| ======================== | |
| START TIME: Tue Jul 2 15:17:40 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 15:17:42.666000 140396671518528 torch/distributed/run.py:757] | |
| W0702 15:17:42.666000 140396671518528 torch/distributed/run.py:757] ***************************************** | |
| W0702 15:17:42.666000 140396671518528 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 15:17:42.666000 140396671518528 torch/distributed/run.py:757] ***************************************** | |
| W0702 15:17:42.671000 140347040614208 torch/distributed/run.py:757] | |
| W0702 15:17:42.671000 140347040614208 torch/distributed/run.py:757] ***************************************** | |
| W0702 15:17:42.671000 140347040614208 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 15:17:42.671000 140347040614208 torch/distributed/run.py:757] ***************************************** | |
| [default0]:07/02/2024 15:18:00 [WARNING|DP=0|PP=0|TP=0|ip-26-0-171-56]: [Vocab Size Padding] Padded vocab (size: 50257) with 7 dummy tokens (new size: 50264) | |
| [default0]:07/02/2024 15:18:00 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-56]: Config: | |
| [default0]:07/02/2024 15:18:00 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-56]: Config(general=GeneralArgs(project='bench_cluster', | |
| [default0]:07/02/2024 15:18:00 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-56]: run='%date_%jobid', | |
| [default0]:07/02/2024 15:18:00 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-56]: seed=42, | |
| [default0]:07/02/2024 15:18:00 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-56]: step=None, | |
| [default0]:07/02/2024 15:18:00 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-56]: consumed_train_samples=None, | |
| [default0]:07/02/2024 15:18:00 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-56]: benchmark_csv_path=None, | |
| [default0]:07/02/2024 15:18:00 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-56]: ignore_sanity_checks=True), | |
| [default0]:07/02/2024 15:18:00 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-56]: parallelism=ParallelismArgs(dp=2, | |
| [default0]:07/02/2024 15:18:00 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-56]: pp=1, | |
| [default0]:07/02/2024 15:18:00 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-56]: tp=8, | |
| [default0]:07/02/2024 15:18:00 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-56]: pp_engine=<nanotron.parallel.pipeline_parallel.engine.OneForwardOneBackwardPipelineEngine object at 0x7f9b1bfe0910>, | |
| [default0]:07/02/2024 15:18:00 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-56]: tp_mode=<TensorParallelLinearMode.REDUCE_SCATTER: 2>, | |
| [default0]:07/02/2024 15:18:00 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-56]: tp_linear_async_communication=False, | |
| [default0]:07/02/2024 15:18:00 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-56]: expert_parallel_size=1), | |
| [default0]:07/02/2024 15:18:00 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-56]: model=ModelArgs(model_config=LlamaConfig(bos_token_id=1, | |
| [default0]:07/02/2024 15:18:00 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-56]: eos_token_id=2, | |
| [default0]:07/02/2024 15:18:00 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-56]: hidden_act='silu', | |
| [default0]:07/02/2024 15:18:00 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-56]: hidden_size=2048, | |
| [default0]:07/02/2024 15:18:00 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-56]: initializer_range=0.02, | |
| [default0]:07/02/2024 15:18:00 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-56]: intermediate_size=4096, | |
| [default0]:07/02/2024 15:18:00 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-56]: is_llama_config=True, | |
| [default0]:07/02/2024 15:18:00 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-56]: max_position_embeddings=4096, | |
| [default0]:07/02/2024 15:18:00 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-56]: num_attention_heads=32, | |
| [default0]:07/02/2024 15:18:00 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-56]: num_hidden_layers=24, | |
| [default0]:07/02/2024 15:18:00 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-56]: num_key_value_heads=32, | |
| [default0]:07/02/2024 15:18:00 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-56]: pad_token_id=None, | |
| [default0]:07/02/2024 15:18:00 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-56]: pretraining_tp=1, | |
| [default0]:07/02/2024 15:18:00 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-56]: rms_norm_eps=1e-05, | |
| [default0]:07/02/2024 15:18:00 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-56]: rope_scaling=None, | |
| [default0]:07/02/2024 15:18:00 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-56]: rope_theta=10000.0, | |
| [default0]:07/02/2024 15:18:00 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-56]: tie_word_embeddings=True, | |
| [default0]:07/02/2024 15:18:00 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-56]: use_cache=True, | |
| [default0]:07/02/2024 15:18:00 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-56]: vocab_size=50264), | |
