llama-1B/64_GPUS/dp-2_tp-32_pp-1_mbz-64/log.out

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START TIME: Wed Jul  3 01:52:43 UTC 2024
python3 version = Python 3.10.14
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M	examples/config_tiny_llama.py
M	examples/config_tiny_llama.yaml
M	examples/train_tiny_llama.sh
M	src/nanotron/models/llama.py
M	src/nanotron/trainer.py
Your branch is up to date with 'origin/bench_cluster'.
Job status: RUNNING
W0703 01:52:51.151000 139793192929088 torch/distributed/run.py:757] 
W0703 01:52:51.151000 139793192929088 torch/distributed/run.py:757] *****************************************
W0703 01:52:51.151000 139793192929088 torch/distributed/run.py:757] Setting OMP_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. 
W0703 01:52:51.151000 139793192929088 torch/distributed/run.py:757] *****************************************
W0703 01:52:51.237000 139671689652032 torch/distributed/run.py:757] 
W0703 01:52:51.237000 139671689652032 torch/distributed/run.py:757] *****************************************
W0703 01:52:51.237000 139671689652032 torch/distributed/run.py:757] Setting OMP_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. 
W0703 01:52:51.237000 139671689652032 torch/distributed/run.py:757] *****************************************
W0703 01:52:51.238000 140172849612608 torch/distributed/run.py:757] 
W0703 01:52:51.238000 140172849612608 torch/distributed/run.py:757] *****************************************
W0703 01:52:51.238000 140172849612608 torch/distributed/run.py:757] Setting OMP_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. 
W0703 01:52:51.238000 140172849612608 torch/distributed/run.py:757] *****************************************
W0703 01:52:51.241000 139825456662336 torch/distributed/run.py:757] 
W0703 01:52:51.241000 139825456662336 torch/distributed/run.py:757] *****************************************
W0703 01:52:51.241000 139825456662336 torch/distributed/run.py:757] Setting OMP_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. 
W0703 01:52:51.241000 139825456662336 torch/distributed/run.py:757] *****************************************
W0703 01:52:51.255000 140245753120576 torch/distributed/run.py:757] 
W0703 01:52:51.255000 140245753120576 torch/distributed/run.py:757] *****************************************
W0703 01:52:51.255000 140245753120576 torch/distributed/run.py:757] Setting OMP_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. 
W0703 01:52:51.255000 140245753120576 torch/distributed/run.py:757] *****************************************
W0703 01:52:51.263000 139910532134720 torch/distributed/run.py:757] 
W0703 01:52:51.263000 139910532134720 torch/distributed/run.py:757] *****************************************
W0703 01:52:51.263000 139910532134720 torch/distributed/run.py:757] Setting OMP_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. 
W0703 01:52:51.263000 139910532134720 torch/distributed/run.py:757] *****************************************
W0703 01:52:51.286000 139658693232448 torch/distributed/run.py:757] 
W0703 01:52:51.286000 139658693232448 torch/distributed/run.py:757] *****************************************
W0703 01:52:51.286000 139658693232448 torch/distributed/run.py:757] Setting OMP_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. 
W0703 01:52:51.286000 139658693232448 torch/distributed/run.py:757] *****************************************
W0703 01:52:51.559000 140696109762368 torch/distributed/run.py:757] 
W0703 01:52:51.559000 140696109762368 torch/distributed/run.py:757] *****************************************
W0703 01:52:51.559000 140696109762368 torch/distributed/run.py:757] Setting OMP_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. 
W0703 01:52:51.559000 140696109762368 torch/distributed/run.py:757] *****************************************
[default0]:07/03/2024 01:53:16 [WARNING|DP=0|PP=0|TP=0|ip-26-0-160-192]: [Vocab Size Padding] Padded vocab (size: 50257) with 15 dummy tokens (new size: 50272)
[default0]:07/03/2024 01:53:16 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: Config:
[default0]:07/03/2024 01:53:16 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: Config(general=GeneralArgs(project='bench_cluster',
[default0]:07/03/2024 01:53:16 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:                            run='%date_%jobid',
[default0]:07/03/2024 01:53:16 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:                            seed=42,
[default0]:07/03/2024 01:53:16 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:                            step=None,
[default0]:07/03/2024 01:53:16 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:                            consumed_train_samples=None,
[default0]:07/03/2024 01:53:16 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:                            benchmark_csv_path=None,
[default0]:07/03/2024 01:53:16 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:                            ignore_sanity_checks=True),
[default0]:07/03/2024 01:53:16 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:        parallelism=ParallelismArgs(dp=2,
[default0]:07/03/2024 01:53:16 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:                                    pp=1,
[default0]:07/03/2024 01:53:16 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:                                    tp=32,
[default0]:07/03/2024 01:53:16 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:                                    pp_engine=<nanotron.parallel.pipeline_parallel.engine.OneForwardOneBackwardPipelineEngine object at 0x7fce4110c670>,
[default0]:07/03/2024 01:53:16 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:                                    tp_mode=<TensorParallelLinearMode.REDUCE_SCATTER: 2>,
[default0]:07/03/2024 01:53:16 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:                                    tp_linear_async_communication=False,
[default0]:07/03/2024 01:53:16 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:                                    expert_parallel_size=1),
[default0]:07/03/2024 01:53:16 [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 01:53:16 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:                                                 eos_token_id=2,
[default0]:07/03/2024 01:53:16 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:                                                 hidden_act='silu',
[default0]:07/03/2024 01:53:16 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:                                                 hidden_size=2048,
[default0]:07/03/2024 01:53:16 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:                                                 initializer_range=0.02,
[default0]:07/03/2024 01:53:16 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:                                                 intermediate_size=4096,
[default0]:07/03/2024 01:53:16 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:                                                 is_llama_config=True,
[default0]:07/03/2024 01:53:16 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:                                                 max_position_embeddings=4096,
[default0]:07/03/2024 01:53:16 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:                                                 num_attention_heads=32,
[default0]:07/03/2024 01:53:16 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:                                                 num_hidden_layers=24,
[default0]:07/03/2024 01:53:16 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:                                                 num_key_value_heads=32,
[default0]:07/03/2024 01:53:16 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:                                                 pad_token_id=None,
[default0]:07/03/2024 01:53:16 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:                                                 pretraining_tp=1,
[default0]:07/03/2024 01:53:16 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:                                                 rms_norm_eps=1e-05,
[default0]:07/03/2024 01:53:16 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:                                                 rope_scaling=None,
[default0]:07/03/2024 01:53:16 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:                                                 rope_theta=10000.0,
[default0]:07/03/2024 01:53:16 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:                                                 tie_word_embeddings=True,
[default0]:07/03/2024 01:53:16 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:                                                 use_cache=True,
[default0]:07/03/2024 01:53:16 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:                                                 vocab_size=50272),
[default0]:07/03/2024 01:53:16 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:                        init_method=RandomInit(std=0.025),
[default0]:07/03/2024 01:53:16 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:                        dtype=torch.bfloat16,
[default0]:07/03/2024 01:53:16 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:                        make_vocab_size_divisible_by=1,
[default0]:07/03/2024 01:53:16 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:                        ddp_bucket_cap_mb=25),
[default0]:07/03/2024 01:53:16 [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 01:53:16 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:                                tokenizer_revision=None,
[default0]:07/03/2024 01:53:16 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:                                tokenizer_max_length=None),
[default0]:07/03/2024 01:53:16 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:        checkpoints=CheckpointsArgs(checkpoints_path=Path('/dev/null'),
[default0]:07/03/2024 01:53:16 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:                                    checkpoint_interval=100000,
[default0]:07/03/2024 01:53:16 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:                                    save_initial_state=False,
[default0]:07/03/2024 01:53:16 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:                                    resume_checkpoint_path=None,
[default0]:07/03/2024 01:53:16 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:                                    checkpoints_path_is_shared_file_system=False),
[default0]:07/03/2024 01:53:16 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:        logging=LoggingArgs(log_level='info',
[default0]:07/03/2024 01:53:16 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:                            log_level_replica='info',
[default0]:07/03/2024 01:53:16 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:                            iteration_step_info_interval=1),
[default0]:07/03/2024 01:53:16 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:        tokens=TokensArgs(sequence_length=4096,
[default0]:07/03/2024 01:53:16 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:                          train_steps=20,
[default0]:07/03/2024 01:53:16 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:                          micro_batch_size=64,
[default0]:07/03/2024 01:53:16 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:                          batch_accumulation_per_replica=8,
[default0]:07/03/2024 01:53:16 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:                          val_check_interval=-1,
[default0]:07/03/2024 01:53:16 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:                          limit_val_batches=0,
[default0]:07/03/2024 01:53:16 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:                          limit_test_batches=0),
[default0]:07/03/2024 01:53:16 [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 01:53:16 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:                                                                     adam_beta1=0.9,
[default0]:07/03/2024 01:53:16 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:                                                                     adam_beta2=0.95,
[default0]:07/03/2024 01:53:16 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:                                                                     torch_adam_is_fused=True,
[default0]:07/03/2024 01:53:16 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:                                                                     name='adamW'),
[default0]:07/03/2024 01:53:16 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:                                zero_stage=1,
[default0]:07/03/2024 01:53:16 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:                                weight_decay=0.01,
[default0]:07/03/2024 01:53:16 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:                                clip_grad=1.0,
[default0]:07/03/2024 01:53:16 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:                                accumulate_grad_in_fp32=True,
[default0]:07/03/2024 01:53:16 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:                                learning_rate_scheduler=LRSchedulerArgs(learning_rate=0.0001,
[default0]:07/03/2024 01:53:16 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:                                                                        lr_warmup_steps=1,
[default0]:07/03/2024 01:53:16 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:                                                                        lr_warmup_style='linear',
[default0]:07/03/2024 01:53:16 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:                                                                        lr_decay_style='linear',
[default0]:07/03/2024 01:53:16 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:                                                                        lr_decay_steps=19,
[default0]:07/03/2024 01:53:16 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:                                                                        lr_decay_starting_step=None,
[default0]:07/03/2024 01:53:16 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:                                                                        min_decay_lr=1e-05)),
[default0]:07/03/2024 01:53:16 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:        data_stages=[DatasetStageArgs(name='Training Stage',
[default0]:07/03/2024 01:53:16 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:                                      start_training_step=1,
[default0]:07/03/2024 01:53:16 [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 01:53:16 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:                                                                                 hf_dataset_splits='train',
[default0]:07/03/2024 01:53:16 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:                                                                                 hf_dataset_config_name=None,
[default0]:07/03/2024 01:53:16 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:                                                                                 dataset_processing_num_proc_per_process=64,
[default0]:07/03/2024 01:53:16 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:                                                                                 dataset_overwrite_cache=False,
[default0]:07/03/2024 01:53:16 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:                                                                                 text_column_name='text'),
[default0]:07/03/2024 01:53:16 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:                                                    seed=42,
[default0]:07/03/2024 01:53:16 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:                                                    num_loading_workers=0))],
[default0]:07/03/2024 01:53:16 [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-2_tp-32_pp-1_mbz-64')),
[default0]:07/03/2024 01:53:16 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:        lighteval=None)
[default0]:07/03/2024 01:53:16 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: Model Config:
[default0]:07/03/2024 01:53:16 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: LlamaConfig(bos_token_id=1,
[default0]:07/03/2024 01:53:16 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:             eos_token_id=2,
[default0]:07/03/2024 01:53:16 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:             hidden_act='silu',
[default0]:07/03/2024 01:53:16 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:             hidden_size=2048,
[default0]:07/03/2024 01:53:16 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:             initializer_range=0.02,
[default0]:07/03/2024 01:53:16 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:             intermediate_size=4096,
[default0]:07/03/2024 01:53:16 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:             is_llama_config=True,
[default0]:07/03/2024 01:53:16 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:             max_position_embeddings=4096,
[default0]:07/03/2024 01:53:16 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:             num_attention_heads=32,
[default0]:07/03/2024 01:53:16 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:             num_hidden_layers=24,
[default0]:07/03/2024 01:53:16 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:             num_key_value_heads=32,
[default0]:07/03/2024 01:53:16 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:             pad_token_id=None,
[default0]:07/03/2024 01:53:16 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:             pretraining_tp=1,
[default0]:07/03/2024 01:53:16 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:             rms_norm_eps=1e-05,
[default0]:07/03/2024 01:53:16 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:             rope_scaling=None,
[default0]:07/03/2024 01:53:16 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:             rope_theta=10000.0,
[default0]:07/03/2024 01:53:16 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:             tie_word_embeddings=True,
[default0]:07/03/2024 01:53:16 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:             use_cache=True,
[default0]:07/03/2024 01:53:16 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:             vocab_size=50272)
[default0]:07/03/2024 01:53:16 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: Building model..
