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

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START TIME: Tue Jul  2 19:53:08 UTC 2024
python3 version = Python 3.10.14
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M	examples/config_tiny_llama.py
M	examples/config_tiny_llama.yaml
M	examples/train_tiny_llama.sh
M	src/nanotron/models/llama.py
M	src/nanotron/trainer.py
Your branch is up to date with 'origin/bench_cluster'.
Job status: RUNNING
W0702 19:53:10.966000 140397096003392 torch/distributed/run.py:757] 
W0702 19:53:10.966000 140397096003392 torch/distributed/run.py:757] *****************************************
W0702 19:53:10.966000 140397096003392 torch/distributed/run.py:757] Setting OMP_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. 
W0702 19:53:10.966000 140397096003392 torch/distributed/run.py:757] *****************************************
W0702 19:53:10.967000 140166790031168 torch/distributed/run.py:757] 
W0702 19:53:10.967000 140166790031168 torch/distributed/run.py:757] *****************************************
W0702 19:53:10.967000 140166790031168 torch/distributed/run.py:757] Setting OMP_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. 
W0702 19:53:10.967000 140166790031168 torch/distributed/run.py:757] *****************************************
[default0]:07/02/2024 19:53:28 [WARNING|DP=0|PP=0|TP=0|ip-26-0-169-139]: [Vocab Size Padding] Padded vocab (size: 50257) with 3 dummy tokens (new size: 50260)
[default0]:07/02/2024 19:53:28 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-139]: Config:
[default0]:07/02/2024 19:53:28 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-139]: Config(general=GeneralArgs(project='bench_cluster',
[default0]:07/02/2024 19:53:28 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-139]:                            run='%date_%jobid',
[default0]:07/02/2024 19:53:28 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-139]:                            seed=42,
[default0]:07/02/2024 19:53:28 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-139]:                            step=None,
[default0]:07/02/2024 19:53:28 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-139]:                            consumed_train_samples=None,
[default0]:07/02/2024 19:53:28 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-139]:                            benchmark_csv_path=None,
[default0]:07/02/2024 19:53:28 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-139]:                            ignore_sanity_checks=True),
[default0]:07/02/2024 19:53:28 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-139]:        parallelism=ParallelismArgs(dp=1,
[default0]:07/02/2024 19:53:28 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-139]:                                    pp=4,
[default0]:07/02/2024 19:53:28 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-139]:                                    tp=4,
[default0]:07/02/2024 19:53:28 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-139]:                                    pp_engine=<nanotron.parallel.pipeline_parallel.engine.OneForwardOneBackwardPipelineEngine object at 0x7f38d5dd06a0>,
[default0]:07/02/2024 19:53:28 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-139]:                                    tp_mode=<TensorParallelLinearMode.REDUCE_SCATTER: 2>,
[default0]:07/02/2024 19:53:28 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-139]:                                    tp_linear_async_communication=False,
[default0]:07/02/2024 19:53:28 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-139]:                                    expert_parallel_size=1),
[default0]:07/02/2024 19:53:28 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-139]:        model=ModelArgs(model_config=LlamaConfig(bos_token_id=1,
[default0]:07/02/2024 19:53:28 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-139]:                                                 eos_token_id=2,
[default0]:07/02/2024 19:53:28 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-139]:                                                 hidden_act='silu',
[default0]:07/02/2024 19:53:28 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-139]:                                                 hidden_size=2048,
[default0]:07/02/2024 19:53:28 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-139]:                                                 initializer_range=0.02,
[default0]:07/02/2024 19:53:28 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-139]:                                                 intermediate_size=4096,
[default0]:07/02/2024 19:53:28 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-139]:                                                 is_llama_config=True,
[default0]:07/02/2024 19:53:28 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-139]:                                                 max_position_embeddings=4096,
[default0]:07/02/2024 19:53:28 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-139]:                                                 num_attention_heads=32,
[default0]:07/02/2024 19:53:28 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-139]:                                                 num_hidden_layers=24,
[default0]:07/02/2024 19:53:28 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-139]:                                                 num_key_value_heads=32,
[default0]:07/02/2024 19:53:28 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-139]:                                                 pad_token_id=None,
[default0]:07/02/2024 19:53:28 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-139]:                                                 pretraining_tp=1,
[default0]:07/02/2024 19:53:28 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-139]:                                                 rms_norm_eps=1e-05,
[default0]:07/02/2024 19:53:28 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-139]:                                                 rope_scaling=None,
[default0]:07/02/2024 19:53:28 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-139]:                                                 rope_theta=10000.0,
[default0]:07/02/2024 19:53:28 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-139]:                                                 tie_word_embeddings=True,
[default0]:07/02/2024 19:53:28 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-139]:                                                 use_cache=True,
[default0]:07/02/2024 19:53:28 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-139]:                                                 vocab_size=50260),
[default0]:07/02/2024 19:53:28 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-139]:                        init_method=RandomInit(std=0.025),
[default0]:07/02/2024 19:53:28 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-139]:                        dtype=torch.bfloat16,
[default0]:07/02/2024 19:53:28 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-139]:                        make_vocab_size_divisible_by=1,
[default0]:07/02/2024 19:53:28 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-139]:                        ddp_bucket_cap_mb=25),
[default0]:07/02/2024 19:53:28 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-139]:        tokenizer=TokenizerArgs(tokenizer_name_or_path='openai-community/gpt2',
[default0]:07/02/2024 19:53:28 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-139]:                                tokenizer_revision=None,
[default0]:07/02/2024 19:53:28 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-139]:                                tokenizer_max_length=None),
[default0]:07/02/2024 19:53:28 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-139]:        checkpoints=CheckpointsArgs(checkpoints_path=Path('/dev/null'),
[default0]:07/02/2024 19:53:28 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-139]:                                    checkpoint_interval=100000,
[default0]:07/02/2024 19:53:28 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-139]:                                    save_initial_state=False,
[default0]:07/02/2024 19:53:28 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-139]:                                    resume_checkpoint_path=None,
[default0]:07/02/2024 19:53:28 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-139]:                                    checkpoints_path_is_shared_file_system=False),
[default0]:07/02/2024 19:53:28 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-139]:        logging=LoggingArgs(log_level='info',
[default0]:07/02/2024 19:53:28 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-139]:                            log_level_replica='info',
[default0]:07/02/2024 19:53:28 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-139]:                            iteration_step_info_interval=1),
[default0]:07/02/2024 19:53:28 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-139]:        tokens=TokensArgs(sequence_length=4096,
[default0]:07/02/2024 19:53:28 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-139]:                          train_steps=20,
[default0]:07/02/2024 19:53:28 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-139]:                          micro_batch_size=8,
[default0]:07/02/2024 19:53:28 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-139]:                          batch_accumulation_per_replica=128,
[default0]:07/02/2024 19:53:28 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-139]:                          val_check_interval=-1,
[default0]:07/02/2024 19:53:28 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-139]:                          limit_val_batches=0,
[default0]:07/02/2024 19:53:28 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-139]:                          limit_test_batches=0),