| [default0]:07/02/2024 15:18:00 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-56]: init_method=RandomInit(std=0.025), | |
| [default0]:07/02/2024 15:18:00 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-56]: dtype=torch.bfloat16, | |
| [default0]:07/02/2024 15:18:00 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-56]: make_vocab_size_divisible_by=1, | |
| [default0]:07/02/2024 15:18:00 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-56]: ddp_bucket_cap_mb=25), | |
| [default0]:07/02/2024 15:18:00 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-56]: tokenizer=TokenizerArgs(tokenizer_name_or_path='openai-community/gpt2', | |
| [default0]:07/02/2024 15:18:00 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-56]: tokenizer_revision=None, | |
| [default0]:07/02/2024 15:18:00 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-56]: tokenizer_max_length=None), | |
| [default0]:07/02/2024 15:18:00 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-56]: checkpoints=CheckpointsArgs(checkpoints_path=Path('/dev/null'), | |
| [default0]:07/02/2024 15:18:00 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-56]: checkpoint_interval=100000, | |
| [default0]:07/02/2024 15:18:00 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-56]: save_initial_state=False, | |
| [default0]:07/02/2024 15:18:00 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-56]: resume_checkpoint_path=None, | |
| [default0]:07/02/2024 15:18:00 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-56]: checkpoints_path_is_shared_file_system=False), | |
| [default0]:07/02/2024 15:18:00 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-56]: logging=LoggingArgs(log_level='info', | |
| [default0]:07/02/2024 15:18:00 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-56]: log_level_replica='info', | |
| [default0]:07/02/2024 15:18:00 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-56]: iteration_step_info_interval=1), | |
| [default0]:07/02/2024 15:18:00 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-56]: tokens=TokensArgs(sequence_length=4096, | |
| [default0]:07/02/2024 15:18:00 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-56]: train_steps=20, | |
| [default0]:07/02/2024 15:18:00 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-56]: micro_batch_size=128, | |
| [default0]:07/02/2024 15:18:00 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-56]: batch_accumulation_per_replica=4, | |
| [default0]:07/02/2024 15:18:00 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-56]: val_check_interval=-1, | |
| [default0]:07/02/2024 15:18:00 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-56]: limit_val_batches=0, | |
| [default0]:07/02/2024 15:18:00 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-56]: limit_test_batches=0), | |
| [default0]:07/02/2024 15:18:00 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-56]: optimizer=OptimizerArgs(optimizer_factory=AdamWOptimizerArgs(adam_eps=1e-08, | |
| [default0]:07/02/2024 15:18:00 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-56]: adam_beta1=0.9, | |
| [default0]:07/02/2024 15:18:00 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-56]: adam_beta2=0.95, | |
| [default0]:07/02/2024 15:18:00 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-56]: torch_adam_is_fused=True, | |
| [default0]:07/02/2024 15:18:00 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-56]: name='adamW'), | |
| [default0]:07/02/2024 15:18:00 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-56]: zero_stage=1, | |
| [default0]:07/02/2024 15:18:00 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-56]: weight_decay=0.01, | |
| [default0]:07/02/2024 15:18:00 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-56]: clip_grad=1.0, | |
| [default0]:07/02/2024 15:18:00 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-56]: accumulate_grad_in_fp32=True, | |
| [default0]:07/02/2024 15:18:00 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-56]: learning_rate_scheduler=LRSchedulerArgs(learning_rate=0.0001, | |
| [default0]:07/02/2024 15:18:00 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-56]: lr_warmup_steps=1, | |
| [default0]:07/02/2024 15:18:00 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-56]: lr_warmup_style='linear', | |
| [default0]:07/02/2024 15:18:00 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-56]: lr_decay_style='linear', | |
| [default0]:07/02/2024 15:18:00 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-56]: lr_decay_steps=19, | |
| [default0]:07/02/2024 15:18:00 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-56]: lr_decay_starting_step=None, | |
| [default0]:07/02/2024 15:18:00 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-56]: min_decay_lr=1e-05)), | |
| [default0]:07/02/2024 15:18:00 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-56]: data_stages=[DatasetStageArgs(name='Training Stage', | |
| [default0]:07/02/2024 15:18:00 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-56]: start_training_step=1, | |
| [default0]:07/02/2024 15:18:00 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-56]: data=DataArgs(dataset=PretrainDatasetsArgs(hf_dataset_or_datasets='roneneldan/TinyStories', | |
