| ======================== | |
| START TIME: Tue Jul 2 18:31:59 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 18:32:07.594000 139743167231808 torch/distributed/run.py:757] | |
| W0702 18:32:07.594000 139743167231808 torch/distributed/run.py:757] ***************************************** | |
| W0702 18:32:07.594000 139743167231808 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 18:32:07.594000 139743167231808 torch/distributed/run.py:757] ***************************************** | |
| W0702 18:32:07.761000 139682410350400 torch/distributed/run.py:757] | |
| W0702 18:32:07.761000 139682410350400 torch/distributed/run.py:757] ***************************************** | |
| W0702 18:32:07.761000 139682410350400 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 18:32:07.761000 139682410350400 torch/distributed/run.py:757] ***************************************** | |
| [default0]:07/02/2024 18:32:30 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: Config: | |
| [default0]:07/02/2024 18:32:30 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: Config(general=GeneralArgs(project='bench_cluster', | |
| [default0]:07/02/2024 18:32:30 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: run='%date_%jobid', | |
| [default0]:07/02/2024 18:32:30 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: seed=42, | |
| [default0]:07/02/2024 18:32:30 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: step=None, | |
| [default0]:07/02/2024 18:32:30 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: consumed_train_samples=None, | |
| [default0]:07/02/2024 18:32:30 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: benchmark_csv_path=None, | |
| [default0]:07/02/2024 18:32:30 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: ignore_sanity_checks=True), | |
| [default0]:07/02/2024 18:32:30 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: parallelism=ParallelismArgs(dp=1, | |
| [default0]:07/02/2024 18:32:30 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: pp=16, | |
| [default0]:07/02/2024 18:32:30 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: tp=1, | |
| [default0]:07/02/2024 18:32:30 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: pp_engine=<nanotron.parallel.pipeline_parallel.engine.OneForwardOneBackwardPipelineEngine object at 0x7f7301914910>, | |
| [default0]:07/02/2024 18:32:30 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: tp_mode=<TensorParallelLinearMode.REDUCE_SCATTER: 2>, | |
| [default0]:07/02/2024 18:32:30 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: tp_linear_async_communication=False, | |
| [default0]:07/02/2024 18:32:30 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: expert_parallel_size=1), | |
| [default0]:07/02/2024 18:32:30 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: model=ModelArgs(model_config=LlamaConfig(bos_token_id=1, | |
| [default0]:07/02/2024 18:32:30 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: eos_token_id=2, | |
| [default0]:07/02/2024 18:32:30 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: hidden_act='silu', | |
| [default0]:07/02/2024 18:32:30 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: hidden_size=2048, | |
| [default0]:07/02/2024 18:32:30 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: initializer_range=0.02, | |
| [default0]:07/02/2024 18:32:30 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: intermediate_size=4096, | |
| [default0]:07/02/2024 18:32:30 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: is_llama_config=True, | |
| [default0]:07/02/2024 18:32:30 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: max_position_embeddings=4096, | |
| [default0]:07/02/2024 18:32:30 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: num_attention_heads=32, | |
| [default0]:07/02/2024 18:32:30 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: num_hidden_layers=24, | |
| [default0]:07/02/2024 18:32:30 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: num_key_value_heads=32, | |
| [default0]:07/02/2024 18:32:30 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: pad_token_id=None, | |
| [default0]:07/02/2024 18:32:30 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: pretraining_tp=1, | |
| [default0]:07/02/2024 18:32:30 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: rms_norm_eps=1e-05, | |
| [default0]:07/02/2024 18:32:30 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: rope_scaling=None, | |
| [default0]:07/02/2024 18:32:30 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: rope_theta=10000.0, | |
| [default0]:07/02/2024 18:32:30 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: tie_word_embeddings=True, | |
| [default0]:07/02/2024 18:32:30 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: use_cache=True, | |
| [default0]:07/02/2024 18:32:30 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: vocab_size=50257), | |
