llama-1B/16_GPUS/dp-1_tp-16_pp-1_mbz-1024/log.out

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START TIME: Tue Jul  2 19:50:11 UTC 2024
python3 version = Python 3.10.14
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M	examples/config_tiny_llama.py
M	examples/config_tiny_llama.yaml
M	examples/train_tiny_llama.sh
M	src/nanotron/models/llama.py
M	src/nanotron/trainer.py
Your branch is up to date with 'origin/bench_cluster'.
Job status: RUNNING
W0702 19:50:18.079000 139733262489408 torch/distributed/run.py:757] 
W0702 19:50:18.079000 139733262489408 torch/distributed/run.py:757] *****************************************
W0702 19:50:18.079000 139733262489408 torch/distributed/run.py:757] Setting OMP_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. 
W0702 19:50:18.079000 139733262489408 torch/distributed/run.py:757] *****************************************
W0702 19:50:18.270000 140152547391296 torch/distributed/run.py:757] 
W0702 19:50:18.270000 140152547391296 torch/distributed/run.py:757] *****************************************
W0702 19:50:18.270000 140152547391296 torch/distributed/run.py:757] Setting OMP_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. 
W0702 19:50:18.270000 140152547391296 torch/distributed/run.py:757] *****************************************
[default0]:07/02/2024 19:50:41 [WARNING|DP=0|PP=0|TP=0|ip-26-0-169-139]: [Vocab Size Padding] Padded vocab (size: 50257) with 15 dummy tokens (new size: 50272)
[default0]:07/02/2024 19:50:41 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-139]: Config:
[default0]:07/02/2024 19:50:41 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-139]: Config(general=GeneralArgs(project='bench_cluster',
[default0]:07/02/2024 19:50:41 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-139]:                            run='%date_%jobid',
[default0]:07/02/2024 19:50:41 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-139]:                            seed=42,
[default0]:07/02/2024 19:50:41 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-139]:                            step=None,
[default0]:07/02/2024 19:50:41 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-139]:                            consumed_train_samples=None,
[default0]:07/02/2024 19:50:41 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-139]:                            benchmark_csv_path=None,
[default0]:07/02/2024 19:50:41 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-139]:                            ignore_sanity_checks=True),
[default0]:07/02/2024 19:50:41 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-139]:        parallelism=ParallelismArgs(dp=1,
[default0]:07/02/2024 19:50:41 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-139]:                                    pp=1,
[default0]:07/02/2024 19:50:41 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-139]:                                    tp=16,
[default0]:07/02/2024 19:50:41 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-139]:                                    pp_engine=<nanotron.parallel.pipeline_parallel.engine.OneForwardOneBackwardPipelineEngine object at 0x7ff49b738910>,
[default0]:07/02/2024 19:50:41 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-139]:                                    tp_mode=<TensorParallelLinearMode.REDUCE_SCATTER: 2>,
[default0]:07/02/2024 19:50:41 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-139]:                                    tp_linear_async_communication=False,
[default0]:07/02/2024 19:50:41 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-139]:                                    expert_parallel_size=1),
[default0]:07/02/2024 19:50:41 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-139]:        model=ModelArgs(model_config=LlamaConfig(bos_token_id=1,
[default0]:07/02/2024 19:50:41 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-139]:                                                 eos_token_id=2,
[default0]:07/02/2024 19:50:41 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-139]:                                                 hidden_act='silu',
[default0]:07/02/2024 19:50:41 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-139]:                                                 hidden_size=2048,
[default0]:07/02/2024 19:50:41 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-139]:                                                 initializer_range=0.02,
[default0]:07/02/2024 19:50:41 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-139]:                                                 intermediate_size=4096,
[default0]:07/02/2024 19:50:41 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-139]:                                                 is_llama_config=True,
[default0]:07/02/2024 19:50:41 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-139]:                                                 max_position_embeddings=4096,
[default0]:07/02/2024 19:50:41 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-139]:                                                 num_attention_heads=32,
[default0]:07/02/2024 19:50:41 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-139]:                                                 num_hidden_layers=24,
[default0]:07/02/2024 19:50:41 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-139]:                                                 num_key_value_heads=32,
[default0]:07/02/2024 19:50:41 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-139]:                                                 pad_token_id=None,
[default0]:07/02/2024 19:50:41 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-139]:                                                 pretraining_tp=1,
[default0]:07/02/2024 19:50:41 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-139]:                                                 rms_norm_eps=1e-05,
[default0]:07/02/2024 19:50:41 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-139]:                                                 rope_scaling=None,
[default0]:07/02/2024 19:50:41 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-139]:                                                 rope_theta=10000.0,
[default0]:07/02/2024 19:50:41 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-139]:                                                 tie_word_embeddings=True,
[default0]:07/02/2024 19:50:41 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-139]:                                                 use_cache=True,
[default0]:07/02/2024 19:50:41 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-139]:                                                 vocab_size=50272),
[default0]:07/02/2024 19:50:41 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-139]:                        init_method=RandomInit(std=0.025),
[default0]:07/02/2024 19:50:41 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-139]:                        dtype=torch.bfloat16,
[default0]:07/02/2024 19:50:41 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-139]:                        make_vocab_size_divisible_by=1,
[default0]:07/02/2024 19:50:41 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-139]:                        ddp_bucket_cap_mb=25),
[default0]:07/02/2024 19:50:41 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-139]:        tokenizer=TokenizerArgs(tokenizer_name_or_path='openai-community/gpt2',
[default0]:07/02/2024 19:50:41 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-139]:                                tokenizer_revision=None,
[default0]:07/02/2024 19:50:41 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-139]:                                tokenizer_max_length=None),
[default0]:07/02/2024 19:50:41 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-139]:        checkpoints=CheckpointsArgs(checkpoints_path=Path('/dev/null'),
[default0]:07/02/2024 19:50:41 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-139]:                                    checkpoint_interval=100000,
[default0]:07/02/2024 19:50:41 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-139]:                                    save_initial_state=False,
[default0]:07/02/2024 19:50:41 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-139]:                                    resume_checkpoint_path=None,
[default0]:07/02/2024 19:50:41 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-139]:                                    checkpoints_path_is_shared_file_system=False),
[default0]:07/02/2024 19:50:41 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-139]:        logging=LoggingArgs(log_level='info',
[default0]:07/02/2024 19:50:41 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-139]:                            log_level_replica='info',
[default0]:07/02/2024 19:50:41 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-139]:                            iteration_step_info_interval=1),
[default0]:07/02/2024 19:50:41 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-139]:        tokens=TokensArgs(sequence_length=4096,
[default0]:07/02/2024 19:50:41 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-139]:                          train_steps=20,
[default0]:07/02/2024 19:50:41 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-139]:                          micro_batch_size=1024,
[default0]:07/02/2024 19:50:41 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-139]:                          batch_accumulation_per_replica=1,
[default0]:07/02/2024 19:50:41 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-139]:                          val_check_interval=-1,
