') + parser.add_argument('dirs', metavar='DIR', nargs='+', help='Directories of training data files') + parser.add_argument('-old-summary-file-to-assume-correct', required=False, help='Summary json file for directory contents') + parser.add_argument('-new-summary-file', required=True, help='Summary json file for directory contents') + parser.add_argument('-num-parallel-processes', required=False, type=int, help='Number of parallel processes to use, default 4') + + args = parser.parse_args() + dirs = args.dirs + old_summary_file_to_assume_correct = args.old_summary_file_to_assume_correct + new_summary_file = args.new_summary_file + + num_processes = 4 + if args.num_parallel_processes is not None: + num_processes = args.num_parallel_processes + + summary_data_by_dirpath = {} + if old_summary_file_to_assume_correct is not None and os.path.exists(old_summary_file_to_assume_correct): + with TimeStuff("Loading " + old_summary_file_to_assume_correct): + with open(old_summary_file_to_assume_correct) as fp: + summary_data_by_dirpath = json.load(fp) + + # Update old summary data into new format + dirpaths = list(summary_data_by_dirpath.keys()) + format_was_updated = False + for dirpath in dirpaths: + if "dir_mtime" not in summary_data_by_dirpath[dirpath]: + format_was_updated = True + filename_mtime_num_rowss = summary_data_by_dirpath[dirpath] + summary_data_by_dirpath[dirpath] = { + "dir_mtime": os.path.getmtime(dirpath), + "filename_mtime_num_rowss": filename_mtime_num_rowss, + } + + dirs_to_handle = [] + with TimeStuff("Finding files"): + for d in dirs: + for (path,dirnames,filenames) in os.walk(d, followlinks=True): + had_no_dirnames = len(dirnames) == 0 + i = 0 + while i < len(dirnames): + dirname = dirnames[i] + dirpath = os.path.normpath(os.path.join(path, dirname)) + if dirpath in summary_data_by_dirpath: + if os.path.getmtime(dirpath) == summary_data_by_dirpath[dirpath]["dir_mtime"]: + # Skip + del dirnames[i] + continue + + if dirname == "tdata": + # Handle this dir and don't recurse further + del dirnames[i] + dirs_to_handle.append(dirpath) + continue + + else: + parseddate = None + try: + parseddate = dateutil.parser.parse(dirname) + except ValueError: + parseddate = None + if parseddate is not None and parseddate < datetime.datetime.now() - datetime.timedelta(days=2.0): + # Handle this dir and don't recurse further + del dirnames[i] + dirs_to_handle.append(dirpath) + continue + + i += 1 + + with TimeStuff("Parallel summarizing %d dirs" % len(dirs_to_handle)): + with multiprocessing.Pool(num_processes) as pool: + results = pool.map(summarize_dir,dirs_to_handle) + + num_total_rows = 0 + with TimeStuff("Merging %d results" % len(results)): + for result in results: + if result is None: + continue + (dirpath, filename_mtime_num_rowss, num_rows_this_dir) = result + num_total_rows += num_rows_this_dir + summary_data_by_dirpath[os.path.abspath(dirpath)] = { + "dir_mtime": os.path.getmtime(os.path.abspath(dirpath)), + "filename_mtime_num_rowss": filename_mtime_num_rowss, + } + + if len(dirs_to_handle) == 0 and old_summary_file_to_assume_correct is not None and os.path.exists(old_summary_file_to_assume_correct) and not format_was_updated: + shutil.copy(old_summary_file_to_assume_correct,new_summary_file) + print("Not writing any new summary, no results, just copying old file") + else: + with TimeStuff("Writing result"): + with open(new_summary_file,"w") as fp: + json.dump(summary_data_by_dirpath,fp) + print("Summary file written adding %d additional rows: %s" % (num_total_rows,new_summary_file),flush=True) + + print("Done computing new summary",flush=True) + sys.stdout.flush() diff --git a/training/scripts/dated/20241110-122511/summarize_sgfs.py b/training/scripts/dated/20241110-122511/summarize_sgfs.py new file mode 100644 index 0000000..6b21f38 --- /dev/null +++ b/training/scripts/dated/20241110-122511/summarize_sgfs.py @@ -0,0 +1,128 @@ +import argparse +import elo +from elo import GameRecord +import itertools +import math +import os +import re + +from dataclasses import dataclass +from sgfmill import sgf +from typing import List, Dict, Tuple, Set, Sequence + +class GoGameResultSummary(elo.GameResultSummary): + + def __init__( + self, + elo_prior_games: float, + estimate_first_player_advantage: bool, + ): + super().__init__(elo_prior_games, estimate_first_player_advantage) + self._should_warn_handicap_komi = False + + # @override + def print_elos(self): + super().print_elos() + if self._should_warn_handicap_komi: + print("WARNING: There are handicap games or games with komi < 5.5 or komi > 7.5, these games may not be fair?") + + # @override + def is_game_file(self, input_file: str) -> bool: + lower = input_file.lower() + return input_file.endswith(".sgf") or input_file.endswith(".sgfs") + + # @override + def get_game_records(self, input_file: str) -> List[GameRecord]: + if input_file.lower().endswith(".sgfs"): + with open(input_file, "rb") as f: + sgfs_strings = f.readlines() + + records = [] + for sgf in sgfs_strings: + record = self.sgf_string_to_game_record(sgf, input_file) + if record is not None: + records.append(record) + return records + else: + with open(input_file, "rb") as f: + sgf = f.read() + + records = [] + record = self.sgf_string_to_game_record(sgf, input_file) + if record is not None: + records.append(record) + return records + + def sgf_string_to_game_record(self, sgf_string, debug_source = None) -> GameRecord: + try: + # sgfmill for some reason can't handle rectangular boards, even though it's part of the SGF spec. + # So lie and say that they're square, so that we can load them. + sgf_string = re.sub(r'SZ\[(\d+):\d+\]', r'SZ[\1]', sgf_string.decode("utf-8")) + sgf_string = sgf_string.encode("utf-8") + + game = sgf.Sgf_game.from_bytes(sgf_string) + winner = game.get_winner() + except ValueError: + print ('\033[91m'+f"A sgf string is damaged in {debug_source}, and its record has been skipped!"+ '\x1b[0m') + return + pla_black = game.get_player_name('b') + pla_white = game.get_player_name('w') + if (game.get_handicap() is not None) or game.get_komi() < 5.5 or game.get_komi() > 7.5: + self._should_warn_handicap_komi = True + + game_record = GameRecord(player1=pla_black,player2=pla_white) + if (winner == 'b'): + game_record.win += 1 + elif (winner == 'w'): + game_record.loss += 1 + else: + game_record.draw += 1 + return game_record + + + +if __name__ == "__main__": + description = """ + Summarize SGF/SGFs files and estimate Bayes Elo score for each of the player. + """ + parser = argparse.ArgumentParser(description=description) + parser.add_argument( + "input-files-or-dirs", + help="sgf/sgfs files or directories of them", + nargs="+", + ) + parser.add_argument( + "-recursive", + help="Recursively search subdirectories of input directories", + required=False, + action="store_true", + ) + parser.add_argument( + "-elo-prior-games", + help="Prior for Bayes Elo calculation, using input as the prior number of games to stabilize the results", + required=False, + type=float, + default=2, + ) + parser.add_argument( + "-estimate-first-player-advantage", + help="Attempt to estimate first player advantage instead of assuming fair game", + required=False, + action="store_true", + ) + args = vars(parser.parse_args()) + print(args) + + input_files_or_dirs = args["input-files-or-dirs"] + recursive = args["recursive"] + elo_prior_games = args["elo_prior_games"] + estimate_first_player_advantage = args["estimate_first_player_advantage"] + + game_result_summary = GoGameResultSummary( + elo_prior_games=elo_prior_games, + estimate_first_player_advantage=estimate_first_player_advantage, + ) + for input_file_or_dir in input_files_or_dirs: + game_result_summary.add_games_from_file_or_dir(input_file_or_dir, recursive=recursive) + + game_result_summary.print_elos() diff --git a/training/scripts/dated/20241110-122511/synchronous_loop.sh b/training/scripts/dated/20241110-122511/synchronous_loop.sh new file mode 100644 index 0000000..33f4be3 --- /dev/null +++ b/training/scripts/dated/20241110-122511/synchronous_loop.sh @@ -0,0 +1,103 @@ +#!/bin/bash -eu +set -o pipefail +{ + +# Runs the entire self-play process synchronously in a loop, training a single size of neural net appropriately. +# Assumes you have the cpp directory compiled and the katago executable is there. + +# If using multiple machines, or even possibly many GPUs on one machine in some cases, then this is NOT the +# recommended method, instead it is better to run all steps simultaneously and asynchronously. See SelfplayTraining.md in +# the root of the KataGo repo for more details. + +if [[ $# -lt 5 ]] +then + echo "Usage: $0 NAMEPREFIX BASEDIR TRAININGNAME MODELKIND USEGATING" + echo "Assumes katago is already built in the 'cpp' directory of the KataGo repo and the executable is present at cpp/katago.exe." + echo "NAMEPREFIX string prefix for this training run, try to pick something globally unique. Will be displayed to users when KataGo loads the model." + echo "BASEDIR containing selfplay data and models and related directories" + echo "TRANINGNAME name to prefix models with, specific to this training daemon" + echo "MODELKIND what size model to train, like 'b10c128', see ../modelconfigs.py" + echo "USEGATING = 1 to use gatekeeper, 0 to not use gatekeeper" + exit 0 +fi +NAMEPREFIX="$1" +shift +BASEDIRRAW="$1" +shift +TRAININGNAME="$1" +shift +MODELKIND="$1" +shift +USEGATING="$1" +shift + +BASEDIR="$(realpath "$BASEDIRRAW")" +GITROOTDIR="$(git rev-parse --show-toplevel)" +LOGSDIR="$BASEDIR"/logs +SCRATCHDIR="$BASEDIR"/shufflescratch + +# Create all the directories we need +mkdir -p "$BASEDIR" +mkdir -p "$LOGSDIR" +mkdir -p "$SCRATCHDIR" +mkdir -p "$BASEDIR"/selfplay +mkdir -p "$BASEDIR"/gatekeepersgf + +# Parameters for the training run +NUM_GAMES_PER_CYCLE=90 # Every cycle, play this many games +NUM_THREADS_FOR_SHUFFLING=8 +NUM_TRAIN_SAMPLES_PER_EPOCH=100000 # Training will proceed in chunks of this many rows, subject to MAX_TRAIN_PER_DATA. +MAX_TRAIN_PER_DATA=8 # On average, train only this many times on each data row. Larger numbers may cause overfitting. +NUM_TRAIN_SAMPLES_PER_SWA=80000 # Stochastic weight averaging frequency. +BATCHSIZE=128 # For lower-end GPUs 64 or smaller may be needed to avoid running out of GPU memory. +SHUFFLE_MINROWS=1 # Require this many rows at the very start before beginning training. +MAX_TRAIN_SAMPLES_PER_CYCLE=500000 # Each cycle will do at most this many training steps. +TAPER_WINDOW_SCALE=50000 # Parameter setting the scale at which the shuffler will make the training window grow sublinearly. +SHUFFLE_KEEPROWS=600000 # Needs to be larger than MAX_TRAIN_SAMPLES_PER_CYCLE, so the shuffler samples enough rows each cycle for the training to use. + +# Paths to the selfplay and gatekeeper configs that contain board sizes, rules, search parameters, etc. +SELFPLAY_CONFIG="$GITROOTDIR"/cpp/configs/training/selfplay1_maxsize9.cfg +GATING_CONFIG="$GITROOTDIR"/cpp/configs/training/gatekeeper1_maxsize9.cfg + +# Copy all the relevant scripts and configs and the katago executable to a dated directory. +DATE_FOR_FILENAME=$(date "+%Y%m%d-%H%M%S") +DATED_ARCHIVE="$BASEDIR"/scripts/dated/"$DATE_FOR_FILENAME" +mkdir -p "$DATED_ARCHIVE" +cp "$GITROOTDIR"/python/*.py "$GITROOTDIR"/python/selfplay/*.sh "$DATED_ARCHIVE" +cp "$GITROOTDIR"/cpp/katago.exe "$DATED_ARCHIVE"/katago.exe # Ensure the executable is copied +cp "$SELFPLAY_CONFIG" "$DATED_ARCHIVE"/selfplay.cfg +cp "$GATING_CONFIG" "$DATED_ARCHIVE"/gatekeeper.cfg +git show --no-patch --no-color > "$DATED_ARCHIVE"/version.txt +git diff --no-color > "$DATED_ARCHIVE"/diff.txt +git diff --staged --no-color > "$DATED_ARCHIVE"/diffstaged.txt + +# Also run the code out of the archive, so that we don't unexpectedly crash or change behavior if the local repo changes. +cd "$DATED_ARCHIVE" + +# Begin cycling forever, running each step in order. +set -x +while true +do + echo "Gatekeeper" + time "C:\Users\chang\Downloads\katago-v1.15.1-opencl-windows-x64\katago.exe" gatekeeper -rejected-models-dir "$BASEDIR"/rejectedmodels -accepted-models-dir "$BASEDIR"/models/ -sgf-output-dir "$BASEDIR"/gatekeepersgf/ -test-models-dir "$BASEDIR"/modelstobetested/ -config "$DATED_ARCHIVE"/gatekeeper.cfg -quit-if-no-nets-to-test | tee -a "$BASEDIR"/gatekeepersgf/stdout.txt + + echo "Selfplay" + time "C:\Users\chang\Downloads\katago-v1.15.1-opencl-windows-x64\katago.exe" selfplay -max-games-total "$NUM_GAMES_PER_CYCLE" -output-dir "$BASEDIR"/selfplay -models-dir "$BASEDIR"/models -config "$DATED_ARCHIVE"/selfplay.cfg | tee -a "$BASEDIR"/selfplay/stdout.txt + + echo "Shuffle" + ( + time SKIP_VALIDATE=1 ./shuffle.sh "$BASEDIR" "$SCRATCHDIR" "$NUM_THREADS_FOR_SHUFFLING" "$BATCHSIZE" -min-rows "$SHUFFLE_MINROWS" -keep-target-rows "$SHUFFLE_KEEPROWS" -taper-window-scale "$TAPER_WINDOW_SCALE" | tee -a "$BASEDIR"/logs/outshuffle.txt + ) + + echo "Train" + time ./train.sh "$BASEDIR" "$TRAININGNAME" "$MODELKIND" "$BATCHSIZE" main -samples-per-epoch "$NUM_TRAIN_SAMPLES_PER_EPOCH" -swa-period-samples "$NUM_TRAIN_SAMPLES_PER_SWA" -quit-if-no-data -stop-when-train-bucket-limited -no-repeat-files -max-train-bucket-per-new-data "$MAX_TRAIN_PER_DATA" -max-train-bucket-size "$MAX_TRAIN_SAMPLES_PER_CYCLE" + + echo "Export" + ( + time ./export_model_for_selfplay.sh "$NAMEPREFIX" "$BASEDIR" "$USEGATING" | tee -a "$BASEDIR"/logs/outexport.txt + ) + +done + +exit 0 +} diff --git a/training/scripts/dated/20241110-122511/test.py b/training/scripts/dated/20241110-122511/test.py new file mode 100644 index 0000000..92065df --- /dev/null +++ b/training/scripts/dated/20241110-122511/test.py @@ -0,0 +1,279 @@ +#!/usr/bin/python3 +import sys +import os +import argparse +import traceback +import random +import math +import time +import logging +import json +import numpy as np +from collections import defaultdict +from typing import Dict, List + +import torch +import torch.nn +from torch.optim.swa_utils import AveragedModel + +import modelconfigs +from model_pytorch import Model, ExtraOutputs, MetadataEncoder +from metrics_pytorch import Metrics +import data_processing_pytorch +from load_model import load_model + +# HANDLE COMMAND AND ARGS ------------------------------------------------------------------- + +if __name__ == "__main__": + + description = """ + Test neural net on Go positions from npz files of batches from selfplay. + """ + + parser = argparse.ArgumentParser(description=description) + parser.add_argument('-npzdir', help='Directory with npz data', required=True) + parser.add_argument('-model-kind', help='If specified, use this model kind instead of config', required=False) + parser.add_argument('-config', help='Path to model.config.json', required=False) + parser.add_argument('-checkpoint', help='Checkpoint to test', required=False) + parser.add_argument('-pos-len', help='Spatial length of expected training data', type=int, required=True) + parser.add_argument('-batch-size', help='Batch size to use for testing', type=int, required=True) + parser.add_argument('-use-swa', help='Use SWA model', action="store_true", required=False) + parser.add_argument('-max-batches', help='Maximum number of batches for testing', type=int, required=False) + parser.add_argument('-gpu-idx', help='GPU idx', type=int, required=False) + parser.add_argument('-print-norm', help='Names of outputs to print norms comma separated', type=str, required=False) + parser.add_argument('-list-available-outputs', help='Print names of outputs available', action="store_true", required=False) + + args = vars(parser.parse_args()) + +def main(args): + npzdir = args["npzdir"] + model_kind = args["model_kind"] + config_file = args["config"] + checkpoint_file = args["checkpoint"] + pos_len = args["pos_len"] + batch_size = args["batch_size"] + use_swa = args["use_swa"] + max_batches = args["max_batches"] + gpu_idx = args["gpu_idx"] + list_available_outputs = args["list_available_outputs"] + + norm_layer_names = [] + if args["print_norm"]: + for layername in args["print_norm"].split(','): + if layername.strip(): + norm_layer_names.append(layername.strip()) + + soft_policy_weight_scale = 1.0 + value_loss_scale = 1.0 + td_value_loss_scales = [0.4, 0.4, 0.4] + + world_size = 1 + rank = 0 + + # SET UP LOGGING ------------------------------------------------------------- + + logging.root.handlers = [] + logging.basicConfig( + level=logging.INFO, + format="%(message)s", + handlers=[ + logging.StreamHandler(stream=sys.stdout) + ], + ) + np.set_printoptions(linewidth=150) + + logging.info(str(sys.argv)) + + # FIGURE OUT GPU ------------------------------------------------------------ + if gpu_idx is not None: + torch.cuda.set_device(gpu_idx) + logging.info("Using GPU device: " + torch.cuda.get_device_name()) + device = torch.device("cuda", gpu_idx) + elif torch.cuda.is_available(): + logging.info("Using GPU device: " + torch.cuda.get_device_name()) + device = torch.device("cuda") + else: + logging.warning("WARNING: No GPU, using CPU") + device = torch.device("cpu") + + # LOAD MODEL --------------------------------------------------------------------- + + if checkpoint_file is None: + logging.info("Initializing new model since no checkpoint provided") + assert (model_kind is None) != (config_file is None), "Must provide exactly one of -model-kind and -config if no checkpoint" + + if model_kind is not None: + model_config = modelconfigs.config_of_name[model_kind] + else: + with open(config_file,"r") as f: + model_config = json.load(f) + logging.info(str(model_config)) + + model = Model(model_config,pos_len) + model.initialize() + model.to(device) + else: + model, swa_model, _ = load_model(checkpoint_file, use_swa, device=device, pos_len=pos_len, verbose=True) + model_config = model.config + + metrics_obj = Metrics(batch_size,world_size,model) + + # METRICS ----------------------------------------------------------------------------------- + def detensorify_metrics(metrics): + ret = {} + for key in metrics: + if isinstance(metrics[key], torch.Tensor): + ret[key] = metrics[key].detach().cpu().item() + else: + ret[key] = metrics[key] + return ret + + def accumulate_metrics(metric_sums, metric_weights, metrics, batch_size, decay): + if decay != 1.0: + for metric in metric_sums: + if metric.endswith("_sum"): + metric_sums[metric] *= decay + metric_weights[metric] *= decay + + for metric in metrics: + if not metric.endswith("_batch"): + metric_sums[metric] += metrics[metric] + metric_weights[metric] += batch_size + else: + metric_sums[metric] += metrics[metric] + metric_weights[metric] += 1 + + def log_metrics(prefix, metric_sums, metric_weights, metrics, sum_norms, num_samples_tested, metrics_out): + metrics_to_print = {} + for metric in metric_sums: + if metric.endswith("_sum"): + metrics_to_print[metric[:-4]] = metric_sums[metric] / metric_weights[metric] + elif metric.endswith("_batch"): + metrics_to_print[metric] = metric_sums[metric] / metric_weights[metric] + metric_sums[metric] *= 0.001 + metric_weights[metric] *= 0.001 + else: + metrics_to_print[metric] = metric_sums[metric] + for metric in metrics: + if metric not in metric_sums: + metrics_to_print[metric] = metrics[metric] + + logging.info(prefix + ", ".join(["%s = %f" % (metric, metrics_to_print[metric]) for metric in metrics_to_print])) + if metrics_out: + metrics_out.write(json.dumps(metrics_to_print) + "\n") + + for name in sum_norms: + avg_norm = sum_norms[name] / num_samples_tested + logging.info(f"{name} {avg_norm:.6f}") + + if metrics_out: + metrics_out.flush() + + torch.backends.cudnn.benchmark = True + + # Validate + logging.info("Beginning test!") + val_files = [os.path.join(npzdir,fname) for fname in os.listdir(npzdir) if fname.endswith(".npz")] + if len(val_files) == 0: + raise Exception("No npz files in " + npzdir) + + with torch.no_grad(): + model.eval() + if swa_model is not None: + swa_model.eval() + val_metric_sums = defaultdict(float) + val_metric_weights = defaultdict(float) + sum_norms = defaultdict(float) + num_batches_tested = 0 + num_samples_tested = 0 + total_inference_time = 0.0 + is_first_batch = True + for batch in data_processing_pytorch.read_npz_training_data( + val_files, + batch_size, + world_size, + rank, + pos_len, + device, + randomize_symmetries=True, + model_config=model_config, + ): + if max_batches is not None and num_batches_tested >= max_batches: + break + + start = torch.cuda.Event(enable_timing=True) + end = torch.cuda.Event(enable_timing=True) + + extra_outputs = ExtraOutputs(norm_layer_names) + # if model.get_has_metadata_encoder(): + # extra_outputs.add_requested([MetadataEncoder.OUTMEAN_KEY,MetadataEncoder.OUTLOGVAR_KEY]) + + start.record() + if swa_model is not None: + model_outputs = swa_model(batch["binaryInputNCHW"],batch["globalInputNC"],extra_outputs=extra_outputs) + else: + model_outputs = model(batch["binaryInputNCHW"],batch["globalInputNC"],extra_outputs=extra_outputs) + end.record() + torch.cuda.synchronize() + time_elapsed = start.elapsed_time(end) / 1000.0 + + if list_available_outputs: + for available_output in extra_outputs.available: + logging.info(f"Available output: {available_output}") + list_available_outputs = False + + postprocessed = model.postprocess_output(model_outputs) + metrics = metrics_obj.metrics_dict_batchwise( + model, + postprocessed, + extra_outputs, + batch, + is_training=False, + soft_policy_weight_scale=soft_policy_weight_scale, + disable_optimistic_policy=False, + meta_kata_only_soft_policy=False, + value_loss_scale=value_loss_scale, + td_value_loss_scales=td_value_loss_scales, + seki_loss_scale=1.0, + variance_time_loss_scale=1.0, + main_loss_scale=1.0, + intermediate_loss_scale=None, + ) + metrics = detensorify_metrics(metrics) + + # Ignore first batch, treat as a warmup so timings are a bit more accurate. + if is_first_batch: + is_first_batch = False + continue + + for norm_layer_name in norm_layer_names: + activations = extra_outputs.returned[norm_layer_name] + non_batch_dims = list(range(1,len(activations.shape))) + sum_norms[norm_layer_name] += torch.sum(torch.sqrt(torch.mean(torch.square(activations),dim=non_batch_dims)),dim=0).item() + + accumulate_metrics(val_metric_sums, val_metric_weights, metrics, batch_size, decay=1.0) + + num_batches_tested += 1 + num_samples_tested += batch_size + total_inference_time += time_elapsed + + if num_batches_tested % 5 == 0: + remainder = num_batches_tested // 5 + while remainder > 1 and remainder % 2 == 0: + remainder = remainder // 2 + if remainder in [1,3,5,7,9]: + metrics["num_batches"] = num_batches_tested + metrics["num_samples"] = num_samples_tested + metrics["inferencetime"] = total_inference_time + metrics["time/1ksamp"] = total_inference_time / num_samples_tested * 1000.0 + log_metrics("STATS SO FAR: ", val_metric_sums, val_metric_weights, metrics, sum_norms, num_samples_tested, None) + + metrics["num_batches"] = num_batches_tested + metrics["num_samples"] = num_samples_tested + metrics["inferencetime"] = total_inference_time + metrics["time/1ksamp"] = total_inference_time / num_samples_tested * 1000.0 + log_metrics("FINAL: ", val_metric_sums, val_metric_weights, metrics, sum_norms, num_samples_tested, None) + + +if __name__ == "__main__": + main(args) diff --git a/training/scripts/dated/20241110-122511/train.py b/training/scripts/dated/20241110-122511/train.py new file mode 100644 index 0000000..6717e4f --- /dev/null +++ b/training/scripts/dated/20241110-122511/train.py @@ -0,0 +1,1368 @@ +#!/usr/bin/python3 +import sys +import os +import argparse +import traceback +import random +import math +import time +import logging +import contextlib +import json +import datetime +from datetime import timezone +import gc +import shutil +import glob +import numpy as np +import itertools +import copy +import atexit +from collections import defaultdict +from typing import Dict, List + +import torch +import torch.nn +import torch.optim +import torch.distributed +import torch.multiprocessing +from torch.nn.parallel import DistributedDataParallel +from torch.optim.swa_utils import AveragedModel +from torch.cuda.amp import GradScaler, autocast + +import modelconfigs +from model_pytorch import Model, ExtraOutputs, MetadataEncoder +from metrics_pytorch import Metrics +import load_model +import data_processing_pytorch +from metrics_logging import accumulate_metrics, log_metrics, clear_metric_nonfinite + +# HANDLE COMMAND AND ARGS ------------------------------------------------------------------- + +if __name__ == "__main__": + + description = """ + Train neural net on Go positions from npz files of batches from selfplay. + """ + + parser = argparse.ArgumentParser(description=description,add_help=False) + required_args = parser.add_argument_group('required arguments') + optional_args = parser.add_argument_group('optional arguments') + optional_args.add_argument( + '-h', + '--help', + action='help', + default=argparse.SUPPRESS, + help='show this help message and exit' + ) + + required_args.add_argument('-traindir', help='Dir to write to for recording training results', required=True) + required_args.add_argument('-datadir', help='Directory with a train and val subdir of npz data, output by shuffle.py', required=True) + optional_args.add_argument('-exportdir', help='Directory to export models periodically', required=False) + optional_args.add_argument('-exportprefix', help='Prefix to append to names of models', required=False) + optional_args.add_argument('-initial-checkpoint', help='If no training checkpoint exists, initialize from this checkpoint', required=False) + + required_args.add_argument('-pos-len', help='Spatial edge length of expected training data, e.g. 19 for 19x19 Go', type=int, required=True) + required_args.add_argument('-batch-size', help='Per-GPU batch size to use for training', type=int, required=True) + optional_args.add_argument('-samples-per-epoch', help='Number of data samples to consider as one epoch', type=int, required=False) + optional_args.add_argument('-model-kind', help='String name for what model config to use', required=False) + optional_args.add_argument('-lr-scale', help='LR multiplier on the hardcoded schedule', type=float, required=False) + optional_args.add_argument('-lr-scale-auto', help='LR auto scaling', required=False, action='store_true') + optional_args.add_argument('-gnorm-clip-scale', help='Multiplier on gradient clipping threshold', type=float, required=False) + optional_args.add_argument('-sub-epochs', help='Reload training data up to this many times per epoch', type=int, default=1, required=False) + optional_args.add_argument('-swa-period-samples', help='How frequently to average an SWA sample, in samples', type=float, required=False) + optional_args.add_argument('-swa-scale', help='Number of samples to average in expectation together for SWA', type=float, required=False) + optional_args.add_argument('-lookahead-k', help='Use lookahead optimizer', type=int, default=6, required=False) + optional_args.add_argument('-lookahead-alpha', help='Use lookahead optimizer', type=float, default=0.5, required=False) + optional_args.add_argument('-lookahead-print', help='Only print on lookahead syncs', required=False, action='store_true') + + optional_args.add_argument('-multi-gpus', help='Use multiple gpus, comma-separated device ids', required=False) + optional_args.add_argument('-use-fp16', help='Use fp16 training', required=False, action='store_true') + + optional_args.add_argument('-epochs-per-export', help='Export model once every this many epochs', type=int, required=False) + optional_args.add_argument('-export-prob', help='Export model with this probablity', type=float, required=False) + optional_args.add_argument('-max-epochs-this-instance', help='Terminate training after this many more epochs', type=int, required=False) + optional_args.add_argument('-max-training-samples', help='Terminate training after about this many training steps in samples', type=int, required=False) + optional_args.add_argument('-sleep-seconds-per-epoch', help='Sleep this long between epochs', type=int, required=False) + optional_args.add_argument('-max-train-bucket-per-new-data', help='When data added, add this many train rows per data row to bucket', type=float, required=False) + optional_args.add_argument('-max-train-bucket-size', help='Approx total number of train rows allowed if data stops', type=float, required=False) + optional_args.add_argument('-max-train-steps-since-last-reload', help='Approx total of training allowed if shuffling stops', type=float, required=False) + optional_args.add_argument('-stop-when-train-bucket-limited', help='Terminate due to train bucket rather than waiting for more', required=False, action='store_true') + optional_args.add_argument('-max-val-samples', help='Approx max of validation samples per epoch', type=int, required=False) + optional_args.add_argument('-randomize-val', help='Randomize order of validation files', required=False, action='store_true') + optional_args.add_argument('-no-export', help='Do not export models', required=False, action='store_true') + optional_args.add_argument('-no-repeat-files', help='Track what shuffled data was used and do not repeat, even when killed and resumed', required=False, action='store_true') + optional_args.add_argument('-quit-if-no-data', help='If no data, quit instead of waiting for data', required=False, action='store_true') + + optional_args.add_argument('-gnorm-stats-debug', required=False, action='store_true') + + optional_args.add_argument('-brenorm-avg-momentum', type=float, help='Set brenorm running avg rate to this value', required=False) + optional_args.add_argument('-brenorm-target-rmax', type=float, help='Gradually adjust brenorm rmax to this value', required=False) + optional_args.add_argument('-brenorm-target-dmax', type=float, help='Gradually adjust brenorm dmax to this value', required=False) + optional_args.add_argument('-brenorm-adjustment-scale', type=float, help='How many samples to adjust brenorm params all but 1/e of the way to target', required=False) + + optional_args.add_argument('-soft-policy-weight-scale', type=float, default=8.0, help='Soft policy loss coeff', required=False) + optional_args.add_argument('-disable-optimistic-policy', help='Disable optimistic policy', required=False, action='store_true') + optional_args.add_argument('-meta-kata-only-soft-policy', help='Mask soft policy on non-kata rows using sgfmeta', required=False, action='store_true') + optional_args.add_argument('-value-loss-scale', type=float, default=0.6, help='Additional value loss coeff', required=False) + optional_args.add_argument('-td-value-loss-scales', type=str, default="0.6,0.6,0.6", help='Additional td value loss coeffs, 3 comma separated values', required=False) + optional_args.add_argument('-seki-loss-scale', type=float, default=1.0, help='Additional seki loss coeff', required=False) + optional_args.add_argument('-variance-time-loss-scale', type=float, default=1.0, help='Additional variance time loss coeff', required=False) + + optional_args.add_argument('-main-loss-scale', type=float, help='Loss factor scale for main head', required=False) + optional_args.add_argument('-intermediate-loss-scale', type=float, help='Loss factor scale for intermediate head', required=False) + + args = vars(parser.parse_args()) + + +def get_longterm_checkpoints_dir(traindir): + return os.path.join(traindir,"longterm_checkpoints") + +def make_dirs(args): + traindir = args["traindir"] + exportdir = args["exportdir"] + + if not os.path.exists(traindir): + os.makedirs(traindir) + if exportdir is not None and not os.path.exists(exportdir): + os.makedirs(exportdir) + + longterm_checkpoints_dir = get_longterm_checkpoints_dir(traindir) + if not os.path.exists(longterm_checkpoints_dir): + os.makedirs(longterm_checkpoints_dir) + +def multiprocessing_setup(rank: int, world_size: int): + os.environ['MASTER_ADDR'] = 'localhost' + os.environ['MASTER_PORT'] = '23456' + logging.info("Running torch.distributed.init_process_group") + torch.distributed.init_process_group("nccl", rank=rank, world_size=world_size) + logging.info(f"Returned from torch.distributed.init_process_group, my rank = {rank}, world_size={world_size}") + +def multiprocessing_cleanup(): + torch.distributed.destroy_process_group() + + +def main(rank: int, world_size: int, args, multi_gpu_device_ids, readpipes, writepipes, barrier): + traindir = args["traindir"] + datadir = args["datadir"] + exportdir = args["exportdir"] + exportprefix = args["exportprefix"] + initial_checkpoint = args["initial_checkpoint"] + + pos_len = args["pos_len"] + batch_size = args["batch_size"] + samples_per_epoch = args["samples_per_epoch"] + model_kind = args["model_kind"] + lr_scale = args["lr_scale"] + lr_scale_auto = args["lr_scale_auto"] + gnorm_clip_scale = args["gnorm_clip_scale"] + sub_epochs = args["sub_epochs"] + swa_period_samples = args["swa_period_samples"] + swa_scale = args["swa_scale"] + lookahead_k = args["lookahead_k"] + lookahead_alpha = args["lookahead_alpha"] + lookahead_print = args["lookahead_print"] + + use_fp16 = args["use_fp16"] + + epochs_per_export = args["epochs_per_export"] + export_prob = args["export_prob"] + max_epochs_this_instance = args["max_epochs_this_instance"] + max_training_samples = args["max_training_samples"] + sleep_seconds_per_epoch = args["sleep_seconds_per_epoch"] + max_train_bucket_per_new_data = args["max_train_bucket_per_new_data"] + max_train_bucket_size = args["max_train_bucket_size"] + max_train_steps_since_last_reload = args["max_train_steps_since_last_reload"] + stop_when_train_bucket_limited = args["stop_when_train_bucket_limited"] + max_val_samples = args["max_val_samples"] + randomize_val = args["randomize_val"] + no_export = args["no_export"] + no_repeat_files = args["no_repeat_files"] + quit_if_no_data = args["quit_if_no_data"] + + gnorm_stats_debug = args["gnorm_stats_debug"] + + brenorm_target_rmax = args["brenorm_target_rmax"] + brenorm_target_dmax = args["brenorm_target_dmax"] + brenorm_avg_momentum = args["brenorm_avg_momentum"] + brenorm_adjustment_scale = args["brenorm_adjustment_scale"] + + soft_policy_weight_scale = args["soft_policy_weight_scale"] + disable_optimistic_policy = args["disable_optimistic_policy"] + meta_kata_only_soft_policy = args["meta_kata_only_soft_policy"] + value_loss_scale = args["value_loss_scale"] + td_value_loss_scales = [float(x) for x in args["td_value_loss_scales"].split(",")] + seki_loss_scale = args["seki_loss_scale"] + variance_time_loss_scale = args["variance_time_loss_scale"] + + main_loss_scale = args["main_loss_scale"] + intermediate_loss_scale = args["intermediate_loss_scale"] + + if lr_scale is None: + lr_scale = 1.0 + if lr_scale_auto: + assert lr_scale == 1.0, "Cannot specify both lr_scale and lr_scale_auto" + + if samples_per_epoch is None: + samples_per_epoch = 1000000 + if max_train_bucket_size is None: + max_train_bucket_size = 1.0e30 + if epochs_per_export is None: + epochs_per_export = 1 + if swa_period_samples is None: + swa_period_samples = max(1, samples_per_epoch // 2) + if swa_scale is None: + swa_scale = 8 + + assert lookahead_alpha > 0.0 and lookahead_alpha <= 1.0 + if lookahead_alpha >= 1.0: # 1.0 means to disable lookahead optimizer + lookahead_alpha = None + lookahead_k = None + + longterm_checkpoints_dir = get_longterm_checkpoints_dir(traindir) + + assert (swa_period_samples is None) == (swa_scale is None) + assert (lookahead_k is None) == (lookahead_alpha is None) + + # SET UP LOGGING ------------------------------------------------------------- + + logging.root.handlers = [] + if rank == 0: + logging.basicConfig( + level=logging.INFO, + format="%(message)s", + handlers=[ + logging.FileHandler(os.path.join(traindir,f"train{rank}.log"), mode="a"), + logging.StreamHandler() + ], + ) + else: + logging.basicConfig( + level=logging.INFO, + format="%(message)s", + handlers=[ + logging.FileHandler(os.path.join(traindir,f"train{rank}.log"), mode="a"), + ], + ) + np.set_printoptions(linewidth=150) + + logging.info(str(sys.argv)) + + # FIGURE OUT MULTIGPU ------------------------------------------------------------ + if world_size > 1: + multiprocessing_setup(rank, world_size) + atexit.register(multiprocessing_cleanup) + assert torch.cuda.is_available() + + if True or torch.cuda.is_available(): + my_gpu_id = multi_gpu_device_ids[rank] + torch.cuda.set_device(my_gpu_id) + logging.info("Using GPU device: " + torch.cuda.get_device_name()) + device = torch.device("cuda", my_gpu_id) + else: + logging.warning("WARNING: No GPU, using CPU") + device = torch.device("cpu") + + seed = int.from_bytes(os.urandom(7), sys.byteorder) + logging.info(f"Seeding torch with {seed}") + torch.manual_seed(seed) + + # LOAD MODEL --------------------------------------------------------------------- + + def lr_scale_auto_factor(train_state): + if not lr_scale_auto: + return 1.0 + + if train_state["global_step_samples"] < 200_000_000: + return 8.00 + if train_state["global_step_samples"] < 400_000_000: + return 4.00 + if train_state["global_step_samples"] < 500_000_000: + return 2.00 + if train_state["global_step_samples"] < 550_000_000: + return 1.00 + if train_state["global_step_samples"] < 600_000_000: + return 0.50 + if train_state["global_step_samples"] < 650_000_000: + return 0.25 + return 0.25 + + def get_checkpoint_path(): + return os.path.join(traindir,"checkpoint.ckpt") + def get_checkpoint_prev_path(i): + return os.path.join(traindir,f"checkpoint_prev{i}.ckpt") + + NUM_SHORTTERM_CHECKPOINTS_TO_KEEP = 4 + def save(ddp_model, swa_model, optimizer, metrics_obj, running_metrics, train_state, last_val_metrics, path=None): + if gnorm_stats_debug: + logging.warning("Skipping save since debugging gnorm stats") + return + if rank == 0: + state_dict = {} + state_dict["model"] = ddp_model.state_dict() + state_dict["optimizer"] = optimizer.state_dict() + state_dict["metrics"] = metrics_obj.state_dict() + state_dict["running_metrics"] = running_metrics + state_dict["train_state"] = train_state + state_dict["last_val_metrics"] = last_val_metrics + state_dict["config"] = model_config + + if swa_model is not None: + state_dict["swa_model"] = swa_model.state_dict() + + if path is not None: + logging.info("Saving checkpoint: " + path) + torch.save(state_dict, path + ".tmp") + time.sleep(1) + os.replace(path + ".tmp", path) + else: + logging.info("Saving checkpoint: " + get_checkpoint_path()) + for i in reversed(range(NUM_SHORTTERM_CHECKPOINTS_TO_KEEP-1)): + if os.path.exists(get_checkpoint_prev_path(i)): + os.replace(get_checkpoint_prev_path(i), get_checkpoint_prev_path(i+1)) + if os.path.exists(get_checkpoint_path()): + shutil.copy(get_checkpoint_path(), get_checkpoint_prev_path(0)) + torch.save(state_dict, get_checkpoint_path() + ".tmp") + os.replace(get_checkpoint_path() + ".tmp", get_checkpoint_path()) + + def get_weight_decay(raw_model, lr_scale, warmup_scale, train_state, running_metrics, group_name): + lr_scale_with_auto = lr_scale * lr_scale_auto_factor(train_state) + if raw_model.get_norm_kind() == "fixup" or raw_model.get_norm_kind() == "fixscale": + if group_name == "normal" or group_name == "normal_gamma" or group_name == "output": + return 0.000001 * world_size * batch_size / 256.0 + elif group_name == "noreg": + return 0.00000001 * world_size * batch_size / 256.0 + elif group_name == "output_noreg": + return 0.00000001 * world_size * batch_size / 256.0 + else: + assert False + elif ( + raw_model.get_norm_kind() == "bnorm" or + raw_model.get_norm_kind() == "brenorm" or + raw_model.get_norm_kind() == "fixbrenorm" or + raw_model.get_norm_kind() == "fixscaleonenorm" + ): + if group_name == "normal" or group_name == "normal_gamma": + adaptive_scale = 1.0 + if "sums" in running_metrics and "norm_normal_batch" in running_metrics["sums"]: + norm_normal_batch = running_metrics["sums"]["norm_normal_batch"] / running_metrics["weights"]["norm_normal_batch"] + baseline = train_state["modelnorm_normal_baseline"] + ratio = norm_normal_batch / (baseline + 1e-30) + # Adaptive weight decay keeping model norm around the baseline level so that batchnorm effective lr is held constant + # throughout training, covering a range of 16x from bottom to top. + adaptive_scale = math.pow(2.0, 2.0 * math.tanh(math.log(ratio+1e-30) * 1.5)) + + # Batch norm gammas can be regularized a bit less, doing them just as much empirically seemed to be a bit more unstable + gamma_scale = 0.125 if group_name == "normal_gamma" else 1.0 + + # The theoretical scaling for keeping us confined to a surface of equal model norm should go proportionally with lr_scale. + # because the strength of drift away from that surface goes as lr^2 and weight decay itself is scaled by lr, so we need + # one more factor of lr to make weight decay strength equal drift strength. + # However, at low lr it tends to be the case that gradient norm increases slightly + # while at high lr it tends to be the case that gradient norm decreases, which means drift strength scales a bit slower + # than expected. + # So we scale sublinearly with lr_scale so as to slightly preadjust to this effect. + # Adaptive scale should then help keep us there thereafter. + return 0.00125 * world_size * batch_size / 256.0 * math.pow(lr_scale_with_auto * warmup_scale,0.75) * adaptive_scale * gamma_scale + elif group_name == "output": + return 0.000001 * world_size * batch_size / 256.0 + elif group_name == "noreg": + return 0.000001 * world_size * batch_size / 256.0 * math.pow(lr_scale_with_auto * warmup_scale,0.75) + elif group_name == "output_noreg": + return 0.00000001 * world_size * batch_size / 256.0 + else: + assert False + else: + assert False + + def get_param_groups(raw_model,train_state,running_metrics): + reg_dict : Dict[str,List] = {} + raw_model.add_reg_dict(reg_dict) + param_groups = [] + param_groups.append({ + "params": reg_dict["normal"], + "weight_decay": get_weight_decay(raw_model, lr_scale, warmup_scale=1.0, train_state=train_state, running_metrics=running_metrics, group_name="normal"), + "group_name": "normal", + }) + if len(reg_dict["normal_gamma"]) > 0: + param_groups.append({ + "params": reg_dict["normal_gamma"], + "weight_decay": get_weight_decay(raw_model, lr_scale, warmup_scale=1.0, train_state=train_state, running_metrics=running_metrics, group_name="normal_gamma"), + "group_name": "normal_gamma", + }) + param_groups.append({ + "params": reg_dict["output"], + "weight_decay": get_weight_decay(raw_model, lr_scale, warmup_scale=1.0, train_state=train_state, running_metrics=running_metrics, group_name="output"), + "group_name": "output", + }) + param_groups.append({ + "params": reg_dict["noreg"], + "weight_decay": get_weight_decay(raw_model, lr_scale, warmup_scale=1.0, train_state=train_state, running_metrics=running_metrics, group_name="noreg"), + "group_name": "noreg", + }) + param_groups.append({ + "params": reg_dict["output_noreg"], + "weight_decay": get_weight_decay(raw_model, lr_scale, warmup_scale=1.0, train_state=train_state, running_metrics=running_metrics, group_name="output_noreg"), + "group_name": "output_noreg", + }) + num_params = len(list(raw_model.parameters())) + num_reg_dict_params = len(reg_dict["normal"]) + len(reg_dict["normal_gamma"]) + len(reg_dict["output"]) + len(reg_dict["noreg"]) + len(reg_dict["output_noreg"]) + assert num_params == num_reg_dict_params, "Reg dict does not have entries for all params in model" + return param_groups + + def load(): + if not os.path.exists(get_checkpoint_path()): + logging.info("No preexisting checkpoint found at: " + get_checkpoint_path()) + for i in range(NUM_SHORTTERM_CHECKPOINTS_TO_KEEP): + if os.path.exists(get_checkpoint_prev_path(i)): + raise Exception(f"No preexisting checkpoint found, but {get_checkpoint_prev_path(i)} exists, something is wrong with the training dir") + + if initial_checkpoint is not None: + if os.path.exists(initial_checkpoint): + logging.info("Using initial checkpoint: {initial_checkpoint}") + path_to_load_from = initial_checkpoint + else: + raise Exception("No preexisting checkpoint found, initial checkpoint provided is invalid: {initial_checkpoint}") + else: + path_to_load_from = None + else: + path_to_load_from = get_checkpoint_path() + + if path_to_load_from is None: + logging.info("Initializing new model!") + assert model_kind is not None, "Model kind is none or unspecified but the model is being created fresh" + model_config = modelconfigs.config_of_name[model_kind] + logging.info(str(model_config)) + raw_model = Model(model_config,pos_len) + raw_model.initialize() + + raw_model.to(device) + if world_size > 1: + ddp_model = torch.nn.parallel.DistributedDataParallel(raw_model, device_ids=[device]) + else: + ddp_model = raw_model + + swa_model = None + if rank == 0 and swa_scale is not None: + new_factor = 1.0 / swa_scale + ema_avg = lambda avg_param, cur_param, num_averaged: avg_param + new_factor * (cur_param - avg_param) + swa_model = AveragedModel(raw_model, avg_fn=ema_avg) + + metrics_obj = Metrics(batch_size,world_size,raw_model) + running_metrics = {} + train_state = {} + last_val_metrics = {} + + train_state["global_step_samples"] = 0 + + with torch.no_grad(): + (modelnorm_normal, modelnorm_normal_gamma, modelnorm_output, modelnorm_noreg, modelnorm_output_noreg) = Metrics.get_model_norms(raw_model) + modelnorm_normal_baseline = modelnorm_normal.detach().cpu().item() + train_state["modelnorm_normal_baseline"] = modelnorm_normal_baseline + logging.info(f"Model norm normal baseline computed: {modelnorm_normal_baseline}") + + optimizer = torch.optim.SGD(get_param_groups(raw_model,train_state,running_metrics), lr=1.0, momentum=0.9) + + return (model_config, ddp_model, raw_model, swa_model, optimizer, metrics_obj, running_metrics, train_state, last_val_metrics) + else: + state_dict = torch.load(path_to_load_from, map_location=device) + model_config = state_dict["config"] if "config" in state_dict else modelconfigs.config_of_name[model_kind] + logging.info(str(model_config)) + raw_model = Model(model_config,pos_len) + raw_model.initialize() + + train_state = {} + if "train_state" in state_dict: + train_state = state_dict["train_state"] + else: + logging.info("WARNING: Train state not found in state dict, using fresh train state") + + # Do this before loading the state dict, while the model is initialized to fresh values, to get a good baseline + if "modelnorm_normal_baseline" not in train_state: + logging.info("Computing modelnorm_normal_baseline since not in train state") + with torch.no_grad(): + (modelnorm_normal, modelnorm_normal_gamma, modelnorm_output, modelnorm_noreg, modelnorm_output_noreg) = Metrics.get_model_norms(raw_model) + modelnorm_normal_baseline = modelnorm_normal.detach().cpu().item() + train_state["modelnorm_normal_baseline"] = modelnorm_normal_baseline + logging.info(f"Model norm normal baseline computed: {modelnorm_normal_baseline}") + + # Strip off any "module." from when the model was saved with DDP or other things + model_state_dict = load_model.load_model_state_dict(state_dict) + raw_model.load_state_dict(model_state_dict) + + raw_model.to(device) + if world_size > 1: + ddp_model = torch.nn.parallel.DistributedDataParallel(raw_model, device_ids=[device]) + else: + ddp_model = raw_model + + swa_model = None + if rank == 0 and swa_scale is not None: + new_factor = 1.0 / swa_scale + ema_avg = lambda avg_param, cur_param, num_averaged: avg_param + new_factor * (cur_param - avg_param) + swa_model = AveragedModel(raw_model, avg_fn=ema_avg) + swa_model_state_dict = load_model.load_swa_model_state_dict(state_dict) + if swa_model_state_dict is not None: + swa_model.load_state_dict(swa_model_state_dict) + + metrics_obj = Metrics(batch_size,world_size,raw_model) + if "metrics" in state_dict: + metrics_obj.load_state_dict(state_dict["metrics"]) + else: + logging.info("WARNING: Metrics not found in state dict, using fresh metrics") + + running_metrics = {} + if "running_metrics" in state_dict: + running_metrics = state_dict["running_metrics"] + else: + logging.info("WARNING: Running metrics not found in state dict, using fresh running metrics") + + last_val_metrics = {} + if "last_val_metrics" in state_dict: + last_val_metrics = state_dict["last_val_metrics"] + else: + logging.info("WARNING: Running metrics not found in state dict, using fresh last val metrics") + + optimizer = torch.optim.SGD(get_param_groups(raw_model,train_state,running_metrics), lr=1.0, momentum=0.9) + if "optimizer" in state_dict: + optimizer.load_state_dict(state_dict["optimizer"]) + else: + logging.info("WARNING: Optimizer not found in state dict, using fresh optimizer") + + return (model_config, ddp_model, raw_model, swa_model, optimizer, metrics_obj, running_metrics, train_state, last_val_metrics) + + (model_config, ddp_model, raw_model, swa_model, optimizer, metrics_obj, running_metrics, train_state, last_val_metrics) = load() + + + if "global_step_samples" not in train_state: + train_state["global_step_samples"] = 0 + if max_train_bucket_per_new_data is not None and "train_bucket_level" not in train_state: + train_state["train_bucket_level"] = samples_per_epoch + if "train_steps_since_last_reload" not in train_state: + train_state["train_steps_since_last_reload"] = 0 + if "export_cycle_counter" not in train_state: + train_state["export_cycle_counter"] = 0 + if "total_num_data_rows" not in train_state: + train_state["total_num_data_rows"] = 0 + if "old_train_data_dirs" not in train_state: + train_state["old_train_data_dirs"] = [] + if "data_files_used" not in train_state: + train_state["data_files_used"] = set() + if "swa_sample_accum" not in train_state: + train_state["swa_sample_accum"] = 0.0 + + + if intermediate_loss_scale is not None: + assert raw_model.get_has_intermediate_head(), "Model must have intermediate head to use intermediate loss" + + # If the user specified an intermediate head but no loss scale, pick something reasonable by default + if raw_model.get_has_intermediate_head(): + if intermediate_loss_scale is None and main_loss_scale is None: + if model_config["trunk_normless"]: + # fson-bnh default + assert model_config["intermediate_head_blocks"] == len(model_config["block_kind"]), "If these are unequal, don't know what you intend, please specify intermediate_loss_scale" + intermediate_loss_scale = 0.8 + main_loss_scale = 0.2 + else: + # Intermediate head in the middle of the trunk + intermediate_loss_scale = 0.5 + main_loss_scale = 0.5 + elif intermediate_loss_scale is None: + assert False, "Please specify both of main_loss_scale and intermediate_loss_scale or neither when using an architecture with an intermediate head." + + logging.info(f"swa_period_samples {swa_period_samples}") + logging.info(f"swa_scale {swa_scale}") + logging.info(f"lookahead_alpha {lookahead_alpha}") + logging.info(f"lookahead_k {lookahead_k}") + logging.info(f"soft_policy_weight_scale {soft_policy_weight_scale}") + logging.info(f"disable_optimistic_policy {disable_optimistic_policy}") + logging.info(f"meta_kata_only_soft_policy {meta_kata_only_soft_policy}") + logging.info(f"value_loss_scale {value_loss_scale}") + logging.info(f"td_value_loss_scales {td_value_loss_scales}") + logging.info(f"seki_loss_scale {seki_loss_scale}") + logging.info(f"variance_time_loss_scale {variance_time_loss_scale}") + logging.info(f"main_loss_scale {main_loss_scale}") + logging.info(f"intermediate_loss_scale {intermediate_loss_scale}") + + # Print all model parameters just to get a summary + total_num_params = 0 + total_trainable_params = 0 + logging.info("Parameters in model:") + for name, param in raw_model.named_parameters(): + product = 1 + for dim in param.shape: + product *= int(dim) + if param.requires_grad: + total_trainable_params += product + total_num_params += product + logging.info(f"{name}, {list(param.shape)}, {product} params") + logging.info(f"Total num params: {total_num_params}") + logging.info(f"Total trainable params: {total_trainable_params}") + + lookahead_cache = {} + if lookahead_k is not None: + for param_group in optimizer.param_groups: + for param in param_group["params"]: + lookahead_cache[param] = torch.zeros_like(param.data) + lookahead_cache[param] = lookahead_cache[param].copy_(param.data) + logging.info(f"Using lookahead optimizer {lookahead_alpha} {lookahead_k}") + + # EPOCHS AND LR --------------------------------------------------------------------- + + def update_and_return_lr_and_wd(): + per_sample_lr = 0.00003 * lr_scale * lr_scale_auto_factor(train_state) + + # Warmup for initial training + warmup_scale = 1.0 + if model_config["norm_kind"] == "fixup" or model_config["norm_kind"] == "fixscale" or model_config["norm_kind"] == "fixscaleonenorm": + if train_state["global_step_samples"] < 1000000: + warmup_scale = 1.0 / 5.0 + elif train_state["global_step_samples"] < 2000000: + warmup_scale = 1.0 / 3.0 + elif train_state["global_step_samples"] < 4000000: + warmup_scale = 1.0 / 2.0 + elif train_state["global_step_samples"] < 6000000: + warmup_scale = 1.0 / 1.4 + elif model_config["norm_kind"] == "bnorm" or model_config["norm_kind"] == "brenorm" or model_config["norm_kind"] == "fixbrenorm": + if train_state["global_step_samples"] < 250000: + warmup_scale = 1.0 / 20.0 + elif train_state["global_step_samples"] < 500000: + warmup_scale = 1.0 / 14.0 + elif train_state["global_step_samples"] < 750000: + warmup_scale = 1.0 / 10.0 + elif train_state["global_step_samples"] < 1000000: + warmup_scale = 1.0 / 7.0 + elif train_state["global_step_samples"] < 1250000: + warmup_scale = 1.0 / 5.0 + elif train_state["global_step_samples"] < 1500000: + warmup_scale = 1.0 / 3.0 + elif train_state["global_step_samples"] < 1750000: + warmup_scale = 1.0 / 2.0 + elif train_state["global_step_samples"] < 2000000: + warmup_scale = 1.0 / 1.4 + else: + warmup_scale = 1.0 / 1.0 + else: + assert False + + normal_weight_decay = 0.0 + + for param_group in optimizer.param_groups: + group_name = param_group["group_name"] + if group_name == "normal": + group_scale = 1.0 + elif group_name == "normal_gamma": + group_scale = 1.0 + elif group_name == "output": + group_scale = 0.5 + elif group_name == "noreg": + group_scale = 1.0 + elif group_name == "output_noreg": + group_scale = 0.5 + else: + assert False + + changed = False + + # For lookahead optimizer, use weight decay appropriate for lr scale, + # but tell optimizer to take larger steps so as to maintain the same + # effective learning rate after lookahead averaging. + if lookahead_alpha is not None: + new_lr_this_group = per_sample_lr * warmup_scale * group_scale / lookahead_alpha + else: + new_lr_this_group = per_sample_lr * warmup_scale * group_scale + + if param_group["lr"] != new_lr_this_group: + param_group["lr"] = new_lr_this_group + changed = True + + new_weight_decay_this_group = get_weight_decay( + raw_model, + lr_scale, + warmup_scale=warmup_scale, + train_state=train_state, + running_metrics=running_metrics, + group_name=group_name, + ) + if param_group["weight_decay"] != new_weight_decay_this_group: + param_group["weight_decay"] = new_weight_decay_this_group + changed = True + + if group_name == "normal": + normal_weight_decay = param_group["weight_decay"] + + if changed: + logging.info(f"Param group {param_group['group_name']} lr {param_group['lr']} weight_decay {param_group['weight_decay']}") + + return per_sample_lr * warmup_scale, normal_weight_decay + + last_brenorm_update_samples_this_instance = train_state["global_step_samples"] + def maybe_update_brenorm_params(): + nonlocal last_brenorm_update_samples_this_instance + + if model_config["norm_kind"] == "brenorm" or model_config["norm_kind"] == "fixbrenorm": + if "brenorm_rmax" not in train_state: + train_state["brenorm_rmax"] = 1.0 + if "brenorm_dmax" not in train_state: + train_state["brenorm_dmax"] = 0.0 + + num_samples_elapsed = train_state["global_step_samples"] - last_brenorm_update_samples_this_instance + factor = math.exp(-num_samples_elapsed / brenorm_adjustment_scale) + train_state["brenorm_rmax"] = train_state["brenorm_rmax"] + (1.0 - factor) * (brenorm_target_rmax - train_state["brenorm_rmax"]) + train_state["brenorm_dmax"] = train_state["brenorm_dmax"] + (1.0 - factor) * (brenorm_target_dmax - train_state["brenorm_dmax"]) + + raw_model.set_brenorm_params(brenorm_avg_momentum, train_state["brenorm_rmax"], train_state["brenorm_dmax"]) + last_brenorm_update_samples_this_instance = train_state["global_step_samples"] + + # DATA RELOADING GENERATOR ------------------------------------------------------------ + + # Some globals + last_curdatadir = None + trainfilegenerator = None + vdatadir = None + + def maybe_reload_training_data(): + nonlocal last_curdatadir + nonlocal trainfilegenerator + nonlocal vdatadir + + assert rank == 0, "Helper ddp training processes should not call maybe_reload_training_data" + + while True: + curdatadir = os.path.realpath(datadir) + + # Different directory - new shuffle + if curdatadir != last_curdatadir: + if not os.path.exists(curdatadir): + if quit_if_no_data: + logging.info("Shuffled data path does not exist, there seems to be no data or not enough data yet, qutting: %s" % curdatadir) + sys.exit(0) + logging.info("Shuffled data path does not exist, there seems to be no shuffled data yet, waiting and trying again later: %s" % curdatadir) + time.sleep(30) + continue + + trainjsonpath = os.path.join(curdatadir,"train.json") + if not os.path.exists(trainjsonpath): + if quit_if_no_data: + logging.info("Shuffled data train.json file does not exist, there seems to be no data or not enough data yet, qutting: %s" % trainjsonpath) + sys.exit(0) + logging.info("Shuffled data train.json file does not exist, there seems to be no shuffled data yet, waiting and trying again later: %s" % trainjsonpath) + time.sleep(30) + continue + + logging.info("Updated training data: " + curdatadir) + last_curdatadir = curdatadir + + with open(trainjsonpath) as f: + datainfo = json.load(f) + train_state["total_num_data_rows"] = datainfo["range"][1] + + # Fill the buckets + if max_train_bucket_per_new_data is not None: + if "train_bucket_level_at_row" not in train_state: + train_state["train_bucket_level_at_row"] = train_state["total_num_data_rows"] + if train_state["total_num_data_rows"] > train_state["train_bucket_level_at_row"]: + new_row_count = train_state["total_num_data_rows"] - train_state["train_bucket_level_at_row"] + logging.info("Advancing trainbucket row %.0f to %.0f, %.0f new rows" % ( + train_state["train_bucket_level_at_row"], train_state["total_num_data_rows"], new_row_count + )) + train_state["train_bucket_level_at_row"] = train_state["total_num_data_rows"] + logging.info("Fill per data %.3f, Max bucket size %.0f" % (max_train_bucket_per_new_data, max_train_bucket_size)) + logging.info("Old rows in bucket: %.0f" % train_state["train_bucket_level"]) + train_state["train_bucket_level"] += new_row_count * max_train_bucket_per_new_data + cap = max(max_train_bucket_size, samples_per_epoch) + if train_state["train_bucket_level"] > cap: + train_state["train_bucket_level"] = cap + logging.info("New rows in bucket: %.0f" % train_state["train_bucket_level"]) + if train_state["total_num_data_rows"] < train_state["train_bucket_level_at_row"]: + # Bucket went backward! This must be a network imported from a different run, reset the train bucket level + logging.warning("Train bucket last filled at %d rows but now there are only %d rows!" % ( + train_state["train_bucket_level_at_row"], train_state["total_num_data_rows"] + )) + logging.warning("Data was deleted or this network was transplanted into a new run, resetting the train bucket fill rows") + train_state["train_bucket_level_at_row"] = train_state["total_num_data_rows"] + + logging.info("Train steps since last reload: %.0f -> 0" % train_state["train_steps_since_last_reload"]) + train_state["train_steps_since_last_reload"] = 0 + + # Load training data files + tdatadir = os.path.join(curdatadir,"train") + train_files = [os.path.join(tdatadir,fname) for fname in os.listdir(tdatadir) if fname.endswith(".npz")] + epoch0_train_files = [path for path in train_files if path not in train_state["data_files_used"]] + if no_repeat_files: + logging.info(f"Dropping {len(train_files)-len(epoch0_train_files)}/{len(train_files)} files in: {tdatadir} as already used") + else: + logging.info(f"Skipping {len(train_files)-len(epoch0_train_files)}/{len(train_files)} files in: {tdatadir} as already used first pass") + + if len(train_files) <= 0 or (no_repeat_files and len(epoch0_train_files) <= 0): + if quit_if_no_data: + logging.info(f"No new training files found in: {tdatadir}, quitting") + sys.exit(0) + logging.info(f"No new training files found in: {tdatadir}, waiting 30s and trying again") + time.sleep(30) + continue + + # Update history of what training data we used + if tdatadir not in train_state["old_train_data_dirs"]: + train_state["old_train_data_dirs"].append(tdatadir) + # Clear out tracking of sufficiently old files + while len(train_state["old_train_data_dirs"]) > 20: + old_dir = train_state["old_train_data_dirs"][0] + train_state["old_train_data_dirs"] = train_state["old_train_data_dirs"][1:] + for filename in list(train_state["data_files_used"]): + if filename.startswith(old_dir): + train_state["data_files_used"].remove(filename) + + def train_files_gen(): + train_files_shuffled = epoch0_train_files.copy() + while True: + random.shuffle(train_files_shuffled) + for filename in train_files_shuffled: + logging.info("Yielding training file for dataset: " + filename) + train_state["data_files_used"].add(filename) + yield filename + if no_repeat_files: + break + else: + train_files_shuffled = train_files.copy() + train_state["data_files_used"] = set() + + trainfilegenerator = train_files_gen() + vdatadir = os.path.join(curdatadir,"val") + + # Same directory as before, no new shuffle + else: + if max_train_steps_since_last_reload is not None: + if train_state["train_steps_since_last_reload"] + 0.99 * samples_per_epoch/sub_epochs > max_train_steps_since_last_reload: + logging.info( + "Too many train steps since last reload, waiting 5m and retrying (current %f)" % + train_state["train_steps_since_last_reload"] + ) + time.sleep(300) + continue + + break + + # Load all the files we should train on during a subepoch + def get_files_for_subepoch(): + nonlocal trainfilegenerator + + assert rank == 0, "Helper ddp training processes should not call get_files_for_subepoch" + + num_batches_per_epoch = int(round(samples_per_epoch / batch_size)) + num_batches_per_subepoch = num_batches_per_epoch / sub_epochs + + # Pick enough files to get the number of batches we want + train_files_to_use = [] + batches_to_use_so_far = 0 + found_enough = False + for filename in trainfilegenerator: + jsonfilename = os.path.splitext(filename)[0] + ".json" + with open(jsonfilename) as f: + trainfileinfo = json.load(f) + + num_batches_this_file = trainfileinfo["num_rows"] // batch_size + if num_batches_this_file <= 0: + continue + + if batches_to_use_so_far + num_batches_this_file > num_batches_per_subepoch: + # If we're going over the desired amount, randomly skip the file with probability equal to the + # proportion of batches over - this makes it so that in expectation, we have the desired number of batches + if batches_to_use_so_far > 0 and random.random() >= (batches_to_use_so_far + num_batches_this_file - num_batches_per_subepoch) / num_batches_this_file: + found_enough = True + break + + train_files_to_use.append(filename) + batches_to_use_so_far += num_batches_this_file + + #Sanity check - load a max of 100000 files. + if batches_to_use_so_far >= num_batches_per_subepoch or len(train_files_to_use) > 100000: + found_enough = True + break + + if found_enough: + return train_files_to_use + return None + + # METRICS ----------------------------------------------------------------------------------- + def detensorify_metrics(metrics): + ret = {} + for key in metrics: + if isinstance(metrics[key], torch.Tensor): + ret[key] = metrics[key].detach().cpu().item() + else: + ret[key] = metrics[key] + return ret + + if rank == 0: + train_metrics_out = open(os.path.join(traindir,"metrics_train.json"),"a") + val_metrics_out = open(os.path.join(traindir,"metrics_val.json"),"a") + else: + train_metrics_out = open(os.path.join(traindir,f"metrics_train_rank{rank}.json"),"a") + val_metrics_out = open(os.path.join(traindir,f"metrics_val_rank{rank}.json"),"a") + + # TRAIN! ----------------------------------------------------------------------------------- + + last_longterm_checkpoint_save_time = datetime.datetime.now() + num_epochs_this_instance = 0 + print_train_loss_every_batches = 100 if not gnorm_stats_debug else 1000 + + if "sums" not in running_metrics: + running_metrics["sums"] = defaultdict(float) + else: + running_metrics["sums"] = defaultdict(float,running_metrics["sums"]) + if "weights" not in running_metrics: + running_metrics["weights"] = defaultdict(float) + else: + running_metrics["weights"] = defaultdict(float,running_metrics["weights"]) + + torch.backends.cudnn.benchmark = True + + if use_fp16: + logging.info("Training in FP16! Creating scaler") + scaler = GradScaler() + else: + logging.info("Training in FP32.") + + # All ddp threads should be lined up at this point before continuing + if barrier is not None: + barrier.wait() + + while True: + if max_epochs_this_instance is not None and max_epochs_this_instance >= 0 and num_epochs_this_instance >= max_epochs_this_instance: + logging.info("Hit max epochs this instance, done") + break + if max_training_samples is not None and train_state["global_step_samples"] >= max_training_samples: + logging.info("Hit max training samples, done") + break + + if rank == 0: + maybe_reload_training_data() + + if max_train_bucket_per_new_data is not None: + if train_state["train_bucket_level"] > 0.99 * samples_per_epoch: + logging.info("Consuming %.0f rows from train bucket (%.0f -> %.0f)" % ( + samples_per_epoch, train_state["train_bucket_level"], train_state["train_bucket_level"]-samples_per_epoch + )) + train_state["train_bucket_level"] -= samples_per_epoch + else: + if stop_when_train_bucket_limited: + logging.info( + "Exceeding train bucket, not enough new data rows, terminating (current level %f)" % + train_state["train_bucket_level"] + ) + break + else: + logging.info( + "Exceeding train bucket, not enough new data rows, waiting 5m and retrying (current level %f)" % + train_state["train_bucket_level"] + ) + time.sleep(300) + continue + + # DDP need to wait on the main process after reloading data and/or training bucket waiting + if barrier is not None: + barrier.wait() + + logging.info("GC collect") + gc.collect() + + clear_metric_nonfinite(running_metrics["sums"], running_metrics["weights"]) + + logging.info("=========================================================================") + logging.info("BEGINNING NEXT EPOCH " + str(num_epochs_this_instance)) + logging.info("=========================================================================") + logging.info("Current time: " + str(datetime.datetime.now())) + logging.info("Global step: %d samples" % (train_state["global_step_samples"])) + logging.info("Currently up to data row " + str(train_state["total_num_data_rows"])) + logging.info(f"Training dir: {traindir}") + logging.info(f"Export dir: {exportdir}") + if use_fp16: + logging.info(f"Current grad scale: {scaler.get_scale()}") + + lr_right_now, normal_weight_decay_right_now = update_and_return_lr_and_wd() + maybe_update_brenorm_params() + + # SUB EPOCH LOOP ----------- + batch_count_this_epoch = 0 + last_train_stats_time = time.perf_counter() + for i in range(sub_epochs): + + if rank == 0: + if i != 0: + maybe_reload_training_data() + train_files_to_use = get_files_for_subepoch() + while train_files_to_use is None or len(train_files_to_use) <= 0: + if quit_if_no_data: + logging.info("Not enough data files to fill a subepoch! Quitting.") + sys.exit(0) + logging.info("Not enough data files to fill a subepoch! Waiting 5m before retrying.") + time.sleep(300) + maybe_reload_training_data() + train_files_to_use = get_files_for_subepoch() + + if barrier is not None: + barrier.wait() + for wpipe in writepipes: + wpipe.send(train_files_to_use) + # Wait briefly just in case to reduce chance of races with filesystem or anything else + time.sleep(5) + else: + if barrier is not None: + barrier.wait() + train_files_to_use = readpipes[rank-1].recv() + + # DDP need to wait on the main process after reloading data and sending files to train with + if barrier is not None: + barrier.wait() + + logging.info("Beginning training subepoch!") + logging.info("This subepoch, using files: " + str(train_files_to_use)) + logging.info("Currently up to data row " + str(train_state["total_num_data_rows"])) + lookahead_counter = 0 + for batch in data_processing_pytorch.read_npz_training_data( + train_files_to_use, + batch_size, + world_size, + rank, + pos_len=pos_len, + device=device, + randomize_symmetries=True, + include_meta=raw_model.get_has_metadata_encoder(), + model_config=model_config + ): + optimizer.zero_grad(set_to_none=True) + extra_outputs = None + # if raw_model.get_has_metadata_encoder(): + # extra_outputs = ExtraOutputs([MetadataEncoder.OUTMEAN_KEY,MetadataEncoder.OUTLOGVAR_KEY]) + + if use_fp16: + with autocast(): + model_outputs = ddp_model( + batch["binaryInputNCHW"], + batch["globalInputNC"], + input_meta=(batch["metadataInputNC"] if raw_model.get_has_metadata_encoder() else None), + extra_outputs=extra_outputs, + ) + model_outputs = raw_model.float32ify_output(model_outputs) + else: + model_outputs = ddp_model( + batch["binaryInputNCHW"], + batch["globalInputNC"], + input_meta=(batch["metadataInputNC"] if raw_model.get_has_metadata_encoder() else None), + extra_outputs=extra_outputs, + ) + + postprocessed = raw_model.postprocess_output(model_outputs) + metrics = metrics_obj.metrics_dict_batchwise( + raw_model, + postprocessed, + extra_outputs, + batch, + is_training=True, + soft_policy_weight_scale=soft_policy_weight_scale, + disable_optimistic_policy=disable_optimistic_policy, + meta_kata_only_soft_policy=meta_kata_only_soft_policy, + value_loss_scale=value_loss_scale, + td_value_loss_scales=td_value_loss_scales, + seki_loss_scale=seki_loss_scale, + variance_time_loss_scale=variance_time_loss_scale, + main_loss_scale=main_loss_scale, + intermediate_loss_scale=intermediate_loss_scale, + ) + + # DDP averages loss across instances, so to preserve LR as per-sample lr, we scale by world size. + loss = metrics["loss_sum"] * world_size + + # Reduce gradients across DDP + if use_fp16: + scaler.scale(loss).backward() + scaler.unscale_(optimizer) + else: + loss.backward() + + if model_config["norm_kind"] == "fixup" or model_config["norm_kind"] == "fixscale" or model_config["norm_kind"] == "fixscaleonenorm": + gnorm_cap = 2500.0 * (1.0 if gnorm_clip_scale is None else gnorm_clip_scale) + elif model_config["norm_kind"] == "bnorm" or model_config["norm_kind"] == "brenorm" or model_config["norm_kind"] == "fixbrenorm": + gnorm_cap = 5500.0 * (1.0 if gnorm_clip_scale is None else gnorm_clip_scale) + else: + assert False + + if gnorm_stats_debug: + stats = metrics_obj.get_specific_norms_and_gradient_stats(raw_model) + for stat, value in stats.items(): + metrics[stat] = value + + if "use_repvgg_learning_rate" in model_config and model_config["use_repvgg_learning_rate"]: + gradscale_constant = torch.tensor([[1.0,1.0,1.0],[1.0,2.0,1.0],[1.0,1.0,1.0]],dtype=torch.float32,device=device,requires_grad=False).view(1,1,3,3) + for name, param in ddp_model.named_parameters(): + if "normactconv" in name and ".conv.weight" in name and len(param.shape) == 4 and param.shape[2] == 3 and param.shape[3] == 3: + param.grad *= gradscale_constant + + # Loosen gradient clipping as we shift to smaller learning rates + gnorm_cap = gnorm_cap / math.sqrt(max(0.0000001,lr_scale * lr_scale_auto_factor(train_state))) + + gnorm = torch.nn.utils.clip_grad_norm_(ddp_model.parameters(), gnorm_cap).detach().cpu().item() + + if math.isfinite(gnorm) and abs(gnorm < 1e30): + metrics["gnorm_batch"] = gnorm + exgnorm = max(0.0, gnorm - gnorm_cap) + metrics["exgnorm_sum"] = exgnorm * batch_size + + metrics["pslr_batch"] = lr_right_now + metrics["wdnormal_batch"] = normal_weight_decay_right_now + metrics["gnorm_cap_batch"] = gnorm_cap + + if use_fp16: + scaler.step(optimizer) + scaler.update() + else: + optimizer.step() + + batch_count_this_epoch += 1 + train_state["train_steps_since_last_reload"] += batch_size * world_size + train_state["global_step_samples"] += batch_size * world_size + + metrics = detensorify_metrics(metrics) + + if lookahead_k is not None and lookahead_print: + # Only accumulate metrics when lookahead is synced if lookahead_print is True + if lookahead_counter == 0: + accumulate_metrics(running_metrics["sums"], running_metrics["weights"], metrics, batch_size, decay=math.exp(-0.001 * lookahead_k), new_weight=1.0) + else: + accumulate_metrics(running_metrics["sums"], running_metrics["weights"], metrics, batch_size, decay=1.0, new_weight=0.0) + else: + accumulate_metrics(running_metrics["sums"], running_metrics["weights"], metrics, batch_size, decay=0.999, new_weight=1.0) + + + if batch_count_this_epoch % print_train_loss_every_batches == 0: + + if model_config["norm_kind"] == "brenorm" or model_config["norm_kind"] == "fixbrenorm": + metrics["brn_rmax"] = train_state["brenorm_rmax"] + metrics["brn_dmax"] = train_state["brenorm_dmax"] + metrics["brn_mmnt"] = brenorm_avg_momentum + upper_rclippage = [] + lower_rclippage = [] + dclippage = [] + raw_model.add_brenorm_clippage(upper_rclippage, lower_rclippage, dclippage) + metrics["brn_ruclip"] = sum(upper_rclippage) / max(len(upper_rclippage),1.0) + metrics["brn_rlclip"] = sum(lower_rclippage) / max(len(lower_rclippage),1.0) + metrics["brn_dclip"] = sum(dclippage) / max(len(dclippage),1.0) + + t1 = time.perf_counter() + timediff = t1 - last_train_stats_time + last_train_stats_time = t1 + metrics["time_since_last_print"] = timediff + log_metrics(running_metrics["sums"], running_metrics["weights"], metrics, train_metrics_out) + + # Update LR more frequently at the start for smoother warmup ramp and wd adjustment + if train_state["global_step_samples"] <= 50000000 and batch_count_this_epoch % 50 == 0: + lr_right_now, normal_weight_decay_right_now = update_and_return_lr_and_wd() + + # Update batch renorm parameters + if batch_count_this_epoch % 500 == 0: + maybe_update_brenorm_params() + + # Perform lookahead + in_between_lookaheads = False + if lookahead_k is not None: + lookahead_counter += 1 + if lookahead_counter >= lookahead_k: + for param_group in optimizer.param_groups: + for param in param_group["params"]: + slow_param_data = lookahead_cache[param] + slow_param_data.add_(param.data.detach() - slow_param_data, alpha=lookahead_alpha) + param.data.copy_(slow_param_data) + lookahead_counter = 0 + in_between_lookaheads = False + else: + in_between_lookaheads = True + + # Perform SWA + if swa_model is not None and swa_scale is not None: + train_state["swa_sample_accum"] += batch_size * world_size + # Only snap SWA when lookahead slow params are in sync. + if train_state["swa_sample_accum"] >= swa_period_samples and not in_between_lookaheads: + train_state["swa_sample_accum"] = 0 + logging.info("Accumulating SWA") + swa_model.update_parameters(raw_model) + + logging.info("Finished training subepoch!") + + # END SUB EPOCH LOOP ------------ + + # Discard the gradient updates from the leftover batches in the sub epoch from lookahead. + # This wastes a very tiny bit, but makes it so that we can be in sync and deterministic on ends of subepochs/epochs. + if lookahead_k is not None: + for param_group in optimizer.param_groups: + for param in param_group["params"]: + slow_param_data = lookahead_cache[param] + param.data.copy_(slow_param_data) + + if rank == 0: + train_state["export_cycle_counter"] += 1 + + save(ddp_model, swa_model, optimizer, metrics_obj, running_metrics, train_state, last_val_metrics) + + num_epochs_this_instance += 1 + + # Validate + if rank == 0: + logging.info("Beginning validation after epoch!") + val_files = [] + if os.path.exists(vdatadir): + val_files = [os.path.join(vdatadir,fname) for fname in os.listdir(vdatadir) if fname.endswith(".npz")] + if randomize_val: + random.shuffle(val_files) + else: + # Sort to ensure deterministic order to validation files in case we use only a subset + val_files = sorted(val_files) + if len(val_files) == 0: + logging.info("No validation files, skipping validation step") + else: + with torch.no_grad(): + ddp_model.eval() + val_metric_sums = defaultdict(float) + val_metric_weights = defaultdict(float) + val_samples = 0 + t0 = time.perf_counter() + for batch in data_processing_pytorch.read_npz_training_data( + val_files, + batch_size, + world_size=1, # Only the main process validates + rank=0, # Only the main process validates + pos_len=pos_len, + device=device, + randomize_symmetries=True, + include_meta=raw_model.get_has_metadata_encoder(), + model_config=model_config + ): + model_outputs = ddp_model( + batch["binaryInputNCHW"], + batch["globalInputNC"], + input_meta=(batch["metadataInputNC"] if raw_model.get_has_metadata_encoder() else None), + ) + postprocessed = raw_model.postprocess_output(model_outputs) + extra_outputs = None + metrics = metrics_obj.metrics_dict_batchwise( + raw_model, + postprocessed, + extra_outputs, + batch, + is_training=False, + soft_policy_weight_scale=soft_policy_weight_scale, + disable_optimistic_policy=disable_optimistic_policy, + meta_kata_only_soft_policy=meta_kata_only_soft_policy, + value_loss_scale=value_loss_scale, + td_value_loss_scales=td_value_loss_scales, + seki_loss_scale=seki_loss_scale, + variance_time_loss_scale=variance_time_loss_scale, + main_loss_scale=main_loss_scale, + intermediate_loss_scale=intermediate_loss_scale, + ) + metrics = detensorify_metrics(metrics) + accumulate_metrics(val_metric_sums, val_metric_weights, metrics, batch_size, decay=1.0, new_weight=1.0) + val_samples += batch_size + if max_val_samples is not None and val_samples > max_val_samples: + break + val_metric_sums["nsamp_train"] = running_metrics["sums"]["nsamp"] + val_metric_weights["nsamp_train"] = running_metrics["weights"]["nsamp"] + val_metric_sums["wsum_train"] = running_metrics["sums"]["wsum"] + val_metric_weights["wsum_train"] = running_metrics["weights"]["wsum"] + last_val_metrics["sums"] = val_metric_sums + last_val_metrics["weights"] = val_metric_weights + log_metrics(val_metric_sums, val_metric_weights, metrics, val_metrics_out) + t1 = time.perf_counter() + logging.info(f"Validation took {t1-t0} seconds") + ddp_model.train() + + if rank == 0: + logging.info("Export cycle counter = " + str(train_state["export_cycle_counter"])) + + is_time_to_export = False + if train_state["export_cycle_counter"] >= epochs_per_export: + if no_export: + train_state["export_cycle_counter"] = epochs_per_export + else: + train_state["export_cycle_counter"] = 0 + is_time_to_export = True + + skip_export_this_time = False + if export_prob is not None: + if random.random() > export_prob: + skip_export_this_time = True + logging.info("Skipping export model this time") + + if not no_export and is_time_to_export and not skip_export_this_time and exportdir is not None and not gnorm_stats_debug: + # Export a model for testing, unless somehow it already exists + modelname = "%s-s%d-d%d" % ( + exportprefix, + train_state["global_step_samples"], + train_state["total_num_data_rows"], + ) + savepath = os.path.join(exportdir,modelname) + savepathtmp = os.path.join(exportdir,modelname+".tmp") + if os.path.exists(savepath): + logging.info("NOT saving model, already exists at: " + savepath) + else: + os.mkdir(savepathtmp) + logging.info("SAVING MODEL FOR EXPORT TO: " + savepath) + save(ddp_model, swa_model, optimizer, metrics_obj, running_metrics, train_state, last_val_metrics, path=os.path.join(savepathtmp,"model.ckpt")) + time.sleep(2) + os.rename(savepathtmp,savepath) + + + if sleep_seconds_per_epoch is None: + time.sleep(1) + else: + time.sleep(sleep_seconds_per_epoch) + + if rank == 0: + now = datetime.datetime.now() + if now - last_longterm_checkpoint_save_time >= datetime.timedelta(hours=12): + last_longterm_checkpoint_save_time = now + dated_name = datetime.datetime.now(timezone.utc).strftime("%Y%m%d-%H%M%S") + save(ddp_model, swa_model, optimizer, metrics_obj, running_metrics, train_state, last_val_metrics, path=os.path.join(longterm_checkpoints_dir,f"{dated_name}.ckpt")) + + train_metrics_out.close() + val_metrics_out.close() + + +if __name__ == "__main__": + multi_gpus = args["multi_gpus"] + num_gpus_used = 1 + multi_gpu_device_ids = [] + if multi_gpus is not None: + for piece in multi_gpus.split(","): + piece = piece.strip() + multi_gpu_device_ids.append(int(piece)) + num_gpus_used = len(multi_gpu_device_ids) + else: + multi_gpu_device_ids = [0] + + make_dirs(args) + + readpipes = [] + writepipes = [] + + if num_gpus_used > 1: + torch.multiprocessing.set_start_method("spawn") + + world_size = num_gpus_used + barrier = torch.multiprocessing.Barrier(num_gpus_used) + + for i in range(world_size - 1): + rpipe, wpipe = torch.multiprocessing.Pipe() + readpipes.append(rpipe) + writepipes.append(wpipe) + + torch.multiprocessing.spawn( + main, + nprocs=num_gpus_used, + args=(world_size, args, multi_gpu_device_ids, readpipes, writepipes, barrier) + ) + else: + rank = 0 + world_size = 1 + barrier = None + main(rank, world_size, args, multi_gpu_device_ids, readpipes, writepipes, barrier) diff --git a/training/scripts/dated/20241110-122511/train.sh b/training/scripts/dated/20241110-122511/train.sh new file mode 100644 index 0000000..e26d70d --- /dev/null +++ b/training/scripts/dated/20241110-122511/train.sh @@ -0,0 +1,87 @@ +#!/bin/bash -eu +set -o pipefail +{ +# Runs training in $BASEDIR/train/$TRAININGNAME +# Should be run once per persistent training process. +# Outputs results in torchmodels_toexport/ in an ongoing basis (EXPORTMODE == "main"). +# Or, to torchmodels_toexport_extra/ (EXPORTMODE == "extra"). +# Or just trains without exporting (EXPORTMODE == "trainonly"). + +if [[ $# -lt 5 ]] +then + echo "Usage: $0 BASEDIR TRAININGNAME MODELKIND BATCHSIZE EXPORTMODE OTHERARGS" + echo "BASEDIR containing selfplay data and models and related directories" + echo "TRAININGNAME name to prefix models with, specific to this training daemon" + echo "MODELKIND what size model to train, like b10c128, see ../modelconfigs.py" + echo "BATCHSIZE number of samples to concat together per batch for training, must match shuffle" + echo "EXPORTMODE 'main': train and export for selfplay. 'extra': train and export extra non-selfplay model. 'trainonly': train without export" + exit 0 +fi +BASEDIR="$1" +shift +TRAININGNAME="$1" +shift +MODELKIND="$1" +shift +BATCHSIZE="$1" +shift +EXPORTMODE="$1" +shift + +#------------------------------------------------------------------------------ +set -x + +mkdir -p "$BASEDIR"/train/"$TRAININGNAME" + +if [[ -n $(pwd | grep "^$BASEDIR/scripts/") ]] +then + echo "Already running out of snapshotted scripts directory, not snapshotting again" +else + GITROOTDIR="$(git rev-parse --show-toplevel)" + + git show --no-patch --no-color > "$BASEDIR"/train/"$TRAININGNAME"/version.txt + git diff --no-color > "$BASEDIR"/train/"$TRAININGNAME"/diff.txt + git diff --staged --no-color > "$BASEDIR"/train/"$TRAININGNAME"/diffstaged.txt + + # For archival and logging purposes - you can look back and see exactly the python code on a particular date + DATE_FOR_FILENAME=$(date "+%Y%m%d-%H%M%S") + DATED_ARCHIVE="$BASEDIR"/scripts/train/dated/"$DATE_FOR_FILENAME" + mkdir -p "$DATED_ARCHIVE" + cp "$GITROOTDIR"/python/*.py "$GITROOTDIR"/python/selfplay/train.sh "$DATED_ARCHIVE" + git show --no-patch --no-color > "$DATED_ARCHIVE"/version.txt + git diff --no-color > "$DATED_ARCHIVE"/diff.txt + git diff --staged --no-color > "$DATED_ARCHIVE"/diffstaged.txt + cd "$DATED_ARCHIVE" +fi + +if [ "$EXPORTMODE" == "main" ] +then + EXPORT_SUBDIR=torchmodels_toexport + EXTRAFLAG="" +elif [ "$EXPORTMODE" == "extra" ] +then + EXPORT_SUBDIR=torchmodels_toexport_extra + EXTRAFLAG="" +elif [ "$EXPORTMODE" == "trainonly" ] +then + EXPORT_SUBDIR=torchmodels_toexport_extra + EXTRAFLAG="-no-export" +else + echo "EXPORTMODE was not 'main' or 'extra' or 'trainonly', run with no arguments for usage" + exit 1 +fi + +time python3 ./train.py \ + -traindir "$BASEDIR"/train/"$TRAININGNAME" \ + -datadir "$BASEDIR"/shuffleddata/current/ \ + -exportdir "$BASEDIR"/"$EXPORT_SUBDIR" \ + -exportprefix "$TRAININGNAME" \ + -pos-len 19 \ + -batch-size "$BATCHSIZE" \ + -model-kind "$MODELKIND" \ + $EXTRAFLAG \ + "$@" \ + 2>&1 | tee -a "$BASEDIR"/train/"$TRAININGNAME"/stdout.txt + +exit 0 +} diff --git a/training/scripts/dated/20241110-122511/upload_model.py b/training/scripts/dated/20241110-122511/upload_model.py new file mode 100644 index 0000000..cfd9606 --- /dev/null +++ b/training/scripts/dated/20241110-122511/upload_model.py @@ -0,0 +1,178 @@ +#!/usr/bin/python3 +import sys +import os +import argparse +import logging +import requests +import hashlib +import configparser +import datetime +import json + +from requests_toolbelt.adapters import host_header_ssl +from requests.auth import HTTPBasicAuth + +#Command and args------------------------------------------------------------------- + +description = """ +Upload neural net weights to server +""" + +parser = argparse.ArgumentParser(description=description) +parser.add_argument('-run-name', help='run name', required=True) +parser.add_argument('-model-name', help='model name', required=True) +parser.add_argument('-model-file', help='model file for kg engine alone', required=True) +parser.add_argument('-model-zip', help='zipped model file with tf weights', required=True) +parser.add_argument('-upload-log-file', help='log upload data to this file', required=True) +parser.add_argument('-metadata-file', help='metadata.json file for recording some stats', required=False) +parser.add_argument('-parents-dir', help='dir with uploaded models dirs for finding parent', required=False) +parser.add_argument('-connection-config', help='config with serverUrl and username and password', required=True) +parser.add_argument('-not-enabled', help='upload model where it is not enabled for train/rating to begin with', required=False, action='store_true') +parser.add_argument('-rating-only', help='upload for rating only or not', type=int, default=0, required=False) +parser.add_argument('-notes', help='extra notes to record for model', required=False) +args = vars(parser.parse_args()) + +run_name = args["run_name"] +model_name = args["model_name"] +model_file = args["model_file"] +model_zip = args["model_zip"] +upload_log_file = args["upload_log_file"] +metadata_file = args["metadata_file"] +parents_dir = args["parents_dir"] +connection_config_file = args["connection_config"] +not_enabled = args["not_enabled"] +rating_only = args["rating_only"] +notes = args["notes"] + +loglines = [] +def log(s): + loglines.append(s) + print(s,flush=True) + +def write_log(): + with open(upload_log_file,"w+") as f: + for line in loglines: + f.write(line + "\n") + +with open(connection_config_file,'r') as f: + connection_config_content = "[DEFAULT]\n" + f.read() +config_parser = configparser.ConfigParser() +config_parser.read_string(connection_config_content) +base_server_url = config_parser["DEFAULT"]["serverUrl"] + +if not base_server_url.endswith("/"): + base_server_url = base_server_url + "/" + +username = config_parser["DEFAULT"]["username"] +password = config_parser["DEFAULT"]["password"] +sslVerificationHost = None +if "sslVerificationHost" in config_parser["DEFAULT"]: + sslVerificationHost = config_parser["DEFAULT"]["sslVerificationHost"] +sslVerifyPemPath = None +if "sslVerifyPemPath" in config_parser["DEFAULT"]: + sslVerifyPemPath = config_parser["DEFAULT"]["sslVerifyPemPath"] + +log("now" + ": " + str(datetime.datetime.now())) +log("run_name" + ": " + run_name) +log("model_name" + ": " + model_name) +log("model_file" + ": " + model_file) +log("model_zip" + ": " + model_zip) +log("parents_dir" + ": " + str(parents_dir)) +log("metadata_file" + ": " + str(metadata_file)) +log("username" + ": " + username) +log("base_server_url" + ": " + base_server_url) + +network_size = model_name.split("-")[1] + +model_file_extension = None +if model_file.endswith(".bin.gz"): + model_file_extension = ".bin.gz" +elif model_file.endswith(".txt.gz"): + model_file_extension = ".txt.gz" +else: + raise Exception("Model file must end in .bin.gz or .txt.gz") + +possible_parents = [] +if parents_dir is not None: + for fname in os.listdir(parents_dir): + if fname.startswith(network_size): + pieces = fname.split("-") + datasamples = int(pieces[2][1:]) + possible_parents.append((fname, datasamples)) + possible_parents.sort(key=(lambda x: x[1])) + +parent_network_name_without_run = None +if len(possible_parents) > 0: + parent_network_name_without_run = possible_parents[-1][0] + +metadata = None +if metadata_file is not None: + with open(metadata_file) as f: + metadata = json.load(f) + +with open(model_file,"rb") as f: + model_file_contents = f.read() + model_file_bytes = len(model_file_contents) + model_file_sha256 = hashlib.sha256(model_file_contents).hexdigest() + del model_file_contents + +url = base_server_url + "api/networks/" + +with open(model_file,"rb") as model_file_handle: + with open(model_zip,"rb") as model_zip_handle: + log_gamma_offset = -1.0 if network_size == "b60c320" else 0.0 + data = { + "run": (None, base_server_url + "api/runs/" + run_name + "/"), + "name": (None, model_name), + "network_size": (None, network_size), + "is_random": (None, "false"), + "model_file": (model_name + model_file_extension, model_file_handle, "application/octet-stream"), + "model_file_bytes": (None, model_file_bytes), + "model_file_sha256": (None, model_file_sha256), + "training_games_enabled": (None, ("false" if (not_enabled or rating_only != 0) else "true")), + "rating_games_enabled": (None, ("false" if not_enabled else "true")), + "log_gamma_offset": (None, str(log_gamma_offset)), + "model_zip_file": (model_name + ".zip", model_zip_handle, "application/octet-stream"), + } + + if parent_network_name_without_run is not None: + data["parent_network"] = (None, base_server_url + "api/networks/" + run_name + "-" + parent_network_name_without_run + "/") + + if notes is not None: + data["notes"] = (None, notes) + + if metadata is not None: + if "global_step_samples" in metadata: + data["train_step"] = (None, metadata["global_step_samples"]) + if "total_num_data_rows" in metadata: + data["total_num_data_rows"] = (None, metadata["total_num_data_rows"]) + if "extra_stats" in metadata: + data["extra_stats"] = (None, json.dumps(metadata["extra_stats"])) + + # print(requests.Request('POST', base_server_url, files=data).prepare().body) + + if sslVerificationHost is not None: + sess = requests.Session() + sess.mount('https://', host_header_ssl.HostHeaderSSLAdapter()) + if sslVerifyPemPath is not None: + result = sess.post(url, files=data, auth=HTTPBasicAuth(username,password), headers={"Host": sslVerificationHost}, verify=sslVerifyPemPath) + else: + result = sess.post(url, files=data, auth=HTTPBasicAuth(username,password), headers={"Host": sslVerificationHost}) + else: + if sslVerifyPemPath is not None: + result = requests.post(url, files=data, auth=HTTPBasicAuth(username,password), verify=sslVerifyPemPath) + else: + result = requests.post(url,files=data,auth=HTTPBasicAuth(username,password)) + +log("Post status code: " + str(result.status_code)) +log("Post result: " + str(result.text)) +if result.status_code == 400 and str(result.text).find("already exist") != -1: + log("Got 400 error with substring 'already exist', network already uploaded? So assuming everything is good") + write_log() +elif result.status_code == 200 or result.status_code == 201 or result.status_code == 202: + log("Post success") + write_log() +else: + log("Post failed") + write_log() + sys.exit(1) diff --git a/training/scripts/dated/20241110-122511/upload_poses.py b/training/scripts/dated/20241110-122511/upload_poses.py new file mode 100644 index 0000000..ae38189 --- /dev/null +++ b/training/scripts/dated/20241110-122511/upload_poses.py @@ -0,0 +1,219 @@ +#!/usr/bin/python3 +import sys +import os +import argparse +import logging +import requests +import hashlib +import configparser +import json + +from requests.auth import HTTPBasicAuth + +#Command and args------------------------------------------------------------------- + +description = """ +Upload neural net weights to server +""" + +parser = argparse.ArgumentParser(description=description) +parser.add_argument('-run-name', help='run name', required=True) +parser.add_argument('-poses', help='startposes or hintposes file or dir', required=True, action='append') +parser.add_argument('-upload-log-file', help='log upload data to this file', required=False) +parser.add_argument('-connection-config', help='config with serverUrl and username and password', required=False) +parser.add_argument('-set-total-weight', help='set total weight of poses to this', type=float, required=False) +parser.add_argument('-weight-factor', help='multiply weight of poses by this', type=float, required=False) +parser.add_argument('-separate-summaries', help='summarize dirs separately', action="store_true", required=False) +parser.add_argument('-extra-stats', help='show some extra stats', action="store_true", required=False) +parser.add_argument('-notes', help='notes or description label for poses', required=False) +args = vars(parser.parse_args()) + +run_name = args["run_name"] +poses_files_or_dirs = args["poses"] +upload_log_file = args["upload_log_file"] +connection_config_file = args["connection_config"] +set_total_weight = args["set_total_weight"] +weight_factor = args["weight_factor"] +separate_summaries = args["separate_summaries"] +extra_stats = args["extra_stats"] +notes = args["notes"] + +loglines = [] +def log(s): + loglines.append(s) + print(s,flush=True) + +def write_log(): + if upload_log_file is not None: + with open(upload_log_file,"a+") as f: + for line in loglines: + f.write(line + "\n") + +if notes is None: + notes = "" + +if connection_config_file is not None: + with open(connection_config_file,'r') as f: + connection_config_content = "[DEFAULT]\n" + f.read() + config_parser = configparser.ConfigParser() + config_parser.read_string(connection_config_content) + base_server_url = config_parser["DEFAULT"]["serverUrl"] + + if not base_server_url.endswith("/"): + base_server_url = base_server_url + "/" + +log("run_name" + ": " + run_name) +log("poses" + ": " + str(poses_files_or_dirs)) +log("notes" + ": " + notes) + +if connection_config_file is not None: + username = config_parser["DEFAULT"]["username"] + password = config_parser["DEFAULT"]["password"] + log("username" + ": " + username) + log("base_server_url" + ": " + base_server_url) + +def compute_sum_sumsq(poses): + sumweight = 0.0 + sumweightsq = 0.0 + for pos in poses: + sumweight += pos["weight"] + sumweightsq += pos["weight"] * pos["weight"] + return (sumweight, sumweightsq) + +def print_extra_stats(poses): + sumweight = 0.0 + sumturnnum = 0 + for pos in poses: + sumturnnum += pos["weight"] * (pos["initialTurnNumber"] + len(pos["moveLocs"])) + sumweight += pos["weight"] + print("Avg turn number:", sumturnnum/sumweight) + +log("Loading positions") +poses_by_key = {} + +def handle_file(poses_by_key, poses_file): + log("Loading" + ": " + poses_file) + poses_by_key_this_file = {} + with open(poses_file,"r") as f: + for line in f: + line = line.strip() + if len(line) <= 0: + continue + pos = json.loads(line) + key = ( + str(pos["initialTurnNumber"]) + "$" + + "@".join(pos["moveLocs"]) + "$" + + "@".join(pos["movePlas"]) + "$" + + str(pos["xSize"]) + "$" + + str(pos["ySize"]) + "$" + + pos["nextPla"] + "$" + + pos["board"] + "$" + + pos["hintLoc"] + ) + + if len(pos["movePlas"]) > 0: + assert pos["nextPla"] == "B" or pos["nextPla"] == "W" + assert pos["movePlas"][0] == pos["nextPla"] + for i in range(len(pos["movePlas"])): + assert pos["movePlas"][i] == "B" or pos["movePlas"][i] == "W" + for i in range(1,len(pos["movePlas"])): + assert pos["movePlas"][i] != pos["movePlas"][i-1] + + if "weight" in pos: + weight = pos["weight"] + else: + pos["weight"] = 1.0 + weight = 1.0 + + if key in poses_by_key: + poses_by_key[key]["weight"] += weight + else: + poses_by_key[key] = pos.copy() + if key in poses_by_key_this_file: + poses_by_key_this_file[key]["weight"] += weight + else: + poses_by_key_this_file[key] = pos.copy() + if separate_summaries: + sumweight,sumweightsq = compute_sum_sumsq(poses_by_key_this_file.values()) + if sumweight > 0 and sumweightsq > 0: + log("Found %d unique positions" % len(poses_by_key_this_file.values())) + log("Found %f total weight" % sumweight) + log("Found %f ess" % (sumweight * sumweight / sumweightsq)) + if extra_stats: + print_extra_stats(poses_by_key_this_file.values()) + log("%d %f %f" % (len(poses_by_key_this_file.values()), sumweight, (sumweight * sumweight / sumweightsq))) + +poses_files_or_dirs = sorted(poses_files_or_dirs) +for poses_file_or_dir in poses_files_or_dirs: + if os.path.isdir(poses_file_or_dir): + for (path,dirnames,filenames) in os.walk(poses_file_or_dir): + dirnames.sort() + filenames = sorted(filenames) + for filename in filenames: + if filename.endswith(".startposes.txt") or filename.endswith(".hintposes.txt") or filename.endswith(".bookposes.txt"): + handle_file(poses_by_key, os.path.join(path,filename)) + else: + handle_file(poses_by_key, poses_file_or_dir) + +poses = poses_by_key.values() +sumweight,sumweightsq = compute_sum_sumsq(poses) +log("Found %d unique positions" % len(poses)) +log("Found %f total weight" % sumweight) +log("Found %f ess" % (sumweight * sumweight / sumweightsq)) +if extra_stats: + print_extra_stats(poses) +log("%d %f %f" % (len(poses), sumweight, (sumweight * sumweight / sumweightsq))) + +if set_total_weight is not None: + log("Setting total weight of data to " + str(set_total_weight)) + scale = set_total_weight / sumweight + for pos in poses: + pos["weight"] *= scale +elif weight_factor is not None: + log("Scaling weight of data by " + str(weight_factor)) + for pos in poses: + pos["weight"] *= weight_factor + +def postStuff(to_post): + url = base_server_url + "api/startposes/" + result = requests.post(url,json=to_post,auth=HTTPBasicAuth(username,password)) + log("Post status code: " + str(result.status_code)) + if result.status_code == 200 or result.status_code == 201 or result.status_code == 202: + log("Post success") + else: + log("Post failed") + if result.text: + log(result.text) + write_log() + sys.exit(1) + + +if connection_config_file is None: + log("No connection config specified, quitting WITHOUT uploading anything") +else: + num_total = len(poses) + num_posted = 0 + + to_post = [] + for pos in poses: + weight = pos["weight"] + data = { + "run": base_server_url + "api/runs/" + run_name + "/", + "weight": weight, + "data": pos, + "notes": notes, + } + to_post.append(data) + if len(to_post) >= 5000: + num_posted += len(to_post) + log("Posting " + str(num_posted) + "/" + str(num_total)) + postStuff(to_post) + to_post = [] + + num_posted += len(to_post) + log("Posting " + str(num_posted) + "/" + str(num_total)) + postStuff(to_post) + to_post = [] + + log("Done") + write_log() diff --git a/training/scripts/dated/20241110-122511/version.txt b/training/scripts/dated/20241110-122511/version.txt new file mode 100644 index 0000000..0a99568 --- /dev/null +++ b/training/scripts/dated/20241110-122511/version.txt @@ -0,0 +1,5 @@ +commit 827f7555bd393e2389f74f1ca147595d95a716b7 +Author: David Wu +Date: Sun Jul 28 12:02:09 2024 -0400 + + Allow analysis data output even when no play selection values diff --git a/training/scripts/dated/20241110-123407/add_kata_selfplay_meta_to_npzs.py b/training/scripts/dated/20241110-123407/add_kata_selfplay_meta_to_npzs.py new file mode 100644 index 0000000..8e48045 --- /dev/null +++ b/training/scripts/dated/20241110-123407/add_kata_selfplay_meta_to_npzs.py @@ -0,0 +1,73 @@ +import os +import sys +import random +import numpy as np + +from sgfmetadata import SGFMetadata + +expected_keys = [ + "binaryInputNCHWPacked", + "globalInputNC", + "policyTargetsNCMove", + "globalTargetsNC", + "scoreDistrN", + "valueTargetsNCHW", +] + +def process_npz_files(in_dir, out_dir): + rand = random.Random() + for filename in os.listdir(in_dir): + if filename.endswith(".npz"): + print("Processing " + filename, flush=True) + with np.load(os.path.join(in_dir, filename)) as npz: + assert(set(npz.keys()) == set(expected_keys)) + binaryInputNCHWPacked = npz["binaryInputNCHWPacked"] + globalInputNC = npz["globalInputNC"] + policyTargetsNCMove = npz["policyTargetsNCMove"] + globalTargetsNC = npz["globalTargetsNC"] + scoreDistrN = npz["scoreDistrN"] + valueTargetsNCHW = npz["valueTargetsNCHW"] + + pos_len = valueTargetsNCHW.shape[2] + + binaryInputNCHW = np.unpackbits(binaryInputNCHWPacked,axis=2) + assert len(binaryInputNCHW.shape) == 3 + assert binaryInputNCHW.shape[2] == ((pos_len * pos_len + 7) // 8) * 8 + binaryInputNCHW = binaryInputNCHW[:,:,:pos_len*pos_len] + binaryInputNCHW = np.reshape(binaryInputNCHW, ( + binaryInputNCHW.shape[0], binaryInputNCHW.shape[1], pos_len, pos_len + )).astype(np.float32) + + metadataInputNC = np.zeros((binaryInputNCHW.shape[0],SGFMetadata.METADATA_INPUT_NUM_CHANNELS), dtype=np.float32) + for i in range(binaryInputNCHW.shape[0]): + board_area = int(np.sum(binaryInputNCHW[i,0])) + sgfmeta = SGFMetadata.get_katago_selfplay_metadata(rand=rand) + # nextPlayer doesn't matter in selfplay because it only is used to swap pla/opp metadata + # and in this case both sides are identically katago + metadataInputNC[i] = sgfmeta.get_metadata_row(nextPlayer=Board.WHITE,boardArea=board_area) + + np.savez_compressed( + os.path.join(out_dir, filename), + binaryInputNCHWPacked = binaryInputNCHWPacked, + globalInputNC = globalInputNC, + policyTargetsNCMove = policyTargetsNCMove, + globalTargetsNC = globalTargetsNC, + scoreDistrN = scoreDistrN, + valueTargetsNCHW = valueTargetsNCHW, + metadataInputNC = metadataInputNC, + ) + print("Done") + +if __name__ == "__main__": + if len(sys.argv) != 3: + print("Usage: python add_kata_selfplay_meta_to_npzs.py ") + sys.exit(1) + + in_dir = sys.argv[1] + out_dir = sys.argv[2] + + if os.path.exists(out_dir): + raise Exception(out_dir + " already exists") + os.mkdir(out_dir) + + process_npz_files(in_dir, out_dir) diff --git a/training/scripts/dated/20241110-123407/board.py b/training/scripts/dated/20241110-123407/board.py new file mode 100644 index 0000000..daa4902 --- /dev/null +++ b/training/scripts/dated/20241110-123407/board.py @@ -0,0 +1,1436 @@ +import random +import numpy as np + +class IllegalMoveError(ValueError): + pass + +Pos = int +Loc = int +Player = int + +#Implements legal moves without superko +class Board: + EMPTY = 0 + BLACK = 1 + WHITE = 2 + WALL = 3 + + ZOBRIST_STONE = [[],[],[],[]] + ZOBRIST_PLA = [] + + ZOBRIST_RAND = random.Random() + ZOBRIST_RAND.seed(123987456) + + PASS_LOC = 0 + + for i in range((19+1)*(19+2)+1): + ZOBRIST_STONE[BLACK].append(ZOBRIST_RAND.getrandbits(64)) + ZOBRIST_STONE[WHITE].append(ZOBRIST_RAND.getrandbits(64)) + for i in range(4): + ZOBRIST_PLA.append(ZOBRIST_RAND.getrandbits(64)) + + def __init__(self,size,copy_other=None): + if size < 2 or size > 39: + raise ValueError("Invalid board size: " + str(size)) + self.size = size + self.arrsize = (size+1)*(size+2)+1 + self.dy = size+1 + self.adj = [-self.dy,-1,1,self.dy] + self.diag = [-self.dy-1,-self.dy+1,self.dy-1,self.dy+1] + + if copy_other is not None: + self.pla = copy_other.pla + self.board = np.copy(copy_other.board) + self.group_head = np.copy(copy_other.group_head) + self.group_stone_count = np.copy(copy_other.group_stone_count) + self.group_liberty_count = np.copy(copy_other.group_liberty_count) + self.group_next = np.copy(copy_other.group_next) + self.group_prev = np.copy(copy_other.group_prev) + self.zobrist = copy_other.zobrist + self.simple_ko_point = copy_other.simple_ko_point + self.num_captures_made = copy_other.num_captures_made.copy() + self.num_non_pass_moves_made = copy_other.num_non_pass_moves_made.copy() + else: + self.pla = Board.BLACK + self.board = np.zeros(shape=(self.arrsize), dtype=np.int8) + self.group_head = np.zeros(shape=(self.arrsize), dtype=np.int16) + self.group_stone_count = np.zeros(shape=(self.arrsize), dtype=np.int16) + self.group_liberty_count = np.zeros(shape=(self.arrsize), dtype=np.int16) + self.group_next = np.zeros(shape=(self.arrsize), dtype=np.int16) + self.group_prev = np.zeros(shape=(self.arrsize), dtype=np.int16) + self.zobrist = 0 + self.simple_ko_point = None + self.num_captures_made = {Board.BLACK: 0, Board.WHITE: 0} + self.num_non_pass_moves_made = {Board.BLACK: 0, Board.WHITE: 0} + + for i in range(-1,size+1): + self.board[self.loc(i,-1)] = Board.WALL + self.board[self.loc(i,size)] = Board.WALL + self.board[self.loc(-1,i)] = Board.WALL + self.board[self.loc(size,i)] = Board.WALL + + #More-easily catch errors + self.group_head[0] = -1 + self.group_next[0] = -1 + self.group_prev[0] = -1 + + def copy(self): + return Board(self.size,copy_other=self) + + @staticmethod + def get_opp(pla): + return 3-pla + @staticmethod + def loc_static(x,y,size): + return (x+1) + (size+1)*(y+1) + + def loc(self,x,y): + return (x+1) + self.dy*(y+1) + def loc_x(self,loc): + return (loc % self.dy)-1 + def loc_y(self,loc): + return (loc // self.dy)-1 + + def is_adjacent(self,loc1,loc2): + return loc1 == loc2 + self.adj[0] or loc1 == loc2 + self.adj[1] or loc1 == loc2 + self.adj[2] or loc1 == loc2 + self.adj[3] + + def pos_zobrist(self): + return self.zobrist + def sit_zobrist(self): + return self.zobrist ^ Board.ZOBRIST_PLA[self.pla] + + def num_liberties(self,loc): + if self.board[loc] == Board.EMPTY or self.board[loc] == Board.WALL: + return 0 + return self.group_liberty_count[self.group_head[loc]] + + def is_simple_eye(self,pla,loc): + adj0 = loc + self.adj[0] + adj1 = loc + self.adj[1] + adj2 = loc + self.adj[2] + adj3 = loc + self.adj[3] + + if (self.board[adj0] != pla and self.board[adj0] != Board.WALL) or \ + (self.board[adj1] != pla and self.board[adj1] != Board.WALL) or \ + (self.board[adj2] != pla and self.board[adj2] != Board.WALL) or \ + (self.board[adj3] != pla and self.board[adj3] != Board.WALL): + return False + + opp = Board.get_opp(pla) + opp_corners = 0 + diag0 = loc + self.diag[0] + diag1 = loc + self.diag[1] + diag2 = loc + self.diag[2] + diag3 = loc + self.diag[3] + if self.board[diag0] == opp: + opp_corners += 1 + if self.board[diag1] == opp: + opp_corners += 1 + if self.board[diag2] == opp: + opp_corners += 1 + if self.board[diag3] == opp: + opp_corners += 1 + + if opp_corners >= 2: + return False + if opp_corners <= 0: + return True + + against_wall = ( + self.board[adj0] == Board.WALL or \ + self.board[adj1] == Board.WALL or \ + self.board[adj2] == Board.WALL or \ + self.board[adj3] == Board.WALL + ) + + if against_wall: + return False + return True + + + def would_be_legal(self,pla,loc): + if pla != Board.BLACK and pla != Board.WHITE: + return False + if loc == Board.PASS_LOC: + return True + if not self.is_on_board(loc): + return False + if self.board[loc] != Board.EMPTY: + return False + if self.would_be_single_stone_suicide(pla,loc): + return False + if loc == self.simple_ko_point: + return False + return True + + def would_be_suicide(self,pla,loc): + adj0 = loc + self.adj[0] + adj1 = loc + self.adj[1] + adj2 = loc + self.adj[2] + adj3 = loc + self.adj[3] + + opp = Board.get_opp(pla) + + #If empty or capture, then not suicide + if self.board[adj0] == Board.EMPTY or (self.board[adj0] == opp and self.group_liberty_count[self.group_head[adj0]] == 1) or \ + self.board[adj1] == Board.EMPTY or (self.board[adj1] == opp and self.group_liberty_count[self.group_head[adj1]] == 1) or \ + self.board[adj2] == Board.EMPTY or (self.board[adj2] == opp and self.group_liberty_count[self.group_head[adj2]] == 1) or \ + self.board[adj3] == Board.EMPTY or (self.board[adj3] == opp and self.group_liberty_count[self.group_head[adj3]] == 1): + return False + #If connects to own stone with enough liberties, then not suicide + if self.board[adj0] == pla and self.group_liberty_count[self.group_head[adj0]] > 1 or \ + self.board[adj1] == pla and self.group_liberty_count[self.group_head[adj1]] > 1 or \ + self.board[adj2] == pla and self.group_liberty_count[self.group_head[adj2]] > 1 or \ + self.board[adj3] == pla and self.group_liberty_count[self.group_head[adj3]] > 1: + return False + return True + + def would_be_single_stone_suicide(self,pla,loc): + adj0 = loc + self.adj[0] + adj1 = loc + self.adj[1] + adj2 = loc + self.adj[2] + adj3 = loc + self.adj[3] + + opp = Board.get_opp(pla) + + #If empty or capture, then not suicide + if self.board[adj0] == Board.EMPTY or (self.board[adj0] == opp and self.group_liberty_count[self.group_head[adj0]] == 1) or \ + self.board[adj1] == Board.EMPTY or (self.board[adj1] == opp and self.group_liberty_count[self.group_head[adj1]] == 1) or \ + self.board[adj2] == Board.EMPTY or (self.board[adj2] == opp and self.group_liberty_count[self.group_head[adj2]] == 1) or \ + self.board[adj3] == Board.EMPTY or (self.board[adj3] == opp and self.group_liberty_count[self.group_head[adj3]] == 1): + return False + #If connects to own stone, then not single stone suicide + if self.board[adj0] == pla or \ + self.board[adj1] == pla or \ + self.board[adj2] == pla or \ + self.board[adj3] == pla: + return False + return True + + #Returns the number of liberties a new stone placed here would have, or maxLibs if it would be >= maxLibs. + def get_liberties_after_play(self,pla,loc,maxLibs): + opp = Board.get_opp(pla) + libs = [] + capturedGroupHeads = [] + + #First, count immediate liberties and groups that would be captured + for i in range(4): + adj = loc + self.adj[i] + if self.board[adj] == Board.EMPTY: + libs.append(adj) + if len(libs) >= maxLibs: + return maxLibs + + elif self.board[adj] == opp and self.num_liberties(adj) == 1: + libs.append(adj) + if len(libs) >= maxLibs: + return maxLibs + + head = self.group_head[adj] + if head not in capturedGroupHeads: + capturedGroupHeads.append(head) + + def wouldBeEmpty(possibleLib): + if self.board[possibleLib] == Board.EMPTY: + return True + elif self.board[possibleLib] == opp: + return (self.group_head[possibleLib] in capturedGroupHeads) + return False + + #Next, walk through all stones of all surrounding groups we would connect with and count liberties, avoiding overlap. + connectingGroupHeads = [] + for i in range(4): + adj = loc + self.adj[i] + if self.board[adj] == pla: + head = self.group_head[adj] + if head not in connectingGroupHeads: + connectingGroupHeads.append(head) + + cur = adj + while True: + for k in range(4): + possibleLib = cur + self.adj[k] + if possibleLib != loc and wouldBeEmpty(possibleLib) and possibleLib not in libs: + libs.append(possibleLib) + if len(libs) >= maxLibs: + return maxLibs + + cur = self.group_next[cur] + if cur == adj: + break + + return len(libs) + + + def to_string(self): + def get_piece(x,y): + loc = self.loc(x,y) + if self.board[loc] == Board.BLACK: + return 'X ' + elif self.board[loc] == Board.WHITE: + return 'O ' + elif (x == 3 or x == self.size/2 or x == self.size-1-3) and (y == 3 or y == self.size/2 or y == self.size-1-3): + return '* ' + else: + return '. ' + + return "\n".join("".join(get_piece(x,y) for x in range(self.size)) for y in range(self.size)) + + def to_liberty_string(self): + def get_piece(x,y): + loc = self.loc(x,y) + if self.board[loc] == Board.BLACK or self.board[loc] == Board.WHITE: + libs = self.group_liberty_count[self.group_head[loc]] + if libs <= 9: + return str(libs) + ' ' + else: + return '@ ' + elif (x == 3 or x == self.size/2 or x == self.size-1-3) and (y == 3 or y == self.size/2 or y == self.size-1-3): + return '* ' + else: + return '. ' + + return "\n".join("".join(get_piece(x,y) for x in range(self.size)) for y in range(self.size)) + + def set_pla(self,pla): + self.pla = pla + def is_on_board(self,loc): + return loc >= 0 and loc < self.arrsize and self.board[loc] != Board.WALL + + #Set a given location with error checking. Suicide setting allowed. + def set_stone(self,pla,loc): + if pla != Board.EMPTY and pla != Board.BLACK and pla != Board.WHITE: + raise IllegalMoveError("Invalid pla for board.set") + if not self.is_on_board(loc): + raise IllegalMoveError("Invalid loc for board.set") + + if self.board[loc] == pla: + pass + elif self.board[loc] == Board.EMPTY: + self.add_unsafe(pla,loc) + elif pla == Board.EMPTY: + self.remove_single_stone_unsafe(loc) + else: + self.remove_single_stone_unsafe(loc) + self.add_unsafe(pla,loc) + + #Clear any ko restrictions + self.simple_ko_point = None + + + #Play a stone at the given location, with non-superko legality checking and updating the pla and simple ko point + #Single stone suicide is disallowed but suicide is allowed, to support rule sets and sgfs that have suicide + def play(self,pla,loc): + if pla != Board.BLACK and pla != Board.WHITE: + raise IllegalMoveError("Invalid pla for board.play") + + if loc != Board.PASS_LOC: + if not self.is_on_board(loc): + raise IllegalMoveError("Invalid loc for board.set") + if self.board[loc] != Board.EMPTY: + raise IllegalMoveError("Location is nonempty") + if self.would_be_single_stone_suicide(pla,loc): + raise IllegalMoveError("Move would be illegal single stone suicide") + if loc == self.simple_ko_point: + raise IllegalMoveError("Move would be illegal simple ko recapture") + + self.playUnsafe(pla,loc) + + def playUnsafe(self,pla,loc): + if loc == Board.PASS_LOC: + self.simple_ko_point = None + self.pla = Board.get_opp(pla) + else: + self.add_unsafe(pla,loc) + self.pla = Board.get_opp(pla) + + def playRecordedUnsafe(self,pla,loc): + capDirs = [] + opp = Board.get_opp(pla) + old_simple_ko_point = self.simple_ko_point + for i in range(4): + adj = loc + self.adj[i] + if self.board[adj] == opp and self.group_liberty_count[self.group_head[adj]] == 1: + capDirs.append(i) + old_num_captures_made = self.num_captures_made.copy() + old_num_non_pass_moves_made = self.num_non_pass_moves_made.copy() + + self.playUnsafe(pla,loc) + + #Suicide + selfCap = False + if self.board[loc] == Board.EMPTY: + selfCap = True + return (pla,loc,old_simple_ko_point,capDirs,selfCap,old_num_captures_made,old_num_non_pass_moves_made) + + def undo(self,record): + (pla,loc,simple_ko_point,capDirs,selfCap,old_num_captures_made,old_num_non_pass_moves_made) = record + opp = Board.get_opp(pla) + + self.simple_ko_point = simple_ko_point + self.pla = pla + self.num_captures_made = old_num_captures_made.copy() + self.num_non_pass_moves_made = old_num_non_pass_moves_made.copy() + + if loc == Board.PASS_LOC: + return + + #Re-fill stones in all captured directions + for capdir in capDirs: + adj = loc + self.adj[capdir] + if self.board[adj] == Board.EMPTY: + self.floodFillStones(opp,adj) + + if selfCap: + self.floodFillStones(pla,loc) + + #Delete the stone played here. + self.zobrist ^= Board.ZOBRIST_STONE[pla][loc] + self.board[loc] = Board.EMPTY + + #Zero out stuff in preparation for rebuilding + head = self.group_head[loc] + stone_count = self.group_stone_count[head] + self.group_stone_count[head] = 0 + self.group_liberty_count[head] = 0 + + #Uneat enemy liberties + self.changeSurroundingLiberties(loc,Board.get_opp(pla),+1) + + #If this was not a single stone, we need to recompute the chain from scratch + if stone_count > 1: + #Run through the whole chain and make their heads point to nothing + cur = loc + while True: + self.group_head[cur] = Board.PASS_LOC + cur = self.group_next[cur] + if cur == loc: + break + + #Rebuild each chain adjacent now + for i in range(4): + adj = loc + self.adj[i] + if self.board[adj] == pla and self.group_head[adj] == Board.PASS_LOC: + self.rebuildChain(pla,adj) + + self.group_head[loc] = 0 + self.group_next[loc] = 0 + self.group_prev[loc] = 0 + + + #Add a chain of the given player to the given region of empty space, floodfilling it. + #Assumes that this region does not border any chains of the desired color already + def floodFillStones(self,pla,loc): + head = loc + self.group_liberty_count[head] = 0 + self.group_stone_count[head] = 0 + + #Add a chain with links front <-> ... <-> head <-> head with all head pointers towards head + front = self.floodFillStonesHelper(head, head, head, pla) + + #Now, we make head point to front, and that completes the circle! + self.group_next[head] = front + self.group_prev[front] = head + + #Floodfill a chain of the given color into this region of empty spaces + #Make the specified loc the head for all the chains and updates the chainData of head with the number of stones. + #Does NOT connect the stones into a circular list. Rather, it produces an linear linked list with the tail pointing + #to tailTarget, and returns the head of the list. The tail is guaranteed to be loc. + def floodFillStonesHelper(self, head, tailTarget, loc, pla): + self.board[loc] = pla + self.zobrist ^= Board.ZOBRIST_STONE[pla][loc] + + self.group_head[loc] = head + self.group_stone_count[head] += 1 + self.group_next[loc] = tailTarget + self.group_prev[tailTarget] = loc + + #Eat enemy liberties + self.changeSurroundingLiberties(loc,Board.get_opp(pla),-1) + + #Recursively add stones around us. + nextTailTarget = loc + for i in range(4): + adj = loc + self.adj[i] + if self.board[adj] == Board.EMPTY: + nextTailTarget = self.floodFillStonesHelper(head,nextTailTarget,adj,pla) + return nextTailTarget + + #Floods through a chain of the specified player already on the board + #rebuilding its links and counting its liberties as we go. + #Requires that all their heads point towards + #some invalid location, such as PASS_LOC or a location not of color. + #The head of the chain will be loc. + def rebuildChain(self,pla,loc): + head = loc + self.group_liberty_count[head] = 0 + self.group_stone_count[head] = 0 + + #Rebuild chain with links front <-> ... <-> head <-> head with all head pointers towards head + front = self.rebuildChainHelper(head, head, head, pla) + + #Now, we make head point to front, and that completes the circle! + self.group_next[head] = front + self.group_prev[front] = head + + + #Does same thing as addChain, but floods through a chain of the specified color already on the board + #rebuilding its links and also counts its liberties as we go. Requires that all their heads point towards + #some invalid location, such as NULL_LOC or a location not of color. + def rebuildChainHelper(self, head, tailTarget, loc, pla): + #Count new liberties + for dloc in self.adj: + if self.board[loc+dloc] == Board.EMPTY and not self.is_group_adjacent(head,loc+dloc): + self.group_liberty_count[head] += 1 + + #Add stone here to the chain by setting its head + self.group_head[loc] = head + self.group_stone_count[head] += 1 + self.group_next[loc] = tailTarget + self.group_prev[tailTarget] = loc + + #Recursively add stones around us. + nextTailTarget = loc + for i in range(4): + adj = loc + self.adj[i] + if self.board[adj] == pla and self.group_head[adj] != head: + nextTailTarget = self.rebuildChainHelper(head,nextTailTarget,adj,pla) + return nextTailTarget + + + #Add a stone, assumes that the location is empty without checking + def add_unsafe(self,pla,loc): + opp = Board.get_opp(pla) + + #Put the stone down + self.board[loc] = pla + self.zobrist ^= Board.ZOBRIST_STONE[pla][loc] + + #Initialize the group for that stone + self.group_head[loc] = loc + self.group_stone_count[loc] = 1 + liberties = 0 + for dloc in self.adj: + if self.board[loc+dloc] == Board.EMPTY: + liberties += 1 + self.group_liberty_count[loc] = liberties + self.group_next[loc] = loc + self.group_prev[loc] = loc + + #Fill surrounding liberties of all adjacent groups + #Carefully avoid doublecounting + adj0 = loc + self.adj[0] + adj1 = loc + self.adj[1] + adj2 = loc + self.adj[2] + adj3 = loc + self.adj[3] + if self.board[adj0] == Board.BLACK or self.board[adj0] == Board.WHITE: + self.group_liberty_count[self.group_head[adj0]] -= 1 + if self.board[adj1] == Board.BLACK or self.board[adj1] == Board.WHITE: + if self.group_head[adj1] != self.group_head[adj0]: + self.group_liberty_count[self.group_head[adj1]] -= 1 + if self.board[adj2] == Board.BLACK or self.board[adj2] == Board.WHITE: + if self.group_head[adj2] != self.group_head[adj0] and \ + self.group_head[adj2] != self.group_head[adj1]: + self.group_liberty_count[self.group_head[adj2]] -= 1 + if self.board[adj3] == Board.BLACK or self.board[adj3] == Board.WHITE: + if self.group_head[adj3] != self.group_head[adj0] and \ + self.group_head[adj3] != self.group_head[adj1] and \ + self.group_head[adj3] != self.group_head[adj2]: + self.group_liberty_count[self.group_head[adj3]] -= 1 + + #Merge groups + if self.board[adj0] == pla: + self.merge_unsafe(loc,adj0) + if self.board[adj1] == pla: + self.merge_unsafe(loc,adj1) + if self.board[adj2] == pla: + self.merge_unsafe(loc,adj2) + if self.board[adj3] == pla: + self.merge_unsafe(loc,adj3) + + #Resolve captures + opp_stones_captured = 0 + caploc = 0 + if self.board[adj0] == opp and self.group_liberty_count[self.group_head[adj0]] == 0: + opp_stones_captured += self.group_stone_count[self.group_head[adj0]] + caploc = adj0 + self.remove_unsafe(adj0) + if self.board[adj1] == opp and self.group_liberty_count[self.group_head[adj1]] == 0: + opp_stones_captured += self.group_stone_count[self.group_head[adj1]] + caploc = adj1 + self.remove_unsafe(adj1) + if self.board[adj2] == opp and self.group_liberty_count[self.group_head[adj2]] == 0: + opp_stones_captured += self.group_stone_count[self.group_head[adj2]] + caploc = adj2 + self.remove_unsafe(adj2) + if self.board[adj3] == opp and self.group_liberty_count[self.group_head[adj3]] == 0: + opp_stones_captured += self.group_stone_count[self.group_head[adj3]] + caploc = adj3 + self.remove_unsafe(adj3) + + #Suicide + pla_stones_captured = 0 + if self.group_liberty_count[self.group_head[loc]] == 0: + pla_stones_captured += self.group_stone_count[self.group_head[loc]] + self.remove_unsafe(loc) + + self.num_captures_made[pla] += pla_stones_captured + self.num_captures_made[opp] += opp_stones_captured + self.num_non_pass_moves_made[pla] += 1 + + #Update ko point for legality checking + if opp_stones_captured == 1 and \ + self.group_stone_count[self.group_head[loc]] == 1 and \ + self.group_liberty_count[self.group_head[loc]] == 1: + self.simple_ko_point = caploc + else: + self.simple_ko_point = None + + #Apply the specified delta to the liberties of all adjacent groups of the specified color + def changeSurroundingLiberties(self,loc,pla,delta): + #Carefully avoid doublecounting + adj0 = loc + self.adj[0] + adj1 = loc + self.adj[1] + adj2 = loc + self.adj[2] + adj3 = loc + self.adj[3] + if self.board[adj0] == pla: + self.group_liberty_count[self.group_head[adj0]] += delta + if self.board[adj1] == pla: + if self.group_head[adj1] != self.group_head[adj0]: + self.group_liberty_count[self.group_head[adj1]] += delta + if self.board[adj2] == pla: + if self.group_head[adj2] != self.group_head[adj0] and \ + self.group_head[adj2] != self.group_head[adj1]: + self.group_liberty_count[self.group_head[adj2]] += delta + if self.board[adj3] == pla: + if self.group_head[adj3] != self.group_head[adj0] and \ + self.group_head[adj3] != self.group_head[adj1] and \ + self.group_head[adj3] != self.group_head[adj2]: + self.group_liberty_count[self.group_head[adj3]] += delta + + def countImmediateLiberties(self,loc): + adj0 = loc + self.adj[0] + adj1 = loc + self.adj[1] + adj2 = loc + self.adj[2] + adj3 = loc + self.adj[3] + count = 0 + if self.board[adj0] == Board.EMPTY: + count += 1 + if self.board[adj1] == Board.EMPTY: + count += 1 + if self.board[adj2] == Board.EMPTY: + count += 1 + if self.board[adj3] == Board.EMPTY: + count += 1 + return count + + def is_group_adjacent(self,head,loc): + return ( + self.group_head[loc+self.adj[0]] == head or \ + self.group_head[loc+self.adj[1]] == head or \ + self.group_head[loc+self.adj[2]] == head or \ + self.group_head[loc+self.adj[3]] == head + ) + + #Helper, merge two groups assuming they're owned by the same player and adjacent + def merge_unsafe(self,loc0,loc1): + if self.group_stone_count[self.group_head[loc0]] >= self.group_stone_count[self.group_head[loc1]]: + parent = loc0 + child = loc1 + else: + child = loc0 + parent = loc1 + + phead = self.group_head[parent] + chead = self.group_head[child] + if phead == chead: + return + + #Walk the child group assigning the new head and simultaneously counting liberties + new_stone_count = self.group_stone_count[phead] + self.group_stone_count[chead] + new_liberties = self.group_liberty_count[phead] + loc = child + while True: + adj0 = loc + self.adj[0] + adj1 = loc + self.adj[1] + adj2 = loc + self.adj[2] + adj3 = loc + self.adj[3] + + #Any adjacent empty space is a new liberty as long as it isn't adjacent to the parent + if self.board[adj0] == Board.EMPTY and not self.is_group_adjacent(phead,adj0): + new_liberties += 1 + if self.board[adj1] == Board.EMPTY and not self.is_group_adjacent(phead,adj1): + new_liberties += 1 + if self.board[adj2] == Board.EMPTY and not self.is_group_adjacent(phead,adj2): + new_liberties += 1 + if self.board[adj3] == Board.EMPTY and not self.is_group_adjacent(phead,adj3): + new_liberties += 1 + + #Now assign the new parent head to take over the child (this also + #prevents double-counting liberties) + self.group_head[loc] = phead + + #Advance around the linked list + loc = self.group_next[loc] + if loc == child: + break + + #Zero out the old head + self.group_stone_count[chead] = 0 + self.group_liberty_count[chead] = 0 + + #Update the new head + self.group_stone_count[phead] = new_stone_count + self.group_liberty_count[phead] = new_liberties + + #Combine the linked lists + plast = self.group_prev[phead] + clast = self.group_prev[chead] + self.group_next[clast] = phead + self.group_next[plast] = chead + self.group_prev[chead] = plast + self.group_prev[phead] = clast + + #Remove all stones in a group + def remove_unsafe(self,group): + head = self.group_head[group] + pla = self.board[group] + opp = Board.get_opp(pla) + + #Walk all the stones in the group and delete them + loc = group + while True: + #Add a liberty to all surrounding opposing groups, taking care to avoid double counting + adj0 = loc + self.adj[0] + adj1 = loc + self.adj[1] + adj2 = loc + self.adj[2] + adj3 = loc + self.adj[3] + if self.board[adj0] == opp: + self.group_liberty_count[self.group_head[adj0]] += 1 + if self.board[adj1] == opp: + if self.group_head[adj1] != self.group_head[adj0]: + self.group_liberty_count[self.group_head[adj1]] += 1 + if self.board[adj2] == opp: + if self.group_head[adj2] != self.group_head[adj0] and \ + self.group_head[adj2] != self.group_head[adj1]: + self.group_liberty_count[self.group_head[adj2]] += 1 + if self.board[adj3] == opp: + if self.group_head[adj3] != self.group_head[adj0] and \ + self.group_head[adj3] != self.group_head[adj1] and \ + self.group_head[adj3] != self.group_head[adj2]: + self.group_liberty_count[self.group_head[adj3]] += 1 + + next_loc = self.group_next[loc] + + #Zero out all the stuff + self.board[loc] = Board.EMPTY + self.zobrist ^= Board.ZOBRIST_STONE[opp][loc] + self.group_head[loc] = 0 + self.group_next[loc] = 0 + self.group_prev[loc] = 0 + + #Advance around the linked list + loc = next_loc + if loc == group: + break + + #Zero out the head + self.group_stone_count[head] = 0 + self.group_liberty_count[head] = 0 + + #Remove a single stone + def remove_single_stone_unsafe(self,rloc): + pla = self.board[rloc] + + #Record all the stones in the group + stones = [] + loc = rloc + while True: + stones.append(loc) + loc = self.group_next[loc] + if loc == rloc: + break + + #Remove them all + self.remove_unsafe(rloc) + + #Then add them back one by one + for loc in stones: + if loc != rloc: + self.add_unsafe(pla,loc) + + + #Helper, find liberties of group at loc. Fills in buf. + def findLiberties(self, loc, buf): + cur = loc + while True: + for i in range(4): + lib = cur + self.adj[i] + if self.board[lib] == Board.EMPTY: + if lib not in buf: + buf.append(lib) + + cur = self.group_next[cur] + if cur == loc: + break + + #Helper, find captures that gain liberties for the group at loc. Fills in buf + def findLibertyGainingCaptures(self, loc, buf): + pla = self.board[loc] + opp = Board.get_opp(pla) + + #For performance, avoid checking for captures on any chain twice + chainHeadsChecked = [] + + cur = loc + while True: + for i in range(4): + adj = cur + self.adj[i] + if self.board[adj] == opp: + head = self.group_head[adj] + + if self.group_liberty_count[head] == 1: + if head not in chainHeadsChecked: + #Capturing moves are precisely the liberties of the groups around us with 1 liberty. + self.findLiberties(adj, buf) + chainHeadsChecked.append(head) + + cur = self.group_next[cur] + if cur == loc: + break + + #Helper, does the group at loc have at least one opponent group adjacent to it in atari? + def hasLibertyGainingCaptures(self, loc): + pla = self.board[loc] + opp = Board.get_opp(pla) + + cur = loc + while True: + for i in range(4): + adj = cur + self.adj[i] + if self.board[adj] == opp: + head = self.group_head[adj] + if self.group_liberty_count[head] == 1: + return True + + cur = self.group_next[cur] + if cur == loc: + break + + return False + + def wouldBeKoCapture(self, loc, pla): + if self.board[loc] != Board.EMPTY: + return False + #Check that surounding points are are all opponent owned and exactly one of them is capturable + opp = Board.get_opp(pla) + oppCapturableLoc = None + for i in range(4): + adj = loc + self.adj[i] + if self.board[adj] != Board.WALL and self.board[adj] != opp: + return False + if self.board[adj] == opp and self.group_liberty_count[self.group_head[adj]] == 1: + if oppCapturableLoc is not None: + return False + oppCapturableLoc = adj + + if oppCapturableLoc is None: + return False + + #Check that the capturable loc has exactly one stone + if self.group_stone_count[self.group_head[oppCapturableLoc]] != 1: + return False + return True + + def countHeuristicConnectionLiberties(self,loc,pla): + adj0 = loc + self.adj[0] + adj1 = loc + self.adj[1] + adj2 = loc + self.adj[2] + adj3 = loc + self.adj[3] + count = 0.0 + if self.board[adj0] == pla: + count += max(0.0,self.group_liberty_count[self.group_head[adj0]]-1.5) + if self.board[adj1] == pla: + count += max(0.0,self.group_liberty_count[self.group_head[adj1]]-1.5) + if self.board[adj2] == pla: + count += max(0.0,self.group_liberty_count[self.group_head[adj2]]-1.5) + if self.board[adj3] == pla: + count += max(0.0,self.group_liberty_count[self.group_head[adj3]]-1.5) + return count + + def searchIsLadderCapturedAttackerFirst2Libs(self,loc): + if not self.is_on_board(loc): + return [] + if self.board[loc] != Board.BLACK and self.board[loc] != Board.WHITE: + return [] + if self.group_liberty_count[self.group_head[loc]] != 2: + return [] + + #Make it so that pla is always the defender + pla = self.board[loc] + opp = Board.get_opp(pla) + + moves = [] + self.findLiberties(loc,moves) + assert(len(moves) == 2) + + move0 = moves[0] + move1 = moves[1] + move0Works = False + move1Works = False + + if self.would_be_legal(opp,move0): + record = self.playRecordedUnsafe(opp,move0) + move0Works = self.searchIsLadderCaptured(loc,True) + self.undo(record) + if self.would_be_legal(opp,move1): + record = self.playRecordedUnsafe(opp,move1) + move1Works = self.searchIsLadderCaptured(loc,True) + self.undo(record) + + workingMoves = [] + if move0Works: + workingMoves.append(move0) + if move1Works: + workingMoves.append(move1) + + return workingMoves + + + def searchIsLadderCaptured(self,loc,defenderFirst): + if not self.is_on_board(loc): + return False + if self.board[loc] != Board.BLACK and self.board[loc] != Board.WHITE: + return False + + if self.group_liberty_count[self.group_head[loc]] > 2 or (defenderFirst and self.group_liberty_count[self.group_head[loc]] > 1): + return False + + #Make it so that pla is always the defender + pla = self.board[loc] + opp = Board.get_opp(pla) + + arrSize = self.size * self.size * 2 #A bit bigger due to paranoia about recaptures making the sequence longer. + + #Stack for the search. These are lists of possible moves to search at each level of the stack + moveLists = [[] for i in range(arrSize)] + moveListCur = [0 for i in range(arrSize)] #Current move list idx searched, equal to -1 if list has not been generated. + records = [None for i in range(arrSize)] #Records so that we can undo moves as we search back up. + stackIdx = 0 + + moveLists[0] = [] + moveListCur[0] = -1 + + returnValue = False + returnedFromDeeper = False + + #Clear the ko loc for the defender at the root node - assume all kos work for the defender + saved_simple_ko_point = self.simple_ko_point + if defenderFirst: + self.simple_ko_point = None + + # debug = True + # if debug: + # print("SEARCHING " + str(self.loc_x(loc)) + " " + str(self.loc_y(loc))) + + while True: + # if debug: + # print(str(stackIdx) + " " + str(moveListCur[stackIdx]) + "/" + str(len(moveLists[stackIdx])) + " " + str(returnValue) + " " + str(returnedFromDeeper)) + + #Returned from the root - so that's the answer + if stackIdx <= -1: + assert(stackIdx == -1) + self.simple_ko_point = saved_simple_ko_point + return returnValue + + isDefender = (defenderFirst and (stackIdx % 2) == 0) or (not defenderFirst and (stackIdx % 2) == 1) + + #We just entered this level? + if moveListCur[stackIdx] == -1: + libs = self.group_liberty_count[self.group_head[loc]] + + #Base cases. + #If we are the attacker and the group has only 1 liberty, we already win. + if not isDefender and libs <= 1: + returnValue = True + returnedFromDeeper = True + stackIdx -= 1 + continue + + #If we are the attacker and the group has 3 liberties, we already lose. + if not isDefender and libs >= 3: + returnValue = False + returnedFromDeeper = True + stackIdx -= 1 + continue + + #If we are the defender and the group has 2 liberties, we already win. + if isDefender and libs >= 2: + returnValue = False + returnedFromDeeper = True + stackIdx -= 1 + continue + + #If we are the defender and the attacker left a simple ko point, assume we already win + #because we don't want to say yes on ladders that depend on kos + #This should also hopefully prevent any possible infinite loops - I don't know of any infinite loop + #that would come up in a continuous atari sequence that doesn't ever leave a simple ko point. + if isDefender and self.simple_ko_point is not None: + returnValue = False + returnedFromDeeper = True + stackIdx -= 1 + continue + + #Otherwise we need to keep searching. + #Generate the move list. Attacker and defender generate moves on the group's liberties, but only the defender + #generates moves on surrounding capturable opposing groups. + if isDefender: + moveLists[stackIdx] = [] + self.findLibertyGainingCaptures(loc,moveLists[stackIdx]) + self.findLiberties(loc,moveLists[stackIdx]) + else: + moveLists[stackIdx] = [] + self.findLiberties(loc,moveLists[stackIdx]) + assert(len(moveLists[stackIdx]) == 2) + + #Early quitouts if the liberties are not adjacent + #(so that filling one doesn't fill an immediate liberty of the other) + move0 = moveLists[stackIdx][0] + move1 = moveLists[stackIdx][1] + + libs0 = self.countImmediateLiberties(move0) + libs1 = self.countImmediateLiberties(move1) + + #If we are the attacker and we're in a double-ko death situation, then assume we win. + #Both defender liberties must be ko mouths, connecting either ko mouth must not increase the defender's + #liberties, and none of the attacker's surrounding stones can currently be in atari. + #This is not complete - there are situations where the defender's connections increase liberties, or where + #the attacker has stones in atari, but where the defender is still in inescapable atari even if they have + #a large finite number of ko threats. But it's better than nothing. + if libs0 == 0 and libs1 == 0 and self.wouldBeKoCapture(move0,opp) and self.wouldBeKoCapture(move1,opp) : + if self.get_liberties_after_play(pla,move0,3) <= 2 and self.get_liberties_after_play(pla,move1,3) <= 2: + if self.hasLibertyGainingCaptures(loc): + returnValue = True + returnedFromDeeper = True + stackIdx -= 1 + continue + + if not self.is_adjacent(move0,move1): + #We lose automatically if both escapes get the defender too many libs + if libs0 >= 3 and libs1 >= 3: + returnValue = False + returnedFromDeeper = True + stackIdx -= 1 + continue + #Move 1 is not possible, so shrink the list + elif libs0 >= 3: + moveLists[stackIdx] = [move0] + #Move 0 is not possible, so shrink the list + elif libs1 >= 3: + moveLists[stackIdx] = [move1] + + #Order the two moves based on a simple heuristic - for each neighboring group with any liberties + #count that the opponent could connect to, count liberties - 1.5. + if len(moveLists[stackIdx]) > 1: + libs0 += self.countHeuristicConnectionLiberties(move0,pla) + libs1 += self.countHeuristicConnectionLiberties(move1,pla) + if libs1 > libs0: + moveLists[stackIdx][0] = move1 + moveLists[stackIdx][1] = move0 + + #And indicate to begin search on the first move generated. + moveListCur[stackIdx] = 0 + + #Else, we returned from a deeper level (or the same level, via illegal move) + else: + assert(moveListCur[stackIdx] >= 0) + assert(moveListCur[stackIdx] < len(moveLists[stackIdx])) + #If we returned from deeper we need to undo the move we made + if returnedFromDeeper: + self.undo(records[stackIdx]) + + #Defender has a move that is not ladder captured? + if isDefender and not returnValue: + #Return! (returnValue is still false, as desired) + returnedFromDeeper = True + stackIdx -= 1 + continue + + #Attacker has a move that does ladder capture? + if not isDefender and returnValue: + #Return! (returnValue is still true, as desired) + returnedFromDeeper = True + stackIdx -= 1 + continue + + #Move on to the next move to search + moveListCur[stackIdx] += 1 + + #If there is no next move to search, then we lose. + if moveListCur[stackIdx] >= len(moveLists[stackIdx]): + #For a defender, that means a ladder capture. + #For an attacker, that means no ladder capture found. + returnValue = isDefender + returnedFromDeeper = True + stackIdx -= 1 + continue + + + #Otherwise we do have an next move to search. Grab it. + move = moveLists[stackIdx][moveListCur[stackIdx]] + p = (pla if isDefender else opp) + + # if debug: + # print("play " + str(self.loc_x(move)) + " " + str(self.loc_y(move)) + " " + str(p)) + # print(self.to_string()) + + #Illegal move - treat it the same as a failed move, but don't return up a level so that we + #loop again and just try the next move. + if not self.would_be_legal(p,move): + returnValue = isDefender + returnedFromDeeper = False + #if(print) cout << "illegal " << endl; + continue + + #Play and record the move! + records[stackIdx] = self.playRecordedUnsafe(p,move) + + #And recurse to the next level + stackIdx += 1 + moveListCur[stackIdx] = -1 + moveLists[stackIdx] = [] + + + def calculateArea(self, result, nonPassAliveStones, safeBigTerritories, unsafeBigTerritories, isMultiStoneSuicideLegal): + for i in range(self.arrsize): + result[i] = Board.EMPTY + self.calculateAreaForPla(Board.BLACK,safeBigTerritories,unsafeBigTerritories,isMultiStoneSuicideLegal,result) + self.calculateAreaForPla(Board.WHITE,safeBigTerritories,unsafeBigTerritories,isMultiStoneSuicideLegal,result) + + if nonPassAliveStones: + for y in range(self.size): + for x in range(self.size): + loc = self.loc(x,y) + if result[loc] == Board.EMPTY: + result[loc] = self.board[loc] + + def calculateNonDameTouchingArea(self, result, keepTerritories, keepStones, isMultiStoneSuicideLegal): + #First, just compute basic area. + basicArea = [Board.EMPTY for i in range(self.arrsize)] + for i in range(self.arrsize): + result[i] = Board.EMPTY + self.calculateAreaForPla(Board.BLACK,True,True,isMultiStoneSuicideLegal,basicArea) + self.calculateAreaForPla(Board.WHITE,True,True,isMultiStoneSuicideLegal,basicArea) + + for y in range(self.size): + for x in range(self.size): + loc = self.loc(x,y) + if basicArea[loc] == Board.EMPTY: + basicArea[loc] = self.board[loc] + + self.calculateNonDameTouchingAreaHelper(basicArea,result) + + if keepTerritories: + for y in range(self.size): + for x in range(self.size): + loc = self.loc(x,y) + if basicArea[loc] != Board.EMPTY and basicArea[loc] != self.board[loc]: + result[loc] = basicArea[loc] + + if keepStones: + for y in range(self.size): + for x in range(self.size): + loc = self.loc(x,y) + if basicArea[loc] != Board.EMPTY and basicArea[loc] == self.board[loc]: + result[loc] = basicArea[loc] + + + def calculateAreaForPla(self, pla, safeBigTerritories, unsafeBigTerritories, isMultiStoneSuicideLegal, result): + opp = self.get_opp(pla) + #First compute all empty-or-opp regions + + #For each loc, if it's empty or opp, the head of the region + regionHeadByLoc = [Board.PASS_LOC for i in range(self.arrsize)] + #For each loc, if it's empty or opp, the next empty or opp belonging to the same region + nextEmptyOrOpp = [Board.PASS_LOC for i in range(self.arrsize)] + #Does this border a pla group that has been marked as not pass alive? + bordersNonPassAlivePlaByHead = [False for i in range(self.arrsize)] + + #A list for each region head, indicating which pla group heads the region is vital for. + #A region is vital for a pla group if all its spaces are adjacent to that pla group. + #All lists are concatenated together, the most we can have is bounded by (MAX_LEN * MAX_LEN+1) / 2 + #independent regions, each one vital for at most 4 pla groups, add some extra just in case. + maxRegions = (self.size * self.size + 1)//2 + 1 + vitalForPlaHeadsListsMaxLen = maxRegions * 4 + vitalForPlaHeadsLists = [-1 for i in range(vitalForPlaHeadsListsMaxLen)] + vitalForPlaHeadsListsTotal = 0 + + #A list of region heads + numRegions = 0 + regionHeads = [-1 for i in range(maxRegions)] + #Start indices and list lengths in vitalForPlaHeadsLists + vitalStart = [-1 for i in range(maxRegions)] + vitalLen = [-1 for i in range(maxRegions)] + #For each region, are there 0, 1, or 2+ spaces of that region not bordering any pla? + numInternalSpacesMax2 = [-1 for i in range(maxRegions)] + containsOpp = [False for i in range(maxRegions)] + + def isAdjacentToPlaHead(loc,plaHead): + for i in range(4): + adj = loc + self.adj[i] + if self.board[adj] == pla and self.group_head[adj] == plaHead: + return True + return False + + #Recursively trace maximal non-pla regions of the board and record their properties and join them into a + #linked list through nextEmptyOrOpp. + #Takes as input the location serving as the head, the tip node of the linked list so far, the next loc, and the + #numeric index of the region + #Returns the loc serving as the current tip node ("tailTarget") of the linked list. + def buildRegion(head, tailTarget, loc, regionIdx): + #Already traced this location, skip + if regionHeadByLoc[loc] != Board.PASS_LOC: + return tailTarget + regionHeadByLoc[loc] = head + + #First, filter out any pla heads it turns out we're not vital for because we're not adjacent to them + #In the case where suicide is allowed, we only do this filtering on intersections that are actually empty + if isMultiStoneSuicideLegal or self.board[loc] == Board.EMPTY: + vStart = vitalStart[regionIdx] + oldVLen = vitalLen[regionIdx] + newVLen = 0 + for i in range(oldVLen): + if isAdjacentToPlaHead(loc,vitalForPlaHeadsLists[vStart+i]): + vitalForPlaHeadsLists[vStart+newVLen] = vitalForPlaHeadsLists[vStart+i] + newVLen += 1 + vitalLen[regionIdx] = newVLen + + #Determine if this point is internal, unless we already have many internal points + if numInternalSpacesMax2[regionIdx] < 2: + isInternal = True + for i in range(4): + adj = loc + self.adj[i] + if self.board[adj] == pla: + isInternal = False + break + if isInternal: + numInternalSpacesMax2[regionIdx] += 1 + + if self.board[loc] == opp: + containsOpp[regionIdx] = True + + #Next, recurse everywhere + nextEmptyOrOpp[loc] = tailTarget + nextTailTarget = loc + for i in range(4): + adj = loc + self.adj[i] + if self.board[adj] == Board.EMPTY or self.board[adj] == opp: + nextTailTarget = buildRegion(head,nextTailTarget,adj,regionIdx) + + return nextTailTarget + + atLeastOnePla = False + for y in range(self.size): + for x in range(self.size): + loc = self.loc(x,y) + + if regionHeadByLoc[loc] != Board.PASS_LOC: + continue + if self.board[loc] != Board.EMPTY: + atLeastOnePla |= (self.board[loc] == pla) + continue + + regionIdx = numRegions + numRegions += 1 + assert(numRegions <= maxRegions) + + #Initialize region metadata + head = loc + regionHeads[regionIdx] = head + vitalStart[regionIdx] = vitalForPlaHeadsListsTotal + vitalLen[regionIdx] = 0 + numInternalSpacesMax2[regionIdx] = 0 + containsOpp[regionIdx] = False + + #Fill in all adjacent pla heads as vital, which will get filtered during buildRegion + vStart = vitalStart[regionIdx] + assert(vStart + 4 <= vitalForPlaHeadsListsMaxLen) + initialVLen = 0 + for i in range(4): + adj = loc + self.adj[i] + if self.board[adj] == pla: + plaHead = self.group_head[adj] + alreadyPresent = False + for j in range(initialVLen): + if vitalForPlaHeadsLists[vStart+j] == plaHead: + alreadyPresent = True + break + + if not alreadyPresent: + vitalForPlaHeadsLists[vStart+initialVLen] = plaHead + initialVLen += 1 + + vitalLen[regionIdx] = initialVLen + + tailTarget = buildRegion(head,head,loc,regionIdx) + nextEmptyOrOpp[head] = tailTarget + + vitalForPlaHeadsListsTotal += vitalLen[regionIdx] + + #Also accumulate all player heads + numPlaHeads = 0 + allPlaHeads = [] + + #Accumulate with duplicates + for y in range(self.size): + for x in range(self.size): + loc = self.loc(x,y) + if self.board[loc] == pla: + allPlaHeads.append(self.group_head[loc]) + numPlaHeads += 1 + + #Filter duplicates + allPlaHeads = list(set(allPlaHeads)) + numPlaHeads = len(allPlaHeads) + + plaHasBeenKilled = [False for i in range(numPlaHeads)] + + #Now, we can begin the benson iteration + vitalCountByPlaHead = [0 for i in range(self.arrsize)] + while(True): + #Zero out vital liberties by head + for i in range(numPlaHeads): + vitalCountByPlaHead[allPlaHeads[i]] = 0 + + #Walk all regions that are still bordered only by pass-alive stuff and accumulate a vital liberty to each pla it is vital for. + for i in range(numRegions): + head = regionHeads[i] + if bordersNonPassAlivePlaByHead[head]: + continue + + vStart = vitalStart[i] + vLen = vitalLen[i] + for j in range(vLen): + plaHead = vitalForPlaHeadsLists[vStart+j] + vitalCountByPlaHead[plaHead] += 1 + + #Walk all player heads and kill them if they haven't accumulated at least 2 vital liberties + killedAnything = False + for i in range(numPlaHeads): + #Already killed - skip + if plaHasBeenKilled[i]: + continue + + plaHead = allPlaHeads[i] + if vitalCountByPlaHead[plaHead] < 2: + plaHasBeenKilled[i] = True + killedAnything = True + #Walk the pla chain to update bordering regions + cur = plaHead + while(True): + for j in range(4): + adj = cur + self.adj[j] + if self.board[adj] == Board.EMPTY or self.board[adj] == opp: + bordersNonPassAlivePlaByHead[regionHeadByLoc[adj]] = True + + cur = self.group_next[cur] + + if cur == plaHead: + break + + if not killedAnything: + break + + #Mark result with pass-alive groups + for i in range(numPlaHeads): + if not plaHasBeenKilled[i]: + plaHead = allPlaHeads[i] + cur = plaHead + while(True): + result[cur] = pla + cur = self.group_next[cur] + if cur == plaHead: + break + + #Mark result with territory + for i in range(numRegions): + head = regionHeads[i] + shouldMark = numInternalSpacesMax2[i] <= 1 and atLeastOnePla and not bordersNonPassAlivePlaByHead[head] + shouldMark = shouldMark or (safeBigTerritories and atLeastOnePla and not containsOpp[i] and not bordersNonPassAlivePlaByHead[head]) + shouldMark = shouldMark or (unsafeBigTerritories and atLeastOnePla and not containsOpp[i]) + + if shouldMark: + cur = head + while(True): + result[cur] = pla + cur = nextEmptyOrOpp[cur] + if cur == head: + break + + def calculateNonDameTouchingAreaHelper(self, basicArea, result): + queue = [Board.PASS_LOC for i in range(self.arrsize)] + + #Iterate through all the regions that players own via area scoring and mark + #all the ones that are touching dame + isDameTouching = [False for i in range(self.arrsize)] + + queueHead = 0 + queueTail = 0 + + ADJ0 = self.adj[0] + ADJ1 = self.adj[1] + ADJ2 = self.adj[2] + ADJ3 = self.adj[3] + + for y in range(self.size): + for x in range(self.size): + loc = self.loc(x,y) + if basicArea[loc] != Board.EMPTY and not isDameTouching[loc]: + #Touches dame? + if((self.board[loc+ADJ0] == Board.EMPTY and basicArea[loc+ADJ0] == Board.EMPTY) or + (self.board[loc+ADJ1] == Board.EMPTY and basicArea[loc+ADJ1] == Board.EMPTY) or + (self.board[loc+ADJ2] == Board.EMPTY and basicArea[loc+ADJ2] == Board.EMPTY) or + (self.board[loc+ADJ3] == Board.EMPTY and basicArea[loc+ADJ3] == Board.EMPTY)): + + pla = basicArea[loc] + isDameTouching[loc] = True + queue[queueTail] = loc + queueTail += 1 + while queueHead != queueTail: + #Pop next location off queue + nextLoc = queue[queueHead] + queueHead += 1 + + #Look all around it, floodfill + for j in range(4): + adj = nextLoc + self.adj[j] + if basicArea[adj] == pla and not isDameTouching[adj]: + isDameTouching[adj] = True + queue[queueTail] = adj + queueTail += 1 + + queueHead = 0 + queueTail = 0 + + #Now, walk through and copy all non-dame-touching basic areas into the result counting + #how many there are. + for y in range(self.size): + for x in range(self.size): + loc = self.loc(x,y) + if basicArea[loc] != Board.EMPTY and not isDameTouching[loc] and result[loc] != basicArea[loc]: + pla = basicArea[loc] + result[loc] = basicArea[loc] + queue[queueTail] = loc + queueTail += 1 + while queueHead != queueTail: + #Pop next location off queue + nextLoc = queue[queueHead] + queueHead += 1 + + #Look all around it, floodfill + for j in range(4): + adj = nextLoc + self.adj[j] + if basicArea[adj] == pla and result[adj] != basicArea[adj]: + result[adj] = basicArea[adj] + queue[queueTail] = adj + queueTail += 1 diff --git a/training/scripts/dated/20241110-123407/clean_checkpoint.py b/training/scripts/dated/20241110-123407/clean_checkpoint.py new file mode 100644 index 0000000..e5e1802 --- /dev/null +++ b/training/scripts/dated/20241110-123407/clean_checkpoint.py @@ -0,0 +1,34 @@ +#!/usr/bin/python3 +import sys +import os +import argparse +import math +import torch +import json + +description = """ +Utility for cleaning torch checkpoints for release. +""" + +parser = argparse.ArgumentParser(description=description) +parser.add_argument('-checkpoint', help='Checkpoint file path', required=True) +parser.add_argument('-output', help='Output checkpoint file path', required=True) +args = vars(parser.parse_args()) + +checkpoint_path = args["checkpoint"] +output_path = args["output"] + +data = torch.load(checkpoint_path,map_location="cpu") + +if "optimizer" in data: + del data["optimizer"] +del data["running_metrics"] +del data["metrics"] +del data["train_state"]["old_train_data_dirs"] +del data["train_state"]["data_files_used"] + +if "last_val_metrics" in data: + del data["last_val_metrics"] + +torch.save(data, output_path) +print(f"Cleaned {checkpoint_path} -> {output_path} for release") diff --git a/training/scripts/dated/20241110-123407/data.py b/training/scripts/dated/20241110-123407/data.py new file mode 100644 index 0000000..cb530b5 --- /dev/null +++ b/training/scripts/dated/20241110-123407/data.py @@ -0,0 +1,163 @@ + +from sgfmill import sgf as Sgf +from sgfmill import sgf_properties as Sgf_properties + +from board import Board + +class Metadata: + def __init__(self, size, bname, wname, brank, wrank, komi, handicap): + self.size = size + self.bname = bname + self.wname = wname + self.brank = brank + self.wrank = wrank + self.komi = komi + self.handicap = handicap + +#Returns (metadata, list of setup stones, list of move stones) +#Setup and move stones are both pairs of (pla,loc) +def load_sgf_moves_exn(path): + sgf_file = open(path,"rb") + contents = sgf_file.read() + sgf_file.close() + + game = Sgf.Sgf_game.from_bytes(contents) + size = game.get_size() + + root = game.get_root() + ab, aw, ae = root.get_setup_stones() + setup = [] + if ab or aw: + for (row,col) in ab: + loc = Board.loc_static(col,size-1-row,size) + setup.append((Board.BLACK,loc)) + for (row,col) in aw: + loc = Board.loc_static(col,size-1-row,size) + setup.append((Board.WHITE,loc)) + + color,raw = root.get_raw_move() + if color is not None: + raise Exception("Found both setup stones and normal moves in root node") + + #Walk down the leftmost branch and assume that this is the game + moves = [] + prev_pla = None + seen_white_moves = False + node = root + while node: + node = node[0] + if node.has_setup_stones(): + raise Exception("Found setup stones after the root node") + + color,raw = node.get_raw_move() + if color is None: + raise Exception("Found node without move color") + + if color == 'b': + pla = Board.BLACK + elif color == 'w': + pla = Board.WHITE + else: + raise Exception("Invalid move color: " + color) + + rc = Sgf_properties.interpret_go_point(raw, size) + if rc is None: #pass + loc = Board.PASS_LOC + else: + (row,col) = rc + loc = Board.loc_static(col,size-1-row,size) + + #Forbid consecutive moves by the same player, unless the previous player was black and we've seen no white moves yet (handicap setup) + if pla == prev_pla and not (prev_pla == Board.BLACK and not seen_white_moves): + raise Exception("Multiple moves in a row by same player") + moves.append((pla,loc)) + + prev_pla = pla + if pla == Board.WHITE: + seen_white_moves = True + + #If there are multiple black moves in a row at the start, assume they are more handicap stones + first_white_move_idx = 0 + while first_white_move_idx < len(moves) and moves[first_white_move_idx][0] == Board.BLACK: + first_white_move_idx += 1 + if first_white_move_idx >= 2: + setup.extend((pla,loc) for (pla,loc) in moves[:first_white_move_idx] if loc is not None) + moves = moves[first_white_move_idx:] + + bname = root.get("PB") + wname = root.get("PW") + brank = (root.get("BR") if root.has_property("BR") else None) + wrank = (root.get("WR") if root.has_property("WR") else None) + komi = (root.get("KM") if root.has_property("KM") else None) + rulesstr = (root.get("RU") if root.has_property("RU") else None) + handicap = (root.get("HA") if root.has_property("HA") else None) + + rules = None + if rulesstr is not None: + if rulesstr.lower() == "japanese" or rulesstr.lower() == "jp": + rules = { + "koRule": "KO_SIMPLE", + "scoringRule": "SCORING_TERRITORY", + "multiStoneSuicideLegal": False, + "encorePhase": 0, + "passWouldEndPhase": False, + "whiteKomi": komi + } + elif rulesstr.lower() == "chinese": + rules = { + "koRule": "KO_SIMPLE", + "scoringRule": "SCORING_AREA", + "multiStoneSuicideLegal": False, + "encorePhase": 0, + "passWouldEndPhase": False, + "whiteKomi": komi + } + elif rulesstr.startswith("ko"): + rules = {} + origrulesstr = rulesstr + rulesstr = rulesstr[2:] + if rulesstr.startswith("SIMPLE"): + rules["koRule"] = "KO_SIMPLE" + rulesstr = rulesstr[6:] + elif rulesstr.startswith("POSITIONAL"): + rules["koRule"] = "KO_POSITIONAL" + rulesstr = rulesstr[10:] + elif rulesstr.startswith("SITUATIONAL"): + rules["koRule"] = "KO_SITUATIONAL" + rulesstr = rulesstr[11:] + elif rulesstr.startswith("SPIGHT"): + rules["koRule"] = "KO_SPIGHT" + rulesstr = rulesstr[6:] + else: + raise Exception("Could not parse rules: " + origrulesstr) + + if rulesstr.startswith("score"): + rulesstr = rulesstr[5:] + else: + raise Exception("Could not parse rules: " + origrulesstr) + + if rulesstr.startswith("AREA"): + rules["scoringRule"] = "SCORING_AREA" + rulesstr = rulesstr[4:] + elif rulesstr.startswith("TERRITORY"): + rules["scoringRule"] = "SCORING_TERRITORY" + rulesstr = rulesstr[9:] + else: + raise Exception("Could not parse rules: " + origrulesstr) + + if rulesstr.startswith("sui"): + rulesstr = rulesstr[3:] + else: + raise Exception("Could not parse rules: " + origrulesstr) + + if rulesstr.startswith("false"): + rules["multiStoneSuicideLegal"] = False + rulesstr = rulesstr[5:] + elif rulesstr.startswith("true"): + rules["multiStoneSuicideLegal"] = True + rulesstr = rulesstr[4:] + else: + raise Exception("Could not parse rules: " + origrulesstr) + + metadata = Metadata(size, bname, wname, brank, wrank, komi, handicap) + return metadata, setup, moves, rules diff --git a/training/scripts/dated/20241110-123407/data_processing_pytorch.py b/training/scripts/dated/20241110-123407/data_processing_pytorch.py new file mode 100644 index 0000000..55fcc58 --- /dev/null +++ b/training/scripts/dated/20241110-123407/data_processing_pytorch.py @@ -0,0 +1,238 @@ +import logging +import os + +import numpy as np + +import torch +import torch.nn.functional + +import modelconfigs + +def read_npz_training_data( + npz_files, + batch_size: int, + world_size: int, + rank: int, + pos_len: int, + device, + randomize_symmetries: bool, + include_meta: bool, + model_config: modelconfigs.ModelConfig, +): + rand = np.random.default_rng(seed=list(os.urandom(12))) + num_bin_features = modelconfigs.get_num_bin_input_features(model_config) + num_global_features = modelconfigs.get_num_global_input_features(model_config) + (h_base,h_builder) = build_history_matrices(model_config, device) + + for npz_file in npz_files: + with np.load(npz_file) as npz: + binaryInputNCHWPacked = npz["binaryInputNCHWPacked"] + globalInputNC = npz["globalInputNC"] + policyTargetsNCMove = npz["policyTargetsNCMove"].astype(np.float32) + globalTargetsNC = npz["globalTargetsNC"] + scoreDistrN = npz["scoreDistrN"].astype(np.float32) + valueTargetsNCHW = npz["valueTargetsNCHW"].astype(np.float32) + if include_meta: + metadataInputNC = npz["metadataInputNC"].astype(np.float32) + del npz + + binaryInputNCHW = np.unpackbits(binaryInputNCHWPacked,axis=2) + assert len(binaryInputNCHW.shape) == 3 + assert binaryInputNCHW.shape[2] == ((pos_len * pos_len + 7) // 8) * 8 + binaryInputNCHW = binaryInputNCHW[:,:,:pos_len*pos_len] + binaryInputNCHW = np.reshape(binaryInputNCHW, ( + binaryInputNCHW.shape[0], binaryInputNCHW.shape[1], pos_len, pos_len + )).astype(np.float32) + + assert binaryInputNCHW.shape[1] == num_bin_features + assert globalInputNC.shape[1] == num_global_features + + num_samples = binaryInputNCHW.shape[0] + # Just discard stuff that doesn't divide evenly + num_whole_steps = num_samples // (batch_size * world_size) + + logging.info(f"Beginning {npz_file} with {num_whole_steps * world_size} usable batches, my rank is {rank}") + for n in range(num_whole_steps): + start = (n * world_size + rank) * batch_size + end = start + batch_size + + batch_binaryInputNCHW = torch.from_numpy(binaryInputNCHW[start:end]).to(device) + batch_globalInputNC = torch.from_numpy(globalInputNC[start:end]).to(device) + batch_policyTargetsNCMove = torch.from_numpy(policyTargetsNCMove[start:end]).to(device) + batch_globalTargetsNC = torch.from_numpy(globalTargetsNC[start:end]).to(device) + batch_scoreDistrN = torch.from_numpy(scoreDistrN[start:end]).to(device) + batch_valueTargetsNCHW = torch.from_numpy(valueTargetsNCHW[start:end]).to(device) + if include_meta: + batch_metadataInputNC = torch.from_numpy(metadataInputNC[start:end]).to(device) + + (batch_binaryInputNCHW, batch_globalInputNC) = apply_history_matrices( + model_config, batch_binaryInputNCHW, batch_globalInputNC, batch_globalTargetsNC, h_base, h_builder + ) + + if randomize_symmetries: + symm = int(rand.integers(0,8)) + batch_binaryInputNCHW = apply_symmetry(batch_binaryInputNCHW, symm) + batch_policyTargetsNCMove = apply_symmetry_policy(batch_policyTargetsNCMove, symm, pos_len) + batch_valueTargetsNCHW = apply_symmetry(batch_valueTargetsNCHW, symm) + batch_binaryInputNCHW = batch_binaryInputNCHW.contiguous() + batch_policyTargetsNCMove = batch_policyTargetsNCMove.contiguous() + batch_valueTargetsNCHW = batch_valueTargetsNCHW.contiguous() + + if include_meta: + batch = dict( + binaryInputNCHW = batch_binaryInputNCHW, + globalInputNC = batch_globalInputNC, + policyTargetsNCMove = batch_policyTargetsNCMove, + globalTargetsNC = batch_globalTargetsNC, + scoreDistrN = batch_scoreDistrN, + valueTargetsNCHW = batch_valueTargetsNCHW, + metadataInputNC = batch_metadataInputNC, + ) + else: + batch = dict( + binaryInputNCHW = batch_binaryInputNCHW, + globalInputNC = batch_globalInputNC, + policyTargetsNCMove = batch_policyTargetsNCMove, + globalTargetsNC = batch_globalTargetsNC, + scoreDistrN = batch_scoreDistrN, + valueTargetsNCHW = batch_valueTargetsNCHW, + ) + yield batch + + +def apply_symmetry_policy(tensor, symm, pos_len): + """Same as apply_symmetry but also handles the pass index""" + batch_size = tensor.shape[0] + channels = tensor.shape[1] + tensor_without_pass = tensor[:,:,:-1].view((batch_size, channels, pos_len, pos_len)) + tensor_transformed = apply_symmetry(tensor_without_pass, symm) + return torch.cat(( + tensor_transformed.reshape(batch_size, channels, pos_len*pos_len), + tensor[:,:,-1:] + ), dim=2) + +def apply_symmetry(tensor, symm): + """ + Apply a symmetry operation to the given tensor. + + Args: + tensor (torch.Tensor): Tensor to be rotated. (..., W, W) + symm (int): + 0, 1, 2, 3: Rotation by symm * pi / 2 radians. + 4, 5, 6, 7: Mirror symmetry on top of rotation. + """ + assert tensor.shape[-1] == tensor.shape[-2] + + if symm == 0: + return tensor + if symm == 1: + return tensor.transpose(-2, -1).flip(-2) + if symm == 2: + return tensor.flip(-1).flip(-2) + if symm == 3: + return tensor.transpose(-2, -1).flip(-1) + if symm == 4: + return tensor.transpose(-2, -1) + if symm == 5: + return tensor.flip(-1) + if symm == 6: + return tensor.transpose(-2, -1).flip(-1).flip(-2) + if symm == 7: + return tensor.flip(-2) + + +def build_history_matrices(model_config: modelconfigs.ModelConfig, device): + num_bin_features = modelconfigs.get_num_bin_input_features(model_config) + assert num_bin_features == 22, "Currently this code is hardcoded for this many features" + + h_base = torch.diag( + torch.tensor( + [ + 1.0, # 0 + 1.0, # 1 + 1.0, # 2 + 1.0, # 3 + 1.0, # 4 + 1.0, # 5 + 1.0, # 6 + 1.0, # 7 + 1.0, # 8 + 0.0, # 9 Location of move 1 turn ago + 0.0, # 10 Location of move 2 turns ago + 0.0, # 11 Location of move 3 turns ago + 0.0, # 12 Location of move 4 turns ago + 0.0, # 13 Location of move 5 turns ago + 1.0, # 14 Ladder-threatened stone + 0.0, # 15 Ladder-threatened stone, 1 turn ago + 0.0, # 16 Ladder-threatened stone, 2 turns ago + 1.0, # 17 + 1.0, # 18 + 1.0, # 19 + 1.0, # 20 + 1.0, # 21 + ], + device=device, + requires_grad=False, + ) + ) + # Because we have ladder features that express past states rather than past diffs, + # the most natural encoding when we have no history is that they were always the + # same, rather than that they were all zero. So rather than zeroing them we have no + # history, we add entries in the matrix to copy them over. + # By default, without history, the ladder features 15 and 16 just copy over from 14. + h_base[14, 15] = 1.0 + h_base[14, 16] = 1.0 + + h0 = torch.zeros(num_bin_features, num_bin_features, device=device, requires_grad=False) + # When have the prev move, we enable feature 9 and 15 + h0[9, 9] = 1.0 # Enable 9 -> 9 + h0[14, 15] = -1.0 # Stop copying 14 -> 15 + h0[14, 16] = -1.0 # Stop copying 14 -> 16 + h0[15, 15] = 1.0 # Enable 15 -> 15 + h0[15, 16] = 1.0 # Start copying 15 -> 16 + + h1 = torch.zeros(num_bin_features, num_bin_features, device=device, requires_grad=False) + # When have the prevprev move, we enable feature 10 and 16 + h1[10, 10] = 1.0 # Enable 10 -> 10 + h1[15, 16] = -1.0 # Stop copying 15 -> 16 + h1[16, 16] = 1.0 # Enable 16 -> 16 + + h2 = torch.zeros(num_bin_features, num_bin_features, device=device, requires_grad=False) + h2[11, 11] = 1.0 + + h3 = torch.zeros(num_bin_features, num_bin_features, device=device, requires_grad=False) + h3[12, 12] = 1.0 + + h4 = torch.zeros(num_bin_features, num_bin_features, device=device, requires_grad=False) + h4[13, 13] = 1.0 + + # (1, n_bin, n_bin) + h_base = h_base.reshape((1, num_bin_features, num_bin_features)) + # (5, n_bin, n_bin) + h_builder = torch.stack((h0, h1, h2, h3, h4), dim=0) + + return (h_base, h_builder) + + +def apply_history_matrices(model_config, batch_binaryInputNCHW, batch_globalInputNC, batch_globalTargetsNC, h_base, h_builder): + num_global_features = modelconfigs.get_num_global_input_features(model_config) + # include_history = batch_globalTargetsNC[:,36:41] + should_stop_history = torch.rand_like(batch_globalTargetsNC[:,36:41]) >= 0.98 + include_history = (torch.cumsum(should_stop_history,axis=1,dtype=torch.float32) <= 0.1).to(torch.float32) + + # include_history: (N, 5) + # bi * ijk -> bjk, (N, 5) * (5, n_bin, n_bin) -> (N, n_bin, n_bin) + h_matrix = h_base + torch.einsum("bi,ijk->bjk", include_history, h_builder) + + + # batch_binaryInputNCHW: (N, n_bin_in, 19, 19) + # h_matrix: (N, n_bin_in, n_bin_out) + # Result: (N, n_bin_out, 19, 19) + batch_binaryInputNCHW = torch.einsum("bijk,bil->bljk", batch_binaryInputNCHW, h_matrix) + + # First 5 global input features exactly correspond to include_history, pointwise multiply to + # enable/disable them + batch_globalInputNC = batch_globalInputNC * torch.nn.functional.pad( + include_history, ((0, num_global_features - include_history.shape[1])), value=1.0 + ) + return batch_binaryInputNCHW, batch_globalInputNC diff --git a/training/scripts/dated/20241110-123407/diff.txt b/training/scripts/dated/20241110-123407/diff.txt new file mode 100644 index 0000000..df24a9e --- /dev/null +++ b/training/scripts/dated/20241110-123407/diff.txt @@ -0,0 +1,61 @@ +diff --git a/python/selfplay/shuffle.sh b/python/selfplay/shuffle.sh +index 0ac0124b..4b4348de 100755 +--- a/python/selfplay/shuffle.sh ++++ b/python/selfplay/shuffle.sh +@@ -1,8 +1,8 @@ + #!/bin/bash -eu + set -o pipefail + { +-#Shuffles and copies selfplay training from selfplay/ to shuffleddata/current/ +-#Should be run periodically. ++# Shuffles and copies selfplay training from selfplay/ to shuffleddata/current/ ++# Should be run periodically. + + if [[ $# -lt 4 ]] + then +@@ -96,25 +96,25 @@ else + fi + #set +x + +-#Just in case, give a little time for nfs ++# Just in case, give a little time for nfs + sleep 10 + +-#rm if it already exists +-rm -f "$BASEDIR"/shuffleddata/current_tmp ++# rm if it already exists ++rm -rf "$BASEDIR"/shuffleddata/current_tmp + + ln -s $OUTDIR "$BASEDIR"/shuffleddata/current_tmp + mv -Tf "$BASEDIR"/shuffleddata/current_tmp "$BASEDIR"/shuffleddata/current + + # CLEANUP --------------------------------------------------------------- + +-#Among shuffled dirs older than 2 hours, remove all but the most recent 5 of them. +-#This should be VERY conservative and allow plenty of time for the training to switch +-#to newer ones as they get generated. ++# Among shuffled dirs older than 2 hours, remove all but the most recent 5 of them. ++# This should be VERY conservative and allow plenty of time for the training to switch ++# to newer ones as they get generated. + echo "Cleaning up any old dirs" + find "$BASEDIR"/shuffleddata/ -mindepth 1 -maxdepth 1 -type d -mmin +120 | sort | head -n -5 | xargs --no-run-if-empty rm -r + + echo "Finished shuffle at" $(date "+%Y-%m-%d %H:%M:%S") +-#Make a little space between shuffles ++# Make a little space between shuffles + echo "" + echo "" + +diff --git a/python/selfplay/synchronous_loop.sh b/python/selfplay/synchronous_loop.sh +index 33f4be30..d21423e2 100755 +--- a/python/selfplay/synchronous_loop.sh ++++ b/python/selfplay/synchronous_loop.sh +@@ -44,7 +44,7 @@ mkdir -p "$BASEDIR"/selfplay + mkdir -p "$BASEDIR"/gatekeepersgf + + # Parameters for the training run +-NUM_GAMES_PER_CYCLE=90 # Every cycle, play this many games ++NUM_GAMES_PER_CYCLE=30 # Every cycle, play this many games + NUM_THREADS_FOR_SHUFFLING=8 + NUM_TRAIN_SAMPLES_PER_EPOCH=100000 # Training will proceed in chunks of this many rows, subject to MAX_TRAIN_PER_DATA. + MAX_TRAIN_PER_DATA=8 # On average, train only this many times on each data row. Larger numbers may cause overfitting. diff --git a/training/scripts/dated/20241110-123407/diffstaged.txt b/training/scripts/dated/20241110-123407/diffstaged.txt new file mode 100644 index 0000000..10d775f --- /dev/null +++ b/training/scripts/dated/20241110-123407/diffstaged.txt @@ -0,0 +1,19665 @@ +diff --git a/cpp/KataGoData/opencltuning/tune11_gpuNVIDIAGeForceRTX4060LaptopGPU_x19_y19_c320_mv10.txt b/cpp/KataGoData/opencltuning/tune11_gpuNVIDIAGeForceRTX4060LaptopGPU_x19_y19_c320_mv10.txt +new file mode 100644 +index 00000000..5b66d67d +--- /dev/null ++++ b/cpp/KataGoData/opencltuning/tune11_gpuNVIDIAGeForceRTX4060LaptopGPU_x19_y19_c320_mv10.txt +@@ -0,0 +1,33 @@ ++VERSION=11 ++#canUseFP16Storage ++1 ++#canUseFP16Compute ++0 ++#canUseFP16TensorCores ++1 ++#canUseFP16TensorCoresFor1x1 ++0 ++#shouldUseFP16Storage ++1 ++#shouldUseFP16Compute ++0 ++#shouldUseFP16TensorCores ++1 ++#shouldUseFP16TensorCoresFor1x1 ++0 ++#xGemmDirect ++WGD=32 MDIMCD=8 NDIMCD=8 MDIMAD=16 NDIMBD=16 KWID=2 VWMD=2 VWND=2 PADA=1 PADB=1 ++#xGemm ++MWG=64 NWG=64 KWG=16 MDIMC=16 NDIMC=8 MDIMA=16 NDIMB=8 KWI=2 VWM=4 VWN=4 STRM=0 STRN=0 SA=1 SB=1 ++#xGemm16 ++MWG=64 NWG=64 KWG=16 MDIMC=16 NDIMC=8 MDIMA=16 NDIMB=8 KWI=2 VWM=4 VWN=4 STRM=0 STRN=0 SA=1 SB=1 ++#hGemmWmma ++MWG=64 NWG=64 KWG=16 MWAVE=32 NWAVE=8 MWARP=32 NWARP=8 VWM=2 VWN=2 SA=0 SB=0 ++#hGemmWmmaNCHW ++MWG=16 NWG=16 KWG=16 MWAVE=16 NWAVE=16 MWARP=16 NWARP=16 VWM=1 VWN=2 SB=0 ++#conv3x3 ++INTILE_XSIZE=6 INTILE_YSIZE=6 OUTTILE_XSIZE=4 OUTTILE_YSIZE=4 transLocalSize0=128 transLocalSize1=1 untransLocalSize0=8 untransLocalSize1=4 untransLocalSize2=2 ++#conv5x5 ++INTILE_XSIZE=6 INTILE_YSIZE=6 OUTTILE_XSIZE=2 OUTTILE_YSIZE=2 transLocalSize0=128 transLocalSize1=1 untransLocalSize0=8 untransLocalSize1=4 untransLocalSize2=2 ++#gPool ++XYSTRIDE=16 CHANNELSTRIDE=2 BATCHSTRIDE=2 +diff --git a/cpp/KataGoData/opencltuning/tune11_gpuNVIDIAGeForceRTX4060LaptopGPU_x19_y19_c384_mv14.txt b/cpp/KataGoData/opencltuning/tune11_gpuNVIDIAGeForceRTX4060LaptopGPU_x19_y19_c384_mv14.txt +new file mode 100644 +index 00000000..71421834 +--- /dev/null ++++ b/cpp/KataGoData/opencltuning/tune11_gpuNVIDIAGeForceRTX4060LaptopGPU_x19_y19_c384_mv14.txt +@@ -0,0 +1,33 @@ ++VERSION=11 ++#canUseFP16Storage ++1 ++#canUseFP16Compute ++0 ++#canUseFP16TensorCores ++1 ++#canUseFP16TensorCoresFor1x1 ++0 ++#shouldUseFP16Storage ++1 ++#shouldUseFP16Compute ++0 ++#shouldUseFP16TensorCores ++1 ++#shouldUseFP16TensorCoresFor1x1 ++0 ++#xGemmDirect ++WGD=32 MDIMCD=8 NDIMCD=8 MDIMAD=8 NDIMBD=8 KWID=8 VWMD=2 VWND=4 PADA=1 PADB=1 ++#xGemm ++MWG=64 NWG=64 KWG=16 MDIMC=16 NDIMC=16 MDIMA=16 NDIMB=16 KWI=2 VWM=4 VWN=4 STRM=0 STRN=0 SA=1 SB=1 ++#xGemm16 ++MWG=64 NWG=64 KWG=16 MDIMC=16 NDIMC=16 MDIMA=16 NDIMB=16 KWI=2 VWM=4 VWN=4 STRM=0 STRN=0 SA=1 SB=1 ++#hGemmWmma ++MWG=64 NWG=64 KWG=64 MWAVE=16 NWAVE=16 MWARP=16 NWARP=16 VWM=2 VWN=2 SA=0 SB=0 ++#hGemmWmmaNCHW ++MWG=16 NWG=16 KWG=16 MWAVE=16 NWAVE=16 MWARP=16 NWARP=16 VWM=1 VWN=2 SB=0 ++#conv3x3 ++INTILE_XSIZE=6 INTILE_YSIZE=6 OUTTILE_XSIZE=4 OUTTILE_YSIZE=4 transLocalSize0=64 transLocalSize1=1 untransLocalSize0=16 untransLocalSize1=2 untransLocalSize2=2 ++#conv5x5 ++INTILE_XSIZE=6 INTILE_YSIZE=6 OUTTILE_XSIZE=2 OUTTILE_YSIZE=2 transLocalSize0=64 transLocalSize1=1 untransLocalSize0=16 untransLocalSize1=2 untransLocalSize2=2 ++#gPool ++XYSTRIDE=32 CHANNELSTRIDE=1 BATCHSTRIDE=2 +diff --git a/cpp/README.txt b/cpp/README.txt +new file mode 100644 +index 00000000..fb82db4b +--- /dev/null ++++ b/cpp/README.txt +@@ -0,0 +1,118 @@ ++KataGo v1.15.1 ++https://github.com/lightvector/KataGo ++ ++For neural nets from the latest run, download from here: ++https://katagotraining.org/ ++For nets from earlier runs, see: ++https://katagoarchive.org/ ++ ++For the human SL net (b18c384nbt-humanv0.bin.gz), it was released with: ++https://github.com/lightvector/KataGo/releases/tag/v1.15.0 ++ ++For differences between this version and older versions, see releases page at ++https://github.com/lightvector/KataGo/releases/ ++ ++----------------------------------------------------- ++USAGE: ++----------------------------------------------------- ++KataGo is just an engine and does not have its own graphical interface. So generally you will want to use KataGo along with a GUI or analysis program. ++(https://github.com/lightvector/KataGo#guis) ++ ++FIRST: Run a command like this to make sure KataGo is working, with the neural net file you downloaded. On OpenCL, it will also tune for your GPU. ++ ++./katago.exe benchmark # if you have default_gtp.cfg and default_model.bin.gz ++./katago.exe benchmark -model .bin.gz # if you have default_gtp.cfg ++./katago.exe benchmark -model .bin.gz -config gtp_custom.cfg # use this .bin.gz neural net and this .cfg file ++ ++It will tell you a good number of threads. Edit your .cfg file and set "numSearchThreads" to that many to get best performance. ++ ++OR: Run this command to have KataGo generate a custom gtp config for you based on answering some questions: ++ ++./katago.exe genconfig -model .bin.gz -output gtp_custom.cfg ++ ++NEXT: A command like this will run KataGo's engine. This is the command to give to your [GUI or analysis program](#guis) so that it can run KataGo. ++ ++./katago.exe gtp # if you have default_gtp.cfg and default_model.bin.gz ++./katago.exe gtp -model .bin.gz # if you have default_gtp.cfg ++./katago.exe gtp -model .bin.gz -config gtp_custom.cfg # use this .bin.gz neural net and this .cfg file ++ ++You may need to specify different paths when entering KataGo's command for a GUI program, e.g.: ++ ++path/to/katago.exe gtp -model path/to/.bin.gz ++path/to/katago.exe gtp -model path/to/.bin.gz -config path/to/gtp_custom.cfg ++ ++KataGo should be able to work with any GUI program that supports GTP, as well as any analysis program that supports Leela Zero's `lz-analyze` command, such as Lizzie (https://github.com/featurecat/lizzie) or Sabaki (https://sabaki.yichuanshen.de/). ++ ++NOTE for Windows users: ++If you encounter errors due to a missing "msvcp140.dll" or "msvcp140_1.dll" or "msvcp140_2.dll" or "vcruntime140.dll" or similar, you need to download and install the Microsoft Visual C++ Redistributable, here: ++https://www.microsoft.com/en-us/download/details.aspx?id=48145 ++If this is for a 64-bit Windows version of KataGo, these dll files have already been included for you, otherwise you will need to install them yourself. On a 64-bit Windows version, there is a rare chance that you may need to delete them if you already have it installed yourself separately and the pre-included files are actually causing problems running KataGo. ++ ++----------------------------------------------------- ++HUMAN-STYLE PLAY AND ANALYSIS: ++----------------------------------------------------- ++ ++You can also have KataGo imitate human play if you download the human SL model b18c384nbt-humanv0.bin.gz from https://github.com/lightvector/KataGo/releases/tag/v1.15.0, and run a command like the following, providing both a model and the human SL model: ++ ++./katago.exe gtp -model .bin.gz -human-model b18c384nbt-humanv0.bin.gz -config gtp_human5k_example.cfg ++ ++The gtp_human5k_example.cfg configures KataGo to imitate 5-kyu-level players. You can change it to imitate other ranks too, as well as to do many more things, including making KataGo play in a human style but still at a strong level or analyze in interesting ways. Read the config file itself for documentation on some of these possibilities! ++ ++And see also this guide to using the human SL model, which is written from the perspective of the JSON-based analysis engine mentioned below, but is also applicable to gtp as well. ++https://github.com/lightvector/KataGo/blob/master/docs/Analysis_Engine.md#human-sl-analysis-guide ++ ++----------------------------------------------------- ++OTHER THINGS YOU CAN DO: ++----------------------------------------------------- ++ ++Run a JSON-based analysis engine (https://github.com/lightvector/KataGo/blob/master/docs/Analysis_Engine.md) that can do efficient batched evaluations for a backend Go service: ++ ++./katago.exe analysis -model .gz -config .cfg ++ ++Run a high-performance match engine that will play a pool of bots against each other sharing the same GPU batches and CPUs with each other: ++ ++./katago.exe match -config .cfg -log-file match.log -sgf-output-dir ++ ++Force OpenCL tuner to re-tune: ++ ++./katago.exe tuner -config .cfg ++ ++Print version: ++ ++./katago.exe version ++ ++ ++----------------------------------------------------- ++OPENCL VS CUDA VS EIGEN: ++----------------------------------------------------- ++Explanation of the various versions available at https://github.com/lightvector/KataGo/releases ++ ++OpenCL - Use this if you have a modern GPU and want the easiest setup. ++ ++TensorRT, CUDA11 - Try one of these if you have a top-end NVIDIA GPU, are willing to do some more technical setup work, and care about getting every bit of performance. It MIGHT OR MIGHT NOT be faster than OpenCL! You have to test it on your specific GPU. TensorRT requires CUDA11 as well, and is likely to be the fastest. ++ ++Eigen AVX2 - Use this if you have no good GPU, but you have an Intel or AMD CPU from the last several years. This is a pure CPU version of KataGo. ++ ++Eigen - Use this if you don't have a GPU or your GPU is too old to work, and your CPU turns out not to support AVX2 or FMA. This is a pure CPU version for KataGo for ++This is the pure CPU version of KataGo, with no special instructions, which should hopefully run just about anywhere. ++ ++ ++----------------------------------------------------- ++TUNING FOR PERFORMANCE: ++----------------------------------------------------- ++You will very likely want to tune some of the parameters in `default_gtp.cfg` for your system for good performance, including the number of threads, fp16 usage, NN cache size, pondering settings, and so on. You can also adjust things like KataGo's resign threshold or utility function. Most of the relevant parameters should be be reasonably well documented directly inline in that config. ++ ++There are other a few notes about usage and performance at : https://github.com/lightvector/KataGo ++ ++----------------------------------------------------- ++TROUBLESHOOTING: ++----------------------------------------------------- ++Some common issues are described here: ++https://github.com/lightvector/KataGo#common-causes-of-errors ++ ++Or, feel free to hop into the Leela Zero discord chat, which has become a general chatroom for a variety of computer Go hobbyists and users, and which you can often find people willing to help. ++https://discord.gg/fhDHgfk ++ ++ ++ ++ +diff --git a/cpp/analysis_example.cfg b/cpp/analysis_example.cfg +new file mode 100644 +index 00000000..ca648321 +--- /dev/null ++++ b/cpp/analysis_example.cfg +@@ -0,0 +1,402 @@ ++# Config for KataGo C++ Analysis engine, i.e. "./katago.exe analysis" ++ ++# Example config for C++ (non-python) analysis engine ++ ++# SEE NOTES ABOUT PERFORMANCE AND MEMORY USAGE IN gtp_example.cfg ++# SEE NOTES ABOUT numSearchThreads AND OTHER IMPORTANT PARAMS BELOW! ++ ++# Logs------------------------------------------------------------------------------------ ++ ++# Where to output log? ++logDir = analysis_logs # Each run of KataGo will log to a separate file in this dir ++# logDirDated = analysis_logs # Use this instead of logDir to also write separate dated subdirs ++# logFile = analysis.log # Use this instead of logDir to just specify a single file directly ++# logToStderr = true # Echo everything output to log file to stderr as well ++# logAllRequests = false # Log all input lines received to the analysis engine. ++# logAllResponses = false # Log all lines output to stdout from the analysis engine. ++# logErrorsAndWarnings = true # Log all lines output to stdout from the analysis engine that are errors and warnings ++# logSearchInfo = false # Log debug info for every search performed ++ ++# Report a warning whenever a query contains a field that is unused. Helps to guard against ++# typos when writing code that queries the analysis engine. ++# warnUnusedFields = true ++ ++# Analysis------------------------------------------------------------------------------------ ++ ++# Controls the number of moves after the first move in a variation. ++# analysisPVLen = 15 ++ ++# Report winrates for analysis as (BLACK|WHITE|SIDETOMOVE). ++reportAnalysisWinratesAs = BLACK ++ ++# Larger values will make KataGo explore the top move(s) less deeply and accurately, ++# but explore and give evaluations to a greater variety of moves. ++# An extreme value like 1 will distribute many playouts across every move on the board, even very bad moves. ++# NOTE: defaults to 0.04, under the presumption that the analysis engine will be used mainly for analysis. ++# If you are intending to use the analysis engine to also play games and you want to maximize playing strength, ++# set this to 0.0 either in this config or in the overrides. ++# wideRootNoise = 0.04 ++ ++# Try to limit the effect of possible bad or bogus move sequences in the ++# history leading to this position from affecting KataGo's move predictions. ++# ignorePreRootHistory = true ++ ++# Bot behavior--------------------------------------------------------------------------------------- ++ ++# Handicap ------------- ++ ++# Assume that if black makes many moves in a row right at the start of the game, then the game is a handicap game. ++# This is necessary on some servers and for some GUIs and also when initializing from many SGF files, which may ++# set up a handicap game using repeated GTP "play" commands for black rather than GTP "place_free_handicap" commands. ++# However, it may also lead to incorrect understanding of komi if whiteHandicapBonus is used and a server does NOT ++# have such a practice. ++# Defaults to true! Uncomment and set to false to disable this behavior. ++# assumeMultipleStartingBlackMovesAreHandicap = true ++ ++# Passing and cleanup ------------- ++ ++# Make the bot never assume that its pass will end the game, even if passing would end and "win" under Tromp-Taylor rules. ++# Usually this is a good idea when using it for analysis or playing on servers where scoring may be implemented non-tromp-taylorly. ++# Defaults to true! Uncomment and set to false to disable this. ++# conservativePass = true ++ ++# When using territory scoring, self-play games continue beyond two passes with special cleanup ++# rules that may be confusing for human players. This option prevents the special cleanup phases from being ++# reachable when using the bot for GTP play. ++# Defaults to true! Uncomment and set to false if you want KataGo to be able to enter special cleanup. ++# For example, if you are testing it against itself, or against another bot that has precisely implemented the rules ++# documented at https://lightvector.github.io/KataGo/rules.html ++# preventCleanupPhase = true ++ ++# Search limits----------------------------------------------------------------------------------- ++ ++# By default, if NOT specified in an individual request, limit maximum number of root visits per search to this much ++maxVisits = 500 ++# If provided, cap search time at this many seconds ++# maxTime = 60 ++ ++# Search threads, batching, GPUs-------------------------------------------------------------------------- ++ ++# Try a configuration like this if you only expect the engine to be handling a few queries at a time and you want ++# individual queries to return more quickly, and are okay with the results being a bit lower-quality and the overall ++# peak throughput on queries to be lower. ++numAnalysisThreads = 2 ++numSearchThreadsPerAnalysisThread = 16 ++ ++# Try a configuration like this if you expect to be sending large numbers of queries at a time, and want to maximize ++# total throughput and also the evaluation quality of all the queries and you never care about the response latency ++# of the individual queries, only the throughput as a whole. ++# numAnalysisThreads = 32 ++# numSearchThreadsPerAnalysisThread = 1 ++ ++# You will want to increase one or both numbers if you have a powerful GPU, and possibly decrease one or both if you ++# have a very weak GPU, and play with the balance between them depending on your use case. ++# Read the explanation below to understand how to set these parameters: ++ ++# EXPLANATION: ++# numAnalysisThreads: the number of POSITIONS to be able to search in parallel. ++# numSearchThreadsPerAnalysisThread: the number of threads to use in the tree search for EACH position. ++# (older analysis configs might just have 'numSearchThreads', this is an alias for 'numSearchThreadsPerAnalysisThread') ++ ++# Therefore, the total number of search threads that may be active at a given time could be as large as the product: ++# numAnalysisThreads * numSearchThreadsPerAnalysisThread ++ ++# Searching more positions in parallel is more efficient since the different threads aren't conflicting with each ++# other on the same MCTS search tree. Using multiple threads on the same search will both make things slower ++# and weaken the search (holding playouts fixed) due to out of date statistics on nodes and suboptimal exploration, ++# although the cost is minor for only 2,4,8 threads. ++ ++# So unlike in GTP, which only ever searches one position at a time and where therefore you might as well make ++# numSearchThreads as large as possible, in the analysis engine you often want you often want to keep numSearchThreads small, ++# and instead parallelize across positions, so you can reduce conflict between threads and improve the overall throughput ++# and strength of the search. ++ ++# But obviously you only get the benefit of parallelization across positions when you actually have lots of positions ++# that you are querying at once! For example, setting numAnalysisThreads = 8 is useless if you only ever send one or two ++# queries at a time! ++ ++# Therefore: ++# * If you plan to use the analysis engine only for batch processing large numbers of positions, ++# it's preferable to numSearchThreadsPerAnalysisThread to only a small number (e.g. 1,2,4) and use a higher numAnalysisThreads. ++# * But if you sometimes plan to query the analysis engine for single positions, or otherwise in smaller quantities ++# than -num-analysis-threads, or if you plan to be user-interactive such that the response time on some individual ++# analysis requests is important to keep low, then set numSearchThreadsPerAnalysisThread to a larger number and use ++# a lower numAnalysisThreads. That way, individual searches complete faster due to having more threads on each one. ++ ++# For 19x19 boards, weaker GPUs probably want a TOTAL number of threads (numAnalysisThreads * numSearchThreadsPerAnalysisThread) ++# between 4 and 32. Mid-tier GPUs probably between 16 and 64. Strong GPUs probably between 32 and 256. ++# But there's no substitute for experimenting and seeing what's best for your hardware and your usage case. ++ ++# Keep in mind that the number of threads you want does NOT necessarily have much to do with how many cores you have on your ++# system. The optimal may easily exceed the number of cores! GPU batching is (usually) the dominant consideration. ++ ++# ------------- ++ ++# nnMaxBatchSize is the max number of positions to send to a single GPU at once. Generally, it should be the case that: ++# (number of GPUs you will use * nnMaxBatchSize) >= (numSearchThreads * num-analysis-threads) ++# That way, when each threads tries to request a GPU eval, your batch size summed across GPUs is large enough to handle them ++# all at once. However, it can be sensible to set this a little smaller if you are limited on GPU memory, ++# too large a number may fail if the GPU doesn't have enough memory. ++nnMaxBatchSize = 64 ++ ++# Uncomment and set these smaller if you are going to use the analysis engine EXCLUSIVELY for smaller boards (or plan to ++# run multiple instances, with some instances only handling smaller boards). It should improve performance. ++# It may also mean you can use more threads profitably. ++# maxBoardXSizeForNNBuffer = 19 ++# maxBoardYSizeForNNBuffer = 19 ++ ++# Uncomment and set this to true if you are going to use the analysis engine EXCLUSIVELY for exactly the board size ++# specified by maxBoardXSizeForNNBuffer and maxBoardYSizeForNNBuffer. It may slightly improve performance on some GPUs. ++# requireMaxBoardSize = true ++ ++# TO USE MULTIPLE GPUS: ++# Uncomment and set this to the number of GPUs you have and/or would like to use... ++# AND if it is more than 1, uncomment the appropriate CUDA or OpenCL section below. ++# numNNServerThreadsPerModel = 1 ++ ++# Other General GPU Settings------------------------------------------------------------------------------- ++ ++# Cache up to 2 ** this many neural net evaluations in case of transpositions in the tree. ++nnCacheSizePowerOfTwo = 23 ++# Size of mutex pool for nnCache is 2 ** this ++nnMutexPoolSizePowerOfTwo = 17 ++# Randomize board orientation when running neural net evals? ++nnRandomize = true ++ ++ ++# TENSORRT GPU settings-------------------------------------- ++# These only apply when using the TENSORRT version of KataGo. ++ ++# IF USING ONE GPU: optionally uncomment and change this if the GPU you want to use turns out to be not device 0 ++# trtDeviceToUse = 0 ++ ++# IF USING TWO GPUS: Uncomment these two lines (AND set numNNServerThreadsPerModel above): ++# trtDeviceToUseThread0 = 0 # change this if the first GPU you want to use turns out to be not device 0 ++# trtDeviceToUseThread1 = 1 # change this if the second GPU you want to use turns out to be not device 1 ++ ++# IF USING THREE GPUS: Uncomment these three lines (AND set numNNServerThreadsPerModel above): ++# trtDeviceToUseThread0 = 0 # change this if the first GPU you want to use turns out to be not device 0 ++# trtDeviceToUseThread1 = 1 # change this if the second GPU you want to use turns out to be not device 1 ++# trtDeviceToUseThread2 = 2 # change this if the third GPU you want to use turns out to be not device 2 ++ ++# You can probably guess the pattern if you have four, five, etc. GPUs. ++ ++ ++# CUDA-specific GPU settings-------------------------------------- ++# These only apply when using the CUDA version of KataGo. ++ ++# IF USING ONE GPU: optionally uncomment and change this if the GPU you want to use turns out to be not device 0 ++# cudaDeviceToUse = 0 ++ ++# IF USING TWO GPUS: Uncomment these two lines (AND set numNNServerThreadsPerModel above): ++# cudaDeviceToUseThread0 = 0 # change this if the first GPU you want to use turns out to be not device 0 ++# cudaDeviceToUseThread1 = 1 # change this if the second GPU you want to use turns out to be not device 1 ++ ++# IF USING THREE GPUS: Uncomment these three lines (AND set numNNServerThreadsPerModel above): ++# cudaDeviceToUseThread0 = 0 # change this if the first GPU you want to use turns out to be not device 0 ++# cudaDeviceToUseThread1 = 1 # change this if the second GPU you want to use turns out to be not device 1 ++# cudaDeviceToUseThread2 = 2 # change this if the third GPU you want to use turns out to be not device 2 ++ ++# You can probably guess the pattern if you have four, five, etc. GPUs. ++ ++# KataGo will automatically use FP16 or not based on the compute capability of your NVIDIA GPU. If you ++# want to try to force a particular behavior though you can uncomment these lines and change them ++# to "true" or "false". E.g. it's using FP16 but on your card that's giving an error, or it's not using ++# FP16 but you think it should. ++# cudaUseFP16 = auto ++# cudaUseNHWC = auto ++ ++ ++# OpenCL-specific GPU settings-------------------------------------- ++# These only apply when using the OpenCL version of KataGo. ++ ++# Uncomment to tune OpenCL for every board size separately, rather than only the largest possible size ++# openclReTunePerBoardSize = true ++ ++# IF USING ONE GPU: optionally uncomment and change this if the best device to use is guessed incorrectly. ++# The default behavior tries to guess the 'best' GPU or device on your system to use, usually it will be a good guess. ++# openclDeviceToUse = 0 ++ ++# IF USING TWO GPUS: Uncomment these two lines and replace X and Y with the device ids of the devices you want to use. ++# It might NOT be 0 and 1, some computers will have many OpenCL devices. You can see what the devices are when ++# KataGo starts up - it should print or log all the devices it finds. ++# (AND also set numNNServerThreadsPerModel above) ++# openclDeviceToUseThread0 = X ++# openclDeviceToUseThread1 = Y ++ ++# IF USING THREE GPUS: Uncomment these three lines and replace X and Y and Z with the device ids of the devices you want to use. ++# It might NOT be 0 and 1 and 2, some computers will have many OpenCL devices. You can see what the devices are when ++# KataGo starts up - it should print or log all the devices it finds. ++# (AND also set numNNServerThreadsPerModel above) ++# openclDeviceToUseThread0 = X ++# openclDeviceToUseThread1 = Y ++# openclDeviceToUseThread2 = Z ++ ++# You can probably guess the pattern if you have four, five, etc. GPUs. ++ ++# KataGo will automatically use FP16 or not based on testing your GPU during tuning. If you ++# want to try to force a particular behavior though you can uncomment this lines and change it ++# to "true" or "false". This is a fairly blunt setting - more detailed settings are testable ++# by rerunning the tuner with various arguments. ++# openclUseFP16 = auto ++ ++ ++# Eigen-specific settings-------------------------------------- ++# These only apply when using the Eigen (pure CPU) version of KataGo. ++ ++# This is the number of CPU threads for evaluating the neural net on the Eigen backend. ++# It defaults to min(numAnalysisThreads * numSearchThreadsPerAnalysisThread, numCPUCores). ++# numEigenThreadsPerModel = X ++ ++ ++# Misc Behavior -------------------- ++ ++# If the board is symmetric, search only one copy of each equivalent move. Attempts to also account for ko/superko, will not theoretically perfect for superko. ++# Uncomment and set to false to disable this. ++# rootSymmetryPruning = true ++ ++# Uncomment and set to true to make KataGo avoid a particular joseki that some KataGo nets misevaluate, ++# and also to improve opening diversity versus some particular other bots that like to play it all the time. ++# avoidMYTDaggerHack = false ++ ++# Have KataGo mildly prefer to avoid playing the same joseki in every corner of the board. ++# Uncomment to set to a specific value. A small value like 0.005 should produce already a noticeable behavior change. ++# avoidRepeatedPatternUtility = 0.0 ++ ++# Enable some hacks that mitigate rare instances when passing messes up deeper searches. ++# enablePassingHacks = true ++ ++# Root move selection and biases------------------------------------------------------------------------------ ++# Uncomment and edit any of the below values to change them from their default. ++# Not all of these parameters are applicable to analysis, some are only used for actual play ++ ++# Temperature for the early game, randomize between chosen moves with this temperature ++# chosenMoveTemperatureEarly = 0.5 ++# Decay temperature for the early game by 0.5 every this many moves, scaled with board size. ++# chosenMoveTemperatureHalflife = 19 ++# At the end of search after the early game, randomize between chosen moves with this temperature ++# chosenMoveTemperature = 0.10 ++# Subtract this many visits from each move prior to applying chosenMoveTemperature ++# (unless all moves have too few visits) to downweight unlikely moves ++# chosenMoveSubtract = 0 ++# The same as chosenMoveSubtract but only prunes moves that fall below the threshold, does not affect moves above ++# chosenMovePrune = 1 ++ ++# Number of symmetries to sample (WITHOUT replacement) and average at the root ++# rootNumSymmetriesToSample = 1 ++ ++# Using LCB for move selection? ++# useLcbForSelection = true ++# How many stdevs a move needs to be better than another for LCB selection ++# lcbStdevs = 5.0 ++# Only use LCB override when a move has this proportion of visits as the top move ++# minVisitPropForLCB = 0.15 ++ ++# Internal params------------------------------------------------------------------------------ ++# Uncomment and edit any of the below values to change them from their default. ++ ++# Scales the utility of winning/losing ++# winLossUtilityFactor = 1.0 ++# Scales the utility for trying to maximize score ++# staticScoreUtilityFactor = 0.10 ++# dynamicScoreUtilityFactor = 0.30 ++# Adjust dynamic score center this proportion of the way towards zero, capped at a reasonable amount. ++# dynamicScoreCenterZeroWeight = 0.20 ++# dynamicScoreCenterScale = 0.75 ++# The utility of getting a "no result" due to triple ko or other long cycle in non-superko rulesets (-1 to 1) ++# noResultUtilityForWhite = 0.0 ++# The number of wins that a draw counts as, for white. (0 to 1) ++# drawEquivalentWinsForWhite = 0.5 ++ ++# Exploration constant for mcts ++# cpuctExploration = 1.0 ++# cpuctExplorationLog = 0.45 ++ ++# Parameters that control exploring more in volatile positions, exploring less in stable positions. ++# cpuctUtilityStdevPrior = 0.40 ++# cpuctUtilityStdevPriorWeight = 2.0 ++# cpuctUtilityStdevScale = 0.85 ++ ++# FPU reduction constant for mcts ++# fpuReductionMax = 0.2 ++# rootFpuReductionMax = 0.1 ++# fpuParentWeightByVisitedPolicy = true ++ ++# Parameters that control weighting of evals based on the net's own self-reported uncertainty. ++# useUncertainty = true ++# uncertaintyExponent = 1.0 ++# uncertaintyCoeff = 0.25 ++ ++# Explore using optimistic policy ++# rootPolicyOptimism = 0.2 ++# policyOptimism = 1.0 ++ ++# Amount to apply a downweighting of children with very bad values relative to good ones ++# valueWeightExponent = 0.25 ++ ++# Slight incentive for the bot to behave human-like with regard to passing at the end, filling the dame, ++# not wasting time playing in its own territory, etc, and not play moves that are equivalent in terms of ++# points but a bit more unfriendly to humans. ++# rootEndingBonusPoints = 0.5 ++ ++# Make the bot prune useless moves that are just prolonging the game to avoid losing yet ++# rootPruneUselessMoves = true ++ ++# Apply bias correction based on local pattern keys ++# subtreeValueBiasFactor = 0.45 ++# subtreeValueBiasWeightExponent = 0.85 ++ ++# Use graph search rather than tree search - identify and share search for transpositions. ++# useGraphSearch = true ++ ++# How much to shard the node table for search synchronization ++# nodeTableShardsPowerOfTwo = 16 ++# How many virtual losses to add when a thread descends through a node ++# numVirtualLossesPerThread = 1 ++ ++# Improve the quality of evals under heavy multithreading ++# useNoisePruning = true ++ ++ ++# Avoid SGF Patterns ------------------------------------------------------------------------------ ++# The parameters in this section provide a powerful way to customize KataGo to avoid moves that follow specific patterns ++# based on a set of provided SGF files loaded upon startup. Uncomment them to use this feature. ++# Additionally, if the SGF file contains the string %SKIP% in a comment on a move, that move will be ignored for this purpose. ++ ++# Load sgf files from this directory when the engine is started (ONLY on startup, will not reload unless engine is restarted) ++# avoidSgfPatternDirs = path/to/directory/with/sgfs/ ++# You can also surround the file path in double quotes if the file path contains trailing spaces or hash signs. ++# Within double quotes, backslashes are escape characters. ++# avoidSgfPatternDirs = "path/to/directory/with/sgfs/" ++ ++# Penalize this much utility per matching move. ++# Set this negative if you instead want to make KataGo favor the SGF patterns instead of penalizing it! ++# This number does not need to be large, even 0.001 will make a difference. Too-large values may lead to bad play. ++# avoidSgfPatternUtility = 0.001 ++ ++# Optional - load only the newest this many files ++# avoidSgfPatternMaxFiles = 20 ++ ++# Optional - Penalty is multiplied by this per each older SGF file, so that old sgf files matter less than newer ones. ++# avoidSgfPatternLambda = 0.90 ++ ++# Optional - pay attention only to moves that were made by players with this name. ++# For example you can set it to the name that your bot's past games will show up as in the SGF, so that the bot will only avoid repeating ++# moves that itself made in past games, not the moves that its opponents made. ++# avoidSgfPatternAllowedNames = my-ogs-bot-name1,my-ogs-bot-name2 ++ ++# Optional - Ignore any moves in SGF files that occurred before this turn number. ++# avoidSgfPatternMinTurnNumber = 0 ++ ++# For more avoid patterns: ++# You can also specify a second set of parameters, and a third, fourth, etc by numbering 2,3,4,... ++# avoidSgf2PatternDirs = ... ++# avoidSgf2PatternUtility = ... ++# avoidSgf2PatternMaxFiles = ... ++# avoidSgf2PatternLambda = ... ++# avoidSgf2PatternAllowedNames = ... ++# avoidSgf2PatternMinTurnNumber = ... ++ ++ ++ ++ +diff --git a/cpp/b28.cfg b/cpp/b28.cfg +new file mode 100644 +index 00000000..302e8a40 +--- /dev/null ++++ b/cpp/b28.cfg +@@ -0,0 +1,721 @@ ++# Configuration for KataGo C++ GTP engine ++ ++# Run the program using: `./katago.exe gtp` ++ ++# In this example config, when a parameter is given as a commented out value, ++# that value also is the default value, unless described otherwise. You can ++# uncomment it (remove the pound sign) and change it if you want. ++ ++# =========================================================================== ++# Running on an online server or in a real tournament or match ++# =========================================================================== ++# If you plan to run online or in a tournament, read through the "Rules" ++# section below for proper handling of komi, handicaps, end-of-game cleanup, ++# and other details. ++ ++# =========================================================================== ++# Notes about performance and memory usage ++# =========================================================================== ++# Important: For good performance, you will very likely want to tune the ++# "numSearchThreads" parameter in the Search limits section below! Run ++# "./katago benchmark" to test KataGo and to suggest a reasonable value ++# of this parameter. ++ ++# For multi-GPU systems, read "OpenCL GPU settings" or "CUDA GPU settings". ++# ++# When using OpenCL, verify that KataGo picks the correct device! Some systems ++# may have both an Intel CPU OpenCL and GPU OpenCL. If # KataGo picks the wrong ++# one, correct this by specifying "openclGpuToUse". ++# ++# Consider adjusting "maxVisits", "ponderingEnabled", "resignThreshold", and ++# other parameters depending on your intended usage. ++ ++# =========================================================================== ++# Command-line usage ++# =========================================================================== ++# All of the below values may be set or overridden via command-line arguments: ++# ++# -override-config KEY=VALUE,KEY=VALUE,... ++ ++# =========================================================================== ++# Logs and files ++# =========================================================================== ++# This section defines where and what logging information is produced. ++ ++# Each run of KataGo will log to a separate file in this dir. ++# This is the default. ++logDir = gtp_logs ++# Uncomment and specify this instead of logDir to write separate dated subdirs ++# logDirDated = gtp_logs ++# Uncomment and specify this instead of logDir to log to only a single file ++# logFile = gtp.log ++ ++# Logging options ++logAllGTPCommunication = true ++logSearchInfo = true ++logSearchInfoForChosenMove = false ++logToStderr = true ++ ++# KataGo will display some info to stderr on GTP startup ++# Uncomment the next line and set it to false to suppress that and remain silent ++# startupPrintMessageToStderr = true ++ ++# Write information to stderr, for use in things like malkovich chat to OGS. ++ ogsChatToStderr = true ++ ++# Uncomment and set this to a directory to override where openCLTuner files ++# and other cached data is written. By default it saves into a subdir of the ++# current directory on windows, and a subdir of ~/.katago on Linux. ++# homeDataDir = PATH_TO_DIRECTORY ++ ++# =========================================================================== ++# Analysis ++# =========================================================================== ++# This section configures analysis settings. ++# ++# The maximum number of moves after the first move displayed in variations ++# from analysis commands like kata-analyze or lz-analyze. ++# analysisPVLen = 15 ++ ++# Report winrates for chat and analysis as (BLACK|WHITE|SIDETOMOVE). ++# Most GUIs and analysis tools will expect SIDETOMOVE. ++# reportAnalysisWinratesAs = SIDETOMOVE ++ ++# Extra noise for wider exploration. Large values will force KataGo to ++# analyze a greater variety of moves than it normally would. ++# An extreme value like 1 distributes playouts across every move on the board, ++# even very bad moves. ++# Affects analysis only, does not affect play. ++# analysisWideRootNoise = 0.04 ++ ++# Try to limit the effect of possible bad or bogus move sequences in the ++# history leading to this position from affecting KataGo's move predictions. ++# analysisIgnorePreRootHistory = true ++ ++# =========================================================================== ++# Rules ++# =========================================================================== ++# This section configures the scoring and playing rules. Rules can also be ++# changed mid-run by issuing custom GTP commands. ++# ++# See https://lightvector.github.io/KataGo/rules.html for rules details. ++# ++# See https://github.com/lightvector/KataGo/blob/master/docs/GTP_Extensions.md ++# for GTP commands. ++ ++# Specify the rules as a string. ++# Some legal values include: ++# chinese, japanese, korean, aga, chinese-ogs, new-zealand, stone-scoring, ++# ancient-territory, bga, aga-button ++# ++# For some human rulesets that require complex adjudication in tricky cases ++# (e.g. japanese, korean) KataGo may not precisely match the ruleset in such ++# cases but will do its best. ++rules = tromp-taylor ++ ++# By default, the "rules" parameter is used, but if you comment it out and ++# uncomment one option in each of the sections below, you can specify an ++# arbitrary combination of individual rules. ++ ++# koRule = SIMPLE # Simple ko rules (triple ko = no result) ++# koRule = POSITIONAL # Positional superko ++# koRule = SITUATIONAL # Situational superko ++ ++# scoringRule = AREA # Area scoring ++# scoringRule = TERRITORY # Territory scoring (special computer-friendly territory rules) ++ ++# taxRule = NONE # All surrounded empty points are scored ++# taxRule = SEKI # Eyes in seki do NOT count as points ++# taxRule = ALL # All groups are taxed up to 2 points for the two eyes needed to live ++ ++# Is multiple-stone suicide legal? (Single-stone suicide is always illegal). ++# multiStoneSuicideLegal = false ++# multiStoneSuicideLegal = true ++ ++# "Button go" - the first pass when area scoring awards 0.5 points and does ++# not count for ending the game. ++# Allows area scoring rulesets that have far simpler rules to achieve the same ++# final scoring precision and reward for precise play as territory scoring. ++# hasButton = false ++# hasButton = true ++ ++# Is this a human ruleset where it's okay to pass before having physically ++# captured and removed all dead stones? ++# friendlyPassOk = false ++# friendlyPassOk = true ++ ++# How handicap stones in handicap games are compensated ++# whiteHandicapBonus = 0 # White gets no compensation for black's handicap stones (Tromp-taylor, NZ, JP) ++# whiteHandicapBonus = N-1 # White gets N-1 points for black's N handicap stones (AGA) ++# whiteHandicapBonus = N # White gets N points for black's N handicap stones (Chinese) ++ ++# ------------------------------ ++# Other rules hacks ++# ------------------------------ ++# Uncomment and change to adjust what board size KataGo uses upon startup ++# by default when GTP doesn't specify. ++# defaultBoardSize = 19 ++ ++# By default, Katago will use the komi that the GUI or GTP controller tries to set. ++# Uncomment and set this to have KataGo ignore the controller and always use this komi. ++# ignoreGTPAndForceKomi = 7 ++ ++# =========================================================================== ++# Bot behavior ++# =========================================================================== ++ ++# ------------------------------ ++# Resignation ++# ------------------------------ ++ ++# Resignation occurs if for at least resignConsecTurns in a row, the ++# winLossUtility (on a [-1,1] scale) is below resignThreshold. ++allowResignation = true ++resignThreshold = -0.90 ++resignConsecTurns = 3 ++ ++# By default, KataGo may resign games that it is confidently losing even if they ++# are very close in score. Uncomment and set this to avoid resigning games ++# if the estimated difference is points is less than or equal to this. ++# resignMinScoreDifference = 10 ++ ++# ------------------------------ ++# Handicap ++# ------------------------------ ++# Assume that if black makes many moves in a row right at the start of the ++# game, then the game is a handicap game. This is necessary on some servers ++# and for some GUIs and also when initializing from many SGF files, which may ++# set up a handicap game using repeated GTP "play" commands for black rather ++# than GTP "place_free_handicap" commands; however, it may also lead to ++# incorrect understanding of komi if whiteHandicapBonus is used and a server ++# does not have such a practice. Uncomment and set to false to disable. ++# assumeMultipleStartingBlackMovesAreHandicap = true ++ ++# Makes katago dynamically adjust in handicap or altered-komi games to assume ++# based on those game settings that it must be stronger or weaker than the ++# opponent and to play accordingly. Greatly improves handicap strength by ++# biasing winrates and scores to favor appropriate safe/aggressive play. ++# Does NOT affect analysis (lz-analyze, kata-analyze, used by programs like ++# Lizzie) so analysis remains unbiased. Uncomment and set this to 0 to disable ++# this and make KataGo play the same always. ++# dynamicPlayoutDoublingAdvantageCapPerOppLead = 0.045 ++ ++# Instead of "dynamicPlayoutDoublingAdvantageCapPerOppLead", you can comment ++# that out and uncomment and set "playoutDoublingAdvantage" to a fixed value ++# from -3.0 to 3.0 that will not change dynamically. ++# ALSO affects analysis tools (lz-analyze, kata-analyze, used by e.g. Lizzie). ++# Negative makes KataGo behave as if it is much weaker than the opponent. ++# Positive makes KataGo behave as if it is much stronger than the opponent. ++# KataGo will adjust to favor safe/aggressive play as appropriate based on ++# the combination of who is ahead and how much stronger/weaker it thinks it is, ++# and report winrates and scores taking the strength difference into account. ++# ++# If this and "dynamicPlayoutDoublingAdvantageCapPerOppLead" are both set ++# then dynamic will be used for all games and this fixed value will be used ++# for analysis tools. ++# playoutDoublingAdvantage = 0.0 ++ ++# Uncomment one of these when using "playoutDoublingAdvantage" to enforce ++# that it will only apply when KataGo plays as the specified color and will be ++# negated when playing as the opposite color. ++# playoutDoublingAdvantagePla = BLACK ++# playoutDoublingAdvantagePla = WHITE ++ ++# ------------------------------ ++# Passing and cleanup ++# ------------------------------ ++# Make the bot never assume that its pass will end the game, even if passing ++# would end and "win" under Tromp-Taylor rules. Usually this is a good idea ++# when using it for analysis or playing on servers where scoring may be ++# implemented non-tromp-taylorly. Uncomment and set to false to disable. ++# conservativePass = true ++ ++# When using territory scoring, self-play games continue beyond two passes ++# with special cleanup rules that may be confusing for human players. This ++# option prevents the special cleanup phases from being reachable when using ++# the bot for GTP play. Uncomment and set to false to enable entering special ++# cleanup. For example, if you are testing it against itself, or against ++# another bot that has precisely implemented the rules documented at ++# https://lightvector.github.io/KataGo/rules.html ++# preventCleanupPhase = true ++ ++# ------------------------------ ++# Miscellaneous behavior ++# ------------------------------ ++# If the board is symmetric, search only one copy of each equivalent move. ++# Attempts to also account for ko/superko, will not theoretically perfect for ++# superko. Uncomment and set to false to disable. ++# rootSymmetryPruning = true ++ ++# Uncomment and set to true to avoid a particular joseki that some networks ++# misevaluate, and also to improve opening diversity versus some particular ++# other bots that like to play it all the time. ++# avoidMYTDaggerHack = false ++ ++# Prefer to avoid playing the same joseki in every corner of the board. ++# Uncomment to set to a specific value. See "Avoid SGF patterns" section. ++# By default: 0 (even games), 0.005 (handicap games) ++# avoidRepeatedPatternUtility = 0.0 ++ ++# Experimental logic to fight against mirror Go even with unfavorable komi. ++# Uncomment to set to a specific value to use for both playing and analysis. ++# By default: true when playing via GTP, but false when analyzing. ++# antiMirror = true ++ ++# Enable some hacks that mitigate rare instances when passing messes up deeper searches. ++# enablePassingHacks = true ++ ++# Uncomment and set this to true to prevent bad or bogus move sequences ++# in the history leading to this position from affecting KataGo's move choices. ++# Same as analysisIgnorePreRootHistory (see above) but applies to actual play. ++# You can enable this if KataGo is being asked to play from positions that it did not ++# choose the moves to reach. ++# ignorePreRootHistory = false ++ ++# =========================================================================== ++# Search limits ++# =========================================================================== ++ ++# Terminology: ++# "Playouts" is the number of new playouts of search performed each turn. ++# "Visits" is the same as "Playouts" but also counts search performed on ++# previous turns that is still applicable to this turn. ++# "Time" is the time in seconds. ++ ++# For example, if KataGo searched 200 nodes on the previous turn, and then ++# after the opponent's reply, 50 nodes of its search tree was still valid, ++# then a visit limit of 200 would allow KataGo to search 150 new nodes ++# (for a final tree size of 200 nodes), whereas a playout limit of of 200 ++# would allow KataGo to search 200 nodes (for a final tree size of 250 nodes). ++ ++# Additionally, KataGo may also move before than the limit in order to ++# obey time controls (e.g. byo-yomi, etc) if the GTP controller has ++# told KataGo that the game has is being played with a given time control. ++ ++# Limits for search on the current turn. ++# If commented out or unspecified, the default is to have no limit. ++# maxVisits = 30000 ++# maxPlayouts = 300 ++maxTime = 1.0 ++ ++# Ponder on the opponent's turn? ++ponderingEnabled = true ++ ++# Limits for search when pondering on the opponent's turn. ++# If commented out or unspecified, the default is to have no limit. ++# Limiting the maximum time is recommended so that KataGo won't burn CPU/GPU ++# forever and/or run out of RAM if left unattended while pondering is enabled. ++# maxVisitsPondering = 5000 ++# maxPlayoutsPondering = 3000 ++maxTimePondering = 120.0 ++ ++ ++# ------------------------------ ++# Other search limits and behavior ++# ------------------------------ ++ ++# Approx number of seconds to buffer for lag for GTP time controls - will ++# move a bit faster assuming there is this much lag per move. ++lagBuffer = 1.0 ++ ++# YOU PROBABLY WANT TO TUNE THIS PARAMETER! ++# The number of threads to use when searching. On powerful GPUs the optimal ++# threads may be much higher than the number of CPU cores you have because ++# many threads are needed to feed efficient large batches to the GPU. ++# ++# Run "./katago benchmark" to tune this parameter and test the effect ++# of changes to any of other parameters. ++numSearchThreads = 10 ++ ++# Play a little faster if the opponent is passing, for human-friendliness. ++# Comment these out to disable them, such as if running a controlled match ++# where you are testing KataGo with fixed compute per move vs other bots. ++searchFactorAfterOnePass = 0.50 ++searchFactorAfterTwoPass = 0.25 ++ ++# Play a little faster if super-winning, for human-friendliness. ++# Comment these out to disable them, such as if running a controlled match ++# where you are testing KataGo with fixed compute per move vs other bots. ++searchFactorWhenWinning = 0.40 ++searchFactorWhenWinningThreshold = 0.95 ++ ++# =========================================================================== ++# GPU settings ++# =========================================================================== ++# This section configures GPU settings. ++# ++# Maximum number of positions to send to a single GPU at once. The default ++# value is roughly equal to numSearchThreads, but can be specified manually ++# if running out of memory, or using multiple GPUs that expect to share work. ++# nnMaxBatchSize = ++ ++# Controls the neural network cache size, which is the primary RAM/memory use. ++# KataGo will cache up to (2 ** nnCacheSizePowerOfTwo) many neural net ++# evaluations in case of transpositions in the tree. ++# Increase this to improve performance for searches with tens of thousands ++# of visits or more. Decrease this to limit memory usage. ++# If you're happy to do some math - each neural net entry takes roughly ++# 1.5KB, except when using whole-board ownership/territory ++# visualizations, where each entry will take roughly 3KB. The number of ++# entries is (2 ** nnCacheSizePowerOfTwo). (E.g. 2 ** 18 = 262144.) ++# You can compute roughly how much memory the cache will use based on this. ++# nnCacheSizePowerOfTwo = 20 ++ ++# Size of mutex pool for nnCache is (2 ** this). ++# nnMutexPoolSizePowerOfTwo = 16 ++ ++# Randomize board orientation when running neural net evals? Uncomment and ++# set to false to disable. ++# nnRandomize = true ++ ++# If provided, force usage of a specific seed for nnRandomize. ++# The default is to use a randomly generated seed. ++# nnRandSeed = abcdefg ++ ++# Uncomment and set to true to force GTP to use the maximum board size for ++# internal buffers for the neural net. This will make KataGo slower when ++# evaluating small boards, but will avoid a lengthy initialization time on every ++# change of board size due to having to re-size the neural net buffers on the GPU. ++# This can be useful for example, for OGS's persistent bot mode that uses a single ++# bot instance to handle multiple games and may thrash between different board sizes ++# if there are concurrent games of multiple sizes. ++# gtpForceMaxNNSize = false ++ ++# ------------------------------ ++# Multiple GPUs ++# ------------------------------ ++# Set this to the number of GPUs to use or that are available. ++# IMPORTANT: If more than 1, also uncomment the appropriate TensorRT ++# or CUDA or OpenCL section. ++# numNNServerThreadsPerModel = 1 ++ ++# ------------------------------ ++# TENSORRT GPU settings ++# ------------------------------ ++# These only apply when using the TENSORRT version of KataGo. ++ ++# For one GPU: optionally uncomment this option and change if the GPU to ++# use is not device 0. ++# trtDeviceToUse = 0 ++ ++# For two GPUs: Uncomment these options, AND set numNNServerThreadsPerModel above. ++# Also, change their values if the devices you want to use are not 0 and 1. ++# trtDeviceToUseThread0 = 0 ++# trtDeviceToUseThread1 = 1 ++ ++# For three GPUs: Uncomment these options, AND set numNNServerThreadsPerModel above. ++# Also, change their values if the devices you want to use are not 0 and 1 and 2. ++# trtDeviceToUseThread0 = 0 ++# trtDeviceToUseThread1 = 1 ++# trtDeviceToUseThread2 = 2 ++ ++# The pattern continues for additional GPUs. ++ ++# ------------------------------ ++# CUDA GPU settings ++# ------------------------------ ++# These only apply when using the CUDA version of KataGo. ++ ++# For one GPU: optionally uncomment and change this if the GPU you want to ++# use is not device 0 ++# cudaDeviceToUse = 0 ++ ++# For two GPUs: Uncomment these options, AND set numNNServerThreadsPerModel above. ++# Also, change their values if the devices you want to use are not 0 and 1. ++# cudaDeviceToUseThread0 = 0 ++# cudaDeviceToUseThread1 = 1 ++ ++# For three GPUs: Uncomment these options, AND set numNNServerThreadsPerModel above. ++# Also, change their values if the devices you want to use are not 0 and 1 and 2. ++# cudaDeviceToUseThread0 = 0 ++# cudaDeviceToUseThread1 = 1 ++# cudaDeviceToUseThread2 = 2 ++ ++# The pattern continues for additional GPUs. ++ ++# KataGo will automatically use FP16 or not based on the compute capability ++# of your NVIDIA GPU. If you want to try to force a particular behavior ++# you can uncomment these lines and change them to "true" or "false". ++# cudaUseFP16 = auto ++# cudaUseNHWC = auto ++ ++# ------------------------------ ++# OpenCL GPU settings ++# ------------------------------ ++# These only apply when using the OpenCL version of KataGo. ++ ++# Uncomment and set to true to tune OpenCL for every board size separately, ++# rather than only the largest possible size. ++# openclReTunePerBoardSize = false ++ ++# For one GPU: optionally uncomment and change this if the best device to use is guessed incorrectly. ++# The default behavior tries to guess the 'best' GPU or device on your system to use, usually it will be a good guess. ++# openclDeviceToUse = 0 ++ ++# For two GPUs: Uncomment these two lines and replace X and Y with the device ids of the devices you want to use. ++# It might NOT be 0 and 1, some computers will have many OpenCL devices. You can see what the devices are when ++# KataGo starts up - it should print or log all the devices it finds. ++# (AND also set numNNServerThreadsPerModel above) ++# openclDeviceToUseThread0 = X ++# openclDeviceToUseThread1 = Y ++ ++# For three GPUs: Uncomment these three lines and replace X and Y and Z with the device ids of the devices you want to use. ++# It might NOT be 0 and 1 and 2, some computers will have many OpenCL devices. You can see what the devices are when ++# KataGo starts up - it should print or log all the devices it finds. ++# (AND also set numNNServerThreadsPerModel above) ++# openclDeviceToUseThread0 = X ++# openclDeviceToUseThread1 = Y ++# openclDeviceToUseThread2 = Z ++ ++# The pattern continues for additional GPUs. ++ ++# KataGo will automatically use FP16 or not based on testing your GPU during ++# tuning. If you want to try to force a particular behavior though you can ++# uncomment this option and change it to "true" or "false". This is a fairly ++# blunt setting - more detailed settings are testable by rerunning the tuner ++# with various arguments (./katago tuner). ++# openclUseFP16 = auto ++ ++# ------------------------------ ++# Eigen-specific settings ++# ------------------------------ ++# These only apply when using the Eigen (pure CPU) version of KataGo. ++ ++# Number of CPU threads for evaluating the neural net on the Eigen backend. ++# ++# Default: numSearchThreads ++# numEigenThreadsPerModel = X ++ ++# =========================================================================== ++# Root move selection and biases ++# =========================================================================== ++# Uncomment and edit any of the below values to change them from their default. ++ ++# If provided, force usage of a specific seed for various random things in ++# the search. The default is to use a random seed. ++# searchRandSeed = hijklmn ++ ++# Temperature for the early game, randomize between chosen moves with ++# this temperature ++# chosenMoveTemperatureEarly = 0.5 ++ ++# Decay temperature for the early game by 0.5 every this many moves, ++# scaled with board size. ++# chosenMoveTemperatureHalflife = 19 ++ ++# At the end of search after the early game, randomize between chosen ++# moves with this temperature ++# chosenMoveTemperature = 0.10 ++ ++# Subtract this many visits from each move prior to applying ++# chosenMoveTemperature (unless all moves have too few visits) to downweight ++# unlikely moves ++# chosenMoveSubtract = 0 ++ ++# The same as chosenMoveSubtract but only prunes moves that fall below ++# the threshold. This setting does not affect chosenMoveSubtract. ++# chosenMovePrune = 1 ++ ++# Number of symmetries to sample (without replacement) and average at the root ++# rootNumSymmetriesToSample = 1 ++ ++# Using LCB for move selection? ++# useLcbForSelection = true ++ ++# How many stdevs a move needs to be better than another for LCB selection ++# lcbStdevs = 5.0 ++ ++# Only use LCB override when a move has this proportion of visits as the ++# top move. ++# minVisitPropForLCB = 0.15 ++ ++# =========================================================================== ++# Internal params ++# =========================================================================== ++# Uncomment and edit any of the below values to change them from their default. ++ ++# Scales the utility of winning/losing ++# winLossUtilityFactor = 1.0 ++ ++# Scales the utility for trying to maximize score ++# staticScoreUtilityFactor = 0.10 ++# dynamicScoreUtilityFactor = 0.30 ++ ++# Adjust dynamic score center this proportion of the way towards zero, ++# capped at a reasonable amount. ++# dynamicScoreCenterZeroWeight = 0.20 ++# dynamicScoreCenterScale = 0.75 ++ ++# The utility of getting a "no result" due to triple ko or other long cycle ++# in non-superko rulesets (-1 to 1) ++# noResultUtilityForWhite = 0.0 ++ ++# The number of wins that a draw counts as, for white. (0 to 1) ++# drawEquivalentWinsForWhite = 0.5 ++ ++# Exploration constant for mcts ++# cpuctExploration = 1.0 ++# cpuctExplorationLog = 0.45 ++ ++# Parameters that control exploring more in volatile positions, exploring ++# less in stable positions. ++# cpuctUtilityStdevPrior = 0.40 ++# cpuctUtilityStdevPriorWeight = 2.0 ++# cpuctUtilityStdevScale = 0.85 ++ ++# FPU reduction constant for mcts ++# fpuReductionMax = 0.2 ++# rootFpuReductionMax = 0.1 ++# fpuParentWeightByVisitedPolicy = true ++ ++# Parameters that control weighting of evals based on the net's own ++# self-reported uncertainty. ++# useUncertainty = true ++# uncertaintyExponent = 1.0 ++# uncertaintyCoeff = 0.25 ++ ++# Explore using optimistic policy ++# rootPolicyOptimism = 0.2 ++# policyOptimism = 1.0 ++ ++# Amount to apply a downweighting of children with very bad values relative ++# to good ones. ++# valueWeightExponent = 0.25 ++ ++# Slight incentive for the bot to behave human-like with regard to passing at ++# the end, filling the dame, not wasting time playing in its own territory, ++# etc., and not play moves that are equivalent in terms of points but a bit ++# more unfriendly to humans. ++# rootEndingBonusPoints = 0.5 ++ ++# Make the bot prune useless moves that are just prolonging the game to ++# avoid losing yet. ++# rootPruneUselessMoves = true ++ ++# Apply bias correction based on local pattern keys ++# subtreeValueBiasFactor = 0.45 ++# subtreeValueBiasWeightExponent = 0.85 ++ ++# Use graph search rather than tree search - identify and share search for ++# transpositions. ++# useGraphSearch = true ++ ++# How much to shard the node table for search synchronization ++# nodeTableShardsPowerOfTwo = 16 ++ ++# How many virtual losses to add when a thread descends through a node ++# numVirtualLossesPerThread = 1 ++ ++# Improve the quality of evals under heavy multithreading ++# useNoisePruning = true ++ ++# =========================================================================== ++# Automatic avoid patterns ++# =========================================================================== ++# The parameters in this section provide a way to bias away from moves that match ++# patterns that this instance of KataGo has played in previous games, by auto-saving ++# moves to a directory and then auto-loading and biasing against them each new game. ++# Uncomment them to use them. When using this feature, all parameters must be specified. ++ ++# Different board sizes are tracked separately, but all board sizes share the same ++# parameters by default. Every parameter *except* for autoAvoidRepeatDir and ++# autoAvoidRepeatSaveChunkSize can be overridden per board size, ++# e.g. "autoAvoidRepeatMinTurnNumber13x13". You must ALSO specify the ++# defaults even if you have specified board-size-specific values. ++ ++# Directory to auto-save moves KataGo plays, to avoid them in future games. ++# You can create a new empty directory and put its path here. ++# If you run parallel instances of KataGo, use different directories if you ++# want them not to share their biases, use the same directory if you want ++# all of them to bias away from past moves that any of them have played. ++# KataGo will also automatically DELETE old data in this directory, so it is ++# recommended that if you do share the same directory between parallel instances, ++# that they all use the same settings that affect data saving/deletion. ++# autoAvoidRepeatDir = PATH_TO_NEW_DIRECTORY ++ ++# Penalize this much utility per matching move. ++# Values that are too large may lead to bad play. The value of 0.004 is fairly large ++# and might be large enough to result in some early weird/bad moves when trying ++# to avoid past games' moves if enough games begin the same way. You can experiment with it. ++# autoAvoidRepeatUtility = 0.004 ++ ++# Per each new move saved, exponentially decay prior saved moves by this factor. ++# This way, the bias against moves from many games ago is gradually phased out. ++# For example, 0.9995 = 1 - 1/2000, so would be roughly 2000 prior moves worth of ++# penalty weight remembered, in steady state. Depending on what turn number range ++# you are saving, this might equate to a different number of games For example ++# saving the first 50 moves per game would make this roughly 2000 / 50 = 40 games ++# worth of memory. ++# autoAvoidRepeatLambda = 0.9995 ++ ++# Affects data saving/deletion. ++# When the number of saved moves exceeds this, outright delete them to avoid too many ++# files and disk space building up. Also may affect the speed of saving/loading on start ++# of each game if this is set large and a lot of data builds up. ++# autoAvoidRepeatMaxPoses = 10000 ++ ++# Affects data saving/deletion. ++# Only save data for moves within this turn number range of those games. ++# E.g. setting autoAvoidRepeatMinTurnNumber to a number like 4 or 5 would tend to make ++# KataGo not develop a bias against the initial 3-4 and 4-4 corner moves in almost every game. ++# autoAvoidRepeatMinTurnNumber = 0 ++# autoAvoidRepeatMaxTurnNumber = 50 ++ ++# Affects data saving/deletion. ++# Within a single run of a program, wait to accumulate this many samples ++# (possibly across multiple clear_boards/games) before saving the data. ++# Can help to avoid writing too many small files to disk, especially when GTP is used ++# in a way that clears the board very frequently (e.g. gtp2ogs pooled manager). ++# autoAvoidRepeatSaveChunkSize = 200 ++ ++# =========================================================================== ++# Avoid SGF patterns ++# =========================================================================== ++# The parameters in this section provide a way to avoid moves that follow ++# specific patterns based on a set of SGF files loaded upon startup. ++# This is basically the same as the above "Automatic avoid patterns" section ++# above except you supply your own SGF files to avoid moves from. ++# Uncomment them to use this feature. Additionally, if the SGF file ++# contains the string %SKIP% in a comment on a move, that move will be ++# ignored for this purpose. ++ ++# Load SGF files from this directory when the engine is started ++# (only on startup, will not reload unless engine is restarted) ++# avoidSgfPatternDirs = path/to/directory/with/sgfs/ ++# You can also surround the file path in double quotes if the file path contains trailing spaces or hash signs. ++# Within double quotes, backslashes are escape characters. ++# avoidSgfPatternDirs = "path/to/directory/with/sgfs/" ++ ++# Penalize this much utility per matching move. ++# Set this negative if you instead want to favor SGF patterns instead of ++# penalizing them. This number does not need to be large, even 0.001 will ++# make a difference. Values that are too large may lead to bad play. ++# avoidSgfPatternUtility = 0.001 ++ ++# Optional - load only the newest this many files ++# avoidSgfPatternMaxFiles = 20 ++ ++# Optional - Penalty is multiplied by this per each older SGF file, so that ++# old SGF files matter less than newer ones. ++# avoidSgfPatternLambda = 0.90 ++ ++# Optional - pay attention only to moves made by players with this name. ++# For example, set it to the name that your bot's past games will show up ++# as in the SGF, so that the bot will only avoid repeating moves that itself ++# made in past games, not the moves that its opponents made. ++# avoidSgfPatternAllowedNames = my-ogs-bot-name1,my-ogs-bot-name2 ++ ++# Optional - Ignore moves in SGF files that occurred before this turn number. ++# avoidSgfPatternMinTurnNumber = 0 ++ ++# For more avoid patterns: ++# You can also specify a second set of parameters, and a third, fourth, ++# etc. by numbering 2,3,4,... ++# ++# avoidSgf2PatternDirs = ... ++# avoidSgf2PatternUtility = ... ++# avoidSgf2PatternMaxFiles = ... ++# avoidSgf2PatternLambda = ... ++# avoidSgf2PatternAllowedNames = ... ++# avoidSgf2PatternMinTurnNumber = ... ++ +diff --git a/cpp/cacert.pem b/cpp/cacert.pem +new file mode 100644 +index 00000000..f9bd706b +--- /dev/null ++++ b/cpp/cacert.pem +@@ -0,0 +1,3447 @@ ++## ++## Bundle of CA Root Certificates ++## ++## Certificate data from Mozilla as of: Wed Jul 22 03:12:14 2020 GMT ++## ++## This is a bundle of X.509 certificates of public Certificate Authorities ++## (CA). These were automatically extracted from Mozilla's root certificates ++## file (certdata.txt). This file can be found in the mozilla source tree: ++## https://hg.mozilla.org/releases/mozilla-release/raw-file/default/security/nss/lib/ckfw/builtins/certdata.txt ++## ++## It contains the certificates in PEM format and therefore ++## can be directly used with curl / libcurl / php_curl, or with ++## an Apache+mod_ssl webserver for SSL client authentication. ++## Just configure this file as the SSLCACertificateFile. ++## ++## Conversion done with mk-ca-bundle.pl version 1.28. ++## SHA256: cc6408bd4be7fbfb8699bdb40ccb7f6de5780d681d87785ea362646e4dad5e8e ++## ++ ++ ++GlobalSign Root CA ++================== ++-----BEGIN CERTIFICATE----- ++MIIDdTCCAl2gAwIBAgILBAAAAAABFUtaw5QwDQYJKoZIhvcNAQEFBQAwVzELMAkGA1UEBhMCQkUx ++GTAXBgNVBAoTEEdsb2JhbFNpZ24gbnYtc2ExEDAOBgNVBAsTB1Jvb3QgQ0ExGzAZBgNVBAMTEkds ++b2JhbFNpZ24gUm9vdCBDQTAeFw05ODA5MDExMjAwMDBaFw0yODAxMjgxMjAwMDBaMFcxCzAJBgNV ++BAYTAkJFMRkwFwYDVQQKExBHbG9iYWxTaWduIG52LXNhMRAwDgYDVQQLEwdSb290IENBMRswGQYD 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++======================= ++-----BEGIN CERTIFICATE----- ++MIIDujCCAqKgAwIBAgILBAAAAAABD4Ym5g0wDQYJKoZIhvcNAQEFBQAwTDEgMB4GA1UECxMXR2xv ++YmFsU2lnbiBSb290IENBIC0gUjIxEzARBgNVBAoTCkdsb2JhbFNpZ24xEzARBgNVBAMTCkdsb2Jh ++bFNpZ24wHhcNMDYxMjE1MDgwMDAwWhcNMjExMjE1MDgwMDAwWjBMMSAwHgYDVQQLExdHbG9iYWxT ++aWduIFJvb3QgQ0EgLSBSMjETMBEGA1UEChMKR2xvYmFsU2lnbjETMBEGA1UEAxMKR2xvYmFsU2ln ++bjCCASIwDQYJKoZIhvcNAQEBBQADggEPADCCAQoCggEBAKbPJA6+Lm8omUVCxKs+IVSbC9N/hHD6 ++ErPLv4dfxn+G07IwXNb9rfF73OX4YJYJkhD10FPe+3t+c4isUoh7SqbKSaZeqKeMWhG8eoLrvozp ++s6yWJQeXSpkqBy+0Hne/ig+1AnwblrjFuTosvNYSuetZfeLQBoZfXklqtTleiDTsvHgMCJiEbKjN ++S7SgfQx5TfC4LcshytVsW33hoCmEofnTlEnLJGKRILzdC9XZzPnqJworc5HGnRusyMvo4KD0L5CL ++TfuwNhv2GXqF4G3yYROIXJ/gkwpRl4pazq+r1feqCapgvdzZX99yqWATXgAByUr6P6TqBwMhAo6C ++ygPCm48CAwEAAaOBnDCBmTAOBgNVHQ8BAf8EBAMCAQYwDwYDVR0TAQH/BAUwAwEB/zAdBgNVHQ4E ++FgQUm+IHV2ccHsBqBt5ZtJot39wZhi4wNgYDVR0fBC8wLTAroCmgJ4YlaHR0cDovL2NybC5nbG9i 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true # In non-compensated handicap and fork games, vary komi + + # Match----------------------------------------------------------------------------------- + +-numGameThreads = 128 ++numGameThreads = 30 + maxMovesPerGame = 1600 + + # Rules------------------------------------------------------------------------------------ +diff --git a/cpp/contribute_example.cfg b/cpp/contribute_example.cfg +new file mode 100644 +index 00000000..a6c28531 +--- /dev/null ++++ b/cpp/contribute_example.cfg +@@ -0,0 +1,125 @@ ++ ++# Be careful not to share any config files where you have entered your password. ++ ++serverUrl = https://katagotraining.org/ ++username = Fill in your username here ++password = Fill in your password here ++# If your password has hash signs or trailing spaces, you can also surround the password with double quotes, like this. ++# Within double quotes, backslashes are escape characters. ++# password = "Fill in your password here" ++ ++# How many games should KataGo play at once? For good GPUs, putting too small a number here will be *very* inefficient. ++# For absolute-top-tier GPUs, try numbers like 32, 40, 64, etc. ++# For modern good middle-tier GPUs, you can try numbers like 8 or 16. ++# For very old or weak GPUs, or if you want KataGo to put less load on your system, try 4. ++maxSimultaneousGames = Fill in a number here ++ ++# Set to true if you want one of KataGo's games to be streamed to a text file, so you can watch. ++# Follow in a separate shell with a command like the following: ++# Linux: "tail -f watchgame.txt" ++# Windows Powershell: "Get-Content .\watchgame.txt -Tail 50 -Wait" ++watchOngoingGameInFile = false ++watchOngoingGameInFileName = watchgame.txt ++ ++# If you use an https proxy to connect to the internet, set these to the host and port of your proxy. ++# proxyHost = HOSTNAME ++# proxyPort = 1234 ++ ++# Special-purpose options. Most users should not need to adjust these. ++# onlyPlayRatingMatches = false ++# maxRatingMatches = 1 ++# disablePredownloadLoop = false ++# warnTaskUnusedKeys = false ++ ++# KataGo will only use one GPU by default. You can edit the below if you have multiple GPUs and want to use them all. ++ ++ ++# TO USE MULTIPLE GPUS: ++# Set this to the number of GPUs you have and/or would like to use. ++# **AND** if it is more than 1, uncomment the appropriate CUDA or OpenCL section below. ++# numNNServerThreadsPerModel = 1 ++ ++ ++# TENSORRT GPU settings-------------------------------------- ++# These only apply when using the TENSORRT version of KataGo. ++ ++# IF USING ONE GPU: optionally uncomment and change this if the GPU you want to use turns out to be not device 0 ++# trtDeviceToUse = 0 ++ ++# IF USING TWO GPUS: Uncomment these two lines (AND set numNNServerThreadsPerModel above): ++# trtDeviceToUseThread0 = 0 # change this if the first GPU you want to use turns out to be not device 0 ++# trtDeviceToUseThread1 = 1 # change this if the second GPU you want to use turns out to be not device 1 ++ ++# IF USING THREE GPUS: Uncomment these three lines (AND set numNNServerThreadsPerModel above): ++# trtDeviceToUseThread0 = 0 # change this if the first GPU you want to use turns out to be not device 0 ++# trtDeviceToUseThread1 = 1 # change this if the second GPU you want to use turns out to be not device 1 ++# trtDeviceToUseThread2 = 2 # change this if the third GPU you want to use turns out to be not device 2 ++ ++# You can probably guess the pattern if you have four, five, etc. GPUs. ++ ++ ++# CUDA GPU settings-------------------------------------- ++# These only apply when using the CUDA version of KataGo. ++ ++# IF USING ONE GPU: optionally uncomment and change this if the GPU you want to use turns out to be not device 0 ++# cudaDeviceToUse = 0 ++ ++# IF USING TWO GPUS: Uncomment these two lines (AND set numNNServerThreadsPerModel above): ++# cudaDeviceToUseThread0 = 0 # change this if the first GPU you want to use turns out to be not device 0 ++# cudaDeviceToUseThread1 = 1 # change this if the second GPU you want to use turns out to be not device 1 ++ ++# IF USING THREE GPUS: Uncomment these three lines (AND set numNNServerThreadsPerModel above): ++# cudaDeviceToUseThread0 = 0 # change this if the first GPU you want to use turns out to be not device 0 ++# cudaDeviceToUseThread1 = 1 # change this if the second GPU you want to use turns out to be not device 1 ++# cudaDeviceToUseThread2 = 2 # change this if the third GPU you want to use turns out to be not device 2 ++ ++# You can probably guess the pattern if you have four, five, etc. GPUs. ++ ++# KataGo will automatically use FP16 or not based on the compute capability of your NVIDIA GPU. If you ++# want to try to force a particular behavior though you can uncomment these lines and change them ++# to "true" or "false". E.g. it's using FP16 but on your card that's giving an error, or it's not using ++# FP16 but you think it should. ++# cudaUseFP16 = auto ++# cudaUseNHWC = auto ++ ++ ++# OpenCL GPU settings-------------------------------------- ++# These only apply when using the OpenCL version of KataGo. ++ ++# Uncomment to tune OpenCL for every board size separately, rather than only the largest possible size ++# openclReTunePerBoardSize = true ++ ++# IF USING ONE GPU: optionally uncomment and change this if the best device to use is guessed incorrectly. ++# The default behavior tries to guess the 'best' GPU or device on your system to use, usually it will be a good guess. ++# openclDeviceToUse = 0 ++ ++# IF USING TWO GPUS: Uncomment these two lines and replace X and Y with the device ids of the devices you want to use. ++# It might NOT be 0 and 1, some computers will have many OpenCL devices. You can see what the devices are when ++# KataGo starts up - it should print or log all the devices it finds. ++# (AND also set numNNServerThreadsPerModel above) ++# openclDeviceToUseThread0 = X ++# openclDeviceToUseThread1 = Y ++ ++# IF USING THREE GPUS: Uncomment these three lines and replace X and Y and Z with the device ids of the devices you want to use. ++# It might NOT be 0 and 1 and 2, some computers will have many OpenCL devices. You can see what the devices are when ++# KataGo starts up - it should print or log all the devices it finds. ++# (AND also set numNNServerThreadsPerModel above) ++# openclDeviceToUseThread0 = X ++# openclDeviceToUseThread1 = Y ++# openclDeviceToUseThread2 = Z ++ ++# You can probably guess the pattern if you have four, five, etc. GPUs. ++ ++# KataGo will automatically use FP16 or not based on testing your GPU during tuning. If you ++# want to try to force a particular behavior though you can uncomment this lines and change it ++# to "true" or "false". This is a fairly blunt setting - more detailed settings are testable ++# by rerunning the tuner with various arguments. ++# openclUseFP16 = auto ++ ++ ++# Eigen-specific settings-------------------------------------- ++# These only apply when using the Eigen (pure CPU) version of KataGo. ++ ++# This is the number of CPU threads for evaluating the neural net on the Eigen backend. ++# It defaults to numSearchThreads. ++# numEigenThreadsPerModel = X +diff --git a/cpp/default_gtp.cfg b/cpp/default_gtp.cfg +new file mode 100644 +index 00000000..8bf19d85 +--- /dev/null ++++ b/cpp/default_gtp.cfg +@@ -0,0 +1,721 @@ ++# Configuration for KataGo C++ GTP engine ++ ++# Run the program using: `./katago.exe gtp` ++ ++# In this example config, when a parameter is given as a commented out value, ++# that value also is the default value, unless described otherwise. You can ++# uncomment it (remove the pound sign) and change it if you want. ++ ++# =========================================================================== ++# Running on an online server or in a real tournament or match ++# =========================================================================== ++# If you plan to run online or in a tournament, read through the "Rules" ++# section below for proper handling of komi, handicaps, end-of-game cleanup, ++# and other details. ++ ++# =========================================================================== ++# Notes about performance and memory usage ++# =========================================================================== ++# Important: For good performance, you will very likely want to tune the ++# "numSearchThreads" parameter in the Search limits section below! Run ++# "./katago benchmark" to test KataGo and to suggest a reasonable value ++# of this parameter. ++ ++# For multi-GPU systems, read "OpenCL GPU settings" or "CUDA GPU settings". ++# ++# When using OpenCL, verify that KataGo picks the correct device! Some systems ++# may have both an Intel CPU OpenCL and GPU OpenCL. If # KataGo picks the wrong ++# one, correct this by specifying "openclGpuToUse". ++# ++# Consider adjusting "maxVisits", "ponderingEnabled", "resignThreshold", and ++# other parameters depending on your intended usage. ++ ++# =========================================================================== ++# Command-line usage ++# =========================================================================== ++# All of the below values may be set or overridden via command-line arguments: ++# ++# -override-config KEY=VALUE,KEY=VALUE,... ++ ++# =========================================================================== ++# Logs and files ++# =========================================================================== ++# This section defines where and what logging information is produced. ++ ++# Each run of KataGo will log to a separate file in this dir. ++# This is the default. ++logDir = gtp_logs ++# Uncomment and specify this instead of logDir to write separate dated subdirs ++# logDirDated = gtp_logs ++# Uncomment and specify this instead of logDir to log to only a single file ++# logFile = gtp.log ++ ++# Logging options ++logAllGTPCommunication = true ++logSearchInfo = true ++logSearchInfoForChosenMove = false ++logToStderr = false ++ ++# KataGo will display some info to stderr on GTP startup ++# Uncomment the next line and set it to false to suppress that and remain silent ++# startupPrintMessageToStderr = true ++ ++# Write information to stderr, for use in things like malkovich chat to OGS. ++ ogsChatToStderr = true ++ ++# Uncomment and set this to a directory to override where openCLTuner files ++# and other cached data is written. By default it saves into a subdir of the ++# current directory on windows, and a subdir of ~/.katago on Linux. ++# homeDataDir = PATH_TO_DIRECTORY ++ ++# =========================================================================== ++# Analysis ++# =========================================================================== ++# This section configures analysis settings. ++# ++# The maximum number of moves after the first move displayed in variations ++# from analysis commands like kata-analyze or lz-analyze. ++# analysisPVLen = 15 ++ ++# Report winrates for chat and analysis as (BLACK|WHITE|SIDETOMOVE). ++# Most GUIs and analysis tools will expect SIDETOMOVE. ++# reportAnalysisWinratesAs = SIDETOMOVE ++ ++# Extra noise for wider exploration. Large values will force KataGo to ++# analyze a greater variety of moves than it normally would. ++# An extreme value like 1 distributes playouts across every move on the board, ++# even very bad moves. ++# Affects analysis only, does not affect play. ++# analysisWideRootNoise = 0.04 ++ ++# Try to limit the effect of possible bad or bogus move sequences in the ++# history leading to this position from affecting KataGo's move predictions. ++# analysisIgnorePreRootHistory = true ++ ++# =========================================================================== ++# Rules ++# =========================================================================== ++# This section configures the scoring and playing rules. Rules can also be ++# changed mid-run by issuing custom GTP commands. ++# ++# See https://lightvector.github.io/KataGo/rules.html for rules details. ++# ++# See https://github.com/lightvector/KataGo/blob/master/docs/GTP_Extensions.md ++# for GTP commands. ++ ++# Specify the rules as a string. ++# Some legal values include: ++# chinese, japanese, korean, aga, chinese-ogs, new-zealand, stone-scoring, ++# ancient-territory, bga, aga-button ++# ++# For some human rulesets that require complex adjudication in tricky cases ++# (e.g. japanese, korean) KataGo may not precisely match the ruleset in such ++# cases but will do its best. ++rules = tromp-taylor ++ ++# By default, the "rules" parameter is used, but if you comment it out and ++# uncomment one option in each of the sections below, you can specify an ++# arbitrary combination of individual rules. ++ ++# koRule = SIMPLE # Simple ko rules (triple ko = no result) ++# koRule = POSITIONAL # Positional superko ++# koRule = SITUATIONAL # Situational superko ++ ++# scoringRule = AREA # Area scoring ++# scoringRule = TERRITORY # Territory scoring (special computer-friendly territory rules) ++ ++# taxRule = NONE # All surrounded empty points are scored ++# taxRule = SEKI # Eyes in seki do NOT count as points ++# taxRule = ALL # All groups are taxed up to 2 points for the two eyes needed to live ++ ++# Is multiple-stone suicide legal? (Single-stone suicide is always illegal). ++# multiStoneSuicideLegal = false ++# multiStoneSuicideLegal = true ++ ++# "Button go" - the first pass when area scoring awards 0.5 points and does ++# not count for ending the game. ++# Allows area scoring rulesets that have far simpler rules to achieve the same ++# final scoring precision and reward for precise play as territory scoring. ++# hasButton = false ++# hasButton = true ++ ++# Is this a human ruleset where it's okay to pass before having physically ++# captured and removed all dead stones? ++# friendlyPassOk = false ++# friendlyPassOk = true ++ ++# How handicap stones in handicap games are compensated ++# whiteHandicapBonus = 0 # White gets no compensation for black's handicap stones (Tromp-taylor, NZ, JP) ++# whiteHandicapBonus = N-1 # White gets N-1 points for black's N handicap stones (AGA) ++# whiteHandicapBonus = N # White gets N points for black's N handicap stones (Chinese) ++ ++# ------------------------------ ++# Other rules hacks ++# ------------------------------ ++# Uncomment and change to adjust what board size KataGo uses upon startup ++# by default when GTP doesn't specify. ++# defaultBoardSize = 19 ++ ++# By default, Katago will use the komi that the GUI or GTP controller tries to set. ++# Uncomment and set this to have KataGo ignore the controller and always use this komi. ++# ignoreGTPAndForceKomi = 7 ++ ++# =========================================================================== ++# Bot behavior ++# =========================================================================== ++ ++# ------------------------------ ++# Resignation ++# ------------------------------ ++ ++# Resignation occurs if for at least resignConsecTurns in a row, the ++# winLossUtility (on a [-1,1] scale) is below resignThreshold. ++allowResignation = true ++resignThreshold = -0.90 ++resignConsecTurns = 3 ++ ++# By default, KataGo may resign games that it is confidently losing even if they ++# are very close in score. Uncomment and set this to avoid resigning games ++# if the estimated difference is points is less than or equal to this. ++# resignMinScoreDifference = 10 ++ ++# ------------------------------ ++# Handicap ++# ------------------------------ ++# Assume that if black makes many moves in a row right at the start of the ++# game, then the game is a handicap game. This is necessary on some servers ++# and for some GUIs and also when initializing from many SGF files, which may ++# set up a handicap game using repeated GTP "play" commands for black rather ++# than GTP "place_free_handicap" commands; however, it may also lead to ++# incorrect understanding of komi if whiteHandicapBonus is used and a server ++# does not have such a practice. Uncomment and set to false to disable. ++# assumeMultipleStartingBlackMovesAreHandicap = true ++ ++# Makes katago dynamically adjust in handicap or altered-komi games to assume ++# based on those game settings that it must be stronger or weaker than the ++# opponent and to play accordingly. Greatly improves handicap strength by ++# biasing winrates and scores to favor appropriate safe/aggressive play. ++# Does NOT affect analysis (lz-analyze, kata-analyze, used by programs like ++# Lizzie) so analysis remains unbiased. Uncomment and set this to 0 to disable ++# this and make KataGo play the same always. ++# dynamicPlayoutDoublingAdvantageCapPerOppLead = 0.045 ++ ++# Instead of "dynamicPlayoutDoublingAdvantageCapPerOppLead", you can comment ++# that out and uncomment and set "playoutDoublingAdvantage" to a fixed value ++# from -3.0 to 3.0 that will not change dynamically. ++# ALSO affects analysis tools (lz-analyze, kata-analyze, used by e.g. Lizzie). ++# Negative makes KataGo behave as if it is much weaker than the opponent. ++# Positive makes KataGo behave as if it is much stronger than the opponent. ++# KataGo will adjust to favor safe/aggressive play as appropriate based on ++# the combination of who is ahead and how much stronger/weaker it thinks it is, ++# and report winrates and scores taking the strength difference into account. ++# ++# If this and "dynamicPlayoutDoublingAdvantageCapPerOppLead" are both set ++# then dynamic will be used for all games and this fixed value will be used ++# for analysis tools. ++# playoutDoublingAdvantage = 0.0 ++ ++# Uncomment one of these when using "playoutDoublingAdvantage" to enforce ++# that it will only apply when KataGo plays as the specified color and will be ++# negated when playing as the opposite color. ++# playoutDoublingAdvantagePla = BLACK ++# playoutDoublingAdvantagePla = WHITE ++ ++# ------------------------------ ++# Passing and cleanup ++# ------------------------------ ++# Make the bot never assume that its pass will end the game, even if passing ++# would end and "win" under Tromp-Taylor rules. Usually this is a good idea ++# when using it for analysis or playing on servers where scoring may be ++# implemented non-tromp-taylorly. Uncomment and set to false to disable. ++# conservativePass = true ++ ++# When using territory scoring, self-play games continue beyond two passes ++# with special cleanup rules that may be confusing for human players. This ++# option prevents the special cleanup phases from being reachable when using ++# the bot for GTP play. Uncomment and set to false to enable entering special ++# cleanup. For example, if you are testing it against itself, or against ++# another bot that has precisely implemented the rules documented at ++# https://lightvector.github.io/KataGo/rules.html ++# preventCleanupPhase = true ++ ++# ------------------------------ ++# Miscellaneous behavior ++# ------------------------------ ++# If the board is symmetric, search only one copy of each equivalent move. ++# Attempts to also account for ko/superko, will not theoretically perfect for ++# superko. Uncomment and set to false to disable. ++# rootSymmetryPruning = true ++ ++# Uncomment and set to true to avoid a particular joseki that some networks ++# misevaluate, and also to improve opening diversity versus some particular ++# other bots that like to play it all the time. ++# avoidMYTDaggerHack = false ++ ++# Prefer to avoid playing the same joseki in every corner of the board. ++# Uncomment to set to a specific value. See "Avoid SGF patterns" section. ++# By default: 0 (even games), 0.005 (handicap games) ++# avoidRepeatedPatternUtility = 0.0 ++ ++# Experimental logic to fight against mirror Go even with unfavorable komi. ++# Uncomment to set to a specific value to use for both playing and analysis. ++# By default: true when playing via GTP, but false when analyzing. ++# antiMirror = true ++ ++# Enable some hacks that mitigate rare instances when passing messes up deeper searches. ++# enablePassingHacks = true ++ ++# Uncomment and set this to true to prevent bad or bogus move sequences ++# in the history leading to this position from affecting KataGo's move choices. ++# Same as analysisIgnorePreRootHistory (see above) but applies to actual play. ++# You can enable this if KataGo is being asked to play from positions that it did not ++# choose the moves to reach. ++# ignorePreRootHistory = false ++ ++# =========================================================================== ++# Search limits ++# =========================================================================== ++ ++# Terminology: ++# "Playouts" is the number of new playouts of search performed each turn. ++# "Visits" is the same as "Playouts" but also counts search performed on ++# previous turns that is still applicable to this turn. ++# "Time" is the time in seconds. ++ ++# For example, if KataGo searched 200 nodes on the previous turn, and then ++# after the opponent's reply, 50 nodes of its search tree was still valid, ++# then a visit limit of 200 would allow KataGo to search 150 new nodes ++# (for a final tree size of 200 nodes), whereas a playout limit of of 200 ++# would allow KataGo to search 200 nodes (for a final tree size of 250 nodes). ++ ++# Additionally, KataGo may also move before than the limit in order to ++# obey time controls (e.g. byo-yomi, etc) if the GTP controller has ++# told KataGo that the game has is being played with a given time control. ++ ++# Limits for search on the current turn. ++# If commented out or unspecified, the default is to have no limit. ++# maxVisits = 30000 ++# maxPlayouts = 300 ++maxTime = 1.0 ++ ++# Ponder on the opponent's turn? ++ponderingEnabled = true ++ ++# Limits for search when pondering on the opponent's turn. ++# If commented out or unspecified, the default is to have no limit. ++# Limiting the maximum time is recommended so that KataGo won't burn CPU/GPU ++# forever and/or run out of RAM if left unattended while pondering is enabled. ++# maxVisitsPondering = 5000 ++# maxPlayoutsPondering = 3000 ++maxTimePondering = 120.0 ++ ++ ++# ------------------------------ ++# Other search limits and behavior ++# ------------------------------ ++ ++# Approx number of seconds to buffer for lag for GTP time controls - will ++# move a bit faster assuming there is this much lag per move. ++lagBuffer = 1.0 ++ ++# YOU PROBABLY WANT TO TUNE THIS PARAMETER! ++# The number of threads to use when searching. On powerful GPUs the optimal ++# threads may be much higher than the number of CPU cores you have because ++# many threads are needed to feed efficient large batches to the GPU. ++# ++# Run "./katago benchmark" to tune this parameter and test the effect ++# of changes to any of other parameters. ++numSearchThreads = 40 ++ ++# Play a little faster if the opponent is passing, for human-friendliness. ++# Comment these out to disable them, such as if running a controlled match ++# where you are testing KataGo with fixed compute per move vs other bots. ++searchFactorAfterOnePass = 0.50 ++searchFactorAfterTwoPass = 0.25 ++ ++# Play a little faster if super-winning, for human-friendliness. ++# Comment these out to disable them, such as if running a controlled match ++# where you are testing KataGo with fixed compute per move vs other bots. ++searchFactorWhenWinning = 0.40 ++searchFactorWhenWinningThreshold = 0.95 ++ ++# =========================================================================== ++# GPU settings ++# =========================================================================== ++# This section configures GPU settings. ++# ++# Maximum number of positions to send to a single GPU at once. The default ++# value is roughly equal to numSearchThreads, but can be specified manually ++# if running out of memory, or using multiple GPUs that expect to share work. ++# nnMaxBatchSize = ++ ++# Controls the neural network cache size, which is the primary RAM/memory use. ++# KataGo will cache up to (2 ** nnCacheSizePowerOfTwo) many neural net ++# evaluations in case of transpositions in the tree. ++# Increase this to improve performance for searches with tens of thousands ++# of visits or more. Decrease this to limit memory usage. ++# If you're happy to do some math - each neural net entry takes roughly ++# 1.5KB, except when using whole-board ownership/territory ++# visualizations, where each entry will take roughly 3KB. The number of ++# entries is (2 ** nnCacheSizePowerOfTwo). (E.g. 2 ** 18 = 262144.) ++# You can compute roughly how much memory the cache will use based on this. ++# nnCacheSizePowerOfTwo = 20 ++ ++# Size of mutex pool for nnCache is (2 ** this). ++# nnMutexPoolSizePowerOfTwo = 16 ++ ++# Randomize board orientation when running neural net evals? Uncomment and ++# set to false to disable. ++# nnRandomize = true ++ ++# If provided, force usage of a specific seed for nnRandomize. ++# The default is to use a randomly generated seed. ++# nnRandSeed = abcdefg ++ ++# Uncomment and set to true to force GTP to use the maximum board size for ++# internal buffers for the neural net. This will make KataGo slower when ++# evaluating small boards, but will avoid a lengthy initialization time on every ++# change of board size due to having to re-size the neural net buffers on the GPU. ++# This can be useful for example, for OGS's persistent bot mode that uses a single ++# bot instance to handle multiple games and may thrash between different board sizes ++# if there are concurrent games of multiple sizes. ++# gtpForceMaxNNSize = false ++ ++# ------------------------------ ++# Multiple GPUs ++# ------------------------------ ++# Set this to the number of GPUs to use or that are available. ++# IMPORTANT: If more than 1, also uncomment the appropriate TensorRT ++# or CUDA or OpenCL section. ++# numNNServerThreadsPerModel = 1 ++ ++# ------------------------------ ++# TENSORRT GPU settings ++# ------------------------------ ++# These only apply when using the TENSORRT version of KataGo. ++ ++# For one GPU: optionally uncomment this option and change if the GPU to ++# use is not device 0. ++# trtDeviceToUse = 0 ++ ++# For two GPUs: Uncomment these options, AND set numNNServerThreadsPerModel above. ++# Also, change their values if the devices you want to use are not 0 and 1. ++# trtDeviceToUseThread0 = 0 ++# trtDeviceToUseThread1 = 1 ++ ++# For three GPUs: Uncomment these options, AND set numNNServerThreadsPerModel above. ++# Also, change their values if the devices you want to use are not 0 and 1 and 2. ++# trtDeviceToUseThread0 = 0 ++# trtDeviceToUseThread1 = 1 ++# trtDeviceToUseThread2 = 2 ++ ++# The pattern continues for additional GPUs. ++ ++# ------------------------------ ++# CUDA GPU settings ++# ------------------------------ ++# These only apply when using the CUDA version of KataGo. ++ ++# For one GPU: optionally uncomment and change this if the GPU you want to ++# use is not device 0 ++# cudaDeviceToUse = 0 ++ ++# For two GPUs: Uncomment these options, AND set numNNServerThreadsPerModel above. ++# Also, change their values if the devices you want to use are not 0 and 1. ++# cudaDeviceToUseThread0 = 0 ++# cudaDeviceToUseThread1 = 1 ++ ++# For three GPUs: Uncomment these options, AND set numNNServerThreadsPerModel above. ++# Also, change their values if the devices you want to use are not 0 and 1 and 2. ++# cudaDeviceToUseThread0 = 0 ++# cudaDeviceToUseThread1 = 1 ++# cudaDeviceToUseThread2 = 2 ++ ++# The pattern continues for additional GPUs. ++ ++# KataGo will automatically use FP16 or not based on the compute capability ++# of your NVIDIA GPU. If you want to try to force a particular behavior ++# you can uncomment these lines and change them to "true" or "false". ++# cudaUseFP16 = auto ++# cudaUseNHWC = auto ++ ++# ------------------------------ ++# OpenCL GPU settings ++# ------------------------------ ++# These only apply when using the OpenCL version of KataGo. ++ ++# Uncomment and set to true to tune OpenCL for every board size separately, ++# rather than only the largest possible size. ++# openclReTunePerBoardSize = false ++ ++# For one GPU: optionally uncomment and change this if the best device to use is guessed incorrectly. ++# The default behavior tries to guess the 'best' GPU or device on your system to use, usually it will be a good guess. ++# openclDeviceToUse = 0 ++ ++# For two GPUs: Uncomment these two lines and replace X and Y with the device ids of the devices you want to use. ++# It might NOT be 0 and 1, some computers will have many OpenCL devices. You can see what the devices are when ++# KataGo starts up - it should print or log all the devices it finds. ++# (AND also set numNNServerThreadsPerModel above) ++# openclDeviceToUseThread0 = X ++# openclDeviceToUseThread1 = Y ++ ++# For three GPUs: Uncomment these three lines and replace X and Y and Z with the device ids of the devices you want to use. ++# It might NOT be 0 and 1 and 2, some computers will have many OpenCL devices. You can see what the devices are when ++# KataGo starts up - it should print or log all the devices it finds. ++# (AND also set numNNServerThreadsPerModel above) ++# openclDeviceToUseThread0 = X ++# openclDeviceToUseThread1 = Y ++# openclDeviceToUseThread2 = Z ++ ++# The pattern continues for additional GPUs. ++ ++# KataGo will automatically use FP16 or not based on testing your GPU during ++# tuning. If you want to try to force a particular behavior though you can ++# uncomment this option and change it to "true" or "false". This is a fairly ++# blunt setting - more detailed settings are testable by rerunning the tuner ++# with various arguments (./katago tuner). ++# openclUseFP16 = auto ++ ++# ------------------------------ ++# Eigen-specific settings ++# ------------------------------ ++# These only apply when using the Eigen (pure CPU) version of KataGo. ++ ++# Number of CPU threads for evaluating the neural net on the Eigen backend. ++# ++# Default: numSearchThreads ++# numEigenThreadsPerModel = X ++ ++# =========================================================================== ++# Root move selection and biases ++# =========================================================================== ++# Uncomment and edit any of the below values to change them from their default. ++ ++# If provided, force usage of a specific seed for various random things in ++# the search. The default is to use a random seed. ++# searchRandSeed = hijklmn ++ ++# Temperature for the early game, randomize between chosen moves with ++# this temperature ++# chosenMoveTemperatureEarly = 0.5 ++ ++# Decay temperature for the early game by 0.5 every this many moves, ++# scaled with board size. ++# chosenMoveTemperatureHalflife = 19 ++ ++# At the end of search after the early game, randomize between chosen ++# moves with this temperature ++# chosenMoveTemperature = 0.10 ++ ++# Subtract this many visits from each move prior to applying ++# chosenMoveTemperature (unless all moves have too few visits) to downweight ++# unlikely moves ++# chosenMoveSubtract = 0 ++ ++# The same as chosenMoveSubtract but only prunes moves that fall below ++# the threshold. This setting does not affect chosenMoveSubtract. ++# chosenMovePrune = 1 ++ ++# Number of symmetries to sample (without replacement) and average at the root ++# rootNumSymmetriesToSample = 1 ++ ++# Using LCB for move selection? ++# useLcbForSelection = true ++ ++# How many stdevs a move needs to be better than another for LCB selection ++# lcbStdevs = 5.0 ++ ++# Only use LCB override when a move has this proportion of visits as the ++# top move. ++# minVisitPropForLCB = 0.15 ++ ++# =========================================================================== ++# Internal params ++# =========================================================================== ++# Uncomment and edit any of the below values to change them from their default. ++ ++# Scales the utility of winning/losing ++# winLossUtilityFactor = 1.0 ++ ++# Scales the utility for trying to maximize score ++# staticScoreUtilityFactor = 0.10 ++# dynamicScoreUtilityFactor = 0.30 ++ ++# Adjust dynamic score center this proportion of the way towards zero, ++# capped at a reasonable amount. ++# dynamicScoreCenterZeroWeight = 0.20 ++# dynamicScoreCenterScale = 0.75 ++ ++# The utility of getting a "no result" due to triple ko or other long cycle ++# in non-superko rulesets (-1 to 1) ++# noResultUtilityForWhite = 0.0 ++ ++# The number of wins that a draw counts as, for white. (0 to 1) ++# drawEquivalentWinsForWhite = 0.5 ++ ++# Exploration constant for mcts ++# cpuctExploration = 1.0 ++# cpuctExplorationLog = 0.45 ++ ++# Parameters that control exploring more in volatile positions, exploring ++# less in stable positions. ++# cpuctUtilityStdevPrior = 0.40 ++# cpuctUtilityStdevPriorWeight = 2.0 ++# cpuctUtilityStdevScale = 0.85 ++ ++# FPU reduction constant for mcts ++# fpuReductionMax = 0.2 ++# rootFpuReductionMax = 0.1 ++# fpuParentWeightByVisitedPolicy = true ++ ++# Parameters that control weighting of evals based on the net's own ++# self-reported uncertainty. ++# useUncertainty = true ++# uncertaintyExponent = 1.0 ++# uncertaintyCoeff = 0.25 ++ ++# Explore using optimistic policy ++# rootPolicyOptimism = 0.2 ++# policyOptimism = 1.0 ++ ++# Amount to apply a downweighting of children with very bad values relative ++# to good ones. ++# valueWeightExponent = 0.25 ++ ++# Slight incentive for the bot to behave human-like with regard to passing at ++# the end, filling the dame, not wasting time playing in its own territory, ++# etc., and not play moves that are equivalent in terms of points but a bit ++# more unfriendly to humans. ++# rootEndingBonusPoints = 0.5 ++ ++# Make the bot prune useless moves that are just prolonging the game to ++# avoid losing yet. ++# rootPruneUselessMoves = true ++ ++# Apply bias correction based on local pattern keys ++# subtreeValueBiasFactor = 0.45 ++# subtreeValueBiasWeightExponent = 0.85 ++ ++# Use graph search rather than tree search - identify and share search for ++# transpositions. ++# useGraphSearch = true ++ ++# How much to shard the node table for search synchronization ++# nodeTableShardsPowerOfTwo = 16 ++ ++# How many virtual losses to add when a thread descends through a node ++# numVirtualLossesPerThread = 1 ++ ++# Improve the quality of evals under heavy multithreading ++# useNoisePruning = true ++ ++# =========================================================================== ++# Automatic avoid patterns ++# =========================================================================== ++# The parameters in this section provide a way to bias away from moves that match ++# patterns that this instance of KataGo has played in previous games, by auto-saving ++# moves to a directory and then auto-loading and biasing against them each new game. ++# Uncomment them to use them. When using this feature, all parameters must be specified. ++ ++# Different board sizes are tracked separately, but all board sizes share the same ++# parameters by default. Every parameter *except* for autoAvoidRepeatDir and ++# autoAvoidRepeatSaveChunkSize can be overridden per board size, ++# e.g. "autoAvoidRepeatMinTurnNumber13x13". You must ALSO specify the ++# defaults even if you have specified board-size-specific values. ++ ++# Directory to auto-save moves KataGo plays, to avoid them in future games. ++# You can create a new empty directory and put its path here. ++# If you run parallel instances of KataGo, use different directories if you ++# want them not to share their biases, use the same directory if you want ++# all of them to bias away from past moves that any of them have played. ++# KataGo will also automatically DELETE old data in this directory, so it is ++# recommended that if you do share the same directory between parallel instances, ++# that they all use the same settings that affect data saving/deletion. ++# autoAvoidRepeatDir = PATH_TO_NEW_DIRECTORY ++ ++# Penalize this much utility per matching move. ++# Values that are too large may lead to bad play. The value of 0.004 is fairly large ++# and might be large enough to result in some early weird/bad moves when trying ++# to avoid past games' moves if enough games begin the same way. You can experiment with it. ++# autoAvoidRepeatUtility = 0.004 ++ ++# Per each new move saved, exponentially decay prior saved moves by this factor. ++# This way, the bias against moves from many games ago is gradually phased out. ++# For example, 0.9995 = 1 - 1/2000, so would be roughly 2000 prior moves worth of ++# penalty weight remembered, in steady state. Depending on what turn number range ++# you are saving, this might equate to a different number of games For example ++# saving the first 50 moves per game would make this roughly 2000 / 50 = 40 games ++# worth of memory. ++# autoAvoidRepeatLambda = 0.9995 ++ ++# Affects data saving/deletion. ++# When the number of saved moves exceeds this, outright delete them to avoid too many ++# files and disk space building up. Also may affect the speed of saving/loading on start ++# of each game if this is set large and a lot of data builds up. ++# autoAvoidRepeatMaxPoses = 10000 ++ ++# Affects data saving/deletion. ++# Only save data for moves within this turn number range of those games. ++# E.g. setting autoAvoidRepeatMinTurnNumber to a number like 4 or 5 would tend to make ++# KataGo not develop a bias against the initial 3-4 and 4-4 corner moves in almost every game. ++# autoAvoidRepeatMinTurnNumber = 0 ++# autoAvoidRepeatMaxTurnNumber = 50 ++ ++# Affects data saving/deletion. ++# Within a single run of a program, wait to accumulate this many samples ++# (possibly across multiple clear_boards/games) before saving the data. ++# Can help to avoid writing too many small files to disk, especially when GTP is used ++# in a way that clears the board very frequently (e.g. gtp2ogs pooled manager). ++# autoAvoidRepeatSaveChunkSize = 200 ++ ++# =========================================================================== ++# Avoid SGF patterns ++# =========================================================================== ++# The parameters in this section provide a way to avoid moves that follow ++# specific patterns based on a set of SGF files loaded upon startup. ++# This is basically the same as the above "Automatic avoid patterns" section ++# above except you supply your own SGF files to avoid moves from. ++# Uncomment them to use this feature. Additionally, if the SGF file ++# contains the string %SKIP% in a comment on a move, that move will be ++# ignored for this purpose. ++ ++# Load SGF files from this directory when the engine is started ++# (only on startup, will not reload unless engine is restarted) ++# avoidSgfPatternDirs = path/to/directory/with/sgfs/ ++# You can also surround the file path in double quotes if the file path contains trailing spaces or hash signs. ++# Within double quotes, backslashes are escape characters. ++# avoidSgfPatternDirs = "path/to/directory/with/sgfs/" ++ ++# Penalize this much utility per matching move. ++# Set this negative if you instead want to favor SGF patterns instead of ++# penalizing them. This number does not need to be large, even 0.001 will ++# make a difference. Values that are too large may lead to bad play. ++# avoidSgfPatternUtility = 0.001 ++ ++# Optional - load only the newest this many files ++# avoidSgfPatternMaxFiles = 20 ++ ++# Optional - Penalty is multiplied by this per each older SGF file, so that ++# old SGF files matter less than newer ones. ++# avoidSgfPatternLambda = 0.90 ++ ++# Optional - pay attention only to moves made by players with this name. ++# For example, set it to the name that your bot's past games will show up ++# as in the SGF, so that the bot will only avoid repeating moves that itself ++# made in past games, not the moves that its opponents made. ++# avoidSgfPatternAllowedNames = my-ogs-bot-name1,my-ogs-bot-name2 ++ ++# Optional - Ignore moves in SGF files that occurred before this turn number. ++# avoidSgfPatternMinTurnNumber = 0 ++ ++# For more avoid patterns: ++# You can also specify a second set of parameters, and a third, fourth, ++# etc. by numbering 2,3,4,... ++# ++# avoidSgf2PatternDirs = ... ++# avoidSgf2PatternUtility = ... ++# avoidSgf2PatternMaxFiles = ... ++# avoidSgf2PatternLambda = ... ++# avoidSgf2PatternAllowedNames = ... ++# avoidSgf2PatternMinTurnNumber = ... ++ +diff --git a/cpp/doudoubot.cfg b/cpp/doudoubot.cfg +new file mode 100644 +index 00000000..bf81254f +--- /dev/null ++++ b/cpp/doudoubot.cfg +@@ -0,0 +1,721 @@ ++# Configuration for KataGo C++ GTP engine ++ ++# Run the program using: `./katago.exe gtp` ++ ++# In this example config, when a parameter is given as a commented out value, ++# that value also is the default value, unless described otherwise. You can ++# uncomment it (remove the pound sign) and change it if you want. ++ ++# =========================================================================== ++# Running on an online server or in a real tournament or match ++# =========================================================================== ++# If you plan to run online or in a tournament, read through the "Rules" ++# section below for proper handling of komi, handicaps, end-of-game cleanup, ++# and other details. ++ ++# =========================================================================== ++# Notes about performance and memory usage ++# =========================================================================== ++# Important: For good performance, you will very likely want to tune the ++# "numSearchThreads" parameter in the Search limits section below! Run ++# "./katago benchmark" to test KataGo and to suggest a reasonable value ++# of this parameter. ++ ++# For multi-GPU systems, read "OpenCL GPU settings" or "CUDA GPU settings". ++# ++# When using OpenCL, verify that KataGo picks the correct device! Some systems ++# may have both an Intel CPU OpenCL and GPU OpenCL. If # KataGo picks the wrong ++# one, correct this by specifying "openclGpuToUse". ++# ++# Consider adjusting "maxVisits", "ponderingEnabled", "resignThreshold", and ++# other parameters depending on your intended usage. ++ ++# =========================================================================== ++# Command-line usage ++# =========================================================================== ++# All of the below values may be set or overridden via command-line arguments: ++# ++# -override-config KEY=VALUE,KEY=VALUE,... ++ ++# =========================================================================== ++# Logs and files ++# =========================================================================== ++# This section defines where and what logging information is produced. ++ ++# Each run of KataGo will log to a separate file in this dir. ++# This is the default. ++logDir = gtp_logs ++# Uncomment and specify this instead of logDir to write separate dated subdirs ++# logDirDated = gtp_logs ++# Uncomment and specify this instead of logDir to log to only a single file ++# logFile = gtp.log ++ ++# Logging options ++logAllGTPCommunication = true ++logSearchInfo = true ++logSearchInfoForChosenMove = false ++logToStderr = false ++ ++# KataGo will display some info to stderr on GTP startup ++# Uncomment the next line and set it to false to suppress that and remain silent ++# startupPrintMessageToStderr = true ++ ++# Write information to stderr, for use in things like malkovich chat to OGS. ++ ogsChatToStderr = true ++ ++# Uncomment and set this to a directory to override where openCLTuner files ++# and other cached data is written. By default it saves into a subdir of the ++# current directory on windows, and a subdir of ~/.katago on Linux. ++# homeDataDir = PATH_TO_DIRECTORY ++ ++# =========================================================================== ++# Analysis ++# =========================================================================== ++# This section configures analysis settings. ++# ++# The maximum number of moves after the first move displayed in variations ++# from analysis commands like kata-analyze or lz-analyze. ++# analysisPVLen = 15 ++ ++# Report winrates for chat and analysis as (BLACK|WHITE|SIDETOMOVE). ++# Most GUIs and analysis tools will expect SIDETOMOVE. ++# reportAnalysisWinratesAs = SIDETOMOVE ++ ++# Extra noise for wider exploration. Large values will force KataGo to ++# analyze a greater variety of moves than it normally would. ++# An extreme value like 1 distributes playouts across every move on the board, ++# even very bad moves. ++# Affects analysis only, does not affect play. ++# analysisWideRootNoise = 0.04 ++ ++# Try to limit the effect of possible bad or bogus move sequences in the ++# history leading to this position from affecting KataGo's move predictions. ++# analysisIgnorePreRootHistory = true ++ ++# =========================================================================== ++# Rules ++# =========================================================================== ++# This section configures the scoring and playing rules. Rules can also be ++# changed mid-run by issuing custom GTP commands. ++# ++# See https://lightvector.github.io/KataGo/rules.html for rules details. ++# ++# See https://github.com/lightvector/KataGo/blob/master/docs/GTP_Extensions.md ++# for GTP commands. ++ ++# Specify the rules as a string. ++# Some legal values include: ++# chinese, japanese, korean, aga, chinese-ogs, new-zealand, stone-scoring, ++# ancient-territory, bga, aga-button ++# ++# For some human rulesets that require complex adjudication in tricky cases ++# (e.g. japanese, korean) KataGo may not precisely match the ruleset in such ++# cases but will do its best. ++rules = tromp-taylor ++ ++# By default, the "rules" parameter is used, but if you comment it out and ++# uncomment one option in each of the sections below, you can specify an ++# arbitrary combination of individual rules. ++ ++# koRule = SIMPLE # Simple ko rules (triple ko = no result) ++# koRule = POSITIONAL # Positional superko ++# koRule = SITUATIONAL # Situational superko ++ ++# scoringRule = AREA # Area scoring ++# scoringRule = TERRITORY # Territory scoring (special computer-friendly territory rules) ++ ++# taxRule = NONE # All surrounded empty points are scored ++# taxRule = SEKI # Eyes in seki do NOT count as points ++# taxRule = ALL # All groups are taxed up to 2 points for the two eyes needed to live ++ ++# Is multiple-stone suicide legal? (Single-stone suicide is always illegal). ++# multiStoneSuicideLegal = false ++# multiStoneSuicideLegal = true ++ ++# "Button go" - the first pass when area scoring awards 0.5 points and does ++# not count for ending the game. ++# Allows area scoring rulesets that have far simpler rules to achieve the same ++# final scoring precision and reward for precise play as territory scoring. ++# hasButton = false ++# hasButton = true ++ ++# Is this a human ruleset where it's okay to pass before having physically ++# captured and removed all dead stones? ++# friendlyPassOk = false ++# friendlyPassOk = true ++ ++# How handicap stones in handicap games are compensated ++# whiteHandicapBonus = 0 # White gets no compensation for black's handicap stones (Tromp-taylor, NZ, JP) ++# whiteHandicapBonus = N-1 # White gets N-1 points for black's N handicap stones (AGA) ++# whiteHandicapBonus = N # White gets N points for black's N handicap stones (Chinese) ++ ++# ------------------------------ ++# Other rules hacks ++# ------------------------------ ++# Uncomment and change to adjust what board size KataGo uses upon startup ++# by default when GTP doesn't specify. ++# defaultBoardSize = 19 ++ ++# By default, Katago will use the komi that the GUI or GTP controller tries to set. ++# Uncomment and set this to have KataGo ignore the controller and always use this komi. ++# ignoreGTPAndForceKomi = 7 ++ ++# =========================================================================== ++# Bot behavior ++# =========================================================================== ++ ++# ------------------------------ ++# Resignation ++# ------------------------------ ++ ++# Resignation occurs if for at least resignConsecTurns in a row, the ++# winLossUtility (on a [-1,1] scale) is below resignThreshold. ++allowResignation = true ++resignThreshold = -0.90 ++resignConsecTurns = 3 ++ ++# By default, KataGo may resign games that it is confidently losing even if they ++# are very close in score. Uncomment and set this to avoid resigning games ++# if the estimated difference is points is less than or equal to this. ++# resignMinScoreDifference = 10 ++ ++# ------------------------------ ++# Handicap ++# ------------------------------ ++# Assume that if black makes many moves in a row right at the start of the ++# game, then the game is a handicap game. This is necessary on some servers ++# and for some GUIs and also when initializing from many SGF files, which may ++# set up a handicap game using repeated GTP "play" commands for black rather ++# than GTP "place_free_handicap" commands; however, it may also lead to ++# incorrect understanding of komi if whiteHandicapBonus is used and a server ++# does not have such a practice. Uncomment and set to false to disable. ++# assumeMultipleStartingBlackMovesAreHandicap = true ++ ++# Makes katago dynamically adjust in handicap or altered-komi games to assume ++# based on those game settings that it must be stronger or weaker than the ++# opponent and to play accordingly. Greatly improves handicap strength by ++# biasing winrates and scores to favor appropriate safe/aggressive play. ++# Does NOT affect analysis (lz-analyze, kata-analyze, used by programs like ++# Lizzie) so analysis remains unbiased. Uncomment and set this to 0 to disable ++# this and make KataGo play the same always. ++# dynamicPlayoutDoublingAdvantageCapPerOppLead = 0.045 ++ ++# Instead of "dynamicPlayoutDoublingAdvantageCapPerOppLead", you can comment ++# that out and uncomment and set "playoutDoublingAdvantage" to a fixed value ++# from -3.0 to 3.0 that will not change dynamically. ++# ALSO affects analysis tools (lz-analyze, kata-analyze, used by e.g. Lizzie). ++# Negative makes KataGo behave as if it is much weaker than the opponent. ++# Positive makes KataGo behave as if it is much stronger than the opponent. ++# KataGo will adjust to favor safe/aggressive play as appropriate based on ++# the combination of who is ahead and how much stronger/weaker it thinks it is, ++# and report winrates and scores taking the strength difference into account. ++# ++# If this and "dynamicPlayoutDoublingAdvantageCapPerOppLead" are both set ++# then dynamic will be used for all games and this fixed value will be used ++# for analysis tools. ++# playoutDoublingAdvantage = 0.0 ++ ++# Uncomment one of these when using "playoutDoublingAdvantage" to enforce ++# that it will only apply when KataGo plays as the specified color and will be ++# negated when playing as the opposite color. ++# playoutDoublingAdvantagePla = BLACK ++# playoutDoublingAdvantagePla = WHITE ++ ++# ------------------------------ ++# Passing and cleanup ++# ------------------------------ ++# Make the bot never assume that its pass will end the game, even if passing ++# would end and "win" under Tromp-Taylor rules. Usually this is a good idea ++# when using it for analysis or playing on servers where scoring may be ++# implemented non-tromp-taylorly. Uncomment and set to false to disable. ++# conservativePass = true ++ ++# When using territory scoring, self-play games continue beyond two passes ++# with special cleanup rules that may be confusing for human players. This ++# option prevents the special cleanup phases from being reachable when using ++# the bot for GTP play. Uncomment and set to false to enable entering special ++# cleanup. For example, if you are testing it against itself, or against ++# another bot that has precisely implemented the rules documented at ++# https://lightvector.github.io/KataGo/rules.html ++# preventCleanupPhase = true ++ ++# ------------------------------ ++# Miscellaneous behavior ++# ------------------------------ ++# If the board is symmetric, search only one copy of each equivalent move. ++# Attempts to also account for ko/superko, will not theoretically perfect for ++# superko. Uncomment and set to false to disable. ++# rootSymmetryPruning = true ++ ++# Uncomment and set to true to avoid a particular joseki that some networks ++# misevaluate, and also to improve opening diversity versus some particular ++# other bots that like to play it all the time. ++# avoidMYTDaggerHack = false ++ ++# Prefer to avoid playing the same joseki in every corner of the board. ++# Uncomment to set to a specific value. See "Avoid SGF patterns" section. ++# By default: 0 (even games), 0.005 (handicap games) ++# avoidRepeatedPatternUtility = 0.0 ++ ++# Experimental logic to fight against mirror Go even with unfavorable komi. ++# Uncomment to set to a specific value to use for both playing and analysis. ++# By default: true when playing via GTP, but false when analyzing. ++# antiMirror = true ++ ++# Enable some hacks that mitigate rare instances when passing messes up deeper searches. ++# enablePassingHacks = true ++ ++# Uncomment and set this to true to prevent bad or bogus move sequences ++# in the history leading to this position from affecting KataGo's move choices. ++# Same as analysisIgnorePreRootHistory (see above) but applies to actual play. ++# You can enable this if KataGo is being asked to play from positions that it did not ++# choose the moves to reach. ++# ignorePreRootHistory = false ++ ++# =========================================================================== ++# Search limits ++# =========================================================================== ++ ++# Terminology: ++# "Playouts" is the number of new playouts of search performed each turn. ++# "Visits" is the same as "Playouts" but also counts search performed on ++# previous turns that is still applicable to this turn. ++# "Time" is the time in seconds. ++ ++# For example, if KataGo searched 200 nodes on the previous turn, and then ++# after the opponent's reply, 50 nodes of its search tree was still valid, ++# then a visit limit of 200 would allow KataGo to search 150 new nodes ++# (for a final tree size of 200 nodes), whereas a playout limit of of 200 ++# would allow KataGo to search 200 nodes (for a final tree size of 250 nodes). ++ ++# Additionally, KataGo may also move before than the limit in order to ++# obey time controls (e.g. byo-yomi, etc) if the GTP controller has ++# told KataGo that the game has is being played with a given time control. ++ ++# Limits for search on the current turn. ++# If commented out or unspecified, the default is to have no limit. ++# maxVisits = 500 ++# maxPlayouts = 300 ++ maxTime = 0.5 ++ ++# Ponder on the opponent's turn? ++ponderingEnabled = true ++ ++# Limits for search when pondering on the opponent's turn. ++# If commented out or unspecified, the default is to have no limit. ++# Limiting the maximum time is recommended so that KataGo won't burn CPU/GPU ++# forever and/or run out of RAM if left unattended while pondering is enabled. ++# maxVisitsPondering = 5000 ++# maxPlayoutsPondering = 3000 ++maxTimePondering = 120.0 ++ ++ ++# ------------------------------ ++# Other search limits and behavior ++# ------------------------------ ++ ++# Approx number of seconds to buffer for lag for GTP time controls - will ++# move a bit faster assuming there is this much lag per move. ++lagBuffer = 1.0 ++ ++# YOU PROBABLY WANT TO TUNE THIS PARAMETER! ++# The number of threads to use when searching. On powerful GPUs the optimal ++# threads may be much higher than the number of CPU cores you have because ++# many threads are needed to feed efficient large batches to the GPU. ++# ++# Run "./katago benchmark" to tune this parameter and test the effect ++# of changes to any of other parameters. ++numSearchThreads = 16 ++ ++# Play a little faster if the opponent is passing, for human-friendliness. ++# Comment these out to disable them, such as if running a controlled match ++# where you are testing KataGo with fixed compute per move vs other bots. ++searchFactorAfterOnePass = 0.50 ++searchFactorAfterTwoPass = 0.25 ++ ++# Play a little faster if super-winning, for human-friendliness. ++# Comment these out to disable them, such as if running a controlled match ++# where you are testing KataGo with fixed compute per move vs other bots. ++searchFactorWhenWinning = 0.40 ++searchFactorWhenWinningThreshold = 0.95 ++ ++# =========================================================================== ++# GPU settings ++# =========================================================================== ++# This section configures GPU settings. ++# ++# Maximum number of positions to send to a single GPU at once. The default ++# value is roughly equal to numSearchThreads, but can be specified manually ++# if running out of memory, or using multiple GPUs that expect to share work. ++# nnMaxBatchSize = ++ ++# Controls the neural network cache size, which is the primary RAM/memory use. ++# KataGo will cache up to (2 ** nnCacheSizePowerOfTwo) many neural net ++# evaluations in case of transpositions in the tree. ++# Increase this to improve performance for searches with tens of thousands ++# of visits or more. Decrease this to limit memory usage. ++# If you're happy to do some math - each neural net entry takes roughly ++# 1.5KB, except when using whole-board ownership/territory ++# visualizations, where each entry will take roughly 3KB. The number of ++# entries is (2 ** nnCacheSizePowerOfTwo). (E.g. 2 ** 18 = 262144.) ++# You can compute roughly how much memory the cache will use based on this. ++# nnCacheSizePowerOfTwo = 20 ++ ++# Size of mutex pool for nnCache is (2 ** this). ++# nnMutexPoolSizePowerOfTwo = 16 ++ ++# Randomize board orientation when running neural net evals? Uncomment and ++# set to false to disable. ++# nnRandomize = true ++ ++# If provided, force usage of a specific seed for nnRandomize. ++# The default is to use a randomly generated seed. ++# nnRandSeed = abcdefg ++ ++# Uncomment and set to true to force GTP to use the maximum board size for ++# internal buffers for the neural net. This will make KataGo slower when ++# evaluating small boards, but will avoid a lengthy initialization time on every ++# change of board size due to having to re-size the neural net buffers on the GPU. ++# This can be useful for example, for OGS's persistent bot mode that uses a single ++# bot instance to handle multiple games and may thrash between different board sizes ++# if there are concurrent games of multiple sizes. ++# gtpForceMaxNNSize = false ++ ++# ------------------------------ ++# Multiple GPUs ++# ------------------------------ ++# Set this to the number of GPUs to use or that are available. ++# IMPORTANT: If more than 1, also uncomment the appropriate TensorRT ++# or CUDA or OpenCL section. ++# numNNServerThreadsPerModel = 1 ++ ++# ------------------------------ ++# TENSORRT GPU settings ++# ------------------------------ ++# These only apply when using the TENSORRT version of KataGo. ++ ++# For one GPU: optionally uncomment this option and change if the GPU to ++# use is not device 0. ++# trtDeviceToUse = 0 ++ ++# For two GPUs: Uncomment these options, AND set numNNServerThreadsPerModel above. ++# Also, change their values if the devices you want to use are not 0 and 1. ++# trtDeviceToUseThread0 = 0 ++# trtDeviceToUseThread1 = 1 ++ ++# For three GPUs: Uncomment these options, AND set numNNServerThreadsPerModel above. ++# Also, change their values if the devices you want to use are not 0 and 1 and 2. ++# trtDeviceToUseThread0 = 0 ++# trtDeviceToUseThread1 = 1 ++# trtDeviceToUseThread2 = 2 ++ ++# The pattern continues for additional GPUs. ++ ++# ------------------------------ ++# CUDA GPU settings ++# ------------------------------ ++# These only apply when using the CUDA version of KataGo. ++ ++# For one GPU: optionally uncomment and change this if the GPU you want to ++# use is not device 0 ++# cudaDeviceToUse = 0 ++ ++# For two GPUs: Uncomment these options, AND set numNNServerThreadsPerModel above. ++# Also, change their values if the devices you want to use are not 0 and 1. ++# cudaDeviceToUseThread0 = 0 ++# cudaDeviceToUseThread1 = 1 ++ ++# For three GPUs: Uncomment these options, AND set numNNServerThreadsPerModel above. ++# Also, change their values if the devices you want to use are not 0 and 1 and 2. ++# cudaDeviceToUseThread0 = 0 ++# cudaDeviceToUseThread1 = 1 ++# cudaDeviceToUseThread2 = 2 ++ ++# The pattern continues for additional GPUs. ++ ++# KataGo will automatically use FP16 or not based on the compute capability ++# of your NVIDIA GPU. If you want to try to force a particular behavior ++# you can uncomment these lines and change them to "true" or "false". ++# cudaUseFP16 = auto ++# cudaUseNHWC = auto ++ ++# ------------------------------ ++# OpenCL GPU settings ++# ------------------------------ ++# These only apply when using the OpenCL version of KataGo. ++ ++# Uncomment and set to true to tune OpenCL for every board size separately, ++# rather than only the largest possible size. ++# openclReTunePerBoardSize = false ++ ++# For one GPU: optionally uncomment and change this if the best device to use is guessed incorrectly. ++# The default behavior tries to guess the 'best' GPU or device on your system to use, usually it will be a good guess. ++# openclDeviceToUse = 0 ++ ++# For two GPUs: Uncomment these two lines and replace X and Y with the device ids of the devices you want to use. ++# It might NOT be 0 and 1, some computers will have many OpenCL devices. You can see what the devices are when ++# KataGo starts up - it should print or log all the devices it finds. ++# (AND also set numNNServerThreadsPerModel above) ++# openclDeviceToUseThread0 = X ++# openclDeviceToUseThread1 = Y ++ ++# For three GPUs: Uncomment these three lines and replace X and Y and Z with the device ids of the devices you want to use. ++# It might NOT be 0 and 1 and 2, some computers will have many OpenCL devices. You can see what the devices are when ++# KataGo starts up - it should print or log all the devices it finds. ++# (AND also set numNNServerThreadsPerModel above) ++# openclDeviceToUseThread0 = X ++# openclDeviceToUseThread1 = Y ++# openclDeviceToUseThread2 = Z ++ ++# The pattern continues for additional GPUs. ++ ++# KataGo will automatically use FP16 or not based on testing your GPU during ++# tuning. If you want to try to force a particular behavior though you can ++# uncomment this option and change it to "true" or "false". This is a fairly ++# blunt setting - more detailed settings are testable by rerunning the tuner ++# with various arguments (./katago tuner). ++# openclUseFP16 = auto ++ ++# ------------------------------ ++# Eigen-specific settings ++# ------------------------------ ++# These only apply when using the Eigen (pure CPU) version of KataGo. ++ ++# Number of CPU threads for evaluating the neural net on the Eigen backend. ++# ++# Default: numSearchThreads ++# numEigenThreadsPerModel = X ++ ++# =========================================================================== ++# Root move selection and biases ++# =========================================================================== ++# Uncomment and edit any of the below values to change them from their default. ++ ++# If provided, force usage of a specific seed for various random things in ++# the search. The default is to use a random seed. ++# searchRandSeed = hijklmn ++ ++# Temperature for the early game, randomize between chosen moves with ++# this temperature ++# chosenMoveTemperatureEarly = 0.5 ++ ++# Decay temperature for the early game by 0.5 every this many moves, ++# scaled with board size. ++# chosenMoveTemperatureHalflife = 19 ++ ++# At the end of search after the early game, randomize between chosen ++# moves with this temperature ++# chosenMoveTemperature = 0.10 ++ ++# Subtract this many visits from each move prior to applying ++# chosenMoveTemperature (unless all moves have too few visits) to downweight ++# unlikely moves ++# chosenMoveSubtract = 0 ++ ++# The same as chosenMoveSubtract but only prunes moves that fall below ++# the threshold. This setting does not affect chosenMoveSubtract. ++# chosenMovePrune = 1 ++ ++# Number of symmetries to sample (without replacement) and average at the root ++# rootNumSymmetriesToSample = 1 ++ ++# Using LCB for move selection? ++# useLcbForSelection = true ++ ++# How many stdevs a move needs to be better than another for LCB selection ++# lcbStdevs = 5.0 ++ ++# Only use LCB override when a move has this proportion of visits as the ++# top move. ++# minVisitPropForLCB = 0.15 ++ ++# =========================================================================== ++# Internal params ++# =========================================================================== ++# Uncomment and edit any of the below values to change them from their default. ++ ++# Scales the utility of winning/losing ++# winLossUtilityFactor = 1.0 ++ ++# Scales the utility for trying to maximize score ++# staticScoreUtilityFactor = 0.10 ++# dynamicScoreUtilityFactor = 0.30 ++ ++# Adjust dynamic score center this proportion of the way towards zero, ++# capped at a reasonable amount. ++# dynamicScoreCenterZeroWeight = 0.20 ++# dynamicScoreCenterScale = 0.75 ++ ++# The utility of getting a "no result" due to triple ko or other long cycle ++# in non-superko rulesets (-1 to 1) ++# noResultUtilityForWhite = 0.0 ++ ++# The number of wins that a draw counts as, for white. (0 to 1) ++# drawEquivalentWinsForWhite = 0.5 ++ ++# Exploration constant for mcts ++# cpuctExploration = 1.0 ++# cpuctExplorationLog = 0.45 ++ ++# Parameters that control exploring more in volatile positions, exploring ++# less in stable positions. ++# cpuctUtilityStdevPrior = 0.40 ++# cpuctUtilityStdevPriorWeight = 2.0 ++# cpuctUtilityStdevScale = 0.85 ++ ++# FPU reduction constant for mcts ++# fpuReductionMax = 0.2 ++# rootFpuReductionMax = 0.1 ++# fpuParentWeightByVisitedPolicy = true ++ ++# Parameters that control weighting of evals based on the net's own ++# self-reported uncertainty. ++# useUncertainty = true ++# uncertaintyExponent = 1.0 ++# uncertaintyCoeff = 0.25 ++ ++# Explore using optimistic policy ++# rootPolicyOptimism = 0.2 ++# policyOptimism = 1.0 ++ ++# Amount to apply a downweighting of children with very bad values relative ++# to good ones. ++# valueWeightExponent = 0.25 ++ ++# Slight incentive for the bot to behave human-like with regard to passing at ++# the end, filling the dame, not wasting time playing in its own territory, ++# etc., and not play moves that are equivalent in terms of points but a bit ++# more unfriendly to humans. ++# rootEndingBonusPoints = 0.5 ++ ++# Make the bot prune useless moves that are just prolonging the game to ++# avoid losing yet. ++# rootPruneUselessMoves = true ++ ++# Apply bias correction based on local pattern keys ++# subtreeValueBiasFactor = 0.45 ++# subtreeValueBiasWeightExponent = 0.85 ++ ++# Use graph search rather than tree search - identify and share search for ++# transpositions. ++# useGraphSearch = true ++ ++# How much to shard the node table for search synchronization ++# nodeTableShardsPowerOfTwo = 16 ++ ++# How many virtual losses to add when a thread descends through a node ++# numVirtualLossesPerThread = 1 ++ ++# Improve the quality of evals under heavy multithreading ++# useNoisePruning = true ++ ++# =========================================================================== ++# Automatic avoid patterns ++# =========================================================================== ++# The parameters in this section provide a way to bias away from moves that match ++# patterns that this instance of KataGo has played in previous games, by auto-saving ++# moves to a directory and then auto-loading and biasing against them each new game. ++# Uncomment them to use them. When using this feature, all parameters must be specified. ++ ++# Different board sizes are tracked separately, but all board sizes share the same ++# parameters by default. Every parameter *except* for autoAvoidRepeatDir and ++# autoAvoidRepeatSaveChunkSize can be overridden per board size, ++# e.g. "autoAvoidRepeatMinTurnNumber13x13". You must ALSO specify the ++# defaults even if you have specified board-size-specific values. ++ ++# Directory to auto-save moves KataGo plays, to avoid them in future games. ++# You can create a new empty directory and put its path here. ++# If you run parallel instances of KataGo, use different directories if you ++# want them not to share their biases, use the same directory if you want ++# all of them to bias away from past moves that any of them have played. ++# KataGo will also automatically DELETE old data in this directory, so it is ++# recommended that if you do share the same directory between parallel instances, ++# that they all use the same settings that affect data saving/deletion. ++# autoAvoidRepeatDir = PATH_TO_NEW_DIRECTORY ++ ++# Penalize this much utility per matching move. ++# Values that are too large may lead to bad play. The value of 0.004 is fairly large ++# and might be large enough to result in some early weird/bad moves when trying ++# to avoid past games' moves if enough games begin the same way. You can experiment with it. ++# autoAvoidRepeatUtility = 0.004 ++ ++# Per each new move saved, exponentially decay prior saved moves by this factor. ++# This way, the bias against moves from many games ago is gradually phased out. ++# For example, 0.9995 = 1 - 1/2000, so would be roughly 2000 prior moves worth of ++# penalty weight remembered, in steady state. Depending on what turn number range ++# you are saving, this might equate to a different number of games For example ++# saving the first 50 moves per game would make this roughly 2000 / 50 = 40 games ++# worth of memory. ++# autoAvoidRepeatLambda = 0.9995 ++ ++# Affects data saving/deletion. ++# When the number of saved moves exceeds this, outright delete them to avoid too many ++# files and disk space building up. Also may affect the speed of saving/loading on start ++# of each game if this is set large and a lot of data builds up. ++# autoAvoidRepeatMaxPoses = 10000 ++ ++# Affects data saving/deletion. ++# Only save data for moves within this turn number range of those games. ++# E.g. setting autoAvoidRepeatMinTurnNumber to a number like 4 or 5 would tend to make ++# KataGo not develop a bias against the initial 3-4 and 4-4 corner moves in almost every game. ++# autoAvoidRepeatMinTurnNumber = 0 ++# autoAvoidRepeatMaxTurnNumber = 50 ++ ++# Affects data saving/deletion. ++# Within a single run of a program, wait to accumulate this many samples ++# (possibly across multiple clear_boards/games) before saving the data. ++# Can help to avoid writing too many small files to disk, especially when GTP is used ++# in a way that clears the board very frequently (e.g. gtp2ogs pooled manager). ++# autoAvoidRepeatSaveChunkSize = 200 ++ ++# =========================================================================== ++# Avoid SGF patterns ++# =========================================================================== ++# The parameters in this section provide a way to avoid moves that follow ++# specific patterns based on a set of SGF files loaded upon startup. ++# This is basically the same as the above "Automatic avoid patterns" section ++# above except you supply your own SGF files to avoid moves from. ++# Uncomment them to use this feature. Additionally, if the SGF file ++# contains the string %SKIP% in a comment on a move, that move will be ++# ignored for this purpose. ++ ++# Load SGF files from this directory when the engine is started ++# (only on startup, will not reload unless engine is restarted) ++# avoidSgfPatternDirs = path/to/directory/with/sgfs/ ++# You can also surround the file path in double quotes if the file path contains trailing spaces or hash signs. ++# Within double quotes, backslashes are escape characters. ++# avoidSgfPatternDirs = "path/to/directory/with/sgfs/" ++ ++# Penalize this much utility per matching move. ++# Set this negative if you instead want to favor SGF patterns instead of ++# penalizing them. This number does not need to be large, even 0.001 will ++# make a difference. Values that are too large may lead to bad play. ++# avoidSgfPatternUtility = 0.001 ++ ++# Optional - load only the newest this many files ++# avoidSgfPatternMaxFiles = 20 ++ ++# Optional - Penalty is multiplied by this per each older SGF file, so that ++# old SGF files matter less than newer ones. ++# avoidSgfPatternLambda = 0.90 ++ ++# Optional - pay attention only to moves made by players with this name. ++# For example, set it to the name that your bot's past games will show up ++# as in the SGF, so that the bot will only avoid repeating moves that itself ++# made in past games, not the moves that its opponents made. ++# avoidSgfPatternAllowedNames = my-ogs-bot-name1,my-ogs-bot-name2 ++ ++# Optional - Ignore moves in SGF files that occurred before this turn number. ++# avoidSgfPatternMinTurnNumber = 0 ++ ++# For more avoid patterns: ++# You can also specify a second set of parameters, and a third, fourth, ++# etc. by numbering 2,3,4,... ++# ++# avoidSgf2PatternDirs = ... ++# avoidSgf2PatternUtility = ... ++# avoidSgf2PatternMaxFiles = ... ++# avoidSgf2PatternLambda = ... ++# avoidSgf2PatternAllowedNames = ... ++# avoidSgf2PatternMinTurnNumber = ... ++ +diff --git a/cpp/golaxy_9d.cfg b/cpp/golaxy_9d.cfg +new file mode 100644 +index 00000000..31e66efd +--- /dev/null ++++ b/cpp/golaxy_9d.cfg +@@ -0,0 +1,713 @@ ++# Configuration for KataGo C++ GTP engine ++ ++# Run the program using: `./katago.exe gtp` ++ ++# In this example config, when a parameter is given as a commented out value, ++# that value also is the default value, unless described otherwise. You can ++# uncomment it (remove the pound sign) and change it if you want. ++ ++# =========================================================================== ++# Running on an online server or in a real tournament or match ++# =========================================================================== ++# If you plan to run online or in a tournament, read through the "Rules" ++# section below for proper handling of komi, handicaps, end-of-game cleanup, ++# and other details. ++ ++# =========================================================================== ++# Notes about performance and memory usage ++# =========================================================================== ++# Important: For good performance, you will very likely want to tune the ++# "numSearchThreads" parameter in the Search limits section below! Run ++# "./katago benchmark" to test KataGo and to suggest a reasonable value ++# of this parameter. ++ ++# For multi-GPU systems, read "OpenCL GPU settings" or "CUDA GPU settings". ++# ++# When using OpenCL, verify that KataGo picks the correct device! Some systems ++# may have both an Intel CPU OpenCL and GPU OpenCL. If # KataGo picks the wrong ++# one, correct this by specifying "openclGpuToUse". ++# ++# Consider adjusting "maxVisits", "ponderingEnabled", "resignThreshold", and ++# other parameters depending on your intended usage. ++ ++# =========================================================================== ++# Command-line usage ++# =========================================================================== ++# All of the below values may be set or overridden via command-line arguments: ++# ++# -override-config KEY=VALUE,KEY=VALUE,... ++ ++# =========================================================================== ++# Logs and files ++# =========================================================================== ++# This section defines where and what logging information is produced. ++ ++# Each run of KataGo will log to a separate file in this dir. ++# This is the default. ++logDir = gtp_logs ++# Uncomment and specify this instead of logDir to write separate dated subdirs ++# logDirDated = gtp_logs ++# Uncomment and specify this instead of logDir to log to only a single file ++# logFile = gtp.log ++ ++# Logging options ++logAllGTPCommunication = true ++logSearchInfo = true ++logSearchInfoForChosenMove = false ++logToStderr = true ++ ++# KataGo will display some info to stderr on GTP startup ++# Uncomment the next line and set it to false to suppress that and remain silent ++# startupPrintMessageToStderr = true ++ ++# Write information to stderr, for use in things like malkovich chat to OGS. ++ ogsChatToStderr = true ++ ++# Uncomment and set this to a directory to override where openCLTuner files ++# and other cached data is written. By default it saves into a subdir of the ++# current directory on windows, and a subdir of ~/.katago on Linux. ++# homeDataDir = PATH_TO_DIRECTORY ++ ++# =========================================================================== ++# Analysis ++# =========================================================================== ++# This section configures analysis settings. ++# ++# The maximum number of moves after the first move displayed in variations ++# from analysis commands like kata-analyze or lz-analyze. ++# analysisPVLen = 15 ++ ++# Report winrates for chat and analysis as (BLACK|WHITE|SIDETOMOVE). ++# Most GUIs and analysis tools will expect SIDETOMOVE. ++# reportAnalysisWinratesAs = SIDETOMOVE ++ ++# Extra noise for wider exploration. Large values will force KataGo to ++# analyze a greater variety of moves than it normally would. ++# An extreme value like 1 distributes playouts across every move on the board, ++# even very bad moves. ++# Affects analysis only, does not affect play. ++# analysisWideRootNoise = 0.04 ++ ++# Try to limit the effect of possible bad or bogus move sequences in the ++# history leading to this position from affecting KataGo's move predictions. ++# analysisIgnorePreRootHistory = true ++ ++# =========================================================================== ++# Rules ++# =========================================================================== ++# This section configures the scoring and playing rules. Rules can also be ++# changed mid-run by issuing custom GTP commands. ++# ++# See https://lightvector.github.io/KataGo/rules.html for rules details. ++# ++# See https://github.com/lightvector/KataGo/blob/master/docs/GTP_Extensions.md ++# for GTP commands. ++ ++# Specify the rules as a string. ++# Some legal values include: ++# chinese, japanese, korean, aga, chinese-ogs, new-zealand, stone-scoring, ++# ancient-territory, bga, aga-button ++# ++# For some human rulesets that require complex adjudication in tricky cases ++# (e.g. japanese, korean) KataGo may not precisely match the ruleset in such ++# cases but will do its best. ++# rules = tromp-taylor ++ ++# By default, the "rules" parameter is used, but if you comment it out and ++# uncomment one option in each of the sections below, you can specify an ++# arbitrary combination of individual rules. ++ ++# Ko Rule Settings ++koRule = POSITIONAL # Positional superko ++ ++# Scoring Rule Settings ++scoringRule = AREA # Area scoring ++ ++# Tax Rule Settings ++taxRule = NONE # All surrounded empty points are scored ++ ++# Is multiple-stone suicide legal? (Single-stone suicide is always illegal). ++multiStoneSuicideLegal = false ++ ++# "Button go" - the first pass when area scoring awards 0.5 points and does ++# not count for ending the game. ++hasButton = false ++ ++# Is this a human ruleset where it's okay to pass before having physically ++# captured and removed all dead stones? ++friendlyPassOk = false ++ ++# How handicap stones in handicap games are compensated ++whiteHandicapBonus = 0 # White gets no compensation for black's handicap stones (Tromp-taylor, NZ, JP) ++ ++ ++# ------------------------------ ++# Other rules hacks ++# ------------------------------ ++# Uncomment and change to adjust what board size KataGo uses upon startup ++# by default when GTP doesn't specify. ++# defaultBoardSize = 19 ++ ++# By default, Katago will use the komi that the GUI or GTP controller tries to set. ++# Uncomment and set this to have KataGo ignore the controller and always use this komi. ++# ignoreGTPAndForceKomi = 7 ++ ++# =========================================================================== ++# Bot behavior ++# =========================================================================== ++ ++# ------------------------------ ++# Resignation ++# ------------------------------ ++ ++# Resignation occurs if for at least resignConsecTurns in a row, the ++# winLossUtility (on a [-1,1] scale) is below resignThreshold. ++allowResignation = true ++resignThreshold = -0.90 ++resignConsecTurns = 3 ++ ++# By default, KataGo may resign games that it is confidently losing even if they ++# are very close in score. Uncomment and set this to avoid resigning games ++# if the estimated difference is points is less than or equal to this. ++# resignMinScoreDifference = 10 ++ ++# ------------------------------ ++# Handicap ++# ------------------------------ ++# Assume that if black makes many moves in a row right at the start of the ++# game, then the game is a handicap game. This is necessary on some servers ++# and for some GUIs and also when initializing from many SGF files, which may ++# set up a handicap game using repeated GTP "play" commands for black rather ++# than GTP "place_free_handicap" commands; however, it may also lead to ++# incorrect understanding of komi if whiteHandicapBonus is used and a server ++# does not have such a practice. Uncomment and set to false to disable. ++# assumeMultipleStartingBlackMovesAreHandicap = true ++ ++# Makes katago dynamically adjust in handicap or altered-komi games to assume ++# based on those game settings that it must be stronger or weaker than the ++# opponent and to play accordingly. Greatly improves handicap strength by ++# biasing winrates and scores to favor appropriate safe/aggressive play. ++# Does NOT affect analysis (lz-analyze, kata-analyze, used by programs like ++# Lizzie) so analysis remains unbiased. Uncomment and set this to 0 to disable ++# this and make KataGo play the same always. ++# dynamicPlayoutDoublingAdvantageCapPerOppLead = 0.045 ++ ++# Instead of "dynamicPlayoutDoublingAdvantageCapPerOppLead", you can comment ++# that out and uncomment and set "playoutDoublingAdvantage" to a fixed value ++# from -3.0 to 3.0 that will not change dynamically. ++# ALSO affects analysis tools (lz-analyze, kata-analyze, used by e.g. Lizzie). ++# Negative makes KataGo behave as if it is much weaker than the opponent. ++# Positive makes KataGo behave as if it is much stronger than the opponent. ++# KataGo will adjust to favor safe/aggressive play as appropriate based on ++# the combination of who is ahead and how much stronger/weaker it thinks it is, ++# and report winrates and scores taking the strength difference into account. ++# ++# If this and "dynamicPlayoutDoublingAdvantageCapPerOppLead" are both set ++# then dynamic will be used for all games and this fixed value will be used ++# for analysis tools. ++# playoutDoublingAdvantage = 0.0 ++ ++# Uncomment one of these when using "playoutDoublingAdvantage" to enforce ++# that it will only apply when KataGo plays as the specified color and will be ++# negated when playing as the opposite color. ++# playoutDoublingAdvantagePla = BLACK ++# playoutDoublingAdvantagePla = WHITE ++ ++# ------------------------------ ++# Passing and cleanup ++# ------------------------------ ++# Make the bot never assume that its pass will end the game, even if passing ++# would end and "win" under Tromp-Taylor rules. Usually this is a good idea ++# when using it for analysis or playing on servers where scoring may be ++# implemented non-tromp-taylorly. Uncomment and set to false to disable. ++# conservativePass = true ++ ++# When using territory scoring, self-play games continue beyond two passes ++# with special cleanup rules that may be confusing for human players. This ++# option prevents the special cleanup phases from being reachable when using ++# the bot for GTP play. Uncomment and set to false to enable entering special ++# cleanup. For example, if you are testing it against itself, or against ++# another bot that has precisely implemented the rules documented at ++# https://lightvector.github.io/KataGo/rules.html ++# preventCleanupPhase = true ++ ++# ------------------------------ ++# Miscellaneous behavior ++# ------------------------------ ++# If the board is symmetric, search only one copy of each equivalent move. ++# Attempts to also account for ko/superko, will not theoretically perfect for ++# superko. Uncomment and set to false to disable. ++# rootSymmetryPruning = true ++ ++# Uncomment and set to true to avoid a particular joseki that some networks ++# misevaluate, and also to improve opening diversity versus some particular ++# other bots that like to play it all the time. ++# avoidMYTDaggerHack = false ++ ++# Prefer to avoid playing the same joseki in every corner of the board. ++# Uncomment to set to a specific value. See "Avoid SGF patterns" section. ++# By default: 0 (even games), 0.005 (handicap games) ++# avoidRepeatedPatternUtility = 0.0 ++ ++# Experimental logic to fight against mirror Go even with unfavorable komi. ++# Uncomment to set to a specific value to use for both playing and analysis. ++# By default: true when playing via GTP, but false when analyzing. ++# antiMirror = true ++ ++# Enable some hacks that mitigate rare instances when passing messes up deeper searches. ++# enablePassingHacks = true ++ ++# Uncomment and set this to true to prevent bad or bogus move sequences ++# in the history leading to this position from affecting KataGo's move choices. ++# Same as analysisIgnorePreRootHistory (see above) but applies to actual play. ++# You can enable this if KataGo is being asked to play from positions that it did not ++# choose the moves to reach. ++# ignorePreRootHistory = false ++ ++# =========================================================================== ++# Search limits ++# =========================================================================== ++ ++# Terminology: ++# "Playouts" is the number of new playouts of search performed each turn. ++# "Visits" is the same as "Playouts" but also counts search performed on ++# previous turns that is still applicable to this turn. ++# "Time" is the time in seconds. ++ ++# For example, if KataGo searched 200 nodes on the previous turn, and then ++# after the opponent's reply, 50 nodes of its search tree was still valid, ++# then a visit limit of 200 would allow KataGo to search 150 new nodes ++# (for a final tree size of 200 nodes), whereas a playout limit of of 200 ++# would allow KataGo to search 200 nodes (for a final tree size of 250 nodes). ++ ++# Additionally, KataGo may also move before than the limit in order to ++# obey time controls (e.g. byo-yomi, etc) if the GTP controller has ++# told KataGo that the game has is being played with a given time control. ++ ++# Limits for search on the current turn. ++# If commented out or unspecified, the default is to have no limit. ++# maxVisits = 50000 ++# maxPlayouts = 300 ++ maxTime = 1.0 ++ ++# Ponder on the opponent's turn? ++ponderingEnabled = true ++ ++# Limits for search when pondering on the opponent's turn. ++# If commented out or unspecified, the default is to have no limit. ++# Limiting the maximum time is recommended so that KataGo won't burn CPU/GPU ++# forever and/or run out of RAM if left unattended while pondering is enabled. ++# maxVisitsPondering = 5000 ++# maxPlayoutsPondering = 3000 ++maxTimePondering = 120.0 ++ ++ ++# ------------------------------ ++# Other search limits and behavior ++# ------------------------------ ++ ++# Approx number of seconds to buffer for lag for GTP time controls - will ++# move a bit faster assuming there is this much lag per move. ++lagBuffer = 1.0 ++ ++# YOU PROBABLY WANT TO TUNE THIS PARAMETER! ++# The number of threads to use when searching. On powerful GPUs the optimal ++# threads may be much higher than the number of CPU cores you have because ++# many threads are needed to feed efficient large batches to the GPU. ++# ++# Run "./katago benchmark" to tune this parameter and test the effect ++# of changes to any of other parameters. ++numSearchThreads = 40 ++ ++# Play a little faster if the opponent is passing, for human-friendliness. ++# Comment these out to disable them, such as if running a controlled match ++# where you are testing KataGo with fixed compute per move vs other bots. ++searchFactorAfterOnePass = 0.50 ++searchFactorAfterTwoPass = 0.25 ++ ++# Play a little faster if super-winning, for human-friendliness. ++# Comment these out to disable them, such as if running a controlled match ++# where you are testing KataGo with fixed compute per move vs other bots. ++searchFactorWhenWinning = 0.40 ++searchFactorWhenWinningThreshold = 0.95 ++ ++# =========================================================================== ++# GPU settings ++# =========================================================================== ++# This section configures GPU settings. ++# ++# Maximum number of positions to send to a