| [default0]:07/02/2024 18:32:30 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: init_method=RandomInit(std=0.025), | |
| [default0]:07/02/2024 18:32:30 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: dtype=torch.bfloat16, | |
| [default0]:07/02/2024 18:32:30 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: make_vocab_size_divisible_by=1, | |
| [default0]:07/02/2024 18:32:30 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: ddp_bucket_cap_mb=25), | |
| [default0]:07/02/2024 18:32:30 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: tokenizer=TokenizerArgs(tokenizer_name_or_path='openai-community/gpt2', | |
| [default0]:07/02/2024 18:32:30 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: tokenizer_revision=None, | |
| [default0]:07/02/2024 18:32:30 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: tokenizer_max_length=None), | |
| [default0]:07/02/2024 18:32:30 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: checkpoints=CheckpointsArgs(checkpoints_path=Path('/dev/null'), | |
| [default0]:07/02/2024 18:32:30 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: checkpoint_interval=100000, | |
| [default0]:07/02/2024 18:32:30 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: save_initial_state=False, | |
| [default0]:07/02/2024 18:32:30 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: resume_checkpoint_path=None, | |
| [default0]:07/02/2024 18:32:30 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: checkpoints_path_is_shared_file_system=False), | |
| [default0]:07/02/2024 18:32:30 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: logging=LoggingArgs(log_level='info', | |
| [default0]:07/02/2024 18:32:30 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: log_level_replica='info', | |
| [default0]:07/02/2024 18:32:30 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: iteration_step_info_interval=1), | |
| [default0]:07/02/2024 18:32:30 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: tokens=TokensArgs(sequence_length=4096, | |
| [default0]:07/02/2024 18:32:30 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: train_steps=20, | |
| [default0]:07/02/2024 18:32:30 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: micro_batch_size=2, | |
| [default0]:07/02/2024 18:32:30 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: batch_accumulation_per_replica=512, | |
| [default0]:07/02/2024 18:32:30 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: val_check_interval=-1, | |
| [default0]:07/02/2024 18:32:30 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: limit_val_batches=0, | |
| [default0]:07/02/2024 18:32:30 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: limit_test_batches=0), | |
| [default0]:07/02/2024 18:32:30 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: optimizer=OptimizerArgs(optimizer_factory=AdamWOptimizerArgs(adam_eps=1e-08, | |
| [default0]:07/02/2024 18:32:30 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: adam_beta1=0.9, | |
| [default0]:07/02/2024 18:32:30 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: adam_beta2=0.95, | |
| [default0]:07/02/2024 18:32:30 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: torch_adam_is_fused=True, | |
| [default0]:07/02/2024 18:32:30 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: name='adamW'), | |
| [default0]:07/02/2024 18:32:30 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: zero_stage=1, | |
| [default0]:07/02/2024 18:32:30 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: weight_decay=0.01, | |
| [default0]:07/02/2024 18:32:30 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: clip_grad=1.0, | |
| [default0]:07/02/2024 18:32:30 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: accumulate_grad_in_fp32=True, | |
| [default0]:07/02/2024 18:32:30 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: learning_rate_scheduler=LRSchedulerArgs(learning_rate=0.0001, | |
| [default0]:07/02/2024 18:32:30 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: lr_warmup_steps=1, | |
| [default0]:07/02/2024 18:32:30 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: lr_warmup_style='linear', | |
| [default0]:07/02/2024 18:32:30 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: lr_decay_style='linear', | |
| [default0]:07/02/2024 18:32:30 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: lr_decay_steps=19, | |
| [default0]:07/02/2024 18:32:30 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: lr_decay_starting_step=None, | |
| [default0]:07/02/2024 18:32:30 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: min_decay_lr=1e-05)), | |
| [default0]:07/02/2024 18:32:30 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: data_stages=[DatasetStageArgs(name='Training Stage', | |
| [default0]:07/02/2024 18:32:30 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: start_training_step=1, | |
| [default0]:07/02/2024 18:32:30 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: data=DataArgs(dataset=PretrainDatasetsArgs(hf_dataset_or_datasets='roneneldan/TinyStories', | |