[default0]:07/03/2024 01:53:16 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: Setting PP block ranks...
[default5]:07/03/2024 01:53:35 [INFO|DP=0|PP=0|TP=21|ip-26-0-163-220]: Local number of parameters: 34.8M (66.33MiB)
[default5]:07/03/2024 01:53:35 [INFO|DP=0|PP=0|TP=21|ip-26-0-163-220]: [After model building] Memory usage: 90.35MiB. Peak allocated: 98.52MiB Peak reserved: 108.00MiB
[default5]:07/03/2024 01:53:35 [INFO|DP=0|PP=0|TP=21|ip-26-0-163-220]: No checkpoint path provided.
[default4]:07/03/2024 01:53:35 [INFO|DP=0|PP=0|TP=20|ip-26-0-163-220]: Local number of parameters: 34.8M (66.33MiB)
[default4]:07/03/2024 01:53:35 [INFO|DP=0|PP=0|TP=20|ip-26-0-163-220]: [After model building] Memory usage: 90.35MiB. Peak allocated: 98.52MiB Peak reserved: 108.00MiB
[default4]:07/03/2024 01:53:35 [INFO|DP=0|PP=0|TP=20|ip-26-0-163-220]: No checkpoint path provided.
[default7]:07/03/2024 01:53:35 [INFO|DP=0|PP=0|TP=23|ip-26-0-163-220]: Local number of parameters: 34.8M (66.33MiB)
[default7]:07/03/2024 01:53:35 [INFO|DP=0|PP=0|TP=23|ip-26-0-163-220]: [After model building] Memory usage: 90.35MiB. Peak allocated: 98.52MiB Peak reserved: 108.00MiB
[default7]:07/03/2024 01:53:35 [INFO|DP=0|PP=0|TP=23|ip-26-0-163-220]: No checkpoint path provided.
[default1]:07/03/2024 01:53:35 [INFO|DP=0|PP=0|TP=25|ip-26-0-163-226]: Local number of parameters: 34.8M (66.33MiB)
[default1]:07/03/2024 01:53:35 [INFO|DP=0|PP=0|TP=25|ip-26-0-163-226]: [After model building] Memory usage: 90.35MiB. Peak allocated: 98.52MiB Peak reserved: 108.00MiB
[default1]:07/03/2024 01:53:35 [INFO|DP=0|PP=0|TP=25|ip-26-0-163-226]: No checkpoint path provided.
[default3]:07/03/2024 01:53:35 [INFO|DP=0|PP=0|TP=27|ip-26-0-163-226]: Local number of parameters: 34.8M (66.33MiB)
[default3]:07/03/2024 01:53:35 [INFO|DP=0|PP=0|TP=27|ip-26-0-163-226]: [After model building] Memory usage: 90.35MiB. Peak allocated: 98.52MiB Peak reserved: 108.00MiB
[default3]:07/03/2024 01:53:35 [INFO|DP=0|PP=0|TP=27|ip-26-0-163-226]: No checkpoint path provided.
[default0]:07/03/2024 01:53:35 [INFO|DP=0|PP=0|TP=8|ip-26-0-161-178]: Local number of parameters: 34.8M (66.33MiB)
[default0]:07/03/2024 01:53:35 [INFO|DP=0|PP=0|TP=8|ip-26-0-161-178]: [After model building] Memory usage: 90.35MiB. Peak allocated: 98.52MiB Peak reserved: 108.00MiB
[default2]:07/03/2024 01:53:35 [INFO|DP=0|PP=0|TP=26|ip-26-0-163-226]: Local number of parameters: 34.8M (66.33MiB)
[default2]:07/03/2024 01:53:35 [INFO|DP=0|PP=0|TP=26|ip-26-0-163-226]: [After model building] Memory usage: 90.35MiB. Peak allocated: 98.52MiB Peak reserved: 108.00MiB
[default2]:07/03/2024 01:53:35 [INFO|DP=0|PP=0|TP=26|ip-26-0-163-226]: No checkpoint path provided.
[default1]:07/03/2024 01:53:35 [INFO|DP=0|PP=0|TP=17|ip-26-0-163-220]: Local number of parameters: 34.8M (66.33MiB)
[default1]:07/03/2024 01:53:35 [INFO|DP=0|PP=0|TP=17|ip-26-0-163-220]: [After model building] Memory usage: 90.35MiB. Peak allocated: 98.52MiB Peak reserved: 108.00MiB
[default1]:07/03/2024 01:53:35 [INFO|DP=0|PP=0|TP=17|ip-26-0-163-220]: No checkpoint path provided.
[default0]:07/03/2024 01:53:35 [INFO|DP=0|PP=0|TP=8|ip-26-0-161-178]: No checkpoint path provided.
[default1]:07/03/2024 01:53:35 [INFO|DP=0|PP=0|TP=9|ip-26-0-161-178]: Local number of parameters: 34.8M (66.33MiB)
[default1]:07/03/2024 01:53:35 [INFO|DP=0|PP=0|TP=9|ip-26-0-161-178]: [After model building] Memory usage: 90.35MiB. Peak allocated: 98.52MiB Peak reserved: 108.00MiB
[default1]:07/03/2024 01:53:35 [INFO|DP=0|PP=0|TP=9|ip-26-0-161-178]: No checkpoint path provided.
[default3]:07/03/2024 01:53:35 [INFO|DP=0|PP=0|TP=11|ip-26-0-161-178]: Local number of parameters: 34.8M (66.33MiB)
[default3]:07/03/2024 01:53:35 [INFO|DP=0|PP=0|TP=11|ip-26-0-161-178]: [After model building] Memory usage: 90.35MiB. Peak allocated: 98.52MiB Peak reserved: 108.00MiB
[default3]:07/03/2024 01:53:35 [INFO|DP=0|PP=0|TP=11|ip-26-0-161-178]: No checkpoint path provided.
[default6]:07/03/2024 01:53:35 [INFO|DP=0|PP=0|TP=30|ip-26-0-163-226]: Local number of parameters: 34.8M (66.33MiB)
[default6]:07/03/2024 01:53:35 [INFO|DP=0|PP=0|TP=30|ip-26-0-163-226]: [After model building] Memory usage: 90.35MiB. Peak allocated: 98.52MiB Peak reserved: 108.00MiB
[default6]:07/03/2024 01:53:35 [INFO|DP=0|PP=0|TP=30|ip-26-0-163-226]: No checkpoint path provided.
[default6]:07/03/2024 01:53:35 [INFO|DP=0|PP=0|TP=14|ip-26-0-161-178]: Local number of parameters: 34.8M (66.33MiB)
[default6]:07/03/2024 01:53:35 [INFO|DP=0|PP=0|TP=14|ip-26-0-161-178]: [After model building] Memory usage: 90.35MiB. Peak allocated: 98.52MiB Peak reserved: 108.00MiB
[default2]:07/03/2024 01:53:35 [INFO|DP=0|PP=0|TP=10|ip-26-0-161-178]: Local number of parameters: 34.8M (66.33MiB)
[default2]:07/03/2024 01:53:35 [INFO|DP=0|PP=0|TP=10|ip-26-0-161-178]: [After model building] Memory usage: 90.35MiB. Peak allocated: 98.52MiB Peak reserved: 108.00MiB
[default6]:07/03/2024 01:53:35 [INFO|DP=0|PP=0|TP=14|ip-26-0-161-178]: No checkpoint path provided.
[default2]:07/03/2024 01:53:35 [INFO|DP=0|PP=0|TP=10|ip-26-0-161-178]: No checkpoint path provided.
[default5]:07/03/2024 01:53:35 [INFO|DP=0|PP=0|TP=13|ip-26-0-161-178]: Local number of parameters: 34.8M (66.33MiB)
[default2]:07/03/2024 01:53:35 [INFO|DP=0|PP=0|TP=18|ip-26-0-163-220]: Local number of parameters: 34.8M (66.33MiB)
[default2]:07/03/2024 01:53:35 [INFO|DP=0|PP=0|TP=18|ip-26-0-163-220]: [After model building] Memory usage: 90.35MiB. Peak allocated: 98.52MiB Peak reserved: 108.00MiB
[default2]:07/03/2024 01:53:35 [INFO|DP=0|PP=0|TP=18|ip-26-0-163-220]: No checkpoint path provided.
[default5]:07/03/2024 01:53:35 [INFO|DP=0|PP=0|TP=13|ip-26-0-161-178]: [After model building] Memory usage: 90.35MiB. Peak allocated: 98.52MiB Peak reserved: 108.00MiB
[default5]:07/03/2024 01:53:35 [INFO|DP=0|PP=0|TP=13|ip-26-0-161-178]: No checkpoint path provided.
[default4]:07/03/2024 01:53:35 [INFO|DP=0|PP=0|TP=12|ip-26-0-161-178]: Local number of parameters: 34.8M (66.33MiB)
[default4]:07/03/2024 01:53:35 [INFO|DP=0|PP=0|TP=12|ip-26-0-161-178]: [After model building] Memory usage: 90.35MiB. Peak allocated: 98.52MiB Peak reserved: 108.00MiB
[default4]:07/03/2024 01:53:35 [INFO|DP=0|PP=0|TP=12|ip-26-0-161-178]: No checkpoint path provided.
[default3]:07/03/2024 01:53:35 [INFO|DP=0|PP=0|TP=19|ip-26-0-163-220]: Local number of parameters: 34.8M (66.33MiB)
[default3]:07/03/2024 01:53:35 [INFO|DP=0|PP=0|TP=19|ip-26-0-163-220]: [After model building] Memory usage: 90.35MiB. Peak allocated: 98.52MiB Peak reserved: 108.00MiB
[default3]:07/03/2024 01:53:35 [INFO|DP=0|PP=0|TP=19|ip-26-0-163-220]: No checkpoint path provided.
[default0]:07/03/2024 01:53:35 [INFO|DP=0|PP=0|TP=24|ip-26-0-163-226]: Local number of parameters: 34.8M (66.33MiB)
[default0]:07/03/2024 01:53:35 [INFO|DP=0|PP=0|TP=24|ip-26-0-163-226]: [After model building] Memory usage: 90.35MiB. Peak allocated: 98.52MiB Peak reserved: 108.00MiB
[default0]:07/03/2024 01:53:35 [INFO|DP=0|PP=0|TP=24|ip-26-0-163-226]: No checkpoint path provided.
[default4]:07/03/2024 01:53:35 [INFO|DP=0|PP=0|TP=4|ip-26-0-160-192]: Local number of parameters: 34.8M (66.33MiB)
[default4]:07/03/2024 01:53:35 [INFO|DP=0|PP=0|TP=4|ip-26-0-160-192]: [After model building] Memory usage: 90.35MiB. Peak allocated: 98.52MiB Peak reserved: 108.00MiB
[default4]:07/03/2024 01:53:35 [INFO|DP=0|PP=0|TP=4|ip-26-0-160-192]: No checkpoint path provided.
[default3]:07/03/2024 01:53:35 [INFO|DP=0|PP=0|TP=3|ip-26-0-160-192]: Local number of parameters: 34.8M (66.33MiB)
[default3]:07/03/2024 01:53:35 [INFO|DP=0|PP=0|TP=3|ip-26-0-160-192]: [After model building] Memory usage: 90.35MiB. Peak allocated: 98.52MiB Peak reserved: 108.00MiB
[default3]:07/03/2024 01:53:35 [INFO|DP=0|PP=0|TP=3|ip-26-0-160-192]: No checkpoint path provided.
