llama-1B/64_GPUS/dp-1_tp-4_pp-16_mbz-8/log.out

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START TIME: Sat Jul  6 09:18:29 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
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Job status: RUNNING
[2024-07-06 09:18:38,069] torch.distributed.run: [WARNING] 
[2024-07-06 09:18:38,069] torch.distributed.run: [WARNING] *****************************************
[2024-07-06 09:18:38,069] torch.distributed.run: [WARNING] 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. 
[2024-07-06 09:18:38,069] torch.distributed.run: [WARNING] *****************************************
[2024-07-06 09:18:38,068] torch.distributed.run: [WARNING] 
[2024-07-06 09:18:38,068] torch.distributed.run: [WARNING] *****************************************
[2024-07-06 09:18:38,068] torch.distributed.run: [WARNING] 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. 
[2024-07-06 09:18:38,068] torch.distributed.run: [WARNING] *****************************************
[2024-07-06 09:18:38,081] torch.distributed.run: [WARNING] 
[2024-07-06 09:18:38,081] torch.distributed.run: [WARNING] *****************************************
[2024-07-06 09:18:38,081] torch.distributed.run: [WARNING] 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. 
[2024-07-06 09:18:38,081] torch.distributed.run: [WARNING] *****************************************
[2024-07-06 09:18:38,112] torch.distributed.run: [WARNING] 
[2024-07-06 09:18:38,112] torch.distributed.run: [WARNING] *****************************************
[2024-07-06 09:18:38,112] torch.distributed.run: [WARNING] 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. 
[2024-07-06 09:18:38,112] torch.distributed.run: [WARNING] *****************************************
[2024-07-06 09:18:38,184] torch.distributed.run: [WARNING] 
[2024-07-06 09:18:38,184] torch.distributed.run: [WARNING] *****************************************
[2024-07-06 09:18:38,184] torch.distributed.run: [WARNING] 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. 
[2024-07-06 09:18:38,184] torch.distributed.run: [WARNING] *****************************************
[2024-07-06 09:18:38,210] torch.distributed.run: [WARNING] 
[2024-07-06 09:18:38,210] torch.distributed.run: [WARNING] *****************************************
[2024-07-06 09:18:38,210] torch.distributed.run: [WARNING] 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. 
[2024-07-06 09:18:38,210] torch.distributed.run: [WARNING] *****************************************
[2024-07-06 09:18:38,235] torch.distributed.run: [WARNING] 
[2024-07-06 09:18:38,235] torch.distributed.run: [WARNING] *****************************************
[2024-07-06 09:18:38,235] torch.distributed.run: [WARNING] 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. 
[2024-07-06 09:18:38,235] torch.distributed.run: [WARNING] *****************************************
[2024-07-06 09:18:38,704] torch.distributed.run: [WARNING] 
[2024-07-06 09:18:38,704] torch.distributed.run: [WARNING] *****************************************
[2024-07-06 09:18:38,704] torch.distributed.run: [WARNING] 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. 
[2024-07-06 09:18:38,704] torch.distributed.run: [WARNING] *****************************************
[default0]:07/06/2024 09:19:00 [WARNING|DP=0|PP=0|TP=0|ip-26-0-160-103]: [Vocab Size Padding] Padded vocab (size: 50257) with 3 dummy tokens (new size: 50260)
[default0]:07/06/2024 09:19:00 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-103]: Config:
[default0]:07/06/2024 09:19:00 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-103]: Config(general=GeneralArgs(project='bench_cluster',
[default0]:07/06/2024 09:19:00 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-103]:                            run='%date_%jobid',
[default0]:07/06/2024 09:19:00 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-103]:                            seed=42,
[default0]:07/06/2024 09:19:00 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-103]:                            step=None,
[default0]:07/06/2024 09:19:00 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-103]:                            consumed_train_samples=None,
[default0]:07/06/2024 09:19:00 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-103]:                            benchmark_csv_path=None,
[default0]:07/06/2024 09:19:00 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-103]:                            ignore_sanity_checks=True),
[default0]:07/06/2024 09:19:00 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-103]:        parallelism=ParallelismArgs(dp=1,
[default0]:07/06/2024 09:19:00 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-103]:                                    pp=16,
[default0]:07/06/2024 09:19:00 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-103]:                                    tp=4,
[default0]:07/06/2024 09:19:00 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-103]:                                    pp_engine=<nanotron.parallel.pipeline_parallel.engine.AllForwardAllBackwardPipelineEngine object at 0x7f56a1c7c730>,
[default0]:07/06/2024 09:19:00 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-103]:                                    tp_mode=<TensorParallelLinearMode.REDUCE_SCATTER: 2>,
[default0]:07/06/2024 09:19:00 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-103]:                                    tp_linear_async_communication=False,
[default0]:07/06/2024 09:19:00 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-103]:                                    expert_parallel_size=1),
[default0]:07/06/2024 09:19:00 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-103]:        model=ModelArgs(model_config=LlamaConfig(bos_token_id=1,
[default0]:07/06/2024 09:19:00 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-103]:                                                 eos_token_id=2,
[default0]:07/06/2024 09:19:00 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-103]:                                                 hidden_act='silu',
[default0]:07/06/2024 09:19:00 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-103]:                                                 hidden_size=2048,
[default0]:07/06/2024 09:19:00 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-103]:                                                 initializer_range=0.02,
[default0]:07/06/2024 09:19:00 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-103]:                                                 intermediate_size=4096,
[default0]:07/06/2024 09:19:00 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-103]:                                                 is_llama_config=True,
[default0]:07/06/2024 09:19:00 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-103]:                                                 max_position_embeddings=4096,
[default0]:07/06/2024 09:19:00 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-103]:                                                 num_attention_heads=32,
[default0]:07/06/2024 09:19:00 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-103]:                                                 num_hidden_layers=24,
[default0]:07/06/2024 09:19:00 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-103]:                                                 num_key_value_heads=32,
[default0]:07/06/2024 09:19:00 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-103]:                                                 pad_token_id=None,
[default0]:07/06/2024 09:19:00 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-103]:                                                 pretraining_tp=1,
[default0]:07/06/2024 09:19:00 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-103]:                                                 rms_norm_eps=1e-05,
[default0]:07/06/2024 09:19:00 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-103]:                                                 rope_scaling=None,
[default0]:07/06/2024 09:19:00 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-103]:                                                 rope_theta=10000.0,
[default0]:07/06/2024 09:19:00 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-103]:                                                 tie_word_embeddings=True,
[default0]:07/06/2024 09:19:00 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-103]:                                                 use_cache=True,
[default0]:07/06/2024 09:19:00 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-103]:                                                 vocab_size=50260),
[default0]:07/06/2024 09:19:00 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-103]:                        init_method=RandomInit(std=0.025),
[default0]:07/06/2024 09:19:00 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-103]:                        dtype=torch.bfloat16,
[default0]:07/06/2024 09:19:00 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-103]:                        make_vocab_size_divisible_by=1,
[default0]:07/06/2024 09:19:00 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-103]:                        ddp_bucket_cap_mb=25),
[default0]:07/06/2024 09:19:00 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-103]:        tokenizer=TokenizerArgs(tokenizer_name_or_path='openai-community/gpt2',
[default0]:07/06/2024 09:19:00 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-103]:                                tokenizer_revision=None,
[default0]:07/06/2024 09:19:00 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-103]:                                tokenizer_max_length=None),
[default0]:07/06/2024 09:19:00 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-103]:        checkpoints=CheckpointsArgs(checkpoints_path=PosixPath('/dev/null'),
[default0]:07/06/2024 09:19:00 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-103]:                                    checkpoint_interval=100000,
[default0]:07/06/2024 09:19:00 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-103]:                                    save_initial_state=False,
[default0]:07/06/2024 09:19:00 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-103]:                                    resume_checkpoint_path=None,
[default0]:07/06/2024 09:19:00 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-103]:                                    checkpoints_path_is_shared_file_system=False),
[default0]:07/06/2024 09:19:00 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-103]:        logging=LoggingArgs(log_level='info',
[default0]:07/06/2024 09:19:00 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-103]:                            log_level_replica='info',
[default0]:07/06/2024 09:19:00 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-103]:                            iteration_step_info_interval=1),
[default0]:07/06/2024 09:19:00 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-103]:        tokens=TokensArgs(sequence_length=4096,
[default0]:07/06/2024 09:19:00 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-103]:                          train_steps=20,
[default0]:07/06/2024 09:19:00 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-103]:                          micro_batch_size=8,
[default0]:07/06/2024 09:19:00 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-103]:                          batch_accumulation_per_replica=128,
[default0]:07/06/2024 09:19:00 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-103]:                          val_check_interval=-1,
[default0]:07/06/2024 09:19:00 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-103]:                          limit_val_batches=0,
[default0]:07/06/2024 09:19:00 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-103]:                          limit_test_batches=0),
[default0]:07/06/2024 09:19:00 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-103]:        optimizer=OptimizerArgs(optimizer_factory=AdamWOptimizerArgs(adam_eps=1e-08,
[default0]:07/06/2024 09:19:00 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-103]:                                                                     adam_beta1=0.9,
[default0]:07/06/2024 09:19:00 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-103]:                                                                     adam_beta2=0.95,
[default0]:07/06/2024 09:19:00 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-103]:                                                                     torch_adam_is_fused=True,
[default0]:07/06/2024 09:19:00 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-103]:                                                                     name='adamW'),
[default0]:07/06/2024 09:19:00 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-103]:                                zero_stage=1,
[default0]:07/06/2024 09:19:00 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-103]:                                weight_decay=0.01,
[default0]:07/06/2024 09:19:00 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-103]:                                clip_grad=1.0,
[default0]:07/06/2024 09:19:00 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-103]:                                accumulate_grad_in_fp32=True,
[default0]:07/06/2024 09:19:00 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-103]:                                learning_rate_scheduler=LRSchedulerArgs(learning_rate=0.0001,
[default0]:07/06/2024 09:19:00 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-103]:                                                                        lr_warmup_steps=1,
[default0]:07/06/2024 09:19:00 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-103]:                                                                        lr_warmup_style='linear',
[default0]:07/06/2024 09:19:00 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-103]:                                                                        lr_decay_style='linear',
[default0]:07/06/2024 09:19:00 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-103]:                                                                        lr_decay_steps=19,
[default0]:07/06/2024 09:19:00 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-103]:                                                                        lr_decay_starting_step=None,
[default0]:07/06/2024 09:19:00 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-103]:                                                                        min_decay_lr=1e-05)),
[default0]:07/06/2024 09:19:00 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-103]:        data_stages=[DatasetStageArgs(name='Training Stage',
[default0]:07/06/2024 09:19:00 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-103]:                                      start_training_step=1,
[default0]:07/06/2024 09:19:00 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-103]:                                      data=DataArgs(dataset=PretrainDatasetsArgs(hf_dataset_or_datasets='roneneldan/TinyStories',
[default0]:07/06/2024 09:19:00 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-103]:                                                                                 hf_dataset_splits='train',
[default0]:07/06/2024 09:19:00 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-103]:                                                                                 hf_dataset_config_name=None,
[default0]:07/06/2024 09:19:00 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-103]:                                                                                 dataset_processing_num_proc_per_process=64,
[default0]:07/06/2024 09:19:00 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-103]:                                                                                 dataset_overwrite_cache=False,
[default0]:07/06/2024 09:19:00 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-103]:                                                                                 text_column_name='text'),
[default0]:07/06/2024 09:19:00 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-103]:                                                    seed=42,
[default0]:07/06/2024 09:19:00 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-103]:                                                    num_loading_workers=0))],
[default0]:07/06/2024 09:19:00 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-103]:        profiler=ProfilerArgs(profiler_export_path=PosixPath('/fsx/ferdinandmom/ferdinand-hf/bench_cluster/results/llama-1B/64_GPUS/dp-1_tp-4_pp-16_mbz-8')),
[default0]:07/06/2024 09:19:00 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-103]:        lighteval=None)
[default0]:07/06/2024 09:19:00 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-103]: Model Config:
[default0]:07/06/2024 09:19:00 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-103]: LlamaConfig(bos_token_id=1,
[default0]:07/06/2024 09:19:00 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-103]:             eos_token_id=2,
[default0]:07/06/2024 09:19:00 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-103]:             hidden_act='silu',
[default0]:07/06/2024 09:19:00 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-103]:             hidden_size=2048,
[default0]:07/06/2024 09:19:00 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-103]:             initializer_range=0.02,
[default0]:07/06/2024 09:19:00 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-103]:             intermediate_size=4096,
[default0]:07/06/2024 09:19:00 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-103]:             is_llama_config=True,
[default0]:07/06/2024 09:19:00 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-103]:             max_position_embeddings=4096,
[default0]:07/06/2024 09:19:00 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-103]:             num_attention_heads=32,
[default0]:07/06/2024 09:19:00 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-103]:             num_hidden_layers=24,
[default0]:07/06/2024 09:19:00 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-103]:             num_key_value_heads=32,
[default0]:07/06/2024 09:19:00 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-103]:             pad_token_id=None,
[default0]:07/06/2024 09:19:00 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-103]:             pretraining_tp=1,
[default0]:07/06/2024 09:19:00 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-103]:             rms_norm_eps=1e-05,
[default0]:07/06/2024 09:19:00 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-103]:             rope_scaling=None,
[default0]:07/06/2024 09:19:00 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-103]:             rope_theta=10000.0,
[default0]:07/06/2024 09:19:00 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-103]:             tie_word_embeddings=True,
[default0]:07/06/2024 09:19:00 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-103]:             use_cache=True,
[default0]:07/06/2024 09:19:00 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-103]:             vocab_size=50260)
[default0]:07/06/2024 09:19:00 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-103]: Building model..
[default0]:07/06/2024 09:19:00 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-103]: Setting PP block ranks...
