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START TIME: Tue Jul 2 16:12:28 UTC 2024 |
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python3 version = Python 3.10.14 |
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======================== |
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Already on 'bench_cluster' |
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M examples/config_tiny_llama.py |
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M examples/config_tiny_llama.yaml |
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M examples/train_tiny_llama.sh |
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M src/nanotron/models/llama.py |
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M src/nanotron/trainer.py |
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Your branch is up to date with 'origin/bench_cluster'. |
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Job status: RUNNING |
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W0702 16:12:30.571000 140296171427648 torch/distributed/run.py:757] |
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W0702 16:12:30.571000 140296171427648 torch/distributed/run.py:757] ***************************************** |
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W0702 16:12:30.571000 140296171427648 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. |
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W0702 16:12:30.571000 140296171427648 torch/distributed/run.py:757] ***************************************** |
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W0702 16:12:30.582000 139639115892544 torch/distributed/run.py:757] |
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W0702 16:12:30.582000 139639115892544 torch/distributed/run.py:757] ***************************************** |
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W0702 16:12:30.582000 139639115892544 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. |
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W0702 16:12:30.582000 139639115892544 torch/distributed/run.py:757] ***************************************** |
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[default0]:07/02/2024 16:12:48 [WARNING|DP=0|PP=0|TP=0|ip-26-0-169-132]: [Vocab Size Padding] Padded vocab (size: 50257) with 3 dummy tokens (new size: 50260) |
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[default0]:07/02/2024 16:12:48 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-132]: Config: |
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[default0]:07/02/2024 16:12:48 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-132]: Config(general=GeneralArgs(project='bench_cluster', |
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[default0]:07/02/2024 16:12:48 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-132]: run='%date_%jobid', |
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[default0]:07/02/2024 16:12:48 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-132]: seed=42, |
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[default0]:07/02/2024 16:12:48 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-132]: step=None, |
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[default0]:07/02/2024 16:12:48 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-132]: consumed_train_samples=None, |
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[default0]:07/02/2024 16:12:48 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-132]: benchmark_csv_path=None, |
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[default0]:07/02/2024 16:12:48 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-132]: ignore_sanity_checks=True), |
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[default0]:07/02/2024 16:12:48 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-132]: parallelism=ParallelismArgs(dp=1, |
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[default0]:07/02/2024 16:12:48 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-132]: pp=4, |
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[default0]:07/02/2024 16:12:48 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-132]: tp=4, |
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[default0]:07/02/2024 16:12:48 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-132]: pp_engine=<nanotron.parallel.pipeline_parallel.engine.OneForwardOneBackwardPipelineEngine object at 0x7f601ca8c910>, |
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[default0]:07/02/2024 16:12:48 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-132]: tp_mode=<TensorParallelLinearMode.REDUCE_SCATTER: 2>, |
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[default0]:07/02/2024 16:12:48 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-132]: tp_linear_async_communication=False, |
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[default0]:07/02/2024 16:12:48 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-132]: expert_parallel_size=1), |
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[default0]:07/02/2024 16:12:48 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-132]: model=ModelArgs(model_config=LlamaConfig(bos_token_id=1, |
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[default0]:07/02/2024 16:12:48 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-132]: eos_token_id=2, |
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[default0]:07/02/2024 16:12:48 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-132]: hidden_act='silu', |
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[default0]:07/02/2024 16:12:48 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-132]: hidden_size=2048, |
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[default0]:07/02/2024 16:12:48 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-132]: initializer_range=0.02, |
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[default0]:07/02/2024 16:12:48 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-132]: intermediate_size=4096, |
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[default0]:07/02/2024 16:12:48 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-132]: is_llama_config=True, |
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[default0]:07/02/2024 16:12:48 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-132]: max_position_embeddings=4096, |
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[default0]:07/02/2024 16:12:48 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-132]: num_attention_heads=32, |
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[default0]:07/02/2024 16:12:48 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-132]: num_hidden_layers=24, |
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[default0]:07/02/2024 16:12:48 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-132]: num_key_value_heads=32, |
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[default0]:07/02/2024 16:12:48 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-132]: pad_token_id=None, |
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[default0]:07/02/2024 16:12:48 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-132]: pretraining_tp=1, |
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[default0]:07/02/2024 16:12:48 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-132]: rms_norm_eps=1e-05, |
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[default0]:07/02/2024 16:12:48 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-132]: rope_scaling=None, |
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[default0]:07/02/2024 16:12:48 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-132]: rope_theta=10000.0, |
