parformer大规模数据集预训练cpu内存持续增加的问题

[chenpaopao]

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在训练paraformer 使用train.py进行大规模预训练时候，cpu内存会不断增加，最后达到100%，进而数据加载报错，应该如何解决

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What is your question?

在训练paraformer 使用train.py进行大规模预训练时候，cpu内存会不断增加，最后达到100%，进而数据加载报错，应该如何解决

Code

[2024-08-14 21:57:58,231][root][INFO] - train, rank: 1, epoch: 0/500, data_slice: 0/5, step_in_slice: 44000/156088, step_in_epoch: 80000, total step: 80000, (loss_avg_rank: 0.679), (loss_avg_slice: 0.623), (ppl_avg_slice: 1.865e+00), (acc_avg_slice: 0.694), (lr: 6.124e-04), [('loss_att', 0.453), ('acc', 0.758), ('loss_pre', 0.048), ('loss', 0.501), ('batch_size', 172)], {'data_load': '0.001', 'forward_time': '0.324', 'backward_and_AllReaduce_time': '0.407', 'optim_time': '0.075', 'total_time': '0.808'}, GPU, memory: usage: 4.356 GB, peak: 39.970 GB, cache: 40.660 GB, cache_peak: 40.660 GB [2024-08-14 21:57:58,231][root][INFO] - train, rank: 7, epoch: 0/500, data_slice: 0/5, step_in_slice: 44000/156088, step_in_epoch: 80000, total step: 80000, (loss_avg_rank: 0.338), (loss_avg_slice: 0.623), (ppl_avg_slice: 1.865e+00), (acc_avg_slice: 0.694), (lr: 6.124e-04), [('loss_att', 0.845), ('acc', 0.616), ('loss_pre', 0.148), ('loss', 0.993), ('batch_size', 200)], {'data_load': '0.000', 'forward_time': '0.339', 'backward_and_AllReaduce_time': '0.235', 'optim_time': '0.242', 'total_time': '0.816'}, GPU, memory: usage: 3.791 GB, peak: 38.241 GB, cache: 38.902 GB, cache_peak: 38.902 GB [2024-08-14 21:57:58,232][root][INFO] - train, rank: 0, epoch: 0/500, data_slice: 0/5, step_in_slice: 44000/156088, step_in_epoch: 80000, total step: 80000, (loss_avg_rank: 0.639), (loss_avg_slice: 0.623), (ppl_avg_slice: 1.865e+00), (acc_avg_slice: 0.694), (lr: 6.124e-04), [('loss_att', 0.393), ('acc', 0.801), ('loss_pre', 0.047), ('loss', 0.441), ('batch_size', 185)], {'data_load': '0.000', 'forward_time': '0.331', 'backward_and_AllReaduce_time': '0.397', 'optim_time': '0.084', 'total_time': '0.813'}, GPU, memory: usage: 4.306 GB, peak: 41.477 GB, cache: 42.143 GB, cache_peak: 42.143 GB [2024-08-14 21:57:58,235][root][INFO] - train, rank: 2, epoch: 0/500, data_slice: 0/5, step_in_slice: 44000/156088, step_in_epoch: 80000, total step: 80000, (loss_avg_rank: 