llama-1B/8_GPUS/dp-4_tp-2_pp-1_mbz-16/log.out

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START TIME: Wed Jul  3 22:42:50 UTC 2024
python3 version = Python 3.10.14
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Job status: RUNNING
W0703 22:42:58.927000 140633245345600 torch/distributed/run.py:757] 
W0703 22:42:58.927000 140633245345600 torch/distributed/run.py:757] *****************************************
W0703 22:42:58.927000 140633245345600 torch/distributed/run.py:757] Setting OMP_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. 
W0703 22:42:58.927000 140633245345600 torch/distributed/run.py:757] *****************************************
[default0]:07/03/2024 22:43:20 [WARNING|DP=0|PP=0|TP=0|ip-26-0-161-178]: [Vocab Size Padding] Padded vocab (size: 50257) with 1 dummy tokens (new size: 50258)
[default0]:07/03/2024 22:43:20 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]: Config:
[default0]:07/03/2024 22:43:20 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]: Config(general=GeneralArgs(project='bench_cluster',
[default0]:07/03/2024 22:43:20 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]:                            run='%date_%jobid',
[default0]:07/03/2024 22:43:20 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]:                            seed=42,
[default0]:07/03/2024 22:43:20 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]:                            step=None,
[default0]:07/03/2024 22:43:20 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]:                            consumed_train_samples=None,
[default0]:07/03/2024 22:43:20 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]:                            benchmark_csv_path=None,
[default0]:07/03/2024 22:43:20 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]:                            ignore_sanity_checks=True),
[default0]:07/03/2024 22:43:20 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]:        parallelism=ParallelismArgs(dp=4,
[default0]:07/03/2024 22:43:20 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]:                                    pp=1,
[default0]:07/03/2024 22:43:20 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]:                                    tp=2,
[default0]:07/03/2024 22:43:20 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]:                                    pp_engine=<nanotron.parallel.pipeline_parallel.engine.OneForwardOneBackwardPipelineEngine object at 0x7f0d9451c820>,
[default0]:07/03/2024 22:43:20 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]:                                    tp_mode=<TensorParallelLinearMode.REDUCE_SCATTER: 2>,
[default0]:07/03/2024 22:43:20 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]:                                    tp_linear_async_communication=False,
[default0]:07/03/2024 22:43:20 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]:                                    expert_parallel_size=1),
[default0]:07/03/2024 22:43:20 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]:        model=ModelArgs(model_config=LlamaConfig(bos_token_id=1,
[default0]:07/03/2024 22:43:20 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]:                                                 eos_token_id=2,
[default0]:07/03/2024 22:43:20 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]:                                                 hidden_act='silu',
[default0]:07/03/2024 22:43:20 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]:                                                 hidden_size=2048,
[default0]:07/03/2024 22:43:20 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]:                                                 initializer_range=0.02,
[default0]:07/03/2024 22:43:20 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]:                                                 intermediate_size=4096,
[default0]:07/03/2024 22:43:20 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]:                                                 is_llama_config=True,
[default0]:07/03/2024 22:43:20 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]:                                                 max_position_embeddings=4096,
[default0]:07/03/2024 22:43:20 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]:                                                 num_attention_heads=32,
[default0]:07/03/2024 22:43:20 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]:                                                 num_hidden_layers=24,
[default0]:07/03/2024 22:43:20 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]:                                                 num_key_value_heads=32,
[default0]:07/03/2024 22:43:20 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]:                                                 pad_token_id=None,
[default0]:07/03/2024 22:43:20 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]:                                                 pretraining_tp=1,
[default0]:07/03/2024 22:43:20 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]:                                                 rms_norm_eps=1e-05,
[default0]:07/03/2024 22:43:20 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]:                                                 rope_scaling=None,
[default0]:07/03/2024 22:43:20 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]:                                                 rope_theta=10000.0,
[default0]:07/03/2024 22:43:20 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]:                                                 tie_word_embeddings=True,
[default0]:07/03/2024 22:43:20 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]:                                                 use_cache=True,
[default0]:07/03/2024 22:43:20 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]:                                                 vocab_size=50258),
[default0]:07/03/2024 22:43:20 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]:                        init_method=RandomInit(std=0.025),
[default0]:07/03/2024 22:43:20 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]:                        dtype=torch.bfloat16,
[default0]:07/03/2024 22:43:20 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]:                        make_vocab_size_divisible_by=1,
[default0]:07/03/2024 22:43:20 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]:                        ddp_bucket_cap_mb=25),
[default0]:07/03/2024 22:43:20 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]:        tokenizer=TokenizerArgs(tokenizer_name_or_path='openai-community/gpt2',
[default0]:07/03/2024 22:43:20 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]:                                tokenizer_revision=None,
[default0]:07/03/2024 22:43:20 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]:                                tokenizer_max_length=None),
[default0]:07/03/2024 22:43:20 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]:        checkpoints=CheckpointsArgs(checkpoints_path=Path('/dev/null'),
[default0]:07/03/2024 22:43:20 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]:                                    checkpoint_interval=100000,
[default0]:07/03/2024 22:43:20 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]:                                    save_initial_state=False,
[default0]:07/03/2024 22:43:20 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]:                                    resume_checkpoint_path=None,
[default0]:07/03/2024 22:43:20 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]:                                    checkpoints_path_is_shared_file_system=False),
[default0]:07/03/2024 22:43:20 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]:        logging=LoggingArgs(log_level='info',
[default0]:07/03/2024 22:43:20 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]:                            log_level_replica='info',
[default0]:07/03/2024 22:43:20 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]:                            iteration_step_info_interval=1),