single GPU at once. The default ++# value is roughly equal to numSearchThreads, but can be specified manually ++# if running out of memory, or using multiple GPUs that expect to share work. ++# nnMaxBatchSize = ++ ++# Controls the neural network cache size, which is the primary RAM/memory use. ++# KataGo will cache up to (2 ** nnCacheSizePowerOfTwo) many neural net ++# evaluations in case of transpositions in the tree. ++# Increase this to improve performance for searches with tens of thousands ++# of visits or more. Decrease this to limit memory usage. ++# If you're happy to do some math - each neural net entry takes roughly ++# 1.5KB, except when using whole-board ownership/territory ++# visualizations, where each entry will take roughly 3KB. The number of ++# entries is (2 ** nnCacheSizePowerOfTwo). (E.g. 2 ** 18 = 262144.) ++# You can compute roughly how much memory the cache will use based on this. ++# nnCacheSizePowerOfTwo = 20 ++ ++# Size of mutex pool for nnCache is (2 ** this). ++# nnMutexPoolSizePowerOfTwo = 16 ++ ++# Randomize board orientation when running neural net evals? Uncomment and ++# set to false to disable. ++# nnRandomize = true ++ ++# If provided, force usage of a specific seed for nnRandomize. ++# The default is to use a randomly generated seed. ++# nnRandSeed = abcdefg ++ ++# Uncomment and set to true to force GTP to use the maximum board size for ++# internal buffers for the neural net. This will make KataGo slower when ++# evaluating small boards, but will avoid a lengthy initialization time on every ++# change of board size due to having to re-size the neural net buffers on the GPU. ++# This can be useful for example, for OGS's persistent bot mode that uses a single ++# bot instance to handle multiple games and may thrash between different board sizes ++# if there are concurrent games of multiple sizes. ++# gtpForceMaxNNSize = false ++ ++# ------------------------------ ++# Multiple GPUs ++# ------------------------------ ++# Set this to the number of GPUs to use or that are available. ++# IMPORTANT: If more than 1, also uncomment the appropriate TensorRT ++# or CUDA or OpenCL section. ++# numNNServerThreadsPerModel = 1 ++ ++# ------------------------------ ++# TENSORRT GPU settings ++# ------------------------------ ++# These only apply when using the TENSORRT version of KataGo. ++ ++# For one GPU: optionally uncomment this option and change if the GPU to ++# use is not device 0. ++# trtDeviceToUse = 0 ++ ++# For two GPUs: Uncomment these options, AND set numNNServerThreadsPerModel above. ++# Also, change their values if the devices you want to use are not 0 and 1. ++# trtDeviceToUseThread0 = 0 ++# trtDeviceToUseThread1 = 1 ++ ++# For three GPUs: Uncomment these options, AND set numNNServerThreadsPerModel above. ++# Also, change their values if the devices you want to use are not 0 and 1 and 2. ++# trtDeviceToUseThread0 = 0 ++# trtDeviceToUseThread1 = 1 ++# trtDeviceToUseThread2 = 2 ++ ++# The pattern continues for additional GPUs. ++ ++# ------------------------------ ++# CUDA GPU settings ++# ------------------------------ ++# These only apply when using the CUDA version of KataGo. ++ ++# For one GPU: optionally uncomment and change this if the GPU you want to ++# use is not device 0 ++# cudaDeviceToUse = 0 ++ ++# For two GPUs: Uncomment these options, AND set numNNServerThreadsPerModel above. ++# Also, change their values if the devices you want to use are not 0 and 1. ++# cudaDeviceToUseThread0 = 0 ++# cudaDeviceToUseThread1 = 1 ++ ++# For three GPUs: Uncomment these options, AND set numNNServerThreadsPerModel above. ++# Also, change their values if the devices you want to use are not 0 and 1 and 2. ++# cudaDeviceToUseThread0 = 0 ++# cudaDeviceToUseThread1 = 1 ++# cudaDeviceToUseThread2 = 2 ++ ++# The pattern continues for additional GPUs. ++ ++# KataGo will automatically use FP16 or not based on the compute capability ++# of your NVIDIA GPU. If you want to try to force a particular behavior ++# you can uncomment these lines and change them to "true" or "false". ++# cudaUseFP16 = auto ++# cudaUseNHWC = auto ++ ++# ------------------------------ ++# OpenCL GPU settings ++# ------------------------------ ++# These only apply when using the OpenCL version of KataGo. ++ ++# Uncomment and set to true to tune OpenCL for every board size separately, ++# rather than only the largest possible size. ++# openclReTunePerBoardSize = false ++ ++# For one GPU: optionally uncomment and change this if the best device to use is guessed incorrectly. ++# The default behavior tries to guess the 'best' GPU or device on your system to use, usually it will be a good guess. ++# openclDeviceToUse = 0 ++ ++# For two GPUs: Uncomment these two lines and replace X and Y with the device ids of the devices you want to use. ++# It might NOT be 0 and 1, some computers will have many OpenCL devices. You can see what the devices are when ++# KataGo starts up - it should print or log all the devices it finds. ++# (AND also set numNNServerThreadsPerModel above) ++# openclDeviceToUseThread0 = X ++# openclDeviceToUseThread1 = Y ++ ++# For three GPUs: Uncomment these three lines and replace X and Y and Z with the device ids of the devices you want to use. ++# It might NOT be 0 and 1 and 2, some computers will have many OpenCL devices. You can see what the devices are when ++# KataGo starts up - it should print or log all the devices it finds. ++# (AND also set numNNServerThreadsPerModel above) ++# openclDeviceToUseThread0 = X ++# openclDeviceToUseThread1 = Y ++# openclDeviceToUseThread2 = Z ++ ++# The pattern continues for additional GPUs. ++ ++# KataGo will automatically use FP16 or not based on testing your GPU during ++# tuning. If you want to try to force a particular behavior though you can ++# uncomment this option and change it to "true" or "false". This is a fairly ++# blunt setting - more detailed settings are testable by rerunning the tuner ++# with various arguments (./katago tuner). ++# openclUseFP16 = auto ++ ++# ------------------------------ ++# Eigen-specific settings ++# ------------------------------ ++# These only apply when using the Eigen (pure CPU) version of KataGo. ++ ++# Number of CPU threads for evaluating the neural net on the Eigen backend. ++# ++# Default: numSearchThreads ++# numEigenThreadsPerModel = X ++ ++# =========================================================================== ++# Root move selection and biases ++# =========================================================================== ++# Uncomment and edit any of the below values to change them from their default. ++ ++# If provided, force usage of a specific seed for various random things in ++# the search. The default is to use a random seed. ++# searchRandSeed = hijklmn ++ ++# Temperature for the early game, randomize between chosen moves with ++# this temperature ++# chosenMoveTemperatureEarly = 0.5 ++ ++# Decay temperature for the early game by 0.5 every this many moves, ++# scaled with board size. ++# chosenMoveTemperatureHalflife = 19 ++ ++# At the end of search after the early game, randomize between chosen ++# moves with this temperature ++# chosenMoveTemperature = 0.10 ++ ++# Subtract this many visits from each move prior to applying ++# chosenMoveTemperature (unless all moves have too few visits) to downweight ++# unlikely moves ++# chosenMoveSubtract = 0 ++ ++# The same as chosenMoveSubtract but only prunes moves that fall below ++# the threshold. This setting does not affect chosenMoveSubtract. ++# chosenMovePrune = 1 ++ ++# Number of symmetries to sample (without replacement) and average at the root ++# rootNumSymmetriesToSample = 1 ++ ++# Using LCB for move selection? ++# useLcbForSelection = true ++ ++# How many stdevs a move needs to be better than another for LCB selection ++# lcbStdevs = 5.0 ++ ++# Only use LCB override when a move has this proportion of visits as the ++# top move. ++# minVisitPropForLCB = 0.15 ++ ++# =========================================================================== ++# Internal params ++# =========================================================================== ++# Uncomment and edit any of the below values to change them from their default. ++ ++# Scales the utility of winning/losing ++# winLossUtilityFactor = 1.0 ++ ++# Scales the utility for trying to maximize score ++# staticScoreUtilityFactor = 0.10 ++# dynamicScoreUtilityFactor = 0.30 ++ ++# Adjust dynamic score center this proportion of the way towards zero, ++# capped at a reasonable amount. ++# dynamicScoreCenterZeroWeight = 0.20 ++# dynamicScoreCenterScale = 0.75 ++ ++# The utility of getting a "no result" due to triple ko or other long cycle ++# in non-superko rulesets (-1 to 1) ++# noResultUtilityForWhite = 0.0 ++ ++# The number of wins that a draw counts as, for white. (0 to 1) ++# drawEquivalentWinsForWhite = 0.5 ++ ++# Exploration constant for mcts ++# cpuctExploration = 1.0 ++# cpuctExplorationLog = 0.45 ++ ++# Parameters that control exploring more in volatile positions, exploring ++# less in stable positions. ++# cpuctUtilityStdevPrior = 0.40 ++# cpuctUtilityStdevPriorWeight = 2.0 ++# cpuctUtilityStdevScale = 0.85 ++ ++# FPU reduction constant for mcts ++# fpuReductionMax = 0.2 ++# rootFpuReductionMax = 0.1 ++# fpuParentWeightByVisitedPolicy = true ++ ++# Parameters that control weighting of evals based on the net's own ++# self-reported uncertainty. ++# useUncertainty = true ++# uncertaintyExponent = 1.0 ++# uncertaintyCoeff = 0.25 ++ ++# Explore using optimistic policy ++# rootPolicyOptimism = 0.2 ++# policyOptimism = 1.0 ++ ++# Amount to apply a downweighting of children with very bad values relative ++# to good ones. ++# valueWeightExponent = 0.25 ++ ++# Slight incentive for the bot to behave human-like with regard to passing at ++# the end, filling the dame, not wasting time playing in its own territory, ++# etc., and not play moves that are equivalent in terms of points but a bit ++# more unfriendly to humans. ++# rootEndingBonusPoints = 0.5 ++ ++# Make the bot prune useless moves that are just prolonging the game to ++# avoid losing yet. ++# rootPruneUselessMoves = true ++ ++# Apply bias correction based on local pattern keys ++# subtreeValueBiasFactor = 0.45 ++# subtreeValueBiasWeightExponent = 0.85 ++ ++# Use graph search rather than tree search - identify and share search for ++# transpositions. ++# useGraphSearch = true ++ ++# How much to shard the node table for search synchronization ++# nodeTableShardsPowerOfTwo = 16 ++ ++# How many virtual losses to add when a thread descends through a node ++# numVirtualLossesPerThread = 1 ++ ++# Improve the quality of evals under heavy multithreading ++# useNoisePruning = true ++ ++# =========================================================================== ++# Automatic avoid patterns ++# =========================================================================== ++# The parameters in this section provide a way to bias away from moves that match ++# patterns that this instance of KataGo has played in previous games, by auto-saving ++# moves to a directory and then auto-loading and biasing against them each new game. ++# Uncomment them to use them. When using this feature, all parameters must be specified. ++ ++# Different board sizes are tracked separately, but all board sizes share the same ++# parameters by default. Every parameter *except* for autoAvoidRepeatDir and ++# autoAvoidRepeatSaveChunkSize can be overridden per board size, ++# e.g. "autoAvoidRepeatMinTurnNumber13x13". You must ALSO specify the ++# defaults even if you have specified board-size-specific values. ++ ++# Directory to auto-save moves KataGo plays, to avoid them in future games. ++# You can create a new empty directory and put its path here. ++# If you run parallel instances of KataGo, use different directories if you ++# want them not to share their biases, use the same directory if you want ++# all of them to bias away from past moves that any of them have played. ++# KataGo will also automatically DELETE old data in this directory, so it is ++# recommended that if you do share the same directory between parallel instances, ++# that they all use the same settings that affect data saving/deletion. ++# autoAvoidRepeatDir = PATH_TO_NEW_DIRECTORY ++ ++# Penalize this much utility per matching move. ++# Values that are too large may lead to bad play. The value of 0.004 is fairly large ++# and might be large enough to result in some early weird/bad moves when trying ++# to avoid past games' moves if enough games begin the same way. You can experiment with it. ++# autoAvoidRepeatUtility = 0.004 ++ ++# Per each new move saved, exponentially decay prior saved moves by this factor. ++# This way, the bias against moves from many games ago is gradually phased out. ++# For example, 0.9995 = 1 - 1/2000, so would be roughly 2000 prior moves worth of ++# penalty weight remembered, in steady state. Depending on what turn number range ++# you are saving, this might equate to a different number of games For example ++# saving the first 50 moves per game would make this roughly 2000 / 50 = 40 games ++# worth of memory. ++# autoAvoidRepeatLambda = 0.9995 ++ ++# Affects data saving/deletion. ++# When the number of saved moves exceeds this, outright delete them to avoid too many ++# files and disk space building up. Also may affect the speed of saving/loading on start ++# of each game if this is set large and a lot of data builds up. ++# autoAvoidRepeatMaxPoses = 10000 ++ ++# Affects data saving/deletion. ++# Only save data for moves within this turn number range of those games. ++# E.g. setting autoAvoidRepeatMinTurnNumber to a number like 4 or 5 would tend to make ++# KataGo not develop a bias against the initial 3-4 and 4-4 corner moves in almost every game. ++# autoAvoidRepeatMinTurnNumber = 0 ++# autoAvoidRepeatMaxTurnNumber = 50 ++ ++# Affects data saving/deletion. ++# Within a single run of a program, wait to accumulate this many samples ++# (possibly across multiple clear_boards/games) before saving the data. ++# Can help to avoid writing too many small files to disk, especially when GTP is used ++# in a way that clears the board very frequently (e.g. gtp2ogs pooled manager). ++# autoAvoidRepeatSaveChunkSize = 200 ++ ++# =========================================================================== ++# Avoid SGF patterns ++# =========================================================================== ++# The parameters in this section provide a way to avoid moves that follow ++# specific patterns based on a set of SGF files loaded upon startup. ++# This is basically the same as the above "Automatic avoid patterns" section ++# above except you supply your own SGF files to avoid moves from. ++# Uncomment them to use this feature. Additionally, if the SGF file ++# contains the string %SKIP% in a comment on a move, that move will be ++# ignored for this purpose. ++ ++# Load SGF files from this directory when the engine is started ++# (only on startup, will not reload unless engine is restarted) ++# avoidSgfPatternDirs = path/to/directory/with/sgfs/ ++# You can also surround the file path in double quotes if the file path contains trailing spaces or hash signs. ++# Within double quotes, backslashes are escape characters. ++# avoidSgfPatternDirs = "path/to/directory/with/sgfs/" ++ ++# Penalize this much utility per matching move. ++# Set this negative if you instead want to favor SGF patterns instead of ++# penalizing them. This number does not need to be large, even 0.001 will ++# make a difference. Values that are too large may lead to bad play. ++# avoidSgfPatternUtility = 0.001 ++ ++# Optional - load only the newest this many files ++# avoidSgfPatternMaxFiles = 20 ++ ++# Optional - Penalty is multiplied by this per each older SGF file, so that ++# old SGF files matter less than newer ones. ++# avoidSgfPatternLambda = 0.90 ++ ++# Optional - pay attention only to moves made by players with this name. ++# For example, set it to the name that your bot's past games will show up ++# as in the SGF, so that the bot will only avoid repeating moves that itself ++# made in past games, not the moves that its opponents made. ++# avoidSgfPatternAllowedNames = my-ogs-bot-name1,my-ogs-bot-name2 ++ ++# Optional - Ignore moves in SGF files that occurred before this turn number. ++# avoidSgfPatternMinTurnNumber = 0 ++ ++# For more avoid patterns: ++# You can also specify a second set of parameters, and a third, fourth, ++# etc. by numbering 2,3,4,... ++# ++# avoidSgf2PatternDirs = ... ++# avoidSgf2PatternUtility = ... ++# avoidSgf2PatternMaxFiles = ... ++# avoidSgf2PatternLambda = ... ++# avoidSgf2PatternAllowedNames = ... ++# avoidSgf2PatternMinTurnNumber = ... ++ +diff --git a/cpp/gtp_human5k_example.cfg b/cpp/gtp_human5k_example.cfg +new file mode 100644 +index 00000000..9098f5ab +--- /dev/null ++++ b/cpp/gtp_human5k_example.cfg +@@ -0,0 +1,189 @@ ++ ++# This is an example config for configuring KataGo to attempt to imitate a weaker human player. ++# Running with this config requires giving a human SL model b18c384nbt-humanv0.bin.gz ++# on the command line such as: ++# ./katago gtp -config gtp_human5k_example.cfg -model your_favorite_normal_model_for_katago.bin.gz -human-model b18c384nbt-humanv0.bin.gz ++# You can obtain the human model at https://github.com/lightvector/KataGo/releases/tag/v1.15.0 ++ ++# Below, the most important parts of the config for human-like play are commented. ++# See the original gtp_example for comments on other parameters. ++ ++# For another useful guide on human-style analysis, see here: ++# https://github.com/lightvector/KataGo/blob/master/docs/Analysis_Engine.md#human-sl-analysis-guide ++ ++# It is ALSO possible to pass in simply '-model b18c384nbt-humanv0.bin.gz' and NOT ++# pass in -human-model, i.e. use the human model as if it were KataGo's normal neural net. ++# If you do that, you need to use a config more like the normal gtp_example.cfg, not this config! ++# Keep in mind that if using the model normally, or if using -human-model but also altering ++# parameters below to blend in some of KataGo's search, KataGo's play might NOT be very human-like, ++# or might be human-like but play at a strength very different than the humanSLProfile. ++# You can experiment, some of the comments below hopefully will help illustrate things too. ++ ++logDir = gtp_logs ++logAllGTPCommunication = true ++logSearchInfo = true ++logSearchInfoForChosenMove = false ++logToStderr = false ++ ++# Use these rules by default, but a GUI or GTP controller might override this. ++rules = japanese ++ ++# When using -human-model, we only resign when far behind since a weaker player ++# might continue to fight much longer than a strong bot normally would. ++allowResignation = true ++resignThreshold = -0.99 ++resignConsecTurns = 20 ++resignMinScoreDifference = 40 ++resignMinMovesPerBoardArea = 0.4 ++ ++# Note: unless you change other parameters too, by default increasing visits won't do much. ++# If humanSLChosenMoveProp = 1.0 AND humanSLChosenMovePiklLambda is a large number, ++# then KataGo's normal search is ignored except for possibly choosing whether to pass/resign, ++# so more visits will have no effect on play. Still, having some visits is good for ++# ensuring good pass/resign judgment. ++maxVisits = 40 ++numSearchThreads = 1 ++lagBuffer = 1.0 ++ ++# Rough scale in seconds to randomly delay moving, so as not to respond instantly. ++# Some moves will delay longer, some moves will delay a little less. ++delayMoveScale = 2.0 ++delayMoveMax = 10.0 ++ ++# =========================================================================== ++# HUMAN SL PARAMETERS ++# =========================================================================== ++ ++# The most important parameter for human-like play configuration! ++# Choose the "profile" of players that the human SL model will imitate. ++# Available options are: ++# preaz_{RANK from 20k to 9d} - imitate player of given rank, before AlphaZero opening style became popular ++# rank_{RANK from 20k to 9d} - imitate player of given rank, after human openings changed due to AlphaZero. ++# preaz_{BRANK}_{WRANK} or rank_{BRANK}_{WRANK} - same, but imitate how black with the rank BR and white ++# with the rank WR would play against each other, knowing that the other player is stronger/weaker than them. ++# Warning: for rank differences > 9 ranks, or drastically mis-matched to the handicap used in the game, ++# this may be out of distribution due to lack of training data and the model might not behave well! Experiment with care. ++# proyear_{YEAR from 1800 to 2023} - imitate historical pros or insei from given year. ++humanSLProfile = preaz_5k ++ ++# The probability that we should play a HUMAN-like move, rather than playing KataGo's move. ++# Applies BEFORE temperature. ++humanSLChosenMoveProp = 1.0 ++ ++# If true, ignore the human SL model's choice of when to pass, and still use KataGo to determine that. ++# The human SL model, in theory, is not guaranteed to be reliable at when to pass for all profiles, ++# since e.g. some historical games omit passes. ++humanSLChosenMoveIgnorePass = true ++ ++# By default humanSLChosenMovePiklLambda is a large number which effectively disables it. ++# Setting it to a smaller number will "suppress" human-like moves that KataGo disapproves of. ++# In particular, if set to, for example, 0.4 when KataGo judges a human SL move to lose 0.4 utility, ++# it will substantially suppress the chance of playing that move (in particular, by a factor of exp(1)). ++# Less-bad moves will also be suppressed, but not by as much, e.g. a move losing 0.2 would get lowered ++# by a factor of exp(0.5). ++# As configured lower down, utilities by default range from -1.0 (loss) to +1.0 (win), plus up to +/- 0.3 for score. ++# WARNING: ONLY moves that KataGo actually searches will get suppressed! If a move is so bad that KataGo ++# rejects it without searching it, it will NOT get suppressed. ++# Therefore, to use humanSLChosenMovePiklLambda, it is STRONGLY recommended that you also use something ++# like humanSLRootExploreProbWeightless to ensure most human moves including bad moves get searched, ++# and ALSO use at least hundreds and ideally thousands of maxVisits, to ensure enough visits. ++humanSLChosenMovePiklLambda = 100000000 ++ ++# These parameters tell KataGo to use the human SL policy for exploration during search. ++# Each of these specifies the probability that KataGo will perform PUCT using the Human SL policy to ++# explore different moves, rather than using KataGo's normal policy, after a certain minimal number of visits. ++# "Root": applies only at the root of the search ++# "Pla": applies during non-root nodes of the search where it is katago's turn. ++# "Opp": applies during non-root nodes of the search where it is the opponent's turn. ++# "Weightless": search the move to evaluate it, but do NOT allow this visit to affect the parent's average utility. ++# "Weightful": search the move to evaluate it, and DO allow this visit to affect the parent's average utility. ++# For example, humanSLRootExploreProbWeightless = 0.5 would tell KataGo at the root of the search to spend ++# 50% of its visits to judge different possible human moves, but NOT to use those visits for determining the ++# value of the position (avoiding biasing the utility if some human SL moves are very bad). ++# If you don't understand these well, ask for help or look up some online explainers for MCTS (Monte-Carlo Tree Search). ++humanSLRootExploreProbWeightless = 0.0 ++humanSLRootExploreProbWeightful = 0.0 ++humanSLPlaExploreProbWeightless = 0.0 ++humanSLPlaExploreProbWeightful = 0.0 ++humanSLOppExploreProbWeightless = 0.0 ++humanSLOppExploreProbWeightful = 0.0 ++ ++# When using the human SL policy for exploration during search, use this cPUCT. ++# This only has an effect if at least one of humanSL{Root,Pla,Opp}ExploreProbWeight{less,ful} is nonzero. ++humanSLCpuctExploration = 0.50 ++ ++# Same as humanSLCpuctExploration, but NEVER diminshes its exploration no matter how many visits are used. ++# Normally, PUCT will sharpen with visits and spend a diminishing proportion of visits on moves with lower utility. ++# This is the coefficient for a term that does NOT diminish, i.e. if this is 0.2, then roughly moves within ++# 0.2 utility (about 10% winrate) of the best move will forever continue getting a decent fraction of visits, ++# smoothly falling off for greater utility differences. ++# Note that in combination with Weightful exploration above, if used for Opp exploration, this could be used ++# to model an opponent that will always have some chance to make small mistakes no matter how deep they search. ++# If further this was increased to a very large value, it would model an opponent that always played according ++# to the human SL raw policy. These might be interesting to experiment with for handicap play. ++humanSLCpuctPermanent = 0.2 ++ ++ ++# =========================================================================== ++# OTHER USEFUL PARAMETERS FOR HUMAN PLAY ADJUSTMENT ++# =========================================================================== ++ ++# Choosing temperature near 1, and restricting it to only affect moves already below 1% chance, ++# so that we sample close to the full range of human play. ++# You can also reduce the temperature to settings more like the plain gtp_example.cfg. ++# Then, rather than imitating a realistic human player, it will be more like imitating the ++# *majority vote* of players at that rank. For example it would avoid a lot of blunders ++# that players of that level would make, because even if players often blunder, the *majority vote* ++# of players would be much less likely to select any given blunder that an individual player would. ++chosenMoveTemperatureEarly = 0.85 ++chosenMoveTemperature = 0.70 ++chosenMoveTemperatureHalflife = 80 ++chosenMoveTemperatureOnlyBelowProb = 0.01 # temperature only starts to dampen moves below this ++chosenMoveSubtract = 0 ++chosenMovePrune = 0 ++ ++# Use a small NN cache to save memory since we're using very low visits anyways. You can increase ++# these back to more like the plain gtp_example.cfg if you are doing more extensive searches to ++# improve performance. ++nnCacheSizePowerOfTwo = 17 ++nnMutexPoolSizePowerOfTwo = 14 ++ ++# =========================================================================== ++# PARAMETERS CHANGED FROM DEFAULT TO MAKE SURE HUMAN SL USAGE WORKS WELL ++# =========================================================================== ++ ++# Make sure to take into account the recent moves in the game, don't ignore history. ++# This will produce the best imitation/prediction, since humans definitely do play differently based on where the ++# most recent moves in the game were, rather than coming fresh to the board position on every turn. ++ignorePreRootHistory = false ++analysisIgnorePreRootHistory = false ++ ++# Average 2 neural net samples at the root - ensures a bit smoother probabilities and results in ++# 8 * 7 / 2 = 28 possible policies instead of 8 possibilities. ++rootNumSymmetriesToSample = 2 ++# LCB improves strength for KataGo, but we disable it so it doesn't mess up move selection when blending human play. ++useLcbForSelection = false ++ ++# We disable dynamicScoreUtilityFactor - the human SL model can make score predictions that are a bit swingy, so ++# if we do want to do a search that blends human SL values in (TODO there isn't a way to do this anyways yet), using ++# static score utility might be a bit more stable. ++winLossUtilityFactor = 1.0 ++staticScoreUtilityFactor = 0.30 ++dynamicScoreUtilityFactor = 0.00 ++ ++# Uncertainty improves strength for KataGo normally, but messes with the weights of playouts in complicated ways, ++# so lets turn it off when doing human SL stuff. ++useUncertainty = false ++ ++# Subtree value bias improves strength for KataGo normally, but messes with the values of nodes in complicated ways, ++# so let's turn it off when doing human SL stuff. ++subtreeValueBiasFactor = 0.0 ++ ++# Noise pruning prunes out weight from moves that KataGo thinks are bad, but if we are doing human SL we might actively ++# want to be playing or exploring and weighting "bad" but human-like moves. So disable this. ++# Warning: when this is false, there is much less protection against the search severely misbehaving when you use too many threads. ++# Make sure not to set numSearchThreads to be too large - at a minimum, keep at least a 20x buffer between ++# the number of visits you use and the number of threads you use. ++# (as an aside, ideally, you want to have visits be a sufficient factor larger than threads EVEN when ++# useNoisePruning is true, this parameter just blunts the worst effects but doesn't entirely fix the badness). ++useNoisePruning = false +diff --git a/cpp/leela_zero_strong.cfg b/cpp/leela_zero_strong.cfg +new file mode 100644 +index 00000000..e1167b84 +--- /dev/null ++++ b/cpp/leela_zero_strong.cfg +@@ -0,0 +1,719 @@ ++# Configuration for KataGo C++ GTP engine ++ ++# Run the program using: `./katago.exe gtp` ++ ++# In this example config, when a parameter is given as a commented out value, ++# that value also is the default value, unless described otherwise. You can ++# uncomment it (remove the pound sign) and change it if you want. ++ ++# =========================================================================== ++# Running on an online server or in a real tournament or match ++# =========================================================================== ++# If you plan to run online or in a tournament, read through the "Rules" ++# section below for proper handling of komi, handicaps, end-of-game cleanup, ++# and other details. ++ ++# =========================================================================== ++# Notes about performance and memory usage ++# =========================================================================== ++# Important: For good performance, you will very likely want to tune the ++# "numSearchThreads" parameter in the Search limits section below! Run ++# "./katago benchmark" to test KataGo and to suggest a reasonable value ++# of this parameter. ++ ++# For multi-GPU systems, read "OpenCL GPU settings" or "CUDA GPU settings". ++# ++# When using OpenCL, verify that KataGo picks the correct device! Some systems ++# may have both an Intel CPU OpenCL and GPU OpenCL. If # KataGo picks the wrong ++# one, correct this by specifying "openclGpuToUse". ++# ++# Consider adjusting "maxVisits", "ponderingEnabled", "resignThreshold", and ++# other parameters depending on your intended usage. ++ ++# =========================================================================== ++# Command-line usage ++# =========================================================================== ++# All of the below values may be set or overridden via command-line arguments: ++# ++# -override-config KEY=VALUE,KEY=VALUE,... ++ ++# =========================================================================== ++# Logs and files ++# =========================================================================== ++# This section defines where and what logging information is produced. ++ ++# Each run of KataGo will log to a separate file in this dir. ++# This is the default. ++logDir = gtp_logs ++# Uncomment and specify this instead of logDir to write separate dated subdirs ++# logDirDated = gtp_logs ++# Uncomment and specify this instead of logDir to log to only a single file ++# logFile = gtp.log ++ ++# Logging options ++logAllGTPCommunication = true ++logSearchInfo = true ++logSearchInfoForChosenMove = false ++logToStderr = true ++ ++# KataGo will display some info to stderr on GTP startup ++# Uncomment the next line and set it to false to suppress that and remain silent ++# startupPrintMessageToStderr = true ++ ++# Write information to stderr, for use in things like malkovich chat to OGS. ++ ogsChatToStderr = true ++ ++# Uncomment and set this to a directory to override where openCLTuner files ++# and other cached data is written. By default it saves into a subdir of the ++# current directory on windows, and a subdir of ~/.katago on Linux. ++# homeDataDir = PATH_TO_DIRECTORY ++ ++# =========================================================================== ++# Analysis ++# =========================================================================== ++# This section configures analysis settings. ++# ++# The maximum number of moves after the first move displayed in variations ++# from analysis commands like kata-analyze or lz-analyze. ++# analysisPVLen = 15 ++ ++# Report winrates for chat and analysis as (BLACK|WHITE|SIDETOMOVE). ++# Most GUIs and analysis tools will expect SIDETOMOVE. ++# reportAnalysisWinratesAs = SIDETOMOVE ++ ++# Extra noise for wider exploration. Large values will force KataGo to ++# analyze a greater variety of moves than it normally would. ++# An extreme value like 1 distributes playouts across every move on the board, ++# even very bad moves. ++# Affects analysis only, does not affect play. ++# analysisWideRootNoise = 0.04 ++ ++# Try to limit the effect of possible bad or bogus move sequences in the ++# history leading to this position from affecting KataGo's move predictions. ++# analysisIgnorePreRootHistory = true ++ ++# =========================================================================== ++# Rules ++# =========================================================================== ++# This section configures the scoring and playing rules. Rules can also be ++# changed mid-run by issuing custom GTP commands. ++# ++# See https://lightvector.github.io/KataGo/rules.html for rules details. ++# ++# See https://github.com/lightvector/KataGo/blob/master/docs/GTP_Extensions.md ++# for GTP commands. ++ ++# Specify the rules as a string. ++# Some legal values include: ++# chinese, japanese, korean, aga, chinese-ogs, new-zealand, stone-scoring, ++# ancient-territory, bga, aga-button ++# ++# For some human rulesets that require complex adjudication in tricky cases ++# (e.g. japanese, korean) KataGo may not precisely match the ruleset in such ++# cases but will do its best. ++# rules = tromp-taylor ++ ++# By default, the "rules" parameter is used, but if you comment it out and ++# uncomment one option in each of the sections below, you can specify an ++# arbitrary combination of individual rules. ++ ++koRule = SIMPLE # Simple ko rules (triple ko = no result) ++# koRule = POSITIONAL # Positional superko ++# koRule = SITUATIONAL # Situational superko ++ ++scoringRule = AREA # Area scoring ++# scoringRule = TERRITORY # Territory scoring (special computer-friendly territory rules) ++ ++taxRule = NONE # All surrounded empty points are scored ++# taxRule = SEKI # Eyes in seki do NOT count as points ++# taxRule = ALL # All groups are taxed up to 2 points for the two eyes needed to live ++ ++# Is multiple-stone suicide legal? (Single-stone suicide is always illegal). ++multiStoneSuicideLegal = true ++# multiStoneSuicideLegal = false ++ ++# "Button go" - the first pass when area scoring awards 0.5 points and does ++# not count for ending the game. ++hasButton = false ++# hasButton = true ++ ++# Is this a human ruleset where it's okay to pass before having physically ++# captured and removed all dead stones? ++friendlyPassOk = true ++# friendlyPassOk = false ++ ++# How handicap stones in handicap games are compensated ++whiteHandicapBonus = 0 # White gets no compensation for black's handicap stones (Tromp-taylor, NZ, JP) ++# whiteHandicapBonus = N-1 # White gets N-1 points for black's N handicap stones (AGA) ++# whiteHandicapBonus = N # White gets N points for black's N handicap stones (Chinese) ++ ++# ------------------------------ ++# Other rules hacks ++# ------------------------------ ++# Uncomment and change to adjust what board size KataGo uses upon startup ++# by default when GTP doesn't specify. ++# defaultBoardSize = 19 ++ ++# By default, Katago will use the komi that the GUI or GTP controller tries to set. ++# Uncomment and set this to have KataGo ignore the controller and always use this komi. ++# ignoreGTPAndForceKomi = 7 ++ ++# =========================================================================== ++# Bot behavior ++# =========================================================================== ++ ++# ------------------------------ ++# Resignation ++# ------------------------------ ++ ++# Resignation occurs if for at least resignConsecTurns in a row, the ++# winLossUtility (on a [-1,1] scale) is below resignThreshold. ++allowResignation = true ++resignThreshold = -0.90 ++resignConsecTurns = 3 ++ ++# By default, KataGo may resign games that it is confidently losing even if they ++# are very close in score. Uncomment and set this to avoid resigning games ++# if the estimated difference is points is less than or equal to this. ++# resignMinScoreDifference = 10 ++ ++# ------------------------------ ++# Handicap ++# ------------------------------ ++# Assume that if black makes many moves in a row right at the start of the ++# game, then the game is a handicap game. This is necessary on some servers ++# and for some GUIs and also when initializing from many SGF files, which may ++# set up a handicap game using repeated GTP "play" commands for black rather ++# than GTP "place_free_handicap" commands; however, it may also lead to ++# incorrect understanding of komi if whiteHandicapBonus is used and a server ++# does not have such a practice. Uncomment and set to false to disable. ++# assumeMultipleStartingBlackMovesAreHandicap = true ++ ++# Makes katago dynamically adjust in handicap or altered-komi games to assume ++# based on those game settings that it must be stronger or weaker than the ++# opponent and to play accordingly. Greatly improves handicap strength by ++# biasing winrates and scores to favor appropriate safe/aggressive play. ++# Does NOT affect analysis (lz-analyze, kata-analyze, used by programs like ++# Lizzie) so analysis remains unbiased. Uncomment and set this to 0 to disable ++# this and make KataGo play the same always. ++# dynamicPlayoutDoublingAdvantageCapPerOppLead = 0.045 ++ ++# Instead of "dynamicPlayoutDoublingAdvantageCapPerOppLead", you can comment ++# that out and uncomment and set "playoutDoublingAdvantage" to a fixed value ++# from -3.0 to 3.0 that will not change dynamically. ++# ALSO affects analysis tools (lz-analyze, kata-analyze, used by e.g. Lizzie). ++# Negative makes KataGo behave as if it is much weaker than the opponent. ++# Positive makes KataGo behave as if it is much stronger than the opponent. ++# KataGo will adjust to favor safe/aggressive play as appropriate based on ++# the combination of who is ahead and how much stronger/weaker it thinks it is, ++# and report winrates and scores taking the strength difference into account. ++# ++# If this and "dynamicPlayoutDoublingAdvantageCapPerOppLead" are both set ++# then dynamic will be used for all games and this fixed value will be used ++# for analysis tools. ++# playoutDoublingAdvantage = 0.0 ++ ++# Uncomment one of these when using "playoutDoublingAdvantage" to enforce ++# that it will only apply when KataGo plays as the specified color and will be ++# negated when playing as the opposite color. ++# playoutDoublingAdvantagePla = BLACK ++# playoutDoublingAdvantagePla = WHITE ++ ++# ------------------------------ ++# Passing and cleanup ++# ------------------------------ ++# Make the bot never assume that its pass will end the game, even if passing ++# would end and "win" under Tromp-Taylor rules. Usually this is a good idea ++# when using it for analysis or playing on servers where scoring may be ++# implemented non-tromp-taylorly. Uncomment and set to false to disable. ++# conservativePass = true ++ ++# When using territory scoring, self-play games continue beyond two passes ++# with special cleanup rules that may be confusing for human players. This ++# option prevents the special cleanup phases from being reachable when using ++# the bot for GTP play. Uncomment and set to false to enable entering special ++# cleanup. For example, if you are testing it against itself, or against ++# another bot that has precisely implemented the rules documented at ++# https://lightvector.github.io/KataGo/rules.html ++# preventCleanupPhase = true ++ ++# ------------------------------ ++# Miscellaneous behavior ++# ------------------------------ ++# If the board is symmetric, search only one copy of each equivalent move. ++# Attempts to also account for ko/superko, will not theoretically perfect for ++# superko. Uncomment and set to false to disable. ++# rootSymmetryPruning = true ++ ++# Uncomment and set to true to avoid a particular joseki that some networks ++# misevaluate, and also to improve opening diversity versus some particular ++# other bots that like to play it all the time. ++# avoidMYTDaggerHack = false ++ ++# Prefer to avoid playing the same joseki in every corner of the board. ++# Uncomment to set to a specific value. See "Avoid SGF patterns" section. ++# By default: 0 (even games), 0.005 (handicap games) ++# avoidRepeatedPatternUtility = 0.0 ++ ++# Experimental logic to fight against mirror Go even with unfavorable komi. ++# Uncomment to set to a specific value to use for both playing and analysis. ++# By default: true when playing via GTP, but false when analyzing. ++# antiMirror = true ++ ++# Enable some hacks that mitigate rare instances when passing messes up deeper searches. ++# enablePassingHacks = true ++ ++# Uncomment and set this to true to prevent bad or bogus move sequences ++# in the history leading to this position from affecting KataGo's move choices. ++# Same as analysisIgnorePreRootHistory (see above) but applies to actual play. ++# You can enable this if KataGo is being asked to play from positions that it did not ++# choose the moves to reach. ++# ignorePreRootHistory = false ++ ++# =========================================================================== ++# Search limits ++# =========================================================================== ++ ++# Terminology: ++# "Playouts" is the number of new playouts of search performed each turn. ++# "Visits" is the same as "Playouts" but also counts search performed on ++# previous turns that is still applicable to this turn. ++# "Time" is the time in seconds. ++ ++# For example, if KataGo searched 200 nodes on the previous turn, and then ++# after the opponent's reply, 50 nodes of its search tree was still valid, ++# then a visit limit of 200 would allow KataGo to search 150 new nodes ++# (for a final tree size of 200 nodes), whereas a playout limit of of 200 ++# would allow KataGo to search 200 nodes (for a final tree size of 250 nodes). ++ ++# Additionally, KataGo may also move before than the limit in order to ++# obey time controls (e.g. byo-yomi, etc) if the GTP controller has ++# told KataGo that the game has is being played with a given time control. ++ ++# Limits for search on the current turn. ++# If commented out or unspecified, the default is to have no limit. ++# maxVisits = 30000 ++# maxPlayouts = 300 ++maxTime = 1.0 ++ ++# Ponder on the opponent's turn? ++ponderingEnabled = true ++ ++# Limits for search when pondering on the opponent's turn. ++# If commented out or unspecified, the default is to have no limit. ++# Limiting the maximum time is recommended so that KataGo won't burn CPU/GPU ++# forever and/or run out of RAM if left unattended while pondering is enabled. ++# maxVisitsPondering = 5000 ++# maxPlayoutsPondering = 3000 ++maxTimePondering = 120.0 ++ ++ ++# ------------------------------ ++# Other search limits and behavior ++# ------------------------------ ++ ++# Approx number of seconds to buffer for lag for GTP time controls - will ++# move a bit faster assuming there is this much lag per move. ++lagBuffer = 1.0 ++ ++# YOU PROBABLY WANT TO TUNE THIS PARAMETER! ++# The number of threads to use when searching. On powerful GPUs the optimal ++# threads may be much higher than the number of CPU cores you have because ++# many threads are needed to feed efficient large batches to the GPU. ++# ++# Run "./katago benchmark" to tune this parameter and test the effect ++# of changes to any of other parameters. ++numSearchThreads = 40 ++ ++# Play a little faster if the opponent is passing, for human-friendliness. ++# Comment these out to disable them, such as if running a controlled match ++# where you are testing KataGo with fixed compute per move vs other bots. ++searchFactorAfterOnePass = 0.50 ++searchFactorAfterTwoPass = 0.25 ++ ++# Play a little faster if super-winning, for human-friendliness. ++# Comment these out to disable them, such as if running a controlled match ++# where you are testing KataGo with fixed compute per move vs other bots. ++searchFactorWhenWinning = 0.40 ++searchFactorWhenWinningThreshold = 0.95 ++ ++# =========================================================================== ++# GPU settings ++# =========================================================================== ++# This section configures GPU settings. ++# ++# Maximum number of positions to send to a single GPU at once. The default ++# value is roughly equal to numSearchThreads, but can be specified manually ++# if running out of memory, or using multiple GPUs that expect to share work. ++# nnMaxBatchSize = ++ ++# Controls the neural network cache size, which is the primary RAM/memory use. ++# KataGo will cache up to (2 ** nnCacheSizePowerOfTwo) many neural net ++# evaluations in case of transpositions in the tree. ++# Increase this to improve performance for searches with tens of thousands ++# of visits or more. Decrease this to limit memory usage. ++# If you're happy to do some math - each neural net entry takes roughly ++# 1.5KB, except when using whole-board ownership/territory ++# visualizations, where each entry will take roughly 3KB. The number of ++# entries is (2 ** nnCacheSizePowerOfTwo). (E.g. 2 ** 18 = 262144.) ++# You can compute roughly how much memory the cache will use based on this. ++# nnCacheSizePowerOfTwo = 20 ++ ++# Size of mutex pool for nnCache is (2 ** this). ++# nnMutexPoolSizePowerOfTwo = 16 ++ ++# Randomize board orientation when running neural net evals? Uncomment and ++# set to false to disable. ++# nnRandomize = true ++ ++# If provided, force usage of a specific seed for nnRandomize. ++# The default is to use a randomly generated seed. ++# nnRandSeed = abcdefg ++ ++# Uncomment and set to true to force GTP to use the maximum board size for ++# internal buffers for the neural net. This will make KataGo slower when ++# evaluating small boards, but will avoid a lengthy initialization time on every ++# change of board size due to having to re-size the neural net buffers on the GPU. ++# This can be useful for example, for OGS's persistent bot mode that uses a single ++# bot instance to handle multiple games and may thrash between different board sizes ++# if there are concurrent games of multiple sizes. ++# gtpForceMaxNNSize = false ++ ++# ------------------------------ ++# Multiple GPUs ++# ------------------------------ ++# Set this to the number of GPUs to use or that are available. ++# IMPORTANT: If more than 1, also uncomment the appropriate TensorRT ++# or CUDA or OpenCL section. ++# numNNServerThreadsPerModel = 1 ++ ++# ------------------------------ ++# TENSORRT GPU settings ++# ------------------------------ ++# These only apply when using the TENSORRT version of KataGo. ++ ++# For one GPU: optionally uncomment this option and change if the GPU to ++# use is not device 0. ++# trtDeviceToUse = 0 ++ ++# For two GPUs: Uncomment these options, AND set numNNServerThreadsPerModel above. ++# Also, change their values if the devices you want to use are not 0 and 1. ++# trtDeviceToUseThread0 = 0 ++# trtDeviceToUseThread1 = 1 ++ ++# For three GPUs: Uncomment these options, AND set numNNServerThreadsPerModel above. ++# Also, change their values if the devices you want to use are not 0 and 1 and 2. ++# trtDeviceToUseThread0 = 0 ++# trtDeviceToUseThread1 = 1 ++# trtDeviceToUseThread2 = 2 ++ ++# The pattern continues for additional GPUs. ++ ++# ------------------------------ ++# CUDA GPU settings ++# ------------------------------ ++# These only apply when using the CUDA version of KataGo. ++ ++# For one GPU: optionally uncomment and change this if the GPU you want to ++# use is not device 0 ++# cudaDeviceToUse = 0 ++ ++# For two GPUs: Uncomment these options, AND set numNNServerThreadsPerModel above. ++# Also, change their values if the devices you want to use are not 0 and 1. ++# cudaDeviceToUseThread0 = 0 ++# cudaDeviceToUseThread1 = 1 ++ ++# For three GPUs: Uncomment these options, AND set numNNServerThreadsPerModel above. ++# Also, change their values if the devices you want to use are not 0 and 1 and 2. ++# cudaDeviceToUseThread0 = 0 ++# cudaDeviceToUseThread1 = 1 ++# cudaDeviceToUseThread2 = 2 ++ ++# The pattern continues for additional GPUs. ++ ++# KataGo will automatically use FP16 or not based on the compute capability ++# of your NVIDIA GPU. If you want to try to force a particular behavior ++# you can uncomment these lines and change them to "true" or "false". ++# cudaUseFP16 = auto ++# cudaUseNHWC = auto ++ ++# ------------------------------ ++# OpenCL GPU settings ++# ------------------------------ ++# These only apply when using the OpenCL version of KataGo. ++ ++# Uncomment and set to true to tune OpenCL for every board size separately, ++# rather than only the largest possible size. ++# openclReTunePerBoardSize = false ++ ++# For one GPU: optionally uncomment and change this if the best device to use is guessed incorrectly. ++# The default behavior tries to guess the 'best' GPU or device on your system to use, usually it will be a good guess. ++# openclDeviceToUse = 0 ++ ++# For two GPUs: Uncomment these two lines and replace X and Y with the device ids of the devices you want to use. ++# It might NOT be 0 and 1, some computers will have many OpenCL devices. You can see what the devices are when ++# KataGo starts up - it should print or log all the devices it finds. ++# (AND also set numNNServerThreadsPerModel above) ++# openclDeviceToUseThread0 = X ++# openclDeviceToUseThread1 = Y ++ ++# For three GPUs: Uncomment these three lines and replace X and Y and Z with the device ids of the devices you want to use. ++# It might NOT be 0 and 1 and 2, some computers will have many OpenCL devices. You can see what the devices are when ++# KataGo starts up - it should print or log all the devices it finds. ++# (AND also set numNNServerThreadsPerModel above) ++# openclDeviceToUseThread0 = X ++# openclDeviceToUseThread1 = Y ++# openclDeviceToUseThread2 = Z ++ ++# The pattern continues for additional GPUs. ++ ++# KataGo will automatically use FP16 or not based on testing your GPU during ++# tuning. If you want to try to force a particular behavior though you can ++# uncomment this option and change it to "true" or "false". This is a fairly ++# blunt setting - more detailed settings are testable by rerunning the tuner ++# with various arguments (./katago tuner). ++# openclUseFP16 = auto ++ ++# ------------------------------ ++# Eigen-specific settings ++# ------------------------------ ++# These only apply when using the Eigen (pure CPU) version of KataGo. ++ ++# Number of CPU threads for evaluating the neural net on the Eigen backend. ++# ++# Default: numSearchThreads ++# numEigenThreadsPerModel = X ++ ++# =========================================================================== ++# Root move selection and biases ++# =========================================================================== ++# Uncomment and edit any of the below values to change them from their default. ++ ++# If provided, force usage of a specific seed for various random things in ++# the search. The default is to use a random seed. ++# searchRandSeed = hijklmn ++ ++# Temperature for the early game, randomize between chosen moves with ++# this temperature ++# chosenMoveTemperatureEarly = 0.5 ++ ++# Decay temperature for the early game by 0.5 every this many moves, ++# scaled with board size. ++# chosenMoveTemperatureHalflife = 19 ++ ++# At the end of search after the early game, randomize between chosen ++# moves with this temperature ++# chosenMoveTemperature = 0.10 ++ ++# Subtract this many visits from each move prior to applying ++# chosenMoveTemperature (unless all moves have too few visits) to downweight ++# unlikely moves ++# chosenMoveSubtract = 0 ++ ++# The same as chosenMoveSubtract but only prunes moves that fall below ++# the threshold. This setting does not affect chosenMoveSubtract. ++# chosenMovePrune = 1 ++ ++# Number of symmetries to sample (without replacement) and average at the root ++# rootNumSymmetriesToSample = 1 ++ ++# Using LCB for move selection? ++# useLcbForSelection = true ++ ++# How many stdevs a move needs to be better than another for LCB selection ++# lcbStdevs = 5.0 ++ ++# Only use LCB override when a move has this proportion of visits as the ++# top move. ++# minVisitPropForLCB = 0.15 ++ ++# =========================================================================== ++# Internal params ++# =========================================================================== ++# Uncomment and edit any of the below values to change them from their default. ++ ++# Scales the utility of winning/losing ++# winLossUtilityFactor = 1.0 ++ ++# Scales the utility for trying to maximize score ++# staticScoreUtilityFactor = 0.10 ++# dynamicScoreUtilityFactor = 0.30 ++ ++# Adjust dynamic score center this proportion of the way towards zero, ++# capped at a reasonable amount. ++# dynamicScoreCenterZeroWeight = 0.20 ++# dynamicScoreCenterScale = 0.75 ++ ++# The utility of getting a "no result" due to triple ko or other long cycle ++# in non-superko rulesets (-1 to 1) ++# noResultUtilityForWhite = 0.0 ++ ++# The number of wins that a draw counts as, for white. (0 to 1) ++# drawEquivalentWinsForWhite = 0.5 ++ ++# Exploration constant for mcts ++# cpuctExploration = 1.0 ++# cpuctExplorationLog = 0.45 ++ ++# Parameters that control exploring more in volatile positions, exploring ++# less in stable positions. ++# cpuctUtilityStdevPrior = 0.40 ++# cpuctUtilityStdevPriorWeight = 2.0 ++# cpuctUtilityStdevScale = 0.85 ++ ++# FPU reduction constant for mcts ++# fpuReductionMax = 0.2 ++# rootFpuReductionMax = 0.1 ++# fpuParentWeightByVisitedPolicy = true ++ ++# Parameters that control weighting of evals based on the net's own ++# self-reported uncertainty. ++# useUncertainty = true ++# uncertaintyExponent = 1.0 ++# uncertaintyCoeff = 0.25 ++ ++# Explore using optimistic policy ++# rootPolicyOptimism = 0.2 ++# policyOptimism = 1.0 ++ ++# Amount to apply a downweighting of children with very bad values relative ++# to good ones. ++# valueWeightExponent = 0.25 ++ ++# Slight incentive for the bot to behave human-like with regard to passing at ++# the end, filling the dame, not wasting time playing in its own territory, ++# etc., and not play moves that are equivalent in terms of points but a bit ++# more unfriendly to humans. ++# rootEndingBonusPoints = 0.5 ++ ++# Make the bot prune useless moves that are just prolonging the game to ++# avoid losing yet. ++# rootPruneUselessMoves = true ++ ++# Apply bias correction based on local pattern keys ++# subtreeValueBiasFactor = 0.45 ++# subtreeValueBiasWeightExponent = 0.85 ++ ++# Use graph search rather than tree search - identify and share search for ++# transpositions. ++# useGraphSearch = true ++ ++# How much to shard the node table for search synchronization ++# nodeTableShardsPowerOfTwo = 16 ++ ++# How many virtual losses to add when a thread descends through a node ++# numVirtualLossesPerThread = 1 ++ ++# Improve the quality of evals under heavy multithreading ++# useNoisePruning = true ++ ++# =========================================================================== ++# Automatic avoid patterns ++# =========================================================================== ++# The parameters in this section provide a way to bias away from moves that match ++# patterns that this instance of KataGo has played in previous games, by auto-saving ++# moves to a directory and then auto-loading and biasing against them each new game. ++# Uncomment them to use them. When using this feature, all parameters must be specified. ++ ++# Different board sizes are tracked separately, but all board sizes share the same ++# parameters by default. Every parameter *except* for autoAvoidRepeatDir and ++# autoAvoidRepeatSaveChunkSize can be overridden per board size, ++# e.g. "autoAvoidRepeatMinTurnNumber13x13". You must ALSO specify the ++# defaults even if you have specified board-size-specific values. ++ ++# Directory to auto-save moves KataGo plays, to avoid them in future games. ++# You can create a new empty directory and put its path here. ++# If you run parallel instances of KataGo, use different directories if you ++# want them not to share their biases, use the same directory if you want ++# all of them to bias away from past moves that any of them have played. ++# KataGo will also automatically DELETE old data in this directory, so it is ++# recommended that if you do share the same directory between parallel instances, ++# that they all use the same settings that affect data saving/deletion. ++# autoAvoidRepeatDir = PATH_TO_NEW_DIRECTORY ++ ++# Penalize this much utility per matching move. ++# Values that are too large may lead to bad play. The value of 0.004 is fairly large ++# and might be large enough to result in some early weird/bad moves when trying ++# to avoid past games' moves if enough games begin the same way. You can experiment with it. ++# autoAvoidRepeatUtility = 0.004 ++ ++# Per each new move saved, exponentially decay prior saved moves by this factor. ++# This way, the bias against moves from many games ago is gradually phased out. ++# For example, 0.9995 = 1 - 1/2000, so would be roughly 2000 prior moves worth of ++# penalty weight remembered, in steady state. Depending on what turn number range ++# you are saving, this might equate to a different number of games For example ++# saving the first 50 moves per game would make this roughly 2000 / 50 = 40 games ++# worth of memory. ++# autoAvoidRepeatLambda = 0.9995 ++ ++# Affects data saving/deletion. ++# When the number of saved moves exceeds this, outright delete them to avoid too many ++# files and disk space building up. Also may affect the speed of saving/loading on start ++# of each game if this is set large and a lot of data builds up. ++# autoAvoidRepeatMaxPoses = 10000 ++ ++# Affects data saving/deletion. ++# Only save data for moves within this turn number range of those games. ++# E.g. setting autoAvoidRepeatMinTurnNumber to a number like 4 or 5 would tend to make ++# KataGo not develop a bias against the initial 3-4 and 4-4 corner moves in almost every game. ++# autoAvoidRepeatMinTurnNumber = 0 ++# autoAvoidRepeatMaxTurnNumber = 50 ++ ++# Affects data saving/deletion. ++# Within a single run of a program, wait to accumulate this many samples ++# (possibly across multiple clear_boards/games) before saving the data. ++# Can help to avoid writing too many small files to disk, especially when GTP is used ++# in a way that clears the board very frequently (e.g. gtp2ogs pooled manager). ++# autoAvoidRepeatSaveChunkSize = 200 ++ ++# =========================================================================== ++# Avoid SGF patterns ++# =========================================================================== ++# The parameters in this section provide a way to avoid moves that follow ++# specific patterns based on a set of SGF files loaded upon startup. ++# This is basically the same as the above "Automatic avoid patterns" section ++# above except you supply your own SGF files to avoid moves from. ++# Uncomment them to use this feature. Additionally, if the SGF file ++# contains the string %SKIP% in a comment on a move, that move will be ++# ignored for this purpose. ++ ++# Load SGF files from this directory when the engine is started ++# (only on startup, will not reload unless engine is restarted) ++# avoidSgfPatternDirs = path/to/directory/with/sgfs/ ++# You can also surround the file path in double quotes if the file path contains trailing spaces or hash signs. ++# Within double quotes, backslashes are escape characters. ++# avoidSgfPatternDirs = "path/to/directory/with/sgfs/" ++ ++# Penalize this much utility per matching move. ++# Set this negative if you instead want to favor SGF patterns instead of ++# penalizing them. This number does not need to be large, even 0.001 will ++# make a difference. Values that are too large may lead to bad play. ++# avoidSgfPatternUtility = 0.001 ++ ++# Optional - load only the newest this many files ++# avoidSgfPatternMaxFiles = 20 ++ ++# Optional - Penalty is multiplied by this per each older SGF file, so that ++# old SGF files matter less than newer ones. ++# avoidSgfPatternLambda = 0.90 ++ ++# Optional - pay attention only to moves made by players with this name. ++# For example, set it to the name that your bot's past games will show up ++# as in the SGF, so that the bot will only avoid repeating moves that itself ++# made in past games, not the moves that its opponents made. ++# avoidSgfPatternAllowedNames = my-ogs-bot-name1,my-ogs-bot-name2 ++ ++# Optional - Ignore moves in SGF files that occurred before this turn number. ++# avoidSgfPatternMinTurnNumber = 0 ++ ++# For more avoid patterns: ++# You can also specify a second set of parameters, and a third, fourth, ++# etc. by numbering 2,3,4,... ++# ++# avoidSgf2PatternDirs = ... ++# avoidSgf2PatternUtility = ... ++# avoidSgf2PatternMaxFiles = ... ++# avoidSgf2PatternLambda = ... ++# avoidSgf2PatternAllowedNames = ... ++# avoidSgf2PatternMinTurnNumber = ... ++ +diff --git a/cpp/libcrypto-3-x64.dll b/cpp/libcrypto-3-x64.dll +new file mode 100644 +index 00000000..cac7f4ec +Binary files /dev/null and b/cpp/libcrypto-3-x64.dll differ +diff --git a/cpp/libssl-3-x64.dll b/cpp/libssl-3-x64.dll +new file mode 100644 +index 00000000..8ad0908e +Binary files /dev/null and b/cpp/libssl-3-x64.dll differ +diff --git a/cpp/libz.dll b/cpp/libz.dll +new file mode 100644 +index 00000000..930a0421 +Binary files /dev/null and b/cpp/libz.dll differ +diff --git a/cpp/libzip.dll b/cpp/libzip.dll +new file mode 100644 +index 00000000..167e69f7 +Binary files /dev/null and b/cpp/libzip.dll differ +diff --git a/cpp/lizgoban.cfg b/cpp/lizgoban.cfg +new file mode 100644 +index 00000000..53f4832b +--- /dev/null ++++ b/cpp/lizgoban.cfg +@@ -0,0 +1,721 @@ ++# Configuration for KataGo C++ GTP engine ++ ++# Run the program using: `./katago.exe gtp` ++ ++# In this example config, when a parameter is given as a commented out value, ++# that value also is the default value, unless described otherwise. You can ++# uncomment it (remove the pound sign) and change it if you want. ++ ++# =========================================================================== ++# Running on an online server or in a real tournament or match ++# =========================================================================== ++# If you plan to run online or in a tournament, read through the "Rules" ++# section below for proper handling of komi, handicaps, end-of-game cleanup, ++# and other details. ++ ++# =========================================================================== ++# Notes about performance and memory usage ++# =========================================================================== ++# Important: For good performance, you will very likely want to tune the ++# "numSearchThreads" parameter in the Search limits section below! Run ++# "./katago benchmark" to test KataGo and to suggest a reasonable value ++# of this parameter. ++ ++# For multi-GPU systems, read "OpenCL GPU settings" or "CUDA GPU settings". ++# ++# When using OpenCL, verify that KataGo picks the correct device! Some systems ++# may have both an Intel CPU OpenCL and GPU OpenCL. If # KataGo picks the wrong ++# one, correct this by specifying "openclGpuToUse". ++# ++# Consider adjusting "maxVisits", "ponderingEnabled", "resignThreshold", and ++# other parameters depending on your intended usage. ++ ++# =========================================================================== ++# Command-line usage ++# =========================================================================== ++# All of the below values may be set or overridden via command-line arguments: ++# ++# -override-config KEY=VALUE,KEY=VALUE,... ++ ++# =========================================================================== ++# Logs and files ++# =========================================================================== ++# This section defines where and what logging information is produced. ++ ++# Each run of KataGo will log to a separate file in this dir. ++# This is the default. ++logDir = gtp_logs ++# Uncomment and specify this instead of logDir to write separate dated subdirs ++# logDirDated = gtp_logs ++# Uncomment and specify this instead of logDir to log to only a single file ++# logFile = gtp.log ++ ++# Logging options ++logAllGTPCommunication = true ++logSearchInfo = true ++logSearchInfoForChosenMove = false ++logToStderr = true ++ ++# KataGo will display some info to stderr on GTP startup ++# Uncomment the next line and set it to false to suppress that and remain silent ++# startupPrintMessageToStderr = true ++ ++# Write information to stderr, for use in things like malkovich chat to OGS. ++ ogsChatToStderr = true ++ ++# Uncomment and set this to a directory to override where openCLTuner files ++# and other cached data is written. By default it saves into a subdir of the ++# current directory on windows, and a subdir of ~/.katago on Linux. ++# homeDataDir = PATH_TO_DIRECTORY ++ ++# =========================================================================== ++# Analysis ++# =========================================================================== ++# This section configures analysis settings. ++# ++# The maximum number of moves after the first move displayed in variations ++# from analysis commands like kata-analyze or lz-analyze. ++# analysisPVLen = 15 ++ ++# Report winrates for chat and analysis as (BLACK|WHITE|SIDETOMOVE). ++# Most GUIs and analysis tools will expect SIDETOMOVE. ++# reportAnalysisWinratesAs = SIDETOMOVE ++ ++# Extra noise for wider exploration. Large values will force KataGo to ++# analyze a greater variety of moves than it normally would. ++# An extreme value like 1 distributes playouts across every move on the board, ++# even very bad moves. ++# Affects analysis only, does not affect play. ++# analysisWideRootNoise = 0.04 ++ ++# Try to limit the effect of possible bad or bogus move sequences in the ++# history leading to this position from affecting KataGo's move predictions. ++# analysisIgnorePreRootHistory = true ++ ++# =========================================================================== ++# Rules ++# =========================================================================== ++# This section configures the scoring and playing rules. Rules can also be ++# changed mid-run by issuing custom GTP commands. ++# ++# See https://lightvector.github.io/KataGo/rules.html for rules details. ++# ++# See https://github.com/lightvector/KataGo/blob/master/docs/GTP_Extensions.md ++# for GTP commands. ++ ++# Specify the rules as a string. ++# Some legal values include: ++# chinese, japanese, korean, aga, chinese-ogs, new-zealand, stone-scoring, ++# ancient-territory, bga, aga-button ++# ++# For some human rulesets that require complex adjudication in tricky cases ++# (e.g. japanese, korean) KataGo may not precisely match the ruleset in such ++# cases but will do its best. ++rules = tromp-taylor ++ ++# By default, the "rules" parameter is used, but if you comment it out and ++# uncomment one option in each of the sections below, you can specify an ++# arbitrary combination of individual rules. ++ ++# koRule = SIMPLE # Simple ko rules (triple ko = no result) ++# koRule = POSITIONAL # Positional superko ++# koRule = SITUATIONAL # Situational superko ++ ++# scoringRule = AREA # Area scoring ++# scoringRule = TERRITORY # Territory scoring (special computer-friendly territory rules) ++ ++# taxRule = NONE # All surrounded empty points are scored ++# taxRule = SEKI # Eyes in seki do NOT count as points ++# taxRule = ALL # All groups are taxed up to 2 points for the two eyes needed to live ++ ++# Is multiple-stone suicide legal? (Single-stone suicide is always illegal). ++# multiStoneSuicideLegal = false ++# multiStoneSuicideLegal = true ++ ++# "Button go" - the first pass when area scoring awards 0.5 points and does ++# not count for ending the game. ++# Allows area scoring rulesets that have far simpler rules to achieve the same ++# final scoring precision and reward for precise play as territory scoring. ++# hasButton = false ++# hasButton = true ++ ++# Is this a human ruleset where it's okay to pass before having physically ++# captured and removed all dead stones? ++# friendlyPassOk = false ++# friendlyPassOk = true ++ ++# How handicap stones in handicap games are compensated ++# whiteHandicapBonus = 0 # White gets no compensation for black's handicap stones (Tromp-taylor, NZ, JP) ++# whiteHandicapBonus = N-1 # White gets N-1 points for black's N handicap stones (AGA) ++# whiteHandicapBonus = N # White gets N points for black's N handicap stones (Chinese) ++ ++# ------------------------------ ++# Other rules hacks ++# ------------------------------ ++# Uncomment and change to adjust what board size KataGo uses upon startup ++# by default when GTP doesn't specify. ++# defaultBoardSize = 19 ++ ++# By default, Katago will use the komi that the GUI or GTP controller tries to set. ++# Uncomment and set this to have KataGo ignore the controller and always use this komi. ++# ignoreGTPAndForceKomi = 7 ++ ++# =========================================================================== ++# Bot behavior ++# =========================================================================== ++ ++# ------------------------------ ++# Resignation ++# ------------------------------ ++ ++# Resignation occurs if for at least resignConsecTurns in a row, the ++# winLossUtility (on a [-1,1] scale) is below resignThreshold. ++allowResignation = true ++resignThreshold = -0.90 ++resignConsecTurns = 3 ++ ++# By default, KataGo may resign games that it is confidently losing even if they ++# are very close in score. Uncomment and set this to avoid resigning games ++# if the estimated difference is points is less than or equal to this. ++# resignMinScoreDifference = 10 ++ ++# ------------------------------ ++# Handicap ++# ------------------------------ ++# Assume that if black makes many moves in a row right at the start of the ++# game, then the game is a handicap game. This is necessary on some servers ++# and for some GUIs and also when initializing from many SGF files, which may ++# set up a handicap game using repeated GTP "play" commands for black rather ++# than GTP "place_free_handicap" commands; however, it may also lead to ++# incorrect understanding of komi if whiteHandicapBonus is used and a server ++# does not have such a practice. Uncomment and set to false to disable. ++# assumeMultipleStartingBlackMovesAreHandicap = true ++ ++# Makes katago dynamically adjust in handicap or altered-komi games to assume ++# based on those game settings that it must be stronger or weaker than the ++# opponent and to play accordingly. Greatly improves handicap strength by ++# biasing winrates and scores to favor appropriate safe/aggressive play. ++# Does NOT affect analysis (lz-analyze, kata-analyze, used by programs like ++# Lizzie) so analysis remains unbiased. Uncomment and set this to 0 to disable ++# this and make KataGo play the same always. ++# dynamicPlayoutDoublingAdvantageCapPerOppLead = 0.045 ++ ++# Instead of "dynamicPlayoutDoublingAdvantageCapPerOppLead", you can comment ++# that out and uncomment and set "playoutDoublingAdvantage" to a fixed value ++# from -3.0 to 3.0 that will not change dynamically. ++# ALSO affects analysis tools (lz-analyze, kata-analyze, used by e.g. Lizzie). ++# Negative makes KataGo behave as if it is much weaker than the opponent. ++# Positive makes KataGo behave as if it is much stronger than the opponent. ++# KataGo will adjust to favor safe/aggressive play as appropriate based on ++# the combination of who is ahead and how much stronger/weaker it thinks it is, ++# and report winrates and scores taking the strength difference into account. ++# ++# If this and "dynamicPlayoutDoublingAdvantageCapPerOppLead" are both set ++# then dynamic will be used for all games and this fixed value will be used ++# for analysis tools. ++# playoutDoublingAdvantage = 0.0 ++ ++# Uncomment one of these when using "playoutDoublingAdvantage" to enforce ++# that it will only apply when KataGo plays as the specified color and will be ++# negated when playing as the opposite color. ++# playoutDoublingAdvantagePla = BLACK ++# playoutDoublingAdvantagePla = WHITE ++ ++# ------------------------------ ++# Passing and cleanup ++# ------------------------------ ++# Make the bot never assume that its pass will end the game, even if passing ++# would end and "win" under Tromp-Taylor rules. Usually this is a good idea ++# when using it for analysis or playing on servers where scoring may be ++# implemented non-tromp-taylorly. Uncomment and set to false to disable. ++# conservativePass = true ++ ++# When using territory scoring, self-play games continue beyond two passes ++# with special cleanup rules that may be confusing for human players. This ++# option prevents the special cleanup phases from being reachable when using ++# the bot for GTP play. Uncomment and set to false to enable entering special ++# cleanup. For example, if you are testing it against itself, or against ++# another bot that has precisely implemented the rules documented at ++# https://lightvector.github.io/KataGo/rules.html ++# preventCleanupPhase = true ++ ++# ------------------------------ ++# Miscellaneous behavior ++# ------------------------------ ++# If the board is symmetric, search only one copy of each equivalent move. ++# Attempts to also account for ko/superko, will not theoretically perfect for ++# superko. Uncomment and set to false to disable. ++# rootSymmetryPruning = true ++ ++# Uncomment and set to true to avoid a particular joseki that some networks ++# misevaluate, and also to improve opening diversity versus some particular ++# other bots that like to play it all the time. ++# avoidMYTDaggerHack = false ++ ++# Prefer to avoid playing the same joseki in every corner of the board. ++# Uncomment to set to a specific value. See "Avoid SGF patterns" section. ++# By default: 0 (even games), 0.005 (handicap games) ++# avoidRepeatedPatternUtility = 0.0 ++ ++# Experimental logic to fight against mirror Go even with unfavorable komi. ++# Uncomment to set to a specific value to use for both playing and analysis. ++# By default: true when playing via GTP, but false when analyzing. ++# antiMirror = true ++ ++# Enable some hacks that mitigate rare instances when passing messes up deeper searches. ++# enablePassingHacks = true ++ ++# Uncomment and set this to true to prevent bad or bogus move sequences ++# in the history leading to this position from affecting KataGo's move choices. ++# Same as analysisIgnorePreRootHistory (see above) but applies to actual play. ++# You can enable this if KataGo is being asked to play from positions that it did not ++# choose the moves to reach. ++# ignorePreRootHistory = false ++ ++# =========================================================================== ++# Search limits ++# =========================================================================== ++ ++# Terminology: ++# "Playouts" is the number of new playouts of search performed each turn. ++# "Visits" is the same as "Playouts" but also counts search performed on ++# previous turns that is still applicable to this turn. ++# "Time" is the time in seconds. ++ ++# For example, if KataGo searched 200 nodes on the previous turn, and then ++# after the opponent's reply, 50 nodes of its search tree was still valid, ++# then a visit limit of 200 would allow KataGo to search 150 new nodes ++# (for a final tree size of 200 nodes), whereas a playout limit of of 200 ++# would allow KataGo to search 200 nodes (for a final tree size of 250 nodes). ++ ++# Additionally, KataGo may also move before than the limit in order to ++# obey time controls (e.g. byo-yomi, etc) if the GTP controller has ++# told KataGo that the game has is being played with a given time control. ++ ++# Limits for search on the current turn. ++# If commented out or unspecified, the default is to have no limit. ++# maxVisits = 500 ++# maxPlayouts = 300 ++ maxTime = 0.5 ++ ++# Ponder on the opponent's turn? ++ponderingEnabled = true ++ ++# Limits for search when pondering on the opponent's turn. ++# If commented out or unspecified, the default is to have no limit. ++# Limiting the maximum time is recommended so that KataGo won't burn CPU/GPU ++# forever and/or run out of RAM if left unattended while pondering is enabled. ++# maxVisitsPondering = 5000 ++# maxPlayoutsPondering = 3000 ++maxTimePondering = 60.0 ++ ++ ++# ------------------------------ ++# Other search limits and behavior ++# ------------------------------ ++ ++# Approx number of seconds to buffer for lag for GTP time controls - will ++# move a bit faster assuming there is this much lag per move. ++lagBuffer = 1.0 ++ ++# YOU PROBABLY WANT TO TUNE THIS PARAMETER! ++# The number of threads to use when searching. On powerful GPUs the optimal ++# threads may be much higher than the number of CPU cores you have because ++# many threads are needed to feed efficient large batches to the GPU. ++# ++# Run "./katago benchmark" to tune this parameter and test the effect ++# of changes to any of other parameters. ++numSearchThreads = 16 ++ ++# Play a little faster if the opponent is passing, for human-friendliness. ++# Comment these out to disable them, such as if running a controlled match ++# where you are testing KataGo with fixed compute per move vs other bots. ++searchFactorAfterOnePass = 0.50 ++searchFactorAfterTwoPass = 0.25 ++ ++# Play a little faster if super-winning, for human-friendliness. ++# Comment these out to disable them, such as if running a controlled match ++# where you are testing KataGo with fixed compute per move vs other bots. ++searchFactorWhenWinning = 0.40 ++searchFactorWhenWinningThreshold = 0.95 ++ ++# =========================================================================== ++# GPU settings ++# =========================================================================== ++# This section configures GPU settings. ++# ++# Maximum number of positions to send to a single GPU at once. The default ++# value is roughly equal to numSearchThreads, but can be specified manually ++# if running out of memory, or using multiple GPUs that expect to share work. ++# nnMaxBatchSize = ++ ++# Controls the neural network cache size, which is the primary RAM/memory use. ++# KataGo will cache up to (2 ** nnCacheSizePowerOfTwo) many neural net ++# evaluations in case of transpositions in the tree. ++# Increase this to improve performance for searches with tens of thousands ++# of visits or more. Decrease this to limit memory usage. ++# If you're happy to do some math - each neural net entry takes roughly ++# 1.5KB, except when using whole-board ownership/territory ++# visualizations, where each entry will take roughly 3KB. The number of ++# entries is (2 ** nnCacheSizePowerOfTwo). (E.g. 2 ** 18 = 262144.) ++# You can compute roughly how much memory the cache will use based on this. ++# nnCacheSizePowerOfTwo = 20 ++ ++# Size of mutex pool for nnCache is (2 ** this). ++# nnMutexPoolSizePowerOfTwo = 16 ++ ++# Randomize board orientation when running neural net evals? Uncomment and ++# set to false to disable. ++# nnRandomize = true ++ ++# If provided, force usage of a specific seed for nnRandomize. ++# The default is to use a randomly generated seed. ++# nnRandSeed = abcdefg ++ ++# Uncomment and set to true to force GTP to use the maximum board size for ++# internal buffers for the neural net. This will make KataGo slower when ++# evaluating small boards, but will avoid a lengthy initialization time on every ++# change of board size due to having to re-size the neural net buffers on the GPU. ++# This can be useful for example, for OGS's persistent bot mode that uses a single ++# bot instance to handle multiple games and may thrash between different board sizes ++# if there are concurrent games of multiple sizes. ++# gtpForceMaxNNSize = false ++ ++# ------------------------------ ++# Multiple GPUs ++# ------------------------------ ++# Set this to the number of GPUs to use or that are available. ++# IMPORTANT: If more than 1, also uncomment the appropriate TensorRT ++# or CUDA or OpenCL section. ++# numNNServerThreadsPerModel = 1 ++ ++# ------------------------------ ++# TENSORRT GPU settings ++# ------------------------------ ++# These only apply when using the TENSORRT version of KataGo. ++ ++# For one GPU: optionally uncomment this option and change if the GPU to ++# use is not device 0. ++# trtDeviceToUse = 0 ++ ++# For two GPUs: Uncomment these options, AND set numNNServerThreadsPerModel above. ++# Also, change their values if the devices you want to use are not 0 and 1. ++# trtDeviceToUseThread0 = 0 ++# trtDeviceToUseThread1 = 1 ++ ++# For three GPUs: Uncomment these options, AND set numNNServerThreadsPerModel above. ++# Also, change their values if the devices you want to use are not 0 and 1 and 2. ++# trtDeviceToUseThread0 = 0 ++# trtDeviceToUseThread1 = 1 ++# trtDeviceToUseThread2 = 2 ++ ++# The pattern continues for additional GPUs. ++ ++# ------------------------------ ++# CUDA GPU settings ++# ------------------------------ ++# These only apply when using the CUDA version of KataGo. ++ ++# For one GPU: optionally uncomment and change this if the GPU you want to ++# use is not device 0 ++# cudaDeviceToUse = 0 ++ ++# For two GPUs: Uncomment these options, AND set numNNServerThreadsPerModel above. ++# Also, change their values if the devices you want to use are not 0 and 1. ++# cudaDeviceToUseThread0 = 0 ++# cudaDeviceToUseThread1 = 1 ++ ++# For three GPUs: Uncomment these options, AND set numNNServerThreadsPerModel above. ++# Also, change their values if the devices you want to use are not 0 and 1 and 2. ++# cudaDeviceToUseThread0 = 0 ++# cudaDeviceToUseThread1 = 1 ++# cudaDeviceToUseThread2 = 2 ++ ++# The pattern continues for additional GPUs. ++ ++# KataGo will automatically use FP16 or not based on the compute capability ++# of your NVIDIA GPU. If you want to try to force a particular behavior ++# you can uncomment these lines and change them to "true" or "false". ++# cudaUseFP16 = auto ++# cudaUseNHWC = auto ++ ++# ------------------------------ ++# OpenCL GPU settings ++# ------------------------------ ++# These only apply when using the OpenCL version of KataGo. ++ ++# Uncomment and set to true to tune OpenCL for every board size separately, ++# rather than only the largest possible size. ++# openclReTunePerBoardSize = false ++ ++# For one GPU: optionally uncomment and change this if the best device to use is guessed incorrectly. ++# The default behavior tries to guess the 'best' GPU or device on your system to use, usually it will be a good guess. ++# openclDeviceToUse = 0 ++ ++# For two GPUs: Uncomment these two lines and replace X and Y with the device ids of the devices you want to use. ++# It might NOT be 0 and 1, some computers will have many OpenCL devices. You can see what the devices are when ++# KataGo starts up - it should print or log all the devices it finds. ++# (AND also set numNNServerThreadsPerModel above) ++# openclDeviceToUseThread0 = X ++# openclDeviceToUseThread1 = Y ++ ++# For three GPUs: Uncomment these three lines and replace X and Y and Z with the device ids of the devices you want to use. ++# It might NOT be 0 and 1 and 2, some computers will have many OpenCL devices. You can see what the devices are when ++# KataGo starts up - it should print or log all the devices it finds. ++# (AND also set numNNServerThreadsPerModel above) ++# openclDeviceToUseThread0 = X ++# openclDeviceToUseThread1 = Y ++# openclDeviceToUseThread2 = Z ++ ++# The pattern continues for additional GPUs. ++ ++# KataGo will automatically use FP16 or not based on testing your GPU during ++# tuning. If you want to try to force a particular behavior though you can ++# uncomment this option and change it to "true" or "false". This is a fairly ++# blunt setting - more detailed settings are testable by rerunning the tuner ++# with various arguments (./katago tuner). ++# openclUseFP16 = auto ++ ++# ------------------------------ ++# Eigen-specific settings ++# ------------------------------ ++# These only apply when using the Eigen (pure CPU) version of KataGo. ++ ++# Number of CPU threads for evaluating the neural net on the Eigen backend. ++# ++# Default: numSearchThreads ++# numEigenThreadsPerModel = X ++ ++# =========================================================================== ++# Root move selection and biases ++# =========================================================================== ++# Uncomment and edit any of the below values to change them from their default. ++ ++# If provided, force usage of a specific seed for various random things in ++# the search. The default is to use a random seed. ++# searchRandSeed = hijklmn ++ ++# Temperature for the early game, randomize between chosen moves with ++# this temperature ++# chosenMoveTemperatureEarly = 0.5 ++ ++# Decay temperature for the early game by 0.5 every this many moves, ++# scaled with board size. ++# chosenMoveTemperatureHalflife = 19 ++ ++# At the end of search after the early game, randomize between chosen ++# moves with this temperature ++# chosenMoveTemperature = 0.10 ++ ++# Subtract this many visits from each move prior to applying ++# chosenMoveTemperature (unless all moves have too few visits) to downweight ++# unlikely moves ++# chosenMoveSubtract = 0 ++ ++# The same as chosenMoveSubtract but only prunes moves that fall below ++# the threshold. This setting does not affect chosenMoveSubtract. ++# chosenMovePrune = 1 ++ ++# Number of symmetries to sample (without replacement) and average at the root ++# rootNumSymmetriesToSample = 1 ++ ++# Using LCB for move selection? ++# useLcbForSelection = true ++ ++# How many stdevs a move needs to be better than another for LCB selection ++# lcbStdevs = 5.0 ++ ++# Only use LCB override when a move has this proportion of visits as the ++# top move. ++# minVisitPropForLCB = 0.15 ++ ++# =========================================================================== ++# Internal params ++# =========================================================================== ++# Uncomment and edit any of the below values to change them from their default. ++ ++# Scales the utility of winning/losing ++# winLossUtilityFactor = 1.0 ++ ++# Scales the utility for trying to maximize score ++# staticScoreUtilityFactor = 0.10 ++# dynamicScoreUtilityFactor = 0.30 ++ ++# Adjust dynamic score center this proportion of the way towards zero, ++# capped at a reasonable amount. ++# dynamicScoreCenterZeroWeight = 0.20 ++# dynamicScoreCenterScale = 0.75 ++ ++# The utility of getting a "no result" due to triple ko or other long cycle ++# in non-superko rulesets (-1 to 1) ++# noResultUtilityForWhite = 0.0 ++ ++# The number of wins that a draw counts as, for white. (0 to 1) ++# drawEquivalentWinsForWhite = 0.5 ++ ++# Exploration constant for mcts ++# cpuctExploration = 1.0 ++# cpuctExplorationLog = 0.45 ++ ++# Parameters that control exploring more in volatile positions, exploring ++# less in stable positions. ++# cpuctUtilityStdevPrior = 0.40 ++# cpuctUtilityStdevPriorWeight = 2.0 ++# cpuctUtilityStdevScale = 0.85 ++ ++# FPU reduction constant for mcts ++# fpuReductionMax = 0.2 ++# rootFpuReductionMax = 0.1 ++# fpuParentWeightByVisitedPolicy = true ++ ++# Parameters that control weighting of evals based on the net's own ++# self-reported uncertainty. ++# useUncertainty = true ++# uncertaintyExponent = 1.0 ++# uncertaintyCoeff = 0.25 ++ ++# Explore using optimistic policy ++# rootPolicyOptimism = 0.2 ++# policyOptimism = 1.0 ++ ++# Amount to apply a downweighting of children with very bad values relative ++# to good ones. ++# valueWeightExponent = 0.25 ++ ++# Slight incentive for the bot to behave human-like with regard to passing at ++# the end, filling the dame, not wasting time playing in its own territory, ++# etc., and not play moves that are equivalent in terms of points but a bit ++# more unfriendly to humans. ++# rootEndingBonusPoints = 0.5 ++ ++# Make the bot prune useless moves that are just prolonging the game to ++# avoid losing yet. ++# rootPruneUselessMoves = true ++ ++# Apply bias correction based on local pattern keys ++# subtreeValueBiasFactor = 0.45 ++# subtreeValueBiasWeightExponent = 0.85 ++ ++# Use graph search rather than tree search - identify and share search for ++# transpositions. ++# useGraphSearch = true ++ ++# How much to shard the node table for search synchronization ++# nodeTableShardsPowerOfTwo = 16 ++ ++# How many virtual losses to add when a thread descends through a node ++# numVirtualLossesPerThread = 1 ++ ++# Improve the quality of evals under heavy multithreading ++# useNoisePruning = true ++ ++# =========================================================================== ++# Automatic avoid patterns ++# =========================================================================== ++# The parameters in this section provide a way to bias away from moves that match ++# patterns that this instance of KataGo has played in previous games, by auto-saving ++# moves to a directory and then auto-loading and biasing against them each new game. ++# Uncomment them to use them. When using this feature, all parameters must be specified. ++ ++# Different board sizes are tracked separately, but all board sizes share the same ++# parameters by default. Every parameter *except* for autoAvoidRepeatDir and ++# autoAvoidRepeatSaveChunkSize can be overridden per board size, ++# e.g. "autoAvoidRepeatMinTurnNumber13x13". You must ALSO specify the ++# defaults even if you have specified board-size-specific values. ++ ++# Directory to auto-save moves KataGo plays, to avoid them in future games. ++# You can create a new empty directory and put its path here. ++# If you run parallel instances of KataGo, use different directories if you ++# want them not to share their biases, use the same directory if you want ++# all of them to bias away from past moves that any of them have played. ++# KataGo will also automatically DELETE old data in this directory, so it is ++# recommended that if you do share the same directory between parallel instances, ++# that they all use the same settings that affect data saving/deletion. ++# autoAvoidRepeatDir = PATH_TO_NEW_DIRECTORY ++ ++# Penalize this much utility per matching move. ++# Values that are too large may lead to bad play. The value of 0.004 is fairly large ++# and might be large enough to result in some early weird/bad moves when trying ++# to avoid past games' moves if enough games begin the same way. You can experiment with it. ++# autoAvoidRepeatUtility = 0.004 ++ ++# Per each new move saved, exponentially decay prior saved moves by this factor. ++# This way, the bias against moves from many games ago is gradually phased out. ++# For example, 0.9995 = 1 - 1/2000, so would be roughly 2000 prior moves worth of ++# penalty weight remembered, in steady state. Depending on what turn number range ++# you are saving, this might equate to a different number of games For example ++# saving the first 50 moves per game would make this roughly 2000 / 50 = 40 games ++# worth of memory. ++# autoAvoidRepeatLambda = 0.9995 ++ ++# Affects data saving/deletion. ++# When the number of saved moves exceeds this, outright delete them to avoid too many ++# files and disk space building up. Also may affect the speed of saving/loading on start ++# of each game if this is set large and a lot of data builds up. ++# autoAvoidRepeatMaxPoses = 10000 ++ ++# Affects data saving/deletion. ++# Only save data for moves within this turn number range of those games. ++# E.g. setting autoAvoidRepeatMinTurnNumber to a number like 4 or 5 would tend to make ++# KataGo not develop a bias against the initial 3-4 and 4-4 corner moves in almost every game. ++# autoAvoidRepeatMinTurnNumber = 0 ++# autoAvoidRepeatMaxTurnNumber = 50 ++ ++# Affects data saving/deletion. ++# Within a single run of a program, wait to accumulate this many samples ++# (possibly across multiple clear_boards/games) before saving the data. ++# Can help to avoid writing too many small files to disk, especially when GTP is used ++# in a way that clears the board very frequently (e.g. gtp2ogs pooled manager). ++# autoAvoidRepeatSaveChunkSize = 200 ++ ++# =========================================================================== ++# Avoid SGF patterns ++# =========================================================================== ++# The parameters in this section provide a way to avoid moves that follow ++# specific patterns based on a set of SGF files loaded upon startup. ++# This is basically the same as the above "Automatic avoid patterns" section ++# above except you supply your own SGF files to avoid moves from. ++# Uncomment them to use this feature. Additionally, if the SGF file ++# contains the string %SKIP% in a comment on a move, that move will be ++# ignored for this purpose. ++ ++# Load SGF files from this directory when the engine is started ++# (only on startup, will not reload unless engine is restarted) ++# avoidSgfPatternDirs = path/to/directory/with/sgfs/ ++# You can also surround the file path in double quotes if the file path contains trailing spaces or hash signs. ++# Within double quotes, backslashes are escape characters. ++# avoidSgfPatternDirs = "path/to/directory/with/sgfs/" ++ ++# Penalize this much utility per matching move. ++# Set this negative if you instead want to favor SGF patterns instead of ++# penalizing them. This number does not need to be large, even 0.001 will ++# make a difference. Values that are too large may lead to bad play. ++# avoidSgfPatternUtility = 0.001 ++ ++# Optional - load only the newest this many files ++# avoidSgfPatternMaxFiles = 20 ++ ++# Optional - Penalty is multiplied by this per each older SGF file, so that ++# old SGF files matter less than newer ones. ++# avoidSgfPatternLambda = 0.90 ++ ++# Optional - pay attention only to moves made by players with this name. ++# For example, set it to the name that your bot's past games will show up ++# as in the SGF, so that the bot will only avoid repeating moves that itself ++# made in past games, not the moves that its opponents made. ++# avoidSgfPatternAllowedNames = my-ogs-bot-name1,my-ogs-bot-name2 ++ ++# Optional - Ignore moves in SGF files that occurred before this turn number. ++# avoidSgfPatternMinTurnNumber = 0 ++ ++# For more avoid patterns: ++# You can also specify a second set of parameters, and a third, fourth, ++# etc. by numbering 2,3,4,... ++# ++# avoidSgf2PatternDirs = ... ++# avoidSgf2PatternUtility = ... ++# avoidSgf2PatternMaxFiles = ... ++# avoidSgf2PatternLambda = ... ++# avoidSgf2PatternAllowedNames = ... ++# avoidSgf2PatternMinTurnNumber = ... ++ +diff --git a/cpp/match_example.cfg b/cpp/match_example.cfg +new file mode 100644 +index 00000000..945ac44e +--- /dev/null ++++ b/cpp/match_example.cfg +@@ -0,0 +1,228 @@ ++# Example config for C++ match runner ++# This is an example template config for the "match" subcommand of KataGo. e.g: ++# ./katago match -config-file configs/match_example.cfg -log-file match.log -sgf-output-dir match_sgfs/ ++# ++# On a good GPU, the match subcommand enables testing of KataGo nets against each other ++# or a net against itself with different parameters vastly faster than anything ++# else, because multiple games can share GPU batching. ++# ++# Beware however of using this to test differing numbers of threads or test time-based search limits. ++# Because many games will be run simultaneously, they will compete from each other for compute power, ++# and although the total will run much faster than if you had run them one by one, you might also get ++# substantially different results not reflective of the strength of a configuration when run in a real ++# match setting, on a machine by itself. For fixed numbers of visits or playouts instead of fixed time, ++# and with numSearchThreads = 1, there should be no problem though, because then the compute time has ++# no influence on the result of the computation. ++# ++# See gtp config for descriptions of most of these params. ++# ++# For almost any parameter in this config that is related to a bot, rather than to the match as a whole ++# (so, visits, search parameters, model files, etc. but NOT the rules, max games, log info, etc) ++# you can specify them differentially between different bots by appending the index of the bot. ++# For example, if you were testing different numbers of visits, you could try: ++# ++# numBots = 3 ++# botName0 = lowVisits ++# botName1 = midVisits ++# botName2 = highVisits ++# ++# maxVisits0 = 100 ++# maxVisits1 = 300 ++# maxVisits2 = 1000 ++# ++# Or, if you were testing different neural nets, with different search configurations, you could do: ++# ++# nnModelFile0 = path/to/first/model/file.bin.gz ++# nnModelFile1 = path/to/second/model/file.bin.gz ++# ++# And specify different search parameters for them if you wanted: ++# cpuctExploration0 = 1.5 ++# cpuctExploration1 = 1.3 ++ ++# Logs------------------------------------------------------------------------------------ ++ ++logSearchInfo = false ++logMoves = false ++logGamesEvery = 50 ++logToStdout = true ++ ++# Bots------------------------------------------------------------------------------------- ++# For multiple bots, you can specify their names as botName0,botName1, etc. ++# If the bots are using different models, specify nnModelFile0, nnModelFile1, etc. ++ ++numBots=1 ++botName=FOO ++nnModelFile=PATH_TO_MODEL ++ ++# These bots will not play each other, but will still be opponents for other bots ++# secondaryBots = 0,1,3 ++# Only these bots will actually play games. Useful if you have a config file with many more ++# bots defined but you want to only selectively enable a few. ++# includeBots = 0,2,6 ++ ++# Specify extra pairings of bots to play. ++# extraPairs = 0-4,1-4 ++# Uncomment and set this to true if you don't want extraPairs to be mirrored by color. ++# (i.e. 0-4 should mean 0 plays black and 4 plays white only, rather than having two games with swapped colors). ++# extraPairsAreOneSidedBW = false ++ ++# Match----------------------------------------------------------------------------------- ++numGameThreads=8 # How many games to run in parallel at a time? ++ ++numGamesTotal=1000000 ++maxMovesPerGame=1200 ++ ++allowResignation = true ++resignThreshold = -0.95 ++resignConsecTurns = 6 ++ ++# Rules------------------------------------------------------------------------------------ ++# See https://lightvector.github.io/KataGo/rules.html for a description of the rules. ++ ++koRules = SIMPLE,POSITIONAL,SITUATIONAL ++scoringRules = AREA,TERRITORY ++taxRules = NONE,SEKI,ALL ++multiStoneSuicideLegals = false,true ++hasButtons = false,true ++ ++bSizes = 19,13,9 ++bSizeRelProbs = 90,5,5 ++ ++# If you want to specify exact distributions of boards, including rectangles, specify this instead ++# of bSizes. E.g. to randomize between a 7x5, a 9x12, and a 10x10 board with probabilities 25%, 25%, 50%: ++# bSizesXY=7-5,9-12,10-10 ++# bSizeRelProbs = 1,1,2 ++ ++komiAuto = True # Automatically adjust komi to what the neural nets think are fair ++# komiMean = 7.5 # Specify explicit komi ++# policyInitAreaProp = 0 ++# compensateAfterPolicyInitProb = 1.0 # Additionally make komi fair this often after the high-temperature moves. ++# policyInitAreaTemperature = 1 ++handicapProb = 0.0 ++handicapCompensateKomiProb = 1.0 ++# numExtraBlackFixed = 3 # When playing handicap games, always use exactly this many extra black moves ++ ++# Search limits----------------------------------------------------------------------------------- ++ ++maxVisits = 500 ++# maxPlayouts = 300 ++# maxTime = 60 ++ ++numSearchThreads = 1 ++ ++# GPU Settings------------------------------------------------------------------------------- ++ ++nnMaxBatchSize = 32 ++nnCacheSizePowerOfTwo = 21 ++nnMutexPoolSizePowerOfTwo = 17 ++nnRandomize = true ++ ++ ++# How many threads should there be to feed positions to the neural net? ++# Server threads are indexed 0,1,...(n-1) for the purposes of the below GPU settings arguments ++# that specify which threads should use which GPUs. ++# NOTE: This parameter is probably ONLY useful if you have multiple GPUs, since each GPU will need a thread. ++# If you're tuning single-GPU performance, use numSearchThreads instead. ++numNNServerThreadsPerModel = 1 ++ ++ ++# TENSORRT GPU settings-------------------------------------- ++# These only apply when using the TENSORRT version of KataGo. ++ ++# IF USING ONE GPU: optionally uncomment and change this if the GPU you want to use turns out to be not device 0 ++# trtDeviceToUse = 0 ++ ++# IF USING TWO GPUS: Uncomment these two lines (AND set numNNServerThreadsPerModel above): ++# trtDeviceToUseThread0 = 0 # change this if the first GPU you want to use turns out to be not device 0 ++# trtDeviceToUseThread1 = 1 # change this if the second GPU you want to use turns out to be not device 1 ++ ++# IF USING THREE GPUS: Uncomment these three lines (AND set numNNServerThreadsPerModel above): ++# trtDeviceToUseThread0 = 0 # change this if the first GPU you want to use turns out to be not device 0 ++# trtDeviceToUseThread1 = 1 # change this if the second GPU you want to use turns out to be not device 1 ++# trtDeviceToUseThread2 = 2 # change this if the third GPU you want to use turns out to be not device 2 ++ ++# You can probably guess the pattern if you have four, five, etc. GPUs. ++ ++ ++# CUDA GPU settings-------------------------------------- ++# For the below, "model" refers to a neural net, from the nnModelFile parameter(s) above. ++# cudaGpuToUse = 0 #use gpu 0 for all server threads (numNNServerThreadsPerModel) unless otherwise specified per-model or per-thread-per-model ++# cudaGpuToUseModel0 = 3 #use gpu 3 for model 0 for all threads unless otherwise specified per-thread for this model ++# cudaGpuToUseModel1 = 2 #use gpu 2 for model 1 for all threads unless otherwise specified per-thread for this model ++# cudaGpuToUseModel0Thread0 = 3 #use gpu 3 for model 0, server thread 0 ++# cudaGpuToUseModel0Thread1 = 2 #use gpu 2 for model 0, server thread 1 ++ ++# cudaUseFP16 = auto ++# cudaUseNHWC = auto ++ ++ ++# OpenCL GPU settings-------------------------------------- ++# These only apply when using OpenCL as the backend for inference. ++# (For GTP, we only ever have one model, when playing matches, we might have more than one, see match_example.cfg) ++ ++# Default behavior is just to always use gpu 0, you will want to uncomment and adjust one or more of these lines ++# to take advantage of a multi-gpu machine ++# openclGpuToUse = 0 #use gpu 0 for all server threads (numNNServerThreadsPerModel) unless otherwise specified per-model or per-thread-per-model ++# openclGpuToUseModel0 = 3 #use gpu 3 for model 0 for all threads unless otherwise specified per-thread for this model ++# openclGpuToUseModel1 = 2 #use gpu 2 for model 1 for all threads unless otherwise specified per-thread for this model ++# openclGpuToUseModel0Thread0 = 3 #use gpu 3 for model 0, server thread 0 ++# openclGpuToUseModel0Thread1 = 2 #use gpu 2 for model 0, server thread 1 ++ ++# Uncomment to tune OpenCL for every board size separately, rather than only the largest possible size ++# openclReTunePerBoardSize = true ++ ++# openclUseFP16 = auto ++ ++ ++# Eigen-specific settings-------------------------------------- ++# These only apply when using the Eigen (pure CPU) version of KataGo. ++ ++# This is the number of CPU threads for evaluating the neural net on the Eigen backend. ++# It defaults to numSearchThreads. ++# numEigenThreadsPerModel = X ++ ++ ++# Root move selection and biases------------------------------------------------------------------------------ ++# Uncomment and edit any of the below values to change them from their default. ++# Values in this section can be specified per-bot as well ++ ++chosenMoveTemperatureEarly = 0.60 ++# chosenMoveTemperatureHalflife = 19 ++chosenMoveTemperature = 0.20 ++# chosenMoveSubtract = 0 ++# chosenMovePrune = 1 ++ ++# rootNumSymmetriesToSample = 1 ++ ++# useLcbForSelection = true ++# lcbStdevs = 5.0 ++# minVisitPropForLCB = 0.15 ++ ++# Internal params------------------------------------------------------------------------------ ++# Uncomment and edit any of the below values to change them from their default. ++# Values in this section can be specified per-bot as well ++ ++# winLossUtilityFactor = 1.0 ++# staticScoreUtilityFactor = 0.10 ++# dynamicScoreUtilityFactor = 0.30 ++# dynamicScoreCenterZeroWeight = 0.20 ++# dynamicScoreCenterScale = 0.75 ++# noResultUtilityForWhite = 0.0 ++# drawEquivalentWinsForWhite = 0.5 ++ ++# cpuctExploration = 0.9 ++# cpuctExplorationLog = 0.4 ++# fpuReductionMax = 0.2 ++# rootFpuReductionMax = 0.1 ++# fpuParentWeightByVisitedPolicy = true ++# valueWeightExponent = 0.25 ++# rootEndingBonusPoints = 0.5 ++# rootPruneUselessMoves = true ++# subtreeValueBiasFactor = 0.45 ++# subtreeValueBiasWeightExponent = 0.85 ++# useGraphSearch = true ++# rootPolicyOptimism = 0.2 ++# policyOptimism = 1.0 ++ ++# nodeTableShardsPowerOfTwo = 16 ++# numVirtualLossesPerThread = 1 +diff --git a/cpp/msvcp140.dll b/cpp/msvcp140.dll +new file mode 100644 +index 00000000..f7c342d1 +Binary files /dev/null and b/cpp/msvcp140.dll differ +diff --git a/cpp/msvcp140_1.dll b/cpp/msvcp140_1.dll +new file mode 100644 +index 00000000..6f55c9d1 +Binary files /dev/null and b/cpp/msvcp140_1.dll differ +diff --git a/cpp/msvcp140_2.dll b/cpp/msvcp140_2.dll +new file mode 100644 +index 00000000..b127e0c8 +Binary files /dev/null and b/cpp/msvcp140_2.dll differ +diff --git a/cpp/msvcp140_atomic_wait.dll b/cpp/msvcp140_atomic_wait.dll +new file mode 100644 +index 00000000..b8fa2030 +Binary files /dev/null and b/cpp/msvcp140_atomic_wait.dll differ +diff --git a/cpp/msvcp140_codecvt_ids.dll b/cpp/msvcp140_codecvt_ids.dll +new file mode 100644 +index 00000000..7549b2b5 +Binary files /dev/null and b/cpp/msvcp140_codecvt_ids.dll differ +diff --git a/cpp/vcruntime140.dll b/cpp/vcruntime140.dll +new file mode 100644 +index 00000000..5d5b81dc +Binary files /dev/null and b/cpp/vcruntime140.dll differ +diff --git a/cpp/vcruntime140_1.dll b/cpp/vcruntime140_1.dll +new file mode 100644 +index 00000000..3a02196f +Binary files /dev/null and b/cpp/vcruntime140_1.dll differ +diff --git "a/cpp/\344\277\261\344\271\220\351\203\250AI.cfg" "b/cpp/\344\277\261\344\271\220\351\203\250AI.cfg" +new file mode 100644 +index 00000000..9b9287b0 +--- /dev/null ++++ "b/cpp/\344\277\261\344\271\220\351\203\250AI.cfg" +@@ -0,0 +1,721 @@ ++# Configuration for KataGo C++ GTP engine ++ ++# Run the program using: `./katago.exe gtp` ++ ++# In this example config, when a parameter is given as a commented out value, ++# that value also is the default value, unless described otherwise. You can ++# uncomment it (remove the pound sign) and change it if you want. ++ ++# =========================================================================== ++# Running on an online server or in a real tournament or match ++# =========================================================================== ++# If you plan to run online or in a tournament, read through the "Rules" ++# section below for proper handling of komi, handicaps, end-of-game cleanup, ++# and other details. ++ ++# =========================================================================== ++# Notes about performance and memory usage ++# =========================================================================== ++# Important: For good performance, you will very likely want to tune the ++# "numSearchThreads" parameter in the Search limits section below! Run ++# "./katago benchmark" to test KataGo and to suggest a reasonable value ++# of this parameter. ++ ++# For multi-GPU systems, read "OpenCL GPU settings" or "CUDA GPU settings". ++# ++# When using OpenCL, verify that KataGo picks the correct device! Some systems ++# may have both an Intel CPU OpenCL and GPU OpenCL. If # KataGo picks the wrong ++# one, correct this by specifying "openclGpuToUse". ++# ++# Consider adjusting "maxVisits", "ponderingEnabled", "resignThreshold", and ++# other parameters depending on your intended usage. ++ ++# =========================================================================== ++# Command-line usage ++# =========================================================================== ++# All of the below values may be set or overridden via command-line arguments: ++# ++# -override-config KEY=VALUE,KEY=VALUE,... ++ ++# =========================================================================== ++# Logs and files ++# =========================================================================== ++# This section defines where and what logging information is produced. ++ ++# Each run of KataGo will log to a separate file in this dir. ++# This is the default. ++logDir = gtp_logs ++# Uncomment and specify this instead of logDir to write separate dated subdirs ++# logDirDated = gtp_logs ++# Uncomment and specify this instead of logDir to log to only a single file ++# logFile = gtp.log ++ ++# Logging options ++logAllGTPCommunication = true ++logSearchInfo = true ++logSearchInfoForChosenMove = false ++logToStderr = false ++ ++# KataGo will display some info to stderr on GTP startup ++# Uncomment the next line and set it to false to suppress that and remain silent ++# startupPrintMessageToStderr = true ++ ++# Write information to stderr, for use in things like malkovich chat to OGS. ++ ogsChatToStderr = true ++ ++# Uncomment and set this to a directory to override where openCLTuner files ++# and other cached data is written. By default it saves into a subdir of the ++# current directory on windows, and a subdir of ~/.katago on Linux. ++# homeDataDir = PATH_TO_DIRECTORY ++ ++# =========================================================================== ++# Analysis ++# =========================================================================== ++# This section configures analysis settings. ++# ++# The maximum number of moves after the first move displayed in variations ++# from analysis commands like kata-analyze or lz-analyze. ++# analysisPVLen = 15 ++ ++# Report winrates for chat and analysis as (BLACK|WHITE|SIDETOMOVE). ++# Most GUIs and analysis tools will expect SIDETOMOVE. ++# reportAnalysisWinratesAs = SIDETOMOVE ++ ++# Extra noise for wider exploration. Large values will force KataGo to ++# analyze a greater variety of moves than it normally would. ++# An extreme value like 1 distributes playouts across every move on the board, ++# even very bad moves. ++# Affects analysis only, does not affect play. ++# analysisWideRootNoise = 0.04 ++ ++# Try to limit the effect of possible bad or bogus move sequences in the ++# history leading to this position from affecting KataGo's move predictions. ++# analysisIgnorePreRootHistory = true ++ ++# =========================================================================== ++# Rules ++# =========================================================================== ++# This section configures the scoring and playing rules. Rules can also be ++# changed mid-run by issuing custom GTP commands. ++# ++# See https://lightvector.github.io/KataGo/rules.html for rules details. ++# ++# See https://github.com/lightvector/KataGo/blob/master/docs/GTP_Extensions.md ++# for GTP commands. ++ ++# Specify the rules as a string. ++# Some legal values include: ++# chinese, japanese, korean, aga, chinese-ogs, new-zealand, stone-scoring, ++# ancient-territory, bga, aga-button ++# ++# For some human rulesets that require complex adjudication in tricky cases ++# (e.g. japanese, korean) KataGo may not precisely match the ruleset in such ++# cases but will do its best. ++rules = tromp-taylor ++ ++# By default, the "rules" parameter is used, but if you comment it out and ++# uncomment one option in each of the sections below, you can specify an ++# arbitrary combination of individual rules. ++ ++# koRule = SIMPLE # Simple ko rules (triple ko = no result) ++# koRule = POSITIONAL # Positional superko ++# koRule = SITUATIONAL # Situational superko ++ ++# scoringRule = AREA # Area scoring ++# scoringRule = TERRITORY # Territory scoring (special computer-friendly territory rules) ++ ++# taxRule = NONE # All surrounded empty points are scored ++# taxRule = SEKI # Eyes in seki do NOT count as points ++# taxRule = ALL # All groups are taxed up to 2 points for the two eyes needed to live ++ ++# Is multiple-stone suicide legal? (Single-stone suicide is always illegal). ++# multiStoneSuicideLegal = false ++# multiStoneSuicideLegal = true ++ ++# "Button go" - the first pass when area scoring awards 0.5 points and does ++# not count for ending the game. ++# Allows area scoring rulesets that have far simpler rules to achieve the same ++# final scoring precision and reward for precise play as territory scoring. ++# hasButton = false ++# hasButton = true ++ ++# Is this a human ruleset where it's okay to pass before having physically ++# captured and removed all dead stones? ++# friendlyPassOk = false ++# friendlyPassOk = true ++ ++# How handicap stones in handicap games are compensated ++# whiteHandicapBonus = 0 # White gets no compensation for black's handicap stones (Tromp-taylor, NZ, JP) ++# whiteHandicapBonus = N-1 # White gets N-1 points for black's N handicap stones (AGA) ++# whiteHandicapBonus = N # White gets N points for black's N handicap stones (Chinese) ++ ++# ------------------------------ ++# Other rules hacks ++# ------------------------------ ++# Uncomment and change to adjust what board size KataGo uses upon startup ++# by default when GTP doesn't specify. ++# defaultBoardSize = 19 ++ ++# By default, Katago will use the komi that the GUI or GTP controller tries to set. ++# Uncomment and set this to have KataGo ignore the controller and always use this komi. ++# ignoreGTPAndForceKomi = 7 ++ ++# =========================================================================== ++# Bot behavior ++# =========================================================================== ++ ++# ------------------------------ ++# Resignation ++# ------------------------------ ++ ++# Resignation occurs if for at least resignConsecTurns in a row, the ++# winLossUtility (on a [-1,1] scale) is below resignThreshold. ++allowResignation = true ++resignThreshold = -0.90 ++resignConsecTurns = 3 ++ ++# By default, KataGo may resign games that it is confidently losing even if they ++# are very close in score. Uncomment and set this to avoid resigning games ++# if the estimated difference is points is less than or equal to this. ++# resignMinScoreDifference = 10 ++ ++# ------------------------------ ++# Handicap ++# ------------------------------ ++# Assume that if black makes many moves in a row right at the start of the ++# game, then the game is a handicap game. This is necessary on some servers ++# and for some GUIs and also when initializing from many SGF files, which may ++# set up a handicap game using repeated GTP "play" commands for black rather ++# than GTP "place_free_handicap" commands; however, it may also lead to ++# incorrect understanding of komi if whiteHandicapBonus is used and a server ++# does not have such a practice. Uncomment and set to false to disable. ++# assumeMultipleStartingBlackMovesAreHandicap = true ++ ++# Makes katago dynamically adjust in handicap or altered-komi games to assume ++# based on those game settings that it must be stronger or weaker than the ++# opponent and to play accordingly. Greatly improves handicap strength by ++# biasing winrates and scores to favor appropriate safe/aggressive play. ++# Does NOT affect analysis (lz-analyze, kata-analyze, used by programs like ++# Lizzie) so analysis remains unbiased. Uncomment and set this to 0 to disable ++# this and make KataGo play the same always. ++# dynamicPlayoutDoublingAdvantageCapPerOppLead = 0.045 ++ ++# Instead of "dynamicPlayoutDoublingAdvantageCapPerOppLead", you can comment ++# that out and uncomment and set "playoutDoublingAdvantage" to a fixed value ++# from -3.0 to 3.0 that will not change dynamically. ++# ALSO affects analysis tools (lz-analyze, kata-analyze, used by e.g. Lizzie). ++# Negative makes KataGo behave as if it is much weaker than the opponent. ++# Positive makes KataGo behave as if it is much stronger than the opponent. ++# KataGo will adjust to favor safe/aggressive play as appropriate based on ++# the combination of who is ahead and how much stronger/weaker it thinks it is, ++# and report winrates and scores taking the strength difference into account. ++# ++# If this and "dynamicPlayoutDoublingAdvantageCapPerOppLead" are both set ++# then dynamic will be used for all games and this fixed value will be used ++# for analysis tools. ++# playoutDoublingAdvantage = 0.0 ++ ++# Uncomment one of these when using "playoutDoublingAdvantage" to enforce ++# that it will only apply when KataGo plays as the specified color and will be ++# negated when playing as the opposite color. ++# playoutDoublingAdvantagePla = BLACK ++# playoutDoublingAdvantagePla = WHITE ++ ++# ------------------------------ ++# Passing and cleanup ++# ------------------------------ ++# Make the bot never assume that its pass will end the game, even if passing ++# would end and "win" under Tromp-Taylor rules. Usually this is a good idea ++# when using it for analysis or playing on servers where scoring may be ++# implemented non-tromp-taylorly. Uncomment and set to false to disable. ++# conservativePass = true ++ ++# When using territory scoring, self-play games continue beyond two passes ++# with special cleanup rules that may be confusing for human players. This ++# option prevents the special cleanup phases from being reachable when using ++# the bot for GTP play. Uncomment and set to false to enable entering special ++# cleanup. For example, if you are testing it against itself, or against ++# another bot that has precisely implemented the rules documented at ++# https://lightvector.github.io/KataGo/rules.html ++# preventCleanupPhase = true ++ ++# ------------------------------ ++# Miscellaneous behavior ++# ------------------------------ ++# If the board is symmetric, search only one copy of each equivalent move. ++# Attempts to also account for ko/superko, will not theoretically perfect for ++# superko. Uncomment and set to false to disable. ++# rootSymmetryPruning = true ++ ++# Uncomment and set to true to avoid a particular joseki that some networks ++# misevaluate, and also to improve opening diversity versus some particular ++# other bots that like to play it all the time. ++# avoidMYTDaggerHack = false ++ ++# Prefer to avoid playing the same joseki in every corner of the board. ++# Uncomment to set to a specific value. See "Avoid SGF patterns" section. ++# By default: 0 (even games), 0.005 (handicap games) ++# avoidRepeatedPatternUtility = 0.0 ++ ++# Experimental logic to fight against mirror Go even with unfavorable komi. ++# Uncomment to set to a specific value to use for both playing and analysis. ++# By default: true when playing via GTP, but false when analyzing. ++# antiMirror = true ++ ++# Enable some hacks that mitigate rare instances when passing messes up deeper searches. ++# enablePassingHacks = true ++ ++# Uncomment and set this to true to prevent bad or bogus move sequences ++# in the history leading to this position from affecting KataGo's move choices. ++# Same as analysisIgnorePreRootHistory (see above) but applies to actual play. ++# You can enable this if KataGo is being asked to play from positions that it did not ++# choose the moves to reach. ++# ignorePreRootHistory = false ++ ++# =========================================================================== ++# Search limits ++# =========================================================================== ++ ++# Terminology: ++# "Playouts" is the number of new playouts of search performed each turn. ++# "Visits" is the same as "Playouts" but also counts search performed on ++# previous turns that is still applicable to this turn. ++# "Time" is the time in seconds. ++ ++# For example, if KataGo searched 200 nodes on the previous turn, and then ++# after the opponent's reply, 50 nodes of its search tree was still valid, ++# then a visit limit of 200 would allow KataGo to search 150 new nodes ++# (for a final tree size of 200 nodes), whereas a playout limit of of 200 ++# would allow KataGo to search 200 nodes (for a final tree size of 250 nodes). ++ ++# Additionally, KataGo may also move before than the limit in order to ++# obey time controls (e.g. byo-yomi, etc) if the GTP controller has ++# told KataGo that the game has is being played with a given time control. ++ ++# Limits for search on the current turn. ++# If commented out or unspecified, the default is to have no limit. ++# maxVisits = 500 ++# maxPlayouts = 300 ++ maxTime = 1.0 ++ ++# Ponder on the opponent's turn? ++ponderingEnabled = true ++ ++# Limits for search when pondering on the opponent's turn. ++# If commented out or unspecified, the default is to have no limit. ++# Limiting the maximum time is recommended so that KataGo won't burn CPU/GPU ++# forever and/or run out of RAM if left unattended while pondering is enabled. ++# maxVisitsPondering = 5000 ++# maxPlayoutsPondering = 3000 ++maxTimePondering = 120.0 ++ ++ ++# ------------------------------ ++# Other search limits and behavior ++# ------------------------------ ++ ++# Approx number of seconds to buffer for lag for GTP time controls - will ++# move a bit faster assuming there is this much lag per move. ++lagBuffer = 1.0 ++ ++# YOU PROBABLY WANT TO TUNE THIS PARAMETER! ++# The number of threads to use when searching. On powerful GPUs the optimal ++# threads may be much higher than the number of CPU cores you have because ++# many threads are needed to feed efficient large batches to the GPU. ++# ++# Run "./katago benchmark" to tune this parameter and test the effect ++# of changes to any of other parameters. ++numSearchThreads = 12 ++ ++# Play a little faster if the opponent is passing, for human-friendliness. ++# Comment these out to disable them, such as if running a controlled match ++# where you are testing KataGo with fixed compute per move vs other bots. ++searchFactorAfterOnePass = 0.50 ++searchFactorAfterTwoPass = 0.25 ++ ++# Play a little faster if super-winning, for human-friendliness. ++# Comment these out to disable them, such as if running a controlled match ++# where you are testing KataGo with fixed compute per move vs other bots. ++searchFactorWhenWinning = 0.40 ++searchFactorWhenWinningThreshold = 0.95 ++ ++# =========================================================================== ++# GPU settings ++# =========================================================================== ++# This section configures GPU settings. ++# ++# Maximum number of positions to send to a single GPU at once. The default ++# value is roughly equal to numSearchThreads, but can be specified manually ++# if running out of memory, or using multiple GPUs that expect to share work. ++# nnMaxBatchSize = ++ ++# Controls the neural network cache size, which is the primary RAM/memory use. ++# KataGo will cache up to (2 ** nnCacheSizePowerOfTwo) many neural net ++# evaluations in case of transpositions in the tree. ++# Increase this to improve performance for searches with tens of thousands ++# of visits or more. Decrease this to limit memory usage. ++# If you're happy to do some math - each neural net entry takes roughly ++# 1.5KB, except when using whole-board ownership/territory ++# visualizations, where each entry will take roughly 3KB. The number of ++# entries is (2 ** nnCacheSizePowerOfTwo). (E.g. 2 ** 18 = 262144.) ++# You can compute roughly how much memory the cache will use based on this. ++# nnCacheSizePowerOfTwo = 20 ++ ++# Size of mutex pool for nnCache is (2 ** this). ++# nnMutexPoolSizePowerOfTwo = 16 ++ ++# Randomize board orientation when running neural net evals? Uncomment and ++# set to false to disable. ++# nnRandomize = true ++ ++# If provided, force usage of a specific seed for nnRandomize. ++# The default is to use a randomly generated seed. ++# nnRandSeed = abcdefg ++ ++# Uncomment and set to true to force GTP to use the maximum board size for ++# internal buffers for the neural net. This will make KataGo slower when ++# evaluating small boards, but will avoid a lengthy initialization time on every ++# change of board size due to having to re-size the neural net buffers on the GPU. ++# This can be useful for example, for OGS's persistent bot mode that uses a single ++# bot instance to handle multiple games and may thrash between different board sizes ++# if there are concurrent games of multiple sizes. ++# gtpForceMaxNNSize = false ++ ++# ------------------------------ ++# Multiple GPUs ++# ------------------------------ ++# Set this to the number of GPUs to use or that are available. ++# IMPORTANT: If more than 1, also uncomment the appropriate TensorRT ++# or CUDA or OpenCL section. ++# numNNServerThreadsPerModel = 1 ++ ++# ------------------------------ ++# TENSORRT GPU settings ++# ------------------------------ ++# These only apply when using the TENSORRT version of KataGo. ++ ++# For one GPU: optionally uncomment this option and change if the GPU to ++# use is not device 0. ++# trtDeviceToUse = 0 ++ ++# For two GPUs: Uncomment these options, AND set numNNServerThreadsPerModel above. ++# Also, change their values if the devices you want to use are not 0 and 1. ++# trtDeviceToUseThread0 = 0 ++# trtDeviceToUseThread1 = 1 ++ ++# For three GPUs: Uncomment these options, AND set numNNServerThreadsPerModel above. ++# Also, change their values if the devices you want to use are not 0 and 1 and 2. ++# trtDeviceToUseThread0 = 0 ++# trtDeviceToUseThread1 = 1 ++# trtDeviceToUseThread2 = 2 ++ ++# The pattern continues for additional GPUs. ++ ++# ------------------------------ ++# CUDA GPU settings ++# ------------------------------ ++# These only apply when using the CUDA version of KataGo. ++ ++# For one GPU: optionally uncomment and change this if the GPU you want to ++# use is not device 0 ++# cudaDeviceToUse = 0 ++ ++# For two GPUs: Uncomment these options, AND set numNNServerThreadsPerModel above. ++# Also, change their values if the devices you want to use are not 0 and 1. ++# cudaDeviceToUseThread0 = 0 ++# cudaDeviceToUseThread1 = 1 ++ ++# For three GPUs: Uncomment these options, AND set numNNServerThreadsPerModel above. ++# Also, change their values if the devices you want to use are not 0 and 1 and 2. ++# cudaDeviceToUseThread0 = 0 ++# cudaDeviceToUseThread1 = 1 ++# cudaDeviceToUseThread2 = 2 ++ ++# The pattern continues for additional GPUs. ++ ++# KataGo will automatically use FP16 or not based on the compute capability ++# of your NVIDIA GPU. If you want to try to force a particular behavior ++# you can uncomment these lines and change them to "true" or "false". ++# cudaUseFP16 = auto ++# cudaUseNHWC = auto ++ ++# ------------------------------ ++# OpenCL GPU settings ++# ------------------------------ ++# These only apply when using the OpenCL version of KataGo. ++ ++# Uncomment and set to true to tune OpenCL for every board size separately, ++# rather than only the largest possible size. ++# openclReTunePerBoardSize = false ++ ++# For one GPU: optionally uncomment and change this if the best device to use is guessed incorrectly. ++# The default behavior tries to guess the 'best' GPU or device on your system to use, usually it will be a good guess. ++# openclDeviceToUse = 0 ++ ++# For two GPUs: Uncomment these two lines and replace X and Y with the device ids of the devices you want to use. ++# It might NOT be 0 and 1, some computers will have many OpenCL devices. You can see what the devices are when ++# KataGo starts up - it should print or log all the devices it finds. ++# (AND also set numNNServerThreadsPerModel above) ++# openclDeviceToUseThread0 = X ++# openclDeviceToUseThread1 = Y ++ ++# For three GPUs: Uncomment these three lines and replace X and Y and Z with the device ids of the devices you want to use. ++# It might NOT be 0 and 1 and 2, some computers will have many OpenCL devices. You can see what the devices are when ++# KataGo starts up - it should print or log all the devices it finds. ++# (AND also set numNNServerThreadsPerModel above) ++# openclDeviceToUseThread0 = X ++# openclDeviceToUseThread1 = Y ++# openclDeviceToUseThread2 = Z ++ ++# The pattern continues for additional GPUs. ++ ++# KataGo will automatically use FP16 or not based on testing your GPU during ++# tuning. If you want to try to force a particular behavior though you can ++# uncomment this option and change it to "true" or "false". This is a fairly ++# blunt setting - more detailed settings are testable by rerunning the tuner ++# with various arguments (./katago tuner). ++# openclUseFP16 = auto ++ ++# ------------------------------ ++# Eigen-specific settings ++# ------------------------------ ++# These only apply when using the Eigen (pure CPU) version of KataGo. ++ ++# Number of CPU threads for evaluating the neural net on the Eigen backend. ++# ++# Default: numSearchThreads ++# numEigenThreadsPerModel = X ++ ++# =========================================================================== ++# Root move selection and biases ++# =========================================================================== ++# Uncomment and edit any of the below values to change them from their default. ++ ++# If provided, force usage of a specific seed for various random things in ++# the search. The default is to use a random seed. ++# searchRandSeed = hijklmn ++ ++# Temperature for the early game, randomize between chosen moves with ++# this temperature ++# chosenMoveTemperatureEarly = 0.5 ++ ++# Decay temperature for the early game by 0.5 every this many moves, ++# scaled with board size. ++# chosenMoveTemperatureHalflife = 19 ++ ++# At the end of search after the early game, randomize between chosen ++# moves with this temperature ++# chosenMoveTemperature = 0.10 ++ ++# Subtract this many visits from each move prior to applying ++# chosenMoveTemperature (unless all moves have too few visits) to downweight ++# unlikely moves ++# chosenMoveSubtract = 0 ++ ++# The same as chosenMoveSubtract but only prunes moves that fall below ++# the threshold. This setting does not affect chosenMoveSubtract. ++# chosenMovePrune = 1 ++ ++# Number of symmetries to sample (without replacement) and average at the root ++# rootNumSymmetriesToSample = 1 ++ ++# Using LCB for move selection? ++# useLcbForSelection = true ++ ++# How many stdevs a move needs to be better than another for LCB selection ++# lcbStdevs = 5.0 ++ ++# Only use LCB override when a move has this proportion of visits as the ++# top move. ++# minVisitPropForLCB = 0.15 ++ ++# =========================================================================== ++# Internal params ++# =========================================================================== ++# Uncomment and edit any of the below values to change them from their default. ++ ++# Scales the utility of winning/losing ++# winLossUtilityFactor = 1.0 ++ ++# Scales the utility for trying to maximize score ++# staticScoreUtilityFactor = 0.10 ++# dynamicScoreUtilityFactor = 0.30 ++ ++# Adjust dynamic score center this proportion of the way towards zero, ++# capped at a reasonable amount. ++# dynamicScoreCenterZeroWeight = 0.20 ++# dynamicScoreCenterScale = 0.75 ++ ++# The utility of getting a "no result" due to triple ko or other long cycle ++# in non-superko rulesets (-1 to 1) ++# noResultUtilityForWhite = 0.0 ++ ++# The number of wins that a draw counts as, for white. (0 to 1) ++# drawEquivalentWinsForWhite = 0.5 ++ ++# Exploration constant for mcts ++# cpuctExploration = 1.0 ++# cpuctExplorationLog = 0.45 ++ ++# Parameters that control exploring more in volatile positions, exploring ++# less in stable positions. ++# cpuctUtilityStdevPrior = 0.40 ++# cpuctUtilityStdevPriorWeight = 2.0 ++# cpuctUtilityStdevScale = 0.85 ++ ++# FPU reduction constant for mcts ++# fpuReductionMax = 0.2 ++# rootFpuReductionMax = 0.1 ++# fpuParentWeightByVisitedPolicy = true ++ ++# Parameters that control weighting of evals based on the net's own ++# self-reported uncertainty. ++# useUncertainty = true ++# uncertaintyExponent = 1.0 ++# uncertaintyCoeff = 0.25 ++ ++# Explore using optimistic policy ++# rootPolicyOptimism = 0.2 ++# policyOptimism = 1.0 ++ ++# Amount to apply a downweighting of children with very bad values relative ++# to good ones. ++# valueWeightExponent = 0.25 ++ ++# Slight incentive for the bot to behave human-like with regard to passing at ++# the end, filling the dame, not wasting time playing in its own territory, ++# etc., and not play moves that are equivalent in terms of points but a bit ++# more unfriendly to humans. ++# rootEndingBonusPoints = 0.5 ++ ++# Make the bot prune useless moves that are just prolonging the game to ++# avoid losing yet. ++# rootPruneUselessMoves = true ++ ++# Apply bias correction based on local pattern keys ++# subtreeValueBiasFactor = 0.45 ++# subtreeValueBiasWeightExponent = 0.85 ++ ++# Use graph search rather than tree search - identify and share search for ++# transpositions. ++# useGraphSearch = true ++ ++# How much to shard the node table for search synchronization ++# nodeTableShardsPowerOfTwo = 16 ++ ++# How many virtual losses to add when a thread descends through a node ++# numVirtualLossesPerThread = 1 ++ ++# Improve the quality of evals under heavy multithreading ++# useNoisePruning = true ++ ++# =========================================================================== ++# Automatic avoid patterns ++# =========================================================================== ++# The parameters in this section provide a way to bias away from moves that match ++# patterns that this instance of KataGo has played in previous games, by auto-saving ++# moves to a directory and then auto-loading and biasing against them each new game. ++# Uncomment them to use them. When using this feature, all parameters must be specified. ++ ++# Different board sizes are tracked separately, but all board sizes share the same ++# parameters by default. Every parameter *except* for autoAvoidRepeatDir and ++# autoAvoidRepeatSaveChunkSize can be overridden per board size, ++# e.g. "autoAvoidRepeatMinTurnNumber13x13". You must ALSO specify the ++# defaults even if you have specified board-size-specific values. ++ ++# Directory to auto-save moves KataGo plays, to avoid them in future games. ++# You can create a new empty directory and put its path here. ++# If you run parallel instances of KataGo, use different directories if you ++# want them not to share their biases, use the same directory if you want ++# all of them to bias away from past moves that any of them have played. ++# KataGo will also automatically DELETE old data in this directory, so it is ++# recommended that if you do share the same directory between parallel instances, ++# that they all use the same settings that affect data saving/deletion. ++# autoAvoidRepeatDir = PATH_TO_NEW_DIRECTORY ++ ++# Penalize this much utility per matching move. ++# Values that are too large may lead to bad play. The value of 0.004 is fairly large ++# and might be large enough to result in some early weird/bad moves when trying ++# to avoid past games' moves if enough games begin the same way. You can experiment with it. ++# autoAvoidRepeatUtility = 0.004 ++ ++# Per each new move saved, exponentially decay prior saved moves by this factor. ++# This way, the bias against moves from many games ago is gradually phased out. ++# For example, 0.9995 = 1 - 1/2000, so would be roughly 2000 prior moves worth of ++# penalty weight remembered, in steady state. Depending on what turn number range ++# you are saving, this might equate to a different number of games For example ++# saving the first 50 moves per game would make this roughly 2000 / 50 = 40 games ++# worth of memory. ++# autoAvoidRepeatLambda = 0.9995 ++ ++# Affects data saving/deletion. ++# When the number of saved moves exceeds this, outright delete them to avoid too many ++# files and disk space building up. Also may affect the speed of saving/loading on start ++# of each game if this is set large and a lot of data builds up. ++# autoAvoidRepeatMaxPoses = 10000 ++ ++# Affects data saving/deletion. ++# Only save data for moves within this turn number range of those games. ++# E.g. setting autoAvoidRepeatMinTurnNumber to a number like 4 or 5 would tend to make ++# KataGo not develop a bias against the initial 3-4 and 4-4 corner moves in almost every game. ++# autoAvoidRepeatMinTurnNumber = 0 ++# autoAvoidRepeatMaxTurnNumber = 50 ++ ++# Affects data saving/deletion. ++# Within a single run of a program, wait to accumulate this many samples ++# (possibly across multiple clear_boards/games) before saving the data. ++# Can help to avoid writing too many small files to disk, especially when GTP is used ++# in a way that clears the board very frequently (e.g. gtp2ogs pooled manager). ++# autoAvoidRepeatSaveChunkSize = 200 ++ ++# =========================================================================== ++# Avoid SGF patterns ++# =========================================================================== ++# The parameters in this section provide a way to avoid moves that follow ++# specific patterns based on a set of SGF files loaded upon startup. ++# This is basically the same as the above "Automatic avoid patterns" section ++# above except you supply your own SGF files to avoid moves from. ++# Uncomment them to use this feature. Additionally, if the SGF file ++# contains the string %SKIP% in a comment on a move, that move will be ++# ignored for this purpose. ++ ++# Load SGF files from this directory when the engine is started ++# (only on startup, will not reload unless engine is restarted) ++# avoidSgfPatternDirs = path/to/directory/with/sgfs/ ++# You can also surround the file path in double quotes if the file path contains trailing spaces or hash signs. ++# Within double quotes, backslashes are escape characters. ++# avoidSgfPatternDirs = "path/to/directory/with/sgfs/" ++ ++# Penalize this much utility per matching move. ++# Set this negative if you instead want to favor SGF patterns instead of ++# penalizing them. This number does not need to be large, even 0.001 will ++# make a difference. Values that are too large may lead to bad play. ++# avoidSgfPatternUtility = 0.001 ++ ++# Optional - load only the newest this many files ++# avoidSgfPatternMaxFiles = 20 ++ ++# Optional - Penalty is multiplied by this per each older SGF file, so that ++# old SGF files matter less than newer ones. ++# avoidSgfPatternLambda = 0.90 ++ ++# Optional - pay attention only to moves made by players with this name. ++# For example, set it to the name that your bot's past games will show up ++# as in the SGF, so that the bot will only avoid repeating moves that itself ++# made in past games, not the moves that its opponents made. ++# avoidSgfPatternAllowedNames = my-ogs-bot-name1,my-ogs-bot-name2 ++ ++# Optional - Ignore moves in SGF files that occurred before this turn number. ++# avoidSgfPatternMinTurnNumber = 0 ++ ++# For more avoid patterns: ++# You can also specify a second set of parameters, and a third, fourth, ++# etc. by numbering 2,3,4,... ++# ++# avoidSgf2PatternDirs = ... ++# avoidSgf2PatternUtility = ... ++# avoidSgf2PatternMaxFiles = ... ++# avoidSgf2PatternLambda = ... ++# avoidSgf2PatternAllowedNames = ... ++# avoidSgf2PatternMinTurnNumber = ... ++ +diff --git a/python/selfplay/KataGoData/opencltuning/tune11_gpuNVIDIAGeForceRTX4060LaptopGPU_x19_y19_c320_mv10.txt b/python/selfplay/KataGoData/opencltuning/tune11_gpuNVIDIAGeForceRTX4060LaptopGPU_x19_y19_c320_mv10.txt +new file mode 100644 +index 00000000..5b66d67d +--- /dev/null ++++ b/python/selfplay/KataGoData/opencltuning/tune11_gpuNVIDIAGeForceRTX4060LaptopGPU_x19_y19_c320_mv10.txt +@@ -0,0 +1,33 @@ ++VERSION=11 ++#canUseFP16Storage ++1 ++#canUseFP16Compute ++0 ++#canUseFP16TensorCores ++1 ++#canUseFP16TensorCoresFor1x1 ++0 ++#shouldUseFP16Storage ++1 ++#shouldUseFP16Compute ++0 ++#shouldUseFP16TensorCores ++1 ++#shouldUseFP16TensorCoresFor1x1 ++0 ++#xGemmDirect ++WGD=32 MDIMCD=8 NDIMCD=8 MDIMAD=16 NDIMBD=16 KWID=2 VWMD=2 VWND=2 PADA=1 PADB=1 ++#xGemm ++MWG=64 NWG=64 KWG=16 MDIMC=16 NDIMC=8 MDIMA=16 NDIMB=8 KWI=2 VWM=4 VWN=4 STRM=0 STRN=0 SA=1 SB=1 ++#xGemm16 ++MWG=64 NWG=64 KWG=16 MDIMC=16 NDIMC=8 MDIMA=16 NDIMB=8 KWI=2 VWM=4 VWN=4 STRM=0 STRN=0 SA=1 SB=1 ++#hGemmWmma ++MWG=64 NWG=64 KWG=16 MWAVE=32 NWAVE=8 MWARP=32 NWARP=8 VWM=2 VWN=2 SA=0 SB=0 ++#hGemmWmmaNCHW ++MWG=16 NWG=16 KWG=16 MWAVE=16 NWAVE=16 MWARP=16 NWARP=16 VWM=1 VWN=2 SB=0 ++#conv3x3 ++INTILE_XSIZE=6 INTILE_YSIZE=6 OUTTILE_XSIZE=4 OUTTILE_YSIZE=4 transLocalSize0=128 transLocalSize1=1 untransLocalSize0=8 untransLocalSize1=4 untransLocalSize2=2 ++#conv5x5 ++INTILE_XSIZE=6 INTILE_YSIZE=6 OUTTILE_XSIZE=2 OUTTILE_YSIZE=2 transLocalSize0=128 transLocalSize1=1 untransLocalSize0=8 untransLocalSize1=4 untransLocalSize2=2 ++#gPool ++XYSTRIDE=16 CHANNELSTRIDE=2 BATCHSTRIDE=2 +diff --git a/python/selfplay/KataGoData/opencltuning/tune11_gpuNVIDIAGeForceRTX4060LaptopGPU_x19_y19_c384_mv14.txt b/python/selfplay/KataGoData/opencltuning/tune11_gpuNVIDIAGeForceRTX4060LaptopGPU_x19_y19_c384_mv14.txt +new file mode 100644 +index 00000000..71421834 +--- /dev/null ++++ b/python/selfplay/KataGoData/opencltuning/tune11_gpuNVIDIAGeForceRTX4060LaptopGPU_x19_y19_c384_mv14.txt +@@ -0,0 +1,33 @@ ++VERSION=11 ++#canUseFP16Storage ++1 ++#canUseFP16Compute ++0 ++#canUseFP16TensorCores ++1 ++#canUseFP16TensorCoresFor1x1 ++0 ++#shouldUseFP16Storage ++1 ++#shouldUseFP16Compute ++0 ++#shouldUseFP16TensorCores ++1 ++#shouldUseFP16TensorCoresFor1x1 ++0 ++#xGemmDirect ++WGD=32 MDIMCD=8 NDIMCD=8 MDIMAD=8 NDIMBD=8 KWID=8 VWMD=2 VWND=4 PADA=1 PADB=1 ++#xGemm ++MWG=64 NWG=64 KWG=16 MDIMC=16 NDIMC=16 MDIMA=16 NDIMB=16 KWI=2 VWM=4 VWN=4 STRM=0 STRN=0 SA=1 SB=1 ++#xGemm16 ++MWG=64 NWG=64 KWG=16 MDIMC=16 NDIMC=16 MDIMA=16 NDIMB=16 KWI=2 VWM=4 VWN=4 STRM=0 STRN=0 SA=1 SB=1 ++#hGemmWmma ++MWG=64 NWG=64 KWG=64 MWAVE=16 NWAVE=16 MWARP=16 NWARP=16 VWM=2 VWN=2 SA=0 SB=0 ++#hGemmWmmaNCHW ++MWG=16 NWG=16 KWG=16 MWAVE=16 NWAVE=16 MWARP=16 NWARP=16 VWM=1 VWN=2 SB=0 ++#conv3x3 ++INTILE_XSIZE=6 INTILE_YSIZE=6 OUTTILE_XSIZE=4 OUTTILE_YSIZE=4 transLocalSize0=64 transLocalSize1=1 untransLocalSize0=16 untransLocalSize1=2 untransLocalSize2=2 ++#conv5x5 ++INTILE_XSIZE=6 INTILE_YSIZE=6 OUTTILE_XSIZE=2 OUTTILE_YSIZE=2 transLocalSize0=64 transLocalSize1=1 untransLocalSize0=16 untransLocalSize1=2 untransLocalSize2=2 ++#gPool ++XYSTRIDE=32 CHANNELSTRIDE=1 BATCHSTRIDE=2 +diff --git a/python/selfplay/README.txt b/python/selfplay/README.txt +new file mode 100644 +index 00000000..fb82db4b +--- /dev/null ++++ b/python/selfplay/README.txt +@@ -0,0 +1,118 @@ ++KataGo v1.15.1 ++https://github.com/lightvector/KataGo ++ ++For neural nets from the latest run, download from here: ++https://katagotraining.org/ ++For nets from earlier runs, see: ++https://katagoarchive.org/ ++ ++For the human SL net (b18c384nbt-humanv0.bin.gz), it was released with: ++https://github.com/lightvector/KataGo/releases/tag/v1.15.0 ++ ++For differences between this version and older versions, see releases page at ++https://github.com/lightvector/KataGo/releases/ ++ ++----------------------------------------------------- ++USAGE: ++----------------------------------------------------- ++KataGo is just an engine and does not have its own graphical interface. So generally you will want to use KataGo along with a GUI or analysis program. ++(https://github.com/lightvector/KataGo#guis) ++ ++FIRST: Run a command like this to make sure KataGo is working, with the neural net file you downloaded. On OpenCL, it will also tune for your GPU. ++ ++./katago.exe benchmark # if you have default_gtp.cfg and default_model.bin.gz ++./katago.exe benchmark -model .bin.gz # if you have default_gtp.cfg ++./katago.exe benchmark -model .bin.gz -config gtp_custom.cfg # use this .bin.gz neural net and this .cfg file ++ ++It will tell you a good number of threads. Edit your .cfg file and set "numSearchThreads" to that many to get best performance. ++ ++OR: Run this command to have KataGo generate a custom gtp config for you based on answering some questions: ++ ++./katago.exe genconfig -model .bin.gz -output gtp_custom.cfg ++ ++NEXT: A command like this will run KataGo's engine. This is the command to give to your [GUI or analysis program](#guis) so that it can run KataGo. ++ ++./katago.exe gtp # if you have default_gtp.cfg and default_model.bin.gz ++./katago.exe gtp -model .bin.gz # if you have default_gtp.cfg ++./katago.exe gtp -model .bin.gz -config gtp_custom.cfg # use this .bin.gz neural net and this .cfg file ++ ++You may need to specify different paths when entering KataGo's command for a GUI program, e.g.: ++ ++path/to/katago.exe gtp -model path/to/.bin.gz ++path/to/katago.exe gtp -model path/to/.bin.gz -config path/to/gtp_custom.cfg ++ ++KataGo should be able to work with any GUI program that supports GTP, as well as any analysis program that supports Leela Zero's `lz-analyze` command, such as Lizzie (https://github.com/featurecat/lizzie) or Sabaki (https://sabaki.yichuanshen.de/). ++ ++NOTE for Windows users: ++If you encounter errors due to a missing "msvcp140.dll" or "msvcp140_1.dll" or "msvcp140_2.dll" or "vcruntime140.dll" or similar, you need to download and install the Microsoft Visual C++ Redistributable, here: ++https://www.microsoft.com/en-us/download/details.aspx?id=48145 ++If this is for a 64-bit Windows version of KataGo, these dll files have already been included for you, otherwise you will need to install them yourself. On a 64-bit Windows version, there is a rare chance that you may need to delete them if you already have it installed yourself separately and the pre-included files are actually causing problems running KataGo. ++ ++----------------------------------------------------- ++HUMAN-STYLE PLAY AND ANALYSIS: ++----------------------------------------------------- ++ ++You can also have KataGo imitate human play if you download the human SL model b18c384nbt-humanv0.bin.gz from https://github.com/lightvector/KataGo/releases/tag/v1.15.0, and run a command like the following, providing both a model and the human SL model: ++ ++./katago.exe gtp -model .bin.gz -human-model b18c384nbt-humanv0.bin.gz -config gtp_human5k_example.cfg ++ ++The gtp_human5k_example.cfg configures KataGo to imitate 5-kyu-level players. You can change it to imitate other ranks too, as well as to do many more things, including making KataGo play in a human style but still at a strong level or analyze in interesting ways. Read the config file itself for documentation on some of these possibilities! ++ ++And see also this guide to using the human SL model, which is written from the perspective of the JSON-based analysis engine mentioned below, but is also applicable to gtp as well. ++https://github.com/lightvector/KataGo/blob/master/docs/Analysis_Engine.md#human-sl-analysis-guide ++ ++----------------------------------------------------- ++OTHER THINGS YOU CAN DO: ++----------------------------------------------------- ++ ++Run a JSON-based analysis engine (https://github.com/lightvector/KataGo/blob/master/docs/Analysis_Engine.md) that can do efficient batched evaluations for a backend Go service: ++ ++./katago.exe analysis -model .gz -config .cfg ++ ++Run a high-performance match engine that will play a pool of bots against each other sharing the same GPU batches and CPUs with each other: ++ ++./katago.exe match -config .cfg -log-file match.log -sgf-output-dir ++ ++Force OpenCL tuner to re-tune: ++ ++./katago.exe tuner -config .cfg ++ ++Print version: ++ ++./katago.exe version ++ ++ ++----------------------------------------------------- ++OPENCL VS CUDA VS EIGEN: ++----------------------------------------------------- ++Explanation of the various versions available at https://github.com/lightvector/KataGo/releases ++ ++OpenCL - Use this if you have a modern GPU and want the easiest setup. ++ ++TensorRT, CUDA11 - Try one of these if you have a top-end NVIDIA GPU, are willing to do some more technical setup work, and care about getting every bit of performance. It MIGHT OR MIGHT NOT be faster than OpenCL! You have to test it on your specific GPU. TensorRT requires CUDA11 as well, and is likely to be the fastest. ++ ++Eigen AVX2 - Use this if you have no good GPU, but you have an Intel or AMD CPU from the last several years. This is a pure CPU version of KataGo. ++ ++Eigen - Use this if you don't have a GPU or your GPU is too old to work, and your CPU turns out not to support AVX2 or FMA. This is a pure CPU version for KataGo for ++This is the pure CPU version of KataGo, with no special instructions, which should hopefully run just about anywhere. ++ ++ ++----------------------------------------------------- ++TUNING FOR PERFORMANCE: ++----------------------------------------------------- ++You will very likely want to tune some of the parameters in `default_gtp.cfg` for your system for good performance, including the number of threads, fp16 usage, NN cache size, pondering settings, and so on. You can also adjust things like KataGo's resign threshold or utility function. Most of the relevant parameters should be be reasonably well documented directly inline in that config. ++ ++There are other a few notes about usage and performance at : https://github.com/lightvector/KataGo ++ ++----------------------------------------------------- ++TROUBLESHOOTING: ++----------------------------------------------------- ++Some common issues are described here: ++https://github.com/lightvector/KataGo#common-causes-of-errors ++ ++Or, feel free to hop into the Leela Zero discord chat, which has become a general chatroom for a variety of computer Go hobbyists and users, and which you can often find people willing to help. ++https://discord.gg/fhDHgfk ++ ++ ++ ++ +diff --git a/python/selfplay/analysis_example.cfg b/python/selfplay/analysis_example.cfg +new file mode 100644 +index 00000000..ca648321 +--- /dev/null ++++ b/python/selfplay/analysis_example.cfg +@@ -0,0 +1,402 @@ ++# Config for KataGo C++ Analysis engine, i.e. "./katago.exe analysis" ++ ++# Example config for C++ (non-python) analysis engine ++ ++# SEE NOTES ABOUT PERFORMANCE AND MEMORY USAGE IN gtp_example.cfg ++# SEE NOTES ABOUT numSearchThreads AND OTHER IMPORTANT PARAMS BELOW! ++ ++# Logs------------------------------------------------------------------------------------ ++ ++# Where to output log? ++logDir = analysis_logs # Each run of KataGo will log to a separate file in this dir ++# logDirDated = analysis_logs # Use this instead of logDir to also write separate dated subdirs ++# logFile = analysis.log # Use this instead of logDir to just specify a single file directly ++# logToStderr = true # Echo everything output to log file to stderr as well ++# logAllRequests = false # Log all input lines received to the analysis engine. ++# logAllResponses = false # Log all lines output to stdout from the analysis engine. ++# logErrorsAndWarnings = true # Log all lines output to stdout from the analysis engine that are errors and warnings ++# logSearchInfo = false # Log debug info for every search performed ++ ++# Report a warning whenever a query contains a field that is unused. Helps to guard against ++# typos when writing code that queries the analysis engine. ++# warnUnusedFields = true ++ ++# Analysis------------------------------------------------------------------------------------ ++ ++# Controls the number of moves after the first move in a variation. ++# analysisPVLen = 15 ++ ++# Report winrates for analysis as (BLACK|WHITE|SIDETOMOVE). ++reportAnalysisWinratesAs = BLACK ++ ++# Larger values will make KataGo explore the top move(s) less deeply and accurately, ++# but explore and give evaluations to a greater variety of moves. ++# An extreme value like 1 will distribute many playouts across every move on the board, even very bad moves. ++# NOTE: defaults to 0.04, under the presumption that the analysis engine will be used mainly for analysis. ++# If you are intending to use the analysis engine to also play games and you want to maximize playing strength, ++# set this to 0.0 either in this config or in the overrides. ++# wideRootNoise = 0.04 ++ ++# Try to limit the effect of possible bad or bogus move sequences in the ++# history leading to this position from affecting KataGo's move predictions. ++# ignorePreRootHistory = true ++ ++# Bot behavior--------------------------------------------------------------------------------------- ++ ++# Handicap ------------- ++ ++# Assume that if black makes many moves in a row right at the start of the game, then the game is a handicap game. ++# This is necessary on some servers and for some GUIs and also when initializing from many SGF files, which may ++# set up a handicap game using repeated GTP "play" commands for black rather than GTP "place_free_handicap" commands. ++# However, it may also lead to incorrect understanding of komi if whiteHandicapBonus is used and a server does NOT ++# have such a practice. ++# Defaults to true! Uncomment and set to false to disable this behavior. ++# assumeMultipleStartingBlackMovesAreHandicap = true ++ ++# Passing and cleanup ------------- ++ ++# Make the bot never assume that its pass will end the game, even if passing would end and "win" under Tromp-Taylor rules. ++# Usually this is a good idea when using it for analysis or playing on servers where scoring may be implemented non-tromp-taylorly. ++# Defaults to true! Uncomment and set to false to disable this. ++# conservativePass = true ++ ++# When using territory scoring, self-play games continue beyond two passes with special cleanup ++# rules that may be confusing for human players. This option prevents the special cleanup phases from being ++# reachable when using the bot for GTP play. ++# Defaults to true! Uncomment and set to false if you want KataGo to be able to enter special cleanup. ++# For example, if you are testing it against itself, or against another bot that has precisely implemented the rules ++# documented at https://lightvector.github.io/KataGo/rules.html ++# preventCleanupPhase = true ++ ++# Search limits----------------------------------------------------------------------------------- ++ ++# By default, if NOT specified in an individual request, limit maximum number of root visits per search to this much ++maxVisits = 500 ++# If provided, cap search time at this many seconds ++# maxTime = 60 ++ ++# Search threads, batching, GPUs-------------------------------------------------------------------------- ++ ++# Try a configuration like this if you only expect the engine to be handling a few queries at a time and you want ++# individual queries to return more quickly, and are okay with the results being a bit lower-quality and the overall ++# peak throughput on queries to be lower. ++numAnalysisThreads = 2 ++numSearchThreadsPerAnalysisThread = 16 ++ ++# Try a configuration like this if you expect to be sending large numbers of queries at a time, and want to maximize ++# total throughput and also the evaluation quality of all the queries and you never care about the response latency ++# of the individual queries, only the throughput as a whole. ++# numAnalysisThreads = 32 ++# numSearchThreadsPerAnalysisThread = 1 ++ ++# You will want to increase one or both numbers if you have a powerful GPU, and possibly decrease one or both if you ++# have a very weak GPU, and play with the balance between them depending on your use case. ++# Read the explanation below to understand how to set these parameters: ++ ++# EXPLANATION: ++# numAnalysisThreads: the number of POSITIONS to be able to search in parallel. ++# numSearchThreadsPerAnalysisThread: the number of threads to use in the tree search for EACH position. ++# (older analysis configs might just have 'numSearchThreads', this is an alias for 'numSearchThreadsPerAnalysisThread') ++ ++# Therefore, the total number of search threads that may be active at a given time could be as large as the product: ++# numAnalysisThreads * numSearchThreadsPerAnalysisThread ++ ++# Searching more positions in parallel is more efficient since the different threads aren't conflicting with each ++# other on the same MCTS search tree. Using multiple threads on the same search will both make things slower ++# and weaken the search (holding playouts fixed) due to out of date statistics on nodes and suboptimal exploration, ++# although the cost is minor for only 2,4,8 threads. ++ ++# So unlike in GTP, which only ever searches one position at a time and where therefore you might as well make ++# numSearchThreads as large as possible, in the analysis engine you often want you often want to keep numSearchThreads small, ++# and instead parallelize across positions, so you can reduce conflict between threads and improve the overall throughput ++# and strength of the search. ++ ++# But obviously you only get the benefit of parallelization across positions when you actually have lots of positions ++# that you are querying at once! For example, setting numAnalysisThreads = 8 is useless if you only ever send one or two ++# queries at a time! ++ ++# Therefore: ++# * If you plan to use the analysis engine only for batch processing large numbers of positions, ++# it's preferable to numSearchThreadsPerAnalysisThread to only a small number (e.g. 1,2,4) and use a higher numAnalysisThreads. ++# * But if you sometimes plan to query the analysis engine for single positions, or otherwise in smaller quantities ++# than -num-analysis-threads, or if you plan to be user-interactive such that the response time on some individual ++# analysis requests is important to keep low, then set numSearchThreadsPerAnalysisThread to a larger number and use ++# a lower numAnalysisThreads. That way, individual searches complete faster due to having more threads on each one. ++ ++# For 19x19 boards, weaker GPUs probably want a TOTAL number of threads (numAnalysisThreads * numSearchThreadsPerAnalysisThread) ++# between 4 and 32. Mid-tier GPUs probably between 16 and 64. Strong GPUs probably between 32 and 256. ++# But there's no substitute for experimenting and seeing what's best for your hardware and your usage case. ++ ++# Keep in mind that the number of threads you want does NOT necessarily have much to do with how many cores you have on your ++# system. The optimal may easily exceed the number of cores! GPU batching is (usually) the dominant consideration. ++ ++# ------------- ++ ++# nnMaxBatchSize is the max number of positions to send to a single GPU at once. Generally, it should be the case that: ++# (number of GPUs you will use * nnMaxBatchSize) >= (numSearchThreads * num-analysis-threads) ++# That way, when each threads tries to request a GPU eval, your batch size summed across GPUs is large enough to handle them ++# all at once. However, it can be sensible to set this a little smaller if you are limited on GPU memory, ++# too large a number may fail if the GPU doesn't have enough memory. ++nnMaxBatchSize = 64 ++ ++# Uncomment and set these smaller if you are going to use the analysis engine EXCLUSIVELY for smaller boards (or plan to ++# run multiple instances, with some instances only handling smaller boards). It should improve performance. ++# It may also mean you can use more threads profitably. ++# maxBoardXSizeForNNBuffer = 19 ++# maxBoardYSizeForNNBuffer = 19 ++ ++# Uncomment and set this to true if you are going to use the analysis engine EXCLUSIVELY for exactly the board size ++# specified by maxBoardXSizeForNNBuffer and maxBoardYSizeForNNBuffer. It may slightly improve performance on some GPUs. ++# requireMaxBoardSize = true ++ ++# TO USE MULTIPLE GPUS: ++# Uncomment and set this to the number of GPUs you have and/or would like to use... ++# AND if it is more than 1, uncomment the appropriate CUDA or OpenCL section below. ++# numNNServerThreadsPerModel = 1 ++ ++# Other General GPU Settings------------------------------------------------------------------------------- ++ ++# Cache up to 2 ** this many neural net evaluations in case of transpositions in the tree. ++nnCacheSizePowerOfTwo = 23 ++# Size of mutex pool for nnCache is 2 ** this ++nnMutexPoolSizePowerOfTwo = 17 ++# Randomize board orientation when running neural net evals? ++nnRandomize = true ++ ++ ++# TENSORRT GPU settings-------------------------------------- ++# These only apply when using the TENSORRT version of KataGo. ++ ++# IF USING ONE GPU: optionally uncomment and change this if the GPU you want to use turns out to be not device 0 ++# trtDeviceToUse = 0 ++ ++# IF USING TWO GPUS: Uncomment these two lines (AND set numNNServerThreadsPerModel above): ++# trtDeviceToUseThread0 = 0 # change this if the first GPU you want to use turns out to be not device 0 ++# trtDeviceToUseThread1 = 1 # change this if the second GPU you want to use turns out to be not device 1 ++ ++# IF USING THREE GPUS: Uncomment these three lines (AND set numNNServerThreadsPerModel above): ++# trtDeviceToUseThread0 = 0 # change this if the first GPU you want to use turns out to be not device 0 ++# trtDeviceToUseThread1 = 1 # change this if the second GPU you want to use turns out to be not device 1 ++# trtDeviceToUseThread2 = 2 # change this if the third GPU you want to use turns out to be not device 2 ++ ++# You can probably guess the pattern if you have four, five, etc. GPUs. ++ ++ ++# CUDA-specific GPU settings-------------------------------------- ++# These only apply when using the CUDA version of KataGo. ++ ++# IF USING ONE GPU: optionally uncomment and change this if the GPU you want to use turns out to be not device 0 ++# cudaDeviceToUse = 0 ++ ++# IF USING TWO GPUS: Uncomment these two lines (AND set numNNServerThreadsPerModel above): ++# cudaDeviceToUseThread0 = 0 # change this if the first GPU you want to use turns out to be not device 0 ++# cudaDeviceToUseThread1 = 1 # change this if the second GPU you want to use turns out to be not device 1 ++ ++# IF USING THREE GPUS: Uncomment these three lines (AND set numNNServerThreadsPerModel above): ++# cudaDeviceToUseThread0 = 0 # change this if the first GPU you want to use turns out to be not device 0 ++# cudaDeviceToUseThread1 = 1 # change this if the second GPU you want to use turns out to be not device 1 ++# cudaDeviceToUseThread2 = 2 # change this if the third GPU you want to use turns out to be not device 2 ++ ++# You can probably guess the pattern if you have four, five, etc. GPUs. ++ ++# KataGo will automatically use FP16 or not based on the compute capability of your NVIDIA GPU. If you ++# want to try to force a particular behavior though you can uncomment these lines and change them ++# to "true" or "false". E.g. it's using FP16 but on your card that's giving an error, or it's not using ++# FP16 but you think it should. ++# cudaUseFP16 = auto ++# cudaUseNHWC = auto ++ ++ ++# OpenCL-specific GPU settings-------------------------------------- ++# These only apply when using the OpenCL version of KataGo. ++ ++# Uncomment to tune OpenCL for every board size separately, rather than only the largest possible size ++# openclReTunePerBoardSize = true ++ ++# IF USING ONE GPU: optionally uncomment and change this if the best device to use is guessed incorrectly. ++# The default behavior tries to guess the 'best' GPU or device on your system to use, usually it will be a good guess. ++# openclDeviceToUse = 0 ++ ++# IF USING TWO GPUS: Uncomment these two lines and replace X and Y with the device ids of the devices you want to use. ++# It might NOT be 0 and 1, some computers will have many OpenCL devices. You can see what the devices are when ++# KataGo starts up - it should print or log all the devices it finds. ++# (AND also set numNNServerThreadsPerModel above) ++# openclDeviceToUseThread0 = X ++# openclDeviceToUseThread1 = Y ++ ++# IF USING THREE GPUS: Uncomment these three lines and replace X and Y and Z with the device ids of the devices you want to use. ++# It might NOT be 0 and 1 and 2, some computers will have many OpenCL devices. You can see what the devices are when ++# KataGo starts up - it should print or log all the devices it finds. ++# (AND also set numNNServerThreadsPerModel above) ++# openclDeviceToUseThread0 = X ++# openclDeviceToUseThread1 = Y ++# openclDeviceToUseThread2 = Z ++ ++# You can probably guess the pattern if you have four, five, etc. GPUs. ++ ++# KataGo will automatically use FP16 or not based on testing your GPU during tuning. If you ++# want to try to force a particular behavior though you can uncomment this lines and change it ++# to "true" or "false". This is a fairly blunt setting - more detailed settings are testable ++# by rerunning the tuner with various arguments. ++# openclUseFP16 = auto ++ ++ ++# Eigen-specific settings-------------------------------------- ++# These only apply when using the Eigen (pure CPU) version of KataGo. ++ ++# This is the number of CPU threads for evaluating the neural net on the Eigen backend. ++# It defaults to min(numAnalysisThreads * numSearchThreadsPerAnalysisThread, numCPUCores). ++# numEigenThreadsPerModel = X ++ ++ ++# Misc Behavior -------------------- ++ ++# If the board is symmetric, search only one copy of each equivalent move. Attempts to also account for ko/superko, will not theoretically perfect for superko. ++# Uncomment and set to false to disable this. ++# rootSymmetryPruning = true ++ ++# Uncomment and set to true to make KataGo avoid a particular joseki that some KataGo nets misevaluate, ++# and also to improve opening diversity versus some particular other bots that like to play it all the time. ++# avoidMYTDaggerHack = false ++ ++# Have KataGo mildly prefer to avoid playing the same joseki in every corner of the board. ++# Uncomment to set to a specific value. A small value like 0.005 should produce already a noticeable behavior change. ++# avoidRepeatedPatternUtility = 0.0 ++ ++# Enable some hacks that mitigate rare instances when passing messes up deeper searches. ++# enablePassingHacks = true ++ ++# Root move selection and biases------------------------------------------------------------------------------ ++# Uncomment and edit any of the below values to change them from their default. ++# Not all of these parameters are applicable to analysis, some are only used for actual play ++ ++# Temperature for the early game, randomize between chosen moves with this temperature ++# chosenMoveTemperatureEarly = 0.5 ++# Decay temperature for the early game by 0.5 every this many moves, scaled with board size. ++# chosenMoveTemperatureHalflife = 19 ++# At the end of search after the early game, randomize between chosen moves with this temperature ++# chosenMoveTemperature = 0.10 ++# Subtract this many visits from each move prior to applying chosenMoveTemperature ++# (unless all moves have too few visits) to downweight unlikely moves ++# chosenMoveSubtract = 0 ++# The same as chosenMoveSubtract but only prunes moves that fall below the threshold, does not affect moves above ++# chosenMovePrune = 1 ++ ++# Number of symmetries to sample (WITHOUT replacement) and average at the root ++# rootNumSymmetriesToSample = 1 ++ ++# Using LCB for move selection? ++# useLcbForSelection = true ++# How many stdevs a move needs to be better than another for LCB selection ++# lcbStdevs = 5.0 ++# Only use LCB override when a move has this proportion of visits as the top move ++# minVisitPropForLCB = 0.15 ++ ++# Internal params------------------------------------------------------------------------------ ++# Uncomment and edit any of the below values to change them from their default. ++ ++# Scales the utility of winning/losing ++# winLossUtilityFactor = 1.0 ++# Scales the utility for trying to maximize score ++# staticScoreUtilityFactor = 0.10 ++# dynamicScoreUtilityFactor = 0.30 ++# Adjust dynamic score center this proportion of the way towards zero, capped at a reasonable amount. ++# dynamicScoreCenterZeroWeight = 0.20 ++# dynamicScoreCenterScale = 0.75 ++# The utility of getting a "no result" due to triple ko or other long cycle in non-superko rulesets (-1 to 1) ++# noResultUtilityForWhite = 0.0 ++# The number of wins that a draw counts as, for white. (0 to 1) ++# drawEquivalentWinsForWhite = 0.5 ++ ++# Exploration constant for mcts ++# cpuctExploration = 1.0 ++# cpuctExplorationLog = 0.45 ++ ++# Parameters that control exploring more in volatile positions, exploring less in stable positions. ++# cpuctUtilityStdevPrior = 0.40 ++# cpuctUtilityStdevPriorWeight = 2.0 ++# cpuctUtilityStdevScale = 0.85 ++ ++# FPU reduction constant for mcts ++# fpuReductionMax = 0.2 ++# rootFpuReductionMax = 0.1 ++# fpuParentWeightByVisitedPolicy = true ++ ++# Parameters that control weighting of evals based on the net's own self-reported uncertainty. ++# useUncertainty = true ++# uncertaintyExponent = 1.0 ++# uncertaintyCoeff = 0.25 ++ ++# Explore using optimistic policy ++# rootPolicyOptimism = 0.2 ++# policyOptimism = 1.0 ++ ++# Amount to apply a downweighting of children with very bad values relative to good ones ++# valueWeightExponent = 0.25 ++ ++# Slight incentive for the bot to behave human-like with regard to passing at the end, filling the dame, ++# not wasting time playing in its own territory, etc, and not play moves that are equivalent in terms of ++# points but a bit more unfriendly to humans. ++# rootEndingBonusPoints = 0.5 ++ ++# Make the bot prune useless moves that are just prolonging the game to avoid losing yet ++# rootPruneUselessMoves = true ++ ++# Apply bias correction based on local pattern keys ++# subtreeValueBiasFactor = 0.45 ++# subtreeValueBiasWeightExponent = 0.85 ++ ++# Use graph search rather than tree search - identify and share search for transpositions. ++# useGraphSearch = true ++ ++# How much to shard the node table for search synchronization ++# nodeTableShardsPowerOfTwo = 16 ++# How many virtual losses to add when a thread descends through a node ++# numVirtualLossesPerThread = 1 ++ ++# Improve the quality of evals under heavy multithreading ++# useNoisePruning = true ++ ++ ++# Avoid SGF Patterns ------------------------------------------------------------------------------ ++# The parameters in this section provide a powerful way to customize KataGo to avoid moves that follow specific patterns ++# based on a set of provided SGF files loaded upon startup. Uncomment them to use this feature. ++# Additionally, if the SGF file contains the string %SKIP% in a comment on a move, that move will be ignored for this purpose. ++ ++# Load sgf files from this directory when the engine is started (ONLY on startup, will not reload unless engine is restarted) ++# avoidSgfPatternDirs = path/to/directory/with/sgfs/ ++# You can also surround the file path in double quotes if the file path contains trailing spaces or hash signs. ++# Within double quotes, backslashes are escape characters. ++# avoidSgfPatternDirs = "path/to/directory/with/sgfs/" ++ ++# Penalize this much utility per matching move. ++# Set this negative if you instead want to make KataGo favor the SGF patterns instead of penalizing it! ++# This number does not need to be large, even 0.001 will make a difference. Too-large values may lead to bad play. ++# avoidSgfPatternUtility = 0.001 ++ ++# Optional - load only the newest this many files ++# avoidSgfPatternMaxFiles = 20 ++ ++# Optional - Penalty is multiplied by this per each older SGF file, so that old sgf files matter less than newer ones. ++# avoidSgfPatternLambda = 0.90 ++ ++# Optional - pay attention only to moves that were made by players with this name. ++# For example you can set it to the name that your bot's past games will show up as in the SGF, so that the bot will only avoid repeating ++# moves that itself made in past games, not the moves that its opponents made. ++# avoidSgfPatternAllowedNames = my-ogs-bot-name1,my-ogs-bot-name2 ++ ++# Optional - Ignore any moves in SGF files that occurred before this turn number. ++# avoidSgfPatternMinTurnNumber = 0 ++ ++# For more avoid patterns: ++# You can also specify a second set of parameters, and a third, fourth, etc by numbering 2,3,4,... ++# avoidSgf2PatternDirs = ... ++# avoidSgf2PatternUtility = ... ++# avoidSgf2PatternMaxFiles = ... ++# avoidSgf2PatternLambda = ... ++# avoidSgf2PatternAllowedNames = ... ++# avoidSgf2PatternMinTurnNumber = ... ++ ++ ++ ++ +diff --git a/python/selfplay/b28.cfg b/python/selfplay/b28.cfg +new file mode 100644 +index 00000000..302e8a40 +--- /dev/null ++++ b/python/selfplay/b28.cfg +@@ -0,0 +1,721 @@ ++# Configuration for KataGo C++ GTP engine ++ ++# Run the program using: `./katago.exe gtp` ++ ++# In this example config, when a parameter is given as a commented out value, ++# that value also is the default value, unless described otherwise. You can ++# uncomment it (remove the pound sign) and change it if you want. ++ ++# =========================================================================== ++# Running on an online server or in a real tournament or match ++# =========================================================================== ++# If you plan to run online or in a tournament, read through the "Rules" ++# section below for proper handling of komi, handicaps, end-of-game cleanup, ++# and other details. ++ ++# =========================================================================== ++# Notes about performance and memory usage ++# =========================================================================== ++# Important: For good performance, you will very likely want to tune the ++# "numSearchThreads" parameter in the Search limits section below! Run ++# "./katago benchmark" to test KataGo and to suggest a reasonable value ++# of this parameter. ++ ++# For multi-GPU systems, read "OpenCL GPU settings" or "CUDA GPU settings". ++# ++# When using OpenCL, verify that KataGo picks the correct device! Some systems ++# may have both an Intel CPU OpenCL and GPU OpenCL. If # KataGo picks the wrong ++# one, correct this by specifying "openclGpuToUse". ++# ++# Consider adjusting "maxVisits", "ponderingEnabled", "resignThreshold", and ++# other parameters depending on your intended usage. ++ ++# =========================================================================== ++# Command-line usage ++# =========================================================================== ++# All of the below values may be set or overridden via command-line arguments: ++# ++# -override-config KEY=VALUE,KEY=VALUE,... ++ ++# =========================================================================== ++# Logs and files ++# =========================================================================== ++# This section defines where and what logging information is produced. ++ ++# Each run of KataGo will log to a separate file in this dir. ++# This is the default. ++logDir = gtp_logs ++# Uncomment and specify this instead of logDir to write separate dated subdirs ++# logDirDated = gtp_logs ++# Uncomment and specify this instead of logDir to log to only a single file ++# logFile = gtp.log ++ ++# Logging options ++logAllGTPCommunication = true ++logSearchInfo = true ++logSearchInfoForChosenMove = false ++logToStderr = true ++ ++# KataGo will display some info to stderr on GTP startup ++# Uncomment the next line and set it to false to suppress that and remain silent ++# startupPrintMessageToStderr = true ++ ++# Write information to stderr, for use in things like malkovich chat to OGS. ++ ogsChatToStderr = true ++ ++# Uncomment and set this to a directory to override where openCLTuner files ++# and other cached data is written. By default it saves into a subdir of the ++# current directory on windows, and a subdir of ~/.katago on Linux. ++# homeDataDir = PATH_TO_DIRECTORY ++ ++# =========================================================================== ++# Analysis ++# =========================================================================== ++# This section configures analysis settings. ++# ++# The maximum number of moves after the first move displayed in variations ++# from analysis commands like kata-analyze or lz-analyze. ++# analysisPVLen = 15 ++ ++# Report winrates for chat and analysis as (BLACK|WHITE|SIDETOMOVE). ++# Most GUIs and analysis tools will expect SIDETOMOVE. ++# reportAnalysisWinratesAs = SIDETOMOVE ++ ++# Extra noise for wider exploration. Large values will force KataGo to ++# analyze a greater variety of moves than it normally would. ++# An extreme value like 1 distributes playouts across every move on the board, ++# even very bad moves. ++# Affects analysis only, does not affect play. ++# analysisWideRootNoise = 0.04 ++ ++# Try to limit the effect of possible bad or bogus move sequences in the ++# history leading to this position from affecting KataGo's move predictions. ++# analysisIgnorePreRootHistory = true ++ ++# =========================================================================== ++# Rules ++# =========================================================================== ++# This section configures the scoring and playing rules. Rules can also be ++# changed mid-run by issuing custom GTP commands. ++# ++# See https://lightvector.github.io/KataGo/rules.html for rules details. ++# ++# See https://github.com/lightvector/KataGo/blob/master/docs/GTP_Extensions.md ++# for GTP commands. ++ ++# Specify the rules as a string. ++# Some legal values include: ++# chinese, japanese, korean, aga, chinese-ogs, new-zealand, stone-scoring, ++# ancient-territory, bga, aga-button ++# ++# For some human rulesets that require complex adjudication in tricky cases ++# (e.g. japanese, korean) KataGo may not precisely match the ruleset in such ++# cases but will do its best. ++rules = tromp-taylor ++ ++# By default, the "rules" parameter is used, but if you comment it out and ++# uncomment one option in each of the sections below, you can specify an ++# arbitrary combination of individual rules. ++ ++# koRule = SIMPLE # Simple ko rules (triple ko = no result) ++# koRule = POSITIONAL # Positional superko ++# koRule = SITUATIONAL # Situational superko ++ ++# scoringRule = AREA # Area scoring ++# scoringRule = TERRITORY # Territory scoring (special computer-friendly territory rules) ++ ++# taxRule = NONE # All surrounded empty points are scored ++# taxRule = SEKI # Eyes in seki do NOT count as points ++# taxRule = ALL # All groups are taxed up to 2 points for the two eyes needed to live ++ ++# Is multiple-stone suicide legal? (Single-stone suicide is always illegal). ++# multiStoneSuicideLegal = false ++# multiStoneSuicideLegal = true ++ ++# "Button go" - the first pass when area scoring awards 0.5 points and does ++# not count for ending the game. ++# Allows area scoring rulesets that have far simpler rules to achieve the same ++# final scoring precision and reward for precise play as territory scoring. ++# hasButton = false ++# hasButton = true ++ ++# Is this a human ruleset where it's okay to pass before having physically ++# captured and removed all dead stones? ++# friendlyPassOk = false ++# friendlyPassOk = true ++ ++# How handicap stones in handicap games are compensated ++# whiteHandicapBonus = 0 # White gets no compensation for black's handicap stones (Tromp-taylor, NZ, JP) ++# whiteHandicapBonus = N-1 # White gets N-1 points for black's N handicap stones (AGA) ++# whiteHandicapBonus = N # White gets N points for black's N handicap stones (Chinese) ++ ++# ------------------------------ ++# Other rules hacks ++# ------------------------------ ++# Uncomment and change to adjust what board size KataGo uses upon startup ++# by default when GTP doesn't specify. ++# defaultBoardSize = 19 ++ ++# By default, Katago will use the komi that the GUI or GTP controller tries to set. ++# Uncomment and set this to have KataGo ignore the controller and always use this komi. ++# ignoreGTPAndForceKomi = 7 ++ ++# =========================================================================== ++# Bot behavior ++# =========================================================================== ++ ++# ------------------------------ ++# Resignation ++# ------------------------------ ++ ++# Resignation occurs if for at least resignConsecTurns in a row, the ++# winLossUtility (on a [-1,1] scale) is below resignThreshold. ++allowResignation = true ++resignThreshold = -0.90 ++resignConsecTurns = 3 ++ ++# By default, KataGo may resign games that it is confidently losing even if they ++# are very close in score. Uncomment and set this to avoid resigning games ++# if the estimated difference is points is less than or equal to this. ++# resignMinScoreDifference = 10 ++ ++# ------------------------------ ++# Handicap ++# ------------------------------ ++# Assume that if black makes many moves in a row right at the start of the ++# game, then the game is a handicap game. This is necessary on some servers ++# and for some GUIs and also when initializing from many SGF files, which may ++# set up a handicap game using repeated GTP "play" commands for black rather ++# than GTP "place_free_handicap" commands; however, it may also lead to ++# incorrect understanding of komi if whiteHandicapBonus is used and a server ++# does not have such a practice. Uncomment and set to false to disable. ++# assumeMultipleStartingBlackMovesAreHandicap = true ++ ++# Makes katago dynamically adjust in handicap or altered-komi games to assume ++# based on those game settings that it must be stronger or weaker than the ++# opponent and to play accordingly. Greatly improves handicap strength by ++# biasing winrates and scores to favor appropriate safe/aggressive play. ++# Does NOT affect analysis (lz-analyze, kata-analyze, used by programs like ++# Lizzie) so analysis remains unbiased. Uncomment and set this to 0 to disable ++# this and make KataGo play the same always. ++# dynamicPlayoutDoublingAdvantageCapPerOppLead = 0.045 ++ ++# Instead of "dynamicPlayoutDoublingAdvantageCapPerOppLead", you can comment ++# that out and uncomment and set "playoutDoublingAdvantage" to a fixed value ++# from -3.0 to 3.0 that will not change dynamically. ++# ALSO affects analysis tools (lz-analyze, kata-analyze, used by e.g. Lizzie). ++# Negative makes KataGo behave as if it is much weaker than the opponent. ++# Positive makes KataGo behave as if it is much stronger than the opponent. ++# KataGo will adjust to favor safe/aggressive play as appropriate based on ++# the combination of who is ahead and how much stronger/weaker it thinks it is, ++# and report winrates and scores taking the strength difference into account. ++# ++# If this and "dynamicPlayoutDoublingAdvantageCapPerOppLead" are both set ++# then dynamic will be used for all games and this fixed value will be used ++# for analysis tools. ++# playoutDoublingAdvantage = 0.0 ++ ++# Uncomment one of these when using "playoutDoublingAdvantage" to enforce ++# that it will only apply when KataGo plays as the specified color and will be ++# negated when playing as the opposite color. ++# playoutDoublingAdvantagePla = BLACK ++# playoutDoublingAdvantagePla = WHITE ++ ++# ------------------------------ ++# Passing and cleanup ++# ------------------------------ ++# Make the bot never assume that its pass will end the game, even if passing ++# would end and "win" under Tromp-Taylor rules. Usually this is a good idea ++# when using it for analysis or playing on servers where scoring may be ++# implemented non-tromp-taylorly. Uncomment and set to false to disable. ++# conservativePass = true ++ ++# When using territory scoring, self-play games continue beyond two passes ++# with special cleanup rules that may be confusing for human players. This ++# option prevents the special cleanup phases from being reachable when using ++# the bot for GTP play. Uncomment and set to false to enable entering special ++# cleanup. For example, if you are testing it against itself, or against ++# another bot that has precisely implemented the rules documented at ++# https://lightvector.github.io/KataGo/rules.html ++# preventCleanupPhase = true ++ ++# ------------------------------ ++# Miscellaneous behavior ++# ------------------------------ ++# If the board is symmetric, search only one copy of each equivalent move. ++# Attempts to also account for ko/superko, will not theoretically perfect for ++# superko. Uncomment and set to false to disable. ++# rootSymmetryPruning = true ++ ++# Uncomment and set to true to avoid a particular joseki that some networks ++# misevaluate, and also to improve opening diversity versus some particular ++# other bots that like to play it all the time. ++# avoidMYTDaggerHack = false ++ ++# Prefer to avoid playing the same joseki in every corner of the board. ++# Uncomment to set to a specific value. See "Avoid SGF patterns" section. ++# By default: 0 (even games), 0.005 (handicap games) ++# avoidRepeatedPatternUtility = 0.0 ++ ++# Experimental logic to fight against mirror Go even with unfavorable komi. ++# Uncomment to set to a specific value to use for both playing and analysis. ++# By default: true when playing via GTP, but false when analyzing. ++# antiMirror = true ++ ++# Enable some hacks that mitigate rare instances when passing messes up deeper searches. ++# enablePassingHacks = true ++ ++# Uncomment and set this to true to prevent bad or bogus move sequences ++# in the history leading to this position from affecting KataGo's move choices. ++# Same as analysisIgnorePreRootHistory (see above) but applies to actual play. ++# You can enable this if KataGo is being asked to play from positions that it did not ++# choose the moves to reach. ++# ignorePreRootHistory = false ++ ++# =========================================================================== ++# Search limits ++# =========================================================================== ++ ++# Terminology: ++# "Playouts" is the number of new playouts of search performed each turn. ++# "Visits" is the same as "Playouts" but also counts search performed on ++# previous turns that is still applicable to this turn. ++# "Time" is the time in seconds. ++ ++# For example, if KataGo searched 200 nodes on the previous turn, and then ++# after the opponent's reply, 50 nodes of its search tree was still valid, ++# then a visit limit of 200 would allow KataGo to search 150 new nodes ++# (for a final tree size of 200 nodes), whereas a playout limit of of 200 ++# would allow KataGo to search 200 nodes (for a final tree size of 250 nodes). ++ ++# Additionally, KataGo may also move before than the limit in order to ++# obey time controls (e.g. byo-yomi, etc) if the GTP controller has ++# told KataGo that the game has is being played with a given time control. ++ ++# Limits for search on the current turn. ++# If commented out or unspecified, the default is to have no limit. ++# maxVisits = 30000 ++# maxPlayouts = 300 ++maxTime = 1.0 ++ ++# Ponder on the opponent's turn? ++ponderingEnabled = true ++ ++# Limits for search when pondering on the opponent's turn. ++# If commented out or unspecified, the default is to have no limit. ++# Limiting the maximum time is recommended so that KataGo won't burn CPU/GPU ++# forever and/or run out of RAM if left unattended while pondering is enabled. ++# maxVisitsPondering = 5000 ++# maxPlayoutsPondering = 3000 ++maxTimePondering = 120.0 ++ ++ ++# ------------------------------ ++# Other search limits and behavior ++# ------------------------------ ++ ++# Approx number of seconds to buffer for lag for GTP time controls - will ++# move a bit faster assuming there is this much lag per move. ++lagBuffer = 1.0 ++ ++# YOU PROBABLY WANT TO TUNE THIS PARAMETER! ++# The number of threads to use when searching. On powerful GPUs the optimal ++# threads may be much higher than the number of CPU cores you have because ++# many threads are needed to feed efficient large batches to the GPU. ++# ++# Run "./katago benchmark" to tune this parameter and test the effect ++# of changes to any of other parameters. ++numSearchThreads = 10 ++ ++# Play a little faster if the opponent is passing, for human-friendliness. ++# Comment these out to disable them, such as if running a controlled match ++# where you are testing KataGo with fixed compute per move vs other bots. ++searchFactorAfterOnePass = 0.50 ++searchFactorAfterTwoPass = 0.25 ++ ++# Play a little faster if super-winning, for human-friendliness. ++# Comment these out to disable them, such as if running a controlled match ++# where you are testing KataGo with fixed compute per move vs other bots. ++searchFactorWhenWinning = 0.40 ++searchFactorWhenWinningThreshold = 0.95 ++ ++# =========================================================================== ++# GPU settings ++# =========================================================================== ++# This section configures GPU settings. ++# ++# Maximum number of positions to send to a single GPU at once. The default ++# value is roughly equal to numSearchThreads, but can be specified manually ++# if running out of memory, or using multiple GPUs that expect to share work. ++# nnMaxBatchSize = ++ ++# Controls the neural network cache size, which is the primary RAM/memory use. ++# KataGo will cache up to (2 ** nnCacheSizePowerOfTwo) many neural net ++# evaluations in case of transpositions in the tree. ++# Increase this to improve performance for searches with tens of thousands ++# of visits or more. Decrease this to limit memory usage. ++# If you're happy to do some math - each neural net entry takes roughly ++# 1.5KB, except when using whole-board ownership/territory ++# visualizations, where each entry will take roughly 3KB. The number of ++# entries is (2 ** nnCacheSizePowerOfTwo). (E.g. 2 ** 18 = 262144.) ++# You can compute roughly how much memory the cache will use based on this. ++# nnCacheSizePowerOfTwo = 20 ++ ++# Size of mutex pool for nnCache is (2 ** this). ++# nnMutexPoolSizePowerOfTwo = 16 ++ ++# Randomize board orientation when running neural net evals? Uncomment and ++# set to false to disable. ++# nnRandomize = true ++ ++# If provided, force usage of a specific seed for nnRandomize. ++# The default is to use a randomly generated seed. ++# nnRandSeed = abcdefg ++ ++# Uncomment and set to true to force GTP to use the maximum board size for ++# internal buffers for the neural net. This will make KataGo slower when ++# evaluating small boards, but will avoid a lengthy initialization time on every ++# change of board size due to having to re-size the neural net buffers on the GPU. ++# This can be useful for example, for OGS's persistent bot mode that uses a single ++# bot instance to handle multiple games and may thrash between different board sizes ++# if there are concurrent games of multiple sizes. ++# gtpForceMaxNNSize = false ++ ++# ------------------------------ ++# Multiple GPUs ++# ------------------------------ ++# Set this to the number of GPUs to use or that are available. ++# IMPORTANT: If more than 1, also uncomment the appropriate TensorRT ++# or CUDA or OpenCL section. ++# numNNServerThreadsPerModel = 1 ++ ++# ------------------------------ ++# TENSORRT GPU settings ++# ------------------------------ ++# These only apply when using the TENSORRT version of KataGo. ++ ++# For one GPU: optionally uncomment this option and change if the GPU to ++# use is not device 0. ++# trtDeviceToUse = 0 ++ ++# For two GPUs: Uncomment these options, AND set numNNServerThreadsPerModel above. ++# Also, change their values if the devices you want to use are not 0 and 1. ++# trtDeviceToUseThread0 = 0 ++# trtDeviceToUseThread1 = 1 ++ ++# For three GPUs: Uncomment these options, AND set numNNServerThreadsPerModel above. ++# Also, change their values if the devices you want to use are not 0 and 1 and 2. ++# trtDeviceToUseThread0 = 0 ++# trtDeviceToUseThread1 = 1 ++# trtDeviceToUseThread2 = 2 ++ ++# The pattern continues for additional GPUs. ++ ++# ------------------------------ ++# CUDA GPU settings ++# ------------------------------ ++# These only apply when using the CUDA version of KataGo. ++ ++# For one GPU: optionally uncomment and change this if the GPU you want to ++# use is not device 0 ++# cudaDeviceToUse = 0 ++ ++# For two GPUs: Uncomment these options, AND set numNNServerThreadsPerModel above. ++# Also, change their values if the devices you want to use are not 0 and 1. ++# cudaDeviceToUseThread0 = 0 ++# cudaDeviceToUseThread1 = 1 ++ ++# For three GPUs: Uncomment these options, AND set numNNServerThreadsPerModel above. ++# Also, change their values if the devices you want to use are not 0 and 1 and 2. ++# cudaDeviceToUseThread0 = 0 ++# cudaDeviceToUseThread1 = 1 ++# cudaDeviceToUseThread2 = 2 ++ ++# The pattern continues for additional GPUs. ++ ++# KataGo will automatically use FP16 or not based on the compute capability ++# of your NVIDIA GPU. If you want to try to force a particular behavior ++# you can uncomment these lines and change them to "true" or "false". ++# cudaUseFP16 = auto ++# cudaUseNHWC = auto ++ ++# ------------------------------ ++# OpenCL GPU settings ++# ------------------------------ ++# These only apply when using the OpenCL version of KataGo. ++ ++# Uncomment and set to true to tune OpenCL for every board size separately, ++# rather than only the largest possible size. ++# openclReTunePerBoardSize = false ++ ++# For one GPU: optionally uncomment and change this if the best device to use is guessed incorrectly. ++# The default behavior tries to guess the 'best' GPU or device on your system to use, usually it will be a good guess. ++# openclDeviceToUse = 0 ++ ++# For two GPUs: Uncomment these two lines and replace X and Y with the device ids of the devices you want to use. ++# It might NOT be 0 and 1, some computers will have many OpenCL devices. You can see what the devices are when ++# KataGo starts up - it should print or log all the devices it finds. ++# (AND also set numNNServerThreadsPerModel above) ++# openclDeviceToUseThread0 = X ++# openclDeviceToUseThread1 = Y ++ ++# For three GPUs: Uncomment these three lines and replace X and Y and Z with the device ids of the devices you want to use. ++# It might NOT be 0 and 1 and 2, some computers will have many OpenCL devices. You can see what the devices are when ++# KataGo starts up - it should print or log all the devices it finds. ++# (AND also set numNNServerThreadsPerModel above) ++# openclDeviceToUseThread0 = X ++# openclDeviceToUseThread1 = Y ++# openclDeviceToUseThread2 = Z ++ ++# The pattern continues for additional GPUs. ++ ++# KataGo will automatically use FP16 or not based on testing your GPU during ++# tuning. If you want to try to force a particular behavior though you can ++# uncomment this option and change it to "true" or "false". This is a fairly ++# blunt setting - more detailed settings are testable by rerunning the tuner ++# with various arguments (./katago tuner). ++# openclUseFP16 = auto ++ ++# ------------------------------ ++# Eigen-specific settings ++# ------------------------------ ++# These only apply when using the Eigen (pure CPU) version of KataGo. ++ ++# Number of CPU threads for evaluating the neural net on the Eigen backend. ++# ++# Default: numSearchThreads ++# numEigenThreadsPerModel = X ++ ++# =========================================================================== ++# Root move selection and biases ++# =========================================================================== ++# Uncomment and edit any of the below values to change them from their default. ++ ++# If provided, force usage of a specific seed for various random things in ++# the search. The default is to use a random seed. ++# searchRandSeed = hijklmn ++ ++# Temperature for the early game, randomize between chosen moves with ++# this temperature ++# chosenMoveTemperatureEarly = 0.5 ++ ++# Decay temperature for the early game by 0.5 every this many moves, ++# scaled with board size. ++# chosenMoveTemperatureHalflife = 19 ++ ++# At the end of search after the early game, randomize between chosen ++# moves with this temperature ++# chosenMoveTemperature = 0.10 ++ ++# Subtract this many visits from each move prior to applying ++# chosenMoveTemperature (unless all moves have too few visits) to downweight ++# unlikely moves ++# chosenMoveSubtract = 0 ++ ++# The same as chosenMoveSubtract but only prunes moves that fall below ++# the threshold. This setting does not affect chosenMoveSubtract. ++# chosenMovePrune = 1 ++ ++# Number of symmetries to sample (without replacement) and average at the root ++# rootNumSymmetriesToSample = 1 ++ ++# Using LCB for move selection? ++# useLcbForSelection = true ++ ++# How many stdevs a move needs to be better than another for LCB selection ++# lcbStdevs = 5.0 ++ ++# Only use LCB override when a move has this proportion of visits as the ++# top move. ++# minVisitPropForLCB = 0.15 ++ ++# =========================================================================== ++# Internal params ++# =========================================================================== ++# Uncomment and edit any of the below values to change them from their default. ++ ++# Scales the utility of winning/losing ++# winLossUtilityFactor = 1.0 ++ ++# Scales the utility for trying to maximize score ++# staticScoreUtilityFactor = 0.10 ++# dynamicScoreUtilityFactor = 0.30 ++ ++# Adjust dynamic score center this proportion of the way towards zero, ++# capped at a reasonable amount. ++# dynamicScoreCenterZeroWeight = 0.20 ++# dynamicScoreCenterScale = 0.75 ++ ++# The utility of getting a "no result" due to triple ko or other long cycle ++# in non-superko rulesets (-1 to 1) ++# noResultUtilityForWhite = 0.0 ++ ++# The number of wins that a draw counts as, for white. (0 to 1) ++# drawEquivalentWinsForWhite = 0.5 ++ ++# Exploration constant for mcts ++# cpuctExploration = 1.0 ++# cpuctExplorationLog = 0.45 ++ ++# Parameters that control exploring more in volatile positions, exploring ++# less in stable positions. ++# cpuctUtilityStdevPrior = 0.40 ++# cpuctUtilityStdevPriorWeight = 2.0 ++# cpuctUtilityStdevScale = 0.85 ++ ++# FPU reduction constant for mcts ++# fpuReductionMax = 0.2 ++# rootFpuReductionMax = 0.1 ++# fpuParentWeightByVisitedPolicy = true ++ ++# Parameters that control weighting of evals based on the net's own ++# self-reported uncertainty. ++# useUncertainty = true ++# uncertaintyExponent = 1.0 ++# uncertaintyCoeff = 0.25 ++ ++# Explore using optimistic policy ++# rootPolicyOptimism = 0.2 ++# policyOptimism = 1.0 ++ ++# Amount to apply a downweighting of children with very bad values relative ++# to good ones. ++# valueWeightExponent = 0.25 ++ ++# Slight incentive for the bot to behave human-like with regard to passing at ++# the end, filling the dame, not wasting time playing in its own territory, ++# etc., and not play moves that are equivalent in terms of points but a bit ++# more unfriendly to humans. ++# rootEndingBonusPoints = 0.5 ++ ++# Make the bot prune useless moves that are just prolonging the game to ++# avoid losing yet. ++# rootPruneUselessMoves = true ++ ++# Apply bias correction based on local pattern keys ++# subtreeValueBiasFactor = 0.45 ++# subtreeValueBiasWeightExponent = 0.85 ++ ++# Use graph search rather than tree search - identify and share search for ++# transpositions. ++# useGraphSearch = true ++ ++# How much to shard the node table for search synchronization ++# nodeTableShardsPowerOfTwo = 16 ++ ++# How many virtual losses to add when a thread descends through a node ++# numVirtualLossesPerThread = 1 ++ ++# Improve the quality of evals under heavy multithreading ++# useNoisePruning = true ++ ++# =========================================================================== ++# Automatic avoid patterns ++# =========================================================================== ++# The parameters in this section provide a way to bias away from moves that match ++# patterns that this instance of KataGo has played in previous games, by auto-saving ++# moves to a directory and then auto-loading and biasing against them each new game. ++# Uncomment them to use them. When using this feature, all parameters must be specified. ++ ++# Different board sizes are tracked separately, but all board sizes share the same ++# parameters by default. Every parameter *except* for autoAvoidRepeatDir and ++# autoAvoidRepeatSaveChunkSize can be overridden per board size, ++# e.g. "autoAvoidRepeatMinTurnNumber13x13". You must ALSO specify the ++# defaults even if you have specified board-size-specific values. ++ ++# Directory to auto-save moves KataGo plays, to avoid them in future games. ++# You can create a new empty directory and put its path here. ++# If you run parallel instances of KataGo, use different directories if you ++# want them not to share their biases, use the same directory if you want ++# all of them to bias away from past moves that any of them have played. ++# KataGo will also automatically DELETE old data in this directory, so it is ++# recommended that if you do share the same directory between parallel instances, ++# that they all use the same settings that affect data saving/deletion. ++# autoAvoidRepeatDir = PATH_TO_NEW_DIRECTORY ++ ++# Penalize this much utility per matching move. ++# Values that are too large may lead to bad play. The value of 0.004 is fairly large ++# and might be large enough to result in some early weird/bad moves when trying ++# to avoid past games' moves if enough games begin the same way. You can experiment with it. ++# autoAvoidRepeatUtility = 0.004 ++ ++# Per each new move saved, exponentially decay prior saved moves by this factor. ++# This way, the bias against moves from many games ago is gradually phased out. ++# For example, 0.9995 = 1 - 1/2000, so would be roughly 2000 prior moves worth of ++# penalty weight remembered, in steady state. Depending on what turn number range ++# you are saving, this might equate to a different number of games For example ++# saving the first 50 moves per game would make this roughly 2000 / 50 = 40 games ++# worth of memory. ++# autoAvoidRepeatLambda = 0.9995 ++ ++# Affects data saving/deletion. ++# When the number of saved moves exceeds this, outright delete them to avoid too many ++# files and disk space building up. Also may affect the speed of saving/loading on start ++# of each game if this is set large and a lot of data builds up. ++# autoAvoidRepeatMaxPoses = 10000 ++ ++# Affects data saving/deletion. ++# Only save data for moves within this turn number range of those games. ++# E.g. setting autoAvoidRepeatMinTurnNumber to a number like 4 or 5 would tend to make ++# KataGo not develop a bias against the initial 3-4 and 4-4 corner moves in almost every game. ++# autoAvoidRepeatMinTurnNumber = 0 ++# autoAvoidRepeatMaxTurnNumber = 50 ++ ++# Affects data saving/deletion. ++# Within a single run of a program, wait to accumulate this many samples ++# (possibly across multiple clear_boards/games) before saving the data. ++# Can help to avoid writing too many small files to disk, especially when GTP is used ++# in a way that clears the board very frequently (e.g. gtp2ogs pooled manager). ++# autoAvoidRepeatSaveChunkSize = 200 ++ ++# =========================================================================== ++# Avoid SGF patterns ++# =========================================================================== ++# The parameters in this section provide a way to avoid moves that follow ++# specific patterns based on a set of SGF files loaded upon startup. ++# This is basically the same as the above "Automatic avoid patterns" section ++# above except you supply your own SGF files to avoid moves from. ++# Uncomment them to use this feature. Additionally, if the SGF file ++# contains the string %SKIP% in a comment on a move, that move will be ++# ignored for this purpose. ++ ++# Load SGF files from this directory when the engine is started ++# (only on startup, will not reload unless engine is restarted) ++# avoidSgfPatternDirs = path/to/directory/with/sgfs/ ++# You can also surround the file path in double quotes if the file path contains trailing spaces or hash signs. ++# Within double quotes, backslashes are escape characters. ++# avoidSgfPatternDirs = "path/to/directory/with/sgfs/" ++ ++# Penalize this much utility per matching move. ++# Set this negative if you instead want to favor SGF patterns instead of ++# penalizing them. This number does not need to be large, even 0.001 will ++# make a difference. Values that are too large may lead to bad play. ++# avoidSgfPatternUtility = 0.001 ++ ++# Optional - load only the newest this many files ++# avoidSgfPatternMaxFiles = 20 ++ ++# Optional - Penalty is multiplied by this per each older SGF file, so that ++# old SGF files matter less than newer ones. ++# avoidSgfPatternLambda = 0.90 ++ ++# Optional - pay attention only to moves made by players with this name. ++# For example, set it to the name that your bot's past games will show up ++# as in the SGF, so that the bot will only avoid repeating moves that itself ++# made in past games, not the moves that its opponents made. ++# avoidSgfPatternAllowedNames = my-ogs-bot-name1,my-ogs-bot-name2 ++ ++# Optional - Ignore moves in SGF files that occurred before this turn number. ++# avoidSgfPatternMinTurnNumber = 0 ++ ++# For more avoid patterns: ++# You can also specify a second set of parameters, and a third, fourth, ++# etc. by numbering 2,3,4,... ++# ++# avoidSgf2PatternDirs = ... ++# avoidSgf2PatternUtility = ... ++# avoidSgf2PatternMaxFiles = ... ++# avoidSgf2PatternLambda = ... ++# avoidSgf2PatternAllowedNames = ... ++# avoidSgf2PatternMinTurnNumber = ... ++ +diff --git a/python/selfplay/cacert.pem b/python/selfplay/cacert.pem +new file mode 100644 +index 00000000..f9bd706b +--- /dev/null ++++ b/python/selfplay/cacert.pem +@@ -0,0 +1,3447 @@ ++## ++## Bundle of CA Root Certificates ++## ++## Certificate data from Mozilla as of: Wed Jul 22 03:12:14 2020 GMT ++## ++## This is a bundle of X.509 certificates of public Certificate Authorities ++## (CA). These were automatically extracted from Mozilla's root certificates ++## file (certdata.txt). This file can be found in the mozilla source tree: ++## https://hg.mozilla.org/releases/mozilla-release/raw-file/default/security/nss/lib/ckfw/builtins/certdata.txt ++## ++## It contains the certificates in PEM format and therefore ++## can be directly used with curl / libcurl / php_curl, or with ++## an Apache+mod_ssl webserver for SSL client authentication. ++## Just configure this file as the SSLCACertificateFile. ++## ++## Conversion done with mk-ca-bundle.pl version 1.28. ++## SHA256: cc6408bd4be7fbfb8699bdb40ccb7f6de5780d681d87785ea362646e4dad5e8e ++## ++ ++ ++GlobalSign Root CA ++================== ++-----BEGIN CERTIFICATE----- ++MIIDdTCCAl2gAwIBAgILBAAAAAABFUtaw5QwDQYJKoZIhvcNAQEFBQAwVzELMAkGA1UEBhMCQkUx ++GTAXBgNVBAoTEEdsb2JhbFNpZ24gbnYtc2ExEDAOBgNVBAsTB1Jvb3QgQ0ExGzAZBgNVBAMTEkds ++b2JhbFNpZ24gUm9vdCBDQTAeFw05ODA5MDExMjAwMDBaFw0yODAxMjgxMjAwMDBaMFcxCzAJBgNV ++BAYTAkJFMRkwFwYDVQQKExBHbG9iYWxTaWduIG52LXNhMRAwDgYDVQQLEwdSb290IENBMRswGQYD ++VQQDExJHbG9iYWxTaWduIFJvb3QgQ0EwggEiMA0GCSqGSIb3DQEBAQUAA4IBDwAwggEKAoIBAQDa ++DuaZjc6j40+Kfvvxi4Mla+pIH/EqsLmVEQS98GPR4mdmzxzdzxtIK+6NiY6arymAZavpxy0Sy6sc ++THAHoT0KMM0VjU/43dSMUBUc71DuxC73/OlS8pF94G3VNTCOXkNz8kHp1Wrjsok6Vjk4bwY8iGlb ++Kk3Fp1S4bInMm/k8yuX9ifUSPJJ4ltbcdG6TRGHRjcdGsnUOhugZitVtbNV4FpWi6cgKOOvyJBNP ++c1STE4U6G7weNLWLBYy5d4ux2x8gkasJU26Qzns3dLlwR5EiUWMWea6xrkEmCMgZK9FGqkjWZCrX ++gzT/LCrBbBlDSgeF59N89iFo7+ryUp9/k5DPAgMBAAGjQjBAMA4GA1UdDwEB/wQEAwIBBjAPBgNV ++HRMBAf8EBTADAQH/MB0GA1UdDgQWBBRge2YaRQ2XyolQL30EzTSo//z9SzANBgkqhkiG9w0BAQUF ++AAOCAQEA1nPnfE920I2/7LqivjTFKDK1fPxsnCwrvQmeU79rXqoRSLblCKOzyj1hTdNGCbM+w6Dj ++Y1Ub8rrvrTnhQ7k4o+YviiY776BQVvnGCv04zcQLcFGUl5gE38NflNUVyRRBnMRddWQVDf9VMOyG ++j/8N7yy5Y0b2qvzfvGn9LhJIZJrglfCm7ymPAbEVtQwdpf5pLGkkeB6zpxxxYu7KyJesF12KwvhH ++hm4qxFYxldBniYUr+WymXUadDKqC5JlR3XC321Y9YeRq4VzW9v493kHMB65jUr9TU/Qr6cf9tveC ++X4XSQRjbgbMEHMUfpIBvFSDJ3gyICh3WZlXi/EjJKSZp4A== ++-----END CERTIFICATE----- ++ ++GlobalSign Root CA - R2 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you have entered your password. ++ ++serverUrl = https://katagotraining.org/ ++username = Fill in your username here ++password = Fill in your password here ++# If your password has hash signs or trailing spaces, you can also surround the password with double quotes, like this. ++# Within double quotes, backslashes are escape characters. ++# password = "Fill in your password here" ++ ++# How many games should KataGo play at once? For good GPUs, putting too small a number here will be *very* inefficient. ++# For absolute-top-tier GPUs, try numbers like 32, 40, 64, etc. ++# For modern good middle-tier GPUs, you can try numbers like 8 or 16. ++# For very old or weak GPUs, or if you want KataGo to put less load on your system, try 4. ++maxSimultaneousGames = Fill in a number here ++ ++# Set to true if you want one of KataGo's games to be streamed to a text file, so you can watch. ++# Follow in a separate shell with a command like the following: ++# Linux: "tail -f watchgame.txt" ++# Windows Powershell: "Get-Content .\watchgame.txt -Tail 50 -Wait" ++watchOngoingGameInFile = false ++watchOngoingGameInFileName = watchgame.txt ++ ++# If you use an https proxy to connect to the internet, set these to the host and port of your proxy. ++# proxyHost = HOSTNAME ++# proxyPort = 1234 ++ ++# Special-purpose options. Most users should not need to adjust these. ++# onlyPlayRatingMatches = false ++# maxRatingMatches = 1 ++# disablePredownloadLoop = false ++# warnTaskUnusedKeys = false ++ ++# KataGo will only use one GPU by default. You can edit the below if you have multiple GPUs and want to use them all. ++ ++ ++# TO USE MULTIPLE GPUS: ++# Set this to the number of GPUs you have and/or would like to use. ++# **AND** if it is more than 1, uncomment the appropriate CUDA or OpenCL section below. ++# numNNServerThreadsPerModel = 1 ++ ++ ++# TENSORRT GPU settings-------------------------------------- ++# These only apply when using the TENSORRT version of KataGo. ++ ++# IF USING ONE GPU: optionally uncomment and change this if the GPU you want to use turns out to be not device 0 ++# trtDeviceToUse = 0 ++ ++# IF USING TWO GPUS: Uncomment these two lines (AND set numNNServerThreadsPerModel above): ++# trtDeviceToUseThread0 = 0 # change this if the first GPU you want to use turns out to be not device 0 ++# trtDeviceToUseThread1 = 1 # change this if the second GPU you want to use turns out to be not device 1 ++ ++# IF USING THREE GPUS: Uncomment these three lines (AND set numNNServerThreadsPerModel above): ++# trtDeviceToUseThread0 = 0 # change this if the first GPU you want to use turns out to be not device 0 ++# trtDeviceToUseThread1 = 1 # change this if the second GPU you want to use turns out to be not device 1 ++# trtDeviceToUseThread2 = 2 # change this if the third GPU you want to use turns out to be not device 2 ++ ++# You can probably guess the pattern if you have four, five, etc. GPUs. ++ ++ ++# CUDA GPU settings-------------------------------------- ++# These only apply when using the CUDA version of KataGo. ++ ++# IF USING ONE GPU: optionally uncomment and change this if the GPU you want to use turns out to be not device 0 ++# cudaDeviceToUse = 0 ++ ++# IF USING TWO GPUS: Uncomment these two lines (AND set numNNServerThreadsPerModel above): ++# cudaDeviceToUseThread0 = 0 # change this if the first GPU you want to use turns out to be not device 0 ++# cudaDeviceToUseThread1 = 1 # change this if the second GPU you want to use turns out to be not device 1 ++ ++# IF USING THREE GPUS: Uncomment these three lines (AND set numNNServerThreadsPerModel above): ++# cudaDeviceToUseThread0 = 0 # change this if the first GPU you want to use turns out to be not device 0 ++# cudaDeviceToUseThread1 = 1 # change this if the second GPU you want to use turns out to be not device 1 ++# cudaDeviceToUseThread2 = 2 # change this if the third GPU you want to use turns out to be not device 2 ++ ++# You can probably guess the pattern if you have four, five, etc. GPUs. ++ ++# KataGo will automatically use FP16 or not based on the compute capability of your NVIDIA GPU. If you ++# want to try to force a particular behavior though you can uncomment these lines and change them ++# to "true" or "false". E.g. it's using FP16 but on your card that's giving an error, or it's not using ++# FP16 but you think it should. ++# cudaUseFP16 = auto ++# cudaUseNHWC = auto ++ ++ ++# OpenCL GPU settings-------------------------------------- ++# These only apply when using the OpenCL version of KataGo. ++ ++# Uncomment to tune OpenCL for every board size separately, rather than only the largest possible size ++# openclReTunePerBoardSize = true ++ ++# IF USING ONE GPU: optionally uncomment and change this if the best device to use is guessed incorrectly. ++# The default behavior tries to guess the 'best' GPU or device on your system to use, usually it will be a good guess. ++# openclDeviceToUse = 0 ++ ++# IF USING TWO GPUS: Uncomment these two lines and replace X and Y with the device ids of the devices you want to use. ++# It might NOT be 0 and 1, some computers will have many OpenCL devices. You can see what the devices are when ++# KataGo starts up - it should print or log all the devices it finds. ++# (AND also set numNNServerThreadsPerModel above) ++# openclDeviceToUseThread0 = X ++# openclDeviceToUseThread1 = Y ++ ++# IF USING THREE GPUS: Uncomment these three lines and replace X and Y and Z with the device ids of the devices you want to use. ++# It might NOT be 0 and 1 and 2, some computers will have many OpenCL devices. You can see what the devices are when ++# KataGo starts up - it should print or log all the devices it finds. ++# (AND also set numNNServerThreadsPerModel above) ++# openclDeviceToUseThread0 = X ++# openclDeviceToUseThread1 = Y ++# openclDeviceToUseThread2 = Z ++ ++# You can probably guess the pattern if you have four, five, etc. GPUs. ++ ++# KataGo will automatically use FP16 or not based on testing your GPU during tuning. If you ++# want to try to force a particular behavior though you can uncomment this lines and change it ++# to "true" or "false". This is a fairly blunt setting - more detailed settings are testable ++# by rerunning the tuner with various arguments. ++# openclUseFP16 = auto ++ ++ ++# Eigen-specific settings-------------------------------------- ++# These only apply when using the Eigen (pure CPU) version of KataGo. ++ ++# This is the number of CPU threads for evaluating the neural net on the Eigen backend. ++# It defaults to numSearchThreads. ++# numEigenThreadsPerModel = X +diff --git a/python/selfplay/default_gtp.cfg b/python/selfplay/default_gtp.cfg +new file mode 100644 +index 00000000..8bf19d85 +--- /dev/null ++++ b/python/selfplay/default_gtp.cfg +@@ -0,0 +1,721 @@ ++# Configuration for KataGo C++ GTP engine ++ ++# Run the program using: `./katago.exe gtp` ++ ++# In this example config, when a parameter is given as a commented out value, ++# that value also is the default value, unless described otherwise. You can ++# uncomment it (remove the pound sign) and change it if you want. ++ ++# =========================================================================== ++# Running on an online server or in a real tournament or match ++# =========================================================================== ++# If you plan to run online or in a tournament, read through the "Rules" ++# section below for proper handling of komi, handicaps, end-of-game cleanup, ++# and other details. ++ ++# =========================================================================== ++# Notes about performance and memory usage ++# =========================================================================== ++# Important: For good performance, you will very likely want to tune the ++# "numSearchThreads" parameter in the Search limits section below! Run ++# "./katago benchmark" to test KataGo and to suggest a reasonable value ++# of this parameter. ++ ++# For multi-GPU systems, read "OpenCL GPU settings" or "CUDA GPU settings". ++# ++# When using OpenCL, verify that KataGo picks the correct device! Some systems ++# may have both an Intel CPU OpenCL and GPU OpenCL. If # KataGo picks the wrong ++# one, correct this by specifying "openclGpuToUse". ++# ++# Consider adjusting "maxVisits", "ponderingEnabled", "resignThreshold", and ++# other parameters depending on your intended usage. ++ ++# =========================================================================== ++# Command-line usage ++# =========================================================================== ++# All of the below values may be set or overridden via command-line arguments: ++# ++# -override-config KEY=VALUE,KEY=VALUE,... ++ ++# =========================================================================== ++# Logs and files ++# =========================================================================== ++# This section defines where and what logging information is produced. ++ ++# Each run of KataGo will log to a separate file in this dir. ++# This is the default. ++logDir = gtp_logs ++# Uncomment and specify this instead of logDir to write separate dated subdirs ++# logDirDated = gtp_logs ++# Uncomment and specify this instead of logDir to log to only a single file ++# logFile = gtp.log ++ ++# Logging options ++logAllGTPCommunication = true ++logSearchInfo = true ++logSearchInfoForChosenMove = false ++logToStderr = false ++ ++# KataGo will display some info to stderr on GTP startup ++# Uncomment the next line and set it to false to suppress that and remain silent ++# startupPrintMessageToStderr = true ++ ++# Write information to stderr, for use in things like malkovich chat to OGS. ++ ogsChatToStderr = true ++ ++# Uncomment and set this to a directory to override where openCLTuner files ++# and other cached data is written. By default it saves into a subdir of the ++# current directory on windows, and a subdir of ~/.katago on Linux. ++# homeDataDir = PATH_TO_DIRECTORY ++ ++# =========================================================================== ++# Analysis ++# =========================================================================== ++# This section configures analysis settings. ++# ++# The maximum number of moves after the first move displayed in variations ++# from analysis commands like kata-analyze or lz-analyze. ++# analysisPVLen = 15 ++ ++# Report winrates for chat and analysis as (BLACK|WHITE|SIDETOMOVE). ++# Most GUIs and analysis tools will expect SIDETOMOVE. ++# reportAnalysisWinratesAs = SIDETOMOVE ++ ++# Extra noise for wider exploration. Large values will force KataGo to ++# analyze a greater variety of moves than it normally would. ++# An extreme value like 1 distributes playouts across every move on the board, ++# even very bad moves. ++# Affects analysis only, does not affect play. ++# analysisWideRootNoise = 0.04 ++ ++# Try to limit the effect of possible bad or bogus move sequences in the ++# history leading to this position from affecting KataGo's move predictions. ++# analysisIgnorePreRootHistory = true ++ ++# =========================================================================== ++# Rules ++# =========================================================================== ++# This section configures the scoring and playing rules. Rules can also be ++# changed mid-run by issuing custom GTP commands. ++# ++# See https://lightvector.github.io/KataGo/rules.html for rules details. ++# ++# See https://github.com/lightvector/KataGo/blob/master/docs/GTP_Extensions.md ++# for GTP commands. ++ ++# Specify the rules as a string. ++# Some legal values include: ++# chinese, japanese, korean, aga, chinese-ogs, new-zealand, stone-scoring, ++# ancient-territory, bga, aga-button ++# ++# For some human rulesets that require complex adjudication in tricky cases ++# (e.g. japanese, korean) KataGo may not precisely match the ruleset in such ++# cases but will do its best. ++rules = tromp-taylor ++ ++# By default, the "rules" parameter is used, but if you comment it out and ++# uncomment one option in each of the sections below, you can specify an ++# arbitrary combination of individual rules. ++ ++# koRule = SIMPLE # Simple ko rules (triple ko = no result) ++# koRule = POSITIONAL # Positional superko ++# koRule = SITUATIONAL # Situational superko ++ ++# scoringRule = AREA # Area scoring ++# scoringRule = TERRITORY # Territory scoring (special computer-friendly territory rules) ++ ++# taxRule = NONE # All surrounded empty points are scored ++# taxRule = SEKI # Eyes in seki do NOT count as points ++# taxRule = ALL # All groups are taxed up to 2 points for the two eyes needed to live ++ ++# Is multiple-stone suicide legal? (Single-stone suicide is always illegal). ++# multiStoneSuicideLegal = false ++# multiStoneSuicideLegal = true ++ ++# "Button go" - the first pass when area scoring awards 0.5 points and does ++# not count for ending the game. ++# Allows area scoring rulesets that have far simpler rules to achieve the same ++# final scoring precision and reward for precise play as territory scoring. ++# hasButton = false ++# hasButton = true ++ ++# Is this a human ruleset where it's okay to pass before having physically ++# captured and removed all dead stones? ++# friendlyPassOk = false ++# friendlyPassOk = true ++ ++# How handicap stones in handicap games are compensated ++# whiteHandicapBonus = 0 # White gets no compensation for black's handicap stones (Tromp-taylor, NZ, JP) ++# whiteHandicapBonus = N-1 # White gets N-1 points for black's N handicap stones (AGA) ++# whiteHandicapBonus = N # White gets N points for black's N handicap stones (Chinese) ++ ++# ------------------------------ ++# Other rules hacks ++# ------------------------------ ++# Uncomment and change to adjust what board size KataGo uses upon startup ++# by default when GTP doesn't specify. ++# defaultBoardSize = 19 ++ ++# By default, Katago will use the komi that the GUI or GTP controller tries to set. ++# Uncomment and set this to have KataGo ignore the controller and always use this komi. ++# ignoreGTPAndForceKomi = 7 ++ ++# =========================================================================== ++# Bot behavior ++# =========================================================================== ++ ++# ------------------------------ ++# Resignation ++# ------------------------------ ++ ++# Resignation occurs if for at least resignConsecTurns in a row, the ++# winLossUtility (on a [-1,1] scale) is below resignThreshold. ++allowResignation = true ++resignThreshold = -0.90 ++resignConsecTurns = 3 ++ ++# By default, KataGo may resign games that it is confidently losing even if they ++# are very close in score. Uncomment and set this to avoid resigning games ++# if the estimated difference is points is less than or equal to this. ++# resignMinScoreDifference = 10 ++ ++# ------------------------------ ++# Handicap ++# ------------------------------ ++# Assume that if black makes many moves in a row right at the start of the ++# game, then the game is a handicap game. This is necessary on some servers ++# and for some GUIs and also when initializing from many SGF files, which may ++# set up a handicap game using repeated GTP "play" commands for black rather ++# than GTP "place_free_handicap" commands; however, it may also lead to ++# incorrect understanding of komi if whiteHandicapBonus is used and a server ++# does not have such a practice. Uncomment and set to false to disable. ++# assumeMultipleStartingBlackMovesAreHandicap = true ++ ++# Makes katago dynamically adjust in handicap or altered-komi games to assume ++# based on those game settings that it must be stronger or weaker than the ++# opponent and to play accordingly. Greatly improves handicap strength by ++# biasing winrates and scores to favor appropriate safe/aggressive play. ++# Does NOT affect analysis (lz-analyze, kata-analyze, used by programs like ++# Lizzie) so analysis remains unbiased. Uncomment and set this to 0 to disable ++# this and make KataGo play the same always. ++# dynamicPlayoutDoublingAdvantageCapPerOppLead = 0.045 ++ ++# Instead of "dynamicPlayoutDoublingAdvantageCapPerOppLead", you can comment ++# that out and uncomment and set "playoutDoublingAdvantage" to a fixed value ++# from -3.0 to 3.0 that will not change dynamically. ++# ALSO affects analysis tools (lz-analyze, kata-analyze, used by e.g. Lizzie). ++# Negative makes KataGo behave as if it is much weaker than the opponent. ++# Positive makes KataGo behave as if it is much stronger than the opponent. ++# KataGo will adjust to favor safe/aggressive play as appropriate based on ++# the combination of who is ahead and how much stronger/weaker it thinks it is, ++# and report winrates and scores taking the strength difference into account. ++# ++# If this and "dynamicPlayoutDoublingAdvantageCapPerOppLead" are both set ++# then dynamic will be used for all games and this fixed value will be used ++# for analysis tools. ++# playoutDoublingAdvantage = 0.0 ++ ++# Uncomment one of these when using "playoutDoublingAdvantage" to enforce ++# that it will only apply when KataGo plays as the specified color and will be ++# negated when playing as the opposite color. ++# playoutDoublingAdvantagePla = BLACK ++# playoutDoublingAdvantagePla = WHITE ++ ++# ------------------------------ ++# Passing and cleanup ++# ------------------------------ ++# Make the bot never assume that its pass will end the game, even if passing ++# would end and "win" under Tromp-Taylor rules. Usually this is a good idea ++# when using it for analysis or playing on servers where scoring may be ++# implemented non-tromp-taylorly. Uncomment and set to false to disable. ++# conservativePass = true ++ ++# When using territory scoring, self-play games continue beyond two passes ++# with special cleanup rules that may be confusing for human players. This ++# option prevents the special cleanup phases from being reachable when using ++# the bot for GTP play. Uncomment and set to false to enable entering special ++# cleanup. For example, if you are testing it against itself, or against ++# another bot that has precisely implemented the rules documented at ++# https://lightvector.github.io/KataGo/rules.html ++# preventCleanupPhase = true ++ ++# ------------------------------ ++# Miscellaneous behavior ++# ------------------------------ ++# If the board is symmetric, search only one copy of each equivalent move. ++# Attempts to also account for ko/superko, will not theoretically perfect for ++# superko. Uncomment and set to false to disable. ++# rootSymmetryPruning = true ++ ++# Uncomment and set to true to avoid a particular joseki that some networks ++# misevaluate, and also to improve opening diversity versus some particular ++# other bots that like to play it all the time. ++# avoidMYTDaggerHack = false ++ ++# Prefer to avoid playing the same joseki in every corner of the board. ++# Uncomment to set to a specific value. See "Avoid SGF patterns" section. ++# By default: 0 (even games), 0.005 (handicap games) ++# avoidRepeatedPatternUtility = 0.0 ++ ++# Experimental logic to fight against mirror Go even with unfavorable komi. ++# Uncomment to set to a specific value to use for both playing and analysis. ++# By default: true when playing via GTP, but false when analyzing. ++# antiMirror = true ++ ++# Enable some hacks that mitigate rare instances when passing messes up deeper searches. ++# enablePassingHacks = true ++ ++# Uncomment and set this to true to prevent bad or bogus move sequences ++# in the history leading to this position from affecting KataGo's move choices. ++# Same as analysisIgnorePreRootHistory (see above) but applies to actual play. ++# You can enable this if KataGo is being asked to play from positions that it did not ++# choose the moves to reach. ++# ignorePreRootHistory = false ++ ++# =========================================================================== ++# Search limits ++# =========================================================================== ++ ++# Terminology: ++# "Playouts" is the number of new playouts of search performed each turn. ++# "Visits" is the same as "Playouts" but also counts search performed on ++# previous turns that is still applicable to this turn. ++# "Time" is the time in seconds. ++ ++# For example, if KataGo searched 200 nodes on the previous turn, and then ++# after the opponent's reply, 50 nodes of its search tree was still valid, ++# then a visit limit of 200 would allow KataGo to search 150 new nodes ++# (for a final tree size of 200 nodes), whereas a playout limit of of 200 ++# would allow KataGo to search 200 nodes (for a final tree size of 250 nodes). ++ ++# Additionally, KataGo may also move before than the limit in order to ++# obey time controls (e.g. byo-yomi, etc) if the GTP controller has ++# told KataGo that the game has is being played with a given time control. ++ ++# Limits for search on the current turn. ++# If commented out or unspecified, the default is to have no limit. ++# maxVisits = 30000 ++# maxPlayouts = 300 ++maxTime = 1.0 ++ ++# Ponder on the opponent's turn? ++ponderingEnabled = true ++ ++# Limits for search when pondering on the opponent's turn. ++# If commented out or unspecified, the default is to have no limit. ++# Limiting the maximum time is recommended so that KataGo won't burn CPU/GPU ++# forever and/or run out of RAM if left unattended while pondering is enabled. ++# maxVisitsPondering = 5000 ++# maxPlayoutsPondering = 3000 ++maxTimePondering = 120.0 ++ ++ ++# ------------------------------ ++# Other search limits and behavior ++# ------------------------------ ++ ++# Approx number of seconds to buffer for lag for GTP time controls - will ++# move a bit faster assuming there is this much lag per move. ++lagBuffer = 1.0 ++ ++# YOU PROBABLY WANT TO TUNE THIS PARAMETER! ++# The number of threads to use when searching. On powerful GPUs the optimal ++# threads may be much higher than the number of CPU cores you have because ++# many threads are needed to feed efficient large batches to the GPU. ++# ++# Run "./katago benchmark" to tune this parameter and test the effect ++# of changes to any of other parameters. ++numSearchThreads = 40 ++ ++# Play a little faster if the opponent is passing, for human-friendliness. ++# Comment these out to disable them, such as if running a controlled match ++# where you are testing KataGo with fixed compute per move vs other bots. ++searchFactorAfterOnePass = 0.50 ++searchFactorAfterTwoPass = 0.25 ++ ++# Play a little faster if super-winning, for human-friendliness. ++# Comment these out to disable them, such as if running a controlled match ++# where you are testing KataGo with fixed compute per move vs other bots. ++searchFactorWhenWinning = 0.40 ++searchFactorWhenWinningThreshold = 0.95 ++ ++# =========================================================================== ++# GPU settings ++# =========================================================================== ++# This section configures GPU settings. ++# ++# Maximum number of positions to send to a single GPU at once. The default ++# value is roughly equal to numSearchThreads, but can be specified manually ++# if running out of memory, or using multiple GPUs that expect to share work. ++# nnMaxBatchSize = ++ ++# Controls the neural network cache size, which is the primary RAM/memory use. ++# KataGo will cache up to (2 ** nnCacheSizePowerOfTwo) many neural net ++# evaluations in case of transpositions in the tree. ++# Increase this to improve performance for searches with tens of thousands ++# of visits or more. Decrease this to limit memory usage. ++# If you're happy to do some math - each neural net entry takes roughly ++# 1.5KB, except when using whole-board ownership/territory ++# visualizations, where each entry will take roughly 3KB. The number of ++# entries is (2 ** nnCacheSizePowerOfTwo). (E.g. 2 ** 18 = 262144.) ++# You can compute roughly how much memory the cache will use based on this. ++# nnCacheSizePowerOfTwo = 20 ++ ++# Size of mutex pool for nnCache is (2 ** this). ++# nnMutexPoolSizePowerOfTwo = 16 ++ ++# Randomize board orientation when running neural net evals? Uncomment and ++# set to false to disable. ++# nnRandomize = true ++ ++# If provided, force usage of a specific seed for nnRandomize. ++# The default is to use a randomly generated seed. ++# nnRandSeed = abcdefg ++ ++# Uncomment and set to true to force GTP to use the maximum board size for ++# internal buffers for the neural net. This will make KataGo slower when ++# evaluating small boards, but will avoid a lengthy initialization time on every ++# change of board size due to having to re-size the neural net buffers on the GPU. ++# This can be useful for example, for OGS's persistent bot mode that uses a single ++# bot instance to handle multiple games and may thrash between different board sizes ++# if there are concurrent games of multiple sizes. ++# gtpForceMaxNNSize = false ++ ++# ------------------------------ ++# Multiple GPUs ++# ------------------------------ ++# Set this to the number of GPUs to use or that are available. ++# IMPORTANT: If more than 1, also uncomment the appropriate TensorRT ++# or CUDA or OpenCL section. ++# numNNServerThreadsPerModel = 1 ++ ++# ------------------------------ ++# TENSORRT GPU settings ++# ------------------------------ ++# These only apply when using the TENSORRT version of KataGo. ++ ++# For one GPU: optionally uncomment this option and change if the GPU to ++# use is not device 0. ++# trtDeviceToUse = 0 ++ ++# For two GPUs: Uncomment these options, AND set numNNServerThreadsPerModel above. ++# Also, change their values if the devices you want to use are not 0 and 1. ++# trtDeviceToUseThread0 = 0 ++# trtDeviceToUseThread1 = 1 ++ ++# For three GPUs: Uncomment these options, AND set numNNServerThreadsPerModel above. ++# Also, change their values if the devices you want to use are not 0 and 1 and 2. ++# trtDeviceToUseThread0 = 0 ++# trtDeviceToUseThread1 = 1 ++# trtDeviceToUseThread2 = 2 ++ ++# The pattern continues for additional GPUs. ++ ++# ------------------------------ ++# CUDA GPU settings ++# ------------------------------ ++# These only apply when using the CUDA version of KataGo. ++ ++# For one GPU: optionally uncomment and change this if the GPU you want to ++# use is not device 0 ++# cudaDeviceToUse = 0 ++ ++# For two GPUs: Uncomment these options, AND set numNNServerThreadsPerModel above. ++# Also, change their values if the devices you want to use are not 0 and 1. ++# cudaDeviceToUseThread0 = 0 ++# cudaDeviceToUseThread1 = 1 ++ ++# For three GPUs: Uncomment these options, AND set numNNServerThreadsPerModel above. ++# Also, change their values if the devices you want to use are not 0 and 1 and 2. ++# cudaDeviceToUseThread0 = 0 ++# cudaDeviceToUseThread1 = 1 ++# cudaDeviceToUseThread2 = 2 ++ ++# The pattern continues for additional GPUs. ++ ++# KataGo will automatically use FP16 or not based on the compute capability ++# of your NVIDIA GPU. If you want to try to force a particular behavior ++# you can uncomment these lines and change them to "true" or "false". ++# cudaUseFP16 = auto ++# cudaUseNHWC = auto ++ ++# ------------------------------ ++# OpenCL GPU settings ++# ------------------------------ ++# These only apply when using the OpenCL version of KataGo. ++ ++# Uncomment and set to true to tune OpenCL for every board size separately, ++# rather than only the largest possible size. ++# openclReTunePerBoardSize = false ++ ++# For one GPU: optionally uncomment and change this if the best device to use is guessed incorrectly. ++# The default behavior tries to guess the 'best' GPU or device on your system to use, usually it will be a good guess. ++# openclDeviceToUse = 0 ++ ++# For two GPUs: Uncomment these two lines and replace X and Y with the device ids of the devices you want to use. ++# It might NOT be 0 and 1, some computers will have many OpenCL devices. You can see what the devices are when ++# KataGo starts up - it should print or log all the devices it finds. ++# (AND also set numNNServerThreadsPerModel above) ++# openclDeviceToUseThread0 = X ++# openclDeviceToUseThread1 = Y ++ ++# For three GPUs: Uncomment these three lines and replace X and Y and Z with the device ids of the devices you want to use. ++# It might NOT be 0 and 1 and 2, some computers will have many OpenCL devices. You can see what the devices are when ++# KataGo starts up - it should print or log all the devices it finds. ++# (AND also set numNNServerThreadsPerModel above) ++# openclDeviceToUseThread0 = X ++# openclDeviceToUseThread1 = Y ++# openclDeviceToUseThread2 = Z ++ ++# The pattern continues for additional GPUs. ++ ++# KataGo will automatically use FP16 or not based on testing your GPU during ++# tuning. If you want to try to force a particular behavior though you can ++# uncomment this option and change it to "true" or "false". This is a fairly ++# blunt setting - more detailed settings are testable by rerunning the tuner ++# with various arguments (./katago tuner). ++# openclUseFP16 = auto ++ ++# ------------------------------ ++# Eigen-specific settings ++# ------------------------------ ++# These only apply when using the Eigen (pure CPU) version of KataGo. ++ ++# Number of CPU threads for evaluating the neural net on the Eigen backend. ++# ++# Default: numSearchThreads ++# numEigenThreadsPerModel = X ++ ++# =========================================================================== ++# Root move selection and biases ++# =========================================================================== ++# Uncomment and edit any of the below values to change them from their default. ++ ++# If provided, force usage of a specific seed for various random things in ++# the search. The default is to use a random seed. ++# searchRandSeed = hijklmn ++ ++# Temperature for the early game, randomize between chosen moves with ++# this temperature ++# chosenMoveTemperatureEarly = 0.5 ++ ++# Decay temperature for the early game by 0.5 every this many moves, ++# scaled with board size. ++# chosenMoveTemperatureHalflife = 19 ++ ++# At the end of search after the early game, randomize between chosen ++# moves with this temperature ++# chosenMoveTemperature = 0.10 ++ ++# Subtract this many visits from each move prior to applying ++# chosenMoveTemperature (unless all moves have too few visits) to downweight ++# unlikely moves ++# chosenMoveSubtract = 0 ++ ++# The same as chosenMoveSubtract but only prunes moves that fall below ++# the threshold. This setting does not affect chosenMoveSubtract. ++# chosenMovePrune = 1 ++ ++# Number of symmetries to sample (without replacement) and average at the root ++# rootNumSymmetriesToSample = 1 ++ ++# Using LCB for move selection? ++# useLcbForSelection = true ++ ++# How many stdevs a move needs to be better than another for LCB selection ++# lcbStdevs = 5.0 ++ ++# Only use LCB override when a move has this proportion of visits as the ++# top move. ++# minVisitPropForLCB = 0.15 ++ ++# =========================================================================== ++# Internal params ++# =========================================================================== ++# Uncomment and edit any of the below values to change them from their default. ++ ++# Scales the utility of winning/losing ++# winLossUtilityFactor = 1.0 ++ ++# Scales the utility for trying to maximize score ++# staticScoreUtilityFactor = 0.10 ++# dynamicScoreUtilityFactor = 0.30 ++ ++# Adjust dynamic score center this proportion of the way towards zero, ++# capped at a reasonable amount. ++# dynamicScoreCenterZeroWeight = 0.20 ++# dynamicScoreCenterScale = 0.75 ++ ++# The utility of getting a "no result" due to triple ko or other long cycle ++# in non-superko rulesets (-1 to 1) ++# noResultUtilityForWhite = 0.0 ++ ++# The number of wins that a draw counts as, for white. (0 to 1) ++# drawEquivalentWinsForWhite = 0.5 ++ ++# Exploration constant for mcts ++# cpuctExploration = 1.0 ++# cpuctExplorationLog = 0.45 ++ ++# Parameters that control exploring more in volatile positions, exploring ++# less in stable positions. ++# cpuctUtilityStdevPrior = 0.40 ++# cpuctUtilityStdevPriorWeight = 2.0 ++# cpuctUtilityStdevScale = 0.85 ++ ++# FPU reduction constant for mcts ++# fpuReductionMax = 0.2 ++# rootFpuReductionMax = 0.1 ++# fpuParentWeightByVisitedPolicy = true ++ ++# Parameters that control weighting of evals based on the net's own ++# self-reported uncertainty. ++# useUncertainty = true ++# uncertaintyExponent = 1.0 ++# uncertaintyCoeff = 0.25 ++ ++# Explore using optimistic policy ++# rootPolicyOptimism = 0.2 ++# policyOptimism = 1.0 ++ ++# Amount to apply a downweighting of children with very bad values relative ++# to good ones. ++# valueWeightExponent = 0.25 ++ ++# Slight incentive for the bot to behave human-like with regard to passing at ++# the end, filling the dame, not wasting time playing in its own territory, ++# etc., and not play moves that are equivalent in terms of points but a bit ++# more unfriendly to humans. ++# rootEndingBonusPoints = 0.5 ++ ++# Make the bot prune useless moves that are just prolonging the game to ++# avoid losing yet. ++# rootPruneUselessMoves = true ++ ++# Apply bias correction based on local pattern keys ++# subtreeValueBiasFactor = 0.45 ++# subtreeValueBiasWeightExponent = 0.85 ++ ++# Use graph search rather than tree search - identify and share search for ++# transpositions. ++# useGraphSearch = true ++ ++# How much to shard the node table for search synchronization ++# nodeTableShardsPowerOfTwo = 16 ++ ++# How many virtual losses to add when a thread descends through a node ++# numVirtualLossesPerThread = 1 ++ ++# Improve the quality of evals under heavy multithreading ++# useNoisePruning = true ++ ++# =========================================================================== ++# Automatic avoid patterns ++# =========================================================================== ++# The parameters in this section provide a way to bias away from moves that match ++# patterns that this instance of KataGo has played in previous games, by auto-saving ++# moves to a directory and then auto-loading and biasing against them each new game. ++# Uncomment them to use them. When using this feature, all parameters must be specified. ++ ++# Different board sizes are tracked separately, but all board sizes share the same ++# parameters by default. Every parameter *except* for autoAvoidRepeatDir and ++# autoAvoidRepeatSaveChunkSize can be overridden per board size, ++# e.g. "autoAvoidRepeatMinTurnNumber13x13". You must ALSO specify the ++# defaults even if you have specified board-size-specific values. ++ ++# Directory to auto-save moves KataGo plays, to avoid them in future games. ++# You can create a new empty directory and put its path here. ++# If you run parallel instances of KataGo, use different directories if you ++# want them not to share their biases, use the same directory if you want ++# all of them to bias away from past moves that any of them have played. ++# KataGo will also automatically DELETE old data in this directory, so it is ++# recommended that if you do share the same directory between parallel instances, ++# that they all use the same settings that affect data saving/deletion. ++# autoAvoidRepeatDir = PATH_TO_NEW_DIRECTORY ++ ++# Penalize this much utility per matching move. ++# Values that are too large may lead to bad play. The value of 0.004 is fairly large ++# and might be large enough to result in some early weird/bad moves when trying ++# to avoid past games' moves if enough games begin the same way. You can experiment with it. ++# autoAvoidRepeatUtility = 0.004 ++ ++# Per each new move saved, exponentially decay prior saved moves by this factor. ++# This way, the bias against moves from many games ago is gradually phased out. ++# For example, 0.9995 = 1 - 1/2000, so would be roughly 2000 prior moves worth of ++# penalty weight remembered, in steady state. Depending on what turn number range ++# you are saving, this might equate to a different number of games For example ++# saving the first 50 moves per game would make this roughly 2000 / 50 = 40 games ++# worth of memory. ++# autoAvoidRepeatLambda = 0.9995 ++ ++# Affects data saving/deletion. ++# When the number of saved moves exceeds this, outright delete them to avoid too many ++# files and disk space building up. Also may affect the speed of saving/loading on start ++# of each game if this is set large and a lot of data builds up. ++# autoAvoidRepeatMaxPoses = 10000 ++ ++# Affects data saving/deletion. ++# Only save data for moves within this turn number range of those games. ++# E.g. setting autoAvoidRepeatMinTurnNumber to a number like 4 or 5 would tend to make ++# KataGo not develop a bias against the initial 3-4 and 4-4 corner moves in almost every game. ++# autoAvoidRepeatMinTurnNumber = 0 ++# autoAvoidRepeatMaxTurnNumber = 50 ++ ++# Affects data saving/deletion. ++# Within a single run of a program, wait to accumulate this many samples ++# (possibly across multiple clear_boards/games) before saving the data. ++# Can help to avoid writing too many small files to disk, especially when GTP is used ++# in a way that clears the board very frequently (e.g. gtp2ogs pooled manager). ++# autoAvoidRepeatSaveChunkSize = 200 ++ ++# =========================================================================== ++# Avoid SGF patterns ++# =========================================================================== ++# The parameters in this section provide a way to avoid moves that follow ++# specific patterns based on a set of SGF files loaded upon startup. ++# This is basically the same as the above "Automatic avoid patterns" section ++# above except you supply your own SGF files to avoid moves from. ++# Uncomment them to use this feature. Additionally, if the SGF file ++# contains the string %SKIP% in a comment on a move, that move will be ++# ignored for this purpose. ++ ++# Load SGF files from this directory when the engine is started ++# (only on startup, will not reload unless engine is restarted) ++# avoidSgfPatternDirs = path/to/directory/with/sgfs/ ++# You can also surround the file path in double quotes if the file path contains trailing spaces or hash signs. ++# Within double quotes, backslashes are escape characters. ++# avoidSgfPatternDirs = "path/to/directory/with/sgfs/" ++ ++# Penalize this much utility per matching move. ++# Set this negative if you instead want to favor SGF patterns instead of ++# penalizing them. This number does not need to be large, even 0.001 will ++# make a difference. Values that are too large may lead to bad play. ++# avoidSgfPatternUtility = 0.001 ++ ++# Optional - load only the newest this many files ++# avoidSgfPatternMaxFiles = 20 ++ ++# Optional - Penalty is multiplied by this per each older SGF file, so that ++# old SGF files matter less than newer ones. ++# avoidSgfPatternLambda = 0.90 ++ ++# Optional - pay attention only to moves made by players with this name. ++# For example, set it to the name that your bot's past games will show up ++# as in the SGF, so that the bot will only avoid repeating moves that itself ++# made in past games, not the moves that its opponents made. ++# avoidSgfPatternAllowedNames = my-ogs-bot-name1,my-ogs-bot-name2 ++ ++# Optional - Ignore moves in SGF files that occurred before this turn number. ++# avoidSgfPatternMinTurnNumber = 0 ++ ++# For more avoid patterns: ++# You can also specify a second set of parameters, and a third, fourth, ++# etc. by numbering 2,3,4,... ++# ++# avoidSgf2PatternDirs = ... ++# avoidSgf2PatternUtility = ... ++# avoidSgf2PatternMaxFiles = ... ++# avoidSgf2PatternLambda = ... ++# avoidSgf2PatternAllowedNames = ... ++# avoidSgf2PatternMinTurnNumber = ... ++ +diff --git a/python/selfplay/doudoubot.cfg b/python/selfplay/doudoubot.cfg +new file mode 100644 +index 00000000..bf81254f +--- /dev/null ++++ b/python/selfplay/doudoubot.cfg +@@ -0,0 +1,721 @@ ++# Configuration for KataGo C++ GTP engine ++ ++# Run the program using: `./katago.exe gtp` ++ ++# In this example config, when a parameter is given as a commented out value, ++# that value also is the default value, unless described otherwise. You can ++# uncomment it (remove the pound sign) and change it if you want. ++ ++# =========================================================================== ++# Running on an online server or in a real tournament or match ++# =========================================================================== ++# If you plan to run online or in a tournament, read through the "Rules" ++# section below for proper handling of komi, handicaps, end-of-game cleanup, ++# and other details. ++ ++# =========================================================================== ++# Notes about performance and memory usage ++# =========================================================================== ++# Important: For good performance, you will very likely want to tune the ++# "numSearchThreads" parameter in the Search limits section below! Run ++# "./katago benchmark" to test KataGo and to suggest a reasonable value ++# of this parameter. ++ ++# For multi-GPU systems, read "OpenCL GPU settings" or "CUDA GPU settings". ++# ++# When using OpenCL, verify that KataGo picks the correct device! Some systems ++# may have both an Intel CPU OpenCL and GPU OpenCL. If # KataGo picks the wrong ++# one, correct this by specifying "openclGpuToUse". ++# ++# Consider adjusting "maxVisits", "ponderingEnabled", "resignThreshold", and ++# other parameters depending on your intended usage. ++ ++# =========================================================================== ++# Command-line usage ++# =========================================================================== ++# All of the below values may be set or overridden via command-line arguments: ++# ++# -override-config KEY=VALUE,KEY=VALUE,... ++ ++# =========================================================================== ++# Logs and files ++# =========================================================================== ++# This section defines where and what logging information is produced. ++ ++# Each run of KataGo will log to a separate file in this dir. ++# This is the default. ++logDir = gtp_logs ++# Uncomment and specify this instead of logDir to write separate dated subdirs ++# logDirDated = gtp_logs ++# Uncomment and specify this instead of logDir to log to only a single file ++# logFile = gtp.log ++ ++# Logging options ++logAllGTPCommunication = true ++logSearchInfo = true ++logSearchInfoForChosenMove = false ++logToStderr = false ++ ++# KataGo will display some info to stderr on GTP startup ++# Uncomment the next line and set it to false to suppress that and remain silent ++# startupPrintMessageToStderr = true ++ ++# Write information to stderr, for use in things like malkovich chat to OGS. ++ ogsChatToStderr = true ++ ++# Uncomment and set this to a directory to override where openCLTuner files ++# and other cached data is written. By default it saves into a subdir of the ++# current directory on windows, and a subdir of ~/.katago on Linux. ++# homeDataDir = PATH_TO_DIRECTORY ++ ++# =========================================================================== ++# Analysis ++# =========================================================================== ++# This section configures analysis settings. ++# ++# The maximum number of moves after the first move displayed in variations ++# from analysis commands like kata-analyze or lz-analyze. ++# analysisPVLen = 15 ++ ++# Report winrates for chat and analysis as (BLACK|WHITE|SIDETOMOVE). ++# Most GUIs and analysis tools will expect SIDETOMOVE. ++# reportAnalysisWinratesAs = SIDETOMOVE ++ ++# Extra noise for wider exploration. Large values will force KataGo to ++# analyze a greater variety of moves than it normally would. ++# An extreme value like 1 distributes playouts across every move on the board, ++# even very bad moves. ++# Affects analysis only, does not affect play. ++# analysisWideRootNoise = 0.04 ++ ++# Try to limit the effect of possible bad or bogus move sequences in the ++# history leading to this position from affecting KataGo's move predictions. ++# analysisIgnorePreRootHistory = true ++ ++# =========================================================================== ++# Rules ++# =========================================================================== ++# This section configures the scoring and playing rules. Rules can also be ++# changed mid-run by issuing custom GTP commands. ++# ++# See https://lightvector.github.io/KataGo/rules.html for rules details. ++# ++# See https://github.com/lightvector/KataGo/blob/master/docs/GTP_Extensions.md ++# for GTP commands. ++ ++# Specify the rules as a string. ++# Some legal values include: ++# chinese, japanese, korean, aga, chinese-ogs, new-zealand, stone-scoring, ++# ancient-territory, bga, aga-button ++# ++# For some human rulesets that require complex adjudication in tricky cases ++# (e.g. japanese, korean) KataGo may not precisely match the ruleset in such ++# cases but will do its best. ++rules = tromp-taylor ++ ++# By default, the "rules" parameter is used, but if you comment it out and ++# uncomment one option in each of the sections below, you can specify an ++# arbitrary combination of individual rules. ++ ++# koRule = SIMPLE # Simple ko rules (triple ko = no result) ++# koRule = POSITIONAL # Positional superko ++# koRule = SITUATIONAL # Situational superko ++ ++# scoringRule = AREA # Area scoring ++# scoringRule = TERRITORY # Territory scoring (special computer-friendly territory rules) ++ ++# taxRule = NONE # All surrounded empty points are scored ++# taxRule = SEKI # Eyes in seki do NOT count as points ++# taxRule = ALL # All groups are taxed up to 2 points for the two eyes needed to live ++ ++# Is multiple-stone suicide legal? (Single-stone suicide is always illegal). ++# multiStoneSuicideLegal = false ++# multiStoneSuicideLegal = true ++ ++# "Button go" - the first pass when area scoring awards 0.5 points and does ++# not count for ending the game. ++# Allows area scoring rulesets that have far simpler rules to achieve the same ++# final scoring precision and reward for precise play as territory scoring. ++# hasButton = false ++# hasButton = true ++ ++# Is this a human ruleset where it's okay to pass before having physically ++# captured and removed all dead stones? ++# friendlyPassOk = false ++# friendlyPassOk = true ++ ++# How handicap stones in handicap games are compensated ++# whiteHandicapBonus = 0 # White gets no compensation for black's handicap stones (Tromp-taylor, NZ, JP) ++# whiteHandicapBonus = N-1 # White gets N-1 points for black's N handicap stones (AGA) ++# whiteHandicapBonus = N # White gets N points for black's N handicap stones (Chinese) ++ ++# ------------------------------ ++# Other rules hacks ++# ------------------------------ ++# Uncomment and change to adjust what board size KataGo uses upon startup ++# by default when GTP doesn't specify. ++# defaultBoardSize = 19 ++ ++# By default, Katago will use the komi that the GUI or GTP controller tries to set. ++# Uncomment and set this to have KataGo ignore the controller and always use this komi. ++# ignoreGTPAndForceKomi = 7 ++ ++# =========================================================================== ++# Bot behavior ++# =========================================================================== ++ ++# ------------------------------ ++# Resignation ++# ------------------------------ ++ ++# Resignation occurs if for at least resignConsecTurns in a row, the ++# winLossUtility (on a [-1,1] scale) is below resignThreshold. ++allowResignation = true ++resignThreshold = -0.90 ++resignConsecTurns = 3 ++ ++# By default, KataGo may resign games that it is confidently losing even if they ++# are very close in score. Uncomment and set this to avoid resigning games ++# if the estimated difference is points is less than or equal to this. ++# resignMinScoreDifference = 10 ++ ++# ------------------------------ ++# Handicap ++# ------------------------------ ++# Assume that if black makes many moves in a row right at the start of the ++# game, then the game is a handicap game. This is necessary on some servers ++# and for some GUIs and also when initializing from many SGF files, which may ++# set up a handicap game using repeated GTP "play" commands for black rather ++# than GTP "place_free_handicap" commands; however, it may also lead to ++# incorrect understanding of komi if whiteHandicapBonus is used and a server ++# does not have such a practice. Uncomment and set to false to disable. ++# assumeMultipleStartingBlackMovesAreHandicap = true ++ ++# Makes katago dynamically adjust in handicap or altered-komi games to assume ++# based on those game settings that it must be stronger or weaker than the ++# opponent and to play accordingly. Greatly improves handicap strength by ++# biasing winrates and scores to favor appropriate safe/aggressive play. ++# Does NOT affect analysis (lz-analyze, kata-analyze, used by programs like ++# Lizzie) so analysis remains unbiased. Uncomment and set this to 0 to disable ++# this and make KataGo play the same always. ++# dynamicPlayoutDoublingAdvantageCapPerOppLead = 0.045 ++ ++# Instead of "dynamicPlayoutDoublingAdvantageCapPerOppLead", you can comment ++# that out and uncomment and set "playoutDoublingAdvantage" to a fixed value ++# from -3.0 to 3.0 that will not change dynamically. ++# ALSO affects analysis tools (lz-analyze, kata-analyze, used by e.g. Lizzie). ++# Negative makes KataGo behave as if it is much weaker than the opponent. ++# Positive makes KataGo behave as if it is much stronger than the opponent. ++# KataGo will adjust to favor safe/aggressive play as appropriate based on ++# the combination of who is ahead and how much stronger/weaker it thinks it is, ++# and report winrates and scores taking the strength difference into account. ++# ++# If this and "dynamicPlayoutDoublingAdvantageCapPerOppLead" are both set ++# then dynamic will be used for all games and this fixed value will be used ++# for analysis tools. ++# playoutDoublingAdvantage = 0.0 ++ ++# Uncomment one of these when using "playoutDoublingAdvantage" to enforce ++# that it will only apply when KataGo plays as the specified color and will be ++# negated when playing as the opposite color. ++# playoutDoublingAdvantagePla = BLACK ++# playoutDoublingAdvantagePla = WHITE ++ ++# ------------------------------ ++# Passing and cleanup ++# ------------------------------ ++# Make the bot never assume that its pass will end the game, even if passing ++# would end and "win" under Tromp-Taylor rules. Usually this is a good idea ++# when using it for analysis or playing on servers where scoring may be ++# implemented non-tromp-taylorly. Uncomment and set to false to disable. ++# conservativePass = true ++ ++# When using territory scoring, self-play games continue beyond two passes ++# with special cleanup rules that may be confusing for human players. This ++# option prevents the special cleanup phases from being reachable when using ++# the bot for GTP play. Uncomment and set to false to enable entering special ++# cleanup. For example, if you are testing it against itself, or against ++# another bot that has precisely implemented the rules documented at ++# https://lightvector.github.io/KataGo/rules.html ++# preventCleanupPhase = true ++ ++# ------------------------------ ++# Miscellaneous behavior ++# ------------------------------ ++# If the board is symmetric, search only one copy of each equivalent move. ++# Attempts to also account for ko/superko, will not theoretically perfect for ++# superko. Uncomment and set to false to disable. ++# rootSymmetryPruning = true ++ ++# Uncomment and set to true to avoid a particular joseki that some networks ++# misevaluate, and also to improve opening diversity versus some particular ++# other bots that like to play it all the time. ++# avoidMYTDaggerHack = false ++ ++# Prefer to avoid playing the same joseki in every corner of the board. ++# Uncomment to set to a specific value. See "Avoid SGF patterns" section. ++# By default: 0 (even games), 0.005 (handicap games) ++# avoidRepeatedPatternUtility = 0.0 ++ ++# Experimental logic to fight against mirror Go even with unfavorable komi. ++# Uncomment to set to a specific value to use for both playing and analysis. ++# By default: true when playing via GTP, but false when analyzing. ++# antiMirror = true ++ ++# Enable some hacks that mitigate rare instances when passing messes up deeper searches. ++# enablePassingHacks = true ++ ++# Uncomment and set this to true to prevent bad or bogus move sequences ++# in the history leading to this position from affecting KataGo's move choices. ++# Same as analysisIgnorePreRootHistory (see above) but applies to actual play. ++# You can enable this if KataGo is being asked to play from positions that it did not ++# choose the moves to reach. ++# ignorePreRootHistory = false ++ ++# =========================================================================== ++# Search limits ++# =========================================================================== ++ ++# Terminology: ++# "Playouts" is the number of new playouts of search performed each turn. ++# "Visits" is the same as "Playouts" but also counts search performed on ++# previous turns that is still applicable to this turn. ++# "Time" is the time in seconds. ++ ++# For example, if KataGo searched 200 nodes on the previous turn, and then ++# after the opponent's reply, 50 nodes of its search tree was still valid, ++# then a visit limit of 200 would allow KataGo to search 150 new nodes ++# (for a final tree size of 200 nodes), whereas a playout limit of of 200 ++# would allow KataGo to search 200 nodes (for a final tree size of 250 nodes). ++ ++# Additionally, KataGo may also move before than the limit in order to ++# obey time controls (e.g. byo-yomi, etc) if the GTP controller has ++# told KataGo that the game has is being played with a given time control. ++ ++# Limits for search on the current turn. ++# If commented out or unspecified, the default is to have no limit. ++# maxVisits = 500 ++# maxPlayouts = 300 ++ maxTime = 0.5 ++ ++# Ponder on the opponent's turn? ++ponderingEnabled = true ++ ++# Limits for search when pondering on the opponent's turn. ++# If commented out or unspecified, the default is to have no limit. ++# Limiting the maximum time is recommended so that KataGo won't burn CPU/GPU ++# forever and/or run out of RAM if left unattended while pondering is enabled. ++# maxVisitsPondering = 5000 ++# maxPlayoutsPondering = 3000 ++maxTimePondering = 120.0 ++ ++ ++# ------------------------------ ++# Other search limits and behavior ++# ------------------------------ ++ ++# Approx number of seconds to buffer for lag for GTP time controls - will ++# move a bit faster assuming there is this much lag per move. ++lagBuffer = 1.0 ++ ++# YOU PROBABLY WANT TO TUNE THIS PARAMETER! ++# The number of threads to use when searching. On powerful GPUs the optimal ++# threads may be much higher than the number of CPU cores you have because ++# many threads are needed to feed efficient large batches to the GPU. ++# ++# Run "./katago benchmark" to tune this parameter and test the effect ++# of changes to any of other parameters. ++numSearchThreads = 16 ++ ++# Play a little faster if the opponent is passing, for human-friendliness. ++# Comment these out to disable them, such as if running a controlled match ++# where you are testing KataGo with fixed compute per move vs other bots. ++searchFactorAfterOnePass = 0.50 ++searchFactorAfterTwoPass = 0.25 ++ ++# Play a little faster if super-winning, for human-friendliness. ++# Comment these out to disable them, such as if running a controlled match ++# where you are testing KataGo with fixed compute per move vs other bots. ++searchFactorWhenWinning = 0.40 ++searchFactorWhenWinningThreshold = 0.95 ++ ++# =========================================================================== ++# GPU settings ++# =========================================================================== ++# This section configures GPU settings. ++# ++# Maximum number of positions to send to a single GPU at once. The default ++# value is roughly equal to numSearchThreads, but can be specified manually ++# if running out of memory, or using multiple GPUs that expect to share work. ++# nnMaxBatchSize = ++ ++# Controls the neural network cache size, which is the primary RAM/memory use. ++# KataGo will cache up to (2 ** nnCacheSizePowerOfTwo) many neural net ++# evaluations in case of transpositions in the tree. ++# Increase this to improve performance for searches with tens of thousands ++# of visits or more. Decrease this to limit memory usage. ++# If you're happy to do some math - each neural net entry takes roughly ++# 1.5KB, except when using whole-board ownership/territory ++# visualizations, where each entry will take roughly 3KB. The number of ++# entries is (2 ** nnCacheSizePowerOfTwo). (E.g. 2 ** 18 = 262144.) ++# You can compute roughly how much memory the cache will use based on this. ++# nnCacheSizePowerOfTwo = 20 ++ ++# Size of mutex pool for nnCache is (2 ** this). ++# nnMutexPoolSizePowerOfTwo = 16 ++ ++# Randomize board orientation when running neural net evals? Uncomment and ++# set to false to disable. ++# nnRandomize = true ++ ++# If provided, force usage of a specific seed for nnRandomize. ++# The default is to use a randomly generated seed. ++# nnRandSeed = abcdefg ++ ++# Uncomment and set to true to force GTP to use the maximum board size for ++# internal buffers for the neural net. This will make KataGo slower when ++# evaluating small boards, but will avoid a lengthy initialization time on every ++# change of board size due to having to re-size the neural net buffers on the GPU. ++# This can be useful for example, for OGS's persistent bot mode that uses a single ++# bot instance to handle multiple games and may thrash between different board sizes ++# if there are concurrent games of multiple sizes. ++# gtpForceMaxNNSize = false ++ ++# ------------------------------ ++# Multiple GPUs ++# ------------------------------ ++# Set this to the number of GPUs to use or that are available. ++# IMPORTANT: If more than 1, also uncomment the appropriate TensorRT ++# or CUDA or OpenCL section. ++# numNNServerThreadsPerModel = 1 ++ ++# ------------------------------ ++# TENSORRT GPU settings ++# ------------------------------ ++# These only apply when using the TENSORRT version of KataGo. ++ ++# For one GPU: optionally uncomment this option and change if the GPU to ++# use is not device 0. ++# trtDeviceToUse = 0 ++ ++# For two GPUs: Uncomment these options, AND set numNNServerThreadsPerModel above. ++# Also, change their values if the devices you want to use are not 0 and 1. ++# trtDeviceToUseThread0 = 0 ++# trtDeviceToUseThread1 = 1 ++ ++# For three GPUs: Uncomment these options, AND set numNNServerThreadsPerModel above. ++# Also, change their values if the devices you want to use are not 0 and 1 and 2. ++# trtDeviceToUseThread0 = 0 ++# trtDeviceToUseThread1 = 1 ++# trtDeviceToUseThread2 = 2 ++ ++# The pattern continues for additional GPUs. ++ ++# ------------------------------ ++# CUDA GPU settings ++# ------------------------------ ++# These only apply when using the CUDA version of KataGo. ++ ++# For one GPU: optionally uncomment and change this if the GPU you want to ++# use is not device 0 ++# cudaDeviceToUse = 0 ++ ++# For two GPUs: Uncomment these options, AND set numNNServerThreadsPerModel above. ++# Also, change their values if the devices you want to use are not 0 and 1. ++# cudaDeviceToUseThread0 = 0 ++# cudaDeviceToUseThread1 = 1 ++ ++# For three GPUs: Uncomment these options, AND set numNNServerThreadsPerModel above. ++# Also, change their values if the devices you want to use are not 0 and 1 and 2. ++# cudaDeviceToUseThread0 = 0 ++# cudaDeviceToUseThread1 = 1 ++# cudaDeviceToUseThread2 = 2 ++ ++# The pattern continues for additional GPUs. ++ ++# KataGo will automatically use FP16 or not based on the compute capability ++# of your NVIDIA GPU. If you want to try to force a particular behavior ++# you can uncomment these lines and change them to "true" or "false". ++# cudaUseFP16 = auto ++# cudaUseNHWC = auto ++ ++# ------------------------------ ++# OpenCL GPU settings ++# ------------------------------ ++# These only apply when using the OpenCL version of KataGo. ++ ++# Uncomment and set to true to tune OpenCL for every board size separately, ++# rather than only the largest possible size. ++# openclReTunePerBoardSize = false ++ ++# For one GPU: optionally uncomment and change this if the best device to use is guessed incorrectly. ++# The default behavior tries to guess the 'best' GPU or device on your system to use, usually it will be a good guess. ++# openclDeviceToUse = 0 ++ ++# For two GPUs: Uncomment these two lines and replace X and Y with the device ids of the devices you want to use. ++# It might NOT be 0 and 1, some computers will have many OpenCL devices. You can see what the devices are when ++# KataGo starts up - it should print or log all the devices it finds. ++# (AND also set numNNServerThreadsPerModel above) ++# openclDeviceToUseThread0 = X ++# openclDeviceToUseThread1 = Y ++ ++# For three GPUs: Uncomment these three lines and replace X and Y and Z with the device ids of the devices you want to use. ++# It might NOT be 0 and 1 and 2, some computers will have many OpenCL devices. You can see what the devices are when ++# KataGo starts up - it should print or log all the devices it finds. ++# (AND also set numNNServerThreadsPerModel above) ++# openclDeviceToUseThread0 = X ++# openclDeviceToUseThread1 = Y ++# openclDeviceToUseThread2 = Z ++ ++# The pattern continues for additional GPUs. ++ ++# KataGo will automatically use FP16 or not based on testing your GPU during ++# tuning. If you want to try to force a particular behavior though you can ++# uncomment this option and change it to "true" or "false". This is a fairly ++# blunt setting - more detailed settings are testable by rerunning the tuner ++# with various arguments (./katago tuner). ++# openclUseFP16 = auto ++ ++# ------------------------------ ++# Eigen-specific settings ++# ------------------------------ ++# These only apply when using the Eigen (pure CPU) version of KataGo. ++ ++# Number of CPU threads for evaluating the neural net on the Eigen backend. ++# ++# Default: numSearchThreads ++# numEigenThreadsPerModel = X ++ ++# =========================================================================== ++# Root move selection and biases ++# =========================================================================== ++# Uncomment and edit any of the below values to change them from their default. ++ ++# If provided, force usage of a specific seed for various random things in ++# the search. The default is to use a random seed. ++# searchRandSeed = hijklmn ++ ++# Temperature for the early game, randomize between chosen moves with ++# this temperature ++# chosenMoveTemperatureEarly = 0.5 ++ ++# Decay temperature for the early game by 0.5 every this many moves, ++# scaled with board size. ++# chosenMoveTemperatureHalflife = 19 ++ ++# At the end of search after the early game, randomize between chosen ++# moves with this temperature ++# chosenMoveTemperature = 0.10 ++ ++# Subtract this many visits from each move prior to applying ++# chosenMoveTemperature (unless all moves have too few visits) to downweight ++# unlikely moves ++# chosenMoveSubtract = 0 ++ ++# The same as chosenMoveSubtract but only prunes moves that fall below ++# the threshold. This setting does not affect chosenMoveSubtract. ++# chosenMovePrune = 1 ++ ++# Number of symmetries to sample (without replacement) and average at the root ++# rootNumSymmetriesToSample = 1 ++ ++# Using LCB for move selection? ++# useLcbForSelection = true ++ ++# How many stdevs a move needs to be better than another for LCB selection ++# lcbStdevs = 5.0 ++ ++# Only use LCB override when a move has this proportion of visits as the ++# top move. ++# minVisitPropForLCB = 0.15 ++ ++# =========================================================================== ++# Internal params ++# =========================================================================== ++# Uncomment and edit any of the below values to change them from their default. ++ ++# Scales the utility of winning/losing ++# winLossUtilityFactor = 1.0 ++ ++# Scales the utility for trying to maximize score ++# staticScoreUtilityFactor = 0.10 ++# dynamicScoreUtilityFactor = 0.30 ++ ++# Adjust dynamic score center this proportion of the way towards zero, ++# capped at a reasonable amount. ++# dynamicScoreCenterZeroWeight = 0.20 ++# dynamicScoreCenterScale = 0.75 ++ ++# The utility of getting a "no result" due to triple ko or other long cycle ++# in non-superko rulesets (-1 to 1) ++# noResultUtilityForWhite = 0.0 ++ ++# The number of wins that a draw counts as, for white. (0 to 1) ++# drawEquivalentWinsForWhite = 0.5 ++ ++# Exploration constant for mcts ++# cpuctExploration = 1.0 ++# cpuctExplorationLog = 0.45 ++ ++# Parameters that control exploring more in volatile positions, exploring ++# less in stable positions. ++# cpuctUtilityStdevPrior = 0.40 ++# cpuctUtilityStdevPriorWeight = 2.0 ++# cpuctUtilityStdevScale = 0.85 ++ ++# FPU reduction constant for mcts ++# fpuReductionMax = 0.2 ++# rootFpuReductionMax = 0.1 ++# fpuParentWeightByVisitedPolicy = true ++ ++# Parameters that control weighting of evals based on the net's own ++# self-reported uncertainty. ++# useUncertainty = true ++# uncertaintyExponent = 1.0 ++# uncertaintyCoeff = 0.25 ++ ++# Explore using optimistic policy ++# rootPolicyOptimism = 0.2 ++# policyOptimism = 1.0 ++ ++# Amount to apply a downweighting of children with very bad values relative ++# to good ones. ++# valueWeightExponent = 0.25 ++ ++# Slight incentive for the bot to behave human-like with regard to passing at ++# the end, filling the dame, not wasting time playing in its own territory, ++# etc., and not play moves that are equivalent in terms of points but a bit ++# more unfriendly to humans. ++# rootEndingBonusPoints = 0.5 ++ ++# Make the bot prune useless moves that are just prolonging the game to ++# avoid losing yet. ++# rootPruneUselessMoves = true ++ ++# Apply bias correction based on local pattern keys ++# subtreeValueBiasFactor = 0.45 ++# subtreeValueBiasWeightExponent = 0.85 ++ ++# Use graph search rather than tree search - identify and share search for ++# transpositions. ++# useGraphSearch = true ++ ++# How much to shard the node table for search synchronization ++# nodeTableShardsPowerOfTwo = 16 ++ ++# How many virtual losses to add when a thread descends through a node ++# numVirtualLossesPerThread = 1 ++ ++# Improve the quality of evals under heavy multithreading ++# useNoisePruning = true ++ ++# =========================================================================== ++# Automatic avoid patterns ++# =========================================================================== ++# The parameters in this section provide a way to bias away from moves that match ++# patterns that this instance of KataGo has played in previous games, by auto-saving ++# moves to a directory and then auto-loading and biasing against them each new game. ++# Uncomment them to use them. When using this feature, all parameters must be specified. ++ ++# Different board sizes are tracked separately, but all board sizes share the same ++# parameters by default. Every parameter *except* for autoAvoidRepeatDir and ++# autoAvoidRepeatSaveChunkSize can be overridden per board size, ++# e.g. "autoAvoidRepeatMinTurnNumber13x13". You must ALSO specify the ++# defaults even if you have specified board-size-specific values. ++ ++# Directory to auto-save moves KataGo plays, to avoid them in future games. ++# You can create a new empty directory and put its path here. ++# If you run parallel instances of KataGo, use different directories if you ++# want them not to share their biases, use the same directory if you want ++# all of them to bias away from past moves that any of them have played. ++# KataGo will also automatically DELETE old data in this directory, so it is ++# recommended that if you do share the same directory between parallel instances, ++# that they all use the same settings that affect data saving/deletion. ++# autoAvoidRepeatDir = PATH_TO_NEW_DIRECTORY ++ ++# Penalize this much utility per matching move. ++# Values that are too large may lead to bad play. The value of 0.004 is fairly large ++# and might be large enough to result in some early weird/bad moves when trying ++# to avoid past games' moves if enough games begin the same way. You can experiment with it. ++# autoAvoidRepeatUtility = 0.004 ++ ++# Per each new move saved, exponentially decay prior saved moves by this factor. ++# This way, the bias against moves from many games ago is gradually phased out. ++# For example, 0.9995 = 1 - 1/2000, so would be roughly 2000 prior moves worth of ++# penalty weight remembered, in steady state. Depending on what turn number range ++# you are saving, this might equate to a different number of games For example ++# saving the first 50 moves per game would make this roughly 2000 / 50 = 40 games ++# worth of memory. ++# autoAvoidRepeatLambda = 0.9995 ++ ++# Affects data saving/deletion. ++# When the number of saved moves exceeds this, outright delete them to avoid too many ++# files and disk space building up. Also may affect the speed of saving/loading on start ++# of each game if this is set large and a lot of data builds up. ++# autoAvoidRepeatMaxPoses = 10000 ++ ++# Affects data saving/deletion. ++# Only save data for moves within this turn number range of those games. ++# E.g. setting autoAvoidRepeatMinTurnNumber to a number like 4 or 5 would tend to make ++# KataGo not develop a bias against the initial 3-4 and 4-4 corner moves in almost every game. ++# autoAvoidRepeatMinTurnNumber = 0 ++# autoAvoidRepeatMaxTurnNumber = 50 ++ ++# Affects data saving/deletion. ++# Within a single run of a program, wait to accumulate this many samples ++# (possibly across multiple clear_boards/games) before saving the data. ++# Can help to avoid writing too many small files to disk, especially when GTP is used ++# in a way that clears the board very frequently (e.g. gtp2ogs pooled manager). ++# autoAvoidRepeatSaveChunkSize = 200 ++ ++# =========================================================================== ++# Avoid SGF patterns ++# =========================================================================== ++# The parameters in this section provide a way to avoid moves that follow ++# specific patterns based on a set of SGF files loaded upon startup. ++# This is basically the same as the above "Automatic avoid patterns" section ++# above except you supply your own SGF files to avoid moves from. ++# Uncomment them to use this feature. Additionally, if the SGF file ++# contains the string %SKIP% in a comment on a move, that move will be ++# ignored for this purpose. ++ ++# Load SGF files from this directory when the engine is started ++# (only on startup, will not reload unless engine is restarted) ++# avoidSgfPatternDirs = path/to/directory/with/sgfs/ ++# You can also surround the file path in double quotes if the file path contains trailing spaces or hash signs. ++# Within double quotes, backslashes are escape characters. ++# avoidSgfPatternDirs = "path/to/directory/with/sgfs/" ++ ++# Penalize this much utility per matching move. ++# Set this negative if you instead want to favor SGF patterns instead of ++# penalizing them. This number does not need to be large, even 0.001 will ++# make a difference. Values that are too large may lead to bad play. ++# avoidSgfPatternUtility = 0.001 ++ ++# Optional - load only the newest this many files ++# avoidSgfPatternMaxFiles = 20 ++ ++# Optional - Penalty is multiplied by this per each older SGF file, so that ++# old SGF files matter less than newer ones. ++# avoidSgfPatternLambda = 0.90 ++ ++# Optional - pay attention only to moves made by players with this name. ++# For example, set it to the name that your bot's past games will show up ++# as in the SGF, so that the bot will only avoid repeating moves that itself ++# made in past games, not the moves that its opponents made. ++# avoidSgfPatternAllowedNames = my-ogs-bot-name1,my-ogs-bot-name2 ++ ++# Optional - Ignore moves in SGF files that occurred before this turn number. ++# avoidSgfPatternMinTurnNumber = 0 ++ ++# For more avoid patterns: ++# You can also specify a second set of parameters, and a third, fourth, ++# etc. by numbering 2,3,4,... ++# ++# avoidSgf2PatternDirs = ... ++# avoidSgf2PatternUtility = ... ++# avoidSgf2PatternMaxFiles = ... ++# avoidSgf2PatternLambda = ... ++# avoidSgf2PatternAllowedNames = ... ++# avoidSgf2PatternMinTurnNumber = ... ++ +diff --git a/python/selfplay/golaxy_9d.cfg b/python/selfplay/golaxy_9d.cfg +new file mode 100644 +index 00000000..31e66efd +--- /dev/null ++++ b/python/selfplay/golaxy_9d.cfg +@@ -0,0 +1,713 @@ ++# Configuration for KataGo C++ GTP engine ++ ++# Run the program using: `./katago.exe gtp` ++ ++# In this example config, when a parameter is given as a commented out value, ++# that value also is the default value, unless described otherwise. You can ++# uncomment it (remove the pound sign) and change it if you want. ++ ++# =========================================================================== ++# Running on an online server or in a real tournament or match ++# =========================================================================== ++# If you plan to run online or in a tournament, read through the "Rules" ++# section below for proper handling of komi, handicaps, end-of-game cleanup, ++# and other details. ++ ++# =========================================================================== ++# Notes about performance and memory usage ++# =========================================================================== ++# Important: For good performance, you will very likely want to tune the ++# "numSearchThreads" parameter in the Search limits section below! Run ++# "./katago benchmark" to test KataGo and to suggest a reasonable value ++# of this parameter. ++ ++# For multi-GPU systems, read "OpenCL GPU settings" or "CUDA GPU settings". ++# ++# When using OpenCL, verify that KataGo picks the correct device! Some systems ++# may have both an Intel CPU OpenCL and GPU OpenCL. If # KataGo picks the wrong ++# one, correct this by specifying "openclGpuToUse". ++# ++# Consider adjusting "maxVisits", "ponderingEnabled", "resignThreshold", and ++# other parameters depending on your intended usage. ++ ++# =========================================================================== ++# Command-line usage ++# =========================================================================== ++# All of the below values may be set or overridden via command-line arguments: ++# ++# -override-config KEY=VALUE,KEY=VALUE,... ++ ++# =========================================================================== ++# Logs and files ++# =========================================================================== ++# This section defines where and what logging information is produced. ++ ++# Each run of KataGo will log to a separate file in this dir. ++# This is the default. ++logDir = gtp_logs ++# Uncomment and specify this instead of logDir to write separate dated subdirs ++# logDirDated = gtp_logs ++# Uncomment and specify this instead of logDir to log to only a single file ++# logFile = gtp.log ++ ++# Logging options ++logAllGTPCommunication = true ++logSearchInfo = true ++logSearchInfoForChosenMove = false ++logToStderr = true ++ ++# KataGo will display some info to stderr on GTP startup ++# Uncomment the next line and set it to false to suppress that and remain silent ++# startupPrintMessageToStderr = true ++ ++# Write information to stderr, for use in things like malkovich chat to OGS. ++ ogsChatToStderr = true ++ ++# Uncomment and set this to a directory to override where openCLTuner files ++# and other cached data is written. By default it saves into a subdir of the ++# current directory on windows, and a subdir of ~/.katago on Linux. ++# homeDataDir = PATH_TO_DIRECTORY ++ ++# =========================================================================== ++# Analysis ++# =========================================================================== ++# This section configures analysis settings. ++# ++# The maximum number of moves after the first move displayed in variations ++# from analysis commands like kata-analyze or lz-analyze. ++# analysisPVLen = 15 ++ ++# Report winrates for chat and analysis as (BLACK|WHITE|SIDETOMOVE). ++# Most GUIs and analysis tools will expect SIDETOMOVE. ++# reportAnalysisWinratesAs = SIDETOMOVE ++ ++# Extra noise for wider exploration. Large values will force KataGo to ++# analyze a greater variety of moves than it normally would. ++# An extreme value like 1 distributes playouts across every move on the board, ++# even very bad moves. ++# Affects analysis only, does not affect play. ++# analysisWideRootNoise = 0.04 ++ ++# Try to limit the effect of possible bad or bogus move sequences in the ++# history leading to this position from affecting KataGo's move predictions. ++# analysisIgnorePreRootHistory = true ++ ++# =========================================================================== ++# Rules ++# =========================================================================== ++# This section configures the scoring and playing rules. Rules can also be ++# changed mid-run by issuing custom GTP commands. ++# ++# See https://lightvector.github.io/KataGo/rules.html for rules details. ++# ++# See https://github.com/lightvector/KataGo/blob/master/docs/GTP_Extensions.md ++# for GTP commands. ++ ++# Specify the rules as a string. ++# Some legal values include: ++# chinese, japanese, korean, aga, chinese-ogs, new-zealand, stone-scoring, ++# ancient-territory, bga, aga-button ++# ++# For some human rulesets that require complex adjudication in tricky cases ++# (e.g. japanese, korean) KataGo may not precisely match the ruleset in such ++# cases but will do its best. ++# rules = tromp-taylor ++ ++# By default, the "rules" parameter is used, but if you comment it out and ++# uncomment one option in each of the sections below, you can specify an ++# arbitrary combination of individual rules. ++ ++# Ko Rule Settings ++koRule = POSITIONAL # Positional superko ++ ++# Scoring Rule Settings ++scoringRule = AREA # Area scoring ++ ++# Tax Rule Settings ++taxRule = NONE # All surrounded empty points are scored ++ ++# Is multiple-stone suicide legal? (Single-stone suicide is always illegal). ++multiStoneSuicideLegal = false ++ ++# "Button go" - the first pass when area scoring awards 0.5 points and does ++# not count for ending the game. ++hasButton = false ++ ++# Is this a human ruleset where it's okay to pass before having physically ++# captured and removed all dead stones? ++friendlyPassOk = false ++ ++# How handicap stones in handicap games are compensated ++whiteHandicapBonus = 0 # White gets no compensation for black's handicap stones (Tromp-taylor, NZ, JP) ++ ++ ++# ------------------------------ ++# Other rules hacks ++# ------------------------------ ++# Uncomment and change to adjust what board size KataGo uses upon startup ++# by default when GTP doesn't specify. ++# defaultBoardSize = 19 ++ ++# By default, Katago will use the komi that the GUI or GTP controller tries to set. ++# Uncomment and set this to have KataGo ignore the controller and always use this komi. ++# ignoreGTPAndForceKomi = 7 ++ ++# =========================================================================== ++# Bot behavior ++# =========================================================================== ++ ++# ------------------------------ ++# Resignation ++# ------------------------------ ++ ++# Resignation occurs if for at least resignConsecTurns in a row, the ++# winLossUtility (on a [-1,1] scale) is below resignThreshold. ++allowResignation = true ++resignThreshold = -0.90 ++resignConsecTurns = 3 ++ ++# By default, KataGo may resign games that it is confidently losing even if they ++# are very close in score. Uncomment and set this to avoid resigning games ++# if the estimated difference is points is less than or equal to this. ++# resignMinScoreDifference = 10 ++ ++# ------------------------------ ++# Handicap ++# ------------------------------ ++# Assume that if black makes many moves in a row right at the start of the ++# game, then the game is a handicap game. This is necessary on some servers ++# and for some GUIs and also when initializing from many SGF files, which may ++# set up a handicap game using repeated GTP "play" commands for black rather ++# than GTP "place_free_handicap" commands; however, it may also lead to ++# incorrect understanding of komi if whiteHandicapBonus is used and a server ++# does not have such a practice. Uncomment and set to false to disable. ++# assumeMultipleStartingBlackMovesAreHandicap = true ++ ++# Makes katago dynamically adjust in handicap or altered-komi games to assume ++# based on those game settings that it must be stronger or weaker than the ++# opponent and to play accordingly. Greatly improves handicap strength by ++# biasing winrates and scores to favor appropriate safe/aggressive play. ++# Does NOT affect analysis (lz-analyze, kata-analyze, used by programs like ++# Lizzie) so analysis remains unbiased. Uncomment and set this to 0 to disable ++# this and make KataGo play the same always. ++# dynamicPlayoutDoublingAdvantageCapPerOppLead = 0.045 ++ ++# Instead of "dynamicPlayoutDoublingAdvantageCapPerOppLead", you can comment ++# that out and uncomment and set "playoutDoublingAdvantage" to a fixed value ++# from -3.0 to 3.0 that will not change dynamically. ++# ALSO affects analysis tools (lz-analyze, kata-analyze, used by e.g. Lizzie). ++# Negative makes KataGo behave as if it is much weaker than the opponent. ++# Positive makes KataGo behave as if it is much stronger than the opponent. ++# KataGo will adjust to favor safe/aggressive play as appropriate based on ++# the combination of who is ahead and how much stronger/weaker it thinks it is, ++# and report winrates and scores taking the strength difference into account. ++# ++# If this and "dynamicPlayoutDoublingAdvantageCapPerOppLead" are both set ++# then dynamic will be used for all games and this fixed value will be used ++# for analysis tools. ++# playoutDoublingAdvantage = 0.0 ++ ++# Uncomment one of these when using "playoutDoublingAdvantage" to enforce ++# that it will only apply when KataGo plays as the specified color and will be ++# negated when playing as the opposite color. ++# playoutDoublingAdvantagePla = BLACK ++# playoutDoublingAdvantagePla = WHITE ++ ++# ------------------------------ ++# Passing and cleanup ++# ------------------------------ ++# Make the bot never assume that its pass will end the game, even if passing ++# would end and "win" under Tromp-Taylor rules. Usually this is a good idea ++# when using it for analysis or playing on servers where scoring may be ++# implemented non-tromp-taylorly. Uncomment and set to false to disable. ++# conservativePass = true ++ ++# When using territory scoring, self-play games continue beyond two passes ++# with special cleanup rules that may be confusing for human players. This ++# option prevents the special cleanup phases from being reachable when using ++# the bot for GTP play. Uncomment and set to false to enable entering special ++# cleanup. For example, if you are testing it against itself, or against ++# another bot that has precisely implemented the rules documented at ++# https://lightvector.github.io/KataGo/rules.html ++# preventCleanupPhase = true ++ ++# ------------------------------ ++# Miscellaneous behavior ++# ------------------------------ ++# If the board is symmetric, search only one copy of each equivalent move. ++# Attempts to also account for ko/superko, will not theoretically perfect for ++# superko. Uncomment and set to false to disable. ++# rootSymmetryPruning = true ++ ++# Uncomment and set to true to avoid a particular joseki that some networks ++# misevaluate, and also to improve opening diversity versus some particular ++# other bots that like to play it all the time. ++# avoidMYTDaggerHack = false ++ ++# Prefer to avoid playing the same joseki in every corner of the board. ++# Uncomment to set to a specific value. See "Avoid SGF patterns" section. ++# By default: 0 (even games), 0.005 (handicap games) ++# avoidRepeatedPatternUtility = 0.0 ++ ++# Experimental logic to fight against mirror Go even with unfavorable komi. ++# Uncomment to set to a specific value to use for both playing and analysis. ++# By default: true when playing via GTP, but false when analyzing. ++# antiMirror = true ++ ++# Enable some hacks that mitigate rare instances when passing messes up deeper searches. ++# enablePassingHacks = true ++ ++# Uncomment and set this to true to prevent bad or bogus move sequences ++# in the history leading to this position from affecting KataGo's move choices. ++# Same as analysisIgnorePreRootHistory (see above) but applies to actual play. ++# You can enable this if KataGo is being asked to play from positions that it did not ++# choose the moves to reach. ++# ignorePreRootHistory = false ++ ++# =========================================================================== ++# Search limits ++# =========================================================================== ++ ++# Terminology: ++# "Playouts" is the number of new playouts of search performed each turn. ++# "Visits" is the same as "Playouts" but also counts search performed on ++# previous turns that is still applicable to this turn. ++# "Time" is the time in seconds. ++ ++# For example, if KataGo searched 200 nodes on the previous turn, and then ++# after the opponent's reply, 50 nodes of its search tree was still valid, ++# then a visit limit of 200 would allow KataGo to search 150 new nodes ++# (for a final tree size of 200 nodes), whereas a playout limit of of 200 ++# would allow KataGo to search 200 nodes (for a final tree size of 250 nodes). ++ ++# Additionally, KataGo may also move before than the limit in order to ++# obey time controls (e.g. byo-yomi, etc) if the GTP controller has ++# told KataGo that the game has is being played with a given time control. ++ ++# Limits for search on the current turn. ++# If commented out or unspecified, the default is to have no limit. ++# maxVisits = 50000 ++# maxPlayouts = 300 ++ maxTime = 1.0 ++ ++# Ponder on the opponent's turn? ++ponderingEnabled = true ++ ++# Limits for search when pondering on the opponent's turn. ++# If commented out or unspecified, the default is to have no limit. ++# Limiting the maximum time is recommended so that KataGo won't burn CPU/GPU ++# forever and/or run out of RAM if left unattended while pondering is enabled. ++# maxVisitsPondering = 5000 ++# maxPlayoutsPondering = 3000 ++maxTimePondering = 120.0 ++ ++ ++# ------------------------------ ++# Other search limits and behavior ++# ------------------------------ ++ ++# Approx number of seconds to buffer for lag for GTP time controls - will ++# move a bit faster assuming there is this much lag per move. ++lagBuffer = 1.0 ++ ++# YOU PROBABLY WANT TO TUNE THIS PARAMETER! ++# The number of threads to use when searching. On powerful GPUs the optimal ++# threads may be much higher than the number of CPU cores you have because ++# many threads are needed to feed efficient large batches to the GPU. ++# ++# Run "./katago benchmark" to tune this parameter and test the effect ++# of changes to any of other parameters. ++numSearchThreads = 40 ++ ++# Play a little faster if the opponent is passing, for human-friendliness. ++# Comment these out to disable them, such as if running a controlled match ++# where you are testing KataGo with fixed compute per move vs other bots. ++searchFactorAfterOnePass = 0.50 ++searchFactorAfterTwoPass = 0.25 ++ ++# Play a little faster if super-winning, for human-friendliness. ++# Comment these out to disable them, such as if running a controlled match ++# where you are testing KataGo with fixed compute per move vs other bots. ++searchFactorWhenWinning = 0.40 ++searchFactorWhenWinningThreshold = 0.95 ++ ++# =========================================================================== ++# GPU settings ++# =========================================================================== ++# This section configures GPU settings. ++# ++# Maximum number of positions to send to a single GPU at once. The default ++# value is roughly equal to numSearchThreads, but can be specified manually ++# if running out of memory, or using multiple GPUs that expect to share work. ++# nnMaxBatchSize = ++ ++# Controls the neural network cache size, which is the primary RAM/memory use. ++# KataGo will cache up to (2 ** nnCacheSizePowerOfTwo) many neural net ++# evaluations in case of transpositions in the tree. ++# Increase this to improve performance for searches with tens of thousands ++# of visits or more. Decrease this to limit memory usage. ++# If you're happy to do some math - each neural net entry takes roughly ++# 1.5KB, except when using whole-board ownership/territory ++# visualizations, where each entry will take roughly 3KB. The number of ++# entries is (2 ** nnCacheSizePowerOfTwo). (E.g. 2 ** 18 = 262144.) ++# You can compute roughly how much memory the cache will use based on this. ++# nnCacheSizePowerOfTwo = 20 ++ ++# Size of mutex pool for nnCache is (2 ** this). ++# nnMutexPoolSizePowerOfTwo = 16 ++ ++# Randomize board orientation when running neural net evals? Uncomment and ++# set to false to disable. ++# nnRandomize = true ++ ++# If provided, force usage of a specific seed for nnRandomize. ++# The default is to use a randomly generated seed. ++# nnRandSeed = abcdefg ++ ++# Uncomment and set to true to force GTP to use the maximum board size for ++# internal buffers for the neural net. This will make KataGo slower when ++# evaluating small boards, but will avoid a lengthy initialization time on every ++# change of board size due to having to re-size the neural net buffers on the GPU. ++# This can be useful for example, for OGS's persistent bot mode that uses a single ++# bot instance to handle multiple games and may thrash between different board sizes ++# if there are concurrent games of multiple sizes. ++# gtpForceMaxNNSize = false ++ ++# ------------------------------ ++# Multiple GPUs ++# ------------------------------ ++# Set this to the number of GPUs to use or that are available. ++# IMPORTANT: If more than 1, also uncomment the appropriate TensorRT ++# or CUDA or OpenCL section. ++# numNNServerThreadsPerModel = 1 ++ ++# ------------------------------ ++# TENSORRT GPU settings ++# ------------------------------ ++# These only apply when using the TENSORRT version of KataGo. ++ ++# For one GPU: optionally uncomment this option and change if the GPU to ++# use is not device 0. ++# trtDeviceToUse = 0 ++ ++# For two GPUs: Uncomment these options, AND set numNNServerThreadsPerModel above. ++# Also, change their values if the devices you want to use are not 0 and 1. ++# trtDeviceToUseThread0 = 0 ++# trtDeviceToUseThread1 = 1 ++ ++# For three GPUs: Uncomment these options, AND set numNNServerThreadsPerModel above. ++# Also, change their values if the devices you want to use are not 0 and 1 and 2. ++# trtDeviceToUseThread0 = 0 ++# trtDeviceToUseThread1 = 1 ++# trtDeviceToUseThread2 = 2 ++ ++# The pattern continues for additional GPUs. ++ ++# ------------------------------ ++# CUDA GPU settings ++# ------------------------------ ++# These only apply when using the CUDA version of KataGo. ++ ++# For one GPU: optionally uncomment and change this if the GPU you want to ++# use is not device 0 ++# cudaDeviceToUse = 0 ++ ++# For two GPUs: Uncomment these options, AND set numNNServerThreadsPerModel above. ++# Also, change their values if the devices you want to use are not 0 and 1. ++# cudaDeviceToUseThread0 = 0 ++# cudaDeviceToUseThread1 = 1 ++ ++# For three GPUs: Uncomment these options, AND set numNNServerThreadsPerModel above. ++# Also, change their values if the devices you want to use are not 0 and 1 and 2. ++# cudaDeviceToUseThread0 = 0 ++# cudaDeviceToUseThread1 = 1 ++# cudaDeviceToUseThread2 = 2 ++ ++# The pattern continues for additional GPUs. ++ ++# KataGo will automatically use FP16 or not based on the compute capability ++# of your NVIDIA GPU. If you want to try to force a particular behavior ++# you can uncomment these lines and change them to "true" or "false". ++# cudaUseFP16 = auto ++# cudaUseNHWC = auto ++ ++# ------------------------------ ++# OpenCL GPU settings ++# ------------------------------ ++# These only apply when using the OpenCL version of KataGo. ++ ++# Uncomment and set to true to tune OpenCL for every board size separately, ++# rather than only the largest possible size. ++# openclReTunePerBoardSize = false ++ ++# For one GPU: optionally uncomment and change this if the best device to use is guessed incorrectly. ++# The default behavior tries to guess the 'best' GPU or device on your system to use, usually it will be a good guess. ++# openclDeviceToUse = 0 ++ ++# For two GPUs: Uncomment these two lines and replace X and Y with the device ids of the devices you want to use. ++# It might NOT be 0 and 1, some computers will have many OpenCL devices. You can see what the devices are when ++# KataGo starts up - it should print or log all the devices it finds. ++# (AND also set numNNServerThreadsPerModel above) ++# openclDeviceToUseThread0 = X ++# openclDeviceToUseThread1 = Y ++ ++# For three GPUs: Uncomment these three lines and replace X and Y and Z with the device ids of the devices you want to use. ++# It might NOT be 0 and 1 and 2, some computers will have many OpenCL devices. You can see what the devices are when ++# KataGo starts up - it should print or log all the devices it finds. ++# (AND also set numNNServerThreadsPerModel above) ++# openclDeviceToUseThread0 = X ++# openclDeviceToUseThread1 = Y ++# openclDeviceToUseThread2 = Z ++ ++# The pattern continues for additional GPUs. ++ ++# KataGo will automatically use FP16 or not based on testing your GPU during ++# tuning. If you want to try to force a particular behavior though you can ++# uncomment this option and change it to "true" or "false". This is a fairly ++# blunt setting - more detailed settings are testable by rerunning the tuner ++# with various arguments (./katago tuner). ++# openclUseFP16 = auto ++ ++# ------------------------------ ++# Eigen-specific settings ++# ------------------------------ ++# These only apply when using the Eigen (pure CPU) version of KataGo. ++ ++# Number of CPU threads for evaluating the neural net on the Eigen backend. ++# ++# Default: numSearchThreads ++# numEigenThreadsPerModel = X ++ ++# =========================================================================== ++# Root move selection and biases ++# =========================================================================== ++# Uncomment and edit any of the below values to change them from their default. ++ ++# If provided, force usage of a specific seed for various random things in ++# the search. The default is to use a random seed. ++# searchRandSeed = hijklmn ++ ++# Temperature for the early game, randomize between chosen moves with ++# this temperature ++# chosenMoveTemperatureEarly = 0.5 ++ ++# Decay temperature for the early game by 0.5 every this many moves, ++# scaled with board size. ++# chosenMoveTemperatureHalflife = 19 ++ ++# At the end of search after the early game, randomize between chosen ++# moves with this temperature ++# chosenMoveTemperature = 0.10 ++ ++# Subtract this many visits from each move prior to applying ++# chosenMoveTemperature (unless all moves have too few visits) to downweight ++# unlikely moves ++# chosenMoveSubtract = 0 ++ ++# The same as chosenMoveSubtract but only prunes moves that fall below ++# the threshold. This setting does not affect chosenMoveSubtract. ++# chosenMovePrune = 1 ++ ++# Number of symmetries to sample (without replacement) and average at the root ++# rootNumSymmetriesToSample = 1 ++ ++# Using LCB for move selection? ++# useLcbForSelection = true ++ ++# How many stdevs a move needs to be better than another for LCB selection ++# lcbStdevs = 5.0 ++ ++# Only use LCB override when a move has this proportion of visits as the ++# top move. ++# minVisitPropForLCB = 0.15 ++ ++# =========================================================================== ++# Internal params ++# =========================================================================== ++# Uncomment and edit any of the below values to change them from their default. ++ ++# Scales the utility of winning/losing ++# winLossUtilityFactor = 1.0 ++ ++# Scales the utility for trying to maximize score ++# staticScoreUtilityFactor = 0.10 ++# dynamicScoreUtilityFactor = 0.30 ++ ++# Adjust dynamic score center this proportion of the way towards zero, ++# capped at a reasonable amount. ++# dynamicScoreCenterZeroWeight = 0.20 ++# dynamicScoreCenterScale = 0.75 ++ ++# The utility of getting a "no result" due to triple ko or other long cycle ++# in non-superko rulesets (-1 to 1) ++# noResultUtilityForWhite = 0.0 ++ ++# The number of wins that a draw counts as, for white. (0 to 1) ++# drawEquivalentWinsForWhite = 0.5 ++ ++# Exploration constant for mcts ++# cpuctExploration = 1.0 ++# cpuctExplorationLog = 0.45 ++ ++# Parameters that control exploring more in volatile positions, exploring ++# less in stable positions. ++# cpuctUtilityStdevPrior = 0.40 ++# cpuctUtilityStdevPriorWeight = 2.0 ++# cpuctUtilityStdevScale = 0.85 ++ ++# FPU reduction constant for mcts ++# fpuReductionMax = 0.2 ++# rootFpuReductionMax = 0.1 ++# fpuParentWeightByVisitedPolicy = true ++ ++# Parameters that control weighting of evals based on the net's own ++# self-reported uncertainty. ++# useUncertainty = true ++# uncertaintyExponent = 1.0 ++# uncertaintyCoeff = 0.25 ++ ++# Explore using optimistic policy ++# rootPolicyOptimism = 0.2 ++# policyOptimism = 1.0 ++ ++# Amount to apply a downweighting of children with very bad values relative ++# to good ones. ++# valueWeightExponent = 0.25 ++ ++# Slight incentive for the bot to behave human-like with regard to passing at ++# the end, filling the dame, not wasting time playing in its own territory, ++# etc., and not play moves that are equivalent in terms of points but a bit ++# more unfriendly to humans. ++# rootEndingBonusPoints = 0.5 ++ ++# Make the bot prune useless moves that are just prolonging the game to ++# avoid losing yet. ++# rootPruneUselessMoves = true ++ ++# Apply bias correction based on local pattern keys ++# subtreeValueBiasFactor = 0.45 ++# subtreeValueBiasWeightExponent = 0.85 ++ ++# Use graph search rather than tree search - identify and share search for ++# transpositions. ++# useGraphSearch = true ++ ++# How much to shard the node table for search synchronization ++# nodeTableShardsPowerOfTwo = 16 ++ ++# How many virtual losses to add when a thread descends through a node ++# numVirtualLossesPerThread = 1 ++ ++# Improve the quality of evals under heavy multithreading ++# useNoisePruning = true ++ ++# =========================================================================== ++# Automatic avoid patterns ++# =========================================================================== ++# The parameters in this section provide a way to bias away from moves that match ++# patterns that this instance of KataGo has played in previous games, by auto-saving ++# moves to a directory and then auto-loading and biasing against them each new game. ++# Uncomment them to use them. When using this feature, all parameters must be specified. ++ ++# Different board sizes are tracked separately, but all board sizes share the same ++# parameters by default. Every parameter *except* for autoAvoidRepeatDir and ++# autoAvoidRepeatSaveChunkSize can be overridden per board size, ++# e.g. "autoAvoidRepeatMinTurnNumber13x13". You must ALSO specify the ++# defaults even if you have specified board-size-specific values. ++ ++# Directory to auto-save moves KataGo plays, to avoid them in future games. ++# You can create a new empty directory and put its path here. ++# If you run parallel instances of KataGo, use different directories if you ++# want them not to share their biases, use the same directory if you want ++# all of them to bias away from past moves that any of them have played. ++# KataGo will also automatically DELETE old data in this directory, so it is ++# recommended that if you do share the same directory between parallel instances, ++# that they all use the same settings that affect data saving/deletion. ++# autoAvoidRepeatDir = PATH_TO_NEW_DIRECTORY ++ ++# Penalize this much utility per matching move. ++# Values that are too large may lead to bad play. The value of 0.004 is fairly large ++# and might be large enough to result in some early weird/bad moves when trying ++# to avoid past games' moves if enough games begin the same way. You can experiment with it. ++# autoAvoidRepeatUtility = 0.004 ++ ++# Per each new move saved, exponentially decay prior saved moves by this factor. ++# This way, the bias against moves from many games ago is gradually phased out. ++# For example, 0.9995 = 1 - 1/2000, so would be roughly 2000 prior moves worth of ++# penalty weight remembered, in steady state. Depending on what turn number range ++# you are saving, this might equate to a different number of games For example ++# saving the first 50 moves per game would make this roughly 2000 / 50 = 40 games ++# worth of memory. ++# autoAvoidRepeatLambda = 0.9995 ++ ++# Affects data saving/deletion. ++# When the number of saved moves exceeds this, outright delete them to avoid too many ++# files and disk space building up. Also may affect the speed of saving/loading on start ++# of each game if this is set large and a lot of data builds up. ++# autoAvoidRepeatMaxPoses = 10000 ++ ++# Affects data saving/deletion. ++# Only save data for moves within this turn number range of those games. ++# E.g. setting autoAvoidRepeatMinTurnNumber to a number like 4 or 5 would tend to make ++# KataGo not develop a bias against the initial 3-4 and 4-4 corner moves in almost every game. ++# autoAvoidRepeatMinTurnNumber = 0 ++# autoAvoidRepeatMaxTurnNumber = 50 ++ ++# Affects data saving/deletion. ++# Within a single run of a program, wait to accumulate this many samples ++# (possibly across multiple clear_boards/games) before saving the data. ++# Can help to avoid writing too many small files to disk, especially when GTP is used ++# in a way that clears the board very frequently (e.g. gtp2ogs pooled manager). ++# autoAvoidRepeatSaveChunkSize = 200 ++ ++# =========================================================================== ++# Avoid SGF patterns ++# =========================================================================== ++# The parameters in this section provide a way to avoid moves that follow ++# specific patterns based on a set of SGF files loaded upon startup. ++# This is basically the same as the above "Automatic avoid patterns" section ++# above except you supply your own SGF files to avoid moves from. ++# Uncomment them to use this feature. Additionally, if the SGF file ++# contains the string %SKIP% in a comment on a move, that move will be ++# ignored for this purpose. ++ ++# Load SGF files from this directory when the engine is started ++# (only on startup, will not reload unless engine is restarted) ++# avoidSgfPatternDirs = path/to/directory/with/sgfs/ ++# You can also surround the file path in double quotes if the file path contains trailing spaces or hash signs. ++# Within double quotes, backslashes are escape characters. ++# avoidSgfPatternDirs = "path/to/directory/with/sgfs/" ++ ++# Penalize this much utility per matching move. ++# Set this negative if you instead want to favor SGF patterns instead of ++# penalizing them. This number does not need to be large, even 0.001 will ++# make a difference. Values that are too large may lead to bad play. ++# avoidSgfPatternUtility = 0.001 ++ ++# Optional - load only the newest this many files ++# avoidSgfPatternMaxFiles = 20 ++ ++# Optional - Penalty is multiplied by this per each older SGF file, so that ++# old SGF files matter less than newer ones. ++# avoidSgfPatternLambda = 0.90 ++ ++# Optional - pay attention only to moves made by players with this name. ++# For example, set it to the name that your bot's past games will show up ++# as in the SGF, so that the bot will only avoid repeating moves that itself ++# made in past games, not the moves that its opponents made. ++# avoidSgfPatternAllowedNames = my-ogs-bot-name1,my-ogs-bot-name2 ++ ++# Optional - Ignore moves in SGF files that occurred before this turn number. ++# avoidSgfPatternMinTurnNumber = 0 ++ ++# For more avoid patterns: ++# You can also specify a second set of parameters, and a third, fourth, ++# etc. by numbering 2,3,4,... ++# ++# avoidSgf2PatternDirs = ... ++# avoidSgf2PatternUtility = ... ++# avoidSgf2PatternMaxFiles = ... ++# avoidSgf2PatternLambda = ... ++# avoidSgf2PatternAllowedNames = ... ++# avoidSgf2PatternMinTurnNumber = ... ++ +diff --git a/python/selfplay/gtp_human5k_example.cfg b/python/selfplay/gtp_human5k_example.cfg +new file mode 100644 +index 00000000..9098f5ab +--- /dev/null ++++ b/python/selfplay/gtp_human5k_example.cfg +@@ -0,0 +1,189 @@ ++ ++# This is an example config for configuring KataGo to attempt to imitate a weaker human player. ++# Running with this config requires giving a human SL model b18c384nbt-humanv0.bin.gz ++# on the command line such as: ++# ./katago gtp -config gtp_human5k_example.cfg -model your_favorite_normal_model_for_katago.bin.gz -human-model b18c384nbt-humanv0.bin.gz ++# You can obtain the human model at https://github.com/lightvector/KataGo/releases/tag/v1.15.0 ++ ++# Below, the most important parts of the config for human-like play are commented. ++# See the original gtp_example for comments on other parameters. ++ ++# For another useful guide on human-style analysis, see here: ++# https://github.com/lightvector/KataGo/blob/master/docs/Analysis_Engine.md#human-sl-analysis-guide ++ ++# It is ALSO possible to pass in simply '-model b18c384nbt-humanv0.bin.gz' and NOT ++# pass in -human-model, i.e. use the human model as if it were KataGo's normal neural net. ++# If you do that, you need to use a config more like the normal gtp_example.cfg, not this config! ++# Keep in mind that if using the model normally, or if using -human-model but also altering ++# parameters below to blend in some of KataGo's search, KataGo's play might NOT be very human-like, ++# or might be human-like but play at a strength very different than the humanSLProfile. ++# You can experiment, some of the comments below hopefully will help illustrate things too. ++ ++logDir = gtp_logs ++logAllGTPCommunication = true ++logSearchInfo = true ++logSearchInfoForChosenMove = false ++logToStderr = false ++ ++# Use these rules by default, but a GUI or GTP controller might override this. ++rules = japanese ++ ++# When using -human-model, we only resign when far behind since a weaker player ++# might continue to fight much longer than a strong bot normally would. ++allowResignation = true ++resignThreshold = -0.99 ++resignConsecTurns = 20 ++resignMinScoreDifference = 40 ++resignMinMovesPerBoardArea = 0.4 ++ ++# Note: unless you change other parameters too, by default increasing visits won't do much. ++# If humanSLChosenMoveProp = 1.0 AND humanSLChosenMovePiklLambda is a large number, ++# then KataGo's normal search is ignored except for possibly choosing whether to pass/resign, ++# so more visits will have no effect on play. Still, having some visits is good for ++# ensuring good pass/resign judgment. ++maxVisits = 40 ++numSearchThreads = 1 ++lagBuffer = 1.0 ++ ++# Rough scale in seconds to randomly delay moving, so as not to respond instantly. ++# Some moves will delay longer, some moves will delay a little less. ++delayMoveScale = 2.0 ++delayMoveMax = 10.0 ++ ++# =========================================================================== ++# HUMAN SL PARAMETERS ++# =========================================================================== ++ ++# The most important parameter for human-like play configuration! ++# Choose the "profile" of players that the human SL model will imitate. ++# Available options are: ++# preaz_{RANK from 20k to 9d} - imitate player of given rank, before AlphaZero opening style became popular ++# rank_{RANK from 20k to 9d} - imitate player of given rank, after human openings changed due to AlphaZero. ++# preaz_{BRANK}_{WRANK} or rank_{BRANK}_{WRANK} - same, but imitate how black with the rank BR and white ++# with the rank WR would play against each other, knowing that the other player is stronger/weaker than them. ++# Warning: for rank differences > 9 ranks, or drastically mis-matched to the handicap used in the game, ++# this may be out of distribution due to lack of training data and the model might not behave well! Experiment with care. ++# proyear_{YEAR from 1800 to 2023} - imitate historical pros or insei from given year. ++humanSLProfile = preaz_5k ++ ++# The probability that we should play a HUMAN-like move, rather than playing KataGo's move. ++# Applies BEFORE temperature. ++humanSLChosenMoveProp = 1.0 ++ ++# If true, ignore the human SL model's choice of when to pass, and still use KataGo to determine that. ++# The human SL model, in theory, is not guaranteed to be reliable at when to pass for all profiles, ++# since e.g. some historical games omit passes. ++humanSLChosenMoveIgnorePass = true ++ ++# By default humanSLChosenMovePiklLambda is a large number which effectively disables it. ++# Setting it to a smaller number will "suppress" human-like moves that KataGo disapproves of. ++# In particular, if set to, for example, 0.4 when KataGo judges a human SL move to lose 0.4 utility, ++# it will substantially suppress the chance of playing that move (in particular, by a factor of exp(1)). ++# Less-bad moves will also be suppressed, but not by as much, e.g. a move losing 0.2 would get lowered ++# by a factor of exp(0.5). ++# As configured lower down, utilities by default range from -1.0 (loss) to +1.0 (win), plus up to +/- 0.3 for score. ++# WARNING: ONLY moves that KataGo actually searches will get suppressed! If a move is so bad that KataGo ++# rejects it without searching it, it will NOT get suppressed. ++# Therefore, to use humanSLChosenMovePiklLambda, it is STRONGLY recommended that you also use something ++# like humanSLRootExploreProbWeightless to ensure most human moves including bad moves get searched, ++# and ALSO use at least hundreds and ideally thousands of maxVisits, to ensure enough visits. ++humanSLChosenMovePiklLambda = 100000000 ++ ++# These parameters tell KataGo to use the human SL policy for exploration during search. ++# Each of these specifies the probability that KataGo will perform PUCT using the Human SL policy to ++# explore different moves, rather than using KataGo's normal policy, after a certain minimal number of visits. ++# "Root": applies only at the root of the search ++# "Pla": applies during non-root nodes of the search where it is katago's turn. ++# "Opp": applies during non-root nodes of the search where it is the opponent's turn. ++# "Weightless": search the move to evaluate it, but do NOT allow this visit to affect the parent's average utility. ++# "Weightful": search the move to evaluate it, and DO allow this visit to affect the parent's average utility. ++# For example, humanSLRootExploreProbWeightless = 0.5 would tell KataGo at the root of the search to spend ++# 50% of its visits to judge different possible human moves, but NOT to use those visits for determining the ++# value of the position (avoiding biasing the utility if some human SL moves are very bad). ++# If you don't understand these well, ask for help or look up some online explainers for MCTS (Monte-Carlo Tree Search). ++humanSLRootExploreProbWeightless = 0.0 ++humanSLRootExploreProbWeightful = 0.0 ++humanSLPlaExploreProbWeightless = 0.0 ++humanSLPlaExploreProbWeightful = 0.0 ++humanSLOppExploreProbWeightless = 0.0 ++humanSLOppExploreProbWeightful = 0.0 ++ ++# When using the human SL policy for exploration during search, use this cPUCT. ++# This only has an effect if at least one of humanSL{Root,Pla,Opp}ExploreProbWeight{less,ful} is nonzero. ++humanSLCpuctExploration = 0.50 ++ ++# Same as humanSLCpuctExploration, but NEVER diminshes its exploration no matter how many visits are used. ++# Normally, PUCT will sharpen with visits and spend a diminishing proportion of visits on moves with lower utility. ++# This is the coefficient for a term that does NOT diminish, i.e. if this is 0.2, then roughly moves within ++# 0.2 utility (about 10% winrate) of the best move will forever continue getting a decent fraction of visits, ++# smoothly falling off for greater utility differences. ++# Note that in combination with Weightful exploration above, if used for Opp exploration, this could be used ++# to model an opponent that will always have some chance to make small mistakes no matter how deep they search. ++# If further this was increased to a very large value, it would model an opponent that always played according ++# to the human SL raw policy. These might be interesting to experiment with for handicap play. ++humanSLCpuctPermanent = 0.2 ++ ++ ++# =========================================================================== ++# OTHER USEFUL PARAMETERS FOR HUMAN PLAY ADJUSTMENT ++# =========================================================================== ++ ++# Choosing temperature near 1, and restricting it to only affect moves already below 1% chance, ++# so that we sample close to the full range of human play. ++# You can also reduce the temperature to settings more like the plain gtp_example.cfg. ++# Then, rather than imitating a realistic human player, it will be more like imitating the ++# *majority vote* of players at that rank. For example it would avoid a lot of blunders ++# that players of that level would make, because even if players often blunder, the *majority vote* ++# of players would be much less likely to select any given blunder that an individual player would. ++chosenMoveTemperatureEarly = 0.85 ++chosenMoveTemperature = 0.70 ++chosenMoveTemperatureHalflife = 80 ++chosenMoveTemperatureOnlyBelowProb = 0.01 # temperature only starts to dampen moves below this ++chosenMoveSubtract = 0 ++chosenMovePrune = 0 ++ ++# Use a small NN cache to save memory since we're using very low visits anyways. You can increase ++# these back to more like the plain gtp_example.cfg if you are doing more extensive searches to ++# improve performance. ++nnCacheSizePowerOfTwo = 17 ++nnMutexPoolSizePowerOfTwo = 14 ++ ++# =========================================================================== ++# PARAMETERS CHANGED FROM DEFAULT TO MAKE SURE HUMAN SL USAGE WORKS WELL ++# =========================================================================== ++ ++# Make sure to take into account the recent moves in the game, don't ignore history. ++# This will produce the best imitation/prediction, since humans definitely do play differently based on where the ++# most recent moves in the game were, rather than coming fresh to the board position on every turn. ++ignorePreRootHistory = false ++analysisIgnorePreRootHistory = false ++ ++# Average 2 neural net samples at the root - ensures a bit smoother probabilities and results in ++# 8 * 7 / 2 = 28 possible policies instead of 8 possibilities. ++rootNumSymmetriesToSample = 2 ++# LCB improves strength for KataGo, but we disable it so it doesn't mess up move selection when blending human play. ++useLcbForSelection = false ++ ++# We disable dynamicScoreUtilityFactor - the human SL model can make score predictions that are a bit swingy, so ++# if we do want to do a search that blends human SL values in (TODO there isn't a way to do this anyways yet), using ++# static score utility might be a bit more stable. ++winLossUtilityFactor = 1.0 ++staticScoreUtilityFactor = 0.30 ++dynamicScoreUtilityFactor = 0.00 ++ ++# Uncertainty improves strength for KataGo normally, but messes with the weights of playouts in complicated ways, ++# so lets turn it off when