| [default0]:07/02/2024 15:18:00 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-56]: hf_dataset_splits='train', | |
| [default0]:07/02/2024 15:18:00 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-56]: hf_dataset_config_name=None, | |
| [default0]:07/02/2024 15:18:00 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-56]: dataset_processing_num_proc_per_process=64, | |
| [default0]:07/02/2024 15:18:00 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-56]: dataset_overwrite_cache=False, | |
| [default0]:07/02/2024 15:18:00 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-56]: text_column_name='text'), | |
| [default0]:07/02/2024 15:18:00 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-56]: seed=42, | |
| [default0]:07/02/2024 15:18:00 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-56]: num_loading_workers=32))], | |
| [default0]:07/02/2024 15:18:00 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-56]: profiler=ProfilerArgs(profiler_export_path=Path('/fsx/ferdinandmom/ferdinand-hf/bench_cluster/results/llama-1B/16_GPUS/dp-2_tp-8_pp-1_mbz-128')), | |
| [default0]:07/02/2024 15:18:00 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-56]: lighteval=None) | |
| [default0]:07/02/2024 15:18:00 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-56]: Model Config: | |
| [default0]:07/02/2024 15:18:00 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-56]: LlamaConfig(bos_token_id=1, | |
| [default0]:07/02/2024 15:18:00 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-56]: eos_token_id=2, | |
| [default0]:07/02/2024 15:18:00 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-56]: hidden_act='silu', | |
| [default0]:07/02/2024 15:18:00 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-56]: hidden_size=2048, | |
| [default0]:07/02/2024 15:18:00 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-56]: initializer_range=0.02, | |
| [default0]:07/02/2024 15:18:00 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-56]: intermediate_size=4096, | |
| [default0]:07/02/2024 15:18:00 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-56]: is_llama_config=True, | |
| [default0]:07/02/2024 15:18:00 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-56]: max_position_embeddings=4096, | |
| [default0]:07/02/2024 15:18:00 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-56]: num_attention_heads=32, | |
| [default0]:07/02/2024 15:18:00 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-56]: num_hidden_layers=24, | |
| [default0]:07/02/2024 15:18:00 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-56]: num_key_value_heads=32, | |
| [default0]:07/02/2024 15:18:00 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-56]: pad_token_id=None, | |
| [default0]:07/02/2024 15:18:00 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-56]: pretraining_tp=1, | |
| [default0]:07/02/2024 15:18:00 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-56]: rms_norm_eps=1e-05, | |
| [default0]:07/02/2024 15:18:00 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-56]: rope_scaling=None, | |
| [default0]:07/02/2024 15:18:00 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-56]: rope_theta=10000.0, | |
| [default0]:07/02/2024 15:18:00 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-56]: tie_word_embeddings=True, | |
| [default0]:07/02/2024 15:18:00 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-56]: use_cache=True, | |
| [default0]:07/02/2024 15:18:00 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-56]: vocab_size=50264) | |
| [default0]:07/02/2024 15:18:00 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-56]: Building model.. | |
| [default0]:07/02/2024 15:18:00 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-56]: Setting PP block ranks... | |
| [default1]:07/02/2024 15:18:17 [INFO|DP=0|PP=0|TP=1|ip-26-0-171-56]: Local number of parameters: 139M (264.73MiB) | |
| [default1]:07/02/2024 15:18:17 [INFO|DP=0|PP=0|TP=1|ip-26-0-171-56]: [After model building] Memory usage: 290.76MiB. Peak allocated: 317.33MiB Peak reserved: 324.00MiB | |
| [default1]:07/02/2024 15:18:17 [INFO|DP=0|PP=0|TP=1|ip-26-0-171-56]: No checkpoint path provided. | |
| [default0]:07/02/2024 15:18:17 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-56]: Total number of parameters: 1.11G (2117.88MiB) | |
| [default0]:07/02/2024 15:18:17 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-56]: Local number of parameters: 139M (264.73MiB) | |
| [default0]:07/02/2024 15:18:17 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-56]: [After model building] Memory usage: 290.76MiB. Peak allocated: 317.33MiB Peak reserved: 324.00MiB | |
| [default0]:07/02/2024 15:18:17 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-56]: No checkpoint path provided. | |
| [default0]:07/02/2024 15:18:17 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-56]: Parametrizing model parameters using StandardParametrizator | |
| [default6]:07/02/2024 15:18:17 [INFO|DP=0|PP=0|TP=6|ip-26-0-171-56]: Local number of parameters: 139M (264.73MiB) | |
| [default6]:07/02/2024 15:18:17 [INFO|DP=0|PP=0|TP=6|ip-26-0-171-56]: [After model building] Memory usage: 290.76MiB. Peak allocated: 317.33MiB Peak reserved: 324.00MiB | |
| [default6]:07/02/2024 15:18:17 [INFO|DP=0|PP=0|TP=6|ip-26-0-171-56]: No checkpoint path provided. | |