[default0]:07/03/2024 22:43:20 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]:        tokens=TokensArgs(sequence_length=4096,
[default0]:07/03/2024 22:43:20 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]:                          train_steps=20,
[default0]:07/03/2024 22:43:20 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]:                          micro_batch_size=16,
[default0]:07/03/2024 22:43:20 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]:                          batch_accumulation_per_replica=16,
[default0]:07/03/2024 22:43:20 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]:                          val_check_interval=-1,
[default0]:07/03/2024 22:43:20 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]:                          limit_val_batches=0,
[default0]:07/03/2024 22:43:20 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]:                          limit_test_batches=0),
[default0]:07/03/2024 22:43:20 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]:        optimizer=OptimizerArgs(optimizer_factory=AdamWOptimizerArgs(adam_eps=1e-08,
[default0]:07/03/2024 22:43:20 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]:                                                                     adam_beta1=0.9,
[default0]:07/03/2024 22:43:20 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]:                                                                     adam_beta2=0.95,
[default0]:07/03/2024 22:43:20 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]:                                                                     torch_adam_is_fused=True,
[default0]:07/03/2024 22:43:20 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]:                                                                     name='adamW'),
[default0]:07/03/2024 22:43:20 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]:                                zero_stage=1,
[default0]:07/03/2024 22:43:20 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]:                                weight_decay=0.01,
[default0]:07/03/2024 22:43:20 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]:                                clip_grad=1.0,
[default0]:07/03/2024 22:43:20 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]:                                accumulate_grad_in_fp32=True,
[default0]:07/03/2024 22:43:20 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]:                                learning_rate_scheduler=LRSchedulerArgs(learning_rate=0.0001,
[default0]:07/03/2024 22:43:20 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]:                                                                        lr_warmup_steps=1,
[default0]:07/03/2024 22:43:20 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]:                                                                        lr_warmup_style='linear',
[default0]:07/03/2024 22:43:20 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]:                                                                        lr_decay_style='linear',
[default0]:07/03/2024 22:43:20 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]:                                                                        lr_decay_steps=19,
[default0]:07/03/2024 22:43:20 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]:                                                                        lr_decay_starting_step=None,
[default0]:07/03/2024 22:43:20 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]:                                                                        min_decay_lr=1e-05)),
[default0]:07/03/2024 22:43:20 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]:        data_stages=[DatasetStageArgs(name='Training Stage',
[default0]:07/03/2024 22:43:20 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]:                                      start_training_step=1,
[default0]:07/03/2024 22:43:20 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]:                                      data=DataArgs(dataset=PretrainDatasetsArgs(hf_dataset_or_datasets='roneneldan/TinyStories',
[default0]:07/03/2024 22:43:20 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]:                                                                                 hf_dataset_splits='train',
[default0]:07/03/2024 22:43:20 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]:                                                                                 hf_dataset_config_name=None,
[default0]:07/03/2024 22:43:20 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]:                                                                                 dataset_processing_num_proc_per_process=64,
[default0]:07/03/2024 22:43:20 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]:                                                                                 dataset_overwrite_cache=False,
[default0]:07/03/2024 22:43:20 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]:                                                                                 text_column_name='text'),
[default0]:07/03/2024 22:43:20 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]:                                                    seed=42,
[default0]:07/03/2024 22:43:20 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]:                                                    num_loading_workers=0))],
[default0]:07/03/2024 22:43:20 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]:        profiler=ProfilerArgs(profiler_export_path=Path('/fsx/ferdinandmom/ferdinand-hf/bench_cluster/results/llama-1B/8_GPUS/dp-4_tp-2_pp-1_mbz-16')),
[default0]:07/03/2024 22:43:20 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]:        lighteval=None)
[default0]:07/03/2024 22:43:20 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]: Model Config:
[default0]:07/03/2024 22:43:20 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]: LlamaConfig(bos_token_id=1,
[default0]:07/03/2024 22:43:20 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]:             eos_token_id=2,
[default0]:07/03/2024 22:43:20 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]:             hidden_act='silu',
[default0]:07/03/2024 22:43:20 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]:             hidden_size=2048,
[default0]:07/03/2024 22:43:20 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]:             initializer_range=0.02,
[default0]:07/03/2024 22:43:20 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]:             intermediate_size=4096,
[default0]:07/03/2024 22:43:20 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]:             is_llama_config=True,
[default0]:07/03/2024 22:43:20 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]:             max_position_embeddings=4096,
[default0]:07/03/2024 22:43:20 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]:             num_attention_heads=32,
[default0]:07/03/2024 22:43:20 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]:             num_hidden_layers=24,
[default0]:07/03/2024 22:43:20 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]:             num_key_value_heads=32,
[default0]:07/03/2024 22:43:20 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]:             pad_token_id=None,
[default0]:07/03/2024 22:43:20 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]:             pretraining_tp=1,
[default0]:07/03/2024 22:43:20 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]:             rms_norm_eps=1e-05,
[default0]:07/03/2024 22:43:20 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]:             rope_scaling=None,
[default0]:07/03/2024 22:43:20 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]:             rope_theta=10000.0,
[default0]:07/03/2024 22:43:20 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]:             tie_word_embeddings=True,
[default0]:07/03/2024 22:43:20 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]:             use_cache=True,
[default0]:07/03/2024 22:43:20 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]:             vocab_size=50258)
[default0]:07/03/2024 22:43:20 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]: Building model..