doing human SL stuff. ++useUncertainty = false ++ ++# Subtree value bias improves strength for KataGo normally, but messes with the values of nodes in complicated ways, ++# so let's turn it off when doing human SL stuff. ++subtreeValueBiasFactor = 0.0 ++ ++# Noise pruning prunes out weight from moves that KataGo thinks are bad, but if we are doing human SL we might actively ++# want to be playing or exploring and weighting "bad" but human-like moves. So disable this. ++# Warning: when this is false, there is much less protection against the search severely misbehaving when you use too many threads. ++# Make sure not to set numSearchThreads to be too large - at a minimum, keep at least a 20x buffer between ++# the number of visits you use and the number of threads you use. ++# (as an aside, ideally, you want to have visits be a sufficient factor larger than threads EVEN when ++# useNoisePruning is true, this parameter just blunts the worst effects but doesn't entirely fix the badness). ++useNoisePruning = false +diff --git a/python/selfplay/leela_zero_strong.cfg b/python/selfplay/leela_zero_strong.cfg +new file mode 100644 +index 00000000..e1167b84 +--- /dev/null ++++ b/python/selfplay/leela_zero_strong.cfg +@@ -0,0 +1,719 @@ ++# Configuration for KataGo C++ GTP engine ++ ++# Run the program using: `./katago.exe gtp` ++ ++# In this example config, when a parameter is given as a commented out value, ++# that value also is the default value, unless described otherwise. You can ++# uncomment it (remove the pound sign) and change it if you want. ++ ++# =========================================================================== ++# Running on an online server or in a real tournament or match ++# =========================================================================== ++# If you plan to run online or in a tournament, read through the "Rules" ++# section below for proper handling of komi, handicaps, end-of-game cleanup, ++# and other details. ++ ++# =========================================================================== ++# Notes about performance and memory usage ++# =========================================================================== ++# Important: For good performance, you will very likely want to tune the ++# "numSearchThreads" parameter in the Search limits section below! Run ++# "./katago benchmark" to test KataGo and to suggest a reasonable value ++# of this parameter. ++ ++# For multi-GPU systems, read "OpenCL GPU settings" or "CUDA GPU settings". ++# ++# When using OpenCL, verify that KataGo picks the correct device! Some systems ++# may have both an Intel CPU OpenCL and GPU OpenCL. If # KataGo picks the wrong ++# one, correct this by specifying "openclGpuToUse". ++# ++# Consider adjusting "maxVisits", "ponderingEnabled", "resignThreshold", and ++# other parameters depending on your intended usage. ++ ++# =========================================================================== ++# Command-line usage ++# =========================================================================== ++# All of the below values may be set or overridden via command-line arguments: ++# ++# -override-config KEY=VALUE,KEY=VALUE,... ++ ++# =========================================================================== ++# Logs and files ++# =========================================================================== ++# This section defines where and what logging information is produced. ++ ++# Each run of KataGo will log to a separate file in this dir. ++# This is the default. ++logDir = gtp_logs ++# Uncomment and specify this instead of logDir to write separate dated subdirs ++# logDirDated = gtp_logs ++# Uncomment and specify this instead of logDir to log to only a single file ++# logFile = gtp.log ++ ++# Logging options ++logAllGTPCommunication = true ++logSearchInfo = true ++logSearchInfoForChosenMove = false ++logToStderr = true ++ ++# KataGo will display some info to stderr on GTP startup ++# Uncomment the next line and set it to false to suppress that and remain silent ++# startupPrintMessageToStderr = true ++ ++# Write information to stderr, for use in things like malkovich chat to OGS. ++ ogsChatToStderr = true ++ ++# Uncomment and set this to a directory to override where openCLTuner files ++# and other cached data is written. By default it saves into a subdir of the ++# current directory on windows, and a subdir of ~/.katago on Linux. ++# homeDataDir = PATH_TO_DIRECTORY ++ ++# =========================================================================== ++# Analysis ++# =========================================================================== ++# This section configures analysis settings. ++# ++# The maximum number of moves after the first move displayed in variations ++# from analysis commands like kata-analyze or lz-analyze. ++# analysisPVLen = 15 ++ ++# Report winrates for chat and analysis as (BLACK|WHITE|SIDETOMOVE). ++# Most GUIs and analysis tools will expect SIDETOMOVE. ++# reportAnalysisWinratesAs = SIDETOMOVE ++ ++# Extra noise for wider exploration. Large values will force KataGo to ++# analyze a greater variety of moves than it normally would. ++# An extreme value like 1 distributes playouts across every move on the board, ++# even very bad moves. ++# Affects analysis only, does not affect play. ++# analysisWideRootNoise = 0.04 ++ ++# Try to limit the effect of possible bad or bogus move sequences in the ++# history leading to this position from affecting KataGo's move predictions. ++# analysisIgnorePreRootHistory = true ++ ++# =========================================================================== ++# Rules ++# =========================================================================== ++# This section configures the scoring and playing rules. Rules can also be ++# changed mid-run by issuing custom GTP commands. ++# ++# See https://lightvector.github.io/KataGo/rules.html for rules details. ++# ++# See https://github.com/lightvector/KataGo/blob/master/docs/GTP_Extensions.md ++# for GTP commands. ++ ++# Specify the rules as a string. ++# Some legal values include: ++# chinese, japanese, korean, aga, chinese-ogs, new-zealand, stone-scoring, ++# ancient-territory, bga, aga-button ++# ++# For some human rulesets that require complex adjudication in tricky cases ++# (e.g. japanese, korean) KataGo may not precisely match the ruleset in such ++# cases but will do its best. ++# rules = tromp-taylor ++ ++# By default, the "rules" parameter is used, but if you comment it out and ++# uncomment one option in each of the sections below, you can specify an ++# arbitrary combination of individual rules. ++ ++koRule = SIMPLE # Simple ko rules (triple ko = no result) ++# koRule = POSITIONAL # Positional superko ++# koRule = SITUATIONAL # Situational superko ++ ++scoringRule = AREA # Area scoring ++# scoringRule = TERRITORY # Territory scoring (special computer-friendly territory rules) ++ ++taxRule = NONE # All surrounded empty points are scored ++# taxRule = SEKI # Eyes in seki do NOT count as points ++# taxRule = ALL # All groups are taxed up to 2 points for the two eyes needed to live ++ ++# Is multiple-stone suicide legal? (Single-stone suicide is always illegal). ++multiStoneSuicideLegal = true ++# multiStoneSuicideLegal = false ++ ++# "Button go" - the first pass when area scoring awards 0.5 points and does ++# not count for ending the game. ++hasButton = false ++# hasButton = true ++ ++# Is this a human ruleset where it's okay to pass before having physically ++# captured and removed all dead stones? ++friendlyPassOk = true ++# friendlyPassOk = false ++ ++# How handicap stones in handicap games are compensated ++whiteHandicapBonus = 0 # White gets no compensation for black's handicap stones (Tromp-taylor, NZ, JP) ++# whiteHandicapBonus = N-1 # White gets N-1 points for black's N handicap stones (AGA) ++# whiteHandicapBonus = N # White gets N points for black's N handicap stones (Chinese) ++ ++# ------------------------------ ++# Other rules hacks ++# ------------------------------ ++# Uncomment and change to adjust what board size KataGo uses upon startup ++# by default when GTP doesn't specify. ++# defaultBoardSize = 19 ++ ++# By default, Katago will use the komi that the GUI or GTP controller tries to set. ++# Uncomment and set this to have KataGo ignore the controller and always use this komi. ++# ignoreGTPAndForceKomi = 7 ++ ++# =========================================================================== ++# Bot behavior ++# =========================================================================== ++ ++# ------------------------------ ++# Resignation ++# ------------------------------ ++ ++# Resignation occurs if for at least resignConsecTurns in a row, the ++# winLossUtility (on a [-1,1] scale) is below resignThreshold. ++allowResignation = true ++resignThreshold = -0.90 ++resignConsecTurns = 3 ++ ++# By default, KataGo may resign games that it is confidently losing even if they ++# are very close in score. Uncomment and set this to avoid resigning games ++# if the estimated difference is points is less than or equal to this. ++# resignMinScoreDifference = 10 ++ ++# ------------------------------ ++# Handicap ++# ------------------------------ ++# Assume that if black makes many moves in a row right at the start of the ++# game, then the game is a handicap game. This is necessary on some servers ++# and for some GUIs and also when initializing from many SGF files, which may ++# set up a handicap game using repeated GTP "play" commands for black rather ++# than GTP "place_free_handicap" commands; however, it may also lead to ++# incorrect understanding of komi if whiteHandicapBonus is used and a server ++# does not have such a practice. Uncomment and set to false to disable. ++# assumeMultipleStartingBlackMovesAreHandicap = true ++ ++# Makes katago dynamically adjust in handicap or altered-komi games to assume ++# based on those game settings that it must be stronger or weaker than the ++# opponent and to play accordingly. Greatly improves handicap strength by ++# biasing winrates and scores to favor appropriate safe/aggressive play. ++# Does NOT affect analysis (lz-analyze, kata-analyze, used by programs like ++# Lizzie) so analysis remains unbiased. Uncomment and set this to 0 to disable ++# this and make KataGo play the same always. ++# dynamicPlayoutDoublingAdvantageCapPerOppLead = 0.045 ++ ++# Instead of "dynamicPlayoutDoublingAdvantageCapPerOppLead", you can comment ++# that out and uncomment and set "playoutDoublingAdvantage" to a fixed value ++# from -3.0 to 3.0 that will not change dynamically. ++# ALSO affects analysis tools (lz-analyze, kata-analyze, used by e.g. Lizzie). ++# Negative makes KataGo behave as if it is much weaker than the opponent. ++# Positive makes KataGo behave as if it is much stronger than the opponent. ++# KataGo will adjust to favor safe/aggressive play as appropriate based on ++# the combination of who is ahead and how much stronger/weaker it thinks it is, ++# and report winrates and scores taking the strength difference into account. ++# ++# If this and "dynamicPlayoutDoublingAdvantageCapPerOppLead" are both set ++# then dynamic will be used for all games and this fixed value will be used ++# for analysis tools. ++# playoutDoublingAdvantage = 0.0 ++ ++# Uncomment one of these when using "playoutDoublingAdvantage" to enforce ++# that it will only apply when KataGo plays as the specified color and will be ++# negated when playing as the opposite color. ++# playoutDoublingAdvantagePla = BLACK ++# playoutDoublingAdvantagePla = WHITE ++ ++# ------------------------------ ++# Passing and cleanup ++# ------------------------------ ++# Make the bot never assume that its pass will end the game, even if passing ++# would end and "win" under Tromp-Taylor rules. Usually this is a good idea ++# when using it for analysis or playing on servers where scoring may be ++# implemented non-tromp-taylorly. Uncomment and set to false to disable. ++# conservativePass = true ++ ++# When using territory scoring, self-play games continue beyond two passes ++# with special cleanup rules that may be confusing for human players. This ++# option prevents the special cleanup phases from being reachable when using ++# the bot for GTP play. Uncomment and set to false to enable entering special ++# cleanup. For example, if you are testing it against itself, or against ++# another bot that has precisely implemented the rules documented at ++# https://lightvector.github.io/KataGo/rules.html ++# preventCleanupPhase = true ++ ++# ------------------------------ ++# Miscellaneous behavior ++# ------------------------------ ++# If the board is symmetric, search only one copy of each equivalent move. ++# Attempts to also account for ko/superko, will not theoretically perfect for ++# superko. Uncomment and set to false to disable. ++# rootSymmetryPruning = true ++ ++# Uncomment and set to true to avoid a particular joseki that some networks ++# misevaluate, and also to improve opening diversity versus some particular ++# other bots that like to play it all the time. ++# avoidMYTDaggerHack = false ++ ++# Prefer to avoid playing the same joseki in every corner of the board. ++# Uncomment to set to a specific value. See "Avoid SGF patterns" section. ++# By default: 0 (even games), 0.005 (handicap games) ++# avoidRepeatedPatternUtility = 0.0 ++ ++# Experimental logic to fight against mirror Go even with unfavorable komi. ++# Uncomment to set to a specific value to use for both playing and analysis. ++# By default: true when playing via GTP, but false when analyzing. ++# antiMirror = true ++ ++# Enable some hacks that mitigate rare instances when passing messes up deeper searches. ++# enablePassingHacks = true ++ ++# Uncomment and set this to true to prevent bad or bogus move sequences ++# in the history leading to this position from affecting KataGo's move choices. ++# Same as analysisIgnorePreRootHistory (see above) but applies to actual play. ++# You can enable this if KataGo is being asked to play from positions that it did not ++# choose the moves to reach. ++# ignorePreRootHistory = false ++ ++# =========================================================================== ++# Search limits ++# =========================================================================== ++ ++# Terminology: ++# "Playouts" is the number of new playouts of search performed each turn. ++# "Visits" is the same as "Playouts" but also counts search performed on ++# previous turns that is still applicable to this turn. ++# "Time" is the time in seconds. ++ ++# For example, if KataGo searched 200 nodes on the previous turn, and then ++# after the opponent's reply, 50 nodes of its search tree was still valid, ++# then a visit limit of 200 would allow KataGo to search 150 new nodes ++# (for a final tree size of 200 nodes), whereas a playout limit of of 200 ++# would allow KataGo to search 200 nodes (for a final tree size of 250 nodes). ++ ++# Additionally, KataGo may also move before than the limit in order to ++# obey time controls (e.g. byo-yomi, etc) if the GTP controller has ++# told KataGo that the game has is being played with a given time control. ++ ++# Limits for search on the current turn. ++# If commented out or unspecified, the default is to have no limit. ++# maxVisits = 30000 ++# maxPlayouts = 300 ++maxTime = 1.0 ++ ++# Ponder on the opponent's turn? ++ponderingEnabled = true ++ ++# Limits for search when pondering on the opponent's turn. ++# If commented out or unspecified, the default is to have no limit. ++# Limiting the maximum time is recommended so that KataGo won't burn CPU/GPU ++# forever and/or run out of RAM if left unattended while pondering is enabled. ++# maxVisitsPondering = 5000 ++# maxPlayoutsPondering = 3000 ++maxTimePondering = 120.0 ++ ++ ++# ------------------------------ ++# Other search limits and behavior ++# ------------------------------ ++ ++# Approx number of seconds to buffer for lag for GTP time controls - will ++# move a bit faster assuming there is this much lag per move. ++lagBuffer = 1.0 ++ ++# YOU PROBABLY WANT TO TUNE THIS PARAMETER! ++# The number of threads to use when searching. On powerful GPUs the optimal ++# threads may be much higher than the number of CPU cores you have because ++# many threads are needed to feed efficient large batches to the GPU. ++# ++# Run "./katago benchmark" to tune this parameter and test the effect ++# of changes to any of other parameters. ++numSearchThreads = 40 ++ ++# Play a little faster if the opponent is passing, for human-friendliness. ++# Comment these out to disable them, such as if running a controlled match ++# where you are testing KataGo with fixed compute per move vs other bots. ++searchFactorAfterOnePass = 0.50 ++searchFactorAfterTwoPass = 0.25 ++ ++# Play a little faster if super-winning, for human-friendliness. ++# Comment these out to disable them, such as if running a controlled match ++# where you are testing KataGo with fixed compute per move vs other bots. ++searchFactorWhenWinning = 0.40 ++searchFactorWhenWinningThreshold = 0.95 ++ ++# =========================================================================== ++# GPU settings ++# =========================================================================== ++# This section configures GPU settings. ++# ++# Maximum number of positions to send to a single GPU at once. The default ++# value is roughly equal to numSearchThreads, but can be specified manually ++# if running out of memory, or using multiple GPUs that expect to share work. ++# nnMaxBatchSize = ++ ++# Controls the neural network cache size, which is the primary RAM/memory use. ++# KataGo will cache up to (2 ** nnCacheSizePowerOfTwo) many neural net ++# evaluations in case of transpositions in the tree. ++# Increase this to improve performance for searches with tens of thousands ++# of visits or more. Decrease this to limit memory usage. ++# If you're happy to do some math - each neural net entry takes roughly ++# 1.5KB, except when using whole-board ownership/territory ++# visualizations, where each entry will take roughly 3KB. The number of ++# entries is (2 ** nnCacheSizePowerOfTwo). (E.g. 2 ** 18 = 262144.) ++# You can compute roughly how much memory the cache will use based on this. ++# nnCacheSizePowerOfTwo = 20 ++ ++# Size of mutex pool for nnCache is (2 ** this). ++# nnMutexPoolSizePowerOfTwo = 16 ++ ++# Randomize board orientation when running neural net evals? Uncomment and ++# set to false to disable. ++# nnRandomize = true ++ ++# If provided, force usage of a specific seed for nnRandomize. ++# The default is to use a randomly generated seed. ++# nnRandSeed = abcdefg ++ ++# Uncomment and set to true to force GTP to use the maximum board size for ++# internal buffers for the neural net. This will make KataGo slower when ++# evaluating small boards, but will avoid a lengthy initialization time on every ++# change of board size due to having to re-size the neural net buffers on the GPU. ++# This can be useful for example, for OGS's persistent bot mode that uses a single ++# bot instance to handle multiple games and may thrash between different board sizes ++# if there are concurrent games of multiple sizes. ++# gtpForceMaxNNSize = false ++ ++# ------------------------------ ++# Multiple GPUs ++# ------------------------------ ++# Set this to the number of GPUs to use or that are available. ++# IMPORTANT: If more than 1, also uncomment the appropriate TensorRT ++# or CUDA or OpenCL section. ++# numNNServerThreadsPerModel = 1 ++ ++# ------------------------------ ++# TENSORRT GPU settings ++# ------------------------------ ++# These only apply when using the TENSORRT version of KataGo. ++ ++# For one GPU: optionally uncomment this option and change if the GPU to ++# use is not device 0. ++# trtDeviceToUse = 0 ++ ++# For two GPUs: Uncomment these options, AND set numNNServerThreadsPerModel above. ++# Also, change their values if the devices you want to use are not 0 and 1. ++# trtDeviceToUseThread0 = 0 ++# trtDeviceToUseThread1 = 1 ++ ++# For three GPUs: Uncomment these options, AND set numNNServerThreadsPerModel above. ++# Also, change their values if the devices you want to use are not 0 and 1 and 2. ++# trtDeviceToUseThread0 = 0 ++# trtDeviceToUseThread1 = 1 ++# trtDeviceToUseThread2 = 2 ++ ++# The pattern continues for additional GPUs. ++ ++# ------------------------------ ++# CUDA GPU settings ++# ------------------------------ ++# These only apply when using the CUDA version of KataGo. ++ ++# For one GPU: optionally uncomment and change this if the GPU you want to ++# use is not device 0 ++# cudaDeviceToUse = 0 ++ ++# For two GPUs: Uncomment these options, AND set numNNServerThreadsPerModel above. ++# Also, change their values if the devices you want to use are not 0 and 1. ++# cudaDeviceToUseThread0 = 0 ++# cudaDeviceToUseThread1 = 1 ++ ++# For three GPUs: Uncomment these options, AND set numNNServerThreadsPerModel above. ++# Also, change their values if the devices you want to use are not 0 and 1 and 2. ++# cudaDeviceToUseThread0 = 0 ++# cudaDeviceToUseThread1 = 1 ++# cudaDeviceToUseThread2 = 2 ++ ++# The pattern continues for additional GPUs. ++ ++# KataGo will automatically use FP16 or not based on the compute capability ++# of your NVIDIA GPU. If you want to try to force a particular behavior ++# you can uncomment these lines and change them to "true" or "false". ++# cudaUseFP16 = auto ++# cudaUseNHWC = auto ++ ++# ------------------------------ ++# OpenCL GPU settings ++# ------------------------------ ++# These only apply when using the OpenCL version of KataGo. ++ ++# Uncomment and set to true to tune OpenCL for every board size separately, ++# rather than only the largest possible size. ++# openclReTunePerBoardSize = false ++ ++# For one GPU: optionally uncomment and change this if the best device to use is guessed incorrectly. ++# The default behavior tries to guess the 'best' GPU or device on your system to use, usually it will be a good guess. ++# openclDeviceToUse = 0 ++ ++# For two GPUs: Uncomment these two lines and replace X and Y with the device ids of the devices you want to use. ++# It might NOT be 0 and 1, some computers will have many OpenCL devices. You can see what the devices are when ++# KataGo starts up - it should print or log all the devices it finds. ++# (AND also set numNNServerThreadsPerModel above) ++# openclDeviceToUseThread0 = X ++# openclDeviceToUseThread1 = Y ++ ++# For three GPUs: Uncomment these three lines and replace X and Y and Z with the device ids of the devices you want to use. ++# It might NOT be 0 and 1 and 2, some computers will have many OpenCL devices. You can see what the devices are when ++# KataGo starts up - it should print or log all the devices it finds. ++# (AND also set numNNServerThreadsPerModel above) ++# openclDeviceToUseThread0 = X ++# openclDeviceToUseThread1 = Y ++# openclDeviceToUseThread2 = Z ++ ++# The pattern continues for additional GPUs. ++ ++# KataGo will automatically use FP16 or not based on testing your GPU during ++# tuning. If you want to try to force a particular behavior though you can ++# uncomment this option and change it to "true" or "false". This is a fairly ++# blunt setting - more detailed settings are testable by rerunning the tuner ++# with various arguments (./katago tuner). ++# openclUseFP16 = auto ++ ++# ------------------------------ ++# Eigen-specific settings ++# ------------------------------ ++# These only apply when using the Eigen (pure CPU) version of KataGo. ++ ++# Number of CPU threads for evaluating the neural net on the Eigen backend. ++# ++# Default: numSearchThreads ++# numEigenThreadsPerModel = X ++ ++# =========================================================================== ++# Root move selection and biases ++# =========================================================================== ++# Uncomment and edit any of the below values to change them from their default. ++ ++# If provided, force usage of a specific seed for various random things in ++# the search. The default is to use a random seed. ++# searchRandSeed = hijklmn ++ ++# Temperature for the early game, randomize between chosen moves with ++# this temperature ++# chosenMoveTemperatureEarly = 0.5 ++ ++# Decay temperature for the early game by 0.5 every this many moves, ++# scaled with board size. ++# chosenMoveTemperatureHalflife = 19 ++ ++# At the end of search after the early game, randomize between chosen ++# moves with this temperature ++# chosenMoveTemperature = 0.10 ++ ++# Subtract this many visits from each move prior to applying ++# chosenMoveTemperature (unless all moves have too few visits) to downweight ++# unlikely moves ++# chosenMoveSubtract = 0 ++ ++# The same as chosenMoveSubtract but only prunes moves that fall below ++# the threshold. This setting does not affect chosenMoveSubtract. ++# chosenMovePrune = 1 ++ ++# Number of symmetries to sample (without replacement) and average at the root ++# rootNumSymmetriesToSample = 1 ++ ++# Using LCB for move selection? ++# useLcbForSelection = true ++ ++# How many stdevs a move needs to be better than another for LCB selection ++# lcbStdevs = 5.0 ++ ++# Only use LCB override when a move has this proportion of visits as the ++# top move. ++# minVisitPropForLCB = 0.15 ++ ++# =========================================================================== ++# Internal params ++# =========================================================================== ++# Uncomment and edit any of the below values to change them from their default. ++ ++# Scales the utility of winning/losing ++# winLossUtilityFactor = 1.0 ++ ++# Scales the utility for trying to maximize score ++# staticScoreUtilityFactor = 0.10 ++# dynamicScoreUtilityFactor = 0.30 ++ ++# Adjust dynamic score center this proportion of the way towards zero, ++# capped at a reasonable amount. ++# dynamicScoreCenterZeroWeight = 0.20 ++# dynamicScoreCenterScale = 0.75 ++ ++# The utility of getting a "no result" due to triple ko or other long cycle ++# in non-superko rulesets (-1 to 1) ++# noResultUtilityForWhite = 0.0 ++ ++# The number of wins that a draw counts as, for white. (0 to 1) ++# drawEquivalentWinsForWhite = 0.5 ++ ++# Exploration constant for mcts ++# cpuctExploration = 1.0 ++# cpuctExplorationLog = 0.45 ++ ++# Parameters that control exploring more in volatile positions, exploring ++# less in stable positions. ++# cpuctUtilityStdevPrior = 0.40 ++# cpuctUtilityStdevPriorWeight = 2.0 ++# cpuctUtilityStdevScale = 0.85 ++ ++# FPU reduction constant for mcts ++# fpuReductionMax = 0.2 ++# rootFpuReductionMax = 0.1 ++# fpuParentWeightByVisitedPolicy = true ++ ++# Parameters that control weighting of evals based on the net's own ++# self-reported uncertainty. ++# useUncertainty = true ++# uncertaintyExponent = 1.0 ++# uncertaintyCoeff = 0.25 ++ ++# Explore using optimistic policy ++# rootPolicyOptimism = 0.2 ++# policyOptimism = 1.0 ++ ++# Amount to apply a downweighting of children with very bad values relative ++# to good ones. ++# valueWeightExponent = 0.25 ++ ++# Slight incentive for the bot to behave human-like with regard to passing at ++# the end, filling the dame, not wasting time playing in its own territory, ++# etc., and not play moves that are equivalent in terms of points but a bit ++# more unfriendly to humans. ++# rootEndingBonusPoints = 0.5 ++ ++# Make the bot prune useless moves that are just prolonging the game to ++# avoid losing yet. ++# rootPruneUselessMoves = true ++ ++# Apply bias correction based on local pattern keys ++# subtreeValueBiasFactor = 0.45 ++# subtreeValueBiasWeightExponent = 0.85 ++ ++# Use graph search rather than tree search - identify and share search for ++# transpositions. ++# useGraphSearch = true ++ ++# How much to shard the node table for search synchronization ++# nodeTableShardsPowerOfTwo = 16 ++ ++# How many virtual losses to add when a thread descends through a node ++# numVirtualLossesPerThread = 1 ++ ++# Improve the quality of evals under heavy multithreading ++# useNoisePruning = true ++ ++# =========================================================================== ++# Automatic avoid patterns ++# =========================================================================== ++# The parameters in this section provide a way to bias away from moves that match ++# patterns that this instance of KataGo has played in previous games, by auto-saving ++# moves to a directory and then auto-loading and biasing against them each new game. ++# Uncomment them to use them. When using this feature, all parameters must be specified. ++ ++# Different board sizes are tracked separately, but all board sizes share the same ++# parameters by default. Every parameter *except* for autoAvoidRepeatDir and ++# autoAvoidRepeatSaveChunkSize can be overridden per board size, ++# e.g. "autoAvoidRepeatMinTurnNumber13x13". You must ALSO specify the ++# defaults even if you have specified board-size-specific values. ++ ++# Directory to auto-save moves KataGo plays, to avoid them in future games. ++# You can create a new empty directory and put its path here. ++# If you run parallel instances of KataGo, use different directories if you ++# want them not to share their biases, use the same directory if you want ++# all of them to bias away from past moves that any of them have played. ++# KataGo will also automatically DELETE old data in this directory, so it is ++# recommended that if you do share the same directory between parallel instances, ++# that they all use the same settings that affect data saving/deletion. ++# autoAvoidRepeatDir = PATH_TO_NEW_DIRECTORY ++ ++# Penalize this much utility per matching move. ++# Values that are too large may lead to bad play. The value of 0.004 is fairly large ++# and might be large enough to result in some early weird/bad moves when trying ++# to avoid past games' moves if enough games begin the same way. You can experiment with it. ++# autoAvoidRepeatUtility = 0.004 ++ ++# Per each new move saved, exponentially decay prior saved moves by this factor. ++# This way, the bias against moves from many games ago is gradually phased out. ++# For example, 0.9995 = 1 - 1/2000, so would be roughly 2000 prior moves worth of ++# penalty weight remembered, in steady state. Depending on what turn number range ++# you are saving, this might equate to a different number of games For example ++# saving the first 50 moves per game would make this roughly 2000 / 50 = 40 games ++# worth of memory. ++# autoAvoidRepeatLambda = 0.9995 ++ ++# Affects data saving/deletion. ++# When the number of saved moves exceeds this, outright delete them to avoid too many ++# files and disk space building up. Also may affect the speed of saving/loading on start ++# of each game if this is set large and a lot of data builds up. ++# autoAvoidRepeatMaxPoses = 10000 ++ ++# Affects data saving/deletion. ++# Only save data for moves within this turn number range of those games. ++# E.g. setting autoAvoidRepeatMinTurnNumber to a number like 4 or 5 would tend to make ++# KataGo not develop a bias against the initial 3-4 and 4-4 corner moves in almost every game. ++# autoAvoidRepeatMinTurnNumber = 0 ++# autoAvoidRepeatMaxTurnNumber = 50 ++ ++# Affects data saving/deletion. ++# Within a single run of a program, wait to accumulate this many samples ++# (possibly across multiple clear_boards/games) before saving the data. ++# Can help to avoid writing too many small files to disk, especially when GTP is used ++# in a way that clears the board very frequently (e.g. gtp2ogs pooled manager). ++# autoAvoidRepeatSaveChunkSize = 200 ++ ++# =========================================================================== ++# Avoid SGF patterns ++# =========================================================================== ++# The parameters in this section provide a way to avoid moves that follow ++# specific patterns based on a set of SGF files loaded upon startup. ++# This is basically the same as the above "Automatic avoid patterns" section ++# above except you supply your own SGF files to avoid moves from. ++# Uncomment them to use this feature. Additionally, if the SGF file ++# contains the string %SKIP% in a comment on a move, that move will be ++# ignored for this purpose. ++ ++# Load SGF files from this directory when the engine is started ++# (only on startup, will not reload unless engine is restarted) ++# avoidSgfPatternDirs = path/to/directory/with/sgfs/ ++# You can also surround the file path in double quotes if the file path contains trailing spaces or hash signs. ++# Within double quotes, backslashes are escape characters. ++# avoidSgfPatternDirs = "path/to/directory/with/sgfs/" ++ ++# Penalize this much utility per matching move. ++# Set this negative if you instead want to favor SGF patterns instead of ++# penalizing them. This number does not need to be large, even 0.001 will ++# make a difference. Values that are too large may lead to bad play. ++# avoidSgfPatternUtility = 0.001 ++ ++# Optional - load only the newest this many files ++# avoidSgfPatternMaxFiles = 20 ++ ++# Optional - Penalty is multiplied by this per each older SGF file, so that ++# old SGF files matter less than newer ones. ++# avoidSgfPatternLambda = 0.90 ++ ++# Optional - pay attention only to moves made by players with this name. ++# For example, set it to the name that your bot's past games will show up ++# as in the SGF, so that the bot will only avoid repeating moves that itself ++# made in past games, not the moves that its opponents made. ++# avoidSgfPatternAllowedNames = my-ogs-bot-name1,my-ogs-bot-name2 ++ ++# Optional - Ignore moves in SGF files that occurred before this turn number. ++# avoidSgfPatternMinTurnNumber = 0 ++ ++# For more avoid patterns: ++# You can also specify a second set of parameters, and a third, fourth, ++# etc. by numbering 2,3,4,... ++# ++# avoidSgf2PatternDirs = ... ++# avoidSgf2PatternUtility = ... ++# avoidSgf2PatternMaxFiles = ... ++# avoidSgf2PatternLambda = ... ++# avoidSgf2PatternAllowedNames = ... ++# avoidSgf2PatternMinTurnNumber = ... ++ +diff --git a/python/selfplay/libcrypto-3-x64.dll b/python/selfplay/libcrypto-3-x64.dll +new file mode 100644 +index 00000000..cac7f4ec +Binary files /dev/null and b/python/selfplay/libcrypto-3-x64.dll differ +diff --git a/python/selfplay/libssl-3-x64.dll b/python/selfplay/libssl-3-x64.dll +new file mode 100644 +index 00000000..8ad0908e +Binary files /dev/null and b/python/selfplay/libssl-3-x64.dll differ +diff --git a/python/selfplay/libz.dll b/python/selfplay/libz.dll +new file mode 100644 +index 00000000..930a0421 +Binary files /dev/null and b/python/selfplay/libz.dll differ +diff --git a/python/selfplay/libzip.dll b/python/selfplay/libzip.dll +new file mode 100644 +index 00000000..167e69f7 +Binary files /dev/null and b/python/selfplay/libzip.dll differ +diff --git a/python/selfplay/lizgoban.cfg b/python/selfplay/lizgoban.cfg +new file mode 100644 +index 00000000..53f4832b +--- /dev/null ++++ b/python/selfplay/lizgoban.cfg +@@ -0,0 +1,721 @@ ++# Configuration for KataGo C++ GTP engine ++ ++# Run the program using: `./katago.exe gtp` ++ ++# In this example config, when a parameter is given as a commented out value, ++# that value also is the default value, unless described otherwise. You can ++# uncomment it (remove the pound sign) and change it if you want. ++ ++# =========================================================================== ++# Running on an online server or in a real tournament or match ++# =========================================================================== ++# If you plan to run online or in a tournament, read through the "Rules" ++# section below for proper handling of komi, handicaps, end-of-game cleanup, ++# and other details. ++ ++# =========================================================================== ++# Notes about performance and memory usage ++# =========================================================================== ++# Important: For good performance, you will very likely want to tune the ++# "numSearchThreads" parameter in the Search limits section below! Run ++# "./katago benchmark" to test KataGo and to suggest a reasonable value ++# of this parameter. ++ ++# For multi-GPU systems, read "OpenCL GPU settings" or "CUDA GPU settings". ++# ++# When using OpenCL, verify that KataGo picks the correct device! Some systems ++# may have both an Intel CPU OpenCL and GPU OpenCL. If # KataGo picks the wrong ++# one, correct this by specifying "openclGpuToUse". ++# ++# Consider adjusting "maxVisits", "ponderingEnabled", "resignThreshold", and ++# other parameters depending on your intended usage. ++ ++# =========================================================================== ++# Command-line usage ++# =========================================================================== ++# All of the below values may be set or overridden via command-line arguments: ++# ++# -override-config KEY=VALUE,KEY=VALUE,... ++ ++# =========================================================================== ++# Logs and files ++# =========================================================================== ++# This section defines where and what logging information is produced. ++ ++# Each run of KataGo will log to a separate file in this dir. ++# This is the default. ++logDir = gtp_logs ++# Uncomment and specify this instead of logDir to write separate dated subdirs ++# logDirDated = gtp_logs ++# Uncomment and specify this instead of logDir to log to only a single file ++# logFile = gtp.log ++ ++# Logging options ++logAllGTPCommunication = true ++logSearchInfo = true ++logSearchInfoForChosenMove = false ++logToStderr = true ++ ++# KataGo will display some info to stderr on GTP startup ++# Uncomment the next line and set it to false to suppress that and remain silent ++# startupPrintMessageToStderr = true ++ ++# Write information to stderr, for use in things like malkovich chat to OGS. ++ ogsChatToStderr = true ++ ++# Uncomment and set this to a directory to override where openCLTuner files ++# and other cached data is written. By default it saves into a subdir of the ++# current directory on windows, and a subdir of ~/.katago on Linux. ++# homeDataDir = PATH_TO_DIRECTORY ++ ++# =========================================================================== ++# Analysis ++# =========================================================================== ++# This section configures analysis settings. ++# ++# The maximum number of moves after the first move displayed in variations ++# from analysis commands like kata-analyze or lz-analyze. ++# analysisPVLen = 15 ++ ++# Report winrates for chat and analysis as (BLACK|WHITE|SIDETOMOVE). ++# Most GUIs and analysis tools will expect SIDETOMOVE. ++# reportAnalysisWinratesAs = SIDETOMOVE ++ ++# Extra noise for wider exploration. Large values will force KataGo to ++# analyze a greater variety of moves than it normally would. ++# An extreme value like 1 distributes playouts across every move on the board, ++# even very bad moves. ++# Affects analysis only, does not affect play. ++# analysisWideRootNoise = 0.04 ++ ++# Try to limit the effect of possible bad or bogus move sequences in the ++# history leading to this position from affecting KataGo's move predictions. ++# analysisIgnorePreRootHistory = true ++ ++# =========================================================================== ++# Rules ++# =========================================================================== ++# This section configures the scoring and playing rules. Rules can also be ++# changed mid-run by issuing custom GTP commands. ++# ++# See https://lightvector.github.io/KataGo/rules.html for rules details. ++# ++# See https://github.com/lightvector/KataGo/blob/master/docs/GTP_Extensions.md ++# for GTP commands. ++ ++# Specify the rules as a string. ++# Some legal values include: ++# chinese, japanese, korean, aga, chinese-ogs, new-zealand, stone-scoring, ++# ancient-territory, bga, aga-button ++# ++# For some human rulesets that require complex adjudication in tricky cases ++# (e.g. japanese, korean) KataGo may not precisely match the ruleset in such ++# cases but will do its best. ++rules = tromp-taylor ++ ++# By default, the "rules" parameter is used, but if you comment it out and ++# uncomment one option in each of the sections below, you can specify an ++# arbitrary combination of individual rules. ++ ++# koRule = SIMPLE # Simple ko rules (triple ko = no result) ++# koRule = POSITIONAL # Positional superko ++# koRule = SITUATIONAL # Situational superko ++ ++# scoringRule = AREA # Area scoring ++# scoringRule = TERRITORY # Territory scoring (special computer-friendly territory rules) ++ ++# taxRule = NONE # All surrounded empty points are scored ++# taxRule = SEKI # Eyes in seki do NOT count as points ++# taxRule = ALL # All groups are taxed up to 2 points for the two eyes needed to live ++ ++# Is multiple-stone suicide legal? (Single-stone suicide is always illegal). ++# multiStoneSuicideLegal = false ++# multiStoneSuicideLegal = true ++ ++# "Button go" - the first pass when area scoring awards 0.5 points and does ++# not count for ending the game. ++# Allows area scoring rulesets that have far simpler rules to achieve the same ++# final scoring precision and reward for precise play as territory scoring. ++# hasButton = false ++# hasButton = true ++ ++# Is this a human ruleset where it's okay to pass before having physically ++# captured and removed all dead stones? ++# friendlyPassOk = false ++# friendlyPassOk = true ++ ++# How handicap stones in handicap games are compensated ++# whiteHandicapBonus = 0 # White gets no compensation for black's handicap stones (Tromp-taylor, NZ, JP) ++# whiteHandicapBonus = N-1 # White gets N-1 points for black's N handicap stones (AGA) ++# whiteHandicapBonus = N # White gets N points for black's N handicap stones (Chinese) ++ ++# ------------------------------ ++# Other rules hacks ++# ------------------------------ ++# Uncomment and change to adjust what board size KataGo uses upon startup ++# by default when GTP doesn't specify. ++# defaultBoardSize = 19 ++ ++# By default, Katago will use the komi that the GUI or GTP controller tries to set. ++# Uncomment and set this to have KataGo ignore the controller and always use this komi. ++# ignoreGTPAndForceKomi = 7 ++ ++# =========================================================================== ++# Bot behavior ++# =========================================================================== ++ ++# ------------------------------ ++# Resignation ++# ------------------------------ ++ ++# Resignation occurs if for at least resignConsecTurns in a row, the ++# winLossUtility (on a [-1,1] scale) is below resignThreshold. ++allowResignation = true ++resignThreshold = -0.90 ++resignConsecTurns = 3 ++ ++# By default, KataGo may resign games that it is confidently losing even if they ++# are very close in score. Uncomment and set this to avoid resigning games ++# if the estimated difference is points is less than or equal to this. ++# resignMinScoreDifference = 10 ++ ++# ------------------------------ ++# Handicap ++# ------------------------------ ++# Assume that if black makes many moves in a row right at the start of the ++# game, then the game is a handicap game. This is necessary on some servers ++# and for some GUIs and also when initializing from many SGF files, which may ++# set up a handicap game using repeated GTP "play" commands for black rather ++# than GTP "place_free_handicap" commands; however, it may also lead to ++# incorrect understanding of komi if whiteHandicapBonus is used and a server ++# does not have such a practice. Uncomment and set to false to disable. ++# assumeMultipleStartingBlackMovesAreHandicap = true ++ ++# Makes katago dynamically adjust in handicap or altered-komi games to assume ++# based on those game settings that it must be stronger or weaker than the ++# opponent and to play accordingly. Greatly improves handicap strength by ++# biasing winrates and scores to favor appropriate safe/aggressive play. ++# Does NOT affect analysis (lz-analyze, kata-analyze, used by programs like ++# Lizzie) so analysis remains unbiased. Uncomment and set this to 0 to disable ++# this and make KataGo play the same always. ++# dynamicPlayoutDoublingAdvantageCapPerOppLead = 0.045 ++ ++# Instead of "dynamicPlayoutDoublingAdvantageCapPerOppLead", you can comment ++# that out and uncomment and set "playoutDoublingAdvantage" to a fixed value ++# from -3.0 to 3.0 that will not change dynamically. ++# ALSO affects analysis tools (lz-analyze, kata-analyze, used by e.g. Lizzie). ++# Negative makes KataGo behave as if it is much weaker than the opponent. ++# Positive makes KataGo behave as if it is much stronger than the opponent. ++# KataGo will adjust to favor safe/aggressive play as appropriate based on ++# the combination of who is ahead and how much stronger/weaker it thinks it is, ++# and report winrates and scores taking the strength difference into account. ++# ++# If this and "dynamicPlayoutDoublingAdvantageCapPerOppLead" are both set ++# then dynamic will be used for all games and this fixed value will be used ++# for analysis tools. ++# playoutDoublingAdvantage = 0.0 ++ ++# Uncomment one of these when using "playoutDoublingAdvantage" to enforce ++# that it will only apply when KataGo plays as the specified color and will be ++# negated when playing as the opposite color. ++# playoutDoublingAdvantagePla = BLACK ++# playoutDoublingAdvantagePla = WHITE ++ ++# ------------------------------ ++# Passing and cleanup ++# ------------------------------ ++# Make the bot never assume that its pass will end the game, even if passing ++# would end and "win" under Tromp-Taylor rules. Usually this is a good idea ++# when using it for analysis or playing on servers where scoring may be ++# implemented non-tromp-taylorly. Uncomment and set to false to disable. ++# conservativePass = true ++ ++# When using territory scoring, self-play games continue beyond two passes ++# with special cleanup rules that may be confusing for human players. This ++# option prevents the special cleanup phases from being reachable when using ++# the bot for GTP play. Uncomment and set to false to enable entering special ++# cleanup. For example, if you are testing it against itself, or against ++# another bot that has precisely implemented the rules documented at ++# https://lightvector.github.io/KataGo/rules.html ++# preventCleanupPhase = true ++ ++# ------------------------------ ++# Miscellaneous behavior ++# ------------------------------ ++# If the board is symmetric, search only one copy of each equivalent move. ++# Attempts to also account for ko/superko, will not theoretically perfect for ++# superko. Uncomment and set to false to disable. ++# rootSymmetryPruning = true ++ ++# Uncomment and set to true to avoid a particular joseki that some networks ++# misevaluate, and also to improve opening diversity versus some particular ++# other bots that like to play it all the time. ++# avoidMYTDaggerHack = false ++ ++# Prefer to avoid playing the same joseki in every corner of the board. ++# Uncomment to set to a specific value. See "Avoid SGF patterns" section. ++# By default: 0 (even games), 0.005 (handicap games) ++# avoidRepeatedPatternUtility = 0.0 ++ ++# Experimental logic to fight against mirror Go even with unfavorable komi. ++# Uncomment to set to a specific value to use for both playing and analysis. ++# By default: true when playing via GTP, but false when analyzing. ++# antiMirror = true ++ ++# Enable some hacks that mitigate rare instances when passing messes up deeper searches. ++# enablePassingHacks = true ++ ++# Uncomment and set this to true to prevent bad or bogus move sequences ++# in the history leading to this position from affecting KataGo's move choices. ++# Same as analysisIgnorePreRootHistory (see above) but applies to actual play. ++# You can enable this if KataGo is being asked to play from positions that it did not ++# choose the moves to reach. ++# ignorePreRootHistory = false ++ ++# =========================================================================== ++# Search limits ++# =========================================================================== ++ ++# Terminology: ++# "Playouts" is the number of new playouts of search performed each turn. ++# "Visits" is the same as "Playouts" but also counts search performed on ++# previous turns that is still applicable to this turn. ++# "Time" is the time in seconds. ++ ++# For example, if KataGo searched 200 nodes on the previous turn, and then ++# after the opponent's reply, 50 nodes of its search tree was still valid, ++# then a visit limit of 200 would allow KataGo to search 150 new nodes ++# (for a final tree size of 200 nodes), whereas a playout limit of of 200 ++# would allow KataGo to search 200 nodes (for a final tree size of 250 nodes). ++ ++# Additionally, KataGo may also move before than the limit in order to ++# obey time controls (e.g. byo-yomi, etc) if the GTP controller has ++# told KataGo that the game has is being played with a given time control. ++ ++# Limits for search on the current turn. ++# If commented out or unspecified, the default is to have no limit. ++# maxVisits = 500 ++# maxPlayouts = 300 ++ maxTime = 0.5 ++ ++# Ponder on the opponent's turn? ++ponderingEnabled = true ++ ++# Limits for search when pondering on the opponent's turn. ++# If commented out or unspecified, the default is to have no limit. ++# Limiting the maximum time is recommended so that KataGo won't burn CPU/GPU ++# forever and/or run out of RAM if left unattended while pondering is enabled. ++# maxVisitsPondering = 5000 ++# maxPlayoutsPondering = 3000 ++maxTimePondering = 60.0 ++ ++ ++# ------------------------------ ++# Other search limits and behavior ++# ------------------------------ ++ ++# Approx number of seconds to buffer for lag for GTP time controls - will ++# move a bit faster assuming there is this much lag per move. ++lagBuffer = 1.0 ++ ++# YOU PROBABLY WANT TO TUNE THIS PARAMETER! ++# The number of threads to use when searching. On powerful GPUs the optimal ++# threads may be much higher than the number of CPU cores you have because ++# many threads are needed to feed efficient large batches to the GPU. ++# ++# Run "./katago benchmark" to tune this parameter and test the effect ++# of changes to any of other parameters. ++numSearchThreads = 16 ++ ++# Play a little faster if the opponent is passing, for human-friendliness. ++# Comment these out to disable them, such as if running a controlled match ++# where you are testing KataGo with fixed compute per move vs other bots. ++searchFactorAfterOnePass = 0.50 ++searchFactorAfterTwoPass = 0.25 ++ ++# Play a little faster if super-winning, for human-friendliness. ++# Comment these out to disable them, such as if running a controlled match ++# where you are testing KataGo with fixed compute per move vs other bots. ++searchFactorWhenWinning = 0.40 ++searchFactorWhenWinningThreshold = 0.95 ++ ++# =========================================================================== ++# GPU settings ++# =========================================================================== ++# This section configures GPU settings. ++# ++# Maximum number of positions to send to a single GPU at once. The default ++# value is roughly equal to numSearchThreads, but can be specified manually ++# if running out of memory, or using multiple GPUs that expect to share work. ++# nnMaxBatchSize = ++ ++# Controls the neural network cache size, which is the primary RAM/memory use. ++# KataGo will cache up to (2 ** nnCacheSizePowerOfTwo) many neural net ++# evaluations in case of transpositions in the tree. ++# Increase this to improve performance for searches with tens of thousands ++# of visits or more. Decrease this to limit memory usage. ++# If you're happy to do some math - each neural net entry takes roughly ++# 1.5KB, except when using whole-board ownership/territory ++# visualizations, where each entry will take roughly 3KB. The number of ++# entries is (2 ** nnCacheSizePowerOfTwo). (E.g. 2 ** 18 = 262144.) ++# You can compute roughly how much memory the cache will use based on this. ++# nnCacheSizePowerOfTwo = 20 ++ ++# Size of mutex pool for nnCache is (2 ** this). ++# nnMutexPoolSizePowerOfTwo = 16 ++ ++# Randomize board orientation when running neural net evals? Uncomment and ++# set to false to disable. ++# nnRandomize = true ++ ++# If provided, force usage of a specific seed for nnRandomize. ++# The default is to use a randomly generated seed. ++# nnRandSeed = abcdefg ++ ++# Uncomment and set to true to force GTP to use the maximum board size for ++# internal buffers for the neural net. This will make KataGo slower when ++# evaluating small boards, but will avoid a lengthy initialization time on every ++# change of board size due to having to re-size the neural net buffers on the GPU. ++# This can be useful for example, for OGS's persistent bot mode that uses a single ++# bot instance to handle multiple games and may thrash between different board sizes ++# if there are concurrent games of multiple sizes. ++# gtpForceMaxNNSize = false ++ ++# ------------------------------ ++# Multiple GPUs ++# ------------------------------ ++# Set this to the number of GPUs to use or that are available. ++# IMPORTANT: If more than 1, also uncomment the appropriate TensorRT ++# or CUDA or OpenCL section. ++# numNNServerThreadsPerModel = 1 ++ ++# ------------------------------ ++# TENSORRT GPU settings ++# ------------------------------ ++# These only apply when using the TENSORRT version of KataGo. ++ ++# For one GPU: optionally uncomment this option and change if the GPU to ++# use is not device 0. ++# trtDeviceToUse = 0 ++ ++# For two GPUs: Uncomment these options, AND set numNNServerThreadsPerModel above. ++# Also, change their values if the devices you want to use are not 0 and 1. ++# trtDeviceToUseThread0 = 0 ++# trtDeviceToUseThread1 = 1 ++ ++# For three GPUs: Uncomment these options, AND set numNNServerThreadsPerModel above. ++# Also, change their values if the devices you want to use are not 0 and 1 and 2. ++# trtDeviceToUseThread0 = 0 ++# trtDeviceToUseThread1 = 1 ++# trtDeviceToUseThread2 = 2 ++ ++# The pattern continues for additional GPUs. ++ ++# ------------------------------ ++# CUDA GPU settings ++# ------------------------------ ++# These only apply when using the CUDA version of KataGo. ++ ++# For one GPU: optionally uncomment and change this if the GPU you want to ++# use is not device 0 ++# cudaDeviceToUse = 0 ++ ++# For two GPUs: Uncomment these options, AND set numNNServerThreadsPerModel above. ++# Also, change their values if the devices you want to use are not 0 and 1. ++# cudaDeviceToUseThread0 = 0 ++# cudaDeviceToUseThread1 = 1 ++ ++# For three GPUs: Uncomment these options, AND set numNNServerThreadsPerModel above. ++# Also, change their values if the devices you want to use are not 0 and 1 and 2. ++# cudaDeviceToUseThread0 = 0 ++# cudaDeviceToUseThread1 = 1 ++# cudaDeviceToUseThread2 = 2 ++ ++# The pattern continues for additional GPUs. ++ ++# KataGo will automatically use FP16 or not based on the compute capability ++# of your NVIDIA GPU. If you want to try to force a particular behavior ++# you can uncomment these lines and change them to "true" or "false". ++# cudaUseFP16 = auto ++# cudaUseNHWC = auto ++ ++# ------------------------------ ++# OpenCL GPU settings ++# ------------------------------ ++# These only apply when using the OpenCL version of KataGo. ++ ++# Uncomment and set to true to tune OpenCL for every board size separately, ++# rather than only the largest possible size. ++# openclReTunePerBoardSize = false ++ ++# For one GPU: optionally uncomment and change this if the best device to use is guessed incorrectly. ++# The default behavior tries to guess the 'best' GPU or device on your system to use, usually it will be a good guess. ++# openclDeviceToUse = 0 ++ ++# For two GPUs: Uncomment these two lines and replace X and Y with the device ids of the devices you want to use. ++# It might NOT be 0 and 1, some computers will have many OpenCL devices. You can see what the devices are when ++# KataGo starts up - it should print or log all the devices it finds. ++# (AND also set numNNServerThreadsPerModel above) ++# openclDeviceToUseThread0 = X ++# openclDeviceToUseThread1 = Y ++ ++# For three GPUs: Uncomment these three lines and replace X and Y and Z with the device ids of the devices you want to use. ++# It might NOT be 0 and 1 and 2, some computers will have many OpenCL devices. You can see what the devices are when ++# KataGo starts up - it should print or log all the devices it finds. ++# (AND also set numNNServerThreadsPerModel above) ++# openclDeviceToUseThread0 = X ++# openclDeviceToUseThread1 = Y ++# openclDeviceToUseThread2 = Z ++ ++# The pattern continues for additional GPUs. ++ ++# KataGo will automatically use FP16 or not based on testing your GPU during ++# tuning. If you want to try to force a particular behavior though you can ++# uncomment this option and change it to "true" or "false". This is a fairly ++# blunt setting - more detailed settings are testable by rerunning the tuner ++# with various arguments (./katago tuner). ++# openclUseFP16 = auto ++ ++# ------------------------------ ++# Eigen-specific settings ++# ------------------------------ ++# These only apply when using the Eigen (pure CPU) version of KataGo. ++ ++# Number of CPU threads for evaluating the neural net on the Eigen backend. ++# ++# Default: numSearchThreads ++# numEigenThreadsPerModel = X ++ ++# =========================================================================== ++# Root move selection and biases ++# =========================================================================== ++# Uncomment and edit any of the below values to change them from their default. ++ ++# If provided, force usage of a specific seed for various random things in ++# the search. The default is to use a random seed. ++# searchRandSeed = hijklmn ++ ++# Temperature for the early game, randomize between chosen moves with ++# this temperature ++# chosenMoveTemperatureEarly = 0.5 ++ ++# Decay temperature for the early game by 0.5 every this many moves, ++# scaled with board size. ++# chosenMoveTemperatureHalflife = 19 ++ ++# At the end of search after the early game, randomize between chosen ++# moves with this temperature ++# chosenMoveTemperature = 0.10 ++ ++# Subtract this many visits from each move prior to applying ++# chosenMoveTemperature (unless all moves have too few visits) to downweight ++# unlikely moves ++# chosenMoveSubtract = 0 ++ ++# The same as chosenMoveSubtract but only prunes moves that fall below ++# the threshold. This setting does not affect chosenMoveSubtract. ++# chosenMovePrune = 1 ++ ++# Number of symmetries to sample (without replacement) and average at the root ++# rootNumSymmetriesToSample = 1 ++ ++# Using LCB for move selection? ++# useLcbForSelection = true ++ ++# How many stdevs a move needs to be better than another for LCB selection ++# lcbStdevs = 5.0 ++ ++# Only use LCB override when a move has this proportion of visits as the ++# top move. ++# minVisitPropForLCB = 0.15 ++ ++# =========================================================================== ++# Internal params ++# =========================================================================== ++# Uncomment and edit any of the below values to change them from their default. ++ ++# Scales the utility of winning/losing ++# winLossUtilityFactor = 1.0 ++ ++# Scales the utility for trying to maximize score ++# staticScoreUtilityFactor = 0.10 ++# dynamicScoreUtilityFactor = 0.30 ++ ++# Adjust dynamic score center this proportion of the way towards zero, ++# capped at a reasonable amount. ++# dynamicScoreCenterZeroWeight = 0.20 ++# dynamicScoreCenterScale = 0.75 ++ ++# The utility of getting a "no result" due to triple ko or other long cycle ++# in non-superko rulesets (-1 to 1) ++# noResultUtilityForWhite = 0.0 ++ ++# The number of wins that a draw counts as, for white. (0 to 1) ++# drawEquivalentWinsForWhite = 0.5 ++ ++# Exploration constant for mcts ++# cpuctExploration = 1.0 ++# cpuctExplorationLog = 0.45 ++ ++# Parameters that control exploring more in volatile positions, exploring ++# less in stable positions. ++# cpuctUtilityStdevPrior = 0.40 ++# cpuctUtilityStdevPriorWeight = 2.0 ++# cpuctUtilityStdevScale = 0.85 ++ ++# FPU reduction constant for mcts ++# fpuReductionMax = 0.2 ++# rootFpuReductionMax = 0.1 ++# fpuParentWeightByVisitedPolicy = true ++ ++# Parameters that control weighting of evals based on the net's own ++# self-reported uncertainty. ++# useUncertainty = true ++# uncertaintyExponent = 1.0 ++# uncertaintyCoeff = 0.25 ++ ++# Explore using optimistic policy ++# rootPolicyOptimism = 0.2 ++# policyOptimism = 1.0 ++ ++# Amount to apply a downweighting of children with very bad values relative ++# to good ones. ++# valueWeightExponent = 0.25 ++ ++# Slight incentive for the bot to behave human-like with regard to passing at ++# the end, filling the dame, not wasting time playing in its own territory, ++# etc., and not play moves that are equivalent in terms of points but a bit ++# more unfriendly to humans. ++# rootEndingBonusPoints = 0.5 ++ ++# Make the bot prune useless moves that are just prolonging the game to ++# avoid losing yet. ++# rootPruneUselessMoves = true ++ ++# Apply bias correction based on local pattern keys ++# subtreeValueBiasFactor = 0.45 ++# subtreeValueBiasWeightExponent = 0.85 ++ ++# Use graph search rather than tree search - identify and share search for ++# transpositions. ++# useGraphSearch = true ++ ++# How much to shard the node table for search synchronization ++# nodeTableShardsPowerOfTwo = 16 ++ ++# How many virtual losses to add when a thread descends through a node ++# numVirtualLossesPerThread = 1 ++ ++# Improve the quality of evals under heavy multithreading ++# useNoisePruning = true ++ ++# =========================================================================== ++# Automatic avoid patterns ++# =========================================================================== ++# The parameters in this section provide a way to bias away from moves that match ++# patterns that this instance of KataGo has played in previous games, by auto-saving ++# moves to a directory and then auto-loading and biasing against them each new game. ++# Uncomment them to use them. When using this feature, all parameters must be specified. ++ ++# Different board sizes are tracked separately, but all board sizes share the same ++# parameters by default. Every parameter *except* for autoAvoidRepeatDir and ++# autoAvoidRepeatSaveChunkSize can be overridden per board size, ++# e.g. "autoAvoidRepeatMinTurnNumber13x13". You must ALSO specify the ++# defaults even if you have specified board-size-specific values. ++ ++# Directory to auto-save moves KataGo plays, to avoid them in future games. ++# You can create a new empty directory and put its path here. ++# If you run parallel instances of KataGo, use different directories if you ++# want them not to share their biases, use the same directory if you want ++# all of them to bias away from past moves that any of them have played. ++# KataGo will also automatically DELETE old data in this directory, so it is ++# recommended that if you do share the same directory between parallel instances, ++# that they all use the same settings that affect data saving/deletion. ++# autoAvoidRepeatDir = PATH_TO_NEW_DIRECTORY ++ ++# Penalize this much utility per matching move. ++# Values that are too large may lead to bad play. The value of 0.004 is fairly large ++# and might be large enough to result in some early weird/bad moves when trying ++# to avoid past games' moves if enough games begin the same way. You can experiment with it. ++# autoAvoidRepeatUtility = 0.004 ++ ++# Per each new move saved, exponentially decay prior saved moves by this factor. ++# This way, the bias against moves from many games ago is gradually phased out. ++# For example, 0.9995 = 1 - 1/2000, so would be roughly 2000 prior moves worth of ++# penalty weight remembered, in steady state. Depending on what turn number range ++# you are saving, this might equate to a different number of games For example ++# saving the first 50 moves per game would make this roughly 2000 / 50 = 40 games ++# worth of memory. ++# autoAvoidRepeatLambda = 0.9995 ++ ++# Affects data saving/deletion. ++# When the number of saved moves exceeds this, outright delete them to avoid too many ++# files and disk space building up. Also may affect the speed of saving/loading on start ++# of each game if this is set large and a lot of data builds up. ++# autoAvoidRepeatMaxPoses = 10000 ++ ++# Affects data saving/deletion. ++# Only save data for moves within this turn number range of those games. ++# E.g. setting autoAvoidRepeatMinTurnNumber to a number like 4 or 5 would tend to make ++# KataGo not develop a bias against the initial 3-4 and 4-4 corner moves in almost every game. ++# autoAvoidRepeatMinTurnNumber = 0 ++# autoAvoidRepeatMaxTurnNumber = 50 ++ ++# Affects data saving/deletion. ++# Within a single run of a program, wait to accumulate this many samples ++# (possibly across multiple clear_boards/games) before saving the data. ++# Can help to avoid writing too many small files to disk, especially when GTP is used ++# in a way that clears the board very frequently (e.g. gtp2ogs pooled manager). ++# autoAvoidRepeatSaveChunkSize = 200 ++ ++# =========================================================================== ++# Avoid SGF patterns ++# =========================================================================== ++# The parameters in this section provide a way to avoid moves that follow ++# specific patterns based on a set of SGF files loaded upon startup. ++# This is basically the same as the above "Automatic avoid patterns" section ++# above except you supply your own SGF files to avoid moves from. ++# Uncomment them to use this feature. Additionally, if the SGF file ++# contains the string %SKIP% in a comment on a move, that move will be ++# ignored for this purpose. ++ ++# Load SGF files from this directory when the engine is started ++# (only on startup, will not reload unless engine is restarted) ++# avoidSgfPatternDirs = path/to/directory/with/sgfs/ ++# You can also surround the file path in double quotes if the file path contains trailing spaces or hash signs. ++# Within double quotes, backslashes are escape characters. ++# avoidSgfPatternDirs = "path/to/directory/with/sgfs/" ++ ++# Penalize this much utility per matching move. ++# Set this negative if you instead want to favor SGF patterns instead of ++# penalizing them. This number does not need to be large, even 0.001 will ++# make a difference. Values that are too large may lead to bad play. ++# avoidSgfPatternUtility = 0.001 ++ ++# Optional - load only the newest this many files ++# avoidSgfPatternMaxFiles = 20 ++ ++# Optional - Penalty is multiplied by this per each older SGF file, so that ++# old SGF files matter less than newer ones. ++# avoidSgfPatternLambda = 0.90 ++ ++# Optional - pay attention only to moves made by players with this name. ++# For example, set it to the name that your bot's past games will show up ++# as in the SGF, so that the bot will only avoid repeating moves that itself ++# made in past games, not the moves that its opponents made. ++# avoidSgfPatternAllowedNames = my-ogs-bot-name1,my-ogs-bot-name2 ++ ++# Optional - Ignore moves in SGF files that occurred before this turn number. ++# avoidSgfPatternMinTurnNumber = 0 ++ ++# For more avoid patterns: ++# You can also specify a second set of parameters, and a third, fourth, ++# etc. by numbering 2,3,4,... ++# ++# avoidSgf2PatternDirs = ... ++# avoidSgf2PatternUtility = ... ++# avoidSgf2PatternMaxFiles = ... ++# avoidSgf2PatternLambda = ... ++# avoidSgf2PatternAllowedNames = ... ++# avoidSgf2PatternMinTurnNumber = ... ++ +diff --git a/python/selfplay/match_example.cfg b/python/selfplay/match_example.cfg +new file mode 100644 +index 00000000..945ac44e +--- /dev/null ++++ b/python/selfplay/match_example.cfg +@@ -0,0 +1,228 @@ ++# Example config for C++ match runner ++# This is an example template config for the "match" subcommand of KataGo. e.g: ++# ./katago match -config-file configs/match_example.cfg -log-file match.log -sgf-output-dir match_sgfs/ ++# ++# On a good GPU, the match subcommand enables testing of KataGo nets against each other ++# or a net against itself with different parameters vastly faster than anything ++# else, because multiple games can share GPU batching. ++# ++# Beware however of using this to test differing numbers of threads or test time-based search limits. ++# Because many games will be run simultaneously, they will compete from each other for compute power, ++# and although the total will run much faster than if you had run them one by one, you might also get ++# substantially different results not reflective of the strength of a configuration when run in a real ++# match setting, on a machine by itself. For fixed numbers of visits or playouts instead of fixed time, ++# and with numSearchThreads = 1, there should be no problem though, because then the compute time has ++# no influence on the result of the computation. ++# ++# See gtp config for descriptions of most of these params. ++# ++# For almost any parameter in this config that is related to a bot, rather than to the match as a whole ++# (so, visits, search parameters, model files, etc. but NOT the rules, max games, log info, etc) ++# you can specify them differentially between different bots by appending the index of the bot. ++# For example, if you were testing different numbers of visits, you could try: ++# ++# numBots = 3 ++# botName0 = lowVisits ++# botName1 = midVisits ++# botName2 = highVisits ++# ++# maxVisits0 = 100 ++# maxVisits1 = 300 ++# maxVisits2 = 1000 ++# ++# Or, if you were testing different neural nets, with different search configurations, you could do: ++# ++# nnModelFile0 = path/to/first/model/file.bin.gz ++# nnModelFile1 = path/to/second/model/file.bin.gz ++# ++# And specify different search parameters for them if you wanted: ++# cpuctExploration0 = 1.5 ++# cpuctExploration1 = 1.3 ++ ++# Logs------------------------------------------------------------------------------------ ++ ++logSearchInfo = false ++logMoves = false ++logGamesEvery = 50 ++logToStdout = true ++ ++# Bots------------------------------------------------------------------------------------- ++# For multiple bots, you can specify their names as botName0,botName1, etc. ++# If the bots are using different models, specify nnModelFile0, nnModelFile1, etc. ++ ++numBots=1 ++botName=FOO ++nnModelFile=PATH_TO_MODEL ++ ++# These bots will not play each other, but will still be opponents for other bots ++# secondaryBots = 0,1,3 ++# Only these bots will actually play games. Useful if you have a config file with many more ++# bots defined but you want to only selectively enable a few. ++# includeBots = 0,2,6 ++ ++# Specify extra pairings of bots to play. ++# extraPairs = 0-4,1-4 ++# Uncomment and set this to true if you don't want extraPairs to be mirrored by color. ++# (i.e. 0-4 should mean 0 plays black and 4 plays white only, rather than having two games with swapped colors). ++# extraPairsAreOneSidedBW = false ++ ++# Match----------------------------------------------------------------------------------- ++numGameThreads=8 # How many games to run in parallel at a time? ++ ++numGamesTotal=1000000 ++maxMovesPerGame=1200 ++ ++allowResignation = true ++resignThreshold = -0.95 ++resignConsecTurns = 6 ++ ++# Rules------------------------------------------------------------------------------------ ++# See https://lightvector.github.io/KataGo/rules.html for a description of the rules. ++ ++koRules = SIMPLE,POSITIONAL,SITUATIONAL ++scoringRules = AREA,TERRITORY ++taxRules = NONE,SEKI,ALL ++multiStoneSuicideLegals = false,true ++hasButtons = false,true ++ ++bSizes = 19,13,9 ++bSizeRelProbs = 90,5,5 ++ ++# If you want to specify exact distributions of boards, including rectangles, specify this instead ++# of bSizes. E.g. to randomize between a 7x5, a 9x12, and a 10x10 board with probabilities 25%, 25%, 50%: ++# bSizesXY=7-5,9-12,10-10 ++# bSizeRelProbs = 1,1,2 ++ ++komiAuto = True # Automatically adjust komi to what the neural nets think are fair ++# komiMean = 7.5 # Specify explicit komi ++# policyInitAreaProp = 0 ++# compensateAfterPolicyInitProb = 1.0 # Additionally make komi fair this often after the high-temperature moves. ++# policyInitAreaTemperature = 1 ++handicapProb = 0.0 ++handicapCompensateKomiProb = 1.0 ++# numExtraBlackFixed = 3 # When playing handicap games, always use exactly this many extra black moves ++ ++# Search limits----------------------------------------------------------------------------------- ++ ++maxVisits = 500 ++# maxPlayouts = 300 ++# maxTime = 60 ++ ++numSearchThreads = 1 ++ ++# GPU Settings------------------------------------------------------------------------------- ++ ++nnMaxBatchSize = 32 ++nnCacheSizePowerOfTwo = 21 ++nnMutexPoolSizePowerOfTwo = 17 ++nnRandomize = true ++ ++ ++# How many threads should there be to feed positions to the neural net? ++# Server threads are indexed 0,1,...(n-1) for the purposes of the below GPU settings arguments ++# that specify which threads should use which GPUs. ++# NOTE: This parameter is probably ONLY useful if you have multiple GPUs, since each GPU will need a thread. ++# If you're tuning single-GPU performance, use numSearchThreads instead. ++numNNServerThreadsPerModel = 1 ++ ++ ++# TENSORRT GPU settings-------------------------------------- ++# These only apply when using the TENSORRT version of KataGo. ++ ++# IF USING ONE GPU: optionally uncomment and change this if the GPU you want to use turns out to be not device 0 ++# trtDeviceToUse = 0 ++ ++# IF USING TWO GPUS: Uncomment these two lines (AND set numNNServerThreadsPerModel above): ++# trtDeviceToUseThread0 = 0 # change this if the first GPU you want to use turns out to be not device 0 ++# trtDeviceToUseThread1 = 1 # change this if the second GPU you want to use turns out to be not device 1 ++ ++# IF USING THREE GPUS: Uncomment these three lines (AND set numNNServerThreadsPerModel above): ++# trtDeviceToUseThread0 = 0 # change this if the first GPU you want to use turns out to be not device 0 ++# trtDeviceToUseThread1 = 1 # change this if the second GPU you want to use turns out to be not device 1 ++# trtDeviceToUseThread2 = 2 # change this if the third GPU you want to use turns out to be not device 2 ++ ++# You can probably guess the pattern if you have four, five, etc. GPUs. ++ ++ ++# CUDA GPU settings-------------------------------------- ++# For the below, "model" refers to a neural net, from the nnModelFile parameter(s) above. ++# cudaGpuToUse = 0 #use gpu 0 for all server threads (numNNServerThreadsPerModel) unless otherwise specified per-model or per-thread-per-model ++# cudaGpuToUseModel0 = 3 #use gpu 3 for model 0 for all threads unless otherwise specified per-thread for this model ++# cudaGpuToUseModel1 = 2 #use gpu 2 for model 1 for all threads unless otherwise specified per-thread for this model ++# cudaGpuToUseModel0Thread0 = 3 #use gpu 3 for model 0, server thread 0 ++# cudaGpuToUseModel0Thread1 = 2 #use gpu 2 for model 0, server thread 1 ++ ++# cudaUseFP16 = auto ++# cudaUseNHWC = auto ++ ++ ++# OpenCL GPU settings-------------------------------------- ++# These only apply when using OpenCL as the backend for inference. ++# (For GTP, we only ever have one model, when playing matches, we might have more than one, see match_example.cfg) ++ ++# Default behavior is just to always use gpu 0, you will want to uncomment and adjust one or more of these lines ++# to take advantage of a multi-gpu machine ++# openclGpuToUse = 0 #use gpu 0 for all server threads (numNNServerThreadsPerModel) unless otherwise specified per-model or per-thread-per-model ++# openclGpuToUseModel0 = 3 #use gpu 3 for model 0 for all threads unless otherwise specified per-thread for this model ++# openclGpuToUseModel1 = 2 #use gpu 2 for model 1 for all threads unless otherwise specified per-thread for this model ++# openclGpuToUseModel0Thread0 = 3 #use gpu 3 for model 0, server thread 0 ++# openclGpuToUseModel0Thread1 = 2 #use gpu 2 for model 0, server thread 1 ++ ++# Uncomment to tune OpenCL for every board size separately, rather than only the largest possible size ++# openclReTunePerBoardSize = true ++ ++# openclUseFP16 = auto ++ ++ ++# Eigen-specific settings-------------------------------------- ++# These only apply when using the Eigen (pure CPU) version of KataGo. ++ ++# This is the number of CPU threads for evaluating the neural net on the Eigen backend. ++# It defaults to numSearchThreads. ++# numEigenThreadsPerModel = X ++ ++ ++# Root move selection and biases------------------------------------------------------------------------------ ++# Uncomment and edit any of the below values to change them from their default. ++# Values in this section can be specified per-bot as well ++ ++chosenMoveTemperatureEarly = 0.60 ++# chosenMoveTemperatureHalflife = 19 ++chosenMoveTemperature = 0.20 ++# chosenMoveSubtract = 0 ++# chosenMovePrune = 1 ++ ++# rootNumSymmetriesToSample = 1 ++ ++# useLcbForSelection = true ++# lcbStdevs = 5.0 ++# minVisitPropForLCB = 0.15 ++ ++# Internal params------------------------------------------------------------------------------ ++# Uncomment and edit any of the below values to change them from their default. ++# Values in this section can be specified per-bot as well ++ ++# winLossUtilityFactor = 1.0 ++# staticScoreUtilityFactor = 0.10 ++# dynamicScoreUtilityFactor = 0.30 ++# dynamicScoreCenterZeroWeight = 0.20 ++# dynamicScoreCenterScale = 0.75 ++# noResultUtilityForWhite = 0.0 ++# drawEquivalentWinsForWhite = 0.5 ++ ++# cpuctExploration = 0.9 ++# cpuctExplorationLog = 0.4 ++# fpuReductionMax = 0.2 ++# rootFpuReductionMax = 0.1 ++# fpuParentWeightByVisitedPolicy = true ++# valueWeightExponent = 0.25 ++# rootEndingBonusPoints = 0.5 ++# rootPruneUselessMoves = true ++# subtreeValueBiasFactor = 0.45 ++# subtreeValueBiasWeightExponent = 0.85 ++# useGraphSearch = true ++# rootPolicyOptimism = 0.2 ++# policyOptimism = 1.0 ++ ++# nodeTableShardsPowerOfTwo = 16 ++# numVirtualLossesPerThread = 1 +diff --git a/python/selfplay/msvcp140.dll b/python/selfplay/msvcp140.dll +new file mode 100644 +index 00000000..f7c342d1 +Binary files /dev/null and b/python/selfplay/msvcp140.dll differ +diff --git a/python/selfplay/msvcp140_1.dll b/python/selfplay/msvcp140_1.dll +new file mode 100644 +index 00000000..6f55c9d1 +Binary files /dev/null and b/python/selfplay/msvcp140_1.dll differ +diff --git a/python/selfplay/msvcp140_2.dll b/python/selfplay/msvcp140_2.dll +new file mode 100644 +index 00000000..b127e0c8 +Binary files /dev/null and b/python/selfplay/msvcp140_2.dll differ +diff --git a/python/selfplay/msvcp140_atomic_wait.dll b/python/selfplay/msvcp140_atomic_wait.dll +new file mode 100644 +index 00000000..b8fa2030 +Binary files /dev/null and b/python/selfplay/msvcp140_atomic_wait.dll differ +diff --git a/python/selfplay/msvcp140_codecvt_ids.dll b/python/selfplay/msvcp140_codecvt_ids.dll +new file mode 100644 +index 00000000..7549b2b5 +Binary files /dev/null and b/python/selfplay/msvcp140_codecvt_ids.dll differ +diff --git a/python/selfplay/synchronous_loop.bat b/python/selfplay/synchronous_loop.bat +new file mode 100644 +index 00000000..a8e01370 +--- /dev/null ++++ b/python/selfplay/synchronous_loop.bat +@@ -0,0 +1,77 @@ ++@echo off ++setlocal EnableDelayedExpansion ++ ++set "-o" "pipefail" ++IF [[ "%#%" LSS "5" "]]" ( ++ echo "Usage: %~0 NAMEPREFIX BASEDIR TRAININGNAME MODELKIND USEGATING" ++ echo "Assumes katago is already built in the 'cpp' directory of the KataGo repo and the executable is present at cpp\katago." ++ echo "NAMEPREFIX string prefix for this training run, try to pick something globally unique. Will be displayed to users when KataGo loads the model." ++ echo "BASEDIR containing selfplay data and models and related directories" ++ echo "TRANINGNAME name to prefix models with, specific to this training daemon" ++ echo "MODELKIND what size model to train, like 'b10c128', see %CD%\..\modelconfigs.py" ++ echo "USEGATING = 1 to use gatekeeper, 0 to not use gatekeeper" ++ exit "0" ++) ++SET "NAMEPREFIX=%~1" ++shift ++SET "%~1BASEDIRRAW=%~1" ++shift ++SET "%~1%~1TRAININGNAME=%~1" ++shift ++SET "%~1%~1%~1MODELKIND=%~1" ++shift ++SET "%~1%~1%~1%~1USEGATING=%~1" ++shift ++SET _INTERPOLATION_0= ++FOR /f "delims=" %%a in ('realpath "$BASEDIRRAW"') DO (SET "_INTERPOLATION_0=!_INTERPOLATION_0! %%a") ++SET "%~1%~1%~1%~1%~1BASEDIR=!_INTERPOLATION_0:~1!" ++SET _INTERPOLATION_1= ++FOR /f "delims=" %%a in ('git rev-parse --show-toplevel') DO (SET "_INTERPOLATION_1=!_INTERPOLATION_1! %%a") ++SET _INTERPOLATION_2= ++FOR /f "delims=" %%a in ('realpath "$BASEDIRRAW"') DO (SET "_INTERPOLATION_2=!_INTERPOLATION_2! %%a") ++SET "%~1%~1%~1%~1%~1!_INTERPOLATION_2:~1!GITROOTDIR=!_INTERPOLATION_1:~1!" ++SET _INTERPOLATION_3= ++FOR /f "delims=" %%a in ('git rev-parse --show-toplevel') DO (SET "_INTERPOLATION_3=!_INTERPOLATION_3! %%a") ++SET _INTERPOLATION_4= ++FOR /f "delims=" %%a in ('realpath "$BASEDIRRAW"') DO (SET "_INTERPOLATION_4=!_INTERPOLATION_4! %%a") ++SET "%~1%~1%~1%~1%~1!_INTERPOLATION_4:~1!!_INTERPOLATION_3:~1!LOGSDIR="!BASEDIR!"\logs" ++SET _INTERPOLATION_5= ++FOR /f "delims=" %%a in ('git rev-parse --show-toplevel') DO (SET "_INTERPOLATION_5=!_INTERPOLATION_5! %%a") ++SET _INTERPOLATION_6= ++FOR /f "delims=" %%a in ('realpath "$BASEDIRRAW"') DO (SET "_INTERPOLATION_6=!_INTERPOLATION_6! %%a") ++SET "%~1%~1%~1%~1%~1!_INTERPOLATION_6:~1!!_INTERPOLATION_5:~1!!BASEDIR!SCRATCHDIR="!BASEDIR!"\shufflescratch" ++mkdir "-p" "!BASEDIR!" ++mkdir "-p" "!LOGSDIR!" ++mkdir "-p" "!SCRATCHDIR!" ++mkdir "-p" "!BASEDIR!"\selfplay ++mkdir "-p" "!BASEDIR!"\gatekeepersgf ++SET "!BASEDIR!NUM_GAMES_PER_CYCLE=500" ++SET "NUM_THREADS_FOR_SHUFFLING=8" ++SET "NUM_TRAIN_SAMPLES_PER_EPOCH=100000" ++SET "MAX_TRAIN_PER_DATA=8" ++SET "NUM_TRAIN_SAMPLES_PER_SWA=80000" ++SET "BATCHSIZE=128" ++SET "SHUFFLE_MINROWS=100000" ++SET "MAX_TRAIN_SAMPLES_PER_CYCLE=500000" ++SET "TAPER_WINDOW_SCALE=50000" ++SET "SHUFFLE_KEEPROWS=600000" ++SET "SELFPLAY_CONFIG="!GITROOTDIR!"\cpp\configs\training\selfplay1.cfg" ++SET "!GITROOTDIR!GATING_CONFIG="!GITROOTDIR!"\cpp\configs\training\gatekeeper1.cfg" ++SET _INTERPOLATION_7= ++FOR /f "delims=" %%a in ('date "+%Y%m%d-%H%M%S"') DO (SET "_INTERPOLATION_7=!_INTERPOLATION_7! %%a") ++SET "!GITROOTDIR!!GITROOTDIR!DATE_FOR_FILENAME=!_INTERPOLATION_7:~1!" ++SET _INTERPOLATION_8= ++FOR /f "delims=" %%a in ('date "+%Y%m%d-%H%M%S"') DO (SET "_INTERPOLATION_8=!_INTERPOLATION_8! %%a") ++SET "!GITROOTDIR!!GITROOTDIR!!_INTERPOLATION_8:~1!DATED_ARCHIVE=!BASEDIR!"/scripts/dated/"!DATE_FOR_FILENAME!" ++mkdir "-p" "!DATED_ARCHIVE!" ++COPY "!GITROOTDIR!"/python/*.py "!GITROOTDIR!"/python/selfplay/*.sh "!DATED_ARCHIVE!" ++COPY "!GITROOTDIR!"\cpp\katago "!DATED_ARCHIVE!" ++COPY "!SELFPLAY_CONFIG!" "!DATED_ARCHIVE!"\selfplay.cfg ++COPY "!GATING_CONFIG!" "!DATED_ARCHIVE!"\gatekeeper.cfg ++git "show" "--no-patch" "--no-color" REM UNKNOWN: {"type":"Redirect","op":{"text":">","type":"great"},"file":{"text":"\"$DATED_ARCHIVE\"/version.txt","expansion":[{"loc":{"start":1,"end":14},"parameter":"DATED_ARCHIVE","type":"ParameterExpansion"}],"type":"Word"}} ++git "diff" "--no-color" REM UNKNOWN: {"type":"Redirect","op":{"text":">","type":"great"},"file":{"text":"\"$DATED_ARCHIVE\"/diff.txt","expansion":[{"loc":{"start":1,"end":14},"parameter":"DATED_ARCHIVE","type":"ParameterExpansion"}],"type":"Word"}} ++git "diff" "--staged" "--no-color" REM UNKNOWN: {"type":"Redirect","op":{"text":">","type":"great"},"file":{"text":"\"$DATED_ARCHIVE\"/diffstaged.txt","expansion":[{"loc":{"start":1,"end":14},"parameter":"DATED_ARCHIVE","type":"ParameterExpansion"}],"type":"Word"}} ++cd "!DATED_ARCHIVE!" ++set "-x" ++REM UNKNOWN: 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ME\"","expansion":[{"loc":{"start":1,"end":13},"parameter":"TRAININGNAME","type":"ParameterExpansion"}],"type":"Word"},{"text":"\"$MODELKIND\"","expansion":[{"loc":{"start":1,"end":10},"parameter":"MODELKIND","type":"ParameterExpansion"}],"type":"Word"},{"text":"\"$BATCHSIZE\"","expansion":[{"loc":{"start":1,"end":10},"parameter":"BATCHSIZE","type":"ParameterExpansion"}],"type":"Word"},{"text":"main","type":"Word"},{"text":"-samples-per-epoch","type":"Word"},{"text":"\"$NUM_TRAIN_SAMPLES_PER_EPOCH\"","expansion":[{"loc":{"start":1,"end":28},"parameter":"NUM_TRAIN_SAMPLES_PER_EPOCH","type":"ParameterExpansion"}],"type":"Word"},{"text":"-swa-period-samples","type":"Word"},{"text":"\"$NUM_TRAIN_SAMPLES_PER_SWA\"","expansion":[{"loc":{"start":1,"end":26},"parameter":"NUM_TRAIN_SAMPLES_PER_SWA","type":"ParameterExpansion"}],"type":"Word"},{"text":"-quit-if-no-data","type":"Word"},{"text":"-stop-when-train-bucket-limited","type":"Word"},{"text":"-no-repeat-files","type":"Word"},{"text":"-max-train-bucket-per-new-data","type":"Word"},{"text":"\"$MAX_TRAIN_PER_DATA\"","expansion":[{"loc":{"start":1,"end":19},"parameter":"MAX_TRAIN_PER_DATA","type":"ParameterExpansion"}],"type":"Word"},{"text":"-max-train-bucket-size","type":"Word"},{"text":"\"$MAX_TRAIN_SAMPLES_PER_CYCLE\"","expansion":[{"loc":{"start":1,"end":28},"parameter":"MAX_TRAIN_SAMPLES_PER_CYCLE","type":"ParameterExpansion"}],"type":"Word"}]},{"type":"Command","name":{"text":"echo","type":"Word"},"suffix":[{"text":"Export","type":"Word"}]},{"type":"Subshell","list":{"type":"CompoundList","commands":[{"type":"Pipeline","commands":[{"type":"Command","name":{"text":"time","type":"Word"},"suffix":[{"text":"./export_model_for_selfplay.sh","type":"Word"},{"text":"\"$NAMEPREFIX\"","expansion":[{"loc":{"start":1,"end":11},"parameter":"NAMEPREFIX","type":"ParameterExpansion"}],"type":"Word"},{"text":"\"$BASEDIR\"","expansion":[{"loc":{"start":1,"end":8},"parameter":"BASEDIR","type":"ParameterExpansion"}],"type":"Word"},{"text":"\"$USEGATING\"","expansion":[{"loc":{"start":1,"end":10},"parameter":"USEGATING","type":"ParameterExpansion"}],"type":"Word"}]},{"type":"Command","name":{"text":"tee","type":"Word"},"suffix":[{"text":"-a","type":"Word"},{"text":"\"$BASEDIR\"/logs/outexport.txt","expansion":[{"loc":{"start":1,"end":8},"parameter":"BASEDIR","type":"ParameterExpansion"}],"type":"Word"}]}]}]}}]}} ++exit "0" +\ No newline at end of file +diff --git a/python/selfplay/synchronous_loop.sh b/python/selfplay/synchronous_loop.sh +index 46cfbc3d..33f4be30 100755 +--- a/python/selfplay/synchronous_loop.sh ++++ b/python/selfplay/synchronous_loop.sh +@@ -12,7 +12,7 @@ set -o pipefail + if [[ $# -lt 5 ]] + then + echo "Usage: $0 NAMEPREFIX BASEDIR TRAININGNAME MODELKIND USEGATING" +- echo "Assumes katago is already built in the 'cpp' directory of the KataGo repo and the executable is present at cpp/katago." ++ echo "Assumes katago is already built in the 'cpp' directory of the KataGo repo and the executable is present at cpp/katago.exe." + echo "NAMEPREFIX string prefix for this training run, try to pick something globally unique. Will be displayed to users when KataGo loads the model." + echo "BASEDIR containing selfplay data and models and related directories" + echo "TRANINGNAME name to prefix models with, specific to this training daemon" +@@ -44,39 +44,27 @@ mkdir -p "$BASEDIR"/selfplay + mkdir -p "$BASEDIR"/gatekeepersgf + + # Parameters for the training run +-# NOTE: You may want to adjust the below numbers. +-# NOTE: You probably want to edit settings in cpp/configs/training/selfplay1.cfg +-# NOTE: You probably want to edit settings in cpp/configs/training/gatekeeper1.cfg +-# Such as what board sizes and rules, you want to learn, number of visits to use, etc. +- +-# Also, the parameters below are relatively small, and probably +-# good for less powerful hardware and tighter turnaround during very early training, but if +-# you have strong hardware or are later into a run you may want to reduce the overhead by scaling +-# these numbers up and doing more games and training per cycle, exporting models less frequently, etc. +- +-NUM_GAMES_PER_CYCLE=500 # Every cycle, play this many games ++NUM_GAMES_PER_CYCLE=90 # Every cycle, play this many games + NUM_THREADS_FOR_SHUFFLING=8 + NUM_TRAIN_SAMPLES_PER_EPOCH=100000 # Training will proceed in chunks of this many rows, subject to MAX_TRAIN_PER_DATA. + MAX_TRAIN_PER_DATA=8 # On average, train only this many times on each data row. Larger numbers may cause overfitting. + NUM_TRAIN_SAMPLES_PER_SWA=80000 # Stochastic weight averaging frequency. + BATCHSIZE=128 # For lower-end GPUs 64 or smaller may be needed to avoid running out of GPU memory. +-SHUFFLE_MINROWS=100000 # Require this many rows at the very start before beginning training. ++SHUFFLE_MINROWS=1 # Require this many rows at the very start before beginning training. + MAX_TRAIN_SAMPLES_PER_CYCLE=500000 # Each cycle will do at most this many training steps. + TAPER_WINDOW_SCALE=50000 # Parameter setting the scale at which the shuffler will make the training window grow sublinearly. + SHUFFLE_KEEPROWS=600000 # Needs to be larger than MAX_TRAIN_SAMPLES_PER_CYCLE, so the shuffler samples enough rows each cycle for the training to use. + + # Paths to the selfplay and gatekeeper configs that contain board sizes, rules, search parameters, etc. +-# See cpp/configs/training/README.md for some notes on other selfplay configs. +-SELFPLAY_CONFIG="$GITROOTDIR"/cpp/configs/training/selfplay1.cfg +-GATING_CONFIG="$GITROOTDIR"/cpp/configs/training/gatekeeper1.cfg ++SELFPLAY_CONFIG="$GITROOTDIR"/cpp/configs/training/selfplay1_maxsize9.cfg ++GATING_CONFIG="$GITROOTDIR"/cpp/configs/training/gatekeeper1_maxsize9.cfg + + # Copy all the relevant scripts and configs and the katago executable to a dated directory. +-# For archival and logging purposes - you can look back and see exactly the python code on a particular date + DATE_FOR_FILENAME=$(date "+%Y%m%d-%H%M%S") + DATED_ARCHIVE="$BASEDIR"/scripts/dated/"$DATE_FOR_FILENAME" + mkdir -p "$DATED_ARCHIVE" + cp "$GITROOTDIR"/python/*.py "$GITROOTDIR"/python/selfplay/*.sh "$DATED_ARCHIVE" +-cp "$GITROOTDIR"/cpp/katago "$DATED_ARCHIVE" ++cp "$GITROOTDIR"/cpp/katago.exe "$DATED_ARCHIVE"/katago.exe # Ensure the executable is copied + cp "$SELFPLAY_CONFIG" "$DATED_ARCHIVE"/selfplay.cfg + cp "$GATING_CONFIG" "$DATED_ARCHIVE"/gatekeeper.cfg + git show --no-patch --no-color > "$DATED_ARCHIVE"/version.txt +@@ -91,15 +79,13 @@ set -x + while true + do + echo "Gatekeeper" +- time ./katago gatekeeper -rejected-models-dir "$BASEDIR"/rejectedmodels -accepted-models-dir "$BASEDIR"/models/ -sgf-output-dir "$BASEDIR"/gatekeepersgf/ -test-models-dir "$BASEDIR"/modelstobetested/ -config "$DATED_ARCHIVE"/gatekeeper.cfg -quit-if-no-nets-to-test | tee -a "$BASEDIR"/gatekeepersgf/stdout.txt ++ time "C:\Users\chang\Downloads\katago-v1.15.1-opencl-windows-x64\katago.exe" gatekeeper -rejected-models-dir "$BASEDIR"/rejectedmodels -accepted-models-dir "$BASEDIR"/models/ -sgf-output-dir "$BASEDIR"/gatekeepersgf/ -test-models-dir "$BASEDIR"/modelstobetested/ -config "$DATED_ARCHIVE"/gatekeeper.cfg -quit-if-no-nets-to-test | tee -a "$BASEDIR"/gatekeepersgf/stdout.txt + + echo "Selfplay" +- time ./katago selfplay -max-games-total "$NUM_GAMES_PER_CYCLE" -output-dir "$BASEDIR"/selfplay -models-dir "$BASEDIR"/models -config "$DATED_ARCHIVE"/selfplay.cfg | tee -a "$BASEDIR"/selfplay/stdout.txt ++ time "C:\Users\chang\Downloads\katago-v1.15.1-opencl-windows-x64\katago.exe" selfplay -max-games-total "$NUM_GAMES_PER_CYCLE" -output-dir "$BASEDIR"/selfplay -models-dir "$BASEDIR"/models -config "$DATED_ARCHIVE"/selfplay.cfg | tee -a "$BASEDIR"/selfplay/stdout.txt + + echo "Shuffle" + ( +- # Skip validate since peeling off 5% of data is actually a bit too chunky and discrete when running at a small scale, and validation data +- # doesn't actually add much to debugging a fast-changing RL training. + time SKIP_VALIDATE=1 ./shuffle.sh "$BASEDIR" "$SCRATCHDIR" "$NUM_THREADS_FOR_SHUFFLING" "$BATCHSIZE" -min-rows "$SHUFFLE_MINROWS" -keep-target-rows "$SHUFFLE_KEEPROWS" -taper-window-scale "$TAPER_WINDOW_SCALE" | tee -a "$BASEDIR"/logs/outshuffle.txt + ) + +diff --git a/python/selfplay/vcruntime140.dll b/python/selfplay/vcruntime140.dll +new file mode 100644 +index 00000000..5d5b81dc +Binary files /dev/null and b/python/selfplay/vcruntime140.dll differ +diff --git a/python/selfplay/vcruntime140_1.dll b/python/selfplay/vcruntime140_1.dll +new file mode 100644 +index 00000000..3a02196f +Binary files /dev/null and b/python/selfplay/vcruntime140_1.dll differ +diff --git "a/python/selfplay/\344\277\261\344\271\220\351\203\250AI.cfg" "b/python/selfplay/\344\277\261\344\271\220\351\203\250AI.cfg" +new file mode 100644 +index 00000000..9b9287b0 +--- /dev/null ++++ "b/python/selfplay/\344\277\261\344\271\220\351\203\250AI.cfg" +@@ -0,0 +1,721 @@ ++# Configuration for KataGo C++ GTP engine ++ ++# Run the program using: `./katago.exe gtp` ++ ++# In this example config, when a parameter is given as a commented out value, ++# that value also is the default value, unless described otherwise. You can ++# uncomment it (remove the pound sign) and change it if you want. ++ ++# =========================================================================== ++# Running on an online server or in a real tournament or match ++# =========================================================================== ++# If you plan to run online or in a tournament, read through the "Rules" ++# section below for proper handling of komi, handicaps, end-of-game cleanup, ++# and other details. ++ ++# =========================================================================== ++# Notes about performance and memory usage ++# =========================================================================== ++# Important: For good performance, you will very likely want to tune the ++# "numSearchThreads" parameter in the Search limits section below! Run ++# "./katago benchmark" to test KataGo and to suggest a reasonable value ++# of this parameter. ++ ++# For multi-GPU systems, read "OpenCL GPU settings" or "CUDA GPU settings". ++# ++# When using OpenCL, verify that KataGo picks the correct device! Some systems ++# may have both an Intel CPU OpenCL and GPU OpenCL. If # KataGo picks the wrong ++# one, correct this by specifying "openclGpuToUse". ++# ++# Consider adjusting "maxVisits", "ponderingEnabled", "resignThreshold", and ++# other parameters depending on your intended usage. ++ ++# =========================================================================== ++# Command-line usage ++# =========================================================================== ++# All of the below values may be set or overridden via command-line arguments: ++# ++# -override-config KEY=VALUE,KEY=VALUE,... ++ ++# =========================================================================== ++# Logs and files ++# =========================================================================== ++# This section defines where and what logging information is produced. ++ ++# Each run of KataGo will log to a separate file in this dir. ++# This is the default. ++logDir = gtp_logs ++# Uncomment and specify this instead of logDir to write separate dated subdirs ++# logDirDated = gtp_logs ++# Uncomment and specify this instead of logDir to log to only a single file ++# logFile = gtp.log ++ ++# Logging options ++logAllGTPCommunication = true ++logSearchInfo = true ++logSearchInfoForChosenMove = false ++logToStderr = false ++ ++# KataGo will display some info to stderr on GTP startup ++# Uncomment the next line and set it to false to suppress that and remain silent ++# startupPrintMessageToStderr = true ++ ++# Write information to stderr, for use in things like malkovich chat to OGS. ++ ogsChatToStderr = true ++ ++# Uncomment and set this to a directory to override where openCLTuner files ++# and other cached data is written. By default it saves into a subdir of the ++# current directory on windows, and a subdir of ~/.katago on Linux. ++# homeDataDir = PATH_TO_DIRECTORY ++ ++# =========================================================================== ++# Analysis ++# =========================================================================== ++# This section configures analysis settings. ++# ++# The maximum number of moves after the first move displayed in variations ++# from analysis commands like kata-analyze or lz-analyze. ++# analysisPVLen = 15 ++ ++# Report winrates for chat and analysis as (BLACK|WHITE|SIDETOMOVE). ++# Most GUIs and analysis tools will expect SIDETOMOVE. ++# reportAnalysisWinratesAs = SIDETOMOVE ++ ++# Extra noise for wider exploration. Large values will force KataGo to ++# analyze a greater variety of moves than it normally would. ++# An extreme value like 1 distributes playouts across every move on the board, ++# even very bad moves. ++# Affects analysis only, does not affect play. ++# analysisWideRootNoise = 0.04 ++ ++# Try to limit the effect of possible bad or bogus move sequences in the ++# history leading to this position from affecting KataGo's move predictions. ++# analysisIgnorePreRootHistory = true ++ ++# =========================================================================== ++# Rules ++# =========================================================================== ++# This section configures the scoring and playing rules. Rules can also be ++# changed mid-run by issuing custom GTP commands. ++# ++# See https://lightvector.github.io/KataGo/rules.html for rules details. ++# ++# See https://github.com/lightvector/KataGo/blob/master/docs/GTP_Extensions.md ++# for GTP commands. ++ ++# Specify the rules as a string. ++# Some legal values include: ++# chinese, japanese, korean, aga, chinese-ogs, new-zealand, stone-scoring, ++# ancient-territory, bga, aga-button ++# ++# For some human rulesets that require complex adjudication in tricky cases ++# (e.g. japanese, korean) KataGo may not precisely match the ruleset in such ++# cases but will do its best. ++rules = tromp-taylor ++ ++# By default, the "rules" parameter is used, but if you comment it out and ++# uncomment one option in each of the sections below, you can specify an ++# arbitrary combination of individual rules. ++ ++# koRule = SIMPLE # Simple ko rules (triple ko = no result) ++# koRule = POSITIONAL # Positional superko ++# koRule = SITUATIONAL # Situational superko ++ ++# scoringRule = AREA # Area scoring ++# scoringRule = TERRITORY # Territory scoring (special computer-friendly territory rules) ++ ++# taxRule = NONE # All surrounded empty points are scored ++# taxRule = SEKI # Eyes in seki do NOT count as points ++# taxRule = ALL # All groups are taxed up to 2 points for the two eyes needed to live ++ ++# Is multiple-stone suicide legal? (Single-stone suicide is always illegal). ++# multiStoneSuicideLegal = false ++# multiStoneSuicideLegal = true ++ ++# "Button go" - the first pass when area scoring awards 0.5 points and does ++# not count for ending the game. ++# Allows area scoring rulesets that have far simpler rules to achieve the same ++# final scoring precision and reward for precise play as territory scoring. ++# hasButton = false ++# hasButton = true ++ ++# Is this a human ruleset where it's okay to pass before having physically ++# captured and removed all dead stones? ++# friendlyPassOk = false ++# friendlyPassOk = true ++ ++# How handicap stones in handicap games are compensated ++# whiteHandicapBonus = 0 # White gets no compensation for black's handicap stones (Tromp-taylor, NZ, JP) ++# whiteHandicapBonus = N-1 # White gets N-1 points for black's N handicap stones (AGA) ++# whiteHandicapBonus = N # White gets N points for black's N handicap stones (Chinese) ++ ++# ------------------------------ ++# Other rules hacks ++# ------------------------------ ++# Uncomment and change to adjust what board size KataGo uses upon startup ++# by default when GTP doesn't specify. ++# defaultBoardSize = 19 ++ ++# By default, Katago will use the komi that the GUI or GTP controller tries to set. ++# Uncomment and set this to have KataGo ignore the controller and always use this komi. ++# ignoreGTPAndForceKomi = 7 ++ ++# =========================================================================== ++# Bot behavior ++# =========================================================================== ++ ++# ------------------------------ ++# Resignation ++# ------------------------------ ++ ++# Resignation occurs if for at least resignConsecTurns in a row, the ++# winLossUtility (on a [-1,1] scale) is below resignThreshold. ++allowResignation = true ++resignThreshold = -0.90 ++resignConsecTurns = 3 ++ ++# By default, KataGo may resign games that it is confidently losing even if they ++# are very close in score. Uncomment and set this to avoid resigning games ++# if the estimated difference is points is less than or equal to this. ++# resignMinScoreDifference = 10 ++ ++# ------------------------------ ++# Handicap ++# ------------------------------ ++# Assume that if black makes many moves in a row right at the start of the ++# game, then the game is a handicap game. This is necessary on some servers ++# and for some GUIs and also when initializing from many SGF files, which may ++# set up a handicap game using repeated GTP "play" commands for black rather ++# than GTP "place_free_handicap" commands; however, it may also lead to ++# incorrect understanding of komi if whiteHandicapBonus is used and a server ++# does not have such a practice. Uncomment and set to false to disable. ++# assumeMultipleStartingBlackMovesAreHandicap = true ++ ++# Makes katago dynamically adjust in handicap or altered-komi games to assume ++# based on those game settings that it must be stronger or weaker than the ++# opponent and to play accordingly. Greatly improves handicap strength by ++# biasing winrates and scores to favor appropriate safe/aggressive play. ++# Does NOT affect analysis (lz-analyze, kata-analyze, used by programs like ++# Lizzie) so analysis remains unbiased. Uncomment and set this to 0 to disable ++# this and make KataGo play the same always. ++# dynamicPlayoutDoublingAdvantageCapPerOppLead = 0.045 ++ ++# Instead of "dynamicPlayoutDoublingAdvantageCapPerOppLead", you can comment ++# that out and uncomment and set "playoutDoublingAdvantage" to a fixed value ++# from -3.0 to 3.0 that will not change dynamically. ++# ALSO affects analysis tools (lz-analyze, kata-analyze, used by e.g. Lizzie). ++# Negative makes KataGo behave as if it is much weaker than the opponent. ++# Positive makes KataGo behave as if it is much stronger than the opponent. ++# KataGo will adjust to favor safe/aggressive play as appropriate based on ++# the combination of who is ahead and how much stronger/weaker it thinks it is, ++# and report winrates and scores taking the strength difference into account. ++# ++# If this and "dynamicPlayoutDoublingAdvantageCapPerOppLead" are both set ++# then dynamic will be used for all games and this fixed value will be used ++# for analysis tools. ++# playoutDoublingAdvantage = 0.0 ++ ++# Uncomment one of these when using "playoutDoublingAdvantage" to enforce ++# that it will only apply when KataGo plays as the specified color and will be ++# negated when playing as the opposite color. ++# playoutDoublingAdvantagePla = BLACK ++# playoutDoublingAdvantagePla = WHITE ++ ++# ------------------------------ ++# Passing and cleanup ++# ------------------------------ ++# Make the bot never assume that its pass will end the game, even if passing ++# would end and "win" under Tromp-Taylor rules. Usually this is a good idea ++# when using it for analysis or playing on servers where scoring may be ++# implemented non-tromp-taylorly. Uncomment and set to false to disable. ++# conservativePass = true ++ ++# When using territory scoring, self-play games continue beyond two passes ++# with special cleanup rules that may be confusing for human players. This ++# option prevents the special cleanup phases from being reachable when using ++# the bot for GTP play. Uncomment and set to false to enable entering special ++# cleanup. For example, if you are testing it against itself, or against ++# another bot that has precisely implemented the rules documented at ++# https://lightvector.github.io/KataGo/rules.html ++# preventCleanupPhase = true ++ ++# ------------------------------ ++# Miscellaneous behavior ++# ------------------------------ ++# If the board is symmetric, search only one copy of each equivalent move. ++# Attempts to also account for ko/superko, will not theoretically perfect for ++# superko. Uncomment and set to false to disable. ++# rootSymmetryPruning = true ++ ++# Uncomment and set to true to avoid a particular joseki that some networks ++# misevaluate, and also to improve opening diversity versus some particular ++# other bots that like to play it all the time. ++# avoidMYTDaggerHack = false ++ ++# Prefer to avoid playing the same joseki in every corner of the board. ++# Uncomment to set to a specific value. See "Avoid SGF patterns" section. ++# By default: 0 (even games), 0.005 (handicap games) ++# avoidRepeatedPatternUtility = 0.0 ++ ++# Experimental logic to fight against mirror Go even with unfavorable komi. ++# Uncomment to set to a specific value to use for both playing and analysis. ++# By default: true when playing via GTP, but false when analyzing. ++# antiMirror = true ++ ++# Enable some hacks that mitigate rare instances when passing messes up deeper searches. ++# enablePassingHacks = true ++ ++# Uncomment and set this to true to prevent bad or bogus move sequences ++# in the history leading to this position from affecting KataGo's move choices. ++# Same as analysisIgnorePreRootHistory (see above) but applies to actual play. ++# You can enable this if KataGo is being asked to play from positions that it did not ++# choose the moves to reach. ++# ignorePreRootHistory = false ++ ++# =========================================================================== ++# Search limits ++# =========================================================================== ++ ++# Terminology: ++# "Playouts" is the number of new playouts of search performed each turn. ++# "Visits" is the same as "Playouts" but also counts search performed on ++# previous turns that is still applicable to this turn. ++# "Time" is the time in seconds. ++ ++# For example, if KataGo searched 200 nodes on the previous turn, and then ++# after the opponent's reply, 50 nodes of its search tree was still valid, ++# then a visit limit of 200 would allow KataGo to search 150 new nodes ++# (for a final tree size of 200 nodes), whereas a playout limit of of 200 ++# would allow KataGo to search 200 nodes (for a final tree size of 250 nodes). ++ ++# Additionally, KataGo may also move before than the limit in order to ++# obey time controls (e.g. byo-yomi, etc) if the GTP controller has ++# told KataGo that the game has is being played with a given time control. ++ ++# Limits for search on the current turn. ++# If commented out or unspecified, the default is to have no limit. ++# maxVisits = 500 ++# maxPlayouts = 300 ++ maxTime = 1.0 ++ ++# Ponder on the opponent's turn? ++ponderingEnabled = true ++ ++# Limits for search when pondering on the opponent's turn. ++# If commented out or unspecified, the default is to have no limit. ++# Limiting the maximum time is recommended so that KataGo won't burn CPU/GPU ++# forever and/or run out of RAM if left unattended while pondering is enabled. ++# maxVisitsPondering = 5000 ++# maxPlayoutsPondering = 3000 ++maxTimePondering = 120.0 ++ ++ ++# ------------------------------ ++# Other search limits and behavior ++# ------------------------------ ++ ++# Approx number of seconds to buffer for lag for GTP time controls - will ++# move a bit faster assuming there is this much lag per move. ++lagBuffer = 1.0 ++ ++# YOU PROBABLY WANT TO TUNE THIS PARAMETER! ++# The number of threads to use when searching. On powerful GPUs the optimal ++# threads may be much higher than the number of CPU cores you have because ++# many threads are needed to feed efficient large batches to the GPU. ++# ++# Run "./katago benchmark" to tune this parameter and test the effect ++# of changes to any of other parameters. ++numSearchThreads = 12 ++ ++# Play a little faster if the opponent is passing, for human-friendliness. ++# Comment these out to disable them, such as if running a controlled match ++# where you are testing KataGo with fixed compute per move vs other bots. ++searchFactorAfterOnePass = 0.50 ++searchFactorAfterTwoPass = 0.25 ++ ++# Play a little faster if super-winning, for human-friendliness. ++# Comment these out to disable them, such as if running a controlled match ++# where you are testing KataGo with fixed compute per move vs other bots. ++searchFactorWhenWinning = 0.40 ++searchFactorWhenWinningThreshold = 0.95 ++ ++# =========================================================================== ++# GPU settings ++# =========================================================================== ++# This section configures GPU settings. ++# ++# Maximum number of positions to send to a single GPU at once. The default ++# value is roughly equal to numSearchThreads, but can be specified manually ++# if running out of memory, or using multiple GPUs that expect to share work. ++# nnMaxBatchSize = ++ ++# Controls the neural network cache size, which is the primary RAM/memory use. ++# KataGo will cache up to (2 ** nnCacheSizePowerOfTwo) many neural net ++# evaluations in case of transpositions in the tree. ++# Increase this to improve performance for searches with tens of thousands ++# of visits or more. Decrease this to limit memory usage. ++# If you're happy to do some math - each neural net entry takes roughly ++# 1.5KB, except when using whole-board ownership/territory ++# visualizations, where each entry will take roughly 3KB. The number of ++# entries is (2 ** nnCacheSizePowerOfTwo). (E.g. 2 ** 18 = 262144.) ++# You can compute roughly how much memory the cache will use based on this. ++# nnCacheSizePowerOfTwo = 20 ++ ++# Size of mutex pool for nnCache is (2 ** this). ++# nnMutexPoolSizePowerOfTwo = 16 ++ ++# Randomize board orientation when running neural net evals? Uncomment and ++# set to false to disable. ++# nnRandomize = true ++ ++# If provided, force usage of a specific seed for nnRandomize. ++# The default is to use a randomly generated seed. ++# nnRandSeed = abcdefg ++ ++# Uncomment and set to true to force GTP to use the maximum board size for ++# internal buffers for the neural net. This will make KataGo slower when ++# evaluating small boards, but will avoid a lengthy initialization time on every ++# change of board size due to having to re-size the neural net buffers on the GPU. ++# This can be useful for example, for OGS's persistent bot mode that uses a single ++# bot instance to handle multiple games and may thrash between different board sizes ++# if there are concurrent games of multiple sizes. ++# gtpForceMaxNNSize = false ++ ++# ------------------------------ ++# Multiple GPUs ++# ------------------------------ ++# Set this to the number of GPUs to use or that are available. ++# IMPORTANT: If more than 1, also uncomment the appropriate TensorRT ++# or CUDA or OpenCL section. ++# numNNServerThreadsPerModel = 1 ++ ++# ------------------------------ ++# TENSORRT GPU settings ++# ------------------------------ ++# These only apply when using the TENSORRT version of KataGo. ++ ++# For one GPU: optionally uncomment this option and change if the GPU to ++# use is not device 0. ++# trtDeviceToUse = 0 ++ ++# For two GPUs: Uncomment these options, AND set numNNServerThreadsPerModel above. ++# Also, change their values if the devices you want to use are not 0 and 1. ++# trtDeviceToUseThread0 = 0 ++# trtDeviceToUseThread1 = 1 ++ ++# For three GPUs: Uncomment these options, AND set numNNServerThreadsPerModel above. ++# Also, change their values if the devices you want to use are not 0 and 1 and 2. ++# trtDeviceToUseThread0 = 0 ++# trtDeviceToUseThread1 = 1 ++# trtDeviceToUseThread2 = 2 ++ ++# The pattern continues for additional GPUs. ++ ++# ------------------------------ ++# CUDA GPU settings ++# ------------------------------ ++# These only apply when using the CUDA version of KataGo. ++ ++# For one GPU: optionally uncomment and change this if the GPU you want to ++# use is not device 0 ++# cudaDeviceToUse = 0 ++ ++# For two GPUs: Uncomment these options, AND set numNNServerThreadsPerModel above. ++# Also, change their values if the devices you want to use are not 0 and 1. ++# cudaDeviceToUseThread0 = 0 ++# cudaDeviceToUseThread1 = 1 ++ ++# For three GPUs: Uncomment these options, AND set numNNServerThreadsPerModel above. ++# Also, change their values if the devices you want to use are not 0 and 1 and 2. ++# cudaDeviceToUseThread0 = 0 ++# cudaDeviceToUseThread1 = 1 ++# cudaDeviceToUseThread2 = 2 ++ ++# The pattern continues for additional GPUs. ++ ++# KataGo will automatically use FP16 or not based on the compute capability ++# of your NVIDIA GPU. If you want to try to force a particular behavior ++# you can uncomment these lines and change them to "true" or "false". ++# cudaUseFP16 = auto ++# cudaUseNHWC = auto ++ ++# ------------------------------ ++# OpenCL GPU settings ++# ------------------------------ ++# These only apply when using the OpenCL version of KataGo. ++ ++# Uncomment and set to true to tune OpenCL for every board size separately, ++# rather than only the largest possible size. ++# openclReTunePerBoardSize = false ++ ++# For one GPU: optionally uncomment and change this if the best device to use is guessed incorrectly. ++# The default behavior tries to guess the 'best' GPU or device on your system to use, usually it will be a good guess. ++# openclDeviceToUse = 0 ++ ++# For two GPUs: Uncomment these two lines and replace X and Y with the device ids of the devices you want to use. ++# It might NOT be 0 and 1, some computers will have many OpenCL devices. You can see what the devices are when ++# KataGo starts up - it should print or log all the devices it finds. ++# (AND also set numNNServerThreadsPerModel above) ++# openclDeviceToUseThread0 = X ++# openclDeviceToUseThread1 = Y ++ ++# For three GPUs: Uncomment these three lines and replace X and Y and Z with the device ids of the devices you want to use. ++# It might NOT be 0 and 1 and 2, some computers will have many OpenCL devices. You can see what the devices are when ++# KataGo starts up - it should print or log all the devices it finds. ++# (AND also set numNNServerThreadsPerModel above) ++# openclDeviceToUseThread0 = X ++# openclDeviceToUseThread1 = Y ++# openclDeviceToUseThread2 = Z ++ ++# The pattern continues for additional GPUs. ++ ++# KataGo will automatically use FP16 or not based on testing your GPU during ++# tuning. If you want to try to force a particular behavior though you can ++# uncomment this option and change it to "true" or "false". This is a fairly ++# blunt setting - more detailed settings are testable by rerunning the tuner ++# with various arguments (./katago tuner). ++# openclUseFP16 = auto ++ ++# ------------------------------ ++# Eigen-specific settings ++# ------------------------------ ++# These only apply when using the Eigen (pure CPU) version of KataGo. ++ ++# Number of CPU threads for evaluating the neural net on the Eigen backend. ++# ++# Default: numSearchThreads ++# numEigenThreadsPerModel = X ++ ++# =========================================================================== ++# Root move selection and biases ++# =========================================================================== ++# Uncomment and edit any of the below values to change them from their default. ++ ++# If provided, force usage of a specific seed for various random things in ++# the search. The default is to use a random seed. ++# searchRandSeed = hijklmn ++ ++# Temperature for the early game, randomize between chosen moves with ++# this temperature ++# chosenMoveTemperatureEarly = 0.5 ++ ++# Decay temperature for the early game by 0.5 every this many moves, ++# scaled with board size. ++# chosenMoveTemperatureHalflife = 19 ++ ++# At the end of search after the early game, randomize between chosen ++# moves with this temperature ++# chosenMoveTemperature = 0.10 ++ ++# Subtract this many visits from each move prior to applying ++# chosenMoveTemperature (unless all moves have too few visits) to downweight ++# unlikely moves ++# chosenMoveSubtract = 0 ++ ++# The same as chosenMoveSubtract but only prunes moves that fall below ++# the threshold. This setting does not affect chosenMoveSubtract. ++# chosenMovePrune = 1 ++ ++# Number of symmetries to sample (without replacement) and average at the root ++# rootNumSymmetriesToSample = 1 ++ ++# Using LCB for move selection? ++# useLcbForSelection = true ++ ++# How many stdevs a move needs to be better than another for LCB selection ++# lcbStdevs = 5.0 ++ ++# Only use LCB override when a move has this proportion of visits as the ++# top move. ++# minVisitPropForLCB = 0.15 ++ ++# =========================================================================== ++# Internal params ++# =========================================================================== ++# Uncomment and edit any of the below values to change them from their default. ++ ++# Scales the utility of winning/losing ++# winLossUtilityFactor = 1.0 ++ ++# Scales the utility for trying to maximize score ++# staticScoreUtilityFactor = 0.10 ++# dynamicScoreUtilityFactor = 0.30 ++ ++# Adjust dynamic score center this proportion of the way towards zero, ++# capped at a reasonable amount. ++# dynamicScoreCenterZeroWeight = 0.20 ++# dynamicScoreCenterScale = 0.75 ++ ++# The utility of getting a "no result" due to triple ko or other long cycle ++# in non-superko rulesets (-1 to 1) ++# noResultUtilityForWhite = 0.0 ++ ++# The number of wins that a draw counts as, for white. (0 to 1) ++# drawEquivalentWinsForWhite = 0.5 ++ ++# Exploration constant for mcts ++# cpuctExploration = 1.0 ++# cpuctExplorationLog = 0.45 ++ ++# Parameters that control exploring more in volatile positions, exploring ++# less in stable positions. ++# cpuctUtilityStdevPrior = 0.40 ++# cpuctUtilityStdevPriorWeight = 2.0 ++# cpuctUtilityStdevScale = 0.85 ++ ++# FPU reduction constant for mcts ++# fpuReductionMax = 0.2 ++# rootFpuReductionMax = 0.1 ++# fpuParentWeightByVisitedPolicy = true ++ ++# Parameters that control weighting of evals based on the net's own ++# self-reported uncertainty. ++# useUncertainty = true ++# uncertaintyExponent = 1.0 ++# uncertaintyCoeff = 0.25 ++ ++# Explore using optimistic policy ++# rootPolicyOptimism = 0.2 ++# policyOptimism = 1.0 ++ ++# Amount to apply a downweighting of children with very bad values relative ++# to good ones. ++# valueWeightExponent = 0.25 ++ ++# Slight incentive for the bot to behave human-like with regard to passing at ++# the end, filling the dame, not wasting time playing in its own territory, ++# etc., and not play moves that are equivalent in terms of points but a bit ++# more unfriendly to humans. ++# rootEndingBonusPoints = 0.5 ++ ++# Make the bot prune useless moves that are just prolonging the game to ++# avoid losing yet. ++# rootPruneUselessMoves = true ++ ++# Apply bias correction based on local pattern keys ++# subtreeValueBiasFactor = 0.45 ++# subtreeValueBiasWeightExponent = 0.85 ++ ++# Use graph search rather than tree search - identify and share search for ++# transpositions. ++# useGraphSearch = true ++ ++# How much to shard the node table for search synchronization ++# nodeTableShardsPowerOfTwo = 16 ++ ++# How many virtual losses to add when a thread descends through a node ++# numVirtualLossesPerThread = 1 ++ ++# Improve the quality of evals under heavy multithreading ++# useNoisePruning = true ++ ++# =========================================================================== ++# Automatic avoid patterns ++# =========================================================================== ++# The parameters in this section provide a way to bias away from moves that match ++# patterns that this instance of KataGo has played in previous games, by auto-saving ++# moves to a directory and then auto-loading and biasing against them each new game. ++# Uncomment them to use them. When using this feature, all parameters must be specified. ++ ++# Different board sizes are tracked separately, but all board sizes share the same ++# parameters by default. Every parameter *except* for autoAvoidRepeatDir and ++# autoAvoidRepeatSaveChunkSize can be overridden per board size, ++# e.g. "autoAvoidRepeatMinTurnNumber13x13". You must ALSO specify the ++# defaults even if you have specified board-size-specific values. ++ ++# Directory to auto-save moves KataGo plays, to avoid them in future games. ++# You can create a new empty directory and put its path here. ++# If you run parallel instances of KataGo, use different directories if you ++# want them not to share their biases, use the same directory if you want ++# all of them to bias away from past moves that any of them have played. ++# KataGo will also automatically DELETE old data in this directory, so it is ++# recommended that if you do share the same directory between parallel instances, ++# that they all use the same settings that affect data saving/deletion. ++# autoAvoidRepeatDir = PATH_TO_NEW_DIRECTORY ++ ++# Penalize this much utility per matching move. ++# Values that are too large may lead to bad play. The value of 0.004 is fairly large ++# and might be large enough to result in some early weird/bad moves when trying ++# to avoid past games' moves if enough games begin the same way. You can experiment with it. ++# autoAvoidRepeatUtility = 0.004 ++ ++# Per each new move saved, exponentially decay prior saved moves by this factor. ++# This way, the bias against moves from many games ago is gradually phased out. ++# For example, 0.9995 = 1 - 1/2000, so would be roughly 2000 prior moves worth of ++# penalty weight remembered, in steady state. Depending on what turn number range ++# you are saving, this might equate to a different number of games For example ++# saving the first 50 moves per game would make this roughly 2000 / 50 = 40 games ++# worth of memory. ++# autoAvoidRepeatLambda = 0.9995 ++ ++# Affects data saving/deletion. ++# When the number of saved moves exceeds this, outright delete them to avoid too many ++# files and disk space building up. Also may affect the speed of saving/loading on start ++# of each game if this is set large and a lot of data builds up. ++# autoAvoidRepeatMaxPoses = 10000 ++ ++# Affects data saving/deletion. ++# Only save data for moves within this turn number range of those games. ++# E.g. setting autoAvoidRepeatMinTurnNumber to a number like 4 or 5 would tend to make ++# KataGo not develop a bias against the initial 3-4 and 4-4 corner moves in almost every game. ++# autoAvoidRepeatMinTurnNumber = 0 ++# autoAvoidRepeatMaxTurnNumber = 50 ++ ++# Affects data saving/deletion. ++# Within a single run of a program, wait to accumulate this many samples ++# (possibly across multiple clear_boards/games) before saving the data. ++# Can help to avoid writing too many small files to disk, especially when GTP is used ++# in a way that clears the board very frequently (e.g. gtp2ogs pooled manager). ++# autoAvoidRepeatSaveChunkSize = 200 ++ ++# =========================================================================== ++# Avoid SGF patterns ++# =========================================================================== ++# The parameters in this section provide a way to avoid moves that follow ++# specific patterns based on a set of SGF files loaded upon startup. ++# This is basically the same as the above "Automatic avoid patterns" section ++# above except you supply your own SGF files to avoid moves from. ++# Uncomment them to use this feature. Additionally, if the SGF file ++# contains the string %SKIP% in a comment on a move, that move will be ++# ignored for this purpose. ++ ++# Load SGF files from this directory when the engine is started ++# (only on startup, will not reload unless engine is restarted) ++# avoidSgfPatternDirs = path/to/directory/with/sgfs/ ++# You can also surround the file path in double quotes if the file path contains trailing spaces or hash signs. ++# Within double quotes, backslashes are escape characters. ++# avoidSgfPatternDirs = "path/to/directory/with/sgfs/" ++ ++# Penalize this much utility per matching move. ++# Set this negative if you instead want to favor SGF patterns instead of ++# penalizing them. This number does not need to be large, even 0.001 will ++# make a difference. Values that are too large may lead to bad play. ++# avoidSgfPatternUtility = 0.001 ++ ++# Optional - load only the newest this many files ++# avoidSgfPatternMaxFiles = 20 ++ ++# Optional - Penalty is multiplied by this per each older SGF file, so that ++# old SGF files matter less than newer ones. ++# avoidSgfPatternLambda = 0.90 ++ ++# Optional - pay attention only to moves made by players with this name. ++# For example, set it to the name that your bot's past games will show up ++# as in the SGF, so that the bot will only avoid repeating moves that itself ++# made in past games, not the moves that its opponents made. ++# avoidSgfPatternAllowedNames = my-ogs-bot-name1,my-ogs-bot-name2 ++ ++# Optional - Ignore moves in SGF files that occurred before this turn number. ++# avoidSgfPatternMinTurnNumber = 0 ++ ++# For more avoid patterns: ++# You can also specify a second set of parameters, and a third, fourth, ++# etc. by numbering 2,3,4,... ++# ++# avoidSgf2PatternDirs = ... ++# avoidSgf2PatternUtility = ... ++# avoidSgf2PatternMaxFiles = ... ++# avoidSgf2PatternLambda = ... ++# avoidSgf2PatternAllowedNames = ... ++# avoidSgf2PatternMinTurnNumber = ... ++ diff --git a/training/scripts/dated/20241110-123407/edit_checkpoint.py b/training/scripts/dated/20241110-123407/edit_checkpoint.py new file mode 100644 index 0000000..62c4189 --- /dev/null +++ b/training/scripts/dated/20241110-123407/edit_checkpoint.py @@ -0,0 +1,58 @@ +#!/usr/bin/python3 +import sys +import os +import argparse +import math +import torch +import json + +description = """ +Utility for dumping or modifying torch checkpoint file contents +""" + +parser = argparse.ArgumentParser(description=description) +parser.add_argument('-checkpoint', help='Checkpoint file path', required=True) +parser.add_argument('-output-json-to', help='Output json contents to here, except for model parameters and optimizer state') +parser.add_argument('-overwrite-checkpoint-from-json', help='Use this json contents to overwrite the apprpriate fields of the state dict of the checkpoint') +args = vars(parser.parse_args()) + +checkpoint_path = args["checkpoint"] +output_json_to = args["output_json_to"] +overwrite_checkpoint_from_json = args["overwrite_checkpoint_from_json"] + +data = torch.load(checkpoint_path,map_location="cpu") + +if output_json_to is not None: + assert output_json_to.endswith(".json") + data_to_write = dict( + running_metrics = data["running_metrics"], + train_state = data["train_state"], + config = data["config"] if "config" in data else None, + ) + with open(output_json_to,"w") as f: + json.dump(data,f,indent=2,default=repr) + print(f"Dumped to {output_json_to}") + +elif overwrite_checkpoint_from_json: + with open(overwrite_checkpoint_from_json) as f: + data_to_use = json.load(f) + if "running_metrics" in data_to_use: + print("Overwriting running_metrics") + data["running_metrics"] = data_to_use["running_metrics"] + if "train_state" in data_to_use: + print("Overwriting train_state") + data["train_state"] = data_to_use["train_state"] + if "config" in data_to_use: + print("Overwriting config") + data["config"] = data_to_use["config"] + + torch.save(data, checkpoint_path) + print(f"Updated {checkpoint_path} with new fields") + +else: + data_to_write = dict( + running_metrics = data["running_metrics"], + train_state = data["train_state"], + config = data["config"] if "config" in data else None, + ) + print(json.dumps(data_to_write,indent=2,default=repr)) diff --git a/training/scripts/dated/20241110-123407/elo.py b/training/scripts/dated/20241110-123407/elo.py new file mode 100644 index 0000000..f54d1a4 --- /dev/null +++ b/training/scripts/dated/20241110-123407/elo.py @@ -0,0 +1,855 @@ +import copy +import math +import numpy as np +import os +import scipy.stats +import scipy.special +import itertools +from abc import ABC, abstractmethod +from typing import List, Dict, Tuple, Set, Sequence +from dataclasses import dataclass + +Player = str +PlayerIdx = int + +ELO_PER_STRENGTH = 400.0 * math.log10(math.exp(1.0)) +P1_ADVANTAGE_NAME = "P1Advantage" + +class EloInfo: + """Summarizes maximum likelihood Elos and uncertainties for a group of players.""" + + def __init__( + self, + players: List[Player], + elo: Dict[Player,float], + elo_stderr: Dict[Player,float], + elo_covariance: Dict[Tuple[Player,Player],float], + effective_game_count: Dict[Player,float], + ): + self.players = players + self.elo = elo + self.elo_stderr = elo_stderr + self.elo_covariance = elo_covariance + self.effective_game_count = effective_game_count + + self.players = sorted(self.players, key=(lambda player: -elo[player] if player != P1_ADVANTAGE_NAME else 1e50)) + + def get_players(self) -> List[Player]: + return self.players + + def get_elo(self, p1: Player) -> float: + """Returns the maximum likelihood Elo of p1""" + return self.elo[p1] + + def get_approx_elo_stderr(self, p1: Player) -> float: + """Returns an approximation of the standard error on the Elo of p1, ASSUMING all other players Elos are equal to their maximum likelihood value. + This approximation may underestimate if the amount of data is very small.""" + return self.elo_stderr[p1] + + def get_elo_difference(self, p1: Player, p2: Player) -> float: + """Returns the maximum likelhood difference in Elo between p1 and p2""" + return self.elo[p1] - self.elo[p2] + + def get_approx_elo_difference_stderr(self, p1: Player, p2: Player) -> float: + """Returns an approximation of the standard error on difference in Elo between p1 and p2. + This approximation may underestimate if the amount of data is very small.""" + return math.sqrt( + self.elo_covariance[(p1,p1)] - self.elo_covariance[(p1,p2)] - self.elo_covariance[(p2,p1)] + self.elo_covariance[(p2,p2)] + ) + + def get_approx_likelihood_of_superiority(self, p1: Player, p2: Player) -> float: + """Returns an approximation of the likelihood that elo(p1) - elo(p2) > 0, given the data. + This approximation may be overconfident or inaccurate if the amount of data is very small.""" + if p1 == p2: + return 0.5 + mean = self.get_elo_difference(p1,p2) + stderr = self.get_approx_elo_difference_stderr(p1,p2) + return scipy.stats.t.cdf(mean/stderr,df=self.effective_game_count[p1]-1) + + def get_log10_odds_surprise_max_likelihood(self, p1: Player, p2: Player, g1: float, total: int) -> float: + """Returns an indication of how surprised we would be for p1 to win g1 games out of total versus p2, given the maximum likeihood Elos. + Doesn't explicitly model draw prob, if g1 is fractional then assumes the fractional part randomly rounds to a full 1 or 0 + and averages the probabilities. + A value of 3.0 means that g1 is larger than we would expect given the players' estimated Elos, such that a result + that extreme or more has less than 1 : 10^3.0 odds of happening under the posterior. + A value of -3.0 means that g1 is smaller larger than we would expect given the players' estimated Elos, such that a result + that extreme or more has less than 1 : 10^3.0 odds of happening under the posterior. + """ + if total <= 0: + return 0 + mean = self.get_elo_difference(p1,p2) / ELO_PER_STRENGTH + + max_likelihood_winprob = 1.0 / (1.0 + math.exp(-mean)) + max_likelihood_g1 = max_likelihood_winprob * total + if max_likelihood_g1 < g1: + signflip = -1 + mean = -mean + g1 = total - g1 + else: + signflip = 1 + + winprob = 1.0 / (1.0 + math.exp(-mean)) + + g1_floor = int(math.floor(g1)) + g1_frac = g1 - g1_floor + if g1_frac != 0.0: + # Average endpoints for draws + logxf = scipy.stats.binom.logcdf(g1_floor,total,winprob) + logxp1 = scipy.stats.binom.logcdf(g1_floor+1,total,winprob) + log_prob = scipy.special.logsumexp([logxf+math.log(1.0-g1_frac),logxp1+math.log(g1_frac)]) + else: + log_prob = scipy.stats.binom.logcdf(g1,total,winprob) + + log_odds = log_prob - (math.log(-log_prob) if log_prob > -1e-10 else math.log(1.0 - math.exp(log_prob))) + return -signflip * max(0.0, -log_odds / math.log(10.0)) + + def get_approx_log10_odds_surprise_bayes(self, p1: Player, p2: Player, g1: float, total: int) -> float: + """Returns an indication of how surprised we would be for p1 to win g1 games out of total versus p2, given the posterior. + Computed via numeric integration over the pairwise posteriors. + Doesn't explicitly model draw prob, if g1 is fractional then assumes the fractional part randomly rounds to a full 1 or 0 + and averages the probabilities. + A value of 3.0 means that g1 is larger than we would expect given the players' estimated Elos, such that a result + that extreme or more has less than 1 : 10^3.0 odds of happening under the posterior. + A value of -3.0 means that g1 is smaller larger than we would expect given the players' estimated Elos, such that a result + that extreme or more has less than 1 : 10^3.0 odds of happening under the posterior. + """ + mean = self.get_elo_difference(p1,p2) / ELO_PER_STRENGTH + stderr = self.get_approx_elo_difference_stderr(p1,p2) / ELO_PER_STRENGTH + + max_likelihood_winprob = 1.0 / (1.0 + math.exp(-mean)) + max_likelihood_g1 = max_likelihood_winprob * total + if max_likelihood_g1 < g1: + signflip = -1 + mean = -mean + g1 = total - g1 + else: + signflip = 1 + + logw_list = [] + logwx_list = [] + for x in np.linspace(-15.0,15.0,num=1000,endpoint=False): + logw = scipy.stats.norm.logpdf(x) + winprob = 1.0 / (1.0 + math.exp(-(mean + x * stderr))) + # print("X", x) + # print("winprob", winprob) + # print("g1 total", g1, total, g1/total) + + g1_floor = int(math.floor(g1)) + g1_frac = g1 - g1_floor + if g1_frac != 0.0: + # Average endpoints for draws + logxf = scipy.stats.binom.logcdf(g1_floor,total,winprob) + logxp1 = scipy.stats.binom.logcdf(g1_floor+1,total,winprob) + log_prob = scipy.special.logsumexp([logxf+math.log(1.0-g1_frac),logxp1+math.log(g1_frac)]) + else: + logx = scipy.stats.binom.logcdf(g1,total,winprob) + + # print("logw logx", logw, logx) + logw_list.append(logw) + logwx_list.append(logw+logx) + + log_wsum = scipy.special.logsumexp(logw_list) + log_wxsum = scipy.special.logsumexp(logwx_list) + # print("log_wsum",log_wsum) + # print("log_wxsum",log_wxsum) + + log_prob = log_wxsum - log_wsum + log_odds = log_prob - (math.log(-log_prob) if log_prob > -1e-10 else math.log(1.0 - math.exp(log_prob))) + return -signflip * max(0.0, -log_odds / math.log(10.0)) + + def __str__(self) -> str: + lines = [] + for player in self.players: + lines.append(f"{player:20s}: {self.elo[player]:8.2f} +/- {self.elo_stderr[player]:5.2f}") + return "\n".join(lines) + + +class Likelihood: + """Summarizes the information in an observed datapoint about player Elos, or a prior about them. + + Represents that sum_{p in playercombo} Strength(p)*playercombo[p] + offset has likelihood function f raised to the power of weight. + If kind is SIGMOID_KIND, f is the sigmoid function. + If kind is GAUSSIAN_KIND, f is the pdf of a unit gaussian + + where strength is such that 1 unit of strength is e:1 odds of winning in a head-to-head game. + """ + + SIGMOID_KIND = 1 + GAUSSIAN_KIND = 2 + + def __init__( + self, + playercombo: Dict[Player,float], + offset: float, + weight: float, + gamecount: float, + kind: int, + ): + self.playercombo = playercombo + self.offset = offset + self.weight = weight + self.gamecount = gamecount + self.kind = kind + assert kind == Likelihood.SIGMOID_KIND or kind == Likelihood.GAUSSIAN_KIND, "invalid kind" + + def add_idxs(self, player_to_idx: Dict[Player,PlayerIdx]): + self.pidxcombo : List[Tuple[PlayerIdx,float]] = [(player_to_idx[player],coeff) for (player,coeff) in self.playercombo.items()] + + LOG_ONE_OVER_SQRT_TWO_PI = math.log(1.0 / math.sqrt(2.0 * math.pi)) + + def get_loglikelihood(self, strengths: np.array) -> float: + strength_total = self.offset + sum(strengths[pidx] * coeff for (pidx,coeff) in self.pidxcombo) + if self.kind == Likelihood.SIGMOID_KIND: + if strength_total < -40: + return strength_total + return -self.weight * math.log(1.0 + math.exp(-strength_total)) + else: + return self.weight * (Likelihood.LOG_ONE_OVER_SQRT_TWO_PI - 0.5 * strength_total * strength_total) + + def accum_dloglikelihood_dstrength(self, strengths: np.array, accum: np.array): + strength_total = self.offset + sum(strengths[pidx] * coeff for (pidx,coeff) in self.pidxcombo) + if self.kind == Likelihood.SIGMOID_KIND: + dloglikelihood_dstrength_total = self.weight / (1.0 + math.exp(strength_total)) + else: + dloglikelihood_dstrength_total = -self.weight * strength_total + for (pidx,coeff) in self.pidxcombo: + accum[pidx] += coeff * dloglikelihood_dstrength_total + + def accum_d2loglikelihood_dstrength2(self, strengths: np.array, accum: np.array): + strength_total = self.offset + sum(strengths[pidx] * coeff for (pidx,coeff) in self.pidxcombo) + if self.kind == Likelihood.SIGMOID_KIND: + denom = math.exp(-0.5 * strength_total) + math.exp(0.5 * strength_total) + d2loglikelihood_dstrength_total2 = -self.weight / (denom * denom) + else: + d2loglikelihood_dstrength_total2 = -self.weight + + for (pidx1,coeff1) in self.pidxcombo: + for (pidx2,coeff2) in self.pidxcombo: + accum[pidx1,pidx2] += coeff1 * coeff2 * d2loglikelihood_dstrength_total2 + + def accum_d2loglikelihood_dstrength2_scalepow(self, strengths: np.array, accum: np.array, scale: float, power: float): + strength_total = self.offset + sum(strengths[pidx] * coeff for (pidx,coeff) in self.pidxcombo) + if self.kind == Likelihood.SIGMOID_KIND: + denom = math.exp(-0.5 * strength_total) + math.exp(0.5 * strength_total) + d2loglikelihood_dstrength_total2 = -self.weight / (denom * denom) + else: + d2loglikelihood_dstrength_total2 = -self.weight + + for (pidx1,coeff1) in self.pidxcombo: + for (pidx2,coeff2) in self.pidxcombo: + x = coeff1 * coeff2 * d2loglikelihood_dstrength_total2 + accum[pidx1,pidx2] += scale * (x ** power) + + +def likelihood_of_games( + p1: Player, + p2: Player, + num_games: float, + p1_won_proportion: float, + include_first_player_advantage: bool, +) -> List[Likelihood]: + """Return a list of Likelihood objects representing the result of set of games between p1 and p2 + + These Likelihoods can accumulated with any other Likelihoods, and then all passed to compute_elos + to compute maximum likelihood Elos for all the players. + + NOTE: For performance reasons, you should try to minimize the number of these you create. If p1 and p2 played + a large number of games, don't call this function once per game. Instead, call it once for all the games + together (or twice, if you are using include_first_player_advantage=True, separately reporting the stats + that occured when each side was the first player). + + NOTE: If specifying include_first_player_advantage=True, make sure to add a make_single_player_prior on + P1_ADVANTAGE_NAME. + + Arguments: + p1: Name of the first player + p2: Name of the second player + num_games: The number of games played + p1_won_proportion: The proportion of games that p1 won among those games played, counting draws as 0.5. + include_first_player_advantage: If true, will also make the computation take into account that the first player + might have an advantage (or a disadvantage!) and it will try to estimate the amount of that advantage. + + Returns: + List of likelihood objects summarizing the information. + """ + + ret = [] + assert p1_won_proportion >= 0.0 and p1_won_proportion <= 1.0 + assert num_games >= 0.0 + assert p1 != p2 + + if num_games > 0.0: + if not include_first_player_advantage: + if p1_won_proportion > 0.0: + ret.append(Likelihood( + playercombo={p1: 1.0, p2: -1.0}, + offset=0.0, + weight=p1_won_proportion*num_games, + gamecount=p1_won_proportion*num_games, + kind=Likelihood.SIGMOID_KIND + )) + if p1_won_proportion < 1.0: + ret.append(Likelihood( + playercombo={p2: 1.0, p1: -1.0}, + offset=0.0, + weight=(1.0-p1_won_proportion)*num_games, + gamecount=(1.0-p1_won_proportion)*num_games, + kind=Likelihood.SIGMOID_KIND + )) + else: + if p1_won_proportion > 0.0: + ret.append(Likelihood( + playercombo={p1: 1.0, p2: -1.0, P1_ADVANTAGE_NAME: 1.0}, + offset=0.0, + weight=p1_won_proportion*num_games, + gamecount=p1_won_proportion*num_games, + kind=Likelihood.SIGMOID_KIND + )) + if p1_won_proportion < 1.0: + ret.append(Likelihood( + playercombo={p2: 1.0, p1: -1.0, P1_ADVANTAGE_NAME: -1.0}, + offset=0.0, + weight=(1.0-p1_won_proportion)*num_games, + gamecount=(1.0-p1_won_proportion)*num_games, + kind=Likelihood.SIGMOID_KIND + )) + + return ret + +def make_single_player_prior( + p1: Player, + num_games: float, + elo: float, +) -> List[Likelihood]: + """Return a list of Likelihood objects representing a Bayesian prior that p1 is the specified Elo. + + The strength of the prior that p1 is the specified Elo is as if p1 were observed to have played + num_games many games against a known player of that Elo and won half and lost half. + + Returns: + List of likelihood objects summarizing the information. + """ + ret = [] + assert num_games >= 0.0 + assert np.isfinite(elo) + if num_games > 0.0: + ret.append(Likelihood( + playercombo={p1: 1.0}, + offset=(-elo / ELO_PER_STRENGTH), + weight=0.5*num_games, + gamecount=0.5*num_games, + kind=Likelihood.SIGMOID_KIND + )) + ret.append(Likelihood( + playercombo={p1: -1.0}, + offset=(elo / ELO_PER_STRENGTH), + weight=0.5*num_games, + gamecount=0.5*num_games, + kind=Likelihood.SIGMOID_KIND + )) + return ret + + +def make_sequential_prior( + players: List[Player], + num_games: float, +) -> List[Likelihood]: + """Return a list of Likelihood objects representing a Bayesian prior that each player in the sequence is similar in strength to the previous. + + This can be used, for example, if there were a sequence of changes between different versions, such that each version on average + is expected to be more similar to its neighbors. + + The strength of the prior between each sequential pair of players is as if they were observed to have played + num_games many games against each other and won half and lost half. + + Returns: + List of likelihood objects summarizing the information. + """ + ret = [] + assert num_games >= 0.0 + assert len(set(players)) == len(players), "players must not contain any duplicates" + + if len(players) < 1: + return ret + + for i in range(len(players)-1): + ret.extend(likelihood_of_games( + p1=players[i], + p2=players[i+1], + num_games=num_games, + p1_won_proportion=0.5, + include_first_player_advantage=False, + )) + return ret + + +def make_center_elos_prior( + players: Sequence[Player], + elo: float, +) -> List[Likelihood]: + """Return a list of Likelihood objects representing a Bayesian prior that the mean of all player Elos is the specified Elo. + + This prior will have no effect on the relative Elos of the players, unless it fights with another that sets players to + specific Elos, such as make_single_player_prior. It can simply be used to center all the Elos of the players. + + Returns: + List of likelihood objects summarizing the information. + """ + ret = [] + assert np.isfinite(elo) + assert len(set(players)) == len(players), "players must not contain any duplicates" + playercombo = { player: 1.0 for player in players } + ret.append(Likelihood( + playercombo=playercombo, + offset=-len(players) * elo / ELO_PER_STRENGTH, + weight=0.001, + gamecount=0.0, + kind=Likelihood.GAUSSIAN_KIND + )) + return ret + + +def compute_elos( + data: List[Likelihood], + tolerance: float = 0.001, + max_iters: int = 1000, + verbose: bool = False, +) -> EloInfo: + """Compute maximum-likelihood Elo ratings given the data and any priors. + + NOTE: It is recommend that you specify some sort of prior, even if relatively weak, for numerical stability. + When you call this function, it is up to you to make sure that the data and priors you have provided result in all + all Elos of all players being "anchored". For example, this function may crash or fail or return bad values if: + + * There is a player with no data at all, and no prior for that player. + (since that would mean an Elo for that player cannot be defined). + + * There is a player who has only won and never lost, and there is no prior that restricts that player's rating. + (since then nothing could stop player's Elo from going to infinity). + + * All players *have* won and lost against other players, but there is nothing that anchors the Elo of the + population as a whole, such no player having a make_single_player_prior AND there being no make_center_elos_prior. + (since even if all players Elos are known relative to one another, the Elos to report are undefined - there is nothing + to say where the Elos should be centered or what value they should start from). + + Examples of things that are normally sufficient to anchor everyone: + If all players have won and lost against other players, adding a make_center_elos_prior is sufficient. + If all players have won and lost against other players, AND every player has transitively beat every other player and + transitively lost to every other player, then adding a make_single_player_prior to one player is sufficient. + Regardless of whether the players have won or lost, adding a make_single_player_prior to every player is sufficient. + Regardless of whether the players have won or lost, adding both a make_sequential_prior and a make_center_elos_prior is sufficient. + + NOTE: Even aside from ensuring numeric stability and anchoring as above, it is probably good practice to add some mild prior + beyond that anyways. If your players are sequential (e.g. a series of different nets), a weak make_sequential_prior could be good. + If you want to just put all players on equal footing unbiasedly, adding a weak make_single_player_prior to each player that its Elo + is 0 (or 1000, or whatever) is also good. + + And if you have actual prior beliefs about the players Elos, feel free to add those. + + Arguments: + data: A single list of all the likelihoods from all your games and priors. + tolerance: Stop soon after the Elos stop changing by more than this. + max_iters: Bail out if the optimization takes more than this many iterations. + verbose: Print out the iteration as it proceeds. + + Returns: + Elos. Yay! + """ + + players = [] + for d in data: + players.extend(d.playercombo.keys()) + players = list(set(players)) + players.sort() + player_to_idx = { player: i for (i,player) in enumerate(players) } + + data = [copy.copy(d) for d in data] + for d in data: + d.add_idxs(player_to_idx) + + num_players = len(players) + + def compute_loglikelihood(strengths: np.array) -> float: + total = 0.0 + for d in data: + total += d.get_loglikelihood(strengths) + return total + + # Gauss newton + def find_ascent_vector(strengths: np.array) -> np.array: + dloglikelihood_dstrength = np.zeros(num_players,dtype=np.float64) + d2loglikelihood_dstrength2 = np.zeros((num_players,num_players),dtype=np.float64) + + for d in data: + d.accum_dloglikelihood_dstrength(strengths, dloglikelihood_dstrength) + d.accum_d2loglikelihood_dstrength2(strengths, d2loglikelihood_dstrength2) + + ascent = -np.linalg.solve(d2loglikelihood_dstrength2,dloglikelihood_dstrength) + return ascent + + def line_search_ascend(strengths: np.array, cur_loglikelihood: float) -> Tuple[np.array,float]: + ascent = find_ascent_vector(strengths) + # Try up to this many times to find an improvement + for i in range(30): + new_strengths = strengths + ascent + new_loglikelihood = compute_loglikelihood(new_strengths) + if new_loglikelihood > cur_loglikelihood: + return (new_strengths, new_loglikelihood) + # Shrink ascent step and try again + ascent *= 0.6 + return (strengths,cur_loglikelihood) + + strengths = np.zeros(num_players,dtype=np.float64) + loglikelihood = compute_loglikelihood(strengths) + iters_since_big_change = 0 + last_elo_change = None + for i in range(max_iters): + if verbose: + print(f"Beginning iteration {i}, cur log likelihood {loglikelihood}, last elo change {last_elo_change}") + + (new_strengths, new_loglikelihood) = line_search_ascend(strengths, loglikelihood) + elodiff = (new_strengths - strengths) * ELO_PER_STRENGTH + last_elo_change = 0 if len(elodiff) <= 0 else np.max(np.abs(elodiff)) + + strengths = new_strengths + loglikelihood = new_loglikelihood + + iters_since_big_change += 1 + if np.any(elodiff > tolerance): + iters_since_big_change = 0 + + if iters_since_big_change > 3: + break + + + d2loglikelihood_dstrength2 = np.zeros((num_players,num_players),dtype=np.float64) + for d in data: + d.accum_d2loglikelihood_dstrength2(strengths, d2loglikelihood_dstrength2) + strength_precision = -d2loglikelihood_dstrength2 + elo_precision = strength_precision / (ELO_PER_STRENGTH * ELO_PER_STRENGTH) + elo_covariance = np.linalg.inv(elo_precision) + + sqrt_ess_numerator = np.zeros((num_players,num_players),dtype=np.float64) + ess_denominator = np.zeros((num_players,num_players),dtype=np.float64) + for d in data: + if d.gamecount > 0.0: + d.accum_d2loglikelihood_dstrength2_scalepow(strengths, sqrt_ess_numerator, scale = 1.0, power=1.0) + d.accum_d2loglikelihood_dstrength2_scalepow(strengths, ess_denominator, scale = 1.0 / d.gamecount, power=2.0) + + info = EloInfo( + players = players, + elo = { player: ELO_PER_STRENGTH * strengths[player_to_idx[player]] for player in players }, + elo_stderr = { player: math.sqrt(1.0 / elo_precision[player_to_idx[player],player_to_idx[player]]) for player in players }, + elo_covariance = { (p1,p2): elo_covariance[player_to_idx[p1],player_to_idx[p2]] for p1 in players for p2 in players }, + effective_game_count = { + player: (np.square(sqrt_ess_numerator[player_to_idx[player],player_to_idx[player]]) / + ess_denominator[player_to_idx[player],player_to_idx[player]]) + for player in players + }, + ) + return info + +def has_only_factors_of_2_and_3(n: int) -> bool: + while n > 1: + if n % 2 == 0: + n //= 2 + elif n % 3 == 0: + n //= 3 + else: + return False + return True + +@dataclass +class GameRecord: + player1: str + player2: str + win: int = 0 + loss: int = 0 + draw: int = 0 + +class GameResultSummary: + + def __init__( + self, + elo_prior_games: float, + estimate_first_player_advantage: bool, + ): + self.results = {} # dict of { (player1_name, player2_name) : GameRecord } + + self._all_game_files = set() + self._elo_prior_games = elo_prior_games # number of games for bayesian prior around Elo 0 + self._estimate_first_player_advantage = estimate_first_player_advantage + self._elo_info = None + self._game_count = 0 + + def add_games_from_file_or_dir(self, input_file_or_dir: str, recursive=False): + """Add games found in input_file_or_dir into the results. Repeated paths to the same file across multiple calls will be ignored.""" + new_files = self._add_files(input_file_or_dir, recursive) + + def add_game_record(self, record: GameRecord): + """Add game record to results.""" + if (record.player1, record.player2) not in self.results: + self.results[(record.player1, record.player2)] = GameRecord(player1=record.player1,player2=record.player2) + self.results[(record.player1, record.player2)].win += record.win + self.results[(record.player1, record.player2)].loss += record.loss + self.results[(record.player1, record.player2)].draw += record.draw + self._game_count += record.win + record.loss + record.draw + + def clear(self): + """Clear all data added.""" + self.results = {} + self._all_game_files = set() + self._elo_info = None + + def print_game_results(self): + """Print tables of wins and win percentage.""" + pla_names = set(itertools.chain(*(name_pair for name_pair in self.results.keys()))) + self._print_result_matrix(pla_names) + + def print_elos(self): + """Print game results and maximum likelihood posterior Elos.""" + elo_info = self._compute_elos_if_needed() + real_players = [player for player in elo_info.players if player != P1_ADVANTAGE_NAME] + self._print_result_matrix(real_players) + print("Elos (+/- one approx standard error):") + print(elo_info) + + print("Pairwise approx % likelihood of superiority of row over column:") + los_matrix = [] + for player in real_players: + los_row = [] + for player2 in real_players: + los = elo_info.get_approx_likelihood_of_superiority(player,player2) + los_row.append(f"{los*100:.2f}") + los_matrix.append(los_row) + self._print_matrix(real_players,los_matrix) + + surprise_matrix = [] + for pla1 in real_players: + row = [] + for pla2 in real_players: + if (pla1 == pla2): + row.append("-") + continue + else: + pla1_pla2 = self.results[(pla1, pla2)] if (pla1, pla2) in self.results else GameRecord(pla1,pla2) + pla2_pla1 = self.results[(pla2, pla1)] if (pla2, pla1) in self.results else GameRecord(pla2,pla1) + win = pla1_pla2.win + pla2_pla1.loss + 0.5 * (pla1_pla2.draw + pla2_pla1.draw) + total = pla1_pla2.win + pla2_pla1.win + pla1_pla2.loss + pla2_pla1.loss + pla1_pla2.draw + pla2_pla1.draw + surprise = elo_info.get_log10_odds_surprise_max_likelihood(pla1, pla2, win, total) + if total <= 0: + row.append("-") + else: + row.append(f"{surprise:.1f}") + surprise_matrix.append(row) + print("Log10odds surprise matrix given the maximum-likelihood Elos:") + print("E.g. +3.0 means a 1:1000 unexpected good performance by row vs column.") + print("E.g. -4.0 means a 1:10000 unexpected bad performance by row vs column.") + print("Extreme numbers may indicate rock/paper/scissors, or Elo is not a good model for the data.") + self._print_matrix(real_players,surprise_matrix) + + print(f"Used a prior of {self._elo_prior_games} games worth that each player is near Elo 0.") + + def get_elos(self) -> EloInfo: + return self._compute_elos_if_needed() + + def get_game_results(self) -> Dict: + """Return a dictionary of game results as { (player1_name, player2_name) : GameRecord } + + You can retrieve results by player's name like: + results[(player1_name, player2_name)].win + results[(player1_name, player2_name)].loss + results[(player1_name, player2_name)].draw + """ + return self.results + + # Functions that can be implemented by subclasses ----------------------------------------------------- + # You can override these methods if you want add_games_from_file_or_dir to work. + # Otherwise, you can just add game records yourself via add_game_record. + @abstractmethod + def is_game_file(self, input_file: str) -> bool: + """Returns true if this file or directory is one that should have game results in it somewhere""" + raise NotImplementedError() + + @abstractmethod + def get_game_records(self, input_file: str) -> List[GameRecord]: + """Return all game records contained in this file""" + raise NotImplementedError() + + # Private functions ------------------------------------------------------------------------------------ + + def _compute_elos_if_needed(self): + if self._elo_info is None: + self._elo_info = self._estimate_elo() + return self._elo_info + + def _add_files(self, input_file_or_dir, recursive): + print(f"Searching and adding files in {input_file_or_dir}, please wait...") + + if not os.path.exists(input_file_or_dir): + raise Exception(f"There is no file or directory with name: {input_file_or_dir}") + + files = [] + if os.path.isdir(input_file_or_dir): + if recursive: + for (dirpath, dirnames, filenames) in os.walk(input_file_or_dir): + files += [os.path.join(dirpath, file) for file in filenames if self.is_game_file(os.path.join(dirpath, file))] + else: + files = [os.path.join(input_file_or_dir, file) for file in os.listdir(input_file_or_dir) if self.is_game_file(os.path.join(input_file_or_dir, file))] + else: + if self.is_game_file(input_file_or_dir): + files.append(input_file_or_dir) + + # Remove duplicates + new_game_files = set(files) + new_game_files = new_game_files.difference(self._all_game_files) + self._all_game_files = self._all_game_files.union(new_game_files) + self._add_new_games_to_result_dict(new_game_files, input_file_or_dir) + + print(f"Added {len(new_game_files)} new game files from {input_file_or_dir}") + + def _add_new_games_to_result_dict(self, new_game_files, source): + idx = 0 + for game_file in new_game_files: + records = self.get_game_records(game_file) + idx += 1 + if idx % 10 == 0 and has_only_factors_of_2_and_3(idx // 10): + print(f"Added {idx}/{len(new_game_files)} game files for {source}") + + for record in records: + self.add_game_record(record) + + def _estimate_elo(self) -> EloInfo: + """Estimate and print elo values. This function must be called after adding all games""" + pla_names = set(itertools.chain(*(name_pair for name_pair in self.results.keys()))) + data = [] + for pla_first in pla_names: + for pla_second in pla_names: + if (pla_first == pla_second): + continue + else: + if (pla_first, pla_second) not in self.results: + continue + record = self.results[(pla_first, pla_second)] + total = record.win + record.loss + record.draw + assert total >= 0 + if total == 0: + continue + + win = record.win + 0.5 * record.draw + winrate = win / total + data.extend(likelihood_of_games( + pla_first, + pla_second, + total, + winrate, + include_first_player_advantage=self._estimate_first_player_advantage + )) + + for pla in pla_names: + data.extend(make_single_player_prior(pla, self._elo_prior_games,0)) + data.extend(make_center_elos_prior(list(pla_names),0)) # Add this in case user put elo_prior_games = 0 + if self._estimate_first_player_advantage: + data.extend(make_single_player_prior(P1_ADVANTAGE_NAME, (1.0 + self._elo_prior_games) * 2.0, 0)) + + info = compute_elos(data, verbose=True) + return info + + def _print_matrix(self,pla_names,results_matrix): + per_elt_space = 2 + for sublist in results_matrix: + for elt in sublist: + per_elt_space = max(per_elt_space, len(str(elt))) + per_elt_space += 2 + + per_name_space = 1 if len(pla_names) == 0 else max(len(name) for name in pla_names) + per_name_space += 1 + if per_name_space > per_elt_space: + per_elt_space += 1 + + row_format = f"{{:>{per_name_space}}}" + f"{{:>{per_elt_space}}}" * len(results_matrix) + print(row_format.format("", *[name[:per_elt_space-2] for name in pla_names])) + for name, row in zip(pla_names, results_matrix): + print(row_format.format(name, *row)) + + def _print_result_matrix(self, pla_names): + print(f"Total games: {self._game_count}") + print("Games by player:") + for pla1 in pla_names: + total = 0 + for pla2 in pla_names: + if (pla1 == pla2): + continue + else: + pla1_pla2 = self.results[(pla1, pla2)] if (pla1, pla2) in self.results else GameRecord(pla1,pla2) + pla2_pla1 = self.results[(pla2, pla1)] if (pla2, pla1) in self.results else GameRecord(pla2,pla1) + total += pla1_pla2.win + pla2_pla1.win + pla1_pla2.loss + pla2_pla1.loss + pla1_pla2.draw + pla2_pla1.draw + print(f"{pla1}: {total:.1f}") + + print("Wins by row player against column player:") + result_matrix = [] + for pla1 in pla_names: + row = [] + for pla2 in pla_names: + if (pla1 == pla2): + row.append("-") + continue + else: + pla1_pla2 = self.results[(pla1, pla2)] if (pla1, pla2) in self.results else GameRecord(pla1,pla2) + pla2_pla1 = self.results[(pla2, pla1)] if (pla2, pla1) in self.results else GameRecord(pla2,pla1) + win = pla1_pla2.win + pla2_pla1.loss + 0.5 * (pla1_pla2.draw + pla2_pla1.draw) + total = pla1_pla2.win + pla2_pla1.win + pla1_pla2.loss + pla2_pla1.loss + pla1_pla2.draw + pla2_pla1.draw + row.append(f"{win:.1f}/{total:.1f}") + result_matrix.append(row) + self._print_matrix(pla_names,result_matrix) + + print("Win% by row player against column player:") + result_matrix = [] + for pla1 in pla_names: + row = [] + for pla2 in pla_names: + if (pla1 == pla2): + row.append("-") + continue + else: + pla1_pla2 = self.results[(pla1, pla2)] if (pla1, pla2) in self.results else GameRecord(pla1,pla2) + pla2_pla1 = self.results[(pla2, pla1)] if (pla2, pla1) in self.results else GameRecord(pla2,pla1) + win = pla1_pla2.win + pla2_pla1.loss + 0.5 * (pla1_pla2.draw + pla2_pla1.draw) + total = pla1_pla2.win + pla2_pla1.win + pla1_pla2.loss + pla2_pla1.loss + pla1_pla2.draw + pla2_pla1.draw + if total <= 0: + row.append("-") + else: + row.append(f"{win/total*100.0:.1f}%") + result_matrix.append(row) + + self._print_matrix(pla_names,result_matrix) + +# Testing code +if __name__ == "__main__": + # Example 1 + # data = [] + # data.extend(likelihood_of_games("Alice","Bob", 18, 12/18, False)) + # data.extend(likelihood_of_games("Bob","Carol", 18, 12/18, False)) + # data.extend(likelihood_of_games("Carol","Dan", 36, 12/18, False)) + # data.extend(likelihood_of_games("Dan","Eve", 48, 40/48, False)) + # data.extend(make_center_elos_prior(["Alice","Bob","Carol","Dan","Eve"],0)) + # info = compute_elos(data,verbose=True) + + # for player in info.players: + # for player2 in info.players: + # print(info.get_approx_likelihood_of_superiority(player,player2),end=" ") + # print() + + # Example 2 + summary = GameResultSummary( + elo_prior_games=1.0, + estimate_first_player_advantage=False, + ) + summary.add_game_record(GameRecord("Alice","Bob",win=12,loss=6,draw=0)) + summary.add_game_record(GameRecord("Bob","Carol",win=12,loss=6,draw=0)) + summary.add_game_record(GameRecord("Carol","Dan",win=36,loss=18,draw=0)) + summary.add_game_record(GameRecord("Dan","Eve",win=48,loss=24,draw=0)) + summary.print_elos() + + + diff --git a/training/scripts/dated/20241110-123407/export_model_for_selfplay.sh b/training/scripts/dated/20241110-123407/export_model_for_selfplay.sh new file mode 100644 index 0000000..eac659b --- /dev/null +++ b/training/scripts/dated/20241110-123407/export_model_for_selfplay.sh @@ -0,0 +1,115 @@ +#!/bin/bash -eu +set -o pipefail +{ +#Takes any models in torchmodels_toexport/ and outputs a cuda-runnable model file to modelstobetested/ +#Takes any models in torchmodels_toexport_extra/ and outputs a cuda-runnable model file to models_extra/ +#Should be run periodically. + +if [[ $# -ne 3 ]] +then + echo "Usage: $0 NAMEPREFIX BASEDIR USEGATING" + echo "Currently expects to be run from within the 'python' directory of the KataGo repo, or otherwise in the same dir as export_model.py." + echo "NAMEPREFIX string prefix for this training run, try to pick something globally unique. Will be displayed to users when KataGo loads the model." + echo "BASEDIR containing selfplay data and models and related directories" + echo "USEGATING = 1 to use gatekeeper, 0 to not use gatekeeper and output directly to models/" + exit 0 +fi +NAMEPREFIX="$1" +shift +BASEDIR="$1" +shift +USEGATING="$1" +shift + +#------------------------------------------------------------------------------ + +mkdir -p "$BASEDIR"/torchmodels_toexport +mkdir -p "$BASEDIR"/torchmodels_toexport_extra +mkdir -p "$BASEDIR"/modelstobetested +mkdir -p "$BASEDIR"/models_extra +mkdir -p "$BASEDIR"/models + +function exportStuff() { + FROMDIR="$1" + TODIR="$2" + + #Sort by timestamp so that we process in order of oldest to newest if there are multiple + for FILEPATH in $(find "$BASEDIR"/"$FROMDIR"/ -mindepth 1 -maxdepth 1 -printf "%T@ %p\n" | sort -n | cut -d ' ' -f 2) + do + #Make sure to skip tmp directories that are transiently there by the training, + #they are probably in the process of being written + if [ ${FILEPATH: -4} == ".tmp" ] + then + echo "Skipping tmp file:" "$FILEPATH" + elif [ ${FILEPATH: -9} == ".exported" ] + then + echo "Skipping self tmp file:" "$FILEPATH" + else + echo "Found model to export:" "$FILEPATH" + NAME="$(basename "$FILEPATH")" + + SRC="$BASEDIR"/"$FROMDIR"/"$NAME" + TMPDST="$BASEDIR"/"$FROMDIR"/"$NAME".exported + TARGET="$BASEDIR"/"$TODIR"/"$NAME" + + if [ -d "$BASEDIR"/modelstobetested/"$NAME" ] || \ + [ -d "$BASEDIR"/rejectedmodels/"$NAME" ] || \ + [ -d "$BASEDIR"/models/"$NAME" ] || \ + [ -d "$BASEDIR"/models_extra/"$NAME" ] || \ + [ -d "$BASEDIR"/modelsuploaded/"$NAME" ] + then + echo "Model with same name aleady exists, so skipping:" "$SRC" + else + rm -rf "$TMPDST" + mkdir "$TMPDST" + + set -x + python3 ./export_model_pytorch.py \ + -checkpoint "$SRC"/model.ckpt \ + -export-dir "$TMPDST" \ + -model-name "$NAMEPREFIX""-""$NAME" \ + -filename-prefix model \ + -use-swa + + python3 ./clean_checkpoint.py \ + -checkpoint "$SRC"/model.ckpt \ + -output "$TMPDST"/model.ckpt + set +x + + rm -r "$SRC" + gzip "$TMPDST"/model.bin + + #Make a bunch of the directories that selfplay will need so that there isn't a race on the selfplay + #machines to concurrently make it, since sometimes concurrent making of the same directory can corrupt + #a filesystem + #Only when not gating. When gating, gatekeeper is responsible. + if [ "$USEGATING" -eq 0 ] + then + if [ "$TODIR" != "models_extra" ] + then + mkdir -p "$BASEDIR"/selfplay/"$NAME" + mkdir -p "$BASEDIR"/selfplay/"$NAME"/sgfs + mkdir -p "$BASEDIR"/selfplay/"$NAME"/tdata + fi + fi + + #Sleep a little to allow some tolerance on the filesystem + sleep 5 + + mv "$TMPDST" "$TARGET" + echo "Done exporting:" "$NAME" "to" "$TARGET" + fi + fi + done +} + +if [ "$USEGATING" -eq 0 ] +then + exportStuff "torchmodels_toexport" "models" +else + exportStuff "torchmodels_toexport" "modelstobetested" +fi +exportStuff "torchmodels_toexport_extra" "models_extra" + +exit 0 +} diff --git a/training/scripts/dated/20241110-123407/export_model_pytorch.py b/training/scripts/dated/20241110-123407/export_model_pytorch.py new file mode 100644 index 0000000..a2b23b4 --- /dev/null +++ b/training/scripts/dated/20241110-123407/export_model_pytorch.py @@ -0,0 +1,461 @@ +#!/usr/bin/python3 +import sys +import os +import argparse +import traceback +import random +import math +import time +import struct +import json +import datetime +import logging +import numpy as np +from collections import defaultdict +from typing import Dict, List + +import torch +import torch.nn +from torch.optim.swa_utils import AveragedModel + +import modelconfigs +from model_pytorch import Model, ResBlock, NestedBottleneckResBlock +from load_model import load_model + +#Command and args------------------------------------------------------------------- + +description = """ +Export neural net weights to file for KataGo engine. +""" + +parser = argparse.ArgumentParser(description=description) +parser.add_argument('-checkpoint', help='Checkpoint to test', required=True) +parser.add_argument('-export-dir', help='model file dir to save to', required=True) +parser.add_argument('-model-name', help='name to record in model file', required=True) +parser.add_argument('-filename-prefix', help='filename prefix to save to within dir', required=True) +parser.add_argument('-use-swa', help='Use SWA model', action="store_true", required=False) +parser.add_argument('-export-14-as-15', help='Export model version 14 as 15', action="store_true", required=False) +args = vars(parser.parse_args()) + + +def main(args): + checkpoint_file = args["checkpoint"] + export_dir = args["export_dir"] + model_name = args["model_name"] + filename_prefix = args["filename_prefix"] + use_swa = args["use_swa"] + export_14_as_15 = args["export_14_as_15"] + + os.makedirs(export_dir,exist_ok=True) + + logging.root.handlers = [] + logging.basicConfig( + level=logging.INFO, + format="%(message)s", + handlers=[ + logging.StreamHandler(stream=sys.stdout), + logging.FileHandler(export_dir + "/log.txt"), + ], + ) + np.set_printoptions(linewidth=150) + + logging.info(str(sys.argv)) + + # LOAD MODEL --------------------------------------------------------------------- + model, swa_model, other_state_dict = load_model(checkpoint_file, use_swa, device="cpu", verbose=True) + model_config = model.config + + # WRITING MODEL ---------------------------------------------------------------- + extension = ".bin" + mode = "wb" + f = open(export_dir + "/" + filename_prefix + extension, mode) + def writeln(s): + f.write((str(s)+"\n").encode(encoding="ascii",errors="backslashreplace")) + def writestr(s): + f.write(s.encode(encoding="ascii",errors="backslashreplace")) + + # Ignore what's in the config if less than 11 since a lot of testing models + # are on old version but actually have various new architectures. + version = max(model_config["version"],11) + true_version = version + # Hack to be able to export version 14 as version 15 + if version == 14 and export_14_as_15: + version = 15 + + writeln(model_name) + writeln(version) + writeln(modelconfigs.get_num_bin_input_features(model_config)) + writeln(modelconfigs.get_num_global_input_features(model_config)) + + if version <= 12: + assert model.td_score_multiplier == 20.0 + assert model.scoremean_multiplier == 20.0 + assert model.scorestdev_multiplier == 20.0 + assert model.lead_multiplier == 20.0 + assert model.variance_time_multiplier == 40.0 + assert model.shortterm_value_error_multiplier == 0.25 + assert model.shortterm_score_error_multiplier == 30.0 + else: + writeln(model.td_score_multiplier) + writeln(model.scoremean_multiplier) + writeln(model.scorestdev_multiplier) + writeln(model.lead_multiplier) + writeln(model.variance_time_multiplier) + writeln(model.shortterm_value_error_multiplier) + writeln(model.shortterm_score_error_multiplier) + + if version >= 15: + if model.metadata_encoder is not None: + writeln(model.metadata_encoder.meta_encoder_version) + else: + writeln(0) + + # Write some dummy placeholders for future features + writeln(0) + writeln(0) + writeln(0) + writeln(0) + writeln(0) + writeln(0) + writeln(0) + + + def write_weights(weights): + # Little endian + reshaped = np.reshape(weights.detach().numpy(),[-1]) + num_weights = len(reshaped) + writestr("@BIN@") + f.write(struct.pack(f'<{num_weights}f',*reshaped)) + writestr("\n") + + def write_conv_weight(name,convweight): + (out_channels, in_channels, diamy, diamx) = convweight.shape + dilation = 1 + writeln(name) + writeln(diamy) #y + writeln(diamx) #x + writeln(in_channels) + writeln(out_channels) + writeln(dilation) #y + writeln(dilation) #x + # Torch order is oc,ic,y,x + # Desired output order is y,x,ic,oc + write_weights(torch.permute(convweight,(2,3,1,0))) + + def write_conv(name,conv): + assert conv.bias is None + write_conv_weight(name, conv.weight) + + def write_bn(name,normmask): + writeln(name) + + writeln(normmask.c_in) + epsilon = 1e-20 + writeln(epsilon) + has_gamma_or_scale = normmask.scale is not None or normmask.gamma is not None + has_beta = True + writeln(1 if has_gamma_or_scale else 0) + writeln(1 if has_beta else 0) + + if hasattr(normmask,"running_mean") and normmask.running_mean is not None: + assert normmask.is_using_batchnorm + assert normmask.running_mean.shape == (normmask.c_in,) + write_weights(normmask.running_mean) + else: + assert not normmask.is_using_batchnorm + write_weights(torch.zeros(normmask.c_in, dtype=torch.float)) + + if hasattr(normmask,"running_std") and normmask.running_std is not None: + assert normmask.is_using_batchnorm + assert normmask.running_std.shape == (normmask.c_in,) + write_weights(torch.maximum(torch.tensor(1e-20), normmask.running_std * normmask.running_std - epsilon)) + else: + assert not normmask.is_using_batchnorm + write_weights((1.0-epsilon) * torch.ones(normmask.c_in, dtype=torch.float)) + + if normmask.scale is not None: + if normmask.gamma is not None: + assert normmask.gamma.shape == (1, normmask.c_in, 1, 1) + assert has_gamma_or_scale + write_weights(normmask.scale * normmask.gamma) + else: + assert has_gamma_or_scale + write_weights(normmask.scale * torch.ones(normmask.c_in, dtype=torch.float, device="cpu")) + else: + if normmask.gamma is not None: + assert normmask.gamma.shape == (1, normmask.c_in, 1, 1) + assert has_gamma_or_scale + write_weights(normmask.gamma) + else: + assert not has_gamma_or_scale + pass + + assert normmask.beta.shape == (1, normmask.c_in, 1, 1) + write_weights(normmask.beta) + + def write_biasmask(name,biasmask): + writeln(name) + + writeln(biasmask.c_in) + epsilon = 1e-20 + writeln(epsilon) + has_gamma_or_scale = biasmask.scale is not None + has_beta = True + writeln(1 if has_gamma_or_scale else 0) + writeln(1 if has_beta else 0) + + write_weights(torch.zeros(biasmask.c_in, dtype=torch.float)) + write_weights((1.0-epsilon) * torch.ones(biasmask.c_in, dtype=torch.float)) + + if biasmask.scale is not None: + write_weights(biasmask.scale * torch.ones(biasmask.c_in, dtype=torch.float, device="cpu")) + + assert biasmask.beta.shape == (1, biasmask.c_in, 1, 1) + write_weights(biasmask.beta) + + def write_activation(name, activation): + writeln(name) + if isinstance(activation,torch.nn.ReLU): + writeln("ACTIVATION_RELU") + elif isinstance(activation,torch.nn.Mish): + writeln("ACTIVATION_MISH") + elif isinstance(activation,torch.nn.Identity): + writeln("ACTIVATION_IDENTITY") + else: + assert False, f"Activation not supported for export: {activation}" + + + def write_matmul(name,linearweight): + writeln(name) + (out_channels,in_channels) = linearweight.shape + writeln(in_channels) + writeln(out_channels) + # Torch order is oc,ic + # Desired output order is ic,oc + write_weights(torch.permute(linearweight,(1,0))) + + def write_matbias(name,linearbias): + writeln(name) + (out_channels,) = linearbias.shape + writeln(out_channels) + write_weights(linearbias) + + def write_normactconv(name,normactconv): + if normactconv.c_gpool is None: + assert normactconv.convpool is None + if normactconv.conv1x1 is None: + write_bn(name+".norm", normactconv.norm) + write_activation(name+".act", normactconv.act) + write_conv(name+".conv", normactconv.conv) + else: + write_bn(name+".norm", normactconv.norm) + write_activation(name+".act", normactconv.act) + # Torch conv order is oc,ic,h,w + # We want to add the 1x1 conv to the center of the h,w + h,w = (normactconv.conv.weight.shape[2],normactconv.conv.weight.shape[3]) + assert h % 2 == 1, "Conv1x1 can't be merged with even-sized convolution kernel" + assert w % 2 == 1, "Conv1x1 can't be merged with even-sized convolution kernel" + combined_conv = normactconv.conv.weight.detach().clone() + combined_conv[:,:,h//2:h//2+1,w//2:w//2+1] += normactconv.conv1x1.weight + assert normactconv.conv.bias is None + assert normactconv.conv1x1.bias is None + write_conv_weight(name+".conv", combined_conv) + else: + assert normactconv.convpool is not None + assert normactconv.conv1x1 is None + write_bn(name+".norm", normactconv.norm) + write_activation(name+".act", normactconv.act) + write_conv(name+".convpool.conv1r", normactconv.convpool.conv1r) + write_conv(name+".convpool.conv1g", normactconv.convpool.conv1g) + write_bn(name+".convpool.normg", normactconv.convpool.normg) + write_activation(name+".convpool.actg", normactconv.convpool.actg) + write_matmul(name+".convpool.linear_g", normactconv.convpool.linear_g.weight) + assert normactconv.convpool.linear_g.bias is None + + def write_block(name,block): + if isinstance(block,ResBlock) and block.normactconv1.c_gpool is None: + assert block.normactconv2.c_gpool is None + writeln("ordinary_block") + writeln(name) + write_normactconv(name+".normactconv1", block.normactconv1) + write_normactconv(name+".normactconv2", block.normactconv2) + elif isinstance(block,ResBlock) and block.normactconv1.c_gpool is not None: + assert block.normactconv2.c_gpool is None + writeln("gpool_block") + writeln(name) + write_normactconv(name+".normactconv1", block.normactconv1) + write_normactconv(name+".normactconv2", block.normactconv2) + elif isinstance(block,NestedBottleneckResBlock): + writeln("nested_bottleneck_block") + writeln(name) + writeln(block.internal_length) + assert block.internal_length == len(block.blockstack) + write_normactconv(name+".normactconvp", block.normactconvp) + for i,subblock in enumerate(block.blockstack): + write_block(name+".blockstack."+str(i),subblock) + write_normactconv(name+".normactconvq", block.normactconvq) + else: + assert False, "This kind of block is not supported for export right now" + + def write_metadata_encoder(name,encoder): + writeln(name) + writeln(encoder.c_input) + # Torch order is oc,ic. Flatten feature mask into the first mul + write_matmul(name+".mul1", encoder.linear1.weight * encoder.feature_mask.reshape((1,-1))) + write_matbias(name+".bias1", encoder.linear1.bias) + write_activation(name+".act1", encoder.act1) + write_matmul(name+".mul2", encoder.linear2.weight) + write_matbias(name+".bias2", encoder.linear2.bias) + write_activation(name+".act2", encoder.act2) + write_matmul(name+".mul3", encoder.out_scale * encoder.linear_output_to_trunk.weight) + assert encoder.linear_output_to_trunk.bias is None + + def write_trunk(name,model): + writeln("trunk") + writeln(len(model.blocks)) + writeln(model.c_trunk) + writeln(model.c_mid) + writeln(model.c_mid-model.c_gpool) + writeln(model.c_gpool) + writeln(model.c_gpool) + if version >= 15: + # Write some dummy placeholders for future features + writeln(0) + writeln(0) + writeln(0) + writeln(0) + writeln(0) + writeln(0) + + write_conv("model.conv_spatial", model.conv_spatial) + write_matmul("model.linear_global", model.linear_global.weight) + assert model.linear_global.bias is None + if model.metadata_encoder is not None: + assert version >= 15 + write_metadata_encoder("model.sgf_metadata_encoder",model.metadata_encoder) + + for i,block in enumerate(model.blocks): + write_block("model.blocks."+str(i), block) + if model.trunk_normless: + write_biasmask("model.norm_trunkfinal", model.norm_trunkfinal) + else: + write_bn("model.norm_trunkfinal", model.norm_trunkfinal) + write_activation("model.act_trunkfinal", model.act_trunkfinal) + + def write_policy_head(name,policyhead): + writeln(name) + write_conv(name+".conv1p", policyhead.conv1p) + write_conv(name+".conv1g", policyhead.conv1g) + write_biasmask(name+".biasg", policyhead.biasg) + write_activation(name+".actg", policyhead.actg) + write_matmul(name+".linear_g", policyhead.linear_g.weight) + assert policyhead.linear_g.bias is None + write_biasmask(name+".bias2", policyhead.bias2) + write_activation(name+".act2", policyhead.act2) + + # Write the this-move prediction and the optimistic policy prediction + if version <= 11: + assert policyhead.conv2p.weight.shape[0] == 4 + write_conv_weight(name+".conv2p", torch.stack((policyhead.conv2p.weight[0],), dim=0)) + assert policyhead.linear_pass.weight.shape[0] == 4 + write_matmul(name+".linear_pass", torch.stack((policyhead.linear_pass.weight[0],), dim=0)) + assert policyhead.linear_pass.bias is None + elif version <= 14: + assert policyhead.conv2p.weight.shape[0] == 6 + write_conv_weight(name+".conv2p", torch.stack((policyhead.conv2p.weight[0], policyhead.conv2p.weight[5]), dim=0)) + assert policyhead.linear_pass.weight.shape[0] == 6 + write_matmul(name+".linear_pass", torch.stack((policyhead.linear_pass.weight[0], policyhead.linear_pass.weight[5]), dim=0)) + assert policyhead.linear_pass.bias is None + elif version == 15 and true_version == 14: + assert policyhead.conv2p.weight.shape[0] == 6 + write_conv_weight(name+".conv2p", torch.stack((policyhead.conv2p.weight[0], policyhead.conv2p.weight[5]), dim=0)) + assert policyhead.linear_pass.weight.shape[0] == 6 + linear_pass_stack = [policyhead.linear_pass.weight[0], policyhead.linear_pass.weight[5]] + c_p1 = int(policyhead.linear_g.weight.shape[0]) + for _ in range(c_p1-2): + linear_pass_stack.append(torch.zeros_like(linear_pass_stack[0])) + write_matmul(name+".linear_pass", torch.stack(linear_pass_stack, dim=0)) + assert policyhead.linear_pass.bias is None + write_matbias(name+".linear_pass_bias", torch.tensor([0.0]*c_p1,dtype=torch.float32,device="cpu")) + write_activation(name+".act_pass", torch.nn.Identity()) + write_matmul(name+".linear_pass2", torch.tensor([[1.0,0.0]+[0.0]*(c_p1-2),[0.0,1.0]+[0.0]*(c_p1-2)],dtype=torch.float32,device="cpu")) + else: + assert policyhead.conv2p.weight.shape[0] == 6 + write_conv_weight(name+".conv2p", torch.stack((policyhead.conv2p.weight[0], policyhead.conv2p.weight[5]), dim=0)) + write_matmul(name+".linear_pass", policyhead.linear_pass.weight) + write_matbias(name+".linear_pass_bias", policyhead.linear_pass.bias) + write_activation(name+".act_pass", policyhead.act_pass) + assert policyhead.linear_pass2.weight.shape[0] == 6 + write_matmul(name+".linear_pass2", torch.stack((policyhead.linear_pass2.weight[0], policyhead.linear_pass2.weight[5]), dim=0)) + assert policyhead.linear_pass2.bias is None + + assert policyhead.conv2p.bias is None + + + def write_value_head(name, valuehead): + writeln(name) + write_conv(name+".conv1", valuehead.conv1) + write_biasmask(name+".bias1", valuehead.bias1) + write_activation(name+".act1", valuehead.act1) + write_matmul(name+".linear2", valuehead.linear2.weight) + write_matbias(name+".bias2", valuehead.linear2.bias) + write_activation(name+".act2", valuehead.act2) + write_matmul(name+".linear_valuehead", valuehead.linear_valuehead.weight) + write_matbias(name+".bias_valuehead", valuehead.linear_valuehead.bias) + + # For now, only output the scoremean and scorestdev and lead and vtime channels + w = valuehead.linear_miscvaluehead.weight[0:4] + b = valuehead.linear_miscvaluehead.bias[0:4] + # Grab the shortterm channels + w2 = valuehead.linear_moremiscvaluehead.weight[0:2] + b2 = valuehead.linear_moremiscvaluehead.bias[0:2] + w = torch.cat((w,w2),dim=0) + b = torch.cat((b,b2),dim=0) + write_matmul(name+".linear_miscvaluehead", w) + write_matbias(name+".bias_miscvaluehead", b) + + write_conv(name+".conv_ownership",valuehead.conv_ownership) + + def write_model(model): + write_trunk("model",model) + write_policy_head("model.policy_head",model.policy_head) + write_value_head("model.value_head",model.value_head) + + if swa_model is not None: + logging.info("Writing SWA model") + write_model(swa_model) + else: + logging.info("Writing model") + write_model(model) + f.close() + + with open(os.path.join(export_dir,"metadata.json"),"w") as f: + train_state = other_state_dict["train_state"] + data = {} + if "global_step_samples" in train_state: + data["global_step_samples"] = train_state["global_step_samples"] + if "total_num_data_rows" in train_state: + data["total_num_data_rows"] = train_state["total_num_data_rows"] + if "running_metrics" in other_state_dict: + assert sorted(list(other_state_dict["running_metrics"].keys())) == ["sums", "weights"] + data["extra_stats"] = { + "sums": { key: value for (key,value) in other_state_dict["running_metrics"]["sums"].items() if "sopt" not in key and "lopt" not in key }, + "weights": { key: value for (key,value) in other_state_dict["running_metrics"]["weights"].items() if "sopt" not in key and "lopt" not in key }, + } + if "last_val_metrics" in other_state_dict and "sums" in other_state_dict["last_val_metrics"] and "weights" in other_state_dict["last_val_metrics"]: + data["extra_stats"]["last_val_metrics"] = { + "sums": { key: value for (key,value) in other_state_dict["last_val_metrics"]["sums"].items() if "sopt" not in key and "lopt" not in key }, + "weights": { key: value for (key,value) in other_state_dict["last_val_metrics"]["weights"].items() if "sopt" not in key and "lopt" not in key }, + } + json.dump(data,f) + + + logging.info("Exported at: ") + logging.info(str(datetime.datetime.utcnow()) + " UTC") + + sys.stdout.flush() + sys.stderr.flush() + + +if __name__ == "__main__": + main(args) diff --git a/training/scripts/dated/20241110-123407/features.py b/training/scripts/dated/20241110-123407/features.py new file mode 100644 index 0000000..1452e42 --- /dev/null +++ b/training/scripts/dated/20241110-123407/features.py @@ -0,0 +1,340 @@ +import math +import numpy as np +from typing import Callable, List + +from board import Board, Loc, Pos, Player +import modelconfigs + +class Features: + def __init__(self, config: modelconfigs.ModelConfig, pos_len: int): + self.config = config + self.pos_len = pos_len + self.version = modelconfigs.get_version(config) + self.pass_pos = self.pos_len * self.pos_len + self.bin_input_shape = [modelconfigs.get_num_bin_input_features(config), pos_len, pos_len] + self.global_input_shape = [modelconfigs.get_num_global_input_features(config)] + + def xy_to_tensor_pos(self,x,y): + return y * self.pos_len + x + def loc_to_tensor_pos(self,loc,board): + if loc == Board.PASS_LOC: + return self.pass_pos + return board.loc_y(loc) * self.pos_len + board.loc_x(loc) + + def tensor_pos_to_loc(self,pos,board): + if pos == self.pass_pos: + return None + pos_len = self.pos_len + bsize = board.size + assert(self.pos_len >= bsize) + x = pos % pos_len + y = pos // pos_len + if x < 0 or x >= bsize or y < 0 or y >= bsize: + return board.loc(-10,-10) #Return an illegal move since this is offboard + return board.loc(x,y) + + def sym_tensor_pos(self,pos,symmetry): + if pos == self.pass_pos: + return pos + pos_len = self.pos_len + x = pos % pos_len + y = pos // pos_len + if symmetry >= 4: + symmetry -= 4 + tmp = x + x = y + y = tmp + if symmetry >= 2: + symmetry -= 2 + x = pos_len-x-1 + if symmetry >= 1: + symmetry -= 1 + y = pos_len-y-1 + return y * pos_len + x + + #Calls f on each location that is part of an inescapable atari, or a group that can be put into inescapable atari + def iterLadders(self, board, f): + chainHeadsSolved = {} + copy = board.copy() + + bsize = board.size + assert(self.pos_len >= bsize) + + for y in range(bsize): + for x in range(bsize): + pos = self.xy_to_tensor_pos(x,y) + loc = board.loc(x,y) + stone = board.board[loc] + + if stone == Board.BLACK or stone == Board.WHITE: + libs = board.num_liberties(loc) + if libs == 1 or libs == 2: + head = board.group_head[loc] + if head in chainHeadsSolved: + laddered = chainHeadsSolved[head] + if laddered: + f(loc,pos,[]) + else: + #Perform search on copy so as not to mess up tracking of solved heads + if libs == 1: + workingMoves = [] + laddered = copy.searchIsLadderCaptured(loc,True) + else: + workingMoves = copy.searchIsLadderCapturedAttackerFirst2Libs(loc) + laddered = len(workingMoves) > 0 + + chainHeadsSolved[head] = laddered + if laddered: + f(loc,pos,workingMoves) + + + #Returns the new idx, which could be the same as idx if this isn't a good training row + def fill_row_features(self, board, pla, opp, boards, moves, move_idx, rules, bin_input_data, global_input_data, idx): + assert(self.version >= 10) + + bsize = board.size + assert(self.pos_len >= bsize) + assert(len(boards) > 0) + assert(board.zobrist == boards[move_idx].zobrist) + + for y in range(bsize): + for x in range(bsize): + pos = self.xy_to_tensor_pos(x,y) + bin_input_data[idx,pos,0] = 1.0 + loc = board.loc(x,y) + stone = board.board[loc] + if stone == pla: + bin_input_data[idx,pos,1] = 1.0 + elif stone == opp: + bin_input_data[idx,pos,2] = 1.0 + + if stone == pla or stone == opp: + libs = board.num_liberties(loc) + if libs == 1: + bin_input_data[idx,pos,3] = 1.0 + elif libs == 2: + bin_input_data[idx,pos,4] = 1.0 + elif libs == 3: + bin_input_data[idx,pos,5] = 1.0 + + #Python code does NOT handle superko + if board.simple_ko_point is not None: + pos = self.loc_to_tensor_pos(board.simple_ko_point,board) + bin_input_data[idx,pos,6] = 1.0 + #Python code does NOT handle ko-prohibited encore spots or anything relating to the encore + #so features 7 and 8 leave that blank + + if move_idx >= 1 and moves[move_idx-1][0] == opp: + prev1_loc = moves[move_idx-1][1] + if prev1_loc is not None and prev1_loc != Board.PASS_LOC: + pos = self.loc_to_tensor_pos(prev1_loc,board) + bin_input_data[idx,pos,9] = 1.0 + elif prev1_loc == Board.PASS_LOC: + global_input_data[idx,0] = 1.0 + + if move_idx >= 2 and moves[move_idx-2][0] == pla: + prev2_loc = moves[move_idx-2][1] + if prev2_loc is not None and prev2_loc != Board.PASS_LOC: + pos = self.loc_to_tensor_pos(prev2_loc,board) + bin_input_data[idx,pos,10] = 1.0 + elif prev2_loc == Board.PASS_LOC: + global_input_data[idx,1] = 1.0 + + if move_idx >= 3 and moves[move_idx-3][0] == opp: + prev3_loc = moves[move_idx-3][1] + if prev3_loc is not None and prev3_loc != Board.PASS_LOC: + pos = self.loc_to_tensor_pos(prev3_loc,board) + bin_input_data[idx,pos,11] = 1.0 + elif prev3_loc == Board.PASS_LOC: + global_input_data[idx,2] = 1.0 + + if move_idx >= 4 and moves[move_idx-4][0] == pla: + prev4_loc = moves[move_idx-4][1] + if prev4_loc is not None and prev4_loc != Board.PASS_LOC: + pos = self.loc_to_tensor_pos(prev4_loc,board) + bin_input_data[idx,pos,12] = 1.0 + elif prev4_loc == Board.PASS_LOC: + global_input_data[idx,3] = 1.0 + + if move_idx >= 5 and moves[move_idx-5][0] == opp: + prev5_loc = moves[move_idx-5][1] + if prev5_loc is not None and prev5_loc != Board.PASS_LOC: + pos = self.loc_to_tensor_pos(prev5_loc,board) + bin_input_data[idx,pos,13] = 1.0 + elif prev5_loc == Board.PASS_LOC: + global_input_data[idx,4] = 1.0 + + def addLadderFeature(loc,pos,workingMoves): + assert(board.board[loc] == Board.BLACK or board.board[loc] == Board.WHITE) + bin_input_data[idx,pos,14] = 1.0 + if board.board[loc] == opp and board.num_liberties(loc) > 1: + for workingMove in workingMoves: + workingPos = self.loc_to_tensor_pos(workingMove,board) + bin_input_data[idx,workingPos,17] = 1.0 + + self.iterLadders(board, addLadderFeature) + + if move_idx > 0: + prevBoard = boards[move_idx-1] + else: + prevBoard = board + def addPrevLadderFeature(loc,pos,workingMoves): + assert(prevBoard.board[loc] == Board.BLACK or prevBoard.board[loc] == Board.WHITE) + bin_input_data[idx,pos,15] = 1.0 + self.iterLadders(prevBoard, addPrevLadderFeature) + + if move_idx > 1: + prevPrevBoard = boards[move_idx-2] + else: + prevPrevBoard = prevBoard + def addPrevPrevLadderFeature(loc,pos,workingMoves): + assert(prevPrevBoard.board[loc] == Board.BLACK or prevPrevBoard.board[loc] == Board.WHITE) + bin_input_data[idx,pos,16] = 1.0 + self.iterLadders(prevPrevBoard, addPrevPrevLadderFeature) + + #Features 18,19 - area + area = [0 for i in range(board.arrsize)] + + if rules["scoringRule"] == "SCORING_AREA" and rules["taxRule"] == "TAX_NONE": + safeBigTerritories = True + nonPassAliveStones = True + unsafeBigTerritories = True + board.calculateArea(area,nonPassAliveStones,safeBigTerritories,unsafeBigTerritories,rules["multiStoneSuicideLegal"]) + else: + hasAreaFeature = False + keepTerritories = False + keepStones = False + if rules["scoringRule"] == "SCORING_AREA" and (rules["taxRule"] == "TAX_SEKI" or rules["taxRule"] == "TAX_ALL"): + hasAreaFeature = True + keepTerritories = False + keepStones = True + elif rules["scoringRule"] == "SCORING_TERRITORY" and rules["taxRule"] == "TAX_NONE": + if rules["encorePhase"] >= 2: + hasAreaFeature = True + keepTerritories = True + keepStones = False + elif rules["scoringRule"] == "SCORING_TERRITORY" and (rules["taxRule"] == "TAX_SEKI" or rules["taxRule"] == "TAX_ALL"): + if rules["encorePhase"] >= 2: + hasAreaFeature = True + keepTerritories = False + keepStones = False + else: + assert(False) + + if hasAreaFeature: + board.calculateNonDameTouchingArea( + area, + keepTerritories, + keepStones, + rules["multiStoneSuicideLegal"] + ) + + for y in range(bsize): + for x in range(bsize): + loc = board.loc(x,y) + pos = self.xy_to_tensor_pos(x,y) + + if area[loc] == pla: + bin_input_data[idx,pos,18] = 1.0 + elif area[loc] == opp: + bin_input_data[idx,pos,19] = 1.0 + + #Features 20,21 - second encore phase starting stones, we just set them to the current stones in pythong + #since we don't really have a jp rules impl + if rules["encorePhase"] >= 2: + for y in range(bsize): + for x in range(bsize): + pos = self.xy_to_tensor_pos(x,y) + loc = board.loc(x,y) + stone = board.board[loc] + if stone == pla: + bin_input_data[idx,pos,20] = 1.0 + elif stone == opp: + bin_input_data[idx,pos,21] = 1.0 + + bArea = board.size * board.size + whiteKomi = rules["whiteKomi"] + if rules["scoringRule"] == "SCORING_TERRITORY": + whiteSelfKomi = whiteKomi + board.num_non_pass_moves_made[Board.BLACK] - board.num_non_pass_moves_made[Board.WHITE] + selfKomi = (whiteSelfKomi if pla == Board.WHITE else -whiteSelfKomi) + else: + selfKomi = (whiteKomi if pla == Board.WHITE else -whiteKomi) + + if selfKomi > bArea+1: + selfKomi = bArea+1 + if selfKomi < -bArea-1: + selfKomi = -bArea-1 + global_input_data[idx,5] = selfKomi/20.0 + + if rules["koRule"] == "KO_SIMPLE": + pass + elif rules["koRule"] == "KO_POSITIONAL" or rules["koRule"] == "KO_SPIGHT": + global_input_data[idx,6] = 1.0 + global_input_data[idx,7] = 0.5 + elif rules["koRule"] == "KO_SITUATIONAL": + global_input_data[idx,6] = 1.0 + global_input_data[idx,7] = -0.5 + else: + assert(False) + + if rules["multiStoneSuicideLegal"]: + global_input_data[idx,8] = 1.0 + + if rules["scoringRule"] == "SCORING_AREA": + pass + elif rules["scoringRule"] == "SCORING_TERRITORY": + global_input_data[idx,9] = 1.0 + else: + assert(False) + + if rules["taxRule"] == "TAX_NONE": + pass + elif rules["taxRule"] == "TAX_SEKI": + global_input_data[idx,10] = 1.0 + elif rules["taxRule"] == "TAX_ALL": + global_input_data[idx,10] = 1.0 + global_input_data[idx,11] = 1.0 + else: + assert(False) + + if rules["encorePhase"] > 0: + global_input_data[idx,12] = 1.0 + if rules["encorePhase"] > 1: + global_input_data[idx,13] = 1.0 + passWouldEndPhase = rules["passWouldEndPhase"] + global_input_data[idx,14] = (1.0 if passWouldEndPhase else 0.0) + + global_input_data[idx,15] = 1.0 if rules["asymPowersOfTwo"] != 0 else 0.0 + global_input_data[idx,16] = rules["asymPowersOfTwo"] + + if "hasButton" in rules and rules["hasButton"] and Board.PASS_LOC not in [move[1] for move in moves]: + global_input_data[idx,17] = 1.0 + + if rules["scoringRule"] == "SCORING_AREA" or rules["encorePhase"] > 1: + boardAreaIsEven = (board.size % 2 == 0) + + drawableKomisAreEven = boardAreaIsEven + + if drawableKomisAreEven: + komiFloor = math.floor(selfKomi / 2.0) * 2.0 + else: + komiFloor = math.floor((selfKomi-1.0) / 2.0) * 2.0 + 1.0 + + delta = selfKomi - komiFloor + assert(delta >= -0.0001) + assert(delta <= 2.0001) + if delta < 0.0: + delta = 0.0 + if delta > 2.0: + delta = 2.0 + + if delta < 0.5: + wave = delta + elif delta < 1.5: + wave = 1.0-delta + else: + wave = delta-2.0 + + global_input_data[idx,18] = wave + + return idx+1 diff --git a/training/scripts/dated/20241110-123407/forward_model.py b/training/scripts/dated/20241110-123407/forward_model.py new file mode 100644 index 0000000..97a46df --- /dev/null +++ b/training/scripts/dated/20241110-123407/forward_model.py @@ -0,0 +1,146 @@ +#!/usr/bin/python3 +import sys +import os +import argparse +import traceback +import random +import math +import time +import logging +import json +import numpy as np +from collections import defaultdict +from typing import Dict, List + +import torch +import torch.nn +from torch.optim.swa_utils import AveragedModel + +import modelconfigs +from model_pytorch import Model +from metrics_pytorch import Metrics +import data_processing_pytorch +from load_model import load_model + +# HANDLE COMMAND AND ARGS ------------------------------------------------------------------- + +if __name__ == "__main__": + + description = """ + Test neural net on Go positions from npz files of batches from selfplay. + """ + + parser = argparse.ArgumentParser(description=description) + parser.add_argument('-npz', help='NPZ file to evaluate', required=True) + parser.add_argument('-checkpoint', help='Checkpoint to test', required=False) + parser.add_argument('-pos-len', help='Spatial length of expected training data', type=int, required=True) + parser.add_argument('-use-swa', help='Use SWA model', action="store_true", required=False) + parser.add_argument('-gpu-idx', help='GPU idx', type=int, required=False) + + args = vars(parser.parse_args()) + +def main(args): + npz_file = args["npz"] + checkpoint_file = args["checkpoint"] + pos_len = args["pos_len"] + use_swa = args["use_swa"] + gpu_idx = args["gpu_idx"] + + world_size = 1 + rank = 0 + + # SET UP LOGGING ------------------------------------------------------------- + + logging.root.handlers = [] + logging.basicConfig( + level=logging.INFO, + format="%(message)s", + handlers=[ + logging.StreamHandler(stream=sys.stdout) + ], + ) + np.set_printoptions(linewidth=150) + + # FIGURE OUT GPU ------------------------------------------------------------ + if gpu_idx is not None: + torch.cuda.set_device(gpu_idx) + logging.info("Using GPU device: " + torch.cuda.get_device_name()) + device = torch.device("cuda", gpu_idx) + elif torch.cuda.is_available(): + logging.info("Using GPU device: " + torch.cuda.get_device_name()) + device = torch.device("cuda") + else: + logging.warning("WARNING: No GPU, using CPU") + device = torch.device("cpu") + + # LOAD MODEL --------------------------------------------------------------------- + + model, swa_model, _ = load_model(checkpoint_file, use_swa, device=device, pos_len=pos_len, verbose=False) + model_config = model.config + + batch = np.load(npz_file) + + with torch.no_grad(): + model.eval() + if swa_model is not None: + swa_model.eval() + + if swa_model is not None: + model_outputs = swa_model(torch.tensor(batch["binaryInputNCHW"],device=device),torch.tensor(batch["globalInputNC"],device=device)) + else: + model_outputs = model(torch.tensor(batch["binaryInputNCHW"],device=device),torch.tensor(batch["globalInputNC"],device=device)) + + postprocessed = model.postprocess_output(model_outputs) + + results = tuple(tensor.cpu() for tensor in postprocessed[0]) + + ( + policy_logits, # N, num_policy_outputs, move + value_logits, # N, {win,loss,noresult} + td_value_logits, # N, {long, mid, short} {win,loss,noresult} + pred_td_score, # N, {long, mid, short} + ownership_pretanh, # N, 1, y, x + pred_scoring, # N, 1, y, x + futurepos_pretanh, # N, 2, y, x + seki_logits, # N, 4, y, x + pred_scoremean, # N + pred_scorestdev, # N + pred_lead, # N + pred_variance_time, # N + pred_shortterm_value_error, # N + pred_shortterm_score_error, # N + scorebelief_logits, # N, 2 * (self.pos_len*self.pos_len + EXTRA_SCORE_DISTR_RADIUS) + ) = results + + for batch_idx in range(batch["binaryInputNCHW"].shape[0]): + print(f"Batch idx {batch_idx}") + + policy = torch.softmax(policy_logits[batch_idx],dim=1).numpy() + print("Policy") + print(f"Pass {policy[0,pos_len*pos_len]}") + for y in range(pos_len): + print(" ".join(["%5.2f%%" % (100.0 * prob) for prob in policy[0,y*pos_len:(y+1)*pos_len]])) + if model_config["version"] >= 12: + print("LongOptPolicy") + print(f"Pass {policy[4,pos_len*pos_len]}") + for y in range(pos_len): + print(" ".join(["%5.2f%%" % (100.0 * prob) for prob in policy[4,y*pos_len:(y+1)*pos_len]])) + print("ShortOptPolicy") + print(f"Pass {policy[5,pos_len*pos_len]}") + for y in range(pos_len): + print(" ".join("%5.2f%%" % (100.0 * prob) for prob in policy[5,y*pos_len:(y+1)*pos_len])) + ownership = torch.tanh(ownership_pretanh[batch_idx,0]) + print("Ownership") + for y in range(pos_len): + print(" ".join("%6.2f" % (100.0 * prob) for prob in ownership[y])) + + print("Value " + " ".join("%5.2fc" % (100.0 * prob) for prob in torch.softmax(value_logits[batch_idx],dim=0).numpy())) + print("ScoreMean " + str(pred_scoremean[batch_idx].item())) + print("ScoreMeanSq " + str(pred_scorestdev[batch_idx].item() ** 2 + pred_scoremean[batch_idx].item() ** 2)) + print("Lead " + str(pred_lead[batch_idx].item())) + print("Vartime " + str(pred_variance_time[batch_idx].item())) + print("STWinLossError " + str(100.0 * math.sqrt(pred_shortterm_value_error[batch_idx].item())) + "c") + print("STScoreError " + str(math.sqrt(pred_shortterm_score_error[batch_idx].item()))) + +if __name__ == "__main__": + main(args) diff --git a/training/scripts/dated/20241110-123407/gamestate.py b/training/scripts/dated/20241110-123407/gamestate.py new file mode 100644 index 0000000..dbfec57 --- /dev/null +++ b/training/scripts/dated/20241110-123407/gamestate.py @@ -0,0 +1,314 @@ + +from typing import Dict, Any, List, Optional, TYPE_CHECKING +import math + +import numpy as np + +from board import Board +from features import Features +from sgfmetadata import SGFMetadata + +if TYPE_CHECKING: + from model_pytorch import Model + +class GameState: + RULES_TT = { + "koRule": "KO_POSITIONAL", + "scoringRule": "SCORING_AREA", + "taxRule": "TAX_NONE", + "multiStoneSuicideLegal": True, + "hasButton": False, + "encorePhase": 0, + "passWouldEndPhase": False, + "whiteKomi": 7.5, + "asymPowersOfTwo": 0.0, + } + RULES_CHINESE = { + "koRule": "KO_SIMPLE", + "scoringRule": "SCORING_AREA", + "taxRule": "TAX_NONE", + "multiStoneSuicideLegal": False, + "hasButton": False, + "encorePhase": 0, + "passWouldEndPhase": False, + "whiteKomi": 7.5, + "asymPowersOfTwo": 0.0, + } + RULES_JAPANESE = { + "koRule": "KO_SIMPLE", + "scoringRule": "SCORING_TERRITORY", + "taxRule": "TAX_SEKI", + "multiStoneSuicideLegal": False, + "hasButton": False, + "encorePhase": 0, + "passWouldEndPhase": False, + "whiteKomi": 6.5, + "asymPowersOfTwo": 0.0, + } + + + def __init__(self, board_size: int, rules: Dict[str,Any]): + self.board_size = board_size + self.board = Board(size=board_size) + self.moves = [] + self.boards = [self.board.copy()] + self.rules = rules.copy() + self.redo_stack = [] + + def play(self, pla, loc): + self.board.play(pla,loc) + self.moves.append((pla,loc)) + self.boards.append(self.board.copy()) + if len(self.redo_stack) > 0: + move, _ = self.redo_stack[-1] + if move == (pla,loc): + self.redo_stack.pop() + else: + self.redo_stack.clear() + + def can_undo(self) -> bool: + return len(self.moves) > 0 + + def undo(self): + assert self.can_undo() + move = self.moves.pop() + board = self.boards.pop() + self.board = self.boards[-1].copy() + self.redo_stack.append((move,board)) + + def can_redo(self) -> bool: + return len(self.redo_stack) > 0 + + def redo(self): + assert self.can_redo() + move, board = self.redo_stack.pop() + self.moves.append(move) + self.boards.append(board) + self.board = self.boards[-1].copy() + + def get_input_features(self, features: Features): + bin_input_data = np.zeros(shape=[1]+features.bin_input_shape, dtype=np.float32) + global_input_data = np.zeros(shape=[1]+features.global_input_shape, dtype=np.float32) + pos_len = features.pos_len + pla = self.board.pla + opp = Board.get_opp(pla) + move_idx = len(self.moves) + # fill_row_features assumes N(HW)C order but we actually use NCHW order in the model, so work with it and revert + bin_input_data = np.transpose(bin_input_data,axes=(0,2,3,1)) + bin_input_data = bin_input_data.reshape([1,pos_len*pos_len,-1]) + features.fill_row_features(self.board,pla,opp,self.boards,self.moves,move_idx,self.rules,bin_input_data,global_input_data,idx=0) + bin_input_data = bin_input_data.reshape([1,pos_len,pos_len,-1]) + bin_input_data = np.transpose(bin_input_data,axes=(0,3,1,2)) + return bin_input_data, global_input_data + + def get_model_outputs(self, model: "Model", sgfmeta: Optional[SGFMetadata] = None, extra_output_names: List[str] = []): + import torch + from model_pytorch import Model, ExtraOutputs + with torch.no_grad(): + model.eval() + features = Features(model.config, model.pos_len) + + bin_input_data, global_input_data = self.get_input_features(features) + # Currently we don't actually do any symmetries + # symmetry = 0 + # model_outputs = model(apply_symmetry(batch["binaryInputNCHW"],symmetry),batch["globalInputNC"]) + + input_meta = None + if sgfmeta is not None: + metarow = sgfmeta.get_metadata_row(nextPlayer=self.board.pla, boardArea=self.board.size*self.board.size) + input_meta = torch.tensor(metarow, dtype=torch.float32, device=model.device) + input_meta = input_meta.reshape([1,-1]) + + extra_outputs = ExtraOutputs(extra_output_names) + + model_outputs = model( + torch.tensor(bin_input_data, dtype=torch.float32, device=model.device), + torch.tensor(global_input_data, dtype=torch.float32, device=model.device), + input_meta = input_meta, + extra_outputs=extra_outputs, + ) + + available_extra_outputs = extra_outputs.available + + outputs = model.postprocess_output(model_outputs) + ( + policy_logits, # N, num_policy_outputs, move + value_logits, # N, {win,loss,noresult} + td_value_logits, # N, {long, mid, short} {win,loss,noresult} + pred_td_score, # N, {long, mid, short} + ownership_pretanh, # N, 1, y, x + pred_scoring, # N, 1, y, x + futurepos_pretanh, # N, 2, y, x + seki_logits, # N, 4, y, x + pred_scoremean, # N + pred_scorestdev, # N + pred_lead, # N + pred_variance_time, # N + pred_shortterm_value_error, # N + pred_shortterm_score_error, # N + scorebelief_logits, # N, 2 * (self.pos_len*self.pos_len + EXTRA_SCORE_DISTR_RADIUS) + ) = (x[0] for x in outputs[0]) # N = 0 + + policy0 = torch.nn.functional.softmax(policy_logits[0,:],dim=0).cpu().numpy() + policy1 = torch.nn.functional.softmax(policy_logits[1,:],dim=0).cpu().numpy() + value = torch.nn.functional.softmax(value_logits,dim=0).cpu().numpy() + td_value = torch.nn.functional.softmax(td_value_logits[0,:],dim=0).cpu().numpy() + td_value2 = torch.nn.functional.softmax(td_value_logits[1,:],dim=0).cpu().numpy() + td_value3 = torch.nn.functional.softmax(td_value_logits[2,:],dim=0).cpu().numpy() + scoremean = pred_scoremean.cpu().item() + td_score = pred_td_score.cpu().numpy() + scorestdev = pred_scorestdev.cpu().item() + lead = pred_lead.cpu().item() + vtime = pred_variance_time.cpu().item() + estv = math.sqrt(pred_shortterm_value_error.cpu().item()) + ests = math.sqrt(pred_shortterm_score_error.cpu().item()) + ownership = torch.tanh(ownership_pretanh).cpu().numpy() + scoring = pred_scoring.cpu().numpy() + futurepos = torch.tanh(futurepos_pretanh).cpu().numpy() + seki_probs = torch.nn.functional.softmax(seki_logits[0:3,:,:],dim=0).cpu().numpy() + seki = seki_probs[1] - seki_probs[2] + seki2 = torch.sigmoid(seki_logits[3,:,:]).cpu().numpy() + scorebelief = torch.nn.functional.softmax(scorebelief_logits,dim=0).cpu().numpy() + + board = self.board + + moves_and_probs0 = [] + for i in range(len(policy0)): + move = features.tensor_pos_to_loc(i,board) + if i == len(policy0)-1: + moves_and_probs0.append((Board.PASS_LOC,policy0[i])) + elif board.would_be_legal(board.pla,move): + moves_and_probs0.append((move,policy0[i])) + + moves_and_probs1 = [] + for i in range(len(policy1)): + move = features.tensor_pos_to_loc(i,board) + if i == len(policy1)-1: + moves_and_probs1.append((Board.PASS_LOC,policy1[i])) + elif board.would_be_legal(board.pla,move): + moves_and_probs1.append((move,policy1[i])) + + ownership_flat = ownership.reshape([features.pos_len * features.pos_len]) + ownership_by_loc = [] + board = self.board + for y in range(board.size): + for x in range(board.size): + loc = board.loc(x,y) + pos = features.loc_to_tensor_pos(loc,board) + if board.pla == Board.WHITE: + ownership_by_loc.append((loc,ownership_flat[pos])) + else: + ownership_by_loc.append((loc,-ownership_flat[pos])) + + scoring_flat = scoring.reshape([features.pos_len * features.pos_len]) + scoring_by_loc = [] + board = self.board + for y in range(board.size): + for x in range(board.size): + loc = board.loc(x,y) + pos = features.loc_to_tensor_pos(loc,board) + if board.pla == Board.WHITE: + scoring_by_loc.append((loc,scoring_flat[pos])) + else: + scoring_by_loc.append((loc,-scoring_flat[pos])) + + futurepos0_flat = futurepos[0,:,:].reshape([features.pos_len * features.pos_len]) + futurepos0_by_loc = [] + board = self.board + for y in range(board.size): + for x in range(board.size): + loc = board.loc(x,y) + pos = features.loc_to_tensor_pos(loc,board) + if board.pla == Board.WHITE: + futurepos0_by_loc.append((loc,futurepos0_flat[pos])) + else: + futurepos0_by_loc.append((loc,-futurepos0_flat[pos])) + + futurepos1_flat = futurepos[1,:,:].reshape([features.pos_len * features.pos_len]) + futurepos1_by_loc = [] + board = self.board + for y in range(board.size): + for x in range(board.size): + loc = board.loc(x,y) + pos = features.loc_to_tensor_pos(loc,board) + if board.pla == Board.WHITE: + futurepos1_by_loc.append((loc,futurepos1_flat[pos])) + else: + futurepos1_by_loc.append((loc,-futurepos1_flat[pos])) + + seki_flat = seki.reshape([features.pos_len * features.pos_len]) + seki_by_loc = [] + board = self.board + for y in range(board.size): + for x in range(board.size): + loc = board.loc(x,y) + pos = features.loc_to_tensor_pos(loc,board) + if board.pla == Board.WHITE: + seki_by_loc.append((loc,seki_flat[pos])) + else: + seki_by_loc.append((loc,-seki_flat[pos])) + + seki_flat2 = seki2.reshape([features.pos_len * features.pos_len]) + seki_by_loc2 = [] + board = self.board + for y in range(board.size): + for x in range(board.size): + loc = board.loc(x,y) + pos = features.loc_to_tensor_pos(loc,board) + seki_by_loc2.append((loc,seki_flat2[pos])) + + moves_and_probs = sorted(moves_and_probs0, key=lambda moveandprob: moveandprob[1], reverse=True) + # Generate a random number biased small and then find the appropriate move to make + # Interpolate from moving uniformly to choosing from the triangular distribution + alpha = 1 + beta = 1 + math.sqrt(max(0,len(self.moves)-20)) + r = np.random.beta(alpha,beta) + probsum = 0.0 + i = 0 + genmove_result = Board.PASS_LOC + while True: + (move,prob) = moves_and_probs[i] + probsum += prob + if i >= len(moves_and_probs)-1 or probsum > r: + genmove_result = move + break + i += 1 + + # Transpose attention so that both it and reverse attention are in n c (hw) format. + for name in list(extra_outputs.returned.keys()): + if name.endswith(".attention"): + extra_outputs.returned[name] = torch.transpose(extra_outputs.returned[name],1,2) + + return { + "policy0": policy0, + "policy1": policy1, + "moves_and_probs0": moves_and_probs0, + "moves_and_probs1": moves_and_probs1, + "value": value, + "td_value": td_value, + "td_value2": td_value2, + "td_value3": td_value3, + "scoremean": scoremean, + "td_score": td_score, + "scorestdev": scorestdev, + "lead": lead, + "vtime": vtime, + "estv": estv, + "ests": ests, + "ownership": ownership, + "ownership_by_loc": ownership_by_loc, + "scoring": scoring, + "scoring_by_loc": scoring_by_loc, + "futurepos": futurepos, + "futurepos0_by_loc": futurepos0_by_loc, + "futurepos1_by_loc": futurepos1_by_loc, + "seki": seki, + "seki_by_loc": seki_by_loc, + "seki2": seki2, + "seki_by_loc2": seki_by_loc2, + "scorebelief": scorebelief, + "genmove_result": genmove_result, + **{ name:activation[0].numpy() for name, activation in extra_outputs.returned.items() }, + "available_extra_outputs": available_extra_outputs, + } + diff --git a/training/scripts/dated/20241110-123407/gatekeeper.cfg b/training/scripts/dated/20241110-123407/gatekeeper.cfg new file mode 100644 index 0000000..6dbedc4 --- /dev/null +++ b/training/scripts/dated/20241110-123407/gatekeeper.cfg @@ -0,0 +1,112 @@ + +# Logs------------------------------------------------------------------------------------ + +logSearchInfo = false +logMoves = false +logGamesEvery = 10 +logToStdout = true + +# Fancy game selfplay settings-------------------------------------------------------------------- + +# startPosesProb = 0.0 # Play this proportion of games starting from SGF positions +# startPosesFromSgfDir = DIRECTORYPATH # Load SGFs from this dir +# startPosesLoadProb = 1.0 # Only load each position from each SGF with this chance (save memory) +# startPosesTurnWeightLambda = 0 # 0 = equal weight 0.01 = decrease probability by 1% per turn -0.01 = increase probability by 1% per turn. + +# Match----------------------------------------------------------------------------------- + +numGameThreads = 128 +maxMovesPerGame = 1600 +numGamesPerGating = 200 + +allowResignation = true +resignThreshold = -0.90 +resignConsecTurns = 5 + +# Disabled, since we're not using any root noise and such +# Could have a slight weirdness on rootEndingBonusPoints, but shouldn't be a big deal. +# clearBotBeforeSearch = true + +# Rules------------------------------------------------------------------------------------ + +koRules = SIMPLE,POSITIONAL,SITUATIONAL +scoringRules = AREA,TERRITORY +taxRules = NONE,NONE,SEKI,SEKI,ALL +multiStoneSuicideLegals = false,true +hasButtons = false,false,true + +bSizes = 7,8,9 +bSizeRelProbs = 1,1,8 +allowRectangleProb = 0.50 + +komiAuto = True # Automatically adjust komi to what the neural nets think are fair +#komiMean = 7.5 # Specify explicit komi +handicapProb = 0.0 # Probability of handicap game +handicapCompensateKomiProb = 1.0 # Probability of compensating komi to fair during handicap game +# numExtraBlackFixed = 3 # When playing handicap games, always use exactly this many extra black moves + +# Search limits----------------------------------------------------------------------------------- +maxVisits = 150 +numSearchThreads = 1 + +# GPU Settings------------------------------------------------------------------------------- + +nnMaxBatchSize = 128 +nnCacheSizePowerOfTwo = 21 +nnMutexPoolSizePowerOfTwo = 15 +numNNServerThreadsPerModel = 1 +nnRandomize = true + +# CUDA GPU settings-------------------------------------- +# cudaDeviceToUse = 0 #use device 0 for all server threads (numNNServerThreadsPerModel) unless otherwise specified per-model or per-thread-per-model +# cudaDeviceToUseModel0 = 3 #use device 3 for model 0 for all threads unless otherwise specified per-thread for this model +# cudaDeviceToUseModel1 = 2 #use device 2 for model 1 for all threads unless otherwise specified per-thread for this model +# cudaDeviceToUseModel0Thread0 = 3 #use device 3 for model 0, server thread 0 +# cudaDeviceToUseModel0Thread1 = 2 #use device 2 for model 0, server thread 1 + +cudaUseFP16 = auto +cudaUseNHWC = auto + +# Root move selection and biases------------------------------------------------------------------------------ + +chosenMoveTemperatureEarly = 0.5 +chosenMoveTemperatureHalflife = 19 +chosenMoveTemperature = 0.2 +chosenMoveSubtract = 0 +chosenMovePrune = 1 + +useLcbForSelection = true +lcbStdevs = 5.0 +minVisitPropForLCB = 0.15 + +# Internal params------------------------------------------------------------------------------ + +winLossUtilityFactor = 1.0 +staticScoreUtilityFactor = 0.00 +dynamicScoreUtilityFactor = 0.25 +dynamicScoreCenterZeroWeight = 0.25 +dynamicScoreCenterScale = 0.50 +noResultUtilityForWhite = 0.0 +drawEquivalentWinsForWhite = 0.5 + +rootEndingBonusPoints = 0.5 +rootPruneUselessMoves = true + +cpuctExploration = 1.1 +cpuctExplorationLog = 0.0 +fpuReductionMax = 0.2 +rootFpuReductionMax = 0.1 +valueWeightExponent = 0.5 +policyOptimism = 1.0 +rootPolicyOptimism = 0.0 + +subtreeValueBiasFactor = 0.35 +subtreeValueBiasWeightExponent = 0.8 +useNonBuggyLcb = true +useGraphSearch = true + +useUncertainty = true +uncertaintyExponent = 1.0 +uncertaintyCoeff = 0.25 + +numVirtualLossesPerThread = 1 diff --git a/training/scripts/dated/20241110-123407/genboard_common.py b/training/scripts/dated/20241110-123407/genboard_common.py new file mode 100644 index 0000000..7873988 --- /dev/null +++ b/training/scripts/dated/20241110-123407/genboard_common.py @@ -0,0 +1,129 @@ +import traceback +import json +import math +import torch +import torch.nn as nn +import torch.nn.functional as F + +class ResBlock(nn.Module): + def __init__(self,num_channels,scale_init): + super(ResBlock, self).__init__() + kernel_size = 3 + self.biasa = nn.Parameter(torch.zeros(num_channels,1,1)) + self.conva = nn.Conv2d(in_channels=num_channels, out_channels=num_channels, kernel_size=kernel_size, padding=1, bias=False) + torch.nn.init.normal_(self.conva.weight,std=math.sqrt(2.0 / num_channels / kernel_size / kernel_size)*scale_init) + self.biasb = nn.Parameter(torch.zeros(num_channels,1,1)) + self.scalb = nn.Parameter(torch.ones(num_channels,1,1)) + self.convb = nn.Conv2d(in_channels=num_channels, out_channels=num_channels, kernel_size=kernel_size, padding=1, bias=False) + torch.nn.init.zeros_(self.convb.weight) + + def forward(self, trunk): + x = F.relu(trunk+self.biasa) + x = self.conva(x) + x = F.relu(x*self.scalb+self.biasb) + x = self.convb(x) + return trunk+x + +class GPoolResBlock(nn.Module): + def __init__(self,num_channels,scale_init): + super(GPoolResBlock, self).__init__() + kernel_size = 3 + self.biasa = nn.Parameter(torch.zeros(num_channels,1,1)) + self.conva = nn.Conv2d(in_channels=num_channels, out_channels=num_channels, kernel_size=kernel_size, padding=1, bias=False) + torch.nn.init.normal_(self.conva.weight,std=math.sqrt(1.0 / num_channels / kernel_size / kernel_size)*scale_init) + self.convg = nn.Conv2d(in_channels=num_channels, out_channels=num_channels, kernel_size=kernel_size, padding=1, bias=False) + torch.nn.init.normal_(self.convg.weight,std=math.sqrt(1.0 / num_channels / kernel_size / kernel_size)*math.sqrt(scale_init)) + self.matg = nn.Parameter(torch.zeros(num_channels,num_channels)) + torch.nn.init.normal_(self.matg,std=math.sqrt(1.0 / num_channels)*math.sqrt(scale_init)) + self.biasb = nn.Parameter(torch.zeros(num_channels,1,1)) + self.scalb = nn.Parameter(torch.ones(num_channels,1,1)) + self.convb = nn.Conv2d(in_channels=num_channels, out_channels=num_channels, kernel_size=kernel_size, padding=1, bias=False) + torch.nn.init.zeros_(self.convb.weight) + + def forward(self, trunk): + x = F.relu(trunk+self.biasa) + x = self.conva(x) + g = self.convg(x) + gsize = g.size() + g = torch.sum(g,(2,3)) / (gsize[2] * gsize[3]) # nchw -> nc + g = torch.matmul(g,self.matg) + g = g.view(gsize[0],gsize[1],1,1) + x = x + g + x = F.relu(x*self.scalb+self.biasb) + x = self.convb(x) + return trunk+x + + +class Model(nn.Module): + def __init__(self, num_channels, num_blocks): + super(Model, self).__init__() + # Channel 0: Next inference point + # Channel 1: On-board + # Channel 2: Black + # Channel 3: White + # Channel 4: Unknown + # Channel 5: Turn number / 100 + # Channel 6: Noise stdev in turn number / 50 + # Channel 7: Source + + self.inference_channel = 0 + self.num_channels = num_channels + self.num_blocks = num_blocks + self.conv0 = nn.Conv2d(in_channels=8, out_channels=self.num_channels, kernel_size=3, padding=1, bias=False) + + self.blocks = nn.ModuleList([]) + self.fixup_scale_init = 1.0 / math.sqrt(self.num_blocks) + self.blocks.append(ResBlock(self.num_channels,self.fixup_scale_init)) + self.blocks.append(ResBlock(self.num_channels,self.fixup_scale_init)) + + next_is_gpool = True + for b in range(num_blocks-2): + if next_is_gpool: + self.blocks.append(GPoolResBlock(self.num_channels,self.fixup_scale_init)) + else: + self.blocks.append(ResBlock(self.num_channels,self.fixup_scale_init)) + next_is_gpool = not next_is_gpool + + assert(len(self.blocks) == self.num_blocks) + + self.endtrunk_bias_focus = nn.Parameter(torch.zeros(self.num_channels,1,1)) + self.endtrunk_bias_g = nn.Parameter(torch.zeros(self.num_channels,1,1)) + self.convg = nn.Conv2d(in_channels=self.num_channels, out_channels=self.num_channels, kernel_size=1, padding=0, bias=False) + + self.fc1 = nn.Linear(self.num_channels*2, self.num_channels) + self.fc2 = nn.Linear(self.num_channels,3) + self.convaux = nn.Conv2d(in_channels=self.num_channels, out_channels=3, kernel_size=1, padding=0, bias=True) + + def forward(self, inputs): + trunk = self.conv0(inputs) + for i in range(self.num_blocks): + trunk = self.blocks[i](trunk) + + head_focus = F.relu(trunk+self.endtrunk_bias_focus) + head_g = F.relu(trunk+self.endtrunk_bias_g) + aux = self.convaux(head_focus) + gsize = head_g.size() + + x = torch.sum(head_focus * inputs[:,self.inference_channel:self.inference_channel+1,:,:],(2,3)) + g = torch.sum(head_g,(2,3)) / (gsize[2] * gsize[3]) # nchw -> nc + + x = torch.cat((x,g),dim=1) + + x = F.relu(self.fc1(x)) + x = self.fc2(x) + return x,aux + + def save_to_file(self, filename): + state_dict = self.state_dict() + data = {} + data["num_channels"] = self.num_channels + data["num_blocks"] = self.num_blocks + data["state_dict"] = state_dict + torch.save(data, filename) + + @staticmethod + def load_from_file(filename): + data = torch.load(filename) + model = Model(data["num_channels"], data["num_blocks"]) + model.load_state_dict(data["state_dict"]) + return model diff --git a/training/scripts/dated/20241110-123407/genboard_run.py b/training/scripts/dated/20241110-123407/genboard_run.py new file mode 100644 index 0000000..6d57139 --- /dev/null +++ b/training/scripts/dated/20241110-123407/genboard_run.py @@ -0,0 +1,203 @@ +#!/usr/bin/python3 +import sys +import os +import argparse +import traceback +import logging +import json +import math +import random +import hashlib +import numpy as np +import torch +import torch.nn as nn +import torch.nn.functional as F +import torch.optim as optim +from copy import deepcopy + +import data +from board import Board, IllegalMoveError +from genboard_common import Model + +if __name__ == '__main__': + + description = """ + Generate completions of Go positions + """ + + parser = argparse.ArgumentParser(description=description) + parser.add_argument('-model', help='Model file to load', required=True) + parser.add_argument('-board', help='Board pattern using {.,*},X,O,? for empty, black, white, unknown', required=True) + parser.add_argument('-turn', help='Approx turn number to tell the net to generate for, [0,300]', required=True, type=float) + parser.add_argument('-turnstdev', help='Approx turn number randomness [0,100]', required=True, type=float) + parser.add_argument('-source', help='Tell the net to mimic positions from source, {-1,0,1}', required=True, type=int) + parser.add_argument('-verbose', help='Print various info and debug messages instead of only the board', required=False, action='store_true') + parser.add_argument('-n', help='How many batches to generate, default 1', required=False, type=int, default=1) + parser.add_argument('-batchsize', help='How many positions to generate, default 1', required=False, type=int, default=1) + args = vars(parser.parse_args()) + + modelfile = args["model"] + boardstr = args["board"] + turn = args["turn"] + turnstdev = args["turnstdev"] + source = args["source"] + verbose = args["verbose"] + numbatches = args["n"] + batchsize = args["batchsize"] + + if turn < 0 or turn > 300: + raise Exception("Turn must be in [0,300]") + if turnstdev < 0 or turnstdev > 100: + raise Exception("Turn must be in [0,100]") + if source != -1 and source != 0 and source != 1: + raise Exception("Source must be in {-1,0,1}") + if numbatches < 0: + raise Exception("Num batches must be nonnegative") + if batchsize < 1: + raise Exception("Batchsize must be positive") + + cpudevice = torch.device("cpu") + if torch.cuda.is_available(): + if verbose: + print("CUDA is available, using it",flush=True) + gpudevice = torch.device("cuda:0") + else: + gpudevice = cpudevice + model = Model.load_from_file(modelfile).to(gpudevice) + + size = 19 + boardbase = [["." for x in range(size)] for y in range(size)] + boardbase[3][3] = "," + boardbase[9][3] = "," + boardbase[15][3] = "," + boardbase[3][9] = "," + boardbase[9][9] = "," + boardbase[15][9] = "," + boardbase[3][15] = "," + boardbase[9][15] = "," + boardbase[15][15] = "," + + num_channels = 8 + inputsbase = torch.zeros((1,num_channels,size,size)) + + inference_point_channel = 0 + black_channel = 2 + white_channel = 3 + unknown_channel = 4 + + # Channel 1: On-board + inputsbase[:,1,:,:].fill_(1.0) + + def fail_if_idx_too_large(idx): + if idx >= size * size: + raise Exception("Provided board is larger than 19x19") + + idx = 0 + for c in boardstr: + y = idx // 19 + x = idx % 19 + if c == "." or c == "*" or c == ",": + fail_if_idx_too_large(idx) + elif c == "X" or c == "x" or c == "B" or c == "b": + fail_if_idx_too_large(idx) + boardbase[y][x] = "X" + inputsbase[0,black_channel,y,x] = 1.0 + elif c == "O" or c == "o" or c == "W" or c == "w": + fail_if_idx_too_large(idx) + boardbase[y][x] = "O" + inputsbase[0,white_channel,y,x] = 1.0 + elif c == "?": + fail_if_idx_too_large(idx) + inputsbase[0,unknown_channel,y,x] = 1.0 + else: + # Ignore this char, counteract the += 1 at the end + idx -= 1 + idx += 1 + + # Channel 5: Turn number / 100 + inputsbase[:,5,:,:].fill_(turn / 100.0) + # Channel 6: Noise stdev in turn number / 50 + inputsbase[:,6,:,:].fill_(turnstdev / 50.0) + # Channel 7: Source + inputsbase[:,7,:,:].fill_(float(source)) + + rand = random.Random(os.urandom(32) + hashlib.md5(boardstr.encode()).hexdigest().encode()) + + with torch.no_grad(): + + for i in range(numbatches): + + flipx = rand.random() < 0.5 + flipy = rand.random() < 0.5 + swapxy = rand.random() < 0.5 + + flipx2 = rand.random() < 0.5 + flipy2 = rand.random() < 0.5 + swapxy2 = rand.random() < 0.5 + + def query_model(inputs): + inputstransformed = inputs.detach().clone() + if flipx: + if flipy: + inputstransformed = torch.flip(inputstransformed,[2,3]) + else: + inputstransformed = torch.flip(inputstransformed,[2]) + else: + if flipx: + inputstransformed = torch.flip(inputstransformed,[3]) + else: + pass + if swapxy: + inputstransformed = torch.transpose(inputstransformed,2,3) + + preds, auxpreds = model(inputstransformed.to(gpudevice)) + preds = F.softmax(preds,dim=1) + assert(len(preds.size()) == 2) + assert(preds.size()[0] == batchsize) + assert(preds.size()[1] == 3) + choices = [] + for b in range(batchsize): + weights = [preds[b,0],preds[b,1],preds[b,2]] + choice = rand.choices([0,1,2],weights=weights)[0] + choices.append(choice) + return choices + + inputs = inputsbase.expand([batchsize,-1,-1,-1]).detach().clone() + boards = [ deepcopy(boardbase) for b in range(batchsize) ] + + for y in range(size): + for x in range(size): + sx = x + sy = y + if flipx2: + sx = size - sx - 1 + if flipy2: + sy = size - sy - 1 + if swapxy2: + tmp = sx + sx = sy + sy = tmp + + if inputs[0,unknown_channel,sy,sx] == 1.0: + for b in range(batchsize): + inputs[b,unknown_channel,sy,sx] = 0.0 + inputs[b,inference_point_channel,sy,sx] = 1.0 + choices = query_model(inputs) + for b in range(batchsize): + inputs[b,inference_point_channel,sy,sx] = 0.0 + + choice = choices[b] + if choice == 0: + pass + elif choice == 1: + inputs[b,black_channel,sy,sx] = 1.0 + boards[b][sy][sx] = "X" + elif choice == 2: + inputs[b,white_channel,sy,sx] = 1.0 + boards[b][sy][sx] = "O" + + for b in range(batchsize): + s = "\n".join([" ".join(row) for row in boards[b]]) + s += "\n" + print(s) + sys.stdout.flush() diff --git a/training/scripts/dated/20241110-123407/genboard_train.py b/training/scripts/dated/20241110-123407/genboard_train.py new file mode 100644 index 0000000..2799d2f --- /dev/null +++ b/training/scripts/dated/20241110-123407/genboard_train.py @@ -0,0 +1,492 @@ +#!/usr/bin/python3 +import sys +import os +import argparse +import traceback +import logging +import json +import math +import random +import hashlib +import numpy as np +import torch +import torch.nn as nn +import torch.nn.functional as F +import torch.optim as optim + +import data +from board import Board, IllegalMoveError +from genboard_common import Model + +class ShuffledDataset(torch.utils.data.IterableDataset): + def __init__(self, dataset, shuffle_buffer_size): + super().__init__() + self.dataset = dataset + self.shuffle_buffer_size = shuffle_buffer_size + + def __iter__(self): + worker_info = torch.utils.data.get_worker_info() + if worker_info is None: + rand = random.Random(os.urandom(32)) + else: + rand = random.Random(os.urandom(32)+ "#ShuffledDataset#".encode() + str(worker_info.id).encode()) + + shuffle_buffer = [] + try: + it = iter(self.dataset) + while len(shuffle_buffer) < self.shuffle_buffer_size: + item = next(it) + if isinstance(item, Exception): + yield item + else: + shuffle_buffer.append(item) + except StopIteration: + self.shuffle_buffer_size = len(shuffle_buffer) + + print("Initial shuffle buffer filled", flush=True) + rand.shuffle(shuffle_buffer) + try: + while True: + try: + item = next(it) + if isinstance(item, Exception): + yield item + else: + idx = rand.randint(0, self.shuffle_buffer_size-1) + old_item = shuffle_buffer[idx] + shuffle_buffer[idx] = item + yield old_item + except StopIteration: + break + while len(shuffle_buffer) > 0: + yield shuffle_buffer.pop() + except GeneratorExit: + pass + +def rand_triangular(rand,maxvalue): + r = (maxvalue+1) * (1.0 - math.sqrt(rand.random())) + r = int(math.floor(r)) + if r <= 0: + return 0 + if r >= maxvalue: + return maxvalue + return r + +def random_subinterval(rand,size): + # Anchor rectangles near the edge more often + if rand.random() < 0.5: + x0 = rand_triangular(rand,size)-1 + x1 = rand_triangular(rand,size)-1 + else: + x0 = rand.randint(0,size-1) + x1 = rand.randint(0,size-1) + + if rand.random() < 0.5: + x0 = size - x0 - 1 + x1 = size - x1 - 1 + + if x0 > x1: + return (x1,x0) + return (x0,x1) + + +class SgfDataset(torch.utils.data.IterableDataset): + def __init__(self, files, max_turn, break_prob_per_turn, sample_prob, endless): + self.files = files + self.max_turn = max_turn + self.break_prob_per_turn = break_prob_per_turn + self.sample_prob = sample_prob + self.endless = endless + + def __iter__(self): + worker_info = torch.utils.data.get_worker_info() + if worker_info is None: + rand = random.Random(os.urandom(32)) + else: + rand = random.Random(os.urandom(32)+ "#SgfDataset#".encode() + str(worker_info.id).encode()) + + files = self.files + cpudevice = torch.device("cpu") + + try: + while True: + rand.shuffle(files) + file_count = 0 + error_count = 0 + print("Iterator beginning reading of files %d / %d" % (file_count, len(files)), flush=True) + for filename in files: + try: + (metadata,setup,moves,rules) = data.load_sgf_moves_exn(filename) + except Exception as e: + error_count += 1 + continue + # Only even 19x19 games! + if metadata.size != 19 or len(setup) != 0 or (metadata.handicap is not None and metadata.handicap != 0): + continue + board = Board(size=metadata.size) + turn_number = 0 + for (pla,loc) in moves: + + if rand.random() < self.sample_prob: + inputs = torch.zeros((8,metadata.size,metadata.size),dtype=torch.float32,device=cpudevice) + result = torch.zeros((3,),dtype=torch.float32,device=cpudevice) + aux = torch.zeros((3,metadata.size,metadata.size),dtype=torch.float32,device=cpudevice) + + (alwaysknownxmin,alwaysknownxmax) = random_subinterval(rand,metadata.size) + (alwaysknownymin,alwaysknownymax) = random_subinterval(rand,metadata.size) + + if alwaysknownxmin <= 0 and alwaysknownxmax >= metadata.size-1 and alwaysknownymin <= 0 and alwaysknownymax >= metadata.size-1: + pass + else: + # Channel 1: On-board + inputs[1,:,:].fill_(1.0) + + num_always_known_poses = 0 + if alwaysknownxmax < 0 or alwaysknownxmin >= metadata.size or alwaysknownymax < 0 or alwaysknownymin >= metadata.size: + num_always_known_poses = 0 + else: + num_always_known_poses = ( + ( min(alwaysknownxmax, metadata.size-1) - max(alwaysknownxmin, 0) + 1) * + ( min(alwaysknownymax, metadata.size-1) - max(alwaysknownymin, 0) + 1) + ) + num_not_always_known_poses = metadata.size * metadata.size - num_always_known_poses + inferenceidx = rand.randint(0,num_not_always_known_poses-1) + + flipx = rand.random() < 0.5 + flipy = rand.random() < 0.5 + swapxy = rand.random() < 0.5 + + idx = 0 + for y in range(metadata.size): + for x in range(metadata.size): + pos = y * metadata.size + x + always_known = (x >= alwaysknownxmin and x <= alwaysknownxmax and y >= alwaysknownymin and y <= alwaysknownymax) + + sx = x + sy = y + if flipx: + sx = metadata.size - sx - 1 + if flipy: + sy = metadata.size - sy - 1 + if swapxy: + tmp = sx + sx = sy + sy = tmp + stone = board.board[board.loc(sx,sy)] + + # Channel 4: Unknown + if idx > inferenceidx and not always_known: + inputs[4,y,x] = 1.0 + # Channel 0: Next inference point + elif idx == inferenceidx and not always_known: + inputs[0,y,x] = 1.0 + result + if stone == Board.BLACK: + result[1] = 1.0 + elif stone == Board.WHITE: + result[2] = 1.0 + else: + result[0] = 1.0 + else: + # Channel 2: Black + if stone == Board.BLACK: + inputs[2,y,x] = 1.0 + # Channel 3: White + elif stone == Board.WHITE: + inputs[3,y,x] = 1.0 + + if stone == Board.BLACK: + aux[1,y,x] = 1.0 + elif stone == Board.WHITE: + aux[2,y,x] = 1.0 + else: + aux[0,y,x] = 1.0 + + if not always_known: + idx += 1 + + assert(idx == num_not_always_known_poses) + + if rand.random() < 0.3: + turn_noise_stdev = 0.0 + reported_turn = turn_number + else: + turn_noise_stdev = (rand.random() ** 2.0) * 100 + reported_turn = turn_number + rand.normalvariate(0.0,turn_noise_stdev) + + # Channel 5: Turn number / 100 + inputs[5,:,:].fill_(reported_turn / 100.0) + # Channel 6: Noise stdev in turn number / 50 + inputs[6,:,:].fill_(turn_noise_stdev / 50.0) + # Channel 7: Source + is_kgs = ("/kgs" in filename) or ("\\KGS" in filename) or ("/KGS" in filename) or ("\\KGS" in filename) + is_fox = ("/fox" in filename) or ("\\fox" in filename) or ("/FOX" in filename) or ("\\FOX" in filename) + if is_kgs: + inputs[7,:,:].fill_(1.0) + elif is_fox: + inputs[7,:,:].fill_(-1.0) + + if rand.random() < 0.5: + if rand.random() < 0.5: + inputs = torch.flip(inputs,[1,2]) + aux = torch.flip(aux,[1,2]) + else: + inputs = torch.flip(inputs,[1]) + aux = torch.flip(aux,[1]) + else: + if rand.random() < 0.5: + inputs = torch.flip(inputs,[2]) + aux = torch.flip(aux,[2]) + else: + pass + + if rand.random() < 0.5: + inputs = torch.transpose(inputs,1,2) + aux = torch.transpose(aux,1,2) + + yield (inputs,result,aux) + + try: + board.play(pla,loc) + except IllegalMoveError as e: + # On illegal move in the SGF, don't attempt to recover, just move on to new game + print("Illegal move, skipping file " + filename + ":" + str(e), flush=True) + break + turn_number += 1 + if turn_number > self.max_turn: + break + if rand.random() < self.break_prob_per_turn: + break + + file_count += 1 + if file_count % 200 == 0: + print("Read through file %d / %d (error count %d)" % (file_count, len(files), error_count), flush=True) + + if not self.endless: + break + + except GeneratorExit: + pass + except Exception as e: + print("EXCEPTION IN GENERATOR: " + str(e)) + traceback.print_exc() + print("---",flush=True) + yield e + + +def save_json(data,filename): + with open(filename,"w") as f: + json.dump(data,f) + f.flush() + os.fsync(f.fileno()) + +def load_json(filename): + with open(filename) as f: + data = json.load(f) + return data + + +if __name__ == '__main__': + + description = """ + Train net to predict Go positions one stone at a time + """ + + parser = argparse.ArgumentParser(description=description) + parser.add_argument('-traindir', help='Dir to write to for recording training results', required=True) + parser.add_argument('-datadirs', help='Directory with sgfs', required=True) + parser.add_argument('-testprop', help='Proportion of data for test', type=float, required=True) + parser.add_argument('-lr-scale', help='LR multiplier', type=float, required=False) + parser.add_argument('-channels', help='Channels', type=int, required=True) + parser.add_argument('-blocks', help='Blocks', type=int, required=True) + parser.add_argument('-grad-clip-scale', help='Gradient clip multiplier', type=float, required=False) + parser.add_argument('-num-data-workers', help='Number of processes for data loading', type=int, required=False) + args = vars(parser.parse_args()) + + traindir = args["traindir"] + datadirs = args["datadirs"] + testprop = args["testprop"] + lr_scale = args["lr_scale"] + num_channels = args["channels"] + num_blocks = args["blocks"] + grad_clip_scale = args["grad_clip_scale"] + num_data_workers = args["num_data_workers"] + logfilemode = "a" + + if lr_scale is None: + lr_scale = 1.0 + if grad_clip_scale is None: + grad_clip_scale = 1.0 + + if num_data_workers is None: + num_data_workers = 0 + + if not os.path.exists(traindir): + os.mkdir(traindir) + + bareformatter = logging.Formatter("%(asctime)s %(message)s") + fh = logging.FileHandler(os.path.join(traindir,"train.log"), mode=logfilemode) + fh.setFormatter(bareformatter) + stdouthandler = logging.StreamHandler(sys.stdout) + stdouthandler.setFormatter(bareformatter) + trainlogger = logging.getLogger("trainlogger") + trainlogger.setLevel(logging.INFO) + trainlogger.addHandler(fh) + trainlogger.addHandler(stdouthandler) + trainlogger.propagate=False + np.set_printoptions(linewidth=150) + def trainlog(s): + trainlogger.info(s) + sys.stdout.flush() + + shuffle_buffer_size = 100000 + + files_found = 0 + trainfiles = [] + testfiles = [] + for datadir in datadirs.split(","): + for parent, subdirs, files in os.walk(datadir): + for name in files: + if name.endswith(".sgf"): + files_found += 1 + if files_found % 10000 == 0: + trainlog("Found %d sgfs..." % files_found) + r = float.fromhex("0."+hashlib.md5(os.path.join(parent,name).encode()).hexdigest()[:16]) + if r < testprop: + testfiles.append(os.path.join(parent,name)) + else: + trainfiles.append(os.path.join(parent,name)) + + trainlog("Found %d training sgfs" % len(trainfiles)) + trainlog("Found %d testing sgfs" % len(testfiles)) + + max_turn = 300 + break_prob_per_turn = 0.01 + + traindataset = ShuffledDataset(SgfDataset(trainfiles,max_turn,break_prob_per_turn,sample_prob=0.5,endless=True),shuffle_buffer_size) + testdataset = SgfDataset(testfiles,max_turn,break_prob_per_turn,sample_prob=0.2,endless=True) + + batch_size = 128 + trainloader = torch.utils.data.DataLoader(traindataset, batch_size=batch_size, shuffle=False, num_workers=num_data_workers, drop_last=True) + testloader = torch.utils.data.DataLoader(testdataset, batch_size=batch_size, shuffle=False, num_workers=num_data_workers, drop_last=True) + + trainlog("Made data loaders") + + samples_per_epoch = 400000 + samples_per_test = 25600 + batches_per_epoch = samples_per_epoch // batch_size + batches_per_test = samples_per_test // batch_size + + def lossfunc(inputs, results, preds, aux, auxpreds): + assert(preds.size()[1] == 3) + assert(auxpreds.size()[1] == 3) + main_loss = -torch.sum(results * F.log_softmax(preds,dim=1)) + aux_loss = -torch.sum(aux * F.log_softmax(auxpreds,dim=1) * inputs[:,4:5,:,:] / torch.sum(inputs[:,1:2,:,:], dim=[2,3], keepdim=True)) * 0.3 + return main_loss, aux_loss + + cpudevice = torch.device("cpu") + if torch.cuda.is_available(): + trainlog("CUDA is available, using it") + gpudevice = torch.device("cuda:0") + else: + gpudevice = cpudevice + + modelpath = os.path.join(traindir,"model.data") + optimpath = os.path.join(traindir,"optim.data") + traindatapath = os.path.join(traindir,"traindata.json") + if os.path.exists(modelpath): + trainlog("Loading preexisting model!") + model = Model.load_from_file(modelpath).to(gpudevice) + if model.num_channels != num_channels: + raise Exception("Number of channels in model is %d but command line arg was %d" % (model.num_channels,num_channels)) + if model.num_blocks != num_blocks: + raise Exception("Number of blocks in model is %d but command line arg was %d" % (model.num_blocks,num_blocks)) + optimizer = optim.SGD(model.parameters(), lr=0.00001*lr_scale, momentum=0.9) + optimizer.load_state_dict(torch.load(optimpath)) + traindata = load_json(traindatapath) + else: + model = Model(num_channels=num_channels, num_blocks=num_blocks).to(gpudevice) + optimizer = optim.SGD(model.parameters(), lr=0.00001*lr_scale, momentum=0.9) + traindata = {"samples_so_far":0, "batches_so_far":0} + + trainlog("Saving!") + model.save_to_file(modelpath) + torch.save(optimizer.state_dict(), optimpath) + save_json(traindata,traindatapath) + + grad_clip_max = 400 * grad_clip_scale + #Loosen gradient clipping as we shift to smaller learning rates + grad_clip_max = grad_clip_max / math.sqrt(lr_scale) + + running_batch_count = 0 + running_main_loss = 0.0 + running_aux_loss = 0.0 + running_gnorm = 0.0 + running_ewms_exgnorm = 0.0 + print_every_batches = 100 + trainiter = iter(trainloader) + testiter = iter(testloader) + while True: + for i in range(batches_per_epoch): + inputs, results, auxs = next(trainiter) + inputs = inputs.to(gpudevice) + results = results.to(gpudevice) + auxs = auxs.to(gpudevice) + + optimizer.zero_grad() + + preds, auxpreds = model(inputs) + main_loss,aux_loss = lossfunc(inputs, results, preds, auxs, auxpreds) + loss = main_loss + aux_loss + loss.backward() + gnorm = torch.nn.utils.clip_grad_norm_(model.parameters(), grad_clip_max) + optimizer.step() + + traindata["samples_so_far"] += batch_size + traindata["batches_so_far"] += 1 + + running_batch_count += 1 + running_main_loss += main_loss.item() + running_aux_loss += aux_loss.item() + running_gnorm += gnorm + running_ewms_exgnorm += max(0.0, gnorm - grad_clip_max) + if running_batch_count >= print_every_batches: + trainlog("TRAIN samples: %d, batches: %d, main loss: %.5f, aux loss: %.5f, gnorm: %.2f, ewms_exgnorm: %.3g" % ( + traindata["samples_so_far"], + traindata["batches_so_far"], + running_main_loss / (running_batch_count * batch_size), + running_aux_loss / (running_batch_count * batch_size), + running_gnorm / (running_batch_count), + running_ewms_exgnorm / (running_batch_count), + )) + running_batch_count = 0 + running_main_loss = 0.0 + running_aux_loss = 0.0 + running_gnorm = 0.0 + running_ewms_exgnorm *= 0.5 + + trainlog("Saving!") + model.save_to_file(modelpath) + torch.save(optimizer.state_dict(), optimpath) + save_json(traindata,traindatapath) + + trainlog("Testing!") + test_samples = 0 + test_main_loss = 0.0 + test_aux_loss = 0.0 + with torch.no_grad(): + for i in range(batches_per_test): + inputs, results, auxs = next(testiter) + inputs = inputs.to(gpudevice) + results = results.to(gpudevice) + auxs = auxs.to(gpudevice) + + preds, auxpreds = model(inputs) + main_loss, aux_loss = lossfunc(inputs, results, preds, auxs, auxpreds) + test_samples += batch_size + test_main_loss += main_loss.item() + test_aux_loss += aux_loss.item() + trainlog("TEST samples %d, main loss: %.5f, aux loss %.5f" % (test_samples, test_main_loss / test_samples, test_aux_loss / test_samples)) + + +trainlog('Finished Training') diff --git a/training/scripts/dated/20241110-123407/genclumpyposes.py b/training/scripts/dated/20241110-123407/genclumpyposes.py new file mode 100644 index 0000000..c6aa590 --- /dev/null +++ b/training/scripts/dated/20241110-123407/genclumpyposes.py @@ -0,0 +1,705 @@ +import json +import random +import argparse +import os +import sgfmill +import math +import sys +from dataclasses import dataclass +from collections import defaultdict + +from PIL import Image, ImageDraw + +from board import Board + +# . stone or rest of board +# e empty +# s stone +# c critical +eyepatterns = [ + (50, [ + "s", + ]), + (20, [ + "e", + ]), + (10, [ + "ee", + ]), + (5, [ + "ece", + ]), + (5, [ + "ec", + ".e", + ]), + (1, [ + "ecce", + ]), + (1, [ + "ec.", + ".ce", + ]), + (1, [ + "ecc", + "..e", + ]), + (2, [ + "ee", + "ee", + ]), + (3, [ + "ee.", + "ece", + ]), + (1, [ + ".e.", + "ece", + ".e.", + ]), + (1, [ + "eccce", + ]), + (1, [ + "eccc", + "...e" + ]), + (1.5, [ + ".ee", + "ece", + ".e.", + ]), + (1, [ + "ece", + "ece", + ".e.", + ]), + (1, [ + ".ee", + "ccc", + "ee.", + ]), + (1, [ + "ece", + "ece", + ]), +] + +def randint_exponential(scale): + r = 0 + while r <= 0: + r = random.random() + return int(math.floor(-math.log(r) * scale)) + +@dataclass +class GroupInfo: + has_eye: bool = False + pla: int = 0 + size: int = 0 + +def gen(xsize,ysize): + stones = [ [ 0 for _ in range(xsize) ] for _ in range(ysize) ] + owned = [ [ 0 for _ in range(xsize) ] for _ in range(ysize) ] + kinds = [ [ '.' for _ in range(xsize) ] for _ in range(ysize) ] + patternids = [ [ 0 for _ in range(xsize) ] for _ in range(ysize) ] + + next_pattern_id = 1 + def place_pattern(pla): + nonlocal next_pattern_id + pattern = random.choices( + [pattern for (weight,pattern) in eyepatterns], + weights=[weight for (weight,pattern) in eyepatterns], + k=1 + )[0] + pattern = [ [ c for c in row ] for row in pattern ] + if random.random() >= 0.5: + pattern = list(zip(*pattern)) + if random.random() >= 0.5: + pattern = list(list(reversed(row)) for row in pattern) + if random.random() >= 0.5: + pattern = list(reversed(pattern)) + + pysize = len(pattern) + pxsize = len(pattern[0]) + + # Generate too large and clamp to border, to make borders more likely + yoffset = random.randint(-1,ysize-pysize+1) + xoffset = random.randint(-1,xsize-pxsize+1) + yoffset = min(yoffset,ysize-pysize) + xoffset = min(xoffset,xsize-pxsize) + yoffset = max(yoffset,0) + xoffset = max(xoffset,0) + + numcs = sum(kind == 'c' for row in pattern for kind in row) + for dy in range(pysize): + for dx in range(pxsize): + kind = pattern[dy][dx] + if kind == 'c': + cpla = 0 + if numcs == 1: + r = random.random() + if r < 0.7: + cpla = 3-pla + elif r < 0.9: + cpla = 0 + else: + cpla = pla + elif numcs == 2: + r = random.random() + if r < 0.3: + cpla = 3-pla + elif r < 0.95: + cpla = 0 + else: + cpla = pla + kinds[yoffset+dy][xoffset+dx] = 'e' + owned[yoffset+dy][xoffset+dx] = pla + stones[yoffset+dy][xoffset+dx] = cpla + patternids[yoffset+dy][xoffset+dx] = next_pattern_id + else: + kinds[yoffset+dy][xoffset+dx] = kind + owned[yoffset+dy][xoffset+dx] = pla + patternids[yoffset+dy][xoffset+dx] = next_pattern_id + + next_pattern_id += 1 + + num_b_patterns = 1 + num_w_patterns = 1 + if random.random() < 0.5: + place_pattern(1) + place_pattern(2) + else: + place_pattern(2) + place_pattern(1) + + num_extra_patterns = 0 + num_extra_patterns = randint_exponential(10) + randint_exponential(3) + for _ in range(num_extra_patterns): + if random.random() < num_b_patterns / (num_b_patterns + num_w_patterns): + num_w_patterns += 1 + place_pattern(2) + else: + num_b_patterns += 1 + place_pattern(1) + + def filleyewall(y,x,pla): + if owned[y][x] != 0 and owned[y][x] != pla: + stones[y][x] = random.randint(1,2) + owned[y][x] = 3 + kinds[y][x] = 'x' + else: + owned[y][x] = pla + stones[y][x] = pla + + # Fill in single stones + for y in range(ysize): + for x in range(xsize): + if kinds[y][x] == 's': + stones[y][x] = owned[y][x] + + # Fill in sides + for y in range(ysize): + for x in range(xsize-1): + if kinds[y][x] == 'e' and kinds[y][x+1] == '.': + filleyewall(y,x+1,owned[y][x]) + elif kinds[y][x+1] == 'e' and kinds[y][x] == '.': + filleyewall(y,x,owned[y][x+1]) + for x in range(xsize): + for y in range(ysize-1): + if kinds[y][x] == 'e' and kinds[y+1][x] == '.': + filleyewall(y+1,x,owned[y][x]) + elif kinds[y+1][x] == 'e' and kinds[y][x] == '.': + filleyewall(y,x,owned[y+1][x]) + + # Fill in corners + corneroppprob = random.random() * 0.24 + 0.18 + corneropps_by_patternid = defaultdict(list) + for y in range(ysize-1): + for x in range(xsize-1): + ecount = ( + int(kinds[y][x] == 'e') + + int(kinds[y][x+1] == 'e') + + int(kinds[y+1][x] == 'e') + + int(kinds[y+1][x+1] == 'e') + ) + ccount = ( + int(kinds[y][x] == 'c') + + int(kinds[y][x+1] == 'c') + + int(kinds[y+1][x] == 'c') + + int(kinds[y+1][x+1] == 'c') + ) + dotcount = ( + int(kinds[y][x] == '.') + + int(kinds[y][x+1] == '.') + + int(kinds[y+1][x] == '.') + + int(kinds[y+1][x+1] == '.') + ) + scount = ( + int(kinds[y][x] == 's') + + int(kinds[y][x+1] == 's') + + int(kinds[y+1][x] == 's') + + int(kinds[y+1][x+1] == 's') + ) + if ecount + ccount == 1 and dotcount + scount == 3: + if kinds[y][x] == 'e': + cornerx = x+1 + cornery = y+1 + cornerpla = owned[y][x] + patternid = patternids[y][x] + elif kinds[y+1][x] == 'e': + cornerx = x+1 + cornery = y + cornerpla = owned[y+1][x] + patternid = patternids[y+1][x] + elif kinds[y][x+1] == 'e': + cornerx = x + cornery = y+1 + cornerpla = owned[y][x+1] + patternid = patternids[y][x+1] + elif kinds[y+1][x+1] == 'e': + cornerx = x + cornery = y + cornerpla = owned[y+1][x+1] + patternid = patternids[y+1][x+1] + else: + assert False + + if random.random() < corneroppprob: + corneropps_by_patternid[patternid].append((cornerx,cornery)) + filleyewall(cornery,cornerx,3-cornerpla) + elif random.random() < 0.60: + pass + else: + filleyewall(cornery,cornerx,cornerpla) + + info_by_label = {} + owned_labels = [ [ 0 for _ in range(xsize) ] for _ in range(ysize) ] + def label_owned_region(label, pla, x, y): + if owned_labels[y][x] == 0 and owned[y][x] == pla: + if stones[y][x] == 0: + info_by_label[label].has_eye = True + owned_labels[y][x] = label + info_by_label[label].size += 1 + if x > 0: + label_owned_region(label, pla, x-1, y) + if x < xsize-1: + label_owned_region(label, pla, x+1, y) + if y > 0: + label_owned_region(label, pla, x, y-1) + if y < ysize-1: + label_owned_region(label, pla, x, y+1) + + next_owned_label = 1 + for y in range(ysize): + for x in range(xsize): + if owned_labels[y][x] == 0 and owned[y][x] != 0: + info_by_label[next_owned_label] = GroupInfo() + info_by_label[next_owned_label].pla = owned[y][x] + label_owned_region(next_owned_label, owned[y][x], x, y) + next_owned_label += 1 + + # print(corneropps_by_patternid) + + def merge(label1, label2): + assert info_by_label[label1].pla == info_by_label[label2].pla + info_by_label[label1].has_eye = (info_by_label[label1].has_eye or info_by_label[label2].has_eye) + info_by_label[label1].size = (info_by_label[label1].size + info_by_label[label2].size) + for y in range(ysize): + for x in range(xsize): + if owned_labels[y][x] == label2: + owned_labels[y][x] = label1 + del info_by_label[label2] + + num_merges = random.random() * 0.25 * len(info_by_label) + num_merges = int(num_merges) + (1 if random.random() < (num_merges - int(num_merges)) else 0) + for _ in range(num_merges): + keys = list(info_by_label.keys()) + key1 = random.choice(keys) + otherkeys = [key for key in keys if info_by_label[key].pla == info_by_label[key1].pla and key != key1] + if len(otherkeys) > 0: + key2 = random.choice(otherkeys) + merge(key1,key2) + + for corneropps in corneropps_by_patternid.values(): + if random.random() < 0.7: + for (x1,y1) in corneropps: + for (x2,y2) in corneropps: + label1 = owned_labels[y1][x1] + label2 = owned_labels[y2][x2] + if label1 != label2: + if info_by_label[label1].pla == info_by_label[label2].pla: + merge(label1,label2) + + def adj(x,y): + if x > 0: + yield (x-1,y) + if x < xsize-1: + yield (x+1,y) + if y > 0: + yield (x,y-1) + if y < ysize-1: + yield (x,y+1) + def diag_reachable(x,y): + if x > 0: + if y > 0: + if owned[y][x-1] == 0 or owned[y-1][x] == 0: + yield (x-1,y-1) + if y < ysize-1: + if owned[y][x-1] == 0 or owned[y+1][x] == 0: + yield (x-1,y+1) + if x < xsize-1: + if y > 0: + if owned[y][x+1] == 0 or owned[y-1][x] == 0: + yield (x+1,y-1) + if y < ysize-1: + if owned[y][x+1] == 0 or owned[y+1][x] == 0: + yield (x+1,y+1) + + # Is this a location with 2 adjacent opponents and 1 adjacent player that + # is NOT the given label + def is_crossroads(x,y,pla,label): + if owned[y][x] != 0: + return False + pcount = 0 + ocount = 0 + opp = 3-pla + for (ax,ay) in adj(x,y): + if owned[ay][ax] == pla and owned_labels[ay][ax] != label: + pcount += 1 + elif owned[ay][ax] == opp: + ocount += 1 + if pcount == 1 and ocount == 2: + return True + return False + + black_grow_prob = sum(random.random() for i in range(8)) / 8 + def grow(allow_diag_blocker): + pla = 1 if random.random() < black_grow_prob else 2 + growspots = [] + for y in range(ysize): + for x in range(xsize): + if owned[y][x] == 0: + adjacent_labels = [] + blocker_labels = [] + for (ax,ay) in adj(x,y): + if owned[ay][ax] == pla: + adjacent_labels.append(owned_labels[ay][ax]) + for (ax,ay) in diag_reachable(x,y): + if owned[ay][ax] == pla: + blocker_labels.append(owned_labels[ay][ax]) + + adjacent_labels = list(set(adjacent_labels)) + blocker_labels = list(set(blocker_labels).difference(set(adjacent_labels))) + if len(adjacent_labels) == 1 and (allow_diag_blocker or len(blocker_labels) == 0): + label = adjacent_labels[0] + has_crossroads = False + for (ax,ay) in adj(x,y): + if is_crossroads(ax,ay,pla,label): + has_crossroads = True + if not has_crossroads: + weight = 1.0 / math.sqrt(0.1 + info_by_label[label].size) + growspots.append(((x,y),label,weight)) + + # print(growspots) + if len(growspots) > 0: + ((x,y),label,weight) = random.choices(growspots,weights=(weight for (_,_,weight) in growspots))[0] + owned[y][x] = pla + owned_labels[y][x] = label + stones[y][x] = pla + info_by_label[label].size += 1 + + for i in range(700): + grow(allow_diag_blocker=False) + for i in range(100): + grow(allow_diag_blocker=True) + + def shrink(allow_far_shrink, allow_disconnect, replace_with_opp): + shrinkables = [] + for y in range(ysize): + for x in range(xsize): + if stones[y][x] != 0: + pla = stones[y][x] + opp = 3-pla + adjplalabels = [] + adjopplabels = [] + for (ax,ay) in adj(x,y): + if owned[ay][ax] == pla: + adjplalabels.append(owned_labels[ay][ax]) + if owned[ay][ax] == opp and (allow_far_shrink or stones[ay][ax] == opp): + adjopplabels.append(owned_labels[ay][ax]) + + adjplalabelset = set(adjplalabels) + adjopplabelset = set(adjopplabels) + if not (len(adjplalabelset) == 1 and len(adjopplabelset) == 1): + continue + if replace_with_opp and len(adjopplabels) != 1: + continue + + allow = True + if not allow_disconnect: + # Set temporarily + stones[y][x] = 0 + for (ax,ay) in adj(x,y): + if stones[ay][ax] == pla: + floodfill = [ [ 0 for _ in range(xsize) ] for _ in range(ysize) ] + queue = [(ax,ay)] + idx = 0 + while idx < len(queue): + fx, fy = queue[idx] + idx += 1 + if floodfill[fy][fx] == 0: + floodfill[fy][fx] = 1 + for (fx2,fy2) in adj(fx,fy): + if stones[fy2][fx2] == pla: + queue.append((fx2,fy2)) + break + disconnected = False + for (ax,ay) in adj(x,y): + if stones[ay][ax] == pla and floodfill[ay][ax] != 1: + disconnected = True + break + if disconnected: + allow = False + # Undo + stones[y][x] = pla + + if allow: + shrinkables.append((x,y,pla,adjopplabels[0])) + + if len(shrinkables) > 0: + (x,y,pla,adjopplabel) = random.choice(shrinkables) + if replace_with_opp: + stones[y][x] = 3-pla + owned_labels[y][x] = adjopplabel + owned[y][x] = 3-pla + else: + stones[y][x] = 0 + + num_replaces = randint_exponential(25) + for _ in range(num_replaces): + shrink(allow_far_shrink=False, allow_disconnect=False, replace_with_opp=True) + + shrinks = [] + num_shrinks = randint_exponential(22 + int(len(info_by_label) * 0.3)) + num_far_shrinks = randint_exponential(20) + for _ in range(num_shrinks): + shrinks.append("shrink") + for _ in range(num_far_shrinks): + shrinks.append("far_shrink") + random.shuffle(shrinks) + for s in shrinks: + if s == "shrink": + shrink(allow_far_shrink=False, allow_disconnect=False, replace_with_opp=False) + elif s == "far_shrink": + shrink(allow_far_shrink=True, allow_disconnect=False, replace_with_opp=False) + + num_disconnect_shrinks = randint_exponential(5) + for _ in range(num_disconnect_shrinks): + shrink(allow_far_shrink=True, allow_disconnect=True, replace_with_opp=False) + + # print("=========================================================") + # # print("\n".join("".join(row) for row in kinds)) + # # print("-----------------------------------------") + # print("\n".join("".join(str("x" if elt == 1 else "o" if elt == 2 else ".") for elt in row) for row in owned)) + # print("-----------------------------------------") + # print("\n".join("".join(str("0123456789abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ"[elt]) for elt in row) for row in owned_labels)) + # print(info_by_label) + # print("-----------------------------------------") + # print("\n".join("".join(str("x" if elt == 1 else "o" if elt == 2 else ".") for elt in row) for row in stones)) + + locs = [] + for y in range(ysize): + for x in range(xsize): + locs.append((x,y)) + + assert xsize == ysize + b = Board(xsize) + for (x,y) in locs: + loc = b.loc(x,y) + # Final slight omission to poke occasional eyes into huge solid stone areas. + if random.random() < 1.0 / 2000.0: + stones[y][x] = 0 + + pla = stones[y][x] + if b.would_be_legal(pla,loc): + b.play(pla,loc) + + num_diffs = 0 + for (x,y) in locs: + loc = b.loc(x,y) + if b.board[loc] != stones[y][x]: + num_diffs += 1 + + if num_diffs > 10: + return None + + return b + + +def save_image(board, xsize, ysize): + CELL_SIZE = 20 + STONE_RADIUS = 8 + + # Create blank board image + img = Image.new('RGB', (CELL_SIZE*ysize, CELL_SIZE*xsize), color=(160,82,45)) + + # Draw grid lines + draw = ImageDraw.Draw(img) + for x in range(xsize): + draw.line((CELL_SIZE*x, 0, CELL_SIZE*x, CELL_SIZE*ysize), fill=(0,0,0)) + for y in range(ysize): + draw.line((0, CELL_SIZE*y, CELL_SIZE*xsize, CELL_SIZE*y), fill=(0,0,0)) + + # Draw stones + for y in range(ysize): + for x in range(xsize): + if board[y][x] == 1: + draw.ellipse((CELL_SIZE*y+CELL_SIZE/2-STONE_RADIUS, CELL_SIZE*x+CELL_SIZE/2-STONE_RADIUS, CELL_SIZE*y+CELL_SIZE/2+STONE_RADIUS, CELL_SIZE*x+CELL_SIZE/2+STONE_RADIUS), fill=(0,0,0)) + elif board[y][x] == 2: + draw.ellipse((CELL_SIZE*y+CELL_SIZE/2-STONE_RADIUS, CELL_SIZE*x+CELL_SIZE/2-STONE_RADIUS, CELL_SIZE*y+CELL_SIZE/2+STONE_RADIUS, CELL_SIZE*x+CELL_SIZE/2+STONE_RADIUS), fill=(255,255,255)) + + # Save image + img.save('tmpclumpypos.png') + + +def gen_sgfs_debug(): + for i in range(100): + xsize = 19 + ysize = 19 + b = None + while b is None: + b = gen(xsize,ysize) + initial_pla = random.choice([1,2]) + + with open(f"tmp/clumpysgfs/{i}.sgf","w") as f: + f.write("(;FF[4]GM[1]SZ[19]HA[0]KM[6.5]") + whitepieces = [] + blackpieces = [] + for y in range(ysize): + for x in range(xsize): + loc = b.loc(x,y) + color = b.board[loc] + if color == 1: + xc = "abcdefghijklmnopqrstuvwxyz"[x] + yc = "abcdefghijklmnopqrstuvwxyz"[y] + blackpieces.append(f"[{xc}{yc}]") + elif color == 2: + xc = "abcdefghijklmnopqrstuvwxyz"[x] + yc = "abcdefghijklmnopqrstuvwxyz"[y] + whitepieces.append(f"[{xc}{yc}]") + if len(whitepieces) > 0: + f.write("AW"+"".join(whitepieces)) + if len(blackpieces) > 0: + f.write("AB"+"".join(blackpieces)) + f.write("PL[W]" if initial_pla == 2 else "") + f.write(")") + + print(i) + print(b.to_string()) + + # board = [ [ 0 for _ in range(xsize) ] for _ in range(ysize) ] + # for y in range(ysize): + # for x in range(xsize): + # loc = b.loc(x,y) + # board[y][x] = b.board[loc] + # save_image(board,xsize,ysize) + + +def maybe_generate_one_pos(out, training_weight, max_lopsidedness, soft_filter_large_group_scale): + xsize = 19 + ysize = 19 + board = None + while board is None: + board = gen(xsize,ysize) + + soft_keep_prob = 1.0 + visited_group_heads = set() + for y in range(ysize): + for x in range(xsize): + loc = board.loc(x,y) + if board.board[loc] == 1 or board.board[loc] == 2: + head = board.group_head[loc] + if head not in visited_group_heads: + visited_group_heads.add(head) + stonecount = board.group_stone_count[head] + if stonecount > 2 * soft_filter_large_group_scale: + soft_keep_prob = 0.0 + elif stonecount > soft_filter_large_group_scale: + excess = stonecount - soft_filter_large_group_scale + soft_keep_prob *= math.exp(-excess * 3 / soft_filter_large_group_scale) + + initial_pla = random.choice([1,2]) + + to_write = {} + to_write["board"] = "" + num_stones = 0 + num_black = 0 + num_white = 0 + for y in range(ysize): + for x in range(xsize): + loc = board.loc(x,y) + if board.board[loc] == 1: + to_write["board"] += "X" + num_stones += 1 + num_black += 1 + elif board.board[loc] == 2: + to_write["board"] += "O" + num_stones += 1 + num_white += 1 + else: + to_write["board"] += "." + to_write["board"] += "/" + + if abs(num_black-num_white) > max_lopsidedness: + return False + + # print(str(soft_keep_prob) + "\n" + to_write["board"].replace("/","\n")) + if soft_keep_prob < 0.05: + return False + if random.random() >= soft_keep_prob: + return False + + to_write["hintLoc"] = "null" + to_write["nextPla"] = ("B" if initial_pla == 1 else "W") + to_write["initialTurnNumber"] = num_stones + to_write["moveLocs"] = [] + to_write["movePlas"] = [] + to_write["weight"] = 1.0 + to_write["trainingWeight"] = training_weight + to_write["xSize"] = board.size + to_write["ySize"] = board.size + out.write(json.dumps(to_write) + "\n") + out.flush() + return True + +def main(num_to_generate, out_file, training_weight, max_lopsidedness, soft_filter_large_group_scale): + with open(out_file,"w") as out: + num_kept = 0 + while True: + suc = maybe_generate_one_pos(out, training_weight, max_lopsidedness, soft_filter_large_group_scale) + if suc: + num_kept += 1 + if num_kept >= num_to_generate: + break + if num_kept % 100 == 0: + print(f"Kept {num_kept} so far") + sys.stdout.flush() + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + parser.add_argument("-num-to-generate", required=True, type=int) + parser.add_argument("-out-file", required=True) + parser.add_argument("-training-weight", required=True, type=float) + parser.add_argument("-max-lopsidedness", required=True, type=int) + parser.add_argument("-soft-filter-large-group-scale", required=True, type=float) + args = vars(parser.parse_args()) + + main( + args["num_to_generate"], + args["out_file"], + args["training_weight"], + args["max_lopsidedness"], + args["soft_filter_large_group_scale"], + ) + + diff --git a/training/scripts/dated/20241110-123407/genrandomposes.py b/training/scripts/dated/20241110-123407/genrandomposes.py new file mode 100644 index 0000000..9c170a2 --- /dev/null +++ b/training/scripts/dated/20241110-123407/genrandomposes.py @@ -0,0 +1,163 @@ +#!/usr/bin/python + +import argparse +import os +import re +import json +import random +import math +import sys +from pathlib import Path +from typing import List + +import sgfmill +import sgfmill.sgf +import sgfmill.sgf_moves +from query_analysis_engine_example import KataGo, sgfmill_to_str +from board import Board + +def is_fair_enough_pos(board: Board, katago: KataGo, initial_player: int, score_rejection_pickiness: float) -> bool: + query = {} + query["id"] = "" + query["moves"] = [] + query["initialStones"] = [] + for y in range(board.size): + for x in range(board.size): + loc = board.loc(x,y) + pla = board.board[loc] + if pla != 0: + query["initialStones"].append((("B" if pla == 1 else "W"), sgfmill_to_str((y,x)))) + + query["rules"] = "Japanese" + query["initialPlayer"] = ("B" if initial_player == 1 else "W") + query["komi"] = 7.0 + query["boardXSize"] = board.size + query["boardYSize"] = board.size + query["maxVisits"] = 400 + + result = katago.query_raw(query) + winrate = result['rootInfo']['winrate'] + score = result['rootInfo']['scoreLead'] + + if abs(score) > 150: + return False + keep_prob = 1.0 / (1.0 + score_rejection_pickiness * abs(score)) + if random.random() < keep_prob: + # print(board.to_string()) + # print(query["initialPlayer"]) + print(winrate, score) + sys.stdout.flush() + return True + return False + +def randint_exponential(scale): + r = 0 + while r <= 0: + r = random.random() + return int(math.floor(-math.log(r) * scale)) + +def maybe_generate_one_pos(katago, score_rejection_pickiness, out): + board = Board(19) + both_plays = 1 + randint_exponential(5) + randint_exponential(5) + randint_exponential(12) + extra_b_plays = randint_exponential(1.5) + extra_w_plays = randint_exponential(1) + + plays = [] + for _ in range(both_plays+extra_b_plays): + plays.append(1) + for _ in range(both_plays+extra_w_plays): + plays.append(2) + random.shuffle(plays) + + if len(plays) > 140: + return False + + for pla in plays: + choices = [] + weights = [] + for y in range(board.size): + for x in range(board.size): + line = min(y+1,x+1,19-y,19-x) + if line <= 1: + relprob = 1 + elif line <= 2: + relprob = 4 + else: + relprob = 20 + choices.append((x,y)) + weights.append(relprob) + (x,y) = random.choices(choices,weights=weights,k=1)[0] + loc = board.loc(x,y) + if board.would_be_legal(pla,loc): + board.play(pla,loc) + + initial_pla = random.choice([1,2]) + if is_fair_enough_pos(board, katago, initial_pla, score_rejection_pickiness): + to_write = {} + to_write["board"] = "" + num_stones = 0 + for y in range(board.size): + for x in range(board.size): + loc = board.loc(x,y) + if board.board[loc] == 1: + to_write["board"] += "X" + num_stones += 1 + elif board.board[loc] == 2: + to_write["board"] += "O" + num_stones += 1 + else: + to_write["board"] += "." + to_write["board"] += "/" + to_write["hintLoc"] = "null" + to_write["nextPla"] = ("B" if initial_pla == 1 else "W") + to_write["initialTurnNumber"] = num_stones + to_write["moveLocs"] = [] + to_write["movePlas"] = [] + to_write["weight"] = 1.0 + to_write["xSize"] = board.size + to_write["ySize"] = board.size + out.write(json.dumps(to_write) + "\n") + out.flush() + return True + return False + +def main(katago_path, config_path, model_path, score_rejection_pickiness, num_to_generate, out_file): + with open(out_file,"w") as out: + + katago = KataGo( + katago_path, + config_path, + model_path, + additional_args=["-override-config","numSearchThreadsPerAnalysisThread=8,reportAnalysisWinratesAs=BLACK"] + ) + + num_kept = 0 + while True: + kept = maybe_generate_one_pos(katago, score_rejection_pickiness, out) + if kept: + num_kept += 1 + if num_kept >= num_to_generate: + break + if num_kept % 100 == 0: + print(f"Kept {num_kept} so far") + sys.stdout.flush() + katago.close() + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + parser.add_argument("-katago-path", required=True) + parser.add_argument("-config-path", required=True) + parser.add_argument("-model-path", required=True) + parser.add_argument("-score-rejection-pickiness", required=True, type=float) + parser.add_argument("-num-to-generate", required=True, type=int) + parser.add_argument("-out-file", required=True) + args = vars(parser.parse_args()) + + main( + args["katago_path"], + args["config_path"], + args["model_path"], + args["score_rejection_pickiness"], + args["num_to_generate"], + args["out_file"] + ) diff --git a/training/scripts/dated/20241110-123407/humanslnet_gui.py b/training/scripts/dated/20241110-123407/humanslnet_gui.py new file mode 100644 index 0000000..535e139 --- /dev/null +++ b/training/scripts/dated/20241110-123407/humanslnet_gui.py @@ -0,0 +1,634 @@ +import wx +import subprocess +import json +import sys +import os +from threading import Thread +import atexit +import datetime + +from gamestate import GameState +from board import Board +from sgfmetadata import SGFMetadata + +from sgfmill import sgf, sgf_moves + +import matplotlib.pyplot as plt +import matplotlib.colors as mcolors +import numpy as np + +def interpolateColor(points,x): + for i in range(len(points)): + x1,c1 = points[i] + if x < x1: + if i <= 0: + return c1 + x0,c0 = points[i-1] + interp = (x-x0)/(x1-x0) + return c0 + (c1-c0)*interp + return points[-1][1] + +POLICY_COLORS = [ + (0.00, np.array([100,0,0,15])), + (0.35, np.array([184,0,0,255])), + (0.50, np.array([220,0,0,255])), + (0.65, np.array([255,100,0,255])), + (0.85, np.array([205,220,60,255])), + (0.94, np.array([120,235,130,255])), + (1.00, np.array([100,255,245,255])), +] + +def policy_color(prob): + r,g,b,a = interpolateColor(POLICY_COLORS,prob**0.25) + return (round(r), round(g), round(b), round(a)) + +def load_sgf_game_state(file_path): + with open(file_path, 'rb') as f: + game = sgf.Sgf_game.from_bytes(f.read()) + + size = game.get_size() + if size < 9 or size > 19: + raise ValueError("Board size must be between 9 and 19 inclusive.") + + board, plays = sgf_moves.get_setup_and_moves(game) + + moves = [] + for y in range(size): + for x in range(size): + color = board.get(y, x) + if color is not None: + moves.append((x, size - 1 - y, (Board.BLACK if color == "b" else Board.WHITE))) + + for color, move in plays: + if move is not None: + y, x = move + moves.append((x, size - 1 - y, (Board.BLACK if color == "b" else Board.WHITE))) + + game_state = GameState(size, GameState.RULES_JAPANESE) + for (x,y,color) in moves: + game_state.play(color, game_state.board.loc(x,y)) + + return game_state + +class GoBoard(wx.Panel): + def __init__(self, parent, game_state, cell_size=30, margin=30): + super().__init__(parent) + self.game_state = game_state + self.board_size = game_state.board.size + self.cell_size = cell_size + self.margin = margin + + self.sgfmeta = SGFMetadata() + self.latest_model_response = None + + self.SetBackgroundStyle(wx.BG_STYLE_CUSTOM) + self.Bind(wx.EVT_PAINT, self.on_paint) + self.Bind(wx.EVT_LEFT_UP, self.on_click) + + def get_desired_size(self): + board_width = self.board_size * self.cell_size + 2 * self.margin + board_height = self.board_size * self.cell_size + self.cell_size + 2 * self.margin + return board_width, board_height + + def px_of_x(self, x): + return round(self.cell_size * x + self.margin + self.cell_size / 2) + def py_of_y(self, y): + return round(self.cell_size * y + self.margin + self.cell_size / 2) + + def x_of_px(self, px): + return round((px - self.margin - self.cell_size / 2) / self.cell_size) + def y_of_py(self, py): + return round((py - self.margin - self.cell_size / 2) / self.cell_size) + + + def on_paint(self, event): + dc = wx.AutoBufferedPaintDC(self) + gc = wx.GraphicsContext.Create(dc) + + gc.SetBrush(wx.Brush(wx.Colour(200, 150, 100))) + gc.SetPen(wx.TRANSPARENT_PEN) + gc.DrawRectangle(0, 0, self.GetSize().Width, self.GetSize().Height) + + gc.SetPen(wx.Pen(wx.BLACK, 1)) + + for i in range(self.board_size): + gc.StrokeLine( + self.px_of_x(0), + self.py_of_y(i), + self.px_of_x(self.board_size - 1), + self.py_of_y(i), + ) + gc.StrokeLine( + self.px_of_x(i), + self.py_of_y(0), + self.px_of_x(i), + self.py_of_y(self.board_size - 1), + ) + + for x in range(self.board_size): + for y in range(self.board_size): + loc = self.game_state.board.loc(x, y) + + if self.game_state.board.board[loc] == Board.BLACK: + gc.SetBrush(wx.Brush(wx.BLACK)) + gc.DrawEllipse(self.px_of_x(x) - (self.cell_size // 2 - 2), self.py_of_y(y) - (self.cell_size // 2 - 2), self.cell_size - 4, self.cell_size - 4) + elif self.game_state.board.board[loc] == Board.WHITE: + gc.SetBrush(wx.Brush(wx.WHITE)) + gc.DrawEllipse(self.px_of_x(x) - (self.cell_size // 2 - 2), self.py_of_y(y) - (self.cell_size // 2 - 2), self.cell_size - 4, self.cell_size - 4) + + gc.SetBrush(wx.Brush(wx.BLACK, wx.TRANSPARENT)) + for x in range(self.board_size): + for y in range(self.board_size): + loc = self.game_state.board.loc(x, y) + + if len(self.game_state.moves) > 0 and self.game_state.moves[-1][1] == loc: + if self.game_state.moves[-1][0] == Board.BLACK: + gc.SetPen(wx.Pen(wx.Colour(0, 120, 255), 2)) + else: + gc.SetPen(wx.Pen(wx.Colour(0, 50, 255), 2)) + gc.DrawEllipse(self.px_of_x(x) - (self.cell_size // 2 - 6), self.py_of_y(y) - (self.cell_size // 2 - 6), self.cell_size - 12, self.cell_size - 12) + + # Draw column labels + gc.SetFont(wx.Font(10, wx.FONTFAMILY_DEFAULT, wx.FONTSTYLE_NORMAL, wx.FONTWEIGHT_NORMAL), wx.BLACK) + for x in range(self.board_size): + col_label = "ABCDEFGHJKLMNOPQRSTUVWXYZ"[x] + text_width, text_height = gc.GetTextExtent(col_label) + text_x = self.px_of_x(x) - text_width // 2 + gc.DrawText(col_label, text_x, self.py_of_y(-0.8)-text_height//2) + gc.DrawText(col_label, text_x, self.py_of_y(self.board_size-0.2)-text_height//2) + + # Draw row labels + for y in range(self.board_size): + row_label = str(self.board_size - y) + text_width, text_height = gc.GetTextExtent(row_label) + text_y = self.py_of_y(y) - text_height // 2 + gc.DrawText(row_label, self.px_of_x(-0.8)-text_width//2, text_y) + gc.DrawText(row_label, self.px_of_x(self.board_size-0.2)-text_width//2, text_y) + + if self.latest_model_response is not None: + bigger_font = wx.Font(10, wx.FONTFAMILY_DEFAULT, wx.FONTSTYLE_NORMAL, wx.FONTWEIGHT_NORMAL) + small_font = wx.Font(8, wx.FONTFAMILY_DEFAULT, wx.FONTSTYLE_NORMAL, wx.FONTWEIGHT_NORMAL) + + value_prediction = self.latest_model_response["value"] + score_prediction = self.latest_model_response["lead"] + scorestdev_prediction = self.latest_model_response["scorestdev"] + sign = (1 if self.game_state.board.pla == Board.BLACK else -1) + + gc.SetBrush(wx.Brush(wx.BLACK)) + gc.SetPen(wx.Pen(wx.BLACK,1)) + gc.SetFont(bigger_font,wx.BLACK) + + black_wr = 0.5 + 0.5 * (value_prediction[0] - value_prediction[1]) * sign + label = "Raw NN Black Win%% %.1f" % (black_wr*100.0) + text_width, text_height = gc.GetTextExtent(label) + text_x = self.px_of_x(0) + text_y = self.py_of_y(self.board_size+0.6) - text_height // 2 + gc.DrawText(label, text_x, text_y) + + black_score = score_prediction * sign + label = "Raw NN Black ScoreMean %.1f +/- %.1f (2 stdv)" % (black_score, 2 * scorestdev_prediction) + text_width, text_height = gc.GetTextExtent(label) + text_x = self.px_of_x(8) + text_y = self.py_of_y(self.board_size+0.6) - text_height // 2 + gc.DrawText(label, text_x, text_y) + + moves_and_probs0 = dict(self.latest_model_response["moves_and_probs0"]) + # print(moves_and_probs0) + for y in range(self.board_size): + for x in range(self.board_size): + loc = self.game_state.board.loc(x, y) + max_prob = max(moves_and_probs0.values()) + if loc in moves_and_probs0: + prob = moves_and_probs0[loc] + r,g,b,a = policy_color(prob / max_prob ** 0.7) + # print(r,g,b,a,prob) + gc.SetBrush(wx.Brush(wx.Colour(r,g,b,alpha=round(a*0.9)))) + gc.SetPen(wx.Pen(wx.Colour(0,0,0,alpha=a),1)) + gc.SetFont(small_font,wx.Colour(0,0,0,alpha=a)) + gc.DrawRectangle( + self.px_of_x(x-0.45), + self.py_of_y(y-0.45), + self.px_of_x(x+0.45)-self.px_of_x(x-0.45), + self.py_of_y(y+0.45)-self.py_of_y(y-0.45), + ) + + label = "%.1f" % (prob*100.0) + text_width, text_height = gc.GetTextExtent(label) + text_x = self.px_of_x(x) - text_width // 2 + text_y = self.py_of_y(y) - text_height // 2 + gc.DrawText(label, text_x, text_y) + + + def on_click(self, event): + x = self.x_of_px(event.GetX()) + y = self.y_of_py(event.GetY()) + + if 0 <= x < self.board_size and 0 <= y < self.board_size: + loc = self.game_state.board.loc(x, y) + pla = self.game_state.board.pla + + if self.game_state.board.would_be_legal(pla,loc): + self.game_state.play(pla, loc) + + command = {"command": "play", "pla": pla, "loc": loc} + parent = self.GetParent().GetParent() + parent.send_command(command) + response = parent.receive_response() + if response != {"outputs": ""}: + parent.handle_error(f"Unexpected response from server: {response}") + + self.Refresh() + self.refresh_model() + + def set_sgfmeta(self, sgfmeta): + self.sgfmeta = sgfmeta + + def refresh_model(self): + sgfmeta = self.sgfmeta + command = {"command": "get_model_outputs", "sgfmeta": sgfmeta.to_dict()} + parent = self.GetParent().GetParent() + parent.send_command(command) + response = parent.receive_response() + if "outputs" not in response: + parent.handle_error(f"Unexpected response from server: {response}") + self.latest_model_response = response["outputs"] + self.Refresh() + + +class LabeledSlider(wx.Panel): + def __init__(self, parent, title, options, on_scroll_callback=None, start_option=None): + super().__init__(parent) + + self.options = options + self.on_scroll_callback = on_scroll_callback + self.title = title + self.is_extrapolation = False + + # Create the slider + start_idx = 0 if start_option is None else options.index(start_option) + self.slider = wx.Slider(self, value=start_idx, minValue=0, maxValue=len(options) - 1, style=wx.SL_HORIZONTAL | wx.SL_AUTOTICKS) + self.slider.SetTickFreq(1) # Set the tick frequency to 1 + self.slider.Bind(wx.EVT_SCROLL, self.on_slider_scroll) + + # Create the label to display the selected option + self.label = wx.StaticText(self, label = self.title + ": " + str(self.options[start_idx])) + + font_size = 12 + + font = self.label.GetFont() + font.SetPointSize(font_size) + self.label.SetFont(font) + font = self.slider.GetFont() + font.SetPointSize(font_size) + self.slider.SetFont(font) + + # Create a sizer to arrange the widgets + sizer = wx.BoxSizer(wx.VERTICAL) + sizer.Add(self.label, 0, wx.ALIGN_LEFT | wx.ALL, 10) + sizer.Add(self.slider, 0, wx.EXPAND | wx.ALL, 10) + self.SetSizer(sizer) + + def get_selected_index(self): + return self.slider.GetValue() + + def get_selected_option(self): + selected_index = self.get_selected_index() + return self.options[selected_index] + + def refresh_label(self): + option_index = self.slider.GetValue() + selected_option = self.options[option_index] + self.label.SetLabel(self.title + ": " + str(selected_option) + ("" if not self.is_extrapolation else " (No Training Data)")) + + def set_is_extrapolation(self, b): + if self.is_extrapolation != b: + self.is_extrapolation = b + self.refresh_label() + + def on_slider_scroll(self, event): + option_index = self.slider.GetValue() + selected_option = self.options[option_index] + self.refresh_label() + if self.on_scroll_callback: + self.on_scroll_callback(option_index, selected_option) + + +class SliderWindow(wx.Frame): + def __init__(self, parent): + super().__init__(parent, title="Sliders") + panel = wx.Panel(self) + panel_sizer = wx.BoxSizer(wx.VERTICAL) + panel_sizer.Add(400,0,0) + + self.source_slider = LabeledSlider(panel, title="Source", options=["KG","OGS","KGS","Fox","Tygem(Unused)","GoGoD","Go4Go"], + on_scroll_callback = (lambda idx, option: self.update_metadata()), + start_option="GoGoD", + ) + panel_sizer.Add(self.source_slider, 0, wx.EXPAND | wx.LEFT | wx.RIGHT | wx.BOTTOM, 10) + + self.rank_slider = LabeledSlider(panel, title="Rank", options=[ + "KG","9d","8d","7d","6d","5d","4d","3d","2d","1d","1k","2k","3k","4k","5k","6k","7k","8k","9k","10k","11k","12k","13k","14k","15k","16k","17k","18k","19k","20k" + ], + on_scroll_callback = (lambda idx, option: self.update_metadata()), + start_option="9d", + ) + panel_sizer.Add(self.rank_slider, 0, wx.EXPAND | wx.LEFT | wx.RIGHT | wx.BOTTOM, 10) + + self.date_slider = LabeledSlider(panel, title="Date", options=[ + 1800,1825,1850,1875,1900,1915,1930,1940,1950,1960,1970,1980,1985,1990,1995,2000,2005,2008,2010,2012,2013,2014,2015,2016,2017,2018,2019,2020,2021,2022,2023], + on_scroll_callback = (lambda idx, option: self.update_metadata()), + start_option=2020, + ) + panel_sizer.Add(self.date_slider, 0, wx.EXPAND | wx.LEFT | wx.RIGHT | wx.BOTTOM, 10) + + self.tc_slider = LabeledSlider(panel, title="TimeControl", options=["Blitz","Fast","Slow","Unknown"], + on_scroll_callback = (lambda idx, option: self.update_metadata()), + start_option="Unknown", + ) + panel_sizer.Add(self.tc_slider, 0, wx.EXPAND | wx.LEFT | wx.RIGHT | wx.BOTTOM, 10) + + + panel.SetSizer(panel_sizer) + sizer = wx.BoxSizer(wx.VERTICAL) + sizer.Add(panel, 1, wx.EXPAND) + self.SetSizerAndFit(sizer) + + self.Bind(wx.EVT_CLOSE, self.on_close) + + def on_close(self, event): + self.GetParent().server_process.terminate() + self.GetParent().Close() + + def update_metadata(self): + sgfmeta = SGFMetadata( + inverseBRank = self.rank_slider.get_selected_index(), + inverseWRank = self.rank_slider.get_selected_index(), + bIsHuman = self.rank_slider.get_selected_index() != 0, + wIsHuman = self.rank_slider.get_selected_index() != 0, + gameIsUnrated = False, + gameRatednessIsUnknown = self.source_slider.get_selected_option() == "KGS", + tcIsUnknown = self.tc_slider.get_selected_option() == "Unknown", + tcIsByoYomi = self.tc_slider.get_selected_option() != "Unknown", + mainTimeSeconds = [300,900,1800,0][self.tc_slider.get_selected_index()], + periodTimeSeconds = [10,15,30,0][self.tc_slider.get_selected_index()], + byoYomiPeriods = [5,5,5,0][self.tc_slider.get_selected_index()], + gameDate = datetime.date(self.date_slider.get_selected_option(),6,1), + source = self.source_slider.get_selected_index(), + ) + + source = self.source_slider.get_selected_option() + if source == "KG": + self.rank_slider.set_is_extrapolation(self.rank_slider.get_selected_index() != 0) + self.tc_slider.set_is_extrapolation(self.tc_slider.get_selected_option() == "Unknown") + self.date_slider.set_is_extrapolation(self.date_slider.get_selected_option() < 2022) + elif source == "OGS": + self.rank_slider.set_is_extrapolation(self.rank_slider.get_selected_index() == 0) + self.tc_slider.set_is_extrapolation(self.tc_slider.get_selected_option() == "Unknown") + self.date_slider.set_is_extrapolation(self.date_slider.get_selected_option() < 2007) + elif source == "KGS": + self.rank_slider.set_is_extrapolation(self.rank_slider.get_selected_index() == 0) + self.tc_slider.set_is_extrapolation(self.tc_slider.get_selected_option() == "Unknown") + self.date_slider.set_is_extrapolation(self.date_slider.get_selected_option() < 2016) + elif source == "Fox": + self.rank_slider.set_is_extrapolation(self.rank_slider.get_selected_index() == 0 or self.rank_slider.get_selected_index() >= 28) + self.tc_slider.set_is_extrapolation(self.tc_slider.get_selected_option() == "Unknown") + self.date_slider.set_is_extrapolation(self.date_slider.get_selected_option() < 2014 or self.date_slider.get_selected_option() > 2019) + elif source == "Tygem(Unused)": + self.rank_slider.set_is_extrapolation(True) + self.tc_slider.set_is_extrapolation(True) + self.date_slider.set_is_extrapolation(True) + elif source == "GoGoD": + self.rank_slider.set_is_extrapolation(self.rank_slider.get_selected_index() == 0 or self.rank_slider.get_selected_index() > 5) + self.tc_slider.set_is_extrapolation(self.tc_slider.get_selected_option() != "Unknown") + self.date_slider.set_is_extrapolation(self.date_slider.get_selected_option() > 2020) + elif source == "Go4Go": + self.rank_slider.set_is_extrapolation(self.rank_slider.get_selected_index() != 1) + self.tc_slider.set_is_extrapolation(self.tc_slider.get_selected_option() != "Unknown") + self.date_slider.set_is_extrapolation(self.date_slider.get_selected_option() < 2020) + + self.GetParent().board.set_sgfmeta(sgfmeta) + self.GetParent().board.refresh_model() + +class FileDropTarget(wx.FileDropTarget): + def __init__(self, window): + super().__init__() + self.window = window + + def OnDropFiles(self, x, y, sgf_files): + return self.window.on_drop_files(sgf_files) + +class GoClient(wx.Frame): + def __init__(self, server_command, game_state): + super().__init__(parent=None, title="HumanSLNetViz") + self.server_command = server_command + self.game_state = game_state + self.board_size = self.game_state.board_size + + self.SetDropTarget(FileDropTarget(self)) + + self.start_server() + self.init_ui() + + + def init_ui(self): + panel = wx.Panel(self) + self.board = GoBoard(panel, self.game_state) + sizer = wx.BoxSizer(wx.VERTICAL) + sizer.Add(self.board, 1, wx.EXPAND) + panel.SetSizer(sizer) + + self.Bind(wx.EVT_CHAR_HOOK, self.on_key_down) + + # Set the initial size of the window based on the board size + board_width, board_height = self.board.get_desired_size() + self.SetClientSize(board_width, board_height) + + screen_width, screen_height = wx.DisplaySize() + frame_width, frame_height = self.GetSize() + pos_x = (screen_width - frame_width) // 2 - 300 + pos_y = (screen_height - frame_height) // 2 + self.SetPosition((pos_x, pos_y)) + + self.slider_window = SliderWindow(self) + + frame_width, frame_height = self.slider_window.GetSize() + pos_x = (screen_width - frame_width) // 2 + 240 + pos_y = (screen_height - frame_height) // 2 + self.slider_window.SetPosition((pos_x, pos_y)) + + + def start_server(self): + print(f"Starting server with command: {self.server_command}") + self.server_process = subprocess.Popen( + self.server_command, + stdin=subprocess.PIPE, + stdout=subprocess.PIPE, + stderr=subprocess.PIPE, + universal_newlines=True, + ) + atexit.register(self.server_process.terminate) + + def print_stderr(): + while True: + line = self.server_process.stderr.readline() + if not line: + returncode = self.server_process.poll() + if returncode is not None: + return + print(line,end="") + + t = Thread(target=print_stderr) + t.daemon = True + t.start() + + self.init_server() + + def init_server(self): + command = {"command": "start", "board_size": self.board_size, "rules": GameState.RULES_JAPANESE} + self.send_command(command) + response = self.receive_response() + if response != {"outputs": ""}: + self.handle_error(f"Unexpected response from server: {response}") + + for (pla,loc) in self.game_state.moves: + command = {"command": "play", "pla": pla, "loc": loc} + self.send_command(command) + response = self.receive_response() + if response != {"outputs": ""}: + self.handle_error(f"Unexpected response from server: {response}") + + def send_command(self, command): + print(f"Sending: {json.dumps(command)}") + self.server_process.stdin.write(json.dumps(command) + "\n") + self.server_process.stdin.flush() + + def receive_response(self): + print(f"Waiting for response") + while True: + returncode = self.server_process.poll() + if returncode is not None: + raise OSError(f"Server terminated unexpectedly with {returncode=}") + response = self.server_process.stdout.readline().strip() + if response != "": + break + print(f"Got response (first 100 chars): {str(response[:100])}") + return json.loads(response) + + def handle_error(self, error_message): + print(f"Error: {error_message}") + self.server_process.terminate() + + sys.exit(1) + + def on_key_down(self, event): + key_code = event.GetKeyCode() + if (key_code == wx.WXK_LEFT or key_code == wx.WXK_BACK) and event.ShiftDown(): + self.undo(10) + elif key_code == wx.WXK_LEFT or key_code == wx.WXK_BACK: + self.undo() + elif key_code == wx.WXK_RIGHT and event.ShiftDown(): + self.redo(10) + elif key_code == wx.WXK_RIGHT: + self.redo() + elif key_code == wx.WXK_DOWN: + self.undo(len(self.game_state.moves)) + elif key_code == wx.WXK_UP: + self.redo(len(self.game_state.redo_stack)) + event.Skip() + + def undo(self, undo_count = 1): + is_refresh_needed = False + for i in range(undo_count): + if not self.game_state.can_undo(): + break + + is_refresh_needed = True + + self.game_state.undo() + + command = {"command": "undo"} + self.send_command(command) + response = self.receive_response() + if response != {"outputs": ""}: + self.handle_error(f"Unexpected response from server: {response}") + + if is_refresh_needed: + self.board.Refresh() + self.board.refresh_model() + + def redo(self, redo_count = 1): + is_refresh_needed = False + for i in range(redo_count): + if not self.game_state.can_redo(): + break + + is_refresh_needed = True + + self.game_state.redo() + + command = {"command": "redo"} + self.send_command(command) + response = self.receive_response() + if response != {"outputs": ""}: + self.handle_error(f"Unexpected response from server: {response}") + + if is_refresh_needed: + self.board.Refresh() + self.board.refresh_model() + + def on_drop_files(self, sgf_files): + if len(sgf_files) == 0: + return False + + sgf_file = sgf_files[0] + file_extension = os.path.splitext(sgf_file)[1] + if file_extension != ".sgf": + return False + + game_state = load_sgf_game_state(sgf_file) + + self.game_state = game_state + self.board_size = self.game_state.board_size + + self.board.game_state = game_state + self.board.board_size = self.board_size + + self.init_server() + + self.board.Refresh() + self.board.refresh_model() + + return True + + def on_close(self, event): + self.server_process.terminate() + event.Skip() + +def main(): + sgf_file = None + server_command = sys.argv[1:] + + if len(server_command) >= 2 and server_command[0] == "-sgf": + sgf_file = server_command[1] + server_command = server_command[2:] + + if not server_command: + print("Usage: python humanslnet_gui.py [-sgf FILENAME] ") + sys.exit(1) + + if sgf_file is not None: + game_state = load_sgf_game_state(sgf_file) + else: + game_state = GameState(19, GameState.RULES_JAPANESE) + + app = wx.App() + client = GoClient(server_command, game_state) + client.Bind(wx.EVT_CLOSE, client.on_close) + client.Show() + client.slider_window.Show() + client.slider_window.update_metadata() + + app.MainLoop() + +if __name__ == "__main__": + main() diff --git a/training/scripts/dated/20241110-123407/humanslnet_server.py b/training/scripts/dated/20241110-123407/humanslnet_server.py new file mode 100644 index 0000000..9d6e351 --- /dev/null +++ b/training/scripts/dated/20241110-123407/humanslnet_server.py @@ -0,0 +1,81 @@ +import sys +import json +import numpy as np +from load_model import load_model +from gamestate import GameState +from features import Features +from sgfmetadata import SGFMetadata +import argparse + +def numpy_array_encoder(obj): + if isinstance(obj, np.ndarray): + return obj.tolist() + elif isinstance(obj, np.float32): + return float(obj) + raise TypeError(f'Object of type {obj.__class__.__name__} is not JSON serializable') + +def main(): + parser = argparse.ArgumentParser() + parser.add_argument('-checkpoint', help='Checkpoint to test', required=True) + parser.add_argument('-use-swa', help='Use SWA model', action="store_true", required=False) + parser.add_argument('-device', help='Device to use, such as cpu or cuda:0', required=True) + args = parser.parse_args() + + model, swa_model, _ = load_model(args.checkpoint, use_swa=args.use_swa, device=args.device, pos_len=19, verbose=False) + if swa_model is not None: + model = swa_model + game_state = None + + def write(output): + sys.stdout.write(json.dumps(output,default=numpy_array_encoder) + "\n") + sys.stdout.flush() + + # DEBUGGING + # game_state = GameState(board_size=19, rules=GameState.RULES_JAPANESE) + # sgfmeta = SGFMetadata() + # outputs = game_state.get_model_outputs(model, sgfmeta=sgfmeta) + # write(dict(outputs=outputs)) + + for line in sys.stdin: + line = line.strip() + if not line: + continue + + data = json.loads(line) + + if data["command"] == "start": + board_size = data["board_size"] + rules = data["rules"] + game_state = GameState(board_size, rules) + write(dict(outputs="")) + + elif data["command"] == "play": + pla = data["pla"] + loc = data["loc"] + game_state.play(pla, loc) + write(dict(outputs="")) + + elif data["command"] == "undo": + game_state.undo() + write(dict(outputs="")) + + elif data["command"] == "redo": + game_state.redo() + write(dict(outputs="")) + + elif data["command"] == "get_model_outputs": + sgfmeta = SGFMetadata.of_dict(data["sgfmeta"]) + # features = Features(model.config, model.pos_len) + # foo = game_state.get_input_features(features) + outputs = game_state.get_model_outputs(model, sgfmeta=sgfmeta) + filtered_outputs = {} + for key in outputs: + if key in ["moves_and_probs0", "value", "lead", "scorestdev"]: + filtered_outputs[key] = outputs[key] + write(dict(outputs=filtered_outputs)) + + else: + raise ValueError(f"Unknown command: {data['command']}") + +if __name__ == "__main__": + main() diff --git a/training/scripts/dated/20241110-123407/katago.exe b/training/scripts/dated/20241110-123407/katago.exe new file mode 100644 index 0000000000000000000000000000000000000000..2ad6e330a7efb336105529a3ff1a4731e23c8e39 GIT binary patch literal 4685824 zcmd?SeSB2K^*_ELS%^T`4YCpxWYvh#AT9(o8q{6L!d=)%RMc2OqaZ~^O+x~)A_kL{ z-CowlYFpb%KlrJ(w)Fv@0{EB!CgDK{S`@?we7-TLpe+Pc_V<3Dxp#N-0AfGi|9*MB 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z*Pv&#Blp5DeoC19iJ#^xKR@2lANj;g1#{FS_S0^C>AQJ;&^)U%`g`6{sAx|1JU!qE zTs`kRhO>7X-V5GW9Ckhuvwn$vu6@STuQu7V@6V zp~KF=%_@3y>w@aQoT{Q3JN)?+n1D|_c^^1idkSXIpM z5$_dT-US_Mvlyr4c8B*e25-%hPK)!U+~x2r+~D0~$h>6mJ~1p+(gR6PCHvpV$c(uw zv3c4%+Gq0oc8K?UHt+D_}pUg-X`pG`%TU@#rxi@?eSUJ`+l4EEQ%|V;ytV4eU##zlHv`Y;`zVg8NT8j zsp37S;wULv=W*_Ow94aMvZu#IlCQ^OOn8h3kGF^(`|a_L!sBhA$3A;J@AsIiJl=_V z%m!X>xi_LcX}`-`X_q(4>{ptv$L0Nw%X?v$G37FzT*i{i{<(}Hm+|8=c3j4d%UeO0 z_q#67!^3`u@!{n2M>$`QLq9pp8xC`Z!+hZ|S2!F6hdIk(zH*qW9Ofy9z7Oc}xX)&^ z>Atq}{!#PzO$4S!uCO-mj%?nP+Psal>2I4io;L48Y~CmOMT{ z-pePtkIZ?hA!}1WFWJ1=GB|2{3j*W7;JmXqY9ak(ai-byQ^eSEIJYd`iyNF(W%@i} zAESXvMnNMpZp=GTq zdc4z&89^p*o;|h@bM~}w delta, + (0.5 * delta * delta) + delta * (abs_diff - delta), + 0.5 * abs_diff * abs_diff, + ) + +def constant_like(data, other_tensor): + return torch.tensor(data, dtype=other_tensor.dtype, device=other_tensor.device, requires_grad=False) + +class Metrics: + def __init__(self, batch_size: int, world_size: int, raw_model: Model): + self.n = batch_size + self.world_size = world_size + self.pos_len = raw_model.pos_len + self.pos_area = raw_model.pos_len * raw_model.pos_len + self.policy_len = raw_model.pos_len * raw_model.pos_len + 1 + self.value_len = 3 + self.num_td_values = 3 + self.num_futurepos_values = 2 + self.num_seki_logits = 4 + self.scorebelief_len = 2 * (self.pos_len*self.pos_len + EXTRA_SCORE_DISTR_RADIUS) + + self.score_belief_offset_vector = raw_model.value_head.score_belief_offset_vector + self.moving_unowned_proportion_sum = 0.0 + self.moving_unowned_proportion_weight = 0.0 + + def state_dict(self): + return dict( + moving_unowned_proportion_sum = self.moving_unowned_proportion_sum, + moving_unowned_proportion_weight = self.moving_unowned_proportion_weight, + ) + def load_state_dict(self, state_dict: Dict[str,Any]): + if isinstance(state_dict["moving_unowned_proportion_sum"],torch.Tensor): + self.moving_unowned_proportion_sum = state_dict["moving_unowned_proportion_sum"].item() + else: + self.moving_unowned_proportion_sum = state_dict["moving_unowned_proportion_sum"] + self.moving_unowned_proportion_weight = state_dict["moving_unowned_proportion_weight"] + + def loss_policy_player_samplewise(self, pred_logits, target_probs, weight, global_weight): + assert pred_logits.shape == (self.n, self.policy_len) + assert target_probs.shape == (self.n, self.policy_len) + loss = cross_entropy(pred_logits, target_probs, dim=1) + return global_weight * weight * loss + + def loss_policy_opponent_samplewise(self, pred_logits, target_probs, weight, global_weight): + assert pred_logits.shape == (self.n, self.policy_len) + assert target_probs.shape == (self.n, self.policy_len) + loss = cross_entropy(pred_logits, target_probs, dim=1) + return 0.15 * global_weight * weight * loss + + + def loss_value_samplewise(self, pred_logits, target_probs, weight, global_weight): + assert pred_logits.shape == (self.n, self.value_len) + assert target_probs.shape == (self.n, self.value_len) + assert weight.shape == (self.n,) + loss = cross_entropy(pred_logits, target_probs, dim=1) + return 1.20 * global_weight * weight * loss + + def loss_td_value_samplewise(self, pred_logits, target_probs, weight, global_weight): + assert pred_logits.shape == (self.n, self.num_td_values, self.value_len) + assert target_probs.shape == (self.n, self.num_td_values, self.value_len) + assert weight.shape == (self.n,) + assert global_weight.shape == (self.n,) + loss = cross_entropy(pred_logits, target_probs, dim=2) - cross_entropy(torch.log(target_probs + 1.0e-30), target_probs, dim=2) + return 1.20 * global_weight.unsqueeze(1) * weight.unsqueeze(1) * loss + + def loss_td_score_samplewise(self, pred, target, weight, global_weight): + assert pred.shape == (self.n, self.num_td_values) + assert target.shape == (self.n, self.num_td_values) + loss = torch.sum(huber_loss(pred, target, delta = 12.0), dim=1) + return 0.0004 * global_weight * weight * loss + + + def loss_ownership_samplewise(self, pred_pretanh, target, weight, mask, mask_sum_hw, global_weight): + # This uses a formulation where each batch element cares about its average loss. + # In particular this means that ownership loss predictions on small boards "count more" per spot. + # Not unlike the way that policy and value loss are also equal-weighted by batch element. + assert pred_pretanh.shape == (self.n, 1, self.pos_len, self.pos_len) + assert target.shape == (self.n, self.pos_len, self.pos_len) + assert mask.shape == (self.n, self.pos_len, self.pos_len) + assert mask_sum_hw.shape == (self.n,) + pred_logits = torch.cat((pred_pretanh, -pred_pretanh), dim=1).view(self.n,2,self.pos_area) + target_probs = torch.stack(((1.0 + target) / 2.0, (1.0 - target) / 2.0), dim=1).view(self.n,2,self.pos_area) + loss = torch.sum(cross_entropy(pred_logits, target_probs, dim=1) * mask.view(self.n,self.pos_area), dim=1) / mask_sum_hw + return 1.5 * global_weight * weight * loss + + + def loss_scoring_samplewise(self, pred_scoring, target, weight, mask, mask_sum_hw, global_weight): + assert pred_scoring.shape == (self.n, 1, self.pos_len, self.pos_len) + assert target.shape == (self.n, self.pos_len, self.pos_len) + assert mask.shape == (self.n, self.pos_len, self.pos_len) + assert mask_sum_hw.shape == (self.n,) + + loss = torch.sum(torch.square(pred_scoring.squeeze(1) - target) * mask, dim=(1,2)) / mask_sum_hw + # Simple huberlike transform to reduce crazy values + loss = 4.0 * (torch.sqrt(loss * 0.5 + 1.0) - 1.0) + return global_weight * weight * loss + + + def loss_futurepos_samplewise(self, pred_pretanh, target, weight, mask, mask_sum_hw, global_weight): + # The futurepos targets extrapolate a fixed number of steps into the future independent + # of board size. So unlike the ownership above, generally a fixed number of spots are going to be + # "wrong" independent of board size, so we should just equal-weight the prediction per spot. + # However, on larger boards often the entropy of where the future moves will be should be greater + # and also in the event of capture, there may be large captures that don't occur on small boards, + # causing some scaling with board size. So, I dunno, let's compromise and scale by sqrt(boardarea). + # Also, the further out targets should be weighted a little less due to them being higher entropy + # due to simply being farther in the future, so multiply by [1,0.25]. + assert pred_pretanh.shape == (self.n, self.num_futurepos_values, self.pos_len, self.pos_len) + assert target.shape == (self.n, self.num_futurepos_values, self.pos_len, self.pos_len) + assert mask.shape == (self.n, self.pos_len, self.pos_len) + assert mask_sum_hw.shape == (self.n,) + loss = torch.square(torch.tanh(pred_pretanh) - target) * mask.unsqueeze(1) + loss = loss * constant_like([1.0,0.25], loss).view(1,2,1,1) + loss = torch.sum(loss, dim=(1, 2, 3)) / torch.sqrt(mask_sum_hw) + return 0.25 * global_weight * weight * loss + + + def loss_seki_samplewise(self, pred_logits, target, target_ownership, weight, mask, mask_sum_hw, global_weight, is_training, skip_moving_update): + assert self.num_seki_logits == 4 + assert pred_logits.shape == (self.n, self.num_seki_logits, self.pos_len, self.pos_len) + assert target.shape == (self.n, self.pos_len, self.pos_len) + assert target_ownership.shape == (self.n, self.pos_len, self.pos_len) + assert mask.shape == (self.n, self.pos_len, self.pos_len) + assert mask_sum_hw.shape == (self.n,) + + owned_target = torch.square(target_ownership) + unowned_target = 1.0 - owned_target + unowned_proportion = torch.sum(unowned_target * mask, dim=(1, 2)) / (1.0 + mask_sum_hw) + unowned_proportion = torch.mean(unowned_proportion * weight) + if is_training: + if not skip_moving_update: + self.moving_unowned_proportion_sum *= 0.998 + self.moving_unowned_proportion_weight *= 0.998 + self.moving_unowned_proportion_sum += unowned_proportion.item() + self.moving_unowned_proportion_weight += 1.0 + moving_unowned_proportion = self.moving_unowned_proportion_sum / self.moving_unowned_proportion_weight + seki_weight_scale = 8.0 * 0.005 / (0.005 + moving_unowned_proportion) + else: + seki_weight_scale = 7.0 + + # Loss for predicting the exact sign of seki points + sign_pred = pred_logits[:, 0:3, :, :] + sign_target = torch.stack( + ( + 1.0 - torch.square(target), + torch.nn.functional.relu(target), + torch.nn.functional.relu(-target), + ), + dim=1, + ) + loss_sign = torch.sum(cross_entropy(sign_pred, sign_target, dim=1) * mask, dim=(1, 2)) + + # Loss for generally predicting points that nobody will own + neutral_pred = torch.stack( + (pred_logits[:, 3, :, :], torch.zeros_like(target_ownership)), dim=1 + ) + neutral_target = torch.stack((unowned_target, owned_target), dim=1) + loss_neutral = torch.sum(cross_entropy(neutral_pred, neutral_target, dim=1) * mask, dim=(1, 2)) + + loss = loss_sign + 0.5 * loss_neutral + loss = loss / mask_sum_hw + return (global_weight * seki_weight_scale * weight * loss, seki_weight_scale) + + + def loss_scoremean_samplewise(self, pred, target, weight, global_weight): + # Huber will incentivize this to not actually converge to the mean, + #but rather something meanlike locally and something medianlike + # for very large possible losses. This seems... okay - it might actually + # be what users want. + assert pred.shape == (self.n,) + assert target.shape == (self.n,) + loss = huber_loss(pred, target, delta = 12.0) + return 0.0015 * global_weight * weight * loss + + + def loss_scorebelief_cdf_samplewise(self, pred_logits, target_probs, weight, global_weight): + assert pred_logits.shape == (self.n,self.scorebelief_len) + assert target_probs.shape == (self.n,self.scorebelief_len) + pred_cdf = torch.cumsum(torch.nn.functional.softmax(pred_logits, dim=1), dim=1) + target_cdf = torch.cumsum(target_probs, dim=1) + loss = torch.sum(torch.square(pred_cdf-target_cdf),axis=1) + return 0.020 * global_weight * weight * loss + + def loss_scorebelief_pdf_samplewise(self, pred_logits, target_probs, weight, global_weight): + assert pred_logits.shape == (self.n,self.scorebelief_len) + assert target_probs.shape == (self.n,self.scorebelief_len) + loss = cross_entropy(pred_logits, target_probs, dim=1) + return 0.020 * global_weight * weight * loss + + def loss_scorestdev_samplewise(self, pred, scorebelief_logits, global_weight): + assert pred.shape == (self.n,) + assert scorebelief_logits.shape == (self.n,self.scorebelief_len) + assert self.score_belief_offset_vector.shape == (self.scorebelief_len,) + scorebelief_probs = torch.nn.functional.softmax(scorebelief_logits, dim=1) + expected_score_from_belief = torch.sum(scorebelief_probs * self.score_belief_offset_vector.view(1,-1),dim=1,keepdim=True) + stdev_of_belief = torch.sqrt(0.001 + torch.sum( + scorebelief_probs * torch.square( + self.score_belief_offset_vector.view(1,-1) - expected_score_from_belief + ), + dim=1 + )) + loss = huber_loss(pred, stdev_of_belief, delta = 10.0) + return 0.001 * global_weight * loss + + def loss_lead_samplewise(self, pred, target, weight, global_weight): + # Huber will incentivize this to not actually converge to the mean, + #but rather something meanlike locally and something medianlike + # for very large possible losses. This seems... okay - it might actually + # be what users want. + assert pred.shape == (self.n,) + assert target.shape == (self.n,) + loss = huber_loss(pred, target, delta = 8.0) + return 0.0060 * global_weight * weight * loss + + def loss_variance_time_samplewise(self, pred, target, weight, global_weight): + assert pred.shape == (self.n,) + assert target.shape == (self.n,) + # Even if the training target is 0, add a tiny bit of irreducible error for regularizing the prediction. + loss = huber_loss(pred, target + 1.0e-5, delta = 50.0) + return 0.0003 * global_weight * weight * loss + + + def loss_shortterm_value_error_samplewise(self, pred, td_value_pred_logits, td_value_target_probs, weight, global_weight): + td_value_pred_probs = torch.softmax(td_value_pred_logits[:,2,:],axis=1) + predvalue = (td_value_pred_probs[:,0] - td_value_pred_probs[:,1]).detach() + realvalue = td_value_target_probs[:,2,0] - td_value_target_probs[:,2,1] + # Even if the training target is 0, add a tiny bit of irreducible error for regularizing the prediction, 0.01%. + sqerror = torch.square(predvalue-realvalue) + 1.0e-8 + loss = huber_loss(pred, sqerror, delta = 0.4) + return 2.0 * global_weight * weight * loss + + def loss_shortterm_score_error_samplewise(self, pred, td_score_pred, td_score_target, weight, global_weight): + predscore = td_score_pred[:,2].detach() + realscore = td_score_target[:,2] + # Even if the training target is 0, add a tiny bit of irreducible error for regularizing the prediction, one hundredth of a point. + sqerror = torch.square(predscore-realscore) + 1.0e-4 + loss = huber_loss(pred, sqerror, delta = 100.0) + return 0.00002 * global_weight * weight * loss + + def accuracy1(self, pred_logits, target_probs, weight, global_weight): + return torch.sum(global_weight * weight * (torch.argmax(pred_logits,dim=1) == torch.argmax(target_probs,dim=1))) + + def target_entropy(self, target_probs, weight, global_weight): + return torch.sum(global_weight * weight * -torch.sum(target_probs * torch.log(target_probs + 1e-30), dim=-1)) + + def square_value(self, value_logits, global_weight): + return torch.sum(global_weight * torch.square(torch.sum(torch.softmax(value_logits,dim=1) * constant_like([1,-1,0],global_weight), dim=1))) + + # Returns 0.5 times the sum of squared model weights, for each reg group of model weights + @staticmethod + def get_model_norms(raw_model): + reg_dict : Dict[str,List] = {} + raw_model.add_reg_dict(reg_dict) + + device = reg_dict["normal"][0].device + dtype = torch.float32 + + modelnorm_normal = torch.zeros([],device=device,dtype=dtype) + modelnorm_normal_gamma = torch.zeros([],device=device,dtype=dtype) + modelnorm_output = torch.zeros([],device=device,dtype=dtype) + modelnorm_noreg = torch.zeros([],device=device,dtype=dtype) + modelnorm_output_noreg = torch.zeros([],device=device,dtype=dtype) + for tensor in reg_dict["normal"]: + modelnorm_normal += torch.sum(tensor * tensor) + for tensor in reg_dict["normal_gamma"]: + modelnorm_normal_gamma += torch.sum(tensor * tensor) + for tensor in reg_dict["output"]: + modelnorm_output += torch.sum(tensor * tensor) + for tensor in reg_dict["noreg"]: + modelnorm_noreg += torch.sum(tensor * tensor) + for tensor in reg_dict["output_noreg"]: + modelnorm_output_noreg += torch.sum(tensor * tensor) + modelnorm_normal *= 0.5 + modelnorm_normal_gamma *= 0.5 + modelnorm_output *= 0.5 + modelnorm_noreg *= 0.5 + modelnorm_output_noreg *= 0.5 + return (modelnorm_normal, modelnorm_normal_gamma, modelnorm_output, modelnorm_noreg, modelnorm_output_noreg) + + def get_specific_norms_and_gradient_stats(self,raw_model): + with torch.no_grad(): + params = {} + for name, param in raw_model.named_parameters(): + params[name] = param + + stats = {} + def add_norm_and_grad_stats(name): + param = params[name] + if name.endswith(".weight"): + fanin = param.shape[1] + elif name.endswith(".gamma"): + fanin = 1 + elif name.endwith(".beta"): + fanin = 1 + else: + assert False, "unimplemented case to compute stats on parameter" + + # 1.0 means that the average squared magnitude of a parameter in this tensor is around where + # it would be at initialization, assuming it uses the activation that the model generally + # uses (e.g. relu or mish) + param_scale = torch.sqrt(torch.mean(torch.square(param))) / compute_gain(raw_model.activation) * math.sqrt(fanin) + stats[f"{name}.SCALE_batch"] = param_scale + + # How large is the gradient, on the same scale? + stats[f"{name}.GRADSC_batch"] = torch.sqrt(torch.mean(torch.square(param.grad))) / compute_gain(raw_model.activation) * math.sqrt(fanin) + + # And how large is the component of the gradient that is orthogonal to the overall magnitude of the parameters? + orthograd = param.grad - param * (torch.sum(param.grad * param) / (1e-20 + torch.sum(torch.square(param)))) + stats[f"{name}.OGRADSC_batch"] = torch.sqrt(torch.mean(torch.square(orthograd))) / compute_gain(raw_model.activation) * math.sqrt(fanin) + + add_norm_and_grad_stats("blocks.1.normactconvp.conv.weight") + add_norm_and_grad_stats("blocks.1.blockstack.0.normactconv1.conv.weight") + add_norm_and_grad_stats("blocks.1.blockstack.0.normactconv2.conv.weight") + add_norm_and_grad_stats("blocks.1.blockstack.1.normactconv2.norm.gamma") + add_norm_and_grad_stats("blocks.1.normactconvq.conv.weight") + add_norm_and_grad_stats("blocks.1.normactconvq.norm.gamma") + + add_norm_and_grad_stats("blocks.6.normactconvp.conv.weight") + add_norm_and_grad_stats("blocks.6.blockstack.0.normactconv1.conv.weight") + add_norm_and_grad_stats("blocks.6.blockstack.0.normactconv2.conv.weight") + add_norm_and_grad_stats("blocks.6.blockstack.1.normactconv2.norm.gamma") + add_norm_and_grad_stats("blocks.6.normactconvq.conv.weight") + add_norm_and_grad_stats("blocks.6.normactconvq.norm.gamma") + + add_norm_and_grad_stats("blocks.10.normactconvp.conv.weight") + add_norm_and_grad_stats("blocks.10.blockstack.0.normactconv1.conv.weight") + add_norm_and_grad_stats("blocks.10.blockstack.0.normactconv2.conv.weight") + add_norm_and_grad_stats("blocks.10.blockstack.1.normactconv2.norm.gamma") + add_norm_and_grad_stats("blocks.10.normactconvq.conv.weight") + add_norm_and_grad_stats("blocks.10.normactconvq.norm.gamma") + + add_norm_and_grad_stats("blocks.16.normactconvp.conv.weight") + add_norm_and_grad_stats("blocks.16.blockstack.0.normactconv1.conv.weight") + add_norm_and_grad_stats("blocks.16.blockstack.0.normactconv2.conv.weight") + add_norm_and_grad_stats("blocks.16.blockstack.1.normactconv2.norm.gamma") + add_norm_and_grad_stats("blocks.16.normactconvq.conv.weight") + add_norm_and_grad_stats("blocks.16.normactconvq.norm.gamma") + + add_norm_and_grad_stats("policy_head.conv1p.weight") + add_norm_and_grad_stats("value_head.conv1.weight") + add_norm_and_grad_stats("intermediate_policy_head.conv1p.weight") + add_norm_and_grad_stats("intermediate_value_head.conv1.weight") + + return stats + + def metrics_dict_batchwise( + self, + raw_model, + model_output_postprocessed_byheads, + extra_outputs, + batch, + is_training, + soft_policy_weight_scale, + disable_optimistic_policy, + meta_kata_only_soft_policy, + value_loss_scale, + td_value_loss_scales, + seki_loss_scale, + variance_time_loss_scale, + main_loss_scale, + intermediate_loss_scale, + ): + results = self.metrics_dict_batchwise_single_heads_output( + raw_model, + model_output_postprocessed_byheads[0], + batch, + is_training=is_training, + soft_policy_weight_scale=soft_policy_weight_scale, + disable_optimistic_policy=disable_optimistic_policy, + meta_kata_only_soft_policy=meta_kata_only_soft_policy, + value_loss_scale=value_loss_scale, + td_value_loss_scales=td_value_loss_scales, + seki_loss_scale=seki_loss_scale, + variance_time_loss_scale=variance_time_loss_scale, + is_intermediate=False, + ) + if main_loss_scale is not None: + results["loss_sum"] = main_loss_scale * results["loss_sum"] + + if raw_model.get_has_intermediate_head(): + assert len(model_output_postprocessed_byheads) > 1 + if raw_model.training: + assert intermediate_loss_scale is not None + else: + if intermediate_loss_scale is None: + intermediate_loss_scale = 1.0 + + if intermediate_loss_scale is not None: + iresults = self.metrics_dict_batchwise_single_heads_output( + raw_model, + model_output_postprocessed_byheads[1], + batch, + is_training=is_training, + soft_policy_weight_scale=soft_policy_weight_scale, + disable_optimistic_policy=disable_optimistic_policy, + meta_kata_only_soft_policy=meta_kata_only_soft_policy, + value_loss_scale=value_loss_scale, + td_value_loss_scales=td_value_loss_scales, + seki_loss_scale=seki_loss_scale, + variance_time_loss_scale=variance_time_loss_scale, + is_intermediate=True, + ) + for key,value in iresults.items(): + if key != "loss_sum": + results["I"+key] = value + results["loss_sum"] = results["loss_sum"] + intermediate_loss_scale * iresults["loss_sum"] + + return results + + def metrics_dict_batchwise_single_heads_output( + self, + raw_model, + model_output_postprocessed, + batch, + is_training, + soft_policy_weight_scale, + disable_optimistic_policy, + meta_kata_only_soft_policy, + value_loss_scale, + td_value_loss_scales, + seki_loss_scale, + variance_time_loss_scale, + is_intermediate, + ): + ( + policy_logits, + value_logits, + td_value_logits, + pred_td_score, + ownership_pretanh, + pred_scoring, + futurepos_pretanh, + seki_logits, + pred_scoremean, + pred_scorestdev, + pred_lead, + pred_variance_time, + pred_shortterm_value_error, + pred_shortterm_score_error, + scorebelief_logits, + ) = model_output_postprocessed + + input_binary_nchw = batch["binaryInputNCHW"] + input_global_nc = batch["globalInputNC"] + target_policy_ncmove = batch["policyTargetsNCMove"] + target_global_nc = batch["globalTargetsNC"] + score_distribution_ns = batch["scoreDistrN"] + target_value_nchw = batch["valueTargetsNCHW"] + + mask = input_binary_nchw[:, 0, :, :].contiguous() + mask_sum_hw = torch.sum(mask,dim=(1,2)) + + n = input_binary_nchw.shape[0] + h = input_binary_nchw.shape[2] + w = input_binary_nchw.shape[3] + + policymask = torch.cat((mask.view(n,h*w),mask.new_ones((n,1))),dim=1) + + target_policy_player = target_policy_ncmove[:, 0, :] + target_policy_player = target_policy_player / torch.sum(target_policy_player, dim=1, keepdim=True) + target_policy_opponent = target_policy_ncmove[:, 1, :] + target_policy_opponent = target_policy_opponent / torch.sum(target_policy_opponent, dim=1, keepdim=True) + target_policy_player_soft = (target_policy_player + 1e-7) * policymask + target_policy_player_soft = torch.pow(target_policy_player_soft, 0.25) + target_policy_player_soft /= torch.sum(target_policy_player_soft, dim=1, keepdim=True) + target_policy_opponent_soft = (target_policy_opponent + 1e-7) * policymask + target_policy_opponent_soft = torch.pow(target_policy_opponent_soft, 0.25) + target_policy_opponent_soft /= torch.sum(target_policy_opponent_soft, dim=1, keepdim=True) + + target_weight_policy_player = target_global_nc[:, 26] + target_weight_policy_opponent = target_global_nc[:, 28] + + target_value = target_global_nc[:, 0:3] + target_scoremean = target_global_nc[:, 3] + target_td_value = torch.stack( + (target_global_nc[:, 4:7], target_global_nc[:, 8:11], target_global_nc[:, 12:15]), dim=1 + ) + target_td_score = torch.cat( + (target_global_nc[:, 7:8], target_global_nc[:, 11:12], target_global_nc[:, 15:16]), dim=1 + ) + target_lead = target_global_nc[:, 21] + target_variance_time = target_global_nc[:, 22] + global_weight = target_global_nc[:, 25] + target_weight_ownership = target_global_nc[:, 27] + target_weight_lead = target_global_nc[:, 29] + target_weight_futurepos = target_global_nc[:, 33] + target_weight_scoring = target_global_nc[:, 34] + target_weight_value = 1.0 - target_global_nc[:, 35] + target_weight_td_value = 1.0 - target_global_nc[:, 24] + + target_score_distribution = score_distribution_ns / 100.0 + + target_ownership = target_value_nchw[:, 0, :, :] + target_seki = target_value_nchw[:, 1, :, :] + target_futurepos = target_value_nchw[:, 2:4, :, :] + target_scoring = target_value_nchw[:, 4, :, :] / 120.0 + + if raw_model.config["version"] <= 11: + assert raw_model.policy_head.num_policy_outputs == 4 + policy_opt_loss_scale = 1.000 + long_policy_opt_loss_scale = 0.0 + short_policy_opt_loss_scale = 0.0 + else: + assert raw_model.policy_head.num_policy_outputs == 6 + policy_opt_loss_scale = 0.930 + long_policy_opt_loss_scale = 0.100 + short_policy_opt_loss_scale = 0.200 + + loss_policy_player = self.loss_policy_player_samplewise( + policy_logits[:, 0, :], + target_policy_player, + target_weight_policy_player, + global_weight, + ).sum() + loss_policy_opponent = self.loss_policy_opponent_samplewise( + policy_logits[:, 1, :], + target_policy_opponent, + target_weight_policy_opponent, + global_weight, + ).sum() + + target_weight_policy_player_soft = target_weight_policy_player + target_weight_policy_opponent_soft = target_weight_policy_opponent + if meta_kata_only_soft_policy: + metadata_input_nc = batch["metadataInputNC"] + assert metadata_input_nc.shape[0] == target_weight_policy_player_soft.shape[0] + # 151 indicates source 0 = katago + target_weight_policy_player_soft = target_weight_policy_player_soft * metadata_input_nc[:,151] + target_weight_policy_opponent_soft = target_weight_policy_opponent_soft * metadata_input_nc[:,151] + + loss_policy_player_soft = self.loss_policy_player_samplewise( + policy_logits[:, 2, :], + target_policy_player_soft, + target_weight_policy_player_soft, + global_weight, + ).sum() + loss_policy_opponent_soft = self.loss_policy_opponent_samplewise( + policy_logits[:, 3, :], + target_policy_opponent_soft, + target_weight_policy_opponent_soft, + global_weight, + ).sum() + + if raw_model.config["version"] <= 11: + target_weight_longoptimistic_policy = torch.zeros_like(global_weight) + loss_longoptimistic_policy = torch.zeros_like(loss_policy_player) + elif disable_optimistic_policy: + target_weight_longoptimistic_policy = target_weight_policy_player * 0.5 + loss_longoptimistic_policy = self.loss_policy_player_samplewise( + policy_logits[:, 4, :], + target_policy_player, + target_weight_longoptimistic_policy, + global_weight, + ).sum() + else: + # Long-term optimistic policy. Weight policy by: + # Final game win squared (squaring discourages draws) + win_squared = torch.square( + target_global_nc[:, 0] # win (or draw, weighted by draw utility) + + 0.5 * target_global_nc[:, 2] # noresult + ) + # Or the score outcome of the game being around 1.5 sigma more than expected + # Add a small amount to the variance to avoid division by zero or overly small numbers + longterm_score_stdevs_excess = (target_global_nc[:, 3] - pred_scoremean.detach()) / torch.sqrt(torch.square(pred_scorestdev.detach()) + 0.25) + target_weight_longoptimistic_policy = torch.clamp( + win_squared + torch.sigmoid((longterm_score_stdevs_excess - 1.5) * 3.0), + min=0.0, + max=1.0, + ) + target_weight_longoptimistic_policy = ( + target_weight_longoptimistic_policy + * target_weight_policy_player # game has normal target + * target_weight_ownership # and also actually ended in full ownership and score, not a sidepos + ) + loss_longoptimistic_policy = self.loss_policy_player_samplewise( + policy_logits[:, 4, :], + target_policy_player, + target_weight_longoptimistic_policy, + global_weight, + ).sum() + + assert len(loss_longoptimistic_policy.shape) == 0 + assert len(target_weight_longoptimistic_policy.shape) == 1 + assert target_weight_longoptimistic_policy.shape[0] == n + target_weight_longoptimistic_policy_sum = (global_weight * target_weight_longoptimistic_policy).sum() + + if raw_model.config["version"] <= 11: + target_weight_shortoptimistic_policy = torch.zeros_like(global_weight) + loss_shortoptimistic_policy = torch.zeros_like(loss_policy_player) + elif disable_optimistic_policy: + target_weight_shortoptimistic_policy = target_weight_policy_player * 0.5 + loss_shortoptimistic_policy = self.loss_policy_player_samplewise( + policy_logits[:, 5, :], + target_policy_player, + target_weight_shortoptimistic_policy, + global_weight, + ).sum() + else: + # Short-term optimistic policy. Weight policy by: + # The shortterm value outcome being around 1.5 sigma more than expected + # Add a small amount to the variance to avoid division by zero or overly small numbers + shortterm_value_actual = target_global_nc[:, 12] - target_global_nc[:, 13] + shortterm_value_pred = torch.nn.functional.softmax(td_value_logits[:, 2, :].detach(), dim=1) + shortterm_value_pred = shortterm_value_pred[:, 0] - shortterm_value_pred[:, 1] + shortterm_value_stdevs_excess = (shortterm_value_actual - shortterm_value_pred) / torch.sqrt(pred_shortterm_value_error.detach() + 0.0001) + # Or the shortterm score outcome being around 1.5 sigma more than expected + # Add a small amount to the variance to avoid division by zero or overly small numbers + shortterm_score_stdevs_excess = (target_global_nc[:, 15] - pred_td_score[:,2].detach()) / torch.sqrt(pred_shortterm_score_error.detach() + 0.25) + target_weight_shortoptimistic_policy = torch.clamp( + torch.sigmoid((shortterm_value_stdevs_excess - 1.5) * 3.0) + torch.sigmoid((shortterm_score_stdevs_excess - 1.5) * 3.0), + min=0.0, + max=1.0, + ) + target_weight_shortoptimistic_policy = ( + target_weight_shortoptimistic_policy + * target_weight_policy_player # game has normal target + * target_weight_ownership # and also actually ended in full ownership and score, not a sidepos + ) + loss_shortoptimistic_policy = self.loss_policy_player_samplewise( + policy_logits[:, 5, :], + target_policy_player, + target_weight_shortoptimistic_policy, + global_weight, + ).sum() + + assert len(loss_shortoptimistic_policy.shape) == 0 + assert len(target_weight_shortoptimistic_policy.shape) == 1 + assert target_weight_shortoptimistic_policy.shape[0] == n + target_weight_shortoptimistic_policy_sum = (global_weight * target_weight_shortoptimistic_policy).sum() + + + loss_value = self.loss_value_samplewise( + value_logits, target_value, target_weight_value, global_weight + ).sum() + + loss_td_value_unsummed = self.loss_td_value_samplewise( + td_value_logits, target_td_value, target_weight_td_value, global_weight + ) + assert self.num_td_values == 3 + loss_td_value1 = loss_td_value_unsummed[:,0].sum() + loss_td_value2 = loss_td_value_unsummed[:,1].sum() + loss_td_value3 = loss_td_value_unsummed[:,2].sum() + + loss_td_score = self.loss_td_score_samplewise( + pred_td_score, target_td_score, target_weight_ownership, global_weight + ).sum() + + loss_ownership = self.loss_ownership_samplewise( + ownership_pretanh, + target_ownership, + target_weight_ownership, + mask, + mask_sum_hw, + global_weight, + ).sum() + loss_scoring = self.loss_scoring_samplewise( + pred_scoring, + target_scoring, + target_weight_scoring, + mask, + mask_sum_hw, + global_weight, + ).sum() + loss_futurepos = self.loss_futurepos_samplewise( + futurepos_pretanh, + target_futurepos, + target_weight_futurepos, + mask, + mask_sum_hw, + global_weight, + ).sum() + (loss_seki,seki_weight_scale) = self.loss_seki_samplewise( + seki_logits, + target_seki, + target_ownership, + target_weight_ownership, + mask, + mask_sum_hw, + global_weight, + is_training, + skip_moving_update=is_intermediate, + ) + loss_seki = loss_seki.sum() + seki_weight_scale = seki_weight_scale.sum() if not isinstance(seki_weight_scale,float) else seki_weight_scale + loss_scoremean = self.loss_scoremean_samplewise( + pred_scoremean, + target_scoremean, + target_weight_ownership, + global_weight, + ).sum() + loss_scorebelief_cdf = self.loss_scorebelief_cdf_samplewise( + scorebelief_logits, + target_score_distribution, + target_weight_ownership, + global_weight, + ).sum() + loss_scorebelief_pdf = self.loss_scorebelief_pdf_samplewise( + scorebelief_logits, + target_score_distribution, + target_weight_ownership, + global_weight, + ).sum() + loss_scorestdev = self.loss_scorestdev_samplewise( + pred_scorestdev, + scorebelief_logits, + global_weight, + ).sum() + loss_lead = self.loss_lead_samplewise( + pred_lead, + target_lead, + target_weight_lead, + global_weight, + ).sum() + loss_variance_time = self.loss_variance_time_samplewise( + pred_variance_time, + target_variance_time, + target_weight_ownership, + global_weight, + ).sum() + loss_shortterm_value_error = self.loss_shortterm_value_error_samplewise( + pred_shortterm_value_error, + td_value_logits, + target_td_value, + target_weight_ownership, + global_weight, + ).sum() + loss_shortterm_score_error = self.loss_shortterm_score_error_samplewise( + pred_shortterm_score_error, + pred_td_score, + target_td_score, + target_weight_ownership, + global_weight, + ).sum() + + loss_sum = ( + loss_policy_player * policy_opt_loss_scale + + loss_policy_opponent + + loss_policy_player_soft * soft_policy_weight_scale + + loss_policy_opponent_soft * soft_policy_weight_scale + + loss_longoptimistic_policy * long_policy_opt_loss_scale + + loss_shortoptimistic_policy * short_policy_opt_loss_scale + + loss_value * value_loss_scale + + loss_td_value1 * td_value_loss_scales[0] + + loss_td_value2 * td_value_loss_scales[1] + + loss_td_value3 * td_value_loss_scales[2] + + loss_td_score + + loss_ownership + + loss_scoring * 0.5 + + loss_futurepos + + loss_seki * seki_loss_scale + + loss_scoremean + + loss_scorebelief_cdf + + loss_scorebelief_pdf + + loss_scorestdev + + loss_lead + + loss_variance_time * variance_time_loss_scale + + loss_shortterm_value_error + + loss_shortterm_score_error + ) + + policy_acc1 = self.accuracy1( + policy_logits[:, 0, :], + target_policy_player, + target_weight_policy_player, + global_weight, + ) + square_value = self.square_value(value_logits, global_weight) + + results = { + "p0loss_sum": loss_policy_player, + "p1loss_sum": loss_policy_opponent, + "p0softloss_sum": loss_policy_player_soft, + "p1softloss_sum": loss_policy_opponent_soft, + "p0lopt_sum": loss_longoptimistic_policy, + "p0loptw_sum": target_weight_longoptimistic_policy_sum, + "p0sopt_sum": loss_shortoptimistic_policy, + "p0soptw_sum": target_weight_shortoptimistic_policy_sum, + "vloss_sum": loss_value, + "tdvloss1_sum": loss_td_value1, + "tdvloss2_sum": loss_td_value2, + "tdvloss3_sum": loss_td_value3, + "tdsloss_sum": loss_td_score, + "oloss_sum": loss_ownership, + "sloss_sum": loss_scoring, + "fploss_sum": loss_futurepos, + "skloss_sum": loss_seki, + "smloss_sum": loss_scoremean, + "sbcdfloss_sum": loss_scorebelief_cdf, + "sbpdfloss_sum": loss_scorebelief_pdf, + "sdregloss_sum": loss_scorestdev, + "leadloss_sum": loss_lead, + "vtimeloss_sum": loss_variance_time, + "evstloss_sum": loss_shortterm_value_error, + "esstloss_sum": loss_shortterm_score_error, + "loss_sum": loss_sum, + "pacc1_sum": policy_acc1, + "vsquare_sum": square_value, + } + + if is_intermediate: + return results + else: + weight = global_weight.sum() + nsamples = int(global_weight.shape[0]) + policy_target_entropy = self.target_entropy( + target_policy_player, + target_weight_policy_player, + global_weight, + ) + soft_policy_target_entropy = self.target_entropy( + target_policy_player_soft, + target_weight_policy_player, + global_weight, + ) + + (modelnorm_normal, modelnorm_normal_gamma, modelnorm_output, modelnorm_noreg, modelnorm_output_noreg) = self.get_model_norms(raw_model) + + extra_results = { + "wsum": weight * self.world_size, + "nsamp": nsamples * self.world_size, + "ptentr_sum": policy_target_entropy, + "ptsoftentr_sum": soft_policy_target_entropy, + "sekiweightscale_sum": seki_weight_scale * weight, + "norm_normal_batch": modelnorm_normal, + "norm_normal_gamma_batch": modelnorm_normal_gamma, + "norm_output_batch": modelnorm_output, + "norm_noreg_batch": modelnorm_noreg, + "norm_output_noreg_batch": modelnorm_output_noreg, + } + for key,value in extra_results.items(): + results[key] = value + return results diff --git a/training/scripts/dated/20241110-123407/migrate_double_v1.py b/training/scripts/dated/20241110-123407/migrate_double_v1.py new file mode 100644 index 0000000..1f6cf5a --- /dev/null +++ b/training/scripts/dated/20241110-123407/migrate_double_v1.py @@ -0,0 +1,90 @@ +#!