[default0]:07/02/2024 19:53:28 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-139]:        optimizer=OptimizerArgs(optimizer_factory=AdamWOptimizerArgs(adam_eps=1e-08,
[default0]:07/02/2024 19:53:28 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-139]:                                                                     adam_beta1=0.9,
[default0]:07/02/2024 19:53:28 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-139]:                                                                     adam_beta2=0.95,
[default0]:07/02/2024 19:53:28 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-139]:                                                                     torch_adam_is_fused=True,
[default0]:07/02/2024 19:53:28 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-139]:                                                                     name='adamW'),
[default0]:07/02/2024 19:53:28 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-139]:                                zero_stage=1,
[default0]:07/02/2024 19:53:28 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-139]:                                weight_decay=0.01,
[default0]:07/02/2024 19:53:28 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-139]:                                clip_grad=1.0,
[default0]:07/02/2024 19:53:28 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-139]:                                accumulate_grad_in_fp32=True,
[default0]:07/02/2024 19:53:28 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-139]:                                learning_rate_scheduler=LRSchedulerArgs(learning_rate=0.0001,
[default0]:07/02/2024 19:53:28 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-139]:                                                                        lr_warmup_steps=1,
[default0]:07/02/2024 19:53:28 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-139]:                                                                        lr_warmup_style='linear',
[default0]:07/02/2024 19:53:28 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-139]:                                                                        lr_decay_style='linear',
[default0]:07/02/2024 19:53:28 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-139]:                                                                        lr_decay_steps=19,
[default0]:07/02/2024 19:53:28 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-139]:                                                                        lr_decay_starting_step=None,
[default0]:07/02/2024 19:53:28 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-139]:                                                                        min_decay_lr=1e-05)),
[default0]:07/02/2024 19:53:28 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-139]:        data_stages=[DatasetStageArgs(name='Training Stage',
[default0]:07/02/2024 19:53:28 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-139]:                                      start_training_step=1,
[default0]:07/02/2024 19:53:28 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-139]:                                      data=DataArgs(dataset=PretrainDatasetsArgs(hf_dataset_or_datasets='roneneldan/TinyStories',
[default0]:07/02/2024 19:53:28 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-139]:                                                                                 hf_dataset_splits='train',
[default0]:07/02/2024 19:53:28 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-139]:                                                                                 hf_dataset_config_name=None,
[default0]:07/02/2024 19:53:28 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-139]:                                                                                 dataset_processing_num_proc_per_process=64,
[default0]:07/02/2024 19:53:28 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-139]:                                                                                 dataset_overwrite_cache=False,
[default0]:07/02/2024 19:53:28 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-139]:                                                                                 text_column_name='text'),
[default0]:07/02/2024 19:53:28 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-139]:                                                    seed=42,
[default0]:07/02/2024 19:53:28 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-139]:                                                    num_loading_workers=32))],
[default0]:07/02/2024 19:53:28 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-139]:        profiler=ProfilerArgs(profiler_export_path=Path('/fsx/ferdinandmom/ferdinand-hf/bench_cluster/results/llama-1B/16_GPUS/dp-1_tp-4_pp-4_mbz-8')),
[default0]:07/02/2024 19:53:28 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-139]:        lighteval=None)
[default0]:07/02/2024 19:53:28 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-139]: Model Config:
[default0]:07/02/2024 19:53:28 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-139]: LlamaConfig(bos_token_id=1,
[default0]:07/02/2024 19:53:28 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-139]:             eos_token_id=2,
[default0]:07/02/2024 19:53:28 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-139]:             hidden_act='silu',
[default0]:07/02/2024 19:53:28 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-139]:             hidden_size=2048,
[default0]:07/02/2024 19:53:28 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-139]:             initializer_range=0.02,
[default0]:07/02/2024 19:53:28 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-139]:             intermediate_size=4096,
[default0]:07/02/2024 19:53:28 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-139]:             is_llama_config=True,
[default0]:07/02/2024 19:53:28 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-139]:             max_position_embeddings=4096,
[default0]:07/02/2024 19:53:28 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-139]:             num_attention_heads=32,
[default0]:07/02/2024 19:53:28 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-139]:             num_hidden_layers=24,
[default0]:07/02/2024 19:53:28 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-139]:             num_key_value_heads=32,
[default0]:07/02/2024 19:53:28 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-139]:             pad_token_id=None,
[default0]:07/02/2024 19:53:28 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-139]:             pretraining_tp=1,
[default0]:07/02/2024 19:53:28 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-139]:             rms_norm_eps=1e-05,
[default0]:07/02/2024 19:53:28 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-139]:             rope_scaling=None,
[default0]:07/02/2024 19:53:28 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-139]:             rope_theta=10000.0,
[default0]:07/02/2024 19:53:28 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-139]:             tie_word_embeddings=True,
[default0]:07/02/2024 19:53:28 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-139]:             use_cache=True,
[default0]:07/02/2024 19:53:28 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-139]:             vocab_size=50260)
[default0]:07/02/2024 19:53:28 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-139]: Building model..
[default0]:07/02/2024 19:53:28 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-139]: Setting PP block ranks...
[default2]:07/02/2024 19:53:43 [INFO|DP=0|PP=2|TP=2|ip-26-0-172-73]: Local number of parameters: 62.9M (120.05MiB)
[default2]:07/02/2024 19:53:43 [INFO|DP=0|PP=2|TP=2|ip-26-0-172-73]: [After model building] Memory usage: 126.06MiB. Peak allocated: 128.09MiB Peak reserved: 130.00MiB
[default2]:07/02/2024 19:53:43 [INFO|DP=0|PP=2|TP=2|ip-26-0-172-73]: No checkpoint path provided.
[default0]:07/02/2024 19:53:43 [INFO|DP=0|PP=2|TP=0|ip-26-0-172-73]: Local number of parameters: 62.9M (120.05MiB)
[default0]:07/02/2024 19:53:43 [INFO|DP=0|PP=2|TP=0|ip-26-0-172-73]: [After model building] Memory usage: 126.06MiB. Peak allocated: 128.09MiB Peak reserved: 130.00MiB
[default5]:07/02/2024 19:53:43 [INFO|DP=0|PP=1|TP=1|ip-26-0-169-139]: Local number of parameters: 73.4M (140.05MiB)
[default5]:07/02/2024 19:53:43 [INFO|DP=0|PP=1|TP=1|ip-26-0-169-139]: [After model building] Memory usage: 147.07MiB. Peak allocated: 149.10MiB Peak reserved: 150.00MiB
[default5]:07/02/2024 19:53:43 [INFO|DP=0|PP=1|TP=1|ip-26-0-169-139]: No checkpoint path provided.
[default2]:07/02/2024 19:53:43 [INFO|DP=0|PP=0|TP=2|ip-26-0-169-139]: Local number of parameters: 99.2M (189.14MiB)
[default2]:07/02/2024 19:53:43 [INFO|DP=0|PP=0|TP=2|ip-26-0-169-139]: [After model building] Memory usage: 197.07MiB. Peak allocated: 199.10MiB Peak reserved: 200.00MiB
[default2]:07/02/2024 19:53:43 [INFO|DP=0|PP=0|TP=2|ip-26-0-169-139]: No checkpoint path provided.
[default0]:07/02/2024 19:53:43 [INFO|DP=0|PP=2|TP=0|ip-26-0-172-73]: No checkpoint path provided.
[default1]:07/02/2024 19:53:43 [INFO|DP=0|PP=2|TP=1|ip-26-0-172-73]: Local number of parameters: 62.9M (120.05MiB)
[default6]:07/02/2024 19:53:43 [INFO|DP=0|PP=1|TP=2|ip-26-0-169-139]: Local number of parameters: 73.4M (140.05MiB)
[default6]:07/02/2024 19:53:43 [INFO|DP=0|PP=1|TP=2|ip-26-0-169-139]: [After model building] Memory usage: 147.07MiB. Peak allocated: 149.10MiB Peak reserved: 150.00MiB
[default1]:07/02/2024 19:53:43 [INFO|DP=0|PP=2|TP=1|ip-26-0-172-73]: [After model building] Memory usage: 126.06MiB. Peak allocated: 128.09MiB Peak reserved: 130.00MiB
[default1]:07/02/2024 19:53:43 [INFO|DP=0|PP=2|TP=1|ip-26-0-172-73]: No checkpoint path provided.
