llama-1B/64_GPUS/dp-16_tp-4_pp-1_mbz-2/log.out

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START TIME: Wed Jul  3 04:03:49 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 04:03:54.924000 139838662874944 torch/distributed/run.py:757] 
W0703 04:03:54.924000 139838662874944 torch/distributed/run.py:757] *****************************************
W0703 04:03:54.924000 139838662874944 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 04:03:54.924000 139838662874944 torch/distributed/run.py:757] *****************************************
W0703 04:03:55.749000 140451006445376 torch/distributed/run.py:757] 
W0703 04:03:55.749000 140451006445376 torch/distributed/run.py:757] *****************************************
W0703 04:03:55.749000 140451006445376 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 04:03:55.749000 140451006445376 torch/distributed/run.py:757] *****************************************
W0703 04:03:55.846000 139761591097152 torch/distributed/run.py:757] 
W0703 04:03:55.846000 139761591097152 torch/distributed/run.py:757] *****************************************
W0703 04:03:55.846000 139761591097152 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 04:03:55.846000 139761591097152 torch/distributed/run.py:757] *****************************************
W0703 04:03:55.865000 140709875398464 torch/distributed/run.py:757] 
W0703 04:03:55.865000 140709875398464 torch/distributed/run.py:757] *****************************************
W0703 04:03:55.865000 140709875398464 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 04:03:55.865000 140709875398464 torch/distributed/run.py:757] *****************************************
W0703 04:03:55.922000 139943634851648 torch/distributed/run.py:757] 
W0703 04:03:55.922000 139943634851648 torch/distributed/run.py:757] *****************************************
W0703 04:03:55.922000 139943634851648 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 04:03:55.922000 139943634851648 torch/distributed/run.py:757] *****************************************
W0703 04:03:56.067000 140544874116928 torch/distributed/run.py:757] 
W0703 04:03:56.067000 140544874116928 torch/distributed/run.py:757] *****************************************
W0703 04:03:56.067000 140544874116928 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 04:03:56.067000 140544874116928 torch/distributed/run.py:757] *****************************************
W0703 04:03:56.117000 139834290403136 torch/distributed/run.py:757] 
W0703 04:03:56.117000 139834290403136 torch/distributed/run.py:757] *****************************************
W0703 04:03:56.117000 139834290403136 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 04:03:56.117000 139834290403136 torch/distributed/run.py:757] *****************************************
W0703 04:03:56.263000 139639773333312 torch/distributed/run.py:757] 
W0703 04:03:56.263000 139639773333312 torch/distributed/run.py:757] *****************************************
W0703 04:03:56.263000 139639773333312 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 04:03:56.263000 139639773333312 torch/distributed/run.py:757] *****************************************
[default0]:07/03/2024 04:04:21 [WARNING|DP=0|PP=0|TP=0|ip-26-0-160-192]: [Vocab Size Padding] Padded vocab (size: 50257) with 3 dummy tokens (new size: 50260)
[default0]:07/03/2024 04:04:21 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: Config:
[default0]:07/03/2024 04:04:21 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: Config(general=GeneralArgs(project='bench_cluster',
[default0]:07/03/2024 04:04:21 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:                            run='%date_%jobid',
[default0]:07/03/2024 04:04:21 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:                            seed=42,
[default0]:07/03/2024 04:04:21 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:                            step=None,
[default0]:07/03/2024 04:04:21 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:                            consumed_train_samples=None,
[default0]:07/03/2024 04:04:21 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:                            benchmark_csv_path=None,
[default0]:07/03/2024 04:04:21 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:                            ignore_sanity_checks=True),
[default0]:07/03/2024 04:04:21 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:        parallelism=ParallelismArgs(dp=16,
[default0]:07/03/2024 04:04:21 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:                                    pp=1,
[default0]:07/03/2024 04:04:21 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:                                    tp=4,
[default0]:07/03/2024 04:04:21 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:                                    pp_engine=<nanotron.parallel.pipeline_parallel.engine.OneForwardOneBackwardPipelineEngine object at 0x7f30e95588b0>,
[default0]:07/03/2024 04:04:21 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:                                    tp_mode=<TensorParallelLinearMode.REDUCE_SCATTER: 2>,
[default0]:07/03/2024 04:04:21 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:                                    tp_linear_async_communication=False,
[default0]:07/03/2024 04:04:21 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:                                    expert_parallel_size=1),
[default0]:07/03/2024 04:04:21 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:        model=ModelArgs(model_config=LlamaConfig(bos_token_id=1,
[default0]:07/03/2024 04:04:21 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:                                                 eos_token_id=2,
[default0]:07/03/2024 04:04:21 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:                                                 hidden_act='silu',
[default0]:07/03/2024 04:04:21 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:                                                 hidden_size=2048,
[default0]:07/03/2024 04:04:21 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:                                                 initializer_range=0.02,
[default0]:07/03/2024 04:04:21 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:                                                 intermediate_size=4096,
[default0]:07/03/2024 04:04:21 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:                                                 is_llama_config=True,
[default0]:07/03/2024 04:04:21 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:                                                 max_position_embeddings=4096,
[default0]:07/03/2024 04:04:21 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:                                                 num_attention_heads=32,
[default0]:07/03/2024 04:04:21 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:                                                 num_hidden_layers=24,
[default0]:07/03/2024 04:04:21 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:                                                 num_key_value_heads=32,
[default0]:07/03/2024 04:04:21 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:                                                 pad_token_id=None,
[default0]:07/03/2024 04:04:21 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:                                                 pretraining_tp=1,
[default0]:07/03/2024 04:04:21 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:                                                 rms_norm_eps=1e-05,
[default0]:07/03/2024 04:04:21 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:                                                 rope_scaling=None,
[default0]:07/03/2024 04:04:21 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:                                                 rope_theta=10000.0,
[default0]:07/03/2024 04:04:21 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:                                                 tie_word_embeddings=True,
[default0]:07/03/2024 04:04:21 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:                                                 use_cache=True,
[default0]:07/03/2024 04:04:21 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:                                                 vocab_size=50260),
[default0]:07/03/2024 04:04:21 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:                        init_method=RandomInit(std=0.025),
[default0]:07/03/2024 04:04:21 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:                        dtype=torch.bfloat16,
[default0]:07/03/2024 04:04:21 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:                        make_vocab_size_divisible_by=1,
[default0]:07/03/2024 04:04:21 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:                        ddp_bucket_cap_mb=25),
[default0]:07/03/2024 04:04:21 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:        tokenizer=TokenizerArgs(tokenizer_name_or_path='openai-community/gpt2',
[default0]:07/03/2024 04:04:21 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:                                tokenizer_revision=None,
[default0]:07/03/2024 04:04:21 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:                                tokenizer_max_length=None),
[default0]:07/03/2024 04:04:21 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:        checkpoints=CheckpointsArgs(checkpoints_path=Path('/dev/null'),
[default0]:07/03/2024 04:04:21 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:                                    checkpoint_interval=100000,
[default0]:07/03/2024 04:04:21 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:                                    save_initial_state=False,
[default0]:07/03/2024 04:04:21 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:                                    resume_checkpoint_path=None,
[default0]:07/03/2024 04:04:21 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:                                    checkpoints_path_is_shared_file_system=False),
[default0]:07/03/2024 04:04:21 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:        logging=LoggingArgs(log_level='info',
[default0]:07/03/2024 04:04:21 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:                            log_level_replica='info',
[default0]:07/03/2024 04:04:21 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:                            iteration_step_info_interval=1),
[default0]:07/03/2024 04:04:21 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:        tokens=TokensArgs(sequence_length=4096,
[default0]:07/03/2024 04:04:21 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:                          train_steps=20,
[default0]:07/03/2024 04:04:21 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:                          micro_batch_size=2,
[default0]:07/03/2024 04:04:21 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:                          batch_accumulation_per_replica=32,
[default0]:07/03/2024 04:04:21 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:                          val_check_interval=-1,
[default0]:07/03/2024 04:04:21 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:                          limit_val_batches=0,
[default0]:07/03/2024 04:04:21 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:                          limit_test_batches=0),
[default0]:07/03/2024 04:04:21 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:        optimizer=OptimizerArgs(optimizer_factory=AdamWOptimizerArgs(adam_eps=1e-08,
[default0]:07/03/2024 04:04:21 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:                                                                     adam_beta1=0.9,
[default0]:07/03/2024 04:04:21 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:                                                                     adam_beta2=0.95,
[default0]:07/03/2024 04:04:21 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:                                                                     torch_adam_is_fused=True,
[default0]:07/03/2024 04:04:21 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:                                                                     name='adamW'),
[default0]:07/03/2024 04:04:21 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:                                zero_stage=1,
[default0]:07/03/2024 04:04:21 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:                                weight_decay=0.01,
[default0]:07/03/2024 04:04:21 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:                                clip_grad=1.0,
[default0]:07/03/2024 04:04:21 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:                                accumulate_grad_in_fp32=True,
[default0]:07/03/2024 04:04:21 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:                                learning_rate_scheduler=LRSchedulerArgs(learning_rate=0.0001,
[default0]:07/03/2024 04:04:21 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:                                                                        lr_warmup_steps=1,
[default0]:07/03/2024 04:04:21 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:                                                                        lr_warmup_style='linear',
[default0]:07/03/2024 04:04:21 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:                                                                        lr_decay_style='linear',
[default0]:07/03/2024 04:04:21 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:                                                                        lr_decay_steps=19,
[default0]:07/03/2024 04:04:21 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:                                                                        lr_decay_starting_step=None,
[default0]:07/03/2024 04:04:21 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:                                                                        min_decay_lr=1e-05)),
[default0]:07/03/2024 04:04:21 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:        data_stages=[DatasetStageArgs(name='Training Stage',
[default0]:07/03/2024 04:04:21 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:                                      start_training_step=1,
[default0]:07/03/2024 04:04:21 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:                                      data=DataArgs(dataset=PretrainDatasetsArgs(hf_dataset_or_datasets='roneneldan/TinyStories',
[default0]:07/03/2024 04:04:21 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:                                                                                 hf_dataset_splits='train',
[default0]:07/03/2024 04:04:21 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:                                                                                 hf_dataset_config_name=None,
[default0]:07/03/2024 04:04:21 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:                                                                                 dataset_processing_num_proc_per_process=64,
[default0]:07/03/2024 04:04:21 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:                                                                                 dataset_overwrite_cache=False,
[default0]:07/03/2024 04:04:21 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:                                                                                 text_column_name='text'),
[default0]:07/03/2024 04:04:21 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:                                                    seed=42,
[default0]:07/03/2024 04:04:21 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:                                                    num_loading_workers=0))],
[default0]:07/03/2024 04:04:21 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:        profiler=ProfilerArgs(profiler_export_path=Path('/fsx/ferdinandmom/ferdinand-hf/bench_cluster/results/llama-1B/64_GPUS/dp-16_tp-4_pp-1_mbz-2')),
[default0]:07/03/2024 04:04:21 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:        lighteval=None)
[default0]:07/03/2024 04:04:21 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: Model Config:
[default0]:07/03/2024 04:04:21 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: LlamaConfig(bos_token_id=1,
[default0]:07/03/2024 04:04:21 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:             eos_token_id=2,
[default0]:07/03/2024 04:04:21 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:             hidden_act='silu',
[default0]:07/03/2024 04:04:21 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:             hidden_size=2048,
[default0]:07/03/2024 04:04:21 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:             initializer_range=0.02,
[default0]:07/03/2024 04:04:21 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:             intermediate_size=4096,
[default0]:07/03/2024 04:04:21 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:             is_llama_config=True,
[default0]:07/03/2024 04:04:21 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:             max_position_embeddings=4096,
[default0]:07/03/2024 04:04:21 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:             num_attention_heads=32,
[default0]:07/03/2024 04:04:21 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:             num_hidden_layers=24,
[default0]:07/03/2024 04:04:21 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:             num_key_value_heads=32,
[default0]:07/03/2024 04:04:21 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:             pad_token_id=None,
[default0]:07/03/2024 04:04:21 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:             pretraining_tp=1,
[default0]:07/03/2024 04:04:21 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:             rms_norm_eps=1e-05,
[default0]:07/03/2024 04:04:21 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:             rope_scaling=None,
[default0]:07/03/2024 04:04:21 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:             rope_theta=10000.0,
[default0]:07/03/2024 04:04:21 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:             tie_word_embeddings=True,
[default0]:07/03/2024 04:04:21 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:             use_cache=True,
[default0]:07/03/2024 04:04:21 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:             vocab_size=50260)
[default0]:07/03/2024 04:04:21 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: Building model..
