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

========================
START TIME: Tue Jul  2 16:29:42 UTC 2024
python3 version = Python 3.10.14
========================
The token has not been saved to the git credentials helper. Pass `add_to_git_credential=True` in this function directly or `--add-to-git-credential` if using via `huggingface-cli` if you want to set the git credential as well.
Token is valid (permission: write).
OSError: [Errno 122] Disk quota exceeded

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
  File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/bin/huggingface-cli", line 8, in <module>
    sys.exit(main())
  File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/huggingface_hub/commands/huggingface_cli.py", line 51, in main
    service.run()
  File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/huggingface_hub/commands/user.py", line 98, in run
    login(token=self.args.token, add_to_git_credential=self.args.add_to_git_credential)
  File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/huggingface_hub/_login.py", line 111, in login
    _login(token, add_to_git_credential=add_to_git_credential, write_permission=write_permission)
  File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/huggingface_hub/_login.py", line 328, in _login
    path.write_text(token)
  File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/pathlib.py", line 1154, in write_text
    with self.open(mode='w', encoding=encoding, errors=errors, newline=newline) as f:
OSError: [Errno 122] Disk quota exceeded
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 16:29:44.579000 140518449399616 torch/distributed/run.py:757] 
W0702 16:29:44.579000 140518449399616 torch/distributed/run.py:757] *****************************************
W0702 16:29:44.579000 140518449399616 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 16:29:44.579000 140518449399616 torch/distributed/run.py:757] *****************************************
W0702 16:29:44.625000 140310946895680 torch/distributed/run.py:757] 
W0702 16:29:44.625000 140310946895680 torch/distributed/run.py:757] *****************************************
W0702 16:29:44.625000 140310946895680 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 16:29:44.625000 140310946895680 torch/distributed/run.py:757] *****************************************
[default0]:07/02/2024 16:30:03 [INFO|DP=0|PP=0|TP=0|ip-26-0-167-177]: Config:
[default0]:07/02/2024 16:30:03 [INFO|DP=0|PP=0|TP=0|ip-26-0-167-177]: Config(general=GeneralArgs(project='bench_cluster',
[default0]:07/02/2024 16:30:03 [INFO|DP=0|PP=0|TP=0|ip-26-0-167-177]:                            run='%date_%jobid',
[default0]:07/02/2024 16:30:03 [INFO|DP=0|PP=0|TP=0|ip-26-0-167-177]:                            seed=42,
[default0]:07/02/2024 16:30:03 [INFO|DP=0|PP=0|TP=0|ip-26-0-167-177]:                            step=None,
[default0]:07/02/2024 16:30:03 [INFO|DP=0|PP=0|TP=0|ip-26-0-167-177]:                            consumed_train_samples=None,
[default0]:07/02/2024 16:30:03 [INFO|DP=0|PP=0|TP=0|ip-26-0-167-177]:                            benchmark_csv_path=None,
[default0]:07/02/2024 16:30:03 [INFO|DP=0|PP=0|TP=0|ip-26-0-167-177]:                            ignore_sanity_checks=True),
[default0]:07/02/2024 16:30:03 [INFO|DP=0|PP=0|TP=0|ip-26-0-167-177]:        parallelism=ParallelismArgs(dp=16,
[default0]:07/02/2024 16:30:03 [INFO|DP=0|PP=0|TP=0|ip-26-0-167-177]:                                    pp=1,
[default0]:07/02/2024 16:30:03 [INFO|DP=0|PP=0|TP=0|ip-26-0-167-177]:                                    tp=1,
[default0]:07/02/2024 16:30:03 [INFO|DP=0|PP=0|TP=0|ip-26-0-167-177]:                                    pp_engine=<nanotron.parallel.pipeline_parallel.engine.OneForwardOneBackwardPipelineEngine object at 0x7f636dfec6a0>,
[default0]:07/02/2024 16:30:03 [INFO|DP=0|PP=0|TP=0|ip-26-0-167-177]:                                    tp_mode=<TensorParallelLinearMode.REDUCE_SCATTER: 2>,
[default0]:07/02/2024 16:30:03 [INFO|DP=0|PP=0|TP=0|ip-26-0-167-177]:                                    tp_linear_async_communication=False,
[default0]:07/02/2024 16:30:03 [INFO|DP=0|PP=0|TP=0|ip-26-0-167-177]:                                    expert_parallel_size=1),
[default0]:07/02/2024 16:30:03 [INFO|DP=0|PP=0|TP=0|ip-26-0-167-177]:        model=ModelArgs(model_config=LlamaConfig(bos_token_id=1,
[default0]:07/02/2024 16:30:03 [INFO|DP=0|PP=0|TP=0|ip-26-0-167-177]:                                                 eos_token_id=2,
[default0]:07/02/2024 16:30:03 [INFO|DP=0|PP=0|TP=0|ip-26-0-167-177]:                                                 hidden_act='silu',
[default0]:07/02/2024 16:30:03 [INFO|DP=0|PP=0|TP=0|ip-26-0-167-177]:                                                 hidden_size=2048,
[default0]:07/02/2024 16:30:03 [INFO|DP=0|PP=0|TP=0|ip-26-0-167-177]:                                                 initializer_range=0.02,
[default0]:07/02/2024 16:30:03 [INFO|DP=0|PP=0|TP=0|ip-26-0-167-177]:                                                 intermediate_size=4096,
[default0]:07/02/2024 16:30:03 [INFO|DP=0|PP=0|TP=0|ip-26-0-167-177]:                                                 is_llama_config=True,
[default0]:07/02/2024 16:30:03 [INFO|DP=0|PP=0|TP=0|ip-26-0-167-177]:                                                 max_position_embeddings=4096,
[default0]:07/02/2024 16:30:03 [INFO|DP=0|PP=0|TP=0|ip-26-0-167-177]:                                                 num_attention_heads=32,
[default0]:07/02/2024 16:30:03 [INFO|DP=0|PP=0|TP=0|ip-26-0-167-177]:                                                 num_hidden_layers=24,
[default0]:07/02/2024 16:30:03 [INFO|DP=0|PP=0|TP=0|ip-26-0-167-177]:                                                 num_key_value_heads=32,
[default0]:07/02/2024 16:30:03 [INFO|DP=0|PP=0|TP=0|ip-26-0-167-177]:                                                 pad_token_id=None,
[default0]:07/02/2024 16:30:03 [INFO|DP=0|PP=0|TP=0|ip-26-0-167-177]:                                                 pretraining_tp=1,
[default0]:07/02/2024 16:30:03 [INFO|DP=0|PP=0|TP=0|ip-26-0-167-177]:                                                 rms_norm_eps=1e-05,
[default0]:07/02/2024 16:30:03 [INFO|DP=0|PP=0|TP=0|ip-26-0-167-177]:                                                 rope_scaling=None,
[default0]:07/02/2024 16:30:03 [INFO|DP=0|PP=0|TP=0|ip-26-0-167-177]:                                                 rope_theta=10000.0,
[default0]:07/02/2024 16:30:03 [INFO|DP=0|PP=0|TP=0|ip-26-0-167-177]:                                                 tie_word_embeddings=True,
[default0]:07/02/2024 16:30:03 [INFO|DP=0|PP=0|TP=0|ip-26-0-167-177]:                                                 use_cache=True,
[default0]:07/02/2024 16:30:03 [INFO|DP=0|PP=0|TP=0|ip-26-0-167-177]:                                                 vocab_size=50257),
[default0]:07/02/2024 16:30:03 [INFO|DP=0|PP=0|TP=0|ip-26-0-167-177]:                        init_method=RandomInit(std=0.025),
[default0]:07/02/2024 16:30:03 [INFO|DP=0|PP=0|TP=0|ip-26-0-167-177]:                        dtype=torch.bfloat16,
[default0]:07/02/2024 16:30:03 [INFO|DP=0|PP=0|TP=0|ip-26-0-167-177]:                        make_vocab_size_divisible_by=1,
[default0]:07/02/2024 16:30:03 [INFO|DP=0|PP=0|TP=0|ip-26-0-167-177]:                        ddp_bucket_cap_mb=25),
[default0]:07/02/2024 16:30:03 [INFO|DP=0|PP=0|TP=0|ip-26-0-167-177]:        tokenizer=TokenizerArgs(tokenizer_name_or_path='openai-community/gpt2',
[default0]:07/02/2024 16:30:03 [INFO|DP=0|PP=0|TP=0|ip-26-0-167-177]:                                tokenizer_revision=None,
[default0]:07/02/2024 16:30:03 [INFO|DP=0|PP=0|TP=0|ip-26-0-167-177]:                                tokenizer_max_length=None),
[default0]:07/02/2024 16:30:03 [INFO|DP=0|PP=0|TP=0|ip-26-0-167-177]:        checkpoints=CheckpointsArgs(checkpoints_path=Path('/dev/null'),
[default0]:07/02/2024 16:30:03 [INFO|DP=0|PP=0|TP=0|ip-26-0-167-177]:                                    checkpoint_interval=100000,
[default0]:07/02/2024 16:30:03 [INFO|DP=0|PP=0|TP=0|ip-26-0-167-177]:                                    save_initial_state=False,
[default0]:07/02/2024 16:30:03 [INFO|DP=0|PP=0|TP=0|ip-26-0-167-177]:                                    resume_checkpoint_path=None,
[default0]:07/02/2024 16:30:03 [INFO|DP=0|PP=0|TP=0|ip-26-0-167-177]:                                    checkpoints_path_is_shared_file_system=False),
[default0]:07/02/2024 16:30:03 [INFO|DP=0|PP=0|TP=0|ip-26-0-167-177]:        logging=LoggingArgs(log_level='info',
[default0]:07/02/2024 16:30:03 [INFO|DP=0|PP=0|TP=0|ip-26-0-167-177]:                            log_level_replica='info',
[default0]:07/02/2024 16:30:03 [INFO|DP=0|PP=0|TP=0|ip-26-0-167-177]:                            iteration_step_info_interval=1),
[default0]:07/02/2024 16:30:03 [INFO|DP=0|PP=0|TP=0|ip-26-0-167-177]:        tokens=TokensArgs(sequence_length=4096,
[default0]:07/02/2024 16:30:03 [INFO|DP=0|PP=0|TP=0|ip-26-0-167-177]:                          train_steps=20,
[default0]:07/02/2024 16:30:03 [INFO|DP=0|PP=0|TP=0|ip-26-0-167-177]:                          micro_batch_size=16,