[default0]:07/02/2024 19:50:41 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-139]:                          limit_val_batches=0,
[default0]:07/02/2024 19:50:41 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-139]:                          limit_test_batches=0),
[default0]:07/02/2024 19:50:41 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-139]:        optimizer=OptimizerArgs(optimizer_factory=AdamWOptimizerArgs(adam_eps=1e-08,
[default0]:07/02/2024 19:50:41 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-139]:                                                                     adam_beta1=0.9,
[default0]:07/02/2024 19:50:41 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-139]:                                                                     adam_beta2=0.95,
[default0]:07/02/2024 19:50:41 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-139]:                                                                     torch_adam_is_fused=True,
[default0]:07/02/2024 19:50:41 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-139]:                                                                     name='adamW'),
[default0]:07/02/2024 19:50:41 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-139]:                                zero_stage=1,
[default0]:07/02/2024 19:50:41 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-139]:                                weight_decay=0.01,
[default0]:07/02/2024 19:50:41 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-139]:                                clip_grad=1.0,
[default0]:07/02/2024 19:50:41 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-139]:                                accumulate_grad_in_fp32=True,
[default0]:07/02/2024 19:50:41 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-139]:                                learning_rate_scheduler=LRSchedulerArgs(learning_rate=0.0001,
[default0]:07/02/2024 19:50:41 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-139]:                                                                        lr_warmup_steps=1,
[default0]:07/02/2024 19:50:41 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-139]:                                                                        lr_warmup_style='linear',
[default0]:07/02/2024 19:50:41 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-139]:                                                                        lr_decay_style='linear',
[default0]:07/02/2024 19:50:41 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-139]:                                                                        lr_decay_steps=19,
[default0]:07/02/2024 19:50:41 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-139]:                                                                        lr_decay_starting_step=None,
[default0]:07/02/2024 19:50:41 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-139]:                                                                        min_decay_lr=1e-05)),
[default0]:07/02/2024 19:50:41 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-139]:        data_stages=[DatasetStageArgs(name='Training Stage',
[default0]:07/02/2024 19:50:41 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-139]:                                      start_training_step=1,
[default0]:07/02/2024 19:50:41 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-139]:                                      data=DataArgs(dataset=PretrainDatasetsArgs(hf_dataset_or_datasets='roneneldan/TinyStories',
[default0]:07/02/2024 19:50:41 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-139]:                                                                                 hf_dataset_splits='train',
[default0]:07/02/2024 19:50:41 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-139]:                                                                                 hf_dataset_config_name=None,
[default0]:07/02/2024 19:50:41 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-139]:                                                                                 dataset_processing_num_proc_per_process=64,
[default0]:07/02/2024 19:50:41 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-139]:                                                                                 dataset_overwrite_cache=False,
[default0]:07/02/2024 19:50:41 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-139]:                                                                                 text_column_name='text'),
[default0]:07/02/2024 19:50:41 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-139]:                                                    seed=42,
[default0]:07/02/2024 19:50:41 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-139]:                                                    num_loading_workers=32))],
[default0]:07/02/2024 19:50:41 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-139]:        profiler=ProfilerArgs(profiler_export_path=Path('/fsx/ferdinandmom/ferdinand-hf/bench_cluster/results/llama-1B/16_GPUS/dp-1_tp-16_pp-1_mbz-1024')),
[default0]:07/02/2024 19:50:41 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-139]:        lighteval=None)
[default0]:07/02/2024 19:50:41 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-139]: Model Config:
[default0]:07/02/2024 19:50:41 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-139]: LlamaConfig(bos_token_id=1,
[default0]:07/02/2024 19:50:41 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-139]:             eos_token_id=2,
[default0]:07/02/2024 19:50:41 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-139]:             hidden_act='silu',
[default0]:07/02/2024 19:50:41 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-139]:             hidden_size=2048,
[default0]:07/02/2024 19:50:41 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-139]:             initializer_range=0.02,
[default0]:07/02/2024 19:50:41 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-139]:             intermediate_size=4096,
[default0]:07/02/2024 19:50:41 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-139]:             is_llama_config=True,
[default0]:07/02/2024 19:50:41 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-139]:             max_position_embeddings=4096,
[default0]:07/02/2024 19:50:41 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-139]:             num_attention_heads=32,
[default0]:07/02/2024 19:50:41 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-139]:             num_hidden_layers=24,
[default0]:07/02/2024 19:50:41 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-139]:             num_key_value_heads=32,
[default0]:07/02/2024 19:50:41 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-139]:             pad_token_id=None,
[default0]:07/02/2024 19:50:41 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-139]:             pretraining_tp=1,
[default0]:07/02/2024 19:50:41 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-139]:             rms_norm_eps=1e-05,
[default0]:07/02/2024 19:50:41 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-139]:             rope_scaling=None,
[default0]:07/02/2024 19:50:41 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-139]:             rope_theta=10000.0,
[default0]:07/02/2024 19:50:41 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-139]:             tie_word_embeddings=True,
[default0]:07/02/2024 19:50:41 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-139]:             use_cache=True,
[default0]:07/02/2024 19:50:41 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-139]:             vocab_size=50272)
[default0]:07/02/2024 19:50:41 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-139]: Building model..
[default0]:07/02/2024 19:50:41 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-139]: Setting PP block ranks...
[default6]:07/02/2024 19:50:58 [INFO|DP=0|PP=0|TP=6|ip-26-0-169-139]: Local number of parameters: 69.4M (132.46MiB)
[default6]:07/02/2024 19:50:58 [INFO|DP=0|PP=0|TP=6|ip-26-0-169-139]: [After model building] Memory usage: 159.71MiB. Peak allocated: 174.02MiB Peak reserved: 178.00MiB
[default6]:07/02/2024 19:50:58 [INFO|DP=0|PP=0|TP=6|ip-26-0-169-139]: No checkpoint path provided.
[default0]:07/02/2024 19:50:58 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-139]: Total number of parameters: 1.11G (2119.44MiB)
[default0]:07/02/2024 19:50:58 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-139]: Local number of parameters: 69.4M (132.46MiB)
[default6]:07/02/2024 19:50:58 [INFO|DP=0|PP=0|TP=14|ip-26-0-172-73]: Local number of parameters: 69.4M (132.46MiB)
[default6]:07/02/2024 19:50:58 [INFO|DP=0|PP=0|TP=14|ip-26-0-172-73]: [After model building] Memory usage: 159.71MiB. Peak allocated: 174.02MiB Peak reserved: 178.00MiB
[default6]:07/02/2024 19:50:58 [INFO|DP=0|PP=0|TP=14|ip-26-0-172-73]: No checkpoint path provided.
[default0]:07/02/2024 19:50:58 [INFO|DP=0|PP=0|TP=8|ip-26-0-172-73]: Local number of parameters: 69.4M (132.46MiB)
[default0]:07/02/2024 19:50:58 [INFO|DP=0|PP=0|TP=8|ip-26-0-172-73]: [After model building] Memory usage: 159.71MiB. Peak allocated: 174.02MiB Peak reserved: 178.00MiB
[default0]:07/02/2024 19:50:58 [INFO|DP=0|PP=0|TP=8|ip-26-0-172-73]: No checkpoint path provided.
[default0]:07/02/2024 19:50:58 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-139]: [After model building] Memory usage: 159.71MiB. Peak allocated: 174.02MiB Peak reserved: 178.00MiB
[default0]:07/02/2024 19:50:58 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-139]: No checkpoint path provided.