[default0]:07/03/2024 22:43:20 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]: Setting PP block ranks...
[default1]:07/03/2024 22:43:31 [INFO|DP=0|PP=0|TP=1|ip-26-0-161-178]: Local number of parameters: 555M (1058.35MiB)
[default1]:07/03/2024 22:43:31 [INFO|DP=0|PP=0|TP=1|ip-26-0-161-178]: [After model building] Memory usage: 1082.37MiB. Peak allocated: 1182.56MiB Peak reserved: 1200.00MiB
[default1]:07/03/2024 22:43:31 [INFO|DP=0|PP=0|TP=1|ip-26-0-161-178]: No checkpoint path provided.
[default0]:07/03/2024 22:43:31 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]: Total number of parameters: 1.11G (2116.70MiB)
[default0]:07/03/2024 22:43:31 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]: Local number of parameters: 555M (1058.35MiB)
[default0]:07/03/2024 22:43:31 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]: [After model building] Memory usage: 1082.37MiB. Peak allocated: 1182.56MiB Peak reserved: 1200.00MiB
[default0]:07/03/2024 22:43:31 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]: No checkpoint path provided.
[default0]:07/03/2024 22:43:31 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]: Parametrizing model parameters using StandardParametrizator
[default2]:07/03/2024 22:43:31 [INFO|DP=1|PP=0|TP=0|ip-26-0-161-178]: No checkpoint path provided.
[default3]:07/03/2024 22:43:31 [INFO|DP=1|PP=0|TP=1|ip-26-0-161-178]: No checkpoint path provided.
[default5]:07/03/2024 22:43:31 [INFO|DP=2|PP=0|TP=1|ip-26-0-161-178]: No checkpoint path provided.
[default4]:07/03/2024 22:43:31 [INFO|DP=2|PP=0|TP=0|ip-26-0-161-178]: No checkpoint path provided.
[default6]:07/03/2024 22:43:31 [INFO|DP=3|PP=0|TP=0|ip-26-0-161-178]: No checkpoint path provided.
[default7]:07/03/2024 22:43:31 [INFO|DP=3|PP=0|TP=1|ip-26-0-161-178]: No checkpoint path provided.
[default0]:07/03/2024 22:43:35 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]: [Optimizer Building] Using LearningRateForSP as learning rate
[default0]:07/03/2024 22:43:35 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]: [ZeRO sharding] Size of optimizer params per rank:
[default0]:07/03/2024 22:43:35 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]: [ZeRO sharding] DP Rank 0 has 139M out of 555M (25.00%) params' optimizer states
[default0]:07/03/2024 22:43:35 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]: [ZeRO sharding] DP Rank 1 has 139M out of 555M (25.00%) params' optimizer states
[default0]:07/03/2024 22:43:35 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]: [ZeRO sharding] DP Rank 2 has 139M out of 555M (25.00%) params' optimizer states
[default0]:07/03/2024 22:43:35 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]: [ZeRO sharding] DP Rank 3 has 139M out of 555M (25.00%) params' optimizer states
[default0]:07/03/2024 22:43:37 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]: [Training Plan] Stage Training Stage has 19 remaining training steps and has consumed 0 samples
[default0]:07/03/2024 22:43:37 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]: Using `datasets` library
[default0]:07/03/2024 22:43:37 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]: Loading tokenizer from openai-community/gpt2 and transformers/hf_hub versions ('4.41.2', '0.23.4')
[default0]:Repo card metadata block was not found. Setting CardData to empty.
