From f11dc38b45817fe5ad8dc2f768074e2a0eaa60f3 Mon Sep 17 00:00:00 2001 From: CjhHa1 Date: Thu, 19 Oct 2023 21:41:21 +0800 Subject: [PATCH 1/2] test --- .../dynamic_batching/ray_dist_init.py | 2 +- colossalai/inference/manager.py | 9 +- .../inference/tensor_parallel/engine.py | 1 + .../tensor_parallel/modeling/llama.py | 2 +- .../test_infer/test_dynamic_batching/out.txt | 1176 +++++++++++++++++ ...rd.py => test_offline_dynamic_batching.py} | 20 +- .../test_offline_dynamic_batching.py.py | 84 ++ 7 files changed, 1285 insertions(+), 9 deletions(-) create mode 100644 tests/test_infer/test_dynamic_batching/out.txt rename tests/test_infer/test_dynamic_batching/{test_forward.py => test_offline_dynamic_batching.py} (82%) create mode 100644 tests/test_infer/test_dynamic_batching/test_offline_dynamic_batching.py.py diff --git a/colossalai/inference/dynamic_batching/ray_dist_init.py b/colossalai/inference/dynamic_batching/ray_dist_init.py index 7639633eaa79..e3a261f96a86 100644 --- a/colossalai/inference/dynamic_batching/ray_dist_init.py +++ b/colossalai/inference/dynamic_batching/ray_dist_init.py @@ -145,7 +145,7 @@ def step(self): outputs = results[0] # get any one of the copies return outputs - def add_req(self, prompt_ids: List[int], sampling_params: SamplingParams, request_id: str, prompt: str): + def add_req(self, request_id: str, prompt_ids: List[int], sampling_params: SamplingParams, prompt: str): ray.get([w.add_req.remote(prompt_ids, sampling_params, request_id, prompt) for w in self.workers]) def is_running(self): diff --git a/colossalai/inference/manager.py b/colossalai/inference/manager.py index 18226d78c20c..7b9c44f3718f 100644 --- a/colossalai/inference/manager.py +++ b/colossalai/inference/manager.py @@ -58,7 +58,7 @@ def __init__( self.mem_usage_interval = log_stats_interval * 2 self.tokenizer = get_tokenizer(tokenizer_name=self.model) - def add_req(self, prompt_ids: List[int], sampling_params: SamplingParams, request_id: str, prompts: str = ""): + def add_req(self, request_id: str, prompt_ids: List[int], sampling_params: SamplingParams, prompts: str = ""): """ Add new request to req queue, during initialization all requests are held in waiting list. """ @@ -75,7 +75,7 @@ def add_input(self, request_id, prompts, sampling_params): if prompt_len > self.engine.max_input_len: raise ValueError(f"the input prompt token len {prompt_len} is too long > {self.engine.max_input_len}") sampling_params.stop_sentences_to_token_ids(self.tokenizer) - self.add_req(prompt_ids, sampling_params, request_id, prompts) + self.add_req(request_id, prompt_ids, sampling_params, prompts) return def abort(self, request_id): @@ -119,6 +119,7 @@ def _step(self): if self.running_batch is None: new_batch = self.req_queue.generate_new_batch(self.running_batch) + print(new_batch) if new_batch is not None: self.stats_tool.count_prompt_tokens(new_batch) self.running_batch = new_batch @@ -258,11 +259,11 @@ def clean_up(self): # this logic should be implemented in the future. pass - def generate(self, prompts, sampling_params, request_id): + def generate(self, request_id, prompts, sampling_params): """ Generate the output of a request. """ - self.add_input(request_id, sampling_params, prompts) + self.add_input(request_id, prompts, sampling_params) return self.loop_for_fwd() def is_running(self): diff --git a/colossalai/inference/tensor_parallel/engine.py b/colossalai/inference/tensor_parallel/engine.py index a98b96565c50..27984691b18b 100644 --- a/colossalai/inference/tensor_parallel/engine.py +++ b/colossalai/inference/tensor_parallel/engine.py @@ -402,6 +402,7 @@ def forward(self, batch_id, is_prefill): model = self.model.transformer setattr(model, "infer_state", infer_state) + print("input_ shape", input_.shape) output = self.model.forward(input_ids=input_) logits = output.logits # bsz, seq_len, vocab_size diff --git a/colossalai/inference/tensor_parallel/modeling/llama.py b/colossalai/inference/tensor_parallel/modeling/llama.py index 958868a0974e..700d80e32e70 100644 --- a/colossalai/inference/tensor_parallel/modeling/llama.py +++ b/colossalai/inference/tensor_parallel/modeling/llama.py @@ -76,7 +76,7 @@ def llama_model_forward( batch_size, seq_length, _ = inputs_embeds.shape else: raise ValueError("You have to specify either decoder_input_ids or decoder_inputs_embeds") - + print(batch_size, infer_state.seq_len) # NOT READY FOR PRIME TIME # dummy but work, revise it if infer_state.is_context_stage: diff --git a/tests/test_infer/test_dynamic_batching/out.txt b/tests/test_infer/test_dynamic_batching/out.txt new file mode 100644 index 000000000000..709de2acff3f --- /dev/null +++ b/tests/test_infer/test_dynamic_batching/out.txt @@ -0,0 +1,1176 @@ +/mnt/vepfs/lccjh/work/dynamic-batching/ColossalAI/colossalai/kernel/cuda_native/mha/flash_attn_2.py:28: UserWarning: please install flash_attn from https://github.com/HazyResearch/flash-attention + warnings.warn("please install flash_attn from https://github.com/HazyResearch/flash-attention") +/mnt/vepfs/lccjh/work/dynamic-batching/ColossalAI/colossalai/kernel/cuda_native/mha/mem_eff_attn.py:15: UserWarning: please install xformers from https://github.com/facebookresearch/xformers + warnings.warn("please install xformers from https://github.com/facebookresearch/xformers") +/home/lccjh/share/miniconda3/envs/lightllm/lib/python3.9/site-packages/torch/cuda/memory.py:282: FutureWarning: torch.cuda.reset_max_memory_allocated now calls torch.cuda.reset_peak_memory_stats, which resets /all/ peak memory stats. + warnings.warn( +/home/lccjh/share/miniconda3/envs/lightllm/lib/python3.9/site-packages/torch/cuda/memory.py:308: FutureWarning: torch.cuda.reset_max_memory_cached now calls torch.cuda.reset_peak_memory_stats, which resets /all/ peak memory stats. + warnings.warn( +/mnt/vepfs/lccjh/work/dynamic-batching/ColossalAI/colossalai/kernel/cuda_native/mha/flash_attn_2.py:28: UserWarning: please install flash_attn from https://github.com/HazyResearch/flash-attention + warnings.warn("please install flash_attn from https://github.com/HazyResearch/flash-attention") +/mnt/vepfs/lccjh/work/dynamic-batching/ColossalAI/colossalai/kernel/cuda_native/mha/mem_eff_attn.py:15: UserWarning: please install xformers from https://github.com/facebookresearch/xformers + warnings.warn("please install xformers from https://github.com/facebookresearch/xformers") +/mnt/vepfs/lccjh/work/dynamic-batching/ColossalAI/colossalai/initialize.py:48: UserWarning: `config` is deprecated and will be removed soon. + warnings.warn("`config` is deprecated and will be removed soon.") +[10/19/2023 06:38:34 PM] INFO colossalai - colossalai - INFO: /mnt/vepfs/lccjh/work/dynamic-batching/ColossalAI/colossalai/initialize.py:63 launch + INFO colossalai - colossalai - INFO: Distributed environment is initialized, world size: 1 +/opt/conda/conda-bld/pytorch_1670525551200/work/aten/src/ATen/native/cuda/Indexing.cu:1088: indexSelectSmallIndex: block: [0,0,0], thread: [32,0,0] Assertion `srcIndex < srcSelectDimSize` failed. 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+/opt/conda/conda-bld/pytorch_1670525551200/work/aten/src/ATen/native/cuda/Indexing.cu:1088: indexSelectSmallIndex: block: [4,0,0], thread: [93,0,0] Assertion `srcIndex < srcSelectDimSize` failed. +/opt/conda/conda-bld/pytorch_1670525551200/work/aten/src/ATen/native/cuda/Indexing.cu:1088: indexSelectSmallIndex: block: [4,0,0], thread: [94,0,0] Assertion `srcIndex < srcSelectDimSize` failed. +/opt/conda/conda-bld/pytorch_1670525551200/work/aten/src/ATen/native/cuda/Indexing.cu:1088: indexSelectSmallIndex: block: [4,0,0], thread: [95,0,0] Assertion `srcIndex < srcSelectDimSize` failed. +For some LLaMA-based models, initializing the fast tokenizer may take a long time. To eliminate the initialization time, consider using 'hf-internal-testing/llama-tokenizer' instead of the original tokenizer. This is done automatically in Colossalai. +batch_id=4a51112363ac4e0786a360db888a0712, reqs=[request_id(n=0, prompt_ids=[0, 0, 10, 6, 8], , request_id(n=1, prompt_ids=[10, 10, 10, 10, 10], ], +input_ shape torch.Size([2, 5]) +2 tensor([5, 5], device='cuda:0', dtype=torch.int32) +bsz tgt_len 2 5 +cuda:0 +input_ shape torch.Size([2, 1]) +2 tensor([6, 6], device='cuda:0', dtype=torch.int32) +input_ shape torch.Size([2, 1]) +2 tensor([7, 7], device='cuda:0', dtype=torch.int32) +input_ shape torch.Size([2, 1]) +2 tensor([8, 8], device='cuda:0', dtype=torch.int32) +input_ shape torch.Size([2, 1]) +2 tensor([9, 9], device='cuda:0', dtype=torch.int32) +input_ shape torch.Size([2, 1]) +2 tensor([10, 10], device='cuda:0', dtype=torch.int32) +input_ shape torch.Size([2, 1]) +2 tensor([11, 11], device='cuda:0', dtype=torch.int32) +input_ shape torch.Size([2, 1]) +2 tensor([12, 12], device='cuda:0', dtype=torch.int32) +input_ shape torch.Size([2, 1]) +2 tensor([13, 13], device='cuda:0', dtype=torch.int32) +input_ shape torch.Size([2, 1]) +2 tensor([14, 14], device='cuda:0', dtype=torch.int32) +input_ shape torch.Size([2, 1]) +2 tensor([15, 15], device='cuda:0', dtype=torch.int32) +input_ shape torch.Size([2, 1]) +2 tensor([16, 16], device='cuda:0', dtype=torch.int32) +input_ shape torch.Size([2, 1]) +2 tensor([17, 17], device='cuda:0', dtype=torch.int32) +input_ shape torch.Size([2, 1]) +2 tensor([18, 18], device='cuda:0', dtype=torch.int32) +input_ shape torch.Size([2, 1]) +2 tensor([19, 19], device='cuda:0', dtype=torch.int32) +input_ shape torch.Size([2, 1]) +2 tensor([20, 20], device='cuda:0', dtype=torch.int32) +[ [ad was his his his me�� over v dis� duck +���ellellportportellinararXellar [ in +batch_id=8725fe92c6a04b94a52e511db630743d, reqs=[request_id(n=2, prompt_ids=[0, 0, 10, 10, 10], , request_id(n=3, prompt_ids=[0, 0, 10, 10, 10], ], +input_ shape torch.Size([2, 5]) +2 tensor([5, 