| ======================== | |
| START TIME: Tue Jul 2 19:31:51 UTC 2024 | |
| python3 version = Python 3.10.14 | |
| ======================== | |
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| Already on 'bench_cluster' | |
| M examples/config_tiny_llama.py | |
| M examples/config_tiny_llama.yaml | |
| M examples/train_tiny_llama.sh | |
| M src/nanotron/models/llama.py | |
| M src/nanotron/trainer.py | |
| Your branch is up to date with 'origin/bench_cluster'. | |
| Job status: RUNNING | |
| W0702 19:31:53.993000 140423668332352 torch/distributed/run.py:757] | |
| W0702 19:31:53.993000 140423668332352 torch/distributed/run.py:757] ***************************************** | |
| W0702 19:31:53.993000 140423668332352 torch/distributed/run.py:757] Setting OMP_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. | |
| W0702 19:31:53.993000 140423668332352 torch/distributed/run.py:757] ***************************************** | |
| W0702 19:31:53.997000 140241535268672 torch/distributed/run.py:757] | |
| W0702 19:31:53.997000 140241535268672 torch/distributed/run.py:757] ***************************************** | |
| W0702 19:31:53.997000 140241535268672 torch/distributed/run.py:757] Setting OMP_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. | |
| W0702 19:31:53.997000 140241535268672 torch/distributed/run.py:757] ***************************************** | |
| [default0]:07/02/2024 19:32:12 [WARNING|DP=0|PP=0|TP=0|ip-26-0-165-24]: [Vocab Size Padding] Padded vocab (size: 50257) with 15 dummy tokens (new size: 50272) | |
| [default0]:07/02/2024 19:32:12 [INFO|DP=0|PP=0|TP=0|ip-26-0-165-24]: Config: | |
| [default0]:07/02/2024 19:32:12 [INFO|DP=0|PP=0|TP=0|ip-26-0-165-24]: Config(general=GeneralArgs(project='bench_cluster', | |
| [default0]:07/02/2024 19:32:12 [INFO|DP=0|PP=0|TP=0|ip-26-0-165-24]: run='%date_%jobid', | |
| [default0]:07/02/2024 19:32:12 [INFO|DP=0|PP=0|TP=0|ip-26-0-165-24]: seed=42, | |
| [default0]:07/02/2024 19:32:12 [INFO|DP=0|PP=0|TP=0|ip-26-0-165-24]: step=None, | |
| [default0]:07/02/2024 19:32:12 [INFO|DP=0|PP=0|TP=0|ip-26-0-165-24]: consumed_train_samples=None, | |
| [default0]:07/02/2024 19:32:12 [INFO|DP=0|PP=0|TP=0|ip-26-0-165-24]: benchmark_csv_path=None, | |
| [default0]:07/02/2024 19:32:12 [INFO|DP=0|PP=0|TP=0|ip-26-0-165-24]: ignore_sanity_checks=True), | |
| [default0]:07/02/2024 19:32:12 [INFO|DP=0|PP=0|TP=0|ip-26-0-165-24]: parallelism=ParallelismArgs(dp=1, | |
| [default0]:07/02/2024 19:32:12 [INFO|DP=0|PP=0|TP=0|ip-26-0-165-24]: pp=1, | |
| [default0]:07/02/2024 19:32:12 [INFO|DP=0|PP=0|TP=0|ip-26-0-165-24]: tp=16, | |
| [default0]:07/02/2024 19:32:12 [INFO|DP=0|PP=0|TP=0|ip-26-0-165-24]: pp_engine=<nanotron.parallel.pipeline_parallel.engine.OneForwardOneBackwardPipelineEngine object at 0x7fd6cea44790>, | |
| [default0]:07/02/2024 19:32:12 [INFO|DP=0|PP=0|TP=0|ip-26-0-165-24]: tp_mode=<TensorParallelLinearMode.REDUCE_SCATTER: 2>, | |
| [default0]:07/02/2024 19:32:12 [INFO|DP=0|PP=0|TP=0|ip-26-0-165-24]: tp_linear_async_communication=False, | |
| [default0]:07/02/2024 19:32:12 [INFO|DP=0|PP=0|TP=0|ip-26-0-165-24]: expert_parallel_size=1), | |
| [default0]:07/02/2024 19:32:12 [INFO|DP=0|PP=0|TP=0|ip-26-0-165-24]: model=ModelArgs(model_config=LlamaConfig(bos_token_id=1, | |
| [default0]:07/02/2024 19:32:12 [INFO|DP=0|PP=0|TP=0|ip-26-0-165-24]: eos_token_id=2, | |
| [default0]:07/02/2024 19:32:12 [INFO|DP=0|PP=0|TP=0|ip-26-0-165-24]: hidden_act='silu', | |
| [default0]:07/02/2024 19:32:12 [INFO|DP=0|PP=0|TP=0|ip-26-0-165-24]: hidden_size=2048, | |
| [default0]:07/02/2024 19:32:12 [INFO|DP=0|PP=0|TP=0|ip-26-0-165-24]: initializer_range=0.02, | |
| [default0]:07/02/2024 19:32:12 [INFO|DP=0|PP=0|TP=0|ip-26-0-165-24]: intermediate_size=4096, | |
| [default0]:07/02/2024 19:32:12 [INFO|DP=0|PP=0|TP=0|ip-26-0-165-24]: is_llama_config=True, | |
| [default0]:07/02/2024 19:32:12 [INFO|DP=0|PP=0|TP=0|ip-26-0-165-24]: max_position_embeddings=4096, | |
| [default0]:07/02/2024 19:32:12 [INFO|DP=0|PP=0|TP=0|ip-26-0-165-24]: num_attention_heads=32, | |
| [default0]:07/02/2024 19:32:12 [INFO|DP=0|PP=0|TP=0|ip-26-0-165-24]: num_hidden_layers=24, | |
| [default0]:07/02/2024 19:32:12 [INFO|DP=0|PP=0|TP=0|ip-26-0-165-24]: num_key_value_heads=32, | |
| [default0]:07/02/2024 19:32:12 [INFO|DP=0|PP=0|TP=0|ip-26-0-165-24]: pad_token_id=None, | |
| [default0]:07/02/2024 19:32:12 [INFO|DP=0|PP=0|TP=0|ip-26-0-165-24]: pretraining_tp=1, | |
| [default0]:07/02/2024 19:32:12 [INFO|DP=0|PP=0|TP=0|ip-26-0-165-24]: rms_norm_eps=1e-05, | |
| [default0]:07/02/2024 19:32:12 [INFO|DP=0|PP=0|TP=0|ip-26-0-165-24]: rope_scaling=None, | |
| [default0]:07/02/2024 19:32:12 [INFO|DP=0|PP=0|TP=0|ip-26-0-165-24]: rope_theta=10000.0, | |
| [default0]:07/02/2024 19:32:12 [INFO|DP=0|PP=0|TP=0|ip-26-0-165-24]: tie_word_embeddings=True, | |
| [default0]:07/02/2024 19:32:12 [INFO|DP=0|PP=0|TP=0|ip-26-0-165-24]: use_cache=True, | |
| [default0]:07/02/2024 19:32:12 [INFO|DP=0|PP=0|TP=0|ip-26-0-165-24]: vocab_size=50272), | |
| [default0]:07/02/2024 19:32:12 [INFO|DP=0|PP=0|TP=0|ip-26-0-165-24]: init_method=RandomInit(std=0.025), | |
| [default0]:07/02/2024 19:32:12 [INFO|DP=0|PP=0|TP=0|ip-26-0-165-24]: dtype=torch.bfloat16, | |
| [default0]:07/02/2024 19:32:12 [INFO|DP=0|PP=0|TP=0|ip-26-0-165-24]: make_vocab_size_divisible_by=1, | |
| [default0]:07/02/2024 19:32:12 [INFO|DP=0|PP=0|TP=0|ip-26-0-165-24]: ddp_bucket_cap_mb=25), | |
| [default0]:07/02/2024 19:32:12 [INFO|DP=0|PP=0|TP=0|ip-26-0-165-24]: tokenizer=TokenizerArgs(tokenizer_name_or_path='openai-community/gpt2', | |
| [default0]:07/02/2024 19:32:12 [INFO|DP=0|PP=0|TP=0|ip-26-0-165-24]: tokenizer_revision=None, | |
| [default0]:07/02/2024 19:32:12 [INFO|DP=0|PP=0|TP=0|ip-26-0-165-24]: tokenizer_max_length=None), | |
| [default0]:07/02/2024 19:32:12 [INFO|DP=0|PP=0|TP=0|ip-26-0-165-24]: checkpoints=CheckpointsArgs(checkpoints_path=Path('/dev/null'), | |
| [default0]:07/02/2024 19:32:12 [INFO|DP=0|PP=0|TP=0|ip-26-0-165-24]: checkpoint_interval=100000, | |
| [default0]:07/02/2024 19:32:12 [INFO|DP=0|PP=0|TP=0|ip-26-0-165-24]: save_initial_state=False, | |
| [default0]:07/02/2024 19:32:12 [INFO|DP=0|PP=0|TP=0|ip-26-0-165-24]: resume_checkpoint_path=None, | |
| [default0]:07/02/2024 19:32:12 [INFO|DP=0|PP=0|TP=0|ip-26-0-165-24]: checkpoints_path_is_shared_file_system=False), | |
| [default0]:07/02/2024 19:32:12 [INFO|DP=0|PP=0|TP=0|ip-26-0-165-24]: logging=LoggingArgs(log_level='info', | |
| [default0]:07/02/2024 19:32:12 [INFO|DP=0|PP=0|TP=0|ip-26-0-165-24]: log_level_replica='info', | |
| [default0]:07/02/2024 19:32:12 [INFO|DP=0|PP=0|TP=0|ip-26-0-165-24]: iteration_step_info_interval=1), | |
| [default0]:07/02/2024 19:32:12 [INFO|DP=0|PP=0|TP=0|ip-26-0-165-24]: tokens=TokensArgs(sequence_length=4096, | |
| [default0]:07/02/2024 19:32:12 [INFO|DP=0|PP=0|TP=0|ip-26-0-165-24]: train_steps=20, | |
| [default0]:07/02/2024 19:32:12 [INFO|DP=0|PP=0|TP=0|ip-26-0-165-24]: micro_batch_size=128, | |
| [default0]:07/02/2024 19:32:12 [INFO|DP=0|PP=0|TP=0|ip-26-0-165-24]: batch_accumulation_per_replica=8, | |
| [default0]:07/02/2024 19:32:12 [INFO|DP=0|PP=0|TP=0|ip-26-0-165-24]: val_check_interval=-1, | |
| [default0]:07/02/2024 19:32:12 [INFO|DP=0|PP=0|TP=0|ip-26-0-165-24]: limit_val_batches=0, | |
| [default0]:07/02/2024 19:32:12 [INFO|DP=0|PP=0|TP=0|ip-26-0-165-24]: limit_test_batches=0), | |
| [default0]:07/02/2024 19:32:12 [INFO|DP=0|PP=0|TP=0|ip-26-0-165-24]: optimizer=OptimizerArgs(optimizer_factory=AdamWOptimizerArgs(adam_eps=1e-08, | |
| [default0]:07/02/2024 19:32:12 [INFO|DP=0|PP=0|TP=0|ip-26-0-165-24]: adam_beta1=0.9, | |
| [default0]:07/02/2024 19:32:12 [INFO|DP=0|PP=0|TP=0|ip-26-0-165-24]: adam_beta2=0.95, | |
| [default0]:07/02/2024 19:32:12 [INFO|DP=0|PP=0|TP=0|ip-26-0-165-24]: torch_adam_is_fused=True, | |
| [default0]:07/02/2024 19:32:12 [INFO|DP=0|PP=0|TP=0|ip-26-0-165-24]: name='adamW'), | |
| [default0]:07/02/2024 19:32:12 [INFO|DP=0|PP=0|TP=0|ip-26-0-165-24]: zero_stage=1, | |
| [default0]:07/02/2024 19:32:12 [INFO|DP=0|PP=0|TP=0|ip-26-0-165-24]: weight_decay=0.01, | |
| [default0]:07/02/2024 19:32:12 [INFO|DP=0|PP=0|TP=0|ip-26-0-165-24]: clip_grad=1.0, | |
| [default0]:07/02/2024 19:32:12 [INFO|DP=0|PP=0|TP=0|ip-26-0-165-24]: accumulate_grad_in_fp32=True, | |
| [default0]:07/02/2024 19:32:12 [INFO|DP=0|PP=0|TP=0|ip-26-0-165-24]: learning_rate_scheduler=LRSchedulerArgs(learning_rate=0.0001, | |
| [default0]:07/02/2024 19:32:12 [INFO|DP=0|PP=0|TP=0|ip-26-0-165-24]: lr_warmup_steps=1, | |
| [default0]:07/02/2024 19:32:12 [INFO|DP=0|PP=0|TP=0|ip-26-0-165-24]: lr_warmup_style='linear', | |
| [default0]:07/02/2024 19:32:12 [INFO|DP=0|PP=0|TP=0|ip-26-0-165-24]: lr_decay_style='linear', | |
| [default0]:07/02/2024 19:32:12 [INFO|DP=0|PP=0|TP=0|ip-26-0-165-24]: lr_decay_steps=19, | |
| [default0]:07/02/2024 19:32:12 [INFO|DP=0|PP=0|TP=0|ip-26-0-165-24]: lr_decay_starting_step=None, | |
| [default0]:07/02/2024 19:32:12 [INFO|DP=0|PP=0|TP=0|ip-26-0-165-24]: min_decay_lr=1e-05)), | |
| [default0]:07/02/2024 19:32:12 [INFO|DP=0|PP=0|TP=0|ip-26-0-165-24]: data_stages=[DatasetStageArgs(name='Training Stage', | |
| [default0]:07/02/2024 19:32:12 [INFO|DP=0|PP=0|TP=0|ip-26-0-165-24]: start_training_step=1, | |
| [default0]:07/02/2024 19:32:12 [INFO|DP=0|PP=0|TP=0|ip-26-0-165-24]: data=DataArgs(dataset=PretrainDatasetsArgs(hf_dataset_or_datasets='roneneldan/TinyStories', | |
| [default0]:07/02/2024 19:32:12 [INFO|DP=0|PP=0|TP=0|ip-26-0-165-24]: hf_dataset_splits='train', | |
| [default0]:07/02/2024 19:32:12 [INFO|DP=0|PP=0|TP=0|ip-26-0-165-24]: hf_dataset_config_name=None, | |
| [default0]:07/02/2024 19:32:12 [INFO|DP=0|PP=0|TP=0|ip-26-0-165-24]: dataset_processing_num_proc_per_process=64, | |
| [default0]:07/02/2024 19:32:12 [INFO|DP=0|PP=0|TP=0|ip-26-0-165-24]: dataset_overwrite_cache=False, | |
| [default0]:07/02/2024 19:32:12 [INFO|DP=0|PP=0|TP=0|ip-26-0-165-24]: text_column_name='text'), | |
| [default0]:07/02/2024 19:32:12 [INFO|DP=0|PP=0|TP=0|ip-26-0-165-24]: seed=42, | |
| [default0]:07/02/2024 19:32:12 [INFO|DP=0|PP=0|TP=0|ip-26-0-165-24]: num_loading_workers=32))], | |
| [default0]:07/02/2024 19:32:12 [INFO|DP=0|PP=0|TP=0|ip-26-0-165-24]: profiler=ProfilerArgs(profiler_export_path=Path('/fsx/ferdinandmom/ferdinand-hf/bench_cluster/results/llama-1B/16_GPUS/dp-1_tp-16_pp-1_mbz-128')), | |
| [default0]:07/02/2024 19:32:12 [INFO|DP=0|PP=0|TP=0|ip-26-0-165-24]: lighteval=None) | |
| [default0]:07/02/2024 19:32:12 [INFO|DP=0|PP=0|TP=0|ip-26-0-165-24]: Model Config: | |
| [default0]:07/02/2024 19:32:12 [INFO|DP=0|PP=0|TP=0|ip-26-0-165-24]: LlamaConfig(bos_token_id=1, | |
| [default0]:07/02/2024 19:32:12 [INFO|DP=0|PP=0|TP=0|ip-26-0-165-24]: eos_token_id=2, | |
| [default0]:07/02/2024 19:32:12 [INFO|DP=0|PP=0|TP=0|ip-26-0-165-24]: hidden_act='silu', | |
| [default0]:07/02/2024 19:32:12 [INFO|DP=0|PP=0|TP=0|ip-26-0-165-24]: hidden_size=2048, | |
| [default0]:07/02/2024 19:32:12 [INFO|DP=0|PP=0|TP=0|ip-26-0-165-24]: initializer_range=0.02, | |
| [default0]:07/02/2024 19:32:12 [INFO|DP=0|PP=0|TP=0|ip-26-0-165-24]: intermediate_size=4096, | |
| [default0]:07/02/2024 19:32:12 [INFO|DP=0|PP=0|TP=0|ip-26-0-165-24]: is_llama_config=True, | |
| [default0]:07/02/2024 19:32:12 [INFO|DP=0|PP=0|TP=0|ip-26-0-165-24]: max_position_embeddings=4096, | |
| [default0]:07/02/2024 19:32:12 [INFO|DP=0|PP=0|TP=0|ip-26-0-165-24]: num_attention_heads=32, | |
| [default0]:07/02/2024 19:32:12 [INFO|DP=0|PP=0|TP=0|ip-26-0-165-24]: num_hidden_layers=24, | |
| [default0]:07/02/2024 19:32:12 [INFO|DP=0|PP=0|TP=0|ip-26-0-165-24]: num_key_value_heads=32, | |
| [default0]:07/02/2024 19:32:12 [INFO|DP=0|PP=0|TP=0|ip-26-0-165-24]: pad_token_id=None, | |
| [default0]:07/02/2024 19:32:12 [INFO|DP=0|PP=0|TP=0|ip-26-0-165-24]: pretraining_tp=1, | |
| [default0]:07/02/2024 19:32:12 [INFO|DP=0|PP=0|TP=0|ip-26-0-165-24]: rms_norm_eps=1e-05, | |
| [default0]:07/02/2024 19:32:12 [INFO|DP=0|PP=0|TP=0|ip-26-0-165-24]: rope_scaling=None, | |
| [default0]:07/02/2024 19:32:12 [INFO|DP=0|PP=0|TP=0|ip-26-0-165-24]: rope_theta=10000.0, | |
| [default0]:07/02/2024 19:32:12 [INFO|DP=0|PP=0|TP=0|ip-26-0-165-24]: tie_word_embeddings=True, | |
| [default0]:07/02/2024 19:32:12 [INFO|DP=0|PP=0|TP=0|ip-26-0-165-24]: use_cache=True, | |
| [default0]:07/02/2024 19:32:12 [INFO|DP=0|PP=0|TP=0|ip-26-0-165-24]: vocab_size=50272) | |
| [default0]:07/02/2024 19:32:12 [INFO|DP=0|PP=0|TP=0|ip-26-0-165-24]: Building model.. | |
| [default0]:07/02/2024 19:32:12 [INFO|DP=0|PP=0|TP=0|ip-26-0-165-24]: Setting PP block ranks... | |
| [default1]:07/02/2024 19:32:29 [INFO|DP=0|PP=0|TP=9|ip-26-0-170-160]: Local number of parameters: 69.4M (132.46MiB) | |
| [default1]:07/02/2024 19:32:29 [INFO|DP=0|PP=0|TP=9|ip-26-0-170-160]: [After model building] Memory usage: 159.71MiB. Peak allocated: 174.02MiB Peak reserved: 178.00MiB | |
| [default1]:07/02/2024 19:32:29 [INFO|DP=0|PP=0|TP=9|ip-26-0-170-160]: No checkpoint path provided. | |
| [default4]:07/02/2024 19:32:29 [INFO|DP=0|PP=0|TP=12|ip-26-0-170-160]: Local number of parameters: 69.4M (132.46MiB) | |
| [default4]:07/02/2024 19:32:29 [INFO|DP=0|PP=0|TP=12|ip-26-0-170-160]: [After model building] Memory usage: 159.71MiB. Peak allocated: 174.02MiB Peak reserved: 178.00MiB | |
