llama-1B/16_GPUS/dp-1_tp-2_pp-8_mbz-2/log.out

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START TIME: Tue Jul  2 14:30:03 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
W0702 14:30:11.703000 139637441115968 torch/distributed/run.py:757] 
W0702 14:30:11.703000 139637441115968 torch/distributed/run.py:757] *****************************************
W0702 14:30:11.703000 139637441115968 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. 
W0702 14:30:11.703000 139637441115968 torch/distributed/run.py:757] *****************************************
W0702 14:30:11.807000 140117242140480 torch/distributed/run.py:757] 
W0702 14:30:11.807000 140117242140480 torch/distributed/run.py:757] *****************************************
W0702 14:30:11.807000 140117242140480 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. 
W0702 14:30:11.807000 140117242140480 torch/distributed/run.py:757] *****************************************
[default0]:07/02/2024 14:30:34 [WARNING|DP=0|PP=0|TP=0|ip-26-0-163-134]: [Vocab Size Padding] Padded vocab (size: 50257) with 1 dummy tokens (new size: 50258)
[default0]:07/02/2024 14:30:34 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-134]: Config:
[default0]:07/02/2024 14:30:34 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-134]: Config(general=GeneralArgs(project='bench_cluster',
[default0]:07/02/2024 14:30:34 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-134]:                            run='%date_%jobid',
[default0]:07/02/2024 14:30:34 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-134]:                            seed=42,
[default0]:07/02/2024 14:30:34 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-134]:                            step=None,
[default0]:07/02/2024 14:30:34 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-134]:                            consumed_train_samples=None,
[default0]:07/02/2024 14:30:34 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-134]:                            benchmark_csv_path=None,
[default0]:07/02/2024 14:30:34 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-134]:                            ignore_sanity_checks=True),
[default0]:07/02/2024 14:30:34 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-134]:        parallelism=ParallelismArgs(dp=1,
[default0]:07/02/2024 14:30:34 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-134]:                                    pp=8,
[default0]:07/02/2024 14:30:34 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-134]:                                    tp=2,
[default0]:07/02/2024 14:30:34 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-134]:                                    pp_engine=<nanotron.parallel.pipeline_parallel.engine.OneForwardOneBackwardPipelineEngine object at 0x7f6c01460910>,
[default0]:07/02/2024 14:30:34 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-134]:                                    tp_mode=<TensorParallelLinearMode.REDUCE_SCATTER: 2>,
[default0]:07/02/2024 14:30:34 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-134]:                                    tp_linear_async_communication=False,
[default0]:07/02/2024 14:30:34 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-134]:                                    expert_parallel_size=1),
[default0]:07/02/2024 14:30:34 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-134]:        model=ModelArgs(model_config=LlamaConfig(bos_token_id=1,
[default0]:07/02/2024 14:30:34 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-134]:                                                 eos_token_id=2,
[default0]:07/02/2024 14:30:34 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-134]:                                                 hidden_act='silu',
[default0]:07/02/2024 14:30:34 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-134]:                                                 hidden_size=2048,
[default0]:07/02/2024 14:30:34 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-134]:                                                 initializer_range=0.02,
[default0]:07/02/2024 14:30:34 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-134]:                                                 intermediate_size=4096,
[default0]:07/02/2024 14:30:34 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-134]:                                                 is_llama_config=True,
[default0]:07/02/2024 14:30:34 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-134]:                                                 max_position_embeddings=4096,
[default0]:07/02/2024 14:30:34 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-134]:                                                 num_attention_heads=32,
[default0]:07/02/2024 14:30:34 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-134]:                                                 num_hidden_layers=24,
[default0]:07/02/2024 14:30:34 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-134]:                                                 num_key_value_heads=32,
[default0]:07/02/2024 14:30:34 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-134]:                                                 pad_token_id=None,
[default0]:07/02/2024 14:30:34 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-134]:                                                 pretraining_tp=1,
[default0]:07/02/2024 14:30:34 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-134]:                                                 rms_norm_eps=1e-05,
[default0]:07/02/2024 14:30:34 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-134]:                                                 rope_scaling=None,
[default0]:07/02/2024 14:30:34 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-134]:                                                 rope_theta=10000.0,
[default0]:07/02/2024 14:30:34 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-134]:                                                 tie_word_embeddings=True,
[default0]:07/02/2024 14:30:34 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-134]:                                                 use_cache=True,
[default0]:07/02/2024 14:30:34 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-134]:                                                 vocab_size=50258),
[default0]:07/02/2024 14:30:34 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-134]:                        init_method=RandomInit(std=0.025),
[default0]:07/02/2024 14:30:34 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-134]:                        dtype=torch.bfloat16,
[default0]:07/02/2024 14:30:34 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-134]:                        make_vocab_size_divisible_by=1,
[default0]:07/02/2024 14:30:34 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-134]:                        ddp_bucket_cap_mb=25),
[default0]:07/02/2024 14:30:34 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-134]:        tokenizer=TokenizerArgs(tokenizer_name_or_path='openai-community/gpt2',
[default0]:07/02/2024 14:30:34 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-134]:                                tokenizer_revision=None,
[default0]:07/02/2024 14:30:34 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-134]:                                tokenizer_max_length=None),
[default0]:07/02/2024 14:30:34 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-134]:        checkpoints=CheckpointsArgs(checkpoints_path=Path('/dev/null'),
[default0]:07/02/2024 14:30:34 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-134]:                                    checkpoint_interval=100000,
[default0]:07/02/2024 14:30:34 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-134]:                                    save_initial_state=False,
[default0]:07/02/2024 14:30:34 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-134]:                                    resume_checkpoint_path=None,
[default0]:07/02/2024 14:30:34 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-134]:                                    checkpoints_path_is_shared_file_system=False),
[default0]:07/02/2024 14:30:34 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-134]:        logging=LoggingArgs(log_level='info',
[default0]:07/02/2024 14:30:34 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-134]:                            log_level_replica='info',
[default0]:07/02/2024 14:30:34 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-134]:                            iteration_step_info_interval=1),
[default0]:07/02/2024 14:30:34 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-134]:        tokens=TokensArgs(sequence_length=4096,
[default0]:07/02/2024 14:30:34 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-134]:                          train_steps=20,
