llama-1B/64_GPUS/dp-64_tp-1_pp-1_mbz-2/log.out

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START TIME: Wed Jul  3 01:01:47 UTC 2024
python3 version = Python 3.10.14
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M	examples/config_tiny_llama.py
M	examples/config_tiny_llama.yaml
M	examples/train_tiny_llama.sh
M	src/nanotron/models/llama.py
M	src/nanotron/trainer.py
Your branch is up to date with 'origin/bench_cluster'.
Job status: RUNNING
W0703 01:01:50.304000 139848241071936 torch/distributed/run.py:757] 
W0703 01:01:50.304000 139848241071936 torch/distributed/run.py:757] *****************************************
W0703 01:01:50.304000 139848241071936 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 01:01:50.304000 139848241071936 torch/distributed/run.py:757] *****************************************
W0703 01:01:50.306000 140174290601792 torch/distributed/run.py:757] 
W0703 01:01:50.306000 140174290601792 torch/distributed/run.py:757] *****************************************
W0703 01:01:50.306000 140174290601792 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 01:01:50.306000 140174290601792 torch/distributed/run.py:757] *****************************************
W0703 01:01:50.311000 140430853101376 torch/distributed/run.py:757] 
W0703 01:01:50.311000 140430853101376 torch/distributed/run.py:757] *****************************************
W0703 01:01:50.311000 140430853101376 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 01:01:50.311000 140430853101376 torch/distributed/run.py:757] *****************************************
W0703 01:01:50.312000 140553062291264 torch/distributed/run.py:757] 
W0703 01:01:50.312000 140553062291264 torch/distributed/run.py:757] *****************************************
W0703 01:01:50.312000 140553062291264 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 01:01:50.312000 140553062291264 torch/distributed/run.py:757] *****************************************
W0703 01:01:50.317000 140071857948480 torch/distributed/run.py:757] 
W0703 01:01:50.317000 140071857948480 torch/distributed/run.py:757] *****************************************
W0703 01:01:50.317000 140071857948480 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 01:01:50.317000 140071857948480 torch/distributed/run.py:757] *****************************************
W0703 01:01:50.317000 140653719533376 torch/distributed/run.py:757] 
W0703 01:01:50.317000 140653719533376 torch/distributed/run.py:757] *****************************************
W0703 01:01:50.317000 140653719533376 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 01:01:50.317000 140653719533376 torch/distributed/run.py:757] *****************************************
W0703 01:01:50.336000 139880512571200 torch/distributed/run.py:757] 
W0703 01:01:50.336000 139880512571200 torch/distributed/run.py:757] *****************************************
W0703 01:01:50.336000 139880512571200 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 01:01:50.336000 139880512571200 torch/distributed/run.py:757] *****************************************
W0703 01:01:50.346000 139821835138880 torch/distributed/run.py:757] 
W0703 01:01:50.346000 139821835138880 torch/distributed/run.py:757] *****************************************
W0703 01:01:50.346000 139821835138880 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 01:01:50.346000 139821835138880 torch/distributed/run.py:757] *****************************************
[default0]:07/03/2024 01:02:10 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: Config:
[default0]:07/03/2024 01:02:10 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: Config(general=GeneralArgs(project='bench_cluster',
[default0]:07/03/2024 01:02:10 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:                            run='%date_%jobid',
[default0]:07/03/2024 01:02:10 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:                            seed=42,
[default0]:07/03/2024 01:02:10 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:                            step=None,
[default0]:07/03/2024 01:02:10 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:                            consumed_train_samples=None,
[default0]:07/03/2024 01:02:10 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:                            benchmark_csv_path=None,
[default0]:07/03/2024 01:02:10 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:                            ignore_sanity_checks=True),
[default0]:07/03/2024 01:02:10 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:        parallelism=ParallelismArgs(dp=64,
[default0]:07/03/2024 01:02:10 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:                                    pp=1,
[default0]:07/03/2024 01:02:10 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:                                    tp=1,
[default0]:07/03/2024 01:02:10 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:                                    pp_engine=<nanotron.parallel.pipeline_parallel.engine.OneForwardOneBackwardPipelineEngine object at 0x7f4e5e7bc670>,
[default0]:07/03/2024 01:02:10 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:                                    tp_mode=<TensorParallelLinearMode.REDUCE_SCATTER: 2>,
[default0]:07/03/2024 01:02:10 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:                                    tp_linear_async_communication=False,
[default0]:07/03/2024 01:02:10 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:                                    expert_parallel_size=1),
[default0]:07/03/2024 01:02:10 [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 01:02:10 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:                                                 eos_token_id=2,
[default0]:07/03/2024 01:02:10 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:                                                 hidden_act='silu',
[default0]:07/03/2024 01:02:10 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:                                                 hidden_size=2048,
[default0]:07/03/2024 01:02:10 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:                                                 initializer_range=0.02,
[default0]:07/03/2024 01:02:10 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:                                                 intermediate_size=4096,
[default0]:07/03/2024 01:02:10 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:                                                 is_llama_config=True,
[default0]:07/03/2024 01:02:10 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:                                                 max_position_embeddings=4096,
[default0]:07/03/2024 01:02:10 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:                                                 num_attention_heads=32,
[default0]:07/03/2024 01:02:10 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:                                                 num_hidden_layers=24,
[default0]:07/03/2024 01:02:10 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:                                                 num_key_value_heads=32,
[default0]:07/03/2024 01:02:10 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:                                                 pad_token_id=None,
[default0]:07/03/2024 01:02:10 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:                                                 pretraining_tp=1,
[default0]:07/03/2024 01:02:10 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:                                                 rms_norm_eps=1e-05,
[default0]:07/03/2024 01:02:10 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:                                                 rope_scaling=None,
[default0]:07/03/2024 01:02:10 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:                                                 rope_theta=10000.0,
[default0]:07/03/2024 01:02:10 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:                                                 tie_word_embeddings=True,
[default0]:07/03/2024 01:02:10 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:                                                 use_cache=True,
[default0]:07/03/2024 01:02:10 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:                                                 vocab_size=50257),
[default0]:07/03/2024 01:02:10 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:                        init_method=RandomInit(std=0.025),
[default0]:07/03/2024 01:02:10 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:                        dtype=torch.bfloat16,
[default0]:07/03/2024 01:02:10 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:                        make_vocab_size_divisible_by=1,
[default0]:07/03/2024 01:02:10 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:                        ddp_bucket_cap_mb=25),
[default0]:07/03/2024 01:02:10 [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 01:02:10 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:                                tokenizer_revision=None,
[default0]:07/03/2024 01:02:10 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:                                tokenizer_max_length=None),
[default0]:07/03/2024 01:02:10 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:        checkpoints=CheckpointsArgs(checkpoints_path=Path('/dev/null'),
[default0]:07/03/2024 01:02:10 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:                                    checkpoint_interval=100000,
[default0]:07/03/2024 01:02:10 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:                                    save_initial_state=False,
[default0]:07/03/2024 01:02:10 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:                                    resume_checkpoint_path=None,
[default0]:07/03/2024 01:02:10 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:                                    checkpoints_path_is_shared_file_system=False),
[default0]:07/03/2024 01:02:10 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:        logging=LoggingArgs(log_level='info',
[default0]:07/03/2024 01:02:10 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:                            log_level_replica='info',
[default0]:07/03/2024 01:02:10 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:                            iteration_step_info_interval=1),
[default0]:07/03/2024 01:02:10 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:        tokens=TokensArgs(sequence_length=4096,
[default0]:07/03/2024 01:02:10 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:                          train_steps=20,
[default0]:07/03/2024 01:02:10 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:                          micro_batch_size=2,
[default0]:07/03/2024 01:02:10 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:                          batch_accumulation_per_replica=8,
[default0]:07/03/2024 01:02:10 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:                          val_check_interval=-1,
[default0]:07/03/2024 01:02:10 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:                          limit_val_batches=0,
[default0]:07/03/2024 01:02:10 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:                          limit_test_batches=0),
[default0]:07/03/2024 01:02:10 [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 01:02:10 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:                                                                     adam_beta1=0.9,
[default0]:07/03/2024 01:02:10 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:                                                                     adam_beta2=0.95,
[default0]:07/03/2024 01:02:10 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:                                                                     torch_adam_is_fused=True,
[default0]:07/03/2024 01:02:10 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:                                                                     name='adamW'),
[default0]:07/03/2024 01:02:10 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:                                zero_stage=1,
[default0]:07/03/2024 01:02:10 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:                                weight_decay=0.01,
[default0]:07/03/2024 01:02:10 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:                                clip_grad=1.0,
[default0]:07/03/2024 01:02:10 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:                                accumulate_grad_in_fp32=True,
[default0]:07/03/2024 01:02:10 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:                                learning_rate_scheduler=LRSchedulerArgs(learning_rate=0.0001,
[default0]:07/03/2024 01:02:10 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:                                                                        lr_warmup_steps=1,
[default0]:07/03/2024 01:02:10 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:                                                                        lr_warmup_style='linear',
[default0]:07/03/2024 01:02:10 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:                                                                        lr_decay_style='linear',
[default0]:07/03/2024 01:02:10 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:                                                                        lr_decay_steps=19,
[default0]:07/03/2024 01:02:10 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:                                                                        lr_decay_starting_step=None,
[default0]:07/03/2024 01:02:10 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:                                                                        min_decay_lr=1e-05)),
[default0]:07/03/2024 01:02:10 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:        data_stages=[DatasetStageArgs(name='Training Stage',
[default0]:07/03/2024 01:02:10 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:                                      start_training_step=1,
[default0]:07/03/2024 01:02:10 [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 01:02:10 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:                                                                                 hf_dataset_splits='train',
[default0]:07/03/2024 01:02:10 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:                                                                                 hf_dataset_config_name=None,
[default0]:07/03/2024 01:02:10 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:                                                                                 dataset_processing_num_proc_per_process=64,
[default0]:07/03/2024 01:02:10 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:                                                                                 dataset_overwrite_cache=False,
[default0]:07/03/2024 01:02:10 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:                                                                                 text_column_name='text'),
[default0]:07/03/2024 01:02:10 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:                                                    seed=42,
[default0]:07/03/2024 01:02:10 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:                                                    num_loading_workers=0))],
[default0]:07/03/2024 01:02:10 [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-64_tp-1_pp-1_mbz-2')),
[default0]:07/03/2024 01:02:10 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:        lighteval=None)
[default0]:07/03/2024 01:02:10 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: Model Config:
[default0]:07/03/2024 01:02:10 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: LlamaConfig(bos_token_id=1,
[default0]:07/03/2024 01:02:10 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:             eos_token_id=2,
[default0]:07/03/2024 01:02:10 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:             hidden_act='silu',
[default0]:07/03/2024 01:02:10 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:             hidden_size=2048,
[default0]:07/03/2024 01:02:10 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:             initializer_range=0.02,
[default0]:07/03/2024 01:02:10 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:             intermediate_size=4096,
[default0]:07/03/2024 01:02:10 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:             is_llama_config=True,
[default0]:07/03/2024 01:02:10 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:             max_position_embeddings=4096,
[default0]:07/03/2024 01:02:10 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:             num_attention_heads=32,
[default0]:07/03/2024 01:02:10 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:             num_hidden_layers=24,
[default0]:07/03/2024 01:02:10 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:             num_key_value_heads=32,
[default0]:07/03/2024 01:02:10 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:             pad_token_id=None,
[default0]:07/03/2024 01:02:10 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:             pretraining_tp=1,
[default0]:07/03/2024 01:02:10 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:             rms_norm_eps=1e-05,
[default0]:07/03/2024 01:02:10 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:             rope_scaling=None,
[default0]:07/03/2024 01:02:10 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:             rope_theta=10000.0,
[default0]:07/03/2024 01:02:10 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:             tie_word_embeddings=True,
[default0]:07/03/2024 01:02:10 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:             use_cache=True,
[default0]:07/03/2024 01:02:10 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:             vocab_size=50257)
[default0]:07/03/2024 01:02:10 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: Building model..
