======================== START TIME: Wed Jul 3 01:00:24 UTC 2024 python3 version = Python 3.10.14 ======================== The token has not been saved to the git credentials helper. Pass `add_to_git_credential=True` in this function directly or `--add-to-git-credential` if using via `huggingface-cli` if you want to set the git credential as well. Token is valid (permission: write). Your token has been saved to /admin/home/ferdinand_mom/.cache/huggingface/token Login successful Already on 'bench_cluster' M examples/config_tiny_llama.py M examples/config_tiny_llama.yaml M examples/train_tiny_llama.sh M src/nanotron/models/llama.py M src/nanotron/trainer.py Your branch is up to date with 'origin/bench_cluster'. Job status: RUNNING W0703 01:00:27.238000 140015942473536 torch/distributed/run.py:757] W0703 01:00:27.238000 140015942473536 torch/distributed/run.py:757] ***************************************** W0703 01:00:27.238000 140015942473536 torch/distributed/run.py:757] Setting OMP_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. W0703 01:00:27.238000 140015942473536 torch/distributed/run.py:757] ***************************************** W0703 01:00:29.222000 140709557507904 torch/distributed/run.py:757] W0703 01:00:29.222000 140709557507904 torch/distributed/run.py:757] ***************************************** W0703 01:00:29.222000 140709557507904 torch/distributed/run.py:757] Setting OMP_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. W0703 01:00:29.222000 140709557507904 torch/distributed/run.py:757] ***************************************** W0703 01:00:29.222000 139854410950464 torch/distributed/run.py:757] W0703 01:00:29.222000 139854410950464 torch/distributed/run.py:757] ***************************************** W0703 01:00:29.222000 139854410950464 torch/distributed/run.py:757] Setting OMP_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. W0703 01:00:29.222000 139854410950464 torch/distributed/run.py:757] ***************************************** W0703 01:00:29.233000 140540211869504 torch/distributed/run.py:757] W0703 01:00:29.233000 140540211869504 torch/distributed/run.py:757] ***************************************** W0703 01:00:29.233000 140540211869504 torch/distributed/run.py:757] Setting OMP_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. W0703 01:00:29.233000 140540211869504 torch/distributed/run.py:757] ***************************************** W0703 01:00:29.237000 140092964255552 torch/distributed/run.py:757] W0703 01:00:29.237000 140092964255552 torch/distributed/run.py:757] ***************************************** W0703 01:00:29.237000 140092964255552 torch/distributed/run.py:757] Setting OMP_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. W0703 01:00:29.237000 140092964255552 torch/distributed/run.py:757] ***************************************** W0703 01:00:29.342000 140574819604288 torch/distributed/run.py:757] W0703 01:00:29.342000 140574819604288 torch/distributed/run.py:757] ***************************************** W0703 01:00:29.342000 140574819604288 torch/distributed/run.py:757] Setting OMP_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. W0703 01:00:29.342000 140574819604288 torch/distributed/run.py:757] ***************************************** W0703 01:00:29.369000 140324900312896 torch/distributed/run.py:757] W0703 01:00:29.369000 140324900312896 torch/distributed/run.py:757] ***************************************** W0703 01:00:29.369000 140324900312896 torch/distributed/run.py:757] Setting OMP_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. W0703 01:00:29.369000 140324900312896 torch/distributed/run.py:757] ***************************************** W0703 01:00:30.315000 140432057993024 torch/distributed/run.py:757] W0703 01:00:30.315000 140432057993024 torch/distributed/run.py:757] ***************************************** W0703 01:00:30.315000 140432057993024 torch/distributed/run.py:757] Setting OMP_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. W0703 01:00:30.315000 140432057993024 torch/distributed/run.py:757] ***************************************** [default0]:07/03/2024 01:00:54 [WARNING|DP=0|PP=0|TP=0|ip-26-0-160-225]: [Vocab Size Padding] Padded vocab (size: 50257) with 15 dummy tokens (new size: 50272) [default0]:07/03/2024 01:00:54 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: Config: [default0]:07/03/2024 01:00:54 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: Config(general=GeneralArgs(project='bench_cluster', [default0]:07/03/2024 01:00:54 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: run='%date_%jobid', [default0]:07/03/2024 01:00:54 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: seed=42, [default0]:07/03/2024 01:00:54 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: step=None, [default0]:07/03/2024 01:00:54 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: consumed_train_samples=None, [default0]:07/03/2024 01:00:54 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: benchmark_csv_path=None, [default0]:07/03/2024 01:00:54 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: ignore_sanity_checks=True), [default0]:07/03/2024 01:00:54 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: parallelism=ParallelismArgs(dp=4, [default0]:07/03/2024 01:00:54 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: pp=1, [default0]:07/03/2024 01:00:54 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: tp=16, [default0]:07/03/2024 01:00:54 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: pp_engine=, [default0]:07/03/2024 01:00:54 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: tp_mode=, [default0]:07/03/2024 01:00:54 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: tp_linear_async_communication=False, [default0]:07/03/2024 01:00:54 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: expert_parallel_size=1), [default0]:07/03/2024 01:00:54 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: model=ModelArgs(model_config=LlamaConfig(bos_token_id=1, [default0]:07/03/2024 01:00:54 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: eos_token_id=2, [default0]:07/03/2024 01:00:54 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: hidden_act='silu', [default0]:07/03/2024 01:00:54 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: hidden_size=2048, [default0]:07/03/2024 01:00:54 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: initializer_range=0.02, [default0]:07/03/2024 01:00:54 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: intermediate_size=4096, [default0]:07/03/2024 01:00:54 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: is_llama_config=True, [default0]:07/03/2024 01:00:54 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: max_position_embeddings=4096, [default0]:07/03/2024 01:00:54 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: num_attention_heads=32, [default0]:07/03/2024 01:00:54 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: num_hidden_layers=24, [default0]:07/03/2024 01:00:54 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: num_key_value_heads=32, [default0]:07/03/2024 01:00:54 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: pad_token_id=None, [default0]:07/03/2024 01:00:54 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: pretraining_tp=1, [default0]:07/03/2024 01:00:54 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: rms_norm_eps=1e-05, [default0]:07/03/2024 01:00:54 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: rope_scaling=None, [default0]:07/03/2024 01:00:54 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: rope_theta=10000.0, [default0]:07/03/2024 01:00:54 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: tie_word_embeddings=True, [default0]:07/03/2024 01:00:54 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: use_cache=True, [default0]:07/03/2024 01:00:54 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: vocab_size=50272), [default0]:07/03/2024 01:00:54 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: init_method=RandomInit(std=0.025), [default0]:07/03/2024 01:00:54 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: dtype=torch.bfloat16, [default0]:07/03/2024 01:00:54 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: make_vocab_size_divisible_by=1, [default0]:07/03/2024 01:00:54 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: ddp_bucket_cap_mb=25), [default0]:07/03/2024 01:00:54 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: tokenizer=TokenizerArgs(tokenizer_name_or_path='openai-community/gpt2', [default0]:07/03/2024 01:00:54 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: tokenizer_revision=None, [default0]:07/03/2024 01:00:54 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: tokenizer_max_length=None), [default0]:07/03/2024 01:00:54 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: checkpoints=CheckpointsArgs(checkpoints_path=Path('/dev/null'), [default0]:07/03/2024 01:00:54 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: checkpoint_interval=100000, [default0]:07/03/2024 01:00:54 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: save_initial_state=False, [default0]:07/03/2024 01:00:54 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: resume_checkpoint_path=None, [default0]:07/03/2024 01:00:54 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: checkpoints_path_is_shared_file_system=False), [default0]:07/03/2024 01:00:54 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: logging=LoggingArgs(log_level='info', [default0]:07/03/2024 01:00:54 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: log_level_replica='info', [default0]:07/03/2024 01:00:54 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: iteration_step_info_interval=1), [default0]:07/03/2024 01:00:54 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: tokens=TokensArgs(sequence_length=4096, [default0]:07/03/2024 01:00:54 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: train_steps=20, [default0]:07/03/2024 01:00:54 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: micro_batch_size=64, [default0]:07/03/2024 01:00:54 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: batch_accumulation_per_replica=4, [default0]:07/03/2024 01:00:54 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: val_check_interval=-1, [default0]:07/03/2024 01:00:54 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: limit_val_batches=0, [default0]:07/03/2024 01:00:54 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: limit_test_batches=0), [default0]:07/03/2024 01:00:54 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: optimizer=OptimizerArgs(optimizer_factory=AdamWOptimizerArgs(adam_eps=1e-08, [default0]:07/03/2024 01:00:54 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: adam_beta1=0.9, [default0]:07/03/2024 01:00:54 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: adam_beta2=0.95, [default0]:07/03/2024 01:00:54 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: torch_adam_is_fused=True, [default0]:07/03/2024 01:00:54 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: name='adamW'), [default0]:07/03/2024 01:00:54 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: zero_stage=1, [default0]:07/03/2024 01:00:54 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: weight_decay=0.01, [default0]:07/03/2024 01:00:54 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: clip_grad=1.0, [default0]:07/03/2024 01:00:54 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: accumulate_grad_in_fp32=True, [default0]:07/03/2024 01:00:54 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: learning_rate_scheduler=LRSchedulerArgs(learning_rate=0.0001, [default0]:07/03/2024 01:00:54 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: lr_warmup_steps=1, [default0]:07/03/2024 01:00:54 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: lr_warmup_style='linear', [default0]:07/03/2024 01:00:54 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: lr_decay_style='linear', [default0]:07/03/2024 01:00:54 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: lr_decay_steps=19, [default0]:07/03/2024 01:00:54 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: lr_decay_starting_step=None, [default0]:07/03/2024 01:00:54 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: min_decay_lr=1e-05)), [default0]:07/03/2024 01:00:54 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: data_stages=[DatasetStageArgs(name='Training Stage', [default0]:07/03/2024 01:00:54 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: start_training_step=1, [default0]:07/03/2024 01:00:54 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: data=DataArgs(dataset=PretrainDatasetsArgs(hf_dataset_or_datasets='roneneldan/TinyStories', [default0]:07/03/2024 01:00:54 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: hf_dataset_splits='train', [default0]:07/03/2024 01:00:54 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: hf_dataset_config_name=None, [default0]:07/03/2024 01:00:54 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: dataset_processing_num_proc_per_process=64, [default0]:07/03/2024 01:00:54 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: dataset_overwrite_cache=False, [default0]:07/03/2024 01:00:54 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: text_column_name='text'), [default0]:07/03/2024 01:00:54 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: seed=42, [default0]:07/03/2024 01:00:54 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: num_loading_workers=0))], [default0]:07/03/2024 01:00:54 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: profiler=ProfilerArgs(profiler_export_path=Path('/fsx/ferdinandmom/ferdinand-hf/bench_cluster/results/llama-1B/64_GPUS/dp-4_tp-16_pp-1_mbz-64')), [default0]:07/03/2024 01:00:54 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: lighteval=None) [default0]:07/03/2024 01:00:54 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: Model Config: [default0]:07/03/2024 01:00:54 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: LlamaConfig(bos_token_id=1, [default0]:07/03/2024 01:00:54 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: eos_token_id=2, [default0]:07/03/2024 01:00:54 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: hidden_act='silu', [default0]:07/03/2024 01:00:54 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: hidden_size=2048, [default0]:07/03/2024 01:00:54 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: initializer_range=0.02, [default0]:07/03/2024 01:00:54 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: intermediate_size=4096, [default0]:07/03/2024 01:00:54 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: is_llama_config=True, [default0]:07/03/2024 01:00:54 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: max_position_embeddings=4096, [default0]:07/03/2024 01:00:54 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: num_attention_heads=32, [default0]:07/03/2024 01:00:54 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: num_hidden_layers=24, [default0]:07/03/2024 01:00:54 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: num_key_value_heads=32, [default0]:07/03/2024 01:00:54 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: pad_token_id=None, [default0]:07/03/2024 01:00:54 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: pretraining_tp=1, [default0]:07/03/2024 01:00:54 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: rms_norm_eps=1e-05, [default0]:07/03/2024 01:00:54 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: rope_scaling=None, [default0]:07/03/2024 01:00:54 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: rope_theta=10000.0, [default0]:07/03/2024 01:00:54 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: tie_word_embeddings=True, [default0]:07/03/2024 01:00:54 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: use_cache=True, [default0]:07/03/2024 01:00:54 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: vocab_size=50272) [default0]:07/03/2024 01:00:54 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: Building model.. [default0]:07/03/2024 01:00:54 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: Setting PP block ranks... [default5]:07/03/2024 01:01:11 [INFO|DP=1|PP=0|TP=13|ip-26-0-161-78]: No checkpoint path provided. [default1]:07/03/2024 01:01:11 [INFO|DP=1|PP=0|TP=9|ip-26-0-161-78]: No checkpoint path provided. [default3]:07/03/2024 01:01:11 [INFO|DP=1|PP=0|TP=3|ip-26-0-161-153]: No checkpoint path provided. [default5]:07/03/2024 01:01:11 [INFO|DP=1|PP=0|TP=5|ip-26-0-161-153]: No checkpoint path provided. [default4]:07/03/2024 01:01:11 [INFO|DP=1|PP=0|TP=4|ip-26-0-161-153]: No checkpoint path provided. [default0]:07/03/2024 01:01:11 [INFO|DP=1|PP=0|TP=0|ip-26-0-161-153]: No checkpoint path provided. [default6]:07/03/2024 01:01:11 [INFO|DP=1|PP=0|TP=6|ip-26-0-161-153]: No checkpoint path provided. [default0]:07/03/2024 01:01:11 [INFO|DP=1|PP=0|TP=8|ip-26-0-161-78]: No checkpoint path provided. [default1]:07/03/2024 01:01:11 [INFO|DP=1|PP=0|TP=1|ip-26-0-161-153]: No checkpoint path provided. [default6]:07/03/2024 01:01:11 [INFO|DP=1|PP=0|TP=14|ip-26-0-161-78]: No checkpoint path provided. [default7]:07/03/2024 01:01:11 [INFO|DP=1|PP=0|TP=7|ip-26-0-161-153]: No checkpoint path provided. [default4]:07/03/2024 01:01:11 [INFO|DP=1|PP=0|TP=12|ip-26-0-161-78]: No checkpoint path provided. [default3]:07/03/2024 01:01:11 [INFO|DP=1|PP=0|TP=11|ip-26-0-161-78]: No checkpoint path provided. [default2]:07/03/2024 01:01:11 [INFO|DP=1|PP=0|TP=10|ip-26-0-161-78]: No checkpoint path provided. [default2]:07/03/2024 01:01:11 [INFO|DP=1|PP=0|TP=2|ip-26-0-161-153]: No checkpoint path provided. [default7]:07/03/2024 01:01:11 [INFO|DP=1|PP=0|TP=15|ip-26-0-161-78]: No checkpoint path provided. [default4]:07/03/2024 01:01:11 [INFO|DP=3|PP=0|TP=12|ip-26-0-171-88]: No checkpoint path provided. [default0]:07/03/2024 01:01:11 [INFO|DP=3|PP=0|TP=8|ip-26-0-171-88]: No checkpoint path provided. [default0]:07/03/2024 01:01:11 [INFO|DP=3|PP=0|TP=0|ip-26-0-171-62]: No checkpoint path provided. [default2]:07/03/2024 01:01:11 [INFO|DP=3|PP=0|TP=10|ip-26-0-171-88]: No checkpoint path provided. [default3]:07/03/2024 01:01:11 [INFO|DP=3|PP=0|TP=11|ip-26-0-171-88]: No checkpoint path provided. [default1]:07/03/2024 01:01:11 [INFO|DP=3|PP=0|TP=9|ip-26-0-171-88]: No checkpoint path provided. [default6]:07/03/2024 01:01:11 [INFO|DP=3|PP=0|TP=14|ip-26-0-171-88]: No checkpoint path provided. [default1]:07/03/2024 01:01:11 [INFO|DP=3|PP=0|TP=1|ip-26-0-171-62]: No checkpoint path provided. [default5]:07/03/2024 01:01:11 [INFO|DP=3|PP=0|TP=13|ip-26-0-171-88]: No checkpoint path provided. [default7]:07/03/2024 01:01:11 [INFO|DP=3|PP=0|TP=15|ip-26-0-171-88]: No checkpoint path provided. [default6]:07/03/2024 01:01:11 [INFO|DP=3|PP=0|TP=6|ip-26-0-171-62]: No checkpoint path provided. [default5]:07/03/2024 01:01:11 [INFO|DP=3|PP=0|TP=5|ip-26-0-171-62]: No checkpoint path provided. [default2]:07/03/2024 01:01:11 [INFO|DP=3|PP=0|TP=2|ip-26-0-171-62]: No checkpoint path provided. [default7]:07/03/2024 01:01:11 [INFO|DP=3|PP=0|TP=7|ip-26-0-171-62]: No checkpoint path provided. [default3]:07/03/2024 01:01:11 [INFO|DP=3|PP=0|TP=3|ip-26-0-171-62]: No checkpoint path provided. [default4]:07/03/2024 01:01:11 [INFO|DP=3|PP=0|TP=4|ip-26-0-171-62]: No checkpoint path provided. [default1]:07/03/2024 01:01:11 [INFO|DP=2|PP=0|TP=1|ip-26-0-162-233]: No checkpoint path provided. [default7]:07/03/2024 01:01:11 [INFO|DP=2|PP=0|TP=7|ip-26-0-162-233]: No checkpoint path provided. [default0]:07/03/2024 01:01:11 [INFO|DP=2|PP=0|TP=0|ip-26-0-162-233]: No checkpoint path provided. [default6]:07/03/2024 01:01:11 [INFO|DP=2|PP=0|TP=6|ip-26-0-162-233]: No checkpoint path provided. [default4]:07/03/2024 01:01:11 [INFO|DP=2|PP=0|TP=4|ip-26-0-162-233]: No checkpoint path provided. [default5]:07/03/2024 01:01:11 [INFO|DP=2|PP=0|TP=5|ip-26-0-162-233]: No checkpoint path provided. [default7]:07/03/2024 01:01:11 [INFO|DP=2|PP=0|TP=15|ip-26-0-171-102]: No checkpoint path provided. [default2]:07/03/2024 01:01:11 [INFO|DP=2|PP=0|TP=10|ip-26-0-171-102]: No checkpoint path provided. [default1]:07/03/2024 01:01:11 [INFO|DP=2|PP=0|TP=9|ip-26-0-171-102]: No checkpoint path provided. [default0]:07/03/2024 01:01:11 [INFO|DP=2|PP=0|TP=8|ip-26-0-171-102]: No checkpoint path provided. [default2]:07/03/2024 01:01:11 [INFO|DP=2|PP=0|TP=2|ip-26-0-162-233]: No checkpoint path provided. [default3]:07/03/2024 01:01:11 [INFO|DP=2|PP=0|TP=3|ip-26-0-162-233]: No checkpoint path provided. [default3]:07/03/2024 01:01:11 [INFO|DP=2|PP=0|TP=11|ip-26-0-171-102]: No checkpoint path provided. [default6]:07/03/2024 01:01:11 [INFO|DP=2|PP=0|TP=14|ip-26-0-171-102]: No checkpoint path provided. [default4]:07/03/2024 01:01:11 [INFO|DP=2|PP=0|TP=12|ip-26-0-171-102]: No checkpoint path provided. [default5]:07/03/2024 01:01:11 [INFO|DP=2|PP=0|TP=13|ip-26-0-171-102]: No checkpoint path provided. [default1]:07/03/2024 01:01:12 [INFO|DP=0|PP=0|TP=9|ip-26-0-161-103]: Local number of parameters: 69.4M (132.46MiB) [default2]:07/03/2024 01:01:12 [INFO|DP=0|PP=0|TP=10|ip-26-0-161-103]: Local number of parameters: 69.4M (132.46MiB) [default1]:07/03/2024 01:01:12 [INFO|DP=0|PP=0|TP=9|ip-26-0-161-103]: [After model building] Memory usage: 159.71MiB. Peak allocated: 174.02MiB Peak reserved: 178.00MiB [default5]:07/03/2024 01:01:12 [INFO|DP=0|PP=0|TP=13|ip-26-0-161-103]: Local number of parameters: 69.4M (132.46MiB) [default1]:07/03/2024 01:01:12 [INFO|DP=0|PP=0|TP=9|ip-26-0-161-103]: No checkpoint path provided. [default5]:07/03/2024 01:01:12 [INFO|DP=0|PP=0|TP=13|ip-26-0-161-103]: [After model building] Memory usage: 159.71MiB. Peak allocated: 174.02MiB Peak reserved: 178.00MiB [default5]:07/03/2024 01:01:12 [INFO|DP=0|PP=0|TP=13|ip-26-0-161-103]: No checkpoint path provided. [default2]:07/03/2024 01:01:12 [INFO|DP=0|PP=0|TP=10|ip-26-0-161-103]: [After model building] Memory usage: 159.71MiB. Peak allocated: 174.02MiB Peak reserved: 178.00MiB [default2]:07/03/2024 01:01:12 [INFO|DP=0|PP=0|TP=10|ip-26-0-161-103]: No checkpoint path provided. [default3]:07/03/2024 01:01:12 [INFO|DP=0|PP=0|TP=11|ip-26-0-161-103]: Local number of parameters: 69.4M (132.46MiB) [default3]:07/03/2024 01:01:12 [INFO|DP=0|PP=0|TP=11|ip-26-0-161-103]: [After model building] Memory usage: 159.71MiB. Peak allocated: 174.02MiB Peak reserved: 178.00MiB [default3]:07/03/2024 01:01:12 [INFO|DP=0|PP=0|TP=11|ip-26-0-161-103]: No checkpoint path provided. [default0]:07/03/2024 01:01:12 [INFO|DP=0|PP=0|TP=8|ip-26-0-161-103]: Local number of parameters: 69.4M (132.46MiB) [default0]:07/03/2024 01:01:12 [INFO|DP=0|PP=0|TP=8|ip-26-0-161-103]: [After model building] Memory usage: 159.71MiB. Peak allocated: 174.02MiB Peak reserved: 178.00MiB [default0]:07/03/2024 01:01:12 [INFO|DP=0|PP=0|TP=8|ip-26-0-161-103]: No checkpoint path provided. [default4]:07/03/2024 01:01:12 [INFO|DP=0|PP=0|TP=12|ip-26-0-161-103]: Local number of parameters: 69.4M (132.46MiB) [default4]:07/03/2024 01:01:12 [INFO|DP=0|PP=0|TP=12|ip-26-0-161-103]: [After model building] Memory usage: 159.71MiB. Peak allocated: 174.02MiB Peak reserved: 178.00MiB [default4]:07/03/2024 01:01:12 [INFO|DP=0|PP=0|TP=12|ip-26-0-161-103]: No checkpoint path provided. [default5]:07/03/2024 01:01:12 [INFO|DP=0|PP=0|TP=5|ip-26-0-160-225]: Local number of parameters: 69.4M (132.46MiB) [default5]:07/03/2024 01:01:12 [INFO|DP=0|PP=0|TP=5|ip-26-0-160-225]: [After model building] Memory usage: 159.71MiB. Peak allocated: 174.02MiB Peak reserved: 178.00MiB [default5]:07/03/2024 01:01:12 [INFO|DP=0|PP=0|TP=5|ip-26-0-160-225]: No checkpoint path provided. [default7]:07/03/2024 01:01:12 [INFO|DP=0|PP=0|TP=7|ip-26-0-160-225]: Local number of parameters: 69.4M (132.46MiB) [default7]:07/03/2024 01:01:12 [INFO|DP=0|PP=0|TP=7|ip-26-0-160-225]: [After model building] Memory usage: 159.71MiB. Peak allocated: 174.02MiB Peak reserved: 178.00MiB [default7]:07/03/2024 01:01:12 [INFO|DP=0|PP=0|TP=7|ip-26-0-160-225]: No checkpoint path provided. [default6]:07/03/2024 01:01:12 [INFO|DP=0|PP=0|TP=6|ip-26-0-160-225]: Local number of parameters: 69.4M (132.46MiB) [default6]:07/03/2024 01:01:12 [INFO|DP=0|PP=0|TP=6|ip-26-0-160-225]: [After model building] Memory usage: 159.71MiB. Peak allocated: 174.02MiB Peak reserved: 178.00MiB [default6]:07/03/2024 01:01:12 [INFO|DP=0|PP=0|TP=6|ip-26-0-160-225]: No checkpoint path provided. [default3]:07/03/2024 01:01:12 [INFO|DP=0|PP=0|TP=3|ip-26-0-160-225]: Local number of parameters: 69.4M (132.46MiB) [default0]:07/03/2024 01:01:12 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: Total number of parameters: 1.11G (2119.44MiB) [default0]:07/03/2024 01:01:12 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: Local number of parameters: 69.4M (132.46MiB) [default0]:07/03/2024 01:01:12 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: [After model building] Memory usage: 159.71MiB. Peak allocated: 174.02MiB Peak reserved: 178.00MiB [default0]:07/03/2024 01:01:12 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: No checkpoint path provided. [default0]:07/03/2024 01:01:12 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: Parametrizing model parameters using StandardParametrizator [default3]:07/03/2024 01:01:12 [INFO|DP=0|PP=0|TP=3|ip-26-0-160-225]: [After model building] Memory usage: 159.71MiB. Peak allocated: 174.02MiB Peak reserved: 178.00MiB [default3]:07/03/2024 01:01:12 [INFO|DP=0|PP=0|TP=3|ip-26-0-160-225]: No checkpoint path provided. [default7]:07/03/2024 01:01:12 [INFO|DP=0|PP=0|TP=15|ip-26-0-161-103]: Local number of parameters: 69.4M (132.46MiB) [default7]:07/03/2024 01:01:12 [INFO|DP=0|PP=0|TP=15|ip-26-0-161-103]: [After model building] Memory usage: 159.71MiB. Peak allocated: 174.02MiB Peak reserved: 178.00MiB [default7]:07/03/2024 01:01:12 [INFO|DP=0|PP=0|TP=15|ip-26-0-161-103]: No checkpoint path provided. [default6]:07/03/2024 01:01:12 [INFO|DP=0|PP=0|TP=14|ip-26-0-161-103]: Local number of parameters: 69.4M (132.46MiB) [default6]:07/03/2024 01:01:12 [INFO|DP=0|PP=0|TP=14|ip-26-0-161-103]: [After model building] Memory usage: 159.71MiB. Peak allocated: 174.02MiB Peak reserved: 178.00MiB [default6]:07/03/2024 01:01:12 [INFO|DP=0|PP=0|TP=14|ip-26-0-161-103]: No checkpoint path provided. [default2]:07/03/2024 01:01:12 [INFO|DP=0|PP=0|TP=2|ip-26-0-160-225]: Local number of parameters: 69.4M (132.46MiB) [default2]:07/03/2024 01:01:12 [INFO|DP=0|PP=0|TP=2|ip-26-0-160-225]: [After model building] Memory usage: 159.71MiB. Peak allocated: 174.02MiB Peak reserved: 178.00MiB [default2]:07/03/2024 01:01:12 [INFO|DP=0|PP=0|TP=2|ip-26-0-160-225]: No checkpoint path provided. [default4]:07/03/2024 01:01:12 [INFO|DP=0|PP=0|TP=4|ip-26-0-160-225]: Local number of parameters: 69.4M (132.46MiB) [default4]:07/03/2024 01:01:12 [INFO|DP=0|PP=0|TP=4|ip-26-0-160-225]: [After model building] Memory usage: 159.71MiB. Peak allocated: 174.02MiB Peak reserved: 178.00MiB [default4]:07/03/2024 01:01:12 [INFO|DP=0|PP=0|TP=4|ip-26-0-160-225]: No checkpoint path provided. [default1]:07/03/2024 01:01:12 [INFO|DP=0|PP=0|TP=1|ip-26-0-160-225]: Local number of parameters: 69.4M (132.46MiB) [default1]:07/03/2024 01:01:12 [INFO|DP=0|PP=0|TP=1|ip-26-0-160-225]: [After model building] Memory usage: 159.71MiB. Peak allocated: 174.02MiB Peak reserved: 178.00MiB [default1]:07/03/2024 01:01:12 [INFO|DP=0|PP=0|TP=1|ip-26-0-160-225]: No checkpoint path provided. [default0]:07/03/2024 01:01:13 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: [Optimizer Building] Using LearningRateForSP as learning rate [default0]:07/03/2024 01:01:13 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: [ZeRO sharding] Size of optimizer params per rank: [default0]:07/03/2024 01:01:13 