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START TIME: Tue Jul 2 18:57:49 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 18:57:52.390000 140570191664960 torch/distributed/run.py:757]
W0702 18:57:52.390000 140570191664960 torch/distributed/run.py:757] *****************************************
W0702 18:57:52.390000 140570191664960 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 18:57:52.390000 140570191664960 torch/distributed/run.py:757] *****************************************
W0702 18:57:55.127000 139912312178496 torch/distributed/run.py:757]
W0702 18:57:55.127000 139912312178496 torch/distributed/run.py:757] *****************************************
W0702 18:57:55.127000 139912312178496 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 18:57:55.127000 139912312178496 torch/distributed/run.py:757] *****************************************
[default0]:07/02/2024 18:58:16 [WARNING|DP=0|PP=0|TP=0|ip-26-0-165-24]: [Vocab Size Padding] Padded vocab (size: 50257) with 1 dummy tokens (new size: 50258)
[default0]:07/02/2024 18:58:16 [INFO|DP=0|PP=0|TP=0|ip-26-0-165-24]: Config:
[default0]:07/02/2024 18:58:16 [INFO|DP=0|PP=0|TP=0|ip-26-0-165-24]: Config(general=GeneralArgs(project='bench_cluster',
[default0]:07/02/2024 18:58:16 [INFO|DP=0|PP=0|TP=0|ip-26-0-165-24]: run='%date_%jobid',
[default0]:07/02/2024 18:58:16 [INFO|DP=0|PP=0|TP=0|ip-26-0-165-24]: seed=42,
[default0]:07/02/2024 18:58:16 [INFO|DP=0|PP=0|TP=0|ip-26-0-165-24]: step=None,
[default0]:07/02/2024 18:58:16 [INFO|DP=0|PP=0|TP=0|ip-26-0-165-24]: consumed_train_samples=None,
[default0]:07/02/2024 18:58:16 [INFO|DP=0|PP=0|TP=0|ip-26-0-165-24]: benchmark_csv_path=None,
[default0]:07/02/2024 18:58:16 [INFO|DP=0|PP=0|TP=0|ip-26-0-165-24]: ignore_sanity_checks=True),
[default0]:07/02/2024 18:58:16 [INFO|DP=0|PP=0|TP=0|ip-26-0-165-24]: parallelism=ParallelismArgs(dp=1,
[default0]:07/02/2024 18:58:16 [INFO|DP=0|PP=0|TP=0|ip-26-0-165-24]: pp=8,
[default0]:07/02/2024 18:58:16 [INFO|DP=0|PP=0|TP=0|ip-26-0-165-24]: tp=2,
[default0]:07/02/2024 18:58:16 [INFO|DP=0|PP=0|TP=0|ip-26-0-165-24]: pp_engine=<nanotron.parallel.pipeline_parallel.engine.OneForwardOneBackwardPipelineEngine object at 0x7f1c504d4730>,
[default0]:07/02/2024 18:58:16 [INFO|DP=0|PP=0|TP=0|ip-26-0-165-24]: tp_mode=<TensorParallelLinearMode.REDUCE_SCATTER: 2>,
[default0]:07/02/2024 18:58:16 [INFO|DP=0|PP=0|TP=0|ip-26-0-165-24]: tp_linear_async_communication=False,
[default0]:07/02/2024 18:58:16 [INFO|DP=0|PP=0|TP=0|ip-26-0-165-24]: expert_parallel_size=1),
[default0]:07/02/2024 18:58:16 [INFO|DP=0|PP=0|TP=0|ip-26-0-165-24]: model=ModelArgs(model_config=LlamaConfig(bos_token_id=1,
[default0]:07/02/2024 18:58:16 [INFO|DP=0|PP=0|TP=0|ip-26-0-165-24]: eos_token_id=2,
[default0]:07/02/2024 18:58:16 [INFO|DP=0|PP=0|TP=0|ip-26-0-165-24]: hidden_act='silu',
[default0]:07/02/2024 18:58:16 [INFO|DP=0|PP=0|TP=0|ip-26-0-165-24]: hidden_size=2048,
[default0]:07/02/2024 18:58:16 [INFO|DP=0|PP=0|TP=0|ip-26-0-165-24]: initializer_range=0.02,