[default0]:07/02/2024 16:30:03 [INFO|DP=0|PP=0|TP=0|ip-26-0-167-177]:                          batch_accumulation_per_replica=4,
[default0]:07/02/2024 16:30:03 [INFO|DP=0|PP=0|TP=0|ip-26-0-167-177]:                          val_check_interval=-1,
[default0]:07/02/2024 16:30:03 [INFO|DP=0|PP=0|TP=0|ip-26-0-167-177]:                          limit_val_batches=0,
[default0]:07/02/2024 16:30:03 [INFO|DP=0|PP=0|TP=0|ip-26-0-167-177]:                          limit_test_batches=0),
[default0]:07/02/2024 16:30:03 [INFO|DP=0|PP=0|TP=0|ip-26-0-167-177]:        optimizer=OptimizerArgs(optimizer_factory=AdamWOptimizerArgs(adam_eps=1e-08,
[default0]:07/02/2024 16:30:03 [INFO|DP=0|PP=0|TP=0|ip-26-0-167-177]:                                                                     adam_beta1=0.9,
[default0]:07/02/2024 16:30:03 [INFO|DP=0|PP=0|TP=0|ip-26-0-167-177]:                                                                     adam_beta2=0.95,
[default0]:07/02/2024 16:30:03 [INFO|DP=0|PP=0|TP=0|ip-26-0-167-177]:                                                                     torch_adam_is_fused=True,
[default0]:07/02/2024 16:30:03 [INFO|DP=0|PP=0|TP=0|ip-26-0-167-177]:                                                                     name='adamW'),
[default0]:07/02/2024 16:30:03 [INFO|DP=0|PP=0|TP=0|ip-26-0-167-177]:                                zero_stage=1,
[default0]:07/02/2024 16:30:03 [INFO|DP=0|PP=0|TP=0|ip-26-0-167-177]:                                weight_decay=0.01,
[default0]:07/02/2024 16:30:03 [INFO|DP=0|PP=0|TP=0|ip-26-0-167-177]:                                clip_grad=1.0,
[default0]:07/02/2024 16:30:03 [INFO|DP=0|PP=0|TP=0|ip-26-0-167-177]:                                accumulate_grad_in_fp32=True,
[default0]:07/02/2024 16:30:03 [INFO|DP=0|PP=0|TP=0|ip-26-0-167-177]:                                learning_rate_scheduler=LRSchedulerArgs(learning_rate=0.0001,
[default0]:07/02/2024 16:30:03 [INFO|DP=0|PP=0|TP=0|ip-26-0-167-177]:                                                                        lr_warmup_steps=1,
[default0]:07/02/2024 16:30:03 [INFO|DP=0|PP=0|TP=0|ip-26-0-167-177]:                                                                        lr_warmup_style='linear',
[default0]:07/02/2024 16:30:03 [INFO|DP=0|PP=0|TP=0|ip-26-0-167-177]:                                                                        lr_decay_style='linear',
[default0]:07/02/2024 16:30:03 [INFO|DP=0|PP=0|TP=0|ip-26-0-167-177]:                                                                        lr_decay_steps=19,
[default0]:07/02/2024 16:30:03 [INFO|DP=0|PP=0|TP=0|ip-26-0-167-177]:                                                                        lr_decay_starting_step=None,
[default0]:07/02/2024 16:30:03 [INFO|DP=0|PP=0|TP=0|ip-26-0-167-177]:                                                                        min_decay_lr=1e-05)),
[default0]:07/02/2024 16:30:03 [INFO|DP=0|PP=0|TP=0|ip-26-0-167-177]:        data_stages=[DatasetStageArgs(name='Training Stage',
[default0]:07/02/2024 16:30:03 [INFO|DP=0|PP=0|TP=0|ip-26-0-167-177]:                                      start_training_step=1,
[default0]:07/02/2024 16:30:03 [INFO|DP=0|PP=0|TP=0|ip-26-0-167-177]:                                      data=DataArgs(dataset=PretrainDatasetsArgs(hf_dataset_or_datasets='roneneldan/TinyStories',
[default0]:07/02/2024 16:30:03 [INFO|DP=0|PP=0|TP=0|ip-26-0-167-177]:                                                                                 hf_dataset_splits='train',
[default0]:07/02/2024 16:30:03 [INFO|DP=0|PP=0|TP=0|ip-26-0-167-177]:                                                                                 hf_dataset_config_name=None,
[default0]:07/02/2024 16:30:03 [INFO|DP=0|PP=0|TP=0|ip-26-0-167-177]:                                                                                 dataset_processing_num_proc_per_process=64,
[default0]:07/02/2024 16:30:03 [INFO|DP=0|PP=0|TP=0|ip-26-0-167-177]:                                                                                 dataset_overwrite_cache=False,
[default0]:07/02/2024 16:30:03 [INFO|DP=0|PP=0|TP=0|ip-26-0-167-177]:                                                                                 text_column_name='text'),
[default0]:07/02/2024 16:30:03 [INFO|DP=0|PP=0|TP=0|ip-26-0-167-177]:                                                    seed=42,
[default0]:07/02/2024 16:30:03 [INFO|DP=0|PP=0|TP=0|ip-26-0-167-177]:                                                    num_loading_workers=32))],
[default0]:07/02/2024 16:30:03 [INFO|DP=0|PP=0|TP=0|ip-26-0-167-177]:        profiler=ProfilerArgs(profiler_export_path=Path('/fsx/ferdinandmom/ferdinand-hf/bench_cluster/results/llama-1B/16_GPUS/dp-16_tp-1_pp-1_mbz-16')),
[default0]:07/02/2024 16:30:03 [INFO|DP=0|PP=0|TP=0|ip-26-0-167-177]:        lighteval=None)
[default0]:07/02/2024 16:30:03 [INFO|DP=0|PP=0|TP=0|ip-26-0-167-177]: Model Config:
[default0]:07/02/2024 16:30:03 [INFO|DP=0|PP=0|TP=0|ip-26-0-167-177]: LlamaConfig(bos_token_id=1,
[default0]:07/02/2024 16:30:03 [INFO|DP=0|PP=0|TP=0|ip-26-0-167-177]:             eos_token_id=2,
[default0]:07/02/2024 16:30:03 [INFO|DP=0|PP=0|TP=0|ip-26-0-167-177]:             hidden_act='silu',
[default0]:07/02/2024 16:30:03 [INFO|DP=0|PP=0|TP=0|ip-26-0-167-177]:             hidden_size=2048,
[default0]:07/02/2024 16:30:03 [INFO|DP=0|PP=0|TP=0|ip-26-0-167-177]:             initializer_range=0.02,
[default0]:07/02/2024 16:30:03 [INFO|DP=0|PP=0|TP=0|ip-26-0-167-177]:             intermediate_size=4096,
[default0]:07/02/2024 16:30:03 [INFO|DP=0|PP=0|TP=0|ip-26-0-167-177]:             is_llama_config=True,
[default0]:07/02/2024 16:30:03 [INFO|DP=0|PP=0|TP=0|ip-26-0-167-177]:             max_position_embeddings=4096,
[default0]:07/02/2024 16:30:03 [INFO|DP=0|PP=0|TP=0|ip-26-0-167-177]:             num_attention_heads=32,
[default0]:07/02/2024 16:30:03 [INFO|DP=0|PP=0|TP=0|ip-26-0-167-177]:             num_hidden_layers=24,
[default0]:07/02/2024 16:30:03 [INFO|DP=0|PP=0|TP=0|ip-26-0-167-177]:             num_key_value_heads=32,
[default0]:07/02/2024 16:30:03 [INFO|DP=0|PP=0|TP=0|ip-26-0-167-177]:             pad_token_id=None,
[default0]:07/02/2024 16:30:03 [INFO|DP=0|PP=0|TP=0|ip-26-0-167-177]:             pretraining_tp=1,
[default0]:07/02/2024 16:30:03 [INFO|DP=0|PP=0|TP=0|ip-26-0-167-177]:             rms_norm_eps=1e-05,
[default0]:07/02/2024 16:30:03 [INFO|DP=0|PP=0|TP=0|ip-26-0-167-177]:             rope_scaling=None,
[default0]:07/02/2024 16:30:03 [INFO|DP=0|PP=0|TP=0|ip-26-0-167-177]:             rope_theta=10000.0,
[default0]:07/02/2024 16:30:03 [INFO|DP=0|PP=0|TP=0|ip-26-0-167-177]:             tie_word_embeddings=True,
[default0]:07/02/2024 16:30:03 [INFO|DP=0|PP=0|TP=0|ip-26-0-167-177]:             use_cache=True,
[default0]:07/02/2024 16:30:03 [INFO|DP=0|PP=0|TP=0|ip-26-0-167-177]:             vocab_size=50257)
[default0]:07/02/2024 16:30:03 [INFO|DP=0|PP=0|TP=0|ip-26-0-167-177]: Building model..
[default0]:07/02/2024 16:30:03 [INFO|DP=0|PP=0|TP=0|ip-26-0-167-177]: Setting PP block ranks...
[default0]:07/02/2024 16:30:12 [INFO|DP=0|PP=0|TP=0|ip-26-0-167-177]: Total number of parameters: 1.11G (2116.51MiB)
[default0]:07/02/2024 16:30:12 [INFO|DP=0|PP=0|TP=0|ip-26-0-167-177]: Local number of parameters: 1.11G (2116.51MiB)
[default0]:07/02/2024 16:30:12 [INFO|DP=0|PP=0|TP=0|ip-26-0-167-177]: [After model building] Memory usage: 2140.53MiB. Peak allocated: 2338.88MiB Peak reserved: 2392.00MiB
[default6]:07/02/2024 16:30:12 [INFO|DP=6|PP=0|TP=0|ip-26-0-167-177]: No checkpoint path provided.
[default0]:07/02/2024 16:30:12 [INFO|DP=0|PP=0|TP=0|ip-26-0-167-177]: No checkpoint path provided.
[default0]:07/02/2024 16:30:12 [INFO|DP=0|PP=0|TP=0|ip-26-0-167-177]: Parametrizing model parameters using StandardParametrizator
[default2]:07/02/2024 16:30:12 [INFO|DP=2|PP=0|TP=0|ip-26-0-167-177]: No checkpoint path provided.
[default1]:07/02/2024 16:30:12 [INFO|DP=1|PP=0|TP=0|ip-26-0-167-177]: No checkpoint path provided.
[default3]:07/02/2024 16:30:12 [INFO|DP=3|PP=0|TP=0|ip-26-0-167-177]: No checkpoint path provided.
[default5]:07/02/2024 16:30:12 [INFO|DP=5|PP=0|TP=0|ip-26-0-167-177]: No checkpoint path provided.
[default4]:07/02/2024 16:30:12 [INFO|DP=4|PP=0|TP=0|ip-26-0-167-177]: No checkpoint path provided.
[default7]:07/02/2024 16:30:12 [INFO|DP=7|PP=0|TP=0|ip-26-0-167-177]: No checkpoint path provided.
[default0]:07/02/2024 16:30:12 [INFO|DP=8|PP=0|TP=0|ip-26-0-170-31]: No checkpoint path provided.
[default5]:07/02/2024 16:30:12 [INFO|DP=13|PP=0|TP=0|ip-26-0-170-31]: No checkpoint path provided.
[default1]:07/02/2024 16:30:12 [INFO|DP=9|PP=0|TP=0|ip-26-0-170-31]: No checkpoint path provided.
[default3]:07/02/2024 16:30:12 [INFO|DP=11|PP=0|TP=0|ip-26-0-170-31]: No checkpoint path provided.
[default7]:07/02/2024 16:30:12 [INFO|DP=15|PP=0|TP=0|ip-26-0-170-31]: No checkpoint path provided.
[default2]:07/02/2024 16:30:12 [INFO|DP=10|PP=0|TP=0|ip-26-0-170-31]: No checkpoint path provided.
[default4]:07/02/2024 16:30:12 [INFO|DP=12|PP=0|TP=0|ip-26-0-170-31]: No checkpoint path provided.
[default6]:07/02/2024 16:30:12 [INFO|DP=14|PP=0|TP=0|ip-26-0-170-31]: No checkpoint path provided.