[default6]:07/02/2024 19:53:43 [INFO|DP=0|PP=1|TP=2|ip-26-0-169-139]: No checkpoint path provided.
[default1]:07/02/2024 19:53:43 [INFO|DP=0|PP=0|TP=1|ip-26-0-169-139]: Local number of parameters: 99.2M (189.14MiB)
[default1]:07/02/2024 19:53:43 [INFO|DP=0|PP=0|TP=1|ip-26-0-169-139]: [After model building] Memory usage: 197.07MiB. Peak allocated: 199.10MiB Peak reserved: 200.00MiB
[default1]:07/02/2024 19:53:43 [INFO|DP=0|PP=0|TP=1|ip-26-0-169-139]: No checkpoint path provided.
[default4]:07/02/2024 19:53:43 [INFO|DP=0|PP=1|TP=0|ip-26-0-169-139]: Local number of parameters: 73.4M (140.05MiB)
[default4]:07/02/2024 19:53:43 [INFO|DP=0|PP=1|TP=0|ip-26-0-169-139]: [After model building] Memory usage: 147.07MiB. Peak allocated: 149.10MiB Peak reserved: 150.00MiB
[default4]:07/02/2024 19:53:43 [INFO|DP=0|PP=1|TP=0|ip-26-0-169-139]: No checkpoint path provided.
[default3]:07/02/2024 19:53:43 [INFO|DP=0|PP=0|TP=3|ip-26-0-169-139]: Local number of parameters: 99.2M (189.14MiB)
[default3]:07/02/2024 19:53:43 [INFO|DP=0|PP=0|TP=3|ip-26-0-169-139]: [After model building] Memory usage: 197.07MiB. Peak allocated: 199.10MiB Peak reserved: 200.00MiB
[default3]:07/02/2024 19:53:43 [INFO|DP=0|PP=0|TP=3|ip-26-0-169-139]: No checkpoint path provided.
[default0]:07/02/2024 19:53:43 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-139]: Total number of parameters: 1.21G (2313.42MiB)
[default0]:07/02/2024 19:53:43 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-139]: Local number of parameters: 99.2M (189.14MiB)
[default0]:07/02/2024 19:53:43 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-139]: [After model building] Memory usage: 197.07MiB. Peak allocated: 199.10MiB Peak reserved: 200.00MiB
[default0]:07/02/2024 19:53:43 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-139]: No checkpoint path provided.
[default0]:07/02/2024 19:53:43 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-139]: Parametrizing model parameters using StandardParametrizator
[default7]:07/02/2024 19:53:43 [INFO|DP=0|PP=3|TP=3|ip-26-0-172-73]: Local number of parameters: 67.7M (129.12MiB)
[default7]:07/02/2024 19:53:43 [INFO|DP=0|PP=3|TP=3|ip-26-0-172-73]: [After model building] Memory usage: 134.05MiB. Peak allocated: 136.08MiB Peak reserved: 138.00MiB
[default7]:07/02/2024 19:53:43 [INFO|DP=0|PP=3|TP=3|ip-26-0-172-73]: No checkpoint path provided.
[default6]:07/02/2024 19:53:43 [INFO|DP=0|PP=3|TP=2|ip-26-0-172-73]: Local number of parameters: 67.7M (129.12MiB)
[default6]:07/02/2024 19:53:43 [INFO|DP=0|PP=3|TP=2|ip-26-0-172-73]: [After model building] Memory usage: 134.05MiB. Peak allocated: 136.08MiB Peak reserved: 138.00MiB
[default6]:07/02/2024 19:53:43 [INFO|DP=0|PP=3|TP=2|ip-26-0-172-73]: No checkpoint path provided.
[default5]:07/02/2024 19:53:43 [INFO|DP=0|PP=3|TP=1|ip-26-0-172-73]: Local number of parameters: 67.7M (129.12MiB)
[default5]:07/02/2024 19:53:43 [INFO|DP=0|PP=3|TP=1|ip-26-0-172-73]: [After model building] Memory usage: 134.05MiB. Peak allocated: 136.08MiB Peak reserved: 138.00MiB
[default5]:07/02/2024 19:53:43 [INFO|DP=0|PP=3|TP=1|ip-26-0-172-73]: No checkpoint path provided.
[default7]:07/02/2024 19:53:43 [INFO|DP=0|PP=1|TP=3|ip-26-0-169-139]: Local number of parameters: 73.4M (140.05MiB)
[default7]:07/02/2024 19:53:43 [INFO|DP=0|PP=1|TP=3|ip-26-0-169-139]: [After model building] Memory usage: 147.07MiB. Peak allocated: 149.10MiB Peak reserved: 150.00MiB
[default7]:07/02/2024 19:53:43 [INFO|DP=0|PP=1|TP=3|ip-26-0-169-139]: No checkpoint path provided.
[default4]:07/02/2024 19:53:43 [INFO|DP=0|PP=3|TP=0|ip-26-0-172-73]: Local number of parameters: 67.7M (129.12MiB)
[default4]:07/02/2024 19:53:43 [INFO|DP=0|PP=3|TP=0|ip-26-0-172-73]: [After model building] Memory usage: 134.05MiB. Peak allocated: 136.08MiB Peak reserved: 138.00MiB
[default4]:07/02/2024 19:53:43 [INFO|DP=0|PP=3|TP=0|ip-26-0-172-73]: No checkpoint path provided.
[default3]:07/02/2024 19:53:43 [INFO|DP=0|PP=2|TP=3|ip-26-0-172-73]: Local number of parameters: 62.9M (120.05MiB)
[default3]:07/02/2024 19:53:43 [INFO|DP=0|PP=2|TP=3|ip-26-0-172-73]: [After model building] Memory usage: 126.06MiB. Peak allocated: 128.09MiB Peak reserved: 130.00MiB
[default3]:07/02/2024 19:53:43 [INFO|DP=0|PP=2|TP=3|ip-26-0-172-73]: No checkpoint path provided.
[default0]:07/02/2024 19:53:44 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-139]: [Optimizer Building] Using LearningRateForSP as learning rate
[default0]:07/02/2024 19:53:44 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-139]: [ZeRO sharding] Size of optimizer params per rank:
[default0]:07/02/2024 19:53:44 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-139]: [ZeRO sharding] DP Rank 0 has 99.2M out of 99.2M (100.00%) params' optimizer states
[default0]:07/02/2024 19:53:45 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-139]: [Training Plan] Stage Training Stage has 19 remaining training steps and has consumed 0 samples
[default0]:07/02/2024 19:53:45 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-139]: Using `datasets` library
[default0]:07/02/2024 19:53:45 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-139]: Loading tokenizer from openai-community/gpt2 and transformers/hf_hub versions ('4.41.2', '0.23.4')
[default0]:Repo card metadata block was not found. Setting CardData to empty.
[default0]:07/02/2024 19:53:45 [WARNING|DP=0|PP=0|TP=0|ip-26-0-169-139]: Repo card metadata block was not found. Setting CardData to empty.
