| ======================== | |
| START TIME: Wed Jul 3 21:43:03 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 | |
| W0703 21:43:07.092000 140123394443072 torch/distributed/run.py:757] | |
| W0703 21:43:07.092000 140123394443072 torch/distributed/run.py:757] ***************************************** | |
| W0703 21:43:07.092000 140123394443072 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. | |
| W0703 21:43:07.092000 140123394443072 torch/distributed/run.py:757] ***************************************** | |
| [default0]:07/03/2024 21:43:26 [WARNING|DP=0|PP=0|TP=0|ip-26-0-163-220]: [Vocab Size Padding] Padded vocab (size: 50257) with 7 dummy tokens (new size: 50264) | |
| [default0]:07/03/2024 21:43:26 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-220]: Config: | |
| [default0]:07/03/2024 21:43:26 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-220]: Config(general=GeneralArgs(project='bench_cluster', | |
| [default0]:07/03/2024 21:43:26 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-220]: run='%date_%jobid', | |
| [default0]:07/03/2024 21:43:26 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-220]: seed=42, | |
| [default0]:07/03/2024 21:43:26 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-220]: step=None, | |
| [default0]:07/03/2024 21:43:26 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-220]: consumed_train_samples=None, | |
| [default0]:07/03/2024 21:43:26 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-220]: benchmark_csv_path=None, | |
| [default0]:07/03/2024 21:43:26 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-220]: ignore_sanity_checks=True), | |
| [default0]:07/03/2024 21:43:26 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-220]: parallelism=ParallelismArgs(dp=1, | |
| [default0]:07/03/2024 21:43:26 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-220]: pp=1, | |
| [default0]:07/03/2024 21:43:26 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-220]: tp=8, | |
| [default0]:07/03/2024 21:43:26 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-220]: pp_engine=<nanotron.parallel.pipeline_parallel.engine.OneForwardOneBackwardPipelineEngine object at 0x7ff563268940>, | |
| [default0]:07/03/2024 21:43:26 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-220]: tp_mode=<TensorParallelLinearMode.REDUCE_SCATTER: 2>, | |
| [default0]:07/03/2024 21:43:26 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-220]: tp_linear_async_communication=False, | |
| [default0]:07/03/2024 21:43:26 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-220]: expert_parallel_size=1), | |
| [default0]:07/03/2024 21:43:26 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-220]: model=ModelArgs(model_config=LlamaConfig(bos_token_id=1, | |
| [default0]:07/03/2024 21:43:26 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-220]: eos_token_id=2, | |
| [default0]:07/03/2024 21:43:26 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-220]: hidden_act='silu', | |
| [default0]:07/03/2024 21:43:26 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-220]: hidden_size=2048, | |
| [default0]:07/03/2024 21:43:26 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-220]: initializer_range=0.02, | |
| [default0]:07/03/2024 21:43:26 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-220]: intermediate_size=4096, | |
| [default0]:07/03/2024 21:43:26 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-220]: is_llama_config=True, | |
| [default0]:07/03/2024 21:43:26 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-220]: max_position_embeddings=4096, | |
| [default0]:07/03/2024 21:43:26 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-220]: num_attention_heads=32, | |
| [default0]:07/03/2024 21:43:26 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-220]: num_hidden_layers=24, | |
| [default0]:07/03/2024 21:43:26 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-220]: num_key_value_heads=32, | |
| [default0]:07/03/2024 21:43:26 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-220]: pad_token_id=None, | |
| [default0]:07/03/2024 21:43:26 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-220]: pretraining_tp=1, | |
| [default0]:07/03/2024 21:43:26 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-220]: rms_norm_eps=1e-05, | |
| [default0]:07/03/2024 21:43:26 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-220]: rope_scaling=None, | |
| [default0]:07/03/2024 21:43:26 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-220]: rope_theta=10000.0, | |
| [default0]:07/03/2024 21:43:26 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-220]: tie_word_embeddings=True, | |
| [default0]:07/03/2024 21:43:26 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-220]: use_cache=True, | |
| [default0]:07/03/2024 21:43:26 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-220]: vocab_size=50264), | |
| [default0]:07/03/2024 21:43:26 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-220]: init_method=RandomInit(std=0.025), | |
| [default0]:07/03/2024 21:43:26 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-220]: dtype=torch.bfloat16, | |
| [default0]:07/03/2024 21:43:26 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-220]: make_vocab_size_divisible_by=1, | |
| [default0]:07/03/2024 21:43:26 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-220]: ddp_bucket_cap_mb=25), | |
| [default0]:07/03/2024 21:43:26 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-220]: tokenizer=TokenizerArgs(tokenizer_name_or_path='openai-community/gpt2', | |
| [default0]:07/03/2024 21:43:26 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-220]: tokenizer_revision=None, | |
| [default0]:07/03/2024 21:43:26 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-220]: tokenizer_max_length=None), | |