/usr/bin/python3 +import sys +import os +import argparse +import math +import torch + +description = """ +Hacky script to double number of v1 channels. +Run on a torch checkpoint.ckpt file. Replace the old one with the new one after a backup. +""" + +parser = argparse.ArgumentParser(description=description) +parser.add_argument('-checkpoint', help='Checkpoint file path', required=True) +parser.add_argument('-output', help='Output new checkpoint to here', required=True) +args = vars(parser.parse_args()) + +checkpoint_path = args["checkpoint"] +output_path = args["output"] + +data = torch.load(checkpoint_path,map_location="cpu") + +def noisify(tensor): + scale = 0.02 * math.sqrt(torch.mean(torch.square(tensor)).item()) + return tensor * (0.9 + 0.15 * torch.rand(tensor.shape, device=tensor.device)) + scale * (2.0 * torch.rand(tensor.shape, device=tensor.device) - 1.0) + +# Torch input channel dimension is dim 1. +def double_linear_in_dim(name, tensor, scale): + ic = tensor.shape[1] + print(f"{name} tensor has {ic} input channels, doubling to {ic*2}") + tensor = torch.cat((noisify(tensor),noisify(tensor)), dim=1) * scale + return tensor + +# Torch output channel dimension is dim 0. +def double_linear_out_dim(name, tensor, scale): + oc = tensor.shape[0] + print(f"{name} tensor has {oc} output channels, doubling to {oc*2}") + tensor = torch.cat((noisify(tensor),noisify(tensor)), dim=0) * scale + return tensor + +def expand_in_dim_for(name, scale): + if f"module.{name}" in data["model"]: + data["model"][f"module.{name}"] = double_linear_in_dim(f"module.{name}", data["model"][f"module.{name}"], scale) + elif name in data["model"]: + data["model"][name] = double_linear_in_dim(name, data["model"][name], scale) + else: + assert False, f"{name} not found in saved model" + +def expand_out_dim_for(name, scale): + if f"module.{name}" in data["model"]: + data["model"][f"module.{name}"] = double_linear_out_dim(f"module.{name}", data["model"][f"module.{name}"], scale) + elif name in data["model"]: + data["model"][name] = double_linear_out_dim(name, data["model"][name], scale) + else: + assert False, f"{name} not found in saved model" + +expand_out_dim_for("value_head.conv1.weight", scale=1.0) +expand_in_dim_for("value_head.bias1.beta", scale=1.0) +expand_in_dim_for("value_head.linear2.weight", scale=math.sqrt(0.5)) +expand_in_dim_for("value_head.conv_ownership.weight", scale=math.sqrt(0.5)) +expand_in_dim_for("value_head.conv_scoring.weight", scale=math.sqrt(0.5)) +expand_in_dim_for("value_head.linear_s2.weight", scale=math.sqrt(0.5)) +expand_in_dim_for("value_head.linear_smix.weight", scale=math.sqrt(0.5)) + + +if any("intermediate_value_head" in key for key in data["model"].keys()): + expand_out_dim_for("intermediate_value_head.conv1.weight", scale=1.0) + expand_in_dim_for("intermediate_value_head.bias1.beta", scale=1.0) + expand_in_dim_for("intermediate_value_head.linear2.weight", scale=math.sqrt(0.5)) + expand_in_dim_for("intermediate_value_head.conv_ownership.weight", scale=math.sqrt(0.5)) + expand_in_dim_for("intermediate_value_head.conv_scoring.weight", scale=math.sqrt(0.5)) + expand_in_dim_for("intermediate_value_head.linear_s2.weight", scale=math.sqrt(0.5)) + expand_in_dim_for("intermediate_value_head.linear_smix.weight", scale=math.sqrt(0.5)) + + +old_v1_num_channels = data["config"]["v1_num_channels"] +print(f"Doubling v1_num_channels {old_v1_num_channels}") +data["config"]["v1_num_channels"] = old_v1_num_channels * 2 + + +if "optimizer" in data: + print("Deleting optimizer state") + del data["optimizer"] +if "swa_model" in data: + print("Deleting swa model state") + del data["swa_model"] + +print(f"Saving to {output_path}") +torch.save(data, output_path) +print("Done") diff --git a/training/scripts/dated/20241110-123407/migrate_optimistic_policy.py b/training/scripts/dated/20241110-123407/migrate_optimistic_policy.py new file mode 100644 index 0000000..290411f --- /dev/null +++ b/training/scripts/dated/20241110-123407/migrate_optimistic_policy.py @@ -0,0 +1,73 @@ +#!/usr/bin/python3 +import sys +import os +import argparse +import math +import torch + +description = """ +Hacky script to migrate v11 model to v12 with optimistic policy. +Run on a torch checkpoint.ckpt file. Replace the old one with the new one after a backup. +And then manually edit the model.config.json to also bump the json file version to 12. +""" + +parser = argparse.ArgumentParser(description=description) +parser.add_argument('-checkpoint', help='Checkpoint file path', required=True) +parser.add_argument('-output', help='Output new checkpoint to here', required=True) +args = vars(parser.parse_args()) + +checkpoint_path = args["checkpoint"] +output_path = args["output"] + +data = torch.load(checkpoint_path,map_location="cpu") +assert data["config"]["version"] == 11 + +def noisify(tensor): + scale = 0.01 * math.sqrt(torch.mean(torch.square(tensor)).item()) + return tensor * (0.95 + 0.05 * torch.rand(tensor.shape, device=tensor.device)) + scale * (2.0 * torch.rand(tensor.shape, device=tensor.device) - 1.0) + + +# Torch output channel dimension is dim 0. +def expand_policy_out_dim(name, tensor): + if tensor.shape[0] == 6: + print(f"{name} tensor already has 6 output channels, not modifying it") + return tensor + elif tensor.shape[0] == 5: + print(f"{name} tensor has 5 output channels, adding one more initialized from channel 0") + slice0 = tensor[0:1] + tensor = torch.cat((tensor,noisify(slice0)), dim=0) + return tensor + elif tensor.shape[0] == 4: + print(f"{name} tensor has 4 output channels, adding two more initialized from channel 0") + slice0 = tensor[0:1] + tensor = torch.cat((tensor,noisify(slice0),noisify(slice0)), dim=0) + return tensor + else: + assert False, f"Unexpected {name} tensor shape: {tensor.shape}" + +def expand_policy_out_dim_for(name): + if f"module.{name}" in data["model"]: + data["model"][f"module.{name}"] = expand_policy_out_dim(f"module.{name}", data["model"][f"module.{name}"]) + elif name in data["model"]: + data["model"][name] = expand_policy_out_dim(name, data["model"][name]) + else: + assert False, f"{name} not found in saved model" + +expand_policy_out_dim_for("policy_head.conv2p.weight") +expand_policy_out_dim_for("policy_head.linear_pass.weight") +if any("intermediate_policy_head" in key for key in data["model"].keys()): + expand_policy_out_dim_for("intermediate_policy_head.conv2p.weight") + expand_policy_out_dim_for("intermediate_policy_head.linear_pass.weight") + +if "optimizer" in data: + print("Deleting optimizer state") + del data["optimizer"] +if "swa_model" in data: + print("Deleting swa model state") + del data["swa_model"] +print("Setting version to 12") +data["config"]["version"] = 12 + +print(f"Saving to {output_path}") +torch.save(data, output_path) +print("Done, don't forget to edit the model.config.json to version 12 if applicable") diff --git a/training/scripts/dated/20241110-123407/migrate_softplus_fix.py b/training/scripts/dated/20241110-123407/migrate_softplus_fix.py new file mode 100644 index 0000000..e9639d3 --- /dev/null +++ b/training/scripts/dated/20241110-123407/migrate_softplus_fix.py @@ -0,0 +1,64 @@ +#!/usr/bin/python3 +import sys +import os +import argparse +import math +import torch + +description = """ +Hacky script to partially migrate v12 optimistic policy to v13 changing score error. +Run on a torch checkpoint.ckpt file. Replace the old one with the new one after a backup. +""" + +parser = argparse.ArgumentParser(description=description) +parser.add_argument('-checkpoint', help='Checkpoint file path', required=True) +parser.add_argument('-output', help='Output new checkpoint to here', required=True) +args = vars(parser.parse_args()) + +checkpoint_path = args["checkpoint"] +output_path = args["output"] + +data = torch.load(checkpoint_path,map_location="cpu") +assert data["config"]["version"] == 12 + +def shift_bias(name, channel_idx): + if f"module.{name}" in data["model"]: + data["model"][f"module.{name}"][channel_idx] -= math.log(150.0 / 30.0) + elif name in data["model"]: + data["model"][name][channel_idx] -= math.log(150.0 / 30.0) + else: + assert False, f"{name} not found in saved model" + if f"module.{name}" in data["swa_model"]: + data["swa_model"][f"module.{name}"][channel_idx] -= math.log(150.0 / 30.0) + elif name in data["swa_model"]: + data["swa_model"][name][channel_idx] -= math.log(150.0 / 30.0) + else: + assert False, f"{name} not found in swa model" + +shift_bias("value_head.linear_moremiscvaluehead.bias", channel_idx=1) +if any("intermediate_value_head" in key for key in data["model"].keys()): + shift_bias("intermediate_value_head.linear_moremiscvaluehead.bias", channel_idx=1) + +if "optimizer" in data: + print("Deleting optimizer state") + del data["optimizer"] +# if "swa_model" in data: +# print("Deleting swa model state") +# del data["swa_model"] +if "running_metrics" in data: + print("Resetting shortterm score error running metrics") + data["running_metrics"]["sums"]["esstloss_sum"] /= 100000.0 + data["running_metrics"]["weights"]["esstloss_sum"] /= 100000.0 + data["running_metrics"]["sums"]["Iesstloss_sum"] /= 100000.0 + data["running_metrics"]["weights"]["Iesstloss_sum"] /= 100000.0 + +print("Clearing export cycle counter to give time to reconverge") +assert "export_cycle_counter" in data["train_state"] +data["train_state"]["export_cycle_counter"] = 0 + +print("Setting version to 13") +data["config"]["version"] = 13 + +print(f"Saving to {output_path}") +torch.save(data, output_path) +print("Done") diff --git a/training/scripts/dated/20241110-123407/migrate_squared_softplus.py b/training/scripts/dated/20241110-123407/migrate_squared_softplus.py new file mode 100644 index 0000000..62cff9b --- /dev/null +++ b/training/scripts/dated/20241110-123407/migrate_squared_softplus.py @@ -0,0 +1,54 @@ +#!/usr/bin/python3 +import sys +import os +import argparse +import math +import torch + +description = """ +Hacky script to partially migrate v13 to v14 new squared softplus activation for error preds. +Run on a torch checkpoint.ckpt file. Replace the old one with the new one after a backup. +""" + +parser = argparse.ArgumentParser(description=description) +parser.add_argument('-checkpoint', help='Checkpoint file path', required=True) +parser.add_argument('-output', help='Output new checkpoint to here', required=True) +args = vars(parser.parse_args()) + +checkpoint_path = args["checkpoint"] +output_path = args["output"] + +data = torch.load(checkpoint_path,map_location="cpu") +assert data["config"]["version"] == 13 + +# No modification of any weight is needed since for values that aren't large positive values, +# due to the fact that softplus(x) ~= exp(x) and exp(0.5*x) ** 2 ~= exp(x), the existing +# weights of the model already should be a good starting point for the new activation. + +if "optimizer" in data: + print("Deleting optimizer state") + del data["optimizer"] +# if "swa_model" in data: +# print("Deleting swa model state") +# del data["swa_model"] +if "running_metrics" in data: + print("Resetting shortterm value and score error running metrics") + data["running_metrics"]["sums"]["evstloss_sum"] /= 100000.0 + data["running_metrics"]["weights"]["evstloss_sum"] /= 100000.0 + data["running_metrics"]["sums"]["Ievstloss_sum"] /= 100000.0 + data["running_metrics"]["weights"]["Ievstloss_sum"] /= 100000.0 + data["running_metrics"]["sums"]["esstloss_sum"] /= 100000.0 + data["running_metrics"]["weights"]["esstloss_sum"] /= 100000.0 + data["running_metrics"]["sums"]["Iesstloss_sum"] /= 100000.0 + data["running_metrics"]["weights"]["Iesstloss_sum"] /= 100000.0 + +print("Clearing export cycle counter to give time to reconverge") +assert "export_cycle_counter" in data["train_state"] +data["train_state"]["export_cycle_counter"] = 0 + +print("Setting version to 14") +data["config"]["version"] = 14 + +print(f"Saving to {output_path}") +torch.save(data, output_path) +print("Done") diff --git a/training/scripts/dated/20241110-123407/model_pytorch.py b/training/scripts/dated/20241110-123407/model_pytorch.py new file mode 100644 index 0000000..dcae5b4 --- /dev/null +++ b/training/scripts/dated/20241110-123407/model_pytorch.py @@ -0,0 +1,1921 @@ +import math +import numpy as np +import torch +import torch.nn +import torch.nn.functional +import torch.nn.init +import packaging +import packaging.version +from typing import List, Dict, Optional, Set + +import modelconfigs + +EXTRA_SCORE_DISTR_RADIUS = 60 + +class ExtraOutputs: + def __init__(self, requested: List[str]): + self.requested: Set[str] = set(requested) + self.available: List[str] = [] + self.returned: Dict[str,torch.Tensor] = {} + + def add_requested(self, requested: List[str]): + self.requested = self.requested.union(set(requested)) + + def report(self, name: str, value: torch.Tensor): + self.available.append(name) + if name in self.requested: + self.returned[name] = value.detach() + +def act(activation, inplace=False): + if activation == "relu": + return torch.nn.ReLU(inplace=inplace) + if activation == "elu": + return torch.nn.ELU(inplace=inplace) + if activation == "mish": + return torch.nn.Mish(inplace=inplace) + if activation == "gelu": + return torch.nn.GELU(inplace=inplace) + if activation == "hardswish": + if packaging.version.parse(torch.__version__) > packaging.version.parse("1.6.0"): + return torch.nn.Hardswish(inplace=inplace) + else: + return torch.nn.Hardswish() + if activation == "identity": + return torch.nn.Identity() + assert False, f"Unknown activation name: {activation}" + +def compute_gain(activation): + if activation == "relu" or activation == "hardswish": + gain = math.sqrt(2.0) + elif activation == "elu": + gain = math.sqrt(1.55052) + elif activation == "mish": + gain = math.sqrt(2.210277) + elif activation == "gelu": + gain = math.sqrt(2.351718) + elif activation == "identity": + gain = 1.0 + else: + assert False, f"Unknown activation name: {activation}" + return gain + +def init_weights(tensor, activation, scale, fan_tensor=None): + gain = compute_gain(activation) + + if fan_tensor is not None: + (fan_in, _) = torch.nn.init._calculate_fan_in_and_fan_out(fan_tensor) + else: + (fan_in, _) = torch.nn.init._calculate_fan_in_and_fan_out(tensor) + + target_std = scale * gain / math.sqrt(fan_in) + # Multiply slightly since we use truncated normal + std = target_std / 0.87962566103423978 + if std < 1e-10: + tensor.fill_(0.0) + else: + torch.nn.init.trunc_normal_(tensor, mean=0.0, std=std, a=-2.0*std, b=2.0*std) + +class SoftPlusWithGradientFloorFunction(torch.autograd.Function): + """ + Same as softplus, except on backward pass, we never let the gradient decrease below grad_floor. + Equivalent to having a dynamic learning rate depending on stop_grad(x) where x is the input. + If square, then also squares the result while halving the input, and still also keeping the same gradient. + """ + @staticmethod + def forward(ctx, x: torch.Tensor, grad_floor: float, square: bool): + ctx.save_for_backward(x) + ctx.grad_floor = grad_floor # grad_floor is not a tensor + if square: + return torch.square(torch.nn.functional.softplus(0.5 * x)) + else: + return torch.nn.functional.softplus(x) + + @staticmethod + def backward(ctx, grad_output: torch.Tensor): + (x,) = ctx.saved_tensors + grad_floor = ctx.grad_floor + grad_x = None + grad_grad_floor = None + grad_square = None + if ctx.needs_input_grad[0]: + grad_x = grad_output * (grad_floor + (1.0 - grad_floor) / (1.0 + torch.exp(-x))) + return grad_x, grad_grad_floor, grad_square + +class BiasMask(torch.nn.Module): + def __init__( + self, + c_in, + config: modelconfigs.ModelConfig, + is_after_batchnorm: bool = False, + ): + super(BiasMask, self).__init__() + self.c_in = c_in + self.beta = torch.nn.Parameter(torch.zeros(1, c_in, 1, 1)) + self.is_after_batchnorm = is_after_batchnorm + self.scale = None + + def set_scale(self, scale: Optional[float]): + self.scale = scale + + def add_reg_dict(self, reg_dict:Dict[str,List]): + if self.is_after_batchnorm: + reg_dict["output_noreg"].append(self.beta) + else: + reg_dict["noreg"].append(self.beta) + + def set_brenorm_params(self, renorm_avg_momentum: float, rmax: float, dmax: float): + pass + + def add_brenorm_clippage(self, upper_rclippage, lower_rclippage, dclippage): + pass + + def forward(self, x, mask, mask_sum: float): + """ + Parameters: + x: NCHW + mask: N1HW + mask_sum: scalar + + Returns: NCHW + """ + if self.scale is not None: + return (x * self.scale + self.beta) * mask + else: + return (x + self.beta) * mask + + +class NormMask(torch.nn.Module): + def __init__( + self, + c_in, + config: modelconfigs.ModelConfig, + fixup_use_gamma: bool, + force_use_gamma: bool = False, + is_last_batchnorm: bool = False, + ): + """Various kinds of normalization. + + bnorm - batch norm + brenorm - batch renorm + fixup - fixup initialization https://arxiv.org/abs/1901.09321 + fixscale - fixed scaling initialization. Normalization layers simply multiply a constant scalar according + to what batchnorm *would* do if all inputs were unit variance and all linear layers or convolutions + preserved variance. + fixbrenorm - fixed scaling normalization PLUS batch renorm. + fixscaleonenorm - fixed scaling normalization PLUS only have one batch norm layer in the entire net, at the end of the residual trunk. + """ + + super(NormMask, self).__init__() + self.norm_kind = config["norm_kind"] + self.epsilon = config["bnorm_epsilon"] + self.running_avg_momentum = config["bnorm_running_avg_momentum"] + self.fixup_use_gamma = fixup_use_gamma + self.is_last_batchnorm = is_last_batchnorm + self.use_gamma = ( + ("bnorm_use_gamma" in config and config["bnorm_use_gamma"]) or + ((self.norm_kind == "fixup" or self.norm_kind == "fixscale" or self.norm_kind == "fixscaleonenorm") and fixup_use_gamma) or + force_use_gamma + ) + self.c_in = c_in + + self.scale = None + self.gamma = None + if self.norm_kind == "bnorm" or (self.norm_kind == "fixscaleonenorm" and self.is_last_batchnorm): + self.is_using_batchnorm = True + if self.use_gamma: + self.gamma = torch.nn.Parameter(torch.ones(1, c_in, 1, 1)) + self.beta = torch.nn.Parameter(torch.zeros(1, c_in, 1, 1)) + self.register_buffer( + "running_mean", torch.zeros(c_in, dtype=torch.float) + ) + self.register_buffer( + "running_std", torch.ones(c_in, dtype=torch.float) + ) + elif self.norm_kind == "brenorm" or self.norm_kind == "fixbrenorm": + self.is_using_batchnorm = True + if self.use_gamma: + self.gamma = torch.nn.Parameter(torch.ones(1, c_in, 1, 1)) + self.beta = torch.nn.Parameter(torch.zeros(1, c_in, 1, 1)) + self.register_buffer( + "running_mean", torch.zeros(c_in, dtype=torch.float) + ) + self.register_buffer( + "running_std", torch.ones(c_in, dtype=torch.float) + ) + self.register_buffer( + "renorm_running_mean", torch.zeros(c_in, dtype=torch.float) + ) + self.register_buffer( + "renorm_running_std", torch.ones(c_in, dtype=torch.float) + ) + self.register_buffer( + "renorm_upper_rclippage", torch.zeros((), dtype=torch.float) + ) + self.register_buffer( + "renorm_lower_rclippage", torch.zeros((), dtype=torch.float) + ) + self.register_buffer( + "renorm_dclippage", torch.zeros((), dtype=torch.float) + ) + + elif self.norm_kind == "fixup" or self.norm_kind == "fixscale" or (self.norm_kind == "fixscaleonenorm" and not self.is_last_batchnorm): + self.is_using_batchnorm = False + self.beta = torch.nn.Parameter(torch.zeros(1, c_in, 1, 1)) + if self.use_gamma: + self.gamma = torch.nn.Parameter(torch.ones(1, c_in, 1, 1)) + else: + assert False, f"Unimplemented norm_kind: {self.norm_kind}" + + def set_scale(self, scale: Optional[float]): + self.scale = scale + + def add_reg_dict(self, reg_dict:Dict[str,List]): + if self.is_last_batchnorm: + if self.gamma is not None: + reg_dict["output"].append(self.gamma) + reg_dict["output_noreg"].append(self.beta) + else: + if self.gamma is not None: + reg_dict["normal_gamma"].append(self.gamma) + reg_dict["noreg"].append(self.beta) + + def set_brenorm_params(self, renorm_avg_momentum: float, rmax: float, dmax: float): + self.renorm_avg_momentum = renorm_avg_momentum + self.rmax = rmax + self.dmax = dmax + + def add_brenorm_clippage(self, upper_rclippage, lower_rclippage, dclippage): + upper_rclippage.append(self.renorm_upper_rclippage.cpu().item()) + lower_rclippage.append(self.renorm_lower_rclippage.cpu().item()) + dclippage.append(self.renorm_dclippage.cpu().item()) + + def _compute_bnorm_values(self, x, mask, mask_sum: float): + # This is the mean, computed only over exactly the areas of the mask, weighting each spot equally, + # even across different elements in the batch that might have different board sizes. + mean = torch.sum(x * mask, dim=(0,2,3),keepdim=True) / mask_sum + zeromean_x = x - mean + # Similarly, the variance computed exactly only over those spots + var = torch.sum(torch.square(zeromean_x * mask),dim=(0,2,3),keepdim=True) / mask_sum + std = torch.sqrt(var + self.epsilon) + return zeromean_x, mean, std + + def apply_gamma_beta_scale_mask(self, x, mask): + if self.scale is not None: + if self.gamma is not None: + return (x * (self.gamma * self.scale) + self.beta) * mask + else: + return (x * self.scale + self.beta) * mask + else: + if self.gamma is not None: + return (x * self.gamma + self.beta) * mask + else: + return (x + self.beta) * mask + + + def forward(self, x, mask, mask_sum: float): + """ + Parameters: + x: NCHW + mask: N1HW + mask_sum: scalar + + Returns: NCHW + """ + + if self.norm_kind == "bnorm" or (self.norm_kind == "fixscaleonenorm" and self.is_last_batchnorm): + assert x.shape[1] == self.c_in + if self.training: + zeromean_x, mean, std = self._compute_bnorm_values(x, mask, mask_sum) + + detached_mean = mean.view(self.c_in).detach() + detached_std = std.view(self.c_in).detach() + with torch.no_grad(): + self.running_mean += self.running_avg_momentum * (detached_mean - self.running_mean) + self.running_std += self.running_avg_momentum * (detached_std - self.running_std) + + return self.apply_gamma_beta_scale_mask(zeromean_x / std, mask) + else: + return self.apply_gamma_beta_scale_mask((x - self.running_mean.view(1,self.c_in,1,1)) / self.running_std.view(1,self.c_in,1,1), mask) + + elif self.norm_kind == "brenorm" or self.norm_kind == "fixbrenorm": + assert x.shape[1] == self.c_in + if self.training: + zeromean_x, mean, std = self._compute_bnorm_values(x, mask, mask_sum) + + detached_mean = mean.view(self.c_in).detach() + detached_std = std.view(self.c_in).detach() + with torch.no_grad(): + unclipped_r = detached_std / self.renorm_running_std + unclipped_d = (detached_mean - self.renorm_running_mean) / self.renorm_running_std + r = unclipped_r.clamp(1.0 / self.rmax, self.rmax) + d = unclipped_d.clamp(-self.dmax, self.dmax) + + self.renorm_running_mean += self.renorm_avg_momentum * (detached_mean - self.renorm_running_mean) + self.renorm_running_std += self.renorm_avg_momentum * (detached_std - self.renorm_running_std) + self.running_mean += self.running_avg_momentum * (detached_mean - self.running_mean) + self.running_std += self.running_avg_momentum * (detached_std - self.running_std) + + upper_rclippage = torch.mean(torch.nn.functional.relu(torch.log(unclipped_r / r))) + lower_rclippage = torch.mean(torch.nn.functional.relu(-torch.log(unclipped_r / r))) + dclippage = torch.mean(torch.abs(unclipped_d - d)) + self.renorm_upper_rclippage += 0.01 * (upper_rclippage - self.renorm_upper_rclippage) + self.renorm_lower_rclippage += 0.01 * (lower_rclippage - self.renorm_lower_rclippage) + self.renorm_dclippage += 0.01 * (dclippage - self.renorm_dclippage) + + if self.rmax > 1.00000001 or self.dmax > 0.00000001: + return self.apply_gamma_beta_scale_mask(zeromean_x / std * r.detach().view(1,self.c_in,1,1) + d.detach().view(1,self.c_in,1,1), mask) + else: + return self.apply_gamma_beta_scale_mask(zeromean_x / std, mask) + + else: + return self.apply_gamma_beta_scale_mask((x - self.running_mean.view(1,self.c_in,1,1)) / self.running_std.view(1,self.c_in,1,1), mask) + + elif self.norm_kind == "fixup" or self.norm_kind == "fixscale" or (self.norm_kind == "fixscaleonenorm" and not self.is_last_batchnorm): + return self.apply_gamma_beta_scale_mask(x, mask) + + else: + assert False + + +class KataGPool(torch.nn.Module): + def __init__(self): + super(KataGPool, self).__init__() + + def forward(self, x, mask, mask_sum_hw): + """ + Parameters: + x: NCHW + mask: N1HW + mask_sum_hw: N111 + + Returns: NC11 + """ + mask_sum_hw_sqrt_offset = torch.sqrt(mask_sum_hw) - 14.0 + + layer_mean = torch.sum(x, dim=(2, 3), keepdim=True, dtype=torch.float32) / mask_sum_hw + # All activation functions we use right now are always greater than -1.0, and map 0 -> 0. + # So off-board areas will equal 0, and then this max is mask-safe if we assign -1.0 to off-board areas. + (layer_max,_argmax) = torch.max((x+(mask-1.0)).view(x.shape[0],x.shape[1],-1).to(torch.float32), dim=2) + layer_max = layer_max.view(x.shape[0],x.shape[1],1,1) + + out_pool1 = layer_mean + out_pool2 = layer_mean * (mask_sum_hw_sqrt_offset / 10.0) + out_pool3 = layer_max + + out = torch.cat((out_pool1, out_pool2, out_pool3), dim=1) + return out + + +class KataValueHeadGPool(torch.nn.Module): + def __init__(self): + super(KataValueHeadGPool, self).__init__() + + def forward(self, x, mask, mask_sum_hw): + """ + Parameters: + x: NCHW + mask: N1HW + mask_sum_hw: N111 + + Returns: NC11 + """ + mask_sum_hw_sqrt_offset = torch.sqrt(mask_sum_hw) - 14.0 + + layer_mean = torch.sum(x, dim=(2, 3), keepdim=True, dtype=torch.float32) / mask_sum_hw + + out_pool1 = layer_mean + out_pool2 = layer_mean * (mask_sum_hw_sqrt_offset / 10.0) + out_pool3 = layer_mean * ((mask_sum_hw_sqrt_offset * mask_sum_hw_sqrt_offset) / 100.0 - 0.1) + + out = torch.cat((out_pool1, out_pool2, out_pool3), dim=1) + return out + +class KataConvAndGPool(torch.nn.Module): + def __init__(self, name, c_in, c_out, c_gpool, config, activation): + super(KataConvAndGPool, self).__init__() + self.name = name + self.norm_kind = config["norm_kind"] + self.activation = activation + self.conv1r = torch.nn.Conv2d(c_in, c_out, kernel_size=3, padding="same", bias=False) + self.conv1g = torch.nn.Conv2d(c_in, c_gpool, kernel_size=3, padding="same", bias=False) + self.normg = NormMask( + c_gpool, + config=config, + fixup_use_gamma=False, + ) + self.actg = act(self.activation, inplace=True) + self.gpool = KataGPool() + self.linear_g = torch.nn.Linear(3 * c_gpool, c_out, bias=False) + + def initialize(self, scale): + # Scaling so that variance on the r and g branches adds up to 1.0 + r_scale = 0.8 + g_scale = 0.6 + if self.norm_kind == "fixup" or self.norm_kind == "fixscale" or self.norm_kind == "fixbrenorm" or self.norm_kind == "fixscaleonenorm": + init_weights(self.conv1r.weight, self.activation, scale=scale * r_scale) + init_weights(self.conv1g.weight, self.activation, scale=math.sqrt(scale) * math.sqrt(g_scale)) + init_weights(self.linear_g.weight, self.activation, scale=math.sqrt(scale) * math.sqrt(g_scale)) + else: + init_weights(self.conv1r.weight, self.activation, scale=scale*r_scale) + init_weights(self.conv1g.weight, self.activation, scale=math.sqrt(scale) * 1.0) + init_weights(self.linear_g.weight, self.activation, scale=math.sqrt(scale) * g_scale) + + def add_reg_dict(self, reg_dict:Dict[str,List]): + reg_dict["normal"].append(self.conv1r.weight) + reg_dict["normal"].append(self.conv1g.weight) + self.normg.add_reg_dict(reg_dict) + reg_dict["normal"].append(self.linear_g.weight) + + def set_brenorm_params(self, renorm_avg_momentum: float, rmax: float, dmax: float): + self.normg.set_brenorm_params(renorm_avg_momentum, rmax, dmax) + + def add_brenorm_clippage(self, upper_rclippage, lower_rclippage, dclippage): + self.normg.add_brenorm_clippage(upper_rclippage, lower_rclippage, dclippage) + + + def forward(self, x, mask, mask_sum_hw, mask_sum:float, extra_outputs: Optional[ExtraOutputs]): + """ + Parameters: + x: NCHW + mask: N1HW + mask_sum_hw: N111 + mask_sum: scalar + + Returns: NCHW + """ + out = x + outr = self.conv1r(out) + outg = self.conv1g(out) + + outg = self.normg(outg, mask=mask, mask_sum=mask_sum) + outg = self.actg(outg) + outg = self.gpool(outg, mask=mask, mask_sum_hw=mask_sum_hw).squeeze(-1).squeeze(-1) + outg = self.linear_g(outg).unsqueeze(-1).unsqueeze(-1) + + out = outr + outg + return out + + +class KataConvAndAttentionPool(torch.nn.Module): + def __init__(self, name, c_in, c_out, c_gpool, config, activation): + super(KataConvAndAttentionPool, self).__init__() + self.name = name + self.norm_kind = config["norm_kind"] + self.c_gpool = c_gpool + self.c_apheads = config["num_attention_pool_heads"] + self.activation = activation + self.conv1r = torch.nn.Conv2d(c_in, c_out, kernel_size=3, padding="same", bias=False) + self.conv1g = torch.nn.Conv2d(c_in, c_gpool, kernel_size=3, padding="same", bias=False) + self.conv1k = torch.nn.Conv2d(c_in, c_gpool, kernel_size=1, padding="same", bias=False) + self.conv1q = torch.nn.Conv2d(c_in, c_gpool, kernel_size=1, padding="same", bias=False) + + assert c_gpool % self.c_apheads == 0, "Gpool channels must be divisible by num_attention_pool_heads" + + self.normg = NormMask( + c_gpool, + config=config, + fixup_use_gamma=False, + ) + self.actg = act(activation, inplace=True) + self.conv_mix = torch.nn.Conv2d(c_gpool*2, c_out, kernel_size=1, padding="same", bias=False) + + def initialize(self, scale): + # Scaling so that variance on the r and g branches adds up to 1.0 + r_scale = 0.8 + g_scale = 0.6 + if self.norm_kind == "fixup" or self.norm_kind == "fixscale" or self.norm_kind == "fixbrenorm" or self.norm_kind == "fixscaleonenorm": + init_weights(self.conv1r.weight, self.activation, scale=scale * r_scale) + init_weights(self.conv1g.weight, self.activation, scale=math.sqrt(scale) * math.sqrt(g_scale)) + init_weights(self.conv1k.weight, "identity", scale=math.sqrt(2.0)) + init_weights(self.conv1q.weight, "identity", scale=math.sqrt(2.0)) + init_weights(self.conv_mix.weight, self.activation, scale=math.sqrt(scale) * math.sqrt(g_scale)) + else: + init_weights(self.conv1r.weight, self.activation, scale=scale*r_scale) + init_weights(self.conv1g.weight, self.activation, scale=math.sqrt(scale) * 1.0) + init_weights(self.conv1k.weight, "identity", scale=math.sqrt(2.0)) + init_weights(self.conv1q.weight, "identity", scale=math.sqrt(2.0)) + init_weights(self.conv_mix.weight, self.activation, scale=math.sqrt(scale) * g_scale) + + def add_reg_dict(self, reg_dict:Dict[str,List]): + reg_dict["normal"].append(self.conv1r.weight) + reg_dict["normal"].append(self.conv1g.weight) + reg_dict["output"].append(self.conv1k.weight) + reg_dict["output"].append(self.conv1q.weight) + self.normg.add_reg_dict(reg_dict) + reg_dict["normal"].append(self.conv_mix.weight) + + def set_brenorm_params(self, renorm_avg_momentum: float, rmax: float, dmax: float): + self.normg.set_brenorm_params(renorm_avg_momentum, rmax, dmax) + + def add_brenorm_clippage(self, upper_rclippage, lower_rclippage, dclippage): + self.normg.add_brenorm_clippage(upper_rclippage, lower_rclippage, dclippage) + + def forward(self, x, mask, mask_sum_hw, mask_sum:float, extra_outputs: Optional[ExtraOutputs]): + """ + Parameters: + x: NCHW + mask: N1HW + mask_sum_hw: N111 + mask_sum: scalar + + Returns: NCHW + """ + n = x.shape[0] + h = x.shape[2] + w = x.shape[3] + + out = x + outr = self.conv1r(out) + outg = self.conv1g(out) + outk = self.conv1k(out).view(n*self.c_apheads, self.c_gpool//self.c_apheads, h*w) + outq = self.conv1q(out).view(n*self.c_apheads, self.c_gpool//self.c_apheads, h*w) + attention_logits = torch.bmm(torch.transpose(outk,1,2), outq) # n*heads, src h*w, dst h*w + attention_logits = attention_logits.view(n, self.c_apheads, h*w, h*w) + attention_logits = attention_logits - (1.0 - mask.view(n,1,h*w,1)) * 6000.0 + attention_logits = attention_logits.view(n * self.c_apheads, h*w, h*w) + attention = torch.nn.functional.softmax(attention_logits, dim=1) + attention_scale = 0.1 / torch.sqrt(torch.sum(torch.square(attention), dim=1, keepdim=True)) # n*heads, 1, h*w + + outg = self.normg(outg, mask=mask, mask_sum=mask_sum) + outg = self.actg(outg).view(n*self.c_apheads, self.c_gpool//self.c_apheads, h*w) + + out_pool1 = torch.bmm(outg, attention) + out_pool2 = out_pool1 * attention_scale + out_pool1 = out_pool1.view(n, self.c_gpool, h*w) + out_pool2 = out_pool2.view(n, self.c_gpool, h*w) + + outg = torch.cat((out_pool1, out_pool2), dim=1).view(n, 2 * self.c_gpool, h, w) * mask + outg = self.conv_mix(outg) + out = outr + outg + return out + + # def forward(self, x, mask, mask_sum_hw, mask_sum:float): + # """ + # Parameters: + # x: NCHW + # mask: N1HW + # mask_sum_hw: N111 + # mask_sum: scalar + + # Returns: NCHW + # """ + # n = x.shape[0] + # h = x.shape[2] + # w = x.shape[3] + + # out = x + # outr = self.conv1r(out) + # outg = self.conv1g(out) + # outk = self.conv1k(out) - (1.0 - mask) * 5000.0 + # outq = self.conv1q(out) + + # outk = outk.view(n*self.c_apheads, self.c_gpool//self.c_apheads, h*w) + # outq = outq.view(n*self.c_apheads, self.c_gpool//self.c_apheads, h*w) + + # mask_sum_hw_sqrt_offset = torch.sqrt(mask_sum_hw) - 14.0 + + # outg = self.normg(outg, mask=mask, mask_sum=mask_sum) + # outg = self.actg(outg).view(n*self.c_apheads, self.c_gpool//self.c_apheads, h*w) + # # Shen et al. Efficient Attention: Attention with Linear Complexities + # outg = torch.bmm(outg, torch.transpose(torch.nn.functional.softmax(outk,dim=2),1,2)) + # outg = torch.bmm(outg, torch.nn.functional.softmax(outq,dim=1)) + # outg = outg.view(n, self.c_gpool, h, w) * mask + # outg = torch.cat(( + # outg, + # outg * (mask_sum_hw_sqrt_offset / 10.0) + # ),dim=1) + + # outg = self.conv_mix(outg) + # out = outr + outg + # return out + + +class NormActConv(torch.nn.Module): + def __init__( + self, + name: str, + c_in: int, + c_out: int, + c_gpool: Optional[int], + config: modelconfigs.ModelConfig, + activation: str, + kernel_size: int, + fixup_use_gamma: bool, + ): + super(NormActConv, self).__init__() + self.name = name + self.c_in = c_in + self.c_out = c_out + self.c_gpool = c_gpool + self.norm = NormMask( + c_in, + config=config, + fixup_use_gamma=fixup_use_gamma, + ) + self.activation = activation + self.act = act(activation, inplace=True) + self.use_repvgg_init = kernel_size > 1 and "use_repvgg_init" in config and config["use_repvgg_init"] + + if c_gpool is not None: + if config["use_attention_pool"]: + self.convpool = KataConvAndAttentionPool(name=name+".convpool",c_in=c_in, c_out=c_out, c_gpool=c_gpool, config=config, activation=activation) + self.conv = None + else: + self.convpool = KataConvAndGPool(name=name+".convpool",c_in=c_in, c_out=c_out, c_gpool=c_gpool, config=config, activation=activation) + self.conv = None + else: + self.conv = torch.nn.Conv2d(c_in, c_out, kernel_size=kernel_size, padding="same", bias=False) + self.convpool = None + + self.conv1x1 = None + if self.conv is not None and kernel_size > 1 and "use_repvgg_linear" in config and config["use_repvgg_linear"]: + self.conv1x1 = torch.nn.Conv2d(c_in, c_out, kernel_size=1, padding="same", bias=False) + + def initialize(self, scale, norm_scale=None): + self.norm.set_scale(norm_scale) + if self.convpool is not None: + self.convpool.initialize(scale=scale) + else: + if self.conv1x1 is not None: + init_weights(self.conv1x1.weight, self.activation, scale=scale*0.6) + init_weights(self.conv.weight, self.activation, scale=scale*0.8) + else: + if self.use_repvgg_init: + init_weights(self.conv.weight, self.activation, scale=scale*0.8) + center_bonus = self.conv.weight.new_zeros((self.conv.weight.shape[0],self.conv.weight.shape[1]),requires_grad=False) + init_weights(center_bonus, self.activation, scale=scale*0.6) + self.conv.weight[:,:,1,1] += center_bonus + else: + init_weights(self.conv.weight, self.activation, scale=scale) + + + def add_reg_dict(self, reg_dict:Dict[str,List]): + self.norm.add_reg_dict(reg_dict) + if self.convpool is not None: + self.convpool.add_reg_dict(reg_dict) + else: + if self.conv1x1 is not None: + reg_dict["normal"].append(self.conv1x1.weight) + reg_dict["normal"].append(self.conv.weight) + + def set_brenorm_params(self, renorm_avg_momentum: float, rmax: float, dmax: float): + self.norm.set_brenorm_params(renorm_avg_momentum, rmax, dmax) + if self.convpool is not None: + self.convpool.set_brenorm_params(renorm_avg_momentum, rmax, dmax) + + def add_brenorm_clippage(self, upper_rclippage, lower_rclippage, dclippage): + self.norm.add_brenorm_clippage(upper_rclippage, lower_rclippage, dclippage) + if self.convpool is not None: + self.convpool.add_brenorm_clippage(upper_rclippage, lower_rclippage, dclippage) + + def forward(self, x, mask, mask_sum_hw, mask_sum: float, extra_outputs: Optional[ExtraOutputs]): + """ + Parameters: + x: NCHW + mask: N1HW + mask_sum_hw: N111 + mask_sum: scalar + + Returns: NCHW + """ + out = x + out = self.norm(out, mask=mask, mask_sum=mask_sum) + out = self.act(out) + # print("TENSOR AFTER NORMACT") + # print(out) + if self.convpool is not None: + out = self.convpool(out, mask=mask, mask_sum_hw=mask_sum_hw, mask_sum=mask_sum, extra_outputs=extra_outputs) + else: + if self.conv1x1 is not None: + out = self.conv(out) + self.conv1x1(out) + else: + out = self.conv(out) + if extra_outputs is not None: + extra_outputs.report(self.name+".out", out) + return out + + +class ResBlock(torch.nn.Module): + def __init__( + self, + name: str, + c_main: int, + c_mid: int, + c_gpool: Optional[int], + config: modelconfigs.ModelConfig, + activation: str, + ): + super(ResBlock, self).__init__() + self.name = name + self.norm_kind = config["norm_kind"] + self.normactconv1 = NormActConv( + name=name+".normactconv1", + c_in=c_main, + c_out=c_mid - (0 if c_gpool is None else c_gpool), + c_gpool=c_gpool, + config=config, + activation=activation, + kernel_size=3, + fixup_use_gamma=False, + ) + self.normactconv2 = NormActConv( + name=name+".normactconv2", + c_in=c_mid - (0 if c_gpool is None else c_gpool), + c_out=c_main, + c_gpool=None, + config=config, + activation=activation, + kernel_size=3, + fixup_use_gamma=True, + ) + + def initialize(self, fixup_scale): + if self.norm_kind == "fixup": + self.normactconv1.initialize(scale=fixup_scale) + self.normactconv2.initialize(scale=0.0) + elif self.norm_kind == "fixscale" or self.norm_kind == "fixbrenorm" or self.norm_kind == "fixscaleonenorm": + self.normactconv1.initialize(scale=1.0, norm_scale=fixup_scale) + self.normactconv2.initialize(scale=1.0) + else: + self.normactconv1.initialize(scale=1.0) + self.normactconv2.initialize(scale=1.0) + + def add_reg_dict(self, reg_dict:Dict[str,List]): + self.normactconv1.add_reg_dict(reg_dict) + self.normactconv2.add_reg_dict(reg_dict) + + def set_brenorm_params(self, renorm_avg_momentum: float, rmax: float, dmax: float): + self.normactconv1.set_brenorm_params(renorm_avg_momentum, rmax, dmax) + self.normactconv2.set_brenorm_params(renorm_avg_momentum, rmax, dmax) + + def add_brenorm_clippage(self, upper_rclippage, lower_rclippage, dclippage): + self.normactconv1.add_brenorm_clippage(upper_rclippage, lower_rclippage, dclippage) + self.normactconv2.add_brenorm_clippage(upper_rclippage, lower_rclippage, dclippage) + + def forward(self, x, mask, mask_sum_hw, mask_sum: float, extra_outputs: Optional[ExtraOutputs]): + """ + Parameters: + x: NCHW + mask: N1HW + mask_sum_hw: N111 + mask_sum: scalar + + Returns: NCHW + """ + out = x + out = self.normactconv1(out, mask=mask, mask_sum_hw=mask_sum_hw, mask_sum=mask_sum, extra_outputs=extra_outputs) + out = self.normactconv2(out, mask=mask, mask_sum_hw=mask_sum_hw, mask_sum=mask_sum, extra_outputs=extra_outputs) + result = x + out + if extra_outputs is not None: + extra_outputs.report(self.name+".out", result) + return result + + +class BottleneckResBlock(torch.nn.Module): + def __init__( + self, + name: str, + internal_length: int, + c_main: int, + c_mid: int, + c_gpool: Optional[int], + config: modelconfigs.ModelConfig, + activation: str, + ): + super(BottleneckResBlock, self).__init__() + self.name = name + self.norm_kind = config["norm_kind"] + self.internal_length = internal_length + assert internal_length >= 1 + + self.normactconvp = NormActConv( + name=name+".normactconvp", + c_in=c_main, + c_out=c_mid, + c_gpool=None, + config=config, + activation=activation, + kernel_size=1, + fixup_use_gamma=False, + ) + + self.normactconvstack = torch.nn.ModuleList() + self.normactconvstack.append(NormActConv( + name=name+".normactconvstack."+str(0), + c_in=c_mid, + c_out=c_mid - (0 if c_gpool is None else c_gpool), + c_gpool=c_gpool, + config=config, + activation=activation, + kernel_size=3, + fixup_use_gamma=False, + )) + for i in range(self.internal_length-1): + self.normactconvstack.append(NormActConv( + name=name+".normactconvstack."+str(i+1), + c_in=self.normactconvstack[-1].c_out, + c_out=c_mid, + c_gpool=None, + config=config, + activation=activation, + kernel_size=3, + fixup_use_gamma=False, + )) + + self.normactconvq = NormActConv( + name=name+".normactconvq", + c_in=self.normactconvstack[-1].c_out, + c_out=c_main, + c_gpool=None, + config=config, + activation=activation, + kernel_size=1, + fixup_use_gamma=True, + ) + + def initialize(self, fixup_scale): + if self.norm_kind == "fixup": + self.normactconvp.initialize(scale=math.pow(fixup_scale, 1.0 / (1.0 + self.internal_length))) + for i in range(self.internal_length): + self.normactconvstack[i].initialize(scale=math.pow(fixup_scale, 1.0 / (1.0 + self.internal_length))) + self.normactconvq.initialize(scale=0.0) + elif self.norm_kind == "fixscale" or self.norm_kind == "fixbrenorm" or self.norm_kind == "fixscaleonenorm": + self.normactconvp.initialize(scale=1.0, norm_scale=fixup_scale) + for i in range(self.internal_length): + self.normactconvstack[i].initialize(scale=1.0) + self.normactconvq.initialize(scale=1.0) + else: + self.normactconvp.initialize(scale=1.0) + for i in range(self.internal_length): + self.normactconvstack[i].initialize(scale=1.0) + self.normactconvq.initialize(scale=1.0) + + def add_reg_dict(self, reg_dict:Dict[str,List]): + self.normactconvp.add_reg_dict(reg_dict) + for i in range(self.internal_length): + self.normactconvstack[i].add_reg_dict(reg_dict) + self.normactconvq.add_reg_dict(reg_dict) + + def set_brenorm_params(self, renorm_avg_momentum: float, rmax: float, dmax: float): + self.normactconvp.set_brenorm_params(renorm_avg_momentum, rmax, dmax) + for i in range(self.internal_length): + self.normactconvstack[i].set_brenorm_params(renorm_avg_momentum, rmax, dmax) + self.normactconvq.set_brenorm_params(renorm_avg_momentum, rmax, dmax) + + def add_brenorm_clippage(self, upper_rclippage, lower_rclippage, dclippage): + self.normactconvp.add_brenorm_clippage(upper_rclippage, lower_rclippage, dclippage) + for i in range(self.internal_length): + self.normactconvstack[i].add_brenorm_clippage(upper_rclippage, lower_rclippage, dclippage) + self.normactconvq.add_brenorm_clippage(upper_rclippage, lower_rclippage, dclippage) + + def forward(self, x, mask, mask_sum_hw, mask_sum: float, extra_outputs: Optional[ExtraOutputs]): + """ + Parameters: + x: NCHW + mask: N1HW + mask_sum_hw: N111 + mask_sum: scalar + + Returns: NCHW + """ + out = x + out = self.normactconvp(out, mask=mask, mask_sum_hw=mask_sum_hw, mask_sum=mask_sum, extra_outputs=extra_outputs) + for i in range(self.internal_length): + out = self.normactconvstack[i](out, mask=mask, mask_sum_hw=mask_sum_hw, mask_sum=mask_sum, extra_outputs=extra_outputs) + out = self.normactconvq(out, mask=mask, mask_sum_hw=mask_sum_hw, mask_sum=mask_sum, extra_outputs=extra_outputs) + result = x + out + if extra_outputs is not None: + extra_outputs.report(self.name+".out", result) + return result + + +class NestedBottleneckResBlock(torch.nn.Module): + def __init__( + self, + name: str, + internal_length: int, + c_main: int, + c_mid: int, + c_gpool: Optional[int], + config: modelconfigs.ModelConfig, + activation: str, + ): + super(NestedBottleneckResBlock, self).__init__() + self.name = name + self.norm_kind = config["norm_kind"] + self.internal_length = internal_length + assert internal_length >= 1 + + self.normactconvp = NormActConv( + name=name+".normactconvp", + c_in=c_main, + c_out=c_mid, + c_gpool=None, + config=config, + activation=activation, + kernel_size=1, + fixup_use_gamma=False, + ) + + self.blockstack = torch.nn.ModuleList() + for i in range(self.internal_length): + self.blockstack.append(ResBlock( + name=name+".blockstack."+str(i), + c_main=c_mid, + c_mid=c_mid, + c_gpool=(c_gpool if i == 0 else None), + config=config, + activation=activation, + )) + + self.normactconvq = NormActConv( + name=name+".normactconvq", + c_in=c_mid, + c_out=c_main, + c_gpool=None, + config=config, + activation=activation, + kernel_size=1, + fixup_use_gamma=True, + ) + + def initialize(self, fixup_scale): + if self.norm_kind == "fixup": + self.normactconvp.initialize(scale=math.pow(fixup_scale, 1.0 / (1.0 + self.internal_length))) + for i in range(self.internal_length): + self.blockstack[i].initialize(fixup_scale=math.pow(fixup_scale, 1.0 / (1.0 + self.internal_length))) + self.normactconvq.initialize(scale=0.0) + elif self.norm_kind == "fixscale" or self.norm_kind == "fixbrenorm" or self.norm_kind == "fixscaleonenorm": + self.normactconvp.initialize(scale=1.0, norm_scale=fixup_scale) + for i in range(self.internal_length): + self.blockstack[i].initialize(fixup_scale=1.0 / math.sqrt(i+1.0)) + self.normactconvq.initialize(scale=1.0, norm_scale=1.0 / math.sqrt(self.internal_length+1.0)) + else: + self.normactconvp.initialize(scale=1.0) + for i in range(self.internal_length): + self.blockstack[i].initialize(fixup_scale=1.0) + self.normactconvq.initialize(scale=1.0) + + def add_reg_dict(self, reg_dict:Dict[str,List]): + self.normactconvp.add_reg_dict(reg_dict) + for i in range(self.internal_length): + self.blockstack[i].add_reg_dict(reg_dict) + self.normactconvq.add_reg_dict(reg_dict) + + def set_brenorm_params(self, renorm_avg_momentum: float, rmax: float, dmax: float): + self.normactconvp.set_brenorm_params(renorm_avg_momentum, rmax, dmax) + for i in range(self.internal_length): + self.blockstack[i].set_brenorm_params(renorm_avg_momentum, rmax, dmax) + self.normactconvq.set_brenorm_params(renorm_avg_momentum, rmax, dmax) + + def add_brenorm_clippage(self, upper_rclippage, lower_rclippage, dclippage): + self.normactconvp.add_brenorm_clippage(upper_rclippage, lower_rclippage, dclippage) + for i in range(self.internal_length): + self.blockstack[i].add_brenorm_clippage(upper_rclippage, lower_rclippage, dclippage) + self.normactconvq.add_brenorm_clippage(upper_rclippage, lower_rclippage, dclippage) + + def forward(self, x, mask, mask_sum_hw, mask_sum: float, extra_outputs: Optional[ExtraOutputs]): + """ + Parameters: + x: NCHW + mask: N1HW + mask_sum_hw: N111 + mask_sum: scalar + + Returns: NCHW + """ + out = x + out = self.normactconvp(out, mask=mask, mask_sum_hw=mask_sum_hw, mask_sum=mask_sum, extra_outputs=extra_outputs) + for i in range(self.internal_length): + out = self.blockstack[i](out, mask=mask, mask_sum_hw=mask_sum_hw, mask_sum=mask_sum, extra_outputs=extra_outputs) + out = self.normactconvq(out, mask=mask, mask_sum_hw=mask_sum_hw, mask_sum=mask_sum, extra_outputs=extra_outputs) + result = x + out + if extra_outputs is not None: + extra_outputs.report(self.name+".out", result) + return result + + + +class NestedNestedBottleneckResBlock(torch.nn.Module): + def __init__( + self, + name: str, + internal_length: int, + sub_internal_length: int, + c_main: int, + c_outermid: int, + c_mid: int, + c_gpool: Optional[int], + config: modelconfigs.ModelConfig, + activation: str, + ): + super(NestedNestedBottleneckResBlock, self).__init__() + self.name = name + self.norm_kind = config["norm_kind"] + self.internal_length = internal_length + assert internal_length >= 1 + + self.normactconvp = NormActConv( + name=name+".normactconvp", + c_in=c_main, + c_out=c_outermid, + c_gpool=None, + config=config, + activation=activation, + kernel_size=1, + fixup_use_gamma=False, + ) + + self.blockstack = torch.nn.ModuleList() + for i in range(self.internal_length): + self.blockstack.append(NestedBottleneckResBlock( + name=name+".blockstack."+str(i), + internal_length=sub_internal_length, + c_main=c_outermid, + c_mid=c_mid, + c_gpool=(c_gpool if i == 0 else None), + config=config, + activation=activation, + )) + + self.normactconvq = NormActConv( + name=name+".normactconvq", + c_in=c_outermid, + c_out=c_main, + c_gpool=None, + config=config, + activation=activation, + kernel_size=1, + fixup_use_gamma=True, + ) + + def initialize(self, fixup_scale): + if self.norm_kind == "fixup": + self.normactconvp.initialize(scale=math.pow(fixup_scale, 1.0 / (1.0 + self.internal_length))) + for i in range(self.internal_length): + self.blockstack[i].initialize(fixup_scale=math.pow(fixup_scale, 1.0 / (1.0 + self.internal_length))) + self.normactconvq.initialize(scale=0.0) + elif self.norm_kind == "fixscale" or self.norm_kind == "fixbrenorm" or self.norm_kind == "fixscaleonenorm": + self.normactconvp.initialize(scale=1.0, norm_scale=fixup_scale) + for i in range(self.internal_length): + self.blockstack[i].initialize(fixup_scale=1.0 / math.sqrt(i+1.0)) + self.normactconvq.initialize(scale=1.0, norm_scale=1.0 / math.sqrt(self.internal_length+1.0)) + else: + self.normactconvp.initialize(scale=1.0) + for i in range(self.internal_length): + self.blockstack[i].initialize(fixup_scale=1.0) + self.normactconvq.initialize(scale=1.0) + + def add_reg_dict(self, reg_dict:Dict[str,List]): + self.normactconvp.add_reg_dict(reg_dict) + for i in range(self.internal_length): + self.blockstack[i].add_reg_dict(reg_dict) + self.normactconvq.add_reg_dict(reg_dict) + + def set_brenorm_params(self, renorm_avg_momentum: float, rmax: float, dmax: float): + self.normactconvp.set_brenorm_params(renorm_avg_momentum, rmax, dmax) + for i in range(self.internal_length): + self.blockstack[i].set_brenorm_params(renorm_avg_momentum, rmax, dmax) + self.normactconvq.set_brenorm_params(renorm_avg_momentum, rmax, dmax) + + def add_brenorm_clippage(self, upper_rclippage, lower_rclippage, dclippage): + self.normactconvp.add_brenorm_clippage(upper_rclippage, lower_rclippage, dclippage) + for i in range(self.internal_length): + self.blockstack[i].add_brenorm_clippage(upper_rclippage, lower_rclippage, dclippage) + self.normactconvq.add_brenorm_clippage(upper_rclippage, lower_rclippage, dclippage) + + def forward(self, x, mask, mask_sum_hw, mask_sum: float, extra_outputs: Optional[ExtraOutputs]): + """ + Parameters: + x: NCHW + mask: N1HW + mask_sum_hw: N111 + mask_sum: scalar + + Returns: NCHW + """ + out = x + out = self.normactconvp(out, mask=mask, mask_sum_hw=mask_sum_hw, mask_sum=mask_sum, extra_outputs=extra_outputs) + for i in range(self.internal_length): + out = self.blockstack[i](out, mask=mask, mask_sum_hw=mask_sum_hw, mask_sum=mask_sum, extra_outputs=extra_outputs) + out = self.normactconvq(out, mask=mask, mask_sum_hw=mask_sum_hw, mask_sum=mask_sum, extra_outputs=extra_outputs) + result = x + out + if extra_outputs is not None: + extra_outputs.report(self.name+".out", result) + return result + + +class PolicyHead(torch.nn.Module): + def __init__(self, c_in, c_p1, c_g1, config, activation): + super(PolicyHead, self).__init__() + self.config = config + self.activation = activation + + if config["version"] <= 11: + self.num_policy_outputs = 4 + else: + self.num_policy_outputs = 6 + # Output 0: policy prediction + # Output 1: opponent reply policy prediction + # Output 2: soft policy prediction + # Output 3: soft opponent reply policy prediction + # Output 4: long-term-optimistic policy prediction + # Output 5: short-term-optimistic policy prediction + + self.conv1p = torch.nn.Conv2d(c_in, c_p1, kernel_size=1, padding="same", bias=False) + self.conv1g = torch.nn.Conv2d(c_in, c_g1, kernel_size=1, padding="same", bias=False) + + self.biasg = BiasMask( + c_g1, + config=config, + is_after_batchnorm=True, + ) + self.actg = act(self.activation) + self.gpool = KataGPool() + + self.linear_g = torch.nn.Linear(3 * c_g1, c_p1, bias=False) + if config["version"] <= 14: + self.linear_pass = torch.nn.Linear(3 * c_g1, self.num_policy_outputs, bias=False) + else: + self.linear_pass = torch.nn.Linear(3 * c_g1, c_p1, bias=True) + self.act_pass = act(self.activation) + self.linear_pass2 = torch.nn.Linear(c_p1, self.num_policy_outputs, bias=False) + + self.bias2 = BiasMask( + c_p1, + config=config, + is_after_batchnorm=True, + ) + self.act2 = act(activation) + self.conv2p = torch.nn.Conv2d(c_p1, self.num_policy_outputs, kernel_size=1, padding="same", bias=False) + + + def initialize(self): + # Scaling so that variance on the p and g branches adds up to 1.0 + p_scale = 0.8 + g_scale = 0.6 + bias_scale = 0.2 + # Extra scaling for outputs + scale_output = 0.3 + init_weights(self.conv1p.weight, self.activation, scale=p_scale) + init_weights(self.conv1g.weight, self.activation, scale=1.0) + init_weights(self.linear_g.weight, self.activation, scale=g_scale) + if self.config["version"] <= 14: + init_weights(self.linear_pass.weight, "identity", scale=scale_output) + else: + init_weights(self.linear_pass.weight, self.activation, scale=1.0) + init_weights(self.linear_pass.bias, self.activation, scale=bias_scale, fan_tensor=self.linear_pass.weight) + init_weights(self.linear_pass2.weight, "identity", scale=scale_output) + init_weights(self.conv2p.weight, "identity", scale=scale_output) + + def add_reg_dict(self, reg_dict:Dict[str,List]): + reg_dict["output"].append(self.conv1p.weight) + reg_dict["output"].append(self.conv1g.weight) + reg_dict["output"].append(self.linear_g.weight) + if self.config["version"] <= 14: + reg_dict["output"].append(self.linear_pass.weight) + else: + reg_dict["output"].append(self.linear_pass.weight) + reg_dict["output_noreg"].append(self.linear_pass.bias) + reg_dict["output"].append(self.linear_pass2.weight) + + reg_dict["output"].append(self.conv2p.weight) + self.biasg.add_reg_dict(reg_dict) + self.bias2.add_reg_dict(reg_dict) + + def set_brenorm_params(self, renorm_avg_momentum: float, rmax: float, dmax: float): + pass + + def add_brenorm_clippage(self, upper_rclippage, lower_rclippage, dclippage): + pass + + def forward(self, x, mask, mask_sum_hw, mask_sum:float, extra_outputs: Optional[ExtraOutputs]): + outp = self.conv1p(x) + outg = self.conv1g(x) + + outg = self.biasg(outg, mask=mask, mask_sum=mask_sum) + outg = self.actg(outg) + outg = self.gpool(outg, mask=mask, mask_sum_hw=mask_sum_hw).squeeze(-1).squeeze(-1) # NC + + if self.config["version"] <= 14: + outpass = self.linear_pass(outg) # NC + else: + outpass = self.linear_pass(outg) # NC + outpass = self.act_pass(outpass) # NC + outpass = self.linear_pass2(outpass) # NC + + outg = self.linear_g(outg).unsqueeze(-1).unsqueeze(-1) # NCHW + + outp = outp + outg + outp = self.bias2(outp, mask=mask, mask_sum=mask_sum) + outp = self.act2(outp) + outp = self.conv2p(outp) + outpolicy = outp + + # mask out parts outside the board by making them a huge neg number, so that they're 0 after softmax + outpolicy = outpolicy - (1.0 - mask) * 5000.0 + # NC(HW) concat with NC1 + return torch.cat((outpolicy.view(outpolicy.shape[0],outpolicy.shape[1],-1), outpass.unsqueeze(-1)),dim=2) + + +class ValueHead(torch.nn.Module): + def __init__(self, c_in, c_v1, c_v2, c_sv2, num_scorebeliefs, config, activation, pos_len): + super(ValueHead, self).__init__() + self.activation = activation + self.conv1 = torch.nn.Conv2d(c_in, c_v1, kernel_size=1, padding="same", bias=False) + self.bias1 = BiasMask( + c_v1, + config=config, + is_after_batchnorm=True, + ) + self.act1 = act(activation) + self.gpool = KataValueHeadGPool() + + self.linear2 = torch.nn.Linear(3 * c_v1, c_v2, bias=True) + self.act2 = act(activation) + + self.linear_valuehead = torch.nn.Linear(c_v2, 3, bias=True) + self.linear_miscvaluehead = torch.nn.Linear(c_v2, 10, bias=True) + self.linear_moremiscvaluehead = torch.nn.Linear(c_v2, 8, bias=True) + self.conv_ownership = torch.nn.Conv2d(c_v1, 1, kernel_size=1, padding="same", bias=False) + self.conv_scoring = torch.nn.Conv2d(c_v1, 1, kernel_size=1, padding="same", bias=False) + self.conv_futurepos = torch.nn.Conv2d(c_in, 2, kernel_size=1, padding="same", bias=False) + self.conv_seki = torch.nn.Conv2d(c_in, 4, kernel_size=1, padding="same", bias=False) + + self.pos_len = pos_len + self.scorebelief_mid = self.pos_len*self.pos_len + EXTRA_SCORE_DISTR_RADIUS + self.scorebelief_len = self.scorebelief_mid * 2 + self.num_scorebeliefs = num_scorebeliefs + self.c_sv2 = c_sv2 + + self.linear_s2 = torch.nn.Linear(3 * c_v1, c_sv2, bias=True) + self.linear_s2off = torch.nn.Linear(1, c_sv2, bias=False) + self.linear_s2par = torch.nn.Linear(1, c_sv2, bias=False) + self.linear_s3 = torch.nn.Linear(c_sv2, num_scorebeliefs, bias=True) + self.linear_smix = torch.nn.Linear(3 * c_v1, num_scorebeliefs, bias=True) + + self.register_buffer("score_belief_offset_vector", torch.tensor( + data=[(float(i-self.scorebelief_mid)+0.5) for i in range(self.scorebelief_len)], + dtype=torch.float32, + requires_grad=False, + ), persistent=False) + self.register_buffer("score_belief_offset_bias_vector", torch.tensor( + data=[0.05 * (float(i-self.scorebelief_mid)+0.5) for i in range(self.scorebelief_len)], + dtype=torch.float32, + requires_grad=False, + ), persistent=False) + self.register_buffer("score_belief_parity_vector", torch.tensor( + [0.5-float((i-self.scorebelief_mid) % 2) for i in range(self.scorebelief_len)], + dtype=torch.float32, + requires_grad=False, + ), persistent=False) + + + def initialize(self): + bias_scale = 0.2 + init_weights(self.conv1.weight, self.activation, scale=1.0) + init_weights(self.linear2.weight, self.activation, scale=1.0) + init_weights(self.linear2.bias, self.activation, scale=bias_scale, fan_tensor=self.linear2.weight) + + init_weights(self.linear_valuehead.weight, "identity", scale=1.0) + init_weights(self.linear_valuehead.bias, "identity", scale=bias_scale, fan_tensor=self.linear_valuehead.weight) + + init_weights(self.linear_miscvaluehead.weight, "identity", scale=1.0) + init_weights(self.linear_miscvaluehead.bias, "identity", scale=bias_scale, fan_tensor=self.linear_miscvaluehead.weight) + + init_weights(self.linear_moremiscvaluehead.weight, "identity", scale=1.0) + init_weights(self.linear_moremiscvaluehead.bias, "identity", scale=bias_scale, fan_tensor=self.linear_moremiscvaluehead.weight) + + aux_spatial_output_scale = 0.2 + init_weights(self.conv_ownership.weight, "identity", scale=aux_spatial_output_scale) + init_weights(self.conv_scoring.weight, "identity", scale=aux_spatial_output_scale) + init_weights(self.conv_futurepos.weight, "identity", scale=aux_spatial_output_scale) + init_weights(self.conv_seki.weight, "identity", scale=aux_spatial_output_scale) + + init_weights(self.linear_s2.weight, self.activation, scale=1.0) + init_weights(self.linear_s2.bias, self.activation, scale=1.0, fan_tensor=self.linear_s2.weight) + init_weights(self.linear_s2off.weight, self.activation, scale=1.0, fan_tensor=self.linear_s2.weight) + init_weights(self.linear_s2par.weight, self.activation, scale=1.0, fan_tensor=self.linear_s2.weight) + + scorebelief_output_scale = 0.5 + init_weights(self.linear_s3.weight, "identity", scale=scorebelief_output_scale) + init_weights(self.linear_s3.bias, "identity", scale=scorebelief_output_scale*bias_scale, fan_tensor=self.linear_s3.weight) + init_weights(self.linear_smix.weight, "identity", scale=1.0) + init_weights(self.linear_smix.bias, "identity", scale=bias_scale, fan_tensor=self.linear_smix.weight) + + def add_reg_dict(self, reg_dict:Dict[str,List]): + reg_dict["output"].append(self.conv1.weight) + reg_dict["output"].append(self.linear2.weight) + reg_dict["output_noreg"].append(self.linear2.bias) + reg_dict["output"].append(self.linear_valuehead.weight) + reg_dict["output_noreg"].append(self.linear_valuehead.bias) + reg_dict["output"].append(self.linear_miscvaluehead.weight) + reg_dict["output_noreg"].append(self.linear_miscvaluehead.bias) + reg_dict["output"].append(self.linear_moremiscvaluehead.weight) + reg_dict["output_noreg"].append(self.linear_moremiscvaluehead.bias) + reg_dict["output"].append(self.conv_ownership.weight) + reg_dict["output"].append(self.conv_scoring.weight) + reg_dict["output"].append(self.conv_futurepos.weight) + reg_dict["output"].append(self.conv_seki.weight) + reg_dict["output"].append(self.linear_s2.weight) + reg_dict["output_noreg"].append(self.linear_s2.bias) + reg_dict["output"].append(self.linear_s2off.weight) + reg_dict["output"].append(self.linear_s2par.weight) + reg_dict["output"].append(self.linear_s3.weight) + reg_dict["output_noreg"].append(self.linear_s3.bias) + reg_dict["output"].append(self.linear_smix.weight) + reg_dict["output_noreg"].append(self.linear_smix.bias) + self.bias1.add_reg_dict(reg_dict) + + def set_brenorm_params(self, renorm_avg_momentum: float, rmax: float, dmax: float): + pass + + def add_brenorm_clippage(self, upper_rclippage, lower_rclippage, dclippage): + pass + + def forward(self, x, mask, mask_sum_hw, mask_sum:float, input_global, extra_outputs: Optional[ExtraOutputs]): + outv1 = x + outv1 = self.conv1(outv1) + outv1 = self.bias1(outv1, mask=mask, mask_sum=mask_sum) + outv1 = self.act1(outv1) + + outpooled = self.gpool(outv1, mask=mask, mask_sum_hw=mask_sum_hw).squeeze(-1).squeeze(-1) + + outv2 = self.linear2(outpooled) + outv2 = self.act2(outv2) + + # Different subheads + out_value = self.linear_valuehead(outv2) + out_miscvalue = self.linear_miscvaluehead(outv2) + out_moremiscvalue = self.linear_moremiscvaluehead(outv2) + out_ownership = self.conv_ownership(outv1) * mask + out_scoring = self.conv_scoring(outv1) * mask + out_futurepos = self.conv_futurepos(x) * mask + out_seki = self.conv_seki(x) * mask + + # Score belief head + batch_size = x.shape[0] + outsv2 = ( + self.linear_s2(outpooled).view(batch_size,1,self.c_sv2) + + self.linear_s2off(self.score_belief_offset_bias_vector.view(1,self.scorebelief_len,1)) + + self.linear_s2par((self.score_belief_parity_vector.view(1,self.scorebelief_len) * input_global[:,-1:]).view(batch_size,self.scorebelief_len,1)) + ) # N,scorebelief_len,c_sv2 + + outsv2 = self.act2(outsv2) + outsv3 = self.linear_s3(outsv2) # N, scorebelief_len, num_scorebeliefs + + outsmix = self.linear_smix(outpooled) # N, num_scorebeliefs + outsmix_logweights = torch.nn.functional.log_softmax(outsmix, dim=1) + # For each of num_scorebeliefs, compute softmax to make it into probability distribution + out_scorebelief_logprobs = torch.nn.functional.log_softmax(outsv3, dim=1) + # Take the mixture distribution weighted by outsmix_weights + out_scorebelief_logprobs = torch.logsumexp(out_scorebelief_logprobs + outsmix_logweights.view(-1, 1, self.num_scorebeliefs), dim=2) + + return ( + out_value, + out_miscvalue, + out_moremiscvalue, + out_ownership, + out_scoring, + out_futurepos, + out_seki, + out_scorebelief_logprobs, + ) + +class MetadataEncoder(torch.nn.Module): + def __init__(self, config: modelconfigs.ModelConfig): + super(MetadataEncoder, self).__init__() + + self.config = config + self.activation = config["activation"] + self.meta_encoder_version = 1 if "meta_encoder_version" not in config["metadata_encoder"] else config["metadata_encoder"]["meta_encoder_version"] + + self.c_input = modelconfigs.get_num_meta_encoder_input_features(self.meta_encoder_version) + assert self.c_input == 192 + + self.c_internal = self.config["metadata_encoder"]["internal_num_channels"] + self.c_trunk = self.config["trunk_num_channels"] + self.out_scale = 0.5 + + self.register_buffer("feature_mask", torch.tensor( + # 86 is board area + data=[(0.0 if i == 86 else 1.0) for i in range(self.c_input)], + dtype=torch.float32, + requires_grad=False, + ), persistent=True) + + self.linear1 = torch.nn.Linear(self.c_input, self.c_internal, bias=True) + self.act1 = act(self.activation, inplace=True) + self.linear2 = torch.nn.Linear(self.c_internal, self.c_internal, bias=True) + self.act2 = act(self.activation, inplace=True) + self.linear_output_to_trunk = torch.nn.Linear(self.c_internal, self.c_trunk, bias=False) + + def initialize(self): + weight_scale = 0.8 + bias_scale = 0.2 + with torch.no_grad(): + init_weights(self.linear1.weight, self.activation, scale=weight_scale) + init_weights(self.linear1.bias, self.activation, scale=bias_scale, fan_tensor=self.linear1.weight) + init_weights(self.linear2.weight, self.activation, scale=weight_scale) + init_weights(self.linear2.bias, self.activation, scale=bias_scale, fan_tensor=self.linear2.weight) + init_weights(self.linear_output_to_trunk.weight, self.activation, scale=weight_scale) + + def add_reg_dict(self, reg_dict:Dict[str,List]): + reg_dict["output"].append(self.linear1.weight) + reg_dict["output_noreg"].append(self.linear1.bias) + reg_dict["output"].append(self.linear2.weight) + reg_dict["output_noreg"].append(self.linear2.bias) + reg_dict["normal"].append(self.linear_output_to_trunk.weight) + + def forward(self, input_meta, extra_outputs: Optional[ExtraOutputs]): + x = input_meta + x = x * self.feature_mask.reshape((1,-1)) + x = self.linear1(x) + x = self.act1(x) + x = self.linear2(x) + x = self.act2(x) + return self.out_scale * self.linear_output_to_trunk(x) + + +class Model(torch.nn.Module): + def __init__(self, config: modelconfigs.ModelConfig, pos_len: int): + super(Model, self).__init__() + + self.config = config + self.norm_kind = config["norm_kind"] + self.block_kind = config["block_kind"] + self.c_trunk = config["trunk_num_channels"] + self.c_mid = config["mid_num_channels"] + self.c_gpool = config["gpool_num_channels"] + self.c_outermid = config["outermid_num_channels"] if "outermid_num_channels" in config else self.c_mid + self.c_p1 = config["p1_num_channels"] + self.c_g1 = config["g1_num_channels"] + self.c_v1 = config["v1_num_channels"] + self.c_v2 = config["v2_size"] + self.c_sv2 = config["sbv2_num_channels"] + self.num_scorebeliefs = config["num_scorebeliefs"] + self.num_total_blocks = len(self.block_kind) + self.pos_len = pos_len + + if config["version"] <= 12: + self.td_score_multiplier = 20.0 + self.scoremean_multiplier = 20.0 + self.scorestdev_multiplier = 20.0 + self.lead_multiplier = 20.0 + self.variance_time_multiplier = 40.0 + self.shortterm_value_error_multiplier = 0.25 + self.shortterm_score_error_multiplier = 30.0 + else: + self.td_score_multiplier = 20.0 + self.scoremean_multiplier = 20.0 + self.scorestdev_multiplier = 20.0 + self.lead_multiplier = 20.0 + self.variance_time_multiplier = 40.0 + self.shortterm_value_error_multiplier = 0.25 + self.shortterm_score_error_multiplier = 150.0 + + self.trunk_normless = "trunk_normless" in config and config["trunk_normless"] + + if "has_intermediate_head" in config and config["has_intermediate_head"]: + self.has_intermediate_head = True + self.intermediate_head_blocks = config["intermediate_head_blocks"] + else: + self.has_intermediate_head = False + self.intermediate_head_blocks = 0 + + self.activation = "relu" if "activation" not in config else config["activation"] + + if config["initial_conv_1x1"]: + self.conv_spatial = torch.nn.Conv2d(22, self.c_trunk, kernel_size=1, padding="same", bias=False) + else: + self.conv_spatial = torch.nn.Conv2d(22, self.c_trunk, kernel_size=3, padding="same", bias=False) + self.linear_global = torch.nn.Linear(19, self.c_trunk, bias=False) + + if "metadata_encoder" in config and config["metadata_encoder"] is not None: + self.metadata_encoder = MetadataEncoder(config) + else: + self.metadata_encoder = None + + self.bin_input_shape = [22, pos_len, pos_len] + self.global_input_shape = [19] + + self.blocks = torch.nn.ModuleList() + for block_config in self.block_kind: + block_name = block_config[0] + block_kind = block_config[1] + use_gpool_this_block = False + if block_kind.endswith("gpool"): + use_gpool_this_block = True + block_kind = block_kind[:-5] + + if block_kind == "regular": + self.blocks.append(ResBlock( + name=block_name, + c_main=self.c_trunk, + c_mid=self.c_mid, + c_gpool=(self.c_gpool if use_gpool_this_block else None), + config=self.config, + activation=self.activation, + )) + elif block_kind == "bottle1" or block_kind == "bottle": + self.blocks.append(BottleneckResBlock( + name=block_name, + internal_length=1, + c_main=self.c_trunk, + c_mid=self.c_mid, + c_gpool=(self.c_gpool if use_gpool_this_block else None), + config=self.config, + activation=self.activation, + )) + elif block_kind == "bottle2": + self.blocks.append(BottleneckResBlock( + name=block_name, + internal_length=2, + c_main=self.c_trunk, + c_mid=self.c_mid, + c_gpool=(self.c_gpool if use_gpool_this_block else None), + config=self.config, + activation=self.activation, + )) + elif block_kind == "bottle3": + self.blocks.append(BottleneckResBlock( + name=block_name, + internal_length=3, + c_main=self.c_trunk, + c_mid=self.c_mid, + c_gpool=(self.c_gpool if use_gpool_this_block else None), + config=self.config, + activation=self.activation, + )) + elif block_kind == "bottlenest2": + self.blocks.append(NestedBottleneckResBlock( + name=block_name, + internal_length=2, + c_main=self.c_trunk, + c_mid=self.c_mid, + c_gpool=(self.c_gpool if use_gpool_this_block else None), + config=self.config, + activation=self.activation, + )) + elif block_kind == "bottlenest3": + self.blocks.append(NestedBottleneckResBlock( + name=block_name, + internal_length=3, + c_main=self.c_trunk, + c_mid=self.c_mid, + c_gpool=(self.c_gpool if use_gpool_this_block else None), + config=self.config, + activation=self.activation, + )) + elif block_kind == "bottlenest2bottlenest2": + self.blocks.append(NestedNestedBottleneckResBlock( + name=block_name, + internal_length=2, + sub_internal_length=2, + c_main=self.c_trunk, + c_outermid=self.c_outermid, + c_mid=self.c_mid, + c_gpool=(self.c_gpool if use_gpool_this_block else None), + config=self.config, + activation=self.activation, + )) + else: + assert False, f"Unknown block kind: {block_config[1]}" + + if self.trunk_normless: + self.norm_trunkfinal = BiasMask(self.c_trunk, self.config, is_after_batchnorm=True) + else: + self.norm_trunkfinal = NormMask(self.c_trunk, self.config, fixup_use_gamma=False, is_last_batchnorm=True) + self.act_trunkfinal = act(self.activation) + + self.policy_head = PolicyHead( + self.c_trunk, + self.c_p1, + self.c_g1, + self.config, + self.activation, + ) + self.value_head = ValueHead( + self.c_trunk, + self.c_v1, + self.c_v2, + self.c_sv2, + self.num_scorebeliefs, + self.config, + self.activation, + self.pos_len, + ) + if self.has_intermediate_head: + self.norm_intermediate_trunkfinal = NormMask(self.c_trunk, self.config, fixup_use_gamma=False, is_last_batchnorm=True) + self.act_intermediate_trunkfinal = act(self.activation) + self.intermediate_policy_head = PolicyHead( + self.c_trunk, + self.c_p1, + self.c_g1, + self.config, + self.activation, + ) + self.intermediate_value_head = ValueHead( + self.c_trunk, + self.c_v1, + self.c_v2, + self.c_sv2, + self.num_scorebeliefs, + self.config, + self.activation, + self.pos_len, + ) + + @property + def device(self): + return self.linear_global.weight.device + + def initialize(self): + with torch.no_grad(): + spatial_scale = 0.8 + global_scale = 0.6 + init_weights(self.conv_spatial.weight, self.activation, scale=spatial_scale) + init_weights(self.linear_global.weight, self.activation, scale=global_scale) + + if self.metadata_encoder is not None: + self.metadata_encoder.initialize() + + if self.norm_kind == "fixup": + fixup_scale = 1.0 / math.sqrt(self.num_total_blocks) + for block in self.blocks: + block.initialize(fixup_scale=fixup_scale) + elif self.norm_kind == "fixscale" or self.norm_kind == "fixbrenorm" or self.norm_kind == "fixscaleonenorm": + for i, block in enumerate(self.blocks): + block.initialize(fixup_scale=1.0 / math.sqrt(i+1.0)) + self.norm_trunkfinal.set_scale(1.0 / math.sqrt(self.num_total_blocks+1.0)) + else: + for block in self.blocks: + block.initialize(fixup_scale=1.0) + + self.policy_head.initialize() + self.value_head.initialize() + if self.has_intermediate_head: + self.intermediate_policy_head.initialize() + self.intermediate_value_head.initialize() + + def get_norm_kind(self) -> bool: + return self.norm_kind + + def get_has_intermediate_head(self) -> bool: + return self.has_intermediate_head + def get_has_metadata_encoder(self) -> bool: + return self.metadata_encoder is not None + + def add_reg_dict(self, reg_dict:Dict[str,List]): + reg_dict["normal"] = [] + reg_dict["normal_gamma"] = [] + reg_dict["output"] = [] + reg_dict["noreg"] = [] + reg_dict["output_noreg"] = [] + + reg_dict["normal"].append(self.conv_spatial.weight) + reg_dict["normal"].append(self.linear_global.weight) + if self.metadata_encoder is not None: + self.metadata_encoder.add_reg_dict(reg_dict) + for block in self.blocks: + block.add_reg_dict(reg_dict) + self.norm_trunkfinal.add_reg_dict(reg_dict) + self.policy_head.add_reg_dict(reg_dict) + self.value_head.add_reg_dict(reg_dict) + if self.has_intermediate_head: + self.norm_intermediate_trunkfinal.add_reg_dict(reg_dict) + self.intermediate_policy_head.add_reg_dict(reg_dict) + self.intermediate_value_head.add_reg_dict(reg_dict) + + + def set_brenorm_params(self, renorm_avg_momentum: float, rmax: float, dmax: float): + for block in self.blocks: + block.set_brenorm_params(renorm_avg_momentum, rmax, dmax) + self.norm_trunkfinal.set_brenorm_params(renorm_avg_momentum, rmax, dmax) + self.policy_head.set_brenorm_params(renorm_avg_momentum, rmax, dmax) + self.value_head.set_brenorm_params(renorm_avg_momentum, rmax, dmax) + if self.has_intermediate_head: + self.norm_intermediate_trunkfinal.set_brenorm_params(renorm_avg_momentum, rmax, dmax) + self.intermediate_policy_head.set_brenorm_params(renorm_avg_momentum, rmax, dmax) + self.intermediate_value_head.set_brenorm_params(renorm_avg_momentum, rmax, dmax) + + def add_brenorm_clippage(self, upper_rclippage, lower_rclippage, dclippage): + for block in self.blocks: + block.add_brenorm_clippage(upper_rclippage, lower_rclippage, dclippage) + self.norm_trunkfinal.add_brenorm_clippage(upper_rclippage, lower_rclippage, dclippage) + self.policy_head.add_brenorm_clippage(upper_rclippage, lower_rclippage, dclippage) + self.value_head.add_brenorm_clippage(upper_rclippage, lower_rclippage, dclippage) + if self.has_intermediate_head: + self.norm_intermediate_trunkfinal.add_brenorm_clippage(upper_rclippage, lower_rclippage, dclippage) + self.intermediate_policy_head.add_brenorm_clippage(upper_rclippage, lower_rclippage, dclippage) + self.intermediate_value_head.add_brenorm_clippage(upper_rclippage, lower_rclippage, dclippage) + + # Returns a tuple of tuples of outputs + # The outer tuple indexes different sets of heads, such as if the net also computes intermediate heads. + # 0 is the main output, 1 is intermediate. + # The inner tuple ranges over the outputs of a set of heads (policy, value, etc). + def forward( + self, + input_spatial, + input_global, + input_meta = None, + extra_outputs: Optional[ExtraOutputs] = None, + ): + # float_formatter = "{:.3f}".format + # np.set_printoptions(formatter={'float_kind':float_formatter}, threshold=1000000, linewidth=10000) + + mask = input_spatial[:, 0:1, :, :].contiguous() + mask_sum_hw = torch.sum(mask,dim=(2,3),keepdim=True) + mask_sum = torch.sum(mask) + + x_spatial = self.conv_spatial(input_spatial) + x_global = self.linear_global(input_global).unsqueeze(-1).unsqueeze(-1) + + out = x_spatial + x_global + + if self.metadata_encoder is not None: + assert input_meta is not None + x_meta = self.metadata_encoder.forward(input_meta,extra_outputs) + out = out + x_meta.unsqueeze(-1).unsqueeze(-1) + + # print("TENSOR BEFORE TRUNK") + # print(out) + + if self.has_intermediate_head: + count = 0 + for block in self.blocks[:self.intermediate_head_blocks]: + # print("TENSOR BEFORE BLOCK") + # print(count) + # print(out) + out = block(out, mask=mask, mask_sum_hw=mask_sum_hw, mask_sum=mask_sum, extra_outputs=extra_outputs) + count += 1 + + # print("INTERMEDIATE") + iout = out + iout = self.norm_intermediate_trunkfinal(iout, mask=mask, mask_sum=mask_sum) + iout = self.act_intermediate_trunkfinal(iout) + iout_policy = self.intermediate_policy_head(iout, mask=mask, mask_sum_hw=mask_sum_hw, mask_sum=mask_sum, extra_outputs=extra_outputs) + ( + iout_value, + iout_miscvalue, + iout_moremiscvalue, + iout_ownership, + iout_scoring, + iout_futurepos, + iout_seki, + iout_scorebelief_logprobs, + ) = self.intermediate_value_head(iout, mask=mask, mask_sum_hw=mask_sum_hw, mask_sum=mask_sum, input_global=input_global, extra_outputs=extra_outputs) + + for block in self.blocks[self.intermediate_head_blocks:]: + # print("TENSOR BEFORE BLOCK") + # print(count) + # print(out) + out = block(out, mask=mask, mask_sum_hw=mask_sum_hw, mask_sum=mask_sum, extra_outputs=extra_outputs) + count += 1 + + else: + count = 0 + for block in self.blocks: + # print("TENSOR BEFORE BLOCK") + # print(count) + # print(out) + out = block(out, mask=mask, mask_sum_hw=mask_sum_hw, mask_sum=mask_sum, extra_outputs=extra_outputs) + count += 1 + + out = self.norm_trunkfinal(out, mask=mask, mask_sum=mask_sum) + out = self.act_trunkfinal(out) + + if extra_outputs is not None: + extra_outputs.report("trunkfinal", out) + + # print("MAIN") + out_policy = self.policy_head(out, mask=mask, mask_sum_hw=mask_sum_hw, mask_sum=mask_sum, extra_outputs=extra_outputs) + ( + out_value, + out_miscvalue, + out_moremiscvalue, + out_ownership, + out_scoring, + out_futurepos, + out_seki, + out_scorebelief_logprobs, + ) = self.value_head(out, mask=mask, mask_sum_hw=mask_sum_hw, mask_sum=mask_sum, input_global=input_global, extra_outputs=extra_outputs) + + if self.has_intermediate_head: + return ( + ( + out_policy, + out_value, + out_miscvalue, + out_moremiscvalue, + out_ownership, + out_scoring, + out_futurepos, + out_seki, + out_scorebelief_logprobs, + ), + ( + iout_policy, + iout_value, + iout_miscvalue, + iout_moremiscvalue, + iout_ownership, + iout_scoring, + iout_futurepos, + iout_seki, + iout_scorebelief_logprobs, + ), + ) + else: + return (( + out_policy, + out_value, + out_miscvalue, + out_moremiscvalue, + out_ownership, + out_scoring, + out_futurepos, + out_seki, + out_scorebelief_logprobs, + ),) + + def float32ify_output(self, outputs_byheads): + return tuple(self.float32ify_single_heads_output(outputs) for outputs in outputs_byheads) + + def float32ify_single_heads_output(self, outputs): + ( + out_policy, + out_value, + out_miscvalue, + out_moremiscvalue, + out_ownership, + out_scoring, + out_futurepos, + out_seki, + out_scorebelief_logprobs, + ) = outputs + return ( + out_policy.to(torch.float32), + out_value.to(torch.float32), + out_miscvalue.to(torch.float32), + out_moremiscvalue.to(torch.float32), + out_ownership.to(torch.float32), + out_scoring.to(torch.float32), + out_futurepos.to(torch.float32), + out_seki.to(torch.float32), + out_scorebelief_logprobs.to(torch.float32), + ) + + def postprocess_output(self, outputs_byheads): + return tuple(self.postprocess_single_heads_output(outputs) for outputs in outputs_byheads) + + def postprocess_single_heads_output(self, outputs): + ( + out_policy, + out_value, + out_miscvalue, + out_moremiscvalue, + out_ownership, + out_scoring, + out_futurepos, + out_seki, + out_scorebelief_logprobs, + ) = outputs + + policy_logits = out_policy + value_logits = out_value + td_value_logits = torch.stack((out_miscvalue[:,4:7], out_miscvalue[:,7:10], out_moremiscvalue[:,2:5]), dim=1) + pred_td_score = out_moremiscvalue[:,5:8] * self.td_score_multiplier + ownership_pretanh = out_ownership + pred_scoring = out_scoring + futurepos_pretanh = out_futurepos + seki_logits = out_seki + pred_scoremean = out_miscvalue[:, 0] * self.scoremean_multiplier + pred_scorestdev = SoftPlusWithGradientFloorFunction.apply(out_miscvalue[:, 1], 0.05, False) * self.scorestdev_multiplier + pred_lead = out_miscvalue[:, 2] * self.lead_multiplier + pred_variance_time = SoftPlusWithGradientFloorFunction.apply(out_miscvalue[:, 3], 0.05, False) * self.variance_time_multiplier + if self.config["version"] < 14: + pred_shortterm_value_error = SoftPlusWithGradientFloorFunction.apply(out_moremiscvalue[:,0], 0.05, False) * self.shortterm_value_error_multiplier + pred_shortterm_score_error = SoftPlusWithGradientFloorFunction.apply(out_moremiscvalue[:,1], 0.05, False) * self.shortterm_score_error_multiplier + else: + pred_shortterm_value_error = SoftPlusWithGradientFloorFunction.apply(out_moremiscvalue[:,0], 0.05, True) * self.shortterm_value_error_multiplier + pred_shortterm_score_error = SoftPlusWithGradientFloorFunction.apply(out_moremiscvalue[:,1], 0.05, True) * self.shortterm_score_error_multiplier + scorebelief_logits = out_scorebelief_logprobs + + return ( + policy_logits, # N, num_policy_outputs, move + value_logits, # N, {win,loss,noresult} + td_value_logits, # N, {long, mid, short} {win,loss,noresult} + pred_td_score, # N, {long, mid, short} + ownership_pretanh, # N, 1, y, x + pred_scoring, # N, 1, y, x + futurepos_pretanh, # N, 2, y, x + seki_logits, # N, 4, y, x + pred_scoremean, # N + pred_scorestdev, # N + pred_lead, # N + pred_variance_time, # N + pred_shortterm_value_error, # N + pred_shortterm_score_error, # N + scorebelief_logits, # N, 2 * (self.pos_len*self.pos_len + EXTRA_SCORE_DISTR_RADIUS) + ) diff --git a/training/scripts/dated/20241110-123407/modelconfigs.py b/training/scripts/dated/20241110-123407/modelconfigs.py new file mode 100644 index 0000000..7bfe24a --- /dev/null +++ b/training/scripts/dated/20241110-123407/modelconfigs.py @@ -0,0 +1,1525 @@ +#!/usr/bin/python3 +""" +This file contains a bunch of configs for models of different sizes. +See the bottom of this file "base_config_of_name" for a dictionary of all the different +base model architectures, and which ones are recommended of each different model size. + +For each base model, additional configs are also pregenerated with different suffixes. + +For example, for b10c384nbt, we also have models like: +b10c384nbt-mish (use mish instead of relu) +b10c384nbt-bn-mish-rvgl (use batchnorm, mish, and repvgg-linear-style convolutions). + +KataGo's main models for the distributed training run currently find the following to work +well or best: "-fson-mish-rvgl-bnh" +* Use fixed activation scale initialization + one batch norm for the whole net +* Mish activation +* Repvgg-linear-style convolutions +* Batch norm output head + non-batch-norm output head, where the former drives optimization + but the latter is used for inference. +""" + +from typing import Dict, Any, Union + +ModelConfig = Dict[str,Any] + +# version = 0 # V1 features, with old head architecture using crelus (no longer supported) +# version = 1 # V1 features, with new head architecture, no crelus +# version = 2 # V2 features, no internal architecture change. +# version = 3 # V3 features, selfplay-planned features with lots of aux targets +# version = 4 # V3 features, but supporting belief stdev and dynamic scorevalue +# version = 5 # V4 features, slightly different pass-alive stones feature +# version = 6 # V5 features, most higher-level go features removed +# version = 7 # V6 features, more rules support +# version = 8 # V7 features, asym, lead, variance time +# version = 9 # V7 features, shortterm value error prediction, inference actually uses variance time, unsupported now +# version = 10 # V7 features, shortterm value error prediction done properly +# version = 11 # V7 features, New architectures! +# version = 12 # V7 features, Optimistic policy head +# version = 13 # V7 features, Adjusted scaling on shortterm score variance, and made C++ side read in scalings. +# version = 14 # V7 features, Squared softplus for error variance predictions +# version = 15 # V7 features, Extra nonlinearity for pass output + +def get_version(config: ModelConfig): + return config["version"] + +def get_num_bin_input_features(config: ModelConfig): + version = get_version(config) + if version == 10 or version == 11 or version == 12 or version == 13 or version == 14 or version == 15: + return 22 + else: + assert(False) + +def get_num_global_input_features(config: ModelConfig): + version = get_version(config) + if version == 10 or version == 11 or version == 12 or version == 13 or version == 14 or version == 15: + return 19 + else: + assert(False) + +def get_num_meta_encoder_input_features(config_or_meta_encoder_version: Union[ModelConfig,int]): + if isinstance(config_or_meta_encoder_version,int): + version = config_or_meta_encoder_version + else: + if "metadata_encoder" not in config: + version = 0 + elif "meta_encoder_version" not in config["metadata_encoder"]: + version = 1 + else: + version = config["metadata_encoder"]["meta_encoder_version"] + assert version == 1 + return 192 + +b1c6nbt = { + "version":15, + "norm_kind":"fixup", + "bnorm_epsilon": 1e-4, + "bnorm_running_avg_momentum": 0.001, + "initial_conv_1x1": False, + "trunk_num_channels":6, + "mid_num_channels":4, + "gpool_num_channels":4, + "use_attention_pool":False, + "num_attention_pool_heads":2, + "block_kind": [ + ["rconv1","bottlenest2"], + ], + "p1_num_channels":4, + "g1_num_channels":4, + "v1_num_channels":4, + "sbv2_num_channels":4, + "num_scorebeliefs":2, + "v2_size":6, +} + +b2c16 = { + "version":15, + "norm_kind":"fixup", + "bnorm_epsilon": 1e-4, + "bnorm_running_avg_momentum": 0.001, + "initial_conv_1x1": False, + "trunk_num_channels":16, + "mid_num_channels":16, + "gpool_num_channels":8, + "use_attention_pool":False, + "num_attention_pool_heads":2, + "block_kind": [ + ["rconv1","regular"], + ["rconv2","regulargpool"], + ], + "p1_num_channels":8, + "g1_num_channels":8, + "v1_num_channels":8, + "sbv2_num_channels":12, + "num_scorebeliefs":2, + "v2_size":12, +} + +b4c32 = { + "version":15, + "norm_kind":"fixup", + "bnorm_epsilon": 1e-4, + "bnorm_running_avg_momentum": 0.001, + "initial_conv_1x1": False, + "trunk_num_channels":32, + "mid_num_channels":32, + "gpool_num_channels":16, + "use_attention_pool":False, + "num_attention_pool_heads":2, + "block_kind": [ + ["rconv1","regular"], + ["rconv2","regular"], + ["rconv3","regulargpool"], + ["rconv4","regular"], + ], + "p1_num_channels":12, + "g1_num_channels":12, + "v1_num_channels":12, + "sbv2_num_channels":24, + "num_scorebeliefs":4, + "v2_size":24, +} + +b6c96 = { + "version":15, + "norm_kind":"fixup", + "bnorm_epsilon": 1e-4, + "bnorm_running_avg_momentum": 0.001, + "initial_conv_1x1": False, + "trunk_num_channels":96, + "mid_num_channels":96, + "gpool_num_channels":32, + "use_attention_pool":False, + "num_attention_pool_heads":4, + "block_kind": [ + ["rconv1","regular"], + ["rconv2","regular"], + ["rconv3","regulargpool"], + ["rconv4","regular"], + ["rconv5","regulargpool"], + ["rconv6","regular"], + ], + "p1_num_channels":32, + "g1_num_channels":32, + "v1_num_channels":32, + "sbv2_num_channels":48, + "num_scorebeliefs":4, + "v2_size":64, +} + +b10c128 = { + "version":15, + "norm_kind":"fixup", + "bnorm_epsilon": 1e-4, + "bnorm_running_avg_momentum": 0.001, + "initial_conv_1x1": False, + "trunk_num_channels":128, + "mid_num_channels":128, + "gpool_num_channels":32, + "use_attention_pool":False, + "num_attention_pool_heads":4, + "block_kind": [ + ["rconv1","regular"], + ["rconv2","regular"], + ["rconv3","regular"], + ["rconv4","regular"], + ["rconv5","regulargpool"], + ["rconv6","regular"], + ["rconv7","regular"], + ["rconv8","regulargpool"], + ["rconv9","regular"], + ["rconv10","regular"], + ], + "p1_num_channels":32, + "g1_num_channels":32, + "v1_num_channels":32, + "sbv2_num_channels":64, + "num_scorebeliefs":6, + "v2_size":80, +} + +b5c192nbt = { + "version":15, + "norm_kind":"fixup", + "bnorm_epsilon": 1e-4, + "bnorm_running_avg_momentum": 0.001, + "initial_conv_1x1": False, + "trunk_num_channels":192, + "mid_num_channels":96, + "gpool_num_channels":32, + "use_attention_pool":False, + "num_attention_pool_heads":4, + "block_kind": [ + ["rconv1","bottlenest2"], + ["rconv2","bottlenest2gpool"], + ["rconv3","bottlenest2"], + ["rconv4","bottlenest2gpool"], + ["rconv5","bottlenest2"], + ], + "p1_num_channels":32, + "g1_num_channels":32, + "v1_num_channels":32, + "sbv2_num_channels":64, + "num_scorebeliefs":6, + "v2_size":80, +} + +b15c192 = { + "version":15, + "norm_kind":"fixup", + "bnorm_epsilon": 1e-4, + "bnorm_running_avg_momentum": 0.001, + "initial_conv_1x1": False, + "trunk_num_channels":192, + "mid_num_channels":192, + "gpool_num_channels":64, + "use_attention_pool":False, + "num_attention_pool_heads":4, + "block_kind": [ + ["rconv1","regular"], + ["rconv2","regular"], + ["rconv3","regular"], + ["rconv4","regular"], + ["rconv5","regular"], + ["rconv6","regular"], + ["rconv7","regulargpool"], + ["rconv8","regular"], + ["rconv9","regular"], + ["rconv10","regular"], + ["rconv11","regular"], + ["rconv12","regulargpool"], + ["rconv13","regular"], + ["rconv14","regular"], + ["rconv15","regular"], + ], + "p1_num_channels":32, + "g1_num_channels":32, + "v1_num_channels":32, + "sbv2_num_channels":80, + "num_scorebeliefs":8, + "v2_size":96, +} + +b20c256 = { + "version":15, + "norm_kind":"fixup", + "bnorm_epsilon": 1e-4, + "bnorm_running_avg_momentum": 0.001, + "initial_conv_1x1": False, + "trunk_num_channels":256, + "mid_num_channels":256, + "gpool_num_channels":64, + "use_attention_pool":False, + "num_attention_pool_heads":4, + "block_kind": [ + ["rconv1","regular"], + ["rconv2","regular"], + ["rconv3","regular"], + ["rconv4","regular"], + ["rconv5","regular"], + ["rconv6","regular"], + ["rconv7","regulargpool"], + ["rconv8","regular"], + ["rconv9","regular"], + ["rconv10","regular"], + ["rconv11","regular"], + ["rconv12","regulargpool"], + ["rconv13","regular"], + ["rconv14","regular"], + ["rconv15","regular"], + ["rconv16","regular"], + ["rconv17","regulargpool"], + ["rconv18","regular"], + ["rconv19","regular"], + ["rconv20","regular"], + ], + "p1_num_channels":48, + "g1_num_channels":48, + "v1_num_channels":48, + "sbv2_num_channels":96, + "num_scorebeliefs":8, + "v2_size":112, +} + +b30c256bt = { + "version":15, + "norm_kind":"fixup", + "bnorm_epsilon": 1e-4, + "bnorm_running_avg_momentum": 0.001, + "initial_conv_1x1": False, + "trunk_num_channels":256, + "mid_num_channels":128, + "gpool_num_channels":64, + "use_attention_pool":False, + "num_attention_pool_heads":4, + "block_kind": [ + ["rconv0","bottle"], + ["rconv1","bottle"], + ["rconv2","bottle"], + ["rconv3","bottle"], + ["rconv4","bottle"], + ["rconv5","bottle"], + ["rconv6","bottlegpool"], + ["rconv7","bottle"], + ["rconv8","bottle"], + ["rconv9","bottle"], + ["rconv10","bottle"], + ["rconv11","bottle"], + ["rconv12","bottlegpool"], + ["rconv13","bottle"], + ["rconv14","bottle"], + ["rconv15","bottle"], + ["rconv16","bottle"], + ["rconv17","bottle"], + ["rconv18","bottlegpool"], + ["rconv19","bottle"], + ["rconv20","bottle"], + ["rconv21","bottle"], + ["rconv22","bottle"], + ["rconv23","bottle"], + ["rconv24","bottlegpool"], + ["rconv25","bottle"], + ["rconv26","bottle"], + ["rconv27","bottle"], + ["rconv28","bottle"], + ["rconv29","bottle"], + ], + "p1_num_channels":48, + "g1_num_channels":48, + "v1_num_channels":48, + "sbv2_num_channels":96, + "num_scorebeliefs":8, + "v2_size":112, +} + +b24c320bt = { + "version":15, + "norm_kind":"fixup", + "bnorm_epsilon": 1e-4, + "bnorm_running_avg_momentum": 0.001, + "initial_conv_1x1": False, + "trunk_num_channels":320, + "mid_num_channels":160, + "gpool_num_channels":64, + "use_attention_pool":False, + "num_attention_pool_heads":4, + "block_kind": [ + ["rconv1","bottle"], + ["rconv2","bottle"], + ["rconv3","bottle"], + ["rconv4","bottle"], + ["rconv5","bottle"], + ["rconv6","bottle"], + ["rconv7","bottlegpool"], + ["rconv8","bottle"], + ["rconv9","bottle"], + ["rconv10","bottle"], + ["rconv11","bottle"], + ["rconv12","bottle"], + ["rconv13","bottlegpool"], + ["rconv14","bottle"], + ["rconv15","bottle"], + ["rconv16","bottle"], + ["rconv17","bottle"], + ["rconv18","bottle"], + ["rconv19","bottlegpool"], + ["rconv20","bottle"], + ["rconv21","bottle"], + ["rconv22","bottle"], + ["rconv23","bottle"], + ["rconv24","bottle"], + ], + "p1_num_channels":48, + "g1_num_channels":48, + "v1_num_channels":48, + "sbv2_num_channels":96, + "num_scorebeliefs":8, + "v2_size":112, +} + +b20c384bt = { + "version":15, + "norm_kind":"fixup", + "bnorm_epsilon": 1e-4, + "bnorm_running_avg_momentum": 0.001, + "initial_conv_1x1": False, + "trunk_num_channels":384, + "mid_num_channels":192, + "gpool_num_channels":64, + "use_attention_pool":False, + "num_attention_pool_heads":4, + "block_kind": [ + ["rconv1","bottle"], + ["rconv2","bottle"], + ["rconv3","bottle"], + ["rconv4","bottle"], + ["rconv5","bottle"], + ["rconv6","bottlegpool"], + ["rconv7","bottle"], + ["rconv8","bottle"], + ["rconv9","bottle"], + ["rconv10","bottle"], + ["rconv11","bottlegpool"], + ["rconv12","bottle"], + ["rconv13","bottle"], + ["rconv14","bottle"], + ["rconv15","bottle"], + ["rconv16","bottlegpool"], + ["rconv17","bottle"], + ["rconv18","bottle"], + ["rconv19","bottle"], + ["rconv20","bottle"], + ], + "p1_num_channels":48, + "g1_num_channels":48, + "v1_num_channels":48, + "sbv2_num_channels":96, + "num_scorebeliefs":8, + "v2_size":112, +} + + +b10c512lbt = { + "version":15, + "norm_kind":"fixup", + "bnorm_epsilon": 1e-4, + "bnorm_running_avg_momentum": 0.001, + "initial_conv_1x1": False, + "trunk_num_channels":512, + "mid_num_channels":256, + "gpool_num_channels":128, + "use_attention_pool":False, + "num_attention_pool_heads":4, + "block_kind": [ + ["rconv1","bottle2"], + ["rconv2","bottle2"], + ["rconv3","bottle2"], + ["rconv4","bottle2gpool"], + ["rconv5","bottle2"], + ["rconv6","bottle2"], + ["rconv7","bottle2"], + ["rconv8","bottle2gpool"], + ["rconv9","bottle2"], + ["rconv10","bottle2"], + ], + "p1_num_channels":48, + "g1_num_channels":48, + "v1_num_channels":48, + "sbv2_num_channels":96, + "num_scorebeliefs":8, + "v2_size":112, +} + + +b15c384lbt = { + "version":15, + "norm_kind":"fixup", + "bnorm_epsilon": 1e-4, + "bnorm_running_avg_momentum": 0.001, + "initial_conv_1x1": False, + "trunk_num_channels":384, + "mid_num_channels":192, + "gpool_num_channels":64, + "use_attention_pool":False, + "num_attention_pool_heads":4, + "block_kind": [ + ["rconv1","bottle2"], + ["rconv2","bottle2"], + ["rconv3","bottle2"], + ["rconv4","bottle2gpool"], + ["rconv5","bottle2"], + ["rconv6","bottle2"], + ["rconv7","bottle2"], + ["rconv8","bottle2gpool"], + ["rconv9","bottle2"], + ["rconv10","bottle2"], + ["rconv11","bottle2"], + ["rconv12","bottle2gpool"], + ["rconv13","bottle2"], + ["rconv14","bottle2"], + ["rconv15","bottle2"], + ], + "p1_num_channels":48, + "g1_num_channels":48, + "v1_num_channels":48, + "sbv2_num_channels":96, + "num_scorebeliefs":8, + "v2_size":112, +} + +b18c320lbt = { + "version":15, + "norm_kind":"fixup", + "bnorm_epsilon": 1e-4, + "bnorm_running_avg_momentum": 0.001, + "initial_conv_1x1": False, + "trunk_num_channels":320, + "mid_num_channels":160, + "gpool_num_channels":64, + "use_attention_pool":False, + "num_attention_pool_heads":4, + "block_kind": [ + ["rconv1","bottle2"], + ["rconv2","bottle2"], + ["rconv3","bottle2"], + ["rconv4","bottle2"], + ["rconv5","bottle2gpool"], + ["rconv6","bottle2"], + ["rconv7","bottle2"], + ["rconv8","bottle2"], + ["rconv9","bottle2"], + ["rconv10","bottle2gpool"], + ["rconv11","bottle2"], + ["rconv12","bottle2"], + ["rconv13","bottle2"], + ["rconv14","bottle2"], + ["rconv15","bottle2gpool"], + ["rconv16","bottle2"], + ["rconv17","bottle2"], + ["rconv18","bottle2"], + ], + "p1_num_channels":48, + "g1_num_channels":48, + "v1_num_channels":48, + "sbv2_num_channels":96, + "num_scorebeliefs":8, + "v2_size":112, +} + +b23c256lbt = { + "version":15, + "norm_kind":"fixup", + "bnorm_epsilon": 1e-4, + "bnorm_running_avg_momentum": 0.001, + "initial_conv_1x1": False, + "trunk_num_channels":256, + "mid_num_channels":128, + "gpool_num_channels":64, + "use_attention_pool":False, + "num_attention_pool_heads":4, + "block_kind": [ + ["rconv1","bottle2"], + ["rconv2","bottle2"], + ["rconv3","bottle2"], + ["rconv4","bottle2"], + ["rconv5","bottle2"], + ["rconv6","bottle2gpool"], + ["rconv7","bottle2"], + ["rconv8","bottle2"], + ["rconv9","bottle2"], + ["rconv10","bottle2"], + ["rconv11","bottle2"], + ["rconv12","bottle2gpool"], + ["rconv13","bottle2"], + ["rconv14","bottle2"], + ["rconv15","bottle2"], + ["rconv16","bottle2"], + ["rconv17","bottle2"], + ["rconv18","bottle2gpool"], + ["rconv19","bottle2"], + ["rconv20","bottle2"], + ["rconv21","bottle2"], + ["rconv22","bottle2"], + ["rconv23","bottle2"], + ], + "p1_num_channels":48, + "g1_num_channels":48, + "v1_num_channels":48, + "sbv2_num_channels":96, + "num_scorebeliefs":8, + "v2_size":112, +} + +b12c384llbt = { + "version":15, + "norm_kind":"fixup", + "bnorm_epsilon": 1e-4, + "bnorm_running_avg_momentum": 0.001, + "initial_conv_1x1": False, + "trunk_num_channels":384, + "mid_num_channels":192, + "gpool_num_channels":64, + "use_attention_pool":False, + "num_attention_pool_heads":4, + "block_kind": [ + ["rconv1","bottle3"], + ["rconv2","bottle3"], + ["rconv3","bottle3"], + ["rconv4","bottle3gpool"], + ["rconv5","bottle3"], + ["rconv6","bottle3"], + ["rconv7","bottle3gpool"], + ["rconv8","bottle3"], + ["rconv9","bottle3"], + ["rconv10","bottle3gpool"], + ["rconv11","bottle3"], + ["rconv12","bottle3"], + ], + "p1_num_channels":48, + "g1_num_channels":48, + "v1_num_channels":48, + "sbv2_num_channels":96, + "num_scorebeliefs":8, + "v2_size":112, +} + + +b10c384nbt = { + "version":15, + "norm_kind":"fixup", + "bnorm_epsilon": 1e-4, + "bnorm_running_avg_momentum": 0.001, + "initial_conv_1x1": False, + "trunk_num_channels":384, + "mid_num_channels":192, + "gpool_num_channels":64, + "use_attention_pool":False, + "num_attention_pool_heads":4, + "block_kind": [ + ["rconv1","bottlenest2"], + ["rconv2","bottlenest2"], + ["rconv3","bottlenest2gpool"], + ["rconv4","bottlenest2"], + ["rconv5","bottlenest2"], + ["rconv6","bottlenest2gpool"], + ["rconv7","bottlenest2"], + ["rconv8","bottlenest2"], + ["rconv9","bottlenest2gpool"], + ["rconv10","bottlenest2"], + ], + "p1_num_channels":48, + "g1_num_channels":48, + "v1_num_channels":48, + "sbv2_num_channels":96, + "num_scorebeliefs":8, + "v2_size":112, +} + + +b10c480nb3t = { + "version":15, + "norm_kind":"fixup", + "bnorm_epsilon": 1e-4, + "bnorm_running_avg_momentum": 0.001, + "initial_conv_1x1": False, + "trunk_num_channels":480, + "mid_num_channels":160, + "gpool_num_channels":64, + "use_attention_pool":False, + "num_attention_pool_heads":4, + "block_kind": [ + ["rconv1","bottlenest2"], + ["rconv2","bottlenest2"], + ["rconv3","bottlenest2gpool"], + ["rconv4","bottlenest2"], + ["rconv5","bottlenest2"], + ["rconv6","bottlenest2gpool"], + ["rconv7","bottlenest2"], + ["rconv8","bottlenest2"], + ["rconv9","bottlenest2gpool"], + ["rconv10","bottlenest2"], + ], + "p1_num_channels":48, + "g1_num_channels":48, + "v1_num_channels":48, + "sbv2_num_channels":96, + "num_scorebeliefs":8, + "v2_size":112, +} + + +b7c384lnbt = { + "version":15, + "norm_kind":"fixup", + "bnorm_epsilon": 1e-4, + "bnorm_running_avg_momentum": 0.001, + "initial_conv_1x1": False, + "trunk_num_channels":384, + "mid_num_channels":192, + "gpool_num_channels":64, + "use_attention_pool":False, + "num_attention_pool_heads":4, + "block_kind": [ + ["rconv1","bottlenest3"], + ["rconv3","bottlenest3gpool"], + ["rconv5","bottlenest3"], + ["rconv6","bottlenest3gpool"], + ["rconv8","bottlenest3"], + ["rconv9","bottlenest3gpool"], + ["rconv10","bottlenest3"], + ], + "p1_num_channels":48, + "g1_num_channels":48, + "v1_num_channels":48, + "sbv2_num_channels":96, + "num_scorebeliefs":8, + "v2_size":112, +} + +b5c512nnbt = { + "version":15, + "norm_kind":"fixup", + "bnorm_epsilon": 1e-4, + "bnorm_running_avg_momentum": 0.001, + "initial_conv_1x1": True, + "trunk_num_channels":512, + "outermid_num_channels":256, + "mid_num_channels":128, + "gpool_num_channels":64, + "use_attention_pool":False, + "num_attention_pool_heads":4, + "block_kind": [ + ["rconv1","bottlenest2bottlenest2"], + ["rconv2","bottlenest2bottlenest2gpool"], + ["rconv3","bottlenest2bottlenest2"], + ["rconv4","bottlenest2bottlenest2gpool"], + ["rconv5","bottlenest2bottlenest2gpool"], + ], + "p1_num_channels":48, + "g1_num_channels":48, + "v1_num_channels":48, + "sbv2_num_channels":96, + "num_scorebeliefs":8, + "v2_size":112, +} + + +b20c384lbt = { + "version":15, + "norm_kind":"fixup", + "bnorm_epsilon": 1e-4, + "bnorm_running_avg_momentum": 0.001, + "initial_conv_1x1": False, + "trunk_num_channels":384, + "mid_num_channels":192, + "gpool_num_channels":64, + "use_attention_pool":False, + "num_attention_pool_heads":4, + "block_kind": [ + ["rconv1","bottle2"], + ["rconv2","bottle2"], + ["rconv3","bottle2"], + ["rconv4","bottle2"], + ["rconv5","bottle2"], + ["rconv6","bottle2gpool"], + ["rconv7","bottle2"], + ["rconv8","bottle2"], + ["rconv9","bottle2"], + ["rconv10","bottle2"], + ["rconv11","bottle2gpool"], + ["rconv12","bottle2"], + ["rconv13","bottle2"], + ["rconv14","bottle2"], + ["rconv15","bottle2"], + ["rconv16","bottle2gpool"], + ["rconv17","bottle2"], + ["rconv18","bottle2"], + ["rconv19","bottle2"], + ["rconv20","bottle2"], + ], + "p1_num_channels":48, + "g1_num_channels":48, + "v1_num_channels":48, + "sbv2_num_channels":96, + "num_scorebeliefs":8, + "v2_size":112, +} + + +b30c320 = { + "version":15, + "norm_kind":"fixup", + "bnorm_epsilon": 1e-4, + "bnorm_running_avg_momentum": 0.001, + "initial_conv_1x1": False, + "trunk_num_channels":320, + "mid_num_channels":320, + "gpool_num_channels":96, + "use_attention_pool":False, + "num_attention_pool_heads":4, + "block_kind": [ + ["rconv1","regular"], + ["rconv2","regular"], + ["rconv3","regular"], + ["rconv4","regular"], + ["rconv5","regular"], + ["rconv6","regulargpool"], + ["rconv7","regular"], + ["rconv8","regular"], + ["rconv9","regular"], + ["rconv10","regular"], + ["rconv11","regulargpool"], + ["rconv12","regular"], + ["rconv13","regular"], + ["rconv14","regular"], + ["rconv15","regular"], + ["rconv16","regulargpool"], + ["rconv17","regular"], + ["rconv18","regular"], + ["rconv19","regular"], + ["rconv20","regular"], + ["rconv21","regulargpool"], + ["rconv22","regular"], + ["rconv23","regular"], + ["rconv24","regular"], + ["rconv25","regular"], + ["rconv26","regulargpool"], + ["rconv27","regular"], + ["rconv28","regular"], + ["rconv29","regular"], + ["rconv30","regular"], + ], + "p1_num_channels":48, + "g1_num_channels":48, + "v1_num_channels":96, + "sbv2_num_channels":112, + "num_scorebeliefs":8, + "v2_size":128, +} + +b40c256 = { + "version":15, + "norm_kind":"fixup", + "bnorm_epsilon": 1e-4, + "bnorm_running_avg_momentum": 0.001, + "initial_conv_1x1": False, + "trunk_num_channels":256, + "mid_num_channels":256, + "gpool_num_channels":64, + "use_attention_pool":False, + "num_attention_pool_heads":4, + "block_kind": [ + ["rconv1","regular"], + ["rconv2","regular"], + ["rconv3","regular"], + ["rconv4","regular"], + ["rconv5","regular"], + ["rconv6","regulargpool"], + ["rconv7","regular"], + ["rconv8","regular"], + ["rconv9","regular"], + ["rconv10","regular"], + ["rconv11","regulargpool"], + ["rconv12","regular"], + ["rconv13","regular"], + ["rconv14","regular"], + ["rconv15","regular"], + ["rconv16","regulargpool"], + ["rconv17","regular"], + ["rconv18","regular"], + ["rconv19","regular"], + ["rconv20","regular"], + ["rconv21","regulargpool"], + ["rconv22","regular"], + ["rconv23","regular"], + ["rconv24","regular"], + ["rconv25","regular"], + ["rconv26","regulargpool"], + ["rconv27","regular"], + ["rconv28","regular"], + ["rconv29","regular"], + ["rconv30","regular"], + ["rconv31","regulargpool"], + ["rconv32","regular"], + ["rconv33","regular"], + ["rconv34","regular"], + ["rconv35","regular"], + ["rconv36","regulargpool"], + ["rconv37","regular"], + ["rconv38","regular"], + ["rconv39","regular"], + ["rconv40","regular"], + ], + "p1_num_channels":48, + "g1_num_channels":48, + "v1_num_channels":96, + "sbv2_num_channels":112, + "num_scorebeliefs":8, + "v2_size":128, +} + +b18c384nbt = { + "version":15, + "norm_kind":"fixup", + "bnorm_epsilon": 1e-4, + "bnorm_running_avg_momentum": 0.001, + "initial_conv_1x1": False, + "trunk_num_channels":384, + "mid_num_channels":192, + "gpool_num_channels":64, + "use_attention_pool":False, + "num_attention_pool_heads":4, + "block_kind": [ + ["rconv1","bottlenest2"], + ["rconv2","bottlenest2"], + ["rconv3","bottlenest2gpool"], + ["rconv4","bottlenest2"], + ["rconv5","bottlenest2"], + ["rconv6","bottlenest2gpool"], + ["rconv7","bottlenest2"], + ["rconv8","bottlenest2"], + ["rconv9","bottlenest2gpool"], + ["rconv10","bottlenest2"], + ["rconv11","bottlenest2"], + ["rconv12","bottlenest2gpool"], + ["rconv13","bottlenest2"], + ["rconv14","bottlenest2"], + ["rconv15","bottlenest2gpool"], + ["rconv16","bottlenest2"], + ["rconv17","bottlenest2"], + ["rconv18","bottlenest2"], + ], + "p1_num_channels":48, + "g1_num_channels":48, + "v1_num_channels":96, + "sbv2_num_channels":112, + "num_scorebeliefs":8, + "v2_size":128, +} + +b14c448nbt = { + "version":15, + "norm_kind":"fixup", + "bnorm_epsilon": 1e-4, + "bnorm_running_avg_momentum": 0.001, + "initial_conv_1x1": False, + "trunk_num_channels":448, + "mid_num_channels":224, + "gpool_num_channels":64, + "use_attention_pool":False, + "num_attention_pool_heads":4, + "block_kind": [ + ["rconv1","bottlenest2"], + ["rconv2","bottlenest2"], + ["rconv3","bottlenest2gpool"], + ["rconv4","bottlenest2"], + ["rconv5","bottlenest2"], + ["rconv6","bottlenest2gpool"], + ["rconv7","bottlenest2"], + ["rconv8","bottlenest2"], + ["rconv9","bottlenest2gpool"], + ["rconv10","bottlenest2"], + ["rconv11","bottlenest2"], + ["rconv12","bottlenest2gpool"], + ["rconv13","bottlenest2"], + ["rconv14","bottlenest2"], + ], + "p1_num_channels":48, + "g1_num_channels":48, + "v1_num_channels":96, + "sbv2_num_channels":112, + "num_scorebeliefs":8, + "v2_size":128, +} + +b40c384 = { + "version":15, + "norm_kind":"fixup", + "bnorm_epsilon": 1e-4, + "bnorm_running_avg_momentum": 0.001, + "initial_conv_1x1": False, + "trunk_num_channels":384, + "mid_num_channels":384, + "gpool_num_channels":128, + "use_attention_pool":False, + "num_attention_pool_heads":4, + "block_kind": [ + ["rconv1","regular"], + ["rconv2","regular"], + ["rconv3","regular"], + ["rconv4","regular"], + ["rconv5","regular"], + ["rconv6","regulargpool"], + ["rconv7","regular"], + ["rconv8","regular"], + ["rconv9","regular"], + ["rconv10","regular"], + ["rconv11","regulargpool"], + ["rconv12","regular"], + ["rconv13","regular"], + ["rconv14","regular"], + ["rconv15","regular"], + ["rconv16","regulargpool"], + ["rconv17","regular"], + ["rconv18","regular"], + ["rconv19","regular"], + ["rconv20","regular"], + ["rconv21","regulargpool"], + ["rconv22","regular"], + ["rconv23","regular"], + ["rconv24","regular"], + ["rconv25","regular"], + ["rconv26","regulargpool"], + ["rconv27","regular"], + ["rconv28","regular"], + ["rconv29","regular"], + ["rconv30","regular"], + ["rconv31","regulargpool"], + ["rconv32","regular"], + ["rconv33","regular"], + ["rconv34","regular"], + ["rconv35","regular"], + ["rconv36","regulargpool"], + ["rconv37","regular"], + ["rconv38","regular"], + ["rconv39","regular"], + ["rconv40","regular"], + ], + "p1_num_channels":64, + "g1_num_channels":64, + "v1_num_channels":96, + "sbv2_num_channels":128, + "num_scorebeliefs":8, + "v2_size":144, +} + + +b60c320 = { + "version":15, + "norm_kind":"fixup", + "bnorm_epsilon": 1e-4, + "bnorm_running_avg_momentum": 0.001, + "initial_conv_1x1": False, + "trunk_num_channels":320, + "mid_num_channels":320, + "gpool_num_channels":96, + "use_attention_pool":False, + "num_attention_pool_heads":4, + "block_kind": [ + ["rconv1","regular"], + ["rconv2","regular"], + ["rconv3","regular"], + ["rconv4","regular"], + ["rconv5","regular"], + ["rconv6","regulargpool"], + ["rconv7","regular"], + ["rconv8","regular"], + ["rconv9","regular"], + ["rconv10","regular"], + ["rconv11","regulargpool"], + ["rconv12","regular"], + ["rconv13","regular"], + ["rconv14","regular"], + ["rconv15","regular"], + ["rconv16","regulargpool"], + ["rconv17","regular"], + ["rconv18","regular"], + ["rconv19","regular"], + ["rconv20","regular"], + ["rconv21","regulargpool"], + ["rconv22","regular"], + ["rconv23","regular"], + ["rconv24","regular"], + ["rconv25","regular"], + ["rconv26","regulargpool"], + ["rconv27","regular"], + ["rconv28","regular"], + ["rconv29","regular"], + ["rconv30","regular"], + ["rconv31","regulargpool"], + ["rconv32","regular"], + ["rconv33","regular"], + ["rconv34","regular"], + ["rconv35","regular"], + ["rconv36","regulargpool"], + ["rconv37","regular"], + ["rconv38","regular"], + ["rconv39","regular"], + ["rconv40","regular"], + ["rconv41","regulargpool"], + ["rconv42","regular"], + ["rconv43","regular"], + ["rconv44","regular"], + ["rconv45","regular"], + ["rconv46","regulargpool"], + ["rconv47","regular"], + ["rconv48","regular"], + ["rconv49","regular"], + ["rconv50","regular"], + ["rconv51","regulargpool"], + ["rconv52","regular"], + ["rconv53","regular"], + ["rconv54","regular"], + ["rconv55","regular"], + ["rconv56","regulargpool"], + ["rconv57","regular"], + ["rconv58","regular"], + ["rconv59","regular"], + ["rconv60","regular"], + ], + "p1_num_channels":64, + "g1_num_channels":64, + "v1_num_channels":96, + "sbv2_num_channels":128, + "num_scorebeliefs":8, + "v2_size":144, +} + + +b41c384nbt = { + "version":15, + "norm_kind":"fixup", + "bnorm_epsilon": 1e-4, + "bnorm_running_avg_momentum": 0.001, + "initial_conv_1x1": False, + "trunk_num_channels":384, + "mid_num_channels":192, + "gpool_num_channels":64, + "use_attention_pool":False, + "num_attention_pool_heads":4, + "block_kind": [ + ["rconv1","bottlenest2"], + ["rconv2","bottlenest2"], + ["rconv3","bottlenest2gpool"], + ["rconv4","bottlenest2"], + ["rconv5","bottlenest2"], + ["rconv6","bottlenest2gpool"], + ["rconv7","bottlenest2"], + ["rconv8","bottlenest2"], + ["rconv9","bottlenest2gpool"], + ["rconv10","bottlenest2"], + ["rconv11","bottlenest2"], + ["rconv12","bottlenest2gpool"], + ["rconv13","bottlenest2"], + ["rconv14","bottlenest2"], + ["rconv15","bottlenest2gpool"], + ["rconv16","bottlenest2"], + ["rconv17","bottlenest2"], + ["rconv18","bottlenest2gpool"], + ["rconv19","bottlenest2"], + ["rconv20","bottlenest2"], + ["rconv21","bottlenest2gpool"], + ["rconv22","bottlenest2"], + ["rconv23","bottlenest2"], + ["rconv24","bottlenest2gpool"], + ["rconv25","bottlenest2"], + ["rconv26","bottlenest2"], + ["rconv27","bottlenest2gpool"], + ["rconv28","bottlenest2"], + ["rconv29","bottlenest2"], + ["rconv30","bottlenest2gpool"], + ["rconv31","bottlenest2"], + ["rconv32","bottlenest2"], + ["rconv33","bottlenest2gpool"], + ["rconv34","bottlenest2"], + ["rconv35","bottlenest2"], + ["rconv36","bottlenest2gpool"], + ["rconv37","bottlenest2"], + ["rconv38","bottlenest2"], + ["rconv39","bottlenest2gpool"], + ["rconv40","bottlenest2"], + ["rconv41","bottlenest2"], + ], + "p1_num_channels":64, + "g1_num_channels":64, + "v1_num_channels":96, + "sbv2_num_channels":128, + "num_scorebeliefs":8, + "v2_size":144, +} + +b32c448nbt = { + "version":15, + "norm_kind":"fixup", + "bnorm_epsilon": 1e-4, + "bnorm_running_avg_momentum": 0.001, + "initial_conv_1x1": False, + "trunk_num_channels":448, + "mid_num_channels":224, + "gpool_num_channels":64, + "use_attention_pool":False, + "num_attention_pool_heads":4, + "block_kind": [ + ["rconv1","bottlenest2"], + ["rconv2","bottlenest2"], + ["rconv3","bottlenest2gpool"], + ["rconv4","bottlenest2"], + ["rconv5","bottlenest2"], + ["rconv6","bottlenest2gpool"], + ["rconv7","bottlenest2"], + ["rconv8","bottlenest2"], + ["rconv9","bottlenest2gpool"], + ["rconv10","bottlenest2"], + ["rconv11","bottlenest2"], + ["rconv12","bottlenest2gpool"], + ["rconv13","bottlenest2"], + ["rconv14","bottlenest2"], + ["rconv15","bottlenest2gpool"], + ["rconv16","bottlenest2"], + ["rconv17","bottlenest2"], + ["rconv18","bottlenest2gpool"], + ["rconv19","bottlenest2"], + ["rconv20","bottlenest2"], + ["rconv21","bottlenest2gpool"], + ["rconv22","bottlenest2"], + ["rconv23","bottlenest2"], + ["rconv24","bottlenest2gpool"], + ["rconv25","bottlenest2"], + ["rconv26","bottlenest2"], + ["rconv27","bottlenest2gpool"], + ["rconv28","bottlenest2"], + ["rconv29","bottlenest2"], + ["rconv30","bottlenest2gpool"], + ["rconv31","bottlenest2"], + ["rconv32","bottlenest2"], + ], + "p1_num_channels":64, + "g1_num_channels":64, + "v1_num_channels":96, + "sbv2_num_channels":128, + "num_scorebeliefs":8, + "v2_size":144, +} + + +b28c512nbt = { + "version":15, + "norm_kind":"fixup", + "bnorm_epsilon": 1e-4, + "bnorm_running_avg_momentum": 0.001, + "initial_conv_1x1": False, + "trunk_num_channels":512, + "mid_num_channels":256, + "gpool_num_channels":64, + "use_attention_pool":False, + "num_attention_pool_heads":4, + "block_kind": [ + ["rconv1","bottlenest2"], + ["rconv2","bottlenest2"], + ["rconv3","bottlenest2gpool"], + ["rconv4","bottlenest2"], + ["rconv5","bottlenest2"], + ["rconv6","bottlenest2gpool"], + ["rconv7","bottlenest2"], + ["rconv8","bottlenest2"], + ["rconv9","bottlenest2gpool"], + ["rconv10","bottlenest2"], + ["rconv11","bottlenest2"], + ["rconv12","bottlenest2gpool"], + ["rconv13","bottlenest2"], + ["rconv14","bottlenest2"], + ["rconv15","bottlenest2gpool"], + ["rconv16","bottlenest2"], + ["rconv17","bottlenest2"], + ["rconv18","bottlenest2gpool"], + ["rconv19","bottlenest2"], + ["rconv20","bottlenest2"], + ["rconv21","bottlenest2gpool"], + ["rconv22","bottlenest2"], + ["rconv23","bottlenest2"], + ["rconv24","bottlenest2gpool"], + ["rconv25","bottlenest2"], + ["rconv26","bottlenest2"], + ["rconv27","bottlenest2gpool"], + ["rconv28","bottlenest2"], + ], + "p1_num_channels":64, + "g1_num_channels":64, + "v1_num_channels":128, + "sbv2_num_channels":128, + "num_scorebeliefs":8, + "v2_size":144, +} + +b20c640nbt = { + "version":15, + "norm_kind":"fixup", + "bnorm_epsilon": 1e-4, + "bnorm_running_avg_momentum": 0.001, + "initial_conv_1x1": False, + "trunk_num_channels":640, + "mid_num_channels":320, + "gpool_num_channels":96, + "use_attention_pool":False, + "num_attention_pool_heads":4, + "block_kind": [ + ["rconv1","bottlenest2"], + ["rconv2","bottlenest2"], + ["rconv3","bottlenest2gpool"], + ["rconv4","bottlenest2"], + ["rconv5","bottlenest2"], + ["rconv6","bottlenest2gpool"], + ["rconv7","bottlenest2"], + ["rconv8","bottlenest2"], + ["rconv9","bottlenest2gpool"], + ["rconv10","bottlenest2"], + ["rconv11","bottlenest2"], + ["rconv12","bottlenest2gpool"], + ["rconv13","bottlenest2"], + ["rconv14","bottlenest2"], + ["rconv15","bottlenest2gpool"], + ["rconv16","bottlenest2"], + ["rconv17","bottlenest2"], + ["rconv18","bottlenest2gpool"], + ["rconv19","bottlenest2"], + ["rconv20","bottlenest2"], + ], + "p1_num_channels":64, + "g1_num_channels":64, + "v1_num_channels":96, + "sbv2_num_channels":128, + "num_scorebeliefs":8, + "v2_size":144, +} + +sandbox = { + "version":15, + "norm_kind":"fixup", + "bnorm_epsilon": 1e-4, + "bnorm_running_avg_momentum": 0.001, + "initial_conv_1x1": False, + "trunk_num_channels":256, + "mid_num_channels":256, + "gpool_num_channels":64, + "use_attention_pool":False, + "num_attention_pool_heads":4, + "block_kind": [ + ["rconv1","regular"], + ["rconv2","regular"], + ["rconv3","regular"], + ["rconv4","regular"], + ["rconv5","regular"], + ["rconv6","regular"], + ["rconv7","regulargpool"], + ["rconv8","regular"], + ["rconv9","regular"], + ["rconv10","regular"], + ["rconv11","regular"], + ["rconv12","regulargpool"], + ["rconv13","regular"], + ["rconv14","regular"], + ["rconv15","regular"], + ["rconv16","regular"], + ["rconv17","regulargpool"], + ["rconv18","regular"], + ["rconv19","regular"], + ["rconv20","regular"], + ], + "p1_num_channels":48, + "g1_num_channels":48, + "v1_num_channels":96, + "sbv2_num_channels":96, + "num_scorebeliefs":8, + "v2_size":112, +} + + +base_config_of_name = { + # Micro-sized model configs + "b1c6nbt": b1c6nbt, + "b2c16": b2c16, + "b4c32": b4c32, + "b6c96": b6c96, + + # Small model configs, not too different in inference cost from b10c128 + "b10c128": b10c128, + "b5c192nbt": b5c192nbt, + + # Medium model configs, not too different in inference cost from b15c192 + "b15c192": b15c192, + + # Roughly AlphaZero-sized, not too different in inference cost from b20c256 + "b20c256": b20c256, + "b30c256bt": b30c256bt, + "b24c320bt": b24c320bt, + "b20c384bt": b20c384bt, + "b23c256lbt": b23c256lbt, + "b18c320lbt": b18c320lbt, + "b15c384lbt": b15c384lbt, + "b10c512lbt": b10c512lbt, + "b12c384llbt": b12c384llbt, + "b10c384nbt": b10c384nbt, # Recommended best config for this cost + "b10c480nb3t": b10c480nb3t, + "b7c384lnbt": b7c384lnbt, + "b5c512nnbt": b5c512nnbt, + "b20c384lbt": b20c384lbt, + + # Roughly AlphaGoZero-sized, not too different in inference cost from b40c256 + "b30c320": b30c320, + "b40c256": b40c256, + "b18c384nbt": b18c384nbt, # Recommended best config for this cost + "b14c448nbt": b14c448nbt, + + # Large model configs, not too different in inference cost from b60c320 + "b40c384": b40c384, + "b60c320": b60c320, + "b41c384nbt": b41c384nbt, + "b32c448nbt": b32c448nbt, + "b28c512nbt": b28c512nbt, # Recommended best config for this cost + "b20c640nbt": b20c640nbt, + + "sandbox": sandbox, +} + +config_of_name = {} +for name, base_config in base_config_of_name.items(): + config = base_config.copy() + config_of_name[name] = config + + +for name, base_config in list(config_of_name.items()): + # Fixup initialization + config = base_config.copy() + config["norm_kind"] = "fixup" + config_of_name[name+""] = config + + # Fixed scaling normalization + config = base_config.copy() + config["norm_kind"] = "fixscale" + config_of_name[name+"-fs"] = config + + # Batchnorm without gamma terms + config = base_config.copy() + config["norm_kind"] = "bnorm" + config_of_name[name+"-bn"] = config + + # Batchrenorm without gamma terms + config = base_config.copy() + config["norm_kind"] = "brenorm" + config_of_name[name+"-brn"] = config + + # Fixed scaling normalization + Batchrenorm without gamma terms + config = base_config.copy() + config["norm_kind"] = "fixbrenorm" + config_of_name[name+"-fbrn"] = config + + # Batchnorm with gamma terms + config = base_config.copy() + config["norm_kind"] = "bnorm" + config["bnorm_use_gamma"] = True + config_of_name[name+"-bng"] = config + + # Batchrenorm with gamma terms + config = base_config.copy() + config["norm_kind"] = "brenorm" + config["bnorm_use_gamma"] = True + config_of_name[name+"-brng"] = config + + # Fixed scaling normalization + Batchrenorm with gamma terms + config = base_config.copy() + config["norm_kind"] = "fixbrenorm" + config["bnorm_use_gamma"] = True + config_of_name[name+"-fbrng"] = config + + # Fixed scaling normalization + ONE batch norm layer in the entire net. + config = base_config.copy() + config["norm_kind"] = "fixscaleonenorm" + config["bnorm_use_gamma"] = True + config_of_name[name+"-fson"] = config + +for name, base_config in list(config_of_name.items()): + config = base_config.copy() + config["activation"] = "elu" + config_of_name[name+"-elu"] = config + + config = base_config.copy() + config["activation"] = "gelu" + config_of_name[name+"-gelu"] = config + + config = base_config.copy() + config["activation"] = "mish" + config_of_name[name+"-mish"] = config + +for name, base_config in list(config_of_name.items()): + config = base_config.copy() + config["use_attention_pool"] = True + config_of_name[name+"-ap"] = config + +for name, base_config in list(config_of_name.items()): + config = base_config.copy() + config["use_repvgg_init"] = True + config_of_name[name+"-rvgi"] = config + + config = base_config.copy() + config["use_repvgg_linear"] = True + config_of_name[name+"-rvgl"] = config + + config = base_config.copy() + config["use_repvgg_init"] = True + config["use_repvgg_learning_rate"] = True + config_of_name[name+"-rvglr"] = config + +for name, base_config in list(config_of_name.items()): + # Add intermediate heads, for use with self-distillation or embedding small net in big one. + config = base_config.copy() + config["has_intermediate_head"] = True + config["intermediate_head_blocks"] = len(config["block_kind"]) // 2 + config_of_name[name+"-ih"] = config + + # Add parallel heads that uses the final trunk batchnorm. + # The original normal heads disables the final trunk batchnorm + # This only makes sense for configs that use some form of batchnorm. + if "norm" in config["norm_kind"]: + config = base_config.copy() + config["has_intermediate_head"] = True + config["intermediate_head_blocks"] = len(config["block_kind"]) + config["trunk_normless"] = True + config_of_name[name+"-bnh"] = config + +for name, base_config in list(config_of_name.items()): + config = base_config.copy() + config["metadata_encoder"] = { + "meta_encoder_version": 1, + "internal_num_channels": config["trunk_num_channels"], + } + config_of_name[name+"-meta"] = config diff --git a/training/scripts/dated/20241110-123407/play.py b/training/scripts/dated/20241110-123407/play.py new file mode 100644 index 0000000..6121db5 --- /dev/null +++ b/training/scripts/dated/20241110-123407/play.py @@ -0,0 +1,648 @@ +#!/usr/bin/python3 +import sys +import os +import argparse +import traceback +import random +import math +import time +import re +import logging +import colorsys +import json +import numpy as np + +from board import Board +from features import Features +from gamestate import GameState + +from typing import Dict, Any, List + +import torch +import torch.nn + +import modelconfigs +from model_pytorch import Model, EXTRA_SCORE_DISTR_RADIUS, ExtraOutputs +from data_processing_pytorch import apply_symmetry +from load_model import load_model + +description = """ +Play go with a trained neural net! +Implements a basic GTP engine that uses the neural net directly to play moves. +""" + +parser = argparse.ArgumentParser(description=description) +parser.add_argument('-checkpoint', help='Checkpoint to test', required=False) +parser.add_argument('-use-swa', help='Use SWA model', action="store_true", required=False) +parser.add_argument('-device', help='Pytorch device, like cpu or cuda:0', required=False) + +args = vars(parser.parse_args()) + +checkpoint_file = args["checkpoint"] +use_swa = args["use_swa"] +device = args["device"] + +# Hardcoded max board size +pos_len = 19 + +# Model ---------------------------------------------------------------- + +logging.root.handlers = [] +logging.basicConfig( + level=logging.INFO, + format="%(message)s", + handlers=[ + logging.StreamHandler(stream=sys.stderr), + ], +) +np.set_printoptions(linewidth=150) +torch.set_printoptions(precision=7,sci_mode=False,linewidth=100000,edgeitems=1000,threshold=1000000) + +model, swa_model, _ = load_model(checkpoint_file, use_swa, device=device, pos_len=pos_len, verbose=True) +if swa_model is not None: + model = swa_model +model_config = model.config +model.eval() + +features = Features(model_config, pos_len) + + +# Moves ---------------------------------------------------------------- + +def get_input_feature(gs, feature_idx): + bin_input_data, global_input_data = gs.get_input_features(features) + locs_and_values = [] + for y in range(gs.board.size): + for x in range(gs.board.size): + loc = board.loc(x,y) + locs_and_values.append((loc,bin_input_data[0,feature_idx,y,x])) + return locs_and_values + +def get_pass_alive(gs): + board = gs.board + rules = gs.rules + pla = board.pla + opp = Board.get_opp(pla) + area = [-1 for i in range(board.arrsize)] + nonPassAliveStones = False + safeBigTerritories = True + unsafeBigTerritories = False + board.calculateArea(area,nonPassAliveStones,safeBigTerritories,unsafeBigTerritories,rules["multiStoneSuicideLegal"]) + + locs_and_values = [] + for y in range(board.size): + for x in range(board.size): + loc = board.loc(x,y) + locs_and_values.append((loc,area[loc])) + return locs_and_values + + +def get_gfx_commands_for_heatmap(locs_and_values, board, normalization_div, is_percent, value_and_score_from=None, hotcold=False): + gfx_commands = [] + divisor = 1.0 + if normalization_div == "max": + max_abs_value = max(abs(value) for (loc,value) in locs_and_values) + divisor = max(0.0000000001,max_abs_value) # avoid divide by zero + elif normalization_div is not None: + divisor = normalization_div + + # Caps value at 1.0, using an asymptotic curve + def loose_cap(x): + def transformed_softplus(x): + return -math.log(math.exp(-(x-1.0)*8.0)+1.0)/8.0+1.0 + base = transformed_softplus(0.0) + return (transformed_softplus(x) - base) / (1.0 - base) + + # Softly curves a value so that it ramps up faster than linear in that range + def soft_curve(x,x0,x1): + p = (x-x0)/(x1-x0) + def curve(p): + return math.sqrt(p+0.16)-0.4 + p = curve(p) / curve(1.0) + return x0 + p * (x1-x0) + + if hotcold: + for (loc,value) in locs_and_values: + if loc != Board.PASS_LOC: + value = value / divisor + + if value < 0: + value = -loose_cap(-value) + else: + value = loose_cap(value) + + interpoints = [ + (-1.00,(0,0,0)), + (-0.85,(15,0,50)), + (-0.60,(60,0,160)), + (-0.35,(0,0,255)), + (-0.15,(0,100,255)), + ( 0.00,(115,115,115)), + ( 0.15,(250,45,40)), + ( 0.25,(255,55,0)), + ( 0.60,(255,255,20)), + ( 0.85,(255,255,128)), + ( 1.00,(255,255,255)), + ] + + def lerp(p,y0,y1): + return y0 + p*(y1-y0) + + i = 0 + while i < len(interpoints): + if value <= interpoints[i][0]: + break + i += 1 + i -= 1 + + if i < 0: + (r,g,b) = interpoints[0][1] + if i >= len(interpoints)-1: + (r,g,b) = interpoints[len(interpoints)-1][1] + + p = (value - interpoints[i][0]) / (interpoints[i+1][0] - interpoints[i][0]) + + (r0,g0,b0) = interpoints[i][1] + (r1,g1,b1) = interpoints[i+1][1] + r = lerp(p,r0,r1) + g = lerp(p,g0,g1) + b = lerp(p,b0,b1) + + r = ("%02x" % int(r)) + g = ("%02x" % int(g)) + b = ("%02x" % int(b)) + gfx_commands.append("COLOR #%s%s%s %s" % (r,g,b,str_coord(loc,board))) + + else: + for (loc,value) in locs_and_values: + if loc != Board.PASS_LOC: + value = value / divisor + if value < 0: + value = -value + huestart = 0.50 + huestop = 0.86 + else: + huestart = -0.02 + huestop = 0.38 + + value = loose_cap(value) + + def lerp(p,x0,x1,y0,y1): + return y0 + (y1-y0) * (p-x0)/(x1-x0) + + if value <= 0.03: + hue = huestart + lightness = 0.00 + 0.50 * (value / 0.03) + saturation = value / 0.03 + (r,g,b) = colorsys.hls_to_rgb((hue+1)%1, lightness, saturation) + elif value <= 0.60: + hue = lerp(value,0.03,0.60,huestart,huestop) + val = 1.0 + saturation = 1.0 + (r,g,b) = colorsys.hsv_to_rgb((hue+1)%1, val, saturation) + else: + hue = huestop + lightness = lerp(value,0.60,1.00,0.5,0.95) + saturation = 1.0 + (r,g,b) = colorsys.hls_to_rgb((hue+1)%1, lightness, saturation) + + r = ("%02x" % int(r*255)) + g = ("%02x" % int(g*255)) + b = ("%02x" % int(b*255)) + gfx_commands.append("COLOR #%s%s%s %s" % (r,g,b,str_coord(loc,board))) + + locs_and_values = sorted(locs_and_values, key=lambda loc_and_value: loc_and_value[1]) + locs_and_values_rev = sorted(locs_and_values, key=lambda loc_and_value: loc_and_value[1], reverse=True) + texts = [] + texts_rev = [] + texts_value = [] + maxlen_per_side = 1000 + if len(locs_and_values) > 0 and locs_and_values[0][1] < 0: + maxlen_per_side = 500 + + for i in range(min(len(locs_and_values),maxlen_per_side)): + (loc,value) = locs_and_values[i] + if is_percent: + texts.append("%s %4.1f%%" % (str_coord(loc,board),value*100)) + else: + texts.append("%s %.3f" % (str_coord(loc,board),value)) + texts.reverse() + + for i in range(min(len(locs_and_values_rev),maxlen_per_side)): + (loc,value) = locs_and_values_rev[i] + if is_percent: + texts_rev.append("%s %4.1f%%" % (str_coord(loc,board),value*100)) + else: + texts_rev.append("%s %.3f" % (str_coord(loc,board),value)) + + if value_and_score_from is not None: + value = value_and_score_from["value"] + score = value_and_score_from["scoremean"] + lead = value_and_score_from["lead"] + vtime = value_and_score_from["vtime"] + texts_value.append("wv %.2fc nr %.2f%% ws %.1f wl %.1f vt %.1f" % ( + 100*(value[0]-value[1] if board.pla == Board.WHITE else value[1] - value[0]), + 100*value[2], + (score if board.pla == Board.WHITE else -score), + (lead if board.pla == Board.WHITE else -lead), + vtime + )) + + gfx_commands.append("TEXT " + ", ".join(texts_value + texts_rev + texts)) + return gfx_commands + +def print_scorebelief(gs,outputs): + board = gs.board + scorebelief = outputs["scorebelief"] + lead = outputs["lead"] + scoremean = outputs["scoremean"] + scorestdev = outputs["scorestdev"] + + scorebelief = list(scorebelief) + # Flip so that it's in perspective of the player playing + if board.pla != Board.WHITE: + scorebelief.reverse() + scoremean = -scoremean + lead = -lead + + scoredistrmid = pos_len * pos_len + EXTRA_SCORE_DISTR_RADIUS + ret = "" + ret += "TEXT " + ret += "ScoreBelief: \n" + for i in range(17,-1,-1): + ret += "TEXT " + ret += "%+6.1f" %(-(i*20+0.5)) + for j in range(20): + idx = scoredistrmid-(i*20+j)-1 + ret += " %4.0f" % (scorebelief[idx] * 10000) + ret += "\n" + for i in range(18): + ret += "TEXT " + ret += "%+6.1f" %((i*20+0.5)) + for j in range(20): + idx = scoredistrmid+(i*20+j) + ret += " %4.0f" % (scorebelief[idx] * 10000) + ret += "\n" + + beliefscore = 0 + beliefscoresq = 0 + beliefwin = 0 + belieftotal = 0 + for idx in range(scoredistrmid*2): + score = idx-scoredistrmid+0.5 + if score > 0: + beliefwin += scorebelief[idx] + else: + beliefwin -= scorebelief[idx] + belieftotal += scorebelief[idx] + beliefscore += score*scorebelief[idx] + beliefscoresq += score*score*scorebelief[idx] + + beliefscoremean = beliefscore/belieftotal + beliefscoremeansq = beliefscoresq/belieftotal + beliefscorevar = max(0,beliefscoremeansq-beliefscoremean*beliefscoremean) + beliefscorestdev = math.sqrt(beliefscorevar) + + ret += "TEXT BeliefWin: %.2fc\n" % (100*beliefwin/belieftotal) + ret += "TEXT BeliefScoreMean: %.2f\n" % (beliefscoremean) + ret += "TEXT BeliefScoreStdev: %.2f\n" % (beliefscorestdev) + ret += "TEXT Lead: %.3f\n" % (lead) + ret += "TEXT ScoreMean: %.3f\n" % (scoremean) + ret += "TEXT ScoreStdev: %.3f\n" % (scorestdev) + ret += "TEXT Value: %s\n" % str(["%.3f" % x for x in outputs["value"]]) + ret += "TEXT TDValue: %s\n" % str(["%.3f" % x for x in outputs["td_value"]]) + ret += "TEXT TDValue2: %s\n" % str(["%.3f" % x for x in outputs["td_value2"]]) + ret += "TEXT TDValue3: %s\n" % str(["%.3f" % x for x in outputs["td_value3"]]) + ret += "TEXT TDScore: %s\n" % str(["%.3f" % x for x in outputs["td_score"]]) + ret += "TEXT Estv: %s\n" % str(outputs["estv"]) + ret += "TEXT Ests: %s\n" % str(outputs["ests"]) + ret += "TEXT Vtime: %s\n" % str(outputs["vtime"]) + return ret + + +# Basic parsing -------------------------------------------------------- +colstr = 'ABCDEFGHJKLMNOPQRST' +def parse_coord(s,board): + if s == 'pass': + return Board.PASS_LOC + return board.loc(colstr.index(s[0].upper()), board.size - int(s[1:])) + +def str_coord(loc,board): + if loc == Board.PASS_LOC: + return 'pass' + x = board.loc_x(loc) + y = board.loc_y(loc) + return '%c%d' % (colstr[x], board.size - y) + + +# GTP Implementation ----------------------------------------------------- + +# Adapted from https://github.com/pasky/michi/blob/master/michi.py, which is distributed under MIT license +# https://opensource.org/licenses/MIT + +known_commands = [ + 'boardsize', + 'clear_board', + 'showboard', + 'komi', + 'play', + 'genmove', + 'quit', + 'name', + 'version', + 'known_command', + 'list_commands', + 'protocol_version', + 'gogui-analyze_commands', + 'setrule', + 'policy', + 'policy1', + 'logpolicy', + 'ownership', + 'scoring', + 'futurepos0', + 'futurepos1', + 'seki', + 'seki2', + 'scorebelief', + 'passalive', +] +known_analyze_commands = [ + 'gfx/Policy/policy', + 'gfx/Policy1/policy1', + 'gfx/LogPolicy/logpolicy', + 'gfx/Ownership/ownership', + 'gfx/Scoring/scoring', + 'gfx/FuturePos0/futurepos0', + 'gfx/FuturePos1/futurepos1', + 'gfx/Seki/seki', + 'gfx/Seki2/seki2', + 'gfx/ScoreBelief/scorebelief', + 'gfx/PassAlive/passalive', +] + +board_size = 19 +gs = GameState(board_size, GameState.RULES_TT) + +input_feature_command_lookup = dict() +def add_input_feature_visualizations(layer_name, feature_idx, normalization_div): + command_name = layer_name + command_name = command_name.replace("/",":") + known_commands.append(command_name) + known_analyze_commands.append("gfx/" + command_name + "/" + command_name) + input_feature_command_lookup[command_name] = (feature_idx,normalization_div) +for i in range(model.bin_input_shape[1]): + add_input_feature_visualizations("input-" + str(i),i, normalization_div=1) + +attention_feature_command_lookup = dict() +def add_attention_visualizations(extra_output_name, extra_output): + for c in range(extra_output.shape[0]): + command_name = extra_output_name + command_name = command_name.replace("/",":") + command_name += ":" + str(c) + known_commands.append(command_name) + known_analyze_commands.append("gfx/" + command_name + "/" + command_name) + attention_feature_command_lookup[command_name] = (extra_output_name, c) +with torch.no_grad(): + dummy_outputs = gs.get_model_outputs(model) + extra_attention_output_names = [name for name in dummy_outputs["available_extra_outputs"] if name.endswith(".attention") or name.endswith(".reverse_attention")] + dummy_outputs = gs.get_model_outputs(model) + for name in extra_attention_output_names: + add_attention_visualizations(name,dummy_outputs[name]) + del dummy_outputs + + +def get_board_matrix_str(matrix, scale, formatstr): + ret = "" + matrix = matrix.reshape([features.pos_len,features.pos_len]) + for y in range(features.pos_len): + for x in range(features.pos_len): + ret += formatstr % (scale * matrix[y,x]) + ret += " " + ret += "\n" + return ret + + +while True: + try: + line = input().strip() + except EOFError: + break + if line == '': + continue + command = [s.lower() for s in line.split()] + if re.match('\d+', command[0]): + cmdid = command[0] + command = command[1:] + else: + cmdid = '' + + ret = '' + if command[0] == "boardsize": + if int(command[1]) > features.pos_len: + print("Warning: Trying to set incompatible boardsize %s (!= %d)" % (command[1], N), file=sys.stderr) + ret = None + board_size = int(command[1]) + gs = GameState(board_size) + elif command[0] == "clear_board": + gs = GameState(board_size) + elif command[0] == "showboard": + ret = "\n" + gs.board.to_string().strip() + elif command[0] == "komi": + gs.rules["whiteKomi"] = float(command[1]) + elif command[0] == "play": + pla = (Board.BLACK if command[1] == "B" or command[1] == "b" else Board.WHITE) + loc = parse_coord(command[2],gs.board) + gs.play(pla,loc) + elif command[0] == "genmove": + outputs = gs.get_model_outputs(model) + loc = outputs["genmove_result"] + pla = gs.board.pla + + if len(command) > 1: + pla = (Board.BLACK if command[1] == "B" or command[1] == "b" else Board.WHITE) + gs.play(pla,loc) + ret = str_coord(loc,gs.board) + + elif command[0] == "name": + ret = 'KataGo Raw Neural Net Debug/Test Script' + elif command[0] == "version": + ret = '1.0' + elif command[0] == "list_commands": + ret = '\n'.join(known_commands) + elif command[0] == "known_command": + ret = 'true' if command[1] in known_commands else 'false' + elif command[0] == "gogui-analyze_commands": + ret = '\n'.join(known_analyze_commands) + elif command[0] == "setrule": + ret = "" + if command[1] == "korule": + gs.rules["koRule"] = command[2].upper() + elif command[1] == "scoringrule": + gs.rules["scoringRule"] = command[2].upper() + elif command[1] == "taxrule": + gs.rules["taxRule"] = command[2].upper() + elif command[1] == "multistonesuicidelegal": + gs.rules["multiStoneSuicideLegal"] = (command[2].lower() == "true") + elif command[1] == "hasbutton": + gs.rules["hasButton"] = (command[2].lower() == "true") + elif command[1] == "encorephase": + gs.rules["encorePhase"] = int(command[2]) + elif command[1] == "passwouldendphase": + gs.rules["passWouldEndPhase"] = (command[2].lower() == "true") + elif command[1] == "whitekomi" or command[1] == "komi": + gs.rules["whiteKomi"] = float(command[2]) + elif command[1] == "asym": + gs.rules["asymPowersOfTwo"] = float(command[2]) + else: + ret = "Unknown rules setting" + elif command[0] == "policy": + outputs = gs.get_model_outputs(model) + gfx_commands = get_gfx_commands_for_heatmap(outputs["moves_and_probs0"], gs.board, normalization_div=None, is_percent=True, value_and_score_from=outputs) + ret = "\n".join(gfx_commands) + elif command[0] == "policy1": + outputs = gs.get_model_outputs(model) + gfx_commands = get_gfx_commands_for_heatmap(outputs["moves_and_probs1"], gs.board, normalization_div=None, is_percent=True, value_and_score_from=outputs) + ret = "\n".join(gfx_commands) + elif command[0] == "logpolicy": + outputs = gs.get_model_outputs(model) + moves_and_logprobs = [(move,max(0.0,4.9+math.log10(prob))) for (move,prob) in outputs["moves_and_probs0"]] + gfx_commands = get_gfx_commands_for_heatmap(moves_and_logprobs, gs.board, normalization_div=6, is_percent=False, value_and_score_from=outputs) + ret = "\n".join(gfx_commands) + elif command[0] == "ownership": + outputs = gs.get_model_outputs(model) + gfx_commands = get_gfx_commands_for_heatmap(outputs["ownership_by_loc"], gs.board, normalization_div=None, is_percent=True, value_and_score_from=None, hotcold=True) + ret = "\n".join(gfx_commands) + elif command[0] == "scoring": + outputs = gs.get_model_outputs(model) + gfx_commands = get_gfx_commands_for_heatmap(outputs["scoring_by_loc"], gs.board, normalization_div=None, is_percent=True, value_and_score_from=None, hotcold=True) + ret = "\n".join(gfx_commands) + elif command[0] == "futurepos0": + outputs = gs.get_model_outputs(model) + gfx_commands = get_gfx_commands_for_heatmap(outputs["futurepos0_by_loc"], gs.board, normalization_div=None, is_percent=True, value_and_score_from=None, hotcold=True) + ret = "\n".join(gfx_commands) + elif command[0] == "futurepos1": + outputs = gs.get_model_outputs(model) + gfx_commands = get_gfx_commands_for_heatmap(outputs["futurepos1_by_loc"], gs.board, normalization_div=None, is_percent=True, value_and_score_from=None, hotcold=True) + ret = "\n".join(gfx_commands) + elif command[0] == "seki": + outputs = gs.get_model_outputs(model) + gfx_commands = get_gfx_commands_for_heatmap(outputs["seki_by_loc"], gs.board, normalization_div=None, is_percent=True, value_and_score_from=None) + ret = "\n".join(gfx_commands) + elif command[0] == "seki2": + outputs = gs.get_model_outputs(model) + gfx_commands = get_gfx_commands_for_heatmap(outputs["seki_by_loc2"], gs.board, normalization_div=None, is_percent=True, value_and_score_from=None) + ret = "\n".join(gfx_commands) + + elif command[0] == "policy_raw": + outputs = gs.get_model_outputs(model) + ret = "\n" + + policysum = 0.0 + for y in range(gs.board.size): + for x in range(gs.board.size): + loc = gs.board.loc(x,y) + pos = features.loc_to_tensor_pos(loc,gs.board) + gs.board.would_be_legal(gs.board.pla,loc) + policysum += outputs["policy0"][pos] + loc = Board.PASS_LOC + pos = features.loc_to_tensor_pos(loc,gs.board) + policysum += outputs["policy0"][pos] + + for y in range(gs.board.size): + for x in range(gs.board.size): + loc = gs.board.loc(x,y) + pos = features.loc_to_tensor_pos(loc,gs.board) + if gs.board.would_be_legal(gs.board.pla,loc): + ret += "%6.3f" % (100.0 * outputs["policy0"][pos] / policysum) + else: + ret += " - " + ret += " " + ret += "\n" + loc = Board.PASS_LOC + pos = features.loc_to_tensor_pos(loc,gs.board) + ret += "Pass: %6.3f" % (100.0 * outputs["policy0"][pos] / policysum) + + elif command[0] == "policy1_raw": + outputs = gs.get_model_outputs(model) + ret = "\n" + + for y in range(gs.board.size): + for x in range(gs.board.size): + loc = gs.board.loc(x,y) + pos = features.loc_to_tensor_pos(loc,gs.board) + if gs.board.would_be_legal(gs.board.pla,loc): + ret += "%6.3f" % (100.0 * outputs["policy1"][pos]) + else: + ret += " - " + ret += " " + ret += "\n" + loc = Board.PASS_LOC + pos = features.loc_to_tensor_pos(loc,gs.board) + ret += "Pass: %6.3f" % (100.0 * outputs["policy1"][pos]) + + elif command[0] == "ownership_raw": + outputs = gs.get_model_outputs(model) + ret = get_board_matrix_str(outputs["ownership"], 100.0, "%+7.3f") + elif command[0] == "scoring_raw": + outputs = gs.get_model_outputs(model) + ret = get_board_matrix_str(outputs["scoring"], 100.0, "%+7.3f") + elif command[0] == "futurepos0_raw": + outputs = gs.get_model_outputs(model) + ret = get_board_matrix_str(outputs["futurepos"][0], 100.0, "%+7.3f") + elif command[0] == "futurepos1_raw": + outputs = gs.get_model_outputs(model) + ret = get_board_matrix_str(outputs["futurepos"][1], 100.0, "%+7.3f") + elif command[0] == "seki_raw": + outputs = gs.get_model_outputs(model) + ret = get_board_matrix_str(outputs["seki"], 100.0, "%+7.3f") + elif command[0] == "seki2_raw": + outputs = gs.get_model_outputs(model) + ret = get_board_matrix_str(outputs["seki2"], 100.0, "%+7.3f") + + elif command[0] in input_feature_command_lookup: + (feature_idx,normalization_div) = input_feature_command_lookup[command[0]] + locs_and_values = get_input_feature(gs, feature_idx) + gfx_commands = get_gfx_commands_for_heatmap(locs_and_values, gs.board, normalization_div, is_percent=False) + ret = "\n".join(gfx_commands) + + elif command[0] in attention_feature_command_lookup: + (extra_output_name,channel_idx) = attention_feature_command_lookup[command[0]] + outputs = gs.get_model_outputs(model, extra_output_names=[extra_output_name]) + output = outputs[extra_output_name] # shape c, hw + output = output[channel_idx] + locs_and_values = [] + board = gs.board + for y in range(board.size): + for x in range(board.size): + loc = board.loc(x,y) + pos = features.loc_to_tensor_pos(loc,board) + locs_and_values.append((loc,output[pos])) + + normalization_div = "max" + gfx_commands = get_gfx_commands_for_heatmap(locs_and_values, gs.board, normalization_div, is_percent=True) + ret = "\n".join(gfx_commands) + + elif command[0] == "passalive": + locs_and_values = get_pass_alive(gs) + gfx_commands = get_gfx_commands_for_heatmap(locs_and_values, gs.board, normalization_div=None, is_percent=False) + ret = "\n".join(gfx_commands) + + elif command[0] == "scorebelief": + outputs = gs.get_model_outputs(model) + ret = print_scorebelief(gs,outputs) + + elif command[0] == "protocol_version": + ret = '2' + elif command[0] == "quit": + print('=%s \n\n' % (cmdid,), end='') + break + else: + print('Warning: Ignoring unknown command - %s' % (line,), file=sys.stderr) + ret = None + + if ret is not None: + print('=%s %s\n\n' % (cmdid, ret,), end='') + else: + print('?%s ???\n\n' % (cmdid,), end='') + sys.stdout.flush() diff --git a/training/scripts/dated/20241110-123407/query_analysis_engine_example.py b/training/scripts/dated/20241110-123407/query_analysis_engine_example.py new file mode 100644 index 0000000..2b803e9 --- /dev/null +++ b/training/scripts/dated/20241110-123407/query_analysis_engine_example.py @@ -0,0 +1,136 @@ +""" +This is a simple python program that demonstrates how to run KataGo's +analysis engine as a subprocess and send it a query. It queries the +result of playing the 4-4 point on an empty board and prints out +the json response. +""" + +import argparse +import json +import subprocess +import time +from threading import Thread +import sgfmill +import sgfmill.boards +import sgfmill.ascii_boards +from typing import Tuple, List, Optional, Union, Literal, Any, Dict + +Color = Union[Literal["b"],Literal["w"]] +Move = Union[None,Literal["pass"],Tuple[int,int]] + +def sgfmill_to_str(move: Move) -> str: + if move is None: + return "pass" + if move == "pass": + return "pass" + (y,x) = move + return "ABCDEFGHJKLMNOPQRSTUVWXYZ"[x] + str(y+1) + +class KataGo: + + def __init__(self, katago_path: str, config_path: str, model_path: str, additional_args: List[str] = []): + self.query_counter = 0 + katago = subprocess.Popen( + [katago_path, "analysis", "-config", config_path, "-model", model_path, *additional_args], + stdin=subprocess.PIPE, + stdout=subprocess.PIPE, + stderr=subprocess.PIPE, + ) + self.katago = katago + def printforever(): + while katago.poll() is None: + data = katago.stderr.readline() + time.sleep(0) + if data: + print("KataGo: ", data.decode(), end="") + data = katago.stderr.read() + if data: + print("KataGo: ", data.decode(), end="") + self.stderrthread = Thread(target=printforever) + self.stderrthread.start() + + def close(self): + self.katago.stdin.close() + + + def query(self, initial_board: sgfmill.boards.Board, moves: List[Tuple[Color,Move]], komi: float, max_visits=None): + query = {} + + query["id"] = str(self.query_counter) + self.query_counter += 1 + + query["moves"] = [(color,sgfmill_to_str(move)) for color, move in moves] + query["initialStones"] = [] + for y in range(initial_board.side): + for x in range(initial_board.side): + color = initial_board.get(y,x) + if color: + query["initialStones"].append((color,sgfmill_to_str((y,x)))) + query["rules"] = "Chinese" + query["komi"] = komi + query["boardXSize"] = initial_board.side + query["boardYSize"] = initial_board.side + query["includePolicy"] = True + if max_visits is not None: + query["maxVisits"] = max_visits + return self.query_raw(query) + + def query_raw(self, query: Dict[str,Any]): + self.katago.stdin.write((json.dumps(query) + "\n").encode()) + self.katago.stdin.flush() + + # print(json.dumps(query)) + + line = "" + while line == "": + if self.katago.poll(): + time.sleep(1) + raise Exception("Unexpected katago exit") + line = self.katago.stdout.readline() + line = line.decode().strip() + # print("Got: " + line) + response = json.loads(line) + + # print(response) + return response + +if __name__ == "__main__": + description = """ + Example script showing how to run KataGo analysis engine and query it from python. + """ + parser = argparse.ArgumentParser(description=description) + parser.add_argument( + "-katago-path", + help="Path to katago executable", + required=True, + ) + parser.add_argument( + "-config-path", + help="Path to KataGo analysis config (e.g. cpp/configs/analysis_example.cfg in KataGo repo)", + required=True, + ) + parser.add_argument( + "-model-path", + help="Path to neural network .bin.gz file", + required=True, + ) + args = vars(parser.parse_args()) + print(args) + + katago = KataGo(args["katago_path"], args["config_path"], args["model_path"]) + + board = sgfmill.boards.Board(19) + komi = 6.5 + moves = [("b",(3,3))] + + displayboard = board.copy() + for color, move in moves: + if move != "pass": + row,col = move + displayboard.play(row,col,color) + print(sgfmill.ascii_boards.render_board(displayboard)) + + print("Query result: ") + print(katago.query(board, moves, komi)) + + katago.close() diff --git a/training/scripts/dated/20241110-123407/save_model_for_export_manual.py b/training/scripts/dated/20241110-123407/save_model_for_export_manual.py new file mode 100644 index 0000000..b0d670a --- /dev/null +++ b/training/scripts/dated/20241110-123407/save_model_for_export_manual.py @@ -0,0 +1,162 @@ +#!/usr/bin/python3 +import sys +import os +import argparse +import traceback +import random +import math +import time +import logging +import contextlib +import json +import datetime +from datetime import timezone +import gc +import shutil +import glob +import numpy as np +import itertools +import copy +import atexit +from collections import defaultdict +from typing import Dict, List + +import torch +import torch.nn +import torch.optim +import torch.distributed +import torch.multiprocessing + +import modelconfigs +from model_pytorch import Model +from metrics_pytorch import Metrics +import load_model +import data_processing_pytorch + +# HANDLE COMMAND AND ARGS ------------------------------------------------------------------- + +if __name__ == "__main__": + + description = """ + Script for one-off taking a checkpoint training under train.py and making it save itself for one export, + without having to use train.py arguments where it would do so repeatedly periodically. + """ + + parser = argparse.ArgumentParser(description=description) + parser.add_argument('-traindir', help='Dir to write to for recording training results', required=True) + parser.add_argument('-exportdir', help='Directory to export models periodically', required=True) + parser.add_argument('-exportprefix', help='Prefix to append to names of models', required=True) + + args = vars(parser.parse_args()) + + +def make_dirs(args): + exportdir = args["exportdir"] + + if exportdir is not None and not os.path.exists(exportdir): + os.makedirs(exportdir) + + +def main(args): + traindir = args["traindir"] + exportdir = args["exportdir"] + exportprefix = args["exportprefix"] + + # SET UP LOGGING ------------------------------------------------------------- + + logging.root.handlers = [] + logging.basicConfig( + level=logging.INFO, + format="%(message)s", + handlers=[ + logging.FileHandler(os.path.join(traindir,f"save_model_for_export_manual.log"), mode="a"), + logging.StreamHandler() + ], + ) + np.set_printoptions(linewidth=150) + + logging.info(str(sys.argv)) + + # LOAD MODEL --------------------------------------------------------------------- + + def get_checkpoint_path(): + return os.path.join(traindir,"checkpoint.ckpt") + + def save(model_state_dict, swa_model_state_dict, optimizer_state_dict, metrics_obj_state_dict, running_metrics, train_state, last_val_metrics, path): + assert path is not None + + state_dict = {} + state_dict["model"] = model_state_dict + state_dict["optimizer"] = optimizer_state_dict + state_dict["metrics"] = metrics_obj_state_dict + state_dict["running_metrics"] = running_metrics + state_dict["train_state"] = train_state + state_dict["last_val_metrics"] = last_val_metrics + state_dict["config"] = model_config + + if swa_model_state_dict is not None: + state_dict["swa_model"] = swa_model_state_dict + + logging.info("Saving checkpoint: " + path) + torch.save(state_dict, path + ".tmp") + time.sleep(1) + os.replace(path + ".tmp", path) + + def load(): + path_to_load_from = get_checkpoint_path() + assert path_to_load_from is not None + + state_dict = torch.load(path_to_load_from, map_location="cpu") + model_config = state_dict["config"] + logging.info(str(model_config)) + + train_state = state_dict["train_state"] + model_state_dict = state_dict["model"] + swa_model_state_dict = None + if "swa_model" in state_dict: + swa_model_state_dict = state_dict["swa_model"] + + metrics_obj_state_dict = state_dict["metrics"] + running_metrics = state_dict["running_metrics"] + + optimizer_state_dict = state_dict["optimizer"] + last_val_metrics = state_dict["last_val_metrics"] + + return (model_config, model_state_dict, swa_model_state_dict, optimizer_state_dict, metrics_obj_state_dict, running_metrics, train_state, last_val_metrics) + + (model_config, model_state_dict, swa_model_state_dict, optimizer_state_dict, metrics_obj_state_dict, running_metrics, train_state, last_val_metrics) = load() + + assert "global_step_samples" in train_state + assert "total_num_data_rows" in train_state + + logging.info("=========================================================================") + logging.info("SAVING MODEL FOR EXPORT MANUAL") + logging.info("=========================================================================") + logging.info("Current time: " + str(datetime.datetime.now())) + logging.info("Global step: %d samples" % (train_state["global_step_samples"])) + logging.info("Currently up to data row " + str(train_state["total_num_data_rows"])) + logging.info(f"Training dir: {traindir}") + logging.info(f"Export dir: {exportdir}") + + # Export a model for testing, unless somehow it already exists + modelname = "%s-s%d-d%d" % ( + exportprefix, + train_state["global_step_samples"], + train_state["total_num_data_rows"], + ) + savepath = os.path.join(exportdir,modelname) + savepathtmp = os.path.join(exportdir,modelname+".tmp") + if os.path.exists(savepath): + logging.info("NOT saving model, already exists at: " + savepath) + else: + os.mkdir(savepathtmp) + logging.info("SAVING MODEL FOR EXPORT TO: " + savepath) + save(model_state_dict, swa_model_state_dict, optimizer_state_dict, metrics_obj_state_dict, running_metrics, train_state, last_val_metrics, path=os.path.join(savepathtmp,"model.ckpt")) + time.sleep(2) + os.rename(savepathtmp,savepath) + + + +if __name__ == "__main__": + make_dirs(args) + main(args) diff --git a/training/scripts/dated/20241110-123407/selfplay.cfg b/training/scripts/dated/20241110-123407/selfplay.cfg new file mode 100644 index 0000000..50bc598 --- /dev/null +++ b/training/scripts/dated/20241110-123407/selfplay.cfg @@ -0,0 +1,185 @@ + +# Logs------------------------------------------------------------------------------------ + +logSearchInfo = false +logMoves = false +logGamesEvery = 10 +logToStdout = true + +# Data writing----------------------------------------------------------------------------------- + +# Spatial size of tensors of data, must match -pos-len in python/train.py and be at least as large as the +# largest boardsize in the data. If you train only on smaller board sizes, you can decrease this and +# pass in a smaller -pos-len to python/train.py (e.g. modify "-pos-len 19" in selfplay/train.sh script), +# to train faster, although it may be awkward if you ever want to increase it again later since data with +# different values cannot be shuffled together. +dataBoardLen = 19 + +maxDataQueueSize = 2000 +maxRowsPerTrainFile = 10000 +firstFileRandMinProp = 0.15 + +# Fancy game selfplay settings-------------------------------------------------------------------- + +# These take dominance - if a game is forked for any of these reasons, that will be the next game played. +# For forked games, randomization of rules and "initGamesWithPolicy" is disabled, komi is made fair with prob "forkCompensateKomiProb". +earlyForkGameProb = 0.04 # Fork to try alternative opening variety with this probability +earlyForkGameExpectedMoveProp = 0.025 # Fork roughly within the first (boardArea * this) many moves +forkGameProb = 0.01 # Fork to try alternative crazy move anywhere in game with this probability if not early forking +forkGameMinChoices = 3 # Choose from the best of at least this many random choices +earlyForkGameMaxChoices = 12 # Choose from the best of at most this many random choices +forkGameMaxChoices = 36 # Choose from the best of at most this many random choices +# Otherwise, with this probability, learn a bit more about the differing evaluation of seki in different rulesets. +sekiForkHackProb = 0.02 + +# Otherwise, play some proportion of games starting from SGF positions, with randomized rules (ignoring the sgf rules) +# On SGF positions, high temperature policy init is allowed +# startPosesProb = 0.0 # Play this proportion of games starting from SGF positions +# startPosesFromSgfDir = DIRECTORYPATH # Load SGFs from this dir +# startPosesLoadProb = 1.0 # Only load each position from each SGF with this chance (save memory) +# startPosesTurnWeightLambda = 0 # 0 = equal weight 0.01 = decrease probability by 1% per turn -0.01 = increase probability by 1% per turn. +# startPosesPolicyInitAreaProp = 0.0 # Same as policyInitAreaProp but for SGF positions + +# Otherwise, play some proportion of games starting from hint positions (generated using "dataminesgfs" command), with randomized rules. +# On hint positions, "initGamesWithPolicy" does not apply. +# hintPosesProb = 0.0 +# hintPosesDir = DIRECTORYPATH + +# Otherwise we are playing a "normal" game, potentially with handicap stones, depending on "handicapProb", and +# potentially with komi randomization, depending on things like "komiStdev", and potentially with different +# board sizes, etc. + +# Most of the remaining parameters here below apply regardless of the initialization, although a few of them +# vary depending on handicap vs normal game, and some are explicitly disabled (e.g. initGamesWithPolicy on hint positions). + +initGamesWithPolicy = true # Play the first few moves of a game high-temperaturely from policy +policyInitAreaProp = 0.04 # The avg number of moves to play +compensateAfterPolicyInitProb = 0.2 # Additionally make komi fair this often after the high-temperature moves. +sidePositionProb = 0.020 # With this probability, train on refuting bad alternative moves. + +cheapSearchProb = 0.75 # Do cheap searches with this probaiblity +cheapSearchVisits = 100 # Number of visits for cheap search +cheapSearchTargetWeight = 0.0 # Training weight for cheap search + +reduceVisits = true # Reduce visits when one side is winning +reduceVisitsThreshold = 0.9 # How winning a side needs to be (winrate) +reduceVisitsThresholdLookback = 3 # How many consecutive turns needed to be that winning +reducedVisitsMin = 100 # Minimum number of visits (never reduce below this) +reducedVisitsWeight = 0.1 # Minimum training weight + +handicapAsymmetricPlayoutProb = 0.5 # In handicap games, play with unbalanced players with this probablity +normalAsymmetricPlayoutProb = 0.01 # In regular games, play with unbalanced players with this probability +maxAsymmetricRatio = 8.0 # Max ratio to unbalance visits between players +minAsymmetricCompensateKomiProb = 0.4 # Compensate komi with at least this probability for unbalanced players + +policySurpriseDataWeight = 0.5 # This proportion of training weight should be concentrated on surprising moves +valueSurpriseDataWeight = 0.1 # This proportion of training weight should be concentrated on surprising position results + +estimateLeadProb = 0.05 # Train lead, rather than just scoremean. Consumes a decent number of extra visits, can be quite slow using low visits to set too high. +switchNetsMidGame = true # When a new neural net is loaded, switch to it immediately instead of waiting for new game +fancyKomiVarying = true # In non-compensated handicap and fork games, vary komi to better learn komi and large score differences that would never happen in even games. + +# Match----------------------------------------------------------------------------------- + +numGameThreads = 30 +maxMovesPerGame = 1600 + +# Rules------------------------------------------------------------------------------------ + +koRules = SIMPLE,POSITIONAL,SITUATIONAL +scoringRules = AREA,TERRITORY +taxRules = NONE,NONE,SEKI,SEKI,ALL +multiStoneSuicideLegals = false,true +hasButtons = false,false,true + +bSizes = 7,8,9 +bSizeRelProbs = 1,1,8 +allowRectangleProb = 0.50 + +komiAuto = True # Automatically adjust komi to what the neural nets think are fair based on the empty board, but still apply komiStdev. +# komiMean = 7.0 # Specify explicit komi +komiStdev = 1.0 # Standard deviation of random variation to komi. +komiBigStdevProb = 0.06 # Probability of applying komiBigStdev +komiBigStdev = 12.0 # Standard deviation of random big variation to komi + +handicapProb = 0.10 # Probability of handicap game +handicapCompensateKomiProb = 0.50 # In handicap games, adjust komi to fair with this probability based on the handicap placement +forkCompensateKomiProb = 0.80 # For forks, adjust komi to fair with this probability based on the forked position +sgfCompensateKomiProb = 0.90 # For sgfs, adjust komi to fair with this probability based on the specific starting position + +drawRandRadius = 0.5 +noResultStdev = 0.166666666 + +# Search limits----------------------------------------------------------------------------------- + +maxVisits = 600 +numSearchThreads = 1 + +# GPU Settings------------------------------------------------------------------------------- + +nnMaxBatchSize = 128 +nnCacheSizePowerOfTwo = 21 +nnMutexPoolSizePowerOfTwo = 15 +numNNServerThreadsPerModel = 1 +nnRandomize = true + +# CUDA GPU settings-------------------------------------- +# cudaDeviceToUse = 0 #use device 0 for all server threads (numNNServerThreadsPerModel) unless otherwise specified per-model or per-thread-per-model +# cudaDeviceToUseModel0 = 3 #use device 3 for model 0 for all threads unless otherwise specified per-thread for this model +# cudaDeviceToUseModel1 = 2 #use device 2 for model 1 for all threads unless otherwise specified per-thread for this model +# cudaDeviceToUseModel0Thread0 = 3 #use device 3 for model 0, server thread 0 +# cudaDeviceToUseModel0Thread1 = 2 #use device 2 for model 0, server thread 1 + +cudaUseFP16 = auto +cudaUseNHWC = auto + +# Root move selection and biases------------------------------------------------------------------------------ + +chosenMoveTemperatureEarly = 0.75 +chosenMoveTemperatureHalflife = 19 +chosenMoveTemperature = 0.15 +chosenMoveSubtract = 0 +chosenMovePrune = 1 + +rootNoiseEnabled = true +rootDirichletNoiseTotalConcentration = 10.83 +rootDirichletNoiseWeight = 0.25 + +rootDesiredPerChildVisitsCoeff = 2 +rootNumSymmetriesToSample = 4 + +useLcbForSelection = true +lcbStdevs = 5.0 +minVisitPropForLCB = 0.15 + +# Internal params------------------------------------------------------------------------------ + +winLossUtilityFactor = 1.0 +staticScoreUtilityFactor = 0.00 +dynamicScoreUtilityFactor = 0.40 +dynamicScoreCenterZeroWeight = 0.25 +dynamicScoreCenterScale = 0.50 +noResultUtilityForWhite = 0.0 +drawEquivalentWinsForWhite = 0.5 + +rootEndingBonusPoints = 0.5 +rootPruneUselessMoves = true + +rootPolicyTemperatureEarly = 1.25 +rootPolicyTemperature = 1.1 + +cpuctExploration = 1.1 +cpuctExplorationLog = 0.0 +fpuReductionMax = 0.2 +rootFpuReductionMax = 0.0 + +numVirtualLossesPerThread = 1 + +# These parameters didn't exist historically during early KataGo runs +valueWeightExponent = 0.5 +subtreeValueBiasFactor = 0.30 +subtreeValueBiasWeightExponent = 0.8 +useNonBuggyLcb = true +useGraphSearch = true +fpuParentWeightByVisitedPolicy = true +fpuParentWeightByVisitedPolicyPow = 2.0 diff --git a/training/scripts/dated/20241110-123407/sgfmetadata.py b/training/scripts/dated/20241110-123407/sgfmetadata.py new file mode 100644 index 0000000..55d1b21 --- /dev/null +++ b/training/scripts/dated/20241110-123407/sgfmetadata.py @@ -0,0 +1,186 @@ +# See the equivalents in trainingwrite.h and trainingwrite.cpp in the cpp directory. +# Translated from the c++ implementation. + +from dataclasses import dataclass +from typing import ClassVar +import datetime +import math +import numpy as np +import random +from board import Board +import modelconfigs + +@dataclass +class SGFMetadata: + inverseBRank: int = 0 + inverseWRank: int = 0 + bIsUnranked: bool = False + wIsUnranked: bool = False + bRankIsUnknown: bool = False + wRankIsUnknown: bool = False + bIsHuman: bool = False + wIsHuman: bool = False + + gameIsUnrated: bool = False + gameRatednessIsUnknown: bool = False + + tcIsUnknown: bool = True + tcIsNone: bool = False + tcIsAbsolute: bool = False + tcIsSimple: bool = False + tcIsByoYomi: bool = False + tcIsCanadian: bool = False + tcIsFischer: bool = False + + mainTimeSeconds: float = 0.0 + periodTimeSeconds: float = 0.0 + byoYomiPeriods: int = 0 + canadianMoves: int = 0 + + gameDate: datetime.date = datetime.date(1970, 1, 1) + + source: int = 0 + + SOURCE_OGS: ClassVar[int] = 1 + SOURCE_KGS: ClassVar[int] = 2 + SOURCE_FOX: ClassVar[int] = 3 + SOURCE_TYGEM: ClassVar[int] = 4 + SOURCE_GOGOD: ClassVar[int] = 5 + SOURCE_GO4GO: ClassVar[int] = 6 + + METADATA_INPUT_NUM_CHANNELS: ClassVar[int] = 192 + + @classmethod + def of_dict(cls, data: dict): + data["gameDate"] = datetime.date.fromisoformat(data["gameDate"]) + return cls(**data) + + def to_dict(self): + data = self.__dict__.copy() + data["gameDate"] = data["gameDate"].isoformat() + return data + + @classmethod + def get_katago_selfplay_metadata(cls, rand: random.Random) -> "SGFMetadata": + return SGFMetadata( + inverseBRank = 0, + inverseWRank = 0, + bIsUnranked = False, + wIsUnranked = False, + bRankIsUnknown = False, + wRankIsUnknown = False, + bIsHuman = False, + wIsHuman = False, + + gameIsUnrated = False, + gameRatednessIsUnknown = False, + + tcIsUnknown = False, + tcIsNone = False, + tcIsAbsolute = False, + tcIsSimple = False, + tcIsByoYomi = True, + tcIsCanadian = False, + tcIsFischer = False, + + mainTimeSeconds = rand.choice([300,600,900,1200]), + periodTimeSeconds = rand.choice([10.0,15.0,30.0]), + byoYomiPeriods = 5, + canadianMoves = 0, + + gameDate = datetime.date(2022, 1, 1) + datetime.timedelta(days=rand.randint(0,722)), + source = 0, + ) + + + def get_metadata_row(self, nextPlayer, boardArea) -> np.ndarray: + if isinstance(nextPlayer,str): + if nextPlayer.lower() == "w": + nextPlayer = Board.WHITE + elif nextPlayer.lower() == "b": + nextPlayer = Board.BLACK + + # This class implements "version 1" of sgf metadata encoding, make sure channel dims match up. + meta_encoder_version = 1 + assert modelconfigs.get_num_meta_encoder_input_features(meta_encoder_version) == self.METADATA_INPUT_NUM_CHANNELS + + rowMetadata = np.zeros(self.METADATA_INPUT_NUM_CHANNELS, dtype=np.float32) + + plaIsHuman = self.wIsHuman if nextPlayer == Board.WHITE else self.bIsHuman + oppIsHuman = self.bIsHuman if nextPlayer == Board.WHITE else self.wIsHuman + rowMetadata[0] = 1.0 if plaIsHuman else 0.0 + rowMetadata[1] = 1.0 if oppIsHuman else 0.0 + + plaIsUnranked = self.wIsUnranked if nextPlayer == Board.WHITE else self.bIsUnranked + oppIsUnranked = self.bIsUnranked if nextPlayer == Board.WHITE else self.wIsUnranked + rowMetadata[2] = 1.0 if plaIsUnranked else 0.0 + rowMetadata[3] = 1.0 if oppIsUnranked else 0.0 + + plaRankIsUnknown = self.wRankIsUnknown if nextPlayer == Board.WHITE else self.bRankIsUnknown + oppRankIsUnknown = self.bRankIsUnknown if nextPlayer == Board.WHITE else self.wRankIsUnknown + rowMetadata[4] = 1.0 if plaRankIsUnknown else 0.0 + rowMetadata[5] = 1.0 if oppRankIsUnknown else 0.0 + + RANK_START_IDX = 6 + invPlaRank = self.inverseWRank if nextPlayer == Board.WHITE else self.inverseBRank + invOppRank = self.inverseBRank if nextPlayer == Board.WHITE else self.inverseWRank + RANK_LEN_PER_PLA = 34 + if not plaIsUnranked: + for i in range(min(invPlaRank, RANK_LEN_PER_PLA)): + rowMetadata[RANK_START_IDX + i] = 1.0 + if not oppIsUnranked: + for i in range(min(invOppRank, RANK_LEN_PER_PLA)): + rowMetadata[RANK_START_IDX + RANK_LEN_PER_PLA + i] = 1.0 + + assert RANK_START_IDX + 2 * RANK_LEN_PER_PLA == 74 + rowMetadata[74] = 0.5 if self.gameRatednessIsUnknown else 1.0 if self.gameIsUnrated else 0.0 + + rowMetadata[75] = 1.0 if self.tcIsUnknown else 0.0 + rowMetadata[76] = 1.0 if self.tcIsNone else 0.0 + rowMetadata[77] = 1.0 if self.tcIsAbsolute else 0.0 + rowMetadata[78] = 1.0 if self.tcIsSimple else 0.0 + rowMetadata[79] = 1.0 if self.tcIsByoYomi else 0.0 + rowMetadata[80] = 1.0 if self.tcIsCanadian else 0.0 + rowMetadata[81] = 1.0 if self.tcIsFischer else 0.0 + assert ( + rowMetadata[75] + + rowMetadata[76] + + rowMetadata[77] + + rowMetadata[78] + + rowMetadata[79] + + rowMetadata[80] + + rowMetadata[81] + == 1.0 + ) + + mainTimeSecondsCapped = min(max(self.mainTimeSeconds, 0.0), 3.0 * 86400) + periodTimeSecondsCapped = min(max(self.periodTimeSeconds, 0.0), 1.0 * 86400) + rowMetadata[82] = 0.4 * (math.log(mainTimeSecondsCapped + 60.0) - 6.5) + rowMetadata[83] = 0.3 * (math.log(periodTimeSecondsCapped + 1.0) - 3.0) + byoYomiPeriodsCapped = min(max(self.byoYomiPeriods, 0), 50) + canadianMovesCapped = min(max(self.canadianMoves, 0), 50) + rowMetadata[84] = 0.5 * (math.log(byoYomiPeriodsCapped + 2.0) - 1.5) + rowMetadata[85] = 0.25 * (math.log(canadianMovesCapped + 2.0) - 1.5) + + rowMetadata[86] = 0.5 * math.log(boardArea / 361.0) + + daysDifference = (self.gameDate - datetime.date(1970, 1, 1)).days + DATE_START_IDX = 87 + DATE_LEN = 32 + period = 7.0 + factor = pow(80000.0, 1.0 / (DATE_LEN - 1)) + twopi = 6.283185307179586476925 + for i in range(DATE_LEN): + numRevolutions = daysDifference / period + rowMetadata[DATE_START_IDX + i * 2 + 0] = math.cos(numRevolutions * twopi) + rowMetadata[DATE_START_IDX + i * 2 + 1] = math.sin(numRevolutions * twopi) + period *= factor + + assert DATE_START_IDX + 2 * DATE_LEN == 151 + + assert 0 <= self.source < 16 + rowMetadata[151 + self.source] = 1.0 + + assert 151 + 16 < self.METADATA_INPUT_NUM_CHANNELS + + return rowMetadata diff --git a/training/scripts/dated/20241110-123407/shuffle.py b/training/scripts/dated/20241110-123407/shuffle.py new file mode 100644 index 0000000..8a535d2 --- /dev/null +++ b/training/scripts/dated/20241110-123407/shuffle.py @@ -0,0 +1,814 @@ +#!/usr/bin/python3 +# Shuffles npz selfplay data for training, choosing a window size based on a power law. +# Run 'python shuffle.py --help' for details on how the window size is chosen and how to use this script. +import sys +import os +import argparse +import traceback +import math +import time +import logging +import zipfile +import shutil +import psutil +import json +import hashlib +import datetime +import gc + +import multiprocessing + +import numpy as np + +def assert_keys(npz,include_meta): + keys = [ + "binaryInputNCHWPacked", + "globalInputNC", + "policyTargetsNCMove", + "globalTargetsNC", + "scoreDistrN", + "valueTargetsNCHW", + ] + if include_meta: + keys.append("metadataInputNC") + assert(set(npz.keys()) == set(keys)) + +def is_temp_npz_like(filename): + return "_" in filename + +def joint_shuffle_take_first_n(n,arrs): + for arr in arrs: + assert(len(arr) == len(arrs[0])) + perm = np.random.permutation(len(arrs[0])) + perm = perm[:n] + shuffled_arrs = [] + for arr in arrs: + shuffled_arrs.append(arr[perm]) + return shuffled_arrs + +def memusage_mb(): + return psutil.Process(os.getpid()).memory_info().rss // 1048576 + +def shardify(input_idx, input_file_group, num_out_files, out_tmp_dirs, keep_prob, include_meta): + np.random.seed([int.from_bytes(os.urandom(4), byteorder='little') for i in range(4)]) + + assert(len(input_file_group) > 0) + num_files_not_found = 0 + + if len(input_file_group) == 1: + try: + with np.load(input_file_group[0]) as npz: + assert_keys(npz,include_meta) + ### + # WARNING - if adding anything here, also add it to joint_shuffle below! + ### + binaryInputNCHWPacked = npz["binaryInputNCHWPacked"] + globalInputNC = npz["globalInputNC"] + policyTargetsNCMove = npz["policyTargetsNCMove"] + globalTargetsNC = npz["globalTargetsNC"] + scoreDistrN = npz["scoreDistrN"] + valueTargetsNCHW = npz["valueTargetsNCHW"] + metadataInputNC = npz["metadataInputNC"] if include_meta else None + except FileNotFoundError: + num_files_not_found += 1 + print("WARNING: file not found by shardify: ", input_file_group[0]) + return num_files_not_found # Early quit since we don't know shapes + else: + binaryInputNCHWPackedList = [] + globalInputNCList = [] + policyTargetsNCMoveList = [] + globalTargetsNCList = [] + scoreDistrNList = [] + valueTargetsNCHWList = [] + metadataInputNCList = [] + + for input_file in input_file_group: + try: + with np.load(input_file) as npz: + assert_keys(npz,include_meta) + binaryInputNCHWPackedList.append(npz["binaryInputNCHWPacked"]) + globalInputNCList.append(npz["globalInputNC"]) + policyTargetsNCMoveList.append(npz["policyTargetsNCMove"]) + globalTargetsNCList.append(npz["globalTargetsNC"]) + scoreDistrNList.append(npz["scoreDistrN"]) + valueTargetsNCHWList.append(npz["valueTargetsNCHW"]) + metadataInputNCList.append(npz["metadataInputNC"] if include_meta else None) + except FileNotFoundError: + num_files_not_found += 1 + print("WARNING: file not found by shardify: ", input_file) + pass + if len(binaryInputNCHWPackedList) <= 0: + return num_files_not_found # Early quit since we don't know shapes + + binaryInputNCHWPacked = np.concatenate(binaryInputNCHWPackedList,axis=0) + globalInputNC = np.concatenate(globalInputNCList,axis=0) + policyTargetsNCMove = np.concatenate(policyTargetsNCMoveList,axis=0) + globalTargetsNC = np.concatenate(globalTargetsNCList,axis=0) + scoreDistrN = np.concatenate(scoreDistrNList,axis=0) + valueTargetsNCHW = np.concatenate(valueTargetsNCHWList,axis=0) + metadataInputNC = np.concatenate(metadataInputNCList,axis=0) if include_meta else None + + num_rows_to_keep = binaryInputNCHWPacked.shape[0] + assert(globalInputNC.shape[0] == num_rows_to_keep) + assert(policyTargetsNCMove.shape[0] == num_rows_to_keep) + assert(globalTargetsNC.shape[0] == num_rows_to_keep) + assert(scoreDistrN.shape[0] == num_rows_to_keep) + assert(valueTargetsNCHW.shape[0] == num_rows_to_keep) + assert(metadataInputNC.shape[0] == num_rows_to_keep if include_meta else True) + + if keep_prob < 1.0: + num_rows_to_keep = min(num_rows_to_keep,int(round(num_rows_to_keep * keep_prob))) + + if include_meta: + [binaryInputNCHWPacked,globalInputNC,policyTargetsNCMove,globalTargetsNC,scoreDistrN,valueTargetsNCHW,metadataInputNC] = ( + joint_shuffle_take_first_n( + num_rows_to_keep, + [binaryInputNCHWPacked,globalInputNC,policyTargetsNCMove,globalTargetsNC,scoreDistrN,valueTargetsNCHW,metadataInputNC] + ) + ) + else: + [binaryInputNCHWPacked,globalInputNC,policyTargetsNCMove,globalTargetsNC,scoreDistrN,valueTargetsNCHW] = ( + joint_shuffle_take_first_n( + num_rows_to_keep, + [binaryInputNCHWPacked,globalInputNC,policyTargetsNCMove,globalTargetsNC,scoreDistrN,valueTargetsNCHW] + ) + ) + + assert(binaryInputNCHWPacked.shape[0] == num_rows_to_keep) + assert(globalInputNC.shape[0] == num_rows_to_keep) + assert(policyTargetsNCMove.shape[0] == num_rows_to_keep) + assert(globalTargetsNC.shape[0] == num_rows_to_keep) + assert(scoreDistrN.shape[0] == num_rows_to_keep) + assert(valueTargetsNCHW.shape[0] == num_rows_to_keep) + assert(metadataInputNC.shape[0] == num_rows_to_keep if include_meta else True) + + rand_assts = np.random.randint(num_out_files,size=[num_rows_to_keep]) + counts = np.bincount(rand_assts,minlength=num_out_files) + countsums = np.cumsum(counts) + assert(countsums[len(countsums)-1] == num_rows_to_keep) + + # if input_idx % 29 == 0: + # print("%s: Shardify writing... (mem usage %dMB)" % (str(datetime.datetime.now()),memusage_mb()), flush=True) + + for out_idx in range(num_out_files): + start = countsums[out_idx]-counts[out_idx] + stop = countsums[out_idx] + if include_meta: + np.savez_compressed( + os.path.join(out_tmp_dirs[out_idx], str(input_idx) + ".npz"), + binaryInputNCHWPacked = binaryInputNCHWPacked[start:stop], + globalInputNC = globalInputNC[start:stop], + policyTargetsNCMove = policyTargetsNCMove[start:stop], + globalTargetsNC = globalTargetsNC[start:stop], + scoreDistrN = scoreDistrN[start:stop], + valueTargetsNCHW = valueTargetsNCHW[start:stop], + metadataInputNC = metadataInputNC[start:stop], + ) + else: + np.savez_compressed( + os.path.join(out_tmp_dirs[out_idx], str(input_idx) + ".npz"), + binaryInputNCHWPacked = binaryInputNCHWPacked[start:stop], + globalInputNC = globalInputNC[start:stop], + policyTargetsNCMove = policyTargetsNCMove[start:stop], + globalTargetsNC = globalTargetsNC[start:stop], + scoreDistrN = scoreDistrN[start:stop], + valueTargetsNCHW = valueTargetsNCHW[start:stop], + ) + return num_files_not_found + +def merge_shards(filename, num_shards_to_merge, out_tmp_dir, batch_size, ensure_batch_multiple, output_npz, include_meta): + np.random.seed([int.from_bytes(os.urandom(4), byteorder='little') for i in range(5)]) + + if output_npz: + record_writer = None + else: + assert False, "No longer supports outputting tensorflow data" + + binaryInputNCHWPackeds = [] + globalInputNCs = [] + policyTargetsNCMoves = [] + globalTargetsNCs = [] + scoreDistrNs = [] + valueTargetsNCHWs = [] + metadataInputNCs = [] + + for input_idx in range(num_shards_to_merge): + shard_filename = os.path.join(out_tmp_dir, str(input_idx) + ".npz") + try: + with np.load(shard_filename) as npz: + assert_keys(npz,include_meta) + + binaryInputNCHWPacked = npz["binaryInputNCHWPacked"] + globalInputNC = npz["globalInputNC"] + policyTargetsNCMove = npz["policyTargetsNCMove"] + globalTargetsNC = npz["globalTargetsNC"] + scoreDistrN = npz["scoreDistrN"] + valueTargetsNCHW = npz["valueTargetsNCHW"] + metadataInputNC = npz["metadataInputNC"] if include_meta else None + + binaryInputNCHWPackeds.append(binaryInputNCHWPacked) + globalInputNCs.append(globalInputNC) + policyTargetsNCMoves.append(policyTargetsNCMove) + globalTargetsNCs.append(globalTargetsNC) + scoreDistrNs.append(scoreDistrN) + valueTargetsNCHWs.append(valueTargetsNCHW) + metadataInputNCs.append(metadataInputNC) + except FileNotFoundError: + print("WARNING: Empty shard in merge_shards for shard :", input_idx, filename) + + if len(binaryInputNCHWPackeds) <= 0: + print("WARNING: empty merge file: ", filename) + return 0 + + ### + # WARNING - if adding anything here, also add it to joint_shuffle below! + ### + binaryInputNCHWPacked = np.concatenate(binaryInputNCHWPackeds) + globalInputNC = np.concatenate(globalInputNCs) + policyTargetsNCMove = np.concatenate(policyTargetsNCMoves) + globalTargetsNC = np.concatenate(globalTargetsNCs) + scoreDistrN = np.concatenate(scoreDistrNs) + valueTargetsNCHW = np.concatenate(valueTargetsNCHWs) + metadataInputNC = np.concatenate(metadataInputNCs) if include_meta else None + + num_rows = binaryInputNCHWPacked.shape[0] + assert(globalInputNC.shape[0] == num_rows) + assert(policyTargetsNCMove.shape[0] == num_rows) + assert(globalTargetsNC.shape[0] == num_rows) + assert(scoreDistrN.shape[0] == num_rows) + assert(valueTargetsNCHW.shape[0] == num_rows) + assert(metadataInputNC.shape[0] == num_rows if include_meta else True) + + if include_meta: + [binaryInputNCHWPacked,globalInputNC,policyTargetsNCMove,globalTargetsNC,scoreDistrN,valueTargetsNCHW,metadataInputNC] = ( + joint_shuffle_take_first_n( + num_rows, + [binaryInputNCHWPacked,globalInputNC,policyTargetsNCMove,globalTargetsNC,scoreDistrN,valueTargetsNCHW,metadataInputNC], + ) + ) + else: + [binaryInputNCHWPacked,globalInputNC,policyTargetsNCMove,globalTargetsNC,scoreDistrN,valueTargetsNCHW] = ( + joint_shuffle_take_first_n( + num_rows, + [binaryInputNCHWPacked,globalInputNC,policyTargetsNCMove,globalTargetsNC,scoreDistrN,valueTargetsNCHW], + ) + ) + + assert(binaryInputNCHWPacked.shape[0] == num_rows) + assert(globalInputNC.shape[0] == num_rows) + assert(policyTargetsNCMove.shape[0] == num_rows) + assert(globalTargetsNC.shape[0] == num_rows) + assert(scoreDistrN.shape[0] == num_rows) + assert(valueTargetsNCHW.shape[0] == num_rows) + assert(metadataInputNC.shape[0] == num_rows if include_meta else True) + + # print("%s: Merge writing... (mem usage %dMB)" % (str(datetime.datetime.now()),memusage_mb()), flush=True) + + # Just truncate and lose the batch at the end, it's fine + num_batches = (num_rows // (batch_size * ensure_batch_multiple)) * ensure_batch_multiple + if output_npz: + start = 0 + stop = num_batches*batch_size + if include_meta: + np.savez_compressed( + filename, + binaryInputNCHWPacked = binaryInputNCHWPacked[start:stop], + globalInputNC = globalInputNC[start:stop], + policyTargetsNCMove = policyTargetsNCMove[start:stop], + globalTargetsNC = globalTargetsNC[start:stop], + scoreDistrN = scoreDistrN[start:stop], + valueTargetsNCHW = valueTargetsNCHW[start:stop], + metadataInputNC = metadataInputNC[start:stop], + ) + else: + np.savez_compressed( + filename, + binaryInputNCHWPacked = binaryInputNCHWPacked[start:stop], + globalInputNC = globalInputNC[start:stop], + policyTargetsNCMove = policyTargetsNCMove[start:stop], + globalTargetsNC = globalTargetsNC[start:stop], + scoreDistrN = scoreDistrN[start:stop], + valueTargetsNCHW = valueTargetsNCHW[start:stop], + ) + else: + assert False, "No longer supports outputting tensorflow data" + + jsonfilename = os.path.splitext(filename)[0] + ".json" + with open(jsonfilename,"w") as f: + json.dump({"num_rows":num_rows,"num_batches":num_batches},f) + + if record_writer is not None: + record_writer.close() + return num_batches * batch_size + +def get_numpy_npz_headers(filename): + with zipfile.ZipFile(filename) as z: + wasbad = False + numrows = 0 + npzheaders = {} + for subfilename in z.namelist(): + npyfile = z.open(subfilename) + try: + version = np.lib.format.read_magic(npyfile) + except ValueError: + wasbad = True + print("WARNING: bad file, skipping it: %s (bad array %s)" % (filename,subfilename)) + else: + (shape, is_fortran, dtype) = np.lib.format._read_array_header(npyfile,version) + npzheaders[subfilename] = (shape, is_fortran, dtype) + if wasbad: + return None + return npzheaders + + +def compute_num_rows(filename): + try: + npheaders = get_numpy_npz_headers(filename) + except PermissionError: + print("WARNING: No permissions for reading file: ", filename) + return (filename,None) + except zipfile.BadZipFile: + print("WARNING: Bad zip file: ", filename) + return (filename,None) + if npheaders is None or len(npheaders) <= 0: + print("WARNING: bad npz headers for file: ", filename) + return (filename,None) + + if "binaryInputNCHWPacked" in npheaders: + (shape, is_fortran, dtype) = npheaders["binaryInputNCHWPacked"] + else: + (shape, is_fortran, dtype) = npheaders["binaryInputNCHWPacked.npy"] + num_rows = shape[0] + return (filename,num_rows) + + +class TimeStuff(object): + + def __init__(self,taskstr): + self.taskstr = taskstr + + def __enter__(self): + print("Beginning: %s" % self.taskstr, flush=True) + self.t0 = time.time() + + def __exit__(self, exception_type, exception_val, trace): + self.t1 = time.time() + print("Finished: %s in %s seconds" % (self.taskstr, str(self.t1 - self.t0)), flush=True) + return False + + +if __name__ == '__main__': + parser = argparse.ArgumentParser(add_help=False,formatter_class=argparse.RawTextHelpFormatter,description=""" + Shuffle data files! + + This shuffle script is designed for ongoing self-play training. It shuffles the most recent window of data among the data it's provided. It chooses the window size dynamically based on the total amount of data in the run so far, assuming that the directories provided contain all of the data for the run so far. If you don't actually have all of the data, e.g. you've archived or deleted the older data, or else want to compute the window size as if there were more data, use -add-to-data-rows. + + The window size is a power law based on the number of rows in the run N: + WINDOWSIZE(N) = (N^EXPONENT - MIN_ROWS^EXPONENT) / (EXPONENT * MIN_ROWS^(EXPONENT-1)) * INITIAL_WINDOW_PER_ROW + MIN_ROWS + + given arguments: + -taper-window-exponent EXPONENT \\ + -expand-window-per-row INITIAL_WINDOW_PER_ROW \\ + -min-rows MIN_ROWS (default 250k) + + This may look a bit complex, but basically it is simply the power law N^EXPONENT with shifting and scaling such that: + WINDOWSIZE(MIN_ROWS) = MIN_ROWS + (dWINDOWSIZE/dN)(MIN_ROWS) = INITIAL_WINDOW_PER_ROW + + Reasonable arguments similar to those used for KataGo's main runs would be + -taper-window-exponent 0.65 or 0.675 \\ + -expand-window-per-row 0.4 \\ + -min-rows 250000 (default) + + If you want to control the "scale" of the power law differently than the min rows, you can specify -taper-window-scale as well. + There is also a bit of a hack to cap the number of random rows (rows generated by random play without a neural net), since random row generation at the start of a run can be very fast due to not hitting the GPU, and overpopulate the run. + + Additionally, NOT all of the shuffled window is output, only a random shuffled 20M rows will be kept. Adjust this using -keep-target-rows. The intention is that this script will be repeatedly run as new data comes in, such that well before train.py would need more than 20M rows, the data would have been shuffled again and a new random 20M rows chosen. + + If you are NOT doing ongoing self-play training, but simply want to shuffle an entire dataset (not just a window of it) and want to output all of it (not just 20M of it) then you can use arguments like: + -taper-window-exponent 1.0 \\ + -expand-window-per-row 1.0 \\ + -keep-target-rows SOME_VERY_LARGE_NUMBER + + If you ARE doing ongoing self-play training, but want a fixed window size, then you can use arguments like: + -min-rows YOUR_DESIRED_SIZE \\ + -taper-window-exponent 1.0 \\ + -expand-window-per-row 0.0 + """) + parser.add_argument('dirs', metavar='DIR', nargs='+', help='Directories of training data files') + + required_args = parser.add_argument_group('required arguments') + optional_args = parser.add_argument_group('optional arguments') + optional_args.add_argument( + '-h', + '--help', + action='help', + default=argparse.SUPPRESS, + help='show this help message and exit' + ) + optional_args.add_argument('-min-rows', type=int, required=False, help='Minimum training rows to use, default 250k') + optional_args.add_argument('-max-rows', type=int, required=False, help='Maximum training rows to use, default unbounded') + optional_args.add_argument('-keep-target-rows', type=int, required=False, help='Target number of rows to actually keep in the final data set, default 20M') + required_args.add_argument('-expand-window-per-row', type=float, required=True, help='Beyond min rows, initially expand the window by this much every post-random data row') + required_args.add_argument('-taper-window-exponent', type=float, required=True, help='Make the window size asymtotically grow as this power of the data rows') + optional_args.add_argument('-taper-window-scale', type=float, required=False, help='The scale at which the power law applies, defaults to -min-rows') + optional_args.add_argument('-add-to-data-rows', type=float, required=False, help='Compute the window size as if the number of data rows were this much larger/smaller') + optional_args.add_argument('-add-to-window-size', type=float, required=False, help='DEPRECATED due to being misnamed name, use -add-to-data-rows') + optional_args.add_argument('-summary-file', required=False, help='Summary json file for directory contents') + required_args.add_argument('-out-dir', required=True, help='Dir to output training files') + required_args.add_argument('-out-tmp-dir', required=True, help='Dir to use as scratch space') + optional_args.add_argument('-approx-rows-per-out-file', type=int, required=False, default=70000, help='Number of rows per output file, default 70k') + required_args.add_argument('-num-processes', type=int, required=True, help='Number of multiprocessing processes for shuffling in parallel') + required_args.add_argument('-batch-size', type=int, required=True, help='Batch size to write training examples in') + optional_args.add_argument('-ensure-batch-multiple', type=int, required=False, help='Ensure each file is a multiple of this many batches') + optional_args.add_argument('-worker-group-size', type=int, required=False, default=80000, help='Internally, target having many rows per parallel sharding worker (doesnt affect merge)') + optional_args.add_argument('-exclude', required=False, help='Text file with npzs to ignore, one per line') + optional_args.add_argument('-exclude-prefix', required=False, help='Prefix to concat to lines in exclude to produce the full file path') + optional_args.add_argument('-exclude-basename', required=False, action="store_true", help='Consider an exclude to match if basename matches') + optional_args.add_argument('-only-include-md5-path-prop-lbound', type=float, required=False, help='Just before sharding, include only filepaths hashing to float >= this') + optional_args.add_argument('-only-include-md5-path-prop-ubound', type=float, required=False, help='Just before sharding, include only filepaths hashing to float < this') + optional_args.add_argument('-output-npz', action="store_true", required=False, help='Output results as npz files') + optional_args.add_argument('-include-meta', action="store_true", required=False, help='Include sgf metadata inputs') + + args = parser.parse_args() + dirs = args.dirs + min_rows = args.min_rows + max_rows = args.max_rows + keep_target_rows = args.keep_target_rows + expand_window_per_row = args.expand_window_per_row + taper_window_exponent = args.taper_window_exponent + taper_window_scale = args.taper_window_scale + add_to_data_rows = args.add_to_data_rows + if args.add_to_data_rows is not None and args.add_to_window_size is not None: + print("Cannot specify both -add-to-data-rows and -add-to-window-size. Please use only -add-to-data-rows, -add-to-window-size is deprecated") + if args.add_to_data_rows is None and args.add_to_window_size is not None: + print("WARNING: -add-to-window-size is deprecated due to being misnamed, use -add-to-data-rows") + add_to_data_rows = args.add_to_window_size + + summary_file = args.summary_file + out_dir = args.out_dir + out_tmp_dir = args.out_tmp_dir + approx_rows_per_out_file = args.approx_rows_per_out_file + num_processes = args.num_processes + batch_size = args.batch_size + ensure_batch_multiple = 1 + if args.ensure_batch_multiple is not None: + ensure_batch_multiple = args.ensure_batch_multiple + worker_group_size = args.worker_group_size + exclude = args.exclude + exclude_prefix = args.exclude_prefix + if exclude_prefix is None: + exclude_prefix = "" + exclude_basename = args.exclude_basename + only_include_md5_path_prop_lbound = args.only_include_md5_path_prop_lbound + only_include_md5_path_prop_ubound = args.only_include_md5_path_prop_ubound + output_npz = args.output_npz + include_meta = args.include_meta + + if min_rows is None: + print("NOTE: -min-rows was not specified, defaulting to requiring 250K rows before shuffling.") + min_rows = 250000 + if keep_target_rows is None: + print("NOTE: -keep-target-rows was not specified, defaulting to sampling a random 20M rows out of the computed window.") + print("If you intended to shuffle the whole dataset instead, pass in -keep-target-rows ") + keep_target_rows = 20000000 + if add_to_data_rows is None: + add_to_data_rows = 0 + + summary_data_by_dirpath = {} + if summary_file is not None: + with TimeStuff("Loading " + summary_file): + # Try a bunch of times, just to be robust to if the file is being swapped out in nfs + for i in range(10): + success = False + try: + with open(summary_file) as fp: + summary_data_by_dirpath = json.load(fp) + success = True + except OSError: + success = False + except ValueError: + success = False + if success: + break + time.sleep(1) + if not success: + raise RuntimeError("Could not load summary file") + + exclude_set = set() + if exclude is not None: + with TimeStuff("Loading " + exclude): + # Try a bunch of times, just to be robust to if the file is being swapped out in nfs + for i in range(10): + success = False + try: + with open(exclude,"r") as exclude_in: + excludes = exclude_in.readlines() + excludes = [x.strip() for x in excludes] + excludes = [x for x in excludes if len(x) > 0] + excludes = [exclude_prefix + x for x in excludes] + exclude_set = set(excludes) + success = True + except OSError: + success = False + except ValueError: + success = False + if success: + break + time.sleep(1) + if not success: + raise RuntimeError("Could not load summary file") + + # If excluding basenames, also add them to the set + if exclude_basename: + basenames = [os.path.basename(path) for path in exclude_set] + exclude_set.update(basenames) + + all_files = [] + files_with_unknown_num_rows = [] + excluded_count = 0 + excluded_due_to_excludes_count = 0 + tempfilelike_count = 0 + with TimeStuff("Finding files"): + for d in dirs: + for (path,dirnames,filenames) in os.walk(d, followlinks=True): + i = 0 + while i < len(dirnames): + dirname = dirnames[i] + summary_data = summary_data_by_dirpath.get(os.path.abspath(os.path.join(path, dirname))) + if summary_data is not None: + filename_mtime_num_rowss = summary_data["filename_mtime_num_rowss"] + del dirnames[i] + i -= 1 + for (filename,mtime,num_rows) in filename_mtime_num_rowss: + if is_temp_npz_like(filename): + # print("WARNING: file looks like a temp file, treating as exclude: ", os.path.join(path,dirname,filename)) + excluded_count += 1 + tempfilelike_count += 1 + continue + if exclude_basename and os.path.basename(filename) in exclude_set: + excluded_count += 1 + excluded_due_to_excludes_count += 1 + continue + filename = os.path.join(path,dirname,filename) + if not exclude_basename and filename in exclude_set: + excluded_count += 1 + excluded_due_to_excludes_count += 1 + continue + if num_rows is None: + print("WARNING: Skipping bad rowless file, treating as exclude: ", filename) + excluded_count += 1 + continue + all_files.append((filename,mtime,num_rows)) + i += 1 + + filtered_filenames = [] + for filename in filenames: + if not filename.endswith(".npz"): + continue + if is_temp_npz_like(filename): + # print("WARNING: file looks like a temp file, treating as exclude: ", os.path.join(path,filename)) + excluded_count += 1 + tempfilelike_count += 1 + continue + if exclude_basename and os.path.basename(filename) in exclude_set: + excluded_count += 1 + excluded_due_to_excludes_count += 1 + continue + filename = os.path.join(path,filename) + if not exclude_basename and filename in exclude_set: + excluded_count += 1 + excluded_due_to_excludes_count += 1 + continue + filtered_filenames.append(filename) + filenames = filtered_filenames + + files_with_unknown_num_rows.extend(filenames) + filenames = [(filename,os.path.getmtime(filename)) for filename in filenames] + all_files.extend(filenames) + print("Total number of files: %d" % len(all_files), flush=True) + print("Total number of files with unknown row count: %d" % len(files_with_unknown_num_rows), flush=True) + print("Excluded count: %d" % excluded_count, flush=True) + print("Excluded count due to looking like temp file: %d" % tempfilelike_count, flush=True) + print("Excluded count due to cmdline excludes file: %d" % excluded_due_to_excludes_count, flush=True) + + print("GC collect", flush=True) + del summary_data_by_dirpath + gc.collect() + + with TimeStuff("Sorting"): + all_files.sort(key=(lambda x: x[1]), reverse=False) + + # Wait a few seconds just in case to limit the chance of filesystem races, now that we know exactly + # the set of filenames we want + time.sleep(3) + + with TimeStuff("Computing rows for unsummarized files"): + with multiprocessing.Pool(num_processes) as pool: + results = pool.map(compute_num_rows,files_with_unknown_num_rows) + results = dict(results) + for i in range(len(all_files)): + info = all_files[i] + if len(info) < 3: + num_rows = results[info[0]] + all_files[i] = (info[0], info[1], num_rows) + + num_rows_total = 0 # Number of data rows + num_random_rows_capped = 0 # Number of random data rows, capped at min_rows - we never keep more than min_rows many data rows if they're from random. + num_postrandom_rows = 0 # Number of NON-random rows + + # How far offset do we start on the power-law window tail? E.g. what number of postrandom rows do we need before the window size grows by a factor + # of 2^(taper_window_exponent)? For now, we set it equal to the min rows + if taper_window_scale is not None: + window_taper_offset = taper_window_scale + else: + window_taper_offset = min_rows + + def num_usable_rows(): + global num_random_rows_capped + global num_postrandom_rows + return num_random_rows_capped + num_postrandom_rows + def num_desired_rows(): + # Every post-random row moves one row beyond window_taper_offset + power_law_x = num_usable_rows() - min_rows + window_taper_offset + add_to_data_rows + # Apply power law and correct for window_taper_offset so we're still anchored at 0 + unscaled_power_law = (power_law_x ** taper_window_exponent) - (window_taper_offset ** taper_window_exponent) + # Scale so that we have an initial derivative of 1 + scaled_power_law = unscaled_power_law / (taper_window_exponent * (window_taper_offset ** (taper_window_exponent-1))) + # Scale so that we have the desired initial slope, and add back the minimum random rows + return int(scaled_power_law * expand_window_per_row + min_rows) + + with TimeStuff("Processing found files"): + for (filename,mtime,num_rows) in all_files: + if num_rows is None: + print("WARNING: Skipping bad file: ", filename) + continue + if num_rows <= 0: + continue + num_rows_total += num_rows + if "random/tdata/" not in filename and "random\\tdata\\" not in filename: + num_postrandom_rows += num_rows + else: + num_random_rows_capped = min(num_random_rows_capped + num_rows, min_rows) + + if os.path.exists(out_dir): + raise Exception(out_dir + " already exists") + os.mkdir(out_dir) + + if num_rows_total <= 0: + print("No rows found") + sys.exit(0) + + # If we don't have enough rows, then quit out + if num_rows_total < min_rows: + print("Not enough rows, only %d (fewer than %d)" % (num_rows_total,min_rows)) + sys.exit(0) + + print("Total rows found: %d (%d usable)" % (num_rows_total,num_usable_rows()), flush=True) + + # Reverse so that recent files are first + all_files.reverse() + + # Now assemble only the files we need to hit our desired window size + desired_num_rows = num_desired_rows() + desired_num_rows = max(desired_num_rows,min_rows) + desired_num_rows = min(desired_num_rows,max_rows) if max_rows is not None else desired_num_rows + print("Desired num rows: %d / %d" % (desired_num_rows,num_rows_total), flush=True) + + desired_input_files = [] + min_start_row = num_rows_total + max_end_row = num_rows_total + num_rows_used = 0 + print_stride = 1 + len(all_files) // 80 + end_row = num_rows_total + with TimeStuff("Computing desired rows"): + for i in range(len(all_files)): + (filename,mtime,num_rows) = all_files[i] + + # This could happen if the .summary.json file is inaccurate after file deletions + # Actually we just handle that in shardify - and accept that it might make our window slightly not far back enough + # if not os.path.exists(filename): + # continue + + if num_rows is not None and num_rows > 0: + desired_input_files.append((filename,num_rows)) + start_row = end_row - num_rows + min_start_row = min(start_row, min_start_row) + num_rows_used += num_rows + end_row -= num_rows + else: + start_row = end_row + + if i % print_stride == 0 or num_rows_used >= desired_num_rows: + print("Using: %s (%d-%d) (%d/%d desired rows)" % (filename,start_row,end_row,num_rows_used,desired_num_rows), flush=True) + if num_rows_used >= desired_num_rows: + break + + print("Finally, using: (%d-%d) (%d/%d desired rows)" % (min_start_row,max_end_row,num_rows_used,desired_num_rows), flush=True) + + print("GC collect", flush=True) + del all_files + gc.collect() + + np.random.seed() + np.random.shuffle(desired_input_files) + + approx_rows_to_keep = num_rows_used + if keep_target_rows is not None: + approx_rows_to_keep = min(approx_rows_to_keep, keep_target_rows) + keep_prob = approx_rows_to_keep / num_rows_used + + num_out_files = int(round(approx_rows_to_keep / approx_rows_per_out_file)) + num_out_files = max(num_out_files,1) + + if output_npz: + out_files = [os.path.join(out_dir, "data%d.npz" % i) for i in range(num_out_files)] + else: + assert False, "No longer supports outputting tensorflow data" + + out_tmp_dirs = [os.path.join(out_tmp_dir, "tmp.shuf%d" % i) for i in range(num_out_files)] + print("Writing %d output files with %d kept / %d desired rows" % (num_out_files, approx_rows_to_keep, desired_num_rows), flush=True) + + def clean_tmp_dirs(): + for tmp_dir in out_tmp_dirs: + if os.path.exists(tmp_dir): + print("Cleaning up tmp dir: " + tmp_dir) + shutil.rmtree(tmp_dir) + + clean_tmp_dirs() + for tmp_dir in out_tmp_dirs: + os.mkdir(tmp_dir) + + num_rows_in_desired_files = 0 + if only_include_md5_path_prop_lbound is not None or only_include_md5_path_prop_ubound is not None: + new_desired_input_files = [] + for (input_file,num_rows_in_file) in desired_input_files: + input_file_base = os.path.basename(input_file) + hashfloat = int("0x"+hashlib.md5(str(input_file_base).encode('utf-8')).hexdigest()[:13],16) / 2 ** 52 + ok = True + if only_include_md5_path_prop_lbound is not None and hashfloat < only_include_md5_path_prop_lbound: + ok = False + if only_include_md5_path_prop_ubound is not None and hashfloat >= only_include_md5_path_prop_ubound: + ok = False + if ok: + new_desired_input_files.append((input_file,num_rows_in_file)) + num_rows_in_desired_files += num_rows_in_file + print("Due to only_include_md5, filtering down to %d/%d files" % (len(new_desired_input_files),len(desired_input_files))) + desired_input_files = new_desired_input_files + else: + for (input_file,num_rows_in_file) in desired_input_files: + num_rows_in_desired_files += num_rows_in_file + + if len(desired_input_files) <= 0: + print("No files after filtering for desired range") + sys.exit(0) + if num_rows_in_desired_files <= 0: + print("No rows in desired files") + sys.exit(0) + + # Clump files into sharding groups. More efficient if shuffling a ton of small npz files + # since we aren't doing separate tasks for every individual file but rather handling a bunch + # of files at once, and also makes chunkier shards on disk when it comes time to shuffle. + desired_input_file_groups = [] + group_size_so_far = 0 + group_so_far = [] + for (input_file,num_rows_in_file) in desired_input_files: + if num_rows_in_file <= 0: + continue + group_so_far.append(input_file) + group_size_so_far += num_rows_in_file + if group_size_so_far >= worker_group_size: + desired_input_file_groups.append(group_so_far) + group_so_far = [] + group_size_so_far = 0 + if group_size_so_far > 0: + desired_input_file_groups.append(group_so_far) + group_so_far = [] + group_size_so_far = 0 + print("Grouping %d input files into %d sharding groups" % (len(desired_input_files),len(desired_input_file_groups)),flush=True) + + with multiprocessing.Pool(num_processes) as pool: + with TimeStuff("Sharding"): + shard_results = pool.starmap(shardify, [ + (input_idx, desired_input_file_groups[input_idx], num_out_files, out_tmp_dirs, keep_prob, include_meta) + for input_idx in range(len(desired_input_file_groups)) + ]) + + with TimeStuff("Merging"): + num_shards_to_merge = len(desired_input_file_groups) + merge_results = pool.starmap(merge_shards, [ + (out_files[idx],num_shards_to_merge,out_tmp_dirs[idx],batch_size,ensure_batch_multiple,output_npz,include_meta) + for idx in range(len(out_files)) + ]) + print("Number of rows by output file:",flush=True) + print(list(zip(out_files,merge_results)),flush=True) + sys.stdout.flush() + + clean_tmp_dirs() + + dump_value = { + "range": (min_start_row, max_end_row) + } + + with open(out_dir + ".json", 'w') as f: + json.dump(dump_value, f) diff --git a/training/scripts/dated/20241110-123407/shuffle.sh b/training/scripts/dated/20241110-123407/shuffle.sh new file mode 100644 index 0000000..4b4348d --- /dev/null +++ b/training/scripts/dated/20241110-123407/shuffle.sh @@ -0,0 +1,122 @@ +#!/bin/bash -eu +set -o pipefail +{ +# Shuffles and copies selfplay training from selfplay/ to shuffleddata/current/ +# Should be run periodically. + +if [[ $# -lt 4 ]] +then + echo "Usage: $0 BASEDIR TMPDIR NTHREADS BATCHSIZE" + echo "Currently expects to be run from within the 'python' directory of the KataGo repo, or otherwise in the same dir as shuffle.py." + echo "BASEDIR containing selfplay data and models and related directories" + echo "TMPDIR scratch space, ideally on fast local disk, unique to this loop" + echo "NTHREADS number of parallel threads/processes to use in shuffle" + echo "BATCHSIZE number of samples to concat together per batch for training" + exit 0 +fi +BASEDIR="$1" +shift +TMPDIR="$1" +shift +NTHREADS="$1" +shift +BATCHSIZE="$1" +shift + +#------------------------------------------------------------------------------ + +OUTDIR=$(date "+%Y%m%d-%H%M%S") +OUTDIRTRAIN=$OUTDIR/train +OUTDIRVAL=$OUTDIR/val + +mkdir -p "$BASEDIR"/shuffleddata/$OUTDIR +mkdir -p "$TMPDIR"/train +mkdir -p "$TMPDIR"/val + +echo "Beginning shuffle at" $(date "+%Y-%m-%d %H:%M:%S") + +if [[ -n "${SKIP_VALIDATE:-}" ]] +then + ( + time python3 ./shuffle.py \ + "$BASEDIR"/selfplay/ \ + -expand-window-per-row 0.4 \ + -taper-window-exponent 0.65 \ + -out-dir "$BASEDIR"/shuffleddata/$OUTDIRTRAIN \ + -out-tmp-dir "$TMPDIR"/train \ + -approx-rows-per-out-file 70000 \ + -num-processes "$NTHREADS" \ + -batch-size "$BATCHSIZE" \ + -only-include-md5-path-prop-lbound 0.00 \ + -only-include-md5-path-prop-ubound 1.00 \ + -output-npz \ + "$@" \ + 2>&1 | tee "$BASEDIR"/shuffleddata/$OUTDIR/outtrain.txt & + + wait + ) +else + # Randomly peels off 5% of files generated by selfplay as validation data + ( + time python3 ./shuffle.py \ + "$BASEDIR"/selfplay/ \ + -expand-window-per-row 0.4 \ + -taper-window-exponent 0.65 \ + -out-dir "$BASEDIR"/shuffleddata/$OUTDIRVAL \ + -out-tmp-dir "$TMPDIR"/val \ + -approx-rows-per-out-file 70000 \ + -num-processes "$NTHREADS" \ + -batch-size "$BATCHSIZE" \ + -only-include-md5-path-prop-lbound 0.95 \ + -only-include-md5-path-prop-ubound 1.00 \ + -output-npz \ + "$@" \ + 2>&1 | tee "$BASEDIR"/shuffleddata/$OUTDIR/outval.txt & + + wait + ) + ( + time python3 ./shuffle.py \ + "$BASEDIR"/selfplay/ \ + -expand-window-per-row 0.4 \ + -taper-window-exponent 0.65 \ + -out-dir "$BASEDIR"/shuffleddata/$OUTDIRTRAIN \ + -out-tmp-dir "$TMPDIR"/train \ + -approx-rows-per-out-file 70000 \ + -num-processes "$NTHREADS" \ + -batch-size "$BATCHSIZE" \ + -only-include-md5-path-prop-lbound 0.00 \ + -only-include-md5-path-prop-ubound 0.95 \ + -output-npz \ + "$@" \ + 2>&1 | tee "$BASEDIR"/shuffleddata/$OUTDIR/outtrain.txt & + + wait + ) +fi +#set +x + +# Just in case, give a little time for nfs +sleep 10 + +# rm if it already exists +rm -rf "$BASEDIR"/shuffleddata/current_tmp + +ln -s $OUTDIR "$BASEDIR"/shuffleddata/current_tmp +mv -Tf "$BASEDIR"/shuffleddata/current_tmp "$BASEDIR"/shuffleddata/current + +# CLEANUP --------------------------------------------------------------- + +# Among shuffled dirs older than 2 hours, remove all but the most recent 5 of them. +# This should be VERY conservative and allow plenty of time for the training to switch +# to newer ones as they get generated. +echo "Cleaning up any old dirs" +find "$BASEDIR"/shuffleddata/ -mindepth 1 -maxdepth 1 -type d -mmin +120 | sort | head -n -5 | xargs --no-run-if-empty rm -r + +echo "Finished shuffle at" $(date "+%Y-%m-%d %H:%M:%S") +# Make a little space between shuffles +echo "" +echo "" + +exit 0 +} diff --git a/training/scripts/dated/20241110-123407/shuffle_and_export_loop.sh b/training/scripts/dated/20241110-123407/shuffle_and_export_loop.sh new file mode 100644 index 0000000..d9ae159 --- /dev/null +++ b/training/scripts/dated/20241110-123407/shuffle_and_export_loop.sh @@ -0,0 +1,62 @@ +#!/bin/bash -eu +set -o pipefail +{ +if [[ $# -lt 6 ]] +then + echo "Usage: $0 NAMEPREFIX BASEDIR TMPDIR NTHREADS BATCHSIZE USEGATING" + echo "NAMEPREFIX string prefix for this training run, try to pick something globally unique. Will be displayed to users when KataGo loads the model." + echo "BASEDIR containing selfplay data and models and related directories" + echo "TMPDIR scratch space, ideally on fast local disk, unique to this loop" + echo "NTHREADS number of parallel threads/processes to use in shuffle" + echo "BATCHSIZE number of samples to concat together per batch for training, must match training" + echo "USEGATING = 1 to use gatekeeper, 0 to not use gatekeeper" + exit 0 +fi +NAMEPREFIX="$1" +shift +BASEDIRRAW="$1" +shift +TMPDIRRAW="$1" +shift +NTHREADS="$1" +shift +BATCHSIZE="$1" +shift +USEGATING="$1" +shift + +GITROOTDIR="$(git rev-parse --show-toplevel)" + +basedir="$(realpath "$BASEDIRRAW")" +tmpdir="$(realpath "$TMPDIRRAW")" + +mkdir -p "$basedir"/scripts +mkdir -p "$basedir"/logs +cp "$GITROOTDIR"/python/*.py "$GITROOTDIR"/python/selfplay/*.sh "$basedir"/scripts + +# For archival and logging purposes - you can look back and see exactly the python code on a particular date +DATE_FOR_FILENAME=$(date "+%Y%m%d-%H%M%S") +DATED_ARCHIVE="$basedir"/scripts/dated/"$DATE_FOR_FILENAME" +mkdir -p "$DATED_ARCHIVE" +cp "$GITROOTDIR"/python/*.py "$GITROOTDIR"/python/selfplay/*.sh "$DATED_ARCHIVE" + +( + cd "$basedir"/scripts + while true + do + ./shuffle.sh "$basedir" "$tmpdir" "$NTHREADS" "$BATCHSIZE" "$@" + sleep 20 + done +) >> "$basedir"/logs/outshuffle.txt 2>&1 & disown + +( + cd "$basedir"/scripts + while true + do + ./export_model_for_selfplay.sh "$NAMEPREFIX" "$basedir" "$USEGATING" + sleep 10 + done +) >> "$basedir"/logs/outexport.txt 2>&1 & disown + +exit 0 +} diff --git a/training/scripts/dated/20241110-123407/shuffle_loop.sh b/training/scripts/dated/20241110-123407/shuffle_loop.sh new file mode 100644 index 0000000..6f75b0c --- /dev/null +++ b/training/scripts/dated/20241110-123407/shuffle_loop.sh @@ -0,0 +1,60 @@ +#!/bin/bash -eu +set -o pipefail +{ +if [[ $# -lt 4 ]] +then + echo "Usage: $0 BASEDIR TMPDIR NTHREADS BATCHSIZE" + echo "Currently expects to be run from within the 'python' directory of the KataGo repo, or otherwise in the same dir as export_model.py." + echo "BASEDIR containing selfplay data and models and related directories" + echo "TMPDIR scratch space, ideally on fast local disk, unique to this loop" + echo "NTHREADS number of parallel threads/processes to use in shuffle" + echo "BATCHSIZE number of samples to concat together per batch for training, must match training" + exit 0 +fi +BASEDIRRAW="$1" +shift +TMPDIRRAW="$1" +shift +NTHREADS="$1" +shift +BATCHSIZE="$1" +shift + +GITROOTDIR="$(git rev-parse --show-toplevel)" + +basedir="$(realpath "$BASEDIRRAW")" +tmpdir="$(realpath "$TMPDIRRAW")" + +mkdir -p "$basedir"/scripts +mkdir -p "$basedir"/logs +cp "$GITROOTDIR"/python/*.py "$GITROOTDIR"/python/selfplay/*.sh "$GITROOTDIR"/python/selfplay/distributed/*.sh "$basedir"/scripts + +# For archival and logging purposes - you can look back and see exactly the python code on a particular date +DATE_FOR_FILENAME=$(date "+%Y%m%d-%H%M%S") +DATED_ARCHIVE="$basedir"/scripts/dated/"$DATE_FOR_FILENAME" +mkdir -p "$DATED_ARCHIVE" +cp "$GITROOTDIR"/python/*.py "$DATED_ARCHIVE" +cp -r "$GITROOTDIR"/python/selfplay "$DATED_ARCHIVE" + + +( + cd "$basedir"/scripts + while true + do + rm -f "$basedir"/selfplay.summary.json.tmp + time python3 ./summarize_old_selfplay_files.py "$basedir"/selfplay/ \ + -old-summary-file-to-assume-correct "$basedir"/selfplay.summary.json \ + -new-summary-file "$basedir"/selfplay.summary.json.tmp + mv "$basedir"/selfplay.summary.json.tmp "$basedir"/selfplay.summary.json + sleep 10 + + for i in {1..10} + do + ./shuffle.sh "$basedir" "$tmpdir" "$NTHREADS" "$BATCHSIZE" -summary-file "$basedir"/selfplay.summary.json "$@" + sleep 600 + done + done +) >> "$basedir"/logs/outshuffle.txt 2>&1 & disown + +exit 0 +} diff --git a/training/scripts/dated/20241110-123407/summarize_old_selfplay_files.py b/training/scripts/dated/20241110-123407/summarize_old_selfplay_files.py new file mode 100644 index 0000000..712374b --- /dev/null +++ b/training/scripts/dated/20241110-123407/summarize_old_selfplay_files.py @@ -0,0 +1,194 @@ +#!/usr/bin/python3 +import sys +import os +import argparse +import traceback +import time +import logging +import zipfile +import json +import datetime +import dateutil.parser +import shutil + +import multiprocessing + +import numpy as np + +def get_numpy_npz_headers(filename): + with zipfile.ZipFile(filename) as z: + wasbad = False + numrows = 0 + npzheaders = {} + for subfilename in z.namelist(): + npyfile = z.open(subfilename) + try: + version = np.lib.format.read_magic(npyfile) + except ValueError: + wasbad = True + print("WARNING: bad file, skipping it: %s (bad array %s)" % (filename,subfilename)) + else: + (shape, is_fortran, dtype) = np.lib.format._read_array_header(npyfile,version) + npzheaders[subfilename] = (shape, is_fortran, dtype) + if wasbad: + return None + return npzheaders + +def is_temp_npz_like(filename): + return "_" in filename + +def summarize_dir(dirpath): + filenames = [filename for filename in os.listdir(dirpath) if filename.endswith('.npz')] + + num_rows_this_dir = 0 + filename_mtime_num_rowss = [] + for filename in filenames: + filepath = os.path.join(dirpath,filename) + mtime = os.path.getmtime(filepath) + + # Files that look like they are temp files should be recorded and warned + if is_temp_npz_like(filename): + print("WARNING: file looks like a temp file: ", filepath) + filename_mtime_num_rowss.append((filename,mtime,None)) + continue + + try: + npheaders = get_numpy_npz_headers(filepath) + except PermissionError: + print("WARNING: No permissions for reading file: ", filepath) + filename_mtime_num_rowss.append((filename,mtime,None)) + continue + except zipfile.BadZipFile: + print("WARNING: Bad zip file: ", filepath) + filename_mtime_num_rowss.append((filename,mtime,None)) + continue + + if npheaders is None or len(npheaders) <= 0: + print("WARNING: bad npz headers for file: ", filepath) + filename_mtime_num_rowss.append((filename,mtime,None)) + continue + + if "binaryInputNCHWPacked" in npheaders: + (shape, is_fortran, dtype) = npheaders["binaryInputNCHWPacked"] + else: + (shape, is_fortran, dtype) = npheaders["binaryInputNCHWPacked.npy"] + num_rows = shape[0] + num_rows_this_dir += num_rows + + filename_mtime_num_rowss.append((filename,mtime,num_rows)) + + print("Summarizing new dir with %d rows: %s" % (num_rows_this_dir,dirpath),flush=True) + return (dirpath, filename_mtime_num_rowss, num_rows_this_dir) + + +class TimeStuff(object): + + def __init__(self,taskstr): + self.taskstr = taskstr + + def __enter__(self): + print("Beginning: %s" % self.taskstr, flush=True) + self.t0 = time.time() + + def __exit__(self, exception_type, exception_val, trace): + self.t1 = time.time() + print("Finished: %s in %s seconds" % (self.taskstr, str(self.t1 - self.t0)), flush=True) + return True + + +if __name__ == '__main__': + parser = argparse.ArgumentParser(description='Shuffle data files') + parser.add_argument('dirs', metavar='DIR', nargs='+', help='Directories of training data files') + parser.add_argument('-old-summary-file-to-assume-correct', required=False, help='Summary json file for directory contents') + parser.add_argument('-new-summary-file', required=True, help='Summary json file for directory contents') + parser.add_argument('-num-parallel-processes', required=False, type=int, help='Number of parallel processes to use, default 4') + + args = parser.parse_args() + dirs = args.dirs + old_summary_file_to_assume_correct = args.old_summary_file_to_assume_correct + new_summary_file = args.new_summary_file + + num_processes = 4 + if args.num_parallel_processes is not None: + num_processes = args.num_parallel_processes + + summary_data_by_dirpath = {} + if old_summary_file_to_assume_correct is not None and os.path.exists(old_summary_file_to_assume_correct): + with TimeStuff("Loading " + old_summary_file_to_assume_correct): + with open(old_summary_file_to_assume_correct) as fp: + summary_data_by_dirpath = json.load(fp) + + # Update old summary data into new format + dirpaths = list(summary_data_by_dirpath.keys()) + format_was_updated = False + for dirpath in dirpaths: + if "dir_mtime" not in summary_data_by_dirpath[dirpath]: + format_was_updated = True + filename_mtime_num_rowss = summary_data_by_dirpath[dirpath] + summary_data_by_dirpath[dirpath] = { + "dir_mtime": os.path.getmtime(dirpath), + "filename_mtime_num_rowss": filename_mtime_num_rowss, + } + + dirs_to_handle = [] + with TimeStuff("Finding files"): + for d in dirs: + for (path,dirnames,filenames) in os.walk(d, followlinks=True): + had_no_dirnames = len(dirnames) == 0 + i = 0 + while i < len(dirnames): + dirname = dirnames[i] + dirpath = os.path.normpath(os.path.join(path, dirname)) + if dirpath in summary_data_by_dirpath: + if os.path.getmtime(dirpath) == summary_data_by_dirpath[dirpath]["dir_mtime"]: + # Skip + del dirnames[i] + continue + + if dirname == "tdata": + # Handle this dir and don't recurse further + del dirnames[i] + dirs_to_handle.append(dirpath) + continue + + else: + parseddate = None + try: + parseddate = dateutil.parser.parse(dirname) + except ValueError: + parseddate = None + if parseddate is not None and parseddate < datetime.datetime.now() - datetime.timedelta(days=2.0): + # Handle this dir and don't recurse further + del dirnames[i] + dirs_to_handle.append(dirpath) + continue + + i += 1 + + with TimeStuff("Parallel summarizing %d dirs" % len(dirs_to_handle)): + with multiprocessing.Pool(num_processes) as pool: + results = pool.map(summarize_dir,dirs_to_handle) + + num_total_rows = 0 + with TimeStuff("Merging %d results" % len(results)): + for result in results: + if result is None: + continue + (dirpath, filename_mtime_num_rowss, num_rows_this_dir) = result + num_total_rows += num_rows_this_dir + summary_data_by_dirpath[os.path.abspath(dirpath)] = { + "dir_mtime": os.path.getmtime(os.path.abspath(dirpath)), + "filename_mtime_num_rowss": filename_mtime_num_rowss, + } + + if len(dirs_to_handle) == 0 and old_summary_file_to_assume_correct is not None and os.path.exists(old_summary_file_to_assume_correct) and not format_was_updated: + shutil.copy(old_summary_file_to_assume_correct,new_summary_file) + print("Not writing any new summary, no results, just copying old file") + else: + with TimeStuff("Writing result"): + with open(new_summary_file,"w") as fp: + json.dump(summary_data_by_dirpath,fp) + print("Summary file written adding %d additional rows: %s" % (num_total_rows,new_summary_file),flush=True) + + print("Done computing new summary",flush=True) + sys.stdout.flush() diff --git a/training/scripts/dated/20241110-123407/summarize_sgfs.py b/training/scripts/dated/20241110-123407/summarize_sgfs.py new file mode 100644 index 0000000..6b21f38 --- /dev/null +++ b/training/scripts/dated/20241110-123407/summarize_sgfs.py @@ -0,0 +1,128 @@ +import argparse +import elo +from elo import GameRecord +import itertools +import math +import os +import re + +from dataclasses import dataclass +from sgfmill import sgf +from typing import List, Dict, Tuple, Set, Sequence + +class GoGameResultSummary(elo.GameResultSummary): + + def __init__( + self, + elo_prior_games: float, + estimate_first_player_advantage: bool, + ): + super().__init__(elo_prior_games, estimate_first_player_advantage) + self._should_warn_handicap_komi = False + + # @override + def print_elos(self): + super().print_elos() + if self._should_warn_handicap_komi: + print("WARNING: There are handicap games or games with komi < 5.5 or komi > 7.5, these games may not be fair?") + + # @override + def is_game_file(self, input_file: str) -> bool: + lower = input_file.lower() + return input_file.endswith(".sgf") or input_file.endswith(".sgfs") + + # @override + def get_game_records(self, input_file: str) -> List[GameRecord]: + if input_file.lower().endswith(".sgfs"): + with open(input_file, "rb") as f: + sgfs_strings = f.readlines() + + records = [] + for sgf in sgfs_strings: + record = self.sgf_string_to_game_record(sgf, input_file) + if record is not None: + records.append(record) + return records + else: + with open(input_file, "rb") as f: + sgf = f.read() + + records = [] + record = self.sgf_string_to_game_record(sgf, input_file) + if record is not None: + records.append(record) + return records + + def sgf_string_to_game_record(self, sgf_string, debug_source = None) -> GameRecord: + try: + # sgfmill for some reason can't handle rectangular boards, even though it's part of the SGF spec. + # So lie and say that they're square, so that we can load them. + sgf_string = re.sub(r'SZ\[(\d+):\d+\]', r'SZ[\1]', sgf_string.decode("utf-8")) + sgf_string = sgf_string.encode("utf-8") + + game = sgf.Sgf_game.from_bytes(sgf_string) + winner = game.get_winner() + except ValueError: + print ('\033[91m'+f"A sgf string is damaged in {debug_source}, and its record has been skipped!"+ '\x1b[0m') + return + pla_black = game.get_player_name('b') + pla_white = game.get_player_name('w') + if (game.get_handicap() is not None) or game.get_komi() < 5.5 or game.get_komi() > 7.5: + self._should_warn_handicap_komi = True + + game_record = GameRecord(player1=pla_black,player2=pla_white) + if (winner == 'b'): + game_record.win += 1 + elif (winner == 'w'): + game_record.loss += 1 + else: + game_record.draw += 1 + return game_record + + + +if __name__ == "__main__": + description = """ + Summarize SGF/SGFs files and estimate Bayes Elo score for each of the player. + """ + parser = argparse.ArgumentParser(description=description) + parser.add_argument( + "input-files-or-dirs", + help="sgf/sgfs files or directories of them", + nargs="+", + ) + parser.add_argument( + "-recursive", + help="Recursively search subdirectories of input directories", + required=False, + action="store_true", + ) + parser.add_argument( + "-elo-prior-games", + help="Prior for Bayes Elo calculation, using input as the prior number of games to stabilize the results", + required=False, + type=float, + default=2, + ) + parser.add_argument( + "-estimate-first-player-advantage", + help="Attempt to estimate first player advantage instead of assuming fair game", + required=False, + action="store_true", + ) + args = vars(parser.parse_args()) + print(args) + + input_files_or_dirs = args["input-files-or-dirs"] + recursive = args["recursive"] + elo_prior_games = args["elo_prior_games"] + estimate_first_player_advantage = args["estimate_first_player_advantage"] + + game_result_summary = GoGameResultSummary( + elo_prior_games=elo_prior_games, + estimate_first_player_advantage=estimate_first_player_advantage, + ) + for input_file_or_dir in input_files_or_dirs: + game_result_summary.add_games_from_file_or_dir(input_file_or_dir, recursive=recursive) + + game_result_summary.print_elos() diff --git a/training/scripts/dated/20241110-123407/synchronous_loop.sh b/training/scripts/dated/20241110-123407/synchronous_loop.sh new file mode 100644 index 0000000..d21423e --- /dev/null +++ b/training/scripts/dated/20241110-123407/synchronous_loop.sh @@ -0,0 +1,103 @@ +#!/bin/bash -eu +set -o pipefail +{ + +# Runs the entire self-play process synchronously in a loop, training a single size of neural net appropriately. +# Assumes you have the cpp directory compiled and the katago executable is there. + +# If using multiple machines, or even possibly many GPUs on one machine in some cases, then this is NOT the +# recommended method, instead it is better to run all steps simultaneously and asynchronously. See SelfplayTraining.md in +# the root of the KataGo repo for more details. + +if [[ $# -lt 5 ]] +then + echo "Usage: $0 NAMEPREFIX BASEDIR TRAININGNAME MODELKIND USEGATING" + echo "Assumes katago is already built in the 'cpp' directory of the KataGo repo and the executable is present at cpp/katago.exe." + echo "NAMEPREFIX string prefix for this training run, try to pick something globally unique. Will be displayed to users when KataGo loads the model." + echo "BASEDIR containing selfplay data and models and related directories" + echo "TRANINGNAME name to prefix models with, specific to this training daemon" + echo "MODELKIND what size model to train, like 'b10c128', see ../modelconfigs.py" + echo "USEGATING = 1 to use gatekeeper, 0 to not use gatekeeper" + exit 0 +fi +NAMEPREFIX="$1" +shift +BASEDIRRAW="$1" +shift +TRAININGNAME="$1" +shift +MODELKIND="$1" +shift +USEGATING="$1" +shift + +BASEDIR="$(realpath "$BASEDIRRAW")" +GITROOTDIR="$(git rev-parse --show-toplevel)" +LOGSDIR="$BASEDIR"/logs +SCRATCHDIR="$BASEDIR"/shufflescratch + +# Create all the directories we need +mkdir -p "$BASEDIR" +mkdir -p "$LOGSDIR" +mkdir -p "$SCRATCHDIR" +mkdir -p "$BASEDIR"/selfplay +mkdir -p "$BASEDIR"/gatekeepersgf + +# Parameters for the training run +NUM_GAMES_PER_CYCLE=30 # Every cycle, play this many games +NUM_THREADS_FOR_SHUFFLING=8 +NUM_TRAIN_SAMPLES_PER_EPOCH=100000 # Training will proceed in chunks of this many rows, subject to MAX_TRAIN_PER_DATA. +MAX_TRAIN_PER_DATA=8 # On average, train only this many times on each data row. Larger numbers may cause overfitting. +NUM_TRAIN_SAMPLES_PER_SWA=80000 # Stochastic weight averaging frequency. +BATCHSIZE=128 # For lower-end GPUs 64 or smaller may be needed to avoid running out of GPU memory. +SHUFFLE_MINROWS=1 # Require this many rows at the very start before beginning training. +MAX_TRAIN_SAMPLES_PER_CYCLE=500000 # Each cycle will do at most this many training steps. +TAPER_WINDOW_SCALE=50000 # Parameter setting the scale at which the shuffler will make the training window grow sublinearly. +SHUFFLE_KEEPROWS=600000 # Needs to be larger than MAX_TRAIN_SAMPLES_PER_CYCLE, so the shuffler samples enough rows each cycle for the training to use. + +# Paths to the selfplay and gatekeeper configs that contain board sizes, rules, search parameters, etc. +SELFPLAY_CONFIG="$GITROOTDIR"/cpp/configs/training/selfplay1_maxsize9.cfg +GATING_CONFIG="$GITROOTDIR"/cpp/configs/training/gatekeeper1_maxsize9.cfg + +# Copy all the relevant scripts and configs and the katago executable to a dated directory. +DATE_FOR_FILENAME=$(date "+%Y%m%d-%H%M%S") +DATED_ARCHIVE="$BASEDIR"/scripts/dated/"$DATE_FOR_FILENAME" +mkdir -p "$DATED_ARCHIVE" +cp "$GITROOTDIR"/python/*.py "$GITROOTDIR"/python/selfplay/*.sh "$DATED_ARCHIVE" +cp "$GITROOTDIR"/cpp/katago.exe "$DATED_ARCHIVE"/katago.exe # Ensure the executable is copied +cp "$SELFPLAY_CONFIG" "$DATED_ARCHIVE"/selfplay.cfg +cp "$GATING_CONFIG" "$DATED_ARCHIVE"/gatekeeper.cfg +git show --no-patch --no-color > "$DATED_ARCHIVE"/version.txt +git diff --no-color > "$DATED_ARCHIVE"/diff.txt +git diff --staged --no-color > "$DATED_ARCHIVE"/diffstaged.txt + +# Also run the code out of the archive, so that we don't unexpectedly crash or change behavior if the local repo changes. +cd "$DATED_ARCHIVE" + +# Begin cycling forever, running each step in order. +set -x +while true +do + echo "Gatekeeper" + time "C:\Users\chang\Downloads\katago-v1.15.1-opencl-windows-x64\katago.exe" gatekeeper -rejected-models-dir "$BASEDIR"/rejectedmodels -accepted-models-dir "$BASEDIR"/models/ -sgf-output-dir "$BASEDIR"/gatekeepersgf/ -test-models-dir "$BASEDIR"/modelstobetested/ -config "$DATED_ARCHIVE"/gatekeeper.cfg -quit-if-no-nets-to-test | tee -a "$BASEDIR"/gatekeepersgf/stdout.txt + + echo "Selfplay" + time "C:\Users\chang\Downloads\katago-v1.15.1-opencl-windows-x64\katago.exe" selfplay -max-games-total "$NUM_GAMES_PER_CYCLE" -output-dir "$BASEDIR"/selfplay -models-dir "$BASEDIR"/models -config "$DATED_ARCHIVE"/selfplay.cfg | tee -a "$BASEDIR"/selfplay/stdout.txt + + echo "Shuffle" + ( + time SKIP_VALIDATE=1 ./shuffle.sh "$BASEDIR" "$SCRATCHDIR" "$NUM_THREADS_FOR_SHUFFLING" "$BATCHSIZE" -min-rows "$SHUFFLE_MINROWS" -keep-target-rows "$SHUFFLE_KEEPROWS" -taper-window-scale "$TAPER_WINDOW_SCALE" | tee -a "$BASEDIR"/logs/outshuffle.txt + ) + + echo "Train" + time ./train.sh "$BASEDIR" "$TRAININGNAME" "$MODELKIND" "$BATCHSIZE" main -samples-per-epoch "$NUM_TRAIN_SAMPLES_PER_EPOCH" -swa-period-samples "$NUM_TRAIN_SAMPLES_PER_SWA" -quit-if-no-data -stop-when-train-bucket-limited -no-repeat-files -max-train-bucket-per-new-data "$MAX_TRAIN_PER_DATA" -max-train-bucket-size "$MAX_TRAIN_SAMPLES_PER_CYCLE" + + echo "Export" + ( + time ./export_model_for_selfplay.sh "$NAMEPREFIX" "$BASEDIR" "$USEGATING" | tee -a "$BASEDIR"/logs/outexport.txt + ) + +done + +exit 0 +} diff --git a/training/scripts/dated/20241110-123407/test.py b/training/scripts/dated/20241110-123407/test.py new file mode 100644 index 0000000..92065df --- /dev/null +++ b/training/scripts/dated/20241110-123407/test.py @@ -0,0 +1,279 @@ +#!/usr/bin/python3 +import sys +import os +import argparse +import traceback +import random +import math +import time +import logging +import json +import numpy as np +from collections import defaultdict +from typing import Dict, List + +import torch +import torch.nn +from torch.optim.swa_utils import AveragedModel + +import modelconfigs +from model_pytorch import Model, ExtraOutputs, MetadataEncoder +from metrics_pytorch import Metrics +import data_processing_pytorch +from load_model import load_model + +# HANDLE COMMAND AND ARGS ------------------------------------------------------------------- + +if __name__ == "__main__": + + description = """ + Test neural net on Go positions from npz files of batches from selfplay. + """ + + parser = argparse.ArgumentParser(description=description) + parser.add_argument('-npzdir', help='Directory with npz data', required=True) + parser.add_argument('-model-kind', help='If specified, use this model kind instead of config', required=False) + parser.add_argument('-config', help='Path to model.config.json', required=False) + parser.add_argument('-checkpoint', help='Checkpoint to test', required=False) + parser.add_argument('-pos-len', help='Spatial length of expected training data', type=int, required=True) + parser.add_argument('-batch-size', help='Batch size to use for testing', type=int, required=True) + parser.add_argument('-use-swa', help='Use SWA model', action="store_true", required=False) + parser.add_argument('-max-batches', help='Maximum number of batches for testing', type=int, required=False) + parser.add_argument('-gpu-idx', help='GPU idx', type=int, required=False) + parser.add_argument('-print-norm', help='Names of outputs to print norms comma separated', type=str, required=False) + parser.add_argument('-list-available-outputs', help='Print names of outputs available', action="store_true", required=False) + + args = vars(parser.parse_args()) + +def main(args): + npzdir = args["npzdir"] + model_kind = args["model_kind"] + config_file = args["config"] + checkpoint_file = args["checkpoint"] + pos_len = args["pos_len"] + batch_size = args["batch_size"] + use_swa = args["use_swa"] + max_batches = args["max_batches"] + gpu_idx = args["gpu_idx"] + list_available_outputs = args["list_available_outputs"] + + norm_layer_names = [] + if args["print_norm"]: + for layername in args["print_norm"].split(','): + if layername.strip(): + norm_layer_names.append(layername.strip()) + + soft_policy_weight_scale = 1.0 + value_loss_scale = 1.0 + td_value_loss_scales = [0.4, 0.4, 0.4] + + world_size = 1 + rank = 0 + + # SET UP LOGGING ------------------------------------------------------------- + + logging.root.handlers = [] + logging.basicConfig( + level=logging.INFO, + format="%(message)s", + handlers=[ + logging.StreamHandler(stream=sys.stdout) + ], + ) + np.set_printoptions(linewidth=150) + + logging.info(str(sys.argv)) + + # FIGURE OUT GPU ------------------------------------------------------------ + if gpu_idx is not None: + torch.cuda.set_device(gpu_idx) + logging.info("Using GPU device: " + torch.cuda.get_device_name()) + device = torch.device("cuda", gpu_idx) + elif torch.cuda.is_available(): + logging.info("Using GPU device: " + torch.cuda.get_device_name()) + device = torch.device("cuda") + else: + logging.warning("WARNING: No GPU, using CPU") + device = torch.device("cpu") + + # LOAD MODEL --------------------------------------------------------------------- + + if checkpoint_file is None: + logging.info("Initializing new model since no checkpoint provided") + assert (model_kind is None) != (config_file is None), "Must provide exactly one of -model-kind and -config if no checkpoint" + + if model_kind is not None: + model_config = modelconfigs.config_of_name[model_kind] + else: + with open(config_file,"r") as f: + model_config = json.load(f) + logging.info(str(model_config)) + + model = Model(model_config,pos_len) + model.initialize() + model.to(device) + else: + model, swa_model, _ = load_model(checkpoint_file, use_swa, device=device, pos_len=pos_len, verbose=True) + model_config = model.config + + metrics_obj = Metrics(batch_size,world_size,model) + + # METRICS ----------------------------------------------------------------------------------- + def detensorify_metrics(metrics): + ret = {} + for key in metrics: + if isinstance(metrics[key], torch.Tensor): + ret[key] = metrics[key].detach().cpu().item() + else: + ret[key] = metrics[key] + return ret + + def accumulate_metrics(metric_sums, metric_weights, metrics, batch_size, decay): + if decay != 1.0: + for metric in metric_sums: + if metric.endswith("_sum"): + metric_sums[metric] *= decay + metric_weights[metric] *= decay + + for metric in metrics: + if not metric.endswith("_batch"): + metric_sums[metric] += metrics[metric] + metric_weights[metric] += batch_size + else: + metric_sums[metric] += metrics[metric] + metric_weights[metric] += 1 + + def log_metrics(prefix, metric_sums, metric_weights, metrics, sum_norms, num_samples_tested, metrics_out): + metrics_to_print = {} + for metric in metric_sums: + if metric.endswith("_sum"): + metrics_to_print[metric[:-4]] = metric_sums[metric] / metric_weights[metric] + elif metric.endswith("_batch"): + metrics_to_print[metric] = metric_sums[metric] / metric_weights[metric] + metric_sums[metric] *= 0.001 + metric_weights[metric] *= 0.001 + else: + metrics_to_print[metric] = metric_sums[metric] + for metric in metrics: + if metric not in metric_sums: + metrics_to_print[metric] = metrics[metric] + + logging.info(prefix + ", ".join(["%s = %f" % (metric, metrics_to_print[metric]) for metric in metrics_to_print])) + if metrics_out: + metrics_out.write(json.dumps(metrics_to_print) + "\n") + + for name in sum_norms: + avg_norm = sum_norms[name] / num_samples_tested + logging.info(f"{name} {avg_norm:.6f}") + + if metrics_out: + metrics_out.flush() + + torch.backends.cudnn.benchmark = True + + # Validate + logging.info("Beginning test!") + val_files = [os.path.join(npzdir,fname) for fname in os.listdir(npzdir) if fname.endswith(".npz")] + if len(val_files) == 0: + raise Exception("No npz files in " + npzdir) + + with torch.no_grad(): + model.eval() + if swa_model is not None: + swa_model.eval() + val_metric_sums = defaultdict(float) + val_metric_weights = defaultdict(float) + sum_norms = defaultdict(float) + num_batches_tested = 0 + num_samples_tested = 0 + total_inference_time = 0.0 + is_first_batch = True + for batch in data_processing_pytorch.read_npz_training_data( + val_files, + batch_size, + world_size, + rank, + pos_len, + device, + randomize_symmetries=True, + model_config=model_config, + ): + if max_batches is not None and num_batches_tested >= max_batches: + break + + start = torch.cuda.Event(enable_timing=True) + end = torch.cuda.Event(enable_timing=True) + + extra_outputs = ExtraOutputs(norm_layer_names) + # if model.get_has_metadata_encoder(): + # extra_outputs.add_requested([MetadataEncoder.OUTMEAN_KEY,MetadataEncoder.OUTLOGVAR_KEY]) + + start.record() + if swa_model is not None: + model_outputs = swa_model(batch["binaryInputNCHW"],batch["globalInputNC"],extra_outputs=extra_outputs) + else: + model_outputs = model(batch["binaryInputNCHW"],batch["globalInputNC"],extra_outputs=extra_outputs) + end.record() + torch.cuda.synchronize() + time_elapsed = start.elapsed_time(end) / 1000.0 + + if list_available_outputs: + for available_output in extra_outputs.available: + logging.info(f"Available output: {available_output}") + list_available_outputs = False + + postprocessed = model.postprocess_output(model_outputs) + metrics = metrics_obj.metrics_dict_batchwise( + model, + postprocessed, + extra_outputs, + batch, + is_training=False, + soft_policy_weight_scale=soft_policy_weight_scale, + disable_optimistic_policy=False, + meta_kata_only_soft_policy=False, + value_loss_scale=value_loss_scale, + td_value_loss_scales=td_value_loss_scales, + seki_loss_scale=1.0, + variance_time_loss_scale=1.0, + main_loss_scale=1.0, + intermediate_loss_scale=None, + ) + metrics = detensorify_metrics(metrics) + + # Ignore first batch, treat as a warmup so timings are a bit more accurate. + if is_first_batch: + is_first_batch = False + continue + + for norm_layer_name in norm_layer_names: + activations = extra_outputs.returned[norm_layer_name] + non_batch_dims = list(range(1,len(activations.shape))) + sum_norms[norm_layer_name] += torch.sum(torch.sqrt(torch.mean(torch.square(activations),dim=non_batch_dims)),dim=0).item() + + accumulate_metrics(val_metric_sums, val_metric_weights, metrics, batch_size, decay=1.0) + + num_batches_tested += 1 + num_samples_tested += batch_size + total_inference_time += time_elapsed + + if num_batches_tested % 5 == 0: + remainder = num_batches_tested // 5 + while remainder > 1 and remainder % 2 == 0: + remainder = remainder // 2 + if remainder in [1,3,5,7,9]: + metrics["num_batches"] = num_batches_tested + metrics["num_samples"] = num_samples_tested + metrics["inferencetime"] = total_inference_time + metrics["time/1ksamp"] = total_inference_time / num_samples_tested * 1000.0 + log_metrics("STATS SO FAR: ", val_metric_sums, val_metric_weights, metrics, sum_norms, num_samples_tested, None) + + metrics["num_batches"] = num_batches_tested + metrics["num_samples"] = num_samples_tested + metrics["inferencetime"] = total_inference_time + metrics["time/1ksamp"] = total_inference_time / num_samples_tested * 1000.0 + log_metrics("FINAL: ", val_metric_sums, val_metric_weights, metrics, sum_norms, num_samples_tested, None) + + +if __name__ == "__main__": + main(args) diff --git a/training/scripts/dated/20241110-123407/train.py b/training/scripts/dated/20241110-123407/train.py new file mode 100644 index 0000000..6717e4f --- /dev/null +++ b/training/scripts/dated/20241110-123407/train.py @@ -0,0 +1,1368 @@ +#!/usr/bin/python3 +import sys +import os +import argparse +import traceback +import random +import math +import time +import logging +import contextlib +import json +import datetime +from datetime import timezone +import gc +import shutil +import glob +import numpy as np +import itertools +import copy +import atexit +from collections import defaultdict +from typing import Dict, List + +import torch +import torch.nn +import torch.optim +import torch.distributed +import torch.multiprocessing +from torch.nn.parallel import DistributedDataParallel +from torch.optim.swa_utils import AveragedModel +from torch.cuda.amp import GradScaler, autocast + +import modelconfigs +from model_pytorch import Model, ExtraOutputs, MetadataEncoder +from metrics_pytorch import Metrics +import load_model +import data_processing_pytorch +from metrics_logging import accumulate_metrics, log_metrics, clear_metric_nonfinite + +# HANDLE COMMAND AND ARGS ------------------------------------------------------------------- + +if __name__ == "__main__": + + description = """ + Train neural net on Go positions from npz files of batches from selfplay. + """ + + parser = argparse.ArgumentParser(description=description,add_help=False) + required_args = parser.add_argument_group('required arguments') + optional_args = parser.add_argument_group('optional arguments') + optional_args.add_argument( + '-h', + '--help', + action='help', + default=argparse.SUPPRESS, + help='show this help message and exit' + ) + + required_args.add_argument('-traindir', help='Dir to write to for recording training results', required=True) + required_args.add_argument('-datadir', help='Directory with a train and val subdir of npz data, output by shuffle.py', required=True) + optional_args.add_argument('-exportdir', help='Directory to export models periodically', required=False) + optional_args.add_argument('-exportprefix', help='Prefix to append to names of models', required=False) + optional_args.add_argument('-initial-checkpoint', help='If no training checkpoint exists, initialize from this checkpoint', required=False) + + required_args.add_argument('-pos-len', help='Spatial edge length of expected training data, e.g. 19 for 19x19 Go', type=int, required=True) + required_args.add_argument('-batch-size', help='Per-GPU batch size to use for training', type=int, required=True) + optional_args.add_argument('-samples-per-epoch', help='Number of data samples to consider as one epoch', type=int, required=False) + optional_args.add_argument('-model-kind', help='String name for what model config to use', required=False) + optional_args.add_argument('-lr-scale', help='LR multiplier on the hardcoded schedule', type=float, required=False) + optional_args.add_argument('-lr-scale-auto', help='LR auto scaling', required=False, action='store_true') + optional_args.add_argument('-gnorm-clip-scale', help='Multiplier on gradient clipping threshold', type=float, required=False) + optional_args.add_argument('-sub-epochs', help='Reload training data up to this many times per epoch', type=int, default=1, required=False) + optional_args.add_argument('-swa-period-samples', help='How frequently to average an SWA sample, in samples', type=float, required=False) + optional_args.add_argument('-swa-scale', help='Number of samples to average in expectation together for SWA', type=float, required=False) + optional_args.add_argument('-lookahead-k', help='Use lookahead optimizer', type=int, default=6, required=False) + optional_args.add_argument('-lookahead-alpha', help='Use lookahead optimizer', type=float, default=0.5, required=False) + optional_args.add_argument('-lookahead-print', help='Only print on lookahead syncs', required=False, action='store_true') + + optional_args.add_argument('-multi-gpus', help='Use multiple gpus, comma-separated device ids', required=False) + optional_args.add_argument('-use-fp16', help='Use fp16 training', required=False, action='store_true') + + optional_args.add_argument('-epochs-per-export', help='Export model once every this many epochs', type=int, required=False) + optional_args.add_argument('-export-prob', help='Export model with this probablity', type=float, required=False) + optional_args.add_argument('-max-epochs-this-instance', help='Terminate training after this many more epochs', type=int, required=False) + optional_args.add_argument('-max-training-samples', help='Terminate training after about this many training steps in samples', type=int, required=False) + optional_args.add_argument('-sleep-seconds-per-epoch', help='Sleep this long between epochs', type=int, required=False) + optional_args.add_argument('-max-train-bucket-per-new-data', help='When data added, add this many train rows per data row to bucket', type=float, required=False) + optional_args.add_argument('-max-train-bucket-size', help='Approx total number of train rows allowed if data stops', type=float, required=False) + optional_args.add_argument('-max-train-steps-since-last-reload', help='Approx total of training allowed if shuffling stops', type=float, required=False) + optional_args.add_argument('-stop-when-train-bucket-limited', help='Terminate due to train bucket rather than waiting for more', required=False, action='store_true') + optional_args.add_argument('-max-val-samples', help='Approx max of validation samples per epoch', type=int, required=False) + optional_args.add_argument('-randomize-val', help='Randomize order of validation files', required=False, action='store_true') + optional_args.add_argument('-no-export', help='Do not export models', required=False, action='store_true') + optional_args.add_argument('-no-repeat-files', help='Track what shuffled data was used and do not repeat, even when killed and resumed', required=False, action='store_true') + optional_args.add_argument('-quit-if-no-data', help='If no data, quit instead of waiting for data', required=False, action='store_true') + + optional_args.add_argument('-gnorm-stats-debug', required=False, action='store_true') + + optional_args.add_argument('-brenorm-avg-momentum', type=float, help='Set brenorm running avg rate to this value', required=False) + optional_args.add_argument('-brenorm-target-rmax', type=float, help='Gradually adjust brenorm rmax to this value', required=False) + optional_args.add_argument('-brenorm-target-dmax', type=float, help='Gradually adjust brenorm dmax to this value', required=False) + optional_args.add_argument('-brenorm-adjustment-scale', type=float, help='How many samples to adjust brenorm params all but 1/e of the way to target', required=False) + + optional_args.add_argument('-soft-policy-weight-scale', type=float, default=8.0, help='Soft policy loss coeff', required=False) + optional_args.add_argument('-disable-optimistic-policy', help='Disable optimistic policy', required=False, action='store_true') + optional_args.add_argument('-meta-kata-only-soft-policy', help='Mask soft policy on non-kata rows using sgfmeta', required=False, action='store_true') + optional_args.add_argument('-value-loss-scale', type=float, default=0.6, help='Additional value loss coeff', required=False) + optional_args.add_argument('-td-value-loss-scales', type=str, default="0.6,0.6,0.6", help='Additional td value loss coeffs, 3 comma separated values', required=False) + optional_args.add_argument('-seki-loss-scale', type=float, default=1.0, help='Additional seki loss coeff', required=False) + optional_args.add_argument('-variance-time-loss-scale', type=float, default=1.0, help='Additional variance time loss coeff', required=False) + + optional_args.add_argument('-main-loss-scale', type=float, help='Loss factor scale for main head', required=False) + optional_args.add_argument('-intermediate-loss-scale', type=float, help='Loss factor scale for intermediate head', required=False) + + args = vars(parser.parse_args()) + + +def get_longterm_checkpoints_dir(traindir): + return os.path.join(traindir,"longterm_checkpoints") + +def make_dirs(args): + traindir = args["traindir"] + exportdir = args["exportdir"] + + if not os.path.exists(traindir): + os.makedirs(traindir) + if exportdir is not None and not os.path.exists(exportdir): + os.makedirs(exportdir) + + longterm_checkpoints_dir = get_longterm_checkpoints_dir(traindir) + if not os.path.exists(longterm_checkpoints_dir): + os.makedirs(longterm_checkpoints_dir) + +def multiprocessing_setup(rank: int, world_size: int): + os.environ['MASTER_ADDR'] = 'localhost' + os.environ['MASTER_PORT'] = '23456' + logging.info("Running torch.distributed.init_process_group") + torch.distributed.init_process_group("nccl", rank=rank, world_size=world_size) + logging.info(f"Returned from torch.distributed.init_process_group, my rank = {rank}, world_size={world_size}") + +def multiprocessing_cleanup(): + torch.distributed.destroy_process_group() + + +def main(rank: int, world_size: int, args, multi_gpu_device_ids, readpipes, writepipes, barrier): + traindir = args["traindir"] + datadir = args["datadir"] + exportdir = args["exportdir"] + exportprefix = args["exportprefix"] + initial_checkpoint = args["initial_checkpoint"] + + pos_len = args["pos_len"] + batch_size = args["batch_size"] + samples_per_epoch = args["samples_per_epoch"] + model_kind = args["model_kind"] + lr_scale = args["lr_scale"] + lr_scale_auto = args["lr_scale_auto"] + gnorm_clip_scale = args["gnorm_clip_scale"] + sub_epochs = args["sub_epochs"] + swa_period_samples = args["swa_period_samples"] + swa_scale = args["swa_scale"] + lookahead_k = args["lookahead_k"] + lookahead_alpha = args["lookahead_alpha"] + lookahead_print = args["lookahead_print"] + + use_fp16 = args["use_fp16"] + + epochs_per_export = args["epochs_per_export"] + export_prob = args["export_prob"] + max_epochs_this_instance = args["max_epochs_this_instance"] + max_training_samples = args["max_training_samples"] + sleep_seconds_per_epoch = args["sleep_seconds_per_epoch"] + max_train_bucket_per_new_data = args["max_train_bucket_per_new_data"] + max_train_bucket_size = args["max_train_bucket_size"] + max_train_steps_since_last_reload = args["max_train_steps_since_last_reload"] + stop_when_train_bucket_limited = args["stop_when_train_bucket_limited"] + max_val_samples = args["max_val_samples"] + randomize_val = args["randomize_val"] + no_export = args["no_export"] + no_repeat_files = args["no_repeat_files"] + quit_if_no_data = args["quit_if_no_data"] + + gnorm_stats_debug = args["gnorm_stats_debug"] + + brenorm_target_rmax = args["brenorm_target_rmax"] + brenorm_target_dmax = args["brenorm_target_dmax"] + brenorm_avg_momentum = args["brenorm_avg_momentum"] + brenorm_adjustment_scale = args["brenorm_adjustment_scale"] + + soft_policy_weight_scale = args["soft_policy_weight_scale"] + disable_optimistic_policy = args["disable_optimistic_policy"] + meta_kata_only_soft_policy = args["meta_kata_only_soft_policy"] + value_loss_scale = args["value_loss_scale"] + td_value_loss_scales = [float(x) for x in args["td_value_loss_scales"].split(",")] + seki_loss_scale = args["seki_loss_scale"] + variance_time_loss_scale = args["variance_time_loss_scale"] + + main_loss_scale = args["main_loss_scale"] + intermediate_loss_scale = args["intermediate_loss_scale"] + + if lr_scale is None: + lr_scale = 1.0 + if lr_scale_auto: + assert lr_scale == 1.0, "Cannot specify both lr_scale and lr_scale_auto" + + if samples_per_epoch is None: + samples_per_epoch = 1000000 + if max_train_bucket_size is None: + max_train_bucket_size = 1.0e30 + if epochs_per_export is None: + epochs_per_export = 1 + if swa_period_samples is None: + swa_period_samples = max(1, samples_per_epoch // 2) + if swa_scale is None: + swa_scale = 8 + + assert lookahead_alpha > 0.0 and lookahead_alpha <= 1.0 + if lookahead_alpha >= 1.0: # 1.0 means to disable lookahead optimizer + lookahead_alpha = None + lookahead_k = None + + longterm_checkpoints_dir = get_longterm_checkpoints_dir(traindir) + + assert (swa_period_samples is None) == (swa_scale is None) + assert (lookahead_k is None) == (lookahead_alpha is None) + + # SET UP LOGGING ------------------------------------------------------------- + + logging.root.handlers = [] + if rank == 0: + logging.basicConfig( + level=logging.INFO, + format="%(message)s", + handlers=[ + logging.FileHandler(os.path.join(traindir,f"train{rank}.log"), mode="a"), + logging.StreamHandler() + ], + ) + else: + logging.basicConfig( + level=logging.INFO, + format="%(message)s", + handlers=[ + logging.FileHandler(os.path.join(traindir,f"train{rank}.log"), mode="a"), + ], + ) + np.set_printoptions(linewidth=150) + + logging.info(str(sys.argv)) + + # FIGURE OUT MULTIGPU ------------------------------------------------------------ + if world_size > 1: + multiprocessing_setup(rank, world_size) + atexit.register(multiprocessing_cleanup) + assert torch.cuda.is_available() + + if True or torch.cuda.is_available(): + my_gpu_id = multi_gpu_device_ids[rank] + torch.cuda.set_device(my_gpu_id) + logging.info("Using GPU device: " + torch.cuda.get_device_name()) + device = torch.device("cuda", my_gpu_id) + else: + logging.warning("WARNING: No GPU, using CPU") + device = torch.device("cpu") + + seed = int.from_bytes(os.urandom(7), sys.byteorder) + logging.info(f"Seeding torch with {seed}") + torch.manual_seed(seed) + + # LOAD MODEL --------------------------------------------------------------------- + + def lr_scale_auto_factor(train_state): + if not lr_scale_auto: + return 1.0 + + if train_state["global_step_samples"] < 200_000_000: + return 8.00 + if train_state["global_step_samples"] < 400_000_000: + return 4.00 + if train_state["global_step_samples"] < 500_000_000: + return 2.00 + if train_state["global_step_samples"] < 550_000_000: + return 1.00 + if train_state["global_step_samples"] < 600_000_000: + return 0.50 + if train_state["global_step_samples"] < 650_000_000: + return 0.25 + return 0.25 + + def get_checkpoint_path(): + return os.path.join(traindir,"checkpoint.ckpt") + def get_checkpoint_prev_path(i): + return os.path.join(traindir,f"checkpoint_prev{i}.ckpt") + + NUM_SHORTTERM_CHECKPOINTS_TO_KEEP = 4 + def save(ddp_model, swa_model, optimizer, metrics_obj, running_metrics, train_state, last_val_metrics, path=None): + if gnorm_stats_debug: + logging.warning("Skipping save since debugging gnorm stats") + return + if rank == 0: + state_dict = {} + state_dict["model"] = ddp_model.state_dict() + state_dict["optimizer"] = optimizer.state_dict() + state_dict["metrics"] = metrics_obj.state_dict() + state_dict["running_metrics"] = running_metrics + state_dict["train_state"] = train_state + state_dict["last_val_metrics"] = last_val_metrics + state_dict["config"] = model_config + + if swa_model is not None: + state_dict["swa_model"] = swa_model.state_dict() + + if path is not None: + logging.info("Saving checkpoint: " + path) + torch.save(state_dict, path + ".tmp") + time.sleep(1) + os.replace(path + ".tmp", path) + else: + logging.info("Saving checkpoint: " + get_checkpoint_path()) + for i in reversed(range(NUM_SHORTTERM_CHECKPOINTS_TO_KEEP-1)): + if os.path.exists(get_checkpoint_prev_path(i)): + os.replace(get_checkpoint_prev_path(i), get_checkpoint_prev_path(i+1)) + if os.path.exists(get_checkpoint_path()): + shutil.copy(get_checkpoint_path(), get_checkpoint_prev_path(0)) + torch.save(state_dict, get_checkpoint_path() + ".tmp") + os.replace(get_checkpoint_path() + ".tmp", get_checkpoint_path()) + + def get_weight_decay(raw_model, lr_scale, warmup_scale, train_state, running_metrics, group_name): + lr_scale_with_auto = lr_scale * lr_scale_auto_factor(train_state) + if raw_model.get_norm_kind() == "fixup" or raw_model.get_norm_kind() == "fixscale": + if group_name == "normal" or group_name == "normal_gamma" or group_name == "output": + return 0.000001 * world_size * batch_size / 256.0 + elif group_name == "noreg": + return 0.00000001 * world_size * batch_size / 256.0 + elif group_name == "output_noreg": + return 0.00000001 * world_size * batch_size / 256.0 + else: + assert False + elif ( + raw_model.get_norm_kind() == "bnorm" or + raw_model.get_norm_kind() == "brenorm" or + raw_model.get_norm_kind() == "fixbrenorm" or + raw_model.get_norm_kind() == "fixscaleonenorm" + ): + if group_name == "normal" or group_name == "normal_gamma": + adaptive_scale = 1.0 + if "sums" in running_metrics and "norm_normal_batch" in running_metrics["sums"]: + norm_normal_batch = running_metrics["sums"]["norm_normal_batch"] / running_metrics["weights"]["norm_normal_batch"] + baseline = train_state["modelnorm_normal_baseline"] + ratio = norm_normal_batch / (baseline + 1e-30) + # Adaptive weight decay keeping model norm around the baseline level so that batchnorm effective lr is held constant + # throughout training, covering a range of 16x from bottom to top. + adaptive_scale = math.pow(2.0, 2.0 * math.tanh(math.log(ratio+1e-30) * 1.5)) + + # Batch norm gammas can be regularized a bit less, doing them just as much empirically seemed to be a bit more unstable + gamma_scale = 0.125 if group_name == "normal_gamma" else 1.0 + + # The theoretical scaling for keeping us confined to a surface of equal model norm should go proportionally with lr_scale. + # because the strength of drift away from that surface goes as lr^2 and weight decay itself is scaled by lr, so we need + # one more factor of lr to make weight decay strength equal drift strength. + # However, at low lr it tends to be the case that gradient norm increases slightly + # while at high lr it tends to be the case that gradient norm decreases, which means drift strength scales a bit slower + # than expected. + # So we scale sublinearly with lr_scale so as to slightly preadjust to this effect. + # Adaptive scale should then help keep us there thereafter. + return 0.00125 * world_size * batch_size / 256.0 * math.pow(lr_scale_with_auto * warmup_scale,0.75) * adaptive_scale * gamma_scale + elif group_name == "output": + return 0.000001 * world_size * batch_size / 256.0 + elif group_name == "noreg": + return 0.000001 * world_size * batch_size / 256.0 * math.pow(lr_scale_with_auto * warmup_scale,0.75) + elif group_name == "output_noreg": + return 0.00000001 * world_size * batch_size / 256.0 + else: + assert False + else: + assert False + + def get_param_groups(raw_model,train_state,running_metrics): + reg_dict : Dict[str,List] = {} + raw_model.add_reg_dict(reg_dict) + param_groups = [] + param_groups.append({ + "params": reg_dict["normal"], + "weight_decay": get_weight_decay(raw_model, lr_scale, warmup_scale=1.0, train_state=train_state, running_metrics=running_metrics, group_name="normal"), + "group_name": "normal", + }) + if len(reg_dict["normal_gamma"]) > 0: + param_groups.append({ + "params": reg_dict["normal_gamma"], + "weight_decay": get_weight_decay(raw_model, lr_scale, warmup_scale=1.0, train_state=train_state, running_metrics=running_metrics, group_name="normal_gamma"), + "group_name": "normal_gamma", + }) + param_groups.append({ + "params": reg_dict["output"], + "weight_decay": get_weight_decay(raw_model, lr_scale, warmup_scale=1.0, train_state=train_state, running_metrics=running_metrics, group_name="output"), + "group_name": "output", + }) + param_groups.append({ + "params": reg_dict["noreg"], + "weight_decay": get_weight_decay(raw_model, lr_scale, warmup_scale=1.0, train_state=train_state, running_metrics=running_metrics, group_name="noreg"), + "group_name": "noreg", + }) + param_groups.append({ + "params": reg_dict["output_noreg"], + "weight_decay": get_weight_decay(raw_model, lr_scale, warmup_scale=1.0, train_state=train_state, running_metrics=running_metrics, group_name="output_noreg"), + "group_name": "output_noreg", + }) + num_params = len(list(raw_model.parameters())) + num_reg_dict_params = len(reg_dict["normal"]) + len(reg_dict["normal_gamma"]) + len(reg_dict["output"]) + len(reg_dict["noreg"]) + len(reg_dict["output_noreg"]) + assert num_params == num_reg_dict_params, "Reg dict does not have entries for all params in model" + return param_groups + + def load(): + if not os.path.exists(get_checkpoint_path()): + logging.info("No preexisting checkpoint found at: " + get_checkpoint_path()) + for i in range(NUM_SHORTTERM_CHECKPOINTS_TO_KEEP): + if os.path.exists(get_checkpoint_prev_path(i)): + raise Exception(f"No preexisting checkpoint found, but {get_checkpoint_prev_path(i)} exists, something is wrong with the training dir") + + if initial_checkpoint is not None: + if os.path.exists(initial_checkpoint): + logging.info("Using initial checkpoint: {initial_checkpoint}") + path_to_load_from = initial_checkpoint + else: + raise Exception("No preexisting checkpoint found, initial checkpoint provided is invalid: {initial_checkpoint}") + else: + path_to_load_from = None + else: + path_to_load_from = get_checkpoint_path() + + if path_to_load_from is None: + logging.info("Initializing new model!") + assert model_kind is not None, "Model kind is none or unspecified but the model is being created fresh" + model_config = modelconfigs.config_of_name[model_kind] + logging.info(str(model_config)) + raw_model = Model(model_config,pos_len) + raw_model.initialize() + + raw_model.to(device) + if world_size > 1: + ddp_model = torch.nn.parallel.DistributedDataParallel(raw_model, device_ids=[device]) + else: + ddp_model = raw_model + + swa_model = None + if rank == 0 and swa_scale is not None: + new_factor = 1.0 / swa_scale + ema_avg = lambda avg_param, cur_param, num_averaged: avg_param + new_factor * (cur_param - avg_param) + swa_model = AveragedModel(raw_model, avg_fn=ema_avg) + + metrics_obj = Metrics(batch_size,world_size,raw_model) + running_metrics = {} + train_state = {} + last_val_metrics = {} + + train_state["global_step_samples"] = 0 + + with torch.no_grad(): + (modelnorm_normal, modelnorm_normal_gamma, modelnorm_output, modelnorm_noreg, modelnorm_output_noreg) = Metrics.get_model_norms(raw_model) + modelnorm_normal_baseline = modelnorm_normal.detach().cpu().item() + train_state["modelnorm_normal_baseline"] = modelnorm_normal_baseline + logging.info(f"Model norm normal baseline computed: {modelnorm_normal_baseline}") + + optimizer = torch.optim.SGD(get_param_groups(raw_model,train_state,running_metrics), lr=1.0, momentum=0.9) + + return (model_config, ddp_model, raw_model, swa_model, optimizer, metrics_obj, running_metrics, train_state, last_val_metrics) + else: + state_dict = torch.load(path_to_load_from, map_location=device) + model_config = state_dict["config"] if "config" in state_dict else modelconfigs.config_of_name[model_kind] + logging.info(str(model_config)) + raw_model = Model(model_config,pos_len) + raw_model.initialize() + + train_state = {} + if "train_state" in state_dict: + train_state = state_dict["train_state"] + else: + logging.info("WARNING: Train state not found in state dict, using fresh train state") + + # Do this before loading the state dict, while the model is initialized to fresh values, to get a good baseline + if "modelnorm_normal_baseline" not in train_state: + logging.info("Computing modelnorm_normal_baseline since not in train state") + with torch.no_grad(): + (modelnorm_normal, modelnorm_normal_gamma, modelnorm_output, modelnorm_noreg, modelnorm_output_noreg) = Metrics.get_model_norms(raw_model) + modelnorm_normal_baseline = modelnorm_normal.detach().cpu().item() + train_state["modelnorm_normal_baseline"] = modelnorm_normal_baseline + logging.info(f"Model norm normal baseline computed: {modelnorm_normal_baseline}") + + # Strip off any "module." from when the model was saved with DDP or other things + model_state_dict = load_model.load_model_state_dict(state_dict) + raw_model.load_state_dict(model_state_dict) + + raw_model.to(device) + if world_size > 1: + ddp_model = torch.nn.parallel.DistributedDataParallel(raw_model, device_ids=[device]) + else: + ddp_model = raw_model + + swa_model = None + if rank == 0 and swa_scale is not None: + new_factor = 1.0 / swa_scale + ema_avg = lambda avg_param, cur_param, num_averaged: avg_param + new_factor * (cur_param - avg_param) + swa_model = AveragedModel(raw_model, avg_fn=ema_avg) + swa_model_state_dict = load_model.load_swa_model_state_dict(state_dict) + if swa_model_state_dict is not None: + swa_model.load_state_dict(swa_model_state_dict) + + metrics_obj = Metrics(batch_size,world_size,raw_model) + if "metrics" in state_dict: + metrics_obj.load_state_dict(state_dict["metrics"]) + else: + logging.info("WARNING: Metrics not found in state dict, using fresh metrics") + + running_metrics = {} + if "running_metrics" in state_dict: + running_metrics = state_dict["running_metrics"] + else: + logging.info("WARNING: Running metrics not found in state dict, using fresh running metrics") + + last_val_metrics = {} + if "last_val_metrics" in state_dict: + last_val_metrics = state_dict["last_val_metrics"] + else: + logging.info("WARNING: Running metrics not found in state dict, using fresh last val metrics") + + optimizer = torch.optim.SGD(get_param_groups(raw_model,train_state,running_metrics), lr=1.0, momentum=0.9) + if "optimizer" in state_dict: + optimizer.load_state_dict(state_dict["optimizer"]) + else: + logging.info("WARNING: Optimizer not found in state dict, using fresh optimizer") + + return (model_config, ddp_model, raw_model, swa_model, optimizer, metrics_obj, running_metrics, train_state, last_val_metrics) + + (model_config, ddp_model, raw_model, swa_model, optimizer, metrics_obj, running_metrics, train_state, last_val_metrics) = load() + + + if "global_step_samples" not in train_state: + train_state["global_step_samples"] = 0 + if max_train_bucket_per_new_data is not None and "train_bucket_level" not in train_state: + train_state["train_bucket_level"] = samples_per_epoch + if "train_steps_since_last_reload" not in train_state: + train_state["train_steps_since_last_reload"] = 0 + if "export_cycle_counter" not in train_state: + train_state["export_cycle_counter"] = 0 + if "total_num_data_rows" not in train_state: + train_state["total_num_data_rows"] = 0 + if "old_train_data_dirs" not in train_state: + train_state["old_train_data_dirs"] = [] + if "data_files_used" not in train_state: + train_state["data_files_used"] = set() + if "swa_sample_accum" not in train_state: + train_state["swa_sample_accum"] = 0.0 + + + if intermediate_loss_scale is not None: + assert raw_model.get_has_intermediate_head(), "Model must have intermediate head to use intermediate loss" + + # If the user specified an intermediate head but no loss scale, pick something reasonable by default + if raw_model.get_has_intermediate_head(): + if intermediate_loss_scale is None and main_loss_scale is None: + if model_config["trunk_normless"]: + # fson-bnh default + assert model_config["intermediate_head_blocks"] == len(model_config["block_kind"]), "If these are unequal, don't know what you intend, please specify intermediate_loss_scale" + intermediate_loss_scale = 0.8 + main_loss_scale = 0.2 + else: + # Intermediate head in the middle of the trunk + intermediate_loss_scale = 0.5 + main_loss_scale = 0.5 + elif intermediate_loss_scale is None: + assert False, "Please specify both of main_loss_scale and intermediate_loss_scale or neither when using an architecture with an intermediate head." + + logging.info(f"swa_period_samples {swa_period_samples}") + logging.info(f"swa_scale {swa_scale}") + logging.info(f"lookahead_alpha {lookahead_alpha}") + logging.info(f"lookahead_k {lookahead_k}") + logging.info(f"soft_policy_weight_scale {soft_policy_weight_scale}") + logging.info(f"disable_optimistic_policy {disable_optimistic_policy}") + logging.info(f"meta_kata_only_soft_policy {meta_kata_only_soft_policy}") + logging.info(f"value_loss_scale {value_loss_scale}") + logging.info(f"td_value_loss_scales {td_value_loss_scales}") + logging.info(f"seki_loss_scale {seki_loss_scale}") + logging.info(f"variance_time_loss_scale {variance_time_loss_scale}") + logging.info(f"main_loss_scale {main_loss_scale}") + logging.info(f"intermediate_loss_scale {intermediate_loss_scale}") + + # Print all model parameters just to get a summary + total_num_params = 0 + total_trainable_params = 0 + logging.info("Parameters in model:") + for name, param in raw_model.named_parameters(): + product = 1 + for dim in param.shape: + product *= int(dim) + if param.requires_grad: + total_trainable_params += product + total_num_params += product + logging.info(f"{name}, {list(param.shape)}, {product} params") + logging.info(f"Total num params: {total_num_params}") + logging.info(f"Total trainable params: {total_trainable_params}") + + lookahead_cache = {} + if lookahead_k is not None: + for param_group in optimizer.param_groups: + for param in param_group["params"]: + lookahead_cache[param] = torch.zeros_like(param.data) + lookahead_cache[param] = lookahead_cache[param].copy_(param.data) + logging.info(f"Using lookahead optimizer {lookahead_alpha} {lookahead_k}") + + # EPOCHS AND LR --------------------------------------------------------------------- + + def update_and_return_lr_and_wd(): + per_sample_lr = 0.00003 * lr_scale * lr_scale_auto_factor(train_state) + + # Warmup for initial training + warmup_scale = 1.0 + if model_config["norm_kind"] == "fixup" or model_config["norm_kind"] == "fixscale" or model_config["norm_kind"] == "fixscaleonenorm": + if train_state["global_step_samples"] < 1000000: + warmup_scale = 1.0 / 5.0 + elif train_state["global_step_samples"] < 2000000: + warmup_scale = 1.0 / 3.0 + elif train_state["global_step_samples"] < 4000000: + warmup_scale = 1.0 / 2.0 + elif train_state["global_step_samples"] < 6000000: + warmup_scale = 1.0 / 1.4 + elif model_config["norm_kind"] == "bnorm" or model_config["norm_kind"] == "brenorm" or model_config["norm_kind"] == "fixbrenorm": + if train_state["global_step_samples"] < 250000: + warmup_scale = 1.0 / 20.0 + elif train_state["global_step_samples"] < 500000: + warmup_scale = 1.0 / 14.0 + elif train_state["global_step_samples"] < 750000: + warmup_scale = 1.0 / 10.0 + elif train_state["global_step_samples"] < 1000000: + warmup_scale = 1.0 / 7.0 + elif train_state["global_step_samples"] < 1250000: + warmup_scale = 1.0 / 5.0 + elif train_state["global_step_samples"] < 1500000: + warmup_scale = 1.0 / 3.0 + elif train_state["global_step_samples"] < 1750000: + warmup_scale = 1.0 / 2.0 + elif train_state["global_step_samples"] < 2000000: + warmup_scale = 1.0 / 1.4 + else: + warmup_scale = 1.0 / 1.0 + else: + assert False + + normal_weight_decay = 0.0 + + for param_group in optimizer.param_groups: + group_name = param_group["group_name"] + if group_name == "normal": + group_scale = 1.0 + elif group_name == "normal_gamma": + group_scale = 1.0 + elif group_name == "output": + group_scale = 0.5 + elif group_name == "noreg": + group_scale = 1.0 + elif group_name == "output_noreg": + group_scale = 0.5 + else: + assert False + + changed = False + + # For lookahead optimizer, use weight decay appropriate for lr scale, + # but tell optimizer to take larger steps so as to maintain the same + # effective learning rate after lookahead averaging. + if lookahead_alpha is not None: + new_lr_this_group = per_sample_lr * warmup_scale * group_scale / lookahead_alpha + else: + new_lr_this_group = per_sample_lr * warmup_scale * group_scale + + if param_group["lr"] != new_lr_this_group: + param_group["lr"] = new_lr_this_group + changed = True + + new_weight_decay_this_group = get_weight_decay( + raw_model, + lr_scale, + warmup_scale=warmup_scale, + train_state=train_state, + running_metrics=running_metrics, + group_name=group_name, + ) + if param_group["weight_decay"] != new_weight_decay_this_group: + param_group["weight_decay"] = new_weight_decay_this_group + changed = True + + if group_name == "normal": + normal_weight_decay = param_group["weight_decay"] + + if changed: + logging.info(f"Param group {param_group['group_name']} lr {param_group['lr']} weight_decay {param_group['weight_decay']}") + + return per_sample_lr * warmup_scale, normal_weight_decay + + last_brenorm_update_samples_this_instance = train_state["global_step_samples"] + def maybe_update_brenorm_params(): + nonlocal last_brenorm_update_samples_this_instance + + if model_config["norm_kind"] == "brenorm" or model_config["norm_kind"] == "fixbrenorm": + if "brenorm_rmax" not in train_state: + train_state["brenorm_rmax"] = 1.0 + if "brenorm_dmax" not in train_state: + train_state["brenorm_dmax"] = 0.0 + + num_samples_elapsed = train_state["global_step_samples"] - last_brenorm_update_samples_this_instance + factor = math.exp(-num_samples_elapsed / brenorm_adjustment_scale) + train_state["brenorm_rmax"] = train_state["brenorm_rmax"] + (1.0 - factor) * (brenorm_target_rmax - train_state["brenorm_rmax"]) + train_state["brenorm_dmax"] = train_state["brenorm_dmax"] + (1.0 - factor) * (brenorm_target_dmax - train_state["brenorm_dmax"]) + + raw_model.set_brenorm_params(brenorm_avg_momentum, train_state["brenorm_rmax"], train_state["brenorm_dmax"]) + last_brenorm_update_samples_this_instance = train_state["global_step_samples"] + + # DATA RELOADING GENERATOR ------------------------------------------------------------ + + # Some globals + last_curdatadir = None + trainfilegenerator = None + vdatadir = None + + def maybe_reload_training_data(): + nonlocal last_curdatadir + nonlocal trainfilegenerator + nonlocal vdatadir + + assert rank == 0, "Helper ddp training processes should not call maybe_reload_training_data" + + while True: + curdatadir = os.path.realpath(datadir) + + # Different directory - new shuffle + if curdatadir != last_curdatadir: + if not os.path.exists(curdatadir): + if quit_if_no_data: + logging.info("Shuffled data path does not exist, there seems to be no data or not enough data yet, qutting: %s" % curdatadir) + sys.exit(0) + logging.info("Shuffled data path does not exist, there seems to be no shuffled data yet, waiting and trying again later: %s" % curdatadir) + time.sleep(30) + continue + + trainjsonpath = os.path.join(curdatadir,"train.json") + if not os.path.exists(trainjsonpath): + if quit_if_no_data: + logging.info("Shuffled data train.json file does not exist, there seems to be no data or not enough data yet, qutting: %s" % trainjsonpath) + sys.exit(0) + logging.info("Shuffled data train.json file does not exist, there seems to be no shuffled data yet, waiting and trying again later: %s" % trainjsonpath) + time.sleep(30) + continue + + logging.info("Updated training data: " + curdatadir) + last_curdatadir = curdatadir + + with open(trainjsonpath) as f: + datainfo = json.load(f) + train_state["total_num_data_rows"] = datainfo["range"][1] + + # Fill the buckets + if max_train_bucket_per_new_data is not None: + if "train_bucket_level_at_row" not in train_state: + train_state["train_bucket_level_at_row"] = train_state["total_num_data_rows"] + if train_state["total_num_data_rows"] > train_state["train_bucket_level_at_row"]: + new_row_count = train_state["total_num_data_rows"] - train_state["train_bucket_level_at_row"] + logging.info("Advancing trainbucket row %.0f to %.0f, %.0f new rows" % ( + train_state["train_bucket_level_at_row"], train_state["total_num_data_rows"], new_row_count + )) + train_state["train_bucket_level_at_row"] = train_state["total_num_data_rows"] + logging.info("Fill per data %.3f, Max bucket size %.0f" % (max_train_bucket_per_new_data, max_train_bucket_size)) + logging.info("Old rows in bucket: %.0f" % train_state["train_bucket_level"]) + train_state["train_bucket_level"] += new_row_count * max_train_bucket_per_new_data + cap = max(max_train_bucket_size, samples_per_epoch) + if train_state["train_bucket_level"] > cap: + train_state["train_bucket_level"] = cap + logging.info("New rows in bucket: %.0f" % train_state["train_bucket_level"]) + if train_state["total_num_data_rows"] < train_state["train_bucket_level_at_row"]: + # Bucket went backward! This must be a network imported from a different run, reset the train bucket level + logging.warning("Train bucket last filled at %d rows but now there are only %d rows!" % ( + train_state["train_bucket_level_at_row"], train_state["total_num_data_rows"] + )) + logging.warning("Data was deleted or this network was transplanted into a new run, resetting the train bucket fill rows") + train_state["train_bucket_level_at_row"] = train_state["total_num_data_rows"] + + logging.info("Train steps since last reload: %.0f -> 0" % train_state["train_steps_since_last_reload"]) + train_state["train_steps_since_last_reload"] = 0 + + # Load training data files + tdatadir = os.path.join(curdatadir,"train") + train_files = [os.path.join(tdatadir,fname) for fname in os.listdir(tdatadir) if fname.endswith(".npz")] + epoch0_train_files = [path for path in train_files if path not in train_state["data_files_used"]] + if no_repeat_files: + logging.info(f"Dropping {len(train_files)-len(epoch0_train_files)}/{len(train_files)} files in: {tdatadir} as already used") + else: + logging.info(f"Skipping {len(train_files)-len(epoch0_train_files)}/{len(train_files)} files in: {tdatadir} as already used first pass") + + if len(train_files) <= 0 or (no_repeat_files and len(epoch0_train_files) <= 0): + if quit_if_no_data: + logging.info(f"No new training files found in: {tdatadir}, quitting") + sys.exit(0) + logging.info(f"No new training files found in: {tdatadir}, waiting 30s and trying again") + time.sleep(30) + continue + + # Update history of what training data we used + if tdatadir not in train_state["old_train_data_dirs"]: + train_state["old_train_data_dirs"].append(tdatadir) + # Clear out tracking of sufficiently old files + while len(train_state["old_train_data_dirs"]) > 20: + old_dir = train_state["old_train_data_dirs"][0] + train_state["old_train_data_dirs"] = train_state["old_train_data_dirs"][1:] + for filename in list(train_state["data_files_used"]): + if filename.startswith(old_dir): + train_state["data_files_used"].remove(filename) + + def train_files_gen(): + train_files_shuffled = epoch0_train_files.copy() + while True: + random.shuffle(train_files_shuffled) + for filename in train_files_shuffled: + logging.info("Yielding training file for dataset: " + filename) + train_state["data_files_used"].add(filename) + yield filename + if no_repeat_files: + break + else: + train_files_shuffled = train_files.copy() + train_state["data_files_used"] = set() + + trainfilegenerator = train_files_gen() + vdatadir = os.path.join(curdatadir,"val") + + # Same directory as before, no new shuffle + else: + if max_train_steps_since_last_reload is not None: + if train_state["train_steps_since_last_reload"] + 0.99 * samples_per_epoch/sub_epochs > max_train_steps_since_last_reload: + logging.info( + "Too many train steps since last reload, waiting 5m and retrying (current %f)" % + train_state["train_steps_since_last_reload"] + ) + time.sleep(300) + continue + + break + + # Load all the files we should train on during a subepoch + def get_files_for_subepoch(): + nonlocal trainfilegenerator + + assert rank == 0, "Helper ddp training processes should not call get_files_for_subepoch" + + num_batches_per_epoch = int(round(samples_per_epoch / batch_size)) + num_batches_per_subepoch = num_batches_per_epoch / sub_epochs + + # Pick enough files to get the number of batches we want + train_files_to_use = [] + batches_to_use_so_far = 0 + found_enough = False + for filename in trainfilegenerator: + jsonfilename = os.path.splitext(filename)[0] + ".json" + with open(jsonfilename) as f: + trainfileinfo = json.load(f) + + num_batches_this_file = trainfileinfo["num_rows"] // batch_size + if num_batches_this_file <= 0: + continue + + if batches_to_use_so_far + num_batches_this_file > num_batches_per_subepoch: + # If we're going over the desired amount, randomly skip the file with probability equal to the + # proportion of batches over - this makes it so that in expectation, we have the desired number of batches + if batches_to_use_so_far > 0 and random.random() >= (batches_to_use_so_far + num_batches_this_file - num_batches_per_subepoch) / num_batches_this_file: + found_enough = True + break + + train_files_to_use.append(filename) + batches_to_use_so_far += num_batches_this_file + + #Sanity check - load a max of 100000 files. + if batches_to_use_so_far >= num_batches_per_subepoch or len(train_files_to_use) > 100000: + found_enough = True + break + + if found_enough: + return train_files_to_use + return None + + # METRICS ----------------------------------------------------------------------------------- + def detensorify_metrics(metrics): + ret = {} + for key in metrics: + if isinstance(metrics[key], torch.Tensor): + ret[key] = metrics[key].detach().cpu().item() + else: + ret[key] = metrics[key] + return ret + + if rank == 0: + train_metrics_out = open(os.path.join(traindir,"metrics_train.json"),"a") + val_metrics_out = open(os.path.join(traindir,"metrics_val.json"),"a") + else: + train_metrics_out = open(os.path.join(traindir,f"metrics_train_rank{rank}.json"),"a") + val_metrics_out = open(os.path.join(traindir,f"metrics_val_rank{rank}.json"),"a") + + # TRAIN! ----------------------------------------------------------------------------------- + + last_longterm_checkpoint_save_time = datetime.datetime.now() + num_epochs_this_instance = 0 + print_train_loss_every_batches = 100 if not gnorm_stats_debug else 1000 + + if "sums" not in running_metrics: + running_metrics["sums"] = defaultdict(float) + else: + running_metrics["sums"] = defaultdict(float,running_metrics["sums"]) + if "weights" not in running_metrics: + running_metrics["weights"] = defaultdict(float) + else: + running_metrics["weights"] = defaultdict(float,running_metrics["weights"]) + + torch.backends.cudnn.benchmark = True + + if use_fp16: + logging.info("Training in FP16! Creating scaler") + scaler = GradScaler() + else: + logging.info("Training in FP32.") + + # All ddp threads should be lined up at this point before continuing + if barrier is not None: + barrier.wait() + + while True: + if max_epochs_this_instance is not None and max_epochs_this_instance >= 0 and num_epochs_this_instance >= max_epochs_this_instance: + logging.info("Hit max epochs this instance, done") + break + if max_training_samples is not None and train_state["global_step_samples"] >= max_training_samples: + logging.info("Hit max training samples, done") + break + + if rank == 0: + maybe_reload_training_data() + + if max_train_bucket_per_new_data is not None: + if train_state["train_bucket_level"] > 0.99 * samples_per_epoch: + logging.info("Consuming %.0f rows from train bucket (%.0f -> %.0f)" % ( + samples_per_epoch, train_state["train_bucket_level"], train_state["train_bucket_level"]-samples_per_epoch + )) + train_state["train_bucket_level"] -= samples_per_epoch + else: + if stop_when_train_bucket_limited: + logging.info( + "Exceeding train bucket, not enough new data rows, terminating (current level %f)" % + train_state["train_bucket_level"] + ) + break + else: + logging.info( + "Exceeding train bucket, not enough new data rows, waiting 5m and retrying (current level %f)" % + train_state["train_bucket_level"] + ) + time.sleep(300) + continue + + # DDP need to wait on the main process after reloading data and/or training bucket waiting + if barrier is not None: + barrier.wait() + + logging.info("GC collect") + gc.collect() + + clear_metric_nonfinite(running_metrics["sums"], running_metrics["weights"]) + + logging.info("=========================================================================") + logging.info("BEGINNING NEXT EPOCH " + str(num_epochs_this_instance)) + logging.info("=========================================================================") + logging.info("Current time: " + str(datetime.datetime.now())) + logging.info("Global step: %d samples" % (train_state["global_step_samples"])) + logging.info("Currently up to data row " + str(train_state["total_num_data_rows"])) + logging.info(f"Training dir: {traindir}") + logging.info(f"Export dir: {exportdir}") + if use_fp16: + logging.info(f"Current grad scale: {scaler.get_scale()}") + + lr_right_now, normal_weight_decay_right_now = update_and_return_lr_and_wd() + maybe_update_brenorm_params() + + # SUB EPOCH LOOP ----------- + batch_count_this_epoch = 0 + last_train_stats_time = time.perf_counter() + for i in range(sub_epochs): + + if rank == 0: + if i != 0: + maybe_reload_training_data() + train_files_to_use = get_files_for_subepoch() + while train_files_to_use is None or len(train_files_to_use) <= 0: + if quit_if_no_data: + logging.info("Not enough data files to fill a subepoch! Quitting.") + sys.exit(0) + logging.info("Not enough data files to fill a subepoch! Waiting 5m before retrying.") + time.sleep(300) + maybe_reload_training_data() + train_files_to_use = get_files_for_subepoch() + + if barrier is not None: + barrier.wait() + for wpipe in writepipes: + wpipe.send(train_files_to_use) + # Wait briefly just in case to reduce chance of races with filesystem or anything else + time.sleep(5) + else: + if barrier is not None: + barrier.wait() + train_files_to_use = readpipes[rank-1].recv() + + # DDP need to wait on the main process after reloading data and sending files to train with + if barrier is not None: + barrier.wait() + + logging.info("Beginning training subepoch!") + logging.info("This subepoch, using files: " + str(train_files_to_use)) + logging.info("Currently up to data row " + str(train_state["total_num_data_rows"])) + lookahead_counter = 0 + for batch in data_processing_pytorch.read_npz_training_data( + train_files_to_use, + batch_size, + world_size, + rank, + pos_len=pos_len, + device=device, + randomize_symmetries=True, + include_meta=raw_model.get_has_metadata_encoder(), + model_config=model_config + ): + optimizer.zero_grad(set_to_none=True) + extra_outputs = None + # if raw_model.get_has_metadata_encoder(): + # extra_outputs = ExtraOutputs([MetadataEncoder.OUTMEAN_KEY,MetadataEncoder.OUTLOGVAR_KEY]) + + if use_fp16: + with autocast(): + model_outputs = ddp_model( + batch["binaryInputNCHW"], + batch["globalInputNC"], + input_meta=(batch["metadataInputNC"] if raw_model.get_has_metadata_encoder() else None), + extra_outputs=extra_outputs, + ) + model_outputs = raw_model.float32ify_output(model_outputs) + else: + model_outputs = ddp_model( + batch["binaryInputNCHW"], + batch["globalInputNC"], + input_meta=(batch["metadataInputNC"] if raw_model.get_has_metadata_encoder() else None), + extra_outputs=extra_outputs, + ) + + postprocessed = raw_model.postprocess_output(model_outputs) + metrics = metrics_obj.metrics_dict_batchwise( + raw_model, + postprocessed, + extra_outputs, + batch, + is_training=True, + soft_policy_weight_scale=soft_policy_weight_scale, + disable_optimistic_policy=disable_optimistic_policy, + meta_kata_only_soft_policy=meta_kata_only_soft_policy, + value_loss_scale=value_loss_scale, + td_value_loss_scales=td_value_loss_scales, + seki_loss_scale=seki_loss_scale, + variance_time_loss_scale=variance_time_loss_scale, + main_loss_scale=main_loss_scale, + intermediate_loss_scale=intermediate_loss_scale, + ) + + # DDP averages loss across instances, so to preserve LR as per-sample lr, we scale by world size. + loss = metrics["loss_sum"] * world_size + + # Reduce gradients across DDP + if use_fp16: + scaler.scale(loss).backward() + scaler.unscale_(optimizer) + else: + loss.backward() + + if model_config["norm_kind"] == "fixup" or model_config["norm_kind"] == "fixscale" or model_config["norm_kind"] == "fixscaleonenorm": + gnorm_cap = 2500.0 * (1.0 if gnorm_clip_scale is None else gnorm_clip_scale) + elif model_config["norm_kind"] == "bnorm" or model_config["norm_kind"] == "brenorm" or model_config["norm_kind"] == "fixbrenorm": + gnorm_cap = 5500.0 * (1.0 if gnorm_clip_scale is None else gnorm_clip_scale) + else: + assert False + + if gnorm_stats_debug: + stats = metrics_obj.get_specific_norms_and_gradient_stats(raw_model) + for stat, value in stats.items(): + metrics[stat] = value + + if "use_repvgg_learning_rate" in model_config and model_config["use_repvgg_learning_rate"]: + gradscale_constant = torch.tensor([[1.0,1.0,1.0],[1.0,2.0,1.0],[1.0,1.0,1.0]],dtype=torch.float32,device=device,requires_grad=False).view(1,1,3,3) + for name, param in ddp_model.named_parameters(): + if "normactconv" in name and ".conv.weight" in name and len(param.shape) == 4 and param.shape[2] == 3 and param.shape[3] == 3: + param.grad *= gradscale_constant + + # Loosen gradient clipping as we shift to smaller learning rates + gnorm_cap = gnorm_cap / math.sqrt(max(0.0000001,lr_scale * lr_scale_auto_factor(train_state))) + + gnorm = torch.nn.utils.clip_grad_norm_(ddp_model.parameters(), gnorm_cap).detach().cpu().item() + + if math.isfinite(gnorm) and abs(gnorm < 1e30): + metrics["gnorm_batch"] = gnorm + exgnorm = max(0.0, gnorm - gnorm_cap) + metrics["exgnorm_sum"] = exgnorm * batch_size + + metrics["pslr_batch"] = lr_right_now + metrics["wdnormal_batch"] = normal_weight_decay_right_now + metrics["gnorm_cap_batch"] = gnorm_cap + + if use_fp16: + scaler.step(optimizer) + scaler.update() + else: + optimizer.step() + + batch_count_this_epoch += 1 + train_state["train_steps_since_last_reload"] += batch_size * world_size + train_state["global_step_samples"] += batch_size * world_size + + metrics = detensorify_metrics(metrics) + + if lookahead_k is not None and lookahead_print: + # Only accumulate metrics when lookahead is synced if lookahead_print is True + if lookahead_counter == 0: + accumulate_metrics(running_metrics["sums"], running_metrics["weights"], metrics, batch_size, decay=math.exp(-0.001 * lookahead_k), new_weight=1.0) + else: + accumulate_metrics(running_metrics["sums"], running_metrics["weights"], metrics, batch_size, decay=1.0, new_weight=0.0) + else: + accumulate_metrics(running_metrics["sums"], running_metrics["weights"], metrics, batch_size, decay=0.999, new_weight=1.0) + + + if batch_count_this_epoch % print_train_loss_every_batches == 0: + + if model_config["norm_kind"] == "brenorm" or model_config["norm_kind"] == "fixbrenorm": + metrics["brn_rmax"] = train_state["brenorm_rmax"] + metrics["brn_dmax"] = train_state["brenorm_dmax"] + metrics["brn_mmnt"] = brenorm_avg_momentum + upper_rclippage = [] + lower_rclippage = [] + dclippage = [] + raw_model.add_brenorm_clippage(upper_rclippage, lower_rclippage, dclippage) + metrics["brn_ruclip"] = sum(upper_rclippage) / max(len(upper_rclippage),1.0) + metrics["brn_rlclip"] = sum(lower_rclippage) / max(len(lower_rclippage),1.0) + metrics["brn_dclip"] = sum(dclippage) / max(len(dclippage),1.0) + + t1 = time.perf_counter() + timediff = t1 - last_train_stats_time + last_train_stats_time = t1 + metrics["time_since_last_print"] = timediff + log_metrics(running_metrics["sums"], running_metrics["weights"], metrics, train_metrics_out) + + # Update LR more frequently at the start for smoother warmup ramp and wd adjustment + if train_state["global_step_samples"] <= 50000000 and batch_count_this_epoch % 50 == 0: + lr_right_now, normal_weight_decay_right_now = update_and_return_lr_and_wd() + + # Update batch renorm parameters + if batch_count_this_epoch % 500 == 0: + maybe_update_brenorm_params() + + # Perform lookahead + in_between_lookaheads = False + if lookahead_k is not None: + lookahead_counter += 1 + if lookahead_counter >= lookahead_k: + for param_group in optimizer.param_groups: + for param in param_group["params"]: + slow_param_data = lookahead_cache[param] + slow_param_data.add_(param.data.detach() - slow_param_data, alpha=lookahead_alpha) + param.data.copy_(slow_param_data) + lookahead_counter = 0 + in_between_lookaheads = False + else: + in_between_lookaheads = True + + # Perform SWA + if swa_model is not None and swa_scale is not None: + train_state["swa_sample_accum"] += batch_size * world_size + # Only snap SWA when lookahead slow params are in sync. + if train_state["swa_sample_accum"] >= swa_period_samples and not in_between_lookaheads: + train_state["swa_sample_accum"] = 0 + logging.info("Accumulating SWA") + swa_model.update_parameters(raw_model) + + logging.info("Finished training subepoch!") + + # END SUB EPOCH LOOP ------------ + + # Discard the gradient updates from the leftover batches in the sub epoch from lookahead. + # This wastes a very tiny bit, but makes it so that we can be in sync and deterministic on ends of subepochs/epochs. + if lookahead_k is not None: + for param_group in optimizer.param_groups: + for param in param_group["params"]: + slow_param_data = lookahead_cache[param] + param.data.copy_(slow_param_data) + + if rank == 0: + train_state["export_cycle_counter"] += 1 + + save(ddp_model, swa_model, optimizer, metrics_obj, running_metrics, train_state, last_val_metrics) + + num_epochs_this_instance += 1 + + # Validate + if rank == 0: + logging.info("Beginning validation after epoch!") + val_files = [] + if os.path.exists(vdatadir): + val_files = [os.path.join(vdatadir,fname) for fname in os.listdir(vdatadir) if fname.endswith(".npz")] + if randomize_val: + random.shuffle(val_files) + else: + # Sort to ensure deterministic order to validation files in case we use only a subset + val_files = sorted(val_files) + if len(val_files) == 0: + logging.info("No validation files, skipping validation step") + else: + with torch.no_grad(): + ddp_model.eval() + val_metric_sums = defaultdict(float) + val_metric_weights = defaultdict(float) + val_samples = 0 + t0 = time.perf_counter() + for batch in data_processing_pytorch.read_npz_training_data( + val_files, + batch_size, + world_size=1, # Only the main process validates + rank=0, # Only the main process validates + pos_len=pos_len, + device=device, + randomize_symmetries=True, + include_meta=raw_model.get_has_metadata_encoder(), + model_config=model_config + ): + model_outputs = ddp_model( + batch["binaryInputNCHW"], + batch["globalInputNC"], + input_meta=(batch["metadataInputNC"] if raw_model.get_has_metadata_encoder() else None), + ) + postprocessed = raw_model.postprocess_output(model_outputs) + extra_outputs = None + metrics = metrics_obj.metrics_dict_batchwise( + raw_model, + postprocessed, + extra_outputs, + batch, + is_training=False, + soft_policy_weight_scale=soft_policy_weight_scale, + disable_optimistic_policy=disable_optimistic_policy, + meta_kata_only_soft_policy=meta_kata_only_soft_policy, + value_loss_scale=value_loss_scale, + td_value_loss_scales=td_value_loss_scales, + seki_loss_scale=seki_loss_scale, + variance_time_loss_scale=variance_time_loss_scale, + main_loss_scale=main_loss_scale, + intermediate_loss_scale=intermediate_loss_scale, + ) + metrics = detensorify_metrics(metrics) + accumulate_metrics(val_metric_sums, val_metric_weights, metrics, batch_size, decay=1.0, new_weight=1.0) + val_samples += batch_size + if max_val_samples is not None and val_samples > max_val_samples: + break + val_metric_sums["nsamp_train"] = running_metrics["sums"]["nsamp"] + val_metric_weights["nsamp_train"] = running_metrics["weights"]["nsamp"] + val_metric_sums["wsum_train"] = running_metrics["sums"]["wsum"] + val_metric_weights["wsum_train"] = running_metrics["weights"]["wsum"] + last_val_metrics["sums"] = val_metric_sums + last_val_metrics["weights"] = val_metric_weights + log_metrics(val_metric_sums, val_metric_weights, metrics, val_metrics_out) + t1 = time.perf_counter() + logging.info(f"Validation took {t1-t0} seconds") + ddp_model.train() + + if rank == 0: + logging.info("Export cycle counter = " + str(train_state["export_cycle_counter"])) + + is_time_to_export = False + if train_state["export_cycle_counter"] >= epochs_per_export: + if no_export: + train_state["export_cycle_counter"] = epochs_per_export + else: + train_state["export_cycle_counter"] = 0 + is_time_to_export = True + + skip_export_this_time = False + if export_prob is not None: + if random.random() > export_prob: + skip_export_this_time = True + logging.info("Skipping export model this time") + + if not no_export and is_time_to_export and not skip_export_this_time and exportdir is not None and not gnorm_stats_debug: + # Export a model for testing, unless somehow it already exists + modelname = "%s-s%d-d%d" % ( + exportprefix, + train_state["global_step_samples"], + train_state["total_num_data_rows"], + ) + savepath = os.path.join(exportdir,modelname) + savepathtmp = os.path.join(exportdir,modelname+".tmp") + if os.path.exists(savepath): + logging.info("NOT saving model, already exists at: " + savepath) + else: + os.mkdir(savepathtmp) + logging.info("SAVING MODEL FOR EXPORT TO: " + savepath) + save(ddp_model, swa_model, optimizer, metrics_obj, running_metrics, train_state, last_val_metrics, path=os.path.join(savepathtmp,"model.ckpt")) + time.sleep(2) + os.rename(savepathtmp,savepath) + + + if sleep_seconds_per_epoch is None: + time.sleep(1) + else: + time.sleep(sleep_seconds_per_epoch) + + if rank == 0: + now = datetime.datetime.now() + if now - last_longterm_checkpoint_save_time >= datetime.timedelta(hours=12): + last_longterm_checkpoint_save_time = now + dated_name = datetime.datetime.now(timezone.utc).strftime("%Y%m%d-%H%M%S") + save(ddp_model, swa_model, optimizer, metrics_obj, running_metrics, train_state, last_val_metrics, path=os.path.join(longterm_checkpoints_dir,f"{dated_name}.ckpt")) + + train_metrics_out.close() + val_metrics_out.close() + + +if __name__ == "__main__": + multi_gpus = args["multi_gpus"] + num_gpus_used = 1 + multi_gpu_device_ids = [] + if multi_gpus is not None: + for piece in multi_gpus.split(","): + piece = piece.strip() + multi_gpu_device_ids.append(int(piece)) + num_gpus_used = len(multi_gpu_device_ids) + else: + multi_gpu_device_ids = [0] + + make_dirs(args) + + readpipes = [] + writepipes = [] + + if num_gpus_used > 1: + torch.multiprocessing.set_start_method("spawn") + + world_size = num_gpus_used + barrier = torch.multiprocessing.Barrier(num_gpus_used) + + for i in range(world_size - 1): + rpipe, wpipe = torch.multiprocessing.Pipe() + readpipes.append(rpipe) + writepipes.append(wpipe) + + torch.multiprocessing.spawn( + main, + nprocs=num_gpus_used, + args=(world_size, args, multi_gpu_device_ids, readpipes, writepipes, barrier) + ) + else: + rank = 0 + world_size = 1 + barrier = None + main(rank, world_size, args, multi_gpu_device_ids, readpipes, writepipes, barrier) diff --git a/training/scripts/dated/20241110-123407/train.sh b/training/scripts/dated/20241110-123407/train.sh new file mode 100644 index 0000000..e26d70d --- /dev/null +++ b/training/scripts/dated/20241110-123407/train.sh @@ -0,0 +1,87 @@ +#!/bin/bash -eu +set -o pipefail +{ +# Runs training in $BASEDIR/train/$TRAININGNAME +# Should be run once per persistent training process. +# Outputs results in torchmodels_toexport/ in an ongoing basis (EXPORTMODE == "main"). +# Or, to torchmodels_toexport_extra/ (EXPORTMODE == "extra"). +# Or just trains without exporting (EXPORTMODE == "trainonly"). + +if [[ $# -lt 5 ]] +then + echo "Usage: $0 BASEDIR TRAININGNAME MODELKIND BATCHSIZE EXPORTMODE OTHERARGS" + echo "BASEDIR containing selfplay data and models and related directories" + echo "TRAININGNAME name to prefix models with, specific to this training daemon" + echo "MODELKIND what size model to train, like b10c128, see ../modelconfigs.py" + echo "BATCHSIZE number of samples to concat together per batch for training, must match shuffle" + echo "EXPORTMODE 'main': train and export for selfplay. 'extra': train and export extra non-selfplay model. 'trainonly': train without export" + exit 0 +fi +BASEDIR="$1" +shift +TRAININGNAME="$1" +shift +MODELKIND="$1" +shift +BATCHSIZE="$1" +shift +EXPORTMODE="$1" +shift + +#------------------------------------------------------------------------------ +set -x + +mkdir -p "$BASEDIR"/train/"$TRAININGNAME" + +if [[ -n $(pwd | grep "^$BASEDIR/scripts/") ]] +then + echo "Already running out of snapshotted scripts directory, not snapshotting again" +else + GITROOTDIR="$(git rev-parse --show-toplevel)" + + git show --no-patch --no-color > "$BASEDIR"/train/"$TRAININGNAME"/version.txt + git diff --no-color > "$BASEDIR"/train/"$TRAININGNAME"/diff.txt + git diff --staged --no-color > "$BASEDIR"/train/"$TRAININGNAME"/diffstaged.txt + + # For archival and logging purposes - you can look back and see exactly the python code on a particular date + DATE_FOR_FILENAME=$(date "+%Y%m%d-%H%M%S") + DATED_ARCHIVE="$BASEDIR"/scripts/train/dated/"$DATE_FOR_FILENAME" + mkdir -p "$DATED_ARCHIVE" + cp "$GITROOTDIR"/python/*.py "$GITROOTDIR"/python/selfplay/train.sh "$DATED_ARCHIVE" + git show --no-patch --no-color > "$DATED_ARCHIVE"/version.txt + git diff --no-color > "$DATED_ARCHIVE"/diff.txt + git diff --staged --no-color > "$DATED_ARCHIVE"/diffstaged.txt + cd "$DATED_ARCHIVE" +fi + +if [ "$EXPORTMODE" == "main" ] +then + EXPORT_SUBDIR=torchmodels_toexport + EXTRAFLAG="" +elif [ "$EXPORTMODE" == "extra" ] +then + EXPORT_SUBDIR=torchmodels_toexport_extra + EXTRAFLAG="" +elif [ "$EXPORTMODE" == "trainonly" ] +then + EXPORT_SUBDIR=torchmodels_toexport_extra + EXTRAFLAG="-no-export" +else + echo "EXPORTMODE was not 'main' or 'extra' or 'trainonly', run with no arguments for usage" + exit 1 +fi + +time python3 ./train.py \ + -traindir "$BASEDIR"/train/"$TRAININGNAME" \ + -datadir "$BASEDIR"/shuffleddata/current/ \ + -exportdir "$BASEDIR"/"$EXPORT_SUBDIR" \ + -exportprefix "$TRAININGNAME" \ + -pos-len 19 \ + -batch-size "$BATCHSIZE" \ + -model-kind "$MODELKIND" \ + $EXTRAFLAG \ + "$@" \ + 2>&1 | tee -a "$BASEDIR"/train/"$TRAININGNAME"/stdout.txt + +exit 0 +} diff --git a/training/scripts/dated/20241110-123407/upload_model.py b/training/scripts/dated/20241110-123407/upload_model.py new file mode 100644 index 0000000..cfd9606 --- /dev/null +++ b/training/scripts/dated/20241110-123407/upload_model.py @@ -0,0 +1,178 @@ +#!/usr/bin/python3 +import sys +import os +import argparse +import logging +import requests +import hashlib +import configparser +import datetime +import json + +from requests_toolbelt.adapters import host_header_ssl +from requests.auth import HTTPBasicAuth + +#Command and args------------------------------------------------------------------- + +description = """ +Upload neural net weights to server +""" + +parser = argparse.ArgumentParser(description=description) +parser.add_argument('-run-name', help='run name', required=True) +parser.add_argument('-model-name', help='model name', required=True) +parser.add_argument('-model-file', help='model file for kg engine alone', required=True) +parser.add_argument('-model-zip', help='zipped model file with tf weights', required=True) +parser.add_argument('-upload-log-file', help='log upload data to this file', required=True) +parser.add_argument('-metadata-file', help='metadata.json file for recording some stats', required=False) +parser.add_argument('-parents-dir', help='dir with uploaded models dirs for finding parent', required=False) +parser.add_argument('-connection-config', help='config with serverUrl and username and password', required=True) +parser.add_argument('-not-enabled', help='upload model where it is not enabled for train/rating to begin with', required=False, action='store_true') +parser.add_argument('-rating-only', help='upload for rating only or not', type=int, default=0, required=False) +parser.add_argument('-notes', help='extra notes to record for model', required=False) +args = vars(parser.parse_args()) + +run_name = args["run_name"] +model_name = args["model_name"] +model_file = args["model_file"] +model_zip = args["model_zip"] +upload_log_file = args["upload_log_file"] +metadata_file = args["metadata_file"] +parents_dir = args["parents_dir"] +connection_config_file = args["connection_config"] +not_enabled = args["not_enabled"] +rating_only = args["rating_only"] +notes = args["notes"] + +loglines = [] +def log(s): + loglines.append(s) + print(s,flush=True) + +def write_log(): + with open(upload_log_file,"w+") as f: + for line in loglines: + f.write(line + "\n") + +with open(connection_config_file,'r') as f: + connection_config_content = "[DEFAULT]\n" + f.read() +config_parser = configparser.ConfigParser() +config_parser.read_string(connection_config_content) +base_server_url = config_parser["DEFAULT"]["serverUrl"] + +if not base_server_url.endswith("/"): + base_server_url = base_server_url + "/" + +username = config_parser["DEFAULT"]["username"] +password = config_parser["DEFAULT"]["password"] +sslVerificationHost = None +if "sslVerificationHost" in config_parser["DEFAULT"]: + sslVerificationHost = config_parser["DEFAULT"]["sslVerificationHost"] +sslVerifyPemPath = None +if "sslVerifyPemPath" in config_parser["DEFAULT"]: + sslVerifyPemPath = config_parser["DEFAULT"]["sslVerifyPemPath"] + +log("now" + ": " + str(datetime.datetime.now())) +log("run_name" + ": " + run_name) +log("model_name" + ": " + model_name) +log("model_file" + ": " + model_file) +log("model_zip" + ": " + model_zip) +log("parents_dir" + ": " + str(parents_dir)) +log("metadata_file" + ": " + str(metadata_file)) +log("username" + ": " + username) +log("base_server_url" + ": " + base_server_url) + +network_size = model_name.split("-")[1] + +model_file_extension = None +if model_file.endswith(".bin.gz"): + model_file_extension = ".bin.gz" +elif model_file.endswith(".txt.gz"): + model_file_extension = ".txt.gz" +else: + raise Exception("Model file must end in .bin.gz or .txt.gz") + +possible_parents = [] +if parents_dir is not None: + for fname in os.listdir(parents_dir): + if fname.startswith(network_size): + pieces = fname.split("-") + datasamples = int(pieces[2][1:]) + possible_parents.append((fname, datasamples)) + possible_parents.sort(key=(lambda x: x[1])) + +parent_network_name_without_run = None +if len(possible_parents) > 0: + parent_network_name_without_run = possible_parents[-1][0] + +metadata = None +if metadata_file is not None: + with open(metadata_file) as f: + metadata = json.load(f) + +with open(model_file,"rb") as f: + model_file_contents = f.read() + model_file_bytes = len(model_file_contents) + model_file_sha256 = hashlib.sha256(model_file_contents).hexdigest() + del model_file_contents + +url = base_server_url + "api/networks/" + +with open(model_file,"rb") as model_file_handle: + with open(model_zip,"rb") as model_zip_handle: + log_gamma_offset = -1.0 if network_size == "b60c320" else 0.0 + data = { + "run": (None, base_server_url + "api/runs/" + run_name + "/"), + "name": (None, model_name), + "network_size": (None, network_size), + "is_random": (None, "false"), + "model_file": (model_name + model_file_extension, model_file_handle, "application/octet-stream"), + "model_file_bytes": (None, model_file_bytes), + "model_file_sha256": (None, model_file_sha256), + "training_games_enabled": (None, ("false" if (not_enabled or rating_only != 0) else "true")), + "rating_games_enabled": (None, ("false" if not_enabled else "true")), + "log_gamma_offset": (None, str(log_gamma_offset)), + "model_zip_file": (model_name + ".zip", model_zip_handle, "application/octet-stream"), + } + + if parent_network_name_without_run is not None: + data["parent_network"] = (None, base_server_url + "api/networks/" + run_name + "-" + parent_network_name_without_run + "/") + + if notes is not None: + data["notes"] = (None, notes) + + if metadata is not None: + if "global_step_samples" in metadata: + data["train_step"] = (None, metadata["global_step_samples"]) + if "total_num_data_rows" in metadata: + data["total_num_data_rows"] = (None, metadata["total_num_data_rows"]) + if "extra_stats" in metadata: + data["extra_stats"] = (None, json.dumps(metadata["extra_stats"])) + + # print(requests.Request('POST', base_server_url, files=data).prepare().body) + + if sslVerificationHost is not None: + sess = requests.Session() + sess.mount('https://', host_header_ssl.HostHeaderSSLAdapter()) + if sslVerifyPemPath is not None: + result = sess.post(url, files=data, auth=HTTPBasicAuth(username,password), headers={"Host": sslVerificationHost}, verify=sslVerifyPemPath) + else: + result = sess.post(url, files=data, auth=HTTPBasicAuth(username,password), headers={"Host": sslVerificationHost}) + else: + if sslVerifyPemPath is not None: + result = requests.post(url, files=data, auth=HTTPBasicAuth(username,password), verify=sslVerifyPemPath) + else: + result = requests.post(url,files=data,auth=HTTPBasicAuth(username,password)) + +log("Post status code: " + str(result.status_code)) +log("Post result: " + str(result.text)) +if result.status_code == 400 and str(result.text).find("already exist") != -1: + log("Got 400 error with substring 'already exist', network already uploaded? So assuming everything is good") + write_log() +elif result.status_code == 200 or result.status_code == 201 or result.status_code == 202: + log("Post success") + write_log() +else: + log("Post failed") + write_log() + sys.exit(1) diff --git a/training/scripts/dated/20241110-123407/upload_poses.py b/training/scripts/dated/20241110-123407/upload_poses.py new file mode 100644 index 0000000..ae38189 --- /dev/null +++ b/training/scripts/dated/20241110-123407/upload_poses.py @@ -0,0 +1,219 @@ +#!