| ======================== | |
| START TIME: Tue Jul 2 14:14:26 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 14:14:28.665000 140151942731584 torch/distributed/run.py:757] | |
| W0702 14:14:28.665000 140151942731584 torch/distributed/run.py:757] ***************************************** | |
| W0702 14:14:28.665000 140151942731584 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 14:14:28.665000 140151942731584 torch/distributed/run.py:757] ***************************************** | |
| W0702 14:14:28.664000 140620972070720 torch/distributed/run.py:757] | |
| W0702 14:14:28.664000 140620972070720 torch/distributed/run.py:757] ***************************************** | |
| W0702 14:14:28.664000 140620972070720 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 14:14:28.664000 140620972070720 torch/distributed/run.py:757] ***************************************** | |
| [default0]:07/02/2024 14:14:46 [WARNING|DP=0|PP=0|TP=0|ip-26-0-161-178]: [Vocab Size Padding] Padded vocab (size: 50257) with 1 dummy tokens (new size: 50258) | |
| [default0]:07/02/2024 14:14:46 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]: Config: | |
| [default0]:07/02/2024 14:14:46 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]: Config(general=GeneralArgs(project='bench_cluster', | |
| [default0]:07/02/2024 14:14:46 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]: run='%date_%jobid', | |
| [default0]:07/02/2024 14:14:46 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]: seed=42, | |
| [default0]:07/02/2024 14:14:46 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]: step=None, | |
| [default0]:07/02/2024 14:14:46 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]: consumed_train_samples=None, | |
| [default0]:07/02/2024 14:14:46 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]: benchmark_csv_path=None, | |
| [default0]:07/02/2024 14:14:46 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]: ignore_sanity_checks=True), | |
| [default0]:07/02/2024 14:14:46 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]: parallelism=ParallelismArgs(dp=8, | |
| [default0]:07/02/2024 14:14:46 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]: pp=1, | |
| [default0]:07/02/2024 14:14:46 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]: tp=2, | |
| [default0]:07/02/2024 14:14:46 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]: pp_engine=<nanotron.parallel.pipeline_parallel.engine.OneForwardOneBackwardPipelineEngine object at 0x7fc99f7a4910>, | |
| [default0]:07/02/2024 14:14:46 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]: tp_mode=<TensorParallelLinearMode.REDUCE_SCATTER: 2>, | |
| [default0]:07/02/2024 14:14:46 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]: tp_linear_async_communication=False, | |
| [default0]:07/02/2024 14:14:46 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]: expert_parallel_size=1), | |
| [default0]:07/02/2024 14:14:46 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]: model=ModelArgs(model_config=LlamaConfig(bos_token_id=1, | |
| [default0]:07/02/2024 14:14:46 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]: eos_token_id=2, | |
| [default0]:07/02/2024 14:14:46 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]: hidden_act='silu', | |
| [default0]:07/02/2024 14:14:46 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]: hidden_size=2048, | |
| [default0]:07/02/2024 14:14:46 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]: initializer_range=0.02, | |
| [default0]:07/02/2024 14:14:46 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]: intermediate_size=4096, | |
| [default0]:07/02/2024 14:14:46 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]: is_llama_config=True, | |
| [default0]:07/02/2024 14:14:46 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]: max_position_embeddings=4096, | |
| [default0]:07/02/2024 14:14:46 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]: num_attention_heads=32, | |
| [default0]:07/02/2024 14:14:46 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]: num_hidden_layers=24, | |
| [default0]:07/02/2024 14:14:46 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]: num_key_value_heads=32, | |
| [default0]:07/02/2024 14:14:46 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]: pad_token_id=None, | |
| [default0]:07/02/2024 14:14:46 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]: pretraining_tp=1, | |
| [default0]:07/02/2024 14:14:46 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]: rms_norm_eps=1e-05, | |
| [default0]:07/02/2024 14:14:46 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]: rope_scaling=None, | |
| [default0]:07/02/2024 14:14:46 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]: rope_theta=10000.0, | |
| [default0]:07/02/2024 14:14:46 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]: tie_word_embeddings=True, | |
| [default0]:07/02/2024 14:14:46 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]: use_cache=True, | |
| [default0]:07/02/2024 14:14:46 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]: vocab_size=50258), | |
| [default0]:07/02/2024 14:14:46 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]: init_method=RandomInit(std=0.025), | |
| [default0]:07/02/2024 14:14:46 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]: dtype=torch.bfloat16, | |
| [default0]:07/02/2024 14:14:46 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]: make_vocab_size_divisible_by=1, | |
| [default0]:07/02/2024 14:14:46 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]: ddp_bucket_cap_mb=25), | |
| [default0]:07/02/2024 14:14:46 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]: tokenizer=TokenizerArgs(tokenizer_name_or_path='openai-community/gpt2', | |
| [default0]:07/02/2024 14:14:46 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]: tokenizer_revision=None, | |
| [default0]:07/02/2024 14:14:46 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]: tokenizer_max_length=None), | |
| [default0]:07/02/2024 14:14:46 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]: checkpoints=CheckpointsArgs(checkpoints_path=Path('/dev/null'), | |
| [default0]:07/02/2024 14:14:46 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]: checkpoint_interval=100000, | |