| [default0]:07/03/2024 21:43:26 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-220]: checkpoints=CheckpointsArgs(checkpoints_path=Path('/dev/null'), | |
| [default0]:07/03/2024 21:43:26 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-220]: checkpoint_interval=100000, | |
| [default0]:07/03/2024 21:43:26 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-220]: save_initial_state=False, | |
| [default0]:07/03/2024 21:43:26 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-220]: resume_checkpoint_path=None, | |
| [default0]:07/03/2024 21:43:26 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-220]: checkpoints_path_is_shared_file_system=False), | |
| [default0]:07/03/2024 21:43:26 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-220]: logging=LoggingArgs(log_level='info', | |
| [default0]:07/03/2024 21:43:26 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-220]: log_level_replica='info', | |
| [default0]:07/03/2024 21:43:26 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-220]: iteration_step_info_interval=1), | |
| [default0]:07/03/2024 21:43:26 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-220]: tokens=TokensArgs(sequence_length=4096, | |
| [default0]:07/03/2024 21:43:26 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-220]: train_steps=20, | |
| [default0]:07/03/2024 21:43:26 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-220]: micro_batch_size=1024, | |
| [default0]:07/03/2024 21:43:26 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-220]: batch_accumulation_per_replica=1, | |
| [default0]:07/03/2024 21:43:26 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-220]: val_check_interval=-1, | |
| [default0]:07/03/2024 21:43:26 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-220]: limit_val_batches=0, | |
| [default0]:07/03/2024 21:43:26 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-220]: limit_test_batches=0), | |
| [default0]:07/03/2024 21:43:26 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-220]: optimizer=OptimizerArgs(optimizer_factory=AdamWOptimizerArgs(adam_eps=1e-08, | |
| [default0]:07/03/2024 21:43:26 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-220]: adam_beta1=0.9, | |
| [default0]:07/03/2024 21:43:26 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-220]: adam_beta2=0.95, | |
| [default0]:07/03/2024 21:43:26 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-220]: torch_adam_is_fused=True, | |
| [default0]:07/03/2024 21:43:26 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-220]: name='adamW'), | |
| [default0]:07/03/2024 21:43:26 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-220]: zero_stage=1, | |
| [default0]:07/03/2024 21:43:26 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-220]: weight_decay=0.01, | |
| [default0]:07/03/2024 21:43:26 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-220]: clip_grad=1.0, | |
| [default0]:07/03/2024 21:43:26 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-220]: accumulate_grad_in_fp32=True, | |
| [default0]:07/03/2024 21:43:26 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-220]: learning_rate_scheduler=LRSchedulerArgs(learning_rate=0.0001, | |
| [default0]:07/03/2024 21:43:26 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-220]: lr_warmup_steps=1, | |
| [default0]:07/03/2024 21:43:26 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-220]: lr_warmup_style='linear', | |
| [default0]:07/03/2024 21:43:26 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-220]: lr_decay_style='linear', | |
| [default0]:07/03/2024 21:43:26 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-220]: lr_decay_steps=19, | |
| [default0]:07/03/2024 21:43:26 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-220]: lr_decay_starting_step=None, | |
| [default0]:07/03/2024 21:43:26 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-220]: min_decay_lr=1e-05)), | |
| [default0]:07/03/2024 21:43:26 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-220]: data_stages=[DatasetStageArgs(name='Training Stage', | |
| [default0]:07/03/2024 21:43:26 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-220]: start_training_step=1, | |
| [default0]:07/03/2024 21:43:26 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-220]: data=DataArgs(dataset=PretrainDatasetsArgs(hf_dataset_or_datasets='roneneldan/TinyStories', | |
| [default0]:07/03/2024 21:43:26 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-220]: hf_dataset_splits='train', | |
| [default0]:07/03/2024 21:43:26 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-220]: hf_dataset_config_name=None, | |
| [default0]:07/03/2024 21:43:26 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-220]: dataset_processing_num_proc_per_process=64, | |
| [default0]:07/03/2024 21:43:26 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-220]: dataset_overwrite_cache=False, | |
| [default0]:07/03/2024 21:43:26 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-220]: text_column_name='text'), | |
| [default0]:07/03/2024 21:43:26 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-220]: seed=42, | |
| [default0]:07/03/2024 21:43:26 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-220]: num_loading_workers=0))], | |
| [default0]:07/03/2024 21:43:26 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-220]: profiler=ProfilerArgs(profiler_export_path=Path('/fsx/ferdinandmom/ferdinand-hf/bench_cluster/results/llama-1B/8_GPUS/dp-1_tp-8_pp-1_mbz-1024')), | |
| [default0]:07/03/2024 21:43:26 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-220]: lighteval=None) | |
| [default0]:07/03/2024 21:43:26 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-220]: Model Config: | |
| [default0]:07/03/2024 21:43:26 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-220]: LlamaConfig(bos_token_id=1, | |