[default0]:07/02/2024 18:58:16 [INFO|DP=0|PP=0|TP=0|ip-26-0-165-24]: intermediate_size=4096,
[default0]:07/02/2024 18:58:16 [INFO|DP=0|PP=0|TP=0|ip-26-0-165-24]: is_llama_config=True,
[default0]:07/02/2024 18:58:16 [INFO|DP=0|PP=0|TP=0|ip-26-0-165-24]: max_position_embeddings=4096,
[default0]:07/02/2024 18:58:16 [INFO|DP=0|PP=0|TP=0|ip-26-0-165-24]: num_attention_heads=32,
[default0]:07/02/2024 18:58:16 [INFO|DP=0|PP=0|TP=0|ip-26-0-165-24]: num_hidden_layers=24,
[default0]:07/02/2024 18:58:16 [INFO|DP=0|PP=0|TP=0|ip-26-0-165-24]: num_key_value_heads=32,
[default0]:07/02/2024 18:58:16 [INFO|DP=0|PP=0|TP=0|ip-26-0-165-24]: pad_token_id=None,
[default0]:07/02/2024 18:58:16 [INFO|DP=0|PP=0|TP=0|ip-26-0-165-24]: pretraining_tp=1,
[default0]:07/02/2024 18:58:16 [INFO|DP=0|PP=0|TP=0|ip-26-0-165-24]: rms_norm_eps=1e-05,
[default0]:07/02/2024 18:58:16 [INFO|DP=0|PP=0|TP=0|ip-26-0-165-24]: rope_scaling=None,
[default0]:07/02/2024 18:58:16 [INFO|DP=0|PP=0|TP=0|ip-26-0-165-24]: rope_theta=10000.0,
[default0]:07/02/2024 18:58:16 [INFO|DP=0|PP=0|TP=0|ip-26-0-165-24]: tie_word_embeddings=True,
[default0]:07/02/2024 18:58:16 [INFO|DP=0|PP=0|TP=0|ip-26-0-165-24]: use_cache=True,
[default0]:07/02/2024 18:58:16 [INFO|DP=0|PP=0|TP=0|ip-26-0-165-24]: vocab_size=50258),
[default0]:07/02/2024 18:58:16 [INFO|DP=0|PP=0|TP=0|ip-26-0-165-24]: init_method=RandomInit(std=0.025),
[default0]:07/02/2024 18:58:16 [INFO|DP=0|PP=0|TP=0|ip-26-0-165-24]: dtype=torch.bfloat16,
[default0]:07/02/2024 18:58:16 [INFO|DP=0|PP=0|TP=0|ip-26-0-165-24]: make_vocab_size_divisible_by=1,
[default0]:07/02/2024 18:58:16 [INFO|DP=0|PP=0|TP=0|ip-26-0-165-24]: ddp_bucket_cap_mb=25),
[default0]:07/02/2024 18:58:16 [INFO|DP=0|PP=0|TP=0|ip-26-0-165-24]: tokenizer=TokenizerArgs(tokenizer_name_or_path='openai-community/gpt2',
[default0]:07/02/2024 18:58:16 [INFO|DP=0|PP=0|TP=0|ip-26-0-165-24]: tokenizer_revision=None,
[default0]:07/02/2024 18:58:16 [INFO|DP=0|PP=0|TP=0|ip-26-0-165-24]: tokenizer_max_length=None),
[default0]:07/02/2024 18:58:16 [INFO|DP=0|PP=0|TP=0|ip-26-0-165-24]: checkpoints=CheckpointsArgs(checkpoints_path=Path('/dev/null'),
[default0]:07/02/2024 18:58:16 [INFO|DP=0|PP=0|TP=0|ip-26-0-165-24]: checkpoint_interval=100000,
[default0]:07/02/2024 18:58:16 [INFO|DP=0|PP=0|TP=0|ip-26-0-165-24]: save_initial_state=False,
[default0]:07/02/2024 18:58:16 [INFO|DP=0|PP=0|TP=0|ip-26-0-165-24]: resume_checkpoint_path=None,
[default0]:07/02/2024 18:58:16 [INFO|DP=0|PP=0|TP=0|ip-26-0-165-24]: checkpoints_path_is_shared_file_system=False),
[default0]:07/02/2024 18:58:16 [INFO|DP=0|PP=0|TP=0|ip-26-0-165-24]: logging=LoggingArgs(log_level='info',
[default0]:07/02/2024 18:58:16 [INFO|DP=0|PP=0|TP=0|ip-26-0-165-24]: log_level_replica='info',
[default0]:07/02/2024 18:58:16 [INFO|DP=0|PP=0|TP=0|ip-26-0-165-24]: iteration_step_info_interval=1),
[default0]:07/02/2024 18:58:16 [INFO|DP=0|PP=0|TP=0|ip-26-0-165-24]: tokens=TokensArgs(sequence_length=4096,
[default0]:07/02/2024 18:58:16 [INFO|DP=0|PP=0|TP=0|ip-26-0-165-24]: train_steps=20,