5], device='cuda:0', dtype=torch.int32) +bsz tgt_len 2 5 +cuda:0 +input_ shape torch.Size([2, 1]) +2 tensor([6, 6], device='cuda:0', dtype=torch.int32) +input_ shape torch.Size([2, 1]) +2 tensor([7, 7], device='cuda:0', dtype=torch.int32) +input_ shape torch.Size([2, 1]) +2 tensor([8, 8], device='cuda:0', dtype=torch.int32) +current batch size: 2 token used ratio: 0.42857142857142855 +input_ shape torch.Size([2, 1]) +2 tensor([9, 9], device='cuda:0', dtype=torch.int32) +input_ shape torch.Size([2, 1]) +2 tensor([10, 10], device='cuda:0', dtype=torch.int32) +input_ shape torch.Size([2, 1]) +2 tensor([11, 11], device='cuda:0', dtype=torch.int32) +input_ shape torch.Size([2, 1]) +2 tensor([12, 12], device='cuda:0', dtype=torch.int32) +input_ shape torch.Size([2, 1]) +2 tensor([13, 13], device='cuda:0', dtype=torch.int32) +input_ shape torch.Size([2, 1]) +2 tensor([14, 14], device='cuda:0', dtype=torch.int32) +input_ shape torch.Size([2, 1]) +2 tensor([15, 15], device='cuda:0', dtype=torch.int32) +input_ shape torch.Size([2, 1]) +2 tensor([16, 16], device='cuda:0', dtype=torch.int32) +input_ shape torch.Size([2, 1]) +2 tensor([17, 17], device='cuda:0', dtype=torch.int32) +input_ shape torch.Size([2, 1]) +2 tensor([18, 18], device='cuda:0', dtype=torch.int32) +input_ shape torch.Size([2, 1]) +2 tensor([19, 19], device='cuda:0', dtype=torch.int32) +input_ shape torch.Size([2, 1]) +2 tensor([20, 20], device='cuda:0', dtype=torch.int32) +� ver Com [ [ [letnerend� Yapp Y Y Y Y +� ver Com [ [ [letnerend� Yapp Y Y Y Y +batch_id=c0599e71d99e4ee989ebf470946fa475, reqs=[request_id(n=5, prompt_ids=[1, 29871, 30919, 31076], ], +input_ shape torch.Size([1, 4]) +1 tensor([4], device='cuda:0', dtype=torch.int32) +bsz tgt_len 1 4 +cuda:0 +Maximum number of attempts is reached or pattern is not matched, no more retrying... +Traceback (most recent call last): + File "/mnt/vepfs/lccjh/work/dynamic-batching/ColossalAI/tests/test_infer/test_dynamic_batching/test_offline_dynamic_batching.py", line 75, in + test_dynamic_batching() + File "/mnt/vepfs/lccjh/work/dynamic-batching/ColossalAI/colossalai/testing/utils.py", line 142, in _run_until_success + raise e + File "/mnt/vepfs/lccjh/work/dynamic-batching/ColossalAI/colossalai/testing/utils.py", line 131, in _run_until_success + ret = func(*args, **kwargs) + File "/mnt/vepfs/lccjh/work/dynamic-batching/ColossalAI/colossalai/testing/utils.py", line 271, in _clear_cache + f(*args, **kwargs) + File "/mnt/vepfs/lccjh/work/dynamic-batching/ColossalAI/tests/test_infer/test_dynamic_batching/test_offline_dynamic_batching.py", line 71, in test_dynamic_batching + spawn(check_dynamic_forward, TP_SIZE) + File "/mnt/vepfs/lccjh/work/dynamic-batching/ColossalAI/colossalai/testing/utils.py", line 250, in spawn + mp.spawn(wrapped_func, nprocs=nprocs) + File "/home/lccjh/share/miniconda3/envs/lightllm/lib/python3.9/site-packages/torch/multiprocessing/spawn.py", line 240, in spawn + return start_processes(fn, args, nprocs, join, daemon, start_method='spawn') + File "/home/lccjh/share/miniconda3/envs/lightllm/lib/python3.9/site-packages/torch/multiprocessing/spawn.py", line 198, in start_processes + while not context.join(): + File "/home/lccjh/share/miniconda3/envs/lightllm/lib/python3.9/site-packages/torch/multiprocessing/spawn.py", line 160, in join + raise ProcessRaisedException(msg, error_index, failed_process.pid) +torch.multiprocessing.spawn.ProcessRaisedException: + +-- Process 0 terminated with the following error: +Traceback (most recent call last): + File "/home/lccjh/share/miniconda3/envs/lightllm/lib/python3.9/site-packages/torch/multiprocessing/spawn.py", line 69, in _wrap + fn(i, *args) + File "/mnt/vepfs/lccjh/work/dynamic-batching/ColossalAI/tests/test_infer/test_dynamic_batching/test_offline_dynamic_batching.py", line 63, in check_dynamic_forward + run() + File "/mnt/vepfs/lccjh/work/dynamic-batching/ColossalAI/tests/test_infer/test_dynamic_batching/test_offline_dynamic_batching.py", line 57, in run + for result in ans_gen: + File "/mnt/vepfs/lccjh/work/dynamic-batching/ColossalAI/colossalai/inference/manager.py", line 100, in loop_for_fwd + yield from self._step() + File "/mnt/vepfs/lccjh/work/dynamic-batching/ColossalAI/colossalai/inference/manager.py", line 126, in _step + yield from self._prefill_batch(self.running_batch) + File "/mnt/vepfs/lccjh/work/dynamic-batching/ColossalAI/colossalai/inference/manager.py", line 184, in _prefill_batch + ans = self.engine._prefill_batch(batch.batch_id) + File "/home/lccjh/share/miniconda3/envs/lightllm/lib/python3.9/site-packages/torch/autograd/grad_mode.py", line 27, in decorate_context + return func(*args, **kwargs) + File "/mnt/vepfs/lccjh/work/dynamic-batching/ColossalAI/colossalai/inference/tensor_parallel/engine.py", line 450, in _prefill_batch + return self.forward(batch_id, is_prefill=True) + File "/home/lccjh/share/miniconda3/envs/lightllm/lib/python3.9/site-packages/torch/autograd/grad_mode.py", line 27, in decorate_context + return func(*args, **kwargs) + File "/mnt/vepfs/lccjh/work/dynamic-batching/ColossalAI/colossalai/inference/tensor_parallel/engine.py", line 406, in forward + output = self.model.forward(input_ids=input_) + File "/home/lccjh/share/miniconda3/envs/lightllm/lib/python3.9/site-packages/transformers/models/llama/modeling_llama.py", line 689, in forward + outputs = self.model( + File "/home/lccjh/share/miniconda3/envs/lightllm/lib/python3.9/site-packages/torch/nn/modules/module.py", line 1194, in _call_impl + return forward_call(*input, **kwargs) + File "/mnt/vepfs/lccjh/work/dynamic-batching/ColossalAI/colossalai/inference/tensor_parallel/modeling/llama.py", line 148, in llama_model_forward + attention_mask = self._prepare_decoder_attention_mask( + File "/home/lccjh/share/miniconda3/envs/lightllm/lib/python3.9/site-packages/transformers/models/llama/modeling_llama.py", line 467, in _prepare_decoder_attention_mask + combined_attention_mask = _make_causal_mask( + File "/home/lccjh/share/miniconda3/envs/lightllm/lib/python3.9/site-packages/transformers/models/llama/modeling_llama.py", line 51, in _make_causal_mask + mask = torch.full((tgt_len, tgt_len), torch.tensor(torch.finfo(dtype).min, device=device), device=device) +RuntimeError: CUDA error: device-side assert triggered diff --git a/tests/test_infer/test_dynamic_batching/test_forward.py b/tests/test_infer/test_dynamic_batching/test_offline_dynamic_batching.py similarity index 82% rename from tests/test_infer/test_dynamic_batching/test_forward.py rename to tests/test_infer/test_dynamic_batching/test_offline_dynamic_batching.py index 63df491e5b52..f8fa18f69a0c 100644 --- a/tests/test_infer/test_dynamic_batching/test_forward.py +++ b/tests/test_infer/test_dynamic_batching/test_offline_dynamic_batching.py @@ -1,3 +1,5 @@ +from dataclasses import dataclass + import pytest import torch from packaging import version @@ -5,7 +7,6 @@ from transformers.models.llama.configuration_llama import LlamaConfig import colossalai -from dataclasses import dataclass from colossalai.inference.dynamic_batching.io_struct import Req from colossalai.inference.dynamic_batching.sampling_params import SamplingParams from colossalai.inference.manager import start_dynamic_batching @@ -19,17 +20,26 @@ MAX_OUTPUT_LEN = 16 CUDA_SUPPORT = version.parse(torch.version.cuda) > version.parse("11.5") + @dataclass class args: max_total_token_num: int batch_max_tokens: int + model: str eos_id: int disable_log_stats: bool log_stats_interval: int def run(): - arg = args(max_total_token_num=42, batch_max_tokens=42, eos_id=0, disable_log_stats=False, log_stats_interval=10) + arg = args( + max_total_token_num=42, + model="llama", + batch_max_tokens=42, + eos_id=0, + disable_log_stats=False, + log_stats_interval=10, + ) sampling_params = SamplingParams() req1 = Req(0, [0, 0, 10, 6, 8], sampling_params) @@ -50,7 +60,11 @@ def run(): shard_config = ShardConfig(enable_tensor_parallelism=True if TP_SIZE > 1 else False, inference_only=True) infer_engine = TPInferEngine(model, shard_config, MAX_BATCH_SIZE, MAX_INPUT_LEN, MAX_OUTPUT_LEN) - start_dynamic_batching(arg, tp_engine=infer_engine, waiting_req_list=waiting_list) + batch_manager = start_dynamic_batching(arg, tp_engine=infer_engine, waiting_req_list=waiting_list) + + ans_gen = batch_manager.generate(request_id=5, prompts="你好", sampling_params=sampling_params) + for result in ans_gen: + print(result) def check_dynamic_forward(rank, world_size, port): diff --git a/tests/test_infer/test_dynamic_batching/test_offline_dynamic_batching.py.py b/tests/test_infer/test_dynamic_batching/test_offline_dynamic_batching.py.py new file mode 100644 index 000000000000..c7b3f8c9cf17 --- /dev/null +++ b/tests/test_infer/test_dynamic_batching/test_offline_dynamic_batching.py.py @@ -0,0 +1,84 @@ +from dataclasses import dataclass + +import pytest +import torch +from packaging import version +from transformers import LlamaForCausalLM +from transformers.models.llama.configuration_llama import LlamaConfig + +import colossalai +from colossalai.inference.dynamic_batching.io_struct import Req +from colossalai.inference.dynamic_batching.sampling_params import SamplingParams +from colossalai.inference.manager import start_dynamic_batching +from colossalai.inference.tensor_parallel import TPInferEngine +from colossalai.shardformer import ShardConfig +from colossalai.testing import clear_cache_before_run, rerun_if_address_is_in_use, spawn + +TP_SIZE = 1 +MAX_BATCH_SIZE = 2 +MAX_INPUT_LEN = 5 +MAX_OUTPUT_LEN = 16 +CUDA_SUPPORT = version.parse(torch.version.cuda) > version.parse("11.5") + + +@dataclass +class args: + max_total_token_num: int + batch_max_tokens: int + model: str + eos_id: int + disable_log_stats: bool + log_stats_interval: int + + +def run(): + arg = args( + max_total_token_num=42, + model="llama", + batch_max_tokens=42, + eos_id=0, + disable_log_stats=False, + log_stats_interval=10, + ) + sampling_params = SamplingParams() + + req1 = Req(0, [0, 0, 10, 6, 8], sampling_params) + req2 = Req(1, [10, 10, 10, 10, 10], sampling_params) + req3 = Req(2, [0, 0, 10, 10, 10], sampling_params) + req4 = Req(3, [0, 0, 10, 10, 10], sampling_params) + + waiting_list = [] + waiting_list.append(req1) + waiting_list.append(req2) + waiting_list.append(req3) + waiting_list.append(req4) + + llama_config = LlamaConfig(num_hidden_layers=2, bos_token_id=0, eos_token_id=1, vocab_size=1200, hidden_size=1024) + model = LlamaForCausalLM(llama_config) + model = model.half() + + shard_config = ShardConfig(enable_tensor_parallelism=True if TP_SIZE > 1 else False, inference_only=True) + + infer_engine = TPInferEngine(model, shard_config, MAX_BATCH_SIZE, MAX_INPUT_LEN, MAX_OUTPUT_LEN) + batch_manager = start_dynamic_batching(arg, tp_engine=infer_engine, waiting_req_list=waiting_list) + + ans_gen = batch_manager.generate(request_id=5, prompt=[10, 10, 10], sampling_params=sampling_params) + for result in ans_gen: + print(result) + + +def check_dynamic_forward(rank, world_size, port): + colossalai.launch(config={}, rank=rank, world_size=world_size, host="localhost", port=port, backend="nccl") + run() + + +@pytest.mark.skipif(not CUDA_SUPPORT, reason="kv-cache manager engine requires cuda version to be higher than 11.5") +@pytest.mark.dist +@rerun_if_address_is_in_use() +@clear_cache_before_run() +def test_dynamic_batching(): + spawn(check_dynamic_forward, TP_SIZE) + + +if __name__ == "__main__": + test_dynamic_batching() From 35aadba0faa6e1400f899b5d86d5d46e7c37b77c Mon Sep 17 00:00:00 2001 From: CjhHa1 Date: Thu, 19 Oct 2023 22:19:13 +0800 Subject: [PATCH 2/2] fix test --- colossalai/inference/manager.py | 1 - .../inference/tensor_parallel/engine.py | 3 +- .../tensor_parallel/modeling/llama.py | 3 +- .../test_infer/test_dynamic_batching/out.txt | 1176 ----------------- .../test_offline_dynamic_batching.py | 6 +- .../test_offline_dynamic_batching.py.py | 84 -- 6 files changed, 5 insertions(+), 1268 deletions(-) delete mode 100644 tests/test_infer/test_dynamic_batching/out.txt delete mode 100644 tests/test_infer/test_dynamic_batching/test_offline_dynamic_batching.py.py diff --git a/colossalai/inference/manager.py b/colossalai/inference/manager.py index 7b9c44f3718f..55a75d5e3925 100644 --- a/colossalai/inference/manager.py +++ b/colossalai/inference/manager.py @@ -119,7 +119,6 @@ def _step(self): if self.running_batch is None: new_batch = self.req_queue.generate_new_batch(self.running_batch) - print(new_batch) if new_batch is not None: self.stats_tool.count_prompt_tokens(new_batch) self.running_batch = new_batch diff --git a/colossalai/inference/tensor_parallel/engine.py b/colossalai/inference/tensor_parallel/engine.py index 27984691b18b..e75004d506a3 100644 --- a/colossalai/inference/tensor_parallel/engine.py +++ b/colossalai/inference/tensor_parallel/engine.py @@ -400,9 +400,8 @@ def forward(self, batch_id, is_prefill): model = self.model.model elif isinstance(model, BloomForCausalLM): model = self.model.transformer + setattr(model, "infer_state", infer_state) - - print("input_ shape", input_.shape) output = self.model.forward(input_ids=input_) logits = output.logits # bsz, seq_len, vocab_size diff --git a/colossalai/inference/tensor_parallel/modeling/llama.py b/colossalai/inference/tensor_parallel/modeling/llama.py index 700d80e32e70..7e6978ad815b 100644 --- a/colossalai/inference/tensor_parallel/modeling/llama.py +++ b/colossalai/inference/tensor_parallel/modeling/llama.py @@ -76,14 +76,13 @@ def llama_model_forward( batch_size, seq_length, _ = inputs_embeds.shape else: raise ValueError("You have to specify either decoder_input_ids or decoder_inputs_embeds") - print(batch_size, infer_state.seq_len) # NOT READY FOR PRIME TIME # dummy but work, revise it if infer_state.is_context_stage: past_key_values_length = 0 else: past_key_values_length = infer_state.max_len_in_batch - 1 - + # NOTE: differentiate with prefill stage # block_loc require different value-assigning method for two different stage if use_cache and seq_length != 1: diff --git a/tests/test_infer/test_dynamic_batching/out.txt b/tests/test_infer/test_dynamic_batching/out.txt deleted file mode 100644 index 709de2acff3f..000000000000 --- a/tests/test_infer/test_dynamic_batching/out.txt +++ /dev/null @@ -1,1176 +0,0 @@ -/mnt/vepfs/lccjh/work/dynamic-batching/ColossalAI/colossalai/kernel/cuda_native/mha/flash_attn_2.py:28: UserWarning: please install flash_attn from https://github.com/HazyResearch/flash-attention - warnings.warn("please install flash_attn from https://github.com/HazyResearch/flash-attention") -/mnt/vepfs/lccjh/work/dynamic-batching/ColossalAI/colossalai/kernel/cuda_native/mha/mem_eff_attn.py:15: UserWarning: please install xformers from