[default0]:07/03/2024 01:02:10 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: Setting PP block ranks...
[default3]:07/03/2024 01:02:20 [INFO|DP=59|PP=0|TP=0|ip-26-0-171-88]: No checkpoint path provided.
[default1]:07/03/2024 01:02:20 [INFO|DP=57|PP=0|TP=0|ip-26-0-171-88]: No checkpoint path provided.
[default2]:07/03/2024 01:02:20 [INFO|DP=58|PP=0|TP=0|ip-26-0-171-88]: No checkpoint path provided.
[default5]:07/03/2024 01:02:20 [INFO|DP=61|PP=0|TP=0|ip-26-0-171-88]: No checkpoint path provided.
[default4]:07/03/2024 01:02:20 [INFO|DP=60|PP=0|TP=0|ip-26-0-171-88]: No checkpoint path provided.
[default2]:07/03/2024 01:02:20 [INFO|DP=2|PP=0|TP=0|ip-26-0-160-225]: No checkpoint path provided.
[default4]:07/03/2024 01:02:20 [INFO|DP=28|PP=0|TP=0|ip-26-0-161-78]: No checkpoint path provided.
[default6]:07/03/2024 01:02:20 [INFO|DP=30|PP=0|TP=0|ip-26-0-161-78]: No checkpoint path provided.
[default1]:07/03/2024 01:02:20 [INFO|DP=25|PP=0|TP=0|ip-26-0-161-78]: No checkpoint path provided.
[default2]:07/03/2024 01:02:20 [INFO|DP=10|PP=0|TP=0|ip-26-0-161-103]: No checkpoint path provided.
[default1]:07/03/2024 01:02:20 [INFO|DP=1|PP=0|TP=0|ip-26-0-160-225]: No checkpoint path provided.
[default4]:07/03/2024 01:02:20 [INFO|DP=12|PP=0|TP=0|ip-26-0-161-103]: No checkpoint path provided.
[default5]:07/03/2024 01:02:20 [INFO|DP=5|PP=0|TP=0|ip-26-0-160-225]: No checkpoint path provided.
[default0]:07/03/2024 01:02:20 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: Total number of parameters: 1.11G (2116.51MiB)
[default0]:07/03/2024 01:02:20 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: Local number of parameters: 1.11G (2116.51MiB)
[default0]:07/03/2024 01:02:20 [INFO|DP=8|PP=0|TP=0|ip-26-0-161-103]: No checkpoint path provided.
[default0]:07/03/2024 01:02:20 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: [After model building] Memory usage: 2140.53MiB. Peak allocated: 2338.88MiB Peak reserved: 2392.00MiB
[default0]:07/03/2024 01:02:20 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: No checkpoint path provided.
[default0]:07/03/2024 01:02:20 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: Parametrizing model parameters using StandardParametrizator
[default3]:07/03/2024 01:02:20 [INFO|DP=11|PP=0|TP=0|ip-26-0-161-103]: No checkpoint path provided.
[default1]:07/03/2024 01:02:20 [INFO|DP=9|PP=0|TP=0|ip-26-0-161-103]: No checkpoint path provided.
[default7]:07/03/2024 01:02:20 [INFO|DP=15|PP=0|TP=0|ip-26-0-161-103]: No checkpoint path provided.
[default6]:07/03/2024 01:02:20 [INFO|DP=6|PP=0|TP=0|ip-26-0-160-225]: No checkpoint path provided.
[default7]:07/03/2024 01:02:20 [INFO|DP=63|PP=0|TP=0|ip-26-0-171-88]: No checkpoint path provided.
[default2]:07/03/2024 01:02:20 [INFO|DP=34|PP=0|TP=0|ip-26-0-162-233]: No checkpoint path provided.
[default5]:07/03/2024 01:02:20 [INFO|DP=37|PP=0|TP=0|ip-26-0-162-233]: No checkpoint path provided.
[default1]:07/03/2024 01:02:20 [INFO|DP=33|PP=0|TP=0|ip-26-0-162-233]: No checkpoint path provided.
[default4]:07/03/2024 01:02:20 [INFO|DP=4|PP=0|TP=0|ip-26-0-160-225]: No checkpoint path provided.
[default3]:07/03/2024 01:02:20 [INFO|DP=3|PP=0|TP=0|ip-26-0-160-225]: No checkpoint path provided.
[default0]:07/03/2024 01:02:20 [INFO|DP=24|PP=0|TP=0|ip-26-0-161-78]: No checkpoint path provided.
[default7]:07/03/2024 01:02:20 [INFO|DP=31|PP=0|TP=0|ip-26-0-161-78]: No checkpoint path provided.
[default0]:07/03/2024 01:02:20 [INFO|DP=40|PP=0|TP=0|ip-26-0-171-102]: No checkpoint path provided.
[default5]:07/03/2024 01:02:20 [INFO|DP=45|PP=0|TP=0|ip-26-0-171-102]: No checkpoint path provided.
[default2]:07/03/2024 01:02:20 [INFO|DP=26|PP=0|TP=0|ip-26-0-161-78]: No checkpoint path provided.
[default4]:07/03/2024 01:02:20 [INFO|DP=44|PP=0|TP=0|ip-26-0-171-102]: No checkpoint path provided.
[default7]:07/03/2024 01:02:20 [INFO|DP=47|PP=0|TP=0|ip-26-0-171-102]: No checkpoint path provided.
[default3]:07/03/2024 01:02:20 [INFO|DP=27|PP=0|TP=0|ip-26-0-161-78]: No checkpoint path provided.
[default3]:07/03/2024 01:02:20 [INFO|DP=43|PP=0|TP=0|ip-26-0-171-102]: No checkpoint path provided.
[default6]:07/03/2024 01:02:20 [INFO|DP=62|PP=0|TP=0|ip-26-0-171-88]: No checkpoint path provided.
[default5]:07/03/2024 01:02:20 [INFO|DP=29|PP=0|TP=0|ip-26-0-161-78]: No checkpoint path provided.
[default5]:07/03/2024 01:02:20 [INFO|DP=13|PP=0|TP=0|ip-26-0-161-103]: No checkpoint path provided.
[default6]:07/03/2024 01:02:20 [INFO|DP=14|PP=0|TP=0|ip-26-0-161-103]: No checkpoint path provided.
[default0]:07/03/2024 01:02:20 [INFO|DP=32|PP=0|TP=0|ip-26-0-162-233]: No checkpoint path provided.
[default3]:07/03/2024 01:02:20 [INFO|DP=19|PP=0|TP=0|ip-26-0-161-153]: No checkpoint path provided.
[default0]:07/03/2024 01:02:20 [INFO|DP=16|PP=0|TP=0|ip-26-0-161-153]: No checkpoint path provided.
[default7]:07/03/2024 01:02:20 [INFO|DP=7|PP=0|TP=0|ip-26-0-160-225]: No checkpoint path provided.
[default1]:07/03/2024 01:02:20 [INFO|DP=17|PP=0|TP=0|ip-26-0-161-153]: No checkpoint path provided.
[default5]:07/03/2024 01:02:20 [INFO|DP=21|PP=0|TP=0|ip-26-0-161-153]: No checkpoint path provided.
[default2]:07/03/2024 01:02:20 [INFO|DP=42|PP=0|TP=0|ip-26-0-171-102]: No checkpoint path provided.
[default2]:07/03/2024 01:02:20 [INFO|DP=18|PP=0|TP=0|ip-26-0-161-153]: No checkpoint path provided.
[default4]:07/03/2024 01:02:20 [INFO|DP=20|PP=0|TP=0|ip-26-0-161-153]: No checkpoint path provided.
[default7]:07/03/2024 01:02:20 [INFO|DP=39|PP=0|TP=0|ip-26-0-162-233]: No checkpoint path provided.
[default6]:07/03/2024 01:02:20 [INFO|DP=22|PP=0|TP=0|ip-26-0-161-153]: No checkpoint path provided.
[default6]:07/03/2024 01:02:20 [INFO|DP=38|PP=0|TP=0|ip-26-0-162-233]: No checkpoint path provided.
[default6]:07/03/2024 01:02:20 [INFO|DP=46|PP=0|TP=0|ip-26-0-171-102]: No checkpoint path provided.
[default4]:07/03/2024 01:02:20 [INFO|DP=52|PP=0|TP=0|ip-26-0-171-62]: No checkpoint path provided.
[default7]:07/03/2024 01:02:20 [INFO|DP=55|PP=0|TP=0|ip-26-0-171-62]: No checkpoint path provided.
[default1]:07/03/2024 01:02:20 [INFO|DP=49|PP=0|TP=0|ip-26-0-171-62]: No checkpoint path provided.
[default0]:07/03/2024 01:02:20 [INFO|DP=48|PP=0|TP=0|ip-26-0-171-62]: No checkpoint path provided.
[default7]:07/03/2024 01:02:20 [INFO|DP=23|PP=0|TP=0|ip-26-0-161-153]: No checkpoint path provided.
[default5]:07/03/2024 01:02:20 [INFO|DP=53|PP=0|TP=0|ip-26-0-171-62]: No checkpoint path provided.
[default6]:07/03/2024 01:02:20 [INFO|DP=54|PP=0|TP=0|ip-26-0-171-62]: No checkpoint path provided.
[default1]:07/03/2024 01:02:20 [INFO|DP=41|PP=0|TP=0|ip-26-0-171-102]: No checkpoint path provided.
[default0]:07/03/2024 01:02:20 [INFO|DP=56|PP=0|TP=0|ip-26-0-171-88]: No checkpoint path provided.
[default3]:07/03/2024 01:02:20 [INFO|DP=35|PP=0|TP=0|ip-26-0-162-233]: No checkpoint path provided.
[default4]:07/03/2024 01:02:20 [INFO|DP=36|PP=0|TP=0|ip-26-0-162-233]: No checkpoint path provided.