[default0]:07/06/2024 09:19:18 [INFO|DP=0|PP=8|TP=0|ip-26-0-163-58]: Local number of parameters: 10.5M (20.01MiB)
[default0]:07/06/2024 09:19:18 [INFO|DP=0|PP=8|TP=0|ip-26-0-163-58]: [After model building] Memory usage: 21.02MiB. Peak allocated: 23.05MiB Peak reserved: 24.00MiB
[default0]:07/06/2024 09:19:18 [INFO|DP=0|PP=8|TP=0|ip-26-0-163-58]: No checkpoint path provided.
[default1]:07/06/2024 09:19:18 [INFO|DP=0|PP=8|TP=1|ip-26-0-163-58]: Local number of parameters: 10.5M (20.01MiB)
[default1]:07/06/2024 09:19:18 [INFO|DP=0|PP=8|TP=1|ip-26-0-163-58]: [After model building] Memory usage: 21.02MiB. Peak allocated: 23.05MiB Peak reserved: 24.00MiB
[default1]:07/06/2024 09:19:18 [INFO|DP=0|PP=8|TP=1|ip-26-0-163-58]: No checkpoint path provided.
[default1]:07/06/2024 09:19:18 [INFO|DP=0|PP=14|TP=1|ip-26-0-172-57]: Local number of parameters: 25.7M (49.09MiB)
[default1]:07/06/2024 09:19:18 [INFO|DP=0|PP=14|TP=1|ip-26-0-172-57]: [After model building] Memory usage: 50.01MiB. Peak allocated: 50.03MiB Peak reserved: 52.00MiB
[default1]:07/06/2024 09:19:18 [INFO|DP=0|PP=14|TP=1|ip-26-0-172-57]: No checkpoint path provided.
[default2]:07/06/2024 09:19:18 [INFO|DP=0|PP=14|TP=2|ip-26-0-172-57]: Local number of parameters: 25.7M (49.09MiB)
[default2]:07/06/2024 09:19:18 [INFO|DP=0|PP=14|TP=2|ip-26-0-172-57]: [After model building] Memory usage: 50.01MiB. Peak allocated: 50.03MiB Peak reserved: 52.00MiB
[default2]:07/06/2024 09:19:18 [INFO|DP=0|PP=14|TP=2|ip-26-0-172-57]: No checkpoint path provided.
[default4]:07/06/2024 09:19:18 [INFO|DP=0|PP=15|TP=0|ip-26-0-172-57]: Local number of parameters: 0 (0.00MiB)
[default4]:07/06/2024 09:19:18 [INFO|DP=0|PP=15|TP=0|ip-26-0-172-57]: [After model building] Memory usage: 0.01MiB. Peak allocated: 0.03MiB Peak reserved: 2.00MiB
[default4]:07/06/2024 09:19:18 [INFO|DP=0|PP=15|TP=0|ip-26-0-172-57]: No checkpoint path provided.
[default3]:07/06/2024 09:19:18 [INFO|DP=0|PP=8|TP=3|ip-26-0-163-58]: Local number of parameters: 10.5M (20.01MiB)
[default3]:07/06/2024 09:19:18 [INFO|DP=0|PP=8|TP=3|ip-26-0-163-58]: [After model building] Memory usage: 21.02MiB. Peak allocated: 23.05MiB Peak reserved: 24.00MiB
[default3]:07/06/2024 09:19:18 [INFO|DP=0|PP=8|TP=3|ip-26-0-163-58]: No checkpoint path provided.
[default4]:07/06/2024 09:19:18 [INFO|DP=0|PP=9|TP=0|ip-26-0-163-58]: Local number of parameters: 21M (40.02MiB)
[default4]:07/06/2024 09:19:18 [INFO|DP=0|PP=9|TP=0|ip-26-0-163-58]: [After model building] Memory usage: 42.03MiB. Peak allocated: 44.06MiB Peak reserved: 46.00MiB
[default4]:07/06/2024 09:19:18 [INFO|DP=0|PP=9|TP=0|ip-26-0-163-58]: No checkpoint path provided.
[default0]:07/06/2024 09:19:18 [INFO|DP=0|PP=4|TP=0|ip-26-0-163-158]: Local number of parameters: 21M (40.02MiB)
[default0]:07/06/2024 09:19:18 [INFO|DP=0|PP=4|TP=0|ip-26-0-163-158]: [After model building] Memory usage: 42.03MiB. Peak allocated: 44.06MiB Peak reserved: 46.00MiB
[default0]:07/06/2024 09:19:18 [INFO|DP=0|PP=4|TP=0|ip-26-0-163-158]: No checkpoint path provided.
[default6]:07/06/2024 09:19:18 [INFO|DP=0|PP=9|TP=2|ip-26-0-163-58]: Local number of parameters: 21M (40.02MiB)
[default6]:07/06/2024 09:19:18 [INFO|DP=0|PP=9|TP=2|ip-26-0-163-58]: [After model building] Memory usage: 42.03MiB. Peak allocated: 44.06MiB Peak reserved: 46.00MiB
[default6]:07/06/2024 09:19:18 [INFO|DP=0|PP=9|TP=2|ip-26-0-163-58]: No checkpoint path provided.
[default7]:07/06/2024 09:19:18 [INFO|DP=0|PP=9|TP=3|ip-26-0-163-58]: Local number of parameters: 21M (40.02MiB)
[default7]:07/06/2024 09:19:18 [INFO|DP=0|PP=9|TP=3|ip-26-0-163-58]: [After model building] Memory usage: 42.03MiB. Peak allocated: 44.06MiB Peak reserved: 46.00MiB
[default7]:07/06/2024 09:19:18 [INFO|DP=0|PP=9|TP=3|ip-26-0-163-58]: No checkpoint path provided.
[default2]:07/06/2024 09:19:18 [INFO|DP=0|PP=8|TP=2|ip-26-0-163-58]: Local number of parameters: 10.5M (20.01MiB)
[default2]:07/06/2024 09:19:18 [INFO|DP=0|PP=8|TP=2|ip-26-0-163-58]: [After model building] Memory usage: 21.02MiB. Peak allocated: 23.05MiB Peak reserved: 24.00MiB
[default5]:07/06/2024 09:19:18 [INFO|DP=0|PP=9|TP=1|ip-26-0-163-58]: Local number of parameters: 21M (40.02MiB)
[default5]:07/06/2024 09:19:18 [INFO|DP=0|PP=9|TP=1|ip-26-0-163-58]: [After model building] Memory usage: 42.03MiB. Peak allocated: 44.06MiB Peak reserved: 46.00MiB
[default2]:07/06/2024 09:19:18 [INFO|DP=0|PP=8|TP=2|ip-26-0-163-58]: No checkpoint path provided.
[default5]:07/06/2024 09:19:18 [INFO|DP=0|PP=9|TP=1|ip-26-0-163-58]: No checkpoint path provided.
[default1]:07/06/2024 09:19:18 [INFO|DP=0|PP=4|TP=1|ip-26-0-163-158]: Local number of parameters: 21M (40.02MiB)
[default1]:07/06/2024 09:19:18 [INFO|DP=0|PP=4|TP=1|ip-26-0-163-158]: [After model building] Memory usage: 42.03MiB. Peak allocated: 44.06MiB Peak reserved: 46.00MiB
[default1]:07/06/2024 09:19:18 [INFO|DP=0|PP=4|TP=1|ip-26-0-163-158]: No checkpoint path provided.
[default5]:07/06/2024 09:19:18 [INFO|DP=0|PP=3|TP=1|ip-26-0-160-192]: Local number of parameters: 21M (40.02MiB)
[default5]:07/06/2024 09:19:18 [INFO|DP=0|PP=3|TP=1|ip-26-0-160-192]: [After model building] Memory usage: 42.03MiB. Peak allocated: 44.06MiB Peak reserved: 46.00MiB
[default5]:07/06/2024 09:19:18 [INFO|DP=0|PP=3|TP=1|ip-26-0-160-192]: No checkpoint path provided.
[default4]:07/06/2024 09:19:18 [INFO|DP=0|PP=5|TP=0|ip-26-0-163-158]: Local number of parameters: 10.5M (20.01MiB)
[default4]:07/06/2024 09:19:18 [INFO|DP=0|PP=5|TP=0|ip-26-0-163-158]: [After model building] Memory usage: 21.02MiB. Peak allocated: 23.05MiB Peak reserved: 24.00MiB
[default2]:07/06/2024 09:19:18 [INFO|DP=0|PP=4|TP=2|ip-26-0-163-158]: Local number of parameters: 21M (40.02MiB)
[default2]:07/06/2024 09:19:18 [INFO|DP=0|PP=4|TP=2|ip-26-0-163-158]: [After model building] Memory usage: 42.03MiB. Peak allocated: 44.06MiB Peak reserved: 46.00MiB
[default2]:07/06/2024 09:19:18 [INFO|DP=0|PP=4|TP=2|ip-26-0-163-158]: No checkpoint path provided.
[default4]:07/06/2024 09:19:18 [INFO|DP=0|PP=5|TP=0|ip-26-0-163-158]: No checkpoint path provided.
[default0]:07/06/2024 09:19:18 [INFO|DP=0|PP=2|TP=0|ip-26-0-160-192]: Local number of parameters: 10.5M (20.01MiB)
[default0]:07/06/2024 09:19:18 [INFO|DP=0|PP=2|TP=0|ip-26-0-160-192]: [After model building] Memory usage: 21.02MiB. Peak allocated: 23.05MiB Peak reserved: 24.00MiB
[default6]:07/06/2024 09:19:18 [INFO|DP=0|PP=13|TP=2|ip-26-0-172-116]: Local number of parameters: 21M (40.02MiB)
[default6]:07/06/2024 09:19:18 [INFO|DP=0|PP=13|TP=2|ip-26-0-172-116]: [After model building] Memory usage: 42.03MiB. Peak allocated: 44.06MiB Peak reserved: 46.00MiB
[default6]:07/06/2024 09:19:18 [INFO|DP=0|PP=13|TP=2|ip-26-0-172-116]: No checkpoint path provided.
[default0]:07/06/2024 09:19:18 [INFO|DP=0|PP=2|TP=0|ip-26-0-160-192]: No checkpoint path provided.
[default2]:07/06/2024 09:19:18 [INFO|DP=0|PP=2|TP=2|ip-26-0-160-192]: Local number of parameters: 10.5M (20.01MiB)
[default2]:07/06/2024 09:19:18 [INFO|DP=0|PP=2|TP=2|ip-26-0-160-192]: [After model building] Memory usage: 21.02MiB. Peak allocated: 23.05MiB Peak reserved: 24.00MiB
[default1]:07/06/2024 09:19:18 [INFO|DP=0|PP=2|TP=1|ip-26-0-160-192]: Local number of parameters: 10.5M (20.01MiB)
[default3]:07/06/2024 09:19:18 [INFO|DP=0|PP=2|TP=3|ip-26-0-160-192]: Local number of parameters: 10.5M (20.01MiB)
[default2]:07/06/2024 09:19:18 [INFO|DP=0|PP=2|TP=2|ip-26-0-160-192]: No checkpoint path provided.
[default1]:07/06/2024 09:19:18 [INFO|DP=0|PP=2|TP=1|ip-26-0-160-192]: [After model building] Memory usage: 21.02MiB. Peak allocated: 23.05MiB Peak reserved: 24.00MiB
[default1]:07/06/2024 09:19:18 [INFO|DP=0|PP=2|TP=1|ip-26-0-160-192]: No checkpoint path provided.
[default3]:07/06/2024 09:19:18 [INFO|DP=0|PP=2|TP=3|ip-26-0-160-192]: [After model building] Memory usage: 21.02MiB. Peak allocated: 23.05MiB Peak reserved: 24.00MiB
[default3]:07/06/2024 09:19:18 [INFO|DP=0|PP=2|TP=3|ip-26-0-160-192]: No checkpoint path provided.
[default3]:07/06/2024 09:19:18 [INFO|DP=0|PP=4|TP=3|ip-26-0-163-158]: Local number of parameters: 21M (40.02MiB)
[default3]:07/06/2024 09:19:18 [INFO|DP=0|PP=4|TP=3|ip-26-0-163-158]: [After model building] Memory usage: 42.03MiB. Peak allocated: 44.06MiB Peak reserved: 46.00MiB
[default7]:07/06/2024 09:19:18 [INFO|DP=0|PP=15|TP=3|ip-26-0-172-57]: Local number of parameters: 0 (0.00MiB)
[default7]:07/06/2024 09:19:18 [INFO|DP=0|PP=15|TP=3|ip-26-0-172-57]: [After model building] Memory usage: 0.01MiB. Peak allocated: 0.03MiB Peak reserved: 2.00MiB
[default7]:07/06/2024 09:19:18 [INFO|DP=0|PP=15|TP=3|ip-26-0-172-57]: No checkpoint path provided.
[default3]:07/06/2024 09:19:18 [INFO|DP=0|PP=4|TP=3|ip-26-0-163-158]: No checkpoint path provided.
[default6]:07/06/2024 09:19:18 [INFO|DP=0|PP=5|TP=2|ip-26-0-163-158]: Local number of parameters: 10.5M (20.01MiB)
[default7]:07/06/2024 09:19:18 [INFO|DP=0|PP=5|TP=3|ip-26-0-163-158]: Local number of parameters: 10.5M (20.01MiB)
[default6]:07/06/2024 09:19:18 [INFO|DP=0|PP=5|TP=2|ip-26-0-163-158]: [After model building] Memory usage: 21.02MiB. Peak allocated: 23.05MiB Peak reserved: 24.00MiB
[default6]:07/06/2024 09:19:18 [INFO|DP=0|PP=5|TP=2|ip-26-0-163-158]: No checkpoint path provided.