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: [ZeRO sharding] DP Rank 0 has 17.4M out of 69.4M (25.00%) params' optimizer states [default0]:07/03/2024 01:01:13 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: [ZeRO sharding] DP Rank 1 has 17.4M out of 69.4M (25.00%) params' optimizer states [default0]:07/03/2024 01:01:13 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: [ZeRO sharding] DP Rank 2 has 17.4M out of 69.4M (25.00%) params' optimizer states [default0]:07/03/2024 01:01:13 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: [ZeRO sharding] DP Rank 3 has 17.4M out of 69.4M (25.00%) params' optimizer states [default0]:07/03/2024 01:01:15 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: [Training Plan] Stage Training Stage has 19 remaining training steps and has consumed 0 samples [default0]:07/03/2024 01:01:15 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: Using `datasets` library [default0]:07/03/2024 01:01:15 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: Loading tokenizer from openai-community/gpt2 and transformers/hf_hub versions ('4.41.2', '0.23.4') [default0]:07/03/2024 01:01:15 [WARNING|DP=0|PP=0|TP=0|ip-26-0-160-225]: Repo card metadata block was not found. Setting CardData to empty. [default0]:Repo card metadata block was not found. Setting CardData to empty. [default0]:07/03/2024 01:01:17 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: [Training Plan] There are 1 training stages [default0]:07/03/2024 01:01:17 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: [Stage Training Stage] start from step 1 [default0]:07/03/2024 01:01:17 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: [default0]:07/03/2024 01:01:17 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: [Start training] datetime: 2024-07-03 01:01:17.289255 | mbs: 64 | grad_accum: 4 | global_batch_size: 1024 | sequence_length: 4096 | train_steps: 20 | start_iteration_step: 0 | consumed_train_samples: 0 [default0]:07/03/2024 01:01:17 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: Resuming training from stage Training Stage, it has trained for 0 samples and has 19 remaining train steps [default0]:07/03/2024 01:01:17 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: Memory usage: 490.87MiB. Peak allocated 490.87MiB. Peak reserved: 512.00MiB [default5]:07/03/2024 01:01:17 [WARNING|DP=1|PP=0|TP=13|ip-26-0-161-78]: Repo card metadata block was not found. Setting CardData to empty. [default6]:07/03/2024 01:01:17 [WARNING|DP=1|PP=0|TP=14|ip-26-0-161-78]: Repo card metadata block was not found. Setting CardData to empty. [default0]:07/03/2024 01:01:17 [WARNING|DP=1|PP=0|TP=8|ip-26-0-161-78]: Repo card metadata block was not found. Setting CardData to empty. [default6]:07/03/2024 01:01:17 [WARNING|DP=2|PP=0|TP=6|ip-26-0-162-233]: Repo card metadata block was not found. Setting CardData to empty. [default5]:07/03/2024 01:01:17 [WARNING|DP=2|PP=0|TP=5|ip-26-0-162-233]: Repo card metadata block was not found. Setting CardData to empty. [default0]:07/03/2024 01:01:17 [WARNING|DP=2|PP=0|TP=0|ip-26-0-162-233]: Repo card metadata block was not found. Setting CardData to empty. [default7]:07/03/2024 01:01:17 [WARNING|DP=2|PP=0|TP=15|ip-26-0-171-102]: Repo card metadata block was not found. Setting CardData to empty. [default1]:07/03/2024 01:01:17 [WARNING|DP=2|PP=0|TP=9|ip-26-0-171-102]: Repo card metadata block was not found. Setting CardData to empty. [default0]:07/03/2024 01:01:17 [WARNING|DP=2|PP=0|TP=8|ip-26-0-171-102]: Repo card metadata block was not found. Setting CardData to empty. [default0]:Repo card metadata block was not found. Setting CardData to empty. [default5]: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. [default0]:Repo card metadata block was not found. Setting CardData to empty. [default7]:07/03/2024 01:01:17 [WARNING|DP=1|PP=0|TP=15|ip-26-0-161-78]: Repo card metadata block was not found. Setting CardData to empty. [default6]:Repo card metadata block was not found. Setting CardData to empty. [default0]:07/03/2024 01:01:17 [WARNING|DP=3|PP=0|TP=8|ip-26-0-171-88]: Repo card metadata block was not found. Setting CardData to empty. [default6]:07/03/2024 01:01:17 [WARNING|DP=2|PP=0|TP=14|ip-26-0-171-102]: Repo card metadata block was not found. Setting CardData to empty. [default3]:07/03/2024 01:01:17 [WARNING|DP=2|PP=0|TP=11|ip-26-0-171-102]: Repo card metadata block was not found. Setting CardData to empty. [default0]:Repo card metadata block was not found. Setting CardData to empty. [default3]:07/03/2024 01:01:17 [WARNING|DP=3|PP=0|TP=11|ip-26-0-171-88]: Repo card metadata block was not found. Setting CardData to empty. [default1]:Repo card metadata block was not found. Setting CardData to empty. [default1]:Repo card metadata block was not found. Setting CardData to empty. [default7]:Repo card metadata block was not found. Setting CardData to empty. [default1]:07/03/2024 01:01:17 [WARNING|DP=3|PP=0|TP=9|ip-26-0-171-88]: Repo card metadata block was not found. Setting CardData to empty. [default3]:Repo card metadata block was not found. Setting CardData to empty. [default5]:07/03/2024 01:01:17 [WARNING|DP=0|PP=0|TP=5|ip-26-0-160-225]: Repo card metadata block was not found. Setting CardData to empty. [default7]:07/03/2024 01:01:17 [WARNING|DP=0|PP=0|TP=7|ip-26-0-160-225]: Repo card metadata block was not found. Setting CardData to empty. [default7]:07/03/2024 01:01:17 [WARNING|DP=0|PP=0|TP=15|ip-26-0-161-103]: Repo card metadata block was not found. Setting CardData to empty. [default0]:Repo card metadata block was not found. Setting CardData to empty. [default3]:Repo card metadata block was not found. Setting CardData to empty. [default4]:07/03/2024 01:01:17 [WARNING|DP=2|PP=0|TP=12|ip-26-0-171-102]: Repo card metadata block was not found. Setting CardData to empty. [default7]:Repo card metadata block was not found. Setting CardData to empty. [default7]:07/03/2024 01:01:17 [WARNING|DP=3|PP=0|TP=15|ip-26-0-171-88]: Repo card metadata block was not found. Setting CardData to empty. [default4]:Repo card metadata block was not found. Setting CardData to empty. [default6]:07/03/2024 01:01:17 [WARNING|DP=3|PP=0|TP=6|ip-26-0-171-62]: Repo card metadata block was not found. Setting CardData to empty. [default7]:Repo card metadata block was not found. Setting CardData to empty. [default4]:07/03/2024 01:01:17 [WARNING|DP=0|PP=0|TP=4|ip-26-0-160-225]: Repo card metadata block was not found. Setting CardData to empty. [default5]:Repo card metadata block was not found. Setting CardData to empty. [default7]:Repo card metadata block was not found. Setting CardData to empty. [default4]:Repo card metadata block was not found. Setting CardData to empty. [default6]:Repo card metadata block was not found. Setting CardData to empty. [default7]:Repo card metadata block was not found. Setting CardData to empty. [default6]:Repo card metadata block was not found. Setting CardData to empty. [default1]:07/03/2024 01:01:17 [WARNING|DP=1|PP=0|TP=9|ip-26-0-161-78]: Repo card metadata block was not found. Setting CardData to empty. [default3]:07/03/2024 01:01:17 [WARNING|DP=1|PP=0|TP=3|ip-26-0-161-153]: Repo card metadata block was not found. Setting CardData to empty. [default0]:07/03/2024 01:01:17 [WARNING|DP=1|PP=0|TP=0|ip-26-0-161-153]: Repo card metadata block was not found. Setting CardData to empty. [default6]:07/03/2024 01:01:17 [WARNING|DP=1|PP=0|TP=6|ip-26-0-161-153]: Repo card metadata block was not found. Setting CardData to empty. [default5]:07/03/2024 01:01:17 [WARNING|DP=1|PP=0|TP=5|ip-26-0-161-153]: Repo card metadata block was not found. Setting CardData to empty. [default4]:07/03/2024 01:01:17 [WARNING|DP=1|PP=0|TP=4|ip-26-0-161-153]: Repo card metadata block was not found. Setting CardData to empty. [default5]:Repo card metadata block was not found. Setting CardData to empty. [default6]:Repo card metadata block was not found. Setting CardData to empty. [default4]:Repo card metadata block was not found. Setting CardData to empty. [default2]:Repo card metadata block was not found. Setting CardData to empty. [default7]:07/03/2024 01:01:17 [WARNING|DP=1|PP=0|TP=7|ip-26-0-161-153]: Repo card metadata block was not found. Setting CardData to empty. [default7]:07/03/2024 01:01:17 [WARNING|DP=2|PP=0|TP=7|ip-26-0-162-233]: Repo card metadata block was not found. Setting CardData to empty. [default1]:07/03/2024 01:01:17 [WARNING|DP=1|PP=0|TP=1|ip-26-0-161-153]: Repo card metadata block was not found. Setting CardData to empty. [default2]:Repo card metadata block was not found. Setting CardData to empty. [default3]:Repo card metadata block was not found. Setting CardData to empty. [default4]:07/03/2024 01:01:17 [WARNING|DP=2|PP=0|TP=4|ip-26-0-162-233]: Repo card metadata block was not found. Setting CardData to empty. [default1]:Repo card metadata block was not found. Setting CardData to empty. [default2]:Repo card metadata block was not found. Setting CardData to empty. [default2]:07/03/2024 01:01:17 [WARNING|DP=2|PP=0|TP=10|ip-26-0-171-102]: Repo card metadata block was not found. Setting CardData to empty. [default4]:Repo card metadata block was not found. Setting CardData to empty. [default2]:07/03/2024 01:01:17 [WARNING|DP=1|PP=0|TP=10|ip-26-0-161-78]: Repo card metadata block was not found. Setting CardData to empty. [default1]:Repo card metadata block was not found. Setting CardData to empty. [default2]:07/03/2024 01:01:17 [WARNING|DP=2|PP=0|TP=2|ip-26-0-162-233]: Repo card metadata block was not found. Setting CardData to empty. [default3]:07/03/2024 01:01:17 [WARNING|DP=2|PP=0|TP=3|ip-26-0-162-233]: Repo card metadata block was not found. Setting CardData to empty. [default0]:Repo card metadata block was not found. Setting CardData to empty. [default2]:07/03/2024 01:01:17 [WARNING|DP=1|PP=0|TP=2|ip-26-0-161-153]: Repo card metadata block was not found. Setting CardData to empty. [default7]:Repo card metadata block was not found. Setting CardData to empty. [default0]:07/03/2024 01:01:17 [WARNING|DP=3|PP=0|TP=0|ip-26-0-171-62]: Repo card metadata block was not found. Setting CardData to empty. [default4]:07/03/2024 01:01:17 [WARNING|DP=3|PP=0|TP=12|ip-26-0-171-88]: Repo card metadata block was not found. Setting CardData to empty. [default5]:07/03/2024 01:01:17 [WARNING|DP=0|PP=0|TP=13|ip-26-0-161-103]: Repo card metadata block was not found. Setting CardData to empty. [default3]:07/03/2024 01:01:17 [WARNING|DP=0|PP=0|TP=11|ip-26-0-161-103]: Repo card metadata block was not found. Setting CardData to empty. [default5]:Repo card metadata block was not found. Setting CardData to empty. [default2]:07/03/2024 01:01:17 [WARNING|DP=0|PP=0|TP=10|ip-26-0-161-103]: Repo card metadata block was not found. Setting CardData to empty. [default1]:07/03/2024 01:01:17 [WARNING|DP=0|PP=0|TP=9|ip-26-0-161-103]: Repo card metadata block was not found. Setting CardData to empty. [default2]:07/03/2024 01:01:17 [WARNING|DP=3|PP=0|TP=10|ip-26-0-171-88]: Repo card metadata block was not found. Setting CardData to empty. [default4]:Repo card metadata block was not found. Setting CardData to empty. [default4]:07/03/2024 01:01:17 [WARNING|DP=0|PP=0|TP=12|ip-26-0-161-103]: Repo card metadata block was not found. Setting CardData to empty. [default3]:Repo card metadata block was not found. Setting CardData to empty. [default6]:07/03/2024 01:01:17 [WARNING|DP=3|PP=0|TP=14|ip-26-0-171-88]: Repo card metadata block was not found. Setting CardData to empty. [default4]:Repo card metadata block was not found. Setting CardData to empty. [default3]:07/03/2024 01:01:17 [WARNING|DP=0|PP=0|TP=3|ip-26-0-160-225]: Repo card metadata block was not found. Setting CardData to empty. [default6]:07/03/2024 01:01:17 [WARNING|DP=0|PP=0|TP=6|ip-26-0-160-225]: Repo card metadata block was not found. Setting CardData to empty. [default6]:07/03/2024 01:01:17 [WARNING|DP=0|PP=0|TP=14|ip-26-0-161-103]: Repo card metadata block was not found. Setting CardData to empty. [default5]:07/03/2024 01:01:17 [WARNING|DP=2|PP=0|TP=13|ip-26-0-171-102]: Repo card metadata block was not found. Setting CardData to empty. [default6]:Repo card metadata block was not found. Setting CardData to empty. [default5]:07/03/2024 01:01:17 [WARNING|DP=3|PP=0|TP=13|ip-26-0-171-88]: Repo card metadata block was not found. Setting CardData to empty. [default3]:Repo card metadata block was not found. Setting CardData to empty. [default6]:Repo card metadata block was not found. Setting CardData to empty. [default4]:Repo card metadata block was not found. Setting CardData to empty. [default2]:07/03/2024 01:01:17 [WARNING|DP=3|PP=0|TP=2|ip-26-0-171-62]: Repo card metadata block was not found. Setting CardData to empty. [default3]:Repo card metadata block was not found. Setting CardData to empty. [default5]:07/03/2024 01:01:17 [WARNING|DP=3|PP=0|TP=5|ip-26-0-171-62]: Repo card metadata block was not found. Setting CardData to empty. [default7]:Repo card metadata block was not found. Setting CardData to empty. [default7]:Repo card metadata block was not found. Setting CardData to empty. [default2]:Repo card metadata block was not found. Setting CardData to empty. [default2]:07/03/2024 01:01:17 [WARNING|DP=0|PP=0|TP=2|ip-26-0-160-225]: Repo card metadata block was not found. Setting CardData to empty. [default7]:07/03/2024 01:01:17 [WARNING|DP=3|PP=0|TP=7|ip-26-0-171-62]: Repo card metadata block was not found. Setting CardData to empty. [default1]:07/03/2024 01:01:17 [WARNING|DP=0|PP=0|TP=1|ip-26-0-160-225]: Repo card metadata block was not found. Setting CardData to empty. [default2]:Repo card metadata block was not found. Setting CardData to empty. [default6]:Repo card metadata block was not found. Setting CardData to empty. [default2]:Repo card metadata block was not found. Setting CardData to empty. [default0]:Repo card metadata block was not found. Setting CardData to empty. [default4]:07/03/2024 01:01:17 [WARNING|DP=3|PP=0|TP=4|ip-26-0-171-62]: Repo card metadata block was not found. Setting CardData to empty. [default2]:Repo card metadata block was not found. Setting CardData to empty. [default2]:Repo card metadata block was not found. Setting CardData to empty. [default1]:Repo card metadata block was not found. Setting CardData to empty. [default5]:Repo card metadata block was not found. Setting CardData to empty. [default5]:Repo card metadata block was not found. Setting CardData to empty. [default5]:Repo card metadata block was not found. Setting CardData to empty. [default1]:Repo card metadata block was not found. Setting CardData to empty. [default1]:07/03/2024 01:01:17 [WARNING|DP=2|PP=0|TP=1|ip-26-0-162-233]: Repo card metadata block was not found. Setting CardData to empty. [default4]:07/03/2024 01:01:17 [WARNING|DP=1|PP=0|TP=12|ip-26-0-161-78]: Repo card metadata block was not found. Setting CardData to empty. [default4]:Repo card metadata block was not found. Setting CardData to empty. [default1]:Repo card metadata block was not found. Setting CardData to empty. [default0]:07/03/2024 01:01:17 [WARNING|DP=0|PP=0|TP=8|ip-26-0-161-103]: Repo card metadata block was not found. Setting CardData to empty. [default1]:07/03/2024 01:01:17 [WARNING|DP=3|PP=0|TP=1|ip-26-0-171-62]: Repo card metadata block was not found. Setting CardData to empty. [default1]:Repo card metadata block was not found. Setting CardData to empty. [default3]:07/03/2024 01:01:17 [WARNING|DP=3|PP=0|TP=3|ip-26-0-171-62]: Repo card metadata block was not found. Setting CardData to empty. [default3]:Repo card metadata block was not found. Setting CardData to empty. [default0]:Repo card metadata block was not found. Setting CardData to empty. [default3]:07/03/2024 01:01:18 [WARNING|DP=1|PP=0|TP=11|ip-26-0-161-78]: Repo card metadata block was not found. Setting CardData to empty. [default3]:Repo card metadata block was not found. Setting CardData to empty. [default2]:[rank34]: Traceback (most recent call last): [default2]:[rank34]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/run_train.py", line 237, in [default2]:[rank34]: trainer.train(dataloader) [default3]:[rank35]: Traceback (most recent call last): [default3]:[rank35]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/run_train.py", line 237, in [default2]:[rank34]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 429, in train [default2]:[rank34]: outputs, loss_avg = self.training_step(dataloader=self.current_dataloader) [default2]:[rank34]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 462, in training_step [default2]:[rank34]: outputs = self.pipeline_engine.train_batch_iter( [default3]:[rank35]: trainer.train(dataloader) [default3]:[rank35]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 429, in train [default3]:[rank35]: outputs, loss_avg = self.training_step(dataloader=self.current_dataloader) [default3]:[rank35]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 462, in training_step [default3]:[rank35]: outputs = self.pipeline_engine.train_batch_iter( [default3]:[rank35]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 278, in train_batch_iter [default3]:[rank35]: output = self.forward(context=context, state=state, micro_batch=micro_batch, model=model) [default3]:[rank35]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 44, in forward [default3]:[rank35]: output = model(**micro_batch) [default2]:[rank34]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 278, in train_batch_iter [default2]:[rank34]: output = self.forward(context=context, state=state, micro_batch=micro_batch, model=model) [default2]:[rank34]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 44, in forward [default2]:[rank34]: output = model(**micro_batch) [default3]:[rank35]: 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]:[rank34]: 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]:[rank34]: return self._call_impl(*args, **kwargs) [default2]:[rank34]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default4]:[rank36]: Traceback (most recent call last): [default4]:[rank36]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/run_train.py", line 237, in [default4]:[rank36]: trainer.train(dataloader) [default3]:[rank35]: return self._call_impl(*args, **kwargs) [default3]:[rank35]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default6]:[rank38]: Traceback (most recent call last): [default6]:[rank38]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/run_train.py", line 237, in [default3]:[rank35]: return forward_call(*args, **kwargs) [default6]:[rank38]: trainer.train(dataloader) [default6]:[rank38]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 429, in train [default2]:[rank34]: return forward_call(*args, **kwargs) [default3]:[rank35]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 891, in forward [default3]:[rank35]: sharded_logits = self.model( [default4]:[rank36]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 429, in train [default4]:[rank36]: outputs, loss_avg = self.training_step(dataloader=self.current_dataloader) [default6]:[rank38]: outputs, loss_avg = self.training_step(dataloader=self.current_dataloader) [default2]:[rank34]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 891, in forward [default5]:[rank37]: Traceback (most recent call last): [default5]:[rank37]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/run_train.py", line 237, in [default6]:[rank38]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 462, in training_step [default6]:[rank38]: outputs = self.pipeline_engine.train_batch_iter( [default6]:[rank38]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 278, in train_batch_iter [default6]:[rank38]: output = self.forward(context=context, state=state, micro_batch=micro_batch, model=model) [default6]:[rank38]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 44, in forward [default6]:[rank38]: output = model(**micro_batch) [default4]:[rank36]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 462, in training_step [default4]:[rank36]: outputs = self.pipeline_engine.train_batch_iter( [default3]:[rank35]: 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]:[rank35]: return self._call_impl(*args, **kwargs) [default5]:[rank37]: trainer.train(dataloader) [default6]:[rank38]: 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]:[rank32]: Traceback (most recent call last): [default0]:[rank32]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/run_train.py", line 237, in [default5]:[rank37]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 429, in train [default6]:[rank38]: return self._call_impl(*args, **kwargs) [default0]:[rank32]: trainer.train(dataloader) [default5]:[rank37]: outputs, loss_avg = self.training_step(dataloader=self.current_dataloader) [default2]:[rank34]: sharded_logits = self.model( [default3]:[rank35]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default4]:[rank36]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 278, in train_batch_iter [default5]:[rank37]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 462, in training_step [default2]:[rank34]: 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]:[rank34]: return self._call_impl(*args, **kwargs) [default2]:[rank34]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default5]:[rank37]: outputs = self.pipeline_engine.train_batch_iter( [default5]:[rank37]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 278, in train_batch_iter [default3]:[rank35]: return forward_call(*args, **kwargs) [default6]:[rank38]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default2]:[rank34]: return forward_call(*args, **kwargs) [default4]:[rank36]: output = self.forward(context=context, state=state, micro_batch=micro_batch, model=model) [default5]:[rank37]: output = self.forward(context=context, state=state, micro_batch=micro_batch, model=model) [default6]:[rank38]: return forward_call(*args, **kwargs) [default4]:[rank36]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 44, in forward [default3]:[rank35]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 764, in forward [default5]:[rank37]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 44, in forward [default0]:[rank32]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 429, in train [default6]:[rank38]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 891, in forward [default6]:[rank38]: sharded_logits = self.model( [default5]:[rank37]: output = model(**micro_batch) [default0]:[rank32]: outputs, loss_avg = self.training_step(dataloader=self.current_dataloader) [default2]:[rank34]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 764, in forward [default2]:[rank34]: return self.forward_with_hidden_states(input_ids=input_ids, input_mask=input_mask)[0] [default5]:[rank37]: 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]:[rank38]: 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]:[rank38]: return self._call_impl(*args, **kwargs) [default2]:[rank34]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 780, in forward_with_hidden_states [default5]:[rank37]: return self._call_impl(*args, **kwargs) [default4]:[rank36]: output = model(**micro_batch) [default4]:[rank36]: 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]:[rank34]: hidden_encoder_states = encoder_block(**hidden_encoder_states) [default5]:[rank37]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default3]:[rank35]: return self.forward_with_hidden_states(input_ids=input_ids, input_mask=input_mask)[0] [default3]:[rank35]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 780, in forward_with_hidden_states [default3]:[rank35]: hidden_encoder_states = encoder_block(**hidden_encoder_states) [default4]:[rank36]: return self._call_impl(*args, **kwargs) [default2]:[rank34]: 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]:[rank37]: return forward_call(*args, **kwargs) [default3]:[rank35]: 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]:[rank32]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 462, in training_step [default0]:[rank32]: outputs = self.pipeline_engine.train_batch_iter( [default5]:[rank37]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 891, in forward [default5]:[rank37]: sharded_logits = self.model( [default2]:[rank34]: return self._call_impl(*args, **kwargs) [default2]:[rank34]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default3]:[rank35]: return self._call_impl(*args, **kwargs) [default5]:[rank37]: 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]:[rank32]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 278, in train_batch_iter [default3]:[rank35]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default4]:[rank36]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default4]:[rank36]: return forward_call(*args, **kwargs) [default4]:[rank36]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 891, in forward [default6]:[rank38]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default2]:[rank34]: return forward_call(*args, **kwargs) [default3]:[rank35]: return forward_call(*args, **kwargs) [default3]:[rank35]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/block.py", line 151, in forward [default5]:[rank37]: return self._call_impl(*args, **kwargs) [default4]:[rank36]: sharded_logits = self.model( [default0]:[rank32]: output = self.forward(context=context, state=state, micro_batch=micro_batch, model=model) [default6]:[rank38]: return forward_call(*args, **kwargs) [default2]:[rank34]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/block.py", line 151, in forward [default4]:[rank36]: 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]:[rank37]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default6]:[rank38]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 764, in forward [default0]:[rank32]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 44, in forward [default4]:[rank36]: return self._call_impl(*args, **kwargs) [default5]:[rank37]: return forward_call(*args, **kwargs) [default6]:[rank38]: return self.forward_with_hidden_states(input_ids=input_ids, input_mask=input_mask)[0] [default6]:[rank38]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 780, in forward_with_hidden_states [default4]:[rank36]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default4]:[rank36]: return forward_call(*args, **kwargs) [default0]:[rank32]: output = model(**micro_batch) [default3]:[rank35]: output = self.pp_block(**new_kwargs) [default2]:[rank34]: output = self.pp_block(**new_kwargs) [default0]:[rank32]: 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]:[rank36]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 764, in forward [default2]:[rank34]: 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]:[rank38]: hidden_encoder_states = encoder_block(**hidden_encoder_states) [default3]:[rank35]: 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]:[rank36]: return self.forward_with_hidden_states(input_ids=input_ids, input_mask=input_mask)[0] [default0]:[rank32]: return self._call_impl(*args, **kwargs) [default5]:[rank37]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 764, in forward [default2]:[rank34]: return self._call_impl(*args, **kwargs) [default6]:[rank38]: 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]:[rank38]: return self._call_impl(*args, **kwargs) [default5]:[rank37]: return self.forward_with_hidden_states(input_ids=input_ids, input_mask=input_mask)[0] [default0]:[rank32]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default0]:[rank32]: return forward_call(*args, **kwargs) [default4]:[rank36]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 780, in forward_with_hidden_states [default6]:[rank38]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default6]:[rank38]: return forward_call(*args, **kwargs) [default5]:[rank37]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 780, in forward_with_hidden_states [default4]:[rank36]: hidden_encoder_states = encoder_block(**hidden_encoder_states) [default0]:[rank32]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 891, in forward [default6]:[rank38]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/block.py", line 151, in forward [default3]:[rank35]: return self._call_impl(*args, **kwargs) [default0]:[rank32]: sharded_logits = self.model( [default5]:[rank37]: hidden_encoder_states = encoder_block(**hidden_encoder_states) [default6]:[rank38]: output = self.pp_block(**new_kwargs) [default3]:[rank35]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default3]:[rank35]: return forward_call(*args, **kwargs) [default3]:[rank35]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 631, in forward [default3]:[rank35]: output = self.attn(hidden_states=hidden_states, sequence_mask=sequence_mask) [default5]:[rank37]: 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]:[rank32]: 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]:[rank38]: 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]:[rank35]: 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]:[rank37]: return self._call_impl(*args, **kwargs) [default0]:[rank32]: return self._call_impl(*args, **kwargs) [default6]:[rank38]: return self._call_impl(*args, **kwargs) [default6]:[rank38]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default6]:[rank38]: return forward_call(*args, **kwargs) [default5]:[rank37]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default5]:[rank37]: return forward_call(*args, **kwargs) [default1]:[rank33]: Traceback (most recent call last): [default0]:[rank32]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default0]:[rank32]: return forward_call(*args, **kwargs) [default0]:[rank32]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 764, in forward [default0]:[rank32]: return self.forward_with_hidden_states(input_ids=input_ids, input_mask=input_mask)[0] [default6]:[rank38]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 631, in forward [default6]:[rank38]: output = self.attn(hidden_states=hidden_states, sequence_mask=sequence_mask) [default5]:[rank37]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/block.py", line 151, in forward [default0]:[rank32]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 780, in forward_with_hidden_states [default4]:[rank36]: 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]:[rank33]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/run_train.py", line 237, in [default3]:[rank35]: return self._call_impl(*args, **kwargs) [default3]:[rank35]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default3]:[rank35]: return forward_call(*args, **kwargs) [default6]:[rank38]: 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]:[rank33]: trainer.train(dataloader) [default2]:[rank34]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default5]:[rank37]: output = self.pp_block(**new_kwargs) [default7]:[rank39]: Traceback (most recent call last): [default7]:[rank39]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/run_train.py", line 237, in [default0]:[rank32]: hidden_encoder_states = encoder_block(**hidden_encoder_states) [default2]:[rank34]: return forward_call(*args, **kwargs) [default2]:[rank34]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 631, in forward [default7]:[rank39]: trainer.train(dataloader) [default0]:[rank32]: 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]:[rank37]: 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]:[rank33]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 429, in train [default4]:[rank36]: return self._call_impl(*args, **kwargs) [default4]:[rank36]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default6]:[rank38]: return self._call_impl(*args, **kwargs) [default6]:[rank38]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default1]:[rank33]: outputs, loss_avg = self.training_step(dataloader=self.current_dataloader) [default1]:[rank41]: Traceback (most recent call last): [default5]:[rank37]: return self._call_impl(*args, **kwargs) [default5]:[rank37]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default6]:[rank38]: return forward_call(*args, **kwargs) [default6]:[rank38]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 565, in forward [default1]:[rank41]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/run_train.py", line 237, in [default0]:[rank40]: Traceback (most recent call last): [default0]:[rank40]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/run_train.py", line 237, in [default1]:[rank41]: trainer.train(dataloader) [default1]:[rank33]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 462, in training_step [default1]:[rank41]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 429, in train [default1]:[rank41]: outputs, loss_avg = self.training_step(dataloader=self.current_dataloader) [default1]:[rank41]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 462, in training_step [default0]:[rank40]: trainer.train(dataloader) [default3]:[rank35]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 563, in forward [default3]:[rank35]: key_value_states = torch.cat([key_states.unsqueeze(0), value_states.unsqueeze(0)], dim=0) [default0]:[rank40]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 429, in train [default0]:[rank40]: outputs, loss_avg = self.training_step(dataloader=self.current_dataloader) [default0]:[rank32]: return self._call_impl(*args, **kwargs) [default0]:[rank32]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default0]:[rank40]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 462, in training_step [default7]:[rank39]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 429, in train [default2]:[rank34]: output = self.attn(hidden_states=hidden_states, sequence_mask=sequence_mask) [default2]:[rank34]: 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]:[rank41]: outputs = self.pipeline_engine.train_batch_iter( [default2]:[rank34]: return self._call_impl(*args, **kwargs) [default5]:[rank37]: return forward_call(*args, **kwargs) [default3]:[rank35]: torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 128.00 MiB. GPU  has a total capacity of 79.33 GiB of which 37.94 MiB is free. Including non-PyTorch memory, this process has 79.28 GiB memory in use. Of the allocated memory 69.45 GiB is allocated by PyTorch, and 64.03 MiB 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) [default1]:[rank41]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 278, in train_batch_iter [default6]:[rank38]: key_value_states = key_value_states.permute(1, 2, 0, 3, 4).contiguous() [default1]:[rank41]: output = self.forward(context=context, state=state, micro_batch=micro_batch, model=model) [default1]:[rank33]: outputs = self.pipeline_engine.train_batch_iter( [default0]:[rank40]: outputs = self.pipeline_engine.train_batch_iter( [default0]:[rank40]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 278, in train_batch_iter [default1]:[rank33]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 278, in train_batch_iter [default0]:[rank40]: output = self.forward(context=context, state=state, micro_batch=micro_batch, model=model) [default1]:[rank41]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 44, in forward [default7]:[rank39]: outputs, loss_avg = self.training_step(dataloader=self.current_dataloader) [default1]:[rank41]: output = model(**micro_batch) [default1]:[rank41]: 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]:[rank34]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default0]:[rank32]: return forward_call(*args, **kwargs) [default0]:[rank32]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/block.py", line 151, in forward [default1]:[rank41]: return self._call_impl(*args, **kwargs) [default0]:[rank32]: output = self.pp_block(**new_kwargs) [default0]:[rank40]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 44, in forward [default6]:[rank38]: torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 128.00 MiB. GPU  has a total capacity of 79.33 GiB of which 77.94 MiB is free. Including non-PyTorch memory, this process has 79.24 GiB memory in use. Of the allocated memory 69.57 GiB is allocated by PyTorch, and 64.03 MiB 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) [default1]:[rank41]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default4]:[rank36]: return forward_call(*args, **kwargs) [default0]:[rank40]: output = model(**micro_batch) [default0]:[rank40]: 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]:[rank36]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/block.py", line 151, in forward [default2]:[rank34]: return forward_call(*args, **kwargs) [default1]:[rank41]: return forward_call(*args, **kwargs) [default5]:[rank37]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 631, in forward [default5]:[rank37]: output = self.attn(hidden_states=hidden_states, sequence_mask=sequence_mask) [default0]:[rank40]: return self._call_impl(*args, **kwargs) [default1]:[rank33]: output = self.forward(context=context, state=state, micro_batch=micro_batch, model=model) [default0]:[rank40]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default4]:[rank36]: output = self.pp_block(**new_kwargs) [default2]:[rank34]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 565, in forward [default0]:[rank40]: return forward_call(*args, **kwargs) [default7]:[rank39]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 462, in training_step [default1]:[rank33]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 44, in forward [default5]:[rank37]: 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]:[rank37]: return self._call_impl(*args, **kwargs) [default1]:[rank41]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 891, in forward [default0]:[rank32]: 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]:[rank32]: return self._call_impl(*args, **kwargs) [default0]:[rank40]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 891, in forward [default5]:[rank37]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default1]:[rank41]: sharded_logits = self.model( [default0]:[rank32]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default0]:[rank32]: return forward_call(*args, **kwargs) [default0]:[rank40]: sharded_logits = self.model( [default7]:[rank39]: outputs = self.pipeline_engine.train_batch_iter( [default1]:[rank41]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl [default7]:[rank39]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 278, in train_batch_iter [default0]:[rank40]: 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]:[rank33]: output = model(**micro_batch) [default1]:[rank41]: return self._call_impl(*args, **kwargs) [default5]:[rank37]: return forward_call(*args, **kwargs) [default5]:[rank37]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 563, in forward [default0]:[rank40]: return self._call_impl(*args, **kwargs) [default2]:[rank34]: key_value_states = key_value_states.permute(1, 2, 0, 3, 4).contiguous() [default0]:[rank40]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default1]:[rank41]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default1]:[rank33]: 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]:[rank40]: return forward_call(*args, **kwargs) [default0]:[rank32]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 631, in forward [default0]:[rank40]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 764, in forward [default0]:[rank40]: return self.forward_with_hidden_states(input_ids=input_ids, input_mask=input_mask)[0] [default5]:[rank37]: key_value_states = torch.cat([key_states.unsqueeze(0), value_states.unsqueeze(0)], dim=0) [default0]:[rank40]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 780, in forward_with_hidden_states [default1]:[rank41]: return forward_call(*args, **kwargs) [default7]:[rank39]: output = self.forward(context=context, state=state, micro_batch=micro_batch, model=model) [default7]:[rank39]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 44, in forward [default1]:[rank41]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 764, in forward [default1]:[rank41]: return self.forward_with_hidden_states(input_ids=input_ids, input_mask=input_mask)[0] [default5]:[rank37]: torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 128.00 MiB. GPU  has a total capacity of 79.33 GiB of which 37.94 MiB is free. Including non-PyTorch memory, this process has 79.28 GiB memory in use. Of the allocated memory 69.45 GiB is allocated by PyTorch, and 64.03 MiB 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) [default1]:[rank41]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 780, in forward_with_hidden_states [default1]:[rank41]: hidden_encoder_states = encoder_block(**hidden_encoder_states) [default4]:[rank36]: 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]:[rank36]: return self._call_impl(*args, **kwargs) [default1]:[rank33]: return self._call_impl(*args, **kwargs) [default0]:[rank32]: output = self.attn(hidden_states=hidden_states, sequence_mask=sequence_mask) [default1]:[rank41]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl [default7]:[rank39]: output = model(**micro_batch) [default1]:[rank33]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default1]:[rank41]: return self._call_impl(*args, **kwargs) [default0]:[rank32]: 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]:[rank41]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default1]:[rank41]: return forward_call(*args, **kwargs) [default7]:[rank39]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl [default7]:[rank39]: return self._call_impl(*args, **kwargs) [default0]:[rank40]: hidden_encoder_states = encoder_block(**hidden_encoder_states) [default0]:[rank32]: return self._call_impl(*args, **kwargs) [default1]:[rank41]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/block.py", line 151, in forward [default1]:[rank41]: output = self.pp_block(**new_kwargs) [default1]:[rank33]: return forward_call(*args, **kwargs) [default0]:[rank40]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl [default7]:[rank39]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default0]:[rank40]: return self._call_impl(*args, **kwargs) [default1]:[rank41]: 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]:[rank32]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default0]:[rank40]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default0]:[rank32]: return forward_call(*args, **kwargs) [default1]:[rank33]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 891, in forward [default0]:[rank40]: return forward_call(*args, **kwargs) [default7]:[rank39]: return forward_call(*args, **kwargs) [default7]:[rank39]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 891, in forward [default0]:[rank40]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/block.py", line 151, in forward [default0]:[rank40]: output = self.pp_block(**new_kwargs) [default1]:[rank33]: sharded_logits = self.model( [default4]:[rank36]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default4]:[rank36]: return forward_call(*args, **kwargs) [default0]:[rank40]: 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]:[rank40]: return self._call_impl(*args, **kwargs) [default0]:[rank32]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 565, in forward [default0]:[rank40]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default2]:[rank34]: torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 128.00 MiB. GPU  has a total capacity of 79.33 GiB of which 13.94 MiB is free. Including non-PyTorch memory, this process has 79.30 GiB memory in use. Of the allocated memory 69.57 GiB is allocated by PyTorch, and 128.03 MiB 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) [default1]:[rank41]: return self._call_impl(*args, **kwargs) [default7]:[rank39]: sharded_logits = self.model( [default7]:[rank39]: 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]:[rank41]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default1]:[rank33]: 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]:[rank32]: key_value_states = key_value_states.permute(1, 2, 0, 3, 4).contiguous() [default4]:[rank36]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 631, in forward [default4]:[rank36]: output = self.attn(hidden_states=hidden_states, sequence_mask=sequence_mask) [default1]:[rank41]: return forward_call(*args, **kwargs) [default1]:[rank41]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 631, in forward [default1]:[rank41]: output = self.attn(hidden_states=hidden_states, sequence_mask=sequence_mask) [default7]:[rank39]: return self._call_impl(*args, **kwargs) [default7]:[rank39]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default1]:[rank41]: 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]:[rank41]: return self._call_impl(*args, **kwargs) [default0]:[rank32]: torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 128.00 MiB. GPU [default0]:[rank40]: return forward_call(*args, **kwargs) [default4]:[rank36]: 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]:[rank41]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default7]:[rank39]: return forward_call(*args, **kwargs) [default7]:[rank39]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 764, in forward [default0]:[rank40]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 631, in forward [default1]:[rank33]: return self._call_impl(*args, **kwargs) [default4]:[rank36]: return self._call_impl(*args, **kwargs) [default4]:[rank36]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default1]:[rank41]: return forward_call(*args, **kwargs) [default7]:[rank39]: return self.forward_with_hidden_states(input_ids=input_ids, input_mask=input_mask)[0] [default0]:[rank40]: output = self.attn(hidden_states=hidden_states, sequence_mask=sequence_mask) [default1]:[rank33]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default0]:[rank40]: 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]:[rank41]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 565, in forward [default4]:[rank36]: return forward_call(*args, **kwargs) [default1]:[rank41]: key_value_states = key_value_states.permute(1, 2, 0, 3, 4).contiguous() [default4]:[rank36]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 565, in forward [default7]:[rank39]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 780, in forward_with_hidden_states [default7]:[rank39]: hidden_encoder_states = encoder_block(**hidden_encoder_states) [default1]:[rank41]: torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 128.00 MiB. GPU  has a total capacity of 79.33 GiB of which 117.94 MiB is free. Including non-PyTorch memory, this process has 79.20 GiB memory in use. Of the allocated memory 69.57 GiB is allocated by PyTorch, and 64.03 MiB 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) [default4]:[rank36]: key_value_states = key_value_states.permute(1, 2, 0, 3, 4).contiguous() [default1]:[rank33]: return forward_call(*args, **kwargs) [default1]:[rank33]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 764, in forward [default0]:[rank40]: return self._call_impl(*args, **kwargs) [default7]:[rank39]: 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]:[rank40]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default4]:[rank36]: torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 128.00 MiB. GPU  has a total capacity of 79.33 GiB of which 13.94 MiB is free. Including non-PyTorch memory, this process has 79.30 GiB memory in use. Of the allocated memory 69.57 GiB is allocated by PyTorch, and 128.03 MiB 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) [default7]:[rank39]: return self._call_impl(*args, **kwargs) [default0]:[rank40]: return forward_call(*args, **kwargs) [default7]:[rank39]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default0]:[rank40]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 565, in forward [default0]:[rank40]: key_value_states = key_value_states.permute(1, 2, 0, 3, 4).contiguous() [default0]:[rank40]: torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 128.00 MiB. GPU [default7]:[rank39]: return forward_call(*args, **kwargs) [default7]:[rank39]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/block.py", line 151, in forward [default7]:[rank39]: output = self.pp_block(**new_kwargs) [default7]:[rank39]: 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]:[rank33]: return self.forward_with_hidden_states(input_ids=input_ids, input_mask=input_mask)[0] [default1]:[rank33]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 780, in forward_with_hidden_states [default1]:[rank33]: hidden_encoder_states = encoder_block(**hidden_encoder_states) [default1]:[rank33]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl [default7]:[rank39]: return self._call_impl(*args, **kwargs) [default4]:[rank44]: Traceback (most recent call last): [default4]:[rank44]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/run_train.py", line 237, in [default4]:[rank44]: trainer.train(dataloader) [default4]:[rank44]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 429, in train [default4]:[rank44]: outputs, loss_avg = self.training_step(dataloader=self.current_dataloader) [default4]:[rank44]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 462, in training_step [default4]:[rank44]: outputs = self.pipeline_engine.train_batch_iter( [default4]:[rank44]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 278, in train_batch_iter [default4]:[rank44]: output = self.forward(context=context, state=state, micro_batch=micro_batch, model=model) [default4]:[rank44]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanot[default7]:[rank39]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl ron/src/nanotron/parallel/pipeline_parallel/engine.py", line 44, in forward [default4]:[rank44]: output = model(**micro_batch) [default4]:[rank44]: 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]:[rank44]: return self._call_impl(*args, **kwargs) [default4]:[rank44]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default4]:[rank44]: return forward_call(*args, **kwargs) [default4]:[rank44]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 891, in forward [default4]:[rank44]: sharded_logits = self.model( [default4]:[rank44]: 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]:[rank44]: return self._call_impl(*args, **kwargs) [def[default1]:[rank33]: return self._call_impl(*args, **kwargs) ault4]:[rank44]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default4]:[rank44]: return forward_call(*args, **kwargs) [default1]:[rank33]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default4]:[rank44]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 764, in forward [default4]:[rank44]: return self.forward_with_hidden_states(input_ids=input_ids, input_mask=input_mask)[0] [default4]:[rank44]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 780, in forward_with_hidden_states [default4]:[rank44]: hidden_encoder_states = encoder_block(**hidden_encoder_states) [default4]:[rank44]: 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]:[rank44]: return self._call_impl(*args, **kwargs) [default4]:[rank44]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default4]:[rank44]: return forward_call(*args, **kwargs) [default4]:[rank44]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster[default1]:[rank33]: return forward_call(*args, **kwargs) [default7]:[rank39]: return forward_call(*args, **kwargs) [default1]:[rank33]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/block.py", line 151, in forward [default1]:[rank33]: output = self.pp_block(**new_kwargs) [default1]:[rank33]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl /nanotron/src/nanotron/parallel/pipeline_parallel/block.py", line 151, in forward [default4]:[rank44]: output = self.pp_block(**new_kwargs) [default4]:[rank44]: 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]:[rank44]: return self._call_impl(*args, **kwargs) [default4]:[rank44]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default4]:[rank44]: return forward_call(*args, **kwargs) [default4]:[rank44]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 631, in forward [default4]:[rank44]: output = self.attn(hidden_states=hidden_states, sequence_mask=sequence_mask) [default4]:[rank44]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl [default[default1]:[rank33]: return self._call_impl(*args, **kwargs) 4]:[rank44]: return self._call_impl(*args, **kwargs) [default4]:[rank44]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default4]:[rank44]: return forward_call(*args, **kwargs) [default4]:[rank44]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 565, in forward [default4]:[rank44]: key_value_states = key_value_states.permute(1, 2, 0, 3, 4).contiguous() [default4]:[rank44]: torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 128.00 MiB. GPU  has a total capacity of 79.33 GiB of which 29.94 MiB is free. Including non-PyTorch memory, this process has 79.29 GiB memory in use. Of the allocated memory 69.57 GiB is allocated by PyTorch, and 64.03 MiB 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 Memo[default7]:[rank39]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 631, in forward ry Management (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables) [default7]:[rank39]: output = self.attn(hidden_states=hidden_states, sequence_mask=sequence_mask) [default7]:[rank39]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl [default7]:[rank39]: return self._call_impl(*args, **kwargs) [default7]:[rank39]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default1]:[rank33]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default7]:[rank39]: return forward_call(*args, **kwargs) [default7]:[rank39]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 563, in forward [default1]:[rank33]: return forward_call(*args, **kwargs) [default7]:[rank39]: key_value_states = torch.cat([key_states.unsqueeze(0), value_states.unsqueeze(0)], dim=0) [default7]:[rank39]: torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 128.00 MiB. GPU  has a total capacity of 79.33 GiB of which 37.94 MiB is free. Including non-PyTorch memory, this process has 79.28 GiB memory in use. Of the allocated memory 69.45 GiB is allocated by PyTorch, and 64.03 MiB 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) [default1]:[rank33]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 631, in forward [default1]:[rank33]: output = self.attn(hidden_states=hidden_states, sequence_mask=sequence_mask) [default1]:[rank33]: 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]:[rank33]: return self._call_impl(*args, **kwargs) [default1]:[rank33]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default1]:[rank33]: return forward_call(*args, **kwargs) [default1]:[rank33]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 563, in forward [default1]:[rank33]: key_value_states = torch.cat([key_states.unsqueeze(0), value_states.unsqueeze(0)], dim=0) [default1]:[rank33]: torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 128.00 MiB. GPU  has a total capacity of 79.33 GiB of which 37.94 MiB is free. Including non-PyTorch memory, this process has 79.28 GiB memory in use. Of the allocated memory 69.45 GiB is allocated by PyTorch, and 64.03 MiB 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]:[rank43]: Traceback (most recent call last): [default3]:[rank43]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/run_train.py", line 237, in [default3]:[rank43]: trainer.train(dataloader) [default3]:[rank43]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 429, in train [default3]:[rank43]: outputs, loss_avg = self.training_step(dataloader=self.current_dataloader) [default3]:[rank43]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 