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START TIME: Wed Jul  3 03:45:50 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 03:45:54.053000 140249022318400 torch/distributed/run.py:757] 
W0703 03:45:54.053000 140249022318400 torch/distributed/run.py:757] *****************************************
W0703 03:45:54.053000 140249022318400 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 03:45:54.053000 140249022318400 torch/distributed/run.py:757] *****************************************
W0703 03:45:54.056000 139840515249984 torch/distributed/run.py:757] 
W0703 03:45:54.056000 139840515249984 torch/distributed/run.py:757] *****************************************
W0703 03:45:54.056000 139840515249984 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 03:45:54.056000 139840515249984 torch/distributed/run.py:757] *****************************************
W0703 03:45:54.057000 139808106514240 torch/distributed/run.py:757] 
W0703 03:45:54.057000 139808106514240 torch/distributed/run.py:757] *****************************************
W0703 03:45:54.057000 139808106514240 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 03:45:54.057000 139808106514240 torch/distributed/run.py:757] *****************************************
W0703 03:45:54.058000 140167912474432 torch/distributed/run.py:757] 
W0703 03:45:54.058000 140167912474432 torch/distributed/run.py:757] *****************************************
W0703 03:45:54.058000 140167912474432 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 03:45:54.058000 140167912474432 torch/distributed/run.py:757] *****************************************
W0703 03:45:54.064000 139621852018496 torch/distributed/run.py:757] 
W0703 03:45:54.064000 139621852018496 torch/distributed/run.py:757] *****************************************
W0703 03:45:54.064000 139621852018496 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 03:45:54.064000 139621852018496 torch/distributed/run.py:757] *****************************************
W0703 03:45:54.074000 140679655200576 torch/distributed/run.py:757] 
W0703 03:45:54.074000 140679655200576 torch/distributed/run.py:757] *****************************************
W0703 03:45:54.074000 140679655200576 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 03:45:54.074000 140679655200576 torch/distributed/run.py:757] *****************************************
W0703 03:45:54.080000 140610473387840 torch/distributed/run.py:757] 
W0703 03:45:54.080000 140610473387840 torch/distributed/run.py:757] *****************************************
W0703 03:45:54.080000 140610473387840 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 03:45:54.080000 140610473387840 torch/distributed/run.py:757] *****************************************
W0703 03:45:58.150000 139738772428608 torch/distributed/run.py:757] 
W0703 03:45:58.150000 139738772428608 torch/distributed/run.py:757] *****************************************
W0703 03:45:58.150000 139738772428608 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 03:45:58.150000 139738772428608 torch/distributed/run.py:757] *****************************************
[default0]:07/03/2024 03:46:23 [WARNING|DP=0|PP=0|TP=0|ip-26-0-160-225]: [Vocab Size Padding] Padded vocab (size: 50257) with 3 dummy tokens (new size: 50260)
[default0]:07/03/2024 03:46:23 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: Config:
[default0]:07/03/2024 03:46:23 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: Config(general=GeneralArgs(project='bench_cluster',
[default0]:07/03/2024 03:46:23 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:                            run='%date_%jobid',
[default0]:07/03/2024 03:46:23 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:                            seed=42,
[default0]:07/03/2024 03:46:23 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:                            step=None,
[default0]:07/03/2024 03:46:23 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:                            consumed_train_samples=None,
[default0]:07/03/2024 03:46:23 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:                            benchmark_csv_path=None,
[default0]:07/03/2024 03:46:23 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:                            ignore_sanity_checks=True),
[default0]:07/03/2024 03:46:23 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:        parallelism=ParallelismArgs(dp=16,
[default0]:07/03/2024 03:46:23 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:                                    pp=1,
[default0]:07/03/2024 03:46:23 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:                                    tp=4,
[default0]:07/03/2024 03:46:23 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:                                    pp_engine=<nanotron.parallel.pipeline_parallel.engine.OneForwardOneBackwardPipelineEngine object at 0x7fa41cee8820>,
[default0]:07/03/2024 03:46:23 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:                                    tp_mode=<TensorParallelLinearMode.REDUCE_SCATTER: 2>,
[default0]:07/03/2024 03:46:23 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:                                    tp_linear_async_communication=False,
[default0]:07/03/2024 03:46:23 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:                                    expert_parallel_size=1),
[default0]:07/03/2024 03:46:23 [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 03:46:23 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:                                                 eos_token_id=2,
[default0]:07/03/2024 03:46:23 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:                                                 hidden_act='silu',
[default0]:07/03/2024 03:46:23 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:                                                 hidden_size=2048,
[default0]:07/03/2024 03:46:23 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:                                                 initializer_range=0.02,
[default0]:07/03/2024 03:46:23 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:                                                 intermediate_size=4096,
[default0]:07/03/2024 03:46:23 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:                                                 is_llama_config=True,
[default0]:07/03/2024 03:46:23 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:                                                 max_position_embeddings=4096,
[default0]:07/03/2024 03:46:23 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:                                                 num_attention_heads=32,
[default0]:07/03/2024 03:46:23 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:                                                 num_hidden_layers=24,
[default0]:07/03/2024 03:46:23 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:                                                 num_key_value_heads=32,
[default0]:07/03/2024 03:46:23 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:                                                 pad_token_id=None,
[default0]:07/03/2024 03:46:23 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:                                                 pretraining_tp=1,
[default0]:07/03/2024 03:46:23 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:                                                 rms_norm_eps=1e-05,
[default0]:07/03/2024 03:46:23 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:                                                 rope_scaling=None,
[default0]:07/03/2024 03:46:23 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:                                                 rope_theta=10000.0,
[default0]:07/03/2024 03:46:23 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:                                                 tie_word_embeddings=True,
[default0]:07/03/2024 03:46:23 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:                                                 use_cache=True,
[default0]:07/03/2024 03:46:23 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:                                                 vocab_size=50260),
[default0]:07/03/2024 03:46:23 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:                        init_method=RandomInit(std=0.025),
[default0]:07/03/2024 03:46:23 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:                        dtype=torch.bfloat16,
[default0]:07/03/2024 03:46:23 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:                        make_vocab_size_divisible_by=1,
[default0]:07/03/2024 03:46:23 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:                        ddp_bucket_cap_mb=25),
[default0]:07/03/2024 03:46:23 [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 03:46:23 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:                                tokenizer_revision=None,
[default0]:07/03/2024 03:46:23 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:                                tokenizer_max_length=None),
[default0]:07/03/2024 03:46:23 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:        checkpoints=CheckpointsArgs(checkpoints_path=Path('/dev/null'),
[default0]:07/03/2024 03:46:23 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:                                    checkpoint_interval=100000,
[default0]:07/03/2024 03:46:23 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:                                    save_initial_state=False,
[default0]:07/03/2024 03:46:23 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:                                    resume_checkpoint_path=None,
[default0]:07/03/2024 03:46:23 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:                                    checkpoints_path_is_shared_file_system=False),
[default0]:07/03/2024 03:46:23 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:        logging=LoggingArgs(log_level='info',
[default0]:07/03/2024 03:46:23 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:                            log_level_replica='info',
[default0]:07/03/2024 03:46:23 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:                            iteration_step_info_interval=1),
[default0]:07/03/2024 03:46:23 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:        tokens=TokensArgs(sequence_length=4096,
[default0]:07/03/2024 03:46:23 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:                          train_steps=20,
[default0]:07/03/2024 03:46:23 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:                          micro_batch_size=16,
[default0]:07/03/2024 03:46:23 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:                          batch_accumulation_per_replica=4,
[default0]:07/03/2024 03:46:23 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:                          val_check_interval=-1,
[default0]:07/03/2024 03:46:23 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:                          limit_val_batches=0,
[default0]:07/03/2024 03:46:23 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:                          limit_test_batches=0),
[default0]:07/03/2024 03:46:23 [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 03:46:23 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:                                                                     adam_beta1=0.9,
[default0]:07/03/2024 03:46:23 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:                                                                     adam_beta2=0.95,
[default0]:07/03/2024 03:46:23 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:                                                                     torch_adam_is_fused=True,
[default0]:07/03/2024 03:46:23 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:                                                                     name='adamW'),
[default0]:07/03/2024 03:46:23 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:                                zero_stage=1,
[default0]:07/03/2024 03:46:23 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:                                weight_decay=0.01,
[default0]:07/03/2024 03:46:23 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:                                clip_grad=1.0,
[default0]:07/03/2024 03:46:23 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:                                accumulate_grad_in_fp32=True,
[default0]:07/03/2024 03:46:23 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:                                learning_rate_scheduler=LRSchedulerArgs(learning_rate=0.0001,
[default0]:07/03/2024 03:46:23 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:                                                                        lr_warmup_steps=1,
[default0]:07/03/2024 03:46:23 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:                                                                        lr_warmup_style='linear',
[default0]:07/03/2024 03:46:23 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:                                                                        lr_decay_style='linear',
[default0]:07/03/2024 03:46:23 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:                                                                        lr_decay_steps=19,
[default0]:07/03/2024 03:46:23 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:                                                                        lr_decay_starting_step=None,
[default0]:07/03/2024 03:46:23 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:                                                                        min_decay_lr=1e-05)),
[default0]:07/03/2024 03:46:23 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:        data_stages=[DatasetStageArgs(name='Training Stage',
[default0]:07/03/2024 03:46:23 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:                                      start_training_step=1,
[default0]:07/03/2024 03:46:23 [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 03:46:23 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:                                                                                 hf_dataset_splits='train',
[default0]:07/03/2024 03:46:23 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:                                                                                 hf_dataset_config_name=None,
[default0]:07/03/2024 03:46:23 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:                                                                                 dataset_processing_num_proc_per_process=64,
[default0]:07/03/2024 03:46:23 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:                                                                                 dataset_overwrite_cache=False,
[default0]:07/03/2024 03:46:23 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:                                                                                 text_column_name='text'),
[default0]:07/03/2024 03:46:23 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:                                                    seed=42,
[default0]:07/03/2024 03:46:23 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:                                                    num_loading_workers=0))],
[default0]:07/03/2024 03:46:23 [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-16_tp-4_pp-1_mbz-16')),
[default0]:07/03/2024 03:46:23 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:        lighteval=None)
[default0]:07/03/2024 03:46:23 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: Model Config:
[default0]:07/03/2024 03:46:23 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: LlamaConfig(bos_token_id=1,
[default0]:07/03/2024 03:46:23 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:             eos_token_id=2,
[default0]:07/03/2024 03:46:23 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:             hidden_act='silu',
[default0]:07/03/2024 03:46:23 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:             hidden_size=2048,
[default0]:07/03/2024 03:46:23 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:             initializer_range=0.02,
[default0]:07/03/2024 03:46:23 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:             intermediate_size=4096,
[default0]:07/03/2024 03:46:23 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:             is_llama_config=True,
[default0]:07/03/2024 03:46:23 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:             max_position_embeddings=4096,
[default0]:07/03/2024 03:46:23 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:             num_attention_heads=32,
[default0]:07/03/2024 03:46:23 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:             num_hidden_layers=24,
[default0]:07/03/2024 03:46:23 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:             num_key_value_heads=32,
[default0]:07/03/2024 03:46:23 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:             pad_token_id=None,
[default0]:07/03/2024 03:46:23 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:             pretraining_tp=1,
[default0]:07/03/2024 03:46:23 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:             rms_norm_eps=1e-05,
[default0]:07/03/2024 03:46:23 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:             rope_scaling=None,
[default0]:07/03/2024 03:46:23 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:             rope_theta=10000.0,
[default0]:07/03/2024 03:46:23 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:             tie_word_embeddings=True,
[default0]:07/03/2024 03:46:23 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:             use_cache=True,
[default0]:07/03/2024 03:46:23 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:             vocab_size=50260)
[default0]:07/03/2024 03:46:23 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: Building model..
[default0]:07/03/2024 03:46:23 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: Setting PP block ranks...
[default0]:07/03/2024 03:46:37 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: Total number of parameters: 1.11G (2117.09MiB)
[default0]:07/03/2024 03:46:37 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: Local number of parameters: 277M (529.27MiB)
[default0]:07/03/2024 03:46:37 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: [After model building] Memory usage: 554.21MiB. Peak allocated: 606.24MiB Peak reserved: 608.00MiB
[default0]:07/03/2024 03:46:37 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: No checkpoint path provided.