https://github.com/facebookresearch/xformers - warnings.warn("please install xformers from https://github.com/facebookresearch/xformers") -/home/lccjh/share/miniconda3/envs/lightllm/lib/python3.9/site-packages/torch/cuda/memory.py:282: FutureWarning: torch.cuda.reset_max_memory_allocated now calls torch.cuda.reset_peak_memory_stats, which resets /all/ peak memory stats. - warnings.warn( -/home/lccjh/share/miniconda3/envs/lightllm/lib/python3.9/site-packages/torch/cuda/memory.py:308: FutureWarning: torch.cuda.reset_max_memory_cached now calls torch.cuda.reset_peak_memory_stats, which resets /all/ peak memory stats. - warnings.warn( -/mnt/vepfs/lccjh/work/dynamic-batching/ColossalAI/colossalai/kernel/cuda_native/mha/flash_attn_2.py:28: UserWarning: please install flash_attn from https://github.com/HazyResearch/flash-attention - warnings.warn("please install flash_attn from https://github.com/HazyResearch/flash-attention") -/mnt/vepfs/lccjh/work/dynamic-batching/ColossalAI/colossalai/kernel/cuda_native/mha/mem_eff_attn.py:15: UserWarning: please install xformers from https://github.com/facebookresearch/xformers - warnings.warn("please install xformers from https://github.com/facebookresearch/xformers") -/mnt/vepfs/lccjh/work/dynamic-batching/ColossalAI/colossalai/initialize.py:48: UserWarning: `config` is deprecated and will be removed soon. - warnings.warn("`config` is deprecated and will be removed soon.") -[10/19/2023 06:38:34 PM] INFO colossalai - colossalai - INFO: /mnt/vepfs/lccjh/work/dynamic-batching/ColossalAI/colossalai/initialize.py:63 launch - INFO colossalai - colossalai - INFO: Distributed environment is initialized, world size: 1 -/opt/conda/conda-bld/pytorch_1670525551200/work/aten/src/ATen/native/cuda/Indexing.cu:1088: indexSelectSmallIndex: block: [0,0,0], thread: [32,0,0] Assertion `srcIndex < srcSelectDimSize` failed. -/opt/conda/conda-bld/pytorch_1670525551200/work/aten/src/ATen/native/cuda/Indexing.cu:1088: indexSelectSmallIndex: block: [0,0,0], thread: [33,0,0] Assertion `srcIndex < srcSelectDimSize` failed. 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To eliminate the initialization time, consider using 'hf-internal-testing/llama-tokenizer' instead of the original tokenizer. This is done automatically in Colossalai. -batch_id=4a51112363ac4e0786a360db888a0712, reqs=[request_id(n=0, prompt_ids=[0, 0, 10, 6, 8], , request_id(n=1, prompt_ids=[10, 10, 10, 10, 10], ], -input_ shape torch.Size([2, 5]) -2 tensor([5, 5], device='cuda:0', dtype=torch.int32) -bsz tgt_len 2 5 -cuda:0 -input_ shape torch.Size([2, 1]) -2 tensor([6, 6], device='cuda:0', dtype=torch.int32) -input_ shape torch.Size([2, 1]) -2 tensor([7, 7], device='cuda:0', dtype=torch.int32) -input_ shape torch.Size([2, 1]) -2 tensor([8, 8], device='cuda:0', dtype=torch.int32) -input_ shape torch.Size([2, 1]) -2 tensor([9, 9], device='cuda:0', dtype=torch.int32) -input_ shape torch.Size([2, 1]) -2 tensor([10, 10], device='cuda:0', dtype=torch.int32) -input_ shape torch.Size([2, 1]) -2 tensor([11, 11], device='cuda:0', dtype=torch.int32) -input_ shape torch.Size([2, 1]) -2 tensor([12, 12], device='cuda:0', dtype=torch.int32) -input_ shape torch.Size([2, 1]) -2 tensor([13, 13], device='cuda:0', dtype=torch.int32) -input_ shape torch.Size([2, 1]) -2 tensor([14, 14], device='cuda:0', dtype=torch.int32) -input_ shape torch.Size([2, 1]) -2 tensor([15, 15], device='cuda:0', dtype=torch.int32) -input_ shape torch.Size([2, 1]) -2 tensor([16, 16], device='cuda:0', dtype=torch.int32) -input_ shape torch.Size([2, 1]) -2 tensor([17, 17], device='cuda:0', dtype=torch.int32) -input_ shape torch.Size([2, 1]) -2 tensor([18, 18], device='cuda:0', dtype=torch.int32) -input_ shape torch.Size([2, 1]) -2 tensor([19, 19], device='cuda:0', dtype=torch.int32) -input_ shape torch.Size([2, 1]) -2 tensor([20, 20], device='cuda:0', dtype=torch.int32) -[ [ad was his his his me�� over v dis� duck -���ellellportportellinararXellar [ in -batch_id=8725fe92c6a04b94a52e511db630743d, reqs=[request_id(n=2, prompt_ids=[0, 0, 10, 10, 10], , request_id(n=3, prompt_ids=[0, 0, 10, 10, 10], ], -input_ shape torch.Size([2, 5]) -2 tensor([5, 5], device='cuda:0', dtype=torch.int32) -bsz tgt_len 2 5 -cuda:0 -input_ shape torch.Size([2, 1]) -2 tensor([6, 6], device='cuda:0', dtype=torch.int32) -input_ shape torch.Size([2, 1]) -2 tensor([7, 7], device='cuda:0', dtype=torch.int32) -input_ shape torch.Size([2, 1]) -2 tensor([8, 8], device='cuda:0', dtype=torch.int32) -current batch size: 2 token used ratio: 0.42857142857142855 -input_ shape torch.Size([2, 1]) -2 tensor([9, 9], device='cuda:0', dtype=torch.int32) -input_ shape torch.Size([2, 1]) -2 tensor([10, 10], device='cuda:0', dtype=torch.int32) -input_ shape torch.Size([2, 1]) -2 tensor([11, 11], device='cuda:0', dtype=torch.int32) -input_ shape torch.Size([2, 1]) -2 tensor([12, 12], device='cuda:0', dtype=torch.int32) -input_ shape torch.Size([2, 1]) -2 tensor([13, 13], device='cuda:0', dtype=torch.int32) -input_ shape torch.Size([2, 1]) -2 tensor([14, 14], device='cuda:0', dtype=torch.int32) -input_ shape torch.Size([2, 1]) -2 tensor([15, 15], device='cuda:0', dtype=torch.int32) -input_ shape torch.Size([2, 1]) -2 tensor([16, 16], device='cuda:0', dtype=torch.int32) -input_ shape torch.Size([2, 1]) -2 tensor([17, 17], device='cuda:0', dtype=torch.int32) -input_ shape torch.Size([2, 1]) -2 tensor([18, 18], device='cuda:0', dtype=torch.int32) -input_ shape torch.Size([2, 1]) -2 tensor([19, 19], device='cuda:0', dtype=torch.int32) -input_ shape torch.Size([2, 1]) -2 tensor([20, 20], device='cuda:0', dtype=torch.int32) -� ver Com [ [ [letnerend� Yapp Y Y Y Y -� ver Com [ [ [letnerend� Yapp Y Y Y Y -batch_id=c0599e71d99e4ee989ebf470946fa475, reqs=[request_id(n=5, prompt_ids=[1, 29871, 30919, 31076], ], -input_ shape torch.Size([1, 4]) -1 tensor([4], device='cuda:0', dtype=torch.int32) -bsz tgt_len 1 4 -cuda:0 -Maximum number of attempts is reached or pattern is not matched, no more retrying... -Traceback (most recent call last): - File "/mnt/vepfs/lccjh/work/dynamic-batching/ColossalAI/tests/test_infer/test_dynamic_batching/test_offline_dynamic_batching.py", line 75, in - test_dynamic_batching() - File "/mnt/vepfs/lccjh/work/dynamic-batching/ColossalAI/colossalai/testing/utils.py", line 142, in _run_until_success - raise e - File "/mnt/vepfs/lccjh/work/dynamic-batching/ColossalAI/colossalai/testing/utils.py", line 131, in _run_until_success - ret = func(*args, **kwargs) - File "/mnt/vepfs/lccjh/work/dynamic-batching/ColossalAI/colossalai/testing/utils.py", line 271, in _clear_cache - f(*args, **kwargs) - File "/mnt/vepfs/lccjh/work/dynamic-batching/ColossalAI/tests/test_infer/test_dynamic_batching/test_offline_dynamic_batching.py", line 71, in test_dynamic_batching - spawn(check_dynamic_forward, TP_SIZE) - File "/mnt/vepfs/lccjh/work/dynamic-batching/ColossalAI/colossalai/testing/utils.py", line 250, in spawn - mp.spawn(wrapped_func, nprocs=nprocs) - File "/home/lccjh/share/miniconda3/envs/lightllm/lib/python3.9/site-packages/torch/multiprocessing/spawn.py", line 240, in spawn - return start_processes(fn, args, nprocs, join, daemon, start_method='spawn') - File "/home/lccjh/share/miniconda3/envs/lightllm/lib/python3.9/site-packages/torch/multiprocessing/spawn.py", line 198, in start_processes - while not context.join(): - File "/home/lccjh/share/miniconda3/envs/lightllm/lib/python3.9/site-packages/torch/multiprocessing/spawn.py", line 160, in join - raise ProcessRaisedException(msg, error_index, failed_process.pid) -torch.multiprocessing.spawn.ProcessRaisedException: - --- Process 0 terminated with the following error: -Traceback (most recent call last): - File "/home/lccjh/share/miniconda3/envs/lightllm/lib/python3.9/site-packages/torch/multiprocessing/spawn.py", line 69, in _wrap - fn(i, *args) - File "/mnt/vepfs/lccjh/work/dynamic-batching/ColossalAI/tests/test_infer/test_dynamic_batching/test_offline_dynamic_batching.py", line 63, in check_dynamic_forward - run() - File "/mnt/vepfs/lccjh/work/dynamic-batching/ColossalAI/tests/test_infer/test_dynamic_batching/test_offline_dynamic_batching.py", line 57, in run - for result in ans_gen: - File "/mnt/vepfs/lccjh/work/dynamic-batching/ColossalAI/colossalai/inference/manager.py", line 100, in loop_for_fwd - yield from self._step() - File "/mnt/vepfs/lccjh/work/dynamic-batching/ColossalAI/colossalai/inference/manager.py", line 126, in _step - yield from self._prefill_batch(self.running_batch) - File "/mnt/vepfs/lccjh/work/dynamic-batching/ColossalAI/colossalai/inference/manager.py", line 184, in _prefill_batch - ans = self.engine._prefill_batch(batch.batch_id) - File "/home/lccjh/share/miniconda3/envs/lightllm/lib/python3.9/site-packages/torch/autograd/grad_mode.py", line 27, in decorate_context - return func(*args, **kwargs) - File "/mnt/vepfs/lccjh/work/dynamic-batching/ColossalAI/colossalai/inference/tensor_parallel/engine.py", line 450, in _prefill_batch - return self.forward(batch_id, is_prefill=True) - File "/home/lccjh/share/miniconda3/envs/lightllm/lib/python3.9/site-packages/torch/autograd/grad_mode.py", line 27, in decorate_context - return func(*args, **kwargs) - File "/mnt/vepfs/lccjh/work/dynamic-batching/ColossalAI/colossalai/inference/tensor_parallel/engine.py", line 406, in forward - output = self.model.forward(input_ids=input_) - File "/home/lccjh/share/miniconda3/envs/lightllm/lib/python3.9/site-packages/transformers/models/llama/modeling_llama.py", line 689, in forward - outputs = self.model( - File "/home/lccjh/share/miniconda3/envs/lightllm/lib/python3.9/site-packages/torch/nn/modules/module.py", line 1194, in _call_impl - return forward_call(*input, **kwargs) - File "/mnt/vepfs/lccjh/work/dynamic-batching/ColossalAI/colossalai/inference/tensor_parallel/modeling/llama.py", line 