| [default0]:07/02/2024 14:14:46 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]: save_initial_state=False, | |
| [default0]:07/02/2024 14:14:46 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]: resume_checkpoint_path=None, | |
| [default0]:07/02/2024 14:14:46 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]: checkpoints_path_is_shared_file_system=False), | |
| [default0]:07/02/2024 14:14:46 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]: logging=LoggingArgs(log_level='info', | |
| [default0]:07/02/2024 14:14:46 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]: log_level_replica='info', | |
| [default0]:07/02/2024 14:14:46 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]: iteration_step_info_interval=1), | |
| [default0]:07/02/2024 14:14:46 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]: tokens=TokensArgs(sequence_length=4096, | |
| [default0]:07/02/2024 14:14:46 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]: train_steps=20, | |
| [default0]:07/02/2024 14:14:46 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]: micro_batch_size=128, | |
| [default0]:07/02/2024 14:14:46 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]: batch_accumulation_per_replica=1, | |
| [default0]:07/02/2024 14:14:46 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]: val_check_interval=-1, | |
| [default0]:07/02/2024 14:14:46 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]: limit_val_batches=0, | |
| [default0]:07/02/2024 14:14:46 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]: limit_test_batches=0), | |
| [default0]:07/02/2024 14:14:46 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]: optimizer=OptimizerArgs(optimizer_factory=AdamWOptimizerArgs(adam_eps=1e-08, | |
| [default0]:07/02/2024 14:14:46 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]: adam_beta1=0.9, | |
| [default0]:07/02/2024 14:14:46 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]: adam_beta2=0.95, | |
| [default0]:07/02/2024 14:14:46 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]: torch_adam_is_fused=True, | |
| [default0]:07/02/2024 14:14:46 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]: name='adamW'), | |
| [default0]:07/02/2024 14:14:46 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]: zero_stage=1, | |
| [default0]:07/02/2024 14:14:46 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]: weight_decay=0.01, | |
| [default0]:07/02/2024 14:14:46 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]: clip_grad=1.0, | |
| [default0]:07/02/2024 14:14:46 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]: accumulate_grad_in_fp32=True, | |
| [default0]:07/02/2024 14:14:46 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]: learning_rate_scheduler=LRSchedulerArgs(learning_rate=0.0001, | |
| [default0]:07/02/2024 14:14:46 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]: lr_warmup_steps=1, | |
| [default0]:07/02/2024 14:14:46 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]: lr_warmup_style='linear', | |
| [default0]:07/02/2024 14:14:46 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]: lr_decay_style='linear', | |
| [default0]:07/02/2024 14:14:46 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]: lr_decay_steps=19, | |
| [default0]:07/02/2024 14:14:46 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]: lr_decay_starting_step=None, | |
| [default0]:07/02/2024 14:14:46 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]: min_decay_lr=1e-05)), | |
| [default0]:07/02/2024 14:14:46 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]: data_stages=[DatasetStageArgs(name='Training Stage', | |
| [default0]:07/02/2024 14:14:46 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]: start_training_step=1, | |
| [default0]:07/02/2024 14:14:46 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]: data=DataArgs(dataset=PretrainDatasetsArgs(hf_dataset_or_datasets='roneneldan/TinyStories', | |
| [default0]:07/02/2024 14:14:46 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]: hf_dataset_splits='train', | |
| [default0]:07/02/2024 14:14:46 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]: hf_dataset_config_name=None, | |
| [default0]:07/02/2024 14:14:46 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]: dataset_processing_num_proc_per_process=64, | |
| [default0]:07/02/2024 14:14:46 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]: dataset_overwrite_cache=False, | |
| [default0]:07/02/2024 14:14:46 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]: text_column_name='text'), | |
| [default0]:07/02/2024 14:14:46 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]: seed=42, | |
| [default0]:07/02/2024 14:14:46 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]: num_loading_workers=32))], | |
| [default0]:07/02/2024 14:14:46 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]: profiler=ProfilerArgs(profiler_export_path=Path('/fsx/ferdinandmom/ferdinand-hf/bench_cluster/results/llama-1B/16_GPUS/dp-8_tp-2_pp-1_mbz-128')), | |
| [default0]:07/02/2024 14:14:46 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]: lighteval=None) | |
| [default0]:07/02/2024 14:14:46 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]: Model Config: | |
| [default0]:07/02/2024 14:14:46 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]: LlamaConfig(bos_token_id=1, | |
| [default0]:07/02/2024 14:14:46 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]: eos_token_id=2, | |
| [default0]:07/02/2024 14:14:46 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]: hidden_act='silu', | |
| [default0]:07/02/2024 14:14:46 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]: hidden_size=2048, | |
| [default0]:07/02/2024 14:14:46 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]: initializer_range=0.02, | |
| [default0]:07/02/2024 14:14:46 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]: intermediate_size=4096, | |
| [default0]:07/02/2024 14:14:46 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]: is_llama_config=True, | |
| [default0]:07/02/2024 14:14:46 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]: max_position_embeddings=4096, | |
| [default0]:07/02/2024 14:14:46 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]: num_attention_heads=32, | |