[default5]:07/03/2024 01:53:35 [INFO|DP=0|PP=0|TP=29|ip-26-0-163-226]: Local number of parameters: 34.8M (66.33MiB)
[default5]:07/03/2024 01:53:35 [INFO|DP=0|PP=0|TP=29|ip-26-0-163-226]: [After model building] Memory usage: 90.35MiB. Peak allocated: 98.52MiB Peak reserved: 108.00MiB
[default5]:07/03/2024 01:53:35 [INFO|DP=0|PP=0|TP=29|ip-26-0-163-226]: No checkpoint path provided.
[default4]:07/03/2024 01:53:35 [INFO|DP=0|PP=0|TP=28|ip-26-0-163-226]: Local number of parameters: 34.8M (66.33MiB)
[default4]:07/03/2024 01:53:35 [INFO|DP=0|PP=0|TP=28|ip-26-0-163-226]: [After model building] Memory usage: 90.35MiB. Peak allocated: 98.52MiB Peak reserved: 108.00MiB
[default4]:07/03/2024 01:53:35 [INFO|DP=0|PP=0|TP=28|ip-26-0-163-226]: No checkpoint path provided.
[default7]:07/03/2024 01:53:35 [INFO|DP=0|PP=0|TP=15|ip-26-0-161-178]: Local number of parameters: 34.8M (66.33MiB)
[default7]:07/03/2024 01:53:35 [INFO|DP=0|PP=0|TP=15|ip-26-0-161-178]: [After model building] Memory usage: 90.35MiB. Peak allocated: 98.52MiB Peak reserved: 108.00MiB
[default7]:07/03/2024 01:53:35 [INFO|DP=0|PP=0|TP=15|ip-26-0-161-178]: No checkpoint path provided.
[default6]:07/03/2024 01:53:35 [INFO|DP=0|PP=0|TP=22|ip-26-0-163-220]: Local number of parameters: 34.8M (66.33MiB)
[default6]:07/03/2024 01:53:35 [INFO|DP=0|PP=0|TP=22|ip-26-0-163-220]: [After model building] Memory usage: 90.35MiB. Peak allocated: 98.52MiB Peak reserved: 108.00MiB
[default6]:07/03/2024 01:53:35 [INFO|DP=0|PP=0|TP=22|ip-26-0-163-220]: No checkpoint path provided.
[default0]:07/03/2024 01:53:35 [INFO|DP=0|PP=0|TP=16|ip-26-0-163-220]: Local number of parameters: 34.8M (66.33MiB)
[default0]:07/03/2024 01:53:35 [INFO|DP=0|PP=0|TP=16|ip-26-0-163-220]: [After model building] Memory usage: 90.35MiB. Peak allocated: 98.52MiB Peak reserved: 108.00MiB
[default0]:07/03/2024 01:53:35 [INFO|DP=0|PP=0|TP=16|ip-26-0-163-220]: No checkpoint path provided.
[default0]:07/03/2024 01:53:35 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: Total number of parameters: 1.11G (2122.50MiB)
[default0]:07/03/2024 01:53:35 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: Local number of parameters: 34.8M (66.33MiB)
[default0]:07/03/2024 01:53:35 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: [After model building] Memory usage: 90.35MiB. Peak allocated: 98.52MiB Peak reserved: 108.00MiB
[default0]:07/03/2024 01:53:35 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: No checkpoint path provided.
[default0]:07/03/2024 01:53:35 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: Parametrizing model parameters using StandardParametrizator
[default5]:07/03/2024 01:53:35 [INFO|DP=0|PP=0|TP=5|ip-26-0-160-192]: Local number of parameters: 34.8M (66.33MiB)
[default5]:07/03/2024 01:53:35 [INFO|DP=0|PP=0|TP=5|ip-26-0-160-192]: [After model building] Memory usage: 90.35MiB. Peak allocated: 98.52MiB Peak reserved: 108.00MiB
[default5]:07/03/2024 01:53:35 [INFO|DP=0|PP=0|TP=5|ip-26-0-160-192]: No checkpoint path provided.
[default7]:07/03/2024 01:53:35 [INFO|DP=0|PP=0|TP=7|ip-26-0-160-192]: Local number of parameters: 34.8M (66.33MiB)
[default7]:07/03/2024 01:53:35 [INFO|DP=0|PP=0|TP=7|ip-26-0-160-192]: [After model building] Memory usage: 90.35MiB. Peak allocated: 98.52MiB Peak reserved: 108.00MiB
[default7]:07/03/2024 01:53:35 [INFO|DP=0|PP=0|TP=7|ip-26-0-160-192]: No checkpoint path provided.
[default6]:07/03/2024 01:53:35 [INFO|DP=0|PP=0|TP=6|ip-26-0-160-192]: Local number of parameters: 34.8M (66.33MiB)
[default6]:07/03/2024 01:53:35 [INFO|DP=0|PP=0|TP=6|ip-26-0-160-192]: [After model building] Memory usage: 90.35MiB. Peak allocated: 98.52MiB Peak reserved: 108.00MiB
[default6]:07/03/2024 01:53:35 [INFO|DP=0|PP=0|TP=6|ip-26-0-160-192]: No checkpoint path provided.
[default7]:07/03/2024 01:53:35 [INFO|DP=0|PP=0|TP=31|ip-26-0-163-226]: Local number of parameters: 34.8M (66.33MiB)
[default7]:07/03/2024 01:53:35 [INFO|DP=0|PP=0|TP=31|ip-26-0-163-226]: [After model building] Memory usage: 90.35MiB. Peak allocated: 98.52MiB Peak reserved: 108.00MiB
[default7]:07/03/2024 01:53:35 [INFO|DP=0|PP=0|TP=31|ip-26-0-163-226]: No checkpoint path provided.
[default1]:07/03/2024 01:53:35 [INFO|DP=0|PP=0|TP=1|ip-26-0-160-192]: Local number of parameters: 34.8M (66.33MiB)
[default1]:07/03/2024 01:53:35 [INFO|DP=0|PP=0|TP=1|ip-26-0-160-192]: [After model building] Memory usage: 90.35MiB. Peak allocated: 98.52MiB Peak reserved: 108.00MiB
[default1]:07/03/2024 01:53:35 [INFO|DP=0|PP=0|TP=1|ip-26-0-160-192]: No checkpoint path provided.
[default2]:07/03/2024 01:53:35 [INFO|DP=0|PP=0|TP=2|ip-26-0-160-192]: Local number of parameters: 34.8M (66.33MiB)
[default2]:07/03/2024 01:53:35 [INFO|DP=0|PP=0|TP=2|ip-26-0-160-192]: [After model building] Memory usage: 90.35MiB. Peak allocated: 98.52MiB Peak reserved: 108.00MiB
[default2]:07/03/2024 01:53:35 [INFO|DP=0|PP=0|TP=2|ip-26-0-160-192]: No checkpoint path provided.
[default3]:07/03/2024 01:53:35 [INFO|DP=1|PP=0|TP=19|ip-26-0-172-57]: No checkpoint path provided.
[default0]:07/03/2024 01:53:35 [INFO|DP=1|PP=0|TP=24|ip-26-0-172-73]: No checkpoint path provided.
[default5]:07/03/2024 01:53:35 [INFO|DP=1|PP=0|TP=5|ip-26-0-168-238]: No checkpoint path provided.
[default2]:07/03/2024 01:53:35 [INFO|DP=1|PP=0|TP=2|ip-26-0-168-238]: No checkpoint path provided.
[default6]:07/03/2024 01:53:35 [INFO|DP=1|PP=0|TP=6|ip-26-0-168-238]: No checkpoint path provided.
[default4]:07/03/2024 01:53:35 [INFO|DP=1|PP=0|TP=4|ip-26-0-168-238]: No checkpoint path provided.
[default3]:07/03/2024 01:53:35 [INFO|DP=1|PP=0|TP=3|ip-26-0-168-238]: No checkpoint path provided.
[default4]:07/03/2024 01:53:35 [INFO|DP=1|PP=0|TP=12|ip-26-0-169-86]: No checkpoint path provided.
[default5]:07/03/2024 01:53:35 [INFO|DP=1|PP=0|TP=21|ip-26-0-172-57]: No checkpoint path provided.
[default1]:07/03/2024 01:53:35 [INFO|DP=1|PP=0|TP=1|ip-26-0-168-238]: No checkpoint path provided.
[default2]:07/03/2024 01:53:35 [INFO|DP=1|PP=0|TP=18|ip-26-0-172-57]: No checkpoint path provided.
[default7]:07/03/2024 01:53:35 [INFO|DP=1|PP=0|TP=23|ip-26-0-172-57]: No checkpoint path provided.
[default0]:07/03/2024 01:53:35 [INFO|DP=1|PP=0|TP=16|ip-26-0-172-57]: No checkpoint path provided.
[default1]:07/03/2024 01:53:35 [INFO|DP=1|PP=0|TP=17|ip-26-0-172-57]: No checkpoint path provided.
[default6]:07/03/2024 01:53:35 [INFO|DP=1|PP=0|TP=14|ip-26-0-169-86]: No checkpoint path provided.
[default6]:07/03/2024 01:53:35 [INFO|DP=1|PP=0|TP=22|ip-26-0-172-57]: No checkpoint path provided.
[default7]:07/03/2024 01:53:35 [INFO|DP=1|PP=0|TP=15|ip-26-0-169-86]: No checkpoint path provided.
[default4]:07/03/2024 01:53:35 [INFO|DP=1|PP=0|TP=20|ip-26-0-172-57]: No checkpoint path provided.
[default0]:07/03/2024 01:53:35 [INFO|DP=1|PP=0|TP=8|ip-26-0-169-86]: No checkpoint path provided.
[default7]:07/03/2024 01:53:35 [INFO|DP=1|PP=0|TP=7|ip-26-0-168-238]: No checkpoint path provided.
[default1]:07/03/2024 01:53:35 [INFO|DP=1|PP=0|TP=9|ip-26-0-169-86]: No checkpoint path provided.
[default0]:07/03/2024 01:53:35 [INFO|DP=1|PP=0|TP=0|ip-26-0-168-238]: No checkpoint path provided.
[default2]:07/03/2024 01:53:35 [INFO|DP=1|PP=0|TP=26|ip-26-0-172-73]: No checkpoint path provided.
[default3]:07/03/2024 01:53:35 [INFO|DP=1|PP=0|TP=27|ip-26-0-172-73]: No checkpoint path provided.
[default7]:07/03/2024 01:53:35 [INFO|DP=1|PP=0|TP=31|ip-26-0-172-73]: No checkpoint path provided.
[default1]:07/03/2024 01:53:35 [INFO|DP=1|PP=0|TP=25|ip-26-0-172-73]: No checkpoint path provided.
[default5]:07/03/2024 01:53:35 [INFO|DP=1|PP=0|TP=29|ip-26-0-172-73]: No checkpoint path provided.
[default4]:07/03/2024 01:53:35 [INFO|DP=1|PP=0|TP=28|ip-26-0-172-73]: No checkpoint path provided.
[default2]:07/03/2024 01:53:35 [INFO|DP=1|PP=0|TP=10|ip-26-0-169-86]: No checkpoint path provided.
[default5]:07/03/2024 01:53:35 [INFO|DP=1|PP=0|TP=13|ip-26-0-169-86]: No checkpoint path provided.
[default3]:07/03/2024 01:53:35 [INFO|DP=1|PP=0|TP=11|ip-26-0-169-86]: No checkpoint path provided.
[default6]:07/03/2024 01:53:35 [INFO|DP=1|PP=0|TP=30|ip-26-0-172-73]: No checkpoint path provided.
[default0]:07/03/2024 01:53:36 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: [Optimizer Building] Using LearningRateForSP as learning rate
[default0]:07/03/2024 01:53:36 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: [ZeRO sharding] Size of optimizer params per rank:
[default0]:07/03/2024 01:53:36 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: [ZeRO sharding] DP Rank 0 has 17.4M out of 34.8M (50.00%) params' optimizer states
[default0]:07/03/2024 01:53:36 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: [ZeRO sharding] DP Rank 1 has 17.4M out of 34.8M (50.00%) params' optimizer states
[default0]:07/03/2024 01:53:38 [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 01:53:38 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: Using `datasets` library
[default0]:07/03/2024 01:53:38 [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]:Repo card metadata block was not found. Setting CardData to empty.