[default2]:07/03/2024 01:02:20 [INFO|DP=50|PP=0|TP=0|ip-26-0-171-62]: No checkpoint path provided.
[default3]:07/03/2024 01:02:20 [INFO|DP=51|PP=0|TP=0|ip-26-0-171-62]: No checkpoint path provided.
[default0]:07/03/2024 01:02:29 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: [Optimizer Building] Using LearningRateForSP as learning rate
[default0]:07/03/2024 01:02:29 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: [ZeRO sharding] Size of optimizer params per rank:
[default0]:07/03/2024 01:02:29 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: [ZeRO sharding] DP Rank 0 has 17.3M out of 1.11G (1.56%) params' optimizer states
[default0]:07/03/2024 01:02:29 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: [ZeRO sharding] DP Rank 1 has 17.3M out of 1.11G (1.56%) params' optimizer states
[default0]:07/03/2024 01:02:29 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: [ZeRO sharding] DP Rank 2 has 17.3M out of 1.11G (1.56%) params' optimizer states
[default0]:07/03/2024 01:02:29 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: [ZeRO sharding] DP Rank 3 has 17.3M out of 1.11G (1.56%) params' optimizer states
[default0]:07/03/2024 01:02:29 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: [ZeRO sharding] DP Rank 4 has 17.3M out of 1.11G (1.56%) params' optimizer states
[default0]:07/03/2024 01:02:29 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: [ZeRO sharding] DP Rank 5 has 17.3M out of 1.11G (1.56%) params' optimizer states
[default0]:07/03/2024 01:02:29 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: [ZeRO sharding] DP Rank 6 has 17.3M out of 1.11G (1.56%) params' optimizer states
[default0]:07/03/2024 01:02:29 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: [ZeRO sharding] DP Rank 7 has 17.3M out of 1.11G (1.56%) params' optimizer states
[default0]:07/03/2024 01:02:29 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: [ZeRO sharding] DP Rank 8 has 17.3M out of 1.11G (1.56%) params' optimizer states
[default0]:07/03/2024 01:02:29 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: [ZeRO sharding] DP Rank 9 has 17.3M out of 1.11G (1.56%) params' optimizer states
[default0]:07/03/2024 01:02:29 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: [ZeRO sharding] DP Rank 10 has 17.3M out of 1.11G (1.56%) params' optimizer states
[default0]:07/03/2024 01:02:29 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: [ZeRO sharding] DP Rank 11 has 17.3M out of 1.11G (1.56%) params' optimizer states
[default0]:07/03/2024 01:02:29 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: [ZeRO sharding] DP Rank 12 has 17.3M out of 1.11G (1.56%) params' optimizer states
[default0]:07/03/2024 01:02:29 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: [ZeRO sharding] DP Rank 13 has 17.3M out of 1.11G (1.56%) params' optimizer states
[default0]:07/03/2024 01:02:29 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: [ZeRO sharding] DP Rank 14 has 17.3M out of 1.11G (1.56%) params' optimizer states
[default0]:07/03/2024 01:02:29 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: [ZeRO sharding] DP Rank 15 has 17.3M out of 1.11G (1.56%) params' optimizer states
[default0]:07/03/2024 01:02:29 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: [ZeRO sharding] DP Rank 16 has 17.3M out of 1.11G (1.56%) params' optimizer states
[default0]:07/03/2024 01:02:29 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: [ZeRO sharding] DP Rank 17 has 17.3M out of 1.11G (1.56%) params' optimizer states
[default0]:07/03/2024 01:02:29 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: [ZeRO sharding] DP Rank 18 has 17.3M out of 1.11G (1.56%) params' optimizer states
[default0]:07/03/2024 01:02:29 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: [ZeRO sharding] DP Rank 19 has 17.3M out of 1.11G (1.56%) params' optimizer states
[default0]:07/03/2024 01:02:29 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: [ZeRO sharding] DP Rank 20 has 17.3M out of 1.11G (1.56%) params' optimizer states
[default0]:07/03/2024 01:02:29 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: [ZeRO sharding] DP Rank 21 has 17.3M out of 1.11G (1.56%) params' optimizer states
[default0]:07/03/2024 01:02:29 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: [ZeRO sharding] DP Rank 22 has 17.3M out of 1.11G (1.56%) params' optimizer states
[default0]:07/03/2024 01:02:29 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: [ZeRO sharding] DP Rank 23 has 17.3M out of 1.11G (1.56%) params' optimizer states
[default0]:07/03/2024 01:02:29 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: [ZeRO sharding] DP Rank 24 has 17.3M out of 1.11G (1.56%) params' optimizer states
[default0]:07/03/2024 01:02:29 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: [ZeRO sharding] DP Rank 25 has 17.3M out of 1.11G (1.56%) params' optimizer states
[default0]:07/03/2024 01:02:29 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: [ZeRO sharding] DP Rank 26 has 17.3M out of 1.11G (1.56%) params' optimizer states
[default0]:07/03/2024 01:02:29 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: [ZeRO sharding] DP Rank 27 has 17.3M out of 1.11G (1.56%) params' optimizer states
[default0]:07/03/2024 01:02:29 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: [ZeRO sharding] DP Rank 28 has 17.3M out of 1.11G (1.56%) params' optimizer states
[default0]:07/03/2024 01:02:29 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: [ZeRO sharding] DP Rank 29 has 17.3M out of 1.11G (1.56%) params' optimizer states
[default0]:07/03/2024 01:02:29 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: [ZeRO sharding] DP Rank 30 has 17.3M out of 1.11G (1.56%) params' optimizer states
[default0]:07/03/2024 01:02:29 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: [ZeRO sharding] DP Rank 31 has 17.3M out of 1.11G (1.56%) params' optimizer states
[default0]:07/03/2024 01:02:29 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: [ZeRO sharding] DP Rank 32 has 17.3M out of 1.11G (1.56%) params' optimizer states
[default0]:07/03/2024 01:02:29 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: [ZeRO sharding] DP Rank 33 has 17.3M out of 1.11G (1.56%) params' optimizer states
[default0]:07/03/2024 01:02:29 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: [ZeRO sharding] DP Rank 34 has 17.3M out of 1.11G (1.56%) params' optimizer states
[default0]:07/03/2024 01:02:29 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: [ZeRO sharding] DP Rank 35 has 17.3M out of 1.11G (1.56%) params' optimizer states
[default0]:07/03/2024 01:02:29 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: [ZeRO sharding] DP Rank 36 has 17.3M out of 1.11G (1.56%) params' optimizer states
[default0]:07/03/2024 01:02:29 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: [ZeRO sharding] DP Rank 37 has 17.3M out of 1.11G (1.56%) params' optimizer states
[default0]:07/03/2024 01:02:29 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: [ZeRO sharding] DP Rank 38 has 17.3M out of 1.11G (1.56%) params' optimizer states
[default0]:07/03/2024 01:02:29 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: [ZeRO sharding] DP Rank 39 has 17.3M out of 1.11G (1.56%) params' optimizer states
[default0]:07/03/2024 01:02:29 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: [ZeRO sharding] DP Rank 40 has 17.3M out of 1.11G (1.56%) params' optimizer states
[default0]:07/03/2024 01:02:29 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: [ZeRO sharding] DP Rank 41 has 17.3M out of 1.11G (1.56%) params' optimizer states
[default0]:07/03/2024 01:02:29 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: [ZeRO sharding] DP Rank 42 has 17.3M out of 1.11G (1.56%) params' optimizer states
[default0]:07/03/2024 01:02:29 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: [ZeRO sharding] DP Rank 43 has 17.3M out of 1.11G (1.56%) params' optimizer states
[default0]:07/03/2024 01:02:29 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: [ZeRO sharding] DP Rank 44 has 17.3M out of 1.11G (1.56%) params' optimizer states
[default0]:07/03/2024 01:02:29 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: [ZeRO sharding] DP Rank 45 has 17.3M out of 1.11G (1.56%) params' optimizer states
[default0]:07/03/2024 01:02:29 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: [ZeRO sharding] DP Rank 46 has 17.3M out of 1.11G (1.56%) params' optimizer states
[default0]:07/03/2024 01:02:29 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: [ZeRO sharding] DP Rank 47 has 17.3M out of 1.11G (1.56%) params' optimizer states
[default0]:07/03/2024 01:02:29 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: [ZeRO sharding] DP Rank 48 has 17.3M out of 1.11G (1.56%) params' optimizer states
[default0]:07/03/2024 01:02:29 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: [ZeRO sharding] DP Rank 49 has 17.3M out of 1.11G (1.56%) params' optimizer states
[default0]:07/03/2024 01:02:29 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: [ZeRO sharding] DP Rank 50 has 17.3M out of 1.11G (1.56%) params' optimizer states
[default0]:07/03/2024 01:02:29 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: [ZeRO sharding] DP Rank 51 has 17.3M out of 1.11G (1.56%) params' optimizer states
[default0]:07/03/2024 01:02:29 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: [ZeRO sharding] DP Rank 52 has 17.3M out of 1.11G (1.56%) params' optimizer states
[default0]:07/03/2024 01:02:29 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: [ZeRO sharding] DP Rank 53 has 17.3M out of 1.11G (1.56%) params' optimizer states
[default0]:07/03/2024 01:02:29 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: [ZeRO sharding] DP Rank 54 has 17.3M out of 1.11G (1.56%) params' optimizer states
[default0]:07/03/2024 01:02:29 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: [ZeRO sharding] DP Rank 55 has 17.3M out of 1.11G (1.56%) params' optimizer states
[default0]:07/03/2024 01:02:29 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: [ZeRO sharding] DP Rank 56 has 17.3M out of 1.11G (1.56%) params' optimizer states
[default0]:07/03/2024 01:02:29 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: [ZeRO sharding] DP Rank 57 has 17.3M out of 1.11G (1.56%) params' optimizer states
[default0]:07/03/2024 01:02:29 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: [ZeRO sharding] DP Rank 58 has 17.3M out of 1.11G (1.56%) params' optimizer states
[default0]:07/03/2024 01:02:29 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: [ZeRO sharding] DP Rank 59 has 17.3M out of 1.11G (1.56%) params' optimizer states
[default0]:07/03/2024 01:02:29 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: [ZeRO sharding] DP Rank 60 has 17.3M out of 1.11G (1.56%) params' optimizer states
[default0]:07/03/2024 01:02:29 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: [ZeRO sharding] DP Rank 61 has 17.3M out of 1.11G (1.56%) params' optimizer states
[default0]:07/03/2024 01:02:29 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: [ZeRO sharding] DP Rank 62 has 17.3M out of 1.11G (1.56%) params' optimizer states
[default0]:07/03/2024 01:02:29 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: [ZeRO sharding] DP Rank 63 has 17.3M out of 1.11G (1.56%) params' optimizer states
[default0]:07/03/2024 01:02:30 [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 01:02:30 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: Using `datasets` library
[default0]:07/03/2024 01:02:30 [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 01:02:30 [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 01:02:36 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: [Training Plan] There are 1 training stages 
[default0]:07/03/2024 01:02:36 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: [Stage Training Stage] start from step 1 
[default0]:07/03/2024 01:02:36 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: 
[default0]:07/03/2024 01:02:36 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: [Start training] datetime: 2024-07-03 01:02:36.757905 | mbs: 2 | grad_accum: 8 | global_batch_size: 1024 | sequence_length: 4096 | train_steps: 20 | start_iteration_step: 0 | consumed_train_samples: 0
[default0]:07/03/2024 01:02:36 [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 01:02:36 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:  Memory usage: 6440.67MiB. Peak allocated 6440.67MiB. Peak reserved: 6626.00MiB
[default0]:07/03/2024 01:02:36 [WARNING|DP=56|PP=0|TP=0|ip-26-0-171-88]: Repo card metadata block was not found. Setting CardData to empty.