| [default0]:07/02/2024 14:14:46 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]: num_hidden_layers=24, | |
| [default0]:07/02/2024 14:14:46 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]: num_key_value_heads=32, | |
| [default0]:07/02/2024 14:14:46 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]: pad_token_id=None, | |
| [default0]:07/02/2024 14:14:46 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]: pretraining_tp=1, | |
| [default0]:07/02/2024 14:14:46 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]: rms_norm_eps=1e-05, | |
| [default0]:07/02/2024 14:14:46 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]: rope_scaling=None, | |
| [default0]:07/02/2024 14:14:46 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]: rope_theta=10000.0, | |
| [default0]:07/02/2024 14:14:46 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]: tie_word_embeddings=True, | |
| [default0]:07/02/2024 14:14:46 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]: use_cache=True, | |
| [default0]:07/02/2024 14:14:46 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]: vocab_size=50258) | |
| [default0]:07/02/2024 14:14:46 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]: Building model.. | |
| [default0]:07/02/2024 14:14:46 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]: Setting PP block ranks... | |
| [default0]:07/02/2024 14:14:57 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]: Total number of parameters: 1.11G (2116.70MiB) | |
| [default0]:07/02/2024 14:14:57 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]: Local number of parameters: 555M (1058.35MiB) | |
| [default0]:07/02/2024 14:14:57 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]: [After model building] Memory usage: 1082.37MiB. Peak allocated: 1182.56MiB Peak reserved: 1200.00MiB | |
| [default0]:07/02/2024 14:14:57 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]: No checkpoint path provided. | |
| [default0]:07/02/2024 14:14:57 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]: Parametrizing model parameters using StandardParametrizator | |
| [default1]:07/02/2024 14:14:57 [INFO|DP=0|PP=0|TP=1|ip-26-0-161-178]: Local number of parameters: 555M (1058.35MiB) | |
| [default1]:07/02/2024 14:14:57 [INFO|DP=0|PP=0|TP=1|ip-26-0-161-178]: [After model building] Memory usage: 1082.37MiB. Peak allocated: 1182.56MiB Peak reserved: 1200.00MiB | |
| [default1]:07/02/2024 14:14:57 [INFO|DP=0|PP=0|TP=1|ip-26-0-161-178]: No checkpoint path provided. | |
| [default3]:07/02/2024 14:14:57 [INFO|DP=5|PP=0|TP=1|ip-26-0-163-147]: No checkpoint path provided. | |
| [default2]:07/02/2024 14:14:57 [INFO|DP=5|PP=0|TP=0|ip-26-0-163-147]: No checkpoint path provided. | |
| [default0]:07/02/2024 14:14:57 [INFO|DP=4|PP=0|TP=0|ip-26-0-163-147]: No checkpoint path provided. | |
| [default1]:07/02/2024 14:14:57 [INFO|DP=4|PP=0|TP=1|ip-26-0-163-147]: No checkpoint path provided. | |
| [default4]:07/02/2024 14:14:57 [INFO|DP=2|PP=0|TP=0|ip-26-0-161-178]: No checkpoint path provided. | |
| [default6]:07/02/2024 14:14:57 [INFO|DP=3|PP=0|TP=0|ip-26-0-161-178]: No checkpoint path provided. | |
| [default5]:07/02/2024 14:14:57 [INFO|DP=2|PP=0|TP=1|ip-26-0-161-178]: No checkpoint path provided. | |
| [default2]:07/02/2024 14:14:57 [INFO|DP=1|PP=0|TP=0|ip-26-0-161-178]: No checkpoint path provided. | |
| [default3]:07/02/2024 14:14:57 [INFO|DP=1|PP=0|TP=1|ip-26-0-161-178]: No checkpoint path provided. | |
| [default4]:07/02/2024 14:14:57 [INFO|DP=6|PP=0|TP=0|ip-26-0-163-147]: No checkpoint path provided. | |
| [default6]:07/02/2024 14:14:57 [INFO|DP=7|PP=0|TP=0|ip-26-0-163-147]: No checkpoint path provided. | |
| [default5]:07/02/2024 14:14:57 [INFO|DP=6|PP=0|TP=1|ip-26-0-163-147]: No checkpoint path provided. | |
| [default7]:07/02/2024 14:14:57 [INFO|DP=3|PP=0|TP=1|ip-26-0-161-178]: No checkpoint path provided. | |
| [default7]:07/02/2024 14:14:57 [INFO|DP=7|PP=0|TP=1|ip-26-0-163-147]: No checkpoint path provided. | |
| [default0]:07/02/2024 14:15:01 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]: [Optimizer Building] Using LearningRateForSP as learning rate | |
| [default0]:07/02/2024 14:15:01 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]: [ZeRO sharding] Size of optimizer params per rank: | |
| [default0]:07/02/2024 14:15:01 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]: [ZeRO sharding] DP Rank 0 has 69.4M out of 555M (12.50%) params' optimizer states | |
| [default0]:07/02/2024 14:15:01 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]: [ZeRO sharding] DP Rank 1 has 69.4M out of 555M (12.50%) params' optimizer states | |
| [default0]:07/02/2024 14:15:01 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]: [ZeRO sharding] DP Rank 2 has 69.4M out of 555M (12.50%) params' optimizer states | |
| [default0]:07/02/2024 14:15:01 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]: [ZeRO sharding] DP Rank 3 has 69.4M out of 555M (12.50%) params' optimizer states | |
| [default0]:07/02/2024 14:15:01 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]: [ZeRO sharding] DP Rank 4 has 69.4M out of 555M (12.50%) params' optimizer states | |
| [default0]:07/02/2024 14:15:01 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]: [ZeRO sharding] DP Rank 5 has 69.4M out of 555M (12.50%) params' optimizer states | |
| [default0]:07/02/2024 14:15:01 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]: [ZeRO sharding] DP Rank 6 has 69.4M out of 555M (12.50%) params' optimizer states | |
| [default0]:07/02/2024 14:15:01 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]: [ZeRO sharding] DP Rank 7 has 69.4M out of 555M (12.50%) params' optimizer states | |
| [default0]:07/02/2024 14:15:04 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]: [Training Plan] Stage Training Stage has 19 remaining training steps and has consumed 0 samples | |
| [default0]:07/02/2024 14:15:04 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]: Using `datasets` library | |
| [default0]:07/02/2024 14:15:04 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]: Loading tokenizer from openai-community/gpt2 and transformers/hf_hub versions ('4.41.2', '0.23.4') | |
| [default0]:07/02/2024 14:15:04 [WARNING|DP=0|PP=0|TP=0|ip-26-0-161-178]: 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 14:15:05 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]: [Training Plan] There are 1 training stages | |