[default0]:07/02/2024 19:53:46 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-139]: [Training Plan] There are 1 training stages 
[default0]:07/02/2024 19:53:46 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-139]: [Stage Training Stage] start from step 1 
[default0]:07/02/2024 19:53:46 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-139]: 
[default0]:07/02/2024 19:53:46 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-139]: [Start training] datetime: 2024-07-02 19:53:46.512495 | mbs: 8 | grad_accum: 128 | global_batch_size: 1024 | sequence_length: 4096 | train_steps: 20 | start_iteration_step: 0 | consumed_train_samples: 0
[default0]:07/02/2024 19:53:46 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-139]: Resuming training from stage Training Stage, it has trained for 0 samples and has 19 remaining train steps
[default0]:07/02/2024 19:53:46 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-139]:  Memory usage: 953.61MiB. Peak allocated 953.61MiB. Peak reserved: 960.00MiB
[default7]:Repo card metadata block was not found. Setting CardData to empty.
[default1]:07/02/2024 19:53:46 [WARNING|DP=0|PP=2|TP=1|ip-26-0-172-73]: Repo card metadata block was not found. Setting CardData to empty.
[default0]:07/02/2024 19:53:46 [WARNING|DP=0|PP=2|TP=0|ip-26-0-172-73]: Repo card metadata block was not found. Setting CardData to empty.
[default6]:07/02/2024 19:53:46 [WARNING|DP=0|PP=1|TP=2|ip-26-0-169-139]: Repo card metadata block was not found. Setting CardData to empty.
[default2]:Repo card metadata block was not found. Setting CardData to empty.
[default2]:07/02/2024 19:53:46 [WARNING|DP=0|PP=0|TP=2|ip-26-0-169-139]: Repo card metadata block was not found. Setting CardData to empty.
[default1]:07/02/2024 19:53:46 [WARNING|DP=0|PP=0|TP=1|ip-26-0-169-139]: Repo card metadata block was not found. Setting CardData to empty.
[default3]:Repo card metadata block was not found. Setting CardData to empty.
[default3]:07/02/2024 19:53:46 [WARNING|DP=0|PP=0|TP=3|ip-26-0-169-139]: Repo card metadata block was not found. Setting CardData to empty.
[default6]:Repo card metadata block was not found. Setting CardData to empty.
[default7]:07/02/2024 19:53:46 [WARNING|DP=0|PP=1|TP=3|ip-26-0-169-139]: Repo card metadata block was not found. Setting CardData to empty.
[default5]:07/02/2024 19:53:46 [WARNING|DP=0|PP=3|TP=1|ip-26-0-172-73]: Repo card metadata block was not found. Setting CardData to empty.
[default6]:07/02/2024 19:53:46 [WARNING|DP=0|PP=3|TP=2|ip-26-0-172-73]: Repo card metadata block was not found. Setting CardData to empty.
[default1]:Repo card metadata block was not found. Setting CardData to empty.
[default4]:07/02/2024 19:53:46 [WARNING|DP=0|PP=3|TP=0|ip-26-0-172-73]: Repo card metadata block was not found. Setting CardData to empty.
[default6]:Repo card metadata block was not found. Setting CardData to empty.
[default3]:07/02/2024 19:53:46 [WARNING|DP=0|PP=2|TP=3|ip-26-0-172-73]: Repo card metadata block was not found. Setting CardData to empty.
[default3]:Repo card metadata block was not found. Setting CardData to empty.
[default5]:Repo card metadata block was not found. Setting CardData to empty.
[default1]:Repo card metadata block was not found. Setting CardData to empty.
[default4]:Repo card metadata block was not found. Setting CardData to empty.
[default0]:Repo card metadata block was not found. Setting CardData to empty.
[default5]:07/02/2024 19:53:46 [WARNING|DP=0|PP=1|TP=1|ip-26-0-169-139]: Repo card metadata block was not found. Setting CardData to empty.
[default2]:07/02/2024 19:53:46 [WARNING|DP=0|PP=2|TP=2|ip-26-0-172-73]: Repo card metadata block was not found. Setting CardData to empty.
[default4]:07/02/2024 19:53:46 [WARNING|DP=0|PP=1|TP=0|ip-26-0-169-139]: Repo card metadata block was not found. Setting CardData to empty.
[default4]:Repo card metadata block was not found. Setting CardData to empty.
[default7]:07/02/2024 19:53:46 [WARNING|DP=0|PP=3|TP=3|ip-26-0-172-73]: Repo card metadata block was not found. Setting CardData to empty.
[default5]:Repo card metadata block was not found. Setting CardData to empty.
[default2]:Repo card metadata block was not found. Setting CardData to empty.
[default7]:Repo card metadata block was not found. Setting CardData to empty.
[default5]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/autograd/graph.py:744: UserWarning: c10d::allreduce_: an autograd kernel was not registered to the Autograd key(s) but we are trying to backprop through it. This may lead to silently incorrect behavior. This behavior is deprecated and will be removed in a future version of PyTorch. If your operator is differentiable, please ensure you have registered an autograd kernel to the correct Autograd key (e.g. DispatchKey::Autograd, DispatchKey::CompositeImplicitAutograd). If your operator is not differentiable, or to squash this warning and use the previous behavior, please register torch::CppFunction::makeFallthrough() to DispatchKey::Autograd. (Triggered internally at ../torch/csrc/autograd/autograd_not_implemented_fallback.cpp:63.)
[default5]:  return Variable._execution_engine.run_backward(  # Calls into the C++ engine to run the backward pass
[default4]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/autograd/graph.py:744: UserWarning: c10d::allreduce_: an autograd kernel was not registered to the Autograd key(s) but we are trying to backprop through it. This may lead to silently incorrect behavior. This behavior is deprecated and will be removed in a future version of PyTorch. If your operator is differentiable, please ensure you have registered an autograd kernel to the correct Autograd key (e.g. DispatchKey::Autograd, DispatchKey::CompositeImplicitAutograd). If your operator is not differentiable, or to squash this warning and use the previous behavior, please register torch::CppFunction::makeFallthrough() to DispatchKey::Autograd. (Triggered internally at ../torch/csrc/autograd/autograd_not_implemented_fallback.cpp:63.)
[default4]:  return Variable._execution_engine.run_backward(  # Calls into the C++ engine to run the backward pass
[default6]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/autograd/graph.py:744: UserWarning: c10d::allreduce_: an autograd kernel was not registered to the Autograd key(s) but we are trying to backprop through it. This may lead to silently incorrect behavior. This behavior is deprecated and will be removed in a future version of PyTorch. If your operator is differentiable, please ensure you have registered an autograd kernel to the correct Autograd key (e.g. DispatchKey::Autograd, DispatchKey::CompositeImplicitAutograd). If your operator is not differentiable, or to squash this warning and use the previous behavior, please register torch::CppFunction::makeFallthrough() to DispatchKey::Autograd. (Triggered internally at ../torch/csrc/autograd/autograd_not_implemented_fallback.cpp:63.)
[default6]:  return Variable._execution_engine.run_backward(  # Calls into the C++ engine to run the backward pass
[default7]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/autograd/graph.py:744: UserWarning: c10d::allreduce_: an autograd kernel was not registered to the Autograd key(s) but we are trying to backprop through it. This may lead to silently incorrect behavior. This behavior is deprecated and will be removed in a future version of PyTorch. If your operator is differentiable, please ensure you have registered an autograd kernel to the correct Autograd key (e.g. DispatchKey::Autograd, DispatchKey::CompositeImplicitAutograd). If your operator is not differentiable, or to squash this warning and use the previous behavior, please register torch::CppFunction::makeFallthrough() to DispatchKey::Autograd. (Triggered internally at ../torch/csrc/autograd/autograd_not_implemented_fallback.cpp:63.)