| [default0]:07/02/2024 18:32:30 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: hf_dataset_splits='train', | |
| [default0]:07/02/2024 18:32:30 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: hf_dataset_config_name=None, | |
| [default0]:07/02/2024 18:32:30 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: dataset_processing_num_proc_per_process=64, | |
| [default0]:07/02/2024 18:32:30 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: dataset_overwrite_cache=False, | |
| [default0]:07/02/2024 18:32:30 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: text_column_name='text'), | |
| [default0]:07/02/2024 18:32:30 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: seed=42, | |
| [default0]:07/02/2024 18:32:30 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: num_loading_workers=32))], | |
| [default0]:07/02/2024 18:32:30 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: profiler=ProfilerArgs(profiler_export_path=Path('/fsx/ferdinandmom/ferdinand-hf/bench_cluster/results/llama-1B/16_GPUS/dp-1_tp-1_pp-16_mbz-2')), | |
| [default0]:07/02/2024 18:32:30 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: lighteval=None) | |
| [default0]:07/02/2024 18:32:30 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: Model Config: | |
| [default0]:07/02/2024 18:32:30 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: LlamaConfig(bos_token_id=1, | |
| [default0]:07/02/2024 18:32:30 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: eos_token_id=2, | |
| [default0]:07/02/2024 18:32:30 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: hidden_act='silu', | |
| [default0]:07/02/2024 18:32:30 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: hidden_size=2048, | |
| [default0]:07/02/2024 18:32:30 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: initializer_range=0.02, | |
| [default0]:07/02/2024 18:32:30 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: intermediate_size=4096, | |
| [default0]:07/02/2024 18:32:30 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: is_llama_config=True, | |
| [default0]:07/02/2024 18:32:30 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: max_position_embeddings=4096, | |
| [default0]:07/02/2024 18:32:30 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: num_attention_heads=32, | |
| [default0]:07/02/2024 18:32:30 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: num_hidden_layers=24, | |
| [default0]:07/02/2024 18:32:30 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: num_key_value_heads=32, | |
| [default0]:07/02/2024 18:32:30 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: pad_token_id=None, | |
| [default0]:07/02/2024 18:32:30 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: pretraining_tp=1, | |
| [default0]:07/02/2024 18:32:30 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: rms_norm_eps=1e-05, | |
| [default0]:07/02/2024 18:32:30 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: rope_scaling=None, | |
| [default0]:07/02/2024 18:32:30 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: rope_theta=10000.0, | |
| [default0]:07/02/2024 18:32:30 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: tie_word_embeddings=True, | |
| [default0]:07/02/2024 18:32:30 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: use_cache=True, | |
| [default0]:07/02/2024 18:32:30 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: vocab_size=50257) | |
| [default0]:07/02/2024 18:32:30 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: Building model.. | |
| [default0]:07/02/2024 18:32:30 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: Setting PP block ranks... | |
| [default0]:07/02/2024 18:32:48 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: Total number of parameters: 1.21G (2312.82MiB) | |
| [default0]:07/02/2024 18:32:48 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: Local number of parameters: 187M (356.33MiB) | |
| [default0]:07/02/2024 18:32:48 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: [After model building] Memory usage: 358.34MiB. Peak allocated: 360.37MiB Peak reserved: 368.00MiB | |
| [default0]:07/02/2024 18:32:48 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: No checkpoint path provided. | |
| [default0]:07/02/2024 18:32:48 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: Parametrizing model parameters using StandardParametrizator | |
| [default4]:07/02/2024 18:32:48 [INFO|DP=0|PP=4|TP=0|ip-26-0-160-192]: Local number of parameters: 83.9M (160.02MiB) | |
| [default4]:07/02/2024 18:32:48 [INFO|DP=0|PP=4|TP=0|ip-26-0-160-192]: [After model building] Memory usage: 162.03MiB. Peak allocated: 164.06MiB Peak reserved: 170.00MiB | |
| [default4]:07/02/2024 18:32:48 [INFO|DP=0|PP=4|TP=0|ip-26-0-160-192]: No checkpoint path provided. | |
| [default1]:07/02/2024 18:32:48 [INFO|DP=0|PP=1|TP=0|ip-26-0-160-192]: Local number of parameters: 83.9M (160.02MiB) | |
