| ======================== | |
| START TIME: Wed Jul 3 15:34:17 UTC 2024 | |
| python3 version = Python 3.10.14 | |
| ======================== | |
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| Already on 'bench_cluster' | |
| 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 | |
| W0703 15:34:20.490000 139973111215936 torch/distributed/run.py:757] | |
| W0703 15:34:20.490000 139973111215936 torch/distributed/run.py:757] ***************************************** | |
| W0703 15:34:20.490000 139973111215936 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. | |
| W0703 15:34:20.490000 139973111215936 torch/distributed/run.py:757] ***************************************** | |
| W0703 15:34:20.488000 140631068751680 torch/distributed/run.py:757] | |
| W0703 15:34:20.488000 140631068751680 torch/distributed/run.py:757] ***************************************** | |
| W0703 15:34:20.488000 140631068751680 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. | |
| W0703 15:34:20.488000 140631068751680 torch/distributed/run.py:757] ***************************************** | |
| [default0]:07/03/2024 15:34:38 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: Config: | |
| [default0]:07/03/2024 15:34:38 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: Config(general=GeneralArgs(project='bench_cluster', | |
| [default0]:07/03/2024 15:34:38 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: run='%date_%jobid', | |
| [default0]:07/03/2024 15:34:38 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: seed=42, | |
| [default0]:07/03/2024 15:34:38 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: step=None, | |
| [default0]:07/03/2024 15:34:38 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: consumed_train_samples=None, | |
| [default0]:07/03/2024 15:34:38 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: benchmark_csv_path=None, | |
| [default0]:07/03/2024 15:34:38 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: ignore_sanity_checks=True), | |
| [default0]:07/03/2024 15:34:38 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: parallelism=ParallelismArgs(dp=2, | |
| [default0]:07/03/2024 15:34:38 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: pp=8, | |
| [default0]:07/03/2024 15:34:38 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: tp=1, | |
| [default0]:07/03/2024 15:34:38 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: pp_engine=<nanotron.parallel.pipeline_parallel.engine.OneForwardOneBackwardPipelineEngine object at 0x7fd00c7dc910>, | |
| [default0]:07/03/2024 15:34:38 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: tp_mode=<TensorParallelLinearMode.REDUCE_SCATTER: 2>, | |
| [default0]:07/03/2024 15:34:38 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: tp_linear_async_communication=False, | |
| [default0]:07/03/2024 15:34:38 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: expert_parallel_size=1), | |
| [default0]:07/03/2024 15:34:38 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: model=ModelArgs(model_config=LlamaConfig(bos_token_id=1, | |
| [default0]:07/03/2024 15:34:38 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: eos_token_id=2, | |
| [default0]:07/03/2024 15:34:38 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: hidden_act='silu', | |
| [default0]:07/03/2024 15:34:38 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: hidden_size=2048, | |
| [default0]:07/03/2024 15:34:38 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: initializer_range=0.02, | |
| [default0]:07/03/2024 15:34:38 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: intermediate_size=4096, | |
| [default0]:07/03/2024 15:34:38 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: is_llama_config=True, | |
| [default0]:07/03/2024 15:34:38 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: max_position_embeddings=4096, | |
| [default0]:07/03/2024 15:34:38 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: num_attention_heads=32, | |
| [default0]:07/03/2024 15:34:38 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: num_hidden_layers=24, | |
| [default0]:07/03/2024 15:34:38 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: num_key_value_heads=32, | |
| [default0]:07/03/2024 15:34:38 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: pad_token_id=None, | |
