Only use tb_writer from master #11
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taylanbil
approved these changes
Apr 2, 2020
| logger.info('global_step: {global_step}, lr: {lr:.3f}, loss: {loss:.3f}'.format( | ||
| global_step=global_step, lr=scheduler.get_lr()[0], loss=loss_scalar)) | ||
| if xm.is_master_ordinal(): | ||
| for key, value in results.items(): |
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similar comment to prev. pr; all the values here need to be on cpu. if so, can you add a comment? it's a subtle point.
What do you mean by we don't log training related info? We do |
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I mean log to tensorboard during training. not a big deal. |
Oh I see. We actually do log training loss and lr in tensorboard during training. |
jysohn23
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Apr 10, 2020
* Initial commit to get BERT + run_glue.py on TPU * Add README section for TPU and address comments. * Cleanup TPU bits from run_glue.py (pytorch-tpu#3) TPU runner is currently implemented in: https://github.com/pytorch-tpu/transformers/blob/tpu/examples/run_glue_tpu.py. We plan to upstream this directly into `huggingface/transformers` (either `master` or `tpu`) branch once it's been more thoroughly tested. * Cleanup TPU bits from run_glue.py TPU runner is currently implemented in: https://github.com/pytorch-tpu/transformers/blob/tpu/examples/run_glue_tpu.py. We plan to upstream this directly into `huggingface/transformers` (either `master` or `tpu`) branch once it's been more thoroughly tested. * No need to call `xm.mark_step()` explicitly (pytorch-tpu#4) Since for gradient accumulation we're accumulating on batches from `ParallelLoader` instance which on next() marks the step itself. * Resolve R/W conflicts from multiprocessing (pytorch-tpu#5) * Add XLNet in list of models for `run_glue_tpu.py` (pytorch-tpu#6) * Add RoBERTa to list of models in TPU GLUE (pytorch-tpu#7) * Add RoBERTa and DistilBert to list of models in TPU GLUE (pytorch-tpu#8) * Use barriers to reduce duplicate work/resources (pytorch-tpu#9) * Shard eval dataset and aggregate eval metrics (pytorch-tpu#10) * Shard eval dataset and aggregate eval metrics Also, instead of calling `eval_loss.item()` every time do summation with tensors on device. * Change defaultdict to float * Reduce the pred, label tensors instead of metrics As brought up during review some metrics like f1 cannot be aggregated via averaging. GLUE task metrics depends largely on the dataset, so instead we sync the prediction and label tensors so that the metrics can be computed accurately on those instead. * Only use tb_writer from master (pytorch-tpu#11) * Apply huggingface black code formatting * Style * Remove `--do_lower_case` as example uses cased * Add option to specify tensorboard logdir This is needed for our testing framework which checks regressions against key metrics writtern by the summary writer. * Using configuration for `xla_device` * Prefix TPU specific comments. * num_cores clarification and namespace eval metrics * Cache features file under `args.cache_dir` Instead of under `args.data_dir`. This is needed as our test infra uses data_dir with a read-only filesystem. * Rename `run_glue_tpu` to `run_tpu_glue` Co-authored-by: LysandreJik <[email protected]>
alanwaketan
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Apr 17, 2024
* Cohere Model Release (#1) Cohere Model Release * Remove unnecessary files and code (#2) Some cleanup * Delete cohere-model directory (#3) * Make Fix (#5) * Pr fixes (#6) * fixes for pr * pr fixes for the format * pr fixes for the format * src/transformers/models/auto/tokenization_auto.py * Tokenizer test (#8) * tokenizer test * format fix * Adding Docs and other minor changes (#7) * Add modeling tests (#9) * Smol Fix (#11) * tokenization tests are fixed * format fixes * fix pr doc tests * fix pr doc tests * fix pr doc tests * fix pr style check * small changes in cohere.md * FIX: Address final comments for transformers integration (#13) * fix modeling final nits and add proper test file * for now leave empty tests * add integration test * push new test * fix modeling cohere (#14) * Update chat templates to use the new API (#15) --------- Co-authored-by: ahmetustun <[email protected]> Co-authored-by: Younes Belkada <[email protected]> Co-authored-by: Matt <[email protected]>
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