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START TIME: Thu Jul 4 02:27:55 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
W0704 02:27:58.305000 140315521349440 torch/distributed/run.py:757]
W0704 02:27:58.305000 140315521349440 torch/distributed/run.py:757] *****************************************
W0704 02:27:58.305000 140315521349440 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.
W0704 02:27:58.305000 140315521349440 torch/distributed/run.py:757] *****************************************
[default0]:07/04/2024 02:28:14 [WARNING|DP=0|PP=0|TP=0|ip-26-0-171-88]: [Vocab Size Padding] Padded vocab (size: 50257) with 7 dummy tokens (new size: 50264)
[default0]:07/04/2024 02:28:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-88]: Config:
[default0]:07/04/2024 02:28:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-88]: Config(general=GeneralArgs(project='bench_cluster',
[default0]:07/04/2024 02:28:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-88]: run='%date_%jobid',
[default0]:07/04/2024 02:28:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-88]: seed=42,
[default0]:07/04/2024 02:28:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-88]: step=None,
[default0]:07/04/2024 02:28:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-88]: consumed_train_samples=None,
[default0]:07/04/2024 02:28:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-88]: benchmark_csv_path=None,
[default0]:07/04/2024 02:28:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-88]: ignore_sanity_checks=True),
[default0]:07/04/2024 02:28:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-88]: parallelism=ParallelismArgs(dp=1,
[default0]:07/04/2024 02:28:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-88]: pp=1,
[default0]:07/04/2024 02:28:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-88]: tp=8,
[default0]:07/04/2024 02:28:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-88]: pp_engine=<nanotron.parallel.pipeline_parallel.engine.OneForwardOneBackwardPipelineEngine object at 0x7f04e6fd4700>,
[default0]:07/04/2024 02:28:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-88]: tp_mode=<TensorParallelLinearMode.REDUCE_SCATTER: 2>,
[default0]:07/04/2024 02:28:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-88]: tp_linear_async_communication=False,
[default0]:07/04/2024 02:28:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-88]: expert_parallel_size=1),
[default0]:07/04/2024 02:28:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-88]: model=ModelArgs(model_config=LlamaConfig(bos_token_id=1,
[default0]:07/04/2024 02:28:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-88]: eos_token_id=2,
[default0]:07/04/2024 02:28:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-88]: hidden_act='silu',
[default0]:07/04/2024 02:28:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-88]: hidden_size=2048,
[default0]:07/04/2024 02:28:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-88]: initializer_range=0.02,
[default0]:07/04/2024 02:28:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-88]: intermediate_size=4096,
[default0]:07/04/2024 02:28:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-88]: is_llama_config=True,
[default0]:07/04/2024 02:28:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-88]: max_position_embeddings=4096,
[default0]:07/04/2024 02:28:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-88]: num_attention_heads=32,
[default0]:07/04/2024 02:28:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-88]: num_hidden_layers=24,
[default0]:07/04/2024 02:28:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-88]: num_key_value_heads=32,
[default0]:07/04/2024 02:28:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-88]: pad_token_id=None,
[default0]:07/04/2024 02:28:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-88]: pretraining_tp=1,
[default0]:07/04/2024 02:28:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-88]: rms_norm_eps=1e-05,
[default0]:07/04/2024 02:28:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-88]: rope_scaling=None,
[default0]:07/04/2024 02:28:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-88]: rope_theta=10000.0,
[default0]:07/04/2024 02:28:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-88]: tie_word_embeddings=True,
[default0]:07/04/2024 02:28:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-88]: use_cache=True,
[default0]:07/04/2024 02:28:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-88]: vocab_size=50264),
[default0]:07/04/2024 02:28:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-88]: init_method=RandomInit(std=0.025),
[default0]:07/04/2024 02:28:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-88]: dtype=torch.bfloat16,
[default0]:07/04/2024 02:28:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-88]: make_vocab_size_divisible_by=1,
[default0]:07/04/2024 02:28:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-88]: ddp_bucket_cap_mb=25),
[default0]:07/04/2024 02:28:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-88]: tokenizer=TokenizerArgs(tokenizer_name_or_path='openai-community/gpt2',