[default0]:07/03/2024 01:53:38 [WARNING|DP=0|PP=0|TP=0|ip-26-0-160-192]: Repo card metadata block was not found. Setting CardData to empty.
[default0]:07/03/2024 01:53:40 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: [Training Plan] There are 1 training stages 
[default0]:07/03/2024 01:53:40 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: [Stage Training Stage] start from step 1 
[default0]:07/03/2024 01:53:40 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: 
[default0]:07/03/2024 01:53:40 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: [Start training] datetime: 2024-07-03 01:53:40.687873 | mbs: 64 | grad_accum: 8 | global_batch_size: 1024 | sequence_length: 4096 | train_steps: 20 | start_iteration_step: 0 | consumed_train_samples: 0
[default0]:07/03/2024 01:53:40 [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 01:53:40 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:  Memory usage: 289.33MiB. Peak allocated 289.33MiB. Peak reserved: 310.00MiB
[default5]:07/03/2024 01:53:40 [WARNING|DP=0|PP=0|TP=21|ip-26-0-163-220]: Repo card metadata block was not found. Setting CardData to empty.
[default3]:07/03/2024 01:53:40 [WARNING|DP=0|PP=0|TP=3|ip-26-0-160-192]: Repo card metadata block was not found. Setting CardData to empty.
[default5]:Repo card metadata block was not found. Setting CardData to empty.
[default7]:07/03/2024 01:53:40 [WARNING|DP=0|PP=0|TP=7|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.
[default1]:07/03/2024 01:53:40 [WARNING|DP=1|PP=0|TP=1|ip-26-0-168-238]: Repo card metadata block was not found. Setting CardData to empty.
[default6]:07/03/2024 01:53:40 [WARNING|DP=1|PP=0|TP=14|ip-26-0-169-86]: Repo card metadata block was not found. Setting CardData to empty.
[default6]:Repo card metadata block was not found. Setting CardData to empty.
[default3]:Repo card metadata block was not found. Setting CardData to empty.
[default5]:07/03/2024 01:53:40 [WARNING|DP=1|PP=0|TP=29|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.
[default5]:Repo card metadata block was not found. Setting CardData to empty.
[default4]:07/03/2024 01:53:40 [WARNING|DP=0|PP=0|TP=20|ip-26-0-163-220]: Repo card metadata block was not found. Setting CardData to empty.
[default1]:07/03/2024 01:53:40 [WARNING|DP=0|PP=0|TP=17|ip-26-0-163-220]: Repo card metadata block was not found. Setting CardData to empty.
[default6]:07/03/2024 01:53:40 [WARNING|DP=0|PP=0|TP=30|ip-26-0-163-226]: Repo card metadata block was not found. Setting CardData to empty.
[default4]:Repo card metadata block was not found. Setting CardData to empty.
[default1]:07/03/2024 01:53:40 [WARNING|DP=0|PP=0|TP=25|ip-26-0-163-226]: Repo card metadata block was not found. Setting CardData to empty.
[default0]:07/03/2024 01:53:40 [WARNING|DP=0|PP=0|TP=8|ip-26-0-161-178]: Repo card metadata block was not found. Setting CardData to empty.
[default2]:07/03/2024 01:53:40 [WARNING|DP=0|PP=0|TP=18|ip-26-0-163-220]: Repo card metadata block was not found. Setting CardData to empty.
[default1]:07/03/2024 01:53:40 [WARNING|DP=0|PP=0|TP=9|ip-26-0-161-178]: 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]:07/03/2024 01:53:40 [WARNING|DP=0|PP=0|TP=14|ip-26-0-161-178]: Repo card metadata block was not found. Setting CardData to empty.
[default0]:07/03/2024 01:53:40 [WARNING|DP=0|PP=0|TP=24|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]:Repo card metadata block was not found. Setting CardData to empty.
[default4]:07/03/2024 01:53:40 [WARNING|DP=0|PP=0|TP=28|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.
[default1]:Repo card metadata block was not found. Setting CardData to empty.
[default7]:07/03/2024 01:53:40 [WARNING|DP=0|PP=0|TP=15|ip-26-0-161-178]: Repo card metadata block was not found. Setting CardData to empty.
[default0]:Repo card metadata block was not found. Setting CardData to empty.
[default1]:Repo card metadata block was not found. Setting CardData to empty.
[default0]:07/03/2024 01:53:40 [WARNING|DP=0|PP=0|TP=16|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.
[default7]: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 01:53:40 [WARNING|DP=1|PP=0|TP=24|ip-26-0-172-73]: Repo card metadata block was not found. Setting CardData to empty.
[default5]:07/03/2024 01:53:40 [WARNING|DP=0|PP=0|TP=5|ip-26-0-160-192]: Repo card metadata block was not found. Setting CardData to empty.
[default6]:07/03/2024 01:53:40 [WARNING|DP=0|PP=0|TP=6|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.
[default6]:07/03/2024 01:53:40 [WARNING|DP=1|PP=0|TP=6|ip-26-0-168-238]: Repo card metadata block was not found. Setting CardData to empty.
[default6]:Repo card metadata block was not found. Setting CardData to empty.
[default6]:07/03/2024 01:53:40 [WARNING|DP=1|PP=0|TP=22|ip-26-0-172-57]: Repo card metadata block was not found. Setting CardData to empty.
[default2]:07/03/2024 01:53:40 [WARNING|DP=0|PP=0|TP=2|ip-26-0-160-192]: Repo card metadata block was not found. Setting CardData to empty.
[default5]:07/03/2024 01:53:40 [WARNING|DP=1|PP=0|TP=5|ip-26-0-168-238]: Repo card metadata block was not found. Setting CardData to empty.
[default2]:Repo card metadata block was not found. Setting CardData to empty.
[default7]:07/03/2024 01:53:40 [WARNING|DP=1|PP=0|TP=7|ip-26-0-168-238]: 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]:07/03/2024 01:53:40 [WARNING|DP=1|PP=0|TP=8|ip-26-0-169-86]: Repo card metadata block was not found. Setting CardData to empty.
[default7]:07/03/2024 01:53:40 [WARNING|DP=1|PP=0|TP=15|ip-26-0-169-86]: Repo card metadata block was not found. Setting CardData to empty.
[default1]:07/03/2024 01:53:40 [WARNING|DP=1|PP=0|TP=25|ip-26-0-172-73]: Repo card metadata block was not found. Setting CardData to empty.
[default2]:07/03/2024 01:53:40 [WARNING|DP=1|PP=0|TP=26|ip-26-0-172-73]: Repo card metadata block was not found. Setting CardData to empty.
[default7]:07/03/2024 01:53:40 [WARNING|DP=1|PP=0|TP=31|ip-26-0-172-73]: Repo card metadata block was not found. Setting CardData to empty.
[default4]:07/03/2024 01:53:40 [WARNING|DP=1|PP=0|TP=28|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.
[default5]:Repo card metadata block was not found. Setting CardData to empty.
[default7]:Repo card metadata block was not found. Setting CardData to empty.
[default0]:Repo card metadata block was not found. Setting CardData to empty.
[default2]: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.
[default2]:Repo card metadata block was not found. Setting CardData to empty.
[default0]:Repo card metadata block was not found. Setting CardData to empty.
[default2]:07/03/2024 01:53:41 [WARNING|DP=0|PP=0|TP=26|ip-26-0-163-226]: Repo card metadata block was not found. Setting CardData to empty.
[default3]:07/03/2024 01:53:41 [WARNING|DP=0|PP=0|TP=11|ip-26-0-161-178]: Repo card metadata block was not found. Setting CardData to empty.
[default5]:07/03/2024 01:53:41 [WARNING|DP=0|PP=0|TP=13|ip-26-0-161-178]: Repo card metadata block was not found. Setting CardData to empty.
[default4]:07/03/2024 01:53:41 [WARNING|DP=0|PP=0|TP=12|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 01:53:41 [WARNING|DP=0|PP=0|TP=4|ip-26-0-160-192]: 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.
[default2]:Repo card metadata block was not found. Setting CardData to empty.
[default3]:07/03/2024 01:53:40 [WARNING|DP=1|PP=0|TP=19|ip-26-0-172-57]: 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.
[default3]:07/03/2024 01:53:41 [WARNING|DP=1|PP=0|TP=3|ip-26-0-168-238]: Repo card metadata block was not found. Setting CardData to empty.
[default4]:07/03/2024 01:53:41 [WARNING|DP=1|PP=0|TP=12|ip-26-0-169-86]: Repo card metadata block was not found. Setting CardData to empty.
[default7]:07/03/2024 01:53:41 [WARNING|DP=0|PP=0|TP=31|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.
[default1]:Repo card metadata block was not found. Setting CardData to empty.
[default7]:Repo card metadata block was not found. Setting CardData to empty.
[default7]:07/03/2024 01:53:41 [WARNING|DP=1|PP=0|TP=23|ip-26-0-172-57]: Repo card metadata block was not found. Setting CardData to empty.
[default4]:07/03/2024 01:53:41 [WARNING|DP=1|PP=0|TP=4|ip-26-0-168-238]: Repo card metadata block was not found. Setting CardData to empty.
[default2]:07/03/2024 01:53:40 [WARNING|DP=1|PP=0|TP=18|ip-26-0-172-57]: Repo card metadata block was not found. Setting CardData to empty.
[default1]:07/03/2024 01:53:40 [WARNING|DP=1|PP=0|TP=17|ip-26-0-172-57]: Repo card metadata block was not found. Setting CardData to empty.
[default4]:07/03/2024 01:53:40 [WARNING|DP=1|PP=0|TP=20|ip-26-0-172-57]: Repo card metadata block was not found. Setting CardData to empty.
[default2]:07/03/2024 01:53:41 [WARNING|DP=1|PP=0|TP=2|ip-26-0-168-238]: Repo card metadata block was not found. Setting CardData to empty.
[default1]:07/03/2024 01:53:40 [WARNING|DP=0|PP=0|TP=1|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.
[default4]:Repo card metadata block was not found. Setting CardData to empty.
[default0]:07/03/2024 01:53:41 [WARNING|DP=1|PP=0|TP=0|ip-26-0-168-238]: Repo card metadata block was not found. Setting CardData to empty.
[default1]:07/03/2024 01:53:41 [WARNING|DP=1|PP=0|TP=9|ip-26-0-169-86]: Repo card metadata block was not found. Setting CardData to empty.
[default3]:07/03/2024 01:53:41 [WARNING|DP=1|PP=0|TP=27|ip-26-0-172-73]: Repo card metadata block was not found. Setting CardData to empty.
[default3]:Repo card metadata block was not found. Setting CardData to empty.
[default1]:Repo card metadata block was not found. Setting CardData to empty.
[default2]:07/03/2024 01:53:40 [WARNING|DP=1|PP=0|TP=10|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.
[default4]:Repo card metadata block was not found. Setting CardData to empty.
[default3]:Repo card metadata block was not found. Setting CardData to empty.
[default7]:Repo card metadata block was not found. Setting CardData to empty.
[default4]: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 01:53:41 [WARNING|DP=0|PP=0|TP=19|ip-26-0-163-220]: Repo card metadata block was not found. Setting CardData to empty.
[default3]:07/03/2024 01:53:41 [WARNING|DP=0|PP=0|TP=27|ip-26-0-163-226]: Repo card metadata block was not found. Setting CardData to empty.
[default5]:Repo card metadata block was not found. Setting CardData to empty.
[default3]:Repo card metadata block was not found. Setting CardData to empty.
[default5]:07/03/2024 01:53:41 [WARNING|DP=0|PP=0|TP=29|ip-26-0-163-226]: Repo card metadata block was not found. Setting CardData to empty.
[default6]:07/03/2024 01:53:41 [WARNING|DP=0|PP=0|TP=22|ip-26-0-163-220]: 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.
[default5]:07/03/2024 01:53:41 [WARNING|DP=1|PP=0|TP=21|ip-26-0-172-57]: 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.