| [default1]:07/02/2024 18:32:48 [INFO|DP=0|PP=1|TP=0|ip-26-0-160-192]: [After model building] Memory usage: 162.03MiB. Peak allocated: 164.06MiB Peak reserved: 170.00MiB | |
| [default1]:07/02/2024 18:32:48 [INFO|DP=0|PP=1|TP=0|ip-26-0-160-192]: No checkpoint path provided. | |
| [default7]:07/02/2024 18:32:48 [INFO|DP=0|PP=7|TP=0|ip-26-0-160-192]: Local number of parameters: 83.9M (160.02MiB) | |
| [default7]:07/02/2024 18:32:48 [INFO|DP=0|PP=7|TP=0|ip-26-0-160-192]: [After model building] Memory usage: 162.03MiB. Peak allocated: 164.06MiB Peak reserved: 170.00MiB | |
| [default7]:07/02/2024 18:32:48 [INFO|DP=0|PP=7|TP=0|ip-26-0-160-192]: No checkpoint path provided. | |
| [default5]:07/02/2024 18:32:48 [INFO|DP=0|PP=5|TP=0|ip-26-0-160-192]: Local number of parameters: 41.9M (80.01MiB) | |
| [default5]:07/02/2024 18:32:48 [INFO|DP=0|PP=5|TP=0|ip-26-0-160-192]: [After model building] Memory usage: 81.02MiB. Peak allocated: 83.05MiB Peak reserved: 96.00MiB | |
| [default5]:07/02/2024 18:32:48 [INFO|DP=0|PP=5|TP=0|ip-26-0-160-192]: No checkpoint path provided. | |
| [default6]:07/02/2024 18:32:48 [INFO|DP=0|PP=14|TP=0|ip-26-0-162-233]: Local number of parameters: 103M (196.32MiB) | |
| [default6]:07/02/2024 18:32:48 [INFO|DP=0|PP=14|TP=0|ip-26-0-162-233]: [After model building] Memory usage: 196.33MiB. Peak allocated: 196.34MiB Peak reserved: 200.00MiB | |
| [default6]:07/02/2024 18:32:48 [INFO|DP=0|PP=14|TP=0|ip-26-0-162-233]: No checkpoint path provided. | |
| [default0]:07/02/2024 18:32:48 [INFO|DP=0|PP=8|TP=0|ip-26-0-162-233]: Local number of parameters: 41.9M (80.01MiB) | |
| [default0]:07/02/2024 18:32:48 [INFO|DP=0|PP=8|TP=0|ip-26-0-162-233]: [After model building] Memory usage: 81.02MiB. Peak allocated: 83.05MiB Peak reserved: 96.00MiB | |
| [default0]:07/02/2024 18:32:48 [INFO|DP=0|PP=8|TP=0|ip-26-0-162-233]: No checkpoint path provided. | |
| [default2]:07/02/2024 18:32:48 [INFO|DP=0|PP=2|TP=0|ip-26-0-160-192]: Local number of parameters: 41.9M (80.01MiB) | |
| [default2]:07/02/2024 18:32:48 [INFO|DP=0|PP=2|TP=0|ip-26-0-160-192]: [After model building] Memory usage: 81.02MiB. Peak allocated: 83.05MiB Peak reserved: 96.00MiB | |
| [default2]:07/02/2024 18:32:48 [INFO|DP=0|PP=2|TP=0|ip-26-0-160-192]: No checkpoint path provided. | |
| [default3]:07/02/2024 18:32:48 [INFO|DP=0|PP=3|TP=0|ip-26-0-160-192]: Local number of parameters: 83.9M (160.02MiB) | |
| [default3]:07/02/2024 18:32:48 [INFO|DP=0|PP=3|TP=0|ip-26-0-160-192]: [After model building] Memory usage: 162.03MiB. Peak allocated: 164.06MiB Peak reserved: 170.00MiB | |
| [default3]:07/02/2024 18:32:48 [INFO|DP=0|PP=3|TP=0|ip-26-0-160-192]: No checkpoint path provided. | |
| [default7]:07/02/2024 18:32:48 [INFO|DP=0|PP=15|TP=0|ip-26-0-162-233]: Local number of parameters: 0 (0.00MiB) | |
| [default7]:07/02/2024 18:32:48 [INFO|DP=0|PP=15|TP=0|ip-26-0-162-233]: [After model building] Memory usage: 0.01MiB. Peak allocated: 0.02MiB Peak reserved: 2.00MiB | |
| [default7]:07/02/2024 18:32:48 [INFO|DP=0|PP=15|TP=0|ip-26-0-162-233]: No checkpoint path provided. | |
| [default1]:07/02/2024 18:32:48 [INFO|DP=0|PP=9|TP=0|ip-26-0-162-233]: Local number of parameters: 83.9M (160.02MiB) | |
| [default1]:07/02/2024 18:32:48 [INFO|DP=0|PP=9|TP=0|ip-26-0-162-233]: [After model building] Memory usage: 162.03MiB. Peak allocated: 164.06MiB Peak reserved: 170.00MiB | |
| [default2]:07/02/2024 18:32:48 [INFO|DP=0|PP=10|TP=0|ip-26-0-162-233]: Local number of parameters: 83.9M (160.02MiB) | |
| [default2]:07/02/2024 18:32:48 [INFO|DP=0|PP=10|TP=0|ip-26-0-162-233]: [After model building] Memory usage: 162.03MiB. Peak allocated: 164.06MiB Peak reserved: 170.00MiB | |
| [default2]:07/02/2024 18:32:48 [INFO|DP=0|PP=10|TP=0|ip-26-0-162-233]: No checkpoint path provided. | |
| [default4]:07/02/2024 18:32:48 [INFO|DP=0|PP=12|TP=0|ip-26-0-162-233]: Local number of parameters: 83.9M (160.02MiB) | |
| [default5]:07/02/2024 18:32:48 [INFO|DP=0|PP=13|TP=0|ip-26-0-162-233]: Local number of parameters: 83.9M (160.02MiB) | |
| [default3]:07/02/2024 18:32:48 [INFO|DP=0|PP=11|TP=0|ip-26-0-162-233]: Local number of parameters: 41.9M (80.01MiB) | |
| [default4]:07/02/2024 18:32:48 [INFO|DP=0|PP=12|TP=0|ip-26-0-162-233]: [After model building] Memory usage: 162.03MiB. Peak allocated: 164.06MiB Peak reserved: 170.00MiB | |
| [default4]:07/02/2024 18:32:48 [INFO|DP=0|PP=12|TP=0|ip-26-0-162-233]: No checkpoint path provided. | |
| [default5]:07/02/2024 18:32:48 [INFO|DP=0|PP=13|TP=0|ip-26-0-162-233]: [After model building] Memory usage: 162.03MiB. Peak allocated: 164.06MiB Peak reserved: 170.00MiB | |
| [default5]:07/02/2024 18:32:48 [INFO|DP=0|PP=13|TP=0|ip-26-0-162-233]: No checkpoint path provided. | |
| [default3]:07/02/2024 18:32:48 [INFO|DP=0|PP=11|TP=0|ip-26-0-162-233]: [After model building] Memory usage: 81.02MiB. Peak allocated: 83.05MiB Peak reserved: 96.00MiB | |