/usr/bin/python3 +import sys +import os +import argparse +import logging +import requests +import hashlib +import configparser +import json + +from requests.auth import HTTPBasicAuth + +#Command and args------------------------------------------------------------------- + +description = """ +Upload neural net weights to server +""" + +parser = argparse.ArgumentParser(description=description) +parser.add_argument('-run-name', help='run name', required=True) +parser.add_argument('-poses', help='startposes or hintposes file or dir', required=True, action='append') +parser.add_argument('-upload-log-file', help='log upload data to this file', required=False) +parser.add_argument('-connection-config', help='config with serverUrl and username and password', required=False) +parser.add_argument('-set-total-weight', help='set total weight of poses to this', type=float, required=False) +parser.add_argument('-weight-factor', help='multiply weight of poses by this', type=float, required=False) +parser.add_argument('-separate-summaries', help='summarize dirs separately', action="store_true", required=False) +parser.add_argument('-extra-stats', help='show some extra stats', action="store_true", required=False) +parser.add_argument('-notes', help='notes or description label for poses', required=False) +args = vars(parser.parse_args()) + +run_name = args["run_name"] +poses_files_or_dirs = args["poses"] +upload_log_file = args["upload_log_file"] +connection_config_file = args["connection_config"] +set_total_weight = args["set_total_weight"] +weight_factor = args["weight_factor"] +separate_summaries = args["separate_summaries"] +extra_stats = args["extra_stats"] +notes = args["notes"] + +loglines = [] +def log(s): + loglines.append(s) + print(s,flush=True) + +def write_log(): + if upload_log_file is not None: + with open(upload_log_file,"a+") as f: + for line in loglines: + f.write(line + "\n") + +if notes is None: + notes = "" + +if connection_config_file is not None: + with open(connection_config_file,'r') as f: + connection_config_content = "[DEFAULT]\n" + f.read() + config_parser = configparser.ConfigParser() + config_parser.read_string(connection_config_content) + base_server_url = config_parser["DEFAULT"]["serverUrl"] + + if not base_server_url.endswith("/"): + base_server_url = base_server_url + "/" + +log("run_name" + ": " + run_name) +log("poses" + ": " + str(poses_files_or_dirs)) +log("notes" + ": " + notes) + +if connection_config_file is not None: + username = config_parser["DEFAULT"]["username"] + password = config_parser["DEFAULT"]["password"] + log("username" + ": " + username) + log("base_server_url" + ": " + base_server_url) + +def compute_sum_sumsq(poses): + sumweight = 0.0 + sumweightsq = 0.0 + for pos in poses: + sumweight += pos["weight"] + sumweightsq += pos["weight"] * pos["weight"] + return (sumweight, sumweightsq) + +def print_extra_stats(poses): + sumweight = 0.0 + sumturnnum = 0 + for pos in poses: + sumturnnum += pos["weight"] * (pos["initialTurnNumber"] + len(pos["moveLocs"])) + sumweight += pos["weight"] + print("Avg turn number:", sumturnnum/sumweight) + +log("Loading positions") +poses_by_key = {} + +def handle_file(poses_by_key, poses_file): + log("Loading" + ": " + poses_file) + poses_by_key_this_file = {} + with open(poses_file,"r") as f: + for line in f: + line = line.strip() + if len(line) <= 0: + continue + pos = json.loads(line) + key = ( + str(pos["initialTurnNumber"]) + "$" + + "@".join(pos["moveLocs"]) + "$" + + "@".join(pos["movePlas"]) + "$" + + str(pos["xSize"]) + "$" + + str(pos["ySize"]) + "$" + + pos["nextPla"] + "$" + + pos["board"] + "$" + + pos["hintLoc"] + ) + + if len(pos["movePlas"]) > 0: + assert pos["nextPla"] == "B" or pos["nextPla"] == "W" + assert pos["movePlas"][0] == pos["nextPla"] + for i in range(len(pos["movePlas"])): + assert pos["movePlas"][i] == "B" or pos["movePlas"][i] == "W" + for i in range(1,len(pos["movePlas"])): + assert pos["movePlas"][i] != pos["movePlas"][i-1] + + if "weight" in pos: + weight = pos["weight"] + else: + pos["weight"] = 1.0 + weight = 1.0 + + if key in poses_by_key: + poses_by_key[key]["weight"] += weight + else: + poses_by_key[key] = pos.copy() + if key in poses_by_key_this_file: + poses_by_key_this_file[key]["weight"] += weight + else: + poses_by_key_this_file[key] = pos.copy() + if separate_summaries: + sumweight,sumweightsq = compute_sum_sumsq(poses_by_key_this_file.values()) + if sumweight > 0 and sumweightsq > 0: + log("Found %d unique positions" % len(poses_by_key_this_file.values())) + log("Found %f total weight" % sumweight) + log("Found %f ess" % (sumweight * sumweight / sumweightsq)) + if extra_stats: + print_extra_stats(poses_by_key_this_file.values()) + log("%d %f %f" % (len(poses_by_key_this_file.values()), sumweight, (sumweight * sumweight / sumweightsq))) + +poses_files_or_dirs = sorted(poses_files_or_dirs) +for poses_file_or_dir in poses_files_or_dirs: + if os.path.isdir(poses_file_or_dir): + for (path,dirnames,filenames) in os.walk(poses_file_or_dir): + dirnames.sort() + filenames = sorted(filenames) + for filename in filenames: + if filename.endswith(".startposes.txt") or filename.endswith(".hintposes.txt") or filename.endswith(".bookposes.txt"): + handle_file(poses_by_key, os.path.join(path,filename)) + else: + handle_file(poses_by_key, poses_file_or_dir) + +poses = poses_by_key.values() +sumweight,sumweightsq = compute_sum_sumsq(poses) +log("Found %d unique positions" % len(poses)) +log("Found %f total weight" % sumweight) +log("Found %f ess" % (sumweight * sumweight / sumweightsq)) +if extra_stats: + print_extra_stats(poses) +log("%d %f %f" % (len(poses), sumweight, (sumweight * sumweight / sumweightsq))) + +if set_total_weight is not None: + log("Setting total weight of data to " + str(set_total_weight)) + scale = set_total_weight / sumweight + for pos in poses: + pos["weight"] *= scale +elif weight_factor is not None: + log("Scaling weight of data by " + str(weight_factor)) + for pos in poses: + pos["weight"] *= weight_factor + +def postStuff(to_post): + url = base_server_url + "api/startposes/" + result = requests.post(url,json=to_post,auth=HTTPBasicAuth(username,password)) + log("Post status code: " + str(result.status_code)) + if result.status_code == 200 or result.status_code == 201 or result.status_code == 202: + log("Post success") + else: + log("Post failed") + if result.text: + log(result.text) + write_log() + sys.exit(1) + + +if connection_config_file is None: + log("No connection config specified, quitting WITHOUT uploading anything") +else: + num_total = len(poses) + num_posted = 0 + + to_post = [] + for pos in poses: + weight = pos["weight"] + data = { + "run": base_server_url + "api/runs/" + run_name + "/", + "weight": weight, + "data": pos, + "notes": notes, + } + to_post.append(data) + if len(to_post) >= 5000: + num_posted += len(to_post) + log("Posting " + str(num_posted) + "/" + str(num_total)) + postStuff(to_post) + to_post = [] + + num_posted += len(to_post) + log("Posting " + str(num_posted) + "/" + str(num_total)) + postStuff(to_post) + to_post = [] + + log("Done") + write_log() diff --git a/training/scripts/dated/20241110-123407/version.txt b/training/scripts/dated/20241110-123407/version.txt new file mode 100644 index 0000000..0a99568 --- /dev/null +++ b/training/scripts/dated/20241110-123407/version.txt @@ -0,0 +1,5 @@ +commit 827f7555bd393e2389f74f1ca147595d95a716b7 +Author: David Wu +Date: Sun Jul 28 12:02:09 2024 -0400 + + Allow analysis data output even when no play selection values diff --git a/training/selfplay/kata1-b18c384nbt-s9908371456-d4296205232.bin.gz/selfplay-AE001DCEEADF7AB1.cfg b/training/selfplay/kata1-b18c384nbt-s9908371456-d4296205232.bin.gz/selfplay-AE001DCEEADF7AB1.cfg new file mode 100644 index 0000000..50bc598 --- /dev/null +++ b/training/selfplay/kata1-b18c384nbt-s9908371456-d4296205232.bin.gz/selfplay-AE001DCEEADF7AB1.cfg @@ -0,0 +1,185 @@ + +# Logs------------------------------------------------------------------------------------ + +logSearchInfo = false +logMoves = false +logGamesEvery = 10 +logToStdout = true + +# Data writing----------------------------------------------------------------------------------- + +# Spatial size of tensors of data, must match -pos-len in python/train.py and be at least as large as the +# largest boardsize in the data. If you train only on smaller board sizes, you can decrease this and +# pass in a smaller -pos-len to python/train.py (e.g. modify "-pos-len 19" in selfplay/train.sh script), +# to train faster, although it may be awkward if you ever want to increase it again later since data with +# different values cannot be shuffled together. +dataBoardLen = 19 + +maxDataQueueSize = 2000 +maxRowsPerTrainFile = 10000 +firstFileRandMinProp = 0.15 + +# Fancy game selfplay settings-------------------------------------------------------------------- + +# These take dominance - if a game is forked for any of these reasons, that will be the next game played. +# For forked games, randomization of rules and "initGamesWithPolicy" is disabled, komi is made fair with prob "forkCompensateKomiProb". +earlyForkGameProb = 0.04 # Fork to try alternative opening variety with this probability +earlyForkGameExpectedMoveProp = 0.025 # Fork roughly within the first (boardArea * this) many moves +forkGameProb = 0.01 # Fork to try alternative crazy move anywhere in game with this probability if not early forking +forkGameMinChoices = 3 # Choose from the best of at least this many random choices +earlyForkGameMaxChoices = 12 # Choose from the best of at most this many random choices +forkGameMaxChoices = 36 # Choose from the best of at most this many random choices +# Otherwise, with this probability, learn a bit more about the differing evaluation of seki in different rulesets. +sekiForkHackProb = 0.02 + +# Otherwise, play some proportion of games starting from SGF positions, with randomized rules (ignoring the sgf rules) +# On SGF positions, high temperature policy init is allowed +# startPosesProb = 0.0 # Play this proportion of games starting from SGF positions +# startPosesFromSgfDir = DIRECTORYPATH # Load SGFs from this dir +# startPosesLoadProb = 1.0 # Only load each position from each SGF with this chance (save memory) +# startPosesTurnWeightLambda = 0 # 0 = equal weight 0.01 = decrease probability by 1% per turn -0.01 = increase probability by 1% per turn. +# startPosesPolicyInitAreaProp = 0.0 # Same as policyInitAreaProp but for SGF positions + +# Otherwise, play some proportion of games starting from hint positions (generated using "dataminesgfs" command), with randomized rules. +# On hint positions, "initGamesWithPolicy" does not apply. +# hintPosesProb = 0.0 +# hintPosesDir = DIRECTORYPATH + +# Otherwise we are playing a "normal" game, potentially with handicap stones, depending on "handicapProb", and +# potentially with komi randomization, depending on things like "komiStdev", and potentially with different +# board sizes, etc. + +# Most of the remaining parameters here below apply regardless of the initialization, although a few of them +# vary depending on handicap vs normal game, and some are explicitly disabled (e.g. initGamesWithPolicy on hint positions). + +initGamesWithPolicy = true # Play the first few moves of a game high-temperaturely from policy +policyInitAreaProp = 0.04 # The avg number of moves to play +compensateAfterPolicyInitProb = 0.2 # Additionally make komi fair this often after the high-temperature moves. +sidePositionProb = 0.020 # With this probability, train on refuting bad alternative moves. + +cheapSearchProb = 0.75 # Do cheap searches with this probaiblity +cheapSearchVisits = 100 # Number of visits for cheap search +cheapSearchTargetWeight = 0.0 # Training weight for cheap search + +reduceVisits = true # Reduce visits when one side is winning +reduceVisitsThreshold = 0.9 # How winning a side needs to be (winrate) +reduceVisitsThresholdLookback = 3 # How many consecutive turns needed to be that winning +reducedVisitsMin = 100 # Minimum number of visits (never reduce below this) +reducedVisitsWeight = 0.1 # Minimum training weight + +handicapAsymmetricPlayoutProb = 0.5 # In handicap games, play with unbalanced players with this probablity +normalAsymmetricPlayoutProb = 0.01 # In regular games, play with unbalanced players with this probability +maxAsymmetricRatio = 8.0 # Max ratio to unbalance visits between players +minAsymmetricCompensateKomiProb = 0.4 # Compensate komi with at least this probability for unbalanced players + +policySurpriseDataWeight = 0.5 # This proportion of training weight should be concentrated on surprising moves +valueSurpriseDataWeight = 0.1 # This proportion of training weight should be concentrated on surprising position results + +estimateLeadProb = 0.05 # Train lead, rather than just scoremean. Consumes a decent number of extra visits, can be quite slow using low visits to set too high. +switchNetsMidGame = true # When a new neural net is loaded, switch to it immediately instead of waiting for new game +fancyKomiVarying = true # In non-compensated handicap and fork games, vary komi to better learn komi and large score differences that would never happen in even games. + +# Match----------------------------------------------------------------------------------- + +numGameThreads = 30 +maxMovesPerGame = 1600 + +# Rules------------------------------------------------------------------------------------ + +koRules = SIMPLE,POSITIONAL,SITUATIONAL +scoringRules = AREA,TERRITORY +taxRules = NONE,NONE,SEKI,SEKI,ALL +multiStoneSuicideLegals = false,true +hasButtons = false,false,true + +bSizes = 7,8,9 +bSizeRelProbs = 1,1,8 +allowRectangleProb = 0.50 + +komiAuto = True # Automatically adjust komi to what the neural nets think are fair based on the empty board, but still apply komiStdev. +# komiMean = 7.0 # Specify explicit komi +komiStdev = 1.0 # Standard deviation of random variation to komi. +komiBigStdevProb = 0.06 # Probability of applying komiBigStdev +komiBigStdev = 12.0 # Standard deviation of random big variation to komi + +handicapProb = 0.10 # Probability of handicap game +handicapCompensateKomiProb = 0.50 # In handicap games, adjust komi to fair with this probability based on the handicap placement +forkCompensateKomiProb = 0.80 # For forks, adjust komi to fair with this probability based on the forked position +sgfCompensateKomiProb = 0.90 # For sgfs, adjust komi to fair with this probability based on the specific starting position + +drawRandRadius = 0.5 +noResultStdev = 0.166666666 + +# Search limits----------------------------------------------------------------------------------- + +maxVisits = 600 +numSearchThreads = 1 + +# GPU Settings------------------------------------------------------------------------------- + +nnMaxBatchSize = 128 +nnCacheSizePowerOfTwo = 21 +nnMutexPoolSizePowerOfTwo = 15 +numNNServerThreadsPerModel = 1 +nnRandomize = true + +# CUDA GPU settings-------------------------------------- +# cudaDeviceToUse = 0 #use device 0 for all server threads (numNNServerThreadsPerModel) unless otherwise specified per-model or per-thread-per-model +# cudaDeviceToUseModel0 = 3 #use device 3 for model 0 for all threads unless otherwise specified per-thread for this model +# cudaDeviceToUseModel1 = 2 #use device 2 for model 1 for all threads unless otherwise specified per-thread for this model +# cudaDeviceToUseModel0Thread0 = 3 #use device 3 for model 0, server thread 0 +# cudaDeviceToUseModel0Thread1 = 2 #use device 2 for model 0, server thread 1 + +cudaUseFP16 = auto +cudaUseNHWC = auto + +# Root move selection and biases------------------------------------------------------------------------------ + +chosenMoveTemperatureEarly = 0.75 +chosenMoveTemperatureHalflife = 19 +chosenMoveTemperature = 0.15 +chosenMoveSubtract = 0 +chosenMovePrune = 1 + +rootNoiseEnabled = true +rootDirichletNoiseTotalConcentration = 10.83 +rootDirichletNoiseWeight = 0.25 + +rootDesiredPerChildVisitsCoeff = 2 +rootNumSymmetriesToSample = 4 + +useLcbForSelection = true +lcbStdevs = 5.0 +minVisitPropForLCB = 0.15 + +# Internal params------------------------------------------------------------------------------ + +winLossUtilityFactor = 1.0 +staticScoreUtilityFactor = 0.00 +dynamicScoreUtilityFactor = 0.40 +dynamicScoreCenterZeroWeight = 0.25 +dynamicScoreCenterScale = 0.50 +noResultUtilityForWhite = 0.0 +drawEquivalentWinsForWhite = 0.5 + +rootEndingBonusPoints = 0.5 +rootPruneUselessMoves = true + +rootPolicyTemperatureEarly = 1.25 +rootPolicyTemperature = 1.1 + +cpuctExploration = 1.1 +cpuctExplorationLog = 0.0 +fpuReductionMax = 0.2 +rootFpuReductionMax = 0.0 + +numVirtualLossesPerThread = 1 + +# These parameters didn't exist historically during early KataGo runs +valueWeightExponent = 0.5 +subtreeValueBiasFactor = 0.30 +subtreeValueBiasWeightExponent = 0.8 +useNonBuggyLcb = true +useGraphSearch = true +fpuParentWeightByVisitedPolicy = true +fpuParentWeightByVisitedPolicyPow = 2.0 diff --git a/training/selfplay/kata1-b18c384nbt-s9908371456-d4296205232.bin.gz/selfplay-FDE1CB731B80A9AA.cfg b/training/selfplay/kata1-b18c384nbt-s9908371456-d4296205232.bin.gz/selfplay-FDE1CB731B80A9AA.cfg new file mode 100644 index 0000000..50bc598 --- /dev/null +++ b/training/selfplay/kata1-b18c384nbt-s9908371456-d4296205232.bin.gz/selfplay-FDE1CB731B80A9AA.cfg @@ -0,0 +1,185 @@ + +# Logs------------------------------------------------------------------------------------ + +logSearchInfo = false +logMoves = false +logGamesEvery = 10 +logToStdout = true + +# Data writing----------------------------------------------------------------------------------- + +# Spatial size of tensors of data, must match -pos-len in python/train.py and be at least as large as the +# largest boardsize in the data. If you train only on smaller board sizes, you can decrease this and +# pass in a smaller -pos-len to python/train.py (e.g. modify "-pos-len 19" in selfplay/train.sh script), +# to train faster, although it may be awkward if you ever want to increase it again later since data with +# different values cannot be shuffled together. +dataBoardLen = 19 + +maxDataQueueSize = 2000 +maxRowsPerTrainFile = 10000 +firstFileRandMinProp = 0.15 + +# Fancy game selfplay settings-------------------------------------------------------------------- + +# These take dominance - if a game is forked for any of these reasons, that will be the next game played. +# For forked games, randomization of rules and "initGamesWithPolicy" is disabled, komi is made fair with prob "forkCompensateKomiProb". +earlyForkGameProb = 0.04 # Fork to try alternative opening variety with this probability +earlyForkGameExpectedMoveProp = 0.025 # Fork roughly within the first (boardArea * this) many moves +forkGameProb = 0.01 # Fork to try alternative crazy move anywhere in game with this probability if not early forking +forkGameMinChoices = 3 # Choose from the best of at least this many random choices +earlyForkGameMaxChoices = 12 # Choose from the best of at most this many random choices +forkGameMaxChoices = 36 # Choose from the best of at most this many random choices +# Otherwise, with this probability, learn a bit more about the differing evaluation of seki in different rulesets. +sekiForkHackProb = 0.02 + +# Otherwise, play some proportion of games starting from SGF positions, with randomized rules (ignoring the sgf rules) +# On SGF positions, high temperature policy init is allowed +# startPosesProb = 0.0 # Play this proportion of games starting from SGF positions +# startPosesFromSgfDir = DIRECTORYPATH # Load SGFs from this dir +# startPosesLoadProb = 1.0 # Only load each position from each SGF with this chance (save memory) +# startPosesTurnWeightLambda = 0 # 0 = equal weight 0.01 = decrease probability by 1% per turn -0.01 = increase probability by 1% per turn. +# startPosesPolicyInitAreaProp = 0.0 # Same as policyInitAreaProp but for SGF positions + +# Otherwise, play some proportion of games starting from hint positions (generated using "dataminesgfs" command), with randomized rules. +# On hint positions, "initGamesWithPolicy" does not apply. +# hintPosesProb = 0.0 +# hintPosesDir = DIRECTORYPATH + +# Otherwise we are playing a "normal" game, potentially with handicap stones, depending on "handicapProb", and +# potentially with komi randomization, depending on things like "komiStdev", and potentially with different +# board sizes, etc. + +# Most of the remaining parameters here below apply regardless of the initialization, although a few of them +# vary depending on handicap vs normal game, and some are explicitly disabled (e.g. initGamesWithPolicy on hint positions). + +initGamesWithPolicy = true # Play the first few moves of a game high-temperaturely from policy +policyInitAreaProp = 0.04 # The avg number of moves to play +compensateAfterPolicyInitProb = 0.2 # Additionally make komi fair this often after the high-temperature moves. +sidePositionProb = 0.020 # With this probability, train on refuting bad alternative moves. + +cheapSearchProb = 0.75 # Do cheap searches with this probaiblity +cheapSearchVisits = 100 # Number of visits for cheap search +cheapSearchTargetWeight = 0.0 # Training weight for cheap search + +reduceVisits = true # Reduce visits when one side is winning +reduceVisitsThreshold = 0.9 # How winning a side needs to be (winrate) +reduceVisitsThresholdLookback = 3 # How many consecutive turns needed to be that winning +reducedVisitsMin = 100 # Minimum number of visits (never reduce below this) +reducedVisitsWeight = 0.1 # Minimum training weight + +handicapAsymmetricPlayoutProb = 0.5 # In handicap games, play with unbalanced players with this probablity +normalAsymmetricPlayoutProb = 0.01 # In regular games, play with unbalanced players with this probability +maxAsymmetricRatio = 8.0 # Max ratio to unbalance visits between players +minAsymmetricCompensateKomiProb = 0.4 # Compensate komi with at least this probability for unbalanced players + +policySurpriseDataWeight = 0.5 # This proportion of training weight should be concentrated on surprising moves +valueSurpriseDataWeight = 0.1 # This proportion of training weight should be concentrated on surprising position results + +estimateLeadProb = 0.05 # Train lead, rather than just scoremean. Consumes a decent number of extra visits, can be quite slow using low visits to set too high. +switchNetsMidGame = true # When a new neural net is loaded, switch to it immediately instead of waiting for new game +fancyKomiVarying = true # In non-compensated handicap and fork games, vary komi to better learn komi and large score differences that would never happen in even games. + +# Match----------------------------------------------------------------------------------- + +numGameThreads = 30 +maxMovesPerGame = 1600 + +# Rules------------------------------------------------------------------------------------ + +koRules = SIMPLE,POSITIONAL,SITUATIONAL +scoringRules = AREA,TERRITORY +taxRules = NONE,NONE,SEKI,SEKI,ALL +multiStoneSuicideLegals = false,true +hasButtons = false,false,true + +bSizes = 7,8,9 +bSizeRelProbs = 1,1,8 +allowRectangleProb = 0.50 + +komiAuto = True # Automatically adjust komi to what the neural nets think are fair based on the empty board, but still apply komiStdev. +# komiMean = 7.0 # Specify explicit komi +komiStdev = 1.0 # Standard deviation of random variation to komi. +komiBigStdevProb = 0.06 # Probability of applying komiBigStdev +komiBigStdev = 12.0 # Standard deviation of random big variation to komi + +handicapProb = 0.10 # Probability of handicap game +handicapCompensateKomiProb = 0.50 # In handicap games, adjust komi to fair with this probability based on the handicap placement +forkCompensateKomiProb = 0.80 # For forks, adjust komi to fair with this probability based on the forked position +sgfCompensateKomiProb = 0.90 # For sgfs, adjust komi to fair with this probability based on the specific starting position + +drawRandRadius = 0.5 +noResultStdev = 0.166666666 + +# Search limits----------------------------------------------------------------------------------- + +maxVisits = 600 +numSearchThreads = 1 + +# GPU Settings------------------------------------------------------------------------------- + +nnMaxBatchSize = 128 +nnCacheSizePowerOfTwo = 21 +nnMutexPoolSizePowerOfTwo = 15 +numNNServerThreadsPerModel = 1 +nnRandomize = true + +# CUDA GPU settings-------------------------------------- +# cudaDeviceToUse = 0 #use device 0 for all server threads (numNNServerThreadsPerModel) unless otherwise specified per-model or per-thread-per-model +# cudaDeviceToUseModel0 = 3 #use device 3 for model 0 for all threads unless otherwise specified per-thread for this model +# cudaDeviceToUseModel1 = 2 #use device 2 for model 1 for all threads unless otherwise specified per-thread for this model +# cudaDeviceToUseModel0Thread0 = 3 #use device 3 for model 0, server thread 0 +# cudaDeviceToUseModel0Thread1 = 2 #use device 2 for model 0, server thread 1 + +cudaUseFP16 = auto +cudaUseNHWC = auto + +# Root move selection and biases------------------------------------------------------------------------------ + +chosenMoveTemperatureEarly = 0.75 +chosenMoveTemperatureHalflife = 19 +chosenMoveTemperature = 0.15 +chosenMoveSubtract = 0 +chosenMovePrune = 1 + +rootNoiseEnabled = true +rootDirichletNoiseTotalConcentration = 10.83 +rootDirichletNoiseWeight = 0.25 + +rootDesiredPerChildVisitsCoeff = 2 +rootNumSymmetriesToSample = 4 + +useLcbForSelection = true +lcbStdevs = 5.0 +minVisitPropForLCB = 0.15 + +# Internal params------------------------------------------------------------------------------ + +winLossUtilityFactor = 1.0 +staticScoreUtilityFactor = 0.00 +dynamicScoreUtilityFactor = 0.40 +dynamicScoreCenterZeroWeight = 0.25 +dynamicScoreCenterScale = 0.50 +noResultUtilityForWhite = 0.0 +drawEquivalentWinsForWhite = 0.5 + +rootEndingBonusPoints = 0.5 +rootPruneUselessMoves = true + +rootPolicyTemperatureEarly = 1.25 +rootPolicyTemperature = 1.1 + +cpuctExploration = 1.1 +cpuctExplorationLog = 0.0 +fpuReductionMax = 0.2 +rootFpuReductionMax = 0.0 + +numVirtualLossesPerThread = 1 + +# These parameters didn't exist historically during early KataGo runs +valueWeightExponent = 0.5 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index 1df0c9e..c041dba 100644 --- a/training/selfplay/log20241110-121246-02562D1107BCEDB6.log +++ b/training/selfplay/log20241110-121246-02562D1107BCEDB6.log @@ -160,3 +160,45 @@ winLossUtilityFactor = 1.0 2024-11-10 12:14:04-0500: Started 40 games with kata1-b18c384nbt-s9908371456-d4296205232.bin.gz 2024-11-10 12:14:32-0500: Started 50 games with kata1-b18c384nbt-s9908371456-d4296205232.bin.gz 2024-11-10 12:14:58-0500: Started 60 games with kata1-b18c384nbt-s9908371456-d4296205232.bin.gz +2024-11-10 12:15:23-0500: Started 70 games with kata1-b18c384nbt-s9908371456-d4296205232.bin.gz +2024-11-10 12:15:55-0500: Started 80 games with kata1-b18c384nbt-s9908371456-d4296205232.bin.gz +2024-11-10 12:16:23-0500: Started 90 games with kata1-b18c384nbt-s9908371456-d4296205232.bin.gz +2024-11-10 12:16:24-0500: Game loop thread 10 terminating +2024-11-10 12:16:28-0500: Game loop thread 8 terminating +2024-11-10 12:16:32-0500: Game loop thread 27 terminating +2024-11-10 12:16:33-0500: Game loop thread 2 terminating +2024-11-10 12:16:36-0500: Game loop thread 20 terminating +2024-11-10 12:16:37-0500: Game loop thread 7 terminating +2024-11-10 12:16:38-0500: Game loop thread 3 terminating +2024-11-10 12:16:39-0500: Game loop thread 4 terminating +2024-11-10 12:16:41-0500: Game loop thread 6 terminating +2024-11-10 12:16:45-0500: Game loop thread 23 terminating +2024-11-10 12:16:45-0500: Game loop thread 15 terminating +2024-11-10 12:16:46-0500: Game loop thread 19 terminating +2024-11-10 12:16:49-0500: Game loop thread 22 terminating +2024-11-10 12:17:00-0500: Game loop thread 11 terminating +2024-11-10 12:17:00-0500: Game loop thread 17 terminating +2024-11-10 12:17:01-0500: Game loop thread 14 terminating +2024-11-10 12:17:03-0500: Game loop thread 29 terminating +2024-11-10 12:17:05-0500: Game loop thread 0 terminating +2024-11-10 12:17:05-0500: Game loop thread 1 terminating +2024-11-10 12:17:06-0500: Game loop thread 24 terminating +2024-11-10 12:17:10-0500: Game loop thread 28 terminating +2024-11-10 12:17:10-0500: Game loop thread 13 terminating +2024-11-10 12:17:11-0500: Game loop thread 25 terminating +2024-11-10 12:17:12-0500: Game loop thread 5 terminating +2024-11-10 12:17:18-0500: Game loop thread 21 terminating +2024-11-10 12:17:21-0500: Game loop thread 9 terminating +2024-11-10 12:17:28-0500: Game loop thread 16 terminating +2024-11-10 12:17:32-0500: Game loop thread 26 terminating +2024-11-10 12:17:43-0500: Game loop thread 12 terminating +2024-11-10 12:17:44-0500: Game loop thread 18 terminating +2024-11-10 12:17:44-0500: Model loading loop thread terminating +2024-11-10 12:17:45-0500: Data write loop finishing for neural net: kata1-b18c384nbt-s9908371456-d4296205232.bin.gz +2024-11-10 12:17:45-0500: Final cleanup of net: C:\Users\chang\OneDrive\Documents\GitHub\Kata_speed\training\models\kata1-b18c384nbt-s9908371456-d4296205232.bin.gz +2024-11-10 12:17:45-0500: Final NN rows: 403486 +2024-11-10 12:17:45-0500: Final NN batches: 31322 +2024-11-10 12:17:45-0500: Final NN avg batch size: 12.8819 +2024-11-10 12:17:45-0500: GPU -1 finishing, processed 403486 rows 31322 batches +2024-11-10 12:17:45-0500: Data write loop cleaned up and terminating for kata1-b18c384nbt-s9908371456-d4296205232.bin.gz +2024-11-10 12:17:45-0500: All cleaned up, quitting diff --git a/training/selfplay/log20241110-122512-71720487F87C862B.log b/training/selfplay/log20241110-122512-71720487F87C862B.log new file mode 100644 index 0000000..fa58dca --- /dev/null +++ b/training/selfplay/log20241110-122512-71720487F87C862B.log @@ -0,0 +1,204 @@ +2024-11-10 12:25:12-0500: Running with following config: +allowRectangleProb = 0.50 +bSizeRelProbs = 1,1,8 +bSizes = 7,8,9 +cheapSearchProb = 0.75 +cheapSearchTargetWeight = 0.0 +cheapSearchVisits = 100 +chosenMovePrune = 1 +chosenMoveSubtract = 0 +chosenMoveTemperature = 0.15 +chosenMoveTemperatureEarly = 0.75 +chosenMoveTemperatureHalflife = 19 +compensateAfterPolicyInitProb = 0.2 +cpuctExploration = 1.1 +cpuctExplorationLog = 0.0 +cudaUseFP16 = auto +cudaUseNHWC = auto +dataBoardLen = 19 +drawEquivalentWinsForWhite = 0.5 +drawRandRadius = 0.5 +dynamicScoreCenterScale = 0.50 +dynamicScoreCenterZeroWeight = 0.25 +dynamicScoreUtilityFactor = 0.40 +earlyForkGameExpectedMoveProp = 0.025 +earlyForkGameMaxChoices = 12 +earlyForkGameProb = 0.04 +estimateLeadProb = 0.05 +fancyKomiVarying = true +firstFileRandMinProp = 0.15 +forkCompensateKomiProb = 0.80 +forkGameMaxChoices = 36 +forkGameMinChoices = 3 +forkGameProb = 0.01 +fpuParentWeightByVisitedPolicy = true +fpuParentWeightByVisitedPolicyPow = 2.0 +fpuReductionMax = 0.2 +handicapAsymmetricPlayoutProb = 0.5 +handicapCompensateKomiProb = 0.50 +handicapProb = 0.10 +hasButtons = false,false,true +initGamesWithPolicy = true +koRules = SIMPLE,POSITIONAL,SITUATIONAL +komiAuto = True +komiBigStdev = 12.0 +komiBigStdevProb = 0.06 +komiStdev = 1.0 +lcbStdevs = 5.0 +logGamesEvery = 10 +logMoves = false +logSearchInfo = false +logToStdout = true +maxAsymmetricRatio = 8.0 +maxDataQueueSize = 2000 +maxMovesPerGame = 1600 +maxRowsPerTrainFile = 10000 +maxVisits = 600 +minAsymmetricCompensateKomiProb = 0.4 +minVisitPropForLCB = 0.15 +multiStoneSuicideLegals = false,true +nnCacheSizePowerOfTwo = 21 +nnMaxBatchSize = 128 +nnMutexPoolSizePowerOfTwo = 15 +nnRandomize = true +noResultStdev = 0.166666666 +noResultUtilityForWhite = 0.0 +normalAsymmetricPlayoutProb = 0.01 +numGameThreads = 30 +numNNServerThreadsPerModel = 1 +numSearchThreads = 1 +numVirtualLossesPerThread = 1 +policyInitAreaProp = 0.04 +policySurpriseDataWeight = 0.5 +reduceVisits = true +reduceVisitsThreshold = 0.9 +reduceVisitsThresholdLookback = 3 +reducedVisitsMin = 100 +reducedVisitsWeight = 0.1 +rootDesiredPerChildVisitsCoeff = 2 +rootDirichletNoiseTotalConcentration = 10.83 +rootDirichletNoiseWeight = 0.25 +rootEndingBonusPoints = 0.5 +rootFpuReductionMax = 0.0 +rootNoiseEnabled = true +rootNumSymmetriesToSample = 4 +rootPolicyTemperature = 1.1 +rootPolicyTemperatureEarly = 1.25 +rootPruneUselessMoves = true +scoringRules = AREA,TERRITORY +sekiForkHackProb = 0.02 +sgfCompensateKomiProb = 0.90 +sidePositionProb = 0.020 +staticScoreUtilityFactor = 0.00 +subtreeValueBiasFactor = 0.30 +subtreeValueBiasWeightExponent = 0.8 +switchNetsMidGame = true +taxRules = NONE,NONE,SEKI,SEKI,ALL +useGraphSearch = true +useLcbForSelection = true +useNonBuggyLcb = true +valueSurpriseDataWeight = 0.1 +valueWeightExponent = 0.5 +winLossUtilityFactor = 1.0 + +2024-11-10 12:25:12-0500: Self Play Engine starting... +2024-11-10 12:25:12-0500: Git revision: 7939e2c3eb43df04e08de8c166889c6d5e937bfa +2024-11-10 12:25:12-0500: Loaded all config stuff, starting self play +2024-11-10 12:25:12-0500: Found new neural net kata1-b18c384nbt-s9908371456-d4296205232.bin.gz +2024-11-10 12:25:12-0500: nnRandSeed0 = 10060376068512553218 +2024-11-10 12:25:12-0500: After dedups: nnModelFile0 = C:\Users\chang\OneDrive\Documents\GitHub\Kata_speed\training\models\kata1-b18c384nbt-s9908371456-d4296205232.bin.gz useFP16 auto useNHWC auto +2024-11-10 12:25:12-0500: Initializing neural net buffer to be size 9 * 9 allowing smaller boards +2024-11-10 12:25:13-0500: Found OpenCL Platform 0: NVIDIA CUDA (NVIDIA Corporation) (OpenCL 3.0 CUDA 12.6.65) +2024-11-10 12:25:13-0500: Found 1 device(s) on platform 0 with type CPU or GPU or Accelerator +2024-11-10 12:25:13-0500: Found OpenCL Platform 1: Intel(R) OpenCL Graphics (Intel(R) Corporation) (OpenCL 3.0 ) +2024-11-10 12:25:13-0500: Found 1 device(s) on platform 1 with type CPU or GPU or Accelerator +2024-11-10 12:25:13-0500: Found OpenCL Device 0: NVIDIA GeForce RTX 4060 Laptop GPU (NVIDIA Corporation) (score 11000300) +2024-11-10 12:25:13-0500: Found OpenCL Device 1: Intel(R) UHD Graphics (Intel(R) Corporation) (score 6000300) +2024-11-10 12:25:13-0500: Creating context for OpenCL Platform: NVIDIA CUDA (NVIDIA Corporation) (OpenCL 3.0 CUDA 12.6.65) +2024-11-10 12:25:13-0500: Using OpenCL Device 0: NVIDIA GeForce RTX 4060 Laptop GPU (NVIDIA Corporation) OpenCL 3.0 CUDA (Extensions: cl_khr_global_int32_base_atomics cl_khr_global_int32_extended_atomics cl_khr_local_int32_base_atomics cl_khr_local_int32_extended_atomics cl_khr_fp64 cl_khr_3d_image_writes cl_khr_byte_addressable_store cl_khr_icd cl_khr_gl_sharing cl_nv_compiler_options cl_nv_device_attribute_query cl_nv_pragma_unroll cl_nv_d3d10_sharing cl_khr_d3d10_sharing cl_nv_d3d11_sharing cl_nv_copy_opts cl_nv_create_buffer cl_khr_int64_base_atomics cl_khr_int64_extended_atomics cl_khr_device_uuid cl_khr_pci_bus_info cl_khr_external_semaphore cl_khr_external_memory cl_khr_external_semaphore_win32 cl_khr_external_memory_win32) +2024-11-10 12:25:13-0500: Loaded tuning parameters from: C:\Users\chang\Downloads\katago-v1.15.1-opencl-windows-x64/KataGoData/opencltuning/tune11_gpuNVIDIAGeForceRTX4060LaptopGPU_x19_y19_c384_mv14.txt +2024-11-10 12:25:13-0500: OpenCL backend thread 0: Model version 14 +2024-11-10 12:25:13-0500: OpenCL backend thread 0: Model name: kata1-b18c384nbt-s9908371456-d4296205232 +2024-11-10 12:25:13-0500: OpenCL backend thread 0: FP16Storage true FP16Compute false FP16TensorCores true FP16TensorCoresFor1x1 false +2024-11-10 12:25:13-0500: Loaded latest neural net kata1-b18c384nbt-s9908371456-d4296205232.bin.gz from: C:\Users\chang\OneDrive\Documents\GitHub\Kata_speed\training\models\kata1-b18c384nbt-s9908371456-d4296205232.bin.gz +2024-11-10 12:25:13-0500: Model loading loop thread loaded new neural net kata1-b18c384nbt-s9908371456-d4296205232.bin.gz +2024-11-10 12:25:13-0500: Game loop thread 0 starting game on new neural net: kata1-b18c384nbt-s9908371456-d4296205232.bin.gz +2024-11-10 12:25:13-0500: Game loop thread 10 starting game on new neural net: kata1-b18c384nbt-s9908371456-d4296205232.bin.gz +2024-11-10 12:25:13-0500: Game loop thread 15 starting game on new neural net: kata1-b18c384nbt-s9908371456-d4296205232.bin.gz +2024-11-10 12:25:13-0500: Game loop thread 23 starting game on new neural net: kata1-b18c384nbt-s9908371456-d4296205232.bin.gz +2024-11-10 12:25:13-0500: Game loop thread 4 starting game on new neural net: kata1-b18c384nbt-s9908371456-d4296205232.bin.gz +2024-11-10 12:25:13-0500: Data write loop starting for neural net: kata1-b18c384nbt-s9908371456-d4296205232.bin.gz +2024-11-10 12:25:13-0500: Game loop thread 5 starting game on new neural net: kata1-b18c384nbt-s9908371456-d4296205232.bin.gz +2024-11-10 12:25:13-0500: Game loop thread 6 starting game on new neural net: kata1-b18c384nbt-s9908371456-d4296205232.bin.gz +2024-11-10 12:25:13-0500: Game loop thread 8 starting game on new neural net: kata1-b18c384nbt-s9908371456-d4296205232.bin.gz +2024-11-10 12:25:13-0500: Game loop thread 7 starting game on new neural net: kata1-b18c384nbt-s9908371456-d4296205232.bin.gz +2024-11-10 12:25:13-0500: Game loop thread 1 starting game on new neural net: kata1-b18c384nbt-s9908371456-d4296205232.bin.gz +2024-11-10 12:25:13-0500: Game loop thread 9 starting game on new neural net: kata1-b18c384nbt-s9908371456-d4296205232.bin.gz +2024-11-10 12:25:13-0500: Started 10 games with kata1-b18c384nbt-s9908371456-d4296205232.bin.gz +2024-11-10 12:25:13-0500: Game loop thread 11 starting game on new neural net: kata1-b18c384nbt-s9908371456-d4296205232.bin.gz +2024-11-10 12:25:13-0500: Game loop thread 12 starting game on new neural net: kata1-b18c384nbt-s9908371456-d4296205232.bin.gz +2024-11-10 12:25:13-0500: Game loop thread 13 starting game on new neural net: kata1-b18c384nbt-s9908371456-d4296205232.bin.gz +2024-11-10 12:25:13-0500: Game loop thread 14 starting game on new neural net: kata1-b18c384nbt-s9908371456-d4296205232.bin.gz +2024-11-10 12:25:13-0500: Game loop thread 3 starting game on new neural net: kata1-b18c384nbt-s9908371456-d4296205232.bin.gz +2024-11-10 12:25:13-0500: Game loop thread 16 starting game on new neural net: kata1-b18c384nbt-s9908371456-d4296205232.bin.gz +2024-11-10 12:25:13-0500: Game loop thread 18 starting game on new neural net: kata1-b18c384nbt-s9908371456-d4296205232.bin.gz +2024-11-10 12:25:13-0500: Game loop thread 17 starting game on new neural net: kata1-b18c384nbt-s9908371456-d4296205232.bin.gz +2024-11-10 12:25:13-0500: Game loop thread 19 starting game on new neural net: kata1-b18c384nbt-s9908371456-d4296205232.bin.gz +2024-11-10 12:25:13-0500: Started 20 games with kata1-b18c384nbt-s9908371456-d4296205232.bin.gz +2024-11-10 12:25:13-0500: Game loop thread 20 starting game on new neural net: kata1-b18c384nbt-s9908371456-d4296205232.bin.gz +2024-11-10 12:25:13-0500: Game loop thread 2 starting game on new neural net: kata1-b18c384nbt-s9908371456-d4296205232.bin.gz +2024-11-10 12:25:13-0500: Game loop thread 22 starting game on new neural net: kata1-b18c384nbt-s9908371456-d4296205232.bin.gz +2024-11-10 12:25:13-0500: Game loop thread 21 starting game on new neural net: kata1-b18c384nbt-s9908371456-d4296205232.bin.gz +2024-11-10 12:25:13-0500: Game loop thread 24 starting game on new neural net: kata1-b18c384nbt-s9908371456-d4296205232.bin.gz +2024-11-10 12:25:13-0500: Model loading loop thread starting +2024-11-10 12:25:13-0500: Game loop thread 28 starting game on new neural net: kata1-b18c384nbt-s9908371456-d4296205232.bin.gz +2024-11-10 12:25:13-0500: Game loop thread 26 starting game on new neural net: kata1-b18c384nbt-s9908371456-d4296205232.bin.gz +2024-11-10 12:25:13-0500: Game loop thread 29 starting game on new neural net: kata1-b18c384nbt-s9908371456-d4296205232.bin.gz +2024-11-10 12:25:13-0500: Game loop thread 25 starting game on new neural net: kata1-b18c384nbt-s9908371456-d4296205232.bin.gz +2024-11-10 12:25:13-0500: Game loop thread 27 starting game on new neural net: kata1-b18c384nbt-s9908371456-d4296205232.bin.gz +2024-11-10 12:25:13-0500: Started 30 games with kata1-b18c384nbt-s9908371456-d4296205232.bin.gz +2024-11-10 12:26:28-0500: Started 40 games with kata1-b18c384nbt-s9908371456-d4296205232.bin.gz +2024-11-10 12:26:47-0500: Started 50 games with kata1-b18c384nbt-s9908371456-d4296205232.bin.gz +2024-11-10 12:27:16-0500: Started 60 games with kata1-b18c384nbt-s9908371456-d4296205232.bin.gz +2024-11-10 12:27:43-0500: Started 70 games with kata1-b18c384nbt-s9908371456-d4296205232.bin.gz +2024-11-10 12:28:29-0500: Started 80 games with kata1-b18c384nbt-s9908371456-d4296205232.bin.gz +2024-11-10 12:28:53-0500: Started 90 games with kata1-b18c384nbt-s9908371456-d4296205232.bin.gz +2024-11-10 12:28:56-0500: Game loop thread 10 terminating +2024-11-10 12:28:56-0500: Game loop thread 23 terminating +2024-11-10 12:28:59-0500: Game loop thread 22 terminating +2024-11-10 12:29:02-0500: Game loop thread 18 terminating +2024-11-10 12:29:06-0500: Game loop thread 25 terminating +2024-11-10 12:29:07-0500: Game loop thread 11 terminating +2024-11-10 12:29:07-0500: Game loop thread 26 terminating +2024-11-10 12:29:08-0500: Game loop thread 13 terminating +2024-11-10 12:29:13-0500: Game loop thread 6 terminating +2024-11-10 12:29:16-0500: Game loop thread 8 terminating +2024-11-10 12:29:16-0500: Game loop thread 27 terminating +2024-11-10 12:29:23-0500: Game loop thread 17 terminating +2024-11-10 12:29:25-0500: Game loop thread 0 terminating +2024-11-10 12:29:27-0500: Game loop thread 21 terminating +2024-11-10 12:29:28-0500: Game loop thread 1 terminating +2024-11-10 12:29:29-0500: Game loop thread 19 terminating +2024-11-10 12:29:29-0500: Game loop thread 28 terminating +2024-11-10 12:29:34-0500: Game loop thread 16 terminating +2024-11-10 12:29:39-0500: Game loop thread 14 terminating +2024-11-10 12:29:46-0500: Game loop thread 2 terminating +2024-11-10 12:29:47-0500: Game loop thread 7 terminating +2024-11-10 12:29:48-0500: Game loop thread 5 terminating +2024-11-10 12:29:49-0500: Game loop thread 24 terminating +2024-11-10 12:29:50-0500: Game loop thread 3 terminating +2024-11-10 12:29:50-0500: Game loop thread 4 terminating +2024-11-10 12:29:52-0500: Game loop thread 29 terminating +2024-11-10 12:29:58-0500: Game loop thread 12 terminating +2024-11-10 12:29:59-0500: Game loop thread 20 terminating +2024-11-10 12:30:01-0500: Game loop thread 9 terminating +2024-11-10 12:30:12-0500: Game loop thread 15 terminating +2024-11-10 12:30:12-0500: Model loading loop thread terminating +2024-11-10 12:30:12-0500: Data write loop finishing for neural net: kata1-b18c384nbt-s9908371456-d4296205232.bin.gz +2024-11-10 12:30:12-0500: Final cleanup of net: C:\Users\chang\OneDrive\Documents\GitHub\Kata_speed\training\models\kata1-b18c384nbt-s9908371456-d4296205232.bin.gz +2024-11-10 12:30:12-0500: Final NN rows: 429027 +2024-11-10 12:30:12-0500: Final NN batches: 26834 +2024-11-10 12:30:12-0500: Final NN avg batch size: 15.9882 +2024-11-10 12:30:12-0500: GPU -1 finishing, processed 429027 rows 26834 batches +2024-11-10 12:30:12-0500: Data write loop cleaned up and terminating for kata1-b18c384nbt-s9908371456-d4296205232.bin.gz +2024-11-10 12:30:12-0500: All cleaned up, quitting diff --git a/training/selfplay/log20241110-123408-158367354CDDA823.log b/training/selfplay/log20241110-123408-158367354CDDA823.log new file mode 100644 index 0000000..d23663c --- /dev/null +++ b/training/selfplay/log20241110-123408-158367354CDDA823.log @@ -0,0 +1,184 @@ +2024-11-10 12:34:08-0500: Running with following config: +allowRectangleProb = 0.50 +bSizeRelProbs = 1,1,8 +bSizes = 7,8,9 +cheapSearchProb = 0.75 +cheapSearchTargetWeight = 0.0 +cheapSearchVisits = 100 +chosenMovePrune = 1 +chosenMoveSubtract = 0 +chosenMoveTemperature = 0.15 +chosenMoveTemperatureEarly = 0.75 +chosenMoveTemperatureHalflife = 19 +compensateAfterPolicyInitProb = 0.2 +cpuctExploration = 1.1 +cpuctExplorationLog = 0.0 +cudaUseFP16 = auto +cudaUseNHWC = auto +dataBoardLen = 19 +drawEquivalentWinsForWhite = 0.5 +drawRandRadius = 0.5 +dynamicScoreCenterScale = 0.50 +dynamicScoreCenterZeroWeight = 0.25 +dynamicScoreUtilityFactor = 0.40 +earlyForkGameExpectedMoveProp = 0.025 +earlyForkGameMaxChoices = 12 +earlyForkGameProb = 0.04 +estimateLeadProb = 0.05 +fancyKomiVarying = true +firstFileRandMinProp = 0.15 +forkCompensateKomiProb = 0.80 +forkGameMaxChoices = 36 +forkGameMinChoices = 3 +forkGameProb = 0.01 +fpuParentWeightByVisitedPolicy = true +fpuParentWeightByVisitedPolicyPow = 2.0 +fpuReductionMax = 0.2 +handicapAsymmetricPlayoutProb = 0.5 +handicapCompensateKomiProb = 0.50 +handicapProb = 0.10 +hasButtons = false,false,true +initGamesWithPolicy = true +koRules = SIMPLE,POSITIONAL,SITUATIONAL +komiAuto = True +komiBigStdev = 12.0 +komiBigStdevProb = 0.06 +komiStdev = 1.0 +lcbStdevs = 5.0 +logGamesEvery = 10 +logMoves = false +logSearchInfo = false +logToStdout = true +maxAsymmetricRatio = 8.0 +maxDataQueueSize = 2000 +maxMovesPerGame = 1600 +maxRowsPerTrainFile = 10000 +maxVisits = 600 +minAsymmetricCompensateKomiProb = 0.4 +minVisitPropForLCB = 0.15 +multiStoneSuicideLegals = false,true +nnCacheSizePowerOfTwo = 21 +nnMaxBatchSize = 128 +nnMutexPoolSizePowerOfTwo = 15 +nnRandomize = true +noResultStdev = 0.166666666 +noResultUtilityForWhite = 0.0 +normalAsymmetricPlayoutProb = 0.01 +numGameThreads = 30 +numNNServerThreadsPerModel = 1 +numSearchThreads = 1 +numVirtualLossesPerThread = 1 +policyInitAreaProp = 0.04 +policySurpriseDataWeight = 0.5 +reduceVisits = true +reduceVisitsThreshold = 0.9 +reduceVisitsThresholdLookback = 3 +reducedVisitsMin = 100 +reducedVisitsWeight = 0.1 +rootDesiredPerChildVisitsCoeff = 2 +rootDirichletNoiseTotalConcentration = 10.83 +rootDirichletNoiseWeight = 0.25 +rootEndingBonusPoints = 0.5 +rootFpuReductionMax = 0.0 +rootNoiseEnabled = true +rootNumSymmetriesToSample = 4 +rootPolicyTemperature = 1.1 +rootPolicyTemperatureEarly = 1.25 +rootPruneUselessMoves = true +scoringRules = AREA,TERRITORY +sekiForkHackProb = 0.02 +sgfCompensateKomiProb = 0.90 +sidePositionProb = 0.020 +staticScoreUtilityFactor = 0.00 +subtreeValueBiasFactor = 0.30 +subtreeValueBiasWeightExponent = 0.8 +switchNetsMidGame = true +taxRules = NONE,NONE,SEKI,SEKI,ALL +useGraphSearch = true +useLcbForSelection = true +useNonBuggyLcb = true +valueSurpriseDataWeight = 0.1 +valueWeightExponent = 0.5 +winLossUtilityFactor = 1.0 + +2024-11-10 12:34:08-0500: Self Play Engine starting... +2024-11-10 12:34:08-0500: Git revision: 7939e2c3eb43df04e08de8c166889c6d5e937bfa +2024-11-10 12:34:08-0500: Loaded all config stuff, starting self play +2024-11-10 12:34:08-0500: Found new neural net kata1-b18c384nbt-s9908371456-d4296205232.bin.gz +2024-11-10 12:34:08-0500: nnRandSeed0 = 15778205460567257830 +2024-11-10 12:34:08-0500: After dedups: nnModelFile0 = C:\Users\chang\OneDrive\Documents\GitHub\Kata_speed\training\models\kata1-b18c384nbt-s9908371456-d4296205232.bin.gz useFP16 auto useNHWC auto +2024-11-10 12:34:08-0500: Initializing neural net buffer to be size 9 * 9 allowing smaller boards +2024-11-10 12:34:09-0500: Found OpenCL Platform 0: NVIDIA CUDA (NVIDIA Corporation) (OpenCL 3.0 CUDA 12.6.65) +2024-11-10 12:34:09-0500: Found 1 device(s) on platform 0 with type CPU or GPU or Accelerator +2024-11-10 12:34:09-0500: Found OpenCL Platform 1: Intel(R) OpenCL Graphics (Intel(R) Corporation) (OpenCL 3.0 ) +2024-11-10 12:34:09-0500: Found 1 device(s) on platform 1 with type CPU or GPU or Accelerator +2024-11-10 12:34:09-0500: Found OpenCL Device 0: NVIDIA GeForce RTX 4060 Laptop GPU (NVIDIA Corporation) (score 11000300) +2024-11-10 12:34:09-0500: Found OpenCL Device 1: Intel(R) UHD Graphics (Intel(R) Corporation) (score 6000300) +2024-11-10 12:34:09-0500: Creating context for OpenCL Platform: NVIDIA CUDA (NVIDIA Corporation) (OpenCL 3.0 CUDA 12.6.65) +2024-11-10 12:34:09-0500: Using OpenCL Device 0: NVIDIA GeForce RTX 4060 Laptop GPU (NVIDIA Corporation) OpenCL 3.0 CUDA (Extensions: cl_khr_global_int32_base_atomics cl_khr_global_int32_extended_atomics cl_khr_local_int32_base_atomics cl_khr_local_int32_extended_atomics cl_khr_fp64 cl_khr_3d_image_writes cl_khr_byte_addressable_store cl_khr_icd cl_khr_gl_sharing cl_nv_compiler_options cl_nv_device_attribute_query cl_nv_pragma_unroll cl_nv_d3d10_sharing cl_khr_d3d10_sharing cl_nv_d3d11_sharing cl_nv_copy_opts cl_nv_create_buffer cl_khr_int64_base_atomics cl_khr_int64_extended_atomics cl_khr_device_uuid cl_khr_pci_bus_info cl_khr_external_semaphore cl_khr_external_memory cl_khr_external_semaphore_win32 cl_khr_external_memory_win32) +2024-11-10 12:34:09-0500: Loaded tuning parameters from: C:\Users\chang\Downloads\katago-v1.15.1-opencl-windows-x64/KataGoData/opencltuning/tune11_gpuNVIDIAGeForceRTX4060LaptopGPU_x19_y19_c384_mv14.txt +2024-11-10 12:34:09-0500: OpenCL backend thread 0: Model version 14 +2024-11-10 12:34:09-0500: OpenCL backend thread 0: Model name: kata1-b18c384nbt-s9908371456-d4296205232 +2024-11-10 12:34:10-0500: OpenCL backend thread 0: FP16Storage true FP16Compute false FP16TensorCores true FP16TensorCoresFor1x1 false +2024-11-10 12:34:10-0500: Loaded latest neural net kata1-b18c384nbt-s9908371456-d4296205232.bin.gz from: C:\Users\chang\OneDrive\Documents\GitHub\Kata_speed\training\models\kata1-b18c384nbt-s9908371456-d4296205232.bin.gz +2024-11-10 12:34:10-0500: Model loading loop thread loaded new neural net kata1-b18c384nbt-s9908371456-d4296205232.bin.gz +2024-11-10 12:34:10-0500: Data write loop starting for neural net: kata1-b18c384nbt-s9908371456-d4296205232.bin.gz +2024-11-10 12:34:10-0500: Game loop thread 14 starting game on new neural net: kata1-b18c384nbt-s9908371456-d4296205232.bin.gz +2024-11-10 12:34:10-0500: Game loop thread 18 starting game on new neural net: kata1-b18c384nbt-s9908371456-d4296205232.bin.gz +2024-11-10 12:34:10-0500: Game loop thread 2 starting game on new neural net: kata1-b18c384nbt-s9908371456-d4296205232.bin.gz +2024-11-10 12:34:10-0500: Game loop thread 28 starting game on new neural net: kata1-b18c384nbt-s9908371456-d4296205232.bin.gz +2024-11-10 12:34:10-0500: Game loop thread 4 starting game on new neural net: kata1-b18c384nbt-s9908371456-d4296205232.bin.gz +2024-11-10 12:34:10-0500: Game loop thread 5 starting game on new neural net: kata1-b18c384nbt-s9908371456-d4296205232.bin.gz +2024-11-10 12:34:10-0500: Game loop thread 6 starting game on new neural net: kata1-b18c384nbt-s9908371456-d4296205232.bin.gz +2024-11-10 12:34:10-0500: Game loop thread 7 starting game on new neural net: kata1-b18c384nbt-s9908371456-d4296205232.bin.gz +2024-11-10 12:34:10-0500: Game loop thread 8 starting game on new neural net: kata1-b18c384nbt-s9908371456-d4296205232.bin.gz +2024-11-10 12:34:10-0500: Game loop thread 9 starting game on new neural net: kata1-b18c384nbt-s9908371456-d4296205232.bin.gz +2024-11-10 12:34:10-0500: Started 10 games with kata1-b18c384nbt-s9908371456-d4296205232.bin.gz +2024-11-10 12:34:10-0500: Game loop thread 10 starting game on new neural net: kata1-b18c384nbt-s9908371456-d4296205232.bin.gz +2024-11-10 12:34:10-0500: Game loop thread 11 starting game on new neural net: kata1-b18c384nbt-s9908371456-d4296205232.bin.gz +2024-11-10 12:34:10-0500: Game loop thread 12 starting game on new neural net: kata1-b18c384nbt-s9908371456-d4296205232.bin.gz +2024-11-10 12:34:10-0500: Game loop thread 13 starting game on new neural net: kata1-b18c384nbt-s9908371456-d4296205232.bin.gz +2024-11-10 12:34:10-0500: Game loop thread 15 starting game on new neural net: kata1-b18c384nbt-s9908371456-d4296205232.bin.gz +2024-11-10 12:34:10-0500: Game loop thread 16 starting game on new neural net: kata1-b18c384nbt-s9908371456-d4296205232.bin.gz +2024-11-10 12:34:10-0500: Game loop thread 17 starting game on new neural net: kata1-b18c384nbt-s9908371456-d4296205232.bin.gz +2024-11-10 12:34:10-0500: Game loop thread 19 starting game on new neural net: kata1-b18c384nbt-s9908371456-d4296205232.bin.gz +2024-11-10 12:34:10-0500: Game loop thread 0 starting game on new neural net: kata1-b18c384nbt-s9908371456-d4296205232.bin.gz +2024-11-10 12:34:10-0500: Game loop thread 1 starting game on new neural net: kata1-b18c384nbt-s9908371456-d4296205232.bin.gz +2024-11-10 12:34:10-0500: Started 20 games with kata1-b18c384nbt-s9908371456-d4296205232.bin.gz +2024-11-10 12:34:10-0500: Game loop thread 20 starting game on new neural net: kata1-b18c384nbt-s9908371456-d4296205232.bin.gz +2024-11-10 12:34:10-0500: Game loop thread 21 starting game on new neural net: kata1-b18c384nbt-s9908371456-d4296205232.bin.gz +2024-11-10 12:34:10-0500: Game loop thread 25 starting game on new neural net: kata1-b18c384nbt-s9908371456-d4296205232.bin.gz +2024-11-10 12:34:10-0500: Game loop thread 26 starting game on new neural net: kata1-b18c384nbt-s9908371456-d4296205232.bin.gz +2024-11-10 12:34:10-0500: Game loop thread 22 starting game on new neural net: kata1-b18c384nbt-s9908371456-d4296205232.bin.gz +2024-11-10 12:34:10-0500: Game loop thread 24 starting game on new neural net: kata1-b18c384nbt-s9908371456-d4296205232.bin.gz +2024-11-10 12:34:10-0500: Game loop thread 27 starting game on new neural net: kata1-b18c384nbt-s9908371456-d4296205232.bin.gz +2024-11-10 12:34:10-0500: Model loading loop thread starting +2024-11-10 12:34:10-0500: Game loop thread 23 starting game on new neural net: kata1-b18c384nbt-s9908371456-d4296205232.bin.gz +2024-11-10 12:34:10-0500: Game loop thread 3 starting game on new neural net: kata1-b18c384nbt-s9908371456-d4296205232.bin.gz +2024-11-10 12:34:10-0500: Game loop thread 29 starting game on new neural net: kata1-b18c384nbt-s9908371456-d4296205232.bin.gz +2024-11-10 12:34:10-0500: Started 30 games with kata1-b18c384nbt-s9908371456-d4296205232.bin.gz +2024-11-10 12:34:44-0500: Game loop thread 15 terminating +2024-11-10 12:34:48-0500: Game loop thread 6 terminating +2024-11-10 12:34:54-0500: Game loop thread 28 terminating +2024-11-10 12:34:57-0500: Game loop thread 24 terminating +2024-11-10 12:34:59-0500: Game loop thread 7 terminating +2024-11-10 12:35:00-0500: Game loop thread 22 terminating +2024-11-10 12:35:04-0500: Game loop thread 18 terminating +2024-11-10 12:35:07-0500: Game loop thread 19 terminating +2024-11-10 12:35:11-0500: Game loop thread 29 terminating +2024-11-10 12:35:13-0500: Game loop thread 1 terminating +2024-11-10 12:35:16-0500: Game loop thread 4 terminating +2024-11-10 12:35:17-0500: Game loop thread 11 terminating +2024-11-10 12:35:23-0500: Game loop thread 21 terminating +2024-11-10 12:35:25-0500: Game loop thread 0 terminating +2024-11-10 12:35:25-0500: Game loop thread 16 terminating +2024-11-10 12:35:27-0500: Game loop thread 3 terminating +2024-11-10 12:35:27-0500: Game loop thread 27 terminating +2024-11-10 12:35:28-0500: Game loop thread 20 terminating +2024-11-10 12:35:28-0500: Game loop thread 10 terminating +2024-11-10 12:35:29-0500: Game loop thread 12 terminating +2024-11-10 12:35:30-0500: Game loop thread 17 terminating +2024-11-10 12:35:30-0500: Game loop thread 14 terminating +2024-11-10 12:35:36-0500: Game loop thread 2 terminating +2024-11-10 12:35:38-0500: Game loop thread 9 terminating +2024-11-10 12:35:41-0500: Game loop thread 13 terminating diff --git a/training/selfplay/stdout.txt b/training/selfplay/stdout.txt index 8ea4e13..1fa0147 100644 --- a/training/selfplay/stdout.txt +++ b/training/selfplay/stdout.txt @@ -807,3 +807,433 @@ winLossUtilityFactor = 1.0 2024-11-10 12:14:04-0500: Started 40 games with kata1-b18c384nbt-s9908371456-d4296205232.bin.gz 2024-11-10 12:14:32-0500: Started 50 games with kata1-b18c384nbt-s9908371456-d4296205232.bin.gz 2024-11-10 12:14:58-0500: Started 60 games with kata1-b18c384nbt-s9908371456-d4296205232.bin.gz +2024-11-10 12:15:23-0500: Started 70 games with kata1-b18c384nbt-s9908371456-d4296205232.bin.gz +2024-11-10 12:15:55-0500: Started 80 games with kata1-b18c384nbt-s9908371456-d4296205232.bin.gz +2024-11-10 12:16:23-0500: Started 90 games with kata1-b18c384nbt-s9908371456-d4296205232.bin.gz +2024-11-10 12:16:24-0500: Game loop thread 10 terminating +2024-11-10 12:16:28-0500: Game loop thread 8 terminating +2024-11-10 12:16:32-0500: Game loop thread 27 terminating +2024-11-10 12:16:33-0500: Game loop thread 2 terminating +2024-11-10 12:16:36-0500: Game loop thread 20 terminating +2024-11-10 12:16:37-0500: Game loop thread 7 terminating +2024-11-10 12:16:38-0500: Game loop thread 3 terminating +2024-11-10 12:16:39-0500: Game loop thread 4 terminating +2024-11-10 12:16:41-0500: Game loop thread 6 terminating +2024-11-10 12:16:45-0500: Game loop thread 23 terminating +2024-11-10 12:16:45-0500: Game loop thread 15 terminating +2024-11-10 12:16:46-0500: Game loop thread 19 terminating +2024-11-10 12:16:49-0500: Game loop thread 22 terminating +2024-11-10 12:17:00-0500: Game loop thread 11 terminating +2024-11-10 12:17:00-0500: Game loop thread 17 terminating +2024-11-10 12:17:01-0500: Game loop thread 14 terminating +2024-11-10 12:17:03-0500: Game loop thread 29 terminating +2024-11-10 12:17:05-0500: Game loop thread 0 terminating +2024-11-10 12:17:05-0500: Game loop thread 1 terminating +2024-11-10 12:17:06-0500: Game loop thread 24 terminating +2024-11-10 12:17:10-0500: Game loop thread 28 terminating +2024-11-10 12:17:10-0500: Game loop thread 13 terminating +2024-11-10 12:17:11-0500: Game loop thread 25 terminating +2024-11-10 12:17:12-0500: Game loop thread 5 terminating +2024-11-10 12:17:18-0500: Game loop thread 21 terminating +2024-11-10 12:17:21-0500: Game loop thread 9 terminating +2024-11-10 12:17:28-0500: Game loop thread 16 terminating +2024-11-10 12:17:32-0500: Game loop thread 26 terminating +2024-11-10 12:17:43-0500: Game loop thread 12 terminating +2024-11-10 12:17:44-0500: Game loop thread 18 terminating +2024-11-10 12:17:44-0500: Model loading loop thread terminating +2024-11-10 12:17:45-0500: Data write loop finishing for neural net: kata1-b18c384nbt-s9908371456-d4296205232.bin.gz +2024-11-10 12:17:45-0500: Final cleanup of net: C:\Users\chang\OneDrive\Documents\GitHub\Kata_speed\training\models\kata1-b18c384nbt-s9908371456-d4296205232.bin.gz +2024-11-10 12:17:45-0500: Final NN rows: 403486 +2024-11-10 12:17:45-0500: Final NN batches: 31322 +2024-11-10 12:17:45-0500: Final NN avg batch size: 12.8819 +2024-11-10 12:17:45-0500: GPU -1 finishing, processed 403486 rows 31322 batches +2024-11-10 12:17:45-0500: Data write loop cleaned up and terminating for kata1-b18c384nbt-s9908371456-d4296205232.bin.gz +2024-11-10 12:17:45-0500: All cleaned up, quitting +2024-11-10 12:25:12-0500: Running with following config: +allowRectangleProb = 0.50 +bSizeRelProbs = 1,1,8 +bSizes = 7,8,9 +cheapSearchProb = 0.75 +cheapSearchTargetWeight = 0.0 +cheapSearchVisits = 100 +chosenMovePrune = 1 +chosenMoveSubtract = 0 +chosenMoveTemperature = 0.15 +chosenMoveTemperatureEarly = 0.75 +chosenMoveTemperatureHalflife = 19 +compensateAfterPolicyInitProb = 0.2 +cpuctExploration = 1.1 +cpuctExplorationLog = 0.0 +cudaUseFP16 = auto +cudaUseNHWC = auto +dataBoardLen = 19 +drawEquivalentWinsForWhite = 0.5 +drawRandRadius = 0.5 +dynamicScoreCenterScale = 0.50 +dynamicScoreCenterZeroWeight = 0.25 +dynamicScoreUtilityFactor = 0.40 +earlyForkGameExpectedMoveProp = 0.025 +earlyForkGameMaxChoices = 12 +earlyForkGameProb = 0.04 +estimateLeadProb = 0.05 +fancyKomiVarying = true +firstFileRandMinProp = 0.15 +forkCompensateKomiProb = 0.80 +forkGameMaxChoices = 36 +forkGameMinChoices = 3 +forkGameProb = 0.01 +fpuParentWeightByVisitedPolicy = true +fpuParentWeightByVisitedPolicyPow = 2.0 +fpuReductionMax = 0.2 +handicapAsymmetricPlayoutProb = 0.5 +handicapCompensateKomiProb = 0.50 +handicapProb = 0.10 +hasButtons = false,false,true +initGamesWithPolicy = true +koRules = SIMPLE,POSITIONAL,SITUATIONAL +komiAuto = True +komiBigStdev = 12.0 +komiBigStdevProb = 0.06 +komiStdev = 1.0 +lcbStdevs = 5.0 +logGamesEvery = 10 +logMoves = false +logSearchInfo = false +logToStdout = true +maxAsymmetricRatio = 8.0 +maxDataQueueSize = 2000 +maxMovesPerGame = 1600 +maxRowsPerTrainFile = 10000 +maxVisits = 600 +minAsymmetricCompensateKomiProb = 0.4 +minVisitPropForLCB = 0.15 +multiStoneSuicideLegals = false,true +nnCacheSizePowerOfTwo = 21 +nnMaxBatchSize = 128 +nnMutexPoolSizePowerOfTwo = 15 +nnRandomize = true +noResultStdev = 0.166666666 +noResultUtilityForWhite = 0.0 +normalAsymmetricPlayoutProb = 0.01 +numGameThreads = 30 +numNNServerThreadsPerModel = 1 +numSearchThreads = 1 +numVirtualLossesPerThread = 1 +policyInitAreaProp = 0.04 +policySurpriseDataWeight = 0.5 +reduceVisits = true +reduceVisitsThreshold = 0.9 +reduceVisitsThresholdLookback = 3 +reducedVisitsMin = 100 +reducedVisitsWeight = 0.1 +rootDesiredPerChildVisitsCoeff = 2 +rootDirichletNoiseTotalConcentration = 10.83 +rootDirichletNoiseWeight = 0.25 +rootEndingBonusPoints = 0.5 +rootFpuReductionMax = 0.0 +rootNoiseEnabled = true +rootNumSymmetriesToSample = 4 +rootPolicyTemperature = 1.1 +rootPolicyTemperatureEarly = 1.25 +rootPruneUselessMoves = true +scoringRules = AREA,TERRITORY +sekiForkHackProb = 0.02 +sgfCompensateKomiProb = 0.90 +sidePositionProb = 0.020 +staticScoreUtilityFactor = 0.00 +subtreeValueBiasFactor = 0.30 +subtreeValueBiasWeightExponent = 0.8 +switchNetsMidGame = true +taxRules = NONE,NONE,SEKI,SEKI,ALL +useGraphSearch = true +useLcbForSelection = true +useNonBuggyLcb = true +valueSurpriseDataWeight = 0.1 +valueWeightExponent = 0.5 +winLossUtilityFactor = 1.0 + +2024-11-10 12:25:12-0500: Self Play Engine starting... +2024-11-10 12:25:12-0500: Git revision: 7939e2c3eb43df04e08de8c166889c6d5e937bfa +2024-11-10 12:25:12-0500: Loaded all config stuff, starting self play +2024-11-10 12:25:12-0500: Found new neural net kata1-b18c384nbt-s9908371456-d4296205232.bin.gz +2024-11-10 12:25:12-0500: nnRandSeed0 = 10060376068512553218 +2024-11-10 12:25:12-0500: After dedups: nnModelFile0 = C:\Users\chang\OneDrive\Documents\GitHub\Kata_speed\training\models\kata1-b18c384nbt-s9908371456-d4296205232.bin.gz useFP16 auto useNHWC auto +2024-11-10 12:25:12-0500: Initializing neural net buffer to be size 9 * 9 allowing smaller boards +2024-11-10 12:25:13-0500: Found OpenCL Platform 0: NVIDIA CUDA (NVIDIA Corporation) (OpenCL 3.0 CUDA 12.6.65) +2024-11-10 12:25:13-0500: Found 1 device(s) on platform 0 with type CPU or GPU or Accelerator +2024-11-10 12:25:13-0500: Found OpenCL Platform 1: Intel(R) OpenCL Graphics (Intel(R) Corporation) (OpenCL 3.0 ) +2024-11-10 12:25:13-0500: Found 1 device(s) on platform 1 with type CPU or GPU or Accelerator +2024-11-10 12:25:13-0500: Found OpenCL Device 0: NVIDIA GeForce RTX 4060 Laptop GPU (NVIDIA Corporation) (score 11000300) +2024-11-10 12:25:13-0500: Found OpenCL Device 1: Intel(R) UHD Graphics (Intel(R) Corporation) (score 6000300) +2024-11-10 12:25:13-0500: Creating context for OpenCL Platform: NVIDIA CUDA (NVIDIA Corporation) (OpenCL 3.0 CUDA 12.6.65) +2024-11-10 12:25:13-0500: Using OpenCL Device 0: NVIDIA GeForce RTX 4060 Laptop GPU (NVIDIA Corporation) OpenCL 3.0 CUDA (Extensions: cl_khr_global_int32_base_atomics cl_khr_global_int32_extended_atomics cl_khr_local_int32_base_atomics cl_khr_local_int32_extended_atomics cl_khr_fp64 cl_khr_3d_image_writes cl_khr_byte_addressable_store cl_khr_icd cl_khr_gl_sharing cl_nv_compiler_options cl_nv_device_attribute_query cl_nv_pragma_unroll cl_nv_d3d10_sharing cl_khr_d3d10_sharing cl_nv_d3d11_sharing cl_nv_copy_opts cl_nv_create_buffer cl_khr_int64_base_atomics cl_khr_int64_extended_atomics cl_khr_device_uuid cl_khr_pci_bus_info cl_khr_external_semaphore cl_khr_external_memory cl_khr_external_semaphore_win32 cl_khr_external_memory_win32) +2024-11-10 12:25:13-0500: Loaded tuning parameters from: C:\Users\chang\Downloads\katago-v1.15.1-opencl-windows-x64/KataGoData/opencltuning/tune11_gpuNVIDIAGeForceRTX4060LaptopGPU_x19_y19_c384_mv14.txt +2024-11-10 12:25:13-0500: OpenCL backend thread 0: Model version 14 +2024-11-10 12:25:13-0500: OpenCL backend thread 0: Model name: kata1-b18c384nbt-s9908371456-d4296205232 +2024-11-10 12:25:13-0500: OpenCL backend thread 0: FP16Storage true FP16Compute false FP16TensorCores true FP16TensorCoresFor1x1 false +2024-11-10 12:25:13-0500: Loaded latest neural net kata1-b18c384nbt-s9908371456-d4296205232.bin.gz from: C:\Users\chang\OneDrive\Documents\GitHub\Kata_speed\training\models\kata1-b18c384nbt-s9908371456-d4296205232.bin.gz +2024-11-10 12:25:13-0500: Model loading loop thread loaded new neural net kata1-b18c384nbt-s9908371456-d4296205232.bin.gz +2024-11-10 12:25:13-0500: Game loop thread 0 starting game on new neural net: kata1-b18c384nbt-s9908371456-d4296205232.bin.gz +2024-11-10 12:25:13-0500: Game loop thread 10 starting game on new neural net: kata1-b18c384nbt-s9908371456-d4296205232.bin.gz +2024-11-10 12:25:13-0500: Game loop thread 15 starting game on new neural net: kata1-b18c384nbt-s9908371456-d4296205232.bin.gz +2024-11-10 12:25:13-0500: Game loop thread 23 starting game on new neural net: kata1-b18c384nbt-s9908371456-d4296205232.bin.gz +2024-11-10 12:25:13-0500: Game loop thread 4 starting game on new neural net: kata1-b18c384nbt-s9908371456-d4296205232.bin.gz +2024-11-10 12:25:13-0500: Data write loop starting for neural net: kata1-b18c384nbt-s9908371456-d4296205232.bin.gz +2024-11-10 12:25:13-0500: Game loop thread 5 starting game on new neural net: kata1-b18c384nbt-s9908371456-d4296205232.bin.gz +2024-11-10 12:25:13-0500: Game loop thread 6 starting game on new neural net: kata1-b18c384nbt-s9908371456-d4296205232.bin.gz +2024-11-10 12:25:13-0500: Game loop thread 8 starting game on new neural net: kata1-b18c384nbt-s9908371456-d4296205232.bin.gz +2024-11-10 12:25:13-0500: Game loop thread 7 starting game on new neural net: kata1-b18c384nbt-s9908371456-d4296205232.bin.gz +2024-11-10 12:25:13-0500: Game loop thread 1 starting game on new neural net: kata1-b18c384nbt-s9908371456-d4296205232.bin.gz +2024-11-10 12:25:13-0500: Game loop thread 9 starting game on new neural net: kata1-b18c384nbt-s9908371456-d4296205232.bin.gz +2024-11-10 12:25:13-0500: Started 10 games with kata1-b18c384nbt-s9908371456-d4296205232.bin.gz +2024-11-10 12:25:13-0500: Game loop thread 11 starting game on new neural net: kata1-b18c384nbt-s9908371456-d4296205232.bin.gz +2024-11-10 12:25:13-0500: Game loop thread 12 starting game on new neural net: kata1-b18c384nbt-s9908371456-d4296205232.bin.gz +2024-11-10 12:25:13-0500: Game loop thread 13 starting game on new neural net: kata1-b18c384nbt-s9908371456-d4296205232.bin.gz +2024-11-10 12:25:13-0500: Game loop thread 14 starting game on new neural net: kata1-b18c384nbt-s9908371456-d4296205232.bin.gz +2024-11-10 12:25:13-0500: Game loop thread 3 starting game on new neural net: kata1-b18c384nbt-s9908371456-d4296205232.bin.gz +2024-11-10 12:25:13-0500: Game loop thread 16 starting game on new neural net: kata1-b18c384nbt-s9908371456-d4296205232.bin.gz +2024-11-10 12:25:13-0500: Game loop thread 18 starting game on new neural net: kata1-b18c384nbt-s9908371456-d4296205232.bin.gz +2024-11-10 12:25:13-0500: Game loop thread 17 starting game on new neural net: kata1-b18c384nbt-s9908371456-d4296205232.bin.gz +2024-11-10 12:25:13-0500: Game loop thread 19 starting game on new neural net: kata1-b18c384nbt-s9908371456-d4296205232.bin.gz +2024-11-10 12:25:13-0500: Started 20 games with kata1-b18c384nbt-s9908371456-d4296205232.bin.gz +2024-11-10 12:25:13-0500: Game loop thread 20 starting game on new neural net: kata1-b18c384nbt-s9908371456-d4296205232.bin.gz +2024-11-10 12:25:13-0500: Game loop thread 2 starting game on new neural net: kata1-b18c384nbt-s9908371456-d4296205232.bin.gz +2024-11-10 12:25:13-0500: Game loop thread 22 starting game on new neural net: kata1-b18c384nbt-s9908371456-d4296205232.bin.gz +2024-11-10 12:25:13-0500: Game loop thread 21 starting game on new neural net: kata1-b18c384nbt-s9908371456-d4296205232.bin.gz +2024-11-10 12:25:13-0500: Game loop thread 24 starting game on new neural net: kata1-b18c384nbt-s9908371456-d4296205232.bin.gz +2024-11-10 12:25:13-0500: Model loading loop thread starting +2024-11-10 12:25:13-0500: Game loop thread 28 starting game on new neural net: kata1-b18c384nbt-s9908371456-d4296205232.bin.gz +2024-11-10 12:25:13-0500: Game loop thread 26 starting game on new neural net: kata1-b18c384nbt-s9908371456-d4296205232.bin.gz +2024-11-10 12:25:13-0500: Game loop thread 29 starting game on new neural net: kata1-b18c384nbt-s9908371456-d4296205232.bin.gz +2024-11-10 12:25:13-0500: Game loop thread 25 starting game on new neural net: kata1-b18c384nbt-s9908371456-d4296205232.bin.gz +2024-11-10 12:25:13-0500: Game loop thread 27 starting game on new neural net: kata1-b18c384nbt-s9908371456-d4296205232.bin.gz +2024-11-10 12:25:13-0500: Started 30 games with kata1-b18c384nbt-s9908371456-d4296205232.bin.gz +2024-11-10 12:26:28-0500: Started 40 games with kata1-b18c384nbt-s9908371456-d4296205232.bin.gz +2024-11-10 12:26:47-0500: Started 50 games with kata1-b18c384nbt-s9908371456-d4296205232.bin.gz +2024-11-10 12:27:16-0500: Started 60 games with kata1-b18c384nbt-s9908371456-d4296205232.bin.gz +2024-11-10 12:27:43-0500: Started 70 games with kata1-b18c384nbt-s9908371456-d4296205232.bin.gz +2024-11-10 12:28:29-0500: Started 80 games with kata1-b18c384nbt-s9908371456-d4296205232.bin.gz +2024-11-10 12:28:53-0500: Started 90 games with kata1-b18c384nbt-s9908371456-d4296205232.bin.gz +2024-11-10 12:28:56-0500: Game loop thread 10 terminating +2024-11-10 12:28:56-0500: Game loop thread 23 terminating +2024-11-10 12:28:59-0500: Game loop thread 22 terminating +2024-11-10 12:29:02-0500: Game loop thread 18 terminating +2024-11-10 12:29:06-0500: Game loop thread 25 terminating +2024-11-10 12:29:07-0500: Game loop thread 11 terminating +2024-11-10 12:29:07-0500: Game loop thread 26 terminating +2024-11-10 12:29:08-0500: Game loop thread 13 terminating +2024-11-10 12:29:13-0500: Game loop thread 6 terminating +2024-11-10 12:29:16-0500: Game loop thread 8 terminating +2024-11-10 12:29:16-0500: Game loop thread 27 terminating +2024-11-10 12:29:23-0500: Game loop thread 17 terminating +2024-11-10 12:29:25-0500: Game loop thread 0 terminating +2024-11-10 12:29:27-0500: Game loop thread 21 terminating +2024-11-10 12:29:28-0500: Game loop thread 1 terminating +2024-11-10 12:29:29-0500: Game loop thread 19 terminating +2024-11-10 12:29:29-0500: Game loop thread 28 terminating +2024-11-10 12:29:34-0500: Game loop thread 16 terminating +2024-11-10 12:29:39-0500: Game loop thread 14 terminating +2024-11-10 12:29:46-0500: Game loop thread 2 terminating +2024-11-10 12:29:47-0500: Game loop thread 7 terminating +2024-11-10 12:29:48-0500: Game loop thread 5 terminating +2024-11-10 12:29:49-0500: Game loop thread 24 terminating +2024-11-10 12:29:50-0500: Game loop thread 3 terminating +2024-11-10 12:29:50-0500: Game loop thread 4 terminating +2024-11-10 12:29:52-0500: Game loop thread 29 terminating +2024-11-10 12:29:58-0500: Game loop thread 12 terminating +2024-11-10 12:29:59-0500: Game loop thread 20 terminating +2024-11-10 12:30:01-0500: Game loop thread 9 terminating +2024-11-10 12:30:12-0500: Game loop thread 15 terminating +2024-11-10 12:30:12-0500: Model loading loop thread terminating +2024-11-10 12:30:12-0500: Data write loop finishing for neural net: kata1-b18c384nbt-s9908371456-d4296205232.bin.gz +2024-11-10 12:30:12-0500: Final cleanup of net: C:\Users\chang\OneDrive\Documents\GitHub\Kata_speed\training\models\kata1-b18c384nbt-s9908371456-d4296205232.bin.gz +2024-11-10 12:30:12-0500: Final NN rows: 429027 +2024-11-10 12:30:12-0500: Final NN batches: 26834 +2024-11-10 12:30:12-0500: Final NN avg batch size: 15.9882 +2024-11-10 12:30:12-0500: GPU -1 finishing, processed 429027 rows 26834 batches +2024-11-10 12:30:12-0500: Data write loop cleaned up and terminating for kata1-b18c384nbt-s9908371456-d4296205232.bin.gz +2024-11-10 12:30:12-0500: All cleaned up, quitting +2024-11-10 12:34:08-0500: Running with following config: +allowRectangleProb = 0.50 +bSizeRelProbs = 1,1,8 +bSizes = 7,8,9 +cheapSearchProb = 0.75 +cheapSearchTargetWeight = 0.0 +cheapSearchVisits = 100 +chosenMovePrune = 1 +chosenMoveSubtract = 0 +chosenMoveTemperature = 0.15 +chosenMoveTemperatureEarly = 0.75 +chosenMoveTemperatureHalflife = 19 +compensateAfterPolicyInitProb = 0.2 +cpuctExploration = 1.1 +cpuctExplorationLog = 0.0 +cudaUseFP16 = auto +cudaUseNHWC = auto +dataBoardLen = 19 +drawEquivalentWinsForWhite = 0.5 +drawRandRadius = 0.5 +dynamicScoreCenterScale = 0.50 +dynamicScoreCenterZeroWeight = 0.25 +dynamicScoreUtilityFactor = 0.40 +earlyForkGameExpectedMoveProp = 0.025 +earlyForkGameMaxChoices = 12 +earlyForkGameProb = 0.04 +estimateLeadProb = 0.05 +fancyKomiVarying = true +firstFileRandMinProp = 0.15 +forkCompensateKomiProb = 0.80 +forkGameMaxChoices = 36 +forkGameMinChoices = 3 +forkGameProb = 0.01 +fpuParentWeightByVisitedPolicy = true +fpuParentWeightByVisitedPolicyPow = 2.0 +fpuReductionMax = 0.2 +handicapAsymmetricPlayoutProb = 0.5 +handicapCompensateKomiProb = 0.50 +handicapProb = 0.10 +hasButtons = false,false,true +initGamesWithPolicy = true +koRules = SIMPLE,POSITIONAL,SITUATIONAL +komiAuto = True +komiBigStdev = 12.0 +komiBigStdevProb = 0.06 +komiStdev = 1.0 +lcbStdevs = 5.0 +logGamesEvery = 10 +logMoves = false +logSearchInfo = false +logToStdout = true +maxAsymmetricRatio = 8.0 +maxDataQueueSize = 2000 +maxMovesPerGame = 1600 +maxRowsPerTrainFile = 10000 +maxVisits = 600 +minAsymmetricCompensateKomiProb = 0.4 +minVisitPropForLCB = 0.15 +multiStoneSuicideLegals = false,true +nnCacheSizePowerOfTwo = 21 +nnMaxBatchSize = 128 +nnMutexPoolSizePowerOfTwo = 15 +nnRandomize = true +noResultStdev = 0.166666666 +noResultUtilityForWhite = 0.0 +normalAsymmetricPlayoutProb = 0.01 +numGameThreads = 30 +numNNServerThreadsPerModel = 1 +numSearchThreads = 1 +numVirtualLossesPerThread = 1 +policyInitAreaProp = 0.04 +policySurpriseDataWeight = 0.5 +reduceVisits = true +reduceVisitsThreshold = 0.9 +reduceVisitsThresholdLookback = 3 +reducedVisitsMin = 100 +reducedVisitsWeight = 0.1 +rootDesiredPerChildVisitsCoeff = 2 +rootDirichletNoiseTotalConcentration = 10.83 +rootDirichletNoiseWeight = 0.25 +rootEndingBonusPoints = 0.5 +rootFpuReductionMax = 0.0 +rootNoiseEnabled = true +rootNumSymmetriesToSample = 4 +rootPolicyTemperature = 1.1 +rootPolicyTemperatureEarly = 1.25 +rootPruneUselessMoves = true +scoringRules = AREA,TERRITORY +sekiForkHackProb = 0.02 +sgfCompensateKomiProb = 0.90 +sidePositionProb = 0.020 +staticScoreUtilityFactor = 0.00 +subtreeValueBiasFactor = 0.30 +subtreeValueBiasWeightExponent = 0.8 +switchNetsMidGame = true +taxRules = NONE,NONE,SEKI,SEKI,ALL +useGraphSearch = true +useLcbForSelection = true +useNonBuggyLcb = true +valueSurpriseDataWeight = 0.1 +valueWeightExponent = 0.5 +winLossUtilityFactor = 1.0 + +2024-11-10 12:34:08-0500: Self Play Engine starting... +2024-11-10 12:34:08-0500: Git revision: 7939e2c3eb43df04e08de8c166889c6d5e937bfa +2024-11-10 12:34:08-0500: Loaded all config stuff, starting self play +2024-11-10 12:34:08-0500: Found new neural net kata1-b18c384nbt-s9908371456-d4296205232.bin.gz +2024-11-10 12:34:08-0500: nnRandSeed0 = 15778205460567257830 +2024-11-10 12:34:08-0500: After dedups: nnModelFile0 = C:\Users\chang\OneDrive\Documents\GitHub\Kata_speed\training\models\kata1-b18c384nbt-s9908371456-d4296205232.bin.gz useFP16 auto useNHWC auto +2024-11-10 12:34:08-0500: Initializing neural net buffer to be size 9 * 9 allowing smaller boards +2024-11-10 12:34:09-0500: Found OpenCL Platform 0: NVIDIA CUDA (NVIDIA Corporation) (OpenCL 3.0 CUDA 12.6.65) +2024-11-10 12:34:09-0500: Found 1 device(s) on platform 0 with type CPU or GPU or Accelerator +2024-11-10 12:34:09-0500: Found OpenCL Platform 1: Intel(R) OpenCL Graphics (Intel(R) Corporation) (OpenCL 3.0 ) +2024-11-10 12:34:09-0500: Found 1 device(s) on platform 1 with type CPU or GPU or Accelerator +2024-11-10 12:34:09-0500: Found OpenCL Device 0: NVIDIA GeForce RTX 4060 Laptop GPU (NVIDIA Corporation) (score 11000300) +2024-11-10 12:34:09-0500: Found OpenCL Device 1: Intel(R) UHD Graphics (Intel(R) Corporation) (score 6000300) +2024-11-10 12:34:09-0500: Creating context for OpenCL Platform: NVIDIA CUDA (NVIDIA Corporation) (OpenCL 3.0 CUDA 12.6.65) +2024-11-10 12:34:09-0500: Using OpenCL Device 0: NVIDIA GeForce RTX 4060 Laptop GPU (NVIDIA Corporation) OpenCL 3.0 CUDA (Extensions: cl_khr_global_int32_base_atomics cl_khr_global_int32_extended_atomics cl_khr_local_int32_base_atomics cl_khr_local_int32_extended_atomics cl_khr_fp64 cl_khr_3d_image_writes cl_khr_byte_addressable_store cl_khr_icd cl_khr_gl_sharing cl_nv_compiler_options cl_nv_device_attribute_query cl_nv_pragma_unroll cl_nv_d3d10_sharing cl_khr_d3d10_sharing cl_nv_d3d11_sharing cl_nv_copy_opts cl_nv_create_buffer cl_khr_int64_base_atomics cl_khr_int64_extended_atomics cl_khr_device_uuid cl_khr_pci_bus_info cl_khr_external_semaphore cl_khr_external_memory cl_khr_external_semaphore_win32 cl_khr_external_memory_win32) +2024-11-10 12:34:09-0500: Loaded tuning parameters from: C:\Users\chang\Downloads\katago-v1.15.1-opencl-windows-x64/KataGoData/opencltuning/tune11_gpuNVIDIAGeForceRTX4060LaptopGPU_x19_y19_c384_mv14.txt +2024-11-10 12:34:09-0500: OpenCL backend thread 0: Model version 14 +2024-11-10 12:34:09-0500: OpenCL backend thread 0: Model name: kata1-b18c384nbt-s9908371456-d4296205232 +2024-11-10 12:34:10-0500: OpenCL backend thread 0: FP16Storage true FP16Compute false FP16TensorCores true FP16TensorCoresFor1x1 false +2024-11-10 12:34:10-0500: Loaded latest neural net kata1-b18c384nbt-s9908371456-d4296205232.bin.gz from: C:\Users\chang\OneDrive\Documents\GitHub\Kata_speed\training\models\kata1-b18c384nbt-s9908371456-d4296205232.bin.gz +2024-11-10 12:34:10-0500: Model loading loop thread loaded new neural net kata1-b18c384nbt-s9908371456-d4296205232.bin.gz +2024-11-10 12:34:10-0500: Data write loop starting for neural net: kata1-b18c384nbt-s9908371456-d4296205232.bin.gz +2024-11-10 12:34:10-0500: Game loop thread 14 starting game on new neural net: kata1-b18c384nbt-s9908371456-d4296205232.bin.gz +2024-11-10 12:34:10-0500: Game loop thread 18 starting game on new neural net: kata1-b18c384nbt-s9908371456-d4296205232.bin.gz +2024-11-10 12:34:10-0500: Game loop thread 2 starting game on new neural net: kata1-b18c384nbt-s9908371456-d4296205232.bin.gz +2024-11-10 12:34:10-0500: Game loop thread 28 starting game on new neural net: kata1-b18c384nbt-s9908371456-d4296205232.bin.gz +2024-11-10 12:34:10-0500: Game loop thread 4 starting game on new neural net: kata1-b18c384nbt-s9908371456-d4296205232.bin.gz +2024-11-10 12:34:10-0500: Game loop thread 5 starting game on new neural net: kata1-b18c384nbt-s9908371456-d4296205232.bin.gz +2024-11-10 12:34:10-0500: Game loop thread 6 starting game on new neural net: kata1-b18c384nbt-s9908371456-d4296205232.bin.gz +2024-11-10 12:34:10-0500: Game loop thread 7 starting game on new neural net: kata1-b18c384nbt-s9908371456-d4296205232.bin.gz +2024-11-10 12:34:10-0500: Game loop thread 8 starting game on new neural net: kata1-b18c384nbt-s9908371456-d4296205232.bin.gz +2024-11-10 12:34:10-0500: Game loop thread 9 starting game on new neural net: kata1-b18c384nbt-s9908371456-d4296205232.bin.gz +2024-11-10 12:34:10-0500: Started 10 games with kata1-b18c384nbt-s9908371456-d4296205232.bin.gz +2024-11-10 12:34:10-0500: Game loop thread 10 starting game on new neural net: kata1-b18c384nbt-s9908371456-d4296205232.bin.gz +2024-11-10 12:34:10-0500: Game loop thread 11 starting game on new neural net: kata1-b18c384nbt-s9908371456-d4296205232.bin.gz +2024-11-10 12:34:10-0500: Game loop thread 12 starting game on new neural net: kata1-b18c384nbt-s9908371456-d4296205232.bin.gz +2024-11-10 12:34:10-0500: Game loop thread 13 starting game on new neural net: kata1-b18c384nbt-s9908371456-d4296205232.bin.gz +2024-11-10 12:34:10-0500: Game loop thread 15 starting game on new neural net: kata1-b18c384nbt-s9908371456-d4296205232.bin.gz +2024-11-10 12:34:10-0500: Game loop thread 16 starting game on new neural net: kata1-b18c384nbt-s9908371456-d4296205232.bin.gz +2024-11-10 12:34:10-0500: Game loop thread 17 starting game on new neural net: kata1-b18c384nbt-s9908371456-d4296205232.bin.gz +2024-11-10 12:34:10-0500: Game loop thread 19 starting game on new neural net: kata1-b18c384nbt-s9908371456-d4296205232.bin.gz +2024-11-10 12:34:10-0500: Game loop thread 0 starting game on new neural net: kata1-b18c384nbt-s9908371456-d4296205232.bin.gz +2024-11-10 12:34:10-0500: Game loop thread 1 starting game on new neural net: kata1-b18c384nbt-s9908371456-d4296205232.bin.gz +2024-11-10 12:34:10-0500: Started 20 games with kata1-b18c384nbt-s9908371456-d4296205232.bin.gz +2024-11-10 12:34:10-0500: Game loop thread 20 starting game on new neural net: kata1-b18c384nbt-s9908371456-d4296205232.bin.gz +2024-11-10 12:34:10-0500: Game loop thread 21 starting game on new neural net: kata1-b18c384nbt-s9908371456-d4296205232.bin.gz +2024-11-10 12:34:10-0500: Game loop thread 25 starting game on new neural net: kata1-b18c384nbt-s9908371456-d4296205232.bin.gz +2024-11-10 12:34:10-0500: Game loop thread 26 starting game on new neural net: kata1-b18c384nbt-s9908371456-d4296205232.bin.gz +2024-11-10 12:34:10-0500: Game loop thread 22 starting game on new neural net: kata1-b18c384nbt-s9908371456-d4296205232.bin.gz +2024-11-10 12:34:10-0500: Game loop thread 24 starting game on new neural net: kata1-b18c384nbt-s9908371456-d4296205232.bin.gz +2024-11-10 12:34:10-0500: Game loop thread 27 starting game on new neural net: kata1-b18c384nbt-s9908371456-d4296205232.bin.gz +2024-11-10 12:34:10-0500: Model loading loop thread starting +2024-11-10 12:34:10-0500: Game loop thread 23 starting game on new neural net: kata1-b18c384nbt-s9908371456-d4296205232.bin.gz +2024-11-10 12:34:10-0500: Game loop thread 3 starting game on new neural net: kata1-b18c384nbt-s9908371456-d4296205232.bin.gz +2024-11-10 12:34:10-0500: Game loop thread 29 starting game on new neural net: kata1-b18c384nbt-s9908371456-d4296205232.bin.gz +2024-11-10 12:34:10-0500: Started 30 games with kata1-b18c384nbt-s9908371456-d4296205232.bin.gz +2024-11-10 12:34:44-0500: Game loop thread 15 terminating +2024-11-10 12:34:48-0500: Game loop thread 6 terminating +2024-11-10 12:34:54-0500: Game loop thread 28 terminating +2024-11-10 12:34:57-0500: Game loop thread 24 terminating +2024-11-10 12:34:59-0500: Game loop thread 7 terminating +2024-11-10 12:35:00-0500: Game loop thread 22 terminating +2024-11-10 12:35:04-0500: Game loop thread 18 terminating +2024-11-10 12:35:07-0500: Game loop thread 19 terminating +2024-11-10 12:35:11-0500: Game loop thread 29 terminating +2024-11-10 12:35:13-0500: Game loop thread 1 terminating +2024-11-10 12:35:16-0500: Game loop thread 4 terminating +2024-11-10 12:35:17-0500: Game loop thread 11 terminating +2024-11-10 12:35:23-0500: Game loop thread 21 terminating +2024-11-10 12:35:25-0500: Game loop thread 0 terminating +2024-11-10 12:35:25-0500: Game loop thread 16 terminating +2024-11-10 12:35:27-0500: Game loop thread 3 terminating +2024-11-10 12:35:27-0500: Game loop thread 27 terminating +2024-11-10 12:35:28-0500: Game loop thread 20 terminating +2024-11-10 12:35:28-0500: Game loop thread 10 terminating +2024-11-10 12:35:29-0500: Game loop thread 12 terminating +2024-11-10 12:35:30-0500: Game loop thread 17 terminating +2024-11-10 12:35:30-0500: Game loop thread 14 terminating +2024-11-10 12:35:36-0500: Game loop thread 2 terminating +2024-11-10 12:35:38-0500: Game loop thread 9 terminating +2024-11-10 12:35:41-0500: Game loop thread 13 terminating diff --git a/training/shuffleddata/20241110-121745/outtrain.txt b/training/shuffleddata/20241110-121745/outtrain.txt new file mode 100644 index 0000000..e6ffc76 --- /dev/null +++ b/training/shuffleddata/20241110-121745/outtrain.txt @@ -0,0 +1,32 @@ +Beginning: Finding files +Finished: Finding files in 0.0009992122650146484 seconds +Total number of files: 4 +Total number of files with unknown row count: 4 +Excluded count: 0 +Excluded count due to looking like temp file: 0 +Excluded count due to cmdline excludes file: 0 +GC collect +Beginning: Sorting +Finished: Sorting in 0.0 seconds +Beginning: Computing rows for unsummarized files +Finished: Computing rows for unsummarized files in 0.34592175483703613 seconds +Beginning: Processing found files +Finished: Processing found files in 0.0 seconds +Total rows found: 9962 (9962 usable) +Desired num rows: 3857 / 9962 +Beginning: Computing desired rows +Using: C:/Users/chang/OneDrive/Documents/GitHub/Kata_speed/training/selfplay/kata1-b18c384nbt-s9908371456-d4296205232.bin.gz\tdata\BB8D1D4E83F730DE.npz (8800-8800) (1162/3857 desired rows) +Using: C:/Users/chang/OneDrive/Documents/GitHub/Kata_speed/training/selfplay/kata1-b18c384nbt-s9908371456-d4296205232.bin.gz\tdata\9C008A7DD1B87C8D.npz (1677-1677) (8285/3857 desired rows) +Finished: Computing desired rows in 0.0 seconds +Finally, using: (1677-9962) (8285/3857 desired rows) +GC collect +Writing 1 output files with 8285 kept / 3857 desired rows +Due to only_include_md5, filtering down to 2/2 files +Grouping 2 input files into 1 sharding groups +Beginning: Sharding +Finished: Sharding in 0.8459525108337402 seconds +Beginning: Merging +Finished: Merging in 0.6311211585998535 seconds +Number of rows by output file: +[('C:/Users/chang/OneDrive/Documents/GitHub/Kata_speed/training/shuffleddata/20241110-121745/train\\data0.npz', 8192)] +Cleaning up tmp dir: C:/Users/chang/OneDrive/Documents/GitHub/Kata_speed/training/shufflescratch/train\tmp.shuf0 diff --git a/training/shuffleddata/20241110-121745/train.json b/training/shuffleddata/20241110-121745/train.json new file mode 100644 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training/scripts/dated/20241110-124043/migrate_double_v1.py create mode 100644 training/scripts/dated/20241110-124043/migrate_optimistic_policy.py create mode 100644 training/scripts/dated/20241110-124043/migrate_softplus_fix.py create mode 100644 training/scripts/dated/20241110-124043/migrate_squared_softplus.py create mode 100644 training/scripts/dated/20241110-124043/model_pytorch.py create mode 100644 training/scripts/dated/20241110-124043/modelconfigs.py create mode 100644 training/scripts/dated/20241110-124043/play.py create mode 100644 training/scripts/dated/20241110-124043/query_analysis_engine_example.py create mode 100644 training/scripts/dated/20241110-124043/save_model_for_export_manual.py create mode 100644 training/scripts/dated/20241110-124043/selfplay.cfg create mode 100644 training/scripts/dated/20241110-124043/sgfmetadata.py create mode 100644 training/scripts/dated/20241110-124043/shuffle.py create mode 100644 training/scripts/dated/20241110-124043/shuffle.sh create mode 100644 training/scripts/dated/20241110-124043/shuffle_and_export_loop.sh create mode 100644 training/scripts/dated/20241110-124043/shuffle_loop.sh create mode 100644 training/scripts/dated/20241110-124043/summarize_old_selfplay_files.py create mode 100644 training/scripts/dated/20241110-124043/summarize_sgfs.py create mode 100644 training/scripts/dated/20241110-124043/synchronous_loop.sh create mode 100644 training/scripts/dated/20241110-124043/test.py create mode 100644 training/scripts/dated/20241110-124043/train.py create mode 100644 training/scripts/dated/20241110-124043/train.sh create mode 100644 training/scripts/dated/20241110-124043/upload_model.py create mode 100644 training/scripts/dated/20241110-124043/upload_poses.py create mode 100644 training/scripts/dated/20241110-124043/version.txt create mode 100644 training/selfplay/kata1-b18c384nbt-s9908371456-d4296205232.bin.gz/selfplay-CA064897108355B4.cfg create mode 100644 training/selfplay/kata1-b18c384nbt-s9908371456-d4296205232.bin.gz/sgfs/DCED6828E3852802.sgfs create mode 100644 training/selfplay/kata1-b18c384nbt-s9908371456-d4296205232.bin.gz/tdata/DD0089FFD56BCD20.npz create mode 100644 training/selfplay/log20241110-124044-BE55D0F1BE790F89.log create mode 100644 training/shuffleddata/20241110-123553/outtrain.txt create mode 100644 training/shuffleddata/20241110-123553/train.json create mode 100644 training/shuffleddata/20241110-123553/train/data0.json create mode 100644 training/shuffleddata/20241110-123553/train/data0.npz create mode 100644 training/shuffleddata/current_tmp/train.json create mode 100644 training/shuffleddata/current_tmp/train/data0.json create mode 100644 training/shuffleddata/current_tmp/train/data0.npz diff --git a/training/gatekeepersgf/log20241110-124044-30B5A48523C7FF2F.log b/training/gatekeepersgf/log20241110-124044-30B5A48523C7FF2F.log new file mode 100644 index 0000000..5912ee8 --- /dev/null +++ b/training/gatekeepersgf/log20241110-124044-30B5A48523C7FF2F.log @@ -0,0 +1,69 @@ +2024-11-10 12:40:44-0500: Running with following config: +allowRectangleProb = 0.50 +allowResignation = true +bSizeRelProbs = 1,1,8 +bSizes = 7,8,9 +chosenMovePrune = 1 +chosenMoveSubtract = 0 +chosenMoveTemperature = 0.2 +chosenMoveTemperatureEarly = 0.5 +chosenMoveTemperatureHalflife = 19 +cpuctExploration = 1.1 +cpuctExplorationLog = 0.0 +cudaUseFP16 = auto +cudaUseNHWC = auto +drawEquivalentWinsForWhite = 0.5 +dynamicScoreCenterScale = 0.50 +dynamicScoreCenterZeroWeight = 0.25 +dynamicScoreUtilityFactor = 0.25 +fpuReductionMax = 0.2 +handicapCompensateKomiProb = 1.0 +handicapProb = 0.0 +hasButtons = false,false,true +koRules = SIMPLE,POSITIONAL,SITUATIONAL +komiAuto = True +lcbStdevs = 5.0 +logGamesEvery = 10 +logMoves = false +logSearchInfo = false +logToStdout = true +maxMovesPerGame = 1600 +maxVisits = 150 +minVisitPropForLCB = 0.15 +multiStoneSuicideLegals = false,true +nnCacheSizePowerOfTwo = 21 +nnMaxBatchSize = 128 +nnMutexPoolSizePowerOfTwo = 15 +nnRandomize = true +noResultUtilityForWhite = 0.0 +numGameThreads = 128 +numGamesPerGating = 200 +numNNServerThreadsPerModel = 1 +numSearchThreads = 1 +numVirtualLossesPerThread = 1 +policyOptimism = 1.0 +resignConsecTurns = 5 +resignThreshold = -0.90 +rootEndingBonusPoints = 0.5 +rootFpuReductionMax = 0.1 +rootPolicyOptimism = 0.0 +rootPruneUselessMoves = true +scoringRules = AREA,TERRITORY +staticScoreUtilityFactor = 0.00 +subtreeValueBiasFactor = 0.35 +subtreeValueBiasWeightExponent = 0.8 +taxRules = NONE,NONE,SEKI,SEKI,ALL +uncertaintyCoeff = 0.25 +uncertaintyExponent = 1.0 +useGraphSearch = true +useLcbForSelection = true +useNonBuggyLcb = true +useUncertainty = true +valueWeightExponent = 0.5 +winLossUtilityFactor = 1.0 + +2024-11-10 12:40:44-0500: Gatekeeper Engine starting... +2024-11-10 12:40:44-0500: Git revision: 7939e2c3eb43df04e08de8c166889c6d5e937bfa +2024-11-10 12:40:44-0500: Required candidate win prop: 0.5 +2024-11-10 12:40:44-0500: Loaded all config stuff, watching for new neural nets in C:/Users/chang/OneDrive/Documents/GitHub/Kata_speed/training/modelstobetested/ +2024-11-10 12:40:44-0500: All cleaned up, quitting diff --git a/training/gatekeepersgf/stdout.txt b/training/gatekeepersgf/stdout.txt index 8578d05..2716242 100644 --- a/training/gatekeepersgf/stdout.txt +++ b/training/gatekeepersgf/stdout.txt @@ -651,3 +651,72 @@ winLossUtilityFactor = 1.0 2024-11-10 12:34:08-0500: Required candidate win prop: 0.5 2024-11-10 12:34:08-0500: Loaded all config stuff, watching for new neural nets in C:/Users/chang/OneDrive/Documents/GitHub/Kata_speed/training/modelstobetested/ 2024-11-10 12:34:08-0500: All cleaned up, quitting +2024-11-10 12:40:44-0500: Running with following config: +allowRectangleProb = 0.50 +allowResignation = true +bSizeRelProbs = 1,1,8 +bSizes = 7,8,9 +chosenMovePrune = 1 +chosenMoveSubtract = 0 +chosenMoveTemperature = 0.2 +chosenMoveTemperatureEarly = 0.5 +chosenMoveTemperatureHalflife = 19 +cpuctExploration = 1.1 +cpuctExplorationLog = 0.0 +cudaUseFP16 = auto +cudaUseNHWC = auto +drawEquivalentWinsForWhite = 0.5 +dynamicScoreCenterScale = 0.50 +dynamicScoreCenterZeroWeight = 0.25 +dynamicScoreUtilityFactor = 0.25 +fpuReductionMax = 0.2 +handicapCompensateKomiProb = 1.0 +handicapProb = 0.0 +hasButtons = false,false,true +koRules = SIMPLE,POSITIONAL,SITUATIONAL +komiAuto = True +lcbStdevs = 5.0 +logGamesEvery = 10 +logMoves = false +logSearchInfo = false +logToStdout = true +maxMovesPerGame = 1600 +maxVisits = 150 +minVisitPropForLCB = 0.15 +multiStoneSuicideLegals = false,true +nnCacheSizePowerOfTwo = 21 +nnMaxBatchSize = 128 +nnMutexPoolSizePowerOfTwo = 15 +nnRandomize = true +noResultUtilityForWhite = 0.0 +numGameThreads = 128 +numGamesPerGating = 200 +numNNServerThreadsPerModel = 1 +numSearchThreads = 1 +numVirtualLossesPerThread = 1 +policyOptimism = 1.0 +resignConsecTurns = 5 +resignThreshold = -0.90 +rootEndingBonusPoints = 0.5 +rootFpuReductionMax = 0.1 +rootPolicyOptimism = 0.0 +rootPruneUselessMoves = true +scoringRules = AREA,TERRITORY +staticScoreUtilityFactor = 0.00 +subtreeValueBiasFactor = 0.35 +subtreeValueBiasWeightExponent = 0.8 +taxRules = NONE,NONE,SEKI,SEKI,ALL +uncertaintyCoeff = 0.25 +uncertaintyExponent = 1.0 +useGraphSearch = true +useLcbForSelection = true +useNonBuggyLcb = true +useUncertainty = true +valueWeightExponent = 0.5 +winLossUtilityFactor = 1.0 + +2024-11-10 12:40:44-0500: Gatekeeper Engine starting... +2024-11-10 12:40:44-0500: Git revision: 7939e2c3eb43df04e08de8c166889c6d5e937bfa +2024-11-10 12:40:44-0500: Required candidate win prop: 0.5 +2024-11-10 12:40:44-0500: Loaded all config stuff, watching for new neural nets in C:/Users/chang/OneDrive/Documents/GitHub/Kata_speed/training/modelstobetested/ +2024-11-10 12:40:44-0500: All cleaned up, quitting diff --git a/training/logs/outshuffle.txt b/training/logs/outshuffle.txt index 36486c9..4ab439c 100644 --- a/training/logs/outshuffle.txt +++ b/training/logs/outshuffle.txt @@ -106,3 +106,38 @@ Finished: Merging in 0.7125730514526367 seconds Number of rows by output file: [('C:/Users/chang/OneDrive/Documents/GitHub/Kata_speed/training/shuffleddata/20241110-123012/train\\data0.npz', 9472)] Cleaning up tmp dir: C:/Users/chang/OneDrive/Documents/GitHub/Kata_speed/training/shufflescratch/train\tmp.shuf0 +Beginning shuffle at 2024-11-10 12:35:53 +Beginning: Finding files +Finished: Finding files in 0.0005304813385009766 seconds +Total number of files: 6 +Total number of files with unknown row count: 6 +Excluded count: 0 +Excluded count due to looking like temp file: 0 +Excluded count due to cmdline excludes file: 0 +GC collect +Beginning: Sorting +Finished: Sorting in 0.0 seconds +Beginning: Computing rows for unsummarized files +Finished: Computing rows for unsummarized files in 0.3340744972229004 seconds +Beginning: Processing found files +Finished: Processing found files in 0.0 seconds +Total rows found: 11675 (11675 usable) +Desired num rows: 4497 / 11675 +Beginning: Computing desired rows +Using: C:/Users/chang/OneDrive/Documents/GitHub/Kata_speed/training/selfplay/kata1-b18c384nbt-s9908371456-d4296205232.bin.gz\tdata\DD0089FFD56BCD20.npz (11273-11273) (402/4497 desired rows) +Using: C:/Users/chang/OneDrive/Documents/GitHub/Kata_speed/training/selfplay/kata1-b18c384nbt-s9908371456-d4296205232.bin.gz\tdata\F6CD6AF355B27725.npz (9962-9962) (1713/4497 desired rows) +Using: C:/Users/chang/OneDrive/Documents/GitHub/Kata_speed/training/selfplay/kata1-b18c384nbt-s9908371456-d4296205232.bin.gz\tdata\BB8D1D4E83F730DE.npz (8800-8800) (2875/4497 desired rows) +Using: C:/Users/chang/OneDrive/Documents/GitHub/Kata_speed/training/selfplay/kata1-b18c384nbt-s9908371456-d4296205232.bin.gz\tdata\9C008A7DD1B87C8D.npz (1677-1677) (9998/4497 desired rows) +Finished: Computing desired rows in 0.0 seconds +Finally, using: (1677-11675) (9998/4497 desired rows) +GC collect +Writing 1 output files with 9998 kept / 4497 desired rows +Due to only_include_md5, filtering down to 4/4 files +Grouping 4 input files into 1 sharding groups +Beginning: Sharding +Finished: Sharding in 0.9081718921661377 seconds +Beginning: Merging +Finished: Merging in 0.7742702960968018 seconds +Number of rows by output file: +[('C:/Users/chang/OneDrive/Documents/GitHub/Kata_speed/training/shuffleddata/20241110-123553/train\\data0.npz', 9984)] +Cleaning up tmp dir: C:/Users/chang/OneDrive/Documents/GitHub/Kata_speed/training/shufflescratch/train\tmp.shuf0 diff --git a/training/scripts/dated/20241110-124043/add_kata_selfplay_meta_to_npzs.py b/training/scripts/dated/20241110-124043/add_kata_selfplay_meta_to_npzs.py new file mode 100644 index 0000000..8e48045 --- /dev/null +++ b/training/scripts/dated/20241110-124043/add_kata_selfplay_meta_to_npzs.py @@ -0,0 +1,73 @@ +import os +import sys +import random +import numpy as np + +from sgfmetadata import SGFMetadata + +expected_keys = [ + "binaryInputNCHWPacked", + "globalInputNC", + "policyTargetsNCMove", + "globalTargetsNC", + "scoreDistrN", + "valueTargetsNCHW", +] + +def process_npz_files(in_dir, out_dir): + rand = random.Random() + for filename in os.listdir(in_dir): + if filename.endswith(".npz"): + print("Processing " + filename, flush=True) + with np.load(os.path.join(in_dir, filename)) as npz: + assert(set(npz.keys()) == set(expected_keys)) + binaryInputNCHWPacked = npz["binaryInputNCHWPacked"] + globalInputNC = npz["globalInputNC"] + policyTargetsNCMove = npz["policyTargetsNCMove"] + globalTargetsNC = npz["globalTargetsNC"] + scoreDistrN = npz["scoreDistrN"] + valueTargetsNCHW = npz["valueTargetsNCHW"] + + pos_len = valueTargetsNCHW.shape[2] + + binaryInputNCHW = np.unpackbits(binaryInputNCHWPacked,axis=2) + assert len(binaryInputNCHW.shape) == 3 + assert binaryInputNCHW.shape[2] == ((pos_len * pos_len + 7) // 8) * 8 + binaryInputNCHW = binaryInputNCHW[:,:,:pos_len*pos_len] + binaryInputNCHW = np.reshape(binaryInputNCHW, ( + binaryInputNCHW.shape[0], binaryInputNCHW.shape[1], pos_len, pos_len + )).astype(np.float32) + + metadataInputNC = np.zeros((binaryInputNCHW.shape[0],SGFMetadata.METADATA_INPUT_NUM_CHANNELS), dtype=np.float32) + for i in range(binaryInputNCHW.shape[0]): + board_area = int(np.sum(binaryInputNCHW[i,0])) + sgfmeta = SGFMetadata.get_katago_selfplay_metadata(rand=rand) + # nextPlayer doesn't matter in selfplay because it only is used to swap pla/opp metadata + # and in this case both sides are identically katago + metadataInputNC[i] = sgfmeta.get_metadata_row(nextPlayer=Board.WHITE,boardArea=board_area) + + np.savez_compressed( + os.path.join(out_dir, filename), + binaryInputNCHWPacked = binaryInputNCHWPacked, + globalInputNC = globalInputNC, + policyTargetsNCMove = policyTargetsNCMove, + globalTargetsNC = globalTargetsNC, + scoreDistrN = scoreDistrN, + valueTargetsNCHW = valueTargetsNCHW, + metadataInputNC = metadataInputNC, + ) + print("Done") + +if __name__ == "__main__": + if len(sys.argv) != 3: + print("Usage: python add_kata_selfplay_meta_to_npzs.py ") + sys.exit(1) + + in_dir = sys.argv[1] + out_dir = sys.argv[2] + + if os.path.exists(out_dir): + raise Exception(out_dir + " already exists") + os.mkdir(out_dir) + + process_npz_files(in_dir, out_dir) diff --git a/training/scripts/dated/20241110-124043/board.py b/training/scripts/dated/20241110-124043/board.py new file mode 100644 index 0000000..daa4902 --- /dev/null +++ b/training/scripts/dated/20241110-124043/board.py @@ -0,0 +1,1436 @@ +import random +import numpy as np + +class IllegalMoveError(ValueError): + pass + +Pos = int +Loc = int +Player = int + +#Implements legal moves without superko +class Board: + EMPTY = 0 + BLACK = 1 + WHITE = 2 + WALL = 3 + + ZOBRIST_STONE = [[],[],[],[]] + ZOBRIST_PLA = [] + + ZOBRIST_RAND = random.Random() + ZOBRIST_RAND.seed(123987456) + + PASS_LOC = 0 + + for i in range((19+1)*(19+2)+1): + ZOBRIST_STONE[BLACK].append(ZOBRIST_RAND.getrandbits(64)) + ZOBRIST_STONE[WHITE].append(ZOBRIST_RAND.getrandbits(64)) + for i in range(4): + ZOBRIST_PLA.append(ZOBRIST_RAND.getrandbits(64)) + + def __init__(self,size,copy_other=None): + if size < 2 or size > 39: + raise ValueError("Invalid board size: " + str(size)) + self.size = size + self.arrsize = (size+1)*(size+2)+1 + self.dy = size+1 + self.adj = [-self.dy,-1,1,self.dy] + self.diag = [-self.dy-1,-self.dy+1,self.dy-1,self.dy+1] + + if copy_other is not None: + self.pla = copy_other.pla + self.board = np.copy(copy_other.board) + self.group_head = np.copy(copy_other.group_head) + self.group_stone_count = np.copy(copy_other.group_stone_count) + self.group_liberty_count = np.copy(copy_other.group_liberty_count) + self.group_next = np.copy(copy_other.group_next) + self.group_prev = np.copy(copy_other.group_prev) + self.zobrist = copy_other.zobrist + self.simple_ko_point = copy_other.simple_ko_point + self.num_captures_made = copy_other.num_captures_made.copy() + self.num_non_pass_moves_made = copy_other.num_non_pass_moves_made.copy() + else: + self.pla = Board.BLACK + self.board = np.zeros(shape=(self.arrsize), dtype=np.int8) + self.group_head = np.zeros(shape=(self.arrsize), dtype=np.int16) + self.group_stone_count = np.zeros(shape=(self.arrsize), dtype=np.int16) + self.group_liberty_count = np.zeros(shape=(self.arrsize), dtype=np.int16) + self.group_next = np.zeros(shape=(self.arrsize), dtype=np.int16) + self.group_prev = np.zeros(shape=(self.arrsize), dtype=np.int16) + self.zobrist = 0 + self.simple_ko_point = None + self.num_captures_made = {Board.BLACK: 0, Board.WHITE: 0} + self.num_non_pass_moves_made = {Board.BLACK: 0, Board.WHITE: 0} + + for i in range(-1,size+1): + self.board[self.loc(i,-1)] = Board.WALL + self.board[self.loc(i,size)] = Board.WALL + self.board[self.loc(-1,i)] = Board.WALL + self.board[self.loc(size,i)] = Board.WALL + + #More-easily catch errors + self.group_head[0] = -1 + self.group_next[0] = -1 + self.group_prev[0] = -1 + + def copy(self): + return Board(self.size,copy_other=self) + + @staticmethod + def get_opp(pla): + return 3-pla + @staticmethod + def loc_static(x,y,size): + return (x+1) + (size+1)*(y+1) + + def loc(self,x,y): + return (x+1) + self.dy*(y+1) + def loc_x(self,loc): + return (loc % self.dy)-1 + def loc_y(self,loc): + return (loc // self.dy)-1 + + def is_adjacent(self,loc1,loc2): + return loc1 == loc2 + self.adj[0] or loc1 == loc2 + self.adj[1] or loc1 == loc2 + self.adj[2] or loc1 == loc2 + self.adj[3] + + def pos_zobrist(self): + return self.zobrist + def sit_zobrist(self): + return self.zobrist ^ Board.ZOBRIST_PLA[self.pla] + + def num_liberties(self,loc): + if self.board[loc] == Board.EMPTY or self.board[loc] == Board.WALL: + return 0 + return self.group_liberty_count[self.group_head[loc]] + + def is_simple_eye(self,pla,loc): + adj0 = loc + self.adj[0] + adj1 = loc + self.adj[1] + adj2 = loc + self.adj[2] + adj3 = loc + self.adj[3] + + if (self.board[adj0] != pla and self.board[adj0] != Board.WALL) or \ + (self.board[adj1] != pla and self.board[adj1] != Board.WALL) or \ + (self.board[adj2] != pla and self.board[adj2] != Board.WALL) or \ + (self.board[adj3] != pla and self.board[adj3] != Board.WALL): + return False + + opp = Board.get_opp(pla) + opp_corners = 0 + diag0 = loc + self.diag[0] + diag1 = loc + self.diag[1] + diag2 = loc + self.diag[2] + diag3 = loc + self.diag[3] + if self.board[diag0] == opp: + opp_corners += 1 + if self.board[diag1] == opp: + opp_corners += 1 + if self.board[diag2] == opp: + opp_corners += 1 + if self.board[diag3] == opp: + opp_corners += 1 + + if opp_corners >= 2: + return False + if opp_corners <= 0: + return True + + against_wall = ( + self.board[adj0] == Board.WALL or \ + self.board[adj1] == Board.WALL or \ + self.board[adj2] == Board.WALL or \ + self.board[adj3] == Board.WALL + ) + + if against_wall: + return False + return True + + + def would_be_legal(self,pla,loc): + if pla != Board.BLACK and pla != Board.WHITE: + return False + if loc == Board.PASS_LOC: + return True + if not self.is_on_board(loc): + return False + if self.board[loc] != Board.EMPTY: + return False + if self.would_be_single_stone_suicide(pla,loc): + return False + if loc == self.simple_ko_point: + return False + return True + + def would_be_suicide(self,pla,loc): + adj0 = loc + self.adj[0] + adj1 = loc + self.adj[1] + adj2 = loc + self.adj[2] + adj3 = loc + self.adj[3] + + opp = Board.get_opp(pla) + + #If empty or capture, then not suicide + if self.board[adj0] == Board.EMPTY or (self.board[adj0] == opp and self.group_liberty_count[self.group_head[adj0]] == 1) or \ + self.board[adj1] == Board.EMPTY or (self.board[adj1] == opp and self.group_liberty_count[self.group_head[adj1]] == 1) or \ + self.board[adj2] == Board.EMPTY or (self.board[adj2] == opp and self.group_liberty_count[self.group_head[adj2]] == 1) or \ + self.board[adj3] == Board.EMPTY or (self.board[adj3] == opp and self.group_liberty_count[self.group_head[adj3]] == 1): + return False + #If connects to own stone with enough liberties, then not suicide + if self.board[adj0] == pla and self.group_liberty_count[self.group_head[adj0]] > 1 or \ + self.board[adj1] == pla and self.group_liberty_count[self.group_head[adj1]] > 1 or \ + self.board[adj2] == pla and self.group_liberty_count[self.group_head[adj2]] > 1 or \ + self.board[adj3] == pla and self.group_liberty_count[self.group_head[adj3]] > 1: + return False + return True + + def would_be_single_stone_suicide(self,pla,loc): + adj0 = loc + self.adj[0] + adj1 = loc + self.adj[1] + adj2 = loc + self.adj[2] + adj3 = loc + self.adj[3] + + opp = Board.get_opp(pla) + + #If empty or capture, then not suicide + if self.board[adj0] == Board.EMPTY or (self.board[adj0] == opp and self.group_liberty_count[self.group_head[adj0]] == 1) or \ + self.board[adj1] == Board.EMPTY or (self.board[adj1] == opp and self.group_liberty_count[self.group_head[adj1]] == 1) or \ + self.board[adj2] == Board.EMPTY or (self.board[adj2] == opp and self.group_liberty_count[self.group_head[adj2]] == 1) or \ + self.board[adj3] == Board.EMPTY or (self.board[adj3] == opp and self.group_liberty_count[self.group_head[adj3]] == 1): + return False + #If connects to own stone, then not single stone suicide + if self.board[adj0] == pla or \ + self.board[adj1] == pla or \ + self.board[adj2] == pla or \ + self.board[adj3] == pla: + return False + return True + + #Returns the number of liberties a new stone placed here would have, or maxLibs if it would be >= maxLibs. + def get_liberties_after_play(self,pla,loc,maxLibs): + opp = Board.get_opp(pla) + libs = [] + capturedGroupHeads = [] + + #First, count immediate liberties and groups that would be captured + for i in range(4): + adj = loc + self.adj[i] + if self.board[adj] == Board.EMPTY: + libs.append(adj) + if len(libs) >= maxLibs: + return maxLibs + + elif self.board[adj] == opp and self.num_liberties(adj) == 1: + libs.append(adj) + if len(libs) >= maxLibs: + return maxLibs + + head = self.group_head[adj] + if head not in capturedGroupHeads: + capturedGroupHeads.append(head) + + def wouldBeEmpty(possibleLib): + if self.board[possibleLib] == Board.EMPTY: + return True + elif self.board[possibleLib] == opp: + return (self.group_head[possibleLib] in capturedGroupHeads) + return False + + #Next, walk through all stones of all surrounding groups we would connect with and count liberties, avoiding overlap. + connectingGroupHeads = [] + for i in range(4): + adj = loc + self.adj[i] + if self.board[adj] == pla: + head = self.group_head[adj] + if head not in connectingGroupHeads: + connectingGroupHeads.append(head) + + cur = adj + while True: + for k in range(4): + possibleLib = cur + self.adj[k] + if possibleLib != loc and wouldBeEmpty(possibleLib) and possibleLib not in libs: + libs.append(possibleLib) + if len(libs) >= maxLibs: + return maxLibs + + cur = self.group_next[cur] + if cur == adj: + break + + return len(libs) + + + def to_string(self): + def get_piece(x,y): + loc = self.loc(x,y) + if self.board[loc] == Board.BLACK: + return 'X ' + elif self.board[loc] == Board.WHITE: + return 'O ' + elif (x == 3 or x == self.size/2 or x == self.size-1-3) and (y == 3 or y == self.size/2 or y == self.size-1-3): + return '* ' + else: + return '. ' + + return "\n".join("".join(get_piece(x,y) for x in range(self.size)) for y in range(self.size)) + + def to_liberty_string(self): + def get_piece(x,y): + loc = self.loc(x,y) + if self.board[loc] == Board.BLACK or self.board[loc] == Board.WHITE: + libs = self.group_liberty_count[self.group_head[loc]] + if libs <= 9: + return str(libs) + ' ' + else: + return '@ ' + elif (x == 3 or x == self.size/2 or x == self.size-1-3) and (y == 3 or y == self.size/2 or y == self.size-1-3): + return '* ' + else: + return '. ' + + return "\n".join("".join(get_piece(x,y) for x in range(self.size)) for y in range(self.size)) + + def set_pla(self,pla): + self.pla = pla + def is_on_board(self,loc): + return loc >= 0 and loc < self.arrsize and self.board[loc] != Board.WALL + + #Set a given location with error checking. Suicide setting allowed. + def set_stone(self,pla,loc): + if pla != Board.EMPTY and pla != Board.BLACK and pla != Board.WHITE: + raise IllegalMoveError("Invalid pla for board.set") + if not self.is_on_board(loc): + raise IllegalMoveError("Invalid loc for board.set") + + if self.board[loc] == pla: + pass + elif self.board[loc] == Board.EMPTY: + self.add_unsafe(pla,loc) + elif pla == Board.EMPTY: + self.remove_single_stone_unsafe(loc) + else: + self.remove_single_stone_unsafe(loc) + self.add_unsafe(pla,loc) + + #Clear any ko restrictions + self.simple_ko_point = None + + + #Play a stone at the given location, with non-superko legality checking and updating the pla and simple ko point + #Single stone suicide is disallowed but suicide is allowed, to support rule sets and sgfs that have suicide + def play(self,pla,loc): + if pla != Board.BLACK and pla != Board.WHITE: + raise IllegalMoveError("Invalid pla for board.play") + + if loc != Board.PASS_LOC: + if not self.is_on_board(loc): + raise IllegalMoveError("Invalid loc for board.set") + if self.board[loc] != Board.EMPTY: + raise IllegalMoveError("Location is nonempty") + if self.would_be_single_stone_suicide(pla,loc): + raise IllegalMoveError("Move would be illegal single stone suicide") + if loc == self.simple_ko_point: + raise IllegalMoveError("Move would be illegal simple ko recapture") + + self.playUnsafe(pla,loc) + + def playUnsafe(self,pla,loc): + if loc == Board.PASS_LOC: + self.simple_ko_point = None + self.pla = Board.get_opp(pla) + else: + self.add_unsafe(pla,loc) + self.pla = Board.get_opp(pla) + + def playRecordedUnsafe(self,pla,loc): + capDirs = [] + opp = Board.get_opp(pla) + old_simple_ko_point = self.simple_ko_point + for i in range(4): + adj = loc + self.adj[i] + if self.board[adj] == opp and self.group_liberty_count[self.group_head[adj]] == 1: + capDirs.append(i) + old_num_captures_made = self.num_captures_made.copy() + old_num_non_pass_moves_made = self.num_non_pass_moves_made.copy() + + self.playUnsafe(pla,loc) + + #Suicide + selfCap = False + if self.board[loc] == Board.EMPTY: + selfCap = True + return (pla,loc,old_simple_ko_point,capDirs,selfCap,old_num_captures_made,old_num_non_pass_moves_made) + + def undo(self,record): + (pla,loc,simple_ko_point,capDirs,selfCap,old_num_captures_made,old_num_non_pass_moves_made) = record + opp = Board.get_opp(pla) + + self.simple_ko_point = simple_ko_point + self.pla = pla + self.num_captures_made = old_num_captures_made.copy() + self.num_non_pass_moves_made = old_num_non_pass_moves_made.copy() + + if loc == Board.PASS_LOC: + return + + #Re-fill stones in all captured directions + for capdir in capDirs: + adj = loc + self.adj[capdir] + if self.board[adj] == Board.EMPTY: + self.floodFillStones(opp,adj) + + if selfCap: + self.floodFillStones(pla,loc) + + #Delete the stone played here. + self.zobrist ^= Board.ZOBRIST_STONE[pla][loc] + self.board[loc] = Board.EMPTY + + #Zero out stuff in preparation for rebuilding + head = self.group_head[loc] + stone_count = self.group_stone_count[head] + self.group_stone_count[head] = 0 + self.group_liberty_count[head] = 0 + + #Uneat enemy liberties + self.changeSurroundingLiberties(loc,Board.get_opp(pla),+1) + + #If this was not a single stone, we need to recompute the chain from scratch + if stone_count > 1: + #Run through the whole chain and make their heads point to nothing + cur = loc + while True: + self.group_head[cur] = Board.PASS_LOC + cur = self.group_next[cur] + if cur == loc: + break + + #Rebuild each chain adjacent now + for i in range(4): + adj = loc + self.adj[i] + if self.board[adj] == pla and self.group_head[adj] == Board.PASS_LOC: + self.rebuildChain(pla,adj) + + self.group_head[loc] = 0 + self.group_next[loc] = 0 + self.group_prev[loc] = 0 + + + #Add a chain of the given player to the given region of empty space, floodfilling it. + #Assumes that this region does not border any chains of the desired color already + def floodFillStones(self,pla,loc): + head = loc + self.group_liberty_count[head] = 0 + self.group_stone_count[head] = 0 + + #Add a chain with links front <-> ... <-> head <-> head with all head pointers towards head + front = self.floodFillStonesHelper(head, head, head, pla) + + #Now, we make head point to front, and that completes the circle! + self.group_next[head] = front + self.group_prev[front] = head + + #Floodfill a chain of the given color into this region of empty spaces + #Make the specified loc the head for all the chains and updates the chainData of head with the number of stones. + #Does NOT connect the stones into a circular list. Rather, it produces an linear linked list with the tail pointing + #to tailTarget, and returns the head of the list. The tail is guaranteed to be loc. + def floodFillStonesHelper(self, head, tailTarget, loc, pla): + self.board[loc] = pla + self.zobrist ^= Board.ZOBRIST_STONE[pla][loc] + + self.group_head[loc] = head + self.group_stone_count[head] += 1 + self.group_next[loc] = tailTarget + self.group_prev[tailTarget] = loc + + #Eat enemy liberties + self.changeSurroundingLiberties(loc,Board.get_opp(pla),-1) + + #Recursively add stones around us. + nextTailTarget = loc + for i in range(4): + adj = loc + self.adj[i] + if self.board[adj] == Board.EMPTY: + nextTailTarget = self.floodFillStonesHelper(head,nextTailTarget,adj,pla) + return nextTailTarget + + #Floods through a chain of the specified player already on the board + #rebuilding its links and counting its liberties as we go. + #Requires that all their heads point towards + #some invalid location, such as PASS_LOC or a location not of color. + #The head of the chain will be loc. + def rebuildChain(self,pla,loc): + head = loc + self.group_liberty_count[head] = 0 + self.group_stone_count[head] = 0 + + #Rebuild chain with links front <-> ... <-> head <-> head with all head pointers towards head + front = self.rebuildChainHelper(head, head, head, pla) + + #Now, we make head point to front, and that completes the circle! + self.group_next[head] = front + self.group_prev[front] = head + + + #Does same thing as addChain, but floods through a chain of the specified color already on the board + #rebuilding its links and also counts its liberties as we go. Requires that all their heads point towards + #some invalid location, such as NULL_LOC or a location not of color. + def rebuildChainHelper(self, head, tailTarget, loc, pla): + #Count new liberties + for dloc in self.adj: + if self.board[loc+dloc] == Board.EMPTY and not self.is_group_adjacent(head,loc+dloc): + self.group_liberty_count[head] += 1 + + #Add stone here to the chain by setting its head + self.group_head[loc] = head + self.group_stone_count[head] += 1 + self.group_next[loc] = tailTarget + self.group_prev[tailTarget] = loc + + #Recursively add stones around us. + nextTailTarget = loc + for i in range(4): + adj = loc + self.adj[i] + if self.board[adj] == pla and self.group_head[adj] != head: + nextTailTarget = self.rebuildChainHelper(head,nextTailTarget,adj,pla) + return nextTailTarget + + + #Add a stone, assumes that the location is empty without checking + def add_unsafe(self,pla,loc): + opp = Board.get_opp(pla) + + #Put the stone down + self.board[loc] = pla + self.zobrist ^= Board.ZOBRIST_STONE[pla][loc] + + #Initialize the group for that stone + self.group_head[loc] = loc + self.group_stone_count[loc] = 1 + liberties = 0 + for dloc in self.adj: + if self.board[loc+dloc] == Board.EMPTY: + liberties += 1 + self.group_liberty_count[loc] = liberties + self.group_next[loc] = loc + self.group_prev[loc] = loc + + #Fill surrounding liberties of all adjacent groups + #Carefully avoid doublecounting + adj0 = loc + self.adj[0] + adj1 = loc + self.adj[1] + adj2 = loc + self.adj[2] + adj3 = loc + self.adj[3] + if self.board[adj0] == Board.BLACK or self.board[adj0] == Board.WHITE: + self.group_liberty_count[self.group_head[adj0]] -= 1 + if self.board[adj1] == Board.BLACK or self.board[adj1] == Board.WHITE: + if self.group_head[adj1] != self.group_head[adj0]: + self.group_liberty_count[self.group_head[adj1]] -= 1 + if self.board[adj2] == Board.BLACK or self.board[adj2] == Board.WHITE: + if self.group_head[adj2] != self.group_head[adj0] and \ + self.group_head[adj2] != self.group_head[adj1]: + self.group_liberty_count[self.group_head[adj2]] -= 1 + if self.board[adj3] == Board.BLACK or self.board[adj3] == Board.WHITE: + if self.group_head[adj3] != self.group_head[adj0] and \ + self.group_head[adj3] != self.group_head[adj1] and \ + self.group_head[adj3] != self.group_head[adj2]: + self.group_liberty_count[self.group_head[adj3]] -= 1 + + #Merge groups + if self.board[adj0] == pla: + self.merge_unsafe(loc,adj0) + if self.board[adj1] == pla: + self.merge_unsafe(loc,adj1) + if self.board[adj2] == pla: + self.merge_unsafe(loc,adj2) + if self.board[adj3] == pla: + self.merge_unsafe(loc,adj3) + + #Resolve captures + opp_stones_captured = 0 + caploc = 0 + if self.board[adj0] == opp and self.group_liberty_count[self.group_head[adj0]] == 0: + opp_stones_captured += self.group_stone_count[self.group_head[adj0]] + caploc = adj0 + self.remove_unsafe(adj0) + if self.board[adj1] == opp and self.group_liberty_count[self.group_head[adj1]] == 0: + opp_stones_captured += self.group_stone_count[self.group_head[adj1]] + caploc = adj1 + self.remove_unsafe(adj1) + if self.board[adj2] == opp and self.group_liberty_count[self.group_head[adj2]] == 0: + opp_stones_captured += self.group_stone_count[self.group_head[adj2]] + caploc = adj2 + self.remove_unsafe(adj2) + if self.board[adj3] == opp and self.group_liberty_count[self.group_head[adj3]] == 0: + opp_stones_captured += self.group_stone_count[self.group_head[adj3]] + caploc = adj3 + self.remove_unsafe(adj3) + + #Suicide + pla_stones_captured = 0 + if self.group_liberty_count[self.group_head[loc]] == 0: + pla_stones_captured += self.group_stone_count[self.group_head[loc]] + self.remove_unsafe(loc) + + self.num_captures_made[pla] += pla_stones_captured + self.num_captures_made[opp] += opp_stones_captured + self.num_non_pass_moves_made[pla] += 1 + + #Update ko point for legality checking + if opp_stones_captured == 1 and \ + self.group_stone_count[self.group_head[loc]] == 1 and \ + self.group_liberty_count[self.group_head[loc]] == 1: + self.simple_ko_point = caploc + else: + self.simple_ko_point = None + + #Apply the specified delta to the liberties of all adjacent groups of the specified color + def changeSurroundingLiberties(self,loc,pla,delta): + #Carefully avoid doublecounting + adj0 = loc + self.adj[0] + adj1 = loc + self.adj[1] + adj2 = loc + self.adj[2] + adj3 = loc + self.adj[3] + if self.board[adj0] == pla: + self.group_liberty_count[self.group_head[adj0]] += delta + if self.board[adj1] == pla: + if self.group_head[adj1] != self.group_head[adj0]: + self.group_liberty_count[self.group_head[adj1]] += delta + if self.board[adj2] == pla: + if self.group_head[adj2] != self.group_head[adj0] and \ + self.group_head[adj2] != self.group_head[adj1]: + self.group_liberty_count[self.group_head[adj2]] += delta + if self.board[adj3] == pla: + if self.group_head[adj3] != self.group_head[adj0] and \ + self.group_head[adj3] != self.group_head[adj1] and \ + self.group_head[adj3] != self.group_head[adj2]: + self.group_liberty_count[self.group_head[adj3]] += delta + + def countImmediateLiberties(self,loc): + adj0 = loc + self.adj[0] + adj1 = loc + self.adj[1] + adj2 = loc + self.adj[2] + adj3 = loc + self.adj[3] + count = 0 + if self.board[adj0] == Board.EMPTY: + count += 1 + if self.board[adj1] == Board.EMPTY: + count += 1 + if self.board[adj2] == Board.EMPTY: + count += 1 + if self.board[adj3] == Board.EMPTY: + count += 1 + return count + + def is_group_adjacent(self,head,loc): + return ( + self.group_head[loc+self.adj[0]] == head or \ + self.group_head[loc+self.adj[1]] == head or \ + self.group_head[loc+self.adj[2]] == head or \ + self.group_head[loc+self.adj[3]] == head + ) + + #Helper, merge two groups assuming they're owned by the same player and adjacent + def merge_unsafe(self,loc0,loc1): + if self.group_stone_count[self.group_head[loc0]] >= self.group_stone_count[self.group_head[loc1]]: + parent = loc0 + child = loc1 + else: + child = loc0 + parent = loc1 + + phead = self.group_head[parent] + chead = self.group_head[child] + if phead == chead: + return + + #Walk the child group assigning the new head and simultaneously counting liberties + new_stone_count = self.group_stone_count[phead] + self.group_stone_count[chead] + new_liberties = self.group_liberty_count[phead] + loc = child + while True: + adj0 = loc + self.adj[0] + adj1 = loc + self.adj[1] + adj2 = loc + self.adj[2] + adj3 = loc + self.adj[3] + + #Any adjacent empty space is a new liberty as long as it isn't adjacent to the parent + if self.board[adj0] == Board.EMPTY and not self.is_group_adjacent(phead,adj0): + new_liberties += 1 + if self.board[adj1] == Board.EMPTY and not self.is_group_adjacent(phead,adj1): + new_liberties += 1 + if self.board[adj2] == Board.EMPTY and not self.is_group_adjacent(phead,adj2): + new_liberties += 1 + if self.board[adj3] == Board.EMPTY and not self.is_group_adjacent(phead,adj3): + new_liberties += 1 + + #Now assign the new parent head to take over the child (this also + #prevents double-counting liberties) + self.group_head[loc] = phead + + #Advance around the linked list + loc = self.group_next[loc] + if loc == child: + break + + #Zero out the old head + self.group_stone_count[chead] = 0 + self.group_liberty_count[chead] = 0 + + #Update the new head + self.group_stone_count[phead] = new_stone_count + self.group_liberty_count[phead] = new_liberties + + #Combine the linked lists + plast = self.group_prev[phead] + clast = self.group_prev[chead] + self.group_next[clast] = phead + self.group_next[plast] = chead + self.group_prev[chead] = plast + self.group_prev[phead] = clast + + #Remove all stones in a group + def remove_unsafe(self,group): + head = self.group_head[group] + pla = self.board[group] + opp = Board.get_opp(pla) + + #Walk all the stones in the group and delete them + loc = group + while True: + #Add a liberty to all surrounding opposing groups, taking care to avoid double counting + adj0 = loc + self.adj[0] + adj1 = loc + self.adj[1] + adj2 = loc + self.adj[2] + adj3 = loc + self.adj[3] + if self.board[adj0] == opp: + self.group_liberty_count[self.group_head[adj0]] += 1 + if self.board[adj1] == opp: + if self.group_head[adj1] != self.group_head[adj0]: + self.group_liberty_count[self.group_head[adj1]] += 1 + if self.board[adj2] == opp: + if self.group_head[adj2] != self.group_head[adj0] and \ + self.group_head[adj2] != self.group_head[adj1]: + self.group_liberty_count[self.group_head[adj2]] += 1 + if self.board[adj3] == opp: + if self.group_head[adj3] != self.group_head[adj0] and \ + self.group_head[adj3] != self.group_head[adj1] and \ + self.group_head[adj3] != self.group_head[adj2]: + self.group_liberty_count[self.group_head[adj3]] += 1 + + next_loc = self.group_next[loc] + + #Zero out all the stuff + self.board[loc] = Board.EMPTY + self.zobrist ^= Board.ZOBRIST_STONE[opp][loc] + self.group_head[loc] = 0 + self.group_next[loc] = 0 + self.group_prev[loc] = 0 + + #Advance around the linked list + loc = next_loc + if loc == group: + break + + #Zero out the head + self.group_stone_count[head] = 0 + self.group_liberty_count[head] = 0 + + #Remove a single stone + def remove_single_stone_unsafe(self,rloc): + pla = self.board[rloc] + + #Record all the stones in the group + stones = [] + loc = rloc + while True: + stones.append(loc) + loc = self.group_next[loc] + if loc == rloc: + break + + #Remove them all + self.remove_unsafe(rloc) + + #Then add them back one by one + for loc in stones: + if loc != rloc: + self.add_unsafe(pla,loc) + + + #Helper, find liberties of group at loc. Fills in buf. + def findLiberties(self, loc, buf): + cur = loc + while True: + for i in range(4): + lib = cur + self.adj[i] + if self.board[lib] == Board.EMPTY: + if lib not in buf: + buf.append(lib) + + cur = self.group_next[cur] + if cur == loc: + break + + #Helper, find captures that gain liberties for the group at loc. Fills in buf + def findLibertyGainingCaptures(self, loc, buf): + pla = self.board[loc] + opp = Board.get_opp(pla) + + #For performance, avoid checking for captures on any chain twice + chainHeadsChecked = [] + + cur = loc + while True: + for i in range(4): + adj = cur + self.adj[i] + if self.board[adj] == opp: + head = self.group_head[adj] + + if self.group_liberty_count[head] == 1: + if head not in chainHeadsChecked: + #Capturing moves are precisely the liberties of the groups around us with 1 liberty. + self.findLiberties(adj, buf) + chainHeadsChecked.append(head) + + cur = self.group_next[cur] + if cur == loc: + break + + #Helper, does the group at loc have at least one opponent group adjacent to it in atari? + def hasLibertyGainingCaptures(self, loc): + pla = self.board[loc] + opp = Board.get_opp(pla) + + cur = loc + while True: + for i in range(4): + adj = cur + self.adj[i] + if self.board[adj] == opp: + head = self.group_head[adj] + if self.group_liberty_count[head] == 1: + return True + + cur = self.group_next[cur] + if cur == loc: + break + + return False + + def wouldBeKoCapture(self, loc, pla): + if self.board[loc] != Board.EMPTY: + return False + #Check that surounding points are are all opponent owned and exactly one of them is capturable + opp = Board.get_opp(pla) + oppCapturableLoc = None + for i in range(4): + adj = loc + self.adj[i] + if self.board[adj] != Board.WALL and self.board[adj] != opp: + return False + if self.board[adj] == opp and self.group_liberty_count[self.group_head[adj]] == 1: + if oppCapturableLoc is not None: + return False + oppCapturableLoc = adj + + if oppCapturableLoc is None: + return False + + #Check that the capturable loc has exactly one stone + if self.group_stone_count[self.group_head[oppCapturableLoc]] != 1: + return False + return True + + def countHeuristicConnectionLiberties(self,loc,pla): + adj0 = loc + self.adj[0] + adj1 = loc + self.adj[1] + adj2 = loc + self.adj[2] + adj3 = loc + self.adj[3] + count = 0.0 + if self.board[adj0] == pla: + count += max(0.0,self.group_liberty_count[self.group_head[adj0]]-1.5) + if self.board[adj1] == pla: + count += max(0.0,self.group_liberty_count[self.group_head[adj1]]-1.5) + if self.board[adj2] == pla: + count += max(0.0,self.group_liberty_count[self.group_head[adj2]]-1.5) + if self.board[adj3] == pla: + count += max(0.0,self.group_liberty_count[self.group_head[adj3]]-1.5) + return count + + def searchIsLadderCapturedAttackerFirst2Libs(self,loc): + if not self.is_on_board(loc): + return [] + if self.board[loc] != Board.BLACK and self.board[loc] != Board.WHITE: + return [] + if self.group_liberty_count[self.group_head[loc]] != 2: + return [] + + #Make it so that pla is always the defender + pla = self.board[loc] + opp = Board.get_opp(pla) + + moves = [] + self.findLiberties(loc,moves) + assert(len(moves) == 2) + + move0 = moves[0] + move1 = moves[1] + move0Works = False + move1Works = False + + if self.would_be_legal(opp,move0): + record = self.playRecordedUnsafe(opp,move0) + move0Works = self.searchIsLadderCaptured(loc,True) + self.undo(record) + if self.would_be_legal(opp,move1): + record = self.playRecordedUnsafe(opp,move1) + move1Works = self.searchIsLadderCaptured(loc,True) + self.undo(record) + + workingMoves = [] + if move0Works: + workingMoves.append(move0) + if move1Works: + workingMoves.append(move1) + + return workingMoves + + + def searchIsLadderCaptured(self,loc,defenderFirst): + if not self.is_on_board(loc): + return False + if self.board[loc] != Board.BLACK and self.board[loc] != Board.WHITE: + return False + + if self.group_liberty_count[self.group_head[loc]] > 2 or (defenderFirst and self.group_liberty_count[self.group_head[loc]] > 1): + return False + + #Make it so that pla is always the defender + pla = self.board[loc] + opp = Board.get_opp(pla) + + arrSize = self.size * self.size * 2 #A bit bigger due to paranoia about recaptures making the sequence longer. + + #Stack for the search. These are lists of possible moves to search at each level of the stack + moveLists = [[] for i in range(arrSize)] + moveListCur = [0 for i in range(arrSize)] #Current move list idx searched, equal to -1 if list has not been generated. + records = [None for i in range(arrSize)] #Records so that we can undo moves as we search back up. + stackIdx = 0 + + moveLists[0] = [] + moveListCur[0] = -1 + + returnValue = False + returnedFromDeeper = False + + #Clear the ko loc for the defender at the root node - assume all kos work for the defender + saved_simple_ko_point = self.simple_ko_point + if defenderFirst: + self.simple_ko_point = None + + # debug = True + # if debug: + # print("SEARCHING " + str(self.loc_x(loc)) + " " + str(self.loc_y(loc))) + + while True: + # if debug: + # print(str(stackIdx) + " " + str(moveListCur[stackIdx]) + "/" + str(len(moveLists[stackIdx])) + " " + str(returnValue) + " " + str(returnedFromDeeper)) + + #Returned from the root - so that's the answer + if stackIdx <= -1: + assert(stackIdx == -1) + self.simple_ko_point = saved_simple_ko_point + return returnValue + + isDefender = (defenderFirst and (stackIdx % 2) == 0) or (not defenderFirst and (stackIdx % 2) == 1) + + #We just entered this level? + if moveListCur[stackIdx] == -1: + libs = self.group_liberty_count[self.group_head[loc]] + + #Base cases. + #If we are the attacker and the group has only 1 liberty, we already win. + if not isDefender and libs <= 1: + returnValue = True + returnedFromDeeper = True + stackIdx -= 1 + continue + + #If we are the attacker and the group has 3 liberties, we already lose. + if not isDefender and libs >= 3: + returnValue = False + returnedFromDeeper = True + stackIdx -= 1 + continue + + #If we are the defender and the group has 2 liberties, we already win. + if isDefender and libs >= 2: + returnValue = False + returnedFromDeeper = True + stackIdx -= 1 + continue + + #If we are the defender and the attacker left a simple ko point, assume we already win + #because we don't want to say yes on ladders that depend on kos + #This should also hopefully prevent any possible infinite loops - I don't know of any infinite loop + #that would come up in a continuous atari sequence that doesn't ever leave a simple ko point. + if isDefender and self.simple_ko_point is not None: + returnValue = False + returnedFromDeeper = True + stackIdx -= 1 + continue + + #Otherwise we need to keep searching. + #Generate the move list. Attacker and defender generate moves on the group's liberties, but only the defender + #generates moves on surrounding capturable opposing groups. + if isDefender: + moveLists[stackIdx] = [] + self.findLibertyGainingCaptures(loc,moveLists[stackIdx]) + self.findLiberties(loc,moveLists[stackIdx]) + else: + moveLists[stackIdx] = [] + self.findLiberties(loc,moveLists[stackIdx]) + assert(len(moveLists[stackIdx]) == 2) + + #Early quitouts if the liberties are not adjacent + #(so that filling one doesn't fill an immediate liberty of the other) + move0 = moveLists[stackIdx][0] + move1 = moveLists[stackIdx][1] + + libs0 = self.countImmediateLiberties(move0) + libs1 = self.countImmediateLiberties(move1) + + #If we are the attacker and we're in a double-ko death situation, then assume we win. + #Both defender liberties must be ko mouths, connecting either ko mouth must not increase the defender's + #liberties, and none of the attacker's surrounding stones can currently be in atari. + #This is not complete - there are situations where the defender's connections increase liberties, or where + #the attacker has stones in atari, but where the defender is still in inescapable atari even if they have + #a large finite number of ko threats. But it's better than nothing. + if libs0 == 0 and libs1 == 0 and self.wouldBeKoCapture(move0,opp) and self.wouldBeKoCapture(move1,opp) : + if self.get_liberties_after_play(pla,move0,3) <= 2 and self.get_liberties_after_play(pla,move1,3) <= 2: + if self.hasLibertyGainingCaptures(loc): + returnValue = True + returnedFromDeeper = True + stackIdx -= 1 + continue + + if not self.is_adjacent(move0,move1): + #We lose automatically if both escapes get the defender too many libs + if libs0 >= 3 and libs1 >= 3: + returnValue = False + returnedFromDeeper = True + stackIdx -= 1 + continue + #Move 1 is not possible, so shrink the list + elif libs0 >= 3: + moveLists[stackIdx] = [move0] + #Move 0 is not possible, so shrink the list + elif libs1 >= 3: + moveLists[stackIdx] = [move1] + + #Order the two moves based on a simple heuristic - for each neighboring group with any liberties + #count that the opponent could connect to, count liberties - 1.5. + if len(moveLists[stackIdx]) > 1: + libs0 += self.countHeuristicConnectionLiberties(move0,pla) + libs1 += self.countHeuristicConnectionLiberties(move1,pla) + if libs1 > libs0: + moveLists[stackIdx][0] = move1 + moveLists[stackIdx][1] = move0 + + #And indicate to begin search on the first move generated. + moveListCur[stackIdx] = 0 + + #Else, we returned from a deeper level (or the same level, via illegal move) + else: + assert(moveListCur[stackIdx] >= 0) + assert(moveListCur[stackIdx] < len(moveLists[stackIdx])) + #If we returned from deeper we need to undo the move we made + if returnedFromDeeper: + self.undo(records[stackIdx]) + + #Defender has a move that is not ladder captured? + if isDefender and not returnValue: + #Return! (returnValue is still false, as desired) + returnedFromDeeper = True + stackIdx -= 1 + continue + + #Attacker has a move that does ladder capture? + if not isDefender and returnValue: + #Return! (returnValue is still true, as desired) + returnedFromDeeper = True + stackIdx -= 1 + continue + + #Move on to the next move to search + moveListCur[stackIdx] += 1 + + #If there is no next move to search, then we lose. + if moveListCur[stackIdx] >= len(moveLists[stackIdx]): + #For a defender, that means a ladder capture. + #For an attacker, that means no ladder capture found. + returnValue = isDefender + returnedFromDeeper = True + stackIdx -= 1 + continue + + + #Otherwise we do have an next move to search. Grab it. + move = moveLists[stackIdx][moveListCur[stackIdx]] + p = (pla if isDefender else opp) + + # if debug: + # print("play " + str(self.loc_x(move)) + " " + str(self.loc_y(move)) + " " + str(p)) + # print(self.to_string()) + + #Illegal move - treat it the same as a failed move, but don't return up a level so that we + #loop again and just try the next move. + if not self.would_be_legal(p,move): + returnValue = isDefender + returnedFromDeeper = False + #if(print) cout << "illegal " << endl; + continue + + #Play and record the move! + records[stackIdx] = self.playRecordedUnsafe(p,move) + + #And recurse to the next level + stackIdx += 1 + moveListCur[stackIdx] = -1 + moveLists[stackIdx] = [] + + + def calculateArea(self, result, nonPassAliveStones, safeBigTerritories, unsafeBigTerritories, isMultiStoneSuicideLegal): + for i in range(self.arrsize): + result[i] = Board.EMPTY + self.calculateAreaForPla(Board.BLACK,safeBigTerritories,unsafeBigTerritories,isMultiStoneSuicideLegal,result) + self.calculateAreaForPla(Board.WHITE,safeBigTerritories,unsafeBigTerritories,isMultiStoneSuicideLegal,result) + + if nonPassAliveStones: + for y in range(self.size): + for x in range(self.size): + loc = self.loc(x,y) + if result[loc] == Board.EMPTY: + result[loc] = self.board[loc] + + def calculateNonDameTouchingArea(self, result, keepTerritories, keepStones, isMultiStoneSuicideLegal): + #First, just compute basic area. + basicArea = [Board.EMPTY for i in range(self.arrsize)] + for i in range(self.arrsize): + result[i] = Board.EMPTY + self.calculateAreaForPla(Board.BLACK,True,True,isMultiStoneSuicideLegal,basicArea) + self.calculateAreaForPla(Board.WHITE,True,True,isMultiStoneSuicideLegal,basicArea) + + for y in range(self.size): + for x in range(self.size): + loc = self.loc(x,y) + if basicArea[loc] == Board.EMPTY: + basicArea[loc] = self.board[loc] + + self.calculateNonDameTouchingAreaHelper(basicArea,result) + + if keepTerritories: + for y in range(self.size): + for x in range(self.size): + loc = self.loc(x,y) + if basicArea[loc] != Board.EMPTY and basicArea[loc] != self.board[loc]: + result[loc] = basicArea[loc] + + if keepStones: + for y in range(self.size): + for x in range(self.size): + loc = self.loc(x,y) + if basicArea[loc] != Board.EMPTY and basicArea[loc] == self.board[loc]: + result[loc] = basicArea[loc] + + + def calculateAreaForPla(self, pla, safeBigTerritories, unsafeBigTerritories, isMultiStoneSuicideLegal, result): + opp = self.get_opp(pla) + #First compute all empty-or-opp regions + + #For each loc, if it's empty or opp, the head of the region + regionHeadByLoc = [Board.PASS_LOC for i in range(self.arrsize)] + #For each loc, if it's empty or opp, the next empty or opp belonging to the same region + nextEmptyOrOpp = [Board.PASS_LOC for i in range(self.arrsize)] + #Does this border a pla group that has been marked as not pass alive? + bordersNonPassAlivePlaByHead = [False for i in range(self.arrsize)] + + #A list for each region head, indicating which pla group heads the region is vital for. + #A region is vital for a pla group if all its spaces are adjacent to that pla group. + #All lists are concatenated together, the most we can have is bounded by (MAX_LEN * MAX_LEN+1) / 2 + #independent regions, each one vital for at most 4 pla groups, add some extra just in case. + maxRegions = (self.size * self.size + 1)//2 + 1 + vitalForPlaHeadsListsMaxLen = maxRegions * 4 + vitalForPlaHeadsLists = [-1 for i in range(vitalForPlaHeadsListsMaxLen)] + vitalForPlaHeadsListsTotal = 0 + + #A list of region heads + numRegions = 0 + regionHeads = [-1 for i in range(maxRegions)] + #Start indices and list lengths in vitalForPlaHeadsLists + vitalStart = [-1 for i in range(maxRegions)] + vitalLen = [-1 for i in range(maxRegions)] + #For each region, are there 0, 1, or 2+ spaces of that region not bordering any pla? + numInternalSpacesMax2 = [-1 for i in range(maxRegions)] + containsOpp = [False for i in range(maxRegions)] + + def isAdjacentToPlaHead(loc,plaHead): + for i in range(4): + adj = loc + self.adj[i] + if self.board[adj] == pla and self.group_head[adj] == plaHead: + return True + return False + + #Recursively trace maximal non-pla regions of the board and record their properties and join them into a + #linked list through nextEmptyOrOpp. + #Takes as input the location serving as the head, the tip node of the linked list so far, the next loc, and the + #numeric index of the region + #Returns the loc serving as the current tip node ("tailTarget") of the linked list. + def buildRegion(head, tailTarget, loc, regionIdx): + #Already traced this location, skip + if regionHeadByLoc[loc] != Board.PASS_LOC: + return tailTarget + regionHeadByLoc[loc] = head + + #First, filter out any pla heads it turns out we're not vital for because we're not adjacent to them + #In the case where suicide is allowed, we only do this filtering on intersections that are actually empty + if isMultiStoneSuicideLegal or self.board[loc] == Board.EMPTY: + vStart = vitalStart[regionIdx] + oldVLen = vitalLen[regionIdx] + newVLen = 0 + for i in range(oldVLen): + if isAdjacentToPlaHead(loc,vitalForPlaHeadsLists[vStart+i]): + vitalForPlaHeadsLists[vStart+newVLen] = vitalForPlaHeadsLists[vStart+i] + newVLen += 1 + vitalLen[regionIdx] = newVLen + + #Determine if this point is internal, unless we already have many internal points + if numInternalSpacesMax2[regionIdx] < 2: + isInternal = True + for i in range(4): + adj = loc + self.adj[i] + if self.board[adj] == pla: + isInternal = False + break + if isInternal: + numInternalSpacesMax2[regionIdx] += 1 + + if self.board[loc] == opp: + containsOpp[regionIdx] = True + + #Next, recurse everywhere + nextEmptyOrOpp[loc] = tailTarget + nextTailTarget = loc + for i in range(4): + adj = loc + self.adj[i] + if self.board[adj] == Board.EMPTY or self.board[adj] == opp: + nextTailTarget = buildRegion(head,nextTailTarget,adj,regionIdx) + + return nextTailTarget + + atLeastOnePla = False + for y in range(self.size): + for x in range(self.size): + loc = self.loc(x,y) + + if regionHeadByLoc[loc] != Board.PASS_LOC: + continue + if self.board[loc] != Board.EMPTY: + atLeastOnePla |= (self.board[loc] == pla) + continue + + regionIdx = numRegions + numRegions += 1 + assert(numRegions <= maxRegions) + + #Initialize region metadata + head = loc + regionHeads[regionIdx] = head + vitalStart[regionIdx] = vitalForPlaHeadsListsTotal + vitalLen[regionIdx] = 0 + numInternalSpacesMax2[regionIdx] = 0 + containsOpp[regionIdx] = False + + #Fill in all adjacent pla heads as vital, which will get filtered during buildRegion + vStart = vitalStart[regionIdx] + assert(vStart + 4 <= vitalForPlaHeadsListsMaxLen) + initialVLen = 0 + for i in range(4): + adj = loc + self.adj[i] + if self.board[adj] == pla: + plaHead = self.group_head[adj] + alreadyPresent = False + for j in range(initialVLen): + if vitalForPlaHeadsLists[vStart+j] == plaHead: + alreadyPresent = True + break + + if not alreadyPresent: + vitalForPlaHeadsLists[vStart+initialVLen] = plaHead + initialVLen += 1 + + vitalLen[regionIdx] = initialVLen + + tailTarget = buildRegion(head,head,loc,regionIdx) + nextEmptyOrOpp[head] = tailTarget + + vitalForPlaHeadsListsTotal += vitalLen[regionIdx] + + #Also accumulate all player heads + numPlaHeads = 0 + allPlaHeads = [] + + #Accumulate with duplicates + for y in range(self.size): + for x in range(self.size): + loc = self.loc(x,y) + if self.board[loc] == pla: + allPlaHeads.append(self.group_head[loc]) + numPlaHeads += 1 + + #Filter duplicates + allPlaHeads = list(set(allPlaHeads)) + numPlaHeads = len(allPlaHeads) + + plaHasBeenKilled = [False for i in range(numPlaHeads)] + + #Now, we can begin the benson iteration + vitalCountByPlaHead = [0 for i in range(self.arrsize)] + while(True): + #Zero out vital liberties by head + for i in range(numPlaHeads): + vitalCountByPlaHead[allPlaHeads[i]] = 0 + + #Walk all regions that are still bordered only by pass-alive stuff and accumulate a vital liberty to each pla it is vital for. + for i in range(numRegions): + head = regionHeads[i] + if bordersNonPassAlivePlaByHead[head]: + continue + + vStart = vitalStart[i] + vLen = vitalLen[i] + for j in range(vLen): + plaHead = vitalForPlaHeadsLists[vStart+j] + vitalCountByPlaHead[plaHead] += 1 + + #Walk all player heads and kill them if they haven't accumulated at least 2 vital liberties + killedAnything = False + for i in range(numPlaHeads): + #Already killed - skip + if plaHasBeenKilled[i]: + continue + + plaHead = allPlaHeads[i] + if vitalCountByPlaHead[plaHead] < 2: + plaHasBeenKilled[i] = True + killedAnything = True + #Walk the pla chain to update bordering regions + cur = plaHead + while(True): + for j in range(4): + adj = cur + self.adj[j] + if self.board[adj] == Board.EMPTY or self.board[adj] == opp: + bordersNonPassAlivePlaByHead[regionHeadByLoc[adj]] = True + + cur = self.group_next[cur] + + if cur == plaHead: + break + + if not killedAnything: + break + + #Mark result with pass-alive groups + for i in range(numPlaHeads): + if not plaHasBeenKilled[i]: + plaHead = allPlaHeads[i] + cur = plaHead + while(True): + result[cur] = pla + cur = self.group_next[cur] + if cur == plaHead: + break + + #Mark result with territory + for i in range(numRegions): + head = regionHeads[i] + shouldMark = numInternalSpacesMax2[i] <= 1 and atLeastOnePla and not bordersNonPassAlivePlaByHead[head] + shouldMark = shouldMark or (safeBigTerritories and atLeastOnePla and not containsOpp[i] and not bordersNonPassAlivePlaByHead[head]) + shouldMark = shouldMark or (unsafeBigTerritories and atLeastOnePla and not containsOpp[i]) + + if shouldMark: + cur = head + while(True): + result[cur] = pla + cur = nextEmptyOrOpp[cur] + if cur == head: + break + + def calculateNonDameTouchingAreaHelper(self, basicArea, result): + queue = [Board.PASS_LOC for i in range(self.arrsize)] + + #Iterate through all the regions that players own via area scoring and mark + #all the ones that are touching dame + isDameTouching = [False for i in range(self.arrsize)] + + queueHead = 0 + queueTail = 0 + + ADJ0 = self.adj[0] + ADJ1 = self.adj[1] + ADJ2 = self.adj[2] + ADJ3 = self.adj[3] + + for y in range(self.size): + for x in range(self.size): + loc = self.loc(x,y) + if basicArea[loc] != Board.EMPTY and not isDameTouching[loc]: + #Touches dame? + if((self.board[loc+ADJ0] == Board.EMPTY and basicArea[loc+ADJ0] == Board.EMPTY) or + (self.board[loc+ADJ1] == Board.EMPTY and basicArea[loc+ADJ1] == Board.EMPTY) or + (self.board[loc+ADJ2] == Board.EMPTY and basicArea[loc+ADJ2] == Board.EMPTY) or + (self.board[loc+ADJ3] == Board.EMPTY and basicArea[loc+ADJ3] == Board.EMPTY)): + + pla = basicArea[loc] + isDameTouching[loc] = True + queue[queueTail] = loc + queueTail += 1 + while queueHead != queueTail: + #Pop next location off queue + nextLoc = queue[queueHead] + queueHead += 1 + + #Look all around it, floodfill + for j in range(4): + adj = nextLoc + self.adj[j] + if basicArea[adj] == pla and not isDameTouching[adj]: + isDameTouching[adj] = True + queue[queueTail] = adj + queueTail += 1 + + queueHead = 0 + queueTail = 0 + + #Now, walk through and copy all non-dame-touching basic areas into the result counting + #how many there are. + for y in range(self.size): + for x in range(self.size): + loc = self.loc(x,y) + if basicArea[loc] != Board.EMPTY and not isDameTouching[loc] and result[loc] != basicArea[loc]: + pla = basicArea[loc] + result[loc] = basicArea[loc] + queue[queueTail] = loc + queueTail += 1 + while queueHead != queueTail: + #Pop next location off queue + nextLoc = queue[queueHead] + queueHead += 1 + + #Look all around it, floodfill + for j in range(4): + adj = nextLoc + self.adj[j] + if basicArea[adj] == pla and result[adj] != basicArea[adj]: + result[adj] = basicArea[adj] + queue[queueTail] = adj + queueTail += 1 diff --git a/training/scripts/dated/20241110-124043/clean_checkpoint.py b/training/scripts/dated/20241110-124043/clean_checkpoint.py new file mode 100644 index 0000000..e5e1802 --- /dev/null +++ b/training/scripts/dated/20241110-124043/clean_checkpoint.py @@ -0,0 +1,34 @@ +#!/usr/bin/python3 +import sys +import os +import argparse +import math +import torch +import json + +description = """ +Utility for cleaning torch checkpoints for release. +""" + +parser = argparse.ArgumentParser(description=description) +parser.add_argument('-checkpoint', help='Checkpoint file path', required=True) +parser.add_argument('-output', help='Output checkpoint file path', required=True) +args = vars(parser.parse_args()) + +checkpoint_path = args["checkpoint"] +output_path = args["output"] + +data = torch.load(checkpoint_path,map_location="cpu") + +if "optimizer" in data: + del data["optimizer"] +del data["running_metrics"] +del data["metrics"] +del data["train_state"]["old_train_data_dirs"] +del data["train_state"]["data_files_used"] + +if "last_val_metrics" in data: + del data["last_val_metrics"] + +torch.save(data, output_path) +print(f"Cleaned {checkpoint_path} -> {output_path} for release") diff --git a/training/scripts/dated/20241110-124043/data.py b/training/scripts/dated/20241110-124043/data.py new file mode 100644 index 0000000..cb530b5 --- /dev/null +++ b/training/scripts/dated/20241110-124043/data.py @@ -0,0 +1,163 @@ + +from sgfmill import sgf as Sgf +from sgfmill import sgf_properties as Sgf_properties + +from board import Board + +class Metadata: + def __init__(self, size, bname, wname, brank, wrank, komi, handicap): + self.size = size + self.bname = bname + self.wname = wname + self.brank = brank + self.wrank = wrank + self.komi = komi + self.handicap = handicap + +#Returns (metadata, list of setup stones, list of move stones) +#Setup and move stones are both pairs of (pla,loc) +def load_sgf_moves_exn(path): + sgf_file = open(path,"rb") + contents = sgf_file.read() + sgf_file.close() + + game = Sgf.Sgf_game.from_bytes(contents) + size = game.get_size() + + root = game.get_root() + ab, aw, ae = root.get_setup_stones() + setup = [] + if ab or aw: + for (row,col) in ab: + loc = Board.loc_static(col,size-1-row,size) + setup.append((Board.BLACK,loc)) + for (row,col) in aw: + loc = Board.loc_static(col,size-1-row,size) + setup.append((Board.WHITE,loc)) + + color,raw = root.get_raw_move() + if color is not None: + raise Exception("Found both setup stones and normal moves in root node") + + #Walk down the leftmost branch and assume that this is the game + moves = [] + prev_pla = None + seen_white_moves = False + node = root + while node: + node = node[0] + if node.has_setup_stones(): + raise Exception("Found setup stones after the root node") + + color,raw = node.get_raw_move() + if color is None: + raise Exception("Found node without move color") + + if color == 'b': + pla = Board.BLACK + elif color == 'w': + pla = Board.WHITE + else: + raise Exception("Invalid move color: " + color) + + rc = Sgf_properties.interpret_go_point(raw, size) + if rc is None: #pass + loc = Board.PASS_LOC + else: + (row,col) = rc + loc = Board.loc_static(col,size-1-row,size) + + #Forbid consecutive moves by the same player, unless the previous player was black and we've seen no white moves yet (handicap setup) + if pla == prev_pla and not (prev_pla == Board.BLACK and not seen_white_moves): + raise Exception("Multiple moves in a row by same player") + moves.append((pla,loc)) + + prev_pla = pla + if pla == Board.WHITE: + seen_white_moves = True + + #If there are multiple black moves in a row at the start, assume they are more handicap stones + first_white_move_idx = 0 + while first_white_move_idx < len(moves) and moves[first_white_move_idx][0] == Board.BLACK: + first_white_move_idx += 1 + if first_white_move_idx >= 2: + setup.extend((pla,loc) for (pla,loc) in moves[:first_white_move_idx] if loc is not None) + moves = moves[first_white_move_idx:] + + bname = root.get("PB") + wname = root.get("PW") + brank = (root.get("BR") if root.has_property("BR") else None) + wrank = (root.get("WR") if root.has_property("WR") else None) + komi = (root.get("KM") if root.has_property("KM") else None) + rulesstr = (root.get("RU") if root.has_property("RU") else None) + handicap = (root.get("HA") if root.has_property("HA") else None) + + rules = None + if rulesstr is not None: + if rulesstr.lower() == "japanese" or rulesstr.lower() == "jp": + rules = { + "koRule": "KO_SIMPLE", + "scoringRule": "SCORING_TERRITORY", + "multiStoneSuicideLegal": False, + "encorePhase": 0, + "passWouldEndPhase": False, + "whiteKomi": komi + } + elif rulesstr.lower() == "chinese": + rules = { + "koRule": "KO_SIMPLE", + "scoringRule": "SCORING_AREA", + "multiStoneSuicideLegal": False, + "encorePhase": 0, + "passWouldEndPhase": False, + "whiteKomi": komi + } + elif rulesstr.startswith("ko"): + rules = {} + origrulesstr = rulesstr + rulesstr = rulesstr[2:] + if rulesstr.startswith("SIMPLE"): + rules["koRule"] = "KO_SIMPLE" + rulesstr = rulesstr[6:] + elif rulesstr.startswith("POSITIONAL"): + rules["koRule"] = "KO_POSITIONAL" + rulesstr = rulesstr[10:] + elif rulesstr.startswith("SITUATIONAL"): + rules["koRule"] = "KO_SITUATIONAL" + rulesstr = rulesstr[11:] + elif rulesstr.startswith("SPIGHT"): + rules["koRule"] = "KO_SPIGHT" + rulesstr = rulesstr[6:] + else: + raise Exception("Could not parse rules: " + origrulesstr) + + if rulesstr.startswith("score"): + rulesstr = rulesstr[5:] + else: + raise Exception("Could not parse rules: " + origrulesstr) + + if rulesstr.startswith("AREA"): + rules["scoringRule"] = "SCORING_AREA" + rulesstr = rulesstr[4:] + elif rulesstr.startswith("TERRITORY"): + rules["scoringRule"] = "SCORING_TERRITORY" + rulesstr = rulesstr[9:] + else: + raise Exception("Could not parse rules: " + origrulesstr) + + if rulesstr.startswith("sui"): + rulesstr = rulesstr[3:] + else: + raise Exception("Could not parse rules: " + origrulesstr) + + if rulesstr.startswith("false"): + rules["multiStoneSuicideLegal"] = False + rulesstr = rulesstr[5:] + elif rulesstr.startswith("true"): + rules["multiStoneSuicideLegal"] = True + rulesstr = rulesstr[4:] + else: + raise Exception("Could not parse rules: " + origrulesstr) + + metadata = Metadata(size, bname, wname, brank, wrank, komi, handicap) + return metadata, setup, moves, rules diff --git a/training/scripts/dated/20241110-124043/data_processing_pytorch.py b/training/scripts/dated/20241110-124043/data_processing_pytorch.py new file mode 100644 index 0000000..55fcc58 --- /dev/null +++ b/training/scripts/dated/20241110-124043/data_processing_pytorch.py @@ -0,0 +1,238 @@ +import logging +import os + +import numpy as np + +import torch +import torch.nn.functional + +import modelconfigs + +def read_npz_training_data( + npz_files, + batch_size: int, + world_size: int, + rank: int, + pos_len: int, + device, + randomize_symmetries: bool, + include_meta: bool, + model_config: modelconfigs.ModelConfig, +): + rand = np.random.default_rng(seed=list(os.urandom(12))) + num_bin_features = modelconfigs.get_num_bin_input_features(model_config) + num_global_features = modelconfigs.get_num_global_input_features(model_config) + (h_base,h_builder) = build_history_matrices(model_config, device) + + for npz_file in npz_files: + with np.load(npz_file) as npz: + binaryInputNCHWPacked = npz["binaryInputNCHWPacked"] + globalInputNC = npz["globalInputNC"] + policyTargetsNCMove = npz["policyTargetsNCMove"].astype(np.float32) + globalTargetsNC = npz["globalTargetsNC"] + scoreDistrN = npz["scoreDistrN"].astype(np.float32) + valueTargetsNCHW = npz["valueTargetsNCHW"].astype(np.float32) + if include_meta: + metadataInputNC = npz["metadataInputNC"].astype(np.float32) + del npz + + binaryInputNCHW = np.unpackbits(binaryInputNCHWPacked,axis=2) + assert len(binaryInputNCHW.shape) == 3 + assert binaryInputNCHW.shape[2] == ((pos_len * pos_len + 7) // 8) * 8 + binaryInputNCHW = binaryInputNCHW[:,:,:pos_len*pos_len] + binaryInputNCHW = np.reshape(binaryInputNCHW, ( + binaryInputNCHW.shape[0], binaryInputNCHW.shape[1], pos_len, pos_len + )).astype(np.float32) + + assert binaryInputNCHW.shape[1] == num_bin_features + assert globalInputNC.shape[1] == num_global_features + + num_samples = binaryInputNCHW.shape[0] + # Just discard stuff that doesn't divide evenly + num_whole_steps = num_samples // (batch_size * world_size) + + logging.info(f"Beginning {npz_file} with {num_whole_steps * world_size} usable batches, my rank is {rank}") + for n in range(num_whole_steps): + start = (n * world_size + rank) * batch_size + end = start + batch_size + + batch_binaryInputNCHW = torch.from_numpy(binaryInputNCHW[start:end]).to(device) + batch_globalInputNC = torch.from_numpy(globalInputNC[start:end]).to(device) + batch_policyTargetsNCMove = torch.from_numpy(policyTargetsNCMove[start:end]).to(device) + batch_globalTargetsNC = torch.from_numpy(globalTargetsNC[start:end]).to(device) + batch_scoreDistrN = torch.from_numpy(scoreDistrN[start:end]).to(device) + batch_valueTargetsNCHW = torch.from_numpy(valueTargetsNCHW[start:end]).to(device) + if include_meta: + batch_metadataInputNC = torch.from_numpy(metadataInputNC[start:end]).to(device) + + (batch_binaryInputNCHW, batch_globalInputNC) = apply_history_matrices( + model_config, batch_binaryInputNCHW, batch_globalInputNC, batch_globalTargetsNC, h_base, h_builder + ) + + if randomize_symmetries: + symm = int(rand.integers(0,8)) + batch_binaryInputNCHW = apply_symmetry(batch_binaryInputNCHW, symm) + batch_policyTargetsNCMove = apply_symmetry_policy(batch_policyTargetsNCMove, symm, pos_len) + batch_valueTargetsNCHW = apply_symmetry(batch_valueTargetsNCHW, symm) + batch_binaryInputNCHW = batch_binaryInputNCHW.contiguous() + batch_policyTargetsNCMove = batch_policyTargetsNCMove.contiguous() + batch_valueTargetsNCHW = batch_valueTargetsNCHW.contiguous() + + if include_meta: + batch = dict( + binaryInputNCHW = batch_binaryInputNCHW, + globalInputNC = batch_globalInputNC, + policyTargetsNCMove = batch_policyTargetsNCMove, + globalTargetsNC = batch_globalTargetsNC, + scoreDistrN = batch_scoreDistrN, + valueTargetsNCHW = batch_valueTargetsNCHW, + metadataInputNC = batch_metadataInputNC, + ) + else: + batch = dict( + binaryInputNCHW = batch_binaryInputNCHW, + globalInputNC = batch_globalInputNC, + policyTargetsNCMove = batch_policyTargetsNCMove, + globalTargetsNC = batch_globalTargetsNC, + scoreDistrN = batch_scoreDistrN, + valueTargetsNCHW = batch_valueTargetsNCHW, + ) + yield batch + + +def apply_symmetry_policy(tensor, symm, pos_len): + """Same as apply_symmetry but also handles the pass index""" + batch_size = tensor.shape[0] + channels = tensor.shape[1] + tensor_without_pass = tensor[:,:,:-1].view((batch_size, channels, pos_len, pos_len)) + tensor_transformed = apply_symmetry(tensor_without_pass, symm) + return torch.cat(( + tensor_transformed.reshape(batch_size, channels, pos_len*pos_len), + tensor[:,:,-1:] + ), dim=2) + +def apply_symmetry(tensor, symm): + """ + Apply a symmetry operation to the given tensor. + + Args: + tensor (torch.Tensor): Tensor to be rotated. (..., W, W) + symm (int): + 0, 1, 2, 3: Rotation by symm * pi / 2 radians. + 4, 5, 6, 7: Mirror symmetry on top of rotation. + """ + assert tensor.shape[-1] == tensor.shape[-2] + + if symm == 0: + return tensor + if symm == 1: + return tensor.transpose(-2, -1).flip(-2) + if symm == 2: + return tensor.flip(-1).flip(-2) + if symm == 3: + return tensor.transpose(-2, -1).flip(-1) + if symm == 4: + return tensor.transpose(-2, -1) + if symm == 5: + return tensor.flip(-1) + if symm == 6: + return tensor.transpose(-2, -1).flip(-1).flip(-2) + if symm == 7: + return tensor.flip(-2) + + +def build_history_matrices(model_config: modelconfigs.ModelConfig, device): + num_bin_features = modelconfigs.get_num_bin_input_features(model_config) + assert num_bin_features == 22, "Currently this code is hardcoded for this many features" + + h_base = torch.diag( + torch.tensor( + [ + 1.0, # 0 + 1.0, # 1 + 1.0, # 2 + 1.0, # 3 + 1.0, # 4 + 1.0, # 5 + 1.0, # 6 + 1.0, # 7 + 1.0, # 8 + 0.0, # 9 Location of move 1 turn ago + 0.0, # 10 Location of move 2 turns ago + 0.0, # 11 Location of move 3 turns ago + 0.0, # 12 Location of move 4 turns ago + 0.0, # 13 Location of move 5 turns ago + 1.0, # 14 Ladder-threatened stone + 0.0, # 15 Ladder-threatened stone, 1 turn ago + 0.0, # 16 Ladder-threatened stone, 2 turns ago + 1.0, # 17 + 1.0, # 18 + 1.0, # 19 + 1.0, # 20 + 1.0, # 21 + ], + device=device, + requires_grad=False, + ) + ) + # Because we have ladder features that express past states rather than past diffs, + # the most natural encoding when we have no history is that they were always the + # same, rather than that they were all zero. So rather than zeroing them we have no + # history, we add entries in the matrix to copy them over. + # By default, without history, the ladder features 15 and 16 just copy over from 14. + h_base[14, 15] = 1.0 + h_base[14, 16] = 1.0 + + h0 = torch.zeros(num_bin_features, num_bin_features, device=device, requires_grad=False) + # When have the prev move, we enable feature 9 and 15 + h0[9, 9] = 1.0 # Enable 9 -> 9 + h0[14, 15] = -1.0 # Stop copying 14 -> 15 + h0[14, 16] = -1.0 # Stop copying 14 -> 16 + h0[15, 15] = 1.0 # Enable 15 -> 15 + h0[15, 16] = 1.0 # Start copying 15 -> 16 + + h1 = torch.zeros(num_bin_features, num_bin_features, device=device, requires_grad=False) + # When have the prevprev move, we enable feature 10 and 16 + h1[10, 10] = 1.0 # Enable 10 -> 10 + h1[15, 16] = -1.0 # Stop copying 15 -> 16 + h1[16, 16] = 1.0 # Enable 16 -> 16 + + h2 = torch.zeros(num_bin_features, num_bin_features, device=device, requires_grad=False) + h2[11, 11] = 1.0 + + h3 = torch.zeros(num_bin_features, num_bin_features, device=device, requires_grad=False) + h3[12, 12] = 1.0 + + h4 = torch.zeros(num_bin_features, num_bin_features, device=device, requires_grad=False) + h4[13, 13] = 1.0 + + # (1, n_bin, n_bin) + h_base = h_base.reshape((1, num_bin_features, num_bin_features)) + # (5, n_bin, n_bin) + h_builder = torch.stack((h0, h1, h2, h3, h4), dim=0) + + return (h_base, h_builder) + + +def apply_history_matrices(model_config, batch_binaryInputNCHW, batch_globalInputNC, batch_globalTargetsNC, h_base, h_builder): + num_global_features = modelconfigs.get_num_global_input_features(model_config) + # include_history = batch_globalTargetsNC[:,36:41] + should_stop_history = torch.rand_like(batch_globalTargetsNC[:,36:41]) >= 0.98 + include_history = (torch.cumsum(should_stop_history,axis=1,dtype=torch.float32) <= 0.1).to(torch.float32) + + # include_history: (N, 5) + # bi * ijk -> bjk, (N, 5) * (5, n_bin, n_bin) -> (N, n_bin, n_bin) + h_matrix = h_base + torch.einsum("bi,ijk->bjk", include_history, h_builder) + + + # batch_binaryInputNCHW: (N, n_bin_in, 19, 19) + # h_matrix: (N, n_bin_in, n_bin_out) + # Result: (N, n_bin_out, 19, 19) + batch_binaryInputNCHW = torch.einsum("bijk,bil->bljk", batch_binaryInputNCHW, h_matrix) + + # First 5 global input features exactly correspond to include_history, pointwise multiply to + # enable/disable them + batch_globalInputNC = batch_globalInputNC * torch.nn.functional.pad( + include_history, ((0, num_global_features - include_history.shape[1])), value=1.0 + ) + return batch_binaryInputNCHW, batch_globalInputNC diff --git a/training/scripts/dated/20241110-124043/diff.txt b/training/scripts/dated/20241110-124043/diff.txt new file mode 100644 index 0000000..d89dfd3 --- /dev/null +++ b/training/scripts/dated/20241110-124043/diff.txt @@ -0,0 +1,62 @@ +diff --git a/python/selfplay/shuffle.sh b/python/selfplay/shuffle.sh +index 0ac0124b..efaaf34b 100755 +--- a/python/selfplay/shuffle.sh ++++ b/python/selfplay/shuffle.sh +@@ -1,8 +1,8 @@ + #!/bin/bash -eu + set -o pipefail + { +-#Shuffles and copies selfplay training from selfplay/ to shuffleddata/current/ +-#Should be run periodically. ++# Shuffles and copies selfplay training from selfplay/ to shuffleddata/current/ ++# Should be run periodically. + + if [[ $# -lt 4 ]] + then +@@ -96,25 +96,26 @@ else + fi + #set +x + +-#Just in case, give a little time for nfs ++# Just in case, give a little time for nfs + sleep 10 + +-#rm if it already exists +-rm -f "$BASEDIR"/shuffleddata/current_tmp ++# Ensure target directory is empty before moving ++rm -rf "$BASEDIR"/shuffleddata/current + ++# Create symbolic link + ln -s $OUTDIR "$BASEDIR"/shuffleddata/current_tmp + mv -Tf "$BASEDIR"/shuffleddata/current_tmp "$BASEDIR"/shuffleddata/current + + # CLEANUP --------------------------------------------------------------- + +-#Among shuffled dirs older than 2 hours, remove all but the most recent 5 of them. +-#This should be VERY conservative and allow plenty of time for the training to switch +-#to newer ones as they get generated. ++# Among shuffled dirs older than 2 hours, remove all but the most recent 5 of them. ++# This should be VERY conservative and allow plenty of time for the training to switch ++# to newer ones as they get generated. + echo "Cleaning up any old dirs" + find "$BASEDIR"/shuffleddata/ -mindepth 1 -maxdepth 1 -type d -mmin +120 | sort | head -n -5 | xargs --no-run-if-empty rm -r + + echo "Finished shuffle at" $(date "+%Y-%m-%d %H:%M:%S") +-#Make a little space between shuffles ++# Make a little space between shuffles + echo "" + echo "" + +diff --git a/python/selfplay/synchronous_loop.sh b/python/selfplay/synchronous_loop.sh +index 33f4be30..d21423e2 100755 +--- a/python/selfplay/synchronous_loop.sh ++++ b/python/selfplay/synchronous_loop.sh +@@ -44,7 +44,7 @@ mkdir -p "$BASEDIR"/selfplay + mkdir -p "$BASEDIR"/gatekeepersgf + + # Parameters for the training run +-NUM_GAMES_PER_CYCLE=90 # Every cycle, play this many games ++NUM_GAMES_PER_CYCLE=30 # Every cycle, play this many games + NUM_THREADS_FOR_SHUFFLING=8 + NUM_TRAIN_SAMPLES_PER_EPOCH=100000 # Training will proceed in chunks of this many rows, subject to MAX_TRAIN_PER_DATA. + MAX_TRAIN_PER_DATA=8 # On average, train only this many times on each data row. Larger numbers may cause overfitting. diff --git a/training/scripts/dated/20241110-124043/diffstaged.txt b/training/scripts/dated/20241110-124043/diffstaged.txt new file mode 100644 index 0000000..10d775f --- /dev/null +++ b/training/scripts/dated/20241110-124043/diffstaged.txt @@ -0,0 +1,19665 @@ +diff --git a/cpp/KataGoData/opencltuning/tune11_gpuNVIDIAGeForceRTX4060LaptopGPU_x19_y19_c320_mv10.txt b/cpp/KataGoData/opencltuning/tune11_gpuNVIDIAGeForceRTX4060LaptopGPU_x19_y19_c320_mv10.txt +new file mode 100644 +index 00000000..5b66d67d +--- /dev/null ++++ b/cpp/KataGoData/opencltuning/tune11_gpuNVIDIAGeForceRTX4060LaptopGPU_x19_y19_c320_mv10.txt +@@ -0,0 +1,33 @@ ++VERSION=11 ++#canUseFP16Storage ++1 ++#canUseFP16Compute ++0 ++#canUseFP16TensorCores ++1 ++#canUseFP16TensorCoresFor1x1 ++0 ++#shouldUseFP16Storage ++1 ++#shouldUseFP16Compute ++0 ++#shouldUseFP16TensorCores ++1 ++#shouldUseFP16TensorCoresFor1x1 ++0 ++#xGemmDirect ++WGD=32 MDIMCD=8 NDIMCD=8 MDIMAD=16 NDIMBD=16 KWID=2 VWMD=2 VWND=2 PADA=1 PADB=1 ++#xGemm ++MWG=64 NWG=64 KWG=16 MDIMC=16 NDIMC=8 MDIMA=16 NDIMB=8 KWI=2 VWM=4 VWN=4 STRM=0 STRN=0 SA=1 SB=1 ++#xGemm16 ++MWG=64 NWG=64 KWG=16 MDIMC=16 NDIMC=8 MDIMA=16 NDIMB=8 KWI=2 VWM=4 VWN=4 STRM=0 STRN=0 SA=1 SB=1 ++#hGemmWmma ++MWG=64 NWG=64 KWG=16 MWAVE=32 NWAVE=8 MWARP=32 NWARP=8 VWM=2 VWN=2 SA=0 SB=0 ++#hGemmWmmaNCHW ++MWG=16 NWG=16 KWG=16 MWAVE=16 NWAVE=16 MWARP=16 NWARP=16 VWM=1 VWN=2 SB=0 ++#conv3x3 ++INTILE_XSIZE=6 INTILE_YSIZE=6 OUTTILE_XSIZE=4 OUTTILE_YSIZE=4 transLocalSize0=128 transLocalSize1=1 untransLocalSize0=8 untransLocalSize1=4 untransLocalSize2=2 ++#conv5x5 ++INTILE_XSIZE=6 INTILE_YSIZE=6 OUTTILE_XSIZE=2 OUTTILE_YSIZE=2 transLocalSize0=128 transLocalSize1=1 untransLocalSize0=8 untransLocalSize1=4 untransLocalSize2=2 ++#gPool ++XYSTRIDE=16 CHANNELSTRIDE=2 BATCHSTRIDE=2 +diff --git a/cpp/KataGoData/opencltuning/tune11_gpuNVIDIAGeForceRTX4060LaptopGPU_x19_y19_c384_mv14.txt b/cpp/KataGoData/opencltuning/tune11_gpuNVIDIAGeForceRTX4060LaptopGPU_x19_y19_c384_mv14.txt +new file mode 100644 +index 00000000..71421834 +--- /dev/null ++++ b/cpp/KataGoData/opencltuning/tune11_gpuNVIDIAGeForceRTX4060LaptopGPU_x19_y19_c384_mv14.txt +@@ -0,0 +1,33 @@ ++VERSION=11 ++#canUseFP16Storage ++1 ++#canUseFP16Compute ++0 ++#canUseFP16TensorCores ++1 ++#canUseFP16TensorCoresFor1x1 ++0 ++#shouldUseFP16Storage ++1 ++#shouldUseFP16Compute ++0 ++#shouldUseFP16TensorCores ++1 ++#shouldUseFP16TensorCoresFor1x1 ++0 ++#xGemmDirect ++WGD=32 MDIMCD=8 NDIMCD=8 MDIMAD=8 NDIMBD=8 KWID=8 VWMD=2 VWND=4 PADA=1 PADB=1 ++#xGemm ++MWG=64 NWG=64 KWG=16 MDIMC=16 NDIMC=16 MDIMA=16 NDIMB=16 KWI=2 VWM=4 VWN=4 STRM=0 STRN=0 SA=1 SB=1 ++#xGemm16 ++MWG=64 NWG=64 KWG=16 MDIMC=16 NDIMC=16 MDIMA=16 NDIMB=16 KWI=2 VWM=4 VWN=4 STRM=0 STRN=0 SA=1 SB=1 ++#hGemmWmma ++MWG=64 NWG=64 KWG=64 MWAVE=16 NWAVE=16 MWARP=16 NWARP=16 VWM=2 VWN=2 SA=0 SB=0 ++#hGemmWmmaNCHW ++MWG=16 NWG=16 KWG=16 MWAVE=16 NWAVE=16 MWARP=16 NWARP=16 VWM=1 VWN=2 SB=0 ++#conv3x3 ++INTILE_XSIZE=6 INTILE_YSIZE=6 OUTTILE_XSIZE=4 OUTTILE_YSIZE=4 transLocalSize0=64 transLocalSize1=1 untransLocalSize0=16 untransLocalSize1=2 untransLocalSize2=2 ++#conv5x5 ++INTILE_XSIZE=6 INTILE_YSIZE=6 OUTTILE_XSIZE=2 OUTTILE_YSIZE=2 transLocalSize0=64 transLocalSize1=1 untransLocalSize0=16 untransLocalSize1=2 untransLocalSize2=2 ++#gPool ++XYSTRIDE=32 CHANNELSTRIDE=1 BATCHSTRIDE=2 +diff --git a/cpp/README.txt b/cpp/README.txt +new file mode 100644 +index 00000000..fb82db4b +--- /dev/null ++++ b/cpp/README.txt +@@ -0,0 +1,118 @@ ++KataGo v1.15.1 ++https://github.com/lightvector/KataGo ++ ++For neural nets from the latest run, download from here: ++https://katagotraining.org/ ++For nets from earlier runs, see: ++https://katagoarchive.org/ ++ ++For the human SL net (b18c384nbt-humanv0.bin.gz), it was released with: ++https://github.com/lightvector/KataGo/releases/tag/v1.15.0 ++ ++For differences between this version and older versions, see releases page at ++https://github.com/lightvector/KataGo/releases/ ++ ++----------------------------------------------------- ++USAGE: ++----------------------------------------------------- ++KataGo is just an engine and does not have its own graphical interface. So generally you will want to use KataGo along with a GUI or analysis program. ++(https://github.com/lightvector/KataGo#guis) ++ ++FIRST: Run a command like this to make sure KataGo is working, with the neural net file you downloaded. On OpenCL, it will also tune for your GPU. ++ ++./katago.exe benchmark # if you have default_gtp.cfg and default_model.bin.gz ++./katago.exe benchmark -model .bin.gz # if you have default_gtp.cfg ++./katago.exe benchmark -model .bin.gz -config gtp_custom.cfg # use this .bin.gz neural net and this .cfg file ++ ++It will tell you a good number of threads. Edit your .cfg file and set "numSearchThreads" to that many to get best performance. ++ ++OR: Run this command to have KataGo generate a custom gtp config for you based on answering some questions: ++ ++./katago.exe genconfig -model .bin.gz -output gtp_custom.cfg ++ ++NEXT: A command like this will run KataGo's engine. This is the command to give to your [GUI or analysis program](#guis) so that it can run KataGo. ++ ++./katago.exe gtp # if you have default_gtp.cfg and default_model.bin.gz ++./katago.exe gtp -model .bin.gz # if you have default_gtp.cfg ++./katago.exe gtp -model .bin.gz -config gtp_custom.cfg # use this .bin.gz neural net and this .cfg file ++ ++You may need to specify different paths when entering KataGo's command for a GUI program, e.g.: ++ ++path/to/katago.exe gtp -model path/to/.bin.gz ++path/to/katago.exe gtp -model path/to/.bin.gz -config path/to/gtp_custom.cfg ++ ++KataGo should be able to work with any GUI program that supports GTP, as well as any analysis program that supports Leela Zero's `lz-analyze` command, such as Lizzie (https://github.com/featurecat/lizzie) or Sabaki (https://sabaki.yichuanshen.de/). ++ ++NOTE for Windows users: ++If you encounter errors due to a missing "msvcp140.dll" or "msvcp140_1.dll" or "msvcp140_2.dll" or "vcruntime140.dll" or similar, you need to download and install the Microsoft Visual C++ Redistributable, here: ++https://www.microsoft.com/en-us/download/details.aspx?id=48145 ++If this is for a 64-bit Windows version of KataGo, these dll files have already been included for you, otherwise you will need to install them yourself. On a 64-bit Windows version, there is a rare chance that you may need to delete them if you already have it installed yourself separately and the pre-included files are actually causing problems running KataGo. ++ ++----------------------------------------------------- ++HUMAN-STYLE PLAY AND ANALYSIS: ++----------------------------------------------------- ++ ++You can also have KataGo imitate human play if you download the human SL model b18c384nbt-humanv0.bin.gz from https://github.com/lightvector/KataGo/releases/tag/v1.15.0, and run a command like the following, providing both a model and the human SL model: ++ ++./katago.exe gtp -model .bin.gz -human-model b18c384nbt-humanv0.bin.gz -config gtp_human5k_example.cfg ++ ++The gtp_human5k_example.cfg configures KataGo to imitate 5-kyu-level players. You can change it to imitate other ranks too, as well as to do many more things, including making KataGo play in a human style but still at a strong level or analyze in interesting ways. Read the config file itself for documentation on some of these possibilities! ++ ++And see also this guide to using the human SL model, which is written from the perspective of the JSON-based analysis engine mentioned below, but is also applicable to gtp as well. ++https://github.com/lightvector/KataGo/blob/master/docs/Analysis_Engine.md#human-sl-analysis-guide ++ ++----------------------------------------------------- ++OTHER THINGS YOU CAN DO: ++----------------------------------------------------- ++ ++Run a JSON-based analysis engine (https://github.com/lightvector/KataGo/blob/master/docs/Analysis_Engine.md) that can do efficient batched evaluations for a backend Go service: ++ ++./katago.exe analysis -model .gz -config .cfg ++ ++Run a high-performance match engine that will play a pool of bots against each other sharing the same GPU batches and CPUs with each other: ++ ++./katago.exe match -config .cfg -log-file match.log -sgf-output-dir ++ ++Force OpenCL tuner to re-tune: ++ ++./katago.exe tuner -config .cfg ++ ++Print version: ++ ++./katago.exe version ++ ++ ++----------------------------------------------------- ++OPENCL VS CUDA VS EIGEN: ++----------------------------------------------------- ++Explanation of the various versions available at https://github.com/lightvector/KataGo/releases ++ ++OpenCL - Use this if you have a modern GPU and want the easiest setup. ++ ++TensorRT, CUDA11 - Try one of these if you have a top-end NVIDIA GPU, are willing to do some more technical setup work, and care about getting every bit of performance. It MIGHT OR MIGHT NOT be faster than OpenCL! You have to test it on your specific GPU. TensorRT requires CUDA11 as well, and is likely to be the fastest. ++ ++Eigen AVX2 - Use this if you have no good GPU, but you have an Intel or AMD CPU from the last several years. This is a pure CPU version of KataGo. ++ ++Eigen - Use this if you don't have a GPU or your GPU is too old to work, and your CPU turns out not to support AVX2 or FMA. This is a pure CPU version for KataGo for ++This is the pure CPU version of KataGo, with no special instructions, which should hopefully run just about anywhere. ++ ++ ++----------------------------------------------------- ++TUNING FOR PERFORMANCE: ++----------------------------------------------------- ++You will very likely want to tune some of the parameters in `default_gtp.cfg` for your system for good performance, including the number of threads, fp16 usage, NN cache size, pondering settings, and so on. You can also adjust things like KataGo's resign threshold or utility function. Most of the relevant parameters should be be reasonably well documented directly inline in that config. ++ ++There are other a few notes about usage and performance at : https://github.com/lightvector/KataGo ++ ++----------------------------------------------------- ++TROUBLESHOOTING: ++----------------------------------------------------- ++Some common issues are described here: ++https://github.com/lightvector/KataGo#common-causes-of-errors ++ ++Or, feel free to hop into the Leela Zero discord chat, which has become a general chatroom for a variety of computer Go hobbyists and users, and which you can often find people willing to help. ++https://discord.gg/fhDHgfk ++ ++ ++ ++ +diff --git a/cpp/analysis_example.cfg b/cpp/analysis_example.cfg +new file mode 100644 +index 00000000..ca648321 +--- /dev/null ++++ b/cpp/analysis_example.cfg +@@ -0,0 +1,402 @@ ++# Config for KataGo C++ Analysis engine, i.e. "./katago.exe analysis" ++ ++# Example config for C++ (non-python) analysis engine ++ ++# SEE NOTES ABOUT PERFORMANCE AND MEMORY USAGE IN gtp_example.cfg ++# SEE NOTES ABOUT numSearchThreads AND OTHER IMPORTANT PARAMS BELOW! ++ ++# Logs------------------------------------------------------------------------------------ ++ ++# Where to output log? ++logDir = analysis_logs # Each run of KataGo will log to a separate file in this dir ++# logDirDated = analysis_logs # Use this instead of logDir to also write separate dated subdirs ++# logFile = analysis.log # Use this instead of logDir to just specify a single file directly ++# logToStderr = true # Echo everything output to log file to stderr as well ++# logAllRequests = false # Log all input lines received to the analysis engine. ++# logAllResponses = false # Log all lines output to stdout from the analysis engine. ++# logErrorsAndWarnings = true # Log all lines output to stdout from the analysis engine that are errors and warnings ++# logSearchInfo = false # Log debug info for every search performed ++ ++# Report a warning whenever a query contains a field that is unused. Helps to guard against ++# typos when writing code that queries the analysis engine. ++# warnUnusedFields = true ++ ++# Analysis------------------------------------------------------------------------------------ ++ ++# Controls the number of moves after the first move in a variation. ++# analysisPVLen = 15 ++ ++# Report winrates for analysis as (BLACK|WHITE|SIDETOMOVE). ++reportAnalysisWinratesAs = BLACK ++ ++# Larger values will make KataGo explore the top move(s) less deeply and accurately, ++# but explore and give evaluations to a greater variety of moves. ++# An extreme value like 1 will distribute many playouts across every move on the board, even very bad moves. ++# NOTE: defaults to 0.04, under the presumption that the analysis engine will be used mainly for analysis. ++# If you are intending to use the analysis engine to also play games and you want to maximize playing strength, ++# set this to 0.0 either in this config or in the overrides. ++# wideRootNoise = 0.04 ++ ++# Try to limit the effect of possible bad or bogus move sequences in the ++# history leading to this position from affecting KataGo's move predictions. ++# ignorePreRootHistory = true ++ ++# Bot behavior--------------------------------------------------------------------------------------- ++ ++# Handicap ------------- ++ ++# Assume that if black makes many moves in a row right at the start of the game, then the game is a handicap game. ++# This is necessary on some servers and for some GUIs and also when initializing from many SGF files, which may ++# set up a handicap game using repeated GTP "play" commands for black rather than GTP "place_free_handicap" commands. ++# However, it may also lead to incorrect understanding of komi if whiteHandicapBonus is used and a server does NOT ++# have such a practice. ++# Defaults to true! Uncomment and set to false to disable this behavior. ++# assumeMultipleStartingBlackMovesAreHandicap = true ++ ++# Passing and cleanup ------------- ++ ++# Make the bot never assume that its pass will end the game, even if passing would end and "win" under Tromp-Taylor rules. ++# Usually this is a good idea when using it for analysis or playing on servers where scoring may be implemented non-tromp-taylorly. ++# Defaults to true! Uncomment and set to false to disable this. ++# conservativePass = true ++ ++# When using territory scoring, self-play games continue beyond two passes with special cleanup ++# rules that may be confusing for human players. This option prevents the special cleanup phases from being ++# reachable when using the bot for GTP play. ++# Defaults to true! Uncomment and set to false if you want KataGo to be able to enter special cleanup. ++# For example, if you are testing it against itself, or against another bot that has precisely implemented the rules ++# documented at https://lightvector.github.io/KataGo/rules.html ++# preventCleanupPhase = true ++ ++# Search limits----------------------------------------------------------------------------------- ++ ++# By default, if NOT specified in an individual request, limit maximum number of root visits per search to this much ++maxVisits = 500 ++# If provided, cap search time at this many seconds ++# maxTime = 60 ++ ++# Search threads, batching, GPUs-------------------------------------------------------------------------- ++ ++# Try a configuration like this if you only expect the engine to be handling a few queries at a time and you want ++# individual queries to return more quickly, and are okay with the results being a bit lower-quality and the overall ++# peak throughput on queries to be lower. ++numAnalysisThreads = 2 ++numSearchThreadsPerAnalysisThread = 16 ++ ++# Try a configuration like this if you expect to be sending large numbers of queries at a time, and want to maximize ++# total throughput and also the evaluation quality of all the queries and you never care about the response latency ++# of the individual queries, only the throughput as a whole. ++# numAnalysisThreads = 32 ++# numSearchThreadsPerAnalysisThread = 1 ++ ++# You will want to increase one or both numbers if you have a powerful GPU, and possibly decrease one or both if you ++# have a very weak GPU, and play with the balance between them depending on your use case. ++# Read the explanation below to understand how to set these parameters: ++ ++# EXPLANATION: ++# numAnalysisThreads: the number of POSITIONS to be able to search in parallel. ++# numSearchThreadsPerAnalysisThread: the number of threads to use in the tree search for EACH position. ++# (older analysis configs might just have 'numSearchThreads', this is an alias for 'numSearchThreadsPerAnalysisThread') ++ ++# Therefore, the total number of search threads that may be active at a given time could be as large as the product: ++# numAnalysisThreads * numSearchThreadsPerAnalysisThread ++ ++# Searching more positions in parallel is more efficient since the different threads aren't conflicting with each ++# other on the same MCTS search tree. Using multiple threads on the same search will both make things slower ++# and weaken the search (holding playouts fixed) due to out of date statistics on nodes and suboptimal exploration, ++# although the cost is minor for only 2,4,8 threads. ++ ++# So unlike in GTP, which only ever searches one position at a time and where therefore you might as well make ++# numSearchThreads as large as possible, in the analysis engine you often want you often want to keep numSearchThreads small, ++# and instead parallelize across positions, so you can reduce conflict between threads and improve the overall throughput ++# and strength of the search. ++ ++# But obviously you only get the benefit of parallelization across positions when you actually have lots of positions ++# that you are querying at once! For example, setting numAnalysisThreads = 8 is useless if you only ever send one or two ++# queries at a time! ++ ++# Therefore: ++# * If you plan to use the analysis engine only for batch processing large numbers of positions, ++# it's preferable to numSearchThreadsPerAnalysisThread to only a small number (e.g. 1,2,4) and use a higher numAnalysisThreads. ++# * But if you sometimes plan to query the analysis engine for single positions, or otherwise in smaller quantities ++# than -num-analysis-threads, or if you plan to be user-interactive such that the response time on some individual ++# analysis requests is important to keep low, then set numSearchThreadsPerAnalysisThread to a larger number and use ++# a lower numAnalysisThreads. That way, individual searches complete faster due to having more threads on each one. ++ ++# For 19x19 boards, weaker GPUs probably want a TOTAL number of threads (numAnalysisThreads * numSearchThreadsPerAnalysisThread) ++# between 4 and 32. Mid-tier GPUs probably between 16 and 64. Strong GPUs probably between 32 and 256. ++# But there's no substitute for experimenting and seeing what's best for your hardware and your usage case. ++ ++# Keep in mind that the number of threads you want does NOT necessarily have much to do with how many cores you have on your ++# system. The optimal may easily exceed the number of cores! GPU batching is (usually) the dominant consideration. ++ ++# ------------- ++ ++# nnMaxBatchSize is the max number of positions to send to a single GPU at once. Generally, it should be the case that: ++# (number of GPUs you will use * nnMaxBatchSize) >= (numSearchThreads * num-analysis-threads) ++# That way, when each threads tries to request a GPU eval, your batch size summed across GPUs is large enough to handle them ++# all at once. However, it can be sensible to set this a little smaller if you are limited on GPU memory, ++# too large a number may fail if the GPU doesn't have enough memory. ++nnMaxBatchSize = 64 ++ ++# Uncomment and set these smaller if you are going to use the analysis engine EXCLUSIVELY for smaller boards (or plan to ++# run multiple instances, with some instances only handling smaller boards). It should improve performance. ++# It may also mean you can use more threads profitably. ++# maxBoardXSizeForNNBuffer = 19 ++# maxBoardYSizeForNNBuffer = 19 ++ ++# Uncomment and set this to true if you are going to use the analysis engine EXCLUSIVELY for exactly the board size ++# specified by maxBoardXSizeForNNBuffer and maxBoardYSizeForNNBuffer. It may slightly improve performance on some GPUs. ++# requireMaxBoardSize = true ++ ++# TO USE MULTIPLE GPUS: ++# Uncomment and set this to the number of GPUs you have and/or would like to use... ++# AND if it is more than 1, uncomment the appropriate CUDA or OpenCL section below. ++# numNNServerThreadsPerModel = 1 ++ ++# Other General GPU Settings------------------------------------------------------------------------------- ++ ++# Cache up to 2 ** this many neural net evaluations in case of transpositions in the tree. ++nnCacheSizePowerOfTwo = 23 ++# Size of mutex pool for nnCache is 2 ** this ++nnMutexPoolSizePowerOfTwo = 17 ++# Randomize board orientation when running neural net evals? ++nnRandomize = true ++ ++ ++# TENSORRT GPU settings-------------------------------------- ++# These only apply when using the TENSORRT version of KataGo. ++ ++# IF USING ONE GPU: optionally uncomment and change this if the GPU you want to use turns out to be not device 0 ++# trtDeviceToUse = 0 ++ ++# IF USING TWO GPUS: Uncomment these two lines (AND set numNNServerThreadsPerModel above): ++# trtDeviceToUseThread0 = 0 # change this if the first GPU you want to use turns out to be not device 0 ++# trtDeviceToUseThread1 = 1 # change this if the second GPU you want to use turns out to be not device 1 ++ ++# IF USING THREE GPUS: Uncomment these three lines (AND set numNNServerThreadsPerModel above): ++# trtDeviceToUseThread0 = 0 # change this if the first GPU you want to use turns out to be not device 0 ++# trtDeviceToUseThread1 = 1 # change this if the second GPU you want to use turns out to be not device 1 ++# trtDeviceToUseThread2 = 2 # change this if the third GPU you want to use turns out to be not device 2 ++ ++# You can probably guess the pattern if you have four, five, etc. GPUs. ++ ++ ++# CUDA-specific GPU settings-------------------------------------- ++# These only apply when using the CUDA version of KataGo. ++ ++# IF USING ONE GPU: optionally uncomment and change this if the GPU you want to use turns out to be not device 0 ++# cudaDeviceToUse = 0 ++ ++# IF USING TWO GPUS: Uncomment these two lines (AND set numNNServerThreadsPerModel above): ++# cudaDeviceToUseThread0 = 0 # change this if the first GPU you want to use turns out to be not device 0 ++# cudaDeviceToUseThread1 = 1 # change this if the second GPU you want to use turns out to be not device 1 ++ ++# IF USING THREE GPUS: Uncomment these three lines (AND set numNNServerThreadsPerModel above): ++# cudaDeviceToUseThread0 = 0 # change this if the first GPU you want to use turns out to be not device 0 ++# cudaDeviceToUseThread1 = 1 # change this if the second GPU you want to use turns out to be not device 1 ++# cudaDeviceToUseThread2 = 2 # change this if the third GPU you want to use turns out to be not device 2 ++ ++# You can probably guess the pattern if you have four, five, etc. GPUs. ++ ++# KataGo will automatically use FP16 or not based on the compute capability of your NVIDIA GPU. If you ++# want to try to force a particular behavior though you can uncomment these lines and change them ++# to "true" or "false". E.g. it's using FP16 but on your card that's giving an error, or it's not using ++# FP16 but you think it should. ++# cudaUseFP16 = auto ++# cudaUseNHWC = auto ++ ++ ++# OpenCL-specific GPU settings-------------------------------------- ++# These only apply when using the OpenCL version of KataGo. ++ ++# Uncomment to tune OpenCL for every board size separately, rather than only the largest possible size ++# openclReTunePerBoardSize = true ++ ++# IF USING ONE GPU: optionally uncomment and change this if the best device to use is guessed incorrectly. ++# The default behavior tries to guess the 'best' GPU or device on your system to use, usually it will be a good guess. ++# openclDeviceToUse = 0 ++ ++# IF USING TWO GPUS: Uncomment these two lines and replace X and Y with the device ids of the devices you want to use. ++# It might NOT be 0 and 1, some computers will have many OpenCL devices. You can see what the devices are when ++# KataGo starts up - it should print or log all the devices it finds. ++# (AND also set numNNServerThreadsPerModel above) ++# openclDeviceToUseThread0 = X ++# openclDeviceToUseThread1 = Y ++ ++# IF USING THREE GPUS: Uncomment these three lines and replace X and Y and Z with the device ids of the devices you want to use. ++# It might NOT be 0 and 1 and 2, some computers will have many OpenCL devices. You can see what the devices are when ++# KataGo starts up - it should print or log all the devices it finds. ++# (AND also set numNNServerThreadsPerModel above) ++# openclDeviceToUseThread0 = X ++# openclDeviceToUseThread1 = Y ++# openclDeviceToUseThread2 = Z ++ ++# You can probably guess the pattern if you have four, five, etc. GPUs. ++ ++# KataGo will automatically use FP16 or not based on testing your GPU during tuning. If you ++# want to try to force a particular behavior though you can uncomment this lines and change it ++# to "true" or "false". This is a fairly blunt setting - more detailed settings are testable ++# by rerunning the tuner with various arguments. ++# openclUseFP16 = auto ++ ++ ++# Eigen-specific settings-------------------------------------- ++# These only apply when using the Eigen (pure CPU) version of KataGo. ++ ++# This is the number of CPU threads for evaluating the neural net on the Eigen backend. ++# It defaults to min(numAnalysisThreads * numSearchThreadsPerAnalysisThread, numCPUCores). ++# numEigenThreadsPerModel = X ++ ++ ++# Misc Behavior -------------------- ++ ++# If the board is symmetric, search only one copy of each equivalent move. Attempts to also account for ko/superko, will not theoretically perfect for superko. ++# Uncomment and set to false to disable this. ++# rootSymmetryPruning = true ++ ++# Uncomment and set to true to make KataGo avoid a particular joseki that some KataGo nets misevaluate, ++# and also to improve opening diversity versus some particular other bots that like to play it all the time. ++# avoidMYTDaggerHack = false ++ ++# Have KataGo mildly prefer to avoid playing the same joseki in every corner of the board. ++# Uncomment to set to a specific value. A small value like 0.005 should produce already a noticeable behavior change. ++# avoidRepeatedPatternUtility = 0.0 ++ ++# Enable some hacks that mitigate rare instances when passing messes up deeper searches. ++# enablePassingHacks = true ++ ++# Root move selection and biases------------------------------------------------------------------------------ ++# Uncomment and edit any of the below values to change them from their default. ++# Not all of these parameters are applicable to analysis, some are only used for actual play ++ ++# Temperature for the early game, randomize between chosen moves with this temperature ++# chosenMoveTemperatureEarly = 0.5 ++# Decay temperature for the early game by 0.5 every this many moves, scaled with board size. ++# chosenMoveTemperatureHalflife = 19 ++# At the end of search after the early game, randomize between chosen moves with this temperature ++# chosenMoveTemperature = 0.10 ++# Subtract this many visits from each move prior to applying chosenMoveTemperature ++# (unless all moves have too few visits) to downweight unlikely moves ++# chosenMoveSubtract = 0 ++# The same as chosenMoveSubtract but only prunes moves that fall below the threshold, does not affect moves above ++# chosenMovePrune = 1 ++ ++# Number of symmetries to sample (WITHOUT replacement) and average at the root ++# rootNumSymmetriesToSample = 1 ++ ++# Using LCB for move selection? ++# useLcbForSelection = true ++# How many stdevs a move needs to be better than another for LCB selection ++# lcbStdevs = 5.0 ++# Only use LCB override when a move has this proportion of visits as the top move ++# minVisitPropForLCB = 0.15 ++ ++# Internal params------------------------------------------------------------------------------ ++# Uncomment and edit any of the below values to change them from their default. ++ ++# Scales the utility of winning/losing ++# winLossUtilityFactor = 1.0 ++# Scales the utility for trying to maximize score ++# staticScoreUtilityFactor = 0.10 ++# dynamicScoreUtilityFactor = 0.30 ++# Adjust dynamic score center this proportion of the way towards zero, capped at a reasonable amount. ++# dynamicScoreCenterZeroWeight = 0.20 ++# dynamicScoreCenterScale = 0.75 ++# The utility of getting a "no result" due to triple ko or other long cycle in non-superko rulesets (-1 to 1) ++# noResultUtilityForWhite = 0.0 ++# The number of wins that a draw counts as, for white. (0 to 1) ++# drawEquivalentWinsForWhite = 0.5 ++ ++# Exploration constant for mcts ++# cpuctExploration = 1.0 ++# cpuctExplorationLog = 0.45 ++ ++# Parameters that control exploring more in volatile positions, exploring less in stable positions. ++# cpuctUtilityStdevPrior = 0.40 ++# cpuctUtilityStdevPriorWeight = 2.0 ++# cpuctUtilityStdevScale = 0.85 ++ ++# FPU reduction constant for mcts ++# fpuReductionMax = 0.2 ++# rootFpuReductionMax = 0.1 ++# fpuParentWeightByVisitedPolicy = true ++ ++# Parameters that control weighting of evals based on the net's own self-reported uncertainty. ++# useUncertainty = true ++# uncertaintyExponent = 1.0 ++# uncertaintyCoeff = 0.25 ++ ++# Explore using optimistic policy ++# rootPolicyOptimism = 0.2 ++# policyOptimism = 1.0 ++ ++# Amount to apply a downweighting of children with very bad values relative to good ones ++# valueWeightExponent = 0.25 ++ ++# Slight incentive for the bot to behave human-like with regard to passing at the end, filling the dame, ++# not wasting time playing in its own territory, etc, and not play moves that are equivalent in terms of ++# points but a bit more unfriendly to humans. ++# rootEndingBonusPoints = 0.5 ++ ++# Make the bot prune useless moves that are just prolonging the game to avoid losing yet ++# rootPruneUselessMoves = true ++ ++# Apply bias correction based on local pattern keys ++# subtreeValueBiasFactor = 0.45 ++# subtreeValueBiasWeightExponent = 0.85 ++ ++# Use graph search rather than tree search - identify and share search for transpositions. ++# useGraphSearch = true ++ ++# How much to shard the node table for search synchronization ++# nodeTableShardsPowerOfTwo = 16 ++# How many virtual losses to add when a thread descends through a node ++# numVirtualLossesPerThread = 1 ++ ++# Improve the quality of evals under heavy multithreading ++# useNoisePruning = true ++ ++ ++# Avoid SGF Patterns ------------------------------------------------------------------------------ ++# The parameters in this section provide a powerful way to customize KataGo to avoid moves that follow specific patterns ++# based on a set of provided SGF files loaded upon startup. Uncomment them to use this feature. ++# Additionally, if the SGF file contains the string %SKIP% in a comment on a move, that move will be ignored for this purpose. ++ ++# Load sgf files from this directory when the engine is started (ONLY on startup, will not reload unless engine is restarted) ++# avoidSgfPatternDirs = path/to/directory/with/sgfs/ ++# You can also surround the file path in double quotes if the file path contains trailing spaces or hash signs. ++# Within double quotes, backslashes are escape characters. ++# avoidSgfPatternDirs = "path/to/directory/with/sgfs/" ++ ++# Penalize this much utility per matching move. ++# Set this negative if you instead want to make KataGo favor the SGF patterns instead of penalizing it! ++# This number does not need to be large, even 0.001 will make a difference. Too-large values may lead to bad play. ++# avoidSgfPatternUtility = 0.001 ++ ++# Optional - load only the newest this many files ++# avoidSgfPatternMaxFiles = 20 ++ ++# Optional - Penalty is multiplied by this per each older SGF file, so that old sgf files matter less than newer ones. ++# avoidSgfPatternLambda = 0.90 ++ ++# Optional - pay attention only to moves that were made by players with this name. ++# For example you can set it to the name that your bot's past games will show up as in the SGF, so that the bot will only avoid repeating ++# moves that itself made in past games, not the moves that its opponents made. ++# avoidSgfPatternAllowedNames = my-ogs-bot-name1,my-ogs-bot-name2 ++ ++# Optional - Ignore any moves in SGF files that occurred before this turn number. ++# avoidSgfPatternMinTurnNumber = 0 ++ ++# For more avoid patterns: ++# You can also specify a second set of parameters, and a third, fourth, etc by numbering 2,3,4,... ++# avoidSgf2PatternDirs = ... ++# avoidSgf2PatternUtility = ... ++# avoidSgf2PatternMaxFiles = ... ++# avoidSgf2PatternLambda = ... ++# avoidSgf2PatternAllowedNames = ... ++# avoidSgf2PatternMinTurnNumber = ... ++ ++ ++ ++ +diff --git a/cpp/b28.cfg b/cpp/b28.cfg +new file mode 100644 +index 00000000..302e8a40 +--- /dev/null ++++ b/cpp/b28.cfg +@@ -0,0 +1,721 @@ ++# Configuration for KataGo C++ GTP engine ++ ++# Run the program using: `./katago.exe gtp` ++ ++# In this example config, when a parameter is given as a commented out value, ++# that value also is the default value, unless described otherwise. You can ++# uncomment it (remove the pound sign) and change it if you want. ++ ++# =========================================================================== ++# Running on an online server or in a real tournament or match ++# =========================================================================== ++# If you plan to run online or in a tournament, read through the "Rules" ++# section below for proper handling of komi, handicaps, end-of-game cleanup, ++# and other details. ++ ++# =========================================================================== ++# Notes about performance and memory usage ++# =========================================================================== ++# Important: For good performance, you will very likely want to tune the ++# "numSearchThreads" parameter in the Search limits section below! Run ++# "./katago benchmark" to test KataGo and to suggest a reasonable value ++# of this parameter. ++ ++# For multi-GPU systems, read "OpenCL GPU settings" or "CUDA GPU settings". ++# ++# When using OpenCL, verify that KataGo picks the correct device! Some systems ++# may have both an Intel CPU OpenCL and GPU OpenCL. If # KataGo picks the wrong ++# one, correct this by specifying "openclGpuToUse". ++# ++# Consider adjusting "maxVisits", "ponderingEnabled", "resignThreshold", and ++# other parameters depending on your intended usage. ++ ++# =========================================================================== ++# Command-line usage ++# =========================================================================== ++# All of the below values may be set or overridden via command-line arguments: ++# ++# -override-config KEY=VALUE,KEY=VALUE,... ++ ++# =========================================================================== ++# Logs and files ++# =========================================================================== ++# This section defines where and what logging information is produced. ++ ++# Each run of KataGo will log to a separate file in this dir. ++# This is the default. ++logDir = gtp_logs ++# Uncomment and specify this instead of logDir to write separate dated subdirs ++# logDirDated = gtp_logs ++# Uncomment and specify this instead of logDir to log to only a single file ++# logFile = gtp.log ++ ++# Logging options ++logAllGTPCommunication = true ++logSearchInfo = true ++logSearchInfoForChosenMove = false ++logToStderr = true ++ ++# KataGo will display some info to stderr on GTP startup ++# Uncomment the next line and set it to false to suppress that and remain silent ++# startupPrintMessageToStderr = true ++ ++# Write information to stderr, for use in things like malkovich chat to OGS. ++ ogsChatToStderr = true ++ ++# Uncomment and set this to a directory to override where openCLTuner files ++# and other cached data is written. By default it saves into a subdir of the ++# current directory on windows, and a subdir of ~/.katago on Linux. ++# homeDataDir = PATH_TO_DIRECTORY ++ ++# =========================================================================== ++# Analysis ++# =========================================================================== ++# This section configures analysis settings. ++# ++# The maximum number of moves after the first move displayed in variations ++# from analysis commands like kata-analyze or lz-analyze. ++# analysisPVLen = 15 ++ ++# Report winrates for chat and analysis as (BLACK|WHITE|SIDETOMOVE). ++# Most GUIs and analysis tools will expect SIDETOMOVE. ++# reportAnalysisWinratesAs = SIDETOMOVE ++ ++# Extra noise for wider exploration. Large values will force KataGo to ++# analyze a greater variety of moves than it normally would. ++# An extreme value like 1 distributes playouts across every move on the board, ++# even very bad moves. ++# Affects analysis only, does not affect play. ++# analysisWideRootNoise = 0.04 ++ ++# Try to limit the effect of possible bad or bogus move sequences in the ++# history leading to this position from affecting KataGo's move predictions. ++# analysisIgnorePreRootHistory = true ++ ++# =========================================================================== ++# Rules ++# =========================================================================== ++# This section configures the scoring and playing rules. Rules can also be ++# changed mid-run by issuing custom GTP commands. ++# ++# See https://lightvector.github.io/KataGo/rules.html for rules details. ++# ++# See https://github.com/lightvector/KataGo/blob/master/docs/GTP_Extensions.md ++# for GTP commands. ++ ++# Specify the rules as a string. ++# Some legal values include: ++# chinese, japanese, korean, aga, chinese-ogs, new-zealand, stone-scoring, ++# ancient-territory, bga, aga-button ++# ++# For some human rulesets that require complex adjudication in tricky cases ++# (e.g. japanese, korean) KataGo may not precisely match the ruleset in such ++# cases but will do its best. ++rules = tromp-taylor ++ ++# By default, the "rules" parameter is used, but if you comment it out and ++# uncomment one option in each of the sections below, you can specify an ++# arbitrary combination of individual rules. ++ ++# koRule = SIMPLE # Simple ko rules (triple ko = no result) ++# koRule = POSITIONAL # Positional superko ++# koRule = SITUATIONAL # Situational superko ++ ++# scoringRule = AREA # Area scoring ++# scoringRule = TERRITORY # Territory scoring (special computer-friendly territory rules) ++ ++# taxRule = NONE # All surrounded empty points are scored ++# taxRule = SEKI # Eyes in seki do NOT count as points ++# taxRule = ALL # All groups are taxed up to 2 points for the two eyes needed to live ++ ++# Is multiple-stone suicide legal? (Single-stone suicide is always illegal). ++# multiStoneSuicideLegal = false ++# multiStoneSuicideLegal = true ++ ++# "Button go" - the first pass when area scoring awards 0.5 points and does ++# not count for ending the game. ++# Allows area scoring rulesets that have far simpler rules to achieve the same ++# final scoring precision and reward for precise play as territory scoring. ++# hasButton = false ++# hasButton = true ++ ++# Is this a human ruleset where it's okay to pass before having physically ++# captured and removed all dead stones? ++# friendlyPassOk = false ++# friendlyPassOk = true ++ ++# How handicap stones in handicap games are compensated ++# whiteHandicapBonus = 0 # White gets no compensation for black's handicap stones (Tromp-taylor, NZ, JP) ++# whiteHandicapBonus = N-1 # White gets N-1 points for black's N handicap stones (AGA) ++# whiteHandicapBonus = N # White gets N points for black's N handicap stones (Chinese) ++ ++# ------------------------------ ++# Other rules hacks ++# ------------------------------ ++# Uncomment and change to adjust what board size KataGo uses upon startup ++# by default when GTP doesn't specify. ++# defaultBoardSize = 19 ++ ++# By default, Katago will use the komi that the GUI or GTP controller tries to set. ++# Uncomment and set this to have KataGo ignore the controller and always use this komi. ++# ignoreGTPAndForceKomi = 7 ++ ++# =========================================================================== ++# Bot behavior ++# =========================================================================== ++ ++# ------------------------------ ++# Resignation ++# ------------------------------ ++ ++# Resignation occurs if for at least resignConsecTurns in a row, the ++# winLossUtility (on a [-1,1] scale) is below resignThreshold. ++allowResignation = true ++resignThreshold = -0.90 ++resignConsecTurns = 3 ++ ++# By default, KataGo may resign games that it is confidently losing even if they ++# are very close in score. Uncomment and set this to avoid resigning games ++# if the estimated difference is points is less than or equal to this. ++# resignMinScoreDifference = 10 ++ ++# ------------------------------ ++# Handicap ++# ------------------------------ ++# Assume that if black makes many moves in a row right at the start of the ++# game, then the game is a handicap game. This is necessary on some servers ++# and for some GUIs and also when initializing from many SGF files, which may ++# set up a handicap game using repeated GTP "play" commands for black rather ++# than GTP "place_free_handicap" commands; however, it may also lead to ++# incorrect understanding of komi if whiteHandicapBonus is used and a server ++# does not have such a practice. Uncomment and set to false to disable. ++# assumeMultipleStartingBlackMovesAreHandicap = true ++ ++# Makes katago dynamically adjust in handicap or altered-komi games to assume ++# based on those game settings that it must be stronger or weaker than the ++# opponent and to play accordingly. Greatly improves handicap strength by ++# biasing winrates and scores to favor appropriate safe/aggressive play. ++# Does NOT affect analysis (lz-analyze, kata-analyze, used by programs like ++# Lizzie) so analysis remains unbiased. Uncomment and set this to 0 to disable ++# this and make KataGo play the same always. ++# dynamicPlayoutDoublingAdvantageCapPerOppLead = 0.045 ++ ++# Instead of "dynamicPlayoutDoublingAdvantageCapPerOppLead", you can comment ++# that out and uncomment and set "playoutDoublingAdvantage" to a fixed value ++# from -3.0 to 3.0 that will not change dynamically. ++# ALSO affects analysis tools (lz-analyze, kata-analyze, used by e.g. Lizzie). ++# Negative makes KataGo behave as if it is much weaker than the opponent. ++# Positive makes KataGo behave as if it is much stronger than the opponent. ++# KataGo will adjust to favor safe/aggressive play as appropriate based on ++# the combination of who is ahead and how much stronger/weaker it thinks it is, ++# and report winrates and scores taking the strength difference into account. ++# ++# If this and "dynamicPlayoutDoublingAdvantageCapPerOppLead" are both set ++# then dynamic will be used for all games and this fixed value will be used ++# for analysis tools. ++# playoutDoublingAdvantage = 0.0 ++ ++# Uncomment one of these when using "playoutDoublingAdvantage" to enforce ++# that it will only apply when KataGo plays as the specified color and will be ++# negated when playing as the opposite color. ++# playoutDoublingAdvantagePla = BLACK ++# playoutDoublingAdvantagePla = WHITE ++ ++# ------------------------------ ++# Passing and cleanup ++# ------------------------------ ++# Make the bot never assume that its pass will end the game, even if passing ++# would end and "win" under Tromp-Taylor rules. Usually this is a good idea ++# when using it for analysis or playing on servers where scoring may be ++# implemented non-tromp-taylorly. Uncomment and set to false to disable. ++# conservativePass = true ++ ++# When using territory scoring, self-play games continue beyond two passes ++# with special cleanup rules that may be confusing for human players. This ++# option prevents the special cleanup phases from being reachable when using ++# the bot for GTP play. Uncomment and set to false to enable entering special ++# cleanup. For example, if you are testing it against itself, or against ++# another bot that has precisely implemented the rules documented at ++# https://lightvector.github.io/KataGo/rules.html ++# preventCleanupPhase = true ++ ++# ------------------------------ ++# Miscellaneous behavior ++# ------------------------------ ++# If the board is symmetric, search only one copy of each equivalent move. ++# Attempts to also account for ko/superko, will not theoretically perfect for ++# superko. Uncomment and set to false to disable. ++# rootSymmetryPruning = true ++ ++# Uncomment and set to true to avoid a particular joseki that some networks ++# misevaluate, and also to improve opening diversity versus some particular ++# other bots that like to play it all the time. ++# avoidMYTDaggerHack = false ++ ++# Prefer to avoid playing the same joseki in every corner of the board. ++# Uncomment to set to a specific value. See "Avoid SGF patterns" section. ++# By default: 0 (even games), 0.005 (handicap games) ++# avoidRepeatedPatternUtility = 0.0 ++ ++# Experimental logic to fight against mirror Go even with unfavorable komi. ++# Uncomment to set to a specific value to use for both playing and analysis. ++# By default: true when playing via GTP, but false when analyzing. ++# antiMirror = true ++ ++# Enable some hacks that mitigate rare instances when passing messes up deeper searches. ++# enablePassingHacks = true ++ ++# Uncomment and set this to true to prevent bad or bogus move sequences ++# in the history leading to this position from affecting KataGo's move choices. ++# Same as analysisIgnorePreRootHistory (see above) but applies to actual play. ++# You can enable this if KataGo is being asked to play from positions that it did not ++# choose the moves to reach. ++# ignorePreRootHistory = false ++ ++# =========================================================================== ++# Search limits ++# =========================================================================== ++ ++# Terminology: ++# "Playouts" is the number of new playouts of search performed each turn. ++# "Visits" is the same as "Playouts" but also counts search performed on ++# previous turns that is still applicable to this turn. ++# "Time" is the time in seconds. ++ ++# For example, if KataGo searched 200 nodes on the previous turn, and then ++# after the opponent's reply, 50 nodes of its search tree was still valid, ++# then a visit limit of 200 would allow KataGo to search 150 new nodes ++# (for a final tree size of 200 nodes), whereas a playout limit of of 200 ++# would allow KataGo to search 200 nodes (for a final tree size of 250 nodes). ++ ++# Additionally, KataGo may also move before than the limit in order to ++# obey time controls (e.g. byo-yomi, etc) if the GTP controller has ++# told KataGo that the game has is being played with a given time control. ++ ++# Limits for search on the current turn. ++# If commented out or unspecified, the default is to have no limit. ++# maxVisits = 30000 ++# maxPlayouts = 300 ++maxTime = 1.0 ++ ++# Ponder on the opponent's turn? ++ponderingEnabled = true ++ ++# Limits for search when pondering on the opponent's turn. ++# If commented out or unspecified, the default is to have no limit. ++# Limiting the maximum time is recommended so that KataGo won't burn CPU/GPU ++# forever and/or run out of RAM if left unattended while pondering is enabled. ++# maxVisitsPondering = 5000 ++# maxPlayoutsPondering = 3000 ++maxTimePondering = 120.0 ++ ++ ++# ------------------------------ ++# Other search limits and behavior ++# ------------------------------ ++ ++# Approx number of seconds to buffer for lag for GTP time controls - will ++# move a bit faster assuming there is this much lag per move. ++lagBuffer = 1.0 ++ ++# YOU PROBABLY WANT TO TUNE THIS PARAMETER! ++# The number of threads to use when searching. On powerful GPUs the optimal ++# threads may be much higher than the number of CPU cores you have because ++# many threads are needed to feed efficient large batches to the GPU. ++# ++# Run "./katago benchmark" to tune this parameter and test the effect ++# of changes to any of other parameters. ++numSearchThreads = 10 ++ ++# Play a little faster if the opponent is passing, for human-friendliness. ++# Comment these out to disable them, such as if running a controlled match ++# where you are testing KataGo with fixed compute per move vs other bots. ++searchFactorAfterOnePass = 0.50 ++searchFactorAfterTwoPass = 0.25 ++ ++# Play a little faster if super-winning, for human-friendliness. ++# Comment these out to disable them, such as if running a controlled match ++# where you are testing KataGo with fixed compute per move vs other bots. ++searchFactorWhenWinning = 0.40 ++searchFactorWhenWinningThreshold = 0.95 ++ ++# =========================================================================== ++# GPU settings ++# =========================================================================== ++# This section configures GPU settings. ++# ++# Maximum number of positions to send to a single GPU at once. The default ++# value is roughly equal to numSearchThreads, but can be specified manually ++# if running out of memory, or using multiple GPUs that expect to share work. ++# nnMaxBatchSize = ++ ++# Controls the neural network cache size, which is the primary RAM/memory use. ++# KataGo will cache up to (2 ** nnCacheSizePowerOfTwo) many neural net ++# evaluations in case of transpositions in the tree. ++# Increase this to improve performance for searches with tens of thousands ++# of visits or more. Decrease this to limit memory usage. ++# If you're happy to do some math - each neural net entry takes roughly ++# 1.5KB, except when using whole-board ownership/territory ++# visualizations, where each entry will take roughly 3KB. The number of ++# entries is (2 ** nnCacheSizePowerOfTwo). (E.g. 2 ** 18 = 262144.) ++# You can compute roughly how much memory the cache will use based on this. ++# nnCacheSizePowerOfTwo = 20 ++ ++# Size of mutex pool for nnCache is (2 ** this). ++# nnMutexPoolSizePowerOfTwo = 16 ++ ++# Randomize board orientation when running neural net evals? Uncomment and ++# set to false to disable. ++# nnRandomize = true ++ ++# If provided, force usage of a specific seed for nnRandomize. ++# The default is to use a randomly generated seed. ++# nnRandSeed = abcdefg ++ ++# Uncomment and set to true to force GTP to use the maximum board size for ++# internal buffers for the neural net. This will make KataGo slower when ++# evaluating small boards, but will avoid a lengthy initialization time on every ++# change of board size due to having to re-size the neural net buffers on the GPU. ++# This can be useful for example, for OGS's persistent bot mode that uses a single ++# bot instance to handle multiple games and may thrash between different board sizes ++# if there are concurrent games of multiple sizes. ++# gtpForceMaxNNSize = false ++ ++# ------------------------------ ++# Multiple GPUs ++# ------------------------------ ++# Set this to the number of GPUs to use or that are available. ++# IMPORTANT: If more than 1, also uncomment the appropriate TensorRT ++# or CUDA or OpenCL section. ++# numNNServerThreadsPerModel = 1 ++ ++# ------------------------------ ++# TENSORRT GPU settings ++# ------------------------------ ++# These only apply when using the TENSORRT version of KataGo. ++ ++# For one GPU: optionally uncomment this option and change if the GPU to ++# use is not device 0. ++# trtDeviceToUse = 0 ++ ++# For two GPUs: Uncomment these options, AND set numNNServerThreadsPerModel above. ++# Also, change their values if the devices you want to use are not 0 and 1. ++# trtDeviceToUseThread0 = 0 ++# trtDeviceToUseThread1 = 1 ++ ++# For three GPUs: Uncomment these options, AND set numNNServerThreadsPerModel above. ++# Also, change their values if the devices you want to use are not 0 and 1 and 2. ++# trtDeviceToUseThread0 = 0 ++# trtDeviceToUseThread1 = 1 ++# trtDeviceToUseThread2 = 2 ++ ++# The pattern continues for additional GPUs. ++ ++# ------------------------------ ++# CUDA GPU settings ++# ------------------------------ ++# These only apply when using the CUDA version of KataGo. ++ ++# For one GPU: optionally uncomment and change this if the GPU you want to ++# use is not device 0 ++# cudaDeviceToUse = 0 ++ ++# For two GPUs: Uncomment these options, AND set numNNServerThreadsPerModel above. ++# Also, change their values if the devices you want to use are not 0 and 1. ++# cudaDeviceToUseThread0 = 0 ++# cudaDeviceToUseThread1 = 1 ++ ++# For three GPUs: Uncomment these options, AND set numNNServerThreadsPerModel above. ++# Also, change their values if the devices you want to use are not 0 and 1 and 2. ++# cudaDeviceToUseThread0 = 0 ++# cudaDeviceToUseThread1 = 1 ++# cudaDeviceToUseThread2 = 2 ++ ++# The pattern continues for additional GPUs. ++ ++# KataGo will automatically use FP16 or not based on the compute capability ++# of your NVIDIA GPU. If you want to try to force a particular behavior ++# you can uncomment these lines and change them to "true" or "false". ++# cudaUseFP16 = auto ++# cudaUseNHWC = auto ++ ++# ------------------------------ ++# OpenCL GPU settings ++# ------------------------------ ++# These only apply when using the OpenCL version of KataGo. ++ ++# Uncomment and set to true to tune OpenCL for every board size separately, ++# rather than only the largest possible size. ++# openclReTunePerBoardSize = false ++ ++# For one GPU: optionally uncomment and change this if the best device to use is guessed incorrectly. ++# The default behavior tries to guess the 'best' GPU or device on your system to use, usually it will be a good guess. ++# openclDeviceToUse = 0 ++ ++# For two GPUs: Uncomment these two lines and replace X and Y with the device ids of the devices you want to use. ++# It might NOT be 0 and 1, some computers will have many OpenCL devices. You can see what the devices are when ++# KataGo starts up - it should print or log all the devices it finds. ++# (AND also set numNNServerThreadsPerModel above) ++# openclDeviceToUseThread0 = X ++# openclDeviceToUseThread1 = Y ++ ++# For three GPUs: Uncomment these three lines and replace X and Y and Z with the device ids of the devices you want to use. ++# It might NOT be 0 and 1 and 2, some computers will have many OpenCL devices. You can see what the devices are when ++# KataGo starts up - it should print or log all the devices it finds. ++# (AND also set numNNServerThreadsPerModel above) ++# openclDeviceToUseThread0 = X ++# openclDeviceToUseThread1 = Y ++# openclDeviceToUseThread2 = Z ++ ++# The pattern continues for additional GPUs. ++ ++# KataGo will automatically use FP16 or not based on testing your GPU during ++# tuning. If you want to try to force a particular behavior though you can ++# uncomment this option and change it to "true" or "false". This is a fairly ++# blunt setting - more detailed settings are testable by rerunning the tuner ++# with various arguments (./katago tuner). ++# openclUseFP16 = auto ++ ++# ------------------------------ ++# Eigen-specific settings ++# ------------------------------ ++# These only apply when using the Eigen (pure CPU) version of KataGo. ++ ++# Number of CPU threads for evaluating the neural net on the Eigen backend. ++# ++# Default: numSearchThreads ++# numEigenThreadsPerModel = X ++ ++# =========================================================================== ++# Root move selection and biases ++# =========================================================================== ++# Uncomment and edit any of the below values to change them from their default. ++ ++# If provided, force usage of a specific seed for various random things in ++# the search. The default is to use a random seed. ++# searchRandSeed = hijklmn ++ ++# Temperature for the early game, randomize between chosen moves with ++# this temperature ++# chosenMoveTemperatureEarly = 0.5 ++ ++# Decay temperature for the early game by 0.5 every this many moves, ++# scaled with board size. ++# chosenMoveTemperatureHalflife = 19 ++ ++# At the end of search after the early game, randomize between chosen ++# moves with this temperature ++# chosenMoveTemperature = 0.10 ++ ++# Subtract this many visits from each move prior to applying ++# chosenMoveTemperature (unless all moves have too few visits) to downweight ++# unlikely moves ++# chosenMoveSubtract = 0 ++ ++# The same as chosenMoveSubtract but only prunes moves that fall below ++# the threshold. This setting does not affect chosenMoveSubtract. ++# chosenMovePrune = 1 ++ ++# Number of symmetries to sample (without replacement) and average at the root ++# rootNumSymmetriesToSample = 1 ++ ++# Using LCB for move selection? ++# useLcbForSelection = true ++ ++# How many stdevs a move needs to be better than another for LCB selection ++# lcbStdevs = 5.0 ++ ++# Only use LCB override when a move has this proportion of visits as the ++# top move. ++# minVisitPropForLCB = 0.15 ++ ++# =========================================================================== ++# Internal params ++# =========================================================================== ++# Uncomment and edit any of the below values to change them from their default. ++ ++# Scales the utility of winning/losing ++# winLossUtilityFactor = 1.0 ++ ++# Scales the utility for trying to maximize score ++# staticScoreUtilityFactor = 0.10 ++# dynamicScoreUtilityFactor = 0.30 ++ ++# Adjust dynamic score center this proportion of the way towards zero, ++# capped at a reasonable amount. ++# dynamicScoreCenterZeroWeight = 0.20 ++# dynamicScoreCenterScale = 0.75 ++ ++# The utility of getting a "no result" due to triple ko or other long cycle ++# in non-superko rulesets (-1 to 1) ++# noResultUtilityForWhite = 0.0 ++ ++# The number of wins that a draw counts as, for white. (0 to 1) ++# drawEquivalentWinsForWhite = 0.5 ++ ++# Exploration constant for mcts ++# cpuctExploration = 1.0 ++# cpuctExplorationLog = 0.45 ++ ++# Parameters that control exploring more in volatile positions, exploring ++# less in stable positions. ++# cpuctUtilityStdevPrior = 0.40 ++# cpuctUtilityStdevPriorWeight = 2.0 ++# cpuctUtilityStdevScale = 0.85 ++ ++# FPU reduction constant for mcts ++# fpuReductionMax = 0.2 ++# rootFpuReductionMax = 0.1 ++# fpuParentWeightByVisitedPolicy = true ++ ++# Parameters that control weighting of evals based on the net's own ++# self-reported uncertainty. ++# useUncertainty = true ++# uncertaintyExponent = 1.0 ++# uncertaintyCoeff = 0.25 ++ ++# Explore using optimistic policy ++# rootPolicyOptimism = 0.2 ++# policyOptimism = 1.0 ++ ++# Amount to apply a downweighting of children with very bad values relative ++# to good ones. ++# valueWeightExponent = 0.25 ++ ++# Slight incentive for the bot to behave human-like with regard to passing at ++# the end, filling the dame, not wasting time playing in its own territory, ++# etc., and not play moves that are equivalent in terms of points but a bit ++# more unfriendly to humans. ++# rootEndingBonusPoints = 0.5 ++ ++# Make the bot prune useless moves that are just prolonging the game to ++# avoid losing yet. ++# rootPruneUselessMoves = true ++ ++# Apply bias correction based on local pattern keys ++# subtreeValueBiasFactor = 0.45 ++# subtreeValueBiasWeightExponent = 0.85 ++ ++# Use graph search rather than tree search - identify and share search for ++# transpositions. ++# useGraphSearch = true ++ ++# How much to shard the node table for search synchronization ++# nodeTableShardsPowerOfTwo = 16 ++ ++# How many virtual losses to add when a thread descends through a node ++# numVirtualLossesPerThread = 1 ++ ++# Improve the quality of evals under heavy multithreading ++# useNoisePruning = true ++ ++# =========================================================================== ++# Automatic avoid patterns ++# =========================================================================== ++# The parameters in this section provide a way to bias away from moves that match ++# patterns that this instance of KataGo has played in previous games, by auto-saving ++# moves to a directory and then auto-loading and biasing against them each new game. ++# Uncomment them to use them. When using this feature, all parameters must be specified. ++ ++# Different board sizes are tracked separately, but all board sizes share the same ++# parameters by default. Every parameter *except* for autoAvoidRepeatDir and ++# autoAvoidRepeatSaveChunkSize can be overridden per board size, ++# e.g. "autoAvoidRepeatMinTurnNumber13x13". You must ALSO specify the ++# defaults even if you have specified board-size-specific values. ++ ++# Directory to auto-save moves KataGo plays, to avoid them in future games. ++# You can create a new empty directory and put its path here. ++# If you run parallel instances of KataGo, use different directories if you ++# want them not to share their biases, use the same directory if you want ++# all of them to bias away from past moves that any of them have played. ++# KataGo will also automatically DELETE old data in this directory, so it is ++# recommended that if you do share the same directory between parallel instances, ++# that they all use the same settings that affect data saving/deletion. ++# autoAvoidRepeatDir = PATH_TO_NEW_DIRECTORY ++ ++# Penalize this much utility per matching move. ++# Values that are too large may lead to bad play. The value of 0.004 is fairly large ++# and might be large enough to result in some early weird/bad moves when trying ++# to avoid past games' moves if enough games begin the same way. You can experiment with it. ++# autoAvoidRepeatUtility = 0.004 ++ ++# Per each new move saved, exponentially decay prior saved moves by this factor. ++# This way, the bias against moves from many games ago is gradually phased out. ++# For example, 0.9995 = 1 - 1/2000, so would be roughly 2000 prior moves worth of ++# penalty weight remembered, in steady state. Depending on what turn number range ++# you are saving, this might equate to a different number of games For example ++# saving the first 50 moves per game would make this roughly 2000 / 50 = 40 games ++# worth of memory. ++# autoAvoidRepeatLambda = 0.9995 ++ ++# Affects data saving/deletion. ++# When the number of saved moves exceeds this, outright delete them to avoid too many ++# files and disk space building up. Also may affect the speed of saving/loading on start ++# of each game if this is set large and a lot of data builds up. ++# autoAvoidRepeatMaxPoses = 10000 ++ ++# Affects data saving/deletion. ++# Only save data for moves within this turn number range of those games. ++# E.g. setting autoAvoidRepeatMinTurnNumber to a number like 4 or 5 would tend to make ++# KataGo not develop a bias against the initial 3-4 and 4-4 corner moves in almost every game. ++# autoAvoidRepeatMinTurnNumber = 0 ++# autoAvoidRepeatMaxTurnNumber = 50 ++ ++# Affects data saving/deletion. ++# Within a single run of a program, wait to accumulate this many samples ++# (possibly across multiple clear_boards/games) before saving the data. ++# Can help to avoid writing too many small files to disk, especially when GTP is used ++# in a way that clears the board very frequently (e.g. gtp2ogs pooled manager). ++# autoAvoidRepeatSaveChunkSize = 200 ++ ++# =========================================================================== ++# Avoid SGF patterns ++# =========================================================================== ++# The parameters in this section provide a way to avoid moves that follow ++# specific patterns based on a set of SGF files loaded upon startup. ++# This is basically the same as the above "Automatic avoid patterns" section ++# above except you supply your own SGF files to avoid moves from. ++# Uncomment them to use this feature. Additionally, if the SGF file ++# contains the string %SKIP% in a comment on a move, that move will be ++# ignored for this purpose. ++ ++# Load SGF files from this directory when the engine is started ++# (only on startup, will not reload unless engine is restarted) ++# avoidSgfPatternDirs = path/to/directory/with/sgfs/ ++# You can also surround the file path in double quotes if the file path contains trailing spaces or hash signs. ++# Within double quotes, backslashes are escape characters. ++# avoidSgfPatternDirs = "path/to/directory/with/sgfs/" ++ ++# Penalize this much utility per matching move. ++# Set this negative if you instead want to favor SGF patterns instead of ++# penalizing them. This number does not need to be large, even 0.001 will ++# make a difference. Values that are too large may lead to bad play. ++# avoidSgfPatternUtility = 0.001 ++ ++# Optional - load only the newest this many files ++# avoidSgfPatternMaxFiles = 20 ++ ++# Optional - Penalty is multiplied by this per each older SGF file, so that ++# old SGF files matter less than newer ones. ++# avoidSgfPatternLambda = 0.90 ++ ++# Optional - pay attention only to moves made by players with this name. ++# For example, set it to the name that your bot's past games will show up ++# as in the SGF, so that the bot will only avoid repeating moves that itself ++# made in past games, not the moves that its opponents made. ++# avoidSgfPatternAllowedNames = my-ogs-bot-name1,my-ogs-bot-name2 ++ ++# Optional - Ignore moves in SGF files that occurred before this turn number. ++# avoidSgfPatternMinTurnNumber = 0 ++ ++# For more avoid patterns: ++# You can also specify a second set of parameters, and a third, fourth, ++# etc. by numbering 2,3,4,... ++# ++# avoidSgf2PatternDirs = ... ++# avoidSgf2PatternUtility = ... ++# avoidSgf2PatternMaxFiles = ... ++# avoidSgf2PatternLambda = ... ++# avoidSgf2PatternAllowedNames = ... ++# avoidSgf2PatternMinTurnNumber = ... ++ +diff --git a/cpp/cacert.pem b/cpp/cacert.pem +new file mode 100644 +index 00000000..f9bd706b +--- /dev/null ++++ b/cpp/cacert.pem +@@ -0,0 +1,3447 @@ ++## ++## Bundle of CA Root Certificates ++## ++## Certificate data from Mozilla as of: Wed Jul 22 03:12:14 2020 GMT ++## ++## This is a bundle of X.509 certificates of public Certificate Authorities ++## (CA). These were automatically extracted from Mozilla's root certificates ++## file (certdata.txt). This file can be found in the mozilla source tree: ++## https://hg.mozilla.org/releases/mozilla-release/raw-file/default/security/nss/lib/ckfw/builtins/certdata.txt ++## ++## It contains the certificates in PEM format and therefore ++## can be directly used with curl / libcurl / php_curl, or with ++## an Apache+mod_ssl webserver for SSL client authentication. ++## Just configure this file as the SSLCACertificateFile. ++## ++## Conversion done with mk-ca-bundle.pl version 1.28. ++## SHA256: cc6408bd4be7fbfb8699bdb40ccb7f6de5780d681d87785ea362646e4dad5e8e ++## ++ ++ ++GlobalSign Root CA ++================== ++-----BEGIN CERTIFICATE----- ++MIIDdTCCAl2gAwIBAgILBAAAAAABFUtaw5QwDQYJKoZIhvcNAQEFBQAwVzELMAkGA1UEBhMCQkUx ++GTAXBgNVBAoTEEdsb2JhbFNpZ24gbnYtc2ExEDAOBgNVBAsTB1Jvb3QgQ0ExGzAZBgNVBAMTEkds ++b2JhbFNpZ24gUm9vdCBDQTAeFw05ODA5MDExMjAwMDBaFw0yODAxMjgxMjAwMDBaMFcxCzAJBgNV ++BAYTAkJFMRkwFwYDVQQKExBHbG9iYWxTaWduIG52LXNhMRAwDgYDVQQLEwdSb290IENBMRswGQYD 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++======================= ++-----BEGIN CERTIFICATE----- ++MIIDujCCAqKgAwIBAgILBAAAAAABD4Ym5g0wDQYJKoZIhvcNAQEFBQAwTDEgMB4GA1UECxMXR2xv ++YmFsU2lnbiBSb290IENBIC0gUjIxEzARBgNVBAoTCkdsb2JhbFNpZ24xEzARBgNVBAMTCkdsb2Jh ++bFNpZ24wHhcNMDYxMjE1MDgwMDAwWhcNMjExMjE1MDgwMDAwWjBMMSAwHgYDVQQLExdHbG9iYWxT ++aWduIFJvb3QgQ0EgLSBSMjETMBEGA1UEChMKR2xvYmFsU2lnbjETMBEGA1UEAxMKR2xvYmFsU2ln ++bjCCASIwDQYJKoZIhvcNAQEBBQADggEPADCCAQoCggEBAKbPJA6+Lm8omUVCxKs+IVSbC9N/hHD6 ++ErPLv4dfxn+G07IwXNb9rfF73OX4YJYJkhD10FPe+3t+c4isUoh7SqbKSaZeqKeMWhG8eoLrvozp ++s6yWJQeXSpkqBy+0Hne/ig+1AnwblrjFuTosvNYSuetZfeLQBoZfXklqtTleiDTsvHgMCJiEbKjN ++S7SgfQx5TfC4LcshytVsW33hoCmEofnTlEnLJGKRILzdC9XZzPnqJworc5HGnRusyMvo4KD0L5CL ++TfuwNhv2GXqF4G3yYROIXJ/gkwpRl4pazq+r1feqCapgvdzZX99yqWATXgAByUr6P6TqBwMhAo6C ++ygPCm48CAwEAAaOBnDCBmTAOBgNVHQ8BAf8EBAMCAQYwDwYDVR0TAQH/BAUwAwEB/zAdBgNVHQ4E ++FgQUm+IHV2ccHsBqBt5ZtJot39wZhi4wNgYDVR0fBC8wLTAroCmgJ4YlaHR0cDovL2NybC5nbG9i 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true # In non-compensated handicap and fork games, vary komi + + # Match----------------------------------------------------------------------------------- + +-numGameThreads = 128 ++numGameThreads = 30 + maxMovesPerGame = 1600 + + # Rules------------------------------------------------------------------------------------ +diff --git a/cpp/contribute_example.cfg b/cpp/contribute_example.cfg +new file mode 100644 +index 00000000..a6c28531 +--- /dev/null ++++ b/cpp/contribute_example.cfg +@@ -0,0 +1,125 @@ ++ ++# Be careful not to share any config files where you have entered your password. ++ ++serverUrl = https://katagotraining.org/ ++username = Fill in your username here ++password = Fill in your password here ++# If your password has hash signs or trailing spaces, you can also surround the password with double quotes, like this. ++# Within double quotes, backslashes are escape characters. ++# password = "Fill in your password here" ++ ++# How many games should KataGo play at once? For good GPUs, putting too small a number here will be *very* inefficient. ++# For absolute-top-tier GPUs, try numbers like 32, 40, 64, etc. ++# For modern good middle-tier GPUs, you can try numbers like 8 or 16. ++# For very old or weak GPUs, or if you want KataGo to put less load on your system, try 4. ++maxSimultaneousGames = Fill in a number here ++ ++# Set to true if you want one of KataGo's games to be streamed to a text file, so you can watch. ++# Follow in a separate shell with a command like the following: ++# Linux: "tail -f watchgame.txt" ++# Windows Powershell: "Get-Content .\watchgame.txt -Tail 50 -Wait" ++watchOngoingGameInFile = false ++watchOngoingGameInFileName = watchgame.txt ++ ++# If you use an https proxy to connect to the internet, set these to the host and port of your proxy. ++# proxyHost = HOSTNAME ++# proxyPort = 1234 ++ ++# Special-purpose options. Most users should not need to adjust these. ++# onlyPlayRatingMatches = false ++# maxRatingMatches = 1 ++# disablePredownloadLoop = false ++# warnTaskUnusedKeys = false ++ ++# KataGo will only use one GPU by default. You can edit the below if you have multiple GPUs and want to use them all. ++ ++ ++# TO USE MULTIPLE GPUS: ++# Set this to the number of GPUs you have and/or would like to use. ++# **AND** if it is more than 1, uncomment the appropriate CUDA or OpenCL section below. ++# numNNServerThreadsPerModel = 1 ++ ++ ++# TENSORRT GPU settings-------------------------------------- ++# These only apply when using the TENSORRT version of KataGo. ++ ++# IF USING ONE GPU: optionally uncomment and change this if the GPU you want to use turns out to be not device 0 ++# trtDeviceToUse = 0 ++ ++# IF USING TWO GPUS: Uncomment these two lines (AND set numNNServerThreadsPerModel above): ++# trtDeviceToUseThread0 = 0 # change this if the first GPU you want to use turns out to be not device 0 ++# trtDeviceToUseThread1 = 1 # change this if the second GPU you want to use turns out to be not device 1 ++ ++# IF USING THREE GPUS: Uncomment these three lines (AND set numNNServerThreadsPerModel above): ++# trtDeviceToUseThread0 = 0 # change this if the first GPU you want to use turns out to be not device 0 ++# trtDeviceToUseThread1 = 1 # change this if the second GPU you want to use turns out to be not device 1 ++# trtDeviceToUseThread2 = 2 # change this if the third GPU you want to use turns out to be not device 2 ++ ++# You can probably guess the pattern if you have four, five, etc. GPUs. ++ ++ ++# CUDA GPU settings-------------------------------------- ++# These only apply when using the CUDA version of KataGo. ++ ++# IF USING ONE GPU: optionally uncomment and change this if the GPU you want to use turns out to be not device 0 ++# cudaDeviceToUse = 0 ++ ++# IF USING TWO GPUS: Uncomment these two lines (AND set numNNServerThreadsPerModel above): ++# cudaDeviceToUseThread0 = 0 # change this if the first GPU you want to use turns out to be not device 0 ++# cudaDeviceToUseThread1 = 1 # change this if the second GPU you want to use turns out to be not device 1 ++ ++# IF USING THREE GPUS: Uncomment these three lines (AND set numNNServerThreadsPerModel above): ++# cudaDeviceToUseThread0 = 0 # change this if the first GPU you want to use turns out to be not device 0 ++# cudaDeviceToUseThread1 = 1 # change this if the second GPU you want to use turns out to be not device 1 ++# cudaDeviceToUseThread2 = 2 # change this if the third GPU you want to use turns out to be not device 2 ++ ++# You can probably guess the pattern if you have four, five, etc. GPUs. ++ ++# KataGo will automatically use FP16 or not based on the compute capability of your NVIDIA GPU. If you ++# want to try to force a particular behavior though you can uncomment these lines and change them ++# to "true" or "false". E.g. it's using FP16 but on your card that's giving an error, or it's not using ++# FP16 but you think it should. ++# cudaUseFP16 = auto ++# cudaUseNHWC = auto ++ ++ ++# OpenCL GPU settings-------------------------------------- ++# These only apply when using the OpenCL version of KataGo. ++ ++# Uncomment to tune OpenCL for every board size separately, rather than only the largest possible size ++# openclReTunePerBoardSize = true ++ ++# IF USING ONE GPU: optionally uncomment and change this if the best device to use is guessed incorrectly. ++# The default behavior tries to guess the 'best' GPU or device on your system to use, usually it will be a good guess. ++# openclDeviceToUse = 0 ++ ++# IF USING TWO GPUS: Uncomment these two lines and replace X and Y with the device ids of the devices you want to use. ++# It might NOT be 0 and 1, some computers will have many OpenCL devices. You can see what the devices are when ++# KataGo starts up - it should print or log all the devices it finds. ++# (AND also set numNNServerThreadsPerModel above) ++# openclDeviceToUseThread0 = X ++# openclDeviceToUseThread1 = Y ++ ++# IF USING THREE GPUS: Uncomment these three lines and replace X and Y and Z with the device ids of the devices you want to use. ++# It might NOT be 0 and 1 and 2, some computers will have many OpenCL devices. You can see what the devices are when ++# KataGo starts up - it should print or log all the devices it finds. ++# (AND also set numNNServerThreadsPerModel above) ++# openclDeviceToUseThread0 = X ++# openclDeviceToUseThread1 = Y ++# openclDeviceToUseThread2 = Z ++ ++# You can probably guess the pattern if you have four, five, etc. GPUs. ++ ++# KataGo will automatically use FP16 or not based on testing your GPU during tuning. If you ++# want to try to force a particular behavior though you can uncomment this lines and change it ++# to "true" or "false". This is a fairly blunt setting - more detailed settings are testable ++# by rerunning the tuner with various arguments. ++# openclUseFP16 = auto ++ ++ ++# Eigen-specific settings-------------------------------------- ++# These only apply when using the Eigen (pure CPU) version of KataGo. ++ ++# This is the number of CPU threads for evaluating the neural net on the Eigen backend. ++# It defaults to numSearchThreads. ++# numEigenThreadsPerModel = X +diff --git a/cpp/default_gtp.cfg b/cpp/default_gtp.cfg +new file mode 100644 +index 00000000..8bf19d85 +--- /dev/null ++++ b/cpp/default_gtp.cfg +@@ -0,0 +1,721 @@ ++# Configuration for KataGo C++ GTP engine ++ ++# Run the program using: `./katago.exe gtp` ++ ++# In this example config, when a parameter is given as a commented out value, ++# that value also is the default value, unless described otherwise. You can ++# uncomment it (remove the pound sign) and change it if you want. ++ ++# =========================================================================== ++# Running on an online server or in a real tournament or match ++# =========================================================================== ++# If you plan to run online or in a tournament, read through the "Rules" ++# section below for proper handling of komi, handicaps, end-of-game cleanup, ++# and other details. ++ ++# =========================================================================== ++# Notes about performance and memory usage ++# =========================================================================== ++# Important: For good performance, you will very likely want to tune the ++# "numSearchThreads" parameter in the Search limits section below! Run ++# "./katago benchmark" to test KataGo and to suggest a reasonable value ++# of this parameter. ++ ++# For multi-GPU systems, read "OpenCL GPU settings" or "CUDA GPU settings". ++# ++# When using OpenCL, verify that KataGo picks the correct device! Some systems ++# may have both an Intel CPU OpenCL and GPU OpenCL. If # KataGo picks the wrong ++# one, correct this by specifying "openclGpuToUse". ++# ++# Consider adjusting "maxVisits", "ponderingEnabled", "resignThreshold", and ++# other parameters depending on your intended usage. ++ ++# =========================================================================== ++# Command-line usage ++# =========================================================================== ++# All of the below values may be set or overridden via command-line arguments: ++# ++# -override-config KEY=VALUE,KEY=VALUE,... ++ ++# =========================================================================== ++# Logs and files ++# =========================================================================== ++# This section defines where and what logging information is produced. ++ ++# Each run of KataGo will log to a separate file in this dir. ++# This is the default. ++logDir = gtp_logs ++# Uncomment and specify this instead of logDir to write separate dated subdirs ++# logDirDated = gtp_logs ++# Uncomment and specify this instead of logDir to log to only a single file ++# logFile = gtp.log ++ ++# Logging options ++logAllGTPCommunication = true ++logSearchInfo = true ++logSearchInfoForChosenMove = false ++logToStderr = false ++ ++# KataGo will display some info to stderr on GTP startup ++# Uncomment the next line and set it to false to suppress that and remain silent ++# startupPrintMessageToStderr = true ++ ++# Write information to stderr, for use in things like malkovich chat to OGS. ++ ogsChatToStderr = true ++ ++# Uncomment and set this to a directory to override where openCLTuner files ++# and other cached data is written. By default it saves into a subdir of the ++# current directory on windows, and a subdir of ~/.katago on Linux. ++# homeDataDir = PATH_TO_DIRECTORY ++ ++# =========================================================================== ++# Analysis ++# =========================================================================== ++# This section configures analysis settings. ++# ++# The maximum number of moves after the first move displayed in variations ++# from analysis commands like kata-analyze or lz-analyze. ++# analysisPVLen = 15 ++ ++# Report winrates for chat and analysis as (BLACK|WHITE|SIDETOMOVE). ++# Most GUIs and analysis tools will expect SIDETOMOVE. ++# reportAnalysisWinratesAs = SIDETOMOVE ++ ++# Extra noise for wider exploration. Large values will force KataGo to ++# analyze a greater variety of moves than it normally would. ++# An extreme value like 1 distributes playouts across every move on the board, ++# even very bad moves. ++# Affects analysis only, does not affect play. ++# analysisWideRootNoise = 0.04 ++ ++# Try to limit the effect of possible bad or bogus move sequences in the ++# history leading to this position from affecting KataGo's move predictions. ++# analysisIgnorePreRootHistory = true ++ ++# =========================================================================== ++# Rules ++# =========================================================================== ++# This section configures the scoring and playing rules. Rules can also be ++# changed mid-run by issuing custom GTP commands. ++# ++# See https://lightvector.github.io/KataGo/rules.html for rules details. ++# ++# See https://github.com/lightvector/KataGo/blob/master/docs/GTP_Extensions.md ++# for GTP commands. ++ ++# Specify the rules as a string. ++# Some legal values include: ++# chinese, japanese, korean, aga, chinese-ogs, new-zealand, stone-scoring, ++# ancient-territory, bga, aga-button ++# ++# For some human rulesets that require complex adjudication in tricky cases ++# (e.g. japanese, korean) KataGo may not precisely match the ruleset in such ++# cases but will do its best. ++rules = tromp-taylor ++ ++# By default, the "rules" parameter is used, but if you comment it out and ++# uncomment one option in each of the sections below, you can specify an ++# arbitrary combination of individual rules. ++ ++# koRule = SIMPLE # Simple ko rules (triple ko = no result) ++# koRule = POSITIONAL # Positional superko ++# koRule = SITUATIONAL # Situational superko ++ ++# scoringRule = AREA # Area scoring ++# scoringRule = TERRITORY # Territory scoring (special computer-friendly territory rules) ++ ++# taxRule = NONE # All surrounded empty points are scored ++# taxRule = SEKI # Eyes in seki do NOT count as points ++# taxRule = ALL # All groups are taxed up to 2 points for the two eyes needed to live ++ ++# Is multiple-stone suicide legal? (Single-stone suicide is always illegal). ++# multiStoneSuicideLegal = false ++# multiStoneSuicideLegal = true ++ ++# "Button go" - the first pass when area scoring awards 0.5 points and does ++# not count for ending the game. ++# Allows area scoring rulesets that have far simpler rules to achieve the same ++# final scoring precision and reward for precise play as territory scoring. ++# hasButton = false ++# hasButton = true ++ ++# Is this a human ruleset where it's okay to pass before having physically ++# captured and removed all dead stones? ++# friendlyPassOk = false ++# friendlyPassOk = true ++ ++# How handicap stones in handicap games are compensated ++# whiteHandicapBonus = 0 # White gets no compensation for black's handicap stones (Tromp-taylor, NZ, JP) ++# whiteHandicapBonus = N-1 # White gets N-1 points for black's N handicap stones (AGA) ++# whiteHandicapBonus = N # White gets N points for black's N handicap stones (Chinese) ++ ++# ------------------------------ ++# Other rules hacks ++# ------------------------------ ++# Uncomment and change to adjust what board size KataGo uses upon startup ++# by default when GTP doesn't specify. ++# defaultBoardSize = 19 ++ ++# By default, Katago will use the komi that the GUI or GTP controller tries to set. ++# Uncomment and set this to have KataGo ignore the controller and always use this komi. ++# ignoreGTPAndForceKomi = 7 ++ ++# =========================================================================== ++# Bot behavior ++# =========================================================================== ++ ++# ------------------------------ ++# Resignation ++# ------------------------------ ++ ++# Resignation occurs if for at least resignConsecTurns in a row, the ++# winLossUtility (on a [-1,1] scale) is below resignThreshold. ++allowResignation = true ++resignThreshold = -0.90 ++resignConsecTurns = 3 ++ ++# By default, KataGo may resign games that it is confidently losing even if they ++# are very close in score. Uncomment and set this to avoid resigning games ++# if the estimated difference is points is less than or equal to this. ++# resignMinScoreDifference = 10 ++ ++# ------------------------------ ++# Handicap ++# ------------------------------ ++# Assume that if black makes many moves in a row right at the start of the ++# game, then the game is a handicap game. This is necessary on some servers ++# and for some GUIs and also when initializing from many SGF files, which may ++# set up a handicap game using repeated GTP "play" commands for black rather ++# than GTP "place_free_handicap" commands; however, it may also lead to ++# incorrect understanding of komi if whiteHandicapBonus is used and a server ++# does not have such a practice. Uncomment and set to false to disable. ++# assumeMultipleStartingBlackMovesAreHandicap = true ++ ++# Makes katago dynamically adjust in handicap or altered-komi games to assume ++# based on those game settings that it must be stronger or weaker than the ++# opponent and to play accordingly. Greatly improves handicap strength by ++# biasing winrates and scores to favor appropriate safe/aggressive play. ++# Does NOT affect analysis (lz-analyze, kata-analyze, used by programs like ++# Lizzie) so analysis remains unbiased. Uncomment and set this to 0 to disable ++# this and make KataGo play the same always. ++# dynamicPlayoutDoublingAdvantageCapPerOppLead = 0.045 ++ ++# Instead of "dynamicPlayoutDoublingAdvantageCapPerOppLead", you can comment ++# that out and uncomment and set "playoutDoublingAdvantage" to a fixed value ++# from -3.0 to 3.0 that will not change dynamically. ++# ALSO affects analysis tools (lz-analyze, kata-analyze, used by e.g. Lizzie). ++# Negative makes KataGo behave as if it is much weaker than the opponent. ++# Positive makes KataGo behave as if it is much stronger than the opponent. ++# KataGo will adjust to favor safe/aggressive play as appropriate based on ++# the combination of who is ahead and how much stronger/weaker it thinks it is, ++# and report winrates and scores taking the strength difference into account. ++# ++# If this and "dynamicPlayoutDoublingAdvantageCapPerOppLead" are both set ++# then dynamic will be used for all games and this fixed value will be used ++# for analysis tools. ++# playoutDoublingAdvantage = 0.0 ++ ++# Uncomment one of these when using "playoutDoublingAdvantage" to enforce ++# that it will only apply when KataGo plays as the specified color and will be ++# negated when playing as the opposite color. ++# playoutDoublingAdvantagePla = BLACK ++# playoutDoublingAdvantagePla = WHITE ++ ++# ------------------------------ ++# Passing and cleanup ++# ------------------------------ ++# Make the bot never assume that its pass will end the game, even if passing ++# would end and "win" under Tromp-Taylor rules. Usually this is a good idea ++# when using it for analysis or playing on servers where scoring may be ++# implemented non-tromp-taylorly. Uncomment and set to false to disable. ++# conservativePass = true ++ ++# When using territory scoring, self-play games continue beyond two passes ++# with special cleanup rules that may be confusing for human players. This ++# option prevents the special cleanup phases from being reachable when using ++# the bot for GTP play. Uncomment and set to false to enable entering special ++# cleanup. For example, if you are testing it against itself, or against ++# another bot that has precisely implemented the rules documented at ++# https://lightvector.github.io/KataGo/rules.html ++# preventCleanupPhase = true ++ ++# ------------------------------ ++# Miscellaneous behavior ++# ------------------------------ ++# If the board is symmetric, search only one copy of each equivalent move. ++# Attempts to also account for ko/superko, will not theoretically perfect for ++# superko. Uncomment and set to false to disable. ++# rootSymmetryPruning = true ++ ++# Uncomment and set to true to avoid a particular joseki that some networks ++# misevaluate, and also to improve opening diversity versus some particular ++# other bots that like to play it all the time. ++# avoidMYTDaggerHack = false ++ ++# Prefer to avoid playing the same joseki in every corner of the board. ++# Uncomment to set to a specific value. See "Avoid SGF patterns" section. ++# By default: 0 (even games), 0.005 (handicap games) ++# avoidRepeatedPatternUtility = 0.0 ++ ++# Experimental logic to fight against mirror Go even with unfavorable komi. ++# Uncomment to set to a specific value to use for both playing and analysis. ++# By default: true when playing via GTP, but false when analyzing. ++# antiMirror = true ++ ++# Enable some hacks that mitigate rare instances when passing messes up deeper searches. ++# enablePassingHacks = true ++ ++# Uncomment and set this to true to prevent bad or bogus move sequences ++# in the history leading to this position from affecting KataGo's move choices. ++# Same as analysisIgnorePreRootHistory (see above) but applies to actual play. ++# You can enable this if KataGo is being asked to play from positions that it did not ++# choose the moves to reach. ++# ignorePreRootHistory = false ++ ++# =========================================================================== ++# Search limits ++# =========================================================================== ++ ++# Terminology: ++# "Playouts" is the number of new playouts of search performed each turn. ++# "Visits" is the same as "Playouts" but also counts search performed on ++# previous turns that is still applicable to this turn. ++# "Time" is the time in seconds. ++ ++# For example, if KataGo searched 200 nodes on the previous turn, and then ++# after the opponent's reply, 50 nodes of its search tree was still valid, ++# then a visit limit of 200 would allow KataGo to search 150 new nodes ++# (for a final tree size of 200 nodes), whereas a playout limit of of 200 ++# would allow KataGo to search 200 nodes (for a final tree size of 250 nodes). ++ ++# Additionally, KataGo may also move before than the limit in order to ++# obey time controls (e.g. byo-yomi, etc) if the GTP controller has ++# told KataGo that the game has is being played with a given time control. ++ ++# Limits for search on the current turn. ++# If commented out or unspecified, the default is to have no limit. ++# maxVisits = 30000 ++# maxPlayouts = 300 ++maxTime = 1.0 ++ ++# Ponder on the opponent's turn? ++ponderingEnabled = true ++ ++# Limits for search when pondering on the opponent's turn. ++# If commented out or unspecified, the default is to have no limit. ++# Limiting the maximum time is recommended so that KataGo won't burn CPU/GPU ++# forever and/or run out of RAM if left unattended while pondering is enabled. ++# maxVisitsPondering = 5000 ++# maxPlayoutsPondering = 3000 ++maxTimePondering = 120.0 ++ ++ ++# ------------------------------ ++# Other search limits and behavior ++# ------------------------------ ++ ++# Approx number of seconds to buffer for lag for GTP time controls - will ++# move a bit faster assuming there is this much lag per move. ++lagBuffer = 1.0 ++ ++# YOU PROBABLY WANT TO TUNE THIS PARAMETER! ++# The number of threads to use when searching. On powerful GPUs the optimal ++# threads may be much higher than the number of CPU cores you have because ++# many threads are needed to feed efficient large batches to the GPU. ++# ++# Run "./katago benchmark" to tune this parameter and test the effect ++# of changes to any of other parameters. ++numSearchThreads = 40 ++ ++# Play a little faster if the opponent is passing, for human-friendliness. ++# Comment these out to disable them, such as if running a controlled match ++# where you are testing KataGo with fixed compute per move vs other bots. ++searchFactorAfterOnePass = 0.50 ++searchFactorAfterTwoPass = 0.25 ++ ++# Play a little faster if super-winning, for human-friendliness. ++# Comment these out to disable them, such as if running a controlled match ++# where you are testing KataGo with fixed compute per move vs other bots. ++searchFactorWhenWinning = 0.40 ++searchFactorWhenWinningThreshold = 0.95 ++ ++# =========================================================================== ++# GPU settings ++# =========================================================================== ++# This section configures GPU settings. ++# ++# Maximum number of positions to send to a single GPU at once. The default ++# value is roughly equal to numSearchThreads, but can be specified manually ++# if running out of memory, or using multiple GPUs that expect to share work. ++# nnMaxBatchSize = ++ ++# Controls the neural network cache size, which is the primary RAM/memory use. ++# KataGo will cache up to (2 ** nnCacheSizePowerOfTwo) many neural net ++# evaluations in case of transpositions in the tree. ++# Increase this to improve performance for searches with tens of thousands ++# of visits or more. Decrease this to limit memory usage. ++# If you're happy to do some math - each neural net entry takes roughly ++# 1.5KB, except when using whole-board ownership/territory ++# visualizations, where each entry will take roughly 3KB. The number of ++# entries is (2 ** nnCacheSizePowerOfTwo). (E.g. 2 ** 18 = 262144.) ++# You can compute roughly how much memory the cache will use based on this. ++# nnCacheSizePowerOfTwo = 20 ++ ++# Size of mutex pool for nnCache is (2 ** this). ++# nnMutexPoolSizePowerOfTwo = 16 ++ ++# Randomize board orientation when running neural net evals? Uncomment and ++# set to false to disable. ++# nnRandomize = true ++ ++# If provided, force usage of a specific seed for nnRandomize. ++# The default is to use a randomly generated seed. ++# nnRandSeed = abcdefg ++ ++# Uncomment and set to true to force GTP to use the maximum board size for ++# internal buffers for the neural net. This will make KataGo slower when ++# evaluating small boards, but will avoid a lengthy initialization time on every ++# change of board size due to having to re-size the neural net buffers on the GPU. ++# This can be useful for example, for OGS's persistent bot mode that uses a single ++# bot instance to handle multiple games and may thrash between different board sizes ++# if there are concurrent games of multiple sizes. ++# gtpForceMaxNNSize = false ++ ++# ------------------------------ ++# Multiple GPUs ++# ------------------------------ ++# Set this to the number of GPUs to use or that are available. ++# IMPORTANT: If more than 1, also uncomment the appropriate TensorRT ++# or CUDA or OpenCL section. ++# numNNServerThreadsPerModel = 1 ++ ++# ------------------------------ ++# TENSORRT GPU settings ++# ------------------------------ ++# These only apply when using the TENSORRT version of KataGo. ++ ++# For one GPU: optionally uncomment this option and change if the GPU to ++# use is not device 0. ++# trtDeviceToUse = 0 ++ ++# For two GPUs: Uncomment these options, AND set numNNServerThreadsPerModel above. ++# Also, change their values if the devices you want to use are not 0 and 1. ++# trtDeviceToUseThread0 = 0 ++# trtDeviceToUseThread1 = 1 ++ ++# For three GPUs: Uncomment these options, AND set numNNServerThreadsPerModel above. ++# Also, change their values if the devices you want to use are not 0 and 1 and 2. ++# trtDeviceToUseThread0 = 0 ++# trtDeviceToUseThread1 = 1 ++# trtDeviceToUseThread2 = 2 ++ ++# The pattern continues for additional GPUs. ++ ++# ------------------------------ ++# CUDA GPU settings ++# ------------------------------ ++# These only apply when using the CUDA version of KataGo. ++ ++# For one GPU: optionally uncomment and change this if the GPU you want to ++# use is not device 0 ++# cudaDeviceToUse = 0 ++ ++# For two GPUs: Uncomment these options, AND set numNNServerThreadsPerModel above. ++# Also, change their values if the devices you want to use are not 0 and 1. ++# cudaDeviceToUseThread0 = 0 ++# cudaDeviceToUseThread1 = 1 ++ ++# For three GPUs: Uncomment these options, AND set numNNServerThreadsPerModel above. ++# Also, change their values if the devices you want to use are not 0 and 1 and 2. ++# cudaDeviceToUseThread0 = 0 ++# cudaDeviceToUseThread1 = 1 ++# cudaDeviceToUseThread2 = 2 ++ ++# The pattern continues for additional GPUs. ++ ++# KataGo will automatically use FP16 or not based on the compute capability ++# of your NVIDIA GPU. If you want to try to force a particular behavior ++# you can uncomment these lines and change them to "true" or "false". ++# cudaUseFP16 = auto ++# cudaUseNHWC = auto ++ ++# ------------------------------ ++# OpenCL GPU settings ++# ------------------------------ ++# These only apply when using the OpenCL version of KataGo. ++ ++# Uncomment and set to true to tune OpenCL for every board size separately, ++# rather than only the largest possible size. ++# openclReTunePerBoardSize = false ++ ++# For one GPU: optionally uncomment and change this if the best device to use is guessed incorrectly. ++# The default behavior tries to guess the 'best' GPU or device on your system to use, usually it will be a good guess. ++# openclDeviceToUse = 0 ++ ++# For two GPUs: Uncomment these two lines and replace X and Y with the device ids of the devices you want to use. ++# It might NOT be 0 and 1, some computers will have many OpenCL devices. You can see what the devices are when ++# KataGo starts up - it should print or log all the devices it finds. ++# (AND also set numNNServerThreadsPerModel above) ++# openclDeviceToUseThread0 = X ++# openclDeviceToUseThread1 = Y ++ ++# For three GPUs: Uncomment these three lines and replace X and Y and Z with the device ids of the devices you want to use. ++# It might NOT be 0 and 1 and 2, some computers will have many OpenCL devices. You can see what the devices are when ++# KataGo starts up - it should print or log all the devices it finds. ++# (AND also set numNNServerThreadsPerModel above) ++# openclDeviceToUseThread0 = X ++# openclDeviceToUseThread1 = Y ++# openclDeviceToUseThread2 = Z ++ ++# The pattern continues for additional GPUs. ++ ++# KataGo will automatically use FP16 or not based on testing your GPU during ++# tuning. If you want to try to force a particular behavior though you can ++# uncomment this option and change it to "true" or "false". This is a fairly ++# blunt setting - more detailed settings are testable by rerunning the tuner ++# with various arguments (./katago tuner). ++# openclUseFP16 = auto ++ ++# ------------------------------ ++# Eigen-specific settings ++# ------------------------------ ++# These only apply when using the Eigen (pure CPU) version of KataGo. ++ ++# Number of CPU threads for evaluating the neural net on the Eigen backend. ++# ++# Default: numSearchThreads ++# numEigenThreadsPerModel = X ++ ++# =========================================================================== ++# Root move selection and biases ++# =========================================================================== ++# Uncomment and edit any of the below values to change them from their default. ++ ++# If provided, force usage of a specific seed for various random things in ++# the search. The default is to use a random seed. ++# searchRandSeed = hijklmn ++ ++# Temperature for the early game, randomize between chosen moves with ++# this temperature ++# chosenMoveTemperatureEarly = 0.5 ++ ++# Decay temperature for the early game by 0.5 every this many moves, ++# scaled with board size. ++# chosenMoveTemperatureHalflife = 19 ++ ++# At the end of search after the early game, randomize between chosen ++# moves with this temperature ++# chosenMoveTemperature = 0.10 ++ ++# Subtract this many visits from each move prior to applying ++# chosenMoveTemperature (unless all moves have too few visits) to downweight ++# unlikely moves ++# chosenMoveSubtract = 0 ++ ++# The same as chosenMoveSubtract but only prunes moves that fall below ++# the threshold. This setting does not affect chosenMoveSubtract. ++# chosenMovePrune = 1 ++ ++# Number of symmetries to sample (without replacement) and average at the root ++# rootNumSymmetriesToSample = 1 ++ ++# Using LCB for move selection? ++# useLcbForSelection = true ++ ++# How many stdevs a move needs to be better than another for LCB selection ++# lcbStdevs = 5.0 ++ ++# Only use LCB override when a move has this proportion of visits as the ++# top move. ++# minVisitPropForLCB = 0.15 ++ ++# =========================================================================== ++# Internal params ++# =========================================================================== ++# Uncomment and edit any of the below values to change them from their default. ++ ++# Scales the utility of winning/losing ++# winLossUtilityFactor = 1.0 ++ ++# Scales the utility for trying to maximize score ++# staticScoreUtilityFactor = 0.10 ++# dynamicScoreUtilityFactor = 0.30 ++ ++# Adjust dynamic score center this proportion of the way towards zero, ++# capped at a reasonable amount. ++# dynamicScoreCenterZeroWeight = 0.20 ++# dynamicScoreCenterScale = 0.75 ++ ++# The utility of getting a "no result" due to triple ko or other long cycle ++# in non-superko rulesets (-1 to 1) ++# noResultUtilityForWhite = 0.0 ++ ++# The number of wins that a draw counts as, for white. (0 to 1) ++# drawEquivalentWinsForWhite = 0.5 ++ ++# Exploration constant for mcts ++# cpuctExploration = 1.0 ++# cpuctExplorationLog = 0.45 ++ ++# Parameters that control exploring more in volatile positions, exploring ++# less in stable positions. ++# cpuctUtilityStdevPrior = 0.40 ++# cpuctUtilityStdevPriorWeight = 2.0 ++# cpuctUtilityStdevScale = 0.85 ++ ++# FPU reduction constant for mcts ++# fpuReductionMax = 0.2 ++# rootFpuReductionMax = 0.1 ++# fpuParentWeightByVisitedPolicy = true ++ ++# Parameters that control weighting of evals based on the net's own ++# self-reported uncertainty. ++# useUncertainty = true ++# uncertaintyExponent = 1.0 ++# uncertaintyCoeff = 0.25 ++ ++# Explore using optimistic policy ++# rootPolicyOptimism = 0.2 ++# policyOptimism = 1.0 ++ ++# Amount to apply a downweighting of children with very bad values relative ++# to good ones. ++# valueWeightExponent = 0.25 ++ ++# Slight incentive for the bot to behave human-like with regard to passing at ++# the end, filling the dame, not wasting time playing in its own territory, ++# etc., and not play moves that are equivalent in terms of points but a bit ++# more unfriendly to humans. ++# rootEndingBonusPoints = 0.5 ++ ++# Make the bot prune useless moves that are just prolonging the game to ++# avoid losing yet. ++# rootPruneUselessMoves = true ++ ++# Apply bias correction based on local pattern keys ++# subtreeValueBiasFactor = 0.45 ++# subtreeValueBiasWeightExponent = 0.85 ++ ++# Use graph search rather than tree search - identify and share search for ++# transpositions. ++# useGraphSearch = true ++ ++# How much to shard the node table for search synchronization ++# nodeTableShardsPowerOfTwo = 16 ++ ++# How many virtual losses to add when a thread descends through a node ++# numVirtualLossesPerThread = 1 ++ ++# Improve the quality of evals under heavy multithreading ++# useNoisePruning = true ++ ++# =========================================================================== ++# Automatic avoid patterns ++# =========================================================================== ++# The parameters in this section provide a way to bias away from moves that match ++# patterns that this instance of KataGo has played in previous games, by auto-saving ++# moves to a directory and then auto-loading and biasing against them each new game. ++# Uncomment them to use them. When using this feature, all parameters must be specified. ++ ++# Different board sizes are tracked separately, but all board sizes share the same ++# parameters by default. Every parameter *except* for autoAvoidRepeatDir and ++# autoAvoidRepeatSaveChunkSize can be overridden per board size, ++# e.g. "autoAvoidRepeatMinTurnNumber13x13". You must ALSO specify the ++# defaults even if you have specified board-size-specific values. ++ ++# Directory to auto-save moves KataGo plays, to avoid them in future games. ++# You can create a new empty directory and put its path here. ++# If you run parallel instances of KataGo, use different directories if you ++# want them not to share their biases, use the same directory if you want ++# all of them to bias away from past moves that any of them have played. ++# KataGo will also automatically DELETE old data in this directory, so it is ++# recommended that if you do share the same directory between parallel instances, ++# that they all use the same settings that affect data saving/deletion. ++# autoAvoidRepeatDir = PATH_TO_NEW_DIRECTORY ++ ++# Penalize this much utility per matching move. ++# Values that are too large may lead to bad play. The value of 0.004 is fairly large ++# and might be large enough to result in some early weird/bad moves when trying ++# to avoid past games' moves if enough games begin the same way. You can experiment with it. ++# autoAvoidRepeatUtility = 0.004 ++ ++# Per each new move saved, exponentially decay prior saved moves by this factor. ++# This way, the bias against moves from many games ago is gradually phased out. ++# For example, 0.9995 = 1 - 1/2000, so would be roughly 2000 prior moves worth of ++# penalty weight remembered, in steady state. Depending on what turn number range ++# you are saving, this might equate to a different number of games For example ++# saving the first 50 moves per game would make this roughly 2000 / 50 = 40 games ++# worth of memory. ++# autoAvoidRepeatLambda = 0.9995 ++ ++# Affects data saving/deletion. ++# When the number of saved moves exceeds this, outright delete them to avoid too many ++# files and disk space building up. Also may affect the speed of saving/loading on start ++# of each game if this is set large and a lot of data builds up. ++# autoAvoidRepeatMaxPoses = 10000 ++ ++# Affects data saving/deletion. ++# Only save data for moves within this turn number range of those games. ++# E.g. setting autoAvoidRepeatMinTurnNumber to a number like 4 or 5 would tend to make ++# KataGo not develop a bias against the initial 3-4 and 4-4 corner moves in almost every game. ++# autoAvoidRepeatMinTurnNumber = 0 ++# autoAvoidRepeatMaxTurnNumber = 50 ++ ++# Affects data saving/deletion. ++# Within a single run of a program, wait to accumulate this many samples ++# (possibly across multiple clear_boards/games) before saving the data. ++# Can help to avoid writing too many small files to disk, especially when GTP is used ++# in a way that clears the board very frequently (e.g. gtp2ogs pooled manager). ++# autoAvoidRepeatSaveChunkSize = 200 ++ ++# =========================================================================== ++# Avoid SGF patterns ++# =========================================================================== ++# The parameters in this section provide a way to avoid moves that follow ++# specific patterns based on a set of SGF files loaded upon startup. ++# This is basically the same as the above "Automatic avoid patterns" section ++# above except you supply your own SGF files to avoid moves from. ++# Uncomment them to use this feature. Additionally, if the SGF file ++# contains the string %SKIP% in a comment on a move, that move will be ++# ignored for this purpose. ++ ++# Load SGF files from this directory when the engine is started ++# (only on startup, will not reload unless engine is restarted) ++# avoidSgfPatternDirs = path/to/directory/with/sgfs/ ++# You can also surround the file path in double quotes if the file path contains trailing spaces or hash signs. ++# Within double quotes, backslashes are escape characters. ++# avoidSgfPatternDirs = "path/to/directory/with/sgfs/" ++ ++# Penalize this much utility per matching move. ++# Set this negative if you instead want to favor SGF patterns instead of ++# penalizing them. This number does not need to be large, even 0.001 will ++# make a difference. Values that are too large may lead to bad play. ++# avoidSgfPatternUtility = 0.001 ++ ++# Optional - load only the newest this many files ++# avoidSgfPatternMaxFiles = 20 ++ ++# Optional - Penalty is multiplied by this per each older SGF file, so that ++# old SGF files matter less than newer ones. ++# avoidSgfPatternLambda = 0.90 ++ ++# Optional - pay attention only to moves made by players with this name. ++# For example, set it to the name that your bot's past games will show up ++# as in the SGF, so that the bot will only avoid repeating moves that itself ++# made in past games, not the moves that its opponents made. ++# avoidSgfPatternAllowedNames = my-ogs-bot-name1,my-ogs-bot-name2 ++ ++# Optional - Ignore moves in SGF files that occurred before this turn number. ++# avoidSgfPatternMinTurnNumber = 0 ++ ++# For more avoid patterns: ++# You can also specify a second set of parameters, and a third, fourth, ++# etc. by numbering 2,3,4,... ++# ++# avoidSgf2PatternDirs = ... ++# avoidSgf2PatternUtility = ... ++# avoidSgf2PatternMaxFiles = ... ++# avoidSgf2PatternLambda = ... ++# avoidSgf2PatternAllowedNames = ... ++# avoidSgf2PatternMinTurnNumber = ... ++ +diff --git a/cpp/doudoubot.cfg b/cpp/doudoubot.cfg +new file mode 100644 +index 00000000..bf81254f +--- /dev/null ++++ b/cpp/doudoubot.cfg +@@ -0,0 +1,721 @@ ++# Configuration for KataGo C++ GTP engine ++ ++# Run the program using: `./katago.exe gtp` ++ ++# In this example config, when a parameter is given as a commented out value, ++# that value also is the default value, unless described otherwise. You can ++# uncomment it (remove the pound sign) and change it if you want. ++ ++# =========================================================================== ++# Running on an online server or in a real tournament or match ++# =========================================================================== ++# If you plan to run online or in a tournament, read through the "Rules" ++# section below for proper handling of komi, handicaps, end-of-game cleanup, ++# and other details. ++ ++# =========================================================================== ++# Notes about performance and memory usage ++# =========================================================================== ++# Important: For good performance, you will very likely want to tune the ++# "numSearchThreads" parameter in the Search limits section below! Run ++# "./katago benchmark" to test KataGo and to suggest a reasonable value ++# of this parameter. ++ ++# For multi-GPU systems, read "OpenCL GPU settings" or "CUDA GPU settings". ++# ++# When using OpenCL, verify that KataGo picks the correct device! Some systems ++# may have both an Intel CPU OpenCL and GPU OpenCL. If # KataGo picks the wrong ++# one, correct this by specifying "openclGpuToUse". ++# ++# Consider adjusting "maxVisits", "ponderingEnabled", "resignThreshold", and ++# other parameters depending on your intended usage. ++ ++# =========================================================================== ++# Command-line usage ++# =========================================================================== ++# All of the below values may be set or overridden via command-line arguments: ++# ++# -override-config KEY=VALUE,KEY=VALUE,... ++ ++# =========================================================================== ++# Logs and files ++# =========================================================================== ++# This section defines where and what logging information is produced. ++ ++# Each run of KataGo will log to a separate file in this dir. ++# This is the default. ++logDir = gtp_logs ++# Uncomment and specify this instead of logDir to write separate dated subdirs ++# logDirDated = gtp_logs ++# Uncomment and specify this instead of logDir to log to only a single file ++# logFile = gtp.log ++ ++# Logging options ++logAllGTPCommunication = true ++logSearchInfo = true ++logSearchInfoForChosenMove = false ++logToStderr = false ++ ++# KataGo will display some info to stderr on GTP startup ++# Uncomment the next line and set it to false to suppress that and remain silent ++# startupPrintMessageToStderr = true ++ ++# Write information to stderr, for use in things like malkovich chat to OGS. ++ ogsChatToStderr = true ++ ++# Uncomment and set this to a directory to override where openCLTuner files ++# and other cached data is written. By default it saves into a subdir of the ++# current directory on windows, and a subdir of ~/.katago on Linux. ++# homeDataDir = PATH_TO_DIRECTORY ++ ++# =========================================================================== ++# Analysis ++# =========================================================================== ++# This section configures analysis settings. ++# ++# The maximum number of moves after the first move displayed in variations ++# from analysis commands like kata-analyze or lz-analyze. ++# analysisPVLen = 15 ++ ++# Report winrates for chat and analysis as (BLACK|WHITE|SIDETOMOVE). ++# Most GUIs and analysis tools will expect SIDETOMOVE. ++# reportAnalysisWinratesAs = SIDETOMOVE ++ ++# Extra noise for wider exploration. Large values will force KataGo to ++# analyze a greater variety of moves than it normally would. ++# An extreme value like 1 distributes playouts across every move on the board, ++# even very bad moves. ++# Affects analysis only, does not affect play. ++# analysisWideRootNoise = 0.04 ++ ++# Try to limit the effect of possible bad or bogus move sequences in the ++# history leading to this position from affecting KataGo's move predictions. ++# analysisIgnorePreRootHistory = true ++ ++# =========================================================================== ++# Rules ++# =========================================================================== ++# This section configures the scoring and playing rules. Rules can also be ++# changed mid-run by issuing custom GTP commands. ++# ++# See https://lightvector.github.io/KataGo/rules.html for rules details. ++# ++# See https://github.com/lightvector/KataGo/blob/master/docs/GTP_Extensions.md ++# for GTP commands. ++ ++# Specify the rules as a string. ++# Some legal values include: ++# chinese, japanese, korean, aga, chinese-ogs, new-zealand, stone-scoring, ++# ancient-territory, bga, aga-button ++# ++# For some human rulesets that require complex adjudication in tricky cases ++# (e.g. japanese, korean) KataGo may not precisely match the ruleset in such ++# cases but will do its best. ++rules = tromp-taylor ++ ++# By default, the "rules" parameter is used, but if you comment it out and ++# uncomment one option in each of the sections below, you can specify an ++# arbitrary combination of individual rules. ++ ++# koRule = SIMPLE # Simple ko rules (triple ko = no result) ++# koRule = POSITIONAL # Positional superko ++# koRule = SITUATIONAL # Situational superko ++ ++# scoringRule = AREA # Area scoring ++# scoringRule = TERRITORY # Territory scoring (special computer-friendly territory rules) ++ ++# taxRule = NONE # All surrounded empty points are scored ++# taxRule = SEKI # Eyes in seki do NOT count as points ++# taxRule = ALL # All groups are taxed up to 2 points for the two eyes needed to live ++ ++# Is multiple-stone suicide legal? (Single-stone suicide is always illegal). ++# multiStoneSuicideLegal = false ++# multiStoneSuicideLegal = true ++ ++# "Button go" - the first pass when area scoring awards 0.5 points and does ++# not count for ending the game. ++# Allows area scoring rulesets that have far simpler rules to achieve the same ++# final scoring precision and reward for precise play as territory scoring. ++# hasButton = false ++# hasButton = true ++ ++# Is this a human ruleset where it's okay to pass before having physically ++# captured and removed all dead stones? ++# friendlyPassOk = false ++# friendlyPassOk = true ++ ++# How handicap stones in handicap games are compensated ++# whiteHandicapBonus = 0 # White gets no compensation for black's handicap stones (Tromp-taylor, NZ, JP) ++# whiteHandicapBonus = N-1 # White gets N-1 points for black's N handicap stones (AGA) ++# whiteHandicapBonus = N # White gets N points for black's N handicap stones (Chinese) ++ ++# ------------------------------ ++# Other rules hacks ++# ------------------------------ ++# Uncomment and change to adjust what board size KataGo uses upon startup ++# by default when GTP doesn't specify. ++# defaultBoardSize = 19 ++ ++# By default, Katago will use the komi that the GUI or GTP controller tries to set. ++# Uncomment and set this to have KataGo ignore the controller and always use this komi. ++# ignoreGTPAndForceKomi = 7 ++ ++# =========================================================================== ++# Bot behavior ++# =========================================================================== ++ ++# ------------------------------ ++# Resignation ++# ------------------------------ ++ ++# Resignation occurs if for at least resignConsecTurns in a row, the ++# winLossUtility (on a [-1,1] scale) is below resignThreshold. ++allowResignation = true ++resignThreshold = -0.90 ++resignConsecTurns = 3 ++ ++# By default, KataGo may resign games that it is confidently losing even if they ++# are very close in score. Uncomment and set this to avoid resigning games ++# if the estimated difference is points is less than or equal to this. ++# resignMinScoreDifference = 10 ++ ++# ------------------------------ ++# Handicap ++# ------------------------------ ++# Assume that if black makes many moves in a row right at the start of the ++# game, then the game is a handicap game. This is necessary on some servers ++# and for some GUIs and also when initializing from many SGF files, which may ++# set up a handicap game using repeated GTP "play" commands for black rather ++# than GTP "place_free_handicap" commands; however, it may also lead to ++# incorrect understanding of komi if whiteHandicapBonus is used and a server ++# does not have such a practice. Uncomment and set to false to disable. ++# assumeMultipleStartingBlackMovesAreHandicap = true ++ ++# Makes katago dynamically adjust in handicap or altered-komi games to assume ++# based on those game settings that it must be stronger or weaker than the ++# opponent and to play accordingly. Greatly improves handicap strength by ++# biasing winrates and scores to favor appropriate safe/aggressive play. ++# Does NOT affect analysis (lz-analyze, kata-analyze, used by programs like ++# Lizzie) so analysis remains unbiased. Uncomment and set this to 0 to disable ++# this and make KataGo play the same always. ++# dynamicPlayoutDoublingAdvantageCapPerOppLead = 0.045 ++ ++# Instead of "dynamicPlayoutDoublingAdvantageCapPerOppLead", you can comment ++# that out and uncomment and set "playoutDoublingAdvantage" to a fixed value ++# from -3.0 to 3.0 that will not change dynamically. ++# ALSO affects analysis tools (lz-analyze, kata-analyze, used by e.g. Lizzie). ++# Negative makes KataGo behave as if it is much weaker than the opponent. ++# Positive makes KataGo behave as if it is much stronger than the opponent. ++# KataGo will adjust to favor safe/aggressive play as appropriate based on ++# the combination of who is ahead and how much stronger/weaker it thinks it is, ++# and report winrates and scores taking the strength difference into account. ++# ++# If this and "dynamicPlayoutDoublingAdvantageCapPerOppLead" are both set ++# then dynamic will be used for all games and this fixed value will be used ++# for analysis tools. ++# playoutDoublingAdvantage = 0.0 ++ ++# Uncomment one of these when using "playoutDoublingAdvantage" to enforce ++# that it will only apply when KataGo plays as the specified color and will be ++# negated when playing as the opposite color. ++# playoutDoublingAdvantagePla = BLACK ++# playoutDoublingAdvantagePla = WHITE ++ ++# ------------------------------ ++# Passing and cleanup ++# ------------------------------ ++# Make the bot never assume that its pass will end the game, even if passing ++# would end and "win" under Tromp-Taylor rules. Usually this is a good idea ++# when using it for analysis or playing on servers where scoring may be ++# implemented non-tromp-taylorly. Uncomment and set to false to disable. ++# conservativePass = true ++ ++# When using territory scoring, self-play games continue beyond two passes ++# with special cleanup rules that may be confusing for human players. This ++# option prevents the special cleanup phases from being reachable when using ++# the bot for GTP play. Uncomment and set to false to enable entering special ++# cleanup. For example, if you are testing it against itself, or against ++# another bot that has precisely implemented the rules documented at ++# https://lightvector.github.io/KataGo/rules.html ++# preventCleanupPhase = true ++ ++# ------------------------------ ++# Miscellaneous behavior ++# ------------------------------ ++# If the board is symmetric, search only one copy of each equivalent move. ++# Attempts to also account for ko/superko, will not theoretically perfect for ++# superko. Uncomment and set to false to disable. ++# rootSymmetryPruning = true ++ ++# Uncomment and set to true to avoid a particular joseki that some networks ++# misevaluate, and also to improve opening diversity versus some particular ++# other bots that like to play it all the time. ++# avoidMYTDaggerHack = false ++ ++# Prefer to avoid playing the same joseki in every corner of the board. ++# Uncomment to set to a specific value. See "Avoid SGF patterns" section. ++# By default: 0 (even games), 0.005 (handicap games) ++# avoidRepeatedPatternUtility = 0.0 ++ ++# Experimental logic to fight against mirror Go even with unfavorable komi. ++# Uncomment to set to a specific value to use for both playing and analysis. ++# By default: true when playing via GTP, but false when analyzing. ++# antiMirror = true ++ ++# Enable some hacks that mitigate rare instances when passing messes up deeper searches. ++# enablePassingHacks = true ++ ++# Uncomment and set this to true to prevent bad or bogus move sequences ++# in the history leading to this position from affecting KataGo's move choices. ++# Same as analysisIgnorePreRootHistory (see above) but applies to actual play. ++# You can enable this if KataGo is being asked to play from positions that it did not ++# choose the moves to reach. ++# ignorePreRootHistory = false ++ ++# =========================================================================== ++# Search limits ++# =========================================================================== ++ ++# Terminology: ++# "Playouts" is the number of new playouts of search performed each turn. ++# "Visits" is the same as "Playouts" but also counts search performed on ++# previous turns that is still applicable to this turn. ++# "Time" is the time in seconds. ++ ++# For example, if KataGo searched 200 nodes on the previous turn, and then ++# after the opponent's reply, 50 nodes of its search tree was still valid, ++# then a visit limit of 200 would allow KataGo to search 150 new nodes ++# (for a final tree size of 200 nodes), whereas a playout limit of of 200 ++# would allow KataGo to search 200 nodes (for a final tree size of 250 nodes). ++ ++# Additionally, KataGo may also move before than the limit in order to ++# obey time controls (e.g. byo-yomi, etc) if the GTP controller has ++# told KataGo that the game has is being played with a given time control. ++ ++# Limits for search on the current turn. ++# If commented out or unspecified, the default is to have no limit. ++# maxVisits = 500 ++# maxPlayouts = 300 ++ maxTime = 0.5 ++ ++# Ponder on the opponent's turn? ++ponderingEnabled = true ++ ++# Limits for search when pondering on the opponent's turn. ++# If commented out or unspecified, the default is to have no limit. ++# Limiting the maximum time is recommended so that KataGo won't burn CPU/GPU ++# forever and/or run out of RAM if left unattended while pondering is enabled. ++# maxVisitsPondering = 5000 ++# maxPlayoutsPondering = 3000 ++maxTimePondering = 120.0 ++ ++ ++# ------------------------------ ++# Other search limits and behavior ++# ------------------------------ ++ ++# Approx number of seconds to buffer for lag for GTP time controls - will ++# move a bit faster assuming there is this much lag per move. ++lagBuffer = 1.0 ++ ++# YOU PROBABLY WANT TO TUNE THIS PARAMETER! ++# The number of threads to use when searching. On powerful GPUs the optimal ++# threads may be much higher than the number of CPU cores you have because ++# many threads are needed to feed efficient large batches to the GPU. ++# ++# Run "./katago benchmark" to tune this parameter and test the effect ++# of changes to any of other parameters. ++numSearchThreads = 16 ++ ++# Play a little faster if the opponent is passing, for human-friendliness. ++# Comment these out to disable them, such as if running a controlled match ++# where you are testing KataGo with fixed compute per move vs other bots. ++searchFactorAfterOnePass = 0.50 ++searchFactorAfterTwoPass = 0.25 ++ ++# Play a little faster if super-winning, for human-friendliness. ++# Comment these out to disable them, such as if running a controlled match ++# where you are testing KataGo with fixed compute per move vs other bots. ++searchFactorWhenWinning = 0.40 ++searchFactorWhenWinningThreshold = 0.95 ++ ++# =========================================================================== ++# GPU settings ++# =========================================================================== ++# This section configures GPU settings. ++# ++# Maximum number of positions to send to a single GPU at once. The default ++# value is roughly equal to numSearchThreads, but can be specified manually ++# if running out of memory, or using multiple GPUs that expect to share work. ++# nnMaxBatchSize = ++ ++# Controls the neural network cache size, which is the primary RAM/memory use. ++# KataGo will cache up to (2 ** nnCacheSizePowerOfTwo) many neural net ++# evaluations in case of transpositions in the tree. ++# Increase this to improve performance for searches with tens of thousands ++# of visits or more. Decrease this to limit memory usage. ++# If you're happy to do some math - each neural net entry takes roughly ++# 1.5KB, except when using whole-board ownership/territory ++# visualizations, where each entry will take roughly 3KB. The number of ++# entries is (2 ** nnCacheSizePowerOfTwo). (E.g. 2 ** 18 = 262144.) ++# You can compute roughly how much memory the cache will use based on this. ++# nnCacheSizePowerOfTwo = 20 ++ ++# Size of mutex pool for nnCache is (2 ** this). ++# nnMutexPoolSizePowerOfTwo = 16 ++ ++# Randomize board orientation when running neural net evals? Uncomment and ++# set to false to disable. ++# nnRandomize = true ++ ++# If provided, force usage of a specific seed for nnRandomize. ++# The default is to use a randomly generated seed. ++# nnRandSeed = abcdefg ++ ++# Uncomment and set to true to force GTP to use the maximum board size for ++# internal buffers for the neural net. This will make KataGo slower when ++# evaluating small boards, but will avoid a lengthy initialization time on every ++# change of board size due to having to re-size the neural net buffers on the GPU. ++# This can be useful for example, for OGS's persistent bot mode that uses a single ++# bot instance to handle multiple games and may thrash between different board sizes ++# if there are concurrent games of multiple sizes. ++# gtpForceMaxNNSize = false ++ ++# ------------------------------ ++# Multiple GPUs ++# ------------------------------ ++# Set this to the number of GPUs to use or that are available. ++# IMPORTANT: If more than 1, also uncomment the appropriate TensorRT ++# or CUDA or OpenCL section. ++# numNNServerThreadsPerModel = 1 ++ ++# ------------------------------ ++# TENSORRT GPU settings ++# ------------------------------ ++# These only apply when using the TENSORRT version of KataGo. ++ ++# For one GPU: optionally uncomment this option and change if the GPU to ++# use is not device 0. ++# trtDeviceToUse = 0 ++ ++# For two GPUs: Uncomment these options, AND set numNNServerThreadsPerModel above. ++# Also, change their values if the devices you want to use are not 0 and 1. ++# trtDeviceToUseThread0 = 0 ++# trtDeviceToUseThread1 = 1 ++ ++# For three GPUs: Uncomment these options, AND set numNNServerThreadsPerModel above. ++# Also, change their values if the devices you want to use are not 0 and 1 and 2. ++# trtDeviceToUseThread0 = 0 ++# trtDeviceToUseThread1 = 1 ++# trtDeviceToUseThread2 = 2 ++ ++# The pattern continues for additional GPUs. ++ ++# ------------------------------ ++# CUDA GPU settings ++# ------------------------------ ++# These only apply when using the CUDA version of KataGo. ++ ++# For one GPU: optionally uncomment and change this if the GPU you want to ++# use is not device 0 ++# cudaDeviceToUse = 0 ++ ++# For two GPUs: Uncomment these options, AND set numNNServerThreadsPerModel above. ++# Also, change their values if the devices you want to use are not 0 and 1. ++# cudaDeviceToUseThread0 = 0 ++# cudaDeviceToUseThread1 = 1 ++ ++# For three GPUs: Uncomment these options, AND set numNNServerThreadsPerModel above. ++# Also, change their values if the devices you want to use are not 0 and 1 and 2. ++# cudaDeviceToUseThread0 = 0 ++# cudaDeviceToUseThread1 = 1 ++# cudaDeviceToUseThread2 = 2 ++ ++# The pattern continues for additional GPUs. ++ ++# KataGo will automatically use FP16 or not based on the compute capability ++# of your NVIDIA GPU. If you want to try to force a particular behavior ++# you can uncomment these lines and change them to "true" or "false". ++# cudaUseFP16 = auto ++# cudaUseNHWC = auto ++ ++# ------------------------------ ++# OpenCL GPU settings ++# ------------------------------ ++# These only apply when using the OpenCL version of KataGo. ++ ++# Uncomment and set to true to tune OpenCL for every board size separately, ++# rather than only the largest possible size. ++# openclReTunePerBoardSize = false ++ ++# For one GPU: optionally uncomment and change this if the best device to use is guessed incorrectly. ++# The default behavior tries to guess the 'best' GPU or device on your system to use, usually it will be a good guess. ++# openclDeviceToUse = 0 ++ ++# For two GPUs: Uncomment these two lines and replace X and Y with the device ids of the devices you want to use. ++# It might NOT be 0 and 1, some computers will have many OpenCL devices. You can see what the devices are when ++# KataGo starts up - it should print or log all the devices it finds. ++# (AND also set numNNServerThreadsPerModel above) ++# openclDeviceToUseThread0 = X ++# openclDeviceToUseThread1 = Y ++ ++# For three GPUs: Uncomment these three lines and replace X and Y and Z with the device ids of the devices you want to use. ++# It might NOT be 0 and 1 and 2, some computers will have many OpenCL devices. You can see what the devices are when ++# KataGo starts up - it should print or log all the devices it finds. ++# (AND also set numNNServerThreadsPerModel above) ++# openclDeviceToUseThread0 = X ++# openclDeviceToUseThread1 = Y ++# openclDeviceToUseThread2 = Z ++ ++# The pattern continues for additional GPUs. ++ ++# KataGo will automatically use FP16 or not based on testing your GPU during ++# tuning. If you want to try to force a particular behavior though you can ++# uncomment this option and change it to "true" or "false". This is a fairly ++# blunt setting - more detailed settings are testable by rerunning the tuner ++# with various arguments (./katago tuner). ++# openclUseFP16 = auto ++ ++# ------------------------------ ++# Eigen-specific settings ++# ------------------------------ ++# These only apply when using the Eigen (pure CPU) version of KataGo. ++ ++# Number of CPU threads for evaluating the neural net on the Eigen backend. ++# ++# Default: numSearchThreads ++# numEigenThreadsPerModel = X ++ ++# =========================================================================== ++# Root move selection and biases ++# =========================================================================== ++# Uncomment and edit any of the below values to change them from their default. ++ ++# If provided, force usage of a specific seed for various random things in ++# the search. The default is to use a random seed. ++# searchRandSeed = hijklmn ++ ++# Temperature for the early game, randomize between chosen moves with ++# this temperature ++# chosenMoveTemperatureEarly = 0.5 ++ ++# Decay temperature for the early game by 0.5 every this many moves, ++# scaled with board size. ++# chosenMoveTemperatureHalflife = 19 ++ ++# At the end of search after the early game, randomize between chosen ++# moves with this temperature ++# chosenMoveTemperature = 0.10 ++ ++# Subtract this many visits from each move prior to applying ++# chosenMoveTemperature (unless all moves have too few visits) to downweight ++# unlikely moves ++# chosenMoveSubtract = 0 ++ ++# The same as chosenMoveSubtract but only prunes moves that fall below ++# the threshold. This setting does not affect chosenMoveSubtract. ++# chosenMovePrune = 1 ++ ++# Number of symmetries to sample (without replacement) and average at the root ++# rootNumSymmetriesToSample = 1 ++ ++# Using LCB for move selection? ++# useLcbForSelection = true ++ ++# How many stdevs a move needs to be better than another for LCB selection ++# lcbStdevs = 5.0 ++ ++# Only use LCB override when a move has this proportion of visits as the ++# top move. ++# minVisitPropForLCB = 0.15 ++ ++# =========================================================================== ++# Internal params ++# =========================================================================== ++# Uncomment and edit any of the below values to change them from their default. ++ ++# Scales the utility of winning/losing ++# winLossUtilityFactor = 1.0 ++ ++# Scales the utility for trying to maximize score ++# staticScoreUtilityFactor = 0.10 ++# dynamicScoreUtilityFactor = 0.30 ++ ++# Adjust dynamic score center this proportion of the way towards zero, ++# capped at a reasonable amount. ++# dynamicScoreCenterZeroWeight = 0.20 ++# dynamicScoreCenterScale = 0.75 ++ ++# The utility of getting a "no result" due to triple ko or other long cycle ++# in non-superko rulesets (-1 to 1) ++# noResultUtilityForWhite = 0.0 ++ ++# The number of wins that a draw counts as, for white. (0 to 1) ++# drawEquivalentWinsForWhite = 0.5 ++ ++# Exploration constant for mcts ++# cpuctExploration = 1.0 ++# cpuctExplorationLog = 0.45 ++ ++# Parameters that control exploring more in volatile positions, exploring ++# less in stable positions. ++# cpuctUtilityStdevPrior = 0.40 ++# cpuctUtilityStdevPriorWeight = 2.0 ++# cpuctUtilityStdevScale = 0.85 ++ ++# FPU reduction constant for mcts ++# fpuReductionMax = 0.2 ++# rootFpuReductionMax = 0.1 ++# fpuParentWeightByVisitedPolicy = true ++ ++# Parameters that control weighting of evals based on the net's own ++# self-reported uncertainty. ++# useUncertainty = true ++# uncertaintyExponent = 1.0 ++# uncertaintyCoeff = 0.25 ++ ++# Explore using optimistic policy ++# rootPolicyOptimism = 0.2 ++# policyOptimism = 1.0 ++ ++# Amount to apply a downweighting of children with very bad values relative ++# to good ones. ++# valueWeightExponent = 0.25 ++ ++# Slight incentive for the bot to behave human-like with regard to passing at ++# the end, filling the dame, not wasting time playing in its own territory, ++# etc., and not play moves that are equivalent in terms of points but a bit ++# more unfriendly to humans. ++# rootEndingBonusPoints = 0.5 ++ ++# Make the bot prune useless moves that are just prolonging the game to ++# avoid losing yet. ++# rootPruneUselessMoves = true ++ ++# Apply bias correction based on local pattern keys ++# subtreeValueBiasFactor = 0.45 ++# subtreeValueBiasWeightExponent = 0.85 ++ ++# Use graph search rather than tree search - identify and share search for ++# transpositions. ++# useGraphSearch = true ++ ++# How much to shard the node table for search synchronization ++# nodeTableShardsPowerOfTwo = 16 ++ ++# How many virtual losses to add when a thread descends through a node ++# numVirtualLossesPerThread = 1 ++ ++# Improve the quality of evals under heavy multithreading ++# useNoisePruning = true ++ ++# =========================================================================== ++# Automatic avoid patterns ++# =========================================================================== ++# The parameters in this section provide a way to bias away from moves that match ++# patterns that this instance of KataGo has played in previous games, by auto-saving ++# moves to a directory and then auto-loading and biasing against them each new game. ++# Uncomment them to use them. When using this feature, all parameters must be specified. ++ ++# Different board sizes are tracked separately, but all board sizes share the same ++# parameters by default. Every parameter *except* for autoAvoidRepeatDir and ++# autoAvoidRepeatSaveChunkSize can be overridden per board size, ++# e.g. "autoAvoidRepeatMinTurnNumber13x13". You must ALSO specify the ++# defaults even if you have specified board-size-specific values. ++ ++# Directory to auto-save moves KataGo plays, to avoid them in future games. ++# You can create a new empty directory and put its path here. ++# If you run parallel instances of KataGo, use different directories if you ++# want them not to share their biases, use the same directory if you want ++# all of them to bias away from past moves that any of them have played. ++# KataGo will also automatically DELETE old data in this directory, so it is ++# recommended that if you do share the same directory between parallel instances, ++# that they all use the same settings that affect data saving/deletion. ++# autoAvoidRepeatDir = PATH_TO_NEW_DIRECTORY ++ ++# Penalize this much utility per matching move. ++# Values that are too large may lead to bad play. The value of 0.004 is fairly large ++# and might be large enough to result in some early weird/bad moves when trying ++# to avoid past games' moves if enough games begin the same way. You can experiment with it. ++# autoAvoidRepeatUtility = 0.004 ++ ++# Per each new move saved, exponentially decay prior saved moves by this factor. ++# This way, the bias against moves from many games ago is gradually phased out. ++# For example, 0.9995 = 1 - 1/2000, so would be roughly 2000 prior moves worth of ++# penalty weight remembered, in steady state. Depending on what turn number range ++# you are saving, this might equate to a different number of games For example ++# saving the first 50 moves per game would make this roughly 2000 / 50 = 40 games ++# worth of memory. ++# autoAvoidRepeatLambda = 0.9995 ++ ++# Affects data saving/deletion. ++# When the number of saved moves exceeds this, outright delete them to avoid too many ++# files and disk space building up. Also may affect the speed of saving/loading on start ++# of each game if this is set large and a lot of data builds up. ++# autoAvoidRepeatMaxPoses = 10000 ++ ++# Affects data saving/deletion. ++# Only save data for moves within this turn number range of those games. ++# E.g. setting autoAvoidRepeatMinTurnNumber to a number like 4 or 5 would tend to make ++# KataGo not develop a bias against the initial 3-4 and 4-4 corner moves in almost every game. ++# autoAvoidRepeatMinTurnNumber = 0 ++# autoAvoidRepeatMaxTurnNumber = 50 ++ ++# Affects data saving/deletion. ++# Within a single run of a program, wait to accumulate this many samples ++# (possibly across multiple clear_boards/games) before saving the data. ++# Can help to avoid writing too many small files to disk, especially when GTP is used ++# in a way that clears the board very frequently (e.g. gtp2ogs pooled manager). ++# autoAvoidRepeatSaveChunkSize = 200 ++ ++# =========================================================================== ++# Avoid SGF patterns ++# =========================================================================== ++# The parameters in this section provide a way to avoid moves that follow ++# specific patterns based on a set of SGF files loaded upon startup. ++# This is basically the same as the above "Automatic avoid patterns" section ++# above except you supply your own SGF files to avoid moves from. ++# Uncomment them to use this feature. Additionally, if the SGF file ++# contains the string %SKIP% in a comment on a move, that move will be ++# ignored for this purpose. ++ ++# Load SGF files from this directory when the engine is started ++# (only on startup, will not reload unless engine is restarted) ++# avoidSgfPatternDirs = path/to/directory/with/sgfs/ ++# You can also surround the file path in double quotes if the file path contains trailing spaces or hash signs. ++# Within double quotes, backslashes are escape characters. ++# avoidSgfPatternDirs = "path/to/directory/with/sgfs/" ++ ++# Penalize this much utility per matching move. ++# Set this negative if you instead want to favor SGF patterns instead of ++# penalizing them. This number does not need to be large, even 0.001 will ++# make a difference. Values that are too large may lead to bad play. ++# avoidSgfPatternUtility = 0.001 ++ ++# Optional - load only the newest this many files ++# avoidSgfPatternMaxFiles = 20 ++ ++# Optional - Penalty is multiplied by this per each older SGF file, so that ++# old SGF files matter less than newer ones. ++# avoidSgfPatternLambda = 0.90 ++ ++# Optional - pay attention only to moves made by players with this name. ++# For example, set it to the name that your bot's past games will show up ++# as in the SGF, so that the bot will only avoid repeating moves that itself ++# made in past games, not the moves that its opponents made. ++# avoidSgfPatternAllowedNames = my-ogs-bot-name1,my-ogs-bot-name2 ++ ++# Optional - Ignore moves in SGF files that occurred before this turn number. ++# avoidSgfPatternMinTurnNumber = 0 ++ ++# For more avoid patterns: ++# You can also specify a second set of parameters, and a third, fourth, ++# etc. by numbering 2,3,4,... ++# ++# avoidSgf2PatternDirs = ... ++# avoidSgf2PatternUtility = ... ++# avoidSgf2PatternMaxFiles = ... ++# avoidSgf2PatternLambda = ... ++# avoidSgf2PatternAllowedNames = ... ++# avoidSgf2PatternMinTurnNumber = ... ++ +diff --git a/cpp/golaxy_9d.cfg b/cpp/golaxy_9d.cfg +new file mode 100644 +index 00000000..31e66efd +--- /dev/null ++++ b/cpp/golaxy_9d.cfg +@@ -0,0 +1,713 @@ ++# Configuration for KataGo C++ GTP engine ++ ++# Run the program using: `./katago.exe gtp` ++ ++# In this example config, when a parameter is given as a commented out value, ++# that value also is the default value, unless described otherwise. You can ++# uncomment it (remove the pound sign) and change it if you want. ++ ++# =========================================================================== ++# Running on an online server or in a real tournament or match ++# =========================================================================== ++# If you plan to run online or in a tournament, read through the "Rules" ++# section below for proper handling of komi, handicaps, end-of-game cleanup, ++# and other details. ++ ++# =========================================================================== ++# Notes about performance and memory usage ++# =========================================================================== ++# Important: For good performance, you will very likely want to tune the ++# "numSearchThreads" parameter in the Search limits section below! Run ++# "./katago benchmark" to test KataGo and to suggest a reasonable value ++# of this parameter. ++ ++# For multi-GPU systems, read "OpenCL GPU settings" or "CUDA GPU settings". ++# ++# When using OpenCL, verify that KataGo picks the correct device! Some systems ++# may have both an Intel CPU OpenCL and GPU OpenCL. If # KataGo picks the wrong ++# one, correct this by specifying "openclGpuToUse". ++# ++# Consider adjusting "maxVisits", "ponderingEnabled", "resignThreshold", and ++# other parameters depending on your intended usage. ++ ++# =========================================================================== ++# Command-line usage ++# =========================================================================== ++# All of the below values may be set or overridden via command-line arguments: ++# ++# -override-config KEY=VALUE,KEY=VALUE,... ++ ++# =========================================================================== ++# Logs and files ++# =========================================================================== ++# This section defines where and what logging information is produced. ++ ++# Each run of KataGo will log to a separate file in this dir. ++# This is the default. ++logDir = gtp_logs ++# Uncomment and specify this instead of logDir to write separate dated subdirs ++# logDirDated = gtp_logs ++# Uncomment and specify this instead of logDir to log to only a single file ++# logFile = gtp.log ++ ++# Logging options ++logAllGTPCommunication = true ++logSearchInfo = true ++logSearchInfoForChosenMove = false ++logToStderr = true ++ ++# KataGo will display some info to stderr on GTP startup ++# Uncomment the next line and set it to false to suppress that and remain silent ++# startupPrintMessageToStderr = true ++ ++# Write information to stderr, for use in things like malkovich chat to OGS. ++ ogsChatToStderr = true ++ ++# Uncomment and set this to a directory to override where openCLTuner files ++# and other cached data is written. By default it saves into a subdir of the ++# current directory on windows, and a subdir of ~/.katago on Linux. ++# homeDataDir = PATH_TO_DIRECTORY ++ ++# =========================================================================== ++# Analysis ++# =========================================================================== ++# This section configures analysis settings. ++# ++# The maximum number of moves after the first move displayed in variations ++# from analysis commands like kata-analyze or lz-analyze. ++# analysisPVLen = 15 ++ ++# Report winrates for chat and analysis as (BLACK|WHITE|SIDETOMOVE). ++# Most GUIs and analysis tools will expect SIDETOMOVE. ++# reportAnalysisWinratesAs = SIDETOMOVE ++ ++# Extra noise for wider exploration. Large values will force KataGo to ++# analyze a greater variety of moves than it normally would. ++# An extreme value like 1 distributes playouts across every move on the board, ++# even very bad moves. ++# Affects analysis only, does not affect play. ++# analysisWideRootNoise = 0.04 ++ ++# Try to limit the effect of possible bad or bogus move sequences in the ++# history leading to this position from affecting KataGo's move predictions. ++# analysisIgnorePreRootHistory = true ++ ++# =========================================================================== ++# Rules ++# =========================================================================== ++# This section configures the scoring and playing rules. Rules can also be ++# changed mid-run by issuing custom GTP commands. ++# ++# See https://lightvector.github.io/KataGo/rules.html for rules details. ++# ++# See https://github.com/lightvector/KataGo/blob/master/docs/GTP_Extensions.md ++# for GTP commands. ++ ++# Specify the rules as a string. ++# Some legal values include: ++# chinese, japanese, korean, aga, chinese-ogs, new-zealand, stone-scoring, ++# ancient-territory, bga, aga-button ++# ++# For some human rulesets that require complex adjudication in tricky cases ++# (e.g. japanese, korean) KataGo may not precisely match the ruleset in such ++# cases but will do its best. ++# rules = tromp-taylor ++ ++# By default, the "rules" parameter is used, but if you comment it out and ++# uncomment one option in each of the sections below, you can specify an ++# arbitrary combination of individual rules. ++ ++# Ko Rule Settings ++koRule = POSITIONAL # Positional superko ++ ++# Scoring Rule Settings ++scoringRule = AREA # Area scoring ++ ++# Tax Rule Settings ++taxRule = NONE # All surrounded empty points are scored ++ ++# Is multiple-stone suicide legal? (Single-stone suicide is always illegal). ++multiStoneSuicideLegal = false ++ ++# "Button go" - the first pass when area scoring awards 0.5 points and does ++# not count for ending the game. ++hasButton = false ++ ++# Is this a human ruleset where it's okay to pass before having physically ++# captured and removed all dead stones? ++friendlyPassOk = false ++ ++# How handicap stones in handicap games are compensated ++whiteHandicapBonus = 0 # White gets no compensation for black's handicap stones (Tromp-taylor, NZ, JP) ++ ++ ++# ------------------------------ ++# Other rules hacks ++# ------------------------------ ++# Uncomment and change to adjust what board size KataGo uses upon startup ++# by default when GTP doesn't specify. ++# defaultBoardSize = 19 ++ ++# By default, Katago will use the komi that the GUI or GTP controller tries to set. ++# Uncomment and set this to have KataGo ignore the controller and always use this komi. ++# ignoreGTPAndForceKomi = 7 ++ ++# =========================================================================== ++# Bot behavior ++# =========================================================================== ++ ++# ------------------------------ ++# Resignation ++# ------------------------------ ++ ++# Resignation occurs if for at least resignConsecTurns in a row, the ++# winLossUtility (on a [-1,1] scale) is below resignThreshold. ++allowResignation = true ++resignThreshold = -0.90 ++resignConsecTurns = 3 ++ ++# By default, KataGo may resign games that it is confidently losing even if they ++# are very close in score. Uncomment and set this to avoid resigning games ++# if the estimated difference is points is less than or equal to this. ++# resignMinScoreDifference = 10 ++ ++# ------------------------------ ++# Handicap ++# ------------------------------ ++# Assume that if black makes many moves in a row right at the start of the ++# game, then the game is a handicap game. This is necessary on some servers ++# and for some GUIs and also when initializing from many SGF files, which may ++# set up a handicap game using repeated GTP "play" commands for black rather ++# than GTP "place_free_handicap" commands; however, it may also lead to ++# incorrect understanding of komi if whiteHandicapBonus is used and a server ++# does not have such a practice. Uncomment and set to false to disable. ++# assumeMultipleStartingBlackMovesAreHandicap = true ++ ++# Makes katago dynamically adjust in handicap or altered-komi games to assume ++# based on those game settings that it must be stronger or weaker than the ++# opponent and to play accordingly. Greatly improves handicap strength by ++# biasing winrates and scores to favor appropriate safe/aggressive play. ++# Does NOT affect analysis (lz-analyze, kata-analyze, used by programs like ++# Lizzie) so analysis remains unbiased. Uncomment and set this to 0 to disable ++# this and make KataGo play the same always. ++# dynamicPlayoutDoublingAdvantageCapPerOppLead = 0.045 ++ ++# Instead of "dynamicPlayoutDoublingAdvantageCapPerOppLead", you can comment ++# that out and uncomment and set "playoutDoublingAdvantage" to a fixed value ++# from -3.0 to 3.0 that will not change dynamically. ++# ALSO affects analysis tools (lz-analyze, kata-analyze, used by e.g. Lizzie). ++# Negative makes KataGo behave as if it is much weaker than the opponent. ++# Positive makes KataGo behave as if it is much stronger than the opponent. ++# KataGo will adjust to favor safe/aggressive play as appropriate based on ++# the combination of who is ahead and how much stronger/weaker it thinks it is, ++# and report winrates and scores taking the strength difference into account. ++# ++# If this and "dynamicPlayoutDoublingAdvantageCapPerOppLead" are both set ++# then dynamic will be used for all games and this fixed value will be used ++# for analysis tools. ++# playoutDoublingAdvantage = 0.0 ++ ++# Uncomment one of these when using "playoutDoublingAdvantage" to enforce ++# that it will only apply when KataGo plays as the specified color and will be ++# negated when playing as the opposite color. ++# playoutDoublingAdvantagePla = BLACK ++# playoutDoublingAdvantagePla = WHITE ++ ++# ------------------------------ ++# Passing and cleanup ++# ------------------------------ ++# Make the bot never assume that its pass will end the game, even if passing ++# would end and "win" under Tromp-Taylor rules. Usually this is a good idea ++# when using it for analysis or playing on servers where scoring may be ++# implemented non-tromp-taylorly. Uncomment and set to false to disable. ++# conservativePass = true ++ ++# When using territory scoring, self-play games continue beyond two passes ++# with special cleanup rules that may be confusing for human players. This ++# option prevents the special cleanup phases from being reachable when using ++# the bot for GTP play. Uncomment and set to false to enable entering special ++# cleanup. For example, if you are testing it against itself, or against ++# another bot that has precisely implemented the rules documented at ++# https://lightvector.github.io/KataGo/rules.html ++# preventCleanupPhase = true ++ ++# ------------------------------ ++# Miscellaneous behavior ++# ------------------------------ ++# If the board is symmetric, search only one copy of each equivalent move. ++# Attempts to also account for ko/superko, will not theoretically perfect for ++# superko. Uncomment and set to false to disable. ++# rootSymmetryPruning = true ++ ++# Uncomment and set to true to avoid a particular joseki that some networks ++# misevaluate, and also to improve opening diversity versus some particular ++# other bots that like to play it all the time. ++# avoidMYTDaggerHack = false ++ ++# Prefer to avoid playing the same joseki in every corner of the board. ++# Uncomment to set to a specific value. See "Avoid SGF patterns" section. ++# By default: 0 (even games), 0.005 (handicap games) ++# avoidRepeatedPatternUtility = 0.0 ++ ++# Experimental logic to fight against mirror Go even with unfavorable komi. ++# Uncomment to set to a specific value to use for both playing and analysis. ++# By default: true when playing via GTP, but false when analyzing. ++# antiMirror = true ++ ++# Enable some hacks that mitigate rare instances when passing messes up deeper searches. ++# enablePassingHacks = true ++ ++# Uncomment and set this to true to prevent bad or bogus move sequences ++# in the history leading to this position from affecting KataGo's move choices. ++# Same as analysisIgnorePreRootHistory (see above) but applies to actual play. ++# You can enable this if KataGo is being asked to play from positions that it did not ++# choose the moves to reach. ++# ignorePreRootHistory = false ++ ++# =========================================================================== ++# Search limits ++# =========================================================================== ++ ++# Terminology: ++# "Playouts" is the number of new playouts of search performed each turn. ++# "Visits" is the same as "Playouts" but also counts search performed on ++# previous turns that is still applicable to this turn. ++# "Time" is the time in seconds. ++ ++# For example, if KataGo searched 200 nodes on the previous turn, and then ++# after the opponent's reply, 50 nodes of its search tree was still valid, ++# then a visit limit of 200 would allow KataGo to search 150 new nodes ++# (for a final tree size of 200 nodes), whereas a playout limit of of 200 ++# would allow KataGo to search 200 nodes (for a final tree size of 250 nodes). ++ ++# Additionally, KataGo may also move before than the limit in order to ++# obey time controls (e.g. byo-yomi, etc) if the GTP controller has ++# told KataGo that the game has is being played with a given time control. ++ ++# Limits for search on the current turn. ++# If commented out or unspecified, the default is to have no limit. ++# maxVisits = 50000 ++# maxPlayouts = 300 ++ maxTime = 1.0 ++ ++# Ponder on the opponent's turn? ++ponderingEnabled = true ++ ++# Limits for search when pondering on the opponent's turn. ++# If commented out or unspecified, the default is to have no limit. ++# Limiting the maximum time is recommended so that KataGo won't burn CPU/GPU ++# forever and/or run out of RAM if left unattended while pondering is enabled. ++# maxVisitsPondering = 5000 ++# maxPlayoutsPondering = 3000 ++maxTimePondering = 120.0 ++ ++ ++# ------------------------------ ++# Other search limits and behavior ++# ------------------------------ ++ ++# Approx number of seconds to buffer for lag for GTP time controls - will ++# move a bit faster assuming there is this much lag per move. ++lagBuffer = 1.0 ++ ++# YOU PROBABLY WANT TO TUNE THIS PARAMETER! ++# The number of threads to use when searching. On powerful GPUs the optimal ++# threads may be much higher than the number of CPU cores you have because ++# many threads are needed to feed efficient large batches to the GPU. ++# ++# Run "./katago benchmark" to tune this parameter and test the effect ++# of changes to any of other parameters. ++numSearchThreads = 40 ++ ++# Play a little faster if the opponent is passing, for human-friendliness. ++# Comment these out to disable them, such as if running a controlled match ++# where you are testing KataGo with fixed compute per move vs other bots. ++searchFactorAfterOnePass = 0.50 ++searchFactorAfterTwoPass = 0.25 ++ ++# Play a little faster if super-winning, for human-friendliness. ++# Comment these out to disable them, such as if running a controlled match ++# where you are testing KataGo with fixed compute per move vs other bots. ++searchFactorWhenWinning = 0.40 ++searchFactorWhenWinningThreshold = 0.95 ++ ++# =========================================================================== ++# GPU settings ++# =========================================================================== ++# This section configures GPU settings. ++# ++# Maximum number of positions to send to a single GPU at once. The default ++# value is roughly equal to numSearchThreads, but can be specified manually ++# if running out of memory, or using multiple GPUs that expect to share work. ++# nnMaxBatchSize = ++ ++# Controls the neural network cache size, which is the primary RAM/memory use. ++# KataGo will cache up to (2 ** nnCacheSizePowerOfTwo) many neural net ++# evaluations in case of transpositions in the tree. ++# Increase this to improve performance for searches with tens of thousands ++# of visits or more. Decrease this to limit memory usage. ++# If you're happy to do some math - each neural net entry takes roughly ++# 1.5KB, except when using whole-board ownership/territory ++# visualizations, where each entry will take roughly 3KB. The number of ++# entries is (2 ** nnCacheSizePowerOfTwo). (E.g. 2 ** 18 = 262144.) ++# You can compute roughly how much memory the cache will use based on this. ++# nnCacheSizePowerOfTwo = 20 ++ ++# Size of mutex pool for nnCache is (2 ** this). ++# nnMutexPoolSizePowerOfTwo = 16 ++ ++# Randomize board orientation when running neural net evals? Uncomment and ++# set to false to disable. ++# nnRandomize = true ++ ++# If provided, force usage of a specific seed for nnRandomize. ++# The default is to use a randomly generated seed. ++# nnRandSeed = abcdefg ++ ++# Uncomment and set to true to force GTP to use the maximum board size for ++# internal buffers for the neural net. This will make KataGo slower when ++# evaluating small boards, but will avoid a lengthy initialization time on every ++# change of board size due to having to re-size the neural net buffers on the GPU. ++# This can be useful for example, for OGS's persistent bot mode that uses a single ++# bot instance to handle multiple games and may thrash between different board sizes ++# if there are concurrent games of multiple sizes. ++# gtpForceMaxNNSize = false ++ ++# ------------------------------ ++# Multiple GPUs ++# ------------------------------ ++# Set this to the number of GPUs to use or that are available. ++# IMPORTANT: If more than 1, also uncomment the appropriate TensorRT ++# or CUDA or OpenCL section. ++# numNNServerThreadsPerModel = 1 ++ ++# ------------------------------ ++# TENSORRT GPU settings ++# ------------------------------ ++# These only apply when using the TENSORRT version of KataGo. ++ ++# For one GPU: optionally uncomment this option and change if the GPU to ++# use is not device 0. ++# trtDeviceToUse = 0 ++ ++# For two GPUs: Uncomment these options, AND set numNNServerThreadsPerModel above. ++# Also, change their values if the devices you want to use are not 0 and 1. ++# trtDeviceToUseThread0 = 0 ++# trtDeviceToUseThread1 = 1 ++ ++# For three GPUs: Uncomment these options, AND set numNNServerThreadsPerModel above. ++# Also, change their values if the devices you want to use are not 0 and 1 and 2. ++# trtDeviceToUseThread0 = 0 ++# trtDeviceToUseThread1 = 1 ++# trtDeviceToUseThread2 = 2 ++ ++# The pattern continues for additional GPUs. ++ ++# ------------------------------ ++# CUDA GPU settings ++# ------------------------------ ++# These only apply when using the CUDA version of KataGo. ++ ++# For one GPU: optionally uncomment and change this if the GPU you want to ++# use is not device 0 ++# cudaDeviceToUse = 0 ++ ++# For two GPUs: Uncomment these options, AND set numNNServerThreadsPerModel above. ++# Also, change their values if the devices you want to use are not 0 and 1. ++# cudaDeviceToUseThread0 = 0 ++# cudaDeviceToUseThread1 = 1 ++ ++# For three GPUs: Uncomment these options, AND set numNNServerThreadsPerModel above. ++# Also, change their values if the devices you want to use are not 0 and 1 and 2. ++# cudaDeviceToUseThread0 = 0 ++# cudaDeviceToUseThread1 = 1 ++# cudaDeviceToUseThread2 = 2 ++ ++# The pattern continues for additional GPUs. ++ ++# KataGo will automatically use FP16 or not based on the compute capability ++# of your NVIDIA GPU. If you want to try to force a particular behavior ++# you can uncomment these lines and change them to "true" or "false". ++# cudaUseFP16 = auto ++# cudaUseNHWC = auto ++ ++# ------------------------------ ++# OpenCL GPU settings ++# ------------------------------ ++# These only apply when using the OpenCL version of KataGo. ++ ++# Uncomment and set to true to tune OpenCL for every board size separately, ++# rather than only the largest possible size. ++# openclReTunePerBoardSize = false ++ ++# For one GPU: optionally uncomment and change this if the best device to use is guessed incorrectly. ++# The default behavior tries to guess the 'best' GPU or device on your system to use, usually it will be a good guess. ++# openclDeviceToUse = 0 ++ ++# For two GPUs: Uncomment these two lines and replace X and Y with the device ids of the devices you want to use. ++# It might NOT be 0 and 1, some computers will have many OpenCL devices. You can see what the devices are when ++# KataGo starts up - it should print or log all the devices it finds. ++# (AND also set numNNServerThreadsPerModel above) ++# openclDeviceToUseThread0 = X ++# openclDeviceToUseThread1 = Y ++ ++# For three GPUs: Uncomment these three lines and replace X and Y and Z with the device ids of the devices you want to use. ++# It might NOT be 0 and 1 and 2, some computers will have many OpenCL devices. You can see what the devices are when ++# KataGo starts up - it should print or log all the devices it finds. ++# (AND also set numNNServerThreadsPerModel above) ++# openclDeviceToUseThread0 = X ++# openclDeviceToUseThread1 = Y ++# openclDeviceToUseThread2 = Z ++ ++# The pattern continues for additional GPUs. ++ ++# KataGo will automatically use FP16 or not based on testing your GPU during ++# tuning. If you want to try to force a particular behavior though you can ++# uncomment this option and change it to "true" or "false". This is a fairly ++# blunt setting - more detailed settings are testable by rerunning the tuner ++# with various arguments (./katago tuner). ++# openclUseFP16 = auto ++ ++# ------------------------------ ++# Eigen-specific settings ++# ------------------------------ ++# These only apply when using the Eigen (pure CPU) version of KataGo. ++ ++# Number of CPU threads for evaluating the neural net on the Eigen backend. ++# ++# Default: numSearchThreads ++# numEigenThreadsPerModel = X ++ ++# =========================================================================== ++# Root move selection and biases ++# =========================================================================== ++# Uncomment and edit any of the below values to change them from their default. ++ ++# If provided, force usage of a specific seed for various random things in ++# the search. The default is to use a random seed. ++# searchRandSeed = hijklmn ++ ++# Temperature for the early game, randomize between chosen moves with ++# this temperature ++# chosenMoveTemperatureEarly = 0.5 ++ ++# Decay temperature for the early game by 0.5 every this many moves, ++# scaled with board size. ++# chosenMoveTemperatureHalflife = 19 ++ ++# At the end of search after the early game, randomize between chosen ++# moves with this temperature ++# chosenMoveTemperature = 0.10 ++ ++# Subtract this many visits from each move prior to applying ++# chosenMoveTemperature (unless all moves have too few visits) to downweight ++# unlikely moves ++# chosenMoveSubtract = 0 ++ ++# The same as chosenMoveSubtract but only prunes moves that fall below ++# the threshold. This setting does not affect chosenMoveSubtract. ++# chosenMovePrune = 1 ++ ++# Number of symmetries to sample (without replacement) and average at the root ++# rootNumSymmetriesToSample = 1 ++ ++# Using LCB for move selection? ++# useLcbForSelection = true ++ ++# How many stdevs a move needs to be better than another for LCB selection ++# lcbStdevs = 5.0 ++ ++# Only use LCB override when a move has this proportion of visits as the ++# top move. ++# minVisitPropForLCB = 0.15 ++ ++# =========================================================================== ++# Internal params ++# =========================================================================== ++# Uncomment and edit any of the below values to change them from their default. ++ ++# Scales the utility of winning/losing ++# winLossUtilityFactor = 1.0 ++ ++# Scales the utility for trying to maximize score ++# staticScoreUtilityFactor = 0.10 ++# dynamicScoreUtilityFactor = 0.30 ++ ++# Adjust dynamic score center this proportion of the way towards zero, ++# capped at a reasonable amount. ++# dynamicScoreCenterZeroWeight = 0.20 ++# dynamicScoreCenterScale = 0.75 ++ ++# The utility of getting a "no result" due to triple ko or other long cycle ++# in non-superko rulesets (-1 to 1) ++# noResultUtilityForWhite = 0.0 ++ ++# The number of wins that a draw counts as, for white. (0 to 1) ++# drawEquivalentWinsForWhite = 0.5 ++ ++# Exploration constant for mcts ++# cpuctExploration = 1.0 ++# cpuctExplorationLog = 0.45 ++ ++# Parameters that control exploring more in volatile positions, exploring ++# less in stable positions. ++# cpuctUtilityStdevPrior = 0.40 ++# cpuctUtilityStdevPriorWeight = 2.0 ++# cpuctUtilityStdevScale = 0.85 ++ ++# FPU reduction constant for mcts ++# fpuReductionMax = 0.2 ++# rootFpuReductionMax = 0.1 ++# fpuParentWeightByVisitedPolicy = true ++ ++# Parameters that control weighting of evals based on the net's own ++# self-reported uncertainty. ++# useUncertainty = true ++# uncertaintyExponent = 1.0 ++# uncertaintyCoeff = 0.25 ++ ++# Explore using optimistic policy ++# rootPolicyOptimism = 0.2 ++# policyOptimism = 1.0 ++ ++# Amount to apply a downweighting of children with very bad values relative ++# to good ones. ++# valueWeightExponent = 0.25 ++ ++# Slight incentive for the bot to behave human-like with regard to passing at ++# the end, filling the dame, not wasting time playing in its own territory, ++# etc., and not play moves that are equivalent in terms of points but a bit ++# more unfriendly to humans. ++# rootEndingBonusPoints = 0.5 ++ ++# Make the bot prune useless moves that are just prolonging the game to ++# avoid losing yet. ++# rootPruneUselessMoves = true ++ ++# Apply bias correction based on local pattern keys ++# subtreeValueBiasFactor = 0.45 ++# subtreeValueBiasWeightExponent = 0.85 ++ ++# Use graph search rather than tree search - identify and share search for ++# transpositions. ++# useGraphSearch = true ++ ++# How much to shard the node table for search synchronization ++# nodeTableShardsPowerOfTwo = 16 ++ ++# How many virtual losses to add when a thread descends through a node ++# numVirtualLossesPerThread = 1 ++ ++# Improve the quality of evals under heavy multithreading ++# useNoisePruning = true ++ ++# =========================================================================== ++# Automatic avoid patterns ++# =========================================================================== ++# The parameters in this section provide a way to bias away from moves that match ++# patterns that this instance of KataGo has played in previous games, by auto-saving ++# moves to a directory and then auto-loading and biasing against them each new game. ++# Uncomment them to use them. When using this feature, all parameters must be specified. ++ ++# Different board sizes are tracked separately, but all board sizes share the same ++# parameters by default. Every parameter *except* for autoAvoidRepeatDir and ++# autoAvoidRepeatSaveChunkSize can be overridden per board size, ++# e.g. "autoAvoidRepeatMinTurnNumber13x13". You must ALSO specify the ++# defaults even if you have specified board-size-specific values. ++ ++# Directory to auto-save moves KataGo plays, to avoid them in future games. ++# You can create a new empty directory and put its path here. ++# If you run parallel instances of KataGo, use different directories if you ++# want them not to share their biases, use the same directory if you want ++# all of them to bias away from past moves that any of them have played. ++# KataGo will also automatically DELETE old data in this directory, so it is ++# recommended that if you do share the same directory between parallel instances, ++# that they all use the same settings that affect data saving/deletion. ++# autoAvoidRepeatDir = PATH_TO_NEW_DIRECTORY ++ ++# Penalize this much utility per matching move. ++# Values that are too large may lead to bad play. The value of 0.004 is fairly large ++# and might be large enough to result in some early weird/bad moves when trying ++# to avoid past games' moves if enough games begin the same way. You can experiment with it. ++# autoAvoidRepeatUtility = 0.004 ++ ++# Per each new move saved, exponentially decay prior saved moves by this factor. ++# This way, the bias against moves from many games ago is gradually phased out. ++# For example, 0.9995 = 1 - 1/2000, so would be roughly 2000 prior moves worth of ++# penalty weight remembered, in steady state. Depending on what turn number range ++# you are saving, this might equate to a different number of games For example ++# saving the first 50 moves per game would make this roughly 2000 / 50 = 40 games ++# worth of memory. ++# autoAvoidRepeatLambda = 0.9995 ++ ++# Affects data saving/deletion. ++# When the number of saved moves exceeds this, outright delete them to avoid too many ++# files and disk space building up. Also may affect the speed of saving/loading on start ++# of each game if this is set large and a lot of data builds up. ++# autoAvoidRepeatMaxPoses = 10000 ++ ++# Affects data saving/deletion. ++# Only save data for moves within this turn number range of those games. ++# E.g. setting autoAvoidRepeatMinTurnNumber to a number like 4 or 5 would tend to make ++# KataGo not develop a bias against the initial 3-4 and 4-4 corner moves in almost every game. ++# autoAvoidRepeatMinTurnNumber = 0 ++# autoAvoidRepeatMaxTurnNumber = 50 ++ ++# Affects data saving/deletion. ++# Within a single run of a program, wait to accumulate this many samples ++# (possibly across multiple clear_boards/games) before saving the data. ++# Can help to avoid writing too many small files to disk, especially when GTP is used ++# in a way that clears the board very frequently (e.g. gtp2ogs pooled manager). ++# autoAvoidRepeatSaveChunkSize = 200 ++ ++# =========================================================================== ++# Avoid SGF patterns ++# =========================================================================== ++# The parameters in this section provide a way to avoid moves that follow ++# specific patterns based on a set of SGF files loaded upon startup. ++# This is basically the same as the above "Automatic avoid patterns" section ++# above except you supply your own SGF files to avoid moves from. ++# Uncomment them to use this feature. Additionally, if the SGF file ++# contains the string %SKIP% in a comment on a move, that move will be ++# ignored for this purpose. ++ ++# Load SGF files from this directory when the engine is started ++# (only on startup, will not reload unless engine is restarted) ++# avoidSgfPatternDirs = path/to/directory/with/sgfs/ ++# You can also surround the file path in double quotes if the file path contains trailing spaces or hash signs. ++# Within double quotes, backslashes are escape characters. ++# avoidSgfPatternDirs = "path/to/directory/with/sgfs/" ++ ++# Penalize this much utility per matching move. ++# Set this negative if you instead want to favor SGF patterns instead of ++# penalizing them. This number does not need to be large, even 0.001 will ++# make a difference. Values that are too large may lead to bad play. ++# avoidSgfPatternUtility = 0.001 ++ ++# Optional - load only the newest this many files ++# avoidSgfPatternMaxFiles = 20 ++ ++# Optional - Penalty is multiplied by this per each older SGF file, so that ++# old SGF files matter less than newer ones. ++# avoidSgfPatternLambda = 0.90 ++ ++# Optional - pay attention only to moves made by players with this name. ++# For example, set it to the name that your bot's past games will show up ++# as in the SGF, so that the bot will only avoid repeating moves that itself ++# made in past games, not the moves that its opponents made. ++# avoidSgfPatternAllowedNames = my-ogs-bot-name1,my-ogs-bot-name2 ++ ++# Optional - Ignore moves in SGF files that occurred before this turn number. ++# avoidSgfPatternMinTurnNumber = 0 ++ ++# For more avoid patterns: ++# You can also specify a second set of parameters, and a third, fourth, ++# etc. by numbering 2,3,4,... ++# ++# avoidSgf2PatternDirs = ... ++# avoidSgf2PatternUtility = ... ++# avoidSgf2PatternMaxFiles = ... ++# avoidSgf2PatternLambda = ... ++# avoidSgf2PatternAllowedNames = ... ++# avoidSgf2PatternMinTurnNumber = ... ++ +diff --git a/cpp/gtp_human5k_example.cfg b/cpp/gtp_human5k_example.cfg +new file mode 100644 +index 00000000..9098f5ab +--- /dev/null ++++ b/cpp/gtp_human5k_example.cfg +@@ -0,0 +1,189 @@ ++ ++# This is an example config for configuring KataGo to attempt to imitate a weaker human player. ++# Running with this config requires giving a human SL model b18c384nbt-humanv0.bin.gz ++# on the command line such as: ++# ./katago gtp -config gtp_human5k_example.cfg -model your_favorite_normal_model_for_katago.bin.gz -human-model b18c384nbt-humanv0.bin.gz ++# You can obtain the human model at https://github.com/lightvector/KataGo/releases/tag/v1.15.0 ++ ++# Below, the most important parts of the config for human-like play are commented. ++# See the original gtp_example for comments on other parameters. ++ ++# For another useful guide on human-style analysis, see here: ++# https://github.com/lightvector/KataGo/blob/master/docs/Analysis_Engine.md#human-sl-analysis-guide ++ ++# It is ALSO possible to pass in simply '-model b18c384nbt-humanv0.bin.gz' and NOT ++# pass in -human-model, i.e. use the human model as if it were KataGo's normal neural net. ++# If you do that, you need to use a config more like the normal gtp_example.cfg, not this config! ++# Keep in mind that if using the model normally, or if using -human-model but also altering ++# parameters below to blend in some of KataGo's search, KataGo's play might NOT be very human-like, ++# or might be human-like but play at a strength very different than the humanSLProfile. ++# You can experiment, some of the comments below hopefully will help illustrate things too. ++ ++logDir = gtp_logs ++logAllGTPCommunication = true ++logSearchInfo = true ++logSearchInfoForChosenMove = false ++logToStderr = false ++ ++# Use these rules by default, but a GUI or GTP controller might override this. ++rules = japanese ++ ++# When using -human-model, we only resign when far behind since a weaker player ++# might continue to fight much longer than a strong bot normally would. ++allowResignation = true ++resignThreshold = -0.99 ++resignConsecTurns = 20 ++resignMinScoreDifference = 40 ++resignMinMovesPerBoardArea = 0.4 ++ ++# Note: unless you change other parameters too, by default increasing visits won't do much. ++# If humanSLChosenMoveProp = 1.0 AND humanSLChosenMovePiklLambda is a large number, ++# then KataGo's normal search is ignored except for possibly choosing whether to pass/resign, ++# so more visits will have no effect on play. Still, having some visits is good for ++# ensuring good pass/resign judgment. ++maxVisits = 40 ++numSearchThreads = 1 ++lagBuffer = 1.0 ++ ++# Rough scale in seconds to randomly delay moving, so as not to respond instantly. ++# Some moves will delay longer, some moves will delay a little less. ++delayMoveScale = 2.0 ++delayMoveMax = 10.0 ++ ++# =========================================================================== ++# HUMAN SL PARAMETERS ++# =========================================================================== ++ ++# The most important parameter for human-like play configuration! ++# Choose the "profile" of players that the human SL model will imitate. ++# Available options are: ++# preaz_{RANK from 20k to 9d} - imitate player of given rank, before AlphaZero opening style became popular ++# rank_{RANK from 20k to 9d} - imitate player of given rank, after human openings changed due to AlphaZero. ++# preaz_{BRANK}_{WRANK} or rank_{BRANK}_{WRANK} - same, but imitate how black with the rank BR and white ++# with the rank WR would play against each other, knowing that the other player is stronger/weaker than them. ++# Warning: for rank differences > 9 ranks, or drastically mis-matched to the handicap used in the game, ++# this may be out of distribution due to lack of training data and the model might not behave well! Experiment with care. ++# proyear_{YEAR from 1800 to 2023} - imitate historical pros or insei from given year. ++humanSLProfile = preaz_5k ++ ++# The probability that we should play a HUMAN-like move, rather than playing KataGo's move. ++# Applies BEFORE temperature. ++humanSLChosenMoveProp = 1.0 ++ ++# If true, ignore the human SL model's choice of when to pass, and still use KataGo to determine that. ++# The human SL model, in theory, is not guaranteed to be reliable at when to pass for all profiles, ++# since e.g. some historical games omit passes. ++humanSLChosenMoveIgnorePass = true ++ ++# By default humanSLChosenMovePiklLambda is a large number which effectively disables it. ++# Setting it to a smaller number will "suppress" human-like moves that KataGo disapproves of. ++# In particular, if set to, for example, 0.4 when KataGo judges a human SL move to lose 0.4 utility, ++# it will substantially suppress the chance of playing that move (in particular, by a factor of exp(1)). ++# Less-bad moves will also be suppressed, but not by as much, e.g. a move losing 0.2 would get lowered ++# by a factor of exp(0.5). ++# As configured lower down, utilities by default range from -1.0 (loss) to +1.0 (win), plus up to +/- 0.3 for score. ++# WARNING: ONLY moves that KataGo actually searches will get suppressed! If a move is so bad that KataGo ++# rejects it without searching it, it will NOT get suppressed. ++# Therefore, to use humanSLChosenMovePiklLambda, it is STRONGLY recommended that you also use something ++# like humanSLRootExploreProbWeightless to ensure most human moves including bad moves get searched, ++# and ALSO use at least hundreds and ideally thousands of maxVisits, to ensure enough visits. ++humanSLChosenMovePiklLambda = 100000000 ++ ++# These parameters tell KataGo to use the human SL policy for exploration during search. ++# Each of these specifies the probability that KataGo will perform PUCT using the Human SL policy to ++# explore different moves, rather than using KataGo's normal policy, after a certain minimal number of visits. ++# "Root": applies only at the root of the search ++# "Pla": applies during non-root nodes of the search where it is katago's turn. ++# "Opp": applies during non-root nodes of the search where it is the opponent's turn. ++# "Weightless": search the move to evaluate it, but do NOT allow this visit to affect the parent's average utility. ++# "Weightful": search the move to evaluate it, and DO allow this visit to affect the parent's average utility. ++# For example, humanSLRootExploreProbWeightless = 0.5 would tell KataGo at the root of the search to spend ++# 50% of its visits to judge different possible human moves, but NOT to use those visits for determining the ++# value of the position (avoiding biasing the utility if some human SL moves are very bad). ++# If you don't understand these well, ask for help or look up some online explainers for MCTS (Monte-Carlo Tree Search). ++humanSLRootExploreProbWeightless = 0.0 ++humanSLRootExploreProbWeightful = 0.0 ++humanSLPlaExploreProbWeightless = 0.0 ++humanSLPlaExploreProbWeightful = 0.0 ++humanSLOppExploreProbWeightless = 0.0 ++humanSLOppExploreProbWeightful = 0.0 ++ ++# When using the human SL policy for exploration during search, use this cPUCT. ++# This only has an effect if at least one of humanSL{Root,Pla,Opp}ExploreProbWeight{less,ful} is nonzero. ++humanSLCpuctExploration = 0.50 ++ ++# Same as humanSLCpuctExploration, but NEVER diminshes its exploration no matter how many visits are used. ++# Normally, PUCT will sharpen with visits and spend a diminishing proportion of visits on moves with lower utility. ++# This is the coefficient for a term that does NOT diminish, i.e. if this is 0.2, then roughly moves within ++# 0.2 utility (about 10% winrate) of the best move will forever continue getting a decent fraction of visits, ++# smoothly falling off for greater utility differences. ++# Note that in combination with Weightful exploration above, if used for Opp exploration, this could be used ++# to model an opponent that will always have some chance to make small mistakes no matter how deep they search. ++# If further this was increased to a very large value, it would model an opponent that always played according ++# to the human SL raw policy. These might be interesting to experiment with for handicap play. ++humanSLCpuctPermanent = 0.2 ++ ++ ++# =========================================================================== ++# OTHER USEFUL PARAMETERS FOR HUMAN PLAY ADJUSTMENT ++# =========================================================================== ++ ++# Choosing temperature near 1, and restricting it to only affect moves already below 1% chance, ++# so that we sample close to the full range of human play. ++# You can also reduce the temperature to settings more like the plain gtp_example.cfg. ++# Then, rather than imitating a realistic human player, it will be more like imitating the ++# *majority