[default0]:07/03/2024 03:46:37 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: Parametrizing model parameters using StandardParametrizator
[default2]:07/03/2024 03:46:37 [INFO|DP=0|PP=0|TP=2|ip-26-0-160-225]: Local number of parameters: 277M (529.27MiB)
[default2]:07/03/2024 03:46:37 [INFO|DP=0|PP=0|TP=2|ip-26-0-160-225]: [After model building] Memory usage: 554.21MiB. Peak allocated: 606.24MiB Peak reserved: 608.00MiB
[default2]:07/03/2024 03:46:37 [INFO|DP=0|PP=0|TP=2|ip-26-0-160-225]: No checkpoint path provided.
[default3]:07/03/2024 03:46:37 [INFO|DP=0|PP=0|TP=3|ip-26-0-160-225]: Local number of parameters: 277M (529.27MiB)
[default3]:07/03/2024 03:46:37 [INFO|DP=0|PP=0|TP=3|ip-26-0-160-225]: [After model building] Memory usage: 554.21MiB. Peak allocated: 606.24MiB Peak reserved: 608.00MiB
[default3]:07/03/2024 03:46:37 [INFO|DP=0|PP=0|TP=3|ip-26-0-160-225]: No checkpoint path provided.
[default1]:07/03/2024 03:46:37 [INFO|DP=0|PP=0|TP=1|ip-26-0-160-225]: Local number of parameters: 277M (529.27MiB)
[default1]:07/03/2024 03:46:37 [INFO|DP=0|PP=0|TP=1|ip-26-0-160-225]: [After model building] Memory usage: 554.21MiB. Peak allocated: 606.24MiB Peak reserved: 608.00MiB
[default1]:07/03/2024 03:46:37 [INFO|DP=0|PP=0|TP=1|ip-26-0-160-225]: No checkpoint path provided.
[default0]:07/03/2024 03:46:37 [INFO|DP=2|PP=0|TP=0|ip-26-0-161-103]: No checkpoint path provided.
[default1]:07/03/2024 03:46:37 [INFO|DP=2|PP=0|TP=1|ip-26-0-161-103]: No checkpoint path provided.
[default2]:07/03/2024 03:46:37 [INFO|DP=2|PP=0|TP=2|ip-26-0-161-103]: No checkpoint path provided.
[default3]:07/03/2024 03:46:37 [INFO|DP=2|PP=0|TP=3|ip-26-0-161-103]: No checkpoint path provided.
[default4]:07/03/2024 03:46:37 [INFO|DP=1|PP=0|TP=0|ip-26-0-160-225]: No checkpoint path provided.
[default5]:07/03/2024 03:46:37 [INFO|DP=1|PP=0|TP=1|ip-26-0-160-225]: No checkpoint path provided.
[default6]:07/03/2024 03:46:37 [INFO|DP=1|PP=0|TP=2|ip-26-0-160-225]: No checkpoint path provided.
[default7]:07/03/2024 03:46:37 [INFO|DP=1|PP=0|TP=3|ip-26-0-160-225]: No checkpoint path provided.
[default3]:07/03/2024 03:46:37 [INFO|DP=8|PP=0|TP=3|ip-26-0-161-78]: No checkpoint path provided.
[default0]:07/03/2024 03:46:37 [INFO|DP=8|PP=0|TP=0|ip-26-0-161-78]: No checkpoint path provided.
[default2]:07/03/2024 03:46:37 [INFO|DP=8|PP=0|TP=2|ip-26-0-161-78]: No checkpoint path provided.
[default1]:07/03/2024 03:46:37 [INFO|DP=8|PP=0|TP=1|ip-26-0-161-78]: No checkpoint path provided.
[default2]:07/03/2024 03:46:37 [INFO|DP=4|PP=0|TP=2|ip-26-0-161-138]: No checkpoint path provided.
[default1]:07/03/2024 03:46:37 [INFO|DP=4|PP=0|TP=1|ip-26-0-161-138]: No checkpoint path provided.
[default3]:07/03/2024 03:46:37 [INFO|DP=4|PP=0|TP=3|ip-26-0-161-138]: No checkpoint path provided.
[default0]:07/03/2024 03:46:37 [INFO|DP=4|PP=0|TP=0|ip-26-0-161-138]: No checkpoint path provided.
[default4]:07/03/2024 03:46:37 [INFO|DP=9|PP=0|TP=0|ip-26-0-161-78]: No checkpoint path provided.
[default0]:07/03/2024 03:46:37 [INFO|DP=10|PP=0|TP=0|ip-26-0-171-102]: No checkpoint path provided.
[default5]:07/03/2024 03:46:37 [INFO|DP=3|PP=0|TP=1|ip-26-0-161-103]: No checkpoint path provided.
[default7]:07/03/2024 03:46:37 [INFO|DP=7|PP=0|TP=3|ip-26-0-161-153]: No checkpoint path provided.
[default0]:07/03/2024 03:46:37 [INFO|DP=6|PP=0|TP=0|ip-26-0-161-153]: No checkpoint path provided.
[default7]:07/03/2024 03:46:37 [INFO|DP=3|PP=0|TP=3|ip-26-0-161-103]: No checkpoint path provided.
[default1]:07/03/2024 03:46:37 [INFO|DP=6|PP=0|TP=1|ip-26-0-161-153]: No checkpoint path provided.
[default2]:07/03/2024 03:46:37 [INFO|DP=10|PP=0|TP=2|ip-26-0-171-102]: No checkpoint path provided.
[default3]:07/03/2024 03:46:37 [INFO|DP=10|PP=0|TP=3|ip-26-0-171-102]: No checkpoint path provided.
[default4]:07/03/2024 03:46:37 [INFO|DP=11|PP=0|TP=0|ip-26-0-171-102]: No checkpoint path provided.
[default6]:07/03/2024 03:46:37 [INFO|DP=3|PP=0|TP=2|ip-26-0-161-103]: No checkpoint path provided.
[default4]:07/03/2024 03:46:37 [INFO|DP=3|PP=0|TP=0|ip-26-0-161-103]: No checkpoint path provided.
[default1]:07/03/2024 03:46:37 [INFO|DP=12|PP=0|TP=1|ip-26-0-171-62]: No checkpoint path provided.
[default7]:07/03/2024 03:46:37 [INFO|DP=11|PP=0|TP=3|ip-26-0-171-102]: No checkpoint path provided.
[default6]:07/03/2024 03:46:37 [INFO|DP=11|PP=0|TP=2|ip-26-0-171-102]: No checkpoint path provided.
[default2]:07/03/2024 03:46:37 [INFO|DP=6|PP=0|TP=2|ip-26-0-161-153]: No checkpoint path provided.
[default3]:07/03/2024 03:46:37 [INFO|DP=6|PP=0|TP=3|ip-26-0-161-153]: No checkpoint path provided.
[default6]:07/03/2024 03:46:37 [INFO|DP=7|PP=0|TP=2|ip-26-0-161-153]: No checkpoint path provided.
[default4]:07/03/2024 03:46:37 [INFO|DP=7|PP=0|TP=0|ip-26-0-161-153]: No checkpoint path provided.
[default6]:07/03/2024 03:46:37 [INFO|DP=9|PP=0|TP=2|ip-26-0-161-78]: No checkpoint path provided.
[default7]:07/03/2024 03:46:37 [INFO|DP=9|PP=0|TP=3|ip-26-0-161-78]: No checkpoint path provided.
[default3]:07/03/2024 03:46:37 [INFO|DP=12|PP=0|TP=3|ip-26-0-171-62]: No checkpoint path provided.
[default5]:07/03/2024 03:46:37 [INFO|DP=9|PP=0|TP=1|ip-26-0-161-78]: No checkpoint path provided.
[default5]:07/03/2024 03:46:37 [INFO|DP=7|PP=0|TP=1|ip-26-0-161-153]: No checkpoint path provided.
[default2]:07/03/2024 03:46:37 [INFO|DP=12|PP=0|TP=2|ip-26-0-171-62]: No checkpoint path provided.
[default0]:07/03/2024 03:46:37 [INFO|DP=12|PP=0|TP=0|ip-26-0-171-62]: No checkpoint path provided.
[default1]:07/03/2024 03:46:37 [INFO|DP=10|PP=0|TP=1|ip-26-0-171-102]: No checkpoint path provided.
[default5]:07/03/2024 03:46:37 [INFO|DP=11|PP=0|TP=1|ip-26-0-171-102]: No checkpoint path provided.
[default6]:07/03/2024 03:46:37 [INFO|DP=13|PP=0|TP=2|ip-26-0-171-62]: No checkpoint path provided.
[default7]:07/03/2024 03:46:37 [INFO|DP=13|PP=0|TP=3|ip-26-0-171-62]: No checkpoint path provided.
[default4]:07/03/2024 03:46:37 [INFO|DP=13|PP=0|TP=0|ip-26-0-171-62]: No checkpoint path provided.
[default5]:07/03/2024 03:46:37 [INFO|DP=13|PP=0|TP=1|ip-26-0-171-62]: No checkpoint path provided.
[default5]:07/03/2024 03:46:37 [INFO|DP=5|PP=0|TP=1|ip-26-0-161-138]: No checkpoint path provided.
[default4]:07/03/2024 03:46:37 [INFO|DP=5|PP=0|TP=0|ip-26-0-161-138]: No checkpoint path provided.
[default7]:07/03/2024 03:46:37 [INFO|DP=5|PP=0|TP=3|ip-26-0-161-138]: No checkpoint path provided.
[default6]:07/03/2024 03:46:37 [INFO|DP=5|PP=0|TP=2|ip-26-0-161-138]: No checkpoint path provided.
[default1]:07/03/2024 03:46:38 [INFO|DP=14|PP=0|TP=1|ip-26-0-171-88]: No checkpoint path provided.
[default3]:07/03/2024 03:46:38 [INFO|DP=14|PP=0|TP=3|ip-26-0-171-88]: No checkpoint path provided.
[default2]:07/03/2024 03:46:38 [INFO|DP=14|PP=0|TP=2|ip-26-0-171-88]: No checkpoint path provided.
[default0]:07/03/2024 03:46:38 [INFO|DP=14|PP=0|TP=0|ip-26-0-171-88]: No checkpoint path provided.
[default5]:07/03/2024 03:46:38 [INFO|DP=15|PP=0|TP=1|ip-26-0-171-88]: No checkpoint path provided.
[default7]:07/03/2024 03:46:38 [INFO|DP=15|PP=0|TP=3|ip-26-0-171-88]: No checkpoint path provided.
[default4]:07/03/2024 03:46:38 [INFO|DP=15|PP=0|TP=0|ip-26-0-171-88]: No checkpoint path provided.
[default6]:07/03/2024 03:46:38 [INFO|DP=15|PP=0|TP=2|ip-26-0-171-88]: No checkpoint path provided.