| [default0]:07/03/2024 15:34:38 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: pretraining_tp=1, | |
| [default0]:07/03/2024 15:34:38 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: rms_norm_eps=1e-05, | |
| [default0]:07/03/2024 15:34:38 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: rope_scaling=None, | |
| [default0]:07/03/2024 15:34:38 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: rope_theta=10000.0, | |
| [default0]:07/03/2024 15:34:38 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: tie_word_embeddings=True, | |
| [default0]:07/03/2024 15:34:38 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: use_cache=True, | |
| [default0]:07/03/2024 15:34:38 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: vocab_size=50257), | |
| [default0]:07/03/2024 15:34:38 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: init_method=RandomInit(std=0.025), | |
| [default0]:07/03/2024 15:34:38 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: dtype=torch.bfloat16, | |
| [default0]:07/03/2024 15:34:38 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: make_vocab_size_divisible_by=1, | |
| [default0]:07/03/2024 15:34:38 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: ddp_bucket_cap_mb=25), | |
| [default0]:07/03/2024 15:34:38 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: tokenizer=TokenizerArgs(tokenizer_name_or_path='openai-community/gpt2', | |
| [default0]:07/03/2024 15:34:38 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: tokenizer_revision=None, | |
| [default0]:07/03/2024 15:34:38 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: tokenizer_max_length=None), | |
| [default0]:07/03/2024 15:34:38 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: checkpoints=CheckpointsArgs(checkpoints_path=Path('/dev/null'), | |
| [default0]:07/03/2024 15:34:38 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: checkpoint_interval=100000, | |
| [default0]:07/03/2024 15:34:38 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: save_initial_state=False, | |
| [default0]:07/03/2024 15:34:38 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: resume_checkpoint_path=None, | |
| [default0]:07/03/2024 15:34:38 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: checkpoints_path_is_shared_file_system=False), | |
| [default0]:07/03/2024 15:34:38 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: logging=LoggingArgs(log_level='info', | |
| [default0]:07/03/2024 15:34:38 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: log_level_replica='info', | |
| [default0]:07/03/2024 15:34:38 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: iteration_step_info_interval=1), | |
| [default0]:07/03/2024 15:34:38 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: tokens=TokensArgs(sequence_length=4096, | |
| [default0]:07/03/2024 15:34:38 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: train_steps=20, | |
| [default0]:07/03/2024 15:34:38 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: micro_batch_size=1, | |
| [default0]:07/03/2024 15:34:38 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: batch_accumulation_per_replica=512, | |
| [default0]:07/03/2024 15:34:38 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: val_check_interval=-1, | |
| [default0]:07/03/2024 15:34:38 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: limit_val_batches=0, | |
| [default0]:07/03/2024 15:34:38 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: limit_test_batches=0), | |
| [default0]:07/03/2024 15:34:38 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: optimizer=OptimizerArgs(optimizer_factory=AdamWOptimizerArgs(adam_eps=1e-08, | |
| [default0]:07/03/2024 15:34:38 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: adam_beta1=0.9, | |
| [default0]:07/03/2024 15:34:38 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: adam_beta2=0.95, | |
| [default0]:07/03/2024 15:34:38 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: torch_adam_is_fused=True, | |
| [default0]:07/03/2024 15:34:38 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: name='adamW'), | |
| [default0]:07/03/2024 15:34:38 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: zero_stage=1, | |
| [default0]:07/03/2024 15:34:38 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: weight_decay=0.01, | |
| [default0]:07/03/2024 15:34:38 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: clip_grad=1.0, | |