[default0]:07/02/2024 18:58:16 [INFO|DP=0|PP=0|TP=0|ip-26-0-165-24]: micro_batch_size=16,
[default0]:07/02/2024 18:58:16 [INFO|DP=0|PP=0|TP=0|ip-26-0-165-24]: batch_accumulation_per_replica=64,
[default0]:07/02/2024 18:58:16 [INFO|DP=0|PP=0|TP=0|ip-26-0-165-24]: val_check_interval=-1,
[default0]:07/02/2024 18:58:16 [INFO|DP=0|PP=0|TP=0|ip-26-0-165-24]: limit_val_batches=0,
[default0]:07/02/2024 18:58:16 [INFO|DP=0|PP=0|TP=0|ip-26-0-165-24]: limit_test_batches=0),
[default0]:07/02/2024 18:58:16 [INFO|DP=0|PP=0|TP=0|ip-26-0-165-24]: optimizer=OptimizerArgs(optimizer_factory=AdamWOptimizerArgs(adam_eps=1e-08,
[default0]:07/02/2024 18:58:16 [INFO|DP=0|PP=0|TP=0|ip-26-0-165-24]: adam_beta1=0.9,
[default0]:07/02/2024 18:58:16 [INFO|DP=0|PP=0|TP=0|ip-26-0-165-24]: adam_beta2=0.95,
[default0]:07/02/2024 18:58:16 [INFO|DP=0|PP=0|TP=0|ip-26-0-165-24]: torch_adam_is_fused=True,
[default0]:07/02/2024 18:58:16 [INFO|DP=0|PP=0|TP=0|ip-26-0-165-24]: name='adamW'),
[default0]:07/02/2024 18:58:16 [INFO|DP=0|PP=0|TP=0|ip-26-0-165-24]: zero_stage=1,
[default0]:07/02/2024 18:58:16 [INFO|DP=0|PP=0|TP=0|ip-26-0-165-24]: weight_decay=0.01,
[default0]:07/02/2024 18:58:16 [INFO|DP=0|PP=0|TP=0|ip-26-0-165-24]: clip_grad=1.0,
[default0]:07/02/2024 18:58:16 [INFO|DP=0|PP=0|TP=0|ip-26-0-165-24]: accumulate_grad_in_fp32=True,
[default0]:07/02/2024 18:58:16 [INFO|DP=0|PP=0|TP=0|ip-26-0-165-24]: learning_rate_scheduler=LRSchedulerArgs(learning_rate=0.0001,
[default0]:07/02/2024 18:58:16 [INFO|DP=0|PP=0|TP=0|ip-26-0-165-24]: lr_warmup_steps=1,
[default0]:07/02/2024 18:58:16 [INFO|DP=0|PP=0|TP=0|ip-26-0-165-24]: lr_warmup_style='linear',
[default0]:07/02/2024 18:58:16 [INFO|DP=0|PP=0|TP=0|ip-26-0-165-24]: lr_decay_style='linear',
[default0]:07/02/2024 18:58:16 [INFO|DP=0|PP=0|TP=0|ip-26-0-165-24]: lr_decay_steps=19,
[default0]:07/02/2024 18:58:16 [INFO|DP=0|PP=0|TP=0|ip-26-0-165-24]: lr_decay_starting_step=None,
[default0]:07/02/2024 18:58:16 [INFO|DP=0|PP=0|TP=0|ip-26-0-165-24]: min_decay_lr=1e-05)),
[default0]:07/02/2024 18:58:16 [INFO|DP=0|PP=0|TP=0|ip-26-0-165-24]: data_stages=[DatasetStageArgs(name='Training Stage',
[default0]:07/02/2024 18:58:16 [INFO|DP=0|PP=0|TP=0|ip-26-0-165-24]: start_training_step=1,
[default0]:07/02/2024 18:58:16 [INFO|DP=0|PP=0|TP=0|ip-26-0-165-24]: data=DataArgs(dataset=PretrainDatasetsArgs(hf_dataset_or_datasets='roneneldan/TinyStories',
[default0]:07/02/2024 18:58:16 [INFO|DP=0|PP=0|TP=0|ip-26-0-165-24]: hf_dataset_splits='train',
[default0]:07/02/2024 18:58:16 [INFO|DP=0|PP=0|TP=0|ip-26-0-165-24]: hf_dataset_config_name=None,
[default0]:07/02/2024 18:58:16 [INFO|DP=0|PP=0|TP=0|ip-26-0-165-24]: dataset_processing_num_proc_per_process=64,
[default0]:07/02/2024 18:58:16 [INFO|DP=0|PP=0|TP=0|ip-26-0-165-24]: dataset_overwrite_cache=False,
[default0]:07/02/2024 18:58:16 [INFO|DP=0|PP=0|TP=0|ip-26-0-165-24]: text_column_name='text'),
[default0]:07/02/2024 18:58:16 [INFO|DP=0|PP=0|TP=0|ip-26-0-165-24]: seed=42,
[default0]:07/02/2024 18:58:16 [INFO|DP=0|PP=0|TP=0|ip-26-0-165-24]: num_loading_workers=32))],