[default0]:07/03/2024 22:43:37 [WARNING|DP=0|PP=0|TP=0|ip-26-0-161-178]: Repo card metadata block was not found. Setting CardData to empty.
[default0]:07/03/2024 22:43:39 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]: [Training Plan] There are 1 training stages 
[default0]:07/03/2024 22:43:39 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]: [Stage Training Stage] start from step 1 
[default0]:07/03/2024 22:43:39 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]: 
[default0]:07/03/2024 22:43:39 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]: [Start training] datetime: 2024-07-03 22:43:39.757881 | mbs: 16 | grad_accum: 16 | global_batch_size: 1024 | sequence_length: 4096 | train_steps: 20 | start_iteration_step: 0 | consumed_train_samples: 0
[default0]:07/03/2024 22:43:39 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]: Resuming training from stage Training Stage, it has trained for 0 samples and has 19 remaining train steps
[default0]:07/03/2024 22:43:39 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]:  Memory usage: 3729.08MiB. Peak allocated 3729.08MiB. Peak reserved: 3848.00MiB
[default6]:Repo card metadata block was not found. Setting CardData to empty.
[default7]:07/03/2024 22:43:39 [WARNING|DP=3|PP=0|TP=1|ip-26-0-161-178]: Repo card metadata block was not found. Setting CardData to empty.
[default6]:07/03/2024 22:43:39 [WARNING|DP=3|PP=0|TP=0|ip-26-0-161-178]: Repo card metadata block was not found. Setting CardData to empty.
[default1]:07/03/2024 22:43:39 [WARNING|DP=0|PP=0|TP=1|ip-26-0-161-178]: Repo card metadata block was not found. Setting CardData to empty.
[default5]:07/03/2024 22:43:39 [WARNING|DP=2|PP=0|TP=1|ip-26-0-161-178]: Repo card metadata block was not found. Setting CardData to empty.
[default3]:07/03/2024 22:43:39 [WARNING|DP=1|PP=0|TP=1|ip-26-0-161-178]: Repo card metadata block was not found. Setting CardData to empty.
[default4]:07/03/2024 22:43:39 [WARNING|DP=2|PP=0|TP=0|ip-26-0-161-178]: Repo card metadata block was not found. Setting CardData to empty.
[default1]:Repo card metadata block was not found. Setting CardData to empty.
[default2]:07/03/2024 22:43:39 [WARNING|DP=1|PP=0|TP=0|ip-26-0-161-178]: Repo card metadata block was not found. Setting CardData to empty.
[default3]: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.
[default7]:Repo card metadata block was not found. Setting CardData to empty.
[default4]:Repo card metadata block was not found. Setting CardData to empty.
[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
[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)
[default5]:[rank5]: Traceback (most recent call last):
[default5]:[rank5]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/run_train.py", line 237, in <module>
[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
[default3]:[rank3]: Traceback (most recent call last):
[default5]:[rank5]:     trainer.train(dataloader)
[default0]:[rank0]:     output = self.forward(context=context, state=state, micro_batch=micro_batch, model=model)
[default3]:[rank3]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/run_train.py", line 237, in <module>
[default0]:[rank0]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 44, in forward
[default0]:[rank0]:     output = model(**micro_batch)
[default1]:[rank1]:     outputs = self.pipeline_engine.train_batch_iter(
[default3]:[rank3]:     trainer.train(dataloader)
[default3]:[rank3]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 429, in train
[default1]:[rank1]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 278, in train_batch_iter
[default5]:[rank5]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 429, in train
[default0]:[rank0]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
[default0]:[rank0]:     return self._call_impl(*args, **kwargs)
[default5]:[rank5]:     outputs, loss_avg = self.training_step(dataloader=self.current_dataloader)
[default1]:[rank1]:     output = self.forward(context=context, state=state, micro_batch=micro_batch, model=model)
[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)
[default1]:[rank1]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 44, in forward
[default2]:[rank2]: Traceback (most recent call last):
[default2]:[rank2]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/run_train.py", line 237, in <module>
[default5]:[rank5]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 462, in training_step
[default1]:[rank1]:     output = model(**micro_batch)
[default3]:[rank3]:     outputs, loss_avg = self.training_step(dataloader=self.current_dataloader)
[default3]:[rank3]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 462, in training_step
[default1]:[rank1]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
[default0]:[rank0]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 891, in forward
[default5]:[rank5]:     outputs = self.pipeline_engine.train_batch_iter(
[default1]:[rank1]:     return self._call_impl(*args, **kwargs)
[default0]:[rank0]:     sharded_logits = self.model(
[default5]:[rank5]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 278, in train_batch_iter
[default1]:[rank1]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
[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
[default5]:[rank5]:     output = self.forward(context=context, state=state, micro_batch=micro_batch, model=model)
[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(
[default5]:[rank5]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 44, in forward
[default5]:[rank5]:     output = model(**micro_batch)
[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
[default2]:[rank2]:     trainer.train(dataloader)
[default0]:[rank0]:     return self._call_impl(*args, **kwargs)
[default3]:[rank3]:     output = self.forward(context=context, state=state, micro_batch=micro_batch, model=model)
[default5]:[rank5]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