[default0]:07/03/2024 01:53:41 [WARNING|DP=1|PP=0|TP=16|ip-26-0-172-57]: Repo card metadata block was not found. Setting CardData to empty.
[default5]:07/03/2024 01:53:41 [WARNING|DP=1|PP=0|TP=13|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.
[default6]:Repo card metadata block was not found. Setting CardData to empty.
[default6]:07/03/2024 01:53:41 [WARNING|DP=1|PP=0|TP=30|ip-26-0-172-73]: Repo card metadata block was not found. Setting CardData to empty.
[default7]:07/03/2024 01:53:46 [WARNING|DP=0|PP=0|TP=23|ip-26-0-163-220]: Repo card metadata block was not found. Setting CardData to empty.
[default7]:Repo card metadata block was not found. Setting CardData to empty.
[default6]:07/03/2024 01:53:56 [WARNING|DP=0|PP=0|TP=6|ip-26-0-160-192]: Using the latest cached version of the dataset since roneneldan/TinyStories couldn't be found on the Hugging Face Hub
[default6]:07/03/2024 01:53:56 [WARNING|DP=0|PP=0|TP=6|ip-26-0-160-192]: 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).
[default6]:Using the latest cached version of the dataset since roneneldan/TinyStories couldn't be found on the Hugging Face Hub
[default6]: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).
[default2]:07/03/2024 01:54:24 [WARNING|DP=0|PP=0|TP=10|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.
[default3]:07/03/2024 01:54:24 [WARNING|DP=1|PP=0|TP=11|ip-26-0-169-86]: Repo card metadata block was not found. Setting CardData to empty.
[default3]:Repo card metadata block was not found. Setting CardData to empty.
[default4]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/autograd/graph.py:744: UserWarning: c10d::allreduce_: an autograd kernel was not registered to the Autograd key(s) but we are trying to backprop through it. This may lead to silently incorrect behavior. This behavior is deprecated and will be removed in a future version of PyTorch. If your operator is differentiable, please ensure you have registered an autograd kernel to the correct Autograd key (e.g. DispatchKey::Autograd, DispatchKey::CompositeImplicitAutograd). If your operator is not differentiable, or to squash this warning and use the previous behavior, please register torch::CppFunction::makeFallthrough() to DispatchKey::Autograd. (Triggered internally at ../torch/csrc/autograd/autograd_not_implemented_fallback.cpp:63.)
[default4]:  return Variable._execution_engine.run_backward(  # Calls into the C++ engine to run the backward pass
[default4]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/autograd/graph.py:744: UserWarning: c10d::allreduce_: an autograd kernel was not registered to the Autograd key(s) but we are trying to backprop through it. This may lead to silently incorrect behavior. This behavior is deprecated and will be removed in a future version of PyTorch. If your operator is differentiable, please ensure you have registered an autograd kernel to the correct Autograd key (e.g. DispatchKey::Autograd, DispatchKey::CompositeImplicitAutograd). If your operator is not differentiable, or to squash this warning and use the previous behavior, please register torch::CppFunction::makeFallthrough() to DispatchKey::Autograd. (Triggered internally at ../torch/csrc/autograd/autograd_not_implemented_fallback.cpp:63.)
[default4]:  return Variable._execution_engine.run_backward(  # Calls into the C++ engine to run the backward pass
[default6]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/autograd/graph.py:744: UserWarning: c10d::allreduce_: an autograd kernel was not registered to the Autograd key(s) but we are trying to backprop through it. This may lead to silently incorrect behavior. This behavior is deprecated and will be removed in a future version of PyTorch. If your operator is differentiable, please ensure you have registered an autograd kernel to the correct Autograd key (e.g. DispatchKey::Autograd, DispatchKey::CompositeImplicitAutograd). If your operator is not differentiable, or to squash this warning and use the previous behavior, please register torch::CppFunction::makeFallthrough() to DispatchKey::Autograd. (Triggered internally at ../torch/csrc/autograd/autograd_not_implemented_fallback.cpp:63.)
[default6]:  return Variable._execution_engine.run_backward(  # Calls into the C++ engine to run the backward pass
[default0]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/autograd/graph.py:744: UserWarning: c10d::allreduce_: an autograd kernel was not registered to the Autograd key(s) but we are trying to backprop through it. This may lead to silently incorrect behavior. This behavior is deprecated and will be removed in a future version of PyTorch. If your operator is differentiable, please ensure you have registered an autograd kernel to the correct Autograd key (e.g. DispatchKey::Autograd, DispatchKey::CompositeImplicitAutograd). If your operator is not differentiable, or to squash this warning and use the previous behavior, please register torch::CppFunction::makeFallthrough() to DispatchKey::Autograd. (Triggered internally at ../torch/csrc/autograd/autograd_not_implemented_fallback.cpp:63.)
[default0]:  return Variable._execution_engine.run_backward(  # Calls into the C++ engine to run the backward pass
[default5]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/autograd/graph.py:744: UserWarning: c10d::allreduce_: an autograd kernel was not registered to the Autograd key(s) but we are trying to backprop through it. This may lead to silently incorrect behavior. This behavior is deprecated and will be removed in a future version of PyTorch. If your operator is differentiable, please ensure you have registered an autograd kernel to the correct Autograd key (e.g. DispatchKey::Autograd, DispatchKey::CompositeImplicitAutograd). If your operator is not differentiable, or to squash this warning and use the previous behavior, please register torch::CppFunction::makeFallthrough() to DispatchKey::Autograd. (Triggered internally at ../torch/csrc/autograd/autograd_not_implemented_fallback.cpp:63.)
[default5]:  return Variable._execution_engine.run_backward(  # Calls into the C++ engine to run the backward pass
[default7]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/autograd/graph.py:744: UserWarning: c10d::allreduce_: an autograd kernel was not registered to the Autograd key(s) but we are trying to backprop through it. This may lead to silently incorrect behavior. This behavior is deprecated and will be removed in a future version of PyTorch. If your operator is differentiable, please ensure you have registered an autograd kernel to the correct Autograd key (e.g. DispatchKey::Autograd, DispatchKey::CompositeImplicitAutograd). If your operator is not differentiable, or to squash this warning and use the previous behavior, please register torch::CppFunction::makeFallthrough() to DispatchKey::Autograd. (Triggered internally at ../torch/csrc/autograd/autograd_not_implemented_fallback.cpp:63.)
[default7]:  return Variable._execution_engine.run_backward(  # Calls into the C++ engine to run the backward pass
[default5]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/autograd/graph.py:744: UserWarning: c10d::allreduce_: an autograd kernel was not registered to the Autograd key(s) but we are trying to backprop through it. This may lead to silently incorrect behavior. This behavior is deprecated and will be removed in a future version of PyTorch. If your operator is differentiable, please ensure you have registered an autograd kernel to the correct Autograd key (e.g. DispatchKey::Autograd, DispatchKey::CompositeImplicitAutograd). If your operator is not differentiable, or to squash this warning and use the previous behavior, please register torch::CppFunction::makeFallthrough() to DispatchKey::Autograd. (Triggered internally at ../torch/csrc/autograd/autograd_not_implemented_fallback.cpp:63.)
[default5]:  return Variable._execution_engine.run_backward(  # Calls into the C++ engine to run the backward pass
[default4]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/autograd/graph.py:744: UserWarning: c10d::allreduce_: an autograd kernel was not registered to the Autograd key(s) but we are trying to backprop through it. This may lead to silently incorrect behavior. This behavior is deprecated and will be removed in a future version of PyTorch. If your operator is differentiable, please ensure you have registered an autograd kernel to the correct Autograd key (e.g. DispatchKey::Autograd, DispatchKey::CompositeImplicitAutograd). If your operator is not differentiable, or to squash this warning and use the previous behavior, please register torch::CppFunction::makeFallthrough() to DispatchKey::Autograd. (Triggered internally at ../torch/csrc/autograd/autograd_not_implemented_fallback.cpp:63.)
[default4]:  return Variable._execution_engine.run_backward(  # Calls into the C++ engine to run the backward pass
[default2]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/autograd/graph.py:744: UserWarning: c10d::allreduce_: an autograd kernel was not registered to the Autograd key(s) but we are trying to backprop through it. This may lead to silently incorrect behavior. This behavior is deprecated and will be removed in a future version of PyTorch. If your operator is differentiable, please ensure you have registered an autograd kernel to the correct Autograd key (e.g. DispatchKey::Autograd, DispatchKey::CompositeImplicitAutograd). If your operator is not differentiable, or to squash this warning and use the previous behavior, please register torch::CppFunction::makeFallthrough() to DispatchKey::Autograd. (Triggered internally at ../torch/csrc/autograd/autograd_not_implemented_fallback.cpp:63.)
[default2]:  return Variable._execution_engine.run_backward(  # Calls into the C++ engine to run the backward pass
[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
[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.)
[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.)
[default0]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/autograd/graph.py:744: UserWarning: c10d::allreduce_: an autograd kernel was not registered to the Autograd key(s) but we are trying to backprop through it. This may lead to silently incorrect behavior. This behavior is deprecated and will be removed in a future version of PyTorch. If your operator is differentiable, please ensure you have registered an autograd kernel to the correct Autograd key (e.g. DispatchKey::Autograd, DispatchKey::CompositeImplicitAutograd). If your operator is not differentiable, or to squash this warning and use the previous behavior, please register torch::CppFunction::makeFallthrough() to DispatchKey::Autograd. (Triggered internally at ../torch/csrc/autograd/autograd_not_implemented_fallback.cpp:63.)
[default0]:  return Variable._execution_engine.run_backward(  # Calls into the C++ engine to run the backward pass
[default5]:  return Variable._execution_engine.run_backward(  # Calls into the C++ engine to run the backward pass
[default1]:  return Variable._execution_engine.run_backward(  # Calls into the C++ engine to run the backward pass
[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
[default7]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/autograd/graph.py:744: UserWarning: c10d::allreduce_: an autograd kernel was not registered to the Autograd key(s) but we are trying to backprop through it. This may lead to silently incorrect behavior. This behavior is deprecated and will be removed in a future version of PyTorch. If your operator is differentiable, please ensure you have registered an autograd kernel to the correct Autograd key (e.g. DispatchKey::Autograd, DispatchKey::CompositeImplicitAutograd). If your operator is not differentiable, or to squash this warning and use the previous behavior, please register torch::CppFunction::makeFallthrough() to DispatchKey::Autograd. (Triggered internally at ../torch/csrc/autograd/autograd_not_implemented_fallback.cpp:63.)
[default7]:  return Variable._execution_engine.run_backward(  # Calls into the C++ engine to run the backward pass
[default2]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/autograd/graph.py:744: UserWarning: c10d::allreduce_: an autograd kernel was not registered to the Autograd key(s) but we are trying to backprop through it. This may lead to silently incorrect behavior. This behavior is deprecated and will be removed in a future version of PyTorch. If your operator is differentiable, please ensure you have registered an autograd kernel to the correct Autograd key (e.g. DispatchKey::Autograd, DispatchKey::CompositeImplicitAutograd). If your operator is not differentiable, or to squash this warning and use the previous behavior, please register torch::CppFunction::makeFallthrough() to DispatchKey::Autograd. (Triggered internally at ../torch/csrc/autograd/autograd_not_implemented_fallback.cpp:63.)
[default2]:  return Variable._execution_engine.run_backward(  # Calls into the C++ engine to run the backward pass
[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
[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
[default6]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/autograd/graph.py:744: UserWarning: c10d::allreduce_: an autograd kernel was not registered to the Autograd key(s) but we are trying to backprop through it. This may lead to silently incorrect behavior. This behavior is deprecated and will be removed in a future version of PyTorch. If your operator is differentiable, please ensure you have registered an autograd kernel to the correct Autograd key (e.g. DispatchKey::Autograd, DispatchKey::CompositeImplicitAutograd). If your operator is not differentiable, or to squash this warning and use the previous behavior, please register torch::CppFunction::makeFallthrough() to DispatchKey::Autograd. (Triggered internally at ../torch/csrc/autograd/autograd_not_implemented_fallback.cpp:63.)