[default7]:07/06/2024 09:19:18 [INFO|DP=0|PP=5|TP=3|ip-26-0-163-158]: [After model building] Memory usage: 21.02MiB. Peak allocated: 23.05MiB Peak reserved: 24.00MiB
[default7]:07/06/2024 09:19:18 [INFO|DP=0|PP=5|TP=3|ip-26-0-163-158]: No checkpoint path provided.
[default2]:07/06/2024 09:19:18 [INFO|DP=0|PP=10|TP=2|ip-26-0-171-88]: Local number of parameters: 21M (40.02MiB)
[default2]:07/06/2024 09:19:18 [INFO|DP=0|PP=10|TP=2|ip-26-0-171-88]: [After model building] Memory usage: 42.03MiB. Peak allocated: 44.06MiB Peak reserved: 46.00MiB
[default2]:07/06/2024 09:19:18 [INFO|DP=0|PP=10|TP=2|ip-26-0-171-88]: No checkpoint path provided.
[default5]:07/06/2024 09:19:18 [INFO|DP=0|PP=11|TP=1|ip-26-0-171-88]: Local number of parameters: 10.5M (20.01MiB)
[default5]:07/06/2024 09:19:18 [INFO|DP=0|PP=11|TP=1|ip-26-0-171-88]: [After model building] Memory usage: 21.02MiB. Peak allocated: 23.05MiB Peak reserved: 24.00MiB
[default5]:07/06/2024 09:19:18 [INFO|DP=0|PP=11|TP=1|ip-26-0-171-88]: No checkpoint path provided.
[default4]:07/06/2024 09:19:18 [INFO|DP=0|PP=3|TP=0|ip-26-0-160-192]: Local number of parameters: 21M (40.02MiB)
[default4]:07/06/2024 09:19:18 [INFO|DP=0|PP=3|TP=0|ip-26-0-160-192]: [After model building] Memory usage: 42.03MiB. Peak allocated: 44.06MiB Peak reserved: 46.00MiB
[default4]:07/06/2024 09:19:18 [INFO|DP=0|PP=3|TP=0|ip-26-0-160-192]: No checkpoint path provided.
[default7]:07/06/2024 09:19:18 [INFO|DP=0|PP=3|TP=3|ip-26-0-160-192]: Local number of parameters: 21M (40.02MiB)
[default7]:07/06/2024 09:19:18 [INFO|DP=0|PP=3|TP=3|ip-26-0-160-192]: [After model building] Memory usage: 42.03MiB. Peak allocated: 44.06MiB Peak reserved: 46.00MiB
[default7]:07/06/2024 09:19:18 [INFO|DP=0|PP=3|TP=3|ip-26-0-160-192]: No checkpoint path provided.
[default0]:07/06/2024 09:19:18 [INFO|DP=0|PP=14|TP=0|ip-26-0-172-57]: Local number of parameters: 25.7M (49.09MiB)
[default0]:07/06/2024 09:19:18 [INFO|DP=0|PP=14|TP=0|ip-26-0-172-57]: [After model building] Memory usage: 50.01MiB. Peak allocated: 50.03MiB Peak reserved: 52.00MiB
[default0]:07/06/2024 09:19:18 [INFO|DP=0|PP=14|TP=0|ip-26-0-172-57]: No checkpoint path provided.
[default1]:07/06/2024 09:19:18 [INFO|DP=0|PP=12|TP=1|ip-26-0-172-116]: Local number of parameters: 21M (40.02MiB)
[default2]:07/06/2024 09:19:18 [INFO|DP=0|PP=12|TP=2|ip-26-0-172-116]: Local number of parameters: 21M (40.02MiB)
[default2]:07/06/2024 09:19:18 [INFO|DP=0|PP=12|TP=2|ip-26-0-172-116]: [After model building] Memory usage: 42.03MiB. Peak allocated: 44.06MiB Peak reserved: 46.00MiB
[default2]:07/06/2024 09:19:18 [INFO|DP=0|PP=12|TP=2|ip-26-0-172-116]: No checkpoint path provided.
[default1]:07/06/2024 09:19:18 [INFO|DP=0|PP=12|TP=1|ip-26-0-172-116]: [After model building] Memory usage: 42.03MiB. Peak allocated: 44.06MiB Peak reserved: 46.00MiB
[default1]:07/06/2024 09:19:18 [INFO|DP=0|PP=12|TP=1|ip-26-0-172-116]: No checkpoint path provided.
[default4]:07/06/2024 09:19:18 [INFO|DP=0|PP=7|TP=0|ip-26-0-163-220]: Local number of parameters: 21M (40.02MiB)
[default4]:07/06/2024 09:19:18 [INFO|DP=0|PP=7|TP=0|ip-26-0-163-220]: [After model building] Memory usage: 42.03MiB. Peak allocated: 44.06MiB Peak reserved: 46.00MiB
[default4]:07/06/2024 09:19:18 [INFO|DP=0|PP=7|TP=0|ip-26-0-163-220]: No checkpoint path provided.
[default4]:07/06/2024 09:19:18 [INFO|DP=0|PP=11|TP=0|ip-26-0-171-88]: Local number of parameters: 10.5M (20.01MiB)
[default4]:07/06/2024 09:19:18 [INFO|DP=0|PP=11|TP=0|ip-26-0-171-88]: [After model building] Memory usage: 21.02MiB. Peak allocated: 23.05MiB Peak reserved: 24.00MiB
[default4]:07/06/2024 09:19:18 [INFO|DP=0|PP=11|TP=0|ip-26-0-171-88]: No checkpoint path provided.
[default3]:07/06/2024 09:19:18 [INFO|DP=0|PP=14|TP=3|ip-26-0-172-57]: Local number of parameters: 25.7M (49.09MiB)
[default3]:07/06/2024 09:19:18 [INFO|DP=0|PP=6|TP=3|ip-26-0-163-220]: Local number of parameters: 21M (40.02MiB)
[default6]:07/06/2024 09:19:18 [INFO|DP=0|PP=11|TP=2|ip-26-0-171-88]: Local number of parameters: 10.5M (20.01MiB)
[default3]:07/06/2024 09:19:18 [INFO|DP=0|PP=14|TP=3|ip-26-0-172-57]: [After model building] Memory usage: 50.01MiB. Peak allocated: 50.03MiB Peak reserved: 52.00MiB
[default3]:07/06/2024 09:19:18 [INFO|DP=0|PP=14|TP=3|ip-26-0-172-57]: No checkpoint path provided.
[default5]:07/06/2024 09:19:18 [INFO|DP=0|PP=15|TP=1|ip-26-0-172-57]: Local number of parameters: 0 (0.00MiB)
[default5]:07/06/2024 09:19:18 [INFO|DP=0|PP=15|TP=1|ip-26-0-172-57]: [After model building] Memory usage: 0.01MiB. Peak allocated: 0.03MiB Peak reserved: 2.00MiB
[default5]:07/06/2024 09:19:18 [INFO|DP=0|PP=15|TP=1|ip-26-0-172-57]: No checkpoint path provided.
[default3]:07/06/2024 09:19:18 [INFO|DP=0|PP=6|TP=3|ip-26-0-163-220]: [After model building] Memory usage: 42.03MiB. Peak allocated: 44.06MiB Peak reserved: 46.00MiB
[default3]:07/06/2024 09:19:18 [INFO|DP=0|PP=6|TP=3|ip-26-0-163-220]: No checkpoint path provided.
[default7]:07/06/2024 09:19:18 [INFO|DP=0|PP=11|TP=3|ip-26-0-171-88]: Local number of parameters: 10.5M (20.01MiB)
[default7]:07/06/2024 09:19:18 [INFO|DP=0|PP=11|TP=3|ip-26-0-171-88]: [After model building] Memory usage: 21.02MiB. Peak allocated: 23.05MiB Peak reserved: 24.00MiB
[default0]:07/06/2024 09:19:18 [INFO|DP=0|PP=6|TP=0|ip-26-0-163-220]: Local number of parameters: 21M (40.02MiB)
[default0]:07/06/2024 09:19:18 [INFO|DP=0|PP=6|TP=0|ip-26-0-163-220]: [After model building] Memory usage: 42.03MiB. Peak allocated: 44.06MiB Peak reserved: 46.00MiB
[default0]:07/06/2024 09:19:18 [INFO|DP=0|PP=6|TP=0|ip-26-0-163-220]: No checkpoint path provided.
[default6]:07/06/2024 09:19:18 [INFO|DP=0|PP=11|TP=2|ip-26-0-171-88]: [After model building] Memory usage: 21.02MiB. Peak allocated: 23.05MiB Peak reserved: 24.00MiB
[default6]:07/06/2024 09:19:18 [INFO|DP=0|PP=11|TP=2|ip-26-0-171-88]: No checkpoint path provided.
[default7]:07/06/2024 09:19:18 [INFO|DP=0|PP=11|TP=3|ip-26-0-171-88]: No checkpoint path provided.
[default6]:07/06/2024 09:19:18 [INFO|DP=0|PP=15|TP=2|ip-26-0-172-57]: Local number of parameters: 0 (0.00MiB)
[default6]:07/06/2024 09:19:18 [INFO|DP=0|PP=15|TP=2|ip-26-0-172-57]: [After model building] Memory usage: 0.01MiB. Peak allocated: 0.03MiB Peak reserved: 2.00MiB
[default6]:07/06/2024 09:19:18 [INFO|DP=0|PP=3|TP=2|ip-26-0-160-192]: Local number of parameters: 21M (40.02MiB)
[default6]:07/06/2024 09:19:18 [INFO|DP=0|PP=15|TP=2|ip-26-0-172-57]: No checkpoint path provided.
[default6]:07/06/2024 09:19:18 [INFO|DP=0|PP=3|TP=2|ip-26-0-160-192]: [After model building] Memory usage: 42.03MiB. Peak allocated: 44.06MiB Peak reserved: 46.00MiB
[default6]:07/06/2024 09:19:18 [INFO|DP=0|PP=3|TP=2|ip-26-0-160-192]: No checkpoint path provided.
[default0]:07/06/2024 09:19:18 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-103]: Total number of parameters: 1.21G (2313.42MiB)
[default6]:07/06/2024 09:19:18 [INFO|DP=0|PP=1|TP=2|ip-26-0-160-103]: Local number of parameters: 21M (40.02MiB)
[default0]:07/06/2024 09:19:18 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-103]: Local number of parameters: 46.7M (89.10MiB)
[default0]:07/06/2024 09:19:18 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-103]: [After model building] Memory usage: 92.03MiB. Peak allocated: 94.06MiB Peak reserved: 96.00MiB
[default6]:07/06/2024 09:19:18 [INFO|DP=0|PP=1|TP=2|ip-26-0-160-103]: [After model building] Memory usage: 42.03MiB. Peak allocated: 44.06MiB Peak reserved: 46.00MiB
[default6]:07/06/2024 09:19:18 [INFO|DP=0|PP=1|TP=2|ip-26-0-160-103]: No checkpoint path provided.
[default7]:07/06/2024 09:19:18 [INFO|DP=0|PP=13|TP=3|ip-26-0-172-116]: Local number of parameters: 21M (40.02MiB)
[default7]:07/06/2024 09:19:18 [INFO|DP=0|PP=13|TP=3|ip-26-0-172-116]: [After model building] Memory usage: 42.03MiB. Peak allocated: 44.06MiB Peak reserved: 46.00MiB
[default7]:07/06/2024 09:19:18 [INFO|DP=0|PP=13|TP=3|ip-26-0-172-116]: No checkpoint path provided.
[default0]:07/06/2024 09:19:18 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-103]: No checkpoint path provided.
[default7]:07/06/2024 09:19:18 [INFO|DP=0|PP=1|TP=3|ip-26-0-160-103]: Local number of parameters: 21M (40.02MiB)
[default7]:07/06/2024 09:19:18 [INFO|DP=0|PP=1|TP=3|ip-26-0-160-103]: [After model building] Memory usage: 42.03MiB. Peak allocated: 44.06MiB Peak reserved: 46.00MiB
[default7]:07/06/2024 09:19:18 [INFO|DP=0|PP=1|TP=3|ip-26-0-160-103]: No checkpoint path provided.
[default0]:07/06/2024 09:19:18 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-103]: Parametrizing model parameters using StandardParametrizator
[default1]:07/06/2024 09:19:18 [INFO|DP=0|PP=6|TP=1|ip-26-0-163-220]: Local number of parameters: 21M (40.02MiB)
[default1]:07/06/2024 09:19:18 [INFO|DP=0|PP=6|TP=1|ip-26-0-163-220]: [After model building] Memory usage: 42.03MiB. Peak allocated: 44.06MiB Peak reserved: 46.00MiB
[default1]:07/06/2024 09:19:18 [INFO|DP=0|PP=6|TP=1|ip-26-0-163-220]: No checkpoint path provided.
[default5]:07/06/2024 09:19:18 [INFO|DP=0|PP=5|TP=1|ip-26-0-163-158]: Local number of parameters: 10.5M (20.01MiB)
[default5]:07/06/2024 09:19:18 [INFO|DP=0|PP=5|TP=1|ip-26-0-163-158]: [After model building] Memory usage: 21.02MiB. Peak allocated: 23.05MiB Peak reserved: 24.00MiB
[default5]:07/06/2024 09:19:18 [INFO|DP=0|PP=1|TP=1|ip-26-0-160-103]: Local number of parameters: 21M (40.02MiB)
[default5]:07/06/2024 09:19:18 [INFO|DP=0|PP=1|TP=1|ip-26-0-160-103]: [After model building] Memory usage: 42.03MiB. Peak allocated: 44.06MiB Peak reserved: 46.00MiB
[default5]:07/06/2024 09:19:18 [INFO|DP=0|PP=1|TP=1|ip-26-0-160-103]: No checkpoint path provided.