| [default0]:07/03/2024 21:43:26 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-220]: eos_token_id=2, | |
| [default0]:07/03/2024 21:43:26 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-220]: hidden_act='silu', | |
| [default0]:07/03/2024 21:43:26 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-220]: hidden_size=2048, | |
| [default0]:07/03/2024 21:43:26 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-220]: initializer_range=0.02, | |
| [default0]:07/03/2024 21:43:26 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-220]: intermediate_size=4096, | |
| [default0]:07/03/2024 21:43:26 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-220]: is_llama_config=True, | |
| [default0]:07/03/2024 21:43:26 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-220]: max_position_embeddings=4096, | |
| [default0]:07/03/2024 21:43:26 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-220]: num_attention_heads=32, | |
| [default0]:07/03/2024 21:43:26 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-220]: num_hidden_layers=24, | |
| [default0]:07/03/2024 21:43:26 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-220]: num_key_value_heads=32, | |
| [default0]:07/03/2024 21:43:26 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-220]: pad_token_id=None, | |
| [default0]:07/03/2024 21:43:26 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-220]: pretraining_tp=1, | |
| [default0]:07/03/2024 21:43:26 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-220]: rms_norm_eps=1e-05, | |
| [default0]:07/03/2024 21:43:26 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-220]: rope_scaling=None, | |
| [default0]:07/03/2024 21:43:26 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-220]: rope_theta=10000.0, | |
| [default0]:07/03/2024 21:43:26 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-220]: tie_word_embeddings=True, | |
| [default0]:07/03/2024 21:43:26 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-220]: use_cache=True, | |
| [default0]:07/03/2024 21:43:26 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-220]: vocab_size=50264) | |
| [default0]:07/03/2024 21:43:26 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-220]: Building model.. | |
| [default0]:07/03/2024 21:43:26 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-220]: Setting PP block ranks... | |
| [default4]:07/03/2024 21:43:41 [INFO|DP=0|PP=0|TP=4|ip-26-0-163-220]: Local number of parameters: 139M (264.73MiB) | |
| [default5]:07/03/2024 21:43:41 [INFO|DP=0|PP=0|TP=5|ip-26-0-163-220]: Local number of parameters: 139M (264.73MiB) | |
| [default4]:07/03/2024 21:43:41 [INFO|DP=0|PP=0|TP=4|ip-26-0-163-220]: [After model building] Memory usage: 290.76MiB. Peak allocated: 317.33MiB Peak reserved: 324.00MiB | |
| [default4]:07/03/2024 21:43:41 [INFO|DP=0|PP=0|TP=4|ip-26-0-163-220]: No checkpoint path provided. | |
| [default5]:07/03/2024 21:43:41 [INFO|DP=0|PP=0|TP=5|ip-26-0-163-220]: [After model building] Memory usage: 290.76MiB. Peak allocated: 317.33MiB Peak reserved: 324.00MiB | |
| [default5]:07/03/2024 21:43:41 [INFO|DP=0|PP=0|TP=5|ip-26-0-163-220]: No checkpoint path provided. | |
| [default3]:07/03/2024 21:43:41 [INFO|DP=0|PP=0|TP=3|ip-26-0-163-220]: Local number of parameters: 139M (264.73MiB) | |
| [default3]:07/03/2024 21:43:41 [INFO|DP=0|PP=0|TP=3|ip-26-0-163-220]: [After model building] Memory usage: 290.76MiB. Peak allocated: 317.33MiB Peak reserved: 324.00MiB | |
| [default3]:07/03/2024 21:43:41 [INFO|DP=0|PP=0|TP=3|ip-26-0-163-220]: No checkpoint path provided. | |
| [default2]:07/03/2024 21:43:41 [INFO|DP=0|PP=0|TP=2|ip-26-0-163-220]: Local number of parameters: 139M (264.73MiB) | |
| [default2]:07/03/2024 21:43:41 [INFO|DP=0|PP=0|TP=2|ip-26-0-163-220]: [After model building] Memory usage: 290.76MiB. Peak allocated: 317.33MiB Peak reserved: 324.00MiB | |
| [default2]:07/03/2024 21:43:41 [INFO|DP=0|PP=0|TP=2|ip-26-0-163-220]: No checkpoint path provided. | |
| [default0]:07/03/2024 21:43:41 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-220]: Total number of parameters: 1.11G (2117.88MiB) | |
| [default0]:07/03/2024 21:43:41 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-220]: Local number of parameters: 139M (264.73MiB) | |
| [default0]:07/03/2024 21:43:41 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-220]: [After model building] Memory usage: 290.76MiB. Peak allocated: 317.33MiB Peak reserved: 324.00MiB | |
| [default0]:07/03/2024 21:43:41 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-220]: No checkpoint path provided. | |
| [default0]:07/03/2024 21:43:41 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-220]: Parametrizing model parameters using StandardParametrizator | |
| [default0]:07/03/2024 21:43:41 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-220]: [Optimizer Building] Using LearningRateForSP as learning rate | |
| [default0]:07/03/2024 21:43:41 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-220]: [ZeRO sharding] Size of optimizer params per rank: | |
| [default0]:07/03/2024 21:43:41 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-220]: [ZeRO sharding] DP Rank 0 has 139M out of 139M (100.00%) params' optimizer states | |
| [default6]:07/03/2024 21:43:41 [INFO|DP=0|PP=0|TP=6|ip-26-0-163-220]: Local number of parameters: 139M (264.73MiB) | |
| [default6]:07/03/2024 21:43:41 [INFO|DP=0|PP=0|TP=6|ip-26-0-163-220]: [After model building] Memory usage: 290.76MiB. Peak allocated: 317.33MiB Peak reserved: 324.00MiB | |
| [default6]:07/03/2024 21:43:41 [INFO|DP=0|PP=0|TP=6|ip-26-0-163-220]: No checkpoint path provided. | |
| [default1]:07/03/2024 21:43:41 [INFO|DP=0|PP=0|TP=1|ip-26-0-163-220]: Local number of parameters: 139M (264.73MiB) | |