[default0]:07/02/2024 16:30:21 [INFO|DP=0|PP=0|TP=0|ip-26-0-167-177]: [Optimizer Building] Using LearningRateForSP as learning rate
[default0]:07/02/2024 16:30:21 [INFO|DP=0|PP=0|TP=0|ip-26-0-167-177]: [ZeRO sharding] Size of optimizer params per rank:
[default0]:07/02/2024 16:30:21 [INFO|DP=0|PP=0|TP=0|ip-26-0-167-177]: [ZeRO sharding] DP Rank 0 has 69.4M out of 1.11G (6.25%) params' optimizer states
[default0]:07/02/2024 16:30:21 [INFO|DP=0|PP=0|TP=0|ip-26-0-167-177]: [ZeRO sharding] DP Rank 1 has 69.4M out of 1.11G (6.25%) params' optimizer states
[default0]:07/02/2024 16:30:21 [INFO|DP=0|PP=0|TP=0|ip-26-0-167-177]: [ZeRO sharding] DP Rank 2 has 69.4M out of 1.11G (6.25%) params' optimizer states
[default0]:07/02/2024 16:30:21 [INFO|DP=0|PP=0|TP=0|ip-26-0-167-177]: [ZeRO sharding] DP Rank 3 has 69.4M out of 1.11G (6.25%) params' optimizer states
[default0]:07/02/2024 16:30:21 [INFO|DP=0|PP=0|TP=0|ip-26-0-167-177]: [ZeRO sharding] DP Rank 4 has 69.4M out of 1.11G (6.25%) params' optimizer states
[default0]:07/02/2024 16:30:21 [INFO|DP=0|PP=0|TP=0|ip-26-0-167-177]: [ZeRO sharding] DP Rank 5 has 69.4M out of 1.11G (6.25%) params' optimizer states
[default0]:07/02/2024 16:30:21 [INFO|DP=0|PP=0|TP=0|ip-26-0-167-177]: [ZeRO sharding] DP Rank 6 has 69.4M out of 1.11G (6.25%) params' optimizer states
[default0]:07/02/2024 16:30:21 [INFO|DP=0|PP=0|TP=0|ip-26-0-167-177]: [ZeRO sharding] DP Rank 7 has 69.4M out of 1.11G (6.25%) params' optimizer states
[default0]:07/02/2024 16:30:21 [INFO|DP=0|PP=0|TP=0|ip-26-0-167-177]: [ZeRO sharding] DP Rank 8 has 69.4M out of 1.11G (6.25%) params' optimizer states
[default0]:07/02/2024 16:30:21 [INFO|DP=0|PP=0|TP=0|ip-26-0-167-177]: [ZeRO sharding] DP Rank 9 has 69.4M out of 1.11G (6.25%) params' optimizer states
[default0]:07/02/2024 16:30:21 [INFO|DP=0|PP=0|TP=0|ip-26-0-167-177]: [ZeRO sharding] DP Rank 10 has 69.4M out of 1.11G (6.25%) params' optimizer states
[default0]:07/02/2024 16:30:21 [INFO|DP=0|PP=0|TP=0|ip-26-0-167-177]: [ZeRO sharding] DP Rank 11 has 69.4M out of 1.11G (6.25%) params' optimizer states
[default0]:07/02/2024 16:30:21 [INFO|DP=0|PP=0|TP=0|ip-26-0-167-177]: [ZeRO sharding] DP Rank 12 has 69.4M out of 1.11G (6.25%) params' optimizer states
[default0]:07/02/2024 16:30:21 [INFO|DP=0|PP=0|TP=0|ip-26-0-167-177]: [ZeRO sharding] DP Rank 13 has 69.4M out of 1.11G (6.25%) params' optimizer states
[default0]:07/02/2024 16:30:21 [INFO|DP=0|PP=0|TP=0|ip-26-0-167-177]: [ZeRO sharding] DP Rank 14 has 69.4M out of 1.11G (6.25%) params' optimizer states
[default0]:07/02/2024 16:30:21 [INFO|DP=0|PP=0|TP=0|ip-26-0-167-177]: [ZeRO sharding] DP Rank 15 has 69.4M out of 1.11G (6.25%) params' optimizer states
[default0]:07/02/2024 16:30:23 [INFO|DP=0|PP=0|TP=0|ip-26-0-167-177]: [Training Plan] Stage Training Stage has 19 remaining training steps and has consumed 0 samples
[default0]:07/02/2024 16:30:23 [INFO|DP=0|PP=0|TP=0|ip-26-0-167-177]: Using `datasets` library
[default0]:07/02/2024 16:30:23 [INFO|DP=0|PP=0|TP=0|ip-26-0-167-177]: Loading tokenizer from openai-community/gpt2 and transformers/hf_hub versions ('4.41.2', '0.23.4')
[default0]:07/02/2024 16:30:23 [WARNING|DP=0|PP=0|TP=0|ip-26-0-167-177]: 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 16:30:24 [INFO|DP=0|PP=0|TP=0|ip-26-0-167-177]: [Training Plan] There are 1 training stages 
[default0]:07/02/2024 16:30:24 [INFO|DP=0|PP=0|TP=0|ip-26-0-167-177]: [Stage Training Stage] start from step 1 
[default0]:07/02/2024 16:30:24 [INFO|DP=0|PP=0|TP=0|ip-26-0-167-177]: 
[default0]:07/02/2024 16:30:24 [INFO|DP=0|PP=0|TP=0|ip-26-0-167-177]: [Start training] datetime: 2024-07-02 16:30:24.323058 | mbs: 16 | grad_accum: 4 | global_batch_size: 1024 | sequence_length: 4096 | train_steps: 20 | start_iteration_step: 0 | consumed_train_samples: 0
[default0]:07/02/2024 16:30:24 [INFO|DP=0|PP=0|TP=0|ip-26-0-167-177]: Resuming training from stage Training Stage, it has trained for 0 samples and has 19 remaining train steps
[default0]:07/02/2024 16:30:24 [INFO|DP=0|PP=0|TP=0|ip-26-0-167-177]:  Memory usage: 6639.09MiB. Peak allocated 6639.09MiB. Peak reserved: 6892.00MiB
[default7]:07/02/2024 16:30:24 [WARNING|DP=15|PP=0|TP=0|ip-26-0-170-31]: Repo card metadata block was not found. Setting CardData to empty.
[default1]:Repo card metadata block was not found. Setting CardData to empty.
[default0]:07/02/2024 16:30:24 [WARNING|DP=8|PP=0|TP=0|ip-26-0-170-31]: Repo card metadata block was not found. Setting CardData to empty.
[default0]:Repo card metadata block was not found. Setting CardData to empty.
[default3]:07/02/2024 16:30:24 [WARNING|DP=11|PP=0|TP=0|ip-26-0-170-31]: Repo card metadata block was not found. Setting CardData to empty.
[default2]:07/02/2024 16:30:24 [WARNING|DP=10|PP=0|TP=0|ip-26-0-170-31]: Repo card metadata block was not found. Setting CardData to empty.
[default1]:07/02/2024 16:30:24 [WARNING|DP=9|PP=0|TP=0|ip-26-0-170-31]: Repo card metadata block was not found. Setting CardData to empty.
[default4]:Repo card metadata block was not found. Setting CardData to empty.
[default4]:07/02/2024 16:30:24 [WARNING|DP=12|PP=0|TP=0|ip-26-0-170-31]: Repo card metadata block was not found. Setting CardData to empty.
[default6]:07/02/2024 16:30:24 [WARNING|DP=6|PP=0|TP=0|ip-26-0-167-177]: 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 16:30:24 [WARNING|DP=1|PP=0|TP=0|ip-26-0-167-177]: Repo card metadata block was not found. Setting CardData to empty.
[default6]:07/02/2024 16:30:24 [WARNING|DP=14|PP=0|TP=0|ip-26-0-170-31]: Repo card metadata block was not found. Setting CardData to empty.
[default7]:Repo card metadata block was not found. Setting CardData to empty.
[default2]:07/02/2024 16:30:24 [WARNING|DP=2|PP=0|TP=0|ip-26-0-167-177]: Repo card metadata block was not found. Setting CardData to empty.
[default5]:07/02/2024 16:30:24 [WARNING|DP=5|PP=0|TP=0|ip-26-0-167-177]: Repo card metadata block was not found. Setting CardData to empty.
[default6]:Repo card metadata block was not found. Setting CardData to empty.
[default4]:Repo card metadata block was not found. Setting CardData to empty.
[default3]:07/02/2024 16:30:24 [WARNING|DP=3|PP=0|TP=0|ip-26-0-167-177]: Repo card metadata block was not found. Setting CardData to empty.
[default4]:07/02/2024 16:30:24 [WARNING|DP=4|PP=0|TP=0|ip-26-0-167-177]: Repo card metadata block was not found. Setting CardData to empty.
[default2]:Repo card metadata block was not found. Setting CardData to empty.
[default3]:Repo card metadata block was not found. Setting CardData to empty.
[default6]:Repo card metadata block was not found. Setting CardData to empty.
[default7]:Repo card metadata block was not found. Setting CardData to empty.
[default7]:07/02/2024 16:30:24 [WARNING|DP=7|PP=0|TP=0|ip-26-0-167-177]: Repo card metadata block was not found. Setting CardData to empty.
[default2]:Repo card metadata block was not found. Setting CardData to empty.
[default3]:Repo card metadata block was not found. Setting CardData to empty.
[default5]:Repo card metadata block was not found. Setting CardData to empty.
[default5]:07/02/2024 16:30:24 [WARNING|DP=13|PP=0|TP=0|ip-26-0-170-31]: Repo card metadata block was not found. Setting CardData to empty.