| [default2]:07/02/2024 15:18:17 [INFO|DP=0|PP=0|TP=2|ip-26-0-171-56]: Local number of parameters: 139M (264.73MiB) | |
| [default2]:07/02/2024 15:18:17 [INFO|DP=0|PP=0|TP=2|ip-26-0-171-56]: [After model building] Memory usage: 290.76MiB. Peak allocated: 317.33MiB Peak reserved: 324.00MiB | |
| [default2]:07/02/2024 15:18:17 [INFO|DP=0|PP=0|TP=2|ip-26-0-171-56]: No checkpoint path provided. | |
| [default3]:07/02/2024 15:18:17 [INFO|DP=0|PP=0|TP=3|ip-26-0-171-56]: Local number of parameters: 139M (264.73MiB) | |
| [default3]:07/02/2024 15:18:17 [INFO|DP=0|PP=0|TP=3|ip-26-0-171-56]: [After model building] Memory usage: 290.76MiB. Peak allocated: 317.33MiB Peak reserved: 324.00MiB | |
| [default3]:07/02/2024 15:18:17 [INFO|DP=0|PP=0|TP=3|ip-26-0-171-56]: No checkpoint path provided. | |
| [default5]:07/02/2024 15:18:17 [INFO|DP=0|PP=0|TP=5|ip-26-0-171-56]: Local number of parameters: 139M (264.73MiB) | |
| [default5]:07/02/2024 15:18:17 [INFO|DP=0|PP=0|TP=5|ip-26-0-171-56]: [After model building] Memory usage: 290.76MiB. Peak allocated: 317.33MiB Peak reserved: 324.00MiB | |
| [default5]:07/02/2024 15:18:17 [INFO|DP=0|PP=0|TP=5|ip-26-0-171-56]: No checkpoint path provided. | |
| [default4]:07/02/2024 15:18:17 [INFO|DP=0|PP=0|TP=4|ip-26-0-171-56]: Local number of parameters: 139M (264.73MiB) | |
| [default7]:07/02/2024 15:18:17 [INFO|DP=0|PP=0|TP=7|ip-26-0-171-56]: Local number of parameters: 139M (264.73MiB) | |
| [default7]:07/02/2024 15:18:17 [INFO|DP=0|PP=0|TP=7|ip-26-0-171-56]: [After model building] Memory usage: 290.76MiB. Peak allocated: 317.33MiB Peak reserved: 324.00MiB | |
| [default7]:07/02/2024 15:18:17 [INFO|DP=0|PP=0|TP=7|ip-26-0-171-56]: No checkpoint path provided. | |
| [default4]:07/02/2024 15:18:17 [INFO|DP=0|PP=0|TP=4|ip-26-0-171-56]: [After model building] Memory usage: 290.76MiB. Peak allocated: 317.33MiB Peak reserved: 324.00MiB | |
| [default4]:07/02/2024 15:18:17 [INFO|DP=0|PP=0|TP=4|ip-26-0-171-56]: No checkpoint path provided. | |
| [default0]:07/02/2024 15:18:18 [INFO|DP=1|PP=0|TP=0|ip-26-0-175-132]: No checkpoint path provided. | |
| [default1]:07/02/2024 15:18:18 [INFO|DP=1|PP=0|TP=1|ip-26-0-175-132]: No checkpoint path provided. | |
| [default6]:07/02/2024 15:18:18 [INFO|DP=1|PP=0|TP=6|ip-26-0-175-132]: No checkpoint path provided. | |
| [default2]:07/02/2024 15:18:18 [INFO|DP=1|PP=0|TP=2|ip-26-0-175-132]: No checkpoint path provided. | |
| [default4]:07/02/2024 15:18:18 [INFO|DP=1|PP=0|TP=4|ip-26-0-175-132]: No checkpoint path provided. | |
| [default5]:07/02/2024 15:18:18 [INFO|DP=1|PP=0|TP=5|ip-26-0-175-132]: No checkpoint path provided. | |
| [default7]:07/02/2024 15:18:18 [INFO|DP=1|PP=0|TP=7|ip-26-0-175-132]: No checkpoint path provided. | |
| [default3]:07/02/2024 15:18:18 [INFO|DP=1|PP=0|TP=3|ip-26-0-175-132]: No checkpoint path provided. | |
| [default0]:07/02/2024 15:18:18 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-56]: [Optimizer Building] Using LearningRateForSP as learning rate | |
| [default0]:07/02/2024 15:18:18 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-56]: [ZeRO sharding] Size of optimizer params per rank: | |
| [default0]:07/02/2024 15:18:18 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-56]: [ZeRO sharding] DP Rank 0 has 69.4M out of 139M (50.00%) params' optimizer states | |
| [default0]:07/02/2024 15:18:18 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-56]: [ZeRO sharding] DP Rank 1 has 69.4M out of 139M (50.00%) params' optimizer states | |
| [default0]:07/02/2024 15:18:20 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-56]: [Training Plan] Stage Training Stage has 19 remaining training steps and has consumed 0 samples | |
| [default0]:07/02/2024 15:18:20 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-56]: Using `datasets` library | |
| [default0]:07/02/2024 15:18:20 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-56]: Loading tokenizer from openai-community/gpt2 and transformers/hf_hub versions ('4.41.2', '0.23.4') | |
| [default0]:07/02/2024 15:18:21 [WARNING|DP=0|PP=0|TP=0|ip-26-0-171-56]: 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 15:18:21 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-56]: [Training Plan] There are 1 training stages | |
| [default0]:07/02/2024 15:18:21 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-56]: [Stage Training Stage] start from step 1 | |
| [default0]:07/02/2024 15:18:21 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-56]: | |
| [default0]:07/02/2024 15:18:21 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-56]: [Start training] datetime: 2024-07-02 15:18:21.781388 | mbs: 128 | grad_accum: 4 | global_batch_size: 1024 | sequence_length: 4096 | train_steps: 20 | start_iteration_step: 0 | consumed_train_samples: 0 | |
| [default0]:07/02/2024 15:18:21 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-56]: Resuming training from stage Training Stage, it has trained for 0 samples and has 19 remaining train steps | |
| [default0]:07/02/2024 15:18:21 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-56]: Memory usage: 1085.49MiB. Peak allocated 1085.49MiB. Peak reserved: 1120.00MiB | |
| [default2]:Repo card metadata block was not found. Setting CardData to empty. | |