[default6]:  return Variable._execution_engine.run_backward(  # Calls into the C++ engine to run the backward pass
[default6]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/autograd/graph.py:744: UserWarning: c10d::allreduce_: an autograd kernel was not registered to the Autograd key(s) but we are trying to backprop through it. This may lead to silently incorrect behavior. This behavior is deprecated and will be removed in a future version of PyTorch. If your operator is differentiable, please ensure you have registered an autograd kernel to the correct Autograd key (e.g. DispatchKey::Autograd, DispatchKey::CompositeImplicitAutograd). If your operator is not differentiable, or to squash this warning and use the previous behavior, please register torch::CppFunction::makeFallthrough() to DispatchKey::Autograd. (Triggered internally at ../torch/csrc/autograd/autograd_not_implemented_fallback.cpp:63.)
[default6]:  return Variable._execution_engine.run_backward(  # Calls into the C++ engine to run the backward pass
[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.)
[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]:  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.)
[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]:  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
[default6]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/autograd/graph.py:744: UserWarning: c10d::allreduce_: an autograd kernel was not registered to the Autograd key(s) but we are trying to backprop through it. This may lead to silently incorrect behavior. This behavior is deprecated and will be removed in a future version of PyTorch. If your operator is differentiable, please ensure you have registered an autograd kernel to the correct Autograd key (e.g. DispatchKey::Autograd, DispatchKey::CompositeImplicitAutograd). If your operator is not differentiable, or to squash this warning and use the previous behavior, please register torch::CppFunction::makeFallthrough() to DispatchKey::Autograd. (Triggered internally at ../torch/csrc/autograd/autograd_not_implemented_fallback.cpp:63.)
[default6]:  return Variable._execution_engine.run_backward(  # Calls into the C++ engine to run the backward pass
[default3]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/autograd/graph.py:744: UserWarning: c10d::allreduce_: an autograd kernel was not registered to the Autograd key(s) but we are trying to backprop through it. This may lead to silently incorrect behavior. This behavior is deprecated and will be removed in a future version of PyTorch. If your operator is differentiable, please ensure you have registered an autograd kernel to the correct Autograd key (e.g. DispatchKey::Autograd, DispatchKey::CompositeImplicitAutograd). If your operator is not differentiable, or to squash this warning and use the previous behavior, please register torch::CppFunction::makeFallthrough() to DispatchKey::Autograd. (Triggered internally at ../torch/csrc/autograd/autograd_not_implemented_fallback.cpp:63.)
[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]:  return Variable._execution_engine.run_backward(  # Calls into the C++ engine to run the backward pass
[default5]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/autograd/graph.py:744: UserWarning: c10d::allreduce_: an autograd kernel was not registered to the Autograd key(s) but we are trying to backprop through it. This may lead to silently incorrect behavior. This behavior is deprecated and will be removed in a future version of PyTorch. If your operator is differentiable, please ensure you have registered an autograd kernel to the correct Autograd key (e.g. DispatchKey::Autograd, DispatchKey::CompositeImplicitAutograd). If your operator is not differentiable, or to squash this warning and use the previous behavior, please register torch::CppFunction::makeFallthrough() to DispatchKey::Autograd. (Triggered internally at ../torch/csrc/autograd/autograd_not_implemented_fallback.cpp:63.)
[default5]:  return Variable._execution_engine.run_backward(  # Calls into the C++ engine to run the backward pass
[default7]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/autograd/graph.py:744: UserWarning: c10d::allreduce_: an autograd kernel was not registered to the Autograd key(s) but we are trying to backprop through it. This may lead to silently incorrect behavior. This behavior is deprecated and will be removed in a future version of PyTorch. If your operator is differentiable, please ensure you have registered an autograd kernel to the correct Autograd key (e.g. DispatchKey::Autograd, DispatchKey::CompositeImplicitAutograd). If your operator is not differentiable, or to squash this warning and use the previous behavior, please register torch::CppFunction::makeFallthrough() to DispatchKey::Autograd. (Triggered internally at ../torch/csrc/autograd/autograd_not_implemented_fallback.cpp:63.)
[default7]:  return Variable._execution_engine.run_backward(  # Calls into the C++ engine to run the backward pass
[default5]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/autograd/graph.py:744: UserWarning: c10d::allreduce_: an autograd kernel was not registered to the Autograd key(s) but we are trying to backprop through it. This may lead to silently incorrect behavior. This behavior is deprecated and will be removed in a future version of PyTorch. If your operator is differentiable, please ensure you have registered an autograd kernel to the correct Autograd key (e.g. DispatchKey::Autograd, DispatchKey::CompositeImplicitAutograd). If your operator is not differentiable, or to squash this warning and use the previous behavior, please register torch::CppFunction::makeFallthrough() to DispatchKey::Autograd. (Triggered internally at ../torch/csrc/autograd/autograd_not_implemented_fallback.cpp:63.)
[default5]:  return Variable._execution_engine.run_backward(  # Calls into the C++ engine to run the backward pass
[default7]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/autograd/graph.py:744: UserWarning: c10d::allreduce_: an autograd kernel was not registered to the Autograd key(s) but we are trying to backprop through it. This may lead to silently incorrect behavior. This behavior is deprecated and will be removed in a future version of PyTorch. If your operator is differentiable, please ensure you have registered an autograd kernel to the correct Autograd key (e.g. DispatchKey::Autograd, DispatchKey::CompositeImplicitAutograd). If your operator is not differentiable, or to squash this warning and use the previous behavior, please register torch::CppFunction::makeFallthrough() to DispatchKey::Autograd. (Triggered internally at ../torch/csrc/autograd/autograd_not_implemented_fallback.cpp:63.)
[default7]:  return Variable._execution_engine.run_backward(  # Calls into the C++ engine to run the backward pass
[default1]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/autograd/graph.py:744: UserWarning: c10d::allreduce_: an autograd kernel was not registered to the Autograd key(s) but we are trying to backprop through it. This may lead to silently incorrect behavior. This behavior is deprecated and will be removed in a future version of PyTorch. If your operator is differentiable, please ensure you have registered an autograd kernel to the correct Autograd key (e.g. DispatchKey::Autograd, DispatchKey::CompositeImplicitAutograd). If your operator is not differentiable, or to squash this warning and use the previous behavior, please register torch::CppFunction::makeFallthrough() to DispatchKey::Autograd. (Triggered internally at ../torch/csrc/autograd/autograd_not_implemented_fallback.cpp:63.)
[default1]:  return Variable._execution_engine.run_backward(  # Calls into the C++ engine to run the backward pass
[default3]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/autograd/graph.py:744: UserWarning: c10d::allreduce_: an autograd kernel was not registered to the Autograd key(s) but we are trying to backprop through it. This may lead to silently incorrect behavior. This behavior is deprecated and will be removed in a future version of PyTorch. If your operator is differentiable, please ensure you have registered an autograd kernel to the correct Autograd key (e.g. DispatchKey::Autograd, DispatchKey::CompositeImplicitAutograd). If your operator is not differentiable, or to squash this warning and use the previous behavior, please register torch::CppFunction::makeFallthrough() to DispatchKey::Autograd. (Triggered internally at ../torch/csrc/autograd/autograd_not_implemented_fallback.cpp:63.)
[default3]:  return Variable._execution_engine.run_backward(  # Calls into the C++ engine to run the backward pass
[default6]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/autograd/graph.py:744: UserWarning: c10d::allreduce_: an autograd kernel was not registered to the Autograd key(s) but we are trying to backprop through it. This may lead to silently incorrect behavior. This behavior is deprecated and will be removed in a future version of PyTorch. If your operator is differentiable, please ensure you have registered an autograd kernel to the correct Autograd key (e.g. DispatchKey::Autograd, DispatchKey::CompositeImplicitAutograd). If your operator is not differentiable, or to squash this warning and use the previous behavior, please register torch::CppFunction::makeFallthrough() to DispatchKey::Autograd. (Triggered internally at ../torch/csrc/autograd/autograd_not_implemented_fallback.cpp:63.)
[default6]:  return Variable._execution_engine.run_backward(  # Calls into the C++ engine to run the backward pass
[default1]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/autograd/graph.py:744: UserWarning: c10d::allreduce_: an autograd kernel was not registered to the Autograd key(s) but we are trying to backprop through it. This may lead to silently incorrect behavior. This behavior is deprecated and will be removed in a future version of PyTorch. If your operator is differentiable, please ensure you have registered an autograd kernel to the correct Autograd key (e.g. DispatchKey::Autograd, DispatchKey::CompositeImplicitAutograd). If your operator is not differentiable, or to squash this warning and use the previous behavior, please register torch::CppFunction::makeFallthrough() to DispatchKey::Autograd. (Triggered internally at ../torch/csrc/autograd/autograd_not_implemented_fallback.cpp:63.)
[default1]:  return Variable._execution_engine.run_backward(  # Calls into the C++ engine to run the backward pass
[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
[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
[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
[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
[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
[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
[default3]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/autograd/graph.py:744: UserWarning: c10d::allreduce_: an autograd kernel was not registered to the Autograd key(s) but we are trying to backprop through it. This may lead to silently incorrect behavior. This behavior is deprecated and will be removed in a future version of PyTorch. If your operator is differentiable, please ensure you have registered an autograd kernel to the correct Autograd key (e.g. DispatchKey::Autograd, DispatchKey::CompositeImplicitAutograd). If your operator is not differentiable, or to squash this warning and use the previous behavior, please register torch::CppFunction::makeFallthrough() to DispatchKey::Autograd. (Triggered internally at ../torch/csrc/autograd/autograd_not_implemented_fallback.cpp:63.)
[default3]:  return Variable._execution_engine.run_backward(  # Calls into the C++ engine to run the backward pass
[default1]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/autograd/graph.py:744: UserWarning: c10d::allreduce_: an autograd kernel was not registered to the Autograd key(s) but we are trying to backprop through it. This may lead to silently incorrect behavior. This behavior is deprecated and will be removed in a future version of PyTorch. If your operator is differentiable, please ensure you have registered an autograd kernel to the correct Autograd key (e.g. DispatchKey::Autograd, DispatchKey::CompositeImplicitAutograd). If your operator is not differentiable, or to squash this warning and use the previous behavior, please register torch::CppFunction::makeFallthrough() to DispatchKey::Autograd. (Triggered internally at ../torch/csrc/autograd/autograd_not_implemented_fallback.cpp:63.)
[default1]:  return Variable._execution_engine.run_backward(  # Calls into the C++ engine to run the backward pass
[default0]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/autograd/graph.py:744: UserWarning: c10d::allreduce_: an autograd kernel was not registered to the Autograd key(s) but we are trying to backprop through it. This may lead to silently incorrect behavior. This behavior is deprecated and will be removed in a future version of PyTorch. If your operator is differentiable, please ensure you have registered an autograd kernel to the correct Autograd key (e.g. DispatchKey::Autograd, DispatchKey::CompositeImplicitAutograd). If your operator is not differentiable, or to squash this warning and use the previous behavior, please register torch::CppFunction::makeFallthrough() to DispatchKey::Autograd. (Triggered internally at ../torch/csrc/autograd/autograd_not_implemented_fallback.cpp:63.)