| [default0]:07/02/2024 14:15:05 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]: [Stage Training Stage] start from step 1 | |
| [default0]:07/02/2024 14:15:05 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]: | |
| [default0]:07/02/2024 14:15:05 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]: [Start training] datetime: 2024-07-02 14:15:05.595384 | mbs: 128 | grad_accum: 1 | global_batch_size: 1024 | sequence_length: 4096 | train_steps: 20 | start_iteration_step: 0 | consumed_train_samples: 0 | |
| [default0]:07/02/2024 14:15:05 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]: Resuming training from stage Training Stage, it has trained for 0 samples and has 19 remaining train steps | |
| [default0]:07/02/2024 14:15:05 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]: Memory usage: 3463.66MiB. Peak allocated 3463.66MiB. Peak reserved: 3584.00MiB | |
| [default4]:07/02/2024 14:15:05 [WARNING|DP=2|PP=0|TP=0|ip-26-0-161-178]: Repo card metadata block was not found. Setting CardData to empty. | |
| [default5]:07/02/2024 14:15:05 [WARNING|DP=2|PP=0|TP=1|ip-26-0-161-178]: Repo card metadata block was not found. Setting CardData to empty. | |
| [default2]:07/02/2024 14:15:05 [WARNING|DP=1|PP=0|TP=0|ip-26-0-161-178]: Repo card metadata block was not found. Setting CardData to empty. | |
| [default1]:07/02/2024 14:15:05 [WARNING|DP=0|PP=0|TP=1|ip-26-0-161-178]: 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. | |
| [default2]:Repo card metadata block was not found. Setting CardData to empty. | |
| [default4]:07/02/2024 14:15:05 [WARNING|DP=6|PP=0|TP=0|ip-26-0-163-147]: Repo card metadata block was not found. Setting CardData to empty. | |
| [default0]:Repo card metadata block was not found. Setting CardData to empty. | |
| [default1]:Repo card metadata block was not found. Setting CardData to empty. | |
| [default3]:07/02/2024 14:15:05 [WARNING|DP=5|PP=0|TP=1|ip-26-0-163-147]: Repo card metadata block was not found. Setting CardData to empty. | |
| [default2]:07/02/2024 14:15:05 [WARNING|DP=5|PP=0|TP=0|ip-26-0-163-147]: Repo card metadata block was not found. Setting CardData to empty. | |
| [default1]:07/02/2024 14:15:05 [WARNING|DP=4|PP=0|TP=1|ip-26-0-163-147]: Repo card metadata block was not found. Setting CardData to empty. | |
| [default0]:07/02/2024 14:15:05 [WARNING|DP=4|PP=0|TP=0|ip-26-0-163-147]: Repo card metadata block was not found. Setting CardData to empty. | |
| [default6]:07/02/2024 14:15:05 [WARNING|DP=7|PP=0|TP=0|ip-26-0-163-147]: Repo card metadata block was not found. Setting CardData to empty. | |
| [default5]:07/02/2024 14:15:05 [WARNING|DP=6|PP=0|TP=1|ip-26-0-163-147]: Repo card metadata block was not found. Setting CardData to empty. | |
| [default5]:Repo card metadata block was not found. Setting CardData to empty. | |
| [default7]:Repo card metadata block was not found. Setting CardData to empty. | |
| [default3]:Repo card metadata block was not found. Setting CardData to empty. | |
| [default7]:07/02/2024 14:15:05 [WARNING|DP=7|PP=0|TP=1|ip-26-0-163-147]: Repo card metadata block was not found. Setting CardData to empty. | |
| [default7]:07/02/2024 14:15:05 [WARNING|DP=3|PP=0|TP=1|ip-26-0-161-178]: Repo card metadata block was not found. Setting CardData to empty. | |
| [default4]: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. | |
| [default6]:Repo card metadata block was not found. Setting CardData to empty. | |
| [default5]:Repo card metadata block was not found. Setting CardData to empty. | |
| [default6]:07/02/2024 14:15:05 [WARNING|DP=3|PP=0|TP=0|ip-26-0-161-178]: 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. | |
| [default3]:07/02/2024 14:15:05 [WARNING|DP=1|PP=0|TP=1|ip-26-0-161-178]: Repo card metadata block was not found. Setting CardData to empty. | |
| [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 637, in forward | |
| [default1]:[rank1]: hidden_states = self.mlp(hidden_states=hidden_states)["hidden_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/models/llama.py", line 172, in forward | |
| [default1]:[rank1]: hidden_states = self.down_proj(self.split_silu_mul(merged_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/tensor_parallel/nn.py", line 159, in forward | |
| [default1]:[rank1]: return row_linear( | |
| [default1]:[rank1]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/functional.py", line 474, in row_linear | |
| [default1]:[rank1]: out = F.linear(input, weight, bias) | |
| [default1]:[rank1]: torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 2.00 GiB. GPU has a total capacity of 79.33 GiB of which 857.94 MiB is free. Including non-PyTorch memory, this process has 78.47 GiB memory in use. Of the allocated memory 68.53 GiB is allocated by PyTorch, and 2.93 GiB 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 637, in forward | |
| [default0]:[rank0]: hidden_states = self.mlp(hidden_states=hidden_states)["hidden_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/models/llama.py", line 172, in forward | |
| [default0]:[rank0]: hidden_states = self.down_proj(self.split_silu_mul(merged_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/tensor_parallel/nn.py", line 159, in forward | |
| [default0]:[rank0]: return row_linear( | |
| [default0]:[rank0]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/functional.py", line 474, in row_linear | |
| [default0]:[rank0]: out = F.linear(input, weight, bias) | |
| [default0]:[rank0]: torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 2.00 GiB. GPU | |
| [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/na[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 637, in forward | |
| [default2]:[rank10]: hidden_states = self.mlp(hidden_states=hidden_states)["hidden_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/models/llama.py", line 172, in forward | |
| [default2]:[rank10]: hidden_states = self.down_proj(self.split_silu_mul(merged_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/tensor_parallel/nn.py", line 159, in forward | |
| [default2]:[rank10]: return row_linear( | |
| [default2]:[rank10]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/functional.py", line 474, in row_linear | |
