llama-1B/64_GPUS/dp-2_tp-4_pp-8_mbz-32/log.out

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START TIME: Tue Jul  2 23:43:43 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 23:43:46.043000 140527302506304 torch/distributed/run.py:757] 
W0702 23:43:46.043000 140527302506304 torch/distributed/run.py:757] *****************************************
W0702 23:43:46.043000 140527302506304 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 23:43:46.043000 140527302506304 torch/distributed/run.py:757] *****************************************
W0702 23:43:46.057000 140521804085056 torch/distributed/run.py:757] 
W0702 23:43:46.057000 140521804085056 torch/distributed/run.py:757] *****************************************
W0702 23:43:46.057000 140521804085056 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 23:43:46.057000 140521804085056 torch/distributed/run.py:757] *****************************************
W0702 23:43:46.067000 140644327421760 torch/distributed/run.py:757] 
W0702 23:43:46.067000 140644327421760 torch/distributed/run.py:757] *****************************************
W0702 23:43:46.067000 140644327421760 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 23:43:46.067000 140644327421760 torch/distributed/run.py:757] *****************************************
W0702 23:43:46.077000 140640224704320 torch/distributed/run.py:757] 
W0702 23:43:46.077000 140640224704320 torch/distributed/run.py:757] *****************************************
W0702 23:43:46.077000 140640224704320 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 23:43:46.077000 140640224704320 torch/distributed/run.py:757] *****************************************
W0702 23:43:46.099000 139914793371456 torch/distributed/run.py:757] 
W0702 23:43:46.099000 139914793371456 torch/distributed/run.py:757] *****************************************
W0702 23:43:46.099000 139914793371456 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 23:43:46.099000 139914793371456 torch/distributed/run.py:757] *****************************************
W0702 23:43:46.110000 140245506594624 torch/distributed/run.py:757] 
W0702 23:43:46.110000 140245506594624 torch/distributed/run.py:757] *****************************************
W0702 23:43:46.110000 140245506594624 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 23:43:46.110000 140245506594624 torch/distributed/run.py:757] *****************************************
W0702 23:43:46.121000 140586234832704 torch/distributed/run.py:757] 
W0702 23:43:46.121000 140586234832704 torch/distributed/run.py:757] *****************************************
W0702 23:43:46.121000 140586234832704 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 23:43:46.121000 140586234832704 torch/distributed/run.py:757] *****************************************
W0702 23:43:46.175000 139903948736320 torch/distributed/run.py:757] 
W0702 23:43:46.175000 139903948736320 torch/distributed/run.py:757] *****************************************
W0702 23:43:46.175000 139903948736320 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 23:43:46.175000 139903948736320 torch/distributed/run.py:757] *****************************************
[default0]:07/02/2024 23:44:05 [WARNING|DP=0|PP=0|TP=0|ip-26-0-160-192]: [Vocab Size Padding] Padded vocab (size: 50257) with 3 dummy tokens (new size: 50260)
[default0]:07/02/2024 23:44:05 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: Config:
[default0]:07/02/2024 23:44:05 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: Config(general=GeneralArgs(project='bench_cluster',
[default0]:07/02/2024 23:44:05 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:                            run='%date_%jobid',
[default0]:07/02/2024 23:44:05 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:                            seed=42,
[default0]:07/02/2024 23:44:05 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:                            step=None,
[default0]:07/02/2024 23:44:05 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:                            consumed_train_samples=None,
[default0]:07/02/2024 23:44:05 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:                            benchmark_csv_path=None,
[default0]:07/02/2024 23:44:05 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:                            ignore_sanity_checks=True),
[default0]:07/02/2024 23:44:05 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:        parallelism=ParallelismArgs(dp=2,
[default0]:07/02/2024 23:44:05 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:                                    pp=8,
[default0]:07/02/2024 23:44:05 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:                                    tp=4,
[default0]:07/02/2024 23:44:05 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:                                    pp_engine=<nanotron.parallel.pipeline_parallel.engine.OneForwardOneBackwardPipelineEngine object at 0x7f3c0ab20730>,
[default0]:07/02/2024 23:44:05 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:                                    tp_mode=<TensorParallelLinearMode.REDUCE_SCATTER: 2>,
[default0]:07/02/2024 23:44:05 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:                                    tp_linear_async_communication=False,
[default0]:07/02/2024 23:44:05 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:                                    expert_parallel_size=1),
[default0]:07/02/2024 23:44:05 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:        model=ModelArgs(model_config=LlamaConfig(bos_token_id=1,
[default0]:07/02/2024 23:44:05 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:                                                 eos_token_id=2,
[default0]:07/02/2024 23:44:05 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:                                                 hidden_act='silu',
[default0]:07/02/2024 23:44:05 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:                                                 hidden_size=2048,
[default0]:07/02/2024 23:44:05 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:                                                 initializer_range=0.02,
[default0]:07/02/2024 23:44:05 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:                                                 intermediate_size=4096,
[default0]:07/02/2024 23:44:05 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:                                                 is_llama_config=True,
[default0]:07/02/2024 23:44:05 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:                                                 max_position_embeddings=4096,
[default0]:07/02/2024 23:44:05 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:                                                 num_attention_heads=32,
[default0]:07/02/2024 23:44:05 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:                                                 num_hidden_layers=24,
[default0]:07/02/2024 23:44:05 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:                                                 num_key_value_heads=32,
[default0]:07/02/2024 23:44:05 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:                                                 pad_token_id=None,
[default0]:07/02/2024 23:44:05 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:                                                 pretraining_tp=1,
[default0]:07/02/2024 23:44:05 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:                                                 rms_norm_eps=1e-05,
[default0]:07/02/2024 23:44:05 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:                                                 rope_scaling=None,
[default0]:07/02/2024 23:44:05 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:                                                 rope_theta=10000.0,
[default0]:07/02/2024 23:44:05 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:                                                 tie_word_embeddings=True,
[default0]:07/02/2024 23:44:05 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:                                                 use_cache=True,
[default0]:07/02/2024 23:44:05 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:                                                 vocab_size=50260),
[default0]:07/02/2024 23:44:05 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:                        init_method=RandomInit(std=0.025),
[default0]:07/02/2024 23:44:05 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:                        dtype=torch.bfloat16,
[default0]:07/02/2024 23:44:05 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:                        make_vocab_size_divisible_by=1,
[default0]:07/02/2024 23:44:05 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:                        ddp_bucket_cap_mb=25),
[default0]:07/02/2024 23:44:05 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:        tokenizer=TokenizerArgs(tokenizer_name_or_path='openai-community/gpt2',
[default0]:07/02/2024 23:44:05 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:                                tokenizer_revision=None,
[default0]:07/02/2024 23:44:05 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:                                tokenizer_max_length=None),
[default0]:07/02/2024 23:44:05 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:        checkpoints=CheckpointsArgs(checkpoints_path=Path('/dev/null'),
[default0]:07/02/2024 23:44:05 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:                                    checkpoint_interval=100000,
[default0]:07/02/2024 23:44:05 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:                                    save_initial_state=False,
[default0]:07/02/2024 23:44:05 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:                                    resume_checkpoint_path=None,
[default0]:07/02/2024 23:44:05 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:                                    checkpoints_path_is_shared_file_system=False),
[default0]:07/02/2024 23:44:05 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:        logging=LoggingArgs(log_level='info',
[default0]:07/02/2024 23:44:05 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:                            log_level_replica='info',
[default0]:07/02/2024 23:44:05 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:                            iteration_step_info_interval=1),
[default0]:07/02/2024 23:44:05 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:        tokens=TokensArgs(sequence_length=4096,
[default0]:07/02/2024 23:44:05 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:                          train_steps=20,
[default0]:07/02/2024 23:44:05 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:                          micro_batch_size=32,
[default0]:07/02/2024 23:44:05 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:                          batch_accumulation_per_replica=16,
[default0]:07/02/2024 23:44:05 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:                          val_check_interval=-1,
[default0]:07/02/2024 23:44:05 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:                          limit_val_batches=0,
[default0]:07/02/2024 23:44:05 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:                          limit_test_batches=0),
[default0]:07/02/2024 23:44:05 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:        optimizer=OptimizerArgs(optimizer_factory=AdamWOptimizerArgs(adam_eps=1e-08,
[default0]:07/02/2024 23:44:05 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:                                                                     adam_beta1=0.9,
[default0]:07/02/2024 23:44:05 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:                                                                     adam_beta2=0.95,
[default0]:07/02/2024 23:44:05 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:                                                                     torch_adam_is_fused=True,
[default0]:07/02/2024 23:44:05 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:                                                                     name='adamW'),
[default0]:07/02/2024 23:44:05 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:                                zero_stage=1,
[default0]:07/02/2024 23:44:05 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:                                weight_decay=0.01,
[default0]:07/02/2024 23:44:05 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:                                clip_grad=1.0,
[default0]:07/02/2024 23:44:05 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:                                accumulate_grad_in_fp32=True,
[default0]:07/02/2024 23:44:05 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:                                learning_rate_scheduler=LRSchedulerArgs(learning_rate=0.0001,
[default0]:07/02/2024 23:44:05 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:                                                                        lr_warmup_steps=1,
[default0]:07/02/2024 23:44:05 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:                                                                        lr_warmup_style='linear',
[default0]:07/02/2024 23:44:05 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:                                                                        lr_decay_style='linear',
[default0]:07/02/2024 23:44:05 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:                                                                        lr_decay_steps=19,
[default0]:07/02/2024 23:44:05 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:                                                                        lr_decay_starting_step=None,
[default0]:07/02/2024 23:44:05 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:                                                                        min_decay_lr=1e-05)),
[default0]:07/02/2024 23:44:05 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:        data_stages=[DatasetStageArgs(name='Training Stage',
[default0]:07/02/2024 23:44:05 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:                                      start_training_step=1,
[default0]:07/02/2024 23:44:05 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:                                      data=DataArgs(dataset=PretrainDatasetsArgs(hf_dataset_or_datasets='roneneldan/TinyStories',
[default0]:07/02/2024 23:44:05 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:                                                                                 hf_dataset_splits='train',
[default0]:07/02/2024 23:44:05 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:                                                                                 hf_dataset_config_name=None,
[default0]:07/02/2024 23:44:05 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:                                                                                 dataset_processing_num_proc_per_process=64,
[default0]:07/02/2024 23:44:05 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:                                                                                 dataset_overwrite_cache=False,
[default0]:07/02/2024 23:44:05 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:                                                                                 text_column_name='text'),
[default0]:07/02/2024 23:44:05 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:                                                    seed=42,
[default0]:07/02/2024 23:44:05 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:                                                    num_loading_workers=0))],
[default0]:07/02/2024 23:44:05 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:        profiler=ProfilerArgs(profiler_export_path=Path('/fsx/ferdinandmom/ferdinand-hf/bench_cluster/results/llama-1B/64_GPUS/dp-2_tp-4_pp-8_mbz-32')),
[default0]:07/02/2024 23:44:05 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:        lighteval=None)
[default0]:07/02/2024 23:44:05 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: Model Config:
[default0]:07/02/2024 23:44:05 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: LlamaConfig(bos_token_id=1,
[default0]:07/02/2024 23:44:05 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:             eos_token_id=2,
[default0]:07/02/2024 23:44:05 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:             hidden_act='silu',
[default0]:07/02/2024 23:44:05 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:             hidden_size=2048,
[default0]:07/02/2024 23:44:05 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:             initializer_range=0.02,
[default0]:07/02/2024 23:44:05 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:             intermediate_size=4096,
[default0]:07/02/2024 23:44:05 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:             is_llama_config=True,
