| ======================== | |
| START TIME: Wed Jul 3 23:02:27 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 23:02:30.165000 140703814809408 torch/distributed/run.py:757] | |
| W0703 23:02:30.165000 140703814809408 torch/distributed/run.py:757] ***************************************** | |
| W0703 23:02:30.165000 140703814809408 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 23:02:30.165000 140703814809408 torch/distributed/run.py:757] ***************************************** | |
| [default0]:07/03/2024 23:02:46 [WARNING|DP=0|PP=0|TP=0|ip-26-0-164-187]: [Vocab Size Padding] Padded vocab (size: 50257) with 3 dummy tokens (new size: 50260) | |
| [default0]:07/03/2024 23:02:46 [INFO|DP=0|PP=0|TP=0|ip-26-0-164-187]: Config: | |
| [default0]:07/03/2024 23:02:46 [INFO|DP=0|PP=0|TP=0|ip-26-0-164-187]: Config(general=GeneralArgs(project='bench_cluster', | |
| [default0]:07/03/2024 23:02:46 [INFO|DP=0|PP=0|TP=0|ip-26-0-164-187]: run='%date_%jobid', | |
| [default0]:07/03/2024 23:02:46 [INFO|DP=0|PP=0|TP=0|ip-26-0-164-187]: seed=42, | |
| [default0]:07/03/2024 23:02:46 [INFO|DP=0|PP=0|TP=0|ip-26-0-164-187]: step=None, | |
| [default0]:07/03/2024 23:02:46 [INFO|DP=0|PP=0|TP=0|ip-26-0-164-187]: consumed_train_samples=None, | |
| [default0]:07/03/2024 23:02:46 [INFO|DP=0|PP=0|TP=0|ip-26-0-164-187]: benchmark_csv_path=None, | |
| [default0]:07/03/2024 23:02:46 [INFO|DP=0|PP=0|TP=0|ip-26-0-164-187]: ignore_sanity_checks=True), | |
| [default0]:07/03/2024 23:02:46 [INFO|DP=0|PP=0|TP=0|ip-26-0-164-187]: parallelism=ParallelismArgs(dp=2, | |
| [default0]:07/03/2024 23:02:46 [INFO|DP=0|PP=0|TP=0|ip-26-0-164-187]: pp=1, | |
| [default0]:07/03/2024 23:02:46 [INFO|DP=0|PP=0|TP=0|ip-26-0-164-187]: tp=4, | |
| [default0]:07/03/2024 23:02:46 [INFO|DP=0|PP=0|TP=0|ip-26-0-164-187]: pp_engine=<nanotron.parallel.pipeline_parallel.engine.OneForwardOneBackwardPipelineEngine object at 0x7f7964bfc8b0>, | |
| [default0]:07/03/2024 23:02:46 [INFO|DP=0|PP=0|TP=0|ip-26-0-164-187]: tp_mode=<TensorParallelLinearMode.REDUCE_SCATTER: 2>, | |
| [default0]:07/03/2024 23:02:46 [INFO|DP=0|PP=0|TP=0|ip-26-0-164-187]: tp_linear_async_communication=False, | |
| [default0]:07/03/2024 23:02:46 [INFO|DP=0|PP=0|TP=0|ip-26-0-164-187]: expert_parallel_size=1), | |
| [default0]:07/03/2024 23:02:46 [INFO|DP=0|PP=0|TP=0|ip-26-0-164-187]: model=ModelArgs(model_config=LlamaConfig(bos_token_id=1, | |
| [default0]:07/03/2024 23:02:46 [INFO|DP=0|PP=0|TP=0|ip-26-0-164-187]: eos_token_id=2, | |
| [default0]:07/03/2024 23:02:46 [INFO|DP=0|PP=0|TP=0|ip-26-0-164-187]: hidden_act='silu', | |
| [default0]:07/03/2024 23:02:46 [INFO|DP=0|PP=0|TP=0|ip-26-0-164-187]: hidden_size=2048, | |
| [default0]:07/03/2024 23:02:46 [INFO|DP=0|PP=0|TP=0|ip-26-0-164-187]: initializer_range=0.02, | |
| [default0]:07/03/2024 23:02:46 [INFO|DP=0|PP=0|TP=0|ip-26-0-164-187]: intermediate_size=4096, | |
| [default0]:07/03/2024 23:02:46 [INFO|DP=0|PP=0|TP=0|ip-26-0-164-187]: is_llama_config=True, | |
| [default0]:07/03/2024 23:02:46 [INFO|DP=0|PP=0|TP=0|ip-26-0-164-187]: max_position_embeddings=4096, | |
| [default0]:07/03/2024 23:02:46 [INFO|DP=0|PP=0|TP=0|ip-26-0-164-187]: num_attention_heads=32, | |
| [default0]:07/03/2024 23:02:46 [INFO|DP=0|PP=0|TP=0|ip-26-0-164-187]: num_hidden_layers=24, | |
| [default0]:07/03/2024 23:02:46 [INFO|DP=0|PP=0|TP=0|ip-26-0-164-187]: num_key_value_heads=32, | |
| [default0]:07/03/2024 23:02:46 [INFO|DP=0|PP=0|TP=0|ip-26-0-164-187]: pad_token_id=None, | |
| [default0]:07/03/2024 23:02:46 [INFO|DP=0|PP=0|TP=0|ip-26-0-164-187]: pretraining_tp=1, | |
| [default0]:07/03/2024 23:02:46 [INFO|DP=0|PP=0|TP=0|ip-26-0-164-187]: rms_norm_eps=1e-05, | |
| [default0]:07/03/2024 23:02:46 [INFO|DP=0|PP=0|TP=0|ip-26-0-164-187]: rope_scaling=None, | |
| [default0]:07/03/2024 23:02:46 [INFO|DP=0|PP=0|TP=0|ip-26-0-164-187]: rope_theta=10000.0, | |
| [default0]:07/03/2024 23:02:46 [INFO|DP=0|PP=0|TP=0|ip-26-0-164-187]: tie_word_embeddings=True, | |
| [default0]:07/03/2024 23:02:46 [INFO|DP=0|PP=0|TP=0|ip-26-0-164-187]: use_cache=True, | |
| [default0]:07/03/2024 23:02:46 [INFO|DP=0|PP=0|TP=0|ip-26-0-164-187]: vocab_size=50260), | |
| [default0]:07/03/2024 23:02:46 [INFO|DP=0|PP=0|TP=0|ip-26-0-164-187]: init_method=RandomInit(std=0.025), | |
| [default0]:07/03/2024 23:02:46 [INFO|DP=0|PP=0|TP=0|ip-26-0-164-187]: dtype=torch.bfloat16, | |
| [default0]:07/03/2024 23:02:46 [INFO|DP=0|PP=0|TP=0|ip-26-0-164-187]: make_vocab_size_divisible_by=1, | |
| [default0]:07/03/2024 23:02:46 [INFO|DP=0|PP=0|TP=0|ip-26-0-164-187]: ddp_bucket_cap_mb=25), | |
| [default0]:07/03/2024 23:02:46 [INFO|DP=0|PP=0|TP=0|ip-26-0-164-187]: tokenizer=TokenizerArgs(tokenizer_name_or_path='openai-community/gpt2', | |
| [default0]:07/03/2024 23:02:46 [INFO|DP=0|PP=0|TP=0|ip-26-0-164-187]: tokenizer_revision=None, | |
| [default0]:07/03/2024 23:02:46 [INFO|DP=0|PP=0|TP=0|ip-26-0-164-187]: tokenizer_max_length=None), | |
| [default0]:07/03/2024 23:02:46 [INFO|DP=0|PP=0|TP=0|ip-26-0-164-187]: checkpoints=CheckpointsArgs(checkpoints_path=Path('/dev/null'), | |
| [default0]:07/03/2024 23:02:46 [INFO|DP=0|PP=0|TP=0|ip-26-0-164-187]: checkpoint_interval=100000, | |
| [default0]:07/03/2024 23:02:46 [INFO|DP=0|PP=0|TP=0|ip-26-0-164-187]: save_initial_state=False, | |
| [default0]:07/03/2024 23:02:46 [INFO|DP=0|PP=0|TP=0|ip-26-0-164-187]: resume_checkpoint_path=None, | |
| [default0]:07/03/2024 23:02:46 [INFO|DP=0|PP=0|TP=0|ip-26-0-164-187]: checkpoints_path_is_shared_file_system=False), | |
| [default0]:07/03/2024 23:02:46 [INFO|DP=0|PP=0|TP=0|ip-26-0-164-187]: logging=LoggingArgs(log_level='info', | |
| [default0]:07/03/2024 23:02:46 [INFO|DP=0|PP=0|TP=0|ip-26-0-164-187]: log_level_replica='info', | |
| [default0]:07/03/2024 23:02:46 [INFO|DP=0|PP=0|TP=0|ip-26-0-164-187]: iteration_step_info_interval=1), | |
| [default0]:07/03/2024 23:02:46 [INFO|DP=0|PP=0|TP=0|ip-26-0-164-187]: tokens=TokensArgs(sequence_length=4096, | |
| [default0]:07/03/2024 23:02:46 [INFO|DP=0|PP=0|TP=0|ip-26-0-164-187]: train_steps=20, | |
| [default0]:07/03/2024 23:02:46 [INFO|DP=0|PP=0|TP=0|ip-26-0-164-187]: micro_batch_size=512, | |
| [default0]:07/03/2024 23:02:46 [INFO|DP=0|PP=0|TP=0|ip-26-0-164-187]: batch_accumulation_per_replica=1, | |
| [default0]:07/03/2024 23:02:46 [INFO|DP=0|PP=0|TP=0|ip-26-0-164-187]: val_check_interval=-1, | |
| [default0]:07/03/2024 23:02:46 [INFO|DP=0|PP=0|TP=0|ip-26-0-164-187]: limit_val_batches=0, | |
| [default0]:07/03/2024 23:02:46 [INFO|DP=0|PP=0|TP=0|ip-26-0-164-187]: limit_test_batches=0), | |
| [default0]:07/03/2024 23:02:46 [INFO|DP=0|PP=0|TP=0|ip-26-0-164-187]: optimizer=OptimizerArgs(optimizer_factory=AdamWOptimizerArgs(adam_eps=1e-08, | |
| [default0]:07/03/2024 23:02:46 [INFO|DP=0|PP=0|TP=0|ip-26-0-164-187]: adam_beta1=0.9, | |
| [default0]:07/03/2024 23:02:46 [INFO|DP=0|PP=0|TP=0|ip-26-0-164-187]: adam_beta2=0.95, | |
| [default0]:07/03/2024 23:02:46 [INFO|DP=0|PP=0|TP=0|ip-26-0-164-187]: torch_adam_is_fused=True, | |
| [default0]:07/03/2024 23:02:46 [INFO|DP=0|PP=0|TP=0|ip-26-0-164-187]: name='adamW'), | |
| [default0]:07/03/2024 23:02:46 [INFO|DP=0|PP=0|TP=0|ip-26-0-164-187]: zero_stage=1, | |