| [default2]:[rank10]: out = F.linear(input, weight, bias) | |
| [default2]:[rank10]: torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 2.00 GiB. GPU has a total capacity of 79.33 GiB of which 801.94 MiB is free. Including non-PyTorch memory, this process has 78.53 GiB memory in use. Of the allocated memory 68.53 GiB is allocated by PyTorch, and 2.93 GiB 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 637, in forward | |
| [default5]:[rank13]: hidden_states = self.mlp(hidden_states=hidden_states)["hidden_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/models/llama.py", line 172, in forward | |
| [default5]:[rank13]: hidden_states = self.down_proj(self.split_silu_mul(merged_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/tensor_parallel/nn.py", line 159, in forward | |
| [default5]:[rank13]: return row_linear( | |
| [default5]:[rank13]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/functional.py", line 474, in row_linear | |
| [default5]:[rank13]: out = F.linear(input, weight, bias) | |
| [default5]:[rank13]: torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 2.00 GiB. GPU has a total capacity of 79.33 GiB of which 423.94 MiB is free. Including non-PyTorch memory, this process has 78.90 GiB memory in use. Of the allocated memory 68.53 GiB is allocated by PyTorch, and 2.93 GiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation. See documentation for Memory Management (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables) | |
| [default3]:[rank11]: Traceback (most recent call last): | |
| [default3]:[rank11]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/run_train.py", line 237, in <module> | |
| [default3]:[rank11]: trainer.train(dataloader) | |
| [default3]:[rank11]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 429, in train | |
| [default3]:[rank11]: outputs, loss_avg = self.training_step(dataloader=self.current_dataloader) | |
| [default3]:[rank11]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 462, in training_step | |
| [default3]:[rank11]: outputs = self.pipeline_engine.train_batch_iter( | |
| [default3]:[rank11]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 278, in train_batch_iter | |
| [default3]:[rank11]: output = self.forward(context=context, state=state, micro_batch=micro_batch, model=model) | |
| [default3]:[rank11]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 44, in forward | |
| [default3]:[rank11]: output = model(**micro_batch) | |
| [default3]:[rank11]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl | |
| [default3]:[rank11]: return self._call_impl(*args, **kwargs) | |
| [default3]:[rank11]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl | |
| [default3]:[rank11]: return forward_call(*args, **kwargs) | |
| [default3]:[rank11]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 891, in forward | |
| [default3]:[rank11]: sharded_logits = self.model( | |
| [default3]:[rank11]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl | |
| [default3]:[rank11]: return self._call_impl(*args, **kwargs) | |
| [default3]:[rank11]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl | |
| [default3]:[rank11]: return forward_call(*args, **kwargs) | |
| [default3]:[rank11]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 764, in forward | |
| [default3]:[rank11]: return self.forward_with_hidden_states(input_ids=input_ids, input_mask=input_mask)[0] | |
| [default3]:[rank11]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 780, in forward_with_hidden_states | |
| [default3]:[rank11]: hidden_encoder_states = encoder_block(**hidden_encoder_states) | |
| [default3]:[rank11]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl | |
| [default3]:[rank11]: return self._call_impl(*args, **kwargs) | |
| [default3]:[rank11]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl | |
| [default3]:[rank11]: return forward_call(*args, **kwargs) | |
| [default3]:[rank11]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/block.py", line 151, in forward | |
| [default3]:[rank11]: output = self.pp_block(**new_kwargs) | |
| [default3]:[rank11]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl | |
| [default3]:[rank11]: return self._call_impl(*args, **kwargs) | |
| [default3]:[rank11]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl | |
| [default3]:[rank11]: return forward_call(*args, **kwargs) | |
| [default3]:[rank11]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 637, in forward | |
| [default3]:[rank11]: hidden_states = self.mlp(hidden_states=hidden_states)["hidden_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/models/llama.py", line 172, in forward | |
| [default3]:[rank11]: hidden_states = self.down_proj(self.split_silu_mul(merged_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/tensor_parallel/nn.py", line 159, in forward | |
| [default3]:[rank11]: return row_linear( | |
| [default3]:[rank11]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/functional.py", line 474, in row_linear | |
| [default3]:[rank11]: out = F.linear(input, weight, bias) | |
| [default3]:[rank11]: torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 2.00 GiB. GPU has a total capacity of 79.33 GiB of which 421.94 MiB is free. Including non-PyTorch memory, this process has 78.90 GiB memory in use. Of the allocated memory 68.53 GiB is allocated by PyTorch, and 2.93 GiB 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) | |
| notron/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 637, in forward | |
| [default7]:[rank7]: hidden_states = self.mlp(hidden_states=hidden_states)["hidden_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/models/llama.py", line 172, in forward | |
| [default7]:[rank7]: hidden_states = self.down_proj(self.split_silu_mul(merged_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/tensor_parallel/nn.py", line 159, in forward | |
| [default7]:[rank7]: return row_linear( | |
| [default7]:[rank7]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/functional.py", line 474, in row_linear | |
| [default7]:[rank7]: out = F.linear(input, weight, bias) | |
| [default7]:[rank7]: torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 2.00 GiB. GPU has a total capacity of 79.33 GiB of which 639.94 MiB is free. Including non-PyTorch memory, this process has 78.69 GiB memory in use. Of the allocated memory 68.53 GiB is allocated by PyTorch, and 2.93 GiB 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 637, in forward | |