[default1]:[rank1]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
[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
[default3]:[rank3]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 44, in forward
[default3]:[rank3]:     output = model(**micro_batch)
[default5]:[rank5]:     return self._call_impl(*args, **kwargs)
[default1]:[rank1]:     return self._call_impl(*args, **kwargs)
[default0]:[rank0]:     return forward_call(*args, **kwargs)
[default2]:[rank2]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 429, in train
[default5]:[rank5]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
[default5]:[rank5]:     return forward_call(*args, **kwargs)
[default1]:[rank1]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
[default0]:[rank0]:   File "/fsx/ferdinandmom/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
[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
[default5]:[rank5]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 891, in forward
[default5]:[rank5]:     sharded_logits = self.model(
[default3]:[rank3]:     return self._call_impl(*args, **kwargs)
[default1]:[rank1]:     return self.forward_with_hidden_states(input_ids=input_ids, input_mask=input_mask)[0]
[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
[default0]:[rank0]:     return self.forward_with_hidden_states(input_ids=input_ids, input_mask=input_mask)[0]
[default5]:[rank5]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
[default5]:[rank5]:     return self._call_impl(*args, **kwargs)
[default0]:[rank0]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 784, in forward_with_hidden_states
[default3]:[rank3]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
[default1]:[rank1]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 784, in forward_with_hidden_states
[default0]:[rank0]:     sharded_logits = self.lm_head(x=hidden_states)["logits"]
[default2]:[rank2]:     outputs = self.pipeline_engine.train_batch_iter(
[default1]:[rank1]:     sharded_logits = self.lm_head(x=hidden_states)["logits"]
[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
[default2]:[rank2]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 278, in train_batch_iter
[default1]:[rank1]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
[default1]:[rank1]:     return self._call_impl(*args, **kwargs)
[default0]:[rank0]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
[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
[default1]:[rank1]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
[default5]:[rank5]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
[default5]:[rank5]:     return forward_call(*args, **kwargs)
[default1]:[rank1]:     return forward_call(*args, **kwargs)
[default2]:[rank2]:     output = model(**micro_batch)
[default2]:[rank2]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
[default2]:[rank2]:     return self._call_impl(*args, **kwargs)
[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)
[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]:     sharded_logits = self.model(
[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
[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
[default5]:[rank5]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 764, in forward
[default5]:[rank5]:     return self.forward_with_hidden_states(input_ids=input_ids, input_mask=input_mask)[0]
[default1]:[rank1]:     return self._call_impl(*args, **kwargs)
[default2]:[rank2]:     return forward_call(*args, **kwargs)
[default3]:[rank3]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
[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
[default1]:[rank1]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
[default3]:[rank3]:     return self._call_impl(*args, **kwargs)
[default5]:[rank5]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 784, in forward_with_hidden_states
[default2]:[rank2]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 891, in forward
[default3]:[rank3]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
[default1]:[rank1]:     return forward_call(*args, **kwargs)
[default1]:[rank1]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/nn.py", line 87, in forward
[default3]:[rank3]:     return forward_call(*args, **kwargs)
[default0]:[rank0]:     output = self.pp_block(**new_kwargs)
[default1]:[rank1]:     return column_linear(
[default3]:[rank3]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 764, in forward
[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)
[default1]:[rank1]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/functional.py", line 359, in column_linear
[default1]:[rank1]:     return F.linear(input, weight, bias)
[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)
[default5]:[rank5]:     sharded_logits = self.lm_head(x=hidden_states)["logits"]
[default5]:[rank5]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
[default3]:[rank3]:     return self.forward_with_hidden_states(input_ids=input_ids, input_mask=input_mask)[0]
[default3]:[rank3]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 784, in forward_with_hidden_states
[default0]:[rank0]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/nn.py", line 87, in forward
[default5]:[rank5]:     return self._call_impl(*args, **kwargs)
[default5]:[rank5]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
[default0]:[rank0]:     return column_linear(
[default0]:[rank0]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/functional.py", line 359, in column_linear
[default5]:[rank5]:     return forward_call(*args, **kwargs)
[default2]:[rank2]:     sharded_logits = self.model(
[default0]:[rank0]:     return F.linear(input, weight, bias)
[default5]:[rank5]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/block.py", line 151, in forward
[default2]:[rank2]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
[default0]:[rank0]: torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 3.07 GiB. GPU 
[default5]:[rank5]:     output = self.pp_block(**new_kwargs)