[default5]:07/06/2024 09:19:18 [INFO|DP=0|PP=5|TP=1|ip-26-0-163-158]: No checkpoint path provided.
[default2]:07/06/2024 09:19:18 [INFO|DP=0|PP=6|TP=2|ip-26-0-163-220]: Local number of parameters: 21M (40.02MiB)
[default2]:07/06/2024 09:19:18 [INFO|DP=0|PP=6|TP=2|ip-26-0-163-220]: [After model building] Memory usage: 42.03MiB. Peak allocated: 44.06MiB Peak reserved: 46.00MiB
[default7]:07/06/2024 09:19:18 [INFO|DP=0|PP=7|TP=3|ip-26-0-163-220]: Local number of parameters: 21M (40.02MiB)
[default2]:07/06/2024 09:19:18 [INFO|DP=0|PP=0|TP=2|ip-26-0-160-103]: Local number of parameters: 46.7M (89.10MiB)
[default2]:07/06/2024 09:19:18 [INFO|DP=0|PP=0|TP=2|ip-26-0-160-103]: [After model building] Memory usage: 92.03MiB. Peak allocated: 94.06MiB Peak reserved: 96.00MiB
[default3]:07/06/2024 09:19:18 [INFO|DP=0|PP=12|TP=3|ip-26-0-172-116]: Local number of parameters: 21M (40.02MiB)
[default3]:07/06/2024 09:19:18 [INFO|DP=0|PP=12|TP=3|ip-26-0-172-116]: [After model building] Memory usage: 42.03MiB. Peak allocated: 44.06MiB Peak reserved: 46.00MiB
[default3]:07/06/2024 09:19:18 [INFO|DP=0|PP=12|TP=3|ip-26-0-172-116]: No checkpoint path provided.
[default7]:07/06/2024 09:19:18 [INFO|DP=0|PP=7|TP=3|ip-26-0-163-220]: [After model building] Memory usage: 42.03MiB. Peak allocated: 44.06MiB Peak reserved: 46.00MiB
[default7]:07/06/2024 09:19:18 [INFO|DP=0|PP=7|TP=3|ip-26-0-163-220]: No checkpoint path provided.
[default2]:07/06/2024 09:19:18 [INFO|DP=0|PP=0|TP=2|ip-26-0-160-103]: No checkpoint path provided.
[default2]:07/06/2024 09:19:18 [INFO|DP=0|PP=6|TP=2|ip-26-0-163-220]: No checkpoint path provided.
[default3]:07/06/2024 09:19:18 [INFO|DP=0|PP=0|TP=3|ip-26-0-160-103]: Local number of parameters: 46.7M (89.10MiB)
[default5]:07/06/2024 09:19:18 [INFO|DP=0|PP=7|TP=1|ip-26-0-163-220]: Local number of parameters: 21M (40.02MiB)
[default5]:07/06/2024 09:19:18 [INFO|DP=0|PP=7|TP=1|ip-26-0-163-220]: [After model building] Memory usage: 42.03MiB. Peak allocated: 44.06MiB Peak reserved: 46.00MiB
[default3]:07/06/2024 09:19:18 [INFO|DP=0|PP=0|TP=3|ip-26-0-160-103]: [After model building] Memory usage: 92.03MiB. Peak allocated: 94.06MiB Peak reserved: 96.00MiB
[default3]:07/06/2024 09:19:18 [INFO|DP=0|PP=0|TP=3|ip-26-0-160-103]: No checkpoint path provided.
[default5]:07/06/2024 09:19:18 [INFO|DP=0|PP=7|TP=1|ip-26-0-163-220]: No checkpoint path provided.
[default4]:07/06/2024 09:19:18 [INFO|DP=0|PP=1|TP=0|ip-26-0-160-103]: Local number of parameters: 21M (40.02MiB)
[default4]:07/06/2024 09:19:18 [INFO|DP=0|PP=1|TP=0|ip-26-0-160-103]: [After model building] Memory usage: 42.03MiB. Peak allocated: 44.06MiB Peak reserved: 46.00MiB
[default4]:07/06/2024 09:19:18 [INFO|DP=0|PP=1|TP=0|ip-26-0-160-103]: No checkpoint path provided.
[default6]:07/06/2024 09:19:18 [INFO|DP=0|PP=7|TP=2|ip-26-0-163-220]: Local number of parameters: 21M (40.02MiB)
[default6]:07/06/2024 09:19:18 [INFO|DP=0|PP=7|TP=2|ip-26-0-163-220]: [After model building] Memory usage: 42.03MiB. Peak allocated: 44.06MiB Peak reserved: 46.00MiB
[default6]:07/06/2024 09:19:18 [INFO|DP=0|PP=7|TP=2|ip-26-0-163-220]: No checkpoint path provided.
[default1]:07/06/2024 09:19:18 [INFO|DP=0|PP=10|TP=1|ip-26-0-171-88]: Local number of parameters: 21M (40.02MiB)
[default1]:07/06/2024 09:19:18 [INFO|DP=0|PP=10|TP=1|ip-26-0-171-88]: [After model building] Memory usage: 42.03MiB. Peak allocated: 44.06MiB Peak reserved: 46.00MiB
[default1]:07/06/2024 09:19:18 [INFO|DP=0|PP=10|TP=1|ip-26-0-171-88]: No checkpoint path provided.
[default3]:07/06/2024 09:19:18 [INFO|DP=0|PP=10|TP=3|ip-26-0-171-88]: Local number of parameters: 21M (40.02MiB)
[default3]:07/06/2024 09:19:18 [INFO|DP=0|PP=10|TP=3|ip-26-0-171-88]: [After model building] Memory usage: 42.03MiB. Peak allocated: 44.06MiB Peak reserved: 46.00MiB
[default3]:07/06/2024 09:19:18 [INFO|DP=0|PP=10|TP=3|ip-26-0-171-88]: No checkpoint path provided.
[default0]:07/06/2024 09:19:18 [INFO|DP=0|PP=10|TP=0|ip-26-0-171-88]: Local number of parameters: 21M (40.02MiB)
[default0]:07/06/2024 09:19:18 [INFO|DP=0|PP=10|TP=0|ip-26-0-171-88]: [After model building] Memory usage: 42.03MiB. Peak allocated: 44.06MiB Peak reserved: 46.00MiB
[default0]:07/06/2024 09:19:18 [INFO|DP=0|PP=10|TP=0|ip-26-0-171-88]: No checkpoint path provided.
[default1]:07/06/2024 09:19:18 [INFO|DP=0|PP=0|TP=1|ip-26-0-160-103]: Local number of parameters: 46.7M (89.10MiB)
[default1]:07/06/2024 09:19:18 [INFO|DP=0|PP=0|TP=1|ip-26-0-160-103]: [After model building] Memory usage: 92.03MiB. Peak allocated: 94.06MiB Peak reserved: 96.00MiB
[default1]:07/06/2024 09:19:18 [INFO|DP=0|PP=0|TP=1|ip-26-0-160-103]: No checkpoint path provided.
[default4]:07/06/2024 09:19:18 [INFO|DP=0|PP=13|TP=0|ip-26-0-172-116]: Local number of parameters: 21M (40.02MiB)
[default4]:07/06/2024 09:19:18 [INFO|DP=0|PP=13|TP=0|ip-26-0-172-116]: [After model building] Memory usage: 42.03MiB. Peak allocated: 44.06MiB Peak reserved: 46.00MiB
[default4]:07/06/2024 09:19:18 [INFO|DP=0|PP=13|TP=0|ip-26-0-172-116]: No checkpoint path provided.
[default0]:07/06/2024 09:19:18 [INFO|DP=0|PP=12|TP=0|ip-26-0-172-116]: Local number of parameters: 21M (40.02MiB)
[default0]:07/06/2024 09:19:18 [INFO|DP=0|PP=12|TP=0|ip-26-0-172-116]: [After model building] Memory usage: 42.03MiB. Peak allocated: 44.06MiB Peak reserved: 46.00MiB
[default0]:07/06/2024 09:19:18 [INFO|DP=0|PP=12|TP=0|ip-26-0-172-116]: No checkpoint path provided.
[default5]:07/06/2024 09:19:18 [INFO|DP=0|PP=13|TP=1|ip-26-0-172-116]: Local number of parameters: 21M (40.02MiB)
[default5]:07/06/2024 09:19:18 [INFO|DP=0|PP=13|TP=1|ip-26-0-172-116]: [After model building] Memory usage: 42.03MiB. Peak allocated: 44.06MiB Peak reserved: 46.00MiB
[default5]:07/06/2024 09:19:18 [INFO|DP=0|PP=13|TP=1|ip-26-0-172-116]: No checkpoint path provided.
[default0]:07/06/2024 09:19:19 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-103]: [Optimizer Building] Using LearningRateForSP as learning rate
[default0]:07/06/2024 09:19:19 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-103]: [ZeRO sharding] Size of optimizer params per rank:
[default0]:07/06/2024 09:19:19 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-103]: [ZeRO sharding] DP Rank 0 has 46.7M out of 46.7M (100.00%) params' optimizer states
[default0]:07/06/2024 09:19:20 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-103]: [Training Plan] Stage Training Stage has 19 remaining training steps and has consumed 0 samples
[default0]:07/06/2024 09:19:20 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-103]: Using `datasets` library
[default0]:07/06/2024 09:19:20 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-103]: Loading tokenizer from openai-community/gpt2 and transformers/hf_hub versions ('4.41.2', '0.23.4')
[default0]:07/06/2024 09:19:21 [WARNING|DP=0|PP=0|TP=0|ip-26-0-160-103]: Repo card metadata block was not found. Setting CardData to empty.
[default0]:Repo card metadata block was not found. Setting CardData to empty.
[default0]:07/06/2024 09:19:23 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-103]: [Training Plan] There are 1 training stages 
[default0]:07/06/2024 09:19:23 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-103]: [Stage Training Stage] start from step 1 
[default0]:07/06/2024 09:19:23 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-103]: 
[default0]:07/06/2024 09:19:23 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-103]: [Start training] datetime: 2024-07-06 09:19:23.368071 | mbs: 8 | grad_accum: 128 | global_batch_size: 1024 | sequence_length: 4096 | train_steps: 20 | start_iteration_step: 0 | consumed_train_samples: 0
[default0]:Repo card metadata block was not found. Setting CardData to empty.
[default0]:07/06/2024 09:19:23 [WARNING|DP=0|PP=8|TP=0|ip-26-0-163-58]: Repo card metadata block was not found. Setting CardData to empty.
[default1]:07/06/2024 09:19:23 [WARNING|DP=0|PP=8|TP=1|ip-26-0-163-58]: 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.
[default5]:Repo card metadata block was not found. Setting CardData to empty.
[default1]:07/06/2024 09:19:23 [WARNING|DP=0|PP=14|TP=1|ip-26-0-172-57]: Repo card metadata block was not found. Setting CardData to empty.
[default2]:07/06/2024 09:19:23 [WARNING|DP=0|PP=14|TP=2|ip-26-0-172-57]: Repo card metadata block was not found. Setting CardData to empty.
[default4]:07/06/2024 09:19:23 [WARNING|DP=0|PP=15|TP=0|ip-26-0-172-57]: Repo card metadata block was not found. Setting CardData to empty.
[default7]:Repo card metadata block was not found. Setting CardData to empty.
[default4]:07/06/2024 09:19:23 [WARNING|DP=0|PP=9|TP=0|ip-26-0-163-58]: Repo card metadata block was not found. Setting CardData to empty.
[default0]:07/06/2024 09:19:23 [WARNING|DP=0|PP=4|TP=0|ip-26-0-163-158]: Repo card metadata block was not found. Setting CardData to empty.
[default3]:07/06/2024 09:19:23 [WARNING|DP=0|PP=8|TP=3|ip-26-0-163-58]: Repo card metadata block was not found. Setting CardData to empty.
[default0]:Repo card metadata block was not found. Setting CardData to empty.
[default6]:07/06/2024 09:19:23 [WARNING|DP=0|PP=9|TP=2|ip-26-0-163-58]: Repo card metadata block was not found. Setting CardData to empty.
[default5]:Repo card metadata block was not found. Setting CardData to empty.
[default7]:07/06/2024 09:19:23 [WARNING|DP=0|PP=9|TP=3|ip-26-0-163-58]: Repo card metadata block was not found. Setting CardData to empty.
[default5]:07/06/2024 09:19:23 [WARNING|DP=0|PP=9|TP=1|ip-26-0-163-58]: Repo card metadata block was not found. Setting CardData to empty.
[default2]: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]:07/06/2024 09:19:23 [WARNING|DP=0|PP=8|TP=2|ip-26-0-163-58]: Repo card metadata block was not found. Setting CardData to empty.
[default3]:Repo card metadata block was not found. Setting CardData to empty.
[default1]:07/06/2024 09:19:23 [WARNING|DP=0|PP=4|TP=1|ip-26-0-163-158]: 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.
[default1]:Repo card metadata block was not found. Setting CardData to empty.
[default2]:Repo card metadata block was not found. Setting CardData to empty.
[default5]:07/06/2024 09:19:23 [WARNING|DP=0|PP=3|TP=1|ip-26-0-160-192]: Repo card metadata block was not found. Setting CardData to empty.
[default6]:Repo card metadata block was not found. Setting CardData to empty.
[default2]:07/06/2024 09:19:23 [WARNING|DP=0|PP=4|TP=2|ip-26-0-163-158]: Repo card metadata block was not found. Setting CardData to empty.
[default6]:07/06/2024 09:19:23 [WARNING|DP=0|PP=13|TP=2|ip-26-0-172-116]: Repo card metadata block was not found. Setting CardData to empty.
[default0]:07/06/2024 09:19:23 [WARNING|DP=0|PP=2|TP=0|ip-26-0-160-192]: Repo card metadata block was not found. Setting CardData to empty.