| [default6]:[rank6]: hidden_states = self.mlp(hidden_states=hidden_states)["hidden_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/models/llama.py", line 172, in forward | |
| [default6]:[rank6]: hidden_states = self.down_proj(self.split_silu_mul(merged_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/tensor_parallel/nn.py", line 159, in forward | |
| [default6]:[rank6]: return row_linear( | |
| [default6]:[rank6]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/functional.py", line 474, in row_linear | |
| [default6]:[rank6]: out = F.linear(input, weight, bias) | |
| [default6]:[rank6]: torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 2.00 GiB. GPU has a total capacity of 79.33 GiB of which 499.94 MiB is free. Including non-PyTorch memory, this process has 78.83 GiB memory in use. Of the allocated memory 68.53 GiB is allocated by PyTorch, and 2.93 GiB 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) | |
| [default4]:[rank12]: Traceback (most recent call last): | |
| [default4]:[rank12]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/run_train.py", line 237, in <module> | |
| [default1]:[rank9]: Traceback (most recent call last): | |
| [default1]:[rank9]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/run_train.py", line 237, in <module> | |
| [default4]:[rank12]: trainer.train(dataloader) | |
| [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 | |
| [default1]:[rank9]: trainer.train(dataloader) | |
| [default4]:[rank12]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 429, in train | |
| [default0]:[rank8]: return forward_call(*args, **kwargs) | |
| [default0]:[rank8]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 637, in forward | |
| [default1]:[rank9]: 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) | |
| [default0]:[rank8]: hidden_states = self.mlp(hidden_states=hidden_states)["hidden_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 | |
| [default1]:[rank9]: 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 | |
| [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) | |
| [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 | |
| [default0]:[rank8]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 172, in forward | |
| [default1]:[rank9]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 462, in training_step | |
| [default4]:[rank12]: output = self.forward(context=context, state=state, micro_batch=micro_batch, model=model) | |
| [default1]:[rank9]: 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 44, in forward | |
| [default1]:[rank9]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 278, in train_batch_iter | |
| [default0]:[rank8]: hidden_states = self.down_proj(self.split_silu_mul(merged_states)) | |
| [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) | |
| [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 | |
| [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) | |
| [default1]:[rank9]: output = self.forward(context=context, state=state, micro_batch=micro_batch, model=model) | |
| [default0]:[rank8]: return self._call_impl(*args, **kwargs) | |
| [default1]:[rank9]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 44, in forward | |
| [default4]:[rank12]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 891, in forward | |
| [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) | |
| [default1]:[rank9]: output = model(**micro_batch) | |
| [default4]:[rank12]: sharded_logits = self.model( | |
| [default0]:[rank8]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/nn.py", line 159, in forward | |
| [default0]:[rank8]: return row_linear( | |
| [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 | |
| [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) | |
| [default0]:[rank8]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/functional.py", line 474, in row_linear | |
| [default1]:[rank9]: 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 | |
| [default0]:[rank8]: out = F.linear(input, weight, bias) | |
| [default0]:[rank8]: torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 2.00 GiB. GPU | |
| [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 | |
| [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 | |
| [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 | |
| [default1]:[rank9]: return self.forward_with_hidden_states(input_ids=input_ids, input_mask=input_mask)[0] | |
| [default4]:[rank12]: return self._call_impl(*args, **kwargs) | |
| [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) | |
| [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) | |
| [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) | |
| [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) | |
| [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) | |
| [default4]:[rank12]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 637, in forward | |
| [default4]:[rank12]: hidden_states = self.mlp(hidden_states=hidden_states)["hidden_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 | |
| [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) | |
| [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 172, in forward | |
| [default4]:[rank12]: hidden_states = self.down_proj(self.split_silu_mul(merged_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 | |
| [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) | |
| [default4]:[rank12]: return forward_call(*args, **kwargs) | |
| [default4]:[rank12]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/nn.py", line 159, in forward | |
| [default4]:[rank12]: return row_linear( | |
| [default1]:[rank9]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 637, in forward | |
| [default1]:[rank9]: hidden_states = self.mlp(hidden_states=hidden_states)["hidden_states"] | |
| [default4]:[rank12]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/functional.py", line 474, in row_linear | |
| [default4]:[rank12]: out = F.linear(input, weight, bias) | |
| [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) | |