148, in llama_model_forward - attention_mask = self._prepare_decoder_attention_mask( - File "/home/lccjh/share/miniconda3/envs/lightllm/lib/python3.9/site-packages/transformers/models/llama/modeling_llama.py", line 467, in _prepare_decoder_attention_mask - combined_attention_mask = _make_causal_mask( - File "/home/lccjh/share/miniconda3/envs/lightllm/lib/python3.9/site-packages/transformers/models/llama/modeling_llama.py", line 51, in _make_causal_mask - mask = torch.full((tgt_len, tgt_len), torch.tensor(torch.finfo(dtype).min, device=device), device=device) -RuntimeError: CUDA error: device-side assert triggered diff --git a/tests/test_infer/test_dynamic_batching/test_offline_dynamic_batching.py b/tests/test_infer/test_dynamic_batching/test_offline_dynamic_batching.py index f8fa18f69a0c..9925a80b6e77 100644 --- a/tests/test_infer/test_dynamic_batching/test_offline_dynamic_batching.py +++ b/tests/test_infer/test_dynamic_batching/test_offline_dynamic_batching.py @@ -53,7 +53,7 @@ def run(): waiting_list.append(req3) waiting_list.append(req4) - llama_config = LlamaConfig(num_hidden_layers=2, bos_token_id=0, eos_token_id=1, vocab_size=1200, hidden_size=1024) + llama_config = LlamaConfig(num_hidden_layers=2, bos_token_id=0, eos_token_id=1, vocab_size=30000, hidden_size=1024) model = LlamaForCausalLM(llama_config) model = model.half() @@ -62,9 +62,9 @@ def run(): infer_engine = TPInferEngine(model, shard_config, MAX_BATCH_SIZE, MAX_INPUT_LEN, MAX_OUTPUT_LEN) batch_manager = start_dynamic_batching(arg, tp_engine=infer_engine, waiting_req_list=waiting_list) - ans_gen = batch_manager.generate(request_id=5, prompts="你好", sampling_params=sampling_params) + ans_gen = batch_manager.generate(request_id=5, prompts="hello", sampling_params=sampling_params) for result in ans_gen: - print(result) + assert result is not None def check_dynamic_forward(rank, world_size, port): diff --git a/tests/test_infer/test_dynamic_batching/test_offline_dynamic_batching.py.py b/tests/test_infer/test_dynamic_batching/test_offline_dynamic_batching.py.py deleted file mode 100644 index c7b3f8c9cf17..000000000000 --- a/tests/test_infer/test_dynamic_batching/test_offline_dynamic_batching.py.py +++ /dev/null @@ -1,84 +0,0 @@ -from dataclasses import dataclass - -import pytest -import torch -from packaging import version -from transformers import LlamaForCausalLM -from transformers.models.llama.configuration_llama import LlamaConfig - -import colossalai -from colossalai.inference.dynamic_batching.io_struct import Req -from colossalai.inference.dynamic_batching.sampling_params import SamplingParams -from colossalai.inference.manager import start_dynamic_batching -from colossalai.inference.tensor_parallel import TPInferEngine -from colossalai.shardformer import ShardConfig -from colossalai.testing import clear_cache_before_run, rerun_if_address_is_in_use, spawn - -TP_SIZE = 1 -MAX_BATCH_SIZE = 2 -MAX_INPUT_LEN = 5 -MAX_OUTPUT_LEN = 16 -CUDA_SUPPORT = version.parse(torch.version.cuda) > version.parse("11.5") - - -@dataclass -class args: - max_total_token_num: int - batch_max_tokens: int - model: str - eos_id: int - disable_log_stats: bool - log_stats_interval: int - - -def run(): - arg = args( - max_total_token_num=42, - model="llama", - batch_max_tokens=42, - eos_id=0, - disable_log_stats=False, - log_stats_interval=10, - ) - sampling_params = SamplingParams() - - req1 = Req(0, [0, 0, 10, 6, 8], sampling_params) - req2 = Req(1, [10, 10, 10, 10, 10], sampling_params) - req3 = Req(2, [0, 0, 10, 10, 10], sampling_params) - req4 = Req(3, [0, 0, 10, 10, 10], sampling_params) - - waiting_list = [] - waiting_list.append(req1) - waiting_list.append(req2) - waiting_list.append(req3) - waiting_list.append(req4) - - llama_config = LlamaConfig(num_hidden_layers=2, bos_token_id=0, eos_token_id=1, vocab_size=1200, hidden_size=1024) - model = LlamaForCausalLM(llama_config) - model = model.half() - - shard_config = ShardConfig(enable_tensor_parallelism=True if TP_SIZE > 1 else False, inference_only=True) - - infer_engine = TPInferEngine(model, shard_config, MAX_BATCH_SIZE, MAX_INPUT_LEN, MAX_OUTPUT_LEN) - batch_manager = start_dynamic_batching(arg, tp_engine=infer_engine, waiting_req_list=waiting_list) - - ans_gen = batch_manager.generate(request_id=5, prompt=[10, 10, 10], sampling_params=sampling_params) - for result in ans_gen: - print(result) - - -def check_dynamic_forward(rank, world_size, port): - colossalai.launch(config={}, rank=rank, world_size=world_size, host="localhost", port=port, backend="nccl") - run() - - -@pytest.mark.skipif(not CUDA_SUPPORT, reason="kv-cache manager engine requires cuda version to be higher than 11.5") -@pytest.mark.dist -@rerun_if_address_is_in_use() -@clear_cache_before_run() -def test_dynamic_batching(): - spawn(check_dynamic_forward, TP_SIZE) - - -if __name__ == "__main__": - test_dynamic_batching()