[default0]:07/04/2024 02:28:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-88]: tokenizer_revision=None,
[default0]:07/04/2024 02:28:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-88]: tokenizer_max_length=None),
[default0]:07/04/2024 02:28:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-88]: checkpoints=CheckpointsArgs(checkpoints_path=Path('/dev/null'),
[default0]:07/04/2024 02:28:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-88]: checkpoint_interval=100000,
[default0]:07/04/2024 02:28:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-88]: save_initial_state=False,
[default0]:07/04/2024 02:28:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-88]: resume_checkpoint_path=None,
[default0]:07/04/2024 02:28:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-88]: checkpoints_path_is_shared_file_system=False),
[default0]:07/04/2024 02:28:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-88]: logging=LoggingArgs(log_level='info',
[default0]:07/04/2024 02:28:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-88]: log_level_replica='info',
[default0]:07/04/2024 02:28:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-88]: iteration_step_info_interval=1),
[default0]:07/04/2024 02:28:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-88]: tokens=TokensArgs(sequence_length=4096,
[default0]:07/04/2024 02:28:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-88]: train_steps=20,
[default0]:07/04/2024 02:28:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-88]: micro_batch_size=256,
[default0]:07/04/2024 02:28:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-88]: batch_accumulation_per_replica=4,
[default0]:07/04/2024 02:28:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-88]: val_check_interval=-1,
[default0]:07/04/2024 02:28:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-88]: limit_val_batches=0,
[default0]:07/04/2024 02:28:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-88]: limit_test_batches=0),
[default0]:07/04/2024 02:28:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-88]: optimizer=OptimizerArgs(optimizer_factory=AdamWOptimizerArgs(adam_eps=1e-08,
[default0]:07/04/2024 02:28:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-88]: adam_beta1=0.9,
[default0]:07/04/2024 02:28:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-88]: adam_beta2=0.95,
[default0]:07/04/2024 02:28:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-88]: torch_adam_is_fused=True,
[default0]:07/04/2024 02:28:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-88]: name='adamW'),
[default0]:07/04/2024 02:28:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-88]: zero_stage=1,
[default0]:07/04/2024 02:28:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-88]: weight_decay=0.01,
[default0]:07/04/2024 02:28:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-88]: clip_grad=1.0,
[default0]:07/04/2024 02:28:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-88]: accumulate_grad_in_fp32=True,
[default0]:07/04/2024 02:28:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-88]: learning_rate_scheduler=LRSchedulerArgs(learning_rate=0.0001,
[default0]:07/04/2024 02:28:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-88]: lr_warmup_steps=1,
[default0]:07/04/2024 02:28:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-88]: lr_warmup_style='linear',
[default0]:07/04/2024 02:28:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-88]: lr_decay_style='linear',
[default0]:07/04/2024 02:28:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-88]: lr_decay_steps=19,
[default0]:07/04/2024 02:28:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-88]: lr_decay_starting_step=None,
[default0]:07/04/2024 02:28:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-88]: min_decay_lr=1e-05)),
[default0]:07/04/2024 02:28:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-88]: data_stages=[DatasetStageArgs(name='Training Stage',
[default0]:07/04/2024 02:28:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-88]: start_training_step=1,
[default0]:07/04/2024 02:28:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-88]: data=DataArgs(dataset=PretrainDatasetsArgs(hf_dataset_or_datasets='roneneldan/TinyStories',
[default0]:07/04/2024 02:28:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-88]: hf_dataset_splits='train',
[default0]:07/04/2024 02:28:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-88]: hf_dataset_config_name=None,
[default0]:07/04/2024 02:28:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-88]: dataset_processing_num_proc_per_process=64,
[default0]:07/04/2024 02:28:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-88]: dataset_overwrite_cache=False,
[default0]:07/04/2024 02:28:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-88]: text_column_name='text'),
[default0]:07/04/2024 02:28:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-88]: seed=42,
[default0]:07/04/2024 02:28:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-88]: num_loading_workers=0))],