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[default0]:07/02/2024 16:12:48 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-132]: tie_word_embeddings=True, |
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[default0]:07/02/2024 16:12:48 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-132]: use_cache=True, |
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[default0]:07/02/2024 16:12:48 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-132]: vocab_size=50260), |
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[default0]:07/02/2024 16:12:48 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-132]: init_method=RandomInit(std=0.025), |
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[default0]:07/02/2024 16:12:48 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-132]: dtype=torch.bfloat16, |
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[default0]:07/02/2024 16:12:48 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-132]: make_vocab_size_divisible_by=1, |
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[default0]:07/02/2024 16:12:48 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-132]: ddp_bucket_cap_mb=25), |
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[default0]:07/02/2024 16:12:48 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-132]: tokenizer=TokenizerArgs(tokenizer_name_or_path='openai-community/gpt2', |
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[default0]:07/02/2024 16:12:48 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-132]: tokenizer_revision=None, |
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[default0]:07/02/2024 16:12:48 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-132]: tokenizer_max_length=None), |
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[default0]:07/02/2024 16:12:48 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-132]: checkpoints=CheckpointsArgs(checkpoints_path=Path('/dev/null'), |
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[default0]:07/02/2024 16:12:48 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-132]: checkpoint_interval=100000, |
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[default0]:07/02/2024 16:12:48 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-132]: save_initial_state=False, |
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[default0]:07/02/2024 16:12:48 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-132]: resume_checkpoint_path=None, |
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[default0]:07/02/2024 16:12:48 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-132]: checkpoints_path_is_shared_file_system=False), |
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[default0]:07/02/2024 16:12:48 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-132]: logging=LoggingArgs(log_level='info', |
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[default0]:07/02/2024 16:12:48 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-132]: log_level_replica='info', |
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[default0]:07/02/2024 16:12:48 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-132]: iteration_step_info_interval=1), |
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[default0]:07/02/2024 16:12:48 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-132]: tokens=TokensArgs(sequence_length=4096, |
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[default0]:07/02/2024 16:12:48 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-132]: train_steps=20, |
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[default0]:07/02/2024 16:12:48 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-132]: micro_batch_size=4, |
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[default0]:07/02/2024 16:12:48 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-132]: batch_accumulation_per_replica=256, |
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[default0]:07/02/2024 16:12:48 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-132]: val_check_interval=-1, |
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[default0]:07/02/2024 16:12:48 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-132]: limit_val_batches=0, |
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[default0]:07/02/2024 16:12:48 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-132]: limit_test_batches=0), |
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[default0]:07/02/2024 16:12:48 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-132]: optimizer=OptimizerArgs(optimizer_factory=AdamWOptimizerArgs(adam_eps=1e-08, |
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[default0]:07/02/2024 16:12:48 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-132]: adam_beta1=0.9, |
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[default0]:07/02/2024 16:12:48 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-132]: adam_beta2=0.95, |
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[default0]:07/02/2024 16:12:48 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-132]: torch_adam_is_fused=True, |
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[default0]:07/02/2024 16:12:48 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-132]: name='adamW'), |
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[default0]:07/02/2024 16:12:48 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-132]: zero_stage=1, |
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[default0]:07/02/2024 16:12:48 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-132]: weight_decay=0.01, |
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[default0]:07/02/2024 16:12:48 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-132]: clip_grad=1.0, |
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[default0]:07/02/2024 16:12:48 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-132]: accumulate_grad_in_fp32=True, |
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[default0]:07/02/2024 16:12:48 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-132]: learning_rate_scheduler=LRSchedulerArgs(learning_rate=0.0001, |
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[default0]:07/02/2024 16:12:48 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-132]: lr_warmup_steps=1, |
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[default0]:07/02/2024 16:12:48 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-132]: lr_warmup_style='linear', |
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[default0]:07/02/2024 16:12:48 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-132]: lr_decay_style='linear', |
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[default0]:07/02/2024 16:12:48 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-132]: lr_decay_steps=19, |
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[default0]:07/02/2024 16:12:48 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-132]: lr_decay_starting_step=None, |
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[default0]:07/02/2024 16:12:48 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-132]: min_decay_lr=1e-05)), |
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[default0]:07/02/2024 16:12:48 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-132]: data_stages=[DatasetStageArgs(name='Training Stage', |
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[default0]:07/02/2024 16:12:48 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-132]: start_training_step=1, |