[default2]:07/06/2024 09:19:23 [WARNING|DP=0|PP=2|TP=2|ip-26-0-160-192]: Repo card metadata block was not found. Setting CardData to empty.
[default1]:07/06/2024 09:19:23 [WARNING|DP=0|PP=2|TP=1|ip-26-0-160-192]: Repo card metadata block was not found. Setting CardData to empty.
[default6]:07/06/2024 09:19:23 [WARNING|DP=0|PP=5|TP=2|ip-26-0-163-158]: Repo card metadata block was not found. Setting CardData to empty.
[default3]:07/06/2024 09:19:23 [WARNING|DP=0|PP=2|TP=3|ip-26-0-160-192]: Repo card metadata block was not found. Setting CardData to empty.
[default7]:07/06/2024 09:19:23 [WARNING|DP=0|PP=5|TP=3|ip-26-0-163-158]: Repo card metadata block was not found. Setting CardData to empty.
[default3]:07/06/2024 09:19:23 [WARNING|DP=0|PP=4|TP=3|ip-26-0-163-158]: Repo card metadata block was not found. Setting CardData to empty.
[default4]:Repo card metadata block was not found. Setting CardData to empty.
[default7]:Repo card metadata block was not found. Setting CardData to empty.
[default5]:07/06/2024 09:19:23 [WARNING|DP=0|PP=11|TP=1|ip-26-0-171-88]: Repo card metadata block was not found. Setting CardData to empty.
[default2]:07/06/2024 09:19:23 [WARNING|DP=0|PP=10|TP=2|ip-26-0-171-88]: Repo card metadata block was not found. Setting CardData to empty.
[default4]:07/06/2024 09:19:23 [WARNING|DP=0|PP=11|TP=0|ip-26-0-171-88]: Repo card metadata block was not found. Setting CardData to empty.
[default4]:Repo card metadata block was not found. Setting CardData to empty.
[default1]:07/06/2024 09:19:23 [WARNING|DP=0|PP=12|TP=1|ip-26-0-172-116]: Repo card metadata block was not found. Setting CardData to empty.
[default1]:Repo card metadata block was not found. Setting CardData to empty.
[default2]:07/06/2024 09:19:23 [WARNING|DP=0|PP=12|TP=2|ip-26-0-172-116]: Repo card metadata block was not found. Setting CardData to empty.
[default7]:07/06/2024 09:19:23 [WARNING|DP=0|PP=15|TP=3|ip-26-0-172-57]: Repo card metadata block was not found. Setting CardData to empty.
[default7]:07/06/2024 09:19:23 [WARNING|DP=0|PP=3|TP=3|ip-26-0-160-192]: 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]:07/06/2024 09:19:23 [WARNING|DP=0|PP=7|TP=0|ip-26-0-163-220]: Repo card metadata block was not found. Setting CardData to empty.
[default3]:07/06/2024 09:19:23 [WARNING|DP=0|PP=6|TP=3|ip-26-0-163-220]: Repo card metadata block was not found. Setting CardData to empty.
[default0]:07/06/2024 09:19:23 [WARNING|DP=0|PP=6|TP=0|ip-26-0-163-220]: Repo card metadata block was not found. Setting CardData to empty.
[default7]:07/06/2024 09:19:23 [WARNING|DP=0|PP=11|TP=3|ip-26-0-171-88]: Repo card metadata block was not found. Setting CardData to empty.
[default6]:07/06/2024 09:19:23 [WARNING|DP=0|PP=11|TP=2|ip-26-0-171-88]: Repo card metadata block was not found. Setting CardData to empty.
[default6]:07/06/2024 09:19:23 [WARNING|DP=0|PP=15|TP=2|ip-26-0-172-57]: Repo card metadata block was not found. Setting CardData to empty.
[default3]:07/06/2024 09:19:23 [WARNING|DP=0|PP=14|TP=3|ip-26-0-172-57]: Repo card metadata block was not found. Setting CardData to empty.
[default1]:Repo card metadata block was not found. Setting CardData to empty.
[default6]:07/06/2024 09:19:23 [WARNING|DP=0|PP=3|TP=2|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.
[default2]:Repo card metadata block was not found. Setting CardData to empty.
[default7]:07/06/2024 09:19:23 [WARNING|DP=0|PP=13|TP=3|ip-26-0-172-116]: 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.
[default6]: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.
[default4]:Repo card metadata block was not found. Setting CardData to empty.
[default2]: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.
[default2]:Repo card metadata block was not found. Setting CardData to empty.
[default6]:Repo card metadata block was not found. Setting CardData to empty.
[default1]:07/06/2024 09:19:23 [WARNING|DP=0|PP=6|TP=1|ip-26-0-163-220]: Repo card metadata block was not found. Setting CardData to empty.
[default3]:Repo card metadata block was not found. Setting CardData to empty.
[default7]:07/06/2024 09:19:23 [WARNING|DP=0|PP=1|TP=3|ip-26-0-160-103]: Repo card metadata block was not found. Setting CardData to empty.
[default5]:07/06/2024 09:19:23 [WARNING|DP=0|PP=5|TP=1|ip-26-0-163-158]: 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.
[default1]:Repo card metadata block was not found. Setting CardData to empty.
[default2]:Repo card metadata block was not found. Setting CardData to empty.
[default6]:07/06/2024 09:19:23 [WARNING|DP=0|PP=1|TP=2|ip-26-0-160-103]: Repo card metadata block was not found. Setting CardData to empty.
[default3]:07/06/2024 09:19:23 [WARNING|DP=0|PP=12|TP=3|ip-26-0-172-116]: Repo card metadata block was not found. Setting CardData to empty.
[default7]:Repo card metadata block was not found. Setting CardData to empty.
[default0]:Repo card metadata block was not found. Setting CardData to empty.
[default7]:07/06/2024 09:19:23 [WARNING|DP=0|PP=7|TP=3|ip-26-0-163-220]: Repo card metadata block was not found. Setting CardData to empty.
[default5]:07/06/2024 09:19:23 [WARNING|DP=0|PP=7|TP=1|ip-26-0-163-220]: Repo card metadata block was not found. Setting CardData to empty.
[default2]:07/06/2024 09:19:23 [WARNING|DP=0|PP=6|TP=2|ip-26-0-163-220]: Repo card metadata block was not found. Setting CardData to empty.
[default6]:07/06/2024 09:19:23 [WARNING|DP=0|PP=7|TP=2|ip-26-0-163-220]: Repo card metadata block was not found. Setting CardData to empty.
[default2]:07/06/2024 09:19:23 [WARNING|DP=0|PP=0|TP=2|ip-26-0-160-103]: Repo card metadata block was not found. Setting CardData to empty.
[default5]:07/06/2024 09:19:23 [WARNING|DP=0|PP=1|TP=1|ip-26-0-160-103]: Repo card metadata block was not found. Setting CardData to empty.
[default3]:07/06/2024 09:19:23 [WARNING|DP=0|PP=0|TP=3|ip-26-0-160-103]: Repo card metadata block was not found. Setting CardData to empty.
[default4]:07/06/2024 09:19:23 [WARNING|DP=0|PP=1|TP=0|ip-26-0-160-103]: 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.
[default6]:Repo card metadata block was not found. Setting CardData to empty.
[default1]: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.
[default4]:Repo card metadata block was not found. Setting CardData to empty.
[default5]:Repo card metadata block was not found. Setting CardData to empty.
[default1]:07/06/2024 09:19:23 [WARNING|DP=0|PP=10|TP=1|ip-26-0-171-88]: Repo card metadata block was not found. Setting CardData to empty.
[default4]:Repo card metadata block was not found. Setting CardData to empty.
[default7]:Repo card metadata block was not found. Setting CardData to empty.
[default0]:07/06/2024 09:19:23 [WARNING|DP=0|PP=10|TP=0|ip-26-0-171-88]: Repo card metadata block was not found. Setting CardData to empty.
[default3]:07/06/2024 09:19:23 [WARNING|DP=0|PP=10|TP=3|ip-26-0-171-88]: Repo card metadata block was not found. Setting CardData to empty.
[default1]:07/06/2024 09:19:23 [WARNING|DP=0|PP=0|TP=1|ip-26-0-160-103]: Repo card metadata block was not found. Setting CardData to empty.
[default7]:Repo card metadata block was not found. Setting CardData to empty.
[default4]:07/06/2024 09:19:23 [WARNING|DP=0|PP=13|TP=0|ip-26-0-172-116]: 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]:Repo card metadata block was not found. Setting CardData to empty.
[default5]:Repo card metadata block was not found. Setting CardData to empty.
[default0]:07/06/2024 09:19:23 [WARNING|DP=0|PP=12|TP=0|ip-26-0-172-116]: Repo card metadata block was not found. Setting CardData to empty.
[default5]:07/06/2024 09:19:23 [WARNING|DP=0|PP=13|TP=1|ip-26-0-172-116]: Repo card metadata block was not found. Setting CardData to empty.
[default4]:Repo card metadata block was not found. Setting CardData to empty.
[default4]:07/06/2024 09:19:23 [WARNING|DP=0|PP=5|TP=0|ip-26-0-163-158]: Repo card metadata block was not found. Setting CardData to empty.
[default4]:Repo card metadata block was not found. Setting CardData to empty.
[default4]:07/06/2024 09:19:23 [WARNING|DP=0|PP=3|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]:07/06/2024 09:19:23 [WARNING|DP=0|PP=14|TP=0|ip-26-0-172-57]: Repo card metadata block was not found. Setting CardData to empty.
[default0]:Repo card metadata block was not found. Setting CardData to empty.
[default5]:07/06/2024 09:19:23 [WARNING|DP=0|PP=15|TP=1|ip-26-0-172-57]: Repo card metadata block was not found. Setting CardData to empty.
[default5]:Repo card metadata block was not found. Setting CardData to empty.