| [default6]:07/02/2024 15:18:21 [WARNING|DP=0|PP=0|TP=6|ip-26-0-171-56]: Repo card metadata block was not found. Setting CardData to empty. | |
| [default2]:07/02/2024 15:18:21 [WARNING|DP=0|PP=0|TP=2|ip-26-0-171-56]: Repo card metadata block was not found. Setting CardData to empty. | |
| [default5]:Repo card metadata block was not found. Setting CardData to empty. | |
| [default3]:07/02/2024 15:18:21 [WARNING|DP=0|PP=0|TP=3|ip-26-0-171-56]: Repo card metadata block was not found. Setting CardData to empty. | |
| [default5]:07/02/2024 15:18:21 [WARNING|DP=0|PP=0|TP=5|ip-26-0-171-56]: Repo card metadata block was not found. Setting CardData to empty. | |
| [default6]:Repo card metadata block was not found. Setting CardData to empty. | |
| [default3]:Repo card metadata block was not found. Setting CardData to empty. | |
| [default5]:07/02/2024 15:18:21 [WARNING|DP=1|PP=0|TP=5|ip-26-0-175-132]: Repo card metadata block was not found. Setting CardData to empty. | |
| [default7]:07/02/2024 15:18:21 [WARNING|DP=1|PP=0|TP=7|ip-26-0-175-132]: Repo card metadata block was not found. Setting CardData to empty. | |
| [default5]: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. | |
| [default1]:07/02/2024 15:18:21 [WARNING|DP=0|PP=0|TP=1|ip-26-0-171-56]: Repo card metadata block was not found. Setting CardData to empty. | |
| [default4]:Repo card metadata block was not found. Setting CardData to empty. | |
| [default1]:Repo card metadata block was not found. Setting CardData to empty. | |
| [default4]:07/02/2024 15:18:21 [WARNING|DP=0|PP=0|TP=4|ip-26-0-171-56]: Repo card metadata block was not found. Setting CardData to empty. | |
| [default7]:07/02/2024 15:18:21 [WARNING|DP=0|PP=0|TP=7|ip-26-0-171-56]: Repo card metadata block was not found. Setting CardData to empty. | |
| [default7]:Repo card metadata block was not found. Setting CardData to empty. | |
| [default0]:07/02/2024 15:18:21 [WARNING|DP=1|PP=0|TP=0|ip-26-0-175-132]: Repo card metadata block was not found. Setting CardData to empty. | |
| [default1]:07/02/2024 15:18:21 [WARNING|DP=1|PP=0|TP=1|ip-26-0-175-132]: Repo card metadata block was not found. Setting CardData to empty. | |
| [default6]:07/02/2024 15:18:21 [WARNING|DP=1|PP=0|TP=6|ip-26-0-175-132]: Repo card metadata block was not found. Setting CardData to empty. | |
| [default2]:07/02/2024 15:18:21 [WARNING|DP=1|PP=0|TP=2|ip-26-0-175-132]: Repo card metadata block was not found. Setting CardData to empty. | |
| [default4]:07/02/2024 15:18:22 [WARNING|DP=1|PP=0|TP=4|ip-26-0-175-132]: Repo card metadata block was not found. Setting CardData to empty. | |
| [default3]:Repo card metadata block was not found. Setting CardData to empty. | |
| [default3]:07/02/2024 15:18:21 [WARNING|DP=1|PP=0|TP=3|ip-26-0-175-132]: Repo card metadata block was not found. Setting CardData to empty. | |
| [default1]:Repo card metadata block was not found. Setting CardData to empty. | |
| [default0]:Repo card metadata block was not found. Setting CardData to empty. | |
| [default4]:Repo card metadata block was not found. Setting CardData to empty. | |
| [default6]:Repo card metadata block was not found. Setting CardData to empty. | |
| [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 360, in forward | |
| [default2]:[rank2]: qkv_states = self.qkv_proj( | |
| [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 87, in forward | |
| [default2]:[rank2]: return column_linear( | |
| [default2]:[rank2]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/functional.py", line 359, in column_linear | |
| [default2]:[rank2]: return F.linear(input, weight, bias) | |
| [default2]:[rank2]: torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 768.00 MiB. GPU has a total capacity of 79.33 GiB of which 323.94 MiB is free. Including non-PyTorch memory, this process has 79.00 GiB memory in use. Of the allocated memory 69.68 GiB is allocated by PyTorch, and 430.74 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]:[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 | |
| [default7]:[rank7]: 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( | |
| [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 764, in forward | |
| [default7]:[rank7]: return self.forward_with_hidden_states(input_ids=input_ids, input_mask=input_mask)[0] | |
| [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) | |
| [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/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 360, in forward | |
| [default7]:[rank7]: qkv_states = self.qkv_proj( | |
| [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/parallel/tensor_parallel/nn.py", line 87, in forward | |
| [default7]:[rank7]: return column_linear( | |
| [default7]:[rank7]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/functional.py", line 359, in column_linear | |
| [default7]:[rank7]: return F.linear(input, weight, bias) | |
| [default7]:[rank7]: torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 768.00 MiB. GPU has a total capacity of 79.33 GiB of which 451.94 MiB is free. Including non-PyTorch memory, this process has 78.87 GiB memory in use. Of the allocated memory 69.68 GiB is allocated by PyTorch, and 430.74 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 360, in forward | |
