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========================
START TIME: Tue Jul 2 19:20:40 UTC 2024
python3 version = Python 3.10.14
========================
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Already on 'bench_cluster'
M examples/config_tiny_llama.py
M examples/config_tiny_llama.yaml
M examples/train_tiny_llama.sh
M src/nanotron/models/llama.py
M src/nanotron/trainer.py
Your branch is up to date with 'origin/bench_cluster'.
Job status: RUNNING
W0702 19:20:43.905000 140334325937984 torch/distributed/run.py:757]
W0702 19:20:43.905000 140334325937984 torch/distributed/run.py:757] *****************************************
W0702 19:20:43.905000 140334325937984 torch/distributed/run.py:757] Setting OMP_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed.
W0702 19:20:43.905000 140334325937984 torch/distributed/run.py:757] *****************************************
W0702 19:20:48.109000 140360651392832 torch/distributed/run.py:757]
W0702 19:20:48.109000 140360651392832 torch/distributed/run.py:757] *****************************************
W0702 19:20:48.109000 140360651392832 torch/distributed/run.py:757] Setting OMP_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed.
W0702 19:20:48.109000 140360651392832 torch/distributed/run.py:757] *****************************************
[default0]:07/02/2024 19:21:10 [WARNING|DP=0|PP=0|TP=0|ip-26-0-171-62]: [Vocab Size Padding] Padded vocab (size: 50257) with 1 dummy tokens (new size: 50258)
[default0]:07/02/2024 19:21:10 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-62]: Config:
[default0]:07/02/2024 19:21:10 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-62]: Config(general=GeneralArgs(project='bench_cluster',
[default0]:07/02/2024 19:21:10 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-62]: run='%date_%jobid',
[default0]:07/02/2024 19:21:10 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-62]: seed=42,
[default0]:07/02/2024 19:21:10 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-62]: step=None,
[default0]:07/02/2024 19:21:10 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-62]: consumed_train_samples=None,
[default0]:07/02/2024 19:21:10 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-62]: benchmark_csv_path=None,
[default0]:07/02/2024 19:21:10 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-62]: ignore_sanity_checks=True),
[default0]:07/02/2024 19:21:10 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-62]: parallelism=ParallelismArgs(dp=2,
[default0]:07/02/2024 19:21:10 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-62]: pp=4,
[default0]:07/02/2024 19:21:10 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-62]: tp=2,
[default0]:07/02/2024 19:21:10 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-62]: pp_engine=<nanotron.parallel.pipeline_parallel.engine.OneForwardOneBackwardPipelineEngine object at 0x7ff744214910>,
[default0]:07/02/2024 19:21:10 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-62]: tp_mode=<TensorParallelLinearMode.REDUCE_SCATTER: 2>,
[default0]:07/02/2024 19:21:10 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-62]: tp_linear_async_communication=False,
[default0]:07/02/2024 19:21:10 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-62]: expert_parallel_size=1),
[default0]:07/02/2024 19:21:10 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-62]: model=ModelArgs(model_config=LlamaConfig(bos_token_id=1,
[default0]:07/02/2024 19:21:10 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-62]: eos_token_id=2,
[default0]:07/02/2024 19:21:10 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-62]: hidden_act='silu',
[default0]:07/02/2024 19:21:10 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-62]: hidden_size=2048,
[default0]:07/02/2024 19:21:10 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-62]: initializer_range=0.02,
[default0]:07/02/2024 19:21:10 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-62]: intermediate_size=4096,
[default0]:07/02/2024 19:21:10 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-62]: is_llama_config=True,
[default0]:07/02/2024 19:21:10 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-62]: max_position_embeddings=4096,
[default0]:07/02/2024 19:21:10 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-62]: num_attention_heads=32,
[default0]:07/02/2024 19:21:10 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-62]: num_hidden_layers=24,
[default0]:07/02/2024 19:21:10 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-62]: num_key_value_heads=32,
[default0]:07/02/2024 19:21:10 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-62]: pad_token_id=None,
[default0]:07/02/2024 19:21:10 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-62]: pretraining_tp=1,
[default0]:07/02/2024 19:21:10 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-62]: rms_norm_eps=1e-05,
[default0]:07/02/2024 19:21:10 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-62]: rope_scaling=None,
[default0]:07/02/2024 19:21:10 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-62]: rope_theta=10000.0,
[default0]:07/02/2024 19:21:10 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-62]: tie_word_embeddings=True,
[default0]:07/02/2024 19:21:10 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-62]: use_cache=True,
[default0]:07/02/2024 19:21:10 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-62]: vocab_size=50258),
[default0]:07/02/2024 19:21:10 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-62]: init_method=RandomInit(std=0.025),
[default0]:07/02/2024 19:21:10 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-62]: dtype=torch.bfloat16,
[default0]:07/02/2024 19:21:10 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-62]: make_vocab_size_divisible_by=1,
[default0]:07/02/2024 19:21:10 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-62]: ddp_bucket_cap_mb=25),
[default0]:07/02/2024 19:21:10 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-62]: tokenizer=TokenizerArgs(tokenizer_name_or_path='openai-community/gpt2',
[default0]:07/02/2024 19:21:10 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-62]: tokenizer_revision=None,
[default0]:07/02/2024 19:21:10 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-62]: tokenizer_max_length=None),
[default0]:07/02/2024 19:21:10 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-62]: checkpoints=CheckpointsArgs(checkpoints_path=Path('/dev/null'),
[default0]:07/02/2024 19:21:10 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-62]: checkpoint_interval=100000,
[default0]:07/02/2024 19:21:10 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-62]: save_initial_state=False,
[default0]:07/02/2024 19:21:10 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-62]: resume_checkpoint_path=None,
[default0]:07/02/2024 19:21:10 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-62]: checkpoints_path_is_shared_file_system=False),
[default0]:07/02/2024 19:21:10 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-62]: logging=LoggingArgs(log_level='info',
[default0]:07/02/2024 19:21:10 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-62]: log_level_replica='info',
[default0]:07/02/2024 19:21:10 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-62]: iteration_step_info_interval=1),
[default0]:07/02/2024 19:21:10 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-62]: tokens=TokensArgs(sequence_length=4096,
[default0]:07/02/2024 19:21:10 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-62]: train_steps=20,
[default0]:07/02/2024 19:21:10 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-62]: micro_batch_size=16,
[default0]:07/02/2024 19:21:10 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-62]: batch_accumulation_per_replica=32,
[default0]:07/02/2024 19:21:10 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-62]: val_check_interval=-1,
[default0]:07/02/2024 19:21:10 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-62]: limit_val_batches=0,
[default0]:07/02/2024 19:21:10 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-62]: limit_test_batches=0),
[default0]:07/02/2024 19:21:10 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-62]: optimizer=OptimizerArgs(optimizer_factory=AdamWOptimizerArgs(adam_eps=1e-08,
[default0]:07/02/2024 19:21:10 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-62]: adam_beta1=0.9,
[default0]:07/02/2024 19:21:10 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-62]: adam_beta2=0.95,