[default0]:07/02/2024 19:50:58 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-139]: Parametrizing model parameters using StandardParametrizator
[default0]:07/02/2024 19:50:58 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-139]: [Optimizer Building] Using LearningRateForSP as learning rate
[default0]:07/02/2024 19:50:58 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-139]: [ZeRO sharding] Size of optimizer params per rank:
[default0]:07/02/2024 19:50:58 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-139]: [ZeRO sharding] DP Rank 0 has 69.4M out of 69.4M (100.00%) params' optimizer states
[default3]:07/02/2024 19:50:58 [INFO|DP=0|PP=0|TP=3|ip-26-0-169-139]: Local number of parameters: 69.4M (132.46MiB)
[default1]:07/02/2024 19:50:58 [INFO|DP=0|PP=0|TP=9|ip-26-0-172-73]: Local number of parameters: 69.4M (132.46MiB)
[default1]:07/02/2024 19:50:58 [INFO|DP=0|PP=0|TP=9|ip-26-0-172-73]: [After model building] Memory usage: 159.71MiB. Peak allocated: 174.02MiB Peak reserved: 178.00MiB
[default1]:07/02/2024 19:50:58 [INFO|DP=0|PP=0|TP=9|ip-26-0-172-73]: No checkpoint path provided.
[default3]:07/02/2024 19:50:58 [INFO|DP=0|PP=0|TP=3|ip-26-0-169-139]: [After model building] Memory usage: 159.71MiB. Peak allocated: 174.02MiB Peak reserved: 178.00MiB
[default4]:07/02/2024 19:50:58 [INFO|DP=0|PP=0|TP=4|ip-26-0-169-139]: Local number of parameters: 69.4M (132.46MiB)
[default1]:07/02/2024 19:50:58 [INFO|DP=0|PP=0|TP=1|ip-26-0-169-139]: Local number of parameters: 69.4M (132.46MiB)
[default1]:07/02/2024 19:50:58 [INFO|DP=0|PP=0|TP=1|ip-26-0-169-139]: [After model building] Memory usage: 159.71MiB. Peak allocated: 174.02MiB Peak reserved: 178.00MiB
[default2]:07/02/2024 19:50:58 [INFO|DP=0|PP=0|TP=2|ip-26-0-169-139]: Local number of parameters: 69.4M (132.46MiB)
[default2]:07/02/2024 19:50:58 [INFO|DP=0|PP=0|TP=2|ip-26-0-169-139]: [After model building] Memory usage: 159.71MiB. Peak allocated: 174.02MiB Peak reserved: 178.00MiB
[default2]:07/02/2024 19:50:58 [INFO|DP=0|PP=0|TP=2|ip-26-0-169-139]: No checkpoint path provided.
[default4]:07/02/2024 19:50:58 [INFO|DP=0|PP=0|TP=4|ip-26-0-169-139]: [After model building] Memory usage: 159.71MiB. Peak allocated: 174.02MiB Peak reserved: 178.00MiB
[default4]:07/02/2024 19:50:58 [INFO|DP=0|PP=0|TP=4|ip-26-0-169-139]: No checkpoint path provided.
[default5]:07/02/2024 19:50:58 [INFO|DP=0|PP=0|TP=5|ip-26-0-169-139]: Local number of parameters: 69.4M (132.46MiB)
[default5]:07/02/2024 19:50:58 [INFO|DP=0|PP=0|TP=5|ip-26-0-169-139]: [After model building] Memory usage: 159.71MiB. Peak allocated: 174.02MiB Peak reserved: 178.00MiB
[default3]:07/02/2024 19:50:58 [INFO|DP=0|PP=0|TP=3|ip-26-0-169-139]: No checkpoint path provided.
[default1]:07/02/2024 19:50:58 [INFO|DP=0|PP=0|TP=1|ip-26-0-169-139]: No checkpoint path provided.
[default5]:07/02/2024 19:50:58 [INFO|DP=0|PP=0|TP=5|ip-26-0-169-139]: No checkpoint path provided.
[default4]:07/02/2024 19:50:58 [INFO|DP=0|PP=0|TP=12|ip-26-0-172-73]: Local number of parameters: 69.4M (132.46MiB)
[default4]:07/02/2024 19:50:58 [INFO|DP=0|PP=0|TP=12|ip-26-0-172-73]: [After model building] Memory usage: 159.71MiB. Peak allocated: 174.02MiB Peak reserved: 178.00MiB
[default4]:07/02/2024 19:50:58 [INFO|DP=0|PP=0|TP=12|ip-26-0-172-73]: No checkpoint path provided.
[default5]:07/02/2024 19:50:58 [INFO|DP=0|PP=0|TP=13|ip-26-0-172-73]: Local number of parameters: 69.4M (132.46MiB)
[default5]:07/02/2024 19:50:58 [INFO|DP=0|PP=0|TP=13|ip-26-0-172-73]: [After model building] Memory usage: 159.71MiB. Peak allocated: 174.02MiB Peak reserved: 178.00MiB
[default5]:07/02/2024 19:50:58 [INFO|DP=0|PP=0|TP=13|ip-26-0-172-73]: No checkpoint path provided.
[default2]:07/02/2024 19:50:58 [INFO|DP=0|PP=0|TP=10|ip-26-0-172-73]: Local number of parameters: 69.4M (132.46MiB)
[default2]:07/02/2024 19:50:58 [INFO|DP=0|PP=0|TP=10|ip-26-0-172-73]: [After model building] Memory usage: 159.71MiB. Peak allocated: 174.02MiB Peak reserved: 178.00MiB
[default2]:07/02/2024 19:50:58 [INFO|DP=0|PP=0|TP=10|ip-26-0-172-73]: No checkpoint path provided.
[default7]:07/02/2024 19:50:58 [INFO|DP=0|PP=0|TP=15|ip-26-0-172-73]: Local number of parameters: 69.4M (132.46MiB)
[default7]:07/02/2024 19:50:58 [INFO|DP=0|PP=0|TP=15|ip-26-0-172-73]: [After model building] Memory usage: 159.71MiB. Peak allocated: 174.02MiB Peak reserved: 178.00MiB
[default7]:07/02/2024 19:50:58 [INFO|DP=0|PP=0|TP=15|ip-26-0-172-73]: No checkpoint path provided.
[default3]:07/02/2024 19:50:58 [INFO|DP=0|PP=0|TP=11|ip-26-0-172-73]: Local number of parameters: 69.4M (132.46MiB)
[default3]:07/02/2024 19:50:58 [INFO|DP=0|PP=0|TP=11|ip-26-0-172-73]: [After model building] Memory usage: 159.71MiB. Peak allocated: 174.02MiB Peak reserved: 178.00MiB
[default3]:07/02/2024 19:50:58 [INFO|DP=0|PP=0|TP=11|ip-26-0-172-73]: No checkpoint path provided.
[default7]:07/02/2024 19:50:58 [INFO|DP=0|PP=0|TP=7|ip-26-0-169-139]: Local number of parameters: 69.4M (132.46MiB)
[default7]:07/02/2024 19:50:58 [INFO|DP=0|PP=0|TP=7|ip-26-0-169-139]: [After model building] Memory usage: 159.71MiB. Peak allocated: 174.02MiB Peak reserved: 178.00MiB
[default7]:07/02/2024 19:50:58 [INFO|DP=0|PP=0|TP=7|ip-26-0-169-139]: No checkpoint path provided.
[default0]:07/02/2024 19:51:00 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-139]: [Training Plan] Stage Training Stage has 19 remaining training steps and has consumed 0 samples
[default0]:07/02/2024 19:51:00 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-139]: Using `datasets` library
[default0]:07/02/2024 19:51:00 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-139]: Loading tokenizer from openai-community/gpt2 and transformers/hf_hub versions ('4.41.2', '0.23.4')
[default0]:Repo card metadata block was not found. Setting CardData to empty.