[default7]:  return Variable._execution_engine.run_backward(  # Calls into the C++ engine to run the backward pass
[default0]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/autograd/graph.py:744: UserWarning: c10d::allreduce_: an autograd kernel was not registered to the Autograd key(s) but we are trying to backprop through it. This may lead to silently incorrect behavior. This behavior is deprecated and will be removed in a future version of PyTorch. If your operator is differentiable, please ensure you have registered an autograd kernel to the correct Autograd key (e.g. DispatchKey::Autograd, DispatchKey::CompositeImplicitAutograd). If your operator is not differentiable, or to squash this warning and use the previous behavior, please register torch::CppFunction::makeFallthrough() to DispatchKey::Autograd. (Triggered internally at ../torch/csrc/autograd/autograd_not_implemented_fallback.cpp:63.)
[default0]:  return Variable._execution_engine.run_backward(  # Calls into the C++ engine to run the backward pass
[default3]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/autograd/graph.py:744: UserWarning: c10d::allreduce_: an autograd kernel was not registered to the Autograd key(s) but we are trying to backprop through it. This may lead to silently incorrect behavior. This behavior is deprecated and will be removed in a future version of PyTorch. If your operator is differentiable, please ensure you have registered an autograd kernel to the correct Autograd key (e.g. DispatchKey::Autograd, DispatchKey::CompositeImplicitAutograd). If your operator is not differentiable, or to squash this warning and use the previous behavior, please register torch::CppFunction::makeFallthrough() to DispatchKey::Autograd. (Triggered internally at ../torch/csrc/autograd/autograd_not_implemented_fallback.cpp:63.)
[default3]:  return Variable._execution_engine.run_backward(  # Calls into the C++ engine to run the backward pass
[default2]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/autograd/graph.py:744: UserWarning: c10d::allreduce_: an autograd kernel was not registered to the Autograd key(s) but we are trying to backprop through it. This may lead to silently incorrect behavior. This behavior is deprecated and will be removed in a future version of PyTorch. If your operator is differentiable, please ensure you have registered an autograd kernel to the correct Autograd key (e.g. DispatchKey::Autograd, DispatchKey::CompositeImplicitAutograd). If your operator is not differentiable, or to squash this warning and use the previous behavior, please register torch::CppFunction::makeFallthrough() to DispatchKey::Autograd. (Triggered internally at ../torch/csrc/autograd/autograd_not_implemented_fallback.cpp:63.)
[default2]:  return Variable._execution_engine.run_backward(  # Calls into the C++ engine to run the backward pass
[default1]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/autograd/graph.py:744: UserWarning: c10d::allreduce_: an autograd kernel was not registered to the Autograd key(s) but we are trying to backprop through it. This may lead to silently incorrect behavior. This behavior is deprecated and will be removed in a future version of PyTorch. If your operator is differentiable, please ensure you have registered an autograd kernel to the correct Autograd key (e.g. DispatchKey::Autograd, DispatchKey::CompositeImplicitAutograd). If your operator is not differentiable, or to squash this warning and use the previous behavior, please register torch::CppFunction::makeFallthrough() to DispatchKey::Autograd. (Triggered internally at ../torch/csrc/autograd/autograd_not_implemented_fallback.cpp:63.)
[default1]:  return Variable._execution_engine.run_backward(  # Calls into the C++ engine to run the backward pass
[default4]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/autograd/graph.py:744: UserWarning: c10d::allreduce_: an autograd kernel was not registered to the Autograd key(s) but we are trying to backprop through it. This may lead to silently incorrect behavior. This behavior is deprecated and will be removed in a future version of PyTorch. If your operator is differentiable, please ensure you have registered an autograd kernel to the correct Autograd key (e.g. DispatchKey::Autograd, DispatchKey::CompositeImplicitAutograd). If your operator is not differentiable, or to squash this warning and use the previous behavior, please register torch::CppFunction::makeFallthrough() to DispatchKey::Autograd. (Triggered internally at ../torch/csrc/autograd/autograd_not_implemented_fallback.cpp:63.)
[default4]:  return Variable._execution_engine.run_backward(  # Calls into the C++ engine to run the backward pass
[default6]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/autograd/graph.py:744: UserWarning: c10d::allreduce_: an autograd kernel was not registered to the Autograd key(s) but we are trying to backprop through it. This may lead to silently incorrect behavior. This behavior is deprecated and will be removed in a future version of PyTorch. If your operator is differentiable, please ensure you have registered an autograd kernel to the correct Autograd key (e.g. DispatchKey::Autograd, DispatchKey::CompositeImplicitAutograd). If your operator is not differentiable, or to squash this warning and use the previous behavior, please register torch::CppFunction::makeFallthrough() to DispatchKey::Autograd. (Triggered internally at ../torch/csrc/autograd/autograd_not_implemented_fallback.cpp:63.)
[default6]:  return Variable._execution_engine.run_backward(  # Calls into the C++ engine to run the backward pass
[default5]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/autograd/graph.py:744: UserWarning: c10d::allreduce_: an autograd kernel was not registered to the Autograd key(s) but we are trying to backprop through it. This may lead to silently incorrect behavior. This behavior is deprecated and will be removed in a future version of PyTorch. If your operator is differentiable, please ensure you have registered an autograd kernel to the correct Autograd key (e.g. DispatchKey::Autograd, DispatchKey::CompositeImplicitAutograd). If your operator is not differentiable, or to squash this warning and use the previous behavior, please register torch::CppFunction::makeFallthrough() to DispatchKey::Autograd. (Triggered internally at ../torch/csrc/autograd/autograd_not_implemented_fallback.cpp:63.)
[default5]:  return Variable._execution_engine.run_backward(  # Calls into the C++ engine to run the backward pass
[default7]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/autograd/graph.py:744: UserWarning: c10d::allreduce_: an autograd kernel was not registered to the Autograd key(s) but we are trying to backprop through it. This may lead to silently incorrect behavior. This behavior is deprecated and will be removed in a future version of PyTorch. If your operator is differentiable, please ensure you have registered an autograd kernel to the correct Autograd key (e.g. DispatchKey::Autograd, DispatchKey::CompositeImplicitAutograd). If your operator is not differentiable, or to squash this warning and use the previous behavior, please register torch::CppFunction::makeFallthrough() to DispatchKey::Autograd. (Triggered internally at ../torch/csrc/autograd/autograd_not_implemented_fallback.cpp:63.)
[default7]:  return Variable._execution_engine.run_backward(  # Calls into the C++ engine to run the backward pass
[default2]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/autograd/graph.py:744: UserWarning: c10d::allreduce_: an autograd kernel was not registered to the Autograd key(s) but we are trying to backprop through it. This may lead to silently incorrect behavior. This behavior is deprecated and will be removed in a future version of PyTorch. If your operator is differentiable, please ensure you have registered an autograd kernel to the correct Autograd key (e.g. DispatchKey::Autograd, DispatchKey::CompositeImplicitAutograd). If your operator is not differentiable, or to squash this warning and use the previous behavior, please register torch::CppFunction::makeFallthrough() to DispatchKey::Autograd. (Triggered internally at ../torch/csrc/autograd/autograd_not_implemented_fallback.cpp:63.)
[default2]:  return Variable._execution_engine.run_backward(  # Calls into the C++ engine to run the backward pass
[default1]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/autograd/graph.py:744: UserWarning: c10d::allreduce_: an autograd kernel was not registered to the Autograd key(s) but we are trying to backprop through it. This may lead to silently incorrect behavior. This behavior is deprecated and will be removed in a future version of PyTorch. If your operator is differentiable, please ensure you have registered an autograd kernel to the correct Autograd key (e.g. DispatchKey::Autograd, DispatchKey::CompositeImplicitAutograd). If your operator is not differentiable, or to squash this warning and use the previous behavior, please register torch::CppFunction::makeFallthrough() to DispatchKey::Autograd. (Triggered internally at ../torch/csrc/autograd/autograd_not_implemented_fallback.cpp:63.)