[default0]:07/02/2024 19:21:10 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-62]: torch_adam_is_fused=True,
[default0]:07/02/2024 19:21:10 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-62]: name='adamW'),
[default0]:07/02/2024 19:21:10 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-62]: zero_stage=1,
[default0]:07/02/2024 19:21:10 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-62]: weight_decay=0.01,
[default0]:07/02/2024 19:21:10 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-62]: clip_grad=1.0,
[default0]:07/02/2024 19:21:10 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-62]: accumulate_grad_in_fp32=True,
[default0]:07/02/2024 19:21:10 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-62]: learning_rate_scheduler=LRSchedulerArgs(learning_rate=0.0001,
[default0]:07/02/2024 19:21:10 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-62]: lr_warmup_steps=1,
[default0]:07/02/2024 19:21:10 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-62]: lr_warmup_style='linear',
[default0]:07/02/2024 19:21:10 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-62]: lr_decay_style='linear',
[default0]:07/02/2024 19:21:10 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-62]: lr_decay_steps=19,
[default0]:07/02/2024 19:21:10 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-62]: lr_decay_starting_step=None,
[default0]:07/02/2024 19:21:10 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-62]: min_decay_lr=1e-05)),
[default0]:07/02/2024 19:21:10 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-62]: data_stages=[DatasetStageArgs(name='Training Stage',
[default0]:07/02/2024 19:21:10 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-62]: start_training_step=1,
[default0]:07/02/2024 19:21:10 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-62]: data=DataArgs(dataset=PretrainDatasetsArgs(hf_dataset_or_datasets='roneneldan/TinyStories',
[default0]:07/02/2024 19:21:10 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-62]: hf_dataset_splits='train',
[default0]:07/02/2024 19:21:10 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-62]: hf_dataset_config_name=None,
[default0]:07/02/2024 19:21:10 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-62]: dataset_processing_num_proc_per_process=64,
[default0]:07/02/2024 19:21:10 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-62]: dataset_overwrite_cache=False,
[default0]:07/02/2024 19:21:10 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-62]: text_column_name='text'),
[default0]:07/02/2024 19:21:10 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-62]: seed=42,
[default0]:07/02/2024 19:21:10 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-62]: num_loading_workers=32))],
[default0]:07/02/2024 19:21:10 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-62]: profiler=ProfilerArgs(profiler_export_path=Path('/fsx/ferdinandmom/ferdinand-hf/bench_cluster/results/llama-1B/16_GPUS/dp-2_tp-2_pp-4_mbz-16')),
[default0]:07/02/2024 19:21:10 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-62]: lighteval=None)
[default0]:07/02/2024 19:21:10 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-62]: Model Config:
[default0]:07/02/2024 19:21:10 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-62]: LlamaConfig(bos_token_id=1,
[default0]:07/02/2024 19:21:10 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-62]: eos_token_id=2,
[default0]:07/02/2024 19:21:10 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-62]: hidden_act='silu',
[default0]:07/02/2024 19:21:10 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-62]: hidden_size=2048,
[default0]:07/02/2024 19:21:10 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-62]: initializer_range=0.02,
[default0]:07/02/2024 19:21:10 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-62]: intermediate_size=4096,
[default0]:07/02/2024 19:21:10 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-62]: is_llama_config=True,
[default0]:07/02/2024 19:21:10 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-62]: max_position_embeddings=4096,
[default0]:07/02/2024 19:21:10 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-62]: num_attention_heads=32,
[default0]:07/02/2024 19:21:10 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-62]: num_hidden_layers=24,
[default0]:07/02/2024 19:21:10 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-62]: num_key_value_heads=32,
[default0]:07/02/2024 19:21:10 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-62]: pad_token_id=None,
[default0]:07/02/2024 19:21:10 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-62]: pretraining_tp=1,
[default0]:07/02/2024 19:21:10 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-62]: rms_norm_eps=1e-05,
[default0]:07/02/2024 19:21:10 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-62]: rope_scaling=None,
[default0]:07/02/2024 19:21:10 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-62]: rope_theta=10000.0,
[default0]:07/02/2024 19:21:10 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-62]: tie_word_embeddings=True,
[default0]:07/02/2024 19:21:10 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-62]: use_cache=True,
[default0]:07/02/2024 19:21:10 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-62]: vocab_size=50258)
[default0]:07/02/2024 19:21:10 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-62]: Building model..
[default0]:07/02/2024 19:21:10 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-62]: Setting PP block ranks...
[default6]:07/02/2024 19:21:23 [INFO|DP=1|PP=3|TP=0|ip-26-0-171-88]: No checkpoint path provided.
[default2]:07/02/2024 19:21:23 [INFO|DP=1|PP=2|TP=0|ip-26-0-171-88]: No checkpoint path provided.
[default7]:07/02/2024 19:21:23 [INFO|DP=1|PP=3|TP=1|ip-26-0-171-88]: No checkpoint path provided.
[default3]:07/02/2024 19:21:23 [INFO|DP=1|PP=2|TP=1|ip-26-0-171-88]: No checkpoint path provided.
[default3]:07/02/2024 19:21:23 [INFO|DP=1|PP=0|TP=1|ip-26-0-171-62]: No checkpoint path provided.
[default2]:07/02/2024 19:21:23 [INFO|DP=1|PP=0|TP=0|ip-26-0-171-62]: No checkpoint path provided.
[default6]:07/02/2024 19:21:23 [INFO|DP=1|PP=1|TP=0|ip-26-0-171-62]: No checkpoint path provided.
[default7]:07/02/2024 19:21:23 [INFO|DP=1|PP=1|TP=1|ip-26-0-171-62]: No checkpoint path provided.
[default5]:07/02/2024 19:21:23 [INFO|DP=0|PP=3|TP=1|ip-26-0-171-88]: Local number of parameters: 135M (258.20MiB)
[default5]:07/02/2024 19:21:23 [INFO|DP=0|PP=3|TP=1|ip-26-0-171-88]: [After model building] Memory usage: 262.21MiB. Peak allocated: 264.24MiB Peak reserved: 280.00MiB
[default5]:07/02/2024 19:21:23 [INFO|DP=0|PP=3|TP=1|ip-26-0-171-88]: No checkpoint path provided.
[default0]:07/02/2024 19:21:23 [INFO|DP=0|PP=2|TP=0|ip-26-0-171-88]: Local number of parameters: 126M (240.05MiB)
[default0]:07/02/2024 19:21:23 [INFO|DP=0|PP=2|TP=0|ip-26-0-171-88]: [After model building] Memory usage: 246.06MiB. Peak allocated: 248.09MiB Peak reserved: 262.00MiB
[default0]:07/02/2024 19:21:23 [INFO|DP=0|PP=2|TP=0|ip-26-0-171-88]: No checkpoint path provided.
[default1]:07/02/2024 19:21:23 [INFO|DP=0|PP=2|TP=1|ip-26-0-171-88]: Local number of parameters: 126M (240.05MiB)
[default1]:07/02/2024 19:21:23 [INFO|DP=0|PP=2|TP=1|ip-26-0-171-88]: [After model building] Memory usage: 246.06MiB. Peak allocated: 248.09MiB Peak reserved: 262.00MiB
[default1]:07/02/2024 19:21:23 [INFO|DP=0|PP=2|TP=1|ip-26-0-171-88]: No checkpoint path provided.
[default4]:07/02/2024 19:21:23 [INFO|DP=0|PP=3|TP=0|ip-26-0-171-88]: Local number of parameters: 135M (258.20MiB)
[default4]:07/02/2024 19:21:23 [INFO|DP=0|PP=3|TP=0|ip-26-0-171-88]: [After model building] Memory usage: 262.21MiB. Peak allocated: 264.24MiB Peak reserved: 280.00MiB
[default4]:07/02/2024 19:21:23 [INFO|DP=0|PP=3|TP=0|ip-26-0-171-88]: No checkpoint path provided.
[default5]:07/02/2024 19:21:23 [INFO|DP=0|PP=1|TP=1|ip-26-0-171-62]: Local number of parameters: 147M (280.05MiB)
[default5]:07/02/2024 19:21:23 [INFO|DP=0|PP=1|TP=1|ip-26-0-171-62]: [After model building] Memory usage: 287.07MiB. Peak allocated: 289.10MiB Peak reserved: 302.00MiB
[default5]:07/02/2024 19:21:23 [INFO|DP=0|PP=1|TP=1|ip-26-0-171-62]: No checkpoint path provided.
[default1]:07/02/2024 19:21:23 [INFO|DP=0|PP=0|TP=1|ip-26-0-171-62]: Local number of parameters: 198M (378.21MiB)
[default1]:07/02/2024 19:21:23 [INFO|DP=0|PP=0|TP=1|ip-26-0-171-62]: [After model building] Memory usage: 385.23MiB. Peak allocated: 387.26MiB Peak reserved: 402.00MiB
[default1]:07/02/2024 19:21:23 [INFO|DP=0|PP=0|TP=1|ip-26-0-171-62]: No checkpoint path provided.