[default3]:07/03/2024 01:02:36 [WARNING|DP=59|PP=0|TP=0|ip-26-0-171-88]: Repo card metadata block was not found. Setting CardData to empty.
[default2]:07/03/2024 01:02:36 [WARNING|DP=58|PP=0|TP=0|ip-26-0-171-88]: Repo card metadata block was not found. Setting CardData to empty.
[default4]:07/03/2024 01:02:36 [WARNING|DP=60|PP=0|TP=0|ip-26-0-171-88]: 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.
[default6]:07/03/2024 01:02:36 [WARNING|DP=30|PP=0|TP=0|ip-26-0-161-78]: Repo card metadata block was not found. Setting CardData to empty.
[default1]:07/03/2024 01:02:36 [WARNING|DP=25|PP=0|TP=0|ip-26-0-161-78]: Repo card metadata block was not found. Setting CardData to empty.
[default7]:07/03/2024 01:02:36 [WARNING|DP=15|PP=0|TP=0|ip-26-0-161-103]: Repo card metadata block was not found. Setting CardData to empty.
[default0]:Repo card metadata block was not found. Setting CardData to empty.
[default4]:07/03/2024 01:02:36 [WARNING|DP=44|PP=0|TP=0|ip-26-0-171-102]: Repo card metadata block was not found. Setting CardData to empty.
[default5]:07/03/2024 01:02:36 [WARNING|DP=45|PP=0|TP=0|ip-26-0-171-102]: Repo card metadata block was not found. Setting CardData to empty.
[default5]:Repo card metadata block was not found. Setting CardData to empty.
[default0]:07/03/2024 01:02:36 [WARNING|DP=40|PP=0|TP=0|ip-26-0-171-102]: Repo card metadata block was not found. Setting CardData to empty.
[default3]:07/03/2024 01:02:36 [WARNING|DP=43|PP=0|TP=0|ip-26-0-171-102]: Repo card metadata block was not found. Setting CardData to empty.
[default7]:07/03/2024 01:02:36 [WARNING|DP=47|PP=0|TP=0|ip-26-0-171-102]: Repo card metadata block was not found. Setting CardData to empty.
[default3]:07/03/2024 01:02:36 [WARNING|DP=11|PP=0|TP=0|ip-26-0-161-103]: Repo card metadata block was not found. Setting CardData to empty.
[default0]:07/03/2024 01:02:36 [WARNING|DP=8|PP=0|TP=0|ip-26-0-161-103]: Repo card metadata block was not found. Setting CardData to empty.
[default7]:Repo card metadata block was not found. Setting CardData to empty.
[default3]:07/03/2024 01:02:36 [WARNING|DP=3|PP=0|TP=0|ip-26-0-160-225]: Repo card metadata block was not found. Setting CardData to empty.
[default3]:Repo card metadata block was not found. Setting CardData to empty.
[default1]:Repo card metadata block was not found. Setting CardData to empty.
[default7]:07/03/2024 01:02:36 [WARNING|DP=63|PP=0|TP=0|ip-26-0-171-88]: Repo card metadata block was not found. Setting CardData to empty.
[default6]:07/03/2024 01:02:36 [WARNING|DP=62|PP=0|TP=0|ip-26-0-171-88]: Repo card metadata block was not found. Setting CardData to empty.
[default1]:07/03/2024 01:02:36 [WARNING|DP=33|PP=0|TP=0|ip-26-0-162-233]: Repo card metadata block was not found. Setting CardData to empty.
[default2]:07/03/2024 01:02:36 [WARNING|DP=34|PP=0|TP=0|ip-26-0-162-233]: 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.
[default5]:07/03/2024 01:02:36 [WARNING|DP=13|PP=0|TP=0|ip-26-0-161-103]: Repo card metadata block was not found. Setting CardData to empty.
[default0]:07/03/2024 01:02:36 [WARNING|DP=32|PP=0|TP=0|ip-26-0-162-233]: Repo card metadata block was not found. Setting CardData to empty.
[default2]:07/03/2024 01:02:36 [WARNING|DP=26|PP=0|TP=0|ip-26-0-161-78]: Repo card metadata block was not found. Setting CardData to empty.
[default7]:07/03/2024 01:02:36 [WARNING|DP=31|PP=0|TP=0|ip-26-0-161-78]: Repo card metadata block was not found. Setting CardData to empty.
[default5]:07/03/2024 01:02:36 [WARNING|DP=29|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.
[default7]:07/03/2024 01:02:36 [WARNING|DP=7|PP=0|TP=0|ip-26-0-160-225]: Repo card metadata block was not found. Setting CardData to empty.
[default2]:Repo card metadata block was not found. Setting CardData to empty.
[default4]:07/03/2024 01:02:36 [WARNING|DP=36|PP=0|TP=0|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.
[default0]:Repo card metadata block was not found. Setting CardData to empty.
[default2]:07/03/2024 01:02:36 [WARNING|DP=18|PP=0|TP=0|ip-26-0-161-153]: Repo card metadata block was not found. Setting CardData to empty.
[default7]:07/03/2024 01:02:36 [WARNING|DP=39|PP=0|TP=0|ip-26-0-162-233]: Repo card metadata block was not found. Setting CardData to empty.
[default2]:Repo card metadata block was not found. Setting CardData to empty.
[default3]: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.
[default5]:07/03/2024 01:02:36 [WARNING|DP=21|PP=0|TP=0|ip-26-0-161-153]: Repo card metadata block was not found. Setting CardData to empty.
[default1]:07/03/2024 01:02:36 [WARNING|DP=17|PP=0|TP=0|ip-26-0-161-153]: Repo card metadata block was not found. Setting CardData to empty.
[default0]:Repo card metadata block was not found. Setting CardData to empty.
[default1]:Repo card metadata block was not found. Setting CardData to empty.
[default7]:Repo card metadata block was not found. Setting CardData to empty.
[default2]:07/03/2024 01:02:36 [WARNING|DP=42|PP=0|TP=0|ip-26-0-171-102]: Repo card metadata block was not found. Setting CardData to empty.
[default4]:07/03/2024 01:02:36 [WARNING|DP=20|PP=0|TP=0|ip-26-0-161-153]: Repo card metadata block was not found. Setting CardData to empty.
[default3]:Repo card metadata block was not found. Setting CardData to empty.
[default2]: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.
[default7]:Repo card metadata block was not found. Setting CardData to empty.
[default4]:07/03/2024 01:02:36 [WARNING|DP=52|PP=0|TP=0|ip-26-0-171-62]: 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.
[default6]:07/03/2024 01:02:36 [WARNING|DP=22|PP=0|TP=0|ip-26-0-161-153]: Repo card metadata block was not found. Setting CardData to empty.
[default3]:07/03/2024 01:02:36 [WARNING|DP=35|PP=0|TP=0|ip-26-0-162-233]: Repo card metadata block was not found. Setting CardData to empty.
[default6]:07/03/2024 01:02:36 [WARNING|DP=46|PP=0|TP=0|ip-26-0-171-102]: 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 01:02:36 [WARNING|DP=41|PP=0|TP=0|ip-26-0-171-102]: Repo card metadata block was not found. Setting CardData to empty.
[default7]:07/03/2024 01:02:36 [WARNING|DP=55|PP=0|TP=0|ip-26-0-171-62]: Repo card metadata block was not found. Setting CardData to empty.
[default0]:07/03/2024 01:02:36 [WARNING|DP=48|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.
[default7]:Repo card metadata block was not found. Setting CardData to empty.
[default3]:07/03/2024 01:02:36 [WARNING|DP=51|PP=0|TP=0|ip-26-0-171-62]: Repo card metadata block was not found. Setting CardData to empty.
[default6]:07/03/2024 01:02:36 [WARNING|DP=54|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.
[default5]:Repo card metadata block was not found. Setting CardData to empty.
[default2]:Repo card metadata block was not found. Setting CardData to empty.
[default0]:Repo card metadata block was not found. Setting CardData to empty.
[default1]: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]:Repo card metadata block was not found. Setting CardData to empty.
[default5]:Repo card metadata block was not found. Setting CardData to empty.
[default0]:Repo card metadata block was not found. Setting CardData to empty.
[default3]:Repo card metadata block was not found. Setting CardData to empty.
[default4]:Repo card metadata block was not found. Setting CardData to empty.
[default1]:07/03/2024 01:02:36 [WARNING|DP=57|PP=0|TP=0|ip-26-0-171-88]: Repo card metadata block was not found. Setting CardData to empty.
[default5]:07/03/2024 01:02:36 [WARNING|DP=61|PP=0|TP=0|ip-26-0-171-88]: Repo card metadata block was not found. Setting CardData to empty.
[default2]:07/03/2024 01:02:36 [WARNING|DP=2|PP=0|TP=0|ip-26-0-160-225]: Repo card metadata block was not found. Setting CardData to empty.
[default2]:Repo card metadata block was not found. Setting CardData to empty.
[default0]:Repo card metadata block was not found. Setting CardData to empty.
[default1]:07/03/2024 01:02:37 [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 01:02:37 [WARNING|DP=9|PP=0|TP=0|ip-26-0-161-103]: Repo card metadata block was not found. Setting CardData to empty.
[default4]:07/03/2024 01:02:36 [WARNING|DP=12|PP=0|TP=0|ip-26-0-161-103]: Repo card metadata block was not found. Setting CardData to empty.
[default2]:07/03/2024 01:02:36 [WARNING|DP=10|PP=0|TP=0|ip-26-0-161-103]: Repo card metadata block was not found. Setting CardData to empty.
[default0]:07/03/2024 01:02:36 [WARNING|DP=24|PP=0|TP=0|ip-26-0-161-78]: Repo card metadata block was not found. Setting CardData to empty.
[default1]:Repo card metadata block was not found. Setting CardData to empty.
[default5]:Repo card metadata block was not found. Setting CardData to empty.
[default6]:07/03/2024 01:02:36 [WARNING|DP=6|PP=0|TP=0|ip-26-0-160-225]: Repo card metadata block was not found. Setting CardData to empty.