[default0]:07/03/2024 03:46:40 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: [Optimizer Building] Using LearningRateForSP as learning rate
[default0]:07/03/2024 03:46:40 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: [ZeRO sharding] Size of optimizer params per rank:
[default0]:07/03/2024 03:46:40 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: [ZeRO sharding] DP Rank 0 has 17.3M out of 277M (6.25%) params' optimizer states
[default0]:07/03/2024 03:46:40 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: [ZeRO sharding] DP Rank 1 has 17.3M out of 277M (6.25%) params' optimizer states
[default0]:07/03/2024 03:46:40 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: [ZeRO sharding] DP Rank 2 has 17.3M out of 277M (6.25%) params' optimizer states
[default0]:07/03/2024 03:46:40 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: [ZeRO sharding] DP Rank 3 has 17.3M out of 277M (6.25%) params' optimizer states
[default0]:07/03/2024 03:46:40 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: [ZeRO sharding] DP Rank 4 has 17.3M out of 277M (6.25%) params' optimizer states
[default0]:07/03/2024 03:46:40 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: [ZeRO sharding] DP Rank 5 has 17.3M out of 277M (6.25%) params' optimizer states
[default0]:07/03/2024 03:46:40 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: [ZeRO sharding] DP Rank 6 has 17.3M out of 277M (6.25%) params' optimizer states
[default0]:07/03/2024 03:46:40 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: [ZeRO sharding] DP Rank 7 has 17.3M out of 277M (6.25%) params' optimizer states
[default0]:07/03/2024 03:46:40 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: [ZeRO sharding] DP Rank 8 has 17.3M out of 277M (6.25%) params' optimizer states
[default0]:07/03/2024 03:46:40 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: [ZeRO sharding] DP Rank 9 has 17.3M out of 277M (6.25%) params' optimizer states
[default0]:07/03/2024 03:46:40 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: [ZeRO sharding] DP Rank 10 has 17.3M out of 277M (6.25%) params' optimizer states
[default0]:07/03/2024 03:46:40 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: [ZeRO sharding] DP Rank 11 has 17.3M out of 277M (6.25%) params' optimizer states
[default0]:07/03/2024 03:46:40 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: [ZeRO sharding] DP Rank 12 has 17.3M out of 277M (6.25%) params' optimizer states
[default0]:07/03/2024 03:46:40 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: [ZeRO sharding] DP Rank 13 has 17.3M out of 277M (6.25%) params' optimizer states
[default0]:07/03/2024 03:46:40 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: [ZeRO sharding] DP Rank 14 has 17.3M out of 277M (6.25%) params' optimizer states
[default0]:07/03/2024 03:46:40 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: [ZeRO sharding] DP Rank 15 has 17.3M out of 277M (6.25%) params' optimizer states
[default0]:07/03/2024 03:46:42 [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 03:46:42 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: Using `datasets` library
[default0]:07/03/2024 03:46:42 [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 03:46:42 [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 03:46:44 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: [Training Plan] There are 1 training stages 
[default0]:07/03/2024 03:46:44 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: [Stage Training Stage] start from step 1 
[default0]:07/03/2024 03:46:44 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: 
[default0]:07/03/2024 03:46:44 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: [Start training] datetime: 2024-07-03 03:46:44.953437 | mbs: 16 | grad_accum: 4 | global_batch_size: 1024 | sequence_length: 4096 | train_steps: 20 | start_iteration_step: 0 | consumed_train_samples: 0
[default0]:07/03/2024 03:46:44 [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 03:46:44 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:  Memory usage: 1678.92MiB. Peak allocated 1678.92MiB. Peak reserved: 1736.00MiB
[default0]:07/03/2024 03:46:45 [WARNING|DP=8|PP=0|TP=0|ip-26-0-161-78]: Repo card metadata block was not found. Setting CardData to empty.
[default1]:07/03/2024 03:46:45 [WARNING|DP=8|PP=0|TP=1|ip-26-0-161-78]: Repo card metadata block was not found. Setting CardData to empty.
[default3]:07/03/2024 03:46:45 [WARNING|DP=8|PP=0|TP=3|ip-26-0-161-78]: Repo card metadata block was not found. Setting CardData to empty.
[default1]:07/03/2024 03:46:45 [WARNING|DP=4|PP=0|TP=1|ip-26-0-161-138]: Repo card metadata block was not found. Setting CardData to empty.
[default3]:07/03/2024 03:46:45 [WARNING|DP=4|PP=0|TP=3|ip-26-0-161-138]: Repo card metadata block was not found. Setting CardData to empty.
[default2]:07/03/2024 03:46:45 [WARNING|DP=4|PP=0|TP=2|ip-26-0-161-138]: Repo card metadata block was not found. Setting CardData to empty.
[default0]:07/03/2024 03:46:45 [WARNING|DP=4|PP=0|TP=0|ip-26-0-161-138]: Repo card metadata block was not found. Setting CardData to empty.
[default4]:07/03/2024 03:46:45 [WARNING|DP=5|PP=0|TP=0|ip-26-0-161-138]: Repo card metadata block was not found. Setting CardData to empty.
[default4]:07/03/2024 03:46:45 [WARNING|DP=9|PP=0|TP=0|ip-26-0-161-78]: Repo card metadata block was not found. Setting CardData to empty.
[default0]:07/03/2024 03:46:45 [WARNING|DP=10|PP=0|TP=0|ip-26-0-171-102]: Repo card metadata block was not found. Setting CardData to empty.
[default6]:Repo card metadata block was not found. Setting CardData to empty.
[default6]:Repo card metadata block was not found. Setting CardData to empty.
[default7]:07/03/2024 03:46:45 [WARNING|DP=5|PP=0|TP=3|ip-26-0-161-138]: Repo card metadata block was not found. Setting CardData to empty.
[default0]:07/03/2024 03:46:45 [WARNING|DP=2|PP=0|TP=0|ip-26-0-161-103]: Repo card metadata block was not found. Setting CardData to empty.
[default1]:07/03/2024 03:46:45 [WARNING|DP=6|PP=0|TP=1|ip-26-0-161-153]: Repo card metadata block was not found. Setting CardData to empty.
[default2]:07/03/2024 03:46:45 [WARNING|DP=2|PP=0|TP=2|ip-26-0-161-103]: Repo card metadata block was not found. Setting CardData to empty.
[default1]:07/03/2024 03:46:45 [WARNING|DP=14|PP=0|TP=1|ip-26-0-171-88]: Repo card metadata block was not found. Setting CardData to empty.
[default3]:07/03/2024 03:46:45 [WARNING|DP=10|PP=0|TP=3|ip-26-0-171-102]: Repo card metadata block was not found. Setting CardData to empty.
[default2]:Repo card metadata block was not found. Setting CardData to empty.
[default2]:07/03/2024 03:46:45 [WARNING|DP=10|PP=0|TP=2|ip-26-0-171-102]: Repo card metadata block was not found. Setting CardData to empty.
[default7]:07/03/2024 03:46:45 [WARNING|DP=7|PP=0|TP=3|ip-26-0-161-153]: Repo card metadata block was not found. Setting CardData to empty.
[default1]:07/03/2024 03:46:45 [WARNING|DP=12|PP=0|TP=1|ip-26-0-171-62]: Repo card metadata block was not found. Setting CardData to empty.
[default1]:07/03/2024 03:46:45 [WARNING|DP=2|PP=0|TP=1|ip-26-0-161-103]: Repo card metadata block was not found. Setting CardData to empty.
[default5]:07/03/2024 03:46:45 [WARNING|DP=9|PP=0|TP=1|ip-26-0-161-78]: Repo card metadata block was not found. Setting CardData to empty.
[default6]:07/03/2024 03:46:45 [WARNING|DP=9|PP=0|TP=2|ip-26-0-161-78]: Repo card metadata block was not found. Setting CardData to empty.
[default7]:07/03/2024 03:46:45 [WARNING|DP=11|PP=0|TP=3|ip-26-0-171-102]: Repo card metadata block was not found. Setting CardData to empty.
[default7]:07/03/2024 03:46:45 [WARNING|DP=9|PP=0|TP=3|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.
[default4]:Repo card metadata block was not found. Setting CardData to empty.
[default2]:07/03/2024 03:46:45 [WARNING|DP=6|PP=0|TP=2|ip-26-0-161-153]: Repo card metadata block was not found. Setting CardData to empty.
[default3]:07/03/2024 03:46:45 [WARNING|DP=6|PP=0|TP=3|ip-26-0-161-153]: Repo card metadata block was not found. Setting CardData to empty.
[default7]:07/03/2024 03:46:45 [WARNING|DP=15|PP=0|TP=3|ip-26-0-171-88]: 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 03:46:45 [WARNING|DP=15|PP=0|TP=0|ip-26-0-171-88]: Repo card metadata block was not found. Setting CardData to empty.
[default2]:07/03/2024 03:46:45 [WARNING|DP=12|PP=0|TP=2|ip-26-0-171-62]: Repo card metadata block was not found. Setting CardData to empty.
[default4]:07/03/2024 03:46:45 [WARNING|DP=1|PP=0|TP=0|ip-26-0-160-225]: Repo card metadata block was not found. Setting CardData to empty.
[default2]:07/03/2024 03:46:45 [WARNING|DP=0|PP=0|TP=2|ip-26-0-160-225]: Repo card metadata block was not found. Setting CardData to empty.
[default5]:07/03/2024 03:46:45 [WARNING|DP=7|PP=0|TP=1|ip-26-0-161-153]: Repo card metadata block was not found. Setting CardData to empty.
[default1]:Repo card metadata block was not found. Setting CardData to empty.
[default2]:Repo card metadata block was not found. Setting CardData to empty.
[default1]:Repo card metadata block was not found. Setting CardData to empty.
[default0]:07/03/2024 03:46:45 [WARNING|DP=12|PP=0|TP=0|ip-26-0-171-62]: Repo card metadata block was not found. Setting CardData to empty.
[default1]:Repo card metadata block was not found. Setting CardData to empty.
[default7]:Repo card metadata block was not found. Setting CardData to empty.
[default2]:Repo card metadata block was not found. Setting CardData to empty.
[default1]:Repo card metadata block was not found. Setting CardData to empty.
[default5]:07/03/2024 03:46:45 [WARNING|DP=1|PP=0|TP=1|ip-26-0-160-225]: Repo card metadata block was not found. Setting CardData to empty.
[default3]:Repo card metadata block was not found. Setting CardData to empty.
[default7]:Repo card metadata block was not found. Setting CardData to empty.
[default5]:07/03/2024 03:46:45 [WARNING|DP=11|PP=0|TP=1|ip-26-0-171-102]: Repo card metadata block was not found. Setting CardData to empty.
[default1]:07/03/2024 03:46:45 [WARNING|DP=10|PP=0|TP=1|ip-26-0-171-102]: Repo card metadata block was not found. Setting CardData to empty.
[default0]:Repo card metadata block was not found. Setting CardData to empty.
[default2]:Repo card metadata block was not found. Setting CardData to empty.
[default6]:07/03/2024 03:46:45 [WARNING|DP=5|PP=0|TP=2|ip-26-0-161-138]: 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 03:46:45 [WARNING|DP=15|PP=0|TP=2|ip-26-0-171-88]: Repo card metadata block was not found. Setting CardData to empty.
[default0]:Repo card metadata block was not found. Setting CardData to empty.
[default7]: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 03:46:45 [WARNING|DP=13|PP=0|TP=0|ip-26-0-171-62]: Repo card metadata block was not found. Setting CardData to empty.
[default5]:07/03/2024 03:46:45 [WARNING|DP=13|PP=0|TP=1|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.
[default3]:Repo card metadata block was not found. Setting CardData to empty.
[default1]:Repo card metadata block was not found. Setting CardData to empty.
[default3]:Repo card metadata block was not found. Setting CardData to empty.
[default3]:Repo card metadata block was not found. Setting CardData to empty.
[default5]:Repo card metadata block was not found. Setting CardData to empty.
[default0]:Repo card metadata block was not found. Setting CardData to empty.
[default6]: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.
[default0]: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.
[default5]:Repo card metadata block was not found. Setting CardData to empty.
[default1]:Repo card metadata block was not found. Setting CardData to empty.
[default4]:Repo card metadata block was not found. Setting CardData to empty.
[default2]:Repo card metadata block was not found. Setting CardData to empty.
[default5]:Repo card metadata block was not found. Setting CardData to empty.
[default2]:Repo card metadata block was not found. Setting CardData to empty.
[default4]:Repo card metadata block was not found. Setting CardData to empty.
[default7]:Repo card metadata block was not found. Setting CardData to empty.
[default2]:07/03/2024 03:46:45 [WARNING|DP=8|PP=0|TP=2|ip-26-0-161-78]: Repo card metadata block was not found. Setting CardData to empty.
[default5]:Repo card metadata block was not found. Setting CardData to empty.
[default3]:Repo card metadata block was not found. Setting CardData to empty.
[default5]:07/03/2024 03:46:45 [WARNING|DP=5|PP=0|TP=1|ip-26-0-161-138]: Repo card metadata block was not found. Setting CardData to empty.
[default5]:07/03/2024 03:46:45 [WARNING|DP=15|PP=0|TP=1|ip-26-0-171-88]: Repo card metadata block was not found. Setting CardData to empty.
[default4]:07/03/2024 03:46:45 [WARNING|DP=11|PP=0|TP=0|ip-26-0-171-102]: Repo card metadata block was not found. Setting CardData to empty.
[default0]:07/03/2024 03:46:45 [WARNING|DP=6|PP=0|TP=0|ip-26-0-161-153]: Repo card metadata block was not found. Setting CardData to empty.
[default6]:07/03/2024 03:46:45 [WARNING|DP=3|PP=0|TP=2|ip-26-0-161-103]: Repo card metadata block was not found. Setting CardData to empty.
[default4]:07/03/2024 03:46:45 [WARNING|DP=3|PP=0|TP=0|ip-26-0-161-103]: Repo card metadata block was not found. Setting CardData to empty.