[default4]:07/02/2024 19:21:23 [INFO|DP=0|PP=1|TP=0|ip-26-0-171-62]: Local number of parameters: 147M (280.05MiB)
[default4]:07/02/2024 19:21:23 [INFO|DP=0|PP=1|TP=0|ip-26-0-171-62]: [After model building] Memory usage: 287.07MiB. Peak allocated: 289.10MiB Peak reserved: 302.00MiB
[default4]:07/02/2024 19:21:23 [INFO|DP=0|PP=1|TP=0|ip-26-0-171-62]: No checkpoint path provided.
[default0]:07/02/2024 19:21:23 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-62]: Total number of parameters: 1.21G (2313.02MiB)
[default0]:07/02/2024 19:21:23 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-62]: Local number of parameters: 198M (378.21MiB)
[default0]:07/02/2024 19:21:23 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-62]: [After model building] Memory usage: 385.23MiB. Peak allocated: 387.26MiB Peak reserved: 402.00MiB
[default0]:07/02/2024 19:21:23 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-62]: No checkpoint path provided.
[default0]:07/02/2024 19:21:23 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-62]: Parametrizing model parameters using StandardParametrizator
[default0]:07/02/2024 19:21:26 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-62]: [Optimizer Building] Using LearningRateForSP as learning rate
[default0]:07/02/2024 19:21:26 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-62]: [ZeRO sharding] Size of optimizer params per rank:
[default0]:07/02/2024 19:21:26 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-62]: [ZeRO sharding] DP Rank 0 has 99.1M out of 198M (50.00%) params' optimizer states
[default0]:07/02/2024 19:21:26 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-62]: [ZeRO sharding] DP Rank 1 has 99.1M out of 198M (50.00%) params' optimizer states
[default0]:07/02/2024 19:21:27 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-62]: [Training Plan] Stage Training Stage has 19 remaining training steps and has consumed 0 samples
[default0]:07/02/2024 19:21:27 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-62]: Using `datasets` library
[default0]:07/02/2024 19:21:27 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-62]: Loading tokenizer from openai-community/gpt2 and transformers/hf_hub versions ('4.41.2', '0.23.4')
[default0]:Repo card metadata block was not found. Setting CardData to empty.
[default0]:07/02/2024 19:21:27 [WARNING|DP=0|PP=0|TP=0|ip-26-0-171-62]: Repo card metadata block was not found. Setting CardData to empty.
[default0]:07/02/2024 19:21:28 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-62]: [Training Plan] There are 1 training stages
[default0]:07/02/2024 19:21:28 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-62]: [Stage Training Stage] start from step 1
[default0]:07/02/2024 19:21:28 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-62]:
[default0]:07/02/2024 19:21:28 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-62]: [Start training] datetime: 2024-07-02 19:21:28.676020 | mbs: 16 | grad_accum: 32 | global_batch_size: 1024 | sequence_length: 4096 | train_steps: 20 | start_iteration_step: 0 | consumed_train_samples: 0
[default0]:07/02/2024 19:21:28 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-62]: Resuming training from stage Training Stage, it has trained for 0 samples and has 19 remaining train steps
[default0]:07/02/2024 19:21:28 [INFO|DP=0|PP=0|TP=0|ip-26-0-171-62]: Memory usage: 1519.87MiB. Peak allocated 1519.87MiB. Peak reserved: 1540.00MiB
[default3]:Repo card metadata block was not found. Setting CardData to empty.
[default1]:07/02/2024 19:21:28 [WARNING|DP=0|PP=2|TP=1|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.
[default2]:07/02/2024 19:21:28 [WARNING|DP=1|PP=2|TP=0|ip-26-0-171-88]: Repo card metadata block was not found. Setting CardData to empty.
[default5]:07/02/2024 19:21:28 [WARNING|DP=0|PP=3|TP=1|ip-26-0-171-88]: Repo card metadata block was not found. Setting CardData to empty.
[default0]:07/02/2024 19:21:28 [WARNING|DP=0|PP=2|TP=0|ip-26-0-171-88]: Repo card metadata block was not found. Setting CardData to empty.
[default7]:07/02/2024 19:21:28 [WARNING|DP=1|PP=3|TP=1|ip-26-0-171-88]: Repo card metadata block was not found. Setting CardData to empty.
[default6]:07/02/2024 19:21:28 [WARNING|DP=1|PP=3|TP=0|ip-26-0-171-88]: Repo card metadata block was not found. Setting CardData to empty.
[default3]:07/02/2024 19:21:28 [WARNING|DP=1|PP=2|TP=1|ip-26-0-171-88]: Repo card metadata block was not found. Setting CardData to empty.
[default4]:07/02/2024 19:21:28 [WARNING|DP=0|PP=3|TP=0|ip-26-0-171-88]: Repo card metadata block was not found. Setting CardData to empty.
[default4]:Repo card metadata block was not found. Setting CardData to empty.
[default0]:Repo card metadata block was not found. Setting CardData to empty.
[default1]:Repo card metadata block was not found. Setting CardData to empty.
[default5]:Repo card metadata block was not found. Setting CardData to empty.
[default2]:Repo card metadata block was not found. Setting CardData to empty.
[default3]:07/02/2024 19:21:28 [WARNING|DP=1|PP=0|TP=1|ip-26-0-171-62]: Repo card metadata block was not found. Setting CardData to empty.
[default5]:07/02/2024 19:21:28 [WARNING|DP=0|PP=1|TP=1|ip-26-0-171-62]: Repo card metadata block was not found. Setting CardData to empty.
[default2]:07/02/2024 19:21:28 [WARNING|DP=1|PP=0|TP=0|ip-26-0-171-62]: Repo card metadata block was not found. Setting CardData to empty.
[default1]:07/02/2024 19:21:28 [WARNING|DP=0|PP=0|TP=1|ip-26-0-171-62]: Repo card metadata block was not found. Setting CardData to empty.
[default6]:07/02/2024 19:21:28 [WARNING|DP=1|PP=1|TP=0|ip-26-0-171-62]: Repo card metadata block was not found. Setting CardData to empty.
[default4]:07/02/2024 19:21:28 [WARNING|DP=0|PP=1|TP=0|ip-26-0-171-62]: Repo card metadata block was not found. Setting CardData to empty.
[default7]:07/02/2024 19:21:28 [WARNING|DP=1|PP=1|TP=1|ip-26-0-171-62]: Repo card metadata block was not found. Setting CardData to empty.
[default7]: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.
[default3]:Repo card metadata block was not found. Setting CardData to empty.
[default7]:Repo card metadata block was not found. Setting CardData to empty.
[default2]:Repo card metadata block was not found. Setting CardData to empty.
[default1]:Repo card metadata block was not found. Setting CardData to empty.
[default6]:Repo card metadata block was not found. Setting CardData to empty.