[default0]:07/02/2024 18:58:16 [INFO|DP=0|PP=0|TP=0|ip-26-0-165-24]: profiler=ProfilerArgs(profiler_export_path=Path('/fsx/ferdinandmom/ferdinand-hf/bench_cluster/results/llama-1B/16_GPUS/dp-1_tp-2_pp-8_mbz-16')),
[default0]:07/02/2024 18:58:16 [INFO|DP=0|PP=0|TP=0|ip-26-0-165-24]: lighteval=None)
[default0]:07/02/2024 18:58:16 [INFO|DP=0|PP=0|TP=0|ip-26-0-165-24]: Model Config:
[default0]:07/02/2024 18:58:16 [INFO|DP=0|PP=0|TP=0|ip-26-0-165-24]: LlamaConfig(bos_token_id=1,
[default0]:07/02/2024 18:58:16 [INFO|DP=0|PP=0|TP=0|ip-26-0-165-24]: eos_token_id=2,
[default0]:07/02/2024 18:58:16 [INFO|DP=0|PP=0|TP=0|ip-26-0-165-24]: hidden_act='silu',
[default0]:07/02/2024 18:58:16 [INFO|DP=0|PP=0|TP=0|ip-26-0-165-24]: hidden_size=2048,
[default0]:07/02/2024 18:58:16 [INFO|DP=0|PP=0|TP=0|ip-26-0-165-24]: initializer_range=0.02,
[default0]:07/02/2024 18:58:16 [INFO|DP=0|PP=0|TP=0|ip-26-0-165-24]: intermediate_size=4096,
[default0]:07/02/2024 18:58:16 [INFO|DP=0|PP=0|TP=0|ip-26-0-165-24]: is_llama_config=True,
[default0]:07/02/2024 18:58:16 [INFO|DP=0|PP=0|TP=0|ip-26-0-165-24]: max_position_embeddings=4096,
[default0]:07/02/2024 18:58:16 [INFO|DP=0|PP=0|TP=0|ip-26-0-165-24]: num_attention_heads=32,
[default0]:07/02/2024 18:58:16 [INFO|DP=0|PP=0|TP=0|ip-26-0-165-24]: num_hidden_layers=24,
[default0]:07/02/2024 18:58:16 [INFO|DP=0|PP=0|TP=0|ip-26-0-165-24]: num_key_value_heads=32,
[default0]:07/02/2024 18:58:16 [INFO|DP=0|PP=0|TP=0|ip-26-0-165-24]: pad_token_id=None,
[default0]:07/02/2024 18:58:16 [INFO|DP=0|PP=0|TP=0|ip-26-0-165-24]: pretraining_tp=1,
[default0]:07/02/2024 18:58:16 [INFO|DP=0|PP=0|TP=0|ip-26-0-165-24]: rms_norm_eps=1e-05,
[default0]:07/02/2024 18:58:16 [INFO|DP=0|PP=0|TP=0|ip-26-0-165-24]: rope_scaling=None,
[default0]:07/02/2024 18:58:16 [INFO|DP=0|PP=0|TP=0|ip-26-0-165-24]: rope_theta=10000.0,
[default0]:07/02/2024 18:58:16 [INFO|DP=0|PP=0|TP=0|ip-26-0-165-24]: tie_word_embeddings=True,
[default0]:07/02/2024 18:58:16 [INFO|DP=0|PP=0|TP=0|ip-26-0-165-24]: use_cache=True,
[default0]:07/02/2024 18:58:16 [INFO|DP=0|PP=0|TP=0|ip-26-0-165-24]: vocab_size=50258)
[default0]:07/02/2024 18:58:16 [INFO|DP=0|PP=0|TP=0|ip-26-0-165-24]: Building model..
[default0]:07/02/2024 18:58:16 [INFO|DP=0|PP=0|TP=0|ip-26-0-165-24]: Setting PP block ranks...
[default7]:07/02/2024 18:58:32 [INFO|DP=0|PP=7|TP=1|ip-26-0-170-160]: Local number of parameters: 51.5M (98.16MiB)
[default4]:07/02/2024 18:58:32 [INFO|DP=0|PP=6|TP=0|ip-26-0-170-160]: Local number of parameters: 83.9M (160.03MiB)
[default4]:07/02/2024 18:58:32 [INFO|DP=0|PP=6|TP=0|ip-26-0-170-160]: [After model building] Memory usage: 164.04MiB. Peak allocated: 166.07MiB Peak reserved: 180.00MiB
[default0]:07/02/2024 18:58:32 [INFO|DP=0|PP=4|TP=0|ip-26-0-170-160]: Local number of parameters: 62.9M (120.02MiB)
[default0]:07/02/2024 18:58:32 [INFO|DP=0|PP=4|TP=0|ip-26-0-170-160]: [After model building] Memory usage: 123.03MiB. Peak allocated: 125.06MiB Peak reserved: 138.00MiB
[default0]:07/02/2024 18:58:32 [INFO|DP=0|PP=4|TP=0|ip-26-0-170-160]: No checkpoint path provided.