[default3]:[rank3]:     sharded_logits = self.lm_head(x=hidden_states)["logits"]
[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)
[default3]:[rank3]:     return self._call_impl(*args, **kwargs)
[default5]:[rank5]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
[default1]:[rank1]: torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 3.07 GiB. GPU 
 has a total capacity of 79.33 GiB of which 329.94 MiB is free. Including non-PyTorch memory, this process has 79.00 GiB memory in use. Of the allocated memory 67.29 GiB is allocated by PyTorch, and 305.08 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)
[default5]:[rank5]:     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
[default5]:[rank5]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
[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)
[default5]:[rank5]:     return forward_call(*args, **kwargs)
[default3]:[rank3]:     return forward_call(*args, **kwargs)
[default5]:[rank5]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/nn.py", line 87, in forward
[default2]:[rank2]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 764, in forward
[default2]:[rank2]:     return self.forward_with_hidden_states(input_ids=input_ids, input_mask=input_mask)[0]
[default5]:[rank5]:     return column_linear(
[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)
[default5]:[rank5]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/functional.py", line 359, in column_linear
[default5]:[rank5]:     return F.linear(input, weight, bias)
[default2]:[rank2]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 784, in forward_with_hidden_states
[default5]:[rank5]: torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 3.07 GiB. GPU  has a total capacity of 79.33 GiB of which 281.94 MiB is free. Including non-PyTorch memory, this process has 79.04 GiB memory in use. Of the allocated memory 67.29 GiB is allocated by PyTorch, and 305.08 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 1532, in _wrapped_call_impl
[default3]:[rank3]:     return self._call_impl(*args, **kwargs)
[default3]:[rank3]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
[default3]:[rank3]:     return forward_call(*args, **kwargs)
[default2]:[rank2]:     sharded_logits = self.lm_head(x=hidden_states)["logits"]
[default3]:[rank3]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/nn.py", line 87, in forward
[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]:     return column_linear(
[default3]:[rank3]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/functional.py", line 359, in column_linear
[default3]:[rank3]:     return F.linear(input, weight, bias)
[default2]:[rank2]:     return self._call_impl(*args, **kwargs)
[default3]:[rank3]: torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 3.07 GiB. GPU  has a total capacity of 79.33 GiB of which 281.94 MiB is free. Including non-PyTorch memory, this process has 79.04 GiB memory in use. Of the allocated memory 67.29 GiB is allocated by PyTorch, and 305.08 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)
[default2]:[rank2]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
[default2]:[rank2]:     return forward_call(*args, **kwargs)
[default2]:[rank2]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/block.py", line 151, in forward
[default2]:[rank2]:     output = self.pp_block(**new_kwargs)
[default2]:[rank2]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
[default2]:[rank2]:     return self._call_impl(*args, **kwargs)
[default2]:[rank2]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
[default2]:[rank2]:     return forward_call(*args, **kwargs)
[default2]:[rank2]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/nn.py", line 87, in forward
[default2]:[rank2]:     return column_linear(
[default2]:[rank2]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/functional.py", line 359, in column_linear
[default2]:[rank2]:     return F.linear(input, weight, bias)
[default2]:[rank2]: torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 3.07 GiB. GPU  has a total capacity of 79.33 GiB of which 281.94 MiB is free. Including non-PyTorch memory, this process has 79.04 GiB memory in use. Of the allocated memory 67.29 GiB is allocated by PyTorch, and 305.08 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]:[rank4]: Traceback (most recent call last):
[default4]:[rank4]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/run_train.py", line 237, in <module>
[default4]:[rank4]:     trainer.train(dataloader)
[default4]:[rank4]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 429, in train
[default4]:[rank4]:     outputs, loss_avg = self.training_step(dataloader=self.current_dataloader)
[default4]:[rank4]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 462, in training_step
[default4]:[rank4]:     outputs = self.pipeline_engine.train_batch_iter(
[default4]:[rank4]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 278, in train_batch_iter
[default4]:[rank4]:     output = self.forward(context=context, state=state, micro_batch=micro_batch, model=model)
[default4]:[rank4]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 44, in forward
[default4]:[rank4]:     output = model(**micro_batch)
[default4]:[rank4]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
[default4]:[rank4]:     return self._call_impl(*args, **kwargs)
[default4]:[rank4]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
[default4]:[rank4]:     return forward_call(*args, **kwargs)
[default4]:[rank4]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 891, in forward
[default4]:[rank4]:     sharded_logits = self.model(
[default4]:[rank4]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
[default4]:[rank4]:     return self._call_impl(*args, **kwargs)
[default4]:[rank4]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
[default4]:[rank4]:     return forward_call(*args, **kwargs)