[default0]:07/03/2024 04:04:21 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: Setting PP block ranks...
[default2]:07/03/2024 04:04:36 [INFO|DP=0|PP=0|TP=2|ip-26-0-160-192]: Local number of parameters: 277M (529.27MiB)
[default2]:07/03/2024 04:04:36 [INFO|DP=0|PP=0|TP=2|ip-26-0-160-192]: [After model building] Memory usage: 554.21MiB. Peak allocated: 606.24MiB Peak reserved: 608.00MiB
[default2]:07/03/2024 04:04:36 [INFO|DP=0|PP=0|TP=2|ip-26-0-160-192]: No checkpoint path provided.
[default1]:07/03/2024 04:04:36 [INFO|DP=0|PP=0|TP=1|ip-26-0-160-192]: Local number of parameters: 277M (529.27MiB)
[default1]:07/03/2024 04:04:36 [INFO|DP=0|PP=0|TP=1|ip-26-0-160-192]: [After model building] Memory usage: 554.21MiB. Peak allocated: 606.24MiB Peak reserved: 608.00MiB
[default3]:07/03/2024 04:04:36 [INFO|DP=0|PP=0|TP=3|ip-26-0-160-192]: Local number of parameters: 277M (529.27MiB)
[default0]:07/03/2024 04:04:36 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: Total number of parameters: 1.11G (2117.09MiB)
[default0]:07/03/2024 04:04:36 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: Local number of parameters: 277M (529.27MiB)
[default1]:07/03/2024 04:04:36 [INFO|DP=0|PP=0|TP=1|ip-26-0-160-192]: No checkpoint path provided.
[default0]:07/03/2024 04:04:36 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: [After model building] Memory usage: 554.21MiB. Peak allocated: 606.24MiB Peak reserved: 608.00MiB
[default3]:07/03/2024 04:04:36 [INFO|DP=0|PP=0|TP=3|ip-26-0-160-192]: [After model building] Memory usage: 554.21MiB. Peak allocated: 606.24MiB Peak reserved: 608.00MiB
[default3]:07/03/2024 04:04:36 [INFO|DP=0|PP=0|TP=3|ip-26-0-160-192]: No checkpoint path provided.
[default0]:07/03/2024 04:04:36 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: No checkpoint path provided.
[default0]:07/03/2024 04:04:36 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: Parametrizing model parameters using StandardParametrizator
[default7]:07/03/2024 04:04:36 [INFO|DP=13|PP=0|TP=3|ip-26-0-172-57]: No checkpoint path provided.
[default0]:07/03/2024 04:04:36 [INFO|DP=12|PP=0|TP=0|ip-26-0-172-57]: No checkpoint path provided.
[default5]:07/03/2024 04:04:36 [INFO|DP=13|PP=0|TP=1|ip-26-0-172-57]: No checkpoint path provided.
[default3]:07/03/2024 04:04:36 [INFO|DP=12|PP=0|TP=3|ip-26-0-172-57]: No checkpoint path provided.
[default6]:07/03/2024 04:04:36 [INFO|DP=13|PP=0|TP=2|ip-26-0-172-57]: No checkpoint path provided.
[default1]:07/03/2024 04:04:36 [INFO|DP=12|PP=0|TP=1|ip-26-0-172-57]: No checkpoint path provided.
[default4]:07/03/2024 04:04:36 [INFO|DP=13|PP=0|TP=0|ip-26-0-172-57]: No checkpoint path provided.
[default2]:07/03/2024 04:04:36 [INFO|DP=12|PP=0|TP=2|ip-26-0-172-57]: No checkpoint path provided.
[default1]:07/03/2024 04:04:36 [INFO|DP=4|PP=0|TP=1|ip-26-0-163-220]: No checkpoint path provided.
[default1]:07/03/2024 04:04:36 [INFO|DP=2|PP=0|TP=1|ip-26-0-161-178]: No checkpoint path provided.
[default0]:07/03/2024 04:04:36 [INFO|DP=6|PP=0|TP=0|ip-26-0-163-226]: No checkpoint path provided.
[default7]:07/03/2024 04:04:36 [INFO|DP=5|PP=0|TP=3|ip-26-0-163-220]: No checkpoint path provided.
[default6]:07/03/2024 04:04:36 [INFO|DP=3|PP=0|TP=2|ip-26-0-161-178]: No checkpoint path provided.
[default0]:07/03/2024 04:04:36 [INFO|DP=2|PP=0|TP=0|ip-26-0-161-178]: No checkpoint path provided.
[default7]:07/03/2024 04:04:36 [INFO|DP=3|PP=0|TP=3|ip-26-0-161-178]: No checkpoint path provided.
[default3]:07/03/2024 04:04:36 [INFO|DP=2|PP=0|TP=3|ip-26-0-161-178]: No checkpoint path provided.
[default0]:07/03/2024 04:04:36 [INFO|DP=4|PP=0|TP=0|ip-26-0-163-220]: No checkpoint path provided.
[default4]:07/03/2024 04:04:36 [INFO|DP=3|PP=0|TP=0|ip-26-0-161-178]: No checkpoint path provided.
[default6]:07/03/2024 04:04:36 [INFO|DP=5|PP=0|TP=2|ip-26-0-163-220]: No checkpoint path provided.
[default2]:07/03/2024 04:04:36 [INFO|DP=6|PP=0|TP=2|ip-26-0-163-226]: No checkpoint path provided.
[default4]:07/03/2024 04:04:36 [INFO|DP=7|PP=0|TP=0|ip-26-0-163-226]: No checkpoint path provided.
[default1]:07/03/2024 04:04:36 [INFO|DP=6|PP=0|TP=1|ip-26-0-163-226]: No checkpoint path provided.
[default6]:07/03/2024 04:04:36 [INFO|DP=7|PP=0|TP=2|ip-26-0-163-226]: No checkpoint path provided.
[default3]:07/03/2024 04:04:36 [INFO|DP=6|PP=0|TP=3|ip-26-0-163-226]: No checkpoint path provided.
[default3]:07/03/2024 04:04:36 [INFO|DP=4|PP=0|TP=3|ip-26-0-163-220]: No checkpoint path provided.
[default5]:07/03/2024 04:04:36 [INFO|DP=7|PP=0|TP=1|ip-26-0-163-226]: No checkpoint path provided.
[default2]:07/03/2024 04:04:36 [INFO|DP=4|PP=0|TP=2|ip-26-0-163-220]: No checkpoint path provided.
[default5]:07/03/2024 04:04:36 [INFO|DP=5|PP=0|TP=1|ip-26-0-163-220]: No checkpoint path provided.
[default4]:07/03/2024 04:04:36 [INFO|DP=5|PP=0|TP=0|ip-26-0-163-220]: No checkpoint path provided.
[default5]:07/03/2024 04:04:36 [INFO|DP=3|PP=0|TP=1|ip-26-0-161-178]: No checkpoint path provided.
[default2]:07/03/2024 04:04:36 [INFO|DP=2|PP=0|TP=2|ip-26-0-161-178]: No checkpoint path provided.
[default1]:07/03/2024 04:04:36 [INFO|DP=14|PP=0|TP=1|ip-26-0-172-73]: No checkpoint path provided.
[default7]:07/03/2024 04:04:36 [INFO|DP=7|PP=0|TP=3|ip-26-0-163-226]: No checkpoint path provided.
[default2]:07/03/2024 04:04:36 [INFO|DP=14|PP=0|TP=2|ip-26-0-172-73]: No checkpoint path provided.
[default3]:07/03/2024 04:04:36 [INFO|DP=14|PP=0|TP=3|ip-26-0-172-73]: No checkpoint path provided.
[default5]:07/03/2024 04:04:36 [INFO|DP=1|PP=0|TP=1|ip-26-0-160-192]: No checkpoint path provided.
[default6]:07/03/2024 04:04:36 [INFO|DP=1|PP=0|TP=2|ip-26-0-160-192]: No checkpoint path provided.
[default4]:07/03/2024 04:04:36 [INFO|DP=15|PP=0|TP=0|ip-26-0-172-73]: No checkpoint path provided.
[default4]:07/03/2024 04:04:36 [INFO|DP=1|PP=0|TP=0|ip-26-0-160-192]: No checkpoint path provided.
[default6]:07/03/2024 04:04:36 [INFO|DP=15|PP=0|TP=2|ip-26-0-172-73]: No checkpoint path provided.
[default0]:07/03/2024 04:04:36 [INFO|DP=14|PP=0|TP=0|ip-26-0-172-73]: No checkpoint path provided.
[default5]:07/03/2024 04:04:36 [INFO|DP=15|PP=0|TP=1|ip-26-0-172-73]: No checkpoint path provided.
[default7]:07/03/2024 04:04:36 [INFO|DP=15|PP=0|TP=3|ip-26-0-172-73]: No checkpoint path provided.
[default7]:07/03/2024 04:04:36 [INFO|DP=1|PP=0|TP=3|ip-26-0-160-192]: No checkpoint path provided.
[default3]:07/03/2024 04:04:36 [INFO|DP=10|PP=0|TP=3|ip-26-0-169-86]: No checkpoint path provided.
[default0]:07/03/2024 04:04:36 [INFO|DP=10|PP=0|TP=0|ip-26-0-169-86]: No checkpoint path provided.
[default1]:07/03/2024 04:04:36 [INFO|DP=10|PP=0|TP=1|ip-26-0-169-86]: No checkpoint path provided.
[default1]:07/03/2024 04:04:36 [INFO|DP=8|PP=0|TP=1|ip-26-0-168-238]: No checkpoint path provided.
[default0]:07/03/2024 04:04:36 [INFO|DP=8|PP=0|TP=0|ip-26-0-168-238]: No checkpoint path provided.
[default3]:07/03/2024 04:04:36 [INFO|DP=8|PP=0|TP=3|ip-26-0-168-238]: No checkpoint path provided.
[default2]:07/03/2024 04:04:36 [INFO|DP=8|PP=0|TP=2|ip-26-0-168-238]: No checkpoint path provided.
[default2]:07/03/2024 04:04:36 [INFO|DP=10|PP=0|TP=2|ip-26-0-169-86]: No checkpoint path provided.
[default6]:07/03/2024 04:04:36 [INFO|DP=11|PP=0|TP=2|ip-26-0-169-86]: No checkpoint path provided.
[default4]:07/03/2024 04:04:36 [INFO|DP=9|PP=0|TP=0|ip-26-0-168-238]: No checkpoint path provided.
[default5]:07/03/2024 04:04:36 [INFO|DP=9|PP=0|TP=1|ip-26-0-168-238]: No checkpoint path provided.
[default7]:07/03/2024 04:04:36 [INFO|DP=9|PP=0|TP=3|ip-26-0-168-238]: No checkpoint path provided.
[default6]:07/03/2024 04:04:36 [INFO|DP=9|PP=0|TP=2|ip-26-0-168-238]: No checkpoint path provided.
[default7]:07/03/2024 04:04:36 [INFO|DP=11|PP=0|TP=3|ip-26-0-169-86]: No checkpoint path provided.
[default5]:07/03/2024 04:04:36 [INFO|DP=11|PP=0|TP=1|ip-26-0-169-86]: No checkpoint path provided.
[default4]:07/03/2024 04:04:36 [INFO|DP=11|PP=0|TP=0|ip-26-0-169-86]: No checkpoint path provided.