[default4]:07/02/2024 18:58:32 [INFO|DP=0|PP=6|TP=0|ip-26-0-170-160]: No checkpoint path provided.
[default7]:07/02/2024 18:58:32 [INFO|DP=0|PP=7|TP=1|ip-26-0-170-160]: [After model building] Memory usage: 98.17MiB. Peak allocated: 98.18MiB Peak reserved: 102.00MiB
[default7]:07/02/2024 18:58:32 [INFO|DP=0|PP=7|TP=1|ip-26-0-170-160]: No checkpoint path provided.
[default6]:07/02/2024 18:58:32 [INFO|DP=0|PP=7|TP=0|ip-26-0-170-160]: Local number of parameters: 51.5M (98.16MiB)
[default6]:07/02/2024 18:58:32 [INFO|DP=0|PP=7|TP=0|ip-26-0-170-160]: [After model building] Memory usage: 98.17MiB. Peak allocated: 98.18MiB Peak reserved: 102.00MiB
[default6]:07/02/2024 18:58:32 [INFO|DP=0|PP=7|TP=0|ip-26-0-170-160]: No checkpoint path provided.
[default1]:07/02/2024 18:58:32 [INFO|DP=0|PP=4|TP=1|ip-26-0-170-160]: Local number of parameters: 62.9M (120.02MiB)
[default1]:07/02/2024 18:58:32 [INFO|DP=0|PP=4|TP=1|ip-26-0-170-160]: [After model building] Memory usage: 123.03MiB. Peak allocated: 125.06MiB Peak reserved: 138.00MiB
[default2]:07/02/2024 18:58:32 [INFO|DP=0|PP=5|TP=0|ip-26-0-170-160]: Local number of parameters: 62.9M (120.02MiB)
[default2]:07/02/2024 18:58:32 [INFO|DP=0|PP=5|TP=0|ip-26-0-170-160]: [After model building] Memory usage: 123.03MiB. Peak allocated: 125.06MiB Peak reserved: 138.00MiB
[default2]:07/02/2024 18:58:32 [INFO|DP=0|PP=5|TP=0|ip-26-0-170-160]: No checkpoint path provided.
[default1]:07/02/2024 18:58:32 [INFO|DP=0|PP=4|TP=1|ip-26-0-170-160]: No checkpoint path provided.
[default5]:07/02/2024 18:58:32 [INFO|DP=0|PP=6|TP=1|ip-26-0-170-160]: Local number of parameters: 83.9M (160.03MiB)
[default5]:07/02/2024 18:58:32 [INFO|DP=0|PP=6|TP=1|ip-26-0-170-160]: [After model building] Memory usage: 164.04MiB. Peak allocated: 166.07MiB Peak reserved: 180.00MiB
[default5]:07/02/2024 18:58:32 [INFO|DP=0|PP=6|TP=1|ip-26-0-170-160]: No checkpoint path provided.
[default3]:07/02/2024 18:58:32 [INFO|DP=0|PP=5|TP=1|ip-26-0-170-160]: Local number of parameters: 62.9M (120.02MiB)
[default3]:07/02/2024 18:58:32 [INFO|DP=0|PP=5|TP=1|ip-26-0-170-160]: [After model building] Memory usage: 123.03MiB. Peak allocated: 125.06MiB Peak reserved: 138.00MiB
[default3]:07/02/2024 18:58:32 [INFO|DP=0|PP=5|TP=1|ip-26-0-170-160]: No checkpoint path provided.
[default3]:07/02/2024 18:58:32 [INFO|DP=0|PP=1|TP=1|ip-26-0-165-24]: Local number of parameters: 62.9M (120.02MiB)
[default3]:07/02/2024 18:58:32 [INFO|DP=0|PP=1|TP=1|ip-26-0-165-24]: [After model building] Memory usage: 123.03MiB. Peak allocated: 125.06MiB Peak reserved: 138.00MiB
[default0]:07/02/2024 18:58:32 [INFO|DP=0|PP=0|TP=0|ip-26-0-165-24]: Total number of parameters: 1.21G (2313.02MiB)
[default0]:07/02/2024 18:58:32 [INFO|DP=0|PP=0|TP=0|ip-26-0-165-24]: Local number of parameters: 135M (258.19MiB)
[default0]:07/02/2024 18:58:32 [INFO|DP=0|PP=0|TP=0|ip-26-0-165-24]: [After model building] Memory usage: 262.20MiB. Peak allocated: 264.23MiB Peak reserved: 280.00MiB
[default3]:07/02/2024 18:58:32 [INFO|DP=0|PP=1|TP=1|ip-26-0-165-24]: No checkpoint path provided.
[default0]:07/02/2024 18:58:32 [INFO|DP=0|PP=0|TP=0|ip-26-0-165-24]: No checkpoint path provided.