0.544), (loss_avg_slice: 0.623), (ppl_avg_slice: 1.865e+00), (acc_avg_slice: 0.694), (lr: 6.124e-04), [('loss_att', 0.358), ('acc', 0.787), ('loss_pre', 0.044), ('loss', 0.402), ('batch_size', 155)], {'data_load': '0.000', 'forward_time': '0.338', 'backward_and_AllReaduce_time': '0.386', 'optim_time': '0.093', 'total_time': '0.819'}, GPU, memory: usage: 4.394 GB, peak: 55.395 GB, cache: 55.916 GB, cache_peak: 55.916 GB [2024-08-14 21:57:58,235][root][INFO] - train, rank: 4, epoch: 0/500, data_slice: 0/5, step_in_slice: 44000/156088, step_in_epoch: 80000, total step: 80000, (loss_avg_rank: 0.550), (loss_avg_slice: 0.623), (ppl_avg_slice: 1.865e+00), (acc_avg_slice: 0.694), (lr: 6.124e-04), [('loss_att', 0.424), ('acc', 0.697), ('loss_pre', 0.062), ('loss', 0.486), ('batch_size', 103)], {'data_load': '0.000', 'forward_time': '0.334', 'backward_and_AllReaduce_time': '0.405', 'optim_time': '0.073', 'total_time': '0.813'}, GPU, memory: usage: 4.703 GB, peak: 67.206 GB, cache: 67.770 GB, cache_peak: 67.770 GB [2024-08-14 21:57:58,237][root][INFO] - train, rank: 6, epoch: 0/500, data_slice: 0/5, step_in_slice: 44000/156088, step_in_epoch: 80000, total step: 80000, (loss_avg_rank: 0.294), (loss_avg_slice: 0.623), (ppl_avg_slice: 1.865e+00), (acc_avg_slice: 0.694), (lr: 6.124e-04), [('loss_att', 0.788), ('acc', 0.614), ('loss_pre', 0.162), ('loss', 0.95), ('batch_size', 199)], {'data_load': '0.000', 'forward_time': '0.329', 'backward_and_AllReaduce_time': '0.239', 'optim_time': '0.246', 'total_time': '0.814'}, GPU, memory: usage: 3.786 GB, peak: 60.709 GB, cache: 61.479 GB, cache_peak: 61.479 GB [2024-08-14 21:57:58,237][root][INFO] - train, rank: 3, epoch: 0/500, data_slice: 0/5, step_in_slice: 44000/156088, step_in_epoch: 80000, total step: 80000, (loss_avg_rank: 0.547), (loss_avg_slice: 0.623), (ppl_avg_slice: 1.865e+00), (acc_avg_slice: 0.694), (lr: 6.124e-04), [('loss_att', 0.376), ('acc', 0.759), ('loss_pre', 0.041), ('loss', 0.417), ('batch_size', 138)], {'data_load': '0.000', 'forward_time': '0.333', 'backward_and_AllReaduce_time': '0.398', 'optim_time': '0.083', 'total_time': '0.815'}, GPU, memory: usage: 4.479 GB, peak: 47.688 GB, cache: 48.240 GB, cache_peak: 48.240 GB [2024-08-14 21:57:58,246][root][INFO] - Validate epoch: 0, rank: 3