[default0]:07/02/2024 23:44:05 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:             max_position_embeddings=4096,
[default0]:07/02/2024 23:44:05 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:             num_attention_heads=32,
[default0]:07/02/2024 23:44:05 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:             num_hidden_layers=24,
[default0]:07/02/2024 23:44:05 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:             num_key_value_heads=32,
[default0]:07/02/2024 23:44:05 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:             pad_token_id=None,
[default0]:07/02/2024 23:44:05 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:             pretraining_tp=1,
[default0]:07/02/2024 23:44:05 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:             rms_norm_eps=1e-05,
[default0]:07/02/2024 23:44:05 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:             rope_scaling=None,
[default0]:07/02/2024 23:44:05 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:             rope_theta=10000.0,
[default0]:07/02/2024 23:44:05 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:             tie_word_embeddings=True,
[default0]:07/02/2024 23:44:05 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:             use_cache=True,
[default0]:07/02/2024 23:44:05 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:             vocab_size=50260)
[default0]:07/02/2024 23:44:05 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: Building model..
[default0]:07/02/2024 23:44:05 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: Setting PP block ranks...
[default6]:07/02/2024 23:44:20 [INFO|DP=1|PP=4|TP=2|ip-26-0-168-238]: No checkpoint path provided.
[default5]:07/02/2024 23:44:20 [INFO|DP=1|PP=4|TP=1|ip-26-0-168-238]: No checkpoint path provided.
[default0]:07/02/2024 23:44:20 [INFO|DP=0|PP=2|TP=0|ip-26-0-163-226]: Local number of parameters: 31.5M (60.02MiB)
[default0]:07/02/2024 23:44:20 [INFO|DP=0|PP=2|TP=0|ip-26-0-163-226]: [After model building] Memory usage: 63.03MiB. Peak allocated: 65.06MiB Peak reserved: 66.00MiB
[default0]:07/02/2024 23:44:20 [INFO|DP=0|PP=2|TP=0|ip-26-0-163-226]: No checkpoint path provided.
[default3]:07/02/2024 23:44:20 [INFO|DP=0|PP=4|TP=3|ip-26-0-168-238]: Local number of parameters: 31.5M (60.02MiB)
[default3]:07/02/2024 23:44:20 [INFO|DP=0|PP=4|TP=3|ip-26-0-168-238]: [After model building] Memory usage: 63.03MiB. Peak allocated: 65.06MiB Peak reserved: 66.00MiB
[default3]:07/02/2024 23:44:20 [INFO|DP=0|PP=4|TP=3|ip-26-0-168-238]: No checkpoint path provided.
[default2]:07/02/2024 23:44:20 [INFO|DP=0|PP=4|TP=2|ip-26-0-168-238]: Local number of parameters: 31.5M (60.02MiB)
[default2]:07/02/2024 23:44:20 [INFO|DP=0|PP=4|TP=2|ip-26-0-168-238]: [After model building] Memory usage: 63.03MiB. Peak allocated: 65.06MiB Peak reserved: 66.00MiB
[default2]:07/02/2024 23:44:20 [INFO|DP=0|PP=4|TP=2|ip-26-0-168-238]: No checkpoint path provided.
[default2]:07/02/2024 23:44:20 [INFO|DP=0|PP=2|TP=2|ip-26-0-163-226]: Local number of parameters: 31.5M (60.02MiB)
[default2]:07/02/2024 23:44:20 [INFO|DP=0|PP=2|TP=2|ip-26-0-163-226]: [After model building] Memory usage: 63.03MiB. Peak allocated: 65.06MiB Peak reserved: 66.00MiB
[default1]:07/02/2024 23:44:20 [INFO|DP=0|PP=2|TP=1|ip-26-0-163-226]: Local number of parameters: 31.5M (60.02MiB)
[default1]:07/02/2024 23:44:20 [INFO|DP=0|PP=2|TP=1|ip-26-0-163-226]: [After model building] Memory usage: 63.03MiB. Peak allocated: 65.06MiB Peak reserved: 66.00MiB
[default1]:07/02/2024 23:44:20 [INFO|DP=0|PP=2|TP=1|ip-26-0-163-226]: No checkpoint path provided.
[default2]:07/02/2024 23:44:20 [INFO|DP=0|PP=2|TP=2|ip-26-0-163-226]: No checkpoint path provided.
[default7]:07/02/2024 23:44:20 [INFO|DP=1|PP=2|TP=3|ip-26-0-163-226]: No checkpoint path provided.
[default0]:07/02/2024 23:44:20 [INFO|DP=0|PP=1|TP=0|ip-26-0-161-178]: Local number of parameters: 31.5M (60.02MiB)
[default0]:07/02/2024 23:44:20 [INFO|DP=0|PP=1|TP=0|ip-26-0-161-178]: [After model building] Memory usage: 63.03MiB. Peak allocated: 65.06MiB Peak reserved: 66.00MiB
[default0]:07/02/2024 23:44:20 [INFO|DP=0|PP=1|TP=0|ip-26-0-161-178]: No checkpoint path provided.
[default2]:07/02/2024 23:44:20 [INFO|DP=0|PP=1|TP=2|ip-26-0-161-178]: Local number of parameters: 31.5M (60.02MiB)
[default2]:07/02/2024 23:44:20 [INFO|DP=0|PP=1|TP=2|ip-26-0-161-178]: [After model building] Memory usage: 63.03MiB. Peak allocated: 65.06MiB Peak reserved: 66.00MiB
[default1]:07/02/2024 23:44:20 [INFO|DP=0|PP=1|TP=1|ip-26-0-161-178]: Local number of parameters: 31.5M (60.02MiB)
[default1]:07/02/2024 23:44:20 [INFO|DP=0|PP=1|TP=1|ip-26-0-161-178]: [After model building] Memory usage: 63.03MiB. Peak allocated: 65.06MiB Peak reserved: 66.00MiB
[default2]:07/02/2024 23:44:20 [INFO|DP=0|PP=1|TP=2|ip-26-0-161-178]: No checkpoint path provided.
[default1]:07/02/2024 23:44:20 [INFO|DP=0|PP=1|TP=1|ip-26-0-161-178]: No checkpoint path provided.
[default5]:07/02/2024 23:44:20 [INFO|DP=1|PP=1|TP=1|ip-26-0-161-178]: No checkpoint path provided.
[default6]:07/02/2024 23:44:20 [INFO|DP=1|PP=2|TP=2|ip-26-0-163-226]: No checkpoint path provided.
[default3]:07/02/2024 23:44:20 [INFO|DP=0|PP=1|TP=3|ip-26-0-161-178]: Local number of parameters: 31.5M (60.02MiB)
[default3]:07/02/2024 23:44:20 [INFO|DP=0|PP=1|TP=3|ip-26-0-161-178]: [After model building] Memory usage: 63.03MiB. Peak allocated: 65.06MiB Peak reserved: 66.00MiB
[default4]:07/02/2024 23:44:20 [INFO|DP=1|PP=2|TP=0|ip-26-0-163-226]: No checkpoint path provided.
[default3]:07/02/2024 23:44:20 [INFO|DP=0|PP=2|TP=3|ip-26-0-163-226]: Local number of parameters: 31.5M (60.02MiB)
[default3]:07/02/2024 23:44:20 [INFO|DP=0|PP=2|TP=3|ip-26-0-163-226]: [After model building] Memory usage: 63.03MiB. Peak allocated: 65.06MiB Peak reserved: 66.00MiB
[default3]:07/02/2024 23:44:20 [INFO|DP=0|PP=2|TP=3|ip-26-0-163-226]: No checkpoint path provided.
[default3]:07/02/2024 23:44:20 [INFO|DP=0|PP=1|TP=3|ip-26-0-161-178]: No checkpoint path provided.
[default5]:07/02/2024 23:44:20 [INFO|DP=1|PP=2|TP=1|ip-26-0-163-226]: No checkpoint path provided.
[default4]:07/02/2024 23:44:20 [INFO|DP=1|PP=1|TP=0|ip-26-0-161-178]: No checkpoint path provided.
[default0]:07/02/2024 23:44:20 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: Total number of parameters: 1.21G (2313.42MiB)
[default0]:07/02/2024 23:44:20 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: Local number of parameters: 67.7M (129.11MiB)
[default0]:07/02/2024 23:44:20 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: [After model building] Memory usage: 134.04MiB. Peak allocated: 136.07MiB Peak reserved: 138.00MiB
[default0]:07/02/2024 23:44:20 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: No checkpoint path provided.
[default0]:07/02/2024 23:44:20 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: Parametrizing model parameters using StandardParametrizator
[default7]:07/02/2024 23:44:20 [INFO|DP=1|PP=1|TP=3|ip-26-0-161-178]: No checkpoint path provided.
[default1]:07/02/2024 23:44:20 [INFO|DP=0|PP=0|TP=1|ip-26-0-160-192]: Local number of parameters: 67.7M (129.11MiB)
[default1]:07/02/2024 23:44:20 [INFO|DP=0|PP=0|TP=1|ip-26-0-160-192]: [After model building] Memory usage: 134.04MiB. Peak allocated: 136.07MiB Peak reserved: 138.00MiB
[default1]:07/02/2024 23:44:20 [INFO|DP=0|PP=0|TP=1|ip-26-0-160-192]: No checkpoint path provided.
[default4]:07/02/2024 23:44:20 [INFO|DP=1|PP=4|TP=0|ip-26-0-168-238]: No checkpoint path provided.
[default5]:07/02/2024 23:44:20 [INFO|DP=1|PP=0|TP=1|ip-26-0-160-192]: No checkpoint path provided.
[default3]:07/02/2024 23:44:20 [INFO|DP=0|PP=0|TP=3|ip-26-0-160-192]: Local number of parameters: 67.7M (129.11MiB)
[default3]:07/02/2024 23:44:20 [INFO|DP=0|PP=0|TP=3|ip-26-0-160-192]: [After model building] Memory usage: 134.04MiB. Peak allocated: 136.07MiB Peak reserved: 138.00MiB
[default0]:07/02/2024 23:44:20 [INFO|DP=0|PP=6|TP=0|ip-26-0-172-57]: Local number of parameters: 42M (80.03MiB)
[default0]:07/02/2024 23:44:20 [INFO|DP=0|PP=6|TP=0|ip-26-0-172-57]: [After model building] Memory usage: 84.04MiB. Peak allocated: 86.07MiB Peak reserved: 88.00MiB
[default0]:07/02/2024 23:44:20 [INFO|DP=0|PP=6|TP=0|ip-26-0-172-57]: No checkpoint path provided.
[default3]:07/02/2024 23:44:20 [INFO|DP=0|PP=0|TP=3|ip-26-0-160-192]: No checkpoint path provided.
[default7]:07/02/2024 23:44:20 [INFO|DP=1|PP=4|TP=3|ip-26-0-168-238]: No checkpoint path provided.
[default0]:07/02/2024 23:44:20 [INFO|DP=0|PP=4|TP=0|ip-26-0-168-238]: Local number of parameters: 31.5M (60.02MiB)
[default0]:07/02/2024 23:44:20 [INFO|DP=0|PP=4|TP=0|ip-26-0-168-238]: [After model building] Memory usage: 63.03MiB. Peak allocated: 65.06MiB Peak reserved: 66.00MiB
[default0]:07/02/2024 23:44:20 [INFO|DP=0|PP=4|TP=0|ip-26-0-168-238]: No checkpoint path provided.
[default2]:07/02/2024 23:44:20 [INFO|DP=0|PP=7|TP=2|ip-26-0-172-73]: Local number of parameters: 25.7M (49.09MiB)
[default2]:07/02/2024 23:44:20 [INFO|DP=0|PP=7|TP=2|ip-26-0-172-73]: [After model building] Memory usage: 50.01MiB. Peak allocated: 50.03MiB Peak reserved: 52.00MiB
[default2]:07/02/2024 23:44:20 [INFO|DP=0|PP=7|TP=2|ip-26-0-172-73]: No checkpoint path provided.
[default5]:07/02/2024 23:44:20 [INFO|DP=1|PP=7|TP=1|ip-26-0-172-73]: No checkpoint path provided.
[default1]:07/02/2024 23:44:20 [INFO|DP=0|PP=7|TP=1|ip-26-0-172-73]: Local number of parameters: 25.7M (49.09MiB)
[default1]:07/02/2024 23:44:20 [INFO|DP=0|PP=7|TP=1|ip-26-0-172-73]: [After model building] Memory usage: 50.01MiB. Peak allocated: 50.03MiB Peak reserved: 52.00MiB
[default1]:07/02/2024 23:44:20 [INFO|DP=0|PP=7|TP=1|ip-26-0-172-73]: No checkpoint path provided.
[default0]:07/02/2024 23:44:20 [INFO|DP=0|PP=3|TP=0|ip-26-0-165-24]: Local number of parameters: 42M (80.03MiB)
[default0]:07/02/2024 23:44:20 [INFO|DP=0|PP=3|TP=0|ip-26-0-165-24]: [After model building] Memory usage: 84.04MiB. Peak allocated: 86.07MiB Peak reserved: 88.00MiB
[default0]:07/02/2024 23:44:20 [INFO|DP=0|PP=3|TP=0|ip-26-0-165-24]: No checkpoint path provided.
[default4]:07/02/2024 23:44:20 [INFO|DP=1|PP=0|TP=0|ip-26-0-160-192]: No checkpoint path provided.
[default2]:07/02/2024 23:44:20 [INFO|DP=0|PP=0|TP=2|ip-26-0-160-192]: Local number of parameters: 67.7M (129.11MiB)
[default2]:07/02/2024 23:44:20 [INFO|DP=0|PP=0|TP=2|ip-26-0-160-192]: [After model building] Memory usage: 134.04MiB. Peak allocated: 136.07MiB Peak reserved: 138.00MiB
[default2]:07/02/2024 23:44:20 [INFO|DP=0|PP=0|TP=2|ip-26-0-160-192]: No checkpoint path provided.
[default3]:07/02/2024 23:44:20 [INFO|DP=0|PP=7|TP=3|ip-26-0-172-73]: Local number of parameters: 25.7M (49.09MiB)
[default1]:07/02/2024 23:44:20 [INFO|DP=0|PP=4|TP=1|ip-26-0-168-238]: Local number of parameters: 31.5M (60.02MiB)
[default3]:07/02/2024 23:44:20 [INFO|DP=0|PP=7|TP=3|ip-26-0-172-73]: [After model building] Memory usage: 50.01MiB. Peak allocated: 50.03MiB Peak reserved: 52.00MiB
[default3]:07/02/2024 23:44:20 [INFO|DP=0|PP=7|TP=3|ip-26-0-172-73]: No checkpoint path provided.
[default1]:07/02/2024 23:44:20 [INFO|DP=0|PP=4|TP=1|ip-26-0-168-238]: [After model building] Memory usage: 63.03MiB. Peak allocated: 65.06MiB Peak reserved: 66.00MiB
[default1]:07/02/2024 23:44:20 [INFO|DP=0|PP=4|TP=1|ip-26-0-168-238]: No checkpoint path provided.
[default7]:07/02/2024 23:44:20 [INFO|DP=1|PP=6|TP=3|ip-26-0-172-57]: No checkpoint path provided.
[default6]:07/02/2024 23:44:20 [INFO|DP=1|PP=7|TP=2|ip-26-0-172-73]: No checkpoint path provided.
[default0]:07/02/2024 23:44:20 [INFO|DP=0|PP=7|TP=0|ip-26-0-172-73]: Local number of parameters: 25.7M (49.09MiB)
[default0]:07/02/2024 23:44:20 [INFO|DP=0|PP=7|TP=0|ip-26-0-172-73]: [After model building] Memory usage: 50.01MiB. Peak allocated: 50.03MiB Peak reserved: 52.00MiB
[default0]:07/02/2024 23:44:20 [INFO|DP=0|PP=7|TP=0|ip-26-0-172-73]: No checkpoint path provided.
[default6]:07/02/2024 23:44:20 [INFO|DP=1|PP=6|TP=2|ip-26-0-172-57]: No checkpoint path provided.
[default2]:07/02/2024 23:44:20 [INFO|DP=0|PP=3|TP=2|ip-26-0-165-24]: Local number of parameters: 42M (80.03MiB)
[default1]:07/02/2024 23:44:20 [INFO|DP=0|PP=6|TP=1|ip-26-0-172-57]: Local number of parameters: 42M (80.03MiB)
[default3]:07/02/2024 23:44:20 [INFO|DP=0|PP=3|TP=3|ip-26-0-165-24]: Local number of parameters: 42M (80.03MiB)
[default2]:07/02/2024 23:44:20 [INFO|DP=0|PP=3|TP=2|ip-26-0-165-24]: [After model building] Memory usage: 84.04MiB. Peak allocated: 86.07MiB Peak reserved: 88.00MiB
[default1]:07/02/2024 23:44:20 [INFO|DP=0|PP=6|TP=1|ip-26-0-172-57]: [After model building] Memory usage: 84.04MiB. Peak allocated: 86.07MiB Peak reserved: 88.00MiB
[default3]:07/02/2024 23:44:20 [INFO|DP=0|PP=6|TP=3|ip-26-0-172-57]: Local number of parameters: 42M (80.03MiB)
[default2]:07/02/2024 23:44:20 [INFO|DP=0|PP=3|TP=2|ip-26-0-165-24]: No checkpoint path provided.
[default1]:07/02/2024 23:44:20 [INFO|DP=0|PP=3|TP=1|ip-26-0-165-24]: Local number of parameters: 42M (80.03MiB)
[default1]:07/02/2024 23:44:20 [INFO|DP=0|PP=3|TP=1|ip-26-0-165-24]: [After model building] Memory usage: 84.04MiB. Peak allocated: 86.07MiB Peak reserved: 88.00MiB
[default5]:07/02/2024 23:44:20 [INFO|DP=1|PP=3|TP=1|ip-26-0-165-24]: No checkpoint path provided.
[default3]:07/02/2024 23:44:20 [INFO|DP=0|PP=3|TP=3|ip-26-0-165-24]: [After model building] Memory usage: 84.04MiB. Peak allocated: 86.07MiB Peak reserved: 88.00MiB
[default3]:07/02/2024 23:44:20 [INFO|DP=0|PP=6|TP=3|ip-26-0-172-57]: [After model building] Memory usage: 84.04MiB. Peak allocated: 86.07MiB Peak reserved: 88.00MiB
[default3]:07/02/2024 23:44:20 [INFO|DP=0|PP=6|TP=3|ip-26-0-172-57]: No checkpoint path provided.
[default1]:07/02/2024 23:44:20 [INFO|DP=0|PP=6|TP=1|ip-26-0-172-57]: No checkpoint path provided.
[default4]:07/02/2024 23:44:20 [INFO|DP=1|PP=3|TP=0|ip-26-0-165-24]: No checkpoint path provided.
[default3]:07/02/2024 23:44:20 [INFO|DP=0|PP=3|TP=3|ip-26-0-165-24]: No checkpoint path provided.
[default1]:07/02/2024 23:44:20 [INFO|DP=0|PP=3|TP=1|ip-26-0-165-24]: No checkpoint path provided.
[default4]:07/02/2024 23:44:20 [INFO|DP=1|PP=6|TP=0|ip-26-0-172-57]: No checkpoint path provided.
[default5]:07/02/2024 23:44:20 [INFO|DP=1|PP=6|TP=1|ip-26-0-172-57]: No checkpoint path provided.
[default3]:07/02/2024 23:44:20 [INFO|DP=0|PP=5|TP=3|ip-26-0-169-86]: Local number of parameters: 31.5M (60.02MiB)
[default3]:07/02/2024 23:44:20 [INFO|DP=0|PP=5|TP=3|ip-26-0-169-86]: [After model building] Memory usage: 63.03MiB. Peak allocated: 65.06MiB Peak reserved: 66.00MiB
[default3]:07/02/2024 23:44:20 [INFO|DP=0|PP=5|TP=3|ip-26-0-169-86]: No checkpoint path provided.
[default6]:07/02/2024 23:44:20 [INFO|DP=1|PP=3|TP=2|ip-26-0-165-24]: No checkpoint path provided.
[default2]:07/02/2024 23:44:20 [INFO|DP=0|PP=6|TP=2|ip-26-0-172-57]: Local number of parameters: 42M (80.03MiB)
[default2]:07/02/2024 23:44:20 [INFO|DP=0|PP=6|TP=2|ip-26-0-172-57]: [After model building] Memory usage: 84.04MiB. Peak allocated: 86.07MiB Peak reserved: 88.00MiB
[default2]:07/02/2024 23:44:20 [INFO|DP=0|PP=6|TP=2|ip-26-0-172-57]: No checkpoint path provided.
[default0]:07/02/2024 23:44:20 [INFO|DP=0|PP=5|TP=0|ip-26-0-169-86]: Local number of parameters: 31.5M (60.02MiB)
[default0]:07/02/2024 23:44:20 [INFO|DP=0|PP=5|TP=0|ip-26-0-169-86]: [After model building] Memory usage: 63.03MiB. Peak allocated: 65.06MiB Peak reserved: 66.00MiB
[default0]:07/02/2024 23:44:20 [INFO|DP=0|PP=5|TP=0|ip-26-0-169-86]: No checkpoint path provided.
[default7]:07/02/2024 23:44:20 [INFO|DP=1|PP=5|TP=3|ip-26-0-169-86]: No checkpoint path provided.
[default4]:07/02/2024 23:44:20 [INFO|DP=1|PP=7|TP=0|ip-26-0-172-73]: No checkpoint path provided.
[default7]:07/02/2024 23:44:20 [INFO|DP=1|PP=0|TP=3|ip-26-0-160-192]: No checkpoint path provided.
[default7]:07/02/2024 23:44:20 [INFO|DP=1|PP=7|TP=3|ip-26-0-172-73]: No checkpoint path provided.
[default6]:07/02/2024 23:44:20 [INFO|DP=1|PP=0|TP=2|ip-26-0-160-192]: No checkpoint path provided.
[default5]:07/02/2024 23:44:20 [INFO|DP=1|PP=5|TP=1|ip-26-0-169-86]: No checkpoint path provided.
[default6]:07/02/2024 23:44:20 [INFO|DP=1|PP=5|TP=2|ip-26-0-169-86]: No checkpoint path provided.
[default4]:07/02/2024 23:44:20 [INFO|DP=1|PP=5|TP=0|ip-26-0-169-86]: No checkpoint path provided.
[default1]:07/02/2024 23:44:20 [INFO|DP=0|PP=5|TP=1|ip-26-0-169-86]: Local number of parameters: 31.5M (60.02MiB)
[default1]:07/02/2024 23:44:20 [INFO|DP=0|PP=5|TP=1|ip-26-0-169-86]: [After model building] Memory usage: 63.03MiB. Peak allocated: 65.06MiB Peak reserved: 66.00MiB
[default2]:07/02/2024 23:44:20 [INFO|DP=0|PP=5|TP=2|ip-26-0-169-86]: Local number of parameters: 31.5M (60.02MiB)
[default1]:07/02/2024 23:44:20 [INFO|DP=0|PP=5|TP=1|ip-26-0-169-86]: No checkpoint path provided.
[default2]:07/02/2024 23:44:20 [INFO|DP=0|PP=5|TP=2|ip-26-0-169-86]: [After model building] Memory usage: 63.03MiB. Peak allocated: 65.06MiB Peak reserved: 66.00MiB
[default2]:07/02/2024 23:44:20 [INFO|DP=0|PP=5|TP=2|ip-26-0-169-86]: No checkpoint path provided.
[default7]:07/02/2024 23:44:20 [INFO|DP=1|PP=3|TP=3|ip-26-0-165-24]: No checkpoint path provided.
[default6]:07/02/2024 23:44:20 [INFO|DP=1|PP=1|TP=2|ip-26-0-161-178]: No checkpoint path provided.