[default2]:Repo card metadata block was not found. Setting CardData to empty.
[default5]:07/03/2024 01:02:37 [WARNING|DP=5|PP=0|TP=0|ip-26-0-160-225]: 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 01:02:37 [WARNING|DP=37|PP=0|TP=0|ip-26-0-162-233]: Repo card metadata block was not found. Setting CardData to empty.
[default6]:07/03/2024 01:02:37 [WARNING|DP=14|PP=0|TP=0|ip-26-0-161-103]: Repo card metadata block was not found. Setting CardData to empty.
[default5]:Repo card metadata block was not found. Setting CardData to empty.
[default5]: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.
[default7]:Repo card metadata block was not found. Setting CardData to empty.
[default0]:07/03/2024 01:02:36 [WARNING|DP=16|PP=0|TP=0|ip-26-0-161-153]: 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 01:02:37 [WARNING|DP=19|PP=0|TP=0|ip-26-0-161-153]: 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 01:02:36 [WARNING|DP=38|PP=0|TP=0|ip-26-0-162-233]: Repo card metadata block was not found. Setting CardData to empty.
[default5]:Repo card metadata block was not found. Setting CardData to empty.
[default1]:Repo card metadata block was not found. Setting CardData to empty.
[default2]:07/03/2024 01:02:37 [WARNING|DP=50|PP=0|TP=0|ip-26-0-171-62]: Repo card metadata block was not found. Setting CardData to empty.
[default1]:07/03/2024 01:02:36 [WARNING|DP=49|PP=0|TP=0|ip-26-0-171-62]: Repo card metadata block was not found. Setting CardData to empty.
[default7]:07/03/2024 01:02:37 [WARNING|DP=23|PP=0|TP=0|ip-26-0-161-153]: Repo card metadata block was not found. Setting CardData to empty.
[default5]:07/03/2024 01:02:37 [WARNING|DP=53|PP=0|TP=0|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.
[default2]:Repo card metadata block was not found. Setting CardData to empty.
[default4]:07/03/2024 01:02:37 [WARNING|DP=28|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.
[default4]:Repo card metadata block was not found. Setting CardData to empty.
[default4]:07/03/2024 01:02:37 [WARNING|DP=4|PP=0|TP=0|ip-26-0-160-225]: Repo card metadata block was not found. Setting CardData to empty.
[default3]:07/03/2024 01:02:37 [WARNING|DP=27|PP=0|TP=0|ip-26-0-161-78]: Repo card metadata block was not found. Setting CardData to empty.
[default3]:Repo card metadata block was not found. Setting CardData to empty.
[default0]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/autograd/graph.py:744: UserWarning: c10d::allreduce_: an autograd kernel was not registered to the Autograd key(s) but we are trying to backprop through it. This may lead to silently incorrect behavior. This behavior is deprecated and will be removed in a future version of PyTorch. If your operator is differentiable, please ensure you have registered an autograd kernel to the correct Autograd key (e.g. DispatchKey::Autograd, DispatchKey::CompositeImplicitAutograd). If your operator is not differentiable, or to squash this warning and use the previous behavior, please register torch::CppFunction::makeFallthrough() to DispatchKey::Autograd. (Triggered internally at ../torch/csrc/autograd/autograd_not_implemented_fallback.cpp:63.)
[default0]:  return Variable._execution_engine.run_backward(  # Calls into the C++ engine to run the backward pass
[default3]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/autograd/graph.py:744: UserWarning: c10d::allreduce_: an autograd kernel was not registered to the Autograd key(s) but we are trying to backprop through it. This may lead to silently incorrect behavior. This behavior is deprecated and will be removed in a future version of PyTorch. If your operator is differentiable, please ensure you have registered an autograd kernel to the correct Autograd key (e.g. DispatchKey::Autograd, DispatchKey::CompositeImplicitAutograd). If your operator is not differentiable, or to squash this warning and use the previous behavior, please register torch::CppFunction::makeFallthrough() to DispatchKey::Autograd. (Triggered internally at ../torch/csrc/autograd/autograd_not_implemented_fallback.cpp:63.)
[default3]:  return Variable._execution_engine.run_backward(  # Calls into the C++ engine to run the backward pass
[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
[default7]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/autograd/graph.py:744: UserWarning: c10d::allreduce_: an autograd kernel was not registered to the Autograd key(s) but we are trying to backprop through it. This may lead to silently incorrect behavior. This behavior is deprecated and will be removed in a future version of PyTorch. If your operator is differentiable, please ensure you have registered an autograd kernel to the correct Autograd key (e.g. DispatchKey::Autograd, DispatchKey::CompositeImplicitAutograd). If your operator is not differentiable, or to squash this warning and use the previous behavior, please register torch::CppFunction::makeFallthrough() to DispatchKey::Autograd. (Triggered internally at ../torch/csrc/autograd/autograd_not_implemented_fallback.cpp:63.)
[default7]:  return Variable._execution_engine.run_backward(  # Calls into the C++ engine to run the backward pass
[default1]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/autograd/graph.py:744: UserWarning: c10d::allreduce_: an autograd kernel was not registered to the Autograd key(s) but we are trying to backprop through it. This may lead to silently incorrect behavior. This behavior is deprecated and will be removed in a future version of PyTorch. If your operator is differentiable, please ensure you have registered an autograd kernel to the correct Autograd key (e.g. DispatchKey::Autograd, DispatchKey::CompositeImplicitAutograd). If your operator is not differentiable, or to squash this warning and use the previous behavior, please register torch::CppFunction::makeFallthrough() to DispatchKey::Autograd. (Triggered internally at ../torch/csrc/autograd/autograd_not_implemented_fallback.cpp:63.)
[default1]:  return Variable._execution_engine.run_backward(  # Calls into the C++ engine to run the backward pass
[default6]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/autograd/graph.py:744: UserWarning: c10d::allreduce_: an autograd kernel was not registered to the Autograd key(s) but we are trying to backprop through it. This may lead to silently incorrect behavior. This behavior is deprecated and will be removed in a future version of PyTorch. If your operator is differentiable, please ensure you have registered an autograd kernel to the correct Autograd key (e.g. DispatchKey::Autograd, DispatchKey::CompositeImplicitAutograd). If your operator is not differentiable, or to squash this warning and use the previous behavior, please register torch::CppFunction::makeFallthrough() to DispatchKey::Autograd. (Triggered internally at ../torch/csrc/autograd/autograd_not_implemented_fallback.cpp:63.)
[default6]:  return Variable._execution_engine.run_backward(  # Calls into the C++ engine to run the backward pass
[default5]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/autograd/graph.py:744: UserWarning: c10d::allreduce_: an autograd kernel was not registered to the Autograd key(s) but we are trying to backprop through it. This may lead to silently incorrect behavior. This behavior is deprecated and will be removed in a future version of PyTorch. If your operator is differentiable, please ensure you have registered an autograd kernel to the correct Autograd key (e.g. DispatchKey::Autograd, DispatchKey::CompositeImplicitAutograd). If your operator is not differentiable, or to squash this warning and use the previous behavior, please register torch::CppFunction::makeFallthrough() to DispatchKey::Autograd. (Triggered internally at ../torch/csrc/autograd/autograd_not_implemented_fallback.cpp:63.)
[default5]:  return Variable._execution_engine.run_backward(  # Calls into the C++ engine to run the backward pass
[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
[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
[default1]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/autograd/graph.py:744: UserWarning: c10d::allreduce_: an autograd kernel was not registered to the Autograd key(s) but we are trying to backprop through it. This may lead to silently incorrect behavior. This behavior is deprecated and will be removed in a future version of PyTorch. If your operator is differentiable, please ensure you have registered an autograd kernel to the correct Autograd key (e.g. DispatchKey::Autograd, DispatchKey::CompositeImplicitAutograd). If your operator is not differentiable, or to squash this warning and use the previous behavior, please register torch::CppFunction::makeFallthrough() to DispatchKey::Autograd. (Triggered internally at ../torch/csrc/autograd/autograd_not_implemented_fallback.cpp:63.)
[default1]:  return Variable._execution_engine.run_backward(  # Calls into the C++ engine to run the backward pass
[default4]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/autograd/graph.py:744: UserWarning: c10d::allreduce_: an autograd kernel was not registered to the Autograd key(s) but we are trying to backprop through it. This may lead to silently incorrect behavior. This behavior is deprecated and will be removed in a future version of PyTorch. If your operator is differentiable, please ensure you have registered an autograd kernel to the correct Autograd key (e.g. DispatchKey::Autograd, DispatchKey::CompositeImplicitAutograd). If your operator is not differentiable, or to squash this warning and use the previous behavior, please register torch::CppFunction::makeFallthrough() to DispatchKey::Autograd. (Triggered internally at ../torch/csrc/autograd/autograd_not_implemented_fallback.cpp:63.)
[default4]:  return Variable._execution_engine.run_backward(  # Calls into the C++ engine to run the backward pass
[default6]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/autograd/graph.py:744: UserWarning: c10d::allreduce_: an autograd kernel was not registered to the Autograd key(s) but we are trying to backprop through it. This may lead to silently incorrect behavior. This behavior is deprecated and will be removed in a future version of PyTorch. If your operator is differentiable, please ensure you have registered an autograd kernel to the correct Autograd key (e.g. DispatchKey::Autograd, DispatchKey::CompositeImplicitAutograd). If your operator is not differentiable, or to squash this warning and use the previous behavior, please register torch::CppFunction::makeFallthrough() to DispatchKey::Autograd. (Triggered internally at ../torch/csrc/autograd/autograd_not_implemented_fallback.cpp:63.)
[default6]:  return Variable._execution_engine.run_backward(  # Calls into the C++ engine to run the backward pass
[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
[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
[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
[default2]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/autograd/graph.py:744: UserWarning: c10d::allreduce_: an autograd kernel was not registered to the Autograd key(s) but we are trying to backprop through it. This may lead to silently incorrect behavior. This behavior is deprecated and will be removed in a future version of PyTorch. If your operator is differentiable, please ensure you have registered an autograd kernel to the correct Autograd key (e.g. DispatchKey::Autograd, DispatchKey::CompositeImplicitAutograd). If your operator is not differentiable, or to squash this warning and use the previous behavior, please register torch::CppFunction::makeFallthrough() to DispatchKey::Autograd. (Triggered internally at ../torch/csrc/autograd/autograd_not_implemented_fallback.cpp:63.)
[default2]:  return Variable._execution_engine.run_backward(  # Calls into the C++ engine to run the backward pass
[default1]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/autograd/graph.py:744: UserWarning: c10d::allreduce_: an autograd kernel was not registered to the Autograd key(s) but we are trying to backprop through it. This may lead to silently incorrect behavior. This behavior is deprecated and will be removed in a future version of PyTorch. If your operator is differentiable, please ensure you have registered an autograd kernel to the correct Autograd key (e.g. DispatchKey::Autograd, DispatchKey::CompositeImplicitAutograd). If your operator is not differentiable, or to squash this warning and use the previous behavior, please register torch::CppFunction::makeFallthrough() to DispatchKey::Autograd. (Triggered internally at ../torch/csrc/autograd/autograd_not_implemented_fallback.cpp:63.)