[default0]:07/02/2024 14:30:34 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-134]:                          micro_batch_size=2,
[default0]:07/02/2024 14:30:34 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-134]:                          batch_accumulation_per_replica=512,
[default0]:07/02/2024 14:30:34 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-134]:                          val_check_interval=-1,
[default0]:07/02/2024 14:30:34 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-134]:                          limit_val_batches=0,
[default0]:07/02/2024 14:30:34 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-134]:                          limit_test_batches=0),
[default0]:07/02/2024 14:30:34 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-134]:        optimizer=OptimizerArgs(optimizer_factory=AdamWOptimizerArgs(adam_eps=1e-08,
[default0]:07/02/2024 14:30:34 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-134]:                                                                     adam_beta1=0.9,
[default0]:07/02/2024 14:30:34 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-134]:                                                                     adam_beta2=0.95,
[default0]:07/02/2024 14:30:34 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-134]:                                                                     torch_adam_is_fused=True,
[default0]:07/02/2024 14:30:34 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-134]:                                                                     name='adamW'),
[default0]:07/02/2024 14:30:34 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-134]:                                zero_stage=1,
[default0]:07/02/2024 14:30:34 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-134]:                                weight_decay=0.01,
[default0]:07/02/2024 14:30:34 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-134]:                                clip_grad=1.0,
[default0]:07/02/2024 14:30:34 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-134]:                                accumulate_grad_in_fp32=True,
[default0]:07/02/2024 14:30:34 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-134]:                                learning_rate_scheduler=LRSchedulerArgs(learning_rate=0.0001,
[default0]:07/02/2024 14:30:34 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-134]:                                                                        lr_warmup_steps=1,
[default0]:07/02/2024 14:30:34 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-134]:                                                                        lr_warmup_style='linear',
[default0]:07/02/2024 14:30:34 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-134]:                                                                        lr_decay_style='linear',
[default0]:07/02/2024 14:30:34 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-134]:                                                                        lr_decay_steps=19,
[default0]:07/02/2024 14:30:34 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-134]:                                                                        lr_decay_starting_step=None,
[default0]:07/02/2024 14:30:34 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-134]:                                                                        min_decay_lr=1e-05)),
[default0]:07/02/2024 14:30:34 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-134]:        data_stages=[DatasetStageArgs(name='Training Stage',
[default0]:07/02/2024 14:30:34 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-134]:                                      start_training_step=1,
[default0]:07/02/2024 14:30:34 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-134]:                                      data=DataArgs(dataset=PretrainDatasetsArgs(hf_dataset_or_datasets='roneneldan/TinyStories',
[default0]:07/02/2024 14:30:34 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-134]:                                                                                 hf_dataset_splits='train',
[default0]:07/02/2024 14:30:34 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-134]:                                                                                 hf_dataset_config_name=None,
[default0]:07/02/2024 14:30:34 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-134]:                                                                                 dataset_processing_num_proc_per_process=64,
[default0]:07/02/2024 14:30:34 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-134]:                                                                                 dataset_overwrite_cache=False,
[default0]:07/02/2024 14:30:34 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-134]:                                                                                 text_column_name='text'),
[default0]:07/02/2024 14:30:34 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-134]:                                                    seed=42,
[default0]:07/02/2024 14:30:34 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-134]:                                                    num_loading_workers=32))],
[default0]:07/02/2024 14:30:34 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-134]:        profiler=ProfilerArgs(profiler_export_path=Path('/fsx/ferdinandmom/ferdinand-hf/bench_cluster/results/llama-1B/16_GPUS/dp-1_tp-2_pp-8_mbz-2')),
[default0]:07/02/2024 14:30:34 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-134]:        lighteval=None)
[default0]:07/02/2024 14:30:34 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-134]: Model Config:
[default0]:07/02/2024 14:30:34 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-134]: LlamaConfig(bos_token_id=1,
[default0]:07/02/2024 14:30:34 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-134]:             eos_token_id=2,
[default0]:07/02/2024 14:30:34 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-134]:             hidden_act='silu',
[default0]:07/02/2024 14:30:34 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-134]:             hidden_size=2048,
[default0]:07/02/2024 14:30:34 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-134]:             initializer_range=0.02,
[default0]:07/02/2024 14:30:34 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-134]:             intermediate_size=4096,
[default0]:07/02/2024 14:30:34 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-134]:             is_llama_config=True,
[default0]:07/02/2024 14:30:34 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-134]:             max_position_embeddings=4096,
[default0]:07/02/2024 14:30:34 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-134]:             num_attention_heads=32,
[default0]:07/02/2024 14:30:34 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-134]:             num_hidden_layers=24,
[default0]:07/02/2024 14:30:34 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-134]:             num_key_value_heads=32,
[default0]:07/02/2024 14:30:34 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-134]:             pad_token_id=None,
[default0]:07/02/2024 14:30:34 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-134]:             pretraining_tp=1,
[default0]:07/02/2024 14:30:34 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-134]:             rms_norm_eps=1e-05,
[default0]:07/02/2024 14:30:34 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-134]:             rope_scaling=None,
[default0]:07/02/2024 14:30:34 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-134]:             rope_theta=10000.0,
[default0]:07/02/2024 14:30:34 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-134]:             tie_word_embeddings=True,
[default0]:07/02/2024 14:30:34 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-134]:             use_cache=True,
[default0]:07/02/2024 14:30:34 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-134]:             vocab_size=50258)
[default0]:07/02/2024 14:30:34 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-134]: Building model..
[default0]:07/02/2024 14:30:34 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-134]: Setting PP block ranks...
[default5]:07/02/2024 14:30:50 [INFO|DP=0|PP=6|TP=1|ip-26-0-163-147]: Local number of parameters: 83.9M (160.03MiB)
[default5]:07/02/2024 14:30:50 [INFO|DP=0|PP=6|TP=1|ip-26-0-163-147]: [After model building] Memory usage: 164.04MiB. Peak allocated: 166.07MiB Peak reserved: 180.00MiB
[default2]:07/02/2024 14:30:50 [INFO|DP=0|PP=5|TP=0|ip-26-0-163-147]: Local number of parameters: 62.9M (120.02MiB)
[default2]:07/02/2024 14:30:50 [INFO|DP=0|PP=5|TP=0|ip-26-0-163-147]: [After model building] Memory usage: 123.03MiB. Peak allocated: 125.06MiB Peak reserved: 138.00MiB
[default2]:07/02/2024 14:30:50 [INFO|DP=0|PP=5|TP=0|ip-26-0-163-147]: No checkpoint path provided.