| [default1]:07/03/2024 21:43:41 [INFO|DP=0|PP=0|TP=1|ip-26-0-163-220]: [After model building] Memory usage: 290.76MiB. Peak allocated: 317.33MiB Peak reserved: 324.00MiB | |
| [default1]:07/03/2024 21:43:41 [INFO|DP=0|PP=0|TP=1|ip-26-0-163-220]: No checkpoint path provided. | |
| [default7]:07/03/2024 21:43:41 [INFO|DP=0|PP=0|TP=7|ip-26-0-163-220]: Local number of parameters: 139M (264.73MiB) | |
| [default7]:07/03/2024 21:43:41 [INFO|DP=0|PP=0|TP=7|ip-26-0-163-220]: [After model building] Memory usage: 290.76MiB. Peak allocated: 317.33MiB Peak reserved: 324.00MiB | |
| [default7]:07/03/2024 21:43:41 [INFO|DP=0|PP=0|TP=7|ip-26-0-163-220]: No checkpoint path provided. | |
| [default0]:07/03/2024 21:43:42 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-220]: [Training Plan] Stage Training Stage has 19 remaining training steps and has consumed 0 samples | |
| [default0]:07/03/2024 21:43:42 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-220]: Using `datasets` library | |
| [default0]:07/03/2024 21:43:42 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-220]: Loading tokenizer from openai-community/gpt2 and transformers/hf_hub versions ('4.41.2', '0.23.4') | |
| [default0]:07/03/2024 21:43:43 [WARNING|DP=0|PP=0|TP=0|ip-26-0-163-220]: Repo card metadata block was not found. Setting CardData to empty. | |
| [default0]:Repo card metadata block was not found. Setting CardData to empty. | |
| [default0]:07/03/2024 21:43:44 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-220]: [Training Plan] There are 1 training stages | |
| [default0]:07/03/2024 21:43:44 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-220]: [Stage Training Stage] start from step 1 | |
| [default0]:07/03/2024 21:43:44 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-220]: | |
| [default0]:07/03/2024 21:43:44 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-220]: [Start training] datetime: 2024-07-03 21:43:44.038819 | mbs: 1024 | grad_accum: 1 | global_batch_size: 1024 | sequence_length: 4096 | train_steps: 20 | start_iteration_step: 0 | consumed_train_samples: 0 | |
| [default0]:07/03/2024 21:43:44 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-220]: Resuming training from stage Training Stage, it has trained for 0 samples and has 19 remaining train steps | |
| [default0]:07/03/2024 21:43:44 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-220]: Memory usage: 1350.75MiB. Peak allocated 1350.76MiB. Peak reserved: 1384.00MiB | |
| [default4]:07/03/2024 21:43:44 [WARNING|DP=0|PP=0|TP=4|ip-26-0-163-220]: Repo card metadata block was not found. Setting CardData to empty. | |
| [default5]:07/03/2024 21:43:44 [WARNING|DP=0|PP=0|TP=5|ip-26-0-163-220]: Repo card metadata block was not found. Setting CardData to empty. | |
| [default7]:Repo card metadata block was not found. Setting CardData to empty. | |
| [default3]:07/03/2024 21:43:44 [WARNING|DP=0|PP=0|TP=3|ip-26-0-163-220]: Repo card metadata block was not found. Setting CardData to empty. | |
| [default2]:07/03/2024 21:43:44 [WARNING|DP=0|PP=0|TP=2|ip-26-0-163-220]: Repo card metadata block was not found. Setting CardData to empty. | |
| [default6]:Repo card metadata block was not found. Setting CardData to empty. | |
| [default6]:07/03/2024 21:43:44 [WARNING|DP=0|PP=0|TP=6|ip-26-0-163-220]: Repo card metadata block was not found. Setting CardData to empty. | |
| [default1]:07/03/2024 21:43:44 [WARNING|DP=0|PP=0|TP=1|ip-26-0-163-220]: Repo card metadata block was not found. Setting CardData to empty. | |
| [default7]:07/03/2024 21:43:44 [WARNING|DP=0|PP=0|TP=7|ip-26-0-163-220]: Repo card metadata block was not found. Setting CardData to empty. | |
| [default1]:Repo card metadata block was not found. Setting CardData to empty. | |
| [default2]:Repo card metadata block was not found. Setting CardData to empty. | |
| [default3]:Repo card metadata block was not found. Setting CardData to empty. | |
| [default4]:Repo card metadata block was not found. Setting CardData to empty. | |
| [default5]:Repo card metadata block was not found. Setting CardData to empty. | |
| [default7]:[rank7]: Traceback (most recent call last): | |
| [default7]:[rank7]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/run_train.py", line 237, in <module> | |
| [default7]:[rank7]: trainer.train(dataloader) | |
| [default7]:[rank7]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 429, in train | |
| [default7]:[rank7]: outputs, loss_avg = self.training_step(dataloader=self.current_dataloader) | |
| [default7]:[rank7]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 462, in training_step | |
| [default7]:[rank7]: outputs = self.pipeline_engine.train_batch_iter( | |
| [default7]:[rank7]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 278, in train_batch_iter | |
| [default7]:[rank7]: output = self.forward(context=context, state=state, micro_batch=micro_batch, model=model) | |
| [default7]:[rank7]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 44, in forward | |
| [default7]:[rank7]: output = model(**micro_batch) | |
| [default7]:[rank7]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl | |
| [default7]:[rank7]: return self._call_impl(*args, **kwargs) | |
| [default7]:[rank7]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl | |
| [default7]:[rank7]: return forward_call(*args, **kwargs) | |
| [default7]:[rank7]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 891, in forward | |
| [default7]:[rank7]: sharded_logits = self.model( | |
| [default7]:[rank7]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl | |
| [default7]:[rank7]: return self._call_impl(*args, **kwargs) | |
| [default7]:[rank7]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl | |
| [default7]:[rank7]: return forward_call(*args, **kwargs) | |