[default3]:07/03/2024 03:46:45 [WARNING|DP=2|PP=0|TP=3|ip-26-0-161-103]: Repo card metadata block was not found. Setting CardData to empty.
[default6]: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 03:46:45 [WARNING|DP=7|PP=0|TP=0|ip-26-0-161-153]: Repo card metadata block was not found. Setting CardData to empty.
[default0]:07/03/2024 03:46:45 [WARNING|DP=14|PP=0|TP=0|ip-26-0-171-88]: Repo card metadata block was not found. Setting CardData to empty.
[default2]:07/03/2024 03:46:45 [WARNING|DP=14|PP=0|TP=2|ip-26-0-171-88]: Repo card metadata block was not found. Setting CardData to empty.
[default3]:07/03/2024 03:46:45 [WARNING|DP=0|PP=0|TP=3|ip-26-0-160-225]: Repo card metadata block was not found. Setting CardData to empty.
[default3]:07/03/2024 03:46:45 [WARNING|DP=12|PP=0|TP=3|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]:Repo card metadata block was not found. Setting CardData to empty.
[default3]:Repo card metadata block was not found. Setting CardData to empty.
[default0]:Repo card metadata block was not found. Setting CardData to empty.
[default7]:07/03/2024 03:46:45 [WARNING|DP=1|PP=0|TP=3|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.
[default7]:07/03/2024 03:46:45 [WARNING|DP=13|PP=0|TP=3|ip-26-0-171-62]: Repo card metadata block was not found. Setting CardData to empty.
[default6]:07/03/2024 03:46:45 [WARNING|DP=13|PP=0|TP=2|ip-26-0-171-62]: 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.
[default6]: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.
[default1]:Repo card metadata block was not found. Setting CardData to empty.
[default3]:07/03/2024 03:46:45 [WARNING|DP=14|PP=0|TP=3|ip-26-0-171-88]: Repo card metadata block was not found. Setting CardData to empty.
[default7]:Repo card metadata block was not found. Setting CardData to empty.
[default7]:07/03/2024 03:46:45 [WARNING|DP=3|PP=0|TP=3|ip-26-0-161-103]: Repo card metadata block was not found. Setting CardData to empty.
[default6]:07/03/2024 03:46:45 [WARNING|DP=7|PP=0|TP=2|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.
[default1]:07/03/2024 03:46:45 [WARNING|DP=0|PP=0|TP=1|ip-26-0-160-225]: Repo card metadata block was not found. Setting CardData to empty.
[default6]:07/03/2024 03:46:45 [WARNING|DP=1|PP=0|TP=2|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.
[default5]:07/03/2024 03:46:45 [WARNING|DP=3|PP=0|TP=1|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]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/autograd/graph.py:744: UserWarning: c10d::allreduce_: an autograd kernel was not registered to the Autograd key(s) but we are trying to backprop through it. This may lead to silently incorrect behavior. This behavior is deprecated and will be removed in a future version of PyTorch. If your operator is differentiable, please ensure you have registered an autograd kernel to the correct Autograd key (e.g. DispatchKey::Autograd, DispatchKey::CompositeImplicitAutograd). If your operator is not differentiable, or to squash this warning and use the previous behavior, please register torch::CppFunction::makeFallthrough() to DispatchKey::Autograd. (Triggered internally at ../torch/csrc/autograd/autograd_not_implemented_fallback.cpp:63.)
[default5]:  return Variable._execution_engine.run_backward(  # Calls into the C++ engine to run the backward pass
[default6]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/autograd/graph.py:744: UserWarning: c10d::allreduce_: an autograd kernel was not registered to the Autograd key(s) but we are trying to backprop through it. This may lead to silently incorrect behavior. This behavior is deprecated and will be removed in a future version of PyTorch. If your operator is differentiable, please ensure you have registered an autograd kernel to the correct Autograd key (e.g. DispatchKey::Autograd, DispatchKey::CompositeImplicitAutograd). If your operator is not differentiable, or to squash this warning and use the previous behavior, please register torch::CppFunction::makeFallthrough() to DispatchKey::Autograd. (Triggered internally at ../torch/csrc/autograd/autograd_not_implemented_fallback.cpp:63.)
[default6]:  return Variable._execution_engine.run_backward(  # Calls into the C++ engine to run the backward pass
[default7]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/autograd/graph.py:744: UserWarning: c10d::allreduce_: an autograd kernel was not registered to the Autograd key(s) but we are trying to backprop through it. This may lead to silently incorrect behavior. This behavior is deprecated and will be removed in a future version of PyTorch. If your operator is differentiable, please ensure you have registered an autograd kernel to the correct Autograd key (e.g. DispatchKey::Autograd, DispatchKey::CompositeImplicitAutograd). If your operator is not differentiable, or to squash this warning and use the previous behavior, please register torch::CppFunction::makeFallthrough() to DispatchKey::Autograd. (Triggered internally at ../torch/csrc/autograd/autograd_not_implemented_fallback.cpp:63.)
[default7]:  return Variable._execution_engine.run_backward(  # Calls into the C++ engine to run the backward pass
[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]:Repo card metadata block was not found. Setting CardData to empty.
[default6]:07/03/2024 03:46:50 [WARNING|DP=11|PP=0|TP=2|ip-26-0-171-102]: Repo card metadata block was not found. Setting CardData to empty.
[default6]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/autograd/graph.py:744: UserWarning: c10d::allreduce_: an autograd kernel was not registered to the Autograd key(s) but we are trying to backprop through it. This may lead to silently incorrect behavior. This behavior is deprecated and will be removed in a future version of PyTorch. If your operator is differentiable, please ensure you have registered an autograd kernel to the correct Autograd key (e.g. DispatchKey::Autograd, DispatchKey::CompositeImplicitAutograd). If your operator is not differentiable, or to squash this warning and use the previous behavior, please register torch::CppFunction::makeFallthrough() to DispatchKey::Autograd. (Triggered internally at ../torch/csrc/autograd/autograd_not_implemented_fallback.cpp:63.)
[default6]:  return Variable._execution_engine.run_backward(  # Calls into the C++ engine to run the backward pass
[default5]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/autograd/graph.py:744: UserWarning: c10d::allreduce_: an autograd kernel was not registered to the Autograd key(s) but we are trying to backprop through it. This may lead to silently incorrect behavior. This behavior is deprecated and will be removed in a future version of PyTorch. If your operator is differentiable, please ensure you have registered an autograd kernel to the correct Autograd key (e.g. DispatchKey::Autograd, DispatchKey::CompositeImplicitAutograd). If your operator is not differentiable, or to squash this warning and use the previous behavior, please register torch::CppFunction::makeFallthrough() to DispatchKey::Autograd. (Triggered internally at ../torch/csrc/autograd/autograd_not_implemented_fallback.cpp:63.)
[default5]:  return Variable._execution_engine.run_backward(  # Calls into the C++ engine to run the backward pass
[default4]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/autograd/graph.py:744: UserWarning: c10d::allreduce_: an autograd kernel was not registered to the Autograd key(s) but we are trying to backprop through it. This may lead to silently incorrect behavior. This behavior is deprecated and will be removed in a future version of PyTorch. If your operator is differentiable, please ensure you have registered an autograd kernel to the correct Autograd key (e.g. DispatchKey::Autograd, DispatchKey::CompositeImplicitAutograd). If your operator is not differentiable, or to squash this warning and use the previous behavior, please register torch::CppFunction::makeFallthrough() to DispatchKey::Autograd. (Triggered internally at ../torch/csrc/autograd/autograd_not_implemented_fallback.cpp:63.)
[default4]:  return Variable._execution_engine.run_backward(  # Calls into the C++ engine to run the backward pass
[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
[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
[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
[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.)
[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]:  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
[default2]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/autograd/graph.py:744: UserWarning: c10d::allreduce_: an autograd kernel was not registered to the Autograd key(s) but we are trying to backprop through it. This may lead to silently incorrect behavior. This behavior is deprecated and will be removed in a future version of PyTorch. If your operator is differentiable, please ensure you have registered an autograd kernel to the correct Autograd key (e.g. DispatchKey::Autograd, DispatchKey::CompositeImplicitAutograd). If your operator is not differentiable, or to squash this warning and use the previous behavior, please register torch::CppFunction::makeFallthrough() to DispatchKey::Autograd. (Triggered internally at ../torch/csrc/autograd/autograd_not_implemented_fallback.cpp:63.)
[default2]:  return Variable._execution_engine.run_backward(  # Calls into the C++ engine to run the backward pass
[default3]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/autograd/graph.py:744: UserWarning: c10d::allreduce_: an autograd kernel was not registered to the Autograd key(s) but we are trying to backprop through it. This may lead to silently incorrect behavior. This behavior is deprecated and will be removed in a future version of PyTorch. If your operator is differentiable, please ensure you have registered an autograd kernel to the correct Autograd key (e.g. DispatchKey::Autograd, DispatchKey::CompositeImplicitAutograd). If your operator is not differentiable, or to squash this warning and use the previous behavior, please register torch::CppFunction::makeFallthrough() to DispatchKey::Autograd. (Triggered internally at ../torch/csrc/autograd/autograd_not_implemented_fallback.cpp:63.)
[default3]:  return Variable._execution_engine.run_backward(  # Calls into the C++ engine to run the backward pass
[default1]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/autograd/graph.py:744: UserWarning: c10d::allreduce_: an autograd kernel was not registered to the Autograd key(s) but we are trying to backprop through it. This may lead to silently incorrect behavior. This behavior is deprecated and will be removed in a future version of PyTorch. If your operator is differentiable, please ensure you have registered an autograd kernel to the correct Autograd key (e.g. DispatchKey::Autograd, DispatchKey::CompositeImplicitAutograd). If your operator is not differentiable, or to squash this warning and use the previous behavior, please register torch::CppFunction::makeFallthrough() to DispatchKey::Autograd. (Triggered internally at ../torch/csrc/autograd/autograd_not_implemented_fallback.cpp:63.)