462, in training_step [default3]:[rank43]: outputs = self.pipeline_engine.train_batch_iter( [default3]:[rank43]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 278, in train_batch_iter [default3]:[rank43]: output = self.forward(context=context, state=state, micro_batch=micro_batch, model=model) [default3]:[rank43]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 44, in forward [default3]:[rank43]: output = model(**micro_batch) [default3]:[rank43]: 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]:[rank43]: return self._call_impl(*args, **kwargs) [default3]:[rank43]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default3]:[rank43]: return forward_call(*args, **kwargs) [default3]:[rank43]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 891, in forward [default3]:[rank43]: sharded_logits = self.model( [default3]:[rank43]: 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]:[rank43]: return self._call_impl(*args, **kwargs) [default3]:[rank43]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default3]:[rank43]: return forward_call(*args, **kwargs) [default3]:[rank43]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 764, in forward [default3]:[rank43]: return self.forward_with_hidden_states(input_ids=input_ids, input_mask=input_mask)[0] [default3]:[rank43]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 780, in forward_with_hidden_states [default3]:[rank43]: hidden_encoder_states = encoder_block(**hidden_encoder_states) [default3]:[rank43]: 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]:[rank43]: return self._call_impl(*args, **kwargs) [default3]:[rank43]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default3]:[rank43]: return forward_call(*args, **kwargs) [default3]:[rank43]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/block.py", line 151, in forward [default3]:[rank43]: output = self.pp_block(**new_kwargs) [default3]:[rank43]: 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]:[rank43]: return self._call_impl(*args, **kwargs) [default3]:[rank43]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default3]:[rank43]: return forward_call(*args, **kwargs) [default3]:[rank43]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 631, in forward [default3]:[rank43]: output = self.attn(hidden_states=hidden_states, sequence_mask=sequence_mask) [default3]:[rank43]: 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]:[rank43]: return self._call_impl(*args, **kwargs) [default3]:[rank43]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default3]:[rank43]: return forward_call(*args, **kwargs) [default3]:[rank43]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 565, in forward [default3]:[rank43]: key_value_states = key_value_states.permute(1, 2, 0, 3, 4).contiguous() [default3]:[rank43]: torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 128.00 MiB. GPU  has a total capacity of 79.33 GiB of which 117.94 MiB is free. Including non-PyTorch memory, this process has 79.20 GiB memory in use. Of the allocated memory 69.57 GiB is allocated by PyTorch, and 64.03 MiB 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) [default2]:[rank42]: Traceback (most recent call last): [default2]:[rank42]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/run_train.py", line 237, in [default2]:[rank42]: trainer.train(dataloader) [default2]:[rank42]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 429, in train [default2]:[rank42]: outputs, loss_avg = self.training_step(dataloader=self.current_dataloader) [default2]:[rank42]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 462, in training_step [default2]:[rank42]: outputs = self.pipeline_engine.train_batch_iter( [default2]:[rank42]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 278, in train_batch_iter [default2]:[rank42]: output = self.forward(context=context, state=state, micro_batch=micro_batch, model=model) [default2]:[rank42]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 44, in forward [default2]:[rank42]: output = model(**micro_batch) [default2]:[rank42]: 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]:[rank42]: return self._call_impl(*args, **kwargs) [default2]:[rank42]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default2]:[rank42]: return forward_call(*args, **kwargs) [default2]:[rank42]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 891, in forward [default2]:[rank42]: sharded_logits = self.model( [default2]:[rank42]: 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]:[rank42]: return self._call_impl(*args, **kwargs) [default2]:[rank42]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default2]:[rank42]: return forward_call(*args, **kwargs) [default2]:[rank42]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 764, in forward [default2]:[rank42]: return self.forward_with_hidden_states(input_ids=input_ids, input_mask=input_mask)[0] [default2]:[rank42]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 780, in forward_with_hidden_states [default2]:[rank42]: hidden_encoder_states = encoder_block(**hidden_encoder_states) [default2]:[rank42]: 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]:[rank42]: return self._call_impl(*args, **kwargs) [default2]:[rank42]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default2]:[rank42]: return forward_call(*args, **kwargs) [default2]:[rank42]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/block.py", line 151, in forward [default2]:[rank42]: output = self.pp_block(**new_kwargs) [default2]:[rank42]: 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]:[rank42]: return self._call_impl(*args, **kwargs) [default2]:[rank42]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default2]:[rank42]: return forward_call(*args, **kwargs) [default2]:[rank42]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 631, in forward [default2]:[rank42]: output = self.attn(hidden_states=hidden_states, sequence_mask=sequence_mask) [default2]:[rank42]: 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]:[rank42]: return self._call_impl(*args, **kwargs) [default2]:[rank42]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default2]:[rank42]: return forward_call(*args, **kwargs) [default2]:[rank42]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 565, in forward [default2]:[rank42]: key_value_states = key_value_states.permute(1, 2, 0, 3, 4).contiguous() [default2]:[rank42]: torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 128.00 MiB. GPU  has a total capacity of 79.33 GiB of which 29.94 MiB is free. Including non-PyTorch memory, this process has 79.29 GiB memory in use. Of the allocated memory 69.57 GiB is allocated by PyTorch, and 64.03 MiB 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) [default7]:[rank47]: Traceback (most recent call last): [default7]:[rank47]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/run_train.py", line 237, in [default7]:[rank47]: trainer.train(dataloader) [default6]:[rank46]: Traceback (most recent call last): [default5]:[rank45]: Traceback (most recent call last): [default7]:[rank47]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 429, in train [default7]:[rank47]: outputs, loss_avg = self.training_step(dataloader=self.current_dataloader) [default5]:[rank45]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/run_train.py", line 237, in [default7]:[rank47]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 462, in training_step [default5]:[rank45]: trainer.train(dataloader) [default5]:[rank45]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 429, in train [default7]:[rank47]: outputs = self.pipeline_engine.train_batch_iter( [default7]:[rank47]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 278, in train_batch_iter [default5]:[rank45]: outputs, loss_avg = self.training_step(dataloader=self.current_dataloader) [default5]:[rank45]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 462, in training_step [default7]:[rank47]: output = self.forward(context=context, state=state, micro_batch=micro_batch, model=model) [default6]:[rank46]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/run_train.py", line 237, in [default5]:[rank45]: outputs = self.pipeline_engine.train_batch_iter( [default5]:[rank45]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 278, in train_batch_iter [default6]:[rank46]: trainer.train(dataloader) [default6]:[rank46]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 429, in train [default7]:[rank47]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 44, in forward [default5]:[rank45]: output = self.forward(context=context, state=state, micro_batch=micro_batch, model=model) [default6]:[rank46]: outputs, loss_avg = self.training_step(dataloader=self.current_dataloader) [default5]:[rank45]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 44, in forward [default5]:[rank45]: output = model(**micro_batch) [default6]:[rank46]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 462, in training_step [default7]:[rank47]: output = model(**micro_batch) [default5]:[rank45]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl [default7]:[rank47]: 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]:[rank46]: outputs = self.pipeline_engine.train_batch_iter( [default5]:[rank45]: return self._call_impl(*args, **kwargs) [default6]:[rank46]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 278, in train_batch_iter [default5]:[rank45]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default7]:[rank47]: return self._call_impl(*args, **kwargs) [default6]:[rank46]: output = self.forward(context=context, state=state, micro_batch=micro_batch, model=model) [default5]:[rank45]: return forward_call(*args, **kwargs) [default6]:[rank46]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 44, in forward [default7]:[rank47]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default6]:[rank46]: output = model(**micro_batch) [default5]:[rank45]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 891, in forward [default6]:[rank46]: 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]:[rank45]: sharded_logits = self.model( [default7]:[rank47]: return forward_call(*args, **kwargs) [default5]:[rank45]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl [default7]:[rank47]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 891, in forward [default7]:[rank47]: sharded_logits = self.model( [default6]:[rank46]: return self._call_impl(*args, **kwargs) [default5]:[rank45]: return self._call_impl(*args, **kwargs) [default7]:[rank47]: 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]:[rank45]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default6]:[rank46]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default6]:[rank46]: return forward_call(*args, **kwargs) [default7]:[rank47]: return self._call_impl(*args, **kwargs) [default5]:[rank45]: return forward_call(*args, **kwargs) [default7]:[rank47]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default7]:[rank47]: return forward_call(*args, **kwargs) [default6]:[rank46]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 891, in forward [default6]:[rank46]: sharded_logits = self.model( [default7]:[rank47]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 764, in forward [default5]:[rank45]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 764, in forward [default7]:[rank47]: return self.forward_with_hidden_states(input_ids=input_ids, input_mask=input_mask)[0] [default6]:[rank46]: 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]:[rank45]: return self.forward_with_hidden_states(input_ids=input_ids, input_mask=input_mask)[0] [default6]:[rank46]: return self._call_impl(*args, **kwargs) [default7]:[rank47]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 780, in forward_with_hidden_states [default6]:[rank46]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default5]:[rank45]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 780, in forward_with_hidden_states [default7]:[rank47]: hidden_encoder_states = encoder_block(**hidden_encoder_states) [default6]:[rank46]: return forward_call(*args, **kwargs) [default7]:[rank47]: 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]:[rank45]: hidden_encoder_states = encoder_block(**hidden_encoder_states) [default7]:[rank47]: return self._call_impl(*args, **kwargs) [default6]:[rank46]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 764, in forward [default7]:[rank47]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default5]:[rank45]: 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]:[rank46]: return self.forward_with_hidden_states(input_ids=input_ids, input_mask=input_mask)[0] [default5]:[rank45]: return self._call_impl(*args, **kwargs) [default7]:[rank47]: return forward_call(*args, **kwargs) [default6]:[rank46]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 780, in forward_with_hidden_states [default6]:[rank46]: hidden_encoder_states = encoder_block(**hidden_encoder_states) [default7]:[rank47]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/block.py", line 151, in forward [default6]:[rank46]: 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]:[rank45]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default7]:[rank47]: output = self.pp_block(**new_kwargs) [default6]:[rank46]: return self._call_impl(*args, **kwargs) [default5]:[rank45]: return forward_call(*args, **kwargs) [default6]:[rank46]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default7]:[rank47]: 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]:[rank45]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/block.py", line 151, in forward [default7]:[rank47]: return self._call_impl(*args, **kwargs) [default6]:[rank46]: return forward_call(*args, **kwargs) [default7]:[rank47]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default5]:[rank45]: output = self.pp_block(**new_kwargs) [default5]:[rank45]: 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]:[rank46]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/block.py", line 151, in forward [default7]:[rank47]: return forward_call(*args, **kwargs) [default6]:[rank46]: output = self.pp_block(**new_kwargs) [default6]:[rank46]: 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]:[rank45]: return self._call_impl(*args, **kwargs) [default7]:[rank47]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 631, in forward [default6]:[rank46]: return self._call_impl(*args, **kwargs) [default6]:[rank46]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default5]:[rank45]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default7]:[rank47]: output = self.attn(hidden_states=hidden_states, sequence_mask=sequence_mask) [default5]:[rank45]: return forward_call(*args, **kwargs) [default6]:[rank46]: return forward_call(*args, **kwargs) [default7]:[rank47]: 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]:[rank46]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 631, in forward [default7]:[rank47]: return self._call_impl(*args, **kwargs) [default5]:[rank45]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 631, in forward [default6]:[rank46]: output = self.attn(hidden_states=hidden_states, sequence_mask=sequence_mask) [default5]:[rank45]: output = self.attn(hidden_states=hidden_states, sequence_mask=sequence_mask) [default6]:[rank46]: 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]:[rank46]: return self._call_impl(*args, **kwargs) [default5]:[rank45]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl [default7]:[rank47]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default6]:[rank46]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default5]:[rank45]: return self._call_impl(*args, **kwargs) [default7]:[rank47]: return forward_call(*args, **kwargs) [default5]:[rank45]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default6]:[rank46]: return forward_call(*args, **kwargs) [default5]:[rank45]: return forward_call(*args, **kwargs) [default7]:[rank47]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 565, in forward [default5]:[rank45]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 565, in forward [default7]:[rank47]: key_value_states = key_value_states.permute(1, 2, 0, 3, 4).contiguous() [default7]:[rank47]: torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 128.00 MiB. GPU  has a total capacity of 79.33 GiB of which 117.94 MiB is free. Including non-PyTorch memory, this process has 79.20 GiB memory in use. Of the allocated memory 69.57 GiB is allocated by PyTorch, and 64.03 MiB 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]:[rank46]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 563, in forward [default6]:[rank46]: key_value_states = torch.cat([key_states.unsqueeze(0), value_states.unsqueeze(0)], dim=0) [default5]:[rank45]: key_value_states = key_value_states.permute(1, 2, 0, 3, 4).contiguous() [default5]:[rank45]: torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 128.00 MiB. GPU  has a total capacity of 79.33 GiB of which 117.94 MiB is free. Including non-PyTorch memory, this process has 79.20 GiB memory in use. Of the allocated memory 69.57 GiB is allocated by PyTorch, and 64.03 MiB 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]:[rank46]: torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 128.00 MiB. GPU  has a total capacity of 79.33 GiB of which 93.94 MiB is free. Including non-PyTorch memory, this process has 79.23 GiB memory in use. Of the allocated memory 69.45 GiB is allocated by PyTorch, and 128.03 MiB 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]:[rank22]: Traceback (most recent call last): [default6]:[rank22]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/run_train.py", line 237, in [default6]:[rank22]: trainer.train(dataloader) [default6]:[rank22]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 429, in train [default6]:[rank22]: outputs, loss_avg = self.training_step(dataloader=self.current_dataloader) [default6]:[rank22]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 462, in training_step [default6]:[rank22]: outputs = self.pipeline_engine.train_batch_iter( [default6]:[rank22]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 278, in train_batch_iter [default6]:[rank22]: output = self.forward(context=context, state=state, micro_batch=micro_batch, model=model) [default6]:[rank22]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 44, in forward [default6]:[rank22]: output = model(**micro_batch) [default6]:[rank22]: 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]:[rank22]: return self._call_impl(*args, **kwargs) [default6]:[rank22]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default6]:[rank22]: return forward_call(*args, **kwargs) [default6]:[rank22]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 891, in forward [default6]:[rank22]: sharded_logits = self.model( [default6]:[rank22]: 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]:[rank22]: return self._call_impl(*args, **kwargs) [default6]:[rank22]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default6]:[rank22]: return forward_call(*args, **kwargs) [default6]:[rank22]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 764, in forward [default6]:[rank22]: return self.forward_with_hidden_states(input_ids=input_ids, input_mask=input_mask)[0] [default6]:[rank22]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 780, in forward_with_hidden_states [default6]:[rank22]: hidden_encoder_states = encoder_block(**hidden_encoder_states) [default6]:[rank22]: 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]:[rank22]: return self._call_impl(*args, **kwargs) [default6]:[rank22]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default6]:[rank22]: return forward_call(*args, **kwargs) [default6]:[rank22]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/block.py", line 151, in forward [default6]:[rank22]: output = self.pp_block(**new_kwargs) [default6]:[rank22]: 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]:[rank22]: return self._call_impl(*args, **kwargs) [default6]:[rank22]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default6]:[rank22]: return forward_call(*args, **kwargs) [default6]:[rank22]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 631, in forward [default6]:[rank22]: output = self.attn(hidden_states=hidden_states, sequence_mask=sequence_mask) [default6]:[rank22]: 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]:[rank22]: return self._call_impl(*args, **kwargs) [default6]:[rank22]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default6]:[rank22]: return forward_call(*args, **kwargs) [default6]:[rank22]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 565, in forward [default6]:[rank22]: key_value_states = key_value_states.permute(1, 2, 0, 3, 4).contiguous() [default6]:[rank22]: torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 128.00 MiB. GPU  has a total capacity of 79.33 GiB of which 117.94 MiB is free. Including non-PyTorch memory, this process has 79.20 GiB memory in use. Of the allocated memory 69.57 GiB is allocated by PyTorch, and 64.03 MiB 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) [default4]:[rank20]: Traceback (most recent call last): [default4]:[rank20]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/run_train.py", line 237, in [default4]:[rank20]: trainer.train(dataloader) [default4]:[rank20]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 429, in train [default4]:[rank20]: outputs, loss_avg = self.training_step(dataloader=self.current_dataloader) [default4]:[rank20]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 462, in training_step [default4]:[rank20]: outputs = self.pipeline_engine.train_batch_iter( [default4]:[rank20]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 278, in train_batch_iter [default4]:[rank20]: output = self.forward(context=context, state=state, micro_batch=micro_batch, model=model) [default4]:[rank20]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 44, in forward [default4]:[rank20]: output = model(**micro_batch) [default4]:[rank20]: 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]:[rank20]: return self._call_impl(*args, **kwargs) [default4]:[rank20]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default4]:[rank20]: return forward_call(*args, **kwargs) [default4]:[rank20]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 891, in forward [default5]:[rank21]: Traceback (most recent call last): [default4]:[rank20]: sharded_logits = self.model( [default4]:[rank20]: 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]:[rank20]: return self._call_impl(*args, **kwargs) [default5]:[rank21]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/run_train.py", line 237, in [default5]:[rank21]: trainer.train(dataloader) [default4]:[rank20]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default4]:[rank20]: return forward_call(*args, **kwargs) [default4]:[rank20]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 764, in forward [default4]:[rank20]: return self.forward_with_hidden_states(input_ids=input_ids, input_mask=input_mask)[0] [default4]:[rank20]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 780, in forward_with_hidden_states [default4]:[rank20]: hidden_encoder_states = encoder_block(**hidden_encoder_states) [default4]:[rank20]: 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]:[rank20]: return self._call_impl(*args, **kwargs) [default4]:[rank20]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default4]:[rank20]: return forward_call(*args, **kwargs) [default4]:[rank20]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/block.py", line 151, in forward [default4]:[rank20]: output = self.pp_block(**new_kwargs) [default5]:[rank21]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 429, in train [default5]:[rank21]: outputs, loss_avg = self.training_step(dataloader=self.current_dataloader) [default4]:[rank20]: 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]:[rank21]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 462, in training_step [default4]:[rank20]: return self._call_impl(*args, **kwargs) [default4]:[rank20]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default4]:[rank20]: return forward_call(*args, **kwargs) [default5]:[rank21]: outputs = self.pipeline_engine.train_batch_iter( [default4]:[rank20]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 631, in forward [default4]:[rank20]: output = self.attn(hidden_states=hidden_states, sequence_mask=sequence_mask) [default5]:[rank21]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 278, in train_batch_iter [default5]:[rank21]: output = self.forward(context=context, state=state, micro_batch=micro_batch, model=model) [default4]:[rank20]: 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]:[rank20]: return self._call_impl(*args, **kwargs) [default4]:[rank20]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default5]:[rank21]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 44, in forward [default5]:[rank21]: output = model(**micro_batch) [default4]:[rank20]: return forward_call(*args, **kwargs) [default4]:[rank20]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 565, in forward [default4]:[rank20]: key_value_states = key_value_states.permute(1, 2, 0, 3, 4).contiguous() [default5]:[rank21]: 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]:[rank20]: torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 128.00 MiB. GPU  has a total capacity of 79.33 GiB of which 117.94 MiB is free. Including non-PyTorch memory, this process has 79.20 GiB memory in use. Of the allocated memory 69.57 GiB is allocated by PyTorch, and 64.03 MiB 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]:[rank21]: return self._call_impl(*args, **kwargs) [default5]:[rank21]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default5]:[rank21]: return forward_call(*args, **kwargs) [default2]:[rank26]: Traceback (most recent call last): [default2]:[rank26]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/run_train.py", line 237, in [default2]:[rank26]: trainer.train(dataloader) [default2]:[rank26]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 429, in train [default2]:[rank26]: outputs, loss_avg = self.training_step(dataloader=self.current_dataloader) [default2]:[rank26]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 462, in training_step [default2]:[rank26]: outputs = self.pipeline_engine.train_batch_iter( [default2]:[rank26]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 278, in train_batch_iter [default2]:[rank26]: output = self.forward(context=context, state=state, micro_batch=micro_batch, model=model) [default2]:[rank26]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanot[default5]:[rank21]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 891, in forward [default5]:[rank21]: sharded_logits = self.model( [default5]:[rank21]: 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]:[rank21]: return self._call_impl(*args, **kwargs) [default5]:[rank21]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default5]:[rank21]: return forward_call(*args, **kwargs) [default5]:[rank21]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 764, in forward [default5]:[rank21]: return self.forward_with_hidden_states(input_ids=input_ids, input_mask=input_mask)[0] [default5]:[rank21]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 780, in forward_with_hidden_states [default5]:[rank21]: hidden_encoder_states = encoder_block(**hidden_encoder_states) [default5]:[rank21]: 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]:[rank21]: return self._call_impl(*args, **kwargs) [default5]:[rank21]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default5]:[rank21]: return forward_call(*args, **kwargs) [default5]:[rank21]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/block.py", line 151, in forward [default5]:[rank21]: output = self.pp_block(**new_kwargs) [default5]:[rank21]: 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]:[rank21]: return self._call_impl(*args, **kwargs) [default5]:[rank21]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl ron/src/nanotron/parallel/pipeline_parallel/engine.py", line 44, in forward [default2]:[rank26]: output = model(**micro_batch) [default2]:[rank26]: 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]:[rank26]: return self._call_impl(*args, **kwargs) [default2]:[rank26]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default2]:[rank26]: return forward_call(*args, **kwargs) [default2]:[rank26]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 891, in forward [default2]:[rank26]: sharded_logits = self.model( [default2]:[rank26]: 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]:[rank26]: return self._call_impl(*args, **kwargs) [def[default5]:[rank21]: return forward_call(*args, **kwargs) [default5]:[rank21]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 631, in forward ault2]:[rank26]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default5]:[rank21]: output = self.attn(hidden_states=hidden_states, sequence_mask=sequence_mask) [default5]:[rank21]: 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]:[rank21]: return self._call_impl(*args, **kwargs) [default2]:[rank26]: return forward_call(*args, **kwargs) [default2]:[rank26]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 764, in forward [default2]:[rank26]: return self.forward_with_hidden_states(input_ids=input_ids, input_mask=input_mask)[0] [default2]:[rank26]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 780, in forward_with_hidden_states [default2]:[rank26]: hidden_encoder_states = encoder_block(**hidden_encoder_states) [default2]:[rank26]: 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]:[rank26]: return self._call_impl(*args, **kwargs) [default2]:[rank26]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default2]:[rank26]: return forward_call(*args, **kwargs) [default2]:[default5]:[rank21]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default5]:[rank21]: return forward_call(*args, **kwargs) [default5]:[rank21]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 565, in forward [default5]:[rank21]: key_value_states = key_value_states.permute(1, 2, 0, 3, 4).contiguous() [default5]:[rank21]: torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 128.00 MiB. GPU  has a total capacity of 79.33 GiB of which 29.94 MiB is free. Including non-PyTorch memory, this process has 79.29 GiB memory in use. Of the allocated memory 69.57 GiB is allocated by PyTorch, and 64.03 MiB 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/cud[rank26]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/block.py", line 151, in forward [default2]:[rank26]: output = self.pp_block(**new_kwargs) [default2]:[rank26]: 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]:[rank26]: return self._call_impl(*args, **kwargs) [default2]:[rank26]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default2]:[rank26]: return forward_call(*args, **kwargs) [default2]:[rank26]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 631, in forward [default2]:[rank26]: output = self.attn(hidden_states=hidden_states, sequence_mask=sequence_mask) [default2]:[rank26]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nna.html#environment-variables) /modules/module.py", line 1532, in _wrapped_call_impl [default2]:[rank26]: return self._call_impl(*args, **kwargs) [default2]:[rank26]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default2]:[rank26]: return forward_call(*args, **kwargs) [default2]:[rank26]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 389, in forward [default2]:[rank26]: .contiguous() [default2]:[rank26]: torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 192.00 MiB. GPU  has a total capacity of 79.33 GiB of which 165.94 MiB is free. Including non-PyTorch memory, this process has 79.16 GiB memory in use. Of the allocated memory 69.26 GiB is allocated by PyTorch, and 128.03 MiB 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) [default2]:[rank18]: Traceback (most recent call last): [default2]:[rank18]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/run_train.py", line 237, in [default2]:[rank18]: trainer.train(dataloader) [default2]:[rank18]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 429, in train [default2]:[rank18]: outputs, loss_avg = self.training_step(dataloader=self.current_dataloader) [default2]:[rank18]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 462, in training_step [default2]:[rank18]: outputs = self.pipeline_engine.train_batch_iter( [default1]:[rank17]: Traceback (most recent call last): [default1]:[rank17]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/run_train.py", line 237, in [default1]:[rank17]: trainer.train(dataloader) [default2]:[rank18]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 278, in train_batch_iter [default1]:[rank17]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 429, in train [default1]:[rank17]: outputs, loss_avg = self.training_step(dataloader=self.current_dataloader) [default1]:[rank17]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 462, in training_step [default1]:[rank17]: outputs = self.pipeline_engine.train_batch_iter( [default2]:[rank18]: output = self.forward(context=context, state=state, micro_batch=micro_batch, model=model) [default1]:[rank17]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 278, in train_batch_iter [default1]:[rank17]: output = self.forward(context=context, state=state, micro_batch=micro_batch, model=model) [default1]:[rank17]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 44, in forward [default1]:[rank17]: output = model(**micro_batch) [default1]:[rank17]: 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]:[rank18]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 44, in forward [default2]:[rank18]: output = model(**micro_batch) [default1]:[rank17]: return self._call_impl(*args, **kwargs) [default1]:[rank17]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default1]:[rank17]: return forward_call(*args, **kwargs) [default1]:[rank17]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 891, in forward [default1]:[rank17]: sharded_logits = self.model( [default2]:[rank18]: 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]:[rank18]: return self._call_impl(*args, **kwargs) [default2]:[rank18]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default2]:[rank18]: return forward_call(*args, **kwargs) [default2]:[rank18]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 891, in forward [default2]:[rank18]: sharded_logits = self.model( [default1]:[rank17]: 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]:[rank18]: 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]:[rank18]: return self._call_impl(*args, **kwargs) [default2]:[rank18]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default1]:[rank17]: return self._call_impl(*args, **kwargs) [default1]:[rank17]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default2]:[rank18]: return forward_call(*args, **kwargs) [default2]:[rank18]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 764, in forward [default1]:[rank17]: return forward_call(*args, **kwargs) [default2]:[rank18]: return self.forward_with_hidden_states(input_ids=input_ids, input_mask=input_mask)[0] [default2]:[rank18]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 780, in forward_with_hidden_states [default2]:[rank18]: hidden_encoder_states = encoder_block(**hidden_encoder_states) [default2]:[rank18]: 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]:[rank29]: Traceback (most recent call last): [default5]:[rank29]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/run_train.py", line 237, in [default5]:[rank29]: trainer.train(dataloader) [default5]:[rank29]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 429, in train [default5]:[rank29]: outputs, loss_avg = self.training_step(dataloader=self.current_dataloader) [default5]:[rank29]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 462, in training_step [default5]:[rank29]: outputs = self.pipeline_engine.train_batch_iter( [default5]:[rank29]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 278, in train_batch_iter [default5]:[rank29]: output = self.forward(context=context, state=state, micro_batch=micro_batch, model=model) [default5]:[rank29]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanot[default1]:[rank17]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 764, in forward [default1]:[rank17]: return self.forward_with_hidden_states(input_ids=input_ids, input_mask=input_mask)[0] [default1]:[rank17]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 780, in forward_with_hidden_states [default2]:[rank18]: return self._call_impl(*args, **kwargs) [default2]:[rank18]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl ron/src/nanotron/parallel/pipeline_parallel/engine.py", line 44, in forward [default5]:[rank29]: output = model(**micro_batch) [default5]:[rank29]: 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]:[rank29]: return self._call_impl(*args, **kwargs) [default5]:[rank29]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default5]:[rank29]: return forward_call(*args, **kwargs) [default5]:[rank29]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 891, in forward [default5]:[rank29]: sharded_logits = self.model( [default1]:[rank17]: hidden_encoder_states = encoder_block(**hidden_encoder_states) [default1]:[rank17]: 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]:[rank29]: 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]:[rank29]: return self._call_impl(*args, **kwargs) [default5]:[rank29]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default5]:[rank29]: return forward_call(*args, **kwargs) [default5]:[rank29]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 764, in forward [default5]:[rank29]: return self.forward_with_hidden_states(input_ids=input_ids, input_mask=input_mask)[0] [default5]:[rank29]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 780, in forward_with_hidden_states [default5]:[rank29]: hidden_encoder_states = encoder_block(**hidden_encoder_states) [default5]:[rank29]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-[default1]:[rank17]: return self._call_impl(*args, **kwargs) cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl [default5]:[rank29]: return self._call_impl(*args, **kwargs) [default5]:[rank29]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default5]:[rank29]: return forward_call(*args, **kwargs) [default5]:[rank29]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/block.py", line 151, in forward [default5]:[rank29]: output = self.pp_block(**new_kwargs) [default5]:[rank29]: 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]:[rank29]: return self._call_impl(*args, **kwargs) [default5]:[rank29]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default5]:[rank[default1]:[rank17]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl 29]: return forward_call(*args, **kwargs) [default5]:[rank29]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 631, in forward [default5]:[rank29]: output = self.attn(hidden_states=hidden_states, sequence_mask=sequence_mask) [default1]:[rank17]: return forward_call(*args, **kwargs) [default1]:[rank17]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/block.py", line 151, in forward [default5]:[rank29]: 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]:[rank29]: return self._call_impl(*args, **kwargs) [default5]:[rank29]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default5]:[rank29]: return forward_call(*args, **kwargs) [default5]:[rank29]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 565, in forward [default5]:[rank29]: key_value_states = key_value_states.permute(1, 2, 0, 3, 4).contiguous() [default5]:[rank29]: torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 128.00 MiB. GPU  has a total capacity of 79.33 GiB of which 77.94 MiB is free. Including non-PyTorch memory, this process has 79.24 GiB memory in use. Of the allocated memory 69.57 GiB is allocated by PyTorch, and 64.03 MiB is reserved by P[default1]:[rank17]: output = self.pp_block(**new_kwargs) yTorch 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) [default2]:[rank18]: return forward_call(*args, **kwargs) [default2]:[rank18]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/block.py", line 151, in forward [default1]:[rank17]: 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]:[rank25]: Traceback (most recent call last): [default2]:[rank18]: output = self.pp_block(**new_kwargs) [default2]:[rank18]: 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]:[rank25]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/run_train.py", line 237, in [default1]:[rank17]: return self._call_impl(*args, **kwargs) [default1]:[rank25]: trainer.train(dataloader) [default2]:[rank18]: return self._call_impl(*args, **kwargs) [default1]:[rank17]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default1]:[rank17]: return forward_call(*args, **kwargs) [default2]:[rank18]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default1]:[rank17]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 631, in forward [default1]:[rank17]: output = self.attn(hidden_states=hidden_states, sequence_mask=sequence_mask) [default1]:[rank17]: 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]:[rank24]: Traceback (most recent call last): [default0]:[rank24]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/run_train.py", line 237, in [default0]:[rank24]: trainer.train(dataloader) [default0]:[rank24]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 429, in train [default0]:[rank24]: outputs, loss_avg = self.training_step(dataloader=self.current_dataloader) [default0]:[rank24]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 462, in training_step [default0]:[rank24]: outputs = self.pipeline_engine.train_batch_iter( [default0]:[rank24]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 278, in train_batch_iter [default2]:[rank18]: return forward_call(*args, **kwargs) [default1]:[rank17]: return self._call_impl(*args, **kwargs) [default2]:[rank18]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 631, in forward [default1]:[rank17]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default1]:[rank17]: return forward_call(*args, **kwargs) [default2]:[rank18]: output = self.attn(hidden_states=hidden_states, sequence_mask=sequence_mask) [default2]:[rank18]: 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]:[rank18]: return self._call_impl(*args, **kwargs) [default1]:[rank17]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 565, in forward [default2]:[rank18]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default1]:[rank25]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 429, in train [default0]:[rank24]: output = self.forward(context=context, state=state, micro_batch=micro_batch, model=model) [default1]:[rank25]: outputs, loss_avg = self.training_step(dataloader=self.current_dataloader) [default1]:[rank17]: key_value_states = key_value_states.permute(1, 2, 0, 3, 4).contiguous() [default0]:[rank24]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 44, in forward [default0]:[rank24]: output = model(**micro_batch) [default0]:[rank24]: 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]:[rank24]: return self._call_impl(*args, **kwargs) [default1]:[rank25]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 462, in training_step [default2]:[rank18]: return forward_call(*args, **kwargs) [default0]:[rank24]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default1]:[rank25]: outputs = self.pipeline_engine.train_batch_iter( [default1]:[rank17]: torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 128.00 MiB. GPU  has a total capacity of 79.33 GiB of which 29.94 MiB is free. Including non-PyTorch memory, this process has 79.29 GiB memory in use. Of the allocated memory 69.57 GiB is allocated by PyTorch, and 64.03 MiB 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]:[rank24]: return forward_call(*args, **kwargs) [default2]:[rank18]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 565, in forward [default2]:[rank18]: key_value_states = key_value_states.permute(1, 2, 0, 3, 4).contiguous() [default0]:[rank24]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 891, in forward [default2]:[rank18]: torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 128.00 MiB. GPU  has a total capacity of 79.33 GiB of which 117.94 MiB is free. Including non-PyTorch memory, this process has 79.20 GiB memory in use. Of the allocated memory 69.57 GiB is allocated by PyTorch, and 64.03 MiB 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]:[rank24]: sharded_logits = self.model( [default1]:[rank25]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 278, in train_batch_iter [default0]:[rank24]: 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]:[rank24]: return self._call_impl(*args, **kwargs) [default0]:[rank24]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default1]:[rank25]: output = self.forward(context=context, state=state, micro_batch=micro_batch, model=model) [default1]:[rank25]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 44, in forward [default0]:[rank24]: return forward_call(*args, **kwargs) [default0]:[rank24]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 764, in forward [default0]:[rank24]: return self.forward_with_hidden_states(input_ids=input_ids, input_mask=input_mask)[0] [default0]:[rank24]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 780, in forward_with_hidden_states [default0]:[rank24]: hidden_encoder_states = encoder_block(**hidden_encoder_states) [default0]:[rank24]: 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]:[rank24]: return self._call_impl(*args, **kwargs) [default0]:[rank24]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default0]:[rank24]: return forward_call(*args, **kwargs) [default0]:[rank24]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/block.py", line 151, in forward [default0]:[rank24]: output = self.pp_block(**new_kwargs) [default0]:[rank24]: 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]:[rank24]: return self._call_impl(*args, **kwargs) [default0]:[rank24]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default0]:[rank24]: return forward_call(*args, **kwargs) [default0]:[rank24]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 631, in forward [default1]:[rank25]: output = model(**micro_batch) [default1]:[rank25]: 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]:[rank25]: return self._call_impl(*args, **kwargs) [default0]:[rank24]: output = self.attn(hidden_states=hidden_states, sequence_mask=sequence_mask) [default0]:[rank24]: 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]:[rank25]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default0]:[rank24]: return self._call_impl(*args, **kwargs) [default1]:[rank25]: return forward_call(*args, **kwargs) [default1]:[rank25]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 891, in forward [default1]:[rank25]: sharded_logits = self.model( [default0]:[rank24]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default0]:[rank24]: return forward_call(*args, **kwargs) [default1]:[rank25]: 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]:[rank25]: return self._call_impl(*args, **kwargs) [default1]:[rank25]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default1]:[rank25]: return forward_call(*args, **kwargs) [default1]:[rank25]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 764, in forward [default1]:[rank25]: return self.forward_with_hidden_states(input_ids=input_ids, input_mask=input_mask)[0] [default0]:[rank24]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 563, in forward [default1]:[rank25]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 780, in forward_with_hidden_states [default1]:[rank25]: hidden_encoder_states = encoder_block(**hidden_encoder_states) [default1]:[rank25]: 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]:[rank25]: return self._call_impl(*args, **kwargs) [default1]:[rank25]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default0]:[rank24]: key_value_states = torch.cat([key_states.unsqueeze(0), value_states.unsqueeze(0)], dim=0) [default0]:[rank24]: torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 128.00 MiB. GPU [default1]:[rank25]: return forward_call(*args, **kwargs) [default1]:[rank25]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/block.py", line 151, in forward [default1]:[rank25]: output = self.pp_block(**new_kwargs) [default1]:[rank25]: 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]:[rank25]: return self._call_impl(*args, **kwargs) [default1]:[rank25]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default1]:[rank25]: return forward_call(*args, **kwargs) [default1]:[rank25]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 631, in forward [default1]:[rank25]: output = self.attn(hidden_states=hidden_states, sequence_mask=sequence_mask) [default1]:[rank25]: 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]:[rank25]: return self._call_impl(*args, **kwargs) [default1]:[rank25]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default1]:[rank25]: return forward_call(*args, **kwargs) [default1]:[rank25]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 565, in forward [default1]:[rank25]: key_value_states = key_value_states.permute(1, 2, 0, 3, 4).contiguous() [default1]:[rank25]: torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 128.00 MiB. GPU  has a total capacity of 79.33 GiB of which 77.94 MiB is free. Including non-PyTorch memory, this process has 79.24 GiB memory in use. Of the allocated memory 69.57 GiB is allocated by PyTorch, and 64.03 MiB 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]:[rank30]: Traceback (most recent call last): [default6]:[rank30]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/run_train.py", line 237, in [default6]:[rank30]: trainer.train(dataloader) [default6]:[rank30]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 429, in train [default6]:[rank30]: outputs, loss_avg = self.training_step(dataloader=self.current_dataloader) [default6]:[rank30]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 462, in training_step [default6]:[rank30]: outputs = self.pipeline_engine.train_batch_iter( [default6]:[rank30]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 278, in train_batch_iter [default6]:[rank30]: output = self.forward(context=context, state=state, micro_batch=micro_batch, model=model) [default6]:[rank30]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 44, in forward [default6]:[rank30]: output = model(**micro_batch) [default6]:[rank30]: 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]:[rank30]: return self._call_impl(*args, **kwargs) [default6]:[rank30]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default6]:[rank30]: return forward_call(*args, **kwargs) [default6]:[rank30]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 891, in forward [default6]:[rank30]: sharded_logits = self.model( [default6]:[rank30]: 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]:[rank30]: return self._call_impl(*args, **kwargs) [default6]:[rank30]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default6]:[rank30]: return forward_call(*args, **kwargs) [default6]:[rank30]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 764, in forward [default6]:[rank30]: return self.forward_with_hidden_states(input_ids=input_ids, input_mask=input_mask)[0] [default6]:[rank30]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 780, in forward_with_hidden_states [default6]:[rank30]: hidden_encoder_states = encoder_block(**hidden_encoder_states) [default6]:[rank30]: 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]:[rank30]: return self._call_impl(*args, **kwargs) [default6]:[rank30]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default6]:[rank30]: return forward_call(*args, **kwargs) [default6]:[rank30]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/block.py", line 151, in forward [default6]:[rank30]: output = self.pp_block(**new_kwargs) [default6]:[rank30]: 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]:[rank30]: return self._call_impl(*args, **kwargs) [default6]:[rank30]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default6]:[rank30]: return forward_call(*args, **kwargs) [default6]:[rank30]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 631, in forward [default6]:[rank30]: output = self.attn(hidden_states=hidden_states, sequence_mask=sequence_mask) [default6]:[rank30]: 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]:[rank30]: return self._call_impl(*args, **kwargs) [default6]:[rank30]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default6]:[rank30]: return forward_call(*args, **kwargs) [default6]:[rank30]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 563, in forward [default6]:[rank30]: key_value_states = torch.cat([key_states.unsqueeze(0), value_states.unsqueeze(0)], dim=0) [default6]:[rank30]: torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 128.00 MiB. GPU  has a total capacity of 79.33 GiB of which 37.94 MiB is free. Including non-PyTorch memory, this process has 79.28 GiB memory in use. Of the allocated memory 69.45 GiB is allocated by PyTorch, and 64.03 MiB 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) [default4]:[rank28]: Traceback (most recent call last): [default4]:[rank28]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/run_train.py", line 237, in [default4]:[rank28]: trainer.train(dataloader) [default4]:[rank28]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 429, in train [default4]:[rank28]: outputs, loss_avg = self.training_step(dataloader=self.current_dataloader) [default4]:[rank28]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 462, in training_step [default4]:[rank28]: outputs = self.pipeline_engine.train_batch_iter( [default4]:[rank28]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 278, in train_batch_iter [default4]:[rank28]: output = self.forward(context=context, state=state, micro_batch=micro_batch, model=model) [default4]:[rank28]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 44, in forward [default4]:[rank28]: output = model(**micro_batch) [default4]:[rank28]: 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]:[rank28]: return self._call_impl(*args, **kwargs) [default4]:[rank28]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default4]:[rank28]: return forward_call(*args, **kwargs) [default4]:[rank28]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 891, in forward [default4]:[rank28]: sharded_logits = self.model( [default4]:[rank28]: 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]:[rank28]: return self._call_impl(*args, **kwargs) [default4]:[rank28]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default4]:[rank28]: return forward_call(*args, **kwargs) [default4]:[rank28]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 764, in forward [default4]:[rank28]: return self.forward_with_hidden_states(input_ids=input_ids, input_mask=input_mask)[0] [default4]:[rank28]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 780, in forward_with_hidden_states [default4]:[rank28]: hidden_encoder_states = encoder_block(**hidden_encoder_states) [default4]:[rank28]: 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]:[rank28]: return self._call_impl(*args, **kwargs) [default4]:[rank28]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default4]:[rank28]: return forward_call(*args, **kwargs) [default4]:[rank28]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/block.py", line 