[default5]: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
[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 637, in forward
[default0]:[rank0]:     hidden_states = self.mlp(hidden_states=hidden_states)["hidden_states"]
[default0]:[rank0]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
[default0]:[rank0]:     return self._call_impl(*args, **kwargs)
[default0]:[rank0]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
[default0]:[rank0]:     return forward_call(*args, **kwargs)
[default0]:[rank0]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 171, in forward
[default0]:[rank0]:     merged_states = self.gate_up_proj(hidden_states)
[default0]:[rank0]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
[default0]:[rank0]:     return self._call_impl(*args, **kwargs)
[default0]:[rank0]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
[default0]:[rank0]:     return forward_call(*args, **kwargs)
[default0]:[rank0]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/nn.py", line 87, in forward
[default0]:[rank0]:     return column_linear(
[default0]:[rank0]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/functional.py", line 359, in column_linear
[default0]:[rank0]:     return F.linear(input, weight, bias)
[default0]:[rank0]: torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 1024.00 MiB. GPU 
[default3]:[rank3]: OSError: [Errno 122] Disk quota exceeded
[default3]:
[default3]:[rank3]: During handling of the above exception, another exception occurred:
[default3]:
[default3]:[rank3]: Traceback (most recent call last):
[default3]:[rank3]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/run_train.py", line 237, in <module>
[default3]:[rank3]:     trainer.train(dataloader)
[default3]:[rank3]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 429, in train
[default3]:[rank3]:     outputs, loss_avg = self.training_step(dataloader=self.current_dataloader)
[default3]:[rank3]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 462, in training_step
[default3]:[rank3]:     outputs = self.pipeline_engine.train_batch_iter(
[default3]:[rank3]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 278, in train_batch_iter
[default3]:[rank3]:     output = self.forward(context=context, state=state, micro_batch=micro_batch, model=model)
[default3]:[rank3]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 44, in forward
[default3]:[rank3]:     output = model(**micro_batch)
[default3]:[rank3]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
[default3]:[rank3]:     return self._call_impl(*args, **kwargs)
[default3]:[rank3]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
[default3]:[rank3]:     return forward_call(*args, **kwargs)
[default3]:[rank3]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 891, in forward
[default3]:[rank3]:     sharded_logits = self.model(
[default3]:[rank3]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
[default3]:[rank3]:     return self._call_impl(*args, **kwargs)
[default3]:[rank3]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
[default3]:[rank3]:     return forward_call(*args, **kwargs)
[default3]:[rank3]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 764, in forward
[default3]:[rank3]:     return self.forward_with_hidden_states(input_ids=input_ids, input_mask=input_mask)[0]
[default3]:[rank3]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 780, in forward_with_hidden_states
[default3]:[rank3]:     hidden_encoder_states = encoder_block(**hidden_encoder_states)
[default3]:[rank3]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
[default3]:[rank3]:     return self._call_impl(*args, **kwargs)
[default3]:[rank3]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
[default3]:[rank3]:     return forward_call(*args, **kwargs)
[default3]:[rank3]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/block.py", line 151, in forward
[default3]:[rank3]:     output = self.pp_block(**new_kwargs)
[default3]:[rank3]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
[default3]:[rank3]:     return self._call_impl(*args, **kwargs)
[default3]:[rank3]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
[default3]:[rank3]:     return forward_call(*args, **kwargs)
[default3]:[rank3]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 629, in forward
[default3]:[rank3]:     hidden_states = self.input_layernorm(hidden_states)
[default3]:[rank3]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
[default3]:[rank3]:     return self._call_impl(*args, **kwargs)
[default3]:[rank3]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
[default3]:[rank3]:     return forward_call(*args, **kwargs)
[default3]:[rank3]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/nn/layer_norm.py", line 42, in forward
[default3]:[rank3]:     return layer_norm_fn(
[default3]:[rank3]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/flash_attn/ops/triton/layer_norm.py", line 875, in layer_norm_fn
[default3]:[rank3]:     return LayerNormFn.apply(
[default3]:[rank3]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/autograd/function.py", line 598, in apply
[default3]:[rank3]:     return super().apply(*args, **kwargs)  # type: ignore[misc]
[default3]:[rank3]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/flash_attn/ops/triton/layer_norm.py", line 748, in forward
[default3]:[rank3]:     y, y1, mean, rstd, residual_out, seeds, dropout_mask, dropout_mask1 = _layer_norm_fwd(
[default3]:[rank3]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/flash_attn/ops/triton/layer_norm.py", line 335, in _layer_norm_fwd
[default3]:[rank3]:     _layer_norm_fwd_1pass_kernel[(M,)](
[default3]:[rank3]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/triton/runtime/jit.py", line 167, in <lambda>
[default3]:[rank3]:     return lambda *args, **kwargs: self.run(grid=grid, warmup=False, *args, **kwargs)
[default3]:[rank3]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/triton/runtime/autotuner.py", line 143, in run
[default3]:[rank3]:     timings = {config: self._bench(*args, config=config, **kwargs) for config in pruned_configs}
[default3]:[rank3]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/triton/runtime/autotuner.py", line 143, in <dictcomp>
[default3]:[rank3]:     timings = {config: self._bench(*args, config=config, **kwargs) for config in pruned_configs}
[default3]:[rank3]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/triton/runtime/autotuner.py", line 122, in _bench
[default3]:[rank3]:     return do_bench(kernel_call, warmup=self.warmup, rep=self.rep, quantiles=(0.5, 0.2, 0.8))
[default3]:[rank3]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/triton/testing.py", line 102, in do_bench
[default3]:[rank3]:     fn()
[default3]:[rank3]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/triton/runtime/autotuner.py", line 110, in kernel_call
[default3]:[rank3]:     self.fn.run(
[default3]:[rank3]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/triton/runtime/autotuner.py", line 305, in run
[default3]:[rank3]:     return self.fn.run(*args, **kwargs)
[default3]:[rank3]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/triton/runtime/autotuner.py", line 305, in run
[default3]:[rank3]:     return self.fn.run(*args, **kwargs)
[default3]:[rank3]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/triton/runtime/autotuner.py", line 305, in run
[default3]:[rank3]:     return self.fn.run(*args, **kwargs)
[default3]:[rank3]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/triton/runtime/jit.py", line 416, in run
[default3]:[rank3]:     self.cache[device][key] = compile(
[default3]:[rank3]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/triton/compiler/compiler.py", line 194, in compile
[default3]:[rank3]:     metadata_group[f"{src.name}.{ext}"] = fn_cache_manager.put(next_module, f"{src.name}.{ext}")
[default3]:[rank3]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/triton/runtime/cache.py", line 123, in put
[default3]:[rank3]:     with open(temp_path, mode) as f:
[default3]:[rank3]: OSError: [Errno 122] Disk quota exceeded
[default0]:[rank8]: OSError: [Errno 122] Disk quota exceeded
[default0]:
[default0]:[rank8]: During handling of the above exception, another exception occurred:
[default0]:
[default0]:[rank8]: Traceback (most recent call last):
[default0]:[rank8]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/run_train.py", line 237, in <module>
[default0]:[rank8]:     trainer.train(dataloader)
[default0]:[rank8]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 429, in train
[default0]:[rank8]:     outputs, loss_avg = self.training_step(dataloader=self.current_dataloader)
[default0]:[rank8]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 462, in training_step
[default0]:[rank8]:     outputs = self.pipeline_engine.train_batch_iter(
[default0]:[rank8]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 278, in train_batch_iter
[default0]:[rank8]:     output = self.forward(context=context, state=state, micro_batch=micro_batch, model=model)
[default0]:[rank8]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 44, in forward
[default0]:[rank8]:     output = model(**micro_batch)
[default0]:[rank8]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
[default0]:[rank8]:     return self._call_impl(*args, **kwargs)
[default0]:[rank8]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
[default0]:[rank8]:     return forward_call(*args, **kwargs)
[default0]:[rank8]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 891, in forward
[default0]:[rank8]:     sharded_logits = self.model(
[default0]:[rank8]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
[default0]:[rank8]:     return self._call_impl(*args, **kwargs)
[default0]:[rank8]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
[default0]:[rank8]:     return forward_call(*args, **kwargs)
[default0]:[rank8]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 764, in forward
[default0]:[rank8]:     return self.forward_with_hidden_states(input_ids=input_ids, input_mask=input_mask)[0]
[default0]:[rank8]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 780, in forward_with_hidden_states
[default0]:[rank8]:     hidden_encoder_states = encoder_block(**hidden_encoder_states)
[default0]:[rank8]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
[default0]:[rank8]:     return self._call_impl(*args, **kwargs)
[default0]:[rank8]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
[default0]:[rank8]:     return forward_call(*args, **kwargs)
[default0]:[rank8]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/block.py", line 151, in forward
[default0]:[rank8]:     output = self.pp_block(**new_kwargs)
[default0]:[rank8]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
[default0]:[rank8]:     return self._call_impl(*args, **kwargs)
[default0]:[rank8]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
[default0]:[rank8]:     return forward_call(*args, **kwargs)
[default0]:[rank8]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 629, in forward
[default0]:[rank8]:     hidden_states = self.input_layernorm(hidden_states)
[default0]:[rank8]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
[default0]:[rank8]:     return self._call_impl(*args, **kwargs)
[default0]:[rank8]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
[default0]:[rank8]:     return forward_call(*args, **kwargs)
[default0]:[rank8]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/nn/layer_norm.py", line 42, in forward
[default0]:[rank8]:     return layer_norm_fn(
[default0]:[rank8]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/flash_attn/ops/triton/layer_norm.py", line 875, in layer_norm_fn
[default0]:[rank8]:     return LayerNormFn.apply(
[default0]:[rank8]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/autograd/function.py", line 598, in apply
[default0]:[rank8]:     return super().apply(*args, **kwargs)  # type: ignore[misc]
[default0]:[rank8]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/flash_attn/ops/triton/layer_norm.py", line 748, in forward
[default0]:[rank8]:     y, y1, mean, rstd, residual_out, seeds, dropout_mask, dropout_mask1 = _layer_norm_fwd(
[default0]:[rank8]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/flash_attn/ops/triton/layer_norm.py", line 335, in _layer_norm_fwd
[default0]:[rank8]:     _layer_norm_fwd_1pass_kernel[(M,)](
[default0]:[rank8]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/triton/runtime/jit.py", line 167, in <lambda>
[default0]:[rank8]:     return lambda *args, **kwargs: self.run(grid=grid, warmup=False, *args, **kwargs)
[default0]:[rank8]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/triton/runtime/autotuner.py", line 143, in run
[default0]:[rank8]:     timings = {config: self._bench(*args, config=config, **kwargs) for config in pruned_configs}
[default0]:[rank8]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/triton/runtime/autotuner.py", line 143, in <dictcomp>
[default0]:[rank8]:     timings = {config: self._bench(*args, config=config, **kwargs) for config in pruned_configs}
[default0]:[rank8]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/triton/runtime/autotuner.py", line 122, in _bench
[default0]:[rank8]:     return do_bench(kernel_call, warmup=self.warmup, rep=self.rep, quantiles=(0.5, 0.2, 0.8))
[default0]:[rank8]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/triton/testing.py", line 102, in do_bench
[default0]:[rank8]:     fn()
[default0]:[rank8]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/triton/runtime/autotuner.py", line 110, in kernel_call
[default0]:[rank8]:     self.fn.run(
[default0]:[rank8]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/triton/runtime/autotuner.py", line 305, in run
[default0]:[rank8]:     return self.fn.run(*args, **kwargs)
[default0]:[rank8]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/triton/runtime/autotuner.py", line 305, in run
[default0]:[rank8]:     return self.fn.run(*args, **kwargs)
[default0]:[rank8]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/triton/runtime/autotuner.py", line 305, in run
[default0]:[rank8]:     return self.fn.run(*args, **kwargs)
[default0]:[rank8]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/triton/runtime/jit.py", line 416, in run
[default0]:[rank8]:     self.cache[device][key] = compile(
[default0]:[rank8]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/triton/compiler/compiler.py", line 194, in compile
[default0]:[rank8]:     metadata_group[f"{src.name}.{ext}"] = fn_cache_manager.put(next_module, f"{src.name}.{ext}")
[default0]:[rank8]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/triton/runtime/cache.py", line 123, in put