[default5]:07/02/2024 14:30:50 [INFO|DP=0|PP=6|TP=1|ip-26-0-163-147]: No checkpoint path provided.
[default1]:07/02/2024 14:30:50 [INFO|DP=0|PP=4|TP=1|ip-26-0-163-147]: Local number of parameters: 62.9M (120.02MiB)
[default1]:07/02/2024 14:30:50 [INFO|DP=0|PP=4|TP=1|ip-26-0-163-147]: [After model building] Memory usage: 123.03MiB. Peak allocated: 125.06MiB Peak reserved: 138.00MiB
[default1]:07/02/2024 14:30:50 [INFO|DP=0|PP=4|TP=1|ip-26-0-163-147]: No checkpoint path provided.
[default6]:07/02/2024 14:30:50 [INFO|DP=0|PP=7|TP=0|ip-26-0-163-147]: Local number of parameters: 51.5M (98.16MiB)
[default6]:07/02/2024 14:30:50 [INFO|DP=0|PP=7|TP=0|ip-26-0-163-147]: [After model building] Memory usage: 98.17MiB. Peak allocated: 98.18MiB Peak reserved: 102.00MiB
[default6]:07/02/2024 14:30:50 [INFO|DP=0|PP=7|TP=0|ip-26-0-163-147]: No checkpoint path provided.
[default5]:07/02/2024 14:30:50 [INFO|DP=0|PP=2|TP=1|ip-26-0-163-134]: Local number of parameters: 62.9M (120.02MiB)
[default5]:07/02/2024 14:30:50 [INFO|DP=0|PP=2|TP=1|ip-26-0-163-134]: [After model building] Memory usage: 123.03MiB. Peak allocated: 125.06MiB Peak reserved: 138.00MiB
[default5]:07/02/2024 14:30:50 [INFO|DP=0|PP=2|TP=1|ip-26-0-163-134]: No checkpoint path provided.
[default6]:07/02/2024 14:30:50 [INFO|DP=0|PP=3|TP=0|ip-26-0-163-134]: Local number of parameters: 83.9M (160.03MiB)
[default6]:07/02/2024 14:30:50 [INFO|DP=0|PP=3|TP=0|ip-26-0-163-134]: [After model building] Memory usage: 164.04MiB. Peak allocated: 166.07MiB Peak reserved: 180.00MiB
[default6]:07/02/2024 14:30:50 [INFO|DP=0|PP=3|TP=0|ip-26-0-163-134]: No checkpoint path provided.
[default1]:07/02/2024 14:30:50 [INFO|DP=0|PP=0|TP=1|ip-26-0-163-134]: Local number of parameters: 135M (258.19MiB)
[default1]:07/02/2024 14:30:50 [INFO|DP=0|PP=0|TP=1|ip-26-0-163-134]: [After model building] Memory usage: 262.20MiB. Peak allocated: 264.23MiB Peak reserved: 280.00MiB
[default1]:07/02/2024 14:30:50 [INFO|DP=0|PP=0|TP=1|ip-26-0-163-134]: No checkpoint path provided.
[default3]:07/02/2024 14:30:50 [INFO|DP=0|PP=1|TP=1|ip-26-0-163-134]: Local number of parameters: 62.9M (120.02MiB)
[default3]:07/02/2024 14:30:50 [INFO|DP=0|PP=1|TP=1|ip-26-0-163-134]: [After model building] Memory usage: 123.03MiB. Peak allocated: 125.06MiB Peak reserved: 138.00MiB
[default3]:07/02/2024 14:30:50 [INFO|DP=0|PP=1|TP=1|ip-26-0-163-134]: No checkpoint path provided.
[default2]:07/02/2024 14:30:50 [INFO|DP=0|PP=1|TP=0|ip-26-0-163-134]: Local number of parameters: 62.9M (120.02MiB)
[default2]:07/02/2024 14:30:50 [INFO|DP=0|PP=1|TP=0|ip-26-0-163-134]: [After model building] Memory usage: 123.03MiB. Peak allocated: 125.06MiB Peak reserved: 138.00MiB
[default2]:07/02/2024 14:30:50 [INFO|DP=0|PP=1|TP=0|ip-26-0-163-134]: No checkpoint path provided.
[default0]:07/02/2024 14:30:50 [INFO|DP=0|PP=4|TP=0|ip-26-0-163-147]: Local number of parameters: 62.9M (120.02MiB)
[default0]:07/02/2024 14:30:50 [INFO|DP=0|PP=4|TP=0|ip-26-0-163-147]: [After model building] Memory usage: 123.03MiB. Peak allocated: 125.06MiB Peak reserved: 138.00MiB
[default0]:07/02/2024 14:30:50 [INFO|DP=0|PP=4|TP=0|ip-26-0-163-147]: No checkpoint path provided.
[default4]:07/02/2024 14:30:50 [INFO|DP=0|PP=2|TP=0|ip-26-0-163-134]: Local number of parameters: 62.9M (120.02MiB)
[default4]:07/02/2024 14:30:50 [INFO|DP=0|PP=2|TP=0|ip-26-0-163-134]: [After model building] Memory usage: 123.03MiB. Peak allocated: 125.06MiB Peak reserved: 138.00MiB
[default4]:07/02/2024 14:30:50 [INFO|DP=0|PP=2|TP=0|ip-26-0-163-134]: No checkpoint path provided.
[default0]:07/02/2024 14:30:50 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-134]: Total number of parameters: 1.21G (2313.02MiB)
[default0]:07/02/2024 14:30:50 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-134]: Local number of parameters: 135M (258.19MiB)
[default0]:07/02/2024 14:30:50 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-134]: [After model building] Memory usage: 262.20MiB. Peak allocated: 264.23MiB Peak reserved: 280.00MiB
[default0]:07/02/2024 14:30:50 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-134]: No checkpoint path provided.