[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
[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]: outputs, loss_avg = self.training_step(dataloader=self.current_dataloader)
[default3]:[rank3]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 462, in training_step
[default3]:[rank3]: outputs = self.pipeline_engine.train_batch_iter(
[default3]:[rank3]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 278, in train_batch_iter
[default3]:[rank3]: output = self.forward(context=context, state=state, micro_batch=micro_batch, model=model)
[default3]:[rank3]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 44, in forward
[default2]:[rank2]: trainer.train(dataloader)
[default2]:[rank2]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 429, in train
[default2]:[rank2]: outputs, loss_avg = self.training_step(dataloader=self.current_dataloader)
[default2]:[rank2]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 462, in training_step
[default2]:[rank2]: outputs = self.pipeline_engine.train_batch_iter(
[default2]:[rank2]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 278, in train_batch_iter
[default2]:[rank2]: output = self.forward(context=context, state=state, micro_batch=micro_batch, model=model)
[default2]:[rank2]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 44, in forward
[default2]:[rank2]: output = model(**micro_batch)
[default3]:[rank3]: output = model(**micro_batch)
[default2]:[rank2]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
[default2]:[rank2]: return self._call_impl(*args, **kwargs)
[default2]:[rank2]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
[default2]:[rank2]: return forward_call(*args, **kwargs)
[default2]:[rank2]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 891, in forward
[default2]:[rank2]: sharded_logits = self.model(
[default2]:[rank2]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
[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 self._call_impl(*args, **kwargs)
[default2]:[rank2]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
[default2]:[rank2]: return forward_call(*args, **kwargs)
[default3]:[rank3]: return self._call_impl(*args, **kwargs)
[default3]:[rank3]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
[default3]:[rank3]: return forward_call(*args, **kwargs)
[default3]:[rank3]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 891, in forward
[default3]:[rank3]: sharded_logits = self.model(
[default2]:[rank2]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 764, 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
[default2]:[rank2]: return self.forward_with_hidden_states(input_ids=input_ids, input_mask=input_mask)[0]
[default2]:[rank2]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 780, in forward_with_hidden_states
[default2]:[rank2]: hidden_encoder_states = encoder_block(**hidden_encoder_states)
[default3]:[rank3]: 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
[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
[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]: 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]
[default2]:[rank2]: return forward_call(*args, **kwargs)
[default3]:[rank3]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 780, in forward_with_hidden_states
[default2]:[rank2]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/block.py", line 151, in forward
[default2]:[rank2]: output = self.pp_block(**new_kwargs)
[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
[default2]:[rank2]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
[default2]:[rank2]: return self._call_impl(*args, **kwargs)
[default2]:[rank2]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
[default2]:[rank2]: return forward_call(*args, **kwargs)
[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)
[default2]:[rank2]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 631, in forward
[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)
[default2]:[rank2]: output = self.attn(hidden_states=hidden_states, sequence_mask=sequence_mask)
[default3]:[rank3]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
[default3]:[rank3]: 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/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 631, in forward
[default2]:[rank2]: return self._call_impl(*args, **kwargs)
[default3]:[rank3]: output = self.attn(hidden_states=hidden_states, sequence_mask=sequence_mask)
[default2]:[rank2]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 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 1532, in _wrapped_call_impl
[default2]:[rank2]: return forward_call(*args, **kwargs)
[default2]:[rank2]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 563, in forward
[default3]:[rank3]: return self._call_impl(*args, **kwargs)
[default2]:[rank2]: key_value_states = torch.cat([key_states.unsqueeze(0), value_states.unsqueeze(0)], dim=0)
[default2]:[rank2]: torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 256.00 MiB. GPU has a total capacity of 79.33 GiB of which 91.94 MiB is free. Including non-PyTorch memory, this process has 79.22 GiB memory in use. Of the allocated memory 71.13 GiB is allocated by PyTorch, and 299.09 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation. See documentation for Memory Management (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables)
[default3]:[rank3]: 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 563, in forward
[default3]:[rank3]: key_value_states = torch.cat([key_states.unsqueeze(0), value_states.unsqueeze(0)], dim=0)
[default3]:[rank3]: torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 256.00 MiB. GPU has a total capacity of 79.33 GiB of which 237.94 MiB is free. Including non-PyTorch memory, this process has 79.08 GiB memory in use. Of the allocated memory 71.13 GiB is allocated by PyTorch, and 299.09 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation. See documentation for Memory Management (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables)
[default0]:[rank0]: Traceback (most recent call last):
[default0]:[rank0]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/run_train.py", line 237, in <module>
[default0]:[rank0]: trainer.train(dataloader)
[default0]:[rank0]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 429, in train
[default0]:[rank0]: outputs, loss_avg = self.training_step(dataloader=self.current_dataloader)
[default0]:[rank0]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 462, in training_step
[default0]:[rank0]: outputs = self.pipeline_engine.train_batch_iter(
[default0]:[rank0]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 278, in train_batch_iter
[default0]:[rank0]: output = self.forward(context=context, state=state, micro_batch=micro_batch, model=model)
[default0]:[rank0]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 44, in forward
[default0]:[rank0]: output = model(**micro_batch)
[default0]:[rank0]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
[default0]:[rank0]: return self._call_impl(*args, **kwargs)
[default0]:[rank0]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
[default0]:[rank0]: return forward_call(*args, **kwargs)
[default0]:[rank0]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 891, in forward
[default0]:[rank0]: sharded_logits = self.model(
[default0]:[rank0]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
[default0]:[rank0]: return self._call_impl(*args, **kwargs)
[default0]:[rank0]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
[default0]:[rank0]: return forward_call(*args, **kwargs)
[default0]:[rank0]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 764, in forward
[default0]:[rank0]: return self.forward_with_hidden_states(input_ids=input_ids, input_mask=input_mask)[0]
[default0]:[rank0]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 780, in forward_with_hidden_states
[default0]:[rank0]: hidden_encoder_states = encoder_block(**hidden_encoder_states)
[default0]:[rank0]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
[default0]:[rank0]: return self._call_impl(*args, **kwargs)
[default0]:[rank0]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
[default0]:[rank0]: return forward_call(*args, **kwargs)
[default0]:[rank0]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/block.py", line 151, in forward
[default0]:[rank0]: output = self.pp_block(**new_kwargs)
[default0]:[rank0]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
[default0]:[rank0]: return self._call_impl(*args, **kwargs)
[default0]:[rank0]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
[default0]:[rank0]: return forward_call(*args, **kwargs)
[default0]:[rank0]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 631, in forward
[default0]:[rank0]: output = self.attn(hidden_states=hidden_states, sequence_mask=sequence_mask)
[default0]:[rank0]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
[default0]:[rank0]: return self._call_impl(*args, **kwargs)
[default0]:[rank0]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
[default0]:[rank0]: return forward_call(*args, **kwargs)
[default0]:[rank0]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 598, in forward
[default0]:[rank0]: output = self.o_proj(attention_output)
[default0]:[rank0]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
[default0]:[rank0]: return self._call_impl(*args, **kwargs)
[default0]:[rank0]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
[default0]:[rank0]: return forward_call(*args, **kwargs)
[default0]:[rank0]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/nn.py", line 159, in forward
[default0]:[rank0]: return row_linear(
[default0]:[rank0]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/functional.py", line 479, in row_linear
[default0]:[rank0]: out = differentiable_reduce_scatter_sum(out, group=group)
[default0]:[rank0]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/distributed_differentiable_primitives.py", line 145, in differentiable_reduce_scatter_sum
[default0]:[rank0]: return DifferentiableReduceScatterSum.apply(tensor, group)