[default1]:  return Variable._execution_engine.run_backward(  # Calls into the C++ engine to run the backward pass
[default5]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/distributed_c10d.py:2261: UserWarning: torch.distributed.all_reduce_coalesced will be deprecated. If you must use it, please revisit our documentation later at https://pytorch.org/docs/master/distributed.html#collective-functions
[default5]:  warnings.warn(
[default6]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/distributed_c10d.py:2261: UserWarning: torch.distributed.all_reduce_coalesced will be deprecated. If you must use it, please revisit our documentation later at https://pytorch.org/docs/master/distributed.html#collective-functions
[default6]:  warnings.warn(
[default7]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/distributed_c10d.py:2261: UserWarning: torch.distributed.all_reduce_coalesced will be deprecated. If you must use it, please revisit our documentation later at https://pytorch.org/docs/master/distributed.html#collective-functions
[default7]:  warnings.warn(
[default4]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/distributed_c10d.py:2261: UserWarning: torch.distributed.all_reduce_coalesced will be deprecated. If you must use it, please revisit our documentation later at https://pytorch.org/docs/master/distributed.html#collective-functions
[default4]:  warnings.warn(
[default2]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/distributed_c10d.py:2261: UserWarning: torch.distributed.all_reduce_coalesced will be deprecated. If you must use it, please revisit our documentation later at https://pytorch.org/docs/master/distributed.html#collective-functions
[default2]:  warnings.warn(
[default0]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/distributed_c10d.py:2261: UserWarning: torch.distributed.all_reduce_coalesced will be deprecated. If you must use it, please revisit our documentation later at https://pytorch.org/docs/master/distributed.html#collective-functions
[default0]:  warnings.warn(
[default3]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/distributed_c10d.py:2261: UserWarning: torch.distributed.all_reduce_coalesced will be deprecated. If you must use it, please revisit our documentation later at https://pytorch.org/docs/master/distributed.html#collective-functions
[default3]:  warnings.warn(
[default1]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/distributed_c10d.py:2261: UserWarning: torch.distributed.all_reduce_coalesced will be deprecated. If you must use it, please revisit our documentation later at https://pytorch.org/docs/master/distributed.html#collective-functions
[default1]:  warnings.warn(
[default6]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/distributed_c10d.py:2261: UserWarning: torch.distributed.all_reduce_coalesced will be deprecated. If you must use it, please revisit our documentation later at https://pytorch.org/docs/master/distributed.html#collective-functions
[default6]:  warnings.warn(
[default4]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/distributed_c10d.py:2261: UserWarning: torch.distributed.all_reduce_coalesced will be deprecated. If you must use it, please revisit our documentation later at https://pytorch.org/docs/master/distributed.html#collective-functions
[default4]:  warnings.warn(
[default7]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/distributed_c10d.py:2261: UserWarning: torch.distributed.all_reduce_coalesced will be deprecated. If you must use it, please revisit our documentation later at https://pytorch.org/docs/master/distributed.html#collective-functions
[default7]:  warnings.warn(
[default5]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/distributed_c10d.py:2261: UserWarning: torch.distributed.all_reduce_coalesced will be deprecated. If you must use it, please revisit our documentation later at https://pytorch.org/docs/master/distributed.html#collective-functions
[default5]:  warnings.warn(
[default5]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/distributed_c10d.py:2261: UserWarning: torch.distributed.all_reduce_coalesced will be deprecated. If you must use it, please revisit our documentation later at https://pytorch.org/docs/master/distributed.html#collective-functions
[default5]:  warnings.warn(
[default6]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/distributed_c10d.py:2261: UserWarning: torch.distributed.all_reduce_coalesced will be deprecated. If you must use it, please revisit our documentation later at https://pytorch.org/docs/master/distributed.html#collective-functions
[default6]:  warnings.warn(
[default7]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/distributed_c10d.py:2261: UserWarning: torch.distributed.all_reduce_coalesced will be deprecated. If you must use it, please revisit our documentation later at https://pytorch.org/docs/master/distributed.html#collective-functions
[default7]:  warnings.warn(
[default4]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/distributed_c10d.py:2261: UserWarning: torch.distributed.all_reduce_coalesced will be deprecated. If you must use it, please revisit our documentation later at https://pytorch.org/docs/master/distributed.html#collective-functions
[default4]:  warnings.warn(
[default0]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/distributed_c10d.py:2261: UserWarning: torch.distributed.all_reduce_coalesced will be deprecated. If you must use it, please revisit our documentation later at https://pytorch.org/docs/master/distributed.html#collective-functions
[default0]:  warnings.warn(
[default3]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/distributed_c10d.py:2261: UserWarning: torch.distributed.all_reduce_coalesced will be deprecated. If you must use it, please revisit our documentation later at https://pytorch.org/docs/master/distributed.html#collective-functions
[default3]:  warnings.warn(
[default2]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/distributed_c10d.py:2261: UserWarning: torch.distributed.all_reduce_coalesced will be deprecated. If you must use it, please revisit our documentation later at https://pytorch.org/docs/master/distributed.html#collective-functions
[default2]:  warnings.warn(
[default1]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/distributed_c10d.py:2261: UserWarning: torch.distributed.all_reduce_coalesced will be deprecated. If you must use it, please revisit our documentation later at https://pytorch.org/docs/master/distributed.html#collective-functions
[default1]:  warnings.warn(
[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.)
[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]:  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
[default4]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/autograd/graph.py:744: UserWarning: c10d::allreduce_: an autograd kernel was not registered to the Autograd key(s) but we are trying to backprop through it. This may lead to silently incorrect behavior. This behavior is deprecated and will be removed in a future version of PyTorch. If your operator is differentiable, please ensure you have registered an autograd kernel to the correct Autograd key (e.g. DispatchKey::Autograd, DispatchKey::CompositeImplicitAutograd). If your operator is not differentiable, or to squash this warning and use the previous behavior, please register torch::CppFunction::makeFallthrough() to DispatchKey::Autograd. (Triggered internally at ../torch/csrc/autograd/autograd_not_implemented_fallback.cpp:63.)
[default4]:  return Variable._execution_engine.run_backward(  # Calls into the C++ engine to run the backward pass
[default7]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/autograd/graph.py:744: UserWarning: c10d::allreduce_: an autograd kernel was not registered to the Autograd key(s) but we are trying to backprop through it. This may lead to silently incorrect behavior. This behavior is deprecated and will be removed in a future version of PyTorch. If your operator is differentiable, please ensure you have registered an autograd kernel to the correct Autograd key (e.g. DispatchKey::Autograd, DispatchKey::CompositeImplicitAutograd). If your operator is not differentiable, or to squash this warning and use the previous behavior, please register torch::CppFunction::makeFallthrough() to DispatchKey::Autograd. (Triggered internally at ../torch/csrc/autograd/autograd_not_implemented_fallback.cpp:63.)
[default7]:  return Variable._execution_engine.run_backward(  # Calls into the C++ engine to run the backward pass
[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
[default1]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/autograd/graph.py:744: UserWarning: c10d::allreduce_: an autograd kernel was not registered to the Autograd key(s) but we are trying to backprop through it. This may lead to silently incorrect behavior. This behavior is deprecated and will be removed in a future version of PyTorch. If your operator is differentiable, please ensure you have registered an autograd kernel to the correct Autograd key (e.g. DispatchKey::Autograd, DispatchKey::CompositeImplicitAutograd). If your operator is not differentiable, or to squash this warning and use the previous behavior, please register torch::CppFunction::makeFallthrough() to DispatchKey::Autograd. (Triggered internally at ../torch/csrc/autograd/autograd_not_implemented_fallback.cpp:63.)
[default1]:  return Variable._execution_engine.run_backward(  # Calls into the C++ engine to run the backward pass
[default0]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/autograd/graph.py:744: UserWarning: c10d::allreduce_: an autograd kernel was not registered to the Autograd key(s) but we are trying to backprop through it. This may lead to silently incorrect behavior. This behavior is deprecated and will be removed in a future version of PyTorch. If your operator is differentiable, please ensure you have registered an autograd kernel to the correct Autograd key (e.g. DispatchKey::Autograd, DispatchKey::CompositeImplicitAutograd). If your operator is not differentiable, or to squash this warning and use the previous behavior, please register torch::CppFunction::makeFallthrough() to DispatchKey::Autograd. (Triggered internally at ../torch/csrc/autograd/autograd_not_implemented_fallback.cpp:63.)
[default0]:  return Variable._execution_engine.run_backward(  # Calls into the C++ engine to run the backward pass
[default7]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/autograd/graph.py:744: UserWarning: c10d::allreduce_: an autograd kernel was not registered to the Autograd key(s) but we are trying to backprop through it. This may lead to silently incorrect behavior. This behavior is deprecated and will be removed in a future version of PyTorch. If your operator is differentiable, please ensure you have registered an autograd kernel to the correct Autograd key (e.g. DispatchKey::Autograd, DispatchKey::CompositeImplicitAutograd). If your operator is not differentiable, or to squash this warning and use the previous behavior, please register torch::CppFunction::makeFallthrough() to DispatchKey::Autograd. (Triggered internally at ../torch/csrc/autograd/autograd_not_implemented_fallback.cpp:63.)
[default7]:  return Variable._execution_engine.run_backward(  # Calls into the C++ engine to run the backward pass
[default5]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/autograd/graph.py:744: UserWarning: c10d::allreduce_: an autograd kernel was not registered to the Autograd key(s) but we are trying to backprop through it. This may lead to silently incorrect behavior. This behavior is deprecated and will be removed in a future version of PyTorch. If your operator is differentiable, please ensure you have registered an autograd kernel to the correct Autograd key (e.g. DispatchKey::Autograd, DispatchKey::CompositeImplicitAutograd). If your operator is not differentiable, or to squash this warning and use the previous behavior, please register torch::CppFunction::makeFallthrough() to DispatchKey::Autograd. (Triggered internally at ../torch/csrc/autograd/autograd_not_implemented_fallback.cpp:63.)
[default5]:  return Variable._execution_engine.run_backward(  # Calls into the C++ engine to run the backward pass
[default4]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/autograd/graph.py:744: UserWarning: c10d::allreduce_: an autograd kernel was not registered to the Autograd key(s) but we are trying to backprop through it. This may lead to silently incorrect behavior. This behavior is deprecated and will be removed in a future version of PyTorch. If your operator is differentiable, please ensure you have registered an autograd kernel to the correct Autograd key (e.g. DispatchKey::Autograd, DispatchKey::CompositeImplicitAutograd). If your operator is not differentiable, or to squash this warning and use the previous behavior, please register torch::CppFunction::makeFallthrough() to DispatchKey::Autograd. (Triggered internally at ../torch/csrc/autograd/autograd_not_implemented_fallback.cpp:63.)
[default4]:  return Variable._execution_engine.run_backward(  # Calls into the C++ engine to run the backward pass
[default2]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/autograd/graph.py:744: UserWarning: c10d::allreduce_: an autograd kernel was not registered to the Autograd key(s) but we are trying to backprop through it. This may lead to silently incorrect behavior. This behavior is deprecated and will be removed in a future version of PyTorch. If your operator is differentiable, please ensure you have registered an autograd kernel to the correct Autograd key (e.g. DispatchKey::Autograd, DispatchKey::CompositeImplicitAutograd). If your operator is not differentiable, or to squash this warning and use the previous behavior, please register torch::CppFunction::makeFallthrough() to DispatchKey::Autograd. (Triggered internally at ../torch/csrc/autograd/autograd_not_implemented_fallback.cpp:63.)
[default2]:  return Variable._execution_engine.run_backward(  # Calls into the C++ engine to run the backward pass
[default2]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/autograd/graph.py:744: UserWarning: c10d::allreduce_: an autograd kernel was not registered to the Autograd key(s) but we are trying to backprop through it. This may lead to silently incorrect behavior. This behavior is deprecated and will be removed in a future version of PyTorch. If your operator is differentiable, please ensure you have registered an autograd kernel to the correct Autograd key (e.g. DispatchKey::Autograd, DispatchKey::CompositeImplicitAutograd). If your operator is not differentiable, or to squash this warning and use the previous behavior, please register torch::CppFunction::makeFallthrough() to DispatchKey::Autograd. (Triggered internally at ../torch/csrc/autograd/autograd_not_implemented_fallback.cpp:63.)
[default2]:  return Variable._execution_engine.run_backward(  # Calls into the C++ engine to run the backward pass
[default6]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/autograd/graph.py:744: UserWarning: c10d::allreduce_: an autograd kernel was not registered to the Autograd key(s) but we are trying to backprop through it. This may lead to silently incorrect behavior. This behavior is deprecated and will be removed in a future version of PyTorch. If your operator is differentiable, please ensure you have registered an autograd kernel to the correct Autograd key (e.g. DispatchKey::Autograd, DispatchKey::CompositeImplicitAutograd). If your operator is not differentiable, or to squash this warning and use the previous behavior, please register torch::CppFunction::makeFallthrough() to DispatchKey::Autograd. (Triggered internally at ../torch/csrc/autograd/autograd_not_implemented_fallback.cpp:63.)
[default6]:  return Variable._execution_engine.run_backward(  # Calls into the C++ engine to run the backward pass
[default1]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/autograd/graph.py:744: UserWarning: c10d::allreduce_: an autograd kernel was not registered to the Autograd key(s) but we are trying to backprop through it. This may lead to silently incorrect behavior. This behavior is deprecated and will be removed in a future version of PyTorch. If your operator is differentiable, please ensure you have registered an autograd kernel to the correct Autograd key (e.g. DispatchKey::Autograd, DispatchKey::CompositeImplicitAutograd). If your operator is not differentiable, or to squash this warning and use the previous behavior, please register torch::CppFunction::makeFallthrough() to DispatchKey::Autograd. (Triggered internally at ../torch/csrc/autograd/autograd_not_implemented_fallback.cpp:63.)
[default1]:  return Variable._execution_engine.run_backward(  # Calls into the C++ engine to run the backward pass
[default4]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/autograd/graph.py:744: UserWarning: c10d::allreduce_: an autograd kernel was not registered to the Autograd key(s) but we are trying to backprop through it. This may lead to silently incorrect behavior. This behavior is deprecated and will be removed in a future version of PyTorch. If your operator is differentiable, please ensure you have registered an autograd kernel to the correct Autograd key (e.g. DispatchKey::Autograd, DispatchKey::CompositeImplicitAutograd). If your operator is not differentiable, or to squash this warning and use the previous behavior, please register torch::CppFunction::makeFallthrough() to DispatchKey::Autograd. (Triggered internally at ../torch/csrc/autograd/autograd_not_implemented_fallback.cpp:63.)