| [default0]:07/03/2024 23:02:46 [INFO|DP=0|PP=0|TP=0|ip-26-0-164-187]: weight_decay=0.01, | |
| [default0]:07/03/2024 23:02:46 [INFO|DP=0|PP=0|TP=0|ip-26-0-164-187]: clip_grad=1.0, | |
| [default0]:07/03/2024 23:02:46 [INFO|DP=0|PP=0|TP=0|ip-26-0-164-187]: accumulate_grad_in_fp32=True, | |
| [default0]:07/03/2024 23:02:46 [INFO|DP=0|PP=0|TP=0|ip-26-0-164-187]: learning_rate_scheduler=LRSchedulerArgs(learning_rate=0.0001, | |
| [default0]:07/03/2024 23:02:46 [INFO|DP=0|PP=0|TP=0|ip-26-0-164-187]: lr_warmup_steps=1, | |
| [default0]:07/03/2024 23:02:46 [INFO|DP=0|PP=0|TP=0|ip-26-0-164-187]: lr_warmup_style='linear', | |
| [default0]:07/03/2024 23:02:46 [INFO|DP=0|PP=0|TP=0|ip-26-0-164-187]: lr_decay_style='linear', | |
| [default0]:07/03/2024 23:02:46 [INFO|DP=0|PP=0|TP=0|ip-26-0-164-187]: lr_decay_steps=19, | |
| [default0]:07/03/2024 23:02:46 [INFO|DP=0|PP=0|TP=0|ip-26-0-164-187]: lr_decay_starting_step=None, | |
| [default0]:07/03/2024 23:02:46 [INFO|DP=0|PP=0|TP=0|ip-26-0-164-187]: min_decay_lr=1e-05)), | |
| [default0]:07/03/2024 23:02:46 [INFO|DP=0|PP=0|TP=0|ip-26-0-164-187]: data_stages=[DatasetStageArgs(name='Training Stage', | |
| [default0]:07/03/2024 23:02:46 [INFO|DP=0|PP=0|TP=0|ip-26-0-164-187]: start_training_step=1, | |
| [default0]:07/03/2024 23:02:46 [INFO|DP=0|PP=0|TP=0|ip-26-0-164-187]: data=DataArgs(dataset=PretrainDatasetsArgs(hf_dataset_or_datasets='roneneldan/TinyStories', | |
| [default0]:07/03/2024 23:02:46 [INFO|DP=0|PP=0|TP=0|ip-26-0-164-187]: hf_dataset_splits='train', | |
| [default0]:07/03/2024 23:02:46 [INFO|DP=0|PP=0|TP=0|ip-26-0-164-187]: hf_dataset_config_name=None, | |
| [default0]:07/03/2024 23:02:46 [INFO|DP=0|PP=0|TP=0|ip-26-0-164-187]: dataset_processing_num_proc_per_process=64, | |
| [default0]:07/03/2024 23:02:46 [INFO|DP=0|PP=0|TP=0|ip-26-0-164-187]: dataset_overwrite_cache=False, | |
| [default0]:07/03/2024 23:02:46 [INFO|DP=0|PP=0|TP=0|ip-26-0-164-187]: text_column_name='text'), | |
| [default0]:07/03/2024 23:02:46 [INFO|DP=0|PP=0|TP=0|ip-26-0-164-187]: seed=42, | |
| [default0]:07/03/2024 23:02:46 [INFO|DP=0|PP=0|TP=0|ip-26-0-164-187]: num_loading_workers=0))], | |
| [default0]:07/03/2024 23:02:46 [INFO|DP=0|PP=0|TP=0|ip-26-0-164-187]: profiler=ProfilerArgs(profiler_export_path=Path('/fsx/ferdinandmom/ferdinand-hf/bench_cluster/results/llama-1B/8_GPUS/dp-2_tp-4_pp-1_mbz-512')), | |
| [default0]:07/03/2024 23:02:46 [INFO|DP=0|PP=0|TP=0|ip-26-0-164-187]: lighteval=None) | |
| [default0]:07/03/2024 23:02:46 [INFO|DP=0|PP=0|TP=0|ip-26-0-164-187]: Model Config: | |
| [default0]:07/03/2024 23:02:46 [INFO|DP=0|PP=0|TP=0|ip-26-0-164-187]: LlamaConfig(bos_token_id=1, | |
| [default0]:07/03/2024 23:02:46 [INFO|DP=0|PP=0|TP=0|ip-26-0-164-187]: eos_token_id=2, | |
| [default0]:07/03/2024 23:02:46 [INFO|DP=0|PP=0|TP=0|ip-26-0-164-187]: hidden_act='silu', | |
| [default0]:07/03/2024 23:02:46 [INFO|DP=0|PP=0|TP=0|ip-26-0-164-187]: hidden_size=2048, | |
| [default0]:07/03/2024 23:02:46 [INFO|DP=0|PP=0|TP=0|ip-26-0-164-187]: initializer_range=0.02, | |
| [default0]:07/03/2024 23:02:46 [INFO|DP=0|PP=0|TP=0|ip-26-0-164-187]: intermediate_size=4096, | |
| [default0]:07/03/2024 23:02:46 [INFO|DP=0|PP=0|TP=0|ip-26-0-164-187]: is_llama_config=True, | |
| [default0]:07/03/2024 23:02:46 [INFO|DP=0|PP=0|TP=0|ip-26-0-164-187]: max_position_embeddings=4096, | |
| [default0]:07/03/2024 23:02:46 [INFO|DP=0|PP=0|TP=0|ip-26-0-164-187]: num_attention_heads=32, | |
| [default0]:07/03/2024 23:02:46 [INFO|DP=0|PP=0|TP=0|ip-26-0-164-187]: num_hidden_layers=24, | |
| [default0]:07/03/2024 23:02:46 [INFO|DP=0|PP=0|TP=0|ip-26-0-164-187]: num_key_value_heads=32, | |
| [default0]:07/03/2024 23:02:46 [INFO|DP=0|PP=0|TP=0|ip-26-0-164-187]: pad_token_id=None, | |
| [default0]:07/03/2024 23:02:46 [INFO|DP=0|PP=0|TP=0|ip-26-0-164-187]: pretraining_tp=1, | |
| [default0]:07/03/2024 23:02:46 [INFO|DP=0|PP=0|TP=0|ip-26-0-164-187]: rms_norm_eps=1e-05, | |
| [default0]:07/03/2024 23:02:46 [INFO|DP=0|PP=0|TP=0|ip-26-0-164-187]: rope_scaling=None, | |
| [default0]:07/03/2024 23:02:46 [INFO|DP=0|PP=0|TP=0|ip-26-0-164-187]: rope_theta=10000.0, | |
| [default0]:07/03/2024 23:02:46 [INFO|DP=0|PP=0|TP=0|ip-26-0-164-187]: tie_word_embeddings=True, | |
| [default0]:07/03/2024 23:02:46 [INFO|DP=0|PP=0|TP=0|ip-26-0-164-187]: use_cache=True, | |
| [default0]:07/03/2024 23:02:46 [INFO|DP=0|PP=0|TP=0|ip-26-0-164-187]: vocab_size=50260) | |
| [default0]:07/03/2024 23:02:46 [INFO|DP=0|PP=0|TP=0|ip-26-0-164-187]: Building model.. | |
| [default0]:07/03/2024 23:02:46 [INFO|DP=0|PP=0|TP=0|ip-26-0-164-187]: Setting PP block ranks... | |
| [default2]:07/03/2024 23:02:59 [INFO|DP=0|PP=0|TP=2|ip-26-0-164-187]: Local number of parameters: 277M (529.27MiB) | |
| [default2]:07/03/2024 23:02:59 [INFO|DP=0|PP=0|TP=2|ip-26-0-164-187]: [After model building] Memory usage: 554.21MiB. Peak allocated: 606.24MiB Peak reserved: 608.00MiB | |
| [default2]:07/03/2024 23:02:59 [INFO|DP=0|PP=0|TP=2|ip-26-0-164-187]: No checkpoint path provided. | |
| [default4]:07/03/2024 23:02:59 [INFO|DP=1|PP=0|TP=0|ip-26-0-164-187]: No checkpoint path provided. | |
| [default1]:07/03/2024 23:02:59 [INFO|DP=0|PP=0|TP=1|ip-26-0-164-187]: Local number of parameters: 277M (529.27MiB) | |
| [default1]:07/03/2024 23:02:59 [INFO|DP=0|PP=0|TP=1|ip-26-0-164-187]: [After model building] Memory usage: 554.21MiB. Peak allocated: 606.24MiB Peak reserved: 608.00MiB | |
| [default1]:07/03/2024 23:02:59 [INFO|DP=0|PP=0|TP=1|ip-26-0-164-187]: No checkpoint path provided. | |
| [default7]:07/03/2024 23:02:59 [INFO|DP=1|PP=0|TP=3|ip-26-0-164-187]: No checkpoint path provided. | |
| [default3]:07/03/2024 23:02:59 [INFO|DP=0|PP=0|TP=3|ip-26-0-164-187]: Local number of parameters: 277M (529.27MiB) | |
| [default3]:07/03/2024 23:02:59 [INFO|DP=0|PP=0|TP=3|ip-26-0-164-187]: [After model building] Memory usage: 554.21MiB. Peak allocated: 606.24MiB Peak reserved: 608.00MiB | |
| [default3]:07/03/2024 23:02:59 [INFO|DP=0|PP=0|TP=3|ip-26-0-164-187]: No checkpoint path provided. | |
| [default6]:07/03/2024 23:02:59 [INFO|DP=1|PP=0|TP=2|ip-26-0-164-187]: No checkpoint path provided. | |
| [default5]:07/03/2024 23:02:59 [INFO|DP=1|PP=0|TP=1|ip-26-0-164-187]: No checkpoint path provided. | |
| [default0]:07/03/2024 23:02:59 [INFO|DP=0|PP=0|TP=0|ip-26-0-164-187]: Total number of parameters: 1.11G (2117.09MiB) | |
| [default0]:07/03/2024 23:02:59 [INFO|DP=0|PP=0|TP=0|ip-26-0-164-187]: Local number of parameters: 277M (529.27MiB) | |
| [default0]:07/03/2024 23:02:59 [INFO|DP=0|PP=0|TP=0|ip-26-0-164-187]: [After model building] Memory usage: 554.21MiB. Peak allocated: 606.24MiB Peak reserved: 608.00MiB | |
| [default0]:07/03/2024 23:02:59 [INFO|DP=0|PP=0|TP=0|ip-26-0-164-187]: No checkpoint path provided. | |
| [default0]:07/03/2024 23:02:59 [INFO|DP=0|PP=0|TP=0|ip-26-0-164-187]: Parametrizing model parameters using StandardParametrizator | |
| [default0]:07/03/2024 23:03:01 [INFO|DP=0|PP=0|TP=0|ip-26-0-164-187]: [Optimizer Building] Using LearningRateForSP as learning rate | |
| [default0]:07/03/2024 23:03:01 [INFO|DP=0|PP=0|TP=0|ip-26-0-164-187]: [ZeRO sharding] Size of optimizer params per rank: | |