[default1]:  return Variable._execution_engine.run_backward(  # Calls into the C++ engine to run the backward pass
[default3]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/autograd/graph.py:744: UserWarning: c10d::allreduce_: an autograd kernel was not registered to the Autograd key(s) but we are trying to backprop through it. This may lead to silently incorrect behavior. This behavior is deprecated and will be removed in a future version of PyTorch. If your operator is differentiable, please ensure you have registered an autograd kernel to the correct Autograd key (e.g. DispatchKey::Autograd, DispatchKey::CompositeImplicitAutograd). If your operator is not differentiable, or to squash this warning and use the previous behavior, please register torch::CppFunction::makeFallthrough() to DispatchKey::Autograd. (Triggered internally at ../torch/csrc/autograd/autograd_not_implemented_fallback.cpp:63.)
[default3]:  return Variable._execution_engine.run_backward(  # Calls into the C++ engine to run the backward pass
[default0]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/autograd/graph.py:744: UserWarning: c10d::allreduce_: an autograd kernel was not registered to the Autograd key(s) but we are trying to backprop through it. This may lead to silently incorrect behavior. This behavior is deprecated and will be removed in a future version of PyTorch. If your operator is differentiable, please ensure you have registered an autograd kernel to the correct Autograd key (e.g. DispatchKey::Autograd, DispatchKey::CompositeImplicitAutograd). If your operator is not differentiable, or to squash this warning and use the previous behavior, please register torch::CppFunction::makeFallthrough() to DispatchKey::Autograd. (Triggered internally at ../torch/csrc/autograd/autograd_not_implemented_fallback.cpp:63.)
[default0]:  return Variable._execution_engine.run_backward(  # Calls into the C++ engine to run the backward pass
[default7]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/distributed_c10d.py:2261: UserWarning: torch.distributed.all_reduce_coalesced will be deprecated. If you must use it, please revisit our documentation later at https://pytorch.org/docs/master/distributed.html#collective-functions
[default7]:  warnings.warn(
[default6]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/distributed_c10d.py:2261: UserWarning: torch.distributed.all_reduce_coalesced will be deprecated. If you must use it, please revisit our documentation later at https://pytorch.org/docs/master/distributed.html#collective-functions
[default6]:  warnings.warn(
[default3]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/distributed_c10d.py:2261: UserWarning: torch.distributed.all_reduce_coalesced will be deprecated. If you must use it, please revisit our documentation later at https://pytorch.org/docs/master/distributed.html#collective-functions
[default5]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/distributed_c10d.py:2261: UserWarning: torch.distributed.all_reduce_coalesced will be deprecated. If you must use it, please revisit our documentation later at https://pytorch.org/docs/master/distributed.html#collective-functions
[default5]:  warnings.warn(
[default3]:  warnings.warn(
[default0]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/distributed_c10d.py:2261: UserWarning: torch.distributed.all_reduce_coalesced will be deprecated. If you must use it, please revisit our documentation later at https://pytorch.org/docs/master/distributed.html#collective-functions
[default0]:  warnings.warn(
[default2]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/distributed_c10d.py:2261: UserWarning: torch.distributed.all_reduce_coalesced will be deprecated. If you must use it, please revisit our documentation later at https://pytorch.org/docs/master/distributed.html#collective-functions
[default2]:  warnings.warn(
[default4]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/distributed_c10d.py:2261: UserWarning: torch.distributed.all_reduce_coalesced will be deprecated. If you must use it, please revisit our documentation later at https://pytorch.org/docs/master/distributed.html#collective-functions
[default4]:  warnings.warn(
[default1]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/distributed_c10d.py:2261: UserWarning: torch.distributed.all_reduce_coalesced will be deprecated. If you must use it, please revisit our documentation later at https://pytorch.org/docs/master/distributed.html#collective-functions
[default1]:  warnings.warn(
[default2]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/distributed_c10d.py:2261: UserWarning: torch.distributed.all_reduce_coalesced will be deprecated. If you must use it, please revisit our documentation later at https://pytorch.org/docs/master/distributed.html#collective-functions
[default2]:  warnings.warn(
[default6]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/distributed_c10d.py:2261: UserWarning: torch.distributed.all_reduce_coalesced will be deprecated. If you must use it, please revisit our documentation later at https://pytorch.org/docs/master/distributed.html#collective-functions
[default6]:  warnings.warn(
[default5]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/distributed_c10d.py:2261: UserWarning: torch.distributed.all_reduce_coalesced will be deprecated. If you must use it, please revisit our documentation later at https://pytorch.org/docs/master/distributed.html#collective-functions
[default5]:  warnings.warn(
[default1]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/distributed_c10d.py:2261: UserWarning: torch.distributed.all_reduce_coalesced will be deprecated. If you must use it, please revisit our documentation later at https://pytorch.org/docs/master/distributed.html#collective-functions
[default1]:  warnings.warn(
[default4]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/distributed_c10d.py:2261: UserWarning: torch.distributed.all_reduce_coalesced will be deprecated. If you must use it, please revisit our documentation later at https://pytorch.org/docs/master/distributed.html#collective-functions
[default4]:  warnings.warn(
[default3]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/distributed_c10d.py:2261: UserWarning: torch.distributed.all_reduce_coalesced will be deprecated. If you must use it, please revisit our documentation later at https://pytorch.org/docs/master/distributed.html#collective-functions
[default3]:  warnings.warn(