| [default7]:[rank7]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 764, in forward | |
| [default7]:[rank7]: return self.forward_with_hidden_states(input_ids=input_ids, input_mask=input_mask)[0] | |
| [default7]:[rank7]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 780, in forward_with_hidden_states | |
| [default7]:[rank7]: hidden_encoder_states = encoder_block(**hidden_encoder_states) | |
| [default7]:[rank7]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl | |
| [default7]:[rank7]: return self._call_impl(*args, **kwargs) | |
| [default7]:[rank7]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl | |
| [default7]:[rank7]: return forward_call(*args, **kwargs) | |
| [default7]:[rank7]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/block.py", line 151, in forward | |
| [default7]:[rank7]: output = self.pp_block(**new_kwargs) | |
| [default7]:[rank7]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl | |
| [default7]:[rank7]: return self._call_impl(*args, **kwargs) | |
| [default7]:[rank7]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl | |
| [default7]:[rank7]: return forward_call(*args, **kwargs) | |
| [default7]:[rank7]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 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 171, in forward | |
| [default7]:[rank7]: merged_states = self.gate_up_proj(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/parallel/tensor_parallel/nn.py", line 87, in forward | |
| [default7]:[rank7]: return column_linear( | |
| [default7]:[rank7]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/functional.py", line 359, in column_linear | |
| [default7]:[rank7]: return F.linear(input, weight, bias) | |
| [default7]:[rank7]: torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 8.00 GiB. GPU has a total capacity of 79.33 GiB of which 6.81 GiB is free. Including non-PyTorch memory, this process has 72.50 GiB memory in use. Of the allocated memory 55.52 GiB is allocated by PyTorch, and 5.95 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> | |
| [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) | |
| [default0]:[rank0]: trainer.train(dataloader) | |
| [default6]:[rank6]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 44, in forward | |
| [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) | |
| [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 | |
| [default0]:[rank0]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 462, in training_step | |
| [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) | |
| [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 | |
| [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 | |
| [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) | |
| [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) | |
| [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 | |
| [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 | |
| [default0]:[rank0]: return self._call_impl(*args, **kwargs) | |
| [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 | |
| [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 | |
| [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) | |
| [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 | |
| [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) | |
| [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) | |
| [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"] | |
| [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 | |
| [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) | |
| [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 | |
| [default6]:[rank6]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 171, in forward | |
| [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) | |
| [default6]:[rank6]: merged_states = self.gate_up_proj(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 | |
| [default6]:[rank6]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl | |
| [default6]:[rank6]: return self._call_impl(*args, **kwargs) | |
| [default6]:[rank6]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl | |
| [default6]:[rank6]: return forward_call(*args, **kwargs) | |
| [default6]:[rank6]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/nn.py", line 87, in forward | |
| [default6]:[rank6]: return column_linear( | |
| [default6]:[rank6]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/functional.py", line 359, in column_linear | |
| [default6]:[rank6]: return F.linear(input, weight, bias) | |
| [default0]:[rank0]: return self._call_impl(*args, **kwargs) | |
| [default6]:[rank6]: torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 8.00 GiB. GPU has a total capacity of 79.33 GiB of which 6.11 GiB is free. Including non-PyTorch memory, this process has 73.21 GiB memory in use. Of the allocated memory 55.52 GiB is allocated by PyTorch, and 5.95 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]: 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 171, in forward | |
| [default0]:[rank0]: merged_states = self.gate_up_proj(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/parallel/tensor_parallel/nn.py", line 87, in forward | |
| [default0]:[rank0]: return column_linear( | |
| [default0]:[rank0]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/functional.py", line 359, in column_linear | |
| [default0]:[rank0]: return F.linear(input, weight, bias) | |
| [default0]:[rank0]: torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 8.00 GiB. GPU | |
| [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) | |
| [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) | |
| [default2]:[rank2]: Traceback (most recent call last): | |
| [default2]:[rank2]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/run_train.py", line 237, in <module> | |
| [default3]:[rank3]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 429, in train | |