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[default0]:07/02/2024 16:12:48 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-132]: data=DataArgs(dataset=PretrainDatasetsArgs(hf_dataset_or_datasets='roneneldan/TinyStories', |
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[default0]:07/02/2024 16:12:48 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-132]: hf_dataset_splits='train', |
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[default0]:07/02/2024 16:12:48 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-132]: hf_dataset_config_name=None, |
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[default0]:07/02/2024 16:12:48 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-132]: dataset_processing_num_proc_per_process=64, |
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[default0]:07/02/2024 16:12:48 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-132]: dataset_overwrite_cache=False, |
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[default0]:07/02/2024 16:12:48 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-132]: text_column_name='text'), |
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[default0]:07/02/2024 16:12:48 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-132]: seed=42, |
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[default0]:07/02/2024 16:12:48 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-132]: num_loading_workers=32))], |
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[default0]:07/02/2024 16:12:48 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-132]: profiler=ProfilerArgs(profiler_export_path=Path('/fsx/ferdinandmom/ferdinand-hf/bench_cluster/results/llama-1B/16_GPUS/dp-1_tp-4_pp-4_mbz-4')), |
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[default0]:07/02/2024 16:12:48 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-132]: lighteval=None) |
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[default0]:07/02/2024 16:12:48 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-132]: Model Config: |
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[default0]:07/02/2024 16:12:48 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-132]: LlamaConfig(bos_token_id=1, |
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[default0]:07/02/2024 16:12:48 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-132]: eos_token_id=2, |
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[default0]:07/02/2024 16:12:48 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-132]: hidden_act='silu', |
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[default0]:07/02/2024 16:12:48 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-132]: hidden_size=2048, |
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[default0]:07/02/2024 16:12:48 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-132]: initializer_range=0.02, |
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[default0]:07/02/2024 16:12:48 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-132]: intermediate_size=4096, |
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[default0]:07/02/2024 16:12:48 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-132]: is_llama_config=True, |
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[default0]:07/02/2024 16:12:48 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-132]: max_position_embeddings=4096, |
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[default0]:07/02/2024 16:12:48 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-132]: num_attention_heads=32, |
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[default0]:07/02/2024 16:12:48 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-132]: num_hidden_layers=24, |
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[default0]:07/02/2024 16:12:48 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-132]: num_key_value_heads=32, |
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[default0]:07/02/2024 16:12:48 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-132]: pad_token_id=None, |
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[default0]:07/02/2024 16:12:48 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-132]: pretraining_tp=1, |
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[default0]:07/02/2024 16:12:48 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-132]: rms_norm_eps=1e-05, |
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[default0]:07/02/2024 16:12:48 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-132]: rope_scaling=None, |
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[default0]:07/02/2024 16:12:48 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-132]: rope_theta=10000.0, |
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[default0]:07/02/2024 16:12:48 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-132]: tie_word_embeddings=True, |
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[default0]:07/02/2024 16:12:48 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-132]: use_cache=True, |
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[default0]:07/02/2024 16:12:48 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-132]: vocab_size=50260) |
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[default0]:07/02/2024 16:12:48 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-132]: Building model.. |
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[default0]:07/02/2024 16:12:48 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-132]: Setting PP block ranks... |
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[default2]:07/02/2024 16:13:04 [INFO|DP=0|PP=0|TP=2|ip-26-0-169-132]: Local number of parameters: 99.2M (189.14MiB) |
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[default2]:07/02/2024 16:13:04 [INFO|DP=0|PP=0|TP=2|ip-26-0-169-132]: [After model building] Memory usage: 197.07MiB. Peak allocated: 199.10MiB Peak reserved: 200.00MiB |
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[default1]:07/02/2024 16:13:04 [INFO|DP=0|PP=0|TP=1|ip-26-0-169-132]: Local number of parameters: 99.2M (189.14MiB) |
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[default1]:07/02/2024 16:13:04 [INFO|DP=0|PP=0|TP=1|ip-26-0-169-132]: [After model building] Memory usage: 197.07MiB. Peak allocated: 199.10MiB Peak reserved: 200.00MiB |
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[default2]:07/02/2024 16:13:04 [INFO|DP=0|PP=0|TP=2|ip-26-0-169-132]: No checkpoint path provided. |
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[default3]:07/02/2024 16:13:04 [INFO|DP=0|PP=0|TP=3|ip-26-0-169-132]: Local number of parameters: 99.2M (189.14MiB) |
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[default1]:07/02/2024 16:13:04 [INFO|DP=0|PP=0|TP=1|ip-26-0-169-132]: No checkpoint path provided. |
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[default3]:07/02/2024 16:13:04 [INFO|DP=0|PP=0|TP=3|ip-26-0-169-132]: [After model building] Memory usage: 197.07MiB. Peak allocated: 199.10MiB Peak reserved: 200.00MiB |
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[default3]:07/02/2024 16:13:04 [INFO|DP=0|PP=0|TP=3|ip-26-0-169-132]: No checkpoint path provided. |
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[default7]:07/02/2024 16:13:04 [INFO|DP=0|PP=1|TP=3|ip-26-0-169-132]: Local number of parameters: 73.4M (140.05MiB) |
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[default7]:07/02/2024 16:13:04 [INFO|DP=0|PP=1|TP=3|ip-26-0-169-132]: [After model building] Memory usage: 147.07MiB. Peak allocated: 149.10MiB Peak reserved: 150.00MiB |
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[default7]:07/02/2024 16:13:04 [INFO|DP=0|PP=1|TP=3|ip-26-0-169-132]: No checkpoint path provided. |