[default0]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/distributed_c10d.py:2261: UserWarning: torch.distributed.all_reduce_coalesced will be deprecated. If you must use it, please revisit our documentation later at https://pytorch.org/docs/master/distributed.html#collective-functions
[default0]:  warnings.warn(
[default7]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/distributed_c10d.py:2261: UserWarning: torch.distributed.all_reduce_coalesced will be deprecated. If you must use it, please revisit our documentation later at https://pytorch.org/docs/master/distributed.html#collective-functions
[default7]:  warnings.warn(
[default0]:07/02/2024 19:54:30 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-139]:  Memory usage: 1021.40MiB. Peak allocated 23680.34MiB. Peak reserved: 23984.00MiB
[default4]:07/02/2024 19:54:38 [INFO|DP=0|PP=3|TP=0|ip-26-0-172-73]: iteration: 1 / 20 | consumed_tokens: 4.19M | elapsed_time_per_iteration_ms: 50.8K | tokens_per_sec: 82.6K | tokens_per_sec_per_gpu: 5.16K | global_batch_size: 1.02K | lm_loss: 11.2 | lr: 0.0001 | model_tflops_per_gpu: 46.9 | hardware_tflops_per_gpu: 46.9 | grad_norm: 10.9 | cuda_memory_allocated: 1.29G | cuda_max_memory_reserved: 8.46G | hd_total_memory_tb: 312G | hd_used_memory_tb: 65.5G | hd_free_memory_tb: 247G
[default0]:07/02/2024 19:54:38 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-139]:  Memory usage: 1777.96MiB. Peak allocated 1777.96MiB. Peak reserved: 23984.00MiB
[default0]:07/02/2024 19:54:53 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-139]:  Memory usage: 1777.96MiB. Peak allocated 24436.91MiB. Peak reserved: 24816.00MiB
[default4]:07/02/2024 19:54:54 [INFO|DP=0|PP=3|TP=0|ip-26-0-172-73]: iteration: 2 / 20 | consumed_tokens: 8.39M | elapsed_time_per_iteration_ms: 15.8K | tokens_per_sec: 265K | tokens_per_sec_per_gpu: 16.6K | global_batch_size: 1.02K | lm_loss: 11.2 | lr: 9.53e-05 | model_tflops_per_gpu: 150 | hardware_tflops_per_gpu: 150 | grad_norm: 11 | cuda_memory_allocated: 1.29G | cuda_max_memory_reserved: 8.46G | hd_total_memory_tb: 312G | hd_used_memory_tb: 65.5G | hd_free_memory_tb: 247G
[default0]:07/02/2024 19:54:54 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-139]:  Memory usage: 1777.96MiB. Peak allocated 1777.99MiB. Peak reserved: 24816.00MiB
[default0]:07/02/2024 19:55:11 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-139]:  Memory usage: 1777.96MiB. Peak allocated 24436.91MiB. Peak reserved: 24816.00MiB
[default4]:07/02/2024 19:55:11 [INFO|DP=0|PP=3|TP=0|ip-26-0-172-73]: iteration: 3 / 20 | consumed_tokens: 12.6M | elapsed_time_per_iteration_ms: 17.3K | tokens_per_sec: 242K | tokens_per_sec_per_gpu: 15.1K | global_batch_size: 1.02K | lm_loss: 9.83 | lr: 9.05e-05 | model_tflops_per_gpu: 137 | hardware_tflops_per_gpu: 137 | grad_norm: 44.3 | cuda_memory_allocated: 1.29G | cuda_max_memory_reserved: 8.46G | hd_total_memory_tb: 312G | hd_used_memory_tb: 65.5G | hd_free_memory_tb: 247G
[default0]:STAGE:2024-07-02 19:55:11 2571529:2571529 ActivityProfilerController.cpp:314] Completed Stage: Warm Up
[default0]:07/02/2024 19:55:11 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-139]:  Memory usage: 1777.96MiB. Peak allocated 1777.99MiB. Peak reserved: 24816.00MiB
[default0]:07/02/2024 19:55:28 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-139]:  Memory usage: 1777.96MiB. Peak allocated 24436.91MiB. Peak reserved: 24816.00MiB
[default4]:07/02/2024 19:55:28 [INFO|DP=0|PP=3|TP=0|ip-26-0-172-73]: iteration: 4 / 20 | consumed_tokens: 16.8M | elapsed_time_per_iteration_ms: 16.7K | tokens_per_sec: 252K | tokens_per_sec_per_gpu: 15.7K | global_batch_size: 1.02K | lm_loss: 12.1 | lr: 8.58e-05 | model_tflops_per_gpu: 143 | hardware_tflops_per_gpu: 143 | grad_norm: 24.8 | cuda_memory_allocated: 1.29G | cuda_max_memory_reserved: 8.46G | hd_total_memory_tb: 312G | hd_used_memory_tb: 65.5G | hd_free_memory_tb: 247G
[default0]:07/02/2024 19:55:28 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-139]:  Memory usage: 1777.96MiB. Peak allocated 1777.99MiB. Peak reserved: 24816.00MiB
[default0]:07/02/2024 19:55:43 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-139]:  Memory usage: 1777.96MiB. Peak allocated 24436.91MiB. Peak reserved: 24816.00MiB
[default4]:07/02/2024 19:55:43 [INFO|DP=0|PP=3|TP=0|ip-26-0-172-73]: iteration: 5 / 20 | consumed_tokens: 21M | elapsed_time_per_iteration_ms: 15.4K | tokens_per_sec: 272K | tokens_per_sec_per_gpu: 17K | global_batch_size: 1.02K | lm_loss: 10.1 | lr: 8.11e-05 | model_tflops_per_gpu: 154 | hardware_tflops_per_gpu: 154 | grad_norm: 11.4
[default4]:07/02/2024 19:55:59 [INFO|DP=0|PP=3|TP=0|ip-26-0-172-73]: iteration: 6 / 20 | consumed_tokens: 25.2M | elapsed_time_per_iteration_ms: 15.9K | tokens_per_sec: 264K | tokens_per_sec_per_gpu: 16.5K | global_batch_size: 1.02K | lm_loss: 9.39 | lr: 7.63e-05 | model_tflops_per_gpu: 150 | hardware_tflops_per_gpu: 150 | grad_norm: 7.05
[default0]:STAGE:2024-07-02 19:56:21 2571529:2571529 ActivityProfilerController.cpp:320] Completed Stage: Collection
[default0]:STAGE:2024-07-02 19:56:23 2571529:2571529 ActivityProfilerController.cpp:324] Completed Stage: Post Processing
[default0]:07/02/2024 19:59:07 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-139]:  Memory usage: 1777.96MiB. Peak allocated 24436.91MiB. Peak reserved: 24816.00MiB
[default0]:07/02/2024 19:59:23 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-139]:  Memory usage: 1777.96MiB. Peak allocated 24436.91MiB. Peak reserved: 24816.00MiB
[default4]:07/02/2024 19:59:23 [INFO|DP=0|PP=3|TP=0|ip-26-0-172-73]: iteration: 7 / 20 | consumed_tokens: 29.4M | elapsed_time_per_iteration_ms: 204K | tokens_per_sec: 20.6K | tokens_per_sec_per_gpu: 1.29K | global_batch_size: 1.02K | lm_loss: 8.69 | lr: 7.16e-05 | model_tflops_per_gpu: 11.7 | hardware_tflops_per_gpu: 11.7 | grad_norm: 5.43
[default0]:07/02/2024 19:59:39 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-139]:  Memory usage: 1777.96MiB. Peak allocated 24436.91MiB. Peak reserved: 24816.00MiB
[default4]:07/02/2024 19:59:39 [INFO|DP=0|PP=3|TP=0|ip-26-0-172-73]: iteration: 8 / 20 | consumed_tokens: 33.6M | elapsed_time_per_iteration_ms: 16.3K | tokens_per_sec: 257K | tokens_per_sec_per_gpu: 16K | global_batch_size: 1.02K | lm_loss: 8.77 | lr: 6.68e-05 | model_tflops_per_gpu: 146 | hardware_tflops_per_gpu: 146 | grad_norm: 18.4
[default0]:07/02/2024 19:59:57 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-139]:  Memory usage: 1777.96MiB. Peak allocated 24436.91MiB. Peak reserved: 24816.00MiB