[default0]:07/03/2024 04:04:39 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: [Optimizer Building] Using LearningRateForSP as learning rate
[default0]:07/03/2024 04:04:39 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: [ZeRO sharding] Size of optimizer params per rank:
[default0]:07/03/2024 04:04:39 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: [ZeRO sharding] DP Rank 0 has 17.3M out of 277M (6.25%) params' optimizer states
[default0]:07/03/2024 04:04:39 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: [ZeRO sharding] DP Rank 1 has 17.3M out of 277M (6.25%) params' optimizer states
[default0]:07/03/2024 04:04:39 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: [ZeRO sharding] DP Rank 2 has 17.3M out of 277M (6.25%) params' optimizer states
[default0]:07/03/2024 04:04:39 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: [ZeRO sharding] DP Rank 3 has 17.3M out of 277M (6.25%) params' optimizer states
[default0]:07/03/2024 04:04:39 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: [ZeRO sharding] DP Rank 4 has 17.3M out of 277M (6.25%) params' optimizer states
[default0]:07/03/2024 04:04:39 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: [ZeRO sharding] DP Rank 5 has 17.3M out of 277M (6.25%) params' optimizer states
[default0]:07/03/2024 04:04:39 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: [ZeRO sharding] DP Rank 6 has 17.3M out of 277M (6.25%) params' optimizer states
[default0]:07/03/2024 04:04:39 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: [ZeRO sharding] DP Rank 7 has 17.3M out of 277M (6.25%) params' optimizer states
[default0]:07/03/2024 04:04:39 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: [ZeRO sharding] DP Rank 8 has 17.3M out of 277M (6.25%) params' optimizer states
[default0]:07/03/2024 04:04:39 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: [ZeRO sharding] DP Rank 9 has 17.3M out of 277M (6.25%) params' optimizer states
[default0]:07/03/2024 04:04:39 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: [ZeRO sharding] DP Rank 10 has 17.3M out of 277M (6.25%) params' optimizer states
[default0]:07/03/2024 04:04:39 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: [ZeRO sharding] DP Rank 11 has 17.3M out of 277M (6.25%) params' optimizer states
[default0]:07/03/2024 04:04:39 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: [ZeRO sharding] DP Rank 12 has 17.3M out of 277M (6.25%) params' optimizer states
[default0]:07/03/2024 04:04:39 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: [ZeRO sharding] DP Rank 13 has 17.3M out of 277M (6.25%) params' optimizer states
[default0]:07/03/2024 04:04:39 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: [ZeRO sharding] DP Rank 14 has 17.3M out of 277M (6.25%) params' optimizer states
[default0]:07/03/2024 04:04:39 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: [ZeRO sharding] DP Rank 15 has 17.3M out of 277M (6.25%) params' optimizer states
[default0]:07/03/2024 04:04:41 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: [Training Plan] Stage Training Stage has 19 remaining training steps and has consumed 0 samples
[default0]:07/03/2024 04:04:41 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: Using `datasets` library
[default0]:07/03/2024 04:04:41 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: Loading tokenizer from openai-community/gpt2 and transformers/hf_hub versions ('4.41.2', '0.23.4')
[default0]:07/03/2024 04:04:41 [WARNING|DP=0|PP=0|TP=0|ip-26-0-160-192]: 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 04:04:43 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: [Training Plan] There are 1 training stages 
[default0]:07/03/2024 04:04:43 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: [Stage Training Stage] start from step 1 
[default0]:07/03/2024 04:04:43 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: 
[default0]:07/03/2024 04:04:43 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: [Start training] datetime: 2024-07-03 04:04:43.925243 | mbs: 2 | grad_accum: 32 | global_batch_size: 1024 | sequence_length: 4096 | train_steps: 20 | start_iteration_step: 0 | consumed_train_samples: 0
[default0]:07/03/2024 04:04:43 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: Resuming training from stage Training Stage, it has trained for 0 samples and has 19 remaining train steps
[default0]:07/03/2024 04:04:43 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:  Memory usage: 1678.92MiB. Peak allocated 1678.92MiB. Peak reserved: 1736.00MiB
[default1]:07/03/2024 04:04:44 [WARNING|DP=2|PP=0|TP=1|ip-26-0-161-178]: Repo card metadata block was not found. Setting CardData to empty.
[default0]:07/03/2024 04:04:44 [WARNING|DP=12|PP=0|TP=0|ip-26-0-172-57]: Repo card metadata block was not found. Setting CardData to empty.
[default7]:07/03/2024 04:04:44 [WARNING|DP=5|PP=0|TP=3|ip-26-0-163-220]: Repo card metadata block was not found. Setting CardData to empty.
[default0]:Repo card metadata block was not found. Setting CardData to empty.
[default5]:07/03/2024 04:04:44 [WARNING|DP=13|PP=0|TP=1|ip-26-0-172-57]: Repo card metadata block was not found. Setting CardData to empty.
[default7]:07/03/2024 04:04:44 [WARNING|DP=3|PP=0|TP=3|ip-26-0-161-178]: Repo card metadata block was not found. Setting CardData to empty.
[default0]:07/03/2024 04:04:44 [WARNING|DP=6|PP=0|TP=0|ip-26-0-163-226]: Repo card metadata block was not found. Setting CardData to empty.
[default6]:Repo card metadata block was not found. Setting CardData to empty.
[default0]:07/03/2024 04:04:44 [WARNING|DP=4|PP=0|TP=0|ip-26-0-163-220]: Repo card metadata block was not found. Setting CardData to empty.
[default1]:Repo card metadata block was not found. Setting CardData to empty.
[default0]:Repo card metadata block was not found. Setting CardData to empty.
[default3]:07/03/2024 04:04:44 [WARNING|DP=6|PP=0|TP=3|ip-26-0-163-226]: Repo card metadata block was not found. Setting CardData to empty.
[default6]:07/03/2024 04:04:44 [WARNING|DP=7|PP=0|TP=2|ip-26-0-163-226]: Repo card metadata block was not found. Setting CardData to empty.
[default3]:Repo card metadata block was not found. Setting CardData to empty.
[default1]:07/03/2024 04:04:44 [WARNING|DP=6|PP=0|TP=1|ip-26-0-163-226]: 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 04:04:44 [WARNING|DP=7|PP=0|TP=3|ip-26-0-163-226]: Repo card metadata block was not found. Setting CardData to empty.
[default5]:07/03/2024 04:04:44 [WARNING|DP=7|PP=0|TP=1|ip-26-0-163-226]: Repo card metadata block was not found. Setting CardData to empty.
[default2]:Repo card metadata block was not found. Setting CardData to empty.
[default0]:Repo card metadata block was not found. Setting CardData to empty.
[default7]:Repo card metadata block was not found. Setting CardData to empty.
[default2]:07/03/2024 04:04:44 [WARNING|DP=4|PP=0|TP=2|ip-26-0-163-220]: Repo card metadata block was not found. Setting CardData to empty.
[default5]:07/03/2024 04:04:44 [WARNING|DP=5|PP=0|TP=1|ip-26-0-163-220]: Repo card metadata block was not found. Setting CardData to empty.
[default6]:07/03/2024 04:04:44 [WARNING|DP=13|PP=0|TP=2|ip-26-0-172-57]: Repo card metadata block was not found. Setting CardData to empty.
[default2]:07/03/2024 04:04:44 [WARNING|DP=12|PP=0|TP=2|ip-26-0-172-57]: Repo card metadata block was not found. Setting CardData to empty.
[default1]:07/03/2024 04:04:44 [WARNING|DP=12|PP=0|TP=1|ip-26-0-172-57]: 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 04:04:44 [WARNING|DP=13|PP=0|TP=0|ip-26-0-172-57]: Repo card metadata block was not found. Setting CardData to empty.
[default5]:Repo card metadata block was not found. Setting CardData to empty.
[default3]:07/03/2024 04:04:44 [WARNING|DP=0|PP=0|TP=3|ip-26-0-160-192]: Repo card metadata block was not found. Setting CardData to empty.
[default1]:07/03/2024 04:04:44 [WARNING|DP=0|PP=0|TP=1|ip-26-0-160-192]: Repo card metadata block was not found. Setting CardData to empty.
[default1]:Repo card metadata block was not found. Setting CardData to empty.
[default5]:Repo card metadata block was not found. Setting CardData to empty.
[default3]:07/03/2024 04:04:44 [WARNING|DP=14|PP=0|TP=3|ip-26-0-172-73]: Repo card metadata block was not found. Setting CardData to empty.
[default2]:07/03/2024 04:04:44 [WARNING|DP=14|PP=0|TP=2|ip-26-0-172-73]: Repo card metadata block was not found. Setting CardData to empty.
[default3]:07/03/2024 04:04:44 [WARNING|DP=8|PP=0|TP=3|ip-26-0-168-238]: Repo card metadata block was not found. Setting CardData to empty.
[default5]:07/03/2024 04:04:44 [WARNING|DP=9|PP=0|TP=1|ip-26-0-168-238]: Repo card metadata block was not found. Setting CardData to empty.
[default1]:07/03/2024 04:04:44 [WARNING|DP=10|PP=0|TP=1|ip-26-0-169-86]: Repo card metadata block was not found. Setting CardData to empty.
[default5]:Repo card metadata block was not found. Setting CardData to empty.
[default0]:07/03/2024 04:04:44 [WARNING|DP=8|PP=0|TP=0|ip-26-0-168-238]: Repo card metadata block was not found. Setting CardData to empty.
[default1]:07/03/2024 04:04:44 [WARNING|DP=8|PP=0|TP=1|ip-26-0-168-238]: Repo card metadata block was not found. Setting CardData to empty.
[default5]:07/03/2024 04:04:44 [WARNING|DP=1|PP=0|TP=1|ip-26-0-160-192]: Repo card metadata block was not found. Setting CardData to empty.
[default6]:07/03/2024 04:04:44 [WARNING|DP=1|PP=0|TP=2|ip-26-0-160-192]: Repo card metadata block was not found. Setting CardData to empty.
[default4]:07/03/2024 04:04:44 [WARNING|DP=1|PP=0|TP=0|ip-26-0-160-192]: Repo card metadata block was not found. Setting CardData to empty.
[default7]:Repo card metadata block was not found. Setting CardData to empty.
[default1]:Repo card metadata block was not found. Setting CardData to empty.
[default4]:07/03/2024 04:04:44 [WARNING|DP=15|PP=0|TP=0|ip-26-0-172-73]: Repo card metadata block was not found. Setting CardData to empty.
[default6]:07/03/2024 04:04:44 [WARNING|DP=15|PP=0|TP=2|ip-26-0-172-73]: 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.
[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.
[default5]:07/03/2024 04:04:44 [WARNING|DP=11|PP=0|TP=1|ip-26-0-169-86]: Repo card metadata block was not found. Setting CardData to empty.
[default4]:07/03/2024 04:04:44 [WARNING|DP=11|PP=0|TP=0|ip-26-0-169-86]: Repo card metadata block was not found. Setting CardData to empty.
[default5]:Repo card metadata block was not found. Setting CardData to empty.
[default2]:07/03/2024 04:04:44 [WARNING|DP=10|PP=0|TP=2|ip-26-0-169-86]: 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.
[default3]:Repo card metadata block was not found. Setting CardData to empty.
[default5]:Repo card metadata block was not found. Setting CardData to empty.
[default1]: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.
[default1]: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.
[default6]: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.
[default2]: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 04:04:44 [WARNING|DP=4|PP=0|TP=1|ip-26-0-163-220]: Repo card metadata block was not found. Setting CardData to empty.
[default2]:Repo card metadata block was not found. Setting CardData to empty.
[default7]:07/03/2024 04:04:44 [WARNING|DP=13|PP=0|TP=3|ip-26-0-172-57]: Repo card metadata block was not found. Setting CardData to empty.
[default3]:07/03/2024 04:04:44 [WARNING|DP=12|PP=0|TP=3|ip-26-0-172-57]: Repo card metadata block was not found. Setting CardData to empty.
[default0]:Repo card metadata block was not found. Setting CardData to empty.
[default6]:07/03/2024 04:04:44 [WARNING|DP=3|PP=0|TP=2|ip-26-0-161-178]: Repo card metadata block was not found. Setting CardData to empty.
[default0]:07/03/2024 04:04:44 [WARNING|DP=2|PP=0|TP=0|ip-26-0-161-178]: 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 04:04:44 [WARNING|DP=5|PP=0|TP=2|ip-26-0-163-220]: Repo card metadata block was not found. Setting CardData to empty.