[default0]:07/02/2024 23:44:23 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: [Optimizer Building] Using LearningRateForSP as learning rate
[default0]:07/02/2024 23:44:23 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: [ZeRO sharding] Size of optimizer params per rank:
[default0]:07/02/2024 23:44:23 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: [ZeRO sharding] DP Rank 0 has 33.8M out of 67.7M (50.00%) params' optimizer states
[default0]:07/02/2024 23:44:23 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: [ZeRO sharding] DP Rank 1 has 33.8M out of 67.7M (50.00%) params' optimizer states
[default0]:07/02/2024 23:44:24 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: [Training Plan] Stage Training Stage has 19 remaining training steps and has consumed 0 samples
[default0]:07/02/2024 23:44:24 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: Using `datasets` library
[default0]:07/02/2024 23:44:24 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: Loading tokenizer from openai-community/gpt2 and transformers/hf_hub versions ('4.41.2', '0.23.4')
[default0]:07/02/2024 23:44:24 [WARNING|DP=0|PP=0|TP=0|ip-26-0-160-192]: Repo card metadata block was not found. Setting CardData to empty.
[default0]:Repo card metadata block was not found. Setting CardData to empty.
[default0]:07/02/2024 23:44:25 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: [Training Plan] There are 1 training stages 
[default0]:07/02/2024 23:44:25 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: [Stage Training Stage] start from step 1 
[default0]:07/02/2024 23:44:25 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: 
[default0]:07/02/2024 23:44:25 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: [Start training] datetime: 2024-07-02 23:44:25.689180 | mbs: 32 | grad_accum: 16 | global_batch_size: 1024 | sequence_length: 4096 | train_steps: 20 | start_iteration_step: 0 | consumed_train_samples: 0
[default0]:07/02/2024 23:44:25 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: Resuming training from stage Training Stage, it has trained for 0 samples and has 19 remaining train steps
[default0]:07/02/2024 23:44:25 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:  Memory usage: 522.27MiB. Peak allocated 522.27MiB. Peak reserved: 528.00MiB
[default5]:07/02/2024 23:44:25 [WARNING|DP=1|PP=4|TP=1|ip-26-0-168-238]: Repo card metadata block was not found. Setting CardData to empty.
[default6]:07/02/2024 23:44:25 [WARNING|DP=1|PP=4|TP=2|ip-26-0-168-238]: Repo card metadata block was not found. Setting CardData to empty.
[default3]:07/02/2024 23:44:25 [WARNING|DP=0|PP=4|TP=3|ip-26-0-168-238]: Repo card metadata block was not found. Setting CardData to empty.
[default0]:07/02/2024 23:44:25 [WARNING|DP=0|PP=2|TP=0|ip-26-0-163-226]: Repo card metadata block was not found. Setting CardData to empty.
[default2]:07/02/2024 23:44:25 [WARNING|DP=0|PP=4|TP=2|ip-26-0-168-238]: Repo card metadata block was not found. Setting CardData to empty.
[default3]:Repo card metadata block was not found. Setting CardData to empty.
[default1]:07/02/2024 23:44:25 [WARNING|DP=0|PP=2|TP=1|ip-26-0-163-226]: Repo card metadata block was not found. Setting CardData to empty.
[default2]:07/02/2024 23:44:25 [WARNING|DP=0|PP=2|TP=2|ip-26-0-163-226]: Repo card metadata block was not found. Setting CardData to empty.
[default7]:07/02/2024 23:44:25 [WARNING|DP=1|PP=2|TP=3|ip-26-0-163-226]: Repo card metadata block was not found. Setting CardData to empty.
[default0]:07/02/2024 23:44:25 [WARNING|DP=0|PP=1|TP=0|ip-26-0-161-178]: Repo card metadata block was not found. Setting CardData to empty.
[default1]:07/02/2024 23:44:25 [WARNING|DP=0|PP=1|TP=1|ip-26-0-161-178]: Repo card metadata block was not found. Setting CardData to empty.
[default2]:07/02/2024 23:44:25 [WARNING|DP=0|PP=1|TP=2|ip-26-0-161-178]: Repo card metadata block was not found. Setting CardData to empty.
[default5]:07/02/2024 23:44:25 [WARNING|DP=1|PP=1|TP=1|ip-26-0-161-178]: Repo card metadata block was not found. Setting CardData to empty.
[default0]:Repo card metadata block was not found. Setting CardData to empty.
[default6]:07/02/2024 23:44:25 [WARNING|DP=1|PP=1|TP=2|ip-26-0-161-178]: Repo card metadata block was not found. Setting CardData to empty.
[default3]:07/02/2024 23:44:25 [WARNING|DP=0|PP=2|TP=3|ip-26-0-163-226]: Repo card metadata block was not found. Setting CardData to empty.
[default4]:07/02/2024 23:44:25 [WARNING|DP=1|PP=2|TP=0|ip-26-0-163-226]: Repo card metadata block was not found. Setting CardData to empty.
[default5]:07/02/2024 23:44:25 [WARNING|DP=1|PP=2|TP=1|ip-26-0-163-226]: Repo card metadata block was not found. Setting CardData to empty.
[default5]:Repo card metadata block was not found. Setting CardData to empty.
[default3]:07/02/2024 23:44:25 [WARNING|DP=0|PP=1|TP=3|ip-26-0-161-178]: Repo card metadata block was not found. Setting CardData to empty.
[default4]:07/02/2024 23:44:25 [WARNING|DP=1|PP=1|TP=0|ip-26-0-161-178]: Repo card metadata block was not found. Setting CardData to empty.
[default5]:Repo card metadata block was not found. Setting CardData to empty.
[default1]:Repo card metadata block was not found. Setting CardData to empty.
[default0]:Repo card metadata block was not found. Setting CardData to empty.
[default2]:Repo card metadata block was not found. Setting CardData to empty.
[default1]:07/02/2024 23:44:25 [WARNING|DP=0|PP=0|TP=1|ip-26-0-160-192]: Repo card metadata block was not found. Setting CardData to empty.
[default2]:Repo card metadata block was not found. Setting CardData to empty.
[default4]:07/02/2024 23:44:25 [WARNING|DP=1|PP=4|TP=0|ip-26-0-168-238]: Repo card metadata block was not found. Setting CardData to empty.
[default4]:Repo card metadata block was not found. Setting CardData to empty.
[default1]:Repo card metadata block was not found. Setting CardData to empty.
[default6]:Repo card metadata block was not found. Setting CardData to empty.
[default0]:07/02/2024 23:44:25 [WARNING|DP=0|PP=6|TP=0|ip-26-0-172-57]: Repo card metadata block was not found. Setting CardData to empty.
[default5]:07/02/2024 23:44:25 [WARNING|DP=1|PP=0|TP=1|ip-26-0-160-192]: Repo card metadata block was not found. Setting CardData to empty.
[default3]:07/02/2024 23:44:25 [WARNING|DP=0|PP=0|TP=3|ip-26-0-160-192]: Repo card metadata block was not found. Setting CardData to empty.
[default4]:Repo card metadata block was not found. Setting CardData to empty.
[default2]:Repo card metadata block was not found. Setting CardData to empty.
[default0]:Repo card metadata block was not found. Setting CardData to empty.
[default0]:07/02/2024 23:44:25 [WARNING|DP=0|PP=4|TP=0|ip-26-0-168-238]: Repo card metadata block was not found. Setting CardData to empty.
[default7]:07/02/2024 23:44:25 [WARNING|DP=1|PP=4|TP=3|ip-26-0-168-238]: Repo card metadata block was not found. Setting CardData to empty.
[default7]:Repo card metadata block was not found. Setting CardData to empty.
[default5]:07/02/2024 23:44:25 [WARNING|DP=1|PP=7|TP=1|ip-26-0-172-73]: Repo card metadata block was not found. Setting CardData to empty.
[default2]:07/02/2024 23:44:25 [WARNING|DP=0|PP=7|TP=2|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.
[default0]:07/02/2024 23:44:25 [WARNING|DP=0|PP=3|TP=0|ip-26-0-165-24]: Repo card metadata block was not found. Setting CardData to empty.
[default1]:07/02/2024 23:44:25 [WARNING|DP=0|PP=4|TP=1|ip-26-0-168-238]: Repo card metadata block was not found. Setting CardData to empty.
[default7]:07/02/2024 23:44:25 [WARNING|DP=1|PP=6|TP=3|ip-26-0-172-57]: Repo card metadata block was not found. Setting CardData to empty.
[default3]:07/02/2024 23:44:25 [WARNING|DP=0|PP=7|TP=3|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]:07/02/2024 23:44:25 [WARNING|DP=1|PP=6|TP=1|ip-26-0-172-57]: Repo card metadata block was not found. Setting CardData to empty.
[default6]: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.
[default4]:07/02/2024 23:44:25 [WARNING|DP=1|PP=6|TP=0|ip-26-0-172-57]: Repo card metadata block was not found. Setting CardData to empty.
[default2]:Repo card metadata block was not found. Setting CardData to empty.
[default1]:07/02/2024 23:44:25 [WARNING|DP=0|PP=6|TP=1|ip-26-0-172-57]: Repo card metadata block was not found. Setting CardData to empty.
[default6]:07/02/2024 23:44:25 [WARNING|DP=1|PP=7|TP=2|ip-26-0-172-73]: Repo card metadata block was not found. Setting CardData to empty.
[default0]:07/02/2024 23:44:25 [WARNING|DP=0|PP=7|TP=0|ip-26-0-172-73]: Repo card metadata block was not found. Setting CardData to empty.
[default2]:07/02/2024 23:44:25 [WARNING|DP=0|PP=3|TP=2|ip-26-0-165-24]: Repo card metadata block was not found. Setting CardData to empty.
[default7]:Repo card metadata block was not found. Setting CardData to empty.
[default3]:07/02/2024 23:44:25 [WARNING|DP=0|PP=6|TP=3|ip-26-0-172-57]: Repo card metadata block was not found. Setting CardData to empty.
[default6]:Repo card metadata block was not found. Setting CardData to empty.
[default5]:Repo card metadata block was not found. Setting CardData to empty.
[default6]:07/02/2024 23:44:25 [WARNING|DP=1|PP=3|TP=2|ip-26-0-165-24]: Repo card metadata block was not found. Setting CardData to empty.
[default5]:07/02/2024 23:44:25 [WARNING|DP=1|PP=3|TP=1|ip-26-0-165-24]: Repo card metadata block was not found. Setting CardData to empty.
[default3]:07/02/2024 23:44:25 [WARNING|DP=0|PP=3|TP=3|ip-26-0-165-24]: Repo card metadata block was not found. Setting CardData to empty.
[default5]:Repo card metadata block was not found. Setting CardData to empty.
[default7]:07/02/2024 23:44:25 [WARNING|DP=1|PP=0|TP=3|ip-26-0-160-192]: Repo card metadata block was not found. Setting CardData to empty.
[default0]:Repo card metadata block was not found. Setting CardData to empty.
[default5]:Repo card metadata block was not found. Setting CardData to empty.
[default0]: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.
[default4]:07/02/2024 23:44:25 [WARNING|DP=1|PP=3|TP=0|ip-26-0-165-24]: Repo card metadata block was not found. Setting CardData to empty.
[default1]:07/02/2024 23:44:25 [WARNING|DP=0|PP=3|TP=1|ip-26-0-165-24]: Repo card metadata block was not found. Setting CardData to empty.
[default3]:07/02/2024 23:44:25 [WARNING|DP=0|PP=5|TP=3|ip-26-0-169-86]: 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 23:44:25 [WARNING|DP=1|PP=5|TP=3|ip-26-0-169-86]: Repo card metadata block was not found. Setting CardData to empty.
[default3]:Repo card metadata block was not found. Setting CardData to empty.
[default6]:Repo card metadata block was not found. Setting CardData to empty.
[default0]:07/02/2024 23:44:25 [WARNING|DP=0|PP=5|TP=0|ip-26-0-169-86]: Repo card metadata block was not found. Setting CardData to empty.
[default7]:Repo card metadata block was not found. Setting CardData to empty.
[default5]:Repo card metadata block was not found. Setting CardData to empty.
[default4]:07/02/2024 23:44:25 [WARNING|DP=1|PP=7|TP=0|ip-26-0-172-73]: Repo card metadata block was not found. Setting CardData to empty.
[default7]:07/02/2024 23:44:25 [WARNING|DP=1|PP=3|TP=3|ip-26-0-165-24]: Repo card metadata block was not found. Setting CardData to empty.
[default6]:Repo card metadata block was not found. Setting CardData to empty.
[default6]:07/02/2024 23:44:25 [WARNING|DP=1|PP=0|TP=2|ip-26-0-160-192]: Repo card metadata block was not found. Setting CardData to empty.
[default3]:Repo card metadata block was not found. Setting CardData to empty.
[default6]:07/02/2024 23:44:25 [WARNING|DP=1|PP=5|TP=2|ip-26-0-169-86]: Repo card metadata block was not found. Setting CardData to empty.
[default6]: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.
[default1]:07/02/2024 23:44:25 [WARNING|DP=0|PP=5|TP=1|ip-26-0-169-86]: 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 23:44:25 [WARNING|DP=0|PP=5|TP=2|ip-26-0-169-86]: Repo card metadata block was not found. Setting CardData to empty.
[default0]:Repo card metadata block was not found. Setting CardData to empty.
[default4]:07/02/2024 23:44:25 [WARNING|DP=1|PP=5|TP=0|ip-26-0-169-86]: Repo card metadata block was not found. Setting CardData to empty.
[default5]:07/02/2024 23:44:25 [WARNING|DP=1|PP=5|TP=1|ip-26-0-169-86]: Repo card metadata block was not found. Setting CardData to empty.
[default7]:Repo card metadata block was not found. Setting CardData to empty.
[default3]:Repo card metadata block was not found. Setting CardData to empty.
[default4]:Repo card metadata block was not found. Setting CardData to empty.
[default1]:Repo card metadata block was not found. Setting CardData to empty.
[default4]:Repo card metadata block was not found. Setting CardData to empty.
[default0]:Repo card metadata block was not found. Setting CardData to empty.
[default1]:Repo card metadata block was not found. Setting CardData to empty.
[default2]:Repo card metadata block was not found. Setting CardData to empty.
[default4]:Repo card metadata block was not found. Setting CardData to empty.
[default1]:Repo card metadata block was not found. Setting CardData to empty.
[default1]:Repo card metadata block was not found. Setting CardData to empty.
[default4]:Repo card metadata block was not found. Setting CardData to empty.
[default1]:Repo card metadata block was not found. Setting CardData to empty.
[default3]:Repo card metadata block was not found. Setting CardData to empty.
[default6]:07/02/2024 23:44:25 [WARNING|DP=1|PP=2|TP=2|ip-26-0-163-226]: Repo card metadata block was not found. Setting CardData to empty.
[default6]:Repo card metadata block was not found. Setting CardData to empty.
[default7]:07/02/2024 23:44:25 [WARNING|DP=1|PP=1|TP=3|ip-26-0-161-178]: Repo card metadata block was not found. Setting CardData to empty.
[default7]:Repo card metadata block was not found. Setting CardData to empty.
[default1]:07/02/2024 23:44:25 [WARNING|DP=0|PP=7|TP=1|ip-26-0-172-73]: Repo card metadata block was not found. Setting CardData to empty.
[default2]:07/02/2024 23:44:25 [WARNING|DP=0|PP=0|TP=2|ip-26-0-160-192]: Repo card metadata block was not found. Setting CardData to empty.
[default4]:07/02/2024 23:44:25 [WARNING|DP=1|PP=0|TP=0|ip-26-0-160-192]: Repo card metadata block was not found. Setting CardData to empty.
[default4]:Repo card metadata block was not found. Setting CardData to empty.
[default6]:07/02/2024 23:44:25 [WARNING|DP=1|PP=6|TP=2|ip-26-0-172-57]: Repo card metadata block was not found. Setting CardData to empty.
[default6]:Repo card metadata block was not found. Setting CardData to empty.
[default2]:Repo card metadata block was not found. Setting CardData to empty.
[default7]:07/02/2024 23:44:25 [WARNING|DP=1|PP=7|TP=3|ip-26-0-172-73]: Repo card metadata block was not found. Setting CardData to empty.
[default7]:Repo card metadata block was not found. Setting CardData to empty.
[default2]:Repo card metadata block was not found. Setting CardData to empty.
[default2]:07/02/2024 23:44:26 [WARNING|DP=0|PP=6|TP=2|ip-26-0-172-57]: Repo card metadata block was not found. Setting CardData to empty.
[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 252, 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 637, in forward
[default4]:[rank4]:     hidden_states = self.mlp(hidden_states=hidden_states)["hidden_states"]
[default4]:[rank4]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
[default4]:[rank4]:     return self._call_impl(*args, **kwargs)
[default4]:[rank4]:   File "/fsx/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 172, in forward
[default4]:[rank4]:     hidden_states = self.down_proj(self.split_silu_mul(merged_states))
[default4]:[rank4]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
[default4]:[rank4]:     return self._call_impl(*args, **kwargs)
[default4]:[rank4]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
[default4]:[rank4]:     return forward_call(*args, **kwargs)
[default4]:[rank4]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/nn.py", line 159, in forward
[default4]:[rank4]:     return row_linear(
[default4]:[rank4]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/functional.py", line 474, in row_linear
[default4]:[rank4]:     out = F.linear(input, weight, bias)
[default4]:[rank4]: torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 512.00 MiB. GPU  has a total capacity of 79.33 GiB of which 37.94 MiB is free. Including non-PyTorch memory, this process has 79.28 GiB memory in use. Of the allocated memory 69.64 GiB is allocated by PyTorch, and 12.20 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation.  See documentation for Memory Management  (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables)
[default7]:[rank7]: Traceback (most recent call last):
[default7]:[rank7]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/run_train.py", line 237, in <module>
[default7]:[rank7]:     trainer.train(dataloader)
[default7]:[rank7]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 429, in train
[default7]:[rank7]:     outputs, loss_avg = self.training_step(dataloader=self.current_dataloader)
[default7]:[rank7]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 462, in training_step
[default7]:[rank7]:     outputs = self.pipeline_engine.train_batch_iter(