vote* of players at that rank. For example it would avoid a lot of blunders ++# that players of that level would make, because even if players often blunder, the *majority vote* ++# of players would be much less likely to select any given blunder that an individual player would. ++chosenMoveTemperatureEarly = 0.85 ++chosenMoveTemperature = 0.70 ++chosenMoveTemperatureHalflife = 80 ++chosenMoveTemperatureOnlyBelowProb = 0.01 # temperature only starts to dampen moves below this ++chosenMoveSubtract = 0 ++chosenMovePrune = 0 ++ ++# Use a small NN cache to save memory since we're using very low visits anyways. You can increase ++# these back to more like the plain gtp_example.cfg if you are doing more extensive searches to ++# improve performance. ++nnCacheSizePowerOfTwo = 17 ++nnMutexPoolSizePowerOfTwo = 14 ++ ++# =========================================================================== ++# PARAMETERS CHANGED FROM DEFAULT TO MAKE SURE HUMAN SL USAGE WORKS WELL ++# =========================================================================== ++ ++# Make sure to take into account the recent moves in the game, don't ignore history. ++# This will produce the best imitation/prediction, since humans definitely do play differently based on where the ++# most recent moves in the game were, rather than coming fresh to the board position on every turn. ++ignorePreRootHistory = false ++analysisIgnorePreRootHistory = false ++ ++# Average 2 neural net samples at the root - ensures a bit smoother probabilities and results in ++# 8 * 7 / 2 = 28 possible policies instead of 8 possibilities. ++rootNumSymmetriesToSample = 2 ++# LCB improves strength for KataGo, but we disable it so it doesn't mess up move selection when blending human play. ++useLcbForSelection = false ++ ++# We disable dynamicScoreUtilityFactor - the human SL model can make score predictions that are a bit swingy, so ++# if we do want to do a search that blends human SL values in (TODO there isn't a way to do this anyways yet), using ++# static score utility might be a bit more stable. ++winLossUtilityFactor = 1.0 ++staticScoreUtilityFactor = 0.30 ++dynamicScoreUtilityFactor = 0.00 ++ ++# Uncertainty improves strength for KataGo normally, but messes with the weights of playouts in complicated ways, ++# so lets turn it off when doing human SL stuff. ++useUncertainty = false ++ ++# Subtree value bias improves strength for KataGo normally, but messes with the values of nodes in complicated ways, ++# so let's turn it off when doing human SL stuff. ++subtreeValueBiasFactor = 0.0 ++ ++# Noise pruning prunes out weight from moves that KataGo thinks are bad, but if we are doing human SL we might actively ++# want to be playing or exploring and weighting "bad" but human-like moves. So disable this. ++# Warning: when this is false, there is much less protection against the search severely misbehaving when you use too many threads. ++# Make sure not to set numSearchThreads to be too large - at a minimum, keep at least a 20x buffer between ++# the number of visits you use and the number of threads you use. ++# (as an aside, ideally, you want to have visits be a sufficient factor larger than threads EVEN when ++# useNoisePruning is true, this parameter just blunts the worst effects but doesn't entirely fix the badness). ++useNoisePruning = false +diff --git a/cpp/leela_zero_strong.cfg b/cpp/leela_zero_strong.cfg +new file mode 100644 +index 00000000..e1167b84 +--- /dev/null ++++ b/cpp/leela_zero_strong.cfg +@@ -0,0 +1,719 @@ ++# Configuration for KataGo C++ GTP engine ++ ++# Run the program using: `./katago.exe gtp` ++ ++# In this example config, when a parameter is given as a commented out value, ++# that value also is the default value, unless described otherwise. You can ++# uncomment it (remove the pound sign) and change it if you want. ++ ++# =========================================================================== ++# Running on an online server or in a real tournament or match ++# =========================================================================== ++# If you plan to run online or in a tournament, read through the "Rules" ++# section below for proper handling of komi, handicaps, end-of-game cleanup, ++# and other details. ++ ++# =========================================================================== ++# Notes about performance and memory usage ++# =========================================================================== ++# Important: For good performance, you will very likely want to tune the ++# "numSearchThreads" parameter in the Search limits section below! Run ++# "./katago benchmark" to test KataGo and to suggest a reasonable value ++# of this parameter. ++ ++# For multi-GPU systems, read "OpenCL GPU settings" or "CUDA GPU settings". ++# ++# When using OpenCL, verify that KataGo picks the correct device! Some systems ++# may have both an Intel CPU OpenCL and GPU OpenCL. If # KataGo picks the wrong ++# one, correct this by specifying "openclGpuToUse". ++# ++# Consider adjusting "maxVisits", "ponderingEnabled", "resignThreshold", and ++# other parameters depending on your intended usage. ++ ++# =========================================================================== ++# Command-line usage ++# =========================================================================== ++# All of the below values may be set or overridden via command-line arguments: ++# ++# -override-config KEY=VALUE,KEY=VALUE,... ++ ++# =========================================================================== ++# Logs and files ++# =========================================================================== ++# This section defines where and what logging information is produced. ++ ++# Each run of KataGo will log to a separate file in this dir. ++# This is the default. ++logDir = gtp_logs ++# Uncomment and specify this instead of logDir to write separate dated subdirs ++# logDirDated = gtp_logs ++# Uncomment and specify this instead of logDir to log to only a single file ++# logFile = gtp.log ++ ++# Logging options ++logAllGTPCommunication = true ++logSearchInfo = true ++logSearchInfoForChosenMove = false ++logToStderr = true ++ ++# KataGo will display some info to stderr on GTP startup ++# Uncomment the next line and set it to false to suppress that and remain silent ++# startupPrintMessageToStderr = true ++ ++# Write information to stderr, for use in things like malkovich chat to OGS. ++ ogsChatToStderr = true ++ ++# Uncomment and set this to a directory to override where openCLTuner files ++# and other cached data is written. By default it saves into a subdir of the ++# current directory on windows, and a subdir of ~/.katago on Linux. ++# homeDataDir = PATH_TO_DIRECTORY ++ ++# =========================================================================== ++# Analysis ++# =========================================================================== ++# This section configures analysis settings. ++# ++# The maximum number of moves after the first move displayed in variations ++# from analysis commands like kata-analyze or lz-analyze. ++# analysisPVLen = 15 ++ ++# Report winrates for chat and analysis as (BLACK|WHITE|SIDETOMOVE). ++# Most GUIs and analysis tools will expect SIDETOMOVE. ++# reportAnalysisWinratesAs = SIDETOMOVE ++ ++# Extra noise for wider exploration. Large values will force KataGo to ++# analyze a greater variety of moves than it normally would. ++# An extreme value like 1 distributes playouts across every move on the board, ++# even very bad moves. ++# Affects analysis only, does not affect play. ++# analysisWideRootNoise = 0.04 ++ ++# Try to limit the effect of possible bad or bogus move sequences in the ++# history leading to this position from affecting KataGo's move predictions. ++# analysisIgnorePreRootHistory = true ++ ++# =========================================================================== ++# Rules ++# =========================================================================== ++# This section configures the scoring and playing rules. Rules can also be ++# changed mid-run by issuing custom GTP commands. ++# ++# See https://lightvector.github.io/KataGo/rules.html for rules details. ++# ++# See https://github.com/lightvector/KataGo/blob/master/docs/GTP_Extensions.md ++# for GTP commands. ++ ++# Specify the rules as a string. ++# Some legal values include: ++# chinese, japanese, korean, aga, chinese-ogs, new-zealand, stone-scoring, ++# ancient-territory, bga, aga-button ++# ++# For some human rulesets that require complex adjudication in tricky cases ++# (e.g. japanese, korean) KataGo may not precisely match the ruleset in such ++# cases but will do its best. ++# rules = tromp-taylor ++ ++# By default, the "rules" parameter is used, but if you comment it out and ++# uncomment one option in each of the sections below, you can specify an ++# arbitrary combination of individual rules. ++ ++koRule = SIMPLE # Simple ko rules (triple ko = no result) ++# koRule = POSITIONAL # Positional superko ++# koRule = SITUATIONAL # Situational superko ++ ++scoringRule = AREA # Area scoring ++# scoringRule = TERRITORY # Territory scoring (special computer-friendly territory rules) ++ ++taxRule = NONE # All surrounded empty points are scored ++# taxRule = SEKI # Eyes in seki do NOT count as points ++# taxRule = ALL # All groups are taxed up to 2 points for the two eyes needed to live ++ ++# Is multiple-stone suicide legal? (Single-stone suicide is always illegal). ++multiStoneSuicideLegal = true ++# multiStoneSuicideLegal = false ++ ++# "Button go" - the first pass when area scoring awards 0.5 points and does ++# not count for ending the game. ++hasButton = false ++# hasButton = true ++ ++# Is this a human ruleset where it's okay to pass before having physically ++# captured and removed all dead stones? ++friendlyPassOk = true ++# friendlyPassOk = false ++ ++# How handicap stones in handicap games are compensated ++whiteHandicapBonus = 0 # White gets no compensation for black's handicap stones (Tromp-taylor, NZ, JP) ++# whiteHandicapBonus = N-1 # White gets N-1 points for black's N handicap stones (AGA) ++# whiteHandicapBonus = N # White gets N points for black's N handicap stones (Chinese) ++ ++# ------------------------------ ++# Other rules hacks ++# ------------------------------ ++# Uncomment and change to adjust what board size KataGo uses upon startup ++# by default when GTP doesn't specify. ++# defaultBoardSize = 19 ++ ++# By default, Katago will use the komi that the GUI or GTP controller tries to set. ++# Uncomment and set this to have KataGo ignore the controller and always use this komi. ++# ignoreGTPAndForceKomi = 7 ++ ++# =========================================================================== ++# Bot behavior ++# =========================================================================== ++ ++# ------------------------------ ++# Resignation ++# ------------------------------ ++ ++# Resignation occurs if for at least resignConsecTurns in a row, the ++# winLossUtility (on a [-1,1] scale) is below resignThreshold. ++allowResignation = true ++resignThreshold = -0.90 ++resignConsecTurns = 3 ++ ++# By default, KataGo may resign games that it is confidently losing even if they ++# are very close in score. Uncomment and set this to avoid resigning games ++# if the estimated difference is points is less than or equal to this. ++# resignMinScoreDifference = 10 ++ ++# ------------------------------ ++# Handicap ++# ------------------------------ ++# Assume that if black makes many moves in a row right at the start of the ++# game, then the game is a handicap game. This is necessary on some servers ++# and for some GUIs and also when initializing from many SGF files, which may ++# set up a handicap game using repeated GTP "play" commands for black rather ++# than GTP "place_free_handicap" commands; however, it may also lead to ++# incorrect understanding of komi if whiteHandicapBonus is used and a server ++# does not have such a practice. Uncomment and set to false to disable. ++# assumeMultipleStartingBlackMovesAreHandicap = true ++ ++# Makes katago dynamically adjust in handicap or altered-komi games to assume ++# based on those game settings that it must be stronger or weaker than the ++# opponent and to play accordingly. Greatly improves handicap strength by ++# biasing winrates and scores to favor appropriate safe/aggressive play. ++# Does NOT affect analysis (lz-analyze, kata-analyze, used by programs like ++# Lizzie) so analysis remains unbiased. Uncomment and set this to 0 to disable ++# this and make KataGo play the same always. ++# dynamicPlayoutDoublingAdvantageCapPerOppLead = 0.045 ++ ++# Instead of "dynamicPlayoutDoublingAdvantageCapPerOppLead", you can comment ++# that out and uncomment and set "playoutDoublingAdvantage" to a fixed value ++# from -3.0 to 3.0 that will not change dynamically. ++# ALSO affects analysis tools (lz-analyze, kata-analyze, used by e.g. Lizzie). ++# Negative makes KataGo behave as if it is much weaker than the opponent. ++# Positive makes KataGo behave as if it is much stronger than the opponent. ++# KataGo will adjust to favor safe/aggressive play as appropriate based on ++# the combination of who is ahead and how much stronger/weaker it thinks it is, ++# and report winrates and scores taking the strength difference into account. ++# ++# If this and "dynamicPlayoutDoublingAdvantageCapPerOppLead" are both set ++# then dynamic will be used for all games and this fixed value will be used ++# for analysis tools. ++# playoutDoublingAdvantage = 0.0 ++ ++# Uncomment one of these when using "playoutDoublingAdvantage" to enforce ++# that it will only apply when KataGo plays as the specified color and will be ++# negated when playing as the opposite color. ++# playoutDoublingAdvantagePla = BLACK ++# playoutDoublingAdvantagePla = WHITE ++ ++# ------------------------------ ++# Passing and cleanup ++# ------------------------------ ++# Make the bot never assume that its pass will end the game, even if passing ++# would end and "win" under Tromp-Taylor rules. Usually this is a good idea ++# when using it for analysis or playing on servers where scoring may be ++# implemented non-tromp-taylorly. Uncomment and set to false to disable. ++# conservativePass = true ++ ++# When using territory scoring, self-play games continue beyond two passes ++# with special cleanup rules that may be confusing for human players. This ++# option prevents the special cleanup phases from being reachable when using ++# the bot for GTP play. Uncomment and set to false to enable entering special ++# cleanup. For example, if you are testing it against itself, or against ++# another bot that has precisely implemented the rules documented at ++# https://lightvector.github.io/KataGo/rules.html ++# preventCleanupPhase = true ++ ++# ------------------------------ ++# Miscellaneous behavior ++# ------------------------------ ++# If the board is symmetric, search only one copy of each equivalent move. ++# Attempts to also account for ko/superko, will not theoretically perfect for ++# superko. Uncomment and set to false to disable. ++# rootSymmetryPruning = true ++ ++# Uncomment and set to true to avoid a particular joseki that some networks ++# misevaluate, and also to improve opening diversity versus some particular ++# other bots that like to play it all the time. ++# avoidMYTDaggerHack = false ++ ++# Prefer to avoid playing the same joseki in every corner of the board. ++# Uncomment to set to a specific value. See "Avoid SGF patterns" section. ++# By default: 0 (even games), 0.005 (handicap games) ++# avoidRepeatedPatternUtility = 0.0 ++ ++# Experimental logic to fight against mirror Go even with unfavorable komi. ++# Uncomment to set to a specific value to use for both playing and analysis. ++# By default: true when playing via GTP, but false when analyzing. ++# antiMirror = true ++ ++# Enable some hacks that mitigate rare instances when passing messes up deeper searches. ++# enablePassingHacks = true ++ ++# Uncomment and set this to true to prevent bad or bogus move sequences ++# in the history leading to this position from affecting KataGo's move choices. ++# Same as analysisIgnorePreRootHistory (see above) but applies to actual play. ++# You can enable this if KataGo is being asked to play from positions that it did not ++# choose the moves to reach. ++# ignorePreRootHistory = false ++ ++# =========================================================================== ++# Search limits ++# =========================================================================== ++ ++# Terminology: ++# "Playouts" is the number of new playouts of search performed each turn. ++# "Visits" is the same as "Playouts" but also counts search performed on ++# previous turns that is still applicable to this turn. ++# "Time" is the time in seconds. ++ ++# For example, if KataGo searched 200 nodes on the previous turn, and then ++# after the opponent's reply, 50 nodes of its search tree was still valid, ++# then a visit limit of 200 would allow KataGo to search 150 new nodes ++# (for a final tree size of 200 nodes), whereas a playout limit of of 200 ++# would allow KataGo to search 200 nodes (for a final tree size of 250 nodes). ++ ++# Additionally, KataGo may also move before than the limit in order to ++# obey time controls (e.g. byo-yomi, etc) if the GTP controller has ++# told KataGo that the game has is being played with a given time control. ++ ++# Limits for search on the current turn. ++# If commented out or unspecified, the default is to have no limit. ++# maxVisits = 30000 ++# maxPlayouts = 300 ++maxTime = 1.0 ++ ++# Ponder on the opponent's turn? ++ponderingEnabled = true ++ ++# Limits for search when pondering on the opponent's turn. ++# If commented out or unspecified, the default is to have no limit. ++# Limiting the maximum time is recommended so that KataGo won't burn CPU/GPU ++# forever and/or run out of RAM if left unattended while pondering is enabled. ++# maxVisitsPondering = 5000 ++# maxPlayoutsPondering = 3000 ++maxTimePondering = 120.0 ++ ++ ++# ------------------------------ ++# Other search limits and behavior ++# ------------------------------ ++ ++# Approx number of seconds to buffer for lag for GTP time controls - will ++# move a bit faster assuming there is this much lag per move. ++lagBuffer = 1.0 ++ ++# YOU PROBABLY WANT TO TUNE THIS PARAMETER! ++# The number of threads to use when searching. On powerful GPUs the optimal ++# threads may be much higher than the number of CPU cores you have because ++# many threads are needed to feed efficient large batches to the GPU. ++# ++# Run "./katago benchmark" to tune this parameter and test the effect ++# of changes to any of other parameters. ++numSearchThreads = 40 ++ ++# Play a little faster if the opponent is passing, for human-friendliness. ++# Comment these out to disable them, such as if running a controlled match ++# where you are testing KataGo with fixed compute per move vs other bots. ++searchFactorAfterOnePass = 0.50 ++searchFactorAfterTwoPass = 0.25 ++ ++# Play a little faster if super-winning, for human-friendliness. ++# Comment these out to disable them, such as if running a controlled match ++# where you are testing KataGo with fixed compute per move vs other bots. ++searchFactorWhenWinning = 0.40 ++searchFactorWhenWinningThreshold = 0.95 ++ ++# =========================================================================== ++# GPU settings ++# =========================================================================== ++# This section configures GPU settings. ++# ++# Maximum number of positions to send to a single GPU at once. The default ++# value is roughly equal to numSearchThreads, but can be specified manually ++# if running out of memory, or using multiple GPUs that expect to share work. ++# nnMaxBatchSize = ++ ++# Controls the neural network cache size, which is the primary RAM/memory use. ++# KataGo will cache up to (2 ** nnCacheSizePowerOfTwo) many neural net ++# evaluations in case of transpositions in the tree. ++# Increase this to improve performance for searches with tens of thousands ++# of visits or more. Decrease this to limit memory usage. ++# If you're happy to do some math - each neural net entry takes roughly ++# 1.5KB, except when using whole-board ownership/territory ++# visualizations, where each entry will take roughly 3KB. The number of ++# entries is (2 ** nnCacheSizePowerOfTwo). (E.g. 2 ** 18 = 262144.) ++# You can compute roughly how much memory the cache will use based on this. ++# nnCacheSizePowerOfTwo = 20 ++ ++# Size of mutex pool for nnCache is (2 ** this). ++# nnMutexPoolSizePowerOfTwo = 16 ++ ++# Randomize board orientation when running neural net evals? Uncomment and ++# set to false to disable. ++# nnRandomize = true ++ ++# If provided, force usage of a specific seed for nnRandomize. ++# The default is to use a randomly generated seed. ++# nnRandSeed = abcdefg ++ ++# Uncomment and set to true to force GTP to use the maximum board size for ++# internal buffers for the neural net. This will make KataGo slower when ++# evaluating small boards, but will avoid a lengthy initialization time on every ++# change of board size due to having to re-size the neural net buffers on the GPU. ++# This can be useful for example, for OGS's persistent bot mode that uses a single ++# bot instance to handle multiple games and may thrash between different board sizes ++# if there are concurrent games of multiple sizes. ++# gtpForceMaxNNSize = false ++ ++# ------------------------------ ++# Multiple GPUs ++# ------------------------------ ++# Set this to the number of GPUs to use or that are available. ++# IMPORTANT: If more than 1, also uncomment the appropriate TensorRT ++# or CUDA or OpenCL section. ++# numNNServerThreadsPerModel = 1 ++ ++# ------------------------------ ++# TENSORRT GPU settings ++# ------------------------------ ++# These only apply when using the TENSORRT version of KataGo. ++ ++# For one GPU: optionally uncomment this option and change if the GPU to ++# use is not device 0. ++# trtDeviceToUse = 0 ++ ++# For two GPUs: Uncomment these options, AND set numNNServerThreadsPerModel above. ++# Also, change their values if the devices you want to use are not 0 and 1. ++# trtDeviceToUseThread0 = 0 ++# trtDeviceToUseThread1 = 1 ++ ++# For three GPUs: Uncomment these options, AND set numNNServerThreadsPerModel above. ++# Also, change their values if the devices you want to use are not 0 and 1 and 2. ++# trtDeviceToUseThread0 = 0 ++# trtDeviceToUseThread1 = 1 ++# trtDeviceToUseThread2 = 2 ++ ++# The pattern continues for additional GPUs. ++ ++# ------------------------------ ++# CUDA GPU settings ++# ------------------------------ ++# These only apply when using the CUDA version of KataGo. ++ ++# For one GPU: optionally uncomment and change this if the GPU you want to ++# use is not device 0 ++# cudaDeviceToUse = 0 ++ ++# For two GPUs: Uncomment these options, AND set numNNServerThreadsPerModel above. ++# Also, change their values if the devices you want to use are not 0 and 1. ++# cudaDeviceToUseThread0 = 0 ++# cudaDeviceToUseThread1 = 1 ++ ++# For three GPUs: Uncomment these options, AND set numNNServerThreadsPerModel above. ++# Also, change their values if the devices you want to use are not 0 and 1 and 2. ++# cudaDeviceToUseThread0 = 0 ++# cudaDeviceToUseThread1 = 1 ++# cudaDeviceToUseThread2 = 2 ++ ++# The pattern continues for additional GPUs. ++ ++# KataGo will automatically use FP16 or not based on the compute capability ++# of your NVIDIA GPU. If you want to try to force a particular behavior ++# you can uncomment these lines and change them to "true" or "false". ++# cudaUseFP16 = auto ++# cudaUseNHWC = auto ++ ++# ------------------------------ ++# OpenCL GPU settings ++# ------------------------------ ++# These only apply when using the OpenCL version of KataGo. ++ ++# Uncomment and set to true to tune OpenCL for every board size separately, ++# rather than only the largest possible size. ++# openclReTunePerBoardSize = false ++ ++# For one GPU: optionally uncomment and change this if the best device to use is guessed incorrectly. ++# The default behavior tries to guess the 'best' GPU or device on your system to use, usually it will be a good guess. ++# openclDeviceToUse = 0 ++ ++# For two GPUs: Uncomment these two lines and replace X and Y with the device ids of the devices you want to use. ++# It might NOT be 0 and 1, some computers will have many OpenCL devices. You can see what the devices are when ++# KataGo starts up - it should print or log all the devices it finds. ++# (AND also set numNNServerThreadsPerModel above) ++# openclDeviceToUseThread0 = X ++# openclDeviceToUseThread1 = Y ++ ++# For three GPUs: Uncomment these three lines and replace X and Y and Z with the device ids of the devices you want to use. ++# It might NOT be 0 and 1 and 2, some computers will have many OpenCL devices. You can see what the devices are when ++# KataGo starts up - it should print or log all the devices it finds. ++# (AND also set numNNServerThreadsPerModel above) ++# openclDeviceToUseThread0 = X ++# openclDeviceToUseThread1 = Y ++# openclDeviceToUseThread2 = Z ++ ++# The pattern continues for additional GPUs. ++ ++# KataGo will automatically use FP16 or not based on testing your GPU during ++# tuning. If you want to try to force a particular behavior though you can ++# uncomment this option and change it to "true" or "false". This is a fairly ++# blunt setting - more detailed settings are testable by rerunning the tuner ++# with various arguments (./katago tuner). ++# openclUseFP16 = auto ++ ++# ------------------------------ ++# Eigen-specific settings ++# ------------------------------ ++# These only apply when using the Eigen (pure CPU) version of KataGo. ++ ++# Number of CPU threads for evaluating the neural net on the Eigen backend. ++# ++# Default: numSearchThreads ++# numEigenThreadsPerModel = X ++ ++# =========================================================================== ++# Root move selection and biases ++# =========================================================================== ++# Uncomment and edit any of the below values to change them from their default. ++ ++# If provided, force usage of a specific seed for various random things in ++# the search. The default is to use a random seed. ++# searchRandSeed = hijklmn ++ ++# Temperature for the early game, randomize between chosen moves with ++# this temperature ++# chosenMoveTemperatureEarly = 0.5 ++ ++# Decay temperature for the early game by 0.5 every this many moves, ++# scaled with board size. ++# chosenMoveTemperatureHalflife = 19 ++ ++# At the end of search after the early game, randomize between chosen ++# moves with this temperature ++# chosenMoveTemperature = 0.10 ++ ++# Subtract this many visits from each move prior to applying ++# chosenMoveTemperature (unless all moves have too few visits) to downweight ++# unlikely moves ++# chosenMoveSubtract = 0 ++ ++# The same as chosenMoveSubtract but only prunes moves that fall below ++# the threshold. This setting does not affect chosenMoveSubtract. ++# chosenMovePrune = 1 ++ ++# Number of symmetries to sample (without replacement) and average at the root ++# rootNumSymmetriesToSample = 1 ++ ++# Using LCB for move selection? ++# useLcbForSelection = true ++ ++# How many stdevs a move needs to be better than another for LCB selection ++# lcbStdevs = 5.0 ++ ++# Only use LCB override when a move has this proportion of visits as the ++# top move. ++# minVisitPropForLCB = 0.15 ++ ++# =========================================================================== ++# Internal params ++# =========================================================================== ++# Uncomment and edit any of the below values to change them from their default. ++ ++# Scales the utility of winning/losing ++# winLossUtilityFactor = 1.0 ++ ++# Scales the utility for trying to maximize score ++# staticScoreUtilityFactor = 0.10 ++# dynamicScoreUtilityFactor = 0.30 ++ ++# Adjust dynamic score center this proportion of the way towards zero, ++# capped at a reasonable amount. ++# dynamicScoreCenterZeroWeight = 0.20 ++# dynamicScoreCenterScale = 0.75 ++ ++# The utility of getting a "no result" due to triple ko or other long cycle ++# in non-superko rulesets (-1 to 1) ++# noResultUtilityForWhite = 0.0 ++ ++# The number of wins that a draw counts as, for white. (0 to 1) ++# drawEquivalentWinsForWhite = 0.5 ++ ++# Exploration constant for mcts ++# cpuctExploration = 1.0 ++# cpuctExplorationLog = 0.45 ++ ++# Parameters that control exploring more in volatile positions, exploring ++# less in stable positions. ++# cpuctUtilityStdevPrior = 0.40 ++# cpuctUtilityStdevPriorWeight = 2.0 ++# cpuctUtilityStdevScale = 0.85 ++ ++# FPU reduction constant for mcts ++# fpuReductionMax = 0.2 ++# rootFpuReductionMax = 0.1 ++# fpuParentWeightByVisitedPolicy = true ++ ++# Parameters that control weighting of evals based on the net's own ++# self-reported uncertainty. ++# useUncertainty = true ++# uncertaintyExponent = 1.0 ++# uncertaintyCoeff = 0.25 ++ ++# Explore using optimistic policy ++# rootPolicyOptimism = 0.2 ++# policyOptimism = 1.0 ++ ++# Amount to apply a downweighting of children with very bad values relative ++# to good ones. ++# valueWeightExponent = 0.25 ++ ++# Slight incentive for the bot to behave human-like with regard to passing at ++# the end, filling the dame, not wasting time playing in its own territory, ++# etc., and not play moves that are equivalent in terms of points but a bit ++# more unfriendly to humans. ++# rootEndingBonusPoints = 0.5 ++ ++# Make the bot prune useless moves that are just prolonging the game to ++# avoid losing yet. ++# rootPruneUselessMoves = true ++ ++# Apply bias correction based on local pattern keys ++# subtreeValueBiasFactor = 0.45 ++# subtreeValueBiasWeightExponent = 0.85 ++ ++# Use graph search rather than tree search - identify and share search for ++# transpositions. ++# useGraphSearch = true ++ ++# How much to shard the node table for search synchronization ++# nodeTableShardsPowerOfTwo = 16 ++ ++# How many virtual losses to add when a thread descends through a node ++# numVirtualLossesPerThread = 1 ++ ++# Improve the quality of evals under heavy multithreading ++# useNoisePruning = true ++ ++# =========================================================================== ++# Automatic avoid patterns ++# =========================================================================== ++# The parameters in this section provide a way to bias away from moves that match ++# patterns that this instance of KataGo has played in previous games, by auto-saving ++# moves to a directory and then auto-loading and biasing against them each new game. ++# Uncomment them to use them. When using this feature, all parameters must be specified. ++ ++# Different board sizes are tracked separately, but all board sizes share the same ++# parameters by default. Every parameter *except* for autoAvoidRepeatDir and ++# autoAvoidRepeatSaveChunkSize can be overridden per board size, ++# e.g. "autoAvoidRepeatMinTurnNumber13x13". You must ALSO specify the ++# defaults even if you have specified board-size-specific values. ++ ++# Directory to auto-save moves KataGo plays, to avoid them in future games. ++# You can create a new empty directory and put its path here. ++# If you run parallel instances of KataGo, use different directories if you ++# want them not to share their biases, use the same directory if you want ++# all of them to bias away from past moves that any of them have played. ++# KataGo will also automatically DELETE old data in this directory, so it is ++# recommended that if you do share the same directory between parallel instances, ++# that they all use the same settings that affect data saving/deletion. ++# autoAvoidRepeatDir = PATH_TO_NEW_DIRECTORY ++ ++# Penalize this much utility per matching move. ++# Values that are too large may lead to bad play. The value of 0.004 is fairly large ++# and might be large enough to result in some early weird/bad moves when trying ++# to avoid past games' moves if enough games begin the same way. You can experiment with it. ++# autoAvoidRepeatUtility = 0.004 ++ ++# Per each new move saved, exponentially decay prior saved moves by this factor. ++# This way, the bias against moves from many games ago is gradually phased out. ++# For example, 0.9995 = 1 - 1/2000, so would be roughly 2000 prior moves worth of ++# penalty weight remembered, in steady state. Depending on what turn number range ++# you are saving, this might equate to a different number of games For example ++# saving the first 50 moves per game would make this roughly 2000 / 50 = 40 games ++# worth of memory. ++# autoAvoidRepeatLambda = 0.9995 ++ ++# Affects data saving/deletion. ++# When the number of saved moves exceeds this, outright delete them to avoid too many ++# files and disk space building up. Also may affect the speed of saving/loading on start ++# of each game if this is set large and a lot of data builds up. ++# autoAvoidRepeatMaxPoses = 10000 ++ ++# Affects data saving/deletion. ++# Only save data for moves within this turn number range of those games. ++# E.g. setting autoAvoidRepeatMinTurnNumber to a number like 4 or 5 would tend to make ++# KataGo not develop a bias against the initial 3-4 and 4-4 corner moves in almost every game. ++# autoAvoidRepeatMinTurnNumber = 0 ++# autoAvoidRepeatMaxTurnNumber = 50 ++ ++# Affects data saving/deletion. ++# Within a single run of a program, wait to accumulate this many samples ++# (possibly across multiple clear_boards/games) before saving the data. ++# Can help to avoid writing too many small files to disk, especially when GTP is used ++# in a way that clears the board very frequently (e.g. gtp2ogs pooled manager). ++# autoAvoidRepeatSaveChunkSize = 200 ++ ++# =========================================================================== ++# Avoid SGF patterns ++# =========================================================================== ++# The parameters in this section provide a way to avoid moves that follow ++# specific patterns based on a set of SGF files loaded upon startup. ++# This is basically the same as the above "Automatic avoid patterns" section ++# above except you supply your own SGF files to avoid moves from. ++# Uncomment them to use this feature. Additionally, if the SGF file ++# contains the string %SKIP% in a comment on a move, that move will be ++# ignored for this purpose. ++ ++# Load SGF files from this directory when the engine is started ++# (only on startup, will not reload unless engine is restarted) ++# avoidSgfPatternDirs = path/to/directory/with/sgfs/ ++# You can also surround the file path in double quotes if the file path contains trailing spaces or hash signs. ++# Within double quotes, backslashes are escape characters. ++# avoidSgfPatternDirs = "path/to/directory/with/sgfs/" ++ ++# Penalize this much utility per matching move. ++# Set this negative if you instead want to favor SGF patterns instead of ++# penalizing them. This number does not need to be large, even 0.001 will ++# make a difference. Values that are too large may lead to bad play. ++# avoidSgfPatternUtility = 0.001 ++ ++# Optional - load only the newest this many files ++# avoidSgfPatternMaxFiles = 20 ++ ++# Optional - Penalty is multiplied by this per each older SGF file, so that ++# old SGF files matter less than newer ones. ++# avoidSgfPatternLambda = 0.90 ++ ++# Optional - pay attention only to moves made by players with this name. ++# For example, set it to the name that your bot's past games will show up ++# as in the SGF, so that the bot will only avoid repeating moves that itself ++# made in past games, not the moves that its opponents made. ++# avoidSgfPatternAllowedNames = my-ogs-bot-name1,my-ogs-bot-name2 ++ ++# Optional - Ignore moves in SGF files that occurred before this turn number. ++# avoidSgfPatternMinTurnNumber = 0 ++ ++# For more avoid patterns: ++# You can also specify a second set of parameters, and a third, fourth, ++# etc. by numbering 2,3,4,... ++# ++# avoidSgf2PatternDirs = ... ++# avoidSgf2PatternUtility = ... ++# avoidSgf2PatternMaxFiles = ... ++# avoidSgf2PatternLambda = ... ++# avoidSgf2PatternAllowedNames = ... ++# avoidSgf2PatternMinTurnNumber = ... ++ +diff --git a/cpp/libcrypto-3-x64.dll b/cpp/libcrypto-3-x64.dll +new file mode 100644 +index 00000000..cac7f4ec +Binary files /dev/null and b/cpp/libcrypto-3-x64.dll differ +diff --git a/cpp/libssl-3-x64.dll b/cpp/libssl-3-x64.dll +new file mode 100644 +index 00000000..8ad0908e +Binary files /dev/null and b/cpp/libssl-3-x64.dll differ +diff --git a/cpp/libz.dll b/cpp/libz.dll +new file mode 100644 +index 00000000..930a0421 +Binary files /dev/null and b/cpp/libz.dll differ +diff --git a/cpp/libzip.dll b/cpp/libzip.dll +new file mode 100644 +index 00000000..167e69f7 +Binary files /dev/null and b/cpp/libzip.dll differ +diff --git a/cpp/lizgoban.cfg b/cpp/lizgoban.cfg +new file mode 100644 +index 00000000..53f4832b +--- /dev/null ++++ b/cpp/lizgoban.cfg +@@ -0,0 +1,721 @@ ++# Configuration for KataGo C++ GTP engine ++ ++# Run the program using: `./katago.exe gtp` ++ ++# In this example config, when a parameter is given as a commented out value, ++# that value also is the default value, unless described otherwise. You can ++# uncomment it (remove the pound sign) and change it if you want. ++ ++# =========================================================================== ++# Running on an online server or in a real tournament or match ++# =========================================================================== ++# If you plan to run online or in a tournament, read through the "Rules" ++# section below for proper handling of komi, handicaps, end-of-game cleanup, ++# and other details. ++ ++# =========================================================================== ++# Notes about performance and memory usage ++# =========================================================================== ++# Important: For good performance, you will very likely want to tune the ++# "numSearchThreads" parameter in the Search limits section below! Run ++# "./katago benchmark" to test KataGo and to suggest a reasonable value ++# of this parameter. ++ ++# For multi-GPU systems, read "OpenCL GPU settings" or "CUDA GPU settings". ++# ++# When using OpenCL, verify that KataGo picks the correct device! Some systems ++# may have both an Intel CPU OpenCL and GPU OpenCL. If # KataGo picks the wrong ++# one, correct this by specifying "openclGpuToUse". ++# ++# Consider adjusting "maxVisits", "ponderingEnabled", "resignThreshold", and ++# other parameters depending on your intended usage. ++ ++# =========================================================================== ++# Command-line usage ++# =========================================================================== ++# All of the below values may be set or overridden via command-line arguments: ++# ++# -override-config KEY=VALUE,KEY=VALUE,... ++ ++# =========================================================================== ++# Logs and files ++# =========================================================================== ++# This section defines where and what logging information is produced. ++ ++# Each run of KataGo will log to a separate file in this dir. ++# This is the default. ++logDir = gtp_logs ++# Uncomment and specify this instead of logDir to write separate dated subdirs ++# logDirDated = gtp_logs ++# Uncomment and specify this instead of logDir to log to only a single file ++# logFile = gtp.log ++ ++# Logging options ++logAllGTPCommunication = true ++logSearchInfo = true ++logSearchInfoForChosenMove = false ++logToStderr = true ++ ++# KataGo will display some info to stderr on GTP startup ++# Uncomment the next line and set it to false to suppress that and remain silent ++# startupPrintMessageToStderr = true ++ ++# Write information to stderr, for use in things like malkovich chat to OGS. ++ ogsChatToStderr = true ++ ++# Uncomment and set this to a directory to override where openCLTuner files ++# and other cached data is written. By default it saves into a subdir of the ++# current directory on windows, and a subdir of ~/.katago on Linux. ++# homeDataDir = PATH_TO_DIRECTORY ++ ++# =========================================================================== ++# Analysis ++# =========================================================================== ++# This section configures analysis settings. ++# ++# The maximum number of moves after the first move displayed in variations ++# from analysis commands like kata-analyze or lz-analyze. ++# analysisPVLen = 15 ++ ++# Report winrates for chat and analysis as (BLACK|WHITE|SIDETOMOVE). ++# Most GUIs and analysis tools will expect SIDETOMOVE. ++# reportAnalysisWinratesAs = SIDETOMOVE ++ ++# Extra noise for wider exploration. Large values will force KataGo to ++# analyze a greater variety of moves than it normally would. ++# An extreme value like 1 distributes playouts across every move on the board, ++# even very bad moves. ++# Affects analysis only, does not affect play. ++# analysisWideRootNoise = 0.04 ++ ++# Try to limit the effect of possible bad or bogus move sequences in the ++# history leading to this position from affecting KataGo's move predictions. ++# analysisIgnorePreRootHistory = true ++ ++# =========================================================================== ++# Rules ++# =========================================================================== ++# This section configures the scoring and playing rules. Rules can also be ++# changed mid-run by issuing custom GTP commands. ++# ++# See https://lightvector.github.io/KataGo/rules.html for rules details. ++# ++# See https://github.com/lightvector/KataGo/blob/master/docs/GTP_Extensions.md ++# for GTP commands. ++ ++# Specify the rules as a string. ++# Some legal values include: ++# chinese, japanese, korean, aga, chinese-ogs, new-zealand, stone-scoring, ++# ancient-territory, bga, aga-button ++# ++# For some human rulesets that require complex adjudication in tricky cases ++# (e.g. japanese, korean) KataGo may not precisely match the ruleset in such ++# cases but will do its best. ++rules = tromp-taylor ++ ++# By default, the "rules" parameter is used, but if you comment it out and ++# uncomment one option in each of the sections below, you can specify an ++# arbitrary combination of individual rules. ++ ++# koRule = SIMPLE # Simple ko rules (triple ko = no result) ++# koRule = POSITIONAL # Positional superko ++# koRule = SITUATIONAL # Situational superko ++ ++# scoringRule = AREA # Area scoring ++# scoringRule = TERRITORY # Territory scoring (special computer-friendly territory rules) ++ ++# taxRule = NONE # All surrounded empty points are scored ++# taxRule = SEKI # Eyes in seki do NOT count as points ++# taxRule = ALL # All groups are taxed up to 2 points for the two eyes needed to live ++ ++# Is multiple-stone suicide legal? (Single-stone suicide is always illegal). ++# multiStoneSuicideLegal = false ++# multiStoneSuicideLegal = true ++ ++# "Button go" - the first pass when area scoring awards 0.5 points and does ++# not count for ending the game. ++# Allows area scoring rulesets that have far simpler rules to achieve the same ++# final scoring precision and reward for precise play as territory scoring. ++# hasButton = false ++# hasButton = true ++ ++# Is this a human ruleset where it's okay to pass before having physically ++# captured and removed all dead stones? ++# friendlyPassOk = false ++# friendlyPassOk = true ++ ++# How handicap stones in handicap games are compensated ++# whiteHandicapBonus = 0 # White gets no compensation for black's handicap stones (Tromp-taylor, NZ, JP) ++# whiteHandicapBonus = N-1 # White gets N-1 points for black's N handicap stones (AGA) ++# whiteHandicapBonus = N # White gets N points for black's N handicap stones (Chinese) ++ ++# ------------------------------ ++# Other rules hacks ++# ------------------------------ ++# Uncomment and change to adjust what board size KataGo uses upon startup ++# by default when GTP doesn't specify. ++# defaultBoardSize = 19 ++ ++# By default, Katago will use the komi that the GUI or GTP controller tries to set. ++# Uncomment and set this to have KataGo ignore the controller and always use this komi. ++# ignoreGTPAndForceKomi = 7 ++ ++# =========================================================================== ++# Bot behavior ++# =========================================================================== ++ ++# ------------------------------ ++# Resignation ++# ------------------------------ ++ ++# Resignation occurs if for at least resignConsecTurns in a row, the ++# winLossUtility (on a [-1,1] scale) is below resignThreshold. ++allowResignation = true ++resignThreshold = -0.90 ++resignConsecTurns = 3 ++ ++# By default, KataGo may resign games that it is confidently losing even if they ++# are very close in score. Uncomment and set this to avoid resigning games ++# if the estimated difference is points is less than or equal to this. ++# resignMinScoreDifference = 10 ++ ++# ------------------------------ ++# Handicap ++# ------------------------------ ++# Assume that if black makes many moves in a row right at the start of the ++# game, then the game is a handicap game. This is necessary on some servers ++# and for some GUIs and also when initializing from many SGF files, which may ++# set up a handicap game using repeated GTP "play" commands for black rather ++# than GTP "place_free_handicap" commands; however, it may also lead to ++# incorrect understanding of komi if whiteHandicapBonus is used and a server ++# does not have such a practice. Uncomment and set to false to disable. ++# assumeMultipleStartingBlackMovesAreHandicap = true ++ ++# Makes katago dynamically adjust in handicap or altered-komi games to assume ++# based on those game settings that it must be stronger or weaker than the ++# opponent and to play accordingly. Greatly improves handicap strength by ++# biasing winrates and scores to favor appropriate safe/aggressive play. ++# Does NOT affect analysis (lz-analyze, kata-analyze, used by programs like ++# Lizzie) so analysis remains unbiased. Uncomment and set this to 0 to disable ++# this and make KataGo play the same always. ++# dynamicPlayoutDoublingAdvantageCapPerOppLead = 0.045 ++ ++# Instead of "dynamicPlayoutDoublingAdvantageCapPerOppLead", you can comment ++# that out and uncomment and set "playoutDoublingAdvantage" to a fixed value ++# from -3.0 to 3.0 that will not change dynamically. ++# ALSO affects analysis tools (lz-analyze, kata-analyze, used by e.g. Lizzie). ++# Negative makes KataGo behave as if it is much weaker than the opponent. ++# Positive makes KataGo behave as if it is much stronger than the opponent. ++# KataGo will adjust to favor safe/aggressive play as appropriate based on ++# the combination of who is ahead and how much stronger/weaker it thinks it is, ++# and report winrates and scores taking the strength difference into account. ++# ++# If this and "dynamicPlayoutDoublingAdvantageCapPerOppLead" are both set ++# then dynamic will be used for all games and this fixed value will be used ++# for analysis tools. ++# playoutDoublingAdvantage = 0.0 ++ ++# Uncomment one of these when using "playoutDoublingAdvantage" to enforce ++# that it will only apply when KataGo plays as the specified color and will be ++# negated when playing as the opposite color. ++# playoutDoublingAdvantagePla = BLACK ++# playoutDoublingAdvantagePla = WHITE ++ ++# ------------------------------ ++# Passing and cleanup ++# ------------------------------ ++# Make the bot never assume that its pass will end the game, even if passing ++# would end and "win" under Tromp-Taylor rules. Usually this is a good idea ++# when using it for analysis or playing on servers where scoring may be ++# implemented non-tromp-taylorly. Uncomment and set to false to disable. ++# conservativePass = true ++ ++# When using territory scoring, self-play games continue beyond two passes ++# with special cleanup rules that may be confusing for human players. This ++# option prevents the special cleanup phases from being reachable when using ++# the bot for GTP play. Uncomment and set to false to enable entering special ++# cleanup. For example, if you are testing it against itself, or against ++# another bot that has precisely implemented the rules documented at ++# https://lightvector.github.io/KataGo/rules.html ++# preventCleanupPhase = true ++ ++# ------------------------------ ++# Miscellaneous behavior ++# ------------------------------ ++# If the board is symmetric, search only one copy of each equivalent move. ++# Attempts to also account for ko/superko, will not theoretically perfect for ++# superko. Uncomment and set to false to disable. ++# rootSymmetryPruning = true ++ ++# Uncomment and set to true to avoid a particular joseki that some networks ++# misevaluate, and also to improve opening diversity versus some particular ++# other bots that like to play it all the time. ++# avoidMYTDaggerHack = false ++ ++# Prefer to avoid playing the same joseki in every corner of the board. ++# Uncomment to set to a specific value. See "Avoid SGF patterns" section. ++# By default: 0 (even games), 0.005 (handicap games) ++# avoidRepeatedPatternUtility = 0.0 ++ ++# Experimental logic to fight against mirror Go even with unfavorable komi. ++# Uncomment to set to a specific value to use for both playing and analysis. ++# By default: true when playing via GTP, but false when analyzing. ++# antiMirror = true ++ ++# Enable some hacks that mitigate rare instances when passing messes up deeper searches. ++# enablePassingHacks = true ++ ++# Uncomment and set this to true to prevent bad or bogus move sequences ++# in the history leading to this position from affecting KataGo's move choices. ++# Same as analysisIgnorePreRootHistory (see above) but applies to actual play. ++# You can enable this if KataGo is being asked to play from positions that it did not ++# choose the moves to reach. ++# ignorePreRootHistory = false ++ ++# =========================================================================== ++# Search limits ++# =========================================================================== ++ ++# Terminology: ++# "Playouts" is the number of new playouts of search performed each turn. ++# "Visits" is the same as "Playouts" but also counts search performed on ++# previous turns that is still applicable to this turn. ++# "Time" is the time in seconds. ++ ++# For example, if KataGo searched 200 nodes on the previous turn, and then ++# after the opponent's reply, 50 nodes of its search tree was still valid, ++# then a visit limit of 200 would allow KataGo to search 150 new nodes ++# (for a final tree size of 200 nodes), whereas a playout limit of of 200 ++# would allow KataGo to search 200 nodes (for a final tree size of 250 nodes). ++ ++# Additionally, KataGo may also move before than the limit in order to ++# obey time controls (e.g. byo-yomi, etc) if the GTP controller has ++# told KataGo that the game has is being played with a given time control. ++ ++# Limits for search on the current turn. ++# If commented out or unspecified, the default is to have no limit. ++# maxVisits = 500 ++# maxPlayouts = 300 ++ maxTime = 0.5 ++ ++# Ponder on the opponent's turn? ++ponderingEnabled = true ++ ++# Limits for search when pondering on the opponent's turn. ++# If commented out or unspecified, the default is to have no limit. ++# Limiting the maximum time is recommended so that KataGo won't burn CPU/GPU ++# forever and/or run out of RAM if left unattended while pondering is enabled. ++# maxVisitsPondering = 5000 ++# maxPlayoutsPondering = 3000 ++maxTimePondering = 60.0 ++ ++ ++# ------------------------------ ++# Other search limits and behavior ++# ------------------------------ ++ ++# Approx number of seconds to buffer for lag for GTP time controls - will ++# move a bit faster assuming there is this much lag per move. ++lagBuffer = 1.0 ++ ++# YOU PROBABLY WANT TO TUNE THIS PARAMETER! ++# The number of threads to use when searching. On powerful GPUs the optimal ++# threads may be much higher than the number of CPU cores you have because ++# many threads are needed to feed efficient large batches to the GPU. ++# ++# Run "./katago benchmark" to tune this parameter and test the effect ++# of changes to any of other parameters. ++numSearchThreads = 16 ++ ++# Play a little faster if the opponent is passing, for human-friendliness. ++# Comment these out to disable them, such as if running a controlled match ++# where you are testing KataGo with fixed compute per move vs other bots. ++searchFactorAfterOnePass = 0.50 ++searchFactorAfterTwoPass = 0.25 ++ ++# Play a little faster if super-winning, for human-friendliness. ++# Comment these out to disable them, such as if running a controlled match ++# where you are testing KataGo with fixed compute per move vs other bots. ++searchFactorWhenWinning = 0.40 ++searchFactorWhenWinningThreshold = 0.95 ++ ++# =========================================================================== ++# GPU settings ++# =========================================================================== ++# This section configures GPU settings. ++# ++# Maximum number of positions to send to a single GPU at once. The default ++# value is roughly equal to numSearchThreads, but can be specified manually ++# if running out of memory, or using multiple GPUs that expect to share work. ++# nnMaxBatchSize = ++ ++# Controls the neural network cache size, which is the primary RAM/memory use. ++# KataGo will cache up to (2 ** nnCacheSizePowerOfTwo) many neural net ++# evaluations in case of transpositions in the tree. ++# Increase this to improve performance for searches with tens of thousands ++# of visits or more. Decrease this to limit memory usage. ++# If you're happy to do some math - each neural net entry takes roughly ++# 1.5KB, except when using whole-board ownership/territory ++# visualizations, where each entry will take roughly 3KB. The number of ++# entries is (2 ** nnCacheSizePowerOfTwo). (E.g. 2 ** 18 = 262144.) ++# You can compute roughly how much memory the cache will use based on this. ++# nnCacheSizePowerOfTwo = 20 ++ ++# Size of mutex pool for nnCache is (2 ** this). ++# nnMutexPoolSizePowerOfTwo = 16 ++ ++# Randomize board orientation when running neural net evals? Uncomment and ++# set to false to disable. ++# nnRandomize = true ++ ++# If provided, force usage of a specific seed for nnRandomize. ++# The default is to use a randomly generated seed. ++# nnRandSeed = abcdefg ++ ++# Uncomment and set to true to force GTP to use the maximum board size for ++# internal buffers for the neural net. This will make KataGo slower when ++# evaluating small boards, but will avoid a lengthy initialization time on every ++# change of board size due to having to re-size the neural net buffers on the GPU. ++# This can be useful for example, for OGS's persistent bot mode that uses a single ++# bot instance to handle multiple games and may thrash between different board sizes ++# if there are concurrent games of multiple sizes. ++# gtpForceMaxNNSize = false ++ ++# ------------------------------ ++# Multiple GPUs ++# ------------------------------ ++# Set this to the number of GPUs to use or that are available. ++# IMPORTANT: If more than 1, also uncomment the appropriate TensorRT ++# or CUDA or OpenCL section. ++# numNNServerThreadsPerModel = 1 ++ ++# ------------------------------ ++# TENSORRT GPU settings ++# ------------------------------ ++# These only apply when using the TENSORRT version of KataGo. ++ ++# For one GPU: optionally uncomment this option and change if the GPU to ++# use is not device 0. ++# trtDeviceToUse = 0 ++ ++# For two GPUs: Uncomment these options, AND set numNNServerThreadsPerModel above. ++# Also, change their values if the devices you want to use are not 0 and 1. ++# trtDeviceToUseThread0 = 0 ++# trtDeviceToUseThread1 = 1 ++ ++# For three GPUs: Uncomment these options, AND set numNNServerThreadsPerModel above. ++# Also, change their values if the devices you want to use are not 0 and 1 and 2. ++# trtDeviceToUseThread0 = 0 ++# trtDeviceToUseThread1 = 1 ++# trtDeviceToUseThread2 = 2 ++ ++# The pattern continues for additional GPUs. ++ ++# ------------------------------ ++# CUDA GPU settings ++# ------------------------------ ++# These only apply when using the CUDA version of KataGo. ++ ++# For one GPU: optionally uncomment and change this if the GPU you want to ++# use is not device 0 ++# cudaDeviceToUse = 0 ++ ++# For two GPUs: Uncomment these options, AND set numNNServerThreadsPerModel above. ++# Also, change their values if the devices you want to use are not 0 and 1. ++# cudaDeviceToUseThread0 = 0 ++# cudaDeviceToUseThread1 = 1 ++ ++# For three GPUs: Uncomment these options, AND set numNNServerThreadsPerModel above. ++# Also, change their values if the devices you want to use are not 0 and 1 and 2. ++# cudaDeviceToUseThread0 = 0 ++# cudaDeviceToUseThread1 = 1 ++# cudaDeviceToUseThread2 = 2 ++ ++# The pattern continues for additional GPUs. ++ ++# KataGo will automatically use FP16 or not based on the compute capability ++# of your NVIDIA GPU. If you want to try to force a particular behavior ++# you can uncomment these lines and change them to "true" or "false". ++# cudaUseFP16 = auto ++# cudaUseNHWC = auto ++ ++# ------------------------------ ++# OpenCL GPU settings ++# ------------------------------ ++# These only apply when using the OpenCL version of KataGo. ++ ++# Uncomment and set to true to tune OpenCL for every board size separately, ++# rather than only the largest possible size. ++# openclReTunePerBoardSize = false ++ ++# For one GPU: optionally uncomment and change this if the best device to use is guessed incorrectly. ++# The default behavior tries to guess the 'best' GPU or device on your system to use, usually it will be a good guess. ++# openclDeviceToUse = 0 ++ ++# For two GPUs: Uncomment these two lines and replace X and Y with the device ids of the devices you want to use. ++# It might NOT be 0 and 1, some computers will have many OpenCL devices. You can see what the devices are when ++# KataGo starts up - it should print or log all the devices it finds. ++# (AND also set numNNServerThreadsPerModel above) ++# openclDeviceToUseThread0 = X ++# openclDeviceToUseThread1 = Y ++ ++# For three GPUs: Uncomment these three lines and replace X and Y and Z with the device ids of the devices you want to use. ++# It might NOT be 0 and 1 and 2, some computers will have many OpenCL devices. You can see what the devices are when ++# KataGo starts up - it should print or log all the devices it finds. ++# (AND also set numNNServerThreadsPerModel above) ++# openclDeviceToUseThread0 = X ++# openclDeviceToUseThread1 = Y ++# openclDeviceToUseThread2 = Z ++ ++# The pattern continues for additional GPUs. ++ ++# KataGo will automatically use FP16 or not based on testing your GPU during ++# tuning. If you want to try to force a particular behavior though you can ++# uncomment this option and change it to "true" or "false". This is a fairly ++# blunt setting - more detailed settings are testable by rerunning the tuner ++# with various arguments (./katago tuner). ++# openclUseFP16 = auto ++ ++# ------------------------------ ++# Eigen-specific settings ++# ------------------------------ ++# These only apply when using the Eigen (pure CPU) version of KataGo. ++ ++# Number of CPU threads for evaluating the neural net on the Eigen backend. ++# ++# Default: numSearchThreads ++# numEigenThreadsPerModel = X ++ ++# =========================================================================== ++# Root move selection and biases ++# =========================================================================== ++# Uncomment and edit any of the below values to change them from their default. ++ ++# If provided, force usage of a specific seed for various random things in ++# the search. The default is to use a random seed. ++# searchRandSeed = hijklmn ++ ++# Temperature for the early game, randomize between chosen moves with ++# this temperature ++# chosenMoveTemperatureEarly = 0.5 ++ ++# Decay temperature for the early game by 0.5 every this many moves, ++# scaled with board size. ++# chosenMoveTemperatureHalflife = 19 ++ ++# At the end of search after the early game, randomize between chosen ++# moves with this temperature ++# chosenMoveTemperature = 0.10 ++ ++# Subtract this many visits from each move prior to applying ++# chosenMoveTemperature (unless all moves have too few visits) to downweight ++# unlikely moves ++# chosenMoveSubtract = 0 ++ ++# The same as chosenMoveSubtract but only prunes moves that fall below ++# the threshold. This setting does not affect chosenMoveSubtract. ++# chosenMovePrune = 1 ++ ++# Number of symmetries to sample (without replacement) and average at the root ++# rootNumSymmetriesToSample = 1 ++ ++# Using LCB for move selection? ++# useLcbForSelection = true ++ ++# How many stdevs a move needs to be better than another for LCB selection ++# lcbStdevs = 5.0 ++ ++# Only use LCB override when a move has this proportion of visits as the ++# top move. ++# minVisitPropForLCB = 0.15 ++ ++# =========================================================================== ++# Internal params ++# =========================================================================== ++# Uncomment and edit any of the below values to change them from their default. ++ ++# Scales the utility of winning/losing ++# winLossUtilityFactor = 1.0 ++ ++# Scales the utility for trying to maximize score ++# staticScoreUtilityFactor = 0.10 ++# dynamicScoreUtilityFactor = 0.30 ++ ++# Adjust dynamic score center this proportion of the way towards zero, ++# capped at a reasonable amount. ++# dynamicScoreCenterZeroWeight = 0.20 ++# dynamicScoreCenterScale = 0.75 ++ ++# The utility of getting a "no result" due to triple ko or other long cycle ++# in non-superko rulesets (-1 to 1) ++# noResultUtilityForWhite = 0.0 ++ ++# The number of wins that a draw counts as, for white. (0 to 1) ++# drawEquivalentWinsForWhite = 0.5 ++ ++# Exploration constant for mcts ++# cpuctExploration = 1.0 ++# cpuctExplorationLog = 0.45 ++ ++# Parameters that control exploring more in volatile positions, exploring ++# less in stable positions. ++# cpuctUtilityStdevPrior = 0.40 ++# cpuctUtilityStdevPriorWeight = 2.0 ++# cpuctUtilityStdevScale = 0.85 ++ ++# FPU reduction constant for mcts ++# fpuReductionMax = 0.2 ++# rootFpuReductionMax = 0.1 ++# fpuParentWeightByVisitedPolicy = true ++ ++# Parameters that control weighting of evals based on the net's own ++# self-reported uncertainty. ++# useUncertainty = true ++# uncertaintyExponent = 1.0 ++# uncertaintyCoeff = 0.25 ++ ++# Explore using optimistic policy ++# rootPolicyOptimism = 0.2 ++# policyOptimism = 1.0 ++ ++# Amount to apply a downweighting of children with very bad values relative ++# to good ones. ++# valueWeightExponent = 0.25 ++ ++# Slight incentive for the bot to behave human-like with regard to passing at ++# the end, filling the dame, not wasting time playing in its own territory, ++# etc., and not play moves that are equivalent in terms of points but a bit ++# more unfriendly to humans. ++# rootEndingBonusPoints = 0.5 ++ ++# Make the bot prune useless moves that are just prolonging the game to ++# avoid losing yet. ++# rootPruneUselessMoves = true ++ ++# Apply bias correction based on local pattern keys ++# subtreeValueBiasFactor = 0.45 ++# subtreeValueBiasWeightExponent = 0.85 ++ ++# Use graph search rather than tree search - identify and share search for ++# transpositions. ++# useGraphSearch = true ++ ++# How much to shard the node table for search synchronization ++# nodeTableShardsPowerOfTwo = 16 ++ ++# How many virtual losses to add when a thread descends through a node ++# numVirtualLossesPerThread = 1 ++ ++# Improve the quality of evals under heavy multithreading ++# useNoisePruning = true ++ ++# =========================================================================== ++# Automatic avoid patterns ++# =========================================================================== ++# The parameters in this section provide a way to bias away from moves that match ++# patterns that this instance of KataGo has played in previous games, by auto-saving ++# moves to a directory and then auto-loading and biasing against them each new game. ++# Uncomment them to use them. When using this feature, all parameters must be specified. ++ ++# Different board sizes are tracked separately, but all board sizes share the same ++# parameters by default. Every parameter *except* for autoAvoidRepeatDir and ++# autoAvoidRepeatSaveChunkSize can be overridden per board size, ++# e.g. "autoAvoidRepeatMinTurnNumber13x13". You must ALSO specify the ++# defaults even if you have specified board-size-specific values. ++ ++# Directory to auto-save moves KataGo plays, to avoid them in future games. ++# You can create a new empty directory and put its path here. ++# If you run parallel instances of KataGo, use different directories if you ++# want them not to share their biases, use the same directory if you want ++# all of them to bias away from past moves that any of them have played. ++# KataGo will also automatically DELETE old data in this directory, so it is ++# recommended that if you do share the same directory between parallel instances, ++# that they all use the same settings that affect data saving/deletion. ++# autoAvoidRepeatDir = PATH_TO_NEW_DIRECTORY ++ ++# Penalize this much utility per matching move. ++# Values that are too large may lead to bad play. The value of 0.004 is fairly large ++# and might be large enough to result in some early weird/bad moves when trying ++# to avoid past games' moves if enough games begin the same way. You can experiment with it. ++# autoAvoidRepeatUtility = 0.004 ++ ++# Per each new move saved, exponentially decay prior saved moves by this factor. ++# This way, the bias against moves from many games ago is gradually phased out. ++# For example, 0.9995 = 1 - 1/2000, so would be roughly 2000 prior moves worth of ++# penalty weight remembered, in steady state. Depending on what turn number range ++# you are saving, this might equate to a different number of games For example ++# saving the first 50 moves per game would make this roughly 2000 / 50 = 40 games ++# worth of memory. ++# autoAvoidRepeatLambda = 0.9995 ++ ++# Affects data saving/deletion. ++# When the number of saved moves exceeds this, outright delete them to avoid too many ++# files and disk space building up. Also may affect the speed of saving/loading on start ++# of each game if this is set large and a lot of data builds up. ++# autoAvoidRepeatMaxPoses = 10000 ++ ++# Affects data saving/deletion. ++# Only save data for moves within this turn number range of those games. ++# E.g. setting autoAvoidRepeatMinTurnNumber to a number like 4 or 5 would tend to make ++# KataGo not develop a bias against the initial 3-4 and 4-4 corner moves in almost every game. ++# autoAvoidRepeatMinTurnNumber = 0 ++# autoAvoidRepeatMaxTurnNumber = 50 ++ ++# Affects data saving/deletion. ++# Within a single run of a program, wait to accumulate this many samples ++# (possibly across multiple clear_boards/games) before saving the data. ++# Can help to avoid writing too many small files to disk, especially when GTP is used ++# in a way that clears the board very frequently (e.g. gtp2ogs pooled manager). ++# autoAvoidRepeatSaveChunkSize = 200 ++ ++# =========================================================================== ++# Avoid SGF patterns ++# =========================================================================== ++# The parameters in this section provide a way to avoid moves that follow ++# specific patterns based on a set of SGF files loaded upon startup. ++# This is basically the same as the above "Automatic avoid patterns" section ++# above except you supply your own SGF files to avoid moves from. ++# Uncomment them to use this feature. Additionally, if the SGF file ++# contains the string %SKIP% in a comment on a move, that move will be ++# ignored for this purpose. ++ ++# Load SGF files from this directory when the engine is started ++# (only on startup, will not reload unless engine is restarted) ++# avoidSgfPatternDirs = path/to/directory/with/sgfs/ ++# You can also surround the file path in double quotes if the file path contains trailing spaces or hash signs. ++# Within double quotes, backslashes are escape characters. ++# avoidSgfPatternDirs = "path/to/directory/with/sgfs/" ++ ++# Penalize this much utility per matching move. ++# Set this negative if you instead want to favor SGF patterns instead of ++# penalizing them. This number does not need to be large, even 0.001 will ++# make a difference. Values that are too large may lead to bad play. ++# avoidSgfPatternUtility = 0.001 ++ ++# Optional - load only the newest this many files ++# avoidSgfPatternMaxFiles = 20 ++ ++# Optional - Penalty is multiplied by this per each older SGF file, so that ++# old SGF files matter less than newer ones. ++# avoidSgfPatternLambda = 0.90 ++ ++# Optional - pay attention only to moves made by players with this name. ++# For example, set it to the name that your bot's past games will show up ++# as in the SGF, so that the bot will only avoid repeating moves that itself ++# made in past games, not the moves that its opponents made. ++# avoidSgfPatternAllowedNames = my-ogs-bot-name1,my-ogs-bot-name2 ++ ++# Optional - Ignore moves in SGF files that occurred before this turn number. ++# avoidSgfPatternMinTurnNumber = 0 ++ ++# For more avoid patterns: ++# You can also specify a second set of parameters, and a third, fourth, ++# etc. by numbering 2,3,4,... ++# ++# avoidSgf2PatternDirs = ... ++# avoidSgf2PatternUtility = ... ++# avoidSgf2PatternMaxFiles = ... ++# avoidSgf2PatternLambda = ... ++# avoidSgf2PatternAllowedNames = ... ++# avoidSgf2PatternMinTurnNumber = ... ++ +diff --git a/cpp/match_example.cfg b/cpp/match_example.cfg +new file mode 100644 +index 00000000..945ac44e +--- /dev/null ++++ b/cpp/match_example.cfg +@@ -0,0 +1,228 @@ ++# Example config for C++ match runner ++# This is an example template config for the "match" subcommand of KataGo. e.g: ++# ./katago match -config-file configs/match_example.cfg -log-file match.log -sgf-output-dir match_sgfs/ ++# ++# On a good GPU, the match subcommand enables testing of KataGo nets against each other ++# or a net against itself with different parameters vastly faster than anything ++# else, because multiple games can share GPU batching. ++# ++# Beware however of using this to test differing numbers of threads or test time-based search limits. ++# Because many games will be run simultaneously, they will compete from each other for compute power, ++# and although the total will run much faster than if you had run them one by one, you might also get ++# substantially different results not reflective of the strength of a configuration when run in a real ++# match setting, on a machine by itself. For fixed numbers of visits or playouts instead of fixed time, ++# and with numSearchThreads = 1, there should be no problem though, because then the compute time has ++# no influence on the result of the computation. ++# ++# See gtp config for descriptions of most of these params. ++# ++# For almost any parameter in this config that is related to a bot, rather than to the match as a whole ++# (so, visits, search parameters, model files, etc. but NOT the rules, max games, log info, etc) ++# you can specify them differentially between different bots by appending the index of the bot. ++# For example, if you were testing different numbers of visits, you could try: ++# ++# numBots = 3 ++# botName0 = lowVisits ++# botName1 = midVisits ++# botName2 = highVisits ++# ++# maxVisits0 = 100 ++# maxVisits1 = 300 ++# maxVisits2 = 1000 ++# ++# Or, if you were testing different neural nets, with different search configurations, you could do: ++# ++# nnModelFile0 = path/to/first/model/file.bin.gz ++# nnModelFile1 = path/to/second/model/file.bin.gz ++# ++# And specify different search parameters for them if you wanted: ++# cpuctExploration0 = 1.5 ++# cpuctExploration1 = 1.3 ++ ++# Logs------------------------------------------------------------------------------------ ++ ++logSearchInfo = false ++logMoves = false ++logGamesEvery = 50 ++logToStdout = true ++ ++# Bots------------------------------------------------------------------------------------- ++# For multiple bots, you can specify their names as botName0,botName1, etc. ++# If the bots are using different models, specify nnModelFile0, nnModelFile1, etc. ++ ++numBots=1 ++botName=FOO ++nnModelFile=PATH_TO_MODEL ++ ++# These bots will not play each other, but will still be opponents for other bots ++# secondaryBots = 0,1,3 ++# Only these bots will actually play games. Useful if you have a config file with many more ++# bots defined but you want to only selectively enable a few. ++# includeBots = 0,2,6 ++ ++# Specify extra pairings of bots to play. ++# extraPairs = 0-4,1-4 ++# Uncomment and set this to true if you don't want extraPairs to be mirrored by color. ++# (i.e. 0-4 should mean 0 plays black and 4 plays white only, rather than having two games with swapped colors). ++# extraPairsAreOneSidedBW = false ++ ++# Match----------------------------------------------------------------------------------- ++numGameThreads=8 # How many games to run in parallel at a time? ++ ++numGamesTotal=1000000 ++maxMovesPerGame=1200 ++ ++allowResignation = true ++resignThreshold = -0.95 ++resignConsecTurns = 6 ++ ++# Rules------------------------------------------------------------------------------------ ++# See https://lightvector.github.io/KataGo/rules.html for a description of the rules. ++ ++koRules = SIMPLE,POSITIONAL,SITUATIONAL ++scoringRules = AREA,TERRITORY ++taxRules = NONE,SEKI,ALL ++multiStoneSuicideLegals = false,true ++hasButtons = false,true ++ ++bSizes = 19,13,9 ++bSizeRelProbs = 90,5,5 ++ ++# If you want to specify exact distributions of boards, including rectangles, specify this instead ++# of bSizes. E.g. to randomize between a 7x5, a 9x12, and a 10x10 board with probabilities 25%, 25%, 50%: ++# bSizesXY=7-5,9-12,10-10 ++# bSizeRelProbs = 1,1,2 ++ ++komiAuto = True # Automatically adjust komi to what the neural nets think are fair ++# komiMean = 7.5 # Specify explicit komi ++# policyInitAreaProp = 0 ++# compensateAfterPolicyInitProb = 1.0 # Additionally make komi fair this often after the high-temperature moves. ++# policyInitAreaTemperature = 1 ++handicapProb = 0.0 ++handicapCompensateKomiProb = 1.0 ++# numExtraBlackFixed = 3 # When playing handicap games, always use exactly this many extra black moves ++ ++# Search limits----------------------------------------------------------------------------------- ++ ++maxVisits = 500 ++# maxPlayouts = 300 ++# maxTime = 60 ++ ++numSearchThreads = 1 ++ ++# GPU Settings------------------------------------------------------------------------------- ++ ++nnMaxBatchSize = 32 ++nnCacheSizePowerOfTwo = 21 ++nnMutexPoolSizePowerOfTwo = 17 ++nnRandomize = true ++ ++ ++# How many threads should there be to feed positions to the neural net? ++# Server threads are indexed 0,1,...(n-1) for the purposes of the below GPU settings arguments ++# that specify which threads should use which GPUs. ++# NOTE: This parameter is probably ONLY useful if you have multiple GPUs, since each GPU will need a thread. ++# If you're tuning single-GPU performance, use numSearchThreads instead. ++numNNServerThreadsPerModel = 1 ++ ++ ++# TENSORRT GPU settings-------------------------------------- ++# These only apply when using the TENSORRT version of KataGo. ++ ++# IF USING ONE GPU: optionally uncomment and change this if the GPU you want to use turns out to be not device 0 ++# trtDeviceToUse = 0 ++ ++# IF USING TWO GPUS: Uncomment these two lines (AND set numNNServerThreadsPerModel above): ++# trtDeviceToUseThread0 = 0 # change this if the first GPU you want to use turns out to be not device 0 ++# trtDeviceToUseThread1 = 1 # change this if the second GPU you want to use turns out to be not device 1 ++ ++# IF USING THREE GPUS: Uncomment these three lines (AND set numNNServerThreadsPerModel above): ++# trtDeviceToUseThread0 = 0 # change this if the first GPU you want to use turns out to be not device 0 ++# trtDeviceToUseThread1 = 1 # change this if the second GPU you want to use turns out to be not device 1 ++# trtDeviceToUseThread2 = 2 # change this if the third GPU you want to use turns out to be not device 2 ++ ++# You can probably guess the pattern if you have four, five, etc. GPUs. ++ ++ ++# CUDA GPU settings-------------------------------------- ++# For the below, "model" refers to a neural net, from the nnModelFile parameter(s) above. ++# cudaGpuToUse = 0 #use gpu 0 for all server threads (numNNServerThreadsPerModel) unless otherwise specified per-model or per-thread-per-model ++# cudaGpuToUseModel0 = 3 #use gpu 3 for model 0 for all threads unless otherwise specified per-thread for this model ++# cudaGpuToUseModel1 = 2 #use gpu 2 for model 1 for all threads unless otherwise specified per-thread for this model ++# cudaGpuToUseModel0Thread0 = 3 #use gpu 3 for model 0, server thread 0 ++# cudaGpuToUseModel0Thread1 = 2 #use gpu 2 for model 0, server thread 1 ++ ++# cudaUseFP16 = auto ++# cudaUseNHWC = auto ++ ++ ++# OpenCL GPU settings-------------------------------------- ++# These only apply when using OpenCL as the backend for inference. ++# (For GTP, we only ever have one model, when playing matches, we might have more than one, see match_example.cfg) ++ ++# Default behavior is just to always use gpu 0, you will want to uncomment and adjust one or more of these lines ++# to take advantage of a multi-gpu machine ++# openclGpuToUse = 0 #use gpu 0 for all server threads (numNNServerThreadsPerModel) unless otherwise specified per-model or per-thread-per-model ++# openclGpuToUseModel0 = 3 #use gpu 3 for model 0 for all threads unless otherwise specified per-thread for this model ++# openclGpuToUseModel1 = 2 #use gpu 2 for model 1 for all threads unless otherwise specified per-thread for this model ++# openclGpuToUseModel0Thread0 = 3 #use gpu 3 for model 0, server thread 0 ++# openclGpuToUseModel0Thread1 = 2 #use gpu 2 for model 0, server thread 1 ++ ++# Uncomment to tune OpenCL for every board size separately, rather than only the largest possible size ++# openclReTunePerBoardSize = true ++ ++# openclUseFP16 = auto ++ ++ ++# Eigen-specific settings-------------------------------------- ++# These only apply when using the Eigen (pure CPU) version of KataGo. ++ ++# This is the number of CPU threads for evaluating the neural net on the Eigen backend. ++# It defaults to numSearchThreads. ++# numEigenThreadsPerModel = X ++ ++ ++# Root move selection and biases------------------------------------------------------------------------------ ++# Uncomment and edit any of the below values to change them from their default. ++# Values in this section can be specified per-bot as well ++ ++chosenMoveTemperatureEarly = 0.60 ++# chosenMoveTemperatureHalflife = 19 ++chosenMoveTemperature = 0.20 ++# chosenMoveSubtract = 0 ++# chosenMovePrune = 1 ++ ++# rootNumSymmetriesToSample = 1 ++ ++# useLcbForSelection = true ++# lcbStdevs = 5.0 ++# minVisitPropForLCB = 0.15 ++ ++# Internal params------------------------------------------------------------------------------ ++# Uncomment and edit any of the below values to change them from their default. ++# Values in this section can be specified per-bot as well ++ ++# winLossUtilityFactor = 1.0 ++# staticScoreUtilityFactor = 0.10 ++# dynamicScoreUtilityFactor = 0.30 ++# dynamicScoreCenterZeroWeight = 0.20 ++# dynamicScoreCenterScale = 0.75 ++# noResultUtilityForWhite = 0.0 ++# drawEquivalentWinsForWhite = 0.5 ++ ++# cpuctExploration = 0.9 ++# cpuctExplorationLog = 0.4 ++# fpuReductionMax = 0.2 ++# rootFpuReductionMax = 0.1 ++# fpuParentWeightByVisitedPolicy = true ++# valueWeightExponent = 0.25 ++# rootEndingBonusPoints = 0.5 ++# rootPruneUselessMoves = true ++# subtreeValueBiasFactor = 0.45 ++# subtreeValueBiasWeightExponent = 0.85 ++# useGraphSearch = true ++# rootPolicyOptimism = 0.2 ++# policyOptimism = 1.0 ++ ++# nodeTableShardsPowerOfTwo = 16 ++# numVirtualLossesPerThread = 1 +diff --git a/cpp/msvcp140.dll b/cpp/msvcp140.dll +new file mode 100644 +index 00000000..f7c342d1 +Binary files /dev/null and b/cpp/msvcp140.dll differ +diff --git a/cpp/msvcp140_1.dll b/cpp/msvcp140_1.dll +new file mode 100644 +index 00000000..6f55c9d1 +Binary files /dev/null and b/cpp/msvcp140_1.dll differ +diff --git a/cpp/msvcp140_2.dll b/cpp/msvcp140_2.dll +new file mode 100644 +index 00000000..b127e0c8 +Binary files /dev/null and b/cpp/msvcp140_2.dll differ +diff --git a/cpp/msvcp140_atomic_wait.dll b/cpp/msvcp140_atomic_wait.dll +new file mode 100644 +index 00000000..b8fa2030 +Binary files /dev/null and b/cpp/msvcp140_atomic_wait.dll differ +diff --git a/cpp/msvcp140_codecvt_ids.dll b/cpp/msvcp140_codecvt_ids.dll +new file mode 100644 +index 00000000..7549b2b5 +Binary files /dev/null and b/cpp/msvcp140_codecvt_ids.dll differ +diff --git a/cpp/vcruntime140.dll b/cpp/vcruntime140.dll +new file mode 100644 +index 00000000..5d5b81dc +Binary files /dev/null and b/cpp/vcruntime140.dll differ +diff --git a/cpp/vcruntime140_1.dll b/cpp/vcruntime140_1.dll +new file mode 100644 +index 00000000..3a02196f +Binary files /dev/null and b/cpp/vcruntime140_1.dll differ +diff --git "a/cpp/\344\277\261\344\271\220\351\203\250AI.cfg" "b/cpp/\344\277\261\344\271\220\351\203\250AI.cfg" +new file mode 100644 +index 00000000..9b9287b0 +--- /dev/null ++++ "b/cpp/\344\277\261\344\271\220\351\203\250AI.cfg" +@@ -0,0 +1,721 @@ ++# Configuration for KataGo C++ GTP engine ++ ++# Run the program using: `./katago.exe gtp` ++ ++# In this example config, when a parameter is given as a commented out value, ++# that value also is the default value, unless described otherwise. You can ++# uncomment it (remove the pound sign) and change it if you want. ++ ++# =========================================================================== ++# Running on an online server or in a real tournament or match ++# =========================================================================== ++# If you plan to run online or in a tournament, read through the "Rules" ++# section below for proper handling of komi, handicaps, end-of-game cleanup, ++# and other details. ++ ++# =========================================================================== ++# Notes about performance and memory usage ++# =========================================================================== ++# Important: For good performance, you will very likely want to tune the ++# "numSearchThreads" parameter in the Search limits section below! Run ++# "./katago benchmark" to test KataGo and to suggest a reasonable value ++# of this parameter. ++ ++# For multi-GPU systems, read "OpenCL GPU settings" or "CUDA GPU settings". ++# ++# When using OpenCL, verify that KataGo picks the correct device! Some systems ++# may have both an Intel CPU OpenCL and GPU OpenCL. If # KataGo picks the wrong ++# one, correct this by specifying "openclGpuToUse". ++# ++# Consider adjusting "maxVisits", "ponderingEnabled", "resignThreshold", and ++# other parameters depending on your intended usage. ++ ++# =========================================================================== ++# Command-line usage ++# =========================================================================== ++# All of the below values may be set or overridden via command-line arguments: ++# ++# -override-config KEY=VALUE,KEY=VALUE,... ++ ++# =========================================================================== ++# Logs and files ++# =========================================================================== ++# This section defines where and what logging information is produced. ++ ++# Each run of KataGo will log to a separate file in this dir. ++# This is the default. ++logDir = gtp_logs ++# Uncomment and specify this instead of logDir to write separate dated subdirs ++# logDirDated = gtp_logs ++# Uncomment and specify this instead of logDir to log to only a single file ++# logFile = gtp.log ++ ++# Logging options ++logAllGTPCommunication = true ++logSearchInfo = true ++logSearchInfoForChosenMove = false ++logToStderr = false ++ ++# KataGo will display some info to stderr on GTP startup ++# Uncomment the next line and set it to false to suppress that and remain silent ++# startupPrintMessageToStderr = true ++ ++# Write information to stderr, for use in things like malkovich chat to OGS. ++ ogsChatToStderr = true ++ ++# Uncomment and set this to a directory to override where openCLTuner files ++# and other cached data is written. By default it saves into a subdir of the ++# current directory on windows, and a subdir of ~/.katago on Linux. ++# homeDataDir = PATH_TO_DIRECTORY ++ ++# =========================================================================== ++# Analysis ++# =========================================================================== ++# This section configures analysis settings. ++# ++# The maximum number of moves after the first move displayed in variations ++# from analysis commands like kata-analyze or lz-analyze. ++# analysisPVLen = 15 ++ ++# Report winrates for chat and analysis as (BLACK|WHITE|SIDETOMOVE). ++# Most GUIs and analysis tools will expect SIDETOMOVE. ++# reportAnalysisWinratesAs = SIDETOMOVE ++ ++# Extra noise for wider exploration. Large values will force KataGo to ++# analyze a greater variety of moves than it normally would. ++# An extreme value like 1 distributes playouts across every move on the board, ++# even very bad moves. ++# Affects analysis only, does not affect play. ++# analysisWideRootNoise = 0.04 ++ ++# Try to limit the effect of possible bad or bogus move sequences in the ++# history leading to this position from affecting KataGo's move predictions. ++# analysisIgnorePreRootHistory = true ++ ++# =========================================================================== ++# Rules ++# =========================================================================== ++# This section configures the scoring and playing rules. Rules can also be ++# changed mid-run by issuing custom GTP commands. ++# ++# See https://lightvector.github.io/KataGo/rules.html for rules details. ++# ++# See https://github.com/lightvector/KataGo/blob/master/docs/GTP_Extensions.md ++# for GTP commands. ++ ++# Specify the rules as a string. ++# Some legal values include: ++# chinese, japanese, korean, aga, chinese-ogs, new-zealand, stone-scoring, ++# ancient-territory, bga, aga-button ++# ++# For some human rulesets that require complex adjudication in tricky cases ++# (e.g. japanese, korean) KataGo may not precisely match the ruleset in such ++# cases but will do its best. ++rules = tromp-taylor ++ ++# By default, the "rules" parameter is used, but if you comment it out and ++# uncomment one option in each of the sections below, you can specify an ++# arbitrary combination of individual rules. ++ ++# koRule = SIMPLE # Simple ko rules (triple ko = no result) ++# koRule = POSITIONAL # Positional superko ++# koRule = SITUATIONAL # Situational superko ++ ++# scoringRule = AREA # Area scoring ++# scoringRule = TERRITORY # Territory scoring (special computer-friendly territory rules) ++ ++# taxRule = NONE # All surrounded empty points are scored ++# taxRule = SEKI # Eyes in seki do NOT count as points ++# taxRule = ALL # All groups are taxed up to 2 points for the two eyes needed to live ++ ++# Is multiple-stone suicide legal? (Single-stone suicide is always illegal). ++# multiStoneSuicideLegal = false ++# multiStoneSuicideLegal = true ++ ++# "Button go" - the first pass when area scoring awards 0.5 points and does ++# not count for ending the game. ++# Allows area scoring rulesets that have far simpler rules to achieve the same ++# final scoring precision and reward for precise play as territory scoring. ++# hasButton = false ++# hasButton = true ++ ++# Is this a human ruleset where it's okay to pass before having physically ++# captured and removed all dead stones? ++# friendlyPassOk = false ++# friendlyPassOk = true ++ ++# How handicap stones in handicap games are compensated ++# whiteHandicapBonus = 0 # White gets no compensation for black's handicap stones (Tromp-taylor, NZ, JP) ++# whiteHandicapBonus = N-1 # White gets N-1 points for black's N handicap stones (AGA) ++# whiteHandicapBonus = N # White gets N points for black's N handicap stones (Chinese) ++ ++# ------------------------------ ++# Other rules hacks ++# ------------------------------ ++# Uncomment and change to adjust what board size KataGo uses upon startup ++# by default when GTP doesn't specify. ++# defaultBoardSize = 19 ++ ++# By default, Katago will use the komi that the GUI or GTP controller tries to set. ++# Uncomment and set this to have KataGo ignore the controller and always use this komi. ++# ignoreGTPAndForceKomi = 7 ++ ++# =========================================================================== ++# Bot behavior ++# =========================================================================== ++ ++# ------------------------------ ++# Resignation ++# ------------------------------ ++ ++# Resignation occurs if for at least resignConsecTurns in a row, the ++# winLossUtility (on a [-1,1] scale) is below resignThreshold. ++allowResignation = true ++resignThreshold = -0.90 ++resignConsecTurns = 3 ++ ++# By default, KataGo may resign games that it is confidently losing even if they ++# are very close in score. Uncomment and set this to avoid resigning games ++# if the estimated difference is points is less than or equal to this. ++# resignMinScoreDifference = 10 ++ ++# ------------------------------ ++# Handicap ++# ------------------------------ ++# Assume that if black makes many moves in a row right at the start of the ++# game, then the game is a handicap game. This is necessary on some servers ++# and for some GUIs and also when initializing from many SGF files, which may ++# set up a handicap game using repeated GTP "play" commands for black rather ++# than GTP "place_free_handicap" commands; however, it may also lead to ++# incorrect understanding of komi if whiteHandicapBonus is used and a server ++# does not have such a practice. Uncomment and set to false to disable. ++# assumeMultipleStartingBlackMovesAreHandicap = true ++ ++# Makes katago dynamically adjust in handicap or altered-komi games to assume ++# based on those game settings that it must be stronger or weaker than the ++# opponent and to play accordingly. Greatly improves handicap strength by ++# biasing winrates and scores to favor appropriate safe/aggressive play. ++# Does NOT affect analysis (lz-analyze, kata-analyze, used by programs like ++# Lizzie) so analysis remains unbiased. Uncomment and set this to 0 to disable ++# this and make KataGo play the same always. ++# dynamicPlayoutDoublingAdvantageCapPerOppLead = 0.045 ++ ++# Instead of "dynamicPlayoutDoublingAdvantageCapPerOppLead", you can comment ++# that out and uncomment and set "playoutDoublingAdvantage" to a fixed value ++# from -3.0 to 3.0 that will not change dynamically. ++# ALSO affects analysis tools (lz-analyze, kata-analyze, used by e.g. Lizzie). ++# Negative makes KataGo behave as if it is much weaker than the opponent. ++# Positive makes KataGo behave as if it is much stronger than the opponent. ++# KataGo will adjust to favor safe/aggressive play as appropriate based on ++# the combination of who is ahead and how much stronger/weaker it thinks it is, ++# and report winrates and scores taking the strength difference into account. ++# ++# If this and "dynamicPlayoutDoublingAdvantageCapPerOppLead" are both set ++# then dynamic will be used for all games and this fixed value will be used ++# for analysis tools. ++# playoutDoublingAdvantage = 0.0 ++ ++# Uncomment one of these when using "playoutDoublingAdvantage" to enforce ++# that it will only apply when KataGo plays as the specified color and will be ++# negated when playing as the opposite color. ++# playoutDoublingAdvantagePla = BLACK ++# playoutDoublingAdvantagePla = WHITE ++ ++# ------------------------------ ++# Passing and cleanup ++# ------------------------------ ++# Make the bot never assume that its pass will end the game, even if passing ++# would end and "win" under Tromp-Taylor rules. Usually this is a good idea ++# when using it for analysis or playing on servers where scoring may be ++# implemented non-tromp-taylorly. Uncomment and set to false to disable. ++# conservativePass = true ++ ++# When using territory scoring, self-play games continue beyond two passes ++# with special cleanup rules that may be confusing for human players. This ++# option prevents the special cleanup phases from being reachable when using ++# the bot for GTP play. Uncomment and set to false to enable entering special ++# cleanup. For example, if you are testing it against itself, or against ++# another bot that has precisely implemented the rules documented at ++# https://lightvector.github.io/KataGo/rules.html ++# preventCleanupPhase = true ++ ++# ------------------------------ ++# Miscellaneous behavior ++# ------------------------------ ++# If the board is symmetric, search only one copy of each equivalent move. ++# Attempts to also account for ko/superko, will not theoretically perfect for ++# superko. Uncomment and set to false to disable. ++# rootSymmetryPruning = true ++ ++# Uncomment and set to true to avoid a particular joseki that some networks ++# misevaluate, and also to improve opening diversity versus some particular ++# other bots that like to play it all the time. ++# avoidMYTDaggerHack = false ++ ++# Prefer to avoid playing the same joseki in every corner of the board. ++# Uncomment to set to a specific value. See "Avoid SGF patterns" section. ++# By default: 0 (even games), 0.005 (handicap games) ++# avoidRepeatedPatternUtility = 0.0 ++ ++# Experimental logic to fight against mirror Go even with unfavorable komi. ++# Uncomment to set to a specific value to use for both playing and analysis. ++# By default: true when playing via GTP, but false when analyzing. ++# antiMirror = true ++ ++# Enable some hacks that mitigate rare instances when passing messes up deeper searches. ++# enablePassingHacks = true ++ ++# Uncomment and set this to true to prevent bad or bogus move sequences ++# in the history leading to this position from affecting KataGo's move choices. ++# Same as analysisIgnorePreRootHistory (see above) but applies to actual play. ++# You can enable this if KataGo is being asked to play from positions that it did not ++# choose the moves to reach. ++# ignorePreRootHistory = false ++ ++# =========================================================================== ++# Search limits ++# =========================================================================== ++ ++# Terminology: ++# "Playouts" is the number of new playouts of search performed each turn. ++# "Visits" is the same as "Playouts" but also counts search performed on ++# previous turns that is still applicable to this turn. ++# "Time" is the time in seconds. ++ ++# For example, if KataGo searched 200 nodes on the previous turn, and then ++# after the opponent's reply, 50 nodes of its search tree was still valid, ++# then a visit limit of 200 would allow KataGo to search 150 new nodes ++# (for a final tree size of 200 nodes), whereas a playout limit of of 200 ++# would allow KataGo to search 200 nodes (for a final tree size of 250 nodes). ++ ++# Additionally, KataGo may also move before than the limit in order to ++# obey time controls (e.g. byo-yomi, etc) if the GTP controller has ++# told KataGo that the game has is being played with a given time control. ++ ++# Limits for search on the current turn. ++# If commented out or unspecified, the default is to have no limit. ++# maxVisits = 500 ++# maxPlayouts = 300 ++ maxTime = 1.0 ++ ++# Ponder on the opponent's turn? ++ponderingEnabled = true ++ ++# Limits for search when pondering on the opponent's turn. ++# If commented out or unspecified, the default is to have no limit. ++# Limiting the maximum time is recommended so that KataGo won't burn CPU/GPU ++# forever and/or run out of RAM if left unattended while pondering is enabled. ++# maxVisitsPondering = 5000 ++# maxPlayoutsPondering = 3000 ++maxTimePondering = 120.0 ++ ++ ++# ------------------------------ ++# Other search limits and behavior ++# ------------------------------ ++ ++# Approx number of seconds to buffer for lag for GTP time controls - will ++# move a bit faster assuming there is this much lag per move. ++lagBuffer = 1.0 ++ ++# YOU PROBABLY WANT TO TUNE THIS PARAMETER! ++# The number of threads to use when searching. On powerful GPUs the optimal ++# threads may be much higher than the number of CPU cores you have because ++# many threads are needed to feed efficient large batches to the GPU. ++# ++# Run "./katago benchmark" to tune this parameter and test the effect ++# of changes to any of other parameters. ++numSearchThreads = 12 ++ ++# Play a little faster if the opponent is passing, for human-friendliness. ++# Comment these out to disable them, such as if running a controlled match ++# where you are testing KataGo with fixed compute per move vs other bots. ++searchFactorAfterOnePass = 0.50 ++searchFactorAfterTwoPass = 0.25 ++ ++# Play a little faster if super-winning, for human-friendliness. ++# Comment these out to disable them, such as if running a controlled match ++# where you are testing KataGo with fixed compute per move vs other bots. ++searchFactorWhenWinning = 0.40 ++searchFactorWhenWinningThreshold = 0.95 ++ ++# =========================================================================== ++# GPU settings ++# =========================================================================== ++# This section configures GPU settings. ++# ++# Maximum number of positions to send to a single GPU at once. The default ++# value is roughly equal to numSearchThreads, but can be specified manually ++# if running out of memory, or using multiple GPUs that expect to share work. ++# nnMaxBatchSize = ++ ++# Controls the neural network cache size, which is the primary RAM/memory use. ++# KataGo will cache up to (2 ** nnCacheSizePowerOfTwo) many neural net ++# evaluations in case of transpositions in the tree. ++# Increase this to improve performance for searches with tens of thousands ++# of visits or more. Decrease this to limit memory usage. ++# If you're happy to do some math - each neural net entry takes roughly ++# 1.5KB, except when using whole-board ownership/territory ++# visualizations, where each entry will take roughly 3KB. The number of ++# entries is (2 ** nnCacheSizePowerOfTwo). (E.g. 2 ** 18 = 262144.) ++# You can compute roughly how much memory the cache will use based on this. ++# nnCacheSizePowerOfTwo = 20 ++ ++# Size of mutex pool for nnCache is (2 ** this). ++# nnMutexPoolSizePowerOfTwo = 16 ++ ++# Randomize board orientation when running neural net evals? Uncomment and ++# set to false to disable. ++# nnRandomize = true ++ ++# If provided, force usage of a specific seed for nnRandomize. ++# The default is to use a randomly generated seed. ++# nnRandSeed = abcdefg ++ ++# Uncomment and set to true to force GTP to use the maximum board size for ++# internal buffers for the neural net. This will make KataGo slower when ++# evaluating small boards, but will avoid a lengthy initialization time on every ++# change of board size due to having to re-size the neural net buffers on the GPU. ++# This can be useful for example, for OGS's persistent bot mode that uses a single ++# bot instance to handle multiple games and may thrash between different board sizes ++# if there are concurrent games of multiple sizes. ++# gtpForceMaxNNSize = false ++ ++# ------------------------------ ++# Multiple GPUs ++# ------------------------------ ++# Set this to the number of GPUs to use or that are available. ++# IMPORTANT: If more than 1, also uncomment the appropriate TensorRT ++# or CUDA or OpenCL section. ++# numNNServerThreadsPerModel = 1 ++ ++# ------------------------------ ++# TENSORRT GPU settings ++# ------------------------------ ++# These only apply when using the TENSORRT version of KataGo. ++ ++# For one GPU: optionally uncomment this option and change if the GPU to ++# use is not device 0. ++# trtDeviceToUse = 0 ++ ++# For two GPUs: Uncomment these options, AND set numNNServerThreadsPerModel above. ++# Also, change their values if the devices you want to use are not 0 and 1. ++# trtDeviceToUseThread0 = 0 ++# trtDeviceToUseThread1 = 1 ++ ++# For three GPUs: Uncomment these options, AND set numNNServerThreadsPerModel above. ++# Also, change their values if the devices you want to use are not 0 and 1 and 2. ++# trtDeviceToUseThread0 = 0 ++# trtDeviceToUseThread1 = 1 ++# trtDeviceToUseThread2 = 2 ++ ++# The pattern continues for additional GPUs. ++ ++# ------------------------------ ++# CUDA GPU settings ++# ------------------------------ ++# These only apply when using the CUDA version of KataGo. ++ ++# For one GPU: optionally uncomment and change this if the GPU you want to ++# use is not device 0 ++# cudaDeviceToUse = 0 ++ ++# For two GPUs: Uncomment these options, AND set numNNServerThreadsPerModel above. ++# Also, change their values if the devices you want to use are not 0 and 1. ++# cudaDeviceToUseThread0 = 0 ++# cudaDeviceToUseThread1 = 1 ++ ++# For three GPUs: Uncomment these options, AND set numNNServerThreadsPerModel above. ++# Also, change their values if the devices you want to use are not 0 and 1 and 2. ++# cudaDeviceToUseThread0 = 0 ++# cudaDeviceToUseThread1 = 1 ++# cudaDeviceToUseThread2 = 2 ++ ++# The pattern continues for additional GPUs. ++ ++# KataGo will automatically use FP16 or not based on the compute capability ++# of your NVIDIA GPU. If you want to try to force a particular behavior ++# you can uncomment these lines and change them to "true" or "false". ++# cudaUseFP16 = auto ++# cudaUseNHWC = auto ++ ++# ------------------------------ ++# OpenCL GPU settings ++# ------------------------------ ++# These only apply when using the OpenCL version of KataGo. ++ ++# Uncomment and set to true to tune OpenCL for every board size separately, ++# rather than only the largest possible size. ++# openclReTunePerBoardSize = false ++ ++# For one GPU: optionally uncomment and change this if the best device to use is guessed incorrectly. ++# The default behavior tries to guess the 'best' GPU or device on your system to use, usually it will be a good guess. ++# openclDeviceToUse = 0 ++ ++# For two GPUs: Uncomment these two lines and replace X and Y with the device ids of the devices you want to use. ++# It might NOT be 0 and 1, some computers will have many OpenCL devices. You can see what the devices are when ++# KataGo starts up - it should print or log all the devices it finds. ++# (AND also set numNNServerThreadsPerModel above) ++# openclDeviceToUseThread0 = X ++# openclDeviceToUseThread1 = Y ++ ++# For three GPUs: Uncomment these three lines and replace X and Y and Z with the device ids of the devices you want to use. ++# It might NOT be 0 and 1 and 2, some computers will have many OpenCL devices. You can see what the devices are when ++# KataGo starts up - it should print or log all the devices it finds. ++# (AND also set numNNServerThreadsPerModel above) ++# openclDeviceToUseThread0 = X ++# openclDeviceToUseThread1 = Y ++# openclDeviceToUseThread2 = Z ++ ++# The pattern continues for additional GPUs. ++ ++# KataGo will automatically use FP16 or not based on testing your GPU during ++# tuning. If you want to try to force a particular behavior though you can ++# uncomment this option and change it to "true" or "false". This is a fairly ++# blunt setting - more detailed settings are testable by rerunning the tuner ++# with various arguments (./katago tuner). ++# openclUseFP16 = auto ++ ++# ------------------------------ ++# Eigen-specific settings ++# ------------------------------ ++# These only apply when using the Eigen (pure CPU) version of KataGo. ++ ++# Number of CPU threads for evaluating the neural net on the Eigen backend. ++# ++# Default: numSearchThreads ++# numEigenThreadsPerModel = X ++ ++# =========================================================================== ++# Root move selection and biases ++# =========================================================================== ++# Uncomment and edit any of the below values to change them from their default. ++ ++# If provided, force usage of a specific seed for various random things in ++# the search. The default is to use a random seed. ++# searchRandSeed = hijklmn ++ ++# Temperature for the early game, randomize between chosen moves with ++# this temperature ++# chosenMoveTemperatureEarly = 0.5 ++ ++# Decay temperature for the early game by 0.5 every this many moves, ++# scaled with board size. ++# chosenMoveTemperatureHalflife = 19 ++ ++# At the end of search after the early game, randomize between chosen ++# moves with this temperature ++# chosenMoveTemperature = 0.10 ++ ++# Subtract this many visits from each move prior to applying ++# chosenMoveTemperature (unless all moves have too few visits) to downweight ++# unlikely moves ++# chosenMoveSubtract = 0 ++ ++# The same as chosenMoveSubtract but only prunes moves that fall below ++# the threshold. This setting does not affect chosenMoveSubtract. ++# chosenMovePrune = 1 ++ ++# Number of symmetries to sample (without replacement) and average at the root ++# rootNumSymmetriesToSample = 1 ++ ++# Using LCB for move selection? ++# useLcbForSelection = true ++ ++# How many stdevs a move needs to be better than another for LCB selection ++# lcbStdevs = 5.0 ++ ++# Only use LCB override when a move has this proportion of visits as the ++# top move. ++# minVisitPropForLCB = 0.15 ++ ++# =========================================================================== ++# Internal params ++# =========================================================================== ++# Uncomment and edit any of the below values to change them from their default. ++ ++# Scales the utility of winning/losing ++# winLossUtilityFactor = 1.0 ++ ++# Scales the utility for trying to maximize score ++# staticScoreUtilityFactor = 0.10 ++# dynamicScoreUtilityFactor = 0.30 ++ ++# Adjust dynamic score center this proportion of the way towards zero, ++# capped at a reasonable amount. ++# dynamicScoreCenterZeroWeight = 0.20 ++# dynamicScoreCenterScale = 0.75 ++ ++# The utility of getting a "no result" due to triple ko or other long cycle ++# in non-superko rulesets (-1 to 1) ++# noResultUtilityForWhite = 0.0 ++ ++# The number of wins that a draw counts as, for white. (0 to 1) ++# drawEquivalentWinsForWhite = 0.5 ++ ++# Exploration constant for mcts ++# cpuctExploration = 1.0 ++# cpuctExplorationLog = 0.45 ++ ++# Parameters that control exploring more in volatile positions, exploring ++# less in stable positions. ++# cpuctUtilityStdevPrior = 0.40 ++# cpuctUtilityStdevPriorWeight = 2.0 ++# cpuctUtilityStdevScale = 0.85 ++ ++# FPU reduction constant for mcts ++# fpuReductionMax = 0.2 ++# rootFpuReductionMax = 0.1 ++# fpuParentWeightByVisitedPolicy = true ++ ++# Parameters that control weighting of evals based on the net's own ++# self-reported uncertainty. ++# useUncertainty = true ++# uncertaintyExponent = 1.0 ++# uncertaintyCoeff = 0.25 ++ ++# Explore using optimistic policy ++# rootPolicyOptimism = 0.2 ++# policyOptimism = 1.0 ++ ++# Amount to apply a downweighting of children with very bad values relative ++# to good ones. ++# valueWeightExponent = 0.25 ++ ++# Slight incentive for the bot to behave human-like with regard to passing at ++# the end, filling the dame, not wasting time playing in its own territory, ++# etc., and not play moves that are equivalent in terms of points but a bit ++# more unfriendly to humans. ++# rootEndingBonusPoints = 0.5 ++ ++# Make the bot prune useless moves that are just prolonging the game to ++# avoid losing yet. ++# rootPruneUselessMoves = true ++ ++# Apply bias correction based on local pattern keys ++# subtreeValueBiasFactor = 0.45 ++# subtreeValueBiasWeightExponent = 0.85 ++ ++# Use graph search rather than tree search - identify and share search for ++# transpositions. ++# useGraphSearch = true ++ ++# How much to shard the node table for search synchronization ++# nodeTableShardsPowerOfTwo = 16 ++ ++# How many virtual losses to add when a thread descends through a node ++# numVirtualLossesPerThread = 1 ++ ++# Improve the quality of evals under heavy multithreading ++# useNoisePruning = true ++ ++# =========================================================================== ++# Automatic avoid patterns ++# =========================================================================== ++# The parameters in this section provide a way to bias away from moves that match ++# patterns that this instance of KataGo has played in previous games, by auto-saving ++# moves to a directory and then auto-loading and biasing against them each new game. ++# Uncomment them to use them. When using this feature, all parameters must be specified. ++ ++# Different board sizes are tracked separately, but all board sizes share the same ++# parameters by default. Every parameter *except* for autoAvoidRepeatDir and ++# autoAvoidRepeatSaveChunkSize can be overridden per board size, ++# e.g. "autoAvoidRepeatMinTurnNumber13x13". You must ALSO specify the ++# defaults even if you have specified board-size-specific values. ++ ++# Directory to auto-save moves KataGo plays, to avoid them in future games. ++# You can create a new empty directory and put its path here. ++# If you run parallel instances of KataGo, use different directories if you ++# want them not to share their biases, use the same directory if you want ++# all of them to bias away from past moves that any of them have played. ++# KataGo will also automatically DELETE old data in this directory, so it is ++# recommended that if you do share the same directory between parallel instances, ++# that they all use the same settings that affect data saving/deletion. ++# autoAvoidRepeatDir = PATH_TO_NEW_DIRECTORY ++ ++# Penalize this much utility per matching move. ++# Values that are too large may lead to bad play. The value of 0.004 is fairly large ++# and might be large enough to result in some early weird/bad moves when trying ++# to avoid past games' moves if enough games begin the same way. You can experiment with it. ++# autoAvoidRepeatUtility = 0.004 ++ ++# Per each new move saved, exponentially decay prior saved moves by this factor. ++# This way, the bias against moves from many games ago is gradually phased out. ++# For example, 0.9995 = 1 - 1/2000, so would be roughly 2000 prior moves worth of ++# penalty weight remembered, in steady state. Depending on what turn number range ++# you are saving, this might equate to a different number of games For example ++# saving the first 50 moves per game would make this roughly 2000 / 50 = 40 games ++# worth of memory. ++# autoAvoidRepeatLambda = 0.9995 ++ ++# Affects data saving/deletion. ++# When the number of saved moves exceeds this, outright delete them to avoid too many ++# files and disk space building up. Also may affect the speed of saving/loading on start ++# of each game if this is set large and a lot of data builds up. ++# autoAvoidRepeatMaxPoses = 10000 ++ ++# Affects data saving/deletion. ++# Only save data for moves within this turn number range of those games. ++# E.g. setting autoAvoidRepeatMinTurnNumber to a number like 4 or 5 would tend to make ++# KataGo not develop a bias against the initial 3-4 and 4-4 corner moves in almost every game. ++# autoAvoidRepeatMinTurnNumber = 0 ++# autoAvoidRepeatMaxTurnNumber = 50 ++ ++# Affects data saving/deletion. ++# Within a single run of a program, wait to accumulate this many samples ++# (possibly across multiple clear_boards/games) before saving the data. ++# Can help to avoid writing too many small files to disk, especially when GTP is used ++# in a way that clears the board very frequently (e.g. gtp2ogs pooled manager). ++# autoAvoidRepeatSaveChunkSize = 200 ++ ++# =========================================================================== ++# Avoid SGF patterns ++# =========================================================================== ++# The parameters in this section provide a way to avoid moves that follow ++# specific patterns based on a set of SGF files loaded upon startup. ++# This is basically the same as the above "Automatic avoid patterns" section ++# above except you supply your own SGF files to avoid moves from. ++# Uncomment them to use this feature. Additionally, if the SGF file ++# contains the string %SKIP% in a comment on a move, that move will be ++# ignored for this purpose. ++ ++# Load SGF files from this directory when the engine is started ++# (only on startup, will not reload unless engine is restarted) ++# avoidSgfPatternDirs = path/to/directory/with/sgfs/ ++# You can also surround the file path in double quotes if the file path contains trailing spaces or hash signs. ++# Within double quotes, backslashes are escape characters. ++# avoidSgfPatternDirs = "path/to/directory/with/sgfs/" ++ ++# Penalize this much utility per matching move. ++# Set this negative if you instead want to favor SGF patterns instead of ++# penalizing them. This number does not need to be large, even 0.001 will ++# make a difference. Values that are too large may lead to bad play. ++# avoidSgfPatternUtility = 0.001 ++ ++# Optional - load only the newest this many files ++# avoidSgfPatternMaxFiles = 20 ++ ++# Optional - Penalty is multiplied by this per each older SGF file, so that ++# old SGF files matter less than newer ones. ++# avoidSgfPatternLambda = 0.90 ++ ++# Optional - pay attention only to moves made by players with this name. ++# For example, set it to the name that your bot's past games will show up ++# as in the SGF, so that the bot will only avoid repeating moves that itself ++# made in past games, not the moves that its opponents made. ++# avoidSgfPatternAllowedNames = my-ogs-bot-name1,my-ogs-bot-name2 ++ ++# Optional - Ignore moves in SGF files that occurred before this turn number. ++# avoidSgfPatternMinTurnNumber = 0 ++ ++# For more avoid patterns: ++# You can also specify a second set of parameters, and a third, fourth, ++# etc. by numbering 2,3,4,... ++# ++# avoidSgf2PatternDirs = ... ++# avoidSgf2PatternUtility = ... ++# avoidSgf2PatternMaxFiles = ... ++# avoidSgf2PatternLambda = ... ++# avoidSgf2PatternAllowedNames = ... ++# avoidSgf2PatternMinTurnNumber = ... ++ +diff --git a/python/selfplay/KataGoData/opencltuning/tune11_gpuNVIDIAGeForceRTX4060LaptopGPU_x19_y19_c320_mv10.txt b/python/selfplay/KataGoData/opencltuning/tune11_gpuNVIDIAGeForceRTX4060LaptopGPU_x19_y19_c320_mv10.txt +new file mode 100644 +index 00000000..5b66d67d +--- /dev/null ++++ b/python/selfplay/KataGoData/opencltuning/tune11_gpuNVIDIAGeForceRTX4060LaptopGPU_x19_y19_c320_mv10.txt +@@ -0,0 +1,33 @@ ++VERSION=11 ++#canUseFP16Storage ++1 ++#canUseFP16Compute ++0 ++#canUseFP16TensorCores ++1 ++#canUseFP16TensorCoresFor1x1 ++0 ++#shouldUseFP16Storage ++1 ++#shouldUseFP16Compute ++0 ++#shouldUseFP16TensorCores ++1 ++#shouldUseFP16TensorCoresFor1x1 ++0 ++#xGemmDirect ++WGD=32 MDIMCD=8 NDIMCD=8 MDIMAD=16 NDIMBD=16 KWID=2 VWMD=2 VWND=2 PADA=1 PADB=1 ++#xGemm ++MWG=64 NWG=64 KWG=16 MDIMC=16 NDIMC=8 MDIMA=16 NDIMB=8 KWI=2 VWM=4 VWN=4 STRM=0 STRN=0 SA=1 SB=1 ++#xGemm16 ++MWG=64 NWG=64 KWG=16 MDIMC=16 NDIMC=8 MDIMA=16 NDIMB=8 KWI=2 VWM=4 VWN=4 STRM=0 STRN=0 SA=1 SB=1 ++#hGemmWmma ++MWG=64 NWG=64 KWG=16 MWAVE=32 NWAVE=8 MWARP=32 NWARP=8 VWM=2 VWN=2 SA=0 SB=0 ++#hGemmWmmaNCHW ++MWG=16 NWG=16 KWG=16 MWAVE=16 NWAVE=16 MWARP=16 NWARP=16 VWM=1 VWN=2 SB=0 ++#conv3x3 ++INTILE_XSIZE=6 INTILE_YSIZE=6 OUTTILE_XSIZE=4 OUTTILE_YSIZE=4 transLocalSize0=128 transLocalSize1=1 untransLocalSize0=8 untransLocalSize1=4 untransLocalSize2=2 ++#conv5x5 ++INTILE_XSIZE=6 INTILE_YSIZE=6 OUTTILE_XSIZE=2 OUTTILE_YSIZE=2 transLocalSize0=128 transLocalSize1=1 untransLocalSize0=8 untransLocalSize1=4 untransLocalSize2=2 ++#gPool ++XYSTRIDE=16 CHANNELSTRIDE=2 BATCHSTRIDE=2 +diff --git a/python/selfplay/KataGoData/opencltuning/tune11_gpuNVIDIAGeForceRTX4060LaptopGPU_x19_y19_c384_mv14.txt b/python/selfplay/KataGoData/opencltuning/tune11_gpuNVIDIAGeForceRTX4060LaptopGPU_x19_y19_c384_mv14.txt +new file mode 100644 +index 00000000..71421834 +--- /dev/null ++++ b/python/selfplay/KataGoData/opencltuning/tune11_gpuNVIDIAGeForceRTX4060LaptopGPU_x19_y19_c384_mv14.txt +@@ -0,0 +1,33 @@ ++VERSION=11 ++#canUseFP16Storage ++1 ++#canUseFP16Compute ++0 ++#canUseFP16TensorCores ++1 ++#canUseFP16TensorCoresFor1x1 ++0 ++#shouldUseFP16Storage ++1 ++#shouldUseFP16Compute ++0 ++#shouldUseFP16TensorCores ++1 ++#shouldUseFP16TensorCoresFor1x1 ++0 ++#xGemmDirect ++WGD=32 MDIMCD=8 NDIMCD=8 MDIMAD=8 NDIMBD=8 KWID=8 VWMD=2 VWND=4 PADA=1 PADB=1 ++#xGemm ++MWG=64 NWG=64 KWG=16 MDIMC=16 NDIMC=16 MDIMA=16 NDIMB=16 KWI=2 VWM=4 VWN=4 STRM=0 STRN=0 SA=1 SB=1 ++#xGemm16 ++MWG=64 NWG=64 KWG=16 MDIMC=16 NDIMC=16 MDIMA=16 NDIMB=16 KWI=2 VWM=4 VWN=4 STRM=0 STRN=0 SA=1 SB=1 ++#hGemmWmma ++MWG=64 NWG=64 KWG=64 MWAVE=16 NWAVE=16 MWARP=16 NWARP=16 VWM=2 VWN=2 SA=0 SB=0 ++#hGemmWmmaNCHW ++MWG=16 NWG=16 KWG=16 MWAVE=16 NWAVE=16 MWARP=16 NWARP=16 VWM=1 VWN=2 SB=0 ++#conv3x3 ++INTILE_XSIZE=6 INTILE_YSIZE=6 OUTTILE_XSIZE=4 OUTTILE_YSIZE=4 transLocalSize0=64 transLocalSize1=1 untransLocalSize0=16 untransLocalSize1=2 untransLocalSize2=2 ++#conv5x5 ++INTILE_XSIZE=6 INTILE_YSIZE=6 OUTTILE_XSIZE=2 OUTTILE_YSIZE=2 transLocalSize0=64 transLocalSize1=1 untransLocalSize0=16 untransLocalSize1=2 untransLocalSize2=2 ++#gPool ++XYSTRIDE=32 CHANNELSTRIDE=1 BATCHSTRIDE=2 +diff --git a/python/selfplay/README.txt b/python/selfplay/README.txt +new file mode 100644 +index 00000000..fb82db4b +--- /dev/null ++++ b/python/selfplay/README.txt +@@ -0,0 +1,118 @@ ++KataGo v1.15.1 ++https://github.com/lightvector/KataGo ++ ++For neural nets from the latest run, download from here: ++https://katagotraining.org/ ++For nets from earlier runs, see: ++https://katagoarchive.org/ ++ ++For the human SL net (b18c384nbt-humanv0.bin.gz), it was released with: ++https://github.com/lightvector/KataGo/releases/tag/v1.15.0 ++ ++For differences between this version and older versions, see releases page at ++https://github.com/lightvector/KataGo/releases/ ++ ++----------------------------------------------------- ++USAGE: ++----------------------------------------------------- ++KataGo is just an engine and does not have its own graphical interface. So generally you will want to use KataGo along with a GUI or analysis program. ++(https://github.com/lightvector/KataGo#guis) ++ ++FIRST: Run a command like this to make sure KataGo is working, with the neural net file you downloaded. On OpenCL, it will also tune for your GPU. ++ ++./katago.exe benchmark # if you have default_gtp.cfg and default_model.bin.gz ++./katago.exe benchmark -model .bin.gz # if you have default_gtp.cfg ++./katago.exe benchmark -model .bin.gz -config gtp_custom.cfg # use this .bin.gz neural net and this .cfg file ++ ++It will tell you a good number of threads. Edit your .cfg file and set "numSearchThreads" to that many to get best performance. ++ ++OR: Run this command to have KataGo generate a custom gtp config for you based on answering some questions: ++ ++./katago.exe genconfig -model .bin.gz -output gtp_custom.cfg ++ ++NEXT: A command like this will run KataGo's engine. This is the command to give to your [GUI or analysis program](#guis) so that it can run KataGo. ++ ++./katago.exe gtp # if you have default_gtp.cfg and default_model.bin.gz ++./katago.exe gtp -model .bin.gz # if you have default_gtp.cfg ++./katago.exe gtp -model .bin.gz -config gtp_custom.cfg # use this .bin.gz neural net and this .cfg file ++ ++You may need to specify different paths when entering KataGo's command for a GUI program, e.g.: ++ ++path/to/katago.exe gtp -model path/to/.bin.gz ++path/to/katago.exe gtp -model path/to/.bin.gz -config path/to/gtp_custom.cfg ++ ++KataGo should be able to work with any GUI program that supports GTP, as well as any analysis program that supports Leela Zero's `lz-analyze` command, such as Lizzie (https://github.com/featurecat/lizzie) or Sabaki (https://sabaki.yichuanshen.de/). ++ ++NOTE for Windows users: ++If you encounter errors due to a missing "msvcp140.dll" or "msvcp140_1.dll" or "msvcp140_2.dll" or "vcruntime140.dll" or similar, you need to download and install the Microsoft Visual C++ Redistributable, here: ++https://www.microsoft.com/en-us/download/details.aspx?id=48145 ++If this is for a 64-bit Windows version of KataGo, these dll files have already been included for you, otherwise you will need to install them yourself. On a 64-bit Windows version, there is a rare chance that you may need to delete them if you already have it installed yourself separately and the pre-included files are actually causing problems running KataGo. ++ ++----------------------------------------------------- ++HUMAN-STYLE PLAY AND ANALYSIS: ++----------------------------------------------------- ++ ++You can also have KataGo imitate human play if you download the human SL model b18c384nbt-humanv0.bin.gz from https://github.com/lightvector/KataGo/releases/tag/v1.15.0, and run a command like the following, providing both a model and the human SL model: ++ ++./katago.exe gtp -model .bin.gz -human-model b18c384nbt-humanv0.bin.gz -config gtp_human5k_example.cfg ++ ++The gtp_human5k_example.cfg configures KataGo to imitate 5-kyu-level players. You can change it to imitate other ranks too, as well as to do many more things, including making KataGo play in a human style but still at a strong level or analyze in interesting ways. Read the config file itself for documentation on some of these possibilities! ++ ++And see also this guide to using the human SL model, which is written from the perspective of the JSON-based analysis engine mentioned below, but is also applicable to gtp as well. ++https://github.com/lightvector/KataGo/blob/master/docs/Analysis_Engine.md#human-sl-analysis-guide ++ ++----------------------------------------------------- ++OTHER THINGS YOU CAN DO: ++----------------------------------------------------- ++ ++Run a JSON-based analysis engine (https://github.com/lightvector/KataGo/blob/master/docs/Analysis_Engine.md) that can do efficient batched evaluations for a backend Go service: ++ ++./katago.exe analysis -model .gz -config .cfg ++ ++Run a high-performance match engine that will play a pool of bots against each other sharing the same GPU batches and CPUs with each other: ++ ++./katago.exe match -config .cfg -log-file match.log -sgf-output-dir ++ ++Force OpenCL tuner to re-tune: ++ ++./katago.exe tuner -config .cfg ++ ++Print version: ++ ++./katago.exe version ++ ++ ++----------------------------------------------------- ++OPENCL VS CUDA VS EIGEN: ++----------------------------------------------------- ++Explanation of the various versions available at https://github.com/lightvector/KataGo/releases ++ ++OpenCL - Use this if you have a modern GPU and want the easiest setup. ++ ++TensorRT, CUDA11 - Try one of these if you have a top-end NVIDIA GPU, are willing to do some more technical setup work, and care about getting every bit of performance. It MIGHT OR MIGHT NOT be faster than OpenCL! You have to test it on your specific GPU. TensorRT requires CUDA11 as well, and is likely to be the fastest. ++ ++Eigen AVX2 - Use this if you have no good GPU, but you have an Intel or AMD CPU from the last several years. This is a pure CPU version of KataGo. ++ ++Eigen - Use this if you don't have a GPU or your GPU is too old to work, and your CPU turns out not to support AVX2 or FMA. This is a pure CPU version for KataGo for ++This is the pure CPU version of KataGo, with no special instructions, which should hopefully run just about anywhere. ++ ++ ++----------------------------------------------------- ++TUNING FOR PERFORMANCE: ++----------------------------------------------------- ++You will very likely want to tune some of the parameters in `default_gtp.cfg` for your system for good performance, including the number of threads, fp16 usage, NN cache size, pondering settings, and so on. You can also adjust things like KataGo's resign threshold or utility function. Most of the relevant parameters should be be reasonably well documented directly inline in that config. ++ ++There are other a few notes about usage and performance at : https://github.com/lightvector/KataGo ++ ++----------------------------------------------------- ++TROUBLESHOOTING: ++----------------------------------------------------- ++Some common issues are described here: ++https://github.com/lightvector/KataGo#common-causes-of-errors ++ ++Or, feel free to hop into the Leela Zero discord chat, which has become a general chatroom for a variety of computer Go hobbyists and users, and which you can often find people willing to help. ++https://discord.gg/fhDHgfk ++ ++ ++ ++ +diff --git a/python/selfplay/analysis_example.cfg b/python/selfplay/analysis_example.cfg +new file mode 100644 +index 00000000..ca648321 +--- /dev/null ++++ b/python/selfplay/analysis_example.cfg +@@ -0,0 +1,402 @@ ++# Config for KataGo C++ Analysis engine, i.e. "./katago.exe analysis" ++ ++# Example config for C++ (non-python) analysis engine ++ ++# SEE NOTES ABOUT PERFORMANCE AND MEMORY USAGE IN gtp_example.cfg ++# SEE NOTES ABOUT numSearchThreads AND OTHER IMPORTANT PARAMS BELOW! ++ ++# Logs------------------------------------------------------------------------------------ ++ ++# Where to output log? ++logDir = analysis_logs # Each run of KataGo will log to a separate file in this dir ++# logDirDated = analysis_logs # Use this instead of logDir to also write separate dated subdirs ++# logFile = analysis.log # Use this instead of logDir to just specify a single file directly ++# logToStderr = true # Echo everything output to log file to stderr as well ++# logAllRequests = false # Log all input lines received to the analysis engine. ++# logAllResponses = false # Log all lines output to stdout from the analysis engine. ++# logErrorsAndWarnings = true # Log all lines output to stdout from the analysis engine that are errors and warnings ++# logSearchInfo = false # Log debug info for every search performed ++ ++# Report a warning whenever a query contains a field that is unused. Helps to guard against ++# typos when writing code that queries the analysis engine. ++# warnUnusedFields = true ++ ++# Analysis------------------------------------------------------------------------------------ ++ ++# Controls the number of moves after the first move in a variation. ++# analysisPVLen = 15 ++ ++# Report winrates for analysis as (BLACK|WHITE|SIDETOMOVE). ++reportAnalysisWinratesAs = BLACK ++ ++# Larger values will make KataGo explore the top move(s) less deeply and accurately, ++# but explore and give evaluations to a greater variety of moves. ++# An extreme value like 1 will distribute many playouts across every move on the board, even very bad moves. ++# NOTE: defaults to 0.04, under the presumption that the analysis engine will be used mainly for analysis. ++# If you are intending to use the analysis engine to also play games and you want to maximize playing strength, ++# set this to 0.0 either in this config or in the overrides. ++# wideRootNoise = 0.04 ++ ++# Try to limit the effect of possible bad or bogus move sequences in the ++# history leading to this position from affecting KataGo's move predictions. ++# ignorePreRootHistory = true ++ ++# Bot behavior--------------------------------------------------------------------------------------- ++ ++# Handicap ------------- ++ ++# Assume that if black makes many moves in a row right at the start of the game, then the game is a handicap game. ++# This is necessary on some servers and for some GUIs and also when initializing from many SGF files, which may ++# set up a handicap game using repeated GTP "play" commands for black rather than GTP "place_free_handicap" commands. ++# However, it may also lead to incorrect understanding of komi if whiteHandicapBonus is used and a server does NOT ++# have such a practice. ++# Defaults to true! Uncomment and set to false to disable this behavior. ++# assumeMultipleStartingBlackMovesAreHandicap = true ++ ++# Passing and cleanup ------------- ++ ++# Make the bot never assume that its pass will end the game, even if passing would end and "win" under Tromp-Taylor rules. ++# Usually this is a good idea when using it for analysis or playing on servers where scoring may be implemented non-tromp-taylorly. ++# Defaults to true! Uncomment and set to false to disable this. ++# conservativePass = true ++ ++# When using territory scoring, self-play games continue beyond two passes with special cleanup ++# rules that may be confusing for human players. This option prevents the special cleanup phases from being ++# reachable when using the bot for GTP play. ++# Defaults to true! Uncomment and set to false if you want KataGo to be able to enter special cleanup. ++# For example, if you are testing it against itself, or against another bot that has precisely implemented the rules ++# documented at https://lightvector.github.io/KataGo/rules.html ++# preventCleanupPhase = true ++ ++# Search limits----------------------------------------------------------------------------------- ++ ++# By default, if NOT specified in an individual request, limit maximum number of root visits per search to this much ++maxVisits = 500 ++# If provided, cap search time at this many seconds ++# maxTime = 60 ++ ++# Search threads, batching, GPUs-------------------------------------------------------------------------- ++ ++# Try a configuration like this if you only expect the engine to be handling a few queries at a time and you want ++# individual queries to return more quickly, and are okay with the results being a bit lower-quality and the overall ++# peak throughput on queries to be lower. ++numAnalysisThreads = 2 ++numSearchThreadsPerAnalysisThread = 16 ++ ++# Try a configuration like this if you expect to be sending large numbers of queries at a time, and want to maximize ++# total throughput and also the evaluation quality of all the queries and you never care about the response latency ++# of the individual queries, only the throughput as a whole. ++# numAnalysisThreads = 32 ++# numSearchThreadsPerAnalysisThread = 1 ++ ++# You will want to increase one or both numbers if you have a powerful GPU, and possibly decrease one or both if you ++# have a very weak GPU, and play with the balance between them depending on your use case. ++# Read the explanation below to understand how to set these parameters: ++ ++# EXPLANATION: ++# numAnalysisThreads: the number of POSITIONS to be able to search in parallel. ++# numSearchThreadsPerAnalysisThread: the number of threads to use in the tree search for EACH position. ++# (older analysis configs might just have 'numSearchThreads', this is an alias for 'numSearchThreadsPerAnalysisThread') ++ ++# Therefore, the total number of search threads that may be active at a given time could be as large as the product: ++# numAnalysisThreads * numSearchThreadsPerAnalysisThread ++ ++# Searching more positions in parallel is more efficient since the different threads aren't conflicting with each ++# other on the same MCTS search tree. Using multiple threads on the same search will both make things slower ++# and weaken the search (holding playouts fixed) due to out of date statistics on nodes and suboptimal exploration, ++# although the cost is minor for only 2,4,8 threads. ++ ++# So unlike in GTP, which only ever searches one position at a time and where therefore you might as well make ++# numSearchThreads as large as possible, in the analysis engine you often want you often want to keep numSearchThreads small, ++# and instead parallelize across positions, so you can reduce conflict between threads and improve the overall throughput ++# and strength of the search. ++ ++# But obviously you only get the benefit of parallelization across positions when you actually have lots of positions ++# that you are querying at once! For example, setting numAnalysisThreads = 8 is useless if you only ever send one or two ++# queries at a time! ++ ++# Therefore: ++# * If you plan to use the analysis engine only for batch processing large numbers of positions, ++# it's preferable to numSearchThreadsPerAnalysisThread to only a small number (e.g. 1,2,4) and use a higher numAnalysisThreads. ++# * But if you sometimes plan to query the analysis engine for single positions, or otherwise in smaller quantities ++# than -num-analysis-threads, or if you plan to be user-interactive such that the response time on some individual ++# analysis requests is important to keep low, then set numSearchThreadsPerAnalysisThread to a larger number and use ++# a lower numAnalysisThreads. That way, individual searches complete faster due to having more threads on each one. ++ ++# For 19x19 boards, weaker GPUs probably want a TOTAL number of threads (numAnalysisThreads * numSearchThreadsPerAnalysisThread) ++# between 4 and 32. Mid-tier GPUs probably between 16 and 64. Strong GPUs probably between 32 and 256. ++# But there's no substitute for experimenting and seeing what's best for your hardware and your usage case. ++ ++# Keep in mind that the number of threads you want does NOT necessarily have much to do with how many cores you have on your ++# system. The optimal may easily exceed the number of cores! GPU batching is (usually) the dominant consideration. ++ ++# ------------- ++ ++# nnMaxBatchSize is the max number of positions to send to a single GPU at once. Generally, it should be the case that: ++# (number of GPUs you will use * nnMaxBatchSize) >= (numSearchThreads * num-analysis-threads) ++# That way, when each threads tries to request a GPU eval, your batch size summed across GPUs is large enough to handle them ++# all at once. However, it can be sensible to set this a little smaller if you are limited on GPU memory, ++# too large a number may fail if the GPU doesn't have enough memory. ++nnMaxBatchSize = 64 ++ ++# Uncomment and set these smaller if you are going to use the analysis engine EXCLUSIVELY for smaller boards (or plan to ++# run multiple instances, with some instances only handling smaller boards). It should improve performance. ++# It may also mean you can use more threads profitably. ++# maxBoardXSizeForNNBuffer = 19 ++# maxBoardYSizeForNNBuffer = 19 ++ ++# Uncomment and set this to true if you are going to use the analysis engine EXCLUSIVELY for exactly the board size ++# specified by maxBoardXSizeForNNBuffer and maxBoardYSizeForNNBuffer. It may slightly improve performance on some GPUs. ++# requireMaxBoardSize = true ++ ++# TO USE MULTIPLE GPUS: ++# Uncomment and set this to the number of GPUs you have and/or would like to use... ++# AND if it is more than 1, uncomment the appropriate CUDA or OpenCL section below. ++# numNNServerThreadsPerModel = 1 ++ ++# Other General GPU Settings------------------------------------------------------------------------------- ++ ++# Cache up to 2 ** this many neural net evaluations in case of transpositions in the tree. ++nnCacheSizePowerOfTwo = 23 ++# Size of mutex pool for nnCache is 2 ** this ++nnMutexPoolSizePowerOfTwo = 17 ++# Randomize board orientation when running neural net evals? ++nnRandomize = true ++ ++ ++# TENSORRT GPU settings-------------------------------------- ++# These only apply when using the TENSORRT version of KataGo. ++ ++# IF USING ONE GPU: optionally uncomment and change this if the GPU you want to use turns out to be not device 0 ++# trtDeviceToUse = 0 ++ ++# IF USING TWO GPUS: Uncomment these two lines (AND set numNNServerThreadsPerModel above): ++# trtDeviceToUseThread0 = 0 # change this if the first GPU you want to use turns out to be not device 0 ++# trtDeviceToUseThread1 = 1 # change this if the second GPU you want to use turns out to be not device 1 ++ ++# IF USING THREE GPUS: Uncomment these three lines (AND set numNNServerThreadsPerModel above): ++# trtDeviceToUseThread0 = 0 # change this if the first GPU you want to use turns out to be not device 0 ++# trtDeviceToUseThread1 = 1 # change this if the second GPU you want to use turns out to be not device 1 ++# trtDeviceToUseThread2 = 2 # change this if the third GPU you want to use turns out to be not device 2 ++ ++# You can probably guess the pattern if you have four, five, etc. GPUs. ++ ++ ++# CUDA-specific GPU settings-------------------------------------- ++# These only apply when using the CUDA version of KataGo. ++ ++# IF USING ONE GPU: optionally uncomment and change this if the GPU you want to use turns out to be not device 0 ++# cudaDeviceToUse = 0 ++ ++# IF USING TWO GPUS: Uncomment these two lines (AND set numNNServerThreadsPerModel above): ++# cudaDeviceToUseThread0 = 0 # change this if the first GPU you want to use turns out to be not device 0 ++# cudaDeviceToUseThread1 = 1 # change this if the second GPU you want to use turns out to be not device 1 ++ ++# IF USING THREE GPUS: Uncomment these three lines (AND set numNNServerThreadsPerModel above): ++# cudaDeviceToUseThread0 = 0 # change this if the first GPU you want to use turns out to be not device 0 ++# cudaDeviceToUseThread1 = 1 # change this if the second GPU you want to use turns out to be not device 1 ++# cudaDeviceToUseThread2 = 2 # change this if the third GPU you want to use turns out to be not device 2 ++ ++# You can probably guess the pattern if you have four, five, etc. GPUs. ++ ++# KataGo will automatically use FP16 or not based on the compute capability of your NVIDIA GPU. If you ++# want to try to force a particular behavior though you can uncomment these lines and change them ++# to "true" or "false". E.g. it's using FP16 but on your card that's giving an error, or it's not using ++# FP16 but you think it should. ++# cudaUseFP16 = auto ++# cudaUseNHWC = auto ++ ++ ++# OpenCL-specific GPU settings-------------------------------------- ++# These only apply when using the OpenCL version of KataGo. ++ ++# Uncomment to tune OpenCL for every board size separately, rather than only the largest possible size ++# openclReTunePerBoardSize = true ++ ++# IF USING ONE GPU: optionally uncomment and change this if the best device to use is guessed incorrectly. ++# The default behavior tries to guess the 'best' GPU or device on your system to use, usually it will be a good guess. ++# openclDeviceToUse = 0 ++ ++# IF USING TWO GPUS: Uncomment these two lines and replace X and Y with the device ids of the devices you want to use. ++# It might NOT be 0 and 1, some computers will have many OpenCL devices. You can see what the devices are when ++# KataGo starts up - it should print or log all the devices it finds. ++# (AND also set numNNServerThreadsPerModel above) ++# openclDeviceToUseThread0 = X ++# openclDeviceToUseThread1 = Y ++ ++# IF USING THREE GPUS: Uncomment these three lines and replace X and Y and Z with the device ids of the devices you want to use. ++# It might NOT be 0 and 1 and 2, some computers will have many OpenCL devices. You can see what the devices are when ++# KataGo starts up - it should print or log all the devices it finds. ++# (AND also set numNNServerThreadsPerModel above) ++# openclDeviceToUseThread0 = X ++# openclDeviceToUseThread1 = Y ++# openclDeviceToUseThread2 = Z ++ ++# You can probably guess the pattern if you have four, five, etc. GPUs. ++ ++# KataGo will automatically use FP16 or not based on testing your GPU during tuning. If you ++# want to try to force a particular behavior though you can uncomment this lines and change it ++# to "true" or "false". This is a fairly blunt setting - more detailed settings are testable ++# by rerunning the tuner with various arguments. ++# openclUseFP16 = auto ++ ++ ++# Eigen-specific settings-------------------------------------- ++# These only apply when using the Eigen (pure CPU) version of KataGo. ++ ++# This is the number of CPU threads for evaluating the neural net on the Eigen backend. ++# It defaults to min(numAnalysisThreads * numSearchThreadsPerAnalysisThread, numCPUCores). ++# numEigenThreadsPerModel = X ++ ++ ++# Misc Behavior -------------------- ++ ++# If the board is symmetric, search only one copy of each equivalent move. Attempts to also account for ko/superko, will not theoretically perfect for superko. ++# Uncomment and set to false to disable this. ++# rootSymmetryPruning = true ++ ++# Uncomment and set to true to make KataGo avoid a particular joseki that some KataGo nets misevaluate, ++# and also to improve opening diversity versus some particular other bots that like to play it all the time. ++# avoidMYTDaggerHack = false ++ ++# Have KataGo mildly prefer to avoid playing the same joseki in every corner of the board. ++# Uncomment to set to a specific value. A small value like 0.005 should produce already a noticeable behavior change. ++# avoidRepeatedPatternUtility = 0.0 ++ ++# Enable some hacks that mitigate rare instances when passing messes up deeper searches. ++# enablePassingHacks = true ++ ++# Root move selection and biases------------------------------------------------------------------------------ ++# Uncomment and edit any of the below values to change them from their default. ++# Not all of these parameters are applicable to analysis, some are only used for actual play ++ ++# Temperature for the early game, randomize between chosen moves with this temperature ++# chosenMoveTemperatureEarly = 0.5 ++# Decay temperature for the early game by 0.5 every this many moves, scaled with board size. ++# chosenMoveTemperatureHalflife = 19 ++# At the end of search after the early game, randomize between chosen moves with this temperature ++# chosenMoveTemperature = 0.10 ++# Subtract this many visits from each move prior to applying chosenMoveTemperature ++# (unless all moves have too few visits) to downweight unlikely moves ++# chosenMoveSubtract = 0 ++# The same as chosenMoveSubtract but only prunes moves that fall below the threshold, does not affect moves above ++# chosenMovePrune = 1 ++ ++# Number of symmetries to sample (WITHOUT replacement) and average at the root ++# rootNumSymmetriesToSample = 1 ++ ++# Using LCB for move selection? ++# useLcbForSelection = true ++# How many stdevs a move needs to be better than another for LCB selection ++# lcbStdevs = 5.0 ++# Only use LCB override when a move has this proportion of visits as the top move ++# minVisitPropForLCB = 0.15 ++ ++# Internal params------------------------------------------------------------------------------ ++# Uncomment and edit any of the below values to change them from their default. ++ ++# Scales the utility of winning/losing ++# winLossUtilityFactor = 1.0 ++# Scales the utility for trying to maximize score ++# staticScoreUtilityFactor = 0.10 ++# dynamicScoreUtilityFactor = 0.30 ++# Adjust dynamic score center this proportion of the way towards zero, capped at a reasonable amount. ++# dynamicScoreCenterZeroWeight = 0.20 ++# dynamicScoreCenterScale = 0.75 ++# The utility of getting a "no result" due to triple ko or other long cycle in non-superko rulesets (-1 to 1) ++# noResultUtilityForWhite = 0.0 ++# The number of wins that a draw counts as, for white. (0 to 1) ++# drawEquivalentWinsForWhite = 0.5 ++ ++# Exploration constant for mcts ++# cpuctExploration = 1.0 ++# cpuctExplorationLog = 0.45 ++ ++# Parameters that control exploring more in volatile positions, exploring less in stable positions. ++# cpuctUtilityStdevPrior = 0.40 ++# cpuctUtilityStdevPriorWeight = 2.0 ++# cpuctUtilityStdevScale = 0.85 ++ ++# FPU reduction constant for mcts ++# fpuReductionMax = 0.2 ++# rootFpuReductionMax = 0.1 ++# fpuParentWeightByVisitedPolicy = true ++ ++# Parameters that control weighting of evals based on the net's own self-reported uncertainty. ++# useUncertainty = true ++# uncertaintyExponent = 1.0 ++# uncertaintyCoeff = 0.25 ++ ++# Explore using optimistic policy ++# rootPolicyOptimism = 0.2 ++# policyOptimism = 1.0 ++ ++# Amount to apply a downweighting of children with very bad values relative to good ones ++# valueWeightExponent = 0.25 ++ ++# Slight incentive for the bot to behave human-like with regard to passing at the end, filling the dame, ++# not wasting time playing in its own territory, etc, and not play moves that are equivalent in terms of ++# points but a bit more unfriendly to humans. ++# rootEndingBonusPoints = 0.5 ++ ++# Make the bot prune useless moves that are just prolonging the game to avoid losing yet ++# rootPruneUselessMoves = true ++ ++# Apply bias correction based on local pattern keys ++# subtreeValueBiasFactor = 0.45 ++# subtreeValueBiasWeightExponent = 0.85 ++ ++# Use graph search rather than tree search - identify and share search for transpositions. ++# useGraphSearch = true ++ ++# How much to shard the node table for search synchronization ++# nodeTableShardsPowerOfTwo = 16 ++# How many virtual losses to add when a thread descends through a node ++# numVirtualLossesPerThread = 1 ++ ++# Improve the quality of evals under heavy multithreading ++# useNoisePruning = true ++ ++ ++# Avoid SGF Patterns ------------------------------------------------------------------------------ ++# The parameters in this section provide a powerful way to customize KataGo to avoid moves that follow specific patterns ++# based on a set of provided SGF files loaded upon startup. Uncomment them to use this feature. ++# Additionally, if the SGF file contains the string %SKIP% in a comment on a move, that move will be ignored for this purpose. ++ ++# Load sgf files from this directory when the engine is started (ONLY on startup, will not reload unless engine is restarted) ++# avoidSgfPatternDirs = path/to/directory/with/sgfs/ ++# You can also surround the file path in double quotes if the file path contains trailing spaces or hash signs. ++# Within double quotes, backslashes are escape characters. ++# avoidSgfPatternDirs = "path/to/directory/with/sgfs/" ++ ++# Penalize this much utility per matching move. ++# Set this negative if you instead want to make KataGo favor the SGF patterns instead of penalizing it! ++# This number does not need to be large, even 0.001 will make a difference. Too-large values may lead to bad play. ++# avoidSgfPatternUtility = 0.001 ++ ++# Optional - load only the newest this many files ++# avoidSgfPatternMaxFiles = 20 ++ ++# Optional - Penalty is multiplied by this per each older SGF file, so that old sgf files matter less than newer ones. ++# avoidSgfPatternLambda = 0.90 ++ ++# Optional - pay attention only to moves that were made by players with this name. ++# For example you can set it to the name that your bot's past games will show up as in the SGF, so that the bot will only avoid repeating ++# moves that itself made in past games, not the moves that its opponents made. ++# avoidSgfPatternAllowedNames = my-ogs-bot-name1,my-ogs-bot-name2 ++ ++# Optional - Ignore any moves in SGF files that occurred before this turn number. ++# avoidSgfPatternMinTurnNumber = 0 ++ ++# For more avoid patterns: ++# You can also specify a second set of parameters, and a third, fourth, etc by numbering 2,3,4,... ++# avoidSgf2PatternDirs = ... ++# avoidSgf2PatternUtility = ... ++# avoidSgf2PatternMaxFiles = ... ++# avoidSgf2PatternLambda = ... ++# avoidSgf2PatternAllowedNames = ... ++# avoidSgf2PatternMinTurnNumber = ... ++ ++ ++ ++ +diff --git a/python/selfplay/b28.cfg b/python/selfplay/b28.cfg +new file mode 100644 +index 00000000..302e8a40 +--- /dev/null ++++ b/python/selfplay/b28.cfg +@@ -0,0 +1,721 @@ ++# Configuration for KataGo C++ GTP engine ++ ++# Run the program using: `./katago.exe gtp` ++ ++# In this example config, when a parameter is given as a commented out value, ++# that value also is the default value, unless described otherwise. You can ++# uncomment it (remove the pound sign) and change it if you want. ++ ++# =========================================================================== ++# Running on an online server or in a real tournament or match ++# =========================================================================== ++# If you plan to run online or in a tournament, read through the "Rules" ++# section below for proper handling of komi, handicaps, end-of-game cleanup, ++# and other details. ++ ++# =========================================================================== ++# Notes about performance and memory usage ++# =========================================================================== ++# Important: For good performance, you will very likely want to tune the ++# "numSearchThreads" parameter in the Search limits section below! Run ++# "./katago benchmark" to test KataGo and to suggest a reasonable value ++# of this parameter. ++ ++# For multi-GPU systems, read "OpenCL GPU settings" or "CUDA GPU settings". ++# ++# When using OpenCL, verify that KataGo picks the correct device! Some systems ++# may have both an Intel CPU OpenCL and GPU OpenCL. If # KataGo picks the wrong ++# one, correct this by specifying "openclGpuToUse". ++# ++# Consider adjusting "maxVisits", "ponderingEnabled", "resignThreshold", and ++# other parameters depending on your intended usage. ++ ++# =========================================================================== ++# Command-line usage ++# =========================================================================== ++# All of the below values may be set or overridden via command-line arguments: ++# ++# -override-config KEY=VALUE,KEY=VALUE,... ++ ++# =========================================================================== ++# Logs and files ++# =========================================================================== ++# This section defines where and what logging information is produced. ++ ++# Each run of KataGo will log to a separate file in this dir. ++# This is the default. ++logDir = gtp_logs ++# Uncomment and specify this instead of logDir to write separate dated subdirs ++# logDirDated = gtp_logs ++# Uncomment and specify this instead of logDir to log to only a single file ++# logFile = gtp.log ++ ++# Logging options ++logAllGTPCommunication = true ++logSearchInfo = true ++logSearchInfoForChosenMove = false ++logToStderr = true ++ ++# KataGo will display some info to stderr on GTP startup ++# Uncomment the next line and set it to false to suppress that and remain silent ++# startupPrintMessageToStderr = true ++ ++# Write information to stderr, for use in things like malkovich chat to OGS. ++ ogsChatToStderr = true ++ ++# Uncomment and set this to a directory to override where openCLTuner files ++# and other cached data is written. By default it saves into a subdir of the ++# current directory on windows, and a subdir of ~/.katago on Linux. ++# homeDataDir = PATH_TO_DIRECTORY ++ ++# =========================================================================== ++# Analysis ++# =========================================================================== ++# This section configures analysis settings. ++# ++# The maximum number of moves after the first move displayed in variations ++# from analysis commands like kata-analyze or lz-analyze. ++# analysisPVLen = 15 ++ ++# Report winrates for chat and analysis as (BLACK|WHITE|SIDETOMOVE). ++# Most GUIs and analysis tools will expect SIDETOMOVE. ++# reportAnalysisWinratesAs = SIDETOMOVE ++ ++# Extra noise for wider exploration. Large values will force KataGo to ++# analyze a greater variety of moves than it normally would. ++# An extreme value like 1 distributes playouts across every move on the board, ++# even very bad moves. ++# Affects analysis only, does not affect play. ++# analysisWideRootNoise = 0.04 ++ ++# Try to limit the effect of possible bad or bogus move sequences in the ++# history leading to this position from affecting KataGo's move predictions. ++# analysisIgnorePreRootHistory = true ++ ++# =========================================================================== ++# Rules ++# =========================================================================== ++# This section configures the scoring and playing rules. Rules can also be ++# changed mid-run by issuing custom GTP commands. ++# ++# See https://lightvector.github.io/KataGo/rules.html for rules details. ++# ++# See https://github.com/lightvector/KataGo/blob/master/docs/GTP_Extensions.md ++# for GTP commands. ++ ++# Specify the rules as a string. ++# Some legal values include: ++# chinese, japanese, korean, aga, chinese-ogs, new-zealand, stone-scoring, ++# ancient-territory, bga, aga-button ++# ++# For some human rulesets that require complex adjudication in tricky cases ++# (e.g. japanese, korean) KataGo may not precisely match the ruleset in such ++# cases but will do its best. ++rules = tromp-taylor ++ ++# By default, the "rules" parameter is used, but if you comment it out and ++# uncomment one option in each of the sections below, you can specify an ++# arbitrary combination of individual rules. ++ ++# koRule = SIMPLE # Simple ko rules (triple ko = no result) ++# koRule = POSITIONAL # Positional superko ++# koRule = SITUATIONAL # Situational superko ++ ++# scoringRule = AREA # Area scoring ++# scoringRule = TERRITORY # Territory scoring (special computer-friendly territory rules) ++ ++# taxRule = NONE # All surrounded empty points are scored ++# taxRule = SEKI # Eyes in seki do NOT count as points ++# taxRule = ALL # All groups are taxed up to 2 points for the two eyes needed to live ++ ++# Is multiple-stone suicide legal? (Single-stone suicide is always illegal). ++# multiStoneSuicideLegal = false ++# multiStoneSuicideLegal = true ++ ++# "Button go" - the first pass when area scoring awards 0.5 points and does ++# not count for ending the game. ++# Allows area scoring rulesets that have far simpler rules to achieve the same ++# final scoring precision and reward for precise play as territory scoring. ++# hasButton = false ++# hasButton = true ++ ++# Is this a human ruleset where it's okay to pass before having physically ++# captured and removed all dead stones? ++# friendlyPassOk = false ++# friendlyPassOk = true ++ ++# How handicap stones in handicap games are compensated ++# whiteHandicapBonus = 0 # White gets no compensation for black's handicap stones (Tromp-taylor, NZ, JP) ++# whiteHandicapBonus = N-1 # White gets N-1 points for black's N handicap stones (AGA) ++# whiteHandicapBonus = N # White gets N points for black's N handicap stones (Chinese) ++ ++# ------------------------------ ++# Other rules hacks ++# ------------------------------ ++# Uncomment and change to adjust what board size KataGo uses upon startup ++# by default when GTP doesn't specify. ++# defaultBoardSize = 19 ++ ++# By default, Katago will use the komi that the GUI or GTP controller tries to set. ++# Uncomment and set this to have KataGo ignore the controller and always use this komi. ++# ignoreGTPAndForceKomi = 7 ++ ++# =========================================================================== ++# Bot behavior ++# =========================================================================== ++ ++# ------------------------------ ++# Resignation ++# ------------------------------ ++ ++# Resignation occurs if for at least resignConsecTurns in a row, the ++# winLossUtility (on a [-1,1] scale) is below resignThreshold. ++allowResignation = true ++resignThreshold = -0.90 ++resignConsecTurns = 3 ++ ++# By default, KataGo may resign games that it is confidently losing even if they ++# are very close in score. Uncomment and set this to avoid resigning games ++# if the estimated difference is points is less than or equal to this. ++# resignMinScoreDifference = 10 ++ ++# ------------------------------ ++# Handicap ++# ------------------------------ ++# Assume that if black makes many moves in a row right at the start of the ++# game, then the game is a handicap game. This is necessary on some servers ++# and for some GUIs and also when initializing from many SGF files, which may ++# set up a handicap game using repeated GTP "play" commands for black rather ++# than GTP "place_free_handicap" commands; however, it may also lead to ++# incorrect understanding of komi if whiteHandicapBonus is used and a server ++# does not have such a practice. Uncomment and set to false to disable. ++# assumeMultipleStartingBlackMovesAreHandicap = true ++ ++# Makes katago dynamically adjust in handicap or altered-komi games to assume ++# based on those game settings that it must be stronger or weaker than the ++# opponent and to play accordingly. Greatly improves handicap strength by ++# biasing winrates and scores to favor appropriate safe/aggressive play. ++# Does NOT affect analysis (lz-analyze, kata-analyze, used by programs like ++# Lizzie) so analysis remains unbiased. Uncomment and set this to 0 to disable ++# this and make KataGo play the same always. ++# dynamicPlayoutDoublingAdvantageCapPerOppLead = 0.045 ++ ++# Instead of "dynamicPlayoutDoublingAdvantageCapPerOppLead", you can comment ++# that out and uncomment and set "playoutDoublingAdvantage" to a fixed value ++# from -3.0 to 3.0 that will not change dynamically. ++# ALSO affects analysis tools (lz-analyze, kata-analyze, used by e.g. Lizzie). ++# Negative makes KataGo behave as if it is much weaker than the opponent. ++# Positive makes KataGo behave as if it is much stronger than the opponent. ++# KataGo will adjust to favor safe/aggressive play as appropriate based on ++# the combination of who is ahead and how much stronger/weaker it thinks it is, ++# and report winrates and scores taking the strength difference into account. ++# ++# If this and "dynamicPlayoutDoublingAdvantageCapPerOppLead" are both set ++# then dynamic will be used for all games and this fixed value will be used ++# for analysis tools. ++# playoutDoublingAdvantage = 0.0 ++ ++# Uncomment one of these when using "playoutDoublingAdvantage" to enforce ++# that it will only apply when KataGo plays as the specified color and will be ++# negated when playing as the opposite color. ++# playoutDoublingAdvantagePla = BLACK ++# playoutDoublingAdvantagePla = WHITE ++ ++# ------------------------------ ++# Passing and cleanup ++# ------------------------------ ++# Make the bot never assume that its pass will end the game, even if passing ++# would end and "win" under Tromp-Taylor rules. Usually this is a good idea ++# when using it for analysis or playing on servers where scoring may be ++# implemented non-tromp-taylorly. Uncomment and set to false to disable. ++# conservativePass = true ++ ++# When using territory scoring, self-play games continue beyond two passes ++# with special cleanup rules that may be confusing for human players. This ++# option prevents the special cleanup phases from being reachable when using ++# the bot for GTP play. Uncomment and set to false to enable entering special ++# cleanup. For example, if you are testing it against itself, or against ++# another bot that has precisely implemented the rules documented at ++# https://lightvector.github.io/KataGo/rules.html ++# preventCleanupPhase = true ++ ++# ------------------------------ ++# Miscellaneous behavior ++# ------------------------------ ++# If the board is symmetric, search only one copy of each equivalent move. ++# Attempts to also account for ko/superko, will not theoretically perfect for ++# superko. Uncomment and set to false to disable. ++# rootSymmetryPruning = true ++ ++# Uncomment and set to true to avoid a particular joseki that some networks ++# misevaluate, and also to improve opening diversity versus some particular ++# other bots that like to play it all the time. ++# avoidMYTDaggerHack = false ++ ++# Prefer to avoid playing the same joseki in every corner of the board. ++# Uncomment to set to a specific value. See "Avoid SGF patterns" section. ++# By default: 0 (even games), 0.005 (handicap games) ++# avoidRepeatedPatternUtility = 0.0 ++ ++# Experimental logic to fight against mirror Go even with unfavorable komi. ++# Uncomment to set to a specific value to use for both playing and analysis. ++# By default: true when playing via GTP, but false when analyzing. ++# antiMirror = true ++ ++# Enable some hacks that mitigate rare instances when passing messes up deeper searches. ++# enablePassingHacks = true ++ ++# Uncomment and set this to true to prevent bad or bogus move sequences ++# in the history leading to this position from affecting KataGo's move choices. ++# Same as analysisIgnorePreRootHistory (see above) but applies to actual play. ++# You can enable this if KataGo is being asked to play from positions that it did not ++# choose the moves to reach. ++# ignorePreRootHistory = false ++ ++# =========================================================================== ++# Search limits ++# =========================================================================== ++ ++# Terminology: ++# "Playouts" is the number of new playouts of search performed each turn. ++# "Visits" is the same as "Playouts" but also counts search performed on ++# previous turns that is still applicable to this turn. ++# "Time" is the time in seconds. ++ ++# For example, if KataGo searched 200 nodes on the previous turn, and then ++# after the opponent's reply, 50 nodes of its search tree was still valid, ++# then a visit limit of 200 would allow KataGo to search 150 new nodes ++# (for a final tree size of 200 nodes), whereas a playout limit of of 200 ++# would allow KataGo to search 200 nodes (for a final tree size of 250 nodes). ++ ++# Additionally, KataGo may also move before than the limit in order to ++# obey time controls (e.g. byo-yomi, etc) if the GTP controller has ++# told KataGo that the game has is being played with a given time control. ++ ++# Limits for search on the current turn. ++# If commented out or unspecified, the default is to have no limit. ++# maxVisits = 30000 ++# maxPlayouts = 300 ++maxTime = 1.0 ++ ++# Ponder on the opponent's turn? ++ponderingEnabled = true ++ ++# Limits for search when pondering on the opponent's turn. ++# If commented out or unspecified, the default is to have no limit. ++# Limiting the maximum time is recommended so that KataGo won't burn CPU/GPU ++# forever and/or run out of RAM if left unattended while pondering is enabled. ++# maxVisitsPondering = 5000 ++# maxPlayoutsPondering = 3000 ++maxTimePondering = 120.0 ++ ++ ++# ------------------------------ ++# Other search limits and behavior ++# ------------------------------ ++ ++# Approx number of seconds to buffer for lag for GTP time controls - will ++# move a bit faster assuming there is this much lag per move. ++lagBuffer = 1.0 ++ ++# YOU PROBABLY WANT TO TUNE THIS PARAMETER! ++# The number of threads to use when searching. On powerful GPUs the optimal ++# threads may be much higher than the number of CPU cores you have because ++# many threads are needed to feed efficient large batches to the GPU. ++# ++# Run "./katago benchmark" to tune this parameter and test the effect ++# of changes to any of other parameters. ++numSearchThreads = 10 ++ ++# Play a little faster if the opponent is passing, for human-friendliness. ++# Comment these out to disable them, such as if running a controlled match ++# where you are testing KataGo with fixed compute per move vs other bots. ++searchFactorAfterOnePass = 0.50 ++searchFactorAfterTwoPass = 0.25 ++ ++# Play a little faster if super-winning, for human-friendliness. ++# Comment these out to disable them, such as if running a controlled match ++# where you are testing KataGo with fixed compute per move vs other bots. ++searchFactorWhenWinning = 0.40 ++searchFactorWhenWinningThreshold = 0.95 ++ ++# =========================================================================== ++# GPU settings ++# =========================================================================== ++# This section configures GPU settings. ++# ++# Maximum number of positions to send to a single GPU at once. The default ++# value is roughly equal to numSearchThreads, but can be specified manually ++# if running out of memory, or using multiple GPUs that expect to share work. ++# nnMaxBatchSize = ++ ++# Controls the neural network cache size, which is the primary RAM/memory use. ++# KataGo will cache up to (2 ** nnCacheSizePowerOfTwo) many neural net ++# evaluations in case of transpositions in the tree. ++# Increase this to improve performance for searches with tens of thousands ++# of visits or more. Decrease this to limit memory usage. ++# If you're happy to do some math - each neural net entry takes roughly ++# 1.5KB, except when using whole-board ownership/territory ++# visualizations, where each entry will take roughly 3KB. The number of ++# entries is (2 ** nnCacheSizePowerOfTwo). (E.g. 2 ** 18 = 262144.) ++# You can compute roughly how much memory the cache will use based on this. ++# nnCacheSizePowerOfTwo = 20 ++ ++# Size of mutex pool for nnCache is (2 ** this). ++# nnMutexPoolSizePowerOfTwo = 16 ++ ++# Randomize board orientation when running neural net evals? Uncomment and ++# set to false to disable. ++# nnRandomize = true ++ ++# If provided, force usage of a specific seed for nnRandomize. ++# The default is to use a randomly generated seed. ++# nnRandSeed = abcdefg ++ ++# Uncomment and set to true to force GTP to use the maximum board size for ++# internal buffers for the neural net. This will make KataGo slower when ++# evaluating small boards, but will avoid a lengthy initialization time on every ++# change of board size due to having to re-size the neural net buffers on the GPU. ++# This can be useful for example, for OGS's persistent bot mode that uses a single ++# bot instance to handle multiple games and may thrash between different board sizes ++# if there are concurrent games of multiple sizes. ++# gtpForceMaxNNSize = false ++ ++# ------------------------------ ++# Multiple GPUs ++# ------------------------------ ++# Set this to the number of GPUs to use or that are available. ++# IMPORTANT: If more than 1, also uncomment the appropriate TensorRT ++# or CUDA or OpenCL section. ++# numNNServerThreadsPerModel = 1 ++ ++# ------------------------------ ++# TENSORRT GPU settings ++# ------------------------------ ++# These only apply when using the TENSORRT version of KataGo. ++ ++# For one GPU: optionally uncomment this option and change if the GPU to ++# use is not device 0. ++# trtDeviceToUse = 0 ++ ++# For two GPUs: Uncomment these options, AND set numNNServerThreadsPerModel above. ++# Also, change their values if the devices you want to use are not 0 and 1. ++# trtDeviceToUseThread0 = 0 ++# trtDeviceToUseThread1 = 1 ++ ++# For three GPUs: Uncomment these options, AND set numNNServerThreadsPerModel above. ++# Also, change their values if the devices you want to use are not 0 and 1 and 2. ++# trtDeviceToUseThread0 = 0 ++# trtDeviceToUseThread1 = 1 ++# trtDeviceToUseThread2 = 2 ++ ++# The pattern continues for additional GPUs. ++ ++# ------------------------------ ++# CUDA GPU settings ++# ------------------------------ ++# These only apply when using the CUDA version of KataGo. ++ ++# For one GPU: optionally uncomment and change this if the GPU you want to ++# use is not device 0 ++# cudaDeviceToUse = 0 ++ ++# For two GPUs: Uncomment these options, AND set numNNServerThreadsPerModel above. ++# Also, change their values if the devices you want to use are not 0 and 1. ++# cudaDeviceToUseThread0 = 0 ++# cudaDeviceToUseThread1 = 1 ++ ++# For three GPUs: Uncomment these options, AND set numNNServerThreadsPerModel above. ++# Also, change their values if the devices you want to use are not 0 and 1 and 2. ++# cudaDeviceToUseThread0 = 0 ++# cudaDeviceToUseThread1 = 1 ++# cudaDeviceToUseThread2 = 2 ++ ++# The pattern continues for additional GPUs. ++ ++# KataGo will automatically use FP16 or not based on the compute capability ++# of your NVIDIA GPU. If you want to try to force a particular behavior ++# you can uncomment these lines and change them to "true" or "false". ++# cudaUseFP16 = auto ++# cudaUseNHWC = auto ++ ++# ------------------------------ ++# OpenCL GPU settings ++# ------------------------------ ++# These only apply when using the OpenCL version of KataGo. ++ ++# Uncomment and set to true to tune OpenCL for every board size separately, ++# rather than only the largest possible size. ++# openclReTunePerBoardSize = false ++ ++# For one GPU: optionally uncomment and change this if the best device to use is guessed incorrectly. ++# The default behavior tries to guess the 'best' GPU or device on your system to use, usually it will be a good guess. ++# openclDeviceToUse = 0 ++ ++# For two GPUs: Uncomment these two lines and replace X and Y with the device ids of the devices you want to use. ++# It might NOT be 0 and 1, some computers will have many OpenCL devices. You can see what the devices are when ++# KataGo starts up - it should print or log all the devices it finds. ++# (AND also set numNNServerThreadsPerModel above) ++# openclDeviceToUseThread0 = X ++# openclDeviceToUseThread1 = Y ++ ++# For three GPUs: Uncomment these three lines and replace X and Y and Z with the device ids of the devices you want to use. ++# It might NOT be 0 and 1 and 2, some computers will have many OpenCL devices. You can see what the devices are when ++# KataGo starts up - it should print or log all the devices it finds. ++# (AND also set numNNServerThreadsPerModel above) ++# openclDeviceToUseThread0 = X ++# openclDeviceToUseThread1 = Y ++# openclDeviceToUseThread2 = Z ++ ++# The pattern continues for additional GPUs. ++ ++# KataGo will automatically use FP16 or not based on testing your GPU during ++# tuning. If you want to try to force a particular behavior though you can ++# uncomment this option and change it to "true" or "false". This is a fairly ++# blunt setting - more detailed settings are testable by rerunning the tuner ++# with various arguments (./katago tuner). ++# openclUseFP16 = auto ++ ++# ------------------------------ ++# Eigen-specific settings ++# ------------------------------ ++# These only apply when using the Eigen (pure CPU) version of KataGo. ++ ++# Number of CPU threads for evaluating the neural net on the Eigen backend. ++# ++# Default: numSearchThreads ++# numEigenThreadsPerModel = X ++ ++# =========================================================================== ++# Root move selection and biases ++# =========================================================================== ++# Uncomment and edit any of the below values to change them from their default. ++ ++# If provided, force usage of a specific seed for various random things in ++# the search. The default is to use a random seed. ++# searchRandSeed = hijklmn ++ ++# Temperature for the early game, randomize between chosen moves with ++# this temperature ++# chosenMoveTemperatureEarly = 0.5 ++ ++# Decay temperature for the early game by 0.5 every this many moves, ++# scaled with board size. ++# chosenMoveTemperatureHalflife = 19 ++ ++# At the end of search after the early game, randomize between chosen ++# moves with this temperature ++# chosenMoveTemperature = 0.10 ++ ++# Subtract this many visits from each move prior to applying ++# chosenMoveTemperature (unless all moves have too few visits) to downweight ++# unlikely moves ++# chosenMoveSubtract = 0 ++ ++# The same as chosenMoveSubtract but only prunes moves that fall below ++# the threshold. This setting does not affect chosenMoveSubtract. ++# chosenMovePrune = 1 ++ ++# Number of symmetries to sample (without replacement) and average at the root ++# rootNumSymmetriesToSample = 1 ++ ++# Using LCB for move selection? ++# useLcbForSelection = true ++ ++# How many stdevs a move needs to be better than another for LCB selection ++# lcbStdevs = 5.0 ++ ++# Only use LCB override when a move has this proportion of visits as the ++# top move. ++# minVisitPropForLCB = 0.15 ++ ++# =========================================================================== ++# Internal params ++# =========================================================================== ++# Uncomment and edit any of the below values to change them from their default. ++ ++# Scales the utility of winning/losing ++# winLossUtilityFactor = 1.0 ++ ++# Scales the utility for trying to maximize score ++# staticScoreUtilityFactor = 0.10 ++# dynamicScoreUtilityFactor = 0.30 ++ ++# Adjust dynamic score center this proportion of the way towards zero, ++# capped at a reasonable amount. ++# dynamicScoreCenterZeroWeight = 0.20 ++# dynamicScoreCenterScale = 0.75 ++ ++# The utility of getting a "no result" due to triple ko or other long cycle ++# in non-superko rulesets (-1 to 1) ++# noResultUtilityForWhite = 0.0 ++ ++# The number of wins that a draw counts as, for white. (0 to 1) ++# drawEquivalentWinsForWhite = 0.5 ++ ++# Exploration constant for mcts ++# cpuctExploration = 1.0 ++# cpuctExplorationLog = 0.45 ++ ++# Parameters that control exploring more in volatile positions, exploring ++# less in stable positions. ++# cpuctUtilityStdevPrior = 0.40 ++# cpuctUtilityStdevPriorWeight = 2.0 ++# cpuctUtilityStdevScale = 0.85 ++ ++# FPU reduction constant for mcts ++# fpuReductionMax = 0.2 ++# rootFpuReductionMax = 0.1 ++# fpuParentWeightByVisitedPolicy = true ++ ++# Parameters that control weighting of evals based on the net's own ++# self-reported uncertainty. ++# useUncertainty = true ++# uncertaintyExponent = 1.0 ++# uncertaintyCoeff = 0.25 ++ ++# Explore using optimistic policy ++# rootPolicyOptimism = 0.2 ++# policyOptimism = 1.0 ++ ++# Amount to apply a downweighting of children with very bad values relative ++# to good ones. ++# valueWeightExponent = 0.25 ++ ++# Slight incentive for the bot to behave human-like with regard to passing at ++# the end, filling the dame, not wasting time playing in its own territory, ++# etc., and not play moves that are equivalent in terms of points but a bit ++# more unfriendly to humans. ++# rootEndingBonusPoints = 0.5 ++ ++# Make the bot prune useless moves that are just prolonging the game to ++# avoid losing yet. ++# rootPruneUselessMoves = true ++ ++# Apply bias correction based on local pattern keys ++# subtreeValueBiasFactor = 0.45 ++# subtreeValueBiasWeightExponent = 0.85 ++ ++# Use graph search rather than tree search - identify and share search for ++# transpositions. ++# useGraphSearch = true ++ ++# How much to shard the node table for search synchronization ++# nodeTableShardsPowerOfTwo = 16 ++ ++# How many virtual losses to add when a thread descends through a node ++# numVirtualLossesPerThread = 1 ++ ++# Improve the quality of evals under heavy multithreading ++# useNoisePruning = true ++ ++# =========================================================================== ++# Automatic avoid patterns ++# =========================================================================== ++# The parameters in this section provide a way to bias away from moves that match ++# patterns that this instance of KataGo has played in previous games, by auto-saving ++# moves to a directory and then auto-loading and biasing against them each new game. ++# Uncomment them to use them. When using this feature, all parameters must be specified. ++ ++# Different board sizes are tracked separately, but all board sizes share the same ++# parameters by default. Every parameter *except* for autoAvoidRepeatDir and ++# autoAvoidRepeatSaveChunkSize can be overridden per board size, ++# e.g. "autoAvoidRepeatMinTurnNumber13x13". You must ALSO specify the ++# defaults even if you have specified board-size-specific values. ++ ++# Directory to auto-save moves KataGo plays, to avoid them in future games. ++# You can create a new empty directory and put its path here. ++# If you run parallel instances of KataGo, use different directories if you ++# want them not to share their biases, use the same directory if you want ++# all of them to bias away from past moves that any of them have played. ++# KataGo will also automatically DELETE old data in this directory, so it is ++# recommended that if you do share the same directory between parallel instances, ++# that they all use the same settings that affect data saving/deletion. ++# autoAvoidRepeatDir = PATH_TO_NEW_DIRECTORY ++ ++# Penalize this much utility per matching move. ++# Values that are too large may lead to bad play. The value of 0.004 is fairly large ++# and might be large enough to result in some early weird/bad moves when trying ++# to avoid past games' moves if enough games begin the same way. You can experiment with it. ++# autoAvoidRepeatUtility = 0.004 ++ ++# Per each new move saved, exponentially decay prior saved moves by this factor. ++# This way, the bias against moves from many games ago is gradually phased out. ++# For example, 0.9995 = 1 - 1/2000, so would be roughly 2000 prior moves worth of ++# penalty weight remembered, in steady state. Depending on what turn number range ++# you are saving, this might equate to a different number of games For example ++# saving the first 50 moves per game would make this roughly 2000 / 50 = 40 games ++# worth of memory. ++# autoAvoidRepeatLambda = 0.9995 ++ ++# Affects data saving/deletion. ++# When the number of saved moves exceeds this, outright delete them to avoid too many ++# files and disk space building up. Also may affect the speed of saving/loading on start ++# of each game if this is set large and a lot of data builds up. ++# autoAvoidRepeatMaxPoses = 10000 ++ ++# Affects data saving/deletion. ++# Only save data for moves within this turn number range of those games. ++# E.g. setting autoAvoidRepeatMinTurnNumber to a number like 4 or 5 would tend to make ++# KataGo not develop a bias against the initial 3-4 and 4-4 corner moves in almost every game. ++# autoAvoidRepeatMinTurnNumber = 0 ++# autoAvoidRepeatMaxTurnNumber = 50 ++ ++# Affects data saving/deletion. ++# Within a single run of a program, wait to accumulate this many samples ++# (possibly across multiple clear_boards/games) before saving the data. ++# Can help to avoid writing too many small files to disk, especially when GTP is used ++# in a way that clears the board very frequently (e.g. gtp2ogs pooled manager). ++# autoAvoidRepeatSaveChunkSize = 200 ++ ++# =========================================================================== ++# Avoid SGF patterns ++# =========================================================================== ++# The parameters in this section provide a way to avoid moves that follow ++# specific patterns based on a set of SGF files loaded upon startup. ++# This is basically the same as the above "Automatic avoid patterns" section ++# above except you supply your own SGF files to avoid moves from. ++# Uncomment them to use this feature. Additionally, if the SGF file ++# contains the string %SKIP% in a comment on a move, that move will be ++# ignored for this purpose. ++ ++# Load SGF files from this directory when the engine is started ++# (only on startup, will not reload unless engine is restarted) ++# avoidSgfPatternDirs = path/to/directory/with/sgfs/ ++# You can also surround the file path in double quotes if the file path contains trailing spaces or hash signs. ++# Within double quotes, backslashes are escape characters. ++# avoidSgfPatternDirs = "path/to/directory/with/sgfs/" ++ ++# Penalize this much utility per matching move. ++# Set this negative if you instead want to favor SGF patterns instead of ++# penalizing them. This number does not need to be large, even 0.001 will ++# make a difference. Values that are too large may lead to bad play. ++# avoidSgfPatternUtility = 0.001 ++ ++# Optional - load only the newest this many files ++# avoidSgfPatternMaxFiles = 20 ++ ++# Optional - Penalty is multiplied by this per each older SGF file, so that ++# old SGF files matter less than newer ones. ++# avoidSgfPatternLambda = 0.90 ++ ++# Optional - pay attention only to moves made by players with this name. ++# For example, set it to the name that your bot's past games will show up ++# as in the SGF, so that the bot will only avoid repeating moves that itself ++# made in past games, not the moves that its opponents made. ++# avoidSgfPatternAllowedNames = my-ogs-bot-name1,my-ogs-bot-name2 ++ ++# Optional - Ignore moves in SGF files that occurred before this turn number. ++# avoidSgfPatternMinTurnNumber = 0 ++ ++# For more avoid patterns: ++# You can also specify a second set of parameters, and a third, fourth, ++# etc. by numbering 2,3,4,... ++# ++# avoidSgf2PatternDirs = ... ++# avoidSgf2PatternUtility = ... ++# avoidSgf2PatternMaxFiles = ... ++# avoidSgf2PatternLambda = ... ++# avoidSgf2PatternAllowedNames = ... ++# avoidSgf2PatternMinTurnNumber = ... ++ +diff --git a/python/selfplay/cacert.pem b/python/selfplay/cacert.pem +new file mode 100644 +index 00000000..f9bd706b +--- /dev/null ++++ b/python/selfplay/cacert.pem +@@ -0,0 +1,3447 @@ ++## ++## Bundle of CA Root Certificates ++## ++## Certificate data from Mozilla as of: Wed Jul 22 03:12:14 2020 GMT ++## ++## This is a bundle of X.509 certificates of public Certificate Authorities ++## (CA). These were automatically extracted from Mozilla's root certificates ++## file (certdata.txt). This file can be found in the mozilla source tree: ++## https://hg.mozilla.org/releases/mozilla-release/raw-file/default/security/nss/lib/ckfw/builtins/certdata.txt ++## ++## It contains the certificates in PEM format and therefore ++## can be directly used with curl / libcurl / php_curl, or with ++## an Apache+mod_ssl webserver for SSL client authentication. ++## Just configure this file as the SSLCACertificateFile. ++## ++## Conversion done with mk-ca-bundle.pl version 1.28. ++## SHA256: cc6408bd4be7fbfb8699bdb40ccb7f6de5780d681d87785ea362646e4dad5e8e ++## ++ ++ ++GlobalSign Root CA ++================== ++-----BEGIN CERTIFICATE----- ++MIIDdTCCAl2gAwIBAgILBAAAAAABFUtaw5QwDQYJKoZIhvcNAQEFBQAwVzELMAkGA1UEBhMCQkUx ++GTAXBgNVBAoTEEdsb2JhbFNpZ24gbnYtc2ExEDAOBgNVBAsTB1Jvb3QgQ0ExGzAZBgNVBAMTEkds ++b2JhbFNpZ24gUm9vdCBDQTAeFw05ODA5MDExMjAwMDBaFw0yODAxMjgxMjAwMDBaMFcxCzAJBgNV ++BAYTAkJFMRkwFwYDVQQKExBHbG9iYWxTaWduIG52LXNhMRAwDgYDVQQLEwdSb290IENBMRswGQYD ++VQQDExJHbG9iYWxTaWduIFJvb3QgQ0EwggEiMA0GCSqGSIb3DQEBAQUAA4IBDwAwggEKAoIBAQDa ++DuaZjc6j40+Kfvvxi4Mla+pIH/EqsLmVEQS98GPR4mdmzxzdzxtIK+6NiY6arymAZavpxy0Sy6sc ++THAHoT0KMM0VjU/43dSMUBUc71DuxC73/OlS8pF94G3VNTCOXkNz8kHp1Wrjsok6Vjk4bwY8iGlb ++Kk3Fp1S4bInMm/k8yuX9ifUSPJJ4ltbcdG6TRGHRjcdGsnUOhugZitVtbNV4FpWi6cgKOOvyJBNP ++c1STE4U6G7weNLWLBYy5d4ux2x8gkasJU26Qzns3dLlwR5EiUWMWea6xrkEmCMgZK9FGqkjWZCrX ++gzT/LCrBbBlDSgeF59N89iFo7+ryUp9/k5DPAgMBAAGjQjBAMA4GA1UdDwEB/wQEAwIBBjAPBgNV ++HRMBAf8EBTADAQH/MB0GA1UdDgQWBBRge2YaRQ2XyolQL30EzTSo//z9SzANBgkqhkiG9w0BAQUF ++AAOCAQEA1nPnfE920I2/7LqivjTFKDK1fPxsnCwrvQmeU79rXqoRSLblCKOzyj1hTdNGCbM+w6Dj ++Y1Ub8rrvrTnhQ7k4o+YviiY776BQVvnGCv04zcQLcFGUl5gE38NflNUVyRRBnMRddWQVDf9VMOyG ++j/8N7yy5Y0b2qvzfvGn9LhJIZJrglfCm7ymPAbEVtQwdpf5pLGkkeB6zpxxxYu7KyJesF12KwvhH ++hm4qxFYxldBniYUr+WymXUadDKqC5JlR3XC321Y9YeRq4VzW9v493kHMB65jUr9TU/Qr6cf9tveC ++X4XSQRjbgbMEHMUfpIBvFSDJ3gyICh3WZlXi/EjJKSZp4A== ++-----END CERTIFICATE----- ++ ++GlobalSign Root CA - R2 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you have entered your password. ++ ++serverUrl = https://katagotraining.org/ ++username = Fill in your username here ++password = Fill in your password here ++# If your password has hash signs or trailing spaces, you can also surround the password with double quotes, like this. ++# Within double quotes, backslashes are escape characters. ++# password = "Fill in your password here" ++ ++# How many games should KataGo play at once? For good GPUs, putting too small a number here will be *very* inefficient. ++# For absolute-top-tier GPUs, try numbers like 32, 40, 64, etc. ++# For modern good middle-tier GPUs, you can try numbers like 8 or 16. ++# For very old or weak GPUs, or if you want KataGo to put less load on your system, try 4. ++maxSimultaneousGames = Fill in a number here ++ ++# Set to true if you want one of KataGo's games to be streamed to a text file, so you can watch. ++# Follow in a separate shell with a command like the following: ++# Linux: "tail -f watchgame.txt" ++# Windows Powershell: "Get-Content .\watchgame.txt -Tail 50 -Wait" ++watchOngoingGameInFile = false ++watchOngoingGameInFileName = watchgame.txt ++ ++# If you use an https proxy to connect to the internet, set these to the host and port of your proxy. ++# proxyHost = HOSTNAME ++# proxyPort = 1234 ++ ++# Special-purpose options. Most users should not need to adjust these. ++# onlyPlayRatingMatches = false ++# maxRatingMatches = 1 ++# disablePredownloadLoop = false ++# warnTaskUnusedKeys = false ++ ++# KataGo will only use one GPU by default. You can edit the below if you have multiple GPUs and want to use them all. ++ ++ ++# TO USE MULTIPLE GPUS: ++# Set this to the number of GPUs you have and/or would like to use. ++# **AND** if it is more than 1, uncomment the appropriate CUDA or OpenCL section below. ++# numNNServerThreadsPerModel = 1 ++ ++ ++# TENSORRT GPU settings-------------------------------------- ++# These only apply when using the TENSORRT version of KataGo. ++ ++# IF USING ONE GPU: optionally uncomment and change this if the GPU you want to use turns out to be not device 0 ++# trtDeviceToUse = 0 ++ ++# IF USING TWO GPUS: Uncomment these two lines (AND set numNNServerThreadsPerModel above): ++# trtDeviceToUseThread0 = 0 # change this if the first GPU you want to use turns out to be not device 0 ++# trtDeviceToUseThread1 = 1 # change this if the second GPU you want to use turns out to be not device 1 ++ ++# IF USING THREE GPUS: Uncomment these three lines (AND set numNNServerThreadsPerModel above): ++# trtDeviceToUseThread0 = 0 # change this if the first GPU you want to use turns out to be not device 0 ++# trtDeviceToUseThread1 = 1 # change this if the second GPU you want to use turns out to be not device 1 ++# trtDeviceToUseThread2 = 2 # change this if the third GPU you want to use turns out to be not device 2 ++ ++# You can probably guess the pattern if you have four, five, etc. GPUs. ++ ++ ++# CUDA GPU settings-------------------------------------- ++# These only apply when using the CUDA version of KataGo. ++ ++# IF USING ONE GPU: optionally uncomment and change this if the GPU you want to use turns out to be not device 0 ++# cudaDeviceToUse = 0 ++ ++# IF USING TWO GPUS: Uncomment these two lines (AND set numNNServerThreadsPerModel above): ++# cudaDeviceToUseThread0 = 0 # change this if the first GPU you want to use turns out to be not device 0 ++# cudaDeviceToUseThread1 = 1 # change this if the second GPU you want to use turns out to be not device 1 ++ ++# IF USING THREE GPUS: Uncomment these three lines (AND set numNNServerThreadsPerModel above): ++# cudaDeviceToUseThread0 = 0 # change this if the first GPU you want to use turns out to be not device 0 ++# cudaDeviceToUseThread1 = 1 # change this if the second GPU you want to use turns out to be not device 1 ++# cudaDeviceToUseThread2 = 2 # change this if the third GPU you want to use turns out to be not device 2 ++ ++# You can probably guess the pattern if you have four, five, etc. GPUs. ++ ++# KataGo will automatically use FP16 or not based on the compute capability of your NVIDIA GPU. If you ++# want to try to force a particular behavior though you can uncomment these lines and change them ++# to "true" or "false". E.g. it's using FP16 but on your card that's giving an error, or it's not using ++# FP16 but you think it should. ++# cudaUseFP16 = auto ++# cudaUseNHWC = auto ++ ++ ++# OpenCL GPU settings-------------------------------------- ++# These only apply when using the OpenCL version of KataGo. ++ ++# Uncomment to tune OpenCL for every board size separately, rather than only the largest possible size ++# openclReTunePerBoardSize = true ++ ++# IF USING ONE GPU: optionally uncomment and change this if the best device to use is guessed incorrectly. ++# The default behavior tries to guess the 'best' GPU or device on your system to use, usually it will be a good guess. ++# openclDeviceToUse = 0 ++ ++# IF USING TWO GPUS: Uncomment these two lines and replace X and Y with the device ids of the devices you want to use. ++# It might NOT be 0 and 1, some computers will have many OpenCL devices. You can see what the devices are when ++# KataGo starts up - it should print or log all the devices it finds. ++# (AND also set numNNServerThreadsPerModel above) ++# openclDeviceToUseThread0 = X ++# openclDeviceToUseThread1 = Y ++ ++# IF USING THREE GPUS: Uncomment these three lines and replace X and Y and Z with the device ids of the devices you want to use. ++# It might NOT be 0 and 1 and 2, some computers will have many OpenCL devices. You can see what the devices are when ++# KataGo starts up - it should print or log all the devices it finds. ++# (AND also set numNNServerThreadsPerModel above) ++# openclDeviceToUseThread0 = X ++# openclDeviceToUseThread1 = Y ++# openclDeviceToUseThread2 = Z ++ ++# You can probably guess the pattern if you have four, five, etc. GPUs. ++ ++# KataGo will automatically use FP16 or not based on testing your GPU during tuning. If you ++# want to try to force a particular behavior though you can uncomment this lines and change it ++# to "true" or "false". This is a fairly blunt setting - more detailed settings are testable ++# by rerunning the tuner with various arguments. ++# openclUseFP16 = auto ++ ++ ++# Eigen-specific settings-------------------------------------- ++# These only apply when using the Eigen (pure CPU) version of KataGo. ++ ++# This is the number of CPU threads for evaluating the neural net on the Eigen backend. ++# It defaults to numSearchThreads. ++# numEigenThreadsPerModel = X +diff --git a/python/selfplay/default_gtp.cfg b/python/selfplay/default_gtp.cfg +new file mode 100644 +index 00000000..8bf19d85 +--- /dev/null ++++ b/python/selfplay/default_gtp.cfg +@@ -0,0 +1,721 @@ ++# Configuration for KataGo C++ GTP engine ++ ++# Run the program using: `./katago.exe gtp` ++ ++# In this example config, when a parameter is given as a commented out value, ++# that value also is the default value, unless described otherwise. You can ++# uncomment it (remove the pound sign) and change it if you want. ++ ++# =========================================================================== ++# Running on an online server or in a real tournament or match ++# =========================================================================== ++# If you plan to run online or in a tournament, read through the "Rules" ++# section below for proper handling of komi, handicaps, end-of-game cleanup, ++# and other details. ++ ++# =========================================================================== ++# Notes about performance and memory usage ++# =========================================================================== ++# Important: For good performance, you will very likely want to tune the ++# "numSearchThreads" parameter in the Search limits section below! Run ++# "./katago benchmark" to test KataGo and to suggest a reasonable value ++# of this parameter. ++ ++# For multi-GPU systems, read "OpenCL GPU settings" or "CUDA GPU settings". ++# ++# When using OpenCL, verify that KataGo picks the correct device! Some systems ++# may have both an Intel CPU OpenCL and GPU OpenCL. If # KataGo picks the wrong ++# one, correct this by specifying "openclGpuToUse". ++# ++# Consider adjusting "maxVisits", "ponderingEnabled", "resignThreshold", and ++# other parameters depending on your intended usage. ++ ++# =========================================================================== ++# Command-line usage ++# =========================================================================== ++# All of the below values may be set or overridden via command-line arguments: ++# ++# -override-config KEY=VALUE,KEY=VALUE,... ++ ++# =========================================================================== ++# Logs and files ++# =========================================================================== ++# This section defines where and what logging information is produced. ++ ++# Each run of KataGo will log to a separate file in this dir. ++# This is the default. ++logDir = gtp_logs ++# Uncomment and specify this instead of logDir to write separate dated subdirs ++# logDirDated = gtp_logs ++# Uncomment and specify this instead of logDir to log to only a single file ++# logFile = gtp.log ++ ++# Logging options ++logAllGTPCommunication = true ++logSearchInfo = true ++logSearchInfoForChosenMove = false ++logToStderr = false ++ ++# KataGo will display some info to stderr on GTP startup ++# Uncomment the next line and set it to false to suppress that and remain silent ++# startupPrintMessageToStderr = true ++ ++# Write information to stderr, for use in things like malkovich chat to OGS. ++ ogsChatToStderr = true ++ ++# Uncomment and set this to a directory to override where openCLTuner files ++# and other cached data is written. By default it saves into a subdir of the ++# current directory on windows, and a subdir of ~/.katago on Linux. ++# homeDataDir = PATH_TO_DIRECTORY ++ ++# =========================================================================== ++# Analysis ++# =========================================================================== ++# This section configures analysis settings. ++# ++# The maximum number of moves after the first move displayed in variations ++# from analysis commands like kata-analyze or lz-analyze. ++# analysisPVLen = 15 ++ ++# Report winrates for chat and analysis as (BLACK|WHITE|SIDETOMOVE). ++# Most GUIs and analysis tools will expect SIDETOMOVE. ++# reportAnalysisWinratesAs = SIDETOMOVE ++ ++# Extra noise for wider exploration. Large values will force KataGo to ++# analyze a greater variety of moves than it normally would. ++# An extreme value like 1 distributes playouts across every move on the board, ++# even very bad moves. ++# Affects analysis only, does not affect play. ++# analysisWideRootNoise = 0.04 ++ ++# Try to limit the effect of possible bad or bogus move sequences in the ++# history leading to this position from affecting KataGo's move predictions. ++# analysisIgnorePreRootHistory = true ++ ++# =========================================================================== ++# Rules ++# =========================================================================== ++# This section configures the scoring and playing rules. Rules can also be ++# changed mid-run by issuing custom GTP commands. ++# ++# See https://lightvector.github.io/KataGo/rules.html for rules details. ++# ++# See https://github.com/lightvector/KataGo/blob/master/docs/GTP_Extensions.md ++# for GTP commands. ++ ++# Specify the rules as a string. ++# Some legal values include: ++# chinese, japanese, korean, aga, chinese-ogs, new-zealand, stone-scoring, ++# ancient-territory, bga, aga-button ++# ++# For some human rulesets that require complex adjudication in tricky cases ++# (e.g. japanese, korean) KataGo may not precisely match the ruleset in such ++# cases but will do its best. ++rules = tromp-taylor ++ ++# By default, the "rules" parameter is used, but if you comment it out and ++# uncomment one option in each of the sections below, you can specify an ++# arbitrary combination of individual rules. ++ ++# koRule = SIMPLE # Simple ko rules (triple ko = no result) ++# koRule = POSITIONAL # Positional superko ++# koRule = SITUATIONAL # Situational superko ++ ++# scoringRule = AREA # Area scoring ++# scoringRule = TERRITORY # Territory scoring (special computer-friendly territory rules) ++ ++# taxRule = NONE # All surrounded empty points are scored ++# taxRule = SEKI # Eyes in seki do NOT count as points ++# taxRule = ALL # All groups are taxed up to 2 points for the two eyes needed to live ++ ++# Is multiple-stone suicide legal? (Single-stone suicide is always illegal). ++# multiStoneSuicideLegal = false ++# multiStoneSuicideLegal = true ++ ++# "Button go" - the first pass when area scoring awards 0.5 points and does ++# not count for ending the game. ++# Allows area scoring rulesets that have far simpler rules to achieve the same ++# final scoring precision and reward for precise play as territory scoring. ++# hasButton = false ++# hasButton = true ++ ++# Is this a human ruleset where it's okay to pass before having physically ++# captured and removed all dead stones? ++# friendlyPassOk = false ++# friendlyPassOk = true ++ ++# How handicap stones in handicap games are compensated ++# whiteHandicapBonus = 0 # White gets no compensation for black's handicap stones (Tromp-taylor, NZ, JP) ++# whiteHandicapBonus = N-1 # White gets N-1 points for black's N handicap stones (AGA) ++# whiteHandicapBonus = N # White gets N points for black's N handicap stones (Chinese) ++ ++# ------------------------------ ++# Other rules hacks ++# ------------------------------ ++# Uncomment and change to adjust what board size KataGo uses upon startup ++# by default when GTP doesn't specify. ++# defaultBoardSize = 19 ++ ++# By default, Katago will use the komi that the GUI or GTP controller tries to set. ++# Uncomment and set this to have KataGo ignore the controller and always use this komi. ++# ignoreGTPAndForceKomi = 7 ++ ++# =========================================================================== ++# Bot behavior ++# =========================================================================== ++ ++# ------------------------------ ++# Resignation ++# ------------------------------ ++ ++# Resignation occurs if for at least resignConsecTurns in a row, the ++# winLossUtility (on a [-1,1] scale) is below resignThreshold. ++allowResignation = true ++resignThreshold = -0.90 ++resignConsecTurns = 3 ++ ++# By default, KataGo may resign games that it is confidently losing even if they ++# are very close in score. Uncomment and set this to avoid resigning games ++# if the estimated difference is points is less than or equal to this. ++# resignMinScoreDifference = 10 ++ ++# ------------------------------ ++# Handicap ++# ------------------------------ ++# Assume that if black makes many moves in a row right at the start of the ++# game, then the game is a handicap game. This is necessary on some servers ++# and for some GUIs and also when initializing from many SGF files, which may ++# set up a handicap game using repeated GTP "play" commands for black rather ++# than GTP "place_free_handicap" commands; however, it may also lead to ++# incorrect understanding of komi if whiteHandicapBonus is used and a server ++# does not have such a practice. Uncomment and set to false to disable. ++# assumeMultipleStartingBlackMovesAreHandicap = true ++ ++# Makes katago dynamically adjust in handicap or altered-komi games to assume ++# based on those game settings that it must be stronger or weaker than the ++# opponent and to play accordingly. Greatly improves handicap strength by ++# biasing winrates and scores to favor appropriate safe/aggressive play. ++# Does NOT affect analysis (lz-analyze, kata-analyze, used by programs like ++# Lizzie) so analysis remains unbiased. Uncomment and set this to 0 to disable ++# this and make KataGo play the same always. ++# dynamicPlayoutDoublingAdvantageCapPerOppLead = 0.045 ++ ++# Instead of "dynamicPlayoutDoublingAdvantageCapPerOppLead", you can comment ++# that out and uncomment and set "playoutDoublingAdvantage" to a fixed value ++# from -3.0 to 3.0 that will not change dynamically. ++# ALSO affects analysis tools (lz-analyze, kata-analyze, used by e.g. Lizzie). ++# Negative makes KataGo behave as if it is much weaker than the opponent. ++# Positive makes KataGo behave as if it is much stronger than the opponent. ++# KataGo will adjust to favor safe/aggressive play as appropriate based on ++# the combination of who is ahead and how much stronger/weaker it thinks it is, ++# and report winrates and scores taking the strength difference into account. ++# ++# If this and "dynamicPlayoutDoublingAdvantageCapPerOppLead" are both set ++# then dynamic will be used for all games and this fixed value will be used ++# for analysis tools. ++# playoutDoublingAdvantage = 0.0 ++ ++# Uncomment one of these when using "playoutDoublingAdvantage" to enforce ++# that it will only apply when KataGo plays as the specified color and will be ++# negated when playing as the opposite color. ++# playoutDoublingAdvantagePla = BLACK ++# playoutDoublingAdvantagePla = WHITE ++ ++# ------------------------------ ++# Passing and cleanup ++# ------------------------------ ++# Make the bot never assume that its pass will end the game, even if passing ++# would end and "win" under Tromp-Taylor rules. Usually this is a good idea ++# when using it for analysis or playing on servers where scoring may be ++# implemented non-tromp-taylorly. Uncomment and set to false to disable. ++# conservativePass = true ++ ++# When using territory scoring, self-play games continue beyond two passes ++# with special cleanup rules that may be confusing for human players. This ++# option prevents the special cleanup phases from being reachable when using ++# the bot for GTP play. Uncomment and set to false to enable entering special ++# cleanup. For example, if you are testing it against itself, or against ++# another bot that has precisely implemented the rules documented at ++# https://lightvector.github.io/KataGo/rules.html ++# preventCleanupPhase = true ++ ++# ------------------------------ ++# Miscellaneous behavior ++# ------------------------------ ++# If the board is symmetric, search only one copy of each equivalent move. ++# Attempts to also account for ko/superko, will not theoretically perfect for ++# superko. Uncomment and set to false to disable. ++# rootSymmetryPruning = true ++ ++# Uncomment and set to true to avoid a particular joseki that some networks ++# misevaluate, and also to improve opening diversity versus some particular ++# other bots that like to play it all the time. ++# avoidMYTDaggerHack = false ++ ++# Prefer to avoid playing the same joseki in every corner of the board. ++# Uncomment to set to a specific value. See "Avoid SGF patterns" section. ++# By default: 0 (even games), 0.005 (handicap games) ++# avoidRepeatedPatternUtility = 0.0 ++ ++# Experimental logic to fight against mirror Go even with unfavorable komi. ++# Uncomment to set to a specific value to use for both playing and analysis. ++# By default: true when playing via GTP, but false when analyzing. ++# antiMirror = true ++ ++# Enable some hacks that mitigate rare instances when passing messes up deeper searches. ++# enablePassingHacks = true ++ ++# Uncomment and set this to true to prevent bad or bogus move sequences ++# in the history leading to this position from affecting KataGo's move choices. ++# Same as analysisIgnorePreRootHistory (see above) but applies to actual play. ++# You can enable this if KataGo is being asked to play from positions that it did not ++# choose the moves to reach. ++# ignorePreRootHistory = false ++ ++# =========================================================================== ++# Search limits ++# =========================================================================== ++ ++# Terminology: ++# "Playouts" is the number of new playouts of search performed each turn. ++# "Visits" is the same as "Playouts" but also counts search performed on ++# previous turns that is still applicable to this turn. ++# "Time" is the time in seconds. ++ ++# For example, if KataGo searched 200 nodes on the previous turn, and then ++# after the opponent's reply, 50 nodes of its search tree was still valid, ++# then a visit limit of 200 would allow KataGo to search 150 new nodes ++# (for a final tree size of 200 nodes), whereas a playout limit of of 200 ++# would allow KataGo to search 200 nodes (for a final tree size of 250 nodes). ++ ++# Additionally, KataGo may also move before than the limit in order to ++# obey time controls (e.g. byo-yomi, etc) if the GTP controller has ++# told KataGo that the game has is being played with a given time control. ++ ++# Limits for search on the current turn. ++# If commented out or unspecified, the default is to have no limit. ++# maxVisits = 30000 ++# maxPlayouts = 300 ++maxTime = 1.0 ++ ++# Ponder on the opponent's turn? ++ponderingEnabled = true ++ ++# Limits for search when pondering on the opponent's turn. ++# If commented out or unspecified, the default is to have no limit. ++# Limiting the maximum time is recommended so that KataGo won't burn CPU/GPU ++# forever and/or run out of RAM if left unattended while pondering is enabled. ++# maxVisitsPondering = 5000 ++# maxPlayoutsPondering = 3000 ++maxTimePondering = 120.0 ++ ++ ++# ------------------------------ ++# Other search limits and behavior ++# ------------------------------ ++ ++# Approx number of seconds to buffer for lag for GTP time controls - will ++# move a bit faster assuming there is this much lag per move. ++lagBuffer = 1.0 ++ ++# YOU PROBABLY WANT TO TUNE THIS PARAMETER! ++# The number of threads to use when searching. On powerful GPUs the optimal ++# threads may be much higher than the number of CPU cores you have because ++# many threads are needed to feed efficient large batches to the GPU. ++# ++# Run "./katago benchmark" to tune this parameter and test the effect ++# of changes to any of other parameters. ++numSearchThreads = 40 ++ ++# Play a little faster if the opponent is passing, for human-friendliness. ++# Comment these out to disable them, such as if running a controlled match ++# where you are testing KataGo with fixed compute per move vs other bots. ++searchFactorAfterOnePass = 0.50 ++searchFactorAfterTwoPass = 0.25 ++ ++# Play a little faster if super-winning, for human-friendliness. ++# Comment these out to disable them, such as if running a controlled match ++# where you are testing KataGo with fixed compute per move vs other bots. ++searchFactorWhenWinning = 0.40 ++searchFactorWhenWinningThreshold = 0.95 ++ ++# =========================================================================== ++# GPU settings ++# =========================================================================== ++# This section configures GPU settings. ++# ++# Maximum number of positions to send to a single GPU at once. The default ++# value is roughly equal to numSearchThreads, but can be specified manually ++# if running out of memory, or using multiple GPUs that expect to share work. ++# nnMaxBatchSize = ++ ++# Controls the neural network cache size, which is the primary RAM/memory use. ++# KataGo will cache up to (2 ** nnCacheSizePowerOfTwo) many neural net ++# evaluations in case of transpositions in the tree. ++# Increase this to improve performance for searches with tens of thousands ++# of visits or more. Decrease this to limit memory usage. ++# If you're happy to do some math - each neural net entry takes roughly ++# 1.5KB, except when using whole-board ownership/territory ++# visualizations, where each entry will take roughly 3KB. The number of ++# entries is (2 ** nnCacheSizePowerOfTwo). (E.g. 2 ** 18 = 262144.) ++# You can compute roughly how much memory the cache will use based on this. ++# nnCacheSizePowerOfTwo = 20 ++ ++# Size of mutex pool for nnCache is (2 ** this). ++# nnMutexPoolSizePowerOfTwo = 16 ++ ++# Randomize board orientation when running neural net evals? Uncomment and ++# set to false to disable. ++# nnRandomize = true ++ ++# If provided, force usage of a specific seed for nnRandomize. ++# The default is to use a randomly generated seed. ++# nnRandSeed = abcdefg ++ ++# Uncomment and set to true to force GTP to use the maximum board size for ++# internal buffers for the neural net. This will make KataGo slower when ++# evaluating small boards, but will avoid a lengthy initialization time on every ++# change of board size due to having to re-size the neural net buffers on the GPU. ++# This can be useful for example, for OGS's persistent bot mode that uses a single ++# bot instance to handle multiple games and may thrash between different board sizes ++# if there are concurrent games of multiple sizes. ++# gtpForceMaxNNSize = false ++ ++# ------------------------------ ++# Multiple GPUs ++# ------------------------------ ++# Set this to the number of GPUs to use or that are available. ++# IMPORTANT: If more than 1, also uncomment the appropriate TensorRT ++# or CUDA or OpenCL section. ++# numNNServerThreadsPerModel = 1 ++ ++# ------------------------------ ++# TENSORRT GPU settings ++# ------------------------------ ++# These only apply when using the TENSORRT version of KataGo. ++ ++# For one GPU: optionally uncomment this option and change if the GPU to ++# use is not device 0. ++# trtDeviceToUse = 0 ++ ++# For two GPUs: Uncomment these options, AND set numNNServerThreadsPerModel above. ++# Also, change their values if the devices you want to use are not 0 and 1. ++# trtDeviceToUseThread0 = 0 ++# trtDeviceToUseThread1 = 1 ++ ++# For three GPUs: Uncomment these options, AND set numNNServerThreadsPerModel above. ++# Also, change their values if the devices you want to use are not 0 and 1 and 2. ++# trtDeviceToUseThread0 = 0 ++# trtDeviceToUseThread1 = 1 ++# trtDeviceToUseThread2 = 2 ++ ++# The pattern continues for additional GPUs. ++ ++# ------------------------------ ++# CUDA GPU settings ++# ------------------------------ ++# These only apply when using the CUDA version of KataGo. ++ ++# For one GPU: optionally uncomment and change this if the GPU you want to ++# use is not device 0 ++# cudaDeviceToUse = 0 ++ ++# For two GPUs: Uncomment these options, AND set numNNServerThreadsPerModel above. ++# Also, change their values if the devices you want to use are not 0 and 1. ++# cudaDeviceToUseThread0 = 0 ++# cudaDeviceToUseThread1 = 1 ++ ++# For three GPUs: Uncomment these options, AND set numNNServerThreadsPerModel above. ++# Also, change their values if the devices you want to use are not 0 and 1 and 2. ++# cudaDeviceToUseThread0 = 0 ++# cudaDeviceToUseThread1 = 1 ++# cudaDeviceToUseThread2 = 2 ++ ++# The pattern continues for additional GPUs. ++ ++# KataGo will automatically use FP16 or not based on the compute capability ++# of your NVIDIA GPU. If you want to try to force a particular behavior ++# you can uncomment these lines and change them to "true" or "false". ++# cudaUseFP16 = auto ++# cudaUseNHWC = auto ++ ++# ------------------------------ ++# OpenCL GPU settings ++# ------------------------------ ++# These only apply when using the OpenCL version of KataGo. ++ ++# Uncomment and set to true to tune OpenCL for every board size separately, ++# rather than only the largest possible size. ++# openclReTunePerBoardSize = false ++ ++# For one GPU: optionally uncomment and change this if the best device to use is guessed incorrectly. ++# The default behavior tries to guess the 'best' GPU or device on your system to use, usually it will be a good guess. ++# openclDeviceToUse = 0 ++ ++# For two GPUs: Uncomment these two lines and replace X and Y with the device ids of the devices you want to use. ++# It might NOT be 0 and 1, some computers will have many OpenCL devices. You can see what the devices are when ++# KataGo starts up - it should print or log all the devices it finds. ++# (AND also set numNNServerThreadsPerModel above) ++# openclDeviceToUseThread0 = X ++# openclDeviceToUseThread1 = Y ++ ++# For three GPUs: Uncomment these three lines and replace X and Y and Z with the device ids of the devices you want to use. ++# It might NOT be 0 and 1 and 2, some computers will have many OpenCL devices. You can see what the devices are when ++# KataGo starts up - it should print or log all the devices it finds. ++# (AND also set numNNServerThreadsPerModel above) ++# openclDeviceToUseThread0 = X ++# openclDeviceToUseThread1 = Y ++# openclDeviceToUseThread2 = Z ++ ++# The pattern continues for additional GPUs. ++ ++# KataGo will automatically use FP16 or not based on testing your GPU during ++# tuning. If you want to try to force a particular behavior though you can ++# uncomment this option and change it to "true" or "false". This is a fairly ++# blunt setting - more detailed settings are testable by rerunning the tuner ++# with various arguments (./katago tuner). ++# openclUseFP16 = auto ++ ++# ------------------------------ ++# Eigen-specific settings ++# ------------------------------ ++# These only apply when using the Eigen (pure CPU) version of KataGo. ++ ++# Number of CPU threads for evaluating the neural net on the Eigen backend. ++# ++# Default: numSearchThreads ++# numEigenThreadsPerModel = X ++ ++# =========================================================================== ++# Root move selection and biases ++# =========================================================================== ++# Uncomment and edit any of the below values to change them from their default. ++ ++# If provided, force usage of a specific seed for various random things in ++# the search. The default is to use a random seed. ++# searchRandSeed = hijklmn ++ ++# Temperature for the early game, randomize between chosen moves with ++# this temperature ++# chosenMoveTemperatureEarly = 0.5 ++ ++# Decay temperature for the early game by 0.5 every this many moves, ++# scaled with board size. ++# chosenMoveTemperatureHalflife = 19 ++ ++# At the end of search after the early game, randomize between chosen ++# moves with this temperature ++# chosenMoveTemperature = 0.10 ++ ++# Subtract this many visits from each move prior to applying ++# chosenMoveTemperature (unless all moves have too few visits) to downweight ++# unlikely moves ++# chosenMoveSubtract = 0 ++ ++# The same as chosenMoveSubtract but only prunes moves that fall below ++# the threshold. This setting does not affect chosenMoveSubtract. ++# chosenMovePrune = 1 ++ ++# Number of symmetries to sample (without replacement) and average at the root ++# rootNumSymmetriesToSample = 1 ++ ++# Using LCB for move selection? ++# useLcbForSelection = true ++ ++# How many stdevs a move needs to be better than another for LCB selection ++# lcbStdevs = 5.0 ++ ++# Only use LCB override when a move has this proportion of visits as the ++# top move. ++# minVisitPropForLCB = 0.15 ++ ++# =========================================================================== ++# Internal params ++# =========================================================================== ++# Uncomment and edit any of the below values to change them from their default. ++ ++# Scales the utility of winning/losing ++# winLossUtilityFactor = 1.0 ++ ++# Scales the utility for trying to maximize score ++# staticScoreUtilityFactor = 0.10 ++# dynamicScoreUtilityFactor = 0.30 ++ ++# Adjust dynamic score center this proportion of the way towards zero, ++# capped at a reasonable amount. ++# dynamicScoreCenterZeroWeight = 0.20 ++# dynamicScoreCenterScale = 0.75 ++ ++# The utility of getting a "no result" due to triple ko or other long cycle ++# in non-superko rulesets (-1 to 1) ++# noResultUtilityForWhite = 0.0 ++ ++# The number of wins that a draw counts as, for white. (0 to 1) ++# drawEquivalentWinsForWhite = 0.5 ++ ++# Exploration constant for mcts ++# cpuctExploration = 1.0 ++# cpuctExplorationLog = 0.45 ++ ++# Parameters that control exploring more in volatile positions, exploring ++# less in stable positions. ++# cpuctUtilityStdevPrior = 0.40 ++# cpuctUtilityStdevPriorWeight = 2.0 ++# cpuctUtilityStdevScale = 0.85 ++ ++# FPU reduction constant for mcts ++# fpuReductionMax = 0.2 ++# rootFpuReductionMax = 0.1 ++# fpuParentWeightByVisitedPolicy = true ++ ++# Parameters that control weighting of evals based on the net's own ++# self-reported uncertainty. ++# useUncertainty = true ++# uncertaintyExponent = 1.0 ++# uncertaintyCoeff = 0.25 ++ ++# Explore using optimistic policy ++# rootPolicyOptimism = 0.2 ++# policyOptimism = 1.0 ++ ++# Amount to apply a downweighting of children with very bad values relative ++# to good ones. ++# valueWeightExponent = 0.25 ++ ++# Slight incentive for the bot to behave human-like with regard to passing at ++# the end, filling the dame, not wasting time playing in its own territory, ++# etc., and not play moves that are equivalent in terms of points but a bit ++# more unfriendly to humans. ++# rootEndingBonusPoints = 0.5 ++ ++# Make the bot prune useless moves that are just prolonging the game to ++# avoid losing yet. ++# rootPruneUselessMoves = true ++ ++# Apply bias correction based on local pattern keys ++# subtreeValueBiasFactor = 0.45 ++# subtreeValueBiasWeightExponent = 0.85 ++ ++# Use graph search rather than tree search - identify and share search for ++# transpositions. ++# useGraphSearch = true ++ ++# How much to shard the node table for search synchronization ++# nodeTableShardsPowerOfTwo = 16 ++ ++# How many virtual losses to add when a thread descends through a node ++# numVirtualLossesPerThread = 1 ++ ++# Improve the quality of evals under heavy multithreading ++# useNoisePruning = true ++ ++# =========================================================================== ++# Automatic avoid patterns ++# =========================================================================== ++# The parameters in this section provide a way to bias away from moves that match ++# patterns that this instance of KataGo has played in previous games, by auto-saving ++# moves to a directory and then auto-loading and biasing against them each new game. ++# Uncomment them to use them. When using this feature, all parameters must be specified. ++ ++# Different board sizes are tracked separately, but all board sizes share the same ++# parameters by default. Every parameter *except* for autoAvoidRepeatDir and ++# autoAvoidRepeatSaveChunkSize can be overridden per board size, ++# e.g. "autoAvoidRepeatMinTurnNumber13x13". You must ALSO specify the ++# defaults even if you have specified board-size-specific values. ++ ++# Directory to auto-save moves KataGo plays, to avoid them in future games. ++# You can create a new empty directory and put its path here. ++# If you run parallel instances of KataGo, use different directories if you ++# want them not to share their biases, use the same directory if you want ++# all of them to bias away from past moves that any of them have played. ++# KataGo will also automatically DELETE old data in this directory, so it is ++# recommended that if you do share the same directory between parallel instances, ++# that they all use the same settings that affect data saving/deletion. ++# autoAvoidRepeatDir = PATH_TO_NEW_DIRECTORY ++ ++# Penalize this much utility per matching move. ++# Values that are too large may lead to bad play. The value of 0.004 is fairly large ++# and might be large enough to result in some early weird/bad moves when trying ++# to avoid past games' moves if enough games begin the same way. You can experiment with it. ++# autoAvoidRepeatUtility = 0.004 ++ ++# Per each new move saved, exponentially decay prior saved moves by this factor. ++# This way, the bias against moves from many games ago is gradually phased out. ++# For example, 0.9995 = 1 - 1/2000, so would be roughly 2000 prior moves worth of ++# penalty weight remembered, in steady state. Depending on what turn number range ++# you are saving, this might equate to a different number of games For example ++# saving the first 50 moves per game would make this roughly 2000 / 50 = 40 games ++# worth of memory. ++# autoAvoidRepeatLambda = 0.9995 ++ ++# Affects data saving/deletion. ++# When the number of saved moves exceeds this, outright delete them to avoid too many ++# files and disk space building up. Also may affect the speed of saving/loading on start ++# of each game if this is set large and a lot of data builds up. ++# autoAvoidRepeatMaxPoses = 10000 ++ ++# Affects data saving/deletion. ++# Only save data for moves within this turn number range of those games. ++# E.g. setting autoAvoidRepeatMinTurnNumber to a number like 4 or 5 would tend to make ++# KataGo not develop a bias against the initial 3-4 and 4-4 corner moves in almost every game. ++# autoAvoidRepeatMinTurnNumber = 0 ++# autoAvoidRepeatMaxTurnNumber = 50 ++ ++# Affects data saving/deletion. ++# Within a single run of a program, wait to accumulate this many samples ++# (possibly across multiple clear_boards/games) before saving the data. ++# Can help to avoid writing too many small files to disk, especially when GTP is used ++# in a way that clears the board very frequently (e.g. gtp2ogs pooled manager). ++# autoAvoidRepeatSaveChunkSize = 200 ++ ++# =========================================================================== ++# Avoid SGF patterns ++# =========================================================================== ++# The parameters in this section provide a way to avoid moves that follow ++# specific patterns based on a set of SGF files loaded upon startup. ++# This is basically the same as the above "Automatic avoid patterns" section ++# above except you supply your own SGF files to avoid moves from. ++# Uncomment them to use this feature. Additionally, if the SGF file ++# contains the string %SKIP% in a comment on a move, that move will be ++# ignored for this purpose. ++ ++# Load SGF files from this directory when the engine is started ++# (only on startup, will not reload unless engine is restarted) ++# avoidSgfPatternDirs = path/to/directory/with/sgfs/ ++# You can also surround the file path in double quotes if the file path contains trailing spaces or hash signs. ++# Within double quotes, backslashes are escape characters. ++# avoidSgfPatternDirs = "path/to/directory/with/sgfs/" ++ ++# Penalize this much utility per matching move. ++# Set this negative if you instead want to favor SGF patterns instead of ++# penalizing them. This number does not need to be large, even 0.001 will ++# make a difference. Values that are too large may lead to bad play. ++# avoidSgfPatternUtility = 0.001 ++ ++# Optional - load only the newest this many files ++# avoidSgfPatternMaxFiles = 20 ++ ++# Optional - Penalty is multiplied by this per each older SGF file, so that ++# old SGF files matter less than newer ones. ++# avoidSgfPatternLambda = 0.90 ++ ++# Optional - pay attention only to moves made by players with this name. ++# For example, set it to the name that your bot's past games will show up ++# as in the SGF, so that the bot will only avoid repeating moves that itself ++# made in past games, not the moves that its opponents made. ++# avoidSgfPatternAllowedNames = my-ogs-bot-name1,my-ogs-bot-name2 ++ ++# Optional - Ignore moves in SGF files that occurred before this turn number. ++# avoidSgfPatternMinTurnNumber = 0 ++ ++# For more avoid patterns: ++# You can also specify a second set of parameters, and a third, fourth, ++# etc. by numbering 2,3,4,... ++# ++# avoidSgf2PatternDirs = ... ++# avoidSgf2PatternUtility = ... ++# avoidSgf2PatternMaxFiles = ... ++# avoidSgf2PatternLambda = ... ++# avoidSgf2PatternAllowedNames = ... ++# avoidSgf2PatternMinTurnNumber = ... ++ +diff --git a/python/selfplay/doudoubot.cfg b/python/selfplay/doudoubot.cfg +new file mode 100644 +index 00000000..bf81254f +--- /dev/null ++++ b/python/selfplay/doudoubot.cfg +@@ -0,0 +1,721 @@ ++# Configuration for KataGo C++ GTP engine ++ ++# Run the program using: `./katago.exe gtp` ++ ++# In this example config, when a parameter is given as a commented out value, ++# that value also is the default value, unless described otherwise. You can ++# uncomment it (remove the pound sign) and change it if you want. ++ ++# =========================================================================== ++# Running on an online server or in a real tournament or match ++# =========================================================================== ++# If you plan to run online or in a tournament, read through the "Rules" ++# section below for proper handling of komi, handicaps, end-of-game cleanup, ++# and other details. ++ ++# =========================================================================== ++# Notes about performance and memory usage ++# =========================================================================== ++# Important: For good performance, you will very likely want to tune the ++# "numSearchThreads" parameter in the Search limits section below! Run ++# "./katago benchmark" to test KataGo and to suggest a reasonable value ++# of this parameter. ++ ++# For multi-GPU systems, read "OpenCL GPU settings" or "CUDA GPU settings". ++# ++# When using OpenCL, verify that KataGo picks the correct device! Some systems ++# may have both an Intel CPU OpenCL and GPU OpenCL. If # KataGo picks the wrong ++# one, correct this by specifying "openclGpuToUse". ++# ++# Consider adjusting "maxVisits", "ponderingEnabled", "resignThreshold", and ++# other parameters depending on your intended usage. ++ ++# =========================================================================== ++# Command-line usage ++# =========================================================================== ++# All of the below values may be set or overridden via command-line arguments: ++# ++# -override-config KEY=VALUE,KEY=VALUE,... ++ ++# =========================================================================== ++# Logs and files ++# =========================================================================== ++# This section defines where and what logging information is produced. ++ ++# Each run of KataGo will log to a separate file in this dir. ++# This is the default. ++logDir = gtp_logs ++# Uncomment and specify this instead of logDir to write separate dated subdirs ++# logDirDated = gtp_logs ++# Uncomment and specify this instead of logDir to log to only a single file ++# logFile = gtp.log ++ ++# Logging options ++logAllGTPCommunication = true ++logSearchInfo = true ++logSearchInfoForChosenMove = false ++logToStderr = false ++ ++# KataGo will display some info to stderr on GTP startup ++# Uncomment the next line and set it to false to suppress that and remain silent ++# startupPrintMessageToStderr = true ++ ++# Write information to stderr, for use in things like malkovich chat to OGS. ++ ogsChatToStderr = true ++ ++# Uncomment and set this to a directory to override where openCLTuner files ++# and other cached data is written. By default it saves into a subdir of the ++# current directory on windows, and a subdir of ~/.katago on Linux. ++# homeDataDir = PATH_TO_DIRECTORY ++ ++# =========================================================================== ++# Analysis ++# =========================================================================== ++# This section configures analysis settings. ++# ++# The maximum number of moves after the first move displayed in variations ++# from analysis commands like kata-analyze or lz-analyze. ++# analysisPVLen = 15 ++ ++# Report winrates for chat and analysis as (BLACK|WHITE|SIDETOMOVE). ++# Most GUIs and analysis tools will expect SIDETOMOVE. ++# reportAnalysisWinratesAs = SIDETOMOVE ++ ++# Extra noise for wider exploration. Large values will force KataGo to ++# analyze a greater variety of moves than it normally would. ++# An extreme value like 1 distributes playouts across every move on the board, ++# even very bad moves. ++# Affects analysis only, does not affect play. ++# analysisWideRootNoise = 0.04 ++ ++# Try to limit the effect of possible bad or bogus move sequences in the ++# history leading to this position from affecting KataGo's move predictions. ++# analysisIgnorePreRootHistory = true ++ ++# =========================================================================== ++# Rules ++# =========================================================================== ++# This section configures the scoring and playing rules. Rules can also be ++# changed mid-run by issuing custom GTP commands. ++# ++# See https://lightvector.github.io/KataGo/rules.html for rules details. ++# ++# See https://github.com/lightvector/KataGo/blob/master/docs/GTP_Extensions.md ++# for GTP commands. ++ ++# Specify the rules as a string. ++# Some legal values include: ++# chinese, japanese, korean, aga, chinese-ogs, new-zealand, stone-scoring, ++# ancient-territory, bga, aga-button ++# ++# For some human rulesets that require complex adjudication in tricky cases ++# (e.g. japanese, korean) KataGo may not precisely match the ruleset in such ++# cases but will do its best. ++rules = tromp-taylor ++ ++# By default, the "rules" parameter is used, but if you comment it out and ++# uncomment one option in each of the sections below, you can specify an ++# arbitrary combination of individual rules. ++ ++# koRule = SIMPLE # Simple ko rules (triple ko = no result) ++# koRule = POSITIONAL # Positional superko ++# koRule = SITUATIONAL # Situational superko ++ ++# scoringRule = AREA # Area scoring ++# scoringRule = TERRITORY # Territory scoring (special computer-friendly territory rules) ++ ++# taxRule = NONE # All surrounded empty points are scored ++# taxRule = SEKI # Eyes in seki do NOT count as points ++# taxRule = ALL # All groups are taxed up to 2 points for the two eyes needed to live ++ ++# Is multiple-stone suicide legal? (Single-stone suicide is always illegal). ++# multiStoneSuicideLegal = false ++# multiStoneSuicideLegal = true ++ ++# "Button go" - the first pass when area scoring awards 0.5 points and does ++# not count for ending the game. ++# Allows area scoring rulesets that have far simpler rules to achieve the same ++# final scoring precision and reward for precise play as territory scoring. ++# hasButton = false ++# hasButton = true ++ ++# Is this a human ruleset where it's okay to pass before having physically ++# captured and removed all dead stones? ++# friendlyPassOk = false ++# friendlyPassOk = true ++ ++# How handicap stones in handicap games are compensated ++# whiteHandicapBonus = 0 # White gets no compensation for black's handicap stones (Tromp-taylor, NZ, JP) ++# whiteHandicapBonus = N-1 # White gets N-1 points for black's N handicap stones (AGA) ++# whiteHandicapBonus = N # White gets N points for black's N handicap stones (Chinese) ++ ++# ------------------------------ ++# Other rules hacks ++# ------------------------------ ++# Uncomment and change to adjust what board size KataGo uses upon startup ++# by default when GTP doesn't specify. ++# defaultBoardSize = 19 ++ ++# By default, Katago will use the komi that the GUI or GTP controller tries to set. ++# Uncomment and set this to have KataGo ignore the controller and always use this komi. ++# ignoreGTPAndForceKomi = 7 ++ ++# =========================================================================== ++# Bot behavior ++# =========================================================================== ++ ++# ------------------------------ ++# Resignation ++# ------------------------------ ++ ++# Resignation occurs if for at least resignConsecTurns in a row, the ++# winLossUtility (on a [-1,1] scale) is below resignThreshold. ++allowResignation = true ++resignThreshold = -0.90 ++resignConsecTurns = 3 ++ ++# By default, KataGo may resign games that it is confidently losing even if they ++# are very close in score. Uncomment and set this to avoid resigning games ++# if the estimated difference is points is less than or equal to this. ++# resignMinScoreDifference = 10 ++ ++# ------------------------------ ++# Handicap ++# ------------------------------ ++# Assume that if black makes many moves in a row right at the start of the ++# game, then the game is a handicap game. This is necessary on some servers ++# and for some GUIs and also when initializing from many SGF files, which may ++# set up a handicap game using repeated GTP "play" commands for black rather ++# than GTP "place_free_handicap" commands; however, it may also lead to ++# incorrect understanding of komi if whiteHandicapBonus is used and a server ++# does not have such a practice. Uncomment and set to false to disable. ++# assumeMultipleStartingBlackMovesAreHandicap = true ++ ++# Makes katago dynamically adjust in handicap or altered-komi games to assume ++# based on those game settings that it must be stronger or weaker than the ++# opponent and to play accordingly. Greatly improves handicap strength by ++# biasing winrates and scores to favor appropriate safe/aggressive play. ++# Does NOT affect analysis (lz-analyze, kata-analyze, used by programs like ++# Lizzie) so analysis remains unbiased. Uncomment and set this to 0 to disable ++# this and make KataGo play the same always. ++# dynamicPlayoutDoublingAdvantageCapPerOppLead = 0.045 ++ ++# Instead of "dynamicPlayoutDoublingAdvantageCapPerOppLead", you can comment ++# that out and uncomment and set "playoutDoublingAdvantage" to a fixed value ++# from -3.0 to 3.0 that will not change dynamically. ++# ALSO affects analysis tools (lz-analyze, kata-analyze, used by e.g. Lizzie). ++# Negative makes KataGo behave as if it is much weaker than the opponent. ++# Positive makes KataGo behave as if it is much stronger than the opponent. ++# KataGo will adjust to favor safe/aggressive play as appropriate based on ++# the combination of who is ahead and how much stronger/weaker it thinks it is, ++# and report winrates and scores taking the strength difference into account. ++# ++# If this and "dynamicPlayoutDoublingAdvantageCapPerOppLead" are both set ++# then dynamic will be used for all games and this fixed value will be used ++# for analysis tools. ++# playoutDoublingAdvantage = 0.0 ++ ++# Uncomment one of these when using "playoutDoublingAdvantage" to enforce ++# that it will only apply when KataGo plays as the specified color and will be ++# negated when playing as the opposite color. ++# playoutDoublingAdvantagePla = BLACK ++# playoutDoublingAdvantagePla = WHITE ++ ++# ------------------------------ ++# Passing and cleanup ++# ------------------------------ ++# Make the bot never assume that its pass will end the game, even if passing ++# would end and "win" under Tromp-Taylor rules. Usually this is a good idea ++# when using it for analysis or playing on servers where scoring may be ++# implemented non-tromp-taylorly. Uncomment and set to false to disable. ++# conservativePass = true ++ ++# When using territory scoring, self-play games continue beyond two passes ++# with special cleanup rules that may be confusing for human players. This ++# option prevents the special cleanup phases from being reachable when using ++# the bot for GTP play. Uncomment and set to false to enable entering special ++# cleanup. For example, if you are testing it against itself, or against ++# another bot that has precisely implemented the rules documented at ++# https://lightvector.github.io/KataGo/rules.html ++# preventCleanupPhase = true ++ ++# ------------------------------ ++# Miscellaneous behavior ++# ------------------------------ ++# If the board is symmetric, search only one copy of each equivalent move. ++# Attempts to also account for ko/superko, will not theoretically perfect for ++# superko. Uncomment and set to false to disable. ++# rootSymmetryPruning = true ++ ++# Uncomment and set to true to avoid a particular joseki that some networks ++# misevaluate, and also to improve opening diversity versus some particular ++# other bots that like to play it all the time. ++# avoidMYTDaggerHack = false ++ ++# Prefer to avoid playing the same joseki in every corner of the board. ++# Uncomment to set to a specific value. See "Avoid SGF patterns" section. ++# By default: 0 (even games), 0.005 (handicap games) ++# avoidRepeatedPatternUtility = 0.0 ++ ++# Experimental logic to fight against mirror Go even with unfavorable komi. ++# Uncomment to set to a specific value to use for both playing and analysis. ++# By default: true when playing via GTP, but false when analyzing. ++# antiMirror = true ++ ++# Enable some hacks that mitigate rare instances when passing messes up deeper searches. ++# enablePassingHacks = true ++ ++# Uncomment and set this to true to prevent bad or bogus move sequences ++# in the history leading to this position from affecting KataGo's move choices. ++# Same as analysisIgnorePreRootHistory (see above) but applies to actual play. ++# You can enable this if KataGo is being asked to play from positions that it did not ++# choose the moves to reach. ++# ignorePreRootHistory = false ++ ++# =========================================================================== ++# Search limits ++# =========================================================================== ++ ++# Terminology: ++# "Playouts" is the number of new playouts of search performed each turn. ++# "Visits" is the same as "Playouts" but also counts search performed on ++# previous turns that is still applicable to this turn. ++# "Time" is the time in seconds. ++ ++# For example, if KataGo searched 200 nodes on the previous turn, and then ++# after the opponent's reply, 50 nodes of its search tree was still valid, ++# then a visit limit of 200 would allow KataGo to search 150 new nodes ++# (for a final tree size of 200 nodes), whereas a playout limit of of 200 ++# would allow KataGo to search 200 nodes (for a final tree size of 250 nodes). ++ ++# Additionally, KataGo may also move before than the limit in order to ++# obey time controls (e.g. byo-yomi, etc) if the GTP controller has ++# told KataGo that the game has is being played with a given time control. ++ ++# Limits for search on the current turn. ++# If commented out or unspecified, the default is to have no limit. ++# maxVisits = 500 ++# maxPlayouts = 300 ++ maxTime = 0.5 ++ ++# Ponder on the opponent's turn? ++ponderingEnabled = true ++ ++# Limits for search when pondering on the opponent's turn. ++# If commented out or unspecified, the default is to have no limit. ++# Limiting the maximum time is recommended so that KataGo won't burn CPU/GPU ++# forever and/or run out of RAM if left unattended while pondering is enabled. ++# maxVisitsPondering = 5000 ++# maxPlayoutsPondering = 3000 ++maxTimePondering = 120.0 ++ ++ ++# ------------------------------ ++# Other search limits and behavior ++# ------------------------------ ++ ++# Approx number of seconds to buffer for lag for GTP time controls - will ++# move a bit faster assuming there is this much lag per move. ++lagBuffer = 1.0 ++ ++# YOU PROBABLY WANT TO TUNE THIS PARAMETER! ++# The number of threads to use when searching. On powerful GPUs the optimal ++# threads may be much higher than the number of CPU cores you have because ++# many threads are needed to feed efficient large batches to the GPU. ++# ++# Run "./katago benchmark" to tune this parameter and test the effect ++# of changes to any of other parameters. ++numSearchThreads = 16 ++ ++# Play a little faster if the opponent is passing, for human-friendliness. ++# Comment these out to disable them, such as if running a controlled match ++# where you are testing KataGo with fixed compute per move vs other bots. ++searchFactorAfterOnePass = 0.50 ++searchFactorAfterTwoPass = 0.25 ++ ++# Play a little faster if super-winning, for human-friendliness. ++# Comment these out to disable them, such as if running a controlled match ++# where you are testing KataGo with fixed compute per move vs other bots. ++searchFactorWhenWinning = 0.40 ++searchFactorWhenWinningThreshold = 0.95 ++ ++# =========================================================================== ++# GPU settings ++# =========================================================================== ++# This section configures GPU settings. ++# ++# Maximum number of positions to send to a single GPU at once. The default ++# value is roughly equal to numSearchThreads, but can be specified manually ++# if running out of memory, or using multiple GPUs that expect to share work. ++# nnMaxBatchSize = ++ ++# Controls the neural network cache size, which is the primary RAM/memory use. ++# KataGo will cache up to (2 ** nnCacheSizePowerOfTwo) many neural net ++# evaluations in case of transpositions in the tree. ++# Increase this to improve performance for searches with tens of thousands ++# of visits or more. Decrease this to limit memory usage. ++# If you're happy to do some math - each neural net entry takes roughly ++# 1.5KB, except when using whole-board ownership/territory ++# visualizations, where each entry will take roughly 3KB. The number of ++# entries is (2 ** nnCacheSizePowerOfTwo). (E.g. 2 ** 18 = 262144.) ++# You can compute roughly how much memory the cache will use based on this. ++# nnCacheSizePowerOfTwo = 20 ++ ++# Size of mutex pool for nnCache is (2 ** this). ++# nnMutexPoolSizePowerOfTwo = 16 ++ ++# Randomize board orientation when running neural net evals? Uncomment and ++# set to false to disable. ++# nnRandomize = true ++ ++# If provided, force usage of a specific seed for nnRandomize. ++# The default is to use a randomly generated seed. ++# nnRandSeed = abcdefg ++ ++# Uncomment and set to true to force GTP to use the maximum board size for ++# internal buffers for the neural net. This will make KataGo slower when ++# evaluating small boards, but will avoid a lengthy initialization time on every ++# change of board size due to having to re-size the neural net buffers on the GPU. ++# This can be useful for example, for OGS's persistent bot mode that uses a single ++# bot instance to handle multiple games and may thrash between different board sizes ++# if there are concurrent games of multiple sizes. ++# gtpForceMaxNNSize = false ++ ++# ------------------------------ ++# Multiple GPUs ++# ------------------------------ ++# Set this to the number of GPUs to use or that are available. ++# IMPORTANT: If more than 1, also uncomment the appropriate TensorRT ++# or CUDA or OpenCL section. ++# numNNServerThreadsPerModel = 1 ++ ++# ------------------------------ ++# TENSORRT GPU settings ++# ------------------------------ ++# These only apply when using the TENSORRT version of KataGo. ++ ++# For one GPU: optionally uncomment this option and change if the GPU to ++# use is not device 0. ++# trtDeviceToUse = 0 ++ ++# For two GPUs: Uncomment these options, AND set numNNServerThreadsPerModel above. ++# Also, change their values if the devices you want to use are not 0 and 1. ++# trtDeviceToUseThread0 = 0 ++# trtDeviceToUseThread1 = 1 ++ ++# For three GPUs: Uncomment these options, AND set numNNServerThreadsPerModel above. ++# Also, change their values if the devices you want to use are not 0 and 1 and 2. ++# trtDeviceToUseThread0 = 0 ++# trtDeviceToUseThread1 = 1 ++# trtDeviceToUseThread2 = 2 ++ ++# The pattern continues for additional GPUs. ++ ++# ------------------------------ ++# CUDA GPU settings ++# ------------------------------ ++# These only apply when using the CUDA version of KataGo. ++ ++# For one GPU: optionally uncomment and change this if the GPU you want to ++# use is not device 0 ++# cudaDeviceToUse = 0 ++ ++# For two GPUs: Uncomment these options, AND set numNNServerThreadsPerModel above. ++# Also, change their values if the devices you want to use are not 0 and 1. ++# cudaDeviceToUseThread0 = 0 ++# cudaDeviceToUseThread1 = 1 ++ ++# For three GPUs: Uncomment these options, AND set numNNServerThreadsPerModel above. ++# Also, change their values if the devices you want to use are not 0 and 1 and 2. ++# cudaDeviceToUseThread0 = 0 ++# cudaDeviceToUseThread1 = 1 ++# cudaDeviceToUseThread2 = 2 ++ ++# The pattern continues for additional GPUs. ++ ++# KataGo will automatically use FP16 or not based on the compute capability ++# of your NVIDIA GPU. If you want to try to force a particular behavior ++# you can uncomment these lines and change them to "true" or "false". ++# cudaUseFP16 = auto ++# cudaUseNHWC = auto ++ ++# ------------------------------ ++# OpenCL GPU settings ++# ------------------------------ ++# These only apply when using the OpenCL version of KataGo. ++ ++# Uncomment and set to true to tune OpenCL for every board size separately, ++# rather than only the largest possible size. ++# openclReTunePerBoardSize = false ++ ++# For one GPU: optionally uncomment and change this if the best device to use is guessed incorrectly. ++# The default behavior tries to guess the 'best' GPU or device on your system to use, usually it will be a good guess. ++# openclDeviceToUse = 0 ++ ++# For two GPUs: Uncomment these two lines and replace X and Y with the device ids of the devices you want to use. ++# It might NOT be 0 and 1, some computers will have many OpenCL devices. You can see what the devices are when ++# KataGo starts up - it should print or log all the devices it finds. ++# (AND also set numNNServerThreadsPerModel above) ++# openclDeviceToUseThread0 = X ++# openclDeviceToUseThread1 = Y ++ ++# For three GPUs: Uncomment these three lines and replace X and Y and Z with the device ids of the devices you want to use. ++# It might NOT be 0 and 1 and 2, some computers will have many OpenCL devices. You can see what the devices are when ++# KataGo starts up - it should print or log all the devices it finds. ++# (AND also set numNNServerThreadsPerModel above) ++# openclDeviceToUseThread0 = X ++# openclDeviceToUseThread1 = Y ++# openclDeviceToUseThread2 = Z ++ ++# The pattern continues for additional GPUs. ++ ++# KataGo will automatically use FP16 or not based on testing your GPU during ++# tuning. If you want to try to force a particular behavior though you can ++# uncomment this option and change it to "true" or "false". This is a fairly ++# blunt setting - more detailed settings are testable by rerunning the tuner ++# with various arguments (./katago tuner). ++# openclUseFP16 = auto ++ ++# ------------------------------ ++# Eigen-specific settings ++# ------------------------------ ++# These only apply when using the Eigen (pure CPU) version of KataGo. ++ ++# Number of CPU threads for evaluating the neural net on the Eigen backend. ++# ++# Default: numSearchThreads ++# numEigenThreadsPerModel = X ++ ++# =========================================================================== ++# Root move selection and biases ++# =========================================================================== ++# Uncomment and edit any of the below values to change them from their default. ++ ++# If provided, force usage of a specific seed for various random things in ++# the search. The default is to use a random seed. ++# searchRandSeed = hijklmn ++ ++# Temperature for the early game, randomize between chosen moves with ++# this temperature ++# chosenMoveTemperatureEarly = 0.5 ++ ++# Decay temperature for the early game by 0.5 every this many moves, ++# scaled with board size. ++# chosenMoveTemperatureHalflife = 19 ++ ++# At the end of search after the early game, randomize between chosen ++# moves with this temperature ++# chosenMoveTemperature = 0.10 ++ ++# Subtract this many visits from each move prior to applying ++# chosenMoveTemperature (unless all moves have too few visits) to downweight ++# unlikely moves ++# chosenMoveSubtract = 0 ++ ++# The same as chosenMoveSubtract but only prunes moves that fall below ++# the threshold. This setting does not affect chosenMoveSubtract. ++# chosenMovePrune = 1 ++ ++# Number of symmetries to sample (without replacement) and average at the root ++# rootNumSymmetriesToSample = 1 ++ ++# Using LCB for move selection? ++# useLcbForSelection = true ++ ++# How many stdevs a move needs to be better than another for LCB selection ++# lcbStdevs = 5.0 ++ ++# Only use LCB override when a move has this proportion of visits as the ++# top move. ++# minVisitPropForLCB = 0.15 ++ ++# =========================================================================== ++# Internal params ++# =========================================================================== ++# Uncomment and edit any of the below values to change them from their default. ++ ++# Scales the utility of winning/losing ++# winLossUtilityFactor = 1.0 ++ ++# Scales the utility for trying to maximize score ++# staticScoreUtilityFactor = 0.10 ++# dynamicScoreUtilityFactor = 0.30 ++ ++# Adjust dynamic score center this proportion of the way towards zero, ++# capped at a reasonable amount. ++# dynamicScoreCenterZeroWeight = 0.20 ++# dynamicScoreCenterScale = 0.75 ++ ++# The utility of getting a "no result" due to triple ko or other long cycle ++# in non-superko rulesets (-1 to 1) ++# noResultUtilityForWhite = 0.0 ++ ++# The number of wins that a draw counts as, for white. (0 to 1) ++# drawEquivalentWinsForWhite = 0.5 ++ ++# Exploration constant for mcts ++# cpuctExploration = 1.0 ++# cpuctExplorationLog = 0.45 ++ ++# Parameters that control exploring more in volatile positions, exploring ++# less in stable positions. ++# cpuctUtilityStdevPrior = 0.40 ++# cpuctUtilityStdevPriorWeight = 2.0 ++# cpuctUtilityStdevScale = 0.85 ++ ++# FPU reduction constant for mcts ++# fpuReductionMax = 0.2 ++# rootFpuReductionMax = 0.1 ++# fpuParentWeightByVisitedPolicy = true ++ ++# Parameters that control weighting of evals based on the net's own ++# self-reported uncertainty. ++# useUncertainty = true ++# uncertaintyExponent = 1.0 ++# uncertaintyCoeff = 0.25 ++ ++# Explore using optimistic policy ++# rootPolicyOptimism = 0.2 ++# policyOptimism = 1.0 ++ ++# Amount to apply a downweighting of children with very bad values relative ++# to good ones. ++# valueWeightExponent = 0.25 ++ ++# Slight incentive for the bot to behave human-like with regard to passing at ++# the end, filling the dame, not wasting time playing in its own territory, ++# etc., and not play moves that are equivalent in terms of points but a bit ++# more unfriendly to humans. ++# rootEndingBonusPoints = 0.5 ++ ++# Make the bot prune useless moves that are just prolonging the game to ++# avoid losing yet. ++# rootPruneUselessMoves = true ++ ++# Apply bias correction based on local pattern keys ++# subtreeValueBiasFactor = 0.45 ++# subtreeValueBiasWeightExponent = 0.85 ++ ++# Use graph search rather than tree search - identify and share search for ++# transpositions. ++# useGraphSearch = true ++ ++# How much to shard the node table for search synchronization ++# nodeTableShardsPowerOfTwo = 16 ++ ++# How many virtual losses to add when a thread descends through a node ++# numVirtualLossesPerThread = 1 ++ ++# Improve the quality of evals under heavy multithreading ++# useNoisePruning = true ++ ++# =========================================================================== ++# Automatic avoid patterns ++# =========================================================================== ++# The parameters in this section provide a way to bias away from moves that match ++# patterns that this instance of KataGo has played in previous games, by auto-saving ++# moves to a directory and then auto-loading and biasing against them each new game. ++# Uncomment them to use them. When using this feature, all parameters must be specified. ++ ++# Different board sizes are tracked separately, but all board sizes share the same ++# parameters by default. Every parameter *except* for autoAvoidRepeatDir and ++# autoAvoidRepeatSaveChunkSize can be overridden per board size, ++# e.g. "autoAvoidRepeatMinTurnNumber13x13". You must ALSO specify the ++# defaults even if you have specified board-size-specific values. ++ ++# Directory to auto-save moves KataGo plays, to avoid them in future games. ++# You can create a new empty directory and put its path here. ++# If you run parallel instances of KataGo, use different directories if you ++# want them not to share their biases, use the same directory if you want ++# all of them to bias away from past moves that any of them have played. ++# KataGo will also automatically DELETE old data in this directory, so it is ++# recommended that if you do share the same directory between parallel instances, ++# that they all use the same settings that affect data saving/deletion. ++# autoAvoidRepeatDir = PATH_TO_NEW_DIRECTORY ++ ++# Penalize this much utility per matching move. ++# Values that are too large may lead to bad play. The value of 0.004 is fairly large ++# and might be large enough to result in some early weird/bad moves when trying ++# to avoid past games' moves if enough games begin the same way. You can experiment with it. ++# autoAvoidRepeatUtility = 0.004 ++ ++# Per each new move saved, exponentially decay prior saved moves by this factor. ++# This way, the bias against moves from many games ago is gradually phased out. ++# For example, 0.9995 = 1 - 1/2000, so would be roughly 2000 prior moves worth of ++# penalty weight remembered, in steady state. Depending on what turn number range ++# you are saving, this might equate to a different number of games For example ++# saving the first 50 moves per game would make this roughly 2000 / 50 = 40 games ++# worth of memory. ++# autoAvoidRepeatLambda = 0.9995 ++ ++# Affects data saving/deletion. ++# When the number of saved moves exceeds this, outright delete them to avoid too many ++# files and disk space building up. Also may affect the speed of saving/loading on start ++# of each game if this is set large and a lot of data builds up. ++# autoAvoidRepeatMaxPoses = 10000 ++ ++# Affects data saving/deletion. ++# Only save data for moves within this turn number range of those games. ++# E.g. setting autoAvoidRepeatMinTurnNumber to a number like 4 or 5 would tend to make ++# KataGo not develop a bias against the initial 3-4 and 4-4 corner moves in almost every game. ++# autoAvoidRepeatMinTurnNumber = 0 ++# autoAvoidRepeatMaxTurnNumber = 50 ++ ++# Affects data saving/deletion. ++# Within a single run of a program, wait to accumulate this many samples ++# (possibly across multiple clear_boards/games) before saving the data. ++# Can help to avoid writing too many small files to disk, especially when GTP is used ++# in a way that clears the board very frequently (e.g. gtp2ogs pooled manager). ++# autoAvoidRepeatSaveChunkSize = 200 ++ ++# =========================================================================== ++# Avoid SGF patterns ++# =========================================================================== ++# The parameters in this section provide a way to avoid moves that follow ++# specific patterns based on a set of SGF files loaded upon startup. ++# This is basically the same as the above "Automatic avoid patterns" section ++# above except you supply your own SGF files to avoid moves from. ++# Uncomment them to use this feature. Additionally, if the SGF file ++# contains the string %SKIP% in a comment on a move, that move will be ++# ignored for this purpose. ++ ++# Load SGF files from this directory when the engine is started ++# (only on startup, will not reload unless engine is restarted) ++# avoidSgfPatternDirs = path/to/directory/with/sgfs/ ++# You can also surround the file path in double quotes if the file path contains trailing spaces or hash signs. ++# Within double quotes, backslashes are escape characters. ++# avoidSgfPatternDirs = "path/to/directory/with/sgfs/" ++ ++# Penalize this much utility per matching move. ++# Set this negative if you instead want to favor SGF patterns instead of ++# penalizing them. This number does not need to be large, even 0.001 will ++# make a difference. Values that are too large may lead to bad play. ++# avoidSgfPatternUtility = 0.001 ++ ++# Optional - load only the newest this many files ++# avoidSgfPatternMaxFiles = 20 ++ ++# Optional - Penalty is multiplied by this per each older SGF file, so that ++# old SGF files matter less than newer ones. ++# avoidSgfPatternLambda = 0.90 ++ ++# Optional - pay attention only to moves made by players with this name. ++# For example, set it to the name that your bot's past games will show up ++# as in the SGF, so that the bot will only avoid repeating moves that itself ++# made in past games, not the moves that its opponents made. ++# avoidSgfPatternAllowedNames = my-ogs-bot-name1,my-ogs-bot-name2 ++ ++# Optional - Ignore moves in SGF files that occurred before this turn number. ++# avoidSgfPatternMinTurnNumber = 0 ++ ++# For more avoid patterns: ++# You can also specify a second set of parameters, and a third, fourth, ++# etc. by numbering 2,3,4,... ++# ++# avoidSgf2PatternDirs = ... ++# avoidSgf2PatternUtility = ... ++# avoidSgf2PatternMaxFiles = ... ++# avoidSgf2PatternLambda = ... ++# avoidSgf2PatternAllowedNames = ... ++# avoidSgf2PatternMinTurnNumber = ... ++ +diff --git a/python/selfplay/golaxy_9d.cfg b/python/selfplay/golaxy_9d.cfg +new file mode 100644 +index 00000000..31e66efd +--- /dev/null ++++ b/python/selfplay/golaxy_9d.cfg +@@ -0,0 +1,713 @@ ++# Configuration for KataGo C++ GTP engine ++ ++# Run the program using: `./katago.exe gtp` ++ ++# In this example config, when a parameter is given as a commented out value, ++# that value also is the default value, unless described otherwise. You can ++# uncomment it (remove the pound sign) and change it if you want. ++ ++# =========================================================================== ++# Running on an online server or in a real tournament or match ++# =========================================================================== ++# If you plan to run online or in a tournament, read through the "Rules" ++# section below for proper handling of komi, handicaps, end-of-game cleanup, ++# and other details. ++ ++# =========================================================================== ++# Notes about performance and memory usage ++# =========================================================================== ++# Important: For good performance, you will very likely want to tune the ++# "numSearchThreads" parameter in the Search limits section below! Run ++# "./katago benchmark" to test KataGo and to suggest a reasonable value ++# of this parameter. ++ ++# For multi-GPU systems, read "OpenCL GPU settings" or "CUDA GPU settings". ++# ++# When using OpenCL, verify that KataGo picks the correct device! Some systems ++# may have both an Intel CPU OpenCL and GPU OpenCL. If # KataGo picks the wrong ++# one, correct this by specifying "openclGpuToUse". ++# ++# Consider adjusting "maxVisits", "ponderingEnabled", "resignThreshold", and ++# other parameters depending on your intended usage. ++ ++# =========================================================================== ++# Command-line usage ++# =========================================================================== ++# All of the below values may be set or overridden via command-line arguments: ++# ++# -override-config KEY=VALUE,KEY=VALUE,... ++ ++# =========================================================================== ++# Logs and files ++# =========================================================================== ++# This section defines where and what logging information is produced. ++ ++# Each run of KataGo will log to a separate file in this dir. ++# This is the default. ++logDir = gtp_logs ++# Uncomment and specify this instead of logDir to write separate dated subdirs ++# logDirDated = gtp_logs ++# Uncomment and specify this instead of logDir to log to only a single file ++# logFile = gtp.log ++ ++# Logging options ++logAllGTPCommunication = true ++logSearchInfo = true ++logSearchInfoForChosenMove = false ++logToStderr = true ++ ++# KataGo will display some info to stderr on GTP startup ++# Uncomment the next line and set it to false to suppress that and remain silent ++# startupPrintMessageToStderr = true ++ ++# Write information to stderr, for use in things like malkovich chat to OGS. ++ ogsChatToStderr = true ++ ++# Uncomment and set this to a directory to override where openCLTuner files ++# and other cached data is written. By default it saves into a subdir of the ++# current directory on windows, and a subdir of ~/.katago on Linux. ++# homeDataDir = PATH_TO_DIRECTORY ++ ++# =========================================================================== ++# Analysis ++# =========================================================================== ++# This section configures analysis settings. ++# ++# The maximum number of moves after the first move displayed in variations ++# from analysis commands like kata-analyze or lz-analyze. ++# analysisPVLen = 15 ++ ++# Report winrates for chat and analysis as (BLACK|WHITE|SIDETOMOVE). ++# Most GUIs and analysis tools will expect SIDETOMOVE. ++# reportAnalysisWinratesAs = SIDETOMOVE ++ ++# Extra noise for wider exploration. Large values will force KataGo to ++# analyze a greater variety of moves than it normally would. ++# An extreme value like 1 distributes playouts across every move on the board, ++# even very bad moves. ++# Affects analysis only, does not affect play. ++# analysisWideRootNoise = 0.04 ++ ++# Try to limit the effect of possible bad or bogus move sequences in the ++# history leading to this position from affecting KataGo's move predictions. ++# analysisIgnorePreRootHistory = true ++ ++# =========================================================================== ++# Rules ++# =========================================================================== ++# This section configures the scoring and playing rules. Rules can also be ++# changed mid-run by issuing custom GTP commands. ++# ++# See https://lightvector.github.io/KataGo/rules.html for rules details. ++# ++# See https://github.com/lightvector/KataGo/blob/master/docs/GTP_Extensions.md ++# for GTP commands. ++ ++# Specify the rules as a string. ++# Some legal values include: ++# chinese, japanese, korean, aga, chinese-ogs, new-zealand, stone-scoring, ++# ancient-territory, bga, aga-button ++# ++# For some human rulesets that require complex adjudication in tricky cases ++# (e.g. japanese, korean) KataGo may not precisely match the ruleset in such ++# cases but will do its best. ++# rules = tromp-taylor ++ ++# By default, the "rules" parameter is used, but if you comment it out and ++# uncomment one option in each of the sections below, you can specify an ++# arbitrary combination of individual rules. ++ ++# Ko Rule Settings ++koRule = POSITIONAL # Positional superko ++ ++# Scoring Rule Settings ++scoringRule = AREA # Area scoring ++ ++# Tax Rule Settings ++taxRule = NONE # All surrounded empty points are scored ++ ++# Is multiple-stone suicide legal? (Single-stone suicide is always illegal). ++multiStoneSuicideLegal = false ++ ++# "Button go" - the first pass when area scoring awards 0.5 points and does ++# not count for ending the game. ++hasButton = false ++ ++# Is this a human ruleset where it's okay to pass before having physically ++# captured and removed all dead stones? ++friendlyPassOk = false ++ ++# How handicap stones in handicap games are compensated ++whiteHandicapBonus = 0 # White gets no compensation for black's handicap stones (Tromp-taylor, NZ, JP) ++ ++ ++# ------------------------------ ++# Other rules hacks ++# ------------------------------ ++# Uncomment and change to adjust what board size KataGo uses upon startup ++# by default when GTP doesn't specify. ++# defaultBoardSize = 19 ++ ++# By default, Katago will use the komi that the GUI or GTP controller tries to set. ++# Uncomment and set this to have KataGo ignore the controller and always use this komi. ++# ignoreGTPAndForceKomi = 7 ++ ++# =========================================================================== ++# Bot behavior ++# =========================================================================== ++ ++# ------------------------------ ++# Resignation ++# ------------------------------ ++ ++# Resignation occurs if for at least resignConsecTurns in a row, the ++# winLossUtility (on a [-1,1] scale) is below resignThreshold. ++allowResignation = true ++resignThreshold = -0.90 ++resignConsecTurns = 3 ++ ++# By default, KataGo may resign games that it is confidently losing even if they ++# are very close in score. Uncomment and set this to avoid resigning games ++# if the estimated difference is points is less than or equal to this. ++# resignMinScoreDifference = 10 ++ ++# ------------------------------ ++# Handicap ++# ------------------------------ ++# Assume that if black makes many moves in a row right at the start of the ++# game, then the game is a handicap game. This is necessary on some servers ++# and for some GUIs and also when initializing from many SGF files, which may ++# set up a handicap game using repeated GTP "play" commands for black rather ++# than GTP "place_free_handicap" commands; however, it may also lead to ++# incorrect understanding of komi if whiteHandicapBonus is used and a server ++# does not have such a practice. Uncomment and set to false to disable. ++# assumeMultipleStartingBlackMovesAreHandicap = true ++ ++# Makes katago dynamically adjust in handicap or altered-komi games to assume ++# based on those game settings that it must be stronger or weaker than the ++# opponent and to play accordingly. Greatly improves handicap strength by ++# biasing winrates and scores to favor appropriate safe/aggressive play. ++# Does NOT affect analysis (lz-analyze, kata-analyze, used by programs like ++# Lizzie) so analysis remains unbiased. Uncomment and set this to 0 to disable ++# this and make KataGo play the same always. ++# dynamicPlayoutDoublingAdvantageCapPerOppLead = 0.045 ++ ++# Instead of "dynamicPlayoutDoublingAdvantageCapPerOppLead", you can comment ++# that out and uncomment and set "playoutDoublingAdvantage" to a fixed value ++# from -3.0 to 3.0 that will not change dynamically. ++# ALSO affects analysis tools (lz-analyze, kata-analyze, used by e.g. Lizzie). ++# Negative makes KataGo behave as if it is much weaker than the opponent. ++# Positive makes KataGo behave as if it is much stronger than the opponent. ++# KataGo will adjust to favor safe/aggressive play as appropriate based on ++# the combination of who is ahead and how much stronger/weaker it thinks it is, ++# and report winrates and scores taking the strength difference into account. ++# ++# If this and "dynamicPlayoutDoublingAdvantageCapPerOppLead" are both set ++# then dynamic will be used for all games and this fixed value will be used ++# for analysis tools. ++# playoutDoublingAdvantage = 0.0 ++ ++# Uncomment one of these when using "playoutDoublingAdvantage" to enforce ++# that it will only apply when KataGo plays as the specified color and will be ++# negated when playing as the opposite color. ++# playoutDoublingAdvantagePla = BLACK ++# playoutDoublingAdvantagePla = WHITE ++ ++# ------------------------------ ++# Passing and cleanup ++# ------------------------------ ++# Make the bot never assume that its pass will end the game, even if passing ++# would end and "win" under Tromp-Taylor rules. Usually this is a good idea ++# when using it for analysis or playing on servers where scoring may be ++# implemented non-tromp-taylorly. Uncomment and set to false to disable. ++# conservativePass = true ++ ++# When using territory scoring, self-play games continue beyond two passes ++# with special cleanup rules that may be confusing for human players. This ++# option prevents the special cleanup phases from being reachable when using ++# the bot for GTP play. Uncomment and set to false to enable entering special ++# cleanup. For example, if you are testing it against itself, or against ++# another bot that has precisely implemented the rules documented at ++# https://lightvector.github.io/KataGo/rules.html ++# preventCleanupPhase = true ++ ++# ------------------------------ ++# Miscellaneous behavior ++# ------------------------------ ++# If the board is symmetric, search only one copy of each equivalent move. ++# Attempts to also account for ko/superko, will not theoretically perfect for ++# superko. Uncomment and set to false to disable. ++# rootSymmetryPruning = true ++ ++# Uncomment and set to true to avoid a particular joseki that some networks ++# misevaluate, and also to improve opening diversity versus some particular ++# other bots that like to play it all the time. ++# avoidMYTDaggerHack = false ++ ++# Prefer to avoid playing the same joseki in every corner of the board. ++# Uncomment to set to a specific value. See "Avoid SGF patterns" section. ++# By default: 0 (even games), 0.005 (handicap games) ++# avoidRepeatedPatternUtility = 0.0 ++ ++# Experimental logic to fight against mirror Go even with unfavorable komi. ++# Uncomment to set to a specific value to use for both playing and analysis. ++# By default: true when playing via GTP, but false when analyzing. ++# antiMirror = true ++ ++# Enable some hacks that mitigate rare instances when passing messes up deeper searches. ++# enablePassingHacks = true ++ ++# Uncomment and set this to true to prevent bad or bogus move sequences ++# in the history leading to this position from affecting KataGo's move choices. ++# Same as analysisIgnorePreRootHistory (see above) but applies to actual play. ++# You can enable this if KataGo is being asked to play from positions that it did not ++# choose the moves to reach. ++# ignorePreRootHistory = false ++ ++# =========================================================================== ++# Search limits ++# =========================================================================== ++ ++# Terminology: ++# "Playouts" is the number of new playouts of search performed each turn. ++# "Visits" is the same as "Playouts" but also counts search performed on ++# previous turns that is still applicable to this turn. ++# "Time" is the time in seconds. ++ ++# For example, if KataGo searched 200 nodes on the previous turn, and then ++# after the opponent's reply, 50 nodes of its search tree was still valid, ++# then a visit limit of 200 would allow KataGo to search 150 new nodes ++# (for a final tree size of 200 nodes), whereas a playout limit of of 200 ++# would allow KataGo to search 200 nodes (for a final tree size of 250 nodes). ++ ++# Additionally, KataGo may also move before than the limit in order to ++# obey time controls (e.g. byo-yomi, etc) if the GTP controller has ++# told KataGo that the game has is being played with a given time control. ++ ++# Limits for search on the current turn. ++# If commented out or unspecified, the default is to have no limit. ++# maxVisits = 50000 ++# maxPlayouts = 300 ++ maxTime = 1.0 ++ ++# Ponder on the opponent's turn? ++ponderingEnabled = true ++ ++# Limits for search when pondering on the opponent's turn. ++# If commented out or unspecified, the default is to have no limit. ++# Limiting the maximum time is recommended so that KataGo won't burn CPU/GPU ++# forever and/or run out of RAM if left unattended while pondering is enabled. ++# maxVisitsPondering = 5000 ++# maxPlayoutsPondering = 3000 ++maxTimePondering = 120.0 ++ ++ ++# ------------------------------ ++# Other search limits and behavior ++# ------------------------------ ++ ++# Approx number of seconds to buffer for lag for GTP time controls - will ++# move a bit faster assuming there is this much lag per move. ++lagBuffer = 1.0 ++ ++# YOU PROBABLY WANT TO TUNE THIS PARAMETER! ++# The number of threads to use when searching. On powerful GPUs the optimal ++# threads may be much higher than the number of CPU cores you have because ++# many threads are needed to feed efficient large batches to the GPU. ++# ++# Run "./katago benchmark" to tune this parameter and test the effect ++# of changes to any of other parameters. ++numSearchThreads = 40 ++ ++# Play a little faster if the opponent is passing, for human-friendliness. ++# Comment these out to disable them, such as if running a controlled match ++# where you are testing KataGo with fixed compute per move vs other bots. ++searchFactorAfterOnePass = 0.50 ++searchFactorAfterTwoPass = 0.25 ++ ++# Play a little faster if super-winning, for human-friendliness. ++# Comment these out to disable them, such as if running a controlled match ++# where you are testing KataGo with fixed compute per move vs other bots. ++searchFactorWhenWinning = 0.40 ++searchFactorWhenWinningThreshold = 0.95 ++ ++# =========================================================================== ++# GPU settings ++# =========================================================================== ++# This section configures GPU settings. ++# ++# Maximum number of positions to send to a single GPU at once. The default ++# value is roughly equal to numSearchThreads, but can be specified manually ++# if running out of memory, or using multiple GPUs that expect to share work. ++# nnMaxBatchSize = ++ ++# Controls the neural network cache size, which is the primary RAM/memory use. ++# KataGo will cache up to (2 ** nnCacheSizePowerOfTwo) many neural net ++# evaluations in case of transpositions in the tree. ++# Increase this to improve performance for searches with tens of thousands ++# of visits or more. Decrease this to limit memory usage. ++# If you're happy to do some math - each neural net entry takes roughly ++# 1.5KB, except when using whole-board ownership/territory ++# visualizations, where each entry will take roughly 3KB. The number of ++# entries is (2 ** nnCacheSizePowerOfTwo). (E.g. 2 ** 18 = 262144.) ++# You can compute roughly how much memory the cache will use based on this. ++# nnCacheSizePowerOfTwo = 20 ++ ++# Size of mutex pool for nnCache is (2 ** this). ++# nnMutexPoolSizePowerOfTwo = 16 ++ ++# Randomize board orientation when running neural net evals? Uncomment and ++# set to false to disable. ++# nnRandomize = true ++ ++# If provided, force usage of a specific seed for nnRandomize. ++# The default is to use a randomly generated seed. ++# nnRandSeed = abcdefg ++ ++# Uncomment and set to true to force GTP to use the maximum board size for ++# internal buffers for the neural net. This will make KataGo slower when ++# evaluating small boards, but will avoid a lengthy initialization time on every ++# change of board size due to having to re-size the neural net buffers on the GPU. ++# This can be useful for example, for OGS's persistent bot mode that uses a single ++# bot instance to handle multiple games and may thrash between different board sizes ++# if there are concurrent games of multiple sizes. ++# gtpForceMaxNNSize = false ++ ++# ------------------------------ ++# Multiple GPUs ++# ------------------------------ ++# Set this to the number of GPUs to use or that are available. ++# IMPORTANT: If more than 1, also uncomment the appropriate TensorRT ++# or CUDA or OpenCL section. ++# numNNServerThreadsPerModel = 1 ++ ++# ------------------------------ ++# TENSORRT GPU settings ++# ------------------------------ ++# These only apply when using the TENSORRT version of KataGo. ++ ++# For one GPU: optionally uncomment this option and change if the GPU to ++# use is not device 0. ++# trtDeviceToUse = 0 ++ ++# For two GPUs: Uncomment these options, AND set numNNServerThreadsPerModel above. ++# Also, change their values if the devices you want to use are not 0 and 1. ++# trtDeviceToUseThread0 = 0 ++# trtDeviceToUseThread1 = 1 ++ ++# For three GPUs: Uncomment these options, AND set numNNServerThreadsPerModel above. ++# Also, change their values if the devices you want to use are not 0 and 1 and 2. ++# trtDeviceToUseThread0 = 0 ++# trtDeviceToUseThread1 = 1 ++# trtDeviceToUseThread2 = 2 ++ ++# The pattern continues for additional GPUs. ++ ++# ------------------------------ ++# CUDA GPU settings ++# ------------------------------ ++# These only apply when using the CUDA version of KataGo. ++ ++# For one GPU: optionally uncomment and change this if the GPU you want to ++# use is not device 0 ++# cudaDeviceToUse = 0 ++ ++# For two GPUs: Uncomment these options, AND set numNNServerThreadsPerModel above. ++# Also, change their values if the devices you want to use are not 0 and 1. ++# cudaDeviceToUseThread0 = 0 ++# cudaDeviceToUseThread1 = 1 ++ ++# For three GPUs: Uncomment these options, AND set numNNServerThreadsPerModel above. ++# Also, change their values if the devices you want to use are not 0 and 1 and 2. ++# cudaDeviceToUseThread0 = 0 ++# cudaDeviceToUseThread1 = 1 ++# cudaDeviceToUseThread2 = 2 ++ ++# The pattern continues for additional GPUs. ++ ++# KataGo will automatically use FP16 or not based on the compute capability ++# of your NVIDIA GPU. If you want to try to force a particular behavior ++# you can uncomment these lines and change them to "true" or "false". ++# cudaUseFP16 = auto ++# cudaUseNHWC = auto ++ ++# ------------------------------ ++# OpenCL GPU settings ++# ------------------------------ ++# These only apply when using the OpenCL version of KataGo. ++ ++# Uncomment and set to true to tune OpenCL for every board size separately, ++# rather than only the largest possible size. ++# openclReTunePerBoardSize = false ++ ++# For one GPU: optionally uncomment and change this if the best device to use is guessed incorrectly. ++# The default behavior tries to guess the 'best' GPU or device on your system to use, usually it will be a good guess. ++# openclDeviceToUse = 0 ++ ++# For two GPUs: Uncomment these two lines and replace X and Y with the device ids of the devices you want to use. ++# It might NOT be 0 and 1, some computers will have many OpenCL devices. You can see what the devices are when ++# KataGo starts up - it should print or log all the devices it finds. ++# (AND also set numNNServerThreadsPerModel above) ++# openclDeviceToUseThread0 = X ++# openclDeviceToUseThread1 = Y ++ ++# For three GPUs: Uncomment these three lines and replace X and Y and Z with the device ids of the devices you want to use. ++# It might NOT be 0 and 1 and 2, some computers will have many OpenCL devices. You can see what the devices are when ++# KataGo starts up - it should print or log all the devices it finds. ++# (AND also set numNNServerThreadsPerModel above) ++# openclDeviceToUseThread0 = X ++# openclDeviceToUseThread1 = Y ++# openclDeviceToUseThread2 = Z ++ ++# The pattern continues for additional GPUs. ++ ++# KataGo will automatically use FP16 or not based on testing your GPU during ++# tuning. If you want to try to force a particular behavior though you can ++# uncomment this option and change it to "true" or "false". This is a fairly ++# blunt setting - more detailed settings are testable by rerunning the tuner ++# with various arguments (./katago tuner). ++# openclUseFP16 = auto ++ ++# ------------------------------ ++# Eigen-specific settings ++# ------------------------------ ++# These only apply when using the Eigen (pure CPU) version of KataGo. ++ ++# Number of CPU threads for evaluating the neural net on the Eigen backend. ++# ++# Default: numSearchThreads ++# numEigenThreadsPerModel = X ++ ++# =========================================================================== ++# Root move selection and biases ++# =========================================================================== ++# Uncomment and edit any of the below values to change them from their default. ++ ++# If provided, force usage of a specific seed for various random things in ++# the search. The default is to use a random seed. ++# searchRandSeed = hijklmn ++ ++# Temperature for the early game, randomize between chosen moves with ++# this temperature ++# chosenMoveTemperatureEarly = 0.5 ++ ++# Decay temperature for the early game by 0.5 every this many moves, ++# scaled with board size. ++# chosenMoveTemperatureHalflife = 19 ++ ++# At the end of search after the early game, randomize between chosen ++# moves with this temperature ++# chosenMoveTemperature = 0.10 ++ ++# Subtract this many visits from each move prior to applying ++# chosenMoveTemperature (unless all moves have too few visits) to downweight ++# unlikely moves ++# chosenMoveSubtract = 0 ++ ++# The same as chosenMoveSubtract but only prunes moves that fall below ++# the threshold. This setting does not affect chosenMoveSubtract. ++# chosenMovePrune = 1 ++ ++# Number of symmetries to sample (without replacement) and average at the root ++# rootNumSymmetriesToSample = 1 ++ ++# Using LCB for move selection? ++# useLcbForSelection = true ++ ++# How many stdevs a move needs to be better than another for LCB selection ++# lcbStdevs = 5.0 ++ ++# Only use LCB override when a move has this proportion of visits as the ++# top move. ++# minVisitPropForLCB = 0.15 ++ ++# =========================================================================== ++# Internal params ++# =========================================================================== ++# Uncomment and edit any of the below values to change them from their default. ++ ++# Scales the utility of winning/losing ++# winLossUtilityFactor = 1.0 ++ ++# Scales the utility for trying to maximize score ++# staticScoreUtilityFactor = 0.10 ++# dynamicScoreUtilityFactor = 0.30 ++ ++# Adjust dynamic score center this proportion of the way towards zero, ++# capped at a reasonable amount. ++# dynamicScoreCenterZeroWeight = 0.20 ++# dynamicScoreCenterScale = 0.75 ++ ++# The utility of getting a "no result" due to triple ko or other long cycle ++# in non-superko rulesets (-1 to 1) ++# noResultUtilityForWhite = 0.0 ++ ++# The number of wins that a draw counts as, for white. (0 to 1) ++# drawEquivalentWinsForWhite = 0.5 ++ ++# Exploration constant for mcts ++# cpuctExploration = 1.0 ++# cpuctExplorationLog = 0.45 ++ ++# Parameters that control exploring more in volatile positions, exploring ++# less in stable positions. ++# cpuctUtilityStdevPrior = 0.40 ++# cpuctUtilityStdevPriorWeight = 2.0 ++# cpuctUtilityStdevScale = 0.85 ++ ++# FPU reduction constant for mcts ++# fpuReductionMax = 0.2 ++# rootFpuReductionMax = 0.1 ++# fpuParentWeightByVisitedPolicy = true ++ ++# Parameters that control weighting of evals based on the net's own ++# self-reported uncertainty. ++# useUncertainty = true ++# uncertaintyExponent = 1.0 ++# uncertaintyCoeff = 0.25 ++ ++# Explore using optimistic policy ++# rootPolicyOptimism = 0.2 ++# policyOptimism = 1.0 ++ ++# Amount to apply a downweighting of children with very bad values relative ++# to good ones. ++# valueWeightExponent = 0.25 ++ ++# Slight incentive for the bot to behave human-like with regard to passing at ++# the end, filling the dame, not wasting time playing in its own territory, ++# etc., and not play moves that are equivalent in terms of points but a bit ++# more unfriendly to humans. ++# rootEndingBonusPoints = 0.5 ++ ++# Make the bot prune useless moves that are just prolonging the game to ++# avoid losing yet. ++# rootPruneUselessMoves = true ++ ++# Apply bias correction based on local pattern keys ++# subtreeValueBiasFactor = 0.45 ++# subtreeValueBiasWeightExponent = 0.85 ++ ++# Use graph search rather than tree search - identify and share search for ++# transpositions. ++# useGraphSearch = true ++ ++# How much to shard the node table for search synchronization ++# nodeTableShardsPowerOfTwo = 16 ++ ++# How many virtual losses to add when a thread descends through a node ++# numVirtualLossesPerThread = 1 ++ ++# Improve the quality of evals under heavy multithreading ++# useNoisePruning = true ++ ++# =========================================================================== ++# Automatic avoid patterns ++# =========================================================================== ++# The parameters in this section provide a way to bias away from moves that match ++# patterns that this instance of KataGo has played in previous games, by auto-saving ++# moves to a directory and then auto-loading and biasing against them each new game. ++# Uncomment them to use them. When using this feature, all parameters must be specified. ++ ++# Different board sizes are tracked separately, but all board sizes share the same ++# parameters by default. Every parameter *except* for autoAvoidRepeatDir and ++# autoAvoidRepeatSaveChunkSize can be overridden per board size, ++# e.g. "autoAvoidRepeatMinTurnNumber13x13". You must ALSO specify the ++# defaults even if you have specified board-size-specific values. ++ ++# Directory to auto-save moves KataGo plays, to avoid them in future games. ++# You can create a new empty directory and put its path here. ++# If you run parallel instances of KataGo, use different directories if you ++# want them not to share their biases, use the same directory if you want ++# all of them to bias away from past moves that any of them have played. ++# KataGo will also automatically DELETE old data in this directory, so it is ++# recommended that if you do share the same directory between parallel instances, ++# that they all use the same settings that affect data saving/deletion. ++# autoAvoidRepeatDir = PATH_TO_NEW_DIRECTORY ++ ++# Penalize this much utility per matching move. ++# Values that are too large may lead to bad play. The value of 0.004 is fairly large ++# and might be large enough to result in some early weird/bad moves when trying ++# to avoid past games' moves if enough games begin the same way. You can experiment with it. ++# autoAvoidRepeatUtility = 0.004 ++ ++# Per each new move saved, exponentially decay prior saved moves by this factor. ++# This way, the bias against moves from many games ago is gradually phased out. ++# For example, 0.9995 = 1 - 1/2000, so would be roughly 2000 prior moves worth of ++# penalty weight remembered, in steady state. Depending on what turn number range ++# you are saving, this might equate to a different number of games For example ++# saving the first 50 moves per game would make this roughly 2000 / 50 = 40 games ++# worth of memory. ++# autoAvoidRepeatLambda = 0.9995 ++ ++# Affects data saving/deletion. ++# When the number of saved moves exceeds this, outright delete them to avoid too many ++# files and disk space building up. Also may affect the speed of saving/loading on start ++# of each game if this is set large and a lot of data builds up. ++# autoAvoidRepeatMaxPoses = 10000 ++ ++# Affects data saving/deletion. ++# Only save data for moves within this turn number range of those games. ++# E.g. setting autoAvoidRepeatMinTurnNumber to a number like 4 or 5 would tend to make ++# KataGo not develop a bias against the initial 3-4 and 4-4 corner moves in almost every game. ++# autoAvoidRepeatMinTurnNumber = 0 ++# autoAvoidRepeatMaxTurnNumber = 50 ++ ++# Affects data saving/deletion. ++# Within a single run of a program, wait to accumulate this many samples ++# (possibly across multiple clear_boards/games) before saving the data. ++# Can help to avoid writing too many small files to disk, especially when GTP is used ++# in a way that clears the board very frequently (e.g. gtp2ogs pooled manager). ++# autoAvoidRepeatSaveChunkSize = 200 ++ ++# =========================================================================== ++# Avoid SGF patterns ++# =========================================================================== ++# The parameters in this section provide a way to avoid moves that follow ++# specific patterns based on a set of SGF files loaded upon startup. ++# This is basically the same as the above "Automatic avoid patterns" section ++# above except you supply your own SGF files to avoid moves from. ++# Uncomment them to use this feature. Additionally, if the SGF file ++# contains the string %SKIP% in a comment on a move, that move will be ++# ignored for this purpose. ++ ++# Load SGF files from this directory when the engine is started ++# (only on startup, will not reload unless engine is restarted) ++# avoidSgfPatternDirs = path/to/directory/with/sgfs/ ++# You can also surround the file path in double quotes if the file path contains trailing spaces or hash signs. ++# Within double quotes, backslashes are escape characters. ++# avoidSgfPatternDirs = "path/to/directory/with/sgfs/" ++ ++# Penalize this much utility per matching move. ++# Set this negative if you instead want to favor SGF patterns instead of ++# penalizing them. This number does not need to be large, even 0.001 will ++# make a difference. Values that are too large may lead to bad play. ++# avoidSgfPatternUtility = 0.001 ++ ++# Optional - load only the newest this many files ++# avoidSgfPatternMaxFiles = 20 ++ ++# Optional - Penalty is multiplied by this per each older SGF file, so that ++# old SGF files matter less than newer ones. ++# avoidSgfPatternLambda = 0.90 ++ ++# Optional - pay attention only to moves made by players with this name. ++# For example, set it to the name that your bot's past games will show up ++# as in the SGF, so that the bot will only avoid repeating moves that itself ++# made in past games, not the moves that its opponents made. ++# avoidSgfPatternAllowedNames = my-ogs-bot-name1,my-ogs-bot-name2 ++ ++# Optional - Ignore moves in SGF files that occurred before this turn number. ++# avoidSgfPatternMinTurnNumber = 0 ++ ++# For more avoid patterns: ++# You can also specify a second set of parameters, and a third, fourth, ++# etc. by numbering 2,3,4,... ++# ++# avoidSgf2PatternDirs = ... ++# avoidSgf2PatternUtility = ... ++# avoidSgf2PatternMaxFiles = ... ++# avoidSgf2PatternLambda = ... ++# avoidSgf2PatternAllowedNames = ... ++# avoidSgf2PatternMinTurnNumber = ... ++ +diff --git a/python/selfplay/gtp_human5k_example.cfg b/python/selfplay/gtp_human5k_example.cfg +new file mode 100644 +index 00000000..9098f5ab +--- /dev/null ++++ b/python/selfplay/gtp_human5k_example.cfg +@@ -0,0 +1,189 @@ ++ ++# This is an example config for configuring KataGo to attempt to imitate a weaker human player. ++# Running with this config requires giving a human SL model b18c384nbt-humanv0.bin.gz ++# on the command line such as: ++# ./katago gtp -config gtp_human5k_example.cfg -model your_favorite_normal_model_for_katago.bin.gz -human-model b18c384nbt-humanv0.bin.gz ++# You can obtain the human model at https://github.com/lightvector/KataGo/releases/tag/v1.15.0 ++ ++# Below, the most important parts of the config for human-like play are commented. ++# See the original gtp_example for comments on other parameters. ++ ++# For another useful guide on human-style analysis, see here: ++# https://github.com/lightvector/KataGo/blob/master/docs/Analysis_Engine.md#human-sl-analysis-guide ++ ++# It is ALSO possible to pass in simply '-model b18c384nbt-humanv0.bin.gz' and NOT ++# pass in -human-model, i.e. use the human model as if it were KataGo's normal neural net. ++# If you do that, you need to use a config more like the normal gtp_example.cfg, not this config! ++# Keep in mind that if using the model normally, or if using -human-model but also altering ++# parameters below to blend in some of KataGo's search, KataGo's play might NOT be very human-like, ++# or might be human-like but play at a strength very different than the humanSLProfile. ++# You can experiment, some of the comments below hopefully will help illustrate things too. ++ ++logDir = gtp_logs ++logAllGTPCommunication = true ++logSearchInfo = true ++logSearchInfoForChosenMove = false ++logToStderr = false ++ ++# Use these rules by default, but a GUI or GTP controller might override this. ++rules = japanese ++ ++# When using -human-model, we only resign when far behind since a weaker player ++# might continue to fight much longer than a strong bot normally would. ++allowResignation = true ++resignThreshold = -0.99 ++resignConsecTurns = 20 ++resignMinScoreDifference = 40 ++resignMinMovesPerBoardArea = 0.4 ++ ++# Note: unless you change other parameters too, by default increasing visits won't do much. ++# If humanSLChosenMoveProp = 1.0 AND humanSLChosenMovePiklLambda is a large number, ++# then KataGo's normal search is ignored except for possibly choosing whether to pass/resign, ++# so more visits will have no effect on play. Still, having some visits is good for ++# ensuring good pass/resign judgment. ++maxVisits = 40 ++numSearchThreads = 1 ++lagBuffer = 1.0 ++ ++# Rough scale in seconds to randomly delay moving, so as not to respond instantly. ++# Some moves will delay longer, some moves will delay a little less. ++delayMoveScale = 2.0 ++delayMoveMax = 10.0 ++ ++# =========================================================================== ++# HUMAN SL PARAMETERS ++# =========================================================================== ++ ++# The most important parameter for human-like play configuration! ++# Choose the "profile" of players that the human SL model will imitate. ++# Available options are: ++# preaz_{RANK from 20k to 9d} - imitate player of given rank, before AlphaZero opening style became popular ++# rank_{RANK from 20k to 9d} - imitate player of given rank, after human openings changed due to AlphaZero. ++# preaz_{BRANK}_{WRANK} or rank_{BRANK}_{WRANK} - same, but imitate how black with the rank BR and white ++# with the rank WR would play against each other, knowing that the other player is stronger/weaker than them. ++# Warning: for rank differences > 9 ranks, or drastically mis-matched to the handicap used in the game, ++# this may be out of distribution due to lack of training data and the model might not behave well! Experiment with care. ++# proyear_{YEAR from 1800 to 2023} - imitate historical pros or insei from given year. ++humanSLProfile = preaz_5k ++ ++# The probability that we should play a HUMAN-like move, rather than playing KataGo's move. ++# Applies BEFORE temperature. ++humanSLChosenMoveProp = 1.0 ++ ++# If true, ignore the human SL model's choice of when to pass, and still use KataGo to determine that. ++# The human SL model, in theory, is not guaranteed to be reliable at when to pass for all profiles, ++# since e.g. some historical games omit passes. ++humanSLChosenMoveIgnorePass = true ++ ++# By default humanSLChosenMovePiklLambda is a large number which effectively disables it. ++# Setting it to a smaller number will "suppress" human-like moves that KataGo disapproves of. ++# In particular, if set to, for example, 0.4 when KataGo judges a human SL move to lose 0.4 utility, ++# it will substantially suppress the chance of playing that move (in particular, by a factor of exp(1)). ++# Less-bad moves will also be suppressed, but not by as much, e.g. a move losing 0.2 would get lowered ++# by a factor of exp(0.5). ++# As configured lower down, utilities by default range from -1.0 (loss) to +1.0 (win), plus up to +/- 0.3 for score. ++# WARNING: ONLY moves that KataGo actually searches will get suppressed! If a move is so bad that KataGo ++# rejects it without searching it, it will NOT get suppressed. ++# Therefore, to use humanSLChosenMovePiklLambda, it is STRONGLY recommended that you also use something ++# like humanSLRootExploreProbWeightless to ensure most human moves including bad moves get searched, ++# and ALSO use at least hundreds and ideally thousands of maxVisits, to ensure enough visits. ++humanSLChosenMovePiklLambda = 100000000 ++ ++# These parameters tell KataGo to use the human SL policy for exploration during search. ++# Each of these specifies the probability that KataGo will perform PUCT using the Human SL policy to ++# explore different moves, rather than using KataGo's normal policy, after a certain minimal number of visits. ++# "Root": applies only at the root of the search ++# "Pla": applies during non-root nodes of the search where it is katago's turn. ++# "Opp": applies during non-root nodes of the search where it is the opponent's turn. ++# "Weightless": search the move to evaluate it, but do NOT allow this visit to affect the parent's average utility. ++# "Weightful": search the move to evaluate it, and DO allow this visit to affect the parent's average utility. ++# For example, humanSLRootExploreProbWeightless = 0.5 would tell KataGo at the root of the search to spend ++# 50% of its visits to judge different possible human moves, but NOT to use those visits for determining the ++# value of the position (avoiding biasing the utility if some human SL moves are very bad). ++# If you don't understand these well, ask for help or look up some online explainers for MCTS (Monte-Carlo Tree Search). ++humanSLRootExploreProbWeightless = 0.0 ++humanSLRootExploreProbWeightful = 0.0 ++humanSLPlaExploreProbWeightless = 0.0 ++humanSLPlaExploreProbWeightful = 0.0 ++humanSLOppExploreProbWeightless = 0.0 ++humanSLOppExploreProbWeightful = 0.0 ++ ++# When using the human SL policy for exploration during search, use this cPUCT. ++# This only has an effect if at least one of humanSL{Root,Pla,Opp}ExploreProbWeight{less,ful} is nonzero. ++humanSLCpuctExploration = 0.50 ++ ++# Same as humanSLCpuctExploration, but NEVER diminshes its exploration no matter how many visits are used. ++# Normally, PUCT will sharpen with visits and spend a diminishing proportion of visits on moves with lower utility. ++# This is the coefficient for a term that does NOT diminish, i.e. if this is 0.2, then roughly moves within ++# 0.2 utility (about 10% winrate) of the best move will forever continue getting a decent fraction of visits, ++# smoothly falling off for greater utility differences. ++# Note that in combination with Weightful exploration above, if used for Opp exploration, this could be used ++# to model an opponent that will always have some chance to make small mistakes no matter how deep they search. ++# If further this was increased to a very large value, it would model an opponent that always played according ++# to the human SL raw policy. These might be interesting to experiment with for handicap play. ++humanSLCpuctPermanent = 0.2 ++ ++ ++# =========================================================================== ++# OTHER USEFUL PARAMETERS FOR HUMAN PLAY ADJUSTMENT ++# =========================================================================== ++ ++# Choosing temperature near 1, and restricting it to only affect moves already below 1% chance, ++# so that we sample close to the full range of human play. ++# You can also reduce the temperature to settings more like the plain gtp_example.cfg. ++# Then, rather than imitating a realistic human player, it will be more like imitating the ++# *majority vote* of players at that rank. For example it would avoid a lot of blunders ++# that players of that level would make, because even if players often blunder, the *majority vote* ++# of players would be much less likely to select any given blunder that an individual player would. ++chosenMoveTemperatureEarly = 0.85 ++chosenMoveTemperature = 0.70 ++chosenMoveTemperatureHalflife = 80 ++chosenMoveTemperatureOnlyBelowProb = 0.01 # temperature only starts to dampen moves below this ++chosenMoveSubtract = 0 ++chosenMovePrune = 0 ++ ++# Use a small NN cache to save memory since we're using very low visits anyways. You can increase ++# these back to more like the plain gtp_example.cfg if you are doing more extensive searches to ++# improve performance. ++nnCacheSizePowerOfTwo = 17 ++nnMutexPoolSizePowerOfTwo = 14 ++ ++# =========================================================================== ++# PARAMETERS CHANGED FROM DEFAULT TO MAKE SURE HUMAN SL USAGE WORKS WELL ++# =========================================================================== ++ ++# Make sure to take into account the recent moves in the game, don't ignore history. ++# This will produce the best imitation/prediction, since humans definitely do play differently based on where the ++# most recent moves in the game were, rather than coming fresh to the board position on every turn. ++ignorePreRootHistory = false ++analysisIgnorePreRootHistory = false ++ ++# Average 2 neural net samples at the root - ensures a bit smoother probabilities and results in ++# 8 * 7 / 2 = 28 possible policies instead of 8 possibilities. ++rootNumSymmetriesToSample = 2 ++# LCB improves strength for KataGo, but we disable it so it doesn't mess up move selection when blending human play. ++useLcbForSelection = false ++ ++# We disable dynamicScoreUtilityFactor - the human SL model can make score predictions that are a bit swingy, so ++# if we do want to do a search that blends human SL values in (TODO there isn't a way to do this anyways yet), using ++# static score utility might be a bit more stable. ++winLossUtilityFactor = 1.0 ++staticScoreUtilityFactor = 0.30 ++dynamicScoreUtilityFactor = 0.00 ++ ++# Uncertainty improves strength for KataGo normally, but messes with the weights of playouts in complicated ways, ++# so lets turn it off when doing human SL stuff. ++useUncertainty = false ++ ++# Subtree value bias improves strength for KataGo normally, but messes with the values of nodes in complicated ways, ++# so let's turn it off when doing human SL stuff. ++subtreeValueBiasFactor = 0.0 ++ ++# Noise pruning prunes out weight from moves that KataGo thinks are bad, but if we are doing human SL we might actively ++# want to be playing or exploring and weighting "bad" but human-like moves. So disable this. ++# Warning: when this is false, there is much less protection against the search severely misbehaving when you use too many threads. ++# Make sure not to set numSearchThreads to be too large - at a minimum, keep at least a 20x buffer between ++# the number of visits you use and the number of threads you use. ++# (as an aside, ideally, you want to have visits be a sufficient factor larger than threads EVEN when ++# useNoisePruning is true, this parameter just blunts the worst effects but doesn't entirely fix the badness). ++useNoisePruning = false +diff --git a/python/selfplay/leela_zero_strong.cfg b/python/selfplay/leela_zero_strong.cfg +new file mode 100644 +index 00000000..e1167b84 +--- /dev/null ++++ b/python/selfplay/leela_zero_strong.cfg +@@ -0,0 +1,719 @@ ++# Configuration for KataGo C++ GTP engine ++ ++# Run the program using: `./katago.exe gtp` ++ ++# In this example config, when a parameter is given as a commented out value, ++# that value also is the default value, unless described otherwise. You can ++# uncomment it (remove the pound sign) and change it if you want. ++ ++# =========================================================================== ++# Running on an online server or in a real tournament or match ++# =========================================================================== ++# If you plan to run online or in a tournament, read through the "Rules" ++# section below for proper handling of komi, handicaps, end-of-game cleanup, ++# and other details. ++ ++# =========================================================================== ++# Notes about performance and memory usage ++# =========================================================================== ++# Important: For good performance, you will very likely want to tune the ++# "numSearchThreads" parameter in the Search limits section below! Run ++# "./katago benchmark" to test KataGo and to suggest a reasonable value ++# of this parameter. ++ ++# For multi-GPU systems, read "OpenCL GPU settings" or "CUDA GPU settings". ++# ++# When using OpenCL, verify that KataGo picks the correct device! Some systems ++# may have both an Intel CPU OpenCL and GPU OpenCL. If # KataGo picks the wrong ++# one, correct this by specifying "openclGpuToUse". ++# ++# Consider adjusting "maxVisits", "ponderingEnabled", "resignThreshold", and ++# other parameters depending on your intended usage. ++ ++# =========================================================================== ++# Command-line usage ++# =========================================================================== ++# All of the below values may be set or overridden via command-line arguments: ++# ++# -override-config KEY=VALUE,KEY=VALUE,... ++ ++# =========================================================================== ++# Logs and files ++# =========================================================================== ++# This section defines where and what logging information is produced. ++ ++# Each run of KataGo will log to a separate file in this dir. ++# This is the default. ++logDir = gtp_logs ++# Uncomment and specify this instead of logDir to write separate dated subdirs ++# logDirDated = gtp_logs ++# Uncomment and specify this instead of logDir to log to only a single file ++# logFile = gtp.log ++ ++# Logging options ++logAllGTPCommunication = true ++logSearchInfo = true ++logSearchInfoForChosenMove = false ++logToStderr = true ++ ++# KataGo will display some info to stderr on GTP startup ++# Uncomment the next line and set it to false to suppress that and remain silent ++# startupPrintMessageToStderr = true ++ ++# Write information to stderr, for use in things like malkovich chat to OGS. ++ ogsChatToStderr = true ++ ++# Uncomment and set this to a directory to override where openCLTuner files ++# and other cached data is written. By default it saves into a subdir of the ++# current directory on windows, and a subdir of ~/.katago on Linux. ++# homeDataDir = PATH_TO_DIRECTORY ++ ++# =========================================================================== ++# Analysis ++# =========================================================================== ++# This section configures analysis settings. ++# ++# The maximum number of moves after the first move displayed in variations ++# from analysis commands like kata-analyze or lz-analyze. ++# analysisPVLen = 15 ++ ++# Report winrates for chat and analysis as (BLACK|WHITE|SIDETOMOVE). ++# Most GUIs and analysis tools will expect SIDETOMOVE. ++# reportAnalysisWinratesAs = SIDETOMOVE ++ ++# Extra noise for wider exploration. Large values will force KataGo to ++# analyze a greater variety of moves than it normally would. ++# An extreme value like 1 distributes playouts across every move on the board, ++# even very bad moves. ++# Affects analysis only, does not affect play. ++# analysisWideRootNoise = 0.04 ++ ++# Try to limit the effect of possible bad or bogus move sequences in the ++# history leading to this position from affecting KataGo's move predictions. ++# analysisIgnorePreRootHistory = true ++ ++# =========================================================================== ++# Rules ++# =========================================================================== ++# This section configures the scoring and playing rules. Rules can also be ++# changed mid-run by issuing custom GTP commands. ++# ++# See https://lightvector.github.io/KataGo/rules.html for rules details. ++# ++# See https://github.com/lightvector/KataGo/blob/master/docs/GTP_Extensions.md ++# for GTP commands. ++ ++# Specify the rules as a string. ++# Some legal values include: ++# chinese, japanese, korean, aga, chinese-ogs, new-zealand, stone-scoring, ++# ancient-territory, bga, aga-button ++# ++# For some human rulesets that require complex adjudication in tricky cases ++# (e.g. japanese, korean) KataGo may not precisely match the ruleset in such ++# cases but will do its best. ++# rules = tromp-taylor ++ ++# By default, the "rules" parameter is used, but if you comment it out and ++# uncomment one option in each of the sections below, you can specify an ++# arbitrary combination of individual rules. ++ ++koRule = SIMPLE # Simple ko rules (triple ko = no result) ++# koRule = POSITIONAL # Positional superko ++# koRule = SITUATIONAL # Situational superko ++ ++scoringRule = AREA # Area scoring ++# scoringRule = TERRITORY # Territory scoring (special computer-friendly territory rules) ++ ++taxRule = NONE # All surrounded empty points are scored ++# taxRule = SEKI # Eyes in seki do NOT count as points ++# taxRule = ALL # All groups are taxed up to 2 points for the two eyes needed to live ++ ++# Is multiple-stone suicide legal? (Single-stone suicide is always illegal). ++multiStoneSuicideLegal = true ++# multiStoneSuicideLegal = false ++ ++# "Button go" - the first pass when area scoring awards 0.5 points and does ++# not count for ending the game. ++hasButton = false ++# hasButton = true ++ ++# Is this a human ruleset where it's okay to pass before having physically ++# captured and removed all dead stones? ++friendlyPassOk = true ++# friendlyPassOk = false ++ ++# How handicap stones in handicap games are compensated ++whiteHandicapBonus = 0 # White gets no compensation for black's handicap stones (Tromp-taylor, NZ, JP) ++# whiteHandicapBonus = N-1 # White gets N-1 points for black's N handicap stones (AGA) ++# whiteHandicapBonus = N # White gets N points for black's N handicap stones (Chinese) ++ ++# ------------------------------ ++# Other rules hacks ++# ------------------------------ ++# Uncomment and change to adjust what board size KataGo uses upon startup ++# by default when GTP doesn't specify. ++# defaultBoardSize = 19 ++ ++# By default, Katago will use the komi that the GUI or GTP controller tries to set. ++# Uncomment and set this to have KataGo ignore the controller and always use this komi. ++# ignoreGTPAndForceKomi = 7 ++ ++# =========================================================================== ++# Bot behavior ++# =========================================================================== ++ ++# ------------------------------ ++# Resignation ++# ------------------------------ ++ ++# Resignation occurs if for at least resignConsecTurns in a row, the ++# winLossUtility (on a [-1,1] scale) is below resignThreshold. ++allowResignation = true ++resignThreshold = -0.90 ++resignConsecTurns = 3 ++ ++# By default, KataGo may resign games that it is confidently losing even if they ++# are very close in score. Uncomment and set this to avoid resigning games ++# if the estimated difference is points is less than or equal to this. ++# resignMinScoreDifference = 10 ++ ++# ------------------------------ ++# Handicap ++# ------------------------------ ++# Assume that if black makes many moves in a row right at the start of the ++# game, then the game is a handicap game. This is necessary on some servers ++# and for some GUIs and also when initializing from many SGF files, which may ++# set up a handicap game using repeated GTP "play" commands for black rather ++# than GTP "place_free_handicap" commands; however, it may also lead to ++# incorrect understanding of komi if whiteHandicapBonus is used and a server ++# does not have such a practice. Uncomment and set to false to disable. ++# assumeMultipleStartingBlackMovesAreHandicap = true ++ ++# Makes katago dynamically adjust in handicap or altered-komi games to assume ++# based on those game settings that it must be stronger or weaker than the ++# opponent and to play accordingly. Greatly improves handicap strength by ++# biasing winrates and scores to favor appropriate safe/aggressive play. ++# Does NOT affect analysis (lz-analyze, kata-analyze, used by programs like ++# Lizzie) so analysis remains unbiased. Uncomment and set this to 0 to disable ++# this and make KataGo play the same always. ++# dynamicPlayoutDoublingAdvantageCapPerOppLead = 0.045 ++ ++# Instead of "dynamicPlayoutDoublingAdvantageCapPerOppLead", you can comment ++# that out and uncomment and set "playoutDoublingAdvantage" to a fixed value ++# from -3.0 to 3.0 that will not change dynamically. ++# ALSO affects analysis tools (lz-analyze, kata-analyze, used by e.g. Lizzie). ++# Negative makes KataGo behave as if it is much weaker than the opponent. ++# Positive makes KataGo behave as if it is much stronger than the opponent. ++# KataGo will adjust to favor safe/aggressive play as appropriate based on ++# the combination of who is ahead and how much stronger/weaker it thinks it is, ++# and report winrates and scores taking the strength difference into account. ++# ++# If this and "dynamicPlayoutDoublingAdvantageCapPerOppLead" are both set ++# then dynamic will be used for all games and this fixed value will be used ++# for analysis tools. ++# playoutDoublingAdvantage = 0.0 ++ ++# Uncomment one of these when using "playoutDoublingAdvantage" to enforce ++# that it will only apply when KataGo plays as the specified color and will be ++# negated when playing as the opposite color. ++# playoutDoublingAdvantagePla = BLACK ++# playoutDoublingAdvantagePla = WHITE ++ ++# ------------------------------ ++# Passing and cleanup ++# ------------------------------ ++# Make the bot never assume that its pass will end the game, even if passing ++# would end and "win" under Tromp-Taylor rules. Usually this is a good idea ++# when using it for analysis or playing on servers where scoring may be ++# implemented non-tromp-taylorly. Uncomment and set to false to disable. ++# conservativePass = true ++ ++# When using territory scoring, self-play games continue beyond two passes ++# with special cleanup rules that may be confusing for human players. This ++# option prevents the special cleanup phases from being reachable when using ++# the bot for GTP play. Uncomment and set to false to enable entering special ++# cleanup. For example, if you are testing it against itself, or against ++# another bot that has precisely implemented the rules documented at ++# https://lightvector.github.io/KataGo/rules.html ++# preventCleanupPhase = true ++ ++# ------------------------------ ++# Miscellaneous behavior ++# ------------------------------ ++# If the board is symmetric, search only one copy of each equivalent move. ++# Attempts to also account for ko/superko, will not theoretically perfect for ++# superko. Uncomment and set to false to disable. ++# rootSymmetryPruning = true ++ ++# Uncomment and set to true to avoid a particular joseki that some networks ++# misevaluate, and also to improve opening diversity versus some particular ++# other bots that like to play it all the time. ++# avoidMYTDaggerHack = false ++ ++# Prefer to avoid playing the same joseki in every corner of the board. ++# Uncomment to set to a specific value. See "Avoid SGF patterns" section. ++# By default: 0 (even games), 0.005 (handicap games) ++# avoidRepeatedPatternUtility = 0.0 ++ ++# Experimental logic to fight against mirror Go even with unfavorable komi. ++# Uncomment to set to a specific value to use for both playing and analysis. ++# By default: true when playing via GTP, but false when analyzing. ++# antiMirror = true ++ ++# Enable some hacks that mitigate rare instances when passing messes up deeper searches. ++# enablePassingHacks = true ++ ++# Uncomment and set this to true to prevent bad or bogus move sequences ++# in the history leading to this position from affecting KataGo's move choices. ++# Same as analysisIgnorePreRootHistory (see above) but applies to actual play. ++# You can enable this if KataGo is being asked to play from positions that it did not ++# choose the moves to reach. ++# ignorePreRootHistory = false ++ ++# =========================================================================== ++# Search limits ++# =========================================================================== ++ ++# Terminology: ++# "Playouts" is the number of new playouts of search performed each turn. ++# "Visits" is the same as "Playouts" but also counts search performed on ++# previous turns that is still applicable to this turn. ++# "Time" is the time in seconds. ++ ++# For example, if KataGo searched 200 nodes on the previous turn, and then ++# after the opponent's reply, 50 nodes of its search tree was still valid, ++# then a visit limit of 200 would allow KataGo to search 150 new nodes ++# (for a final tree size of 200 nodes), whereas a playout limit of of 200 ++# would allow KataGo to search 200 nodes (for a final tree size of 250 nodes). ++ ++# Additionally, KataGo may also move before than the limit in order to ++# obey time controls (e.g. byo-yomi, etc) if the GTP controller has ++# told KataGo that the game has is being played with a given time control. ++ ++# Limits for search on the current turn. ++# If commented out or unspecified, the default is to have no limit. ++# maxVisits = 30000 ++# maxPlayouts = 300 ++maxTime = 1.0 ++ ++# Ponder on the opponent's turn? ++ponderingEnabled = true ++ ++# Limits for search when pondering on the opponent's turn. ++# If commented out or unspecified, the default is to have no limit. ++# Limiting the maximum time is recommended so that KataGo won't burn CPU/GPU ++# forever and/or run out of RAM if left unattended while pondering is enabled. ++# maxVisitsPondering = 5000 ++# maxPlayoutsPondering = 3000 ++maxTimePondering = 120.0 ++ ++ ++# ------------------------------ ++# Other search limits and behavior ++# ------------------------------ ++ ++# Approx number of seconds to buffer for lag for GTP time controls - will ++# move a bit faster assuming there is this much lag per move. ++lagBuffer = 1.0 ++ ++# YOU PROBABLY WANT TO TUNE THIS PARAMETER! ++# The number of threads to use when searching. On powerful GPUs the optimal ++# threads may be much higher than the number of CPU cores you have because ++# many threads are needed to feed efficient large batches to the GPU. ++# ++# Run "./katago benchmark" to tune this parameter and test the effect ++# of changes to any of other parameters. ++numSearchThreads = 40 ++ ++# Play a little faster if the opponent is passing, for human-friendliness. ++# Comment these out to disable them, such as if running a controlled match ++# where you are testing KataGo with fixed compute per move vs other bots. ++searchFactorAfterOnePass = 0.50 ++searchFactorAfterTwoPass = 0.25 ++ ++# Play a little faster if super-winning, for human-friendliness. ++# Comment these out to disable them, such as if running a controlled match ++# where you are testing KataGo with fixed compute per move vs other bots. ++searchFactorWhenWinning = 0.40 ++searchFactorWhenWinningThreshold = 0.95 ++ ++# =========================================================================== ++# GPU settings ++# =========================================================================== ++# This section configures GPU settings. ++# ++# Maximum number of positions to send to a single GPU at once. The default ++# value is roughly equal to numSearchThreads, but can be specified manually ++# if running out of memory, or using multiple GPUs that expect to share work. ++# nnMaxBatchSize = ++ ++# Controls the neural network cache size, which is the primary RAM/memory use. ++# KataGo will cache up to (2 ** nnCacheSizePowerOfTwo) many neural net ++# evaluations in case of transpositions in the tree. ++# Increase this to improve performance for searches with tens of thousands ++# of visits or more. Decrease this to limit memory usage. ++# If you're happy to do some math - each neural net entry takes roughly ++# 1.5KB, except when using whole-board ownership/territory ++# visualizations, where each entry will take roughly 3KB. The number of ++# entries is (2 ** nnCacheSizePowerOfTwo). (E.g. 2 ** 18 = 262144.) ++# You can compute roughly how much memory the cache will use based on this. ++# nnCacheSizePowerOfTwo = 20 ++ ++# Size of mutex pool for nnCache is (2 ** this). ++# nnMutexPoolSizePowerOfTwo = 16 ++ ++# Randomize board orientation when running neural net evals? Uncomment and ++# set to false to disable. ++# nnRandomize = true ++ ++# If provided, force usage of a specific seed for nnRandomize. ++# The default is to use a randomly generated seed. ++# nnRandSeed = abcdefg ++ ++# Uncomment and set to true to force GTP to use the maximum board size for ++# internal buffers for the neural net. This will make KataGo slower when ++# evaluating small boards, but will avoid a lengthy initialization time on every ++# change of board size due to having to re-size the neural net buffers on the GPU. ++# This can be useful for example, for OGS's persistent bot mode that uses a single ++# bot instance to handle multiple games and may thrash between different board sizes ++# if there are concurrent games of multiple sizes. ++# gtpForceMaxNNSize = false ++ ++# ------------------------------ ++# Multiple GPUs ++# ------------------------------ ++# Set this to the number of GPUs to use or that are available. ++# IMPORTANT: If more than 1, also uncomment the appropriate TensorRT ++# or CUDA or OpenCL section. ++# numNNServerThreadsPerModel = 1 ++ ++# ------------------------------ ++# TENSORRT GPU settings ++# ------------------------------ ++# These only apply when using the TENSORRT version of KataGo. ++ ++# For one GPU: optionally uncomment this option and change if the GPU to ++# use is not device 0. ++# trtDeviceToUse = 0 ++ ++# For two GPUs: Uncomment these options, AND set numNNServerThreadsPerModel above. ++# Also, change their values if the devices you want to use are not 0 and 1. ++# trtDeviceToUseThread0 = 0 ++# trtDeviceToUseThread1 = 1 ++ ++# For three GPUs: Uncomment these options, AND set numNNServerThreadsPerModel above. ++# Also, change their values if the devices you want to use are not 0 and 1 and 2. ++# trtDeviceToUseThread0 = 0 ++# trtDeviceToUseThread1 = 1 ++# trtDeviceToUseThread2 = 2 ++ ++# The pattern continues for additional GPUs. ++ ++# ------------------------------ ++# CUDA GPU settings ++# ------------------------------ ++# These only apply when using the CUDA version of KataGo. ++ ++# For one GPU: optionally uncomment and change this if the GPU you want to ++# use is not device 0 ++# cudaDeviceToUse = 0 ++ ++# For two GPUs: Uncomment these options, AND set numNNServerThreadsPerModel above. ++# Also, change their values if the devices you want to use are not 0 and 1. ++# cudaDeviceToUseThread0 = 0 ++# cudaDeviceToUseThread1 = 1 ++ ++# For three GPUs: Uncomment these options, AND set numNNServerThreadsPerModel above. ++# Also, change their values if the devices you want to use are not 0 and 1 and 2. ++# cudaDeviceToUseThread0 = 0 ++# cudaDeviceToUseThread1 = 1 ++# cudaDeviceToUseThread2 = 2 ++ ++# The pattern continues for additional GPUs. ++ ++# KataGo will automatically use FP16 or not based on the compute capability ++# of your NVIDIA GPU. If you want to try to force a particular behavior ++# you can uncomment these lines and change them to "true" or "false". ++# cudaUseFP16 = auto ++# cudaUseNHWC = auto ++ ++# ------------------------------ ++# OpenCL GPU settings ++# ------------------------------ ++# These only apply when using the OpenCL version of KataGo. ++ ++# Uncomment and set to true to tune OpenCL for every board size separately, ++# rather than only the largest possible size. ++# openclReTunePerBoardSize = false ++ ++# For one GPU: optionally uncomment and change this if the best device to use is guessed incorrectly. ++# The default behavior tries to guess the 'best' GPU or device on your system to use, usually it will be a good guess. ++# openclDeviceToUse = 0 ++ ++# For two GPUs: Uncomment these two lines and replace X and Y with the device ids of the devices you want to use. ++# It might NOT be 0 and 1, some computers will have many OpenCL devices. You can see what the devices are when ++# KataGo starts up - it should print or log all the devices it finds. ++# (AND also set numNNServerThreadsPerModel above) ++# openclDeviceToUseThread0 = X ++# openclDeviceToUseThread1 = Y ++ ++# For three GPUs: Uncomment these three lines and replace X and Y and Z with the device ids of the devices you want to use. ++# It might NOT be 0 and 1 and 2, some computers will have many OpenCL devices. You can see what the devices are when ++# KataGo starts up - it should print or log all the devices it finds. ++# (AND also set numNNServerThreadsPerModel above) ++# openclDeviceToUseThread0 = X ++# openclDeviceToUseThread1 = Y ++# openclDeviceToUseThread2 = Z ++ ++# The pattern continues for additional GPUs. ++ ++# KataGo will automatically use FP16 or not based on testing your GPU during ++# tuning. If you want to try to force a particular behavior though you can ++# uncomment this option and change it to "true" or "false". This is a fairly ++# blunt setting - more detailed settings are testable by rerunning the tuner ++# with various arguments (./katago tuner). ++# openclUseFP16 = auto ++ ++# ------------------------------ ++# Eigen-specific settings ++# ------------------------------ ++# These only apply when using the Eigen (pure CPU) version of KataGo. ++ ++# Number of CPU threads for evaluating the neural net on the Eigen backend. ++# ++# Default: numSearchThreads ++# numEigenThreadsPerModel = X ++ ++# =========================================================================== ++# Root move selection and biases ++# =========================================================================== ++# Uncomment and edit any of the below values to change them from their default. ++ ++# If provided, force usage of a specific seed for various random things in ++# the search. The default is to use a random seed. ++# searchRandSeed = hijklmn ++ ++# Temperature for the early game, randomize between chosen moves with ++# this temperature ++# chosenMoveTemperatureEarly = 0.5 ++ ++# Decay temperature for the early game by 0.5 every this many moves, ++# scaled with board size. ++# chosenMoveTemperatureHalflife = 19 ++ ++# At the end of search after the early game, randomize between chosen ++# moves with this temperature ++# chosenMoveTemperature = 0.10 ++ ++# Subtract this many visits from each move prior to applying ++# chosenMoveTemperature (unless all moves have too few visits) to downweight ++# unlikely moves ++# chosenMoveSubtract = 0 ++ ++# The same as chosenMoveSubtract but only prunes moves that fall below ++# the threshold. This setting does not affect chosenMoveSubtract. ++# chosenMovePrune = 1 ++ ++# Number of symmetries to sample (without replacement) and average at the root ++# rootNumSymmetriesToSample = 1 ++ ++# Using LCB for move selection? ++# useLcbForSelection = true ++ ++# How many stdevs a move needs to be better than another for LCB selection ++# lcbStdevs = 5.0 ++ ++# Only use LCB override when a move has this proportion of visits as the ++# top move. ++# minVisitPropForLCB = 0.15 ++ ++# =========================================================================== ++# Internal params ++# =========================================================================== ++# Uncomment and edit any of the below values to change them from their default. ++ ++# Scales the utility of winning/losing ++# winLossUtilityFactor = 1.0 ++ ++# Scales the utility for trying to maximize score ++# staticScoreUtilityFactor = 0.10 ++# dynamicScoreUtilityFactor = 0.30 ++ ++# Adjust dynamic score center this proportion of the way towards zero, ++# capped at a reasonable amount. ++# dynamicScoreCenterZeroWeight = 0.20 ++# dynamicScoreCenterScale = 0.75 ++ ++# The utility of getting a "no result" due to triple ko or other long cycle ++# in non-superko rulesets (-1 to 1) ++# noResultUtilityForWhite = 0.0 ++ ++# The number of wins that a draw counts as, for white. (0 to 1) ++# drawEquivalentWinsForWhite = 0.5 ++ ++# Exploration constant for mcts ++# cpuctExploration = 1.0 ++# cpuctExplorationLog = 0.45 ++ ++# Parameters that control exploring more in volatile positions, exploring ++# less in stable positions. ++# cpuctUtilityStdevPrior = 0.40 ++# cpuctUtilityStdevPriorWeight = 2.0 ++# cpuctUtilityStdevScale = 0.85 ++ ++# FPU reduction constant for mcts ++# fpuReductionMax = 0.2 ++# rootFpuReductionMax = 0.1 ++# fpuParentWeightByVisitedPolicy = true ++ ++# Parameters that control weighting of evals based on the net's own ++# self-reported uncertainty. ++# useUncertainty = true ++# uncertaintyExponent = 1.0 ++# uncertaintyCoeff = 0.25 ++ ++# Explore using optimistic policy ++# rootPolicyOptimism = 0.2 ++# policyOptimism = 1.0 ++ ++# Amount to apply a downweighting of children with very bad values relative ++# to good ones. ++# valueWeightExponent = 0.25 ++ ++# Slight incentive for the bot to behave human-like with regard to passing at ++# the end, filling the dame, not wasting time playing in its own territory, ++# etc., and not play moves that are equivalent in terms of points but a bit ++# more unfriendly to humans. ++# rootEndingBonusPoints = 0.5 ++ ++# Make the bot prune useless moves that are just prolonging the game to ++# avoid losing yet. ++# rootPruneUselessMoves = true ++ ++# Apply bias correction based on local pattern keys ++# subtreeValueBiasFactor = 0.45 ++# subtreeValueBiasWeightExponent = 0.85 ++ ++# Use graph search rather than tree search - identify and share search for ++# transpositions. ++# useGraphSearch = true ++ ++# How much to shard the node table for search synchronization ++# nodeTableShardsPowerOfTwo = 16 ++ ++# How many virtual losses to add when a thread descends through a node ++# numVirtualLossesPerThread = 1 ++ ++# Improve the quality of evals under heavy multithreading ++# useNoisePruning = true ++ ++# =========================================================================== ++# Automatic avoid patterns ++# =========================================================================== ++# The parameters in this section provide a way to bias away from moves that match ++# patterns that this instance of KataGo has played in previous games, by auto-saving ++# moves to a directory and then auto-loading and biasing against them each new game. ++# Uncomment them to use them. When using this feature, all parameters must be specified. ++ ++# Different board sizes are tracked separately, but all board sizes share the same ++# parameters by default. Every parameter *except* for autoAvoidRepeatDir and ++# autoAvoidRepeatSaveChunkSize can be overridden per board size, ++# e.g. "autoAvoidRepeatMinTurnNumber13x13". You must ALSO specify the ++# defaults even if you have specified board-size-specific values. ++ ++# Directory to auto-save moves KataGo plays, to avoid them in future games. ++# You can create a new empty directory and put its path here. ++# If you run parallel instances of KataGo, use different directories if you ++# want them not to share their biases, use the same directory if you want ++# all of them to bias away from past moves that any of them have played. ++# KataGo will also automatically DELETE old data in this directory, so it is ++# recommended that if you do share the same directory between parallel instances, ++# that they all use the same settings that affect data saving/deletion. ++# autoAvoidRepeatDir = PATH_TO_NEW_DIRECTORY ++ ++# Penalize this much utility per matching move. ++# Values that are too large may lead to bad play. The value of 0.004 is fairly large ++# and might be large enough to result in some early weird/bad moves when trying ++# to avoid past games' moves if enough games begin the same way. You can experiment with it. ++# autoAvoidRepeatUtility = 0.004 ++ ++# Per each new move saved, exponentially decay prior saved moves by this factor. ++# This way, the bias against moves from many games ago is gradually phased out. ++# For example, 0.9995 = 1 - 1/2000, so would be roughly 2000 prior moves worth of ++# penalty weight remembered, in steady state. Depending on what turn number range ++# you are saving, this might equate to a different number of games For example ++# saving the first 50 moves per game would make this roughly 2000 / 50 = 40 games ++# worth of memory. ++# autoAvoidRepeatLambda = 0.9995 ++ ++# Affects data saving/deletion. ++# When the number of saved moves exceeds this, outright delete them to avoid too many ++# files and disk space building up. Also may affect the speed of saving/loading on start ++# of each game if this is set large and a lot of data builds up. ++# autoAvoidRepeatMaxPoses = 10000 ++ ++# Affects data saving/deletion. ++# Only save data for moves within this turn number range of those games. ++# E.g. setting autoAvoidRepeatMinTurnNumber to a number like 4 or 5 would tend to make ++# KataGo not develop a bias against the initial 3-4 and 4-4 corner moves in almost every game. ++# autoAvoidRepeatMinTurnNumber = 0 ++# autoAvoidRepeatMaxTurnNumber = 50 ++ ++# Affects data saving/deletion. ++# Within a single run of a program, wait to accumulate this many samples ++# (possibly across multiple clear_boards/games) before saving the data. ++# Can help to avoid writing too many small files to disk, especially when GTP is used ++# in a way that clears the board very frequently (e.g. gtp2ogs pooled manager). ++# autoAvoidRepeatSaveChunkSize = 200 ++ ++# =========================================================================== ++# Avoid SGF patterns ++# =========================================================================== ++# The parameters in this section provide a way to avoid moves that follow ++# specific patterns based on a set of SGF files loaded upon startup. ++# This is basically the same as the above "Automatic avoid patterns" section ++# above except you supply your own SGF files to avoid moves from. ++# Uncomment them to use this feature. Additionally, if the SGF file ++# contains the string %SKIP% in a comment on a move, that move will be ++# ignored for this purpose. ++ ++# Load SGF files from this directory when the engine is started ++# (only on startup, will not reload unless engine is restarted) ++# avoidSgfPatternDirs = path/to/directory/with/sgfs/ ++# You can also surround the file path in double quotes if the file path contains trailing spaces or hash signs. ++# Within double quotes, backslashes are escape characters. ++# avoidSgfPatternDirs = "path/to/directory/with/sgfs/" ++ ++# Penalize this much utility per matching move. ++# Set this negative if you instead want to favor SGF patterns instead of ++# penalizing them. This number does not need to be large, even 0.001 will ++# make a difference. Values that are too large may lead to bad play. ++# avoidSgfPatternUtility = 0.001 ++ ++# Optional - load only the newest this many files ++# avoidSgfPatternMaxFiles = 20 ++ ++# Optional - Penalty is multiplied by this per each older SGF file, so that ++# old SGF files matter less than newer ones. ++# avoidSgfPatternLambda = 0.90 ++ ++# Optional - pay attention only to moves made by players with this name. ++# For example, set it to the name that your bot's past games will show up ++# as in the SGF, so that the bot will only avoid repeating moves that itself ++# made in past games, not the moves that its opponents made. ++# avoidSgfPatternAllowedNames = my-ogs-bot-name1,my-ogs-bot-name2 ++ ++# Optional - Ignore moves in SGF files that occurred before this turn number. ++# avoidSgfPatternMinTurnNumber = 0 ++ ++# For more avoid patterns: ++# You can also specify a second set of parameters, and a third, fourth, ++# etc. by numbering 2,3,4,... ++# ++# avoidSgf2PatternDirs = ... ++# avoidSgf2PatternUtility = ... ++# avoidSgf2PatternMaxFiles = ... ++# avoidSgf2PatternLambda = ... ++# avoidSgf2PatternAllowedNames = ... ++# avoidSgf2PatternMinTurnNumber = ... ++ +diff --git a/python/selfplay/libcrypto-3-x64.dll b/python/selfplay/libcrypto-3-x64.dll +new file mode 100644 +index 00000000..cac7f4ec +Binary files /dev/null and b/python/selfplay/libcrypto-3-x64.dll differ +diff --git a/python/selfplay/libssl-3-x64.dll b/python/selfplay/libssl-3-x64.dll +new file mode 100644 +index 00000000..8ad0908e +Binary files /dev/null and b/python/selfplay/libssl-3-x64.dll differ +diff --git a/python/selfplay/libz.dll b/python/selfplay/libz.dll +new file mode 100644 +index 00000000..930a0421 +Binary files /dev/null and b/python/selfplay/libz.dll differ +diff --git a/python/selfplay/libzip.dll b/python/selfplay/libzip.dll +new file mode 100644 +index 00000000..167e69f7 +Binary files /dev/null and b/python/selfplay/libzip.dll differ +diff --git a/python/selfplay/lizgoban.cfg b/python/selfplay/lizgoban.cfg +new file mode 100644 +index 00000000..53f4832b +--- /dev/null ++++ b/python/selfplay/lizgoban.cfg +@@ -0,0 +1,721 @@ ++# Configuration for KataGo C++ GTP engine ++ ++# Run the program using: `./katago.exe gtp` ++ ++# In this example config, when a parameter is given as a commented out value, ++# that value also is the default value, unless described otherwise. You can ++# uncomment it (remove the pound sign) and change it if you want. ++ ++# =========================================================================== ++# Running on an online server or in a real tournament or match ++# =========================================================================== ++# If you plan to run online or in a tournament, read through the "Rules" ++# section below for proper handling of komi, handicaps, end-of-game cleanup, ++# and other details. ++ ++# =========================================================================== ++# Notes about performance and memory usage ++# =========================================================================== ++# Important: For good performance, you will very likely want to tune the ++# "numSearchThreads" parameter in the Search limits section below! Run ++# "./katago benchmark" to test KataGo and to suggest a reasonable value ++# of this parameter. ++ ++# For multi-GPU systems, read "OpenCL GPU settings" or "CUDA GPU settings". ++# ++# When using OpenCL, verify that KataGo picks the correct device! Some systems ++# may have both an Intel CPU OpenCL and GPU OpenCL. If # KataGo picks the wrong ++# one, correct this by specifying "openclGpuToUse". ++# ++# Consider adjusting "maxVisits", "ponderingEnabled", "resignThreshold", and ++# other parameters depending on your intended usage. ++ ++# =========================================================================== ++# Command-line usage ++# =========================================================================== ++# All of the below values may be set or overridden via command-line arguments: ++# ++# -override-config KEY=VALUE,KEY=VALUE,... ++ ++# =========================================================================== ++# Logs and files ++# =========================================================================== ++# This section defines where and what logging information is produced. ++ ++# Each run of KataGo will log to a separate file in this dir. ++# This is the default. ++logDir = gtp_logs ++# Uncomment and specify this instead of logDir to write separate dated subdirs ++# logDirDated = gtp_logs ++# Uncomment and specify this instead of logDir to log to only a single file ++# logFile = gtp.log ++ ++# Logging options ++logAllGTPCommunication = true ++logSearchInfo = true ++logSearchInfoForChosenMove = false ++logToStderr = true ++ ++# KataGo will display some info to stderr on GTP startup ++# Uncomment the next line and set it to false to suppress that and remain silent ++# startupPrintMessageToStderr = true ++ ++# Write information to stderr, for use in things like malkovich chat to OGS. ++ ogsChatToStderr = true ++ ++# Uncomment and set this to a directory to override where openCLTuner files ++# and other cached data is written. By default it saves into a subdir of the ++# current directory on windows, and a subdir of ~/.katago on Linux. ++# homeDataDir = PATH_TO_DIRECTORY ++ ++# =========================================================================== ++# Analysis ++# =========================================================================== ++# This section configures analysis settings. ++# ++# The maximum number of moves after the first move displayed in variations ++# from analysis commands like kata-analyze or lz-analyze. ++# analysisPVLen = 15 ++ ++# Report winrates for chat and analysis as (BLACK|WHITE|SIDETOMOVE). ++# Most GUIs and analysis tools will expect SIDETOMOVE. ++# reportAnalysisWinratesAs = SIDETOMOVE ++ ++# Extra noise for wider exploration. Large values will force KataGo to ++# analyze a greater variety of moves than it normally would. ++# An extreme value like 1 distributes playouts across every move on the board, ++# even very bad moves. ++# Affects analysis only, does not affect play. ++# analysisWideRootNoise = 0.04 ++ ++# Try to limit the effect of possible bad or bogus move sequences in the ++# history leading to this position from affecting KataGo's move predictions. ++# analysisIgnorePreRootHistory = true ++ ++# =========================================================================== ++# Rules ++# =========================================================================== ++# This section configures the scoring and playing rules. Rules can also be ++# changed mid-run by issuing custom GTP commands. ++# ++# See https://lightvector.github.io/KataGo/rules.html for rules details. ++# ++# See https://github.com/lightvector/KataGo/blob/master/docs/GTP_Extensions.md ++# for GTP commands. ++ ++# Specify the rules as a string. ++# Some legal values include: ++# chinese, japanese, korean, aga, chinese-ogs, new-zealand, stone-scoring, ++# ancient-territory, bga, aga-button ++# ++# For some human rulesets that require complex adjudication in tricky cases ++# (e.g. japanese, korean) KataGo may not precisely match the ruleset in such ++# cases but will do its best. ++rules = tromp-taylor ++ ++# By default, the "rules" parameter is used, but if you comment it out and ++# uncomment one option in each of the sections below, you can specify an ++# arbitrary combination of individual rules. ++ ++# koRule = SIMPLE # Simple ko rules (triple ko = no result) ++# koRule = POSITIONAL # Positional superko ++# koRule = SITUATIONAL # Situational superko ++ ++# scoringRule = AREA # Area scoring ++# scoringRule = TERRITORY # Territory scoring (special computer-friendly territory rules) ++ ++# taxRule = NONE # All surrounded empty points are scored ++# taxRule = SEKI # Eyes in seki do NOT count as points ++# taxRule = ALL # All groups are taxed up to 2 points for the two eyes needed to live ++ ++# Is multiple-stone suicide legal? (Single-stone suicide is always illegal). ++# multiStoneSuicideLegal = false ++# multiStoneSuicideLegal = true ++ ++# "Button go" - the first pass when area scoring awards 0.5 points and does ++# not count for ending the game. ++# Allows area scoring rulesets that have far simpler rules to achieve the same ++# final scoring precision and reward for precise play as territory scoring. ++# hasButton = false ++# hasButton = true ++ ++# Is this a human ruleset where it's okay to pass before having physically ++# captured and removed all dead stones? ++# friendlyPassOk = false ++# friendlyPassOk = true ++ ++# How handicap stones in handicap games are compensated ++# whiteHandicapBonus = 0 # White gets no compensation for black's handicap stones (Tromp-taylor, NZ, JP) ++# whiteHandicapBonus = N-1 # White gets N-1 points for black's N handicap stones (AGA) ++# whiteHandicapBonus = N # White gets N points for black's N handicap stones (Chinese) ++ ++# ------------------------------ ++# Other rules hacks ++# ------------------------------ ++# Uncomment and change to adjust what board size KataGo uses upon startup ++# by default when GTP doesn't specify. ++# defaultBoardSize = 19 ++ ++# By default, Katago will use the komi that the GUI or GTP controller tries to set. ++# Uncomment and set this to have KataGo ignore the controller and always use this komi. ++# ignoreGTPAndForceKomi = 7 ++ ++# =========================================================================== ++# Bot behavior ++# =========================================================================== ++ ++# ------------------------------ ++# Resignation ++# ------------------------------ ++ ++# Resignation occurs if for at least resignConsecTurns in a row, the ++# winLossUtility (on a [-1,1] scale) is below resignThreshold. ++allowResignation = true ++resignThreshold = -0.90 ++resignConsecTurns = 3 ++ ++# By default, KataGo may resign games that it is confidently losing even if they ++# are very close in score. Uncomment and set this to avoid resigning games ++# if the estimated difference is points is less than or equal to this. ++# resignMinScoreDifference = 10 ++ ++# ------------------------------ ++# Handicap ++# ------------------------------ ++# Assume that if black makes many moves in a row right at the start of the ++# game, then the game is a handicap game. This is necessary on some servers ++# and for some GUIs and also when initializing from many SGF files, which may ++# set up a handicap game using repeated GTP "play" commands for black rather ++# than GTP "place_free_handicap" commands; however, it may also lead to ++# incorrect understanding of komi if whiteHandicapBonus is used and a server ++# does not have such a practice. Uncomment and set to false to disable. ++# assumeMultipleStartingBlackMovesAreHandicap = true ++ ++# Makes katago dynamically adjust in handicap or altered-komi games to assume ++# based on those game settings that it must be stronger or weaker than the ++# opponent and to play accordingly. Greatly improves handicap strength by ++# biasing winrates and scores to favor appropriate safe/aggressive play. ++# Does NOT affect analysis (lz-analyze, kata-analyze, used by programs like ++# Lizzie) so analysis remains unbiased. Uncomment and set this to 0 to disable ++# this and make KataGo play the same always. ++# dynamicPlayoutDoublingAdvantageCapPerOppLead = 0.045 ++ ++# Instead of "dynamicPlayoutDoublingAdvantageCapPerOppLead", you can comment ++# that out and uncomment and set "playoutDoublingAdvantage" to a fixed value ++# from -3.0 to 3.0 that will not change dynamically. ++# ALSO affects analysis tools (lz-analyze, kata-analyze, used by e.g. Lizzie). ++# Negative makes KataGo behave as if it is much weaker than the opponent. ++# Positive makes KataGo behave as if it is much stronger than the opponent. ++# KataGo will adjust to favor safe/aggressive play as appropriate based on ++# the combination of who is ahead and how much stronger/weaker it thinks it is, ++# and report winrates and scores taking the strength difference into account. ++# ++# If this and "dynamicPlayoutDoublingAdvantageCapPerOppLead" are both set ++# then dynamic will be used for all games and this fixed value will be used ++# for analysis tools. ++# playoutDoublingAdvantage = 0.0 ++ ++# Uncomment one of these when using "playoutDoublingAdvantage" to enforce ++# that it will only apply when KataGo plays as the specified color and will be ++# negated when playing as the opposite color. ++# playoutDoublingAdvantagePla = BLACK ++# playoutDoublingAdvantagePla = WHITE ++ ++# ------------------------------ ++# Passing and cleanup ++# ------------------------------ ++# Make the bot never assume that its pass will end the game, even if passing ++# would end and "win" under Tromp-Taylor rules. Usually this is a good idea ++# when using it for analysis or playing on servers where scoring may be ++# implemented non-tromp-taylorly. Uncomment and set to false to disable. ++# conservativePass = true ++ ++# When using territory scoring, self-play games continue beyond two passes ++# with special cleanup rules that may be confusing for human players. This ++# option prevents the special cleanup phases from being reachable when using ++# the bot for GTP play. Uncomment and set to false to enable entering special ++# cleanup. For example, if you are testing it against itself, or against ++# another bot that has precisely implemented the rules documented at ++# https://lightvector.github.io/KataGo/rules.html ++# preventCleanupPhase = true ++ ++# ------------------------------ ++# Miscellaneous behavior ++# ------------------------------ ++# If the board is symmetric, search only one copy of each equivalent move. ++# Attempts to also account for ko/superko, will not theoretically perfect for ++# superko. Uncomment and set to false to disable. ++# rootSymmetryPruning = true ++ ++# Uncomment and set to true to avoid a particular joseki that some networks ++# misevaluate, and also to improve opening diversity versus some particular ++# other bots that like to play it all the time. ++# avoidMYTDaggerHack = false ++ ++# Prefer to avoid playing the same joseki in every corner of the board. ++# Uncomment to set to a specific value. See "Avoid SGF patterns" section. ++# By default: 0 (even games), 0.005 (handicap games) ++# avoidRepeatedPatternUtility = 0.0 ++ ++# Experimental logic to fight against mirror Go even with unfavorable komi. ++# Uncomment to set to a specific value to use for both playing and analysis. ++# By default: true when playing via GTP, but false when analyzing. ++# antiMirror = true ++ ++# Enable some hacks that mitigate rare instances when passing messes up deeper searches. ++# enablePassingHacks = true ++ ++# Uncomment and set this to true to prevent bad or bogus move sequences ++# in the history leading to this position from affecting KataGo's move choices. ++# Same as analysisIgnorePreRootHistory (see above) but applies to actual play. ++# You can enable this if KataGo is being asked to play from positions that it did not ++# choose the moves to reach. ++# ignorePreRootHistory = false ++ ++# =========================================================================== ++# Search limits ++# =========================================================================== ++ ++# Terminology: ++# "Playouts" is the number of new playouts of search performed each turn. ++# "Visits" is the same as "Playouts" but also counts search performed on ++# previous turns that is still applicable to this turn. ++# "Time" is the time in seconds. ++ ++# For example, if KataGo searched 200 nodes on the previous turn, and then ++# after the opponent's reply, 50 nodes of its search tree was still valid, ++# then a visit limit of 200 would allow KataGo to search 150 new nodes ++# (for a final tree size of 200 nodes), whereas a playout limit of of 200 ++# would allow KataGo to search 200 nodes (for a final tree size of 250 nodes). ++ ++# Additionally, KataGo may also move before than the limit in order to ++# obey time controls (e.g. byo-yomi, etc) if the GTP controller has ++# told KataGo that the game has is being played with a given time control. ++ ++# Limits for search on the current turn. ++# If commented out or unspecified, the default is to have no limit. ++# maxVisits = 500 ++# maxPlayouts = 300 ++ maxTime = 0.5 ++ ++# Ponder on the opponent's turn? ++ponderingEnabled = true ++ ++# Limits for search when pondering on the opponent's turn. ++# If commented out or unspecified, the default is to have no limit. ++# Limiting the maximum time is recommended so that KataGo won't burn CPU/GPU ++# forever and/or run out of RAM if left unattended while pondering is enabled. ++# maxVisitsPondering = 5000 ++# maxPlayoutsPondering = 3000 ++maxTimePondering = 60.0 ++ ++ ++# ------------------------------ ++# Other search limits and behavior ++# ------------------------------ ++ ++# Approx number of seconds to buffer for lag for GTP time controls - will ++# move a bit faster assuming there is this much lag per move. ++lagBuffer = 1.0 ++ ++# YOU PROBABLY WANT TO TUNE THIS PARAMETER! ++# The number of threads to use when searching. On powerful GPUs the optimal ++# threads may be much higher than the number of CPU cores you have because ++# many threads are needed to feed efficient large batches to the GPU. ++# ++# Run "./katago benchmark" to tune this parameter and test the effect ++# of changes to any of other parameters. ++numSearchThreads = 16 ++ ++# Play a little faster if the opponent is passing, for human-friendliness. ++# Comment these out to disable them, such as if running a controlled match ++# where you are testing KataGo with fixed compute per move vs other bots. ++searchFactorAfterOnePass = 0.50 ++searchFactorAfterTwoPass = 0.25 ++ ++# Play a little faster if super-winning, for human-friendliness. ++# Comment these out to disable them, such as if running a controlled match ++# where you are testing KataGo with fixed compute per move vs other bots. ++searchFactorWhenWinning = 0.40 ++searchFactorWhenWinningThreshold = 0.95 ++ ++# =========================================================================== ++# GPU settings ++# =========================================================================== ++# This section configures GPU settings. ++# ++# Maximum number of positions to send to a single GPU at once. The default ++# value is roughly equal to numSearchThreads, but can be specified manually ++# if running out of memory, or using multiple GPUs that expect to share work. ++# nnMaxBatchSize = ++ ++# Controls the neural network cache size, which is the primary RAM/memory use. ++# KataGo will cache up to (2 ** nnCacheSizePowerOfTwo) many neural net ++# evaluations in case of transpositions in the tree. ++# Increase this to improve performance for searches with tens of thousands ++# of visits or more. Decrease this to limit memory usage. ++# If you're happy to do some math - each neural net entry takes roughly ++# 1.5KB, except when using whole-board ownership/territory ++# visualizations, where each entry will take roughly 3KB. The number of ++# entries is (2 ** nnCacheSizePowerOfTwo). (E.g. 2 ** 18 = 262144.) ++# You can compute roughly how much memory the cache will use based on this. ++# nnCacheSizePowerOfTwo = 20 ++ ++# Size of mutex pool for nnCache is (2 ** this). ++# nnMutexPoolSizePowerOfTwo = 16 ++ ++# Randomize board orientation when running neural net evals? Uncomment and ++# set to false to disable. ++# nnRandomize = true ++ ++# If provided, force usage of a specific seed for nnRandomize. ++# The default is to use a randomly generated seed. ++# nnRandSeed = abcdefg ++ ++# Uncomment and set to true to force GTP to use the maximum board size for ++# internal buffers for the neural net. This will make KataGo slower when ++# evaluating small boards, but will avoid a lengthy initialization time on every ++# change of board size due to having to re-size the neural net buffers on the GPU. ++# This can be useful for example, for OGS's persistent bot mode that uses a single ++# bot instance to handle multiple games and may thrash between different board sizes ++# if there are concurrent games of multiple sizes. ++# gtpForceMaxNNSize = false ++ ++# ------------------------------ ++# Multiple GPUs ++# ------------------------------ ++# Set this to the number of GPUs to use or that are available. ++# IMPORTANT: If more than 1, also uncomment the appropriate TensorRT ++# or CUDA or OpenCL section. ++# numNNServerThreadsPerModel = 1 ++ ++# ------------------------------ ++# TENSORRT GPU settings ++# ------------------------------ ++# These only apply when using the TENSORRT version of KataGo. ++ ++# For one GPU: optionally uncomment this option and change if the GPU to ++# use is not device 0. ++# trtDeviceToUse = 0 ++ ++# For two GPUs: Uncomment these options, AND set numNNServerThreadsPerModel above. ++# Also, change their values if the devices you want to use are not 0 and 1. ++# trtDeviceToUseThread0 = 0 ++# trtDeviceToUseThread1 = 1 ++ ++# For three GPUs: Uncomment these options, AND set numNNServerThreadsPerModel above. ++# Also, change their values if the devices you want to use are not 0 and 1 and 2. ++# trtDeviceToUseThread0 = 0 ++# trtDeviceToUseThread1 = 1 ++# trtDeviceToUseThread2 = 2 ++ ++# The pattern continues for additional GPUs. ++ ++# ------------------------------ ++# CUDA GPU settings ++# ------------------------------ ++# These only apply when using the CUDA version of KataGo. ++ ++# For one GPU: optionally uncomment and change this if the GPU you want to ++# use is not device 0 ++# cudaDeviceToUse = 0 ++ ++# For two GPUs: Uncomment these options, AND set numNNServerThreadsPerModel above. ++# Also, change their values if the devices you want to use are not 0 and 1. ++# cudaDeviceToUseThread0 = 0 ++# cudaDeviceToUseThread1 = 1 ++ ++# For three GPUs: Uncomment these options, AND set numNNServerThreadsPerModel above. ++# Also, change their values if the devices you want to use are not 0 and 1 and 2. ++# cudaDeviceToUseThread0 = 0 ++# cudaDeviceToUseThread1 = 1 ++# cudaDeviceToUseThread2 = 2 ++ ++# The pattern continues for additional GPUs. ++ ++# KataGo will automatically use FP16 or not based on the compute capability ++# of your NVIDIA GPU. If you want to try to force a particular behavior ++# you can uncomment these lines and change them to "true" or "false". ++# cudaUseFP16 = auto ++# cudaUseNHWC = auto ++ ++# ------------------------------ ++# OpenCL GPU settings ++# ------------------------------ ++# These only apply when using the OpenCL version of KataGo. ++ ++# Uncomment and set to true to tune OpenCL for every board size separately, ++# rather than only the largest possible size. ++# openclReTunePerBoardSize = false ++ ++# For one GPU: optionally uncomment and change this if the best device to use is guessed incorrectly. ++# The default behavior tries to guess the 'best' GPU or device on your system to use, usually it will be a good guess. ++# openclDeviceToUse = 0 ++ ++# For two GPUs: Uncomment these two lines and replace X and Y with the device ids of the devices you want to use. ++# It might NOT be 0 and 1, some computers will have many OpenCL devices. You can see what the devices are when ++# KataGo starts up - it should print or log all the devices it finds. ++# (AND also set numNNServerThreadsPerModel above) ++# openclDeviceToUseThread0 = X ++# openclDeviceToUseThread1 = Y ++ ++# For three GPUs: Uncomment these three lines and replace X and Y and Z with the device ids of the devices you want to use. ++# It might NOT be 0 and 1 and 2, some computers will have many OpenCL devices. You can see what the devices are when ++# KataGo starts up - it should print or log all the devices it finds. ++# (AND also set numNNServerThreadsPerModel above) ++# openclDeviceToUseThread0 = X ++# openclDeviceToUseThread1 = Y ++# openclDeviceToUseThread2 = Z ++ ++# The pattern continues for additional GPUs. ++ ++# KataGo will automatically use FP16 or not based on testing your GPU during ++# tuning. If you want to try to force a particular behavior though you can ++# uncomment this option and change it to "true" or "false". This is a fairly ++# blunt setting - more detailed settings are testable by rerunning the tuner ++# with various arguments (./katago tuner). ++# openclUseFP16 = auto ++ ++# ------------------------------ ++# Eigen-specific settings ++# ------------------------------ ++# These only apply when using the Eigen (pure CPU) version of KataGo. ++ ++# Number of CPU threads for evaluating the neural net on the Eigen backend. ++# ++# Default: numSearchThreads ++# numEigenThreadsPerModel = X ++ ++# =========================================================================== ++# Root move selection and biases ++# =========================================================================== ++# Uncomment and edit any of the below values to change them from their default. ++ ++# If provided, force usage of a specific seed for various random things in ++# the search. The default is to use a random seed. ++# searchRandSeed = hijklmn ++ ++# Temperature for the early game, randomize between chosen moves with ++# this temperature ++# chosenMoveTemperatureEarly = 0.5 ++ ++# Decay temperature for the early game by 0.5 every this many moves, ++# scaled with board size. ++# chosenMoveTemperatureHalflife = 19 ++ ++# At the end of search after the early game, randomize between chosen ++# moves with this temperature ++# chosenMoveTemperature = 0.10 ++ ++# Subtract this many visits from each move prior to applying ++# chosenMoveTemperature (unless all moves have too few visits) to downweight ++# unlikely moves ++# chosenMoveSubtract = 0 ++ ++# The same as chosenMoveSubtract but only prunes moves that fall below ++# the threshold. This setting does not affect chosenMoveSubtract. ++# chosenMovePrune = 1 ++ ++# Number of symmetries to sample (without replacement) and average at the root ++# rootNumSymmetriesToSample = 1 ++ ++# Using LCB for move selection? ++# useLcbForSelection = true ++ ++# How many stdevs a move needs to be better than another for LCB selection ++# lcbStdevs = 5.0 ++ ++# Only use LCB override when a move has this proportion of visits as the ++# top move. ++# minVisitPropForLCB = 0.15 ++ ++# =========================================================================== ++# Internal params ++# =========================================================================== ++# Uncomment and edit any of the below values to change them from their default. ++ ++# Scales the utility of winning/losing ++# winLossUtilityFactor = 1.0 ++ ++# Scales the utility for trying to maximize score ++# staticScoreUtilityFactor = 0.10 ++# dynamicScoreUtilityFactor = 0.30 ++ ++# Adjust dynamic score center this proportion of the way towards zero, ++# capped at a reasonable amount. ++# dynamicScoreCenterZeroWeight = 0.20 ++# dynamicScoreCenterScale = 0.75 ++ ++# The utility of getting a "no result" due to triple ko or other long cycle ++# in non-superko rulesets (-1 to 1) ++# noResultUtilityForWhite = 0.0 ++ ++# The number of wins that a draw counts as, for white. (0 to 1) ++# drawEquivalentWinsForWhite = 0.5 ++ ++# Exploration constant for mcts ++# cpuctExploration = 1.0 ++# cpuctExplorationLog = 0.45 ++ ++# Parameters that control exploring more in volatile positions, exploring ++# less in stable positions. ++# cpuctUtilityStdevPrior = 0.40 ++# cpuctUtilityStdevPriorWeight = 2.0 ++# cpuctUtilityStdevScale = 0.85 ++ ++# FPU reduction constant for mcts ++# fpuReductionMax = 0.2 ++# rootFpuReductionMax = 0.1 ++# fpuParentWeightByVisitedPolicy = true ++ ++# Parameters that control weighting of evals based on the net's own ++# self-reported uncertainty. ++# useUncertainty = true ++# uncertaintyExponent = 1.0 ++# uncertaintyCoeff = 0.25 ++ ++# Explore using optimistic policy ++# rootPolicyOptimism = 0.2 ++# policyOptimism = 1.0 ++ ++# Amount to apply a downweighting of children with very bad values relative ++# to good ones. ++# valueWeightExponent = 0.25 ++ ++# Slight incentive for the bot to behave human-like with regard to passing at ++# the end, filling the dame, not wasting time playing in its own territory, ++# etc., and not play moves that are equivalent in terms of points but a bit ++# more unfriendly to humans. ++# rootEndingBonusPoints = 0.5 ++ ++# Make the bot prune useless moves that are just prolonging the game to ++# avoid losing yet. ++# rootPruneUselessMoves = true ++ ++# Apply bias correction based on local pattern keys ++# subtreeValueBiasFactor = 0.45 ++# subtreeValueBiasWeightExponent = 0.85 ++ ++# Use graph search rather than tree search - identify and share search for ++# transpositions. ++# useGraphSearch = true ++ ++# How much to shard the node table for search synchronization ++# nodeTableShardsPowerOfTwo = 16 ++ ++# How many virtual losses to add when a thread descends through a node ++# numVirtualLossesPerThread = 1 ++ ++# Improve the quality of evals under heavy multithreading ++# useNoisePruning = true ++ ++# =========================================================================== ++# Automatic avoid patterns ++# =========================================================================== ++# The parameters in this section provide a way to bias away from moves that match ++# patterns that this instance of KataGo has played in previous games, by auto-saving ++# moves to a directory and then auto-loading and biasing against them each new game. ++# Uncomment them to use them. When using this feature, all parameters must be specified. ++ ++# Different board sizes are tracked separately, but all board sizes share the same ++# parameters by default. Every parameter *except* for autoAvoidRepeatDir and ++# autoAvoidRepeatSaveChunkSize can be overridden per board size, ++# e.g. "autoAvoidRepeatMinTurnNumber13x13". You must ALSO specify the ++# defaults even if you have specified board-size-specific values. ++ ++# Directory to auto-save moves KataGo plays, to avoid them in future games. ++# You can create a new empty directory and put its path here. ++# If you run parallel instances of KataGo, use different directories if you ++# want them not to share their biases, use the same directory if you want ++# all of them to bias away from past moves that any of them have played. ++# KataGo will also automatically DELETE old data in this directory, so it is ++# recommended that if you do share the same directory between parallel instances, ++# that they all use the same settings that affect data saving/deletion. ++# autoAvoidRepeatDir = PATH_TO_NEW_DIRECTORY ++ ++# Penalize this much utility per matching move. ++# Values that are too large may lead to bad play. The value of 0.004 is fairly large ++# and might be large enough to result in some early weird/bad moves when trying ++# to avoid past games' moves if enough games begin the same way. You can experiment with it. ++# autoAvoidRepeatUtility = 0.004 ++ ++# Per each new move saved, exponentially decay prior saved moves by this factor. ++# This way, the bias against moves from many games ago is gradually phased out. ++# For example, 0.9995 = 1 - 1/2000, so would be roughly 2000 prior moves worth of ++# penalty weight remembered, in steady state. Depending on what turn number range ++# you are saving, this might equate to a different number of games For example ++# saving the first 50 moves per game would make this roughly 2000 / 50 = 40 games ++# worth of memory. ++# autoAvoidRepeatLambda = 0.9995 ++ ++# Affects data saving/deletion. ++# When the number of saved moves exceeds this, outright delete them to avoid too many ++# files and disk space building up. Also may affect the speed of saving/loading on start ++# of each game if this is set large and a lot of data builds up. ++# autoAvoidRepeatMaxPoses = 10000 ++ ++# Affects data saving/deletion. ++# Only save data for moves within this turn number range of those games. ++# E.g. setting autoAvoidRepeatMinTurnNumber to a number like 4 or 5 would tend to make ++# KataGo not develop a bias against the initial 3-4 and 4-4 corner moves in almost every game. ++# autoAvoidRepeatMinTurnNumber = 0 ++# autoAvoidRepeatMaxTurnNumber = 50 ++ ++# Affects data saving/deletion. ++# Within a single run of a program, wait to accumulate this many samples ++# (possibly across multiple clear_boards/games) before saving the data. ++# Can help to avoid writing too many small files to disk, especially when GTP is used ++# in a way that clears the board very frequently (e.g. gtp2ogs pooled manager). ++# autoAvoidRepeatSaveChunkSize = 200 ++ ++# =========================================================================== ++# Avoid SGF patterns ++# =========================================================================== ++# The parameters in this section provide a way to avoid moves that follow ++# specific patterns based on a set of SGF files loaded upon startup. ++# This is basically the same as the above "Automatic avoid patterns" section ++# above except you supply your own SGF files to avoid moves from. ++# Uncomment them to use this feature. Additionally, if the SGF file ++# contains the string %SKIP% in a comment on a move, that move will be ++# ignored for this purpose. ++ ++# Load SGF files from this directory when the engine is started ++# (only on startup, will not reload unless engine is restarted) ++# avoidSgfPatternDirs = path/to/directory/with/sgfs/ ++# You can also surround the file path in double quotes if the file path contains trailing spaces or hash signs. ++# Within double quotes, backslashes are escape characters. ++# avoidSgfPatternDirs = "path/to/directory/with/sgfs/" ++ ++# Penalize this much utility per matching move. ++# Set this negative if you instead want to favor SGF patterns instead of ++# penalizing them. This number does not need to be large, even 0.001 will ++# make a difference. Values that are too large may lead to bad play. ++# avoidSgfPatternUtility = 0.001 ++ ++# Optional - load only the newest this many files ++# avoidSgfPatternMaxFiles = 20 ++ ++# Optional - Penalty is multiplied by this per each older SGF file, so that ++# old SGF files matter less than newer ones. ++# avoidSgfPatternLambda = 0.90 ++ ++# Optional - pay attention only to moves made by players with this name. ++# For example, set it to the name that your bot's past games will show up ++# as in the SGF, so that the bot will only avoid repeating moves that itself ++# made in past games, not the moves that its opponents made. ++# avoidSgfPatternAllowedNames = my-ogs-bot-name1,my-ogs-bot-name2 ++ ++# Optional - Ignore moves in SGF files that occurred before this turn number. ++# avoidSgfPatternMinTurnNumber = 0 ++ ++# For more avoid patterns: ++# You can also specify a second set of parameters, and a third, fourth, ++# etc. by numbering 2,3,4,... ++# ++# avoidSgf2PatternDirs = ... ++# avoidSgf2PatternUtility = ... ++# avoidSgf2PatternMaxFiles = ... ++# avoidSgf2PatternLambda = ... ++# avoidSgf2PatternAllowedNames = ... ++# avoidSgf2PatternMinTurnNumber = ... ++ +diff --git a/python/selfplay/match_example.cfg b/python/selfplay/match_example.cfg +new file mode 100644 +index 00000000..945ac44e +--- /dev/null ++++ b/python/selfplay/match_example.cfg +@@ -0,0 +1,228 @@ ++# Example config for C++ match runner ++# This is an example template config for the "match" subcommand of KataGo. e.g: ++# ./katago match -config-file configs/match_example.cfg -log-file match.log -sgf-output-dir match_sgfs/ ++# ++# On a good GPU, the match subcommand enables testing of KataGo nets against each other ++# or a net against itself with different parameters vastly faster than anything ++# else, because multiple games can share GPU batching. ++# ++# Beware however of using this to test differing numbers of threads or test time-based search limits. ++# Because many games will be run simultaneously, they will compete from each other for compute power, ++# and although the total will run much faster than if you had run them one by one, you might also get ++# substantially different results not reflective of the strength of a configuration when run in a real ++# match setting, on a machine by itself. For fixed numbers of visits or playouts instead of fixed time, ++# and with numSearchThreads = 1, there should be no problem though, because then the compute time has ++# no influence on the result of the computation. ++# ++# See gtp config for descriptions of most of these params. ++# ++# For almost any parameter in this config that is related to a bot, rather than to the match as a whole ++# (so, visits, search parameters, model files, etc. but NOT the rules, max games, log info, etc) ++# you can specify them differentially between different bots by appending the index of the bot. ++# For example, if you were testing different numbers of visits, you could try: ++# ++# numBots = 3 ++# botName0 = lowVisits ++# botName1 = midVisits ++# botName2 = highVisits ++# ++# maxVisits0 = 100 ++# maxVisits1 = 300 ++# maxVisits2 = 1000 ++# ++# Or, if you were testing different neural nets, with different search configurations, you could do: ++# ++# nnModelFile0 = path/to/first/model/file.bin.gz ++# nnModelFile1 = path/to/second/model/file.bin.gz ++# ++# And specify different search parameters for them if you wanted: ++# cpuctExploration0 = 1.5 ++# cpuctExploration1 = 1.3 ++ ++# Logs------------------------------------------------------------------------------------ ++ ++logSearchInfo = false ++logMoves = false ++logGamesEvery = 50 ++logToStdout = true ++ ++# Bots------------------------------------------------------------------------------------- ++# For multiple bots, you can specify their names as botName0,botName1, etc. ++# If the bots are using different models, specify nnModelFile0, nnModelFile1, etc. ++ ++numBots=1 ++botName=FOO ++nnModelFile=PATH_TO_MODEL ++ ++# These bots will not play each other, but will still be opponents for other bots ++# secondaryBots = 0,1,3 ++# Only these bots will actually play games. Useful if you have a config file with many more ++# bots defined but you want to only selectively enable a few. ++# includeBots = 0,2,6 ++ ++# Specify extra pairings of bots to play. ++# extraPairs = 0-4,1-4 ++# Uncomment and set this to true if you don't want extraPairs to be mirrored by color. ++# (i.e. 0-4 should mean 0 plays black and 4 plays white only, rather than having two games with swapped colors). ++# extraPairsAreOneSidedBW = false ++ ++# Match----------------------------------------------------------------------------------- ++numGameThreads=8 # How many games to run in parallel at a time? ++ ++numGamesTotal=1000000 ++maxMovesPerGame=1200 ++ ++allowResignation = true ++resignThreshold = -0.95 ++resignConsecTurns = 6 ++ ++# Rules------------------------------------------------------------------------------------ ++# See https://lightvector.github.io/KataGo/rules.html for a description of the rules. ++ ++koRules = SIMPLE,POSITIONAL,SITUATIONAL ++scoringRules = AREA,TERRITORY ++taxRules = NONE,SEKI,ALL ++multiStoneSuicideLegals = false,true ++hasButtons = false,true ++ ++bSizes = 19,13,9 ++bSizeRelProbs = 90,5,5 ++ ++# If you want to specify exact distributions of boards, including rectangles, specify this instead ++# of bSizes. E.g. to randomize between a 7x5, a 9x12, and a 10x10 board with probabilities 25%, 25%, 50%: ++# bSizesXY=7-5,9-12,10-10 ++# bSizeRelProbs = 1,1,2 ++ ++komiAuto = True # Automatically adjust komi to what the neural nets think are fair ++# komiMean = 7.5 # Specify explicit komi ++# policyInitAreaProp = 0 ++# compensateAfterPolicyInitProb = 1.0 # Additionally make komi fair this often after the high-temperature moves. ++# policyInitAreaTemperature = 1 ++handicapProb = 0.0 ++handicapCompensateKomiProb = 1.0 ++# numExtraBlackFixed = 3 # When playing handicap games, always use exactly this many extra black moves ++ ++# Search limits----------------------------------------------------------------------------------- ++ ++maxVisits = 500 ++# maxPlayouts = 300 ++# maxTime = 60 ++ ++numSearchThreads = 1 ++ ++# GPU Settings------------------------------------------------------------------------------- ++ ++nnMaxBatchSize = 32 ++nnCacheSizePowerOfTwo = 21 ++nnMutexPoolSizePowerOfTwo = 17 ++nnRandomize = true ++ ++ ++# How many threads should there be to feed positions to the neural net? ++# Server threads are indexed 0,1,...