[default0]:07/02/2024 19:51:00 [WARNING|DP=0|PP=0|TP=0|ip-26-0-169-139]: Repo card metadata block was not found. Setting CardData to empty.
[default0]:07/02/2024 19:51:02 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-139]: [Training Plan] There are 1 training stages 
[default0]:07/02/2024 19:51:02 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-139]: [Stage Training Stage] start from step 1 
[default0]:07/02/2024 19:51:02 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-139]: 
[default0]:07/02/2024 19:51:02 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-139]: [Start training] datetime: 2024-07-02 19:51:02.676484 | mbs: 1024 | grad_accum: 1 | global_batch_size: 1024 | sequence_length: 4096 | train_steps: 20 | start_iteration_step: 0 | consumed_train_samples: 0
[default0]:07/02/2024 19:51:02 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-139]: Resuming training from stage Training Stage, it has trained for 0 samples and has 19 remaining train steps
[default0]:07/02/2024 19:51:02 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-139]:  Memory usage: 689.57MiB. Peak allocated 689.57MiB. Peak reserved: 710.00MiB
[default4]:Repo card metadata block was not found. Setting CardData to empty.
[default6]:Repo card metadata block was not found. Setting CardData to empty.
[default6]:07/02/2024 19:51:02 [WARNING|DP=0|PP=0|TP=6|ip-26-0-169-139]: Repo card metadata block was not found. Setting CardData to empty.
[default6]:07/02/2024 19:51:02 [WARNING|DP=0|PP=0|TP=14|ip-26-0-172-73]: Repo card metadata block was not found. Setting CardData to empty.
[default0]:07/02/2024 19:51:02 [WARNING|DP=0|PP=0|TP=8|ip-26-0-172-73]: Repo card metadata block was not found. Setting CardData to empty.
[default1]:07/02/2024 19:51:02 [WARNING|DP=0|PP=0|TP=9|ip-26-0-172-73]: Repo card metadata block was not found. Setting CardData to empty.
[default4]: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 19:51:02 [WARNING|DP=0|PP=0|TP=5|ip-26-0-169-139]: Repo card metadata block was not found. Setting CardData to empty.
[default4]:07/02/2024 19:51:02 [WARNING|DP=0|PP=0|TP=4|ip-26-0-169-139]: Repo card metadata block was not found. Setting CardData to empty.
[default3]:07/02/2024 19:51:02 [WARNING|DP=0|PP=0|TP=3|ip-26-0-169-139]: Repo card metadata block was not found. Setting CardData to empty.
[default4]:07/02/2024 19:51:02 [WARNING|DP=0|PP=0|TP=12|ip-26-0-172-73]: Repo card metadata block was not found. Setting CardData to empty.
[default2]:Repo card metadata block was not found. Setting CardData to empty.
[default2]:07/02/2024 19:51:02 [WARNING|DP=0|PP=0|TP=10|ip-26-0-172-73]: Repo card metadata block was not found. Setting CardData to empty.
[default3]:07/02/2024 19:51:02 [WARNING|DP=0|PP=0|TP=11|ip-26-0-172-73]: Repo card metadata block was not found. Setting CardData to empty.
[default5]:07/02/2024 19:51:02 [WARNING|DP=0|PP=0|TP=13|ip-26-0-172-73]: Repo card metadata block was not found. Setting CardData to empty.
[default1]:Repo card metadata block was not found. Setting CardData to empty.
[default7]:07/02/2024 19:51:02 [WARNING|DP=0|PP=0|TP=15|ip-26-0-172-73]: 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.
[default3]:Repo card metadata block was not found. Setting CardData to empty.
[default7]:Repo card metadata block was not found. Setting CardData to empty.
[default6]:Repo card metadata block was not found. Setting CardData to empty.
[default0]:Repo card metadata block was not found. Setting CardData to empty.
[default1]:Repo card metadata block was not found. Setting CardData to empty.
[default1]:07/02/2024 19:51:02 [WARNING|DP=0|PP=0|TP=1|ip-26-0-169-139]: Repo card metadata block was not found. Setting CardData to empty.
[default2]:07/02/2024 19:51:02 [WARNING|DP=0|PP=0|TP=2|ip-26-0-169-139]: 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 19:51:02 [WARNING|DP=0|PP=0|TP=7|ip-26-0-169-139]: Repo card metadata block was not found. Setting CardData to empty.
[default2]:Repo card metadata block was not found. Setting CardData to empty.
[default3]:[rank3]: Traceback (most recent call last):
[default3]:[rank3]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/run_train.py", line 237, in <module>
[default3]:[rank3]:     trainer.train(dataloader)
[default3]:[rank3]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 429, in train
[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 631, in forward
[default3]:[rank3]:     output = self.attn(hidden_states=hidden_states, sequence_mask=sequence_mask)
[default3]:[rank3]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 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 360, in forward
[default3]:[rank3]:     qkv_states = self.qkv_proj(
[default3]:[rank3]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
[default3]:[rank3]:     return self._call_impl(*args, **kwargs)
[default3]:[rank3]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
[default3]:[rank3]:     return forward_call(*args, **kwargs)
[default3]:[rank3]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/nn.py", line 87, in forward
[default3]:[rank3]:     return column_linear(
[default3]:[rank3]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/functional.py", line 359, in column_linear
[default3]:[rank3]:     return F.linear(input, weight, bias)
[default3]:[rank3]: torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 3.00 GiB. GPU  has a total capacity of 79.33 GiB of which 2.14 GiB is free. Including non-PyTorch memory, this process has 77.16 GiB memory in use. Of the allocated memory 67.84 GiB is allocated by PyTorch, and 2.00 GiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation.  See documentation for Memory Management  (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables)
[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)
[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
[default4]:[rank12]:   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)
[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
[default5]:[rank13]:   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
[default5]:[rank13]:     outputs = self.pipeline_engine.train_batch_iter(
[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)
[default5]:[rank13]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 278, in train_batch_iter
[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
[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)
[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)
[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)
[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
[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
[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
[default5]:[rank13]:     sharded_logits = self.model(
[default4]:[rank12]:     return forward_call(*args, **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
[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)
[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
[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 360, in forward
[default4]:[rank12]:     qkv_states = self.qkv_proj(
[default5]:[rank13]:     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
[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
[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/tensor_parallel/nn.py", line 87, in forward
[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
[default4]:[rank12]:     return column_linear(
[default5]:[rank13]:     return forward_call(*args, **kwargs)
[default4]:[rank12]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/functional.py", line 359, in column_linear
[default5]:[rank13]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/block.py", line 151, in forward
[default4]:[rank12]:     return F.linear(input, weight, bias)
[default5]:[rank13]:     output = self.pp_block(**new_kwargs)
[default4]:[rank12]: torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 3.00 GiB. GPU  has a total capacity of 79.33 GiB of which 2.14 GiB is free. Including non-PyTorch memory, this process has 77.16 GiB memory in use. Of the allocated memory 67.84 GiB is allocated by PyTorch, and 2.00 GiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation.  See documentation for Memory Management  (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables)
[default5]:[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 631, in forward
[default5]:[rank13]:     output = self.attn(hidden_states=hidden_states, sequence_mask=sequence_mask)
[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 360, in forward
[default5]:[rank13]:     qkv_states = self.qkv_proj(
[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 3.00 GiB. GPU  has a total capacity of 79.33 GiB of which 2.14 GiB is free. Including non-PyTorch memory, this process has 77.16 GiB memory in use. Of the allocated memory 67.84 GiB is allocated by PyTorch, and 2.00 GiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation.  See documentation for Memory Management  (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables)
[default4]:[rank4]: Traceback (most recent call last):
[default4]:[rank4]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/run_train.py", line 237, in <module>
[default4]:[rank4]:     trainer.train(dataloader)
[default4]:[rank4]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 429, in train