| [default0]:07/03/2024 23:03:01 [INFO|DP=0|PP=0|TP=0|ip-26-0-164-187]: [ZeRO sharding] DP Rank 0 has 139M out of 277M (50.00%) params' optimizer states | |
| [default0]:07/03/2024 23:03:01 [INFO|DP=0|PP=0|TP=0|ip-26-0-164-187]: [ZeRO sharding] DP Rank 1 has 139M out of 277M (50.00%) params' optimizer states | |
| [default0]:07/03/2024 23:03:02 [INFO|DP=0|PP=0|TP=0|ip-26-0-164-187]: [Training Plan] Stage Training Stage has 19 remaining training steps and has consumed 0 samples | |
| [default0]:07/03/2024 23:03:02 [INFO|DP=0|PP=0|TP=0|ip-26-0-164-187]: Using `datasets` library | |
| [default0]:07/03/2024 23:03:02 [INFO|DP=0|PP=0|TP=0|ip-26-0-164-187]: 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 23:03:03 [WARNING|DP=0|PP=0|TP=0|ip-26-0-164-187]: Repo card metadata block was not found. Setting CardData to empty. | |
| [default0]:07/03/2024 23:03:04 [INFO|DP=0|PP=0|TP=0|ip-26-0-164-187]: [Training Plan] There are 1 training stages | |
| [default0]:07/03/2024 23:03:04 [INFO|DP=0|PP=0|TP=0|ip-26-0-164-187]: [Stage Training Stage] start from step 1 | |
| [default0]:07/03/2024 23:03:04 [INFO|DP=0|PP=0|TP=0|ip-26-0-164-187]: | |
| [default0]:07/03/2024 23:03:04 [INFO|DP=0|PP=0|TP=0|ip-26-0-164-187]: [Start training] datetime: 2024-07-03 23:03:04.007731 | mbs: 512 | grad_accum: 1 | global_batch_size: 1024 | sequence_length: 4096 | train_steps: 20 | start_iteration_step: 0 | consumed_train_samples: 0 | |
| [default0]:07/03/2024 23:03:04 [INFO|DP=0|PP=0|TP=0|ip-26-0-164-187]: Resuming training from stage Training Stage, it has trained for 0 samples and has 19 remaining train steps | |
| [default0]:07/03/2024 23:03:04 [INFO|DP=0|PP=0|TP=0|ip-26-0-164-187]: Memory usage: 2142.76MiB. Peak allocated 2142.76MiB. Peak reserved: 2198.00MiB | |
| [default2]:07/03/2024 23:03:04 [WARNING|DP=0|PP=0|TP=2|ip-26-0-164-187]: Repo card metadata block was not found. Setting CardData to empty. | |
| [default7]:07/03/2024 23:03:04 [WARNING|DP=1|PP=0|TP=3|ip-26-0-164-187]: Repo card metadata block was not found. Setting CardData to empty. | |
| [default4]:07/03/2024 23:03:04 [WARNING|DP=1|PP=0|TP=0|ip-26-0-164-187]: Repo card metadata block was not found. Setting CardData to empty. | |
| [default5]:07/03/2024 23:03:04 [WARNING|DP=1|PP=0|TP=1|ip-26-0-164-187]: Repo card metadata block was not found. Setting CardData to empty. | |
| [default5]: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. | |
| [default7]:Repo card metadata block was not found. Setting CardData to empty. | |
| [default3]:07/03/2024 23:03:04 [WARNING|DP=0|PP=0|TP=3|ip-26-0-164-187]: Repo card metadata block was not found. Setting CardData to empty. | |
| [default6]:07/03/2024 23:03:04 [WARNING|DP=1|PP=0|TP=2|ip-26-0-164-187]: 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. | |
| [default1]:07/03/2024 23:03:04 [WARNING|DP=0|PP=0|TP=1|ip-26-0-164-187]: Repo card metadata block was not found. Setting CardData to empty. | |
| [default1]:Repo card metadata block was not found. Setting CardData to empty. | |
| [default1]:[rank1]: Traceback (most recent call last): | |
| [default1]:[rank1]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/run_train.py", line 237, in <module> | |
| [default1]:[rank1]: trainer.train(dataloader) | |
| [default1]:[rank1]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 429, in train | |
| [default1]:[rank1]: outputs, loss_avg = self.training_step(dataloader=self.current_dataloader) | |
| [default1]:[rank1]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 462, in training_step | |
| [default1]:[rank1]: outputs = self.pipeline_engine.train_batch_iter( | |
| [default1]:[rank1]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 278, in train_batch_iter | |
| [default1]:[rank1]: output = self.forward(context=context, state=state, micro_batch=micro_batch, model=model) | |
| [default1]:[rank1]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 44, in forward | |
| [default1]:[rank1]: output = model(**micro_batch) | |
| [default1]:[rank1]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl | |
| [default1]:[rank1]: return self._call_impl(*args, **kwargs) | |
| [default1]:[rank1]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl | |
| [default1]:[rank1]: return forward_call(*args, **kwargs) | |
| [default1]:[rank1]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 891, in forward | |
| [default1]:[rank1]: sharded_logits = self.model( | |
| [default1]:[rank1]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl | |
| [default1]:[rank1]: return self._call_impl(*args, **kwargs) | |
| [default1]:[rank1]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl | |
| [default1]:[rank1]: return forward_call(*args, **kwargs) | |
| [default1]:[rank1]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 764, in forward | |
| [default1]:[rank1]: return self.forward_with_hidden_states(input_ids=input_ids, input_mask=input_mask)[0] | |
| [default1]:[rank1]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 780, in forward_with_hidden_states | |
| [default1]:[rank1]: hidden_encoder_states = encoder_block(**hidden_encoder_states) | |
| [default1]:[rank1]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl | |
| [default1]:[rank1]: return self._call_impl(*args, **kwargs) | |
| [default1]:[rank1]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl | |
| [default1]:[rank1]: return forward_call(*args, **kwargs) | |
| [default1]:[rank1]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/block.py", line 151, in forward | |
| [default1]:[rank1]: output = self.pp_block(**new_kwargs) | |
| [default1]:[rank1]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl | |
| [default1]:[rank1]: return self._call_impl(*args, **kwargs) | |
| [default1]:[rank1]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl | |
| [default1]:[rank1]: return forward_call(*args, **kwargs) | |
| [default1]:[rank1]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 637, in forward | |
| [default1]:[rank1]: hidden_states = self.mlp(hidden_states=hidden_states)["hidden_states"] | |
| [default1]:[rank1]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl | |
| [default1]:[rank1]: return self._call_impl(*args, **kwargs) | |
| [default1]:[rank1]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl | |
| [default1]:[rank1]: return forward_call(*args, **kwargs) | |
| [default1]:[rank1]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 172, in forward | |
| [default1]:[rank1]: hidden_states = self.down_proj(self.split_silu_mul(merged_states)) | |
| [default1]:[rank1]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl | |
| [default1]:[rank1]: return self._call_impl(*args, **kwargs) | |
| [default1]:[rank1]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl | |
| [default1]:[rank1]: return forward_call(*args, **kwargs) | |
| [default1]:[rank1]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/nn.py", line 159, in forward | |
| [default1]:[rank1]: return row_linear( | |
| [default1]:[rank1]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/functional.py", line 479, in row_linear | |
| [default1]:[rank1]: out = differentiable_reduce_scatter_sum(out, group=group) | |