[default0]:07/04/2024 02:28:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-88]: profiler=ProfilerArgs(profiler_export_path=Path('/fsx/ferdinandmom/ferdinand-hf/bench_cluster/results/llama-1B/8_GPUS/dp-1_tp-8_pp-1_mbz-256')),
[default0]:07/04/2024 02:28:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-88]: lighteval=None)
[default0]:07/04/2024 02:28:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-88]: Model Config:
[default0]:07/04/2024 02:28:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-88]: LlamaConfig(bos_token_id=1,
[default0]:07/04/2024 02:28:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-88]: eos_token_id=2,
[default0]:07/04/2024 02:28:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-88]: hidden_act='silu',
[default0]:07/04/2024 02:28:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-88]: hidden_size=2048,
[default0]:07/04/2024 02:28:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-88]: initializer_range=0.02,
[default0]:07/04/2024 02:28:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-88]: intermediate_size=4096,
[default0]:07/04/2024 02:28:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-88]: is_llama_config=True,
[default0]:07/04/2024 02:28:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-88]: max_position_embeddings=4096,
[default0]:07/04/2024 02:28:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-88]: num_attention_heads=32,
[default0]:07/04/2024 02:28:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-88]: num_hidden_layers=24,
[default0]:07/04/2024 02:28:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-88]: num_key_value_heads=32,
[default0]:07/04/2024 02:28:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-88]: pad_token_id=None,
[default0]:07/04/2024 02:28:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-88]: pretraining_tp=1,
[default0]:07/04/2024 02:28:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-88]: rms_norm_eps=1e-05,
[default0]:07/04/2024 02:28:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-88]: rope_scaling=None,
[default0]:07/04/2024 02:28:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-88]: rope_theta=10000.0,
[default0]:07/04/2024 02:28:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-88]: tie_word_embeddings=True,
[default0]:07/04/2024 02:28:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-88]: use_cache=True,
[default0]:07/04/2024 02:28:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-88]: vocab_size=50264)
[default0]:07/04/2024 02:28:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-88]: Building model..
[default0]:07/04/2024 02:28:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-88]: Setting PP block ranks...
[default4]:07/04/2024 02:28:30 [INFO|DP=0|PP=0|TP=4|ip-26-0-171-88]: Local number of parameters: 139M (264.73MiB)
[default5]:07/04/2024 02:28:30 [INFO|DP=0|PP=0|TP=5|ip-26-0-171-88]: Local number of parameters: 139M (264.73MiB)
[default5]:07/04/2024 02:28:30 [INFO|DP=0|PP=0|TP=5|ip-26-0-171-88]: [After model building] Memory usage: 290.76MiB. Peak allocated: 317.33MiB Peak reserved: 324.00MiB
[default5]:07/04/2024 02:28:30 [INFO|DP=0|PP=0|TP=5|ip-26-0-171-88]: No checkpoint path provided.
[default4]:07/04/2024 02:28:30 [INFO|DP=0|PP=0|TP=4|ip-26-0-171-88]: [After model building] Memory usage: 290.76MiB. Peak allocated: 317.33MiB Peak reserved: 324.00MiB
[default4]:07/04/2024 02:28:30 [INFO|DP=0|PP=0|TP=4|ip-26-0-171-88]: No checkpoint path provided.
[default0]:07/04/2024 02:28:30 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-88]: Total number of parameters: 1.11G (2117.88MiB)
[default0]:07/04/2024 02:28:30 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-88]: Local number of parameters: 139M (264.73MiB)
[default0]:07/04/2024 02:28:30 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-88]: [After model building] Memory usage: 290.76MiB. Peak allocated: 317.33MiB Peak reserved: 324.00MiB
[default0]:07/04/2024 02:28:30 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-88]: No checkpoint path provided.
[default0]:07/04/2024 02:28:30 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-88]: Parametrizing model parameters using StandardParametrizator
[default0]:07/04/2024 02:28:30 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-88]: [Optimizer Building] Using LearningRateForSP as learning rate
[default0]:07/04/2024 02:28:30 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-88]: [ZeRO sharding] Size of optimizer params per rank:
[default0]:07/04/2024 02:28:30 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-88]: [ZeRO sharding] DP Rank 0 has 139M out of 139M (100.00%) params' optimizer states
[default2]:07/04/2024 02:28:30 [INFO|DP=0|PP=0|TP=2|ip-26-0-171-88]: Local number of parameters: 139M (264.73MiB)
[default2]:07/04/2024 02:28:30 [INFO|DP=0|PP=0|TP=2|ip-26-0-171-88]: [After model building] Memory usage: 290.76MiB. Peak allocated: 317.33MiB Peak reserved: 324.00MiB
[default2]:07/04/2024 02:28:30 [INFO|DP=0|PP=0|TP=2|ip-26-0-171-88]: No checkpoint path provided.