[default4]:[rank4]:     outputs, loss_avg = self.training_step(dataloader=self.current_dataloader)
[default4]:[rank4]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 462, in training_step
[default4]:[rank4]:     outputs = self.pipeline_engine.train_batch_iter(
[default4]:[rank4]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 278, in train_batch_iter
[default4]:[rank4]:     output = self.forward(context=context, state=state, micro_batch=micro_batch, model=model)
[default4]:[rank4]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 44, in forward
[default4]:[rank4]:     output = model(**micro_batch)
[default4]:[rank4]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
[default4]:[rank4]:     return self._call_impl(*args, **kwargs)
[default4]:[rank4]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
[default4]:[rank4]:     return forward_call(*args, **kwargs)
[default4]:[rank4]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 891, in forward
[default4]:[rank4]:     sharded_logits = self.model(
[default4]:[rank4]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
[default4]:[rank4]:     return self._call_impl(*args, **kwargs)
[default4]:[rank4]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
[default4]:[rank4]:     return forward_call(*args, **kwargs)
[default4]:[rank4]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 764, in forward
[default4]:[rank4]:     return self.forward_with_hidden_states(input_ids=input_ids, input_mask=input_mask)[0]
[default4]:[rank4]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 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]:[rank4]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
[default4]:[rank4]:     return forward_call(*args, **kwargs)
[default4]:[rank4]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/block.py", line 151, in forward
[default4]:[rank4]:     output = self.pp_block(**new_kwargs)
[default4]:[rank4]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
[default4]:[rank4]:     return self._call_impl(*args, **kwargs)
[default4]:[rank4]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
[default4]:[rank4]:     return forward_call(*args, **kwargs)
[default4]:[rank4]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 631, in forward
[default4]:[rank4]:     output = self.attn(hidden_states=hidden_states, sequence_mask=sequence_mask)
[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 360, in forward
[default4]:[rank4]:     qkv_states = self.qkv_proj(
[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[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 631, in forward
[default6]:[rank14]:     output = self.attn(hidden_states=hidden_states, sequence_mask=sequence_mask)
[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 360, in forward
[default6]:[rank14]:     qkv_states = self.qkv_proj(
[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 3.00 GiB. GPU  has a total capacity of 79.33 GiB of which 2.14 GiB is free. Including non-PyTorch memory, this process has 77.16 GiB memory in use. Of the allocated memory 67.84 GiB is allocated by PyTorch, and 2.00 GiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation.  See documentation for Memory Management  (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables)
._call_impl(*args, **kwargs)
[default4]:[rank4]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
[default4]:[rank4]:     return forward_call(*args, **kwargs)
[default4]:[rank4]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/nn.py", line 87, in forward
[default4]:[rank4]:     return column_linear(
[default4]:[rank4]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/functional.py", line 359, in column_linear
[default4]:[rank4]:     return F.linear(input, weight, bias)
[default4]:[rank4]: torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 3.00 GiB. GPU  has a total capacity of 79.33 GiB of which 2.14 GiB is free. Including non-PyTorch memory, this process has 77.16 GiB memory in use. Of the allocated memory 67.84 GiB is allocated by PyTorch, and 2.00 GiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation.  See documentation for Memory Management  (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables)
[default5]:[rank5]: Traceback (most recent call last):
[default5]:[rank5]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/run_train.py", line 237, in <module>
[default5]:[rank5]:     trainer.train(dataloader)
[default5]:[rank5]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 429, in train
[default5]:[rank5]:     outputs, loss_avg = self.training_step(dataloader=self.current_dataloader)
[default5]:[rank5]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 462, in training_step
[default5]:[rank5]:     outputs = self.pipeline_engine.train_batch_iter(
[default5]:[rank5]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 278, in train_batch_iter
[default5]:[rank5]:     output = self.forward(context=context, state=state, micro_batch=micro_batch, model=model)
[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 360, in forward
[default5]:[rank5]:     qkv_states = self.qkv_proj(
[default5]:[rank5]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
[default5]:[rank5]:     return self._call_impl(*args, **kwargs)
[default5]:[rank5]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
[default5]:[rank5]:     return forward_call(*args, **kwargs)
[default5]:[rank5]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/nn.py", line 87, in forward
[default5]:[rank5]:     return column_linear(
[default5]:[rank5]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/functional.py", line 359, in column_linear
[default5]:[rank5]:     return F.linear(input, weight, bias)
[default5]:[rank5]: torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 3.00 GiB. GPU  has a total capacity of 79.33 GiB of which 2.14 GiB is free. Including non-PyTorch memory, this process has 77.16 GiB memory in use. Of the allocated memory 67.84 GiB is allocated by PyTorch, and 2.00 GiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation.  See documentation for Memory Management  (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables)
[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 631, in forward
[default1]:[rank9]:     output = self.attn(hidden_states=hidden_states, sequence_mask=sequence_mask)
[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 360, in forward
[default1]:[rank9]:     qkv_states = self.qkv_proj(
[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/tensor_parallel/nn.py", line 87, in forward
[default1]:[rank9]:     return column_linear(
[default1]:[rank9]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/functional.py", line 359, in column_linear
[default1]:[rank9]:     return F.linear(input, weight, bias)
[default1]:[rank9]: torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 3.00 GiB. GPU 
 has a total capacity of 79.33 GiB of which 2.54 GiB is free. Including non-PyTorch memory, this process has 76.76 GiB memory in use. Of the allocated memory 67.84 GiB is allocated by PyTorch, and 2.00 GiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation.  See documentation for Memory Management  (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables)
[default7]:[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 631, in forward
[default7]:[rank15]:     output = self.attn(hidden_states=hidden_states, sequence_mask=sequence_mask)
[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 360, in forward
[default7]:[rank15]:     qkv_states = self.qkv_proj(
[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 3.00 GiB. GPU  has a total capacity of 79.33 GiB of which 2.21 GiB is free. Including non-PyTorch memory, this process has 77.09 GiB memory in use. Of the allocated memory 67.84 GiB is allocated by PyTorch, and 2.00 GiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation.  See documentation for Memory Management  (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables)
[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 631, in forward
[default0]:[rank8]:     output = self.attn(hidden_states=hidden_states, sequence_mask=sequence_mask)
[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 360, in forward
[default0]:[rank8]:     qkv_states = self.qkv_proj(
[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/tensor_parallel/nn.py", line 87, in forward
[default0]:[rank8]:     return column_linear(
[default0]:[rank8]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/functional.py", line 359, in column_linear
[default0]:[rank8]:     return F.linear(input, weight, bias)
[default0]:[rank8]: torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 3.00 GiB. GPU 
[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 631, in forward
[default3]:[rank11]:     output = self.attn(hidden_states=hidden_states, sequence_mask=sequence_mask)
[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 360, in forward
[default3]:[rank11]:     qkv_states = self.qkv_proj(
[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 3.00 GiB. GPU  has a total capacity of 79.33 GiB of which 2.14 GiB is free. Including non-PyTorch memory, this process has 77.16 GiB memory in use. Of the allocated memory 67.84 GiB is allocated by PyTorch, and 2.00 GiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation.  See documentation for Memory Management  (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables)