[default2]:07/03/2024 04:04:44 [WARNING|DP=6|PP=0|TP=2|ip-26-0-163-226]: Repo card metadata block was not found. Setting CardData to empty.
[default4]:07/03/2024 04:04:44 [WARNING|DP=7|PP=0|TP=0|ip-26-0-163-226]: 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 04:04:44 [WARNING|DP=4|PP=0|TP=3|ip-26-0-163-220]: Repo card metadata block was not found. Setting CardData to empty.
[default5]:07/03/2024 04:04:44 [WARNING|DP=3|PP=0|TP=1|ip-26-0-161-178]: Repo card metadata block was not found. Setting CardData to empty.
[default6]:Repo card metadata block was not found. Setting CardData to empty.
[default2]:07/03/2024 04:04:44 [WARNING|DP=2|PP=0|TP=2|ip-26-0-161-178]: Repo card metadata block was not found. Setting CardData to empty.
[default5]:Repo card metadata block was not found. Setting CardData to empty.
[default6]:Repo card metadata block was not found. Setting CardData to empty.
[default4]:07/03/2024 04:04:44 [WARNING|DP=5|PP=0|TP=0|ip-26-0-163-220]: Repo card metadata block was not found. Setting CardData to empty.
[default1]:07/03/2024 04:04:44 [WARNING|DP=14|PP=0|TP=1|ip-26-0-172-73]: Repo card metadata block was not found. Setting CardData to empty.
[default3]:07/03/2024 04:04:44 [WARNING|DP=10|PP=0|TP=3|ip-26-0-169-86]: 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 04:04:44 [WARNING|DP=0|PP=0|TP=2|ip-26-0-160-192]: Repo card metadata block was not found. Setting CardData to empty.
[default3]:Repo card metadata block was not found. Setting CardData to empty.
[default6]:07/03/2024 04:04:44 [WARNING|DP=11|PP=0|TP=2|ip-26-0-169-86]: Repo card metadata block was not found. Setting CardData to empty.
[default0]:07/03/2024 04:04:44 [WARNING|DP=10|PP=0|TP=0|ip-26-0-169-86]: Repo card metadata block was not found. Setting CardData to empty.
[default4]:07/03/2024 04:04:44 [WARNING|DP=9|PP=0|TP=0|ip-26-0-168-238]: Repo card metadata block was not found. Setting CardData to empty.
[default0]:Repo card metadata block was not found. Setting CardData to empty.
[default3]:Repo card metadata block was not found. Setting CardData to empty.
[default6]:07/03/2024 04:04:44 [WARNING|DP=9|PP=0|TP=2|ip-26-0-168-238]: Repo card metadata block was not found. Setting CardData to empty.
[default4]: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 04:04:44 [WARNING|DP=14|PP=0|TP=0|ip-26-0-172-73]: Repo card metadata block was not found. Setting CardData to empty.
[default5]:07/03/2024 04:04:44 [WARNING|DP=15|PP=0|TP=1|ip-26-0-172-73]: Repo card metadata block was not found. Setting CardData to empty.
[default2]:07/03/2024 04:04:44 [WARNING|DP=8|PP=0|TP=2|ip-26-0-168-238]: Repo card metadata block was not found. Setting CardData to empty.
[default7]:07/03/2024 04:04:44 [WARNING|DP=15|PP=0|TP=3|ip-26-0-172-73]: 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 04:04:44 [WARNING|DP=11|PP=0|TP=3|ip-26-0-169-86]: Repo card metadata block was not found. Setting CardData to empty.
[default7]:Repo card metadata block was not found. Setting CardData to empty.
[default3]:Repo card metadata block was not found. Setting CardData to empty.
[default6]:Repo card metadata block was not found. Setting CardData to empty.
[default2]:Repo card metadata block was not found. Setting CardData to empty.
[default2]:Repo card metadata block was not found. Setting CardData to empty.
[default7]:Repo card metadata block was not found. Setting CardData to empty.
[default7]:07/03/2024 04:04:44 [WARNING|DP=1|PP=0|TP=3|ip-26-0-160-192]: 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.
[default7]:Repo card metadata block was not found. Setting CardData to empty.
[default3]:07/03/2024 04:04:44 [WARNING|DP=2|PP=0|TP=3|ip-26-0-161-178]: Repo card metadata block was not found. Setting CardData to empty.
[default3]:Repo card metadata block was not found. Setting CardData to empty.
[default4]:07/03/2024 04:04:44 [WARNING|DP=3|PP=0|TP=0|ip-26-0-161-178]: Repo card metadata block was not found. Setting CardData to empty.
[default7]:07/03/2024 04:04:44 [WARNING|DP=9|PP=0|TP=3|ip-26-0-168-238]: Repo card metadata block was not found. Setting CardData to empty.
[default4]:Repo card metadata block was not found. Setting CardData to empty.
[default0]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/autograd/graph.py:744: UserWarning: c10d::allreduce_: an autograd kernel was not registered to the Autograd key(s) but we are trying to backprop through it. This may lead to silently incorrect behavior. This behavior is deprecated and will be removed in a future version of PyTorch. If your operator is differentiable, please ensure you have registered an autograd kernel to the correct Autograd key (e.g. DispatchKey::Autograd, DispatchKey::CompositeImplicitAutograd). If your operator is not differentiable, or to squash this warning and use the previous behavior, please register torch::CppFunction::makeFallthrough() to DispatchKey::Autograd. (Triggered internally at ../torch/csrc/autograd/autograd_not_implemented_fallback.cpp:63.)
[default0]:  return Variable._execution_engine.run_backward(  # Calls into the C++ engine to run the backward pass
[default1]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/autograd/graph.py:744: UserWarning: c10d::allreduce_: an autograd kernel was not registered to the Autograd key(s) but we are trying to backprop through it. This may lead to silently incorrect behavior. This behavior is deprecated and will be removed in a future version of PyTorch. If your operator is differentiable, please ensure you have registered an autograd kernel to the correct Autograd key (e.g. DispatchKey::Autograd, DispatchKey::CompositeImplicitAutograd). If your operator is not differentiable, or to squash this warning and use the previous behavior, please register torch::CppFunction::makeFallthrough() to DispatchKey::Autograd. (Triggered internally at ../torch/csrc/autograd/autograd_not_implemented_fallback.cpp:63.)
[default1]:  return Variable._execution_engine.run_backward(  # Calls into the C++ engine to run the backward pass
[default2]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/autograd/graph.py:744: UserWarning: c10d::allreduce_: an autograd kernel was not registered to the Autograd key(s) but we are trying to backprop through it. This may lead to silently incorrect behavior. This behavior is deprecated and will be removed in a future version of PyTorch. If your operator is differentiable, please ensure you have registered an autograd kernel to the correct Autograd key (e.g. DispatchKey::Autograd, DispatchKey::CompositeImplicitAutograd). If your operator is not differentiable, or to squash this warning and use the previous behavior, please register torch::CppFunction::makeFallthrough() to DispatchKey::Autograd. (Triggered internally at ../torch/csrc/autograd/autograd_not_implemented_fallback.cpp:63.)
[default2]:  return Variable._execution_engine.run_backward(  # Calls into the C++ engine to run the backward pass
[default4]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/autograd/graph.py:744: UserWarning: c10d::allreduce_: an autograd kernel was not registered to the Autograd key(s) but we are trying to backprop through it. This may lead to silently incorrect behavior. This behavior is deprecated and will be removed in a future version of PyTorch. If your operator is differentiable, please ensure you have registered an autograd kernel to the correct Autograd key (e.g. DispatchKey::Autograd, DispatchKey::CompositeImplicitAutograd). If your operator is not differentiable, or to squash this warning and use the previous behavior, please register torch::CppFunction::makeFallthrough() to DispatchKey::Autograd. (Triggered internally at ../torch/csrc/autograd/autograd_not_implemented_fallback.cpp:63.)
[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.)
[default4]:  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
[default6]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/autograd/graph.py:744: UserWarning: c10d::allreduce_: an autograd kernel was not registered to the Autograd key(s) but we are trying to backprop through it. This may lead to silently incorrect behavior. This behavior is deprecated and will be removed in a future version of PyTorch. If your operator is differentiable, please ensure you have registered an autograd kernel to the correct Autograd key (e.g. DispatchKey::Autograd, DispatchKey::CompositeImplicitAutograd). If your operator is not differentiable, or to squash this warning and use the previous behavior, please register torch::CppFunction::makeFallthrough() to DispatchKey::Autograd. (Triggered internally at ../torch/csrc/autograd/autograd_not_implemented_fallback.cpp:63.)
[default6]:  return Variable._execution_engine.run_backward(  # Calls into the C++ engine to run the backward pass
[default7]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/autograd/graph.py:744: UserWarning: c10d::allreduce_: an autograd kernel was not registered to the Autograd key(s) but we are trying to backprop through it. This may lead to silently incorrect behavior. This behavior is deprecated and will be removed in a future version of PyTorch. If your operator is differentiable, please ensure you have registered an autograd kernel to the correct Autograd key (e.g. DispatchKey::Autograd, DispatchKey::CompositeImplicitAutograd). If your operator is not differentiable, or to squash this warning and use the previous behavior, please register torch::CppFunction::makeFallthrough() to DispatchKey::Autograd. (Triggered internally at ../torch/csrc/autograd/autograd_not_implemented_fallback.cpp:63.)
[default7]:  return Variable._execution_engine.run_backward(  # Calls into the C++ engine to run the backward pass
[default5]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/autograd/graph.py:744: UserWarning: c10d::allreduce_: an autograd kernel was not registered to the Autograd key(s) but we are trying to backprop through it. This may lead to silently incorrect behavior. This behavior is deprecated and will be removed in a future version of PyTorch. If your operator is differentiable, please ensure you have registered an autograd kernel to the correct Autograd key (e.g. DispatchKey::Autograd, DispatchKey::CompositeImplicitAutograd). If your operator is not differentiable, or to squash this warning and use the previous behavior, please register torch::CppFunction::makeFallthrough() to DispatchKey::Autograd. (Triggered internally at ../torch/csrc/autograd/autograd_not_implemented_fallback.cpp:63.)
[default5]:  return Variable._execution_engine.run_backward(  # Calls into the C++ engine to run the backward pass
[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
[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
[default2]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/autograd/graph.py:744: UserWarning: c10d::allreduce_: an autograd kernel was not registered to the Autograd key(s) but we are trying to backprop through it. This may lead to silently incorrect behavior. This behavior is deprecated and will be removed in a future version of PyTorch. If your operator is differentiable, please ensure you have registered an autograd kernel to the correct Autograd key (e.g. DispatchKey::Autograd, DispatchKey::CompositeImplicitAutograd). If your operator is not differentiable, or to squash this warning and use the previous behavior, please register torch::CppFunction::makeFallthrough() to DispatchKey::Autograd. (Triggered internally at ../torch/csrc/autograd/autograd_not_implemented_fallback.cpp:63.)
[default2]:  return Variable._execution_engine.run_backward(  # Calls into the C++ engine to run the backward pass
[default0]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/autograd/graph.py:744: UserWarning: c10d::allreduce_: an autograd kernel was not registered to the Autograd key(s) but we are trying to backprop through it. This may lead to silently incorrect behavior. This behavior is deprecated and will be removed in a future version of PyTorch. If your operator is differentiable, please ensure you have registered an autograd kernel to the correct Autograd key (e.g. DispatchKey::Autograd, DispatchKey::CompositeImplicitAutograd). If your operator is not differentiable, or to squash this warning and use the previous behavior, please register torch::CppFunction::makeFallthrough() to DispatchKey::Autograd. (Triggered internally at ../torch/csrc/autograd/autograd_not_implemented_fallback.cpp:63.)
[default0]:  return Variable._execution_engine.run_backward(  # Calls into the C++ engine to run the backward pass
[default1]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/autograd/graph.py:744: UserWarning: c10d::allreduce_: an autograd kernel was not registered to the Autograd key(s) but we are trying to backprop through it. This may lead to silently incorrect behavior. This behavior is deprecated and will be removed in a future version of PyTorch. If your operator is differentiable, please ensure you have registered an autograd kernel to the correct Autograd key (e.g. DispatchKey::Autograd, DispatchKey::CompositeImplicitAutograd). If your operator is not differentiable, or to squash this warning and use the previous behavior, please register torch::CppFunction::makeFallthrough() to DispatchKey::Autograd. (Triggered internally at ../torch/csrc/autograd/autograd_not_implemented_fallback.cpp:63.)