[default7]:[rank7]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 252, in train_batch_iter
[default7]:[rank7]:     output = self.forward(context=context, state=state, micro_batch=micro_batch, model=model)
[default7]:[rank7]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 44, in forward
[default7]:[rank7]:     output = model(**micro_batch)
[default7]:[rank7]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
[default7]:[rank7]:     return self._call_impl(*args, **kwargs)
[default7]:[rank7]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
[default7]:[rank7]:     return forward_call(*args, **kwargs)
[default7]:[rank7]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 891, in forward
[default7]:[rank7]:     sharded_logits = self.model(
[default7]:[rank7]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
[default7]:[rank7]:     return self._call_impl(*args, **kwargs)
[default7]:[rank7]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
[default7]:[rank7]:     return forward_call(*args, **kwargs)
[default7]:[rank7]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 764, in forward
[default7]:[rank7]:     return self.forward_with_hidden_states(input_ids=input_ids, input_mask=input_mask)[0]
[default7]:[rank7]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 780, in forward_with_hidden_states
[default7]:[rank7]:     hidden_encoder_states = encoder_block(**hidden_encoder_states)
[default7]:[rank7]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
[default7]:[rank7]:     return self._call_impl(*args, **kwargs)
[default7]:[rank7]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
[default7]:[rank7]:     return forward_call(*args, **kwargs)
[default7]:[rank7]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/block.py", line 151, in forward
[default7]:[rank7]:     output = self.pp_block(**new_kwargs)
[default7]:[rank7]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
[default7]:[rank7]:     return self._call_impl(*args, **kwargs)
[default7]:[rank7]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
[default7]:[rank7]:     return forward_call(*args, **kwargs)
[default7]:[rank7]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 637, in forward
[default7]:[rank7]:     hidden_states = self.mlp(hidden_states=hidden_states)["hidden_states"]
[default7]:[rank7]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
[default7]:[rank7]:     return self._call_impl(*args, **kwargs)
[default7]:[rank7]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
[default7]:[rank7]:     return forward_call(*args, **kwargs)
[default7]:[rank7]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 172, in forward
[default7]:[rank7]:     hidden_states = self.down_proj(self.split_silu_mul(merged_states))
[default7]:[rank7]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
[default7]:[rank7]:     return self._call_impl(*args, **kwargs)
[default7]:[rank7]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
[default7]:[rank7]:     return forward_call(*args, **kwargs)
[default7]:[rank7]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/nn.py", line 159, in forward
[default7]:[rank7]:     return row_linear(
[default7]:[rank7]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/functional.py", line 474, in row_linear
[default7]:[rank7]:     out = F.linear(input, weight, bias)
[default7]:[rank7]: torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 512.00 MiB. GPU  has a total capacity of 79.33 GiB of which 157.94 MiB is free. Including non-PyTorch memory, this process has 79.16 GiB memory in use. Of the allocated memory 69.64 GiB is allocated by PyTorch, and 12.20 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation.  See documentation for Memory Management  (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables)
[default5]:[rank5]: 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 252, 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 637, in forward
[default5]:[rank5]:     hidden_states = self.mlp(hidden_states=hidden_states)["hidden_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/models/llama.py", line 172, in forward
[default5]:[rank5]:     hidden_states = self.down_proj(self.split_silu_mul(merged_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/models/llama.py", line 128, in forward
[default5]:[rank5]:     return self.act(gate_states) * up_states
[default5]:[rank5]: torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 256.00 MiB. GPU  has a total capacity of 79.33 GiB of which 173.94 MiB is free. Including non-PyTorch memory, this process has 79.15 GiB memory in use. Of the allocated memory 69.39 GiB is allocated by PyTorch, and 12.20 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation.  See documentation for Memory Management  (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables)
[default6]:[rank6]: Traceback (most recent call last):
[default6]:[rank6]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/run_train.py", line 237, in <module>
[default6]:[rank6]:     trainer.train(dataloader)
[default6]:[rank6]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 429, in train
[default6]:[rank6]:     outputs, loss_avg = self.training_step(dataloader=self.current_dataloader)
[default6]:[rank6]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 462, in training_step
[default6]:[rank6]:     outputs = self.pipeline_engine.train_batch_iter(
[default6]:[rank6]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 252, in train_batch_iter
[default6]:[rank6]:     output = self.forward(context=context, state=state, micro_batch=micro_batch, model=model)
[default6]:[rank6]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 44, in forward
[default6]:[rank6]:     output = model(**micro_batch)
[default6]:[rank6]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
[default6]:[rank6]:     return self._call_impl(*args, **kwargs)
[default6]:[rank6]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
[default6]:[rank6]:     return forward_call(*args, **kwargs)
[default6]:[rank6]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 891, in forward
[default6]:[rank6]:     sharded_logits = self.model(
[default6]:[rank6]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
[default6]:[rank6]:     return self._call_impl(*args, **kwargs)
[default6]:[rank6]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
[default6]:[rank6]:     return forward_call(*args, **kwargs)
[default6]:[rank6]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 764, in forward
[default6]:[rank6]:     return self.forward_with_hidden_states(input_ids=input_ids, input_mask=input_mask)[0]
[default6]:[rank6]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 780, in forward_with_hidden_states
[default6]:[rank6]:     hidden_encoder_states = encoder_block(**hidden_encoder_states)
[default6]:[rank6]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
[default6]:[rank6]:     return self._call_impl(*args, **kwargs)
[default6]:[rank6]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
[default6]:[rank6]:     return forward_call(*args, **kwargs)
[default6]:[rank6]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/block.py", line 151, in forward
[default6]:[rank6]:     output = self.pp_block(**new_kwargs)
[default6]:[rank6]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
[default6]:[rank6]:     return self._call_impl(*args, **kwargs)
[default6]:[rank6]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
[default6]:[rank6]:     return forward_call(*args, **kwargs)
[default6]:[rank6]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 637, in forward
[default6]:[rank6]:     hidden_states = self.mlp(hidden_states=hidden_states)["hidden_states"]
[default6]:[rank6]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
[default6]:[rank6]:     return self._call_impl(*args, **kwargs)
[default6]:[rank6]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
[default6]:[rank6]:     return forward_call(*args, **kwargs)
[default6]:[rank6]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 172, in forward
[default6]:[rank6]:     hidden_states = self.down_proj(self.split_silu_mul(merged_states))
[default6]:[rank6]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
[default6]:[rank6]:     return self._call_impl(*args, **kwargs)
[default6]:[rank6]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
[default6]:[rank6]:     return forward_call(*args, **kwargs)
[default6]:[rank6]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 128, in forward
[default6]:[rank6]:     return self.act(gate_states) * up_states
[default6]:[rank6]: torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 256.00 MiB. GPU  has a total capacity of 79.33 GiB of which 245.94 MiB is free. Including non-PyTorch memory, this process has 79.08 GiB memory in use. Of the allocated memory 69.39 GiB is allocated by PyTorch, and 12.20 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation.  See documentation for Memory Management  (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables)
[default0]:[rank0]: Traceback (most recent call last):
[default0]:[rank0]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/run_train.py", line 237, in <module>
[default0]:[rank0]:     trainer.train(dataloader)
[default0]:[rank0]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 429, in train
[default0]:[rank0]:     outputs, loss_avg = self.training_step(dataloader=self.current_dataloader)
[default0]:[rank0]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 462, in training_step
[default0]:[rank0]:     outputs = self.pipeline_engine.train_batch_iter(
[default0]:[rank0]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 252, in train_batch_iter
[default0]:[rank0]:     output = self.forward(context=context, state=state, micro_batch=micro_batch, model=model)
[default0]:[rank0]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 44, in forward
[default0]:[rank0]:     output = model(**micro_batch)
[default0]:[rank0]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
[default0]:[rank0]:     return self._call_impl(*args, **kwargs)
[default0]:[rank0]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
[default0]:[rank0]:     return forward_call(*args, **kwargs)
[default0]:[rank0]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 891, in forward
[default0]:[rank0]:     sharded_logits = self.model(
[default0]:[rank0]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
[default0]:[rank0]:     return self._call_impl(*args, **kwargs)
[default0]:[rank0]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
[default0]:[rank0]:     return forward_call(*args, **kwargs)
[default0]:[rank0]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 764, in forward
[default0]:[rank0]:     return self.forward_with_hidden_states(input_ids=input_ids, input_mask=input_mask)[0]
[default0]:[rank0]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 780, in forward_with_hidden_states
[default0]:[rank0]:     hidden_encoder_states = encoder_block(**hidden_encoder_states)
[default0]:[rank0]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
[default0]:[rank0]:     return self._call_impl(*args, **kwargs)
[default0]:[rank0]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
[default0]:[rank0]:     return forward_call(*args, **kwargs)
[default0]:[rank0]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/block.py", line 151, in forward
[default0]:[rank0]:     output = self.pp_block(**new_kwargs)
[default0]:[rank0]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
[default0]:[rank0]:     return self._call_impl(*args, **kwargs)
[default0]:[rank0]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
[default0]:[rank0]:     return forward_call(*args, **kwargs)
[default0]:[rank0]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 631, in forward
[default0]:[rank0]:     output = self.attn(hidden_states=hidden_states, sequence_mask=sequence_mask)
[default0]:[rank0]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
[default0]:[rank0]:     return self._call_impl(*args, **kwargs)
[default0]:[rank0]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
[default0]:[rank0]:     return forward_call(*args, **kwargs)
[default0]:[rank0]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 389, in forward
[default0]:[rank0]:     .contiguous()
[default0]:[rank0]: torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 384.00 MiB. GPU 
[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 252, in train_batch_iter
[default2]:[rank2]:     output = self.forward(context=context, state=state, micro_batch=micro_batch, model=model)
[default2]:[rank2]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 44, in forward
[default2]:[rank2]:     output = model(**micro_batch)
[default2]:[rank2]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
[default2]:[rank2]:     return self._call_impl(*args, **kwargs)
[default2]:[rank2]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
[default2]:[rank2]:     return forward_call(*args, **kwargs)
[default2]:[rank2]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 891, in forward
[default2]:[rank2]:     sharded_logits = self.model(
[default2]:[rank2]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
[default2]:[rank2]:     return self._call_impl(*args, **kwargs)
[default2]:[rank2]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
[default2]:[rank2]:     return forward_call(*args, **kwargs)
[default2]:[rank2]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 764, in forward
[default2]:[rank2]:     return self.forward_with_hidden_states(input_ids=input_ids, input_mask=input_mask)[0]
[default2]:[rank2]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 780, in forward_with_hidden_states
[default2]:[rank2]:     hidden_encoder_states = encoder_block(**hidden_encoder_states)
[default2]:[rank2]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
[default2]:[rank2]:     return self._call_impl(*args, **kwargs)
[default2]:[rank2]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
[default2]:[rank2]:     return forward_call(*args, **kwargs)
[default2]:[rank2]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/block.py", line 151, in forward
[default2]:[rank2]:     output = self.pp_block(**new_kwargs)
[default2]:[rank2]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
[default2]:[rank2]:     return self._call_impl(*args, **kwargs)
[default2]:[rank2]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
[default2]:[rank2]:     return forward_call(*args, **kwargs)
[default2]:[rank2]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 637, in forward
[default2]:[rank2]:     hidden_states = self.mlp(hidden_states=hidden_states)["hidden_states"]
[default2]:[rank2]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
[default2]:[rank2]:     return self._call_impl(*args, **kwargs)
[default2]:[rank2]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
[default2]:[rank2]:     return forward_call(*args, **kwargs)
[default2]:[rank2]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 172, in forward
[default2]:[rank2]:     hidden_states = self.down_proj(self.split_silu_mul(merged_states))
[default2]:[rank2]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
[default2]:[rank2]:     return self._call_impl(*args, **kwargs)
[default2]:[rank2]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
[default2]:[rank2]:     return forward_call(*args, **kwargs)
[default2]:[rank2]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 128, in forward
[default2]:[rank2]:     return self.act(gate_states) * up_states
[default2]:[rank2]: torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 256.00 MiB. GPU  has a total capacity of 79.33 GiB of which 245.94 MiB is free. Including non-PyTorch memory, this process has 79.08 GiB memory in use. Of the allocated memory 69.39 GiB is allocated by PyTorch, and 12.20 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation.  See documentation for Memory Management  (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables)
[default1]:[rank1]: Traceback (most recent call last):
[default1]:[rank1]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/run_train.py", line 237, in <module>
[default1]:[rank1]:     trainer.train(dataloader)
[default1]:[rank1]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 429, in train
[default1]:[rank1]:     outputs, loss_avg = self.training_step(dataloader=self.current_dataloader)
[default1]:[rank1]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 462, in training_step
[default1]:[rank1]:     outputs = self.pipeline_engine.train_batch_iter(
[default1]:[rank1]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 252, in train_batch_iter
[default1]:[rank1]:     output = self.forward(context=context, state=state, micro_batch=micro_batch, model=model)
[default1]:[rank1]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 44, in forward
[default1]:[rank1]:     output = model(**micro_batch)
[default1]:[rank1]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
[default1]:[rank1]:     return self._call_impl(*args, **kwargs)
[default1]:[rank1]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
[default1]:[rank1]:     return forward_call(*args, **kwargs)
[default1]:[rank1]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 891, in forward