[default0]:07/02/2024 18:58:32 [INFO|DP=0|PP=0|TP=0|ip-26-0-165-24]: Parametrizing model parameters using StandardParametrizator
[default5]:07/02/2024 18:58:32 [INFO|DP=0|PP=2|TP=1|ip-26-0-165-24]: Local number of parameters: 62.9M (120.02MiB)
[default5]:07/02/2024 18:58:32 [INFO|DP=0|PP=2|TP=1|ip-26-0-165-24]: [After model building] Memory usage: 123.03MiB. Peak allocated: 125.06MiB Peak reserved: 138.00MiB
[default4]:07/02/2024 18:58:32 [INFO|DP=0|PP=2|TP=0|ip-26-0-165-24]: Local number of parameters: 62.9M (120.02MiB)
[default4]:07/02/2024 18:58:32 [INFO|DP=0|PP=2|TP=0|ip-26-0-165-24]: [After model building] Memory usage: 123.03MiB. Peak allocated: 125.06MiB Peak reserved: 138.00MiB
[default7]:07/02/2024 18:58:32 [INFO|DP=0|PP=3|TP=1|ip-26-0-165-24]: Local number of parameters: 83.9M (160.03MiB)
[default7]:07/02/2024 18:58:32 [INFO|DP=0|PP=3|TP=1|ip-26-0-165-24]: [After model building] Memory usage: 164.04MiB. Peak allocated: 166.07MiB Peak reserved: 180.00MiB
[default7]:07/02/2024 18:58:32 [INFO|DP=0|PP=3|TP=1|ip-26-0-165-24]: No checkpoint path provided.
[default6]:07/02/2024 18:58:32 [INFO|DP=0|PP=3|TP=0|ip-26-0-165-24]: Local number of parameters: 83.9M (160.03MiB)
[default6]:07/02/2024 18:58:32 [INFO|DP=0|PP=3|TP=0|ip-26-0-165-24]: [After model building] Memory usage: 164.04MiB. Peak allocated: 166.07MiB Peak reserved: 180.00MiB
[default6]:07/02/2024 18:58:32 [INFO|DP=0|PP=3|TP=0|ip-26-0-165-24]: No checkpoint path provided.
[default5]:07/02/2024 18:58:32 [INFO|DP=0|PP=2|TP=1|ip-26-0-165-24]: No checkpoint path provided.
[default1]:07/02/2024 18:58:32 [INFO|DP=0|PP=0|TP=1|ip-26-0-165-24]: Local number of parameters: 135M (258.19MiB)
[default1]:07/02/2024 18:58:32 [INFO|DP=0|PP=0|TP=1|ip-26-0-165-24]: [After model building] Memory usage: 262.20MiB. Peak allocated: 264.23MiB Peak reserved: 280.00MiB
[default4]:07/02/2024 18:58:32 [INFO|DP=0|PP=2|TP=0|ip-26-0-165-24]: No checkpoint path provided.
[default2]:07/02/2024 18:58:32 [INFO|DP=0|PP=1|TP=0|ip-26-0-165-24]: Local number of parameters: 62.9M (120.02MiB)
[default2]:07/02/2024 18:58:32 [INFO|DP=0|PP=1|TP=0|ip-26-0-165-24]: [After model building] Memory usage: 123.03MiB. Peak allocated: 125.06MiB Peak reserved: 138.00MiB
[default1]:07/02/2024 18:58:32 [INFO|DP=0|PP=0|TP=1|ip-26-0-165-24]: No checkpoint path provided.
[default2]:07/02/2024 18:58:32 [INFO|DP=0|PP=1|TP=0|ip-26-0-165-24]: No checkpoint path provided.
[default0]:07/02/2024 18:58:34 [INFO|DP=0|PP=0|TP=0|ip-26-0-165-24]: [Optimizer Building] Using LearningRateForSP as learning rate
[default0]:07/02/2024 18:58:34 [INFO|DP=0|PP=0|TP=0|ip-26-0-165-24]: [ZeRO sharding] Size of optimizer params per rank:
[default0]:07/02/2024 18:58:34 [INFO|DP=0|PP=0|TP=0|ip-26-0-165-24]: [ZeRO sharding] DP Rank 0 has 135M out of 135M (100.00%) params' optimizer states
[default0]:07/02/2024 18:58:35 [INFO|DP=0|PP=0|TP=0|ip-26-0-165-24]: [Training Plan] Stage Training Stage has 19 remaining training steps and has consumed 0 samples
[default0]:07/02/2024 18:58:35 [INFO|DP=0|PP=0|TP=0|ip-26-0-165-24]: Using `datasets` library
[default0]:07/02/2024 18:58:35 [INFO|DP=0|PP=0|TP=0|ip-26-0-165-24]: Loading tokenizer from openai-community/gpt2 and transformers/hf_hub versions ('4.41.2', '0.23.4')
[default0]:07/02/2024 18:58:35 [WARNING|DP=0|PP=0|TP=0|ip-26-0-165-24]: Repo card metadata block was not found. Setting CardData to empty.