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[default4]:07/02/2024 16:13:04 [INFO|DP=0|PP=3|TP=0|ip-26-0-175-132]: Local number of parameters: 67.7M (129.12MiB) |
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[default4]:07/02/2024 16:13:04 [INFO|DP=0|PP=3|TP=0|ip-26-0-175-132]: [After model building] Memory usage: 134.05MiB. Peak allocated: 136.08MiB Peak reserved: 138.00MiB |
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[default4]:07/02/2024 16:13:04 [INFO|DP=0|PP=3|TP=0|ip-26-0-175-132]: No checkpoint path provided. |
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[default2]:07/02/2024 16:13:04 [INFO|DP=0|PP=2|TP=2|ip-26-0-175-132]: Local number of parameters: 62.9M (120.05MiB) |
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[default2]:07/02/2024 16:13:04 [INFO|DP=0|PP=2|TP=2|ip-26-0-175-132]: [After model building] Memory usage: 126.06MiB. Peak allocated: 128.09MiB Peak reserved: 130.00MiB |
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[default2]:07/02/2024 16:13:04 [INFO|DP=0|PP=2|TP=2|ip-26-0-175-132]: No checkpoint path provided. |
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[default0]:07/02/2024 16:13:04 [INFO|DP=0|PP=2|TP=0|ip-26-0-175-132]: Local number of parameters: 62.9M (120.05MiB) |
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[default0]:07/02/2024 16:13:04 [INFO|DP=0|PP=2|TP=0|ip-26-0-175-132]: [After model building] Memory usage: 126.06MiB. Peak allocated: 128.09MiB Peak reserved: 130.00MiB |
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[default0]:07/02/2024 16:13:04 [INFO|DP=0|PP=2|TP=0|ip-26-0-175-132]: No checkpoint path provided. |
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[default7]:07/02/2024 16:13:04 [INFO|DP=0|PP=3|TP=3|ip-26-0-175-132]: Local number of parameters: 67.7M (129.12MiB) |
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[default7]:07/02/2024 16:13:04 [INFO|DP=0|PP=3|TP=3|ip-26-0-175-132]: [After model building] Memory usage: 134.05MiB. Peak allocated: 136.08MiB Peak reserved: 138.00MiB |
|
[default7]:07/02/2024 16:13:04 [INFO|DP=0|PP=3|TP=3|ip-26-0-175-132]: No checkpoint path provided. |
|
[default3]:07/02/2024 16:13:04 [INFO|DP=0|PP=2|TP=3|ip-26-0-175-132]: Local number of parameters: 62.9M (120.05MiB) |
|
[default3]:07/02/2024 16:13:04 [INFO|DP=0|PP=2|TP=3|ip-26-0-175-132]: [After model building] Memory usage: 126.06MiB. Peak allocated: 128.09MiB Peak reserved: 130.00MiB |
|
[default1]:07/02/2024 16:13:04 [INFO|DP=0|PP=2|TP=1|ip-26-0-175-132]: Local number of parameters: 62.9M (120.05MiB) |
|
[default3]:07/02/2024 16:13:04 [INFO|DP=0|PP=2|TP=3|ip-26-0-175-132]: No checkpoint path provided. |
|
[default1]:07/02/2024 16:13:04 [INFO|DP=0|PP=2|TP=1|ip-26-0-175-132]: [After model building] Memory usage: 126.06MiB. Peak allocated: 128.09MiB Peak reserved: 130.00MiB |
|
[default1]:07/02/2024 16:13:04 [INFO|DP=0|PP=2|TP=1|ip-26-0-175-132]: No checkpoint path provided. |
|
[default0]:07/02/2024 16:13:04 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-132]: Total number of parameters: 1.21G (2313.42MiB) |
|
[default0]:07/02/2024 16:13:04 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-132]: Local number of parameters: 99.2M (189.14MiB) |
|
[default0]:07/02/2024 16:13:04 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-132]: [After model building] Memory usage: 197.07MiB. Peak allocated: 199.10MiB Peak reserved: 200.00MiB |
|
[default0]:07/02/2024 16:13:04 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-132]: No checkpoint path provided. |
|
[default0]:07/02/2024 16:13:04 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-132]: Parametrizing model parameters using StandardParametrizator |
|
[default5]:07/02/2024 16:13:04 [INFO|DP=0|PP=3|TP=1|ip-26-0-175-132]: Local number of parameters: 67.7M (129.12MiB) |
|
[default5]:07/02/2024 16:13:04 [INFO|DP=0|PP=3|TP=1|ip-26-0-175-132]: [After model building] Memory usage: 134.05MiB. Peak allocated: 136.08MiB Peak reserved: 138.00MiB |
|
[default5]:07/02/2024 16:13:04 [INFO|DP=0|PP=3|TP=1|ip-26-0-175-132]: No checkpoint path provided. |
|
[default6]:07/02/2024 16:13:04 [INFO|DP=0|PP=1|TP=2|ip-26-0-169-132]: Local number of parameters: 73.4M (140.05MiB) |
|
[default6]:07/02/2024 16:13:04 [INFO|DP=0|PP=1|TP=2|ip-26-0-169-132]: [After model building] Memory usage: 147.07MiB. Peak allocated: 149.10MiB Peak reserved: 150.00MiB |
|
[default6]:07/02/2024 16:13:04 [INFO|DP=0|PP=3|TP=2|ip-26-0-175-132]: Local number of parameters: 67.7M (129.12MiB) |
|
[default6]:07/02/2024 16:13:04 [INFO|DP=0|PP=3|TP=2|ip-26-0-175-132]: [After model building] Memory usage: 134.05MiB. Peak allocated: 136.08MiB Peak reserved: 138.00MiB |
|
[default6]:07/02/2024 16:13:04 [INFO|DP=0|PP=1|TP=2|ip-26-0-169-132]: No checkpoint path provided. |
|
[default4]:07/02/2024 16:13:04 [INFO|DP=0|PP=1|TP=0|ip-26-0-169-132]: Local number of parameters: 73.4M (140.05MiB) |
|
[default6]:07/02/2024 16:13:04 [INFO|DP=0|PP=3|TP=2|ip-26-0-175-132]: No checkpoint path provided. |
|
[default4]:07/02/2024 16:13:04 [INFO|DP=0|PP=1|TP=0|ip-26-0-169-132]: [After model building] Memory usage: 147.07MiB. Peak allocated: 149.10MiB Peak reserved: 150.00MiB |
|
[default5]:07/02/2024 16:13:04 [INFO|DP=0|PP=1|TP=1|ip-26-0-169-132]: Local number of parameters: 73.4M (140.05MiB) |
|
[default5]:07/02/2024 16:13:04 [INFO|DP=0|PP=1|TP=1|ip-26-0-169-132]: [After model building] Memory usage: 147.07MiB. Peak allocated: 149.10MiB Peak reserved: 150.00MiB |
|
[default4]:07/02/2024 16:13:04 [INFO|DP=0|PP=1|TP=0|ip-26-0-169-132]: No checkpoint path provided. |
|
[default5]:07/02/2024 16:13:04 [INFO|DP=0|PP=1|TP=1|ip-26-0-169-132]: No checkpoint path provided. |
|
[default0]:07/02/2024 16:13:05 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-132]: [Optimizer Building] Using LearningRateForSP as learning rate |
|
[default0]:07/02/2024 16:13:05 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-132]: [ZeRO sharding] Size of optimizer params per rank: |
|
[default0]:07/02/2024 16:13:05 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-132]: [ZeRO sharding] DP Rank 0 has 99.2M out of 99.2M (100.00%) params' optimizer states |
|
[default0]:07/02/2024 16:13:06 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-132]: [Training Plan] Stage Training Stage has 19 remaining training steps and has consumed 0 samples |
|
[default0]:07/02/2024 16:13:06 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-132]: Using `datasets` library |
|
[default0]:07/02/2024 16:13:06 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-132]: Loading tokenizer from openai-community/gpt2 and transformers/hf_hub versions ('4.41.2', '0.23.4') |
|
[default0]:07/02/2024 16:13:07 [WARNING|DP=0|PP=0|TP=0|ip-26-0-169-132]: 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 16:13:08 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-132]: [Training Plan] There are 1 training stages |
|
[default0]:07/02/2024 16:13:08 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-132]: [Stage Training Stage] start from step 1 |
|
[default0]:07/02/2024 16:13:08 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-132]: |
|
[default0]:07/02/2024 16:13:08 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-132]: [Start training] datetime: 2024-07-02 16:13:08.759337 | mbs: 4 | grad_accum: 256 | global_batch_size: 1024 | sequence_length: 4096 | train_steps: 20 | start_iteration_step: 0 | consumed_train_samples: 0 |
|
[default0]:07/02/2024 16:13:08 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-132]: Resuming training from stage Training Stage, it has trained for 0 samples and has 19 remaining train steps |