| [default4]:[rank12]: torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 2.00 GiB. GPU has a total capacity of 79.33 GiB of which 475.94 MiB is free. Including non-PyTorch memory, this process has 78.85 GiB memory in use. Of the allocated memory 68.53 GiB is allocated by PyTorch, and 2.93 GiB 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]: 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 172, in forward | |
| [default1]:[rank9]: hidden_states = self.down_proj(self.split_silu_mul(merged_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/tensor_parallel/nn.py", line 159, in forward | |
| [default1]:[rank9]: return row_linear( | |
| [default1]:[rank9]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/functional.py", line 474, in row_linear | |
| [default1]:[rank9]: out = F.linear(input, weight, bias) | |
| [default1]:[rank9]: torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 2.00 GiB. GPU has a total capacity of 79.33 GiB of which 857.94 MiB is free. Including non-PyTorch memory, this process has 78.47 GiB memory in use. Of the allocated memory 68.53 GiB is allocated by PyTorch, and 2.93 GiB 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 637, in forward | |
| [default3]:[rank3]: hidden_states = self.mlp(hidden_states=hidden_states)["hidden_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/models/llama.py", line 172, in forward | |
| [default3]:[rank3]: hidden_states = self.down_proj(self.split_silu_mul(merged_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/tensor_parallel/nn.py", line 159, in forward | |
| [default3]:[rank3]: return row_linear( | |
| [default3]:[rank3]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/functional.py", line 474, in row_linear | |
| [default3]:[rank3]: out = F.linear(input, weight, bias) | |
| [default3]:[rank3]: torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 2.00 GiB. GPU has a total capacity of 79.33 GiB of which 421.94 MiB is free. Including non-PyTorch memory, this process has 78.90 GiB memory in use. Of the allocated memory 68.53 GiB is allocated by PyTorch, and 2.93 GiB 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 637, in forward | |
| [default7]:[rank15]: hidden_states = self.mlp(hidden_states=hidden_states)["hidden_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/models/llama.py", line 172, in forward | |
| [default7]:[rank15]: hidden_states = self.down_proj(self.split_silu_mul(merged_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/tensor_parallel/nn.py", line 159, in forward | |
| [default7]:[rank15]: return row_linear( | |
| [default7]:[rank15]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/functional.py", line 474, in row_linear | |
| [default7]:[rank15]: out = F.linear(input, weight, bias) | |
| [default7]:[rank15]: torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 2.00 GiB. GPU has a total capacity of 79.33 GiB of which 639.94 MiB is free. Including non-PyTorch memory, this process has 78.69 GiB memory in use. Of the allocated memory 68.53 GiB is allocated by PyTorch, and 2.93 GiB 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 637, in forward | |
| [default6]:[rank14]: hidden_states = self.mlp(hidden_states=hidden_states)["hidden_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/models/llama.py", line 172, in forward | |
| [default6]:[rank14]: hidden_states = self.down_proj(self.split_silu_mul(merged_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/tensor_parallel/nn.py", line 159, in forward | |
| [default6]:[rank14]: return row_linear( | |
| [default6]:[rank14]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/functional.py", line 474, in row_linear | |
| [default6]:[rank14]: out = F.linear(input, weight, bias) | |
| [default6]:[rank14]: torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 2.00 GiB. GPU has a total capacity of 79.33 GiB of which 499.94 MiB is free. Including non-PyTorch memory, this process has 78.83 GiB memory in use. Of the allocated memory 68.53 GiB is allocated by PyTorch, and 2.93 GiB 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]:[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 637, in forward | |
| [default2]:[rank2]: hidden_states = self.mlp(hidden_states=hidden_states)["hidden_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/models/llama.py", line 172, in forward | |
| [default2]:[rank2]: hidden_states = self.down_proj(self.split_silu_mul(merged_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/tensor_parallel/nn.py", line 159, in forward | |
| [default2]:[rank2]: return row_linear( | |
| [default2]:[rank2]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/functional.py", line 474, in row_linear | |
| [default2]:[rank2]: out = F.linear(input, weight, bias) | |
| [default2]:[rank2]: torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 2.00 GiB. GPU has a total capacity of 79.33 GiB of which 801.94 MiB is free. Including non-PyTorch memory, this process has 78.53 GiB memory in use. Of the allocated memory 68.53 GiB is allocated by PyTorch, and 2.93 GiB 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 637, in forward | |
| [default4]:[rank4]: hidden_states = self.mlp(hidden_states=hidden_states)["hidden_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/models/llama.py", line 172, in forward | |
| [default4]:[rank4]: hidden_states = self.down_proj(self.split_silu_mul(merged_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/tensor_parallel/nn.py", line 159, in forward | |
| [default4]:[rank4]: return row_linear( | |
| [default4]:[rank4]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/functional.py", line 474, in row_linear | |
| [default4]:[rank4]: out = F.linear(input, weight, bias) | |
| [default4]:[rank4]: torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 2.00 GiB. GPU has a total capacity of 79.33 GiB of which 475.94 MiB is free. Including non-PyTorch memory, this process has 78.85 GiB memory in use. Of the allocated memory 68.53 GiB is allocated by PyTorch, and 2.93 GiB 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 637, in forward | |
| [default5]:[rank5]: hidden_states = self.mlp(hidden_states=hidden_states)["hidden_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/models/llama.py", line 172, in forward | |
| [default5]:[rank5]: hidden_states = self.down_proj(self.split_silu_mul(merged_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/tensor_parallel/nn.py", line 159, in forward | |