[default1]:[rank1]:     sharded_logits = self.model(
[default1]:[rank1]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
[default1]:[rank1]:     return self._call_impl(*args, **kwargs)
[default1]:[rank1]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
[default1]:[rank1]:     return forward_call(*args, **kwargs)
[default1]:[rank1]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 764, in forward
[default1]:[rank1]:     return self.forward_with_hidden_states(input_ids=input_ids, input_mask=input_mask)[0]
[default1]:[rank1]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 780, in forward_with_hidden_states
[default1]:[rank1]:     hidden_encoder_states = encoder_block(**hidden_encoder_states)
[default1]:[rank1]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
[default1]:[rank1]:     return self._call_impl(*args, **kwargs)
[default1]:[rank1]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
[default1]:[rank1]:     return forward_call(*args, **kwargs)
[default1]:[rank1]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/block.py", line 151, in forward
[default1]:[rank1]:     output = self.pp_block(**new_kwargs)
[default1]:[rank1]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
[default1]:[rank1]:     return self._call_impl(*args, **kwargs)
[default1]:[rank1]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
[default1]:[rank1]:     return forward_call(*args, **kwargs)
[default1]:[rank1]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 637, in forward
[default1]:[rank1]:     hidden_states = self.mlp(hidden_states=hidden_states)["hidden_states"]
[default1]:[rank1]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
[default1]:[rank1]:     return self._call_impl(*args, **kwargs)
[default1]:[rank1]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
[default1]:[rank1]:     return forward_call(*args, **kwargs)
[default1]:[rank1]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 172, in forward
[default1]:[rank1]:     hidden_states = self.down_proj(self.split_silu_mul(merged_states))
[default1]:[rank1]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
[default1]:[rank1]:     return self._call_impl(*args, **kwargs)
[default1]:[rank1]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
[default1]:[rank1]:     return forward_call(*args, **kwargs)
[default1]:[rank1]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 128, in forward
[default1]:[rank1]:     return self.act(gate_states) * up_states
[default1]:[rank1]: torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 256.00 MiB. GPU 
 has a total capacity of 79.33 GiB of which 173.94 MiB is free. Including non-PyTorch memory, this process has 79.15 GiB memory in use. Of the allocated memory 69.39 GiB is allocated by PyTorch, and 12.20 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation.  See documentation for Memory Management  (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables)
[default3]:[rank3]: 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 252, 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 637, in forward
[default3]:[rank3]:     hidden_states = self.mlp(hidden_states=hidden_states)["hidden_states"]
[default3]:[rank3]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
[default3]:[rank3]:     return self._call_impl(*args, **kwargs)
[default3]:[rank3]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
[default3]:[rank3]:     return forward_call(*args, **kwargs)
[default3]:[rank3]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 172, in forward
[default3]:[rank3]:     hidden_states = self.down_proj(self.split_silu_mul(merged_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/tensor_parallel/nn.py", line 159, in forward
[default3]:[rank3]:     return row_linear(
[default3]:[rank3]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/functional.py", line 474, in row_linear
[default3]:[rank3]:     out = F.linear(input, weight, bias)
[default3]:[rank3]: torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 512.00 MiB. GPU  has a total capacity of 79.33 GiB of which 157.94 MiB is free. Including non-PyTorch memory, this process has 79.16 GiB memory in use. Of the allocated memory 69.64 GiB is allocated by PyTorch, and 12.20 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation.  See documentation for Memory Management  (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables)
[default6]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/autograd/graph.py:744: UserWarning: c10d::allreduce_: an autograd kernel was not registered to the Autograd key(s) but we are trying to backprop through it. This may lead to silently incorrect behavior. This behavior is deprecated and will be removed in a future version of PyTorch. If your operator is differentiable, please ensure you have registered an autograd kernel to the correct Autograd key (e.g. DispatchKey::Autograd, DispatchKey::CompositeImplicitAutograd). If your operator is not differentiable, or to squash this warning and use the previous behavior, please register torch::CppFunction::makeFallthrough() to DispatchKey::Autograd. (Triggered internally at ../torch/csrc/autograd/autograd_not_implemented_fallback.cpp:63.)
[default6]:  return Variable._execution_engine.run_backward(  # Calls into the C++ engine to run the backward pass
[default5]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/autograd/graph.py:744: UserWarning: c10d::allreduce_: an autograd kernel was not registered to the Autograd key(s) but we are trying to backprop through it. This may lead to silently incorrect behavior. This behavior is deprecated and will be removed in a future version of PyTorch. If your operator is differentiable, please ensure you have registered an autograd kernel to the correct Autograd key (e.g. DispatchKey::Autograd, DispatchKey::CompositeImplicitAutograd). If your operator is not differentiable, or to squash this warning and use the previous behavior, please register torch::CppFunction::makeFallthrough() to DispatchKey::Autograd. (Triggered internally at ../torch/csrc/autograd/autograd_not_implemented_fallback.cpp:63.)
[default5]:  return Variable._execution_engine.run_backward(  # Calls into the C++ engine to run the backward pass
[default7]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/autograd/graph.py:744: UserWarning: c10d::allreduce_: an autograd kernel was not registered to the Autograd key(s) but we are trying to backprop through it. This may lead to silently incorrect behavior. This behavior is deprecated and will be removed in a future version of PyTorch. If your operator is differentiable, please ensure you have registered an autograd kernel to the correct Autograd key (e.g. DispatchKey::Autograd, DispatchKey::CompositeImplicitAutograd). If your operator is not differentiable, or to squash this warning and use the previous behavior, please register torch::CppFunction::makeFallthrough() to DispatchKey::Autograd. (Triggered internally at ../torch/csrc/autograd/autograd_not_implemented_fallback.cpp:63.)
[default7]:  return Variable._execution_engine.run_backward(  # Calls into the C++ engine to run the backward pass
[default4]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/autograd/graph.py:744: UserWarning: c10d::allreduce_: an autograd kernel was not registered to the Autograd key(s) but we are trying to backprop through it. This may lead to silently incorrect behavior. This behavior is deprecated and will be removed in a future version of PyTorch. If your operator is differentiable, please ensure you have registered an autograd kernel to the correct Autograd key (e.g. DispatchKey::Autograd, DispatchKey::CompositeImplicitAutograd). If your operator is not differentiable, or to squash this warning and use the previous behavior, please register torch::CppFunction::makeFallthrough() to DispatchKey::Autograd. (Triggered internally at ../torch/csrc/autograd/autograd_not_implemented_fallback.cpp:63.)
[default4]:  return Variable._execution_engine.run_backward(  # Calls into the C++ engine to run the backward pass
[default0]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/autograd/graph.py:744: UserWarning: c10d::allreduce_: an autograd kernel was not registered to the Autograd key(s) but we are trying to backprop through it. This may lead to silently incorrect behavior. This behavior is deprecated and will be removed in a future version of PyTorch. If your operator is differentiable, please ensure you have registered an autograd kernel to the correct Autograd key (e.g. DispatchKey::Autograd, DispatchKey::CompositeImplicitAutograd). If your operator is not differentiable, or to squash this warning and use the previous behavior, please register torch::CppFunction::makeFallthrough() to DispatchKey::Autograd. (Triggered internally at ../torch/csrc/autograd/autograd_not_implemented_fallback.cpp:63.)
[default0]:  return Variable._execution_engine.run_backward(  # Calls into the C++ engine to run the backward pass
[default1]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/autograd/graph.py:744: UserWarning: c10d::allreduce_: an autograd kernel was not registered to the Autograd key(s) but we are trying to backprop through it. This may lead to silently incorrect behavior. This behavior is deprecated and will be removed in a future version of PyTorch. If your operator is differentiable, please ensure you have registered an autograd kernel to the correct Autograd key (e.g. DispatchKey::Autograd, DispatchKey::CompositeImplicitAutograd). If your operator is not differentiable, or to squash this warning and use the previous behavior, please register torch::CppFunction::makeFallthrough() to DispatchKey::Autograd. (Triggered internally at ../torch/csrc/autograd/autograd_not_implemented_fallback.cpp:63.)
[default1]:  return Variable._execution_engine.run_backward(  # Calls into the C++ engine to run the backward pass
[default3]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/autograd/graph.py:744: UserWarning: c10d::allreduce_: an autograd kernel was not registered to the Autograd key(s) but we are trying to backprop through it. This may lead to silently incorrect behavior. This behavior is deprecated and will be removed in a future version of PyTorch. If your operator is differentiable, please ensure you have registered an autograd kernel to the correct Autograd key (e.g. DispatchKey::Autograd, DispatchKey::CompositeImplicitAutograd). If your operator is not differentiable, or to squash this warning and use the previous behavior, please register torch::CppFunction::makeFallthrough() to DispatchKey::Autograd. (Triggered internally at ../torch/csrc/autograd/autograd_not_implemented_fallback.cpp:63.)
[default3]:  return Variable._execution_engine.run_backward(  # Calls into the C++ engine to run the backward pass
[default2]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/autograd/graph.py:744: UserWarning: c10d::allreduce_: an autograd kernel was not registered to the Autograd key(s) but we are trying to backprop through it. This may lead to silently incorrect behavior. This behavior is deprecated and will be removed in a future version of PyTorch. If your operator is differentiable, please ensure you have registered an autograd kernel to the correct Autograd key (e.g. DispatchKey::Autograd, DispatchKey::CompositeImplicitAutograd). If your operator is not differentiable, or to squash this warning and use the previous behavior, please register torch::CppFunction::makeFallthrough() to DispatchKey::Autograd. (Triggered internally at ../torch/csrc/autograd/autograd_not_implemented_fallback.cpp:63.)
[default2]:  return Variable._execution_engine.run_backward(  # Calls into the C++ engine to run the backward pass
W0702 23:44:52.428000 140527302506304 torch/distributed/elastic/multiprocessing/api.py:851] Sending process 1101280 closing signal SIGTERM
W0702 23:44:52.428000 140527302506304 torch/distributed/elastic/multiprocessing/api.py:851] Sending process 1101281 closing signal SIGTERM
W0702 23:44:52.428000 140527302506304 torch/distributed/elastic/multiprocessing/api.py:851] Sending process 1101282 closing signal SIGTERM
[default7]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/autograd/graph.py:744: UserWarning: c10d::allreduce_: an autograd kernel was not registered to the Autograd key(s) but we are trying to backprop through it. This may lead to silently incorrect behavior. This behavior is deprecated and will be removed in a future version of PyTorch. If your operator is differentiable, please ensure you have registered an autograd kernel to the correct Autograd key (e.g. DispatchKey::Autograd, DispatchKey::CompositeImplicitAutograd). If your operator is not differentiable, or to squash this warning and use the previous behavior, please register torch::CppFunction::makeFallthrough() to DispatchKey::Autograd. (Triggered internally at ../torch/csrc/autograd/autograd_not_implemented_fallback.cpp:63.)
[default7]:  return Variable._execution_engine.run_backward(  # Calls into the C++ engine to run the backward pass
[default4]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/autograd/graph.py:744: UserWarning: c10d::allreduce_: an autograd kernel was not registered to the Autograd key(s) but we are trying to backprop through it. This may lead to silently incorrect behavior. This behavior is deprecated and will be removed in a future version of PyTorch. If your operator is differentiable, please ensure you have registered an autograd kernel to the correct Autograd key (e.g. DispatchKey::Autograd, DispatchKey::CompositeImplicitAutograd). If your operator is not differentiable, or to squash this warning and use the previous behavior, please register torch::CppFunction::makeFallthrough() to DispatchKey::Autograd. (Triggered internally at ../torch/csrc/autograd/autograd_not_implemented_fallback.cpp:63.)
[default4]:  return Variable._execution_engine.run_backward(  # Calls into the C++ engine to run the backward pass
[default6]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/autograd/graph.py:744: UserWarning: c10d::allreduce_: an autograd kernel was not registered to the Autograd key(s) but we are trying to backprop through it. This may lead to silently incorrect behavior. This behavior is deprecated and will be removed in a future version of PyTorch. If your operator is differentiable, please ensure you have registered an autograd kernel to the correct Autograd key (e.g. DispatchKey::Autograd, DispatchKey::CompositeImplicitAutograd). If your operator is not differentiable, or to squash this warning and use the previous behavior, please register torch::CppFunction::makeFallthrough() to DispatchKey::Autograd. (Triggered internally at ../torch/csrc/autograd/autograd_not_implemented_fallback.cpp:63.)
[default6]:  return Variable._execution_engine.run_backward(  # Calls into the C++ engine to run the backward pass
[default5]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/autograd/graph.py:744: UserWarning: c10d::allreduce_: an autograd kernel was not registered to the Autograd key(s) but we are trying to backprop through it. This may lead to silently incorrect behavior. This behavior is deprecated and will be removed in a future version of PyTorch. If your operator is differentiable, please ensure you have registered an autograd kernel to the correct Autograd key (e.g. DispatchKey::Autograd, DispatchKey::CompositeImplicitAutograd). If your operator is not differentiable, or to squash this warning and use the previous behavior, please register torch::CppFunction::makeFallthrough() to DispatchKey::Autograd. (Triggered internally at ../torch/csrc/autograd/autograd_not_implemented_fallback.cpp:63.)
[default5]:  return Variable._execution_engine.run_backward(  # Calls into the C++ engine to run the backward pass
[default3]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/autograd/graph.py:744: UserWarning: c10d::allreduce_: an autograd kernel was not registered to the Autograd key(s) but we are trying to backprop through it. This may lead to silently incorrect behavior. This behavior is deprecated and will be removed in a future version of PyTorch. If your operator is differentiable, please ensure you have registered an autograd kernel to the correct Autograd key (e.g. DispatchKey::Autograd, DispatchKey::CompositeImplicitAutograd). If your operator is not differentiable, or to squash this warning and use the previous behavior, please register torch::CppFunction::makeFallthrough() to DispatchKey::Autograd. (Triggered internally at ../torch/csrc/autograd/autograd_not_implemented_fallback.cpp:63.)
[default3]:  return Variable._execution_engine.run_backward(  # Calls into the C++ engine to run the backward pass
[default2]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/autograd/graph.py:744: UserWarning: c10d::allreduce_: an autograd kernel was not registered to the Autograd key(s) but we are trying to backprop through it. This may lead to silently incorrect behavior. This behavior is deprecated and will be removed in a future version of PyTorch. If your operator is differentiable, please ensure you have registered an autograd kernel to the correct Autograd key (e.g. DispatchKey::Autograd, DispatchKey::CompositeImplicitAutograd). If your operator is not differentiable, or to squash this warning and use the previous behavior, please register torch::CppFunction::makeFallthrough() to DispatchKey::Autograd. (Triggered internally at ../torch/csrc/autograd/autograd_not_implemented_fallback.cpp:63.)