| [default3]:07/02/2024 18:32:48 [INFO|DP=0|PP=11|TP=0|ip-26-0-162-233]: No checkpoint path provided. | |
| [default1]:07/02/2024 18:32:48 [INFO|DP=0|PP=9|TP=0|ip-26-0-162-233]: No checkpoint path provided. | |
| [default6]:07/02/2024 18:32:48 [INFO|DP=0|PP=6|TP=0|ip-26-0-160-192]: Local number of parameters: 83.9M (160.02MiB) | |
| [default6]:07/02/2024 18:32:48 [INFO|DP=0|PP=6|TP=0|ip-26-0-160-192]: [After model building] Memory usage: 162.03MiB. Peak allocated: 164.06MiB Peak reserved: 170.00MiB | |
| [default6]:07/02/2024 18:32:48 [INFO|DP=0|PP=6|TP=0|ip-26-0-160-192]: No checkpoint path provided. | |
| [default0]:07/02/2024 18:32:48 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: [Optimizer Building] Using LearningRateForSP as learning rate | |
| [default0]:07/02/2024 18:32:48 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: [ZeRO sharding] Size of optimizer params per rank: | |
| [default0]:07/02/2024 18:32:48 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: [ZeRO sharding] DP Rank 0 has 187M out of 187M (100.00%) params' optimizer states | |
| [default0]:07/02/2024 18:32:50 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: [Training Plan] Stage Training Stage has 19 remaining training steps and has consumed 0 samples | |
| [default0]:07/02/2024 18:32:50 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: Using `datasets` library | |
| [default0]:07/02/2024 18:32:50 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: Loading tokenizer from openai-community/gpt2 and transformers/hf_hub versions ('4.41.2', '0.23.4') | |
| [default0]:07/02/2024 18:32:50 [WARNING|DP=0|PP=0|TP=0|ip-26-0-160-192]: Repo card metadata block was not found. Setting CardData to empty. | |
| [default0]:Repo card metadata block was not found. Setting CardData to empty. | |
| [default0]:07/02/2024 18:32:52 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: [Training Plan] There are 1 training stages | |
| [default0]:07/02/2024 18:32:52 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: [Stage Training Stage] start from step 1 | |
| [default0]:07/02/2024 18:32:52 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: | |
| [default0]:07/02/2024 18:32:52 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: [Start training] datetime: 2024-07-02 18:32:52.233490 | mbs: 2 | grad_accum: 512 | global_batch_size: 1024 | sequence_length: 4096 | train_steps: 20 | start_iteration_step: 0 | consumed_train_samples: 0 | |
| [default0]:07/02/2024 18:32:52 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: Resuming training from stage Training Stage, it has trained for 0 samples and has 19 remaining train steps | |
| [default0]:07/02/2024 18:32:52 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: Memory usage: 1783.67MiB. Peak allocated 1783.67MiB. Peak reserved: 1796.00MiB | |
| [default4]:07/02/2024 18:32:52 [WARNING|DP=0|PP=4|TP=0|ip-26-0-160-192]: Repo card metadata block was not found. Setting CardData to empty. | |
| [default1]:07/02/2024 18:32:52 [WARNING|DP=0|PP=1|TP=0|ip-26-0-160-192]: Repo card metadata block was not found. Setting CardData to empty. | |
| [default5]:07/02/2024 18:32:52 [WARNING|DP=0|PP=5|TP=0|ip-26-0-160-192]: Repo card metadata block was not found. Setting CardData to empty. | |
| [default6]:07/02/2024 18:32:52 [WARNING|DP=0|PP=14|TP=0|ip-26-0-162-233]: Repo card metadata block was not found. Setting CardData to empty. | |
| [default0]:07/02/2024 18:32:52 [WARNING|DP=0|PP=8|TP=0|ip-26-0-162-233]: Repo card metadata block was not found. Setting CardData to empty. | |
| [default2]:07/02/2024 18:32:52 [WARNING|DP=0|PP=2|TP=0|ip-26-0-160-192]: Repo card metadata block was not found. Setting CardData to empty. | |
| [default3]:07/02/2024 18:32:52 [WARNING|DP=0|PP=3|TP=0|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. | |
| [default1]:Repo card metadata block was not found. Setting CardData to empty. | |
| [default2]:Repo card metadata block was not found. Setting CardData to empty. | |
| [default4]:Repo card metadata block was not found. Setting CardData to empty. | |
| [default7]:07/02/2024 18:32:52 [WARNING|DP=0|PP=15|TP=0|ip-26-0-162-233]: Repo card metadata block was not found. Setting CardData to empty. | |
| [default2]:Repo card metadata block was not found. Setting CardData to empty. | |
| [default0]:Repo card metadata block was not found. Setting CardData to empty. | |
| [default4]:07/02/2024 18:32:52 [WARNING|DP=0|PP=12|TP=0|ip-26-0-162-233]: Repo card metadata block was not found. Setting CardData to empty. | |
| [default7]:Repo card metadata block was not found. Setting CardData to empty. | |
| [default6]:Repo card metadata block was not found. Setting CardData to empty. | |
| [default2]:07/02/2024 18:32:52 [WARNING|DP=0|PP=10|TP=0|ip-26-0-162-233]: Repo card metadata block was not found. Setting CardData to empty. | |
| [default3]:07/02/2024 18:32:52 [WARNING|DP=0|PP=11|TP=0|ip-26-0-162-233]: Repo card metadata block was not found. Setting CardData to empty. | |