[default0]:[rank0]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/autograd/function.py", line 598, in apply
[default0]:[rank0]: return super().apply(*args, **kwargs) # type: ignore[misc]
[default0]:[rank0]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/distributed_differentiable_primitives.py", line 111, in forward
[default0]:[rank0]: sharded_tensor = torch.empty(
[default0]:[rank0]: torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 128.00 MiB. GPU
[default1]:[rank1]: Traceback (most recent call last):
[default1]:[rank1]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/run_train.py", line 237, in <module>
[default1]:[rank1]: trainer.train(dataloader)
[default1]:[rank1]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 429, in train
[default1]:[rank1]: outputs, loss_avg = self.training_step(dataloader=self.current_dataloader)
[default1]:[rank1]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 462, in training_step
[default1]:[rank1]: outputs = self.pipeline_engine.train_batch_iter(
[default1]:[rank1]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 278, in train_batch_iter
[default1]:[rank1]: output = self.forward(context=context, state=state, micro_batch=micro_batch, model=model)
[default1]:[rank1]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 44, in forward
[default1]:[rank1]: output = model(**micro_batch)
[default1]:[rank1]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
[default1]:[rank1]: return self._call_impl(*args, **kwargs)
[default1]:[rank1]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
[default1]:[rank1]: return forward_call(*args, **kwargs)
[default1]:[rank1]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 891, in forward
[default1]:[rank1]: sharded_logits = self.model(
[default1]:[rank1]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
[default1]:[rank1]: return self._call_impl(*args, **kwargs)
[default1]:[rank1]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
[default1]:[rank1]: return forward_call(*args, **kwargs)
[default1]:[rank1]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 764, in forward
[default1]:[rank1]: return self.forward_with_hidden_states(input_ids=input_ids, input_mask=input_mask)[0]
[default1]:[rank1]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 780, in forward_with_hidden_states
[default1]:[rank1]: hidden_encoder_states = encoder_block(**hidden_encoder_states)
[default1]:[rank1]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
[default1]:[rank1]: return self._call_impl(*args, **kwargs)
[default1]:[rank1]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
[default1]:[rank1]: return forward_call(*args, **kwargs)
[default1]:[rank1]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/block.py", line 151, in forward
[default1]:[rank1]: output = self.pp_block(**new_kwargs)
[default1]:[rank1]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
[default1]:[rank1]: return self._call_impl(*args, **kwargs)
[default1]:[rank1]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
[default1]:[rank1]: return forward_call(*args, **kwargs)
[default1]:[rank1]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 631, in forward
[default1]:[rank1]: output = self.attn(hidden_states=hidden_states, sequence_mask=sequence_mask)
[default1]:[rank1]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
[default1]:[rank1]: return self._call_impl(*args, **kwargs)
[default1]:[rank1]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
[default1]:[rank1]: return forward_call(*args, **kwargs)
[default1]:[rank1]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 563, in forward
[default1]:[rank1]: key_value_states = torch.cat([key_states.unsqueeze(0), value_states.unsqueeze(0)], dim=0)
[default1]:[rank1]: torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 256.00 MiB. GPU has a total capacity of 79.33 GiB of which 177.94 MiB is free. Including non-PyTorch memory, this process has 79.14 GiB memory in use. Of the allocated memory 71.13 GiB is allocated by PyTorch, and 299.09 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation. See documentation for Memory Management (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables)
[default4]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/autograd/graph.py:744: UserWarning: c10d::allreduce_: an autograd kernel was not registered to the Autograd key(s) but we are trying to backprop through it. This may lead to silently incorrect behavior. This behavior is deprecated and will be removed in a future version of PyTorch. If your operator is differentiable, please ensure you have registered an autograd kernel to the correct Autograd key (e.g. DispatchKey::Autograd, DispatchKey::CompositeImplicitAutograd). If your operator is not differentiable, or to squash this warning and use the previous behavior, please register torch::CppFunction::makeFallthrough() to DispatchKey::Autograd. (Triggered internally at ../torch/csrc/autograd/autograd_not_implemented_fallback.cpp:63.)
[default4]: return Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass
[default5]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/autograd/graph.py:744: UserWarning: c10d::allreduce_: an autograd kernel was not registered to the Autograd key(s) but we are trying to backprop through it. This may lead to silently incorrect behavior. This behavior is deprecated and will be removed in a future version of PyTorch. If your operator is differentiable, please ensure you have registered an autograd kernel to the correct Autograd key (e.g. DispatchKey::Autograd, DispatchKey::CompositeImplicitAutograd). If your operator is not differentiable, or to squash this warning and use the previous behavior, please register torch::CppFunction::makeFallthrough() to DispatchKey::Autograd. (Triggered internally at ../torch/csrc/autograd/autograd_not_implemented_fallback.cpp:63.)
[default5]: return Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass
W0702 19:21:44.184000 140334325937984 torch/distributed/elastic/multiprocessing/api.py:851] Sending process 3754858 closing signal SIGTERM
W0702 19:21:44.184000 140334325937984 torch/distributed/elastic/multiprocessing/api.py:851] Sending process 3754861 closing signal SIGTERM
W0702 19:21:44.184000 140334325937984 torch/distributed/elastic/multiprocessing/api.py:851] Sending process 3754862 closing signal SIGTERM
W0702 19:21:44.185000 140334325937984 torch/distributed/elastic/multiprocessing/api.py:851] Sending process 3754863 closing signal SIGTERM
W0702 19:21:44.185000 140334325937984 torch/distributed/elastic/multiprocessing/api.py:851] Sending process 3754864 closing signal SIGTERM
[default7]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/autograd/graph.py:744: UserWarning: c10d::allreduce_: an autograd kernel was not registered to the Autograd key(s) but we are trying to backprop through it. This may lead to silently incorrect behavior. This behavior is deprecated and will be removed in a future version of PyTorch. If your operator is differentiable, please ensure you have registered an autograd kernel to the correct Autograd key (e.g. DispatchKey::Autograd, DispatchKey::CompositeImplicitAutograd). If your operator is not differentiable, or to squash this warning and use the previous behavior, please register torch::CppFunction::makeFallthrough() to DispatchKey::Autograd. (Triggered internally at ../torch/csrc/autograd/autograd_not_implemented_fallback.cpp:63.)
[default7]: return Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass
[default6]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/autograd/graph.py:744: UserWarning: c10d::allreduce_: an autograd kernel was not registered to the Autograd key(s) but we are trying to backprop through it. This may lead to silently incorrect behavior. This behavior is deprecated and will be removed in a future version of PyTorch. If your operator is differentiable, please ensure you have registered an autograd kernel to the correct Autograd key (e.g. DispatchKey::Autograd, DispatchKey::CompositeImplicitAutograd). If your operator is not differentiable, or to squash this warning and use the previous behavior, please register torch::CppFunction::makeFallthrough() to DispatchKey::Autograd. (Triggered internally at ../torch/csrc/autograd/autograd_not_implemented_fallback.cpp:63.)
[default6]: return Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass
[default1]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/autograd/graph.py:744: UserWarning: c10d::allreduce_: an autograd kernel was not registered to the Autograd key(s) but we are trying to backprop through it. This may lead to silently incorrect behavior. This behavior is deprecated and will be removed in a future version of PyTorch. If your operator is differentiable, please ensure you have registered an autograd kernel to the correct Autograd key (e.g. DispatchKey::Autograd, DispatchKey::CompositeImplicitAutograd). If your operator is not differentiable, or to squash this warning and use the previous behavior, please register torch::CppFunction::makeFallthrough() to DispatchKey::Autograd. (Triggered internally at ../torch/csrc/autograd/autograd_not_implemented_fallback.cpp:63.)
[default1]: return Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass
[default0]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/autograd/graph.py:744: UserWarning: c10d::allreduce_: an autograd kernel was not registered to the Autograd key(s) but we are trying to backprop through it. This may lead to silently incorrect behavior. This behavior is deprecated and will be removed in a future version of PyTorch. If your operator is differentiable, please ensure you have registered an autograd kernel to the correct Autograd key (e.g. DispatchKey::Autograd, DispatchKey::CompositeImplicitAutograd). If your operator is not differentiable, or to squash this warning and use the previous behavior, please register torch::CppFunction::makeFallthrough() to DispatchKey::Autograd. (Triggered internally at ../torch/csrc/autograd/autograd_not_implemented_fallback.cpp:63.)