| [default5]:[rank5]: return row_linear( | |
| [default5]:[rank5]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/functional.py", line 474, in row_linear | |
| [default5]:[rank5]: out = F.linear(input, weight, bias) | |
| [default5]:[rank5]: torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 2.00 GiB. GPU has a total capacity of 79.33 GiB of which 423.94 MiB is free. Including non-PyTorch memory, this process has 78.90 GiB memory in use. Of the allocated memory 68.53 GiB is allocated by PyTorch, and 2.93 GiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation. See documentation for Memory Management (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables) | |
| E0702 14:15:24.941000 140151942731584 torch/distributed/elastic/multiprocessing/api.py:826] failed (exitcode: 1) local_rank: 0 (pid: 118181) of binary: /fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/bin/python3.10 | |
| E0702 14:15:24.943000 140620972070720 torch/distributed/elastic/multiprocessing/api.py:826] failed (exitcode: 1) local_rank: 0 (pid: 78076) 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> | |
| 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 | |
| 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 | |
| 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 | |
| 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__ | |
| 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 | |
| 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( | |
| 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_14:15:24 | |
| host : ip-26-0-161-178.ec2.internal | |
| rank : 1 (local_rank: 1) | |
| exitcode : 1 (pid: 118182) | |
| error_file: <N/A> | |
| traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html | |
| [2]: | |
| time : 2024-07-02_14:15:24 | |
| host : ip-26-0-161-178.ec2.internal | |
| rank : 2 (local_rank: 2) | |
| exitcode : 1 (pid: 118183) | |
| error_file: <N/A> | |
| traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html | |
| [3]: | |
| time : 2024-07-02_14:15:24 | |
| host : ip-26-0-161-178.ec2.internal | |
| rank : 3 (local_rank: 3) | |
| exitcode : 1 (pid: 118184) | |
| error_file: <N/A> | |
| traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html | |
| [4]: | |
| time : 2024-07-02_14:15:24 | |
| host : ip-26-0-161-178.ec2.internal | |
| rank : 4 (local_rank: 4) | |
| exitcode : 1 (pid: 118185) | |
| error_file: <N/A> | |
| traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html | |
| [5]: | |
| time : 2024-07-02_14:15:24 | |
| host : ip-26-0-161-178.ec2.internal | |
| rank : 5 (local_rank: 5) | |
| exitcode : 1 (pid: 118186) | |
| error_file: <N/A> | |
| traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html | |
| [6]: | |
| time : 2024-07-02_14:15:24 | |
| host : ip-26-0-161-178.ec2.internal | |
| rank : 6 (local_rank: 6) | |
| exitcode : 1 (pid: 118187) | |
| error_file: <N/A> | |
| traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html | |
| [7]: | |
| time : 2024-07-02_14:15:24 | |
| host : ip-26-0-161-178.ec2.internal | |
| rank : 7 (local_rank: 7) | |
| exitcode : 1 (pid: 118188) | |
| error_file: <N/A> | |
| traceback : To enable traceback see: https://pytortorch.distributed.elastic.multiprocessing.errors.ChildFailedError: | |
| ============================================================ | |
| /fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/run_train.py FAILED | |
| ------------------------------------------------------------ | |
| Failures: | |
| [1]: | |
| time : 2024-07-02_14:15:24 | |
| host : ip-26-0-163-147.ec2.internal | |
| rank : 9 (local_rank: 1) | |
| exitcode : 1 (pid: 78077) | |
| error_file: <N/A> | |
| traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html | |
| [2]: | |
| time : 2024-07-02_14:15:24 | |
| host : ip-26-0-163-147.ec2.internal | |
| rank : 10 (local_rank: 2) | |
| exitcode : 1 (pid: 78078) | |
| error_file: <N/A> | |
| traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html | |
| [3]: | |
| time : 2024-07-02_14:15:24 | |
| host : ip-26-0-163-147.ec2.internal | |
| rank : 11 (local_rank: 3) | |
| exitcode : 1 (pid: 78079) | |
| error_file: <N/A> | |
| traceback : To enable traceback see: https://pytorch.org/docs/stable/elastich.org/docs/stable/elastic/errors.html | |
| ------------------------------------------------------------ | |
| Root Cause (first observed failure): | |
| [0]: | |
| time : 2024-07-02_14:15:24 | |
| host : ip-26-0-161-178.ec2.internal | |
| rank : 0 (local_rank: 0) | |
| exitcode : 1 (pid: 118181) | |
| error_file: <N/A> | |
| traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html | |
| ============================================================c/errors.html | |
| [4]: | |
| time : 2024-07-02_14:15:24 | |
| host : ip-26-0-163-147.ec2.internal | |
| rank : 12 (local_rank: 4) | |
| exitcode : 1 (pid: 78080) | |
| error_file: <N/A> | |
| traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html | |
| [5]: | |
| time : 2024-07-02_14:15:24 | |
| host : ip-26-0-163-147.ec2.internal | |
| rank : 13 (local_rank: 5) | |
| exitcode : 1 (pid: 78081) | |
| error_file: <N/A> | |
| traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html | |
| [6]: | |
| time : 2024-07-02_14:15:24 | |
| host : ip-26-0-163-147.ec2.internal | |
| rank : 14 (local_rank: 6) | |
| exitcode : 1 (pid: 78082) | |
| error_file: <N/A> | |
| traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html | |
| [7]: | |
| time : 2024-07-02_14:15:24 | |
| host : ip-26-0-163-147.ec2.internal | |
| rank : 15 (local_rank: 7) | |
| exitcode : 1 (pid: 78083) | |
| error_file: <N/A> | |
| traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/er | |
| rors.html | |
| ------------------------------------------------------------ | |
| Root Cause (first observed failure): | |
| [0]: | |
| time : 2024-07-02_14:15:24 | |
| host : ip-26-0-163-147.ec2.internal | |
| rank : 8 (local_rank: 0) | |
| exitcode : 1 (pid: 78076) | |
| error_file: <N/A> | |
| traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html | |
| ============================================================ | |
| srun: error: ip-26-0-163-147: task 1: Exited with exit code 1 | |
| srun: error: ip-26-0-161-178: task 0: 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. | |