[default0]:07/02/2024 14:30:50 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-134]: Parametrizing model parameters using StandardParametrizator
[default7]:07/02/2024 14:30:50 [INFO|DP=0|PP=7|TP=1|ip-26-0-163-147]: Local number of parameters: 51.5M (98.16MiB)
[default7]:07/02/2024 14:30:50 [INFO|DP=0|PP=7|TP=1|ip-26-0-163-147]: [After model building] Memory usage: 98.17MiB. Peak allocated: 98.18MiB Peak reserved: 102.00MiB
[default7]:07/02/2024 14:30:50 [INFO|DP=0|PP=7|TP=1|ip-26-0-163-147]: No checkpoint path provided.
[default4]:07/02/2024 14:30:50 [INFO|DP=0|PP=6|TP=0|ip-26-0-163-147]: Local number of parameters: 83.9M (160.03MiB)
[default4]:07/02/2024 14:30:50 [INFO|DP=0|PP=6|TP=0|ip-26-0-163-147]: [After model building] Memory usage: 164.04MiB. Peak allocated: 166.07MiB Peak reserved: 180.00MiB
[default4]:07/02/2024 14:30:50 [INFO|DP=0|PP=6|TP=0|ip-26-0-163-147]: No checkpoint path provided.
[default3]:07/02/2024 14:30:50 [INFO|DP=0|PP=5|TP=1|ip-26-0-163-147]: Local number of parameters: 62.9M (120.02MiB)
[default3]:07/02/2024 14:30:50 [INFO|DP=0|PP=5|TP=1|ip-26-0-163-147]: [After model building] Memory usage: 123.03MiB. Peak allocated: 125.06MiB Peak reserved: 138.00MiB
[default3]:07/02/2024 14:30:50 [INFO|DP=0|PP=5|TP=1|ip-26-0-163-147]: No checkpoint path provided.
[default7]:07/02/2024 14:30:50 [INFO|DP=0|PP=3|TP=1|ip-26-0-163-134]: Local number of parameters: 83.9M (160.03MiB)
[default7]:07/02/2024 14:30:50 [INFO|DP=0|PP=3|TP=1|ip-26-0-163-134]: [After model building] Memory usage: 164.04MiB. Peak allocated: 166.07MiB Peak reserved: 180.00MiB
[default7]:07/02/2024 14:30:50 [INFO|DP=0|PP=3|TP=1|ip-26-0-163-134]: No checkpoint path provided.
[default0]:07/02/2024 14:30:51 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-134]: [Optimizer Building] Using LearningRateForSP as learning rate
[default0]:07/02/2024 14:30:51 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-134]: [ZeRO sharding] Size of optimizer params per rank:
[default0]:07/02/2024 14:30:51 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-134]: [ZeRO sharding] DP Rank 0 has 135M out of 135M (100.00%) params' optimizer states
[default0]:07/02/2024 14:30:52 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-134]: [Training Plan] Stage Training Stage has 19 remaining training steps and has consumed 0 samples
[default0]:07/02/2024 14:30:52 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-134]: Using `datasets` library
[default0]:07/02/2024 14:30:52 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-134]: 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]:07/02/2024 14:30:52 [WARNING|DP=0|PP=0|TP=0|ip-26-0-163-134]: Repo card metadata block was not found. Setting CardData to empty.
[default0]:07/02/2024 14:30:54 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-134]: [Training Plan] There are 1 training stages 
[default0]:07/02/2024 14:30:54 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-134]: [Stage Training Stage] start from step 1 
[default0]:07/02/2024 14:30:54 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-134]: 
[default0]:07/02/2024 14:30:54 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-134]: [Start training] datetime: 2024-07-02 14:30:54.958619 | mbs: 2 | grad_accum: 512 | global_batch_size: 1024 | sequence_length: 4096 | train_steps: 20 | start_iteration_step: 0 | consumed_train_samples: 0
[default0]:07/02/2024 14:30:54 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-134]: Resuming training from stage Training Stage, it has trained for 0 samples and has 19 remaining train steps
[default0]:07/02/2024 14:30:54 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-134]:  Memory usage: 1294.97MiB. Peak allocated 1294.97MiB. Peak reserved: 1316.00MiB
[default1]:07/02/2024 14:30:55 [WARNING|DP=0|PP=4|TP=1|ip-26-0-163-147]: Repo card metadata block was not found. Setting CardData to empty.
[default6]:07/02/2024 14:30:55 [WARNING|DP=0|PP=7|TP=0|ip-26-0-163-147]: 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.
[default5]:07/02/2024 14:30:55 [WARNING|DP=0|PP=2|TP=1|ip-26-0-163-134]: Repo card metadata block was not found. Setting CardData to empty.
[default6]:07/02/2024 14:30:55 [WARNING|DP=0|PP=3|TP=0|ip-26-0-163-134]: Repo card metadata block was not found. Setting CardData to empty.
[default7]:Repo card metadata block was not found. Setting CardData to empty.
[default3]:07/02/2024 14:30:55 [WARNING|DP=0|PP=1|TP=1|ip-26-0-163-134]: Repo card metadata block was not found. Setting CardData to empty.
[default1]:07/02/2024 14:30:55 [WARNING|DP=0|PP=0|TP=1|ip-26-0-163-134]: Repo card metadata block was not found. Setting CardData to empty.
[default4]:07/02/2024 14:30:55 [WARNING|DP=0|PP=2|TP=0|ip-26-0-163-134]: Repo card metadata block was not found. Setting CardData to empty.
[default0]:07/02/2024 14:30:55 [WARNING|DP=0|PP=4|TP=0|ip-26-0-163-147]: Repo card metadata block was not found. Setting CardData to empty.
[default4]:Repo card metadata block was not found. Setting CardData to empty.
[default0]:Repo card metadata block was not found. Setting CardData to empty.
[default7]:07/02/2024 14:30:55 [WARNING|DP=0|PP=7|TP=1|ip-26-0-163-147]: Repo card metadata block was not found. Setting CardData to empty.
[default7]:07/02/2024 14:30:55 [WARNING|DP=0|PP=3|TP=1|ip-26-0-163-134]: Repo card metadata block was not found. Setting CardData to empty.
[default1]:Repo card metadata block was not found. Setting CardData to empty.
[default6]:Repo card metadata block was not found. Setting CardData to empty.