| [default4]:07/02/2024 19:32:29 [INFO|DP=0|PP=0|TP=12|ip-26-0-170-160]: No checkpoint path provided. | |
| [default5]:07/02/2024 19:32:29 [INFO|DP=0|PP=0|TP=13|ip-26-0-170-160]: Local number of parameters: 69.4M (132.46MiB) | |
| [default3]:07/02/2024 19:32:29 [INFO|DP=0|PP=0|TP=11|ip-26-0-170-160]: Local number of parameters: 69.4M (132.46MiB) | |
| [default3]:07/02/2024 19:32:29 [INFO|DP=0|PP=0|TP=11|ip-26-0-170-160]: [After model building] Memory usage: 159.71MiB. Peak allocated: 174.02MiB Peak reserved: 178.00MiB | |
| [default5]:07/02/2024 19:32:29 [INFO|DP=0|PP=0|TP=13|ip-26-0-170-160]: [After model building] Memory usage: 159.71MiB. Peak allocated: 174.02MiB Peak reserved: 178.00MiB | |
| [default5]:07/02/2024 19:32:29 [INFO|DP=0|PP=0|TP=13|ip-26-0-170-160]: No checkpoint path provided. | |
| [default3]:07/02/2024 19:32:29 [INFO|DP=0|PP=0|TP=11|ip-26-0-170-160]: No checkpoint path provided. | |
| [default0]:07/02/2024 19:32:29 [INFO|DP=0|PP=0|TP=8|ip-26-0-170-160]: Local number of parameters: 69.4M (132.46MiB) | |
| [default0]:07/02/2024 19:32:29 [INFO|DP=0|PP=0|TP=8|ip-26-0-170-160]: [After model building] Memory usage: 159.71MiB. Peak allocated: 174.02MiB Peak reserved: 178.00MiB | |
| [default0]:07/02/2024 19:32:29 [INFO|DP=0|PP=0|TP=8|ip-26-0-170-160]: No checkpoint path provided. | |
| [default6]:07/02/2024 19:32:29 [INFO|DP=0|PP=0|TP=14|ip-26-0-170-160]: Local number of parameters: 69.4M (132.46MiB) | |
| [default6]:07/02/2024 19:32:29 [INFO|DP=0|PP=0|TP=14|ip-26-0-170-160]: [After model building] Memory usage: 159.71MiB. Peak allocated: 174.02MiB Peak reserved: 178.00MiB | |
| [default6]:07/02/2024 19:32:29 [INFO|DP=0|PP=0|TP=14|ip-26-0-170-160]: No checkpoint path provided. | |
| [default2]:07/02/2024 19:32:29 [INFO|DP=0|PP=0|TP=10|ip-26-0-170-160]: Local number of parameters: 69.4M (132.46MiB) | |
| [default2]:07/02/2024 19:32:29 [INFO|DP=0|PP=0|TP=10|ip-26-0-170-160]: [After model building] Memory usage: 159.71MiB. Peak allocated: 174.02MiB Peak reserved: 178.00MiB | |
| [default2]:07/02/2024 19:32:29 [INFO|DP=0|PP=0|TP=10|ip-26-0-170-160]: No checkpoint path provided. | |
| [default4]:07/02/2024 19:32:29 [INFO|DP=0|PP=0|TP=4|ip-26-0-165-24]: Local number of parameters: 69.4M (132.46MiB) | |
| [default4]:07/02/2024 19:32:29 [INFO|DP=0|PP=0|TP=4|ip-26-0-165-24]: [After model building] Memory usage: 159.71MiB. Peak allocated: 174.02MiB Peak reserved: 178.00MiB | |
| [default4]:07/02/2024 19:32:29 [INFO|DP=0|PP=0|TP=4|ip-26-0-165-24]: No checkpoint path provided. | |
| [default5]:07/02/2024 19:32:29 [INFO|DP=0|PP=0|TP=5|ip-26-0-165-24]: Local number of parameters: 69.4M (132.46MiB) | |
| [default5]:07/02/2024 19:32:29 [INFO|DP=0|PP=0|TP=5|ip-26-0-165-24]: [After model building] Memory usage: 159.71MiB. Peak allocated: 174.02MiB Peak reserved: 178.00MiB | |
| [default5]:07/02/2024 19:32:29 [INFO|DP=0|PP=0|TP=5|ip-26-0-165-24]: No checkpoint path provided. | |
| [default1]:07/02/2024 19:32:29 [INFO|DP=0|PP=0|TP=1|ip-26-0-165-24]: Local number of parameters: 69.4M (132.46MiB) | |
| [default1]:07/02/2024 19:32:29 [INFO|DP=0|PP=0|TP=1|ip-26-0-165-24]: [After model building] Memory usage: 159.71MiB. Peak allocated: 174.02MiB Peak reserved: 178.00MiB | |
| [default2]:07/02/2024 19:32:29 [INFO|DP=0|PP=0|TP=2|ip-26-0-165-24]: Local number of parameters: 69.4M (132.46MiB) | |
| [default2]:07/02/2024 19:32:29 [INFO|DP=0|PP=0|TP=2|ip-26-0-165-24]: [After model building] Memory usage: 159.71MiB. Peak allocated: 174.02MiB Peak reserved: 178.00MiB | |
| [default2]:07/02/2024 19:32:29 [INFO|DP=0|PP=0|TP=2|ip-26-0-165-24]: No checkpoint path provided. | |
| [default6]:07/02/2024 19:32:29 [INFO|DP=0|PP=0|TP=6|ip-26-0-165-24]: Local number of parameters: 69.4M (132.46MiB) | |
| [default6]:07/02/2024 19:32:29 [INFO|DP=0|PP=0|TP=6|ip-26-0-165-24]: [After model building] Memory usage: 159.71MiB. Peak allocated: 174.02MiB Peak reserved: 178.00MiB | |
| [default6]:07/02/2024 19:32:29 [INFO|DP=0|PP=0|TP=6|ip-26-0-165-24]: No checkpoint path provided. | |
| [default1]:07/02/2024 19:32:29 [INFO|DP=0|PP=0|TP=1|ip-26-0-165-24]: No checkpoint path provided. | |
| [default0]:07/02/2024 19:32:29 [INFO|DP=0|PP=0|TP=0|ip-26-0-165-24]: Total number of parameters: 1.11G (2119.44MiB) | |
| [default0]:07/02/2024 19:32:29 [INFO|DP=0|PP=0|TP=0|ip-26-0-165-24]: Local number of parameters: 69.4M (132.46MiB) | |
| [default0]:07/02/2024 19:32:29 [INFO|DP=0|PP=0|TP=0|ip-26-0-165-24]: [After model building] Memory usage: 159.71MiB. Peak allocated: 174.02MiB Peak reserved: 178.00MiB | |
| [default0]:07/02/2024 19:32:29 [INFO|DP=0|PP=0|TP=0|ip-26-0-165-24]: No checkpoint path provided. | |
| [default0]:07/02/2024 19:32:29 [INFO|DP=0|PP=0|TP=0|ip-26-0-165-24]: Parametrizing model parameters using StandardParametrizator | |
| [default0]:07/02/2024 19:32:29 [INFO|DP=0|PP=0|TP=0|ip-26-0-165-24]: [Optimizer Building] Using LearningRateForSP as learning rate | |
| [default0]:07/02/2024 19:32:29 [INFO|DP=0|PP=0|TP=0|ip-26-0-165-24]: [ZeRO sharding] Size of optimizer params per rank: | |
| [default0]:07/02/2024 19:32:29 [INFO|DP=0|PP=0|TP=0|ip-26-0-165-24]: [ZeRO sharding] DP Rank 0 has 69.4M out of 69.4M (100.00%) params' optimizer states | |
| [default3]:07/02/2024 19:32:29 [INFO|DP=0|PP=0|TP=3|ip-26-0-165-24]: Local number of parameters: 69.4M (132.46MiB) | |
| [default3]:07/02/2024 19:32:29 [INFO|DP=0|PP=0|TP=3|ip-26-0-165-24]: [After model building] Memory usage: 159.71MiB. Peak allocated: 174.02MiB Peak reserved: 178.00MiB | |
| [default3]:07/02/2024 19:32:29 [INFO|DP=0|PP=0|TP=3|ip-26-0-165-24]: No checkpoint path provided. | |
| [default7]:07/02/2024 19:32:29 [INFO|DP=0|PP=0|TP=7|ip-26-0-165-24]: Local number of parameters: 69.4M (132.46MiB) | |
| [default7]:07/02/2024 19:32:29 [INFO|DP=0|PP=0|TP=7|ip-26-0-165-24]: [After model building] Memory usage: 159.71MiB. Peak allocated: 174.02MiB Peak reserved: 178.00MiB | |
| [default7]:07/02/2024 19:32:29 [INFO|DP=0|PP=0|TP=7|ip-26-0-165-24]: No checkpoint path provided. | |
| [default7]:07/02/2024 19:32:29 [INFO|DP=0|PP=0|TP=15|ip-26-0-170-160]: Local number of parameters: 69.4M (132.46MiB) | |
| [default7]:07/02/2024 19:32:29 [INFO|DP=0|PP=0|TP=15|ip-26-0-170-160]: [After model building] Memory usage: 159.71MiB. Peak allocated: 174.02MiB Peak reserved: 178.00MiB | |
| [default7]:07/02/2024 19:32:29 [INFO|DP=0|PP=0|TP=15|ip-26-0-170-160]: No checkpoint path provided. | |
| [default0]:07/02/2024 19:32:30 [INFO|DP=0|PP=0|TP=0|ip-26-0-165-24]: [Training Plan] Stage Training Stage has 19 remaining training steps and has consumed 0 samples | |
| [default0]:07/02/2024 19:32:30 [INFO|DP=0|PP=0|TP=0|ip-26-0-165-24]: Using `datasets` library | |
| [default0]:07/02/2024 19:32:30 [INFO|DP=0|PP=0|TP=0|ip-26-0-165-24]: Loading tokenizer from openai-community/gpt2 and transformers/hf_hub versions ('4.41.2', '0.23.4') | |
| [default0]:07/02/2024 19:32:30 [WARNING|DP=0|PP=0|TP=0|ip-26-0-165-24]: Repo card metadata block was not found. Setting CardData to empty. | |
| [default0]:Repo card metadata block was not found. Setting CardData to empty. | |
| [default0]:07/02/2024 19:32:31 [INFO|DP=0|PP=0|TP=0|ip-26-0-165-24]: [Training Plan] There are 1 training stages | |
| [default0]:07/02/2024 19:32:31 [INFO|DP=0|PP=0|TP=0|ip-26-0-165-24]: [Stage Training Stage] start from step 1 | |
| [default0]:07/02/2024 19:32:31 [INFO|DP=0|PP=0|TP=0|ip-26-0-165-24]: | |
| [default0]:07/02/2024 19:32:31 [INFO|DP=0|PP=0|TP=0|ip-26-0-165-24]: [Start training] datetime: 2024-07-02 19:32:31.439930 | mbs: 128 | grad_accum: 8 | global_batch_size: 1024 | sequence_length: 4096 | train_steps: 20 | start_iteration_step: 0 | consumed_train_samples: 0 | |
| [default0]:07/02/2024 19:32:31 [INFO|DP=0|PP=0|TP=0|ip-26-0-165-24]: Resuming training from stage Training Stage, it has trained for 0 samples and has 19 remaining train steps | |
| [default0]:07/02/2024 19:32:31 [INFO|DP=0|PP=0|TP=0|ip-26-0-165-24]: Memory usage: 689.57MiB. Peak allocated 689.57MiB. Peak reserved: 710.00MiB | |
| [default5]:07/02/2024 19:32:31 [WARNING|DP=0|PP=0|TP=13|ip-26-0-170-160]: Repo card metadata block was not found. Setting CardData to empty. | |
| [default0]:07/02/2024 19:32:31 [WARNING|DP=0|PP=0|TP=8|ip-26-0-170-160]: Repo card metadata block was not found. Setting CardData to empty. | |
| [default6]:07/02/2024 19:32:31 [WARNING|DP=0|PP=0|TP=14|ip-26-0-170-160]: Repo card metadata block was not found. Setting CardData to empty. | |
| [default1]:07/02/2024 19:32:31 [WARNING|DP=0|PP=0|TP=9|ip-26-0-170-160]: Repo card metadata block was not found. Setting CardData to empty. | |
| [default4]:Repo card metadata block was not found. Setting CardData to empty. | |
| [default2]:Repo card metadata block was not found. Setting CardData to empty. | |
| [default1]:Repo card metadata block was not found. Setting CardData to empty. | |
| [default5]:Repo card metadata block was not found. Setting CardData to empty. | |
| [default4]:07/02/2024 19:32:31 [WARNING|DP=0|PP=0|TP=4|ip-26-0-165-24]: Repo card metadata block was not found. Setting CardData to empty. | |
| [default5]:07/02/2024 19:32:31 [WARNING|DP=0|PP=0|TP=5|ip-26-0-165-24]: Repo card metadata block was not found. Setting CardData to empty. | |
| [default3]:07/02/2024 19:32:31 [WARNING|DP=0|PP=0|TP=3|ip-26-0-165-24]: Repo card metadata block was not found. Setting CardData to empty. | |
| [default2]:07/02/2024 19:32:31 [WARNING|DP=0|PP=0|TP=2|ip-26-0-165-24]: Repo card metadata block was not found. Setting CardData to empty. | |
| [default1]:07/02/2024 19:32:31 [WARNING|DP=0|PP=0|TP=1|ip-26-0-165-24]: Repo card metadata block was not found. Setting CardData to empty. | |
| [default6]:07/02/2024 19:32:31 [WARNING|DP=0|PP=0|TP=6|ip-26-0-165-24]: Repo card metadata block was not found. Setting CardData to empty. | |
| [default3]:Repo card metadata block was not found. Setting CardData to empty. | |
| [default7]:07/02/2024 19:32:31 [WARNING|DP=0|PP=0|TP=7|ip-26-0-165-24]: Repo card metadata block was not found. Setting CardData to empty. | |
| [default7]:Repo card metadata block was not found. Setting CardData to empty. | |
| [default6]:Repo card metadata block was not found. Setting CardData to empty. | |
| [default7]:07/02/2024 19:32:31 [WARNING|DP=0|PP=0|TP=15|ip-26-0-170-160]: Repo card metadata block was not found. Setting CardData to empty. | |
| [default5]:Repo card metadata block was not found. Setting CardData to empty. | |
| [default0]:Repo card metadata block was not found. Setting CardData to empty. | |
| [default2]:Repo card metadata block was not found. Setting CardData to empty. | |
| [default7]:Repo card metadata block was not found. Setting CardData to empty. | |
| [default6]:Repo card metadata block was not found. Setting CardData to empty. | |
| [default1]:Repo card metadata block was not found. Setting CardData to empty. | |
| [default2]:07/02/2024 19:32:31 [WARNING|DP=0|PP=0|TP=10|ip-26-0-170-160]: Repo card metadata block was not found. Setting CardData to empty. | |
| [default4]:07/02/2024 19:32:31 [WARNING|DP=0|PP=0|TP=12|ip-26-0-170-160]: Repo card metadata block was not found. Setting CardData to empty. | |
| [default4]:Repo card metadata block was not found. Setting CardData to empty. | |
| [default3]:07/02/2024 19:32:33 [WARNING|DP=0|PP=0|TP=11|ip-26-0-170-160]: Repo card metadata block was not found. Setting CardData to empty. | |
| [default3]:Repo card metadata block was not found. Setting CardData to empty. | |
| [default5]:[rank13]: Traceback (most recent call last): | |
| [default5]:[rank13]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/run_train.py", line 237, in <module> | |
| [default5]:[rank13]: trainer.train(dataloader) | |
| [default5]:[rank13]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 429, in train | |
| [default5]:[rank13]: outputs, loss_avg = self.training_step(dataloader=self.current_dataloader) | |
| [default5]:[rank13]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 462, in training_step | |
| [default5]:[rank13]: outputs = self.pipeline_engine.train_batch_iter( | |
| [default5]:[rank13]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 278, in train_batch_iter | |
| [default5]:[rank13]: output = self.forward(context=context, state=state, micro_batch=micro_batch, model=model) | |
| [default5]:[rank13]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 44, in forward | |
| [default5]:[rank13]: output = model(**micro_batch) | |
| [default5]:[rank13]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl | |
| [default5]:[rank13]: return self._call_impl(*args, **kwargs) | |
| [default5]:[rank13]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl | |
| [default5]:[rank13]: return forward_call(*args, **kwargs) | |
| [default5]:[rank13]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 891, in forward | |
| [default5]:[rank13]: sharded_logits = self.model( | |
| [default5]:[rank13]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl | |
| [default5]:[rank13]: return self._call_impl(*args, **kwargs) | |
| [default5]:[rank13]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl | |
| [default5]:[rank13]: return forward_call(*args, **kwargs) | |
| [default5]:[rank13]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 764, in forward | |
| [default5]:[rank13]: return self.forward_with_hidden_states(input_ids=input_ids, input_mask=input_mask)[0] | |
| [default5]:[rank13]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 780, in forward_with_hidden_states | |
| [default5]:[rank13]: hidden_encoder_states = encoder_block(**hidden_encoder_states) | |
| [default5]:[rank13]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl | |
| [default5]:[rank13]: return self._call_impl(*args, **kwargs) | |
| [default5]:[rank13]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl | |
| [default5]:[rank13]: return forward_call(*args, **kwargs) | |
| [default5]:[rank13]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/block.py", line 151, in forward | |
| [default5]:[rank13]: output = self.pp_block(**new_kwargs) | |
| [default5]:[rank13]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl | |
| [default5]:[rank13]: return self._call_impl(*args, **kwargs) | |
| [default5]:[rank13]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl | |
| [default5]:[rank13]: return forward_call(*args, **kwargs) | |
| [default5]:[rank13]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 631, in forward | |
| [default5]:[rank13]: output = self.attn(hidden_states=hidden_states, sequence_mask=sequence_mask) | |
| [default5]:[rank13]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl | |
| [default5]:[rank13]: return self._call_impl(*args, **kwargs) | |
| [default5]:[rank13]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl | |
| [default5]:[rank13]: return forward_call(*args, **kwargs) | |
| [default5]:[rank13]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 598, in forward | |
| [default5]:[rank13]: output = self.o_proj(attention_output) | |
| [default5]:[rank13]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl | |
| [default5]:[rank13]: return self._call_impl(*args, **kwargs) | |
| [default5]:[rank13]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl | |
| [default5]:[rank13]: return forward_call(*args, **kwargs) | |
| [default5]:[rank13]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/nn.py", line 159, in forward | |
| [default5]:[rank13]: return row_linear( | |
| [default5]:[rank13]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/functional.py", line 474, in row_linear | |
| [default5]:[rank13]: out = F.linear(input, weight, bias) | |
| [default5]:[rank13]: torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 2.00 GiB. GPU has a total capacity of 79.33 GiB of which 151.94 MiB is free. Including non-PyTorch memory, this process has 79.17 GiB memory in use. Of the allocated memory 71.65 GiB is allocated by PyTorch, and 202.02 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation. See documentation for Memory Management (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables) | |
| [default4]:[rank12]: Traceback (most recent call last): | |
| [default4]:[rank12]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/run_train.py", line 237, in <module> | |
| [default4]:[rank12]: trainer.train(dataloader) | |
| [default4]:[rank12]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 429, in train | |
| [default4]:[rank12]: outputs, loss_avg = self.training_step(dataloader=self.current_dataloader) | |
| [default4]:[rank12]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 462, in training_step | |
| [default4]:[rank12]: outputs = self.pipeline_engine.train_batch_iter( | |
| [default4]:[rank12]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 278, in train_batch_iter | |
| [default4]:[rank12]: output = self.forward(context=context, state=state, micro_batch=micro_batch, model=model) | |
| [default4]:[rank12]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 44, in forward | |
| [default4]:[rank12]: output = model(**micro_batch) | |
| [default4]:[rank12]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl | |
| [default4]:[rank12]: return self._call_impl(*args, **kwargs) | |
| [default4]:[rank12]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl | |
| [default4]:[rank12]: return forward_call(*args, **kwargs) | |
| [default4]:[rank12]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 891, in forward | |
| [default4]:[rank12]: sharded_logits = self.model( | |
| [default4]:[rank12]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl | |
| [default4]:[rank12]: return self._call_impl(*args, **kwargs) | |
| [default4]:[rank12]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl | |
| [default4]:[rank12]: return forward_call(*args, **kwargs) | |
| [default4]:[rank12]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 764, in forward | |
| [default4]:[rank12]: return self.forward_with_hidden_states(input_ids=input_ids, input_mask=input_mask)[0] | |
| [default4]:[rank12]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 780, in forward_with_hidden_states | |
| [default4]:[rank12]: hidden_encoder_states = encoder_block(**hidden_encoder_states) | |
| [default4]:[rank12]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl | |
| [default4]:[rank12]: return self._call_impl(*args, **kwargs) | |
| [default4]:[rank12]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl | |
| [default4]:[rank12]: return forward_call(*args, **kwargs) | |
| [default4]:[rank12]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/block.py", line 151, in forward | |
| [default4]:[rank12]: output = self.pp_block(**new_kwargs) | |
| [default4]:[rank12]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl | |
| [default4]:[rank12]: return self._call_impl(*args, **kwargs) | |
| [default4]:[rank12]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl | |
| [default4]:[rank12]: return forward_call(*args, **kwargs) | |
| [default4]:[rank12]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 631, in forward | |
| [default4]:[rank12]: output = self.attn(hidden_states=hidden_states, sequence_mask=sequence_mask) | |
| [default4]:[rank12]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl | |
| [default4]:[rank12]: return self._call_impl(*args, **kwargs) | |
| [default4]:[rank12]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl | |
| [default4]:[rank12]: return forward_call(*args, **kwargs) | |
| [default4]:[rank12]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 598, in forward | |
| [default4]:[rank12]: output = self.o_proj(attention_output) | |
| [default4]:[rank12]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl | |
| [default4]:[rank12]: return self._call_impl(*args, **kwargs) | |
| [default4]:[rank12]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl | |
| [default4]:[rank12]: return forward_call(*args, **kwargs) | |
| [default4]:[rank12]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/nn.py", line 159, in forward | |
| [default4]:[rank12]: return row_linear( | |
| [default4]:[rank12]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/functional.py", line 474, in row_linear | |
| [default4]:[rank12]: out = F.linear(input, weight, bias) | |
| [default4]:[rank12]: torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 2.00 GiB. GPU has a total capacity of 79.33 GiB of which 151.94 MiB is free. Including non-PyTorch memory, this process has 79.17 GiB memory in use. Of the allocated memory 71.65 GiB is allocated by PyTorch, and 202.02 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation. See documentation for Memory Management (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables) | |
| [default7]:[rank15]: Traceback (most recent call last): | |
| [default7]:[rank15]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/run_train.py", line 237, in <module> | |
| [default7]:[rank15]: trainer.train(dataloader) | |
| [default7]:[rank15]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 429, in train | |
| [default7]:[rank15]: outputs, loss_avg = self.training_step(dataloader=self.current_dataloader) | |
| [default7]:[rank15]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 462, in training_step | |
| [default7]:[rank15]: outputs = self.pipeline_engine.train_batch_iter( | |
| [default7]:[rank15]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 278, in train_batch_iter | |
| [default7]:[rank15]: output = self.forward(context=context, state=state, micro_batch=micro_batch, model=model) | |
| [default7]:[rank15]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 44, in forward | |
| [default7]:[rank15]: output = model(**micro_batch) | |
| [default7]:[rank15]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl | |
| [default7]:[rank15]: return self._call_impl(*args, **kwargs) | |
| [default7]:[rank15]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl | |
| [default7]:[rank15]: return forward_call(*args, **kwargs) | |
| [default7]:[rank15]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 891, in forward | |
| [default7]:[rank15]: sharded_logits = self.model( | |
| [default7]:[rank15]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl | |
| [default7]:[rank15]: return self._call_impl(*args, **kwargs) | |
| [default7]:[rank15]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl | |
| [default7]:[rank15]: return forward_call(*args, **kwargs) | |
| [default7]:[rank15]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 764, in forward | |
| [default7]:[rank15]: return self.forward_with_hidden_states(input_ids=input_ids, input_mask=input_mask)[0] | |
| [default7]:[rank15]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 780, in forward_with_hidden_states | |
| [default7]:[rank15]: hidden_encoder_states = encoder_block(**hidden_encoder_states) | |
| [default7]:[rank15]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl | |
| [default7]:[rank15]: return self._call_impl(*args, **kwargs) | |
| [default7]:[rank15]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl | |
| [default7]:[rank15]: return forward_call(*args, **kwargs) | |
| [default7]:[rank15]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/block.py", line 151, in forward | |
| [default7]:[rank15]: output = self.pp_block(**new_kwargs) | |
| [default7]:[rank15]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl | |
| [default7]:[rank15]: return self._call_impl(*args, **kwargs) | |
| [default7]:[rank15]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl | |
| [default7]:[rank15]: return forward_call(*args, **kwargs) | |
| [default7]:[rank15]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 631, in forward | |
| [default7]:[rank15]: output = self.attn(hidden_states=hidden_states, sequence_mask=sequence_mask) | |
| [default7]:[rank15]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl | |
| [default7]:[rank15]: return self._call_impl(*args, **kwargs) | |
| [default7]:[rank15]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl | |
| [default7]:[rank15]: return forward_call(*args, **kwargs) | |
| [default7]:[rank15]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 598, in forward | |
| [default7]:[rank15]: output = self.o_proj(attention_output) | |
| [default7]:[rank15]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl | |
| [default7]:[rank15]: return self._call_impl(*args, **kwargs) | |
| [default7]:[rank15]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl | |
| [default7]:[rank15]: return forward_call(*args, **kwargs) | |
| [default7]:[rank15]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/nn.py", line 159, in forward | |
| [default7]:[rank15]: return row_linear( | |
| [default7]:[rank15]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/functional.py", line 474, in row_linear | |
| [default7]:[rank15]: out = F.linear(input, weight, bias) | |
| [default7]:[rank15]: torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 2.00 GiB. GPU has a total capacity of 79.33 GiB of which 223.94 MiB is free. Including non-PyTorch memory, this process has 79.10 GiB memory in use. Of the allocated memory 71.65 GiB is allocated by PyTorch, and 202.02 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation. See documentation for Memory Management (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables) | |
| [default3]:[rank11]: Traceback (most recent call last): | |
| [default3]:[rank11]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/run_train.py", line 237, in <module> | |
| [default3]:[rank11]: trainer.train(dataloader) | |
| [default3]:[rank11]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 429, in train | |
| [default3]:[rank11]: outputs, loss_avg = self.training_step(dataloader=self.current_dataloader) | |
| [default3]:[rank11]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 462, in training_step | |
| [default3]:[rank11]: outputs = self.pipeline_engine.train_batch_iter( | |
| [default3]:[rank11]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 278, in train_batch_iter | |
| [default3]:[rank11]: output = self.forward(context=context, state=state, micro_batch=micro_batch, model=model) | |
| [default3]:[rank11]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 44, in forward | |
| [default3]:[rank11]: output = model(**micro_batch) | |