| [default0]:07/03/2024 15:34:38 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: accumulate_grad_in_fp32=True, | |
| [default0]:07/03/2024 15:34:38 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: learning_rate_scheduler=LRSchedulerArgs(learning_rate=0.0001, | |
| [default0]:07/03/2024 15:34:38 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: lr_warmup_steps=1, | |
| [default0]:07/03/2024 15:34:38 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: lr_warmup_style='linear', | |
| [default0]:07/03/2024 15:34:38 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: lr_decay_style='linear', | |
| [default0]:07/03/2024 15:34:38 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: lr_decay_steps=19, | |
| [default0]:07/03/2024 15:34:38 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: lr_decay_starting_step=None, | |
| [default0]:07/03/2024 15:34:38 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: min_decay_lr=1e-05)), | |
| [default0]:07/03/2024 15:34:38 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: data_stages=[DatasetStageArgs(name='Training Stage', | |
| [default0]:07/03/2024 15:34:38 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: start_training_step=1, | |
| [default0]:07/03/2024 15:34:38 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: data=DataArgs(dataset=PretrainDatasetsArgs(hf_dataset_or_datasets='roneneldan/TinyStories', | |
| [default0]:07/03/2024 15:34:38 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: hf_dataset_splits='train', | |
| [default0]:07/03/2024 15:34:38 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: hf_dataset_config_name=None, | |
| [default0]:07/03/2024 15:34:38 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: dataset_processing_num_proc_per_process=64, | |
| [default0]:07/03/2024 15:34:38 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: dataset_overwrite_cache=False, | |
| [default0]:07/03/2024 15:34:38 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: text_column_name='text'), | |
| [default0]:07/03/2024 15:34:38 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: seed=42, | |
| [default0]:07/03/2024 15:34:38 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: num_loading_workers=32))], | |
| [default0]:07/03/2024 15:34:38 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: profiler=ProfilerArgs(profiler_export_path=Path('/fsx/ferdinandmom/ferdinand-hf/bench_cluster/results/llama-1B/16_GPUS/dp-2_tp-1_pp-8_mbz-1')), | |
| [default0]:07/03/2024 15:34:38 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: lighteval=None) | |
| [default0]:07/03/2024 15:34:38 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: Model Config: | |
| [default0]:07/03/2024 15:34:38 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: LlamaConfig(bos_token_id=1, | |
| [default0]:07/03/2024 15:34:38 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: eos_token_id=2, | |
| [default0]:07/03/2024 15:34:38 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: hidden_act='silu', | |
| [default0]:07/03/2024 15:34:38 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: hidden_size=2048, | |
| [default0]:07/03/2024 15:34:38 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: initializer_range=0.02, | |
| [default0]:07/03/2024 15:34:38 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: intermediate_size=4096, | |
| [default0]:07/03/2024 15:34:38 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: is_llama_config=True, | |
| [default0]:07/03/2024 15:34:38 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: max_position_embeddings=4096, | |
| [default0]:07/03/2024 15:34:38 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: num_attention_heads=32, | |
| [default0]:07/03/2024 15:34:38 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: num_hidden_layers=24, | |
| [default0]:07/03/2024 15:34:38 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: num_key_value_heads=32, | |
| [default0]:07/03/2024 15:34:38 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: pad_token_id=None, | |
| [default0]:07/03/2024 15:34:38 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: pretraining_tp=1, | |
| [default0]:07/03/2024 15:34:38 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: rms_norm_eps=1e-05, | |