[default7]:Repo card metadata block was not found. Setting CardData to empty.
[default6]:Repo card metadata block was not found. Setting CardData to empty.
[default5]:Repo card metadata block was not found. Setting CardData to empty.
[default2]:07/02/2024 14:30:55 [WARNING|DP=0|PP=5|TP=0|ip-26-0-163-147]: Repo card metadata block was not found. Setting CardData to empty.
[default5]:07/02/2024 14:30:55 [WARNING|DP=0|PP=6|TP=1|ip-26-0-163-147]: Repo card metadata block was not found. Setting CardData to empty.
[default2]:Repo card metadata block was not found. Setting CardData to empty.
[default2]:07/02/2024 14:30:55 [WARNING|DP=0|PP=1|TP=0|ip-26-0-163-134]: Repo card metadata block was not found. Setting CardData to empty.
[default5]:Repo card metadata block was not found. Setting CardData to empty.
[default4]:07/02/2024 14:30:55 [WARNING|DP=0|PP=6|TP=0|ip-26-0-163-147]: Repo card metadata block was not found. Setting CardData to empty.
[default2]:Repo card metadata block was not found. Setting CardData to empty.
[default3]:07/02/2024 14:30:55 [WARNING|DP=0|PP=5|TP=1|ip-26-0-163-147]: Repo card metadata block was not found. Setting CardData to empty.
[default3]:Repo card metadata block was not found. Setting CardData to empty.
[default4]:Repo card metadata block was not found. Setting CardData to empty.
[default7]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/autograd/graph.py:744: UserWarning: c10d::allreduce_: an autograd kernel was not registered to the Autograd key(s) but we are trying to backprop through it. This may lead to silently incorrect behavior. This behavior is deprecated and will be removed in a future version of PyTorch. If your operator is differentiable, please ensure you have registered an autograd kernel to the correct Autograd key (e.g. DispatchKey::Autograd, DispatchKey::CompositeImplicitAutograd). If your operator is not differentiable, or to squash this warning and use the previous behavior, please register torch::CppFunction::makeFallthrough() to DispatchKey::Autograd. (Triggered internally at ../torch/csrc/autograd/autograd_not_implemented_fallback.cpp:63.)
[default7]:  return Variable._execution_engine.run_backward(  # Calls into the C++ engine to run the backward pass
[default6]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/autograd/graph.py:744: UserWarning: c10d::allreduce_: an autograd kernel was not registered to the Autograd key(s) but we are trying to backprop through it. This may lead to silently incorrect behavior. This behavior is deprecated and will be removed in a future version of PyTorch. If your operator is differentiable, please ensure you have registered an autograd kernel to the correct Autograd key (e.g. DispatchKey::Autograd, DispatchKey::CompositeImplicitAutograd). If your operator is not differentiable, or to squash this warning and use the previous behavior, please register torch::CppFunction::makeFallthrough() to DispatchKey::Autograd. (Triggered internally at ../torch/csrc/autograd/autograd_not_implemented_fallback.cpp:63.)
[default6]:  return Variable._execution_engine.run_backward(  # Calls into the C++ engine to run the backward pass
[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
[default0]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/autograd/graph.py:744: UserWarning: c10d::allreduce_: an autograd kernel was not registered to the Autograd key(s) but we are trying to backprop through it. This may lead to silently incorrect behavior. This behavior is deprecated and will be removed in a future version of PyTorch. If your operator is differentiable, please ensure you have registered an autograd kernel to the correct Autograd key (e.g. DispatchKey::Autograd, DispatchKey::CompositeImplicitAutograd). If your operator is not differentiable, or to squash this warning and use the previous behavior, please register torch::CppFunction::makeFallthrough() to DispatchKey::Autograd. (Triggered internally at ../torch/csrc/autograd/autograd_not_implemented_fallback.cpp:63.)
[default0]:  return Variable._execution_engine.run_backward(  # Calls into the C++ engine to run the backward pass
[default7]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/autograd/graph.py:744: UserWarning: c10d::allreduce_: an autograd kernel was not registered to the Autograd key(s) but we are trying to backprop through it. This may lead to silently incorrect behavior. This behavior is deprecated and will be removed in a future version of PyTorch. If your operator is differentiable, please ensure you have registered an autograd kernel to the correct Autograd key (e.g. DispatchKey::Autograd, DispatchKey::CompositeImplicitAutograd). If your operator is not differentiable, or to squash this warning and use the previous behavior, please register torch::CppFunction::makeFallthrough() to DispatchKey::Autograd. (Triggered internally at ../torch/csrc/autograd/autograd_not_implemented_fallback.cpp:63.)
[default7]:  return Variable._execution_engine.run_backward(  # Calls into the C++ engine to run the backward pass
[default6]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/autograd/graph.py:744: UserWarning: c10d::allreduce_: an autograd kernel was not registered to the Autograd key(s) but we are trying to backprop through it. This may lead to silently incorrect behavior. This behavior is deprecated and will be removed in a future version of PyTorch. If your operator is differentiable, please ensure you have registered an autograd kernel to the correct Autograd key (e.g. DispatchKey::Autograd, DispatchKey::CompositeImplicitAutograd). If your operator is not differentiable, or to squash this warning and use the previous behavior, please register torch::CppFunction::makeFallthrough() to DispatchKey::Autograd. (Triggered internally at ../torch/csrc/autograd/autograd_not_implemented_fallback.cpp:63.)
[default6]:  return Variable._execution_engine.run_backward(  # Calls into the C++ engine to run the backward pass
[default4]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/autograd/graph.py:744: UserWarning: c10d::allreduce_: an autograd kernel was not registered to the Autograd key(s) but we are trying to backprop through it. This may lead to silently incorrect behavior. This behavior is deprecated and will be removed in a future version of PyTorch. If your operator is differentiable, please ensure you have registered an autograd kernel to the correct Autograd key (e.g. DispatchKey::Autograd, DispatchKey::CompositeImplicitAutograd). If your operator is not differentiable, or to squash this warning and use the previous behavior, please register torch::CppFunction::makeFallthrough() to DispatchKey::Autograd. (Triggered internally at ../torch/csrc/autograd/autograd_not_implemented_fallback.cpp:63.)