[default4]:  return Variable._execution_engine.run_backward(  # Calls into the C++ engine to run the backward pass
[default7]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/autograd/graph.py:744: UserWarning: c10d::allreduce_: an autograd kernel was not registered to the Autograd key(s) but we are trying to backprop through it. This may lead to silently incorrect behavior. This behavior is deprecated and will be removed in a future version of PyTorch. If your operator is differentiable, please ensure you have registered an autograd kernel to the correct Autograd key (e.g. DispatchKey::Autograd, DispatchKey::CompositeImplicitAutograd). If your operator is not differentiable, or to squash this warning and use the previous behavior, please register torch::CppFunction::makeFallthrough() to DispatchKey::Autograd. (Triggered internally at ../torch/csrc/autograd/autograd_not_implemented_fallback.cpp:63.)
[default7]:  return Variable._execution_engine.run_backward(  # Calls into the C++ engine to run the backward pass
[default7]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/autograd/graph.py:744: UserWarning: c10d::allreduce_: an autograd kernel was not registered to the Autograd key(s) but we are trying to backprop through it. This may lead to silently incorrect behavior. This behavior is deprecated and will be removed in a future version of PyTorch. If your operator is differentiable, please ensure you have registered an autograd kernel to the correct Autograd key (e.g. DispatchKey::Autograd, DispatchKey::CompositeImplicitAutograd). If your operator is not differentiable, or to squash this warning and use the previous behavior, please register torch::CppFunction::makeFallthrough() to DispatchKey::Autograd. (Triggered internally at ../torch/csrc/autograd/autograd_not_implemented_fallback.cpp:63.)
[default7]:  return Variable._execution_engine.run_backward(  # Calls into the C++ engine to run the backward pass
[default5]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/autograd/graph.py:744: UserWarning: c10d::allreduce_: an autograd kernel was not registered to the Autograd key(s) but we are trying to backprop through it. This may lead to silently incorrect behavior. This behavior is deprecated and will be removed in a future version of PyTorch. If your operator is differentiable, please ensure you have registered an autograd kernel to the correct Autograd key (e.g. DispatchKey::Autograd, DispatchKey::CompositeImplicitAutograd). If your operator is not differentiable, or to squash this warning and use the previous behavior, please register torch::CppFunction::makeFallthrough() to DispatchKey::Autograd. (Triggered internally at ../torch/csrc/autograd/autograd_not_implemented_fallback.cpp:63.)
[default5]:  return Variable._execution_engine.run_backward(  # Calls into the C++ engine to run the backward pass
[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
[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.)
[default3]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/autograd/graph.py:744: UserWarning: c10d::allreduce_: an autograd kernel was not registered to the Autograd key(s) but we are trying to backprop through it. This may lead to silently incorrect behavior. This behavior is deprecated and will be removed in a future version of PyTorch. If your operator is differentiable, please ensure you have registered an autograd kernel to the correct Autograd key (e.g. DispatchKey::Autograd, DispatchKey::CompositeImplicitAutograd). If your operator is not differentiable, or to squash this warning and use the previous behavior, please register torch::CppFunction::makeFallthrough() to DispatchKey::Autograd. (Triggered internally at ../torch/csrc/autograd/autograd_not_implemented_fallback.cpp:63.)
[default3]:  return Variable._execution_engine.run_backward(  # Calls into the C++ engine to run the backward pass
[default0]:  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
[default1]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/autograd/graph.py:744: UserWarning: c10d::allreduce_: an autograd kernel was not registered to the Autograd key(s) but we are trying to backprop through it. This may lead to silently incorrect behavior. This behavior is deprecated and will be removed in a future version of PyTorch. If your operator is differentiable, please ensure you have registered an autograd kernel to the correct Autograd key (e.g. DispatchKey::Autograd, DispatchKey::CompositeImplicitAutograd). If your operator is not differentiable, or to squash this warning and use the previous behavior, please register torch::CppFunction::makeFallthrough() to DispatchKey::Autograd. (Triggered internally at ../torch/csrc/autograd/autograd_not_implemented_fallback.cpp:63.)
[default1]:  return Variable._execution_engine.run_backward(  # Calls into the C++ engine to run the backward pass
[default0]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/autograd/graph.py:744: UserWarning: c10d::allreduce_: an autograd kernel was not registered to the Autograd key(s) but we are trying to backprop through it. This may lead to silently incorrect behavior. This behavior is deprecated and will be removed in a future version of PyTorch. If your operator is differentiable, please ensure you have registered an autograd kernel to the correct Autograd key (e.g. DispatchKey::Autograd, DispatchKey::CompositeImplicitAutograd). If your operator is not differentiable, or to squash this warning and use the previous behavior, please register torch::CppFunction::makeFallthrough() to DispatchKey::Autograd. (Triggered internally at ../torch/csrc/autograd/autograd_not_implemented_fallback.cpp:63.)
[default0]:  return Variable._execution_engine.run_backward(  # Calls into the C++ engine to run the backward pass
[default3]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/autograd/graph.py:744: UserWarning: c10d::allreduce_: an autograd kernel was not registered to the Autograd key(s) but we are trying to backprop through it. This may lead to silently incorrect behavior. This behavior is deprecated and will be removed in a future version of PyTorch. If your operator is differentiable, please ensure you have registered an autograd kernel to the correct Autograd key (e.g. DispatchKey::Autograd, DispatchKey::CompositeImplicitAutograd). If your operator is not differentiable, or to squash this warning and use the previous behavior, please register torch::CppFunction::makeFallthrough() to DispatchKey::Autograd. (Triggered internally at ../torch/csrc/autograd/autograd_not_implemented_fallback.cpp:63.)
[default3]:  return Variable._execution_engine.run_backward(  # Calls into the C++ engine to run the backward pass
[default0]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/autograd/graph.py:744: UserWarning: c10d::allreduce_: an autograd kernel was not registered to the Autograd key(s) but we are trying to backprop through it. This may lead to silently incorrect behavior. This behavior is deprecated and will be removed in a future version of PyTorch. If your operator is differentiable, please ensure you have registered an autograd kernel to the correct Autograd key (e.g. DispatchKey::Autograd, DispatchKey::CompositeImplicitAutograd). If your operator is not differentiable, or to squash this warning and use the previous behavior, please register torch::CppFunction::makeFallthrough() to DispatchKey::Autograd. (Triggered internally at ../torch/csrc/autograd/autograd_not_implemented_fallback.cpp:63.)
[default0]:  return Variable._execution_engine.run_backward(  # Calls into the C++ engine to run the backward pass
[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
[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.)
[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]:  return Variable._execution_engine.run_backward(  # Calls into the C++ engine to run the backward pass
[default2]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/autograd/graph.py:744: UserWarning: c10d::allreduce_: an autograd kernel was not registered to the Autograd key(s) but we are trying to backprop through it. This may lead to silently incorrect behavior. This behavior is deprecated and will be removed in a future version of PyTorch. If your operator is differentiable, please ensure you have registered an autograd kernel to the correct Autograd key (e.g. DispatchKey::Autograd, DispatchKey::CompositeImplicitAutograd). If your operator is not differentiable, or to squash this warning and use the previous behavior, please register torch::CppFunction::makeFallthrough() to DispatchKey::Autograd. (Triggered internally at ../torch/csrc/autograd/autograd_not_implemented_fallback.cpp:63.)
[default2]:  return Variable._execution_engine.run_backward(  # Calls into the C++ engine to run the backward pass
[default7]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/autograd/graph.py:744: UserWarning: c10d::allreduce_: an autograd kernel was not registered to the Autograd key(s) but we are trying to backprop through it. This may lead to silently incorrect behavior. This behavior is deprecated and will be removed in a future version of PyTorch. If your operator is differentiable, please ensure you have registered an autograd kernel to the correct Autograd key (e.g. DispatchKey::Autograd, DispatchKey::CompositeImplicitAutograd). If your operator is not differentiable, or to squash this warning and use the previous behavior, please register torch::CppFunction::makeFallthrough() to DispatchKey::Autograd. (Triggered internally at ../torch/csrc/autograd/autograd_not_implemented_fallback.cpp:63.)
[default7]:  return Variable._execution_engine.run_backward(  # Calls into the C++ engine to run the backward pass
[default2]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/distributed_c10d.py:2261: UserWarning: torch.distributed.all_reduce_coalesced will be deprecated. If you must use it, please revisit our documentation later at https://pytorch.org/docs/master/distributed.html#collective-functions
[default1]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/distributed_c10d.py:2261: UserWarning: torch.distributed.all_reduce_coalesced will be deprecated. If you must use it, please revisit our documentation later at https://pytorch.org/docs/master/distributed.html#collective-functions
[default1]:  warnings.warn(
[default2]:  warnings.warn(
[default3]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/distributed_c10d.py:2261: UserWarning: torch.distributed.all_reduce_coalesced will be deprecated. If you must use it, please revisit our documentation later at https://pytorch.org/docs/master/distributed.html#collective-functions
[default3]:  warnings.warn(
[default1]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/autograd/graph.py:744: UserWarning: c10d::allreduce_: an autograd kernel was not registered to the Autograd key(s) but we are trying to backprop through it. This may lead to silently incorrect behavior. This behavior is deprecated and will be removed in a future version of PyTorch. If your operator is differentiable, please ensure you have registered an autograd kernel to the correct Autograd key (e.g. DispatchKey::Autograd, DispatchKey::CompositeImplicitAutograd). If your operator is not differentiable, or to squash this warning and use the previous behavior, please register torch::CppFunction::makeFallthrough() to DispatchKey::Autograd. (Triggered internally at ../torch/csrc/autograd/autograd_not_implemented_fallback.cpp:63.)
[default1]:  return Variable._execution_engine.run_backward(  # Calls into the C++ engine to run the backward pass
[default0]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/autograd/graph.py:744: UserWarning: c10d::allreduce_: an autograd kernel was not registered to the Autograd key(s) but we are trying to backprop through it. This may lead to silently incorrect behavior. This behavior is deprecated and will be removed in a future version of PyTorch. If your operator is differentiable, please ensure you have registered an autograd kernel to the correct Autograd key (e.g. DispatchKey::Autograd, DispatchKey::CompositeImplicitAutograd). If your operator is not differentiable, or to squash this warning and use the previous behavior, please register torch::CppFunction::makeFallthrough() to DispatchKey::Autograd. (Triggered internally at ../torch/csrc/autograd/autograd_not_implemented_fallback.cpp:63.)
[default0]:  return Variable._execution_engine.run_backward(  # Calls into the C++ engine to run the backward pass
[default2]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/autograd/graph.py:744: UserWarning: c10d::allreduce_: an autograd kernel was not registered to the Autograd key(s) but we are trying to backprop through it. This may lead to silently incorrect behavior. This behavior is deprecated and will be removed in a future version of PyTorch. If your operator is differentiable, please ensure you have registered an autograd kernel to the correct Autograd key (e.g. DispatchKey::Autograd, DispatchKey::CompositeImplicitAutograd). If your operator is not differentiable, or to squash this warning and use the previous behavior, please register torch::CppFunction::makeFallthrough() to DispatchKey::Autograd. (Triggered internally at ../torch/csrc/autograd/autograd_not_implemented_fallback.cpp:63.)
[default2]:  return Variable._execution_engine.run_backward(  # Calls into the C++ engine to run the backward pass
[default3]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/autograd/graph.py:744: UserWarning: c10d::allreduce_: an autograd kernel was not registered to the Autograd key(s) but we are trying to backprop through it. This may lead to silently incorrect behavior. This behavior is deprecated and will be removed in a future version of PyTorch. If your operator is differentiable, please ensure you have registered an autograd kernel to the correct Autograd key (e.g. DispatchKey::Autograd, DispatchKey::CompositeImplicitAutograd). If your operator is not differentiable, or to squash this warning and use the previous behavior, please register torch::CppFunction::makeFallthrough() to DispatchKey::Autograd. (Triggered internally at ../torch/csrc/autograd/autograd_not_implemented_fallback.cpp:63.)