[default4]:07/02/2024 19:59:57 [INFO|DP=0|PP=3|TP=0|ip-26-0-172-73]: iteration: 9 / 20 | consumed_tokens: 37.7M | elapsed_time_per_iteration_ms: 17.6K | tokens_per_sec: 238K | tokens_per_sec_per_gpu: 14.9K | global_batch_size: 1.02K | lm_loss: 8.11 | lr: 6.21e-05 | model_tflops_per_gpu: 135 | hardware_tflops_per_gpu: 135 | grad_norm: 4.96
[default4]:07/02/2024 20:00:14 [INFO|DP=0|PP=3|TP=0|ip-26-0-172-73]: iteration: 10 / 20 | consumed_tokens: 41.9M | elapsed_time_per_iteration_ms: 16.6K | tokens_per_sec: 253K | tokens_per_sec_per_gpu: 15.8K | global_batch_size: 1.02K | lm_loss: 7.96 | lr: 5.74e-05 | model_tflops_per_gpu: 144 | hardware_tflops_per_gpu: 144 | grad_norm: 4.62
[default0]:07/02/2024 20:00:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-139]:  Memory usage: 1777.96MiB. Peak allocated 24436.91MiB. Peak reserved: 24816.00MiB
[default0]:07/02/2024 20:00:30 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-139]:  Memory usage: 1777.96MiB. Peak allocated 24436.91MiB. Peak reserved: 24816.00MiB
[default4]:07/02/2024 20:00:30 [INFO|DP=0|PP=3|TP=0|ip-26-0-172-73]: iteration: 11 / 20 | consumed_tokens: 46.1M | elapsed_time_per_iteration_ms: 16.3K | tokens_per_sec: 257K | tokens_per_sec_per_gpu: 16.1K | global_batch_size: 1.02K | lm_loss: 7.84 | lr: 5.26e-05 | model_tflops_per_gpu: 146 | hardware_tflops_per_gpu: 146 | grad_norm: 4.93
[default0]:07/02/2024 20:00:46 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-139]:  Memory usage: 1777.96MiB. Peak allocated 24436.91MiB. Peak reserved: 24816.00MiB
[default4]:07/02/2024 20:00:46 [INFO|DP=0|PP=3|TP=0|ip-26-0-172-73]: iteration: 12 / 20 | consumed_tokens: 50.3M | elapsed_time_per_iteration_ms: 16K | tokens_per_sec: 262K | tokens_per_sec_per_gpu: 16.4K | global_batch_size: 1.02K | lm_loss: 7.64 | lr: 4.79e-05 | model_tflops_per_gpu: 148 | hardware_tflops_per_gpu: 148 | grad_norm: 4.08
[default4]:07/02/2024 20:01:02 [INFO|DP=0|PP=3|TP=0|ip-26-0-172-73]: iteration: 13 / 20 | consumed_tokens: 54.5M | elapsed_time_per_iteration_ms: 16.1K | tokens_per_sec: 261K | tokens_per_sec_per_gpu: 16.3K | global_batch_size: 1.02K | lm_loss: 7.48 | lr: 4.32e-05 | model_tflops_per_gpu: 148 | hardware_tflops_per_gpu: 148 | grad_norm: 3.28
[default0]:07/02/2024 20:01:02 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-139]:  Memory usage: 1777.96MiB. Peak allocated 24436.91MiB. Peak reserved: 24816.00MiB
[default4]:07/02/2024 20:01:19 [INFO|DP=0|PP=3|TP=0|ip-26-0-172-73]: iteration: 14 / 20 | consumed_tokens: 58.7M | elapsed_time_per_iteration_ms: 16.5K | tokens_per_sec: 254K | tokens_per_sec_per_gpu: 15.9K | global_batch_size: 1.02K | lm_loss: 7.4 | lr: 3.84e-05 | model_tflops_per_gpu: 144 | hardware_tflops_per_gpu: 144 | grad_norm: 3.52
[default0]:07/02/2024 20:01:19 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-139]:  Memory usage: 1777.96MiB. Peak allocated 24436.91MiB. Peak reserved: 24816.00MiB
[default0]:07/02/2024 20:01:35 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-139]:  Memory usage: 1777.96MiB. Peak allocated 24436.91MiB. Peak reserved: 24816.00MiB
[default4]:07/02/2024 20:01:35 [INFO|DP=0|PP=3|TP=0|ip-26-0-172-73]: iteration: 15 / 20 | consumed_tokens: 62.9M | elapsed_time_per_iteration_ms: 16.1K | tokens_per_sec: 261K | tokens_per_sec_per_gpu: 16.3K | global_batch_size: 1.02K | lm_loss: 7.29 | lr: 3.37e-05 | model_tflops_per_gpu: 148 | hardware_tflops_per_gpu: 148 | grad_norm: 3.13
[default4]:07/02/2024 20:01:51 [INFO|DP=0|PP=3|TP=0|ip-26-0-172-73]: iteration: 16 / 20 | consumed_tokens: 67.1M | elapsed_time_per_iteration_ms: 16K | tokens_per_sec: 263K | tokens_per_sec_per_gpu: 16.4K | global_batch_size: 1.02K | lm_loss: 7.18 | lr: 2.89e-05 | model_tflops_per_gpu: 149 | hardware_tflops_per_gpu: 149 | grad_norm: 3.11
[default0]:07/02/2024 20:01:51 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-139]:  Memory usage: 1777.96MiB. Peak allocated 24436.91MiB. Peak reserved: 24816.00MiB
[default0]:07/02/2024 20:02:06 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-139]:  Memory usage: 1777.96MiB. Peak allocated 24436.91MiB. Peak reserved: 24816.00MiB
[default4]:07/02/2024 20:02:06 [INFO|DP=0|PP=3|TP=0|ip-26-0-172-73]: iteration: 17 / 20 | consumed_tokens: 71.3M | elapsed_time_per_iteration_ms: 15.3K | tokens_per_sec: 275K | tokens_per_sec_per_gpu: 17.2K | global_batch_size: 1.02K | lm_loss: 7.09 | lr: 2.42e-05 | model_tflops_per_gpu: 156 | hardware_tflops_per_gpu: 156 | grad_norm: 3.22
[default0]:07/02/2024 20:02:22 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-139]:  Memory usage: 1777.96MiB. Peak allocated 24436.91MiB. Peak reserved: 24816.00MiB
[default4]:07/02/2024 20:02:22 [INFO|DP=0|PP=3|TP=0|ip-26-0-172-73]: iteration: 18 / 20 | consumed_tokens: 75.5M | elapsed_time_per_iteration_ms: 15.8K | tokens_per_sec: 266K | tokens_per_sec_per_gpu: 16.6K | global_batch_size: 1.02K | lm_loss: 7.02 | lr: 1.95e-05 | model_tflops_per_gpu: 151 | hardware_tflops_per_gpu: 151 | grad_norm: 3.19
[default0]:07/02/2024 20:02:37 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-139]:  Memory usage: 1777.96MiB. Peak allocated 24436.91MiB. Peak reserved: 24816.00MiB
[default4]:07/02/2024 20:02:37 [INFO|DP=0|PP=3|TP=0|ip-26-0-172-73]: iteration: 19 / 20 | consumed_tokens: 79.7M | elapsed_time_per_iteration_ms: 15.6K | tokens_per_sec: 268K | tokens_per_sec_per_gpu: 16.8K | global_batch_size: 1.02K | lm_loss: 6.97 | lr: 1.47e-05 | model_tflops_per_gpu: 152 | hardware_tflops_per_gpu: 152 | grad_norm: 3.06
[default4]:07/02/2024 20:02:53 [INFO|DP=0|PP=3|TP=0|ip-26-0-172-73]: iteration: 20 / 20 | consumed_tokens: 83.9M | elapsed_time_per_iteration_ms: 15.3K | tokens_per_sec: 274K | tokens_per_sec_per_gpu: 17.1K | global_batch_size: 1.02K | lm_loss: 6.92 | lr: 1e-05 | model_tflops_per_gpu: 156 | hardware_tflops_per_gpu: 156 | grad_norm: 2.89
Traceback (most recent call last):
  File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/main.py", line 4, in <module>
    from bench_cluster.submit_jobs import submit_jobs, check_status
ImportError: cannot import name 'check_status' from 'bench_cluster.submit_jobs' (/fsx/ferdinandmom/ferdinand-hf/bench_cluster/bench_cluster/submit_jobs.py)
Traceback (most recent call last):
  File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/main.py", line 4, in <module>
    from bench_cluster.submit_jobs import submit_jobs, check_status
ImportError: cannot import name 'check_status' from 'bench_cluster.submit_jobs' (/fsx/ferdinandmom/ferdinand-hf/bench_cluster/bench_cluster/submit_jobs.py)
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.

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