[default0]:  return Variable._execution_engine.run_backward(  # Calls into the C++ engine to run the backward pass
[default4]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/distributed_c10d.py:2261: UserWarning: torch.distributed.all_reduce_coalesced will be deprecated. If you must use it, please revisit our documentation later at https://pytorch.org/docs/master/distributed.html#collective-functions
[default4]:  warnings.warn(
[default4]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/distributed_c10d.py:2261: UserWarning: torch.distributed.all_reduce_coalesced will be deprecated. If you must use it, please revisit our documentation later at https://pytorch.org/docs/master/distributed.html#collective-functions
[default4]:  warnings.warn(
[default6]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/distributed_c10d.py:2261: UserWarning: torch.distributed.all_reduce_coalesced will be deprecated. If you must use it, please revisit our documentation later at https://pytorch.org/docs/master/distributed.html#collective-functions
[default6]:  warnings.warn(
[default0]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/distributed_c10d.py:2261: UserWarning: torch.distributed.all_reduce_coalesced will be deprecated. If you must use it, please revisit our documentation later at https://pytorch.org/docs/master/distributed.html#collective-functions
[default0]:  warnings.warn(
[default5]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/distributed_c10d.py:2261: UserWarning: torch.distributed.all_reduce_coalesced will be deprecated. If you must use it, please revisit our documentation later at https://pytorch.org/docs/master/distributed.html#collective-functions
[default5]:  warnings.warn(
[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(
[default1]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/distributed_c10d.py:2261: UserWarning: torch.distributed.all_reduce_coalesced will be deprecated. If you must use it, please revisit our documentation later at https://pytorch.org/docs/master/distributed.html#collective-functions
[default1]:  warnings.warn(
[default4]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/distributed_c10d.py:2261: UserWarning: torch.distributed.all_reduce_coalesced will be deprecated. If you must use it, please revisit our documentation later at https://pytorch.org/docs/master/distributed.html#collective-functions
[default4]:  warnings.warn(
[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(
[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(
[default5]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/distributed_c10d.py:2261: UserWarning: torch.distributed.all_reduce_coalesced will be deprecated. If you must use it, please revisit our documentation later at https://pytorch.org/docs/master/distributed.html#collective-functions
[default5]:  warnings.warn(
[default0]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/distributed_c10d.py:2261: UserWarning: torch.distributed.all_reduce_coalesced will be deprecated. If you must use it, please revisit our documentation later at https://pytorch.org/docs/master/distributed.html#collective-functions
[default0]:  warnings.warn(
[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(
[default0]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/distributed_c10d.py:2261: UserWarning: torch.distributed.all_reduce_coalesced will be deprecated. If you must use it, please revisit our documentation later at https://pytorch.org/docs/master/distributed.html#collective-functions
[default0]:  warnings.warn(
[default7]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/distributed_c10d.py:2261: UserWarning: torch.distributed.all_reduce_coalesced will be deprecated. If you must use it, please revisit our documentation later at https://pytorch.org/docs/master/distributed.html#collective-functions
[default7]:  warnings.warn(
[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(
[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(
[default3]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/distributed_c10d.py:2261: UserWarning: torch.distributed.all_reduce_coalesced will be deprecated. If you must use it, please revisit our documentation later at https://pytorch.org/docs/master/distributed.html#collective-functions
[default3]:  warnings.warn(
[default0]:07/03/2024 01:54:39 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:  Memory usage: 369.84MiB. Peak allocated 66816.90MiB. Peak reserved: 67676.00MiB
[default1]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/distributed_c10d.py:2261: UserWarning: torch.distributed.all_reduce_coalesced will be deprecated. If you must use it, please revisit our documentation later at https://pytorch.org/docs/master/distributed.html#collective-functions
[default1]:  warnings.warn(
[default0]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/distributed_c10d.py:2261: UserWarning: torch.distributed.all_reduce_coalesced will be deprecated. If you must use it, please revisit our documentation later at https://pytorch.org/docs/master/distributed.html#collective-functions
[default0]:  warnings.warn(
[default2]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/distributed_c10d.py:2261: UserWarning: torch.distributed.all_reduce_coalesced will be deprecated. If you must use it, please revisit our documentation later at https://pytorch.org/docs/master/distributed.html#collective-functions
[default2]:  warnings.warn(
[default6]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/distributed_c10d.py:2261: UserWarning: torch.distributed.all_reduce_coalesced will be deprecated. If you must use it, please revisit our documentation later at https://pytorch.org/docs/master/distributed.html#collective-functions
[default6]:  warnings.warn(
[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(
[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(
[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(
[default2]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/distributed_c10d.py:2261: UserWarning: torch.distributed.all_reduce_coalesced will be deprecated. If you must use it, please revisit our documentation later at https://pytorch.org/docs/master/distributed.html#collective-functions
[default2]:  warnings.warn(
[default4]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/distributed_c10d.py:2261: UserWarning: torch.distributed.all_reduce_coalesced will be deprecated. If you must use it, please revisit our documentation later at https://pytorch.org/docs/master/distributed.html#collective-functions
[default4]:  warnings.warn(
[default5]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/distributed_c10d.py:2261: UserWarning: torch.distributed.all_reduce_coalesced will be deprecated. If you must use it, please revisit our documentation later at https://pytorch.org/docs/master/distributed.html#collective-functions
[default5]:  warnings.warn(
[default6]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/distributed_c10d.py:2261: UserWarning: torch.distributed.all_reduce_coalesced will be deprecated. If you must use it, please revisit our documentation later at https://pytorch.org/docs/master/distributed.html#collective-functions
[default6]:  warnings.warn(
[default3]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/distributed_c10d.py:2261: UserWarning: torch.distributed.all_reduce_coalesced will be deprecated. If you must use it, please revisit our documentation later at https://pytorch.org/docs/master/distributed.html#collective-functions
[default3]:  warnings.warn(
[default1]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/distributed_c10d.py:2261: UserWarning: torch.distributed.all_reduce_coalesced will be deprecated. If you must use it, please revisit our documentation later at https://pytorch.org/docs/master/distributed.html#collective-functions
[default1]:  warnings.warn(
[default0]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/distributed_c10d.py:2261: UserWarning: torch.distributed.all_reduce_coalesced will be deprecated. If you must use it, please revisit our documentation later at https://pytorch.org/docs/master/distributed.html#collective-functions
[default0]:  warnings.warn(
[default7]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/distributed_c10d.py:2261: UserWarning: torch.distributed.all_reduce_coalesced will be deprecated. If you must use it, please revisit our documentation later at https://pytorch.org/docs/master/distributed.html#collective-functions
[default7]:  warnings.warn(
[default7]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/distributed_c10d.py:2261: UserWarning: torch.distributed.all_reduce_coalesced will be deprecated. If you must use it, please revisit our documentation later at https://pytorch.org/docs/master/distributed.html#collective-functions
[default7]:  warnings.warn(
[default5]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/distributed_c10d.py:2261: UserWarning: torch.distributed.all_reduce_coalesced will be deprecated. If you must use it, please revisit our documentation later at https://pytorch.org/docs/master/distributed.html#collective-functions
[default5]:  warnings.warn(
[default3]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/distributed_c10d.py:2261: UserWarning: torch.distributed.all_reduce_coalesced will be deprecated. If you must use it, please revisit our documentation later at https://pytorch.org/docs/master/distributed.html#collective-functions
[default3]:  warnings.warn(
[default6]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/distributed_c10d.py:2261: UserWarning: torch.distributed.all_reduce_coalesced will be deprecated. If you must use it, please revisit our documentation later at https://pytorch.org/docs/master/distributed.html#collective-functions
[default6]:  warnings.warn(
[default1]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/distributed_c10d.py:2261: UserWarning: torch.distributed.all_reduce_coalesced will be deprecated. If you must use it, please revisit our documentation later at https://pytorch.org/docs/master/distributed.html#collective-functions
[default1]:  warnings.warn(
[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(
[default1]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/distributed_c10d.py:2261: UserWarning: torch.distributed.all_reduce_coalesced will be deprecated. If you must use it, please revisit our documentation later at https://pytorch.org/docs/master/distributed.html#collective-functions
[default1]:  warnings.warn(
[default4]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/distributed_c10d.py:2261: UserWarning: torch.distributed.all_reduce_coalesced will be deprecated. If you must use it, please revisit our documentation later at https://pytorch.org/docs/master/distributed.html#collective-functions
[default4]:  warnings.warn(
[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(
[default5]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/distributed_c10d.py:2261: UserWarning: torch.distributed.all_reduce_coalesced will be deprecated. If you must use it, please revisit our documentation later at https://pytorch.org/docs/master/distributed.html#collective-functions
[default5]:  warnings.warn(
[default4]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/distributed_c10d.py:2261: UserWarning: torch.distributed.all_reduce_coalesced will be deprecated. If you must use it, please revisit our documentation later at https://pytorch.org/docs/master/distributed.html#collective-functions
[default4]:  warnings.warn(
[default2]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/distributed_c10d.py:2261: UserWarning: torch.distributed.all_reduce_coalesced will be deprecated. If you must use it, please revisit our documentation later at https://pytorch.org/docs/master/distributed.html#collective-functions
[default2]:  warnings.warn(
[default3]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/distributed_c10d.py:2261: UserWarning: torch.distributed.all_reduce_coalesced will be deprecated. If you must use it, please revisit our documentation later at https://pytorch.org/docs/master/distributed.html#collective-functions
[default3]:  warnings.warn(
[default7]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/distributed_c10d.py:2261: UserWarning: torch.distributed.all_reduce_coalesced will be deprecated. If you must use it, please revisit our documentation later at https://pytorch.org/docs/master/distributed.html#collective-functions
[default7]:  warnings.warn(
[default3]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/distributed_c10d.py:2261: UserWarning: torch.distributed.all_reduce_coalesced will be deprecated. If you must use it, please revisit our documentation later at https://pytorch.org/docs/master/distributed.html#collective-functions
[default3]:  warnings.warn(
[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(