[default0]: return Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass
E0702 19:21:46.108000 140334325937984 torch/distributed/elastic/multiprocessing/api.py:826] failed (exitcode: 1) local_rank: 0 (pid: 3754857) of binary: /fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/bin/python3.10
Traceback (most recent call last):
File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/bin/torchrun", line 8, in <module>
sys.exit(main())
File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/elastic/multiprocessing/errors/__init__.py", line 347, in wrapper
return f(*args, **kwargs)
File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/run.py", line 879, in main
run(args)
File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/run.py", line 870, in run
elastic_launch(
File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/launcher/api.py", line 132, in __call__
return launch_agent(self._config, self._entrypoint, list(args))
File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/launcher/api.py", line 263, in launch_agent
raise ChildFailedError(
torch.distributed.elastic.multiprocessing.errors.ChildFailedError:
============================================================
/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/run_train.py FAILED
------------------------------------------------------------
Failures:
[1]:
time : 2024-07-02_19:21:44
host : ip-26-0-171-62.ec2.internal
rank : 2 (local_rank: 2)
exitcode : 1 (pid: 3754859)
error_file: <N/A>
traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html
[2]:
time : 2024-07-02_19:21:44
host : ip-26-0-171-62.ec2.internal
rank : 3 (local_rank: 3)
exitcode : 1 (pid: 3754860)
error_file: <N/A>
traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html
------------------------------------------------------------
Root Cause (first observed failure):
[0]:
time : 2024-07-02_19:21:44
host : ip-26-0-171-62.ec2.internal
rank : 0 (local_rank: 0)
exitcode : 1 (pid: 3754857)
error_file: <N/A>
traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html
============================================================
srun: error: ip-26-0-171-62: task 0: Exited with exit code 1
[default2]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/autograd/graph.py:744: UserWarning: c10d::allreduce_: an autograd kernel was not registered to the Autograd key(s) but we are trying to backprop through it. This may lead to silently incorrect behavior. This behavior is deprecated and will be removed in a future version of PyTorch. If your operator is differentiable, please ensure you have registered an autograd kernel to the correct Autograd key (e.g. DispatchKey::Autograd, DispatchKey::CompositeImplicitAutograd). If your operator is not differentiable, or to squash this warning and use the previous behavior, please register torch::CppFunction::makeFallthrough() to DispatchKey::Autograd. (Triggered internally at ../torch/csrc/autograd/autograd_not_implemented_fallback.cpp:63.)
[default2]: return Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass
[default3]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/autograd/graph.py:744: UserWarning: c10d::allreduce_: an autograd kernel was not registered to the Autograd key(s) but we are trying to backprop through it. This may lead to silently incorrect behavior. This behavior is deprecated and will be removed in a future version of PyTorch. If your operator is differentiable, please ensure you have registered an autograd kernel to the correct Autograd key (e.g. DispatchKey::Autograd, DispatchKey::CompositeImplicitAutograd). If your operator is not differentiable, or to squash this warning and use the previous behavior, please register torch::CppFunction::makeFallthrough() to DispatchKey::Autograd. (Triggered internally at ../torch/csrc/autograd/autograd_not_implemented_fallback.cpp:63.)
[default3]: return Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass
[default0]:[rank8]: Traceback (most recent call last):
[default0]:[rank8]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/run_train.py", line 237, in <module>
[default0]:[rank8]: trainer.train(dataloader)
[default1]:[rank9]: Traceback (most recent call last):
[default1]:[rank9]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/run_train.py", line 237, in <module>
[default0]:[rank8]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 429, in train
[default0]:[rank8]: outputs, loss_avg = self.training_step(dataloader=self.current_dataloader)
[default0]:[rank8]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 462, in training_step
[default1]:[rank9]: trainer.train(dataloader)
[default0]:[rank8]: outputs = self.pipeline_engine.train_batch_iter(
[default0]:[rank8]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 278, in train_batch_iter
[default1]:[rank9]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 429, in train
[default1]:[rank9]: outputs, loss_avg = self.training_step(dataloader=self.current_dataloader)
[default1]:[rank9]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 462, in training_step
[default0]:[rank8]: output = self.forward(context=context, state=state, micro_batch=micro_batch, model=model)
[default0]:[rank8]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 44, in forward
[default1]:[rank9]: outputs = self.pipeline_engine.train_batch_iter(
[default0]:[rank8]: output = model(**micro_batch)
[default0]:[rank8]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
[default0]:[rank8]: return self._call_impl(*args, **kwargs)
[default1]:[rank9]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 278, in train_batch_iter
[default1]:[rank9]: output = self.forward(context=context, state=state, micro_batch=micro_batch, model=model)
[default0]:[rank8]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
[default0]:[rank8]: return forward_call(*args, **kwargs)
[default0]:[rank8]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 891, in forward
[default0]:[rank8]: sharded_logits = self.model(
[default0]:[rank8]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
[default1]:[rank9]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 44, in forward
[default0]:[rank8]: return self._call_impl(*args, **kwargs)
[default1]:[rank9]: output = model(**micro_batch)
[default1]:[rank9]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
[default0]:[rank8]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
[default0]:[rank8]: return forward_call(*args, **kwargs)
[default0]:[rank8]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 764, in forward
[default0]:[rank8]: return self.forward_with_hidden_states(input_ids=input_ids, input_mask=input_mask)[0]
[default1]:[rank9]: return self._call_impl(*args, **kwargs)
[default1]:[rank9]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
[default1]:[rank9]: return forward_call(*args, **kwargs)
[default1]:[rank9]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 891, in forward
[default1]:[rank9]: sharded_logits = self.model(
[default1]:[rank9]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
[default0]:[rank8]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 780, in forward_with_hidden_states
[default1]:[rank9]: return self._call_impl(*args, **kwargs)
[default1]:[rank9]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
[default1]:[rank9]: return forward_call(*args, **kwargs)
[default1]:[rank9]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 764, in forward
[default1]:[rank9]: return self.forward_with_hidden_states(input_ids=input_ids, input_mask=input_mask)[0]
[default1]:[rank9]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 780, in forward_with_hidden_states
[default1]:[rank9]: hidden_encoder_states = encoder_block(**hidden_encoder_states)
[default1]:[rank9]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
[default1]:[rank9]: return self._call_impl(*args, **kwargs)
[default1]:[rank9]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
[default1]:[rank9]: return forward_call(*args, **kwargs)
[default1]:[rank9]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/block.py", line 126, in forward
[default0]:[rank8]: hidden_encoder_states = encoder_block(**hidden_encoder_states)
[default1]:[rank9]: new_kwargs[name] = recv_from_pipeline_state_buffer(
[default1]:[rank9]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/functional.py", line 117, in recv_from_pipeline_state_buffer
[default1]:[rank9]: pipeline_state.run_communication()
[default1]:[rank9]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/state.py", line 160, in run_communication
[default1]:[rank9]: send_grad()
[default0]:[rank8]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
[default1]:[rank9]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/state.py", line 41, in __call__
[default0]:[rank8]: return self._call_impl(*args, **kwargs)
[default1]:[rank9]: self.p2p.send_tensors([self.grad], to_rank=self.to_rank)
[default1]:[rank9]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/p2p.py", line 348, in send_tensors
[default0]:[rank8]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
[default1]:[rank9]: futures = self.isend_tensors(tensors=tensors, to_rank=to_rank, tag=tag)
[default1]:[rank9]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/p2p.py", line 295, in isend_tensors
[default1]:[rank9]: self._send_meta(tensor, to_rank=to_rank, tag=tag)
[default0]:[rank8]: return forward_call(*args, **kwargs)
[default1]:[rank9]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/p2p.py", line 221, in _send_meta
[default0]:[rank8]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/block.py", line 126, in forward
[default1]:[rank9]: dist.send(
[default1]:[rank9]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/c10d_logger.py", line 75, in wrapper
[default1]:[rank9]: return func(*args, **kwargs)
[default0]:[rank8]: new_kwargs[name] = recv_from_pipeline_state_buffer(
[default1]:[rank9]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/distributed_c10d.py", line 1886, in send
[default1]:[rank9]: group.send([tensor], group_dst_rank, tag).wait()
[default1]:[rank9]: torch.distributed.DistBackendError: NCCL communicator was aborted on rank 1.
[default0]:[rank8]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/functional.py", line 117, in recv_from_pipeline_state_buffer
[default0]:[rank8]: pipeline_state.run_communication()
[default0]:[rank8]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/state.py", line 160, in run_communication
[default0]:[rank8]: send_grad()
[default0]:[rank8]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/state.py", line 41, in __call__
[default0]:[rank8]: self.p2p.send_tensors([self.grad], to_rank=self.to_rank)
[default0]:[rank8]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/p2p.py", line 348, in send_tensors
[default0]:[rank8]: futures = self.isend_tensors(tensors=tensors, to_rank=to_rank, tag=tag)
[default0]:[rank8]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/p2p.py", line 295, in isend_tensors
[default0]:[rank8]: self._send_meta(tensor, to_rank=to_rank, tag=tag)
[default0]:[rank8]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/p2p.py", line 221, in _send_meta
[default0]:[rank8]: dist.send(
[default0]:[rank8]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/c10d_logger.py", line 75, in wrapper
[default0]:[rank8]: return func(*args, **kwargs)
[default0]:[rank8]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/distributed_c10d.py", line 1886, in send
[default0]:[rank8]: group.send([tensor], group_dst_rank, tag).wait()
[default0]:[rank8]: torch.distributed.DistBackendError: NCCL communicator was aborted on rank 1.