[default7]:[rank7]: 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 631, in forward
[default7]:[rank7]:     output = self.attn(hidden_states=hidden_states, sequence_mask=sequence_mask)
[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 360, in forward
[default7]:[rank7]:     qkv_states = self.qkv_proj(
[default7]:[rank7]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
[default7]:[rank7]:     return self._call_impl(*args, **kwargs)
[default7]:[rank7]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
[default7]:[rank7]:     return forward_call(*args, **kwargs)
[default7]:[rank7]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/nn.py", line 87, in forward
[default7]:[rank7]:     return column_linear(
[default7]:[rank7]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/functional.py", line 359, in column_linear
[default7]:[rank7]:     return F.linear(input, weight, bias)
[default7]:[rank7]: torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 3.00 GiB. GPU  has a total capacity of 79.33 GiB of which 2.21 GiB is free. Including non-PyTorch memory, this process has 77.09 GiB memory in use. Of the allocated memory 67.84 GiB is allocated by PyTorch, and 2.00 GiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation.  See documentation for Memory Management  (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables)
[default4]:Exception in thread Thread-2 (_pin_memory_loop):
[default4]:Traceback (most recent call last):
[default4]:  File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/threading.py", line 1016, in _bootstrap_inner
[default4]:    self.run()
[default4]:  File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/threading.py", line 953, in run
[default4]:    self._target(*self._args, **self._kwargs)
[default4]:  File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/utils/data/_utils/pin_memory.py", line 54, in _pin_memory_loop
[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 631, in forward
[default2]:[rank2]:     output = self.attn(hidden_states=hidden_states, sequence_mask=sequence_mask)
[default2]:[rank2]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 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 360, in forward
[default2]:[rank2]:     qkv_states = self.qkv_proj(
[default2]:[rank2]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
[default2]:[rank2]:     return self._call_impl(*args, **kwargs)
[default2]:[rank2]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
[default2]:[rank2]:     return forward_call(*args, **kwargs)
[default2]:[rank2]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/nn.py", line 87, in forward
[default2]:[rank2]:     return column_linear(
[default2]:[rank2]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/functional.py", line 359, in column_linear
[default2]:[rank2]:     return F.linear(input, weight, bias)
[default2]:[rank2]: torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 3.00 GiB. GPU  has a total capacity of 79.33 GiB of which 2.37 GiB is free. Including non-PyTorch memory, this process has 76.93 GiB memory in use. Of the allocated memory 67.84 GiB is allocated by PyTorch, and 2.00 GiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation.  See documentation for Memory Management  (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables)
[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 631, in forward
[default6]:[rank6]:     output = self.attn(hidden_states=hidden_states, sequence_mask=sequence_mask)
[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 360, in forward
[default6]:[rank6]:     qkv_states = self.qkv_proj(
[default6]:[rank6]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
[default6]:[rank6]:     return self._call_impl(*args, **kwargs)
[default6]:[rank6]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
[default6]:[rank6]:     return forward_call(*args, **kwargs)
[default6]:[rank6]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/nn.py", line 87, in forward
[default6]:[rank6]:     return column_linear(
[default6]:[rank6]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/functional.py", line 359, in column_linear
[default6]:[rank6]:     return F.linear(input, weight, bias)
[default6]:[rank6]: torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 3.00 GiB. GPU  has a total capacity of 79.33 GiB of which 2.15 GiB is free. Including non-PyTorch memory, this process has 77.16 GiB memory in use. Of the allocated memory 67.84 GiB is allocated by PyTorch, and 2.00 GiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation.  See documentation for Memory Management  (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables)
[default0]:[rank0]: Traceback (most recent call last):
[default0]:[rank0]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/run_train.py", line 237, in <module>
[default0]:[rank0]:     trainer.train(dataloader)
[default0]:[rank0]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 429, in train
[default0]:[rank0]:     outputs, loss_avg = self.training_step(dataloader=self.current_dataloader)
[default0]:[rank0]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 462, in training_step
[default0]:[rank0]:     outputs = self.pipeline_engine.train_batch_iter(
[default0]:[rank0]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 278, in train_batch_iter
[default0]:[rank0]:     output = self.forward(context=context, state=state, micro_batch=micro_batch, model=model)
[default0]:[rank0]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 44, in forward
[default0]:[rank0]:     output = model(**micro_batch)
[default0]:[rank0]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
[default0]:[rank0]:     return self._call_impl(*args, **kwargs)
[default0]:[rank0]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
[default0]:[rank0]:     return forward_call(*args, **kwargs)
[default0]:[rank0]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 891, in forward
[default0]:[rank0]:     sharded_logits = self.model(
[default0]:[rank0]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
[default0]:[rank0]:     return self._call_impl(*args, **kwargs)
[default0]:[rank0]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
[default0]:[rank0]:     return forward_call(*args, **kwargs)
[default0]:[rank0]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 764, in forward
[default0]:[rank0]:     return self.forward_with_hidden_states(input_ids=input_ids, input_mask=input_mask)[0]
[default0]:[rank0]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 780, in forward_with_hidden_states
[default0]:[rank0]:     hidden_encoder_states = encoder_block(**hidden_encoder_states)
[default0]:[rank0]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
[default0]:[rank0]:     return self._call_impl(*args, **kwargs)
[default0]:[rank0]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
[default0]:[rank0]:     return forward_call(*args, **kwargs)
[default0]:[rank0]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/block.py", line 151, in forward
[default0]:[rank0]:     output = self.pp_block(**new_kwargs)
[default0]:[rank0]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
[default0]:[rank0]:     return self._call_impl(*args, **kwargs)
[default0]:[rank0]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
[default0]:[rank0]:     return forward_call(*args, **kwargs)
[default0]:[rank0]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 631, in forward
[default0]:[rank0]:     output = self.attn(hidden_states=hidden_states, sequence_mask=sequence_mask)
[default0]:[rank0]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
[default0]:[rank0]:     return self._call_impl(*args, **kwargs)
[default0]:[rank0]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
[default0]:[rank0]:     return forward_call(*args, **kwargs)
[default0]:[rank0]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 360, in forward
[default0]:[rank0]:     qkv_states = self.qkv_proj(
[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 3.00 GiB. GPU 
[default1]:[rank1]: Traceback (most recent call last):
[default1]:[rank1]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/run_train.py", line 237, in <module>
[default1]:[rank1]:     trainer.train(dataloader)
[default1]:[rank1]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 429, in train
[default1]:[rank1]:     outputs, loss_avg = self.training_step(dataloader=self.current_dataloader)
[default1]:[rank1]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 462, in training_step
[default1]:[rank1]:     outputs = self.pipeline_engine.train_batch_iter(
[default1]:[rank1]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 278, in train_batch_iter
[default1]:[rank1]:     output = self.forward(context=context, state=state, micro_batch=micro_batch, model=model)
[default1]:[rank1]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 44, in forward
[default1]:[rank1]:     output = model(**micro_batch)
[default1]:[rank1]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
[default1]:[rank1]:     return self._call_impl(*args, **kwargs)