|
[default0]:07/02/2024 16:13:08 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-132]: Memory usage: 953.61MiB. Peak allocated 953.61MiB. Peak reserved: 960.00MiB |
|
[default3]:07/02/2024 16:13:08 [WARNING|DP=0|PP=0|TP=3|ip-26-0-169-132]: Repo card metadata block was not found. Setting CardData to empty. |
|
[default2]:07/02/2024 16:13:08 [WARNING|DP=0|PP=0|TP=2|ip-26-0-169-132]: Repo card metadata block was not found. Setting CardData to empty. |
|
[default4]:Repo card metadata block was not found. Setting CardData to empty. |
|
[default2]:07/02/2024 16:13:08 [WARNING|DP=0|PP=2|TP=2|ip-26-0-175-132]: Repo card metadata block was not found. Setting CardData to empty. |
|
[default4]:07/02/2024 16:13:08 [WARNING|DP=0|PP=3|TP=0|ip-26-0-175-132]: Repo card metadata block was not found. Setting CardData to empty. |
|
[default4]:07/02/2024 16:13:08 [WARNING|DP=0|PP=1|TP=0|ip-26-0-169-132]: Repo card metadata block was not found. Setting CardData to empty. |
|
[default4]:Repo card metadata block was not found. Setting CardData to empty. |
|
[default5]:07/02/2024 16:13:08 [WARNING|DP=0|PP=1|TP=1|ip-26-0-169-132]: Repo card metadata block was not found. Setting CardData to empty. |
|
[default2]:Repo card metadata block was not found. Setting CardData to empty. |
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[default3]:Repo card metadata block was not found. Setting CardData to empty. |
|
[default2]:Repo card metadata block was not found. Setting CardData to empty. |
|
[default5]:Repo card metadata block was not found. Setting CardData to empty. |
|
[default1]:07/02/2024 16:13:09 [WARNING|DP=0|PP=0|TP=1|ip-26-0-169-132]: Repo card metadata block was not found. Setting CardData to empty. |
|
[default1]:Repo card metadata block was not found. Setting CardData to empty. |
|
[default0]:07/02/2024 16:13:09 [WARNING|DP=0|PP=2|TP=0|ip-26-0-175-132]: Repo card metadata block was not found. Setting CardData to empty. |
|
[default3]:Repo card metadata block was not found. Setting CardData to empty. |
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[default0]:Repo card metadata block was not found. Setting CardData to empty. |
|
[default7]:Repo card metadata block was not found. Setting CardData to empty. |
|
[default3]:07/02/2024 16:13:09 [WARNING|DP=0|PP=2|TP=3|ip-26-0-175-132]: Repo card metadata block was not found. Setting CardData to empty. |
|
[default7]:07/02/2024 16:13:09 [WARNING|DP=0|PP=3|TP=3|ip-26-0-175-132]: Repo card metadata block was not found. Setting CardData to empty. |
|
[default5]:07/02/2024 16:13:08 [WARNING|DP=0|PP=3|TP=1|ip-26-0-175-132]: Repo card metadata block was not found. Setting CardData to empty. |
|
[default6]:07/02/2024 16:13:08 [WARNING|DP=0|PP=3|TP=2|ip-26-0-175-132]: Repo card metadata block was not found. Setting CardData to empty. |
|
[default6]:Repo card metadata block was not found. Setting CardData to empty. |
|
[default5]:Repo card metadata block was not found. Setting CardData to empty. |
|
[default6]:07/02/2024 16:13:09 [WARNING|DP=0|PP=1|TP=2|ip-26-0-169-132]: Repo card metadata block was not found. Setting CardData to empty. |
|
[default6]:Repo card metadata block was not found. Setting CardData to empty. |
|
[default1]:07/02/2024 16:13:09 [WARNING|DP=0|PP=2|TP=1|ip-26-0-175-132]: Repo card metadata block was not found. Setting CardData to empty. |
|
[default1]:Repo card metadata block was not found. Setting CardData to empty. |
|
[default7]:Repo card metadata block was not found. Setting CardData to empty. |
|
[default7]:07/02/2024 16:13:14 [WARNING|DP=0|PP=1|TP=3|ip-26-0-169-132]: Repo card metadata block was not found. Setting CardData to empty. |
|
[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 |
|
[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 |
|
[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 |
|
[default3]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/autograd/graph.py:744: UserWarning: c10d::allreduce_: an autograd kernel was not registered to the Autograd key(s) but we are trying to backprop through it. This may lead to silently incorrect behavior. This behavior is deprecated and will be removed in a future version of PyTorch. If your operator is differentiable, please ensure you have registered an autograd kernel to the correct Autograd key (e.g. DispatchKey::Autograd, DispatchKey::CompositeImplicitAutograd). If your operator is not differentiable, or to squash this warning and use the previous behavior, please register torch::CppFunction::makeFallthrough() to DispatchKey::Autograd. (Triggered internally at ../torch/csrc/autograd/autograd_not_implemented_fallback.cpp:63.) |
|
[default3]: return Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass |
|
[default0]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/autograd/graph.py:744: UserWarning: c10d::allreduce_: an autograd kernel was not registered to the Autograd key(s) but we are trying to backprop through it. This may lead to silently incorrect behavior. This behavior is deprecated and will be removed in a future version of PyTorch. If your operator is differentiable, please ensure you have registered an autograd kernel to the correct Autograd key (e.g. DispatchKey::Autograd, DispatchKey::CompositeImplicitAutograd). If your operator is not differentiable, or to squash this warning and use the previous behavior, please register torch::CppFunction::makeFallthrough() to DispatchKey::Autograd. (Triggered internally at ../torch/csrc/autograd/autograd_not_implemented_fallback.cpp:63.) |
|
[default0]: return Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass |
|
[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 |
|
[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 |
|
[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 |
|
[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 |
|
[default6]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/autograd/graph.py:744: UserWarning: c10d::allreduce_: an autograd kernel was not registered to the Autograd key(s) but we are trying to backprop through it. This may lead to silently incorrect behavior. This behavior is deprecated and will be removed in a future version of PyTorch. If your operator is differentiable, please ensure you have registered an autograd kernel to the correct Autograd key (e.g. DispatchKey::Autograd, DispatchKey::CompositeImplicitAutograd). If your operator is not differentiable, or to squash this warning and use the previous behavior, please register torch::CppFunction::makeFallthrough() to DispatchKey::Autograd. (Triggered internally at ../torch/csrc/autograd/autograd_not_implemented_fallback.cpp:63.) |
|
[default6]: return Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass |
|
[default7]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/autograd/graph.py:744: UserWarning: c10d::allreduce_: an autograd kernel was not registered to the Autograd key(s) but we are trying to backprop through it. This may lead to silently incorrect behavior. This behavior is deprecated and will be removed in a future version of PyTorch. If your operator is differentiable, please ensure you have registered an autograd kernel to the correct Autograd key (e.g. DispatchKey::Autograd, DispatchKey::CompositeImplicitAutograd). If your operator is not differentiable, or to squash this warning and use the previous behavior, please register torch::CppFunction::makeFallthrough() to DispatchKey::Autograd. (Triggered internally at ../torch/csrc/autograd/autograd_not_implemented_fallback.cpp:63.) |
|
[default7]: return Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass |
|