151, in forward [default4]:[rank28]: output = self.pp_block(**new_kwargs) [default4]:[rank28]: 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]:[rank28]: return self._call_impl(*args, **kwargs) [default4]:[rank28]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default4]:[rank28]: return forward_call(*args, **kwargs) [default4]:[rank28]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 631, in forward [default4]:[rank28]: output = self.attn(hidden_states=hidden_states, sequence_mask=sequence_mask) [default4]:[rank28]: 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]:[rank28]: return self._call_impl(*args, **kwargs) [default4]:[rank28]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default4]:[rank28]: return forward_call(*args, **kwargs) [default4]:[rank28]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 563, in forward [default4]:[rank28]: key_value_states = torch.cat([key_states.unsqueeze(0), value_states.unsqueeze(0)], dim=0) [default4]:[rank28]: torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 128.00 MiB. GPU  has a total capacity of 79.33 GiB of which 37.94 MiB is free. Including non-PyTorch memory, this process has 79.28 GiB memory in use. Of the allocated memory 69.45 GiB is allocated by PyTorch, and 64.03 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_C[default0]:[rank16]: Traceback (most recent call last): [default0]:[rank16]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/run_train.py", line 237, in [default0]:[rank16]: trainer.train(dataloader) [default0]:[rank16]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 429, in train [default0]:[rank16]: outputs, loss_avg = self.training_step(dataloader=self.current_dataloader) [default0]:[rank16]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 462, in training_step [default0]:[rank16]: outputs = self.pipeline_engine.train_batch_iter( [default0]:[rank16]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 278, in train_batch_iter [default0]:[rank16]: output = self.forward(context=context, state=state, micro_batch=micro_batch, model=model) [default0]:[rank16]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotONF=expandable_segments:True to avoid fragmentation. See documentation for Memory Management (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables) [default3]:[rank27]: Traceback (most recent call last): [default3]:[rank27]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/run_train.py", line 237, in [default3]:[rank27]: trainer.train(dataloader) [default3]:[rank27]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 429, in train [default3]:[rank27]: outputs, loss_avg = self.training_step(dataloader=self.current_dataloader) [default3]:[rank27]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 462, in training_step [default3]:[rank27]: outputs = self.pipeline_engine.train_batch_iter( [default3]:[rank27]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 278, in train_batch_iter [default3]:[rank27]: output = self.forward(context=context, state=state, micro_batch=micro_batch, model=model) [default3]:[rank27]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 44, in forward [default3]:[rank27]: output = model(**micro_batch) [default3]:[rank27]: 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]:[rank27]: return self._call_impl(*args, **kwargs) [default3]:[rank27]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default3]:[rank27]: return forward_call(*args, **kwargs) [default3]:[rank27]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 891, in forward [default3]:[rank27]: sharded_logits = self.model( [default3]:[rank27]: 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]:[rank27]: return self._call_impl(*args, **kwargs) [defron/src/nanotron/parallel/pipeline_parallel/engine.py", line 44, in forward [default0]:[rank16]: output = model(**micro_batch) [default0]:[rank16]: 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]:[rank16]: return self._call_impl(*args, **kwargs) [default0]:[rank16]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default0]:[rank16]: return forward_call(*args, **kwargs) [default0]:[rank16]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 891, in forward ault3]:[rank27]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default3]:[rank27]: return forward_call(*args, **kwargs) [default0]:[rank16]: sharded_logits = self.model( [default0]:[rank16]: 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]:[rank16]: return self._call_impl(*args, **kwargs) [default0]:[rank16]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default0]:[rank16]: return forward_call(*args, **kwargs) [default0]:[rank16]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 764, in forward [default0]:[rank16]: return self.forward_with_hidden_states(input_ids=input_ids, input_mask=input_mask)[0] [default0]:[rank16]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 780, in forward_with_hidden_states [default0]:[rank16]: hidden_encoder_states = encoder_block(**hidden_encoder_states) [default0]:[rank16][default3]:[rank27]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 764, in forward [default3]:[rank27]: return self.forward_with_hidden_states(input_ids=input_ids, input_mask=input_mask)[0] [default3]:[rank27]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 780, in forward_with_hidden_states [default3]:[rank27]: hidden_encoder_states = encoder_block(**hidden_encoder_states) [default3]:[rank27]: 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]:[rank27]: return self._call_impl(*args, **kwargs) [default3]:[rank27]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default3]:[rank27]: return forward_call(*args, **kwargs) [default3]:[rank27]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster: 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]:[rank16]: return self._call_impl(*args, **kwargs) [default0]:[rank16]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default0]:[rank16]: return forward_call(*args, **kwargs) [default0]:[rank16]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/block.py", line 151, in forward [default0]:[rank16]: output = self.pp_block(**new_kwargs) [default0]:[rank16]: 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]:[rank16]: return self._call_impl(*args, **kwargs) [default0]:[rank16]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/nanotron/src/nanotron/parallel/pipeline_parallel/block.py", line 151, in forward [default3]:[rank27]: output = self.pp_block(**new_kwargs) [default3]:[rank27]: 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]:[rank27]: return self._call_impl(*args, **kwargs) [default3]:[rank27]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default3]:[rank27]: return forward_call(*args, **kwargs) [default3]:[rank27]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 631, in forward [default3]:[rank27]: output = self.attn(hidden_states=hidden_states, sequence_mask=sequence_mask) [default3]:[rank27]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl [default/module.py", line 1541, in _call_impl [default0]:[rank16]: return forward_call(*args, **kwargs) [default0]:[rank16]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 631, in forward 3]:[rank27]: return self._call_impl(*args, **kwargs) [default3]:[rank27]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default3]:[rank27]: return forward_call(*args, **kwargs) [default3]:[rank27]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 565, in forward [default3]:[rank27]: key_value_states = key_value_states.permute(1, 2, 0, 3, 4).contiguous() [default3]:[rank27]: torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 128.00 MiB. GPU  has a total capacity of 79.33 GiB of which 13.94 MiB is free. Including non-PyTorch memory, this process has 79.30 GiB memory in use. Of the allocated memory 69.57 GiB is allocated by PyTorch, and 128.03 MiB 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 Mem[default0]:[rank16]: output = self.attn(hidden_states=hidden_states, sequence_mask=sequence_mask) [default0]:[rank16]: 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]:[rank16]: return self._call_impl(*args, **kwargs) [default0]:[rank16]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default0]:[rank16]: return forward_call(*args, **kwargs) [default0]:[rank16]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 565, in forward [default0]:[rank16]: key_value_states = key_value_states.permute(1, 2, 0, 3, 4).contiguous() [default0]:[rank16]: torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 128.00 MiB. GPU ory Management (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables) [default7]:[rank31]: Traceback (most recent call last): [default7]:[rank31]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/run_train.py", line 237, in [default7]:[rank31]: trainer.train(dataloader) [default7]:[rank31]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 429, in train [default7]:[rank31]: outputs, loss_avg = self.training_step(dataloader=self.current_dataloader) [default7]:[rank31]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 462, in training_step [default7]:[rank31]: outputs = self.pipeline_engine.train_batch_iter( [default7]:[rank31]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 278, in train_batch_iter [default7]:[rank31]: output = self.forward(context=context, state=state, micro_batch=micro_batch, model=model) [default7]:[rank31]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 44, in forward [default7]:[rank31]: output = model(**micro_batch) [default7]:[rank31]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl [default7]:[rank31]: return self._call_impl(*args, **kwargs) [default7]:[rank31]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default7]:[rank31]: return forward_call(*args, **kwargs) [default7]:[rank31]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 891, in forward [default7]:[rank31]: sharded_logits = self.model( [default7]:[rank31]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl [default7]:[rank31]: return self._call_impl(*args, **kwargs) [default7]:[rank31]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default7]:[rank31]: return forward_call(*args, **kwargs) [default7]:[rank31]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 764, in forward [default7]:[rank31]: return self.forward_with_hidden_states(input_ids=input_ids, input_mask=input_mask)[0] [default7]:[rank31]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 780, in forward_with_hidden_states [default7]:[rank31]: hidden_encoder_states = encoder_block(**hidden_encoder_states) [default7]:[rank31]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl [default7]:[rank31]: return self._call_impl(*args, **kwargs) [default7]:[rank31]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default7]:[rank31]: return forward_call(*args, **kwargs) [default7]:[rank31]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/block.py", line 151, in forward [default7]:[rank31]: output = self.pp_block(**new_kwargs) [default7]:[rank31]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl [default7]:[rank31]: return self._call_impl(*args, **kwargs) [default7]:[rank31]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default7]:[rank31]: return forward_call(*args, **kwargs) [default7]:[rank31]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 631, in forward [default7]:[rank31]: output = self.attn(hidden_states=hidden_states, sequence_mask=sequence_mask) [default7]:[rank31]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl [default7]:[rank31]: return self._call_impl(*args, **kwargs) [default7]:[rank31]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default7]:[rank31]: return forward_call(*args, **kwargs) [default7]:[rank31]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 565, in forward [default7]:[rank31]: key_value_states = key_value_states.permute(1, 2, 0, 3, 4).contiguous() [default7]:[rank31]: torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 128.00 MiB. GPU  has a total capacity of 79.33 GiB of which 77.94 MiB is free. Including non-PyTorch memory, this process has 79.24 GiB memory in use. Of the allocated memory 69.57 GiB is allocated by PyTorch, and 64.03 MiB 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]:[rank19]: Traceback (most recent call last): [default3]:[rank19]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/run_train.py", line 237, in [default3]:[rank19]: trainer.train(dataloader) [default3]:[rank19]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 429, in train [default3]:[rank19]: outputs, loss_avg = self.training_step(dataloader=self.current_dataloader) [default3]:[rank19]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 462, in training_step [default3]:[rank19]: outputs = self.pipeline_engine.train_batch_iter( [default3]:[rank19]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 278, in train_batch_iter [default3]:[rank19]: output = self.forward(context=context, state=state, micro_batch=micro_batch, model=model) [default3]:[rank19]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 44, in forward [default3]:[rank19]: output = model(**micro_batch) [default3]:[rank19]: 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]:[rank19]: return self._call_impl(*args, **kwargs) [default3]:[rank19]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default3]:[rank19]: return forward_call(*args, **kwargs) [default3]:[rank19]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 891, in forward [default3]:[rank19]: sharded_logits = self.model( [default3]:[rank19]: 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]:[rank19]: return self._call_impl(*args, **kwargs) [default3]:[rank19]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default3]:[rank19]: return forward_call(*args, **kwargs) [default3]:[rank19]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 764, in forward [default3]:[rank19]: return self.forward_with_hidden_states(input_ids=input_ids, input_mask=input_mask)[0] [default3]:[rank19]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 780, in forward_with_hidden_states [default3]:[rank19]: hidden_encoder_states = encoder_block(**hidden_encoder_states) [default3]:[rank19]: 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]:[rank19]: return self._call_impl(*args, **kwargs) [default3]:[rank19]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default3]:[rank19]: return forward_call(*args, **kwargs) [default3]:[rank19]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/block.py", line 151, in forward [default3]:[rank19]: output = self.pp_block(**new_kwargs) [default3]:[rank19]: 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]:[rank19]: return self._call_impl(*args, **kwargs) [default3]:[rank19]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default3]:[rank19]: return forward_call(*args, **kwargs) [default3]:[rank19]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 631, in forward [default3]:[rank19]: output = self.attn(hidden_states=hidden_states, sequence_mask=sequence_mask) [default3]:[rank19]: 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]:[rank19]: return self._call_impl(*args, **kwargs) [default3]:[rank19]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default3]:[rank19]: return forward_call(*args, **kwargs) [default3]:[rank19]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 565, in forward [default3]:[rank19]: key_value_states = key_value_states.permute(1, 2, 0, 3, 4).contiguous() [default3]:[rank19]: torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 128.00 MiB. GPU  has a total capacity of 79.33 GiB of which 29.94 MiB is free. Including non-PyTorch memory, this process has 79.29 GiB memory in use. Of the allocated memory 69.57 GiB is allocated by PyTorch, and 64.03 MiB 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) [default7]:[rank23]: Traceback (most recent call last): [default7]:[rank23]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/run_train.py", line 237, in [default7]:[rank23]: trainer.train(dataloader) [default7]:[rank23]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 429, in train [default7]:[rank23]: outputs, loss_avg = self.training_step(dataloader=self.current_dataloader) [default7]:[rank23]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 462, in training_step [default7]:[rank23]: outputs = self.pipeline_engine.train_batch_iter( [default7]:[rank23]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 278, in train_batch_iter [default7]:[rank23]: output = self.forward(context=context, state=state, micro_batch=micro_batch, model=model) [default7]:[rank23]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 44, in forward [default7]:[rank23]: output = model(**micro_batch) [default7]:[rank23]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl [default7]:[rank23]: return self._call_impl(*args, **kwargs) [default7]:[rank23]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default7]:[rank23]: return forward_call(*args, **kwargs) [default7]:[rank23]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 891, in forward [default7]:[rank23]: sharded_logits = self.model( [default7]:[rank23]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl [default7]:[rank23]: return self._call_impl(*args, **kwargs) [default7]:[rank23]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default7]:[rank23]: return forward_call(*args, **kwargs) [default7]:[rank23]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 764, in forward [default7]:[rank23]: return self.forward_with_hidden_states(input_ids=input_ids, input_mask=input_mask)[0] [default7]:[rank23]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 780, in forward_with_hidden_states [default7]:[rank23]: hidden_encoder_states = encoder_block(**hidden_encoder_states) [default7]:[rank23]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl [default7]:[rank23]: return self._call_impl(*args, **kwargs) [default7]:[rank23]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default7]:[rank23]: return forward_call(*args, **kwargs) [default7]:[rank23]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/block.py", line 151, in forward [default7]:[rank23]: output = self.pp_block(**new_kwargs) [default7]:[rank23]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl [default7]:[rank23]: return self._call_impl(*args, **kwargs) [default7]:[rank23]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default7]:[rank23]: return forward_call(*args, **kwargs) [default7]:[rank23]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 631, in forward [default7]:[rank23]: output = self.attn(hidden_states=hidden_states, sequence_mask=sequence_mask) [default7]:[rank23]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl [default7]:[rank23]: return self._call_impl(*args, **kwargs) [default7]:[rank23]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default7]:[rank23]: return forward_call(*args, **kwargs) [default7]:[rank23]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 565, in forward [default7]:[rank23]: key_value_states = key_value_states.permute(1, 2, 0, 3, 4).contiguous() [default7]:[rank23]: torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 128.00 MiB. GPU  has a total capacity of 79.33 GiB of which 29.94 MiB is free. Including non-PyTorch memory, this process has 79.29 GiB memory in use. Of the allocated memory 69.57 GiB is allocated by PyTorch, and 64.03 MiB 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]:[rank61]: Traceback (most recent call last): [default5]:[rank61]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/run_train.py", line 237, in [default5]:[rank61]: trainer.train(dataloader) [default5]:[rank61]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 429, in train [default5]:[rank61]: outputs, loss_avg = self.training_step(dataloader=self.current_dataloader) [default5]:[rank61]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 462, in training_step [default5]:[rank61]: outputs = self.pipeline_engine.train_batch_iter( [default5]:[rank61]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 278, in train_batch_iter [default5]:[rank61]: output = self.forward(context=context, state=state, micro_batch=micro_batch, model=model) [default5]:[rank61]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 44, in forward [default5]:[rank61]: output = model(**micro_batch) [default5]:[rank61]: 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]:[rank61]: return self._call_impl(*args, **kwargs) [default5]:[rank61]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default5]:[rank61]: return forward_call(*args, **kwargs) [default5]:[rank61]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 891, in forward [default5]:[rank61]: sharded_logits = self.model( [default5]:[rank61]: 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]:[rank61]: return self._call_impl(*args, **kwargs) [default5]:[rank61]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default5]:[rank61]: return forward_call(*args, **kwargs) [default5]:[rank61]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 764, in forward [default5]:[rank61]: return self.forward_with_hidden_states(input_ids=input_ids, input_mask=input_mask)[0] [default5]:[rank61]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 780, in forward_with_hidden_states [default5]:[rank61]: hidden_encoder_states = encoder_block(**hidden_encoder_states) [default5]:[rank61]: 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]:[rank61]: return self._call_impl(*args, **kwargs) [default5]:[rank61]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default5]:[rank61]: return forward_call(*args, **kwargs) [default5]:[rank61]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/block.py", line 151, in forward [default5]:[rank61]: output = self.pp_block(**new_kwargs) [default5]:[rank61]: 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]:[rank61]: return self._call_impl(*args, **kwargs) [default5]:[rank61]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default5]:[rank61]: return forward_call(*args, **kwargs) [default5]:[rank61]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 631, in forward [default5]:[rank61]: output = self.attn(hidden_states=hidden_states, sequence_mask=sequence_mask) [default5]:[rank61]: 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]:[rank61]: return self._call_impl(*args, **kwargs) [default5]:[rank61]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default5]:[rank61]: return forward_call(*args, **kwargs) [default5]:[rank61]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 598, in forward [default5]:[rank61]: output = self.o_proj(attention_output) [default5]:[rank61]: 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]:[rank61]: return self._call_impl(*args, **kwargs) [default5]:[rank61]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default5]:[rank61]: return forward_call(*args, **kwargs) [default5]:[rank61]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/nn.py", line 159, in forward [default5]:[rank61]: return row_linear( [default5]:[rank61]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/functional.py", line 474, in row_linear [default5]:[rank61]: out = F.linear(input, weight, bias) [default5]:[rank61]: torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 1024.00 MiB. GPU  has a total capacity of 79.33 GiB of which 563.94 MiB is free. Including non-PyTorch memory, this process has 78.77 GiB memory in use. Of the allocated memory 69.70 GiB is allocated by PyTorch, and 127.53 MiB 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) [default4]:[rank60]: Traceback (most recent call last): [default4]:[rank60]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/run_train.py", line 237, in [default4]:[rank60]: trainer.train(dataloader) [default4]:[rank60]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 429, in train [default4]:[rank60]: outputs, loss_avg = self.training_step(dataloader=self.current_dataloader) [default4]:[rank60]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 462, in training_step [default4]:[rank60]: outputs = self.pipeline_engine.train_batch_iter( [default4]:[rank60]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 278, in train_batch_iter [default4]:[rank60]: output = self.forward(context=context, state=state, micro_batch=micro_batch, model=model) [default4]:[rank60]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 44, in forward [default4]:[rank60]: output = model(**micro_batch) [default4]:[rank60]: 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]:[rank60]: return self._call_impl(*args, **kwargs) [default4]:[rank60]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default4]:[rank60]: return forward_call(*args, **kwargs) [default4]:[rank60]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 891, in forward [default4]:[rank60]: sharded_logits = self.model( [default4]:[rank60]: 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]:[rank60]: return self._call_impl(*args, **kwargs) [default4]:[rank60]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default4]:[rank60]: return forward_call(*args, **kwargs) [default4]:[rank60]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 764, in forward [default4]:[rank60]: return self.forward_with_hidden_states(input_ids=input_ids, input_mask=input_mask)[0] [default4]:[rank60]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 780, in forward_with_hidden_states [default4]:[rank60]: hidden_encoder_states = encoder_block(**hidden_encoder_states) [default4]:[rank60]: 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]:[rank60]: return self._call_impl(*args, **kwargs) [default4]:[rank60]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default4]:[rank60]: return forward_call(*args, **kwargs) [default4]:[rank60]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/block.py", line 151, in forward [default4]:[rank60]: output = self.pp_block(**new_kwargs) [default4]:[rank60]: 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]:[rank60]: return self._call_impl(*args, **kwargs) [default4]:[rank60]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default4]:[rank60]: return forward_call(*args, **kwargs) [default4]:[rank60]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 631, in forward [default4]:[rank60]: output = self.attn(hidden_states=hidden_states, sequence_mask=sequence_mask) [default4]:[rank60]: 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]:[rank60]: return self._call_impl(*args, **kwargs) [default4]:[rank60]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default4]:[rank60]: return forward_call(*args, **kwargs) [default4]:[rank60]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 598, in forward [default4]:[rank60]: output = self.o_proj(attention_output) [default4]:[rank60]: 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]:[rank60]: return self._call_impl(*args, **kwargs) [default4]:[rank60]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default4]:[rank60]: return forward_call(*args, **kwargs) [default4]:[rank60]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/nn.py", line 159, in forward [default4]:[rank60]: return row_linear( [default4]:[rank60]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/functional.py", line 474, in row_linear [default4]:[rank60]: out = F.linear(input, weight, bias) [default4]:[rank60]: torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 1024.00 MiB. GPU  has a total capacity of 79.33 GiB of which 475.94 MiB is free. Including non-PyTorch memory, this process has 78.85 GiB memory in use. Of the allocated memory 69.70 GiB is allocated by PyTorch, and 127.53 MiB 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) [default1]:[rank57]: Traceback (most recent call