[default4]:[rank4]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 764, in forward
[default4]:[rank4]:     return self.forward_with_hidden_states(input_ids=input_ids, input_mask=input_mask)[0]
[default4]:[rank4]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 784, in forward_with_hidden_states
[default4]:[rank4]:     sharded_logits = self.lm_head(x=hidden_states)["logits"]
[default4]:[rank4]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
[default4]:[rank4]:     return self._call_impl(*args, **kwargs)
[default4]:[rank4]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
[default4]:[rank4]:     return forward_call(*args, **kwargs)
[default4]:[rank4]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/block.py", line 151, in forward
[default4]:[rank4]:     output = self.pp_block(**new_kwargs)
[default4]:[rank4]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
[default4]:[rank4]:     return self._call_impl(*args, **kwargs)
[default4]:[rank4]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
[default4]:[rank4]:     return forward_call(*args, **kwargs)
[default4]:[rank4]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/nn.py", line 87, in forward
[default4]:[rank4]:     return column_linear(
[default4]:[rank4]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/functional.py", line 359, in column_linear
[default4]:[rank4]:     return F.linear(input, weight, bias)
[default4]:[rank4]: torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 3.07 GiB. GPU  has a total capacity of 79.33 GiB of which 281.94 MiB is free. Including non-PyTorch memory, this process has 79.04 GiB memory in use. Of the allocated memory 67.29 GiB is allocated by PyTorch, and 305.08 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)
[default6]:[rank6]: Traceback (most recent call last):
[default6]:[rank6]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/run_train.py", line 237, in <module>
[default6]:[rank6]:     trainer.train(dataloader)
[default6]:[rank6]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 429, in train
[default6]:[rank6]:     outputs, loss_avg = self.training_step(dataloader=self.current_dataloader)
[default6]:[rank6]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 462, in training_step
[default6]:[rank6]:     outputs = self.pipeline_engine.train_batch_iter(
[default6]:[rank6]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 278, in train_batch_iter
[default6]:[rank6]:     output = self.forward(context=context, state=state, micro_batch=micro_batch, model=model)
[default6]:[rank6]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 44, in forward
[default6]:[rank6]:     output = model(**micro_batch)
[default6]:[rank6]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
[default6]:[rank6]:     return self._call_impl(*args, **kwargs)
[default6]:[rank6]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
[default6]:[rank6]:     return forward_call(*args, **kwargs)
[default6]:[rank6]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 891, in forward
[default6]:[rank6]:     sharded_logits = self.model(
[default6]:[rank6]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
[default6]:[rank6]:     return self._call_impl(*args, **kwargs)
[default6]:[rank6]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
[default6]:[rank6]:     return forward_call(*args, **kwargs)
[default6]:[rank6]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 764, in forward
[default6]:[rank6]:     return self.forward_with_hidden_states(input_ids=input_ids, input_mask=input_mask)[0]
[default6]:[rank6]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 784, in forward_with_hidden_states
[default6]:[rank6]:     sharded_logits = self.lm_head(x=hidden_states)["logits"]
[default6]:[rank6]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
[default6]:[rank6]:     return self._call_impl(*args, **kwargs)
[default6]:[rank6]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
[default6]:[rank6]:     return forward_call(*args, **kwargs)
[default6]:[rank6]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/block.py", line 151, in forward
[default6]:[rank6]:     output = self.pp_block(**new_kwargs)
[default6]:[rank6]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
[default6]:[rank6]:     return self._call_impl(*args, **kwargs)
[default6]:[rank6]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
[default6]:[rank6]:     return forward_call(*args, **kwargs)
[default6]:[rank6]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/nn.py", line 87, in forward
[default6]:[rank6]:     return column_linear(
[default6]:[rank6]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/functional.py", line 359, in column_linear
[default6]:[rank6]:     return F.linear(input, weight, bias)
[default6]:[rank6]: torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 3.07 GiB. GPU  has a total capacity of 79.33 GiB of which 521.94 MiB is free. Including non-PyTorch memory, this process has 78.81 GiB memory in use. Of the allocated memory 67.29 GiB is allocated by PyTorch, and 305.08 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)
[default7]:[rank7]: Traceback (most recent call last):
[default7]:[rank7]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/run_train.py", line 237, in <module>
[default7]:[rank7]:     trainer.train(dataloader)
[default7]:[rank7]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 429, in train
[default7]:[rank7]:     outputs, loss_avg = self.training_step(dataloader=self.current_dataloader)
[default7]:[rank7]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 462, in training_step
[default7]:[rank7]:     outputs = self.pipeline_engine.train_batch_iter(
[default7]:[rank7]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 278, in train_batch_iter
[default7]:[rank7]:     output = self.forward(context=context, state=state, micro_batch=micro_batch, model=model)
[default7]:[rank7]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 44, in forward