[default4]:  return Variable._execution_engine.run_backward(  # Calls into the C++ engine to run the backward pass
[default5]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/autograd/graph.py:744: UserWarning: c10d::allreduce_: an autograd kernel was not registered to the Autograd key(s) but we are trying to backprop through it. This may lead to silently incorrect behavior. This behavior is deprecated and will be removed in a future version of PyTorch. If your operator is differentiable, please ensure you have registered an autograd kernel to the correct Autograd key (e.g. DispatchKey::Autograd, DispatchKey::CompositeImplicitAutograd). If your operator is not differentiable, or to squash this warning and use the previous behavior, please register torch::CppFunction::makeFallthrough() to DispatchKey::Autograd. (Triggered internally at ../torch/csrc/autograd/autograd_not_implemented_fallback.cpp:63.)
[default5]:  return Variable._execution_engine.run_backward(  # Calls into the C++ engine to run the backward pass
[default2]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/autograd/graph.py:744: UserWarning: c10d::allreduce_: an autograd kernel was not registered to the Autograd key(s) but we are trying to backprop through it. This may lead to silently incorrect behavior. This behavior is deprecated and will be removed in a future version of PyTorch. If your operator is differentiable, please ensure you have registered an autograd kernel to the correct Autograd key (e.g. DispatchKey::Autograd, DispatchKey::CompositeImplicitAutograd). If your operator is not differentiable, or to squash this warning and use the previous behavior, please register torch::CppFunction::makeFallthrough() to DispatchKey::Autograd. (Triggered internally at ../torch/csrc/autograd/autograd_not_implemented_fallback.cpp:63.)
[default2]:  return Variable._execution_engine.run_backward(  # Calls into the C++ engine to run the backward pass
[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
[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(
[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(
[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(
[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(
[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(
[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(
[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(
[default0]:07/02/2024 14:32:42 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-134]:  Memory usage: 1361.04MiB. Peak allocated 12288.41MiB. Peak reserved: 12498.00MiB
[default6]:07/02/2024 14:32:50 [INFO|DP=0|PP=7|TP=0|ip-26-0-163-147]: iteration: 1 / 20 | consumed_tokens: 4.19M | elapsed_time_per_iteration_ms: 113K | tokens_per_sec: 37.1K | tokens_per_sec_per_gpu: 2.32K | global_batch_size: 1.02K | lm_loss: 11.2 | lr: 0.0001 | model_tflops_per_gpu: 21 | hardware_tflops_per_gpu: 21 | grad_norm: 17.8 | cuda_memory_allocated: 994M | cuda_max_memory_reserved: 2.88G | hd_total_memory_tb: 312G | hd_used_memory_tb: 68.6G | hd_free_memory_tb: 244G
[default0]:07/02/2024 14:32:50 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-134]:  Memory usage: 2393.82MiB. Peak allocated 2393.82MiB. Peak reserved: 12696.00MiB
[default0]:07/02/2024 14:33:55 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-134]:  Memory usage: 2393.82MiB. Peak allocated 13251.43MiB. Peak reserved: 13676.00MiB
[default6]:07/02/2024 14:33:55 [INFO|DP=0|PP=7|TP=0|ip-26-0-163-147]: iteration: 2 / 20 | consumed_tokens: 8.39M | elapsed_time_per_iteration_ms: 65K | tokens_per_sec: 64.5K | tokens_per_sec_per_gpu: 4.03K | global_batch_size: 1.02K | lm_loss: 11.2 | lr: 9.53e-05 | model_tflops_per_gpu: 36.6 | hardware_tflops_per_gpu: 36.6 | grad_norm: 17.8 | cuda_memory_allocated: 994M | cuda_max_memory_reserved: 2.88G | hd_total_memory_tb: 312G | hd_used_memory_tb: 68.6G | hd_free_memory_tb: 244G
[default0]:07/02/2024 14:33:55 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-134]:  Memory usage: 2393.82MiB. Peak allocated 2393.83MiB. Peak reserved: 13676.00MiB
[default0]:07/02/2024 14:35:00 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-134]:  Memory usage: 2393.82MiB. Peak allocated 13251.43MiB. Peak reserved: 13748.00MiB
[default0]:STAGE:2024-07-02 14:35:00 1435141:1435141 ActivityProfilerController.cpp:314] Completed Stage: Warm Up
[default6]:07/02/2024 14:35:00 [INFO|DP=0|PP=7|TP=0|ip-26-0-163-147]: iteration: 3 / 20 | consumed_tokens: 12.6M | elapsed_time_per_iteration_ms: 64.8K | tokens_per_sec: 64.7K | tokens_per_sec_per_gpu: 4.05K | global_batch_size: 1.02K | lm_loss: 9.62 | lr: 9.05e-05 | model_tflops_per_gpu: 36.7 | hardware_tflops_per_gpu: 36.7 | grad_norm: 21.7 | cuda_memory_allocated: 994M | cuda_max_memory_reserved: 2.88G | hd_total_memory_tb: 312G | hd_used_memory_tb: 68.6G | hd_free_memory_tb: 244G