[default1]:  return Variable._execution_engine.run_backward(  # Calls into the C++ engine to run the backward pass
[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
[default6]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/autograd/graph.py:744: UserWarning: c10d::allreduce_: an autograd kernel was not registered to the Autograd key(s) but we are trying to backprop through it. This may lead to silently incorrect behavior. This behavior is deprecated and will be removed in a future version of PyTorch. If your operator is differentiable, please ensure you have registered an autograd kernel to the correct Autograd key (e.g. DispatchKey::Autograd, DispatchKey::CompositeImplicitAutograd). If your operator is not differentiable, or to squash this warning and use the previous behavior, please register torch::CppFunction::makeFallthrough() to DispatchKey::Autograd. (Triggered internally at ../torch/csrc/autograd/autograd_not_implemented_fallback.cpp:63.)
[default6]:  return Variable._execution_engine.run_backward(  # Calls into the C++ engine to run the backward pass
[default7]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/autograd/graph.py:744: UserWarning: c10d::allreduce_: an autograd kernel was not registered to the Autograd key(s) but we are trying to backprop through it. This may lead to silently incorrect behavior. This behavior is deprecated and will be removed in a future version of PyTorch. If your operator is differentiable, please ensure you have registered an autograd kernel to the correct Autograd key (e.g. DispatchKey::Autograd, DispatchKey::CompositeImplicitAutograd). If your operator is not differentiable, or to squash this warning and use the previous behavior, please register torch::CppFunction::makeFallthrough() to DispatchKey::Autograd. (Triggered internally at ../torch/csrc/autograd/autograd_not_implemented_fallback.cpp:63.)
[default7]:  return Variable._execution_engine.run_backward(  # Calls into the C++ engine to run the backward pass
[default1]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/autograd/graph.py:744: UserWarning: c10d::allreduce_: an autograd kernel was not registered to the Autograd key(s) but we are trying to backprop through it. This may lead to silently incorrect behavior. This behavior is deprecated and will be removed in a future version of PyTorch. If your operator is differentiable, please ensure you have registered an autograd kernel to the correct Autograd key (e.g. DispatchKey::Autograd, DispatchKey::CompositeImplicitAutograd). If your operator is not differentiable, or to squash this warning and use the previous behavior, please register torch::CppFunction::makeFallthrough() to DispatchKey::Autograd. (Triggered internally at ../torch/csrc/autograd/autograd_not_implemented_fallback.cpp:63.)
[default1]:  return Variable._execution_engine.run_backward(  # Calls into the C++ engine to run the backward pass
[default3]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/autograd/graph.py:744: UserWarning: c10d::allreduce_: an autograd kernel was not registered to the Autograd key(s) but we are trying to backprop through it. This may lead to silently incorrect behavior. This behavior is deprecated and will be removed in a future version of PyTorch. If your operator is differentiable, please ensure you have registered an autograd kernel to the correct Autograd key (e.g. DispatchKey::Autograd, DispatchKey::CompositeImplicitAutograd). If your operator is not differentiable, or to squash this warning and use the previous behavior, please register torch::CppFunction::makeFallthrough() to DispatchKey::Autograd. (Triggered internally at ../torch/csrc/autograd/autograd_not_implemented_fallback.cpp:63.)
[default3]:  return Variable._execution_engine.run_backward(  # Calls into the C++ engine to run the backward pass
[default3]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/autograd/graph.py:744: UserWarning: c10d::allreduce_: an autograd kernel was not registered to the Autograd key(s) but we are trying to backprop through it. This may lead to silently incorrect behavior. This behavior is deprecated and will be removed in a future version of PyTorch. If your operator is differentiable, please ensure you have registered an autograd kernel to the correct Autograd key (e.g. DispatchKey::Autograd, DispatchKey::CompositeImplicitAutograd). If your operator is not differentiable, or to squash this warning and use the previous behavior, please register torch::CppFunction::makeFallthrough() to DispatchKey::Autograd. (Triggered internally at ../torch/csrc/autograd/autograd_not_implemented_fallback.cpp:63.)
[default3]:  return Variable._execution_engine.run_backward(  # Calls into the C++ engine to run the backward pass
[default1]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/autograd/graph.py:744: UserWarning: c10d::allreduce_: an autograd kernel was not registered to the Autograd key(s) but we are trying to backprop through it. This may lead to silently incorrect behavior. This behavior is deprecated and will be removed in a future version of PyTorch. If your operator is differentiable, please ensure you have registered an autograd kernel to the correct Autograd key (e.g. DispatchKey::Autograd, DispatchKey::CompositeImplicitAutograd). If your operator is not differentiable, or to squash this warning and use the previous behavior, please register torch::CppFunction::makeFallthrough() to DispatchKey::Autograd. (Triggered internally at ../torch/csrc/autograd/autograd_not_implemented_fallback.cpp:63.)
[default1]:  return Variable._execution_engine.run_backward(  # Calls into the C++ engine to run the backward pass
[default7]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/autograd/graph.py:744: UserWarning: c10d::allreduce_: an autograd kernel was not registered to the Autograd key(s) but we are trying to backprop through it. This may lead to silently incorrect behavior. This behavior is deprecated and will be removed in a future version of PyTorch. If your operator is differentiable, please ensure you have registered an autograd kernel to the correct Autograd key (e.g. DispatchKey::Autograd, DispatchKey::CompositeImplicitAutograd). If your operator is not differentiable, or to squash this warning and use the previous behavior, please register torch::CppFunction::makeFallthrough() to DispatchKey::Autograd. (Triggered internally at ../torch/csrc/autograd/autograd_not_implemented_fallback.cpp:63.)
[default7]:  return Variable._execution_engine.run_backward(  # Calls into the C++ engine to run the backward pass
[default0]:07/03/2024 01:02:41 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:  Memory usage: 6516.81MiB. Peak allocated 24339.79MiB. Peak reserved: 25206.00MiB
[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
[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
[default6]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/autograd/graph.py:744: UserWarning: c10d::allreduce_: an autograd kernel was not registered to the Autograd key(s) but we are trying to backprop through it. This may lead to silently incorrect behavior. This behavior is deprecated and will be removed in a future version of PyTorch. If your operator is differentiable, please ensure you have registered an autograd kernel to the correct Autograd key (e.g. DispatchKey::Autograd, DispatchKey::CompositeImplicitAutograd). If your operator is not differentiable, or to squash this warning and use the previous behavior, please register torch::CppFunction::makeFallthrough() to DispatchKey::Autograd. (Triggered internally at ../torch/csrc/autograd/autograd_not_implemented_fallback.cpp:63.)
[default6]:  return Variable._execution_engine.run_backward(  # Calls into the C++ engine to run the backward pass
[default4]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/autograd/graph.py:744: UserWarning: c10d::allreduce_: an autograd kernel was not registered to the Autograd key(s) but we are trying to backprop through it. This may lead to silently incorrect behavior. This behavior is deprecated and will be removed in a future version of PyTorch. If your operator is differentiable, please ensure you have registered an autograd kernel to the correct Autograd key (e.g. DispatchKey::Autograd, DispatchKey::CompositeImplicitAutograd). If your operator is not differentiable, or to squash this warning and use the previous behavior, please register torch::CppFunction::makeFallthrough() to DispatchKey::Autograd. (Triggered internally at ../torch/csrc/autograd/autograd_not_implemented_fallback.cpp:63.)
[default4]:  return Variable._execution_engine.run_backward(  # Calls into the C++ engine to run the backward pass
[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
[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
[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
[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
[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
[default1]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/autograd/graph.py:744: UserWarning: c10d::allreduce_: an autograd kernel was not registered to the Autograd key(s) but we are trying to backprop through it. This may lead to silently incorrect behavior. This behavior is deprecated and will be removed in a future version of PyTorch. If your operator is differentiable, please ensure you have registered an autograd kernel to the correct Autograd key (e.g. DispatchKey::Autograd, DispatchKey::CompositeImplicitAutograd). If your operator is not differentiable, or to squash this warning and use the previous behavior, please register torch::CppFunction::makeFallthrough() to DispatchKey::Autograd. (Triggered internally at ../torch/csrc/autograd/autograd_not_implemented_fallback.cpp:63.)
[default1]:  return Variable._execution_engine.run_backward(  # Calls into the C++ engine to run the backward pass
[default7]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/autograd/graph.py:744: UserWarning: c10d::allreduce_: an autograd kernel was not registered to the Autograd key(s) but we are trying to backprop through it. This may lead to silently incorrect behavior. This behavior is deprecated and will be removed in a future version of PyTorch. If your operator is differentiable, please ensure you have registered an autograd kernel to the correct Autograd key (e.g. DispatchKey::Autograd, DispatchKey::CompositeImplicitAutograd). If your operator is not differentiable, or to squash this warning and use the previous behavior, please register torch::CppFunction::makeFallthrough() to DispatchKey::Autograd. (Triggered internally at ../torch/csrc/autograd/autograd_not_implemented_fallback.cpp:63.)
[default7]:  return Variable._execution_engine.run_backward(  # Calls into the C++ engine to run the backward pass
[default6]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/autograd/graph.py:744: UserWarning: c10d::allreduce_: an autograd kernel was not registered to the Autograd key(s) but we are trying to backprop through it. This may lead to silently incorrect behavior. This behavior is deprecated and will be removed in a future version of PyTorch. If your operator is differentiable, please ensure you have registered an autograd kernel to the correct Autograd key (e.g. DispatchKey::Autograd, DispatchKey::CompositeImplicitAutograd). If your operator is not differentiable, or to squash this warning and use the previous behavior, please register torch::CppFunction::makeFallthrough() to DispatchKey::Autograd. (Triggered internally at ../torch/csrc/autograd/autograd_not_implemented_fallback.cpp:63.)
[default6]:  return Variable._execution_engine.run_backward(  # Calls into the C++ engine to run the backward pass
[default7]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/autograd/graph.py:744: UserWarning: c10d::allreduce_: an autograd kernel was not registered to the Autograd key(s) but we are trying to backprop through it. This may lead to silently incorrect behavior. This behavior is deprecated and will be removed in a future version of PyTorch. If your operator is differentiable, please ensure you have registered an autograd kernel to the correct Autograd key (e.g. DispatchKey::Autograd, DispatchKey::CompositeImplicitAutograd). If your operator is not differentiable, or to squash this warning and use the previous behavior, please register torch::CppFunction::makeFallthrough() to DispatchKey::Autograd. (Triggered internally at ../torch/csrc/autograd/autograd_not_implemented_fallback.cpp:63.)