W0702 19:21:48.257000 140354984572672 torch/distributed/elastic/rendezvous/dynamic_rendezvous.py:1252] The node 'ip-26-0-171-88.ec2.internal_742831_0' has failed to send a keep-alive heartbeat to the rendezvous 'none' due to an error of type RendezvousConnectionError.
[default3]:[rank11]: Traceback (most recent call last):
[default3]:[rank11]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/run_train.py", line 237, in <module>
[default3]:[rank11]: trainer.train(dataloader)
[default3]:[rank11]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 429, in train
[default3]:[rank11]: outputs, loss_avg = self.training_step(dataloader=self.current_dataloader)
[default3]:[rank11]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 462, in training_step
[default3]:[rank11]: outputs = self.pipeline_engine.train_batch_iter(
[default3]:[rank11]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 278, in train_batch_iter
[default3]:[rank11]: output = self.forward(context=context, state=state, micro_batch=micro_batch, model=model)
[default3]:[rank11]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 44, in forward
[default3]:[rank11]: output = model(**micro_batch)
[default3]:[rank11]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
[default3]:[rank11]: return self._call_impl(*args, **kwargs)
[default3]:[rank11]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
[default3]:[rank11]: return forward_call(*args, **kwargs)
[default3]:[rank11]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 891, in forward
[default3]:[rank11]: sharded_logits = self.model(
[default3]:[rank11]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
[default3]:[rank11]: return self._call_impl(*args, **kwargs)
[default3]:[rank11]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
[default3]:[rank11]: return forward_call(*args, **kwargs)
[default3]:[rank11]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 764, in forward
[default3]:[rank11]: return self.forward_with_hidden_states(input_ids=input_ids, input_mask=input_mask)[0]
[default3]:[rank11]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 780, in forward_with_hidden_states
[default3]:[rank11]: hidden_encoder_states = encoder_block(**hidden_encoder_states)
[default3]:[rank11]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
[default3]:[rank11]: return self._call_impl(*args, **kwargs)
[default3]:[rank11]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
[default3]:[rank11]: return forward_call(*args, **kwargs)
[default3]:[rank11]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/block.py", line 126, in forward
[default3]:[rank11]: new_kwargs[name] = recv_from_pipeline_state_buffer(
[default3]:[rank11]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/functional.py", line 117, in recv_from_pipeline_state_buffer
[default3]:[rank11]: pipeline_state.run_communication()
[default3]:[rank11]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/state.py", line 160, in run_communication
[default3]:[rank11]: send_grad()
[default3]:[rank11]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/state.py", line 41, in __call__
[default3]:[rank11]: self.p2p.send_tensors([self.grad], to_rank=self.to_rank)
[default3]:[rank11]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/p2p.py", line 348, in send_tensors
[default3]:[rank11]: futures = self.isend_tensors(tensors=tensors, to_rank=to_rank, tag=tag)
[default3]:[rank11]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/p2p.py", line 295, in isend_tensors
[default3]:[rank11]: self._send_meta(tensor, to_rank=to_rank, tag=tag)
[default3]:[rank11]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/p2p.py", line 221, in _send_meta
[default3]:[rank11]: dist.send(
[default3]:[rank11]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/c10d_logger.py", line 75, in wrapper
[default3]:[rank11]: return func(*args, **kwargs)
[default3]:[rank11]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/distributed_c10d.py", line 1886, in send
[default3]:[rank11]: group.send([tensor], group_dst_rank, tag).wait()
[default3]:[rank11]: torch.distributed.DistBackendError: NCCL communicator was aborted on rank 1.
[default2]:[rank10]: Traceback (most recent call last):
[default2]:[rank10]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/run_train.py", line 237, in <module>
[default2]:[rank10]: trainer.train(dataloader)
[default2]:[rank10]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 429, in train
[default2]:[rank10]: outputs, loss_avg = self.training_step(dataloader=self.current_dataloader)
[default2]:[rank10]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 462, in training_step
[default2]:[rank10]: outputs = self.pipeline_engine.train_batch_iter(
[default2]:[rank10]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 278, in train_batch_iter
[default2]:[rank10]: output = self.forward(context=context, state=state, micro_batch=micro_batch, model=model)
[default2]:[rank10]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 44, in forward
[default2]:[rank10]: output = model(**micro_batch)
[default2]:[rank10]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
[default2]:[rank10]: return self._call_impl(*args, **kwargs)
[default2]:[rank10]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
[default2]:[rank10]: return forward_call(*args, **kwargs)
[default2]:[rank10]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 891, in forward
[default2]:[rank10]: sharded_logits = self.model(
[default2]:[rank10]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
[default2]:[rank10]: return self._call_impl(*args, **kwargs)
[default2]:[rank10]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
[default2]:[rank10]: return forward_call(*args, **kwargs)
[default2]:[rank10]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 764, in forward
[default2]:[rank10]: return self.forward_with_hidden_states(input_ids=input_ids, input_mask=input_mask)[0]
[default2]:[rank10]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 780, in forward_with_hidden_states
[default2]:[rank10]: hidden_encoder_states = encoder_block(**hidden_encoder_states)
[default2]:[rank10]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
[default2]:[rank10]: return self._call_impl(*args, **kwargs)
[default2]:[rank10]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
[default2]:[rank10]: return forward_call(*args, **kwargs)
[default2]:[rank10]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/block.py", line 126, in forward
[default2]:[rank10]: new_kwargs[name] = recv_from_pipeline_state_buffer(
[default2]:[rank10]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/functional.py", line 117, in recv_from_pipeline_state_buffer
[default2]:[rank10]: pipeline_state.run_communication()
[default2]:[rank10]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/state.py", line 160, in run_communication
[default2]:[rank10]: send_grad()
[default2]:[rank10]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/state.py", line 41, in __call__
[default2]:[rank10]: self.p2p.send_tensors([self.grad], to_rank=self.to_rank)
[default2]:[rank10]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/p2p.py", line 348, in send_tensors
[default2]:[rank10]: futures = self.isend_tensors(tensors=tensors, to_rank=to_rank, tag=tag)
[default2]:[rank10]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/p2p.py", line 295, in isend_tensors
[default2]:[rank10]: self._send_meta(tensor, to_rank=to_rank, tag=tag)
[default2]:[rank10]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/p2p.py", line 221, in _send_meta
[default2]:[rank10]: dist.send(
[default2]:[rank10]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/c10d_logger.py", line 75, in wrapper
[default2]:[rank10]: return func(*args, **kwargs)
[default2]:[rank10]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/distributed_c10d.py", line 1886, in send
[default2]:[rank10]: group.send([tensor], group_dst_rank, tag).wait()
[default2]:[rank10]: torch.distributed.DistBackendError: NCCL communicator was aborted on rank 1.
[default0]:[rank8]:[E ProcessGroupNCCL.cpp:577] [Rank 2] Some NCCL operations have failed or timed out. Due to the asynchronous nature of CUDA kernels, subsequent GPU operations might run on corrupted/incomplete data.
[default0]:[rank8]:[E ProcessGroupNCCL.cpp:583] [Rank 2] To avoid data inconsistency, we are taking the entire process down.
[default0]:[rank8]:[E ProcessGroupNCCL.cpp:1414] [PG 4 Rank 2] Process group watchdog thread terminated with exception: NCCL error: remote process exited or there was a network error, NCCL version 2.20.5
[default0]:ncclRemoteError: A call failed possibly due to a network error or a remote process exiting prematurely.