[default0]:07/02/2024 14:35:00 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-134]:  Memory usage: 2393.82MiB. Peak allocated 2393.83MiB. Peak reserved: 13748.00MiB
[default0]:07/02/2024 14:36:04 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-134]:  Memory usage: 2393.82MiB. Peak allocated 13251.43MiB. Peak reserved: 13748.00MiB
[default6]:07/02/2024 14:36:04 [INFO|DP=0|PP=7|TP=0|ip-26-0-163-147]: iteration: 4 / 20 | consumed_tokens: 16.8M | elapsed_time_per_iteration_ms: 64K | tokens_per_sec: 65.5K | tokens_per_sec_per_gpu: 4.1K | global_batch_size: 1.02K | lm_loss: 10.4 | lr: 8.58e-05 | model_tflops_per_gpu: 37.2 | hardware_tflops_per_gpu: 37.2 | grad_norm: 45.6 | cuda_memory_allocated: 994M | cuda_max_memory_reserved: 2.88G | hd_total_memory_tb: 312G | hd_used_memory_tb: 68.6G | hd_free_memory_tb: 244G
[default0]:07/02/2024 14:36:04 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-134]:  Memory usage: 2393.82MiB. Peak allocated 2393.83MiB. Peak reserved: 13748.00MiB
[default0]:07/02/2024 14:37:05 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-134]:  Memory usage: 2393.82MiB. Peak allocated 13251.43MiB. Peak reserved: 13748.00MiB
[default6]:07/02/2024 14:37:05 [INFO|DP=0|PP=7|TP=0|ip-26-0-163-147]: iteration: 5 / 20 | consumed_tokens: 21M | elapsed_time_per_iteration_ms: 61.7K | tokens_per_sec: 68K | tokens_per_sec_per_gpu: 4.25K | global_batch_size: 1.02K | lm_loss: 9.43 | lr: 8.11e-05 | model_tflops_per_gpu: 38.5 | hardware_tflops_per_gpu: 38.5 | grad_norm: 11.2
[default6]:07/02/2024 14:38:06 [INFO|DP=0|PP=7|TP=0|ip-26-0-163-147]: iteration: 6 / 20 | consumed_tokens: 25.2M | elapsed_time_per_iteration_ms: 60.4K | tokens_per_sec: 69.5K | tokens_per_sec_per_gpu: 4.34K | global_batch_size: 1.02K | lm_loss: 9.37 | lr: 7.63e-05 | model_tflops_per_gpu: 39.4 | hardware_tflops_per_gpu: 39.4 | grad_norm: 7.69
[default0]:STAGE:2024-07-02 14:38:58 1435141:1435141 ActivityProfilerController.cpp:320] Completed Stage: Collection
[default0]:STAGE:2024-07-02 14:39:04 1435141:1435141 ActivityProfilerController.cpp:324] Completed Stage: Post Processing
[default0]:07/02/2024 14:46:04 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-134]:  Memory usage: 2393.82MiB. Peak allocated 13251.43MiB. Peak reserved: 13748.00MiB
[default0]:07/02/2024 14:47:06 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-134]:  Memory usage: 2393.82MiB. Peak allocated 13251.43MiB. Peak reserved: 13748.00MiB
[default6]:07/02/2024 14:47:06 [INFO|DP=0|PP=7|TP=0|ip-26-0-163-147]: iteration: 7 / 20 | consumed_tokens: 29.4M | elapsed_time_per_iteration_ms: 540K | tokens_per_sec: 7.77K | tokens_per_sec_per_gpu: 485 | global_batch_size: 1.02K | lm_loss: 8.96 | lr: 7.16e-05 | model_tflops_per_gpu: 4.4 | hardware_tflops_per_gpu: 4.4 | grad_norm: 5.69
[default0]:07/02/2024 14:48:08 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-134]:  Memory usage: 2393.82MiB. Peak allocated 13251.43MiB. Peak reserved: 13748.00MiB
[default6]:07/02/2024 14:48:08 [INFO|DP=0|PP=7|TP=0|ip-26-0-163-147]: iteration: 8 / 20 | consumed_tokens: 33.6M | elapsed_time_per_iteration_ms: 62.4K | tokens_per_sec: 67.2K | tokens_per_sec_per_gpu: 4.2K | global_batch_size: 1.02K | lm_loss: 8.47 | lr: 6.68e-05 | model_tflops_per_gpu: 38.1 | hardware_tflops_per_gpu: 38.1 | grad_norm: 5.25
[default0]:07/02/2024 14:49:10 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-134]:  Memory usage: 2393.82MiB. Peak allocated 13251.43MiB. Peak reserved: 13748.00MiB
[default6]:07/02/2024 14:49:10 [INFO|DP=0|PP=7|TP=0|ip-26-0-163-147]: iteration: 9 / 20 | consumed_tokens: 37.7M | elapsed_time_per_iteration_ms: 61.3K | tokens_per_sec: 68.4K | tokens_per_sec_per_gpu: 4.27K | global_batch_size: 1.02K | lm_loss: 8.01 | lr: 6.21e-05 | model_tflops_per_gpu: 38.8 | hardware_tflops_per_gpu: 38.8 | grad_norm: 4.65
[default6]:07/02/2024 14:50:13 [INFO|DP=0|PP=7|TP=0|ip-26-0-163-147]: iteration: 10 / 20 | consumed_tokens: 41.9M | elapsed_time_per_iteration_ms: 62.8K | tokens_per_sec: 66.8K | tokens_per_sec_per_gpu: 4.17K | global_batch_size: 1.02K | lm_loss: 7.75 | lr: 5.74e-05 | model_tflops_per_gpu: 37.9 | hardware_tflops_per_gpu: 37.9 | grad_norm: 3.85
[default0]:07/02/2024 14:50:13 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-134]:  Memory usage: 2393.82MiB. Peak allocated 13251.43MiB. Peak reserved: 13748.00MiB
[default0]:07/02/2024 14:51:16 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-134]:  Memory usage: 2393.82MiB. Peak allocated 13251.43MiB. Peak reserved: 13748.00MiB
[default6]:07/02/2024 14:51:16 [INFO|DP=0|PP=7|TP=0|ip-26-0-163-147]: iteration: 11 / 20 | consumed_tokens: 46.1M | elapsed_time_per_iteration_ms: 63.8K | tokens_per_sec: 65.7K | tokens_per_sec_per_gpu: 4.11K | global_batch_size: 1.02K | lm_loss: 7.62 | lr: 5.26e-05 | model_tflops_per_gpu: 37.3 | hardware_tflops_per_gpu: 37.3 | grad_norm: 4.98
[default0]:07/02/2024 14:52:20 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-134]:  Memory usage: 2393.82MiB. Peak allocated 13251.43MiB. Peak reserved: 13748.00MiB