| [default1]:[rank1]: Traceback (most recent call last): | |
| [default4]:[rank4]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 429, in train | |
| [default2]:[rank2]: trainer.train(dataloader) | |
| [default2]:[rank2]: 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) | |
| [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 | |
| [default2]:[rank2]: 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 | |
| [default1]:[rank1]: 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( | |
| [default4]:[rank4]: outputs = self.pipeline_engine.train_batch_iter( | |
| [default1]:[rank1]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 462, in training_step | |
| [default3]:[rank3]: outputs, loss_avg = self.training_step(dataloader=self.current_dataloader) | |
| [default2]:[rank2]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 278, in 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 | |
| [default3]:[rank3]: 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( | |
| [default4]:[rank4]: output = self.forward(context=context, state=state, micro_batch=micro_batch, model=model) | |
| [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 | |
| [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) | |
| [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 | |
| [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) | |
| [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 | |
| [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) | |
| [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 | |
| [default4]:[rank4]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 891, in forward | |
| [default1]:[rank1]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 278, in train_batch_iter | |
| [default3]:[rank3]: outputs = self.pipeline_engine.train_batch_iter( | |
| [default4]:[rank4]: sharded_logits = self.model( | |
| [default1]:[rank1]: 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 278, in train_batch_iter | |
| [default2]:[rank2]: 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 | |
| [default3]:[rank3]: 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) | |
| [default4]:[rank4]: 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 1532, in _wrapped_call_impl | |
| [default1]:[rank1]: 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 1532, in _wrapped_call_impl | |
| [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 | |
| [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 | |
| [default3]:[rank3]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 44, in forward | |
| [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 | |
| [default2]:[rank2]: return self._call_impl(*args, **kwargs) | |
| [default3]:[rank3]: output = model(**micro_batch) | |
| [default1]:[rank1]: 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 1541, in _call_impl | |
| [default4]:[rank4]: return forward_call(*args, **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 | |
| [default2]:[rank2]: return forward_call(*args, **kwargs) | |
| [default1]:[rank1]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl | |
| [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 | |
| [default4]:[rank4]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 764, in forward | |
| [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 | |
| [default4]:[rank4]: return self.forward_with_hidden_states(input_ids=input_ids, input_mask=input_mask)[0] | |
| [default1]:[rank1]: return self._call_impl(*args, **kwargs) | |
| [default2]:[rank2]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 764, in forward | |
| [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) | |
| [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 | |
| [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) | |
| [default2]:[rank2]: 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 1541, in _call_impl | |
| [default3]:[rank3]: sharded_logits = self.model( | |
| [default1]:[rank1]: return forward_call(*args, **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 | |
| [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 | |
| [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] | |
| [default2]:[rank2]: 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 | |
| [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 | |
| [default4]:[rank4]: return forward_call(*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) | |
| [default4]:[rank4]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/block.py", line 151, in forward | |
| [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) | |
| [default2]:[rank2]: 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) | |
| [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) | |
| [default2]:[rank2]: output = self.pp_block(**new_kwargs) | |
| [default3]:[rank3]: return forward_call(*args, **kwargs) | |
| [default1]:[rank1]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl | |
| [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) | |
| [default5]:[rank5]: Traceback (most recent call last): | |
| [default3]:[rank3]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 764, in forward | |
| [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 | |
| [default1]:[rank1]: return forward_call(*args, **kwargs) | |
| [default3]:[rank3]: return self.forward_with_hidden_states(input_ids=input_ids, input_mask=input_mask)[0] | |
| [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 | |
| [default5]:[rank5]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/run_train.py", line 237, in <module> | |
| [default3]:[rank3]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 780, in forward_with_hidden_states | |
| [default1]:[rank1]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/block.py", line 151, in forward | |