[default1]:[rank1]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
[default1]:[rank1]:     return forward_call(*args, **kwargs)
[default1]:[rank1]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 891, in forward
[default1]:[rank1]:     sharded_logits = self.model(
[default1]:[rank1]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
[default1]:[rank1]:     return self._call_impl(*args, **kwargs)
[default1]:[rank1]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
[default1]:[rank1]:     return forward_call(*args, **kwargs)
[default1]:[rank1]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 764, in forward
[default1]:[rank1]:     return self.forward_with_hidden_states(input_ids=input_ids, input_mask=input_mask)[0]
[default1]:[rank1]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 780, in forward_with_hidden_states
[default1]:[rank1]:     hidden_encoder_states = encoder_block(**hidden_encoder_states)
[default1]:[rank1]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
[default1]:[rank1]:     return self._call_impl(*args, **kwargs)
[default1]:[rank1]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
[default1]:[rank1]:     return forward_call(*args, **kwargs)
[default1]:[rank1]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/block.py", line 151, in forward
[default1]:[rank1]:     output = self.pp_block(**new_kwargs)
[default1]:[rank1]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
[default1]:[rank1]:     return self._call_impl(*args, **kwargs)
[default1]:[rank1]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
[default1]:[rank1]:     return forward_call(*args, **kwargs)
[default1]:[rank1]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 631, in forward
[default1]:[rank1]:     output = self.attn(hidden_states=hidden_states, sequence_mask=sequence_mask)
[default1]:[rank1]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
[default1]:[rank1]:     return self._call_impl(*args, **kwargs)
[default1]:[rank1]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
[default1]:[rank1]:     return forward_call(*args, **kwargs)
[default1]:[rank1]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 360, in forward
[default1]:[rank1]:     qkv_states = self.qkv_proj(
[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 3.00 GiB. GPU 
 has a total capacity of 79.33 GiB of which 2.54 GiB is free. Including non-PyTorch memory, this process has 76.76 GiB memory in use. Of the allocated memory 67.84 GiB is allocated by PyTorch, and 2.00 GiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation.  See documentation for Memory Management  (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables)
[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/nanot[default7]:Exception in thread Thread-2 (_pin_memory_loop):
[default7]:Traceback (most recent call last):
[default7]:  File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/threading.py", line 1016, in _bootstrap_inner
[default7]:    self.run()
[default7]:  File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/threading.py", line 953, in run
[default7]:    self._target(*self._args, **self._kwargs)
[default7]:  File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/utils/data/_utils/pin_memory.py", line 54, in _pin_memory_loop
[default7]:    do_one_step()
[default7]:  File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/utils/data/_utils/pin_memory.py", line 31, in do_one_step
[default7]:    r = in_queue.get(timeout=MP_STATUS_CHECK_INTERVAL)
[default7]:  File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/multiprocessing/queues.py", line 122, in get
[default7]:    return _ForkingPickler.loads(res)
[default7]:  File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/multiprocessing/reductions.py", line 495, in rebuild_storage_fd
[default7]:    fd = df.detach()
[default7]:  File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/multiprocessing/resource_sharer.py", line 58, in detach
[default7]:    return reduction.recv_handle(conn)
[default7]:  File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/multiprocessing/reduction.py", line 189, in recv_handle
[default7]:    return recvfds(s, 1)[0]
ron/src/nanotron/parallel/pipeline_parallel/engine.py", line 44, in forward
[default2]:[rank10]:     output = model(**micro_batch)
[default2]:[rank10]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
[default2]:[rank10]:     return self._call_impl(*args, **kwargs)
[default2]:[rank10]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
[default2]:[rank10]:     return forward_call(*args, **kwargs)
[default2]:[rank10]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 891, in forward
[default2]:[rank10]:     sharded_logits = self.model(
[default7]:  File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/multiprocessing/reduction.py", line 157, in recvfds
[default7]:    msg, ancdata, flags, addr = sock.recvmsg(1, socket.CMSG_SPACE(bytes_size))
[default7]:ConnectionResetError: [Errno 104] Connection reset by peer
[default2]:[rank10]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
[default2]:[rank10]:     return self._call_impl(*args, **kwargs)
[default2]:[rank10]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
[default2]:[rank10]:     return forward_call(*args, **kwargs)
[default2]:[rank10]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 764, in forward
[default2]:[rank10]:     return self.forward_with_hidden_states(input_ids=input_ids, input_mask=input_mask)[0]
[default2]:[rank10]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 780, in forward_with_hidden_states
[default2]:[rank10]:     hidden_encoder_states = encoder_block(**hidden_encoder_states)
[default2]:[rank10]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
[default2]:[rank10]:     return self._call_impl(*args, **kwargs)
[default2]:[rank10]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
[default2]:[rank10]:     return forward_call(*args, **kwargs)
[default2]:[rank10]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/block.py", line 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 631, in forward
[default2]:[rank10]:     output = self.attn(hidden_states=hidden_states, sequence_mask=sequence_mask)
[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 360, in forward
[default2]:[rank10]:     qkv_states = self.qkv_proj(
[default2]:[rank10]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
[default2]:[rank10]:     return self._call_impl(*args, **kwargs)
[default2]:[rank10]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
[default2]:[rank10]:     return forward_call(*args, **kwargs)
[default2]:[rank10]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/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 3.00 GiB. GPU  has a total capacity of 79.33 GiB of which 2.38 GiB is free. Including non-PyTorch memory, this process has 76.93 GiB memory in use. Of the allocated memory 67.84 GiB is allocated by PyTorch, and 2.00 GiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation.  See documentation for Memory Management  (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables)
[default1]:Exception in thread Thread-2 (_pin_memory_loop):
[default1]:Traceback (most recent call last):
[default1]:  File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/threading.py", line 1016, in _bootstrap_inner
[default7]:Exception in thread Thread-2 (_pin_memory_loop):
[default7]:Traceback (most recent call last):
[default7]:  File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/threading.py", line 1016, in _bootstrap_inner
[default7]:    self.run()
[default7]:  File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/threading.py", line 953, in run
[default6]:Exception in thread Thread-2 (_pin_memory_loop):
[default6]:Traceback (most recent call last):
[default6]:  File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/threading.py", line 1016, in _bootstrap_inner
[default6]:    self.run()
[default6]:  File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/threading.py", line 953, in run
[default6]:    self._target(*self._args, **self._kwargs)
[default6]:  File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/utils/data/_utils/pin_memory.py", line 54, in _pin_memory_loop
[default6]:    do_one_step()
[default6]:  File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/utils/data/_utils/pin_memory.py", line 31, in do_one_step
[default6]:    r = in_queue.get(timeout=MP_STATUS_CHECK_INTERVAL)
[default6]:  File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/multiprocessing/queues.py", line 122, in get
[default6]:    return _ForkingPickler.loads(res)
[default6]:  File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/multiprocessing/reductions.py", line 495, in rebuild_storage_fd
[default6]:    fd = df.detach()
[default6]:  File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/multiprocessing/resource_sharer.py", line 57, in detach
[default6]:    with _resource_sharer.get_connection(self._id) as conn:
[default6]:  File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/multiprocessing/resource_sharer.py", line 86, in get_connection
[default6]:    c = Client(address, authkey=process.current_process().authkey)
[default6]:  File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/multiprocessing/connection.py", line 508, in Client