| [default3]:[rank11]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl | |
| [default3]:[rank11]: return self._call_impl(*args, **kwargs) | |
| [default3]:[rank11]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl | |
| [default3]:[rank11]: return forward_call(*args, **kwargs) | |
| [default3]:[rank11]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 891, in forward | |
| [default3]:[rank11]: sharded_logits = self.model( | |
| [default3]:[rank11]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl | |
| [default3]:[rank11]: return self._call_impl(*args, **kwargs) | |
| [default3]:[rank11]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl | |
| [default3]:[rank11]: return forward_call(*args, **kwargs) | |
| [default3]:[rank11]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 764, in forward | |
| [default3]:[rank11]: return self.forward_with_hidden_states(input_ids=input_ids, input_mask=input_mask)[0] | |
| [default3]:[rank11]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 780, in forward_with_hidden_states | |
| [default3]:[rank11]: hidden_encoder_states = encoder_block(**hidden_encoder_states) | |
| [default3]:[rank11]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl | |
| [default3]:[rank11]: return self._call_impl(*args, **kwargs) | |
| [default3]:[rank11]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl | |
| [default3]:[rank11]: return forward_call(*args, **kwargs) | |
| [default3]:[rank11]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/block.py", line 151, in forward | |
| [default3]:[rank11]: output = self.pp_block(**new_kwargs) | |
| [default3]:[rank11]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl | |
| [default3]:[rank11]: return self._call_impl(*args, **kwargs) | |
| [default3]:[rank11]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl | |
| [default3]:[rank11]: return forward_call(*args, **kwargs) | |
| [default3]:[rank11]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 631, in forward | |
| [default3]:[rank11]: output = self.attn(hidden_states=hidden_states, sequence_mask=sequence_mask) | |
| [default3]:[rank11]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl | |
| [default3]:[rank11]: return self._call_impl(*args, **kwargs) | |
| [default3]:[rank11]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl | |
| [default5]:[rank5]: Traceback (most recent call last): | |
| [default3]:[rank11]: return forward_call(*args, **kwargs) | |
| [default6]:[rank14]: Traceback (most recent call last): | |
| [default5]:[rank5]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/run_train.py", line 237, in <module> | |
| [default5]:[rank5]: trainer.train(dataloader) | |
| [default6]:[rank14]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/run_train.py", line 237, in <module> | |
| [default5]:[rank5]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 429, in train | |
| [default6]:[rank14]: trainer.train(dataloader) | |
| [default5]:[rank5]: outputs, loss_avg = self.training_step(dataloader=self.current_dataloader) | |
| [default5]:[rank5]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 462, in training_step | |
| [default5]:[rank5]: outputs = self.pipeline_engine.train_batch_iter( | |
| [default5]:[rank5]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 278, in train_batch_iter | |
| [default5]:[rank5]: output = self.forward(context=context, state=state, micro_batch=micro_batch, model=model) | |
| [default5]:[rank5]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 44, in forward | |
| [default5]:[rank5]: output = model(**micro_batch) | |
| [default6]:[rank14]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 429, in train | |
| [default5]:[rank5]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl | |
| [default6]:[rank14]: outputs, loss_avg = self.training_step(dataloader=self.current_dataloader) | |
| [default5]:[rank5]: return self._call_impl(*args, **kwargs) | |
| [default5]:[rank5]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl | |
| [default5]:[rank5]: return forward_call(*args, **kwargs) | |
| [default5]:[rank5]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 891, in forward | |
| [default3]:[rank11]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 598, in forward | |
| [default6]:[rank14]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 462, in training_step | |
| [default3]:[rank11]: output = self.o_proj(attention_output) | |
| [default3]:[rank11]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl | |
| [default5]:[rank5]: sharded_logits = self.model( | |
| [default5]:[rank5]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl | |
| [default5]:[rank5]: return self._call_impl(*args, **kwargs) | |
| [default5]:[rank5]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl | |
| [default5]:[rank5]: return forward_call(*args, **kwargs) | |
| [default5]:[rank5]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 764, in forward | |
| [default5]:[rank5]: return self.forward_with_hidden_states(input_ids=input_ids, input_mask=input_mask)[0] | |
| [default5]:[rank5]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 780, in forward_with_hidden_states | |
| [default5]:[rank5]: hidden_encoder_states = encoder_block(**hidden_encoder_states) | |
| [default5]:[rank5]: File "[default3]:[rank11]: return self._call_impl(*args, **kwargs) | |
| [default3]:[rank11]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl | |
| [default3]:[rank11]: return forward_call(*args, **kwargs) | |
| [default3]:[rank11]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/nn.py", line 159, in forward | |
| [default3]:[rank11]: return row_linear( | |
| [default3]:[rank11]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/functional.py", line 474, in row_linear | |
| [default3]:[rank11]: out = F.linear(input, weight, bias) | |
| [default6]:[rank14]: outputs = self.pipeline_engine.train_batch_iter( | |
| [default6]:[rank14]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 278, in train_batch_iter | |
| [default6]:[rank14]: output = self.forward(context=context, state=state, micro_batch=micro_batch, model=model) | |
| [default6]:[rank14]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 44, in forward | |
| [default6]:[rank14]: output = model(**micro_batch) | |
| [default6]:[rank14]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl | |
| [default6]:[rank14]: return self._call_impl(*args, **kwargs) | |
| [default6]:[rank14]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl | |
| [default6]:[rank14]: return forward_call(*args, **kwargs) | |
| [default6]:[rank14]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 891, in forward | |
| [default6]:[rank14]: sharded_logits = self.model( | |
| [default6]:[rank14]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl | |
| [default6]:[rank14]: return self._call_impl(*args, **kwargs) | |
| [default6]:[rank14]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl | |
| [default6]:[rank14]: return forward_call(*args, **kwargs) | |
| [default6]:[rank14]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 764, in forward | |
| [default6]:[rank14]: return self.forward_with_hidden_states(input_ids=input_ids, input_mask=input_mask)[0] | |
| [default6]:[rank14]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 780, in forward_with_hidden_states | |
| [default6]:[rank14]: hidden_encoder_states = encoder_block(**hidden_encoder_states) | |
| [default6]:[rank14]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl | |
| [default6]:[rank14]: return self._call_impl(*args, **kwargs) | |
| [default6]:[rank14]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl | |
| [default6]:[rank14]: return forward_call(*args, **kwargs) | |
| [default6]:[rank14]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/block.py", line 151, in forward | |
| [default6]:[rank14]: output = self.pp_block(**new_kwargs) | |
| [default6]:[rank14]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl | |
| [default6]:[rank14]: return self._call_impl(*args, **kwargs) | |
| [default6]:[rank14]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl | |
| [default6]:[rank14]: return forward_call(*args, **kwargs) | |
| [default6]:[rank14]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 631, in forward | |
| [default6]:[rank14]: output = self.attn(hidden_states=hidden_states, sequence_mask=sequence_mask) | |
| [default6]:[rank14]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl | |
| [default6]:[rank14]: return self._call_impl(*args, **kwargs) | |
| [default6]:[rank14]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl | |
| [default6]:[rank14]: return forward_call(*args, **kwargs) | |
| [default6]:[rank14]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 598, in forward | |
| [default6]:[rank14]: output = self.o_proj(attention_output) | |
| [default6]:[rank14]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl | |
| [default6]:[rank14]: return self._call_impl(*args, **kwargs) | |
| [default6]:[rank14]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl | |
| [default6]:[rank14]: return forward_call(*args, **kwargs) | |
| [default6]:[rank14]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/nn.py", line 159, in forward | |
| [default6]:[rank14]: return row_linear( | |
| [default6]:[rank14]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/functional.py", line 474, in row_linear | |
| [default6]:[rank14]: out = F.linear(input, weight, bias) | |
| [default3]:[rank11]: torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 2.00 GiB. GPU has a total capacity of 79.33 GiB of which 151.94 MiB is free. Including non-PyTorch memory, this process has 79.17 GiB memory in use. Of the allocated memory 71.65 GiB is allocated by PyTorch, and 202.02 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation. See documentation for Memory Management (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables) | |
| [default6]:[rank14]: torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 2.00 GiB. GPU has a total capacity of 79.33 GiB of which 151.94 MiB is free. Including non-PyTorch memory, this process has 79.17 GiB memory in use. Of the allocated memory 71.65 GiB is allocated by PyTorch, and 202.02 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation. See documentation for Memory Management (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables) | |
| [default0]:[rank8]: Traceback (most recent call last): | |
| [default0]:[rank8]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/run_train.py", line 237, in <module> | |
| [default0]:[rank8]: trainer.train(dataloader) | |
| [default0]:[rank8]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 429, in train | |
| [default0]:[rank8]: outputs, loss_avg = self.training_step(dataloader=self.current_dataloader) | |
| [default0]:[rank8]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 462, in training_step | |
| [default0]:[rank8]: outputs = self.pipeline_engine.train_batch_iter( | |
| [default0]:[rank8]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 278, in train_batch_iter | |
| [default0]:[rank8]: output = self.forward(context=context, state=state, micro_batch=micro_batch, model=model) | |
| [default0]:[rank8]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 44, in forward | |
| [default0]:[rank8]: output = model(**micro_batch) | |
| [default0]:[rank8]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl | |
| [default0]:[rank8]: return self._call_impl(*args, **kwargs) | |
| [default0]:[rank8]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl | |
| [default0]:[rank8]: return forward_call(*args, **kwargs) | |
| [default0]:[rank8]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 891, in forward | |
| [default0]:[rank8]: sharded_logits = self.model( | |
| [default0]:[rank8]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl | |
| [default0]:[rank8]: return self._call_impl(*args, **kwargs) | |
| [default0]:[rank8]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl | |
| [default0]:[rank8]: return forward_call(*args, **kwargs) | |
| [default0]:[rank8]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 764, in forward | |
| [default0]:[rank8]: return self.forward_with_hidden_states(input_ids=input_ids, input_mask=input_mask)[0] | |
| [default0]:[rank8]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 780, in forward_with_hidden_states | |
| [default0]:[rank8]: hidden_encoder_states = encoder_block(**hidden_encoder_states) | |
| [default0]:[rank8]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl | |
| [default0]:[rank8]: return self._call_impl(*args, **kwargs) | |
| [default0]:[rank8]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl | |
| [default0]:[rank8]: return forward_call(*args, **kwargs) | |
| [default0]:[rank8]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/block.py", line 151, in forward | |
| [default0]:[rank8]: output = self.pp_block(**new_kwargs) | |
| [default0]:[rank8]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl | |
| [default0]:[rank8]: return self._call_impl(*args, **kwargs) | |
| [default0]:[rank8]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl | |
| [default0]:[rank8]: return forward_call(*args, **kwargs) | |
| [default0]:[rank8]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 631, in forward | |
| [default0]:[rank8]: output = self.attn(hidden_states=hidden_states, sequence_mask=sequence_mask) | |
| [default0]:[rank8]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl | |
| [default0]:[rank8]: return self._call_impl(*args, **kwargs) | |
| [default0]:[rank8]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl | |
| [default0]:[rank8]: return forward_call(*args, **kwargs) | |
| [default0]:[rank8]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 598, in forward | |
| [default0]:[rank8]: output = self.o_proj(attention_output) | |
| [default0]:[rank8]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl | |
| [default0]:[rank8]: return self._call_impl(*args, **kwargs) | |
| [default0]:[rank8]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl | |
| [default0]:[rank8]: return forward_call(*args, **kwargs) | |
| [default0]:[rank8]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/nn.py", line 159, in forward | |
| [default0]:[rank8]: return row_linear( | |
| [default0]:[rank8]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/functional.py", line 474, in row_linear | |
| [default0]:[rank8]: out = F.linear(input, weight, bias) | |
| [default0]:[rank8]: torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 2.00 GiB. GPU | |
| /fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl | |
| [default5]:[rank5]: return self._call_impl(*args, **kwargs) | |
| [default5]:[rank5]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl | |
| [default5]:[rank5]: return forward_call(*args, **kwargs) | |
| [default5]:[rank5]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/block.py", line 151, in forward | |
| [default5]:[rank5]: output = self.pp_block(**new_kwargs) | |
| [default5]:[rank5]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl | |
| [default5]:[rank5]: return self._call_impl(*args, **kwargs) | |
| [default5]:[rank5]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl | |
| [default5]:[rank5]: return forward_call(*args, **kwargs) | |
| [default5]:[rank5]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 631, in forward | |
| [default5]:[rank5]: output = self.attn(hidden_states=hidden_states, sequence_mask=sequence_mask) | |
| [default5]:[rank5]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl | |
| [default5]:[rank5]: return self._call_impl(*args, **kwargs) | |
| [default5]:[rank5]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl | |
| [default5]:[rank5]: return forward_call(*args, **kwargs) | |
| [default5]:[rank5]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 598, in forward | |
| [default5]:[rank5]: output = self.o_proj(attention_output) | |
| [default5]:[rank5]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl | |
| [default5]:[rank5]: return self._call_impl(*args, **kwargs) | |
| [default5]:[rank5]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl | |
| [default5]:[rank5]: return forward_call(*args, **kwargs) | |
| [default5]:[rank5]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/nn.py", line 159, in forward | |
| [default5]:[rank5]: return row_linear( | |
| [default5]:[rank5]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/functional.py", line 474, in row_linear | |
| [default5]:[rank5]: out = F.linear(input, weight, bias) | |
| [default5]:[rank5]: torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 2.00 GiB. GPU has a total capacity of 79.33 GiB of which 151.94 MiB is free. Including non-PyTorch memory, this process has 79.17 GiB memory in use. Of the allocated memory 71.65 GiB is allocated by PyTorch, and 202.02 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation. See documentation for Memory Management (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables) | |
| [default4]:[rank4]: Traceback (most recent call last): | |
| [default4]:[rank4]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/run_train.py", line 237, in <module> | |
| [default4]:[rank4]: trainer.train(dataloader) | |
| [default4]:[rank4]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 429, in train | |
| [default4]:[rank4]: outputs, loss_avg = self.training_step(dataloader=self.current_dataloader) | |
| [default4]:[rank4]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 462, in training_step | |
| [default4]:[rank4]: outputs = self.pipeline_engine.train_batch_iter( | |
| [default4]:[rank4]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 278, in train_batch_iter | |
| [default4]:[rank4]: output = self.forward(context=context, state=state, micro_batch=micro_batch, model=model) | |
| [default4]:[rank4]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 44, in forward | |
| [default4]:[rank4]: output = model(**micro_batch) | |
| [default4]:[rank4]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl | |
| [default4]:[rank4]: return self._call_impl(*args, **kwargs) | |
| [default4]:[rank4]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl | |
| [default4]:[rank4]: return forward_call(*args, **kwargs) | |
| [default4]:[rank4]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 891, in forward | |
| [default4]:[rank4]: sharded_logits = self.model( | |
| [default4]:[rank4]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl | |
| [default4]:[rank4]: return self._call_impl(*args, **kwargs) | |
| [default4]:[rank4]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl | |
| [default4]:[rank4]: return forward_call(*args, **kwargs) | |
| [default4]:[rank4]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 764, in forward | |
| [default4]:[rank4]: return self.forward_with_hidden_states(input_ids=input_ids, input_mask=input_mask)[0] | |
| [default4]:[rank4]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 780, in forward_with_hidden_states | |
| [default4]:[rank4]: hidden_encoder_states = encoder_block(**hidden_encoder_states) | |
| [default4]:[rank4]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl | |
| [default4]:[rank4]: return self._call_impl(*args, **kwargs) | |
| [default4]:[rank4]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl | |
| [default4]:[rank4]: return forward_call(*args, **kwargs) | |
| [default4]:[rank4]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/block.py", line 151, in forward | |
| [default4]:[rank4]: output = self.pp_block(**new_kwargs) | |
| [default4]:[rank4]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl | |
| [default4]:[rank4]: return self._call_impl(*args, **kwargs) | |
| [default4]:[rank4]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl | |
| [default4]:[rank4]: return forward_call(*args, **kwargs) | |
| [default4]:[rank4]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 631, in forward | |
| [default4]:[rank4]: output = self.attn(hidden_states=hidden_states, sequence_mask=sequence_mask) | |
| [default4]:[rank4]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl | |
| [default4]:[rank4]: return self._call_impl(*args, **kwargs) | |
| [default4]:[rank4]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl | |
| [default4]:[rank4]: return forward_call(*args, **kwargs) | |
| [default4]:[rank4]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 598, in forward | |
| [default4]:[rank4]: output = self.o_proj(attention_output) | |
| [default4]:[rank4]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl | |
| [default4]:[rank4]: return self._call_impl(*args, **kwargs) | |
| [default4]:[rank4]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl | |
| [default4]:[rank4]: return forward_call(*args, **kwargs) | |
| [default4]:[rank4]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/nn.py", line 159, in forward | |
| [default4]:[rank4]: return row_linear( | |
| [default4]:[rank4]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/functional.py", line 474, in row_linear | |
| [default4]:[rank4]: out = F.linear(input, weight, bias) | |
| [default4]:[rank4]: torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 2.00 GiB. GPU has a total capacity of 79.33 GiB of which 151.94 MiB is free. Including non-PyTorch memory, this process has 79.17 GiB memory in use. Of the allocated memory 71.65 GiB is allocated by PyTorch, and 202.02 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation. See documentation for Memory Management (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables) | |
| [default2]:[rank10]: Traceback (most recent call last): | |
| [default2]:[rank10]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/run_train.py", line 237, in <module> | |
| [default2]:[rank10]: trainer.train(dataloader) | |
| [default2]:[rank10]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 429, in train | |
| [default2]:[rank10]: outputs, loss_avg = self.training_step(dataloader=self.current_dataloader) | |
| [default2]:[rank10]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 462, in training_step | |
| [default2]:[rank10]: outputs = self.pipeline_engine.train_batch_iter( | |
| [default2]:[rank10]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 278, in train_batch_iter | |
| [default2]:[rank10]: output = self.forward(context=context, state=state, micro_batch=micro_batch, model=model) | |
| [default2]:[rank10]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 44, in forward | |
| [default2]:[rank10]: output = model(**micro_batch) | |
| [default2]:[rank10]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl | |
| [default2]:[rank10]: return self._call_impl(*args, **kwargs) | |
| [default2]:[rank10]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl | |
| [default2]:[rank10]: return forward_call(*args, **kwargs) | |
| [default2]:[rank10]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 891, in forward | |
| [default2]:[rank10]: sharded_logits = self.model( | |
| [default2]:[rank10]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl | |
| [default2]:[rank10]: return self._call_impl(*args, **kwargs) | |
| [default2]:[rank10]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl | |
| [default2]:[rank10]: return forward_call(*args, **kwargs) | |
| [default2]:[rank10]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 764, in forward | |
| [default2]:[rank10]: return self.forward_with_hidden_states(input_ids=input_ids, input_mask=input_mask)[0] | |
| [default2]:[rank10]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 780, in forward_with_hidden_states | |
| [default2]:[rank10]: hidden_encoder_states = encoder_block(**hidden_encoder_states) | |
| [default2]:[rank10]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl | |
| [default2]:[rank10]: return self._call_impl(*args, **kwargs) | |
| [default2]:[rank10]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl | |
| [default2]:[rank10]: return forward_call(*args, **kwargs) | |
| [default2]:[rank10]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/block.py", line 151, in forward | |
| [default2]:[rank10]: output = self.pp_block(**new_kwargs) | |
| [default2]:[rank10]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl | |
| [default2]:[rank10]: return self._call_impl(*args, **kwargs) | |
| [default2]:[rank10]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl | |
| [default2]:[rank10]: return forward_call(*args, **kwargs) | |
| [default2]:[rank10]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 631, in forward | |
| [default2]:[rank10]: output = self.attn(hidden_states=hidden_states, sequence_mask=sequence_mask) | |
| [default2]:[rank10]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl | |
| [default2]:[rank10]: return self._call_impl(*args, **kwargs) | |
| [default2]:[rank10]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl | |
| [default2]:[rank10]: return forward_call(*args, **kwargs) | |
| [default2]:[rank10]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 598, in forward | |
| [default2]:[rank10]: output = self.o_proj(attention_output) | |
| [default2]:[rank10]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl | |
| [default2]:[rank10]: return self._call_impl(*args, **kwargs) | |
| [default2]:[rank10]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl | |
| [default2]:[rank10]: return forward_call(*args, **kwargs) | |
| [default2]:[rank10]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/nn.py", line 159, in forward | |
| [default2]:[rank10]: return row_linear( | |
| [default2]:[rank10]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/functional.py", line 474, in row_linear | |
| [default2]:[rank10]: out = F.linear(input, weight, bias) | |
| [default2]:[rank10]: torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 2.00 GiB. GPU has a total capacity of 79.33 GiB of which 389.94 MiB is free. Including non-PyTorch memory, this process has 78.93 GiB memory in use. Of the allocated memory 71.65 GiB is allocated by PyTorch, and 202.02 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation. See documentation for Memory Management (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables) | |
| [default6]:[rank6]: Traceback (most recent call last): | |
| [default6]:[rank6]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/run_train.py", line 237, in <module> | |