| [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 | |
| [default5]:[rank5]: trainer.train(dataloader) | |
| [default5]:[rank5]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 429, in train | |
| [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 | |
| [default4]:[rank4]: return self._call_impl(*args, **kwargs) | |
| [default5]:[rank5]: outputs, loss_avg = self.training_step(dataloader=self.current_dataloader) | |
| [default3]:[rank3]: hidden_encoder_states = encoder_block(**hidden_encoder_states) | |
| [default1]:[rank1]: 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 1541, in _call_impl | |
| [default4]:[rank4]: return forward_call(*args, **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) | |
| [default5]:[rank5]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 462, in training_step | |
| [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 | |
| [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 | |
| [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 | |
| [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 | |
| [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 | |
| [default4]:[rank4]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 637, in forward | |
| [default2]:[rank2]: return forward_call(*args, **kwargs) | |
| [default5]:[rank5]: 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/models/llama.py", line 171, in forward | |
| [default2]:[rank2]: merged_states = self.gate_up_proj(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 | |
| [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 | |
| [default5]:[rank5]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 44, in forward | |
| [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 | |
| [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 | |
| [default5]:[rank5]: output = model(**micro_batch) | |
| [default2]:[rank2]: return self._call_impl(*args, **kwargs) | |
| [default3]:[rank3]: return self._call_impl(*args, **kwargs) | |
| [default4]:[rank4]: 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 | |
| [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 | |
| [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 | |
| [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) | |
| [default4]:[rank4]: return forward_call(*args, **kwargs) | |
| [default2]:[rank2]: return forward_call(*args, **kwargs) | |
| [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 | |
| [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) | |
| [default1]:[rank1]: hidden_states = self.mlp(hidden_states=hidden_states)["hidden_states"] | |
| [default2]:[rank2]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/nn.py", line 87, in forward | |
| [default4]:[rank4]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 171, in forward | |
| [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 | |
| [default2]:[rank2]: return column_linear( | |
| [default4]:[rank4]: merged_states = self.gate_up_proj(hidden_states) | |
| [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( | |
| [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 | |
| [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 | |
| [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 | |
| [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 | |
| [default2]:[rank2]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/functional.py", line 359, in column_linear | |
| [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 | |
| [default1]:[rank1]: return forward_call(*args, **kwargs) | |
| [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 | |
| [default1]:[rank1]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 171, in forward | |
| [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 | |
| [default4]:[rank4]: return self._call_impl(*args, **kwargs) | |
| [default3]:[rank3]: return forward_call(*args, **kwargs) | |
| [default3]:[rank3]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 171, in forward | |
| [default3]:[rank3]: merged_states = self.gate_up_proj(hidden_states) | |
| [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) | |
| [default2]:[rank2]: return F.linear(input, weight, bias) | |
| [default4]:[rank4]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/nn.py", line 87, in forward | |
| [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 | |
| [default5]:[rank5]: return self.forward_with_hidden_states(input_ids=input_ids, input_mask=input_mask)[0] | |
| [default4]:[rank4]: return column_linear( | |
| [default4]:[rank4]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/functional.py", line 359, in column_linear | |
| [default1]:[rank1]: merged_states = self.gate_up_proj(hidden_states) | |
| [default3]:[rank3]: return self._call_impl(*args, **kwargs) | |
| [default4]:[rank4]: return F.linear(input, weight, bias) | |
| [default2]:[rank2]: torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 8.00 GiB. GPU has a total capacity of 79.33 GiB of which 6.11 GiB is free. Including non-PyTorch memory, this process has 73.21 GiB memory in use. Of the allocated memory 55.52 GiB is allocated by PyTorch, and 5.95 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]: 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) | |
| [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 | |
| [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 | |
| [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 | |
| [default1]:[rank1]: return forward_call(*args, **kwargs) | |
| [default4]:[rank4]: torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 8.00 GiB. GPU has a total capacity of 79.33 GiB of which 6.11 GiB is free. Including non-PyTorch memory, this process has 73.21 GiB memory in use. Of the allocated memory 55.52 GiB is allocated by PyTorch, and 5.95 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]: return forward_call(*args, **kwargs) | |