| [default1]:07/02/2024 18:32:52 [WARNING|DP=0|PP=9|TP=0|ip-26-0-162-233]: Repo card metadata block was not found. Setting CardData to empty. | |
| [default5]:Repo card metadata block was not found. Setting CardData to empty. | |
| [default6]:07/02/2024 18:32:52 [WARNING|DP=0|PP=6|TP=0|ip-26-0-160-192]: Repo card metadata block was not found. Setting CardData to empty. | |
| [default5]:07/02/2024 18:32:52 [WARNING|DP=0|PP=13|TP=0|ip-26-0-162-233]: Repo card metadata block was not found. Setting CardData to empty. | |
| [default3]:Repo card metadata block was not found. Setting CardData to empty. | |
| [default3]:Repo card metadata block was not found. Setting CardData to empty. | |
| [default7]:Repo card metadata block was not found. Setting CardData to empty. | |
| [default1]:Repo card metadata block was not found. Setting CardData to empty. | |
| [default5]: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 18:32:52 [WARNING|DP=0|PP=7|TP=0|ip-26-0-160-192]: Repo card metadata block was not found. Setting CardData to empty. | |
| [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 | |
| [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 | |
| [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 | |
| [default0]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/autograd/graph.py:744: UserWarning: Attempting to run cuBLAS, but there was no current CUDA context! Attempting to set the primary context... (Triggered internally at ../aten/src/ATen/cuda/CublasHandlePool.cpp:135.) | |
| [default0]: 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/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 | |
| [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 | |
| [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 | |
| [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 | |
| [default0]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/autograd/graph.py:744: UserWarning: Attempting to run cuBLAS, but there was no current CUDA context! Attempting to set the primary context... (Triggered internally at ../aten/src/ATen/cuda/CublasHandlePool.cpp:135.) | |
| [default0]: 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 | |
| [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( | |
| [default0]:07/02/2024 18:34:28 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: Memory usage: 1848.74MiB. Peak allocated 21515.73MiB. Peak reserved: 21810.00MiB | |
| [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( | |
| [default0]:07/02/2024 18:34:28 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: Memory usage: 3274.08MiB. Peak allocated 3274.08MiB. Peak reserved: 22598.00MiB | |
| [default7]:07/02/2024 18:34:28 [INFO|DP=0|PP=15|TP=0|ip-26-0-162-233]: iteration: 1 / 20 | consumed_tokens: 4.19M | elapsed_time_per_iteration_ms: 90.8K | tokens_per_sec: 46.2K | tokens_per_sec_per_gpu: 2.89K | global_batch_size: 1.02K | lm_loss: 11.1 | lr: 0.0001 | model_tflops_per_gpu: 26.2 | hardware_tflops_per_gpu: 26.2 | grad_norm: 25.6 | cuda_memory_allocated: 346K | cuda_max_memory_reserved: 5.77G | hd_total_memory_tb: 312G | hd_used_memory_tb: 66.1G | hd_free_memory_tb: 246G | |
| [default0]:07/02/2024 18:35:00 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: Memory usage: 3274.08MiB. Peak allocated 22941.07MiB. Peak reserved: 23444.00MiB | |
| [default0]:07/02/2024 18:35:00 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: Memory usage: 3274.08MiB. Peak allocated 3274.08MiB. Peak reserved: 23444.00MiB | |
| [default7]:07/02/2024 18:35:00 [INFO|DP=0|PP=15|TP=0|ip-26-0-162-233]: iteration: 2 / 20 | consumed_tokens: 8.39M | elapsed_time_per_iteration_ms: 31.7K | tokens_per_sec: 132K | tokens_per_sec_per_gpu: 8.27K | global_batch_size: 1.02K | lm_loss: 11.1 | lr: 9.53e-05 | model_tflops_per_gpu: 75.1 | hardware_tflops_per_gpu: 75.1 | grad_norm: 25.9 | cuda_memory_allocated: 346K | cuda_max_memory_reserved: 5.77G | hd_total_memory_tb: 312G | hd_used_memory_tb: 66.1G | hd_free_memory_tb: 246G | |
| [default0]:07/02/2024 18:35:32 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: Memory usage: 3274.08MiB. Peak allocated 22941.07MiB. Peak reserved: 23444.00MiB | |
| [default7]:07/02/2024 18:35:32 [INFO|DP=0|PP=15|TP=0|ip-26-0-162-233]: iteration: 3 / 20 | consumed_tokens: 12.6M | elapsed_time_per_iteration_ms: 31.9K | tokens_per_sec: 131K | tokens_per_sec_per_gpu: 8.21K | global_batch_size: 1.02K | lm_loss: 9.9 | lr: 9.05e-05 | model_tflops_per_gpu: 74.5 | hardware_tflops_per_gpu: 74.5 | grad_norm: 40.4 | cuda_memory_allocated: 346K | cuda_max_memory_reserved: 5.77G | hd_total_memory_tb: 312G | hd_used_memory_tb: 66.1G | hd_free_memory_tb: 246G | |
| [default0]:07/02/2024 18:35:32 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: Memory usage: 3274.08MiB. Peak allocated 3274.08MiB. Peak reserved: 23444.00MiB | |
| [default0]:STAGE:2024-07-02 18:35:32 925006:925006 ActivityProfilerController.cpp:314] Completed Stage: Warm Up | |