[default2]:  return Variable._execution_engine.run_backward(  # Calls into the C++ engine to run the backward pass
[default0]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/autograd/graph.py:744: UserWarning: c10d::allreduce_: an autograd kernel was not registered to the Autograd key(s) but we are trying to backprop through it. This may lead to silently incorrect behavior. This behavior is deprecated and will be removed in a future version of PyTorch. If your operator is differentiable, please ensure you have registered an autograd kernel to the correct Autograd key (e.g. DispatchKey::Autograd, DispatchKey::CompositeImplicitAutograd). If your operator is not differentiable, or to squash this warning and use the previous behavior, please register torch::CppFunction::makeFallthrough() to DispatchKey::Autograd. (Triggered internally at ../torch/csrc/autograd/autograd_not_implemented_fallback.cpp:63.)
[default1]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/autograd/graph.py:744: UserWarning: c10d::allreduce_: an autograd kernel was not registered to the Autograd key(s) but we are trying to backprop through it. This may lead to silently incorrect behavior. This behavior is deprecated and will be removed in a future version of PyTorch. If your operator is differentiable, please ensure you have registered an autograd kernel to the correct Autograd key (e.g. DispatchKey::Autograd, DispatchKey::CompositeImplicitAutograd). If your operator is not differentiable, or to squash this warning and use the previous behavior, please register torch::CppFunction::makeFallthrough() to DispatchKey::Autograd. (Triggered internally at ../torch/csrc/autograd/autograd_not_implemented_fallback.cpp:63.)
[default1]:  return Variable._execution_engine.run_backward(  # Calls into the C++ engine to run the backward pass
[default0]:  return Variable._execution_engine.run_backward(  # Calls into the C++ engine to run the backward pass
E0702 23:44:57.153000 140527302506304 torch/distributed/elastic/multiprocessing/api.py:826] failed (exitcode: 1) local_rank: 0 (pid: 1101279) 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_23:44:52
  host      : ip-26-0-160-192.ec2.internal
  rank      : 4 (local_rank: 4)
  exitcode  : 1 (pid: 1101283)
  error_file: <N/A>
  traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html
[2]:
  time      : 2024-07-02_23:44:52
  host      : ip-26-0-160-192.ec2.internal
  rank      : 5 (local_rank: 5)
  exitcode  : 1 (pid: 1101284)
  error_file: <N/A>
  traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html
[3]:
  time      : 2024-07-02_23:44:52
  host      : ip-26-0-160-192.ec2.internal
  rank      : 6 (local_rank: 6)
  exitcode  : 1 (pid: 1101285)
  error_file: <N/A>
  traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html
[4]:
  time      : 2024-07-02_23:44:52
  host      : ip-26-0-160-192.ec2.internal
  rank      : 7 (local_rank: 7)
  exitcode  : 1 (pid: 1101286)
  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_23:44:52
  host      : ip-26-0-160-192.ec2.internal
  rank      : 0 (local_rank: 0)
  exitcode  : 1 (pid: 1101279)
  error_file: <N/A>
  traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html
============================================================
W0702 23:44:57.252000 139898288002816 torch/distributed/elastic/rendezvous/dynamic_rendezvous.py:1252] The node 'ip-26-0-168-238.ec2.internal_1825584_0' has failed to send a keep-alive heartbeat to the rendezvous 'none' due to an error of type RendezvousConnectionError.
W0702 23:44:57.263000 140580574099200 torch/distributed/elastic/rendezvous/dynamic_rendezvous.py:1252] The node 'ip-26-0-169-86.ec2.internal_1797961_0' has failed to send a keep-alive heartbeat to the rendezvous 'none' due to an error of type RendezvousConnectionError.
W0702 23:44:57.277000 140638666688256 torch/distributed/elastic/rendezvous/dynamic_rendezvous.py:1252] The node 'ip-26-0-161-178.ec2.internal_489112_0' has failed to send a keep-alive heartbeat to the rendezvous 'none' due to an error of type RendezvousConnectionError.
W0702 23:44:57.343000 140634563970816 torch/distributed/elastic/rendezvous/dynamic_rendezvous.py:1252] The node 'ip-26-0-172-57.ec2.internal_1024917_0' has failed to send a keep-alive heartbeat to the rendezvous 'none' due to an error of type RendezvousConnectionError.
W0702 23:44:57.389000 140239845861120 torch/distributed/elastic/rendezvous/dynamic_rendezvous.py:1252] The node 'ip-26-0-165-24.ec2.internal_862024_0' has failed to send a keep-alive heartbeat to the rendezvous 'none' due to an error of type RendezvousConnectionError.
W0702 23:44:57.421000 139903948736320 torch/distributed/elastic/multiprocessing/api.py:851] Sending process 1825652 closing signal SIGTERM
W0702 23:44:57.421000 139903948736320 torch/distributed/elastic/multiprocessing/api.py:851] Sending process 1825653 closing signal SIGTERM
W0702 23:44:57.421000 139903948736320 torch/distributed/elastic/multiprocessing/api.py:851] Sending process 1825654 closing signal SIGTERM
W0702 23:44:57.421000 139903948736320 torch/distributed/elastic/multiprocessing/api.py:851] Sending process 1825655 closing signal SIGTERM
W0702 23:44:57.422000 139903948736320 torch/distributed/elastic/multiprocessing/api.py:851] Sending process 1825656 closing signal SIGTERM
W0702 23:44:57.425000 140245506594624 torch/distributed/elastic/multiprocessing/api.py:851] Sending process 862093 closing signal SIGTERM
W0702 23:44:57.425000 140245506594624 torch/distributed/elastic/multiprocessing/api.py:851] Sending process 862094 closing signal SIGTERM
W0702 23:44:57.426000 140245506594624 torch/distributed/elastic/multiprocessing/api.py:851] Sending process 862095 closing signal SIGTERM
W0702 23:44:57.426000 140245506594624 torch/distributed/elastic/multiprocessing/api.py:851] Sending process 862096 closing signal SIGTERM
W0702 23:44:57.425000 139903948736320 torch/distributed/elastic/multiprocessing/api.py:851] Sending process 1825657 closing signal SIGTERM
W0702 23:44:57.427000 139903948736320 torch/distributed/elastic/multiprocessing/api.py:851] Sending process 1825658 closing signal SIGTERM
W0702 23:44:57.428000 139903948736320 torch/distributed/elastic/multiprocessing/api.py:851] Sending process 1825659 closing signal SIGTERM
W0702 23:44:57.430000 140245506594624 torch/distributed/elastic/multiprocessing/api.py:851] Sending process 862097 closing signal SIGTERM
W0702 23:44:57.430000 140245506594624 torch/distributed/elastic/multiprocessing/api.py:851] Sending process 862098 closing signal SIGTERM
W0702 23:44:57.430000 140245506594624 torch/distributed/elastic/multiprocessing/api.py:851] Sending process 862099 closing signal SIGTERM
W0702 23:44:57.432000 140245506594624 torch/distributed/elastic/multiprocessing/api.py:851] Sending process 862100 closing signal SIGTERM
W0702 23:44:57.431000 140644327421760 torch/distributed/elastic/multiprocessing/api.py:851] Sending process 489181 closing signal SIGTERM
W0702 23:44:57.431000 140644327421760 torch/distributed/elastic/multiprocessing/api.py:851] Sending process 489182 closing signal SIGTERM
W0702 23:44:57.431000 140644327421760 torch/distributed/elastic/multiprocessing/api.py:851] Sending process 489183 closing signal SIGTERM
W0702 23:44:57.433000 140644327421760 torch/distributed/elastic/multiprocessing/api.py:851] Sending process 489184 closing signal SIGTERM
W0702 23:44:57.433000 140644327421760 torch/distributed/elastic/multiprocessing/api.py:851] Sending process 489185 closing signal SIGTERM
W0702 23:44:57.433000 140644327421760 torch/distributed/elastic/multiprocessing/api.py:851] Sending process 489186 closing signal SIGTERM
W0702 23:44:57.435000 140644327421760 torch/distributed/elastic/multiprocessing/api.py:851] Sending process 489187 closing signal SIGTERM
W0702 23:44:57.435000 140644327421760 torch/distributed/elastic/multiprocessing/api.py:851] Sending process 489188 closing signal SIGTERM
W0702 23:44:57.439000 140640224704320 torch/distributed/elastic/multiprocessing/api.py:851] Sending process 1024985 closing signal SIGTERM
W0702 23:44:57.439000 140640224704320 torch/distributed/elastic/multiprocessing/api.py:851] Sending process 1024986 closing signal SIGTERM
W0702 23:44:57.440000 140640224704320 torch/distributed/elastic/multiprocessing/api.py:851] Sending process 1024987 closing signal SIGTERM
W0702 23:44:57.440000 140640224704320 torch/distributed/elastic/multiprocessing/api.py:851] Sending process 1024988 closing signal SIGTERM
W0702 23:44:57.438000 140586234832704 torch/distributed/elastic/multiprocessing/api.py:851] Sending process 1798030 closing signal SIGTERM
W0702 23:44:57.438000 140586234832704 torch/distributed/elastic/multiprocessing/api.py:851] Sending process 1798031 closing signal SIGTERM
W0702 23:44:57.438000 140586234832704 torch/distributed/elastic/multiprocessing/api.py:851] Sending process 1798032 closing signal SIGTERM
W0702 23:44:57.438000 140586234832704 torch/distributed/elastic/multiprocessing/api.py:851] Sending process 1798033 closing signal SIGTERM
W0702 23:44:57.441000 140640224704320 torch/distributed/elastic/multiprocessing/api.py:851] Sending process 1024989 closing signal SIGTERM
W0702 23:44:57.444000 140640224704320 torch/distributed/elastic/multiprocessing/api.py:851] Sending process 1024990 closing signal SIGTERM
W0702 23:44:57.439000 140586234832704 torch/distributed/elastic/multiprocessing/api.py:851] Sending process 1798034 closing signal SIGTERM
W0702 23:44:57.444000 140640224704320 torch/distributed/elastic/multiprocessing/api.py:851] Sending process 1024991 closing signal SIGTERM
W0702 23:44:57.444000 140640224704320 torch/distributed/elastic/multiprocessing/api.py:851] Sending process 1024992 closing signal SIGTERM
W0702 23:44:57.443000 140586234832704 torch/distributed/elastic/multiprocessing/api.py:851] Sending process 1798035 closing signal SIGTERM
W0702 23:44:57.444000 140586234832704 torch/distributed/elastic/multiprocessing/api.py:851] Sending process 1798036 closing signal SIGTERM
W0702 23:44:57.445000 140586234832704 torch/distributed/elastic/multiprocessing/api.py:851] Sending process 1798037 closing signal SIGTERM
srun: error: ip-26-0-160-192: task 0: Exited with exit code 1
W0702 23:45:02.171000 140516143351552 torch/distributed/elastic/rendezvous/dynamic_rendezvous.py:1252] The node 'ip-26-0-172-73.ec2.internal_866890_0' has failed to send a keep-alive heartbeat to the rendezvous 'none' due to an error of type RendezvousConnectionError.
W0702 23:45:02.241000 139909132637952 torch/distributed/elastic/rendezvous/dynamic_rendezvous.py:1252] The node 'ip-26-0-163-226.ec2.internal_3185359_0' has failed to send a keep-alive heartbeat to the rendezvous 'none' due to an error of type RendezvousConnectionError.
W0702 23:45:02.257000 139898288002816 torch/distributed/elastic/rendezvous/dynamic_rendezvous.py:1252] The node 'ip-26-0-168-238.ec2.internal_1825584_0' has failed to send a keep-alive heartbeat to the rendezvous 'none' due to an error of type RendezvousConnectionError.
W0702 23:45:02.268000 140580574099200 torch/distributed/elastic/rendezvous/dynamic_rendezvous.py:1252] The node 'ip-26-0-169-86.ec2.internal_1797961_0' has failed to send a keep-alive heartbeat to the rendezvous 'none' due to an error of type RendezvousConnectionError.
W0702 23:45:02.281000 140638666688256 torch/distributed/elastic/rendezvous/dynamic_rendezvous.py:1252] The node 'ip-26-0-161-178.ec2.internal_489112_0' has failed to send a keep-alive heartbeat to the rendezvous 'none' due to an error of type RendezvousConnectionError.
W0702 23:45:02.348000 140634563970816 torch/distributed/elastic/rendezvous/dynamic_rendezvous.py:1252] The node 'ip-26-0-172-57.ec2.internal_1024917_0' has failed to send a keep-alive heartbeat to the rendezvous 'none' due to an error of type RendezvousConnectionError.
W0702 23:45:02.394000 140239845861120 torch/distributed/elastic/rendezvous/dynamic_rendezvous.py:1252] The node 'ip-26-0-165-24.ec2.internal_862024_0' has failed to send a keep-alive heartbeat to the rendezvous 'none' due to an error of type RendezvousConnectionError.
W0702 23:45:02.434000 139914793371456 torch/distributed/elastic/multiprocessing/api.py:851] Sending process 3185428 closing signal SIGTERM
W0702 23:45:02.434000 139914793371456 torch/distributed/elastic/multiprocessing/api.py:851] Sending process 3185429 closing signal SIGTERM
W0702 23:45:02.434000 139914793371456 torch/distributed/elastic/multiprocessing/api.py:851] Sending process 3185430 closing signal SIGTERM
W0702 23:45:02.434000 139914793371456 torch/distributed/elastic/multiprocessing/api.py:851] Sending process 3185431 closing signal SIGTERM
W0702 23:45:02.437000 140521804085056 torch/distributed/elastic/multiprocessing/api.py:851] Sending process 866959 closing signal SIGTERM
W0702 23:45:02.437000 139914793371456 torch/distributed/elastic/multiprocessing/api.py:851] Sending process 3185432 closing signal SIGTERM
W0702 23:45:02.437000 140521804085056 torch/distributed/elastic/multiprocessing/api.py:851] Sending process 866960 closing signal SIGTERM
W0702 23:45:02.437000 140521804085056 torch/distributed/elastic/multiprocessing/api.py:851] Sending process 866961 closing signal SIGTERM
W0702 23:45:02.438000 139914793371456 torch/distributed/elastic/multiprocessing/api.py:851] Sending process 3185433 closing signal SIGTERM
W0702 23:45:02.438000 139914793371456 torch/distributed/elastic/multiprocessing/api.py:851] Sending process 3185434 closing signal SIGTERM
W0702 23:45:02.437000 140521804085056 torch/distributed/elastic/multiprocessing/api.py:851] Sending process 866962 closing signal SIGTERM
W0702 23:45:02.439000 139914793371456 torch/distributed/elastic/multiprocessing/api.py:851] Sending process 3185435 closing signal SIGTERM
W0702 23:45:02.440000 140521804085056 torch/distributed/elastic/multiprocessing/api.py:851] Sending process 866963 closing signal SIGTERM
W0702 23:45:02.442000 140521804085056 torch/distributed/elastic/multiprocessing/api.py:851] Sending process 866964 closing signal SIGTERM
W0702 23:45:02.442000 140521804085056 torch/distributed/elastic/multiprocessing/api.py:851] Sending process 866965 closing signal SIGTERM
W0702 23:45:02.442000 140521804085056 torch/distributed/elastic/multiprocessing/api.py:851] Sending process 866966 closing signal SIGTERM
W0702 23:45:07.175000 140516143351552 torch/distributed/elastic/rendezvous/dynamic_rendezvous.py:1252] The node 'ip-26-0-172-73.ec2.internal_866890_0' has failed to send a keep-alive heartbeat to the rendezvous 'none' due to an error of type RendezvousConnectionError.
W0702 23:45:07.245000 139909132637952 torch/distributed/elastic/rendezvous/dynamic_rendezvous.py:1252] The node 'ip-26-0-163-226.ec2.internal_3185359_0' has failed to send a keep-alive heartbeat to the rendezvous 'none' due to an error of type RendezvousConnectionError.
W0702 23:45:07.261000 139898288002816 torch/distributed/elastic/rendezvous/dynamic_rendezvous.py:1252] The node 'ip-26-0-168-238.ec2.internal_1825584_0' has failed to send a keep-alive heartbeat to the rendezvous 'none' due to an error of type RendezvousConnectionError.
W0702 23:45:07.272000 140580574099200 torch/distributed/elastic/rendezvous/dynamic_rendezvous.py:1252] The node 'ip-26-0-169-86.ec2.internal_1797961_0' has failed to send a keep-alive heartbeat to the rendezvous 'none' due to an error of type RendezvousConnectionError.
W0702 23:45:07.286000 140638666688256 torch/distributed/elastic/rendezvous/dynamic_rendezvous.py:1252] The node 'ip-26-0-161-178.ec2.internal_489112_0' has failed to send a keep-alive heartbeat to the rendezvous 'none' due to an error of type RendezvousConnectionError.
W0702 23:45:07.352000 140634563970816 torch/distributed/elastic/rendezvous/dynamic_rendezvous.py:1252] The node 'ip-26-0-172-57.ec2.internal_1024917_0' has failed to send a keep-alive heartbeat to the rendezvous 'none' due to an error of type RendezvousConnectionError.
W0702 23:45:07.398000 140239845861120 torch/distributed/elastic/rendezvous/dynamic_rendezvous.py:1252] The node 'ip-26-0-165-24.ec2.internal_862024_0' has failed to send a keep-alive heartbeat to the rendezvous 'none' due to an error of type RendezvousConnectionError.
W0702 23:45:08.674000 140640224704320 torch/distributed/elastic/rendezvous/dynamic_rendezvous.py:1203] The node 'ip-26-0-172-57.ec2.internal_1024917_0' has failed to shutdown the rendezvous 'none' due to an error of type RendezvousConnectionError.
W0702 23:45:08.682000 140640224704320 torch/distributed/elastic/rendezvous/dynamic_rendezvous.py:1203] The node 'ip-26-0-172-57.ec2.internal_1024917_0' has failed to shutdown the rendezvous 'none' due to an error of type RendezvousConnectionError.
Traceback (most recent call last):
  File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/elastic/rendezvous/c10d_rendezvous_backend.py", line 113, in _call_store
    return getattr(self._store, store_op)(*args, **kwargs)
torch.distributed.DistNetworkError: Broken pipe