[default7]:07/04/2024 02:28:30 [INFO|DP=0|PP=0|TP=7|ip-26-0-171-88]: Local number of parameters: 139M (264.73MiB)
[default7]:07/04/2024 02:28:30 [INFO|DP=0|PP=0|TP=7|ip-26-0-171-88]: [After model building] Memory usage: 290.76MiB. Peak allocated: 317.33MiB Peak reserved: 324.00MiB
[default7]:07/04/2024 02:28:30 [INFO|DP=0|PP=0|TP=7|ip-26-0-171-88]: No checkpoint path provided.
[default6]:07/04/2024 02:28:30 [INFO|DP=0|PP=0|TP=6|ip-26-0-171-88]: Local number of parameters: 139M (264.73MiB)
[default6]:07/04/2024 02:28:30 [INFO|DP=0|PP=0|TP=6|ip-26-0-171-88]: [After model building] Memory usage: 290.76MiB. Peak allocated: 317.33MiB Peak reserved: 324.00MiB
[default3]:07/04/2024 02:28:30 [INFO|DP=0|PP=0|TP=3|ip-26-0-171-88]: Local number of parameters: 139M (264.73MiB)
[default3]:07/04/2024 02:28:30 [INFO|DP=0|PP=0|TP=3|ip-26-0-171-88]: [After model building] Memory usage: 290.76MiB. Peak allocated: 317.33MiB Peak reserved: 324.00MiB
[default6]:07/04/2024 02:28:30 [INFO|DP=0|PP=0|TP=6|ip-26-0-171-88]: No checkpoint path provided.
[default3]:07/04/2024 02:28:30 [INFO|DP=0|PP=0|TP=3|ip-26-0-171-88]: No checkpoint path provided.
[default1]:07/04/2024 02:28:30 [INFO|DP=0|PP=0|TP=1|ip-26-0-171-88]: Local number of parameters: 139M (264.73MiB)
[default1]:07/04/2024 02:28:30 [INFO|DP=0|PP=0|TP=1|ip-26-0-171-88]: [After model building] Memory usage: 290.76MiB. Peak allocated: 317.33MiB Peak reserved: 324.00MiB
[default1]:07/04/2024 02:28:30 [INFO|DP=0|PP=0|TP=1|ip-26-0-171-88]: No checkpoint path provided.
[default0]:07/04/2024 02:28:31 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-88]: [Training Plan] Stage Training Stage has 19 remaining training steps and has consumed 0 samples
[default0]:07/04/2024 02:28:31 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-88]: Using `datasets` library
[default0]:07/04/2024 02:28:31 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-88]: Loading tokenizer from openai-community/gpt2 and transformers/hf_hub versions ('4.41.2', '0.23.4')
[default0]:07/04/2024 02:28:31 [WARNING|DP=0|PP=0|TP=0|ip-26-0-171-88]: Repo card metadata block was not found. Setting CardData to empty.
[default0]:Repo card metadata block was not found. Setting CardData to empty.
[default0]:07/04/2024 02:28:32 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-88]: [Training Plan] There are 1 training stages
[default0]:07/04/2024 02:28:32 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-88]: [Stage Training Stage] start from step 1
[default0]:07/04/2024 02:28:32 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-88]:
[default0]:07/04/2024 02:28:32 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-88]: [Start training] datetime: 2024-07-04 02:28:32.629932 | mbs: 256 | grad_accum: 4 | global_batch_size: 1024 | sequence_length: 4096 | train_steps: 20 | start_iteration_step: 0 | consumed_train_samples: 0
[default0]:07/04/2024 02:28:32 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-88]: Resuming training from stage Training Stage, it has trained for 0 samples and has 19 remaining train steps
[default0]:07/04/2024 02:28:32 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-88]: Memory usage: 1350.75MiB. Peak allocated 1350.76MiB. Peak reserved: 1384.00MiB
[default4]:07/04/2024 02:28:32 [WARNING|DP=0|PP=0|TP=4|ip-26-0-171-88]: Repo card metadata block was not found. Setting CardData to empty.
[default5]:07/04/2024 02:28:32 [WARNING|DP=0|PP=0|TP=5|ip-26-0-171-88]: Repo card metadata block was not found. Setting CardData to empty.
[default6]:07/04/2024 02:28:32 [WARNING|DP=0|PP=0|TP=6|ip-26-0-171-88]: Repo card metadata block was not found. Setting CardData to empty.
[default6]:Repo card metadata block was not found. Setting CardData to empty.
[default1]:07/04/2024 02:28:32 [WARNING|DP=0|PP=0|TP=1|ip-26-0-171-88]: Repo card metadata block was not found. Setting CardData to empty.