| [default0]:07/02/2024 18:36:02 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: Memory usage: 3274.08MiB. Peak allocated 22941.07MiB. Peak reserved: 23444.00MiB | |
| [default7]:07/02/2024 18:36:02 [INFO|DP=0|PP=15|TP=0|ip-26-0-162-233]: iteration: 4 / 20 | consumed_tokens: 16.8M | elapsed_time_per_iteration_ms: 30.4K | tokens_per_sec: 138K | tokens_per_sec_per_gpu: 8.63K | global_batch_size: 1.02K | lm_loss: 11.9 | lr: 8.58e-05 | model_tflops_per_gpu: 78.3 | hardware_tflops_per_gpu: 78.3 | grad_norm: 61.2 | cuda_memory_allocated: 346K | cuda_max_memory_reserved: 5.77G | hd_total_memory_tb: 312G | hd_used_memory_tb: 66.1G | hd_free_memory_tb: 246G | |
| [default0]:07/02/2024 18:36:02 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: Memory usage: 3274.08MiB. Peak allocated 3274.08MiB. Peak reserved: 23444.00MiB | |
| [default7]:07/02/2024 18:36:33 [INFO|DP=0|PP=15|TP=0|ip-26-0-162-233]: iteration: 5 / 20 | consumed_tokens: 21M | elapsed_time_per_iteration_ms: 30.1K | tokens_per_sec: 139K | tokens_per_sec_per_gpu: 8.7K | global_batch_size: 1.02K | lm_loss: 9.05 | lr: 8.11e-05 | model_tflops_per_gpu: 78.9 | hardware_tflops_per_gpu: 78.9 | grad_norm: 8.32 | |
| [default0]:07/02/2024 18:36:33 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: Memory usage: 3274.08MiB. Peak allocated 22941.07MiB. Peak reserved: 23444.00MiB | |
| [default7]:07/02/2024 18:37:05 [INFO|DP=0|PP=15|TP=0|ip-26-0-162-233]: iteration: 6 / 20 | consumed_tokens: 25.2M | elapsed_time_per_iteration_ms: 32.9K | tokens_per_sec: 128K | tokens_per_sec_per_gpu: 7.97K | global_batch_size: 1.02K | lm_loss: 8.86 | lr: 7.63e-05 | model_tflops_per_gpu: 72.3 | hardware_tflops_per_gpu: 72.3 | grad_norm: 6.61 | |
| [default0]:STAGE:2024-07-02 18:37:34 925006:925006 ActivityProfilerController.cpp:320] Completed Stage: Collection | |
| [default0]:STAGE:2024-07-02 18:37:37 925006:925006 ActivityProfilerController.cpp:324] Completed Stage: Post Processing | |
| [default0]:07/02/2024 18:41:04 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: Memory usage: 3274.08MiB. Peak allocated 22941.07MiB. Peak reserved: 23444.00MiB | |
| [default7]:07/02/2024 18:41:37 [INFO|DP=0|PP=15|TP=0|ip-26-0-162-233]: iteration: 7 / 20 | consumed_tokens: 29.4M | elapsed_time_per_iteration_ms: 272K | tokens_per_sec: 15.4K | tokens_per_sec_per_gpu: 965 | global_batch_size: 1.02K | lm_loss: 8.37 | lr: 7.16e-05 | model_tflops_per_gpu: 8.75 | hardware_tflops_per_gpu: 8.75 | grad_norm: 4.93 | |
| [default0]:07/02/2024 18:41:37 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: Memory usage: 3274.08MiB. Peak allocated 22941.07MiB. Peak reserved: 23444.00MiB | |
| [default0]:07/02/2024 18:42:09 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: Memory usage: 3274.08MiB. Peak allocated 22941.07MiB. Peak reserved: 23444.00MiB | |
| [default7]:07/02/2024 18:42:09 [INFO|DP=0|PP=15|TP=0|ip-26-0-162-233]: iteration: 8 / 20 | consumed_tokens: 33.6M | elapsed_time_per_iteration_ms: 32K | tokens_per_sec: 131K | tokens_per_sec_per_gpu: 8.19K | global_batch_size: 1.02K | lm_loss: 7.97 | lr: 6.68e-05 | model_tflops_per_gpu: 74.3 | hardware_tflops_per_gpu: 74.3 | grad_norm: 3.12 | |
| [default0]:07/02/2024 18:42:42 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: Memory usage: 3274.08MiB. Peak allocated 22941.07MiB. Peak reserved: 23444.00MiB | |
| [default7]:07/02/2024 18:42:42 [INFO|DP=0|PP=15|TP=0|ip-26-0-162-233]: iteration: 9 / 20 | consumed_tokens: 37.7M | elapsed_time_per_iteration_ms: 32.6K | tokens_per_sec: 129K | tokens_per_sec_per_gpu: 8.04K | global_batch_size: 1.02K | lm_loss: 7.83 | lr: 6.21e-05 | model_tflops_per_gpu: 73 | hardware_tflops_per_gpu: 73 | grad_norm: 9.04 | |
| [default0]:07/02/2024 18:43:13 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: Memory usage: 3274.08MiB. Peak allocated 22941.07MiB. Peak reserved: 23444.00MiB | |
| [default7]:07/02/2024 18:43:13 [INFO|DP=0|PP=15|TP=0|ip-26-0-162-233]: iteration: 10 / 20 | consumed_tokens: 41.9M | elapsed_time_per_iteration_ms: 31.1K | tokens_per_sec: 135K | tokens_per_sec_per_gpu: 8.43K | global_batch_size: 1.02K | lm_loss: 7.62 | lr: 5.74e-05 | model_tflops_per_gpu: 76.5 | hardware_tflops_per_gpu: 76.5 | grad_norm: 5.08 | |
| [default7]:07/02/2024 18:43:44 [INFO|DP=0|PP=15|TP=0|ip-26-0-162-233]: iteration: 11 / 20 | consumed_tokens: 46.1M | elapsed_time_per_iteration_ms: 31.1K | tokens_per_sec: 135K | tokens_per_sec_per_gpu: 8.44K | global_batch_size: 1.02K | lm_loss: 7.47 | lr: 5.26e-05 | model_tflops_per_gpu: 76.5 | hardware_tflops_per_gpu: 76.5 | grad_norm: 4.05 | |
| [default0]:07/02/2024 18:43:44 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: Memory usage: 3274.08MiB. Peak allocated 22941.07MiB. Peak reserved: 23444.00MiB | |
| [default0]:07/02/2024 18:44:16 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: Memory usage: 3274.08MiB. Peak allocated 22941.07MiB. Peak reserved: 23444.00MiB | |