[default7]:[rank7]:     output = model(**micro_batch)
[default7]:[rank7]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
[default7]:[rank7]:     return self._call_impl(*args, **kwargs)
[default7]:[rank7]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
[default7]:[rank7]:     return forward_call(*args, **kwargs)
[default7]:[rank7]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 891, in forward
[default7]:[rank7]:     sharded_logits = self.model(
[default7]:[rank7]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
[default7]:[rank7]:     return self._call_impl(*args, **kwargs)
[default7]:[rank7]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
[default7]:[rank7]:     return forward_call(*args, **kwargs)
[default7]:[rank7]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 764, in forward
[default7]:[rank7]:     return self.forward_with_hidden_states(input_ids=input_ids, input_mask=input_mask)[0]
[default7]:[rank7]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 784, in forward_with_hidden_states
[default7]:[rank7]:     sharded_logits = self.lm_head(x=hidden_states)["logits"]
[default7]:[rank7]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
[default7]:[rank7]:     return self._call_impl(*args, **kwargs)
[default7]:[rank7]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
[default7]:[rank7]:     return forward_call(*args, **kwargs)
[default7]:[rank7]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/block.py", line 151, in forward
[default7]:[rank7]:     output = self.pp_block(**new_kwargs)
[default7]:[rank7]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
[default7]:[rank7]:     return self._call_impl(*args, **kwargs)
[default7]:[rank7]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
[default7]:[rank7]:     return forward_call(*args, **kwargs)
[default7]:[rank7]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/nn.py", line 87, in forward
[default7]:[rank7]:     return column_linear(
[default7]:[rank7]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/functional.py", line 359, in column_linear
[default7]:[rank7]:     return F.linear(input, weight, bias)
[default7]:[rank7]: torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 3.07 GiB. GPU  has a total capacity of 79.33 GiB of which 1001.94 MiB is free. Including non-PyTorch memory, this process has 78.34 GiB memory in use. Of the allocated memory 67.29 GiB is allocated by PyTorch, and 305.08 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)
W0703 22:43:49.264000 140633245345600 torch/distributed/elastic/multiprocessing/api.py:851] Sending process 1028021 closing signal SIGTERM
E0703 22:43:49.682000 140633245345600 torch/distributed/elastic/multiprocessing/api.py:826] failed (exitcode: 1) local_rank: 0 (pid: 1028020) of binary: /fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/bin/python3.10
Traceback (most recent call last):
  File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/bin/torchrun", line 8, in <module>
    sys.exit(main())
  File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/elastic/multiprocessing/errors/__init__.py", line 347, in wrapper
    return f(*args, **kwargs)
  File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/run.py", line 879, in main
    run(args)
  File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/run.py", line 870, in run
    elastic_launch(
  File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/launcher/api.py", line 132, in __call__
    return launch_agent(self._config, self._entrypoint, list(args))
  File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/launcher/api.py", line 263, in launch_agent
    raise ChildFailedError(
torch.distributed.elastic.multiprocessing.errors.ChildFailedError: 
============================================================
/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/run_train.py FAILED
------------------------------------------------------------
Failures:
[1]:
  time      : 2024-07-03_22:43:49
  host      : ip-26-0-161-178.ec2.internal
  rank      : 2 (local_rank: 2)
  exitcode  : 1 (pid: 1028022)
  error_file: <N/A>
  traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html
[2]:
  time      : 2024-07-03_22:43:49
  host      : ip-26-0-161-178.ec2.internal
  rank      : 3 (local_rank: 3)
  exitcode  : 1 (pid: 1028023)
  error_file: <N/A>
  traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html
[3]:
  time      : 2024-07-03_22:43:49
  host      : ip-26-0-161-178.ec2.internal
  rank      : 4 (local_rank: 4)
  exitcode  : 1 (pid: 1028024)
  error_file: <N/A>
  traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html
[4]:
  time      : 2024-07-03_22:43:49
  host      : ip-26-0-161-178.ec2.internal
  rank      : 5 (local_rank: 5)
  exitcode  : 1 (pid: 1028025)
  error_file: <N/A>
  traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html
[5]:
  time      : 2024-07-03_22:43:49
  host      : ip-26-0-161-178.ec2.internal
  rank      : 6 (local_rank: 6)
  exitcode  : 1 (pid: 1028026)
  error_file: <N/A>
  traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html
[6]:
  time      : 2024-07-03_22:43:49
  host      : ip-26-0-161-178.ec2.internal
  rank      : 7 (local_rank: 7)
  exitcode  : 1 (pid: 1028027)
  error_file: <N/A>
  traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html
------------------------------------------------------------
Root Cause (first observed failure):
[0]:
  time      : 2024-07-03_22:43:49
  host      : ip-26-0-161-178.ec2.internal
  rank      : 0 (local_rank: 0)
  exitcode  : 1 (pid: 1028020)
  error_file: <N/A>
  traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html
============================================================
srun: error: ip-26-0-161-178: task 0: Exited with exit code 1
Consider using `hf_transfer` for faster uploads. This solution comes with some limitations. See https://huggingface.co/docs/huggingface_hub/hf_transfer for more details.