[default0]:[rank8]:     with open(temp_path, mode) as f:
[default0]:[rank8]: OSError: [Errno 122] Disk quota exceeded
[default1]:[rank9]: OSError: [Errno 122] Disk quota exceeded
[default1]:
[default1]:[rank9]: During handling of the above exception, another exception occurred:
[default1]:
[default1]:[rank9]: Traceback (most recent call last):
[default1]:[rank9]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/run_train.py", line 237, in <module>
[default1]:[rank9]:     trainer.train(dataloader)
[default1]:[rank9]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 429, in train
[default1]:[rank9]:     outputs, loss_avg = self.training_step(dataloader=self.current_dataloader)
[default1]:[rank9]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 462, in training_step
[default1]:[rank9]:     outputs = self.pipeline_engine.train_batch_iter(
[default1]:[rank9]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 278, in train_batch_iter
[default1]:[rank9]:     output = self.forward(context=context, state=state, micro_batch=micro_batch, model=model)
[default1]:[rank9]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 44, in forward
[default1]:[rank9]:     output = model(**micro_batch)
[default1]:[rank9]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
[default1]:[rank9]:     return self._call_impl(*args, **kwargs)
[default1]:[rank9]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
[default1]:[rank9]:     return forward_call(*args, **kwargs)
[default1]:[rank9]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 891, in forward
[default1]:[rank9]:     sharded_logits = self.model(
[default1]:[rank9]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
[default1]:[rank9]:     return self._call_impl(*args, **kwargs)
[default1]:[rank9]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
[default1]:[rank9]:     return forward_call(*args, **kwargs)
[default1]:[rank9]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 764, in forward
[default1]:[rank9]:     return self.forward_with_hidden_states(input_ids=input_ids, input_mask=input_mask)[0]
[default1]:[rank9]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 780, in forward_with_hidden_states
[default1]:[rank9]:     hidden_encoder_states = encoder_block(**hidden_encoder_states)
[default1]:[rank9]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
[default1]:[rank9]:     return self._call_impl(*args, **kwargs)
[default1]:[rank9]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
[default1]:[rank9]:     return forward_call(*args, **kwargs)
[default1]:[rank9]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/block.py", line 151, in forward
[default1]:[rank9]:     output = self.pp_block(**new_kwargs)
[default1]:[rank9]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
[default1]:[rank9]:     return self._call_impl(*args, **kwargs)
[default1]:[rank9]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
[default1]:[rank9]:     return forward_call(*args, **kwargs)
[default1]:[rank9]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 629, in forward
[default1]:[rank9]:     hidden_states = self.input_layernorm(hidden_states)
[default1]:[rank9]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
[default1]:[rank9]:     return self._call_impl(*args, **kwargs)
[default1]:[rank9]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
[default1]:[rank9]:     return forward_call(*args, **kwargs)
[default1]:[rank9]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/nn/layer_norm.py", line 42, in forward
[default1]:[rank9]:     return layer_norm_fn(
[default1]:[rank9]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/flash_attn/ops/triton/layer_norm.py", line 875, in layer_norm_fn
[default1]:[rank9]:     return LayerNormFn.apply(
[default1]:[rank9]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/autograd/function.py", line 598, in apply
[default1]:[rank9]:     return super().apply(*args, **kwargs)  # type: ignore[misc]
[default1]:[rank9]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/flash_attn/ops/triton/layer_norm.py", line 748, in forward
[default1]:[rank9]:     y, y1, mean, rstd, residual_out, seeds, dropout_mask, dropout_mask1 = _layer_norm_fwd(
[default1]:[rank9]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/flash_attn/ops/triton/layer_norm.py", line 335, in _layer_norm_fwd
[default1]:[rank9]:     _layer_norm_fwd_1pass_kernel[(M,)](
[default1]:[rank9]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/triton/runtime/jit.py", line 167, in <lambda>
[default1]:[rank9]:     return lambda *args, **kwargs: self.run(grid=grid, warmup=False, *args, **kwargs)
[default1]:[rank9]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/triton/runtime/autotuner.py", line 143, in run
[default1]:[rank9]:     timings = {config: self._bench(*args, config=config, **kwargs) for config in pruned_configs}
[default1]:[rank9]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/triton/runtime/autotuner.py", line 143, in <dictcomp>
[default1]:[rank9]:     timings = {config: self._bench(*args, config=config, **kwargs) for config in pruned_configs}
[default1]:[rank9]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/triton/runtime/autotuner.py", line 122, in _bench
[default1]:[rank9]:     return do_bench(kernel_call, warmup=self.warmup, rep=self.rep, quantiles=(0.5, 0.2, 0.8))
[default1]:[rank9]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/triton/testing.py", line 102, in do_bench
[default1]:[rank9]:     fn()
[default1]:[rank9]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/triton/runtime/autotuner.py", line 110, in kernel_call
[default1]:[rank9]:     self.fn.run(
[default1]:[rank9]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/triton/runtime/autotuner.py", line 305, in run
[default1]:[rank9]:     return self.fn.run(*args, **kwargs)
[default1]:[rank9]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/triton/runtime/autotuner.py", line 305, in run
[default1]:[rank9]:     return self.fn.run(*args, **kwargs)
[default1]:[rank9]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/triton/runtime/autotuner.py", line 305, in run
[default1]:[rank9]:     return self.fn.run(*args, **kwargs)
[default1]:[rank9]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/triton/runtime/jit.py", line 416, in run
[default1]:[rank9]:     self.cache[device][key] = compile(
[default1]:[rank9]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/triton/compiler/compiler.py", line 194, in compile
[default1]:[rank9]:     metadata_group[f"{src.name}.{ext}"] = fn_cache_manager.put(next_module, f"{src.name}.{ext}")
[default1]:[rank9]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/triton/runtime/cache.py", line 123, in put
[default1]:[rank9]:     with open(temp_path, mode) as f:
[default1]:[rank9]: OSError: [Errno 122] Disk quota exceeded
[default2]:[rank2]: Traceback (most recent call last):
[default2]:[rank2]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/run_train.py", line 237, in <module>
[default2]:[rank2]:     trainer.train(dataloader)
[default2]:[rank2]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 429, in train
[default2]:[rank2]:     outputs, loss_avg = self.training_step(dataloader=self.current_dataloader)
[default2]:[rank2]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 462, in training_step
[default2]:[rank2]:     outputs = self.pipeline_engine.train_batch_iter(
[default2]:[rank2]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 278, in train_batch_iter
[default2]:[rank2]:     output = self.forward(context=context, state=state, micro_batch=micro_batch, model=model)
[default2]:[rank2]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 44, in forward
[default2]:[rank2]:     output = model(**micro_batch)
[default2]:[rank2]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
[default2]:[rank2]:     return self._call_impl(*args, **kwargs)
[default2]:[rank2]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
[default2]:[rank2]:     return forward_call(*args, **kwargs)
[default2]:[rank2]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 891, in forward
[default2]:[rank2]:     sharded_logits = self.model(
[default2]:[rank2]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
[default2]:[rank2]:     return self._call_impl(*args, **kwargs)
[default2]:[rank2]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
[default2]:[rank2]:     return forward_call(*args, **kwargs)
[default2]:[rank2]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 764, in forward
[default2]:[rank2]:     return self.forward_with_hidden_states(input_ids=input_ids, input_mask=input_mask)[0]
[default2]:[rank2]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 780, in forward_with_hidden_states
[default2]:[rank2]:     hidden_encoder_states = encoder_block(**hidden_encoder_states)
[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/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/models/llama.py", line 637, in forward
[default2]:[rank2]:     hidden_states = self.mlp(hidden_states=hidden_states)["hidden_states"]
[default2]:[rank2]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
[default2]:[rank2]:     return self._call_impl(*args, **kwargs)
[default2]:[rank2]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
[default2]:[rank2]:     return forward_call(*args, **kwargs)
[default2]:[rank2]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 171, in forward
[default2]:[rank2]:     merged_states = self.gate_up_proj(hidden_states)
[default2]:[rank2]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
[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 1024.00 MiB. GPU  has a total capacity of 79.33 GiB of which 447.94 MiB is free. Including non-PyTorch memory, this process has 78.88 GiB memory in use. Of the allocated memory 71.39 GiB is allocated by PyTorch, and 411.76 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]: OSError: [Errno 122] Disk quota exceeded
[default5]:
[default5]:[rank5]: During handling of the above exception, another exception occurred:
[default5]:
[default5]:[rank5]: Traceback (most recent call last):
[default5]:[rank5]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/run_train.py", line 237, in <module>
[default5]:[rank5]:     trainer.train(dataloader)
[default5]:[rank5]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 429, in train
[default5]:[rank5]:     outputs, loss_avg = self.training_step(dataloader=self.current_dataloader)
[default5]:[rank5]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 462, in training_step
[default5]:[rank5]:     outputs = self.pipeline_engine.train_batch_iter(
[default5]:[rank5]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 278, in train_batch_iter
[default5]:[rank5]:     output = self.forward(context=context, state=state, micro_batch=micro_batch, model=model)
[default5]:[rank5]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 44, in forward
[default5]:[rank5]:     output = model(**micro_batch)
[default5]:[rank5]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
[default5]:[rank5]:     return self._call_impl(*args, **kwargs)
[default5]:[rank5]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
[default5]:[rank5]:     return forward_call(*args, **kwargs)
[default5]:[rank5]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 891, in forward
[default5]:[rank5]:     sharded_logits = self.model(
[default5]:[rank5]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
[default5]:[rank5]:     return self._call_impl(*args, **kwargs)
[default5]:[rank5]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
[default5]:[rank5]:     return forward_call(*args, **kwargs)
[default5]:[rank5]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 764, in forward
[default5]:[rank5]:     return self.forward_with_hidden_states(input_ids=input_ids, input_mask=input_mask)[0]
[default5]:[rank5]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 780, in forward_with_hidden_states
[default5]:[rank5]:     hidden_encoder_states = encoder_block(**hidden_encoder_states)
[default5]:[rank5]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
[default5]:[rank5]:     return self._call_impl(*args, **kwargs)
[default5]:[rank5]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
[default5]:[rank5]:     return forward_call(*args, **kwargs)
[default5]:[rank5]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/block.py", line 151, in forward
[default5]:[rank5]:     output = self.pp_block(**new_kwargs)
[default5]:[rank5]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
[default5]:[rank5]:     return self._call_impl(*args, **kwargs)
[default5]:[rank5]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
[default5]:[rank5]:     return forward_call(*args, **kwargs)
[default5]:[rank5]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 631, in forward
[default5]:[rank5]:     output = self.attn(hidden_states=hidden_states, sequence_mask=sequence_mask)
[default5]:[rank5]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
[default5]:[rank5]:     return self._call_impl(*args, **kwargs)
[default5]:[rank5]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
[default5]:[rank5]:     return forward_call(*args, **kwargs)
[default5]:[rank5]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 566, in forward
[default5]:[rank5]:     query_states, key_value_states = self.flash_rotary_embedding(query_states, kv=key_value_states)
[default5]:[rank5]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
[default5]:[rank5]:     return self._call_impl(*args, **kwargs)
[default5]:[rank5]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
[default5]:[rank5]:     return forward_call(*args, **kwargs)
[default5]:[rank5]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/flash_attn/layers/rotary.py", line 457, in forward
[default5]:[rank5]:     q = apply_rotary_emb_func(
[default5]:[rank5]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/flash_attn/layers/rotary.py", line 122, in apply_rotary_emb
[default5]:[rank5]:     return ApplyRotaryEmb.apply(
[default5]:[rank5]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/autograd/function.py", line 598, in apply
[default5]:[rank5]:     return super().apply(*args, **kwargs)  # type: ignore[misc]
[default5]:[rank5]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/flash_attn/layers/rotary.py", line 48, in forward
[default5]:[rank5]:     out = apply_rotary(
[default5]:[rank5]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/flash_attn/ops/triton/rotary.py", line 202, in apply_rotary
[default5]:[rank5]:     rotary_kernel[grid](
[default5]:[rank5]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/triton/runtime/jit.py", line 167, in <lambda>