[default0]:07/06/2024 09:19:31 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-103]: Resuming training from stage Training Stage, it has trained for 0 samples and has 19 remaining train steps
[default0]:07/06/2024 09:19:31 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-103]:  Memory usage: 448.42MiB. Peak allocated 448.42MiB. Peak reserved: 456.00MiB
[default6]:Traceback (most recent call last):
[default6]:  File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/run_train.py", line 237, in <module>
[default6]:    trainer.train(dataloader)
[default6]:  File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 430, in train
[default6]:    outputs, loss_avg = self.training_step(dataloader=self.current_dataloader)
[default6]:  File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 459, in training_step
[default6]:    outputs = self.pipeline_engine.train_batch_iter(
[default6]:  File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 187, in train_batch_iter
[default6]:    output = self.forward(context=context, state=state, micro_batch=micro_batch, model=model)
[default6]:  File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 44, in forward
[default6]:    output = model(**micro_batch)
[default6]:  File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1511, in _wrapped_call_impl
[default6]:    return self._call_impl(*args, **kwargs)
[default6]:  File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1520, in _call_impl
[default6]:    return forward_call(*args, **kwargs)
[default6]:  File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 890, in forward
[default6]:    sharded_logits = self.model(
[default6]:  File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1511, in _wrapped_call_impl
[default6]:    return self._call_impl(*args, **kwargs)
[default6]:  File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1520, in _call_impl
[default6]:    return forward_call(*args, **kwargs)
[default6]:  File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 764, in forward
[default6]:    return self.forward_with_hidden_states(input_ids=input_ids, input_mask=input_mask)[0]
[default6]:  File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 780, in forward_with_hidden_states
[default6]:    hidden_encoder_states = encoder_block(**hidden_encoder_states)
[default6]:  File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1511, in _wrapped_call_impl
[default6]:    return self._call_impl(*args, **kwargs)
[default6]:  File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1520, in _call_impl
[default6]:    return forward_call(*args, **kwargs)
[default6]:  File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/block.py", line 151, in forward
[default6]:    output = self.pp_block(**new_kwargs)
[default6]:  File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1511, in _wrapped_call_impl
[default6]:    return self._call_impl(*args, **kwargs)
[default6]:  File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1520, in _call_impl
[default6]:    return forward_call(*args, **kwargs)
[default6]:  File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 636, in forward
[default6]:    hidden_states = self.mlp(hidden_states=hidden_states)["hidden_states"]
[default6]:  File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1511, in _wrapped_call_impl
[default6]:    return self._call_impl(*args, **kwargs)
[default6]:  File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1520, in _call_impl
[default6]:    return forward_call(*args, **kwargs)
[default6]:  File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 171, in forward
[default6]:    hidden_states = self.down_proj(self.split_silu_mul(merged_states))
[default6]:  File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1511, in _wrapped_call_impl
[default6]:    return self._call_impl(*args, **kwargs)
[default6]:  File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1520, in _call_impl
[default6]:    return forward_call(*args, **kwargs)
[default6]:  File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/nn.py", line 159, in forward
[default6]:    return row_linear(
[default6]:  File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/functional.py", line 479, in row_linear
[default6]:    out = differentiable_reduce_scatter_sum(out, group=group)
[default6]:  File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/distributed_differentiable_primitives.py", line 145, in differentiable_reduce_scatter_sum
[default6]:    return DifferentiableReduceScatterSum.apply(tensor, group)
[default6]:  File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/autograd/function.py", line 553, in apply
[default6]:    return super().apply(*args, **kwargs)  # type: ignore[misc]
[default6]:  File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/distributed_differentiable_primitives.py", line 111, in forward
[default6]:    sharded_tensor = torch.empty(
[default6]:torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 32.00 MiB. GPU 6 has a total capacity of 79.33 GiB of which 31.94 MiB is free. Including non-PyTorch memory, this process has 79.29 GiB memory in use. Of the allocated memory 69.26 GiB is allocated by PyTorch, and 27.22 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]:Traceback (most recent call last):
[default1]:  File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/run_train.py", line 237, in <module>
[default1]:    trainer.train(dataloader)
[default1]:  File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 430, in train
[default1]:    outputs, loss_avg = self.training_step(dataloader=self.current_dataloader)
[default1]:  File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 459, in training_step
[default1]:    outputs = self.pipeline_engine.train_batch_iter(
[default1]:  File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 187, in train_batch_iter
[default1]:    output = self.forward(context=context, state=state, micro_batch=micro_batch, model=model)
[default1]:  File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 44, in forward
[default1]:    output = model(**micro_batch)
[default1]:  File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1511, in _wrapped_call_impl
[default1]:    return self._call_impl(*args, **kwargs)
[default1]:  File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1520, in _call_impl
[default1]:    return forward_call(*args, **kwargs)
[default1]:  File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 890, in forward
[default1]:    sharded_logits = self.model(
[default1]:  File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1511, in _wrapped_call_impl
[default1]:    return self._call_impl(*args, **kwargs)
[default1]:  File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1520, in _call_impl
[default1]:    return forward_call(*args, **kwargs)
[default1]:  File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 764, in forward
[default1]:    return self.forward_with_hidden_states(input_ids=input_ids, input_mask=input_mask)[0]
[default1]:  File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 780, in forward_with_hidden_states
[default1]:    hidden_encoder_states = encoder_block(**hidden_encoder_states)
[default1]:  File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1511, in _wrapped_call_impl
[default1]:    return self._call_impl(*args, **kwargs)
[default1]:  File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1520, in _call_impl
[default1]:    return forward_call(*args, **kwargs)
[default1]:  File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/block.py", line 151, in forward
[default1]:    output = self.pp_block(**new_kwargs)
[default1]:  File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1511, in _wrapped_call_impl
[default1]:    return self._call_impl(*args, **kwargs)
[default1]:  File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1520, in _call_impl
[default1]:    return forward_call(*args, **kwargs)
[default1]:  File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 630, in forward
[default1]:    output = self.attn(hidden_states=hidden_states, sequence_mask=sequence_mask)
[default1]:  File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1511, in _wrapped_call_impl
[default1]:    return self._call_impl(*args, **kwargs)
[default1]:  File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1520, in _call_impl
[default1]:    return forward_call(*args, **kwargs)
[default1]:  File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 562, in forward
[default1]:    key_value_states = torch.cat([key_states.unsqueeze(0), value_states.unsqueeze(0)], dim=0)
[default1]:torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 64.00 MiB. GPU 1 has a total capacity of 79.33 GiB of which 17.94 MiB is free. Including non-PyTorch memory, this process has 79.29 GiB memory in use. Of the allocated memory 69.70 GiB is allocated by PyTorch, and 89.96 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]:Traceback (most recent call last):
[default5]:  File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/run_train.py", line 237, in <module>
[default5]:    trainer.train(dataloader)
[default5]:  File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 430, in train
[default5]:    outputs, loss_avg = self.training_step(dataloader=self.current_dataloader)
[default5]:  File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 459, in training_step
[default5]:    outputs = self.pipeline_engine.train_batch_iter(
[default5]:  File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 187, in train_batch_iter
[default5]:    output = self.forward(context=context, state=state, micro_batch=micro_batch, model=model)
[default5]:  File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 44, in forward
[default5]:    output = model(**micro_batch)
[default5]:  File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1511, in _wrapped_call_impl
[default5]:    return self._call_impl(*args, **kwargs)
[default5]:  File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1520, in _call_impl
[default5]:    return forward_call(*args, **kwargs)
[default5]:  File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 890, in forward
[default5]:    sharded_logits = self.model(
[default5]:  File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1511, in _wrapped_call_impl
[default5]:    return self._call_impl(*args, **kwargs)
[default5]:  File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1520, in _call_impl
[default5]:    return forward_call(*args, **kwargs)
[default5]:  File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 764, in forward
[default5]:    return self.forward_with_hidden_states(input_ids=input_ids, input_mask=input_mask)[0]
[default5]:  File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 780, in forward_with_hidden_states
[default5]:    hidden_encoder_states = encoder_block(**hidden_encoder_states)
[default5]:  File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1511, in _wrapped_call_impl
[default5]:    return self._call_impl(*args, **kwargs)
[default5]:  File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1520, in _call_impl
[default5]:    return forward_call(*args, **kwargs)
[default5]:  File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/block.py", line 151, in forward
[default5]:    output = self.pp_block(**new_kwargs)
[default5]:  File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1511, in _wrapped_call_impl
[default5]:    return self._call_impl(*args, **kwargs)
[default5]:  File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1520, in _call_impl
[default5]:    return forward_call(*args, **kwargs)
[default5]:  File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 636, in forward
[default5]:    hidden_states = self.mlp(hidden_states=hidden_states)["hidden_states"]
[default5]:  File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1511, in _wrapped_call_impl
[default5]:    return self._call_impl(*args, **kwargs)
[default5]:  File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1520, in _call_impl
[default5]:    return forward_call(*args, **kwargs)
[default5]:  File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 171, in forward
[default5]:    hidden_states = self.down_proj(self.split_silu_mul(merged_states))
[default5]:  File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1511, in _wrapped_call_impl
[default5]:    return self._call_impl(*args, **kwargs)
[default5]:  File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1520, in _call_impl
[default5]:    return forward_call(*args, **kwargs)
[default5]:  File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/nn.py", line 159, in forward
[default5]:    return row_linear(
[default5]:  File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/functional.py", line 479, in row_linear
[default5]:    out = differentiable_reduce_scatter_sum(out, group=group)
[default5]:  File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/distributed_differentiable_primitives.py", line 145, in differentiable_reduce_scatter_sum
[default5]:    return DifferentiableReduceScatterSum.apply(tensor, group)
[default5]:  File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/autograd/function.py", line 553, in apply
[default5]:    return super().apply(*args, **kwargs)  # type: ignore[misc]
[default5]:  File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/distributed_differentiable_primitives.py", line 111, in forward
[default5]:    sharded_tensor = torch.empty(
[default5]:torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 32.00 MiB. GPU 5 has a total capacity of 79.33 GiB of which 7.94 MiB is free. Including non-PyTorch memory, this process has 79.31 GiB memory in use. Of the allocated memory 69.26 GiB is allocated by PyTorch, and 27.22 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation.  See documentation for Memory Management  (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables)
[default2]:Traceback (most recent call last):
[default2]:  File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/run_train.py", line 237, in <module>
[default2]:    trainer.train(dataloader)
[default2]:  File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 430, in train
[default2]:    outputs, loss_avg = self.training_step(dataloader=self.current_dataloader)
[default2]:  File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 459, in training_step
[default2]:    outputs = self.pipeline_engine.train_batch_iter(
[default2]:  File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 187, in train_batch_iter
[default2]:    output = self.forward(context=context, state=state, micro_batch=micro_batch, model=model)
[default2]:  File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 44, in forward
[default2]:    output = model(**micro_batch)
[default2]:  File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1511, in _wrapped_call_impl
[default2]:    return self._call_impl(*args, **kwargs)
[default2]:  File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1520, in _call_impl
[default2]:    return forward_call(*args, **kwargs)
[default2]:  File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 890, in forward
[default2]:    sharded_logits = self.model(
[default2]:  File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1511, in _wrapped_call_impl
[default2]:    return self._call_impl(*args, **kwargs)
[default2]:  File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1520, in _call_impl
[default2]:    return forward_call(*args, **kwargs)
[default2]:  File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 764, in forward
[default2]:    return self.forward_with_hidden_states(input_ids=input_ids, input_mask=input_mask)[0]
[default2]:  File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 780, in forward_with_hidden_states
[default2]:    hidden_encoder_states = encoder_block(**hidden_encoder_states)
[default2]:  File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1511, in _wrapped_call_impl
[default2]:    return self._call_impl(*args, **kwargs)
[default2]:  File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1520, in _call_impl
[default2]:    return forward_call(*args, **kwargs)
[default2]:  File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/block.py", line 151, in forward
[default2]:    output = self.pp_block(**new_kwargs)
[default2]:  File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1511, in _wrapped_call_impl
[default2]:    return self._call_impl(*args, **kwargs)
[default2]:  File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1520, in _call_impl
[default2]:    return forward_call(*args, **kwargs)
[default2]:  File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 630, in forward
[default2]:    output = self.attn(hidden_states=hidden_states, sequence_mask=sequence_mask)
[default2]:  File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1511, in _wrapped_call_impl
[default2]:    return self._call_impl(*args, **kwargs)
[default2]:  File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1520, in _call_impl
[default2]:    return forward_call(*args, **kwargs)
[default2]:  File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 388, in forward
[default2]:    .contiguous()
[default2]:torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 96.00 MiB. GPU 2 has a total capacity of 79.33 GiB of which 27.94 MiB is free. Including non-PyTorch memory, this process has 79.29 GiB memory in use. Of the allocated memory 69.61 GiB is allocated by PyTorch, and 89.96 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]:STAGE:2024-07-06 09:20:27 208777:208777 ActivityProfilerController.cpp:314] Completed Stage: Warm Up
[default3]:Traceback (most recent call last):
[default3]:  File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/run_train.py", line 237, in <module>
[default3]:    trainer.train(dataloader)
[default3]:  File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 430, in train
[default3]:    outputs, loss_avg = self.training_step(dataloader=self.current_dataloader)
[default3]:  File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 459, in training_step
[default3]:    outputs = self.pipeline_engine.train_batch_iter(
[default3]:  File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 187, in train_batch_iter
[default3]:    output = self.forward(context=context, state=state, micro_batch=micro_batch, model=model)
[default3]:  File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 44, in forward
[default3]:    output = model(**micro_batch)
[default3]:  File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1511, in _wrapped_call_impl
[default3]:    return self._call_impl(*args, **kwargs)
[default3]:  File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1520, in _call_impl
[default3]:    return forward_call(*args, **kwargs)
[default3]:  File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 890, in forward
[default3]:    sharded_logits = self.model(
[default3]:  File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1511, in _wrapped_call_impl
[default3]:    return self._call_impl(*args, **kwargs)
[default3]:  File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1520, in _call_impl
[default3]:    return forward_call(*args, **kwargs)
[default3]:  File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 764, in forward
[default3]:    return self.forward_with_hidden_states(input_ids=input_ids, input_mask=input_mask)[0]
[default3]:  File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 780, in forward_with_hidden_states
[default3]:    hidden_encoder_states = encoder_block(**hidden_encoder_states)
[default3]:  File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1511, in _wrapped_call_impl
[default3]:    return self._call_impl(*args, **kwargs)
[default3]:  File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1520, in _call_impl
[default3]:    return forward_call(*args, **kwargs)
[default3]:  File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/block.py", line 151, in forward
[default3]:    output = self.pp_block(**new_kwargs)
[default3]:  File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1511, in _wrapped_call_impl
[default3]:    return self._call_impl(*args, **kwargs)
[default3]:  File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1520, in _call_impl
[default3]:    return forward_call(*args, **kwargs)
[default3]:  File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 636, in forward
[default3]:    hidden_states = self.mlp(hidden_states=hidden_states)["hidden_states"]
[default3]:  File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1511, in _wrapped_call_impl
[default3]:    return self._call_impl(*args, **kwargs)
[default3]:  File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1520, in _call_impl
[default3]:    return forward_call(*args, **kwargs)
[default3]:  File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 170, in forward
[default3]:    merged_states = self.gate_up_proj(hidden_states)
[default3]:  File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1511, in _wrapped_call_impl
[default3]:    return self._call_impl(*args, **kwargs)