[default0]:Repo card metadata block was not found. Setting CardData to empty.
[default0]:07/02/2024 18:58:36 [INFO|DP=0|PP=0|TP=0|ip-26-0-165-24]: [Training Plan] There are 1 training stages
[default0]:07/02/2024 18:58:36 [INFO|DP=0|PP=0|TP=0|ip-26-0-165-24]: [Stage Training Stage] start from step 1
[default0]:07/02/2024 18:58:36 [INFO|DP=0|PP=0|TP=0|ip-26-0-165-24]:
[default0]:07/02/2024 18:58:36 [INFO|DP=0|PP=0|TP=0|ip-26-0-165-24]: [Start training] datetime: 2024-07-02 18:58:36.988045 | mbs: 16 | grad_accum: 64 | global_batch_size: 1024 | sequence_length: 4096 | train_steps: 20 | start_iteration_step: 0 | consumed_train_samples: 0
[default0]:07/02/2024 18:58:36 [INFO|DP=0|PP=0|TP=0|ip-26-0-165-24]: Resuming training from stage Training Stage, it has trained for 0 samples and has 19 remaining train steps
[default0]:07/02/2024 18:58:36 [INFO|DP=0|PP=0|TP=0|ip-26-0-165-24]: Memory usage: 1294.97MiB. Peak allocated 1294.97MiB. Peak reserved: 1316.00MiB
[default6]:07/02/2024 18:58:37 [WARNING|DP=0|PP=7|TP=0|ip-26-0-170-160]: Repo card metadata block was not found. Setting CardData to empty.
[default0]:07/02/2024 18:58:37 [WARNING|DP=0|PP=4|TP=0|ip-26-0-170-160]: Repo card metadata block was not found. Setting CardData to empty.
[default7]:07/02/2024 18:58:37 [WARNING|DP=0|PP=7|TP=1|ip-26-0-170-160]: Repo card metadata block was not found. Setting CardData to empty.
[default0]:Repo card metadata block was not found. Setting CardData to empty.
[default1]:07/02/2024 18:58:37 [WARNING|DP=0|PP=4|TP=1|ip-26-0-170-160]: Repo card metadata block was not found. Setting CardData to empty.
[default2]:07/02/2024 18:58:37 [WARNING|DP=0|PP=5|TP=0|ip-26-0-170-160]: Repo card metadata block was not found. Setting CardData to empty.
[default2]:Repo card metadata block was not found. Setting CardData to empty.
[default5]:07/02/2024 18:58:37 [WARNING|DP=0|PP=6|TP=1|ip-26-0-170-160]: Repo card metadata block was not found. Setting CardData to empty.
[default5]:Repo card metadata block was not found. Setting CardData to empty.
[default7]:Repo card metadata block was not found. Setting CardData to empty.
[default1]:Repo card metadata block was not found. Setting CardData to empty.
[default4]:Repo card metadata block was not found. Setting CardData to empty.
[default2]:Repo card metadata block was not found. Setting CardData to empty.
[default6]:Repo card metadata block was not found. Setting CardData to empty.
[default6]:07/02/2024 18:58:37 [WARNING|DP=0|PP=3|TP=0|ip-26-0-165-24]: Repo card metadata block was not found. Setting CardData to empty.
[default4]:07/02/2024 18:58:37 [WARNING|DP=0|PP=2|TP=0|ip-26-0-165-24]: Repo card metadata block was not found. Setting CardData to empty.
[default5]:07/02/2024 18:58:37 [WARNING|DP=0|PP=2|TP=1|ip-26-0-165-24]: Repo card metadata block was not found. Setting CardData to empty.
[default7]:07/02/2024 18:58:37 [WARNING|DP=0|PP=3|TP=1|ip-26-0-165-24]: Repo card metadata block was not found. Setting CardData to empty.
[default2]:07/02/2024 18:58:37 [WARNING|DP=0|PP=1|TP=0|ip-26-0-165-24]: Repo card metadata block was not found. Setting CardData to empty.
[default6]:Repo card metadata block was not found. Setting CardData to empty.
[default5]:Repo card metadata block was not found. Setting CardData to empty.
[default7]:Repo card metadata block was not found. Setting CardData to empty.
[default3]:07/02/2024 18:58:37 [WARNING|DP=0|PP=1|TP=1|ip-26-0-165-24]: Repo card metadata block was not found. Setting CardData to empty.
[default3]:Repo card metadata block was not found. Setting CardData to empty.
[default4]:07/02/2024 18:58:37 [WARNING|DP=0|PP=6|TP=0|ip-26-0-170-160]: 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.
[default3]:07/02/2024 18:58:37 [WARNING|DP=0|PP=5|TP=1|ip-26-0-170-160]: Repo card metadata block was not found. Setting CardData to empty.