[default3]:  return Variable._execution_engine.run_backward(  # Calls into the C++ engine to run the backward pass
[default0]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/distributed_c10d.py:2261: UserWarning: torch.distributed.all_reduce_coalesced will be deprecated. If you must use it, please revisit our documentation later at https://pytorch.org/docs/master/distributed.html#collective-functions
[default0]:  warnings.warn(
[default6]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/distributed_c10d.py:2261: UserWarning: torch.distributed.all_reduce_coalesced will be deprecated. If you must use it, please revisit our documentation later at https://pytorch.org/docs/master/distributed.html#collective-functions
[default6]:  warnings.warn(
[default5]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/distributed_c10d.py:2261: UserWarning: torch.distributed.all_reduce_coalesced will be deprecated. If you must use it, please revisit our documentation later at https://pytorch.org/docs/master/distributed.html#collective-functions
[default5]:  warnings.warn(
[default7]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/distributed_c10d.py:2261: UserWarning: torch.distributed.all_reduce_coalesced will be deprecated. If you must use it, please revisit our documentation later at https://pytorch.org/docs/master/distributed.html#collective-functions
[default7]:  warnings.warn(
[default4]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/distributed_c10d.py:2261: UserWarning: torch.distributed.all_reduce_coalesced will be deprecated. If you must use it, please revisit our documentation later at https://pytorch.org/docs/master/distributed.html#collective-functions
[default4]:  warnings.warn(
[default6]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/distributed_c10d.py:2261: UserWarning: torch.distributed.all_reduce_coalesced will be deprecated. If you must use it, please revisit our documentation later at https://pytorch.org/docs/master/distributed.html#collective-functions
[default6]:  warnings.warn(
[default4]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/distributed_c10d.py:2261: UserWarning: torch.distributed.all_reduce_coalesced will be deprecated. If you must use it, please revisit our documentation later at https://pytorch.org/docs/master/distributed.html#collective-functions
[default4]:  warnings.warn(
[default7]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/distributed_c10d.py:2261: UserWarning: torch.distributed.all_reduce_coalesced will be deprecated. If you must use it, please revisit our documentation later at https://pytorch.org/docs/master/distributed.html#collective-functions
[default7]:  warnings.warn(
[default7]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/distributed_c10d.py:2261: UserWarning: torch.distributed.all_reduce_coalesced will be deprecated. If you must use it, please revisit our documentation later at https://pytorch.org/docs/master/distributed.html#collective-functions
[default7]:  warnings.warn(
[default5]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/distributed_c10d.py:2261: UserWarning: torch.distributed.all_reduce_coalesced will be deprecated. If you must use it, please revisit our documentation later at https://pytorch.org/docs/master/distributed.html#collective-functions
[default5]:  warnings.warn(
[default6]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/distributed_c10d.py:2261: UserWarning: torch.distributed.all_reduce_coalesced will be deprecated. If you must use it, please revisit our documentation later at https://pytorch.org/docs/master/distributed.html#collective-functions
[default6]:  warnings.warn(
[default4]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/distributed_c10d.py:2261: UserWarning: torch.distributed.all_reduce_coalesced will be deprecated. If you must use it, please revisit our documentation later at https://pytorch.org/docs/master/distributed.html#collective-functions
[default4]:  warnings.warn(
[default5]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/distributed_c10d.py:2261: UserWarning: torch.distributed.all_reduce_coalesced will be deprecated. If you must use it, please revisit our documentation later at https://pytorch.org/docs/master/distributed.html#collective-functions
[default5]:  warnings.warn(
[default3]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/distributed_c10d.py:2261: UserWarning: torch.distributed.all_reduce_coalesced will be deprecated. If you must use it, please revisit our documentation later at https://pytorch.org/docs/master/distributed.html#collective-functions
[default3]:  warnings.warn(
[default0]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/distributed_c10d.py:2261: UserWarning: torch.distributed.all_reduce_coalesced will be deprecated. If you must use it, please revisit our documentation later at https://pytorch.org/docs/master/distributed.html#collective-functions
[default0]:  warnings.warn(
[default2]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/distributed_c10d.py:2261: UserWarning: torch.distributed.all_reduce_coalesced will be deprecated. If you must use it, please revisit our documentation later at https://pytorch.org/docs/master/distributed.html#collective-functions
[default2]:  warnings.warn(
[default1]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/distributed_c10d.py:2261: UserWarning: torch.distributed.all_reduce_coalesced will be deprecated. If you must use it, please revisit our documentation later at https://pytorch.org/docs/master/distributed.html#collective-functions
[default1]:  warnings.warn(
[default1]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/distributed_c10d.py:2261: UserWarning: torch.distributed.all_reduce_coalesced will be deprecated. If you must use it, please revisit our documentation later at https://pytorch.org/docs/master/distributed.html#collective-functions
[default1]:  warnings.warn(
[default2]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/distributed_c10d.py:2261: UserWarning: torch.distributed.all_reduce_coalesced will be deprecated. If you must use it, please revisit our documentation later at https://pytorch.org/docs/master/distributed.html#collective-functions
[default2]:  warnings.warn(
[default0]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/distributed_c10d.py:2261: UserWarning: torch.distributed.all_reduce_coalesced will be deprecated. If you must use it, please revisit our documentation later at https://pytorch.org/docs/master/distributed.html#collective-functions
[default0]:  warnings.warn(
[default3]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/distributed_c10d.py:2261: UserWarning: torch.distributed.all_reduce_coalesced will be deprecated. If you must use it, please revisit our documentation later at https://pytorch.org/docs/master/distributed.html#collective-functions
[default3]:  warnings.warn(
[default0]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/distributed_c10d.py:2261: UserWarning: torch.distributed.all_reduce_coalesced will be deprecated. If you must use it, please revisit our documentation later at https://pytorch.org/docs/master/distributed.html#collective-functions
[default0]:  warnings.warn(
[default3]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/distributed_c10d.py:2261: UserWarning: torch.distributed.all_reduce_coalesced will be deprecated. If you must use it, please revisit our documentation later at https://pytorch.org/docs/master/distributed.html#collective-functions
[default3]:  warnings.warn(
[default6]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/distributed_c10d.py:2261: UserWarning: torch.distributed.all_reduce_coalesced will be deprecated. If you must use it, please revisit our documentation later at https://pytorch.org/docs/master/distributed.html#collective-functions
[default6]:  warnings.warn(
[default2]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/distributed_c10d.py:2261: UserWarning: torch.distributed.all_reduce_coalesced will be deprecated. If you must use it, please revisit our documentation later at https://pytorch.org/docs/master/distributed.html#collective-functions
[default2]:  warnings.warn(
[default1]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/distributed_c10d.py:2261: UserWarning: torch.distributed.all_reduce_coalesced will be deprecated. If you must use it, please revisit our documentation later at https://pytorch.org/docs/master/distributed.html#collective-functions
[default1]:  warnings.warn(
[default4]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/distributed_c10d.py:2261: UserWarning: torch.distributed.all_reduce_coalesced will be deprecated. If you must use it, please revisit our documentation later at https://pytorch.org/docs/master/distributed.html#collective-functions
[default4]:  warnings.warn(
[default5]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/distributed_c10d.py:2261: UserWarning: torch.distributed.all_reduce_coalesced will be deprecated. If you must use it, please revisit our documentation later at https://pytorch.org/docs/master/distributed.html#collective-functions
[default5]:  warnings.warn(
[default7]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/distributed_c10d.py:2261: UserWarning: torch.distributed.all_reduce_coalesced will be deprecated. If you must use it, please revisit our documentation later at https://pytorch.org/docs/master/distributed.html#collective-functions
[default7]:  warnings.warn(
[default0]:07/03/2024 03:46:57 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:  Memory usage: 1756.04MiB. Peak allocated 46814.52MiB. Peak reserved: 48534.00MiB
[default6]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/distributed_c10d.py:2261: UserWarning: torch.distributed.all_reduce_coalesced will be deprecated. If you must use it, please revisit our documentation later at https://pytorch.org/docs/master/distributed.html#collective-functions
[default6]:  warnings.warn(
[default3]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/distributed_c10d.py:2261: UserWarning: torch.distributed.all_reduce_coalesced will be deprecated. If you must use it, please revisit our documentation later at https://pytorch.org/docs/master/distributed.html#collective-functions
[default3]:  warnings.warn(
[default1]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/distributed_c10d.py:2261: UserWarning: torch.distributed.all_reduce_coalesced will be deprecated. If you must use it, please revisit our documentation later at https://pytorch.org/docs/master/distributed.html#collective-functions
[default1]:  warnings.warn(
[default0]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/distributed_c10d.py:2261: UserWarning: torch.distributed.all_reduce_coalesced will be deprecated. If you must use it, please revisit our documentation later at https://pytorch.org/docs/master/distributed.html#collective-functions
[default0]:  warnings.warn(
[default7]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/distributed_c10d.py:2261: UserWarning: torch.distributed.all_reduce_coalesced will be deprecated. If you must use it, please revisit our documentation later at https://pytorch.org/docs/master/distributed.html#collective-functions
[default7]:  warnings.warn(
[default4]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/distributed_c10d.py:2261: UserWarning: torch.distributed.all_reduce_coalesced will be deprecated. If you must use it, please revisit our documentation later at https://pytorch.org/docs/master/distributed.html#collective-functions
[default4]:  warnings.warn(
[default5]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/distributed_c10d.py:2261: UserWarning: torch.distributed.all_reduce_coalesced will be deprecated. If you must use it, please revisit our documentation later at https://pytorch.org/docs/master/distributed.html#collective-functions
[default5]:  warnings.warn(
[default2]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/distributed_c10d.py:2261: UserWarning: torch.distributed.all_reduce_coalesced will be deprecated. If you must use it, please revisit our documentation later at https://pytorch.org/docs/master/distributed.html#collective-functions
[default2]:  warnings.warn(
[default1]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/distributed_c10d.py:2261: UserWarning: torch.distributed.all_reduce_coalesced will be deprecated. If you must use it, please revisit our documentation later at https://pytorch.org/docs/master/distributed.html#collective-functions
[default1]:  warnings.warn(
[default0]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/distributed_c10d.py:2261: UserWarning: torch.distributed.all_reduce_coalesced will be deprecated. If you must use it, please revisit our documentation later at https://pytorch.org/docs/master/distributed.html#collective-functions
[default0]:  warnings.warn(