| [default6]:[rank6]: trainer.train(dataloader) | |
| [default6]:[rank6]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 429, in train | |
| [default6]:[rank6]: outputs, loss_avg = self.training_step(dataloader=self.current_dataloader) | |
| [default6]:[rank6]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 462, in training_step | |
| [default6]:[rank6]: outputs = self.pipeline_engine.train_batch_iter( | |
| [default6]:[rank6]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 278, in train_batch_iter | |
| [default6]:[rank6]: output = self.forward(context=context, state=state, micro_batch=micro_batch, model=model) | |
| [default6]:[rank6]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 44, in forward | |
| [default6]:[rank6]: output = model(**micro_batch) | |
| [default6]:[rank6]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl | |
| [default6]:[rank6]: return self._call_impl(*args, **kwargs) | |
| [default6]:[rank6]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl | |
| [default6]:[rank6]: return forward_call(*args, **kwargs) | |
| [default6]:[rank6]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 891, in forward | |
| [default6]:[rank6]: sharded_logits = self.model( | |
| [default6]:[rank6]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl | |
| [default6]:[rank6]: return self._call_impl(*args, **kwargs) | |
| [default6]:[rank6]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl | |
| [default6]:[rank6]: return forward_call(*args, **kwargs) | |
| [default6]:[rank6]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 764, in forward | |
| [default6]:[rank6]: return self.forward_with_hidden_states(input_ids=input_ids, input_mask=input_mask)[0] | |
| [default6]:[rank6]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 780, in forward_with_hidden_states | |
| [default6]:[rank6]: hidden_encoder_states = encoder_block(**hidden_encoder_states) | |
| [default6]:[rank6]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl | |
| [default6]:[rank6]: return self._call_impl(*args, **kwargs) | |
| [default6]:[rank6]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl | |
| [default6]:[rank6]: return forward_call(*args, **kwargs) | |
| [default6]:[rank6]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/block.py", line 151, in forward | |
| [default6]:[rank6]: output = self.pp_block(**new_kwargs) | |
| [default6]:[rank6]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl | |
| [default6]:[rank6]: return self._call_impl(*args, **kwargs) | |
| [default6]:[rank6]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl | |
| [default6]:[rank6]: return forward_call(*args, **kwargs) | |
| [default6]:[rank6]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 631, in forward | |
| [default6]:[rank6]: output = self.attn(hidden_states=hidden_states, sequence_mask=sequence_mask) | |
| [default6]:[rank6]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl | |
| [default6]:[rank6]: return self._call_impl(*args, **kwargs) | |
| [default6]:[rank6]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl | |
| [default6]:[rank6]: return forward_call(*args, **kwargs) | |
| [default6]:[rank6]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 598, in forward | |
| [default6]:[rank6]: output = self.o_proj(attention_output) | |
| [default6]:[rank6]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl | |
| [default6]:[rank6]: return self._call_impl(*args, **kwargs) | |
| [default6]:[rank6]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl | |
| [default6]:[rank6]: return forward_call(*args, **kwargs) | |
| [default6]:[rank6]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/nn.py", line 159, in forward | |
| [default6]:[rank6]: return row_linear( | |
| [default6]:[rank6]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/functional.py", line 474, in row_linear | |
| [default6]:[rank6]: out = F.linear(input, weight, bias) | |
| [default6]:[rank6]: torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 2.00 GiB. GPU has a total capacity of 79.33 GiB of which 151.94 MiB is free. Including non-PyTorch memory, this process has 79.17 GiB memory in use. Of the allocated memory 71.65 GiB is allocated by PyTorch, and 202.02 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation. See documentation for Memory Management (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables) | |
| [default1]:[rank1]: Traceback (most recent call last): | |
| [default1]:[rank1]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/run_train.py", line 237, in <module> | |
| [default1]:[rank1]: trainer.train(dataloader) | |
| [default1]:[rank1]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 429, in train | |
| [default1]:[rank1]: outputs, loss_avg = self.training_step(dataloader=self.current_dataloader) | |
| [default1]:[rank1]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 462, in training_step | |
| [default1]:[rank1]: outputs = self.pipeline_engine.train_batch_iter( | |
| [default1]:[rank1]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 278, in train_batch_iter | |
| [default1]:[rank1]: output = self.forward(context=context, state=state, micro_batch=micro_batch, model=model) | |
| [default7]:[rank7]: Traceback (most recent call last): | |
| [default7]:[rank7]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/run_train.py", line 237, in <module> | |
| [default7]:[rank7]: trainer.train(dataloader) | |
| [default7]:[rank7]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 429, in train | |
| [default7]:[rank7]: outputs, loss_avg = self.training_step(dataloader=self.current_dataloader) | |
| [default7]:[rank7]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 462, in training_step | |
| [default7]:[rank7]: outputs = self.pipeline_engine.train_batch_iter( | |
| [default7]:[rank7]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 278, in train_batch_iter | |
| [default7]:[rank7]: output = self.forward(context=context, state=state, micro_batch=micro_batch, model=model) | |
| [default7]:[rank7]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 44, in forward | |
| [default7]:[rank7]: output = model(**micro_batch) | |
| [default7]:[rank7]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl | |
| [default7]:[rank7]: return self._call_impl(*args, **kwargs) | |
| [default7]:[rank7]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl | |
| [default1]:[rank1]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 44, in forward | |
| [default1]:[rank1]: output = model(**micro_batch) | |
| [default1]:[rank1]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl | |
| [default1]:[rank1]: return self._call_impl(*args, **kwargs) | |
| [default7]:[rank7]: return forward_call(*args, **kwargs) | |
| [default1]:[rank1]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl | |
| [default1]:[rank1]: return forward_call(*args, **kwargs) | |
| [default7]:[rank7]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 891, in forward | |
| [default7]:[rank7]: sharded_logits = self.model( | |
| [default1]:[rank1]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 891, in forward | |
| [default1]:[rank1]: sharded_logits = self.model( | |
| [default1]:[rank1]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl | |
| [default1]:[rank1]: return self._call_impl(*args, **kwargs) | |
| [default1]:[rank1]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl | |
| [default1]:[rank1]: return forward_call(*args, **kwargs) | |
| [default1]:[rank1]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 764, in forward | |
| [default7]:[rank7]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl | |
| [default7]:[rank7]: return self._call_impl(*args, **kwargs) | |
| [default1]:[rank1]: return self.forward_with_hidden_states(input_ids=input_ids, input_mask=input_mask)[0] | |
| [default1]:[rank1]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 780, in forward_with_hidden_states | |
| [default1]:[rank1]: hidden_encoder_states = encoder_block(**hidden_encoder_states) | |
| [default7]:[rank7]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl | |
| [default7]:[rank7]: return forward_call(*args, **kwargs) | |
| [default1]:[rank1]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl | |
| [default1]:[rank1]: return self._call_impl(*args, **kwargs) | |
| [default1]:[rank1]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl | |
| [default7]:[rank7]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 764, in forward | |
| [default7]:[rank7]: return self.forward_with_hidden_states(input_ids=input_ids, input_mask=input_mask)[0] | |
| [default1]:[rank1]: return forward_call(*args, **kwargs) | |
| [default1]:[rank1]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/block.py", line 151, in forward | |
| [default1]:[rank1]: output = self.pp_block(**new_kwargs) | |
| [default1]:[rank1]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl | |
| [default1]:[rank1]: return self._call_impl(*args, **kwargs) | |
| [default1]:[rank1]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl | |
| [default1]:[rank1]: return forward_call(*args, **kwargs) | |
| [default1]:[rank1]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 631, in forward | |
| [default7]:[rank7]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 780, in forward_with_hidden_states | |
| [default7]:[rank7]: hidden_encoder_states = encoder_block(**hidden_encoder_states) | |
| [default1]:[rank1]: output = self.attn(hidden_states=hidden_states, sequence_mask=sequence_mask) | |
| [default1]:[rank1]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl | |
| [default7]:[rank7]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl | |
| [default1]:[rank1]: return self._call_impl(*args, **kwargs) | |
| [default1]:[rank1]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl | |
| [default1]:[rank1]: return forward_call(*args, **kwargs) | |
| [default7]:[rank7]: return self._call_impl(*args, **kwargs) | |
| [default7]:[rank7]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl | |
| [default7]:[rank7]: return forward_call(*args, **kwargs) | |
| [default7]:[rank7]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/block.py", line 151, in forward | |
| [default7]:[rank7]: output = self.pp_block(**new_kwargs) | |
| [default7]:[rank7]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl | |
| [default7]:[rank7]: return self._call_impl(*args, **kwargs) | |
| [default7]:[rank7]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl | |
| [default7]:[rank7]: return forward_call(*args, **kwargs) | |
| [default7]:[rank7]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 631, in forward | |
| [default7]:[rank7]: output = self.attn(hidden_states=hidden_states, sequence_mask=sequence_mask) | |
| [default7]:[rank7]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl | |
| [default7]:[rank7]: return self._call_impl(*args, **kwargs) | |
| [default7]:[rank7]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl | |
| [default7]:[rank7]: return forward_call(*args, **kwargs) | |
| [default7]:[rank7]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 598, in forward | |
| [default7]:[rank7]: output = self.o_proj(attention_output) | |
| [default7]:[rank7]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl | |
| [default7]:[rank7]: return self._call_impl(*args, **kwargs) | |
| [default1]:[rank1]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 598, in forward | |
| [default1]:[rank1]: output = self.o_proj(attention_output) | |
| [default7]:[rank7]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl | |
| [default7]:[rank7]: return forward_call(*args, **kwargs) | |
| [default7]:[rank7]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/nn.py", line 159, in forward | |
| [default7]:[rank7]: return row_linear( | |
| [default7]:[rank7]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/functional.py", line 474, in row_linear | |
| [default7]:[rank7]: out = F.linear(input, weight, bias) | |
| [default1]:[rank1]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl | |
| [default1]:[rank1]: return self._call_impl(*args, **kwargs) | |
| [default7]:[rank7]: torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 2.00 GiB. GPU has a total capacity of 79.33 GiB of which 223.94 MiB is free. Including non-PyTorch memory, this process has 79.10 GiB memory in use. Of the allocated memory 71.65 GiB is allocated by PyTorch, and 202.02 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation. See documentation for Memory Management (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables) | |
| [default1]:[rank1]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl | |
| [default1]:[rank1]: return forward_call(*args, **kwargs) | |
| [default1]:[rank1]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/nn.py", line 159, in forward | |
| [default1]:[rank1]: return row_linear( | |
| [default1]:[rank1]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/functional.py", line 474, in row_linear | |
| [default1]:[rank1]: out = F.linear(input, weight, bias) | |