[2024-08-14 21:57:58,248][root][INFO] - Validate epoch: 0, rank: 5

[2024-08-14 21:57:58,250][root][INFO] - Validate epoch: 0, rank: 1

[2024-08-14 21:57:58,250][root][INFO] - Validate epoch: 0, rank: 7

[2024-08-14 21:57:58,251][root][INFO] - Validate epoch: 0, rank: 0

[2024-08-14 21:57:58,253][root][INFO] - Validate epoch: 0, rank: 4

[2024-08-14 21:57:58,254][root][INFO] - Validate epoch: 0, rank: 2

[2024-08-14 21:57:58,255][root][INFO] - Validate epoch: 0, rank: 6

[2024-08-14 21:57:58,409][root][INFO] - rank: 3, dataloader start from step: 0, batch_num: 65, after: 65 [2024-08-14 21:57:58,412][root][INFO] - rank: 5, dataloader start from step: 0, batch_num: 65, after: 65 [2024-08-14 21:57:58,413][root][INFO] - rank: 1, dataloader start from step: 0, batch_num: 65, after: 65 [2024-08-14 21:57:58,414][root][INFO] - rank: 4, dataloader start from step: 0, batch_num: 65, after: 65 [2024-08-14 21:57:58,414][root][INFO] - rank: 0, dataloader start from step: 0, batch_num: 65, after: 65 [2024-08-14 21:57:58,417][root][INFO] - rank: 7, dataloader start from step: 0, batch_num: 65, after: 65 [2024-08-14 21:57:58,418][root][INFO] - rank: 2, dataloader start from step: 0, batch_num: 65, after: 65 [2024-08-14 21:57:58,419][root][INFO] - rank: 6, dataloader start from step: 0, batch_num: 65, after: 65 [2024-08-14 21:58:59,814][root][INFO] - rank: 5, dataloader start from step: 0, batch_num: 65, after: 65 [2024-08-14 21:59:02,055][root][INFO] - rank: 3, dataloader start from step: 0, batch_num: 65, after: 65 [2024-08-14 21:59:02,374][root][INFO] - rank: 6, dataloader start from step: 0, batch_num: 65, after: 65 [2024-08-14 21:59:03,529][root][INFO] - rank: 4, dataloader start from step: 0, batch_num: 65, after: 65 [2024-08-14 21:59:03,584][root][INFO] - rank: 1, dataloader start from step: 0, batch_num: 65, after: 65 [2024-08-14 21:59:05,045][root][INFO] - rank: 0, dataloader start from step: 0, batch_num: 65, after: 65 [2024-08-14 21:59:05,611][root][INFO] - rank: 2, dataloader start from step: 0, batch_num: 65, after: 65 [rank7]: File "/lxc-data/FunASR/examples/industrial_data_pretraining/paraformer/../../../funasr/bin/train.py", line 270, in [rank7]: main_hydra() [rank7]: File "/lxc-data/minianconda3/envs/python39/lib/python3.9/site-packages/hydra/main.py", line 94, in decorated_main [rank7]: _run_hydra( [rank7]: File "/lxc-data/minianconda3/envs/python39/lib/python3.9/site-packages/hydra/_internal/utils.py", line 394, in _run_hydra [rank7]: _run_app( [rank7]: File "/lxc-data/minianconda3/envs/python39/lib/python3.9/site-packages/hydra/_internal/utils.py", line 457, in _run_app [rank7]: run_and_report( [rank7]: File "/lxc-data/minianconda3/envs/python39/lib/python3.9/site-packages/hydra/_internal/utils.py", line 223, in run_and_report [rank7]: raise ex [rank7]: File "/lxc-data/minianconda3/envs/python39/lib/python3.9/site-packages/hydra/_internal/utils.py", line 220, in run_and_report [rank7]: return func() [rank7]: File "/lxc-data/minianconda3/envs/python39/lib/python3.9/site-packages/hydra/_internal/utils.py", line 458, in [rank7]: lambda: hydra.run( [rank7]: File "/lxc-data/minianconda3/envs/python39/lib/python3.9/site-packages/hydra/_internal/hydra.py", line 132, in run [rank7]: _ = ret.return_value [rank7]: File "/lxc-data/minianconda3/envs/python39/lib/python3.9/site-packages/hydra/core/utils.py", line 260, in return_value [rank7]: raise self._return_value [rank7]: File "/lxc-data/minianconda3/envs/python39/lib/python3.9/site-packages/hydra/core/utils.py", line 186, in run_job [rank7]: ret.return_value = task_function(task_cfg) [rank7]: File "/lxc-data/FunASR/examples/industrial_data_pretraining/paraformer/../../../funasr/bin/train.py", line 