[default7]:  return Variable._execution_engine.run_backward(  # Calls into the C++ engine to run the backward pass
[default0]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/autograd/graph.py:744: UserWarning: c10d::allreduce_: an autograd kernel was not registered to the Autograd key(s) but we are trying to backprop through it. This may lead to silently incorrect behavior. This behavior is deprecated and will be removed in a future version of PyTorch. If your operator is differentiable, please ensure you have registered an autograd kernel to the correct Autograd key (e.g. DispatchKey::Autograd, DispatchKey::CompositeImplicitAutograd). If your operator is not differentiable, or to squash this warning and use the previous behavior, please register torch::CppFunction::makeFallthrough() to DispatchKey::Autograd. (Triggered internally at ../torch/csrc/autograd/autograd_not_implemented_fallback.cpp:63.)
[default0]:  return Variable._execution_engine.run_backward(  # Calls into the C++ engine to run the backward pass
[default5]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/autograd/graph.py:744: UserWarning: c10d::allreduce_: an autograd kernel was not registered to the Autograd key(s) but we are trying to backprop through it. This may lead to silently incorrect behavior. This behavior is deprecated and will be removed in a future version of PyTorch. If your operator is differentiable, please ensure you have registered an autograd kernel to the correct Autograd key (e.g. DispatchKey::Autograd, DispatchKey::CompositeImplicitAutograd). If your operator is not differentiable, or to squash this warning and use the previous behavior, please register torch::CppFunction::makeFallthrough() to DispatchKey::Autograd. (Triggered internally at ../torch/csrc/autograd/autograd_not_implemented_fallback.cpp:63.)
[default5]:  return Variable._execution_engine.run_backward(  # Calls into the C++ engine to run the backward pass
[default3]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/autograd/graph.py:744: UserWarning: c10d::allreduce_: an autograd kernel was not registered to the Autograd key(s) but we are trying to backprop through it. This may lead to silently incorrect behavior. This behavior is deprecated and will be removed in a future version of PyTorch. If your operator is differentiable, please ensure you have registered an autograd kernel to the correct Autograd key (e.g. DispatchKey::Autograd, DispatchKey::CompositeImplicitAutograd). If your operator is not differentiable, or to squash this warning and use the previous behavior, please register torch::CppFunction::makeFallthrough() to DispatchKey::Autograd. (Triggered internally at ../torch/csrc/autograd/autograd_not_implemented_fallback.cpp:63.)
[default3]:  return Variable._execution_engine.run_backward(  # Calls into the C++ engine to run the backward pass
[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
[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
[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]:/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
[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
[default3]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/autograd/graph.py:744: UserWarning: c10d::allreduce_: an autograd kernel was not registered to the Autograd key(s) but we are trying to backprop through it. This may lead to silently incorrect behavior. This behavior is deprecated and will be removed in a future version of PyTorch. If your operator is differentiable, please ensure you have registered an autograd kernel to the correct Autograd key (e.g. DispatchKey::Autograd, DispatchKey::CompositeImplicitAutograd). If your operator is not differentiable, or to squash this warning and use the previous behavior, please register torch::CppFunction::makeFallthrough() to DispatchKey::Autograd. (Triggered internally at ../torch/csrc/autograd/autograd_not_implemented_fallback.cpp:63.)
[default3]:  return Variable._execution_engine.run_backward(  # Calls into the C++ engine to run the backward pass
[default1]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/autograd/graph.py:744: UserWarning: c10d::allreduce_: an autograd kernel was not registered to the Autograd key(s) but we are trying to backprop through it. This may lead to silently incorrect behavior. This behavior is deprecated and will be removed in a future version of PyTorch. If your operator is differentiable, please ensure you have registered an autograd kernel to the correct Autograd key (e.g. DispatchKey::Autograd, DispatchKey::CompositeImplicitAutograd). If your operator is not differentiable, or to squash this warning and use the previous behavior, please register torch::CppFunction::makeFallthrough() to DispatchKey::Autograd. (Triggered internally at ../torch/csrc/autograd/autograd_not_implemented_fallback.cpp:63.)
[default1]:  return Variable._execution_engine.run_backward(  # Calls into the C++ engine to run the backward pass
[default7]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/autograd/graph.py:744: UserWarning: c10d::allreduce_: an autograd kernel was not registered to the Autograd key(s) but we are trying to backprop through it. This may lead to silently incorrect behavior. This behavior is deprecated and will be removed in a future version of PyTorch. If your operator is differentiable, please ensure you have registered an autograd kernel to the correct Autograd key (e.g. DispatchKey::Autograd, DispatchKey::CompositeImplicitAutograd). If your operator is not differentiable, or to squash this warning and use the previous behavior, please register torch::CppFunction::makeFallthrough() to DispatchKey::Autograd. (Triggered internally at ../torch/csrc/autograd/autograd_not_implemented_fallback.cpp:63.)
[default7]:  return Variable._execution_engine.run_backward(  # Calls into the C++ engine to run the backward pass
[default6]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/autograd/graph.py:744: UserWarning: c10d::allreduce_: an autograd kernel was not registered to the Autograd key(s) but we are trying to backprop through it. This may lead to silently incorrect behavior. This behavior is deprecated and will be removed in a future version of PyTorch. If your operator is differentiable, please ensure you have registered an autograd kernel to the correct Autograd key (e.g. DispatchKey::Autograd, DispatchKey::CompositeImplicitAutograd). If your operator is not differentiable, or to squash this warning and use the previous behavior, please register torch::CppFunction::makeFallthrough() to DispatchKey::Autograd. (Triggered internally at ../torch/csrc/autograd/autograd_not_implemented_fallback.cpp:63.)
[default6]:  return Variable._execution_engine.run_backward(  # Calls into the C++ engine to run the backward pass
[default4]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/autograd/graph.py:744: UserWarning: c10d::allreduce_: an autograd kernel was not registered to the Autograd key(s) but we are trying to backprop through it. This may lead to silently incorrect behavior. This behavior is deprecated and will be removed in a future version of PyTorch. If your operator is differentiable, please ensure you have registered an autograd kernel to the correct Autograd key (e.g. DispatchKey::Autograd, DispatchKey::CompositeImplicitAutograd). If your operator is not differentiable, or to squash this warning and use the previous behavior, please register torch::CppFunction::makeFallthrough() to DispatchKey::Autograd. (Triggered internally at ../torch/csrc/autograd/autograd_not_implemented_fallback.cpp:63.)
[default4]:  return Variable._execution_engine.run_backward(  # Calls into the C++ engine to run the backward pass
[default0]:07/03/2024 01:02:49 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: iteration: 1 / 20 | consumed_tokens: 4.19M | elapsed_time_per_iteration_ms: 12.6K | tokens_per_sec: 333K | tokens_per_sec_per_gpu: 5.21K | global_batch_size: 1.02K | lm_loss: 11.3 | lr: 0.0001 | model_tflops_per_gpu: 47.2 | hardware_tflops_per_gpu: 47.2 | grad_norm: 33.1 | cuda_memory_allocated: 6.97G | cuda_max_memory_reserved: 28.7G | hd_total_memory_tb: 312G | hd_used_memory_tb: 66.7G | hd_free_memory_tb: 245G
[default0]:07/03/2024 01:02:49 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:  Memory usage: 6649.20MiB. Peak allocated 10915.31MiB. Peak reserved: 27418.00MiB
[default0]:07/03/2024 01:02:51 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:  Memory usage: 6649.21MiB. Peak allocated 24472.19MiB. Peak reserved: 27444.00MiB
[default0]:07/03/2024 01:02:57 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: iteration: 2 / 20 | consumed_tokens: 8.39M | elapsed_time_per_iteration_ms: 8.38K | tokens_per_sec: 500K | tokens_per_sec_per_gpu: 7.82K | global_batch_size: 1.02K | lm_loss: 11.3 | lr: 9.53e-05 | model_tflops_per_gpu: 70.9 | hardware_tflops_per_gpu: 70.9 | grad_norm: 33.3 | cuda_memory_allocated: 6.97G | cuda_max_memory_reserved: 28.8G | hd_total_memory_tb: 312G | hd_used_memory_tb: 66.7G | hd_free_memory_tb: 245G
[default0]:07/03/2024 01:02:57 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:  Memory usage: 6649.20MiB. Peak allocated 10915.32MiB. Peak reserved: 27444.00MiB
[default0]:07/03/2024 01:02:59 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:  Memory usage: 6649.21MiB. Peak allocated 24472.19MiB. Peak reserved: 27444.00MiB
[default0]:07/03/2024 01:03:06 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: iteration: 3 / 20 | consumed_tokens: 12.6M | elapsed_time_per_iteration_ms: 8.25K | tokens_per_sec: 508K | tokens_per_sec_per_gpu: 7.94K | global_batch_size: 1.02K | lm_loss: 16 | lr: 9.05e-05 | model_tflops_per_gpu: 72.1 | hardware_tflops_per_gpu: 72.1 | grad_norm: 249 | cuda_memory_allocated: 6.97G | cuda_max_memory_reserved: 28.8G | hd_total_memory_tb: 312G | hd_used_memory_tb: 66.7G | hd_free_memory_tb: 245G
[default0]:07/03/2024 01:03:06 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:  Memory usage: 6649.20MiB. Peak allocated 10915.32MiB. Peak reserved: 27444.00MiB
[default0]:STAGE:2024-07-03 01:03:06 1777729:1777729 ActivityProfilerController.cpp:314] Completed Stage: Warm Up
[default0]:07/03/2024 01:03:07 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:  Memory usage: 6649.21MiB. Peak allocated 24472.19MiB. Peak reserved: 27444.00MiB