| [default1]:[rank1]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/distributed_differentiable_primitives.py", line 145, in differentiable_reduce_scatter_sum | |
| [default1]:[rank1]: return DifferentiableReduceScatterSum.apply(tensor, group) | |
| [default1]:[rank1]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/autograd/function.py", line 598, in apply | |
| [default1]:[rank1]: return super().apply(*args, **kwargs) # type: ignore[misc] | |
| [default1]:[rank1]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/distributed_differentiable_primitives.py", line 111, in forward | |
| [default1]:[rank1]: sharded_tensor = torch.empty( | |
| [default1]:[rank1]: torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 2.00 GiB. GPU has a total capacity of 79.33 GiB of which 1.68 GiB is free. Including non-PyTorch memory, this process has 77.64 GiB memory in use. Of the allocated memory 64.24 GiB is allocated by PyTorch, and 1.94 GiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation. See documentation for Memory Management (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables) | |
| [default0]:[rank0]: Traceback (most recent call last): | |
| [default0]:[rank0]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/run_train.py", line 237, in <module> | |
| [default0]:[rank0]: trainer.train(dataloader) | |
| [default0]:[rank0]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 429, in train | |
| [default0]:[rank0]: outputs, loss_avg = self.training_step(dataloader=self.current_dataloader) | |
| [default0]:[rank0]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 462, in training_step | |
| [default0]:[rank0]: outputs = self.pipeline_engine.train_batch_iter( | |
| [default0]:[rank0]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 278, in train_batch_iter | |
| [default0]:[rank0]: output = self.forward(context=context, state=state, micro_batch=micro_batch, model=model) | |
| [default0]:[rank0]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 44, in forward | |
| [default0]:[rank0]: output = model(**micro_batch) | |
| [default0]:[rank0]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl | |
| [default0]:[rank0]: return self._call_impl(*args, **kwargs) | |
| [default0]:[rank0]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl | |
| [default0]:[rank0]: return forward_call(*args, **kwargs) | |
| [default0]:[rank0]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 891, in forward | |
| [default0]:[rank0]: sharded_logits = self.model( | |
| [default0]:[rank0]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl | |
| [default0]:[rank0]: return self._call_impl(*args, **kwargs) | |
| [default0]:[rank0]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl | |
| [default0]:[rank0]: return forward_call(*args, **kwargs) | |
| [default0]:[rank0]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 764, in forward | |
| [default0]:[rank0]: return self.forward_with_hidden_states(input_ids=input_ids, input_mask=input_mask)[0] | |
| [default0]:[rank0]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 780, in forward_with_hidden_states | |
| [default0]:[rank0]: hidden_encoder_states = encoder_block(**hidden_encoder_states) | |
| [default0]:[rank0]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl | |
| [default0]:[rank0]: return self._call_impl(*args, **kwargs) | |
| [default0]:[rank0]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl | |
| [default0]:[rank0]: return forward_call(*args, **kwargs) | |
| [default0]:[rank0]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/block.py", line 151, in forward | |
| [default0]:[rank0]: output = self.pp_block(**new_kwargs) | |
| [default0]:[rank0]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl | |
| [default0]:[rank0]: return self._call_impl(*args, **kwargs) | |
| [default0]:[rank0]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl | |
| [default0]:[rank0]: return forward_call(*args, **kwargs) | |
| [default0]:[rank0]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 637, in forward | |
| [default0]:[rank0]: hidden_states = self.mlp(hidden_states=hidden_states)["hidden_states"] | |
| [default0]:[rank0]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl | |
| [default0]:[rank0]: return self._call_impl(*args, **kwargs) | |
| [default0]:[rank0]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl | |
| [default0]:[rank0]: return forward_call(*args, **kwargs) | |
| [default0]:[rank0]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 172, in forward | |
| [default0]:[rank0]: hidden_states = self.down_proj(self.split_silu_mul(merged_states)) | |
| [default0]:[rank0]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl | |
| [default0]:[rank0]: return self._call_impl(*args, **kwargs) | |
| [default0]:[rank0]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl | |
| [default0]:[rank0]: return forward_call(*args, **kwargs) | |
| [default0]:[rank0]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/nn.py", line 159, in forward | |
| [default0]:[rank0]: return row_linear( | |
| [default0]:[rank0]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/functional.py", line 479, in row_linear | |
| [default0]:[rank0]: out = differentiable_reduce_scatter_sum(out, group=group) | |
| [default0]:[rank0]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/distributed_differentiable_primitives.py", line 145, in differentiable_reduce_scatter_sum | |
| [default0]:[rank0]: return DifferentiableReduceScatterSum.apply(tensor, group) | |
| [default0]:[rank0]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/autograd/function.py", line 598, in apply | |
| [default0]:[rank0]: return super().apply(*args, **kwargs) # type: ignore[misc] | |
| [default0]:[rank0]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/distributed_differentiable_primitives.py", line 111, in forward | |
| [default0]:[rank0]: sharded_tensor = torch.empty( | |
| [default0]:[rank0]: torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 2.00 GiB. GPU | |
| [default2]:[rank2]: Traceback (most recent call last): | |
| [default2]:[rank2]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/run_train.py", line 237, in <module> | |
| [default2]:[rank2]: trainer.train(dataloader) | |
| [default2]:[rank2]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 429, in train | |
| [default2]:[rank2]: outputs, loss_avg = self.training_step(dataloader=self.current_dataloader) | |
| [default2]:[rank2]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 462, in training_step | |
| [default2]:[rank2]: outputs = self.pipeline_engine.train_batch_iter( | |
| [default2]:[rank2]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 278, in train_batch_iter | |
| [default2]:[rank2]: output = self.forward(context=context, state=state, micro_batch=micro_batch, model=model) | |
| [default2]:[rank2]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 44, in forward | |
| [default2]:[rank2]: output = model(**micro_batch) | |
| [default2]:[rank2]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl | |
| [default2]:[rank2]: return self._call_impl(*args, **kwargs) | |