[default1]:Repo card metadata block was not found. Setting CardData to empty.
[default1]:07/02/2024 18:58:37 [WARNING|DP=0|PP=0|TP=1|ip-26-0-165-24]: Repo card metadata block was not found. Setting CardData to empty.
[default1]:[rank1]: Traceback (most recent call last):
[default1]:[rank1]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/run_train.py", line 237, in <module>
[default1]:[rank1]: trainer.train(dataloader)
[default1]:[rank1]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 429, in train
[default1]:[rank1]: outputs, loss_avg = self.training_step(dataloader=self.current_dataloader)
[default1]:[rank1]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 462, in training_step
[default1]:[rank1]: outputs = self.pipeline_engine.train_batch_iter(
[default1]:[rank1]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 252, 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 51.94 MiB is free. Including non-PyTorch memory, this process has 79.26 GiB memory in use. Of the allocated memory 70.91 GiB is allocated by PyTorch, and 297.81 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 252, 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 565, in forward
[default0]:[rank0]: key_value_states = key_value_states.permute(1, 2, 0, 3, 4).contiguous()
[default0]:[rank0]: torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 256.00 MiB. GPU
W0702 18:59:00.741000 140570191664960 torch/distributed/elastic/multiprocessing/api.py:851] Sending process 676602 closing signal SIGTERM
W0702 18:59:00.741000 140570191664960 torch/distributed/elastic/multiprocessing/api.py:851] Sending process 676603 closing signal SIGTERM
W0702 18:59:00.742000 140570191664960 torch/distributed/elastic/multiprocessing/api.py:851] Sending process 676604 closing signal SIGTERM
W0702 18:59:00.742000 140570191664960 torch/distributed/elastic/multiprocessing/api.py:851] Sending process 676605 closing signal SIGTERM
W0702 18:59:00.743000 140570191664960 torch/distributed/elastic/multiprocessing/api.py:851] Sending process 676606 closing signal SIGTERM
W0702 18:59:00.743000 140570191664960 torch/distributed/elastic/multiprocessing/api.py:851] Sending process 676607 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
[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
[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
E0702 18:59:03.171000 140570191664960 torch/distributed/elastic/multiprocessing/api.py:826] failed (exitcode: 1) local_rank: 0 (pid: 676600) 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_18:59:00
host : ip-26-0-165-24.ec2.internal
rank : 1 (local_rank: 1)
exitcode : 1 (pid: 676601)
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_18:59:00
host : ip-26-0-165-24.ec2.internal
rank : 0 (local_rank: 0)
exitcode : 1 (pid: 676600)
error_file: <N/A>
traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html
============================================================
srun: error: ip-26-0-165-24: 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
W0702 18:59:05.401000 139906645358336 torch/distributed/elastic/rendezvous/dynamic_rendezvous.py:1252] The node 'ip-26-0-170-160.ec2.internal_700100_0' has failed to send a keep-alive heartbeat to the rendezvous 'none' due to an error of type RendezvousConnectionError.
W0702 18:59:05.756000 139912312178496 torch/distributed/elastic/multiprocessing/api.py:851] Sending process 700172 closing signal SIGTERM
W0702 18:59:05.756000 139912312178496 torch/distributed/elastic/multiprocessing/api.py:851] Sending process 700173 closing signal SIGTERM
W0702 18:59:05.757000 139912312178496 torch/distributed/elastic/multiprocessing/api.py:851] Sending process 700174 closing signal SIGTERM
W0702 18:59:05.758000 139912312178496 torch/distributed/elastic/multiprocessing/api.py:851] Sending process 700175 closing signal SIGTERM
W0702 18:59:05.759000 139912312178496 torch/distributed/elastic/multiprocessing/api.py:851] Sending process 700176 closing signal SIGTERM
W0702 18:59:05.760000 139912312178496 torch/distributed/elastic/multiprocessing/api.py:851] Sending process 700177 closing signal SIGTERM
W0702 18:59:05.761000 139912312178496 torch/distributed/elastic/multiprocessing/api.py:851] Sending process 700178 closing signal SIGTERM
W0702 18:59:05.766000 139912312178496 torch/distributed/elastic/multiprocessing/api.py:851] Sending process 700179 closing signal SIGTERM
W0702 18:59:09.385000 139912312178496 torch/distributed/elastic/rendezvous/dynamic_rendezvous.py:1203] The node 'ip-26-0-170-160.ec2.internal_700100_0' has failed to shutdown the rendezvous 'none' due to an error of type RendezvousConnectionError.
W0702 18:59:09.395000 139912312178496 torch/distributed/elastic/rendezvous/dynamic_rendezvous.py:1203] The node 'ip-26-0-170-160.ec2.internal_700100_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-26-0-170-160: task 1: Exited with exit code 1
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