[default5]:[rank5]:     return lambda *args, **kwargs: self.run(grid=grid, warmup=False, *args, **kwargs)
[default5]:[rank5]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/triton/runtime/jit.py", line 416, in run
[default5]:[rank5]:     self.cache[device][key] = compile(
[default5]:[rank5]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/triton/compiler/compiler.py", line 194, in compile
[default5]:[rank5]:     metadata_group[f"{src.name}.{ext}"] = fn_cache_manager.put(next_module, f"{src.name}.{ext}")
[default5]:[rank5]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/triton/runtime/cache.py", line 123, in put
[default5]:[rank5]:     with open(temp_path, mode) as f:
[default5]:[rank5]: OSError: [Errno 122] Disk quota exceeded
[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 780, in forward_with_hidden_states
[default6]:[rank6]:     hidden_encoder_states = encoder_block(**hidden_encoder_states)
[default6]:[rank6]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
[default6]:[rank6]:     return self._call_impl(*args, **kwargs)
[default6]:[rank6]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
[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/models/llama.py", line 637, in forward
[default6]:[rank6]:     hidden_states = self.mlp(hidden_states=hidden_states)["hidden_states"]
[default6]:[rank6]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
[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 171, in forward
[default6]:[rank6]:     merged_states = self.gate_up_proj(hidden_states)
[default6]:[rank6]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
[default6]:[rank6]:     return self._call_impl(*args, **kwargs)
[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 1024.00 MiB. GPU  has a total capacity of 79.33 GiB of which 209.94 MiB is free. Including non-PyTorch memory, this process has 79.11 GiB memory in use. Of the allocated memory 71.39 GiB is allocated by PyTorch, and 411.76 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]:[rank12]: OSError: [Errno 122] Disk quota exceeded
[default4]:
[default4]:[rank12]: During handling of the above exception, another exception occurred:
[default4]:
[default4]:[rank12]: Traceback (most recent call last):
[default4]:[rank12]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/run_train.py", line 237, in <module>
[default4]:[rank12]:     trainer.train(dataloader)
[default4]:[rank12]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 429, in train
[default4]:[rank12]:     outputs, loss_avg = self.training_step(dataloader=self.current_dataloader)
[default4]:[rank12]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 462, in training_step
[default4]:[rank12]:     outputs = self.pipeline_engine.train_batch_iter(
[default4]:[rank12]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 278, in train_batch_iter
[default4]:[rank12]:     output = self.forward(context=context, state=state, micro_batch=micro_batch, model=model)
[default4]:[rank12]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 44, in forward
[default4]:[rank12]:     output = model(**micro_batch)
[default4]:[rank12]:   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]:[rank12]:     return self._call_impl(*args, **kwargs)
[default4]:[rank12]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
[default4]:[rank12]:     return forward_call(*args, **kwargs)
[default4]:[rank12]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 891, in forward
[default4]:[rank12]:     sharded_logits = self.model(
[default4]:[rank12]:   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]:[rank12]:     return self._call_impl(*args, **kwargs)
[default4]:[rank12]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
[default4]:[rank12]:     return forward_call(*args, **kwargs)
[default4]:[rank12]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 764, in forward
[default4]:[rank12]:     return self.forward_with_hidden_states(input_ids=input_ids, input_mask=input_mask)[0]
[default4]:[rank12]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 780, in forward_with_hidden_states
[default4]:[rank12]:     hidden_encoder_states = encoder_block(**hidden_encoder_states)
[default4]:[rank12]:   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]:[rank12]:     return self._call_impl(*args, **kwargs)
[default4]:[rank12]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
[default4]:[rank12]:     return forward_call(*args, **kwargs)
[default4]:[rank12]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/block.py", line 151, in forward
[default4]:[rank12]:     output = self.pp_block(**new_kwargs)
[default4]:[rank12]:   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]:[rank12]:     return self._call_impl(*args, **kwargs)
[default4]:[rank12]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
[default4]:[rank12]:     return forward_call(*args, **kwargs)
[default4]:[rank12]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 631, in forward
[default4]:[rank12]:     output = self.attn(hidden_states=hidden_states, sequence_mask=sequence_mask)
[default4]:[rank12]:   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]:[rank12]:     return self._call_impl(*args, **kwargs)
[default4]:[rank12]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
[default4]:[rank12]:     return forward_call(*args, **kwargs)
[default4]:[rank12]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 566, in forward
[default4]:[rank12]:     query_states, key_value_states = self.flash_rotary_embedding(query_states, kv=key_value_states)
[default4]:[rank12]:   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]:[rank12]:     return self._call_impl(*args, **kwargs)
[default4]:[rank12]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
[default4]:[rank12]:     return forward_call(*args, **kwargs)
[default4]:[rank12]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/flash_attn/layers/rotary.py", line 457, in forward
[default4]:[rank12]:     q = apply_rotary_emb_func(
[default4]:[rank12]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/flash_attn/layers/rotary.py", line 122, in apply_rotary_emb
[default4]:[rank12]:     return ApplyRotaryEmb.apply(
[default4]:[rank12]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/autograd/function.py", line 598, in apply
[default4]:[rank12]:     return super().apply(*args, **kwargs)  # type: ignore[misc]
[default4]:[rank12]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/flash_attn/layers/rotary.py", line 48, in forward
[default4]:[rank12]:     out = apply_rotary(
[default4]:[rank12]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/flash_attn/ops/triton/rotary.py", line 202, in apply_rotary
[default4]:[rank12]:     rotary_kernel[grid](
[default4]:[rank12]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/triton/runtime/jit.py", line 167, in <lambda>
[default4]:[rank12]:     return lambda *args, **kwargs: self.run(grid=grid, warmup=False, *args, **kwargs)
[default4]:[rank12]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/triton/runtime/jit.py", line 416, in run
[default4]:[rank12]:     self.cache[device][key] = compile(
[default4]:[rank12]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/triton/compiler/compiler.py", line 194, in compile
[default4]:[rank12]:     metadata_group[f"{src.name}.{ext}"] = fn_cache_manager.put(next_module, f"{src.name}.{ext}")
[default4]:[rank12]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/triton/runtime/cache.py", line 123, in put
[default4]:[rank12]:     with open(temp_path, mode) as f:
[default4]:[rank12]: OSError: [Errno 122] Disk quota exceeded
[default7]:[rank7]: Traceback (most recent call last):
[default7]:[rank7]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/run_train.py", line 237, in <module>
[default7]:[rank7]:     trainer.train(dataloader)
[default7]:[rank7]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 429, in train
[default7]:[rank7]:     outputs, loss_avg = self.training_step(dataloader=self.current_dataloader)
[default7]:[rank7]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 462, in training_step
[default7]:[rank7]:     outputs = self.pipeline_engine.train_batch_iter(
[default7]:[rank7]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 278, in train_batch_iter
[default7]:[rank7]:     output = self.forward(context=context, state=state, micro_batch=micro_batch, model=model)
[default7]:[rank7]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 44, in forward
[default7]:[rank7]:     output = model(**micro_batch)
[default7]:[rank7]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
[default7]:[rank7]:     return self._call_impl(*args, **kwargs)
[default7]:[rank7]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
[default7]:[rank7]:     return forward_call(*args, **kwargs)
[default7]:[rank7]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 891, in forward
[default7]:[rank7]:     sharded_logits = self.model(
[default7]:[rank7]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
[default7]:[rank7]:     return self._call_impl(*args, **kwargs)
[default7]:[rank7]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
[default7]:[rank7]:     return forward_call(*args, **kwargs)
[default7]:[rank7]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 764, in forward
[default7]:[rank7]:     return self.forward_with_hidden_states(input_ids=input_ids, input_mask=input_mask)[0]
[default7]:[rank7]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 780, in forward_with_hidden_states
[default7]:[rank7]:     hidden_encoder_states = encoder_block(**hidden_encoder_states)
[default7]:[rank7]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
[default7]:[rank7]:     return self._call_impl(*args, **kwargs)
[default7]:[rank7]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
[default7]:[rank7]:     return forward_call(*args, **kwargs)
[default7]:[rank7]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/block.py", line 151, in forward
[default7]:[rank7]:     output = self.pp_block(**new_kwargs)
[default7]:[rank7]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
[default7]:[rank7]:     return self._call_impl(*args, **kwargs)
[default7]:[rank7]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
[default7]:[rank7]:     return forward_call(*args, **kwargs)
[default7]:[rank7]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 637, in forward
[default7]:[rank7]:     hidden_states = self.mlp(hidden_states=hidden_states)["hidden_states"]
[default7]:[rank7]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
[default7]:[rank7]:     return self._call_impl(*args, **kwargs)
[default7]:[rank7]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
[default7]:[rank7]:     return forward_call(*args, **kwargs)
[default7]:[rank7]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 171, in forward
[default7]:[rank7]:     merged_states = self.gate_up_proj(hidden_states)
[default7]:[rank7]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
[default7]:[rank7]:     return self._call_impl(*args, **kwargs)
[default7]:[rank7]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
[default7]:[rank7]:     return forward_call(*args, **kwargs)
[default7]:[rank7]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/nn.py", line 87, in forward
[default7]:[rank7]:     return column_linear(
[default7]:[rank7]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/functional.py", line 359, in column_linear
[default7]:[rank7]:     return F.linear(input, weight, bias)
[default7]:[rank7]: torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 1024.00 MiB. 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 71.39 GiB is allocated by PyTorch, and 411.76 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]:[rank10]: Traceback (most recent call last):
[default2]:[rank10]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/run_train.py", line 237, in <module>
[default2]:[rank10]:     trainer.train(dataloader)
[default2]:[rank10]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 429, in train
[default2]:[rank10]:     outputs, loss_avg = self.training_step(dataloader=self.current_dataloader)
[default2]:[rank10]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 462, in training_step
[default2]:[rank10]:     outputs = self.pipeline_engine.train_batch_iter(
[default2]:[rank10]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 278, in train_batch_iter
[default2]:[rank10]:     output = self.forward(context=context, state=state, micro_batch=micro_batch, model=model)
[default2]:[rank10]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 44, in forward
[default2]:[rank10]:     output = model(**micro_batch)
[default2]:[rank10]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
[default2]:[rank10]:     return self._call_impl(*args, **kwargs)
[default2]:[rank10]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
[default2]:[rank10]:     return forward_call(*args, **kwargs)
[default2]:[rank10]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_clust[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)
er/nanotron/src/nanotron/models/llama.py", line 891, in forward
[default2]:[rank10]:     sharded_logits = self.model(
[default2]:[rank10]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
[default2]:[rank10]:     return self._call_impl(*args, **kwargs)
[default2]:[rank10]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
[default2]:[rank10]:     return forward_call(*args, **kwargs)
[default2]:[rank10]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 764, in forward
[default2]:[rank10]:     return self.forward_with_hidden_states(input_ids=input_ids, input_mask=input_mask)[0]
[default2]:[rank10]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 780, in forward_with_hidden_states
[default2]:[rank10]:     hidden_encoder_sta[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_impltes = encoder_block(**hidden_encoder_states)
[default2]:[rank10]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
[default2]:[rank10]:     return self._call_impl(*args, **kwargs)
[default2]:[rank10]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl

[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 780, in forward_with_hidden_states
[default4]:[rank4]:     hidden_encoder_states = encoder_block(**hidden_encoder_states)
[default4]:[rank4]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
[default4]:[rank4]:     return self._call_impl(*args, **kwargs)
[default4]:[rank[default2]:[rank10]:     return forward_call(*args, **kwargs)
[default2]:[rank10]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/block.py", line 151, in forward