| [default1]:[rank1]: torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 2.00 GiB. GPU has a total capacity of 79.33 GiB of which 553.94 MiB is free. Including non-PyTorch memory, this process has 78.77 GiB memory in use. Of the allocated memory 71.65 GiB is allocated by PyTorch, and 202.02 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation. See documentation for Memory Management (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables) | |
| [default3]:[rank3]: Traceback (most recent call last): | |
| [default3]:[rank3]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/run_train.py", line 237, in <module> | |
| [default3]:[rank3]: trainer.train(dataloader) | |
| [default3]:[rank3]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 429, in train | |
| [default3]:[rank3]: outputs, loss_avg = self.training_step(dataloader=self.current_dataloader) | |
| [default3]:[rank3]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 462, in training_step | |
| [default3]:[rank3]: outputs = self.pipeline_engine.train_batch_iter( | |
| [default3]:[rank3]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 278, in train_batch_iter | |
| [default3]:[rank3]: output = self.forward(context=context, state=state, micro_batch=micro_batch, model=model) | |
| [default3]:[rank3]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 44, in forward | |
| [default3]:[rank3]: output = model(**micro_batch) | |
| [default3]:[rank3]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl | |
| [default3]:[rank3]: return self._call_impl(*args, **kwargs) | |
| [default3]:[rank3]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl | |
| [default3]:[rank3]: return forward_call(*args, **kwargs) | |
| [default3]:[rank3]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 891, in forward | |
| [default3]:[rank3]: sharded_logits = self.model( | |
| [default3]:[rank3]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl | |
| [default3]:[rank3]: return self._call_impl(*args, **kwargs) | |
| [default3]:[rank3]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl | |
| [default3]:[rank3]: return forward_call(*args, **kwargs) | |
| [default3]:[rank3]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 764, in forward | |
| [default3]:[rank3]: return self.forward_with_hidden_states(input_ids=input_ids, input_mask=input_mask)[0] | |
| [default3]:[rank3]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 780, in forward_with_hidden_states | |
| [default3]:[rank3]: hidden_encoder_states = encoder_block(**hidden_encoder_states) | |
| [default3]:[rank3]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl | |
| [default3]:[rank3]: return self._call_impl(*args, **kwargs) | |
| [default3]:[rank3]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl | |
| [default3]:[rank3]: return forward_call(*args, **kwargs) | |
| [default3]:[rank3]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/block.py", line 151, in forward | |
| [default3]:[rank3]: output = self.pp_block(**new_kwargs) | |
| [default3]:[rank3]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl | |
| [default3]:[rank3]: return self._call_impl(*args, **kwargs) | |
| [default3]:[rank3]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl | |
| [default3]:[rank3]: return forward_call(*args, **kwargs) | |
| [default3]:[rank3]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 631, in forward | |
| [default3]:[rank3]: output = self.attn(hidden_states=hidden_states, sequence_mask=sequence_mask) | |
| [default3]:[rank3]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl | |
| [default3]:[rank3]: return self._call_impl(*args, **kwargs) | |
| [default3]:[rank3]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl | |
| [default3]:[rank3]: return forward_call(*args, **kwargs) | |
| [default3]:[rank3]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 598, in forward | |
| [default3]:[rank3]: output = self.o_proj(attention_output) | |
| [default3]:[rank3]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl | |
| [default3]:[rank3]: return self._call_impl(*args, **kwargs) | |
| [default3]:[rank3]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl | |
| [default3]:[rank3]: return forward_call(*args, **kwargs) | |
| [default3]:[rank3]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/nn.py", line 159, in forward | |
| [default3]:[rank3]: return row_linear( | |
| [default3]:[rank3]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/functional.py", line 474, in row_linear | |
| [default3]:[rank3]: out = F.linear(input, weight, bias) | |
| [default3]:[rank3]: torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 2.00 GiB. GPU has a total capacity of 79.33 GiB of which 151.94 MiB is free. Including non-PyTorch memory, this process has 79.17 GiB memory in use. Of the allocated memory 71.65 GiB is allocated by PyTorch, and 202.02 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation. See documentation for Memory Management (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables) | |
| [default0]:[rank0]: Traceback (most recent call last): | |
| [default0]:[rank0]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/run_train.py", line 237, in <module> | |
| [default0]:[rank0]: trainer.train(dataloader) | |
| [default0]:[rank0]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 429, in train | |
| [default0]:[rank0]: outputs, loss_avg = self.training_step(dataloader=self.current_dataloader) | |
| [default0]:[rank0]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 462, in training_step | |
| [default0]:[rank0]: outputs = self.pipeline_engine.train_batch_iter( | |
| [default0]:[rank0]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 278, in train_batch_iter | |
| [default0]:[rank0]: output = self.forward(context=context, state=state, micro_batch=micro_batch, model=model) | |
| [default0]:[rank0]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 44, in forward | |
| [default0]:[rank0]: output = model(**micro_batch) | |
| [default0]:[rank0]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl | |
| [default0]:[rank0]: return self._call_impl(*args, **kwargs) | |
| [default0]:[rank0]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl | |
| [default0]:[rank0]: return forward_call(*args, **kwargs) | |
| [default0]:[rank0]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 891, in forward | |
| [default0]:[rank0]: sharded_logits = self.model( | |
| [default0]:[rank0]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl | |
| [default0]:[rank0]: return self._call_impl(*args, **kwargs) | |
| [default0]:[rank0]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl | |
| [default0]:[rank0]: return forward_call(*args, **kwargs) | |
| [default0]:[rank0]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 764, in forward | |
| [default0]:[rank0]: return self.forward_with_hidden_states(input_ids=input_ids, input_mask=input_mask)[0] | |
| [default0]:[rank0]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 780, in forward_with_hidden_states | |
| [default0]:[rank0]: hidden_encoder_states = encoder_block(**hidden_encoder_states) | |
| [default0]:[rank0]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl | |
| [default0]:[rank0]: return self._call_impl(*args, **kwargs) | |
| [default0]:[rank0]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl | |
| [default0]:[rank0]: return forward_call(*args, **kwargs) | |
| [default0]:[rank0]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/block.py", line 151, in forward | |
| [default0]:[rank0]: output = self.pp_block(**new_kwargs) | |
| [default0]:[rank0]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl | |
| [default0]:[rank0]: return self._call_impl(*args, **kwargs) | |
| [default0]:[rank0]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl | |
| [default0]:[rank0]: return forward_call(*args, **kwargs) | |
| [default0]:[rank0]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 631, in forward | |
| [default0]:[rank0]: output = self.attn(hidden_states=hidden_states, sequence_mask=sequence_mask) | |
| [default0]:[rank0]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl | |
| [default0]:[rank0]: return self._call_impl(*args, **kwargs) | |
| [default0]:[rank0]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl | |
| [default0]:[rank0]: return forward_call(*args, **kwargs) | |
| [default0]:[rank0]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 598, in forward | |
| [default0]:[rank0]: output = self.o_proj(attention_output) | |
| [default0]:[rank0]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl | |
| [default0]:[rank0]: return self._call_impl(*args, **kwargs) | |
| [default0]:[rank0]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl | |
| [default0]:[rank0]: return forward_call(*args, **kwargs) | |
| [default0]:[rank0]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/nn.py", line 159, in forward | |
| [default0]:[rank0]: return row_linear( | |
| [default0]:[rank0]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/functional.py", line 474, in row_linear | |
| [default0]:[rank0]: out = F.linear(input, weight, bias) | |
| [default0]:[rank0]: torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 2.00 GiB. GPU | |
| [default2]:[rank2]: Traceback (most recent call last): | |
| [default2]:[rank2]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/run_train.py", line 237, in <module> | |
| [default2]:[rank2]: trainer.train(dataloader) | |
| [default2]:[rank2]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 429, in train | |
| [default2]:[rank2]: outputs, loss_avg = self.training_step(dataloader=self.current_dataloader) | |
| [default2]:[rank2]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 462, in training_step | |
| [default2]:[rank2]: outputs = self.pipeline_engine.train_batch_iter( | |
| [default2]:[rank2]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 278, in train_batch_iter | |
| [default2]:[rank2]: output = self.forward(context=context, state=state, micro_batch=micro_batch, model=model) | |
| [default2]:[rank2]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 44, in forward | |
| [default2]:[rank2]: output = model(**micro_batch) | |
| [default2]:[rank2]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl | |
| [default2]:[rank2]: return self._call_impl(*args, **kwargs) | |
| [default2]:[rank2]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl | |
| [default2]:[rank2]: return forward_call(*args, **kwargs) | |
| [default2]:[rank2]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 891, in forward | |
| [default2]:[rank2]: sharded_logits = self.model( | |
| [default2]:[rank2]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl | |
| [default2]:[rank2]: return self._call_impl(*args, **kwargs) | |
| [default2]:[rank2]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl | |
| [default2]:[rank2]: return forward_call(*args, **kwargs) | |
| [default2]:[rank2]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 764, in forward | |
| [default2]:[rank2]: return self.forward_with_hidden_states(input_ids=input_ids, input_mask=input_mask)[0] | |
| [default2]:[rank2]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 780, in forward_with_hidden_states | |
| [default2]:[rank2]: hidden_encoder_states = encoder_block(**hidden_encoder_states) | |
| [default2]:[rank2]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl | |
| [default2]:[rank2]: return self._call_impl(*args, **kwargs) | |
| [default2]:[rank2]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl | |
| [default2]:[rank2]: return forward_call(*args, **kwargs) | |
| [default2]:[rank2]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/block.py", line 151, in forward | |
| [default2]:[rank2]: output = self.pp_block(**new_kwargs) | |
| [default2]:[rank2]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl | |
| [default2]:[rank2]: return self._call_impl(*args, **kwargs) | |
| [default2]:[rank2]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl | |
| [default2]:[rank2]: return forward_call(*args, **kwargs) | |
| [default2]:[rank2]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 631, in forward | |
| [default2]:[rank2]: output = self.attn(hidden_states=hidden_states, sequence_mask=sequence_mask) | |
| [default2]:[rank2]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl | |
| [default2]:[rank2]: return self._call_impl(*args, **kwargs) | |
| [default2]:[rank2]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl | |
| [default2]:[rank2]: return forward_call(*args, **kwargs) | |
| [default2]:[rank2]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 598, in forward | |
| [default2]:[rank2]: output = self.o_proj(attention_output) | |
| [default2]:[rank2]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl | |
| [default2]:[rank2]: return self._call_impl(*args, **kwargs) | |
| [default2]:[rank2]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl | |
| [default2]:[rank2]: return forward_call(*args, **kwargs) | |
| [default2]:[rank2]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/nn.py", line 159, in forward | |
| [default2]:[rank2]: return row_linear( | |
| [default2]:[rank2]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/functional.py", line 474, in row_linear | |