[default1]:  return Variable._execution_engine.run_backward(  # Calls into the C++ engine to run the backward pass
[default3]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/autograd/graph.py:744: UserWarning: c10d::allreduce_: an autograd kernel was not registered to the Autograd key(s) but we are trying to backprop through it. This may lead to silently incorrect behavior. This behavior is deprecated and will be removed in a future version of PyTorch. If your operator is differentiable, please ensure you have registered an autograd kernel to the correct Autograd key (e.g. DispatchKey::Autograd, DispatchKey::CompositeImplicitAutograd). If your operator is not differentiable, or to squash this warning and use the previous behavior, please register torch::CppFunction::makeFallthrough() to DispatchKey::Autograd. (Triggered internally at ../torch/csrc/autograd/autograd_not_implemented_fallback.cpp:63.)
[default3]:  return Variable._execution_engine.run_backward(  # Calls into the C++ engine to run the backward pass
[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
[default1]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/autograd/graph.py:744: UserWarning: c10d::allreduce_: an autograd kernel was not registered to the Autograd key(s) but we are trying to backprop through it. This may lead to silently incorrect behavior. This behavior is deprecated and will be removed in a future version of PyTorch. If your operator is differentiable, please ensure you have registered an autograd kernel to the correct Autograd key (e.g. DispatchKey::Autograd, DispatchKey::CompositeImplicitAutograd). If your operator is not differentiable, or to squash this warning and use the previous behavior, please register torch::CppFunction::makeFallthrough() to DispatchKey::Autograd. (Triggered internally at ../torch/csrc/autograd/autograd_not_implemented_fallback.cpp:63.)
[default1]:  return Variable._execution_engine.run_backward(  # Calls into the C++ engine to run the backward pass
[default3]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/autograd/graph.py:744: UserWarning: c10d::allreduce_: an autograd kernel was not registered to the Autograd key(s) but we are trying to backprop through it. This may lead to silently incorrect behavior. This behavior is deprecated and will be removed in a future version of PyTorch. If your operator is differentiable, please ensure you have registered an autograd kernel to the correct Autograd key (e.g. DispatchKey::Autograd, DispatchKey::CompositeImplicitAutograd). If your operator is not differentiable, or to squash this warning and use the previous behavior, please register torch::CppFunction::makeFallthrough() to DispatchKey::Autograd. (Triggered internally at ../torch/csrc/autograd/autograd_not_implemented_fallback.cpp:63.)
[default3]:  return Variable._execution_engine.run_backward(  # Calls into the C++ engine to run the backward pass
[default1]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/autograd/graph.py:744: UserWarning: c10d::allreduce_: an autograd kernel was not registered to the Autograd key(s) but we are trying to backprop through it. This may lead to silently incorrect behavior. This behavior is deprecated and will be removed in a future version of PyTorch. If your operator is differentiable, please ensure you have registered an autograd kernel to the correct Autograd key (e.g. DispatchKey::Autograd, DispatchKey::CompositeImplicitAutograd). If your operator is not differentiable, or to squash this warning and use the previous behavior, please register torch::CppFunction::makeFallthrough() to DispatchKey::Autograd. (Triggered internally at ../torch/csrc/autograd/autograd_not_implemented_fallback.cpp:63.)
[default1]:  return Variable._execution_engine.run_backward(  # Calls into the C++ engine to run the backward pass
[default3]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/autograd/graph.py:744: UserWarning: c10d::allreduce_: an autograd kernel was not registered to the Autograd key(s) but we are trying to backprop through it. This may lead to silently incorrect behavior. This behavior is deprecated and will be removed in a future version of PyTorch. If your operator is differentiable, please ensure you have registered an autograd kernel to the correct Autograd key (e.g. DispatchKey::Autograd, DispatchKey::CompositeImplicitAutograd). If your operator is not differentiable, or to squash this warning and use the previous behavior, please register torch::CppFunction::makeFallthrough() to DispatchKey::Autograd. (Triggered internally at ../torch/csrc/autograd/autograd_not_implemented_fallback.cpp:63.)
[default3]:  return Variable._execution_engine.run_backward(  # Calls into the C++ engine to run the backward pass
[default7]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/autograd/graph.py:744: UserWarning: c10d::allreduce_: an autograd kernel was not registered to the Autograd key(s) but we are trying to backprop through it. This may lead to silently incorrect behavior. This behavior is deprecated and will be removed in a future version of PyTorch. If your operator is differentiable, please ensure you have registered an autograd kernel to the correct Autograd key (e.g. DispatchKey::Autograd, DispatchKey::CompositeImplicitAutograd). If your operator is not differentiable, or to squash this warning and use the previous behavior, please register torch::CppFunction::makeFallthrough() to DispatchKey::Autograd. (Triggered internally at ../torch/csrc/autograd/autograd_not_implemented_fallback.cpp:63.)
[default7]:  return Variable._execution_engine.run_backward(  # Calls into the C++ engine to run the backward pass
[default0]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/autograd/graph.py:744: UserWarning: c10d::allreduce_: an autograd kernel was not registered to the Autograd key(s) but we are trying to backprop through it. This may lead to silently incorrect behavior. This behavior is deprecated and will be removed in a future version of PyTorch. If your operator is differentiable, please ensure you have registered an autograd kernel to the correct Autograd key (e.g. DispatchKey::Autograd, DispatchKey::CompositeImplicitAutograd). If your operator is not differentiable, or to squash this warning and use the previous behavior, please register torch::CppFunction::makeFallthrough() to DispatchKey::Autograd. (Triggered internally at ../torch/csrc/autograd/autograd_not_implemented_fallback.cpp:63.)
[default0]:  return Variable._execution_engine.run_backward(  # Calls into the C++ engine to run the backward pass
[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
[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
[default4]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/autograd/graph.py:744: UserWarning: c10d::allreduce_: an autograd kernel was not registered to the Autograd key(s) but we are trying to backprop through it. This may lead to silently incorrect behavior. This behavior is deprecated and will be removed in a future version of PyTorch. If your operator is differentiable, please ensure you have registered an autograd kernel to the correct Autograd key (e.g. DispatchKey::Autograd, DispatchKey::CompositeImplicitAutograd). If your operator is not differentiable, or to squash this warning and use the previous behavior, please register torch::CppFunction::makeFallthrough() to DispatchKey::Autograd. (Triggered internally at ../torch/csrc/autograd/autograd_not_implemented_fallback.cpp:63.)
[default4]:  return Variable._execution_engine.run_backward(  # Calls into the C++ engine to run the backward pass
[default0]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/autograd/graph.py:744: UserWarning: c10d::allreduce_: an autograd kernel was not registered to the Autograd key(s) but we are trying to backprop through it. This may lead to silently incorrect behavior. This behavior is deprecated and will be removed in a future version of PyTorch. If your operator is differentiable, please ensure you have registered an autograd kernel to the correct Autograd key (e.g. DispatchKey::Autograd, DispatchKey::CompositeImplicitAutograd). If your operator is not differentiable, or to squash this warning and use the previous behavior, please register torch::CppFunction::makeFallthrough() to DispatchKey::Autograd. (Triggered internally at ../torch/csrc/autograd/autograd_not_implemented_fallback.cpp:63.)
[default0]:  return Variable._execution_engine.run_backward(  # Calls into the C++ engine to run the backward pass
[default1]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/autograd/graph.py:744: UserWarning: c10d::allreduce_: an autograd kernel was not registered to the Autograd key(s) but we are trying to backprop through it. This may lead to silently incorrect behavior. This behavior is deprecated and will be removed in a future version of PyTorch. If your operator is differentiable, please ensure you have registered an autograd kernel to the correct Autograd key (e.g. DispatchKey::Autograd, DispatchKey::CompositeImplicitAutograd). If your operator is not differentiable, or to squash this warning and use the previous behavior, please register torch::CppFunction::makeFallthrough() to DispatchKey::Autograd. (Triggered internally at ../torch/csrc/autograd/autograd_not_implemented_fallback.cpp:63.)
[default1]:  return Variable._execution_engine.run_backward(  # Calls into the C++ engine to run the backward pass
[default7]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/autograd/graph.py:744: UserWarning: c10d::allreduce_: an autograd kernel was not registered to the Autograd key(s) but we are trying to backprop through it. This may lead to silently incorrect behavior. This behavior is deprecated and will be removed in a future version of PyTorch. If your operator is differentiable, please ensure you have registered an autograd kernel to the correct Autograd key (e.g. DispatchKey::Autograd, DispatchKey::CompositeImplicitAutograd). If your operator is not differentiable, or to squash this warning and use the previous behavior, please register torch::CppFunction::makeFallthrough() to DispatchKey::Autograd. (Triggered internally at ../torch/csrc/autograd/autograd_not_implemented_fallback.cpp:63.)
[default7]:  return Variable._execution_engine.run_backward(  # Calls into the C++ engine to run the backward pass
[default6]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/autograd/graph.py:744: UserWarning: c10d::allreduce_: an autograd kernel was not registered to the Autograd key(s) but we are trying to backprop through it. This may lead to silently incorrect behavior. This behavior is deprecated and will be removed in a future version of PyTorch. If your operator is differentiable, please ensure you have registered an autograd kernel to the correct Autograd key (e.g. DispatchKey::Autograd, DispatchKey::CompositeImplicitAutograd). If your operator is not differentiable, or to squash this warning and use the previous behavior, please register torch::CppFunction::makeFallthrough() to DispatchKey::Autograd. (Triggered internally at ../torch/csrc/autograd/autograd_not_implemented_fallback.cpp:63.)
[default6]:  return Variable._execution_engine.run_backward(  # Calls into the C++ engine to run the backward pass
[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
[default5]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/autograd/graph.py:744: UserWarning: c10d::allreduce_: an autograd kernel was not registered to the Autograd key(s) but we are trying to backprop through it. This may lead to silently incorrect behavior. This behavior is deprecated and will be removed in a future version of PyTorch. If your operator is differentiable, please ensure you have registered an autograd kernel to the correct Autograd key (e.g. DispatchKey::Autograd, DispatchKey::CompositeImplicitAutograd). If your operator is not differentiable, or to squash this warning and use the previous behavior, please register torch::CppFunction::makeFallthrough() to DispatchKey::Autograd. (Triggered internally at ../torch/csrc/autograd/autograd_not_implemented_fallback.cpp:63.)
[default5]:  return Variable._execution_engine.run_backward(  # Calls into the C++ engine to run the backward pass
[default3]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/autograd/graph.py:744: UserWarning: c10d::allreduce_: an autograd kernel was not registered to the Autograd key(s) but we are trying to backprop through it. This may lead to silently incorrect behavior. This behavior is deprecated and will be removed in a future version of PyTorch. If your operator is differentiable, please ensure you have registered an autograd kernel to the correct Autograd key (e.g. DispatchKey::Autograd, DispatchKey::CompositeImplicitAutograd). If your operator is not differentiable, or to squash this warning and use the previous behavior, please register torch::CppFunction::makeFallthrough() to DispatchKey::Autograd. (Triggered internally at ../torch/csrc/autograd/autograd_not_implemented_fallback.cpp:63.)
[default3]:  return Variable._execution_engine.run_backward(  # Calls into the C++ engine to run the backward pass
[default4]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/autograd/graph.py:744: UserWarning: c10d::allreduce_: an autograd kernel was not registered to the Autograd key(s) but we are trying to backprop through it. This may lead to silently incorrect behavior. This behavior is deprecated and will be removed in a future version of PyTorch. If your operator is differentiable, please ensure you have registered an autograd kernel to the correct Autograd key (e.g. DispatchKey::Autograd, DispatchKey::CompositeImplicitAutograd). If your operator is not differentiable, or to squash this warning and use the previous behavior, please register torch::CppFunction::makeFallthrough() to DispatchKey::Autograd. (Triggered internally at ../torch/csrc/autograd/autograd_not_implemented_fallback.cpp:63.)
[default4]:  return Variable._execution_engine.run_backward(  # Calls into the C++ engine to run the backward pass
[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/distributed/distributed_c10d.py:2261: UserWarning: torch.distributed.all_reduce_coalesced will be deprecated. If you must use it, please revisit our documentation later at https://pytorch.org/docs/master/distributed.html#collective-functions
[default4]:  warnings.warn(
[default5]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/distributed_c10d.py:2261: UserWarning: torch.distributed.all_reduce_coalesced will be deprecated. If you must use it, please revisit our documentation later at https://pytorch.org/docs/master/distributed.html#collective-functions
[default5]:  warnings.warn(
[default6]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/distributed_c10d.py:2261: UserWarning: torch.distributed.all_reduce_coalesced will be deprecated. If you must use it, please revisit our documentation later at https://pytorch.org/docs/master/distributed.html#collective-functions
[default6]:  warnings.warn(
[default7]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/distributed_c10d.py:2261: UserWarning: torch.distributed.all_reduce_coalesced will be deprecated. If you must use it, please revisit our documentation later at https://pytorch.org/docs/master/distributed.html#collective-functions
[default7]:  warnings.warn(
[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
[default3]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/autograd/graph.py:744: UserWarning: c10d::allreduce_: an autograd kernel was not registered to the Autograd key(s) but we are trying to backprop through it. This may lead to silently incorrect behavior. This behavior is deprecated and will be removed in a future version of PyTorch. If your operator is differentiable, please ensure you have registered an autograd kernel to the correct Autograd key (e.g. DispatchKey::Autograd, DispatchKey::CompositeImplicitAutograd). If your operator is not differentiable, or to squash this warning and use the previous behavior, please register torch::CppFunction::makeFallthrough() to DispatchKey::Autograd. (Triggered internally at ../torch/csrc/autograd/autograd_not_implemented_fallback.cpp:63.)
[default3]:  return Variable._execution_engine.run_backward(  # Calls into the C++ engine to run the backward pass
[default2]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/autograd/graph.py:744: UserWarning: c10d::allreduce_: an autograd kernel was not registered to the Autograd key(s) but we are trying to backprop through it. This may lead to silently incorrect behavior. This behavior is deprecated and will be removed in a future version of PyTorch. If your operator is differentiable, please ensure you have registered an autograd kernel to the correct Autograd key (e.g. DispatchKey::Autograd, DispatchKey::CompositeImplicitAutograd). If your operator is not differentiable, or to squash this warning and use the previous behavior, please register torch::CppFunction::makeFallthrough() to DispatchKey::Autograd. (Triggered internally at ../torch/csrc/autograd/autograd_not_implemented_fallback.cpp:63.)
[default2]:  return Variable._execution_engine.run_backward(  # Calls into the C++ engine to run the backward pass
[default1]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/autograd/graph.py:744: UserWarning: c10d::allreduce_: an autograd kernel was not registered to the Autograd key(s) but we are trying to backprop through it. This may lead to silently incorrect behavior. This behavior is deprecated and will be removed in a future version of PyTorch. If your operator is differentiable, please ensure you have registered an autograd kernel to the correct Autograd key (e.g. DispatchKey::Autograd, DispatchKey::CompositeImplicitAutograd). If your operator is not differentiable, or to squash this warning and use the previous behavior, please register torch::CppFunction::makeFallthrough() to DispatchKey::Autograd. (Triggered internally at ../torch/csrc/autograd/autograd_not_implemented_fallback.cpp:63.)
[default1]:  return Variable._execution_engine.run_backward(  # Calls into the C++ engine to run the backward pass
[default0]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/autograd/graph.py:744: UserWarning: c10d::allreduce_: an autograd kernel was not registered to the Autograd key(s) but we are trying to backprop through it. This may lead to silently incorrect behavior. This behavior is deprecated and will be removed in a future version of PyTorch. If your operator is differentiable, please ensure you have registered an autograd kernel to the correct Autograd key (e.g. DispatchKey::Autograd, DispatchKey::CompositeImplicitAutograd). If your operator is not differentiable, or to squash this warning and use the previous behavior, please register torch::CppFunction::makeFallthrough() to DispatchKey::Autograd. (Triggered internally at ../torch/csrc/autograd/autograd_not_implemented_fallback.cpp:63.)
[default0]:  return Variable._execution_engine.run_backward(  # Calls into the C++ engine to run the backward pass
[default6]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/autograd/graph.py:744: UserWarning: c10d::allreduce_: an autograd kernel was not registered to the Autograd key(s) but we are trying to backprop through it. This may lead to silently incorrect behavior. This behavior is deprecated and will be removed in a future version of PyTorch. If your operator is differentiable, please ensure you have registered an autograd kernel to the correct Autograd key (e.g. DispatchKey::Autograd, DispatchKey::CompositeImplicitAutograd). If your operator is not differentiable, or to squash this warning and use the previous behavior, please register torch::CppFunction::makeFallthrough() to DispatchKey::Autograd. (Triggered internally at ../torch/csrc/autograd/autograd_not_implemented_fallback.cpp:63.)
[default6]:  return Variable._execution_engine.run_backward(  # Calls into the C++ engine to run the backward pass
[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
[default7]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/autograd/graph.py:744: UserWarning: c10d::allreduce_: an autograd kernel was not registered to the Autograd key(s) but we are trying to backprop through it. This may lead to silently incorrect behavior. This behavior is deprecated and will be removed in a future version of PyTorch. If your operator is differentiable, please ensure you have registered an autograd kernel to the correct Autograd key (e.g. DispatchKey::Autograd, DispatchKey::CompositeImplicitAutograd). If your operator is not differentiable, or to squash this warning and use the previous behavior, please register torch::CppFunction::makeFallthrough() to DispatchKey::Autograd. (Triggered internally at ../torch/csrc/autograd/autograd_not_implemented_fallback.cpp:63.)
[default7]:  return Variable._execution_engine.run_backward(  # Calls into the C++ engine to run the backward pass
[default0]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/distributed_c10d.py:2261: UserWarning: torch.distributed.all_reduce_coalesced will be deprecated. If you must use it, please revisit our documentation later at https://pytorch.org/docs/master/distributed.html#collective-functions
[default0]:  warnings.warn(
[default1]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/distributed_c10d.py:2261: UserWarning: torch.distributed.all_reduce_coalesced will be deprecated. If you must use it, please revisit our documentation later at https://pytorch.org/docs/master/distributed.html#collective-functions
[default1]:  warnings.warn(
[default2]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/distributed_c10d.py:2261: UserWarning: torch.distributed.all_reduce_coalesced will be deprecated. If you must use it, please revisit our documentation later at https://pytorch.org/docs/master/distributed.html#collective-functions
[default2]:  warnings.warn(
[default3]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/distributed_c10d.py:2261: UserWarning: torch.distributed.all_reduce_coalesced will be deprecated. If you must use it, please revisit our documentation later at https://pytorch.org/docs/master/distributed.html#collective-functions
[default3]:  warnings.warn(
[default5]:07/03/2024 04:04:54 [WARNING|DP=13|PP=0|TP=1|ip-26-0-172-57]: Using the latest cached version of the dataset since roneneldan/TinyStories couldn't be found on the Hugging Face Hub
[default5]:07/03/2024 04:04:54 [WARNING|DP=13|PP=0|TP=1|ip-26-0-172-57]: Found the latest cached dataset configuration 'default' at /admin/home/ferdinand_mom/.cache/roneneldan___tiny_stories/default/0.0.0/691b0d9bd48ade766778c940011ca1c549f6359b (last modified on Mon Jun 24 07:59:52 2024).
[default5]:Using the latest cached version of the dataset since roneneldan/TinyStories couldn't be found on the Hugging Face Hub
[default5]:Found the latest cached dataset configuration 'default' at /admin/home/ferdinand_mom/.cache/roneneldan___tiny_stories/default/0.0.0/691b0d9bd48ade766778c940011ca1c549f6359b (last modified on Mon Jun 24 07:59:52 2024).
[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(
[default6]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/distributed_c10d.py:2261: UserWarning: torch.distributed.all_reduce_coalesced will be deprecated. If you must use it, please revisit our documentation later at https://pytorch.org/docs/master/distributed.html#collective-functions
[default6]:  warnings.warn(
[default0]:/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(
[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(
[default1]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/distributed_c10d.py:2261: UserWarning: torch.distributed.all_reduce_coalesced will be deprecated. If you must use it, please revisit our documentation later at https://pytorch.org/docs/master/distributed.html#collective-functions
[default1]:  warnings.warn(
[default7]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/distributed_c10d.py:2261: UserWarning: torch.distributed.all_reduce_coalesced will be deprecated. If you must use it, please revisit our documentation later at https://pytorch.org/docs/master/distributed.html#collective-functions
[default7]:  warnings.warn(
[default0]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/distributed_c10d.py:2261: UserWarning: torch.distributed.all_reduce_coalesced will be deprecated. If you must use it, please revisit our documentation later at https://pytorch.org/docs/master/distributed.html#collective-functions
[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]:  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/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/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
[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
[default6]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/autograd/graph.py:744: UserWarning: c10d::allreduce_: an autograd kernel was not registered to the Autograd key(s) but we are trying to backprop through it. This may lead to silently incorrect behavior. This behavior is deprecated and will be removed in a future version of PyTorch. If your operator is differentiable, please ensure you have registered an autograd kernel to the correct Autograd key (e.g. DispatchKey::Autograd, DispatchKey::CompositeImplicitAutograd). If your operator is not differentiable, or to squash this warning and use the previous behavior, please register torch::CppFunction::makeFallthrough() to DispatchKey::Autograd. (Triggered internally at ../torch/csrc/autograd/autograd_not_implemented_fallback.cpp:63.)
[default6]:  return Variable._execution_engine.run_backward(  # Calls into the C++ engine to run the backward pass
[default7]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/autograd/graph.py:744: UserWarning: c10d::allreduce_: an autograd kernel was not registered to the Autograd key(s) but we are trying to backprop through it. This may lead to silently incorrect behavior. This behavior is deprecated and will be removed in a future version of PyTorch. If your operator is differentiable, please ensure you have registered an autograd kernel to the correct Autograd key (e.g. DispatchKey::Autograd, DispatchKey::CompositeImplicitAutograd). If your operator is not differentiable, or to squash this warning and use the previous behavior, please register torch::CppFunction::makeFallthrough() to DispatchKey::Autograd. (Triggered internally at ../torch/csrc/autograd/autograd_not_implemented_fallback.cpp:63.)
[default7]:  return Variable._execution_engine.run_backward(  # Calls into the C++ engine to run the backward pass
[default4]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/autograd/graph.py:744: UserWarning: c10d::allreduce_: an autograd kernel was not registered to the Autograd key(s) but we are trying to backprop through it. This may lead to silently incorrect behavior. This behavior is deprecated and will be removed in a future version of PyTorch. If your operator is differentiable, please ensure you have registered an autograd kernel to the correct Autograd key (e.g. DispatchKey::Autograd, DispatchKey::CompositeImplicitAutograd). If your operator is not differentiable, or to squash this warning and use the previous behavior, please register torch::CppFunction::makeFallthrough() to DispatchKey::Autograd. (Triggered internally at ../torch/csrc/autograd/autograd_not_implemented_fallback.cpp:63.)
[default4]:  return Variable._execution_engine.run_backward(  # Calls into the C++ engine to run the backward pass
[default5]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/autograd/graph.py:744: UserWarning: c10d::allreduce_: an autograd kernel was not registered to the Autograd key(s) but we are trying to backprop through it. This may lead to silently incorrect behavior. This behavior is deprecated and will be removed in a future version of PyTorch. If your operator is differentiable, please ensure you have registered an autograd kernel to the correct Autograd key (e.g. DispatchKey::Autograd, DispatchKey::CompositeImplicitAutograd). If your operator is not differentiable, or to squash this warning and use the previous behavior, please register torch::CppFunction::makeFallthrough() to DispatchKey::Autograd. (Triggered internally at ../torch/csrc/autograd/autograd_not_implemented_fallback.cpp:63.)