| [default2]:[rank2]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl | |
| [default2]:[rank2]: return forward_call(*args, **kwargs) | |
| [default2]:[rank2]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 891, in forward | |
| [default2]:[rank2]: sharded_logits = self.model( | |
| [default2]:[rank2]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl | |
| [default2]:[rank2]: return self._call_impl(*args, **kwargs) | |
| [default2]:[rank2]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl | |
| [default2]:[rank2]: return forward_call(*args, **kwargs) | |
| [default2]:[rank2]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 764, in forward | |
| [default2]:[rank2]: return self.forward_with_hidden_states(input_ids=input_ids, input_mask=input_mask)[0] | |
| [default2]:[rank2]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 780, in forward_with_hidden_states | |
| [default2]:[rank2]: hidden_encoder_states = encoder_block(**hidden_encoder_states) | |
| [default2]:[rank2]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl | |
| [default2]:[rank2]: return self._call_impl(*args, **kwargs) | |
| [default2]:[rank2]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl | |
| [default2]:[rank2]: return forward_call(*args, **kwargs) | |
| [default2]:[rank2]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/block.py", line 151, in forward | |
| [default2]:[rank2]: output = self.pp_block(**new_kwargs) | |
| [default2]:[rank2]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl | |
| [default2]:[rank2]: return self._call_impl(*args, **kwargs) | |
| [default2]:[rank2]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl | |
| [default2]:[rank2]: return forward_call(*args, **kwargs) | |
| [default2]:[rank2]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 637, in forward | |
| [default2]:[rank2]: hidden_states = self.mlp(hidden_states=hidden_states)["hidden_states"] | |
| [default2]:[rank2]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl | |
| [default2]:[rank2]: return self._call_impl(*args, **kwargs) | |
| [default2]:[rank2]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl | |
| [default2]:[rank2]: return forward_call(*args, **kwargs) | |
| [default2]:[rank2]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 172, in forward | |
| [default2]:[rank2]: hidden_states = self.down_proj(self.split_silu_mul(merged_states)) | |
| [default2]:[rank2]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl | |
| [default2]:[rank2]: return self._call_impl(*args, **kwargs) | |
| [default2]:[rank2]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl | |
| [default2]:[rank2]: return forward_call(*args, **kwargs) | |
| [default2]:[rank2]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/nn.py", line 159, in forward | |
| [default2]:[rank2]: return row_linear( | |
| [default2]:[rank2]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/functional.py", line 479, in row_linear | |
| [default2]:[rank2]: out = differentiable_reduce_scatter_sum(out, group=group) | |
| [default2]:[rank2]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/distributed_differentiable_primitives.py", line 145, in differentiable_reduce_scatter_sum | |
| [default2]:[rank2]: return DifferentiableReduceScatterSum.apply(tensor, group) | |
| [default2]:[rank2]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/autograd/function.py", line 598, in apply | |
| [default2]:[rank2]: return super().apply(*args, **kwargs) # type: ignore[misc] | |
| [default2]:[rank2]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/distributed_differentiable_primitives.py", line 111, in forward | |
| [default2]:[rank2]: sharded_tensor = torch.empty( | |
| [default2]:[rank2]: torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 2.00 GiB. GPU has a total capacity of 79.33 GiB of which 1.68 GiB is free. Including non-PyTorch memory, this process has 77.64 GiB memory in use. Of the allocated memory 64.24 GiB is allocated by PyTorch, and 1.94 GiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation. See documentation for Memory Management (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables) | |
| [default3]:[rank3]: Traceback (most recent call last): | |
| [default3]:[rank3]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/run_train.py", line 237, in <module> | |
| [default3]:[rank3]: trainer.train(dataloader) | |
| [default3]:[rank3]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 429, in train | |
| [default3]:[rank3]: outputs, loss_avg = self.training_step(dataloader=self.current_dataloader) | |
| [default3]:[rank3]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 462, in training_step | |
| [default3]:[rank3]: outputs = self.pipeline_engine.train_batch_iter( | |
| [default3]:[rank3]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 278, in train_batch_iter | |
| [default3]:[rank3]: output = self.forward(context=context, state=state, micro_batch=micro_batch, model=model) | |
| [default3]:[rank3]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 44, in forward | |
| [default3]:[rank3]: output = model(**micro_batch) | |
| [default3]:[rank3]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl | |
| [default3]:[rank3]: return self._call_impl(*args, **kwargs) | |
| [default3]:[rank3]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl | |
| [default3]:[rank3]: return forward_call(*args, **kwargs) | |
| [default3]:[rank3]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 891, in forward | |
| [default3]:[rank3]: sharded_logits = self.model( | |
| [default3]:[rank3]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl | |
| [default3]:[rank3]: return self._call_impl(*args, **kwargs) | |
| [default3]:[rank3]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl | |
| [default3]:[rank3]: return forward_call(*args, **kwargs) | |
| [default3]:[rank3]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 764, in forward | |
| [default3]:[rank3]: return self.forward_with_hidden_states(input_ids=input_ids, input_mask=input_mask)[0] | |
| [default3]:[rank3]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 780, in forward_with_hidden_states | |
| [default3]:[rank3]: hidden_encoder_states = encoder_block(**hidden_encoder_states) | |
| [default3]:[rank3]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl | |
| [default3]:[rank3]: return self._call_impl(*args, **kwargs) | |
| [default3]:[rank3]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl | |
| [default3]:[rank3]: return forward_call(*args, **kwargs) | |
| [default3]:[rank3]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/block.py", line 151, in forward | |
| [default3]:[rank3]: output = self.pp_block(**new_kwargs) | |
| [default3]:[rank3]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl | |
| [default3]:[rank3]: return self._call_impl(*args, **kwargs) | |
| [default3]:[rank3]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl | |
| [default3]:[rank3]: return forward_call(*args, **kwargs) | |
| [default3]:[rank3]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 637, in forward | |