| [default2]:[rank2]: out = F.linear(input, weight, bias) | |
| [default2]:[rank2]: torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 2.00 GiB. GPU has a total capacity of 79.33 GiB of which 389.94 MiB is free. Including non-PyTorch memory, this process has 78.93 GiB memory in use. Of the allocated memory 71.65 GiB is allocated by PyTorch, and 202.02 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation. See documentation for Memory Management (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables) | |
| [default1]:[rank9]: Traceback (most recent call last): | |
| [default1]:[rank9]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/run_train.py", line 237, in <module> | |
| [default1]:[rank9]: trainer.train(dataloader) | |
| [default1]:[rank9]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 429, in train | |
| [default1]:[rank9]: outputs, loss_avg = self.training_step(dataloader=self.current_dataloader) | |
| [default1]:[rank9]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 462, in training_step | |
| [default1]:[rank9]: outputs = self.pipeline_engine.train_batch_iter( | |
| [default1]:[rank9]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 278, in train_batch_iter | |
| [default1]:[rank9]: output = self.forward(context=context, state=state, micro_batch=micro_batch, model=model) | |
| [default1]:[rank9]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 44, in forward | |
| [default1]:[rank9]: output = model(**micro_batch) | |
| [default1]:[rank9]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl | |
| [default1]:[rank9]: return self._call_impl(*args, **kwargs) | |
| [default1]:[rank9]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl | |
| [default1]:[rank9]: return forward_call(*args, **kwargs) | |
| [default1]:[rank9]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 891, in forward | |
| [default1]:[rank9]: sharded_logits = self.model( | |
| [default1]:[rank9]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl | |
| [default1]:[rank9]: return self._call_impl(*args, **kwargs) | |
| [default1]:[rank9]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl | |
| [default1]:[rank9]: return forward_call(*args, **kwargs) | |
| [default1]:[rank9]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 764, in forward | |
| [default1]:[rank9]: return self.forward_with_hidden_states(input_ids=input_ids, input_mask=input_mask)[0] | |
| [default1]:[rank9]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 780, in forward_with_hidden_states | |
| [default1]:[rank9]: hidden_encoder_states = encoder_block(**hidden_encoder_states) | |
| [default1]:[rank9]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl | |
| [default1]:[rank9]: return self._call_impl(*args, **kwargs) | |
| [default1]:[rank9]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl | |
| [default1]:[rank9]: return forward_call(*args, **kwargs) | |
| [default1]:[rank9]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/block.py", line 151, in forward | |
| [default1]:[rank9]: output = self.pp_block(**new_kwargs) | |
| [default1]:[rank9]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl | |
| [default1]:[rank9]: return self._call_impl(*args, **kwargs) | |
| [default1]:[rank9]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl | |
| [default1]:[rank9]: return forward_call(*args, **kwargs) | |
| [default1]:[rank9]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 631, in forward | |
| [default1]:[rank9]: output = self.attn(hidden_states=hidden_states, sequence_mask=sequence_mask) | |
| [default1]:[rank9]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl | |
| [default1]:[rank9]: return self._call_impl(*args, **kwargs) | |
| [default1]:[rank9]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl | |
| [default1]:[rank9]: return forward_call(*args, **kwargs) | |
| [default1]:[rank9]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 598, in forward | |
| [default1]:[rank9]: output = self.o_proj(attention_output) | |
| [default1]:[rank9]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl | |
| [default1]:[rank9]: return self._call_impl(*args, **kwargs) | |
| [default1]:[rank9]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl | |
| [default1]:[rank9]: return forward_call(*args, **kwargs) | |
| [default1]:[rank9]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/nn.py", line 159, in forward | |
| [default1]:[rank9]: return row_linear( | |
| [default1]:[rank9]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/functional.py", line 474, in row_linear | |
| [default1]:[rank9]: out = F.linear(input, weight, bias) | |
| [default1]:[rank9]: torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 2.00 GiB. GPU has a total capacity of 79.33 GiB of which 553.94 MiB is free. Including non-PyTorch memory, this process has 78.77 GiB memory in use. Of the allocated memory 71.65 GiB is allocated by PyTorch, and 202.02 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation. See documentation for Memory Management (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables) | |
| E0702 19:33:15.313000 140423668332352 torch/distributed/elastic/multiprocessing/api.py:826] failed (exitcode: 1) local_rank: 0 (pid: 743440) of binary: /fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/bin/python3.10 | |
| Traceback (most recent call last): | |
| File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/bin/torchrun", line 8, in <module> | |
| sys.exit(main()) | |
| File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/elastic/multiprocessing/errors/__init__.py", line 347, in wrapper | |
| return f(*args, **kwargs) | |
| File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/run.py", line 879, in main | |
| run(args) | |
| File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/run.py", line 870, in run | |
| elastic_launch( | |
| File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/launcher/api.py", line 132, in __call__ | |
| return launch_agent(self._config, self._entrypoint, list(args)) | |
| File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/launcher/api.py", line 263, in launch_agent | |
| raise ChildFailedError( | |
| torch.distributed.elastic.multiprocessing.errors.ChildFailedError: | |
| ============================================================ | |
| /fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/run_train.py FAILED | |
| ------------------------------------------------------------ | |
| Failures: | |
| [1]: | |
| time : 2024-07-02_19:33:15 | |
| host : ip-26-0-165-24.ec2.internal | |
| rank : 1 (local_rank: 1) | |
| exitcode : 1 (pid: 743441) | |
| error_file: <N/A> | |
| traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html | |
| [2]: | |
| time : 2024-07-02_19:33:15 | |
| host : ip-26-0-165-24.ec2.internal | |
| rank : 2 (local_rank: 2) | |
| exitcode : 1 (pid: 743442) | |
| error_file: <N/A> | |
| traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html | |
| [3]: | |
| time : 2024-07-02_19:33:15 | |
| host : ip-26-0-165-24.ec2.internal | |
| rank : 3 (local_rank: 3) | |
| exitcode : 1 (pid: 743443) | |
| error_file: <N/A> | |
| traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html | |
| [4]: | |
| time : 2024-07-02_19:33:15 | |
| host : ip-26-0-165-24.ec2.internal | |
| rank : 4 (local_rank: 4) | |
| exitcode : 1 (pid: 743444) | |
| error_file: <N/A> | |
| traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html | |
| [5]: | |
| time : 2024-07-02_19:33:15 | |
| host : ip-26-0-165-24.ec2.internal | |
| rank : 5 (local_rank: 5) | |
| exitcode : 1 (pid: 743445) | |
| error_file: <N/A> | |
| traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html | |
| [6]: | |
| time : 2024-07-02_19:33:15 | |
| host : ip-26-0-165-24.ec2.internal | |
| rank : 6 (local_rank: 6) | |
| exitcode : 1 (pid: 743446) | |
| error_file: <N/A> | |
| traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html | |
| [7]: | |
| time : 2024-07-02_19:33:15 | |
| host : ip-26-0-165-24.ec2.internal | |
| rank : 7 (local_rank: 7) | |
| exitcode : 1 (pid: 743447) | |
| error_file: <N/A> | |
| traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html | |
| ------------------------------------------------------------ | |
| Root Cause (first observed failure): | |
| [0]: | |
| time : 2024-07-02_19:33:15 | |
| host : ip-26-0-165-24.ec2.internal | |
| rank : 0 (local_rank: 0) | |
| exitcode : 1 (pid: 743440) | |
| error_file: <N/A> | |
| traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html | |
| ============================================================ | |
| E0702 19:33:15.511000 140241535268672 torch/distributed/elastic/multiprocessing/api.py:826] failed (exitcode: 1) local_rank: 0 (pid: 767081) of binary: /fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/bin/python3.10 | |
| W0702 19:33:15.517000 140241535268672 torch/distributed/elastic/rendezvous/dynamic_rendezvous.py:1203] The node 'ip-26-0-170-160.ec2.internal_767012_0' has failed to shutdown the rendezvous 'none' due to an error of type RendezvousConnectionError. | |
| W0702 19:33:15.544000 140241535268672 torch/distributed/elastic/rendezvous/dynamic_rendezvous.py:1203] The node 'ip-26-0-170-160.ec2.internal_767012_0' has failed to shutdown the rendezvous 'none' due to an error of type RendezvousConnectionError. | |
| W0702 19:33:15.578000 140241535268672 torch/distributed/elastic/rendezvous/dynamic_rendezvous.py:1203] The node 'ip-26-0-170-160.ec2.internal_767012_0' has failed to shutdown the rendezvous 'none' due to an error of type RendezvousConnectionError. | |
| Traceback (most recent call last): | |
| File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/bin/torchrun", line 8, in <module> | |
| sys.exit(main()) | |
| File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/elastic/multiprocessing/errors/__init__.py", line 347, in wrapper | |
| return f(*args, **kwargs) | |
| File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/run.py", line 879, in main | |
| run(args) | |
| File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/run.py", line 870, in run | |
| elastic_launch( | |
| File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/launcher/api.py", line 132, in __call__ | |
| return launch_agent(self._config, self._entrypoint, list(args)) | |
| File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/launcher/api.py", line 263, in launch_agent | |
| raise ChildFailedError( | |
| torch.distributed.elastic.multiprocessing.errors.ChildFailedError: | |
| ============================================================ | |
| /fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/run_train.py FAILED | |
| ------------------------------------------------------------ | |
| Failures: | |
| [1]: | |
| time : 2024-07-02_19:33:15 | |
| host : ip-26-0-170-160.ec2.internal | |
| rank : 9 (local_rank: 1) | |
| exitcode : 1 (pid: 767082) | |
| error_file: <N/A> | |
| traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html | |
| [2]: | |
| time : 2024-07-02_19:33:15 | |
| host : ip-26-0-170-160.ec2.internal | |
| rank : 10 (local_rank: 2) | |
| exitcode : 1 (pid: 767083) | |
| error_file: <N/A> | |
| traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html | |
| [3]: | |
| time : 2024-07-02_19:33:15 | |
| host : ip-26-0-170-160.ec2.internal | |
| rank : 11 (local_rank: 3) | |
| exitcode : 1 (pid: 767084) | |
| error_file: <N/A> | |
| traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html | |
| [4]: | |
| time : 2024-07-02_19:33:15 | |
| host : ip-26-0-170-160.ec2.internal | |
| rank : 12 (local_rank: 4) | |
| exitcode : 1 (pid: 767085) | |
| error_file: <N/A> | |
| traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html | |
| [5]: | |
| time : 2024-07-02_19:33:15 | |
| host : ip-26-0-170-160.ec2.internal | |
| rank : 13 (local_rank: 5) | |
| exitcode : 1 (pid: 767086) | |
| error_file: <N/A> | |
| traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html | |
| [6]: | |
| time : 2024-07-02_19:33:15 | |
| host : ip-26-0-170-160.ec2.internal | |
| rank : 14 (local_rank: 6) | |
| exitcode : 1 (pid: 767087) | |
| error_file: <N/A> | |
| traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html | |
| [7]: | |
| time : 2024-07-02_19:33:15 | |
| host : ip-26-0-170-160.ec2.internal | |
| rank : 15 (local_rank: 7) | |
| exitcode : 1 (pid: 767088) | |
| error_file: <N/A> | |
| traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html | |
| ------------------------------------------------------------ | |
| Root Cause (first observed failure): | |
| [0]: | |
| time : 2024-07-02_19:33:15 | |
| host : ip-26-0-170-160.ec2.internal | |
| rank : 8 (local_rank: 0) | |
| exitcode : 1 (pid: 767081) | |
| error_file: <N/A> | |
| traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html | |
| ============================================================ | |
| srun: error: ip-26-0-165-24: task 0: Exited with exit code 1 | |
| srun: error: ip-26-0-170-160: task 1: Exited with exit code 1 | |
| Consider using `hf_transfer` for faster uploads. This solution comes with some limitations. See https://huggingface.co/docs/huggingface_hub/hf_transfer for more details. | |