[default5]:  return Variable._execution_engine.run_backward(  # Calls into the C++ engine to run the backward pass
[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(
[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(
[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(
[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(
[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(
[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(
[default4]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/autograd/graph.py:744: UserWarning: c10d::allreduce_: an autograd kernel was not registered to the Autograd key(s) but we are trying to backprop through it. This may lead to silently incorrect behavior. This behavior is deprecated and will be removed in a future version of PyTorch. If your operator is differentiable, please ensure you have registered an autograd kernel to the correct Autograd key (e.g. DispatchKey::Autograd, DispatchKey::CompositeImplicitAutograd). If your operator is not differentiable, or to squash this warning and use the previous behavior, please register torch::CppFunction::makeFallthrough() to DispatchKey::Autograd. (Triggered internally at ../torch/csrc/autograd/autograd_not_implemented_fallback.cpp:63.)
[default4]:  return Variable._execution_engine.run_backward(  # Calls into the C++ engine to run the backward pass
[default7]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/autograd/graph.py:744: UserWarning: c10d::allreduce_: an autograd kernel was not registered to the Autograd key(s) but we are trying to backprop through it. This may lead to silently incorrect behavior. This behavior is deprecated and will be removed in a future version of PyTorch. If your operator is differentiable, please ensure you have registered an autograd kernel to the correct Autograd key (e.g. DispatchKey::Autograd, DispatchKey::CompositeImplicitAutograd). If your operator is not differentiable, or to squash this warning and use the previous behavior, please register torch::CppFunction::makeFallthrough() to DispatchKey::Autograd. (Triggered internally at ../torch/csrc/autograd/autograd_not_implemented_fallback.cpp:63.)
[default7]:  return Variable._execution_engine.run_backward(  # Calls into the C++ engine to run the backward pass
[default5]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/autograd/graph.py:744: UserWarning: c10d::allreduce_: an autograd kernel was not registered to the Autograd key(s) but we are trying to backprop through it. This may lead to silently incorrect behavior. This behavior is deprecated and will be removed in a future version of PyTorch. If your operator is differentiable, please ensure you have registered an autograd kernel to the correct Autograd key (e.g. DispatchKey::Autograd, DispatchKey::CompositeImplicitAutograd). If your operator is not differentiable, or to squash this warning and use the previous behavior, please register torch::CppFunction::makeFallthrough() to DispatchKey::Autograd. (Triggered internally at ../torch/csrc/autograd/autograd_not_implemented_fallback.cpp:63.)
[default5]:  return Variable._execution_engine.run_backward(  # Calls into the C++ engine to run the backward pass
[default6]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/autograd/graph.py:744: UserWarning: c10d::allreduce_: an autograd kernel was not registered to the Autograd key(s) but we are trying to backprop through it. This may lead to silently incorrect behavior. This behavior is deprecated and will be removed in a future version of PyTorch. If your operator is differentiable, please ensure you have registered an autograd kernel to the correct Autograd key (e.g. DispatchKey::Autograd, DispatchKey::CompositeImplicitAutograd). If your operator is not differentiable, or to squash this warning and use the previous behavior, please register torch::CppFunction::makeFallthrough() to DispatchKey::Autograd. (Triggered internally at ../torch/csrc/autograd/autograd_not_implemented_fallback.cpp:63.)
[default6]:  return Variable._execution_engine.run_backward(  # Calls into the C++ engine to run the backward pass
[default0]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/distributed_c10d.py:2261: UserWarning: torch.distributed.all_reduce_coalesced will be deprecated. If you must use it, please revisit our documentation later at https://pytorch.org/docs/master/distributed.html#collective-functions
[default0]:  warnings.warn(
[default1]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/distributed_c10d.py:2261: UserWarning: torch.distributed.all_reduce_coalesced will be deprecated. If you must use it, please revisit our documentation later at https://pytorch.org/docs/master/distributed.html#collective-functions
[default1]:  warnings.warn(
[default6]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/distributed_c10d.py:2261: UserWarning: torch.distributed.all_reduce_coalesced will be deprecated. If you must use it, please revisit our documentation later at https://pytorch.org/docs/master/distributed.html#collective-functions
[default6]:  warnings.warn(
[default2]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/distributed_c10d.py:2261: UserWarning: torch.distributed.all_reduce_coalesced will be deprecated. If you must use it, please revisit our documentation later at https://pytorch.org/docs/master/distributed.html#collective-functions
[default2]:  warnings.warn(
[default5]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/distributed_c10d.py:2261: UserWarning: torch.distributed.all_reduce_coalesced will be deprecated. If you must use it, please revisit our documentation later at https://pytorch.org/docs/master/distributed.html#collective-functions
[default5]:  warnings.warn(
[default3]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/distributed_c10d.py:2261: UserWarning: torch.distributed.all_reduce_coalesced will be deprecated. If you must use it, please revisit our documentation later at https://pytorch.org/docs/master/distributed.html#collective-functions
[default3]:  warnings.warn(
[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(
[default7]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/distributed_c10d.py:2261: UserWarning: torch.distributed.all_reduce_coalesced will be deprecated. If you must use it, please revisit our documentation later at https://pytorch.org/docs/master/distributed.html#collective-functions
[default7]:  warnings.warn(
[default6]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/distributed_c10d.py:2261: UserWarning: torch.distributed.all_reduce_coalesced will be deprecated. If you must use it, please revisit our documentation later at https://pytorch.org/docs/master/distributed.html#collective-functions
[default6]:  warnings.warn(
[default5]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/distributed_c10d.py:2261: UserWarning: torch.distributed.all_reduce_coalesced will be deprecated. If you must use it, please revisit our documentation later at https://pytorch.org/docs/master/distributed.html#collective-functions
[default5]:  warnings.warn(
[default4]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/distributed_c10d.py:2261: UserWarning: torch.distributed.all_reduce_coalesced will be deprecated. If you must use it, please revisit our documentation later at https://pytorch.org/docs/master/distributed.html#collective-functions
[default4]:  warnings.warn(
[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(
[default5]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/distributed_c10d.py:2261: UserWarning: torch.distributed.all_reduce_coalesced will be deprecated. If you must use it, please revisit our documentation later at https://pytorch.org/docs/master/distributed.html#collective-functions
[default5]:  warnings.warn(
[default0]:07/03/2024 04:04:59 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:  Memory usage: 1755.07MiB. Peak allocated 7387.23MiB. Peak reserved: 8068.00MiB
[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(
[default0]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/distributed_c10d.py:2261: UserWarning: torch.distributed.all_reduce_coalesced will be deprecated. If you must use it, please revisit our documentation later at https://pytorch.org/docs/master/distributed.html#collective-functions
[default0]:  warnings.warn(
[default2]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/distributed_c10d.py:2261: UserWarning: torch.distributed.all_reduce_coalesced will be deprecated. If you must use it, please revisit our documentation later at https://pytorch.org/docs/master/distributed.html#collective-functions
[default2]:  warnings.warn(
[default6]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/distributed_c10d.py:2261: UserWarning: torch.distributed.all_reduce_coalesced will be deprecated. If you must use it, please revisit our documentation later at https://pytorch.org/docs/master/distributed.html#collective-functions
[default6]:  warnings.warn(
[default7]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/distributed_c10d.py:2261: UserWarning: torch.distributed.all_reduce_coalesced will be deprecated. If you must use it, please revisit our documentation later at https://pytorch.org/docs/master/distributed.html#collective-functions
[default7]:  warnings.warn(
[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(
[default4]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/distributed_c10d.py:2261: UserWarning: torch.distributed.all_reduce_coalesced will be deprecated. If you must use it, please revisit our documentation later at https://pytorch.org/docs/master/distributed.html#collective-functions
[default4]:  warnings.warn(
[default1]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/distributed_c10d.py:2261: UserWarning: torch.distributed.all_reduce_coalesced will be deprecated. If you must use it, please revisit our documentation later at https://pytorch.org/docs/master/distributed.html#collective-functions
[default1]:  warnings.warn(
[default5]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/distributed_c10d.py:2261: UserWarning: torch.distributed.all_reduce_coalesced will be deprecated. If you must use it, please revisit our documentation later at https://pytorch.org/docs/master/distributed.html#collective-functions
[default5]:  warnings.warn(
[default4]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/distributed_c10d.py:2261: UserWarning: torch.distributed.all_reduce_coalesced will be deprecated. If you must use it, please revisit our documentation later at https://pytorch.org/docs/master/distributed.html#collective-functions
[default4]:  warnings.warn(
[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(
[default6]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/distributed_c10d.py:2261: UserWarning: torch.distributed.all_reduce_coalesced will be deprecated. If you must use it, please revisit our documentation later at https://pytorch.org/docs/master/distributed.html#collective-functions
[default6]:  warnings.warn(
[default0]:07/03/2024 04:05:03 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: iteration: 1 / 20 | consumed_tokens: 4.19M | elapsed_time_per_iteration_ms: 19.4K | tokens_per_sec: 216K | tokens_per_sec_per_gpu: 3.38K | global_batch_size: 1.02K | lm_loss: 11.4 | lr: 0.0001 | model_tflops_per_gpu: 30.6 | hardware_tflops_per_gpu: 30.6 | grad_norm: 20.6 | cuda_memory_allocated: 1.98G | cuda_max_memory_reserved: 9.14G | hd_total_memory_tb: 312G | hd_used_memory_tb: 74G | hd_free_memory_tb: 238G
[default0]:07/03/2024 04:05:03 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:  Memory usage: 1887.46MiB. Peak allocated 2979.82MiB. Peak reserved: 8712.00MiB
[default0]:07/03/2024 04:05:08 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:  Memory usage: 1887.48MiB. Peak allocated 7519.63MiB. Peak reserved: 8718.00MiB
[default0]:07/03/2024 04:05:08 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: iteration: 2 / 20 | consumed_tokens: 8.39M | elapsed_time_per_iteration_ms: 5.39K | tokens_per_sec: 778K | tokens_per_sec_per_gpu: 12.2K | global_batch_size: 1.02K | lm_loss: 11.4 | lr: 9.53e-05 | model_tflops_per_gpu: 110 | hardware_tflops_per_gpu: 110 | grad_norm: 20.7 | cuda_memory_allocated: 1.98G | cuda_max_memory_reserved: 9.14G | hd_total_memory_tb: 312G | hd_used_memory_tb: 74G | hd_free_memory_tb: 238G