| [default0]:07/03/2024 15:34:38 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: rope_scaling=None, | |
| [default0]:07/03/2024 15:34:38 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: rope_theta=10000.0, | |
| [default0]:07/03/2024 15:34:38 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: tie_word_embeddings=True, | |
| [default0]:07/03/2024 15:34:38 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: use_cache=True, | |
| [default0]:07/03/2024 15:34:38 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: vocab_size=50257) | |
| [default0]:07/03/2024 15:34:38 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: Building model.. | |
| [default0]:07/03/2024 15:34:38 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: Setting PP block ranks... | |
| [default0]:07/03/2024 15:34:51 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: Total number of parameters: 1.21G (2312.82MiB) | |
| [default0]:07/03/2024 15:34:51 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: Local number of parameters: 271M (516.35MiB) | |
| [default4]:07/03/2024 15:34:51 [INFO|DP=0|PP=2|TP=0|ip-26-0-160-225]: Local number of parameters: 126M (240.02MiB) | |
| [default4]:07/03/2024 15:34:51 [INFO|DP=0|PP=2|TP=0|ip-26-0-160-225]: [After model building] Memory usage: 243.03MiB. Peak allocated: 245.06MiB Peak reserved: 262.00MiB | |
| [default4]:07/03/2024 15:34:51 [INFO|DP=0|PP=2|TP=0|ip-26-0-160-225]: No checkpoint path provided. | |
| [default6]:07/03/2024 15:34:51 [INFO|DP=0|PP=3|TP=0|ip-26-0-160-225]: Local number of parameters: 168M (320.03MiB) | |
| [default6]:07/03/2024 15:34:51 [INFO|DP=0|PP=3|TP=0|ip-26-0-160-225]: [After model building] Memory usage: 324.04MiB. Peak allocated: 326.07MiB Peak reserved: 336.00MiB | |
| [default6]:07/03/2024 15:34:51 [INFO|DP=0|PP=3|TP=0|ip-26-0-160-225]: No checkpoint path provided. | |
| [default2]:07/03/2024 15:34:51 [INFO|DP=0|PP=5|TP=0|ip-26-0-170-160]: Local number of parameters: 126M (240.02MiB) | |
| [default2]:07/03/2024 15:34:51 [INFO|DP=0|PP=5|TP=0|ip-26-0-170-160]: [After model building] Memory usage: 243.03MiB. Peak allocated: 245.06MiB Peak reserved: 262.00MiB | |
| [default2]:07/03/2024 15:34:51 [INFO|DP=0|PP=5|TP=0|ip-26-0-170-160]: No checkpoint path provided. | |
| [default6]:07/03/2024 15:34:51 [INFO|DP=0|PP=7|TP=0|ip-26-0-170-160]: Local number of parameters: 103M (196.32MiB) | |
| [default6]:07/03/2024 15:34:51 [INFO|DP=0|PP=7|TP=0|ip-26-0-170-160]: [After model building] Memory usage: 196.33MiB. Peak allocated: 196.34MiB Peak reserved: 200.00MiB | |
| [default6]:07/03/2024 15:34:51 [INFO|DP=0|PP=7|TP=0|ip-26-0-170-160]: No checkpoint path provided. | |
| [default4]:07/03/2024 15:34:51 [INFO|DP=0|PP=6|TP=0|ip-26-0-170-160]: Local number of parameters: 168M (320.03MiB) | |
| [default4]:07/03/2024 15:34:51 [INFO|DP=0|PP=6|TP=0|ip-26-0-170-160]: [After model building] Memory usage: 324.04MiB. Peak allocated: 326.07MiB Peak reserved: 336.00MiB | |
| [default4]:07/03/2024 15:34:51 [INFO|DP=0|PP=6|TP=0|ip-26-0-170-160]: No checkpoint path provided. | |
| [default0]:07/03/2024 15:34:51 [INFO|DP=0|PP=4|TP=0|ip-26-0-170-160]: Local number of parameters: 126M (240.02MiB) | |
| [default0]:07/03/2024 15:34:51 [INFO|DP=0|PP=4|TP=0|ip-26-0-170-160]: [After model building] Memory usage: 243.03MiB. Peak allocated: 245.06MiB Peak reserved: 262.00MiB | |
| [default0]:07/03/2024 15:34:51 [INFO|DP=0|PP=4|TP=0|ip-26-0-170-160]: No checkpoint path provided. | |
| [default2]:07/03/2024 15:34:51 [INFO|DP=0|PP=1|TP=0|ip-26-0-160-225]: Local number of parameters: 126M (240.02MiB) | |
| [default2]:07/03/2024 15:34:51 [INFO|DP=0|PP=1|TP=0|ip-26-0-160-225]: [After model building] Memory usage: 243.03MiB. Peak allocated: 245.06MiB Peak reserved: 262.00MiB | |
| [default2]:07/03/2024 15:34:51 [INFO|DP=0|PP=1|TP=0|ip-26-0-160-225]: No checkpoint path provided. | |
| [default0]:07/03/2024 15:34:51 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: [After model building] Memory usage: 520.36MiB. Peak allocated: 522.39MiB Peak reserved: 534.00MiB | |
| [default0]:07/03/2024 15:34:51 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: No checkpoint path provided. | |
| [default0]:07/03/2024 15:34:51 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: Parametrizing model parameters using StandardParametrizator | |
| [default7]:07/03/2024 15:34:52 [INFO|DP=1|PP=7|TP=0|ip-26-0-170-160]: No checkpoint path provided. | |