[default2]:07/04/2024 02:28:32 [WARNING|DP=0|PP=0|TP=2|ip-26-0-171-88]: Repo card metadata block was not found. Setting CardData to empty.
[default2]:Repo card metadata block was not found. Setting CardData to empty.
[default1]:Repo card metadata block was not found. Setting CardData to empty.
[default5]:Repo card metadata block was not found. Setting CardData to empty.
[default4]:Repo card metadata block was not found. Setting CardData to empty.
[default7]:07/04/2024 02:28:32 [WARNING|DP=0|PP=0|TP=7|ip-26-0-171-88]: Repo card metadata block was not found. Setting CardData to empty.
[default3]:07/04/2024 02:28:32 [WARNING|DP=0|PP=0|TP=3|ip-26-0-171-88]: 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.
[default2]:[rank2]: Traceback (most recent call last):
[default0]:[rank0]: Traceback (most recent call last):
[default0]:[rank0]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/run_train.py", line 237, in <module>
[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
[default0]:[rank0]: trainer.train(dataloader)
[default6]:[rank6]: Traceback (most recent call last):
[default6]:[rank6]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/run_train.py", line 237, in <module>
[default2]:[rank2]: 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 429, in train
[default6]:[rank6]: trainer.train(dataloader)
[default2]:[rank2]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 462, in training_step
[default0]:[rank0]: outputs, loss_avg = self.training_step(dataloader=self.current_dataloader)
[default2]:[rank2]: outputs = self.pipeline_engine.train_batch_iter(
[default6]:[rank6]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 429, in train
[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(
[default6]:[rank6]: outputs, loss_avg = self.training_step(dataloader=self.current_dataloader)
[default0]:[rank0]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 278, in 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
[default6]:[rank6]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 462, in training_step
[default0]:[rank0]: output = self.forward(context=context, state=state, micro_batch=micro_batch, model=model)
[default6]:[rank6]: outputs = self.pipeline_engine.train_batch_iter(
[default2]:[rank2]: 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 278, in train_batch_iter
[default0]:[rank0]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 44, in forward
[default6]:[rank6]: output = self.forward(context=context, state=state, micro_batch=micro_batch, model=model)
[default0]:[rank0]: output = model(**micro_batch)
[default6]:[rank6]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 44, in forward
[default2]:[rank2]: 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)
[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)
[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]: 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]: 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 1541, in _call_impl
[default0]:[rank0]: 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
[default0]:[rank0]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 891, in forward
[default6]:[rank6]: return forward_call(*args, **kwargs)
[default2]:[rank2]: return self._call_impl(*args, **kwargs)
[default0]:[rank0]: 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
[default6]:[rank6]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 891, in forward
[default2]:[rank2]: return forward_call(*args, **kwargs)
[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]: 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/models/llama.py", line 891, in forward
[default0]:[rank0]: return self._call_impl(*args, **kwargs)
[default6]:[rank6]: return self._call_impl(*args, **kwargs)
[default2]:[rank2]: 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 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
[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
[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
[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
[default0]:[rank0]: return forward_call(*args, **kwargs)
[default2]:[rank2]: return forward_call(*args, **kwargs)
[default6]:[rank6]: 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 764, in forward
[default2]:[rank2]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 764, in forward
[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.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
[default2]:[rank2]: return self.forward_with_hidden_states(input_ids=input_ids, input_mask=input_mask)[0]
[default0]:[rank0]: hidden_encoder_states = encoder_block(**hidden_encoder_states)
[default6]:[rank6]: 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
[default2]:[rank2]: 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)
[default2]:[rank2]: 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 1541, in _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
[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)
[default0]:[rank0]: return forward_call(*args, **kwargs)
[default6]:[rank6]: return self._call_impl(*args, **kwargs)
[default0]:[rank0]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/block.py", line 151, in forward
[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]: 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 1541, in _call_impl
[default6]:[rank6]: return forward_call(*args, **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
[default6]:[rank6]: 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)
[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
[default2]:[rank2]: 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]: 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
[default2]:[rank2]: output = self.pp_block(**new_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
[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
[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)
[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
[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
[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]: hidden_states = self.mlp(hidden_states=hidden_states)["hidden_states"]
[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