53, in main_hydra [rank7]: main(**kwargs) [rank7]: File "/lxc-data/FunASR/examples/industrial_data_pretraining/paraformer/../../../funasr/bin/train.py", line 215, in main [rank7]: trainer.train_epoch( [rank7]: File "/lxc-data/FunASR/funasr/train_utils/trainer.py", line 499, in train_epoch [rank7]: self.validate_epoch( [rank7]: File "/lxc-data/FunASR/funasr/train_utils/trainer.py", line 562, in validate_epoch [rank7]: for batch_idx, batch in enumerate(dataloader_val): [rank7]: File "/lxc-data/minianconda3/envs/python39/lib/python3.9/site-packages/torch/utils/data/dataloader.py", line 439, in iter [rank7]: return self._get_iterator() [rank7]: File "/lxc-data/minianconda3/envs/python39/lib/python3.9/site-packages/torch/utils/data/dataloader.py", line 387, in _get_iterator [rank7]: return _MultiProcessingDataLoaderIter(self) [rank7]: File "/lxc-data/minianconda3/envs/python39/lib/python3.9/site-packages/torch/utils/data/dataloader.py", line 1085, in init [rank7]: self._reset(loader, first_iter=True) [rank7]: File "/lxc-data/minianconda3/envs/python39/lib/python3.9/site-packages/torch/utils/data/dataloader.py", line 1088, in _reset [rank7]: super()._reset(loader, first_iter) [rank7]: File "/lxc-data/minianconda3/envs/python39/lib/python3.9/site-packages/torch/utils/data/dataloader.py", line 611, in _reset [rank7]: self._sampler_iter = iter(self._index_sampler) [rank7]: File "/lxc-data/FunASR/funasr/datasets/audio_datasets/samplers.py", line 400, in iter [rank7]: potential_batch_length = max(max_len_in_batch, sample_length) * (len(batch) + 1) [rank7]: File "/lxc-data/minianconda3/envs/python39/lib/python3.9/site-packages/torch/utils/data/_utils/signal_handling.py", line 66, in handler [rank7]: _error_if_any_worker_fails() [rank7]: RuntimeError: DataLoader worker (pid 6044) is killed by signal: Killed. Error executing job with overrides: ['++model=/lxc-data/FunASR/examples/industrial_data_pretraining/paraformer/modelscope_models/iic/speech_paraformer-large_big_asr_nat-zh-cn-16k-common-vocab8404-pytorch', '++train_data_set_list=../../../data/10w_list/15Wdata_nochat_5s_train.jsonl', '++valid_data_set_list=../../../data/10w_list/2Wdata_nochat_test.jsonl', '++dataset=AudioDataset', '++dataset_conf.index_ds=IndexDSJsonl', '++dataset_conf.data_split_num=5', '++dataset_conf.batch_sampler=BatchSampler', '++dataset_conf.batch_size=60000', '++dataset_conf.sort_size=1024', '++dataset_conf.batch_type=token', '++dataset_conf.num_workers=12', '++dataset_conf.max_token_length=1024', '++train_conf.max_epoch=500', '++train_conf.log_interval=100', '++train_conf.resume=true', '++train_conf.validate_interval=4000', '++train_conf.save_checkpoint_interval=4000', '++train_conf.keep_nbest_models=20', '++train_conf.avg_nbest_model=10', '++train_conf.use_deepspeed=false', '++train_conf.deepspeed_config=', '++optim_conf.lr=0.0006', '++output_dir=./15Wdata_train_big_large'] [rank1]: Traceback (most recent call last): [rank1]: File "/lxc-data/minianconda3/envs/python39/lib/python3.9/site-packages/torch/utils/data/dataloader.py", line 1133, in _try_get_data [rank1]: data = self._data_queue.get(timeout=timeout) [rank1]: File "/lxc-data/minianconda3/envs/python39/lib/python3.9/queue.py", line 180, in get [rank1]: self.not_empty.wait(remaining) [rank1]: File "/lxc-data/minianconda3/envs/python39/lib/python3.9/threading.py", line 316, in wait [rank1]: gotit = waiter.acquire(True, timeout) [rank1]: File "/lxc-data/minianconda3/envs/python39/lib/python3.9/site-packages/torch/utils/data/_utils/signal_handling.py", line 66, in handler [rank1]: _error_if_any_worker_fails() [rank1]: RuntimeError: DataLoader worker (pid 6070) is killed by signal: Killed.