[default0]:Last error:
[default0]:NET/Socket : socket progress error
[default0]:Exception raised from checkForNCCLErrorsInternal at ../torch/csrc/distributed/c10d/ProcessGroupNCCL.cpp:1723 (most recent call first):
[default0]:frame #0: c10::Error::Error(c10::SourceLocation, std::string) + 0x57 (0x7fbb3c4e9897 in /fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/lib/libc10.so)
[default0]:frame #1: c10d::ProcessGroupNCCL::checkForNCCLErrorsInternal(std::shared_ptr<c10d::NCCLComm>&) + 0x220 (0x7fbb3d7c25f0 in /fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/lib/libtorch_cuda.so)
[default0]:frame #2: c10d::ProcessGroupNCCL::WorkNCCL::checkAndSetException() + 0x7c (0x7fbb3d7c283c in /fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/lib/libtorch_cuda.so)
[default0]:frame #3: c10d::ProcessGroupNCCL::watchdogHandler() + 0x180 (0x7fbb3d7c7a60 in /fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/lib/libtorch_cuda.so)
[default0]:frame #4: c10d::ProcessGroupNCCL::ncclCommWatchdog() + 0x10c (0x7fbb3d7c8dcc in /fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/lib/libtorch_cuda.so)
[default0]:frame #5: <unknown function> + 0xd3e95 (0x7fbb89261e95 in /fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/bin/../lib/libstdc++.so.6)
[default0]:frame #6: <unknown function> + 0x8609 (0x7fbb8e2a8609 in /lib/x86_64-linux-gnu/libpthread.so.0)
[default0]:frame #7: clone + 0x43 (0x7fbb8e073353 in /lib/x86_64-linux-gnu/libc.so.6)
[default0]:
[default0]:terminate called after throwing an instance of 'c10::DistBackendError'
[default0]: what(): [PG 4 Rank 2] Process group watchdog thread terminated with exception: NCCL error: remote process exited or there was a network error, NCCL version 2.20.5
[default0]:ncclRemoteError: A call failed possibly due to a network error or a remote process exiting prematurely.
[default0]:Last error:
[default0]:NET/Socket : socket progress error
[default0]:Exception raised from checkForNCCLErrorsInternal at ../torch/csrc/distributed/c10d/ProcessGroupNCCL.cpp:1723 (most recent call first):
[default0]:frame #0: c10::Error::Error(c10::SourceLocation, std::string) + 0x57 (0x7fbb3c4e9897 in /fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/lib/libc10.so)
[default0]:frame #1: c10d::ProcessGroupNCCL::checkForNCCLErrorsInternal(std::shared_ptr<c10d::NCCLComm>&) + 0x220 (0x7fbb3d7c25f0 in /fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/lib/libtorch_cuda.so)
[default0]:frame #2: c10d::ProcessGroupNCCL::WorkNCCL::checkAndSetException() + 0x7c (0x7fbb3d7c283c in /fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/lib/libtorch_cuda.so)
[default0]:frame #3: c10d::ProcessGroupNCCL::watchdogHandler() + 0x180 (0x7fbb3d7c7a60 in /fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/lib/libtorch_cuda.so)
[default0]:frame #4: c10d::ProcessGroupNCCL::ncclCommWatchdog() + 0x10c (0x7fbb3d7c8dcc in /fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/lib/libtorch_cuda.so)
[default0]:frame #5: <unknown function> + 0xd3e95 (0x7fbb89261e95 in /fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/bin/../lib/libstdc++.so.6)
[default0]:frame #6: <unknown function> + 0x8609 (0x7fbb8e2a8609 in /lib/x86_64-linux-gnu/libpthread.so.0)
[default0]:frame #7: clone + 0x43 (0x7fbb8e073353 in /lib/x86_64-linux-gnu/libc.so.6)
[default0]:
[default0]:Exception raised from ncclCommWatchdog at ../torch/csrc/distributed/c10d/ProcessGroupNCCL.cpp:1418 (most recent call first):
[default0]:frame #0: c10::Error::Error(c10::SourceLocation, std::string) + 0x57 (0x7fbb3c4e9897 in /fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/lib/libc10.so)
[default0]:frame #1: <unknown function> + 0xe32119 (0x7fbb3d44c119 in /fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/lib/libtorch_cuda.so)
[default0]:frame #2: <unknown function> + 0xd3e95 (0x7fbb89261e95 in /fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/bin/../lib/libstdc++.so.6)
[default0]:frame #3: <unknown function> + 0x8609 (0x7fbb8e2a8609 in /lib/x86_64-linux-gnu/libpthread.so.0)
[default0]:frame #4: clone + 0x43 (0x7fbb8e073353 in /lib/x86_64-linux-gnu/libc.so.6)
[default0]:
W0702 19:21:49.191000 140360651392832 torch/distributed/elastic/multiprocessing/api.py:851] Sending process 742905 closing signal SIGTERM
W0702 19:21:49.191000 140360651392832 torch/distributed/elastic/multiprocessing/api.py:851] Sending process 742906 closing signal SIGTERM
W0702 19:21:49.191000 140360651392832 torch/distributed/elastic/multiprocessing/api.py:851] Sending process 742907 closing signal SIGTERM
W0702 19:21:49.191000 140360651392832 torch/distributed/elastic/multiprocessing/api.py:851] Sending process 742908 closing signal SIGTERM
W0702 19:21:49.192000 140360651392832 torch/distributed/elastic/multiprocessing/api.py:851] Sending process 742909 closing signal SIGTERM
W0702 19:21:49.199000 140360651392832 torch/distributed/elastic/multiprocessing/api.py:851] Sending process 742910 closing signal SIGTERM
W0702 19:21:49.203000 140360651392832 torch/distributed/elastic/multiprocessing/api.py:851] Sending process 742911 closing signal SIGTERM
W0702 19:21:49.207000 140360651392832 torch/distributed/elastic/multiprocessing/api.py:851] Sending process 742912 closing signal SIGTERM
W0702 19:21:51.840000 140360651392832 torch/distributed/elastic/rendezvous/dynamic_rendezvous.py:1203] The node 'ip-26-0-171-88.ec2.internal_742831_0' has failed to shutdown the rendezvous 'none' due to an error of type RendezvousConnectionError.
W0702 19:21:51.851000 140360651392832 torch/distributed/elastic/rendezvous/dynamic_rendezvous.py:1203] The node 'ip-26-0-171-88.ec2.internal_742831_0' has failed to shutdown the rendezvous 'none' due to an error of type RendezvousConnectionError.
Traceback (most recent call last):
File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/elastic/rendezvous/c10d_rendezvous_backend.py", line 113, in _call_store
return getattr(self._store, store_op)(*args, **kwargs)
torch.distributed.DistNetworkError: Broken pipe
The above exception was the direct cause of the following exception:
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 254, in launch_agent
result = agent.run()
File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/elastic/metrics/api.py", line 123, in wrapper
result = f(*args, **kwargs)
File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/elastic/agent/server/api.py", line 733, in run
result = self._invoke_run(role)
File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/elastic/agent/server/api.py", line 908, in _invoke_run
num_nodes_waiting = rdzv_handler.num_nodes_waiting()
File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/elastic/rendezvous/dynamic_rendezvous.py", line 1174, in num_nodes_waiting
self._state_holder.sync()
File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/elastic/rendezvous/dynamic_rendezvous.py", line 419, in sync
get_response = self._backend.get_state()
File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/elastic/rendezvous/c10d_rendezvous_backend.py", line 73, in get_state
base64_state: bytes = self._call_store("get", self._key)
File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/elastic/rendezvous/c10d_rendezvous_backend.py", line 115, in _call_store
raise RendezvousConnectionError(
torch.distributed.elastic.rendezvous.api.RendezvousConnectionError: The connection to the C10d store has failed. See inner exception for details.
srun: error: ip-26-0-171-88: task 1: Exited with exit code 1
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