[default1]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/autograd/graph.py:744: UserWarning: c10d::allreduce_: an autograd kernel was not registered to the Autograd key(s) but we are trying to backprop through it. This may lead to silently incorrect behavior. This behavior is deprecated and will be removed in a future version of PyTorch. If your operator is differentiable, please ensure you have registered an autograd kernel to the correct Autograd key (e.g. DispatchKey::Autograd, DispatchKey::CompositeImplicitAutograd). If your operator is not differentiable, or to squash this warning and use the previous behavior, please register torch::CppFunction::makeFallthrough() to DispatchKey::Autograd. (Triggered internally at ../torch/csrc/autograd/autograd_not_implemented_fallback.cpp:63.) |
|
[default1]: return Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass |
|
[default3]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/autograd/graph.py:744: UserWarning: c10d::allreduce_: an autograd kernel was not registered to the Autograd key(s) but we are trying to backprop through it. This may lead to silently incorrect behavior. This behavior is deprecated and will be removed in a future version of PyTorch. If your operator is differentiable, please ensure you have registered an autograd kernel to the correct Autograd key (e.g. DispatchKey::Autograd, DispatchKey::CompositeImplicitAutograd). If your operator is not differentiable, or to squash this warning and use the previous behavior, please register torch::CppFunction::makeFallthrough() to DispatchKey::Autograd. (Triggered internally at ../torch/csrc/autograd/autograd_not_implemented_fallback.cpp:63.) |
|
[default3]: return Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass |
|
[default2]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/autograd/graph.py:744: UserWarning: c10d::allreduce_: an autograd kernel was not registered to the Autograd key(s) but we are trying to backprop through it. This may lead to silently incorrect behavior. This behavior is deprecated and will be removed in a future version of PyTorch. If your operator is differentiable, please ensure you have registered an autograd kernel to the correct Autograd key (e.g. DispatchKey::Autograd, DispatchKey::CompositeImplicitAutograd). If your operator is not differentiable, or to squash this warning and use the previous behavior, please register torch::CppFunction::makeFallthrough() to DispatchKey::Autograd. (Triggered internally at ../torch/csrc/autograd/autograd_not_implemented_fallback.cpp:63.) |
|
[default2]: return Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass |
|
[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.) |
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[default0]: return Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass |
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[default4]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/distributed_c10d.py:2261: UserWarning: torch.distributed.all_reduce_coalesced will be deprecated. If you must use it, please revisit our documentation later at https://pytorch.org/docs/master/distributed.html#collective-functions |
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[default4]: warnings.warn( |
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[default3]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/distributed_c10d.py:2261: UserWarning: torch.distributed.all_reduce_coalesced will be deprecated. If you must use it, please revisit our documentation later at https://pytorch.org/docs/master/distributed.html#collective-functions |
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[default3]: warnings.warn( |
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[default5]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/distributed_c10d.py:2261: UserWarning: torch.distributed.all_reduce_coalesced will be deprecated. If you must use it, please revisit our documentation later at https://pytorch.org/docs/master/distributed.html#collective-functions |
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[default5]: warnings.warn( |
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[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 |
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[default7]: warnings.warn( |
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[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 |
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[default0]: warnings.warn( |
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[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 |
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[default6]: warnings.warn( |
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[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 |
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[default1]: warnings.warn( |
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[default2]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/distributed_c10d.py:2261: UserWarning: torch.distributed.all_reduce_coalesced will be deprecated. If you must use it, please revisit our documentation later at https://pytorch.org/docs/master/distributed.html#collective-functions |
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[default2]: warnings.warn( |
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[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 |
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[default1]: warnings.warn( |
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[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 |
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[default7]: warnings.warn( |
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[default3]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/distributed_c10d.py:2261: UserWarning: torch.distributed.all_reduce_coalesced will be deprecated. If you must use it, please revisit our documentation later at https://pytorch.org/docs/master/distributed.html#collective-functions |
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[default3]: warnings.warn( |
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[default2]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/distributed_c10d.py:2261: UserWarning: torch.distributed.all_reduce_coalesced will be deprecated. If you must use it, please revisit our documentation later at https://pytorch.org/docs/master/distributed.html#collective-functions |
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[default2]: warnings.warn( |
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[default4]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/distributed_c10d.py:2261: UserWarning: torch.distributed.all_reduce_coalesced will be deprecated. If you must use it, please revisit our documentation later at https://pytorch.org/docs/master/distributed.html#collective-functions |
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[default4]: warnings.warn( |
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[default5]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/distributed_c10d.py:2261: UserWarning: torch.distributed.all_reduce_coalesced will be deprecated. If you must use it, please revisit our documentation later at https://pytorch.org/docs/master/distributed.html#collective-functions |
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[default5]: warnings.warn( |
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[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 |
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[default0]: warnings.warn( |
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[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 |
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[default6]: warnings.warn( |
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[default0]:07/02/2024 16:13:57 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-132]: Memory usage: 1021.25MiB. Peak allocated 12352.75MiB. Peak reserved: 12640.00MiB |
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[default4]:07/02/2024 16:14:05 [INFO|DP=0|PP=3|TP=0|ip-26-0-175-132]: iteration: 1 / 20 | consumed_tokens: 4.19M | elapsed_time_per_iteration_ms: 56.1K | tokens_per_sec: 74.8K | tokens_per_sec_per_gpu: 4.67K | global_batch_size: 1.02K | lm_loss: 11.2 | lr: 0.0001 | model_tflops_per_gpu: 42.4 | hardware_tflops_per_gpu: 42.4 | grad_norm: 10.9 | cuda_memory_allocated: 1.29G | cuda_max_memory_reserved: 5.41G | hd_total_memory_tb: 312G | hd_used_memory_tb: 65.5G | hd_free_memory_tb: 247G |
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[default0]:07/02/2024 16:14:05 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-132]: Memory usage: 1777.82MiB. Peak allocated 1777.82MiB. Peak reserved: 12640.00MiB |
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[default0]:07/02/2024 16:14:24 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-132]: Memory usage: 1777.82MiB. Peak allocated 13109.32MiB. Peak reserved: 13264.00MiB |
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[default4]:07/02/2024 16:14:24 [INFO|DP=0|PP=3|TP=0|ip-26-0-175-132]: iteration: 2 / 20 | consumed_tokens: 8.39M | elapsed_time_per_iteration_ms: 18.4K | tokens_per_sec: 228K | tokens_per_sec_per_gpu: 14.2K | global_batch_size: 1.02K | lm_loss: 11.2 | lr: 9.53e-05 | model_tflops_per_gpu: 129 | hardware_tflops_per_gpu: 129 | grad_norm: 11 | cuda_memory_allocated: 1.29G | cuda_max_memory_reserved: 5.41G | hd_total_memory_tb: 312G | hd_used_memory_tb: 65.5G | hd_free_memory_tb: 247G |
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[default0]:07/02/2024 16:14:24 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-132]: Memory usage: 1777.82MiB. Peak allocated 1777.85MiB. Peak reserved: 13264.00MiB |
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[default0]:07/02/2024 16:14:42 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-132]: Memory usage: 1777.82MiB. Peak allocated 13109.32MiB. Peak reserved: 13264.00MiB |
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[default4]:07/02/2024 16:14:42 [INFO|DP=0|PP=3|TP=0|ip-26-0-175-132]: iteration: 3 / 20 | consumed_tokens: 12.6M | elapsed_time_per_iteration_ms: 18K | tokens_per_sec: 233K | tokens_per_sec_per_gpu: 14.5K | global_batch_size: 1.02K | lm_loss: 9.83 | lr: 9.05e-05 | model_tflops_per_gpu: 132 | hardware_tflops_per_gpu: 132 | grad_norm: 44.4 | cuda_memory_allocated: 1.29G | cuda_max_memory_reserved: 5.41G | hd_total_memory_tb: 312G | hd_used_memory_tb: 65.5G | hd_free_memory_tb: 247G |
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[default0]:STAGE:2024-07-02 16:14:42 2343928:2343928 ActivityProfilerController.cpp:314] Completed Stage: Warm Up |
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[default0]:07/02/2024 16:14:42 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-132]: Memory usage: 1777.82MiB. Peak allocated 1777.85MiB. Peak reserved: 13264.00MiB |
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[default0]:07/02/2024 16:14:59 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-132]: Memory usage: 1777.82MiB. Peak allocated 13109.32MiB. Peak reserved: 13264.00MiB |
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[default0]:07/02/2024 16:15:00 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-132]: Memory usage: 1777.82MiB. Peak allocated 1777.85MiB. Peak reserved: 13264.00MiB |
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[default4]:07/02/2024 16:15:00 [INFO|DP=0|PP=3|TP=0|ip-26-0-175-132]: iteration: 4 / 20 | consumed_tokens: 16.8M | elapsed_time_per_iteration_ms: 17.8K | tokens_per_sec: 236K | tokens_per_sec_per_gpu: 14.7K | global_batch_size: 1.02K | lm_loss: 12.1 | lr: 8.58e-05 | model_tflops_per_gpu: 134 | hardware_tflops_per_gpu: 134 | grad_norm: 24.9 | cuda_memory_allocated: 1.29G | cuda_max_memory_reserved: 5.41G | hd_total_memory_tb: 312G | hd_used_memory_tb: 65.5G | hd_free_memory_tb: 247G |
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[default4]:07/02/2024 16:15:18 [INFO|DP=0|PP=3|TP=0|ip-26-0-175-132]: iteration: 5 / 20 | consumed_tokens: 21M | elapsed_time_per_iteration_ms: 18.6K | tokens_per_sec: 225K | tokens_per_sec_per_gpu: 14.1K | global_batch_size: 1.02K | lm_loss: 10.1 | lr: 8.11e-05 | model_tflops_per_gpu: 128 | hardware_tflops_per_gpu: 128 | grad_norm: 11.5 |
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[default0]:07/02/2024 16:15:18 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-132]: Memory usage: 1777.82MiB. Peak allocated 13109.32MiB. Peak reserved: 13264.00MiB |
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[default4]:07/02/2024 16:15:37 [INFO|DP=0|PP=3|TP=0|ip-26-0-175-132]: iteration: 6 / 20 | consumed_tokens: 25.2M | elapsed_time_per_iteration_ms: 18.3K | tokens_per_sec: 229K | tokens_per_sec_per_gpu: 14.3K | global_batch_size: 1.02K | lm_loss: 9.39 | lr: 7.63e-05 | model_tflops_per_gpu: 130 | hardware_tflops_per_gpu: 130 | grad_norm: 7.05 |
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[default0]:STAGE:2024-07-02 16:16:23 2343928:2343928 ActivityProfilerController.cpp:320] Completed Stage: Collection |
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[default0]:STAGE:2024-07-02 16:16:28 2343928:2343928 ActivityProfilerController.cpp:324] Completed Stage: Post Processing |
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[default0]:07/02/2024 16:22:20 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-132]: Memory usage: 1777.82MiB. Peak allocated 13109.32MiB. Peak reserved: 13264.00MiB |
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[default4]:07/02/2024 16:22:38 [INFO|DP=0|PP=3|TP=0|ip-26-0-175-132]: iteration: 7 / 20 | consumed_tokens: 29.4M | elapsed_time_per_iteration_ms: 422K | tokens_per_sec: 9.95K | tokens_per_sec_per_gpu: 622 | global_batch_size: 1.02K | lm_loss: 8.69 | lr: 7.16e-05 | model_tflops_per_gpu: 5.64 | hardware_tflops_per_gpu: 5.64 | grad_norm: 5.44 |
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[default0]:07/02/2024 16:22:38 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-132]: Memory usage: 1777.82MiB. Peak allocated 13109.32MiB. Peak reserved: 13264.00MiB |
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[default4]:07/02/2024 16:22:55 [INFO|DP=0|PP=3|TP=0|ip-26-0-175-132]: iteration: 8 / 20 | consumed_tokens: 33.6M | elapsed_time_per_iteration_ms: 17.4K | tokens_per_sec: 241K | tokens_per_sec_per_gpu: 15K | global_batch_size: 1.02K | lm_loss: 8.76 | lr: 6.68e-05 | model_tflops_per_gpu: 137 | hardware_tflops_per_gpu: 137 | grad_norm: 18.1 |
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[default0]:07/02/2024 16:22:55 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-132]: Memory usage: 1777.82MiB. Peak allocated 13109.32MiB. Peak reserved: 13264.00MiB |
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[default4]:07/02/2024 16:23:13 [INFO|DP=0|PP=3|TP=0|ip-26-0-175-132]: iteration: 9 / 20 | consumed_tokens: 37.7M | elapsed_time_per_iteration_ms: 17.9K | tokens_per_sec: 235K | tokens_per_sec_per_gpu: 14.7K | global_batch_size: 1.02K | lm_loss: 8.11 | lr: 6.21e-05 | model_tflops_per_gpu: 133 | hardware_tflops_per_gpu: 133 | grad_norm: 4.98 |
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[default0]:07/02/2024 16:23:13 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-132]: Memory usage: 1777.82MiB. Peak allocated 13109.32MiB. Peak reserved: 13264.00MiB |
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[default4]:07/02/2024 16:23:32 [INFO|DP=0|PP=3|TP=0|ip-26-0-175-132]: iteration: 10 / 20 | consumed_tokens: 41.9M | elapsed_time_per_iteration_ms: 18.2K | tokens_per_sec: 231K | tokens_per_sec_per_gpu: 14.4K | global_batch_size: 1.02K | lm_loss: 7.96 | lr: 5.74e-05 | model_tflops_per_gpu: 131 | hardware_tflops_per_gpu: 131 | grad_norm: 4.62 |
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[default0]:07/02/2024 16:23:32 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-132]: Memory usage: 1777.82MiB. Peak allocated 13109.32MiB. Peak reserved: 13264.00MiB |
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[default0]:07/02/2024 16:23:49 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-132]: Memory usage: 1777.82MiB. Peak allocated 13109.32MiB. Peak reserved: 13264.00MiB |
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[default4]:07/02/2024 16:23:49 [INFO|DP=0|PP=3|TP=0|ip-26-0-175-132]: iteration: 11 / 20 | consumed_tokens: 46.1M | elapsed_time_per_iteration_ms: 17.6K | tokens_per_sec: 238K | tokens_per_sec_per_gpu: 14.9K | global_batch_size: 1.02K | lm_loss: 7.84 | lr: 5.26e-05 | model_tflops_per_gpu: 135 | hardware_tflops_per_gpu: 135 | grad_norm: 4.92 |
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[default4]:07/02/2024 16:24:07 [INFO|DP=0|PP=3|TP=0|ip-26-0-175-132]: iteration: 12 / 20 | consumed_tokens: 50.3M | elapsed_time_per_iteration_ms: 17.6K | tokens_per_sec: 238K | tokens_per_sec_per_gpu: 14.9K | global_batch_size: 1.02K | lm_loss: 7.64 | lr: 4.79e-05 | model_tflops_per_gpu: 135 | hardware_tflops_per_gpu: 135 | grad_norm: 4.08 |
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[default0]:07/02/2024 16:24:07 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-132]: Memory usage: 1777.82MiB. Peak allocated 13109.32MiB. Peak reserved: 13264.00MiB |
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[default0]:07/02/2024 16:24:24 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-132]: Memory usage: 1777.82MiB. Peak allocated 13109.32MiB. Peak reserved: 13264.00MiB |
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[default4]:07/02/2024 16:24:24 [INFO|DP=0|PP=3|TP=0|ip-26-0-175-132]: iteration: 13 / 20 | consumed_tokens: 54.5M | elapsed_time_per_iteration_ms: 17.5K | tokens_per_sec: 240K | tokens_per_sec_per_gpu: 15K | global_batch_size: 1.02K | lm_loss: 7.48 | lr: 4.32e-05 | model_tflops_per_gpu: 136 | hardware_tflops_per_gpu: 136 | grad_norm: 3.27 |
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[default0]:07/02/2024 16:24:42 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-132]: Memory usage: 1777.82MiB. Peak allocated 13109.32MiB. Peak reserved: 13264.00MiB |
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[default4]:07/02/2024 16:24:42 [INFO|DP=0|PP=3|TP=0|ip-26-0-175-132]: iteration: 14 / 20 | consumed_tokens: 58.7M | elapsed_time_per_iteration_ms: 17.3K | tokens_per_sec: 242K | tokens_per_sec_per_gpu: 15.1K | global_batch_size: 1.02K | lm_loss: 7.4 | lr: 3.84e-05 | model_tflops_per_gpu: 137 | hardware_tflops_per_gpu: 137 | grad_norm: 3.52 |
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[default4]:07/02/2024 16:25:00 [INFO|DP=0|PP=3|TP=0|ip-26-0-175-132]: iteration: 15 / 20 | consumed_tokens: 62.9M | elapsed_time_per_iteration_ms: 18.7K | tokens_per_sec: 225K | tokens_per_sec_per_gpu: 14K | global_batch_size: 1.02K | lm_loss: 7.28 | lr: 3.37e-05 | model_tflops_per_gpu: 127 | hardware_tflops_per_gpu: 127 | grad_norm: 3.12 |
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[default0]:07/02/2024 16:25:00 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-132]: Memory usage: 1777.82MiB. Peak allocated 13109.32MiB. Peak reserved: 13264.00MiB |
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[default4]:07/02/2024 16:25:18 [INFO|DP=0|PP=3|TP=0|ip-26-0-175-132]: iteration: 16 / 20 | consumed_tokens: 67.1M | elapsed_time_per_iteration_ms: 17.6K | tokens_per_sec: 238K | tokens_per_sec_per_gpu: 14.9K | global_batch_size: 1.02K | lm_loss: 7.18 | lr: 2.89e-05 | model_tflops_per_gpu: 135 | hardware_tflops_per_gpu: 135 | grad_norm: 3.12 |
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[default0]:07/02/2024 16:25:18 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-132]: Memory usage: 1777.82MiB. Peak allocated 13109.32MiB. Peak reserved: 13264.00MiB |
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[default0]:07/02/2024 16:25:36 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-132]: Memory usage: 1777.82MiB. Peak allocated 13109.32MiB. Peak reserved: 13264.00MiB |
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[default4]:07/02/2024 16:25:36 [INFO|DP=0|PP=3|TP=0|ip-26-0-175-132]: iteration: 17 / 20 | consumed_tokens: 71.3M | elapsed_time_per_iteration_ms: 18.2K | tokens_per_sec: 231K | tokens_per_sec_per_gpu: 14.4K | global_batch_size: 1.02K | lm_loss: 7.09 | lr: 2.42e-05 | model_tflops_per_gpu: 131 | hardware_tflops_per_gpu: 131 | grad_norm: 3.22 |
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[default0]:07/02/2024 16:25:53 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-132]: Memory usage: 1777.82MiB. Peak allocated 13109.32MiB. Peak reserved: 13264.00MiB |
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[default4]:07/02/2024 16:25:53 [INFO|DP=0|PP=3|TP=0|ip-26-0-175-132]: iteration: 18 / 20 | consumed_tokens: 75.5M | elapsed_time_per_iteration_ms: 17.4K | tokens_per_sec: 241K | tokens_per_sec_per_gpu: 15K | global_batch_size: 1.02K | lm_loss: 7.02 | lr: 1.95e-05 | model_tflops_per_gpu: 136 | hardware_tflops_per_gpu: 136 | grad_norm: 3.19 |
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[default4]:07/02/2024 16:26:12 [INFO|DP=0|PP=3|TP=0|ip-26-0-175-132]: iteration: 19 / 20 | consumed_tokens: 79.7M | elapsed_time_per_iteration_ms: 18.7K | tokens_per_sec: 225K | tokens_per_sec_per_gpu: 14K | global_batch_size: 1.02K | lm_loss: 6.96 | lr: 1.47e-05 | model_tflops_per_gpu: 127 | hardware_tflops_per_gpu: 127 | grad_norm: 3.06 |
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[default0]:07/02/2024 16:26:12 [INFO|DP=0|PP=0|TP=0|ip-26-0-169-132]: Memory usage: 1777.82MiB. Peak allocated 13109.32MiB. Peak reserved: 13264.00MiB |
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[default4]:07/02/2024 16:26:30 [INFO|DP=0|PP=3|TP=0|ip-26-0-175-132]: iteration: 20 / 20 | consumed_tokens: 83.9M | elapsed_time_per_iteration_ms: 18.2K | tokens_per_sec: 230K | tokens_per_sec_per_gpu: 14.4K | global_batch_size: 1.02K | lm_loss: 6.92 | lr: 1e-05 | model_tflops_per_gpu: 131 | hardware_tflops_per_gpu: 131 | grad_norm: 2.88 |
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Saved 1 csv files over 1 completed logs |
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Processing file: /fsx/ferdinandmom/ferdinand-hf/bench_cluster/results/llama-1B/16_GPUS/dp-1_tp-4_pp-4_mbz-4/profiler/ip-26-0-169-132_2343928.1719937262116152276.pt.trace.json |
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Results written to /fsx/ferdinandmom/ferdinand-hf/bench_cluster/results/llama-1B/16_GPUS/dp-1_tp-4_pp-4_mbz-4/profiler.csv |
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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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ip-26-0-169-132_2343928.1719937262116152276.pt.trace.json: 0%| | 0.00/10.9G [00:00<?, ?B/s]
ip-26-0-169-132_2343928.1719937262116152276.pt.trace.json: 0%| | 16.0M/10.9G [00:00<03:27, 52.3MB/s]
ip-26-0-169-132_2343928.1719937262116152276.pt.trace.json: 0%| | 32.0M/10.9G [00:00<03:05, 58.3MB/s]
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