[default6]:    answer_challenge(c, authkey)
[default6]:  File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/multiprocessing/connection.py", line 757, in answer_challenge
[default6]:    response = connection.recv_bytes(256)        # reject large message
[default6]:  File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/multiprocessing/connection.py", line 216, in recv_bytes
[default6]:    buf = self._recv_bytes(maxlength)
[default6]:  File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/multiprocessing/connection.py", line 414, in _recv_bytes
[default6]:    buf = self._recv(4)
[default6]:  File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/multiprocessing/connection.py", line 379, in _recv
[default6]:    chunk = read(handle, remaining)
[default6]:ConnectionResetError: [Errno 104] Connection reset by peer
[default3]:Exception in thread Thread-2 (_pin_memory_loop):
[default3]:Traceback (most recent call last):
[default3]:  File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/threading.py", line 1016, in _bootstrap_inner
[default3]:    self.run()
[default3]:  File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/threading.py", line 953, in run
[default3]:    self._target(*self._args, **self._kwargs)
[default3]:  File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/utils/data/_utils/pin_memory.py", line 54, in _pin_memory_loop
[default2]:Exception in thread Thread-2 (_pin_memory_loop):
[default2]:Traceback (most recent call last):
[default2]:  File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/threading.py", line 1016, in _bootstrap_inner
[default2]:    self.run()
[default2]:  File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/threading.py", line 953, in run
[default2]:    self._target(*self._args, **self._kwargs)
[default2]:  File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/utils/data/_utils/pin_memory.py", line 54, in _pin_memory_loop
[default2]:    do_one_step()
[default2]:  File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/utils/data/_utils/pin_memory.py", line 31, in do_one_step
[default2]:    r = in_queue.get(timeout=MP_STATUS_CHECK_INTERVAL)
[default2]:  File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/multiprocessing/queues.py", line 122, in get
[default0]:Exception in thread Thread-2 (_pin_memory_loop):
[default0]:Traceback (most recent call last):
[default0]:  File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/threading.py", line 1016, in _bootstrap_inner
[default0]:    self.run()
[default0]:  File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/threading.py", line 953, in run
[default0]:    self._target(*self._args, **self._kwargs)
[default0]:  File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/utils/data/_utils/pin_memory.py", line 54, in _pin_memory_loop
[default1]:Exception in thread Thread-2 (_pin_memory_loop):
[default1]:Traceback (most recent call last):
[default1]:  File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/threading.py", line 1016, in _bootstrap_inner
[default1]:    self.run()
[default1]:  File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/threading.py", line 953, in run
[default2]:Exception in thread Thread-2 (_pin_memory_loop):
[default2]:Traceback (most recent call last):
[default2]:  File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/threading.py", line 1016, in _bootstrap_inner
W0702 19:51:49.215000 139733262489408 torch/distributed/elastic/multiprocessing/api.py:851] Sending process 2542724 closing signal SIGTERM
W0702 19:51:49.216000 139733262489408 torch/distributed/elastic/multiprocessing/api.py:851] Sending process 2542725 closing signal SIGTERM
W0702 19:51:49.216000 139733262489408 torch/distributed/elastic/multiprocessing/api.py:851] Sending process 2542726 closing signal SIGTERM
W0702 19:51:49.216000 139733262489408 torch/distributed/elastic/multiprocessing/api.py:851] Sending process 2542727 closing signal SIGTERM
W0702 19:51:49.216000 139733262489408 torch/distributed/elastic/multiprocessing/api.py:851] Sending process 2542729 closing signal SIGTERM
W0702 19:51:49.223000 140152547391296 torch/distributed/elastic/multiprocessing/api.py:851] Sending process 754878 closing signal SIGTERM
W0702 19:51:49.223000 140152547391296 torch/distributed/elastic/multiprocessing/api.py:851] Sending process 754879 closing signal SIGTERM
W0702 19:51:49.223000 140152547391296 torch/distributed/elastic/multiprocessing/api.py:851] Sending process 754880 closing signal SIGTERM
W0702 19:51:49.224000 140152547391296 torch/distributed/elastic/multiprocessing/api.py:851] Sending process 754881 closing signal SIGTERM
W0702 19:51:49.224000 140152547391296 torch/distributed/elastic/multiprocessing/api.py:851] Sending process 754883 closing signal SIGTERM
W0702 19:51:49.224000 140152547391296 torch/distributed/elastic/multiprocessing/api.py:851] Sending process 754884 closing signal SIGTERM
E0702 19:51:53.674000 140152547391296 torch/distributed/elastic/multiprocessing/api.py:826] failed (exitcode: 1) local_rank: 4 (pid: 754882) of binary: /fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/bin/python3.10
Traceback (most recent call last):
  File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/bin/torchrun", line 8, in <module>
    sys.exit(main())
  File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/elastic/multiprocessing/errors/__init__.py", line 347, in wrapper
    return f(*args, **kwargs)
  File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/run.py", line 879, in main
    run(args)
  File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/run.py", line 870, in run
    elastic_launch(
  File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/launcher/api.py", line 132, in __call__
    return launch_agent(self._config, self._entrypoint, list(args))
  File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/launcher/api.py", line 263, in launch_agent
    raise ChildFailedError(
torch.distributed.elastic.multiprocessing.errors.ChildFailedError: 
============================================================
/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/run_train.py FAILED
------------------------------------------------------------
Failures:
[1]:
  time      : 2024-07-02_19:51:49
  host      : ip-26-0-172-73.ec2.internal
  rank      : 15 (local_rank: 7)
  exitcode  : 1 (pid: 754885)
  error_file: <N/A>
  traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html
------------------------------------------------------------
Root Cause (first observed failure):
[0]:
  time      : 2024-07-02_19:51:49
  host      : ip-26-0-172-73.ec2.internal
  rank      : 12 (local_rank: 4)
  exitcode  : 1 (pid: 754882)
  error_file: <N/A>
  traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html
============================================================
E0702 19:51:53.874000 139733262489408 torch/distributed/elastic/multiprocessing/api.py:826] failed (exitcode: 1) local_rank: 4 (pid: 2542728) of binary: /fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/bin/python3.10
Traceback (most recent call last):
  File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/bin/torchrun", line 8, in <module>
    sys.exit(main())
  File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/elastic/multiprocessing/errors/__init__.py", line 347, in wrapper
    return f(*args, **kwargs)
  File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/run.py", line 879, in main
    run(args)
  File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/run.py", line 870, in run
    elastic_launch(
  File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/launcher/api.py", line 132, in __call__
    return launch_agent(self._config, self._entrypoint, list(args))
  File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/launcher/api.py", line 263, in launch_agent
    raise ChildFailedError(
torch.distributed.elastic.multiprocessing.errors.ChildFailedError: 
============================================================
/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/run_train.py FAILED
------------------------------------------------------------
Failures:
[1]:
  time      : 2024-07-02_19:51:49
  host      : ip-26-0-169-139.ec2.internal
  rank      : 6 (local_rank: 6)
  exitcode  : 1 (pid: 2542730)
  error_file: <N/A>
  traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html
[2]:
  time      : 2024-07-02_19:51:49
  host      : ip-26-0-169-139.ec2.internal
  rank      : 7 (local_rank: 7)
  exitcode  : 1 (pid: 2542731)
  error_file: <N/A>
  traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html
------------------------------------------------------------
Root Cause (first observed failure):
[0]:
  time      : 2024-07-02_19:51:49
  host      : ip-26-0-169-139.ec2.internal
  rank      : 4 (local_rank: 4)
  exitcode  : 1 (pid: 2542728)
  error_file: <N/A>
  traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html
============================================================
srun: error: ip-26-0-172-73: task 1: Exited with exit code 1
srun: error: ip-26-0-169-139: task 0: Exited with exit code 1
Consider using `hf_transfer` for faster uploads. This solution comes with some limitations. See https://huggingface.co/docs/huggingface_hub/hf_transfer for more details.