| [default1]:07/03/2024 15:34:52 [INFO|DP=1|PP=4|TP=0|ip-26-0-170-160]: No checkpoint path provided. | |
| [default3]:07/03/2024 15:34:52 [INFO|DP=1|PP=5|TP=0|ip-26-0-170-160]: No checkpoint path provided. | |
| [default5]:07/03/2024 15:34:52 [INFO|DP=1|PP=6|TP=0|ip-26-0-170-160]: No checkpoint path provided. | |
| [default1]:07/03/2024 15:34:52 [INFO|DP=1|PP=0|TP=0|ip-26-0-160-225]: No checkpoint path provided. | |
| [default3]:07/03/2024 15:34:52 [INFO|DP=1|PP=1|TP=0|ip-26-0-160-225]: No checkpoint path provided. | |
| [default5]:07/03/2024 15:34:52 [INFO|DP=1|PP=2|TP=0|ip-26-0-160-225]: No checkpoint path provided. | |
| [default7]:07/03/2024 15:34:52 [INFO|DP=1|PP=3|TP=0|ip-26-0-160-225]: No checkpoint path provided. | |
| [default0]:07/03/2024 15:34:55 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: [Optimizer Building] Using LearningRateForSP as learning rate | |
| [default0]:07/03/2024 15:34:55 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: [ZeRO sharding] Size of optimizer params per rank: | |
| [default0]:07/03/2024 15:34:55 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: [ZeRO sharding] DP Rank 0 has 135M out of 271M (50.00%) params' optimizer states | |
| [default0]:07/03/2024 15:34:55 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: [ZeRO sharding] DP Rank 1 has 135M out of 271M (50.00%) params' optimizer states | |
| [default0]:07/03/2024 15:34:56 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: [Training Plan] Stage Training Stage has 19 remaining training steps and has consumed 0 samples | |
| [default0]:07/03/2024 15:34:56 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: Using `datasets` library | |
| [default0]:07/03/2024 15:34:56 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: Loading tokenizer from openai-community/gpt2 and transformers/hf_hub versions ('4.41.2', '0.23.4') | |
| [default0]:07/03/2024 15:34:56 [WARNING|DP=0|PP=0|TP=0|ip-26-0-160-225]: Repo card metadata block was not found. Setting CardData to empty. | |
| [default0]:Repo card metadata block was not found. Setting CardData to empty. | |
| [default0]:07/03/2024 15:34:57 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: [Training Plan] There are 1 training stages | |
| [default0]:07/03/2024 15:34:57 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: [Stage Training Stage] start from step 1 | |
| [default0]:07/03/2024 15:34:57 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: | |
| [default0]:07/03/2024 15:34:57 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: [Start training] datetime: 2024-07-03 15:34:57.136501 | mbs: 1 | grad_accum: 512 | global_batch_size: 1024 | sequence_length: 4096 | train_steps: 20 | start_iteration_step: 0 | consumed_train_samples: 0 | |
| [default0]:07/03/2024 15:34:57 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: Resuming training from stage Training Stage, it has trained for 0 samples and has 19 remaining train steps | |
| [default0]:07/03/2024 15:34:57 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: Memory usage: 2069.40MiB. Peak allocated 2069.40MiB. Peak reserved: 2086.00MiB | |
| [default6]:07/03/2024 15:34:57 [WARNING|DP=0|PP=7|TP=0|ip-26-0-170-160]: Repo card metadata block was not found. Setting CardData to empty. | |
| [default7]:07/03/2024 15:34:57 [WARNING|DP=1|PP=7|TP=0|ip-26-0-170-160]: Repo card metadata block was not found. Setting CardData to empty. | |
| [default4]:07/03/2024 15:34:57 [WARNING|DP=0|PP=6|TP=0|ip-26-0-170-160]: Repo card metadata block was not found. Setting CardData to empty. | |
| [default7]:Repo card metadata block was not found. Setting CardData to empty. | |
| [default5]:07/03/2024 15:34:57 [WARNING|DP=1|PP=6|TP=0|ip-26-0-170-160]: 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. | |
| [default6]:Repo card metadata block was not found. Setting CardData to empty. | |
| [default4]:07/03/2024 15:34:57 [WARNING|DP=0|PP=2|TP=0|ip-26-0-160-225]: Repo card metadata block was not found. Setting CardData to empty. | |
| [default5]:07/03/2024 15:34:57 [WARNING|DP=1|PP=2|TP=0|ip-26-0-160-225]: Repo card metadata block was not found. Setting CardData to empty. | |
| [default7]:Repo card metadata block was not found. Setting CardData to empty. | |
| [default7]:07/03/2024 15:34:57 [WARNING|DP=1|PP=3|TP=0|ip-26-0-160-225]: Repo card metadata block was not found. Setting CardData to empty. | |