[default6]:07/02/2024 14:52:20 [INFO|DP=0|PP=7|TP=0|ip-26-0-163-147]: iteration: 12 / 20 | consumed_tokens: 50.3M | elapsed_time_per_iteration_ms: 63.4K | tokens_per_sec: 66.1K | tokens_per_sec_per_gpu: 4.13K | global_batch_size: 1.02K | lm_loss: 7.46 | lr: 4.79e-05 | model_tflops_per_gpu: 37.5 | hardware_tflops_per_gpu: 37.5 | grad_norm: 3.44
[default0]:07/02/2024 14:53:22 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-134]:  Memory usage: 2393.82MiB. Peak allocated 13251.43MiB. Peak reserved: 13748.00MiB
[default6]:07/02/2024 14:53:22 [INFO|DP=0|PP=7|TP=0|ip-26-0-163-147]: iteration: 13 / 20 | consumed_tokens: 54.5M | elapsed_time_per_iteration_ms: 62.5K | tokens_per_sec: 67.1K | tokens_per_sec_per_gpu: 4.2K | global_batch_size: 1.02K | lm_loss: 7.34 | lr: 4.32e-05 | model_tflops_per_gpu: 38.1 | hardware_tflops_per_gpu: 38.1 | grad_norm: 3.45
[default0]:07/02/2024 14:54:22 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-134]:  Memory usage: 2393.82MiB. Peak allocated 13251.43MiB. Peak reserved: 13748.00MiB
[default6]:07/02/2024 14:54:22 [INFO|DP=0|PP=7|TP=0|ip-26-0-163-147]: iteration: 14 / 20 | consumed_tokens: 58.7M | elapsed_time_per_iteration_ms: 60K | tokens_per_sec: 69.9K | tokens_per_sec_per_gpu: 4.37K | global_batch_size: 1.02K | lm_loss: 7.22 | lr: 3.84e-05 | model_tflops_per_gpu: 39.6 | hardware_tflops_per_gpu: 39.6 | grad_norm: 3.23
[default0]:07/02/2024 14:55:26 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-134]:  Memory usage: 2393.82MiB. Peak allocated 13251.43MiB. Peak reserved: 13748.00MiB
[default6]:07/02/2024 14:55:26 [INFO|DP=0|PP=7|TP=0|ip-26-0-163-147]: iteration: 15 / 20 | consumed_tokens: 62.9M | elapsed_time_per_iteration_ms: 63.4K | tokens_per_sec: 66.2K | tokens_per_sec_per_gpu: 4.13K | global_batch_size: 1.02K | lm_loss: 7.1 | lr: 3.37e-05 | model_tflops_per_gpu: 37.5 | hardware_tflops_per_gpu: 37.5 | grad_norm: 2.88
[default0]:07/02/2024 14:56:31 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-134]:  Memory usage: 2393.82MiB. Peak allocated 13251.43MiB. Peak reserved: 13748.00MiB
[default6]:07/02/2024 14:56:31 [INFO|DP=0|PP=7|TP=0|ip-26-0-163-147]: iteration: 16 / 20 | consumed_tokens: 67.1M | elapsed_time_per_iteration_ms: 65.7K | tokens_per_sec: 63.9K | tokens_per_sec_per_gpu: 3.99K | global_batch_size: 1.02K | lm_loss: 7.01 | lr: 2.89e-05 | model_tflops_per_gpu: 36.2 | hardware_tflops_per_gpu: 36.2 | grad_norm: 2.57
[default0]:07/02/2024 14:57:32 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-134]:  Memory usage: 2393.82MiB. Peak allocated 13251.43MiB. Peak reserved: 13748.00MiB
[default6]:07/02/2024 14:57:32 [INFO|DP=0|PP=7|TP=0|ip-26-0-163-147]: iteration: 17 / 20 | consumed_tokens: 71.3M | elapsed_time_per_iteration_ms: 60.7K | tokens_per_sec: 69.1K | tokens_per_sec_per_gpu: 4.32K | global_batch_size: 1.02K | lm_loss: 6.94 | lr: 2.42e-05 | model_tflops_per_gpu: 39.2 | hardware_tflops_per_gpu: 39.2 | grad_norm: 2.47
[default0]:07/02/2024 14:58:33 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-134]:  Memory usage: 2393.82MiB. Peak allocated 13251.43MiB. Peak reserved: 13748.00MiB
[default6]:07/02/2024 14:58:33 [INFO|DP=0|PP=7|TP=0|ip-26-0-163-147]: iteration: 18 / 20 | consumed_tokens: 75.5M | elapsed_time_per_iteration_ms: 61.4K | tokens_per_sec: 68.3K | tokens_per_sec_per_gpu: 4.27K | global_batch_size: 1.02K | lm_loss: 6.88 | lr: 1.95e-05 | model_tflops_per_gpu: 38.7 | hardware_tflops_per_gpu: 38.7 | grad_norm: 2.49
[default0]:07/02/2024 14:59:36 [INFO|DP=0|PP=0|TP=0|ip-26-0-163-134]:  Memory usage: 2393.82MiB. Peak allocated 13251.43MiB. Peak reserved: 13748.00MiB
[default6]:07/02/2024 14:59:36 [INFO|DP=0|PP=7|TP=0|ip-26-0-163-147]: iteration: 19 / 20 | consumed_tokens: 79.7M | elapsed_time_per_iteration_ms: 62.2K | tokens_per_sec: 67.4K | tokens_per_sec_per_gpu: 4.21K | global_batch_size: 1.02K | lm_loss: 6.82 | lr: 1.47e-05 | model_tflops_per_gpu: 38.2 | hardware_tflops_per_gpu: 38.2 | grad_norm: 2.42
[default6]:07/02/2024 15:00:36 [INFO|DP=0|PP=7|TP=0|ip-26-0-163-147]: iteration: 20 / 20 | consumed_tokens: 83.9M | elapsed_time_per_iteration_ms: 60.2K | tokens_per_sec: 69.6K | tokens_per_sec_per_gpu: 4.35K | global_batch_size: 1.02K | lm_loss: 6.78 | lr: 1e-05 | model_tflops_per_gpu: 39.5 | hardware_tflops_per_gpu: 39.5 | grad_norm: 2.25
Saved 1 csv files over 1 completed logs
Processing file: /fsx/ferdinandmom/ferdinand-hf/bench_cluster/results/llama-1B/16_GPUS/dp-1_tp-2_pp-8_mbz-2/profiler/ip-26-0-163-134_1435141.1719931469116122110.pt.trace.json
Results written to /fsx/ferdinandmom/ferdinand-hf/bench_cluster/results/llama-1B/16_GPUS/dp-1_tp-2_pp-8_mbz-2/profiler.csv
Consider using `hf_transfer` for faster uploads. This solution comes with some limitations. See https://huggingface.co/docs/huggingface_hub/hf_transfer for more details.

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