[default0]:07/03/2024 04:05:08 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:  Memory usage: 1887.46MiB. Peak allocated 2979.83MiB. Peak reserved: 8718.00MiB
[default0]:07/03/2024 04:05:13 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:  Memory usage: 1887.48MiB. Peak allocated 7519.63MiB. Peak reserved: 8718.00MiB
[default0]:07/03/2024 04:05:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: iteration: 3 / 20 | consumed_tokens: 12.6M | elapsed_time_per_iteration_ms: 5.29K | tokens_per_sec: 792K | tokens_per_sec_per_gpu: 12.4K | global_batch_size: 1.02K | lm_loss: 11.6 | lr: 9.05e-05 | model_tflops_per_gpu: 112 | hardware_tflops_per_gpu: 112 | grad_norm: 194 | cuda_memory_allocated: 1.98G | cuda_max_memory_reserved: 9.14G | hd_total_memory_tb: 312G | hd_used_memory_tb: 74G | hd_free_memory_tb: 238G
[default0]:07/03/2024 04:05:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:  Memory usage: 1887.46MiB. Peak allocated 2979.83MiB. Peak reserved: 8718.00MiB
[default0]:STAGE:2024-07-03 04:05:14 1146591:1146591 ActivityProfilerController.cpp:314] Completed Stage: Warm Up
[default0]:07/03/2024 04:05:20 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:  Memory usage: 1887.48MiB. Peak allocated 7519.63MiB. Peak reserved: 8718.00MiB
[default0]:07/03/2024 04:05:20 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: iteration: 4 / 20 | consumed_tokens: 16.8M | elapsed_time_per_iteration_ms: 6.92K | tokens_per_sec: 606K | tokens_per_sec_per_gpu: 9.47K | global_batch_size: 1.02K | lm_loss: 13.6 | lr: 8.58e-05 | model_tflops_per_gpu: 86 | hardware_tflops_per_gpu: 86 | grad_norm: 28 | cuda_memory_allocated: 1.98G | cuda_max_memory_reserved: 9.14G | hd_total_memory_tb: 312G | hd_used_memory_tb: 74G | hd_free_memory_tb: 238G
[default0]:07/03/2024 04:05:20 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:  Memory usage: 1887.46MiB. Peak allocated 2979.83MiB. Peak reserved: 8718.00MiB
[default0]:07/03/2024 04:05:27 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: iteration: 5 / 20 | consumed_tokens: 21M | elapsed_time_per_iteration_ms: 6.96K | tokens_per_sec: 602K | tokens_per_sec_per_gpu: 9.41K | global_batch_size: 1.02K | lm_loss: 12 | lr: 8.11e-05 | model_tflops_per_gpu: 85.4 | hardware_tflops_per_gpu: 85.4 | grad_norm: 48.9
[default0]:07/03/2024 04:05:27 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:  Memory usage: 1887.46MiB. Peak allocated 7519.63MiB. Peak reserved: 8718.00MiB
[default0]:07/03/2024 04:05:34 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: iteration: 6 / 20 | consumed_tokens: 25.2M | elapsed_time_per_iteration_ms: 6.91K | tokens_per_sec: 607K | tokens_per_sec_per_gpu: 9.48K | global_batch_size: 1.02K | lm_loss: 10.9 | lr: 7.63e-05 | model_tflops_per_gpu: 86 | hardware_tflops_per_gpu: 86 | grad_norm: 19.8
[default0]:STAGE:2024-07-03 04:05:52 1146591:1146591 ActivityProfilerController.cpp:320] Completed Stage: Collection
[default0]:STAGE:2024-07-03 04:05:54 1146591:1146591 ActivityProfilerController.cpp:324] Completed Stage: Post Processing
[default0]:07/03/2024 04:08:09 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:  Memory usage: 1887.46MiB. Peak allocated 7519.63MiB. Peak reserved: 8718.00MiB
[default0]:07/03/2024 04:08:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: iteration: 7 / 20 | consumed_tokens: 29.4M | elapsed_time_per_iteration_ms: 4.81K | tokens_per_sec: 871K | tokens_per_sec_per_gpu: 13.6K | global_batch_size: 1.02K | lm_loss: 10.4 | lr: 7.16e-05 | model_tflops_per_gpu: 124 | hardware_tflops_per_gpu: 124 | grad_norm: 8.62
[default0]:07/03/2024 04:08:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:  Memory usage: 1887.46MiB. Peak allocated 7519.63MiB. Peak reserved: 8718.00MiB
[default0]:07/03/2024 04:08:19 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: iteration: 8 / 20 | consumed_tokens: 33.6M | elapsed_time_per_iteration_ms: 4.8K | tokens_per_sec: 873K | tokens_per_sec_per_gpu: 13.6K | global_batch_size: 1.02K | lm_loss: 9.66 | lr: 6.68e-05 | model_tflops_per_gpu: 124 | hardware_tflops_per_gpu: 124 | grad_norm: 6.89
[default0]:07/03/2024 04:08:19 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:  Memory usage: 1887.46MiB. Peak allocated 7519.63MiB. Peak reserved: 8718.00MiB
[default0]:07/03/2024 04:08:24 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: iteration: 9 / 20 | consumed_tokens: 37.7M | elapsed_time_per_iteration_ms: 4.81K | tokens_per_sec: 873K | tokens_per_sec_per_gpu: 13.6K | global_batch_size: 1.02K | lm_loss: 11.3 | lr: 6.21e-05 | model_tflops_per_gpu: 124 | hardware_tflops_per_gpu: 124 | grad_norm: 53.1
[default0]:07/03/2024 04:08:24 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:  Memory usage: 1887.46MiB. Peak allocated 7519.63MiB. Peak reserved: 8718.00MiB
[default0]:07/03/2024 04:08:29 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: iteration: 10 / 20 | consumed_tokens: 41.9M | elapsed_time_per_iteration_ms: 4.8K | tokens_per_sec: 874K | tokens_per_sec_per_gpu: 13.7K | global_batch_size: 1.02K | lm_loss: 9.11 | lr: 5.74e-05 | model_tflops_per_gpu: 124 | hardware_tflops_per_gpu: 124 | grad_norm: 16.2
[default0]:07/03/2024 04:08:29 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:  Memory usage: 1887.46MiB. Peak allocated 7519.63MiB. Peak reserved: 8718.00MiB
[default0]:07/03/2024 04:08:33 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: iteration: 11 / 20 | consumed_tokens: 46.1M | elapsed_time_per_iteration_ms: 4.79K | tokens_per_sec: 875K | tokens_per_sec_per_gpu: 13.7K | global_batch_size: 1.02K | lm_loss: 8.57 | lr: 5.26e-05 | model_tflops_per_gpu: 124 | hardware_tflops_per_gpu: 124 | grad_norm: 7.53
[default0]:07/03/2024 04:08:33 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:  Memory usage: 1887.46MiB. Peak allocated 7519.63MiB. Peak reserved: 8718.00MiB
[default0]:07/03/2024 04:08:38 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: iteration: 12 / 20 | consumed_tokens: 50.3M | elapsed_time_per_iteration_ms: 4.79K | tokens_per_sec: 875K | tokens_per_sec_per_gpu: 13.7K | global_batch_size: 1.02K | lm_loss: 8.37 | lr: 4.79e-05 | model_tflops_per_gpu: 124 | hardware_tflops_per_gpu: 124 | grad_norm: 5.8
[default0]:07/03/2024 04:08:38 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:  Memory usage: 1887.46MiB. Peak allocated 7519.63MiB. Peak reserved: 8718.00MiB
[default0]:07/03/2024 04:08:43 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: iteration: 13 / 20 | consumed_tokens: 54.5M | elapsed_time_per_iteration_ms: 4.8K | tokens_per_sec: 875K | tokens_per_sec_per_gpu: 13.7K | global_batch_size: 1.02K | lm_loss: 8.16 | lr: 4.32e-05 | model_tflops_per_gpu: 124 | hardware_tflops_per_gpu: 124 | grad_norm: 5.6
[default0]:07/03/2024 04:08:43 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:  Memory usage: 1887.46MiB. Peak allocated 7519.63MiB. Peak reserved: 8718.00MiB
[default0]:07/03/2024 04:08:48 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: iteration: 14 / 20 | consumed_tokens: 58.7M | elapsed_time_per_iteration_ms: 4.79K | tokens_per_sec: 875K | tokens_per_sec_per_gpu: 13.7K | global_batch_size: 1.02K | lm_loss: 7.91 | lr: 3.84e-05 | model_tflops_per_gpu: 124 | hardware_tflops_per_gpu: 124 | grad_norm: 5.4
[default0]:07/03/2024 04:08:48 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:  Memory usage: 1887.46MiB. Peak allocated 7519.63MiB. Peak reserved: 8718.00MiB
[default0]:07/03/2024 04:08:52 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: iteration: 15 / 20 | consumed_tokens: 62.9M | elapsed_time_per_iteration_ms: 4.79K | tokens_per_sec: 875K | tokens_per_sec_per_gpu: 13.7K | global_batch_size: 1.02K | lm_loss: 7.68 | lr: 3.37e-05 | model_tflops_per_gpu: 124 | hardware_tflops_per_gpu: 124 | grad_norm: 4.96
[default0]:07/03/2024 04:08:52 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:  Memory usage: 1887.46MiB. Peak allocated 7519.63MiB. Peak reserved: 8718.00MiB
[default0]:07/03/2024 04:08:57 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: iteration: 16 / 20 | consumed_tokens: 67.1M | elapsed_time_per_iteration_ms: 4.79K | tokens_per_sec: 876K | tokens_per_sec_per_gpu: 13.7K | global_batch_size: 1.02K | lm_loss: 7.54 | lr: 2.89e-05 | model_tflops_per_gpu: 124 | hardware_tflops_per_gpu: 124 | grad_norm: 4.96
[default0]:07/03/2024 04:08:57 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:  Memory usage: 1887.46MiB. Peak allocated 7519.63MiB. Peak reserved: 8718.00MiB
[default0]:07/03/2024 04:09:02 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: iteration: 17 / 20 | consumed_tokens: 71.3M | elapsed_time_per_iteration_ms: 4.8K | tokens_per_sec: 875K | tokens_per_sec_per_gpu: 13.7K | global_batch_size: 1.02K | lm_loss: 7.46 | lr: 2.42e-05 | model_tflops_per_gpu: 124 | hardware_tflops_per_gpu: 124 | grad_norm: 5.07
[default0]:07/03/2024 04:09:02 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:  Memory usage: 1887.46MiB. Peak allocated 7519.63MiB. Peak reserved: 8718.00MiB
[default0]:07/03/2024 04:09:07 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: iteration: 18 / 20 | consumed_tokens: 75.5M | elapsed_time_per_iteration_ms: 4.79K | tokens_per_sec: 875K | tokens_per_sec_per_gpu: 13.7K | global_batch_size: 1.02K | lm_loss: 7.36 | lr: 1.95e-05 | model_tflops_per_gpu: 124 | hardware_tflops_per_gpu: 124 | grad_norm: 5.8
[default0]:07/03/2024 04:09:07 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:  Memory usage: 1887.46MiB. Peak allocated 7519.63MiB. Peak reserved: 8718.00MiB
[default0]:07/03/2024 04:09:12 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: iteration: 19 / 20 | consumed_tokens: 79.7M | elapsed_time_per_iteration_ms: 4.79K | tokens_per_sec: 875K | tokens_per_sec_per_gpu: 13.7K | global_batch_size: 1.02K | lm_loss: 7.23 | lr: 1.47e-05 | model_tflops_per_gpu: 124 | hardware_tflops_per_gpu: 124 | grad_norm: 4.04
[default0]:07/03/2024 04:09:12 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:  Memory usage: 1887.46MiB. Peak allocated 7519.63MiB. Peak reserved: 8718.00MiB
[default0]:07/03/2024 04:09:16 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: iteration: 20 / 20 | consumed_tokens: 83.9M | elapsed_time_per_iteration_ms: 4.79K | tokens_per_sec: 875K | tokens_per_sec_per_gpu: 13.7K | global_batch_size: 1.02K | lm_loss: 7.14 | lr: 1e-05 | model_tflops_per_gpu: 124 | hardware_tflops_per_gpu: 124 | grad_norm: 3.04
W0703 04:09:47.846000 139761591097152 torch/distributed/elastic/rendezvous/dynamic_rendezvous.py:1203] The node 'ip-26-0-169-86.ec2.internal_1842940_0' has failed to shutdown the rendezvous 'none' due to an error of type RendezvousConnectionError.
W0703 04:09:47.851000 139761591097152 torch/distributed/elastic/rendezvous/dynamic_rendezvous.py:1203] The node 'ip-26-0-169-86.ec2.internal_1842940_0' has failed to shutdown the rendezvous 'none' due to an error of type RendezvousConnectionError.
W0703 04:09:47.861000 139639773333312 torch/distributed/elastic/rendezvous/dynamic_rendezvous.py:1203] The node 'ip-26-0-161-178.ec2.internal_532557_0' has failed to shutdown the rendezvous 'none' due to an error of type RendezvousConnectionError.
W0703 04:09:47.865000 139639773333312 torch/distributed/elastic/rendezvous/dynamic_rendezvous.py:1203] The node 'ip-26-0-161-178.ec2.internal_532557_0' has failed to shutdown the rendezvous 'none' due to an error of type RendezvousConnectionError.
Saved 1 csv files over 1 completed logs
Processing file: /fsx/ferdinandmom/ferdinand-hf/bench_cluster/results/llama-1B/64_GPUS/dp-16_tp-4_pp-1_mbz-2/profiler/ip-26-0-160-192_1146591.1719979661459804863.pt.trace.json
Results written to /fsx/ferdinandmom/ferdinand-hf/bench_cluster/results/llama-1B/64_GPUS/dp-16_tp-4_pp-1_mbz-2/profiler.csv
Consider using `hf_transfer` for faster uploads. This solution comes with some limitations. See https://huggingface.co/docs/huggingface_hub/hf_transfer for more details.

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