[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 forward_call(*args, **kwargs)
[default7]:[rank7]: Traceback (most recent call last):
[default7]:[rank7]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/run_train.py", line 237, in <module>
[default0]:[rank0]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 171, in forward
[default6]:[rank6]: return self._call_impl(*args, **kwargs)
[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
[default0]:[rank0]: merged_states = self.gate_up_proj(hidden_states)
[default7]:[rank7]: trainer.train(dataloader)
[default2]:[rank2]: 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 1532, in _wrapped_call_impl
[default0]:[rank0]: return self._call_impl(*args, **kwargs)
[default7]:[rank7]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 429, in train
[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
[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
[default7]:[rank7]: outputs, loss_avg = self.training_step(dataloader=self.current_dataloader)
[default0]:[rank0]: return forward_call(*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
[default2]:[rank2]: return forward_call(*args, **kwargs)
[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(
[default6]:[rank6]: return forward_call(*args, **kwargs)
[default2]:[rank2]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 171, in forward
[default7]:[rank7]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 278, in train_batch_iter
[default6]:[rank6]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 171, in forward
[default0]:[rank0]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/nn.py", line 87, in forward
[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
[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
[default6]:[rank6]: merged_states = self.gate_up_proj(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)
[default2]:[rank2]: merged_states = self.gate_up_proj(hidden_states)
[default0]:[rank0]: return F.linear(input, weight, bias)
[default0]:[rank0]: torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 2.00 GiB. GPU
[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
[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
[default7]:[rank7]: 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)
[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
[default2]:[rank2]: return self._call_impl(*args, **kwargs)
[default7]:[rank7]: 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
[default7]:[rank7]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 891, in forward
[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
[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
[default6]:[rank6]: return column_linear(
[default2]:[rank2]: return forward_call(*args, **kwargs)
[default7]:[rank7]: return self._call_impl(*args, **kwargs)
[default2]:[rank2]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/nn.py", line 87, in forward
[default2]:[rank2]: return column_linear(
[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
[default6]:[rank6]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/functional.py", line 359, in column_linear
[default2]:[rank2]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/functional.py", line 359, in column_linear
[default2]:[rank2]: return F.linear(input, weight, bias)
[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
[default6]:[rank6]: return 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 1.71 GiB is free. Including non-PyTorch memory, this process has 77.61 GiB memory in use. Of the allocated memory 64.45 GiB is allocated by PyTorch, and 1.43 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]:[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
[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 1.71 GiB is free. Including non-PyTorch memory, this process has 77.61 GiB memory in use. Of the allocated memory 64.45 GiB is allocated by PyTorch, and 1.43 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]:[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/models/llama.py", line 128, in forward
[default7]:[rank7]: return self.act(gate_states) * up_states
[default7]:[rank7]: torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 1024.00 MiB. GPU has a total capacity of 79.33 GiB of which 421.94 MiB is free. Including non-PyTorch memory, this process has 78.91 GiB memory in use. Of the allocated memory 67.45 GiB is allocated by PyTorch, and 436.23 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation. See documentation for Memory Management (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables)
[default1]:[rank1]: Traceback (most recent call last):
[default1]:[rank1]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/run_train.py", line 237, in <module>
[default1]:[rank1]: trainer.train(dataloader)
[default1]:[rank1]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 429, in train
[default1]:[rank1]: outputs, loss_avg = self.training_step(dataloader=self.current_dataloader)
[default1]:[rank1]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 462, in training_step
[default1]:[rank1]: outputs = self.pipeline_engine.train_batch_iter(
[default1]:[rank1]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 278, in train_batch_iter
[default1]:[rank1]: output = self.forward(context=context, state=state, micro_batch=micro_batch, model=model)
[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
[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
[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
[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
[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)
[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
[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 171, in forward
[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
[default1]:[rank1]: merged_states = self.gate_up_proj(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
[default3]:[rank3]: output = model(**micro_batch)
[default1]:[rank1]: 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)
[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
[default1]:[rank1]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/nn.py", line 87, in forward
[default1]:[rank1]: return column_linear(