(n-1) for the purposes of the below GPU settings arguments ++# that specify which threads should use which GPUs. ++# NOTE: This parameter is probably ONLY useful if you have multiple GPUs, since each GPU will need a thread. ++# If you're tuning single-GPU performance, use numSearchThreads instead. ++numNNServerThreadsPerModel = 1 ++ ++ ++# TENSORRT GPU settings-------------------------------------- ++# These only apply when using the TENSORRT version of KataGo. ++ ++# IF USING ONE GPU: optionally uncomment and change this if the GPU you want to use turns out to be not device 0 ++# trtDeviceToUse = 0 ++ ++# IF USING TWO GPUS: Uncomment these two lines (AND set numNNServerThreadsPerModel above): ++# trtDeviceToUseThread0 = 0 # change this if the first GPU you want to use turns out to be not device 0 ++# trtDeviceToUseThread1 = 1 # change this if the second GPU you want to use turns out to be not device 1 ++ ++# IF USING THREE GPUS: Uncomment these three lines (AND set numNNServerThreadsPerModel above): ++# trtDeviceToUseThread0 = 0 # change this if the first GPU you want to use turns out to be not device 0 ++# trtDeviceToUseThread1 = 1 # change this if the second GPU you want to use turns out to be not device 1 ++# trtDeviceToUseThread2 = 2 # change this if the third GPU you want to use turns out to be not device 2 ++ ++# You can probably guess the pattern if you have four, five, etc. GPUs. ++ ++ ++# CUDA GPU settings-------------------------------------- ++# For the below, "model" refers to a neural net, from the nnModelFile parameter(s) above. ++# cudaGpuToUse = 0 #use gpu 0 for all server threads (numNNServerThreadsPerModel) unless otherwise specified per-model or per-thread-per-model ++# cudaGpuToUseModel0 = 3 #use gpu 3 for model 0 for all threads unless otherwise specified per-thread for this model ++# cudaGpuToUseModel1 = 2 #use gpu 2 for model 1 for all threads unless otherwise specified per-thread for this model ++# cudaGpuToUseModel0Thread0 = 3 #use gpu 3 for model 0, server thread 0 ++# cudaGpuToUseModel0Thread1 = 2 #use gpu 2 for model 0, server thread 1 ++ ++# cudaUseFP16 = auto ++# cudaUseNHWC = auto ++ ++ ++# OpenCL GPU settings-------------------------------------- ++# These only apply when using OpenCL as the backend for inference. ++# (For GTP, we only ever have one model, when playing matches, we might have more than one, see match_example.cfg) ++ ++# Default behavior is just to always use gpu 0, you will want to uncomment and adjust one or more of these lines ++# to take advantage of a multi-gpu machine ++# openclGpuToUse = 0 #use gpu 0 for all server threads (numNNServerThreadsPerModel) unless otherwise specified per-model or per-thread-per-model ++# openclGpuToUseModel0 = 3 #use gpu 3 for model 0 for all threads unless otherwise specified per-thread for this model ++# openclGpuToUseModel1 = 2 #use gpu 2 for model 1 for all threads unless otherwise specified per-thread for this model ++# openclGpuToUseModel0Thread0 = 3 #use gpu 3 for model 0, server thread 0 ++# openclGpuToUseModel0Thread1 = 2 #use gpu 2 for model 0, server thread 1 ++ ++# Uncomment to tune OpenCL for every board size separately, rather than only the largest possible size ++# openclReTunePerBoardSize = true ++ ++# openclUseFP16 = auto ++ ++ ++# Eigen-specific settings-------------------------------------- ++# These only apply when using the Eigen (pure CPU) version of KataGo. ++ ++# This is the number of CPU threads for evaluating the neural net on the Eigen backend. ++# It defaults to numSearchThreads. ++# numEigenThreadsPerModel = X ++ ++ ++# Root move selection and biases------------------------------------------------------------------------------ ++# Uncomment and edit any of the below values to change them from their default. ++# Values in this section can be specified per-bot as well ++ ++chosenMoveTemperatureEarly = 0.60 ++# chosenMoveTemperatureHalflife = 19 ++chosenMoveTemperature = 0.20 ++# chosenMoveSubtract = 0 ++# chosenMovePrune = 1 ++ ++# rootNumSymmetriesToSample = 1 ++ ++# useLcbForSelection = true ++# lcbStdevs = 5.0 ++# minVisitPropForLCB = 0.15 ++ ++# Internal params------------------------------------------------------------------------------ ++# Uncomment and edit any of the below values to change them from their default. ++# Values in this section can be specified per-bot as well ++ ++# winLossUtilityFactor = 1.0 ++# staticScoreUtilityFactor = 0.10 ++# dynamicScoreUtilityFactor = 0.30 ++# dynamicScoreCenterZeroWeight = 0.20 ++# dynamicScoreCenterScale = 0.75 ++# noResultUtilityForWhite = 0.0 ++# drawEquivalentWinsForWhite = 0.5 ++ ++# cpuctExploration = 0.9 ++# cpuctExplorationLog = 0.4 ++# fpuReductionMax = 0.2 ++# rootFpuReductionMax = 0.1 ++# fpuParentWeightByVisitedPolicy = true ++# valueWeightExponent = 0.25 ++# rootEndingBonusPoints = 0.5 ++# rootPruneUselessMoves = true ++# subtreeValueBiasFactor = 0.45 ++# subtreeValueBiasWeightExponent = 0.85 ++# useGraphSearch = true ++# rootPolicyOptimism = 0.2 ++# policyOptimism = 1.0 ++ ++# nodeTableShardsPowerOfTwo = 16 ++# numVirtualLossesPerThread = 1 +diff --git a/python/selfplay/msvcp140.dll b/python/selfplay/msvcp140.dll +new file mode 100644 +index 00000000..f7c342d1 +Binary files /dev/null and b/python/selfplay/msvcp140.dll differ +diff --git a/python/selfplay/msvcp140_1.dll b/python/selfplay/msvcp140_1.dll +new file mode 100644 +index 00000000..6f55c9d1 +Binary files /dev/null and b/python/selfplay/msvcp140_1.dll differ +diff --git a/python/selfplay/msvcp140_2.dll b/python/selfplay/msvcp140_2.dll +new file mode 100644 +index 00000000..b127e0c8 +Binary files /dev/null and b/python/selfplay/msvcp140_2.dll differ +diff --git a/python/selfplay/msvcp140_atomic_wait.dll b/python/selfplay/msvcp140_atomic_wait.dll +new file mode 100644 +index 00000000..b8fa2030 +Binary files /dev/null and b/python/selfplay/msvcp140_atomic_wait.dll differ +diff --git a/python/selfplay/msvcp140_codecvt_ids.dll b/python/selfplay/msvcp140_codecvt_ids.dll +new file mode 100644 +index 00000000..7549b2b5 +Binary files /dev/null and b/python/selfplay/msvcp140_codecvt_ids.dll differ +diff --git a/python/selfplay/synchronous_loop.bat b/python/selfplay/synchronous_loop.bat +new file mode 100644 +index 00000000..a8e01370 +--- /dev/null ++++ b/python/selfplay/synchronous_loop.bat +@@ -0,0 +1,77 @@ ++@echo off ++setlocal EnableDelayedExpansion ++ ++set "-o" "pipefail" ++IF [[ "%#%" LSS "5" "]]" ( ++ echo "Usage: %~0 NAMEPREFIX BASEDIR TRAININGNAME MODELKIND USEGATING" ++ echo "Assumes katago is already built in the 'cpp' directory of the KataGo repo and the executable is present at cpp\katago." ++ echo "NAMEPREFIX string prefix for this training run, try to pick something globally unique. Will be displayed to users when KataGo loads the model." ++ echo "BASEDIR containing selfplay data and models and related directories" ++ echo "TRANINGNAME name to prefix models with, specific to this training daemon" ++ echo "MODELKIND what size model to train, like 'b10c128', see %CD%\..\modelconfigs.py" ++ echo "USEGATING = 1 to use gatekeeper, 0 to not use gatekeeper" ++ exit "0" ++) ++SET "NAMEPREFIX=%~1" ++shift ++SET "%~1BASEDIRRAW=%~1" ++shift ++SET "%~1%~1TRAININGNAME=%~1" ++shift ++SET "%~1%~1%~1MODELKIND=%~1" ++shift ++SET "%~1%~1%~1%~1USEGATING=%~1" ++shift ++SET _INTERPOLATION_0= ++FOR /f "delims=" %%a in ('realpath "$BASEDIRRAW"') DO (SET "_INTERPOLATION_0=!_INTERPOLATION_0! %%a") ++SET "%~1%~1%~1%~1%~1BASEDIR=!_INTERPOLATION_0:~1!" ++SET _INTERPOLATION_1= ++FOR /f "delims=" %%a in ('git rev-parse --show-toplevel') DO (SET "_INTERPOLATION_1=!_INTERPOLATION_1! %%a") ++SET _INTERPOLATION_2= ++FOR /f "delims=" %%a in ('realpath "$BASEDIRRAW"') DO (SET "_INTERPOLATION_2=!_INTERPOLATION_2! %%a") ++SET "%~1%~1%~1%~1%~1!_INTERPOLATION_2:~1!GITROOTDIR=!_INTERPOLATION_1:~1!" ++SET _INTERPOLATION_3= ++FOR /f "delims=" %%a in ('git rev-parse --show-toplevel') DO (SET "_INTERPOLATION_3=!_INTERPOLATION_3! %%a") ++SET _INTERPOLATION_4= ++FOR /f "delims=" %%a in ('realpath "$BASEDIRRAW"') DO (SET "_INTERPOLATION_4=!_INTERPOLATION_4! %%a") ++SET "%~1%~1%~1%~1%~1!_INTERPOLATION_4:~1!!_INTERPOLATION_3:~1!LOGSDIR="!BASEDIR!"\logs" ++SET _INTERPOLATION_5= ++FOR /f "delims=" %%a in ('git rev-parse --show-toplevel') DO (SET "_INTERPOLATION_5=!_INTERPOLATION_5! %%a") ++SET _INTERPOLATION_6= ++FOR /f "delims=" %%a in ('realpath "$BASEDIRRAW"') DO (SET "_INTERPOLATION_6=!_INTERPOLATION_6! %%a") ++SET "%~1%~1%~1%~1%~1!_INTERPOLATION_6:~1!!_INTERPOLATION_5:~1!!BASEDIR!SCRATCHDIR="!BASEDIR!"\shufflescratch" ++mkdir "-p" "!BASEDIR!" ++mkdir "-p" "!LOGSDIR!" ++mkdir "-p" "!SCRATCHDIR!" ++mkdir "-p" "!BASEDIR!"\selfplay ++mkdir "-p" "!BASEDIR!"\gatekeepersgf ++SET "!BASEDIR!NUM_GAMES_PER_CYCLE=500" ++SET "NUM_THREADS_FOR_SHUFFLING=8" ++SET "NUM_TRAIN_SAMPLES_PER_EPOCH=100000" ++SET "MAX_TRAIN_PER_DATA=8" ++SET "NUM_TRAIN_SAMPLES_PER_SWA=80000" ++SET "BATCHSIZE=128" ++SET "SHUFFLE_MINROWS=100000" ++SET "MAX_TRAIN_SAMPLES_PER_CYCLE=500000" ++SET "TAPER_WINDOW_SCALE=50000" ++SET "SHUFFLE_KEEPROWS=600000" ++SET "SELFPLAY_CONFIG="!GITROOTDIR!"\cpp\configs\training\selfplay1.cfg" ++SET "!GITROOTDIR!GATING_CONFIG="!GITROOTDIR!"\cpp\configs\training\gatekeeper1.cfg" ++SET _INTERPOLATION_7= ++FOR /f "delims=" %%a in ('date "+%Y%m%d-%H%M%S"') DO (SET "_INTERPOLATION_7=!_INTERPOLATION_7! %%a") ++SET "!GITROOTDIR!!GITROOTDIR!DATE_FOR_FILENAME=!_INTERPOLATION_7:~1!" ++SET _INTERPOLATION_8= ++FOR /f "delims=" %%a in ('date "+%Y%m%d-%H%M%S"') DO (SET "_INTERPOLATION_8=!_INTERPOLATION_8! %%a") ++SET "!GITROOTDIR!!GITROOTDIR!!_INTERPOLATION_8:~1!DATED_ARCHIVE=!BASEDIR!"/scripts/dated/"!DATE_FOR_FILENAME!" ++mkdir "-p" "!DATED_ARCHIVE!" ++COPY "!GITROOTDIR!"/python/*.py "!GITROOTDIR!"/python/selfplay/*.sh "!DATED_ARCHIVE!" ++COPY "!GITROOTDIR!"\cpp\katago "!DATED_ARCHIVE!" ++COPY "!SELFPLAY_CONFIG!" "!DATED_ARCHIVE!"\selfplay.cfg ++COPY "!GATING_CONFIG!" "!DATED_ARCHIVE!"\gatekeeper.cfg ++git "show" "--no-patch" "--no-color" REM UNKNOWN: {"type":"Redirect","op":{"text":">","type":"great"},"file":{"text":"\"$DATED_ARCHIVE\"/version.txt","expansion":[{"loc":{"start":1,"end":14},"parameter":"DATED_ARCHIVE","type":"ParameterExpansion"}],"type":"Word"}} ++git "diff" "--no-color" REM UNKNOWN: {"type":"Redirect","op":{"text":">","type":"great"},"file":{"text":"\"$DATED_ARCHIVE\"/diff.txt","expansion":[{"loc":{"start":1,"end":14},"parameter":"DATED_ARCHIVE","type":"ParameterExpansion"}],"type":"Word"}} ++git "diff" "--staged" "--no-color" REM UNKNOWN: {"type":"Redirect","op":{"text":">","type":"great"},"file":{"text":"\"$DATED_ARCHIVE\"/diffstaged.txt","expansion":[{"loc":{"start":1,"end":14},"parameter":"DATED_ARCHIVE","type":"ParameterExpansion"}],"type":"Word"}} ++cd "!DATED_ARCHIVE!" ++set "-x" ++REM UNKNOWN: 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ME\"","expansion":[{"loc":{"start":1,"end":13},"parameter":"TRAININGNAME","type":"ParameterExpansion"}],"type":"Word"},{"text":"\"$MODELKIND\"","expansion":[{"loc":{"start":1,"end":10},"parameter":"MODELKIND","type":"ParameterExpansion"}],"type":"Word"},{"text":"\"$BATCHSIZE\"","expansion":[{"loc":{"start":1,"end":10},"parameter":"BATCHSIZE","type":"ParameterExpansion"}],"type":"Word"},{"text":"main","type":"Word"},{"text":"-samples-per-epoch","type":"Word"},{"text":"\"$NUM_TRAIN_SAMPLES_PER_EPOCH\"","expansion":[{"loc":{"start":1,"end":28},"parameter":"NUM_TRAIN_SAMPLES_PER_EPOCH","type":"ParameterExpansion"}],"type":"Word"},{"text":"-swa-period-samples","type":"Word"},{"text":"\"$NUM_TRAIN_SAMPLES_PER_SWA\"","expansion":[{"loc":{"start":1,"end":26},"parameter":"NUM_TRAIN_SAMPLES_PER_SWA","type":"ParameterExpansion"}],"type":"Word"},{"text":"-quit-if-no-data","type":"Word"},{"text":"-stop-when-train-bucket-limited","type":"Word"},{"text":"-no-repeat-files","type":"Word"},{"text":"-max-train-bucket-per-new-data","type":"Word"},{"text":"\"$MAX_TRAIN_PER_DATA\"","expansion":[{"loc":{"start":1,"end":19},"parameter":"MAX_TRAIN_PER_DATA","type":"ParameterExpansion"}],"type":"Word"},{"text":"-max-train-bucket-size","type":"Word"},{"text":"\"$MAX_TRAIN_SAMPLES_PER_CYCLE\"","expansion":[{"loc":{"start":1,"end":28},"parameter":"MAX_TRAIN_SAMPLES_PER_CYCLE","type":"ParameterExpansion"}],"type":"Word"}]},{"type":"Command","name":{"text":"echo","type":"Word"},"suffix":[{"text":"Export","type":"Word"}]},{"type":"Subshell","list":{"type":"CompoundList","commands":[{"type":"Pipeline","commands":[{"type":"Command","name":{"text":"time","type":"Word"},"suffix":[{"text":"./export_model_for_selfplay.sh","type":"Word"},{"text":"\"$NAMEPREFIX\"","expansion":[{"loc":{"start":1,"end":11},"parameter":"NAMEPREFIX","type":"ParameterExpansion"}],"type":"Word"},{"text":"\"$BASEDIR\"","expansion":[{"loc":{"start":1,"end":8},"parameter":"BASEDIR","type":"ParameterExpansion"}],"type":"Word"},{"text":"\"$USEGATING\"","expansion":[{"loc":{"start":1,"end":10},"parameter":"USEGATING","type":"ParameterExpansion"}],"type":"Word"}]},{"type":"Command","name":{"text":"tee","type":"Word"},"suffix":[{"text":"-a","type":"Word"},{"text":"\"$BASEDIR\"/logs/outexport.txt","expansion":[{"loc":{"start":1,"end":8},"parameter":"BASEDIR","type":"ParameterExpansion"}],"type":"Word"}]}]}]}}]}} ++exit "0" +\ No newline at end of file +diff --git a/python/selfplay/synchronous_loop.sh b/python/selfplay/synchronous_loop.sh +index 46cfbc3d..33f4be30 100755 +--- a/python/selfplay/synchronous_loop.sh ++++ b/python/selfplay/synchronous_loop.sh +@@ -12,7 +12,7 @@ set -o pipefail + if [[ $# -lt 5 ]] + then + echo "Usage: $0 NAMEPREFIX BASEDIR TRAININGNAME MODELKIND USEGATING" +- echo "Assumes katago is already built in the 'cpp' directory of the KataGo repo and the executable is present at cpp/katago." ++ echo "Assumes katago is already built in the 'cpp' directory of the KataGo repo and the executable is present at cpp/katago.exe." + echo "NAMEPREFIX string prefix for this training run, try to pick something globally unique. Will be displayed to users when KataGo loads the model." + echo "BASEDIR containing selfplay data and models and related directories" + echo "TRANINGNAME name to prefix models with, specific to this training daemon" +@@ -44,39 +44,27 @@ mkdir -p "$BASEDIR"/selfplay + mkdir -p "$BASEDIR"/gatekeepersgf + + # Parameters for the training run +-# NOTE: You may want to adjust the below numbers. +-# NOTE: You probably want to edit settings in cpp/configs/training/selfplay1.cfg +-# NOTE: You probably want to edit settings in cpp/configs/training/gatekeeper1.cfg +-# Such as what board sizes and rules, you want to learn, number of visits to use, etc. +- +-# Also, the parameters below are relatively small, and probably +-# good for less powerful hardware and tighter turnaround during very early training, but if +-# you have strong hardware or are later into a run you may want to reduce the overhead by scaling +-# these numbers up and doing more games and training per cycle, exporting models less frequently, etc. +- +-NUM_GAMES_PER_CYCLE=500 # Every cycle, play this many games ++NUM_GAMES_PER_CYCLE=90 # Every cycle, play this many games + NUM_THREADS_FOR_SHUFFLING=8 + NUM_TRAIN_SAMPLES_PER_EPOCH=100000 # Training will proceed in chunks of this many rows, subject to MAX_TRAIN_PER_DATA. + MAX_TRAIN_PER_DATA=8 # On average, train only this many times on each data row. Larger numbers may cause overfitting. + NUM_TRAIN_SAMPLES_PER_SWA=80000 # Stochastic weight averaging frequency. + BATCHSIZE=128 # For lower-end GPUs 64 or smaller may be needed to avoid running out of GPU memory. +-SHUFFLE_MINROWS=100000 # Require this many rows at the very start before beginning training. ++SHUFFLE_MINROWS=1 # Require this many rows at the very start before beginning training. + MAX_TRAIN_SAMPLES_PER_CYCLE=500000 # Each cycle will do at most this many training steps. + TAPER_WINDOW_SCALE=50000 # Parameter setting the scale at which the shuffler will make the training window grow sublinearly. + SHUFFLE_KEEPROWS=600000 # Needs to be larger than MAX_TRAIN_SAMPLES_PER_CYCLE, so the shuffler samples enough rows each cycle for the training to use. + + # Paths to the selfplay and gatekeeper configs that contain board sizes, rules, search parameters, etc. +-# See cpp/configs/training/README.md for some notes on other selfplay configs. +-SELFPLAY_CONFIG="$GITROOTDIR"/cpp/configs/training/selfplay1.cfg +-GATING_CONFIG="$GITROOTDIR"/cpp/configs/training/gatekeeper1.cfg ++SELFPLAY_CONFIG="$GITROOTDIR"/cpp/configs/training/selfplay1_maxsize9.cfg ++GATING_CONFIG="$GITROOTDIR"/cpp/configs/training/gatekeeper1_maxsize9.cfg + + # Copy all the relevant scripts and configs and the katago executable to a dated directory. +-# For archival and logging purposes - you can look back and see exactly the python code on a particular date + DATE_FOR_FILENAME=$(date "+%Y%m%d-%H%M%S") + DATED_ARCHIVE="$BASEDIR"/scripts/dated/"$DATE_FOR_FILENAME" + mkdir -p "$DATED_ARCHIVE" + cp "$GITROOTDIR"/python/*.py "$GITROOTDIR"/python/selfplay/*.sh "$DATED_ARCHIVE" +-cp "$GITROOTDIR"/cpp/katago "$DATED_ARCHIVE" ++cp "$GITROOTDIR"/cpp/katago.exe "$DATED_ARCHIVE"/katago.exe # Ensure the executable is copied + cp "$SELFPLAY_CONFIG" "$DATED_ARCHIVE"/selfplay.cfg + cp "$GATING_CONFIG" "$DATED_ARCHIVE"/gatekeeper.cfg + git show --no-patch --no-color > "$DATED_ARCHIVE"/version.txt +@@ -91,15 +79,13 @@ set -x + while true + do + echo "Gatekeeper" +- time ./katago gatekeeper -rejected-models-dir "$BASEDIR"/rejectedmodels -accepted-models-dir "$BASEDIR"/models/ -sgf-output-dir "$BASEDIR"/gatekeepersgf/ -test-models-dir "$BASEDIR"/modelstobetested/ -config "$DATED_ARCHIVE"/gatekeeper.cfg -quit-if-no-nets-to-test | tee -a "$BASEDIR"/gatekeepersgf/stdout.txt ++ time "C:\Users\chang\Downloads\katago-v1.15.1-opencl-windows-x64\katago.exe" gatekeeper -rejected-models-dir "$BASEDIR"/rejectedmodels -accepted-models-dir "$BASEDIR"/models/ -sgf-output-dir "$BASEDIR"/gatekeepersgf/ -test-models-dir "$BASEDIR"/modelstobetested/ -config "$DATED_ARCHIVE"/gatekeeper.cfg -quit-if-no-nets-to-test | tee -a "$BASEDIR"/gatekeepersgf/stdout.txt + + echo "Selfplay" +- time ./katago selfplay -max-games-total "$NUM_GAMES_PER_CYCLE" -output-dir "$BASEDIR"/selfplay -models-dir "$BASEDIR"/models -config "$DATED_ARCHIVE"/selfplay.cfg | tee -a "$BASEDIR"/selfplay/stdout.txt ++ time "C:\Users\chang\Downloads\katago-v1.15.1-opencl-windows-x64\katago.exe" selfplay -max-games-total "$NUM_GAMES_PER_CYCLE" -output-dir "$BASEDIR"/selfplay -models-dir "$BASEDIR"/models -config "$DATED_ARCHIVE"/selfplay.cfg | tee -a "$BASEDIR"/selfplay/stdout.txt + + echo "Shuffle" + ( +- # Skip validate since peeling off 5% of data is actually a bit too chunky and discrete when running at a small scale, and validation data +- # doesn't actually add much to debugging a fast-changing RL training. + time SKIP_VALIDATE=1 ./shuffle.sh "$BASEDIR" "$SCRATCHDIR" "$NUM_THREADS_FOR_SHUFFLING" "$BATCHSIZE" -min-rows "$SHUFFLE_MINROWS" -keep-target-rows "$SHUFFLE_KEEPROWS" -taper-window-scale "$TAPER_WINDOW_SCALE" | tee -a "$BASEDIR"/logs/outshuffle.txt + ) + +diff --git a/python/selfplay/vcruntime140.dll b/python/selfplay/vcruntime140.dll +new file mode 100644 +index 00000000..5d5b81dc +Binary files /dev/null and b/python/selfplay/vcruntime140.dll differ +diff --git a/python/selfplay/vcruntime140_1.dll b/python/selfplay/vcruntime140_1.dll +new file mode 100644 +index 00000000..3a02196f +Binary files /dev/null and b/python/selfplay/vcruntime140_1.dll differ +diff --git "a/python/selfplay/\344\277\261\344\271\220\351\203\250AI.cfg" "b/python/selfplay/\344\277\261\344\271\220\351\203\250AI.cfg" +new file mode 100644 +index 00000000..9b9287b0 +--- /dev/null ++++ "b/python/selfplay/\344\277\261\344\271\220\351\203\250AI.cfg" +@@ -0,0 +1,721 @@ ++# Configuration for KataGo C++ GTP engine ++ ++# Run the program using: `./katago.exe gtp` ++ ++# In this example config, when a parameter is given as a commented out value, ++# that value also is the default value, unless described otherwise. You can ++# uncomment it (remove the pound sign) and change it if you want. ++ ++# =========================================================================== ++# Running on an online server or in a real tournament or match ++# =========================================================================== ++# If you plan to run online or in a tournament, read through the "Rules" ++# section below for proper handling of komi, handicaps, end-of-game cleanup, ++# and other details. ++ ++# =========================================================================== ++# Notes about performance and memory usage ++# =========================================================================== ++# Important: For good performance, you will very likely want to tune the ++# "numSearchThreads" parameter in the Search limits section below! Run ++# "./katago benchmark" to test KataGo and to suggest a reasonable value ++# of this parameter. ++ ++# For multi-GPU systems, read "OpenCL GPU settings" or "CUDA GPU settings". ++# ++# When using OpenCL, verify that KataGo picks the correct device! Some systems ++# may have both an Intel CPU OpenCL and GPU OpenCL. If # KataGo picks the wrong ++# one, correct this by specifying "openclGpuToUse". ++# ++# Consider adjusting "maxVisits", "ponderingEnabled", "resignThreshold", and ++# other parameters depending on your intended usage. ++ ++# =========================================================================== ++# Command-line usage ++# =========================================================================== ++# All of the below values may be set or overridden via command-line arguments: ++# ++# -override-config KEY=VALUE,KEY=VALUE,... ++ ++# =========================================================================== ++# Logs and files ++# =========================================================================== ++# This section defines where and what logging information is produced. ++ ++# Each run of KataGo will log to a separate file in this dir. ++# This is the default. ++logDir = gtp_logs ++# Uncomment and specify this instead of logDir to write separate dated subdirs ++# logDirDated = gtp_logs ++# Uncomment and specify this instead of logDir to log to only a single file ++# logFile = gtp.log ++ ++# Logging options ++logAllGTPCommunication = true ++logSearchInfo = true ++logSearchInfoForChosenMove = false ++logToStderr = false ++ ++# KataGo will display some info to stderr on GTP startup ++# Uncomment the next line and set it to false to suppress that and remain silent ++# startupPrintMessageToStderr = true ++ ++# Write information to stderr, for use in things like malkovich chat to OGS. ++ ogsChatToStderr = true ++ ++# Uncomment and set this to a directory to override where openCLTuner files ++# and other cached data is written. By default it saves into a subdir of the ++# current directory on windows, and a subdir of ~/.katago on Linux. ++# homeDataDir = PATH_TO_DIRECTORY ++ ++# =========================================================================== ++# Analysis ++# =========================================================================== ++# This section configures analysis settings. ++# ++# The maximum number of moves after the first move displayed in variations ++# from analysis commands like kata-analyze or lz-analyze. ++# analysisPVLen = 15 ++ ++# Report winrates for chat and analysis as (BLACK|WHITE|SIDETOMOVE). ++# Most GUIs and analysis tools will expect SIDETOMOVE. ++# reportAnalysisWinratesAs = SIDETOMOVE ++ ++# Extra noise for wider exploration. Large values will force KataGo to ++# analyze a greater variety of moves than it normally would. ++# An extreme value like 1 distributes playouts across every move on the board, ++# even very bad moves. ++# Affects analysis only, does not affect play. ++# analysisWideRootNoise = 0.04 ++ ++# Try to limit the effect of possible bad or bogus move sequences in the ++# history leading to this position from affecting KataGo's move predictions. ++# analysisIgnorePreRootHistory = true ++ ++# =========================================================================== ++# Rules ++# =========================================================================== ++# This section configures the scoring and playing rules. Rules can also be ++# changed mid-run by issuing custom GTP commands. ++# ++# See https://lightvector.github.io/KataGo/rules.html for rules details. ++# ++# See https://github.com/lightvector/KataGo/blob/master/docs/GTP_Extensions.md ++# for GTP commands. ++ ++# Specify the rules as a string. ++# Some legal values include: ++# chinese, japanese, korean, aga, chinese-ogs, new-zealand, stone-scoring, ++# ancient-territory, bga, aga-button ++# ++# For some human rulesets that require complex adjudication in tricky cases ++# (e.g. japanese, korean) KataGo may not precisely match the ruleset in such ++# cases but will do its best. ++rules = tromp-taylor ++ ++# By default, the "rules" parameter is used, but if you comment it out and ++# uncomment one option in each of the sections below, you can specify an ++# arbitrary combination of individual rules. ++ ++# koRule = SIMPLE # Simple ko rules (triple ko = no result) ++# koRule = POSITIONAL # Positional superko ++# koRule = SITUATIONAL # Situational superko ++ ++# scoringRule = AREA # Area scoring ++# scoringRule = TERRITORY # Territory scoring (special computer-friendly territory rules) ++ ++# taxRule = NONE # All surrounded empty points are scored ++# taxRule = SEKI # Eyes in seki do NOT count as points ++# taxRule = ALL # All groups are taxed up to 2 points for the two eyes needed to live ++ ++# Is multiple-stone suicide legal? (Single-stone suicide is always illegal). ++# multiStoneSuicideLegal = false ++# multiStoneSuicideLegal = true ++ ++# "Button go" - the first pass when area scoring awards 0.5 points and does ++# not count for ending the game. ++# Allows area scoring rulesets that have far simpler rules to achieve the same ++# final scoring precision and reward for precise play as territory scoring. ++# hasButton = false ++# hasButton = true ++ ++# Is this a human ruleset where it's okay to pass before having physically ++# captured and removed all dead stones? ++# friendlyPassOk = false ++# friendlyPassOk = true ++ ++# How handicap stones in handicap games are compensated ++# whiteHandicapBonus = 0 # White gets no compensation for black's handicap stones (Tromp-taylor, NZ, JP) ++# whiteHandicapBonus = N-1 # White gets N-1 points for black's N handicap stones (AGA) ++# whiteHandicapBonus = N # White gets N points for black's N handicap stones (Chinese) ++ ++# ------------------------------ ++# Other rules hacks ++# ------------------------------ ++# Uncomment and change to adjust what board size KataGo uses upon startup ++# by default when GTP doesn't specify. ++# defaultBoardSize = 19 ++ ++# By default, Katago will use the komi that the GUI or GTP controller tries to set. ++# Uncomment and set this to have KataGo ignore the controller and always use this komi. ++# ignoreGTPAndForceKomi = 7 ++ ++# =========================================================================== ++# Bot behavior ++# =========================================================================== ++ ++# ------------------------------ ++# Resignation ++# ------------------------------ ++ ++# Resignation occurs if for at least resignConsecTurns in a row, the ++# winLossUtility (on a [-1,1] scale) is below resignThreshold. ++allowResignation = true ++resignThreshold = -0.90 ++resignConsecTurns = 3 ++ ++# By default, KataGo may resign games that it is confidently losing even if they ++# are very close in score. Uncomment and set this to avoid resigning games ++# if the estimated difference is points is less than or equal to this. ++# resignMinScoreDifference = 10 ++ ++# ------------------------------ ++# Handicap ++# ------------------------------ ++# Assume that if black makes many moves in a row right at the start of the ++# game, then the game is a handicap game. This is necessary on some servers ++# and for some GUIs and also when initializing from many SGF files, which may ++# set up a handicap game using repeated GTP "play" commands for black rather ++# than GTP "place_free_handicap" commands; however, it may also lead to ++# incorrect understanding of komi if whiteHandicapBonus is used and a server ++# does not have such a practice. Uncomment and set to false to disable. ++# assumeMultipleStartingBlackMovesAreHandicap = true ++ ++# Makes katago dynamically adjust in handicap or altered-komi games to assume ++# based on those game settings that it must be stronger or weaker than the ++# opponent and to play accordingly. Greatly improves handicap strength by ++# biasing winrates and scores to favor appropriate safe/aggressive play. ++# Does NOT affect analysis (lz-analyze, kata-analyze, used by programs like ++# Lizzie) so analysis remains unbiased. Uncomment and set this to 0 to disable ++# this and make KataGo play the same always. ++# dynamicPlayoutDoublingAdvantageCapPerOppLead = 0.045 ++ ++# Instead of "dynamicPlayoutDoublingAdvantageCapPerOppLead", you can comment ++# that out and uncomment and set "playoutDoublingAdvantage" to a fixed value ++# from -3.0 to 3.0 that will not change dynamically. ++# ALSO affects analysis tools (lz-analyze, kata-analyze, used by e.g. Lizzie). ++# Negative makes KataGo behave as if it is much weaker than the opponent. ++# Positive makes KataGo behave as if it is much stronger than the opponent. ++# KataGo will adjust to favor safe/aggressive play as appropriate based on ++# the combination of who is ahead and how much stronger/weaker it thinks it is, ++# and report winrates and scores taking the strength difference into account. ++# ++# If this and "dynamicPlayoutDoublingAdvantageCapPerOppLead" are both set ++# then dynamic will be used for all games and this fixed value will be used ++# for analysis tools. ++# playoutDoublingAdvantage = 0.0 ++ ++# Uncomment one of these when using "playoutDoublingAdvantage" to enforce ++# that it will only apply when KataGo plays as the specified color and will be ++# negated when playing as the opposite color. ++# playoutDoublingAdvantagePla = BLACK ++# playoutDoublingAdvantagePla = WHITE ++ ++# ------------------------------ ++# Passing and cleanup ++# ------------------------------ ++# Make the bot never assume that its pass will end the game, even if passing ++# would end and "win" under Tromp-Taylor rules. Usually this is a good idea ++# when using it for analysis or playing on servers where scoring may be ++# implemented non-tromp-taylorly. Uncomment and set to false to disable. ++# conservativePass = true ++ ++# When using territory scoring, self-play games continue beyond two passes ++# with special cleanup rules that may be confusing for human players. This ++# option prevents the special cleanup phases from being reachable when using ++# the bot for GTP play. Uncomment and set to false to enable entering special ++# cleanup. For example, if you are testing it against itself, or against ++# another bot that has precisely implemented the rules documented at ++# https://lightvector.github.io/KataGo/rules.html ++# preventCleanupPhase = true ++ ++# ------------------------------ ++# Miscellaneous behavior ++# ------------------------------ ++# If the board is symmetric, search only one copy of each equivalent move. ++# Attempts to also account for ko/superko, will not theoretically perfect for ++# superko. Uncomment and set to false to disable. ++# rootSymmetryPruning = true ++ ++# Uncomment and set to true to avoid a particular joseki that some networks ++# misevaluate, and also to improve opening diversity versus some particular ++# other bots that like to play it all the time. ++# avoidMYTDaggerHack = false ++ ++# Prefer to avoid playing the same joseki in every corner of the board. ++# Uncomment to set to a specific value. See "Avoid SGF patterns" section. ++# By default: 0 (even games), 0.005 (handicap games) ++# avoidRepeatedPatternUtility = 0.0 ++ ++# Experimental logic to fight against mirror Go even with unfavorable komi. ++# Uncomment to set to a specific value to use for both playing and analysis. ++# By default: true when playing via GTP, but false when analyzing. ++# antiMirror = true ++ ++# Enable some hacks that mitigate rare instances when passing messes up deeper searches. ++# enablePassingHacks = true ++ ++# Uncomment and set this to true to prevent bad or bogus move sequences ++# in the history leading to this position from affecting KataGo's move choices. ++# Same as analysisIgnorePreRootHistory (see above) but applies to actual play. ++# You can enable this if KataGo is being asked to play from positions that it did not ++# choose the moves to reach. ++# ignorePreRootHistory = false ++ ++# =========================================================================== ++# Search limits ++# =========================================================================== ++ ++# Terminology: ++# "Playouts" is the number of new playouts of search performed each turn. ++# "Visits" is the same as "Playouts" but also counts search performed on ++# previous turns that is still applicable to this turn. ++# "Time" is the time in seconds. ++ ++# For example, if KataGo searched 200 nodes on the previous turn, and then ++# after the opponent's reply, 50 nodes of its search tree was still valid, ++# then a visit limit of 200 would allow KataGo to search 150 new nodes ++# (for a final tree size of 200 nodes), whereas a playout limit of of 200 ++# would allow KataGo to search 200 nodes (for a final tree size of 250 nodes). ++ ++# Additionally, KataGo may also move before than the limit in order to ++# obey time controls (e.g. byo-yomi, etc) if the GTP controller has ++# told KataGo that the game has is being played with a given time control. ++ ++# Limits for search on the current turn. ++# If commented out or unspecified, the default is to have no limit. ++# maxVisits = 500 ++# maxPlayouts = 300 ++ maxTime = 1.0 ++ ++# Ponder on the opponent's turn? ++ponderingEnabled = true ++ ++# Limits for search when pondering on the opponent's turn. ++# If commented out or unspecified, the default is to have no limit. ++# Limiting the maximum time is recommended so that KataGo won't burn CPU/GPU ++# forever and/or run out of RAM if left unattended while pondering is enabled. ++# maxVisitsPondering = 5000 ++# maxPlayoutsPondering = 3000 ++maxTimePondering = 120.0 ++ ++ ++# ------------------------------ ++# Other search limits and behavior ++# ------------------------------ ++ ++# Approx number of seconds to buffer for lag for GTP time controls - will ++# move a bit faster assuming there is this much lag per move. ++lagBuffer = 1.0 ++ ++# YOU PROBABLY WANT TO TUNE THIS PARAMETER! ++# The number of threads to use when searching. On powerful GPUs the optimal ++# threads may be much higher than the number of CPU cores you have because ++# many threads are needed to feed efficient large batches to the GPU. ++# ++# Run "./katago benchmark" to tune this parameter and test the effect ++# of changes to any of other parameters. ++numSearchThreads = 12 ++ ++# Play a little faster if the opponent is passing, for human-friendliness. ++# Comment these out to disable them, such as if running a controlled match ++# where you are testing KataGo with fixed compute per move vs other bots. ++searchFactorAfterOnePass = 0.50 ++searchFactorAfterTwoPass = 0.25 ++ ++# Play a little faster if super-winning, for human-friendliness. ++# Comment these out to disable them, such as if running a controlled match ++# where you are testing KataGo with fixed compute per move vs other bots. ++searchFactorWhenWinning = 0.40 ++searchFactorWhenWinningThreshold = 0.95 ++ ++# =========================================================================== ++# GPU settings ++# =========================================================================== ++# This section configures GPU settings. ++# ++# Maximum number of positions to send to a single GPU at once. The default ++# value is roughly equal to numSearchThreads, but can be specified manually ++# if running out of memory, or using multiple GPUs that expect to share work. ++# nnMaxBatchSize = ++ ++# Controls the neural network cache size, which is the primary RAM/memory use. ++# KataGo will cache up to (2 ** nnCacheSizePowerOfTwo) many neural net ++# evaluations in case of transpositions in the tree. ++# Increase this to improve performance for searches with tens of thousands ++# of visits or more. Decrease this to limit memory usage. ++# If you're happy to do some math - each neural net entry takes roughly ++# 1.5KB, except when using whole-board ownership/territory ++# visualizations, where each entry will take roughly 3KB. The number of ++# entries is (2 ** nnCacheSizePowerOfTwo). (E.g. 2 ** 18 = 262144.) ++# You can compute roughly how much memory the cache will use based on this. ++# nnCacheSizePowerOfTwo = 20 ++ ++# Size of mutex pool for nnCache is (2 ** this). ++# nnMutexPoolSizePowerOfTwo = 16 ++ ++# Randomize board orientation when running neural net evals? Uncomment and ++# set to false to disable. ++# nnRandomize = true ++ ++# If provided, force usage of a specific seed for nnRandomize. ++# The default is to use a randomly generated seed. ++# nnRandSeed = abcdefg ++ ++# Uncomment and set to true to force GTP to use the maximum board size for ++# internal buffers for the neural net. This will make KataGo slower when ++# evaluating small boards, but will avoid a lengthy initialization time on every ++# change of board size due to having to re-size the neural net buffers on the GPU. ++# This can be useful for example, for OGS's persistent bot mode that uses a single ++# bot instance to handle multiple games and may thrash between different board sizes ++# if there are concurrent games of multiple sizes. ++# gtpForceMaxNNSize = false ++ ++# ------------------------------ ++# Multiple GPUs ++# ------------------------------ ++# Set this to the number of GPUs to use or that are available. ++# IMPORTANT: If more than 1, also uncomment the appropriate TensorRT ++# or CUDA or OpenCL section. ++# numNNServerThreadsPerModel = 1 ++ ++# ------------------------------ ++# TENSORRT GPU settings ++# ------------------------------ ++# These only apply when using the TENSORRT version of KataGo. ++ ++# For one GPU: optionally uncomment this option and change if the GPU to ++# use is not device 0. ++# trtDeviceToUse = 0 ++ ++# For two GPUs: Uncomment these options, AND set numNNServerThreadsPerModel above. ++# Also, change their values if the devices you want to use are not 0 and 1. ++# trtDeviceToUseThread0 = 0 ++# trtDeviceToUseThread1 = 1 ++ ++# For three GPUs: Uncomment these options, AND set numNNServerThreadsPerModel above. ++# Also, change their values if the devices you want to use are not 0 and 1 and 2. ++# trtDeviceToUseThread0 = 0 ++# trtDeviceToUseThread1 = 1 ++# trtDeviceToUseThread2 = 2 ++ ++# The pattern continues for additional GPUs. ++ ++# ------------------------------ ++# CUDA GPU settings ++# ------------------------------ ++# These only apply when using the CUDA version of KataGo. ++ ++# For one GPU: optionally uncomment and change this if the GPU you want to ++# use is not device 0 ++# cudaDeviceToUse = 0 ++ ++# For two GPUs: Uncomment these options, AND set numNNServerThreadsPerModel above. ++# Also, change their values if the devices you want to use are not 0 and 1. ++# cudaDeviceToUseThread0 = 0 ++# cudaDeviceToUseThread1 = 1 ++ ++# For three GPUs: Uncomment these options, AND set numNNServerThreadsPerModel above. ++# Also, change their values if the devices you want to use are not 0 and 1 and 2. ++# cudaDeviceToUseThread0 = 0 ++# cudaDeviceToUseThread1 = 1 ++# cudaDeviceToUseThread2 = 2 ++ ++# The pattern continues for additional GPUs. ++ ++# KataGo will automatically use FP16 or not based on the compute capability ++# of your NVIDIA GPU. If you want to try to force a particular behavior ++# you can uncomment these lines and change them to "true" or "false". ++# cudaUseFP16 = auto ++# cudaUseNHWC = auto ++ ++# ------------------------------ ++# OpenCL GPU settings ++# ------------------------------ ++# These only apply when using the OpenCL version of KataGo. ++ ++# Uncomment and set to true to tune OpenCL for every board size separately, ++# rather than only the largest possible size. ++# openclReTunePerBoardSize = false ++ ++# For one GPU: optionally uncomment and change this if the best device to use is guessed incorrectly. ++# The default behavior tries to guess the 'best' GPU or device on your system to use, usually it will be a good guess. ++# openclDeviceToUse = 0 ++ ++# For two GPUs: Uncomment these two lines and replace X and Y with the device ids of the devices you want to use. ++# It might NOT be 0 and 1, some computers will have many OpenCL devices. You can see what the devices are when ++# KataGo starts up - it should print or log all the devices it finds. ++# (AND also set numNNServerThreadsPerModel above) ++# openclDeviceToUseThread0 = X ++# openclDeviceToUseThread1 = Y ++ ++# For three GPUs: Uncomment these three lines and replace X and Y and Z with the device ids of the devices you want to use. ++# It might NOT be 0 and 1 and 2, some computers will have many OpenCL devices. You can see what the devices are when ++# KataGo starts up - it should print or log all the devices it finds. ++# (AND also set numNNServerThreadsPerModel above) ++# openclDeviceToUseThread0 = X ++# openclDeviceToUseThread1 = Y ++# openclDeviceToUseThread2 = Z ++ ++# The pattern continues for additional GPUs. ++ ++# KataGo will automatically use FP16 or not based on testing your GPU during ++# tuning. If you want to try to force a particular behavior though you can ++# uncomment this option and change it to "true" or "false". This is a fairly ++# blunt setting - more detailed settings are testable by rerunning the tuner ++# with various arguments (./katago tuner). ++# openclUseFP16 = auto ++ ++# ------------------------------ ++# Eigen-specific settings ++# ------------------------------ ++# These only apply when using the Eigen (pure CPU) version of KataGo. ++ ++# Number of CPU threads for evaluating the neural net on the Eigen backend. ++# ++# Default: numSearchThreads ++# numEigenThreadsPerModel = X ++ ++# =========================================================================== ++# Root move selection and biases ++# =========================================================================== ++# Uncomment and edit any of the below values to change them from their default. ++ ++# If provided, force usage of a specific seed for various random things in ++# the search. The default is to use a random seed. ++# searchRandSeed = hijklmn ++ ++# Temperature for the early game, randomize between chosen moves with ++# this temperature ++# chosenMoveTemperatureEarly = 0.5 ++ ++# Decay temperature for the early game by 0.5 every this many moves, ++# scaled with board size. ++# chosenMoveTemperatureHalflife = 19 ++ ++# At the end of search after the early game, randomize between chosen ++# moves with this temperature ++# chosenMoveTemperature = 0.10 ++ ++# Subtract this many visits from each move prior to applying ++# chosenMoveTemperature (unless all moves have too few visits) to downweight ++# unlikely moves ++# chosenMoveSubtract = 0 ++ ++# The same as chosenMoveSubtract but only prunes moves that fall below ++# the threshold. This setting does not affect chosenMoveSubtract. ++# chosenMovePrune = 1 ++ ++# Number of symmetries to sample (without replacement) and average at the root ++# rootNumSymmetriesToSample = 1 ++ ++# Using LCB for move selection? ++# useLcbForSelection = true ++ ++# How many stdevs a move needs to be better than another for LCB selection ++# lcbStdevs = 5.0 ++ ++# Only use LCB override when a move has this proportion of visits as the ++# top move. ++# minVisitPropForLCB = 0.15 ++ ++# =========================================================================== ++# Internal params ++# =========================================================================== ++# Uncomment and edit any of the below values to change them from their default. ++ ++# Scales the utility of winning/losing ++# winLossUtilityFactor = 1.0 ++ ++# Scales the utility for trying to maximize score ++# staticScoreUtilityFactor = 0.10 ++# dynamicScoreUtilityFactor = 0.30 ++ ++# Adjust dynamic score center this proportion of the way towards zero, ++# capped at a reasonable amount. ++# dynamicScoreCenterZeroWeight = 0.20 ++# dynamicScoreCenterScale = 0.75 ++ ++# The utility of getting a "no result" due to triple ko or other long cycle ++# in non-superko rulesets (-1 to 1) ++# noResultUtilityForWhite = 0.0 ++ ++# The number of wins that a draw counts as, for white. (0 to 1) ++# drawEquivalentWinsForWhite = 0.5 ++ ++# Exploration constant for mcts ++# cpuctExploration = 1.0 ++# cpuctExplorationLog = 0.45 ++ ++# Parameters that control exploring more in volatile positions, exploring ++# less in stable positions. ++# cpuctUtilityStdevPrior = 0.40 ++# cpuctUtilityStdevPriorWeight = 2.0 ++# cpuctUtilityStdevScale = 0.85 ++ ++# FPU reduction constant for mcts ++# fpuReductionMax = 0.2 ++# rootFpuReductionMax = 0.1 ++# fpuParentWeightByVisitedPolicy = true ++ ++# Parameters that control weighting of evals based on the net's own ++# self-reported uncertainty. ++# useUncertainty = true ++# uncertaintyExponent = 1.0 ++# uncertaintyCoeff = 0.25 ++ ++# Explore using optimistic policy ++# rootPolicyOptimism = 0.2 ++# policyOptimism = 1.0 ++ ++# Amount to apply a downweighting of children with very bad values relative ++# to good ones. ++# valueWeightExponent = 0.25 ++ ++# Slight incentive for the bot to behave human-like with regard to passing at ++# the end, filling the dame, not wasting time playing in its own territory, ++# etc., and not play moves that are equivalent in terms of points but a bit ++# more unfriendly to humans. ++# rootEndingBonusPoints = 0.5 ++ ++# Make the bot prune useless moves that are just prolonging the game to ++# avoid losing yet. ++# rootPruneUselessMoves = true ++ ++# Apply bias correction based on local pattern keys ++# subtreeValueBiasFactor = 0.45 ++# subtreeValueBiasWeightExponent = 0.85 ++ ++# Use graph search rather than tree search - identify and share search for ++# transpositions. ++# useGraphSearch = true ++ ++# How much to shard the node table for search synchronization ++# nodeTableShardsPowerOfTwo = 16 ++ ++# How many virtual losses to add when a thread descends through a node ++# numVirtualLossesPerThread = 1 ++ ++# Improve the quality of evals under heavy multithreading ++# useNoisePruning = true ++ ++# =========================================================================== ++# Automatic avoid patterns ++# =========================================================================== ++# The parameters in this section provide a way to bias away from moves that match ++# patterns that this instance of KataGo has played in previous games, by auto-saving ++# moves to a directory and then auto-loading and biasing against them each new game. ++# Uncomment them to use them. When using this feature, all parameters must be specified. ++ ++# Different board sizes are tracked separately, but all board sizes share the same ++# parameters by default. Every parameter *except* for autoAvoidRepeatDir and ++# autoAvoidRepeatSaveChunkSize can be overridden per board size, ++# e.g. "autoAvoidRepeatMinTurnNumber13x13". You must ALSO specify the ++# defaults even if you have specified board-size-specific values. ++ ++# Directory to auto-save moves KataGo plays, to avoid them in future games. ++# You can create a new empty directory and put its path here. ++# If you run parallel instances of KataGo, use different directories if you ++# want them not to share their biases, use the same directory if you want ++# all of them to bias away from past moves that any of them have played. ++# KataGo will also automatically DELETE old data in this directory, so it is ++# recommended that if you do share the same directory between parallel instances, ++# that they all use the same settings that affect data saving/deletion. ++# autoAvoidRepeatDir = PATH_TO_NEW_DIRECTORY ++ ++# Penalize this much utility per matching move. ++# Values that are too large may lead to bad play. The value of 0.004 is fairly large ++# and might be large enough to result in some early weird/bad moves when trying ++# to avoid past games' moves if enough games begin the same way. You can experiment with it. ++# autoAvoidRepeatUtility = 0.004 ++ ++# Per each new move saved, exponentially decay prior saved moves by this factor. ++# This way, the bias against moves from many games ago is gradually phased out. ++# For example, 0.9995 = 1 - 1/2000, so would be roughly 2000 prior moves worth of ++# penalty weight remembered, in steady state. Depending on what turn number range ++# you are saving, this might equate to a different number of games For example ++# saving the first 50 moves per game would make this roughly 2000 / 50 = 40 games ++# worth of memory. ++# autoAvoidRepeatLambda = 0.9995 ++ ++# Affects data saving/deletion. ++# When the number of saved moves exceeds this, outright delete them to avoid too many ++# files and disk space building up. Also may affect the speed of saving/loading on start ++# of each game if this is set large and a lot of data builds up. ++# autoAvoidRepeatMaxPoses = 10000 ++ ++# Affects data saving/deletion. ++# Only save data for moves within this turn number range of those games. ++# E.g. setting autoAvoidRepeatMinTurnNumber to a number like 4 or 5 would tend to make ++# KataGo not develop a bias against the initial 3-4 and 4-4 corner moves in almost every game. ++# autoAvoidRepeatMinTurnNumber = 0 ++# autoAvoidRepeatMaxTurnNumber = 50 ++ ++# Affects data saving/deletion. ++# Within a single run of a program, wait to accumulate this many samples ++# (possibly across multiple clear_boards/games) before saving the data. ++# Can help to avoid writing too many small files to disk, especially when GTP is used ++# in a way that clears the board very frequently (e.g. gtp2ogs pooled manager). ++# autoAvoidRepeatSaveChunkSize = 200 ++ ++# =========================================================================== ++# Avoid SGF patterns ++# =========================================================================== ++# The parameters in this section provide a way to avoid moves that follow ++# specific patterns based on a set of SGF files loaded upon startup. ++# This is basically the same as the above "Automatic avoid patterns" section ++# above except you supply your own SGF files to avoid moves from. ++# Uncomment them to use this feature. Additionally, if the SGF file ++# contains the string %SKIP% in a comment on a move, that move will be ++# ignored for this purpose. ++ ++# Load SGF files from this directory when the engine is started ++# (only on startup, will not reload unless engine is restarted) ++# avoidSgfPatternDirs = path/to/directory/with/sgfs/ ++# You can also surround the file path in double quotes if the file path contains trailing spaces or hash signs. ++# Within double quotes, backslashes are escape characters. ++# avoidSgfPatternDirs = "path/to/directory/with/sgfs/" ++ ++# Penalize this much utility per matching move. ++# Set this negative if you instead want to favor SGF patterns instead of ++# penalizing them. This number does not need to be large, even 0.001 will ++# make a difference. Values that are too large may lead to bad play. ++# avoidSgfPatternUtility = 0.001 ++ ++# Optional - load only the newest this many files ++# avoidSgfPatternMaxFiles = 20 ++ ++# Optional - Penalty is multiplied by this per each older SGF file, so that ++# old SGF files matter less than newer ones. ++# avoidSgfPatternLambda = 0.90 ++ ++# Optional - pay attention only to moves made by players with this name. ++# For example, set it to the name that your bot's past games will show up ++# as in the SGF, so that the bot will only avoid repeating moves that itself ++# made in past games, not the moves that its opponents made. ++# avoidSgfPatternAllowedNames = my-ogs-bot-name1,my-ogs-bot-name2 ++ ++# Optional - Ignore moves in SGF files that occurred before this turn number. ++# avoidSgfPatternMinTurnNumber = 0 ++ ++# For more avoid patterns: ++# You can also specify a second set of parameters, and a third, fourth, ++# etc. by numbering 2,3,4,... ++# ++# avoidSgf2PatternDirs = ... ++# avoidSgf2PatternUtility = ... ++# avoidSgf2PatternMaxFiles = ... ++# avoidSgf2PatternLambda = ... ++# avoidSgf2PatternAllowedNames = ... ++# avoidSgf2PatternMinTurnNumber = ... ++ diff --git a/training/scripts/dated/20241110-124043/edit_checkpoint.py b/training/scripts/dated/20241110-124043/edit_checkpoint.py new file mode 100644 index 0000000..62c4189 --- /dev/null +++ b/training/scripts/dated/20241110-124043/edit_checkpoint.py @@ -0,0 +1,58 @@ +#!/usr/bin/python3 +import sys +import os +import argparse +import math +import torch +import json + +description = """ +Utility for dumping or modifying torch checkpoint file contents +""" + +parser = argparse.ArgumentParser(description=description) +parser.add_argument('-checkpoint', help='Checkpoint file path', required=True) +parser.add_argument('-output-json-to', help='Output json contents to here, except for model parameters and optimizer state') +parser.add_argument('-overwrite-checkpoint-from-json', help='Use this json contents to overwrite the apprpriate fields of the state dict of the checkpoint') +args = vars(parser.parse_args()) + +checkpoint_path = args["checkpoint"] +output_json_to = args["output_json_to"] +overwrite_checkpoint_from_json = args["overwrite_checkpoint_from_json"] + +data = torch.load(checkpoint_path,map_location="cpu") + +if output_json_to is not None: + assert output_json_to.endswith(".json") + data_to_write = dict( + running_metrics = data["running_metrics"], + train_state = data["train_state"], + config = data["config"] if "config" in data else None, + ) + with open(output_json_to,"w") as f: + json.dump(data,f,indent=2,default=repr) + print(f"Dumped to {output_json_to}") + +elif overwrite_checkpoint_from_json: + with open(overwrite_checkpoint_from_json) as f: + data_to_use = json.load(f) + if "running_metrics" in data_to_use: + print("Overwriting running_metrics") + data["running_metrics"] = data_to_use["running_metrics"] + if "train_state" in data_to_use: + print("Overwriting train_state") + data["train_state"] = data_to_use["train_state"] + if "config" in data_to_use: + print("Overwriting config") + data["config"] = data_to_use["config"] + + torch.save(data, checkpoint_path) + print(f"Updated {checkpoint_path} with new fields") + +else: + data_to_write = dict( + running_metrics = data["running_metrics"], + train_state = data["train_state"], + config = data["config"] if "config" in data else None, + ) + print(json.dumps(data_to_write,indent=2,default=repr)) diff --git a/training/scripts/dated/20241110-124043/elo.py b/training/scripts/dated/20241110-124043/elo.py new file mode 100644 index 0000000..f54d1a4 --- /dev/null +++ b/training/scripts/dated/20241110-124043/elo.py @@ -0,0 +1,855 @@ +import copy +import math +import numpy as np +import os +import scipy.stats +import scipy.special +import itertools +from abc import ABC, abstractmethod +from typing import List, Dict, Tuple, Set, Sequence +from dataclasses import dataclass + +Player = str +PlayerIdx = int + +ELO_PER_STRENGTH = 400.0 * math.log10(math.exp(1.0)) +P1_ADVANTAGE_NAME = "P1Advantage" + +class EloInfo: + """Summarizes maximum likelihood Elos and uncertainties for a group of players.""" + + def __init__( + self, + players: List[Player], + elo: Dict[Player,float], + elo_stderr: Dict[Player,float], + elo_covariance: Dict[Tuple[Player,Player],float], + effective_game_count: Dict[Player,float], + ): + self.players = players + self.elo = elo + self.elo_stderr = elo_stderr + self.elo_covariance = elo_covariance + self.effective_game_count = effective_game_count + + self.players = sorted(self.players, key=(lambda player: -elo[player] if player != P1_ADVANTAGE_NAME else 1e50)) + + def get_players(self) -> List[Player]: + return self.players + + def get_elo(self, p1: Player) -> float: + """Returns the maximum likelihood Elo of p1""" + return self.elo[p1] + + def get_approx_elo_stderr(self, p1: Player) -> float: + """Returns an approximation of the standard error on the Elo of p1, ASSUMING all other players Elos are equal to their maximum likelihood value. + This approximation may underestimate if the amount of data is very small.""" + return self.elo_stderr[p1] + + def get_elo_difference(self, p1: Player, p2: Player) -> float: + """Returns the maximum likelhood difference in Elo between p1 and p2""" + return self.elo[p1] - self.elo[p2] + + def get_approx_elo_difference_stderr(self, p1: Player, p2: Player) -> float: + """Returns an approximation of the standard error on difference in Elo between p1 and p2. + This approximation may underestimate if the amount of data is very small.""" + return math.sqrt( + self.elo_covariance[(p1,p1)] - self.elo_covariance[(p1,p2)] - self.elo_covariance[(p2,p1)] + self.elo_covariance[(p2,p2)] + ) + + def get_approx_likelihood_of_superiority(self, p1: Player, p2: Player) -> float: + """Returns an approximation of the likelihood that elo(p1) - elo(p2) > 0, given the data. + This approximation may be overconfident or inaccurate if the amount of data is very small.""" + if p1 == p2: + return 0.5 + mean = self.get_elo_difference(p1,p2) + stderr = self.get_approx_elo_difference_stderr(p1,p2) + return scipy.stats.t.cdf(mean/stderr,df=self.effective_game_count[p1]-1) + + def get_log10_odds_surprise_max_likelihood(self, p1: Player, p2: Player, g1: float, total: int) -> float: + """Returns an indication of how surprised we would be for p1 to win g1 games out of total versus p2, given the maximum likeihood Elos. + Doesn't explicitly model draw prob, if g1 is fractional then assumes the fractional part randomly rounds to a full 1 or 0 + and averages the probabilities. + A value of 3.0 means that g1 is larger than we would expect given the players' estimated Elos, such that a result + that extreme or more has less than 1 : 10^3.0 odds of happening under the posterior. + A value of -3.0 means that g1 is smaller larger than we would expect given the players' estimated Elos, such that a result + that extreme or more has less than 1 : 10^3.0 odds of happening under the posterior. + """ + if total <= 0: + return 0 + mean = self.get_elo_difference(p1,p2) / ELO_PER_STRENGTH + + max_likelihood_winprob = 1.0 / (1.0 + math.exp(-mean)) + max_likelihood_g1 = max_likelihood_winprob * total + if max_likelihood_g1 < g1: + signflip = -1 + mean = -mean + g1 = total - g1 + else: + signflip = 1 + + winprob = 1.0 / (1.0 + math.exp(-mean)) + + g1_floor = int(math.floor(g1)) + g1_frac = g1 - g1_floor + if g1_frac != 0.0: + # Average endpoints for draws + logxf = scipy.stats.binom.logcdf(g1_floor,total,winprob) + logxp1 = scipy.stats.binom.logcdf(g1_floor+1,total,winprob) + log_prob = scipy.special.logsumexp([logxf+math.log(1.0-g1_frac),logxp1+math.log(g1_frac)]) + else: + log_prob = scipy.stats.binom.logcdf(g1,total,winprob) + + log_odds = log_prob - (math.log(-log_prob) if log_prob > -1e-10 else math.log(1.0 - math.exp(log_prob))) + return -signflip * max(0.0, -log_odds / math.log(10.0)) + + def get_approx_log10_odds_surprise_bayes(self, p1: Player, p2: Player, g1: float, total: int) -> float: + """Returns an indication of how surprised we would be for p1 to win g1 games out of total versus p2, given the posterior. + Computed via numeric integration over the pairwise posteriors. + Doesn't explicitly model draw prob, if g1 is fractional then assumes the fractional part randomly rounds to a full 1 or 0 + and averages the probabilities. + A value of 3.0 means that g1 is larger than we would expect given the players' estimated Elos, such that a result + that extreme or more has less than 1 : 10^3.0 odds of happening under the posterior. + A value of -3.0 means that g1 is smaller larger than we would expect given the players' estimated Elos, such that a result + that extreme or more has less than 1 : 10^3.0 odds of happening under the posterior. + """ + mean = self.get_elo_difference(p1,p2) / ELO_PER_STRENGTH + stderr = self.get_approx_elo_difference_stderr(p1,p2) / ELO_PER_STRENGTH + + max_likelihood_winprob = 1.0 / (1.0 + math.exp(-mean)) + max_likelihood_g1 = max_likelihood_winprob * total + if max_likelihood_g1 < g1: + signflip = -1 + mean = -mean + g1 = total - g1 + else: + signflip = 1 + + logw_list = [] + logwx_list = [] + for x in np.linspace(-15.0,15.0,num=1000,endpoint=False): + logw = scipy.stats.norm.logpdf(x) + winprob = 1.0 / (1.0 + math.exp(-(mean + x * stderr))) + # print("X", x) + # print("winprob", winprob) + # print("g1 total", g1, total, g1/total) + + g1_floor = int(math.floor(g1)) + g1_frac = g1 - g1_floor + if g1_frac != 0.0: + # Average endpoints for draws + logxf = scipy.stats.binom.logcdf(g1_floor,total,winprob) + logxp1 = scipy.stats.binom.logcdf(g1_floor+1,total,winprob) + log_prob = scipy.special.logsumexp([logxf+math.log(1.0-g1_frac),logxp1+math.log(g1_frac)]) + else: + logx = scipy.stats.binom.logcdf(g1,total,winprob) + + # print("logw logx", logw, logx) + logw_list.append(logw) + logwx_list.append(logw+logx) + + log_wsum = scipy.special.logsumexp(logw_list) + log_wxsum = scipy.special.logsumexp(logwx_list) + # print("log_wsum",log_wsum) + # print("log_wxsum",log_wxsum) + + log_prob = log_wxsum - log_wsum + log_odds = log_prob - (math.log(-log_prob) if log_prob > -1e-10 else math.log(1.0 - math.exp(log_prob))) + return -signflip * max(0.0, -log_odds / math.log(10.0)) + + def __str__(self) -> str: + lines = [] + for player in self.players: + lines.append(f"{player:20s}: {self.elo[player]:8.2f} +/- {self.elo_stderr[player]:5.2f}") + return "\n".join(lines) + + +class Likelihood: + """Summarizes the information in an observed datapoint about player Elos, or a prior about them. + + Represents that sum_{p in playercombo} Strength(p)*playercombo[p] + offset has likelihood function f raised to the power of weight. + If kind is SIGMOID_KIND, f is the sigmoid function. + If kind is GAUSSIAN_KIND, f is the pdf of a unit gaussian + + where strength is such that 1 unit of strength is e:1 odds of winning in a head-to-head game. + """ + + SIGMOID_KIND = 1 + GAUSSIAN_KIND = 2 + + def __init__( + self, + playercombo: Dict[Player,float], + offset: float, + weight: float, + gamecount: float, + kind: int, + ): + self.playercombo = playercombo + self.offset = offset + self.weight = weight + self.gamecount = gamecount + self.kind = kind + assert kind == Likelihood.SIGMOID_KIND or kind == Likelihood.GAUSSIAN_KIND, "invalid kind" + + def add_idxs(self, player_to_idx: Dict[Player,PlayerIdx]): + self.pidxcombo : List[Tuple[PlayerIdx,float]] = [(player_to_idx[player],coeff) for (player,coeff) in self.playercombo.items()] + + LOG_ONE_OVER_SQRT_TWO_PI = math.log(1.0 / math.sqrt(2.0 * math.pi)) + + def get_loglikelihood(self, strengths: np.array) -> float: + strength_total = self.offset + sum(strengths[pidx] * coeff for (pidx,coeff) in self.pidxcombo) + if self.kind == Likelihood.SIGMOID_KIND: + if strength_total < -40: + return strength_total + return -self.weight * math.log(1.0 + math.exp(-strength_total)) + else: + return self.weight * (Likelihood.LOG_ONE_OVER_SQRT_TWO_PI - 0.5 * strength_total * strength_total) + + def accum_dloglikelihood_dstrength(self, strengths: np.array, accum: np.array): + strength_total = self.offset + sum(strengths[pidx] * coeff for (pidx,coeff) in self.pidxcombo) + if self.kind == Likelihood.SIGMOID_KIND: + dloglikelihood_dstrength_total = self.weight / (1.0 + math.exp(strength_total)) + else: + dloglikelihood_dstrength_total = -self.weight * strength_total + for (pidx,coeff) in self.pidxcombo: + accum[pidx] += coeff * dloglikelihood_dstrength_total + + def accum_d2loglikelihood_dstrength2(self, strengths: np.array, accum: np.array): + strength_total = self.offset + sum(strengths[pidx] * coeff for (pidx,coeff) in self.pidxcombo) + if self.kind == Likelihood.SIGMOID_KIND: + denom = math.exp(-0.5 * strength_total) + math.exp(0.5 * strength_total) + d2loglikelihood_dstrength_total2 = -self.weight / (denom * denom) + else: + d2loglikelihood_dstrength_total2 = -self.weight + + for (pidx1,coeff1) in self.pidxcombo: + for (pidx2,coeff2) in self.pidxcombo: + accum[pidx1,pidx2] += coeff1 * coeff2 * d2loglikelihood_dstrength_total2 + + def accum_d2loglikelihood_dstrength2_scalepow(self, strengths: np.array, accum: np.array, scale: float, power: float): + strength_total = self.offset + sum(strengths[pidx] * coeff for (pidx,coeff) in self.pidxcombo) + if self.kind == Likelihood.SIGMOID_KIND: + denom = math.exp(-0.5 * strength_total) + math.exp(0.5 * strength_total) + d2loglikelihood_dstrength_total2 = -self.weight / (denom * denom) + else: + d2loglikelihood_dstrength_total2 = -self.weight + + for (pidx1,coeff1) in self.pidxcombo: + for (pidx2,coeff2) in self.pidxcombo: + x = coeff1 * coeff2 * d2loglikelihood_dstrength_total2 + accum[pidx1,pidx2] += scale * (x ** power) + + +def likelihood_of_games( + p1: Player, + p2: Player, + num_games: float, + p1_won_proportion: float, + include_first_player_advantage: bool, +) -> List[Likelihood]: + """Return a list of Likelihood objects representing the result of set of games between p1 and p2 + + These Likelihoods can accumulated with any other Likelihoods, and then all passed to compute_elos + to compute maximum likelihood Elos for all the players. + + NOTE: For performance reasons, you should try to minimize the number of these you create. If p1 and p2 played + a large number of games, don't call this function once per game. Instead, call it once for all the games + together (or twice, if you are using include_first_player_advantage=True, separately reporting the stats + that occured when each side was the first player). + + NOTE: If specifying include_first_player_advantage=True, make sure to add a make_single_player_prior on + P1_ADVANTAGE_NAME. + + Arguments: + p1: Name of the first player + p2: Name of the second player + num_games: The number of games played + p1_won_proportion: The proportion of games that p1 won among those games played, counting draws as 0.5. + include_first_player_advantage: If true, will also make the computation take into account that the first player + might have an advantage (or a disadvantage!) and it will try to estimate the amount of that advantage. + + Returns: + List of likelihood objects summarizing the information. + """ + + ret = [] + assert p1_won_proportion >= 0.0 and p1_won_proportion <= 1.0 + assert num_games >= 0.0 + assert p1 != p2 + + if num_games > 0.0: + if not include_first_player_advantage: + if p1_won_proportion > 0.0: + ret.append(Likelihood( + playercombo={p1: 1.0, p2: -1.0}, + offset=0.0, + weight=p1_won_proportion*num_games, + gamecount=p1_won_proportion*num_games, + kind=Likelihood.SIGMOID_KIND + )) + if p1_won_proportion < 1.0: + ret.append(Likelihood( + playercombo={p2: 1.0, p1: -1.0}, + offset=0.0, + weight=(1.0-p1_won_proportion)*num_games, + gamecount=(1.0-p1_won_proportion)*num_games, + kind=Likelihood.SIGMOID_KIND + )) + else: + if p1_won_proportion > 0.0: + ret.append(Likelihood( + playercombo={p1: 1.0, p2: -1.0, P1_ADVANTAGE_NAME: 1.0}, + offset=0.0, + weight=p1_won_proportion*num_games, + gamecount=p1_won_proportion*num_games, + kind=Likelihood.SIGMOID_KIND + )) + if p1_won_proportion < 1.0: + ret.append(Likelihood( + playercombo={p2: 1.0, p1: -1.0, P1_ADVANTAGE_NAME: -1.0}, + offset=0.0, + weight=(1.0-p1_won_proportion)*num_games, + gamecount=(1.0-p1_won_proportion)*num_games, + kind=Likelihood.SIGMOID_KIND + )) + + return ret + +def make_single_player_prior( + p1: Player, + num_games: float, + elo: float, +) -> List[Likelihood]: + """Return a list of Likelihood objects representing a Bayesian prior that p1 is the specified Elo. + + The strength of the prior that p1 is the specified Elo is as if p1 were observed to have played + num_games many games against a known player of that Elo and won half and lost half. + + Returns: + List of likelihood objects summarizing the information. + """ + ret = [] + assert num_games >= 0.0 + assert np.isfinite(elo) + if num_games > 0.0: + ret.append(Likelihood( + playercombo={p1: 1.0}, + offset=(-elo / ELO_PER_STRENGTH), + weight=0.5*num_games, + gamecount=0.5*num_games, + kind=Likelihood.SIGMOID_KIND + )) + ret.append(Likelihood( + playercombo={p1: -1.0}, + offset=(elo / ELO_PER_STRENGTH), + weight=0.5*num_games, + gamecount=0.5*num_games, + kind=Likelihood.SIGMOID_KIND + )) + return ret + + +def make_sequential_prior( + players: List[Player], + num_games: float, +) -> List[Likelihood]: + """Return a list of Likelihood objects representing a Bayesian prior that each player in the sequence is similar in strength to the previous. + + This can be used, for example, if there were a sequence of changes between different versions, such that each version on average + is expected to be more similar to its neighbors. + + The strength of the prior between each sequential pair of players is as if they were observed to have played + num_games many games against each other and won half and lost half. + + Returns: + List of likelihood objects summarizing the information. + """ + ret = [] + assert num_games >= 0.0 + assert len(set(players)) == len(players), "players must not contain any duplicates" + + if len(players) < 1: + return ret + + for i in range(len(players)-1): + ret.extend(likelihood_of_games( + p1=players[i], + p2=players[i+1], + num_games=num_games, + p1_won_proportion=0.5, + include_first_player_advantage=False, + )) + return ret + + +def make_center_elos_prior( + players: Sequence[Player], + elo: float, +) -> List[Likelihood]: + """Return a list of Likelihood objects representing a Bayesian prior that the mean of all player Elos is the specified Elo. + + This prior will have no effect on the relative Elos of the players, unless it fights with another that sets players to + specific Elos, such as make_single_player_prior. It can simply be used to center all the Elos of the players. + + Returns: + List of likelihood objects summarizing the information. + """ + ret = [] + assert np.isfinite(elo) + assert len(set(players)) == len(players), "players must not contain any duplicates" + playercombo = { player: 1.0 for player in players } + ret.append(Likelihood( + playercombo=playercombo, + offset=-len(players) * elo / ELO_PER_STRENGTH, + weight=0.001, + gamecount=0.0, + kind=Likelihood.GAUSSIAN_KIND + )) + return ret + + +def compute_elos( + data: List[Likelihood], + tolerance: float = 0.001, + max_iters: int = 1000, + verbose: bool = False, +) -> EloInfo: + """Compute maximum-likelihood Elo ratings given the data and any priors. + + NOTE: It is recommend that you specify some sort of prior, even if relatively weak, for numerical stability. + When you call this function, it is up to you to make sure that the data and priors you have provided result in all + all Elos of all players being "anchored". For example, this function may crash or fail or return bad values if: + + * There is a player with no data at all, and no prior for that player. + (since that would mean an Elo for that player cannot be defined). + + * There is a player who has only won and never lost, and there is no prior that restricts that player's rating. + (since then nothing could stop player's Elo from going to infinity). + + * All players *have* won and lost against other players, but there is nothing that anchors the Elo of the + population as a whole, such no player having a make_single_player_prior AND there being no make_center_elos_prior. + (since even if all players Elos are known relative to one another, the Elos to report are undefined - there is nothing + to say where the Elos should be centered or what value they should start from). + + Examples of things that are normally sufficient to anchor everyone: + If all players have won and lost against other players, adding a make_center_elos_prior is sufficient. + If all players have won and lost against other players, AND every player has transitively beat every other player and + transitively lost to every other player, then adding a make_single_player_prior to one player is sufficient. + Regardless of whether the players have won or lost, adding a make_single_player_prior to every player is sufficient. + Regardless of whether the players have won or lost, adding both a make_sequential_prior and a make_center_elos_prior is sufficient. + + NOTE: Even aside from ensuring numeric stability and anchoring as above, it is probably good practice to add some mild prior + beyond that anyways. If your players are sequential (e.g. a series of different nets), a weak make_sequential_prior could be good. + If you want to just put all players on equal footing unbiasedly, adding a weak make_single_player_prior to each player that its Elo + is 0 (or 1000, or whatever) is also good. + + And if you have actual prior beliefs about the players Elos, feel free to add those. + + Arguments: + data: A single list of all the likelihoods from all your games and priors. + tolerance: Stop soon after the Elos stop changing by more than this. + max_iters: Bail out if the optimization takes more than this many iterations. + verbose: Print out the iteration as it proceeds. + + Returns: + Elos. Yay! + """ + + players = [] + for d in data: + players.extend(d.playercombo.keys()) + players = list(set(players)) + players.sort() + player_to_idx = { player: i for (i,player) in enumerate(players) } + + data = [copy.copy(d) for d in data] + for d in data: + d.add_idxs(player_to_idx) + + num_players = len(players) + + def compute_loglikelihood(strengths: np.array) -> float: + total = 0.0 + for d in data: + total += d.get_loglikelihood(strengths) + return total + + # Gauss newton + def find_ascent_vector(strengths: np.array) -> np.array: + dloglikelihood_dstrength = np.zeros(num_players,dtype=np.float64) + d2loglikelihood_dstrength2 = np.zeros((num_players,num_players),dtype=np.float64) + + for d in data: + d.accum_dloglikelihood_dstrength(strengths, dloglikelihood_dstrength) + d.accum_d2loglikelihood_dstrength2(strengths, d2loglikelihood_dstrength2) + + ascent = -np.linalg.solve(d2loglikelihood_dstrength2,dloglikelihood_dstrength) + return ascent + + def line_search_ascend(strengths: np.array, cur_loglikelihood: float) -> Tuple[np.array,float]: + ascent = find_ascent_vector(strengths) + # Try up to this many times to find an improvement + for i in range(30): + new_strengths = strengths + ascent + new_loglikelihood = compute_loglikelihood(new_strengths) + if new_loglikelihood > cur_loglikelihood: + return (new_strengths, new_loglikelihood) + # Shrink ascent step and try again + ascent *= 0.6 + return (strengths,cur_loglikelihood) + + strengths = np.zeros(num_players,dtype=np.float64) + loglikelihood = compute_loglikelihood(strengths) + iters_since_big_change = 0 + last_elo_change = None + for i in range(max_iters): + if verbose: + print(f"Beginning iteration {i}, cur log likelihood {loglikelihood}, last elo change {last_elo_change}") + + (new_strengths, new_loglikelihood) = line_search_ascend(strengths, loglikelihood) + elodiff = (new_strengths - strengths) * ELO_PER_STRENGTH + last_elo_change = 0 if len(elodiff) <= 0 else np.max(np.abs(elodiff)) + + strengths = new_strengths + loglikelihood = new_loglikelihood + + iters_since_big_change += 1 + if np.any(elodiff > tolerance): + iters_since_big_change = 0 + + if iters_since_big_change > 3: + break + + + d2loglikelihood_dstrength2 = np.zeros((num_players,num_players),dtype=np.float64) + for d in data: + d.accum_d2loglikelihood_dstrength2(strengths, d2loglikelihood_dstrength2) + strength_precision = -d2loglikelihood_dstrength2 + elo_precision = strength_precision / (ELO_PER_STRENGTH * ELO_PER_STRENGTH) + elo_covariance = np.linalg.inv(elo_precision) + + sqrt_ess_numerator = np.zeros((num_players,num_players),dtype=np.float64) + ess_denominator = np.zeros((num_players,num_players),dtype=np.float64) + for d in data: + if d.gamecount > 0.0: + d.accum_d2loglikelihood_dstrength2_scalepow(strengths, sqrt_ess_numerator, scale = 1.0, power=1.0) + d.accum_d2loglikelihood_dstrength2_scalepow(strengths, ess_denominator, scale = 1.0 / d.gamecount, power=2.0) + + info = EloInfo( + players = players, + elo = { player: ELO_PER_STRENGTH * strengths[player_to_idx[player]] for player in players }, + elo_stderr = { player: math.sqrt(1.0 / elo_precision[player_to_idx[player],player_to_idx[player]]) for player in players }, + elo_covariance = { (p1,p2): elo_covariance[player_to_idx[p1],player_to_idx[p2]] for p1 in players for p2 in players }, + effective_game_count = { + player: (np.square(sqrt_ess_numerator[player_to_idx[player],player_to_idx[player]]) / + ess_denominator[player_to_idx[player],player_to_idx[player]]) + for player in players + }, + ) + return info + +def has_only_factors_of_2_and_3(n: int) -> bool: + while n > 1: + if n % 2 == 0: + n //= 2 + elif n % 3 == 0: + n //= 3 + else: + return False + return True + +@dataclass +class GameRecord: + player1: str + player2: str + win: int = 0 + loss: int = 0 + draw: int = 0 + +class GameResultSummary: + + def __init__( + self, + elo_prior_games: float, + estimate_first_player_advantage: bool, + ): + self.results = {} # dict of { (player1_name, player2_name) : GameRecord } + + self._all_game_files = set() + self._elo_prior_games = elo_prior_games # number of games for bayesian prior around Elo 0 + self._estimate_first_player_advantage = estimate_first_player_advantage + self._elo_info = None + self._game_count = 0 + + def add_games_from_file_or_dir(self, input_file_or_dir: str, recursive=False): + """Add games found in input_file_or_dir into the results. Repeated paths to the same file across multiple calls will be ignored.""" + new_files = self._add_files(input_file_or_dir, recursive) + + def add_game_record(self, record: GameRecord): + """Add game record to results.""" + if (record.player1, record.player2) not in self.results: + self.results[(record.player1, record.player2)] = GameRecord(player1=record.player1,player2=record.player2) + self.results[(record.player1, record.player2)].win += record.win + self.results[(record.player1, record.player2)].loss += record.loss + self.results[(record.player1, record.player2)].draw += record.draw + self._game_count += record.win + record.loss + record.draw + + def clear(self): + """Clear all data added.""" + self.results = {} + self._all_game_files = set() + self._elo_info = None + + def print_game_results(self): + """Print tables of wins and win percentage.""" + pla_names = set(itertools.chain(*(name_pair for name_pair in self.results.keys()))) + self._print_result_matrix(pla_names) + + def print_elos(self): + """Print game results and maximum likelihood posterior Elos.""" + elo_info = self._compute_elos_if_needed() + real_players = [player for player in elo_info.players if player != P1_ADVANTAGE_NAME] + self._print_result_matrix(real_players) + print("Elos (+/- one approx standard error):") + print(elo_info) + + print("Pairwise approx % likelihood of superiority of row over column:") + los_matrix = [] + for player in real_players: + los_row = [] + for player2 in real_players: + los = elo_info.get_approx_likelihood_of_superiority(player,player2) + los_row.append(f"{los*100:.2f}") + los_matrix.append(los_row) + self._print_matrix(real_players,los_matrix) + + surprise_matrix = [] + for pla1 in real_players: + row = [] + for pla2 in real_players: + if (pla1 == pla2): + row.append("-") + continue + else: + pla1_pla2 = self.results[(pla1, pla2)] if (pla1, pla2) in self.results else GameRecord(pla1,pla2) + pla2_pla1 = self.results[(pla2, pla1)] if (pla2, pla1) in self.results else GameRecord(pla2,pla1) + win = pla1_pla2.win + pla2_pla1.loss + 0.5 * (pla1_pla2.draw + pla2_pla1.draw) + total = pla1_pla2.win + pla2_pla1.win + pla1_pla2.loss + pla2_pla1.loss + pla1_pla2.draw + pla2_pla1.draw + surprise = elo_info.get_log10_odds_surprise_max_likelihood(pla1, pla2, win, total) + if total <= 0: + row.append("-") + else: + row.append(f"{surprise:.1f}") + surprise_matrix.append(row) + print("Log10odds surprise matrix given the maximum-likelihood Elos:") + print("E.g. +3.0 means a 1:1000 unexpected good performance by row vs column.") + print("E.g. -4.0 means a 1:10000 unexpected bad performance by row vs column.") + print("Extreme numbers may indicate rock/paper/scissors, or Elo is not a good model for the data.") + self._print_matrix(real_players,surprise_matrix) + + print(f"Used a prior of {self._elo_prior_games} games worth that each player is near Elo 0.") + + def get_elos(self) -> EloInfo: + return self._compute_elos_if_needed() + + def get_game_results(self) -> Dict: + """Return a dictionary of game results as { (player1_name, player2_name) : GameRecord } + + You can retrieve results by player's name like: + results[(player1_name, player2_name)].win + results[(player1_name, player2_name)].loss + results[(player1_name, player2_name)].draw + """ + return self.results + + # Functions that can be implemented by subclasses ----------------------------------------------------- + # You can override these methods if you want add_games_from_file_or_dir to work. + # Otherwise, you can just add game records yourself via add_game_record. + @abstractmethod + def is_game_file(self, input_file: str) -> bool: + """Returns true if this file or directory is one that should have game results in it somewhere""" + raise NotImplementedError() + + @abstractmethod + def get_game_records(self, input_file: str) -> List[GameRecord]: + """Return all game records contained in this file""" + raise NotImplementedError() + + # Private functions ------------------------------------------------------------------------------------ + + def _compute_elos_if_needed(self): + if self._elo_info is None: + self._elo_info = self._estimate_elo() + return self._elo_info + + def _add_files(self, input_file_or_dir, recursive): + print(f"Searching and adding files in {input_file_or_dir}, please wait...") + + if not os.path.exists(input_file_or_dir): + raise Exception(f"There is no file or directory with name: {input_file_or_dir}") + + files = [] + if os.path.isdir(input_file_or_dir): + if recursive: + for (dirpath, dirnames, filenames) in os.walk(input_file_or_dir): + files += [os.path.join(dirpath, file) for file in filenames if self.is_game_file(os.path.join(dirpath, file))] + else: + files = [os.path.join(input_file_or_dir, file) for file in os.listdir(input_file_or_dir) if self.is_game_file(os.path.join(input_file_or_dir, file))] + else: + if self.is_game_file(input_file_or_dir): + files.append(input_file_or_dir) + + # Remove duplicates + new_game_files = set(files) + new_game_files = new_game_files.difference(self._all_game_files) + self._all_game_files = self._all_game_files.union(new_game_files) + self._add_new_games_to_result_dict(new_game_files, input_file_or_dir) + + print(f"Added {len(new_game_files)} new game files from {input_file_or_dir}") + + def _add_new_games_to_result_dict(self, new_game_files, source): + idx = 0 + for game_file in new_game_files: + records = self.get_game_records(game_file) + idx += 1 + if idx % 10 == 0 and has_only_factors_of_2_and_3(idx // 10): + print(f"Added {idx}/{len(new_game_files)} game files for {source}") + + for record in records: + self.add_game_record(record) + + def _estimate_elo(self) -> EloInfo: + """Estimate and print elo values. This function must be called after adding all games""" + pla_names = set(itertools.chain(*(name_pair for name_pair in self.results.keys()))) + data = [] + for pla_first in pla_names: + for pla_second in pla_names: + if (pla_first == pla_second): + continue + else: + if (pla_first, pla_second) not in self.results: + continue + record = self.results[(pla_first, pla_second)] + total = record.win + record.loss + record.draw + assert total >= 0 + if total == 0: + continue + + win = record.win + 0.5 * record.draw + winrate = win / total + data.extend(likelihood_of_games( + pla_first, + pla_second, + total, + winrate, + include_first_player_advantage=self._estimate_first_player_advantage + )) + + for pla in pla_names: + data.extend(make_single_player_prior(pla, self._elo_prior_games,0)) + data.extend(make_center_elos_prior(list(pla_names),0)) # Add this in case user put elo_prior_games = 0 + if self._estimate_first_player_advantage: + data.extend(make_single_player_prior(P1_ADVANTAGE_NAME, (1.0 + self._elo_prior_games) * 2.0, 0)) + + info = compute_elos(data, verbose=True) + return info + + def _print_matrix(self,pla_names,results_matrix): + per_elt_space = 2 + for sublist in results_matrix: + for elt in sublist: + per_elt_space = max(per_elt_space, len(str(elt))) + per_elt_space += 2 + + per_name_space = 1 if len(pla_names) == 0 else max(len(name) for name in pla_names) + per_name_space += 1 + if per_name_space > per_elt_space: + per_elt_space += 1 + + row_format = f"{{:>{per_name_space}}}" + f"{{:>{per_elt_space}}}" * len(results_matrix) + print(row_format.format("", *[name[:per_elt_space-2] for name in pla_names])) + for name, row in zip(pla_names, results_matrix): + print(row_format.format(name, *row)) + + def _print_result_matrix(self, pla_names): + print(f"Total games: {self._game_count}") + print("Games by player:") + for pla1 in pla_names: + total = 0 + for pla2 in pla_names: + if (pla1 == pla2): + continue + else: + pla1_pla2 = self.results[(pla1, pla2)] if (pla1, pla2) in self.results else GameRecord(pla1,pla2) + pla2_pla1 = self.results[(pla2, pla1)] if (pla2, pla1) in self.results else GameRecord(pla2,pla1) + total += pla1_pla2.win + pla2_pla1.win + pla1_pla2.loss + pla2_pla1.loss + pla1_pla2.draw + pla2_pla1.draw + print(f"{pla1}: {total:.1f}") + + print("Wins by row player against column player:") + result_matrix = [] + for pla1 in pla_names: + row = [] + for pla2 in pla_names: + if (pla1 == pla2): + row.append("-") + continue + else: + pla1_pla2 = self.results[(pla1, pla2)] if (pla1, pla2) in self.results else GameRecord(pla1,pla2) + pla2_pla1 = self.results[(pla2, pla1)] if (pla2, pla1) in self.results else GameRecord(pla2,pla1) + win = pla1_pla2.win + pla2_pla1.loss + 0.5 * (pla1_pla2.draw + pla2_pla1.draw) + total = pla1_pla2.win + pla2_pla1.win + pla1_pla2.loss + pla2_pla1.loss + pla1_pla2.draw + pla2_pla1.draw + row.append(f"{win:.1f}/{total:.1f}") + result_matrix.append(row) + self._print_matrix(pla_names,result_matrix) + + print("Win% by row player against column player:") + result_matrix = [] + for pla1 in pla_names: + row = [] + for pla2 in pla_names: + if (pla1 == pla2): + row.append("-") + continue + else: + pla1_pla2 = self.results[(pla1, pla2)] if (pla1, pla2) in self.results else GameRecord(pla1,pla2) + pla2_pla1 = self.results[(pla2, pla1)] if (pla2, pla1) in self.results else GameRecord(pla2,pla1) + win = pla1_pla2.win + pla2_pla1.loss + 0.5 * (pla1_pla2.draw + pla2_pla1.draw) + total = pla1_pla2.win + pla2_pla1.win + pla1_pla2.loss + pla2_pla1.loss + pla1_pla2.draw + pla2_pla1.draw + if total <= 0: + row.append("-") + else: + row.append(f"{win/total*100.0:.1f}%") + result_matrix.append(row) + + self._print_matrix(pla_names,result_matrix) + +# Testing code +if __name__ == "__main__": + # Example 1 + # data = [] + # data.extend(likelihood_of_games("Alice","Bob", 18, 12/18, False)) + # data.extend(likelihood_of_games("Bob","Carol", 18, 12/18, False)) + # data.extend(likelihood_of_games("Carol","Dan", 36, 12/18, False)) + # data.extend(likelihood_of_games("Dan","Eve", 48, 40/48, False)) + # data.extend(make_center_elos_prior(["Alice","Bob","Carol","Dan","Eve"],0)) + # info = compute_elos(data,verbose=True) + + # for player in info.players: + # for player2 in info.players: + # print(info.get_approx_likelihood_of_superiority(player,player2),end=" ") + # print() + + # Example 2 + summary = GameResultSummary( + elo_prior_games=1.0, + estimate_first_player_advantage=False, + ) + summary.add_game_record(GameRecord("Alice","Bob",win=12,loss=6,draw=0)) + summary.add_game_record(GameRecord("Bob","Carol",win=12,loss=6,draw=0)) + summary.add_game_record(GameRecord("Carol","Dan",win=36,loss=18,draw=0)) + summary.add_game_record(GameRecord("Dan","Eve",win=48,loss=24,draw=0)) + summary.print_elos() + + + diff --git a/training/scripts/dated/20241110-124043/export_model_for_selfplay.sh b/training/scripts/dated/20241110-124043/export_model_for_selfplay.sh new file mode 100644 index 0000000..eac659b --- /dev/null +++ b/training/scripts/dated/20241110-124043/export_model_for_selfplay.sh @@ -0,0 +1,115 @@ +#!/bin/bash -eu +set -o pipefail +{ +#Takes any models in torchmodels_toexport/ and outputs a cuda-runnable model file to modelstobetested/ +#Takes any models in torchmodels_toexport_extra/ and outputs a cuda-runnable model file to models_extra/ +#Should be run periodically. + +if [[ $# -ne 3 ]] +then + echo "Usage: $0 NAMEPREFIX BASEDIR USEGATING" + echo "Currently expects to be run from within the 'python' directory of the KataGo repo, or otherwise in the same dir as export_model.py." + echo "NAMEPREFIX string prefix for this training run, try to pick something globally unique. Will be displayed to users when KataGo loads the model." + echo "BASEDIR containing selfplay data and models and related directories" + echo "USEGATING = 1 to use gatekeeper, 0 to not use gatekeeper and output directly to models/" + exit 0 +fi +NAMEPREFIX="$1" +shift +BASEDIR="$1" +shift +USEGATING="$1" +shift + +#------------------------------------------------------------------------------ + +mkdir -p "$BASEDIR"/torchmodels_toexport +mkdir -p "$BASEDIR"/torchmodels_toexport_extra +mkdir -p "$BASEDIR"/modelstobetested +mkdir -p "$BASEDIR"/models_extra +mkdir -p "$BASEDIR"/models + +function exportStuff() { + FROMDIR="$1" + TODIR="$2" + + #Sort by timestamp so that we process in order of oldest to newest if there are multiple + for FILEPATH in $(find "$BASEDIR"/"$FROMDIR"/ -mindepth 1 -maxdepth 1 -printf "%T@ %p\n" | sort -n | cut -d ' ' -f 2) + do + #Make sure to skip tmp directories that are transiently there by the training, + #they are probably in the process of being written + if [ ${FILEPATH: -4} == ".tmp" ] + then + echo "Skipping tmp file:" "$FILEPATH" + elif [ ${FILEPATH: -9} == ".exported" ] + then + echo "Skipping self tmp file:" "$FILEPATH" + else + echo "Found model to export:" "$FILEPATH" + NAME="$(basename "$FILEPATH")" + + SRC="$BASEDIR"/"$FROMDIR"/"$NAME" + TMPDST="$BASEDIR"/"$FROMDIR"/"$NAME".exported + TARGET="$BASEDIR"/"$TODIR"/"$NAME" + + if [ -d "$BASEDIR"/modelstobetested/"$NAME" ] || \ + [ -d "$BASEDIR"/rejectedmodels/"$NAME" ] || \ + [ -d "$BASEDIR"/models/"$NAME" ] || \ + [ -d "$BASEDIR"/models_extra/"$NAME" ] || \ + [ -d "$BASEDIR"/modelsuploaded/"$NAME" ] + then + echo "Model with same name aleady exists, so skipping:" "$SRC" + else + rm -rf "$TMPDST" + mkdir "$TMPDST" + + set -x + python3 ./export_model_pytorch.py \ + -checkpoint "$SRC"/model.ckpt \ + -export-dir "$TMPDST" \ + -model-name "$NAMEPREFIX""-""$NAME" \ + -filename-prefix model \ + -use-swa + + python3 ./clean_checkpoint.py \ + -checkpoint "$SRC"/model.ckpt \ + -output "$TMPDST"/model.ckpt + set +x + + rm -r "$SRC" + gzip "$TMPDST"/model.bin + + #Make a bunch of the directories that selfplay will need so that there isn't a race on the selfplay + #machines to concurrently make it, since sometimes concurrent making of the same directory can corrupt + #a filesystem + #Only when not gating. When gating, gatekeeper is responsible. + if [ "$USEGATING" -eq 0 ] + then + if [ "$TODIR" != "models_extra" ] + then + mkdir -p "$BASEDIR"/selfplay/"$NAME" + mkdir -p "$BASEDIR"/selfplay/"$NAME"/sgfs + mkdir -p "$BASEDIR"/selfplay/"$NAME"/tdata + fi + fi + + #Sleep a little to allow some tolerance on the filesystem + sleep 5 + + mv "$TMPDST" "$TARGET" + echo "Done exporting:" "$NAME" "to" "$TARGET" + fi + fi + done +} + +if [ "$USEGATING" -eq 0 ] +then + exportStuff "torchmodels_toexport" "models" +else + exportStuff "torchmodels_toexport" "modelstobetested" +fi +exportStuff "torchmodels_toexport_extra" "models_extra" + +exit 0 +} diff --git a/training/scripts/dated/20241110-124043/export_model_pytorch.py b/training/scripts/dated/20241110-124043/export_model_pytorch.py new file mode 100644 index 0000000..a2b23b4 --- /dev/null +++ b/training/scripts/dated/20241110-124043/export_model_pytorch.py @@ -0,0 +1,461 @@ +#!/usr/bin/python3 +import sys +import os +import argparse +import traceback +import random +import math +import time +import struct +import json +import datetime +import logging +import numpy as np +from collections import defaultdict +from typing import Dict, List + +import torch +import torch.nn +from torch.optim.swa_utils import AveragedModel + +import modelconfigs +from model_pytorch import Model, ResBlock, NestedBottleneckResBlock +from load_model import load_model + +#Command and args------------------------------------------------------------------- + +description = """ +Export neural net weights to file for KataGo engine. +""" + +parser = argparse.ArgumentParser(description=description) +parser.add_argument('-checkpoint', help='Checkpoint to test', required=True) +parser.add_argument('-export-dir', help='model file dir to save to', required=True) +parser.add_argument('-model-name', help='name to record in model file', required=True) +parser.add_argument('-filename-prefix', help='filename prefix to save to within dir', required=True) +parser.add_argument('-use-swa', help='Use SWA model', action="store_true", required=False) +parser.add_argument('-export-14-as-15', help='Export model version 14 as 15', action="store_true", required=False) +args = vars(parser.parse_args()) + + +def main(args): + checkpoint_file = args["checkpoint"] + export_dir = args["export_dir"] + model_name = args["model_name"] + filename_prefix = args["filename_prefix"] + use_swa = args["use_swa"] + export_14_as_15 = args["export_14_as_15"] + + os.makedirs(export_dir,exist_ok=True) + + logging.root.handlers = [] + logging.basicConfig( + level=logging.INFO, + format="%(message)s", + handlers=[ + logging.StreamHandler(stream=sys.stdout), + logging.FileHandler(export_dir + "/log.txt"), + ], + ) + np.set_printoptions(linewidth=150) + + logging.info(str(sys.argv)) + + # LOAD MODEL --------------------------------------------------------------------- + model, swa_model, other_state_dict = load_model(checkpoint_file, use_swa, device="cpu", verbose=True) + model_config = model.config + + # WRITING MODEL ---------------------------------------------------------------- + extension = ".bin" + mode = "wb" + f = open(export_dir + "/" + filename_prefix + extension, mode) + def writeln(s): + f.write((str(s)+"\n").encode(encoding="ascii",errors="backslashreplace")) + def writestr(s): + f.write(s.encode(encoding="ascii",errors="backslashreplace")) + + # Ignore what's in the config if less than 11 since a lot of testing models + # are on old version but actually have various new architectures. + version = max(model_config["version"],11) + true_version = version + # Hack to be able to export version 14 as version 15 + if version == 14 and export_14_as_15: + version = 15 + + writeln(model_name) + writeln(version) + writeln(modelconfigs.get_num_bin_input_features(model_config)) + writeln(modelconfigs.get_num_global_input_features(model_config)) + + if version <= 12: + assert model.td_score_multiplier == 20.0 + assert model.scoremean_multiplier == 20.0 + assert model.scorestdev_multiplier == 20.0 + assert model.lead_multiplier == 20.0 + assert model.variance_time_multiplier == 40.0 + assert model.shortterm_value_error_multiplier == 0.25 + assert model.shortterm_score_error_multiplier == 30.0 + else: + writeln(model.td_score_multiplier) + writeln(model.scoremean_multiplier) + writeln(model.scorestdev_multiplier) + writeln(model.lead_multiplier) + writeln(model.variance_time_multiplier) + writeln(model.shortterm_value_error_multiplier) + writeln(model.shortterm_score_error_multiplier) + + if version >= 15: + if model.metadata_encoder is not None: + writeln(model.metadata_encoder.meta_encoder_version) + else: + writeln(0) + + # Write some dummy placeholders for future features + writeln(0) + writeln(0) + writeln(0) + writeln(0) + writeln(0) + writeln(0) + writeln(0) + + + def write_weights(weights): + # Little endian + reshaped = np.reshape(weights.detach().numpy(),[-1]) + num_weights = len(reshaped) + writestr("@BIN@") + f.write(struct.pack(f'<{num_weights}f',*reshaped)) + writestr("\n") + + def write_conv_weight(name,convweight): + (out_channels, in_channels, diamy, diamx) = convweight.shape + dilation = 1 + writeln(name) + writeln(diamy) #y + writeln(diamx) #x + writeln(in_channels) + writeln(out_channels) + writeln(dilation) #y + writeln(dilation) #x + # Torch order is oc,ic,y,x + # Desired output order is y,x,ic,oc + write_weights(torch.permute(convweight,(2,3,1,0))) + + def write_conv(name,conv): + assert conv.bias is None + write_conv_weight(name, conv.weight) + + def write_bn(name,normmask): + writeln(name) + + writeln(normmask.c_in) + epsilon = 1e-20 + writeln(epsilon) + has_gamma_or_scale = normmask.scale is not None or normmask.gamma is not None + has_beta = True + writeln(1 if has_gamma_or_scale else 0) + writeln(1 if has_beta else 0) + + if hasattr(normmask,"running_mean") and normmask.running_mean is not None: + assert normmask.is_using_batchnorm + assert normmask.running_mean.shape == (normmask.c_in,) + write_weights(normmask.running_mean) + else: + assert not normmask.is_using_batchnorm + write_weights(torch.zeros(normmask.c_in, dtype=torch.float)) + + if hasattr(normmask,"running_std") and normmask.running_std is not None: + assert normmask.is_using_batchnorm + assert normmask.running_std.shape == (normmask.c_in,) + write_weights(torch.maximum(torch.tensor(1e-20), normmask.running_std * normmask.running_std - epsilon)) + else: + assert not normmask.is_using_batchnorm + write_weights((1.0-epsilon) * torch.ones(normmask.c_in, dtype=torch.float)) + + if normmask.scale is not None: + if normmask.gamma is not None: + assert normmask.gamma.shape == (1, normmask.c_in, 1, 1) + assert has_gamma_or_scale + write_weights(normmask.scale * normmask.gamma) + else: + assert has_gamma_or_scale + write_weights(normmask.scale * torch.ones(normmask.c_in, dtype=torch.float, device="cpu")) + else: + if normmask.gamma is not None: + assert normmask.gamma.shape == (1, normmask.c_in, 1, 1) + assert has_gamma_or_scale + write_weights(normmask.gamma) + else: + assert not has_gamma_or_scale + pass + + assert normmask.beta.shape == (1, normmask.c_in, 1, 1) + write_weights(normmask.beta) + + def write_biasmask(name,biasmask): + writeln(name) + + writeln(biasmask.c_in) + epsilon = 1e-20 + writeln(epsilon) + has_gamma_or_scale = biasmask.scale is not None + has_beta = True + writeln(1 if has_gamma_or_scale else 0) + writeln(1 if has_beta else 0) + + write_weights(torch.zeros(biasmask.c_in, dtype=torch.float)) + write_weights((1.0-epsilon) * torch.ones(biasmask.c_in, dtype=torch.float)) + + if biasmask.scale is not None: + write_weights(biasmask.scale * torch.ones(biasmask.c_in, dtype=torch.float, device="cpu")) + + assert biasmask.beta.shape == (1, biasmask.c_in, 1, 1) + write_weights(biasmask.beta) + + def write_activation(name, activation): + writeln(name) + if isinstance(activation,torch.nn.ReLU): + writeln("ACTIVATION_RELU") + elif isinstance(activation,torch.nn.Mish): + writeln("ACTIVATION_MISH") + elif isinstance(activation,torch.nn.Identity): + writeln("ACTIVATION_IDENTITY") + else: + assert False, f"Activation not supported for export: {activation}" + + + def write_matmul(name,linearweight): + writeln(name) + (out_channels,in_channels) = linearweight.shape + writeln(in_channels) + writeln(out_channels) + # Torch order is oc,ic + # Desired output order is ic,oc + write_weights(torch.permute(linearweight,(1,0))) + + def write_matbias(name,linearbias): + writeln(name) + (out_channels,) = linearbias.shape + writeln(out_channels) + write_weights(linearbias) + + def write_normactconv(name,normactconv): + if normactconv.c_gpool is None: + assert normactconv.convpool is None + if normactconv.conv1x1 is None: + write_bn(name+".norm", normactconv.norm) + write_activation(name+".act", normactconv.act) + write_conv(name+".conv", normactconv.conv) + else: + write_bn(name+".norm", normactconv.norm) + write_activation(name+".act", normactconv.act) + # Torch conv order is oc,ic,h,w + # We want to add the 1x1 conv to the center of the h,w + h,w = (normactconv.conv.weight.shape[2],normactconv.conv.weight.shape[3]) + assert h % 2 == 1, "Conv1x1 can't be merged with even-sized convolution kernel" + assert w % 2 == 1, "Conv1x1 can't be merged with even-sized convolution kernel" + combined_conv = normactconv.conv.weight.detach().clone() + combined_conv[:,:,h//2:h//2+1,w//2:w//2+1] += normactconv.conv1x1.weight + assert normactconv.conv.bias is None + assert normactconv.conv1x1.bias is None + write_conv_weight(name+".conv", combined_conv) + else: + assert normactconv.convpool is not None + assert normactconv.conv1x1 is None + write_bn(name+".norm", normactconv.norm) + write_activation(name+".act", normactconv.act) + write_conv(name+".convpool.conv1r", normactconv.convpool.conv1r) + write_conv(name+".convpool.conv1g", normactconv.convpool.conv1g) + write_bn(name+".convpool.normg", normactconv.convpool.normg) + write_activation(name+".convpool.actg", normactconv.convpool.actg) + write_matmul(name+".convpool.linear_g", normactconv.convpool.linear_g.weight) + assert normactconv.convpool.linear_g.bias is None + + def write_block(name,block): + if isinstance(block,ResBlock) and block.normactconv1.c_gpool is None: + assert block.normactconv2.c_gpool is None + writeln("ordinary_block") + writeln(name) + write_normactconv(name+".normactconv1", block.normactconv1) + write_normactconv(name+".normactconv2", block.normactconv2) + elif isinstance(block,ResBlock) and block.normactconv1.c_gpool is not None: + assert block.normactconv2.c_gpool is None + writeln("gpool_block") + writeln(name) + write_normactconv(name+".normactconv1", block.normactconv1) + write_normactconv(name+".normactconv2", block.normactconv2) + elif isinstance(block,NestedBottleneckResBlock): + writeln("nested_bottleneck_block") + writeln(name) + writeln(block.internal_length) + assert block.internal_length == len(block.blockstack) + write_normactconv(name+".normactconvp", block.normactconvp) + for i,subblock in enumerate(block.blockstack): + write_block(name+".blockstack."+str(i),subblock) + write_normactconv(name+".normactconvq", block.normactconvq) + else: + assert False, "This kind of block is not supported for export right now" + + def write_metadata_encoder(name,encoder): + writeln(name) + writeln(encoder.c_input) + # Torch order is oc,ic. Flatten feature mask into the first mul + write_matmul(name+".mul1", encoder.linear1.weight * encoder.feature_mask.reshape((1,-1))) + write_matbias(name+".bias1", encoder.linear1.bias) + write_activation(name+".act1", encoder.act1) + write_matmul(name+".mul2", encoder.linear2.weight) + write_matbias(name+".bias2", encoder.linear2.bias) + write_activation(name+".act2", encoder.act2) + write_matmul(name+".mul3", encoder.out_scale * encoder.linear_output_to_trunk.weight) + assert encoder.linear_output_to_trunk.bias is None + + def write_trunk(name,model): + writeln("trunk") + writeln(len(model.blocks)) + writeln(model.c_trunk) + writeln(model.c_mid) + writeln(model.c_mid-model.c_gpool) + writeln(model.c_gpool) + writeln(model.c_gpool) + if version >= 15: + # Write some dummy placeholders for future features + writeln(0) + writeln(0) + writeln(0) + writeln(0) + writeln(0) + writeln(0) + + write_conv("model.conv_spatial", model.conv_spatial) + write_matmul("model.linear_global", model.linear_global.weight) + assert model.linear_global.bias is None + if model.metadata_encoder is not None: + assert version >= 15 + write_metadata_encoder("model.sgf_metadata_encoder",model.metadata_encoder) + + for i,block in enumerate(model.blocks): + write_block("model.blocks."+str(i), block) + if model.trunk_normless: + write_biasmask("model.norm_trunkfinal", model.norm_trunkfinal) + else: + write_bn("model.norm_trunkfinal", model.norm_trunkfinal) + write_activation("model.act_trunkfinal", model.act_trunkfinal) + + def write_policy_head(name,policyhead): + writeln(name) + write_conv(name+".conv1p", policyhead.conv1p) + write_conv(name+".conv1g", policyhead.conv1g) + write_biasmask(name+".biasg", policyhead.biasg) + write_activation(name+".actg", policyhead.actg) + write_matmul(name+".linear_g", policyhead.linear_g.weight) + assert policyhead.linear_g.bias is None + write_biasmask(name+".bias2", policyhead.bias2) + write_activation(name+".act2", policyhead.act2) + + # Write the this-move prediction and the optimistic policy prediction + if version <= 11: + assert policyhead.conv2p.weight.shape[0] == 4 + write_conv_weight(name+".conv2p", torch.stack((policyhead.conv2p.weight[0],), dim=0)) + assert policyhead.linear_pass.weight.shape[0] == 4 + write_matmul(name+".linear_pass", torch.stack((policyhead.linear_pass.weight[0],), dim=0)) + assert policyhead.linear_pass.bias is None + elif version <= 14: + assert policyhead.conv2p.weight.shape[0] == 6 + write_conv_weight(name+".conv2p", torch.stack((policyhead.conv2p.weight[0], policyhead.conv2p.weight[5]), dim=0)) + assert policyhead.linear_pass.weight.shape[0] == 6 + write_matmul(name+".linear_pass", torch.stack((policyhead.linear_pass.weight[0], policyhead.linear_pass.weight[5]), dim=0)) + assert policyhead.linear_pass.bias is None + elif version == 15 and true_version == 14: + assert policyhead.conv2p.weight.shape[0] == 6 + write_conv_weight(name+".conv2p", torch.stack((policyhead.conv2p.weight[0], policyhead.conv2p.weight[5]), dim=0)) + assert policyhead.linear_pass.weight.shape[0] == 6 + linear_pass_stack = [policyhead.linear_pass.weight[0], policyhead.linear_pass.weight[5]] + c_p1 = int(policyhead.linear_g.weight.shape[0]) + for _ in range(c_p1-2): + linear_pass_stack.append(torch.zeros_like(linear_pass_stack[0])) + write_matmul(name+".linear_pass", torch.stack(linear_pass_stack, dim=0)) + assert policyhead.linear_pass.bias is None + write_matbias(name+".linear_pass_bias", torch.tensor([0.0]*c_p1,dtype=torch.float32,device="cpu")) + write_activation(name+".act_pass", torch.nn.Identity()) + write_matmul(name+".linear_pass2", torch.tensor([[1.0,0.0]+[0.0]*(c_p1-2),[0.0,1.0]+[0.0]*(c_p1-2)],dtype=torch.float32,device="cpu")) + else: + assert policyhead.conv2p.weight.shape[0] == 6 + write_conv_weight(name+".conv2p", torch.stack((policyhead.conv2p.weight[0], policyhead.conv2p.weight[5]), dim=0)) + write_matmul(name+".linear_pass", policyhead.linear_pass.weight) + write_matbias(name+".linear_pass_bias", policyhead.linear_pass.bias) + write_activation(name+".act_pass", policyhead.act_pass) + assert policyhead.linear_pass2.weight.shape[0] == 6 + write_matmul(name+".linear_pass2", torch.stack((policyhead.linear_pass2.weight[0], policyhead.linear_pass2.weight[5]), dim=0)) + assert policyhead.linear_pass2.bias is None + + assert policyhead.conv2p.bias is None + + + def write_value_head(name, valuehead): + writeln(name) + write_conv(name+".conv1", valuehead.conv1) + write_biasmask(name+".bias1", valuehead.bias1) + write_activation(name+".act1", valuehead.act1) + write_matmul(name+".linear2", valuehead.linear2.weight) + write_matbias(name+".bias2", valuehead.linear2.bias) + write_activation(name+".act2", valuehead.act2) + write_matmul(name+".linear_valuehead", valuehead.linear_valuehead.weight) + write_matbias(name+".bias_valuehead", valuehead.linear_valuehead.bias) + + # For now, only output the scoremean and scorestdev and lead and vtime channels + w = valuehead.linear_miscvaluehead.weight[0:4] + b = valuehead.linear_miscvaluehead.bias[0:4] + # Grab the shortterm channels + w2 = valuehead.linear_moremiscvaluehead.weight[0:2] + b2 = valuehead.linear_moremiscvaluehead.bias[0:2] + w = torch.cat((w,w2),dim=0) + b = torch.cat((b,b2),dim=0) + write_matmul(name+".linear_miscvaluehead", w) + write_matbias(name+".bias_miscvaluehead", b) + + write_conv(name+".conv_ownership",valuehead.conv_ownership) + + def write_model(model): + write_trunk("model",model) + write_policy_head("model.policy_head",model.policy_head) + write_value_head("model.value_head",model.value_head) + + if swa_model is not None: + logging.info("Writing SWA model") + write_model(swa_model) + else: + logging.info("Writing model") + write_model(model) + f.close() + + with open(os.path.join(export_dir,"metadata.json"),"w") as f: + train_state = other_state_dict["train_state"] + data = {} + if "global_step_samples" in train_state: + data["global_step_samples"] = train_state["global_step_samples"] + if "total_num_data_rows" in train_state: + data["total_num_data_rows"] = train_state["total_num_data_rows"] + if "running_metrics" in other_state_dict: + assert sorted(list(other_state_dict["running_metrics"].keys())) == ["sums", "weights"] + data["extra_stats"] = { + "sums": { key: value for (key,value) in other_state_dict["running_metrics"]["sums"].items() if "sopt" not in key and "lopt" not in key }, + "weights": { key: value for (key,value) in other_state_dict["running_metrics"]["weights"].items() if "sopt" not in key and "lopt" not in key }, + } + if "last_val_metrics" in other_state_dict and "sums" in other_state_dict["last_val_metrics"] and "weights" in other_state_dict["last_val_metrics"]: + data["extra_stats"]["last_val_metrics"] = { + "sums": { key: value for (key,value) in other_state_dict["last_val_metrics"]["sums"].items() if "sopt" not in key and "lopt" not in key }, + "weights": { key: value for (key,value) in other_state_dict["last_val_metrics"]["weights"].items() if "sopt" not in key and "lopt" not in key }, + } + json.dump(data,f) + + + logging.info("Exported at: ") + logging.info(str(datetime.datetime.utcnow()) + " UTC") + + sys.stdout.flush() + sys.stderr.flush() + + +if __name__ == "__main__": + main(args) diff --git a/training/scripts/dated/20241110-124043/features.py b/training/scripts/dated/20241110-124043/features.py new file mode 100644 index 0000000..1452e42 --- /dev/null +++ b/training/scripts/dated/20241110-124043/features.py @@ -0,0 +1,340 @@ +import math +import numpy as np +from typing import Callable, List + +from board import Board, Loc, Pos, Player +import modelconfigs + +class Features: + def __init__(self, config: modelconfigs.ModelConfig, pos_len: int): + self.config = config + self.pos_len = pos_len + self.version = modelconfigs.get_version(config) + self.pass_pos = self.pos_len * self.pos_len + self.bin_input_shape = [modelconfigs.get_num_bin_input_features(config), pos_len, pos_len] + self.global_input_shape = [modelconfigs.get_num_global_input_features(config)] + + def xy_to_tensor_pos(self,x,y): + return y * self.pos_len + x + def loc_to_tensor_pos(self,loc,board): + if loc == Board.PASS_LOC: + return self.pass_pos + return board.loc_y(loc) * self.pos_len + board.loc_x(loc) + + def tensor_pos_to_loc(self,pos,board): + if pos == self.pass_pos: + return None + pos_len = self.pos_len + bsize = board.size + assert(self.pos_len >= bsize) + x = pos % pos_len + y = pos // pos_len + if x < 0 or x >= bsize or y < 0 or y >= bsize: + return board.loc(-10,-10) #Return an illegal move since this is offboard + return board.loc(x,y) + + def sym_tensor_pos(self,pos,symmetry): + if pos == self.pass_pos: + return pos + pos_len = self.pos_len + x = pos % pos_len + y = pos // pos_len + if symmetry >= 4: + symmetry -= 4 + tmp = x + x = y + y = tmp + if symmetry >= 2: + symmetry -= 2 + x = pos_len-x-1 + if symmetry >= 1: + symmetry -= 1 + y = pos_len-y-1 + return y * pos_len + x + + #Calls f on each location that is part of an inescapable atari, or a group that can be put into inescapable atari + def iterLadders(self, board, f): + chainHeadsSolved = {} + copy = board.copy() + + bsize = board.size + assert(self.pos_len >= bsize) + + for y in range(bsize): + for x in range(bsize): + pos = self.xy_to_tensor_pos(x,y) + loc = board.loc(x,y) + stone = board.board[loc] + + if stone == Board.BLACK or stone == Board.WHITE: + libs = board.num_liberties(loc) + if libs == 1 or libs == 2: + head = board.group_head[loc] + if head in chainHeadsSolved: + laddered = chainHeadsSolved[head] + if laddered: + f(loc,pos,[]) + else: + #Perform search on copy so as not to mess up tracking of solved heads + if libs == 1: + workingMoves = [] + laddered = copy.searchIsLadderCaptured(loc,True) + else: + workingMoves = copy.searchIsLadderCapturedAttackerFirst2Libs(loc) + laddered = len(workingMoves) > 0 + + chainHeadsSolved[head] = laddered + if laddered: + f(loc,pos,workingMoves) + + + #Returns the new idx, which could be the same as idx if this isn't a good training row + def fill_row_features(self, board, pla, opp, boards, moves, move_idx, rules, bin_input_data, global_input_data, idx): + assert(self.version >= 10) + + bsize = board.size + assert(self.pos_len >= bsize) + assert(len(boards) > 0) + assert(board.zobrist == boards[move_idx].zobrist) + + for y in range(bsize): + for x in range(bsize): + pos = self.xy_to_tensor_pos(x,y) + bin_input_data[idx,pos,0] = 1.0 + loc = board.loc(x,y) + stone = board.board[loc] + if stone == pla: + bin_input_data[idx,pos,1] = 1.0 + elif stone == opp: + bin_input_data[idx,pos,2] = 1.0 + + if stone == pla or stone == opp: + libs = board.num_liberties(loc) + if libs == 1: + bin_input_data[idx,pos,3] = 1.0 + elif libs == 2: + bin_input_data[idx,pos,4] = 1.0 + elif libs == 3: + bin_input_data[idx,pos,5] = 1.0 + + #Python code does NOT handle superko + if board.simple_ko_point is not None: + pos = self.loc_to_tensor_pos(board.simple_ko_point,board) + bin_input_data[idx,pos,6] = 1.0 + #Python code does NOT handle ko-prohibited encore spots or anything relating to the encore + #so features 7 and 8 leave that blank + + if move_idx >= 1 and moves[move_idx-1][0] == opp: + prev1_loc = moves[move_idx-1][1] + if prev1_loc is not None and prev1_loc != Board.PASS_LOC: + pos = self.loc_to_tensor_pos(prev1_loc,board) + bin_input_data[idx,pos,9] = 1.0 + elif prev1_loc == Board.PASS_LOC: + global_input_data[idx,0] = 1.0 + + if move_idx >= 2 and moves[move_idx-2][0] == pla: + prev2_loc = moves[move_idx-2][1] + if prev2_loc is not None and prev2_loc != Board.PASS_LOC: + pos = self.loc_to_tensor_pos(prev2_loc,board) + bin_input_data[idx,pos,10] = 1.0 + elif prev2_loc == Board.PASS_LOC: + global_input_data[idx,1] = 1.0 + + if move_idx >= 3 and moves[move_idx-3][0] == opp: + prev3_loc = moves[move_idx-3][1] + if prev3_loc is not None and prev3_loc != Board.PASS_LOC: + pos = self.loc_to_tensor_pos(prev3_loc,board) + bin_input_data[idx,pos,11] = 1.0 + elif prev3_loc == Board.PASS_LOC: + global_input_data[idx,2] = 1.0 + + if move_idx >= 4 and moves[move_idx-4][0] == pla: + prev4_loc = moves[move_idx-4][1] + if prev4_loc is not None and prev4_loc != Board.PASS_LOC: + pos = self.loc_to_tensor_pos(prev4_loc,board) + bin_input_data[idx,pos,12] = 1.0 + elif prev4_loc == Board.PASS_LOC: + global_input_data[idx,3] = 1.0 + + if move_idx >= 5 and moves[move_idx-5][0] == opp: + prev5_loc = moves[move_idx-5][1] + if prev5_loc is not None and prev5_loc != Board.PASS_LOC: + pos = self.loc_to_tensor_pos(prev5_loc,board) + bin_input_data[idx,pos,13] = 1.0 + elif prev5_loc == Board.PASS_LOC: + global_input_data[idx,4] = 1.0 + + def addLadderFeature(loc,pos,workingMoves): + assert(board.board[loc] == Board.BLACK or board.board[loc] == Board.WHITE) + bin_input_data[idx,pos,14] = 1.0 + if board.board[loc] == opp and board.num_liberties(loc) > 1: + for workingMove in workingMoves: + workingPos = self.loc_to_tensor_pos(workingMove,board) + bin_input_data[idx,workingPos,17] = 1.0 + + self.iterLadders(board, addLadderFeature) + + if move_idx > 0: + prevBoard = boards[move_idx-1] + else: + prevBoard = board + def addPrevLadderFeature(loc,pos,workingMoves): + assert(prevBoard.board[loc] == Board.BLACK or prevBoard.board[loc] == Board.WHITE) + bin_input_data[idx,pos,15] = 1.0 + self.iterLadders(prevBoard, addPrevLadderFeature) + + if move_idx > 1: + prevPrevBoard = boards[move_idx-2] + else: + prevPrevBoard = prevBoard + def addPrevPrevLadderFeature(loc,pos,workingMoves): + assert(prevPrevBoard.board[loc] == Board.BLACK or prevPrevBoard.board[loc] == Board.WHITE) + bin_input_data[idx,pos,16] = 1.0 + self.iterLadders(prevPrevBoard, addPrevPrevLadderFeature) + + #Features 18,19 - area + area = [0 for i in range(board.arrsize)] + + if rules["scoringRule"] == "SCORING_AREA" and rules["taxRule"] == "TAX_NONE": + safeBigTerritories = True + nonPassAliveStones = True + unsafeBigTerritories = True + board.calculateArea(area,nonPassAliveStones,safeBigTerritories,unsafeBigTerritories,rules["multiStoneSuicideLegal"]) + else: + hasAreaFeature = False + keepTerritories = False + keepStones = False + if rules["scoringRule"] == "SCORING_AREA" and (rules["taxRule"] == "TAX_SEKI" or rules["taxRule"] == "TAX_ALL"): + hasAreaFeature = True + keepTerritories = False + keepStones = True + elif rules["scoringRule"] == "SCORING_TERRITORY" and rules["taxRule"] == "TAX_NONE": + if rules["encorePhase"] >= 2: + hasAreaFeature = True + keepTerritories = True + keepStones = False + elif rules["scoringRule"] == "SCORING_TERRITORY" and (rules["taxRule"] == "TAX_SEKI" or rules["taxRule"] == "TAX_ALL"): + if rules["encorePhase"] >= 2: + hasAreaFeature = True + keepTerritories = False + keepStones = False + else: + assert(False) + + if hasAreaFeature: + board.calculateNonDameTouchingArea( + area, + keepTerritories, + keepStones, + rules["multiStoneSuicideLegal"] + ) + + for y in range(bsize): + for x in range(bsize): + loc = board.loc(x,y) + pos = self.xy_to_tensor_pos(x,y) + + if area[loc] == pla: + bin_input_data[idx,pos,18] = 1.0 + elif area[loc] == opp: + bin_input_data[idx,pos,19] = 1.0 + + #Features 20,21 - second encore phase starting stones, we just set them to the current stones in pythong + #since we don't really have a jp rules impl + if rules["encorePhase"] >= 2: + for y in range(bsize): + for x in range(bsize): + pos = self.xy_to_tensor_pos(x,y) + loc = board.loc(x,y) + stone = board.board[loc] + if stone == pla: + bin_input_data[idx,pos,20] = 1.0 + elif stone == opp: + bin_input_data[idx,pos,21] = 1.0 + + bArea = board.size * board.size + whiteKomi = rules["whiteKomi"] + if rules["scoringRule"] == "SCORING_TERRITORY": + whiteSelfKomi = whiteKomi + board.num_non_pass_moves_made[Board.BLACK] - board.num_non_pass_moves_made[Board.WHITE] + selfKomi = (whiteSelfKomi if pla == Board.WHITE else -whiteSelfKomi) + else: + selfKomi = (whiteKomi if pla == Board.WHITE else -whiteKomi) + + if selfKomi > bArea+1: + selfKomi = bArea+1 + if selfKomi < -bArea-1: + selfKomi = -bArea-1 + global_input_data[idx,5] = selfKomi/20.0 + + if rules["koRule"] == "KO_SIMPLE": + pass + elif rules["koRule"] == "KO_POSITIONAL" or rules["koRule"] == "KO_SPIGHT": + global_input_data[idx,6] = 1.0 + global_input_data[idx,7] = 0.5 + elif rules["koRule"] == "KO_SITUATIONAL": + global_input_data[idx,6] = 1.0 + global_input_data[idx,7] = -0.5 + else: + assert(False) + + if rules["multiStoneSuicideLegal"]: + global_input_data[idx,8] = 1.0 + + if rules["scoringRule"] == "SCORING_AREA": + pass + elif rules["scoringRule"] == "SCORING_TERRITORY": + global_input_data[idx,9] = 1.0 + else: + assert(False) + + if rules["taxRule"] == "TAX_NONE": + pass + elif rules["taxRule"] == "TAX_SEKI": + global_input_data[idx,10] = 1.0 + elif rules["taxRule"] == "TAX_ALL": + global_input_data[idx,10] = 1.0 + global_input_data[idx,11] = 1.0 + else: + assert(False) + + if rules["encorePhase"] > 0: + global_input_data[idx,12] = 1.0 + if rules["encorePhase"] > 1: + global_input_data[idx,13] = 1.0 + passWouldEndPhase = rules["passWouldEndPhase"] + global_input_data[idx,14] = (1.0 if passWouldEndPhase else 0.0) + + global_input_data[idx,15] = 1.0 if rules["asymPowersOfTwo"] != 0 else 0.0 + global_input_data[idx,16] = rules["asymPowersOfTwo"] + + if "hasButton" in rules and rules["hasButton"] and Board.PASS_LOC not in [move[1] for move in moves]: + global_input_data[idx,17] = 1.0 + + if rules["scoringRule"] == "SCORING_AREA" or rules["encorePhase"] > 1: + boardAreaIsEven = (board.size % 2 == 0) + + drawableKomisAreEven = boardAreaIsEven + + if drawableKomisAreEven: + komiFloor = math.floor(selfKomi / 2.0) * 2.0 + else: + komiFloor = math.floor((selfKomi-1.0) / 2.0) * 2.0 + 1.0 + + delta = selfKomi - komiFloor + assert(delta >= -0.0001) + assert(delta <= 2.0001) + if delta < 0.0: + delta = 0.0 + if delta > 2.0: + delta = 2.0 + + if delta < 0.5: + wave = delta + elif delta < 1.5: + wave = 1.0-delta + else: + wave = delta-2.0 + + global_input_data[idx,18] = wave + + return idx+1 diff --git a/training/scripts/dated/20241110-124043/forward_model.py b/training/scripts/dated/20241110-124043/forward_model.py new file mode 100644 index 0000000..97a46df --- /dev/null +++ b/training/scripts/dated/20241110-124043/forward_model.py @@ -0,0 +1,146 @@ +#!/usr/bin/python3 +import sys +import os +import argparse +import traceback +import random +import math +import time +import logging +import json +import numpy as np +from collections import defaultdict +from typing import Dict, List + +import torch +import torch.nn +from torch.optim.swa_utils import AveragedModel + +import modelconfigs +from model_pytorch import Model +from metrics_pytorch import Metrics +import data_processing_pytorch +from load_model import load_model + +# HANDLE COMMAND AND ARGS ------------------------------------------------------------------- + +if __name__ == "__main__": + + description = """ + Test neural net on Go positions from npz files of batches from selfplay. + """ + + parser = argparse.ArgumentParser(description=description) + parser.add_argument('-npz', help='NPZ file to evaluate', required=True) + parser.add_argument('-checkpoint', help='Checkpoint to test', required=False) + parser.add_argument('-pos-len', help='Spatial length of expected training data', type=int, required=True) + parser.add_argument('-use-swa', help='Use SWA model', action="store_true", required=False) + parser.add_argument('-gpu-idx', help='GPU idx', type=int, required=False) + + args = vars(parser.parse_args()) + +def main(args): + npz_file = args["npz"] + checkpoint_file = args["checkpoint"] + pos_len = args["pos_len"] + use_swa = args["use_swa"] + gpu_idx = args["gpu_idx"] + + world_size = 1 + rank = 0 + + # SET UP LOGGING ------------------------------------------------------------- + + logging.root.handlers = [] + logging.basicConfig( + level=logging.INFO, + format="%(message)s", + handlers=[ + logging.StreamHandler(stream=sys.stdout) + ], + ) + np.set_printoptions(linewidth=150) + + # FIGURE OUT GPU ------------------------------------------------------------ + if gpu_idx is not None: + torch.cuda.set_device(gpu_idx) + logging.info("Using GPU device: " + torch.cuda.get_device_name()) + device = torch.device("cuda", gpu_idx) + elif torch.cuda.is_available(): + logging.info("Using GPU device: " + torch.cuda.get_device_name()) + device = torch.device("cuda") + else: + logging.warning("WARNING: No GPU, using CPU") + device = torch.device("cpu") + + # LOAD MODEL --------------------------------------------------------------------- + + model, swa_model, _ = load_model(checkpoint_file, use_swa, device=device, pos_len=pos_len, verbose=False) + model_config = model.config + + batch = np.load(npz_file) + + with torch.no_grad(): + model.eval() + if swa_model is not None: + swa_model.eval() + + if swa_model is not None: + model_outputs = swa_model(torch.tensor(batch["binaryInputNCHW"],device=device),torch.tensor(batch["globalInputNC"],device=device)) + else: + model_outputs = model(torch.tensor(batch["binaryInputNCHW"],device=device),torch.tensor(batch["globalInputNC"],device=device)) + + postprocessed = model.postprocess_output(model_outputs) + + results = tuple(tensor.cpu() for tensor in postprocessed[0]) + + ( + policy_logits, # N, num_policy_outputs, move + value_logits, # N, {win,loss,noresult} + td_value_logits, # N, {long, mid, short} {win,loss,noresult} + pred_td_score, # N, {long, mid, short} + ownership_pretanh, # N, 1, y, x + pred_scoring, # N, 1, y, x + futurepos_pretanh, # N, 2, y, x + seki_logits, # N, 4, y, x + pred_scoremean, # N + pred_scorestdev, # N + pred_lead, # N + pred_variance_time, # N + pred_shortterm_value_error, # N + pred_shortterm_score_error, # N + scorebelief_logits, # N, 2 * (self.pos_len*self.pos_len + EXTRA_SCORE_DISTR_RADIUS) + ) = results + + for batch_idx in range(batch["binaryInputNCHW"].shape[0]): + print(f"Batch idx {batch_idx}") + + policy = torch.softmax(policy_logits[batch_idx],dim=1).numpy() + print("Policy") + print(f"Pass {policy[0,pos_len*pos_len]}") + for y in range(pos_len): + print(" ".join(["%5.2f%%" % (100.0 * prob) for prob in policy[0,y*pos_len:(y+1)*pos_len]])) + if model_config["version"] >= 12: + print("LongOptPolicy") + print(f"Pass {policy[4,pos_len*pos_len]}") + for y in range(pos_len): + print(" ".join(["%5.2f%%" % (100.0 * prob) for prob in policy[4,y*pos_len:(y+1)*pos_len]])) + print("ShortOptPolicy") + print(f"Pass {policy[5,pos_len*pos_len]}") + for y in range(pos_len): + print(" ".join("%5.2f%%" % (100.0 * prob) for prob in policy[5,y*pos_len:(y+1)*pos_len])) + ownership = torch.tanh(ownership_pretanh[batch_idx,0]) + print("Ownership") + for y in range(pos_len): + print(" ".join("%6.2f" % (100.0 * prob) for prob in ownership[y])) + + print("Value " + " ".join("%5.2fc" % (100.0 * prob) for prob in torch.softmax(value_logits[batch_idx],dim=0).numpy())) + print("ScoreMean " + str(pred_scoremean[batch_idx].item())) + print("ScoreMeanSq " + str(pred_scorestdev[batch_idx].item() ** 2 + pred_scoremean[batch_idx].item() ** 2)) + print("Lead " + str(pred_lead[batch_idx].item())) + print("Vartime " + str(pred_variance_time[batch_idx].item())) + print("STWinLossError " + str(100.0 * math.sqrt(pred_shortterm_value_error[batch_idx].item())) + "c") + print("STScoreError " + str(math.sqrt(pred_shortterm_score_error[batch_idx].item()))) + +if __name__ == "__main__": + main(args) diff --git a/training/scripts/dated/20241110-124043/gamestate.py b/training/scripts/dated/20241110-124043/gamestate.py new file mode 100644 index 0000000..dbfec57 --- /dev/null +++ b/training/scripts/dated/20241110-124043/gamestate.py @@ -0,0 +1,314 @@ + +from typing import Dict, Any, List, Optional, TYPE_CHECKING +import math + +import numpy as np + +from board import Board +from features import Features +from sgfmetadata import SGFMetadata + +if TYPE_CHECKING: + from model_pytorch import Model + +class GameState: + RULES_TT = { + "koRule": "KO_POSITIONAL", + "scoringRule": "SCORING_AREA", + "taxRule": "TAX_NONE", + "multiStoneSuicideLegal": True, + "hasButton": False, + "encorePhase": 0, + "passWouldEndPhase": False, + "whiteKomi": 7.5, + "asymPowersOfTwo": 0.0, + } + RULES_CHINESE = { + "koRule": "KO_SIMPLE", + "scoringRule": "SCORING_AREA", + "taxRule": "TAX_NONE", + "multiStoneSuicideLegal": False, + "hasButton": False, + "encorePhase": 0, + "passWouldEndPhase": False, + "whiteKomi": 7.5, + "asymPowersOfTwo": 0.0, + } + RULES_JAPANESE = { + "koRule": "KO_SIMPLE", + "scoringRule": "SCORING_TERRITORY", + "taxRule": "TAX_SEKI", + "multiStoneSuicideLegal": False, + "hasButton": False, + "encorePhase": 0, + "passWouldEndPhase": False, + "whiteKomi": 6.5, + "asymPowersOfTwo": 0.0, + } + + + def __init__(self, board_size: int, rules: Dict[str,Any]): + self.board_size = board_size + self.board = Board(size=board_size) + self.moves = [] + self.boards = [self.board.copy()] + self.rules = rules.copy() + self.redo_stack = [] + + def play(self, pla, loc): + self.board.play(pla,loc) + self.moves.append((pla,loc)) + self.boards.append(self.board.copy()) + if len(self.redo_stack) > 0: + move, _ = self.redo_stack[-1] + if move == (pla,loc): + self.redo_stack.pop() + else: + self.redo_stack.clear() + + def can_undo(self) -> bool: + return len(self.moves) > 0 + + def undo(self): + assert self.can_undo() + move = self.moves.pop() + board = self.boards.pop() + self.board = self.boards[-1].copy() + self.redo_stack.append((move,board)) + + def can_redo(self) -> bool: + return len(self.redo_stack) > 0 + + def redo(self): + assert self.can_redo() + move, board = self.redo_stack.pop() + self.moves.append(move) + self.boards.append(board) + self.board = self.boards[-1].copy() + + def get_input_features(self, features: Features): + bin_input_data = np.zeros(shape=[1]+features.bin_input_shape, dtype=np.float32) + global_input_data = np.zeros(shape=[1]+features.global_input_shape, dtype=np.float32) + pos_len = features.pos_len + pla = self.board.pla + opp = Board.get_opp(pla) + move_idx = len(self.moves) + # fill_row_features assumes N(HW)C order but we actually use NCHW order in the model, so work with it and revert + bin_input_data = np.transpose(bin_input_data,axes=(0,2,3,1)) + bin_input_data = bin_input_data.reshape([1,pos_len*pos_len,-1]) + features.fill_row_features(self.board,pla,opp,self.boards,self.moves,move_idx,self.rules,bin_input_data,global_input_data,idx=0) + bin_input_data = bin_input_data.reshape([1,pos_len,pos_len,-1]) + bin_input_data = np.transpose(bin_input_data,axes=(0,3,1,2)) + return bin_input_data, global_input_data + + def get_model_outputs(self, model: "Model", sgfmeta: Optional[SGFMetadata] = None, extra_output_names: List[str] = []): + import torch + from model_pytorch import Model, ExtraOutputs + with torch.no_grad(): + model.eval() + features = Features(model.config, model.pos_len) + + bin_input_data, global_input_data = self.get_input_features(features) + # Currently we don't actually do any symmetries + # symmetry = 0 + # model_outputs = model(apply_symmetry(batch["binaryInputNCHW"],symmetry),batch["globalInputNC"]) + + input_meta = None + if sgfmeta is not None: + metarow = sgfmeta.get_metadata_row(nextPlayer=self.board.pla, boardArea=self.board.size*self.board.size) + input_meta = torch.tensor(metarow, dtype=torch.float32, device=model.device) + input_meta = input_meta.reshape([1,-1]) + + extra_outputs = ExtraOutputs(extra_output_names) + + model_outputs = model( + torch.tensor(bin_input_data, dtype=torch.float32, device=model.device), + torch.tensor(global_input_data, dtype=torch.float32, device=model.device), + input_meta = input_meta, + extra_outputs=extra_outputs, + ) + + available_extra_outputs = extra_outputs.available + + outputs = model.postprocess_output(model_outputs) + ( + policy_logits, # N, num_policy_outputs, move + value_logits, # N, {win,loss,noresult} + td_value_logits, # N, {long, mid, short} {win,loss,noresult} + pred_td_score, # N, {long, mid, short} + ownership_pretanh, # N, 1, y, x + pred_scoring, # N, 1, y, x + futurepos_pretanh, # N, 2, y, x + seki_logits, # N, 4, y, x + pred_scoremean, # N + pred_scorestdev, # N + pred_lead, # N + pred_variance_time, # N + pred_shortterm_value_error, # N + pred_shortterm_score_error, # N + scorebelief_logits, # N, 2 * (self.pos_len*self.pos_len + EXTRA_SCORE_DISTR_RADIUS) + ) = (x[0] for x in outputs[0]) # N = 0 + + policy0 = torch.nn.functional.softmax(policy_logits[0,:],dim=0).cpu().numpy() + policy1 = torch.nn.functional.softmax(policy_logits[1,:],dim=0).cpu().numpy() + value = torch.nn.functional.softmax(value_logits,dim=0).cpu().numpy() + td_value = torch.nn.functional.softmax(td_value_logits[0,:],dim=0).cpu().numpy() + td_value2 = torch.nn.functional.softmax(td_value_logits[1,:],dim=0).cpu().numpy() + td_value3 = torch.nn.functional.softmax(td_value_logits[2,:],dim=0).cpu().numpy() + scoremean = pred_scoremean.cpu().item() + td_score = pred_td_score.cpu().numpy() + scorestdev = pred_scorestdev.cpu().item() + lead = pred_lead.cpu().item() + vtime = pred_variance_time.cpu().item() + estv = math.sqrt(pred_shortterm_value_error.cpu().item()) + ests = math.sqrt(pred_shortterm_score_error.cpu().item()) + ownership = torch.tanh(ownership_pretanh).cpu().numpy() + scoring = pred_scoring.cpu().numpy() + futurepos = torch.tanh(futurepos_pretanh).cpu().numpy() + seki_probs = torch.nn.functional.softmax(seki_logits[0:3,:,:],dim=0).cpu().numpy() + seki = seki_probs[1] - seki_probs[2] + seki2 = torch.sigmoid(seki_logits[3,:,:]).cpu().numpy() + scorebelief = torch.nn.functional.softmax(scorebelief_logits,dim=0).cpu().numpy() + + board = self.board + + moves_and_probs0 = [] + for i in range(len(policy0)): + move = features.tensor_pos_to_loc(i,board) + if i == len(policy0)-1: + moves_and_probs0.append((Board.PASS_LOC,policy0[i])) + elif board.would_be_legal(board.pla,move): + moves_and_probs0.append((move,policy0[i])) + + moves_and_probs1 = [] + for i in range(len(policy1)): + move = features.tensor_pos_to_loc(i,board) + if i == len(policy1)-1: + moves_and_probs1.append((Board.PASS_LOC,policy1[i])) + elif board.would_be_legal(board.pla,move): + moves_and_probs1.append((move,policy1[i])) + + ownership_flat = ownership.reshape([features.pos_len * features.pos_len]) + ownership_by_loc = [] + board = self.board + for y in range(board.size): + for x in range(board.size): + loc = board.loc(x,y) + pos = features.loc_to_tensor_pos(loc,board) + if board.pla == Board.WHITE: + ownership_by_loc.append((loc,ownership_flat[pos])) + else: + ownership_by_loc.append((loc,-ownership_flat[pos])) + + scoring_flat = scoring.reshape([features.pos_len * features.pos_len]) + scoring_by_loc = [] + board = self.board + for y in range(board.size): + for x in range(board.size): + loc = board.loc(x,y) + pos = features.loc_to_tensor_pos(loc,board) + if board.pla == Board.WHITE: + scoring_by_loc.append((loc,scoring_flat[pos])) + else: + scoring_by_loc.append((loc,-scoring_flat[pos])) + + futurepos0_flat = futurepos[0,:,:].reshape([features.pos_len * features.pos_len]) + futurepos0_by_loc = [] + board = self.board + for y in range(board.size): + for x in range(board.size): + loc = board.loc(x,y) + pos = features.loc_to_tensor_pos(loc,board) + if board.pla == Board.WHITE: + futurepos0_by_loc.append((loc,futurepos0_flat[pos])) + else: + futurepos0_by_loc.append((loc,-futurepos0_flat[pos])) + + futurepos1_flat = futurepos[1,:,:].reshape([features.pos_len * features.pos_len]) + futurepos1_by_loc = [] + board = self.board + for y in range(board.size): + for x in range(board.size): + loc = board.loc(x,y) + pos = features.loc_to_tensor_pos(loc,board) + if board.pla == Board.WHITE: + futurepos1_by_loc.append((loc,futurepos1_flat[pos])) + else: + futurepos1_by_loc.append((loc,-futurepos1_flat[pos])) + + seki_flat = seki.reshape([features.pos_len * features.pos_len]) + seki_by_loc = [] + board = self.board + for y in range(board.size): + for x in range(board.size): + loc = board.loc(x,y) + pos = features.loc_to_tensor_pos(loc,board) + if board.pla == Board.WHITE: + seki_by_loc.append((loc,seki_flat[pos])) + else: + seki_by_loc.append((loc,-seki_flat[pos])) + + seki_flat2 = seki2.reshape([features.pos_len * features.pos_len]) + seki_by_loc2 = [] + board = self.board + for y in range(board.size): + for x in range(board.size): + loc = board.loc(x,y) + pos = features.loc_to_tensor_pos(loc,board) + seki_by_loc2.append((loc,seki_flat2[pos])) + + moves_and_probs = sorted(moves_and_probs0, key=lambda moveandprob: moveandprob[1], reverse=True) + # Generate a random number biased small and then find the appropriate move to make + # Interpolate from moving uniformly to choosing from the triangular distribution + alpha = 1 + beta = 1 + math.sqrt(max(0,len(self.moves)-20)) + r = np.random.beta(alpha,beta) + probsum = 0.0 + i = 0 + genmove_result = Board.PASS_LOC + while True: + (move,prob) = moves_and_probs[i] + probsum += prob + if i >= len(moves_and_probs)-1 or probsum > r: + genmove_result = move + break + i += 1 + + # Transpose attention so that both it and reverse attention are in n c (hw) format. + for name in list(extra_outputs.returned.keys()): + if name.endswith(".attention"): + extra_outputs.returned[name] = torch.transpose(extra_outputs.returned[name],1,2) + + return { + "policy0": policy0, + "policy1": policy1, + "moves_and_probs0": moves_and_probs0, + "moves_and_probs1": moves_and_probs1, + "value": value, + "td_value": td_value, + "td_value2": td_value2, + "td_value3": td_value3, + "scoremean": scoremean, + "td_score": td_score, + "scorestdev": scorestdev, + "lead": lead, + "vtime": vtime, + "estv": estv, + "ests": ests, + "ownership": ownership, + "ownership_by_loc": ownership_by_loc, + "scoring": scoring, + "scoring_by_loc": scoring_by_loc, + "futurepos": futurepos, + "futurepos0_by_loc": futurepos0_by_loc, + "futurepos1_by_loc": futurepos1_by_loc, + "seki": seki, + "seki_by_loc": seki_by_loc, + "seki2": seki2, + "seki_by_loc2": seki_by_loc2, + "scorebelief": scorebelief, + "genmove_result": genmove_result, + **{ name:activation[0].numpy() for name, activation in extra_outputs.returned.items() }, + "available_extra_outputs": available_extra_outputs, + } + diff --git a/training/scripts/dated/20241110-124043/gatekeeper.cfg b/training/scripts/dated/20241110-124043/gatekeeper.cfg new file mode 100644 index 0000000..6dbedc4 --- /dev/null +++ b/training/scripts/dated/20241110-124043/gatekeeper.cfg @@ -0,0 +1,112 @@ + +# Logs------------------------------------------------------------------------------------ + +logSearchInfo = false +logMoves = false +logGamesEvery = 10 +logToStdout = true + +# Fancy game selfplay settings-------------------------------------------------------------------- + +# startPosesProb = 0.0 # Play this proportion of games starting from SGF positions +# startPosesFromSgfDir = DIRECTORYPATH # Load SGFs from this dir +# startPosesLoadProb = 1.0 # Only load each position from each SGF with this chance (save memory) +# startPosesTurnWeightLambda = 0 # 0 = equal weight 0.01 = decrease probability by 1% per turn -0.01 = increase probability by 1% per turn. + +# Match----------------------------------------------------------------------------------- + +numGameThreads = 128 +maxMovesPerGame = 1600 +numGamesPerGating = 200 + +allowResignation = true +resignThreshold = -0.90 +resignConsecTurns = 5 + +# Disabled, since we're not using any root noise and such +# Could have a slight weirdness on rootEndingBonusPoints, but shouldn't be a big deal. +# clearBotBeforeSearch = true + +# Rules------------------------------------------------------------------------------------ + +koRules = SIMPLE,POSITIONAL,SITUATIONAL +scoringRules = AREA,TERRITORY +taxRules = NONE,NONE,SEKI,SEKI,ALL +multiStoneSuicideLegals = false,true +hasButtons = false,false,true + +bSizes = 7,8,9 +bSizeRelProbs = 1,1,8 +allowRectangleProb = 0.50 + +komiAuto = True # Automatically adjust komi to what the neural nets think are fair +#komiMean = 7.5 # Specify explicit komi +handicapProb = 0.0 # Probability of handicap game +handicapCompensateKomiProb = 1.0 # Probability of compensating komi to fair during handicap game +# numExtraBlackFixed = 3 # When playing handicap games, always use exactly this many extra black moves + +# Search limits----------------------------------------------------------------------------------- +maxVisits = 150 +numSearchThreads = 1 + +# GPU Settings------------------------------------------------------------------------------- + +nnMaxBatchSize = 128 +nnCacheSizePowerOfTwo = 21 +nnMutexPoolSizePowerOfTwo = 15 +numNNServerThreadsPerModel = 1 +nnRandomize = true + +# CUDA GPU settings-------------------------------------- +# cudaDeviceToUse = 0 #use device 0 for all server threads (numNNServerThreadsPerModel) unless otherwise specified per-model or per-thread-per-model +# cudaDeviceToUseModel0 = 3 #use device 3 for model 0 for all threads unless otherwise specified per-thread for this model +# cudaDeviceToUseModel1 = 2 #use device 2 for model 1 for all threads unless otherwise specified per-thread for this model +# cudaDeviceToUseModel0Thread0 = 3 #use device 3 for model 0, server thread 0 +# cudaDeviceToUseModel0Thread1 = 2 #use device 2 for model 0, server thread 1 + +cudaUseFP16 = auto +cudaUseNHWC = auto + +# Root move selection and biases------------------------------------------------------------------------------ + +chosenMoveTemperatureEarly = 0.5 +chosenMoveTemperatureHalflife = 19 +chosenMoveTemperature = 0.2 +chosenMoveSubtract = 0 +chosenMovePrune = 1 + +useLcbForSelection = true +lcbStdevs = 5.0 +minVisitPropForLCB = 0.15 + +# Internal params------------------------------------------------------------------------------ + +winLossUtilityFactor = 1.0 +staticScoreUtilityFactor = 0.00 +dynamicScoreUtilityFactor = 0.25 +dynamicScoreCenterZeroWeight = 0.25 +dynamicScoreCenterScale = 0.50 +noResultUtilityForWhite = 0.0 +drawEquivalentWinsForWhite = 0.5 + +rootEndingBonusPoints = 0.5 +rootPruneUselessMoves = true + +cpuctExploration = 1.1 +cpuctExplorationLog = 0.0 +fpuReductionMax = 0.2 +rootFpuReductionMax = 0.1 +valueWeightExponent = 0.5 +policyOptimism = 1.0 +rootPolicyOptimism = 0.0 + +subtreeValueBiasFactor = 0.35 +subtreeValueBiasWeightExponent = 0.8 +useNonBuggyLcb = true +useGraphSearch = true + +useUncertainty = true +uncertaintyExponent = 1.0 +uncertaintyCoeff = 0.25 + +numVirtualLossesPerThread = 1 diff --git a/training/scripts/dated/20241110-124043/genboard_common.py b/training/scripts/dated/20241110-124043/genboard_common.py new file mode 100644 index 0000000..7873988 --- /dev/null +++ b/training/scripts/dated/20241110-124043/genboard_common.py @@ -0,0 +1,129 @@ +import traceback +import json +import math +import torch +import torch.nn as nn +import torch.nn.functional as F + +class ResBlock(nn.Module): + def __init__(self,num_channels,scale_init): + super(ResBlock, self).__init__() + kernel_size = 3 + self.biasa = nn.Parameter(torch.zeros(num_channels,1,1)) + self.conva = nn.Conv2d(in_channels=num_channels, out_channels=num_channels, kernel_size=kernel_size, padding=1, bias=False) + torch.nn.init.normal_(self.conva.weight,std=math.sqrt(2.0 / num_channels / kernel_size / kernel_size)*scale_init) + self.biasb = nn.Parameter(torch.zeros(num_channels,1,1)) + self.scalb = nn.Parameter(torch.ones(num_channels,1,1)) + self.convb = nn.Conv2d(in_channels=num_channels, out_channels=num_channels, kernel_size=kernel_size, padding=1, bias=False) + torch.nn.init.zeros_(self.convb.weight) + + def forward(self, trunk): + x = F.relu(trunk+self.biasa) + x = self.conva(x) + x = F.relu(x*self.scalb+self.biasb) + x = self.convb(x) + return trunk+x + +class GPoolResBlock(nn.Module): + def __init__(self,num_channels,scale_init): + super(GPoolResBlock, self).__init__() + kernel_size = 3 + self.biasa = nn.Parameter(torch.zeros(num_channels,1,1)) + self.conva = nn.Conv2d(in_channels=num_channels, out_channels=num_channels, kernel_size=kernel_size, padding=1, bias=False) + torch.nn.init.normal_(self.conva.weight,std=math.sqrt(1.0 / num_channels / kernel_size / kernel_size)*scale_init) + self.convg = nn.Conv2d(in_channels=num_channels, out_channels=num_channels, kernel_size=kernel_size, padding=1, bias=False) + torch.nn.init.normal_(self.convg.weight,std=math.sqrt(1.0 / num_channels / kernel_size / kernel_size)*math.sqrt(scale_init)) + self.matg = nn.Parameter(torch.zeros(num_channels,num_channels)) + torch.nn.init.normal_(self.matg,std=math.sqrt(1.0 / num_channels)*math.sqrt(scale_init)) + self.biasb = nn.Parameter(torch.zeros(num_channels,1,1)) + self.scalb = nn.Parameter(torch.ones(num_channels,1,1)) + self.convb = nn.Conv2d(in_channels=num_channels, out_channels=num_channels, kernel_size=kernel_size, padding=1, bias=False) + torch.nn.init.zeros_(self.convb.weight) + + def forward(self, trunk): + x = F.relu(trunk+self.biasa) + x = self.conva(x) + g = self.convg(x) + gsize = g.size() + g = torch.sum(g,(2,3)) / (gsize[2] * gsize[3]) # nchw -> nc + g = torch.matmul(g,self.matg) + g = g.view(gsize[0],gsize[1],1,1) + x = x + g + x = F.relu(x*self.scalb+self.biasb) + x = self.convb(x) + return trunk+x + + +class Model(nn.Module): + def __init__(self, num_channels, num_blocks): + super(Model, self).__init__() + # Channel 0: Next inference point + # Channel 1: On-board + # Channel 2: Black + # Channel 3: White + # Channel 4: Unknown + # Channel 5: Turn number / 100 + # Channel 6: Noise stdev in turn number / 50 + # Channel 7: Source + + self.inference_channel = 0 + self.num_channels = num_channels + self.num_blocks = num_blocks + self.conv0 = nn.Conv2d(in_channels=8, out_channels=self.num_channels, kernel_size=3, padding=1, bias=False) + + self.blocks = nn.ModuleList([]) + self.fixup_scale_init = 1.0 / math.sqrt(self.num_blocks) + self.blocks.append(ResBlock(self.num_channels,self.fixup_scale_init)) + self.blocks.append(ResBlock(self.num_channels,self.fixup_scale_init)) + + next_is_gpool = True + for b in range(num_blocks-2): + if next_is_gpool: + self.blocks.append(GPoolResBlock(self.num_channels,self.fixup_scale_init)) + else: + self.blocks.append(ResBlock(self.num_channels,self.fixup_scale_init)) + next_is_gpool = not next_is_gpool + + assert(len(self.blocks) == self.num_blocks) + + self.endtrunk_bias_focus = nn.Parameter(torch.zeros(self.num_channels,1,1)) + self.endtrunk_bias_g = nn.Parameter(torch.zeros(self.num_channels,1,1)) + self.convg = nn.Conv2d(in_channels=self.num_channels, out_channels=self.num_channels, kernel_size=1, padding=0, bias=False) + + self.fc1 = nn.Linear(self.num_channels*2, self.num_channels) + self.fc2 = nn.Linear(self.num_channels,3) + self.convaux = nn.Conv2d(in_channels=self.num_channels, out_channels=3, kernel_size=1, padding=0, bias=True) + + def forward(self, inputs): + trunk = self.conv0(inputs) + for i in range(self.num_blocks): + trunk = self.blocks[i](trunk) + + head_focus = F.relu(trunk+self.endtrunk_bias_focus) + head_g = F.relu(trunk+self.endtrunk_bias_g) + aux = self.convaux(head_focus) + gsize = head_g.size() + + x = torch.sum(head_focus * inputs[:,self.inference_channel:self.inference_channel+1,:,:],(2,3)) + g = torch.sum(head_g,(2,3)) / (gsize[2] * gsize[3]) # nchw -> nc + + x = torch.cat((x,g),dim=1) + + x = F.relu(self.fc1(x)) + x = self.fc2(x) + return x,aux + + def save_to_file(self, filename): + state_dict = self.state_dict() + data = {} + data["num_channels"] = self.num_channels + data["num_blocks"] = self.num_blocks + data["state_dict"] = state_dict + torch.save(data, filename) + + @staticmethod + def load_from_file(filename): + data = torch.load(filename) + model = Model(data["num_channels"], data["num_blocks"]) + model.load_state_dict(data["state_dict"]) + return model diff --git a/training/scripts/dated/20241110-124043/genboard_run.py b/training/scripts/dated/20241110-124043/genboard_run.py new file mode 100644 index 0000000..6d57139 --- /dev/null +++ b/training/scripts/dated/20241110-124043/genboard_run.py @@ -0,0 +1,203 @@ +#!/usr/bin/python3 +import sys +import os +import argparse +import traceback +import logging +import json +import math +import random +import hashlib +import numpy as np +import torch +import torch.nn as nn +import torch.nn.functional as F +import torch.optim as optim +from copy import deepcopy + +import data +from board import Board, IllegalMoveError +from genboard_common import Model + +if __name__ == '__main__': + + description = """ + Generate completions of Go positions + """ + + parser = argparse.ArgumentParser(description=description) + parser.add_argument('-model', help='Model file to load', required=True) + parser.add_argument('-board', help='Board pattern using {.,*},X,O,? for empty, black, white, unknown', required=True) + parser.add_argument('-turn', help='Approx turn number to tell the net to generate for, [0,300]', required=True, type=float) + parser.add_argument('-turnstdev', help='Approx turn number randomness [0,100]', required=True, type=float) + parser.add_argument('-source', help='Tell the net to mimic positions from source, {-1,0,1}', required=True, type=int) + parser.add_argument('-verbose', help='Print various info and debug messages instead of only the board', required=False, action='store_true') + parser.add_argument('-n', help='How many batches to generate, default 1', required=False, type=int, default=1) + parser.add_argument('-batchsize', help='How many positions to generate, default 1', required=False, type=int, default=1) + args = vars(parser.parse_args()) + + modelfile = args["model"] + boardstr = args["board"] + turn = args["turn"] + turnstdev = args["turnstdev"] + source = args["source"] + verbose = args["verbose"] + numbatches = args["n"] + batchsize = args["batchsize"] + + if turn < 0 or turn > 300: + raise Exception("Turn must be in [0,300]") + if turnstdev < 0 or turnstdev > 100: + raise Exception("Turn must be in [0,100]") + if source != -1 and source != 0 and source != 1: + raise Exception("Source must be in {-1,0,1}") + if numbatches < 0: + raise Exception("Num batches must be nonnegative") + if batchsize < 1: + raise Exception("Batchsize must be positive") + + cpudevice = torch.device("cpu") + if torch.cuda.is_available(): + if verbose: + print("CUDA is available, using it",flush=True) + gpudevice = torch.device("cuda:0") + else: + gpudevice = cpudevice + model = Model.load_from_file(modelfile).to(gpudevice) + + size = 19 + boardbase = [["." for x in range(size)] for y in range(size)] + boardbase[3][3] = "," + boardbase[9][3] = "," + boardbase[15][3] = "," + boardbase[3][9] = "," + boardbase[9][9] = "," + boardbase[15][9] = "," + boardbase[3][15] = "," + boardbase[9][15] = "," + boardbase[15][15] = "," + + num_channels = 8 + inputsbase = torch.zeros((1,num_channels,size,size)) + + inference_point_channel = 0 + black_channel = 2 + white_channel = 3 + unknown_channel = 4 + + # Channel 1: On-board + inputsbase[:,1,:,:].fill_(1.0) + + def fail_if_idx_too_large(idx): + if idx >= size * size: + raise Exception("Provided board is larger than 19x19") + + idx = 0 + for c in boardstr: + y = idx // 19 + x = idx % 19 + if c == "." or c == "*" or c == ",": + fail_if_idx_too_large(idx) + elif c == "X" or c == "x" or c == "B" or c == "b": + fail_if_idx_too_large(idx) + boardbase[y][x] = "X" + inputsbase[0,black_channel,y,x] = 1.0 + elif c == "O" or c == "o" or c == "W" or c == "w": + fail_if_idx_too_large(idx) + boardbase[y][x] = "O" + inputsbase[0,white_channel,y,x] = 1.0 + elif c == "?": + fail_if_idx_too_large(idx) + inputsbase[0,unknown_channel,y,x] = 1.0 + else: + # Ignore this char, counteract the += 1 at the end + idx -= 1 + idx += 1 + + # Channel 5: Turn number / 100 + inputsbase[:,5,:,:].fill_(turn / 100.0) + # Channel 6: Noise stdev in turn number / 50 + inputsbase[:,6,:,:].fill_(turnstdev / 50.0) + # Channel 7: Source + inputsbase[:,7,:,:].fill_(float(source)) + + rand = random.Random(os.urandom(32) + hashlib.md5(boardstr.encode()).hexdigest().encode()) + + with torch.no_grad(): + + for i in range(numbatches): + + flipx = rand.random() < 0.5 + flipy = rand.random() < 0.5 + swapxy = rand.random() < 0.5 + + flipx2 = rand.random() < 0.5 + flipy2 = rand.random() < 0.5 + swapxy2 = rand.random() < 0.5 + + def query_model(inputs): + inputstransformed = inputs.detach().clone() + if flipx: + if flipy: + inputstransformed = torch.flip(inputstransformed,[2,3]) + else: + inputstransformed = torch.flip(inputstransformed,[2]) + else: + if flipx: + inputstransformed = torch.flip(inputstransformed,[3]) + else: + pass + if swapxy: + inputstransformed = torch.transpose(inputstransformed,2,3) + + preds, auxpreds = model(inputstransformed.to(gpudevice)) + preds = F.softmax(preds,dim=1) + assert(len(preds.size()) == 2) + assert(preds.size()[0] == batchsize) + assert(preds.size()[1] == 3) + choices = [] + for b in range(batchsize): + weights = [preds[b,0],preds[b,1],preds[b,2]] + choice = rand.choices([0,1,2],weights=weights)[0] + choices.append(choice) + return choices + + inputs = inputsbase.expand([batchsize,-1,-1,-1]).detach().clone() + boards = [ deepcopy(boardbase) for b in range(batchsize) ] + + for y in range(size): + for x in range(size): + sx = x + sy = y + if flipx2: + sx = size - sx - 1 + if flipy2: + sy = size - sy - 1 + if swapxy2: + tmp = sx + sx = sy + sy = tmp + + if inputs[0,unknown_channel,sy,sx] == 1.0: + for b in range(batchsize): + inputs[b,unknown_channel,sy,sx] = 0.0 + inputs[b,inference_point_channel,sy,sx] = 1.0 + choices = query_model(inputs) + for b in range(batchsize): + inputs[b,inference_point_channel,sy,sx] = 0.0 + + choice = choices[b] + if choice == 0: + pass + elif choice == 1: + inputs[b,black_channel,sy,sx] = 1.0 + boards[b][sy][sx] = "X" + elif choice == 2: + inputs[b,white_channel,sy,sx] = 1.0 + boards[b][sy][sx] = "O" + + for b in range(batchsize): + s = "\n".join([" ".join(row) for row in boards[b]]) + s += "\n" + print(s) + sys.stdout.flush() diff --git a/training/scripts/dated/20241110-124043/genboard_train.py b/training/scripts/dated/20241110-124043/genboard_train.py new file mode 100644 index 0000000..2799d2f --- /dev/null +++ b/training/scripts/dated/20241110-124043/genboard_train.py @@ -0,0 +1,492 @@ +#!/usr/bin/python3 +import sys +import os +import argparse +import traceback +import logging +import json +import math +import random +import hashlib +import numpy as np +import torch +import torch.nn as nn +import torch.nn.functional as F +import torch.optim as optim + +import data +from board import Board, IllegalMoveError +from genboard_common import Model + +class ShuffledDataset(torch.utils.data.IterableDataset): + def __init__(self, dataset, shuffle_buffer_size): + super().__init__() + self.dataset = dataset + self.shuffle_buffer_size = shuffle_buffer_size + + def __iter__(self): + worker_info = torch.utils.data.get_worker_info() + if worker_info is None: + rand = random.Random(os.urandom(32)) + else: + rand = random.Random(os.urandom(32)+ "#ShuffledDataset#".encode() + str(worker_info.id).encode()) + + shuffle_buffer = [] + try: + it = iter(self.dataset) + while len(shuffle_buffer) < self.shuffle_buffer_size: + item = next(it) + if isinstance(item, Exception): + yield item + else: + shuffle_buffer.append(item) + except StopIteration: + self.shuffle_buffer_size = len(shuffle_buffer) + + print("Initial shuffle buffer filled", flush=True) + rand.shuffle(shuffle_buffer) + try: + while True: + try: + item = next(it) + if isinstance(item, Exception): + yield item + else: + idx = rand.randint(0, self.shuffle_buffer_size-1) + old_item = shuffle_buffer[idx] + shuffle_buffer[idx] = item + yield old_item + except StopIteration: + break + while len(shuffle_buffer) > 0: + yield shuffle_buffer.pop() + except GeneratorExit: + pass + +def rand_triangular(rand,maxvalue): + r = (maxvalue+1) * (1.0 - math.sqrt(rand.random())) + r = int(math.floor(r)) + if r <= 0: + return 0 + if r >= maxvalue: + return maxvalue + return r + +def random_subinterval(rand,size): + # Anchor rectangles near the edge more often + if rand.random() < 0.5: + x0 = rand_triangular(rand,size)-1 + x1 = rand_triangular(rand,size)-1 + else: + x0 = rand.randint(0,size-1) + x1 = rand.randint(0,size-1) + + if rand.random() < 0.5: + x0 = size - x0 - 1 + x1 = size - x1 - 1 + + if x0 > x1: + return (x1,x0) + return (x0,x1) + + +class SgfDataset(torch.utils.data.IterableDataset): + def __init__(self, files, max_turn, break_prob_per_turn, sample_prob, endless): + self.files = files + self.max_turn = max_turn + self.break_prob_per_turn = break_prob_per_turn + self.sample_prob = sample_prob + self.endless = endless + + def __iter__(self): + worker_info = torch.utils.data.get_worker_info() + if worker_info is None: + rand = random.Random(os.urandom(32)) + else: + rand = random.Random(os.urandom(32)+ "#SgfDataset#".encode() + str(worker_info.id).encode()) + + files = self.files + cpudevice = torch.device("cpu") + + try: + while True: + rand.shuffle(files) + file_count = 0 + error_count = 0 + print("Iterator beginning reading of files %d / %d" % (file_count, len(files)), flush=True) + for filename in files: + try: + (metadata,setup,moves,rules) = data.load_sgf_moves_exn(filename) + except Exception as e: + error_count += 1 + continue + # Only even 19x19 games! + if metadata.size != 19 or len(setup) != 0 or (metadata.handicap is not None and metadata.handicap != 0): + continue + board = Board(size=metadata.size) + turn_number = 0 + for (pla,loc) in moves: + + if rand.random() < self.sample_prob: + inputs = torch.zeros((8,metadata.size,metadata.size),dtype=torch.float32,device=cpudevice) + result = torch.zeros((3,),dtype=torch.float32,device=cpudevice) + aux = torch.zeros((3,metadata.size,metadata.size),dtype=torch.float32,device=cpudevice) + + (alwaysknownxmin,alwaysknownxmax) = random_subinterval(rand,metadata.size) + (alwaysknownymin,alwaysknownymax) = random_subinterval(rand,metadata.size) + + if alwaysknownxmin <= 0 and alwaysknownxmax >= metadata.size-1 and alwaysknownymin <= 0 and alwaysknownymax >= metadata.size-1: + pass + else: + # Channel 1: On-board + inputs[1,:,:].fill_(1.0) + + num_always_known_poses = 0 + if alwaysknownxmax < 0 or alwaysknownxmin >= metadata.size or alwaysknownymax < 0 or alwaysknownymin >= metadata.size: + num_always_known_poses = 0 + else: + num_always_known_poses = ( + ( min(alwaysknownxmax, metadata.size-1) - max(alwaysknownxmin, 0) + 1) * + ( min(alwaysknownymax, metadata.size-1) - max(alwaysknownymin, 0) + 1) + ) + num_not_always_known_poses = metadata.size * metadata.size - num_always_known_poses + inferenceidx = rand.randint(0,num_not_always_known_poses-1) + + flipx = rand.random() < 0.5 + flipy = rand.random() < 0.5 + swapxy = rand.random() < 0.5 + + idx = 0 + for y in range(metadata.size): + for x in range(metadata.size): + pos = y * metadata.size + x + always_known = (x >= alwaysknownxmin and x <= alwaysknownxmax and y >= alwaysknownymin and y <= alwaysknownymax) + + sx = x + sy = y + if flipx: + sx = metadata.size - sx - 1 + if flipy: + sy = metadata.size - sy - 1 + if swapxy: + tmp = sx + sx = sy + sy = tmp + stone = board.board[board.loc(sx,sy)] + + # Channel 4: Unknown + if idx > inferenceidx and not always_known: + inputs[4,y,x] = 1.0 + # Channel 0: Next inference point + elif idx == inferenceidx and not always_known: + inputs[0,y,x] = 1.0 + result + if stone == Board.BLACK: + result[1] = 1.0 + elif stone == Board.WHITE: + result[2] = 1.0 + else: + result[0] = 1.0 + else: + # Channel 2: Black + if stone == Board.BLACK: + inputs[2,y,x] = 1.0 + # Channel 3: White + elif stone == Board.WHITE: + inputs[3,y,x] = 1.0 + + if stone == Board.BLACK: + aux[1,y,x] = 1.0 + elif stone == Board.WHITE: + aux[2,y,x] = 1.0 + else: + aux[0,y,x] = 1.0 + + if not always_known: + idx += 1 + + assert(idx == num_not_always_known_poses) + + if rand.random() < 0.3: + turn_noise_stdev = 0.0 + reported_turn = turn_number + else: + turn_noise_stdev = (rand.random() ** 2.0) * 100 + reported_turn = turn_number + rand.normalvariate(0.0,turn_noise_stdev) + + # Channel 5: Turn number / 100 + inputs[5,:,:].fill_(reported_turn / 100.0) + # Channel 6: Noise stdev in turn number / 50 + inputs[6,:,:].fill_(turn_noise_stdev / 50.0) + # Channel 7: Source + is_kgs = ("/kgs" in filename) or ("\\KGS" in filename) or ("/KGS" in filename) or ("\\KGS" in filename) + is_fox = ("/fox" in filename) or ("\\fox" in filename) or ("/FOX" in filename) or ("\\FOX" in filename) + if is_kgs: + inputs[7,:,:].fill_(1.0) + elif is_fox: + inputs[7,:,:].fill_(-1.0) + + if rand.random() < 0.5: + if rand.random() < 0.5: + inputs = torch.flip(inputs,[1,2]) + aux = torch.flip(aux,[1,2]) + else: + inputs = torch.flip(inputs,[1]) + aux = torch.flip(aux,[1]) + else: + if rand.random() < 0.5: + inputs = torch.flip(inputs,[2]) + aux = torch.flip(aux,[2]) + else: + pass + + if rand.random() < 0.5: + inputs = torch.transpose(inputs,1,2) + aux = torch.transpose(aux,1,2) + + yield (inputs,result,aux) + + try: + board.play(pla,loc) + except IllegalMoveError as e: + # On illegal move in the SGF, don't attempt to recover, just move on to new game + print("Illegal move, skipping file " + filename + ":" + str(e), flush=True) + break + turn_number += 1 + if turn_number > self.max_turn: + break + if rand.random() < self.break_prob_per_turn: + break + + file_count += 1 + if file_count % 200 == 0: + print("Read through file %d / %d (error count %d)" % (file_count, len(files), error_count), flush=True) + + if not self.endless: + break + + except GeneratorExit: + pass + except Exception as e: + print("EXCEPTION IN GENERATOR: " + str(e)) + traceback.print_exc() + print("---",flush=True) + yield e + + +def save_json(data,filename): + with open(filename,"w") as f: + json.dump(data,f) + f.flush() + os.fsync(f.fileno()) + +def load_json(filename): + with open(filename) as f: + data = json.load(f) + return data + + +if __name__ == '__main__': + + description = """ + Train net to predict Go positions one stone at a time + """ + + parser = argparse.ArgumentParser(description=description) + parser.add_argument('-traindir', help='Dir to write to for recording training results', required=True) + parser.add_argument('-datadirs', help='Directory with sgfs', required=True) + parser.add_argument('-testprop', help='Proportion of data for test', type=float, required=True) + parser.add_argument('-lr-scale', help='LR multiplier', type=float, required=False) + parser.add_argument('-channels', help='Channels', type=int, required=True) + parser.add_argument('-blocks', help='Blocks', type=int, required=True) + parser.add_argument('-grad-clip-scale', help='Gradient clip multiplier', type=float, required=False) + parser.add_argument('-num-data-workers', help='Number of processes for data loading', type=int, required=False) + args = vars(parser.parse_args()) + + traindir = args["traindir"] + datadirs = args["datadirs"] + testprop = args["testprop"] + lr_scale = args["lr_scale"] + num_channels = args["channels"] + num_blocks = args["blocks"] + grad_clip_scale = args["grad_clip_scale"] + num_data_workers = args["num_data_workers"] + logfilemode = "a" + + if lr_scale is None: + lr_scale = 1.0 + if grad_clip_scale is None: + grad_clip_scale = 1.0 + + if num_data_workers is None: + num_data_workers = 0 + + if not os.path.exists(traindir): + os.mkdir(traindir) + + bareformatter = logging.Formatter("%(asctime)s %(message)s") + fh = logging.FileHandler(os.path.join(traindir,"train.log"), mode=logfilemode) + fh.setFormatter(bareformatter) + stdouthandler = logging.StreamHandler(sys.stdout) + stdouthandler.setFormatter(bareformatter) + trainlogger = logging.getLogger("trainlogger") + trainlogger.setLevel(logging.INFO) + trainlogger.addHandler(fh) + trainlogger.addHandler(stdouthandler) + trainlogger.propagate=False + np.set_printoptions(linewidth=150) + def trainlog(s): + trainlogger.info(s) + sys.stdout.flush() + + shuffle_buffer_size = 100000 + + files_found = 0 + trainfiles = [] + testfiles = [] + for datadir in datadirs.split(","): + for parent, subdirs, files in os.walk(datadir): + for name in files: + if name.endswith(".sgf"): + files_found += 1 + if files_found % 10000 == 0: + trainlog("Found %d sgfs..." % files_found) + r = float.fromhex("0."+hashlib.md5(os.path.join(parent,name).encode()).hexdigest()[:16]) + if r < testprop: + testfiles.append(os.path.join(parent,name)) + else: + trainfiles.append(os.path.join(parent,name)) + + trainlog("Found %d training sgfs" % len(trainfiles)) + trainlog("Found %d testing sgfs" % len(testfiles)) + + max_turn = 300 + break_prob_per_turn = 0.01 + + traindataset = ShuffledDataset(SgfDataset(trainfiles,max_turn,break_prob_per_turn,sample_prob=0.5,endless=True),shuffle_buffer_size) + testdataset = SgfDataset(testfiles,max_turn,break_prob_per_turn,sample_prob=0.2,endless=True) + + batch_size = 128 + trainloader = torch.utils.data.DataLoader(traindataset, batch_size=batch_size, shuffle=False, num_workers=num_data_workers, drop_last=True) + testloader = torch.utils.data.DataLoader(testdataset, batch_size=batch_size, shuffle=False, num_workers=num_data_workers, drop_last=True) + + trainlog("Made data loaders") + + samples_per_epoch = 400000 + samples_per_test = 25600 + batches_per_epoch = samples_per_epoch // batch_size + batches_per_test = samples_per_test // batch_size + + def lossfunc(inputs, results, preds, aux, auxpreds): + assert(preds.size()[1] == 3) + assert(auxpreds.size()[1] == 3) + main_loss = -torch.sum(results * F.log_softmax(preds,dim=1)) + aux_loss = -torch.sum(aux * F.log_softmax(auxpreds,dim=1) * inputs[:,4:5,:,:] / torch.sum(inputs[:,1:2,:,:], dim=[2,3], keepdim=True)) * 0.3 + return main_loss, aux_loss + + cpudevice = torch.device("cpu") + if torch.cuda.is_available(): + trainlog("CUDA is available, using it") + gpudevice = torch.device("cuda:0") + else: + gpudevice = cpudevice + + modelpath = os.path.join(traindir,"model.data") + optimpath = os.path.join(traindir,"optim.data") + traindatapath = os.path.join(traindir,"traindata.json") + if os.path.exists(modelpath): + trainlog("Loading preexisting model!") + model = Model.load_from_file(modelpath).to(gpudevice) + if model.num_channels != num_channels: + raise Exception("Number of channels in model is %d but command line arg was %d" % (model.num_channels,num_channels)) + if model.num_blocks != num_blocks: + raise Exception("Number of blocks in model is %d but command line arg was %d" % (model.num_blocks,num_blocks)) + optimizer = optim.SGD(model.parameters(), lr=0.00001*lr_scale, momentum=0.9) + optimizer.load_state_dict(torch.load(optimpath)) + traindata = load_json(traindatapath) + else: + model = Model(num_channels=num_channels, num_blocks=num_blocks).to(gpudevice) + optimizer = optim.SGD(model.parameters(), lr=0.00001*lr_scale, momentum=0.9) + traindata = {"samples_so_far":0, "batches_so_far":0} + + trainlog("Saving!") + model.save_to_file(modelpath) + torch.save(optimizer.state_dict(), optimpath) + save_json(traindata,traindatapath) + + grad_clip_max = 400 * grad_clip_scale + #Loosen gradient clipping as we shift to smaller learning rates + grad_clip_max = grad_clip_max / math.sqrt(lr_scale) + + running_batch_count = 0 + running_main_loss = 0.0 + running_aux_loss = 0.0 + running_gnorm = 0.0 + running_ewms_exgnorm = 0.0 + print_every_batches = 100 + trainiter = iter(trainloader) + testiter = iter(testloader) + while True: + for i in range(batches_per_epoch): + inputs, results, auxs = next(trainiter) + inputs = inputs.to(gpudevice) + results = results.to(gpudevice) + auxs = auxs.to(gpudevice) + + optimizer.zero_grad() + + preds, auxpreds = model(inputs) + main_loss,aux_loss = lossfunc(inputs, results, preds, auxs, auxpreds) + loss = main_loss + aux_loss + loss.backward() + gnorm = torch.nn.utils.clip_grad_norm_(model.parameters(), grad_clip_max) + optimizer.step() + + traindata["samples_so_far"] += batch_size + traindata["batches_so_far"] += 1 + + running_batch_count += 1 + running_main_loss += main_loss.item() + running_aux_loss += aux_loss.item() + running_gnorm += gnorm + running_ewms_exgnorm += max(0.0, gnorm - grad_clip_max) + if running_batch_count >= print_every_batches: + trainlog("TRAIN samples: %d, batches: %d, main loss: %.5f, aux loss: %.5f, gnorm: %.2f, ewms_exgnorm: %.3g" % ( + traindata["samples_so_far"], + traindata["batches_so_far"], + running_main_loss / (running_batch_count * batch_size), + running_aux_loss / (running_batch_count * batch_size), + running_gnorm / (running_batch_count), + running_ewms_exgnorm / (running_batch_count), + )) + running_batch_count = 0 + running_main_loss = 0.0 + running_aux_loss = 0.0 + running_gnorm = 0.0 + running_ewms_exgnorm *= 0.5 + + trainlog("Saving!") + model.save_to_file(modelpath) + torch.save(optimizer.state_dict(), optimpath) + save_json(traindata,traindatapath) + + trainlog("Testing!") + test_samples = 0 + test_main_loss = 0.0 + test_aux_loss = 0.0 + with torch.no_grad(): + for i in range(batches_per_test): + inputs, results, auxs = next(testiter) + inputs = inputs.to(gpudevice) + results = results.to(gpudevice) + auxs = auxs.to(gpudevice) + + preds, auxpreds = model(inputs) + main_loss, aux_loss = lossfunc(inputs, results, preds, auxs, auxpreds) + test_samples += batch_size + test_main_loss += main_loss.item() + test_aux_loss += aux_loss.item() + trainlog("TEST samples %d, main loss: %.5f, aux loss %.5f" % (test_samples, test_main_loss / test_samples, test_aux_loss / test_samples)) + + +trainlog('Finished Training') diff --git a/training/scripts/dated/20241110-124043/genclumpyposes.py b/training/scripts/dated/20241110-124043/genclumpyposes.py new file mode 100644 index 0000000..c6aa590 --- /dev/null +++ b/training/scripts/dated/20241110-124043/genclumpyposes.py @@ -0,0 +1,705 @@ +import json +import random +import argparse +import os +import sgfmill +import math +import sys +from dataclasses import dataclass +from collections import defaultdict + +from PIL import Image, ImageDraw + +from board import Board + +# . stone or rest of board +# e empty +# s stone +# c critical +eyepatterns = [ + (50, [ + "s", + ]), + (20, [ + "e", + ]), + (10, [ + "ee", + ]), + (5, [ + "ece", + ]), + (5, [ + "ec", + ".e", + ]), + (1, [ + "ecce", + ]), + (1, [ + "ec.", + ".ce", + ]), + (1, [ + "ecc", + "..e", + ]), + (2, [ + "ee", + "ee", + ]), + (3, [ + "ee.", + "ece", + ]), + (1, [ + ".e.", + "ece", + ".e.", + ]), + (1, [ + "eccce", + ]), + (1, [ + "eccc", + "...e" + ]), + (1.5, [ + ".ee", + "ece", + ".e.", + ]), + (1, [ + "ece", + "ece", + ".e.", + ]), + (1, [ + ".ee", + "ccc", + "ee.", + ]), + (1, [ + "ece", + "ece", + ]), +] + +def randint_exponential(scale): + r = 0 + while r <= 0: + r = random.random() + return int(math.floor(-math.log(r) * scale)) + +@dataclass +class GroupInfo: + has_eye: bool = False + pla: int = 0 + size: int = 0 + +def gen(xsize,ysize): + stones = [ [ 0 for _ in range(xsize) ] for _ in range(ysize) ] + owned = [ [ 0 for _ in range(xsize) ] for _ in range(ysize) ] + kinds = [ [ '.' for _ in range(xsize) ] for _ in range(ysize) ] + patternids = [ [ 0 for _ in range(xsize) ] for _ in range(ysize) ] + + next_pattern_id = 1 + def place_pattern(pla): + nonlocal next_pattern_id + pattern = random.choices( + [pattern for (weight,pattern) in eyepatterns], + weights=[weight for (weight,pattern) in eyepatterns], + k=1 + )[0] + pattern = [ [ c for c in row ] for row in pattern ] + if random.random() >= 0.5: + pattern = list(zip(*pattern)) + if random.random() >= 0.5: + pattern = list(list(reversed(row)) for row in pattern) + if random.random() >= 0.5: + pattern = list(reversed(pattern)) + + pysize = len(pattern) + pxsize = len(pattern[0]) + + # Generate too large and clamp to border, to make borders more likely + yoffset = random.randint(-1,ysize-pysize+1) + xoffset = random.randint(-1,xsize-pxsize+1) + yoffset = min(yoffset,ysize-pysize) + xoffset = min(xoffset,xsize-pxsize) + yoffset = max(yoffset,0) + xoffset = max(xoffset,0) + + numcs = sum(kind == 'c' for row in pattern for kind in row) + for dy in range(pysize): + for dx in range(pxsize): + kind = pattern[dy][dx] + if kind == 'c': + cpla = 0 + if numcs == 1: + r = random.random() + if r < 0.7: + cpla = 3-pla + elif r < 0.9: + cpla = 0 + else: + cpla = pla + elif numcs == 2: + r = random.random() + if r < 0.3: + cpla = 3-pla + elif r < 0.95: + cpla = 0 + else: + cpla = pla + kinds[yoffset+dy][xoffset+dx] = 'e' + owned[yoffset+dy][xoffset+dx] = pla + stones[yoffset+dy][xoffset+dx] = cpla + patternids[yoffset+dy][xoffset+dx] = next_pattern_id + else: + kinds[yoffset+dy][xoffset+dx] = kind + owned[yoffset+dy][xoffset+dx] = pla + patternids[yoffset+dy][xoffset+dx] = next_pattern_id + + next_pattern_id += 1 + + num_b_patterns = 1 + num_w_patterns = 1 + if random.random() < 0.5: + place_pattern(1) + place_pattern(2) + else: + place_pattern(2) + place_pattern(1) + + num_extra_patterns = 0 + num_extra_patterns = randint_exponential(10) + randint_exponential(3) + for _ in range(num_extra_patterns): + if random.random() < num_b_patterns / (num_b_patterns + num_w_patterns): + num_w_patterns += 1 + place_pattern(2) + else: + num_b_patterns += 1 + place_pattern(1) + + def filleyewall(y,x,pla): + if owned[y][x] != 0 and owned[y][x] != pla: + stones[y][x] = random.randint(1,2) + owned[y][x] = 3 + kinds[y][x] = 'x' + else: + owned[y][x] = pla + stones[y][x] = pla + + # Fill in single stones + for y in range(ysize): + for x in range(xsize): + if kinds[y][x] == 's': + stones[y][x] = owned[y][x] + + # Fill in sides + for y in range(ysize): + for x in range(xsize-1): + if kinds[y][x] == 'e' and kinds[y][x+1] == '.': + filleyewall(y,x+1,owned[y][x]) + elif kinds[y][x+1] == 'e' and kinds[y][x] == '.': + filleyewall(y,x,owned[y][x+1]) + for x in range(xsize): + for y in range(ysize-1): + if kinds[y][x] == 'e' and kinds[y+1][x] == '.': + filleyewall(y+1,x,owned[y][x]) + elif kinds[y+1][x] == 'e' and kinds[y][x] == '.': + filleyewall(y,x,owned[y+1][x]) + + # Fill in corners + corneroppprob = random.random() * 0.24 + 0.18 + corneropps_by_patternid = defaultdict(list) + for y in range(ysize-1): + for x in range(xsize-1): + ecount = ( + int(kinds[y][x] == 'e') + + int(kinds[y][x+1] == 'e') + + int(kinds[y+1][x] == 'e') + + int(kinds[y+1][x+1] == 'e') + ) + ccount = ( + int(kinds[y][x] == 'c') + + int(kinds[y][x+1] == 'c') + + int(kinds[y+1][x] == 'c') + + int(kinds[y+1][x+1] == 'c') + ) + dotcount = ( + int(kinds[y][x] == '.') + + int(kinds[y][x+1] == '.') + + int(kinds[y+1][x] == '.') + + int(kinds[y+1][x+1] == '.') + ) + scount = ( + int(kinds[y][x] == 's') + + int(kinds[y][x+1] == 's') + + int(kinds[y+1][x] == 's') + + int(kinds[y+1][x+1] == 's') + ) + if ecount + ccount == 1 and dotcount + scount == 3: + if kinds[y][x] == 'e': + cornerx = x+1 + cornery = y+1 + cornerpla = owned[y][x] + patternid = patternids[y][x] + elif kinds[y+1][x] == 'e': + cornerx = x+1 + cornery = y + cornerpla = owned[y+1][x] + patternid = patternids[y+1][x] + elif kinds[y][x+1] == 'e': + cornerx = x + cornery = y+1 + cornerpla = owned[y][x+1] + patternid = patternids[y][x+1] + elif kinds[y+1][x+1] == 'e': + cornerx = x + cornery = y + cornerpla = owned[y+1][x+1] + patternid = patternids[y+1][x+1] + else: + assert False + + if random.random() < corneroppprob: + corneropps_by_patternid[patternid].append((cornerx,cornery)) + filleyewall(cornery,cornerx,3-cornerpla) + elif random.random() < 0.60: + pass + else: + filleyewall(cornery,cornerx,cornerpla) + + info_by_label = {} + owned_labels = [ [ 0 for _ in range(xsize) ] for _ in range(ysize) ] + def label_owned_region(label, pla, x, y): + if owned_labels[y][x] == 0 and owned[y][x] == pla: + if stones[y][x] == 0: + info_by_label[label].has_eye = True + owned_labels[y][x] = label + info_by_label[label].size += 1 + if x > 0: + label_owned_region(label, pla, x-1, y) + if x < xsize-1: + label_owned_region(label, pla, x+1, y) + if y > 0: + label_owned_region(label, pla, x, y-1) + if y < ysize-1: + label_owned_region(label, pla, x, y+1) + + next_owned_label = 1 + for y in range(ysize): + for x in range(xsize): + if owned_labels[y][x] == 0 and owned[y][x] != 0: + info_by_label[next_owned_label] = GroupInfo() + info_by_label[next_owned_label].pla = owned[y][x] + label_owned_region(next_owned_label, owned[y][x], x, y) + next_owned_label += 1 + + # print(corneropps_by_patternid) + + def merge(label1, label2): + assert info_by_label[label1].pla == info_by_label[label2].pla + info_by_label[label1].has_eye = (info_by_label[label1].has_eye or info_by_label[label2].has_eye) + info_by_label[label1].size = (info_by_label[label1].size + info_by_label[label2].size) + for y in range(ysize): + for x in range(xsize): + if owned_labels[y][x] == label2: + owned_labels[y][x] = label1 + del info_by_label[label2] + + num_merges = random.random() * 0.25 * len(info_by_label) + num_merges = int(num_merges) + (1 if random.random() < (num_merges - int(num_merges)) else 0) + for _ in range(num_merges): + keys = list(info_by_label.keys()) + key1 = random.choice(keys) + otherkeys = [key for key in keys if info_by_label[key].pla == info_by_label[key1].pla and key != key1] + if len(otherkeys) > 0: + key2 = random.choice(otherkeys) + merge(key1,key2) + + for corneropps in corneropps_by_patternid.values(): + if random.random() < 0.7: + for (x1,y1) in corneropps: + for (x2,y2) in corneropps: + label1 = owned_labels[y1][x1] + label2 = owned_labels[y2][x2] + if label1 != label2: + if info_by_label[label1].pla == info_by_label[label2].pla: + merge(label1,label2) + + def adj(x,y): + if x > 0: + yield (x-1,y) + if x < xsize-1: + yield (x+1,y) + if y > 0: + yield (x,y-1) + if y < ysize-1: + yield (x,y+1) + def diag_reachable(x,y): + if x > 0: + if y > 0: + if owned[y][x-1] == 0 or owned[y-1][x] == 0: + yield (x-1,y-1) + if y < ysize-1: + if owned[y][x-1] == 0 or owned[y+1][x] == 0: + yield (x-1,y+1) + if x < xsize-1: + if y > 0: + if owned[y][x+1] == 0 or owned[y-1][x] == 0: + yield (x+1,y-1) + if y < ysize-1: + if owned[y][x+1] == 0 or owned[y+1][x] == 0: + yield (x+1,y+1) + + # Is this a location with 2 adjacent opponents and 1 adjacent player that + # is NOT the given label + def is_crossroads(x,y,pla,label): + if owned[y][x] != 0: + return False + pcount = 0 + ocount = 0 + opp = 3-pla + for (ax,ay) in adj(x,y): + if owned[ay][ax] == pla and owned_labels[ay][ax] != label: + pcount += 1 + elif owned[ay][ax] == opp: + ocount += 1 + if pcount == 1 and ocount == 2: + return True + return False + + black_grow_prob = sum(random.random() for i in range(8)) / 8 + def grow(allow_diag_blocker): + pla = 1 if random.random() < black_grow_prob else 2 + growspots = [] + for y in range(ysize): + for x in range(xsize): + if owned[y][x] == 0: + adjacent_labels = [] + blocker_labels = [] + for (ax,ay) in adj(x,y): + if owned[ay][ax] == pla: + adjacent_labels.append(owned_labels[ay][ax]) + for (ax,ay) in diag_reachable(x,y): + if owned[ay][ax] == pla: + blocker_labels.append(owned_labels[ay][ax]) + + adjacent_labels = list(set(adjacent_labels)) + blocker_labels = list(set(blocker_labels).difference(set(adjacent_labels))) + if len(adjacent_labels) == 1 and (allow_diag_blocker or len(blocker_labels) == 0): + label = adjacent_labels[0] + has_crossroads = False + for (ax,ay) in adj(x,y): + if is_crossroads(ax,ay,pla,label): + has_crossroads = True + if not has_crossroads: + weight = 1.0 / math.sqrt(0.1 + info_by_label[label].size) + growspots.append(((x,y),label,weight)) + + # print(growspots) + if len(growspots) > 0: + ((x,y),label,weight) = random.choices(growspots,weights=(weight for (_,_,weight) in growspots))[0] + owned[y][x] = pla + owned_labels[y][x] = label + stones[y][x] = pla + info_by_label[label].size += 1 + + for i in range(700): + grow(allow_diag_blocker=False) + for i in range(100): + grow(allow_diag_blocker=True) + + def shrink(allow_far_shrink, allow_disconnect, replace_with_opp): + shrinkables = [] + for y in range(ysize): + for x in range(xsize): + if stones[y][x] != 0: + pla = stones[y][x] + opp = 3-pla + adjplalabels = [] + adjopplabels = [] + for (ax,ay) in adj(x,y): + if owned[ay][ax] == pla: + adjplalabels.append(owned_labels[ay][ax]) + if owned[ay][ax] == opp and (allow_far_shrink or stones[ay][ax] == opp): + adjopplabels.append(owned_labels[ay][ax]) + + adjplalabelset = set(adjplalabels) + adjopplabelset = set(adjopplabels) + if not (len(adjplalabelset) == 1 and len(adjopplabelset) == 1): + continue + if replace_with_opp and len(adjopplabels) != 1: + continue + + allow = True + if not allow_disconnect: + # Set temporarily + stones[y][x] = 0 + for (ax,ay) in adj(x,y): + if stones[ay][ax] == pla: + floodfill = [ [ 0 for _ in range(xsize) ] for _ in range(ysize) ] + queue = [(ax,ay)] + idx = 0 + while idx < len(queue): + fx, fy = queue[idx] + idx += 1 + if floodfill[fy][fx] == 0: + floodfill[fy][fx] = 1 + for (fx2,fy2) in adj(fx,fy): + if stones[fy2][fx2] == pla: + queue.append((fx2,fy2)) + break + disconnected = False + for (ax,ay) in adj(x,y): + if stones[ay][ax] == pla and floodfill[ay][ax] != 1: + disconnected = True + break + if disconnected: + allow = False + # Undo + stones[y][x] = pla + + if allow: + shrinkables.append((x,y,pla,adjopplabels[0])) + + if len(shrinkables) > 0: + (x,y,pla,adjopplabel) = random.choice(shrinkables) + if replace_with_opp: + stones[y][x] = 3-pla + owned_labels[y][x] = adjopplabel + owned[y][x] = 3-pla + else: + stones[y][x] = 0 + + num_replaces = randint_exponential(25) + for _ in range(num_replaces): + shrink(allow_far_shrink=False, allow_disconnect=False, replace_with_opp=True) + + shrinks = [] + num_shrinks = randint_exponential(22 + int(len(info_by_label) * 0.3)) + num_far_shrinks = randint_exponential(20) + for _ in range(num_shrinks): + shrinks.append("shrink") + for _ in range(num_far_shrinks): + shrinks.append("far_shrink") + random.shuffle(shrinks) + for s in shrinks: + if s == "shrink": + shrink(allow_far_shrink=False, allow_disconnect=False, replace_with_opp=False) + elif s == "far_shrink": + shrink(allow_far_shrink=True, allow_disconnect=False, replace_with_opp=False) + + num_disconnect_shrinks = randint_exponential(5) + for _ in range(num_disconnect_shrinks): + shrink(allow_far_shrink=True, allow_disconnect=True, replace_with_opp=False) + + # print("=========================================================") + # # print("\n".join("".join(row) for row in kinds)) + # # print("-----------------------------------------") + # print("\n".join("".join(str("x" if elt == 1 else "o" if elt == 2 else ".") for elt in row) for row in owned)) + # print("-----------------------------------------") + # print("\n".join("".join(str("0123456789abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ"[elt]) for elt in row) for row in owned_labels)) + # print(info_by_label) + # print("-----------------------------------------") + # print("\n".join("".join(str("x" if elt == 1 else "o" if elt == 2 else ".") for elt in row) for row in stones)) + + locs = [] + for y in range(ysize): + for x in range(xsize): + locs.append((x,y)) + + assert xsize == ysize + b = Board(xsize) + for (x,y) in locs: + loc = b.loc(x,y) + # Final slight omission to poke occasional eyes into huge solid stone areas. + if random.random() < 1.0 / 2000.0: + stones[y][x] = 0 + + pla = stones[y][x] + if b.would_be_legal(pla,loc): + b.play(pla,loc) + + num_diffs = 0 + for (x,y) in locs: + loc = b.loc(x,y) + if b.board[loc] != stones[y][x]: + num_diffs += 1 + + if num_diffs > 10: + return None + + return b + + +def save_image(board, xsize, ysize): + CELL_SIZE = 20 + STONE_RADIUS = 8 + + # Create blank board image + img = Image.new('RGB', (CELL_SIZE*ysize, CELL_SIZE*xsize), color=(160,82,45)) + + # Draw grid lines + draw = ImageDraw.Draw(img) + for x in range(xsize): + draw.line((CELL_SIZE*x, 0, CELL_SIZE*x, CELL_SIZE*ysize), fill=(0,0,0)) + for y in range(ysize): + draw.line((0, CELL_SIZE*y, CELL_SIZE*xsize, CELL_SIZE*y), fill=(0,0,0)) + + # Draw stones + for y in range(ysize): + for x in range(xsize): + if board[y][x] == 1: + draw.ellipse((CELL_SIZE*y+CELL_SIZE/2-STONE_RADIUS, CELL_SIZE*x+CELL_SIZE/2-STONE_RADIUS, CELL_SIZE*y+CELL_SIZE/2+STONE_RADIUS, CELL_SIZE*x+CELL_SIZE/2+STONE_RADIUS), fill=(0,0,0)) + elif board[y][x] == 2: + draw.ellipse((CELL_SIZE*y+CELL_SIZE/2-STONE_RADIUS, CELL_SIZE*x+CELL_SIZE/2-STONE_RADIUS, CELL_SIZE*y+CELL_SIZE/2+STONE_RADIUS, CELL_SIZE*x+CELL_SIZE/2+STONE_RADIUS), fill=(255,255,255)) + + # Save image + img.save('tmpclumpypos.png') + + +def gen_sgfs_debug(): + for i in range(100): + xsize = 19 + ysize = 19 + b = None + while b is None: + b = gen(xsize,ysize) + initial_pla = random.choice([1,2]) + + with open(f"tmp/clumpysgfs/{i}.sgf","w") as f: + f.write("(;FF[4]GM[1]SZ[19]HA[0]KM[6.5]") + whitepieces = [] + blackpieces = [] + for y in range(ysize): + for x in range(xsize): + loc = b.loc(x,y) + color = b.board[loc] + if color == 1: + xc = "abcdefghijklmnopqrstuvwxyz"[x] + yc = "abcdefghijklmnopqrstuvwxyz"[y] + blackpieces.append(f"[{xc}{yc}]") + elif color == 2: + xc = "abcdefghijklmnopqrstuvwxyz"[x] + yc = "abcdefghijklmnopqrstuvwxyz"[y] + whitepieces.append(f"[{xc}{yc}]") + if len(whitepieces) > 0: + f.write("AW"+"".join(whitepieces)) + if len(blackpieces) > 0: + f.write("AB"+"".join(blackpieces)) + f.write("PL[W]" if initial_pla == 2 else "") + f.write(")") + + print(i) + print(b.to_string()) + + # board = [ [ 0 for _ in range(xsize) ] for _ in range(ysize) ] + # for y in range(ysize): + # for x in range(xsize): + # loc = b.loc(x,y) + # board[y][x] = b.board[loc] + # save_image(board,xsize,ysize) + + +def maybe_generate_one_pos(out, training_weight, max_lopsidedness, soft_filter_large_group_scale): + xsize = 19 + ysize = 19 + board = None + while board is None: + board = gen(xsize,ysize) + + soft_keep_prob = 1.0 + visited_group_heads = set() + for y in range(ysize): + for x in range(xsize): + loc = board.loc(x,y) + if board.board[loc] == 1 or board.board[loc] == 2: + head = board.group_head[loc] + if head not in visited_group_heads: + visited_group_heads.add(head) + stonecount = board.group_stone_count[head] + if stonecount > 2 * soft_filter_large_group_scale: + soft_keep_prob = 0.0 + elif stonecount > soft_filter_large_group_scale: + excess = stonecount - soft_filter_large_group_scale + soft_keep_prob *= math.exp(-excess * 3 / soft_filter_large_group_scale) + + initial_pla = random.choice([1,2]) + + to_write = {} + to_write["board"] = "" + num_stones = 0 + num_black = 0 + num_white = 0 + for y in range(ysize): + for x in range(xsize): + loc = board.loc(x,y) + if board.board[loc] == 1: + to_write["board"] += "X" + num_stones += 1 + num_black += 1 + elif board.board[loc] == 2: + to_write["board"] += "O" + num_stones += 1 + num_white += 1 + else: + to_write["board"] += "." + to_write["board"] += "/" + + if abs(num_black-num_white) > max_lopsidedness: + return False + + # print(str(soft_keep_prob) + "\n" + to_write["board"].replace("/","\n")) + if soft_keep_prob < 0.05: + return False + if random.random() >= soft_keep_prob: + return False + + to_write["hintLoc"] = "null" + to_write["nextPla"] = ("B" if initial_pla == 1 else "W") + to_write["initialTurnNumber"] = num_stones + to_write["moveLocs"] = [] + to_write["movePlas"] = [] + to_write["weight"] = 1.0 + to_write["trainingWeight"] = training_weight + to_write["xSize"] = board.size + to_write["ySize"] = board.size + out.write(json.dumps(to_write) + "\n") + out.flush() + return True + +def main(num_to_generate, out_file, training_weight, max_lopsidedness, soft_filter_large_group_scale): + with open(out_file,"w") as out: + num_kept = 0 + while True: + suc = maybe_generate_one_pos(out, training_weight, max_lopsidedness, soft_filter_large_group_scale) + if suc: + num_kept += 1 + if num_kept >= num_to_generate: + break + if num_kept % 100 == 0: + print(f"Kept {num_kept} so far") + sys.stdout.flush() + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + parser.add_argument("-num-to-generate", required=True, type=int) + parser.add_argument("-out-file", required=True) + parser.add_argument("-training-weight", required=True, type=float) + parser.add_argument("-max-lopsidedness", required=True, type=int) + parser.add_argument("-soft-filter-large-group-scale", required=True, type=float) + args = vars(parser.parse_args()) + + main( + args["num_to_generate"], + args["out_file"], + args["training_weight"], + args["max_lopsidedness"], + args["soft_filter_large_group_scale"], + ) + + diff --git a/training/scripts/dated/20241110-124043/genrandomposes.py b/training/scripts/dated/20241110-124043/genrandomposes.py new file mode 100644 index 0000000..9c170a2 --- /dev/null +++ b/training/scripts/dated/20241110-124043/genrandomposes.py @@ -0,0 +1,163 @@ +#!/usr/bin/python + +import argparse +import os +import re +import json +import random +import math +import sys +from pathlib import Path +from typing import List + +import sgfmill +import sgfmill.sgf +import sgfmill.sgf_moves +from query_analysis_engine_example import KataGo, sgfmill_to_str +from board import Board + +def is_fair_enough_pos(board: Board, katago: KataGo, initial_player: int, score_rejection_pickiness: float) -> bool: + query = {} + query["id"] = "" + query["moves"] = [] + query["initialStones"] = [] + for y in range(board.size): + for x in range(board.size): + loc = board.loc(x,y) + pla = board.board[loc] + if pla != 0: + query["initialStones"].append((("B" if pla == 1 else "W"), sgfmill_to_str((y,x)))) + + query["rules"] = "Japanese" + query["initialPlayer"] = ("B" if initial_player == 1 else "W") + query["komi"] = 7.0 + query["boardXSize"] = board.size + query["boardYSize"] = board.size + query["maxVisits"] = 400 + + result = katago.query_raw(query) + winrate = result['rootInfo']['winrate'] + score = result['rootInfo']['scoreLead'] + + if abs(score) > 150: + return False + keep_prob = 1.0 / (1.0 + score_rejection_pickiness * abs(score)) + if random.random() < keep_prob: + # print(board.to_string()) + # print(query["initialPlayer"]) + print(winrate, score) + sys.stdout.flush() + return True + return False + +def randint_exponential(scale): + r = 0 + while r <= 0: + r = random.random() + return int(math.floor(-math.log(r) * scale)) + +def maybe_generate_one_pos(katago, score_rejection_pickiness, out): + board = Board(19) + both_plays = 1 + randint_exponential(5) + randint_exponential(5) + randint_exponential(12) + extra_b_plays = randint_exponential(1.5) + extra_w_plays = randint_exponential(1) + + plays = [] + for _ in range(both_plays+extra_b_plays): + plays.append(1) + for _ in range(both_plays+extra_w_plays): + plays.append(2) + random.shuffle(plays) + + if len(plays) > 140: + return False + + for pla in plays: + choices = [] + weights = [] + for y in range(board.size): + for x in range(board.size): + line = min(y+1,x+1,19-y,19-x) + if line <= 1: + relprob = 1 + elif line <= 2: + relprob = 4 + else: + relprob = 20 + choices.append((x,y)) + weights.append(relprob) + (x,y) = random.choices(choices,weights=weights,k=1)[0] + loc = board.loc(x,y) + if board.would_be_legal(pla,loc): + board.play(pla,loc) + + initial_pla = random.choice([1,2]) + if is_fair_enough_pos(board, katago, initial_pla, score_rejection_pickiness): + to_write = {} + to_write["board"] = "" + num_stones = 0 + for y in range(board.size): + for x in range(board.size): + loc = board.loc(x,y) + if board.board[loc] == 1: + to_write["board"] += "X" + num_stones += 1 + elif board.board[loc] == 2: + to_write["board"] += "O" + num_stones += 1 + else: + to_write["board"] += "." + to_write["board"] += "/" + to_write["hintLoc"] = "null" + to_write["nextPla"] = ("B" if initial_pla == 1 else "W") + to_write["initialTurnNumber"] = num_stones + to_write["moveLocs"] = [] + to_write["movePlas"] = [] + to_write["weight"] = 1.0 + to_write["xSize"] = board.size + to_write["ySize"] = board.size + out.write(json.dumps(to_write) + "\n") + out.flush() + return True + return False + +def main(katago_path, config_path, model_path, score_rejection_pickiness, num_to_generate, out_file): + with open(out_file,"w") as out: + + katago = KataGo( + katago_path, + config_path, + model_path, + additional_args=["-override-config","numSearchThreadsPerAnalysisThread=8,reportAnalysisWinratesAs=BLACK"] + ) + + num_kept = 0 + while True: + kept = maybe_generate_one_pos(katago, score_rejection_pickiness, out) + if kept: + num_kept += 1 + if num_kept >= num_to_generate: + break + if num_kept % 100 == 0: + print(f"Kept {num_kept} so far") + sys.stdout.flush() + katago.close() + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + parser.add_argument("-katago-path", required=True) + parser.add_argument("-config-path", required=True) + parser.add_argument("-model-path", required=True) + parser.add_argument("-score-rejection-pickiness", required=True, type=float) + parser.add_argument("-num-to-generate", required=True, type=int) + parser.add_argument("-out-file", required=True) + args = vars(parser.parse_args()) + + main( + args["katago_path"], + args["config_path"], + args["model_path"], + args["score_rejection_pickiness"], + args["num_to_generate"], + args["out_file"] + ) diff --git a/training/scripts/dated/20241110-124043/humanslnet_gui.py b/training/scripts/dated/20241110-124043/humanslnet_gui.py new file mode 100644 index 0000000..535e139 --- /dev/null +++ b/training/scripts/dated/20241110-124043/humanslnet_gui.py @@ -0,0 +1,634 @@ +import wx +import subprocess +import json +import sys +import os +from threading import Thread +import atexit +import datetime + +from gamestate import GameState +from board import Board +from sgfmetadata import SGFMetadata + +from sgfmill import sgf, sgf_moves + +import matplotlib.pyplot as plt +import matplotlib.colors as mcolors +import numpy as np + +def interpolateColor(points,x): + for i in range(len(points)): + x1,c1 = points[i] + if x < x1: + if i <= 0: + return c1 + x0,c0 = points[i-1] + interp = (x-x0)/(x1-x0) + return c0 + (c1-c0)*interp + return points[-1][1] + +POLICY_COLORS = [ + (0.00, np.array([100,0,0,15])), + (0.35, np.array([184,0,0,255])), + (0.50, np.array([220,0,0,255])), + (0.65, np.array([255,100,0,255])), + (0.85, np.array([205,220,60,255])), + (0.94, np.array([120,235,130,255])), + (1.00, np.array([100,255,245,255])), +] + +def policy_color(prob): + r,g,b,a = interpolateColor(POLICY_COLORS,prob**0.25) + return (round(r), round(g), round(b), round(a)) + +def load_sgf_game_state(file_path): + with open(file_path, 'rb') as f: + game = sgf.Sgf_game.from_bytes(f.read()) + + size = game.get_size() + if size < 9 or size > 19: + raise ValueError("Board size must be between 9 and 19 inclusive.") + + board, plays = sgf_moves.get_setup_and_moves(game) + + moves = [] + for y in range(size): + for x in range(size): + color = board.get(y, x) + if color is not None: + moves.append((x, size - 1 - y, (Board.BLACK if color == "b" else Board.WHITE))) + + for color, move in plays: + if move is not None: + y, x = move + moves.append((x, size - 1 - y, (Board.BLACK if color == "b" else Board.WHITE))) + + game_state = GameState(size, GameState.RULES_JAPANESE) + for (x,y,color) in moves: + game_state.play(color, game_state.board.loc(x,y)) + + return game_state + +class GoBoard(wx.Panel): + def __init__(self, parent, game_state, cell_size=30, margin=30): + super().__init__(parent) + self.game_state = game_state + self.board_size = game_state.board.size + self.cell_size = cell_size + self.margin = margin + + self.sgfmeta = SGFMetadata() + self.latest_model_response = None + + self.SetBackgroundStyle(wx.BG_STYLE_CUSTOM) + self.Bind(wx.EVT_PAINT, self.on_paint) + self.Bind(wx.EVT_LEFT_UP, self.on_click) + + def get_desired_size(self): + board_width = self.board_size * self.cell_size + 2 * self.margin + board_height = self.board_size * self.cell_size + self.cell_size + 2 * self.margin + return board_width, board_height + + def px_of_x(self, x): + return round(self.cell_size * x + self.margin + self.cell_size / 2) + def py_of_y(self, y): + return round(self.cell_size * y + self.margin + self.cell_size / 2) + + def x_of_px(self, px): + return round((px - self.margin - self.cell_size / 2) / self.cell_size) + def y_of_py(self, py): + return round((py - self.margin - self.cell_size / 2) / self.cell_size) + + + def on_paint(self, event): + dc = wx.AutoBufferedPaintDC(self) + gc = wx.GraphicsContext.Create(dc) + + gc.SetBrush(wx.Brush(wx.Colour(200, 150, 100))) + gc.SetPen(wx.TRANSPARENT_PEN) + gc.DrawRectangle(0, 0, self.GetSize().Width, self.GetSize().Height) + + gc.SetPen(wx.Pen(wx.BLACK, 1)) + + for i in range(self.board_size): + gc.StrokeLine( + self.px_of_x(0), + self.py_of_y(i), + self.px_of_x(self.board_size - 1), + self.py_of_y(i), + ) + gc.StrokeLine( + self.px_of_x(i), + self.py_of_y(0), + self.px_of_x(i), + self.py_of_y(self.board_size - 1), + ) + + for x in range(self.board_size): + for y in range(self.board_size): + loc = self.game_state.board.loc(x, y) + + if self.game_state.board.board[loc] == Board.BLACK: + gc.SetBrush(wx.Brush(wx.BLACK)) + gc.DrawEllipse(self.px_of_x(x) - (self.cell_size // 2 - 2), self.py_of_y(y) - (self.cell_size // 2 - 2), self.cell_size - 4, self.cell_size - 4) + elif self.game_state.board.board[loc] == Board.WHITE: + gc.SetBrush(wx.Brush(wx.WHITE)) + gc.DrawEllipse(self.px_of_x(x) - (self.cell_size // 2 - 2), self.py_of_y(y) - (self.cell_size // 2 - 2), self.cell_size - 4, self.cell_size - 4) + + gc.SetBrush(wx.Brush(wx.BLACK, wx.TRANSPARENT)) + for x in range(self.board_size): + for y in range(self.board_size): + loc = self.game_state.board.loc(x, y) + + if len(self.game_state.moves) > 0 and self.game_state.moves[-1][1] == loc: + if self.game_state.moves[-1][0] == Board.BLACK: + gc.SetPen(wx.Pen(wx.Colour(0, 120, 255), 2)) + else: + gc.SetPen(wx.Pen(wx.Colour(0, 50, 255), 2)) + gc.DrawEllipse(self.px_of_x(x) - (self.cell_size // 2 - 6), self.py_of_y(y) - (self.cell_size // 2 - 6), self.cell_size - 12, self.cell_size - 12) + + # Draw column labels + gc.SetFont(wx.Font(10, wx.FONTFAMILY_DEFAULT, wx.FONTSTYLE_NORMAL, wx.FONTWEIGHT_NORMAL), wx.BLACK) + for x in range(self.board_size): + col_label = "ABCDEFGHJKLMNOPQRSTUVWXYZ"[x] + text_width, text_height = gc.GetTextExtent(col_label) + text_x = self.px_of_x(x) - text_width // 2 + gc.DrawText(col_label, text_x, self.py_of_y(-0.8)-text_height//2) + gc.DrawText(col_label, text_x, self.py_of_y(self.board_size-0.2)-text_height//2) + + # Draw row labels + for y in range(self.board_size): + row_label = str(self.board_size - y) + text_width, text_height = gc.GetTextExtent(row_label) + text_y = self.py_of_y(y) - text_height // 2 + gc.DrawText(row_label, self.px_of_x(-0.8)-text_width//2, text_y) + gc.DrawText(row_label, self.px_of_x(self.board_size-0.2)-text_width//2, text_y) + + if self.latest_model_response is not None: + bigger_font = wx.Font(10, wx.FONTFAMILY_DEFAULT, wx.FONTSTYLE_NORMAL, wx.FONTWEIGHT_NORMAL) + small_font = wx.Font(8, wx.FONTFAMILY_DEFAULT, wx.FONTSTYLE_NORMAL, wx.FONTWEIGHT_NORMAL) + + value_prediction = self.latest_model_response["value"] + score_prediction = self.latest_model_response["lead"] + scorestdev_prediction = self.latest_model_response["scorestdev"] + sign = (1 if self.game_state.board.pla == Board.BLACK else -1) + + gc.SetBrush(wx.Brush(wx.BLACK)) + gc.SetPen(wx.Pen(wx.BLACK,1)) + gc.SetFont(bigger_font,wx.BLACK) + + black_wr = 0.5 + 0.5 * (value_prediction[0] - value_prediction[1]) * sign + label = "Raw NN Black Win%% %.1f" % (black_wr*100.0) + text_width, text_height = gc.GetTextExtent(label) + text_x = self.px_of_x(0) + text_y = self.py_of_y(self.board_size+0.6) - text_height // 2 + gc.DrawText(label, text_x, text_y) + + black_score = score_prediction * sign + label = "Raw NN Black ScoreMean %.1f +/- %.1f (2 stdv)" % (black_score, 2 * scorestdev_prediction) + text_width, text_height = gc.GetTextExtent(label) + text_x = self.px_of_x(8) + text_y = self.py_of_y(self.board_size+0.6) - text_height // 2 + gc.DrawText(label, text_x, text_y) + + moves_and_probs0 = dict(self.latest_model_response["moves_and_probs0"]) + # print(moves_and_probs0) + for y in range(self.board_size): + for x in range(self.board_size): + loc = self.game_state.board.loc(x, y) + max_prob = max(moves_and_probs0.values()) + if loc in moves_and_probs0: + prob = moves_and_probs0[loc] + r,g,b,a = policy_color(prob / max_prob ** 0.7) + # print(r,g,b,a,prob) + gc.SetBrush(wx.Brush(wx.Colour(r,g,b,alpha=round(a*0.9)))) + gc.SetPen(wx.Pen(wx.Colour(0,0,0,alpha=a),1)) + gc.SetFont(small_font,wx.Colour(0,0,0,alpha=a)) + gc.DrawRectangle( + self.px_of_x(x-0.45), + self.py_of_y(y-0.45), + self.px_of_x(x+0.45)-self.px_of_x(x-0.45), + self.py_of_y(y+0.45)-self.py_of_y(y-0.45), + ) + + label = "%.1f" % (prob*100.0) + text_width, text_height = gc.GetTextExtent(label) + text_x = self.px_of_x(x) - text_width // 2 + text_y = self.py_of_y(y) - text_height // 2 + gc.DrawText(label, text_x, text_y) + + + def on_click(self, event): + x = self.x_of_px(event.GetX()) + y = self.y_of_py(event.GetY()) + + if 0 <= x < self.board_size and 0 <= y < self.board_size: + loc = self.game_state.board.loc(x, y) + pla = self.game_state.board.pla + + if self.game_state.board.would_be_legal(pla,loc): + self.game_state.play(pla, loc) + + command = {"command": "play", "pla": pla, "loc": loc} + parent = self.GetParent().GetParent() + parent.send_command(command) + response = parent.receive_response() + if response != {"outputs": ""}: + parent.handle_error(f"Unexpected response from server: {response}") + + self.Refresh() + self.refresh_model() + + def set_sgfmeta(self, sgfmeta): + self.sgfmeta = sgfmeta + + def refresh_model(self): + sgfmeta = self.sgfmeta + command = {"command": "get_model_outputs", "sgfmeta": sgfmeta.to_dict()} + parent = self.GetParent().GetParent() + parent.send_command(command) + response = parent.receive_response() + if "outputs" not in response: + parent.handle_error(f"Unexpected response from server: {response}") + self.latest_model_response = response["outputs"] + self.Refresh() + + +class LabeledSlider(wx.Panel): + def __init__(self, parent, title, options, on_scroll_callback=None, start_option=None): + super().__init__(parent) + + self.options = options + self.on_scroll_callback = on_scroll_callback + self.title = title + self.is_extrapolation = False + + # Create the slider + start_idx = 0 if start_option is None else options.index(start_option) + self.slider = wx.Slider(self, value=start_idx, minValue=0, maxValue=len(options) - 1, style=wx.SL_HORIZONTAL | wx.SL_AUTOTICKS) + self.slider.SetTickFreq(1) # Set the tick frequency to 1 + self.slider.Bind(wx.EVT_SCROLL, self.on_slider_scroll) + + # Create the label to display the selected option + self.label = wx.StaticText(self, label = self.title + ": " + str(self.options[start_idx])) + + font_size = 12 + + font = self.label.GetFont() + font.SetPointSize(font_size) + self.label.SetFont(font) + font = self.slider.GetFont() + font.SetPointSize(font_size) + self.slider.SetFont(font) + + # Create a sizer to arrange the widgets + sizer = wx.BoxSizer(wx.VERTICAL) + sizer.Add(self.label, 0, wx.ALIGN_LEFT | wx.ALL, 10) + sizer.Add(self.slider, 0, wx.EXPAND | wx.ALL, 10) + self.SetSizer(sizer) + + def get_selected_index(self): + return self.slider.GetValue() + + def get_selected_option(self): + selected_index = self.get_selected_index() + return self.options[selected_index] + + def refresh_label(self): + option_index = self.slider.GetValue() + selected_option = self.options[option_index] + self.label.SetLabel(self.title + ": " + str(selected_option) + ("" if not self.is_extrapolation else " (No Training Data)")) + + def set_is_extrapolation(self, b): + if self.is_extrapolation != b: + self.is_extrapolation = b + self.refresh_label() + + def on_slider_scroll(self, event): + option_index = self.slider.GetValue() + selected_option = self.options[option_index] + self.refresh_label() + if self.on_scroll_callback: + self.on_scroll_callback(option_index, selected_option) + + +class SliderWindow(wx.Frame): + def __init__(self, parent): + super().__init__(parent, title="Sliders") + panel = wx.Panel(self) + panel_sizer = wx.BoxSizer(wx.VERTICAL) + panel_sizer.Add(400,0,0) + + self.source_slider = LabeledSlider(panel, title="Source", options=["KG","OGS","KGS","Fox","Tygem(Unused)","GoGoD","Go4Go"], + on_scroll_callback = (lambda idx, option: self.update_metadata()), + start_option="GoGoD", + ) + panel_sizer.Add(self.source_slider, 0, wx.EXPAND | wx.LEFT | wx.RIGHT | wx.BOTTOM, 10) + + self.rank_slider = LabeledSlider(panel, title="Rank", options=[ + "KG","9d","8d","7d","6d","5d","4d","3d","2d","1d","1k","2k","3k","4k","5k","6k","7k","8k","9k","10k","11k","12k","13k","14k","15k","16k","17k","18k","19k","20k" + ], + on_scroll_callback = (lambda idx, option: self.update_metadata()), + start_option="9d", + ) + panel_sizer.Add(self.rank_slider, 0, wx.EXPAND | wx.LEFT | wx.RIGHT | wx.BOTTOM, 10) + + self.date_slider = LabeledSlider(panel, title="Date", options=[ + 1800,1825,1850,1875,1900,1915,1930,1940,1950,1960,1970,1980,1985,1990,1995,2000,2005,2008,2010,2012,2013,2014,2015,2016,2017,2018,2019,2020,2021,2022,2023], + on_scroll_callback = (lambda idx, option: self.update_metadata()), + start_option=2020, + ) + panel_sizer.Add(self.date_slider, 0, wx.EXPAND | wx.LEFT | wx.RIGHT | wx.BOTTOM, 10) + + self.tc_slider = LabeledSlider(panel, title="TimeControl", options=["Blitz","Fast","Slow","Unknown"], + on_scroll_callback = (lambda idx, option: self.update_metadata()), + start_option="Unknown", + ) + panel_sizer.Add(self.tc_slider, 0, wx.EXPAND | wx.LEFT | wx.RIGHT | wx.BOTTOM, 10) + + + panel.SetSizer(panel_sizer) + sizer = wx.BoxSizer(wx.VERTICAL) + sizer.Add(panel, 1, wx.EXPAND) + self.SetSizerAndFit(sizer) + + self.Bind(wx.EVT_CLOSE, self.on_close) + + def on_close(self, event): + self.GetParent().server_process.terminate() + self.GetParent().Close() + + def update_metadata(self): + sgfmeta = SGFMetadata( + inverseBRank = self.rank_slider.get_selected_index(), + inverseWRank = self.rank_slider.get_selected_index(), + bIsHuman = self.rank_slider.get_selected_index() != 0, + wIsHuman = self.rank_slider.get_selected_index() != 0, + gameIsUnrated = False, + gameRatednessIsUnknown = self.source_slider.get_selected_option() == "KGS", + tcIsUnknown = self.tc_slider.get_selected_option() == "Unknown", + tcIsByoYomi = self.tc_slider.get_selected_option() != "Unknown", + mainTimeSeconds = [300,900,1800,0][self.tc_slider.get_selected_index()], + periodTimeSeconds = [10,15,30,0][self.tc_slider.get_selected_index()], + byoYomiPeriods = [5,5,5,0][self.tc_slider.get_selected_index()], + gameDate = datetime.date(self.date_slider.get_selected_option(),6,1), + source = self.source_slider.get_selected_index(), + ) + + source = self.source_slider.get_selected_option() + if source == "KG": + self.rank_slider.set_is_extrapolation(self.rank_slider.get_selected_index() != 0) + self.tc_slider.set_is_extrapolation(self.tc_slider.get_selected_option() == "Unknown") + self.date_slider.set_is_extrapolation(self.date_slider.get_selected_option() < 2022) + elif source == "OGS": + self.rank_slider.set_is_extrapolation(self.rank_slider.get_selected_index() == 0) + self.tc_slider.set_is_extrapolation(self.tc_slider.get_selected_option() == "Unknown") + self.date_slider.set_is_extrapolation(self.date_slider.get_selected_option() < 2007) + elif source == "KGS": + self.rank_slider.set_is_extrapolation(self.rank_slider.get_selected_index() == 0) + self.tc_slider.set_is_extrapolation(self.tc_slider.get_selected_option() == "Unknown") + self.date_slider.set_is_extrapolation(self.date_slider.get_selected_option() < 2016) + elif source == "Fox": + self.rank_slider.set_is_extrapolation(self.rank_slider.get_selected_index() == 0 or self.rank_slider.get_selected_index() >= 28) + self.tc_slider.set_is_extrapolation(self.tc_slider.get_selected_option() == "Unknown") + self.date_slider.set_is_extrapolation(self.date_slider.get_selected_option() < 2014 or self.date_slider.get_selected_option() > 2019) + elif source == "Tygem(Unused)": + self.rank_slider.set_is_extrapolation(True) + self.tc_slider.set_is_extrapolation(True) + self.date_slider.set_is_extrapolation(True) + elif source == "GoGoD": + self.rank_slider.set_is_extrapolation(self.rank_slider.get_selected_index() == 0 or self.rank_slider.get_selected_index() > 5) + self.tc_slider.set_is_extrapolation(self.tc_slider.get_selected_option() != "Unknown") + self.date_slider.set_is_extrapolation(self.date_slider.get_selected_option() > 2020) + elif source == "Go4Go": + self.rank_slider.set_is_extrapolation(self.rank_slider.get_selected_index() != 1) + self.tc_slider.set_is_extrapolation(self.tc_slider.get_selected_option() != "Unknown") + self.date_slider.set_is_extrapolation(self.date_slider.get_selected_option() < 2020) + + self.GetParent().board.set_sgfmeta(sgfmeta) + self.GetParent().board.refresh_model() + +class FileDropTarget(wx.FileDropTarget): + def __init__(self, window): + super().__init__() + self.window = window + + def OnDropFiles(self, x, y, sgf_files): + return self.window.on_drop_files(sgf_files) + +class GoClient(wx.Frame): + def __init__(self, server_command, game_state): + super().__init__(parent=None, title="HumanSLNetViz") + self.server_command = server_command + self.game_state = game_state + self.board_size = self.game_state.board_size + + self.SetDropTarget(FileDropTarget(self)) + + self.start_server() + self.init_ui() + + + def init_ui(self): + panel = wx.Panel(self) + self.board = GoBoard(panel, self.game_state) + sizer = wx.BoxSizer(wx.VERTICAL) + sizer.Add(self.board, 1, wx.EXPAND) + panel.SetSizer(sizer) + + self.Bind(wx.EVT_CHAR_HOOK, self.on_key_down) + + # Set the initial size of the window based on the board size + board_width, board_height = self.board.get_desired_size() + self.SetClientSize(board_width, board_height) + + screen_width, screen_height = wx.DisplaySize() + frame_width, frame_height = self.GetSize() + pos_x = (screen_width - frame_width) // 2 - 300 + pos_y = (screen_height - frame_height) // 2 + self.SetPosition((pos_x, pos_y)) + + self.slider_window = SliderWindow(self) + + frame_width, frame_height = self.slider_window.GetSize() + pos_x = (screen_width - frame_width) // 2 + 240 + pos_y = (screen_height - frame_height) // 2 + self.slider_window.SetPosition((pos_x, pos_y)) + + + def start_server(self): + print(f"Starting server with command: {self.server_command}") + self.server_process = subprocess.Popen( + self.server_command, + stdin=subprocess.PIPE, + stdout=subprocess.PIPE, + stderr=subprocess.PIPE, + universal_newlines=True, + ) + atexit.register(self.server_process.terminate) + + def print_stderr(): + while True: + line = self.server_process.stderr.readline() + if not line: + returncode = self.server_process.poll() + if returncode is not None: + return + print(line,end="") + + t = Thread(target=print_stderr) + t.daemon = True + t.start() + + self.init_server() + + def init_server(self): + command = {"command": "start", "board_size": self.board_size, "rules": GameState.RULES_JAPANESE} + self.send_command(command) + response = self.receive_response() + if response != {"outputs": ""}: + self.handle_error(f"Unexpected response from server: {response}") + + for (pla,loc) in self.game_state.moves: + command = {"command": "play", "pla": pla, "loc": loc} + self.send_command(command) + response = self.receive_response() + if response != {"outputs": ""}: + self.handle_error(f"Unexpected response from server: {response}") + + def send_command(self, command): + print(f"Sending: {json.dumps(command)}") + self.server_process.stdin.write(json.dumps(command) + "\n") + self.server_process.stdin.flush() + + def receive_response(self): + print(f"Waiting for response") + while True: + returncode = self.server_process.poll() + if returncode is not None: + raise OSError(f"Server terminated unexpectedly with {returncode=}") + response = self.server_process.stdout.readline().strip() + if response != "": + break + print(f"Got response (first 100 chars): {str(response[:100])}") + return json.loads(response) + + def handle_error(self, error_message): + print(f"Error: {error_message}") + self.server_process.terminate() + + sys.exit(1) + + def on_key_down(self, event): + key_code = event.GetKeyCode() + if (key_code == wx.WXK_LEFT or key_code == wx.WXK_BACK) and event.ShiftDown(): + self.undo(10) + elif key_code == wx.WXK_LEFT or key_code == wx.WXK_BACK: + self.undo() + elif key_code == wx.WXK_RIGHT and event.ShiftDown(): + self.redo(10) + elif key_code == wx.WXK_RIGHT: + self.redo() + elif key_code == wx.WXK_DOWN: + self.undo(len(self.game_state.moves)) + elif key_code == wx.WXK_UP: + self.redo(len(self.game_state.redo_stack)) + event.Skip() + + def undo(self, undo_count = 1): + is_refresh_needed = False + for i in range(undo_count): + if not self.game_state.can_undo(): + break + + is_refresh_needed = True + + self.game_state.undo() + + command = {"command": "undo"} + self.send_command(command) + response = self.receive_response() + if response != {"outputs": ""}: + self.handle_error(f"Unexpected response from server: {response}") + + if is_refresh_needed: + self.board.Refresh() + self.board.refresh_model() + + def redo(self, redo_count = 1): + is_refresh_needed = False + for i in range(redo_count): + if not self.game_state.can_redo(): + break + + is_refresh_needed = True + + self.game_state.redo() + + command = {"command": "redo"} + self.send_command(command) + response = self.receive_response() + if response != {"outputs": ""}: + self.handle_error(f"Unexpected response from server: {response}") + + if is_refresh_needed: + self.board.Refresh() + self.board.refresh_model() + + def on_drop_files(self, sgf_files): + if len(sgf_files) == 0: + return False + + sgf_file = sgf_files[0] + file_extension = os.path.splitext(sgf_file)[1] + if file_extension != ".sgf": + return False + + game_state = load_sgf_game_state(sgf_file) + + self.game_state = game_state + self.board_size = self.game_state.board_size + + self.board.game_state = game_state + self.board.board_size = self.board_size + + self.init_server() + + self.board.Refresh() + self.board.refresh_model() + + return True + + def on_close(self, event): + self.server_process.terminate() + event.Skip() + +def main(): + sgf_file = None + server_command = sys.argv[1:] + + if len(server_command) >= 2 and server_command[0] == "-sgf": + sgf_file = server_command[1] + server_command = server_command[2:] + + if not server_command: + print("Usage: python humanslnet_gui.py [-sgf FILENAME] ") + sys.exit(1) + + if sgf_file is not None: + game_state = load_sgf_game_state(sgf_file) + else: + game_state = GameState(19, GameState.RULES_JAPANESE) + + app = wx.App() + client = GoClient(server_command, game_state) + client.Bind(wx.EVT_CLOSE, client.on_close) + client.Show() + client.slider_window.Show() + client.slider_window.update_metadata() + + app.MainLoop() + +if __name__ == "__main__": + main() diff --git a/training/scripts/dated/20241110-124043/humanslnet_server.py b/training/scripts/dated/20241110-124043/humanslnet_server.py new file mode 100644 index 0000000..9d6e351 --- /dev/null +++ b/training/scripts/dated/20241110-124043/humanslnet_server.py @@ -0,0 +1,81 @@ +import sys +import json +import numpy as np +from load_model import load_model +from gamestate import GameState +from features import Features +from sgfmetadata import SGFMetadata +import argparse + +def numpy_array_encoder(obj): + if isinstance(obj, np.ndarray): + return obj.tolist() + elif isinstance(obj, np.float32): + return float(obj) + raise TypeError(f'Object of type {obj.__class__.__name__} is not JSON serializable') + +def main(): + parser = argparse.ArgumentParser() + parser.add_argument('-checkpoint', help='Checkpoint to test', required=True) + parser.add_argument('-use-swa', help='Use SWA model', action="store_true", required=False) + parser.add_argument('-device', help='Device to use, such as cpu or cuda:0', required=True) + args = parser.parse_args() + + model, swa_model, _ = load_model(args.checkpoint, use_swa=args.use_swa, device=args.device, pos_len=19, verbose=False) + if swa_model is not None: + model = swa_model + game_state = None + + def write(output): + sys.stdout.write(json.dumps(output,default=numpy_array_encoder) + "\n") + sys.stdout.flush() + + # DEBUGGING + # game_state = GameState(board_size=19, rules=GameState.RULES_JAPANESE) + # sgfmeta = SGFMetadata() + # outputs = game_state.get_model_outputs(model, sgfmeta=sgfmeta) + # write(dict(outputs=outputs)) + + for line in sys.stdin: + line = line.strip() + if not line: + continue + + data = json.loads(line) + + if data["command"] == "start": + board_size = data["board_size"] + rules = data["rules"] + game_state = GameState(board_size, rules) + write(dict(outputs="")) + + elif data["command"] == "play": + pla = data["pla"] + loc = data["loc"] + game_state.play(pla, loc) + write(dict(outputs="")) + + elif data["command"] == "undo": + game_state.undo() + write(dict(outputs="")) + + elif data["command"] == "redo": + game_state.redo() + write(dict(outputs="")) + + elif data["command"] == "get_model_outputs": + sgfmeta = SGFMetadata.of_dict(data["sgfmeta"]) + # features = Features(model.config, model.pos_len) + # foo = game_state.get_input_features(features) + outputs = game_state.get_model_outputs(model, sgfmeta=sgfmeta) + filtered_outputs = {} + for key in outputs: + if key in ["moves_and_probs0", "value", "lead", "scorestdev"]: + filtered_outputs[key] = outputs[key] + write(dict(outputs=filtered_outputs)) + + else: + raise ValueError(f"Unknown command: {data['command']}") + +if __name__ == "__main__": + main() diff --git a/training/scripts/dated/20241110-124043/katago.exe b/training/scripts/dated/20241110-124043/katago.exe new file mode 100644 index 0000000000000000000000000000000000000000..2ad6e330a7efb336105529a3ff1a4731e23c8e39 GIT binary patch literal 4685824 zcmd?SeSB2K^*_ELS%^T`4YCpxWYvh#AT9(o8q{6L!d=)%RMc2OqaZ~^O+x~)A_kL{ z-CowlYFpb%KlrJ(w)Fv@0{EB!CgDK{S`@?we7-TLpe+Pc_V<3Dxp#N-0AfGi|9*MB 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z*Pv&#Blp5DeoC19iJ#^xKR@2lANj;g1#{FS_S0^C>AQJ;&^)U%`g`6{sAx|1JU!qE zTs`kRhO>7X-V5GW9Ckhuvwn$vu6@STuQu7V@6V zp~KF=%_@3y>w@aQoT{Q3JN)?+n1D|_c^^1idkSXIpM z5$_dT-US_Mvlyr4c8B*e25-%hPK)!U+~x2r+~D0~$h>6mJ~1p+(gR6PCHvpV$c(uw zv3c4%+Gq0oc8K?UHt+D_}pUg-X`pG`%TU@#rxi@?eSUJ`+l4EEQ%|V;ytV4eU##zlHv`Y;`zVg8NT8j zsp37S;wULv=W*_Ow94aMvZu#IlCQ^OOn8h3kGF^(`|a_L!sBhA$3A;J@AsIiJl=_V z%m!X>xi_LcX}`-`X_q(4>{ptv$L0Nw%X?v$G37FzT*i{i{<(}Hm+|8=c3j4d%UeO0 z_q#67!^3`u@!{n2M>$`QLq9pp8xC`Z!+hZ|S2!F6hdIk(zH*qW9Ofy9z7Oc}xX)&^ z>Atq}{!#PzO$4S!uCO-mj%?nP+Psal>2I4io;L48Y~CmOMT{ z-pePtkIZ?hA!}1WFWJ1=GB|2{3j*W7;JmXqY9ak(ai-byQ^eSEIJYd`iyNF(W%@i} zAESXvMnNMpZp=GTq zdc4z&89^p*o;|h@bM~}w delta, + (0.5 * delta * delta) + delta * (abs_diff - delta), + 0.5 * abs_diff * abs_diff, + ) + +def constant_like(data, other_tensor): + return torch.tensor(data, dtype=other_tensor.dtype, device=other_tensor.device, requires_grad=False) + +class Metrics: + def __init__(self, batch_size: int, world_size: int, raw_model: Model): + self.n = batch_size + self.world_size = world_size + self.pos_len = raw_model.pos_len + self.pos_area = raw_model.pos_len * raw_model.pos_len + self.policy_len = raw_model.pos_len * raw_model.pos_len + 1 + self.value_len = 3 + self.num_td_values = 3 + self.num_futurepos_values = 2 + self.num_seki_logits = 4 + self.scorebelief_len = 2 * (self.pos_len*self.pos_len + EXTRA_SCORE_DISTR_RADIUS) + + self.score_belief_offset_vector = raw_model.value_head.score_belief_offset_vector + self.moving_unowned_proportion_sum = 0.0 + self.moving_unowned_proportion_weight = 0.0 + + def state_dict(self): + return dict( + moving_unowned_proportion_sum = self.moving_unowned_proportion_sum, + moving_unowned_proportion_weight = self.moving_unowned_proportion_weight, + ) + def load_state_dict(self, state_dict: Dict[str,Any]): + if isinstance(state_dict["moving_unowned_proportion_sum"],torch.Tensor): + self.moving_unowned_proportion_sum = state_dict["moving_unowned_proportion_sum"].item() + else: + self.moving_unowned_proportion_sum = state_dict["moving_unowned_proportion_sum"] + self.moving_unowned_proportion_weight = state_dict["moving_unowned_proportion_weight"] + + def loss_policy_player_samplewise(self, pred_logits, target_probs, weight, global_weight): + assert pred_logits.shape == (self.n, self.policy_len) + assert target_probs.shape == (self.n, self.policy_len) + loss = cross_entropy(pred_logits, target_probs, dim=1) + return global_weight * weight * loss + + def loss_policy_opponent_samplewise(self, pred_logits, target_probs, weight, global_weight): + assert pred_logits.shape == (self.n, self.policy_len) + assert target_probs.shape == (self.n, self.policy_len) + loss = cross_entropy(pred_logits, target_probs, dim=1) + return 0.15 * global_weight * weight * loss + + + def loss_value_samplewise(self, pred_logits, target_probs, weight, global_weight): + assert pred_logits.shape == (self.n, self.value_len) + assert target_probs.shape == (self.n, self.value_len) + assert weight.shape == (self.n,) + loss = cross_entropy(pred_logits, target_probs, dim=1) + return 1.20 * global_weight * weight * loss + + def loss_td_value_samplewise(self, pred_logits, target_probs, weight, global_weight): + assert pred_logits.shape == (self.n, self.num_td_values, self.value_len) + assert target_probs.shape == (self.n, self.num_td_values, self.value_len) + assert weight.shape == (self.n,) + assert global_weight.shape == (self.n,) + loss = cross_entropy(pred_logits, target_probs, dim=2) - cross_entropy(torch.log(target_probs + 1.0e-30), target_probs, dim=2) + return 1.20 * global_weight.unsqueeze(1) * weight.unsqueeze(1) * loss + + def loss_td_score_samplewise(self, pred, target, weight, global_weight): + assert pred.shape == (self.n, self.num_td_values) + assert target.shape == (self.n, self.num_td_values) + loss = torch.sum(huber_loss(pred, target, delta = 12.0), dim=1) + return 0.0004 * global_weight * weight * loss + + + def loss_ownership_samplewise(self, pred_pretanh, target, weight, mask, mask_sum_hw, global_weight): + # This uses a formulation where each batch element cares about its average loss. + # In particular this means that ownership loss predictions on small boards "count more" per spot. + # Not unlike the way that policy and value loss are also equal-weighted by batch element. + assert pred_pretanh.shape == (self.n, 1, self.pos_len, self.pos_len) + assert target.shape == (self.n, self.pos_len, self.pos_len) + assert mask.shape == (self.n, self.pos_len, self.pos_len) + assert mask_sum_hw.shape == (self.n,) + pred_logits = torch.cat((pred_pretanh, -pred_pretanh), dim=1).view(self.n,2,self.pos_area) + target_probs = torch.stack(((1.0 + target) / 2.0, (1.0 - target) / 2.0), dim=1).view(self.n,2,self.pos_area) + loss = torch.sum(cross_entropy(pred_logits, target_probs, dim=1) * mask.view(self.n,self.pos_area), dim=1) / mask_sum_hw + return 1.5 * global_weight * weight * loss + + + def loss_scoring_samplewise(self, pred_scoring, target, weight, mask, mask_sum_hw, global_weight): + assert pred_scoring.shape == (self.n, 1, self.pos_len, self.pos_len) + assert target.shape == (self.n, self.pos_len, self.pos_len) + assert mask.shape == (self.n, self.pos_len, self.pos_len) + assert mask_sum_hw.shape == (self.n,) + + loss = torch.sum(torch.square(pred_scoring.squeeze(1) - target) * mask, dim=(1,2)) / mask_sum_hw + # Simple huberlike transform to reduce crazy values + loss = 4.0 * (torch.sqrt(loss * 0.5 + 1.0) - 1.0) + return global_weight * weight * loss + + + def loss_futurepos_samplewise(self, pred_pretanh, target, weight, mask, mask_sum_hw, global_weight): + # The futurepos targets extrapolate a fixed number of steps into the future independent + # of board size. So unlike the ownership above, generally a fixed number of spots are going to be + # "wrong" independent of board size, so we should just equal-weight the prediction per spot. + # However, on larger boards often the entropy of where the future moves will be should be greater + # and also in the event of capture, there may be large captures that don't occur on small boards, + # causing some scaling with board size. So, I dunno, let's compromise and scale by sqrt(boardarea). + # Also, the further out targets should be weighted a little less due to them being higher entropy + # due to simply being farther in the future, so multiply by [1,0.25]. + assert pred_pretanh.shape == (self.n, self.num_futurepos_values, self.pos_len, self.pos_len) + assert target.shape == (self.n, self.num_futurepos_values, self.pos_len, self.pos_len) + assert mask.shape == (self.n, self.pos_len, self.pos_len) + assert mask_sum_hw.shape == (self.n,) + loss = torch.square(torch.tanh(pred_pretanh) - target) * mask.unsqueeze(1) + loss = loss * constant_like([1.0,0.25], loss).view(1,2,1,1) + loss = torch.sum(loss, dim=(1, 2, 3)) / torch.sqrt(mask_sum_hw) + return 0.25 * global_weight * weight * loss + + + def loss_seki_samplewise(self, pred_logits, target, target_ownership, weight, mask, mask_sum_hw, global_weight, is_training, skip_moving_update): + assert self.num_seki_logits == 4 + assert pred_logits.shape == (self.n, self.num_seki_logits, self.pos_len, self.pos_len) + assert target.shape == (self.n, self.pos_len, self.pos_len) + assert target_ownership.shape == (self.n, self.pos_len, self.pos_len) + assert mask.shape == (self.n, self.pos_len, self.pos_len) + assert mask_sum_hw.shape == (self.n,) + + owned_target = torch.square(target_ownership) + unowned_target = 1.0 - owned_target + unowned_proportion = torch.sum(unowned_target * mask, dim=(1, 2)) / (1.0 + mask_sum_hw) + unowned_proportion = torch.mean(unowned_proportion * weight) + if is_training: + if not skip_moving_update: + self.moving_unowned_proportion_sum *= 0.998 + self.moving_unowned_proportion_weight *= 0.998 + self.moving_unowned_proportion_sum += unowned_proportion.item() + self.moving_unowned_proportion_weight += 1.0 + moving_unowned_proportion = self.moving_unowned_proportion_sum / self.moving_unowned_proportion_weight + seki_weight_scale = 8.0 * 0.005 / (0.005 + moving_unowned_proportion) + else: + seki_weight_scale = 7.0 + + # Loss for predicting the exact sign of seki points + sign_pred = pred_logits[:, 0:3, :, :] + sign_target = torch.stack( + ( + 1.0 - torch.square(target), + torch.nn.functional.relu(target), + torch.nn.functional.relu(-target), + ), + dim=1, + ) + loss_sign = torch.sum(cross_entropy(sign_pred, sign_target, dim=1) * mask, dim=(1, 2)) + + # Loss for generally predicting points that nobody will own + neutral_pred = torch.stack( + (pred_logits[:, 3, :, :], torch.zeros_like(target_ownership)), dim=1 + ) + neutral_target = torch.stack((unowned_target, owned_target), dim=1) + loss_neutral = torch.sum(cross_entropy(neutral_pred, neutral_target, dim=1) * mask, dim=(1, 2)) + + loss = loss_sign + 0.5 * loss_neutral + loss = loss / mask_sum_hw + return (global_weight * seki_weight_scale * weight * loss, seki_weight_scale) + + + def loss_scoremean_samplewise(self, pred, target, weight, global_weight): + # Huber will incentivize this to not actually converge to the mean, + #but rather something meanlike locally and something medianlike + # for very large possible losses. This seems... okay - it might actually + # be what users want. + assert pred.shape == (self.n,) + assert target.shape == (self.n,) + loss = huber_loss(pred, target, delta = 12.0) + return 0.0015 * global_weight * weight * loss + + + def loss_scorebelief_cdf_samplewise(self, pred_logits, target_probs, weight, global_weight): + assert pred_logits.shape == (self.n,self.scorebelief_len) + assert target_probs.shape == (self.n,self.scorebelief_len) + pred_cdf = torch.cumsum(torch.nn.functional.softmax(pred_logits, dim=1), dim=1) + target_cdf = torch.cumsum(target_probs, dim=1) + loss = torch.sum(torch.square(pred_cdf-target_cdf),axis=1) + return 0.020 * global_weight * weight * loss + + def loss_scorebelief_pdf_samplewise(self, pred_logits, target_probs, weight, global_weight): + assert pred_logits.shape == (self.n,self.scorebelief_len) + assert target_probs.shape == (self.n,self.scorebelief_len) + loss = cross_entropy(pred_logits, target_probs, dim=1) + return 0.020 * global_weight * weight * loss + + def loss_scorestdev_samplewise(self, pred, scorebelief_logits, global_weight): + assert pred.shape == (self.n,) + assert scorebelief_logits.shape == (self.n,self.scorebelief_len) + assert self.score_belief_offset_vector.shape == (self.scorebelief_len,) + scorebelief_probs = torch.nn.functional.softmax(scorebelief_logits, dim=1) + expected_score_from_belief = torch.sum(scorebelief_probs * self.score_belief_offset_vector.view(1,-1),dim=1,keepdim=True) + stdev_of_belief = torch.sqrt(0.001 + torch.sum( + scorebelief_probs * torch.square( + self.score_belief_offset_vector.view(1,-1) - expected_score_from_belief + ), + dim=1 + )) + loss = huber_loss(pred, stdev_of_belief, delta = 10.0) + return 0.001 * global_weight * loss + + def loss_lead_samplewise(self, pred, target, weight, global_weight): + # Huber will incentivize this to not actually converge to the mean, + #but rather something meanlike locally and something medianlike + # for very large possible losses. This seems... okay - it might actually + # be what users want. + assert pred.shape == (self.n,) + assert target.shape == (self.n,) + loss = huber_loss(pred, target, delta = 8.0) + return 0.0060 * global_weight * weight * loss + + def loss_variance_time_samplewise(self, pred, target, weight, global_weight): + assert pred.shape == (self.n,) + assert target.shape == (self.n,) + # Even if the training target is 0, add a tiny bit of irreducible error for regularizing the prediction. + loss = huber_loss(pred, target + 1.0e-5, delta = 50.0) + return 0.0003 * global_weight * weight * loss + + + def loss_shortterm_value_error_samplewise(self, pred, td_value_pred_logits, td_value_target_probs, weight, global_weight): + td_value_pred_probs = torch.softmax(td_value_pred_logits[:,2,:],axis=1) + predvalue = (td_value_pred_probs[:,0] - td_value_pred_probs[:,1]).detach() + realvalue = td_value_target_probs[:,2,0] - td_value_target_probs[:,2,1] + # Even if the training target is 0, add a tiny bit of irreducible error for regularizing the prediction, 0.01%. + sqerror = torch.square(predvalue-realvalue) + 1.0e-8 + loss = huber_loss(pred, sqerror, delta = 0.4) + return 2.0 * global_weight * weight * loss + + def loss_shortterm_score_error_samplewise(self, pred, td_score_pred, td_score_target, weight, global_weight): + predscore = td_score_pred[:,2].detach() + realscore = td_score_target[:,2] + # Even if the training target is 0, add a tiny bit of irreducible error for regularizing the prediction, one hundredth of a point. + sqerror = torch.square(predscore-realscore) + 1.0e-4 + loss = huber_loss(pred, sqerror, delta = 100.0) + return 0.00002 * global_weight * weight * loss + + def accuracy1(self, pred_logits, target_probs, weight, global_weight): + return torch.sum(global_weight * weight * (torch.argmax(pred_logits,dim=1) == torch.argmax(target_probs,dim=1))) + + def target_entropy(self, target_probs, weight, global_weight): + return torch.sum(global_weight * weight * -torch.sum(target_probs * torch.log(target_probs + 1e-30), dim=-1)) + + def square_value(self, value_logits, global_weight): + return torch.sum(global_weight * torch.square(torch.sum(torch.softmax(value_logits,dim=1) * constant_like([1,-1,0],global_weight), dim=1))) + + # Returns 0.5 times the sum of squared model weights, for each reg group of model weights + @staticmethod + def get_model_norms(raw_model): + reg_dict : Dict[str,List] = {} + raw_model.add_reg_dict(reg_dict) + + device = reg_dict["normal"][0].device + dtype = torch.float32 + + modelnorm_normal = torch.zeros([],device=device,dtype=dtype) + modelnorm_normal_gamma = torch.zeros([],device=device,dtype=dtype) + modelnorm_output = torch.zeros([],device=device,dtype=dtype) + modelnorm_noreg = torch.zeros([],device=device,dtype=dtype) + modelnorm_output_noreg = torch.zeros([],device=device,dtype=dtype) + for tensor in reg_dict["normal"]: + modelnorm_normal += torch.sum(tensor * tensor) + for tensor in reg_dict["normal_gamma"]: + modelnorm_normal_gamma += torch.sum(tensor * tensor) + for tensor in reg_dict["output"]: + modelnorm_output += torch.sum(tensor * tensor) + for tensor in reg_dict["noreg"]: + modelnorm_noreg += torch.sum(tensor * tensor) + for tensor in reg_dict["output_noreg"]: + modelnorm_output_noreg += torch.sum(tensor * tensor) + modelnorm_normal *= 0.5 + modelnorm_normal_gamma *= 0.5 + modelnorm_output *= 0.5 + modelnorm_noreg *= 0.5 + modelnorm_output_noreg *= 0.5 + return (modelnorm_normal, modelnorm_normal_gamma, modelnorm_output, modelnorm_noreg, modelnorm_output_noreg) + + def get_specific_norms_and_gradient_stats(self,raw_model): + with torch.no_grad(): + params = {} + for name, param in raw_model.named_parameters(): + params[name] = param + + stats = {} + def add_norm_and_grad_stats(name): + param = params[name] + if name.endswith(".weight"): + fanin = param.shape[1] + elif name.endswith(".gamma"): + fanin = 1 + elif name.endwith(".beta"): + fanin = 1 + else: + assert False, "unimplemented case to compute stats on parameter" + + # 1.0 means that the average squared magnitude of a parameter in this tensor is around where + # it would be at initialization, assuming it uses the activation that the model generally + # uses (e.g. relu or mish) + param_scale = torch.sqrt(torch.mean(torch.square(param))) / compute_gain(raw_model.activation) * math.sqrt(fanin) + stats[f"{name}.SCALE_batch"] = param_scale + + # How large is the gradient, on the same scale? + stats[f"{name}.GRADSC_batch"] = torch.sqrt(torch.mean(torch.square(param.grad))) / compute_gain(raw_model.activation) * math.sqrt(fanin) + + # And how large is the component of the gradient that is orthogonal to the overall magnitude of the parameters? + orthograd = param.grad - param * (torch.sum(param.grad * param) / (1e-20 + torch.sum(torch.square(param)))) + stats[f"{name}.OGRADSC_batch"] = torch.sqrt(torch.mean(torch.square(orthograd))) / compute_gain(raw_model.activation) * math.sqrt(fanin) + + add_norm_and_grad_stats("blocks.1.normactconvp.conv.weight") + add_norm_and_grad_stats("blocks.1.blockstack.0.normactconv1.conv.weight") + add_norm_and_grad_stats("blocks.1.blockstack.0.normactconv2.conv.weight") + add_norm_and_grad_stats("blocks.1.blockstack.1.normactconv2.norm.gamma") + add_norm_and_grad_stats("blocks.1.normactconvq.conv.weight") + add_norm_and_grad_stats("blocks.1.normactconvq.norm.gamma") + + add_norm_and_grad_stats("blocks.6.normactconvp.conv.weight") + add_norm_and_grad_stats("blocks.6.blockstack.0.normactconv1.conv.weight") + add_norm_and_grad_stats("blocks.6.blockstack.0.normactconv2.conv.weight") + add_norm_and_grad_stats("blocks.6.blockstack.1.normactconv2.norm.gamma") + add_norm_and_grad_stats("blocks.6.normactconvq.conv.weight") + add_norm_and_grad_stats("blocks.6.normactconvq.norm.gamma") + + add_norm_and_grad_stats("blocks.10.normactconvp.conv.weight") + add_norm_and_grad_stats("blocks.10.blockstack.0.normactconv1.conv.weight") + add_norm_and_grad_stats("blocks.10.blockstack.0.normactconv2.conv.weight") + add_norm_and_grad_stats("blocks.10.blockstack.1.normactconv2.norm.gamma") + add_norm_and_grad_stats("blocks.10.normactconvq.conv.weight") + add_norm_and_grad_stats("blocks.10.normactconvq.norm.gamma") + + add_norm_and_grad_stats("blocks.16.normactconvp.conv.weight") + add_norm_and_grad_stats("blocks.16.blockstack.0.normactconv1.conv.weight") + add_norm_and_grad_stats("blocks.16.blockstack.0.normactconv2.conv.weight") + add_norm_and_grad_stats("blocks.16.blockstack.1.normactconv2.norm.gamma") + add_norm_and_grad_stats("blocks.16.normactconvq.conv.weight") + add_norm_and_grad_stats("blocks.16.normactconvq.norm.gamma") + + add_norm_and_grad_stats("policy_head.conv1p.weight") + add_norm_and_grad_stats("value_head.conv1.weight") + add_norm_and_grad_stats("intermediate_policy_head.conv1p.weight") + add_norm_and_grad_stats("intermediate_value_head.conv1.weight") + + return stats + + def metrics_dict_batchwise( + self, + raw_model, + model_output_postprocessed_byheads, + extra_outputs, + batch, + is_training, + soft_policy_weight_scale, + disable_optimistic_policy, + meta_kata_only_soft_policy, + value_loss_scale, + td_value_loss_scales, + seki_loss_scale, + variance_time_loss_scale, + main_loss_scale, + intermediate_loss_scale, + ): + results = self.metrics_dict_batchwise_single_heads_output( + raw_model, + model_output_postprocessed_byheads[0], + batch, + is_training=is_training, + soft_policy_weight_scale=soft_policy_weight_scale, + disable_optimistic_policy=disable_optimistic_policy, + meta_kata_only_soft_policy=meta_kata_only_soft_policy, + value_loss_scale=value_loss_scale, + td_value_loss_scales=td_value_loss_scales, + seki_loss_scale=seki_loss_scale, + variance_time_loss_scale=variance_time_loss_scale, + is_intermediate=False, + ) + if main_loss_scale is not None: + results["loss_sum"] = main_loss_scale * results["loss_sum"] + + if raw_model.get_has_intermediate_head(): + assert len(model_output_postprocessed_byheads) > 1 + if raw_model.training: + assert intermediate_loss_scale is not None + else: + if intermediate_loss_scale is None: + intermediate_loss_scale = 1.0 + + if intermediate_loss_scale is not None: + iresults = self.metrics_dict_batchwise_single_heads_output( + raw_model, + model_output_postprocessed_byheads[1], + batch, + is_training=is_training, + soft_policy_weight_scale=soft_policy_weight_scale, + disable_optimistic_policy=disable_optimistic_policy, + meta_kata_only_soft_policy=meta_kata_only_soft_policy, + value_loss_scale=value_loss_scale, + td_value_loss_scales=td_value_loss_scales, + seki_loss_scale=seki_loss_scale, + variance_time_loss_scale=variance_time_loss_scale, + is_intermediate=True, + ) + for key,value in iresults.items(): + if key != "loss_sum": + results["I"+key] = value + results["loss_sum"] = results["loss_sum"] + intermediate_loss_scale * iresults["loss_sum"] + + return results + + def metrics_dict_batchwise_single_heads_output( + self, + raw_model, + model_output_postprocessed, + batch, + is_training, + soft_policy_weight_scale, + disable_optimistic_policy, + meta_kata_only_soft_policy, + value_loss_scale, + td_value_loss_scales, + seki_loss_scale, + variance_time_loss_scale, + is_intermediate, + ): + ( + policy_logits, + value_logits, + td_value_logits, + pred_td_score, + ownership_pretanh, + pred_scoring, + futurepos_pretanh, + seki_logits, + pred_scoremean, + pred_scorestdev, + pred_lead, + pred_variance_time, + pred_shortterm_value_error, + pred_shortterm_score_error, + scorebelief_logits, + ) = model_output_postprocessed + + input_binary_nchw = batch["binaryInputNCHW"] + input_global_nc = batch["globalInputNC"] + target_policy_ncmove = batch["policyTargetsNCMove"] + target_global_nc = batch["globalTargetsNC"] + score_distribution_ns = batch["scoreDistrN"] + target_value_nchw = batch["valueTargetsNCHW"] + + mask = input_binary_nchw[:, 0, :, :].contiguous() + mask_sum_hw = torch.sum(mask,dim=(1,2)) + + n = input_binary_nchw.shape[0] + h = input_binary_nchw.shape[2] + w = input_binary_nchw.shape[3] + + policymask = torch.cat((mask.view(n,h*w),mask.new_ones((n,1))),dim=1) + + target_policy_player = target_policy_ncmove[:, 0, :] + target_policy_player = target_policy_player / torch.sum(target_policy_player, dim=1, keepdim=True) + target_policy_opponent = target_policy_ncmove[:, 1, :] + target_policy_opponent = target_policy_opponent / torch.sum(target_policy_opponent, dim=1, keepdim=True) + target_policy_player_soft = (target_policy_player + 1e-7) * policymask + target_policy_player_soft = torch.pow(target_policy_player_soft, 0.25) + target_policy_player_soft /= torch.sum(target_policy_player_soft, dim=1, keepdim=True) + target_policy_opponent_soft = (target_policy_opponent + 1e-7) * policymask + target_policy_opponent_soft = torch.pow(target_policy_opponent_soft, 0.25) + target_policy_opponent_soft /= torch.sum(target_policy_opponent_soft, dim=1, keepdim=True) + + target_weight_policy_player = target_global_nc[:, 26] + target_weight_policy_opponent = target_global_nc[:, 28] + + target_value = target_global_nc[:, 0:3] + target_scoremean = target_global_nc[:, 3] + target_td_value = torch.stack( + (target_global_nc[:, 4:7], target_global_nc[:, 8:11], target_global_nc[:, 12:15]), dim=1 + ) + target_td_score = torch.cat( + (target_global_nc[:, 7:8], target_global_nc[:, 11:12], target_global_nc[:, 15:16]), dim=1 + ) + target_lead = target_global_nc[:, 21] + target_variance_time = target_global_nc[:, 22] + global_weight = target_global_nc[:, 25] + target_weight_ownership = target_global_nc[:, 27] + target_weight_lead = target_global_nc[:, 29] + target_weight_futurepos = target_global_nc[:, 33] + target_weight_scoring = target_global_nc[:, 34] + target_weight_value = 1.0 - target_global_nc[:, 35] + target_weight_td_value = 1.0 - target_global_nc[:, 24] + + target_score_distribution = score_distribution_ns / 100.0 + + target_ownership = target_value_nchw[:, 0, :, :] + target_seki = target_value_nchw[:, 1, :, :] + target_futurepos = target_value_nchw[:, 2:4, :, :] + target_scoring = target_value_nchw[:, 4, :, :] / 120.0 + + if raw_model.config["version"] <= 11: + assert raw_model.policy_head.num_policy_outputs == 4 + policy_opt_loss_scale = 1.000 + long_policy_opt_loss_scale = 0.0 + short_policy_opt_loss_scale = 0.0 + else: + assert raw_model.policy_head.num_policy_outputs == 6 + policy_opt_loss_scale = 0.930 + long_policy_opt_loss_scale = 0.100 + short_policy_opt_loss_scale = 0.200 + + loss_policy_player = self.loss_policy_player_samplewise( + policy_logits[:, 0, :], + target_policy_player, + target_weight_policy_player, + global_weight, + ).sum() + loss_policy_opponent = self.loss_policy_opponent_samplewise( + policy_logits[:, 1, :], + target_policy_opponent, + target_weight_policy_opponent, + global_weight, + ).sum() + + target_weight_policy_player_soft = target_weight_policy_player + target_weight_policy_opponent_soft = target_weight_policy_opponent + if meta_kata_only_soft_policy: + metadata_input_nc = batch["metadataInputNC"] + assert metadata_input_nc.shape[0] == target_weight_policy_player_soft.shape[0] + # 151 indicates source 0 = katago + target_weight_policy_player_soft = target_weight_policy_player_soft * metadata_input_nc[:,151] + target_weight_policy_opponent_soft = target_weight_policy_opponent_soft * metadata_input_nc[:,151] + + loss_policy_player_soft = self.loss_policy_player_samplewise( + policy_logits[:, 2, :], + target_policy_player_soft, + target_weight_policy_player_soft, + global_weight, + ).sum() + loss_policy_opponent_soft = self.loss_policy_opponent_samplewise( + policy_logits[:, 3, :], + target_policy_opponent_soft, + target_weight_policy_opponent_soft, + global_weight, + ).sum() + + if raw_model.config["version"] <= 11: + target_weight_longoptimistic_policy = torch.zeros_like(global_weight) + loss_longoptimistic_policy = torch.zeros_like(loss_policy_player) + elif disable_optimistic_policy: + target_weight_longoptimistic_policy = target_weight_policy_player * 0.5 + loss_longoptimistic_policy = self.loss_policy_player_samplewise( + policy_logits[:, 4, :], + target_policy_player, + target_weight_longoptimistic_policy, + global_weight, + ).sum() + else: + # Long-term optimistic policy. Weight policy by: + # Final game win squared (squaring discourages draws) + win_squared = torch.square( + target_global_nc[:, 0] # win (or draw, weighted by draw utility) + + 0.5 * target_global_nc[:, 2] # noresult + ) + # Or the score outcome of the game being around 1.5 sigma more than expected + # Add a small amount to the variance to avoid division by zero or overly small numbers + longterm_score_stdevs_excess = (target_global_nc[:, 3] - pred_scoremean.detach()) / torch.sqrt(torch.square(pred_scorestdev.detach()) + 0.25) + target_weight_longoptimistic_policy = torch.clamp( + win_squared + torch.sigmoid((longterm_score_stdevs_excess - 1.5) * 3.0), + min=0.0, + max=1.0, + ) + target_weight_longoptimistic_policy = ( + target_weight_longoptimistic_policy + * target_weight_policy_player # game has normal target + * target_weight_ownership # and also actually ended in full ownership and score, not a sidepos + ) + loss_longoptimistic_policy = self.loss_policy_player_samplewise( + policy_logits[:, 4, :], + target_policy_player, + target_weight_longoptimistic_policy, + global_weight, + ).sum() + + assert len(loss_longoptimistic_policy.shape) == 0 + assert len(target_weight_longoptimistic_policy.shape) == 1 + assert target_weight_longoptimistic_policy.shape[0] == n + target_weight_longoptimistic_policy_sum = (global_weight * target_weight_longoptimistic_policy).sum() + + if raw_model.config["version"] <= 11: + target_weight_shortoptimistic_policy = torch.zeros_like(global_weight) + loss_shortoptimistic_policy = torch.zeros_like(loss_policy_player) + elif disable_optimistic_policy: + target_weight_shortoptimistic_policy = target_weight_policy_player * 0.5 + loss_shortoptimistic_policy = self.loss_policy_player_samplewise( + policy_logits[:, 5, :], + target_policy_player, + target_weight_shortoptimistic_policy, + global_weight, + ).sum() + else: + # Short-term optimistic policy. Weight policy by: + # The shortterm value outcome being around 1.5 sigma more than expected + # Add a small amount to the variance to avoid division by zero or overly small numbers + shortterm_value_actual = target_global_nc[:, 12] - target_global_nc[:, 13] + shortterm_value_pred = torch.nn.functional.softmax(td_value_logits[:, 2, :].detach(), dim=1) + shortterm_value_pred = shortterm_value_pred[:, 0] - shortterm_value_pred[:, 1] + shortterm_value_stdevs_excess = (shortterm_value_actual - shortterm_value_pred) / torch.sqrt(pred_shortterm_value_error.detach() + 0.0001) + # Or the shortterm score outcome being around 1.5 sigma more than expected + # Add a small amount to the variance to avoid division by zero or overly small numbers + shortterm_score_stdevs_excess = (target_global_nc[:, 15] - pred_td_score[:,2].detach()) / torch.sqrt(pred_shortterm_score_error.detach() + 0.25) + target_weight_shortoptimistic_policy = torch.clamp( + torch.sigmoid((shortterm_value_stdevs_excess - 1.5) * 3.0) + torch.sigmoid((shortterm_score_stdevs_excess - 1.5) * 3.0), + min=0.0, + max=1.0, + ) + target_weight_shortoptimistic_policy = ( + target_weight_shortoptimistic_policy + * target_weight_policy_player # game has normal target + * target_weight_ownership # and also actually ended in full ownership and score, not a sidepos + ) + loss_shortoptimistic_policy = self.loss_policy_player_samplewise( + policy_logits[:, 5, :], + target_policy_player, + target_weight_shortoptimistic_policy, + global_weight, + ).sum() + + assert len(loss_shortoptimistic_policy.shape) == 0 + assert len(target_weight_shortoptimistic_policy.shape) == 1 + assert target_weight_shortoptimistic_policy.shape[0] == n + target_weight_shortoptimistic_policy_sum = (global_weight * target_weight_shortoptimistic_policy).sum() + + + loss_value = self.loss_value_samplewise( + value_logits, target_value, target_weight_value, global_weight + ).sum() + + loss_td_value_unsummed = self.loss_td_value_samplewise( + td_value_logits, target_td_value, target_weight_td_value, global_weight + ) + assert self.num_td_values == 3 + loss_td_value1 = loss_td_value_unsummed[:,0].sum() + loss_td_value2 = loss_td_value_unsummed[:,1].sum() + loss_td_value3 = loss_td_value_unsummed[:,2].sum() + + loss_td_score = self.loss_td_score_samplewise( + pred_td_score, target_td_score, target_weight_ownership, global_weight + ).sum() + + loss_ownership = self.loss_ownership_samplewise( + ownership_pretanh, + target_ownership, + target_weight_ownership, + mask, + mask_sum_hw, + global_weight, + ).sum() + loss_scoring = self.loss_scoring_samplewise( + pred_scoring, + target_scoring, + target_weight_scoring, + mask, + mask_sum_hw, + global_weight, + ).sum() + loss_futurepos = self.loss_futurepos_samplewise( + futurepos_pretanh, + target_futurepos, + target_weight_futurepos, + mask, + mask_sum_hw, + global_weight, + ).sum() + (loss_seki,seki_weight_scale) = self.loss_seki_samplewise( + seki_logits, + target_seki, + target_ownership, + target_weight_ownership, + mask, + mask_sum_hw, + global_weight, + is_training, + skip_moving_update=is_intermediate, + ) + loss_seki = loss_seki.sum() + seki_weight_scale = seki_weight_scale.sum() if not isinstance(seki_weight_scale,float) else seki_weight_scale + loss_scoremean = self.loss_scoremean_samplewise( + pred_scoremean, + target_scoremean, + target_weight_ownership, + global_weight, + ).sum() + loss_scorebelief_cdf = self.loss_scorebelief_cdf_samplewise( + scorebelief_logits, + target_score_distribution, + target_weight_ownership, + global_weight, + ).sum() + loss_scorebelief_pdf = self.loss_scorebelief_pdf_samplewise( + scorebelief_logits, + target_score_distribution, + target_weight_ownership, + global_weight, + ).sum() + loss_scorestdev = self.loss_scorestdev_samplewise( + pred_scorestdev, + scorebelief_logits, + global_weight, + ).sum() + loss_lead = self.loss_lead_samplewise( + pred_lead, + target_lead, + target_weight_lead, + global_weight, + ).sum() + loss_variance_time = self.loss_variance_time_samplewise( + pred_variance_time, + target_variance_time, + target_weight_ownership, + global_weight, + ).sum() + loss_shortterm_value_error = self.loss_shortterm_value_error_samplewise( + pred_shortterm_value_error, + td_value_logits, + target_td_value, + target_weight_ownership, + global_weight, + ).sum() + loss_shortterm_score_error = self.loss_shortterm_score_error_samplewise( + pred_shortterm_score_error, + pred_td_score, + target_td_score, + target_weight_ownership, + global_weight, + ).sum() + + loss_sum = ( + loss_policy_player * policy_opt_loss_scale + + loss_policy_opponent + + loss_policy_player_soft * soft_policy_weight_scale + + loss_policy_opponent_soft * soft_policy_weight_scale + + loss_longoptimistic_policy * long_policy_opt_loss_scale + + loss_shortoptimistic_policy * short_policy_opt_loss_scale + + loss_value * value_loss_scale + + loss_td_value1 * td_value_loss_scales[0] + + loss_td_value2 * td_value_loss_scales[1] + + loss_td_value3 * td_value_loss_scales[2] + + loss_td_score + + loss_ownership + + loss_scoring * 0.5 + + loss_futurepos + + loss_seki * seki_loss_scale + + loss_scoremean + + loss_scorebelief_cdf + + loss_scorebelief_pdf + + loss_scorestdev + + loss_lead + + loss_variance_time * variance_time_loss_scale + + loss_shortterm_value_error + + loss_shortterm_score_error + ) + + policy_acc1 = self.accuracy1( + policy_logits[:, 0, :], + target_policy_player, + target_weight_policy_player, + global_weight, + ) + square_value = self.square_value(value_logits, global_weight) + + results = { + "p0loss_sum": loss_policy_player, + "p1loss_sum": loss_policy_opponent, + "p0softloss_sum": loss_policy_player_soft, + "p1softloss_sum": loss_policy_opponent_soft, + "p0lopt_sum": loss_longoptimistic_policy, + "p0loptw_sum": target_weight_longoptimistic_policy_sum, + "p0sopt_sum": loss_shortoptimistic_policy, + "p0soptw_sum": target_weight_shortoptimistic_policy_sum, + "vloss_sum": loss_value, + "tdvloss1_sum": loss_td_value1, + "tdvloss2_sum": loss_td_value2, + "tdvloss3_sum": loss_td_value3, + "tdsloss_sum": loss_td_score, + "oloss_sum": loss_ownership, + "sloss_sum": loss_scoring, + "fploss_sum": loss_futurepos, + "skloss_sum": loss_seki, + "smloss_sum": loss_scoremean, + "sbcdfloss_sum": loss_scorebelief_cdf, + "sbpdfloss_sum": loss_scorebelief_pdf, + "sdregloss_sum": loss_scorestdev, + "leadloss_sum": loss_lead, + "vtimeloss_sum": loss_variance_time, + "evstloss_sum": loss_shortterm_value_error, + "esstloss_sum": loss_shortterm_score_error, + "loss_sum": loss_sum, + "pacc1_sum": policy_acc1, + "vsquare_sum": square_value, + } + + if is_intermediate: + return results + else: + weight = global_weight.sum() + nsamples = int(global_weight.shape[0]) + policy_target_entropy = self.target_entropy( + target_policy_player, + target_weight_policy_player, + global_weight, + ) + soft_policy_target_entropy = self.target_entropy( + target_policy_player_soft, + target_weight_policy_player, + global_weight, + ) + + (modelnorm_normal, modelnorm_normal_gamma, modelnorm_output, modelnorm_noreg, modelnorm_output_noreg) = self.get_model_norms(raw_model) + + extra_results = { + "wsum": weight * self.world_size, + "nsamp": nsamples * self.world_size, + "ptentr_sum": policy_target_entropy, + "ptsoftentr_sum": soft_policy_target_entropy, + "sekiweightscale_sum": seki_weight_scale * weight, + "norm_normal_batch": modelnorm_normal, + "norm_normal_gamma_batch": modelnorm_normal_gamma, + "norm_output_batch": modelnorm_output, + "norm_noreg_batch": modelnorm_noreg, + "norm_output_noreg_batch": modelnorm_output_noreg, + } + for key,value in extra_results.items(): + results[key] = value + return results diff --git a/training/scripts/dated/20241110-124043/migrate_double_v1.py b/training/scripts/dated/20241110-124043/migrate_double_v1.py new file mode 100644 index 0000000..1f6cf5a --- /dev/null +++ b/training/scripts/dated/20241110-124043/migrate_double_v1.py @@ -0,0 +1,90 @@ +#!/usr/bin/python3 +import sys +import os +import argparse +import math +import torch + +description = """ +Hacky script to double number of v1 channels. +Run on a torch checkpoint.ckpt file. Replace the old one with the new one after a backup. +""" + +parser = argparse.ArgumentParser(description=description) +parser.add_argument('-checkpoint', help='Checkpoint file path', required=True) +parser.add_argument('-output', help='Output new checkpoint to here', required=True) +args = vars(parser.parse_args()) + +checkpoint_path = args["checkpoint"] +output_path = args["output"] + +data = torch.load(checkpoint_path,map_location="cpu") + +def noisify(tensor): + scale = 0.02 * math.sqrt(torch.mean(torch.square(tensor)).item()) + return tensor * (0.9 + 0.15 * torch.rand(tensor.shape, device=tensor.device)) + scale * (2.0 * torch.rand(tensor.shape, device=tensor.device) - 1.0) + +# Torch input channel dimension is dim 1. +def double_linear_in_dim(name, tensor, scale): + ic = tensor.shape[1] + print(f"{name} tensor has {ic} input channels, doubling to {ic*2}") + tensor = torch.cat((noisify(tensor),noisify(tensor)), dim=1) * scale + return tensor + +# Torch output channel dimension is dim 0. +def double_linear_out_dim(name, tensor, scale): + oc = tensor.shape[0] + print(f"{name} tensor has {oc} output channels, doubling to {oc*2}") + tensor = torch.cat((noisify(tensor),noisify(tensor)), dim=0) * scale + return tensor + +def expand_in_dim_for(name, scale): + if f"module.{name}" in data["model"]: + data["model"][f"module.{name}"] = double_linear_in_dim(f"module.{name}", data["model"][f"module.{name}"], scale) + elif name in data["model"]: + data["model"][name] = double_linear_in_dim(name, data["model"][name], scale) + else: + assert False, f"{name} not found in saved model" + +def expand_out_dim_for(name, scale): + if f"module.{name}" in data["model"]: + data["model"][f"module.{name}"] = double_linear_out_dim(f"module.{name}", data["model"][f"module.{name}"], scale) + elif name in data["model"]: + data["model"][name] = double_linear_out_dim(name, data["model"][name], scale) + else: + assert False, f"{name} not found in saved model" + +expand_out_dim_for("value_head.conv1.weight", scale=1.0) +expand_in_dim_for("value_head.bias1.beta", scale=1.0) +expand_in_dim_for("value_head.linear2.weight", scale=math.sqrt(0.5)) +expand_in_dim_for("value_head.conv_ownership.weight", scale=math.sqrt(0.5)) +expand_in_dim_for("value_head.conv_scoring.weight", scale=math.sqrt(0.5)) +expand_in_dim_for("value_head.linear_s2.weight", scale=math.sqrt(0.5)) +expand_in_dim_for("value_head.linear_smix.weight", scale=math.sqrt(0.5)) + + +if any("intermediate_value_head" in key for key in data["model"].keys()): + expand_out_dim_for("intermediate_value_head.conv1.weight", scale=1.0) + expand_in_dim_for("intermediate_value_head.bias1.beta", scale=1.0) + expand_in_dim_for("intermediate_value_head.linear2.weight", scale=math.sqrt(0.5)) + expand_in_dim_for("intermediate_value_head.conv_ownership.weight", scale=math.sqrt(0.5)) + expand_in_dim_for("intermediate_value_head.conv_scoring.weight", scale=math.sqrt(0.5)) + expand_in_dim_for("intermediate_value_head.linear_s2.weight", scale=math.sqrt(0.5)) + expand_in_dim_for("intermediate_value_head.linear_smix.weight", scale=math.sqrt(0.5)) + + +old_v1_num_channels = data["config"]["v1_num_channels"] +print(f"Doubling v1_num_channels {old_v1_num_channels}") +data["config"]["v1_num_channels"] = old_v1_num_channels * 2 + + +if "optimizer" in data: + print("Deleting optimizer state") + del data["optimizer"] +if "swa_model" in data: + print("Deleting swa model state") + del data["swa_model"] + +print(f"Saving to {output_path}") +torch.save(data, output_path) +print("Done") diff --git a/training/scripts/dated/20241110-124043/migrate_optimistic_policy.py b/training/scripts/dated/20241110-124043/migrate_optimistic_policy.py new file mode 100644 index 0000000..290411f --- /dev/null +++ b/training/scripts/dated/20241110-124043/migrate_optimistic_policy.py @@ -0,0 +1,73 @@ +#!/usr/bin/python3 +import sys +import os +import argparse +import math +import torch + +description = """ +Hacky script to migrate v11 model to v12 with optimistic policy. +Run on a torch checkpoint.ckpt file. Replace the old one with the new one after a backup. +And then manually edit the model.config.json to also bump the json file version to 12. +""" + +parser = argparse.ArgumentParser(description=description) +parser.add_argument('-checkpoint', help='Checkpoint file path', required=True) +parser.add_argument('-output', help='Output new checkpoint to here', required=True) +args = vars(parser.parse_args()) + +checkpoint_path = args["checkpoint"] +output_path = args["output"] + +data = torch.load(checkpoint_path,map_location="cpu") +assert data["config"]["version"] == 11 + +def noisify(tensor): + scale = 0.01 * math.sqrt(torch.mean(torch.square(tensor)).item()) + return tensor * (0.95 + 0.05 * torch.rand(tensor.shape, device=tensor.device)) + scale * (2.0 * torch.rand(tensor.shape, device=tensor.device) - 1.0) + + +# Torch output channel dimension is dim 0. +def expand_policy_out_dim(name, tensor): + if tensor.shape[0] == 6: + print(f"{name} tensor already has 6 output channels, not modifying it") + return tensor + elif tensor.shape[0] == 5: + print(f"{name} tensor has 5 output channels, adding one more initialized from channel 0") + slice0 = tensor[0:1] + tensor = torch.cat((tensor,noisify(slice0)), dim=0) + return tensor + elif tensor.shape[0] == 4: + print(f"{name} tensor has 4 output channels, adding two more initialized from channel 0") + slice0 = tensor[0:1] + tensor = torch.cat((tensor,noisify(slice0),noisify(slice0)), dim=0) + return tensor + else: + assert False, f"Unexpected {name} tensor shape: {tensor.shape}" + +def expand_policy_out_dim_for(name): + if f"module.{name}" in data["model"]: + data["model"][f"module.{name}"] = expand_policy_out_dim(f"module.{name}", data["model"][f"module.{name}"]) + elif name in data["model"]: + data["model"][name] = expand_policy_out_dim(name, data["model"][name]) + else: + assert False, f"{name} not found in saved model" + +expand_policy_out_dim_for("policy_head.conv2p.weight") +expand_policy_out_dim_for("policy_head.linear_pass.weight") +if any("intermediate_policy_head" in key for key in data["model"].keys()): + expand_policy_out_dim_for("intermediate_policy_head.conv2p.weight") + expand_policy_out_dim_for("intermediate_policy_head.linear_pass.weight") + +if "optimizer" in data: + print("Deleting optimizer state") + del data["optimizer"] +if "swa_model" in data: + print("Deleting swa model state") + del data["swa_model"] +print("Setting version to 12") +data["config"]["version"] = 12 + +print(f"Saving to {output_path}") +torch.save(data, output_path) +print("Done, don't forget to edit the model.config.json to version 12 if applicable") diff --git a/training/scripts/dated/20241110-124043/migrate_softplus_fix.py b/training/scripts/dated/20241110-124043/migrate_softplus_fix.py new file mode 100644 index 0000000..e9639d3 --- /dev/null +++ b/training/scripts/dated/20241110-124043/migrate_softplus_fix.py @@ -0,0 +1,64 @@ +#!/usr/bin/python3 +import sys +import os +import argparse +import math +import torch + +description = """ +Hacky script to partially migrate v12 optimistic policy to v13 changing score error. +Run on a torch checkpoint.ckpt file. Replace the old one with the new one after a backup. +""" + +parser = argparse.ArgumentParser(description=description) +parser.add_argument('-checkpoint', help='Checkpoint file path', required=True) +parser.add_argument('-output', help='Output new checkpoint to here', required=True) +args = vars(parser.parse_args()) + +checkpoint_path = args["checkpoint"] +output_path = args["output"] + +data = torch.load(checkpoint_path,map_location="cpu") +assert data["config"]["version"] == 12 + +def shift_bias(name, channel_idx): + if f"module.{name}" in data["model"]: + data["model"][f"module.{name}"][channel_idx] -= math.log(150.0 / 30.0) + elif name in data["model"]: + data["model"][name][channel_idx] -= math.log(150.0 / 30.0) + else: + assert False, f"{name} not found in saved model" + if f"module.{name}" in data["swa_model"]: + data["swa_model"][f"module.{name}"][channel_idx] -= math.log(150.0 / 30.0) + elif name in data["swa_model"]: + data["swa_model"][name][channel_idx] -= math.log(150.0 / 30.0) + else: + assert False, f"{name} not found in swa model" + +shift_bias("value_head.linear_moremiscvaluehead.bias", channel_idx=1) +if any("intermediate_value_head" in key for key in data["model"].keys()): + shift_bias("intermediate_value_head.linear_moremiscvaluehead.bias", channel_idx=1) + +if "optimizer" in data: + print("Deleting optimizer state") + del data["optimizer"] +# if "swa_model" in data: +# print("Deleting swa model state") +# del data["swa_model"] +if "running_metrics" in data: + print("Resetting shortterm score error running metrics") + data["running_metrics"]["sums"]["esstloss_sum"] /= 100000.0 + data["running_metrics"]["weights"]["esstloss_sum"] /= 100000.0 + data["running_metrics"]["sums"]["Iesstloss_sum"] /= 100000.0 + data["running_metrics"]["weights"]["Iesstloss_sum"] /= 100000.0 + +print("Clearing export cycle counter to give time to reconverge") +assert "export_cycle_counter" in data["train_state"] +data["train_state"]["export_cycle_counter"] = 0 + +print("Setting version to 13") +data["config"]["version"] = 13 + +print(f"Saving to {output_path}") +torch.save(data, output_path) +print("Done") diff --git a/training/scripts/dated/20241110-124043/migrate_squared_softplus.py b/training/scripts/dated/20241110-124043/migrate_squared_softplus.py new file mode 100644 index 0000000..62cff9b --- /dev/null +++ b/training/scripts/dated/20241110-124043/migrate_squared_softplus.py @@ -0,0 +1,54 @@ +#!/usr/bin/python3 +import sys +import os +import argparse +import math +import torch + +description = """ +Hacky script to partially migrate v13 to v14 new squared softplus activation for error preds. +Run on a torch checkpoint.ckpt file. Replace the old one with the new one after a backup. +""" + +parser = argparse.ArgumentParser(description=description) +parser.add_argument('-checkpoint', help='Checkpoint file path', required=True) +parser.add_argument('-output', help='Output new checkpoint to here', required=True) +args = vars(parser.parse_args()) + +checkpoint_path = args["checkpoint"] +output_path = args["output"] + +data = torch.load(checkpoint_path,map_location="cpu") +assert data["config"]["version"] == 13 + +# No modification of any weight is needed since for values that aren't large positive values, +# due to the fact that softplus(x) ~= exp(x) and exp(0.5*x) ** 2 ~= exp(x), the existing +# weights of the model already should be a good starting point for the new activation. + +if "optimizer" in data: + print("Deleting optimizer state") + del data["optimizer"] +# if "swa_model" in data: +# print("Deleting swa model state") +# del data["swa_model"] +if "running_metrics" in data: + print("Resetting shortterm value and score error running metrics") + data["running_metrics"]["sums"]["evstloss_sum"] /= 100000.0 + data["running_metrics"]["weights"]["evstloss_sum"] /= 100000.0 + data["running_metrics"]["sums"]["Ievstloss_sum"] /= 100000.0 + data["running_metrics"]["weights"]["Ievstloss_sum"] /= 100000.0 + data["running_metrics"]["sums"]["esstloss_sum"] /= 100000.0 + data["running_metrics"]["weights"]["esstloss_sum"] /= 100000.0 + data["running_metrics"]["sums"]["Iesstloss_sum"] /= 100000.0 + data["running_metrics"]["weights"]["Iesstloss_sum"] /= 100000.0 + +print("Clearing export cycle counter to give time to reconverge") +assert "export_cycle_counter" in data["train_state"] +data["train_state"]["export_cycle_counter"] = 0 + +print("Setting version to 14") +data["config"]["version"] = 14 + +print(f"Saving to {output_path}") +torch.save(data, output_path) +print("Done") diff --git a/training/scripts/dated/20241110-124043/model_pytorch.py b/training/scripts/dated/20241110-124043/model_pytorch.py new file mode 100644 index 0000000..dcae5b4 --- /dev/null +++ b/training/scripts/dated/20241110-124043/model_pytorch.py @@ -0,0 +1,1921 @@ +import math +import numpy as np +import torch +import torch.nn +import torch.nn.functional +import torch.nn.init +import packaging +import packaging.version +from typing import List, Dict, Optional, Set + +import modelconfigs + +EXTRA_SCORE_DISTR_RADIUS = 60 + +class ExtraOutputs: + def __init__(self, requested: List[str]): + self.requested: Set[str] = set(requested) + self.available: List[str] = [] + self.returned: Dict[str,torch.Tensor] = {} + + def add_requested(self, requested: List[str]): + self.requested = self.requested.union(set(requested)) + + def report(self, name: str, value: torch.Tensor): + self.available.append(name) + if name in self.requested: + self.returned[name] = value.detach() + +def act(activation, inplace=False): + if activation == "relu": + return torch.nn.ReLU(inplace=inplace) + if activation == "elu": + return torch.nn.ELU(inplace=inplace) + if activation == "mish": + return torch.nn.Mish(inplace=inplace) + if activation == "gelu": + return torch.nn.GELU(inplace=inplace) + if activation == "hardswish": + if packaging.version.parse(torch.__version__) > packaging.version.parse("1.6.0"): + return torch.nn.Hardswish(inplace=inplace) + else: + return torch.nn.Hardswish() + if activation == "identity": + return torch.nn.Identity() + assert False, f"Unknown activation name: {activation}" + +def compute_gain(activation): + if activation == "relu" or activation == "hardswish": + gain = math.sqrt(2.0) + elif activation == "elu": + gain = math.sqrt(1.55052) + elif activation == "mish": + gain = math.sqrt(2.210277) + elif activation == "gelu": + gain = math.sqrt(2.351718) + elif activation == "identity": + gain = 1.0 + else: + assert False, f"Unknown activation name: {activation}" + return gain + +def init_weights(tensor, activation, scale, fan_tensor=None): + gain = compute_gain(activation) + + if fan_tensor is not None: + (fan_in, _) = torch.nn.init._calculate_fan_in_and_fan_out(fan_tensor) + else: + (fan_in, _) = torch.nn.init._calculate_fan_in_and_fan_out(tensor) + + target_std = scale * gain / math.sqrt(fan_in) + # Multiply slightly since we use truncated normal + std = target_std / 0.87962566103423978 + if std < 1e-10: + tensor.fill_(0.0) + else: + torch.nn.init.trunc_normal_(tensor, mean=0.0, std=std, a=-2.0*std, b=2.0*std) + +class SoftPlusWithGradientFloorFunction(torch.autograd.Function): + """ + Same as softplus, except on backward pass, we never let the gradient decrease below grad_floor. + Equivalent to having a dynamic learning rate depending on stop_grad(x) where x is the input. + If square, then also squares the result while halving the input, and still also keeping the same gradient. + """ + @staticmethod + def forward(ctx, x: torch.Tensor, grad_floor: float, square: bool): + ctx.save_for_backward(x) + ctx.grad_floor = grad_floor # grad_floor is not a tensor + if square: + return torch.square(torch.nn.functional.softplus(0.5 * x)) + else: + return torch.nn.functional.softplus(x) + + @staticmethod + def backward(ctx, grad_output: torch.Tensor): + (x,) = ctx.saved_tensors + grad_floor = ctx.grad_floor + grad_x = None + grad_grad_floor = None + grad_square = None + if ctx.needs_input_grad[0]: + grad_x = grad_output * (grad_floor + (1.0 - grad_floor) / (1.0 + torch.exp(-x))) + return grad_x, grad_grad_floor, grad_square + +class BiasMask(torch.nn.Module): + def __init__( + self, + c_in, + config: modelconfigs.ModelConfig, + is_after_batchnorm: bool = False, + ): + super(BiasMask, self).__init__() + self.c_in = c_in + self.beta = torch.nn.Parameter(torch.zeros(1, c_in, 1, 1)) + self.is_after_batchnorm = is_after_batchnorm + self.scale = None + + def set_scale(self, scale: Optional[float]): + self.scale = scale + + def add_reg_dict(self, reg_dict:Dict[str,List]): + if self.is_after_batchnorm: + reg_dict["output_noreg"].append(self.beta) + else: + reg_dict["noreg"].append(self.beta) + + def set_brenorm_params(self, renorm_avg_momentum: float, rmax: float, dmax: float): + pass + + def add_brenorm_clippage(self, upper_rclippage, lower_rclippage, dclippage): + pass + + def forward(self, x, mask, mask_sum: float): + """ + Parameters: + x: NCHW + mask: N1HW + mask_sum: scalar + + Returns: NCHW + """ + if self.scale is not None: + return (x * self.scale + self.beta) * mask + else: + return (x + self.beta) * mask + + +class NormMask(torch.nn.Module): + def __init__( + self, + c_in, + config: modelconfigs.ModelConfig, + fixup_use_gamma: bool, + force_use_gamma: bool = False, + is_last_batchnorm: bool = False, + ): + """Various kinds of normalization. + + bnorm - batch norm + brenorm - batch renorm + fixup - fixup initialization https://arxiv.org/abs/1901.09321 + fixscale - fixed scaling initialization. Normalization layers simply multiply a constant scalar according + to what batchnorm *would* do if all inputs were unit variance and all linear layers or convolutions + preserved variance. + fixbrenorm - fixed scaling normalization PLUS batch renorm. + fixscaleonenorm - fixed scaling normalization PLUS only have one batch norm layer in the entire net, at the end of the residual trunk. + """ + + super(NormMask, self).__init__() + self.norm_kind = config["norm_kind"] + self.epsilon = config["bnorm_epsilon"] + self.running_avg_momentum = config["bnorm_running_avg_momentum"] + self.fixup_use_gamma = fixup_use_gamma + self.is_last_batchnorm = is_last_batchnorm + self.use_gamma = ( + ("bnorm_use_gamma" in config and config["bnorm_use_gamma"]) or + ((self.norm_kind == "fixup" or self.norm_kind == "fixscale" or self.norm_kind == "fixscaleonenorm") and fixup_use_gamma) or + force_use_gamma + ) + self.c_in = c_in + + self.scale = None + self.gamma = None + if self.norm_kind == "bnorm" or (self.norm_kind == "fixscaleonenorm" and self.is_last_batchnorm): + self.is_using_batchnorm = True + if self.use_gamma: + self.gamma = torch.nn.Parameter(torch.ones(1, c_in, 1, 1)) + self.beta = torch.nn.Parameter(torch.zeros(1, c_in, 1, 1)) + self.register_buffer( + "running_mean", torch.zeros(c_in, dtype=torch.float) + ) + self.register_buffer( + "running_std", torch.ones(c_in, dtype=torch.float) + ) + elif self.norm_kind == "brenorm" or self.norm_kind == "fixbrenorm": + self.is_using_batchnorm = True + if self.use_gamma: + self.gamma = torch.nn.Parameter(torch.ones(1, c_in, 1, 1)) + self.beta = torch.nn.Parameter(torch.zeros(1, c_in, 1, 1)) + self.register_buffer( + "running_mean", torch.zeros(c_in, dtype=torch.float) + ) + self.register_buffer( + "running_std", torch.ones(c_in, dtype=torch.float) + ) + self.register_buffer( + "renorm_running_mean", torch.zeros(c_in, dtype=torch.float) + ) + self.register_buffer( + "renorm_running_std", torch.ones(c_in, dtype=torch.float) + ) + self.register_buffer( + "renorm_upper_rclippage", torch.zeros((), dtype=torch.float) + ) + self.register_buffer( + "renorm_lower_rclippage", torch.zeros((), dtype=torch.float) + ) + self.register_buffer( + "renorm_dclippage", torch.zeros((), dtype=torch.float) + ) + + elif self.norm_kind == "fixup" or self.norm_kind == "fixscale" or (self.norm_kind == "fixscaleonenorm" and not self.is_last_batchnorm): + self.is_using_batchnorm = False + self.beta = torch.nn.Parameter(torch.zeros(1, c_in, 1, 1)) + if self.use_gamma: + self.gamma = torch.nn.Parameter(torch.ones(1, c_in, 1, 1)) + else: + assert False, f"Unimplemented norm_kind: {self.norm_kind}" + + def set_scale(self, scale: Optional[float]): + self.scale = scale + + def add_reg_dict(self, reg_dict:Dict[str,List]): + if self.is_last_batchnorm: + if self.gamma is not None: + reg_dict["output"].append(self.gamma) + reg_dict["output_noreg"].append(self.beta) + else: + if self.gamma is not None: + reg_dict["normal_gamma"].append(self.gamma) + reg_dict["noreg"].append(self.beta) + + def set_brenorm_params(self, renorm_avg_momentum: float, rmax: float, dmax: float): + self.renorm_avg_momentum = renorm_avg_momentum + self.rmax = rmax + self.dmax = dmax + + def add_brenorm_clippage(self, upper_rclippage, lower_rclippage, dclippage): + upper_rclippage.append(self.renorm_upper_rclippage.cpu().item()) + lower_rclippage.append(self.renorm_lower_rclippage.cpu().item()) + dclippage.append(self.renorm_dclippage.cpu().item()) + + def _compute_bnorm_values(self, x, mask, mask_sum: float): + # This is the mean, computed only over exactly the areas of the mask, weighting each spot equally, + # even across different elements in the batch that might have different board sizes. + mean = torch.sum(x * mask, dim=(0,2,3),keepdim=True) / mask_sum + zeromean_x = x - mean + # Similarly, the variance computed exactly only over those spots + var = torch.sum(torch.square(zeromean_x * mask),dim=(0,2,3),keepdim=True) / mask_sum + std = torch.sqrt(var + self.epsilon) + return zeromean_x, mean, std + + def apply_gamma_beta_scale_mask(self, x, mask): + if self.scale is not None: + if self.gamma is not None: + return (x * (self.gamma * self.scale) + self.beta) * mask + else: + return (x * self.scale + self.beta) * mask + else: + if self.gamma is not None: + return (x * self.gamma + self.beta) * mask + else: + return (x + self.beta) * mask + + + def forward(self, x, mask, mask_sum: float): + """ + Parameters: + x: NCHW + mask: N1HW + mask_sum: scalar + + Returns: NCHW + """ + + if self.norm_kind == "bnorm" or (self.norm_kind == "fixscaleonenorm" and self.is_last_batchnorm): + assert x.shape[1] == self.c_in + if self.training: + zeromean_x, mean, std = self._compute_bnorm_values(x, mask, mask_sum) + + detached_mean = mean.view(self.c_in).detach() + detached_std = std.view(self.c_in).detach() + with torch.no_grad(): + self.running_mean += self.running_avg_momentum * (detached_mean - self.running_mean) + self.running_std += self.running_avg_momentum * (detached_std - self.running_std) + + return self.apply_gamma_beta_scale_mask(zeromean_x / std, mask) + else: + return self.apply_gamma_beta_scale_mask((x - self.running_mean.view(1,self.c_in,1,1)) / self.running_std.view(1,self.c_in,1,1), mask) + + elif self.norm_kind == "brenorm" or self.norm_kind == "fixbrenorm": + assert x.shape[1] == self.c_in + if self.training: + zeromean_x, mean, std = self._compute_bnorm_values(x, mask, mask_sum) + + detached_mean = mean.view(self.c_in).detach() + detached_std = std.view(self.c_in).detach() + with torch.no_grad(): + unclipped_r = detached_std / self.renorm_running_std + unclipped_d = (detached_mean - self.renorm_running_mean) / self.renorm_running_std + r = unclipped_r.clamp(1.0 / self.rmax, self.rmax) + d = unclipped_d.clamp(-self.dmax, self.dmax) + + self.renorm_running_mean += self.renorm_avg_momentum * (detached_mean - self.renorm_running_mean) + self.renorm_running_std += self.renorm_avg_momentum * (detached_std - self.renorm_running_std) + self.running_mean += self.running_avg_momentum * (detached_mean - self.running_mean) + self.running_std += self.running_avg_momentum * (detached_std - self.running_std) + + upper_rclippage = torch.mean(torch.nn.functional.relu(torch.log(unclipped_r / r))) + lower_rclippage = torch.mean(torch.nn.functional.relu(-torch.log(unclipped_r / r))) + dclippage = torch.mean(torch.abs(unclipped_d - d)) + self.renorm_upper_rclippage += 0.01 * (upper_rclippage - self.renorm_upper_rclippage) + self.renorm_lower_rclippage += 0.01 * (lower_rclippage - self.renorm_lower_rclippage) + self.renorm_dclippage += 0.01 * (dclippage - self.renorm_dclippage) + + if self.rmax > 1.00000001 or self.dmax > 0.00000001: + return self.apply_gamma_beta_scale_mask(zeromean_x / std * r.detach().view(1,self.c_in,1,1) + d.detach().view(1,self.c_in,1,1), mask) + else: + return self.apply_gamma_beta_scale_mask(zeromean_x / std, mask) + + else: + return self.apply_gamma_beta_scale_mask((x - self.running_mean.view(1,self.c_in,1,1)) / self.running_std.view(1,self.c_in,1,1), mask) + + elif self.norm_kind == "fixup" or self.norm_kind == "fixscale" or (self.norm_kind == "fixscaleonenorm" and not self.is_last_batchnorm): + return self.apply_gamma_beta_scale_mask(x, mask) + + else: + assert False + + +class KataGPool(torch.nn.Module): + def __init__(self): + super(KataGPool, self).__init__() + + def forward(self, x, mask, mask_sum_hw): + """ + Parameters: + x: NCHW + mask: N1HW + mask_sum_hw: N111 + + Returns: NC11 + """ + mask_sum_hw_sqrt_offset = torch.sqrt(mask_sum_hw) - 14.0 + + layer_mean = torch.sum(x, dim=(2, 3), keepdim=True, dtype=torch.float32) / mask_sum_hw + # All activation functions we use right now are always greater than -1.0, and map 0 -> 0. + # So off-board areas will equal 0, and then this max is mask-safe if we assign -1.0 to off-board areas. + (layer_max,_argmax) = torch.max((x+(mask-1.0)).view(x.shape[0],x.shape[1],-1).to(torch.float32), dim=2) + layer_max = layer_max.view(x.shape[0],x.shape[1],1,1) + + out_pool1 = layer_mean + out_pool2 = layer_mean * (mask_sum_hw_sqrt_offset / 10.0) + out_pool3 = layer_max + + out = torch.cat((out_pool1, out_pool2, out_pool3), dim=1) + return out + + +class KataValueHeadGPool(torch.nn.Module): + def __init__(self): + super(KataValueHeadGPool, self).__init__() + + def forward(self, x, mask, mask_sum_hw): + """ + Parameters: + x: NCHW + mask: N1HW + mask_sum_hw: N111 + + Returns: NC11 + """ + mask_sum_hw_sqrt_offset = torch.sqrt(mask_sum_hw) - 14.0 + + layer_mean = torch.sum(x, dim=(2, 3), keepdim=True, dtype=torch.float32) / mask_sum_hw + + out_pool1 = layer_mean + out_pool2 = layer_mean * (mask_sum_hw_sqrt_offset / 10.0) + out_pool3 = layer_mean * ((mask_sum_hw_sqrt_offset * mask_sum_hw_sqrt_offset) / 100.0 - 0.1) + + out = torch.cat((out_pool1, out_pool2, out_pool3), dim=1) + return out + +class KataConvAndGPool(torch.nn.Module): + def __init__(self, name, c_in, c_out, c_gpool, config, activation): + super(KataConvAndGPool, self).