[default2]:[rank10]:     output = self.pp_block(**new_kwargs)
[default2]:[rank10]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
[default2]:[rank10]:     return self._call_impl(*args, **kwargs)
[default2]:[rank10]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
[default2]:[rank10]:     return forward_call(*args, **kwargs)
[default2]:[rank10]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 637, in forward
[default2]:[rank10]:     hidden_states = self.mlp(hidden_states=hidden_states)["hidden_states"]
[default2]:[rank10]:   File "/fsx/ferdinandmom/mini4]:   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
forge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
[default2]:[rank10]:     return self._call_impl(*args, **kwargs)
[default2]:[rank10]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
[default2]:[rank10]:     return forward_call(*args, **kwargs)
[default2]:[rank10]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 171, in forward
[default2]:[rank10]:     merged_states = self.gate_up_proj(hidden_states)
[default2]:[rank10]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
[default2]:[rank10]:     return self._call_impl(*args, **kwargs)
[default2]:[rank10]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
[d[default4]:[rank4]:     output = self.pp_block(**new_kwargs)
[default4]:[rank4]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
[default4]:[rank4]:     return self._call_impl(*args, **kwargs)
[default4]:[rank4]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
[default4]:[rank4]:     return forward_call(*args, **kwargs)
[default4]:[rank4]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 637, in forward
[default4]:[rank4]:     hidden_states = self.mlp(hidden_states=hidden_states)["hidden_states"]
[default4]:[rank4]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
[default4]:[rank4]:     return self._call_impl(*args, **kwargs)
[default4]:[rank4]:   File "/fsx/ferdinaefault2]:[rank10]:     return forward_call(*args, **kwargs)
[default2]:[rank10]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/nn.py", line 87, in forward
[default2]:[rank10]:     return column_linear(
[default2]:[rank10]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/functional.py", line 359, in column_linear
[default2]:[rank10]:     return F.linear(input, weight, bias)
[default2]:[rank10]: torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 1024.00 MiB. GPU  has a total capacity of 79.33 GiB of which 447.94 MiB is free. Including non-PyTorch memory, this process has 78.88 GiB memory in use. Of the allocated memory 71.39 GiB is allocated by PyTorch, and 411.76 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://pytorcndmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
[default4]:[rank4]:     return forward_call(*args, **kwargs)
[default4]:[rank4]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 171, in forward
[default4]:[rank4]:     merged_states = self.gate_up_proj(hidden_states)
[default4]:[rank4]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
[default4]:[rank4]:     return self._call_impl(*args, **kwargs)
[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]:     reh.org/docs/stable/notes/cuda.html#environment-variables)
turn 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 1024.00 MiB. GPU  has a total capacity of 79.33 GiB of which 209.94 MiB is free. Including non-PyTorch memory, this process has 79.11 GiB memory in use. Of the allocated memory 71.39 GiB is allocated by PyTorch, and 411.76 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation.  See documentation for Memory Management  (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables)
[default1]:[rank1]: Traceback (most recent call last):
[default1]:[rank1]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/run_train.py", line 237, in <module>
[default1]:[rank1]:     trainer.train(dataloader)
[default1]:[rank1]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 429, in train
[default1]:[rank1]:     outputs, loss_avg = self.training_step(dataloader=self.current_dataloader)
[default1]:[rank1]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 462, in training_step
[default1]:[rank1]:     outputs = self.pipeline_engine.train_batch_iter(
[default1]:[rank1]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 278, in train_batch_iter
[default1]:[rank1]:     output = self.forward(context=context, state=state, micro_batch=micro_batch, model=model)
[default1]:[rank1]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 44, in forward
[default1]:[rank1]:     output = model(**micro_batch)
[default1]:[rank1]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
[default1]:[rank1]:     return self._call_impl(*args, **kwargs)
[default1]:[rank1]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
[default1]:[rank1]:     return forward_call(*args, **kwargs)
[default1]:[rank1]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 891, in forward
[default1]:[rank1]:     sharded_logits = self.model(
[default1]:[rank1]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
[default1]:[rank1]:     return self._call_impl(*args, **kwargs)
[default1]:[rank1]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
[default1]:[rank1]:     return forward_call(*args, **kwargs)
[default1]:[rank1]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 764, in forward
[default1]:[rank1]:     return self.forward_with_hidden_states(input_ids=input_ids, input_mask=input_mask)[0]
[default1]:[rank1]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 780, in forward_with_hidden_states
[default1]:[rank1]:     hidden_encoder_states = encoder_block(**hidden_encoder_states)
[default1]:[rank1]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
[default1]:[rank1]:     return self._call_impl(*args, **kwargs)
[default1]:[rank1]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
[default1]:[rank1]:     return forward_call(*args, **kwargs)
[default1]:[rank1]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/block.py", line 151, in forward
[default1]:[rank1]:     output = self.pp_block(**new_kwargs)
[default1]:[rank1]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
[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 637, in forward
[default1]:[rank1]:     hidden_states = self.mlp(hidden_states=hidden_states)["hidden_states"]
[default1]:[rank1]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
[default1]:[rank1]:     return self._call_impl(*args, **kwargs)
[default1]:[rank1]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
[default1]:[rank1]:     return forward_call(*args, **kwargs)
[default1]:[rank1]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 171, in forward
[default1]:[rank1]:     merged_states = self.gate_up_proj(hidden_states)
[default1]:[rank1]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
[default1]:[rank1]:     return self._call_impl(*args, **kwargs)
[default1]:[rank1]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
[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
[default1]:[rank1]:     return column_linear(
[default1]:[rank1]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/functional.py", line 359, in column_linear
[default1]:[rank1]:     return F.linear(input, weight, bias)
[default1]:[rank1]: torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 1024.00 MiB. GPU 
 has a total capacity of 79.33 GiB of which 611.94 MiB is free. Including non-PyTorch memory, this process has 78.71 GiB memory in use. Of the allocated memory 71.39 GiB is allocated by PyTorch, and 411.76 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]:[rank11]: Traceback (most recent call last):
[default3]:[rank11]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/run_train.py", line 237, in <module>
[default3]:[rank11]:     trainer.train(dataloader)
[default3]:[rank11]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 429, in train
[default3]:[rank11]:     outputs, loss_avg = self.training_step(dataloader=self.current_dataloader)
[default3]:[rank11]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 462, in training_step
[default3]:[rank11]:     outputs = self.pipeline_engine.train_batch_iter(
[default3]:[rank11]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 278, in train_batch_iter
[default3]:[rank11]:     output = self.forward(context=context, state=state, micro_batch=micro_batch, model=model)
[default3]:[rank11]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 44, in forward
[default3]:[rank11]:     output = model(**micro_batch)
[default3]:[rank11]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
[default3]:[rank11]:     return self._call_impl(*args, **kwargs)
[default3]:[rank11]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
[default3]:[rank11]:     return forward_call(*args, **kwargs)
[default3]:[rank11]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 891, in forward
[default3]:[rank11]:     sharded_logits = self.model(
[default3]:[rank11]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
[default3]:[rank11]:     return self._call_impl(*args, **kwargs)
[default3]:[rank11]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
[default3]:[rank11]:     return forward_call(*args, **kwargs)
[default3]:[rank11]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 764, in forward
[default3]:[rank11]:     return self.forward_with_hidden_states(input_ids=input_ids, input_mask=input_mask)[0]
[default3]:[rank11]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 780, in forward_with_hidden_states
[default3]:[rank11]:     hidden_encoder_states = encoder_block(**hidden_encoder_states)
[default3]:[rank11]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
[default3]:[rank11]:     return self._call_impl(*args, **kwargs)
[default3]:[rank11]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
[default3]:[rank11]:     return forward_call(*args, **kwargs)
[default3]:[rank11]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/block.py", line 151, in forward
[default3]:[rank11]:     output = self.pp_block(**new_kwargs)
[default3]:[rank11]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
[default3]:[rank11]:     return self._call_impl(*args, **kwargs)
[default3]:[rank11]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
[default3]:[rank11]:     return forward_call(*args, **kwargs)
[default3]:[rank11]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 637, in forward
[default3]:[rank11]:     hidden_states = self.mlp(hidden_states=hidden_states)["hidden_states"]
[default3]:[rank11]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
[default3]:[rank11]:     return self._call_impl(*args, **kwargs)
[default3]:[rank11]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
[default3]:[rank11]:     return forward_call(*args, **kwargs)
[default3]:[rank11]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 171, in forward
[default3]:[rank11]:     merged_states = self.gate_up_proj(hidden_states)
[default3]:[rank11]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
[default3]:[rank11]:     return self._call_impl(*args, **kwargs)
[default3]:[rank11]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
[default3]:[rank11]:     return forward_call(*args, **kwargs)
[default3]:[rank11]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/nn.py", line 87, in forward
[default3]:[rank11]:     return column_linear(
[default3]:[rank11]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/functional.py", line 359, in column_linear
[default3]:[rank11]:     return F.linear(input, weight, bias)
[default3]:[rank11]: torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 1024.00 MiB. GPU  has a total capacity of 79.33 GiB of which 209.94 MiB is free. Including non-PyTorch memory, this process has 79.11 GiB memory in use. Of the allocated memory 71.39 GiB is allocated by PyTorch, and 411.76 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]:[rank14]: Traceback (most recent call last):
[default6]:[rank14]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/run_train.py", line 237, in <module>
[default6]:[rank14]:     trainer.train(dataloader)
[default6]:[rank14]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 429, in train
[default6]:[rank14]:     outputs, loss_avg = self.training_step(dataloader=self.current_dataloader)
[default6]:[rank14]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 462, in training_step
[default6]:[rank14]:     outputs = self.pipeline_engine.train_batch_iter(
[default6]:[rank14]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 278, in train_batch_iter
[default6]:[rank14]:     output = self.forward(context=context, state=state, micro_batch=micro_batch, model=model)
[default6]:[rank14]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 44, in forward
[default6]:[rank14]:     output = model(**micro_batch)
[default6]:[rank14]:   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]:[rank14]:     return self._call_impl(*args, **kwargs)
[default6]:[rank14]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
[default6]:[rank14]:     return forward_call(*args, **kwargs)
[default6]:[rank14]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 891, in forward
[default6]:[rank14]:     sharded_logits = self.model(
[default6]:[rank14]:   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]:[rank14]:     return self._call_impl(*args, **kwargs)
[default6]:[rank14]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
[default6]:[rank14]:     return forward_call(*args, **kwargs)
[default6]:[rank14]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 764, in forward
[default6]:[rank14]:     return self.forward_with_hidden_states(input_ids=input_ids, input_mask=input_mask)[0]
[default6]:[rank14]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 780, in forward_with_hidden_states
[default6]:[rank14]:     hidden_encoder_states = encoder_block(**hidden_encoder_states)
[default6]:[rank14]:   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]:[rank14]:     return self._call_impl(*args, **kwargs)
[default6]:[rank14]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
[default6]:[rank14]:     return forward_call(*args, **kwargs)
[default6]:[rank14]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/block.py", line 151, in forward
[default6]:[rank14]:     output = self.pp_block(**new_kwargs)
[default6]:[rank14]:   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]:[rank14]:     return self._call_impl(*args, **kwargs)
[default6]:[rank14]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
[default6]:[rank14]:     return forward_call(*args, **kwargs)
[default6]:[rank14]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 637, in forward