| [default6]:07/03/2024 15:34:57 [WARNING|DP=0|PP=3|TP=0|ip-26-0-160-225]: 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. | |
| [default6]:Repo card metadata block was not found. Setting CardData to empty. | |
| [default2]:07/03/2024 15:34:57 [WARNING|DP=0|PP=5|TP=0|ip-26-0-170-160]: Repo card metadata block was not found. Setting CardData to empty. | |
| [default1]:07/03/2024 15:34:57 [WARNING|DP=1|PP=4|TP=0|ip-26-0-170-160]: Repo card metadata block was not found. Setting CardData to empty. | |
| [default3]:07/03/2024 15:34:57 [WARNING|DP=1|PP=5|TP=0|ip-26-0-170-160]: Repo card metadata block was not found. Setting CardData to empty. | |
| [default0]:07/03/2024 15:34:57 [WARNING|DP=0|PP=4|TP=0|ip-26-0-170-160]: 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. | |
| [default3]:Repo card metadata block was not found. Setting CardData to empty. | |
| [default1]:Repo card metadata block was not found. Setting CardData to empty. | |
| [default1]:07/03/2024 15:34:57 [WARNING|DP=1|PP=0|TP=0|ip-26-0-160-225]: Repo card metadata block was not found. Setting CardData to empty. | |
| [default3]:07/03/2024 15:34:57 [WARNING|DP=1|PP=1|TP=0|ip-26-0-160-225]: Repo card metadata block was not found. Setting CardData to empty. | |
| [default3]:Repo card metadata block was not found. Setting CardData to empty. | |
| [default1]:Repo card metadata block was not found. Setting CardData to empty. | |
| [default2]:07/03/2024 15:34:57 [WARNING|DP=0|PP=1|TP=0|ip-26-0-160-225]: Repo card metadata block was not found. Setting CardData to empty. | |
| [default2]:Repo card metadata block was not found. Setting CardData to empty. | |
| [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 | |
| [default5]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/autograd/graph.py:744: UserWarning: c10d::allreduce_: an autograd kernel was not registered to the Autograd key(s) but we are trying to backprop through it. This may lead to silently incorrect behavior. This behavior is deprecated and will be removed in a future version of PyTorch. If your operator is differentiable, please ensure you have registered an autograd kernel to the correct Autograd key (e.g. DispatchKey::Autograd, DispatchKey::CompositeImplicitAutograd). If your operator is not differentiable, or to squash this warning and use the previous behavior, please register torch::CppFunction::makeFallthrough() to DispatchKey::Autograd. (Triggered internally at ../torch/csrc/autograd/autograd_not_implemented_fallback.cpp:63.) | |
| [default5]: return Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass | |
| [default3]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/autograd/graph.py:744: UserWarning: c10d::allreduce_: an autograd kernel was not registered to the Autograd key(s) but we are trying to backprop through it. This may lead to silently incorrect behavior. This behavior is deprecated and will be removed in a future version of PyTorch. If your operator is differentiable, please ensure you have registered an autograd kernel to the correct Autograd key (e.g. DispatchKey::Autograd, DispatchKey::CompositeImplicitAutograd). If your operator is not differentiable, or to squash this warning and use the previous behavior, please register torch::CppFunction::makeFallthrough() to DispatchKey::Autograd. (Triggered internally at ../torch/csrc/autograd/autograd_not_implemented_fallback.cpp:63.) | |
| [default3]: return Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass | |
| [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 | |
| [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 | |
| [default5]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/autograd/graph.py:744: UserWarning: c10d::allreduce_: an autograd kernel was not registered to the Autograd key(s) but we are trying to backprop through it. This may lead to silently incorrect behavior. This behavior is deprecated and will be removed in a future version of PyTorch. If your operator is differentiable, please ensure you have registered an autograd kernel to the correct Autograd key (e.g. DispatchKey::Autograd, DispatchKey::CompositeImplicitAutograd). If your operator is not differentiable, or to squash this warning and use the previous behavior, please register torch::CppFunction::makeFallthrough() to DispatchKey::Autograd. (Triggered internally at ../torch/csrc/autograd/autograd_not_implemented_fallback.cpp:63.) | |