__init__() + self.name = name + self.norm_kind = config["norm_kind"] + self.activation = activation + self.conv1r = torch.nn.Conv2d(c_in, c_out, kernel_size=3, padding="same", bias=False) + self.conv1g = torch.nn.Conv2d(c_in, c_gpool, kernel_size=3, padding="same", bias=False) + self.normg = NormMask( + c_gpool, + config=config, + fixup_use_gamma=False, + ) + self.actg = act(self.activation, inplace=True) + self.gpool = KataGPool() + self.linear_g = torch.nn.Linear(3 * c_gpool, c_out, bias=False) + + def initialize(self, scale): + # Scaling so that variance on the r and g branches adds up to 1.0 + r_scale = 0.8 + g_scale = 0.6 + if self.norm_kind == "fixup" or self.norm_kind == "fixscale" or self.norm_kind == "fixbrenorm" or self.norm_kind == "fixscaleonenorm": + init_weights(self.conv1r.weight, self.activation, scale=scale * r_scale) + init_weights(self.conv1g.weight, self.activation, scale=math.sqrt(scale) * math.sqrt(g_scale)) + init_weights(self.linear_g.weight, self.activation, scale=math.sqrt(scale) * math.sqrt(g_scale)) + else: + init_weights(self.conv1r.weight, self.activation, scale=scale*r_scale) + init_weights(self.conv1g.weight, self.activation, scale=math.sqrt(scale) * 1.0) + init_weights(self.linear_g.weight, self.activation, scale=math.sqrt(scale) * g_scale) + + def add_reg_dict(self, reg_dict:Dict[str,List]): + reg_dict["normal"].append(self.conv1r.weight) + reg_dict["normal"].append(self.conv1g.weight) + self.normg.add_reg_dict(reg_dict) + reg_dict["normal"].append(self.linear_g.weight) + + def set_brenorm_params(self, renorm_avg_momentum: float, rmax: float, dmax: float): + self.normg.set_brenorm_params(renorm_avg_momentum, rmax, dmax) + + def add_brenorm_clippage(self, upper_rclippage, lower_rclippage, dclippage): + self.normg.add_brenorm_clippage(upper_rclippage, lower_rclippage, dclippage) + + + def forward(self, x, mask, mask_sum_hw, mask_sum:float, extra_outputs: Optional[ExtraOutputs]): + """ + Parameters: + x: NCHW + mask: N1HW + mask_sum_hw: N111 + mask_sum: scalar + + Returns: NCHW + """ + out = x + outr = self.conv1r(out) + outg = self.conv1g(out) + + outg = self.normg(outg, mask=mask, mask_sum=mask_sum) + outg = self.actg(outg) + outg = self.gpool(outg, mask=mask, mask_sum_hw=mask_sum_hw).squeeze(-1).squeeze(-1) + outg = self.linear_g(outg).unsqueeze(-1).unsqueeze(-1) + + out = outr + outg + return out + + +class KataConvAndAttentionPool(torch.nn.Module): + def __init__(self, name, c_in, c_out, c_gpool, config, activation): + super(KataConvAndAttentionPool, self).__init__() + self.name = name + self.norm_kind = config["norm_kind"] + self.c_gpool = c_gpool + self.c_apheads = config["num_attention_pool_heads"] + self.activation = activation + self.conv1r = torch.nn.Conv2d(c_in, c_out, kernel_size=3, padding="same", bias=False) + self.conv1g = torch.nn.Conv2d(c_in, c_gpool, kernel_size=3, padding="same", bias=False) + self.conv1k = torch.nn.Conv2d(c_in, c_gpool, kernel_size=1, padding="same", bias=False) + self.conv1q = torch.nn.Conv2d(c_in, c_gpool, kernel_size=1, padding="same", bias=False) + + assert c_gpool % self.c_apheads == 0, "Gpool channels must be divisible by num_attention_pool_heads" + + self.normg = NormMask( + c_gpool, + config=config, + fixup_use_gamma=False, + ) + self.actg = act(activation, inplace=True) + self.conv_mix = torch.nn.Conv2d(c_gpool*2, c_out, kernel_size=1, padding="same", bias=False) + + def initialize(self, scale): + # Scaling so that variance on the r and g branches adds up to 1.0 + r_scale = 0.8 + g_scale = 0.6 + if self.norm_kind == "fixup" or self.norm_kind == "fixscale" or self.norm_kind == "fixbrenorm" or self.norm_kind == "fixscaleonenorm": + init_weights(self.conv1r.weight, self.activation, scale=scale * r_scale) + init_weights(self.conv1g.weight, self.activation, scale=math.sqrt(scale) * math.sqrt(g_scale)) + init_weights(self.conv1k.weight, "identity", scale=math.sqrt(2.0)) + init_weights(self.conv1q.weight, "identity", scale=math.sqrt(2.0)) + init_weights(self.conv_mix.weight, self.activation, scale=math.sqrt(scale) * math.sqrt(g_scale)) + else: + init_weights(self.conv1r.weight, self.activation, scale=scale*r_scale) + init_weights(self.conv1g.weight, self.activation, scale=math.sqrt(scale) * 1.0) + init_weights(self.conv1k.weight, "identity", scale=math.sqrt(2.0)) + init_weights(self.conv1q.weight, "identity", scale=math.sqrt(2.0)) + init_weights(self.conv_mix.weight, self.activation, scale=math.sqrt(scale) * g_scale) + + def add_reg_dict(self, reg_dict:Dict[str,List]): + reg_dict["normal"].append(self.conv1r.weight) + reg_dict["normal"].append(self.conv1g.weight) + reg_dict["output"].append(self.conv1k.weight) + reg_dict["output"].append(self.conv1q.weight) + self.normg.add_reg_dict(reg_dict) + reg_dict["normal"].append(self.conv_mix.weight) + + def set_brenorm_params(self, renorm_avg_momentum: float, rmax: float, dmax: float): + self.normg.set_brenorm_params(renorm_avg_momentum, rmax, dmax) + + def add_brenorm_clippage(self, upper_rclippage, lower_rclippage, dclippage): + self.normg.add_brenorm_clippage(upper_rclippage, lower_rclippage, dclippage) + + def forward(self, x, mask, mask_sum_hw, mask_sum:float, extra_outputs: Optional[ExtraOutputs]): + """ + Parameters: + x: NCHW + mask: N1HW + mask_sum_hw: N111 + mask_sum: scalar + + Returns: NCHW + """ + n = x.shape[0] + h = x.shape[2] + w = x.shape[3] + + out = x + outr = self.conv1r(out) + outg = self.conv1g(out) + outk = self.conv1k(out).view(n*self.c_apheads, self.c_gpool//self.c_apheads, h*w) + outq = self.conv1q(out).view(n*self.c_apheads, self.c_gpool//self.c_apheads, h*w) + attention_logits = torch.bmm(torch.transpose(outk,1,2), outq) # n*heads, src h*w, dst h*w + attention_logits = attention_logits.view(n, self.c_apheads, h*w, h*w) + attention_logits = attention_logits - (1.0 - mask.view(n,1,h*w,1)) * 6000.0 + attention_logits = attention_logits.view(n * self.c_apheads, h*w, h*w) + attention = torch.nn.functional.softmax(attention_logits, dim=1) + attention_scale = 0.1 / torch.sqrt(torch.sum(torch.square(attention), dim=1, keepdim=True)) # n*heads, 1, h*w + + outg = self.normg(outg, mask=mask, mask_sum=mask_sum) + outg = self.actg(outg).view(n*self.c_apheads, self.c_gpool//self.c_apheads, h*w) + + out_pool1 = torch.bmm(outg, attention) + out_pool2 = out_pool1 * attention_scale + out_pool1 = out_pool1.view(n, self.c_gpool, h*w) + out_pool2 = out_pool2.view(n, self.c_gpool, h*w) + + outg = torch.cat((out_pool1, out_pool2), dim=1).view(n, 2 * self.c_gpool, h, w) * mask + outg = self.conv_mix(outg) + out = outr + outg + return out + + # def forward(self, x, mask, mask_sum_hw, mask_sum:float): + # """ + # Parameters: + # x: NCHW + # mask: N1HW + # mask_sum_hw: N111 + # mask_sum: scalar + + # Returns: NCHW + # """ + # n = x.shape[0] + # h = x.shape[2] + # w = x.shape[3] + + # out = x + # outr = self.conv1r(out) + # outg = self.conv1g(out) + # outk = self.conv1k(out) - (1.0 - mask) * 5000.0 + # outq = self.conv1q(out) + + # outk = outk.view(n*self.c_apheads, self.c_gpool//self.c_apheads, h*w) + # outq = outq.view(n*self.c_apheads, self.c_gpool//self.c_apheads, h*w) + + # mask_sum_hw_sqrt_offset = torch.sqrt(mask_sum_hw) - 14.0 + + # outg = self.normg(outg, mask=mask, mask_sum=mask_sum) + # outg = self.actg(outg).view(n*self.c_apheads, self.c_gpool//self.c_apheads, h*w) + # # Shen et al. Efficient Attention: Attention with Linear Complexities + # outg = torch.bmm(outg, torch.transpose(torch.nn.functional.softmax(outk,dim=2),1,2)) + # outg = torch.bmm(outg, torch.nn.functional.softmax(outq,dim=1)) + # outg = outg.view(n, self.c_gpool, h, w) * mask + # outg = torch.cat(( + # outg, + # outg * (mask_sum_hw_sqrt_offset / 10.0) + # ),dim=1) + + # outg = self.conv_mix(outg) + # out = outr + outg + # return out + + +class NormActConv(torch.nn.Module): + def __init__( + self, + name: str, + c_in: int, + c_out: int, + c_gpool: Optional[int], + config: modelconfigs.ModelConfig, + activation: str, + kernel_size: int, + fixup_use_gamma: bool, + ): + super(NormActConv, self).__init__() + self.name = name + self.c_in = c_in + self.c_out = c_out + self.c_gpool = c_gpool + self.norm = NormMask( + c_in, + config=config, + fixup_use_gamma=fixup_use_gamma, + ) + self.activation = activation + self.act = act(activation, inplace=True) + self.use_repvgg_init = kernel_size > 1 and "use_repvgg_init" in config and config["use_repvgg_init"] + + if c_gpool is not None: + if config["use_attention_pool"]: + self.convpool = KataConvAndAttentionPool(name=name+".convpool",c_in=c_in, c_out=c_out, c_gpool=c_gpool, config=config, activation=activation) + self.conv = None + else: + self.convpool = KataConvAndGPool(name=name+".convpool",c_in=c_in, c_out=c_out, c_gpool=c_gpool, config=config, activation=activation) + self.conv = None + else: + self.conv = torch.nn.Conv2d(c_in, c_out, kernel_size=kernel_size, padding="same", bias=False) + self.convpool = None + + self.conv1x1 = None + if self.conv is not None and kernel_size > 1 and "use_repvgg_linear" in config and config["use_repvgg_linear"]: + self.conv1x1 = torch.nn.Conv2d(c_in, c_out, kernel_size=1, padding="same", bias=False) + + def initialize(self, scale, norm_scale=None): + self.norm.set_scale(norm_scale) + if self.convpool is not None: + self.convpool.initialize(scale=scale) + else: + if self.conv1x1 is not None: + init_weights(self.conv1x1.weight, self.activation, scale=scale*0.6) + init_weights(self.conv.weight, self.activation, scale=scale*0.8) + else: + if self.use_repvgg_init: + init_weights(self.conv.weight, self.activation, scale=scale*0.8) + center_bonus = self.conv.weight.new_zeros((self.conv.weight.shape[0],self.conv.weight.shape[1]),requires_grad=False) + init_weights(center_bonus, self.activation, scale=scale*0.6) + self.conv.weight[:,:,1,1] += center_bonus + else: + init_weights(self.conv.weight, self.activation, scale=scale) + + + def add_reg_dict(self, reg_dict:Dict[str,List]): + self.norm.add_reg_dict(reg_dict) + if self.convpool is not None: + self.convpool.add_reg_dict(reg_dict) + else: + if self.conv1x1 is not None: + reg_dict["normal"].append(self.conv1x1.weight) + reg_dict["normal"].append(self.conv.weight) + + def set_brenorm_params(self, renorm_avg_momentum: float, rmax: float, dmax: float): + self.norm.set_brenorm_params(renorm_avg_momentum, rmax, dmax) + if self.convpool is not None: + self.convpool.set_brenorm_params(renorm_avg_momentum, rmax, dmax) + + def add_brenorm_clippage(self, upper_rclippage, lower_rclippage, dclippage): + self.norm.add_brenorm_clippage(upper_rclippage, lower_rclippage, dclippage) + if self.convpool is not None: + self.convpool.add_brenorm_clippage(upper_rclippage, lower_rclippage, dclippage) + + def forward(self, x, mask, mask_sum_hw, mask_sum: float, extra_outputs: Optional[ExtraOutputs]): + """ + Parameters: + x: NCHW + mask: N1HW + mask_sum_hw: N111 + mask_sum: scalar + + Returns: NCHW + """ + out = x + out = self.norm(out, mask=mask, mask_sum=mask_sum) + out = self.act(out) + # print("TENSOR AFTER NORMACT") + # print(out) + if self.convpool is not None: + out = self.convpool(out, mask=mask, mask_sum_hw=mask_sum_hw, mask_sum=mask_sum, extra_outputs=extra_outputs) + else: + if self.conv1x1 is not None: + out = self.conv(out) + self.conv1x1(out) + else: + out = self.conv(out) + if extra_outputs is not None: + extra_outputs.report(self.name+".out", out) + return out + + +class ResBlock(torch.nn.Module): + def __init__( + self, + name: str, + c_main: int, + c_mid: int, + c_gpool: Optional[int], + config: modelconfigs.ModelConfig, + activation: str, + ): + super(ResBlock, self).__init__() + self.name = name + self.norm_kind = config["norm_kind"] + self.normactconv1 = NormActConv( + name=name+".normactconv1", + c_in=c_main, + c_out=c_mid - (0 if c_gpool is None else c_gpool), + c_gpool=c_gpool, + config=config, + activation=activation, + kernel_size=3, + fixup_use_gamma=False, + ) + self.normactconv2 = NormActConv( + name=name+".normactconv2", + c_in=c_mid - (0 if c_gpool is None else c_gpool), + c_out=c_main, + c_gpool=None, + config=config, + activation=activation, + kernel_size=3, + fixup_use_gamma=True, + ) + + def initialize(self, fixup_scale): + if self.norm_kind == "fixup": + self.normactconv1.initialize(scale=fixup_scale) + self.normactconv2.initialize(scale=0.0) + elif self.norm_kind == "fixscale" or self.norm_kind == "fixbrenorm" or self.norm_kind == "fixscaleonenorm": + self.normactconv1.initialize(scale=1.0, norm_scale=fixup_scale) + self.normactconv2.initialize(scale=1.0) + else: + self.normactconv1.initialize(scale=1.0) + self.normactconv2.initialize(scale=1.0) + + def add_reg_dict(self, reg_dict:Dict[str,List]): + self.normactconv1.add_reg_dict(reg_dict) + self.normactconv2.add_reg_dict(reg_dict) + + def set_brenorm_params(self, renorm_avg_momentum: float, rmax: float, dmax: float): + self.normactconv1.set_brenorm_params(renorm_avg_momentum, rmax, dmax) + self.normactconv2.set_brenorm_params(renorm_avg_momentum, rmax, dmax) + + def add_brenorm_clippage(self, upper_rclippage, lower_rclippage, dclippage): + self.normactconv1.add_brenorm_clippage(upper_rclippage, lower_rclippage, dclippage) + self.normactconv2.add_brenorm_clippage(upper_rclippage, lower_rclippage, dclippage) + + def forward(self, x, mask, mask_sum_hw, mask_sum: float, extra_outputs: Optional[ExtraOutputs]): + """ + Parameters: + x: NCHW + mask: N1HW + mask_sum_hw: N111 + mask_sum: scalar + + Returns: NCHW + """ + out = x + out = self.normactconv1(out, mask=mask, mask_sum_hw=mask_sum_hw, mask_sum=mask_sum, extra_outputs=extra_outputs) + out = self.normactconv2(out, mask=mask, mask_sum_hw=mask_sum_hw, mask_sum=mask_sum, extra_outputs=extra_outputs) + result = x + out + if extra_outputs is not None: + extra_outputs.report(self.name+".out", result) + return result + + +class BottleneckResBlock(torch.nn.Module): + def __init__( + self, + name: str, + internal_length: int, + c_main: int, + c_mid: int, + c_gpool: Optional[int], + config: modelconfigs.ModelConfig, + activation: str, + ): + super(BottleneckResBlock, self).__init__() + self.name = name + self.norm_kind = config["norm_kind"] + self.internal_length = internal_length + assert internal_length >= 1 + + self.normactconvp = NormActConv( + name=name+".normactconvp", + c_in=c_main, + c_out=c_mid, + c_gpool=None, + config=config, + activation=activation, + kernel_size=1, + fixup_use_gamma=False, + ) + + self.normactconvstack = torch.nn.ModuleList() + self.normactconvstack.append(NormActConv( + name=name+".normactconvstack."+str(0), + c_in=c_mid, + c_out=c_mid - (0 if c_gpool is None else c_gpool), + c_gpool=c_gpool, + config=config, + activation=activation, + kernel_size=3, + fixup_use_gamma=False, + )) + for i in range(self.internal_length-1): + self.normactconvstack.append(NormActConv( + name=name+".normactconvstack."+str(i+1), + c_in=self.normactconvstack[-1].c_out, + c_out=c_mid, + c_gpool=None, + config=config, + activation=activation, + kernel_size=3, + fixup_use_gamma=False, + )) + + self.normactconvq = NormActConv( + name=name+".normactconvq", + c_in=self.normactconvstack[-1].c_out, + c_out=c_main, + c_gpool=None, + config=config, + activation=activation, + kernel_size=1, + fixup_use_gamma=True, + ) + + def initialize(self, fixup_scale): + if self.norm_kind == "fixup": + self.normactconvp.initialize(scale=math.pow(fixup_scale, 1.0 / (1.0 + self.internal_length))) + for i in range(self.internal_length): + self.normactconvstack[i].initialize(scale=math.pow(fixup_scale, 1.0 / (1.0 + self.internal_length))) + self.normactconvq.initialize(scale=0.0) + elif self.norm_kind == "fixscale" or self.norm_kind == "fixbrenorm" or self.norm_kind == "fixscaleonenorm": + self.normactconvp.initialize(scale=1.0, norm_scale=fixup_scale) + for i in range(self.internal_length): + self.normactconvstack[i].initialize(scale=1.0) + self.normactconvq.initialize(scale=1.0) + else: + self.normactconvp.initialize(scale=1.0) + for i in range(self.internal_length): + self.normactconvstack[i].initialize(scale=1.0) + self.normactconvq.initialize(scale=1.0) + + def add_reg_dict(self, reg_dict:Dict[str,List]): + self.normactconvp.add_reg_dict(reg_dict) + for i in range(self.internal_length): + self.normactconvstack[i].add_reg_dict(reg_dict) + self.normactconvq.add_reg_dict(reg_dict) + + def set_brenorm_params(self, renorm_avg_momentum: float, rmax: float, dmax: float): + self.normactconvp.set_brenorm_params(renorm_avg_momentum, rmax, dmax) + for i in range(self.internal_length): + self.normactconvstack[i].set_brenorm_params(renorm_avg_momentum, rmax, dmax) + self.normactconvq.set_brenorm_params(renorm_avg_momentum, rmax, dmax) + + def add_brenorm_clippage(self, upper_rclippage, lower_rclippage, dclippage): + self.normactconvp.add_brenorm_clippage(upper_rclippage, lower_rclippage, dclippage) + for i in range(self.internal_length): + self.normactconvstack[i].add_brenorm_clippage(upper_rclippage, lower_rclippage, dclippage) + self.normactconvq.add_brenorm_clippage(upper_rclippage, lower_rclippage, dclippage) + + def forward(self, x, mask, mask_sum_hw, mask_sum: float, extra_outputs: Optional[ExtraOutputs]): + """ + Parameters: + x: NCHW + mask: N1HW + mask_sum_hw: N111 + mask_sum: scalar + + Returns: NCHW + """ + out = x + out = self.normactconvp(out, mask=mask, mask_sum_hw=mask_sum_hw, mask_sum=mask_sum, extra_outputs=extra_outputs) + for i in range(self.internal_length): + out = self.normactconvstack[i](out, mask=mask, mask_sum_hw=mask_sum_hw, mask_sum=mask_sum, extra_outputs=extra_outputs) + out = self.normactconvq(out, mask=mask, mask_sum_hw=mask_sum_hw, mask_sum=mask_sum, extra_outputs=extra_outputs) + result = x + out + if extra_outputs is not None: + extra_outputs.report(self.name+".out", result) + return result + + +class NestedBottleneckResBlock(torch.nn.Module): + def __init__( + self, + name: str, + internal_length: int, + c_main: int, + c_mid: int, + c_gpool: Optional[int], + config: modelconfigs.ModelConfig, + activation: str, + ): + super(NestedBottleneckResBlock, self).__init__() + self.name = name + self.norm_kind = config["norm_kind"] + self.internal_length = internal_length + assert internal_length >= 1 + + self.normactconvp = NormActConv( + name=name+".normactconvp", + c_in=c_main, + c_out=c_mid, + c_gpool=None, + config=config, + activation=activation, + kernel_size=1, + fixup_use_gamma=False, + ) + + self.blockstack = torch.nn.ModuleList() + for i in range(self.internal_length): + self.blockstack.append(ResBlock( + name=name+".blockstack."+str(i), + c_main=c_mid, + c_mid=c_mid, + c_gpool=(c_gpool if i == 0 else None), + config=config, + activation=activation, + )) + + self.normactconvq = NormActConv( + name=name+".normactconvq", + c_in=c_mid, + c_out=c_main, + c_gpool=None, + config=config, + activation=activation, + kernel_size=1, + fixup_use_gamma=True, + ) + + def initialize(self, fixup_scale): + if self.norm_kind == "fixup": + self.normactconvp.initialize(scale=math.pow(fixup_scale, 1.0 / (1.0 + self.internal_length))) + for i in range(self.internal_length): + self.blockstack[i].initialize(fixup_scale=math.pow(fixup_scale, 1.0 / (1.0 + self.internal_length))) + self.normactconvq.initialize(scale=0.0) + elif self.norm_kind == "fixscale" or self.norm_kind == "fixbrenorm" or self.norm_kind == "fixscaleonenorm": + self.normactconvp.initialize(scale=1.0, norm_scale=fixup_scale) + for i in range(self.internal_length): + self.blockstack[i].initialize(fixup_scale=1.0 / math.sqrt(i+1.0)) + self.normactconvq.initialize(scale=1.0, norm_scale=1.0 / math.sqrt(self.internal_length+1.0)) + else: + self.normactconvp.initialize(scale=1.0) + for i in range(self.internal_length): + self.blockstack[i].initialize(fixup_scale=1.0) + self.normactconvq.initialize(scale=1.0) + + def add_reg_dict(self, reg_dict:Dict[str,List]): + self.normactconvp.add_reg_dict(reg_dict) + for i in range(self.internal_length): + self.blockstack[i].add_reg_dict(reg_dict) + self.normactconvq.add_reg_dict(reg_dict) + + def set_brenorm_params(self, renorm_avg_momentum: float, rmax: float, dmax: float): + self.normactconvp.set_brenorm_params(renorm_avg_momentum, rmax, dmax) + for i in range(self.internal_length): + self.blockstack[i].set_brenorm_params(renorm_avg_momentum, rmax, dmax) + self.normactconvq.set_brenorm_params(renorm_avg_momentum, rmax, dmax) + + def add_brenorm_clippage(self, upper_rclippage, lower_rclippage, dclippage): + self.normactconvp.add_brenorm_clippage(upper_rclippage, lower_rclippage, dclippage) + for i in range(self.internal_length): + self.blockstack[i].add_brenorm_clippage(upper_rclippage, lower_rclippage, dclippage) + self.normactconvq.add_brenorm_clippage(upper_rclippage, lower_rclippage, dclippage) + + def forward(self, x, mask, mask_sum_hw, mask_sum: float, extra_outputs: Optional[ExtraOutputs]): + """ + Parameters: + x: NCHW + mask: N1HW + mask_sum_hw: N111 + mask_sum: scalar + + Returns: NCHW + """ + out = x + out = self.normactconvp(out, mask=mask, mask_sum_hw=mask_sum_hw, mask_sum=mask_sum, extra_outputs=extra_outputs) + for i in range(self.internal_length): + out = self.blockstack[i](out, mask=mask, mask_sum_hw=mask_sum_hw, mask_sum=mask_sum, extra_outputs=extra_outputs) + out = self.normactconvq(out, mask=mask, mask_sum_hw=mask_sum_hw, mask_sum=mask_sum, extra_outputs=extra_outputs) + result = x + out + if extra_outputs is not None: + extra_outputs.report(self.name+".out", result) + return result + + + +class NestedNestedBottleneckResBlock(torch.nn.Module): + def __init__( + self, + name: str, + internal_length: int, + sub_internal_length: int, + c_main: int, + c_outermid: int, + c_mid: int, + c_gpool: Optional[int], + config: modelconfigs.ModelConfig, + activation: str, + ): + super(NestedNestedBottleneckResBlock, self).__init__() + self.name = name + self.norm_kind = config["norm_kind"] + self.internal_length = internal_length + assert internal_length >= 1 + + self.normactconvp = NormActConv( + name=name+".normactconvp", + c_in=c_main, + c_out=c_outermid, + c_gpool=None, + config=config, + activation=activation, + kernel_size=1, + fixup_use_gamma=False, + ) + + self.blockstack = torch.nn.ModuleList() + for i in range(self.internal_length): + self.blockstack.append(NestedBottleneckResBlock( + name=name+".blockstack."+str(i), + internal_length=sub_internal_length, + c_main=c_outermid, + c_mid=c_mid, + c_gpool=(c_gpool if i == 0 else None), + config=config, + activation=activation, + )) + + self.normactconvq = NormActConv( + name=name+".normactconvq", + c_in=c_outermid, + c_out=c_main, + c_gpool=None, + config=config, + activation=activation, + kernel_size=1, + fixup_use_gamma=True, + ) + + def initialize(self, fixup_scale): + if self.norm_kind == "fixup": + self.normactconvp.initialize(scale=math.pow(fixup_scale, 1.0 / (1.0 + self.internal_length))) + for i in range(self.internal_length): + self.blockstack[i].initialize(fixup_scale=math.pow(fixup_scale, 1.0 / (1.0 + self.internal_length))) + self.normactconvq.initialize(scale=0.0) + elif self.norm_kind == "fixscale" or self.norm_kind == "fixbrenorm" or self.norm_kind == "fixscaleonenorm": + self.normactconvp.initialize(scale=1.0, norm_scale=fixup_scale) + for i in range(self.internal_length): + self.blockstack[i].initialize(fixup_scale=1.0 / math.sqrt(i+1.0)) + self.normactconvq.initialize(scale=1.0, norm_scale=1.0 / math.sqrt(self.internal_length+1.0)) + else: + self.normactconvp.initialize(scale=1.0) + for i in range(self.internal_length): + self.blockstack[i].initialize(fixup_scale=1.0) + self.normactconvq.initialize(scale=1.0) + + def add_reg_dict(self, reg_dict:Dict[str,List]): + self.normactconvp.add_reg_dict(reg_dict) + for i in range(self.internal_length): + self.blockstack[i].add_reg_dict(reg_dict) + self.normactconvq.add_reg_dict(reg_dict) + + def set_brenorm_params(self, renorm_avg_momentum: float, rmax: float, dmax: float): + self.normactconvp.set_brenorm_params(renorm_avg_momentum, rmax, dmax) + for i in range(self.internal_length): + self.blockstack[i].set_brenorm_params(renorm_avg_momentum, rmax, dmax) + self.normactconvq.set_brenorm_params(renorm_avg_momentum, rmax, dmax) + + def add_brenorm_clippage(self, upper_rclippage, lower_rclippage, dclippage): + self.normactconvp.add_brenorm_clippage(upper_rclippage, lower_rclippage, dclippage) + for i in range(self.internal_length): + self.blockstack[i].add_brenorm_clippage(upper_rclippage, lower_rclippage, dclippage) + self.normactconvq.add_brenorm_clippage(upper_rclippage, lower_rclippage, dclippage) + + def forward(self, x, mask, mask_sum_hw, mask_sum: float, extra_outputs: Optional[ExtraOutputs]): + """ + Parameters: + x: NCHW + mask: N1HW + mask_sum_hw: N111 + mask_sum: scalar + + Returns: NCHW + """ + out = x + out = self.normactconvp(out, mask=mask, mask_sum_hw=mask_sum_hw, mask_sum=mask_sum, extra_outputs=extra_outputs) + for i in range(self.internal_length): + out = self.blockstack[i](out, mask=mask, mask_sum_hw=mask_sum_hw, mask_sum=mask_sum, extra_outputs=extra_outputs) + out = self.normactconvq(out, mask=mask, mask_sum_hw=mask_sum_hw, mask_sum=mask_sum, extra_outputs=extra_outputs) + result = x + out + if extra_outputs is not None: + extra_outputs.report(self.name+".out", result) + return result + + +class PolicyHead(torch.nn.Module): + def __init__(self, c_in, c_p1, c_g1, config, activation): + super(PolicyHead, self).__init__() + self.config = config + self.activation = activation + + if config["version"] <= 11: + self.num_policy_outputs = 4 + else: + self.num_policy_outputs = 6 + # Output 0: policy prediction + # Output 1: opponent reply policy prediction + # Output 2: soft policy prediction + # Output 3: soft opponent reply policy prediction + # Output 4: long-term-optimistic policy prediction + # Output 5: short-term-optimistic policy prediction + + self.conv1p = torch.nn.Conv2d(c_in, c_p1, kernel_size=1, padding="same", bias=False) + self.conv1g = torch.nn.Conv2d(c_in, c_g1, kernel_size=1, padding="same", bias=False) + + self.biasg = BiasMask( + c_g1, + config=config, + is_after_batchnorm=True, + ) + self.actg = act(self.activation) + self.gpool = KataGPool() + + self.linear_g = torch.nn.Linear(3 * c_g1, c_p1, bias=False) + if config["version"] <= 14: + self.linear_pass = torch.nn.Linear(3 * c_g1, self.num_policy_outputs, bias=False) + else: + self.linear_pass = torch.nn.Linear(3 * c_g1, c_p1, bias=True) + self.act_pass = act(self.activation) + self.linear_pass2 = torch.nn.Linear(c_p1, self.num_policy_outputs, bias=False) + + self.bias2 = BiasMask( + c_p1, + config=config, + is_after_batchnorm=True, + ) + self.act2 = act(activation) + self.conv2p = torch.nn.Conv2d(c_p1, self.num_policy_outputs, kernel_size=1, padding="same", bias=False) + + + def initialize(self): + # Scaling so that variance on the p and g branches adds up to 1.0 + p_scale = 0.8 + g_scale = 0.6 + bias_scale = 0.2 + # Extra scaling for outputs + scale_output = 0.3 + init_weights(self.conv1p.weight, self.activation, scale=p_scale) + init_weights(self.conv1g.weight, self.activation, scale=1.0) + init_weights(self.linear_g.weight, self.activation, scale=g_scale) + if self.config["version"] <= 14: + init_weights(self.linear_pass.weight, "identity", scale=scale_output) + else: + init_weights(self.linear_pass.weight, self.activation, scale=1.0) + init_weights(self.linear_pass.bias, self.activation, scale=bias_scale, fan_tensor=self.linear_pass.weight) + init_weights(self.linear_pass2.weight, "identity", scale=scale_output) + init_weights(self.conv2p.weight, "identity", scale=scale_output) + + def add_reg_dict(self, reg_dict:Dict[str,List]): + reg_dict["output"].append(self.conv1p.weight) + reg_dict["output"].append(self.conv1g.weight) + reg_dict["output"].append(self.linear_g.weight) + if self.config["version"] <= 14: + reg_dict["output"].append(self.linear_pass.weight) + else: + reg_dict["output"].append(self.linear_pass.weight) + reg_dict["output_noreg"].append(self.linear_pass.bias) + reg_dict["output"].append(self.linear_pass2.weight) + + reg_dict["output"].append(self.conv2p.weight) + self.biasg.add_reg_dict(reg_dict) + self.bias2.add_reg_dict(reg_dict) + + def set_brenorm_params(self, renorm_avg_momentum: float, rmax: float, dmax: float): + pass + + def add_brenorm_clippage(self, upper_rclippage, lower_rclippage, dclippage): + pass + + def forward(self, x, mask, mask_sum_hw, mask_sum:float, extra_outputs: Optional[ExtraOutputs]): + outp = self.conv1p(x) + outg = self.conv1g(x) + + outg = self.biasg(outg, mask=mask, mask_sum=mask_sum) + outg = self.actg(outg) + outg = self.gpool(outg, mask=mask, mask_sum_hw=mask_sum_hw).squeeze(-1).squeeze(-1) # NC + + if self.config["version"] <= 14: + outpass = self.linear_pass(outg) # NC + else: + outpass = self.linear_pass(outg) # NC + outpass = self.act_pass(outpass) # NC + outpass = self.linear_pass2(outpass) # NC + + outg = self.linear_g(outg).unsqueeze(-1).unsqueeze(-1) # NCHW + + outp = outp + outg + outp = self.bias2(outp, mask=mask, mask_sum=mask_sum) + outp = self.act2(outp) + outp = self.conv2p(outp) + outpolicy = outp + + # mask out parts outside the board by making them a huge neg number, so that they're 0 after softmax + outpolicy = outpolicy - (1.0 - mask) * 5000.0 + # NC(HW) concat with NC1 + return torch.cat((outpolicy.view(outpolicy.shape[0],outpolicy.shape[1],-1), outpass.unsqueeze(-1)),dim=2) + + +class ValueHead(torch.nn.Module): + def __init__(self, c_in, c_v1, c_v2, c_sv2, num_scorebeliefs, config, activation, pos_len): + super(ValueHead, self).__init__() + self.activation = activation + self.conv1 = torch.nn.Conv2d(c_in, c_v1, kernel_size=1, padding="same", bias=False) + self.bias1 = BiasMask( + c_v1, + config=config, + is_after_batchnorm=True, + ) + self.act1 = act(activation) + self.gpool = KataValueHeadGPool() + + self.linear2 = torch.nn.Linear(3 * c_v1, c_v2, bias=True) + self.act2 = act(activation) + + self.linear_valuehead = torch.nn.Linear(c_v2, 3, bias=True) + self.linear_miscvaluehead = torch.nn.Linear(c_v2, 10, bias=True) + self.linear_moremiscvaluehead = torch.nn.Linear(c_v2, 8, bias=True) + self.conv_ownership = torch.nn.Conv2d(c_v1, 1, kernel_size=1, padding="same", bias=False) + self.conv_scoring = torch.nn.Conv2d(c_v1, 1, kernel_size=1, padding="same", bias=False) + self.conv_futurepos = torch.nn.Conv2d(c_in, 2, kernel_size=1, padding="same", bias=False) + self.conv_seki = torch.nn.Conv2d(c_in, 4, kernel_size=1, padding="same", bias=False) + + self.pos_len = pos_len + self.scorebelief_mid = self.pos_len*self.pos_len + EXTRA_SCORE_DISTR_RADIUS + self.scorebelief_len = self.scorebelief_mid * 2 + self.num_scorebeliefs = num_scorebeliefs + self.c_sv2 = c_sv2 + + self.linear_s2 = torch.nn.Linear(3 * c_v1, c_sv2, bias=True) + self.linear_s2off = torch.nn.Linear(1, c_sv2, bias=False) + self.linear_s2par = torch.nn.Linear(1, c_sv2, bias=False) + self.linear_s3 = torch.nn.Linear(c_sv2, num_scorebeliefs, bias=True) + self.linear_smix = torch.nn.Linear(3 * c_v1, num_scorebeliefs, bias=True) + + self.register_buffer("score_belief_offset_vector", torch.tensor( + data=[(float(i-self.scorebelief_mid)+0.5) for i in range(self.scorebelief_len)], + dtype=torch.float32, + requires_grad=False, + ), persistent=False) + self.register_buffer("score_belief_offset_bias_vector", torch.tensor( + data=[0.05 * (float(i-self.scorebelief_mid)+0.5) for i in range(self.scorebelief_len)], + dtype=torch.float32, + requires_grad=False, + ), persistent=False) + self.register_buffer("score_belief_parity_vector", torch.tensor( + [0.5-float((i-self.scorebelief_mid) % 2) for i in range(self.scorebelief_len)], + dtype=torch.float32, + requires_grad=False, + ), persistent=False) + + + def initialize(self): + bias_scale = 0.2 + init_weights(self.conv1.weight, self.activation, scale=1.0) + init_weights(self.linear2.weight, self.activation, scale=1.0) + init_weights(self.linear2.bias, self.activation, scale=bias_scale, fan_tensor=self.linear2.weight) + + init_weights(self.linear_valuehead.weight, "identity", scale=1.0) + init_weights(self.linear_valuehead.bias, "identity", scale=bias_scale, fan_tensor=self.linear_valuehead.weight) + + init_weights(self.linear_miscvaluehead.weight, "identity", scale=1.0) + init_weights(self.linear_miscvaluehead.bias, "identity", scale=bias_scale, fan_tensor=self.linear_miscvaluehead.weight) + + init_weights(self.linear_moremiscvaluehead.weight, "identity", scale=1.0) + init_weights(self.linear_moremiscvaluehead.bias, "identity", scale=bias_scale, fan_tensor=self.linear_moremiscvaluehead.weight) + + aux_spatial_output_scale = 0.2 + init_weights(self.conv_ownership.weight, "identity", scale=aux_spatial_output_scale) + init_weights(self.conv_scoring.weight, "identity", scale=aux_spatial_output_scale) + init_weights(self.conv_futurepos.weight, "identity", scale=aux_spatial_output_scale) + init_weights(self.conv_seki.weight, "identity", scale=aux_spatial_output_scale) + + init_weights(self.linear_s2.weight, self.activation, scale=1.0) + init_weights(self.linear_s2.bias, self.activation, scale=1.0, fan_tensor=self.linear_s2.weight) + init_weights(self.linear_s2off.weight, self.activation, scale=1.0, fan_tensor=self.linear_s2.weight) + init_weights(self.linear_s2par.weight, self.activation, scale=1.0, fan_tensor=self.linear_s2.weight) + + scorebelief_output_scale = 0.5 + init_weights(self.linear_s3.weight, "identity", scale=scorebelief_output_scale) + init_weights(self.linear_s3.bias, "identity", scale=scorebelief_output_scale*bias_scale, fan_tensor=self.linear_s3.weight) + init_weights(self.linear_smix.weight, "identity", scale=1.0) + init_weights(self.linear_smix.bias, "identity", scale=bias_scale, fan_tensor=self.linear_smix.weight) + + def add_reg_dict(self, reg_dict:Dict[str,List]): + reg_dict["output"].append(self.conv1.weight) + reg_dict["output"].append(self.linear2.weight) + reg_dict["output_noreg"].append(self.linear2.bias) + reg_dict["output"].append(self.linear_valuehead.weight) + reg_dict["output_noreg"].append(self.linear_valuehead.bias) + reg_dict["output"].append(self.linear_miscvaluehead.weight) + reg_dict["output_noreg"].append(self.linear_miscvaluehead.bias) + reg_dict["output"].append(self.linear_moremiscvaluehead.weight) + reg_dict["output_noreg"].append(self.linear_moremiscvaluehead.bias) + reg_dict["output"].append(self.conv_ownership.weight) + reg_dict["output"].append(self.conv_scoring.weight) + reg_dict["output"].append(self.conv_futurepos.weight) + reg_dict["output"].append(self.conv_seki.weight) + reg_dict["output"].append(self.linear_s2.weight) + reg_dict["output_noreg"].append(self.linear_s2.bias) + reg_dict["output"].append(self.linear_s2off.weight) + reg_dict["output"].append(self.linear_s2par.weight) + reg_dict["output"].append(self.linear_s3.weight) + reg_dict["output_noreg"].append(self.linear_s3.bias) + reg_dict["output"].append(self.linear_smix.weight) + reg_dict["output_noreg"].append(self.linear_smix.bias) + self.bias1.add_reg_dict(reg_dict) + + def set_brenorm_params(self, renorm_avg_momentum: float, rmax: float, dmax: float): + pass + + def add_brenorm_clippage(self, upper_rclippage, lower_rclippage, dclippage): + pass + + def forward(self, x, mask, mask_sum_hw, mask_sum:float, input_global, extra_outputs: Optional[ExtraOutputs]): + outv1 = x + outv1 = self.conv1(outv1) + outv1 = self.bias1(outv1, mask=mask, mask_sum=mask_sum) + outv1 = self.act1(outv1) + + outpooled = self.gpool(outv1, mask=mask, mask_sum_hw=mask_sum_hw).squeeze(-1).squeeze(-1) + + outv2 = self.linear2(outpooled) + outv2 = self.act2(outv2) + + # Different subheads + out_value = self.linear_valuehead(outv2) + out_miscvalue = self.linear_miscvaluehead(outv2) + out_moremiscvalue = self.linear_moremiscvaluehead(outv2) + out_ownership = self.conv_ownership(outv1) * mask + out_scoring = self.conv_scoring(outv1) * mask + out_futurepos = self.conv_futurepos(x) * mask + out_seki = self.conv_seki(x) * mask + + # Score belief head + batch_size = x.shape[0] + outsv2 = ( + self.linear_s2(outpooled).view(batch_size,1,self.c_sv2) + + self.linear_s2off(self.score_belief_offset_bias_vector.view(1,self.scorebelief_len,1)) + + self.linear_s2par((self.score_belief_parity_vector.view(1,self.scorebelief_len) * input_global[:,-1:]).view(batch_size,self.scorebelief_len,1)) + ) # N,scorebelief_len,c_sv2 + + outsv2 = self.act2(outsv2) + outsv3 = self.linear_s3(outsv2) # N, scorebelief_len, num_scorebeliefs + + outsmix = self.linear_smix(outpooled) # N, num_scorebeliefs + outsmix_logweights = torch.nn.functional.log_softmax(outsmix, dim=1) + # For each of num_scorebeliefs, compute softmax to make it into probability distribution + out_scorebelief_logprobs = torch.nn.functional.log_softmax(outsv3, dim=1) + # Take the mixture distribution weighted by outsmix_weights + out_scorebelief_logprobs = torch.logsumexp(out_scorebelief_logprobs + outsmix_logweights.view(-1, 1, self.num_scorebeliefs), dim=2) + + return ( + out_value, + out_miscvalue, + out_moremiscvalue, + out_ownership, + out_scoring, + out_futurepos, + out_seki, + out_scorebelief_logprobs, + ) + +class MetadataEncoder(torch.nn.Module): + def __init__(self, config: modelconfigs.ModelConfig): + super(MetadataEncoder, self).__init__() + + self.config = config + self.activation = config["activation"] + self.meta_encoder_version = 1 if "meta_encoder_version" not in config["metadata_encoder"] else config["metadata_encoder"]["meta_encoder_version"] + + self.c_input = modelconfigs.get_num_meta_encoder_input_features(self.meta_encoder_version) + assert self.c_input == 192 + + self.c_internal = self.config["metadata_encoder"]["internal_num_channels"] + self.c_trunk = self.config["trunk_num_channels"] + self.out_scale = 0.5 + + self.register_buffer("feature_mask", torch.tensor( + # 86 is board area + data=[(0.0 if i == 86 else 1.0) for i in range(self.c_input)], + dtype=torch.float32, + requires_grad=False, + ), persistent=True) + + self.linear1 = torch.nn.Linear(self.c_input, self.c_internal, bias=True) + self.act1 = act(self.activation, inplace=True) + self.linear2 = torch.nn.Linear(self.c_internal, self.c_internal, bias=True) + self.act2 = act(self.activation, inplace=True) + self.linear_output_to_trunk = torch.nn.Linear(self.c_internal, self.c_trunk, bias=False) + + def initialize(self): + weight_scale = 0.8 + bias_scale = 0.2 + with torch.no_grad(): + init_weights(self.linear1.weight, self.activation, scale=weight_scale) + init_weights(self.linear1.bias, self.activation, scale=bias_scale, fan_tensor=self.linear1.weight) + init_weights(self.linear2.weight, self.activation, scale=weight_scale) + init_weights(self.linear2.bias, self.activation, scale=bias_scale, fan_tensor=self.linear2.weight) + init_weights(self.linear_output_to_trunk.weight, self.activation, scale=weight_scale) + + def add_reg_dict(self, reg_dict:Dict[str,List]): + reg_dict["output"].append(self.linear1.weight) + reg_dict["output_noreg"].append(self.linear1.bias) + reg_dict["output"].append(self.linear2.weight) + reg_dict["output_noreg"].append(self.linear2.bias) + reg_dict["normal"].append(self.linear_output_to_trunk.weight) + + def forward(self, input_meta, extra_outputs: Optional[ExtraOutputs]): + x = input_meta + x = x * self.feature_mask.reshape((1,-1)) + x = self.linear1(x) + x = self.act1(x) + x = self.linear2(x) + x = self.act2(x) + return self.out_scale * self.linear_output_to_trunk(x) + + +class Model(torch.nn.Module): + def __init__(self, config: modelconfigs.ModelConfig, pos_len: int): + super(Model, self).__init__() + + self.config = config + self.norm_kind = config["norm_kind"] + self.block_kind = config["block_kind"] + self.c_trunk = config["trunk_num_channels"] + self.c_mid = config["mid_num_channels"] + self.c_gpool = config["gpool_num_channels"] + self.c_outermid = config["outermid_num_channels"] if "outermid_num_channels" in config else self.c_mid + self.c_p1 = config["p1_num_channels"] + self.c_g1 = config["g1_num_channels"] + self.c_v1 = config["v1_num_channels"] + self.c_v2 = config["v2_size"] + self.c_sv2 = config["sbv2_num_channels"] + self.num_scorebeliefs = config["num_scorebeliefs"] + self.num_total_blocks = len(self.block_kind) + self.pos_len = pos_len + + if config["version"] <= 12: + self.td_score_multiplier = 20.0 + self.scoremean_multiplier = 20.0 + self.scorestdev_multiplier = 20.0 + self.lead_multiplier = 20.0 + self.variance_time_multiplier = 40.0 + self.shortterm_value_error_multiplier = 0.25 + self.shortterm_score_error_multiplier = 30.0 + else: + self.td_score_multiplier = 20.0 + self.scoremean_multiplier = 20.0 + self.scorestdev_multiplier = 20.0 + self.lead_multiplier = 20.0 + self.variance_time_multiplier = 40.0 + self.shortterm_value_error_multiplier = 0.25 + self.shortterm_score_error_multiplier = 150.0 + + self.trunk_normless = "trunk_normless" in config and config["trunk_normless"] + + if "has_intermediate_head" in config and config["has_intermediate_head"]: + self.has_intermediate_head = True + self.intermediate_head_blocks = config["intermediate_head_blocks"] + else: + self.has_intermediate_head = False + self.intermediate_head_blocks = 0 + + self.activation = "relu" if "activation" not in config else config["activation"] + + if config["initial_conv_1x1"]: + self.conv_spatial = torch.nn.Conv2d(22, self.c_trunk, kernel_size=1, padding="same", bias=False) + else: + self.conv_spatial = torch.nn.Conv2d(22, self.c_trunk, kernel_size=3, padding="same", bias=False) + self.linear_global = torch.nn.Linear(19, self.c_trunk, bias=False) + + if "metadata_encoder" in config and config["metadata_encoder"] is not None: + self.metadata_encoder = MetadataEncoder(config) + else: + self.metadata_encoder = None + + self.bin_input_shape = [22, pos_len, pos_len] + self.global_input_shape = [19] + + self.blocks = torch.nn.ModuleList() + for block_config in self.block_kind: + block_name = block_config[0] + block_kind = block_config[1] + use_gpool_this_block = False + if block_kind.endswith("gpool"): + use_gpool_this_block = True + block_kind = block_kind[:-5] + + if block_kind == "regular": + self.blocks.append(ResBlock( + name=block_name, + c_main=self.c_trunk, + c_mid=self.c_mid, + c_gpool=(self.c_gpool if use_gpool_this_block else None), + config=self.config, + activation=self.activation, + )) + elif block_kind == "bottle1" or block_kind == "bottle": + self.blocks.append(BottleneckResBlock( + name=block_name, + internal_length=1, + c_main=self.c_trunk, + c_mid=self.c_mid, + c_gpool=(self.c_gpool if use_gpool_this_block else None), + config=self.config, + activation=self.activation, + )) + elif block_kind == "bottle2": + self.blocks.append(BottleneckResBlock( + name=block_name, + internal_length=2, + c_main=self.c_trunk, + c_mid=self.c_mid, + c_gpool=(self.c_gpool if use_gpool_this_block else None), + config=self.config, + activation=self.activation, + )) + elif block_kind == "bottle3": + self.blocks.append(BottleneckResBlock( + name=block_name, + internal_length=3, + c_main=self.c_trunk, + c_mid=self.c_mid, + c_gpool=(self.c_gpool if use_gpool_this_block else None), + config=self.config, + activation=self.activation, + )) + elif block_kind == "bottlenest2": + self.blocks.append(NestedBottleneckResBlock( + name=block_name, + internal_length=2, + c_main=self.c_trunk, + c_mid=self.c_mid, + c_gpool=(self.c_gpool if use_gpool_this_block else None), + config=self.config, + activation=self.activation, + )) + elif block_kind == "bottlenest3": + self.blocks.append(NestedBottleneckResBlock( + name=block_name, + internal_length=3, + c_main=self.c_trunk, + c_mid=self.c_mid, + c_gpool=(self.c_gpool if use_gpool_this_block else None), + config=self.config, + activation=self.activation, + )) + elif block_kind == "bottlenest2bottlenest2": + self.blocks.append(NestedNestedBottleneckResBlock( + name=block_name, + internal_length=2, + sub_internal_length=2, + c_main=self.c_trunk, + c_outermid=self.c_outermid, + c_mid=self.c_mid, + c_gpool=(self.c_gpool if use_gpool_this_block else None), + config=self.config, + activation=self.activation, + )) + else: + assert False, f"Unknown block kind: {block_config[1]}" + + if self.trunk_normless: + self.norm_trunkfinal = BiasMask(self.c_trunk, self.config, is_after_batchnorm=True) + else: + self.norm_trunkfinal = NormMask(self.c_trunk, self.config, fixup_use_gamma=False, is_last_batchnorm=True) + self.act_trunkfinal = act(self.activation) + + self.policy_head = PolicyHead( + self.c_trunk, + self.c_p1, + self.c_g1, + self.config, + self.activation, + ) + self.value_head = ValueHead( + self.c_trunk, + self.c_v1, + self.c_v2, + self.c_sv2, + self.num_scorebeliefs, + self.config, + self.activation, + self.pos_len, + ) + if self.has_intermediate_head: + self.norm_intermediate_trunkfinal = NormMask(self.c_trunk, self.config, fixup_use_gamma=False, is_last_batchnorm=True) + self.act_intermediate_trunkfinal = act(self.activation) + self.intermediate_policy_head = PolicyHead( + self.c_trunk, + self.c_p1, + self.c_g1, + self.config, + self.activation, + ) + self.intermediate_value_head = ValueHead( + self.c_trunk, + self.c_v1, + self.c_v2, + self.c_sv2, + self.num_scorebeliefs, + self.config, + self.activation, + self.pos_len, + ) + + @property + def device(self): + return self.linear_global.weight.device + + def initialize(self): + with torch.no_grad(): + spatial_scale = 0.8 + global_scale = 0.6 + init_weights(self.conv_spatial.weight, self.activation, scale=spatial_scale) + init_weights(self.linear_global.weight, self.activation, scale=global_scale) + + if self.metadata_encoder is not None: + self.metadata_encoder.initialize() + + if self.norm_kind == "fixup": + fixup_scale = 1.0 / math.sqrt(self.num_total_blocks) + for block in self.blocks: + block.initialize(fixup_scale=fixup_scale) + elif self.norm_kind == "fixscale" or self.norm_kind == "fixbrenorm" or self.norm_kind == "fixscaleonenorm": + for i, block in enumerate(self.blocks): + block.initialize(fixup_scale=1.0 / math.sqrt(i+1.0)) + self.norm_trunkfinal.set_scale(1.0 / math.sqrt(self.num_total_blocks+1.0)) + else: + for block in self.blocks: + block.initialize(fixup_scale=1.0) + + self.policy_head.initialize() + self.value_head.initialize() + if self.has_intermediate_head: + self.intermediate_policy_head.initialize() + self.intermediate_value_head.initialize() + + def get_norm_kind(self) -> bool: + return self.norm_kind + + def get_has_intermediate_head(self) -> bool: + return self.has_intermediate_head + def get_has_metadata_encoder(self) -> bool: + return self.metadata_encoder is not None + + def add_reg_dict(self, reg_dict:Dict[str,List]): + reg_dict["normal"] = [] + reg_dict["normal_gamma"] = [] + reg_dict["output"] = [] + reg_dict["noreg"] = [] + reg_dict["output_noreg"] = [] + + reg_dict["normal"].append(self.conv_spatial.weight) + reg_dict["normal"].append(self.linear_global.weight) + if self.metadata_encoder is not None: + self.metadata_encoder.add_reg_dict(reg_dict) + for block in self.blocks: + block.add_reg_dict(reg_dict) + self.norm_trunkfinal.add_reg_dict(reg_dict) + self.policy_head.add_reg_dict(reg_dict) + self.value_head.add_reg_dict(reg_dict) + if self.has_intermediate_head: + self.norm_intermediate_trunkfinal.add_reg_dict(reg_dict) + self.intermediate_policy_head.add_reg_dict(reg_dict) + self.intermediate_value_head.add_reg_dict(reg_dict) + + + def set_brenorm_params(self, renorm_avg_momentum: float, rmax: float, dmax: float): + for block in self.blocks: + block.set_brenorm_params(renorm_avg_momentum, rmax, dmax) + self.norm_trunkfinal.set_brenorm_params(renorm_avg_momentum, rmax, dmax) + self.policy_head.set_brenorm_params(renorm_avg_momentum, rmax, dmax) + self.value_head.set_brenorm_params(renorm_avg_momentum, rmax, dmax) + if self.has_intermediate_head: + self.norm_intermediate_trunkfinal.set_brenorm_params(renorm_avg_momentum, rmax, dmax) + self.intermediate_policy_head.set_brenorm_params(renorm_avg_momentum, rmax, dmax) + self.intermediate_value_head.set_brenorm_params(renorm_avg_momentum, rmax, dmax) + + def add_brenorm_clippage(self, upper_rclippage, lower_rclippage, dclippage): + for block in self.blocks: + block.add_brenorm_clippage(upper_rclippage, lower_rclippage, dclippage) + self.norm_trunkfinal.add_brenorm_clippage(upper_rclippage, lower_rclippage, dclippage) + self.policy_head.add_brenorm_clippage(upper_rclippage, lower_rclippage, dclippage) + self.value_head.add_brenorm_clippage(upper_rclippage, lower_rclippage, dclippage) + if self.has_intermediate_head: + self.norm_intermediate_trunkfinal.add_brenorm_clippage(upper_rclippage, lower_rclippage, dclippage) + self.intermediate_policy_head.add_brenorm_clippage(upper_rclippage, lower_rclippage, dclippage) + self.intermediate_value_head.add_brenorm_clippage(upper_rclippage, lower_rclippage, dclippage) + + # Returns a tuple of tuples of outputs + # The outer tuple indexes different sets of heads, such as if the net also computes intermediate heads. + # 0 is the main output, 1 is intermediate. + # The inner tuple ranges over the outputs of a set of heads (policy, value, etc). + def forward( + self, + input_spatial, + input_global, + input_meta = None, + extra_outputs: Optional[ExtraOutputs] = None, + ): + # float_formatter = "{:.3f}".format + # np.set_printoptions(formatter={'float_kind':float_formatter}, threshold=1000000, linewidth=10000) + + mask = input_spatial[:, 0:1, :, :].contiguous() + mask_sum_hw = torch.sum(mask,dim=(2,3),keepdim=True) + mask_sum = torch.sum(mask) + + x_spatial = self.conv_spatial(input_spatial) + x_global = self.linear_global(input_global).unsqueeze(-1).unsqueeze(-1) + + out = x_spatial + x_global + + if self.metadata_encoder is not None: + assert input_meta is not None + x_meta = self.metadata_encoder.forward(input_meta,extra_outputs) + out = out + x_meta.unsqueeze(-1).unsqueeze(-1) + + # print("TENSOR BEFORE TRUNK") + # print(out) + + if self.has_intermediate_head: + count = 0 + for block in self.blocks[:self.intermediate_head_blocks]: + # print("TENSOR BEFORE BLOCK") + # print(count) + # print(out) + out = block(out, mask=mask, mask_sum_hw=mask_sum_hw, mask_sum=mask_sum, extra_outputs=extra_outputs) + count += 1 + + # print("INTERMEDIATE") + iout = out + iout = self.norm_intermediate_trunkfinal(iout, mask=mask, mask_sum=mask_sum) + iout = self.act_intermediate_trunkfinal(iout) + iout_policy = self.intermediate_policy_head(iout, mask=mask, mask_sum_hw=mask_sum_hw, mask_sum=mask_sum, extra_outputs=extra_outputs) + ( + iout_value, + iout_miscvalue, + iout_moremiscvalue, + iout_ownership, + iout_scoring, + iout_futurepos, + iout_seki, + iout_scorebelief_logprobs, + ) = self.intermediate_value_head(iout, mask=mask, mask_sum_hw=mask_sum_hw, mask_sum=mask_sum, input_global=input_global, extra_outputs=extra_outputs) + + for block in self.blocks[self.intermediate_head_blocks:]: + # print("TENSOR BEFORE BLOCK") + # print(count) + # print(out) + out = block(out, mask=mask, mask_sum_hw=mask_sum_hw, mask_sum=mask_sum, extra_outputs=extra_outputs) + count += 1 + + else: + count = 0 + for block in self.blocks: + # print("TENSOR BEFORE BLOCK") + # print(count) + # print(out) + out = block(out, mask=mask, mask_sum_hw=mask_sum_hw, mask_sum=mask_sum, extra_outputs=extra_outputs) + count += 1 + + out = self.norm_trunkfinal(out, mask=mask, mask_sum=mask_sum) + out = self.act_trunkfinal(out) + + if extra_outputs is not None: + extra_outputs.report("trunkfinal", out) + + # print("MAIN") + out_policy = self.policy_head(out, mask=mask, mask_sum_hw=mask_sum_hw, mask_sum=mask_sum, extra_outputs=extra_outputs) + ( + out_value, + out_miscvalue, + out_moremiscvalue, + out_ownership, + out_scoring, + out_futurepos, + out_seki, + out_scorebelief_logprobs, + ) = self.value_head(out, mask=mask, mask_sum_hw=mask_sum_hw, mask_sum=mask_sum, input_global=input_global, extra_outputs=extra_outputs) + + if self.has_intermediate_head: + return ( + ( + out_policy, + out_value, + out_miscvalue, + out_moremiscvalue, + out_ownership, + out_scoring, + out_futurepos, + out_seki, + out_scorebelief_logprobs, + ), + ( + iout_policy, + iout_value, + iout_miscvalue, + iout_moremiscvalue, + iout_ownership, + iout_scoring, + iout_futurepos, + iout_seki, + iout_scorebelief_logprobs, + ), + ) + else: + return (( + out_policy, + out_value, + out_miscvalue, + out_moremiscvalue, + out_ownership, + out_scoring, + out_futurepos, + out_seki, + out_scorebelief_logprobs, + ),) + + def float32ify_output(self, outputs_byheads): + return tuple(self.float32ify_single_heads_output(outputs) for outputs in outputs_byheads) + + def float32ify_single_heads_output(self, outputs): + ( + out_policy, + out_value, + out_miscvalue, + out_moremiscvalue, + out_ownership, + out_scoring, + out_futurepos, + out_seki, + out_scorebelief_logprobs, + ) = outputs + return ( + out_policy.to(torch.float32), + out_value.to(torch.float32), + out_miscvalue.to(torch.float32), + out_moremiscvalue.to(torch.float32), + out_ownership.to(torch.float32), + out_scoring.to(torch.float32), + out_futurepos.to(torch.float32), + out_seki.to(torch.float32), + out_scorebelief_logprobs.to(torch.float32), + ) + + def postprocess_output(self, outputs_byheads): + return tuple(self.postprocess_single_heads_output(outputs) for outputs in outputs_byheads) + + def postprocess_single_heads_output(self, outputs): + ( + out_policy, + out_value, + out_miscvalue, + out_moremiscvalue, + out_ownership, + out_scoring, + out_futurepos, + out_seki, + out_scorebelief_logprobs, + ) = outputs + + policy_logits = out_policy + value_logits = out_value + td_value_logits = torch.stack((out_miscvalue[:,4:7], out_miscvalue[:,7:10], out_moremiscvalue[:,2:5]), dim=1) + pred_td_score = out_moremiscvalue[:,5:8] * self.td_score_multiplier + ownership_pretanh = out_ownership + pred_scoring = out_scoring + futurepos_pretanh = out_futurepos + seki_logits = out_seki + pred_scoremean = out_miscvalue[:, 0] * self.scoremean_multiplier + pred_scorestdev = SoftPlusWithGradientFloorFunction.apply(out_miscvalue[:, 1], 0.05, False) * self.scorestdev_multiplier + pred_lead = out_miscvalue[:, 2] * self.lead_multiplier + pred_variance_time = SoftPlusWithGradientFloorFunction.apply(out_miscvalue[:, 3], 0.05, False) * self.variance_time_multiplier + if self.config["version"] < 14: + pred_shortterm_value_error = SoftPlusWithGradientFloorFunction.apply(out_moremiscvalue[:,0], 0.05, False) * self.shortterm_value_error_multiplier + pred_shortterm_score_error = SoftPlusWithGradientFloorFunction.apply(out_moremiscvalue[:,1], 0.05, False) * self.shortterm_score_error_multiplier + else: + pred_shortterm_value_error = SoftPlusWithGradientFloorFunction.apply(out_moremiscvalue[:,0], 0.05, True) * self.shortterm_value_error_multiplier + pred_shortterm_score_error = SoftPlusWithGradientFloorFunction.apply(out_moremiscvalue[:,1], 0.05, True) * self.shortterm_score_error_multiplier + scorebelief_logits = out_scorebelief_logprobs + + return ( + policy_logits, # N, num_policy_outputs, move + value_logits, # N, {win,loss,noresult} + td_value_logits, # N, {long, mid, short} {win,loss,noresult} + pred_td_score, # N, {long, mid, short} + ownership_pretanh, # N, 1, y, x + pred_scoring, # N, 1, y, x + futurepos_pretanh, # N, 2, y, x + seki_logits, # N, 4, y, x + pred_scoremean, # N + pred_scorestdev, # N + pred_lead, # N + pred_variance_time, # N + pred_shortterm_value_error, # N + pred_shortterm_score_error, # N + scorebelief_logits, # N, 2 * (self.pos_len*self.pos_len + EXTRA_SCORE_DISTR_RADIUS) + ) diff --git a/training/scripts/dated/20241110-124043/modelconfigs.py b/training/scripts/dated/20241110-124043/modelconfigs.py new file mode 100644 index 0000000..7bfe24a --- /dev/null +++ b/training/scripts/dated/20241110-124043/modelconfigs.py @@ -0,0 +1,1525 @@ +#!/usr/bin/python3 +""" +This file contains a bunch of configs for models of different sizes. +See the bottom of this file "base_config_of_name" for a dictionary of all the different +base model architectures, and which ones are recommended of each different model size. + +For each base model, additional configs are also pregenerated with different suffixes. + +For example, for b10c384nbt, we also have models like: +b10c384nbt-mish (use mish instead of relu) +b10c384nbt-bn-mish-rvgl (use batchnorm, mish, and repvgg-linear-style convolutions). + +KataGo's main models for the distributed training run currently find the following to work +well or best: "-fson-mish-rvgl-bnh" +* Use fixed activation scale initialization + one batch norm for the whole net +* Mish activation +* Repvgg-linear-style convolutions +* Batch norm output head + non-batch-norm output head, where the former drives optimization + but the latter is used for inference. +""" + +from typing import Dict, Any, Union + +ModelConfig = Dict[str,Any] + +# version = 0 # V1 features, with old head architecture using crelus (no longer supported) +# version = 1 # V1 features, with new head architecture, no crelus +# version = 2 # V2 features, no internal architecture change. +# version = 3 # V3 features, selfplay-planned features with lots of aux targets +# version = 4 # V3 features, but supporting belief stdev and dynamic scorevalue +# version = 5 # V4 features, slightly different pass-alive stones feature +# version = 6 # V5 features, most higher-level go features removed +# version = 7 # V6 features, more rules support +# version = 8 # V7 features, asym, lead, variance time +# version = 9 # V7 features, shortterm value error prediction, inference actually uses variance time, unsupported now +# version = 10 # V7 features, shortterm value error prediction done properly +# version = 11 # V7 features, New architectures! +# version = 12 # V7 features, Optimistic policy head +# version = 13 # V7 features, Adjusted scaling on shortterm score variance, and made C++ side read in scalings. +# version = 14 # V7 features, Squared softplus for error variance predictions +# version = 15 # V7 features, Extra nonlinearity for pass output + +def get_version(config: ModelConfig): + return config["version"] + +def get_num_bin_input_features(config: ModelConfig): + version = get_version(config) + if version == 10 or version == 11 or version == 12 or version == 13 or version == 14 or version == 15: + return 22 + else: + assert(False) + +def get_num_global_input_features(config: ModelConfig): + version = get_version(config) + if version == 10 or version == 11 or version == 12 or version == 13 or version == 14 or version == 15: + return 19 + else: + assert(False) + +def get_num_meta_encoder_input_features(config_or_meta_encoder_version: Union[ModelConfig,int]): + if isinstance(config_or_meta_encoder_version,int): + version = config_or_meta_encoder_version + else: + if "metadata_encoder" not in config: + version = 0 + elif "meta_encoder_version" not in config["metadata_encoder"]: + version = 1 + else: + version = config["metadata_encoder"]["meta_encoder_version"] + assert version == 1 + return 192 + +b1c6nbt = { + "version":15, + "norm_kind":"fixup", + "bnorm_epsilon": 1e-4, + "bnorm_running_avg_momentum": 0.001, + "initial_conv_1x1": False, + "trunk_num_channels":6, + "mid_num_channels":4, + "gpool_num_channels":4, + "use_attention_pool":False, + "num_attention_pool_heads":2, + "block_kind": [ + ["rconv1","bottlenest2"], + ], + "p1_num_channels":4, + "g1_num_channels":4, + "v1_num_channels":4, + "sbv2_num_channels":4, + "num_scorebeliefs":2, + "v2_size":6, +} + +b2c16 = { + "version":15, + "norm_kind":"fixup", + "bnorm_epsilon": 1e-4, + "bnorm_running_avg_momentum": 0.001, + "initial_conv_1x1": False, + "trunk_num_channels":16, + "mid_num_channels":16, + "gpool_num_channels":8, + "use_attention_pool":False, + "num_attention_pool_heads":2, + "block_kind": [ + ["rconv1","regular"], + ["rconv2","regulargpool"], + ], + "p1_num_channels":8, + "g1_num_channels":8, + "v1_num_channels":8, + "sbv2_num_channels":12, + "num_scorebeliefs":2, + "v2_size":12, +} + +b4c32 = { + "version":15, + "norm_kind":"fixup", + "bnorm_epsilon": 1e-4, + "bnorm_running_avg_momentum": 0.001, + "initial_conv_1x1": False, + "trunk_num_channels":32, + "mid_num_channels":32, + "gpool_num_channels":16, + "use_attention_pool":False, + "num_attention_pool_heads":2, + "block_kind": [ + ["rconv1","regular"], + ["rconv2","regular"], + ["rconv3","regulargpool"], + ["rconv4","regular"], + ], + "p1_num_channels":12, + "g1_num_channels":12, + "v1_num_channels":12, + "sbv2_num_channels":24, + "num_scorebeliefs":4, + "v2_size":24, +} + +b6c96 = { + "version":15, + "norm_kind":"fixup", + "bnorm_epsilon": 1e-4, + "bnorm_running_avg_momentum": 0.001, + "initial_conv_1x1": False, + "trunk_num_channels":96, + "mid_num_channels":96, + "gpool_num_channels":32, + "use_attention_pool":False, + "num_attention_pool_heads":4, + "block_kind": [ + ["rconv1","regular"], + ["rconv2","regular"], + ["rconv3","regulargpool"], + ["rconv4","regular"], + ["rconv5","regulargpool"], + ["rconv6","regular"], + ], + "p1_num_channels":32, + "g1_num_channels":32, + "v1_num_channels":32, + "sbv2_num_channels":48, + "num_scorebeliefs":4, + "v2_size":64, +} + +b10c128 = { + "version":15, + "norm_kind":"fixup", + "bnorm_epsilon": 1e-4, + "bnorm_running_avg_momentum": 0.001, + "initial_conv_1x1": False, + "trunk_num_channels":128, + "mid_num_channels":128, + "gpool_num_channels":32, + "use_attention_pool":False, + "num_attention_pool_heads":4, + "block_kind": [ + ["rconv1","regular"], + ["rconv2","regular"], + ["rconv3","regular"], + ["rconv4","regular"], + ["rconv5","regulargpool"], + ["rconv6","regular"], + ["rconv7","regular"], + ["rconv8","regulargpool"], + ["rconv9","regular"], + ["rconv10","regular"], + ], + "p1_num_channels":32, + "g1_num_channels":32, + "v1_num_channels":32, + "sbv2_num_channels":64, + "num_scorebeliefs":6, + "v2_size":80, +} + +b5c192nbt = { + "version":15, + "norm_kind":"fixup", + "bnorm_epsilon": 1e-4, + "bnorm_running_avg_momentum": 0.001, + "initial_conv_1x1": False, + "trunk_num_channels":192, + "mid_num_channels":96, + "gpool_num_channels":32, + "use_attention_pool":False, + "num_attention_pool_heads":4, + "block_kind": [ + ["rconv1","bottlenest2"], + ["rconv2","bottlenest2gpool"], + ["rconv3","bottlenest2"], + ["rconv4","bottlenest2gpool"], + ["rconv5","bottlenest2"], + ], + "p1_num_channels":32, + "g1_num_channels":32, + "v1_num_channels":32, + "sbv2_num_channels":64, + "num_scorebeliefs":6, + "v2_size":80, +} + +b15c192 = { + "version":15, + "norm_kind":"fixup", + "bnorm_epsilon": 1e-4, + "bnorm_running_avg_momentum": 0.001, + "initial_conv_1x1": False, + "trunk_num_channels":192, + "mid_num_channels":192, + "gpool_num_channels":64, + "use_attention_pool":False, + "num_attention_pool_heads":4, + "block_kind": [ + ["rconv1","regular"], + ["rconv2","regular"], + ["rconv3","regular"], + ["rconv4","regular"], + ["rconv5","regular"], + ["rconv6","regular"], + ["rconv7","regulargpool"], + ["rconv8","regular"], + ["rconv9","regular"], + ["rconv10","regular"], + ["rconv11","regular"], + ["rconv12","regulargpool"], + ["rconv13","regular"], + ["rconv14","regular"], + ["rconv15","regular"], + ], + "p1_num_channels":32, + "g1_num_channels":32, + "v1_num_channels":32, + "sbv2_num_channels":80, + "num_scorebeliefs":8, + "v2_size":96, +} + +b20c256 = { + "version":15, + "norm_kind":"fixup", + "bnorm_epsilon": 1e-4, + "bnorm_running_avg_momentum": 0.001, + "initial_conv_1x1": False, + "trunk_num_channels":256, + "mid_num_channels":256, + "gpool_num_channels":64, + "use_attention_pool":False, + "num_attention_pool_heads":4, + "block_kind": [ + ["rconv1","regular"], + ["rconv2","regular"], + ["rconv3","regular"], + ["rconv4","regular"], + ["rconv5","regular"], + ["rconv6","regular"], + ["rconv7","regulargpool"], + ["rconv8","regular"], + ["rconv9","regular"], + ["rconv10","regular"], + ["rconv11","regular"], + ["rconv12","regulargpool"], + ["rconv13","regular"], + ["rconv14","regular"], + ["rconv15","regular"], + ["rconv16","regular"], + ["rconv17","regulargpool"], + ["rconv18","regular"], + ["rconv19","regular"], + ["rconv20","regular"], + ], + "p1_num_channels":48, + "g1_num_channels":48, + "v1_num_channels":48, + "sbv2_num_channels":96, + "num_scorebeliefs":8, + "v2_size":112, +} + +b30c256bt = { + "version":15, + "norm_kind":"fixup", + "bnorm_epsilon": 1e-4, + "bnorm_running_avg_momentum": 0.001, + "initial_conv_1x1": False, + "trunk_num_channels":256, + "mid_num_channels":128, + "gpool_num_channels":64, + "use_attention_pool":False, + "num_attention_pool_heads":4, + "block_kind": [ + ["rconv0","bottle"], + ["rconv1","bottle"], + ["rconv2","bottle"], + ["rconv3","bottle"], + ["rconv4","bottle"], + ["rconv5","bottle"], + ["rconv6","bottlegpool"], + ["rconv7","bottle"], + ["rconv8","bottle"], + ["rconv9","bottle"], + ["rconv10","bottle"], + ["rconv11","bottle"], + ["rconv12","bottlegpool"], + ["rconv13","bottle"], + ["rconv14","bottle"], + ["rconv15","bottle"], + ["rconv16","bottle"], + ["rconv17","bottle"], + ["rconv18","bottlegpool"], + ["rconv19","bottle"], + ["rconv20","bottle"], + ["rconv21","bottle"], + ["rconv22","bottle"], + ["rconv23","bottle"], + ["rconv24","bottlegpool"], + ["rconv25","bottle"], + ["rconv26","bottle"], + ["rconv27","bottle"], + ["rconv28","bottle"], + ["rconv29","bottle"], + ], + "p1_num_channels":48, + "g1_num_channels":48, + "v1_num_channels":48, + "sbv2_num_channels":96, + "num_scorebeliefs":8, + "v2_size":112, +} + +b24c320bt = { + "version":15, + "norm_kind":"fixup", + "bnorm_epsilon": 1e-4, + "bnorm_running_avg_momentum": 0.001, + "initial_conv_1x1": False, + "trunk_num_channels":320, + "mid_num_channels":160, + "gpool_num_channels":64, + "use_attention_pool":False, + "num_attention_pool_heads":4, + "block_kind": [ + ["rconv1","bottle"], + ["rconv2","bottle"], + ["rconv3","bottle"], + ["rconv4","bottle"], + ["rconv5","bottle"], + ["rconv6","bottle"], + ["rconv7","bottlegpool"], + ["rconv8","bottle"], + ["rconv9","bottle"], + ["rconv10","bottle"], + ["rconv11","bottle"], + ["rconv12","bottle"], + ["rconv13","bottlegpool"], + ["rconv14","bottle"], + ["rconv15","bottle"], + ["rconv16","bottle"], + ["rconv17","bottle"], + ["rconv18","bottle"], + ["rconv19","bottlegpool"], + ["rconv20","bottle"], + ["rconv21","bottle"], + ["rconv22","bottle"], + ["rconv23","bottle"], + ["rconv24","bottle"], + ], + "p1_num_channels":48, + "g1_num_channels":48, + "v1_num_channels":48, + "sbv2_num_channels":96, + "num_scorebeliefs":8, + "v2_size":112, +} + +b20c384bt = { + "version":15, + "norm_kind":"fixup", + "bnorm_epsilon": 1e-4, + "bnorm_running_avg_momentum": 0.001, + "initial_conv_1x1": False, + "trunk_num_channels":384, + "mid_num_channels":192, + "gpool_num_channels":64, + "use_attention_pool":False, + "num_attention_pool_heads":4, + "block_kind": [ + ["rconv1","bottle"], + ["rconv2","bottle"], + ["rconv3","bottle"], + ["rconv4","bottle"], + ["rconv5","bottle"], + ["rconv6","bottlegpool"], + ["rconv7","bottle"], + ["rconv8","bottle"], + ["rconv9","bottle"], + ["rconv10","bottle"], + ["rconv11","bottlegpool"], + ["rconv12","bottle"], + ["rconv13","bottle"], + ["rconv14","bottle"], + ["rconv15","bottle"], + ["rconv16","bottlegpool"], + ["rconv17","bottle"], + ["rconv18","bottle"], + ["rconv19","bottle"], + ["rconv20","bottle"], + ], + "p1_num_channels":48, + "g1_num_channels":48, + "v1_num_channels":48, + "sbv2_num_channels":96, + "num_scorebeliefs":8, + "v2_size":112, +} + + +b10c512lbt = { + "version":15, + "norm_kind":"fixup", + "bnorm_epsilon": 1e-4, + "bnorm_running_avg_momentum": 0.001, + "initial_conv_1x1": False, + "trunk_num_channels":512, + "mid_num_channels":256, + "gpool_num_channels":128, + "use_attention_pool":False, + "num_attention_pool_heads":4, + "block_kind": [ + ["rconv1","bottle2"], + ["rconv2","bottle2"], + ["rconv3","bottle2"], + ["rconv4","bottle2gpool"], + ["rconv5","bottle2"], + ["rconv6","bottle2"], + ["rconv7","bottle2"], + ["rconv8","bottle2gpool"], + ["rconv9","bottle2"], + ["rconv10","bottle2"], + ], + "p1_num_channels":48, + "g1_num_channels":48, + "v1_num_channels":48, + "sbv2_num_channels":96, + "num_scorebeliefs":8, + "v2_size":112, +} + + +b15c384lbt = { + "version":15, + "norm_kind":"fixup", + "bnorm_epsilon": 1e-4, + "bnorm_running_avg_momentum": 0.001, + "initial_conv_1x1": False, + "trunk_num_channels":384, + "mid_num_channels":192, + "gpool_num_channels":64, + "use_attention_pool":False, + "num_attention_pool_heads":4, + "block_kind": [ + ["rconv1","bottle2"], + ["rconv2","bottle2"], + ["rconv3","bottle2"], + ["rconv4","bottle2gpool"], + ["rconv5","bottle2"], + ["rconv6","bottle2"], + ["rconv7","bottle2"], + ["rconv8","bottle2gpool"], + ["rconv9","bottle2"], + ["rconv10","bottle2"], + ["rconv11","bottle2"], + ["rconv12","bottle2gpool"], + ["rconv13","bottle2"], + ["rconv14","bottle2"], + ["rconv15","bottle2"], + ], + "p1_num_channels":48, + "g1_num_channels":48, + "v1_num_channels":48, + "sbv2_num_channels":96, + "num_scorebeliefs":8, + "v2_size":112, +} + +b18c320lbt = { + "version":15, + "norm_kind":"fixup", + "bnorm_epsilon": 1e-4, + "bnorm_running_avg_momentum": 0.001, + "initial_conv_1x1": False, + "trunk_num_channels":320, + "mid_num_channels":160, + "gpool_num_channels":64, + "use_attention_pool":False, + "num_attention_pool_heads":4, + "block_kind": [ + ["rconv1","bottle2"], + ["rconv2","bottle2"], + ["rconv3","bottle2"], + ["rconv4","bottle2"], + ["rconv5","bottle2gpool"], + ["rconv6","bottle2"], + ["rconv7","bottle2"], + ["rconv8","bottle2"], + ["rconv9","bottle2"], + ["rconv10","bottle2gpool"], + ["rconv11","bottle2"], + ["rconv12","bottle2"], + ["rconv13","bottle2"], + ["rconv14","bottle2"], + ["rconv15","bottle2gpool"], + ["rconv16","bottle2"], + ["rconv17","bottle2"], + ["rconv18","bottle2"], + ], + "p1_num_channels":48, + "g1_num_channels":48, + "v1_num_channels":48, + "sbv2_num_channels":96, + "num_scorebeliefs":8, + "v2_size":112, +} + +b23c256lbt = { + "version":15, + "norm_kind":"fixup", + "bnorm_epsilon": 1e-4, + "bnorm_running_avg_momentum": 0.001, + "initial_conv_1x1": False, + "trunk_num_channels":256, + "mid_num_channels":128, + "gpool_num_channels":64, + "use_attention_pool":False, + "num_attention_pool_heads":4, + "block_kind": [ + ["rconv1","bottle2"], + ["rconv2","bottle2"], + ["rconv3","bottle2"], + ["rconv4","bottle2"], + ["rconv5","bottle2"], + ["rconv6","bottle2gpool"], + ["rconv7","bottle2"], + ["rconv8","bottle2"], + ["rconv9","bottle2"], + ["rconv10","bottle2"], + ["rconv11","bottle2"], + ["rconv12","bottle2gpool"], + ["rconv13","bottle2"], + ["rconv14","bottle2"], + ["rconv15","bottle2"], + ["rconv16","bottle2"], + ["rconv17","bottle2"], + ["rconv18","bottle2gpool"], + ["rconv19","bottle2"], + ["rconv20","bottle2"], + ["rconv21","bottle2"], + ["rconv22","bottle2"], + ["rconv23","bottle2"], + ], + "p1_num_channels":48, + "g1_num_channels":48, + "v1_num_channels":48, + "sbv2_num_channels":96, + "num_scorebeliefs":8, + "v2_size":112, +} + +b12c384llbt = { + "version":15, + "norm_kind":"fixup", + "bnorm_epsilon": 1e-4, + "bnorm_running_avg_momentum": 0.001, + "initial_conv_1x1": False, + "trunk_num_channels":384, + "mid_num_channels":192, + "gpool_num_channels":64, + "use_attention_pool":False, + "num_attention_pool_heads":4, + "block_kind": [ + ["rconv1","bottle3"], + ["rconv2","bottle3"], + ["rconv3","bottle3"], + ["rconv4","bottle3gpool"], + ["rconv5","bottle3"], + ["rconv6","bottle3"], + ["rconv7","bottle3gpool"], + ["rconv8","bottle3"], + ["rconv9","bottle3"], + ["rconv10","bottle3gpool"], + ["rconv11","bottle3"], + ["rconv12","bottle3"], + ], + "p1_num_channels":48, + "g1_num_channels":48, + "v1_num_channels":48, + "sbv2_num_channels":96, + "num_scorebeliefs":8, + "v2_size":112, +} + + +b10c384nbt = { + "version":15, + "norm_kind":"fixup", + "bnorm_epsilon": 1e-4, + "bnorm_running_avg_momentum": 0.001, + "initial_conv_1x1": False, + "trunk_num_channels":384, + "mid_num_channels":192, + "gpool_num_channels":64, + "use_attention_pool":False, + "num_attention_pool_heads":4, + "block_kind": [ + ["rconv1","bottlenest2"], + ["rconv2","bottlenest2"], + ["rconv3","bottlenest2gpool"], + ["rconv4","bottlenest2"], + ["rconv5","bottlenest2"], + ["rconv6","bottlenest2gpool"], + ["rconv7","bottlenest2"], + ["rconv8","bottlenest2"], + ["rconv9","bottlenest2gpool"], + ["rconv10","bottlenest2"], + ], + "p1_num_channels":48, + "g1_num_channels":48, + "v1_num_channels":48, + "sbv2_num_channels":96, + "num_scorebeliefs":8, + "v2_size":112, +} + + +b10c480nb3t = { + "version":15, + "norm_kind":"fixup", + "bnorm_epsilon": 1e-4, + "bnorm_running_avg_momentum": 0.001, + "initial_conv_1x1": False, + "trunk_num_channels":480, + "mid_num_channels":160, + "gpool_num_channels":64, + "use_attention_pool":False, + "num_attention_pool_heads":4, + "block_kind": [ + ["rconv1","bottlenest2"], + ["rconv2","bottlenest2"], + ["rconv3","bottlenest2gpool"], + ["rconv4","bottlenest2"], + ["rconv5","bottlenest2"], + ["rconv6","bottlenest2gpool"], + ["rconv7","bottlenest2"], + ["rconv8","bottlenest2"], + ["rconv9","bottlenest2gpool"], + ["rconv10","bottlenest2"], + ], + "p1_num_channels":48, + "g1_num_channels":48, + "v1_num_channels":48, + "sbv2_num_channels":96, + "num_scorebeliefs":8, + "v2_size":112, +} + + +b7c384lnbt = { + "version":15, + "norm_kind":"fixup", + "bnorm_epsilon": 1e-4, + "bnorm_running_avg_momentum": 0.001, + "initial_conv_1x1": False, + "trunk_num_channels":384, + "mid_num_channels":192, + "gpool_num_channels":64, + "use_attention_pool":False, + "num_attention_pool_heads":4, + "block_kind": [ + ["rconv1","bottlenest3"], + ["rconv3","bottlenest3gpool"], + ["rconv5","bottlenest3"], + ["rconv6","bottlenest3gpool"], + ["rconv8","bottlenest3"], + ["rconv9","bottlenest3gpool"], + ["rconv10","bottlenest3"], + ], + "p1_num_channels":48, + "g1_num_channels":48, + "v1_num_channels":48, + "sbv2_num_channels":96, + "num_scorebeliefs":8, + "v2_size":112, +} + +b5c512nnbt = { + "version":15, + "norm_kind":"fixup", + "bnorm_epsilon": 1e-4, + "bnorm_running_avg_momentum": 0.001, + "initial_conv_1x1": True, + "trunk_num_channels":512, + "outermid_num_channels":256, + "mid_num_channels":128, + "gpool_num_channels":64, + "use_attention_pool":False, + "num_attention_pool_heads":4, + "block_kind": [ + ["rconv1","bottlenest2bottlenest2"], + ["rconv2","bottlenest2bottlenest2gpool"], + ["rconv3","bottlenest2bottlenest2"], + ["rconv4","bottlenest2bottlenest2gpool"], + ["rconv5","bottlenest2bottlenest2gpool"], + ], + "p1_num_channels":48, + "g1_num_channels":48, + "v1_num_channels":48, + "sbv2_num_channels":96, + "num_scorebeliefs":8, + "v2_size":112, +} + + +b20c384lbt = { + "version":15, + "norm_kind":"fixup", + "bnorm_epsilon": 1e-4, + "bnorm_running_avg_momentum": 0.001, + "initial_conv_1x1": False, + "trunk_num_channels":384, + "mid_num_channels":192, + "gpool_num_channels":64, + "use_attention_pool":False, + "num_attention_pool_heads":4, + "block_kind": [ + ["rconv1","bottle2"], + ["rconv2","bottle2"], + ["rconv3","bottle2"], + ["rconv4","bottle2"], + ["rconv5","bottle2"], + ["rconv6","bottle2gpool"], + ["rconv7","bottle2"], + ["rconv8","bottle2"], + ["rconv9","bottle2"], + ["rconv10","bottle2"], + ["rconv11","bottle2gpool"], + ["rconv12","bottle2"], + ["rconv13","bottle2"], + ["rconv14","bottle2"], + ["rconv15","bottle2"], + ["rconv16","bottle2gpool"], + ["rconv17","bottle2"], + ["rconv18","bottle2"], + ["rconv19","bottle2"], + ["rconv20","bottle2"], + ], + "p1_num_channels":48, + "g1_num_channels":48, + "v1_num_channels":48, + "sbv2_num_channels":96, + "num_scorebeliefs":8, + "v2_size":112, +} + + +b30c320 = { + "version":15, + "norm_kind":"fixup", + "bnorm_epsilon": 1e-4, + "bnorm_running_avg_momentum": 0.001, + "initial_conv_1x1": False, + "trunk_num_channels":320, + "mid_num_channels":320, + "gpool_num_channels":96, + "use_attention_pool":False, + "num_attention_pool_heads":4, + "block_kind": [ + ["rconv1","regular"], + ["rconv2","regular"], + ["rconv3","regular"], + ["rconv4","regular"], + ["rconv5","regular"], + ["rconv6","regulargpool"], + ["rconv7","regular"], + ["rconv8","regular"], + ["rconv9","regular"], + ["rconv10","regular"], + ["rconv11","regulargpool"], + ["rconv12","regular"], + ["rconv13","regular"], + ["rconv14","regular"], + ["rconv15","regular"], + ["rconv16","regulargpool"], + ["rconv17","regular"], + ["rconv18","regular"], + ["rconv19","regular"], + ["rconv20","regular"], + ["rconv21","regulargpool"], + ["rconv22","regular"], + ["rconv23","regular"], + ["rconv24","regular"], + ["rconv25","regular"], + ["rconv26","regulargpool"], + ["rconv27","regular"], + ["rconv28","regular"], + ["rconv29","regular"], + ["rconv30","regular"], + ], + "p1_num_channels":48, + "g1_num_channels":48, + "v1_num_channels":96, + "sbv2_num_channels":112, + "num_scorebeliefs":8, + "v2_size":128, +} + +b40c256 = { + "version":15, + "norm_kind":"fixup", + "bnorm_epsilon": 1e-4, + "bnorm_running_avg_momentum": 0.001, + "initial_conv_1x1": False, + "trunk_num_channels":256, + "mid_num_channels":256, + "gpool_num_channels":64, + "use_attention_pool":False, + "num_attention_pool_heads":4, + "block_kind": [ + ["rconv1","regular"], + ["rconv2","regular"], + ["rconv3","regular"], + ["rconv4","regular"], + ["rconv5","regular"], + ["rconv6","regulargpool"], + ["rconv7","regular"], + ["rconv8","regular"], + ["rconv9","regular"], + ["rconv10","regular"], + ["rconv11","regulargpool"], + ["rconv12","regular"], + ["rconv13","regular"], + ["rconv14","regular"], + ["rconv15","regular"], + ["rconv16","regulargpool"], + ["rconv17","regular"], + ["rconv18","regular"], + ["rconv19","regular"], + ["rconv20","regular"], + ["rconv21","regulargpool"], + ["rconv22","regular"], + ["rconv23","regular"], + ["rconv24","regular"], + ["rconv25","regular"], + ["rconv26","regulargpool"], + ["rconv27","regular"], + ["rconv28","regular"], + ["rconv29","regular"], + ["rconv30","regular"], + ["rconv31","regulargpool"], + ["rconv32","regular"], + ["rconv33","regular"], + ["rconv34","regular"], + ["rconv35","regular"], + ["rconv36","regulargpool"], + ["rconv37","regular"], + ["rconv38","regular"], + ["rconv39","regular"], + ["rconv40","regular"], + ], + "p1_num_channels":48, + "g1_num_channels":48, + "v1_num_channels":96, + "sbv2_num_channels":112, + "num_scorebeliefs":8, + "v2_size":128, +} + +b18c384nbt = { + "version":15, + "norm_kind":"fixup", + "bnorm_epsilon": 1e-4, + "bnorm_running_avg_momentum": 0.001, + "initial_conv_1x1": False, + "trunk_num_channels":384, + "mid_num_channels":192, + "gpool_num_channels":64, + "use_attention_pool":False, + "num_attention_pool_heads":4, + "block_kind": [ + ["rconv1","bottlenest2"], + ["rconv2","bottlenest2"], + ["rconv3","bottlenest2gpool"], + ["rconv4","bottlenest2"], + ["rconv5","bottlenest2"], + ["rconv6","bottlenest2gpool"], + ["rconv7","bottlenest2"], + ["rconv8","bottlenest2"], + ["rconv9","bottlenest2gpool"], + ["rconv10","bottlenest2"], + ["rconv11","bottlenest2"], + ["rconv12","bottlenest2gpool"], + ["rconv13","bottlenest2"], + ["rconv14","bottlenest2"], + ["rconv15","bottlenest2gpool"], + ["rconv16","bottlenest2"], + ["rconv17","bottlenest2"], + ["rconv18","bottlenest2"], + ], + "p1_num_channels":48, + "g1_num_channels":48, + "v1_num_channels":96, + "sbv2_num_channels":112, + "num_scorebeliefs":8, + "v2_size":128, +} + +b14c448nbt = { + "version":15, + "norm_kind":"fixup", + "bnorm_epsilon": 1e-4, + "bnorm_running_avg_momentum": 0.001, + "initial_conv_1x1": False, + "trunk_num_channels":448, + "mid_num_channels":224, + "gpool_num_channels":64, + "use_attention_pool":False, + "num_attention_pool_heads":4, + "block_kind": [ + ["rconv1","bottlenest2"], + ["rconv2","bottlenest2"], + ["rconv3","bottlenest2gpool"], + ["rconv4","bottlenest2"], + ["rconv5","bottlenest2"], + ["rconv6","bottlenest2gpool"], + ["rconv7","bottlenest2"], + ["rconv8","bottlenest2"], + ["rconv9","bottlenest2gpool"], + ["rconv10","bottlenest2"], + ["rconv11","bottlenest2"], + ["rconv12","bottlenest2gpool"], + ["rconv13","bottlenest2"], + ["rconv14","bottlenest2"], + ], + "p1_num_channels":48, + "g1_num_channels":48, + "v1_num_channels":96, + "sbv2_num_channels":112, + "num_scorebeliefs":8, + "v2_size":128, +} + +b40c384 = { + "version":15, + "norm_kind":"fixup", + "bnorm_epsilon": 1e-4, + "bnorm_running_avg_momentum": 0.001, + "initial_conv_1x1": False, + "trunk_num_channels":384, + "mid_num_channels":384, + "gpool_num_channels":128, + "use_attention_pool":False, + "num_attention_pool_heads":4, + "block_kind": [ + ["rconv1","regular"], + ["rconv2","regular"], + ["rconv3","regular"], + ["rconv4","regular"], + ["rconv5","regular"], + ["rconv6","regulargpool"], + ["rconv7","regular"], + ["rconv8","regular"], + ["rconv9","regular"], + ["rconv10","regular"], + ["rconv11","regulargpool"], + ["rconv12","regular"], + ["rconv13","regular"], + ["rconv14","regular"], + ["rconv15","regular"], + ["rconv16","regulargpool"], + ["rconv17","regular"], + ["rconv18","regular"], + ["rconv19","regular"], + ["rconv20","regular"], + ["rconv21","regulargpool"], + ["rconv22","regular"], + ["rconv23","regular"], + ["rconv24","regular"], + ["rconv25","regular"], + ["rconv26","regulargpool"], + ["rconv27","regular"], + ["rconv28","regular"], + ["rconv29","regular"], + ["rconv30","regular"], + ["rconv31","regulargpool"], + ["rconv32","regular"], + ["rconv33","regular"], + ["rconv34","regular"], + ["rconv35","regular"], + ["rconv36","regulargpool"], + ["rconv37","regular"], + ["rconv38","regular"], + ["rconv39","regular"], + ["rconv40","regular"], + ], + "p1_num_channels":64, + "g1_num_channels":64, + "v1_num_channels":96, + "sbv2_num_channels":128, + "num_scorebeliefs":8, + "v2_size":144, +} + + +b60c320 = { + "version":15, + "norm_kind":"fixup", + "bnorm_epsilon": 1e-4, + "bnorm_running_avg_momentum": 0.001, + "initial_conv_1x1": False, + "trunk_num_channels":320, + "mid_num_channels":320, + "gpool_num_channels":96, + "use_attention_pool":False, + "num_attention_pool_heads":4, + "block_kind": [ + ["rconv1","regular"], + ["rconv2","regular"], + ["rconv3","regular"], + ["rconv4","regular"], + ["rconv5","regular"], + ["rconv6","regulargpool"], + ["rconv7","regular"], + ["rconv8","regular"], + ["rconv9","regular"], + ["rconv10","regular"], + ["rconv11","regulargpool"], + ["rconv12","regular"], + ["rconv13","regular"], + ["rconv14","regular"], + ["rconv15","regular"], + ["rconv16","regulargpool"], + ["rconv17","regular"], + ["rconv18","regular"], + ["rconv19","regular"], + ["rconv20","regular"], + ["rconv21","regulargpool"], + ["rconv22","regular"], + ["rconv23","regular"], + ["rconv24","regular"], + ["rconv25","regular"], + ["rconv26","regulargpool"], + ["rconv27","regular"], + ["rconv28","regular"], + ["rconv29","regular"], + ["rconv30","regular"], + ["rconv31","regulargpool"], + ["rconv32","regular"], + ["rconv33","regular"], + ["rconv34","regular"], + ["rconv35","regular"], + ["rconv36","regulargpool"], + ["rconv37","regular"], + ["rconv38","regular"], + ["rconv39","regular"], + ["rconv40","regular"], + ["rconv41","regulargpool"], + ["rconv42","regular"], + ["rconv43","regular"], + ["rconv44","regular"], + ["rconv45","regular"], + ["rconv46","regulargpool"], + ["rconv47","regular"], + ["rconv48","regular"], + ["rconv49","regular"], + ["rconv50","regular"], + ["rconv51","regulargpool"], + ["rconv52","regular"], + ["rconv53","regular"], + ["rconv54","regular"], + ["rconv55","regular"], + ["rconv56","regulargpool"], + ["rconv57","regular"], + ["rconv58","regular"], + ["rconv59","regular"], + ["rconv60","regular"], + ], + "p1_num_channels":64, + "g1_num_channels":64, + "v1_num_channels":96, + "sbv2_num_channels":128, + "num_scorebeliefs":8, + "v2_size":144, +} + + +b41c384nbt = { + "version":15, + "norm_kind":"fixup", + "bnorm_epsilon": 1e-4, + "bnorm_running_avg_momentum": 0.001, + "initial_conv_1x1": False, + "trunk_num_channels":384, + "mid_num_channels":192, + "gpool_num_channels":64, + "use_attention_pool":False, + "num_attention_pool_heads":4, + "block_kind": [ + ["rconv1","bottlenest2"], + ["rconv2","bottlenest2"], + ["rconv3","bottlenest2gpool"], + ["rconv4","bottlenest2"], + ["rconv5","bottlenest2"], + ["rconv6","bottlenest2gpool"], + ["rconv7","bottlenest2"], + ["rconv8","bottlenest2"], + ["rconv9","bottlenest2gpool"], + ["rconv10","bottlenest2"], + ["rconv11","bottlenest2"], + ["rconv12","bottlenest2gpool"], + ["rconv13","bottlenest2"], + ["rconv14","bottlenest2"], + ["rconv15","bottlenest2gpool"], + ["rconv16","bottlenest2"], + ["rconv17","bottlenest2"], + ["rconv18","bottlenest2gpool"], + ["rconv19","bottlenest2"], + ["rconv20","bottlenest2"], + ["rconv21","bottlenest2gpool"], + ["rconv22","bottlenest2"], + ["rconv23","bottlenest2"], + ["rconv24","bottlenest2gpool"], + ["rconv25","bottlenest2"], + ["rconv26","bottlenest2"], + ["rconv27","bottlenest2gpool"], + ["rconv28","bottlenest2"], + ["rconv29","bottlenest2"], + ["rconv30","bottlenest2gpool"], + ["rconv31","bottlenest2"], + ["rconv32","bottlenest2"], + ["rconv33","bottlenest2gpool"], + ["rconv34","bottlenest2"], + ["rconv35","bottlenest2"], + ["rconv36","bottlenest2gpool"], + ["rconv37","bottlenest2"], + ["rconv38","bottlenest2"], + ["rconv39","bottlenest2gpool"], + ["rconv40","bottlenest2"], + ["rconv41","bottlenest2"], + ], + "p1_num_channels":64, + "g1_num_channels":64, + "v1_num_channels":96, + "sbv2_num_channels":128, + "num_scorebeliefs":8, + "v2_size":144, +} + +b32c448nbt = { + "version":15, + "norm_kind":"fixup", + "bnorm_epsilon": 1e-4, + "bnorm_running_avg_momentum": 0.001, + "initial_conv_1x1": False, + "trunk_num_channels":448, + "mid_num_channels":224, + "gpool_num_channels":64, + "use_attention_pool":False, + "num_attention_pool_heads":4, + "block_kind": [ + ["rconv1","bottlenest2"], + ["rconv2","bottlenest2"], + ["rconv3","bottlenest2gpool"], + ["rconv4","bottlenest2"], + ["rconv5","bottlenest2"], + ["rconv6","bottlenest2gpool"], + ["rconv7","bottlenest2"], + ["rconv8","bottlenest2"], + ["rconv9","bottlenest2gpool"], + ["rconv10","bottlenest2"], + ["rconv11","bottlenest2"], + ["rconv12","bottlenest2gpool"], + ["rconv13","bottlenest2"], + ["rconv14","bottlenest2"], + ["rconv15","bottlenest2gpool"], + ["rconv16","bottlenest2"], + ["rconv17","bottlenest2"], + ["rconv18","bottlenest2gpool"], + ["rconv19","bottlenest2"], + ["rconv20","bottlenest2"], + ["rconv21","bottlenest2gpool"], + ["rconv22","bottlenest2"], + ["rconv23","bottlenest2"], + ["rconv24","bottlenest2gpool"], + ["rconv25","bottlenest2"], + ["rconv26","bottlenest2"], + ["rconv27","bottlenest2gpool"], + ["rconv28","bottlenest2"], + ["rconv29","bottlenest2"], + ["rconv30","bottlenest2gpool"], + ["rconv31","bottlenest2"], + ["rconv32","bottlenest2"], + ], + "p1_num_channels":64, + "g1_num_channels":64, + "v1_num_channels":96, + "sbv2_num_channels":128, + "num_scorebeliefs":8, + "v2_size":144, +} + + +b28c512nbt = { + "version":15, + "norm_kind":"fixup", + "bnorm_epsilon": 1e-4, + "bnorm_running_avg_momentum": 0.001, + "initial_conv_1x1": False, + "trunk_num_channels":512, + "mid_num_channels":256, + "gpool_num_channels":64, + "use_attention_pool":False, + "num_attention_pool_heads":4, + "block_kind": [ + ["rconv1","bottlenest2"], + ["rconv2","bottlenest2"], + ["rconv3","bottlenest2gpool"], + ["rconv4","bottlenest2"], + ["rconv5","bottlenest2"], + ["rconv6","bottlenest2gpool"], + ["rconv7","bottlenest2"], + ["rconv8","bottlenest2"], + ["rconv9","bottlenest2gpool"], + ["rconv10","bottlenest2"], + ["rconv11","bottlenest2"], + ["rconv12","bottlenest2gpool"], + ["rconv13","bottlenest2"], + ["rconv14","bottlenest2"], + ["rconv15","bottlenest2gpool"], + ["rconv16","bottlenest2"], + ["rconv17","bottlenest2"], + ["rconv18","bottlenest2gpool"], + ["rconv19","bottlenest2"], + ["rconv20","bottlenest2"], + ["rconv21","bottlenest2gpool"], + ["rconv22","bottlenest2"], + ["rconv23","bottlenest2"], + ["rconv24","bottlenest2gpool"], + ["rconv25","bottlenest2"], + ["rconv26","bottlenest2"], + ["rconv27","bottlenest2gpool"], + ["rconv28","bottlenest2"], + ], + "p1_num_channels":64, + "g1_num_channels":64, + "v1_num_channels":128, + "sbv2_num_channels":128, + "num_scorebeliefs":8, + "v2_size":144, +} + +b20c640nbt = { + "version":15, + "norm_kind":"fixup", + "bnorm_epsilon": 1e-4, + "bnorm_running_avg_momentum": 0.001, + "initial_conv_1x1": False, + "trunk_num_channels":640, + "mid_num_channels":320, + "gpool_num_channels":96, + "use_attention_pool":False, + "num_attention_pool_heads":4, + "block_kind": [ + ["rconv1","bottlenest2"], + ["rconv2","bottlenest2"], + ["rconv3","bottlenest2gpool"], + ["rconv4","bottlenest2"], + ["rconv5","bottlenest2"], + ["rconv6","bottlenest2gpool"], + ["rconv7","bottlenest2"], + ["rconv8","bottlenest2"], + ["rconv9","bottlenest2gpool"], + ["rconv10","bottlenest2"], + ["rconv11","bottlenest2"], + ["rconv12","bottlenest2gpool"], + ["rconv13","bottlenest2"], + ["rconv14","bottlenest2"], + ["rconv15","bottlenest2gpool"], + ["rconv16","bottlenest2"], + ["rconv17","bottlenest2"], + ["rconv18","bottlenest2gpool"], + ["rconv19","bottlenest2"], + ["rconv20","bottlenest2"], + ], + "p1_num_channels":64, + "g1_num_channels":64, + "v1_num_channels":96, + "sbv2_num_channels":128, + "num_scorebeliefs":8, + "v2_size":144, +} + +sandbox = { + "version":15, + "norm_kind":"fixup", + "bnorm_epsilon": 1e-4, + "bnorm_running_avg_momentum": 0.001, + "initial_conv_1x1": False, + "trunk_num_channels":256, + "mid_num_channels":256, + "gpool_num_channels":64, + "use_attention_pool":False, + "num_attention_pool_heads":4, + "block_kind": [ + ["rconv1","regular"], + ["rconv2","regular"], + ["rconv3","regular"], + ["rconv4","regular"], + ["rconv5","regular"], + ["rconv6","regular"], + ["rconv7","regulargpool"], + ["rconv8","regular"], + ["rconv9","regular"], + ["rconv10","regular"], + ["rconv11","regular"], + ["rconv12","regulargpool"], + ["rconv13","regular"], + ["rconv14","regular"], + ["rconv15","regular"], + ["rconv16","regular"], + ["rconv17","regulargpool"], + ["rconv18","regular"], + ["rconv19","regular"], + ["rconv20","regular"], + ], + "p1_num_channels":48, + "g1_num_channels":48, + "v1_num_channels":96, + "sbv2_num_channels":96, + "num_scorebeliefs":8, + "v2_size":112, +} + + +base_config_of_name = { + # Micro-sized model configs + "b1c6nbt": b1c6nbt, + "b2c16": b2c16, + "b4c32": b4c32, + "b6c96": b6c96, + + # Small model configs, not too different in inference cost from b10c128 + "b10c128": b10c128, + "b5c192nbt": b5c192nbt, + + # Medium model configs, not too different in inference cost from b15c192 + "b15c192": b15c192, + + # Roughly AlphaZero-sized, not too different in inference cost from b20c256 + "b20c256": b20c256, + "b30c256bt": b30c256bt, + "b24c320bt": b24c320bt, + "b20c384bt": b20c384bt, + "b23c256lbt": b23c256lbt, + "b18c320lbt": b18c320lbt, + "b15c384lbt": b15c384lbt, + "b10c512lbt": b10c512lbt, + "b12c384llbt": b12c384llbt, + "b10c384nbt": b10c384nbt, # Recommended best config for this cost + "b10c480nb3t": b10c480nb3t, + "b7c384lnbt": b7c384lnbt, + "b5c512nnbt": b5c512nnbt, + "b20c384lbt": b20c384lbt, + + # Roughly AlphaGoZero-sized, not too different in inference cost from b40c256 + "b30c320": b30c320, + "b40c256": b40c256, + "b18c384nbt": b18c384nbt, # Recommended best config for this cost + "b14c448nbt": b14c448nbt, + + # Large model configs, not too different in inference cost from b60c320 + "b40c384": b40c384, + "b60c320": b60c320, + "b41c384nbt": b41c384nbt, + "b32c448nbt": b32c448nbt, + "b28c512nbt": b28c512nbt, # Recommended best config for this cost + "b20c640nbt": b20c640nbt, + + "sandbox": sandbox, +} + +config_of_name = {} +for name, base_config in base_config_of_name.items(): + config = base_config.copy() + config_of_name[name] = config + + +for name, base_config in list(config_of_name.items()): + # Fixup initialization + config = base_config.copy() + config["norm_kind"] = "fixup" + config_of_name[name+""] = config + + # Fixed scaling normalization + config = base_config.copy() + config["norm_kind"] = "fixscale" + config_of_name[name+"-fs"] = config + + # Batchnorm without gamma terms + config = base_config.copy() + config["norm_kind"] = "bnorm" + config_of_name[name+"-bn"] = config + + # Batchrenorm without gamma terms + config = base_config.copy() + config["norm_kind"] = "brenorm" + config_of_name[name+"-brn"] = config + + # Fixed scaling normalization + Batchrenorm without gamma terms + config = base_config.copy() + config["norm_kind"] = "fixbrenorm" + config_of_name[name+"-fbrn"] = config + + # Batchnorm with gamma terms + config = base_config.copy() + config["norm_kind"] = "bnorm" + config["bnorm_use_gamma"] = True + config_of_name[name+"-bng"] = config + + # Batchrenorm with gamma terms + config = base_config.copy() + config["norm_kind"] = "brenorm" + config["bnorm_use_gamma"] = True + config_of_name[name+"-brng"] = config + + # Fixed scaling normalization + Batchrenorm with gamma terms + config = base_config.copy() + config["norm_kind"] = "fixbrenorm" + config["bnorm_use_gamma"] = True + config_of_name[name+"-fbrng"] = config + + # Fixed scaling normalization + ONE batch norm layer in the entire net. + config = base_config.copy() + config["norm_kind"] = "fixscaleonenorm" + config["bnorm_use_gamma"] = True + config_of_name[name+"-fson"] = config + +for name, base_config in list(config_of_name.items()): + config = base_config.copy() + config["activation"] = "elu" + config_of_name[name+"-elu"] = config + + config = base_config.copy() + config["activation"] = "gelu" + config_of_name[name+"-gelu"] = config + + config = base_config.copy() + config["activation"] = "mish" + config_of_name[name+"-mish"] = config + +for name, base_config in list(config_of_name.items()): + config = base_config.copy() + config["use_attention_pool"] = True + config_of_name[name+"-ap"] = config + +for name, base_config in list(config_of_name.items()): + config = base_config.copy() + config["use_repvgg_init"] = True + config_of_name[name+"-rvgi"] = config + + config = base_config.copy() + config["use_repvgg_linear"] = True + config_of_name[name+"-rvgl"] = config + + config = base_config.copy() + config["use_repvgg_init"] = True + config["use_repvgg_learning_rate"] = True + config_of_name[name+"-rvglr"] = config + +for name, base_config in list(config_of_name.items()): + # Add intermediate heads, for use with self-distillation or embedding small net in big one. + config = base_config.copy() + config["has_intermediate_head"] = True + config["intermediate_head_blocks"] = len(config["block_kind"]) // 2 + config_of_name[name+"-ih"] = config + + # Add parallel heads that uses the final trunk batchnorm. + # The original normal heads disables the final trunk batchnorm + # This only makes sense for configs that use some form of batchnorm. + if "norm" in config["norm_kind"]: + config = base_config.copy() + config["has_intermediate_head"] = True + config["intermediate_head_blocks"] = len(config["block_kind"]) + config["trunk_normless"] = True + config_of_name[name+"-bnh"] = config + +for name, base_config in list(config_of_name.items()): + config = base_config.copy() + config["metadata_encoder"] = { + "meta_encoder_version": 1, + "internal_num_channels": config["trunk_num_channels"], + } + config_of_name[name+"-meta"] = config diff --git a/training/scripts/dated/20241110-124043/play.py b/training/scripts/dated/20241110-124043/play.py new file mode 100644 index 0000000..6121db5 --- /dev/null +++ b/training/scripts/dated/20241110-124043/play.py @@ -0,0 +1,648 @@ +#!/usr/bin/python3 +import sys +import os +import argparse +import traceback +import random +import math +import time +import re +import logging +import colorsys +import json +import numpy as np + +from board import Board +from features import Features +from gamestate import GameState + +from typing import Dict, Any, List + +import torch +import torch.nn + +import modelconfigs +from model_pytorch import Model, EXTRA_SCORE_DISTR_RADIUS, ExtraOutputs +from data_processing_pytorch import apply_symmetry +from load_model import load_model + +description = """ +Play go with a trained neural net! +Implements a basic GTP engine that uses the neural net directly to play moves. +""" + +parser = argparse.ArgumentParser(description=description) +parser.add_argument('-checkpoint', help='Checkpoint to test', required=False) +parser.add_argument('-use-swa', help='Use SWA model', action="store_true", required=False) +parser.add_argument('-device', help='Pytorch device, like cpu or cuda:0', required=False) + +args = vars(parser.parse_args()) + +checkpoint_file = args["checkpoint"] +use_swa = args["use_swa"] +device = args["device"] + +# Hardcoded max board size +pos_len = 19 + +# Model ---------------------------------------------------------------- + +logging.root.handlers = [] +logging.basicConfig( + level=logging.INFO, + format="%(message)s", + handlers=[ + logging.StreamHandler(stream=sys.stderr), + ], +) +np.set_printoptions(linewidth=150) +torch.set_printoptions(precision=7,sci_mode=False,linewidth=100000,edgeitems=1000,threshold=1000000) + +model, swa_model, _ = load_model(checkpoint_file, use_swa, device=device, pos_len=pos_len, verbose=True) +if swa_model is not None: + model = swa_model +model_config = model.config +model.eval() + +features = Features(model_config, pos_len) + + +# Moves ---------------------------------------------------------------- + +def get_input_feature(gs, feature_idx): + bin_input_data, global_input_data = gs.get_input_features(features) + locs_and_values = [] + for y in range(gs.board.size): + for x in range(gs.board.size): + loc = board.loc(x,y) + locs_and_values.append((loc,bin_input_data[0,feature_idx,y,x])) + return locs_and_values + +def get_pass_alive(gs): + board = gs.board + rules = gs.rules + pla = board.pla + opp = Board.get_opp(pla) + area = [-1 for i in range(board.arrsize)] + nonPassAliveStones = False + safeBigTerritories = True + unsafeBigTerritories = False + board.calculateArea(area,nonPassAliveStones,safeBigTerritories,unsafeBigTerritories,rules["multiStoneSuicideLegal"]) + + locs_and_values = [] + for y in range(board.size): + for x in range(board.size): + loc = board.loc(x,y) + locs_and_values.append((loc,area[loc])) + return locs_and_values + + +def get_gfx_commands_for_heatmap(locs_and_values, board, normalization_div, is_percent, value_and_score_from=None, hotcold=False): + gfx_commands = [] + divisor = 1.0 + if normalization_div == "max": + max_abs_value = max(abs(value) for (loc,value) in locs_and_values) + divisor = max(0.0000000001,max_abs_value) # avoid divide by zero + elif normalization_div is not None: + divisor = normalization_div + + # Caps value at 1.0, using an asymptotic curve + def loose_cap(x): + def transformed_softplus(x): + return -math.log(math.exp(-(x-1.0)*8.0)+1.0)/8.0+1.0 + base = transformed_softplus(0.0) + return (transformed_softplus(x) - base) / (1.0 - base) + + # Softly curves a value so that it ramps up faster than linear in that range + def soft_curve(x,x0,x1): + p = (x-x0)/(x1-x0) + def curve(p): + return math.sqrt(p+0.16)-0.4 + p = curve(p) / curve(1.0) + return x0 + p * (x1-x0) + + if hotcold: + for (loc,value) in locs_and_values: + if loc != Board.PASS_LOC: + value = value / divisor + + if value < 0: + value = -loose_cap(-value) + else: + value = loose_cap(value) + + interpoints = [ + (-1.00,(0,0,0)), + (-0.85,(15,0,50)), + (-0.60,(60,0,160)), + (-0.35,(0,0,255)), + (-0.15,(0,100,255)), + ( 0.00,(115,115,115)), + ( 0.15,(250,45,40)), + ( 0.25,(255,55,0)), + ( 0.60,(255,255,20)), + ( 0.85,(255,255,128)), + ( 1.00,(255,255,255)), + ] + + def lerp(p,y0,y1): + return y0 + p*(y1-y0) + + i = 0 + while i < len(interpoints): + if value <= interpoints[i][0]: + break + i += 1 + i -= 1 + + if i < 0: + (r,g,b) = interpoints[0][1] + if i >= len(interpoints)-1: + (r,g,b) = interpoints[len(interpoints)-1][1] + + p = (value - interpoints[i][0]) / (interpoints[i+1][0] - interpoints[i][0]) + + (r0,g0,b0) = interpoints[i][1] + (r1,g1,b1) = interpoints[i+1][1] + r = lerp(p,r0,r1) + g = lerp(p,g0,g1) + b = lerp(p,b0,b1) + + r = ("%02x" % int(r)) + g = ("%02x" % int(g)) + b = ("%02x" % int(b)) + gfx_commands.append("COLOR #%s%s%s %s" % (r,g,b,str_coord(loc,board))) + + else: + for (loc,value) in locs_and_values: + if loc != Board.PASS_LOC: + value = value / divisor + if value < 0: + value = -value + huestart = 0.50 + huestop = 0.86 + else: + huestart = -0.02 + huestop = 0.38 + + value = loose_cap(value) + + def lerp(p,x0,x1,y0,y1): + return y0 + (y1-y0) * (p-x0)/(x1-x0) + + if value <= 0.03: + hue = huestart + lightness = 0.00 + 0.50 * (value / 0.03) + saturation = value / 0.03 + (r,g,b) = colorsys.hls_to_rgb((hue+1)%1, lightness, saturation) + elif value <= 0.60: + hue = lerp(value,0.03,0.60,huestart,huestop) + val = 1.0 + saturation = 1.0 + (r,g,b) = colorsys.hsv_to_rgb((hue+1)%1, val, saturation) + else: + hue = huestop + lightness = lerp(value,0.60,1.00,0.5,0.95) + saturation = 1.0 + (r,g,b) = colorsys.hls_to_rgb((hue+1)%1, lightness, saturation) + + r = ("%02x" % int(r*255)) + g = ("%02x" % int(g*255)) + b = ("%02x" % int(b*255)) + gfx_commands.append("COLOR #%s%s%s %s" % (r,g,b,str_coord(loc,board))) + + locs_and_values = sorted(locs_and_values, key=lambda loc_and_value: loc_and_value[1]) + locs_and_values_rev = sorted(locs_and_values, key=lambda loc_and_value: loc_and_value[1], reverse=True) + texts = [] + texts_rev = [] + texts_value = [] + maxlen_per_side = 1000 + if len(locs_and_values) > 0 and locs_and_values[0][1] < 0: + maxlen_per_side = 500 + + for i in range(min(len(locs_and_values),maxlen_per_side)): + (loc,value) = locs_and_values[i] + if is_percent: + texts.append("%s %4.1f%%" % (str_coord(loc,board),value*100)) + else: + texts.append("%s %.3f" % (str_coord(loc,board),value)) + texts.reverse() + + for i in range(min(len(locs_and_values_rev),maxlen_per_side)): + (loc,value) = locs_and_values_rev[i] + if is_percent: + texts_rev.append("%s %4.1f%%" % (str_coord(loc,board),value*100)) + else: + texts_rev.append("%s %.3f" % (str_coord(loc,board),value)) + + if value_and_score_from is not None: + value = value_and_score_from["value"] + score = value_and_score_from["scoremean"] + lead = value_and_score_from["lead"] + vtime = value_and_score_from["vtime"] + texts_value.append("wv %.2fc nr %.2f%% ws %.1f wl %.1f vt %.1f" % ( + 100*(value[0]-value[1] if board.pla == Board.WHITE else value[1] - value[0]), + 100*value[2], + (score if board.pla == Board.WHITE else -score), + (lead if board.pla == Board.WHITE else -lead), + vtime + )) + + gfx_commands.append("TEXT " + ", ".join(texts_value + texts_rev + texts)) + return gfx_commands + +def print_scorebelief(gs,outputs): + board = gs.board + scorebelief = outputs["scorebelief"] + lead = outputs["lead"] + scoremean = outputs["scoremean"] + scorestdev = outputs["scorestdev"] + + scorebelief = list(scorebelief) + # Flip so that it's in perspective of the player playing + if board.pla != Board.WHITE: + scorebelief.reverse() + scoremean = -scoremean + lead = -lead + + scoredistrmid = pos_len * pos_len + EXTRA_SCORE_DISTR_RADIUS + ret = "" + ret += "TEXT " + ret += "ScoreBelief: \n" + for i in range(17,-1,-1): + ret += "TEXT " + ret += "%+6.1f" %(-(i*20+0.5)) + for j in range(20): + idx = scoredistrmid-(i*20+j)-1 + ret += " %4.0f" % (scorebelief[idx] * 10000) + ret += "\n" + for i in range(18): + ret += "TEXT " + ret += "%+6.1f" %((i*20+0.5)) + for j in range(20): + idx = scoredistrmid+(i*20+j) + ret += " %4.0f" % (scorebelief[idx] * 10000) + ret += "\n" + + beliefscore = 0 + beliefscoresq = 0 + beliefwin = 0 + belieftotal = 0 + for idx in range(scoredistrmid*2): + score = idx-scoredistrmid+0.5 + if score > 0: + beliefwin += scorebelief[idx] + else: + beliefwin -= scorebelief[idx] + belieftotal += scorebelief[idx] + beliefscore += score*scorebelief[idx] + beliefscoresq += score*score*scorebelief[idx] + + beliefscoremean = beliefscore/belieftotal + beliefscoremeansq = beliefscoresq/belieftotal + beliefscorevar = max(0,beliefscoremeansq-beliefscoremean*beliefscoremean) + beliefscorestdev = math.sqrt(beliefscorevar) + + ret += "TEXT BeliefWin: %.2fc\n" % (100*beliefwin/belieftotal) + ret += "TEXT BeliefScoreMean: %.2f\n" % (beliefscoremean) + ret += "TEXT BeliefScoreStdev: %.2f\n" % (beliefscorestdev) + ret += "TEXT Lead: %.3f\n" % (lead) + ret += "TEXT ScoreMean: %.3f\n" % (scoremean) + ret += "TEXT ScoreStdev: %.3f\n" % (scorestdev) + ret += "TEXT Value: %s\n" % str(["%.3f" % x for x in outputs["value"]]) + ret += "TEXT TDValue: %s\n" % str(["%.3f" % x for x in outputs["td_value"]]) + ret += "TEXT TDValue2: %s\n" % str(["%.3f" % x for x in outputs["td_value2"]]) + ret += "TEXT TDValue3: %s\n" % str(["%.3f" % x for x in outputs["td_value3"]]) + ret += "TEXT TDScore: %s\n" % str(["%.3f" % x for x in outputs["td_score"]]) + ret += "TEXT Estv: %s\n" % str(outputs["estv"]) + ret += "TEXT Ests: %s\n" % str(outputs["ests"]) + ret += "TEXT Vtime: %s\n" % str(outputs["vtime"]) + return ret + + +# Basic parsing -------------------------------------------------------- +colstr = 'ABCDEFGHJKLMNOPQRST' +def parse_coord(s,board): + if s == 'pass': + return Board.PASS_LOC + return board.loc(colstr.index(s[0].upper()), board.size - int(s[1:])) + +def str_coord(loc,board): + if loc == Board.PASS_LOC: + return 'pass' + x = board.loc_x(loc) + y = board.loc_y(loc) + return '%c%d' % (colstr[x], board.size - y) + + +# GTP Implementation ----------------------------------------------------- + +# Adapted from https://github.com/pasky/michi/blob/master/michi.py, which is distributed under MIT license +# https://opensource.org/licenses/MIT + +known_commands = [ + 'boardsize', + 'clear_board', + 'showboard', + 'komi', + 'play', + 'genmove', + 'quit', + 'name', + 'version', + 'known_command', + 'list_commands', + 'protocol_version', + 'gogui-analyze_commands', + 'setrule', + 'policy', + 'policy1', + 'logpolicy', + 'ownership', + 'scoring', + 'futurepos0', + 'futurepos1', + 'seki', + 'seki2', + 'scorebelief', + 'passalive', +] +known_analyze_commands = [ + 'gfx/Policy/policy', + 'gfx/Policy1/policy1', + 'gfx/LogPolicy/logpolicy', + 'gfx/Ownership/ownership', + 'gfx/Scoring/scoring', + 'gfx/FuturePos0/futurepos0', + 'gfx/FuturePos1/futurepos1', + 'gfx/Seki/seki', + 'gfx/Seki2/seki2', + 'gfx/ScoreBelief/scorebelief', + 'gfx/PassAlive/passalive', +] + +board_size = 19 +gs = GameState(board_size, GameState.RULES_TT) + +input_feature_command_lookup = dict() +def add_input_feature_visualizations(layer_name, feature_idx, normalization_div): + command_name = layer_name + command_name = command_name.replace("/",":") + known_commands.append(command_name) + known_analyze_commands.append("gfx/" + command_name + "/" + command_name) + input_feature_command_lookup[command_name] = (feature_idx,normalization_div) +for i in range(model.bin_input_shape[1]): + add_input_feature_visualizations("input-" + str(i),i, normalization_div=1) + +attention_feature_command_lookup = dict() +def add_attention_visualizations(extra_output_name, extra_output): + for c in range(extra_output.shape[0]): + command_name = extra_output_name + command_name = command_name.replace("/",":") + command_name += ":" + str(c) + known_commands.append(command_name) + known_analyze_commands.append("gfx/" + command_name + "/" + command_name) + attention_feature_command_lookup[command_name] = (extra_output_name, c) +with torch.no_grad(): + dummy_outputs = gs.get_model_outputs(model) + extra_attention_output_names = [name for name in dummy_outputs["available_extra_outputs"] if name.endswith(".attention") or name.endswith(".reverse_attention")] + dummy_outputs = gs.get_model_outputs(model) + for name in extra_attention_output_names: + add_attention_visualizations(name,dummy_outputs[name]) + del dummy_outputs + + +def get_board_matrix_str(matrix, scale, formatstr): + ret = "" + matrix = matrix.reshape([features.pos_len,features.pos_len]) + for y in range(features.pos_len): + for x in range(features.pos_len): + ret += formatstr % (scale * matrix[y,x]) + ret += " " + ret += "\n" + return ret + + +while True: + try: + line = input().strip() + except EOFError: + break + if line == '': + continue + command = [s.lower() for s in line.split()] + if re.match('\d+', command[0]): + cmdid = command[0] + command = command[1:] + else: + cmdid = '' + + ret = '' + if command[0] == "boardsize": + if int(command[1]) > features.pos_len: + print("Warning: Trying to set incompatible boardsize %s (!= %d)" % (command[1], N), file=sys.stderr) + ret = None + board_size = int(command[1]) + gs = GameState(board_size) + elif command[0] == "clear_board": + gs = GameState(board_size) + elif command[0] == "showboard": + ret = "\n" + gs.board.to_string().strip() + elif command[0] == "komi": + gs.rules["whiteKomi"] = float(command[1]) + elif command[0] == "play": + pla = (Board.BLACK if command[1] == "B" or command[1] == "b" else Board.WHITE) + loc = parse_coord(command[2],gs.board) + gs.play(pla,loc) + elif command[0] == "genmove": + outputs = gs.get_model_outputs(model) + loc = outputs["genmove_result"] + pla = gs.board.pla + + if len(command) > 1: + pla = (Board.BLACK if command[1] == "B" or command[1] == "b" else Board.WHITE) + gs.play(pla,loc) + ret = str_coord(loc,gs.board) + + elif command[0] == "name": + ret = 'KataGo Raw Neural Net Debug/Test Script' + elif command[0] == "version": + ret = '1.0' + elif command[0] == "list_commands": + ret = '\n'.join(known_commands) + elif command[0] == "known_command": + ret = 'true' if command[1] in known_commands else 'false' + elif command[0] == "gogui-analyze_commands": + ret = '\n'.join(known_analyze_commands) + elif command[0] == "setrule": + ret = "" + if command[1] == "korule": + gs.rules["koRule"] = command[2].upper() + elif command[1] == "scoringrule": + gs.rules["scoringRule"] = command[2].upper() + elif command[1] == "taxrule": + gs.rules["taxRule"] = command[2].upper() + elif command[1] == "multistonesuicidelegal": + gs.rules["multiStoneSuicideLegal"] = (command[2].lower() == "true") + elif command[1] == "hasbutton": + gs.rules["hasButton"] = (command[2].lower() == "true") + elif command[1] == "encorephase": + gs.rules["encorePhase"] = int(command[2]) + elif command[1] == "passwouldendphase": + gs.rules["passWouldEndPhase"] = (command[2].lower() == "true") + elif command[1] == "whitekomi" or command[1] == "komi": + gs.rules["whiteKomi"] = float(command[2]) + elif command[1] == "asym": + gs.rules["asymPowersOfTwo"] = float(command[2]) + else: + ret = "Unknown rules setting" + elif command[0] == "policy": + outputs = gs.get_model_outputs(model) + gfx_commands = get_gfx_commands_for_heatmap(outputs["moves_and_probs0"], gs.board, normalization_div=None, is_percent=True, value_and_score_from=outputs) + ret = "\n".join(gfx_commands) + elif command[0] == "policy1": + outputs = gs.get_model_outputs(model) + gfx_commands = get_gfx_commands_for_heatmap(outputs["moves_and_probs1"], gs.board, normalization_div=None, is_percent=True, value_and_score_from=outputs) + ret = "\n".join(gfx_commands) + elif command[0] == "logpolicy": + outputs = gs.get_model_outputs(model) + moves_and_logprobs = [(move,max(0.0,4.9+math.log10(prob))) for (move,prob) in outputs["moves_and_probs0"]] + gfx_commands = get_gfx_commands_for_heatmap(moves_and_logprobs, gs.board, normalization_div=6, is_percent=False, value_and_score_from=outputs) + ret = "\n".join(gfx_commands) + elif command[0] == "ownership": + outputs = gs.get_model_outputs(model) + gfx_commands = get_gfx_commands_for_heatmap(outputs["ownership_by_loc"], gs.board, normalization_div=None, is_percent=True, value_and_score_from=None, hotcold=True) + ret = "\n".join(gfx_commands) + elif command[0] == "scoring": + outputs = gs.get_model_outputs(model) + gfx_commands = get_gfx_commands_for_heatmap(outputs["scoring_by_loc"], gs.board, normalization_div=None, is_percent=True, value_and_score_from=None, hotcold=True) + ret = "\n".join(gfx_commands) + elif command[0] == "futurepos0": + outputs = gs.get_model_outputs(model) + gfx_commands = get_gfx_commands_for_heatmap(outputs["futurepos0_by_loc"], gs.board, normalization_div=None, is_percent=True, value_and_score_from=None, hotcold=True) + ret = "\n".join(gfx_commands) + elif command[0] == "futurepos1": + outputs = gs.get_model_outputs(model) + gfx_commands = get_gfx_commands_for_heatmap(outputs["futurepos1_by_loc"], gs.board, normalization_div=None, is_percent=True, value_and_score_from=None, hotcold=True) + ret = "\n".join(gfx_commands) + elif command[0] == "seki": + outputs = gs.get_model_outputs(model) + gfx_commands = get_gfx_commands_for_heatmap(outputs["seki_by_loc"], gs.board, normalization_div=None, is_percent=True, value_and_score_from=None) + ret = "\n".join(gfx_commands) + elif command[0] == "seki2": + outputs = gs.get_model_outputs(model) + gfx_commands = get_gfx_commands_for_heatmap(outputs["seki_by_loc2"], gs.board, normalization_div=None, is_percent=True, value_and_score_from=None) + ret = "\n".join(gfx_commands) + + elif command[0] == "policy_raw": + outputs = gs.get_model_outputs(model) + ret = "\n" + + policysum = 0.0 + for y in range(gs.board.size): + for x in range(gs.board.size): + loc = gs.board.loc(x,y) + pos = features.loc_to_tensor_pos(loc,gs.board) + gs.board.would_be_legal(gs.board.pla,loc) + policysum += outputs["policy0"][pos] + loc = Board.PASS_LOC + pos = features.loc_to_tensor_pos(loc,gs.board) + policysum += outputs["policy0"][pos] + + for y in range(gs.board.size): + for x in range(gs.board.size): + loc = gs.board.loc(x,y) + pos = features.loc_to_tensor_pos(loc,gs.board) + if gs.board.would_be_legal(gs.board.pla,loc): + ret += "%6.3f" % (100.0 * outputs["policy0"][pos] / policysum) + else: + ret += " - " + ret += " " + ret += "\n" + loc = Board.PASS_LOC + pos = features.loc_to_tensor_pos(loc,gs.board) + ret += "Pass: %6.3f" % (100.0 * outputs["policy0"][pos] / policysum) + + elif command[0] == "policy1_raw": + outputs = gs.get_model_outputs(model) + ret = "\n" + + for y in range(gs.board.size): + for x in range(gs.board.size): + loc = gs.board.loc(x,y) + pos = features.loc_to_tensor_pos(loc,gs.board) + if gs.board.would_be_legal(gs.board.pla,loc): + ret += "%6.3f" % (100.0 * outputs["policy1"][pos]) + else: + ret += " - " + ret += " " + ret += "\n" + loc = Board.PASS_LOC + pos = features.loc_to_tensor_pos(loc,gs.board) + ret += "Pass: %6.3f" % (100.0 * outputs["policy1"][pos]) + + elif command[0] == "ownership_raw": + outputs = gs.get_model_outputs(model) + ret = get_board_matrix_str(outputs["ownership"], 100.0, "%+7.3f") + elif command[0] == "scoring_raw": + outputs = gs.get_model_outputs(model) + ret = get_board_matrix_str(outputs["scoring"], 100.0, "%+7.3f") + elif command[0] == "futurepos0_raw": + outputs = gs.get_model_outputs(model) + ret = get_board_matrix_str(outputs["futurepos"][0], 100.0, "%+7.3f") + elif command[0] == "futurepos1_raw": + outputs = gs.get_model_outputs(model) + ret = get_board_matrix_str(outputs["futurepos"][1], 100.0, "%+7.3f") + elif command[0] == "seki_raw": + outputs = gs.get_model_outputs(model) + ret = get_board_matrix_str(outputs["seki"], 100.0, "%+7.3f") + elif command[0] == "seki2_raw": + outputs = gs.get_model_outputs(model) + ret = get_board_matrix_str(outputs["seki2"], 100.0, "%+7.3f") + + elif command[0] in input_feature_command_lookup: + (feature_idx,normalization_div) = input_feature_command_lookup[command[0]] + locs_and_values = get_input_feature(gs, feature_idx) + gfx_commands = get_gfx_commands_for_heatmap(locs_and_values, gs.board, normalization_div, is_percent=False) + ret = "\n".join(gfx_commands) + + elif command[0] in attention_feature_command_lookup: + (extra_output_name,channel_idx) = attention_feature_command_lookup[command[0]] + outputs = gs.get_model_outputs(model, extra_output_names=[extra_output_name]) + output = outputs[extra_output_name] # shape c, hw + output = output[channel_idx] + locs_and_values = [] + board = gs.board + for y in range(board.size): + for x in range(board.size): + loc = board.loc(x,y) + pos = features.loc_to_tensor_pos(loc,board) + locs_and_values.append((loc,output[pos])) + + normalization_div = "max" + gfx_commands = get_gfx_commands_for_heatmap(locs_and_values, gs.board, normalization_div, is_percent=True) + ret = "\n".join(gfx_commands) + + elif command[0] == "passalive": + locs_and_values = get_pass_alive(gs) + gfx_commands = get_gfx_commands_for_heatmap(locs_and_values, gs.board, normalization_div=None, is_percent=False) + ret = "\n".join(gfx_commands) + + elif command[0] == "scorebelief": + outputs = gs.get_model_outputs(model) + ret = print_scorebelief(gs,outputs) + + elif command[0] == "protocol_version": + ret = '2' + elif command[0] == "quit": + print('=%s \n\n' % (cmdid,), end='') + break + else: + print('Warning: Ignoring unknown command - %s' % (line,), file=sys.stderr) + ret = None + + if ret is not None: + print('=%s %s\n\n' % (cmdid, ret,), end='') + else: + print('?%s ???\n\n' % (cmdid,), end='') + sys.stdout.flush() diff --git a/training/scripts/dated/20241110-124043/query_analysis_engine_example.py b/training/scripts/dated/20241110-124043/query_analysis_engine_example.py new file mode 100644 index 0000000..2b803e9 --- /dev/null +++ b/training/scripts/dated/20241110-124043/query_analysis_engine_example.py @@ -0,0 +1,136 @@ +""" +This is a simple python program that demonstrates how to run KataGo's +analysis engine as a subprocess and send it a query. It queries the +result of playing the 4-4 point on an empty board and prints out +the json response. +""" + +import argparse +import json +import subprocess +import time +from threading import Thread +import sgfmill +import sgfmill.boards +import sgfmill.ascii_boards +from typing import Tuple, List, Optional, Union, Literal, Any, Dict + +Color = Union[Literal["b"],Literal["w"]] +Move = Union[None,Literal["pass"],Tuple[int,int]] + +def sgfmill_to_str(move: Move) -> str: + if move is None: + return "pass" + if move == "pass": + return "pass" + (y,x) = move + return "ABCDEFGHJKLMNOPQRSTUVWXYZ"[x] + str(y+1) + +class KataGo: + + def __init__(self, katago_path: str, config_path: str, model_path: str, additional_args: List[str] = []): + self.query_counter = 0 + katago = subprocess.Popen( + [katago_path, "analysis", "-config", config_path, "-model", model_path, *additional_args], + stdin=subprocess.PIPE, + stdout=subprocess.PIPE, + stderr=subprocess.PIPE, + ) + self.katago = katago + def printforever(): + while katago.poll() is None: + data = katago.stderr.readline() + time.sleep(0) + if data: + print("KataGo: ", data.decode(), end="") + data = katago.stderr.read() + if data: + print("KataGo: ", data.decode(), end="") + self.stderrthread = Thread(target=printforever) + self.stderrthread.start() + + def close(self): + self.katago.stdin.close() + + + def query(self, initial_board: sgfmill.boards.Board, moves: List[Tuple[Color,Move]], komi: float, max_visits=None): + query = {} + + query["id"] = str(self.query_counter) + self.query_counter += 1 + + query["moves"] = [(color,sgfmill_to_str(move)) for color, move in moves] + query["initialStones"] = [] + for y in range(initial_board.side): + for x in range(initial_board.side): + color = initial_board.get(y,x) + if color: + query["initialStones"].append((color,sgfmill_to_str((y,x)))) + query["rules"] = "Chinese" + query["komi"] = komi + query["boardXSize"] = initial_board.side + query["boardYSize"] = initial_board.side + query["includePolicy"] = True + if max_visits is not None: + query["maxVisits"] = max_visits + return self.query_raw(query) + + def query_raw(self, query: Dict[str,Any]): + self.katago.stdin.write((json.dumps(query) + "\n").encode()) + self.katago.stdin.flush() + + # print(json.dumps(query)) + + line = "" + while line == "": + if self.katago.poll(): + time.sleep(1) + raise Exception("Unexpected katago exit") + line = self.katago.stdout.readline() + line = line.decode().strip() + # print("Got: " + line) + response = json.loads(line) + + # print(response) + return response + +if __name__ == "__main__": + description = """ + Example script showing how to run KataGo analysis engine and query it from python. + """ + parser = argparse.ArgumentParser(description=description) + parser.add_argument( + "-katago-path", + help="Path to katago executable", + required=True, + ) + parser.add_argument( + "-config-path", + help="Path to KataGo analysis config (e.g. cpp/configs/analysis_example.cfg in KataGo repo)", + required=True, + ) + parser.add_argument( + "-model-path", + help="Path to neural network .bin.gz file", + required=True, + ) + args = vars(parser.parse_args()) + print(args) + + katago = KataGo(args["katago_path"], args["config_path"], args["model_path"]) + + board = sgfmill.boards.Board(19) + komi = 6.5 + moves = [("b",(3,3))] + + displayboard = board.copy() + for color, move in moves: + if move != "pass": + row,col = move + displayboard.play(row,col,color) + print(sgfmill.ascii_boards.render_board(displayboard)) + + print("Query result: ") + print(katago.query(board, moves, komi)) + + katago.close() diff --git a/training/scripts/dated/20241110-124043/save_model_for_export_manual.py b/training/scripts/dated/20241110-124043/save_model_for_export_manual.py new file mode 100644 index 0000000..b0d670a --- /dev/null +++ b/training/scripts/dated/20241110-124043/save_model_for_export_manual.py @@ -0,0 +1,162 @@ +#!/usr/bin/python3 +import sys +import os +import argparse +import traceback +import random +import math +import time +import logging +import contextlib +import json +import datetime +from datetime import timezone +import gc +import shutil +import glob +import numpy as np +import itertools +import copy +import atexit +from collections import defaultdict +from typing import Dict, List + +import torch +import torch.nn +import torch.optim +import torch.distributed +import torch.multiprocessing + +import modelconfigs +from model_pytorch import Model +from metrics_pytorch import Metrics +import load_model +import data_processing_pytorch + +# HANDLE COMMAND AND ARGS ------------------------------------------------------------------- + +if __name__ == "__main__": + + description = """ + Script for one-off taking a checkpoint training under train.py and making it save itself for one export, + without having to use train.py arguments where it would do so repeatedly periodically. + """ + + parser = argparse.ArgumentParser(description=description) + parser.add_argument('-traindir', help='Dir to write to for recording training results', required=True) + parser.add_argument('-exportdir', help='Directory to export models periodically', required=True) + parser.add_argument('-exportprefix', help='Prefix to append to names of models', required=True) + + args = vars(parser.parse_args()) + + +def make_dirs(args): + exportdir = args["exportdir"] + + if exportdir is not None and not os.path.exists(exportdir): + os.makedirs(exportdir) + + +def main(args): + traindir = args["traindir"] + exportdir = args["exportdir"] + exportprefix = args["exportprefix"] + + # SET UP LOGGING ------------------------------------------------------------- + + logging.root.handlers = [] + logging.basicConfig( + level=logging.INFO, + format="%(message)s", + handlers=[ + logging.FileHandler(os.path.join(traindir,f"save_model_for_export_manual.log"), mode="a"), + logging.StreamHandler() + ], + ) + np.set_printoptions(linewidth=150) + + logging.info(str(sys.argv)) + + # LOAD MODEL --------------------------------------------------------------------- + + def get_checkpoint_path(): + return os.path.join(traindir,"checkpoint.ckpt") + + def save(model_state_dict, swa_model_state_dict, optimizer_state_dict, metrics_obj_state_dict, running_metrics, train_state, last_val_metrics, path): + assert path is not None + + state_dict = {} + state_dict["model"] = model_state_dict + state_dict["optimizer"] = optimizer_state_dict + state_dict["metrics"] = metrics_obj_state_dict + state_dict["running_metrics"] = running_metrics + state_dict["train_state"] = train_state + state_dict["last_val_metrics"] = last_val_metrics + state_dict["config"] = model_config + + if swa_model_state_dict is not None: + state_dict["swa_model"] = swa_model_state_dict + + logging.info("Saving checkpoint: " + path) + torch.save(state_dict, path + ".tmp") + time.sleep(1) + os.replace(path + ".tmp", path) + + def load(): + path_to_load_from = get_checkpoint_path() + assert path_to_load_from is not None + + state_dict = torch.load(path_to_load_from, map_location="cpu") + model_config = state_dict["config"] + logging.info(str(model_config)) + + train_state = state_dict["train_state"] + model_state_dict = state_dict["model"] + swa_model_state_dict = None + if "swa_model" in state_dict: + swa_model_state_dict = state_dict["swa_model"] + + metrics_obj_state_dict = state_dict["metrics"] + running_metrics = state_dict["running_metrics"] + + optimizer_state_dict = state_dict["optimizer"] + last_val_metrics = state_dict["last_val_metrics"] + + return (model_config, model_state_dict, swa_model_state_dict, optimizer_state_dict, metrics_obj_state_dict, running_metrics, train_state, last_val_metrics) + + (model_config, model_state_dict, swa_model_state_dict, optimizer_state_dict, metrics_obj_state_dict, running_metrics, train_state, last_val_metrics) = load() + + assert "global_step_samples" in train_state + assert "total_num_data_rows" in train_state + + logging.info("=========================================================================") + logging.info("SAVING MODEL FOR EXPORT MANUAL") + logging.info("=========================================================================") + logging.info("Current time: " + str(datetime.datetime.now())) + logging.info("Global step: %d samples" % (train_state["global_step_samples"])) + logging.info("Currently up to data row " + str(train_state["total_num_data_rows"])) + logging.info(f"Training dir: {traindir}") + logging.info(f"Export dir: {exportdir}") + + # Export a model for testing, unless somehow it already exists + modelname = "%s-s%d-d%d" % ( + exportprefix, + train_state["global_step_samples"], + train_state["total_num_data_rows"], + ) + savepath = os.path.join(exportdir,modelname) + savepathtmp = os.path.join(exportdir,modelname+".tmp") + if os.path.exists(savepath): + logging.info("NOT saving model, already exists at: " + savepath) + else: + os.mkdir(savepathtmp) + logging.info("SAVING MODEL FOR EXPORT TO: " + savepath) + save(model_state_dict, swa_model_state_dict, optimizer_state_dict, metrics_obj_state_dict, running_metrics, train_state, last_val_metrics, path=os.path.join(savepathtmp,"model.ckpt")) + time.sleep(2) + os.rename(savepathtmp,savepath) + + + +if __name__ == "__main__": + make_dirs(args) + main(args) diff --git a/training/scripts/dated/20241110-124043/selfplay.cfg b/training/scripts/dated/20241110-124043/selfplay.cfg new file mode 100644 index 0000000..50bc598 --- /dev/null +++ b/training/scripts/dated/20241110-124043/selfplay.cfg @@ -0,0 +1,185 @@ + +# Logs------------------------------------------------------------------------------------ + +logSearchInfo = false +logMoves = false +logGamesEvery = 10 +logToStdout = true + +# Data writing----------------------------------------------------------------------------------- + +# Spatial size of tensors of data, must match -pos-len in python/train.py and be at least as large as the +# largest boardsize in the data. If you train only on smaller board sizes, you can decrease this and +# pass in a smaller -pos-len to python/train.py (e.g. modify "-pos-len 19" in selfplay/train.sh script), +# to train faster, although it may be awkward if you ever want to increase it again later since data with +# different values cannot be shuffled together. +dataBoardLen = 19 + +maxDataQueueSize = 2000 +maxRowsPerTrainFile = 10000 +firstFileRandMinProp = 0.15 + +# Fancy game selfplay settings-------------------------------------------------------------------- + +# These take dominance - if a game is forked for any of these reasons, that will be the next game played. +# For forked games, randomization of rules and "initGamesWithPolicy" is disabled, komi is made fair with prob "forkCompensateKomiProb". +earlyForkGameProb = 0.04 # Fork to try alternative opening variety with this probability +earlyForkGameExpectedMoveProp = 0.025 # Fork roughly within the first (boardArea * this) many moves +forkGameProb = 0.01 # Fork to try alternative crazy move anywhere in game with this probability if not early forking +forkGameMinChoices = 3 # Choose from the best of at least this many random choices +earlyForkGameMaxChoices = 12 # Choose from the best of at most this many random choices +forkGameMaxChoices = 36 # Choose from the best of at most this many random choices +# Otherwise, with this probability, learn a bit more about the differing evaluation of seki in different rulesets. +sekiForkHackProb = 0.02 + +# Otherwise, play some proportion of games starting from SGF positions, with randomized rules (ignoring the sgf rules) +# On SGF positions, high temperature policy init is allowed +# startPosesProb = 0.0 # Play this proportion of games starting from SGF positions +# startPosesFromSgfDir = DIRECTORYPATH # Load SGFs from this dir +# startPosesLoadProb = 1.0 # Only load each position from each SGF with this chance (save memory) +# startPosesTurnWeightLambda = 0 # 0 = equal weight 0.01 = decrease probability by 1% per turn -0.01 = increase probability by 1% per turn. +# startPosesPolicyInitAreaProp = 0.0 # Same as policyInitAreaProp but for SGF positions + +# Otherwise, play some proportion of games starting from hint positions (generated using "dataminesgfs" command), with randomized rules. +# On hint positions, "initGamesWithPolicy" does not apply. +# hintPosesProb = 0.0 +# hintPosesDir = DIRECTORYPATH + +# Otherwise we are playing a "normal" game, potentially with handicap stones, depending on "handicapProb", and +# potentially with komi randomization, depending on things like "komiStdev", and potentially with different +# board sizes, etc. + +# Most of the remaining parameters here below apply regardless of the initialization, although a few of them +# vary depending on handicap vs normal game, and some are explicitly disabled (e.g. initGamesWithPolicy on hint positions). + +initGamesWithPolicy = true # Play the first few moves of a game high-temperaturely from policy +policyInitAreaProp = 0.04 # The avg number of moves to play +compensateAfterPolicyInitProb = 0.2 # Additionally make komi fair this often after the high-temperature moves. +sidePositionProb = 0.020 # With this probability, train on refuting bad alternative moves. + +cheapSearchProb = 0.75 # Do cheap searches with this probaiblity +cheapSearchVisits = 100 # Number of visits for cheap search +cheapSearchTargetWeight = 0.0 # Training weight for cheap search + +reduceVisits = true # Reduce visits when one side is winning +reduceVisitsThreshold = 0.9 # How winning a side needs to be (winrate) +reduceVisitsThresholdLookback = 3 # How many consecutive turns needed to be that winning +reducedVisitsMin = 100 # Minimum number of visits (never reduce below this) +reducedVisitsWeight = 0.1 # Minimum training weight + +handicapAsymmetricPlayoutProb = 0.5 # In handicap games, play with unbalanced players with this probablity +normalAsymmetricPlayoutProb = 0.01 # In regular games, play with unbalanced players with this probability +maxAsymmetricRatio = 8.0 # Max ratio to unbalance visits between players +minAsymmetricCompensateKomiProb = 0.4 # Compensate komi with at least this probability for unbalanced players + +policySurpriseDataWeight = 0.5 # This proportion of training weight should be concentrated on surprising moves +valueSurpriseDataWeight = 0.1 # This proportion of training weight should be concentrated on surprising position results + +estimateLeadProb = 0.05 # Train lead, rather than just scoremean. Consumes a decent number of extra visits, can be quite slow using low visits to set too high. +switchNetsMidGame = true # When a new neural net is loaded, switch to it immediately instead of waiting for new game +fancyKomiVarying = true # In non-compensated handicap and fork games, vary komi to better learn komi and large score differences that would never happen in even games. + +# Match----------------------------------------------------------------------------------- + +numGameThreads = 30 +maxMovesPerGame = 1600 + +# Rules------------------------------------------------------------------------------------ + +koRules = SIMPLE,POSITIONAL,SITUATIONAL +scoringRules = AREA,TERRITORY +taxRules = NONE,NONE,SEKI,SEKI,ALL +multiStoneSuicideLegals = false,true +hasButtons = false,false,true + +bSizes = 7,8,9 +bSizeRelProbs = 1,1,8 +allowRectangleProb = 0.50 + +komiAuto = True # Automatically adjust komi to what the neural nets think are fair based on the empty board, but still apply komiStdev. +# komiMean = 7.0 # Specify explicit komi +komiStdev = 1.0 # Standard deviation of random variation to komi. +komiBigStdevProb = 0.06 # Probability of applying komiBigStdev +komiBigStdev = 12.0 # Standard deviation of random big variation to komi + +handicapProb = 0.10 # Probability of handicap game +handicapCompensateKomiProb = 0.50 # In handicap games, adjust komi to fair with this probability based on the handicap placement +forkCompensateKomiProb = 0.80 # For forks, adjust komi to fair with this probability based on the forked position +sgfCompensateKomiProb = 0.90 # For sgfs, adjust komi to fair with this probability based on the specific starting position + +drawRandRadius = 0.5 +noResultStdev = 0.166666666 + +# Search limits----------------------------------------------------------------------------------- + +maxVisits = 600 +numSearchThreads = 1 + +# GPU Settings------------------------------------------------------------------------------- + +nnMaxBatchSize = 128 +nnCacheSizePowerOfTwo = 21 +nnMutexPoolSizePowerOfTwo = 15 +numNNServerThreadsPerModel = 1 +nnRandomize = true + +# CUDA GPU settings-------------------------------------- +# cudaDeviceToUse = 0 #use device 0 for all server threads (numNNServerThreadsPerModel) unless otherwise specified per-model or per-thread-per-model +# cudaDeviceToUseModel0 = 3 #use device 3 for model 0 for all threads unless otherwise specified per-thread for this model +# cudaDeviceToUseModel1 = 2 #use device 2 for model 1 for all threads unless otherwise specified per-thread for this model +# cudaDeviceToUseModel0Thread0 = 3 #use device 3 for model 0, server thread 0 +# cudaDeviceToUseModel0Thread1 = 2 #use device 2 for model 0, server thread 1 + +cudaUseFP16 = auto +cudaUseNHWC = auto + +# Root move selection and biases------------------------------------------------------------------------------ + +chosenMoveTemperatureEarly = 0.75 +chosenMoveTemperatureHalflife = 19 +chosenMoveTemperature = 0.15 +chosenMoveSubtract = 0 +chosenMovePrune = 1 + +rootNoiseEnabled = true +rootDirichletNoiseTotalConcentration = 10.83 +rootDirichletNoiseWeight = 0.25 + +rootDesiredPerChildVisitsCoeff = 2 +rootNumSymmetriesToSample = 4 + +useLcbForSelection = true +lcbStdevs = 5.0 +minVisitPropForLCB = 0.15 + +# Internal params------------------------------------------------------------------------------ + +winLossUtilityFactor = 1.0 +staticScoreUtilityFactor = 0.00 +dynamicScoreUtilityFactor = 0.40 +dynamicScoreCenterZeroWeight = 0.25 +dynamicScoreCenterScale = 0.50 +noResultUtilityForWhite = 0.0 +drawEquivalentWinsForWhite = 0.5 + +rootEndingBonusPoints = 0.5 +rootPruneUselessMoves = true + +rootPolicyTemperatureEarly = 1.25 +rootPolicyTemperature = 1.1 + +cpuctExploration = 1.1 +cpuctExplorationLog = 0.0 +fpuReductionMax = 0.2 +rootFpuReductionMax = 0.0 + +numVirtualLossesPerThread = 1 + +# These parameters didn't exist historically during early KataGo runs +valueWeightExponent = 0.5 +subtreeValueBiasFactor = 0.30 +subtreeValueBiasWeightExponent = 0.8 +useNonBuggyLcb = true +useGraphSearch = true +fpuParentWeightByVisitedPolicy = true +fpuParentWeightByVisitedPolicyPow = 2.0 diff --git a/training/scripts/dated/20241110-124043/sgfmetadata.py b/training/scripts/dated/20241110-124043/sgfmetadata.py new file mode 100644 index 0000000..55d1b21 --- /dev/null +++ b/training/scripts/dated/20241110-124043/sgfmetadata.py @@ -0,0 +1,186 @@ +# See the equivalents in trainingwrite.h and trainingwrite.cpp in the cpp directory. +# Translated from the c++ implementation. + +from dataclasses import dataclass +from typing import ClassVar +import datetime +import math +import numpy as np +import random +from board import Board +import modelconfigs + +@dataclass +class SGFMetadata: + inverseBRank: int = 0 + inverseWRank: int = 0 + bIsUnranked: bool = False + wIsUnranked: bool = False + bRankIsUnknown: bool = False + wRankIsUnknown: bool = False + bIsHuman: bool = False + wIsHuman: bool = False + + gameIsUnrated: bool = False + gameRatednessIsUnknown: bool = False + + tcIsUnknown: bool = True + tcIsNone: bool = False + tcIsAbsolute: bool = False + tcIsSimple: bool = False + tcIsByoYomi: bool = False + tcIsCanadian: bool = False + tcIsFischer: bool = False + + mainTimeSeconds: float = 0.0 + periodTimeSeconds: float = 0.0 + byoYomiPeriods: int = 0 + canadianMoves: int = 0 + + gameDate: datetime.date = datetime.date(1970, 1, 1) + + source: int = 0 + + SOURCE_OGS: ClassVar[int] = 1 + SOURCE_KGS: ClassVar[int] = 2 + SOURCE_FOX: ClassVar[int] = 3 + SOURCE_TYGEM: ClassVar[int] = 4 + SOURCE_GOGOD: ClassVar[int] = 5 + SOURCE_GO4GO: ClassVar[int] = 6 + + METADATA_INPUT_NUM_CHANNELS: ClassVar[int] = 192 + + @classmethod + def of_dict(cls, data: dict): + data["gameDate"] = datetime.date.fromisoformat(data["gameDate"]) + return cls(**data) + + def to_dict(self): + data = self.__dict__.copy() + data["gameDate"] = data["gameDate"].isoformat() + return data + + @classmethod + def get_katago_selfplay_metadata(cls, rand: random.Random) -> "SGFMetadata": + return SGFMetadata( + inverseBRank = 0, + inverseWRank = 0, + bIsUnranked = False, + wIsUnranked = False, + bRankIsUnknown = False, + wRankIsUnknown = False, + bIsHuman = False, + wIsHuman = False, + + gameIsUnrated = False, + gameRatednessIsUnknown = False, + + tcIsUnknown = False, + tcIsNone = False, + tcIsAbsolute = False, + tcIsSimple = False, + tcIsByoYomi = True, + tcIsCanadian = False, + tcIsFischer = False, + + mainTimeSeconds = rand.choice([300,600,900,1200]), + periodTimeSeconds = rand.choice([10.0,15.0,30.0]), + byoYomiPeriods = 5, + canadianMoves = 0, + + gameDate = datetime.date(2022, 1, 1) + datetime.timedelta(days=rand.randint(0,722)), + source = 0, + ) + + + def get_metadata_row(self, nextPlayer, boardArea) -> np.ndarray: + if isinstance(nextPlayer,str): + if nextPlayer.lower() == "w": + nextPlayer = Board.WHITE + elif nextPlayer.lower() == "b": + nextPlayer = Board.BLACK + + # This class implements "version 1" of sgf metadata encoding, make sure channel dims match up. + meta_encoder_version = 1 + assert modelconfigs.get_num_meta_encoder_input_features(meta_encoder_version) == self.METADATA_INPUT_NUM_CHANNELS + + rowMetadata = np.zeros(self.METADATA_INPUT_NUM_CHANNELS, dtype=np.float32) + + plaIsHuman = self.wIsHuman if nextPlayer == Board.WHITE else self.bIsHuman + oppIsHuman = self.bIsHuman if nextPlayer == Board.WHITE else self.wIsHuman + rowMetadata[0] = 1.0 if plaIsHuman else 0.0 + rowMetadata[1] = 1.0 if oppIsHuman else 0.0 + + plaIsUnranked = self.wIsUnranked if nextPlayer == Board.WHITE else self.bIsUnranked + oppIsUnranked = self.bIsUnranked if nextPlayer == Board.WHITE else self.wIsUnranked + rowMetadata[2] = 1.0 if plaIsUnranked else 0.0 + rowMetadata[3] = 1.0 if oppIsUnranked else 0.0 + + plaRankIsUnknown = self.wRankIsUnknown if nextPlayer == Board.WHITE else self.bRankIsUnknown + oppRankIsUnknown = self.bRankIsUnknown if nextPlayer == Board.WHITE else self.wRankIsUnknown + rowMetadata[4] = 1.0 if plaRankIsUnknown else 0.0 + rowMetadata[5] = 1.0 if oppRankIsUnknown else 0.0 + + RANK_START_IDX = 6 + invPlaRank = self.inverseWRank if nextPlayer == Board.WHITE else self.inverseBRank + invOppRank = self.inverseBRank if nextPlayer == Board.WHITE else self.inverseWRank + RANK_LEN_PER_PLA = 34 + if not plaIsUnranked: + for i in range(min(invPlaRank, RANK_LEN_PER_PLA)): + rowMetadata[RANK_START_IDX + i] = 1.0 + if not oppIsUnranked: + for i in range(min(invOppRank, RANK_LEN_PER_PLA)): + rowMetadata[RANK_START_IDX + RANK_LEN_PER_PLA + i] = 1.0 + + assert RANK_START_IDX + 2 * RANK_LEN_PER_PLA == 74 + rowMetadata[74] = 0.5 if self.gameRatednessIsUnknown else 1.0 if self.gameIsUnrated else 0.0 + + rowMetadata[75] = 1.0 if self.tcIsUnknown else 0.0 + rowMetadata[76] = 1.0 if self.tcIsNone else 0.0 + rowMetadata[77] = 1.0 if self.tcIsAbsolute else 0.0 + rowMetadata[78] = 1.0 if self.tcIsSimple else 0.0 + rowMetadata[79] = 1.0 if self.tcIsByoYomi else 0.0 + rowMetadata[80] = 1.0 if self.tcIsCanadian else 0.0 + rowMetadata[81] = 1.0 if self.tcIsFischer else 0.0 + assert ( + rowMetadata[75] + + rowMetadata[76] + + rowMetadata[77] + + rowMetadata[78] + + rowMetadata[79] + + rowMetadata[80] + + rowMetadata[81] + == 1.0 + ) + + mainTimeSecondsCapped = min(max(self.mainTimeSeconds, 0.0), 3.0 * 86400) + periodTimeSecondsCapped = min(max(self.periodTimeSeconds, 0.0), 1.0 * 86400) + rowMetadata[82] = 0.4 * (math.log(mainTimeSecondsCapped + 60.0) - 6.5) + rowMetadata[83] = 0.3 * (math.log(periodTimeSecondsCapped + 1.0) - 3.0) + byoYomiPeriodsCapped = min(max(self.byoYomiPeriods, 0), 50) + canadianMovesCapped = min(max(self.canadianMoves, 0), 50) + rowMetadata[84] = 0.5 * (math.log(byoYomiPeriodsCapped + 2.0) - 1.5) + rowMetadata[85] = 0.25 * (math.log(canadianMovesCapped + 2.0) - 1.5) + + rowMetadata[86] = 0.5 * math.log(boardArea / 361.0) + + daysDifference = (self.gameDate - datetime.date(1970, 1, 1)).days + DATE_START_IDX = 87 + DATE_LEN = 32 + period = 7.0 + factor = pow(80000.0, 1.0 / (DATE_LEN - 1)) + twopi = 6.283185307179586476925 + for i in range(DATE_LEN): + numRevolutions = daysDifference / period + rowMetadata[DATE_START_IDX + i * 2 + 0] = math.cos(numRevolutions * twopi) + rowMetadata[DATE_START_IDX + i * 2 + 1] = math.sin(numRevolutions * twopi) + period *= factor + + assert DATE_START_IDX + 2 * DATE_LEN == 151 + + assert 0 <= self.source < 16 + rowMetadata[151 + self.source] = 1.0 + + assert 151 + 16 < self.METADATA_INPUT_NUM_CHANNELS + + return rowMetadata diff --git a/training/scripts/dated/20241110-124043/shuffle.py b/training/scripts/dated/20241110-124043/shuffle.py new file mode 100644 index 0000000..8a535d2 --- /dev/null +++ b/training/scripts/dated/20241110-124043/shuffle.py @@ -0,0 +1,814 @@ +#!/usr/bin/python3 +# Shuffles npz selfplay data for training, choosing a window size based on a power law. +# Run 'python shuffle.py --help' for details on how the window size is chosen and how to use this script. +import sys +import os +import argparse +import traceback +import math +import time +import logging +import zipfile +import shutil +import psutil +import json +import hashlib +import datetime +import gc + +import multiprocessing + +import numpy as np + +def assert_keys(npz,include_meta): + keys = [ + "binaryInputNCHWPacked", + "globalInputNC", + "policyTargetsNCMove", + "globalTargetsNC", + "scoreDistrN", + "valueTargetsNCHW", + ] + if include_meta: + keys.append("metadataInputNC") + assert(set(npz.keys()) == set(keys)) + +def is_temp_npz_like(filename): + return "_" in filename + +def joint_shuffle_take_first_n(n,arrs): + for arr in arrs: + assert(len(arr) == len(arrs[0])) + perm = np.random.permutation(len(arrs[0])) + perm = perm[:n] + shuffled_arrs = [] + for arr in arrs: + shuffled_arrs.append(arr[perm]) + return shuffled_arrs + +def memusage_mb(): + return psutil.Process(os.getpid()).memory_info().rss // 1048576 + +def shardify(input_idx, input_file_group, num_out_files, out_tmp_dirs, keep_prob, include_meta): + np.random.seed([int.from_bytes(os.urandom(4), byteorder='little') for i in range(4)]) + + assert(len(input_file_group) > 0) + num_files_not_found = 0 + + if len(input_file_group) == 1: + try: + with np.load(input_file_group[0]) as npz: + assert_keys(npz,include_meta) + ### + # WARNING - if adding anything here, also add it to joint_shuffle below! + ### + binaryInputNCHWPacked = npz["binaryInputNCHWPacked"] + globalInputNC = npz["globalInputNC"] + policyTargetsNCMove = npz["policyTargetsNCMove"] + globalTargetsNC = npz["globalTargetsNC"] + scoreDistrN = npz["scoreDistrN"] + valueTargetsNCHW = npz["valueTargetsNCHW"] + metadataInputNC = npz["metadataInputNC"] if include_meta else None + except FileNotFoundError: + num_files_not_found += 1 + print("WARNING: file not found by shardify: ", input_file_group[0]) + return num_files_not_found # Early quit since we don't know shapes + else: + binaryInputNCHWPackedList = [] + globalInputNCList = [] + policyTargetsNCMoveList = [] + globalTargetsNCList = [] + scoreDistrNList = [] + valueTargetsNCHWList = [] + metadataInputNCList = [] + + for input_file in input_file_group: + try: + with np.load(input_file) as npz: + assert_keys(npz,include_meta) + binaryInputNCHWPackedList.append(npz["binaryInputNCHWPacked"]) + globalInputNCList.append(npz["globalInputNC"]) + policyTargetsNCMoveList.append(npz["policyTargetsNCMove"]) + globalTargetsNCList.append(npz["globalTargetsNC"]) + scoreDistrNList.append(npz["scoreDistrN"]) + valueTargetsNCHWList.append(npz["valueTargetsNCHW"]) + metadataInputNCList.append(npz["metadataInputNC"] if include_meta else None) + except FileNotFoundError: + num_files_not_found += 1 + print("WARNING: file not found by shardify: ", input_file) + pass + if len(binaryInputNCHWPackedList) <= 0: + return num_files_not_found # Early quit since we don't know shapes + + binaryInputNCHWPacked = np.concatenate(binaryInputNCHWPackedList,axis=0) + globalInputNC = np.concatenate(globalInputNCList,axis=0) + policyTargetsNCMove = np.concatenate(policyTargetsNCMoveList,axis=0) + globalTargetsNC = np.concatenate(globalTargetsNCList,axis=0) + scoreDistrN = np.concatenate(scoreDistrNList,axis=0) + valueTargetsNCHW = np.concatenate(valueTargetsNCHWList,axis=0) + metadataInputNC = np.concatenate(metadataInputNCList,axis=0) if include_meta else None + + num_rows_to_keep = binaryInputNCHWPacked.shape[0] + assert(globalInputNC.shape[0] == num_rows_to_keep) + assert(policyTargetsNCMove.shape[0] == num_rows_to_keep) + assert(globalTargetsNC.shape[0] == num_rows_to_keep) + assert(scoreDistrN.shape[0] == num_rows_to_keep) + assert(valueTargetsNCHW.shape[0] == num_rows_to_keep) + assert(metadataInputNC.shape[0] == num_rows_to_keep if include_meta else True) + + if keep_prob < 1.0: + num_rows_to_keep = min(num_rows_to_keep,int(round(num_rows_to_keep * keep_prob))) + + if include_meta: + [binaryInputNCHWPacked,globalInputNC,policyTargetsNCMove,globalTargetsNC,scoreDistrN,valueTargetsNCHW,metadataInputNC] = ( + joint_shuffle_take_first_n( + num_rows_to_keep, + [binaryInputNCHWPacked,globalInputNC,policyTargetsNCMove,globalTargetsNC,scoreDistrN,valueTargetsNCHW,metadataInputNC] + ) + ) + else: + [binaryInputNCHWPacked,globalInputNC,policyTargetsNCMove,globalTargetsNC,scoreDistrN,valueTargetsNCHW] = ( + joint_shuffle_take_first_n( + num_rows_to_keep, + [binaryInputNCHWPacked,globalInputNC,policyTargetsNCMove,globalTargetsNC,scoreDistrN,valueTargetsNCHW] + ) + ) + + assert(binaryInputNCHWPacked.shape[0] == num_rows_to_keep) + assert(globalInputNC.shape[0] == num_rows_to_keep) + assert(policyTargetsNCMove.shape[0] == num_rows_to_keep) + assert(globalTargetsNC.shape[0] == num_rows_to_keep) + assert(scoreDistrN.shape[0] == num_rows_to_keep) + assert(valueTargetsNCHW.shape[0] == num_rows_to_keep) + assert(metadataInputNC.shape[0] == num_rows_to_keep if include_meta else True) + + rand_assts = np.random.randint(num_out_files,size=[num_rows_to_keep]) + counts = np.bincount(rand_assts,minlength=num_out_files) + countsums = np.cumsum(counts) + assert(countsums[len(countsums)-1] == num_rows_to_keep) + + # if input_idx % 29 == 0: + # print("%s: Shardify writing... (mem usage %dMB)" % (str(datetime.datetime.now()),memusage_mb()), flush=True) + + for out_idx in range(num_out_files): + start = countsums[out_idx]-counts[out_idx] + stop = countsums[out_idx] + if include_meta: + np.savez_compressed( + os.path.join(out_tmp_dirs[out_idx], str(input_idx) + ".npz"), + binaryInputNCHWPacked = binaryInputNCHWPacked[start:stop], + globalInputNC = globalInputNC[start:stop], + policyTargetsNCMove = policyTargetsNCMove[start:stop], + globalTargetsNC = globalTargetsNC[start:stop], + scoreDistrN = scoreDistrN[start:stop], + valueTargetsNCHW = valueTargetsNCHW[start:stop], + metadataInputNC = metadataInputNC[start:stop], + ) + else: + np.savez_compressed( + os.path.join(out_tmp_dirs[out_idx], str(input_idx) + ".npz"), + binaryInputNCHWPacked = binaryInputNCHWPacked[start:stop], + globalInputNC = globalInputNC[start:stop], + policyTargetsNCMove = policyTargetsNCMove[start:stop], + globalTargetsNC = globalTargetsNC[start:stop], + scoreDistrN = scoreDistrN[start:stop], + valueTargetsNCHW = valueTargetsNCHW[start:stop], + ) + return num_files_not_found + +def merge_shards(filename, num_shards_to_merge, out_tmp_dir, batch_size, ensure_batch_multiple, output_npz, include_meta): + np.random.seed([int.from_bytes(os.urandom(4), byteorder='little') for i in range(5)]) + + if output_npz: + record_writer = None + else: + assert False, "No longer supports outputting tensorflow data" + + binaryInputNCHWPackeds = [] + globalInputNCs = [] + policyTargetsNCMoves = [] + globalTargetsNCs = [] + scoreDistrNs = [] + valueTargetsNCHWs = [] + metadataInputNCs = [] + + for input_idx in range(num_shards_to_merge): + shard_filename = os.path.join(out_tmp_dir, str(input_idx) + ".npz") + try: + with np.load(shard_filename) as npz: + assert_keys(npz,include_meta) + + binaryInputNCHWPacked = npz["binaryInputNCHWPacked"] + globalInputNC = npz["globalInputNC"] + policyTargetsNCMove = npz["policyTargetsNCMove"] + globalTargetsNC = npz["globalTargetsNC"] + scoreDistrN = npz["scoreDistrN"] + valueTargetsNCHW = npz["valueTargetsNCHW"] + metadataInputNC = npz["metadataInputNC"] if include_meta else None + + binaryInputNCHWPackeds.append(binaryInputNCHWPacked) + globalInputNCs.append(globalInputNC) + policyTargetsNCMoves.append(policyTargetsNCMove) + globalTargetsNCs.append(globalTargetsNC) + scoreDistrNs.append(scoreDistrN) + valueTargetsNCHWs.append(valueTargetsNCHW) + metadataInputNCs.append(metadataInputNC) + except FileNotFoundError: + print("WARNING: Empty shard in merge_shards for shard :", input_idx, filename) + + if len(binaryInputNCHWPackeds) <= 0: + print("WARNING: empty merge file: ", filename) + return 0 + + ### + # WARNING - if adding anything here, also add it to joint_shuffle below! + ### + binaryInputNCHWPacked = np.concatenate(binaryInputNCHWPackeds) + globalInputNC = np.concatenate(globalInputNCs) + policyTargetsNCMove = np.concatenate(policyTargetsNCMoves) + globalTargetsNC = np.concatenate(globalTargetsNCs) + scoreDistrN = np.concatenate(scoreDistrNs) + valueTargetsNCHW = np.concatenate(valueTargetsNCHWs) + metadataInputNC = np.concatenate(metadataInputNCs) if include_meta else None + + num_rows = binaryInputNCHWPacked.shape[0] + assert(globalInputNC.shape[0] == num_rows) + assert(policyTargetsNCMove.shape[0] == num_rows) + assert(globalTargetsNC.shape[0] == num_rows) + assert(scoreDistrN.shape[0] == num_rows) + assert(valueTargetsNCHW.shape[0] == num_rows) + assert(metadataInputNC.shape[0] == num_rows if include_meta else True) + + if include_meta: + [binaryInputNCHWPacked,globalInputNC,policyTargetsNCMove,globalTargetsNC,scoreDistrN,valueTargetsNCHW,metadataInputNC] = ( + joint_shuffle_take_first_n( + num_rows, + [binaryInputNCHWPacked,globalInputNC,policyTargetsNCMove,globalTargetsNC,scoreDistrN,valueTargetsNCHW,metadataInputNC], + ) + ) + else: + [binaryInputNCHWPacked,globalInputNC,policyTargetsNCMove,globalTargetsNC,scoreDistrN,valueTargetsNCHW] = ( + joint_shuffle_take_first_n( + num_rows, + [binaryInputNCHWPacked,globalInputNC,policyTargetsNCMove,globalTargetsNC,scoreDistrN,valueTargetsNCHW], + ) + ) + + assert(binaryInputNCHWPacked.shape[0] == num_rows) + assert(globalInputNC.shape[0] == num_rows) + assert(policyTargetsNCMove.shape[0] == num_rows) + assert(globalTargetsNC.shape[0] == num_rows) + assert(scoreDistrN.shape[0] == num_rows) + assert(valueTargetsNCHW.shape[0] == num_rows) + assert(metadataInputNC.shape[0] == num_rows if include_meta else True) + + # print("%s: Merge writing... (mem usage %dMB)" % (str(datetime.datetime.now()),memusage_mb()), flush=True) + + # Just truncate and lose the batch at the end, it's fine + num_batches = (num_rows // (batch_size * ensure_batch_multiple)) * ensure_batch_multiple + if output_npz: + start = 0 + stop = num_batches*batch_size + if include_meta: + np.savez_compressed( + filename, + binaryInputNCHWPacked = binaryInputNCHWPacked[start:stop], + globalInputNC = globalInputNC[start:stop], + policyTargetsNCMove = policyTargetsNCMove[start:stop], + globalTargetsNC = globalTargetsNC[start:stop], + scoreDistrN = scoreDistrN[start:stop], + valueTargetsNCHW = valueTargetsNCHW[start:stop], + metadataInputNC = metadataInputNC[start:stop], + ) + else: + np.savez_compressed( + filename, + binaryInputNCHWPacked = binaryInputNCHWPacked[start:stop], + globalInputNC = globalInputNC[start:stop], + policyTargetsNCMove = policyTargetsNCMove[start:stop], + globalTargetsNC = globalTargetsNC[start:stop], + scoreDistrN = scoreDistrN[start:stop], + valueTargetsNCHW = valueTargetsNCHW[start:stop], + ) + else: + assert False, "No longer supports outputting tensorflow data" + + jsonfilename = os.path.splitext(filename)[0] + ".json" + with open(jsonfilename,"w") as f: + json.dump({"num_rows":num_rows,"num_batches":num_batches},f) + + if record_writer is not None: + record_writer.close() + return num_batches * batch_size + +def get_numpy_npz_headers(filename): + with zipfile.ZipFile(filename) as z: + wasbad = False + numrows = 0 + npzheaders = {} + for subfilename in z.namelist(): + npyfile = z.open(subfilename) + try: + version = np.lib.format.read_magic(npyfile) + except ValueError: + wasbad = True + print("WARNING: bad file, skipping it: %s (bad array %s)" % (filename,subfilename)) + else: + (shape, is_fortran, dtype) = np.lib.format._read_array_header(npyfile,version) + npzheaders[subfilename] = (shape, is_fortran, dtype) + if wasbad: + return None + return npzheaders + + +def compute_num_rows(filename): + try: + npheaders = get_numpy_npz_headers(filename) + except PermissionError: + print("WARNING: No permissions for reading file: ", filename) + return (filename,None) + except zipfile.BadZipFile: + print("WARNING: Bad zip file: ", filename) + return (filename,None) + if npheaders is None or len(npheaders) <= 0: + print("WARNING: bad npz headers for file: ", filename) + return (filename,None) + + if "binaryInputNCHWPacked" in npheaders: + (shape, is_fortran, dtype) = npheaders["binaryInputNCHWPacked"] + else: + (shape, is_fortran, dtype) = npheaders["binaryInputNCHWPacked.npy"] + num_rows = shape[0] + return (filename,num_rows) + + +class TimeStuff(object): + + def __init__(self,taskstr): + self.taskstr = taskstr + + def __enter__(self): + print("Beginning: %s" % self.taskstr, flush=True) + self.t0 = time.time() + + def __exit__(self, exception_type, exception_val, trace): + self.t1 = time.time() + print("Finished: %s in %s seconds" % (self.taskstr, str(self.t1 - self.t0)), flush=True) + return False + + +if __name__ == '__main__': + parser = argparse.ArgumentParser(add_help=False,formatter_class=argparse.RawTextHelpFormatter,description=""" + Shuffle data files! + + This shuffle script is designed for ongoing self-play training. It shuffles the most recent window of data among the data it's provided. It chooses the window size dynamically based on the total amount of data in the run so far, assuming that the directories provided contain all of the data for the run so far. If you don't actually have all of the data, e.g. you've archived or deleted the older data, or else want to compute the window size as if there were more data, use -add-to-data-rows. + + The window size is a power law based on the number of rows in the run N: + WINDOWSIZE(N) = (N^EXPONENT - MIN_ROWS^EXPONENT) / (EXPONENT * MIN_ROWS^(EXPONENT-1)) * INITIAL_WINDOW_PER_ROW + MIN_ROWS + + given arguments: + -taper-window-exponent EXPONENT \\ + -expand-window-per-row INITIAL_WINDOW_PER_ROW \\ + -min-rows MIN_ROWS (default 250k) + + This may look a bit complex, but basically it is simply the power law N^EXPONENT with shifting and scaling such that: + WINDOWSIZE(MIN_ROWS) = MIN_ROWS + (dWINDOWSIZE/dN)(MIN_ROWS) = INITIAL_WINDOW_PER_ROW + + Reasonable arguments similar to those used for KataGo's main runs would be + -taper-window-exponent 0.65 or 0.675 \\ + -expand-window-per-row 0.4 \\ + -min-rows 250000 (default) + + If you want to control the "scale" of the power law differently than the min rows, you can specify -taper-window-scale as well. + There is also a bit of a hack to cap the number of random rows (rows generated by random play without a neural net), since random row generation at the start of a run can be very fast due to not hitting the GPU, and overpopulate the run. + + Additionally, NOT all of the shuffled window is output, only a random shuffled 20M rows will be kept. Adjust this using -keep-target-rows. The intention is that this script will be repeatedly run as new data comes in, such that well before train.py would need more than 20M rows, the data would have been shuffled again and a new random 20M rows chosen. + + If you are NOT doing ongoing self-play training, but simply want to shuffle an entire dataset (not just a window of it) and want to output all of it (not just 20M of it) then you can use arguments like: + -taper-window-exponent 1.0 \\ + -expand-window-per-row 1.0 \\ + -keep-target-rows SOME_VERY_LARGE_NUMBER + + If you ARE doing ongoing self-play training, but want a fixed window size, then you can use arguments like: + -min-rows YOUR_DESIRED_SIZE \\ + -taper-window-exponent 1.0 \\ + -expand-window-per-row 0.0 + """) + parser.add_argument('dirs', metavar='DIR', nargs='+', help='Directories of training data files') + + required_args = parser.add_argument_group('required arguments') + optional_args = parser.add_argument_group('optional arguments') + optional_args.add_argument( + '-h', + '--help', + action='help', + default=argparse.SUPPRESS, + help='show this help message and exit' + ) + optional_args.add_argument('-min-rows', type=int, required=False, help='Minimum training rows to use, default 250k') + optional_args.add_argument('-max-rows', type=int, required=False, help='Maximum training rows to use, default unbounded') + optional_args.add_argument('-keep-target-rows', type=int, required=False, help='Target number of rows to actually keep in the final data set, default 20M') + required_args.add_argument('-expand-window-per-row', type=float, required=True, help='Beyond min rows, initially expand the window by this much every post-random data row') + required_args.add_argument('-taper-window-exponent', type=float, required=True, help='Make the window size asymtotically grow as this power of the data rows') + optional_args.add_argument('-taper-window-scale', type=float, required=False, help='The scale at which the power law applies, defaults to -min-rows') + optional_args.add_argument('-add-to-data-rows', type=float, required=False, help='Compute the window size as if the number of data rows were this much larger/smaller') + optional_args.add_argument('-add-to-window-size', type=float, required=False, help='DEPRECATED due to being misnamed name, use -add-to-data-rows') + optional_args.add_argument('-summary-file', required=False, help='Summary json file for directory contents') + required_args.add_argument('-out-dir', required=True, help='Dir to output training files') + required_args.add_argument('-out-tmp-dir', required=True, help='Dir to use as scratch space') + optional_args.add_argument('-approx-rows-per-out-file', type=int, required=False, default=70000, help='Number of rows per output file, default 70k') + required_args.add_argument('-num-processes', type=int, required=True, help='Number of multiprocessing processes for shuffling in parallel') + required_args.add_argument('-batch-size', type=int, required=True, help='Batch size to write training examples in') + optional_args.add_argument('-ensure-batch-multiple', type=int, required=False, help='Ensure each file is a multiple of this many batches') + optional_args.add_argument('-worker-group-size', type=int, required=False, default=80000, help='Internally, target having many rows per parallel sharding worker (doesnt affect merge)') + optional_args.add_argument('-exclude', required=False, help='Text file with npzs to ignore, one per line') + optional_args.add_argument('-exclude-prefix', required=False, help='Prefix to concat to lines in exclude to produce the full file path') + optional_args.add_argument('-exclude-basename', required=False, action="store_true", help='Consider an exclude to match if basename matches') + optional_args.add_argument('-only-include-md5-path-prop-lbound', type=float, required=False, help='Just before sharding, include only filepaths hashing to float >= this') + optional_args.add_argument('-only-include-md5-path-prop-ubound', type=float, required=False, help='Just before sharding, include only filepaths hashing to float < this') + optional_args.add_argument('-output-npz', action="store_true", required=False, help='Output results as npz files') + optional_args.add_argument('-include-meta', action="store_true", required=False, help='Include sgf metadata inputs') + + args = parser.parse_args() + dirs = args.dirs + min_rows = args.min_rows + max_rows = args.max_rows + keep_target_rows = args.keep_target_rows + expand_window_per_row = args.expand_window_per_row + taper_window_exponent = args.taper_window_exponent + taper_window_scale = args.taper_window_scale + add_to_data_rows = args.add_to_data_rows + if args.add_to_data_rows is not None and args.add_to_window_size is not None: + print("Cannot specify both -add-to-data-rows and -add-to-window-size. Please use only -add-to-data-rows, -add-to-window-size is deprecated") + if args.add_to_data_rows is None and args.add_to_window_size is not None: + print("WARNING: -add-to-window-size is deprecated due to being misnamed, use -add-to-data-rows") + add_to_data_rows = args.add_to_window_size + + summary_file = args.summary_file + out_dir = args.out_dir + out_tmp_dir = args.out_tmp_dir + approx_rows_per_out_file = args.approx_rows_per_out_file + num_processes = args.num_processes + batch_size = args.batch_size + ensure_batch_multiple = 1 + if args.ensure_batch_multiple is not None: + ensure_batch_multiple = args.ensure_batch_multiple + worker_group_size = args.worker_group_size + exclude = args.exclude + exclude_prefix = args.exclude_prefix + if exclude_prefix is None: + exclude_prefix = "" + exclude_basename = args.exclude_basename + only_include_md5_path_prop_lbound = args.only_include_md5_path_prop_lbound + only_include_md5_path_prop_ubound = args.only_include_md5_path_prop_ubound + output_npz = args.output_npz + include_meta = args.include_meta + + if min_rows is None: + print("NOTE: -min-rows was not specified, defaulting to requiring 250K rows before shuffling.") + min_rows = 250000 + if keep_target_rows is None: + print("NOTE: -keep-target-rows was not specified, defaulting to sampling a random 20M rows out of the computed window.") + print("If you intended to shuffle the whole dataset instead, pass in -keep-target-rows ") + keep_target_rows = 20000000 + if add_to_data_rows is None: + add_to_data_rows = 0 + + summary_data_by_dirpath = {} + if summary_file is not None: + with TimeStuff("Loading " + summary_file): + # Try a bunch of times, just to be robust to if the file is being swapped out in nfs + for i in range(10): + success = False + try: + with open(summary_file) as fp: + summary_data_by_dirpath = json.load(fp) + success = True + except OSError: + success = False + except ValueError: + success = False + if success: + break + time.sleep(1) + if not success: + raise RuntimeError("Could not load summary file") + + exclude_set = set() + if exclude is not None: + with TimeStuff("Loading " + exclude): + # Try a bunch of times, just to be robust to if the file is being swapped out in nfs + for i in range(10): + success = False + try: + with open(exclude,"r") as exclude_in: + excludes = exclude_in.readlines() + excludes = [x.strip() for x in excludes] + excludes = [x for x in excludes if len(x) > 0] + excludes = [exclude_prefix + x for x in excludes] + exclude_set = set(excludes) + success = True + except OSError: + success = False + except ValueError: + success = False + if success: + break + time.sleep(1) + if not success: + raise RuntimeError("Could not load summary file") + + # If excluding basenames, also add them to the set + if exclude_basename: + basenames = [os.path.basename(path) for path in exclude_set] + exclude_set.update(basenames) + + all_files = [] + files_with_unknown_num_rows = [] + excluded_count = 0 + excluded_due_to_excludes_count = 0 + tempfilelike_count = 0 + with TimeStuff("Finding files"): + for d in dirs: + for (path,dirnames,filenames) in os.walk(d, followlinks=True): + i = 0 + while i < len(dirnames): + dirname = dirnames[i] + summary_data = summary_data_by_dirpath.get(os.path.abspath(os.path.join(path, dirname))) + if summary_data is not None: + filename_mtime_num_rowss = summary_data["filename_mtime_num_rowss"] + del dirnames[i] + i -= 1 + for (filename,mtime,num_rows) in filename_mtime_num_rowss: + if is_temp_npz_like(filename): + # print("WARNING: file looks like a temp file, treating as exclude: ", os.path.join(path,dirname,filename)) + excluded_count += 1 + tempfilelike_count += 1 + continue + if exclude_basename and os.path.basename(filename) in exclude_set: + excluded_count += 1 + excluded_due_to_excludes_count += 1 + continue + filename = os.path.join(path,dirname,filename) + if not exclude_basename and filename in exclude_set: + excluded_count += 1 + excluded_due_to_excludes_count += 1 + continue + if num_rows is None: + print("WARNING: Skipping bad rowless file, treating as exclude: ", filename) + excluded_count += 1 + continue + all_files.append((filename,mtime,num_rows)) + i += 1 + + filtered_filenames = [] + for filename in filenames: + if not filename.endswith(".npz"): + continue + if is_temp_npz_like(filename): + # print("WARNING: file looks like a temp file, treating as exclude: ", os.path.join(path,filename)) + excluded_count += 1 + tempfilelike_count += 1 + continue + if exclude_basename and os.path.basename(filename) in exclude_set: + excluded_count += 1 + excluded_due_to_excludes_count += 1 + continue + filename = os.path.join(path,filename) + if not exclude_basename and filename in exclude_set: + excluded_count += 1 + excluded_due_to_excludes_count += 1 + continue + filtered_filenames.append(filename) + filenames = filtered_filenames + + files_with_unknown_num_rows.extend(filenames) + filenames = [(filename,os.path.getmtime(filename)) for filename in filenames] + all_files.extend(filenames) + print("Total number of files: %d" % len(all_files), flush=True) + print("Total number of files with unknown row count: %d" % len(files_with_unknown_num_rows), flush=True) + print("Excluded count: %d" % excluded_count, flush=True) + print("Excluded count due to looking like temp file: %d" % tempfilelike_count, flush=True) + print("Excluded count due to cmdline excludes file: %d" % excluded_due_to_excludes_count, flush=True) + + print("GC collect", flush=True) + del summary_data_by_dirpath + gc.collect() + + with TimeStuff("Sorting"): + all_files.sort(key=(lambda x: x[1]), reverse=False) + + # Wait a few seconds just in case to limit the chance of filesystem races, now that we know exactly + # the set of filenames we want + time.sleep(3) + + with TimeStuff("Computing rows for unsummarized files"): + with multiprocessing.Pool(num_processes) as pool: + results = pool.map(compute_num_rows,files_with_unknown_num_rows) + results = dict(results) + for i in range(len(all_files)): + info = all_files[i] + if len(info) < 3: + num_rows = results[info[0]] + all_files[i] = (info[0], info[1], num_rows) + + num_rows_total = 0 # Number of data rows + num_random_rows_capped = 0 # Number of random data rows, capped at min_rows - we never keep more than min_rows many data rows if they're from random. + num_postrandom_rows = 0 # Number of NON-random rows + + # How far offset do we start on the power-law window tail? E.g. what number of postrandom rows do we need before the window size grows by a factor + # of 2^(taper_window_exponent)? For now, we set it equal to the min rows + if taper_window_scale is not None: + window_taper_offset = taper_window_scale + else: + window_taper_offset = min_rows + + def num_usable_rows(): + global num_random_rows_capped + global num_postrandom_rows + return num_random_rows_capped + num_postrandom_rows + def num_desired_rows(): + # Every post-random row moves one row beyond window_taper_offset + power_law_x = num_usable_rows() - min_rows + window_taper_offset + add_to_data_rows + # Apply power law and correct for window_taper_offset so we're still anchored at 0 + unscaled_power_law = (power_law_x ** taper_window_exponent) - (window_taper_offset ** taper_window_exponent) + # Scale so that we have an initial derivative of 1 + scaled_power_law = unscaled_power_law / (taper_window_exponent * (window_taper_offset ** (taper_window_exponent-1))) + # Scale so that we have the desired initial slope, and add back the minimum random rows + return int(scaled_power_law * expand_window_per_row + min_rows) + + with TimeStuff("Processing found files"): + for (filename,mtime,num_rows) in all_files: + if num_rows is None: + print("WARNING: Skipping bad file: ", filename) + continue + if num_rows <= 0: + continue + num_rows_total += num_rows + if "random/tdata/" not in filename and "random\\tdata\\" not in filename: + num_postrandom_rows += num_rows + else: + num_random_rows_capped = min(num_random_rows_capped + num_rows, min_rows) + + if os.path.exists(out_dir): + raise Exception(out_dir + " already exists") + os.mkdir(out_dir) + + if num_rows_total <= 0: + print("No rows found") + sys.exit(0) + + # If we don't have enough rows, then quit out + if num_rows_total < min_rows: + print("Not enough rows, only %d (fewer than %d)" % (num_rows_total,min_rows)) + sys.exit(0) + + print("Total rows found: %d (%d usable)" % (num_rows_total,num_usable_rows()), flush=True) + + # Reverse so that recent files are first + all_files.reverse() + + # Now assemble only the files we need to hit our desired window size + desired_num_rows = num_desired_rows() + desired_num_rows = max(desired_num_rows,min_rows) + desired_num_rows = min(desired_num_rows,max_rows) if max_rows is not None else desired_num_rows + print("Desired num rows: %d / %d" % (desired_num_rows,num_rows_total), flush=True) + + desired_input_files = [] + min_start_row = num_rows_total + max_end_row = num_rows_total + num_rows_used = 0 + print_stride = 1 + len(all_files) // 80 + end_row = num_rows_total + with TimeStuff("Computing desired rows"): + for i in range(len(all_files)): + (filename,mtime,num_rows) = all_files[i] + + # This could happen if the .summary.json file is inaccurate after file deletions + # Actually we just handle that in shardify - and accept that it might make our window slightly not far back enough + # if not os.path.exists(filename): + # continue + + if num_rows is not None and num_rows > 0: + desired_input_files.append((filename,num_rows)) + start_row = end_row - num_rows + min_start_row = min(start_row, min_start_row) + num_rows_used += num_rows + end_row -= num_rows + else: + start_row = end_row + + if i % print_stride == 0 or num_rows_used >= desired_num_rows: + print("Using: %s (%d-%d) (%d/%d desired rows)" % (filename,start_row,end_row,num_rows_used,desired_num_rows), flush=True) + if num_rows_used >= desired_num_rows: + break + + print("Finally, using: (%d-%d) (%d/%d desired rows)" % (min_start_row,max_end_row,num_rows_used,desired_num_rows), flush=True) + + print("GC collect", flush=True) + del all_files + gc.collect() + + np.random.seed() + np.random.shuffle(desired_input_files) + + approx_rows_to_keep = num_rows_used + if keep_target_rows is not None: + approx_rows_to_keep = min(approx_rows_to_keep, keep_target_rows) + keep_prob = approx_rows_to_keep / num_rows_used + + num_out_files = int(round(approx_rows_to_keep / approx_rows_per_out_file)) + num_out_files = max(num_out_files,1) + + if output_npz: + out_files = [os.path.join(out_dir, "data%d.npz" % i) for i in range(num_out_files)] + else: + assert False, "No longer supports outputting tensorflow data" + + out_tmp_dirs = [os.path.join(out_tmp_dir, "tmp.shuf%d" % i) for i in range(num_out_files)] + print("Writing %d output files with %d kept / %d desired rows" % (num_out_files, approx_rows_to_keep, desired_num_rows), flush=True) + + def clean_tmp_dirs(): + for tmp_dir in out_tmp_dirs: + if os.path.exists(tmp_dir): + print("Cleaning up tmp dir: " + tmp_dir) + shutil.rmtree(tmp_dir) + + clean_tmp_dirs() + for tmp_dir in out_tmp_dirs: + os.mkdir(tmp_dir) + + num_rows_in_desired_files = 0 + if only_include_md5_path_prop_lbound is not None or only_include_md5_path_prop_ubound is not None: + new_desired_input_files = [] + for (input_file,num_rows_in_file) in desired_input_files: + input_file_base = os.path.basename(input_file) + hashfloat = int("0x"+hashlib.md5(str(input_file_base).encode('utf-8')).hexdigest()[:13],16) / 2 ** 52 + ok = True + if only_include_md5_path_prop_lbound is not None and hashfloat < only_include_md5_path_prop_lbound: + ok = False + if only_include_md5_path_prop_ubound is not None and hashfloat >= only_include_md5_path_prop_ubound: + ok = False + if ok: + new_desired_input_files.append((input_file,num_rows_in_file)) + num_rows_in_desired_files += num_rows_in_file + print("Due to only_include_md5, filtering down to %d/%d files" % (len(new_desired_input_files),len(desired_input_files))) + desired_input_files = new_desired_input_files + else: + for (input_file,num_rows_in_file) in desired_input_files: + num_rows_in_desired_files += num_rows_in_file + + if len(desired_input_files) <= 0: + print("No files after filtering for desired range") + sys.exit(0) + if num_rows_in_desired_files <= 0: + print("No rows in desired files") + sys.exit(0) + + # Clump files into sharding groups. More efficient if shuffling a ton of small npz files + # since we aren't doing separate tasks for every individual file but rather handling a bunch + # of files at once, and also makes chunkier shards on disk when it comes time to shuffle. + desired_input_file_groups = [] + group_size_so_far = 0 + group_so_far = [] + for (input_file,num_rows_in_file) in desired_input_files: + if num_rows_in_file <= 0: + continue + group_so_far.append(input_file) + group_size_so_far += num_rows_in_file + if group_size_so_far >= worker_group_size: + desired_input_file_groups.append(group_so_far) + group_so_far = [] + group_size_so_far = 0 + if group_size_so_far > 0: + desired_input_file_groups.append(group_so_far) + group_so_far = [] + group_size_so_far = 0 + print("Grouping %d input files into %d sharding groups" % (len(desired_input_files),len(desired_input_file_groups)),flush=True) + + with multiprocessing.Pool(num_processes) as pool: + with TimeStuff("Sharding"): + shard_results = pool.starmap(shardify, [ + (input_idx, desired_input_file_groups[input_idx], num_out_files, out_tmp_dirs, keep_prob, include_meta) + for input_idx in range(len(desired_input_file_groups)) + ]) + + with TimeStuff("Merging"): + num_shards_to_merge = len(desired_input_file_groups) + merge_results = pool.starmap(merge_shards, [ + (out_files[idx],num_shards_to_merge,out_tmp_dirs[idx],batch_size,ensure_batch_multiple,output_npz,include_meta) + for idx in range(len(out_files)) + ]) + print("Number of rows by output file:",flush=True) + print(list(zip(out_files,merge_results)),flush=True) + sys.stdout.flush() + + clean_tmp_dirs() + + dump_value = { + "range": (min_start_row, max_end_row) + } + + with open(out_dir + ".json", 'w') as f: + json.dump(dump_value, f) diff --git a/training/scripts/dated/20241110-124043/shuffle.sh b/training/scripts/dated/20241110-124043/shuffle.sh new file mode 100644 index 0000000..efaaf34 --- /dev/null +++ b/training/scripts/dated/20241110-124043/shuffle.sh @@ -0,0 +1,123 @@ +#!/bin/bash -eu +set -o pipefail +{ +# Shuffles and copies selfplay training from selfplay/ to shuffleddata/current/ +# Should be run periodically. + +if [[ $# -lt 4 ]] +then + echo "Usage: $0 BASEDIR TMPDIR NTHREADS BATCHSIZE" + echo "Currently expects to be run from within the 'python' directory of the KataGo repo, or otherwise in the same dir as shuffle.py." + echo "BASEDIR containing selfplay data and models and related directories" + echo "TMPDIR scratch space, ideally on fast local disk, unique to this loop" + echo "NTHREADS number of parallel threads/processes to use in shuffle" + echo "BATCHSIZE number of samples to concat together per batch for training" + exit 0 +fi +BASEDIR="$1" +shift +TMPDIR="$1" +shift +NTHREADS="$1" +shift +BATCHSIZE="$1" +shift + +#------------------------------------------------------------------------------ + +OUTDIR=$(date "+%Y%m%d-%H%M%S") +OUTDIRTRAIN=$OUTDIR/train +OUTDIRVAL=$OUTDIR/val + +mkdir -p "$BASEDIR"/shuffleddata/$OUTDIR +mkdir -p "$TMPDIR"/train +mkdir -p "$TMPDIR"/val + +echo "Beginning shuffle at" $(date "+%Y-%m-%d %H:%M:%S") + +if [[ -n "${SKIP_VALIDATE:-}" ]] +then + ( + time python3 ./shuffle.py \ + "$BASEDIR"/selfplay/ \ + -expand-window-per-row 0.4 \ + -taper-window-exponent 0.65 \ + -out-dir "$BASEDIR"/shuffleddata/$OUTDIRTRAIN \ + -out-tmp-dir "$TMPDIR"/train \ + -approx-rows-per-out-file 70000 \ + -num-processes "$NTHREADS" \ + -batch-size "$BATCHSIZE" \ + -only-include-md5-path-prop-lbound 0.00 \ + -only-include-md5-path-prop-ubound 1.00 \ + -output-npz \ + "$@" \ + 2>&1 | tee "$BASEDIR"/shuffleddata/$OUTDIR/outtrain.txt & + + wait + ) +else + # Randomly peels off 5% of files generated by selfplay as validation data + ( + time python3 ./shuffle.py \ + "$BASEDIR"/selfplay/ \ + -expand-window-per-row 0.4 \ + -taper-window-exponent 0.65 \ + -out-dir "$BASEDIR"/shuffleddata/$OUTDIRVAL \ + -out-tmp-dir "$TMPDIR"/val \ + -approx-rows-per-out-file 70000 \ + -num-processes "$NTHREADS" \ + -batch-size "$BATCHSIZE" \ + -only-include-md5-path-prop-lbound 0.95 \ + -only-include-md5-path-prop-ubound 1.00 \ + -output-npz \ + "$@" \ + 2>&1 | tee "$BASEDIR"/shuffleddata/$OUTDIR/outval.txt & + + wait + ) + ( + time python3 ./shuffle.py \ + "$BASEDIR"/selfplay/ \ + -expand-window-per-row 0.4 \ + -taper-window-exponent 0.65 \ + -out-dir "$BASEDIR"/shuffleddata/$OUTDIRTRAIN \ + -out-tmp-dir "$TMPDIR"/train \ + -approx-rows-per-out-file 70000 \ + -num-processes "$NTHREADS" \ + -batch-size "$BATCHSIZE" \ + -only-include-md5-path-prop-lbound 0.00 \ + -only-include-md5-path-prop-ubound 0.95 \ + -output-npz \ + "$@" \ + 2>&1 | tee "$BASEDIR"/shuffleddata/$OUTDIR/outtrain.txt & + + wait + ) +fi +#set +x + +# Just in case, give a little time for nfs +sleep 10 + +# Ensure target directory is empty before moving +rm -rf "$BASEDIR"/shuffleddata/current + +# Create symbolic link +ln -s $OUTDIR "$BASEDIR"/shuffleddata/current_tmp +mv -Tf "$BASEDIR"/shuffleddata/current_tmp "$BASEDIR"/shuffleddata/current + +# CLEANUP --------------------------------------------------------------- + +# Among shuffled dirs older than 2 hours, remove all but the most recent 5 of them. +# This should be VERY conservative and allow plenty of time for the training to switch +# to newer ones as they get generated. +echo "Cleaning up any old dirs" +find "$BASEDIR"/shuffleddata/ -mindepth 1 -maxdepth 1 -type d -mmin +120 | sort | head -n -5 | xargs --no-run-if-empty rm -r + +echo "Finished shuffle at" $(date "+%Y-%m-%d %H:%M:%S") +# Make a little space between shuffles +echo "" +echo "" + +exit 0 +} diff --git a/training/scripts/dated/20241110-124043/shuffle_and_export_loop.sh b/training/scripts/dated/20241110-124043/shuffle_and_export_loop.sh new file mode 100644 index 0000000..d9ae159 --- /dev/null +++ b/training/scripts/dated/20241110-124043/shuffle_and_export_loop.sh @@ -0,0 +1,62 @@ +#!/bin/bash -eu +set -o pipefail +{ +if [[ $# -lt 6 ]] +then + echo "Usage: $0 NAMEPREFIX BASEDIR TMPDIR NTHREADS BATCHSIZE USEGATING" + echo "NAMEPREFIX string prefix for this training run, try to pick something globally unique. Will be displayed to users when KataGo loads the model." + echo "BASEDIR containing selfplay data and models and related directories" + echo "TMPDIR scratch space, ideally on fast local disk, unique to this loop" + echo "NTHREADS number of parallel threads/processes to use in shuffle" + echo "BATCHSIZE number of samples to concat together per batch for training, must match training" + echo "USEGATING = 1 to use gatekeeper, 0 to not use gatekeeper" + exit 0 +fi +NAMEPREFIX="$1" +shift +BASEDIRRAW="$1" +shift +TMPDIRRAW="$1" +shift +NTHREADS="$1" +shift +BATCHSIZE="$1" +shift +USEGATING="$1" +shift + +GITROOTDIR="$(git rev-parse --show-toplevel)" + +basedir="$(realpath "$BASEDIRRAW")" +tmpdir="$(realpath "$TMPDIRRAW")" + +mkdir -p "$basedir"/scripts +mkdir -p "$basedir"/logs +cp "$GITROOTDIR"/python/*.py "$GITROOTDIR"/python/selfplay/*.sh "$basedir"/scripts + +# For archival and logging purposes - you can look back and see exactly the python code on a particular date +DATE_FOR_FILENAME=$(date "+%Y%m%d-%H%M%S") +DATED_ARCHIVE="$basedir"/scripts/dated/"$DATE_FOR_FILENAME" +mkdir -p "$DATED_ARCHIVE" +cp "$GITROOTDIR"/python/*.py "$GITROOTDIR"/python/selfplay/*.sh "$DATED_ARCHIVE" + +( + cd "$basedir"/scripts + while true + do + ./shuffle.sh "$basedir" "$tmpdir" "$NTHREADS" "$BATCHSIZE" "$@" + sleep 20 + done +) >> "$basedir"/logs/outshuffle.txt 2>&1 & disown + +( + cd "$basedir"/scripts + while true + do + ./export_model_for_selfplay.sh "$NAMEPREFIX" "$basedir" "$USEGATING" + sleep 10 + done +) >> "$basedir"/logs/outexport.txt 2>&1 & disown + +exit 0 +} diff --git a/training/scripts/dated/20241110-124043/shuffle_loop.sh b/training/scripts/dated/20241110-124043/shuffle_loop.sh new file mode 100644 index 0000000..6f75b0c --- /dev/null +++ b/training/scripts/dated/20241110-124043/shuffle_loop.sh @@ -0,0 +1,60 @@ +#!/bin/bash -eu +set -o pipefail +{ +if [[ $# -lt 4 ]] +then + echo "Usage: $0 BASEDIR TMPDIR NTHREADS BATCHSIZE" + echo "Currently expects to be run from within the 'python' directory of the KataGo repo, or otherwise in the same dir as export_model.py." + echo "BASEDIR containing selfplay data and models and related directories" + echo "TMPDIR scratch space, ideally on fast local disk, unique to this loop" + echo "NTHREADS number of parallel threads/processes to use in shuffle" + echo "BATCHSIZE number of samples to concat together per batch for training, must match training" + exit 0 +fi +BASEDIRRAW="$1" +shift +TMPDIRRAW="$1" +shift +NTHREADS="$1" +shift +BATCHSIZE="$1" +shift + +GITROOTDIR="$(git rev-parse --show-toplevel)" + +basedir="$(realpath "$BASEDIRRAW")" +tmpdir="$(realpath "$TMPDIRRAW")" + +mkdir -p "$basedir"/scripts +mkdir -p "$basedir"/logs +cp "$GITROOTDIR"/python/*.py "$GITROOTDIR"/python/selfplay/*.sh "$GITROOTDIR"/python/selfplay/distributed/*.sh "$basedir"/scripts + +# For archival and logging purposes - you can look back and see exactly the python code on a particular date +DATE_FOR_FILENAME=$(date "+%Y%m%d-%H%M%S") +DATED_ARCHIVE="$basedir"/scripts/dated/"$DATE_FOR_FILENAME" +mkdir -p "$DATED_ARCHIVE" +cp "$GITROOTDIR"/python/*.py "$DATED_ARCHIVE" +cp -r "$GITROOTDIR"/python/selfplay "$DATED_ARCHIVE" + + +( + cd "$basedir"/scripts + while true + do + rm -f "$basedir"/selfplay.summary.json.tmp + time python3 ./summarize_old_selfplay_files.py "$basedir"/selfplay/ \ + -old-summary-file-to-assume-correct "$basedir"/selfplay.summary.json \ + -new-summary-file "$basedir"/selfplay.summary.json.tmp + mv "$basedir"/selfplay.summary.json.tmp "$basedir"/selfplay.summary.json + sleep 10 + + for i in {1..10} + do + ./shuffle.sh "$basedir" "$tmpdir" "$NTHREADS" "$BATCHSIZE" -summary-file "$basedir"/selfplay.summary.json "$@" + sleep 600 + done + done +) >> "$basedir"/logs/outshuffle.txt 2>&1 & disown + +exit 0 +} diff --git a/training/scripts/dated/20241110-124043/summarize_old_selfplay_files.py b/training/scripts/dated/20241110-124043/summarize_old_selfplay_files.py new file mode 100644 index 0000000..712374b --- /dev/null +++ b/training/scripts/dated/20241110-124043/summarize_old_selfplay_files.py @@ -0,0 +1,194 @@ +#!/usr/bin/python3 +import sys +import os +import argparse +import traceback +import time +import logging +import zipfile +import json +import datetime +import dateutil.parser +import shutil + +import multiprocessing + +import numpy as np + +def get_numpy_npz_headers(filename): + with zipfile.ZipFile(filename) as z: + wasbad = False + numrows = 0 + npzheaders = {} + for subfilename in z.namelist(): + npyfile = z.open(subfilename) + try: + version = np.lib.format.read_magic(npyfile) + except ValueError: + wasbad = True + print("WARNING: bad file, skipping it: %s (bad array %s)" % (filename,subfilename)) + else: + (shape, is_fortran, dtype) = np.lib.format._read_array_header(npyfile,version) + npzheaders[subfilename] = (shape, is_fortran, dtype) + if wasbad: + return None + return npzheaders + +def is_temp_npz_like(filename): + return "_" in filename + +def summarize_dir(dirpath): + filenames = [filename for filename in os.listdir(dirpath) if filename.endswith('.npz')] + + num_rows_this_dir = 0 + filename_mtime_num_rowss = [] + for filename in filenames: + filepath = os.path.join(dirpath,filename) + mtime = os.path.getmtime(filepath) + + # Files that look like they are temp files should be recorded and warned + if is_temp_npz_like(filename): + print("WARNING: file looks like a temp file: ", filepath) + filename_mtime_num_rowss.append((filename,mtime,None)) + continue + + try: + npheaders = get_numpy_npz_headers(filepath) + except PermissionError: + print("WARNING: No permissions for reading file: ", filepath) + filename_mtime_num_rowss.append((filename,mtime,None)) + continue + except zipfile.BadZipFile: + print("WARNING: Bad zip file: ", filepath) + filename_mtime_num_rowss.append((filename,mtime,None)) + continue + + if npheaders is None or len(npheaders) <= 0: + print("WARNING: bad npz headers for file: ", filepath) + filename_mtime_num_rowss.append((filename,mtime,None)) + continue + + if "binaryInputNCHWPacked" in npheaders: + (shape, is_fortran, dtype) = npheaders["binaryInputNCHWPacked"] + else: + (shape, is_fortran, dtype) = npheaders["binaryInputNCHWPacked.npy"] + num_rows = shape[0] + num_rows_this_dir += num_rows + + filename_mtime_num_rowss.append((filename,mtime,num_rows)) + + print("Summarizing new dir with %d rows: %s" % (num_rows_this_dir,dirpath),flush=True) + return (dirpath, filename_mtime_num_rowss, num_rows_this_dir) + + +class TimeStuff(object): + + def __init__(self,taskstr): + self.taskstr = taskstr + + def __enter__(self): + print("Beginning: %s" % self.taskstr, flush=True) + self.t0 = time.time() + + def __exit__(self, exception_type, exception_val, trace): + self.t1 = time.time() + print("Finished: %s in %s seconds" % (self.taskstr, str(self.t1 - self.t0)), flush=True) + return True + + +if __name__ == '__main__': + parser = argparse.ArgumentParser(description='Shuffle data files') + parser.add_argument('dirs', metavar='DIR', nargs='+', help='Directories of training data files') + parser.add_argument('-old-summary-file-to-assume-correct', required=False, help='Summary json file for directory contents') + parser.add_argument('-new-summary-file', required=True, help='Summary json file for directory contents') + parser.add_argument('-num-parallel-processes', required=False, type=int, help='Number of parallel processes to use, default 4') + + args = parser.parse_args() + dirs = args.dirs + old_summary_file_to_assume_correct = args.old_summary_file_to_assume_correct + new_summary_file = args.new_summary_file + + num_processes = 4 + if args.num_parallel_processes is not None: + num_processes = args.num_parallel_processes + + summary_data_by_dirpath = {} + if old_summary_file_to_assume_correct is not None and os.path.exists(old_summary_file_to_assume_correct): + with TimeStuff("Loading " + old_summary_file_to_assume_correct): + with open(old_summary_file_to_assume_correct) as fp: + summary_data_by_dirpath = json.load(fp) + + # Update old summary data into new format + dirpaths = list(summary_data_by_dirpath.keys()) + format_was_updated = False + for dirpath in dirpaths: + if "dir_mtime" not in summary_data_by_dirpath[dirpath]: + format_was_updated = True + filename_mtime_num_rowss = summary_data_by_dirpath[dirpath] + summary_data_by_dirpath[dirpath] = { + "dir_mtime": os.path.getmtime(dirpath), + "filename_mtime_num_rowss": filename_mtime_num_rowss, + } + + dirs_to_handle = [] + with TimeStuff("Finding files"): + for d in dirs: + for (path,dirnames,filenames) in os.walk(d, followlinks=True): + had_no_dirnames = len(dirnames) == 0 + i = 0 + while i < len(dirnames): + dirname = dirnames[i] + dirpath = os.path.normpath(os.path.join(path, dirname)) + if dirpath in summary_data_by_dirpath: + if os.path.getmtime(dirpath) == summary_data_by_dirpath[dirpath]["dir_mtime"]: + # Skip + del dirnames[i] + continue + + if dirname == "tdata": + # Handle this dir and don't recurse further + del dirnames[i] + dirs_to_handle.append(dirpath) + continue + + else: + parseddate = None + try: + parseddate = dateutil.parser.parse(dirname) + except ValueError: + parseddate = None + if parseddate is not None and parseddate < datetime.datetime.now() - datetime.timedelta(days=2.0): + # Handle this dir and don't recurse further + del dirnames[i] + dirs_to_handle.append(dirpath) + continue + + i += 1 + + with TimeStuff("Parallel summarizing %d dirs" % len(dirs_to_handle)): + with multiprocessing.Pool(num_processes) as pool: + results = pool.map(summarize_dir,dirs_to_handle) + + num_total_rows = 0 + with TimeStuff("Merging %d results" % len(results)): + for result in results: + if result is None: + continue + (dirpath, filename_mtime_num_rowss, num_rows_this_dir) = result + num_total_rows += num_rows_this_dir + summary_data_by_dirpath[os.path.abspath(dirpath)] = { + "dir_mtime": os.path.getmtime(os.path.abspath(dirpath)), + "filename_mtime_num_rowss": filename_mtime_num_rowss, + } + + if len(dirs_to_handle) == 0 and old_summary_file_to_assume_correct is not None and os.path.exists(old_summary_file_to_assume_correct) and not format_was_updated: + shutil.copy(old_summary_file_to_assume_correct,new_summary_file) + print("Not writing any new summary, no results, just copying old file") + else: + with TimeStuff("Writing result"): + with open(new_summary_file,"w") as fp: + json.dump(summary_data_by_dirpath,fp) + print("Summary file written adding %d additional rows: %s" % (num_total_rows,new_summary_file),flush=True) + + print("Done computing new summary",flush=True) + sys.stdout.flush() diff --git a/training/scripts/dated/20241110-124043/summarize_sgfs.py b/training/scripts/dated/20241110-124043/summarize_sgfs.py new file mode 100644 index 0000000..6b21f38 --- /dev/null +++ b/training/scripts/dated/20241110-124043/summarize_sgfs.py @@ -0,0 +1,128 @@ +import argparse +import elo +from elo import GameRecord +import itertools +import math +import os +import re + +from dataclasses import dataclass +from sgfmill import sgf +from typing import List, Dict, Tuple, Set, Sequence + +class GoGameResultSummary(elo.GameResultSummary): + + def __init__( + self, + elo_prior_games: float, + estimate_first_player_advantage: bool, + ): + super().__init__(elo_prior_games, estimate_first_player_advantage) + self._should_warn_handicap_komi = False + + # @override + def print_elos(self): + super().print_elos() + if self._should_warn_handicap_komi: + print("WARNING: There are handicap games or games with komi < 5.5 or komi > 7.5, these games may not be fair?") + + # @override + def is_game_file(self, input_file: str) -> bool: + lower = input_file.lower() + return input_file.endswith(".sgf") or input_file.endswith(".sgfs") + + # @override + def get_game_records(self, input_file: str) -> List[GameRecord]: + if input_file.lower().endswith(".sgfs"): + with open(input_file, "rb") as f: + sgfs_strings = f.readlines() + + records = [] + for sgf in sgfs_strings: + record = self.sgf_string_to_game_record(sgf, input_file) + if record is not None: + records.append(record) + return records + else: + with open(input_file, "rb") as f: + sgf = f.read() + + records = [] + record = self.sgf_string_to_game_record(sgf, input_file) + if record is not None: + records.append(record) + return records + + def sgf_string_to_game_record(self, sgf_string, debug_source = None) -> GameRecord: + try: + # sgfmill for some reason can't handle rectangular boards, even though it's part of the SGF spec. + # So lie and say that they're square, so that we can load them. + sgf_string = re.sub(r'SZ\[(\d+):\d+\]', r'SZ[\1]', sgf_string.decode("utf-8")) + sgf_string = sgf_string.encode("utf-8") + + game = sgf.Sgf_game.from_bytes(sgf_string) + winner = game.get_winner() + except ValueError: + print ('\033[91m'+f"A sgf string is damaged in {debug_source}, and its record has been skipped!"+ '\x1b[0m') + return + pla_black = game.get_player_name('b') + pla_white = game.get_player_name('w') + if (game.get_handicap() is not None) or game.get_komi() < 5.5 or game.get_komi() > 7.5: + self._should_warn_handicap_komi = True + + game_record = GameRecord(player1=pla_black,player2=pla_white) + if (winner == 'b'): + game_record.win += 1 + elif (winner == 'w'): + game_record.loss += 1 + else: + game_record.draw += 1 + return game_record + + + +if __name__ == "__main__": + description = """ + Summarize SGF/SGFs files and estimate Bayes Elo score for each of the player. + """ + parser = argparse.ArgumentParser(description=description) + parser.add_argument( + "input-files-or-dirs", + help="sgf/sgfs files or directories of them", + nargs="+", + ) + parser.add_argument( + "-recursive", + help="Recursively search subdirectories of input directories", + required=False, + action="store_true", + ) + parser.add_argument( + "-elo-prior-games", + help="Prior for Bayes Elo calculation, using input as the prior number of games to stabilize the results", + required=False, + type=float, + default=2, + ) + parser.add_argument( + "-estimate-first-player-advantage", + help="Attempt to estimate first player advantage instead of assuming fair game", + required=False, + action="store_true", + ) + args = vars(parser.parse_args()) + print(args) + + input_files_or_dirs = args["input-files-or-dirs"] + recursive = args["recursive"] + elo_prior_games = args["elo_prior_games"] + estimate_first_player_advantage = args["estimate_first_player_advantage"] + + game_result_summary = GoGameResultSummary( + elo_prior_games=elo_prior_games, + estimate_first_player_advantage=estimate_first_player_advantage, + ) + for input_file_or_dir in input_files_or_dirs: + game_result_summary.add_games_from_file_or_dir(input_file_or_dir, recursive=recursive) + + game_result_summary.print_elos() diff --git a/training/scripts/dated/20241110-124043/synchronous_loop.sh b/training/scripts/dated/20241110-124043/synchronous_loop.sh new file mode 100644 index 0000000..d21423e --- /dev/null +++ b/training/scripts/dated/20241110-124043/synchronous_loop.sh @@ -0,0 +1,103 @@ +#!/bin/bash -eu +set -o pipefail +{ + +# Runs the entire self-play process synchronously in a loop, training a single size of neural net appropriately. +# Assumes you have the cpp directory compiled and the katago executable is there. + +# If using multiple machines, or even possibly many GPUs on one machine in some cases, then this is NOT the +# recommended method, instead it is better to run all steps simultaneously and asynchronously. See SelfplayTraining.md in +# the root of the KataGo repo for more details. + +if [[ $# -lt 5 ]] +then + echo "Usage: $0 NAMEPREFIX BASEDIR TRAININGNAME MODELKIND USEGATING" + echo "Assumes katago is already built in the 'cpp' directory of the KataGo repo and the executable is present at cpp/katago.exe." + echo "NAMEPREFIX string prefix for this training run, try to pick something globally unique. Will be displayed to users when KataGo loads the model." + echo "BASEDIR containing selfplay data and models and related directories" + echo "TRANINGNAME name to prefix models with, specific to this training daemon" + echo "MODELKIND what size model to train, like 'b10c128', see ../modelconfigs.py" + echo "USEGATING = 1 to use gatekeeper, 0 to not use gatekeeper" + exit 0 +fi +NAMEPREFIX="$1" +shift +BASEDIRRAW="$1" +shift +TRAININGNAME="$1" +shift +MODELKIND="$1" +shift +USEGATING="$1" +shift + +BASEDIR="$(realpath "$BASEDIRRAW")" +GITROOTDIR="$(git rev-parse --show-toplevel)" +LOGSDIR="$BASEDIR"/logs +SCRATCHDIR="$BASEDIR"/shufflescratch + +# Create all the directories we need +mkdir -p "$BASEDIR" +mkdir -p "$LOGSDIR" +mkdir -p "$SCRATCHDIR" +mkdir -p "$BASEDIR"/selfplay +mkdir -p "$BASEDIR"/gatekeepersgf + +# Parameters for the training run +NUM_GAMES_PER_CYCLE=30 # Every cycle, play this many games +NUM_THREADS_FOR_SHUFFLING=8 +NUM_TRAIN_SAMPLES_PER_EPOCH=100000 # Training will proceed in chunks of this many rows, subject to MAX_TRAIN_PER_DATA. +MAX_TRAIN_PER_DATA=8 # On average, train only this many times on each data row. Larger numbers may cause overfitting. +NUM_TRAIN_SAMPLES_PER_SWA=80000 # Stochastic weight averaging frequency. +BATCHSIZE=128 # For lower-end GPUs 64 or smaller may be needed to avoid running out of GPU memory. +SHUFFLE_MINROWS=1 # Require this many rows at the very start before beginning training. +MAX_TRAIN_SAMPLES_PER_CYCLE=500000 # Each cycle will do at most this many training steps. +TAPER_WINDOW_SCALE=50000 # Parameter setting the scale at which the shuffler will make the training window grow sublinearly. +SHUFFLE_KEEPROWS=600000 # Needs to be larger than MAX_TRAIN_SAMPLES_PER_CYCLE, so the shuffler samples enough rows each cycle for the training to use. + +# Paths to the selfplay and gatekeeper configs that contain board sizes, rules, search parameters, etc. +SELFPLAY_CONFIG="$GITROOTDIR"/cpp/configs/training/selfplay1_maxsize9.cfg +GATING_CONFIG="$GITROOTDIR"/cpp/configs/training/gatekeeper1_maxsize9.cfg + +# Copy all the relevant scripts and configs and the katago executable to a dated directory. +DATE_FOR_FILENAME=$(date "+%Y%m%d-%H%M%S") +DATED_ARCHIVE="$BASEDIR"/scripts/dated/"$DATE_FOR_FILENAME" +mkdir -p "$DATED_ARCHIVE" +cp "$GITROOTDIR"/python/*.py "$GITROOTDIR"/python/selfplay/*.sh "$DATED_ARCHIVE" +cp "$GITROOTDIR"/cpp/katago.exe "$DATED_ARCHIVE"/katago.exe # Ensure the executable is copied +cp "$SELFPLAY_CONFIG" "$DATED_ARCHIVE"/selfplay.cfg +cp "$GATING_CONFIG" "$DATED_ARCHIVE"/gatekeeper.cfg +git show --no-patch --no-color > "$DATED_ARCHIVE"/version.txt +git diff --no-color > "$DATED_ARCHIVE"/diff.txt +git diff --staged --no-color > "$DATED_ARCHIVE"/diffstaged.txt + +# Also run the code out of the archive, so that we don't unexpectedly crash or change behavior if the local repo changes. +cd "$DATED_ARCHIVE" + +# Begin cycling forever, running each step in order. +set -x +while true +do + echo "Gatekeeper" + time "C:\Users\chang\Downloads\katago-v1.15.1-opencl-windows-x64\katago.exe" gatekeeper -rejected-models-dir "$BASEDIR"/rejectedmodels -accepted-models-dir "$BASEDIR"/models/ -sgf-output-dir "$BASEDIR"/gatekeepersgf/ -test-models-dir "$BASEDIR"/modelstobetested/ -config "$DATED_ARCHIVE"/gatekeeper.cfg -quit-if-no-nets-to-test | tee -a "$BASEDIR"/gatekeepersgf/stdout.txt + + echo "Selfplay" + time "C:\Users\chang\Downloads\katago-v1.15.1-opencl-windows-x64\katago.exe" selfplay -max-games-total "$NUM_GAMES_PER_CYCLE" -output-dir "$BASEDIR"/selfplay -models-dir "$BASEDIR"/models -config "$DATED_ARCHIVE"/selfplay.cfg | tee -a "$BASEDIR"/selfplay/stdout.txt + + echo "Shuffle" + ( + time SKIP_VALIDATE=1 ./shuffle.sh "$BASEDIR" "$SCRATCHDIR" "$NUM_THREADS_FOR_SHUFFLING" "$BATCHSIZE" -min-rows "$SHUFFLE_MINROWS" -keep-target-rows "$SHUFFLE_KEEPROWS" -taper-window-scale "$TAPER_WINDOW_SCALE" | tee -a "$BASEDIR"/logs/outshuffle.txt + ) + + echo "Train" + time ./train.sh "$BASEDIR" "$TRAININGNAME" "$MODELKIND" "$BATCHSIZE" main -samples-per-epoch "$NUM_TRAIN_SAMPLES_PER_EPOCH" -swa-period-samples "$NUM_TRAIN_SAMPLES_PER_SWA" -quit-if-no-data -stop-when-train-bucket-limited -no-repeat-files -max-train-bucket-per-new-data "$MAX_TRAIN_PER_DATA" -max-train-bucket-size "$MAX_TRAIN_SAMPLES_PER_CYCLE" + + echo "Export" + ( + time ./export_model_for_selfplay.sh "$NAMEPREFIX" "$BASEDIR" "$USEGATING" | tee -a "$BASEDIR"/logs/outexport.txt + ) + +done + +exit 0 +} diff --git a/training/scripts/dated/20241110-124043/test.py b/training/scripts/dated/20241110-124043/test.py new file mode 100644 index 0000000..92065df --- /dev/null +++ b/training/scripts/dated/20241110-124043/test.py @@ -0,0 +1,279 @@ +#!/usr/bin/python3 +import sys +import os +import argparse +import traceback +import random +import math +import time +import logging +import json +import numpy as np +from collections import defaultdict +from typing import Dict, List + +import torch +import torch.nn +from torch.optim.swa_utils import AveragedModel + +import modelconfigs +from model_pytorch import Model, ExtraOutputs, MetadataEncoder +from metrics_pytorch import Metrics +import data_processing_pytorch +from load_model import load_model + +# HANDLE COMMAND AND ARGS ------------------------------------------------------------------- + +if __name__ == "__main__": + + description = """ + Test neural net on Go positions from npz files of batches from selfplay. + """ + + parser = argparse.ArgumentParser(description=description) + parser.add_argument('-npzdir', help='Directory with npz data', required=True) + parser.add_argument('-model-kind', help='If specified, use this model kind instead of config', required=False) + parser.add_argument('-config', help='Path to model.config.json', required=False) + parser.add_argument('-checkpoint', help='Checkpoint to test', required=False) + parser.add_argument('-pos-len', help='Spatial length of expected training data', type=int, required=True) + parser.add_argument('-batch-size', help='Batch size to use for testing', type=int, required=True) + parser.add_argument('-use-swa', help='Use SWA model', action="store_true", required=False) + parser.add_argument('-max-batches', help='Maximum number of batches for testing', type=int, required=False) + parser.add_argument('-gpu-idx', help='GPU idx', type=int, required=False) + parser.add_argument('-print-norm', help='Names of outputs to print norms comma separated', type=str, required=False) + parser.add_argument('-list-available-outputs', help='Print names of outputs available', action="store_true", required=False) + + args = vars(parser.parse_args()) + +def main(args): + npzdir = args["npzdir"] + model_kind = args["model_kind"] + config_file = args["config"] + checkpoint_file = args["checkpoint"] + pos_len = args["pos_len"] + batch_size = args["batch_size"] + use_swa = args["use_swa"] + max_batches = args["max_batches"] + gpu_idx = args["gpu_idx"] + list_available_outputs = args["list_available_outputs"] + + norm_layer_names = [] + if args["print_norm"]: + for layername in args["print_norm"].split(','): + if layername.strip(): + norm_layer_names.append(layername.strip()) + + soft_policy_weight_scale = 1.0 + value_loss_scale = 1.0 + td_value_loss_scales = [0.4, 0.4, 0.4] + + world_size = 1 + rank = 0 + + # SET UP LOGGING ------------------------------------------------------------- + + logging.root.handlers = [] + logging.basicConfig( + level=logging.INFO, + format="%(message)s", + handlers=[ + logging.StreamHandler(stream=sys.stdout) + ], + ) + np.set_printoptions(linewidth=150) + + logging.info(str(sys.argv)) + + # FIGURE OUT GPU ------------------------------------------------------------ + if gpu_idx is not None: + torch.cuda.set_device(gpu_idx) + logging.info("Using GPU device: " + torch.cuda.get_device_name()) + device = torch.device("cuda", gpu_idx) + elif torch.cuda.is_available(): + logging.info("Using GPU device: " + torch.cuda.get_device_name()) + device = torch.device("cuda") + else: + logging.warning("WARNING: No GPU, using CPU") + device = torch.device("cpu") + + # LOAD MODEL --------------------------------------------------------------------- + + if checkpoint_file is None: + logging.info("Initializing new model since no checkpoint provided") + assert (model_kind is None) != (config_file is None), "Must provide exactly one of -model-kind and -config if no checkpoint" + + if model_kind is not None: + model_config = modelconfigs.config_of_name[model_kind] + else: + with open(config_file,"r") as f: + model_config = json.load(f) + logging.info(str(model_config)) + + model = Model(model_config,pos_len) + model.initialize() + model.to(device) + else: + model, swa_model, _ = load_model(checkpoint_file, use_swa, device=device, pos_len=pos_len, verbose=True) + model_config = model.config + + metrics_obj = Metrics(batch_size,world_size,model) + + # METRICS ----------------------------------------------------------------------------------- + def detensorify_metrics(metrics): + ret = {} + for key in metrics: + if isinstance(metrics[key], torch.Tensor): + ret[key] = metrics[key].detach().cpu().item() + else: + ret[key] = metrics[key] + return ret + + def accumulate_metrics(metric_sums, metric_weights, metrics, batch_size, decay): + if decay != 1.0: + for metric in metric_sums: + if metric.endswith("_sum"): + metric_sums[metric] *= decay + metric_weights[metric] *= decay + + for metric in metrics: + if not metric.endswith("_batch"): + metric_sums[metric] += metrics[metric] + metric_weights[metric] += batch_size + else: + metric_sums[metric] += metrics[metric] + metric_weights[metric] += 1 + + def log_metrics(prefix, metric_sums, metric_weights, metrics, sum_norms, num_samples_tested, metrics_out): + metrics_to_print = {} + for metric in metric_sums: + if metric.endswith("_sum"): + metrics_to_print[metric[:-4]] = metric_sums[metric] / metric_weights[metric] + elif metric.endswith("_batch"): + metrics_to_print[metric] = metric_sums[metric] / metric_weights[metric] + metric_sums[metric] *= 0.001 + metric_weights[metric] *= 0.001 + else: + metrics_to_print[metric] = metric_sums[metric] + for metric in metrics: + if metric not in metric_sums: + metrics_to_print[metric] = metrics[metric] + + logging.info(prefix + ", ".join(["%s = %f" % (metric, metrics_to_print[metric]) for metric in metrics_to_print])) + if metrics_out: + metrics_out.write(json.dumps(metrics_to_print) + "\n") + + for name in sum_norms: + avg_norm = sum_norms[name] / num_samples_tested + logging.info(f"{name} {avg_norm:.6f}") + + if metrics_out: + metrics_out.flush() + + torch.backends.cudnn.benchmark = True + + # Validate + logging.info("Beginning test!") + val_files = [os.path.join(npzdir,fname) for fname in os.listdir(npzdir) if fname.endswith(".npz")] + if len(val_files) == 0: + raise Exception("No npz files in " + npzdir) + + with torch.no_grad(): + model.eval() + if swa_model is not None: + swa_model.eval() + val_metric_sums = defaultdict(float) + val_metric_weights = defaultdict(float) + sum_norms = defaultdict(float) + num_batches_tested = 0 + num_samples_tested = 0 + total_inference_time = 0.0 + is_first_batch = True + for batch in data_processing_pytorch.read_npz_training_data( + val_files, + batch_size, + world_size, + rank, + pos_len, + device, + randomize_symmetries=True, + model_config=model_config, + ): + if max_batches is not None and num_batches_tested >= max_batches: + break + + start = torch.cuda.Event(enable_timing=True) + end = torch.cuda.Event(enable_timing=True) + + extra_outputs = ExtraOutputs(norm_layer_names) + # if model.get_has_metadata_encoder(): + # extra_outputs.add_requested([MetadataEncoder.OUTMEAN_KEY,MetadataEncoder.OUTLOGVAR_KEY]) + + start.record() + if swa_model is not None: + model_outputs = swa_model(batch["binaryInputNCHW"],batch["globalInputNC"],extra_outputs=extra_outputs) + else: + model_outputs = model(batch["binaryInputNCHW"],batch["globalInputNC"],extra_outputs=extra_outputs) + end.record() + torch.cuda.synchronize() + time_elapsed = start.elapsed_time(end) / 1000.0 + + if list_available_outputs: + for available_output in extra_outputs.available: + logging.info(f"Available output: {available_output}") + list_available_outputs = False + + postprocessed = model.postprocess_output(model_outputs) + metrics = metrics_obj.metrics_dict_batchwise( + model, + postprocessed, + extra_outputs, + batch, + is_training=False, + soft_policy_weight_scale=soft_policy_weight_scale, + disable_optimistic_policy=False, + meta_kata_only_soft_policy=False, + value_loss_scale=value_loss_scale, + td_value_loss_scales=td_value_loss_scales, + seki_loss_scale=1.0, + variance_time_loss_scale=1.0, + main_loss_scale=1.0, + intermediate_loss_scale=None, + ) + metrics = detensorify_metrics(metrics) + + # Ignore first batch, treat as a warmup so timings are a bit more accurate. + if is_first_batch: + is_first_batch = False + continue + + for norm_layer_name in norm_layer_names: + activations = extra_outputs.returned[norm_layer_name] + non_batch_dims = list(range(1,len(activations.shape))) + sum_norms[norm_layer_name] += torch.sum(torch.sqrt(torch.mean(torch.square(activations),dim=non_batch_dims)),dim=0).item() + + accumulate_metrics(val_metric_sums, val_metric_weights, metrics, batch_size, decay=1.0) + + num_batches_tested += 1 + num_samples_tested += batch_size + total_inference_time += time_elapsed + + if num_batches_tested % 5 == 0: + remainder = num_batches_tested // 5 + while remainder > 1 and remainder % 2 == 0: + remainder = remainder // 2 + if remainder in [1,3,5,7,9]: + metrics["num_batches"] = num_batches_tested + metrics["num_samples"] = num_samples_tested + metrics["inferencetime"] = total_inference_time + metrics["time/1ksamp"] = total_inference_time / num_samples_tested * 1000.0 + log_metrics("STATS SO FAR: ", val_metric_sums, val_metric_weights, metrics, sum_norms, num_samples_tested, None) + + metrics["num_batches"] = num_batches_tested + metrics["num_samples"] = num_samples_tested + metrics["inferencetime"] = total_inference_time + metrics["time/1ksamp"] = total_inference_time / num_samples_tested * 1000.0 + log_metrics("FINAL: ", val_metric_sums, val_metric_weights, metrics, sum_norms, num_samples_tested, None) + + +if __name__ == "__main__": + main(args) diff --git a/training/scripts/dated/20241110-124043/train.py b/training/scripts/dated/20241110-124043/train.py new file mode 100644 index 0000000..6717e4f --- /dev/null +++ b/training/scripts/dated/20241110-124043/train.py @@ -0,0 +1,1368 @@ +#!/usr/bin/python3 +import sys +import os +import argparse +import traceback +import random +import math +import time +import logging +import contextlib +import json +import datetime +from datetime import timezone +import gc +import shutil +import glob +import numpy as np +import itertools +import copy +import atexit +from collections import defaultdict +from typing import Dict, List + +import torch +import torch.nn +import torch.optim +import torch.distributed +import torch.multiprocessing +from torch.nn.parallel import DistributedDataParallel +from torch.optim.swa_utils import AveragedModel +from torch.cuda.amp import GradScaler, autocast + +import modelconfigs +from model_pytorch import Model, ExtraOutputs, MetadataEncoder +from metrics_pytorch import Metrics +import load_model +import data_processing_pytorch +from metrics_logging import accumulate_metrics, log_metrics, clear_metric_nonfinite + +# HANDLE COMMAND AND ARGS ------------------------------------------------------------------- + +if __name__ == "__main__": + + description = """ + Train neural net on Go positions from npz files of batches from selfplay. + """ + + parser = argparse.ArgumentParser(description=description,add_help=False) + required_args = parser.add_argument_group('required arguments') + optional_args = parser.add_argument_group('optional arguments') + optional_args.add_argument( + '-h', + '--help', + action='help', + default=argparse.SUPPRESS, + help='show this help message and exit' + ) + + required_args.add_argument('-traindir', help='Dir to write to for recording training results', required=True) + required_args.add_argument('-datadir', help='Directory with a train and val subdir of npz data, output by shuffle.py', required=True) + optional_args.add_argument('-exportdir', help='Directory to export models periodically', required=False) + optional_args.add_argument('-exportprefix', help='Prefix to append to names of models', required=False) + optional_args.add_argument('-initial-checkpoint', help='If no training checkpoint exists, initialize from this checkpoint', required=False) + + required_args.add_argument('-pos-len', help='Spatial edge length of expected training data, e.g. 19 for 19x19 Go', type=int, required=True) + required_args.add_argument('-batch-size', help='Per-GPU batch size to use for training', type=int, required=True) + optional_args.add_argument('-samples-per-epoch', help='Number of data samples to consider as one epoch', type=int, required=False) + optional_args.add_argument('-model-kind', help='String name for what model config to use', required=False) + optional_args.add_argument('-lr-scale', help='LR multiplier on the hardcoded schedule', type=float, required=False) + optional_args.add_argument('-lr-scale-auto', help='LR auto scaling', required=False, action='store_true') + optional_args.add_argument('-gnorm-clip-scale', help='Multiplier on gradient clipping threshold', type=float, required=False) + optional_args.add_argument('-sub-epochs', help='Reload training data up to this many times per epoch', type=int, default=1, required=False) + optional_args.add_argument('-swa-period-samples', help='How frequently to average an SWA sample, in samples', type=float, required=False) + optional_args.add_argument('-swa-scale', help='Number of samples to average in expectation together for SWA', type=float, required=False) + optional_args.add_argument('-lookahead-k', help='Use lookahead optimizer', type=int, default=6, required=False) + optional_args.add_argument('-lookahead-alpha', help='Use lookahead optimizer', type=float, default=0.5, required=False) + optional_args.add_argument('-lookahead-print', help='Only print on lookahead syncs', required=False, action='store_true') + + optional_args.add_argument('-multi-gpus', help='Use multiple gpus, comma-separated device ids', required=False) + optional_args.add_argument('-use-fp16', help='Use fp16 training', required=False, action='store_true') + + optional_args.add_argument('-epochs-per-export', help='Export model once every this many epochs', type=int, required=False) + optional_args.add_argument('-export-prob', help='Export model with this probablity', type=float, required=False) + optional_args.add_argument('-max-epochs-this-instance', help='Terminate training after this many more epochs', type=int, required=False) + optional_args.add_argument('-max-training-samples', help='Terminate training after about this many training steps in samples', type=int, required=False) + optional_args.add_argument('-sleep-seconds-per-epoch', help='Sleep this long between epochs', type=int, required=False) + optional_args.add_argument('-max-train-bucket-per-new-data', help='When data added, add this many train rows per data row to bucket', type=float, required=False) + optional_args.add_argument('-max-train-bucket-size', help='Approx total number of train rows allowed if data stops', type=float, required=False) + optional_args.add_argument('-max-train-steps-since-last-reload', help='Approx total of training allowed if shuffling stops', type=float, required=False) + optional_args.add_argument('-stop-when-train-bucket-limited', help='Terminate due to train bucket rather than waiting for more', required=False, action='store_true') + optional_args.add_argument('-max-val-samples', help='Approx max of validation samples per epoch', type=int, required=False) + optional_args.add_argument('-randomize-val', help='Randomize order of validation files', required=False, action='store_true') + optional_args.add_argument('-no-export', help='Do not export models', required=False, action='store_true') + optional_args.add_argument('-no-repeat-files', help='Track what shuffled data was used and do not repeat, even when killed and resumed', required=False, action='store_true') + optional_args.add_argument('-quit-if-no-data', help='If no data, quit instead of waiting for data', required=False, action='store_true') + + optional_args.add_argument('-gnorm-stats-debug', required=False, action='store_true') + + optional_args.add_argument('-brenorm-avg-momentum', type=float, help='Set brenorm running avg rate to this value', required=False) + optional_args.add_argument('-brenorm-target-rmax', type=float, help='Gradually adjust brenorm rmax to this value', required=False) + optional_args.add_argument('-brenorm-target-dmax', type=float, help='Gradually adjust brenorm dmax to this value', required=False) + optional_args.add_argument('-brenorm-adjustment-scale', type=float, help='How many samples to adjust brenorm params all but 1/e of the way to target', required=False) + + optional_args.add_argument('-soft-policy-weight-scale', type=float, default=8.0, help='Soft policy loss coeff', required=False) + optional_args.add_argument('-disable-optimistic-policy', help='Disable optimistic policy', required=False, action='store_true') + optional_args.add_argument('-meta-kata-only-soft-policy', help='Mask soft policy on non-kata rows using sgfmeta', required=False, action='store_true') + optional_args.add_argument('-value-loss-scale', type=float, default=0.6, help='Additional value loss coeff', required=False) + optional_args.add_argument('-td-value-loss-scales', type=str, default="0.6,0.6,0.6", help='Additional td value loss coeffs, 3 comma separated values', required=False) + optional_args.add_argument('-seki-loss-scale', type=float, default=1.0, help='Additional seki loss coeff', required=False) + optional_args.add_argument('-variance-time-loss-scale', type=float, default=1.0, help='Additional variance time loss coeff', required=False) + + optional_args.add_argument('-main-loss-scale', type=float, help='Loss factor scale for main head', required=False) + optional_args.add_argument('-intermediate-loss-scale', type=float, help='Loss factor scale for intermediate head', required=False) + + args = vars(parser.parse_args()) + + +def get_longterm_checkpoints_dir(traindir): + return os.path.join(traindir,"longterm_checkpoints") + +def make_dirs(args): + traindir = args["traindir"] + exportdir = args["exportdir"] + + if not os.path.exists(traindir): + os.makedirs(traindir) + if exportdir is not None and not os.path.exists(exportdir): + os.makedirs(exportdir) + + longterm_checkpoints_dir = get_longterm_checkpoints_dir(traindir) + if not os.path.exists(longterm_checkpoints_dir): + os.makedirs(longterm_checkpoints_dir) + +def multiprocessing_setup(rank: int, world_size: int): + os.environ['MASTER_ADDR'] = 'localhost' + os.environ['MASTER_PORT'] = '23456' + logging.info("Running torch.distributed.init_process_group") + torch.distributed.init_process_group("nccl", rank=rank, world_size=world_size) + logging.info(f"Returned from torch.distributed.init_process_group, my rank = {rank}, world_size={world_size}") + +def multiprocessing_cleanup(): + torch.distributed.destroy_process_group() + + +def main(rank: int, world_size: int, args, multi_gpu_device_ids, readpipes, writepipes, barrier): + traindir = args["traindir"] + datadir = args["datadir"] + exportdir = args["exportdir"] + exportprefix = args["exportprefix"] + initial_checkpoint = args["initial_checkpoint"] + + pos_len = args["pos_len"] + batch_size = args["batch_size"] + samples_per_epoch = args["samples_per_epoch"] + model_kind = args["model_kind"] + lr_scale = args["lr_scale"] + lr_scale_auto = args["lr_scale_auto"] + gnorm_clip_scale = args["gnorm_clip_scale"] + sub_epochs = args["sub_epochs"] + swa_period_samples = args["swa_period_samples"] + swa_scale = args["swa_scale"] + lookahead_k = args["lookahead_k"] + lookahead_alpha = args["lookahead_alpha"] + lookahead_print = args["lookahead_print"] + + use_fp16 = args["use_fp16"] + + epochs_per_export = args["epochs_per_export"] + export_prob = args["export_prob"] + max_epochs_this_instance = args["max_epochs_this_instance"] + max_training_samples = args["max_training_samples"] + sleep_seconds_per_epoch = args["sleep_seconds_per_epoch"] + max_train_bucket_per_new_data = args["max_train_bucket_per_new_data"] + max_train_bucket_size = args["max_train_bucket_size"] + max_train_steps_since_last_reload = args["max_train_steps_since_last_reload"] + stop_when_train_bucket_limited = args["stop_when_train_bucket_limited"] + max_val_samples = args["max_val_samples"] + randomize_val = args["randomize_val"] + no_export = args["no_export"] + no_repeat_files = args["no_repeat_files"] + quit_if_no_data = args["quit_if_no_data"] + + gnorm_stats_debug = args["gnorm_stats_debug"] + + brenorm_target_rmax = args["brenorm_target_rmax"] + brenorm_target_dmax = args["brenorm_target_dmax"] + brenorm_avg_momentum = args["brenorm_avg_momentum"] + brenorm_adjustment_scale = args["brenorm_adjustment_scale"] + + soft_policy_weight_scale = args["soft_policy_weight_scale"] + disable_optimistic_policy = args["disable_optimistic_policy"] + meta_kata_only_soft_policy = args["meta_kata_only_soft_policy"] + value_loss_scale = args["value_loss_scale"] + td_value_loss_scales = [float(x) for x in args["td_value_loss_scales"].split(",")] + seki_loss_scale = args["seki_loss_scale"] + variance_time_loss_scale = args["variance_time_loss_scale"] + + main_loss_scale = args["main_loss_scale"] + intermediate_loss_scale = args["intermediate_loss_scale"] + + if lr_scale is None: + lr_scale = 1.0 + if lr_scale_auto: + assert lr_scale == 1.0, "Cannot specify both lr_scale and lr_scale_auto" + + if samples_per_epoch is None: + samples_per_epoch = 1000000 + if max_train_bucket_size is None: + max_train_bucket_size = 1.0e30 + if epochs_per_export is None: + epochs_per_export = 1 + if swa_period_samples is None: + swa_period_samples = max(1, samples_per_epoch // 2) + if swa_scale is None: + swa_scale = 8 + + assert lookahead_alpha > 0.0 and lookahead_alpha <= 1.0 + if lookahead_alpha >= 1.0: # 1.0 means to disable lookahead optimizer + lookahead_alpha = None + lookahead_k = None + + longterm_checkpoints_dir = get_longterm_checkpoints_dir(traindir) + + assert (swa_period_samples is None) == (swa_scale is None) + assert (lookahead_k is None) == (lookahead_alpha is None) + + # SET UP LOGGING ------------------------------------------------------------- + + logging.root.handlers = [] + if rank == 0: + logging.basicConfig( + level=logging.INFO, + format="%(message)s", + handlers=[ + logging.FileHandler(os.path.join(traindir,f"train{rank}.log"), mode="a"), + logging.StreamHandler() + ], + ) + else: + logging.basicConfig( + level=logging.INFO, + format="%(message)s", + handlers=[ + logging.FileHandler(os.path.join(traindir,f"train{rank}.log"), mode="a"), + ], + ) + np.set_printoptions(linewidth=150) + + logging.info(str(sys.argv)) + + # FIGURE OUT MULTIGPU ------------------------------------------------------------ + if world_size > 1: + multiprocessing_setup(rank, world_size) + atexit.register(multiprocessing_cleanup) + assert torch.cuda.is_available() + + if True or torch.cuda.is_available(): + my_gpu_id = multi_gpu_device_ids[rank] + torch.cuda.set_device(my_gpu_id) + logging.info("Using GPU device: " + torch.cuda.get_device_name()) + device = torch.device("cuda", my_gpu_id) + else: + logging.warning("WARNING: No GPU, using CPU") + device = torch.device("cpu") + + seed = int.from_bytes(os.urandom(7), sys.byteorder) + logging.info(f"Seeding torch with {seed}") + torch.manual_seed(seed) + + # LOAD MODEL --------------------------------------------------------------------- + + def lr_scale_auto_factor(train_state): + if not lr_scale_auto: + return 1.0 + + if train_state["global_step_samples"] < 200_000_000: + return 8.00 + if train_state["global_step_samples"] < 400_000_000: + return 4.00 + if train_state["global_step_samples"] < 500_000_000: + return 2.00 + if train_state["global_step_samples"] < 550_000_000: + return 1.00 + if train_state["global_step_samples"] < 600_000_000: + return 0.50 + if train_state["global_step_samples"] < 650_000_000: + return 0.25 + return 0.25 + + def get_checkpoint_path(): + return os.path.join(traindir,"checkpoint.ckpt") + def get_checkpoint_prev_path(i): + return os.path.join(traindir,f"checkpoint_prev{i}.ckpt") + + NUM_SHORTTERM_CHECKPOINTS_TO_KEEP = 4 + def save(ddp_model, swa_model, optimizer, metrics_obj, running_metrics, train_state, last_val_metrics, path=None): + if gnorm_stats_debug: + logging.warning("Skipping save since debugging gnorm stats") + return + if rank == 0: + state_dict = {} + state_dict["model"] = ddp_model.state_dict() + state_dict["optimizer"] = optimizer.state_dict() + state_dict["metrics"] = metrics_obj.state_dict() + state_dict["running_metrics"] = running_metrics + state_dict["train_state"] = train_state + state_dict["last_val_metrics"] = last_val_metrics + state_dict["config"] = model_config + + if swa_model is not None: + state_dict["swa_model"] = swa_model.state_dict() + + if path is not None: + logging.info("Saving checkpoint: " + path) + torch.save(state_dict, path + ".tmp") + time.sleep(1) + os.replace(path + ".tmp", path) + else: + logging.info("Saving checkpoint: " + get_checkpoint_path()) + for i in reversed(range(NUM_SHORTTERM_CHECKPOINTS_TO_KEEP-1)): + if os.path.exists(get_checkpoint_prev_path(i)): + os.replace(get_checkpoint_prev_path(i), get_checkpoint_prev_path(i+1)) + if os.path.exists(get_checkpoint_path()): + shutil.copy(get_checkpoint_path(), get_checkpoint_prev_path(0)) + torch.save(state_dict, get_checkpoint_path() + ".tmp") + os.replace(get_checkpoint_path() + ".tmp", get_checkpoint_path()) + + def get_weight_decay(raw_model, lr_scale, warmup_scale, train_state, running_metrics, group_name): + lr_scale_with_auto = lr_scale * lr_scale_auto_factor(train_state) + if raw_model.get_norm_kind() == "fixup" or raw_model.get_norm_kind() == "fixscale": + if group_name == "normal" or group_name == "normal_gamma" or group_name == "output": + return 0.000001 * world_size * batch_size / 256.0 + elif group_name == "noreg": + return 0.00000001 * world_size * batch_size / 256.0 + elif group_name == "output_noreg": + return 0.00000001 * world_size * batch_size / 256.0 + else: + assert False + elif ( + raw_model.get_norm_kind() == "bnorm" or + raw_model.get_norm_kind() == "brenorm" or + raw_model.get_norm_kind() == "fixbrenorm" or + raw_model.get_norm_kind() == "fixscaleonenorm" + ): + if group_name == "normal" or group_name == "normal_gamma": + adaptive_scale = 1.0 + if "sums" in running_metrics and "norm_normal_batch" in running_metrics["sums"]: + norm_normal_batch = running_metrics["sums"]["norm_normal_batch"] / running_metrics["weights"]["norm_normal_batch"] + baseline = train_state["modelnorm_normal_baseline"] + ratio = norm_normal_batch / (baseline + 1e-30) + # Adaptive weight decay keeping model norm around the baseline level so that batchnorm effective lr is held constant + # throughout training, covering a range of 16x from bottom to top. + adaptive_scale = math.pow(2.0, 2.0 * math.tanh(math.log(ratio+1e-30) * 1.5)) + + # Batch norm gammas can be regularized a bit less, doing them just as much empirically seemed to be a bit more unstable + gamma_scale = 0.125 if group_name == "normal_gamma" else 1.0 + + # The theoretical scaling for keeping us confined to a surface of equal model norm should go proportionally with lr_scale. + # because the strength of drift away from that surface goes as lr^2 and weight decay itself is scaled by lr, so we need + # one more factor of lr to make weight decay strength equal drift strength. + # However, at low lr it tends to be the case that gradient norm increases slightly + # while at high lr it tends to be the case that gradient norm decreases, which means drift strength scales a bit slower + # than expected. + # So we scale sublinearly with lr_scale so as to slightly preadjust to this effect. + # Adaptive scale should then help keep us there thereafter. + return 0.00125 * world_size * batch_size / 256.0 * math.pow(lr_scale_with_auto * warmup_scale,0.75) * adaptive_scale * gamma_scale + elif group_name == "output": + return 0.000001 * world_size * batch_size / 256.0 + elif group_name == "noreg": + return 0.000001 * world_size * batch_size / 256.0 * math.pow(lr_scale_with_auto * warmup_scale,0.75) + elif group_name == "output_noreg": + return 0.00000001 * world_size * batch_size / 256.0 + else: + assert False + else: + assert False + + def get_param_groups(raw_model,train_state,running_metrics): + reg_dict : Dict[str,List] = {} + raw_model.add_reg_dict(reg_dict) + param_groups = [] + param_groups.append({ + "params": reg_dict["normal"], + "weight_decay": get_weight_decay(raw_model, lr_scale, warmup_scale=1.0, train_state=train_state, running_metrics=running_metrics, group_name="normal"), + "group_name": "normal", + }) + if len(reg_dict["normal_gamma"]) > 0: + param_groups.append({ + "params": reg_dict["normal_gamma"], + "weight_decay": get_weight_decay(raw_model, lr_scale, warmup_scale=1.0, train_state=train_state, running_metrics=running_metrics, group_name="normal_gamma"), + "group_name": "normal_gamma", + }) + param_groups.append({ + "params": reg_dict["output"], + "weight_decay": get_weight_decay(raw_model, lr_scale, warmup_scale=1.0, train_state=train_state, running_metrics=running_metrics, group_name="output"), + "group_name": "output", + }) + param_groups.append({ + "params": reg_dict["noreg"], + "weight_decay": get_weight_decay(raw_model, lr_scale, warmup_scale=1.0, train_state=train_state, running_metrics=running_metrics, group_name="noreg"), + "group_name": "noreg", + }) + param_groups.append({ + "params": reg_dict["output_noreg"], + "weight_decay": get_weight_decay(raw_model, lr_scale, warmup_scale=1.0, train_state=train_state, running_metrics=running_metrics, group_name="output_noreg"), + "group_name": "output_noreg", + }) + num_params = len(list(raw_model.parameters())) + num_reg_dict_params = len(reg_dict["normal"]) + len(reg_dict["normal_gamma"]) + len(reg_dict["output"]) + len(reg_dict["noreg"]) + len(reg_dict["output_noreg"]) + assert num_params == num_reg_dict_params, "Reg dict does not have entries for all params in model" + return param_groups + + def load(): + if not os.path.exists(get_checkpoint_path()): + logging.info("No preexisting checkpoint found at: " + get_checkpoint_path()) + for i in range(NUM_SHORTTERM_CHECKPOINTS_TO_KEEP): + if os.path.exists(get_checkpoint_prev_path(i)): + raise Exception(f"No preexisting checkpoint found, but {get_checkpoint_prev_path(i)} exists, something is wrong with the training dir") + + if initial_checkpoint is not None: + if os.path.exists(initial_checkpoint): + logging.info("Using initial checkpoint: {initial_checkpoint}") + path_to_load_from = initial_checkpoint + else: + raise Exception("No preexisting checkpoint found, initial checkpoint provided is invalid: {initial_checkpoint}") + else: + path_to_load_from = None + else: + path_to_load_from = get_checkpoint_path() + + if path_to_load_from is None: + logging.info("Initializing new model!") + assert model_kind is not None, "Model kind is none or unspecified but the model is being created fresh" + model_config = modelconfigs.config_of_name[model_kind] + logging.info(str(model_config)) + raw_model = Model(model_config,pos_len) + raw_model.initialize() + + raw_model.to(device) + if world_size > 1: + ddp_model = torch.nn.parallel.DistributedDataParallel(raw_model, device_ids=[device]) + else: + ddp_model = raw_model + + swa_model = None + if rank == 0 and swa_scale is not None: + new_factor = 1.0 / swa_scale + ema_avg = lambda avg_param, cur_param, num_averaged: avg_param + new_factor * (cur_param - avg_param) + swa_model = AveragedModel(raw_model, avg_fn=ema_avg) + + metrics_obj = Metrics(batch_size,world_size,raw_model) + running_metrics = {} + train_state = {} + last_val_metrics = {} + + train_state["global_step_samples"] = 0 + + with torch.no_grad(): + (modelnorm_normal, modelnorm_normal_gamma, modelnorm_output, modelnorm_noreg, modelnorm_output_noreg) = Metrics.get_model_norms(raw_model) + modelnorm_normal_baseline = modelnorm_normal.detach().cpu().item() + train_state["modelnorm_normal_baseline"] = modelnorm_normal_baseline + logging.info(f"Model norm normal baseline computed: {modelnorm_normal_baseline}") + + optimizer = torch.optim.SGD(get_param_groups(raw_model,train_state,running_metrics), lr=1.0, momentum=0.9) + + return (model_config, ddp_model, raw_model, swa_model, optimizer, metrics_obj, running_metrics, train_state, last_val_metrics) + else: + state_dict = torch.load(path_to_load_from, map_location=device) + model_config = state_dict["config"] if "config" in state_dict else modelconfigs.config_of_name[model_kind] + logging.info(str(model_config)) + raw_model = Model(model_config,pos_len) + raw_model.initialize() + + train_state = {} + if "train_state" in state_dict: + train_state = state_dict["train_state"] + else: + logging.info("WARNING: Train state not found in state dict, using fresh train state") + + # Do this before loading the state dict, while the model is initialized to fresh values, to get a good baseline + if "modelnorm_normal_baseline" not in train_state: + logging.info("Computing modelnorm_normal_baseline since not in train state") + with torch.no_grad(): + (modelnorm_normal, modelnorm_normal_gamma, modelnorm_output, modelnorm_noreg, modelnorm_output_noreg) = Metrics.get_model_norms(raw_model) + modelnorm_normal_baseline = modelnorm_normal.detach().cpu().item() + train_state["modelnorm_normal_baseline"] = modelnorm_normal_baseline + logging.info(f"Model norm normal baseline computed: {modelnorm_normal_baseline}") + + # Strip off any "module." from when the model was saved with DDP or other things + model_state_dict = load_model.load_model_state_dict(state_dict) + raw_model.load_state_dict(model_state_dict) + + raw_model.to(device) + if world_size > 1: + ddp_model = torch.nn.parallel.DistributedDataParallel(raw_model, device_ids=[device]) + else: + ddp_model = raw_model + + swa_model = None + if rank == 0 and swa_scale is not None: + new_factor = 1.0 / swa_scale + ema_avg = lambda avg_param, cur_param, num_averaged: avg_param + new_factor * (cur_param - avg_param) + swa_model = AveragedModel(raw_model, avg_fn=ema_avg) + swa_model_state_dict = load_model.load_swa_model_state_dict(state_dict) + if swa_model_state_dict is not None: + swa_model.load_state_dict(swa_model_state_dict) + + metrics_obj = Metrics(batch_size,world_size,raw_model) + if "metrics" in state_dict: + metrics_obj.load_state_dict(state_dict["metrics"]) + else: + logging.info("WARNING: Metrics not found in state dict, using fresh metrics") + + running_metrics = {} + if "running_metrics" in state_dict: + running_metrics = state_dict["running_metrics"] + else: + logging.info("WARNING: Running metrics not found in state dict, using fresh running metrics") + + last_val_metrics = {} + if "last_val_metrics" in state_dict: + last_val_metrics = state_dict["last_val_metrics"] + else: + logging.info("WARNING: Running metrics not found in state dict, using fresh last val metrics") + + optimizer = torch.optim.SGD(get_param_groups(raw_model,train_state,running_metrics), lr=1.0, momentum=0.9) + if "optimizer" in state_dict: + optimizer.load_state_dict(state_dict["optimizer"]) + else: + logging.info("WARNING: Optimizer not found in state dict, using fresh optimizer") + + return (model_config, ddp_model, raw_model, swa_model, optimizer, metrics_obj, running_metrics, train_state, last_val_metrics) + + (model_config, ddp_model, raw_model, swa_model, optimizer, metrics_obj, running_metrics, train_state, last_val_metrics) = load() + + + if "global_step_samples" not in train_state: + train_state["global_step_samples"] = 0 + if max_train_bucket_per_new_data is not None and "train_bucket_level" not in train_state: + train_state["train_bucket_level"] = samples_per_epoch + if "train_steps_since_last_reload" not in train_state: + train_state["train_steps_since_last_reload"] = 0 + if "export_cycle_counter" not in train_state: + train_state["export_cycle_counter"] = 0 + if "total_num_data_rows" not in train_state: + train_state["total_num_data_rows"] = 0 + if "old_train_data_dirs" not in train_state: + train_state["old_train_data_dirs"] = [] + if "data_files_used" not in train_state: + train_state["data_files_used"] = set() + if "swa_sample_accum" not in train_state: + train_state["swa_sample_accum"] = 0.0 + + + if intermediate_loss_scale is not None: + assert raw_model.get_has_intermediate_head(), "Model must have intermediate head to use intermediate loss" + + # If the user specified an intermediate head but no loss scale, pick something reasonable by default + if raw_model.get_has_intermediate_head(): + if intermediate_loss_scale is None and main_loss_scale is None: + if model_config["trunk_normless"]: + # fson-bnh default + assert model_config["intermediate_head_blocks"] == len(model_config["block_kind"]), "If these are unequal, don't know what you intend, please specify intermediate_loss_scale" + intermediate_loss_scale = 0.8 + main_loss_scale = 0.2 + else: + # Intermediate head in the middle of the trunk + intermediate_loss_scale = 0.5 + main_loss_scale = 0.5 + elif intermediate_loss_scale is None: + assert False, "Please specify both of main_loss_scale and intermediate_loss_scale or neither when using an architecture with an intermediate head." + + logging.info(f"swa_period_samples {swa_period_samples}") + logging.info(f"swa_scale {swa_scale}") + logging.info(f"lookahead_alpha {lookahead_alpha}") + logging.info(f"lookahead_k {lookahead_k}") + logging.info(f"soft_policy_weight_scale {soft_policy_weight_scale}") + logging.info(f"disable_optimistic_policy {disable_optimistic_policy}") + logging.info(f"meta_kata_only_soft_policy {meta_kata_only_soft_policy}") + logging.info(f"value_loss_scale {value_loss_scale}") + logging.info(f"td_value_loss_scales {td_value_loss_scales}") + logging.info(f"seki_loss_scale {seki_loss_scale}") + logging.info(f"variance_time_loss_scale {variance_time_loss_scale}") + logging.info(f"main_loss_scale {main_loss_scale}") + logging.info(f"intermediate_loss_scale {intermediate_loss_scale}") + + # Print all model parameters just to get a summary + total_num_params = 0 + total_trainable_params = 0 + logging.info("Parameters in model:") + for name, param in raw_model.named_parameters(): + product = 1 + for dim in param.shape: + product *= int(dim) + if param.requires_grad: + total_trainable_params += product + total_num_params += product + logging.info(f"{name}, {list(param.shape)}, {product} params") + logging.info(f"Total num params: {total_num_params}") + logging.info(f"Total trainable params: {total_trainable_params}") + + lookahead_cache = {} + if lookahead_k is not None: + for param_group in optimizer.param_groups: + for param in param_group["params"]: + lookahead_cache[param] = torch.zeros_like(param.data) + lookahead_cache[param] = lookahead_cache[param].copy_(param.data) + logging.info(f"Using lookahead optimizer {lookahead_alpha} {lookahead_k}") + + # EPOCHS AND LR --------------------------------------------------------------------- + + def update_and_return_lr_and_wd(): + per_sample_lr = 0.00003 * lr_scale * lr_scale_auto_factor(train_state) + + # Warmup for initial training + warmup_scale = 1.0 + if model_config["norm_kind"] == "fixup" or model_config["norm_kind"] == "fixscale" or model_config["norm_kind"] == "fixscaleonenorm": + if train_state["global_step_samples"] < 1000000: + warmup_scale = 1.0 / 5.0 + elif train_state["global_step_samples"] < 2000000: + warmup_scale = 1.0 / 3.0 + elif train_state["global_step_samples"] < 4000000: + warmup_scale = 1.0 / 2.0 + elif train_state["global_step_samples"] < 6000000: + warmup_scale = 1.0 / 1.4 + elif model_config["norm_kind"] == "bnorm" or model_config["norm_kind"] == "brenorm" or model_config["norm_kind"] == "fixbrenorm": + if train_state["global_step_samples"] < 250000: + warmup_scale = 1.0 / 20.0 + elif train_state["global_step_samples"] < 500000: + warmup_scale = 1.0 / 14.0 + elif train_state["global_step_samples"] < 750000: + warmup_scale = 1.0 / 10.0 + elif train_state["global_step_samples"] < 1000000: + warmup_scale = 1.0 / 7.0 + elif train_state["global_step_samples"] < 1250000: + warmup_scale = 1.0 / 5.0 + elif train_state["global_step_samples"] < 1500000: + warmup_scale = 1.0 / 3.0 + elif train_state["global_step_samples"] < 1750000: + warmup_scale = 1.0 / 2.0 + elif train_state["global_step_samples"] < 2000000: + warmup_scale = 1.0 / 1.4 + else: + warmup_scale = 1.0 / 1.0 + else: + assert False + + normal_weight_decay = 0.0 + + for param_group in optimizer.param_groups: + group_name = param_group["group_name"] + if group_name == "normal": + group_scale = 1.0 + elif group_name == "normal_gamma": + group_scale = 1.0 + elif group_name == "output": + group_scale = 0.5 + elif group_name == "noreg": + group_scale = 1.0 + elif group_name == "output_noreg": + group_scale = 0.5 + else: + assert False + + changed = False + + # For lookahead optimizer, use weight decay appropriate for lr scale, + # but tell optimizer to take larger steps so as to maintain the same + # effective learning rate after lookahead averaging. + if lookahead_alpha is not None: + new_lr_this_group = per_sample_lr * warmup_scale * group_scale / lookahead_alpha + else: + new_lr_this_group = per_sample_lr * warmup_scale * group_scale + + if param_group["lr"] != new_lr_this_group: + param_group["lr"] = new_lr_this_group + changed = True + + new_weight_decay_this_group = get_weight_decay( + raw_model, + lr_scale, + warmup_scale=warmup_scale, + train_state=train_state, + running_metrics=running_metrics, + group_name=group_name, + ) + if param_group["weight_decay"] != new_weight_decay_this_group: + param_group["weight_decay"] = new_weight_decay_this_group + changed = True + + if group_name == "normal": + normal_weight_decay = param_group["weight_decay"] + + if changed: + logging.info(f"Param group {param_group['group_name']} lr {param_group['lr']} weight_decay {param_group['weight_decay']}") + + return per_sample_lr * warmup_scale, normal_weight_decay + + last_brenorm_update_samples_this_instance = train_state["global_step_samples"] + def maybe_update_brenorm_params(): + nonlocal last_brenorm_update_samples_this_instance + + if model_config["norm_kind"] == "brenorm" or model_config["norm_kind"] == "fixbrenorm": + if "brenorm_rmax" not in train_state: + train_state["brenorm_rmax"] = 1.0 + if "brenorm_dmax" not in train_state: + train_state["brenorm_dmax"] = 0.0 + + num_samples_elapsed = train_state["global_step_samples"] - last_brenorm_update_samples_this_instance + factor = math.exp(-num_samples_elapsed / brenorm_adjustment_scale) + train_state["brenorm_rmax"] = train_state["brenorm_rmax"] + (1.0 - factor) * (brenorm_target_rmax - train_state["brenorm_rmax"]) + train_state["brenorm_dmax"] = train_state["brenorm_dmax"] + (1.0 - factor) * (brenorm_target_dmax - train_state["brenorm_dmax"]) + + raw_model.set_brenorm_params(brenorm_avg_momentum, train_state["brenorm_rmax"], train_state["brenorm_dmax"]) + last_brenorm_update_samples_this_instance = train_state["global_step_samples"] + + # DATA RELOADING GENERATOR ------------------------------------------------------------ + + # Some globals + last_curdatadir = None + trainfilegenerator = None + vdatadir = None + + def maybe_reload_training_data(): + nonlocal last_curdatadir + nonlocal trainfilegenerator + nonlocal vdatadir + + assert rank == 0, "Helper ddp training processes should not call maybe_reload_training_data" + + while True: + curdatadir = os.path.realpath(datadir) + + # Different directory - new shuffle + if curdatadir != last_curdatadir: + if not os.path.exists(curdatadir): + if quit_if_no_data: + logging.info("Shuffled data path does not exist, there seems to be no data or not enough data yet, qutting: %s" % curdatadir) + sys.exit(0) + logging.info("Shuffled data path does not exist, there seems to be no shuffled data yet, waiting and trying again later: %s" % curdatadir) + time.sleep(30) + continue + + trainjsonpath = os.path.join(curdatadir,"train.json") + if not os.path.exists(trainjsonpath): + if quit_if_no_data: + logging.info("Shuffled data train.json file does not exist, there seems to be no data or not enough data yet, qutting: %s" % trainjsonpath) + sys.exit(0) + logging.info("Shuffled data train.json file does not exist, there seems to be no shuffled data yet, waiting and trying again later: %s" % trainjsonpath) + time.sleep(30) + continue + + logging.info("Updated training data: " + curdatadir) + last_curdatadir = curdatadir + + with open(trainjsonpath) as f: + datainfo = json.load(f) + train_state["total_num_data_rows"] = datainfo["range"][1] + + # Fill the buckets + if max_train_bucket_per_new_data is not None: + if "train_bucket_level_at_row" not in train_state: + train_state["train_bucket_level_at_row"] = train_state["total_num_data_rows"] + if train_state["total_num_data_rows"] > train_state["train_bucket_level_at_row"]: + new_row_count = train_state["total_num_data_rows"] - train_state["train_bucket_level_at_row"] + logging.info("Advancing trainbucket row %.0f to %.0f, %.0f new rows" % ( + train_state["train_bucket_level_at_row"], train_state["total_num_data_rows"], new_row_count + )) + train_state["train_bucket_level_at_row"] = train_state["total_num_data_rows"] + logging.info("Fill per data %.3f, Max bucket size %.0f" % (max_train_bucket_per_new_data, max_train_bucket_size)) + logging.info("Old rows in bucket: %.0f" % train_state["train_bucket_level"]) + train_state["train_bucket_level"] += new_row_count * max_train_bucket_per_new_data + cap = max(max_train_bucket_size, samples_per_epoch) + if train_state["train_bucket_level"] > cap: + train_state["train_bucket_level"] = cap + logging.info("New rows in bucket: %.0f" % train_state["train_bucket_level"]) + if train_state["total_num_data_rows"] < train_state["train_bucket_level_at_row"]: + # Bucket went backward! This must be a network imported from a different run, reset the train bucket level + logging.warning("Train bucket last filled at %d rows but now there are only %d rows!" % ( + train_state["train_bucket_level_at_row"], train_state["total_num_data_rows"] + )) + logging.warning("Data was deleted or this network was transplanted into a new run, resetting the train bucket fill rows") + train_state["train_bucket_level_at_row"] = train_state["total_num_data_rows"] + + logging.info("Train steps since last reload: %.0f -> 0" % train_state["train_steps_since_last_reload"]) + train_state["train_steps_since_last_reload"] = 0 + + # Load training data files + tdatadir = os.path.join(curdatadir,"train") + train_files = [os.path.join(tdatadir,fname) for fname in os.listdir(tdatadir) if fname.endswith(".npz")] + epoch0_train_files = [path for path in train_files if path not in train_state["data_files_used"]] + if no_repeat_files: + logging.info(f"Dropping {len(train_files)-len(epoch0_train_files)}/{len(train_files)} files in: {tdatadir} as already used") + else: + logging.info(f"Skipping {len(train_files)-len(epoch0_train_files)}/{len(train_files)} files in: {tdatadir} as already used first pass") + + if len(train_files) <= 0 or (no_repeat_files and len(epoch0_train_files) <= 0): + if quit_if_no_data: + logging.info(f"No new training files found in: {tdatadir}, quitting") + sys.exit(0) + logging.info(f"No new training files found in: {tdatadir}, waiting 30s and trying again") + time.sleep(30) + continue + + # Update history of what training data we used + if tdatadir not in train_state["old_train_data_dirs"]: + train_state["old_train_data_dirs"].append(tdatadir) + # Clear out tracking of sufficiently old files + while len(train_state["old_train_data_dirs"]) > 20: + old_dir = train_state["old_train_data_dirs"][0] + train_state["old_train_data_dirs"] = train_state["old_train_data_dirs"][1:] + for filename in list(train_state["data_files_used"]): + if filename.startswith(old_dir): + train_state["data_files_used"].remove(filename) + + def train_files_gen(): + train_files_shuffled = epoch0_train_files.copy() + while True: + random.shuffle(train_files_shuffled) + for filename in train_files_shuffled: + logging.info("Yielding training file for dataset: " + filename) + train_state["data_files_used"].add(filename) + yield filename + if no_repeat_files: + break + else: + train_files_shuffled = train_files.copy() + train_state["data_files_used"] = set() + + trainfilegenerator = train_files_gen() + vdatadir = os.path.join(curdatadir,"val") + + # Same directory as before, no new shuffle + else: + if max_train_steps_since_last_reload is not None: + if train_state["train_steps_since_last_reload"] + 0.99 * samples_per_epoch/sub_epochs > max_train_steps_since_last_reload: + logging.info( + "Too many train steps since last reload, waiting 5m and retrying (current %f)" % + train_state["train_steps_since_last_reload"] + ) + time.sleep(300) + continue + + break + + # Load all the files we should train on during a subepoch + def get_files_for_subepoch(): + nonlocal trainfilegenerator + + assert rank == 0, "Helper ddp training processes should not call get_files_for_subepoch" + + num_batches_per_epoch = int(round(samples_per_epoch / batch_size)) + num_batches_per_subepoch = num_batches_per_epoch / sub_epochs + + # Pick enough files to get the number of batches we want + train_files_to_use = [] + batches_to_use_so_far = 0 + found_enough = False + for filename in trainfilegenerator: + jsonfilename = os.path.splitext(filename)[0] + ".json" + with open(jsonfilename) as f: + trainfileinfo = json.load(f) + + num_batches_this_file = trainfileinfo["num_rows"] // batch_size + if num_batches_this_file <= 0: + continue + + if batches_to_use_so_far + num_batches_this_file > num_batches_per_subepoch: + # If we're going over the desired amount, randomly skip the file with probability equal to the + # proportion of batches over - this makes it so that in expectation, we have the desired number of batches + if batches_to_use_so_far > 0 and random.random() >= (batches_to_use_so_far + num_batches_this_file - num_batches_per_subepoch) / num_batches_this_file: + found_enough = True + break + + train_files_to_use.append(filename) + batches_to_use_so_far += num_batches_this_file + + #Sanity check - load a max of 100000 files. + if batches_to_use_so_far >= num_batches_per_subepoch or len(train_files_to_use) > 100000: + found_enough = True + break + + if found_enough: + return train_files_to_use + return None + + # METRICS ----------------------------------------------------------------------------------- + def detensorify_metrics(metrics): + ret = {} + for key in metrics: + if isinstance(metrics[key], torch.Tensor): + ret[key] = metrics[key].detach().cpu().item() + else: + ret[key] = metrics[key] + return ret + + if rank == 0: + train_metrics_out = open(os.path.join(traindir,"metrics_train.json"),"a") + val_metrics_out = open(os.path.join(traindir,"metrics_val.json"),"a") + else: + train_metrics_out = open(os.path.join(traindir,f"metrics_train_rank{rank}.json"),"a") + val_metrics_out = open(os.path.join(traindir,f"metrics_val_rank{rank}.json"),"a") + + # TRAIN! ----------------------------------------------------------------------------------- + + last_longterm_checkpoint_save_time = datetime.datetime.now() + num_epochs_this_instance = 0 + print_train_loss_every_batches = 100 if not gnorm_stats_debug else 1000 + + if "sums" not in running_metrics: + running_metrics["sums"] = defaultdict(float) + else: + running_metrics["sums"] = defaultdict(float,running_metrics["sums"]) + if "weights" not in running_metrics: + running_metrics["weights"] = defaultdict(float) + else: + running_metrics["weights"] = defaultdict(float,running_metrics["weights"]) + + torch.backends.cudnn.benchmark = True + + if use_fp16: + logging.info("Training in FP16! Creating scaler") + scaler = GradScaler() + else: + logging.info("Training in FP32.") + + # All ddp threads should be lined up at this point before continuing + if barrier is not None: + barrier.wait() + + while True: + if max_epochs_this_instance is not None and max_epochs_this_instance >= 0 and num_epochs_this_instance >= max_epochs_this_instance: + logging.info("Hit max epochs this instance, done") + break + if max_training_samples is not None and train_state["global_step_samples"] >= max_training_samples: + logging.info("Hit max training samples, done") + break + + if rank == 0: + maybe_reload_training_data() + + if max_train_bucket_per_new_data is not None: + if train_state["train_bucket_level"] > 0.99 * samples_per_epoch: + logging.info("Consuming %.0f rows from train bucket (%.0f -> %.0f)" % ( + samples_per_epoch, train_state["train_bucket_level"], train_state["train_bucket_level"]-samples_per_epoch + )) + train_state["train_bucket_level"] -= samples_per_epoch + else: + if stop_when_train_bucket_limited: + logging.info( + "Exceeding train bucket, not enough new data rows, terminating (current level %f)" % + train_state["train_bucket_level"] + ) + break + else: + logging.info( + "Exceeding train bucket, not enough new data rows, waiting 5m and retrying (current level %f)" % + train_state["train_bucket_level"] + ) + time.sleep(300) + continue + + # DDP need to wait on the main process after reloading data and/or training bucket waiting + if barrier is not None: + barrier.wait() + + logging.info("GC collect") + gc.collect() + + clear_metric_nonfinite(running_metrics["sums"], running_metrics["weights"]) + + logging.info("=========================================================================") + logging.info("BEGINNING NEXT EPOCH " + str(num_epochs_this_instance)) + logging.info("=========================================================================") + logging.info("Current time: " + str(datetime.datetime.now())) + logging.info("Global step: %d samples" % (train_state["global_step_samples"])) + logging.info("Currently up to data row " + str(train_state["total_num_data_rows"])) + logging.info(f"Training dir: {traindir}") + logging.info(f"Export dir: {exportdir}") + if use_fp16: + logging.info(f"Current grad scale: {scaler.get_scale()}") + + lr_right_now, normal_weight_decay_right_now = update_and_return_lr_and_wd() + maybe_update_brenorm_params() + + # SUB EPOCH LOOP ----------- + batch_count_this_epoch = 0 + last_train_stats_time = time.perf_counter() + for i in range(sub_epochs): + + if rank == 0: + if i != 0: + maybe_reload_training_data() + train_files_to_use = get_files_for_subepoch() + while train_files_to_use is None or len(train_files_to_use) <= 0: + if quit_if_no_data: + logging.info("Not enough data files to fill a subepoch! Quitting.") + sys.exit(0) + logging.info("Not enough data files to fill a subepoch! Waiting 5m before retrying.") + time.sleep(300) + maybe_reload_training_data() + train_files_to_use = get_files_for_subepoch() + + if barrier is not None: + barrier.wait() + for wpipe in writepipes: + wpipe.send(train_files_to_use) + # Wait briefly just in case to reduce chance of races with filesystem or anything else + time.sleep(5) + else: + if barrier is not None: + barrier.wait() + train_files_to_use = readpipes[rank-1].recv() + + # DDP need to wait on the main process after reloading data and sending files to train with + if barrier is not None: + barrier.wait() + + logging.info("Beginning training subepoch!") + logging.info("This subepoch, using files: " + str(train_files_to_use)) + logging.info("Currently up to data row " + str(train_state["total_num_data_rows"])) + lookahead_counter = 0 + for batch in data_processing_pytorch.read_npz_training_data( + train_files_to_use, + batch_size, + world_size, + rank, + pos_len=pos_len, + device=device, + randomize_symmetries=True, + include_meta=raw_model.get_has_metadata_encoder(), + model_config=model_config + ): + optimizer.zero_grad(set_to_none=True) + extra_outputs = None + # if raw_model.get_has_metadata_encoder(): + # extra_outputs = ExtraOutputs([MetadataEncoder.OUTMEAN_KEY,MetadataEncoder.OUTLOGVAR_KEY]) + + if use_fp16: + with autocast(): + model_outputs = ddp_model( + batch["binaryInputNCHW"], + batch["globalInputNC"], + input_meta=(batch["metadataInputNC"] if raw_model.get_has_metadata_encoder() else None), + extra_outputs=extra_outputs, + ) + model_outputs = raw_model.float32ify_output(model_outputs) + else: + model_outputs = ddp_model( + batch["binaryInputNCHW"], + batch["globalInputNC"], + input_meta=(batch["metadataInputNC"] if raw_model.get_has_metadata_encoder() else None), + extra_outputs=extra_outputs, + ) + + postprocessed = raw_model.postprocess_output(model_outputs) + metrics = metrics_obj.metrics_dict_batchwise( + raw_model, + postprocessed, + extra_outputs, + batch, + is_training=True, + soft_policy_weight_scale=soft_policy_weight_scale, + disable_optimistic_policy=disable_optimistic_policy, + meta_kata_only_soft_policy=meta_kata_only_soft_policy, + value_loss_scale=value_loss_scale, + td_value_loss_scales=td_value_loss_scales, + seki_loss_scale=seki_loss_scale, + variance_time_loss_scale=variance_time_loss_scale, + main_loss_scale=main_loss_scale, + intermediate_loss_scale=intermediate_loss_scale, + ) + + # DDP averages loss across instances, so to preserve LR as per-sample lr, we scale by world size. + loss = metrics["loss_sum"] * world_size + + # Reduce gradients across DDP + if use_fp16: + scaler.scale(loss).backward() + scaler.unscale_(optimizer) + else: + loss.backward() + + if model_config["norm_kind"] == "fixup" or model_config["norm_kind"] == "fixscale" or model_config["norm_kind"] == "fixscaleonenorm": + gnorm_cap = 2500.0 * (1.0 if gnorm_clip_scale is None else gnorm_clip_scale) + elif model_config["norm_kind"] == "bnorm" or model_config["norm_kind"] == "brenorm" or model_config["norm_kind"] == "fixbrenorm": + gnorm_cap = 5500.0 * (1.0 if gnorm_clip_scale is None else gnorm_clip_scale) + else: + assert False + + if gnorm_stats_debug: + stats = metrics_obj.get_specific_norms_and_gradient_stats(raw_model) + for stat, value in stats.items(): + metrics[stat] = value + + if "use_repvgg_learning_rate" in model_config and model_config["use_repvgg_learning_rate"]: + gradscale_constant = torch.tensor([[1.0,1.0,1.0],[1.0,2.0,1.0],[1.0,1.0,1.0]],dtype=torch.float32,device=device,requires_grad=False).view(1,1,3,3) + for name, param in ddp_model.named_parameters(): + if "normactconv" in name and ".conv.weight" in name and len(param.shape) == 4 and param.shape[2] == 3 and param.shape[3] == 3: + param.grad *= gradscale_constant + + # Loosen gradient clipping as we shift to smaller learning rates + gnorm_cap = gnorm_cap / math.sqrt(max(0.0000001,lr_scale * lr_scale_auto_factor(train_state))) + + gnorm = torch.nn.utils.clip_grad_norm_(ddp_model.parameters(), gnorm_cap).detach().cpu().item() + + if math.isfinite(gnorm) and abs(gnorm < 1e30): + metrics["gnorm_batch"] = gnorm + exgnorm = max(0.0, gnorm - gnorm_cap) + metrics["exgnorm_sum"] = exgnorm * batch_size + + metrics["pslr_batch"] = lr_right_now + metrics["wdnormal_batch"] = normal_weight_decay_right_now + metrics["gnorm_cap_batch"] = gnorm_cap + + if use_fp16: + scaler.step(optimizer) + scaler.update() + else: + optimizer.step() + + batch_count_this_epoch += 1 + train_state["train_steps_since_last_reload"] += batch_size * world_size + train_state["global_step_samples"] += batch_size * world_size + + metrics = detensorify_metrics(metrics) + + if lookahead_k is not None and lookahead_print: + # Only accumulate metrics when lookahead is synced if lookahead_print is True + if lookahead_counter == 0: + accumulate_metrics(running_metrics["sums"], running_metrics["weights"], metrics, batch_size, decay=math.exp(-0.001 * lookahead_k), new_weight=1.0) + else: + accumulate_metrics(running_metrics["sums"], running_metrics["weights"], metrics, batch_size, decay=1.0, new_weight=0.0) + else: + accumulate_metrics(running_metrics["sums"], running_metrics["weights"], metrics, batch_size, decay=0.999, new_weight=1.0) + + + if batch_count_this_epoch % print_train_loss_every_batches == 0: + + if model_config["norm_kind"] == "brenorm" or model_config["norm_kind"] == "fixbrenorm": + metrics["brn_rmax"] = train_state["brenorm_rmax"] + metrics["brn_dmax"] = train_state["brenorm_dmax"] + metrics["brn_mmnt"] = brenorm_avg_momentum + upper_rclippage = [] + lower_rclippage = [] + dclippage = [] + raw_model.add_brenorm_clippage(upper_rclippage, lower_rclippage, dclippage) + metrics["brn_ruclip"] = sum(upper_rclippage) / max(len(upper_rclippage),1.0) + metrics["brn_rlclip"] = sum(lower_rclippage) / max(len(lower_rclippage),1.0) + metrics["brn_dclip"] = sum(dclippage) / max(len(dclippage),1.0) + + t1 = time.perf_counter() + timediff = t1 - last_train_stats_time + last_train_stats_time = t1 + metrics["time_since_last_print"] = timediff + log_metrics(running_metrics["sums"], running_metrics["weights"], metrics, train_metrics_out) + + # Update LR more frequently at the start for smoother warmup ramp and wd adjustment + if train_state["global_step_samples"] <= 50000000 and batch_count_this_epoch % 50 == 0: + lr_right_now, normal_weight_decay_right_now = update_and_return_lr_and_wd() + + # Update batch renorm parameters + if batch_count_this_epoch % 500 == 0: + maybe_update_brenorm_params() + + # Perform lookahead + in_between_lookaheads = False + if lookahead_k is not None: + lookahead_counter += 1 + if lookahead_counter >= lookahead_k: + for param_group in optimizer.param_groups: + for param in param_group["params"]: + slow_param_data = lookahead_cache[param] + slow_param_data.add_(param.data.detach() - slow_param_data, alpha=lookahead_alpha) + param.data.copy_(slow_param_data) + lookahead_counter = 0 + in_between_lookaheads = False + else: + in_between_lookaheads = True + + # Perform SWA + if swa_model is not None and swa_scale is not None: + train_state["swa_sample_accum"] += batch_size * world_size + # Only snap SWA when lookahead slow params are in sync. + if train_state["swa_sample_accum"] >= swa_period_samples and not in_between_lookaheads: + train_state["swa_sample_accum"] = 0 + logging.info("Accumulating SWA") + swa_model.update_parameters(raw_model) + + logging.info("Finished training subepoch!") + + # END SUB EPOCH LOOP ------------ + + # Discard the gradient updates from the leftover batches in the sub epoch from lookahead. + # This wastes a very tiny bit, but makes it so that we can be in sync and deterministic on ends of subepochs/epochs. + if lookahead_k is not None: + for param_group in optimizer.param_groups: + for param in param_group["params"]: + slow_param_data = lookahead_cache[param] + param.data.copy_(slow_param_data) + + if rank == 0: + train_state["export_cycle_counter"] += 1 + + save(ddp_model, swa_model, optimizer, metrics_obj, running_metrics, train_state, last_val_metrics) + + num_epochs_this_instance += 1 + + # Validate + if rank == 0: + logging.info("Beginning validation after epoch!") + val_files = [] + if os.path.exists(vdatadir): + val_files = [os.path.join(vdatadir,fname) for fname in os.listdir(vdatadir) if fname.endswith(".npz")] + if randomize_val: + random.shuffle(val_files) + else: + # Sort to ensure deterministic order to validation files in case we use only a subset + val_files = sorted(val_files) + if len(val_files) == 0: + logging.info("No validation files, skipping validation step") + else: + with torch.no_grad(): + ddp_model.eval() + val_metric_sums = defaultdict(float) + val_metric_weights = defaultdict(float) + val_samples = 0 + t0 = time.perf_counter() + for batch in data_processing_pytorch.read_npz_training_data( + val_files, + batch_size, + world_size=1, # Only the main process validates + rank=0, # Only the main process validates + pos_len=pos_len, + device=device, + randomize_symmetries=True, + include_meta=raw_model.get_has_metadata_encoder(), + model_config=model_config + ): + model_outputs = ddp_model( + batch["binaryInputNCHW"], + batch["globalInputNC"], + input_meta=(batch["metadataInputNC"] if raw_model.get_has_metadata_encoder() else None), + ) + postprocessed = raw_model.postprocess_output(model_outputs) + extra_outputs = None + metrics = metrics_obj.metrics_dict_batchwise( + raw_model, + postprocessed, + extra_outputs, + batch, + is_training=False, + soft_policy_weight_scale=soft_policy_weight_scale, + disable_optimistic_policy=disable_optimistic_policy, + meta_kata_only_soft_policy=meta_kata_only_soft_policy, + value_loss_scale=value_loss_scale, + td_value_loss_scales=td_value_loss_scales, + seki_loss_scale=seki_loss_scale, + variance_time_loss_scale=variance_time_loss_scale, + main_loss_scale=main_loss_scale, + intermediate_loss_scale=intermediate_loss_scale, + ) + metrics = detensorify_metrics(metrics) + accumulate_metrics(val_metric_sums, val_metric_weights, metrics, batch_size, decay=1.0, new_weight=1.0) + val_samples += batch_size + if max_val_samples is not None and val_samples > max_val_samples: + break + val_metric_sums["nsamp_train"] = running_metrics["sums"]["nsamp"] + val_metric_weights["nsamp_train"] = running_metrics["weights"]["nsamp"] + val_metric_sums["wsum_train"] = running_metrics["sums"]["wsum"] + val_metric_weights["wsum_train"] = running_metrics["weights"]["wsum"] + last_val_metrics["sums"] = val_metric_sums + last_val_metrics["weights"] = val_metric_weights + log_metrics(val_metric_sums, val_metric_weights, metrics, val_metrics_out) + t1 = time.perf_counter() + logging.info(f"Validation took {t1-t0} seconds") + ddp_model.train() + + if rank == 0: + logging.info("Export cycle counter = " + str(train_state["export_cycle_counter"])) + + is_time_to_export = False + if train_state["export_cycle_counter"] >= epochs_per_export: + if no_export: + train_state["export_cycle_counter"] = epochs_per_export + else: + train_state["export_cycle_counter"] = 0 + is_time_to_export = True + + skip_export_this_time = False + if export_prob is not None: + if random.random() > export_prob: + skip_export_this_time = True + logging.info("Skipping export model this time") + + if not no_export and is_time_to_export and not skip_export_this_time and exportdir is not None and not gnorm_stats_debug: + # Export a model for testing, unless somehow it already exists + modelname = "%s-s%d-d%d" % ( + exportprefix, + train_state["global_step_samples"], + train_state["total_num_data_rows"], + ) + savepath = os.path.join(exportdir,modelname) + savepathtmp = os.path.join(exportdir,modelname+".tmp") + if os.path.exists(savepath): + logging.info("NOT saving model, already exists at: " + savepath) + else: + os.mkdir(savepathtmp) + logging.info("SAVING MODEL FOR EXPORT TO: " + savepath) + save(ddp_model, swa_model, optimizer, metrics_obj, running_metrics, train_state, last_val_metrics, path=os.path.join(savepathtmp,"model.ckpt")) + time.sleep(2) + os.rename(savepathtmp,savepath) + + + if sleep_seconds_per_epoch is None: + time.sleep(1) + else: + time.sleep(sleep_seconds_per_epoch) + + if rank == 0: + now = datetime.datetime.now() + if now - last_longterm_checkpoint_save_time >= datetime.timedelta(hours=12): + last_longterm_checkpoint_save_time = now + dated_name = datetime.datetime.now(timezone.utc).strftime("%Y%m%d-%H%M%S") + save(ddp_model, swa_model, optimizer, metrics_obj, running_metrics, train_state, last_val_metrics, path=os.path.join(longterm_checkpoints_dir,f"{dated_name}.ckpt")) + + train_metrics_out.close() + val_metrics_out.close() + + +if __name__ == "__main__": + multi_gpus = args["multi_gpus"] + num_gpus_used = 1 + multi_gpu_device_ids = [] + if multi_gpus is not None: + for piece in multi_gpus.split(","): + piece = piece.strip() + multi_gpu_device_ids.append(int(piece)) + num_gpus_used = len(multi_gpu_device_ids) + else: + multi_gpu_device_ids = [0] + + make_dirs(args) + + readpipes = [] + writepipes = [] + + if num_gpus_used > 1: + torch.multiprocessing.set_start_method("spawn") + + world_size = num_gpus_used + barrier = torch.multiprocessing.Barrier(num_gpus_used) + + for i in range(world_size - 1): + rpipe, wpipe = torch.multiprocessing.Pipe() + readpipes.append(rpipe) + writepipes.append(wpipe) + + torch.multiprocessing.spawn( + main, + nprocs=num_gpus_used, + args=(world_size, args, multi_gpu_device_ids, readpipes, writepipes, barrier) + ) + else: + rank = 0 + world_size = 1 + barrier = None + main(rank, world_size, args, multi_gpu_device_ids, readpipes, writepipes, barrier) diff --git a/training/scripts/dated/20241110-124043/train.sh b/training/scripts/dated/20241110-124043/train.sh new file mode 100644 index 0000000..e26d70d --- /dev/null +++ b/training/scripts/dated/20241110-124043/train.sh @@ -0,0 +1,87 @@ +#!/bin/bash -eu +set -o pipefail +{ +# Runs training in $BASEDIR/train/$TRAININGNAME +# Should be run once per persistent training process. +# Outputs results in torchmodels_toexport/ in an ongoing basis (EXPORTMODE == "main"). +# Or, to torchmodels_toexport_extra/ (EXPORTMODE == "extra"). +# Or just trains without exporting (EXPORTMODE == "trainonly"). + +if [[ $# -lt 5 ]] +then + echo "Usage: $0 BASEDIR TRAININGNAME MODELKIND BATCHSIZE EXPORTMODE OTHERARGS" + echo "BASEDIR containing selfplay data and models and related directories" + echo "TRAININGNAME name to prefix models with, specific to this training daemon" + echo "MODELKIND what size model to train, like b10c128, see ../modelconfigs.py" + echo "BATCHSIZE number of samples to concat together per batch for training, must match shuffle" + echo "EXPORTMODE 'main': train and export for selfplay. 'extra': train and export extra non-selfplay model. 'trainonly': train without export" + exit 0 +fi +BASEDIR="$1" +shift +TRAININGNAME="$1" +shift +MODELKIND="$1" +shift +BATCHSIZE="$1" +shift +EXPORTMODE="$1" +shift + +#------------------------------------------------------------------------------ +set -x + +mkdir -p "$BASEDIR"/train/"$TRAININGNAME" + +if [[ -n $(pwd | grep "^$BASEDIR/scripts/") ]] +then + echo "Already running out of snapshotted scripts directory, not snapshotting again" +else + GITROOTDIR="$(git rev-parse --show-toplevel)" + + git show --no-patch --no-color > "$BASEDIR"/train/"$TRAININGNAME"/version.txt + git diff --no-color > "$BASEDIR"/train/"$TRAININGNAME"/diff.txt + git diff --staged --no-color > "$BASEDIR"/train/"$TRAININGNAME"/diffstaged.txt + + # For archival and logging purposes - you can look back and see exactly the python code on a particular date + DATE_FOR_FILENAME=$(date "+%Y%m%d-%H%M%S") + DATED_ARCHIVE="$BASEDIR"/scripts/train/dated/"$DATE_FOR_FILENAME" + mkdir -p "$DATED_ARCHIVE" + cp "$GITROOTDIR"/python/*.py "$GITROOTDIR"/python/selfplay/train.sh "$DATED_ARCHIVE" + git show --no-patch --no-color > "$DATED_ARCHIVE"/version.txt + git diff --no-color > "$DATED_ARCHIVE"/diff.txt + git diff --staged --no-color > "$DATED_ARCHIVE"/diffstaged.txt + cd "$DATED_ARCHIVE" +fi + +if [ "$EXPORTMODE" == "main" ] +then + EXPORT_SUBDIR=torchmodels_toexport + EXTRAFLAG="" +elif [ "$EXPORTMODE" == "extra" ] +then + EXPORT_SUBDIR=torchmodels_toexport_extra + EXTRAFLAG="" +elif [ "$EXPORTMODE" == "trainonly" ] +then + EXPORT_SUBDIR=torchmodels_toexport_extra + EXTRAFLAG="-no-export" +else + echo "EXPORTMODE was not 'main' or 'extra' or 'trainonly', run with no arguments for usage" + exit 1 +fi + +time python3 ./train.py \ + -traindir "$BASEDIR"/train/"$TRAININGNAME" \ + -datadir "$BASEDIR"/shuffleddata/current/ \ + -exportdir "$BASEDIR"/"$EXPORT_SUBDIR" \ + -exportprefix "$TRAININGNAME" \ + -pos-len 19 \ + -batch-size "$BATCHSIZE" \ + -model-kind "$MODELKIND" \ + $EXTRAFLAG \ + "$@" \ + 2>&1 | tee -a "$BASEDIR"/train/"$TRAININGNAME"/stdout.txt + +exit 0 +} diff --git a/training/scripts/dated/20241110-124043/upload_model.py b/training/scripts/dated/20241110-124043/upload_model.py new file mode 100644 index 0000000..cfd9606 --- /dev/null +++ b/training/scripts/dated/20241110-124043/upload_model.py @@ -0,0 +1,178 @@ +#!/usr/bin/python3 +import sys +import os +import argparse +import logging +import requests +import hashlib +import configparser +import datetime +import json + +from requests_toolbelt.adapters import host_header_ssl +from requests.auth import HTTPBasicAuth + +#Command and args------------------------------------------------------------------- + +description = """ +Upload neural net weights to server +""" + +parser = argparse.ArgumentParser(description=description) +parser.add_argument('-run-name', help='run name', required=True) +parser.add_argument('-model-name', help='model name', required=True) +parser.add_argument('-model-file', help='model file for kg engine alone', required=True) +parser.add_argument('-model-zip', help='zipped model file with tf weights', required=True) +parser.add_argument('-upload-log-file', help='log upload data to this file', required=True) +parser.add_argument('-metadata-file', help='metadata.json file for recording some stats', required=False) +parser.add_argument('-parents-dir', help='dir with uploaded models dirs for finding parent', required=False) +parser.add_argument('-connection-config', help='config with serverUrl and username and password', required=True) +parser.add_argument('-not-enabled', help='upload model where it is not enabled for train/rating to begin with', required=False, action='store_true') +parser.add_argument('-rating-only', help='upload for rating only or not', type=int, default=0, required=False) +parser.add_argument('-notes', help='extra notes to record for model', required=False) +args = vars(parser.parse_args()) + +run_name = args["run_name"] +model_name = args["model_name"] +model_file = args["model_file"] +model_zip = args["model_zip"] +upload_log_file = args["upload_log_file"] +metadata_file = args["metadata_file"] +parents_dir = args["parents_dir"] +connection_config_file = args["connection_config"] +not_enabled = args["not_enabled"] +rating_only = args["rating_only"] +notes = args["notes"] + +loglines = [] +def log(s): + loglines.append(s) + print(s,flush=True) + +def write_log(): + with open(upload_log_file,"w+") as f: + for line in loglines: + f.write(line + "\n") + +with open(connection_config_file,'r') as f: + connection_config_content = "[DEFAULT]\n" + f.read() +config_parser = configparser.ConfigParser() +config_parser.read_string(connection_config_content) +base_server_url = config_parser["DEFAULT"]["serverUrl"] + +if not base_server_url.endswith("/"): + base_server_url = base_server_url + "/" + +username = config_parser["DEFAULT"]["username"] +password = config_parser["DEFAULT"]["password"] +sslVerificationHost = None +if "sslVerificationHost" in config_parser["DEFAULT"]: + sslVerificationHost = config_parser["DEFAULT"]["sslVerificationHost"] +sslVerifyPemPath = None +if "sslVerifyPemPath" in config_parser["DEFAULT"]: + sslVerifyPemPath = config_parser["DEFAULT"]["sslVerifyPemPath"] + +log("now" + ": " + str(datetime.datetime.now())) +log("run_name" + ": " + run_name) +log("model_name" + ": " + model_name) +log("model_file" + ": " + model_file) +log("model_zip" + ": " + model_zip) +log("parents_dir" + ": " + str(parents_dir)) +log("metadata_file" + ": " + str(metadata_file)) +log("username" + ": " + username) +log("base_server_url" + ": " + base_server_url) + +network_size = model_name.split("-")[1] + +model_file_extension = None +if model_file.endswith(".bin.gz"): + model_file_extension = ".bin.gz" +elif model_file.endswith(".txt.gz"): + model_file_extension = ".txt.gz" +else: + raise Exception("Model file must end in .bin.gz or .txt.gz") + +possible_parents = [] +if parents_dir is not None: + for fname in os.listdir(parents_dir): + if fname.startswith(network_size): + pieces = fname.split("-") + datasamples = int(pieces[2][1:]) + possible_parents.append((fname, datasamples)) + possible_parents.sort(key=(lambda x: x[1])) + +parent_network_name_without_run = None +if len(possible_parents) > 0: + parent_network_name_without_run = possible_parents[-1][0] + +metadata = None +if metadata_file is not None: + with open(metadata_file) as f: + metadata = json.load(f) + +with open(model_file,"rb") as f: + model_file_contents = f.read() + model_file_bytes = len(model_file_contents) + model_file_sha256 = hashlib.sha256(model_file_contents).hexdigest() + del model_file_contents + +url = base_server_url + "api/networks/" + +with open(model_file,"rb") as model_file_handle: + with open(model_zip,"rb") as model_zip_handle: + log_gamma_offset = -1.0 if network_size == "b60c320" else 0.0 + data = { + "run": (None, base_server_url + "api/runs/" + run_name + "/"), + "name": (None, model_name), + "network_size": (None, network_size), + "is_random": (None, "false"), + "model_file": (model_name + model_file_extension, model_file_handle, "application/octet-stream"), + "model_file_bytes": (None, model_file_bytes), + "model_file_sha256": (None, model_file_sha256), + "training_games_enabled": (None, ("false" if (not_enabled or rating_only != 0) else "true")), + "rating_games_enabled": (None, ("false" if not_enabled else "true")), + "log_gamma_offset": (None, str(log_gamma_offset)), + "model_zip_file": (model_name + ".zip", model_zip_handle, "application/octet-stream"), + } + + if parent_network_name_without_run is not None: + data["parent_network"] = (None, base_server_url + "api/networks/" + run_name + "-" + parent_network_name_without_run + "/") + + if notes is not None: + data["notes"] = (None, notes) + + if metadata is not None: + if "global_step_samples" in metadata: + data["train_step"] = (None, metadata["global_step_samples"]) + if "total_num_data_rows" in metadata: + data["total_num_data_rows"] = (None, metadata["total_num_data_rows"]) + if "extra_stats" in metadata: + data["extra_stats"] = (None, json.dumps(metadata["extra_stats"])) + + # print(requests.Request('POST', base_server_url, files=data).prepare().body) + + if sslVerificationHost is not None: + sess = requests.Session() + sess.mount('https://', host_header_ssl.HostHeaderSSLAdapter()) + if sslVerifyPemPath is not None: + result = sess.post(url, files=data, auth=HTTPBasicAuth(username,password), headers={"Host": sslVerificationHost}, verify=sslVerifyPemPath) + else: + result = sess.post(url, files=data, auth=HTTPBasicAuth(username,password), headers={"Host": sslVerificationHost}) + else: + if sslVerifyPemPath is not None: + result = requests.post(url, files=data, auth=HTTPBasicAuth(username,password), verify=sslVerifyPemPath) + else: + result = requests.post(url,files=data,auth=HTTPBasicAuth(username,password)) + +log("Post status code: " + str(result.status_code)) +log("Post result: " + str(result.text)) +if result.status_code == 400 and str(result.text).find("already exist") != -1: + log("Got 400 error with substring 'already exist', network already uploaded? So assuming everything is good") + write_log() +elif result.status_code == 200 or result.status_code == 201 or result.status_code == 202: + log("Post success") + write_log() +else: + log("Post failed") + write_log() + sys.exit(1) diff --git a/training/scripts/dated/20241110-124043/upload_poses.py b/training/scripts/dated/20241110-124043/upload_poses.py new file mode 100644 index 0000000..ae38189 --- /dev/null +++ b/training/scripts/dated/20241110-124043/upload_poses.py @@ -0,0 +1,219 @@ +#!/usr/bin/python3 +import sys +import os +import argparse +import logging +import requests +import hashlib +import configparser +import json + +from requests.auth import HTTPBasicAuth + +#Command and args------------------------------------------------------------------- + +description = """ +Upload neural net weights to server +""" + +parser = argparse.ArgumentParser(description=description) +parser.add_argument('-run-name', help='run name', required=True) +parser.add_argument('-poses', help='startposes or hintposes file or dir', required=True, action='append') +parser.add_argument('-upload-log-file', help='log upload data to this file', required=False) +parser.add_argument('-connection-config', help='config with serverUrl and username and password', required=False) +parser.add_argument('-set-total-weight', help='set total weight of poses to this', type=float, required=False) +parser.add_argument('-weight-factor', help='multiply weight of poses by this', type=float, required=False) +parser.add_argument('-separate-summaries', help='summarize dirs separately', action="store_true", required=False) +parser.add_argument('-extra-stats', help='show some extra stats', action="store_true", required=False) +parser.add_argument('-notes', help='notes or description label for poses', required=False) +args = vars(parser.parse_args()) + +run_name = args["run_name"] +poses_files_or_dirs = args["poses"] +upload_log_file = args["upload_log_file"] +connection_config_file = args["connection_config"] +set_total_weight = args["set_total_weight"] +weight_factor = args["weight_factor"] +separate_summaries = args["separate_summaries"] +extra_stats = args["extra_stats"] +notes = args["notes"] + +loglines = [] +def log(s): + loglines.append(s) + print(s,flush=True) + +def write_log(): + if upload_log_file is not None: + with open(upload_log_file,"a+") as f: + for line in loglines: + f.write(line + "\n") + +if notes is None: + notes = "" + +if connection_config_file is not None: + with open(connection_config_file,'r') as f: + connection_config_content = "[DEFAULT]\n" + f.read() + config_parser = configparser.ConfigParser() + config_parser.read_string(connection_config_content) + base_server_url = config_parser["DEFAULT"]["serverUrl"] + + if not base_server_url.endswith("/"): + base_server_url = base_server_url + "/" + +log("run_name" + ": " + run_name) +log("poses" + ": " + str(poses_files_or_dirs)) +log("notes" + ": " + notes) + +if connection_config_file is not None: + username = config_parser["DEFAULT"]["username"] + password = config_parser["DEFAULT"]["password"] + log("username" + ": " + username) + log("base_server_url" + ": " + base_server_url) + +def compute_sum_sumsq(poses): + sumweight = 0.0 + sumweightsq = 0.0 + for pos in poses: + sumweight += pos["weight"] + sumweightsq += pos["weight"] * pos["weight"] + return (sumweight, sumweightsq) + +def print_extra_stats(poses): + sumweight = 0.0 + sumturnnum = 0 + for pos in poses: + sumturnnum += pos["weight"] * (pos["initialTurnNumber"] + len(pos["moveLocs"])) + sumweight += pos["weight"] + print("Avg turn number:", sumturnnum/sumweight) + +log("Loading positions") +poses_by_key = {} + +def handle_file(poses_by_key, poses_file): + log("Loading" + ": " + poses_file) + poses_by_key_this_file = {} + with open(poses_file,"r") as f: + for line in f: + line = line.strip() + if len(line) <= 0: + continue + pos = json.loads(line) + key = ( + str(pos["initialTurnNumber"]) + "$" + + "@".join(pos["moveLocs"]) + "$" + + "@".join(pos["movePlas"]) + "$" + + str(pos["xSize"]) + "$" + + str(pos["ySize"]) + "$" + + pos["nextPla"] + "$" + + pos["board"] + "$" + + pos["hintLoc"] + ) + + if len(pos["movePlas"]) > 0: + assert pos["nextPla"] == "B" or pos["nextPla"] == "W" + assert pos["movePlas"][0] == pos["nextPla"] + for i in range(len(pos["movePlas"])): + assert pos["movePlas"][i] == "B" or pos["movePlas"][i] == "W" + for i in range(1,len(pos["movePlas"])): + assert pos["movePlas"][i] != pos["movePlas"][i-1] + + if "weight" in pos: + weight = pos["weight"] + else: + pos["weight"] = 1.0 + weight = 1.0 + + if key in poses_by_key: + poses_by_key[key]["weight"] += weight + else: + poses_by_key[key] = pos.copy() + if key in poses_by_key_this_file: + poses_by_key_this_file[key]["weight"] += weight + else: + poses_by_key_this_file[key] = pos.copy() + if separate_summaries: + sumweight,sumweightsq = compute_sum_sumsq(poses_by_key_this_file.values()) + if sumweight > 0 and sumweightsq > 0: + log("Found %d unique positions" % len(poses_by_key_this_file.values())) + log("Found %f total weight" % sumweight) + log("Found %f ess" % (sumweight * sumweight / sumweightsq)) + if extra_stats: + print_extra_stats(poses_by_key_this_file.values()) + log("%d %f %f" % (len(poses_by_key_this_file.values()), sumweight, (sumweight * sumweight / sumweightsq))) + +poses_files_or_dirs = sorted(poses_files_or_dirs) +for poses_file_or_dir in poses_files_or_dirs: + if os.path.isdir(poses_file_or_dir): + for (path,dirnames,filenames) in os.walk(poses_file_or_dir): + dirnames.sort() + filenames = sorted(filenames) + for filename in filenames: + if filename.endswith(".startposes.txt") or filename.endswith(".hintposes.txt") or filename.endswith(".bookposes.txt"): + handle_file(poses_by_key, os.path.join(path,filename)) + else: + handle_file(poses_by_key, poses_file_or_dir) + +poses = poses_by_key.values() +sumweight,sumweightsq = compute_sum_sumsq(poses) +log("Found %d unique positions" % len(poses)) +log("Found %f total weight" % sumweight) +log("Found %f ess" % (sumweight * sumweight / sumweightsq)) +if extra_stats: + print_extra_stats(poses) +log("%d %f %f" % (len(poses), sumweight, (sumweight * sumweight / sumweightsq))) + +if set_total_weight is not None: + log("Setting total weight of data to " + str(set_total_weight)) + scale = set_total_weight / sumweight + for pos in poses: + pos["weight"] *= scale +elif weight_factor is not None: + log("Scaling weight of data by " + str(weight_factor)) + for pos in poses: + pos["weight"] *= weight_factor + +def postStuff(to_post): + url = base_server_url + "api/startposes/" + result = requests.post(url,json=to_post,auth=HTTPBasicAuth(username,password)) + log("Post status code: " + str(result.status_code)) + if result.status_code == 200 or result.status_code == 201 or result.status_code == 202: + log("Post success") + else: + log("Post failed") + if result.text: + log(result.text) + write_log() + sys.exit(1) + + +if connection_config_file is None: + log("No connection config specified, quitting WITHOUT uploading anything") +else: + num_total = len(poses) + num_posted = 0 + + to_post = [] + for pos in poses: + weight = pos["weight"] + data = { + "run": base_server_url + "api/runs/" + run_name + "/", + "weight": weight, + "data": pos, + "notes": notes, + } + to_post.append(data) + if len(to_post) >= 5000: + num_posted += len(to_post) + log("Posting " + str(num_posted) + "/" + str(num_total)) + postStuff(to_post) + to_post = [] + + num_posted += len(to_post) + log("Posting " + str(num_posted) + "/" + str(num_total)) + postStuff(to_post) + to_post = [] + + log("Done") + write_log() diff --git a/training/scripts/dated/20241110-124043/version.txt b/training/scripts/dated/20241110-124043/version.txt new file mode 100644 index 0000000..0a99568 --- /dev/null +++ b/training/scripts/dated/20241110-124043/version.txt @@ -0,0 +1,5 @@ +commit 827f7555bd393e2389f74f1ca147595d95a716b7 +Author: David Wu +Date: Sun Jul 28 12:02:09 2024 -0400 + + Allow analysis data output even when no play selection values diff --git a/training/selfplay/kata1-b18c384nbt-s9908371456-d4296205232.bin.gz/selfplay-CA064897108355B4.cfg b/training/selfplay/kata1-b18c384nbt-s9908371456-d4296205232.bin.gz/selfplay-CA064897108355B4.cfg new file mode 100644 index 0000000..50bc598 --- /dev/null +++ b/training/selfplay/kata1-b18c384nbt-s9908371456-d4296205232.bin.gz/selfplay-CA064897108355B4.cfg @@ -0,0 +1,185 @@ + +# Logs------------------------------------------------------------------------------------ + +logSearchInfo = false +logMoves = false +logGamesEvery = 10 +logToStdout = true + +# Data writing----------------------------------------------------------------------------------- + +# Spatial size of tensors of data, must match -pos-len in python/train.py and be at least as large as the +# largest boardsize in the data. If you train only on smaller board sizes, you can decrease this and +# pass in a smaller -pos-len to python/train.py (e.g. modify "-pos-len 19" in selfplay/train.sh script), +# to train faster, although it may be awkward if you ever want to increase it again later since data with +# different values cannot be shuffled together. +dataBoardLen = 19 + +maxDataQueueSize = 2000 +maxRowsPerTrainFile = 10000 +firstFileRandMinProp = 0.15 + +# Fancy game selfplay settings-------------------------------------------------------------------- + +# These take dominance - if a game is forked for any of these reasons, that will be the next game played. +# For forked games, randomization of rules and "initGamesWithPolicy" is disabled, komi is made fair with prob "forkCompensateKomiProb". +earlyForkGameProb = 0.04 # Fork to try alternative opening variety with this probability +earlyForkGameExpectedMoveProp = 0.025 # Fork roughly within the first (boardArea * this) many moves +forkGameProb = 0.01 # Fork to try alternative crazy move anywhere in game with this probability if not early forking +forkGameMinChoices = 3 # Choose from the best of at least this many random choices +earlyForkGameMaxChoices = 12 # Choose from the best of at most this many random choices +forkGameMaxChoices = 36 # Choose from the best of at most this many random choices +# Otherwise, with this probability, learn a bit more about the differing evaluation of seki in different rulesets. +sekiForkHackProb = 0.02 + +# Otherwise, play some proportion of games starting from SGF positions, with randomized rules (ignoring the sgf rules) +# On SGF positions, high temperature policy init is allowed +# startPosesProb = 0.0 # Play this proportion of games starting from SGF positions +# startPosesFromSgfDir = DIRECTORYPATH # Load SGFs from this dir +# startPosesLoadProb = 1.0 # Only load each position from each SGF with this chance (save memory) +# startPosesTurnWeightLambda = 0 # 0 = equal weight 0.01 = decrease probability by 1% per turn -0.01 = increase probability by 1% per turn. +# startPosesPolicyInitAreaProp = 0.0 # Same as policyInitAreaProp but for SGF positions + +# Otherwise, play some proportion of games starting from hint positions (generated using "dataminesgfs" command), with randomized rules. +# On hint positions, "initGamesWithPolicy" does not apply. +# hintPosesProb = 0.0 +# hintPosesDir = DIRECTORYPATH + +# Otherwise we are playing a "normal" game, potentially with handicap stones, depending on "handicapProb", and +# potentially with komi randomization, depending on things like "komiStdev", and potentially with different +# board sizes, etc. + +# Most of the remaining parameters here below apply regardless of the initialization, although a few of them +# vary depending on handicap vs normal game, and some are explicitly disabled (e.g. initGamesWithPolicy on hint positions). + +initGamesWithPolicy = true # Play the first few moves of a game high-temperaturely from policy +policyInitAreaProp = 0.04 # The avg number of moves to play +compensateAfterPolicyInitProb = 0.2 # Additionally make komi fair this often after the high-temperature moves. +sidePositionProb = 0.020 # With this probability, train on refuting bad alternative moves. + +cheapSearchProb = 0.75 # Do cheap searches with this probaiblity +cheapSearchVisits = 100 # Number of visits for cheap search +cheapSearchTargetWeight = 0.0 # Training weight for cheap search + +reduceVisits = true # Reduce visits when one side is winning +reduceVisitsThreshold = 0.9 # How winning a side needs to be (winrate) +reduceVisitsThresholdLookback = 3 # How many consecutive turns needed to be that winning +reducedVisitsMin = 100 # Minimum number of visits (never reduce below this) +reducedVisitsWeight = 0.1 # Minimum training weight + +handicapAsymmetricPlayoutProb = 0.5 # In handicap games, play with unbalanced players with this probablity +normalAsymmetricPlayoutProb = 0.01 # In regular games, play with unbalanced players with this probability +maxAsymmetricRatio = 8.0 # Max ratio to unbalance visits between players +minAsymmetricCompensateKomiProb = 0.4 # Compensate komi with at least this probability for unbalanced players + +policySurpriseDataWeight = 0.5 # This proportion of training weight should be concentrated on surprising moves +valueSurpriseDataWeight = 0.1 # This proportion of training weight should be concentrated on surprising position results + +estimateLeadProb = 0.05 # Train lead, rather than just scoremean. Consumes a decent number of extra visits, can be quite slow using low visits to set too high. +switchNetsMidGame = true # When a new neural net is loaded, switch to it immediately instead of waiting for new game +fancyKomiVarying = true # In non-compensated handicap and fork games, vary komi to better learn komi and large score differences that would never happen in even games. + +# Match----------------------------------------------------------------------------------- + +numGameThreads = 30 +maxMovesPerGame = 1600 + +# Rules------------------------------------------------------------------------------------ + +koRules = SIMPLE,POSITIONAL,SITUATIONAL +scoringRules = AREA,TERRITORY +taxRules = NONE,NONE,SEKI,SEKI,ALL +multiStoneSuicideLegals = false,true +hasButtons = false,false,true + +bSizes = 7,8,9 +bSizeRelProbs = 1,1,8 +allowRectangleProb = 0.50 + +komiAuto = True # Automatically adjust komi to what the neural nets think are fair based on the empty board, but still apply komiStdev. +# komiMean = 7.0 # Specify explicit komi +komiStdev = 1.0 # Standard deviation of random variation to komi. +komiBigStdevProb = 0.06 # Probability of applying komiBigStdev +komiBigStdev = 12.0 # Standard deviation of random big variation to komi + +handicapProb = 0.10 # Probability of handicap game +handicapCompensateKomiProb = 0.50 # In handicap games, adjust komi to fair with this probability based on the handicap placement +forkCompensateKomiProb = 0.80 # For forks, adjust komi to fair with this probability based on the forked position +sgfCompensateKomiProb = 0.90 # For sgfs, adjust komi to fair with this probability based on the specific starting position + +drawRandRadius = 0.5 +noResultStdev = 0.166666666 + +# Search limits----------------------------------------------------------------------------------- + +maxVisits = 600 +numSearchThreads = 1 + +# GPU Settings------------------------------------------------------------------------------- + +nnMaxBatchSize = 128 +nnCacheSizePowerOfTwo = 21 +nnMutexPoolSizePowerOfTwo = 15 +numNNServerThreadsPerModel = 1 +nnRandomize = true + +# CUDA GPU settings-------------------------------------- +# cudaDeviceToUse = 0 #use device 0 for all server threads (numNNServerThreadsPerModel) unless otherwise specified per-model or per-thread-per-model +# cudaDeviceToUseModel0 = 3 #use device 3 for model 0 for all threads unless otherwise specified per-thread for this model +# cudaDeviceToUseModel1 = 2 #use device 2 for model 1 for all threads unless otherwise specified per-thread for this model +# cudaDeviceToUseModel0Thread0 = 3 #use device 3 for model 0, server thread 0 +# cudaDeviceToUseModel0Thread1 = 2 #use device 2 for model 0, server thread 1 + +cudaUseFP16 = auto +cudaUseNHWC = auto + +# Root move selection and biases------------------------------------------------------------------------------ + +chosenMoveTemperatureEarly = 0.75 +chosenMoveTemperatureHalflife = 19 +chosenMoveTemperature = 0.15 +chosenMoveSubtract = 0 +chosenMovePrune = 1 + +rootNoiseEnabled = true +rootDirichletNoiseTotalConcentration = 10.83 +rootDirichletNoiseWeight = 0.25 + +rootDesiredPerChildVisitsCoeff = 2 +rootNumSymmetriesToSample = 4 + +useLcbForSelection = true +lcbStdevs = 5.0 +minVisitPropForLCB = 0.15 + +# Internal params------------------------------------------------------------------------------ + +winLossUtilityFactor = 1.0 +staticScoreUtilityFactor = 0.00 +dynamicScoreUtilityFactor = 0.40 +dynamicScoreCenterZeroWeight = 0.25 +dynamicScoreCenterScale = 0.50 +noResultUtilityForWhite = 0.0 +drawEquivalentWinsForWhite = 0.5 + +rootEndingBonusPoints = 0.5 +rootPruneUselessMoves = true + +rootPolicyTemperatureEarly = 1.25 +rootPolicyTemperature = 1.1 + +cpuctExploration = 1.1 +cpuctExplorationLog = 0.0 +fpuReductionMax = 0.2 +rootFpuReductionMax = 0.0 + +numVirtualLossesPerThread = 1 + +# These parameters didn't exist historically during early KataGo runs +valueWeightExponent = 0.5 +subtreeValueBiasFactor = 0.30 +subtreeValueBiasWeightExponent = 0.8 +useNonBuggyLcb = true +useGraphSearch = true +fpuParentWeightByVisitedPolicy = true +fpuParentWeightByVisitedPolicyPow = 2.0 diff --git a/training/selfplay/kata1-b18c384nbt-s9908371456-d4296205232.bin.gz/sgfs/73935E5B0135F8FD.sgfs b/training/selfplay/kata1-b18c384nbt-s9908371456-d4296205232.bin.gz/sgfs/73935E5B0135F8FD.sgfs index 239f727..5f72cb2 100644 --- a/training/selfplay/kata1-b18c384nbt-s9908371456-d4296205232.bin.gz/sgfs/73935E5B0135F8FD.sgfs +++ b/training/selfplay/kata1-b18c384nbt-s9908371456-d4296205232.bin.gz/sgfs/73935E5B0135F8FD.sgfs @@ -23,3 +23,8 @@ (;FF[4]GM[1]SZ[9]PB[kata1-b18c384nbt-s9908371456-d4296205232.bin.gz]PW[kata1-b18c384nbt-s9908371456-d4296205232.bin.gz]HA[0]KM[6]RU[koPOSITIONALscoreTERRITORYtaxSEKIsui0]RE[0]C[startTurnIdx=1,initTurnNum=0,gameHash=7A4D4BD3851BC22678237E33EA0AA0CB,gtype=normal];B[df];W[fd]C[0.75 0.25 0.00 1.0 v=100 weight=0.00];B[ed]C[0.75 0.25 0.00 1.0 v=100 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z5CnoL0_=ZV9Ym{x72B0EsuGa&6I|D?zqmik&#-BK`k;1;sU$Hc#2|d>1)4f{kOA9( z9%2tKYdN0_AIwd(76v<^77R!$-ypL7C8dt zVRp@I=j-SGE>)Zlh75`S-dsI{e~SQ_yz*bzS{XWwd@+j=V>7aZ;_d^xZ4#0&>NX1) zpj^LiVy{O|CMNzq0Ql)@zaE4L$|Z{IV5C=iWQV%ozH!`~Eq~>@PW#!jUwail?(v=} zD?55vTRM_6g24xKqN4eB-i0C6nQ%(u6_L$6N!ABSp?jS@U~1+eHq4ojhR9Ke287*A zmbv>bo&IY)8aM$@Z{;&ExH2pFHc5!%k5-*8ZE|Y;%f$+Qh3PBP@B$@HR^tI-c&;ptO-bar1LJ`$0xs*I}Fr~bUv8R?brF>GN zXz1RxW{DzJ21&;*L*D&0n)^+5$xu-VNYhdsr~1#osz~VP zE3RJXYd5sLeEj{O(_%(w2Y1J-|DFPE-Wl3LO+|Gnh>D8gKQmBOMbgMpQ8{~fUqO59 zP4Vxc8SYmmc}1wGh~E!V9sQr7>k3p`0rD7{~4IM{{PR|8R^o{LVrOE{$Jlm LMO7;fUa0;L>s8Py literal 0 HcmV?d00001 diff --git a/training/selfplay/log20241110-123408-158367354CDDA823.log b/training/selfplay/log20241110-123408-158367354CDDA823.log index d23663c..3441456 100644 --- a/training/selfplay/log20241110-123408-158367354CDDA823.log +++ b/training/selfplay/log20241110-123408-158367354CDDA823.log @@ -182,3 +182,17 @@ winLossUtilityFactor = 1.0 2024-11-10 12:35:36-0500: Game loop thread 2 terminating 2024-11-10 12:35:38-0500: Game loop thread 9 terminating 2024-11-10 12:35:41-0500: Game loop thread 13 terminating +2024-11-10 12:35:45-0500: Game loop thread 8 terminating +2024-11-10 12:35:45-0500: Game loop thread 23 terminating +2024-11-10 12:35:47-0500: Game loop thread 26 terminating +2024-11-10 12:35:48-0500: Game loop thread 5 terminating +2024-11-10 12:35:53-0500: Game loop thread 25 terminating +2024-11-10 12:35:53-0500: Model loading loop thread terminating +2024-11-10 12:35:53-0500: Data write loop finishing for neural net: kata1-b18c384nbt-s9908371456-d4296205232.bin.gz +2024-11-10 12:35:53-0500: Final cleanup of net: C:\Users\chang\OneDrive\Documents\GitHub\Kata_speed\training\models\kata1-b18c384nbt-s9908371456-d4296205232.bin.gz +2024-11-10 12:35:53-0500: Final NN rows: 132765 +2024-11-10 12:35:53-0500: Final NN batches: 11334 +2024-11-10 12:35:53-0500: Final NN avg batch size: 11.7139 +2024-11-10 12:35:53-0500: GPU -1 finishing, processed 132765 rows 11334 batches +2024-11-10 12:35:53-0500: Data write loop cleaned up and terminating for kata1-b18c384nbt-s9908371456-d4296205232.bin.gz +2024-11-10 12:35:53-0500: All cleaned up, quitting diff --git a/training/selfplay/log20241110-124044-BE55D0F1BE790F89.log b/training/selfplay/log20241110-124044-BE55D0F1BE790F89.log new file mode 100644 index 0000000..4a39053 --- /dev/null +++ b/training/selfplay/log20241110-124044-BE55D0F1BE790F89.log @@ -0,0 +1,159 @@ +2024-11-10 12:40:44-0500: Running with following config: +allowRectangleProb = 0.50 +bSizeRelProbs = 1,1,8 +bSizes = 7,8,9 +cheapSearchProb = 0.75 +cheapSearchTargetWeight = 0.0 +cheapSearchVisits = 100 +chosenMovePrune = 1 +chosenMoveSubtract = 0 +chosenMoveTemperature = 0.15 +chosenMoveTemperatureEarly = 0.75 +chosenMoveTemperatureHalflife = 19 +compensateAfterPolicyInitProb = 0.2 +cpuctExploration = 1.1 +cpuctExplorationLog = 0.0 +cudaUseFP16 = auto +cudaUseNHWC = auto +dataBoardLen = 19 +drawEquivalentWinsForWhite = 0.5 +drawRandRadius = 0.5 +dynamicScoreCenterScale = 0.50 +dynamicScoreCenterZeroWeight = 0.25 +dynamicScoreUtilityFactor = 0.40 +earlyForkGameExpectedMoveProp = 0.025 +earlyForkGameMaxChoices = 12 +earlyForkGameProb = 0.04 +estimateLeadProb = 0.05 +fancyKomiVarying = true +firstFileRandMinProp = 0.15 +forkCompensateKomiProb = 0.80 +forkGameMaxChoices = 36 +forkGameMinChoices = 3 +forkGameProb = 0.01 +fpuParentWeightByVisitedPolicy = true +fpuParentWeightByVisitedPolicyPow = 2.0 +fpuReductionMax = 0.2 +handicapAsymmetricPlayoutProb = 0.5 +handicapCompensateKomiProb = 0.50 +handicapProb = 0.10 +hasButtons = false,false,true +initGamesWithPolicy = true +koRules = SIMPLE,POSITIONAL,SITUATIONAL +komiAuto = True +komiBigStdev = 12.0 +komiBigStdevProb = 0.06 +komiStdev = 1.0 +lcbStdevs = 5.0 +logGamesEvery = 10 +logMoves = false +logSearchInfo = false +logToStdout = true +maxAsymmetricRatio = 8.0 +maxDataQueueSize = 2000 +maxMovesPerGame = 1600 +maxRowsPerTrainFile = 10000 +maxVisits = 600 +minAsymmetricCompensateKomiProb = 0.4 +minVisitPropForLCB = 0.15 +multiStoneSuicideLegals = false,true +nnCacheSizePowerOfTwo = 21 +nnMaxBatchSize = 128 +nnMutexPoolSizePowerOfTwo = 15 +nnRandomize = true +noResultStdev = 0.166666666 +noResultUtilityForWhite = 0.0 +normalAsymmetricPlayoutProb = 0.01 +numGameThreads = 30 +numNNServerThreadsPerModel = 1 +numSearchThreads = 1 +numVirtualLossesPerThread = 1 +policyInitAreaProp = 0.04 +policySurpriseDataWeight = 0.5 +reduceVisits = true +reduceVisitsThreshold = 0.9 +reduceVisitsThresholdLookback = 3 +reducedVisitsMin = 100 +reducedVisitsWeight = 0.1 +rootDesiredPerChildVisitsCoeff = 2 +rootDirichletNoiseTotalConcentration = 10.83 +rootDirichletNoiseWeight = 0.25 +rootEndingBonusPoints = 0.5 +rootFpuReductionMax = 0.0 +rootNoiseEnabled = true +rootNumSymmetriesToSample = 4 +rootPolicyTemperature = 1.1 +rootPolicyTemperatureEarly = 1.25 +rootPruneUselessMoves = true +scoringRules = AREA,TERRITORY +sekiForkHackProb = 0.02 +sgfCompensateKomiProb = 0.90 +sidePositionProb = 0.020 +staticScoreUtilityFactor = 0.00 +subtreeValueBiasFactor = 0.30 +subtreeValueBiasWeightExponent = 0.8 +switchNetsMidGame = true +taxRules = NONE,NONE,SEKI,SEKI,ALL +useGraphSearch = true +useLcbForSelection = true +useNonBuggyLcb = true +valueSurpriseDataWeight = 0.1 +valueWeightExponent = 0.5 +winLossUtilityFactor = 1.0 + +2024-11-10 12:40:44-0500: Self Play Engine starting... +2024-11-10 12:40:44-0500: Git revision: 7939e2c3eb43df04e08de8c166889c6d5e937bfa +2024-11-10 12:40:44-0500: Loaded all config stuff, starting self play +2024-11-10 12:40:44-0500: Found new neural net kata1-b18c384nbt-s9908371456-d4296205232.bin.gz +2024-11-10 12:40:44-0500: nnRandSeed0 = 2704693968822434151 +2024-11-10 12:40:44-0500: After dedups: nnModelFile0 = C:\Users\chang\OneDrive\Documents\GitHub\Kata_speed\training\models\kata1-b18c384nbt-s9908371456-d4296205232.bin.gz useFP16 auto useNHWC auto +2024-11-10 12:40:44-0500: Initializing neural net buffer to be size 9 * 9 allowing smaller boards +2024-11-10 12:40:46-0500: Found OpenCL Platform 0: NVIDIA CUDA (NVIDIA Corporation) (OpenCL 3.0 CUDA 12.6.65) +2024-11-10 12:40:46-0500: Found 1 device(s) on platform 0 with type CPU or GPU or Accelerator +2024-11-10 12:40:46-0500: Found OpenCL Platform 1: Intel(R) OpenCL Graphics (Intel(R) Corporation) (OpenCL 3.0 ) +2024-11-10 12:40:46-0500: Found 1 device(s) on platform 1 with type CPU or GPU or Accelerator +2024-11-10 12:40:46-0500: Found OpenCL Device 0: NVIDIA GeForce RTX 4060 Laptop GPU (NVIDIA Corporation) (score 11000300) +2024-11-10 12:40:46-0500: Found OpenCL Device 1: Intel(R) UHD Graphics (Intel(R) Corporation) (score 6000300) +2024-11-10 12:40:46-0500: Creating context for OpenCL Platform: NVIDIA CUDA (NVIDIA Corporation) (OpenCL 3.0 CUDA 12.6.65) +2024-11-10 12:40:46-0500: Using OpenCL Device 0: NVIDIA GeForce RTX 4060 Laptop GPU (NVIDIA Corporation) OpenCL 3.0 CUDA (Extensions: cl_khr_global_int32_base_atomics cl_khr_global_int32_extended_atomics cl_khr_local_int32_base_atomics cl_khr_local_int32_extended_atomics cl_khr_fp64 cl_khr_3d_image_writes cl_khr_byte_addressable_store cl_khr_icd cl_khr_gl_sharing cl_nv_compiler_options cl_nv_device_attribute_query cl_nv_pragma_unroll cl_nv_d3d10_sharing cl_khr_d3d10_sharing cl_nv_d3d11_sharing cl_nv_copy_opts cl_nv_create_buffer cl_khr_int64_base_atomics cl_khr_int64_extended_atomics cl_khr_device_uuid cl_khr_pci_bus_info cl_khr_external_semaphore cl_khr_external_memory cl_khr_external_semaphore_win32 cl_khr_external_memory_win32) +2024-11-10 12:40:46-0500: Loaded tuning parameters from: C:\Users\chang\Downloads\katago-v1.15.1-opencl-windows-x64/KataGoData/opencltuning/tune11_gpuNVIDIAGeForceRTX4060LaptopGPU_x19_y19_c384_mv14.txt +2024-11-10 12:40:46-0500: OpenCL backend thread 0: Model version 14 +2024-11-10 12:40:46-0500: OpenCL backend thread 0: Model name: kata1-b18c384nbt-s9908371456-d4296205232 +2024-11-10 12:40:47-0500: OpenCL backend thread 0: FP16Storage true FP16Compute false FP16TensorCores true FP16TensorCoresFor1x1 false +2024-11-10 12:40:47-0500: Loaded latest neural net kata1-b18c384nbt-s9908371456-d4296205232.bin.gz from: C:\Users\chang\OneDrive\Documents\GitHub\Kata_speed\training\models\kata1-b18c384nbt-s9908371456-d4296205232.bin.gz +2024-11-10 12:40:47-0500: Model loading loop thread loaded new neural net kata1-b18c384nbt-s9908371456-d4296205232.bin.gz +2024-11-10 12:40:47-0500: Data write loop starting for neural net: kata1-b18c384nbt-s9908371456-d4296205232.bin.gz +2024-11-10 12:40:47-0500: Game loop thread 1 starting game on new neural net: kata1-b18c384nbt-s9908371456-d4296205232.bin.gz +2024-11-10 12:40:47-0500: Game loop thread 2 starting game on new neural net: kata1-b18c384nbt-s9908371456-d4296205232.bin.gz +2024-11-10 12:40:47-0500: Game loop thread 5 starting game on new neural net: kata1-b18c384nbt-s9908371456-d4296205232.bin.gz +2024-11-10 12:40:47-0500: Game loop thread 9 starting game on new neural net: kata1-b18c384nbt-s9908371456-d4296205232.bin.gz +2024-11-10 12:40:47-0500: Game loop thread 18 starting game on new neural net: kata1-b18c384nbt-s9908371456-d4296205232.bin.gz +2024-11-10 12:40:47-0500: Game loop thread 4 starting game on new neural net: kata1-b18c384nbt-s9908371456-d4296205232.bin.gz +2024-11-10 12:40:47-0500: Game loop thread 7 starting game on new neural net: kata1-b18c384nbt-s9908371456-d4296205232.bin.gz +2024-11-10 12:40:47-0500: Game loop thread 8 starting game on new neural net: kata1-b18c384nbt-s9908371456-d4296205232.bin.gz +2024-11-10 12:40:47-0500: Game loop thread 25 starting game on new neural net: kata1-b18c384nbt-s9908371456-d4296205232.bin.gz +2024-11-10 12:40:47-0500: Game loop thread 12 starting game on new neural net: kata1-b18c384nbt-s9908371456-d4296205232.bin.gz +2024-11-10 12:40:47-0500: Started 10 games with kata1-b18c384nbt-s9908371456-d4296205232.bin.gz +2024-11-10 12:40:47-0500: Game loop thread 10 starting game on new neural net: kata1-b18c384nbt-s9908371456-d4296205232.bin.gz +2024-11-10 12:40:47-0500: Game loop thread 0 starting game on new neural net: kata1-b18c384nbt-s9908371456-d4296205232.bin.gz +2024-11-10 12:40:47-0500: Game loop thread 13 starting game on new neural net: kata1-b18c384nbt-s9908371456-d4296205232.bin.gz +2024-11-10 12:40:47-0500: Game loop thread 11 starting game on new neural net: kata1-b18c384nbt-s9908371456-d4296205232.bin.gz +2024-11-10 12:40:47-0500: Game loop thread 14 starting game on new neural net: kata1-b18c384nbt-s9908371456-d4296205232.bin.gz +2024-11-10 12:40:47-0500: Game loop thread 15 starting game on new neural net: kata1-b18c384nbt-s9908371456-d4296205232.bin.gz +2024-11-10 12:40:47-0500: Game loop thread 16 starting game on new neural net: kata1-b18c384nbt-s9908371456-d4296205232.bin.gz +2024-11-10 12:40:47-0500: Game loop thread 17 starting game on new neural net: kata1-b18c384nbt-s9908371456-d4296205232.bin.gz +2024-11-10 12:40:47-0500: Game loop thread 6 starting game on new neural net: kata1-b18c384nbt-s9908371456-d4296205232.bin.gz +2024-11-10 12:40:47-0500: Game loop thread 19 starting game on new neural net: kata1-b18c384nbt-s9908371456-d4296205232.bin.gz +2024-11-10 12:40:47-0500: Started 20 games with kata1-b18c384nbt-s9908371456-d4296205232.bin.gz +2024-11-10 12:40:47-0500: Game loop thread 20 starting game on new neural net: kata1-b18c384nbt-s9908371456-d4296205232.bin.gz +2024-11-10 12:40:47-0500: Game loop thread 22 starting game on new neural net: kata1-b18c384nbt-s9908371456-d4296205232.bin.gz +2024-11-10 12:40:47-0500: Game loop thread 24 starting game on new neural net: kata1-b18c384nbt-s9908371456-d4296205232.bin.gz +2024-11-10 12:40:47-0500: Game loop thread 23 starting game on new neural net: kata1-b18c384nbt-s9908371456-d4296205232.bin.gz +2024-11-10 12:40:47-0500: Game loop thread 3 starting game on new neural net: kata1-b18c384nbt-s9908371456-d4296205232.bin.gz +2024-11-10 12:40:47-0500: Game loop thread 21 starting game on new neural net: kata1-b18c384nbt-s9908371456-d4296205232.bin.gz +2024-11-10 12:40:47-0500: Game loop thread 27 starting game on new neural net: kata1-b18c384nbt-s9908371456-d4296205232.bin.gz +2024-11-10 12:40:47-0500: Game loop thread 26 starting game on new neural net: kata1-b18c384nbt-s9908371456-d4296205232.bin.gz +2024-11-10 12:40:47-0500: Model loading loop thread starting +2024-11-10 12:40:47-0500: Game loop thread 29 starting game on new neural net: kata1-b18c384nbt-s9908371456-d4296205232.bin.gz +2024-11-10 12:40:47-0500: Game loop thread 28 starting game on new neural net: kata1-b18c384nbt-s9908371456-d4296205232.bin.gz +2024-11-10 12:40:47-0500: Started 30 games with kata1-b18c384nbt-s9908371456-d4296205232.bin.gz diff --git a/training/selfplay/stdout.txt b/training/selfplay/stdout.txt index 1fa0147..d4a3e6c 100644 --- a/training/selfplay/stdout.txt +++ b/training/selfplay/stdout.txt @@ -1237,3 +1237,176 @@ winLossUtilityFactor = 1.0 2024-11-10 12:35:36-0500: Game loop thread 2 terminating 2024-11-10 12:35:38-0500: Game loop thread 9 terminating 2024-11-10 12:35:41-0500: Game loop thread 13 terminating +2024-11-10 12:35:45-0500: Game loop thread 8 terminating +2024-11-10 12:35:45-0500: Game loop thread 23 terminating +2024-11-10 12:35:47-0500: Game loop thread 26 terminating +2024-11-10 12:35:48-0500: Game loop thread 5 terminating +2024-11-10 12:35:53-0500: Game loop thread 25 terminating +2024-11-10 12:35:53-0500: Model loading loop thread terminating +2024-11-10 12:35:53-0500: Data write loop finishing for neural net: kata1-b18c384nbt-s9908371456-d4296205232.bin.gz +2024-11-10 12:35:53-0500: Final cleanup of net: C:\Users\chang\OneDrive\Documents\GitHub\Kata_speed\training\models\kata1-b18c384nbt-s9908371456-d4296205232.bin.gz +2024-11-10 12:35:53-0500: Final NN rows: 132765 +2024-11-10 12:35:53-0500: Final NN batches: 11334 +2024-11-10 12:35:53-0500: Final NN avg batch size: 11.7139 +2024-11-10 12:35:53-0500: GPU -1 finishing, processed 132765 rows 11334 batches +2024-11-10 12:35:53-0500: Data write loop cleaned up and terminating for kata1-b18c384nbt-s9908371456-d4296205232.bin.gz +2024-11-10 12:35:53-0500: All cleaned up, quitting +2024-11-10 12:40:44-0500: Running with following config: +allowRectangleProb = 0.50 +bSizeRelProbs = 1,1,8 +bSizes = 7,8,9 +cheapSearchProb = 0.75 +cheapSearchTargetWeight = 0.0 +cheapSearchVisits = 100 +chosenMovePrune = 1 +chosenMoveSubtract = 0 +chosenMoveTemperature = 0.15 +chosenMoveTemperatureEarly = 0.75 +chosenMoveTemperatureHalflife = 19 +compensateAfterPolicyInitProb = 0.2 +cpuctExploration = 1.1 +cpuctExplorationLog = 0.0 +cudaUseFP16 = auto +cudaUseNHWC = auto +dataBoardLen = 19 +drawEquivalentWinsForWhite = 0.5 +drawRandRadius = 0.5 +dynamicScoreCenterScale = 0.50 +dynamicScoreCenterZeroWeight = 0.25 +dynamicScoreUtilityFactor = 0.40 +earlyForkGameExpectedMoveProp = 0.025 +earlyForkGameMaxChoices = 12 +earlyForkGameProb = 0.04 +estimateLeadProb = 0.05 +fancyKomiVarying = true +firstFileRandMinProp = 0.15 +forkCompensateKomiProb = 0.80 +forkGameMaxChoices = 36 +forkGameMinChoices = 3 +forkGameProb = 0.01 +fpuParentWeightByVisitedPolicy = true +fpuParentWeightByVisitedPolicyPow = 2.0 +fpuReductionMax = 0.2 +handicapAsymmetricPlayoutProb = 0.5 +handicapCompensateKomiProb = 0.50 +handicapProb = 0.10 +hasButtons = false,false,true +initGamesWithPolicy = true +koRules = SIMPLE,POSITIONAL,SITUATIONAL +komiAuto = True +komiBigStdev = 12.0 +komiBigStdevProb = 0.06 +komiStdev = 1.0 +lcbStdevs = 5.0 +logGamesEvery = 10 +logMoves = false +logSearchInfo = false +logToStdout = true +maxAsymmetricRatio = 8.0 +maxDataQueueSize = 2000 +maxMovesPerGame = 1600 +maxRowsPerTrainFile = 10000 +maxVisits = 600 +minAsymmetricCompensateKomiProb = 0.4 +minVisitPropForLCB = 0.15 +multiStoneSuicideLegals = false,true +nnCacheSizePowerOfTwo = 21 +nnMaxBatchSize = 128 +nnMutexPoolSizePowerOfTwo = 15 +nnRandomize = true +noResultStdev = 0.166666666 +noResultUtilityForWhite = 0.0 +normalAsymmetricPlayoutProb = 0.01 +numGameThreads = 30 +numNNServerThreadsPerModel = 1 +numSearchThreads = 1 +numVirtualLossesPerThread = 1 +policyInitAreaProp = 0.04 +policySurpriseDataWeight = 0.5 +reduceVisits = true +reduceVisitsThreshold = 0.9 +reduceVisitsThresholdLookback = 3 +reducedVisitsMin = 100 +reducedVisitsWeight = 0.1 +rootDesiredPerChildVisitsCoeff = 2 +rootDirichletNoiseTotalConcentration = 10.83 +rootDirichletNoiseWeight = 0.25 +rootEndingBonusPoints = 0.5 +rootFpuReductionMax = 0.0 +rootNoiseEnabled = true +rootNumSymmetriesToSample = 4 +rootPolicyTemperature = 1.1 +rootPolicyTemperatureEarly = 1.25 +rootPruneUselessMoves = true +scoringRules = AREA,TERRITORY +sekiForkHackProb = 0.02 +sgfCompensateKomiProb = 0.90 +sidePositionProb = 0.020 +staticScoreUtilityFactor = 0.00 +subtreeValueBiasFactor = 0.30 +subtreeValueBiasWeightExponent = 0.8 +switchNetsMidGame = true +taxRules = NONE,NONE,SEKI,SEKI,ALL +useGraphSearch = true +useLcbForSelection = true +useNonBuggyLcb = true +valueSurpriseDataWeight = 0.1 +valueWeightExponent = 0.5 +winLossUtilityFactor = 1.0 + +2024-11-10 12:40:44-0500: Self Play Engine starting... +2024-11-10 12:40:44-0500: Git revision: 7939e2c3eb43df04e08de8c166889c6d5e937bfa +2024-11-10 12:40:44-0500: Loaded all config stuff, starting self play +2024-11-10 12:40:44-0500: Found new neural net kata1-b18c384nbt-s9908371456-d4296205232.bin.gz +2024-11-10 12:40:44-0500: nnRandSeed0 = 2704693968822434151 +2024-11-10 12:40:44-0500: After dedups: nnModelFile0 = C:\Users\chang\OneDrive\Documents\GitHub\Kata_speed\training\models\kata1-b18c384nbt-s9908371456-d4296205232.bin.gz useFP16 auto useNHWC auto +2024-11-10 12:40:44-0500: Initializing neural net buffer to be size 9 * 9 allowing smaller boards +2024-11-10 12:40:46-0500: Found OpenCL Platform 0: NVIDIA CUDA (NVIDIA Corporation) (OpenCL 3.0 CUDA 12.6.65) +2024-11-10 12:40:46-0500: Found 1 device(s) on platform 0 with type CPU or GPU or Accelerator +2024-11-10 12:40:46-0500: Found OpenCL Platform 1: Intel(R) OpenCL Graphics (Intel(R) Corporation) (OpenCL 3.0 ) +2024-11-10 12:40:46-0500: Found 1 device(s) on platform 1 with type CPU or GPU or Accelerator +2024-11-10 12:40:46-0500: Found OpenCL Device 0: NVIDIA GeForce RTX 4060 Laptop GPU (NVIDIA Corporation) (score 11000300) +2024-11-10 12:40:46-0500: Found OpenCL Device 1: Intel(R) UHD Graphics (Intel(R) Corporation) (score 6000300) +2024-11-10 12:40:46-0500: Creating context for OpenCL Platform: NVIDIA CUDA (NVIDIA Corporation) (OpenCL 3.0 CUDA 12.6.65) +2024-11-10 12:40:46-0500: Using OpenCL Device 0: NVIDIA GeForce RTX 4060 Laptop GPU (NVIDIA Corporation) OpenCL 3.0 CUDA (Extensions: cl_khr_global_int32_base_atomics cl_khr_global_int32_extended_atomics cl_khr_local_int32_base_atomics cl_khr_local_int32_extended_atomics cl_khr_fp64 cl_khr_3d_image_writes cl_khr_byte_addressable_store cl_khr_icd cl_khr_gl_sharing cl_nv_compiler_options cl_nv_device_attribute_query cl_nv_pragma_unroll cl_nv_d3d10_sharing cl_khr_d3d10_sharing cl_nv_d3d11_sharing cl_nv_copy_opts cl_nv_create_buffer cl_khr_int64_base_atomics cl_khr_int64_extended_atomics cl_khr_device_uuid cl_khr_pci_bus_info cl_khr_external_semaphore cl_khr_external_memory cl_khr_external_semaphore_win32 cl_khr_external_memory_win32) +2024-11-10 12:40:46-0500: Loaded tuning parameters from: C:\Users\chang\Downloads\katago-v1.15.1-opencl-windows-x64/KataGoData/opencltuning/tune11_gpuNVIDIAGeForceRTX4060LaptopGPU_x19_y19_c384_mv14.txt +2024-11-10 12:40:46-0500: OpenCL backend thread 0: Model version 14 +2024-11-10 12:40:46-0500: OpenCL backend thread 0: Model name: kata1-b18c384nbt-s9908371456-d4296205232 +2024-11-10 12:40:47-0500: OpenCL backend thread 0: FP16Storage true FP16Compute false FP16TensorCores true FP16TensorCoresFor1x1 false +2024-11-10 12:40:47-0500: Loaded latest neural net kata1-b18c384nbt-s9908371456-d4296205232.bin.gz from: C:\Users\chang\OneDrive\Documents\GitHub\Kata_speed\training\models\kata1-b18c384nbt-s9908371456-d4296205232.bin.gz +2024-11-10 12:40:47-0500: Model loading loop thread loaded new neural net kata1-b18c384nbt-s9908371456-d4296205232.bin.gz +2024-11-10 12:40:47-0500: Data write loop starting for neural net: kata1-b18c384nbt-s9908371456-d4296205232.bin.gz +2024-11-10 12:40:47-0500: Game loop thread 1 starting game on new neural net: kata1-b18c384nbt-s9908371456-d4296205232.bin.gz +2024-11-10 12:40:47-0500: Game loop thread 2 starting game on new neural net: kata1-b18c384nbt-s9908371456-d4296205232.bin.gz +2024-11-10 12:40:47-0500: Game loop thread 5 starting game on new neural net: kata1-b18c384nbt-s9908371456-d4296205232.bin.gz +2024-11-10 12:40:47-0500: Game loop thread 9 starting game on new neural net: kata1-b18c384nbt-s9908371456-d4296205232.bin.gz +2024-11-10 12:40:47-0500: Game loop thread 18 starting game on new neural net: kata1-b18c384nbt-s9908371456-d4296205232.bin.gz +2024-11-10 12:40:47-0500: Game loop thread 4 starting game on new neural net: kata1-b18c384nbt-s9908371456-d4296205232.bin.gz +2024-11-10 12:40:47-0500: Game loop thread 7 starting game on new neural net: kata1-b18c384nbt-s9908371456-d4296205232.bin.gz +2024-11-10 12:40:47-0500: Game loop thread 8 starting game on new neural net: kata1-b18c384nbt-s9908371456-d4296205232.bin.gz +2024-11-10 12:40:47-0500: Game loop thread 25 starting game on new neural net: kata1-b18c384nbt-s9908371456-d4296205232.bin.gz +2024-11-10 12:40:47-0500: Game loop thread 12 starting game on new neural net: kata1-b18c384nbt-s9908371456-d4296205232.bin.gz +2024-11-10 12:40:47-0500: Started 10 games with kata1-b18c384nbt-s9908371456-d4296205232.bin.gz +2024-11-10 12:40:47-0500: Game loop thread 10 starting game on new neural net: kata1-b18c384nbt-s9908371456-d4296205232.bin.gz +2024-11-10 12:40:47-0500: Game loop thread 0 starting game on new neural net: kata1-b18c384nbt-s9908371456-d4296205232.bin.gz +2024-11-10 12:40:47-0500: Game loop thread 13 starting game on new neural net: kata1-b18c384nbt-s9908371456-d4296205232.bin.gz +2024-11-10 12:40:47-0500: Game loop thread 11 starting game on new neural net: kata1-b18c384nbt-s9908371456-d4296205232.bin.gz +2024-11-10 12:40:47-0500: Game loop thread 14 starting game on new neural net: kata1-b18c384nbt-s9908371456-d4296205232.bin.gz +2024-11-10 12:40:47-0500: Game loop thread 15 starting game on new neural net: kata1-b18c384nbt-s9908371456-d4296205232.bin.gz +2024-11-10 12:40:47-0500: Game loop thread 16 starting game on new neural net: kata1-b18c384nbt-s9908371456-d4296205232.bin.gz +2024-11-10 12:40:47-0500: Game loop thread 17 starting game on new neural net: kata1-b18c384nbt-s9908371456-d4296205232.bin.gz +2024-11-10 12:40:47-0500: Game loop thread 6 starting game on new neural net: kata1-b18c384nbt-s9908371456-d4296205232.bin.gz +2024-11-10 12:40:47-0500: Game loop thread 19 starting game on new neural net: kata1-b18c384nbt-s9908371456-d4296205232.bin.gz +2024-11-10 12:40:47-0500: Started 20 games with kata1-b18c384nbt-s9908371456-d4296205232.bin.gz +2024-11-10 12:40:47-0500: Game loop thread 20 starting game on new neural net: kata1-b18c384nbt-s9908371456-d4296205232.bin.gz +2024-11-10 12:40:47-0500: Game loop thread 22 starting game on new neural net: kata1-b18c384nbt-s9908371456-d4296205232.bin.gz +2024-11-10 12:40:47-0500: Game loop thread 24 starting game on new neural net: kata1-b18c384nbt-s9908371456-d4296205232.bin.gz +2024-11-10 12:40:47-0500: Game loop thread 23 starting game on new neural net: kata1-b18c384nbt-s9908371456-d4296205232.bin.gz +2024-11-10 12:40:47-0500: Game loop thread 3 starting game on new neural net: kata1-b18c384nbt-s9908371456-d4296205232.bin.gz +2024-11-10 12:40:47-0500: Game loop thread 21 starting game on new neural net: kata1-b18c384nbt-s9908371456-d4296205232.bin.gz +2024-11-10 12:40:47-0500: Game loop thread 27 starting game on new neural net: kata1-b18c384nbt-s9908371456-d4296205232.bin.gz +2024-11-10 12:40:47-0500: Game loop thread 26 starting game on new neural net: kata1-b18c384nbt-s9908371456-d4296205232.bin.gz +2024-11-10 12:40:47-0500: Model loading loop thread starting +2024-11-10 12:40:47-0500: Game loop thread 29 starting game on new neural net: kata1-b18c384nbt-s9908371456-d4296205232.bin.gz +2024-11-10 12:40:47-0500: Game loop thread 28 starting game on new neural net: kata1-b18c384nbt-s9908371456-d4296205232.bin.gz +2024-11-10 12:40:47-0500: Started 30 games with kata1-b18c384nbt-s9908371456-d4296205232.bin.gz diff --git a/training/shuffleddata/20241110-123553/outtrain.txt b/training/shuffleddata/20241110-123553/outtrain.txt new file mode 100644 index 0000000..42a8faf --- /dev/null +++ b/training/shuffleddata/20241110-123553/outtrain.txt @@ -0,0 +1,34 @@ +Beginning: Finding files +Finished: Finding files in 0.0005304813385009766 seconds +Total number of files: 6 +Total number of files with unknown row count: 6 +Excluded count: 0 +Excluded count due to looking like temp file: 0 +Excluded count due to cmdline excludes file: 0 +GC collect +Beginning: Sorting +Finished: Sorting in 0.0 seconds +Beginning: Computing rows for unsummarized files +Finished: Computing rows for unsummarized files in 0.3340744972229004 seconds +Beginning: Processing found files +Finished: Processing found files in 0.0 seconds +Total rows found: 11675 (11675 usable) +Desired num rows: 4497 / 11675 +Beginning: Computing desired rows +Using: C:/Users/chang/OneDrive/Documents/GitHub/Kata_speed/training/selfplay/kata1-b18c384nbt-s9908371456-d4296205232.bin.gz\tdata\DD0089FFD56BCD20.npz (11273-11273) (402/4497 desired rows) +Using: C:/Users/chang/OneDrive/Documents/GitHub/Kata_speed/training/selfplay/kata1-b18c384nbt-s9908371456-d4296205232.bin.gz\tdata\F6CD6AF355B27725.npz (9962-9962) (1713/4497 desired rows) +Using: C:/Users/chang/OneDrive/Documents/GitHub/Kata_speed/training/selfplay/kata1-b18c384nbt-s9908371456-d4296205232.bin.gz\tdata\BB8D1D4E83F730DE.npz (8800-8800) (2875/4497 desired rows) +Using: C:/Users/chang/OneDrive/Documents/GitHub/Kata_speed/training/selfplay/kata1-b18c384nbt-s9908371456-d4296205232.bin.gz\tdata\9C008A7DD1B87C8D.npz (1677-1677) (9998/4497 desired rows) +Finished: Computing desired rows in 0.0 seconds +Finally, using: (1677-11675) (9998/4497 desired rows) +GC collect +Writing 1 output files with 9998 kept / 4497 desired rows +Due to only_include_md5, filtering down to 4/4 files +Grouping 4 input files into 1 sharding groups +Beginning: Sharding +Finished: Sharding in 0.9081718921661377 seconds +Beginning: Merging +Finished: Merging in 0.7742702960968018 seconds +Number of rows by output file: +[('C:/Users/chang/OneDrive/Documents/GitHub/Kata_speed/training/shuffleddata/20241110-123553/train\\data0.npz', 9984)] +Cleaning up tmp dir: 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-Finished: Finding files in 0.0 seconds -Total number of files: 2 -Total number of files with unknown row count: 2 +Finished: Finding files in 0.0005304813385009766 seconds +Total number of files: 6 +Total number of files with unknown row count: 6 Excluded count: 0 Excluded count due to looking like temp file: 0 Excluded count due to cmdline excludes file: 0 @@ -9,7 +9,26 @@ GC collect Beginning: Sorting Finished: Sorting in 0.0 seconds Beginning: Computing rows for unsummarized files -Finished: Computing rows for unsummarized files in 0.3282926082611084 seconds +Finished: Computing rows for unsummarized files in 0.3340744972229004 seconds Beginning: Processing found files Finished: Processing found files in 0.0 seconds -Not enough rows, only 1677 (fewer than 100000) +Total rows found: 11675 (11675 usable) +Desired num rows: 4497 / 11675 +Beginning: Computing desired rows +Using: C:/Users/chang/OneDrive/Documents/GitHub/Kata_speed/training/selfplay/kata1-b18c384nbt-s9908371456-d4296205232.bin.gz\tdata\DD0089FFD56BCD20.npz (11273-11273) (402/4497 desired rows) +Using: C:/Users/chang/OneDrive/Documents/GitHub/Kata_speed/training/selfplay/kata1-b18c384nbt-s9908371456-d4296205232.bin.gz\tdata\F6CD6AF355B27725.npz (9962-9962) (1713/4497 desired rows) +Using: C:/Users/chang/OneDrive/Documents/GitHub/Kata_speed/training/selfplay/kata1-b18c384nbt-s9908371456-d4296205232.bin.gz\tdata\BB8D1D4E83F730DE.npz (8800-8800) (2875/4497 desired rows) +Using: C:/Users/chang/OneDrive/Documents/GitHub/Kata_speed/training/selfplay/kata1-b18c384nbt-s9908371456-d4296205232.bin.gz\tdata\9C008A7DD1B87C8D.npz (1677-1677) (9998/4497 desired rows) +Finished: Computing desired rows in 0.0 seconds +Finally, using: (1677-11675) (9998/4497 desired rows) +GC collect +Writing 1 output files with 9998 kept / 4497 desired rows +Due to only_include_md5, filtering down to 4/4 files +Grouping 4 input files into 1 sharding groups +Beginning: Sharding +Finished: Sharding in 0.9081718921661377 seconds +Beginning: Merging +Finished: Merging in 0.7742702960968018 seconds +Number of rows by output file: +[('C:/Users/chang/OneDrive/Documents/GitHub/Kata_speed/training/shuffleddata/20241110-123553/train\\data0.npz', 9984)] +Cleaning up tmp dir: C:/Users/chang/OneDrive/Documents/GitHub/Kata_speed/training/shufflescratch/train\tmp.shuf0 diff --git a/training/shuffleddata/current_tmp/train.json b/training/shuffleddata/current_tmp/train.json new file mode 100644 index 0000000..f53783a --- /dev/null +++ b/training/shuffleddata/current_tmp/train.json @@ -0,0 +1 @@ +{"range": [1677, 11675]} \ No newline at end of file diff --git a/training/shuffleddata/current_tmp/train/data0.json b/training/shuffleddata/current_tmp/train/data0.json new file mode 100644 index 0000000..8b4d735 --- /dev/null +++ b/training/shuffleddata/current_tmp/train/data0.json @@ -0,0 +1 @@ +{"num_rows": 9998, "num_batches": 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