[default2]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/distributed_c10d.py:2261: UserWarning: torch.distributed.all_reduce_coalesced will be deprecated. If you must use it, please revisit our documentation later at https://pytorch.org/docs/master/distributed.html#collective-functions
[default2]:  warnings.warn(
[default3]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/distributed_c10d.py:2261: UserWarning: torch.distributed.all_reduce_coalesced will be deprecated. If you must use it, please revisit our documentation later at https://pytorch.org/docs/master/distributed.html#collective-functions
[default3]:  warnings.warn(
[default0]:07/03/2024 03:47:03 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: iteration: 1 / 20 | consumed_tokens: 4.19M | elapsed_time_per_iteration_ms: 18.8K | tokens_per_sec: 223K | tokens_per_sec_per_gpu: 3.49K | global_batch_size: 1.02K | lm_loss: 11.4 | lr: 0.0001 | model_tflops_per_gpu: 31.6 | hardware_tflops_per_gpu: 31.6 | grad_norm: 20.6 | cuda_memory_allocated: 1.98G | cuda_max_memory_reserved: 51G | hd_total_memory_tb: 312G | hd_used_memory_tb: 66.5G | hd_free_memory_tb: 246G
[default0]:07/03/2024 03:47:03 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:  Memory usage: 1888.44MiB. Peak allocated 2980.06MiB. Peak reserved: 48650.00MiB
[default0]:07/03/2024 03:47:05 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:  Memory usage: 1888.44MiB. Peak allocated 46946.91MiB. Peak reserved: 48656.00MiB
[default0]:07/03/2024 03:47:13 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: iteration: 2 / 20 | consumed_tokens: 8.39M | elapsed_time_per_iteration_ms: 9.76K | tokens_per_sec: 430K | tokens_per_sec_per_gpu: 6.71K | global_batch_size: 1.02K | lm_loss: 11.4 | lr: 9.53e-05 | model_tflops_per_gpu: 60.9 | hardware_tflops_per_gpu: 60.9 | grad_norm: 20.7 | cuda_memory_allocated: 1.98G | cuda_max_memory_reserved: 51G | hd_total_memory_tb: 312G | hd_used_memory_tb: 66.5G | hd_free_memory_tb: 246G
[default0]:07/03/2024 03:47:13 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:  Memory usage: 1888.44MiB. Peak allocated 2980.06MiB. Peak reserved: 48656.00MiB
[default0]:07/03/2024 03:47:15 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:  Memory usage: 1888.44MiB. Peak allocated 46946.91MiB. Peak reserved: 48656.00MiB
[default0]:07/03/2024 03:47:23 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: iteration: 3 / 20 | consumed_tokens: 12.6M | elapsed_time_per_iteration_ms: 9.7K | tokens_per_sec: 432K | tokens_per_sec_per_gpu: 6.76K | global_batch_size: 1.02K | lm_loss: 11.6 | lr: 9.05e-05 | model_tflops_per_gpu: 61.3 | hardware_tflops_per_gpu: 61.3 | grad_norm: 194 | cuda_memory_allocated: 1.98G | cuda_max_memory_reserved: 51G | hd_total_memory_tb: 312G | hd_used_memory_tb: 66.5G | hd_free_memory_tb: 246G
[default0]:07/03/2024 03:47:23 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:  Memory usage: 1888.44MiB. Peak allocated 2980.06MiB. Peak reserved: 48656.00MiB
[default0]:STAGE:2024-07-03 03:47:23 11001:11001 ActivityProfilerController.cpp:314] Completed Stage: Warm Up
[default0]:07/03/2024 03:47:25 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:  Memory usage: 1888.44MiB. Peak allocated 46946.91MiB. Peak reserved: 48656.00MiB
[default0]:07/03/2024 03:47:32 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: iteration: 4 / 20 | consumed_tokens: 16.8M | elapsed_time_per_iteration_ms: 9.65K | tokens_per_sec: 435K | tokens_per_sec_per_gpu: 6.79K | global_batch_size: 1.02K | lm_loss: 13.6 | lr: 8.58e-05 | model_tflops_per_gpu: 61.6 | hardware_tflops_per_gpu: 61.6 | grad_norm: 28 | cuda_memory_allocated: 1.98G | cuda_max_memory_reserved: 51G | hd_total_memory_tb: 312G | hd_used_memory_tb: 66.5G | hd_free_memory_tb: 246G
[default0]:07/03/2024 03:47:32 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:  Memory usage: 1888.44MiB. Peak allocated 2980.06MiB. Peak reserved: 48656.00MiB
[default0]:07/03/2024 03:47:42 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: iteration: 5 / 20 | consumed_tokens: 21M | elapsed_time_per_iteration_ms: 9.69K | tokens_per_sec: 433K | tokens_per_sec_per_gpu: 6.76K | global_batch_size: 1.02K | lm_loss: 12 | lr: 8.11e-05 | model_tflops_per_gpu: 61.4 | hardware_tflops_per_gpu: 61.4 | grad_norm: 48.9
[default0]:07/03/2024 03:47:42 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:  Memory usage: 1888.44MiB. Peak allocated 46946.91MiB. Peak reserved: 48656.00MiB
[default0]:07/03/2024 03:47:52 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: iteration: 6 / 20 | consumed_tokens: 25.2M | elapsed_time_per_iteration_ms: 9.68K | tokens_per_sec: 433K | tokens_per_sec_per_gpu: 6.77K | global_batch_size: 1.02K | lm_loss: 10.9 | lr: 7.63e-05 | model_tflops_per_gpu: 61.4 | hardware_tflops_per_gpu: 61.4 | grad_norm: 19.8
[default0]:STAGE:2024-07-03 03:47:54 11001:11001 ActivityProfilerController.cpp:320] Completed Stage: Collection
[default0]:STAGE:2024-07-03 03:47:54 11001:11001 ActivityProfilerController.cpp:324] Completed Stage: Post Processing
[default0]:07/03/2024 03:48:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:  Memory usage: 1888.44MiB. Peak allocated 46946.91MiB. Peak reserved: 48656.00MiB
[default0]:07/03/2024 03:48:17 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: iteration: 7 / 20 | consumed_tokens: 29.4M | elapsed_time_per_iteration_ms: 2.41K | tokens_per_sec: 1.74M | tokens_per_sec_per_gpu: 27.2K | global_batch_size: 1.02K | lm_loss: 10.4 | lr: 7.16e-05 | model_tflops_per_gpu: 247 | hardware_tflops_per_gpu: 247 | grad_norm: 8.63
[default0]:07/03/2024 03:48:17 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:  Memory usage: 1888.44MiB. Peak allocated 46946.91MiB. Peak reserved: 48656.00MiB
[default0]:07/03/2024 03:48:27 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: iteration: 8 / 20 | consumed_tokens: 33.6M | elapsed_time_per_iteration_ms: 9.87K | tokens_per_sec: 425K | tokens_per_sec_per_gpu: 6.64K | global_batch_size: 1.02K | lm_loss: 9.67 | lr: 6.68e-05 | model_tflops_per_gpu: 60.2 | hardware_tflops_per_gpu: 60.2 | grad_norm: 6.91
[default0]:07/03/2024 03:48:27 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:  Memory usage: 1888.44MiB. Peak allocated 46946.91MiB. Peak reserved: 48656.00MiB
[default0]:07/03/2024 03:48:36 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: iteration: 9 / 20 | consumed_tokens: 37.7M | elapsed_time_per_iteration_ms: 9.68K | tokens_per_sec: 433K | tokens_per_sec_per_gpu: 6.77K | global_batch_size: 1.02K | lm_loss: 11.3 | lr: 6.21e-05 | model_tflops_per_gpu: 61.4 | hardware_tflops_per_gpu: 61.4 | grad_norm: 53.2
[default0]:07/03/2024 03:48:36 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:  Memory usage: 1888.44MiB. Peak allocated 46946.91MiB. Peak reserved: 48656.00MiB
[default0]:07/03/2024 03:48:46 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: iteration: 10 / 20 | consumed_tokens: 41.9M | elapsed_time_per_iteration_ms: 9.62K | tokens_per_sec: 436K | tokens_per_sec_per_gpu: 6.81K | global_batch_size: 1.02K | lm_loss: 9.12 | lr: 5.74e-05 | model_tflops_per_gpu: 61.8 | hardware_tflops_per_gpu: 61.8 | grad_norm: 16.6
[default0]:07/03/2024 03:48:46 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:  Memory usage: 1888.44MiB. Peak allocated 46946.91MiB. Peak reserved: 48656.00MiB
[default0]:07/03/2024 03:48:56 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: iteration: 11 / 20 | consumed_tokens: 46.1M | elapsed_time_per_iteration_ms: 9.64K | tokens_per_sec: 435K | tokens_per_sec_per_gpu: 6.8K | global_batch_size: 1.02K | lm_loss: 8.59 | lr: 5.26e-05 | model_tflops_per_gpu: 61.7 | hardware_tflops_per_gpu: 61.7 | grad_norm: 7.76
[default0]:07/03/2024 03:48:56 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:  Memory usage: 1888.44MiB. Peak allocated 46946.91MiB. Peak reserved: 48656.00MiB
[default0]:07/03/2024 03:49:05 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: iteration: 12 / 20 | consumed_tokens: 50.3M | elapsed_time_per_iteration_ms: 9.72K | tokens_per_sec: 431K | tokens_per_sec_per_gpu: 6.74K | global_batch_size: 1.02K | lm_loss: 8.38 | lr: 4.79e-05 | model_tflops_per_gpu: 61.2 | hardware_tflops_per_gpu: 61.2 | grad_norm: 5.81
[default0]:07/03/2024 03:49:05 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:  Memory usage: 1888.44MiB. Peak allocated 46946.91MiB. Peak reserved: 48656.00MiB
[default0]:07/03/2024 03:49:15 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: iteration: 13 / 20 | consumed_tokens: 54.5M | elapsed_time_per_iteration_ms: 9.71K | tokens_per_sec: 432K | tokens_per_sec_per_gpu: 6.75K | global_batch_size: 1.02K | lm_loss: 8.17 | lr: 4.32e-05 | model_tflops_per_gpu: 61.2 | hardware_tflops_per_gpu: 61.2 | grad_norm: 5.61
[default0]:07/03/2024 03:49:15 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:  Memory usage: 1888.44MiB. Peak allocated 46946.91MiB. Peak reserved: 48656.00MiB
[default0]:07/03/2024 03:49:25 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: iteration: 14 / 20 | consumed_tokens: 58.7M | elapsed_time_per_iteration_ms: 9.74K | tokens_per_sec: 430K | tokens_per_sec_per_gpu: 6.73K | global_batch_size: 1.02K | lm_loss: 7.92 | lr: 3.84e-05 | model_tflops_per_gpu: 61 | hardware_tflops_per_gpu: 61 | grad_norm: 5.41
[default0]:07/03/2024 03:49:25 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:  Memory usage: 1888.44MiB. Peak allocated 46946.91MiB. Peak reserved: 48656.00MiB
[default0]:07/03/2024 03:49:35 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: iteration: 15 / 20 | consumed_tokens: 62.9M | elapsed_time_per_iteration_ms: 9.7K | tokens_per_sec: 432K | tokens_per_sec_per_gpu: 6.76K | global_batch_size: 1.02K | lm_loss: 7.69 | lr: 3.37e-05 | model_tflops_per_gpu: 61.3 | hardware_tflops_per_gpu: 61.3 | grad_norm: 4.98
[default0]:07/03/2024 03:49:35 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:  Memory usage: 1888.44MiB. Peak allocated 46946.91MiB. Peak reserved: 48656.00MiB
[default0]:07/03/2024 03:49:44 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: iteration: 16 / 20 | consumed_tokens: 67.1M | elapsed_time_per_iteration_ms: 9.68K | tokens_per_sec: 433K | tokens_per_sec_per_gpu: 6.77K | global_batch_size: 1.02K | lm_loss: 7.55 | lr: 2.89e-05 | model_tflops_per_gpu: 61.4 | hardware_tflops_per_gpu: 61.4 | grad_norm: 4.93
[default0]:07/03/2024 03:49:44 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:  Memory usage: 1888.44MiB. Peak allocated 46946.91MiB. Peak reserved: 48656.00MiB
[default0]:07/03/2024 03:49:54 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: iteration: 17 / 20 | consumed_tokens: 71.3M | elapsed_time_per_iteration_ms: 9.67K | tokens_per_sec: 434K | tokens_per_sec_per_gpu: 6.78K | global_batch_size: 1.02K | lm_loss: 7.46 | lr: 2.42e-05 | model_tflops_per_gpu: 61.5 | hardware_tflops_per_gpu: 61.5 | grad_norm: 4.98
[default0]:07/03/2024 03:49:54 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:  Memory usage: 1888.44MiB. Peak allocated 46946.91MiB. Peak reserved: 48656.00MiB
[default0]:07/03/2024 03:50:04 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: iteration: 18 / 20 | consumed_tokens: 75.5M | elapsed_time_per_iteration_ms: 9.62K | tokens_per_sec: 436K | tokens_per_sec_per_gpu: 6.81K | global_batch_size: 1.02K | lm_loss: 7.37 | lr: 1.95e-05 | model_tflops_per_gpu: 61.8 | hardware_tflops_per_gpu: 61.8 | grad_norm: 5.81
[default0]:07/03/2024 03:50:04 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:  Memory usage: 1888.44MiB. Peak allocated 46946.91MiB. Peak reserved: 48656.00MiB
[default0]:07/03/2024 03:50:13 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: iteration: 19 / 20 | consumed_tokens: 79.7M | elapsed_time_per_iteration_ms: 9.69K | tokens_per_sec: 433K | tokens_per_sec_per_gpu: 6.76K | global_batch_size: 1.02K | lm_loss: 7.24 | lr: 1.47e-05 | model_tflops_per_gpu: 61.4 | hardware_tflops_per_gpu: 61.4 | grad_norm: 4.29
[default0]:07/03/2024 03:50:13 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:  Memory usage: 1888.44MiB. Peak allocated 46946.91MiB. Peak reserved: 48656.00MiB
[default0]:07/03/2024 03:50:23 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: iteration: 20 / 20 | consumed_tokens: 83.9M | elapsed_time_per_iteration_ms: 9.67K | tokens_per_sec: 434K | tokens_per_sec_per_gpu: 6.78K | global_batch_size: 1.02K | lm_loss: 7.15 | lr: 1e-05 | model_tflops_per_gpu: 61.5 | hardware_tflops_per_gpu: 61.5 | grad_norm: 2.97
Saved 1 csv files over 1 completed logs
Processing file: /fsx/ferdinandmom/ferdinand-hf/bench_cluster/results/llama-1B/64_GPUS/dp-16_tp-4_pp-1_mbz-16/profiler/ip-26-0-160-225_11001.1719978491142150701.pt.trace.json
Results written to /fsx/ferdinandmom/ferdinand-hf/bench_cluster/results/llama-1B/64_GPUS/dp-16_tp-4_pp-1_mbz-16/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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