| [default4]:[rank4]: qkv_states = self.qkv_proj( | |
| [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 87, in forward | |
| [default4]:[rank4]: return column_linear( | |
| [default4]:[rank4]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/functional.py", line 359, in column_linear | |
| [default4]:[rank4]: return F.linear(input, weight, bias) | |
| [default4]:[rank4]: torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 768.00 MiB. GPU has a total capacity of 79.33 GiB of which 163.94 MiB is free. Including non-PyTorch memory, this process has 79.15 GiB memory in use. Of the allocated memory 69.68 GiB is allocated by PyTorch, and 430.74 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 360, in forward | |
| [default6]:[rank6]: qkv_states = self.qkv_proj( | |
| [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 87, in forward | |
| [default6]:[rank6]: return column_linear( | |
| [default6]:[rank6]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/functional.py", line 359, in column_linear | |
| [default6]:[rank6]: return F.linear(input, weight, bias) | |
| [default6]:[rank6]: torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 768.00 MiB. GPU has a total capacity of 79.33 GiB of which 163.94 MiB is free. Including non-PyTorch memory, this process has 79.15 GiB memory in use. Of the allocated memory 69.68 GiB is allocated by PyTorch, and 430.74 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) | |
| [default5]:[rank5]: 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) | |
| [default5]:[rank5]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 429, in train | |
| [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) | |
| [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 891, in forward | |
| [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 "/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 360, in forward | |
| [default5]:[rank5]: qkv_states = self.qkv_proj( | |
| [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 87, in forward | |
| [default5]:[rank5]: return column_linear( | |
| [default5]:[rank5]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/functional.py", line 359, in column_linear | |
| [default5]:[rank5]: return F.linear(input, weight, bias) | |
| [default5]:[rank5]: torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 768.00 MiB. GPU has a total capacity of 79.33 GiB of which 163.94 MiB is free. Including non-PyTorch memory, this process has 79.15 GiB memory in use. Of the allocated memory 69.68 GiB is allocated by PyTorch, and 430.74 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 360, in forward | |
| [default3]:[rank3]: qkv_states = self.qkv_proj( | |
| [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 87, in forward | |
| [default3]:[rank3]: return column_linear( | |
| [default3]:[rank3]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/functional.py", line 359, in column_linear | |
| [default3]:[rank3]: return F.linear(input, weight, bias) | |
| [default3]:[rank3]: torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 768.00 MiB. GPU has a total capacity of 79.33 GiB of which 291.94 MiB is free. Including non-PyTorch memory, this process has 79.03 GiB memory in use. Of the allocated memory 69.68 GiB is allocated by PyTorch, and 430.74 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 360, in forward | |
| [default0]:[rank0]: qkv_states = self.qkv_proj( | |
| [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 87, in forward | |
| [default0]:[rank0]: return column_linear( | |
| [default0]:[rank0]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/functional.py", line 359, in column_linear | |
| [default0]:[rank0]: return F.linear(input, weight, bias) | |
| [default0]:[rank0]: torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 768.00 MiB. GPU | |
| [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) | |
| [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) | |
| [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 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 | |
| [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) | |
| [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/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 | |
| [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 | |
| [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 360, in forward | |
| [default1]:[rank1]: qkv_states = self.qkv_proj( | |
| [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/parallel/tensor_parallel/nn.py", line 87, in forward | |
| [default1]:[rank1]: return column_linear( | |
| [default1]:[rank1]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/functional.py", line 359, in column_linear | |
| [default1]:[rank1]: return F.linear(input, weight, bias) | |
| [default1]:[rank1]: torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 768.00 MiB. GPU has a total capacity of 79.33 GiB of which 433.94 MiB is free. Including non-PyTorch memory, this process has 78.89 GiB memory in use. Of the allocated memory 69.68 GiB is allocated by PyTorch, and 430.74 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 360, in forward | |
| [default1]:[rank9]: qkv_states = self.qkv_proj( | |