[default1]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/distributed_c10d.py:2261: UserWarning: torch.distributed.all_reduce_coalesced will be deprecated. If you must use it, please revisit our documentation later at https://pytorch.org/docs/master/distributed.html#collective-functions
[default1]:  warnings.warn(
[default4]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/distributed_c10d.py:2261: UserWarning: torch.distributed.all_reduce_coalesced will be deprecated. If you must use it, please revisit our documentation later at https://pytorch.org/docs/master/distributed.html#collective-functions
[default4]:  warnings.warn(
[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(
[default0]:07/03/2024 01:54:40 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: iteration: 1 / 20 | consumed_tokens: 4.19M | elapsed_time_per_iteration_ms: 59.9K | tokens_per_sec: 70.1K | tokens_per_sec_per_gpu: 1.09K | global_batch_size: 1.02K | lm_loss: 11.6 | lr: 0.0001 | model_tflops_per_gpu: 9.93 | hardware_tflops_per_gpu: 9.93 | grad_norm: 11.1 | cuda_memory_allocated: 527M | cuda_max_memory_reserved: 71G | hd_total_memory_tb: 312G | hd_used_memory_tb: 74G | hd_free_memory_tb: 238G
[default0]:07/03/2024 01:54:40 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:  Memory usage: 502.57MiB. Peak allocated 668.39MiB. Peak reserved: 67730.00MiB
[default0]:07/03/2024 01:54:48 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:  Memory usage: 502.57MiB. Peak allocated 66949.64MiB. Peak reserved: 67796.00MiB
[default0]:07/03/2024 01:54:49 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: iteration: 2 / 20 | consumed_tokens: 8.39M | elapsed_time_per_iteration_ms: 9.01K | tokens_per_sec: 465K | tokens_per_sec_per_gpu: 7.27K | global_batch_size: 1.02K | lm_loss: 11.6 | lr: 9.53e-05 | model_tflops_per_gpu: 66 | hardware_tflops_per_gpu: 66 | grad_norm: 11.1 | cuda_memory_allocated: 527M | cuda_max_memory_reserved: 71.1G | hd_total_memory_tb: 312G | hd_used_memory_tb: 74G | hd_free_memory_tb: 238G
[default0]:07/03/2024 01:54:49 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:  Memory usage: 502.57MiB. Peak allocated 668.39MiB. Peak reserved: 67796.00MiB
[default0]:07/03/2024 01:54:57 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:  Memory usage: 502.57MiB. Peak allocated 66949.64MiB. Peak reserved: 67796.00MiB
[default0]:07/03/2024 01:54:58 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: iteration: 3 / 20 | consumed_tokens: 12.6M | elapsed_time_per_iteration_ms: 8.85K | tokens_per_sec: 474K | tokens_per_sec_per_gpu: 7.4K | global_batch_size: 1.02K | lm_loss: 11.4 | lr: 9.05e-05 | model_tflops_per_gpu: 67.2 | hardware_tflops_per_gpu: 67.2 | grad_norm: 76 | cuda_memory_allocated: 527M | cuda_max_memory_reserved: 71.1G | hd_total_memory_tb: 312G | hd_used_memory_tb: 74G | hd_free_memory_tb: 238G
[default0]:07/03/2024 01:54:58 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:  Memory usage: 502.57MiB. Peak allocated 668.39MiB. Peak reserved: 67796.00MiB
[default0]:STAGE:2024-07-03 01:54:58 1120367:1120367 ActivityProfilerController.cpp:314] Completed Stage: Warm Up
[default0]:07/03/2024 01:55:06 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:  Memory usage: 502.57MiB. Peak allocated 66949.64MiB. Peak reserved: 67796.00MiB
[default0]:07/03/2024 01:55:07 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: iteration: 4 / 20 | consumed_tokens: 16.8M | elapsed_time_per_iteration_ms: 8.89K | tokens_per_sec: 472K | tokens_per_sec_per_gpu: 7.37K | global_batch_size: 1.02K | lm_loss: 11.5 | lr: 8.58e-05 | model_tflops_per_gpu: 66.9 | hardware_tflops_per_gpu: 66.9 | grad_norm: 15.1 | cuda_memory_allocated: 527M | cuda_max_memory_reserved: 71.1G | hd_total_memory_tb: 312G | hd_used_memory_tb: 74G | hd_free_memory_tb: 238G
[default0]:07/03/2024 01:55:07 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:  Memory usage: 502.57MiB. Peak allocated 668.39MiB. Peak reserved: 67796.00MiB
[default0]:07/03/2024 01:55:16 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: iteration: 5 / 20 | consumed_tokens: 21M | elapsed_time_per_iteration_ms: 8.84K | tokens_per_sec: 474K | tokens_per_sec_per_gpu: 7.41K | global_batch_size: 1.02K | lm_loss: 11.6 | lr: 8.11e-05 | model_tflops_per_gpu: 67.3 | hardware_tflops_per_gpu: 67.3 | grad_norm: 24.2
[default0]:07/03/2024 01:55:16 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:  Memory usage: 502.57MiB. Peak allocated 66949.64MiB. Peak reserved: 67796.00MiB
[default0]:07/03/2024 01:55:25 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: iteration: 6 / 20 | consumed_tokens: 25.2M | elapsed_time_per_iteration_ms: 8.89K | tokens_per_sec: 472K | tokens_per_sec_per_gpu: 7.37K | global_batch_size: 1.02K | lm_loss: 10.1 | lr: 7.63e-05 | model_tflops_per_gpu: 66.9 | hardware_tflops_per_gpu: 66.9 | grad_norm: 12.8
[default0]:STAGE:2024-07-03 01:55:29 1120367:1120367 ActivityProfilerController.cpp:320] Completed Stage: Collection
[default0]:STAGE:2024-07-03 01:55:30 1120367:1120367 ActivityProfilerController.cpp:324] Completed Stage: Post Processing
[default0]:07/03/2024 01:56:06 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:  Memory usage: 502.57MiB. Peak allocated 66949.64MiB. Peak reserved: 67796.00MiB
[default0]:07/03/2024 01:56:15 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: iteration: 7 / 20 | consumed_tokens: 29.4M | elapsed_time_per_iteration_ms: 8.8K | tokens_per_sec: 477K | tokens_per_sec_per_gpu: 7.45K | global_batch_size: 1.02K | lm_loss: 9.9 | lr: 7.16e-05 | model_tflops_per_gpu: 67.6 | hardware_tflops_per_gpu: 67.6 | grad_norm: 9.27
[default0]:07/03/2024 01:56:15 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:  Memory usage: 502.57MiB. Peak allocated 66949.64MiB. Peak reserved: 67796.00MiB
[default0]:07/03/2024 01:56:24 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: iteration: 8 / 20 | consumed_tokens: 33.6M | elapsed_time_per_iteration_ms: 8.84K | tokens_per_sec: 474K | tokens_per_sec_per_gpu: 7.41K | global_batch_size: 1.02K | lm_loss: 9.56 | lr: 6.68e-05 | model_tflops_per_gpu: 67.2 | hardware_tflops_per_gpu: 67.2 | grad_norm: 6.92
[default0]:07/03/2024 01:56:24 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:  Memory usage: 502.57MiB. Peak allocated 66949.64MiB. Peak reserved: 67796.00MiB
[default0]:07/03/2024 01:56:32 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: iteration: 9 / 20 | consumed_tokens: 37.7M | elapsed_time_per_iteration_ms: 8.87K | tokens_per_sec: 473K | tokens_per_sec_per_gpu: 7.39K | global_batch_size: 1.02K | lm_loss: 9.2 | lr: 6.21e-05 | model_tflops_per_gpu: 67 | hardware_tflops_per_gpu: 67 | grad_norm: 6.51
[default0]:07/03/2024 01:56:32 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:  Memory usage: 502.57MiB. Peak allocated 66949.64MiB. Peak reserved: 67796.00MiB
[default0]:07/03/2024 01:56:41 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: iteration: 10 / 20 | consumed_tokens: 41.9M | elapsed_time_per_iteration_ms: 8.87K | tokens_per_sec: 473K | tokens_per_sec_per_gpu: 7.39K | global_batch_size: 1.02K | lm_loss: 8.88 | lr: 5.74e-05 | model_tflops_per_gpu: 67.1 | hardware_tflops_per_gpu: 67.1 | grad_norm: 5.55
[default0]:07/03/2024 01:56:41 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:  Memory usage: 502.57MiB. Peak allocated 66949.64MiB. Peak reserved: 67796.00MiB
[default0]:07/03/2024 01:56:50 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: iteration: 11 / 20 | consumed_tokens: 46.1M | elapsed_time_per_iteration_ms: 8.84K | tokens_per_sec: 474K | tokens_per_sec_per_gpu: 7.41K | global_batch_size: 1.02K | lm_loss: 8.69 | lr: 5.26e-05 | model_tflops_per_gpu: 67.2 | hardware_tflops_per_gpu: 67.2 | grad_norm: 5.87
[default0]:07/03/2024 01:56:50 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:  Memory usage: 502.57MiB. Peak allocated 66949.64MiB. Peak reserved: 67796.00MiB
[default0]:07/03/2024 01:56:59 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: iteration: 12 / 20 | consumed_tokens: 50.3M | elapsed_time_per_iteration_ms: 8.84K | tokens_per_sec: 475K | tokens_per_sec_per_gpu: 7.42K | global_batch_size: 1.02K | lm_loss: 8.48 | lr: 4.79e-05 | model_tflops_per_gpu: 67.3 | hardware_tflops_per_gpu: 67.3 | grad_norm: 5.82
[default0]:07/03/2024 01:56:59 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:  Memory usage: 502.57MiB. Peak allocated 66949.64MiB. Peak reserved: 67796.00MiB
[default0]:07/03/2024 01:57:08 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: iteration: 13 / 20 | consumed_tokens: 54.5M | elapsed_time_per_iteration_ms: 8.85K | tokens_per_sec: 474K | tokens_per_sec_per_gpu: 7.4K | global_batch_size: 1.02K | lm_loss: 8.25 | lr: 4.32e-05 | model_tflops_per_gpu: 67.2 | hardware_tflops_per_gpu: 67.2 | grad_norm: 5.08
[default0]:07/03/2024 01:57:08 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:  Memory usage: 502.57MiB. Peak allocated 66949.64MiB. Peak reserved: 67796.00MiB
[default0]:07/03/2024 01:57:17 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: iteration: 14 / 20 | consumed_tokens: 58.7M | elapsed_time_per_iteration_ms: 8.89K | tokens_per_sec: 472K | tokens_per_sec_per_gpu: 7.37K | global_batch_size: 1.02K | lm_loss: 8.1 | lr: 3.84e-05 | model_tflops_per_gpu: 66.9 | hardware_tflops_per_gpu: 66.9 | grad_norm: 5.08
[default0]:07/03/2024 01:57:17 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:  Memory usage: 502.57MiB. Peak allocated 66949.64MiB. Peak reserved: 67796.00MiB
[default0]:07/03/2024 01:57:26 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: iteration: 15 / 20 | consumed_tokens: 62.9M | elapsed_time_per_iteration_ms: 8.86K | tokens_per_sec: 473K | tokens_per_sec_per_gpu: 7.39K | global_batch_size: 1.02K | lm_loss: 7.99 | lr: 3.37e-05 | model_tflops_per_gpu: 67.1 | hardware_tflops_per_gpu: 67.1 | grad_norm: 5.11
[default0]:07/03/2024 01:57:26 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:  Memory usage: 502.57MiB. Peak allocated 66949.64MiB. Peak reserved: 67796.00MiB
[default0]:07/03/2024 01:57:35 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: iteration: 16 / 20 | consumed_tokens: 67.1M | elapsed_time_per_iteration_ms: 8.87K | tokens_per_sec: 473K | tokens_per_sec_per_gpu: 7.39K | global_batch_size: 1.02K | lm_loss: 7.9 | lr: 2.89e-05 | model_tflops_per_gpu: 67 | hardware_tflops_per_gpu: 67 | grad_norm: 5.13
[default0]:07/03/2024 01:57:35 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:  Memory usage: 502.57MiB. Peak allocated 66949.64MiB. Peak reserved: 67796.00MiB
[default0]:07/03/2024 01:57:43 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: iteration: 17 / 20 | consumed_tokens: 71.3M | elapsed_time_per_iteration_ms: 8.87K | tokens_per_sec: 473K | tokens_per_sec_per_gpu: 7.39K | global_batch_size: 1.02K | lm_loss: 7.78 | lr: 2.42e-05 | model_tflops_per_gpu: 67 | hardware_tflops_per_gpu: 67 | grad_norm: 4.9
[default0]:07/03/2024 01:57:43 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:  Memory usage: 502.57MiB. Peak allocated 66949.64MiB. Peak reserved: 67796.00MiB
[default0]:07/03/2024 01:57:52 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: iteration: 18 / 20 | consumed_tokens: 75.5M | elapsed_time_per_iteration_ms: 8.85K | tokens_per_sec: 474K | tokens_per_sec_per_gpu: 7.4K | global_batch_size: 1.02K | lm_loss: 7.67 | lr: 1.95e-05 | model_tflops_per_gpu: 67.2 | hardware_tflops_per_gpu: 67.2 | grad_norm: 4.65
[default0]:07/03/2024 01:57:52 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:  Memory usage: 502.57MiB. Peak allocated 66949.64MiB. Peak reserved: 67796.00MiB
[default0]:07/03/2024 01:58:01 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: iteration: 19 / 20 | consumed_tokens: 79.7M | elapsed_time_per_iteration_ms: 8.85K | tokens_per_sec: 474K | tokens_per_sec_per_gpu: 7.4K | global_batch_size: 1.02K | lm_loss: 7.58 | lr: 1.47e-05 | model_tflops_per_gpu: 67.2 | hardware_tflops_per_gpu: 67.2 | grad_norm: 4.55
[default0]:07/03/2024 01:58:01 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]:  Memory usage: 502.57MiB. Peak allocated 66949.64MiB. Peak reserved: 67796.00MiB
[default0]:07/03/2024 01:58:10 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: iteration: 20 / 20 | consumed_tokens: 83.9M | elapsed_time_per_iteration_ms: 8.87K | tokens_per_sec: 473K | tokens_per_sec_per_gpu: 7.39K | global_batch_size: 1.02K | lm_loss: 7.53 | lr: 1e-05 | model_tflops_per_gpu: 67 | hardware_tflops_per_gpu: 67 | grad_norm: 4.51
Saved 1 csv files over 1 completed logs
Processing file: /fsx/ferdinandmom/ferdinand-hf/bench_cluster/results/llama-1B/64_GPUS/dp-2_tp-32_pp-1_mbz-64/profiler/ip-26-0-160-192_1120367.1719971759042826136.pt.trace.json
Results written to /fsx/ferdinandmom/ferdinand-hf/bench_cluster/results/llama-1B/64_GPUS/dp-2_tp-32_pp-1_mbz-64/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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ip-26-0-160-192_1120367.1719971759042826136.pt.trace.json: 100%|█████████▉| 1.17G/1.17G [00:22<00:00, 53.1MB/s]
ip-26-0-160-192_1120367.1719971759042826136.pt.trace.json: 100%|██████████| 1.17G/1.17G [00:22<00:00, 51.1MB/s]