| [default7]:07/02/2024 18:44:16 [INFO|DP=0|PP=15|TP=0|ip-26-0-162-233]: iteration: 12 / 20 | consumed_tokens: 50.3M | elapsed_time_per_iteration_ms: 32.4K | tokens_per_sec: 130K | tokens_per_sec_per_gpu: 8.09K | global_batch_size: 1.02K | lm_loss: 7.34 | lr: 4.79e-05 | model_tflops_per_gpu: 73.4 | hardware_tflops_per_gpu: 73.4 | grad_norm: 3.13 | |
| [default0]:07/02/2024 18:44:48 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: Memory usage: 3274.08MiB. Peak allocated 22941.07MiB. Peak reserved: 23444.00MiB | |
| [default7]:07/02/2024 18:44:48 [INFO|DP=0|PP=15|TP=0|ip-26-0-162-233]: iteration: 13 / 20 | consumed_tokens: 54.5M | elapsed_time_per_iteration_ms: 32K | tokens_per_sec: 131K | tokens_per_sec_per_gpu: 8.18K | global_batch_size: 1.02K | lm_loss: 7.23 | lr: 4.32e-05 | model_tflops_per_gpu: 74.2 | hardware_tflops_per_gpu: 74.2 | grad_norm: 2.74 | |
| [default7]:07/02/2024 18:45:21 [INFO|DP=0|PP=15|TP=0|ip-26-0-162-233]: iteration: 14 / 20 | consumed_tokens: 58.7M | elapsed_time_per_iteration_ms: 32.1K | tokens_per_sec: 131K | tokens_per_sec_per_gpu: 8.16K | global_batch_size: 1.02K | lm_loss: 7.14 | lr: 3.84e-05 | model_tflops_per_gpu: 74 | hardware_tflops_per_gpu: 74 | grad_norm: 2.32 | |
| [default0]:07/02/2024 18:45:21 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: Memory usage: 3274.08MiB. Peak allocated 22941.07MiB. Peak reserved: 23444.00MiB | |
| [default7]:07/02/2024 18:45:51 [INFO|DP=0|PP=15|TP=0|ip-26-0-162-233]: iteration: 15 / 20 | consumed_tokens: 62.9M | elapsed_time_per_iteration_ms: 30K | tokens_per_sec: 140K | tokens_per_sec_per_gpu: 8.74K | global_batch_size: 1.02K | lm_loss: 7.06 | lr: 3.37e-05 | model_tflops_per_gpu: 79.3 | hardware_tflops_per_gpu: 79.3 | grad_norm: 2.47 | |
| [default0]:07/02/2024 18:45:51 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: Memory usage: 3274.08MiB. Peak allocated 22941.07MiB. Peak reserved: 23444.00MiB | |
| [default0]:07/02/2024 18:46:25 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: Memory usage: 3274.08MiB. Peak allocated 22941.07MiB. Peak reserved: 23444.00MiB | |
| [default7]:07/02/2024 18:46:25 [INFO|DP=0|PP=15|TP=0|ip-26-0-162-233]: iteration: 16 / 20 | consumed_tokens: 67.1M | elapsed_time_per_iteration_ms: 34K | tokens_per_sec: 123K | tokens_per_sec_per_gpu: 7.7K | global_batch_size: 1.02K | lm_loss: 6.98 | lr: 2.89e-05 | model_tflops_per_gpu: 69.9 | hardware_tflops_per_gpu: 69.9 | grad_norm: 2.66 | |
| [default7]:07/02/2024 18:46:57 [INFO|DP=0|PP=15|TP=0|ip-26-0-162-233]: iteration: 17 / 20 | consumed_tokens: 71.3M | elapsed_time_per_iteration_ms: 32.9K | tokens_per_sec: 128K | tokens_per_sec_per_gpu: 7.97K | global_batch_size: 1.02K | lm_loss: 6.9 | lr: 2.42e-05 | model_tflops_per_gpu: 72.4 | hardware_tflops_per_gpu: 72.4 | grad_norm: 1.88 | |
| [default0]:07/02/2024 18:46:57 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: Memory usage: 3274.08MiB. Peak allocated 22941.07MiB. Peak reserved: 23444.00MiB | |
| [default7]:07/02/2024 18:47:29 [INFO|DP=0|PP=15|TP=0|ip-26-0-162-233]: iteration: 18 / 20 | consumed_tokens: 75.5M | elapsed_time_per_iteration_ms: 31.5K | tokens_per_sec: 133K | tokens_per_sec_per_gpu: 8.34K | global_batch_size: 1.02K | lm_loss: 6.84 | lr: 1.95e-05 | model_tflops_per_gpu: 75.6 | hardware_tflops_per_gpu: 75.6 | grad_norm: 1.61 | |
| [default0]:07/02/2024 18:47:29 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: Memory usage: 3274.08MiB. Peak allocated 22941.07MiB. Peak reserved: 23444.00MiB | |
| [default7]:07/02/2024 18:48:01 [INFO|DP=0|PP=15|TP=0|ip-26-0-162-233]: iteration: 19 / 20 | consumed_tokens: 79.7M | elapsed_time_per_iteration_ms: 31.9K | tokens_per_sec: 132K | tokens_per_sec_per_gpu: 8.23K | global_batch_size: 1.02K | lm_loss: 6.8 | lr: 1.47e-05 | model_tflops_per_gpu: 74.7 | hardware_tflops_per_gpu: 74.7 | grad_norm: 1.83 | |
| [default0]:07/02/2024 18:48:01 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: Memory usage: 3274.08MiB. Peak allocated 22941.07MiB. Peak reserved: 23444.00MiB | |
| [default7]:07/02/2024 18:48:32 [INFO|DP=0|PP=15|TP=0|ip-26-0-162-233]: iteration: 20 / 20 | consumed_tokens: 83.9M | elapsed_time_per_iteration_ms: 31.6K | tokens_per_sec: 133K | tokens_per_sec_per_gpu: 8.29K | global_batch_size: 1.02K | lm_loss: 6.77 | lr: 1e-05 | model_tflops_per_gpu: 75.2 | hardware_tflops_per_gpu: 75.2 | grad_norm: 1.82 | |
| W0702 18:49:14.895000 139682410350400 torch/distributed/elastic/rendezvous/dynamic_rendezvous.py:1203] The node 'ip-26-0-162-233.ec2.internal_1273047_0' has failed to shutdown the rendezvous 'none' due to an error of type RendezvousConnectionError. | |
| W0702 18:49:14.899000 139682410350400 torch/distributed/elastic/rendezvous/dynamic_rendezvous.py:1203] The node 'ip-26-0-162-233.ec2.internal_1273047_0' has failed to shutdown the rendezvous 'none' due to an error of type RendezvousConnectionError. | |
| 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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