| [default5]: return Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass | |
| [default3]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/autograd/graph.py:744: UserWarning: c10d::allreduce_: an autograd kernel was not registered to the Autograd key(s) but we are trying to backprop through it. This may lead to silently incorrect behavior. This behavior is deprecated and will be removed in a future version of PyTorch. If your operator is differentiable, please ensure you have registered an autograd kernel to the correct Autograd key (e.g. DispatchKey::Autograd, DispatchKey::CompositeImplicitAutograd). If your operator is not differentiable, or to squash this warning and use the previous behavior, please register torch::CppFunction::makeFallthrough() to DispatchKey::Autograd. (Triggered internally at ../torch/csrc/autograd/autograd_not_implemented_fallback.cpp:63.) | |
| [default3]: return Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass | |
| [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 | |
| [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/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( | |
| [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 | |
| [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/autograd/graph.py:744: UserWarning: c10d::allreduce_: an autograd kernel was not registered to the Autograd key(s) but we are trying to backprop through it. This may lead to silently incorrect behavior. This behavior is deprecated and will be removed in a future version of PyTorch. If your operator is differentiable, please ensure you have registered an autograd kernel to the correct Autograd key (e.g. DispatchKey::Autograd, DispatchKey::CompositeImplicitAutograd). If your operator is not differentiable, or to squash this warning and use the previous behavior, please register torch::CppFunction::makeFallthrough() to DispatchKey::Autograd. (Triggered internally at ../torch/csrc/autograd/autograd_not_implemented_fallback.cpp:63.) | |
| [default2]: return Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass | |
| [default0]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/autograd/graph.py:744: UserWarning: 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/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 | |
| [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 | |
| [default2]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/autograd/graph.py:744: UserWarning: c10d::allreduce_: an autograd kernel was not registered to the Autograd key(s) but we are trying to backprop through it. This may lead to silently incorrect behavior. This behavior is deprecated and will be removed in a future version of PyTorch. If your operator is differentiable, please ensure you have registered an autograd kernel to the correct Autograd key (e.g. DispatchKey::Autograd, DispatchKey::CompositeImplicitAutograd). If your operator is not differentiable, or to squash this warning and use the previous behavior, please register torch::CppFunction::makeFallthrough() to DispatchKey::Autograd. (Triggered internally at ../torch/csrc/autograd/autograd_not_implemented_fallback.cpp:63.) | |
| [default2]: return Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass | |
| [default0]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/autograd/graph.py:744: UserWarning: 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]:07/03/2024 15:36:21 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: Memory usage: 2135.44MiB. Peak allocated 11566.15MiB. Peak reserved: 11748.00MiB | |
| [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( | |
| [default6]:07/03/2024 15:36:21 [INFO|DP=0|PP=7|TP=0|ip-26-0-170-160]: iteration: 1 / 20 | consumed_tokens: 4.19M | elapsed_time_per_iteration_ms: 83.6K | tokens_per_sec: 50.2K | tokens_per_sec_per_gpu: 3.14K | global_batch_size: 1.02K | lm_loss: 11.1 | lr: 0.0001 | model_tflops_per_gpu: 28.5 | hardware_tflops_per_gpu: 28.5 | grad_norm: 24.9 | cuda_memory_allocated: 1.3G | cuda_max_memory_reserved: 4.01G | hd_total_memory_tb: 312G | hd_used_memory_tb: 68.6G | hd_free_memory_tb: 244G | |
| [default0]:07/03/2024 15:36:21 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: Memory usage: 3168.14MiB. Peak allocated 4459.01MiB. Peak reserved: 13244.00MiB | |