| [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 | |
| [default3]:[rank3]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/nn.py", line 87, in forward | |
| [default3]:[rank3]: return column_linear( | |
| [default1]:[rank1]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/nn.py", line 87, in forward | |
| [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 | |
| [default3]:[rank3]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/functional.py", line 359, in column_linear | |
| [default1]:[rank1]: return column_linear( | |
| [default1]:[rank1]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/functional.py", line 359, in column_linear | |
| [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 | |
| [default3]:[rank3]: return F.linear(input, weight, bias) | |
| [default5]:[rank5]: return self._call_impl(*args, **kwargs) | |
| [default1]:[rank1]: return F.linear(input, weight, bias) | |
| [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) | |
| [default3]:[rank3]: torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 8.00 GiB. GPU has a total capacity of 79.33 GiB of which 6.11 GiB is free. Including non-PyTorch memory, this process has 73.21 GiB memory in use. Of the allocated memory 55.52 GiB is allocated by PyTorch, and 5.95 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]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 637, in forward | |
| [default1]:[rank1]: torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 8.00 GiB. GPU has a total capacity of 79.33 GiB of which 6.11 GiB is free. Including non-PyTorch memory, this process has 73.21 GiB memory in use. Of the allocated memory 55.52 GiB is allocated by PyTorch, and 5.95 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]: 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 171, in forward | |
| [default5]:[rank5]: merged_states = self.gate_up_proj(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/parallel/tensor_parallel/nn.py", line 87, in forward | |
| [default5]:[rank5]: return column_linear( | |
| [default5]:[rank5]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/functional.py", line 359, in column_linear | |
| [default5]:[rank5]: return F.linear(input, weight, bias) | |
| [default5]:[rank5]: torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 8.00 GiB. GPU has a total capacity of 79.33 GiB of which 6.11 GiB is free. Including non-PyTorch memory, this process has 73.21 GiB memory in use. Of the allocated memory 55.52 GiB is allocated by PyTorch, and 5.95 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) | |
| W0703 21:43:57.232000 140123394443072 torch/distributed/elastic/multiprocessing/api.py:851] Sending process 393829 closing signal SIGTERM | |
| W0703 21:43:57.232000 140123394443072 torch/distributed/elastic/multiprocessing/api.py:851] Sending process 393830 closing signal SIGTERM | |
| W0703 21:43:57.233000 140123394443072 torch/distributed/elastic/multiprocessing/api.py:851] Sending process 393831 closing signal SIGTERM | |
| W0703 21:43:57.233000 140123394443072 torch/distributed/elastic/multiprocessing/api.py:851] Sending process 393832 closing signal SIGTERM | |
| W0703 21:43:57.233000 140123394443072 torch/distributed/elastic/multiprocessing/api.py:851] Sending process 393833 closing signal SIGTERM | |
| E0703 21:43:58.351000 140123394443072 torch/distributed/elastic/multiprocessing/api.py:826] failed (exitcode: 1) local_rank: 0 (pid: 393826) of binary: /fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/bin/python3.10 | |
| Traceback (most recent call last): | |
| File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/bin/torchrun", line 8, in <module> | |
| sys.exit(main()) | |
| File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/elastic/multiprocessing/errors/__init__.py", line 347, in wrapper | |
| return f(*args, **kwargs) | |
| File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/run.py", line 879, in main | |
| run(args) | |
| File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/run.py", line 870, in run | |
| elastic_launch( | |
| File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/launcher/api.py", line 132, in __call__ | |
| return launch_agent(self._config, self._entrypoint, list(args)) | |
| File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/launcher/api.py", line 263, in launch_agent | |
| raise ChildFailedError( | |
| torch.distributed.elastic.multiprocessing.errors.ChildFailedError: | |
| ============================================================ | |
| /fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/run_train.py FAILED | |
| ------------------------------------------------------------ | |
| Failures: | |
| [1]: | |
| time : 2024-07-03_21:43:57 | |
| host : ip-26-0-163-220.ec2.internal | |
| rank : 1 (local_rank: 1) | |
| exitcode : 1 (pid: 393827) | |
| error_file: <N/A> | |
| traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html | |
| [2]: | |
| time : 2024-07-03_21:43:57 | |
| host : ip-26-0-163-220.ec2.internal | |
| rank : 2 (local_rank: 2) | |
| exitcode : 1 (pid: 393828) | |
| error_file: <N/A> | |
| traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html | |
| ------------------------------------------------------------ | |
| Root Cause (first observed failure): | |
| [0]: | |
| time : 2024-07-03_21:43:57 | |
| host : ip-26-0-163-220.ec2.internal | |
| rank : 0 (local_rank: 0) | |
| exitcode : 1 (pid: 393826) | |
| error_file: <N/A> | |
| traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html | |
| ============================================================ | |
| srun: error: ip-26-0-163-220: 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. | |