[default3]:  File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1520, in _call_impl
[default3]:    return forward_call(*args, **kwargs)
[default3]:  File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/nn.py", line 87, in forward
[default3]:    return column_linear(
[default3]:  File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/functional.py", line 359, in column_linear
[default3]:    return F.linear(input, weight, bias)
[default3]:torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 128.00 MiB. GPU 3 has a total capacity of 79.33 GiB of which 59.94 MiB is free. Including non-PyTorch memory, this process has 79.26 GiB memory in use. Of the allocated memory 69.92 GiB is allocated by PyTorch, and 122.92 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]:Traceback (most recent call last):
[default7]:  File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/run_train.py", line 237, in <module>
[default7]:    trainer.train(dataloader)
[default7]:  File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 430, in train
[default7]:    outputs, loss_avg = self.training_step(dataloader=self.current_dataloader)
[default7]:  File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 459, in training_step
[default7]:    outputs = self.pipeline_engine.train_batch_iter(
[default7]:  File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 187, in train_batch_iter
[default7]:    output = self.forward(context=context, state=state, micro_batch=micro_batch, model=model)
[default7]:  File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 44, in forward
[default7]:    output = model(**micro_batch)
[default7]:  File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1511, in _wrapped_call_impl
[default7]:    return self._call_impl(*args, **kwargs)
[default7]:  File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1520, in _call_impl
[default7]:    return forward_call(*args, **kwargs)
[default7]:  File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 894, in forward
[default7]:    loss = self.loss(
[default7]:  File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1511, in _wrapped_call_impl
[default7]:    return self._call_impl(*args, **kwargs)
[default7]:  File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1520, in _call_impl
[default7]:    return forward_call(*args, **kwargs)
[default7]:  File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/block.py", line 151, in forward
[default7]:    output = self.pp_block(**new_kwargs)
[default7]:  File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1511, in _wrapped_call_impl
[default7]:    return self._call_impl(*args, **kwargs)
[default7]:  File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1520, in _call_impl
[default7]:    return forward_call(*args, **kwargs)
[default7]:  File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 848, in forward
[default7]:    loss = sharded_cross_entropy(
[default7]:  File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/functional.py", line 116, in sharded_cross_entropy
[default7]:    return _ShardedCrossEntropy.apply(sharded_logits, target, group)
[default7]:  File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/autograd/function.py", line 553, in apply
[default7]:    return super().apply(*args, **kwargs)  # type: ignore[misc]
[default7]:  File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/functional.py", line 44, in forward
[default7]:    sharded_logits = sharded_logits - logits_max.unsqueeze(dim=-1)
[default7]:torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 1.54 GiB. GPU 7 has a total capacity of 79.33 GiB of which 459.94 MiB is free. Including non-PyTorch memory, this process has 78.87 GiB memory in use. Of the allocated memory 69.03 GiB is allocated by PyTorch, and 849.43 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]:Traceback (most recent call last):
[default6]:  File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/run_train.py", line 237, in <module>
[default6]:    trainer.train(dataloader)
[default6]:  File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 430, in train
[default6]:    outputs, loss_avg = self.training_step(dataloader=self.current_dataloader)
[default6]:  File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 459, in training_step
[default6]:    outputs = self.pipeline_engine.train_batch_iter(
[default6]:  File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 187, in train_batch_iter
[default6]:    output = self.forward(context=context, state=state, micro_batch=micro_batch, model=model)
[default6]:  File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 44, in forward
[default6]:    output = model(**micro_batch)
[default6]:  File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1511, in _wrapped_call_impl
[default6]:    return self._call_impl(*args, **kwargs)
[default6]:  File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1520, in _call_impl
[default6]:    return forward_call(*args, **kwargs)
[default6]:  File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 890, in forward
[default6]:    sharded_logits = self.model(
[default6]:  File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1511, in _wrapped_call_impl
[default6]:    return self._call_impl(*args, **kwargs)
[default6]:  File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1520, in _call_impl
[default6]:    return forward_call(*args, **kwargs)
[default6]:  File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 764, in forward
[default6]:    return self.forward_with_hidden_states(input_ids=input_ids, input_mask=input_mask)[0]
[default6]:  File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 786, in forward_with_hidden_states
[default6]:    fp32_sharded_logits = self.cast_to_fp32(x=sharded_logits)["output"]
[default6]:  File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1511, in _wrapped_call_impl
[default6]:    return self._call_impl(*args, **kwargs)
[default6]:  File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1520, in _call_impl
[default6]:    return forward_call(*args, **kwargs)
[default6]:  File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/block.py", line 151, in forward
[default6]:    output = self.pp_block(**new_kwargs)
[default6]:  File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 753, in <lambda>
[default6]:    module_builder=lambda: lambda x: x.float(),
[default6]:torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 1.54 GiB. GPU 6 has a total capacity of 79.33 GiB of which 1.53 GiB is free. Including non-PyTorch memory, this process has 77.79 GiB memory in use. Of the allocated memory 67.50 GiB is allocated by PyTorch, and 848.43 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation.  See documentation for Memory Management  (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables)
[default4]:Traceback (most recent call last):
[default4]:  File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/run_train.py", line 237, in <module>
[default4]:    trainer.train(dataloader)
[default4]:  File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 430, in train
[default4]:    outputs, loss_avg = self.training_step(dataloader=self.current_dataloader)
[default4]:  File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 459, in training_step
[default4]:    outputs = self.pipeline_engine.train_batch_iter(
[default4]:  File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 187, in train_batch_iter
[default4]:    output = self.forward(context=context, state=state, micro_batch=micro_batch, model=model)
[default4]:  File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 44, in forward
[default4]:    output = model(**micro_batch)
[default4]:  File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1511, in _wrapped_call_impl
[default4]:    return self._call_impl(*args, **kwargs)
[default4]:  File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1520, in _call_impl
[default4]:    return forward_call(*args, **kwargs)
[default4]:  File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 894, in forward
[default4]:    loss = self.loss(
[default4]:  File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1511, in _wrapped_call_impl
[default4]:    return self._call_impl(*args, **kwargs)
[default4]:  File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1520, in _call_impl
[default4]:    return forward_call(*args, **kwargs)
[default4]:  File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/block.py", line 151, in forward
[default5]:Traceback (most recent call last):
[default5]:  File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/run_train.py", line 237, in <module>
[default5]:    trainer.train(dataloader)
[default5]:  File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 430, in train
[default5]:    outputs, loss_avg = self.training_step(dataloader=self.current_dataloader)
[default5]:  File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 459, in training_step
[default5]:    outputs = self.pipeline_engine.train_batch_iter(
[default4]:    output = self.pp_block(**new_kwargs)
[default4]:  File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1511, in _wrapped_call_impl
[default4]:    return self._call_impl(*args, **kwargs)
[default4]:  File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1520, in _call_impl
[default4]:    return forward_call(*args, **kwargs)
[default4]:  File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 848, in forward
[default4]:    loss = sharded_cross_entropy(
[default5]:  File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 187, in train_batch_iter
[default5]:    output = self.forward(context=context, state=state, micro_batch=micro_batch, model=model)
[default5]:  File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 44, in forward
[default4]:  File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/functional.py", line 116, in sharded_cross_entropy
[default4]:    return _ShardedCrossEntropy.apply(sharded_logits, target, group)
[default4]:  File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/autograd/function.py", line 553, in apply
[default4]:    return super().apply(*args, **kwargs)  # type: ignore[misc]
[default4]:  File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/functional.py", line 44, in forward
[default4]:    sharded_logits = sharded_logits - logits_max.unsqueeze(dim=-1)
[default5]:    output = model(**micro_batch)
[default5]:  File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1511, in _wrapped_call_impl
[default5]:    return self._call_impl(*args, **kwargs)
[default4]:torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 1.54 GiB. GPU 4 has a total capacity of 79.33 GiB of which 219.94 MiB is free. Including non-PyTorch memory, this process has 79.10 GiB memory in use. Of the allocated memory 69.03 GiB is allocated by PyTorch, and 849.43 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]:  File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1520, in _call_impl
[default5]:    return forward_call(*args, **kwargs)
[default5]:  File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 890, in forward
[default5]:    sharded_logits = self.model(
[default5]:  File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1511, in _wrapped_call_impl
[default5]:    return self._call_impl(*args, **kwargs)
[default5]:  File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1520, in _call_impl
[default5]:    return forward_call(*args, **kwargs)
[default5]:  File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 764, in forward
[default5]:    return self.forward_with_hidden_states(input_ids=input_ids, input_mask=input_mask)[0]
[default5]:  File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 786, in forward_with_hidden_states
[default5]:    fp32_sharded_logits = self.cast_to_fp32(x=sharded_logits)["output"]
[default5]:  File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1511, in _wrapped_call_impl
[default5]:    return self._call_impl(*args, **kwargs)
[default5]:  File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1520, in _call_impl
[default5]:    return forward_call(*args, **kwargs)
[default5]:  File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/block.py", line 151, in forward
[default5]:    output = self.pp_block(**new_kwargs)
[default5]:  File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 753, in <lambda>
[default5]:    module_builder=lambda: lambda x: x.float(),
[default5]:torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 1.54 GiB. GPU 5 has a total capacity of 79.33 GiB of which 1.51 GiB is free. Including non-PyTorch memory, this process has 77.81 GiB memory in use. Of the allocated memory 67.50 GiB is allocated by PyTorch, and 848.43 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation.  See documentation for Memory Management  (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables)
[default2]:Traceback (most recent call last):
[default2]:  File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/run_train.py", line 237, in <module>
[default2]:    trainer.train(dataloader)
[default2]:  File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 430, in train
[default2]:    outputs, loss_avg = self.training_step(dataloader=self.current_dataloader)
[default2]:  File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 459, in training_step
[default2]:    outputs = self.pipeline_engine.train_batch_iter(
[default2]:  File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 187, in train_batch_iter
[default2]:    output = self.forward(context=context, state=state, micro_batch=micro_batch, model=model)
[default2]:  File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 44, in forward
[default2]:    output = model(**micro_batch)
[default2]:  File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1511, in _wrapped_call_impl
[default2]:    return self._call_impl(*args, **kwargs)
[default2]:  File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1520, in _call_impl
[default2]:    return forward_call(*args, **kwargs)
[default2]:  File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 890, in forward
[default2]:    sharded_logits = self.model(
[default2]:  File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1511, in _wrapped_call_impl
[default2]:    return self._call_impl(*args, **kwargs)
[default2]:  File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1520, in _call_impl
[default2]:    return forward_call(*args, **kwargs)
[default2]:  File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 764, in forward
[default2]:    return self.forward_with_hidden_states(input_ids=input_ids, input_mask=input_mask)[0]
[default2]:  File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 780, in forward_with_hidden_states
[default2]:    hidden_encoder_states = encoder_block(**hidden_encoder_states)
[default2]:  File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1511, in _wrapped_call_impl
[default2]:    return self._call_impl(*args, **kwargs)
[default2]:  File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1520, in _call_impl
[default2]:    return forward_call(*args, **kwargs)
[default2]:  File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/block.py", line 126, in forward
[default2]:    new_kwargs[name] = recv_from_pipeline_state_buffer(
[default2]:  File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/functional.py", line 117, in recv_from_pipeline_state_buffer
[default2]:    pipeline_state.run_communication()
[default2]:  File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/state.py", line 150, in run_communication
[default2]:    recv_activation_tensor = recv_activation()
[default2]:  File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/state.py", line 31, in __call__
[default2]:    return self.p2p.recv_tensors(num_tensors=1, from_rank=self.from_rank)[0]
[default2]:  File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/p2p.py", line 353, in recv_tensors
[default2]:    buffers, futures = self.irecv_tensors(num_tensors=num_tensors, from_rank=from_rank, tag=tag)
[default2]:  File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/p2p.py", line 326, in irecv_tensors
[default2]:    meta = self._recv_meta(from_rank=from_rank, tag=tag)
[default2]:  File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/p2p.py", line 269, in _recv_meta
[default2]:    dist.recv(
[default2]:  File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/c10d_logger.py", line 72, in wrapper
[default2]:    return func(*args, **kwargs)
[default2]:  File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/distributed_c10d.py", line 1706, in recv
[default2]:    pg.recv([tensor], group_src_rank, tag).wait()
[default2]:torch.distributed.DistBackendError: NCCL communicator was aborted on rank 1. 
[2024-07-06 09:20:30,433] torch.distributed.elastic.multiprocessing.api: [WARNING] Sending process 1680911 closing signal SIGTERM
[2024-07-06 09:20:30,433] torch.distributed.elastic.multiprocessing.api: [WARNING] Sending process 1680912 closing signal SIGTERM
[2024-07-06 09:20:30,434] torch.distributed.elastic.multiprocessing.api: [WARNING] Sending process 1680913 closing signal SIGTERM
[2024-07-06 09:20:30,434] torch.distributed.elastic.multiprocessing.api: [WARNING] Sending process 1680914 closing signal SIGTERM
[2024-07-06 09:20:30,434] torch.distributed.elastic.multiprocessing.api: [WARNING] Sending process 208777 closing signal SIGTERM
[2024-07-06 09:20:30,435] torch.distributed.elastic.multiprocessing.api: [WARNING] Sending process 208778 closing signal SIGTERM
[2024-07-06 09:20:30,435] torch.distributed.elastic.multiprocessing.api: [WARNING] Sending process 208779 closing signal SIGTERM
[2024-07-06 09:20:30,436] torch.distributed.elastic.multiprocessing.api: [WARNING] Sending process 208780 closing signal SIGTERM
[2024-07-06 09:20:30,436] torch.distributed.elastic.multiprocessing.api: [WARNING] Sending process 208781 closing signal SIGTERM
[2024-07-06 09:20:30,436] torch.distributed.elastic.multiprocessing.api: [WARNING] Sending process 208784 closing signal SIGTERM
[2024-07-06 09:20:32,378] torch.distributed.elastic.multiprocessing.api: [ERROR] failed (exitcode: 1) local_rank: 4 (pid: 1680915) 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 812, in main
    run(args)
  File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/run.py", line 803, in run
    elastic_launch(
  File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/launcher/api.py", line 135, 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 268, 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      : 2024-07-06_09:20:30
  host      : ip-26-0-172-57.ec2.internal
  rank      : 61 (local_rank: 5)
  exitcode  : 1 (pid: 1680916)
  error_file: <N/A>
  traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html
[2]:
  time      : 2024-07-06_09:20:30
  host      : ip-26-0-172-57.ec2.internal
  rank      : 62 (local_rank: 6)
  exitcode  : 1 (pid: 1680917)
  error_file: <N/A>
  traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html
[3]:
  time      : 2024-07-06_09:20:30
  host      : ip-26-0-172-57.ec2.internal
  rank      : 63 (local_rank: 7)
  exitcode  : 1 (pid: 1680918)
  error_file: <N/A>
  traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html
------------------------------------------------------------
Root Cause (first observed failure):
[0]:
  time      : 2024-07-06_09:20:30
  host      : ip-26-0-172-57.ec2.internal
  rank      : 60 (local_rank: 4)
  exitcode  : 1 (pid: 1680915)
  error_file: <N/A>
  traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html
============================================================
srun: error: ip-26-0-172-57: task 6: Exited with exit code 1
[2024-07-06 09:20:33,283] torch.distributed.elastic.multiprocessing.api: [ERROR] failed (exitcode: 1) local_rank: 5 (pid: 208782) 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 812, in main
    run(args)
  File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/run.py", line 803, in run
    elastic_launch(
  File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/launcher/api.py", line 135, 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 268, 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      : 2024-07-06_09:20:30
  host      : ip-26-0-160-103.ec2.internal
  rank      : 6 (local_rank: 6)
  exitcode  : 1 (pid: 208783)
  error_file: <N/A>
  traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html
------------------------------------------------------------
Root Cause (first observed failure):
[0]:
  time      : 2024-07-06_09:20:30
  host      : ip-26-0-160-103.ec2.internal
  rank      : 5 (local_rank: 5)
  exitcode  : 1 (pid: 208782)
  error_file: <N/A>
  traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html
============================================================
srun: error: ip-26-0-160-103: task 0: Exited with exit code 1
[2024-07-06 09:20:34,475] torch.distributed.elastic.rendezvous.dynamic_rendezvous: [WARNING] The node 'ip-26-0-163-58.ec2.internal_1196472_0' has failed to send a keep-alive heartbeat to the rendezvous 'none' due to an error of type RendezvousConnectionError.