| [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 87, in forward | |
| [default1]:[rank9]: return column_linear( | |
| [default1]:[rank9]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/functional.py", line 359, in column_linear | |
| [default1]:[rank9]: return F.linear(input, weight, bias) | |
| [default1]:[rank9]: torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 768.00 MiB. GPU has a total capacity of 79.33 GiB of which 433.94 MiB is free. Including non-PyTorch memory, this process has 78.89 GiB memory in use. Of the allocated memory 69.68 GiB is allocated by PyTorch, and 430.74 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 360, in forward | |
| [default2]:[rank10]: qkv_states = self.qkv_proj( | |
| [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 87, in forward | |
| [default2]:[rank10]: return column_linear( | |
| [default2]:[rank10]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/functional.py", line 359, in column_linear | |
| [default2]:[rank10]: return F.linear(input, weight, bias) | |
| [default2]:[rank10]: torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 768.00 MiB. GPU has a total capacity of 79.33 GiB of which 323.94 MiB is free. Including non-PyTorch memory, this process has 79.00 GiB memory in use. Of the allocated memory 69.68 GiB is allocated by PyTorch, and 430.74 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 360, in forward | |
| [default4]:[rank12]: qkv_states = self.qkv_proj( | |
| [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 87, in forward | |
| [default4]:[rank12]: return column_linear( | |
| [default4]:[rank12]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/functional.py", line 359, in column_linear | |
| [default4]:[rank12]: return F.linear(input, weight, bias) | |
| [default4]:[rank12]: torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 768.00 MiB. GPU has a total capacity of 79.33 GiB of which 163.94 MiB is free. Including non-PyTorch memory, this process has 79.15 GiB memory in use. Of the allocated memory 69.68 GiB is allocated by PyTorch, and 430.74 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) | |
| [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 360, in forward | |
| [default5]:[rank13]: qkv_states = self.qkv_proj( | |
| [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 87, in forward | |
| [default5]:[rank13]: return column_linear( | |
| [default5]:[rank13]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/functional.py", line 359, in column_linear | |
| [default5]:[rank13]: return F.linear(input, weight, bias) | |
| [default5]:[rank13]: torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 768.00 MiB. GPU has a total capacity of 79.33 GiB of which 163.94 MiB is free. Including non-PyTorch memory, this process has 79.15 GiB memory in use. Of the allocated memory 69.68 GiB is allocated by PyTorch, and 430.74 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]: Traceback (most recent call last): | |
| [default6]:[rank14]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/run_train.py", line 237, in <module> | |
| [default6]:[rank14]: trainer.train(dataloader) | |
| [default6]:[rank14]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 429, in train | |
| [default6]:[rank14]: outputs, loss_avg = self.training_step(dataloader=self.current_dataloader) | |
| [default6]:[rank14]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 462, in training_step | |
| [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 360, in forward | |
| [default6]:[rank14]: qkv_states = self.qkv_proj( | |
| [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 87, in forward | |
| [default6]:[rank14]: return column_linear( | |
| [default6]:[rank14]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/functional.py", line 359, in column_linear | |
| [default6]:[rank14]: return F.linear(input, weight, bias) | |
| [default6]:[rank14]: torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 768.00 MiB. GPU has a total capacity of 79.33 GiB of which 163.94 MiB is free. Including non-PyTorch memory, this process has 79.15 GiB memory in use. Of the allocated memory 69.68 GiB is allocated by PyTorch, and 430.74 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 360, in forward | |
| [default0]:[rank8]: qkv_states = self.qkv_proj( | |
| [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 87, in forward | |
| [default0]:[rank8]: return column_linear( | |
| [default0]:[rank8]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/functional.py", line 359, in column_linear | |
| [default0]:[rank8]: return F.linear(input, weight, bias) | |
| [default0]:[rank8]: torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 768.00 MiB. GPU | |
| [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 | |
| [default3]:[rank11]: return forward_call(*args, **kwargs) | |
| [default3]:[rank11]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 360, in forward | |
| [default3]:[rank11]: qkv_states = self.qkv_proj( | |
| [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/tensor_parallel/nn.py", line 87, in forward | |
| [default3]:[rank11]: return column_linear( | |
| [default3]:[rank11]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/functional.py", line 359, in column_linear | |