[default6]:[rank14]:     hidden_states = self.mlp(hidden_states=hidden_states)["hidden_states"]
[default6]:[rank14]:   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]:[rank14]:     return self._call_impl(*args, **kwargs)
[default6]:[rank14]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
[default6]:[rank14]:     return forward_call(*args, **kwargs)
[default6]:[rank14]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 171, in forward
[default6]:[rank14]:     merged_states = self.gate_up_proj(hidden_states)
[default6]:[rank14]:   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]:[rank14]:     return self._call_impl(*args, **kwargs)
[default6]:[rank14]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
[default6]:[rank14]:     return forward_call(*args, **kwargs)
[default6]:[rank14]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/nn.py", line 87, in forward
[default6]:[rank14]:     return column_linear(
[default6]:[rank14]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/functional.py", line 359, in column_linear
[default6]:[rank14]:     return F.linear(input, weight, bias)
[default6]:[rank14]: torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 1024.00 MiB. GPU  has a total capacity of 79.33 GiB of which 209.94 MiB is free. Including non-PyTorch memory, this process has 79.11 GiB memory in use. Of the allocated memory 71.39 GiB is allocated by PyTorch, and 411.76 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]:[rank13]: Traceback (most recent call last):
[default5]:[rank13]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/run_train.py", line 237, in <module>
[default5]:[rank13]:     trainer.train(dataloader)
[default5]:[rank13]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 429, in train
[default5]:[rank13]:     outputs, loss_avg = self.training_step(dataloader=self.current_dataloader)
[default5]:[rank13]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 462, in training_step
[default5]:[rank13]:     outputs = self.pipeline_engine.train_batch_iter(
[default5]:[rank13]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 278, in train_batch_iter
[default5]:[rank13]:     output = self.forward(context=context, state=state, micro_batch=micro_batch, model=model)
[default5]:[rank13]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 44, in forward
[default5]:[rank13]:     output = model(**micro_batch)
[default5]:[rank13]:   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]:[rank13]:     return self._call_impl(*args, **kwargs)
[default5]:[rank13]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
[default5]:[rank13]:     return forward_call(*args, **kwargs)
[default5]:[rank13]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 891, in forward
[default5]:[rank13]:     sharded_logits = self.model(
[default5]:[rank13]:   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]:[rank13]:     return self._call_impl(*args, **kwargs)
[default5]:[rank13]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
[default5]:[rank13]:     return forward_call(*args, **kwargs)
[default5]:[rank13]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 764, in forward
[default5]:[rank13]:     return self.forward_with_hidden_states(input_ids=input_ids, input_mask=input_mask)[0]
[default5]:[rank13]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 780, in forward_with_hidden_states
[default5]:[rank13]:     hidden_encoder_states = encoder_block(**hidden_encoder_states)
[default5]:[rank13]:   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]:[rank13]:     return self._call_impl(*args, **kwargs)
[default5]:[rank13]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
[default5]:[rank13]:     return forward_call(*args, **kwargs)
[default5]:[rank13]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/block.py", line 151, in forward
[default5]:[rank13]:     output = self.pp_block(**new_kwargs)
[default5]:[rank13]:   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]:[rank13]:     return self._call_impl(*args, **kwargs)
[default5]:[rank13]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
[default5]:[rank13]:     return forward_call(*args, **kwargs)
[default5]:[rank13]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 637, in forward
[default5]:[rank13]:     hidden_states = self.mlp(hidden_states=hidden_states)["hidden_states"]
[default5]:[rank13]:   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]:[rank13]:     return self._call_impl(*args, **kwargs)
[default5]:[rank13]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
[default5]:[rank13]:     return forward_call(*args, **kwargs)
[default5]:[rank13]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 171, in forward
[default5]:[rank13]:     merged_states = self.gate_up_proj(hidden_states)
[default5]:[rank13]:   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]:[rank13]:     return self._call_impl(*args, **kwargs)
[default5]:[rank13]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
[default5]:[rank13]:     return forward_call(*args, **kwargs)
[default5]:[rank13]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/nn.py", line 87, in forward
[default5]:[rank13]:     return column_linear(
[default5]:[rank13]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/functional.py", line 359, in column_linear
[default5]:[rank13]:     return F.linear(input, weight, bias)
[default5]:[rank13]: torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 1024.00 MiB. GPU  has a total capacity of 79.33 GiB of which 209.94 MiB is free. Including non-PyTorch memory, this process has 79.11 GiB memory in use. Of the allocated memory 71.39 GiB is allocated by PyTorch, and 411.76 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]:[rank15]: Traceback (most recent call last):
[default7]:[rank15]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/run_train.py", line 237, in <module>
[default7]:[rank15]:     trainer.train(dataloader)
[default7]:[rank15]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 429, in train
[default7]:[rank15]:     outputs, loss_avg = self.training_step(dataloader=self.current_dataloader)
[default7]:[rank15]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 462, in training_step
[default7]:[rank15]:     outputs = self.pipeline_engine.train_batch_iter(
[default7]:[rank15]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 278, in train_batch_iter
[default7]:[rank15]:     output = self.forward(context=context, state=state, micro_batch=micro_batch, model=model)
[default7]:[rank15]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 44, in forward
[default7]:[rank15]:     output = model(**micro_batch)
[default7]:[rank15]:   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]:[rank15]:     return self._call_impl(*args, **kwargs)
[default7]:[rank15]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
[default7]:[rank15]:     return forward_call(*args, **kwargs)
[default7]:[rank15]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 891, in forward
[default7]:[rank15]:     sharded_logits = self.model(
[default7]:[rank15]:   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]:[rank15]:     return self._call_impl(*args, **kwargs)
[default7]:[rank15]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
[default7]:[rank15]:     return forward_call(*args, **kwargs)
[default7]:[rank15]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 764, in forward
[default7]:[rank15]:     return self.forward_with_hidden_states(input_ids=input_ids, input_mask=input_mask)[0]
[default7]:[rank15]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 780, in forward_with_hidden_states
[default7]:[rank15]:     hidden_encoder_states = encoder_block(**hidden_encoder_states)
[default7]:[rank15]:   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]:[rank15]:     return self._call_impl(*args, **kwargs)
[default7]:[rank15]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
[default7]:[rank15]:     return forward_call(*args, **kwargs)
[default7]:[rank15]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/block.py", line 151, in forward
[default7]:[rank15]:     output = self.pp_block(**new_kwargs)
[default7]:[rank15]:   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]:[rank15]:     return self._call_impl(*args, **kwargs)
[default7]:[rank15]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
[default7]:[rank15]:     return forward_call(*args, **kwargs)
[default7]:[rank15]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 637, in forward
[default7]:[rank15]:     hidden_states = self.mlp(hidden_states=hidden_states)["hidden_states"]
[default7]:[rank15]:   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]:[rank15]:     return self._call_impl(*args, **kwargs)
[default7]:[rank15]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
[default7]:[rank15]:     return forward_call(*args, **kwargs)
[default7]:[rank15]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 171, in forward
[default7]:[rank15]:     merged_states = self.gate_up_proj(hidden_states)
[default7]:[rank15]:   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]:[rank15]:     return self._call_impl(*args, **kwargs)
[default7]:[rank15]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
[default7]:[rank15]:     return forward_call(*args, **kwargs)
[default7]:[rank15]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/nn.py", line 87, in forward
[default7]:[rank15]:     return column_linear(
[default7]:[rank15]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/functional.py", line 359, in column_linear
[default7]:[rank15]:     return F.linear(input, weight, bias)
[default7]:[rank15]: torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 1024.00 MiB. 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 71.39 GiB is allocated by PyTorch, and 411.76 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)
W0702 16:30:30.831000 140518449399616 torch/distributed/elastic/multiprocessing/api.py:851] Sending process 673389 closing signal SIGTERM
W0702 16:30:30.831000 140518449399616 torch/distributed/elastic/multiprocessing/api.py:851] Sending process 673391 closing signal SIGTERM
W0702 16:30:30.832000 140518449399616 torch/distributed/elastic/multiprocessing/api.py:851] Sending process 673392 closing signal SIGTERM
W0702 16:30:30.832000 140518449399616 torch/distributed/elastic/multiprocessing/api.py:851] Sending process 673394 closing signal SIGTERM
W0702 16:30:30.832000 140518449399616 torch/distributed/elastic/multiprocessing/api.py:851] Sending process 673395 closing signal SIGTERM
E0702 16:30:32.447000 140518449399616 torch/distributed/elastic/multiprocessing/api.py:826] failed (exitcode: 1) local_rank: 0 (pid: 673388) 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_16:30:30
  host      : ip-26-0-167-177.ec2.internal
  rank      : 2 (local_rank: 2)
  exitcode  : 1 (pid: 673390)
  error_file: <N/A>
  traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html
[2]:
  time      : 2024-07-02_16:30:30
  host      : ip-26-0-167-177.ec2.internal
  rank      : 5 (local_rank: 5)
  exitcode  : 1 (pid: 673393)
  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_16:30:30
  host      : ip-26-0-167-177.ec2.internal
  rank      : 0 (local_rank: 0)
  exitcode  : 1 (pid: 673388)
  error_file: <N/A>
  traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html
============================================================
srun: error: ip-26-0-167-177: task 0: Exited with exit code 1
W0702 16:30:35.798000 140305280075520 torch/distributed/elastic/rendezvous/dynamic_rendezvous.py:1252] The node 'ip-26-0-170-31.ec2.internal_2925807_0' has failed to send a keep-alive heartbeat to the rendezvous 'none' due to an error of type RendezvousConnectionError.
E0702 16:30:35.947000 140310946895680 torch/distributed/elastic/multiprocessing/api.py:826] failed (exitcode: 1) local_rank: 0 (pid: 2925877) of binary: /fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/bin/python3.10
W0702 16:30:35.954000 140310946895680 torch/distributed/elastic/rendezvous/dynamic_rendezvous.py:1203] The node 'ip-26-0-170-31.ec2.internal_2925807_0' has failed to shutdown the rendezvous 'none' due to an error of type RendezvousConnectionError.
W0702 16:30:35.991000 140310946895680 torch/distributed/elastic/rendezvous/dynamic_rendezvous.py:1203] The node 'ip-26-0-170-31.ec2.internal_2925807_0' has failed to shutdown the rendezvous 'none' due to an error of type RendezvousConnectionError.
W0702 16:30:36.019000 140310946895680 torch/distributed/elastic/rendezvous/dynamic_rendezvous.py:1203] The node 'ip-26-0-170-31.ec2.internal_2925807_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/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_16:30:35
  host      : ip-26-0-170-31.ec2.internal
  rank      : 9 (local_rank: 1)
  exitcode  : 1 (pid: 2925878)
  error_file: <N/A>
  traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html
[2]:
  time      : 2024-07-02_16:30:35
  host      : ip-26-0-170-31.ec2.internal
  rank      : 10 (local_rank: 2)
  exitcode  : 1 (pid: 2925879)
  error_file: <N/A>
  traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html
[3]:
  time      : 2024-07-02_16:30:35
  host      : ip-26-0-170-31.ec2.internal
  rank      : 11 (local_rank: 3)
  exitcode  : 1 (pid: 2925880)
  error_file: <N/A>
  traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html
[4]:
  time      : 2024-07-02_16:30:35
  host      : ip-26-0-170-31.ec2.internal
  rank      : 12 (local_rank: 4)
  exitcode  : 1 (pid: 2925881)
  error_file: <N/A>
  traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html
[5]:
  time      : 2024-07-02_16:30:35
  host      : ip-26-0-170-31.ec2.internal
  rank      : 13 (local_rank: 5)
  exitcode  : 1 (pid: 2925882)
  error_file: <N/A>
  traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html
[6]:
  time      : 2024-07-02_16:30:35
  host      : ip-26-0-170-31.ec2.internal
  rank      : 14 (local_rank: 6)
  exitcode  : 1 (pid: 2925883)
  error_file: <N/A>
  traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html
[7]:
  time      : 2024-07-02_16:30:35
  host      : ip-26-0-170-31.ec2.internal
  rank      : 15 (local_rank: 7)
  exitcode  : 1 (pid: 2925884)
  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_16:30:35
  host      : ip-26-0-170-31.ec2.internal
  rank      : 8 (local_rank: 0)
  exitcode  : 1 (pid: 2925877)
  error_file: <N/A>
  traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html
============================================================
srun: error: ip-26-0-170-31: task 1: 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.