[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
[default1]:[rank1]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/functional.py", line 359, in column_linear
[default3]:[rank3]: return self._call_impl(*args, **kwargs)
[default1]:[rank1]: return F.linear(input, weight, bias)
[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]: torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 2.00 GiB. GPU has a total capacity of 79.33 GiB of which 1.71 GiB is free. Including non-PyTorch memory, this process has 77.61 GiB memory in use. Of the allocated memory 64.45 GiB is allocated by PyTorch, and 1.43 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)
[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 171, in forward
[default3]:[rank3]: merged_states = self.gate_up_proj(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/parallel/tensor_parallel/nn.py", line 87, in forward
[default3]:[rank3]: return column_linear(
[default3]:[rank3]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/functional.py", line 359, in column_linear
[default3]:[rank3]: return F.linear(input, weight, bias)
[default3]:[rank3]: torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 2.00 GiB. GPU has a total capacity of 79.33 GiB of which 1.71 GiB is free. Including non-PyTorch memory, this process has 77.61 GiB memory in use. Of the allocated memory 64.45 GiB is allocated by PyTorch, and 1.43 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)
[default4]:[rank4]: Traceback (most recent call last):
[default4]:[rank4]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/run_train.py", line 237, in <module>
[default5]:[rank5]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/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
[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)
[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)
[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)
[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
[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)
[default5]:[rank5]: return forward_call(*args, **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 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)
[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]
[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
[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)
[default4]:[rank4]: return self.forward_with_hidden_states(input_ids=input_ids, input_mask=input_mask)[0]
[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
[default4]:[rank4]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 780, in forward_with_hidden_states
[default5]:[rank5]: output = self.pp_block(**new_kwargs)
[default4]:[rank4]: 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)
[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 171, in forward
[default4]:[rank4]: 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 1532, in _wrapped_call_impl
[default4]:[rank4]: 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
[default4]:[rank4]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
[default4]:[rank4]: return forward_call(*args, **kwargs)
[default4]:[rank4]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/nn.py", line 87, in forward
[default4]:[rank4]: return column_linear(
[default4]:[rank4]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/functional.py", line 359, in column_linear
[default5]:[rank5]: return forward_call(*args, **kwargs)
[default4]:[rank4]: return F.linear(input, weight, bias)
[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)
[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 1.71 GiB is free. Including non-PyTorch memory, this process has 77.61 GiB memory in use. Of the allocated memory 64.45 GiB is allocated by PyTorch, and 1.43 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/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 2.00 GiB. GPU has a total capacity of 79.33 GiB of which 1.71 GiB is free. Including non-PyTorch memory, this process has 77.61 GiB memory in use. Of the allocated memory 64.45 GiB is allocated by PyTorch, and 1.43 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)
E0704 02:28:53.525000 140315521349440 torch/distributed/elastic/multiprocessing/api.py:826] failed (exitcode: 1) local_rank: 0 (pid: 1144719) 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 : 2024-07-04_02:28:53
host : ip-26-0-171-88.ec2.internal
rank : 1 (local_rank: 1)
exitcode : 1 (pid: 1144720)
error_file: <N/A>
traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html
[2]:
time : 2024-07-04_02:28:53
host : ip-26-0-171-88.ec2.internal
rank : 2 (local_rank: 2)
exitcode : 1 (pid: 1144721)
error_file: <N/A>
traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html
[3]:
time : 2024-07-04_02:28:53
host : ip-26-0-171-88.ec2.internal
rank : 3 (local_rank: 3)
exitcode : 1 (pid: 1144722)
error_file: <N/A>
traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html
[4]:
time : 2024-07-04_02:28:53
host : ip-26-0-171-88.ec2.internal
rank : 4 (local_rank: 4)
exitcode : 1 (pid: 1144723)
error_file: <N/A>
traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html
[5]:
time : 2024-07-04_02:28:53
host : ip-26-0-171-88.ec2.internal
rank : 5 (local_rank: 5)
exitcode : 1 (pid: 1144724)
error_file: <N/A>
traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html
[6]:
time : 2024-07-04_02:28:53
host : ip-26-0-171-88.ec2.internal
rank : 6 (local_rank: 6)
exitcode : 1 (pid: 1144725)
error_file: <N/A>
traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html
[7]:
time : 2024-07-04_02:28:53
host : ip-26-0-171-88.ec2.internal
rank : 7 (local_rank: 7)
exitcode : 1 (pid: 1144726)
error_file: <N/A>
traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html
------------------------------------------------------------
Root Cause (first observed failure):
[0]:
time : 2024-07-04_02:28:53
host : ip-26-0-171-88.ec2.internal
rank : 0 (local_rank: 0)
exitcode : 1 (pid: 1144719)
error_file: <N/A>
traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html
============================================================
srun: error: ip-26-0-171-88: 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.
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