| [default3]:[rank11]: return F.linear(input, weight, bias) | |
| [default3]:[rank11]: torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 768.00 MiB. GPU has a total capacity of 79.33 GiB of which 291.94 MiB is free. Including non-PyTorch memory, this process has 79.03 GiB memory in use. Of the allocated memory 69.68 GiB is allocated by PyTorch, and 430.74 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 360, in forward | |
| [default7]:[rank15]: qkv_states = self.qkv_proj( | |
| [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 87, in forward | |
| [default7]:[rank15]: return column_linear( | |
| [default7]:[rank15]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/functional.py", line 359, in column_linear | |
| [default7]:[rank15]: return F.linear(input, weight, bias) | |
| [default7]:[rank15]: torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 768.00 MiB. GPU has a total capacity of 79.33 GiB of which 451.94 MiB is free. Including non-PyTorch memory, this process has 78.87 GiB memory in use. Of the allocated memory 69.68 GiB is allocated by PyTorch, and 430.74 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) | |
| W0702 15:18:33.836000 140396671518528 torch/distributed/elastic/multiprocessing/api.py:851] Sending process 3025975 closing signal SIGTERM | |
| W0702 15:18:33.836000 140396671518528 torch/distributed/elastic/multiprocessing/api.py:851] Sending process 3025976 closing signal SIGTERM | |
| W0702 15:18:33.836000 140396671518528 torch/distributed/elastic/multiprocessing/api.py:851] Sending process 3025977 closing signal SIGTERM | |
| W0702 15:18:33.836000 140396671518528 torch/distributed/elastic/multiprocessing/api.py:851] Sending process 3025978 closing signal SIGTERM | |
| W0702 15:18:33.836000 140396671518528 torch/distributed/elastic/multiprocessing/api.py:851] Sending process 3025979 closing signal SIGTERM | |
| W0702 15:18:33.836000 140396671518528 torch/distributed/elastic/multiprocessing/api.py:851] Sending process 3025981 closing signal SIGTERM | |
| W0702 15:18:33.841000 140347040614208 torch/distributed/elastic/multiprocessing/api.py:851] Sending process 50136 closing signal SIGTERM | |
| W0702 15:18:33.842000 140347040614208 torch/distributed/elastic/multiprocessing/api.py:851] Sending process 50137 closing signal SIGTERM | |
| W0702 15:18:33.842000 140347040614208 torch/distributed/elastic/multiprocessing/api.py:851] Sending process 50138 closing signal SIGTERM | |
| W0702 15:18:33.842000 140347040614208 torch/distributed/elastic/multiprocessing/api.py:851] Sending process 50139 closing signal SIGTERM | |
| W0702 15:18:33.842000 140347040614208 torch/distributed/elastic/multiprocessing/api.py:851] Sending process 50140 closing signal SIGTERM | |
| W0702 15:18:33.842000 140347040614208 torch/distributed/elastic/multiprocessing/api.py:851] Sending process 50141 closing signal SIGTERM | |
| W0702 15:18:33.842000 140347040614208 torch/distributed/elastic/multiprocessing/api.py:851] Sending process 50142 closing signal SIGTERM | |
| E0702 15:18:36.367000 140396671518528 torch/distributed/elastic/multiprocessing/api.py:826] failed (exitcode: 1) local_rank: 0 (pid: 3025974) 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_15:18:33 | |
| host : ip-26-0-171-56.ec2.internal | |
| rank : 6 (local_rank: 6) | |
| exitcode : 1 (pid: 3025980) | |
| 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_15:18:33 | |
| host : ip-26-0-171-56.ec2.internal | |
| rank : 0 (local_rank: 0) | |
| exitcode : 1 (pid: 3025974) | |
| error_file: <N/A> | |
| traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html | |
| ============================================================ | |
| srun: error: ip-26-0-171-56: task 0: Exited with exit code 1 | |
| E0702 15:18:36.780000 140347040614208 torch/distributed/elastic/multiprocessing/api.py:826] failed (exitcode: 1) local_rank: 0 (pid: 50135) of binary: /fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/bin/python3.10 | |
| W0702 15:18:36.789000 140347040614208 torch/distributed/elastic/rendezvous/dynamic_rendezvous.py:1203] The node 'ip-26-0-175-132.ec2.internal_50066_0' has failed to shutdown the rendezvous 'none' due to an error of type RendezvousConnectionError. | |
| W0702 15:18:36.820000 140347040614208 torch/distributed/elastic/rendezvous/dynamic_rendezvous.py:1203] The node 'ip-26-0-175-132.ec2.internal_50066_0' has failed to shutdown the rendezvous 'none' due to an error of type RendezvousConnectionError. | |
| W0702 15:18:36.829000 140347040614208 torch/distributed/elastic/rendezvous/dynamic_rendezvous.py:1203] The node 'ip-26-0-175-132.ec2.internal_50066_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: | |
| <NO_OTHER_FAILURES> | |
| ------------------------------------------------------------ | |
| Root Cause (first observed failure): | |
| [0]: | |
| time : 2024-07-02_15:18:33 | |
| host : ip-26-0-175-132.ec2.internal | |
| rank : 8 (local_rank: 0) | |
| exitcode : 1 (pid: 50135) | |
| error_file: <N/A> | |
| traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html | |
| ============================================================ | |
| srun: error: ip-26-0-175-132: 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. | |