The above exception was the direct cause of the following exception:

Traceback (most recent call last):
  File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/bin/torchrun", line 8, in <module>
    sys.exit(main())
  File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/elastic/multiprocessing/errors/__init__.py", line 347, in wrapper
    return f(*args, **kwargs)
  File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/run.py", line 879, in main
    run(args)
  File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/run.py", line 870, in run
    elastic_launch(
  File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/launcher/api.py", line 132, in __call__
    return launch_agent(self._config, self._entrypoint, list(args))
  File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/launcher/api.py", line 254, in launch_agent
    result = agent.run()
  File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/elastic/metrics/api.py", line 123, in wrapper
    result = f(*args, **kwargs)
  File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/elastic/agent/server/api.py", line 733, in run
    result = self._invoke_run(role)
  File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/elastic/agent/server/api.py", line 908, in _invoke_run
    num_nodes_waiting = rdzv_handler.num_nodes_waiting()
  File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/elastic/rendezvous/dynamic_rendezvous.py", line 1174, in num_nodes_waiting
    self._state_holder.sync()
  File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/elastic/rendezvous/dynamic_rendezvous.py", line 419, in sync
    get_response = self._backend.get_state()
  File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/elastic/rendezvous/c10d_rendezvous_backend.py", line 73, in get_state
    base64_state: bytes = self._call_store("get", self._key)
  File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/elastic/rendezvous/c10d_rendezvous_backend.py", line 115, in _call_store
    raise RendezvousConnectionError(
torch.distributed.elastic.rendezvous.api.RendezvousConnectionError: The connection to the C10d store has failed. See inner exception for details.
W0702 23:45:08.867000 140644327421760 torch/distributed/elastic/rendezvous/dynamic_rendezvous.py:1203] The node 'ip-26-0-161-178.ec2.internal_489112_0' has failed to shutdown the rendezvous 'none' due to an error of type RendezvousConnectionError.
W0702 23:45:08.876000 140644327421760 torch/distributed/elastic/rendezvous/dynamic_rendezvous.py:1203] The node 'ip-26-0-161-178.ec2.internal_489112_0' has failed to shutdown the rendezvous 'none' due to an error of type RendezvousConnectionError.
Traceback (most recent call last):
  File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/elastic/rendezvous/c10d_rendezvous_backend.py", line 113, in _call_store
    return getattr(self._store, store_op)(*args, **kwargs)
torch.distributed.DistNetworkError: Broken pipe