| [default3]:[rank3]: hidden_states = self.mlp(hidden_states=hidden_states)["hidden_states"] | |
| [default3]:[rank3]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl | |
| [default3]:[rank3]: return self._call_impl(*args, **kwargs) | |
| [default3]:[rank3]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl | |
| [default3]:[rank3]: return forward_call(*args, **kwargs) | |
| [default3]:[rank3]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 172, in forward | |
| [default3]:[rank3]: hidden_states = self.down_proj(self.split_silu_mul(merged_states)) | |
| [default3]:[rank3]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl | |
| [default3]:[rank3]: return self._call_impl(*args, **kwargs) | |
| [default3]:[rank3]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl | |
| [default3]:[rank3]: return forward_call(*args, **kwargs) | |
| [default3]:[rank3]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/nn.py", line 159, in forward | |
| [default3]:[rank3]: return row_linear( | |
| [default3]:[rank3]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/functional.py", line 479, in row_linear | |
| [default3]:[rank3]: out = differentiable_reduce_scatter_sum(out, group=group) | |
| [default3]:[rank3]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/distributed_differentiable_primitives.py", line 145, in differentiable_reduce_scatter_sum | |
| [default3]:[rank3]: return DifferentiableReduceScatterSum.apply(tensor, group) | |
| [default3]:[rank3]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/autograd/function.py", line 598, in apply | |
| [default3]:[rank3]: return super().apply(*args, **kwargs) # type: ignore[misc] | |
| [default3]:[rank3]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/distributed_differentiable_primitives.py", line 111, in forward | |
| [default3]:[rank3]: sharded_tensor = torch.empty( | |
| [default3]:[rank3]: torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 2.00 GiB. GPU has a total capacity of 79.33 GiB of which 1.91 GiB is free. Including non-PyTorch memory, this process has 77.41 GiB memory in use. Of the allocated memory 64.24 GiB is allocated by PyTorch, and 1.94 GiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation. See documentation for Memory Management (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables) | |
| [default5]:[rank5]: Traceback (most recent call last): | |
| [default5]:[rank5]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/run_train.py", line 237, in <module> | |
| [default5]:[rank5]: trainer.train(dataloader) | |
| [default5]:[rank5]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 429, in train | |
| [default5]:[rank5]: outputs, loss_avg = self.training_step(dataloader=self.current_dataloader) | |
| [default5]:[rank5]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 462, in training_step | |
| [default4]:[rank4]: Traceback (most recent call last): | |
| [default4]:[rank4]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/run_train.py", line 237, in <module> | |
| [default4]:[rank4]: trainer.train(dataloader) | |
| [default4]:[rank4]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 429, in train | |
| [default4]:[rank4]: outputs, loss_avg = self.training_step(dataloader=self.current_dataloader) | |
| [default4]:[rank4]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 462, in training_step | |
| [default4]:[rank4]: outputs = self.pipeline_engine.train_batch_iter( | |
| [default4]:[rank4]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 278, in train_batch_iter | |
| [default4]:[rank4]: output = self.forward(context=context, state=state, micro_batch=micro_batch, model=model) | |
| [default4]:[rank4]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 44, in forward | |
| [default4]:[rank4]: output = model(**micro_batch) | |
| [default4]:[rank4]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl | |
| [default4]:[rank4]: return self._call_impl(*args, **kwargs) | |
| [default4]:[rank4]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl | |
| [default4]:[rank4]: return forward_call(*args, **kwargs) | |
| [default4]:[rank4]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 891, in forward | |
| [default5]:[rank5]: outputs = self.pipeline_engine.train_batch_iter( | |
| [default5]:[rank5]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 278, in train_batch_iter | |
| [default5]:[rank5]: output = self.forward(context=context, state=state, micro_batch=micro_batch, model=model) | |
| [default5]:[rank5]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 44, in forward | |
| [default4]:[rank4]: sharded_logits = self.model( | |
| [default4]:[rank4]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl | |
| [default5]:[rank5]: output = model(**micro_batch) | |
| [default4]:[rank4]: return self._call_impl(*args, **kwargs) | |
| [default5]:[rank5]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl | |
| [default4]:[rank4]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl | |
| [default5]:[rank5]: return self._call_impl(*args, **kwargs) | |
| [default5]:[rank5]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl | |
| [default5]:[rank5]: return forward_call(*args, **kwargs) | |
| [default4]:[rank4]: return forward_call(*args, **kwargs) | |
| [default4]:[rank4]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 764, in forward | |
| [default5]:[rank5]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 891, in forward | |
| [default4]:[rank4]: return self.forward_with_hidden_states(input_ids=input_ids, input_mask=input_mask)[0] | |
| [default5]:[rank5]: sharded_logits = self.model( | |
| [default5]:[rank5]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl | |
| [default5]:[rank5]: return self._call_impl(*args, **kwargs) | |
| [default4]:[rank4]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 780, in forward_with_hidden_states | |
| [default5]:[rank5]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl | |
| [default4]:[rank4]: hidden_encoder_states = encoder_block(**hidden_encoder_states) | |
| [default5]:[rank5]: return forward_call(*args, **kwargs) | |
| [default4]:[rank4]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl | |
| [default5]:[rank5]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 764, in forward | |
| [default4]:[rank4]: return self._call_impl(*args, **kwargs) | |
| [default5]:[rank5]: return self.forward_with_hidden_states(input_ids=input_ids, input_mask=input_mask)[0] | |
| [default5]:[rank5]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 780, in forward_with_hidden_states | |
| [default5]:[rank5]: hidden_encoder_states = encoder_block(**hidden_encoder_states) | |
| [default4]:[rank4]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl | |
| [default4]:[rank4]: return forward_call(*args, **kwargs) | |
| [default4]:[rank4]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/block.py", line 151, in forward | |
| [default4]:[rank4]: output = self.pp_block(**new_kwargs) | |
| [default4]:[rank4]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl | |
| [default4]:[rank4]: return self._call_impl(*args, **kwargs) | |
| [default4]:[rank4]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl | |
| [default4]:[rank4]: return forward_call(*args, **kwargs) | |
| [default4]:[rank4]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 637, in forward | |
| [default4]:[rank4]: hidden_states = self.mlp(hidden_states=hidden_states)["hidden_states"] | |
| [default5]:[rank5]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl | |
| [default5]:[rank5]: return self._call_impl(*args, **kwargs) | |
| [default5]:[rank5]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl | |
| [default5]:[rank5]: return forward_call(*args, **kwargs) | |
| [default4]:[rank4]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl | |
| [default4]:[rank4]: return self._call_impl(*args, **kwargs) | |
| [default4]:[rank4]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl | |
| [default5]:[rank5]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/block.py", line 151, in forward | |
| [default5]:[rank5]: output = self.pp_block(**new_kwargs) | |
| [default4]:[rank4]: return forward_call(*args, **kwargs) | |
| [default5]:[rank5]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl | |
| [default4]:[rank4]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 172, in forward | |
| [default5]:[rank5]: return self._call_impl(*args, **kwargs) | |
| [default4]:[rank4]: hidden_states = self.down_proj(self.split_silu_mul(merged_states)) | |
| [default4]:[rank4]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl | |
| [default4]:[rank4]: return self._call_impl(*args, **kwargs) | |
| [default4]:[rank4]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl | |
| [default4]:[rank4]: return forward_call(*args, **kwargs) | |
| [default4]:[rank4]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/nn.py", line 159, in forward | |
| [default5]:[rank5]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl | |
| [default4]:[rank4]: return row_linear( | |
| [default4]:[rank4]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/functional.py", line 479, in row_linear | |
| [default5]:[rank5]: return forward_call(*args, **kwargs) | |
| [default5]:[rank5]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 637, in forward | |
| [default4]:[rank4]: out = differentiable_reduce_scatter_sum(out, group=group) | |
| [default5]:[rank5]: hidden_states = self.mlp(hidden_states=hidden_states)["hidden_states"] | |
| [default4]:[rank4]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/distributed_differentiable_primitives.py", line 145, in differentiable_reduce_scatter_sum | |
| [default4]:[rank4]: return DifferentiableReduceScatterSum.apply(tensor, group) | |
| [default5]:[rank5]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl | |
| [default5]:[rank5]: return self._call_impl(*args, **kwargs) | |
| [default4]:[rank4]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/autograd/function.py", line 598, in apply | |
| [default5]:[rank5]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl | |
| [default4]:[rank4]: return super().apply(*args, **kwargs) # type: ignore[misc] | |
| [default4]:[rank4]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/distributed_differentiable_primitives.py", line 111, in forward | |
| [default5]:[rank5]: return forward_call(*args, **kwargs) | |
| [default4]:[rank4]: sharded_tensor = torch.empty( | |
| [default5]:[rank5]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 172, in forward | |
| [default5]:[rank5]: hidden_states = self.down_proj(self.split_silu_mul(merged_states)) | |
| [default5]:[rank5]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl | |
| [default4]:[rank4]: torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 2.00 GiB. GPU has a total capacity of 79.33 GiB of which 1.72 GiB is free. Including non-PyTorch memory, this process has 77.59 GiB memory in use. Of the allocated memory 64.24 GiB is allocated by PyTorch, and 1.94 GiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation. See documentation for Memory Management (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables) | |
| [default5]:[rank5]: return self._call_impl(*args, **kwargs) | |
| [default5]:[rank5]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl | |
| [default5]:[rank5]: return forward_call(*args, **kwargs) | |
| [default5]:[rank5]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/nn.py", line 159, in forward | |
| [default5]:[rank5]: return row_linear( | |
| [default5]:[rank5]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/functional.py", line 479, in row_linear | |
| [default5]:[rank5]: out = differentiable_reduce_scatter_sum(out, group=group) | |
| [default5]:[rank5]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/distributed_differentiable_primitives.py", line 145, in differentiable_reduce_scatter_sum | |
| [default5]:[rank5]: return DifferentiableReduceScatterSum.apply(tensor, group) | |
| [default5]:[rank5]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/autograd/function.py", line 598, in apply | |
| [default5]:[rank5]: return super().apply(*args, **kwargs) # type: ignore[misc] | |
| [default5]:[rank5]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/distributed_differentiable_primitives.py", line 111, in forward | |
| [default5]:[rank5]: sharded_tensor = torch.empty( | |
| [default5]:[rank5]: torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 2.00 GiB. GPU has a total capacity of 79.33 GiB of which 1.68 GiB is free. Including non-PyTorch memory, this process has 77.64 GiB memory in use. Of the allocated memory 64.24 GiB is allocated by PyTorch, and 1.94 GiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation. See documentation for Memory Management (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables) | |
| [default6]:[rank6]: Traceback (most recent call last): | |
| [default6]:[rank6]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/run_train.py", line 237, in <module> | |
| [default6]:[rank6]: trainer.train(dataloader) | |
| [default6]:[rank6]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 429, in train | |
| [default6]:[rank6]: outputs, loss_avg = self.training_step(dataloader=self.current_dataloader) | |
| [default6]:[rank6]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 462, in training_step | |
| [default6]:[rank6]: outputs = self.pipeline_engine.train_batch_iter( | |
| [default6]:[rank6]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 278, in train_batch_iter | |
| [default6]:[rank6]: output = self.forward(context=context, state=state, micro_batch=micro_batch, model=model) | |
| [default6]:[rank6]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 44, in forward | |
| [default6]:[rank6]: output = model(**micro_batch) | |
| [default6]:[rank6]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl | |
| [default6]:[rank6]: return self._call_impl(*args, **kwargs) | |
| [default6]:[rank6]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl | |
| [default6]:[rank6]: return forward_call(*args, **kwargs) | |
| [default6]:[rank6]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 891, in forward | |
| [default6]:[rank6]: sharded_logits = self.model( | |
| [default6]:[rank6]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl | |
| [default6]:[rank6]: return self._call_impl(*args, **kwargs) | |
| [default6]:[rank6]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl | |
| [default6]:[rank6]: return forward_call(*args, **kwargs) | |
| [default6]:[rank6]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 764, in forward | |
| [default6]:[rank6]: return self.forward_with_hidden_states(input_ids=input_ids, input_mask=input_mask)[0] | |
| [default6]:[rank6]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 780, in forward_with_hidden_states | |
| [default6]:[rank6]: hidden_encoder_states = encoder_block(**hidden_encoder_states) | |
| [default6]:[rank6]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl | |
| [default6]:[rank6]: return self._call_impl(*args, **kwargs) | |
| [default6]:[rank6]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl | |
| [default6]:[rank6]: return forward_call(*args, **kwargs) | |
| [default6]:[rank6]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/block.py", line 151, in forward | |
| [default6]:[rank6]: output = self.pp_block(**new_kwargs) | |
| [default6]:[rank6]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl | |
| [default6]:[rank6]: return self._call_impl(*args, **kwargs) | |
| [default6]:[rank6]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl | |
| [default6]:[rank6]: return forward_call(*args, **kwargs) | |
| [default6]:[rank6]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 637, in forward | |
| [default6]:[rank6]: hidden_states = self.mlp(hidden_states=hidden_states)["hidden_states"] | |
| [default6]:[rank6]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl | |
| [default6]:[rank6]: return self._call_impl(*args, **kwargs) | |
| [default6]:[rank6]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl | |
| [default6]:[rank6]: return forward_call(*args, **kwargs) | |
| [default6]:[rank6]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 172, in forward | |
| [default6]:[rank6]: hidden_states = self.down_proj(self.split_silu_mul(merged_states)) | |
| [default6]:[rank6]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl | |
| [default6]:[rank6]: return self._call_impl(*args, **kwargs) | |
| [default6]:[rank6]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl | |
| [default6]:[rank6]: return forward_call(*args, **kwargs) | |
| [default6]:[rank6]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/nn.py", line 159, in forward | |
| [default6]:[rank6]: return row_linear( | |
| [default6]:[rank6]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/functional.py", line 479, in row_linear | |
| [default6]:[rank6]: out = differentiable_reduce_scatter_sum(out, group=group) | |
| [default6]:[rank6]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/distributed_differentiable_primitives.py", line 145, in differentiable_reduce_scatter_sum | |
| [default6]:[rank6]: return DifferentiableReduceScatterSum.apply(tensor, group) | |
| [default6]:[rank6]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/autograd/function.py", line 598, in apply | |
| [default6]:[rank6]: return super().apply(*args, **kwargs) # type: ignore[misc] | |
| [default6]:[rank6]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/distributed_differentiable_primitives.py", line 111, in forward | |
| [default6]:[rank6]: sharded_tensor = torch.empty( | |
| [default6]:[rank6]: torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 2.00 GiB. GPU has a total capacity of 79.33 GiB of which 1.68 GiB is free. Including non-PyTorch memory, this process has 77.64 GiB memory in use. Of the allocated memory 64.24 GiB is allocated by PyTorch, and 1.94 GiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation. See documentation for Memory Management (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables) | |
| W0703 23:03:15.486000 140703814809408 torch/distributed/elastic/multiprocessing/api.py:851] Sending process 26448 closing signal SIGTERM | |
| W0703 23:03:15.486000 140703814809408 torch/distributed/elastic/multiprocessing/api.py:851] Sending process 26449 closing signal SIGTERM | |
| W0703 23:03:15.486000 140703814809408 torch/distributed/elastic/multiprocessing/api.py:851] Sending process 26451 closing signal SIGTERM | |
| E0703 23:03:16.403000 140703814809408 torch/distributed/elastic/multiprocessing/api.py:826] failed (exitcode: 1) local_rank: 0 (pid: 26444) of binary: /fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/bin/python3.10 | |
| Traceback (most recent call last): | |
| File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/bin/torchrun", line 8, in <module> | |
| sys.exit(main()) | |
| File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/elastic/multiprocessing/errors/__init__.py", line 347, in wrapper | |
| return f(*args, **kwargs) | |
| File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/run.py", line 879, in main | |
| run(args) | |
| File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/run.py", line 870, in run | |
| elastic_launch( | |
| File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/launcher/api.py", line 132, in __call__ | |
| return launch_agent(self._config, self._entrypoint, list(args)) | |
| File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/launcher/api.py", line 263, in launch_agent | |
| raise ChildFailedError( | |
| torch.distributed.elastic.multiprocessing.errors.ChildFailedError: | |
| ============================================================ | |
| /fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/run_train.py FAILED | |
| ------------------------------------------------------------ | |
| Failures: | |
| [1]: | |
| time : 2024-07-03_23:03:15 | |
| host : ip-26-0-164-187.ec2.internal | |
| rank : 1 (local_rank: 1) | |
| exitcode : 1 (pid: 26445) | |
| error_file: <N/A> | |
| traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html | |
| [2]: | |
| time : 2024-07-03_23:03:15 | |
| host : ip-26-0-164-187.ec2.internal | |
| rank : 2 (local_rank: 2) | |
| exitcode : 1 (pid: 26446) | |
| error_file: <N/A> | |
| traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html | |
| [3]: | |
| time : 2024-07-03_23:03:15 | |
| host : ip-26-0-164-187.ec2.internal | |
| rank : 3 (local_rank: 3) | |
| exitcode : 1 (pid: 26447) | |
| error_file: <N/A> | |
| traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html | |
| [4]: | |
| time : 2024-07-03_23:03:15 | |
| host : ip-26-0-164-187.ec2.internal | |
| rank : 6 (local_rank: 6) | |
| exitcode : 1 (pid: 26450) | |
| error_file: <N/A> | |
| traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html | |
| ------------------------------------------------------------ | |
| Root Cause (first observed failure): | |
| [0]: | |
| time : 2024-07-03_23:03:15 | |
| host : ip-26-0-164-187.ec2.internal | |
| rank : 0 (local_rank: 0) | |
| exitcode : 1 (pid: 26444) | |
| error_file: <N/A> | |
| traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html | |
| ============================================================ | |
| srun: error: ip-26-0-164-187: task 0: Exited with exit code 1 | |
| 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. | |