[default0]:07/03/2024 01:03:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: iteration: 4 / 20 | consumed_tokens: 16.8M | elapsed_time_per_iteration_ms: 8.39K | tokens_per_sec: 500K | tokens_per_sec_per_gpu: 7.81K | global_batch_size: 1.02K | lm_loss: 15.1 | lr: 8.58e-05 | model_tflops_per_gpu: 70.9 | hardware_tflops_per_gpu: 70.9 | grad_norm: 41.8 | cuda_memory_allocated: 6.97G | cuda_max_memory_reserved: 28.8G | hd_total_memory_tb: 312G | hd_used_memory_tb: 66.7G | hd_free_memory_tb: 245G
[default0]:07/03/2024 01:03:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:  Memory usage: 6649.20MiB. Peak allocated 10915.32MiB. Peak reserved: 27444.00MiB
[default0]:07/03/2024 01:03:22 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: iteration: 5 / 20 | consumed_tokens: 21M | elapsed_time_per_iteration_ms: 8.39K | tokens_per_sec: 500K | tokens_per_sec_per_gpu: 7.81K | global_batch_size: 1.02K | lm_loss: 10.8 | lr: 8.11e-05 | model_tflops_per_gpu: 70.9 | hardware_tflops_per_gpu: 70.9 | grad_norm: 25.9
[default0]:07/03/2024 01:03:22 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:  Memory usage: 6649.20MiB. Peak allocated 24472.19MiB. Peak reserved: 27444.00MiB
[default0]:07/03/2024 01:03:31 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: iteration: 6 / 20 | consumed_tokens: 25.2M | elapsed_time_per_iteration_ms: 8.33K | tokens_per_sec: 503K | tokens_per_sec_per_gpu: 7.86K | global_batch_size: 1.02K | lm_loss: 10.8 | lr: 7.63e-05 | model_tflops_per_gpu: 71.4 | hardware_tflops_per_gpu: 71.4 | grad_norm: 18.8
[default0]:STAGE:2024-07-03 01:03:35 1777729:1777729 ActivityProfilerController.cpp:320] Completed Stage: Collection
[default0]:STAGE:2024-07-03 01:03:36 1777729:1777729 ActivityProfilerController.cpp:324] Completed Stage: Post Processing
[default0]:07/03/2024 01:04:15 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:  Memory usage: 6649.20MiB. Peak allocated 24472.19MiB. Peak reserved: 27444.00MiB
[default0]:07/03/2024 01:04:17 [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: 30.8K | 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: 7.96
[default0]:07/03/2024 01:04:17 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:  Memory usage: 6649.20MiB. Peak allocated 24472.19MiB. Peak reserved: 27444.00MiB
[default0]:07/03/2024 01:04:26 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: iteration: 8 / 20 | consumed_tokens: 33.6M | elapsed_time_per_iteration_ms: 8.34K | tokens_per_sec: 503K | tokens_per_sec_per_gpu: 7.86K | global_batch_size: 1.02K | lm_loss: 9.15 | lr: 6.68e-05 | model_tflops_per_gpu: 71.3 | hardware_tflops_per_gpu: 71.3 | grad_norm: 6.46
[default0]:07/03/2024 01:04:26 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:  Memory usage: 6649.20MiB. Peak allocated 24472.19MiB. Peak reserved: 27444.00MiB
[default0]:07/03/2024 01:04:34 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: iteration: 9 / 20 | consumed_tokens: 37.7M | elapsed_time_per_iteration_ms: 8.25K | tokens_per_sec: 509K | tokens_per_sec_per_gpu: 7.95K | global_batch_size: 1.02K | lm_loss: 11.1 | lr: 6.21e-05 | model_tflops_per_gpu: 72.1 | hardware_tflops_per_gpu: 72.1 | grad_norm: 59
[default0]:07/03/2024 01:04:34 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:  Memory usage: 6649.20MiB. Peak allocated 24472.19MiB. Peak reserved: 27444.00MiB
[default0]:07/03/2024 01:04:42 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: iteration: 10 / 20 | consumed_tokens: 41.9M | elapsed_time_per_iteration_ms: 8.26K | tokens_per_sec: 508K | tokens_per_sec_per_gpu: 7.93K | global_batch_size: 1.02K | lm_loss: 9.52 | lr: 5.74e-05 | model_tflops_per_gpu: 72 | hardware_tflops_per_gpu: 72 | grad_norm: 43.1
[default0]:07/03/2024 01:04:42 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:  Memory usage: 6649.20MiB. Peak allocated 24472.19MiB. Peak reserved: 27444.00MiB
[default0]:07/03/2024 01:04:51 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: iteration: 11 / 20 | consumed_tokens: 46.1M | elapsed_time_per_iteration_ms: 8.36K | tokens_per_sec: 502K | tokens_per_sec_per_gpu: 7.84K | global_batch_size: 1.02K | lm_loss: 8.08 | lr: 5.26e-05 | model_tflops_per_gpu: 71.2 | hardware_tflops_per_gpu: 71.2 | grad_norm: 8.48
[default0]:07/03/2024 01:04:51 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:  Memory usage: 6649.20MiB. Peak allocated 24472.19MiB. Peak reserved: 27444.00MiB
[default0]:07/03/2024 01:04:59 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: iteration: 12 / 20 | consumed_tokens: 50.3M | elapsed_time_per_iteration_ms: 8.25K | tokens_per_sec: 509K | tokens_per_sec_per_gpu: 7.95K | global_batch_size: 1.02K | lm_loss: 7.85 | lr: 4.79e-05 | model_tflops_per_gpu: 72.1 | hardware_tflops_per_gpu: 72.1 | grad_norm: 5.1
[default0]:07/03/2024 01:04:59 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:  Memory usage: 6649.20MiB. Peak allocated 24472.19MiB. Peak reserved: 27444.00MiB
[default0]:07/03/2024 01:05:07 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: iteration: 13 / 20 | consumed_tokens: 54.5M | elapsed_time_per_iteration_ms: 8.3K | tokens_per_sec: 506K | tokens_per_sec_per_gpu: 7.9K | global_batch_size: 1.02K | lm_loss: 7.7 | lr: 4.32e-05 | model_tflops_per_gpu: 71.7 | hardware_tflops_per_gpu: 71.7 | grad_norm: 4.76
[default0]:07/03/2024 01:05:07 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:  Memory usage: 6649.20MiB. Peak allocated 24472.19MiB. Peak reserved: 27444.00MiB
[default0]:07/03/2024 01:05:16 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: iteration: 14 / 20 | consumed_tokens: 58.7M | elapsed_time_per_iteration_ms: 8.39K | tokens_per_sec: 500K | tokens_per_sec_per_gpu: 7.81K | global_batch_size: 1.02K | lm_loss: 7.55 | lr: 3.84e-05 | model_tflops_per_gpu: 70.9 | hardware_tflops_per_gpu: 70.9 | grad_norm: 5.08
[default0]:07/03/2024 01:05:16 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:  Memory usage: 6649.20MiB. Peak allocated 24472.19MiB. Peak reserved: 27444.00MiB
[default0]:07/03/2024 01:05:24 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: iteration: 15 / 20 | consumed_tokens: 62.9M | elapsed_time_per_iteration_ms: 8.34K | tokens_per_sec: 503K | tokens_per_sec_per_gpu: 7.86K | global_batch_size: 1.02K | lm_loss: 7.4 | lr: 3.37e-05 | model_tflops_per_gpu: 71.3 | hardware_tflops_per_gpu: 71.3 | grad_norm: 5.14
[default0]:07/03/2024 01:05:24 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:  Memory usage: 6649.20MiB. Peak allocated 24472.19MiB. Peak reserved: 27444.00MiB
[default0]:07/03/2024 01:05:32 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: iteration: 16 / 20 | consumed_tokens: 67.1M | elapsed_time_per_iteration_ms: 8.35K | tokens_per_sec: 502K | tokens_per_sec_per_gpu: 7.85K | global_batch_size: 1.02K | lm_loss: 7.3 | lr: 2.89e-05 | model_tflops_per_gpu: 71.2 | hardware_tflops_per_gpu: 71.2 | grad_norm: 5.23
[default0]:07/03/2024 01:05:32 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:  Memory usage: 6649.20MiB. Peak allocated 24472.19MiB. Peak reserved: 27444.00MiB
[default0]:07/03/2024 01:05:41 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: iteration: 17 / 20 | consumed_tokens: 71.3M | elapsed_time_per_iteration_ms: 8.31K | tokens_per_sec: 505K | tokens_per_sec_per_gpu: 7.88K | global_batch_size: 1.02K | lm_loss: 7.23 | lr: 2.42e-05 | model_tflops_per_gpu: 71.5 | hardware_tflops_per_gpu: 71.5 | grad_norm: 5.28
[default0]:07/03/2024 01:05:41 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:  Memory usage: 6649.20MiB. Peak allocated 24472.19MiB. Peak reserved: 27444.00MiB
[default0]:07/03/2024 01:05:49 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: iteration: 18 / 20 | consumed_tokens: 75.5M | elapsed_time_per_iteration_ms: 8.23K | tokens_per_sec: 510K | tokens_per_sec_per_gpu: 7.97K | global_batch_size: 1.02K | lm_loss: 7.15 | lr: 1.95e-05 | model_tflops_per_gpu: 72.3 | hardware_tflops_per_gpu: 72.3 | grad_norm: 5.06
[default0]:07/03/2024 01:05:49 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:  Memory usage: 6649.20MiB. Peak allocated 24472.19MiB. Peak reserved: 27444.00MiB
[default0]:07/03/2024 01:05:57 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: iteration: 19 / 20 | consumed_tokens: 79.7M | elapsed_time_per_iteration_ms: 8.27K | tokens_per_sec: 507K | tokens_per_sec_per_gpu: 7.92K | global_batch_size: 1.02K | lm_loss: 7.08 | lr: 1.47e-05 | model_tflops_per_gpu: 71.9 | hardware_tflops_per_gpu: 71.9 | grad_norm: 3.86
[default0]:07/03/2024 01:05:57 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:  Memory usage: 6649.20MiB. Peak allocated 24472.19MiB. Peak reserved: 27444.00MiB
[default0]:07/03/2024 01:06:05 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: iteration: 20 / 20 | consumed_tokens: 83.9M | elapsed_time_per_iteration_ms: 8.33K | tokens_per_sec: 503K | tokens_per_sec_per_gpu: 7.86K | global_batch_size: 1.02K | lm_loss: 7.03 | lr: 1e-05 | model_tflops_per_gpu: 71.3 | hardware_tflops_per_gpu: 71.3 | grad_norm: 2.87
W0703 01:06:27.076000 140547401557760 torch/distributed/elastic/rendezvous/dynamic_rendezvous.py:1252] The node 'ip-26-0-171-88.ec2.internal_881444_0' has failed to send a keep-alive heartbeat to the rendezvous 'none' due to an error of type RendezvousTimeoutError.
W0703 01:06:27.146000 140653719533376 torch/distributed/elastic/rendezvous/dynamic_rendezvous.py:1203] The node 'ip-26-0-161-153.ec2.internal_1419394_0' has failed to shutdown the rendezvous 'none' due to an error of type RendezvousConnectionError.
W0703 01:06:27.146000 139880512571200 torch/distributed/elastic/rendezvous/dynamic_rendezvous.py:1203] The node 'ip-26-0-161-103.ec2.internal_868134_0' has failed to shutdown the rendezvous 'none' due to an error of type RendezvousConnectionError.
W0703 01:06:27.151000 140653719533376 torch/distributed/elastic/rendezvous/dynamic_rendezvous.py:1203] The node 'ip-26-0-161-153.ec2.internal_1419394_0' has failed to shutdown the rendezvous 'none' due to an error of type RendezvousConnectionError.
W0703 01:06:27.151000 139880512571200 torch/distributed/elastic/rendezvous/dynamic_rendezvous.py:1203] The node 'ip-26-0-161-103.ec2.internal_868134_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-64_tp-1_pp-1_mbz-2/profiler/ip-26-0-160-225_1777729.1719968648431741219.pt.trace.json
Results written to /fsx/ferdinandmom/ferdinand-hf/bench_cluster/results/llama-1B/64_GPUS/dp-64_tp-1_pp-1_mbz-2/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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