[2024-07-06 09:20:34,508] torch.distributed.elastic.rendezvous.dynamic_rendezvous: [WARNING] The node 'ip-26-0-172-116.ec2.internal_3378612_0' has failed to send a keep-alive heartbeat to the rendezvous 'none' due to an error of type RendezvousConnectionError.
[2024-07-06 09:20:34,572] torch.distributed.elastic.rendezvous.dynamic_rendezvous: [WARNING] The node 'ip-26-0-160-192.ec2.internal_618059_0' has failed to send a keep-alive heartbeat to the rendezvous 'none' due to an error of type RendezvousConnectionError.
[2024-07-06 09:20:34,955] torch.distributed.elastic.rendezvous.dynamic_rendezvous: [WARNING] The node 'ip-26-0-163-158.ec2.internal_2763820_0' has failed to send a keep-alive heartbeat to the rendezvous 'none' due to an error of type RendezvousConnectionError.
[2024-07-06 09:20:35,253] torch.distributed.elastic.rendezvous.dynamic_rendezvous: [WARNING] The node 'ip-26-0-163-220.ec2.internal_174858_0' has failed to send a keep-alive heartbeat to the rendezvous 'none' due to an error of type RendezvousConnectionError.
[2024-07-06 09:20:35,390] torch.distributed.elastic.rendezvous.dynamic_rendezvous: [WARNING] The node 'ip-26-0-171-88.ec2.internal_169869_0' has failed to send a keep-alive heartbeat to the rendezvous 'none' due to an error of type RendezvousConnectionError.
[2024-07-06 09:20:35,429] torch.distributed.elastic.multiprocessing.api: [WARNING] Sending process 618137 closing signal SIGTERM
[2024-07-06 09:20:35,429] torch.distributed.elastic.multiprocessing.api: [WARNING] Sending process 618138 closing signal SIGTERM
[2024-07-06 09:20:35,430] torch.distributed.elastic.multiprocessing.api: [WARNING] Sending process 618140 closing signal SIGTERM
[2024-07-06 09:20:35,431] torch.distributed.elastic.multiprocessing.api: [WARNING] Sending process 618141 closing signal SIGTERM
[2024-07-06 09:20:35,432] torch.distributed.elastic.multiprocessing.api: [WARNING] Sending process 618142 closing signal SIGTERM
[2024-07-06 09:20:35,432] torch.distributed.elastic.multiprocessing.api: [WARNING] Sending process 618143 closing signal SIGTERM
[2024-07-06 09:20:35,433] torch.distributed.elastic.multiprocessing.api: [WARNING] Sending process 618144 closing signal SIGTERM
[2024-07-06 09:20:35,433] torch.distributed.elastic.multiprocessing.api: [WARNING] Sending process 2763895 closing signal SIGTERM
[2024-07-06 09:20:35,434] torch.distributed.elastic.multiprocessing.api: [WARNING] Sending process 169947 closing signal SIGTERM
[2024-07-06 09:20:35,434] torch.distributed.elastic.multiprocessing.api: [WARNING] Sending process 2763896 closing signal SIGTERM
[2024-07-06 09:20:35,435] torch.distributed.elastic.multiprocessing.api: [WARNING] Sending process 169948 closing signal SIGTERM
[2024-07-06 09:20:35,435] torch.distributed.elastic.multiprocessing.api: [WARNING] Sending process 174934 closing signal SIGTERM
[2024-07-06 09:20:35,435] torch.distributed.elastic.multiprocessing.api: [WARNING] Sending process 3378691 closing signal SIGTERM
[2024-07-06 09:20:35,435] torch.distributed.elastic.multiprocessing.api: [WARNING] Sending process 174935 closing signal SIGTERM
[2024-07-06 09:20:35,436] torch.distributed.elastic.multiprocessing.api: [WARNING] Sending process 3378692 closing signal SIGTERM
[2024-07-06 09:20:35,435] torch.distributed.elastic.multiprocessing.api: [WARNING] Sending process 169949 closing signal SIGTERM
[2024-07-06 09:20:35,434] torch.distributed.elastic.multiprocessing.api: [WARNING] Sending process 2763897 closing signal SIGTERM
[2024-07-06 09:20:35,437] torch.distributed.elastic.multiprocessing.api: [WARNING] Sending process 1196549 closing signal SIGTERM
[2024-07-06 09:20:35,436] torch.distributed.elastic.multiprocessing.api: [WARNING] Sending process 169950 closing signal SIGTERM
[2024-07-06 09:20:35,435] torch.distributed.elastic.multiprocessing.api: [WARNING] Sending process 174936 closing signal SIGTERM
[2024-07-06 09:20:35,437] torch.distributed.elastic.multiprocessing.api: [WARNING] Sending process 1196550 closing signal SIGTERM
[2024-07-06 09:20:35,437] torch.distributed.elastic.multiprocessing.api: [WARNING] Sending process 1196551 closing signal SIGTERM
[2024-07-06 09:20:35,435] torch.distributed.elastic.multiprocessing.api: [WARNING] Sending process 2763898 closing signal SIGTERM
[2024-07-06 09:20:35,436] torch.distributed.elastic.multiprocessing.api: [WARNING] Sending process 169951 closing signal SIGTERM
[2024-07-06 09:20:35,436] torch.distributed.elastic.multiprocessing.api: [WARNING] Sending process 174937 closing signal SIGTERM
[2024-07-06 09:20:35,437] torch.distributed.elastic.multiprocessing.api: [WARNING] Sending process 3378693 closing signal SIGTERM
[2024-07-06 09:20:35,437] torch.distributed.elastic.multiprocessing.api: [WARNING] Sending process 169952 closing signal SIGTERM
[2024-07-06 09:20:35,438] torch.distributed.elastic.multiprocessing.api: [WARNING] Sending process 1196552 closing signal SIGTERM
[2024-07-06 09:20:35,437] torch.distributed.elastic.multiprocessing.api: [WARNING] Sending process 169953 closing signal SIGTERM
[2024-07-06 09:20:35,438] torch.distributed.elastic.multiprocessing.api: [WARNING] Sending process 3378694 closing signal SIGTERM
[2024-07-06 09:20:35,438] torch.distributed.elastic.multiprocessing.api: [WARNING] Sending process 174938 closing signal SIGTERM
[2024-07-06 09:20:35,438] torch.distributed.elastic.multiprocessing.api: [WARNING] Sending process 174939 closing signal SIGTERM
[2024-07-06 09:20:35,437] torch.distributed.elastic.multiprocessing.api: [WARNING] Sending process 2763899 closing signal SIGTERM
[2024-07-06 09:20:35,439] torch.distributed.elastic.multiprocessing.api: [WARNING] Sending process 3378695 closing signal SIGTERM
[2024-07-06 09:20:35,438] torch.distributed.elastic.multiprocessing.api: [WARNING] Sending process 169954 closing signal SIGTERM
[2024-07-06 09:20:35,438] torch.distributed.elastic.multiprocessing.api: [WARNING] Sending process 2763900 closing signal SIGTERM
[2024-07-06 09:20:35,441] torch.distributed.elastic.multiprocessing.api: [WARNING] Sending process 1196553 closing signal SIGTERM
[2024-07-06 09:20:35,439] torch.distributed.elastic.multiprocessing.api: [WARNING] Sending process 174940 closing signal SIGTERM
[2024-07-06 09:20:35,439] torch.distributed.elastic.multiprocessing.api: [WARNING] Sending process 3378696 closing signal SIGTERM
[2024-07-06 09:20:35,440] torch.distributed.elastic.multiprocessing.api: [WARNING] Sending process 3378697 closing signal SIGTERM
[2024-07-06 09:20:35,440] torch.distributed.elastic.multiprocessing.api: [WARNING] Sending process 2763901 closing signal SIGTERM
[2024-07-06 09:20:35,440] torch.distributed.elastic.multiprocessing.api: [WARNING] Sending process 3378698 closing signal SIGTERM
[2024-07-06 09:20:35,442] torch.distributed.elastic.multiprocessing.api: [WARNING] Sending process 1196554 closing signal SIGTERM
[2024-07-06 09:20:35,441] torch.distributed.elastic.multiprocessing.api: [WARNING] Sending process 174941 closing signal SIGTERM
[2024-07-06 09:20:35,440] torch.distributed.elastic.multiprocessing.api: [WARNING] Sending process 2763902 closing signal SIGTERM
[2024-07-06 09:20:35,443] torch.distributed.elastic.multiprocessing.api: [WARNING] Sending process 1196555 closing signal SIGTERM
[2024-07-06 09:20:35,443] torch.distributed.elastic.multiprocessing.api: [WARNING] Sending process 1196556 closing signal SIGTERM
[2024-07-06 09:20:39,018] torch.distributed.elastic.multiprocessing.api: [ERROR] failed (exitcode: 1) local_rank: 2 (pid: 618139) of binary: /fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/bin/python3.10
[2024-07-06 09:20:39,061] torch.distributed.elastic.rendezvous.dynamic_rendezvous: [WARNING] The node 'ip-26-0-160-192.ec2.internal_618059_0' has failed to shutdown the rendezvous 'none' due to an error of type RendezvousConnectionError.
Traceback (most recent call last):
  File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/bin/torchrun", line 8, in <module>
    sys.exit(main())
  File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/elastic/multiprocessing/errors/__init__.py", line 347, in wrapper
    return f(*args, **kwargs)
  File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/run.py", line 812, in main
    run(args)
  File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/run.py", line 803, in run
    elastic_launch(
  File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/launcher/api.py", line 135, 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 268, in launch_agent
    raise ChildFailedError(
torch.distributed.elastic.multiprocessing.errors.ChildFailedError: 
============================================================
/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/run_train.py FAILED
------------------------------------------------------------
Failures:
  <NO_OTHER_FAILURES>
------------------------------------------------------------
Root Cause (first observed failure):
[0]:
  time      : 2024-07-06_09:20:35
  host      : ip-26-0-160-192.ec2.internal
  rank      : 10 (local_rank: 2)
  exitcode  : 1 (pid: 618139)
  error_file: <N/A>
  traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html
============================================================
srun: error: ip-26-0-160-192: task 1: Exited with exit code 1
[2024-07-06 09:20:39,397] torch.distributed.elastic.rendezvous.dynamic_rendezvous: [WARNING] The node 'ip-26-0-171-88.ec2.internal_169869_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 812, in main
    run(args)
  File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/run.py", line 803, in run
    elastic_launch(
  File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/launcher/api.py", line 135, 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 259, 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 727, 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 900, 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 1083, 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 409, 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.
[2024-07-06 09:20:39,479] torch.distributed.elastic.rendezvous.dynamic_rendezvous: [WARNING] The node 'ip-26-0-163-58.ec2.internal_1196472_0' has failed to send a keep-alive heartbeat to the rendezvous 'none' due to an error of type RendezvousConnectionError.
[2024-07-06 09:20:39,513] torch.distributed.elastic.rendezvous.dynamic_rendezvous: [WARNING] The node 'ip-26-0-172-116.ec2.internal_3378612_0' has failed to send a keep-alive heartbeat to the rendezvous 'none' due to an error of type RendezvousConnectionError.
[2024-07-06 09:20:39,522] torch.distributed.elastic.rendezvous.dynamic_rendezvous: [WARNING] The node 'ip-26-0-172-116.ec2.internal_3378612_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 812, in main
    run(args)
  File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/run.py", line 803, in run
    elastic_launch(
  File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/launcher/api.py", line 135, 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 259, 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 727, 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 900, 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 1083, 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 409, 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.
[2024-07-06 09:20:39,589] torch.distributed.elastic.rendezvous.dynamic_rendezvous: [WARNING] The node 'ip-26-0-163-58.ec2.internal_1196472_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 812, in main
    run(args)
  File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/run.py", line 803, in run
    elastic_launch(
  File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/launcher/api.py", line 135, 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 259, 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 727, 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 900, 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 1083, 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 409, 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.
[2024-07-06 09:20:39,596] torch.distributed.elastic.rendezvous.dynamic_rendezvous: [WARNING] The node 'ip-26-0-163-220.ec2.internal_174858_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 812, in main
    run(args)
  File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/run.py", line 803, in run
    elastic_launch(
  File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/launcher/api.py", line 135, 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 259, 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 727, 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 900, 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 1083, 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 409, 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-26-0-171-88: task 5: Exited with exit code 1
srun: error: ip-26-0-163-58: task 2: Exited with exit code 1
srun: error: ip-26-0-172-116: task 7: Exited with exit code 1
[2024-07-06 09:20:39,960] torch.distributed.elastic.rendezvous.dynamic_rendezvous: [WARNING] The node 'ip-26-0-163-158.ec2.internal_2763820_0' has failed to send a keep-alive heartbeat to the rendezvous 'none' due to an error of type RendezvousConnectionError.
[2024-07-06 09:20:39,989] torch.distributed.elastic.rendezvous.dynamic_rendezvous: [WARNING] The node 'ip-26-0-163-158.ec2.internal_2763820_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 812, in main
    run(args)
  File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/run.py", line 803, in run
    elastic_launch(
  File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/launcher/api.py", line 135, 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 259, 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 727, 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 900, 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 1083, 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 409, 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-26-0-163-220: task 4: Exited with exit code 1
srun: error: ip-26-0-163-158: task 3: Exited with exit code 1
Consider using `hf_transfer` for faster uploads. This solution comes with some limitations. See https://huggingface.co/docs/huggingface_hub/hf_transfer for more details.