[rank1]: The above exception was the direct cause of the following exception:

[rank1]: Traceback (most recent call last): [rank1]: File "/lxc-data/FunASR/examples/industrial_data_pretraining/paraformer/../../../funasr/bin/train.py", line 270, in [rank1]: main_hydra() [rank1]: File "/lxc-data/minianconda3/envs/python39/lib/python3.9/site-packages/hydra/main.py", line 94, in decorated_main [rank1]: _run_hydra( [rank1]: File "/lxc-data/minianconda3/envs/python39/lib/python3.9/site-packages/hydra/_internal/utils.py", line 394, in _run_hydra [rank1]: _run_app( [rank1]: File "/lxc-data/minianconda3/envs/python39/lib/python3.9/site-packages/hydra/_internal/utils.py", line 457, in _run_app [rank1]: run_and_report( [rank1]: File "/lxc-data/minianconda3/envs/python39/lib/python3.9/site-packages/hydra/_internal/utils.py", line 223, in run_and_report [rank1]: raise ex [rank1]: File "/lxc-data/minianconda3/envs/python39/lib/python3.9/site-packages/hydra/_internal/utils.py", line 220, in run_and_report [rank1]: return func() [rank1]: File "/lxc-data/minianconda3/envs/python39/lib/python3.9/site-packages/hydra/_internal/utils.py", line 458, in [rank1]: lambda: hydra.run( [rank1]: File "/lxc-data/minianconda3/envs/python39/lib/python3.9/site-packages/hydra/_internal/hydra.py", line 132, in run [rank1]: _ = ret.return_value [rank1]: File "/lxc-data/minianconda3/envs/python39/lib/python3.9/site-packages/hydra/core/utils.py", line 260, in return_value [rank1]: raise self._return_value [rank1]: File "/lxc-data/minianconda3/envs/python39/lib/python3.9/site-packages/hydra/core/utils.py", line 186, in run_job [rank1]: ret.return_value = task_function(task_cfg) [rank1]: File "/lxc-data/FunASR/examples/industrial_data_pretraining/paraformer/../../../funasr/bin/train.py", line 53, in main_hydra [rank1]: main(**kwargs) [rank1]: File "/lxc-data/FunASR/examples/industrial_data_pretraining/paraformer/../../../funasr/bin/train.py", line 215, in main [rank1]: trainer.train_epoch( [rank1]: File "/lxc-data/FunASR/funasr/train_utils/trainer.py", line 499, in train_epoch [rank1]: self.validate_epoch( [rank1]: File "/lxc-data/FunASR/funasr/train_utils/trainer.py", line 562, in validate_epoch [rank1]: for batch_idx, batch in enumerate(dataloader_val): [rank1]: File "/lxc-data/minianconda3/envs/python39/lib/python3.9/site-packages/torch/utils/data/dataloader.py", line 631, in next [rank1]: data = self._next_data() [rank1]: File "/lxc-data/minianconda3/envs/python39/lib/python3.9/site-packages/torch/utils/data/dataloader.py", line 1329, in _next_data [rank1]: idx, data = self._get_data() [rank1]: File "/lxc-data/minianconda3/envs/python39/lib/python3.9/site-packages/torch/utils/data/dataloader.py", line 1285, in _get_data [rank1]: success, data = self._try_get_data() [rank1]: File "/lxc-data/minianconda3/envs/python39/lib/python3.9/site-packages/torch/utils/data/dataloader.py", line 1146, in _try_get_data [rank1]: raise RuntimeError(f'DataLoader worker (pid(s) {pids_str}) exited unexpectedly') from e [rank1]: RuntimeError: DataLoader worker (pid(s) 6030, 6038, 6047, 6070) exited unexpectedly

What have you tried?

What's your environment?

cuda12.2 linux 4* A100-8

• OS (e.g., Linux):
• FunASR Version (e.g., 1.0.0):
• ModelScope Version (e.g., 1.11.0):
• PyTorch Version (e.g., 2.0.0):
• How you installed funasr (pip, source):
• Python version:
• GPU (e.g., V100M32)
• CUDA/cuDNN version (e.g., cuda11.7):
• Docker version (e.g., funasr-runtime-sdk-cpu-0.4.1)
• Any other relevant information: