| ======================== | |
| START TIME: Wed Jul 3 00:26:05 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 00:26:07.774000 140516195460928 torch/distributed/run.py:757] | |
| W0703 00:26:07.774000 140516195460928 torch/distributed/run.py:757] ***************************************** | |
| W0703 00:26:07.774000 140516195460928 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 00:26:07.774000 140516195460928 torch/distributed/run.py:757] ***************************************** | |
| W0703 00:26:07.774000 139630156592960 torch/distributed/run.py:757] | |
| W0703 00:26:07.774000 139630156592960 torch/distributed/run.py:757] ***************************************** | |
| W0703 00:26:07.774000 139630156592960 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 00:26:07.774000 139630156592960 torch/distributed/run.py:757] ***************************************** | |
| W0703 00:26:07.775000 139855154063168 torch/distributed/run.py:757] | |
| W0703 00:26:07.775000 139855154063168 torch/distributed/run.py:757] ***************************************** | |
| W0703 00:26:07.775000 139855154063168 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 00:26:07.775000 139855154063168 torch/distributed/run.py:757] ***************************************** | |
| W0703 00:26:07.784000 140584143710016 torch/distributed/run.py:757] | |
| W0703 00:26:07.784000 140584143710016 torch/distributed/run.py:757] ***************************************** | |
| W0703 00:26:07.784000 140584143710016 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 00:26:07.784000 140584143710016 torch/distributed/run.py:757] ***************************************** | |
| W0703 00:26:07.787000 139859872692032 torch/distributed/run.py:757] | |
| W0703 00:26:07.787000 139859872692032 torch/distributed/run.py:757] ***************************************** | |
| W0703 00:26:07.787000 139859872692032 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 00:26:07.787000 139859872692032 torch/distributed/run.py:757] ***************************************** | |
| W0703 00:26:07.794000 140024378103616 torch/distributed/run.py:757] | |
| W0703 00:26:07.794000 140024378103616 torch/distributed/run.py:757] ***************************************** | |
| W0703 00:26:07.794000 140024378103616 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 00:26:07.794000 140024378103616 torch/distributed/run.py:757] ***************************************** | |
| W0703 00:26:07.799000 140181542528832 torch/distributed/run.py:757] | |
| W0703 00:26:07.799000 140181542528832 torch/distributed/run.py:757] ***************************************** | |
| W0703 00:26:07.799000 140181542528832 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 00:26:07.799000 140181542528832 torch/distributed/run.py:757] ***************************************** | |
| W0703 00:26:07.807000 140302371690304 torch/distributed/run.py:757] | |
| W0703 00:26:07.807000 140302371690304 torch/distributed/run.py:757] ***************************************** | |
| W0703 00:26:07.807000 140302371690304 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 00:26:07.807000 140302371690304 torch/distributed/run.py:757] ***************************************** | |
| [default0]:07/03/2024 00:26:27 [WARNING|DP=0|PP=0|TP=0|ip-26-0-160-225]: [Vocab Size Padding] Padded vocab (size: 50257) with 1 dummy tokens (new size: 50258) | |
| [default0]:07/03/2024 00:26:27 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: Config: | |
| [default0]:07/03/2024 00:26:27 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: Config(general=GeneralArgs(project='bench_cluster', | |
| [default0]:07/03/2024 00:26:27 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: run='%date_%jobid', | |
| [default0]:07/03/2024 00:26:27 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: seed=42, | |
| [default0]:07/03/2024 00:26:27 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: step=None, | |
| [default0]:07/03/2024 00:26:27 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: consumed_train_samples=None, | |
| [default0]:07/03/2024 00:26:27 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: benchmark_csv_path=None, | |
| [default0]:07/03/2024 00:26:27 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: ignore_sanity_checks=True), | |
| [default0]:07/03/2024 00:26:27 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: parallelism=ParallelismArgs(dp=32, | |
| [default0]:07/03/2024 00:26:27 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: pp=1, | |
| [default0]:07/03/2024 00:26:27 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: tp=2, | |
| [default0]:07/03/2024 00:26:27 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: pp_engine=<nanotron.parallel.pipeline_parallel.engine.OneForwardOneBackwardPipelineEngine object at 0x7fc9f2154820>, | |
| [default0]:07/03/2024 00:26:27 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: tp_mode=<TensorParallelLinearMode.REDUCE_SCATTER: 2>, | |
| [default0]:07/03/2024 00:26:27 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: tp_linear_async_communication=False, | |
| [default0]:07/03/2024 00:26:27 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: expert_parallel_size=1), | |
| [default0]:07/03/2024 00:26:27 [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 00:26:27 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: eos_token_id=2, | |
| [default0]:07/03/2024 00:26:27 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: hidden_act='silu', | |
| [default0]:07/03/2024 00:26:27 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: hidden_size=2048, | |
| [default0]:07/03/2024 00:26:27 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: initializer_range=0.02, | |
| [default0]:07/03/2024 00:26:27 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: intermediate_size=4096, | |
| [default0]:07/03/2024 00:26:27 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: is_llama_config=True, | |
| [default0]:07/03/2024 00:26:27 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: max_position_embeddings=4096, | |
| [default0]:07/03/2024 00:26:27 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: num_attention_heads=32, | |
| [default0]:07/03/2024 00:26:27 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: num_hidden_layers=24, | |
| [default0]:07/03/2024 00:26:27 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: num_key_value_heads=32, | |
| [default0]:07/03/2024 00:26:27 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: pad_token_id=None, | |
| [default0]:07/03/2024 00:26:27 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: pretraining_tp=1, | |
| [default0]:07/03/2024 00:26:27 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: rms_norm_eps=1e-05, | |
| [default0]:07/03/2024 00:26:27 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: rope_scaling=None, | |
| [default0]:07/03/2024 00:26:27 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: rope_theta=10000.0, | |
| [default0]:07/03/2024 00:26:27 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: tie_word_embeddings=True, | |
| [default0]:07/03/2024 00:26:27 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: use_cache=True, | |
| [default0]:07/03/2024 00:26:27 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: vocab_size=50258), | |
| [default0]:07/03/2024 00:26:27 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: init_method=RandomInit(std=0.025), | |
| [default0]:07/03/2024 00:26:27 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: dtype=torch.bfloat16, | |
| [default0]:07/03/2024 00:26:27 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: make_vocab_size_divisible_by=1, | |
| [default0]:07/03/2024 00:26:27 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: ddp_bucket_cap_mb=25), | |
| [default0]:07/03/2024 00:26:27 [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 00:26:27 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: tokenizer_revision=None, | |
| [default0]:07/03/2024 00:26:27 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: tokenizer_max_length=None), | |
| [default0]:07/03/2024 00:26:27 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: checkpoints=CheckpointsArgs(checkpoints_path=Path('/dev/null'), | |
| [default0]:07/03/2024 00:26:27 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: checkpoint_interval=100000, | |
| [default0]:07/03/2024 00:26:27 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: save_initial_state=False, | |
| [default0]:07/03/2024 00:26:27 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: resume_checkpoint_path=None, | |
| [default0]:07/03/2024 00:26:27 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: checkpoints_path_is_shared_file_system=False), | |
| [default0]:07/03/2024 00:26:27 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: logging=LoggingArgs(log_level='info', | |
| [default0]:07/03/2024 00:26:27 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: log_level_replica='info', | |
| [default0]:07/03/2024 00:26:27 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: iteration_step_info_interval=1), | |
| [default0]:07/03/2024 00:26:27 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: tokens=TokensArgs(sequence_length=4096, | |
| [default0]:07/03/2024 00:26:27 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: train_steps=20, | |
| [default0]:07/03/2024 00:26:27 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: micro_batch_size=8, | |
| [default0]:07/03/2024 00:26:27 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: batch_accumulation_per_replica=4, | |
| [default0]:07/03/2024 00:26:27 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: val_check_interval=-1, | |
| [default0]:07/03/2024 00:26:27 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: limit_val_batches=0, | |
| [default0]:07/03/2024 00:26:27 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: limit_test_batches=0), | |
| [default0]:07/03/2024 00:26:27 [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 00:26:27 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: adam_beta1=0.9, | |
| [default0]:07/03/2024 00:26:27 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: adam_beta2=0.95, | |
| [default0]:07/03/2024 00:26:27 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: torch_adam_is_fused=True, | |
| [default0]:07/03/2024 00:26:27 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: name='adamW'), | |
| [default0]:07/03/2024 00:26:27 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: zero_stage=1, | |
| [default0]:07/03/2024 00:26:27 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: weight_decay=0.01, | |
| [default0]:07/03/2024 00:26:27 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: clip_grad=1.0, | |
| [default0]:07/03/2024 00:26:27 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: accumulate_grad_in_fp32=True, | |
| [default0]:07/03/2024 00:26:27 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: learning_rate_scheduler=LRSchedulerArgs(learning_rate=0.0001, | |
| [default0]:07/03/2024 00:26:27 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: lr_warmup_steps=1, | |
| [default0]:07/03/2024 00:26:27 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: lr_warmup_style='linear', | |
| [default0]:07/03/2024 00:26:27 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: lr_decay_style='linear', | |
| [default0]:07/03/2024 00:26:27 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: lr_decay_steps=19, | |
| [default0]:07/03/2024 00:26:27 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: lr_decay_starting_step=None, | |
| [default0]:07/03/2024 00:26:27 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: min_decay_lr=1e-05)), | |
| [default0]:07/03/2024 00:26:27 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: data_stages=[DatasetStageArgs(name='Training Stage', | |
| [default0]:07/03/2024 00:26:27 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: start_training_step=1, | |
| [default0]:07/03/2024 00:26:27 [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 00:26:27 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: hf_dataset_splits='train', | |
| [default0]:07/03/2024 00:26:27 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: hf_dataset_config_name=None, | |
| [default0]:07/03/2024 00:26:27 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: dataset_processing_num_proc_per_process=64, | |
| [default0]:07/03/2024 00:26:27 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: dataset_overwrite_cache=False, | |
| [default0]:07/03/2024 00:26:27 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: text_column_name='text'), | |
| [default0]:07/03/2024 00:26:27 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: seed=42, | |
| [default0]:07/03/2024 00:26:27 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: num_loading_workers=0))], | |
| [default0]:07/03/2024 00:26:27 [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/64_GPUS/dp-32_tp-2_pp-1_mbz-8')), | |
| [default0]:07/03/2024 00:26:27 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: lighteval=None) | |
| [default0]:07/03/2024 00:26:27 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: Model Config: | |
| [default0]:07/03/2024 00:26:27 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: LlamaConfig(bos_token_id=1, | |
| [default0]:07/03/2024 00:26:27 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: eos_token_id=2, | |
| [default0]:07/03/2024 00:26:27 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: hidden_act='silu', | |
| [default0]:07/03/2024 00:26:27 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: hidden_size=2048, | |
| [default0]:07/03/2024 00:26:27 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: initializer_range=0.02, | |
| [default0]:07/03/2024 00:26:27 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: intermediate_size=4096, | |
| [default0]:07/03/2024 00:26:27 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: is_llama_config=True, | |
| [default0]:07/03/2024 00:26:27 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: max_position_embeddings=4096, | |
| [default0]:07/03/2024 00:26:27 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: num_attention_heads=32, | |
| [default0]:07/03/2024 00:26:27 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: num_hidden_layers=24, | |
| [default0]:07/03/2024 00:26:27 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: num_key_value_heads=32, | |
| [default0]:07/03/2024 00:26:27 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: pad_token_id=None, | |
| [default0]:07/03/2024 00:26:27 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: pretraining_tp=1, | |
| [default0]:07/03/2024 00:26:27 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: rms_norm_eps=1e-05, | |
| [default0]:07/03/2024 00:26:27 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: rope_scaling=None, | |
| [default0]:07/03/2024 00:26:27 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: rope_theta=10000.0, | |
| [default0]:07/03/2024 00:26:27 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: tie_word_embeddings=True, | |
| [default0]:07/03/2024 00:26:27 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: use_cache=True, | |
| [default0]:07/03/2024 00:26:27 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: vocab_size=50258) | |
| [default0]:07/03/2024 00:26:27 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: Building model.. | |
| [default0]:07/03/2024 00:26:27 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: Setting PP block ranks... | |
| [default3]:07/03/2024 00:26:39 [INFO|DP=29|PP=0|TP=1|ip-26-0-171-88]: No checkpoint path provided. | |
| [default2]:07/03/2024 00:26:39 [INFO|DP=29|PP=0|TP=0|ip-26-0-171-88]: No checkpoint path provided. | |
| [default1]:07/03/2024 00:26:39 [INFO|DP=28|PP=0|TP=1|ip-26-0-171-88]: No checkpoint path provided. | |
| [default0]:07/03/2024 00:26:39 [INFO|DP=28|PP=0|TP=0|ip-26-0-171-88]: No checkpoint path provided. | |
| [default4]:07/03/2024 00:26:39 [INFO|DP=30|PP=0|TP=0|ip-26-0-171-88]: No checkpoint path provided. | |
| [default1]:07/03/2024 00:26:39 [INFO|DP=4|PP=0|TP=1|ip-26-0-161-103]: No checkpoint path provided. | |
| [default0]:07/03/2024 00:26:39 [INFO|DP=16|PP=0|TP=0|ip-26-0-162-233]: No checkpoint path provided. | |
| [default1]:07/03/2024 00:26:39 [INFO|DP=16|PP=0|TP=1|ip-26-0-162-233]: No checkpoint path provided. | |
| [default3]:07/03/2024 00:26:39 [INFO|DP=17|PP=0|TP=1|ip-26-0-162-233]: No checkpoint path provided. | |
| [default2]:07/03/2024 00:26:39 [INFO|DP=17|PP=0|TP=0|ip-26-0-162-233]: No checkpoint path provided. | |
| [default5]:07/03/2024 00:26:39 [INFO|DP=14|PP=0|TP=1|ip-26-0-161-78]: No checkpoint path provided. | |
| [default0]:07/03/2024 00:26:39 [INFO|DP=12|PP=0|TP=0|ip-26-0-161-78]: No checkpoint path provided. | |
| [default4]:07/03/2024 00:26:39 [INFO|DP=14|PP=0|TP=0|ip-26-0-161-78]: No checkpoint path provided. | |
| [default1]:07/03/2024 00:26:39 [INFO|DP=12|PP=0|TP=1|ip-26-0-161-78]: No checkpoint path provided. | |
| [default3]:07/03/2024 00:26:39 [INFO|DP=21|PP=0|TP=1|ip-26-0-171-102]: No checkpoint path provided. | |
| [default0]:07/03/2024 00:26:39 [INFO|DP=20|PP=0|TP=0|ip-26-0-171-102]: No checkpoint path provided. | |
| [default0]:07/03/2024 00:26:39 [INFO|DP=4|PP=0|TP=0|ip-26-0-161-103]: No checkpoint path provided. | |
| [default5]:07/03/2024 00:26:39 [INFO|DP=30|PP=0|TP=1|ip-26-0-171-88]: No checkpoint path provided. | |
| [default1]:07/03/2024 00:26:39 [INFO|DP=20|PP=0|TP=1|ip-26-0-171-102]: No checkpoint path provided. | |
| [default2]:07/03/2024 00:26:39 [INFO|DP=21|PP=0|TP=0|ip-26-0-171-102]: No checkpoint path provided. | |
| [default6]:07/03/2024 00:26:39 [INFO|DP=31|PP=0|TP=0|ip-26-0-171-88]: No checkpoint path provided. | |
| [default4]:07/03/2024 00:26:39 [INFO|DP=18|PP=0|TP=0|ip-26-0-162-233]: No checkpoint path provided. | |
| [default5]:07/03/2024 00:26:39 [INFO|DP=18|PP=0|TP=1|ip-26-0-162-233]: No checkpoint path provided. | |
| [default2]:07/03/2024 00:26:39 [INFO|DP=13|PP=0|TP=0|ip-26-0-161-78]: No checkpoint path provided. | |
| [default3]:07/03/2024 00:26:39 [INFO|DP=13|PP=0|TP=1|ip-26-0-161-78]: No checkpoint path provided. | |
| [default7]:07/03/2024 00:26:39 [INFO|DP=31|PP=0|TP=1|ip-26-0-171-88]: No checkpoint path provided. | |
| [default0]:07/03/2024 00:26:39 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: Total number of parameters: 1.11G (2116.70MiB) | |
| [default0]:07/03/2024 00:26:39 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: Local number of parameters: 555M (1058.35MiB) | |
| [default0]:07/03/2024 00:26:39 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: [After model building] Memory usage: 1082.37MiB. Peak allocated: 1182.56MiB Peak reserved: 1200.00MiB | |
| [default0]:07/03/2024 00:26:39 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: No checkpoint path provided. | |
| [default0]:07/03/2024 00:26:39 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: Parametrizing model parameters using StandardParametrizator | |
| [default1]:07/03/2024 00:26:39 [INFO|DP=0|PP=0|TP=1|ip-26-0-160-225]: Local number of parameters: 555M (1058.35MiB) | |
| [default1]:07/03/2024 00:26:39 [INFO|DP=0|PP=0|TP=1|ip-26-0-160-225]: [After model building] Memory usage: 1082.37MiB. Peak allocated: 1182.56MiB Peak reserved: 1200.00MiB | |
| [default1]:07/03/2024 00:26:39 [INFO|DP=0|PP=0|TP=1|ip-26-0-160-225]: No checkpoint path provided. | |
| [default6]:07/03/2024 00:26:39 [INFO|DP=7|PP=0|TP=0|ip-26-0-161-103]: No checkpoint path provided. | |
| [default6]:07/03/2024 00:26:39 [INFO|DP=15|PP=0|TP=0|ip-26-0-161-78]: No checkpoint path provided. | |
| [default4]:07/03/2024 00:26:39 [INFO|DP=26|PP=0|TP=0|ip-26-0-171-62]: No checkpoint path provided. | |
| [default5]:07/03/2024 00:26:39 [INFO|DP=26|PP=0|TP=1|ip-26-0-171-62]: No checkpoint path provided. | |
| [default3]:07/03/2024 00:26:39 [INFO|DP=25|PP=0|TP=1|ip-26-0-171-62]: No checkpoint path provided. | |
| [default6]:07/03/2024 00:26:39 [INFO|DP=23|PP=0|TP=0|ip-26-0-171-102]: No checkpoint path provided. | |
| [default5]:07/03/2024 00:26:39 [INFO|DP=22|PP=0|TP=1|ip-26-0-171-102]: No checkpoint path provided. | |
| [default2]:07/03/2024 00:26:39 [INFO|DP=25|PP=0|TP=0|ip-26-0-171-62]: No checkpoint path provided. | |
| [default7]:07/03/2024 00:26:39 [INFO|DP=7|PP=0|TP=1|ip-26-0-161-103]: No checkpoint path provided. | |
| [default3]:07/03/2024 00:26:39 [INFO|DP=1|PP=0|TP=1|ip-26-0-160-225]: No checkpoint path provided. | |
| [default1]:07/03/2024 00:26:39 [INFO|DP=24|PP=0|TP=1|ip-26-0-171-62]: No checkpoint path provided. | |
| [default0]:07/03/2024 00:26:39 [INFO|DP=24|PP=0|TP=0|ip-26-0-171-62]: No checkpoint path provided. | |
| [default6]:07/03/2024 00:26:39 [INFO|DP=27|PP=0|TP=0|ip-26-0-171-62]: No checkpoint path provided. | |
| [default7]:07/03/2024 00:26:39 [INFO|DP=15|PP=0|TP=1|ip-26-0-161-78]: No checkpoint path provided. | |
| [default4]:07/03/2024 00:26:39 [INFO|DP=22|PP=0|TP=0|ip-26-0-171-102]: No checkpoint path provided. | |
| [default7]:07/03/2024 00:26:39 [INFO|DP=23|PP=0|TP=1|ip-26-0-171-102]: No checkpoint path provided. | |
| [default2]:07/03/2024 00:26:39 [INFO|DP=1|PP=0|TP=0|ip-26-0-160-225]: No checkpoint path provided. | |
| [default5]:07/03/2024 00:26:39 [INFO|DP=2|PP=0|TP=1|ip-26-0-160-225]: No checkpoint path provided. | |
| [default4]:07/03/2024 00:26:39 [INFO|DP=2|PP=0|TP=0|ip-26-0-160-225]: No checkpoint path provided. | |
| [default7]:07/03/2024 00:26:39 [INFO|DP=27|PP=0|TP=1|ip-26-0-171-62]: No checkpoint path provided. | |
| [default5]:07/03/2024 00:26:39 [INFO|DP=6|PP=0|TP=1|ip-26-0-161-103]: No checkpoint path provided. | |
| [default6]:07/03/2024 00:26:39 [INFO|DP=19|PP=0|TP=0|ip-26-0-162-233]: No checkpoint path provided. | |
| [default2]:07/03/2024 00:26:39 [INFO|DP=5|PP=0|TP=0|ip-26-0-161-103]: No checkpoint path provided. | |
| [default3]:07/03/2024 00:26:39 [INFO|DP=5|PP=0|TP=1|ip-26-0-161-103]: No checkpoint path provided. | |
| [default4]:07/03/2024 00:26:39 [INFO|DP=6|PP=0|TP=0|ip-26-0-161-103]: No checkpoint path provided. | |
| [default7]:07/03/2024 00:26:39 [INFO|DP=19|PP=0|TP=1|ip-26-0-162-233]: No checkpoint path provided. | |
| [default0]:07/03/2024 00:26:39 [INFO|DP=8|PP=0|TP=0|ip-26-0-161-153]: No checkpoint path provided. | |
| [default1]:07/03/2024 00:26:39 [INFO|DP=8|PP=0|TP=1|ip-26-0-161-153]: No checkpoint path provided. | |
| [default7]:07/03/2024 00:26:39 [INFO|DP=3|PP=0|TP=1|ip-26-0-160-225]: No checkpoint path provided. | |
| [default6]:07/03/2024 00:26:39 [INFO|DP=3|PP=0|TP=0|ip-26-0-160-225]: No checkpoint path provided. | |
| [default6]:07/03/2024 00:26:39 [INFO|DP=11|PP=0|TP=0|ip-26-0-161-153]: No checkpoint path provided. | |
| [default4]:07/03/2024 00:26:39 [INFO|DP=10|PP=0|TP=0|ip-26-0-161-153]: No checkpoint path provided. | |
| [default5]:07/03/2024 00:26:39 [INFO|DP=10|PP=0|TP=1|ip-26-0-161-153]: No checkpoint path provided. | |
| [default2]:07/03/2024 00:26:39 [INFO|DP=9|PP=0|TP=0|ip-26-0-161-153]: No checkpoint path provided. | |
| [default3]:07/03/2024 00:26:39 [INFO|DP=9|PP=0|TP=1|ip-26-0-161-153]: No checkpoint path provided. | |
| [default7]:07/03/2024 00:26:39 [INFO|DP=11|PP=0|TP=1|ip-26-0-161-153]: No checkpoint path provided. | |
| [default0]:07/03/2024 00:26:47 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: [Optimizer Building] Using LearningRateForSP as learning rate | |
| [default0]:07/03/2024 00:26:47 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: [ZeRO sharding] Size of optimizer params per rank: | |
| [default0]:07/03/2024 00:26:47 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: [ZeRO sharding] DP Rank 0 has 17.3M out of 555M (3.12%) params' optimizer states | |
| [default0]:07/03/2024 00:26:47 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: [ZeRO sharding] DP Rank 1 has 17.3M out of 555M (3.12%) params' optimizer states | |
| [default0]:07/03/2024 00:26:47 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: [ZeRO sharding] DP Rank 2 has 17.3M out of 555M (3.12%) params' optimizer states | |
| [default0]:07/03/2024 00:26:47 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: [ZeRO sharding] DP Rank 3 has 17.3M out of 555M (3.12%) params' optimizer states | |
| [default0]:07/03/2024 00:26:47 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: [ZeRO sharding] DP Rank 4 has 17.3M out of 555M (3.12%) params' optimizer states | |
| [default0]:07/03/2024 00:26:47 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: [ZeRO sharding] DP Rank 5 has 17.3M out of 555M (3.12%) params' optimizer states | |
| [default0]:07/03/2024 00:26:47 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: [ZeRO sharding] DP Rank 6 has 17.3M out of 555M (3.12%) params' optimizer states | |
| [default0]:07/03/2024 00:26:47 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: [ZeRO sharding] DP Rank 7 has 17.3M out of 555M (3.12%) params' optimizer states | |
| [default0]:07/03/2024 00:26:47 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: [ZeRO sharding] DP Rank 8 has 17.3M out of 555M (3.12%) params' optimizer states | |
| [default0]:07/03/2024 00:26:47 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: [ZeRO sharding] DP Rank 9 has 17.3M out of 555M (3.12%) params' optimizer states | |
| [default0]:07/03/2024 00:26:47 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: [ZeRO sharding] DP Rank 10 has 17.3M out of 555M (3.12%) params' optimizer states | |
| [default0]:07/03/2024 00:26:47 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: [ZeRO sharding] DP Rank 11 has 17.3M out of 555M (3.12%) params' optimizer states | |
| [default0]:07/03/2024 00:26:47 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: [ZeRO sharding] DP Rank 12 has 17.3M out of 555M (3.12%) params' optimizer states | |
| [default0]:07/03/2024 00:26:47 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: [ZeRO sharding] DP Rank 13 has 17.3M out of 555M (3.12%) params' optimizer states | |
| [default0]:07/03/2024 00:26:47 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: [ZeRO sharding] DP Rank 14 has 17.3M out of 555M (3.12%) params' optimizer states | |
| [default0]:07/03/2024 00:26:47 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: [ZeRO sharding] DP Rank 15 has 17.3M out of 555M (3.12%) params' optimizer states | |
| [default0]:07/03/2024 00:26:47 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: [ZeRO sharding] DP Rank 16 has 17.3M out of 555M (3.12%) params' optimizer states | |
| [default0]:07/03/2024 00:26:47 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: [ZeRO sharding] DP Rank 17 has 17.3M out of 555M (3.12%) params' optimizer states | |
| [default0]:07/03/2024 00:26:47 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: [ZeRO sharding] DP Rank 18 has 17.3M out of 555M (3.12%) params' optimizer states | |
| [default0]:07/03/2024 00:26:47 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: [ZeRO sharding] DP Rank 19 has 17.3M out of 555M (3.12%) params' optimizer states | |
| [default0]:07/03/2024 00:26:47 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: [ZeRO sharding] DP Rank 20 has 17.3M out of 555M (3.12%) params' optimizer states | |
| [default0]:07/03/2024 00:26:47 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: [ZeRO sharding] DP Rank 21 has 17.3M out of 555M (3.12%) params' optimizer states | |
| [default0]:07/03/2024 00:26:47 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: [ZeRO sharding] DP Rank 22 has 17.3M out of 555M (3.12%) params' optimizer states | |
| [default0]:07/03/2024 00:26:47 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: [ZeRO sharding] DP Rank 23 has 17.3M out of 555M (3.12%) params' optimizer states | |
| [default0]:07/03/2024 00:26:47 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: [ZeRO sharding] DP Rank 24 has 17.3M out of 555M (3.12%) params' optimizer states | |
| [default0]:07/03/2024 00:26:47 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: [ZeRO sharding] DP Rank 25 has 17.3M out of 555M (3.12%) params' optimizer states | |
| [default0]:07/03/2024 00:26:47 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: [ZeRO sharding] DP Rank 26 has 17.3M out of 555M (3.12%) params' optimizer states | |
| [default0]:07/03/2024 00:26:47 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: [ZeRO sharding] DP Rank 27 has 17.3M out of 555M (3.12%) params' optimizer states | |
| [default0]:07/03/2024 00:26:47 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: [ZeRO sharding] DP Rank 28 has 17.3M out of 555M (3.12%) params' optimizer states | |
| [default0]:07/03/2024 00:26:47 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: [ZeRO sharding] DP Rank 29 has 17.3M out of 555M (3.12%) params' optimizer states | |
| [default0]:07/03/2024 00:26:47 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: [ZeRO sharding] DP Rank 30 has 17.3M out of 555M (3.12%) params' optimizer states | |
| [default0]:07/03/2024 00:26:47 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: [ZeRO sharding] DP Rank 31 has 17.3M out of 555M (3.12%) params' optimizer states | |
| [default0]:07/03/2024 00:26:49 [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 00:26:49 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: Using `datasets` library | |
| [default0]:07/03/2024 00:26:49 [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 00:26:49 [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 00:26:49 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: [Training Plan] There are 1 training stages | |
| [default0]:07/03/2024 00:26:49 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: [Stage Training Stage] start from step 1 | |
| [default0]:07/03/2024 00:26:49 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: | |
| [default0]:07/03/2024 00:26:49 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: [Start training] datetime: 2024-07-03 00:26:49.972442 | mbs: 8 | grad_accum: 4 | global_batch_size: 1024 | sequence_length: 4096 | train_steps: 20 | start_iteration_step: 0 | consumed_train_samples: 0 | |
| [default0]:07/03/2024 00:26:49 [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 00:26:49 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: Memory usage: 3265.22MiB. Peak allocated 3265.22MiB. Peak reserved: 3318.00MiB | |
| [default4]:07/03/2024 00:26:50 [WARNING|DP=14|PP=0|TP=0|ip-26-0-161-78]: Repo card metadata block was not found. Setting CardData to empty. | |
| [default4]:Repo card metadata block was not found. Setting CardData to empty. | |
| [default6]:07/03/2024 00:26:50 [WARNING|DP=7|PP=0|TP=0|ip-26-0-161-103]: Repo card metadata block was not found. Setting CardData to empty. | |
| [default2]:07/03/2024 00:26:50 [WARNING|DP=29|PP=0|TP=0|ip-26-0-171-88]: Repo card metadata block was not found. Setting CardData to empty. | |
| [default3]:07/03/2024 00:26:50 [WARNING|DP=29|PP=0|TP=1|ip-26-0-171-88]: Repo card metadata block was not found. Setting CardData to empty. | |
| [default2]:Repo card metadata block was not found. Setting CardData to empty. | |
| [default6]:07/03/2024 00:26:50 [WARNING|DP=31|PP=0|TP=0|ip-26-0-171-88]: Repo card metadata block was not found. Setting CardData to empty. | |
| [default4]:07/03/2024 00:26:50 [WARNING|DP=30|PP=0|TP=0|ip-26-0-171-88]: Repo card metadata block was not found. Setting CardData to empty. | |
| [default7]:Repo card metadata block was not found. Setting CardData to empty. | |
| [default4]:07/03/2024 00:26:50 [WARNING|DP=18|PP=0|TP=0|ip-26-0-162-233]: Repo card metadata block was not found. Setting CardData to empty. | |
| [default0]:Repo card metadata block was not found. Setting CardData to empty. | |
| [default2]:Repo card metadata block was not found. Setting CardData to empty. | |
| [default6]:07/03/2024 00:26:50 [WARNING|DP=19|PP=0|TP=0|ip-26-0-162-233]: Repo card metadata block was not found. Setting CardData to empty. | |
| [default0]: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. | |
| [default4]:Repo card metadata block was not found. Setting CardData to empty. | |
| [default3]:07/03/2024 00:26:50 [WARNING|DP=17|PP=0|TP=1|ip-26-0-162-233]: Repo card metadata block was not found. Setting CardData to empty. | |
| [default4]:Repo card metadata block was not found. Setting CardData to empty. | |
| [default2]:07/03/2024 00:26:50 [WARNING|DP=17|PP=0|TP=0|ip-26-0-162-233]: Repo card metadata block was not found. Setting CardData to empty. | |
| [default1]:07/03/2024 00:26:50 [WARNING|DP=16|PP=0|TP=1|ip-26-0-162-233]: Repo card metadata block was not found. Setting CardData to empty. | |
| [default0]:07/03/2024 00:26:50 [WARNING|DP=16|PP=0|TP=0|ip-26-0-162-233]: Repo card metadata block was not found. Setting CardData to empty. | |
| [default1]:Repo card metadata block was not found. Setting CardData to empty. | |
| [default5]:07/03/2024 00:26:50 [WARNING|DP=18|PP=0|TP=1|ip-26-0-162-233]: Repo card metadata block was not found. Setting CardData to empty. | |
| [default3]:Repo card metadata block was not found. Setting CardData to empty. | |
| [default3]:Repo card metadata block was not found. Setting CardData to empty. | |
| [default5]:Repo card metadata block was not found. Setting CardData to empty. | |
| [default1]:07/03/2024 00:26:50 [WARNING|DP=12|PP=0|TP=1|ip-26-0-161-78]: Repo card metadata block was not found. Setting CardData to empty. | |
| [default4]:07/03/2024 00:26:50 [WARNING|DP=6|PP=0|TP=0|ip-26-0-161-103]: Repo card metadata block was not found. Setting CardData to empty. | |
| [default3]:07/03/2024 00:26:50 [WARNING|DP=5|PP=0|TP=1|ip-26-0-161-103]: Repo card metadata block was not found. Setting CardData to empty. | |
| [default5]:07/03/2024 00:26:50 [WARNING|DP=26|PP=0|TP=1|ip-26-0-171-62]: Repo card metadata block was not found. Setting CardData to empty. | |
| [default4]:07/03/2024 00:26:50 [WARNING|DP=26|PP=0|TP=0|ip-26-0-171-62]: Repo card metadata block was not found. Setting CardData to empty. | |
| [default6]:07/03/2024 00:26:50 [WARNING|DP=11|PP=0|TP=0|ip-26-0-161-153]: Repo card metadata block was not found. Setting CardData to empty. | |
| [default4]:07/03/2024 00:26:50 [WARNING|DP=10|PP=0|TP=0|ip-26-0-161-153]: Repo card metadata block was not found. Setting CardData to empty. | |
| [default2]:07/03/2024 00:26:50 [WARNING|DP=5|PP=0|TP=0|ip-26-0-161-103]: Repo card metadata block was not found. Setting CardData to empty. | |
| [default3]:07/03/2024 00:26:50 [WARNING|DP=13|PP=0|TP=1|ip-26-0-161-78]: Repo card metadata block was not found. Setting CardData to empty. | |
| [default3]:07/03/2024 00:26:50 [WARNING|DP=25|PP=0|TP=1|ip-26-0-171-62]: Repo card metadata block was not found. Setting CardData to empty. | |
| [default5]:07/03/2024 00:26:50 [WARNING|DP=10|PP=0|TP=1|ip-26-0-161-153]: Repo card metadata block was not found. Setting CardData to empty. | |
| [default2]:Repo card metadata block was not found. Setting CardData to empty. | |
| [default5]:07/03/2024 00:26:50 [WARNING|DP=14|PP=0|TP=1|ip-26-0-161-78]: Repo card metadata block was not found. Setting CardData to empty. | |
| [default6]:07/03/2024 00:26:50 [WARNING|DP=15|PP=0|TP=0|ip-26-0-161-78]: Repo card metadata block was not found. Setting CardData to empty. | |
| [default2]:Repo card metadata block was not found. Setting CardData to empty. | |
| [default2]:07/03/2024 00:26:50 [WARNING|DP=9|PP=0|TP=0|ip-26-0-161-153]: Repo card metadata block was not found. Setting CardData to empty. | |
| [default7]:07/03/2024 00:26:50 [WARNING|DP=19|PP=0|TP=1|ip-26-0-162-233]: Repo card metadata block was not found. Setting CardData to empty. | |
| [default7]:07/03/2024 00:26:50 [WARNING|DP=31|PP=0|TP=1|ip-26-0-171-88]: Repo card metadata block was not found. Setting CardData to empty. | |
| [default6]:Repo card metadata block was not found. Setting CardData to empty. | |
| [default5]:07/03/2024 00:26:50 [WARNING|DP=30|PP=0|TP=1|ip-26-0-171-88]: Repo card metadata block was not found. Setting CardData to empty. | |
| [default0]:07/03/2024 00:26:50 [WARNING|DP=4|PP=0|TP=0|ip-26-0-161-103]: Repo card metadata block was not found. Setting CardData to empty. | |
| [default7]:07/03/2024 00:26:50 [WARNING|DP=7|PP=0|TP=1|ip-26-0-161-103]: Repo card metadata block was not found. Setting CardData to empty. | |
| [default6]:07/03/2024 00:26:50 [WARNING|DP=23|PP=0|TP=0|ip-26-0-171-102]: Repo card metadata block was not found. Setting CardData to empty. | |
| [default5]:07/03/2024 00:26:50 [WARNING|DP=22|PP=0|TP=1|ip-26-0-171-102]: Repo card metadata block was not found. Setting CardData to empty. | |
| [default0]:Repo card metadata block was not found. Setting CardData to empty. | |
| [default3]:07/03/2024 00:26:50 [WARNING|DP=21|PP=0|TP=1|ip-26-0-171-102]: Repo card metadata block was not found. Setting CardData to empty. | |
| [default0]:07/03/2024 00:26:50 [WARNING|DP=20|PP=0|TP=0|ip-26-0-171-102]: Repo card metadata block was not found. Setting CardData to empty. | |
| [default0]:07/03/2024 00:26:50 [WARNING|DP=8|PP=0|TP=0|ip-26-0-161-153]: Repo card metadata block was not found. Setting CardData to empty. | |
| [default7]:Repo card metadata block was not found. Setting CardData to empty. | |
| [default2]:07/03/2024 00:26:50 [WARNING|DP=25|PP=0|TP=0|ip-26-0-171-62]: Repo card metadata block was not found. Setting CardData to empty. | |
| [default6]:Repo card metadata block was not found. Setting CardData to empty. | |
| [default6]:Repo card metadata block was not found. Setting CardData to empty. | |
| [default3]:Repo card metadata block was not found. Setting CardData to empty. | |
| [default0]:Repo card metadata block was not found. Setting CardData to empty. | |
| [default4]:Repo card metadata block was not found. Setting CardData to empty. | |
| [default7]:Repo card metadata block was not found. Setting CardData to empty. | |
| [default5]:Repo card metadata block was not found. Setting CardData to empty. | |
| [default6]:07/03/2024 00:26:50 [WARNING|DP=27|PP=0|TP=0|ip-26-0-171-62]: Repo card metadata block was not found. Setting CardData to empty. | |
| [default1]:Repo card metadata block was not found. Setting CardData to empty. | |
| [default1]:Repo card metadata block was not found. Setting CardData to empty. | |
| [default7]:07/03/2024 00:26:50 [WARNING|DP=15|PP=0|TP=1|ip-26-0-161-78]: Repo card metadata block was not found. Setting CardData to empty. | |
| [default0]:07/03/2024 00:26:50 [WARNING|DP=24|PP=0|TP=0|ip-26-0-171-62]: Repo card metadata block was not found. Setting CardData to empty. | |
| [default7]:Repo card metadata block was not found. Setting CardData to empty. | |
| [default6]:Repo card metadata block was not found. Setting CardData to empty. | |
| [default3]:07/03/2024 00:26:50 [WARNING|DP=9|PP=0|TP=1|ip-26-0-161-153]: Repo card metadata block was not found. Setting CardData to empty. | |
| [default1]:07/03/2024 00:26:50 [WARNING|DP=8|PP=0|TP=1|ip-26-0-161-153]: Repo card metadata block was not found. Setting CardData to empty. | |
| [default7]:Repo card metadata block was not found. Setting CardData to empty. | |
| [default4]:Repo card metadata block was not found. Setting CardData to empty. | |
| [default4]:07/03/2024 00:26:50 [WARNING|DP=22|PP=0|TP=0|ip-26-0-171-102]: Repo card metadata block was not found. Setting CardData to empty. | |
| [default6]:Repo card metadata block was not found. Setting CardData to empty. | |
| [default7]:07/03/2024 00:26:50 [WARNING|DP=27|PP=0|TP=1|ip-26-0-171-62]: Repo card metadata block was not found. Setting CardData to empty. | |
| [default3]:Repo card metadata block was not found. Setting CardData to empty. | |
| [default7]:07/03/2024 00:26:50 [WARNING|DP=23|PP=0|TP=1|ip-26-0-171-102]: Repo card metadata block was not found. Setting CardData to empty. | |
| [default1]:07/03/2024 00:26:50 [WARNING|DP=20|PP=0|TP=1|ip-26-0-171-102]: 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 00:26:50 [WARNING|DP=21|PP=0|TP=0|ip-26-0-171-102]: Repo card metadata block was not found. Setting CardData to empty. | |
| [default2]:Repo card metadata block was not found. Setting CardData to empty. | |
| [default7]:Repo card metadata block was not found. Setting CardData to empty. | |
| [default5]:Repo card metadata block was not found. Setting CardData to empty. | |
| [default7]:Repo card metadata block was not found. Setting CardData to empty. | |
| [default2]:07/03/2024 00:26:50 [WARNING|DP=1|PP=0|TP=0|ip-26-0-160-225]: Repo card metadata block was not found. Setting CardData to empty. | |
| [default1]:07/03/2024 00:26:50 [WARNING|DP=0|PP=0|TP=1|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. | |
| [default5]:Repo card metadata block was not found. Setting CardData to empty. | |
| [default1]:Repo card metadata block was not found. Setting CardData to empty. | |
| [default5]:07/03/2024 00:26:50 [WARNING|DP=2|PP=0|TP=1|ip-26-0-160-225]: Repo card metadata block was not found. Setting CardData to empty. | |
| [default6]:07/03/2024 00:26:50 [WARNING|DP=3|PP=0|TP=0|ip-26-0-160-225]: Repo card metadata block was not found. Setting CardData to empty. | |
| [default7]:07/03/2024 00:26:50 [WARNING|DP=11|PP=0|TP=1|ip-26-0-161-153]: Repo card metadata block was not found. Setting CardData to empty. | |
| [default2]:Repo card metadata block was not found. Setting CardData to empty. | |
| [default7]:Repo card metadata block was not found. Setting CardData to empty. | |
| [default4]:07/03/2024 00:26:50 [WARNING|DP=2|PP=0|TP=0|ip-26-0-160-225]: Repo card metadata block was not found. Setting CardData to empty. | |
| [default7]:07/03/2024 00:26:50 [WARNING|DP=3|PP=0|TP=1|ip-26-0-160-225]: Repo card metadata block was not found. Setting CardData to empty. | |
| [default4]:Repo card metadata block was not found. Setting CardData to empty. | |
| [default3]:Repo card metadata block was not found. Setting CardData to empty. | |
| [default5]:Repo card metadata block was not found. Setting CardData to empty. | |
| [default2]:Repo card metadata block was not found. Setting CardData to empty. | |
| [default1]:Repo card metadata block was not found. Setting CardData to empty. | |
| [default6]:Repo card metadata block was not found. Setting CardData to empty. | |
| [default3]:Repo card metadata block was not found. Setting CardData to empty. | |
| [default4]:Repo card metadata block was not found. Setting CardData to empty. | |
| [default4]:Repo card metadata block was not found. Setting CardData to empty. | |
| [default5]:Repo card metadata block was not found. Setting CardData to empty. | |
| [default3]:Repo card metadata block was not found. Setting CardData to empty. | |
| [default1]:07/03/2024 00:26:50 [WARNING|DP=4|PP=0|TP=1|ip-26-0-161-103]: Repo card metadata block was not found. Setting CardData to empty. | |
| [default1]:Repo card metadata block was not found. Setting CardData to empty. | |
| [default0]:07/03/2024 00:26:50 [WARNING|DP=28|PP=0|TP=0|ip-26-0-171-88]: Repo card metadata block was not found. Setting CardData to empty. | |
| [default5]:07/03/2024 00:26:50 [WARNING|DP=6|PP=0|TP=1|ip-26-0-161-103]: Repo card metadata block was not found. Setting CardData to empty. | |
| [default2]:07/03/2024 00:26:50 [WARNING|DP=13|PP=0|TP=0|ip-26-0-161-78]: Repo card metadata block was not found. Setting CardData to empty. | |
| [default0]:07/03/2024 00:26:50 [WARNING|DP=12|PP=0|TP=0|ip-26-0-161-78]: Repo card metadata block was not found. Setting CardData to empty. | |
| [default0]:Repo card metadata block was not found. Setting CardData to empty. | |
| [default0]:Repo card metadata block was not found. Setting CardData to empty. | |
| [default2]:Repo card metadata block was not found. Setting CardData to empty. | |
| [default5]:Repo card metadata block was not found. Setting CardData to empty. | |
| [default1]:07/03/2024 00:26:50 [WARNING|DP=24|PP=0|TP=1|ip-26-0-171-62]: Repo card metadata block was not found. Setting CardData to empty. | |
| [default3]:07/03/2024 00:26:50 [WARNING|DP=1|PP=0|TP=1|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. | |
| [default1]:07/03/2024 00:26:50 [WARNING|DP=28|PP=0|TP=1|ip-26-0-171-88]: Repo card metadata block was not found. Setting CardData to empty. | |
| [default1]:Repo card metadata block was not found. Setting CardData to empty. | |
| [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.) | |
| [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 | |
| [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 | |
| [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 | |
| [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 | |
| [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 | |
| [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 | |
| [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 | |
| [default5]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/autograd/graph.py:744: UserWarning: c10d::allreduce_: an autograd kernel was not registered to the Autograd key(s) but we are trying to backprop through it. This may lead to silently incorrect behavior. This behavior is deprecated and will be removed in a future version of PyTorch. If your operator is differentiable, please ensure you have registered an autograd kernel to the correct Autograd key (e.g. DispatchKey::Autograd, DispatchKey::CompositeImplicitAutograd). If your operator is not differentiable, or to squash this warning and use the previous behavior, please register torch::CppFunction::makeFallthrough() to DispatchKey::Autograd. (Triggered internally at ../torch/csrc/autograd/autograd_not_implemented_fallback.cpp:63.) | |
| [default5]: return Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass | |
| [default4]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/autograd/graph.py:744: UserWarning: c10d::allreduce_: an autograd kernel was not registered to the Autograd key(s) but we are trying to backprop through it. This may lead to silently incorrect behavior. This behavior is deprecated and will be removed in a future version of PyTorch. If your operator is differentiable, please ensure you have registered an autograd kernel to the correct Autograd key (e.g. DispatchKey::Autograd, DispatchKey::CompositeImplicitAutograd). If your operator is not differentiable, or to squash this warning and use the previous behavior, please register torch::CppFunction::makeFallthrough() to DispatchKey::Autograd. (Triggered internally at ../torch/csrc/autograd/autograd_not_implemented_fallback.cpp:63.) | |
| [default4]: return Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass | |
| [default3]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/autograd/graph.py:744: UserWarning: c10d::allreduce_: an autograd kernel was not registered to the Autograd key(s) but we are trying to backprop through it. This may lead to silently incorrect behavior. This behavior is deprecated and will be removed in a future version of PyTorch. If your operator is differentiable, please ensure you have registered an autograd kernel to the correct Autograd key (e.g. DispatchKey::Autograd, DispatchKey::CompositeImplicitAutograd). If your operator is not differentiable, or to squash this warning and use the previous behavior, please register torch::CppFunction::makeFallthrough() to DispatchKey::Autograd. (Triggered internally at ../torch/csrc/autograd/autograd_not_implemented_fallback.cpp:63.) | |
| [default3]: return Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass | |
| [default5]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/autograd/graph.py:744: UserWarning: c10d::allreduce_: an autograd kernel was not registered to the Autograd key(s) but we are trying to backprop through it. This may lead to silently incorrect behavior. This behavior is deprecated and will be removed in a future version of PyTorch. If your operator is differentiable, please ensure you have registered an autograd kernel to the correct Autograd key (e.g. DispatchKey::Autograd, DispatchKey::CompositeImplicitAutograd). If your operator is not differentiable, or to squash this warning and use the previous behavior, please register torch::CppFunction::makeFallthrough() to DispatchKey::Autograd. (Triggered internally at ../torch/csrc/autograd/autograd_not_implemented_fallback.cpp:63.) | |
| [default5]: return Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass | |
| [default4]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/autograd/graph.py:744: UserWarning: c10d::allreduce_: an autograd kernel was not registered to the Autograd key(s) but we are trying to backprop through it. This may lead to silently incorrect behavior. This behavior is deprecated and will be removed in a future version of PyTorch. If your operator is differentiable, please ensure you have registered an autograd kernel to the correct Autograd key (e.g. DispatchKey::Autograd, DispatchKey::CompositeImplicitAutograd). If your operator is not differentiable, or to squash this warning and use the previous behavior, please register torch::CppFunction::makeFallthrough() to DispatchKey::Autograd. (Triggered internally at ../torch/csrc/autograd/autograd_not_implemented_fallback.cpp:63.) | |
| [default4]: return Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass | |
| [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 | |
| [default7]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/autograd/graph.py:744: UserWarning: c10d::allreduce_: an autograd kernel was not registered to the Autograd key(s) but we are trying to backprop through it. This may lead to silently incorrect behavior. This behavior is deprecated and will be removed in a future version of PyTorch. If your operator is differentiable, please ensure you have registered an autograd kernel to the correct Autograd key (e.g. DispatchKey::Autograd, DispatchKey::CompositeImplicitAutograd). If your operator is not differentiable, or to squash this warning and use the previous behavior, please register torch::CppFunction::makeFallthrough() to DispatchKey::Autograd. (Triggered internally at ../torch/csrc/autograd/autograd_not_implemented_fallback.cpp:63.) | |
| [default7]: return Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass | |
| [default2]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/autograd/graph.py:744: UserWarning: c10d::allreduce_: an autograd kernel was not registered to the Autograd key(s) but we are trying to backprop through it. This may lead to silently incorrect behavior. This behavior is deprecated and will be removed in a future version of PyTorch. If your operator is differentiable, please ensure you have registered an autograd kernel to the correct Autograd key (e.g. DispatchKey::Autograd, DispatchKey::CompositeImplicitAutograd). If your operator is not differentiable, or to squash this warning and use the previous behavior, please register torch::CppFunction::makeFallthrough() to DispatchKey::Autograd. (Triggered internally at ../torch/csrc/autograd/autograd_not_implemented_fallback.cpp:63.) | |
| [default2]: return Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass | |
| [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/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( | |
| [default1]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/distributed_c10d.py:2261: UserWarning: torch.distributed.all_reduce_coalesced will be deprecated. If you must use it, please revisit our documentation later at https://pytorch.org/docs/master/distributed.html#collective-functions | |
| [default1]: warnings.warn( | |
| [default2]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/distributed_c10d.py:2261: UserWarning: torch.distributed.all_reduce_coalesced will be deprecated. If you must use it, please revisit our documentation later at https://pytorch.org/docs/master/distributed.html#collective-functions | |
| [default2]: warnings.warn( | |
| [default0]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/autograd/graph.py:744: UserWarning: c10d::allreduce_: an autograd kernel was not registered to the Autograd key(s) but we are trying to backprop through it. This may lead to silently incorrect behavior. This behavior is deprecated and will be removed in a future version of PyTorch. If your operator is differentiable, please ensure you have registered an autograd kernel to the correct Autograd key (e.g. DispatchKey::Autograd, DispatchKey::CompositeImplicitAutograd). If your operator is not differentiable, or to squash this warning and use the previous behavior, please register torch::CppFunction::makeFallthrough() to DispatchKey::Autograd. (Triggered internally at ../torch/csrc/autograd/autograd_not_implemented_fallback.cpp:63.) | |
| [default0]: return Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass | |
| [default7]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/autograd/graph.py:744: UserWarning: c10d::allreduce_: an autograd kernel was not registered to the Autograd key(s) but we are trying to backprop through it. This may lead to silently incorrect behavior. This behavior is deprecated and will be removed in a future version of PyTorch. If your operator is differentiable, please ensure you have registered an autograd kernel to the correct Autograd key (e.g. DispatchKey::Autograd, DispatchKey::CompositeImplicitAutograd). If your operator is not differentiable, or to squash this warning and use the previous behavior, please register torch::CppFunction::makeFallthrough() to DispatchKey::Autograd. (Triggered internally at ../torch/csrc/autograd/autograd_not_implemented_fallback.cpp:63.) | |
| [default7]: return Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass | |
| [default6]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/autograd/graph.py:744: UserWarning: c10d::allreduce_: an autograd kernel was not registered to the Autograd key(s) but we are trying to backprop through it. This may lead to silently incorrect behavior. This behavior is deprecated and will be removed in a future version of PyTorch. If your operator is differentiable, please ensure you have registered an autograd kernel to the correct Autograd key (e.g. DispatchKey::Autograd, DispatchKey::CompositeImplicitAutograd). If your operator is not differentiable, or to squash this warning and use the previous behavior, please register torch::CppFunction::makeFallthrough() to DispatchKey::Autograd. (Triggered internally at ../torch/csrc/autograd/autograd_not_implemented_fallback.cpp:63.) | |
| [default6]: return Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass | |
| [default3]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/distributed_c10d.py:2261: UserWarning: torch.distributed.all_reduce_coalesced will be deprecated. If you must use it, please revisit our documentation later at https://pytorch.org/docs/master/distributed.html#collective-functions | |
| [default3]: warnings.warn( | |
| [default1]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/autograd/graph.py:744: UserWarning: c10d::allreduce_: an autograd kernel was not registered to the Autograd key(s) but we are trying to backprop through it. This may lead to silently incorrect behavior. This behavior is deprecated and will be removed in a future version of PyTorch. If your operator is differentiable, please ensure you have registered an autograd kernel to the correct Autograd key (e.g. DispatchKey::Autograd, DispatchKey::CompositeImplicitAutograd). If your operator is not differentiable, or to squash this warning and use the previous behavior, please register torch::CppFunction::makeFallthrough() to DispatchKey::Autograd. (Triggered internally at ../torch/csrc/autograd/autograd_not_implemented_fallback.cpp:63.) | |
| [default1]: return Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass | |
| [default4]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/distributed_c10d.py:2261: UserWarning: torch.distributed.all_reduce_coalesced will be deprecated. If you must use it, please revisit our documentation later at https://pytorch.org/docs/master/distributed.html#collective-functions | |
| [default4]: warnings.warn( | |
| [default5]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/distributed_c10d.py:2261: UserWarning: torch.distributed.all_reduce_coalesced will be deprecated. If you must use it, please revisit our documentation later at https://pytorch.org/docs/master/distributed.html#collective-functions | |
| [default5]: warnings.warn( | |
| [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( | |
| [default7]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/distributed_c10d.py:2261: UserWarning: torch.distributed.all_reduce_coalesced will be deprecated. If you must use it, please revisit our documentation later at https://pytorch.org/docs/master/distributed.html#collective-functions | |
| [default7]: warnings.warn( | |
| [default6]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/distributed_c10d.py:2261: UserWarning: torch.distributed.all_reduce_coalesced will be deprecated. If you must use it, please revisit our documentation later at https://pytorch.org/docs/master/distributed.html#collective-functions | |
| [default6]: warnings.warn( | |
| [default7]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/distributed_c10d.py:2261: UserWarning: torch.distributed.all_reduce_coalesced will be deprecated. If you must use it, please revisit our documentation later at https://pytorch.org/docs/master/distributed.html#collective-functions | |
| [default7]: warnings.warn( | |
| [default6]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/distributed_c10d.py:2261: UserWarning: torch.distributed.all_reduce_coalesced will be deprecated. If you must use it, please revisit our documentation later at https://pytorch.org/docs/master/distributed.html#collective-functions | |
| [default6]: warnings.warn( | |
| [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( | |
| [default2]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/distributed_c10d.py:2261: UserWarning: torch.distributed.all_reduce_coalesced will be deprecated. If you must use it, please revisit our documentation later at https://pytorch.org/docs/master/distributed.html#collective-functions | |
| [default2]: warnings.warn( | |
| [default3]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/distributed_c10d.py:2261: UserWarning: torch.distributed.all_reduce_coalesced will be deprecated. If you must use it, please revisit our documentation later at https://pytorch.org/docs/master/distributed.html#collective-functions | |
| [default3]: warnings.warn( | |
| [default5]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/distributed_c10d.py:2261: UserWarning: torch.distributed.all_reduce_coalesced will be deprecated. If you must use it, please revisit our documentation later at https://pytorch.org/docs/master/distributed.html#collective-functions | |
| [default5]: warnings.warn( | |
| [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/distributed/distributed_c10d.py:2261: UserWarning: torch.distributed.all_reduce_coalesced will be deprecated. If you must use it, please revisit our documentation later at https://pytorch.org/docs/master/distributed.html#collective-functions | |
| [default4]: warnings.warn( | |
| [default2]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/distributed_c10d.py:2261: UserWarning: torch.distributed.all_reduce_coalesced will be deprecated. If you must use it, please revisit our documentation later at https://pytorch.org/docs/master/distributed.html#collective-functions | |
| [default2]: warnings.warn( | |
| [default3]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/distributed_c10d.py:2261: UserWarning: torch.distributed.all_reduce_coalesced will be deprecated. If you must use it, please revisit our documentation later at https://pytorch.org/docs/master/distributed.html#collective-functions | |
| [default3]: warnings.warn( | |
| [default6]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/distributed_c10d.py:2261: UserWarning: torch.distributed.all_reduce_coalesced will be deprecated. If you must use it, please revisit our documentation later at https://pytorch.org/docs/master/distributed.html#collective-functions | |
| [default6]: warnings.warn( | |
| [default5]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/distributed_c10d.py:2261: UserWarning: torch.distributed.all_reduce_coalesced will be deprecated. If you must use it, please revisit our documentation later at https://pytorch.org/docs/master/distributed.html#collective-functions | |
| [default5]: warnings.warn( | |
| [default4]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/distributed_c10d.py:2261: UserWarning: torch.distributed.all_reduce_coalesced will be deprecated. If you must use it, please revisit our documentation later at https://pytorch.org/docs/master/distributed.html#collective-functions | |
| [default4]: warnings.warn( | |
| [default2]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/distributed_c10d.py:2261: UserWarning: torch.distributed.all_reduce_coalesced will be deprecated. If you must use it, please revisit our documentation later at https://pytorch.org/docs/master/distributed.html#collective-functions | |
| [default2]: warnings.warn( | |
| [default3]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/distributed_c10d.py:2261: UserWarning: torch.distributed.all_reduce_coalesced will be deprecated. If you must use it, please revisit our documentation later at https://pytorch.org/docs/master/distributed.html#collective-functions | |
| [default3]: warnings.warn( | |
| [default0]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/distributed_c10d.py:2261: UserWarning: torch.distributed.all_reduce_coalesced will be deprecated. If you must use it, please revisit our documentation later at https://pytorch.org/docs/master/distributed.html#collective-functions | |
| [default0]: warnings.warn( | |
| [default7]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/distributed_c10d.py:2261: UserWarning: torch.distributed.all_reduce_coalesced will be deprecated. If you must use it, please revisit our documentation later at https://pytorch.org/docs/master/distributed.html#collective-functions | |
| [default7]: warnings.warn( | |
| [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( | |
| [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( | |
| [default1]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/autograd/graph.py:744: UserWarning: c10d::allreduce_: an autograd kernel was not registered to the Autograd key(s) but we are trying to backprop through it. This may lead to silently incorrect behavior. This behavior is deprecated and will be removed in a future version of PyTorch. If your operator is differentiable, please ensure you have registered an autograd kernel to the correct Autograd key (e.g. DispatchKey::Autograd, DispatchKey::CompositeImplicitAutograd). If your operator is not differentiable, or to squash this warning and use the previous behavior, please register torch::CppFunction::makeFallthrough() to DispatchKey::Autograd. (Triggered internally at ../torch/csrc/autograd/autograd_not_implemented_fallback.cpp:63.) | |
| [default1]: return Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass | |
| [default0]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/autograd/graph.py:744: UserWarning: 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 | |
| [default7]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/autograd/graph.py:744: UserWarning: c10d::allreduce_: an autograd kernel was not registered to the Autograd key(s) but we are trying to backprop through it. This may lead to silently incorrect behavior. This behavior is deprecated and will be removed in a future version of PyTorch. If your operator is differentiable, please ensure you have registered an autograd kernel to the correct Autograd key (e.g. DispatchKey::Autograd, DispatchKey::CompositeImplicitAutograd). If your operator is not differentiable, or to squash this warning and use the previous behavior, please register torch::CppFunction::makeFallthrough() to DispatchKey::Autograd. (Triggered internally at ../torch/csrc/autograd/autograd_not_implemented_fallback.cpp:63.) | |
| [default7]: return Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass | |
| [default2]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/autograd/graph.py:744: UserWarning: c10d::allreduce_: an autograd kernel was not registered to the Autograd key(s) but we are trying to backprop through it. This may lead to silently incorrect behavior. This behavior is deprecated and will be removed in a future version of PyTorch. If your operator is differentiable, please ensure you have registered an autograd kernel to the correct Autograd key (e.g. DispatchKey::Autograd, DispatchKey::CompositeImplicitAutograd). If your operator is not differentiable, or to squash this warning and use the previous behavior, please register torch::CppFunction::makeFallthrough() to DispatchKey::Autograd. (Triggered internally at ../torch/csrc/autograd/autograd_not_implemented_fallback.cpp:63.) | |
| [default2]: return Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass | |
| [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 | |
| [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 | |
| [default5]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/autograd/graph.py:744: UserWarning: c10d::allreduce_: an autograd kernel was not registered to the Autograd key(s) but we are trying to backprop through it. This may lead to silently incorrect behavior. This behavior is deprecated and will be removed in a future version of PyTorch. If your operator is differentiable, please ensure you have registered an autograd kernel to the correct Autograd key (e.g. DispatchKey::Autograd, DispatchKey::CompositeImplicitAutograd). If your operator is not differentiable, or to squash this warning and use the previous behavior, please register torch::CppFunction::makeFallthrough() to DispatchKey::Autograd. (Triggered internally at ../torch/csrc/autograd/autograd_not_implemented_fallback.cpp:63.) | |
| [default5]: return Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass | |
| [default4]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/autograd/graph.py:744: UserWarning: c10d::allreduce_: an autograd kernel was not registered to the Autograd key(s) but we are trying to backprop through it. This may lead to silently incorrect behavior. This behavior is deprecated and will be removed in a future version of PyTorch. If your operator is differentiable, please ensure you have registered an autograd kernel to the correct Autograd key (e.g. DispatchKey::Autograd, DispatchKey::CompositeImplicitAutograd). If your operator is not differentiable, or to squash this warning and use the previous behavior, please register torch::CppFunction::makeFallthrough() to DispatchKey::Autograd. (Triggered internally at ../torch/csrc/autograd/autograd_not_implemented_fallback.cpp:63.) | |
| [default4]: return Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass | |
| [default1]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/autograd/graph.py:744: UserWarning: c10d::allreduce_: an autograd kernel was not registered to the Autograd key(s) but we are trying to backprop through it. This may lead to silently incorrect behavior. This behavior is deprecated and will be removed in a future version of PyTorch. If your operator is differentiable, please ensure you have registered an autograd kernel to the correct Autograd key (e.g. DispatchKey::Autograd, DispatchKey::CompositeImplicitAutograd). If your operator is not differentiable, or to squash this warning and use the previous behavior, please register torch::CppFunction::makeFallthrough() to DispatchKey::Autograd. (Triggered internally at ../torch/csrc/autograd/autograd_not_implemented_fallback.cpp:63.) | |
| [default1]: return Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass | |
| [default0]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/autograd/graph.py:744: UserWarning: 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 | |
| [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 | |
| [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 | |
| [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 | |
| [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.) | |
| [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.) | |
| [default3]: return Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass | |
| [default2]: return Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass | |
| [default1]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/autograd/graph.py:744: UserWarning: c10d::allreduce_: an autograd kernel was not registered to the Autograd key(s) but we are trying to backprop through it. This may lead to silently incorrect behavior. This behavior is deprecated and will be removed in a future version of PyTorch. If your operator is differentiable, please ensure you have registered an autograd kernel to the correct Autograd key (e.g. DispatchKey::Autograd, DispatchKey::CompositeImplicitAutograd). If your operator is not differentiable, or to squash this warning and use the previous behavior, please register torch::CppFunction::makeFallthrough() to DispatchKey::Autograd. (Triggered internally at ../torch/csrc/autograd/autograd_not_implemented_fallback.cpp:63.) | |
| [default1]: return Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass | |
| [default4]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/autograd/graph.py:744: UserWarning: c10d::allreduce_: an autograd kernel was not registered to the Autograd key(s) but we are trying to backprop through it. This may lead to silently incorrect behavior. This behavior is deprecated and will be removed in a future version of PyTorch. If your operator is differentiable, please ensure you have registered an autograd kernel to the correct Autograd key (e.g. DispatchKey::Autograd, DispatchKey::CompositeImplicitAutograd). If your operator is not differentiable, or to squash this warning and use the previous behavior, please register torch::CppFunction::makeFallthrough() to DispatchKey::Autograd. (Triggered internally at ../torch/csrc/autograd/autograd_not_implemented_fallback.cpp:63.) | |
| [default4]: return Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass | |
| [default7]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/autograd/graph.py:744: UserWarning: c10d::allreduce_: an autograd kernel was not registered to the Autograd key(s) but we are trying to backprop through it. This may lead to silently incorrect behavior. This behavior is deprecated and will be removed in a future version of PyTorch. If your operator is differentiable, please ensure you have registered an autograd kernel to the correct Autograd key (e.g. DispatchKey::Autograd, DispatchKey::CompositeImplicitAutograd). If your operator is not differentiable, or to squash this warning and use the previous behavior, please register torch::CppFunction::makeFallthrough() to DispatchKey::Autograd. (Triggered internally at ../torch/csrc/autograd/autograd_not_implemented_fallback.cpp:63.) | |
| [default7]: return Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass | |
| [default6]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/autograd/graph.py:744: UserWarning: c10d::allreduce_: an autograd kernel was not registered to the Autograd key(s) but we are trying to backprop through it. This may lead to silently incorrect behavior. This behavior is deprecated and will be removed in a future version of PyTorch. If your operator is differentiable, please ensure you have registered an autograd kernel to the correct Autograd key (e.g. DispatchKey::Autograd, DispatchKey::CompositeImplicitAutograd). If your operator is not differentiable, or to squash this warning and use the previous behavior, please register torch::CppFunction::makeFallthrough() to DispatchKey::Autograd. (Triggered internally at ../torch/csrc/autograd/autograd_not_implemented_fallback.cpp:63.) | |
| [default6]: return Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass | |
| [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 | |
| [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 | |
| [default5]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/autograd/graph.py:744: UserWarning: c10d::allreduce_: an autograd kernel was not registered to the Autograd key(s) but we are trying to backprop through it. This may lead to silently incorrect behavior. This behavior is deprecated and will be removed in a future version of PyTorch. If your operator is differentiable, please ensure you have registered an autograd kernel to the correct Autograd key (e.g. DispatchKey::Autograd, DispatchKey::CompositeImplicitAutograd). If your operator is not differentiable, or to squash this warning and use the previous behavior, please register torch::CppFunction::makeFallthrough() to DispatchKey::Autograd. (Triggered internally at ../torch/csrc/autograd/autograd_not_implemented_fallback.cpp:63.) | |
| [default5]: return Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass | |
| [default4]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/autograd/graph.py:744: UserWarning: c10d::allreduce_: an autograd kernel was not registered to the Autograd key(s) but we are trying to backprop through it. This may lead to silently incorrect behavior. This behavior is deprecated and will be removed in a future version of PyTorch. If your operator is differentiable, please ensure you have registered an autograd kernel to the correct Autograd key (e.g. DispatchKey::Autograd, DispatchKey::CompositeImplicitAutograd). If your operator is not differentiable, or to squash this warning and use the previous behavior, please register torch::CppFunction::makeFallthrough() to DispatchKey::Autograd. (Triggered internally at ../torch/csrc/autograd/autograd_not_implemented_fallback.cpp:63.) | |
| [default4]: return Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass | |
| [default5]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/autograd/graph.py:744: UserWarning: c10d::allreduce_: an autograd kernel was not registered to the Autograd key(s) but we are trying to backprop through it. This may lead to silently incorrect behavior. This behavior is deprecated and will be removed in a future version of PyTorch. If your operator is differentiable, please ensure you have registered an autograd kernel to the correct Autograd key (e.g. DispatchKey::Autograd, DispatchKey::CompositeImplicitAutograd). If your operator is not differentiable, or to squash this warning and use the previous behavior, please register torch::CppFunction::makeFallthrough() to DispatchKey::Autograd. (Triggered internally at ../torch/csrc/autograd/autograd_not_implemented_fallback.cpp:63.) | |
| [default5]: return Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass | |
| [default4]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/autograd/graph.py:744: UserWarning: c10d::allreduce_: an autograd kernel was not registered to the Autograd key(s) but we are trying to backprop through it. This may lead to silently incorrect behavior. This behavior is deprecated and will be removed in a future version of PyTorch. If your operator is differentiable, please ensure you have registered an autograd kernel to the correct Autograd key (e.g. DispatchKey::Autograd, DispatchKey::CompositeImplicitAutograd). If your operator is not differentiable, or to squash this warning and use the previous behavior, please register torch::CppFunction::makeFallthrough() to DispatchKey::Autograd. (Triggered internally at ../torch/csrc/autograd/autograd_not_implemented_fallback.cpp:63.) | |
| [default4]: return Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass | |
| [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 | |
| [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 | |
| [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 | |
| [default0]:07/03/2024 00:27:00 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: Memory usage: 3341.79MiB. Peak allocated 42126.57MiB. Peak reserved: 43882.00MiB | |
| [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( | |
| [default0]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/distributed_c10d.py:2261: UserWarning: torch.distributed.all_reduce_coalesced will be deprecated. If you must use it, please revisit our documentation later at https://pytorch.org/docs/master/distributed.html#collective-functions | |
| [default0]: warnings.warn( | |
| [default2]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/distributed_c10d.py:2261: UserWarning: torch.distributed.all_reduce_coalesced will be deprecated. If you must use it, please revisit our documentation later at https://pytorch.org/docs/master/distributed.html#collective-functions | |
| [default2]: warnings.warn( | |
| [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( | |
| [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( | |
| [default3]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/distributed_c10d.py:2261: UserWarning: torch.distributed.all_reduce_coalesced will be deprecated. If you must use it, please revisit our documentation later at https://pytorch.org/docs/master/distributed.html#collective-functions | |
| [default3]: warnings.warn( | |
| [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( | |
| [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( | |
| [default5]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/distributed_c10d.py:2261: UserWarning: torch.distributed.all_reduce_coalesced will be deprecated. If you must use it, please revisit our documentation later at https://pytorch.org/docs/master/distributed.html#collective-functions | |
| [default5]: warnings.warn( | |
| [default4]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/distributed_c10d.py:2261: UserWarning: torch.distributed.all_reduce_coalesced will be deprecated. If you must use it, please revisit our documentation later at https://pytorch.org/docs/master/distributed.html#collective-functions | |
| [default4]: warnings.warn( | |
| [default1]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/distributed_c10d.py:2261: UserWarning: torch.distributed.all_reduce_coalesced will be deprecated. If you must use it, please revisit our documentation later at https://pytorch.org/docs/master/distributed.html#collective-functions | |
| [default1]: warnings.warn( | |
| [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( | |
| [default3]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/distributed_c10d.py:2261: UserWarning: torch.distributed.all_reduce_coalesced will be deprecated. If you must use it, please revisit our documentation later at https://pytorch.org/docs/master/distributed.html#collective-functions | |
| [default3]: warnings.warn( | |
| [default0]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/distributed_c10d.py:2261: UserWarning: torch.distributed.all_reduce_coalesced will be deprecated. If you must use it, please revisit our documentation later at https://pytorch.org/docs/master/distributed.html#collective-functions | |
| [default0]: warnings.warn( | |
| [default2]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/distributed_c10d.py:2261: UserWarning: torch.distributed.all_reduce_coalesced will be deprecated. If you must use it, please revisit our documentation later at https://pytorch.org/docs/master/distributed.html#collective-functions | |
| [default2]: warnings.warn( | |
| [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( | |
| [default7]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/distributed_c10d.py:2261: UserWarning: torch.distributed.all_reduce_coalesced will be deprecated. If you must use it, please revisit our documentation later at https://pytorch.org/docs/master/distributed.html#collective-functions | |
| [default7]: warnings.warn( | |
| [default0]:/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( | |
| [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( | |
| [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( | |
| [default2]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/distributed_c10d.py:2261: UserWarning: torch.distributed.all_reduce_coalesced will be deprecated. If you must use it, please revisit our documentation later at https://pytorch.org/docs/master/distributed.html#collective-functions | |
| [default2]: warnings.warn( | |
| [default5]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/distributed_c10d.py:2261: UserWarning: torch.distributed.all_reduce_coalesced will be deprecated. If you must use it, please revisit our documentation later at https://pytorch.org/docs/master/distributed.html#collective-functions | |
| [default5]: warnings.warn( | |
| [default3]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/distributed_c10d.py:2261: UserWarning: torch.distributed.all_reduce_coalesced will be deprecated. If you must use it, please revisit our documentation later at https://pytorch.org/docs/master/distributed.html#collective-functions | |
| [default3]: warnings.warn( | |
| [default4]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/distributed_c10d.py:2261: UserWarning: torch.distributed.all_reduce_coalesced will be deprecated. If you must use it, please revisit our documentation later at https://pytorch.org/docs/master/distributed.html#collective-functions | |
| [default4]: warnings.warn( | |
| [default4]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/distributed_c10d.py:2261: UserWarning: torch.distributed.all_reduce_coalesced will be deprecated. If you must use it, please revisit our documentation later at https://pytorch.org/docs/master/distributed.html#collective-functions | |
| [default4]: warnings.warn( | |
| [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( | |
| [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( | |
| [default5]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/distributed_c10d.py:2261: UserWarning: torch.distributed.all_reduce_coalesced will be deprecated. If you must use it, please revisit our documentation later at https://pytorch.org/docs/master/distributed.html#collective-functions | |
| [default5]: warnings.warn( | |
| [default3]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/distributed_c10d.py:2261: UserWarning: torch.distributed.all_reduce_coalesced will be deprecated. If you must use it, please revisit our documentation later at https://pytorch.org/docs/master/distributed.html#collective-functions | |
| [default3]: warnings.warn( | |
| [default5]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/distributed_c10d.py:2261: UserWarning: torch.distributed.all_reduce_coalesced will be deprecated. If you must use it, please revisit our documentation later at https://pytorch.org/docs/master/distributed.html#collective-functions | |
| [default5]: warnings.warn( | |
| [default4]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/distributed_c10d.py:2261: UserWarning: torch.distributed.all_reduce_coalesced will be deprecated. If you must use it, please revisit our documentation later at https://pytorch.org/docs/master/distributed.html#collective-functions | |
| [default4]: warnings.warn( | |
| [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/distributed/distributed_c10d.py:2261: UserWarning: torch.distributed.all_reduce_coalesced will be deprecated. If you must use it, please revisit our documentation later at https://pytorch.org/docs/master/distributed.html#collective-functions | |
| [default2]: warnings.warn( | |
| [default4]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/distributed_c10d.py:2261: UserWarning: torch.distributed.all_reduce_coalesced will be deprecated. If you must use it, please revisit our documentation later at https://pytorch.org/docs/master/distributed.html#collective-functions | |
| [default4]: warnings.warn( | |
| [default5]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/distributed_c10d.py:2261: UserWarning: torch.distributed.all_reduce_coalesced will be deprecated. If you must use it, please revisit our documentation later at https://pytorch.org/docs/master/distributed.html#collective-functions | |
| [default5]: warnings.warn( | |
| [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( | |
| [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( | |
| [default0]:07/03/2024 00:27:08 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: iteration: 1 / 20 | consumed_tokens: 4.19M | elapsed_time_per_iteration_ms: 18.2K | tokens_per_sec: 230K | tokens_per_sec_per_gpu: 3.6K | global_batch_size: 1.02K | lm_loss: 11.5 | lr: 0.0001 | model_tflops_per_gpu: 32.7 | hardware_tflops_per_gpu: 32.7 | grad_norm: 26.4 | cuda_memory_allocated: 3.64G | cuda_max_memory_reserved: 46.1G | hd_total_memory_tb: 312G | hd_used_memory_tb: 66.7G | hd_free_memory_tb: 245G | |
| [default0]:07/03/2024 00:27:08 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: Memory usage: 3474.18MiB. Peak allocated 5623.96MiB. Peak reserved: 44000.00MiB | |
| [default0]:07/03/2024 00:27:09 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: Memory usage: 3474.18MiB. Peak allocated 42258.96MiB. Peak reserved: 44002.00MiB | |
| [default0]:07/03/2024 00:27:16 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: iteration: 2 / 20 | consumed_tokens: 8.39M | elapsed_time_per_iteration_ms: 8.7K | tokens_per_sec: 482K | tokens_per_sec_per_gpu: 7.54K | global_batch_size: 1.02K | lm_loss: 11.5 | lr: 9.53e-05 | model_tflops_per_gpu: 68.4 | hardware_tflops_per_gpu: 68.4 | grad_norm: 26.6 | cuda_memory_allocated: 3.64G | cuda_max_memory_reserved: 46.1G | hd_total_memory_tb: 312G | hd_used_memory_tb: 66.7G | hd_free_memory_tb: 245G | |
| [default0]:07/03/2024 00:27:16 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: Memory usage: 3474.18MiB. Peak allocated 5623.96MiB. Peak reserved: 44002.00MiB | |
| [default0]:07/03/2024 00:27:18 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: Memory usage: 3474.18MiB. Peak allocated 42258.96MiB. Peak reserved: 44002.00MiB | |
| [default0]:07/03/2024 00:27:25 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: iteration: 3 / 20 | consumed_tokens: 12.6M | elapsed_time_per_iteration_ms: 8.65K | tokens_per_sec: 485K | tokens_per_sec_per_gpu: 7.57K | global_batch_size: 1.02K | lm_loss: 11.5 | lr: 9.05e-05 | model_tflops_per_gpu: 68.7 | hardware_tflops_per_gpu: 68.7 | grad_norm: 262 | cuda_memory_allocated: 3.64G | cuda_max_memory_reserved: 46.1G | hd_total_memory_tb: 312G | hd_used_memory_tb: 66.7G | hd_free_memory_tb: 245G | |
| [default0]:07/03/2024 00:27:25 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: Memory usage: 3474.18MiB. Peak allocated 5623.96MiB. Peak reserved: 44002.00MiB | |
| [default0]:STAGE:2024-07-03 00:27:25 1769215:1769215 ActivityProfilerController.cpp:314] Completed Stage: Warm Up | |
| [default0]:07/03/2024 00:27:27 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: Memory usage: 3474.18MiB. Peak allocated 42258.96MiB. Peak reserved: 44002.00MiB | |
| [default0]:07/03/2024 00:27:34 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: iteration: 4 / 20 | consumed_tokens: 16.8M | elapsed_time_per_iteration_ms: 8.65K | tokens_per_sec: 485K | tokens_per_sec_per_gpu: 7.58K | global_batch_size: 1.02K | lm_loss: 14.6 | lr: 8.58e-05 | model_tflops_per_gpu: 68.8 | hardware_tflops_per_gpu: 68.8 | grad_norm: 29.1 | cuda_memory_allocated: 3.64G | cuda_max_memory_reserved: 46.1G | hd_total_memory_tb: 312G | hd_used_memory_tb: 66.7G | hd_free_memory_tb: 245G | |
| [default0]:07/03/2024 00:27:34 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: Memory usage: 3474.18MiB. Peak allocated 5623.96MiB. Peak reserved: 44002.00MiB | |
| [default0]:07/03/2024 00:27:42 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: iteration: 5 / 20 | consumed_tokens: 21M | elapsed_time_per_iteration_ms: 8.67K | tokens_per_sec: 484K | tokens_per_sec_per_gpu: 7.56K | global_batch_size: 1.02K | lm_loss: 10.8 | lr: 8.11e-05 | model_tflops_per_gpu: 68.6 | hardware_tflops_per_gpu: 68.6 | grad_norm: 30.9 | |
| [default0]:07/03/2024 00:27:42 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: Memory usage: 3474.18MiB. Peak allocated 42258.96MiB. Peak reserved: 44002.00MiB | |
| [default0]:07/03/2024 00:27:51 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: iteration: 6 / 20 | consumed_tokens: 25.2M | elapsed_time_per_iteration_ms: 8.73K | tokens_per_sec: 481K | tokens_per_sec_per_gpu: 7.51K | global_batch_size: 1.02K | lm_loss: 10.6 | lr: 7.63e-05 | model_tflops_per_gpu: 68.1 | hardware_tflops_per_gpu: 68.1 | grad_norm: 27.5 | |
| [default0]:STAGE:2024-07-03 00:27:53 1769215:1769215 ActivityProfilerController.cpp:320] Completed Stage: Collection | |
| [default0]:STAGE:2024-07-03 00:27:54 1769215:1769215 ActivityProfilerController.cpp:324] Completed Stage: Post Processing | |
| [default0]:07/03/2024 00:28:16 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: Memory usage: 3474.18MiB. Peak allocated 42258.96MiB. Peak reserved: 44002.00MiB | |
| [default0]:07/03/2024 00:28:18 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: iteration: 7 / 20 | consumed_tokens: 29.4M | elapsed_time_per_iteration_ms: 2.13K | tokens_per_sec: 1.97M | tokens_per_sec_per_gpu: 30.7K | global_batch_size: 1.02K | lm_loss: 10.2 | lr: 7.16e-05 | model_tflops_per_gpu: 279 | hardware_tflops_per_gpu: 279 | grad_norm: 9.42 | |
| [default0]:07/03/2024 00:28:18 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: Memory usage: 3474.18MiB. Peak allocated 42258.96MiB. Peak reserved: 44002.00MiB | |
| [default0]:07/03/2024 00:28:27 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: iteration: 8 / 20 | consumed_tokens: 33.6M | elapsed_time_per_iteration_ms: 8.65K | tokens_per_sec: 485K | tokens_per_sec_per_gpu: 7.58K | global_batch_size: 1.02K | lm_loss: 12.8 | lr: 6.68e-05 | model_tflops_per_gpu: 68.8 | hardware_tflops_per_gpu: 68.8 | grad_norm: 77.8 | |
| [default0]:07/03/2024 00:28:27 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: Memory usage: 3474.18MiB. Peak allocated 42258.96MiB. Peak reserved: 44002.00MiB | |
| [default0]:07/03/2024 00:28:36 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: iteration: 9 / 20 | consumed_tokens: 37.7M | elapsed_time_per_iteration_ms: 8.69K | tokens_per_sec: 482K | tokens_per_sec_per_gpu: 7.54K | global_batch_size: 1.02K | lm_loss: 9.42 | lr: 6.21e-05 | model_tflops_per_gpu: 68.4 | hardware_tflops_per_gpu: 68.4 | grad_norm: 11.5 | |
| [default0]:07/03/2024 00:28:36 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: Memory usage: 3474.18MiB. Peak allocated 42258.96MiB. Peak reserved: 44002.00MiB | |
| [default0]:07/03/2024 00:28:44 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: iteration: 10 / 20 | consumed_tokens: 41.9M | elapsed_time_per_iteration_ms: 8.65K | tokens_per_sec: 485K | tokens_per_sec_per_gpu: 7.58K | global_batch_size: 1.02K | lm_loss: 9.2 | lr: 5.74e-05 | model_tflops_per_gpu: 68.8 | hardware_tflops_per_gpu: 68.8 | grad_norm: 6.89 | |
| [default0]:07/03/2024 00:28:44 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: Memory usage: 3474.18MiB. Peak allocated 42258.96MiB. Peak reserved: 44002.00MiB | |
| [default0]:07/03/2024 00:28:53 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: iteration: 11 / 20 | consumed_tokens: 46.1M | elapsed_time_per_iteration_ms: 8.67K | tokens_per_sec: 484K | tokens_per_sec_per_gpu: 7.56K | global_batch_size: 1.02K | lm_loss: 8.92 | lr: 5.26e-05 | model_tflops_per_gpu: 68.6 | hardware_tflops_per_gpu: 68.6 | grad_norm: 5.87 | |
| [default0]:07/03/2024 00:28:53 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: Memory usage: 3474.18MiB. Peak allocated 42258.96MiB. Peak reserved: 44002.00MiB | |
| [default0]:07/03/2024 00:29:02 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: iteration: 12 / 20 | consumed_tokens: 50.3M | elapsed_time_per_iteration_ms: 8.73K | tokens_per_sec: 481K | tokens_per_sec_per_gpu: 7.51K | global_batch_size: 1.02K | lm_loss: 8.55 | lr: 4.79e-05 | model_tflops_per_gpu: 68.2 | hardware_tflops_per_gpu: 68.2 | grad_norm: 6.31 | |
| [default0]:07/03/2024 00:29:02 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: Memory usage: 3474.18MiB. Peak allocated 42258.96MiB. Peak reserved: 44002.00MiB | |
| [default0]:07/03/2024 00:29:10 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: iteration: 13 / 20 | consumed_tokens: 54.5M | elapsed_time_per_iteration_ms: 8.7K | tokens_per_sec: 482K | tokens_per_sec_per_gpu: 7.53K | global_batch_size: 1.02K | lm_loss: 8.09 | lr: 4.32e-05 | model_tflops_per_gpu: 68.3 | hardware_tflops_per_gpu: 68.3 | grad_norm: 5.4 | |
| [default0]:07/03/2024 00:29:10 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: Memory usage: 3474.18MiB. Peak allocated 42258.96MiB. Peak reserved: 44002.00MiB | |
| [default0]:07/03/2024 00:29:19 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: iteration: 14 / 20 | consumed_tokens: 58.7M | elapsed_time_per_iteration_ms: 8.69K | tokens_per_sec: 483K | tokens_per_sec_per_gpu: 7.54K | global_batch_size: 1.02K | lm_loss: 7.69 | lr: 3.84e-05 | model_tflops_per_gpu: 68.4 | hardware_tflops_per_gpu: 68.4 | grad_norm: 4.65 | |
| [default0]:07/03/2024 00:29:19 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: Memory usage: 3474.18MiB. Peak allocated 42258.96MiB. Peak reserved: 44002.00MiB | |
| [default0]:07/03/2024 00:29:28 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: iteration: 15 / 20 | consumed_tokens: 62.9M | elapsed_time_per_iteration_ms: 8.68K | tokens_per_sec: 483K | tokens_per_sec_per_gpu: 7.55K | global_batch_size: 1.02K | lm_loss: 7.51 | lr: 3.37e-05 | model_tflops_per_gpu: 68.5 | hardware_tflops_per_gpu: 68.5 | grad_norm: 7.06 | |
| [default0]:07/03/2024 00:29:28 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: Memory usage: 3474.18MiB. Peak allocated 42258.96MiB. Peak reserved: 44002.00MiB | |
| [default0]:07/03/2024 00:29:36 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: iteration: 16 / 20 | consumed_tokens: 67.1M | elapsed_time_per_iteration_ms: 8.64K | tokens_per_sec: 486K | tokens_per_sec_per_gpu: 7.59K | global_batch_size: 1.02K | lm_loss: 7.4 | lr: 2.89e-05 | model_tflops_per_gpu: 68.9 | hardware_tflops_per_gpu: 68.9 | grad_norm: 7.08 | |
| [default0]:07/03/2024 00:29:36 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: Memory usage: 3474.18MiB. Peak allocated 42258.96MiB. Peak reserved: 44002.00MiB | |
| [default0]:07/03/2024 00:29:45 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: iteration: 17 / 20 | consumed_tokens: 71.3M | elapsed_time_per_iteration_ms: 8.7K | tokens_per_sec: 482K | tokens_per_sec_per_gpu: 7.53K | global_batch_size: 1.02K | lm_loss: 7.35 | lr: 2.42e-05 | model_tflops_per_gpu: 68.3 | hardware_tflops_per_gpu: 68.3 | grad_norm: 6.09 | |
| [default0]:07/03/2024 00:29:45 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: Memory usage: 3474.18MiB. Peak allocated 42258.96MiB. Peak reserved: 44002.00MiB | |
| [default0]:07/03/2024 00:29:54 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: iteration: 18 / 20 | consumed_tokens: 75.5M | elapsed_time_per_iteration_ms: 8.69K | tokens_per_sec: 482K | tokens_per_sec_per_gpu: 7.54K | global_batch_size: 1.02K | lm_loss: 7.32 | lr: 1.95e-05 | model_tflops_per_gpu: 68.4 | hardware_tflops_per_gpu: 68.4 | grad_norm: 6.83 | |
| [default0]:07/03/2024 00:29:54 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: Memory usage: 3474.18MiB. Peak allocated 42258.96MiB. Peak reserved: 44002.00MiB | |
| [default0]:07/03/2024 00:30:02 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: iteration: 19 / 20 | consumed_tokens: 79.7M | elapsed_time_per_iteration_ms: 8.69K | tokens_per_sec: 483K | tokens_per_sec_per_gpu: 7.54K | global_batch_size: 1.02K | lm_loss: 7.21 | lr: 1.47e-05 | model_tflops_per_gpu: 68.4 | hardware_tflops_per_gpu: 68.4 | grad_norm: 5.56 | |
| [default0]:07/03/2024 00:30:02 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: Memory usage: 3474.18MiB. Peak allocated 42258.96MiB. Peak reserved: 44002.00MiB | |
| [default0]:07/03/2024 00:30:11 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: iteration: 20 / 20 | consumed_tokens: 83.9M | elapsed_time_per_iteration_ms: 8.7K | tokens_per_sec: 482K | tokens_per_sec_per_gpu: 7.53K | global_batch_size: 1.02K | lm_loss: 7.11 | lr: 1e-05 | model_tflops_per_gpu: 68.3 | hardware_tflops_per_gpu: 68.3 | grad_norm: 4.28 | |
| W0703 00:30:34.494000 140510459066112 torch/distributed/elastic/rendezvous/dynamic_rendezvous.py:1252] The node 'ip-26-0-160-225.ec2.internal_1769145_0' has failed to send a keep-alive heartbeat to the rendezvous 'none' due to an error of type RendezvousTimeoutError. | |
| W0703 00:30:34.494000 140296710956800 torch/distributed/elastic/rendezvous/dynamic_rendezvous.py:1252] The node 'ip-26-0-171-88.ec2.internal_873862_0' has failed to send a keep-alive heartbeat to the rendezvous 'none' due to an error of type RendezvousTimeoutError. | |
| W0703 00:30:34.534000 139630156592960 torch/distributed/elastic/rendezvous/dynamic_rendezvous.py:1203] The node 'ip-26-0-161-78.ec2.internal_1133188_0' has failed to shutdown the rendezvous 'none' due to an error of type RendezvousConnectionError. | |
| W0703 00:30:34.547000 139630156592960 torch/distributed/elastic/rendezvous/dynamic_rendezvous.py:1203] The node 'ip-26-0-161-78.ec2.internal_1133188_0' has failed to shutdown the rendezvous 'none' due to an error of type RendezvousConnectionError. | |
| Saved 1 csv files over 1 completed logs | |
| Processing file: /fsx/ferdinandmom/ferdinand-hf/bench_cluster/results/llama-1B/64_GPUS/dp-32_tp-2_pp-1_mbz-8/profiler/ip-26-0-160-225_1769215.1719966492698530506.pt.trace.json | |
| Results written to /fsx/ferdinandmom/ferdinand-hf/bench_cluster/results/llama-1B/64_GPUS/dp-32_tp-2_pp-1_mbz-8/profiler.csv | |
| 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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