The above exception was the direct cause of the following exception:

Traceback (most recent call last):
  File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/bin/torchrun", line 8, in <module>
    sys.exit(main())
  File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/elastic/multiprocessing/errors/__init__.py", line 347, in wrapper
    return f(*args, **kwargs)
  File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/run.py", line 879, in main
    run(args)
  File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/run.py", line 870, in run
    elastic_launch(
  File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/launcher/api.py", line 132, in __call__
    return launch_agent(self._config, self._entrypoint, list(args))
  File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/launcher/api.py", line 254, in launch_agent
    result = agent.run()
  File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/elastic/metrics/api.py", line 123, in wrapper
    result = f(*args, **kwargs)
  File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/elastic/agent/server/api.py", line 733, in run
    result = self._invoke_run(role)
  File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/elastic/agent/server/api.py", line 908, in _invoke_run
    num_nodes_waiting = rdzv_handler.num_nodes_waiting()
  File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/elastic/rendezvous/dynamic_rendezvous.py", line 1174, in num_nodes_waiting
    self._state_holder.sync()
  File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/elastic/rendezvous/dynamic_rendezvous.py", line 419, in sync
    get_response = self._backend.get_state()
  File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/elastic/rendezvous/c10d_rendezvous_backend.py", line 73, in get_state
    base64_state: bytes = self._call_store("get", self._key)
  File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/elastic/rendezvous/c10d_rendezvous_backend.py", line 115, in _call_store
    raise RendezvousConnectionError(
torch.distributed.elastic.rendezvous.api.RendezvousConnectionError: The connection to the C10d store has failed. See inner exception for details.
srun: error: ip-26-0-172-57: task 6: Exited with exit code 1
W0702 23:45:09.077000 140586234832704 torch/distributed/elastic/rendezvous/dynamic_rendezvous.py:1203] The node 'ip-26-0-169-86.ec2.internal_1797961_0' has failed to shutdown the rendezvous 'none' due to an error of type RendezvousConnectionError.
W0702 23:45:09.086000 140586234832704 torch/distributed/elastic/rendezvous/dynamic_rendezvous.py:1203] The node 'ip-26-0-169-86.ec2.internal_1797961_0' has failed to shutdown the rendezvous 'none' due to an error of type RendezvousConnectionError.
Traceback (most recent call last):
  File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/elastic/rendezvous/c10d_rendezvous_backend.py", line 113, in _call_store
    return getattr(self._store, store_op)(*args, **kwargs)
torch.distributed.DistNetworkError: Broken pipe

The above exception was the direct cause of the following exception:

Traceback (most recent call last):
  File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/bin/torchrun", line 8, in <module>
    sys.exit(main())
  File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/elastic/multiprocessing/errors/__init__.py", line 347, in wrapper
    return f(*args, **kwargs)
  File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/run.py", line 879, in main
    run(args)
  File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/run.py", line 870, in run
    elastic_launch(
  File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/launcher/api.py", line 132, in __call__
    return launch_agent(self._config, self._entrypoint, list(args))
  File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/launcher/api.py", line 254, in launch_agent
    result = agent.run()
  File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/elastic/metrics/api.py", line 123, in wrapper
    result = f(*args, **kwargs)
  File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/elastic/agent/server/api.py", line 733, in run
    result = self._invoke_run(role)
  File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/elastic/agent/server/api.py", line 908, in _invoke_run
    num_nodes_waiting = rdzv_handler.num_nodes_waiting()
  File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/elastic/rendezvous/dynamic_rendezvous.py", line 1174, in num_nodes_waiting
    self._state_holder.sync()
  File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/elastic/rendezvous/dynamic_rendezvous.py", line 419, in sync
    get_response = self._backend.get_state()
  File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/elastic/rendezvous/c10d_rendezvous_backend.py", line 73, in get_state
    base64_state: bytes = self._call_store("get", self._key)
  File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/elastic/rendezvous/c10d_rendezvous_backend.py", line 115, in _call_store
    raise RendezvousConnectionError(
torch.distributed.elastic.rendezvous.api.RendezvousConnectionError: The connection to the C10d store has failed. See inner exception for details.
srun: error: ip-26-0-161-178: task 1: Exited with exit code 1
srun: error: ip-26-0-169-86: task 5: Exited with exit code 1
W0702 23:45:09.674000 140245506594624 torch/distributed/elastic/rendezvous/dynamic_rendezvous.py:1203] The node 'ip-26-0-165-24.ec2.internal_862024_0' has failed to shutdown the rendezvous 'none' due to an error of type RendezvousConnectionError.
W0702 23:45:09.685000 140245506594624 torch/distributed/elastic/rendezvous/dynamic_rendezvous.py:1203] The node 'ip-26-0-165-24.ec2.internal_862024_0' has failed to shutdown the rendezvous 'none' due to an error of type RendezvousConnectionError.
Traceback (most recent call last):
  File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/elastic/rendezvous/c10d_rendezvous_backend.py", line 113, in _call_store
    return getattr(self._store, store_op)(*args, **kwargs)
torch.distributed.DistNetworkError: Broken pipe

The above exception was the direct cause of the following exception:

Traceback (most recent call last):
  File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/bin/torchrun", line 8, in <module>
    sys.exit(main())
  File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/elastic/multiprocessing/errors/__init__.py", line 347, in wrapper
    return f(*args, **kwargs)
  File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/run.py", line 879, in main
    run(args)
  File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/run.py", line 870, in run
    elastic_launch(
  File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/launcher/api.py", line 132, in __call__
    return launch_agent(self._config, self._entrypoint, list(args))
  File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/launcher/api.py", line 254, in launch_agent
    result = agent.run()
  File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/elastic/metrics/api.py", line 123, in wrapper
    result = f(*args, **kwargs)
  File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/elastic/agent/server/api.py", line 733, in run
    result = self._invoke_run(role)
  File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/elastic/agent/server/api.py", line 908, in _invoke_run
    num_nodes_waiting = rdzv_handler.num_nodes_waiting()
  File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/elastic/rendezvous/dynamic_rendezvous.py", line 1174, in num_nodes_waiting
    self._state_holder.sync()
  File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/elastic/rendezvous/dynamic_rendezvous.py", line 419, in sync
    get_response = self._backend.get_state()
  File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/elastic/rendezvous/c10d_rendezvous_backend.py", line 73, in get_state
    base64_state: bytes = self._call_store("get", self._key)
  File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/elastic/rendezvous/c10d_rendezvous_backend.py", line 115, in _call_store
    raise RendezvousConnectionError(
torch.distributed.elastic.rendezvous.api.RendezvousConnectionError: The connection to the C10d store has failed. See inner exception for details.
W0702 23:45:09.776000 140521804085056 torch/distributed/elastic/rendezvous/dynamic_rendezvous.py:1203] The node 'ip-26-0-172-73.ec2.internal_866890_0' has failed to shutdown the rendezvous 'none' due to an error of type RendezvousConnectionError.
W0702 23:45:09.784000 140521804085056 torch/distributed/elastic/rendezvous/dynamic_rendezvous.py:1203] The node 'ip-26-0-172-73.ec2.internal_866890_0' has failed to shutdown the rendezvous 'none' due to an error of type RendezvousConnectionError.
Traceback (most recent call last):
  File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/elastic/rendezvous/c10d_rendezvous_backend.py", line 113, in _call_store
    return getattr(self._store, store_op)(*args, **kwargs)
torch.distributed.DistNetworkError: Broken pipe

The above exception was the direct cause of the following exception:

Traceback (most recent call last):
  File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/bin/torchrun", line 8, in <module>
    sys.exit(main())
  File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/elastic/multiprocessing/errors/__init__.py", line 347, in wrapper
    return f(*args, **kwargs)
  File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/run.py", line 879, in main
    run(args)
  File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/run.py", line 870, in run
    elastic_launch(
  File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/launcher/api.py", line 132, in __call__
    return launch_agent(self._config, self._entrypoint, list(args))
  File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/launcher/api.py", line 254, in launch_agent
    result = agent.run()
  File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/elastic/metrics/api.py", line 123, in wrapper
    result = f(*args, **kwargs)
  File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/elastic/agent/server/api.py", line 733, in run
    result = self._invoke_run(role)
  File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/elastic/agent/server/api.py", line 908, in _invoke_run
    num_nodes_waiting = rdzv_handler.num_nodes_waiting()
  File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/elastic/rendezvous/dynamic_rendezvous.py", line 1174, in num_nodes_waiting
    self._state_holder.sync()
  File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/elastic/rendezvous/dynamic_rendezvous.py", line 419, in sync
    get_response = self._backend.get_state()
  File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/elastic/rendezvous/c10d_rendezvous_backend.py", line 73, in get_state
    base64_state: bytes = self._call_store("get", self._key)
  File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/elastic/rendezvous/c10d_rendezvous_backend.py", line 115, in _call_store
    raise RendezvousConnectionError(
torch.distributed.elastic.rendezvous.api.RendezvousConnectionError: The connection to the C10d store has failed. See inner exception for details.
W0702 23:45:09.874000 139903948736320 torch/distributed/elastic/rendezvous/dynamic_rendezvous.py:1203] The node 'ip-26-0-168-238.ec2.internal_1825584_0' has failed to shutdown the rendezvous 'none' due to an error of type RendezvousConnectionError.
W0702 23:45:09.883000 139903948736320 torch/distributed/elastic/rendezvous/dynamic_rendezvous.py:1203] The node 'ip-26-0-168-238.ec2.internal_1825584_0' has failed to shutdown the rendezvous 'none' due to an error of type RendezvousConnectionError.
Traceback (most recent call last):
  File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/elastic/rendezvous/c10d_rendezvous_backend.py", line 113, in _call_store
    return getattr(self._store, store_op)(*args, **kwargs)
torch.distributed.DistNetworkError: Broken pipe

The above exception was the direct cause of the following exception:

Traceback (most recent call last):
  File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/bin/torchrun", line 8, in <module>
    sys.exit(main())
  File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/elastic/multiprocessing/errors/__init__.py", line 347, in wrapper
    return f(*args, **kwargs)
  File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/run.py", line 879, in main
    run(args)
  File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/run.py", line 870, in run
    elastic_launch(
  File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/launcher/api.py", line 132, in __call__
    return launch_agent(self._config, self._entrypoint, list(args))
  File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/launcher/api.py", line 254, in launch_agent
    result = agent.run()
  File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/elastic/metrics/api.py", line 123, in wrapper
    result = f(*args, **kwargs)
  File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/elastic/agent/server/api.py", line 733, in run
    result = self._invoke_run(role)
  File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/elastic/agent/server/api.py", line 908, in _invoke_run
    num_nodes_waiting = rdzv_handler.num_nodes_waiting()
  File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/elastic/rendezvous/dynamic_rendezvous.py", line 1174, in num_nodes_waiting
    self._state_holder.sync()
  File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/elastic/rendezvous/dynamic_rendezvous.py", line 419, in sync
    get_response = self._backend.get_state()
  File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/elastic/rendezvous/c10d_rendezvous_backend.py", line 73, in get_state
    base64_state: bytes = self._call_store("get", self._key)
  File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/elastic/rendezvous/c10d_rendezvous_backend.py", line 115, in _call_store
    raise RendezvousConnectionError(
torch.distributed.elastic.rendezvous.api.RendezvousConnectionError: The connection to the C10d store has failed. See inner exception for details.
srun: error: ip-26-0-165-24: task 3: Exited with exit code 1
srun: error: ip-26-0-172-73: task 7: Exited with exit code 1
srun: error: ip-26-0-168-238: task 4: Exited with exit code 1
W0702 23:45:12.249000 139909132637952 torch/distributed/elastic/rendezvous/dynamic_rendezvous.py:1252] The node 'ip-26-0-163-226.ec2.internal_3185359_0' has failed to send a keep-alive heartbeat to the rendezvous 'none' due to an error of type RendezvousConnectionError.
W0702 23:45:13.676000 139914793371456 torch/distributed/elastic/rendezvous/dynamic_rendezvous.py:1203] The node 'ip-26-0-163-226.ec2.internal_3185359_0' has failed to shutdown the rendezvous 'none' due to an error of type RendezvousConnectionError.
W0702 23:45:13.685000 139914793371456 torch/distributed/elastic/rendezvous/dynamic_rendezvous.py:1203] The node 'ip-26-0-163-226.ec2.internal_3185359_0' has failed to shutdown the rendezvous 'none' due to an error of type RendezvousConnectionError.
Traceback (most recent call last):
  File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/elastic/rendezvous/c10d_rendezvous_backend.py", line 113, in _call_store
    return getattr(self._store, store_op)(*args, **kwargs)
torch.distributed.DistNetworkError: Broken pipe

The above exception was the direct cause of the following exception:

Traceback (most recent call last):
  File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/bin/torchrun", line 8, in <module>
    sys.exit(main())
  File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/elastic/multiprocessing/errors/__init__.py", line 347, in wrapper
    return f(*args, **kwargs)
  File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/run.py", line 879, in main
    run(args)
  File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/run.py", line 870, in run
    elastic_launch(
  File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/launcher/api.py", line 132, in __call__
    return launch_agent(self._config, self._entrypoint, list(args))
  File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/launcher/api.py", line 254, in launch_agent
    result = agent.run()
  File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/elastic/metrics/api.py", line 123, in wrapper
    result = f(*args, **kwargs)
  File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/elastic/agent/server/api.py", line 733, in run
    result = self._invoke_run(role)
  File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/elastic/agent/server/api.py", line 908, in _invoke_run
    num_nodes_waiting = rdzv_handler.num_nodes_waiting()
  File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/elastic/rendezvous/dynamic_rendezvous.py", line 1174, in num_nodes_waiting
    self._state_holder.sync()
  File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/elastic/rendezvous/dynamic_rendezvous.py", line 419, in sync
    get_response = self._backend.get_state()
  File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/elastic/rendezvous/c10d_rendezvous_backend.py", line 73, in get_state
    base64_state: bytes = self._call_store("get", self._key)
  File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/elastic/rendezvous/c10d_rendezvous_backend.py", line 115, in _call_store
    raise RendezvousConnectionError(
torch.distributed.elastic.rendezvous.api.RendezvousConnectionError: The connection to the C10d store has failed. See inner exception for details.
srun: error: ip-26-0-163-226: task 2: Exited with exit code 1
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