last): [default1]:[rank57]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/run_train.py", line 237, in [default1]:[rank57]: trainer.train(dataloader) [default1]:[rank57]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 429, in train [default1]:[rank57]: outputs, loss_avg = self.training_step(dataloader=self.current_dataloader) [default1]:[rank57]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 462, in training_step [default1]:[rank57]: outputs = self.pipeline_engine.train_batch_iter( [default1]:[rank57]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 278, in train_batch_iter [default1]:[rank57]: output = self.forward(context=context, state=state, micro_batch=micro_batch, model=model) [default1]:[rank57]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 44, in forward [default1]:[rank57]: output = model(**micro_batch) [default1]:[rank57]: 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]:[rank57]: return self._call_impl(*args, **kwargs) [default1]:[rank57]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default1]:[rank57]: return forward_call(*args, **kwargs) [default1]:[rank57]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 891, in forward [default1]:[rank57]: sharded_logits = self.model( [default1]:[rank57]: 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]:[rank57]: return self._call_impl(*args, **kwargs) [default1]:[rank57]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default1]:[rank57]: return forward_call(*args, **kwargs) [default1]:[rank57]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 764, in forward [default1]:[rank57]: return self.forward_with_hidden_states(input_ids=input_ids, input_mask=input_mask)[0] [default1]:[rank57]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 780, in forward_with_hidden_states [default1]:[rank57]: hidden_encoder_states = encoder_block(**hidden_encoder_states) [default1]:[rank57]: 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]:[rank57]: return self._call_impl(*args, **kwargs) [default1]:[rank57]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default1]:[rank57]: return forward_call(*args, **kwargs) [default1]:[rank57]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/block.py", line 151, in forward [default1]:[rank57]: output = self.pp_block(**new_kwargs) [default1]:[rank57]: 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]:[rank57]: return self._call_impl(*args, **kwargs) [default1]:[rank57]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default1]:[rank57]: return forward_call(*args, **kwargs) [default1]:[rank57]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 631, in forward [default1]:[rank57]: output = self.attn(hidden_states=hidden_states, sequence_mask=sequence_mask) [default1]:[rank57]: 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]:[rank57]: return self._call_impl(*args, **kwargs) [default1]:[rank57]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default1]:[rank57]: return forward_call(*args, **kwargs) [default1]:[rank57]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 598, in forward [default1]:[rank57]: output = self.o_proj(attention_output) [default1]:[rank57]: 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]:[rank57]: return self._call_impl(*args, **kwargs) [default1]:[rank57]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default1]:[rank57]: return forward_call(*args, **kwargs) [default1]:[rank57]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/nn.py", line 159, in forward [default1]:[rank57]: return row_linear( [default1]:[rank57]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/functional.py", line 474, in row_linear [default1]:[rank57]: out = F.linear(input, weight, bias) [default1]:[rank57]: torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 1024.00 MiB. GPU  has a total capacity of 79.33 GiB of which 563.94 MiB is free. Including non-PyTorch memory, this process has 78.77 GiB memory in use. Of the allocated memory 69.70 GiB is allocated by PyTorch, and 127.53 MiB 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]:[rank59]: Traceback (most recent call last): [default3]:[rank59]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/run_train.py", line 237, in [default3]:[rank59]: trainer.train(dataloader) [default3]:[rank59]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 429, in train [default3]:[rank59]: outputs, loss_avg = self.training_step(dataloader=self.current_dataloader) [default3]:[rank59]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 462, in training_step [default3]:[rank59]: outputs = self.pipeline_engine.train_batch_iter( [default3]:[rank59]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 278, in train_batch_iter [default3]:[rank59]: output = self.forward(context=context, state=state, micro_batch=micro_batch, model=model) [default3]:[rank59]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 44, in forward [default3]:[rank59]: output = model(**micro_batch) [default3]:[rank59]: 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]:[rank59]: return self._call_impl(*args, **kwargs) [default3]:[rank59]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default3]:[rank59]: return forward_call(*args, **kwargs) [default3]:[rank59]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 891, in forward [default3]:[rank59]: sharded_logits = self.model( [default3]:[rank59]: 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]:[rank59]: return self._call_impl(*args, **kwargs) [default3]:[rank59]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default3]:[rank59]: return forward_call(*args, **kwargs) [default3]:[rank59]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 764, in forward [default3]:[rank59]: return self.forward_with_hidden_states(input_ids=input_ids, input_mask=input_mask)[0] [default3]:[rank59]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 780, in forward_with_hidden_states [default3]:[rank59]: hidden_encoder_states = encoder_block(**hidden_encoder_states) [default3]:[rank59]: 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]:[rank59]: return self._call_impl(*args, **kwargs) [default3]:[rank59]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default3]:[rank59]: return forward_call(*args, **kwargs) [default3]:[rank59]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/block.py", line 151, in forward [default3]:[rank59]: output = self.pp_block(**new_kwargs) [default3]:[rank59]: 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]:[rank59]: return self._call_impl(*args, **kwargs) [default3]:[rank59]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default3]:[rank59]: return forward_call(*args, **kwargs) [default3]:[rank59]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 631, in forward [default3]:[rank59]: output = self.attn(hidden_states=hidden_states, sequence_mask=sequence_mask) [default3]:[rank59]: 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]:[rank59]: return self._call_impl(*args, **kwargs) [default3]:[rank59]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default3]:[rank59]: return forward_call(*args, **kwargs) [default3]:[rank59]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 598, in forward [default3]:[rank59]: output = self.o_proj(attention_output) [default3]:[rank59]: 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]:[rank59]: return self._call_impl(*args, **kwargs) [default3]:[rank59]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default3]:[rank59]: return forward_call(*args, **kwargs) [default3]:[rank59]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/nn.py", line 159, in forward [default3]:[rank59]: return row_linear( [default3]:[rank59]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/functional.py", line 474, in row_linear [default3]:[rank59]: out = F.linear(input, weight, bias) [default3]:[rank59]: torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 1024.00 MiB. GPU  has a total capacity of 79.33 GiB of which 563.94 MiB is free. Including non-PyTorch memory, this process has 78.77 GiB memory in use. Of the allocated memory 69.70 GiB is allocated by PyTorch, and 127.53 MiB 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]:[rank56]: Traceback (most recent call last): [default0]:[rank56]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/run_train.py", line 237, in [default0]:[rank56]: trainer.train(dataloader) [default0]:[rank56]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 429, in train [default0]:[rank56]: outputs, loss_avg = self.training_step(dataloader=self.current_dataloader) [default0]:[rank56]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 462, in training_step [default0]:[rank56]: outputs = self.pipeline_engine.train_batch_iter( [default0]:[rank56]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 278, in train_batch_iter [default0]:[rank56]: output = self.forward(context=context, state=state, micro_batch=micro_batch, model=model) [default0]:[rank56]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 44, in forward [default0]:[rank56]: output = model(**micro_batch) [default0]:[rank56]: 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]:[rank56]: return self._call_impl(*args, **kwargs) [default0]:[rank56]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default0]:[rank56]: return forward_call(*args, **kwargs) [default0]:[rank56]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 891, in forward [default0]:[rank56]: sharded_logits = self.model( [default0]:[rank56]: 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]:[rank56]: return self._call_impl(*args, **kwargs) [default0]:[rank56]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default0]:[rank56]: return forward_call(*args, **kwargs) [default0]:[rank56]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 764, in forward [default0]:[rank56]: return self.forward_with_hidden_states(input_ids=input_ids, input_mask=input_mask)[0] [default0]:[rank56]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 780, in forward_with_hidden_states [default0]:[rank56]: hidden_encoder_states = encoder_block(**hidden_encoder_states) [default0]:[rank56]: 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]:[rank56]: return self._call_impl(*args, **kwargs) [default0]:[rank56]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default0]:[rank56]: return forward_call(*args, **kwargs) [default0]:[rank56]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/block.py", line 151, in forward [default0]:[rank56]: output = self.pp_block(**new_kwargs) [default0]:[rank56]: 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]:[rank56]: return self._call_impl(*args, **kwargs) [default0]:[rank56]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default0]:[rank56]: return forward_call(*args, **kwargs) [default0]:[rank56]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 631, in forward [default0]:[rank56]: output = self.attn(hidden_states=hidden_states, sequence_mask=sequence_mask) [default0]:[rank56]: 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]:[rank56]: return self._call_impl(*args, **kwargs) [default0]:[rank56]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default0]:[rank56]: return forward_call(*args, **kwargs) [default0]:[rank56]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 598, in forward [default0]:[rank56]: output = self.o_proj(attention_output) [default0]:[rank56]: 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]:[rank56]: return self._call_impl(*args, **kwargs) [default0]:[rank56]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default0]:[rank56]: return forward_call(*args, **kwargs) [default0]:[rank56]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/nn.py", line 159, in forward [default0]:[rank56]: return row_linear( [default0]:[rank56]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/functional.py", line 474, in row_linear [default0]:[rank56]: out = F.linear(input, weight, bias) [default0]:[rank56]: torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 1024.00 MiB. GPU [default7]:[rank55]: Traceback (most recent call last): [default7]:[rank55]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/run_train.py", line 237, in [default7]:[rank55]: trainer.train(dataloader) [default7]:[rank55]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 429, in train [default7]:[rank55]: outputs, loss_avg = self.training_step(dataloader=self.current_dataloader) [default7]:[rank55]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 462, in training_step [default7]:[rank55]: outputs = self.pipeline_engine.train_batch_iter( [default7]:[rank55]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 278, in train_batch_iter [default7]:[rank55]: output = self.forward(context=context, state=state, micro_batch=micro_batch, model=model) [default7]:[rank55]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 44, in forward [default7]:[rank55]: output = model(**micro_batch) [default7]:[rank55]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl [default7]:[rank55]: return self._call_impl(*args, **kwargs) [default7]:[rank55]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default7]:[rank55]: return forward_call(*args, **kwargs) [default7]:[rank55]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 891, in forward [default7]:[rank55]: sharded_logits = self.model( [default7]:[rank55]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl [default7]:[rank55]: return self._call_impl(*args, **kwargs) [default7]:[rank55]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default7]:[rank55]: return forward_call(*args, **kwargs) [default7]:[rank55]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 764, in forward [default7]:[rank55]: return self.forward_with_hidden_states(input_ids=input_ids, input_mask=input_mask)[0] [default7]:[rank55]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 780, in forward_with_hidden_states [default7]:[rank55]: hidden_encoder_states = encoder_block(**hidden_encoder_states) [default7]:[rank55]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl [default7]:[rank55]: return self._call_impl(*args, **kwargs) [default7]:[rank55]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default7]:[rank55]: return forward_call(*args, **kwargs) [default7]:[rank55]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/block.py", line 151, in forward [default7]:[rank55]: output = self.pp_block(**new_kwargs) [d[default6]:[rank62]: Traceback (most recent call last): [default6]:[rank62]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/run_train.py", line 237, in [default6]:[rank62]: trainer.train(dataloader) [default6]:[rank62]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 429, in train [default6]:[rank62]: outputs, loss_avg = self.training_step(dataloader=self.current_dataloader) [default6]:[rank62]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 462, in training_step [default6]:[rank62]: outputs = self.pipeline_engine.train_batch_iter( [default6]:[rank62]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 278, in train_batch_iter [default6]:[rank62]: output = self.forward(context=context, state=state, micro_batch=micro_batch, model=model) [default6]:[rank62]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotefault7]:[rank55]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl [default7]:[rank55]: return self._call_impl(*args, **kwargs) [default7]:[rank55]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default7]:[rank55]: return forward_call(*args, **kwargs) [default7]:[rank55]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 631, in forward [default7]:[rank55]: output = self.attn(hidden_states=hidden_states, sequence_mask=sequence_mask) [default7]:[rank55]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl [default7]:[rank55]: return self._call_impl(*args, **kwargs) [default7]:[rank55]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default7]:[rank55]: return forward_call(*args, **kwargs) [default7]:[rank55]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 565, in forward [default7]:[rank55]: key_value_states = key_value_states.permute(1, 2, 0, 3, 4).contiguous() [default7]:[rank55]: torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 128.00 MiB. GPU  has a total capacity of 79.33 GiB of which 69.94 MiB is free. Including non-PyTorch memory, this process has 79.25 GiB meron/src/nanotron/parallel/pipeline_parallel/engine.py", line 44, in forward [default6]:[rank62]: output = model(**micro_batch) [default6]:[rank62]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl mory in use. Of the allocated memory 69.57 GiB is allocated by PyTorch, and 64.03 MiB 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]:[rank62]: return self._call_impl(*args, **kwargs) [default6]:[rank62]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default6]:[rank62]: return forward_call(*args, **kwargs) [default6]:[rank62]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 891, in forward [default6]:[rank62]: sharded_logits = self.model( [default6]:[rank62]: 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]:[rank62]: return self._call_impl(*args, **kwargs) [default6]:[rank62]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default6]:[rank62]: return forward_call(*args, **kwargs) [default6]:[rank62]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 764, in forward [default6]:[rank62]: return self.forward_with_hidden_states(input_ids=input_ids, input_mask=input_mask)[0] [default6]:[rank62]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 780, in forward_with_hidden_states [default6]:[rank62]: hidden_encoder_states = encoder_block(**hidden_encoder_states) [default6]:[rank62]: 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]:[rank62]: return self._call_impl(*args, **kwargs) [default6]:[rank62]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default6]:[rank62]: return forward_call(*args, **kwargs) [default6]:[rank62]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/block.py", line 151, in forward [default6]:[rank62]: output = self.pp_block(**new_kwargs) [default6]:[rank62]: 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]:[rank62]: return self._call_impl(*args, **kwargs) [default6]:[rank62]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default6]:[rank62]: return forward_call(*args, **kwargs) [default6]:[rank62]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 631, in forward [default6]:[rank62]: output = self.attn(hidden_states=hidden_states, sequence_mask=sequence_mask) [default6]:[rank62]: 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]:[rank62]: return self._call_impl(*args, **kwargs) [default6]:[rank62]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default6]:[rank62]: return forward_call(*args, **kwargs) [default6]:[rank62]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 598, in forward [default6]:[rank62]: output = self.o_proj(attention_output) [default6]:[rank62]: 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]:[rank62]: return self._call_impl(*args, **kwargs) [default6]:[rank62]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default6]:[rank62]: return forward_call(*args, **kwargs) [default6]:[rank62]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/nn.py", line 159, in forward [default6]:[rank62]: return row_linear( [default6]:[rank62]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/functional.py", line 474, in row_linear [default6]:[rank62]: out = F.linear(input, weight, bias) [default6]:[rank62]: torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 1024.00 MiB. GPU  has a total capacity of 79.33 GiB of which 475.94 MiB is free. Including non-PyTorch memory, this process has 78.85 GiB memory in use. Of the allocated memory 69.70 GiB is allocated by PyTorch, and 127.53 MiB 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) [default4]:[rank52]: Traceback (most recent call last): [default4]:[rank52]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/run_train.py", line 237, in [default4]:[rank52]: trainer.train(dataloader) [default4]:[rank52]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 429, in train [default4]:[rank52]: outputs, loss_avg = self.training_step(dataloader=self.current_dataloader) [default4]:[rank52]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 462, in training_step [default4]:[rank52]: outputs = self.pipeline_engine.train_batch_iter( [default4]:[rank52]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 278, in train_batch_iter [default4]:[rank52]: output = self.forward(context=context, state=state, micro_batch=micro_batch, model=model) [default4]:[rank52]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 44, in forward [default4]:[rank52]: output = model(**micro_batch) [default4]:[rank52]: 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]:[rank52]: return self._call_impl(*args, **kwargs) [default4]:[rank52]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default4]:[rank52]: return forward_call(*args, **kwargs) [default4]:[rank52]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 891, in forward [default4]:[rank52]: sharded_logits = self.model( [default4]:[rank52]: 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]:[rank52]: return self._call_impl(*args, **kwargs) [default4]:[rank52]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default4]:[rank52]: return forward_call(*args, **kwargs) [default4]:[rank52]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 764, in forward [default4]:[rank52]: return self.forward_with_hidden_states(input_ids=input_ids, input_mask=input_mask)[0] [default4]:[rank52]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 780, in forward_with_hidden_states [default4]:[rank52]: hidden_encoder_states = encoder_block(**hidden_encoder_states) [default4]:[rank52]: 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]:[rank52]: return self._call_impl(*args, **kwargs) [default4]:[rank52]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default4]:[rank52]: return forward_call(*args, **kwargs) [default4]:[rank52]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/block.py", line 151, in forward [default4]:[rank52]: output = self.pp_block(**new_kwargs) [default4]:[rank52]: 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]:[rank52]: return self._call_impl(*args, **kwargs) [default4]:[rank52]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default4]:[rank52]: return forward_call(*args, **kwargs) [default4]:[rank52]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 631, in forward [default4]:[rank52]: output = self.attn(hidden_states=hidden_states, sequence_mask=sequence_mask) [default4]:[rank52]: 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]:[rank52]: return self._call_impl(*args, **kwargs) [default4]:[rank52]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default4]:[rank52]: return forward_call(*args, **kwargs) [default4]:[rank52]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 598, in forward [default4]:[rank52]: output = self.o_proj(attention_output) [default4]:[rank52]: 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]:[rank52]: return self._call_impl(*args, **kwargs) [default4]:[rank52]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default4]:[rank52]: return forward_call(*args, **kwargs) [default4]:[rank52]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/nn.py", line 159, in forward [default4]:[rank52]: return row_linear( [default4]:[rank52]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/functional.py", line 474, in row_linear [default4]:[rank52]: out = F.linear(input, weight, bias) [default4]:[rank52]: torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 1024.00 MiB. GPU  has a total capacity of 79.33 GiB of which 27.94 MiB is free. Including non-PyTorch memory, this process has 79.29 GiB memory in use. Of the allocated memory 69.70 GiB is allocated by PyTorch, and 63.53 MiB 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) [default2]:[rank50]: Traceback (most recent call last): [default2]:[rank50]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/run_train.py", line 237, in [default2]:[rank50]: trainer.train(dataloader) [default2]:[rank50]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 429, in train [default2]:[rank50]: outputs, loss_avg = self.training_step(dataloader=self.current_dataloader) [default2]:[rank50]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 462, in training_step [default2]:[rank50]: outputs = self.pipeline_engine.train_batch_iter( [default2]:[rank50]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 278, in train_batch_iter [default2]:[rank50]: output = self.forward(context=context, state=state, micro_batch=micro_batch, model=model) [default2]:[rank50]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 44, in forward [default2]:[rank50]: output = model(**micro_batch) [default2]:[rank50]: 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]:[rank50]: return self._call_impl(*args, **kwargs) [default2]:[rank50]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default2]:[rank50]: return forward_call(*args, **kwargs) [default2]:[rank50]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 891, in forward [default1]:[rank49]: Traceback (most recent call last): [default1]:[rank49]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/run_train.py", line 237, in [default0]:[rank48]: Traceback (most recent call last): [default1]:[rank49]: trainer.train(dataloader) [default1]:[rank49]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 429, in train [default2]:[rank50]: sharded_logits = self.model( [default1]:[rank49]: outputs, loss_avg = self.training_step(dataloader=self.current_dataloader) [default2]:[rank50]: 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]:[rank48]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/run_train.py", line 237, in [default1]:[rank49]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 462, in training_step [default0]:[rank48]: trainer.train(dataloader) [default2]:[rank50]: return self._call_impl(*args, **kwargs) [default0]:[rank48]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 429, in train [default1]:[rank49]: outputs = self.pipeline_engine.train_batch_iter( [default2]:[rank50]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default1]:[rank49]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 278, in train_batch_iter [default0]:[rank48]: outputs, loss_avg = self.training_step(dataloader=self.current_dataloader) [default2]:[rank50]: return forward_call(*args, **kwargs) [default1]:[rank49]: output = self.forward(context=context, state=state, micro_batch=micro_batch, model=model) [default0]:[rank48]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 462, in training_step [default1]:[rank49]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 44, in forward [default2]:[rank50]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 764, in forward [default1]:[rank49]: output = model(**micro_batch) [default2]:[rank50]: return self.forward_with_hidden_states(input_ids=input_ids, input_mask=input_mask)[0] [default1]:[rank49]: 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]:[rank50]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 780, in forward_with_hidden_states [default1]:[rank49]: return self._call_impl(*args, **kwargs) [default0]:[rank48]: outputs = self.pipeline_engine.train_batch_iter( [default1]:[rank49]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default2]:[rank50]: hidden_encoder_states = encoder_block(**hidden_encoder_states) [default1]:[rank49]: return forward_call(*args, **kwargs) [default0]:[rank48]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 278, in train_batch_iter [default1]:[rank49]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 891, in forward [default2]:[rank50]: 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]:[rank49]: sharded_logits = self.model( [default2]:[rank50]: return self._call_impl(*args, **kwargs) [default1]:[rank49]: 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]:[rank50]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default1]:[rank49]: return self._call_impl(*args, **kwargs) [default2]:[rank50]: return forward_call(*args, **kwargs) [default2]:[rank50]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/block.py", line 151, in forward [default1]:[rank49]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default0]:[rank48]: output = self.forward(context=context, state=state, micro_batch=micro_batch, model=model) [default2]:[rank50]: output = self.pp_block(**new_kwargs) [default1]:[rank49]: return forward_call(*args, **kwargs) [default2]:[rank58]: Traceback (most recent call last): [default2]:[rank58]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/run_train.py", line 237, in [default2]:[rank58]: trainer.train(dataloader) [default2]:[rank58]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 429, in train [default2]:[rank58]: outputs, loss_avg = self.training_step(dataloader=self.current_dataloader) [default2]:[rank58]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 462, in training_step [default2]:[rank58]: outputs = self.pipeline_engine.train_batch_iter( [default2]:[rank58]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 278, in train_batch_iter [default2]:[rank58]: output = self.forward(context=context, state=state, micro_batch=micro_batch, model=model) [default2]:[rank58]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanot[default2]:[rank50]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl ron/src/nanotron/parallel/pipeline_parallel/engine.py", line 44, in forward [default2]:[rank58]: output = model(**micro_batch) [default2]:[rank58]: 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]:[rank58]: return self._call_impl(*args, **kwargs) [default2]:[rank58]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default2]:[rank58]: return forward_call(*args, **kwargs) [default2]:[rank58]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 891, in forward [default2]:[rank58]: sharded_logits = self.model( [default0]:[rank48]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 44, in forward [default1]:[rank49]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 764, in forward [default2]:[rank58]: 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]:[rank58]: return self._call_impl(*args, **kwargs) [default2]:[rank58]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default2]:[rank58]: return forward_call(*args, **kwargs) [default2]:[rank58]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 764, in forward [default2]:[rank58]: return self.forward_with_hidden_states(input_ids=input_ids, input_mask=input_mask)[0] [default2]:[rank58]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 780, in forward_with_hidden_states [default2]:[rank58]: hidden_encoder_states = encoder_block(**hidden_encoder_states) [default2]:[rank58]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-[default2]:[rank50]: return self._call_impl(*args, **kwargs) cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl [default2]:[rank58]: return self._call_impl(*args, **kwargs) [default2]:[rank58]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default2]:[rank58]: return forward_call(*args, **kwargs) [default2]:[rank58]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/block.py", line 151, in forward [default2]:[rank58]: output = self.pp_block(**new_kwargs) [default2]:[rank58]: 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]:[rank58]: return self._call_impl(*args, **kwargs) [default2]:[rank58]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default2]:[rank[default0]:[rank48]: output = model(**micro_batch) 58]: return forward_call(*args, **kwargs) [default2]:[rank58]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 631, in forward [default2]:[rank58]: output = self.attn(hidden_states=hidden_states, sequence_mask=sequence_mask) [default2]:[rank58]: 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]:[rank58]: return self._call_impl(*args, **kwargs) [default2]:[rank58]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default2]:[rank58]: return forward_call(*args, **kwargs) [default2]:[rank58]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 598, in forward [default2]:[rank58]: output = self.o_proj(attention_output) [default2]:[rank58]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluste[default1]:[rank49]: return self.forward_with_hidden_states(input_ids=input_ids, input_mask=input_mask)[0] r/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl [default2]:[rank58]: return self._call_impl(*args, **kwargs) [default2]:[rank58]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default2]:[rank58]: return forward_call(*args, **kwargs) [default2]:[rank58]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/nn.py", line 159, in forward [default2]:[rank58]: return row_linear( [default2]:[rank58]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/functional.py", line 474, in row_linear [default0]:[rank48]: 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]:[rank48]: return self._call_impl(*args, **kwargs) [default2]:[rank58]: out = F.linear(input, weight, bias) [default2]:[rank58]: torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 1024.00 MiB. GPU  has a total capacity of 79.33 GiB of which 475.94 MiB is free. Including non-PyTorch memory, this process has 78.85 GiB memory in use. Of the allocated memory 69.70 GiB is allocated by PyTorch, and 127.53 MiB 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) [default2]:[rank50]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default7]:[rank63]: Traceback (most recent call last): [default7]:[rank63]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/run_train.py", line 237, in [default7]:[rank63]: trainer.train(dataloader) [default7]:[rank63]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 429, in train [default7]:[rank63]: outputs, loss_avg = self.training_step(dataloader=self.current_dataloader) [default7]:[rank63]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 462, in training_step [default7]:[rank63]: outputs = self.pipeline_engine.train_batch_iter( [default1]:[rank49]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 780, in forward_with_hidden_states [default7]:[rank63]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 278, in train_batch_iter [default7]:[rank63]: output = self.forward(context=context, state=state, micro_batch=micro_batch, model=model) [default1]:[rank49]: hidden_encoder_states = encoder_block(**hidden_encoder_states) [default2]:[rank50]: return forward_call(*args, **kwargs) [default0]:[rank48]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default7]:[rank63]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 44, in forward [default7]:[rank63]: output = model(**micro_batch) [default2]:[rank50]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 631, in forward [default7]:[rank63]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl [default7]:[rank63]: return self._call_impl(*args, **kwargs) [default1]:[rank49]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl [default7]:[rank63]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default7]:[rank63]: return forward_call(*args, **kwargs) [default7]:[rank63]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 891, in forward [default7]:[rank63]: sharded_logits = self.model( [default0]:[rank48]: return forward_call(*args, **kwargs) [default7]:[rank63]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl [default7]:[rank63]: return self._call_impl(*args, **kwargs) [default1]:[rank49]: return self._call_impl(*args, **kwargs) [default7]:[rank63]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default7]:[rank63]: return forward_call(*args, **kwargs) [default0]:[rank48]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 891, in forward [default7]:[rank63]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 764, in forward [default7]:[rank63]: return self.forward_with_hidden_states(input_ids=input_ids, input_mask=input_mask)[0] [default7]:[rank63]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 780, in forward_with_hidden_states [default7]:[rank63]: hidden_encoder_states = encoder_block(**hidden_encoder_states) [default1]:[rank49]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default7]:[rank63]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl [default7]:[rank63]: return self._call_impl(*args, **kwargs) [default2]:[rank50]: output = self.attn(hidden_states=hidden_states, sequence_mask=sequence_mask) [default7]:[rank63]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default7]:[rank63]: return forward_call(*args, **kwargs) [default7]:[rank63]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/block.py", line 151, in forward [default7]:[rank63]: output = self.pp_block(**new_kwargs) [default7]:[rank63]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl [default7]:[rank63]: return self._call_impl(*args, **kwargs) [default7]:[rank63]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default7]:[rank63]: return forward_call(*args, **kwargs) [default7]:[rank63]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line[default0]:[rank48]: sharded_logits = self.model( 631, in forward [default7]:[rank63]: output = self.attn(hidden_states=hidden_states, sequence_mask=sequence_mask) [default7]:[rank63]: 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]:[rank50]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl [default7]:[rank63]: return self._call_impl(*args, **kwargs) [default7]:[rank63]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default7]:[rank63]: return forward_call(*args, **kwargs) [default7]:[rank63]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 598, in forward [default7]:[rank63]: output = self.o_proj(attention_output) [default7]:[rank63]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl [default7]:[rank63]: return self._call_impl(*args, **kwargs) [default7]:[rank63]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default7]:[rank63]: return forward_call(*args, **kwargs) [default7]:[rank63]: File "/fsx/ferdinandmom/ferdinand-hf/bench_clust[default1]:[rank49]: return forward_call(*args, **kwargs) [default0]:[rank48]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl er/nanotron/src/nanotron/parallel/tensor_parallel/nn.py", line 159, in forward [default7]:[rank63]: return row_linear( [default7]:[rank63]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/functional.py", line 474, in row_linear [default7]:[rank63]: out = F.linear(input, weight, bias) [default1]:[rank49]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/block.py", line 151, in forward [default7]:[rank63]: torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 1024.00 MiB. GPU  has a total capacity of 79.33 GiB of which 563.94 MiB is free. Including non-PyTorch memory, this process has 78.77 GiB memory in use. Of the allocated memory 69.70 GiB is allocated by PyTorch, and 127.53 MiB 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]:[rank48]: return self._call_impl(*args, **kwargs) [default1]:[rank49]: output = self.pp_block(**new_kwargs) [default0]:[rank48]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default2]:[rank50]: return self._call_impl(*args, **kwargs) [default1]:[rank49]: 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]:[rank48]: return forward_call(*args, **kwargs) [default2]:[rank50]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default1]:[rank49]: return self._call_impl(*args, **kwargs) [default0]:[rank48]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 764, in forward [default1]:[rank49]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default2]:[rank50]: return forward_call(*args, **kwargs) [default1]:[rank49]: return forward_call(*args, **kwargs) [default0]:[rank48]: return self.forward_with_hidden_states(input_ids=input_ids, input_mask=input_mask)[0] [default1]:[rank49]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 631, in forward [default0]:[rank48]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 780, in forward_with_hidden_states [default1]:[rank49]: output = self.attn(hidden_states=hidden_states, sequence_mask=sequence_mask) [default0]:[rank48]: hidden_encoder_states = encoder_block(**hidden_encoder_states) [default1]:[rank49]: 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]:[rank50]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 598, in forward [default0]:[rank48]: 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]:[rank49]: return self._call_impl(*args, **kwargs) [default2]:[rank50]: output = self.o_proj(attention_output) [default0]:[rank48]: return self._call_impl(*args, **kwargs) [default2]:[rank50]: 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]:[rank49]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default1]:[rank49]: return forward_call(*args, **kwargs) [default2]:[rank50]: return self._call_impl(*args, **kwargs) [default1]:[rank49]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 565, in forward [default0]:[rank48]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default2]:[rank50]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default0]:[rank48]: return forward_call(*args, **kwargs) [default0]:[rank48]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/block.py", line 151, in forward [default2]:[rank50]: return forward_call(*args, **kwargs) [default0]:[rank48]: output = self.pp_block(**new_kwargs) [default1]:[rank49]: key_value_states = key_value_states.permute(1, 2, 0, 3, 4).contiguous() [default2]:[rank50]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/nn.py", line 159, in forward [default2]:[rank50]: return row_linear( [default0]:[rank48]: 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]:[rank49]: torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 128.00 MiB. GPU  has a total capacity of 79.33 GiB of which 69.94 MiB is free. Including non-PyTorch memory, this process has 79.25 GiB memory in use. Of the allocated memory 69.57 GiB is allocated by PyTorch, and 64.03 MiB 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]:[rank48]: return self._call_impl(*args, **kwargs) [default2]:[rank50]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/functional.py", line 474, in row_linear [default0]:[rank48]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default2]:[rank50]: out = F.linear(input, weight, bias) [default0]:[rank48]: return forward_call(*args, **kwargs) [default2]:[rank50]: torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 1024.00 MiB. GPU  has a total capacity of 79.33 GiB of which 27.94 MiB is free. Including non-PyTorch memory, this process has 79.29 GiB memory in use. Of the allocated memory 69.70 GiB is allocated by PyTorch, and 63.53 MiB 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]:[rank48]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 631, in forward [default0]:[rank48]: output = self.attn(hidden_states=hidden_states, sequence_mask=sequence_mask) [default0]:[rank48]: 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]:[rank48]: return self._call_impl(*args, **kwargs) [default0]:[rank48]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default0]:[rank48]: return forward_call(*args, **kwargs) [default0]:[rank48]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 598, in forward [default0]:[rank48]: output = self.o_proj(attention_output) [default0]:[rank48]: 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]:[rank48]: return self._call_impl(*args, **kwargs) [default0]:[rank48]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default0]:[rank48]: return forward_call(*args, **kwargs) [default0]:[rank48]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/nn.py", line 159, in forward [default0]:[rank48]: return row_linear( [default0]:[rank48]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/functional.py", line 474, in row_linear [default0]:[rank48]: out = F.linear(input, weight, bias) [default0]:[rank48]: torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 1024.00 MiB. GPU [default3]:[rank51]: Traceback (most recent call last): [default3]:[rank51]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/run_train.py", line 237, in [default5]:[rank53]: Traceback (most recent call last): [default5]:[rank53]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/run_train.py", line 237, in [default5]:[rank53]: trainer.train(dataloader) [default5]:[rank53]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 429, in train [default5]:[rank53]: outputs, loss_avg = self.training_step(dataloader=self.current_dataloader) [default5]:[rank53]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 462, in training_step [default3]:[rank51]: trainer.train(dataloader) [default5]:[rank53]: outputs = self.pipeline_engine.train_batch_iter( [default5]:[rank53]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 278, in train_batch_iter [default3]:[rank51]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 429, in train [default5]:[rank53]: output = self.forward(context=context, state=state, micro_batch=micro_batch, model=model) [default5]:[rank53]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 44, in forward [default5]:[rank53]: output = model(**micro_batch) [default5]:[rank53]: 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]:[rank53]: return self._call_impl(*args, **kwargs) [default5]:[rank53]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default5]:[rank53]: return forward_call(*args, **kwargs) [default5]:[rank53]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 891, in forward [default5]:[rank53]: sharded_logits = self.model( [default5]:[rank53]: 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]:[rank51]: outputs, loss_avg = self.training_step(dataloader=self.current_dataloader) [default5]:[rank53]: return self._call_impl(*args, **kwargs) [default3]:[rank51]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 462, in training_step [default3]:[rank51]: outputs = self.pipeline_engine.train_batch_iter( [default3]:[rank51]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 278, in train_batch_iter [default3]:[rank51]: output = self.forward(context=context, state=state, micro_batch=micro_batch, model=model) [default3]:[rank51]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 44, in forward [default5]:[rank53]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default5]:[rank53]: return forward_call(*args, **kwargs) [default5]:[rank53]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 764, in forward [default5]:[rank53]: return self.forward_with_hidden_states(input_ids=input_ids, input_mask=input_mask)[0] [default5]:[rank53]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 780, in forward_with_hidden_states [default5]:[rank53]: hidden_encoder_states = encoder_block(**hidden_encoder_states) [default3]:[rank51]: output = model(**micro_batch) [default3]:[rank51]: 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]:[rank51]: return self._call_impl(*args, **kwargs) [default3]:[rank51]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default3]:[rank51]: return forward_call(*args, **kwargs) [default3]:[rank51]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 891, in forward [default3]:[rank51]: sharded_logits = self.model( [default5]:[rank53]: 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]:[rank53]: return self._call_impl(*args, **kwargs) [default5]:[rank53]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default3]:[rank51]: 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]:[rank51]: return self._call_impl(*args, **kwargs) [default3]:[rank51]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default3]:[rank51]: return forward_call(*args, **kwargs) [default5]:[rank53]: return forward_call(*args, **kwargs) [default3]:[rank51]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 764, in forward [default3]:[rank51]: return self.forward_with_hidden_states(input_ids=input_ids, input_mask=input_mask)[0] [default3]:[rank51]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 780, in forward_with_hidden_states [default5]:[rank53]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/block.py", line 151, in forward [default5]:[rank53]: output = self.pp_block(**new_kwargs) [default5]:[rank53]: 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]:[rank53]: return self._call_impl(*args, **kwargs) [default3]:[rank51]: hidden_encoder_states = encoder_block(**hidden_encoder_states) [default5]:[rank53]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default5]:[rank53]: return forward_call(*args, **kwargs) [default3]:[rank51]: 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]:[rank51]: return self._call_impl(*args, **kwargs) [default3]:[rank51]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default3]:[rank51]: return forward_call(*args, **kwargs) [default3]:[rank51]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/block.py", line 151, in forward [default5]:[rank53]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 631, in forward [default3]:[rank51]: output = self.pp_block(**new_kwargs) [default5]:[rank53]: output = self.attn(hidden_states=hidden_states, sequence_mask=sequence_mask) [default3]:[rank51]: 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]:[rank51]: return self._call_impl(*args, **kwargs) [default5]:[rank53]: 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]:[rank53]: return self._call_impl(*args, **kwargs) [default5]:[rank53]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default3]:[rank51]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default3]:[rank51]: return forward_call(*args, **kwargs) [default3]:[rank51]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 631, in forward [default3]:[rank51]: output = self.attn(hidden_states=hidden_states, sequence_mask=sequence_mask) [default3]:[rank51]: 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]:[rank51]: return self._call_impl(*args, **kwargs) [default3]:[rank51]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default5]:[rank53]: return forward_call(*args, **kwargs) [default3]:[rank51]: return forward_call(*args, **kwargs) [default3]:[rank51]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 565, in forward [default5]:[rank53]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 565, in forward [default3]:[rank51]: key_value_states = key_value_states.permute(1, 2, 0, 3, 4).contiguous() [default3]:[rank51]: torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 128.00 MiB. GPU  has a total capacity of 79.33 GiB of which 69.94 MiB is free. Including non-PyTorch memory, this process has 79.25 GiB memory in use. Of the allocated memory 69.57 GiB is allocated by PyTorch, and 64.03 MiB 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]:[rank53]: key_value_states = key_value_states.permute(1, 2, 0, 3, 4).contiguous() [default5]:[rank53]: torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 128.00 MiB. GPU  has a total capacity of 79.33 GiB of which 69.94 MiB is free. Including non-PyTorch memory, this process has 79.25 GiB memory in use. Of the allocated memory 69.57 GiB is allocated by PyTorch, and 64.03 MiB 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]:[rank54]: Traceback (most recent call last): [default6]:[rank54]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/run_train.py", line 237, in [default6]:[rank54]: trainer.train(dataloader) [default6]:[rank54]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 429, in train [default6]:[rank54]: outputs, loss_avg = self.training_step(dataloader=self.current_dataloader) [default6]:[rank54]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 462, in training_step [default6]:[rank54]: outputs = self.pipeline_engine.train_batch_iter( [default6]:[rank54]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 278, in train_batch_iter [default6]:[rank54]: output = self.forward(context=context, state=state, micro_batch=micro_batch, model=model) [default6]:[rank54]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 44, in forward [default6]:[rank54]: output = model(**micro_batch) [default6]:[rank54]: 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]:[rank54]: return self._call_impl(*args, **kwargs) [default6]:[rank54]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default6]:[rank54]: return forward_call(*args, **kwargs) [default6]:[rank54]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 891, in forward [default6]:[rank54]: sharded_logits = self.model( [default6]:[rank54]: 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]:[rank54]: return self._call_impl(*args, **kwargs) [default6]:[rank54]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default6]:[rank54]: return forward_call(*args, **kwargs) [default6]:[rank54]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 764, in forward [default6]:[rank54]: return self.forward_with_hidden_states(input_ids=input_ids, input_mask=input_mask)[0] [default6]:[rank54]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 780, in forward_with_hidden_states [default6]:[rank54]: hidden_encoder_states = encoder_block(**hidden_encoder_states) [default6]:[rank54]: 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]:[rank54]: return self._call_impl(*args, **kwargs) [default6]:[rank54]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default6]:[rank54]: return forward_call(*args, **kwargs) [default6]:[rank54]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/block.py", line 151, in forward [default6]:[rank54]: output = self.pp_block(**new_kwargs) [default6]:[rank54]: 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]:[rank54]: return self._call_impl(*args, **kwargs) [default6]:[rank54]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default6]:[rank54]: return forward_call(*args, **kwargs) [default6]:[rank54]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 631, in forward [default6]:[rank54]: output = self.attn(hidden_states=hidden_states, sequence_mask=sequence_mask) [default6]:[rank54]: 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]:[rank54]: return self._call_impl(*args, **kwargs) [default6]:[rank54]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default6]:[rank54]: return forward_call(*args, **kwargs) [default6]:[rank54]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 565, in forward [default6]:[rank54]: key_value_states = key_value_states.permute(1, 2, 0, 3, 4).contiguous() [default6]:[rank54]: torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 128.00 MiB. GPU  has a total capacity of 79.33 GiB of which 93.94 MiB is free. Including non-PyTorch memory, this process has 79.23 GiB memory in use. Of the allocated memory 69.57 GiB is allocated by PyTorch, and 128.03 MiB 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 [default6]:[rank6]: Traceback (most recent call last): [default6]:[rank6]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/run_train.py", line 237, in [default7]:[rank7]: Traceback (most recent call last): [default7]:[rank7]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/run_train.py", line 237, in [default3]:[rank3]: trainer.train(dataloader) [default7]:[rank7]: trainer.train(dataloader) [default6]:[rank6]: trainer.train(dataloader) [default3]:[rank3]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 429, in train [default6]:[rank6]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 429, in train [default7]:[rank7]: 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) [default7]:[rank7]: outputs, loss_avg = self.training_step(dataloader=self.current_dataloader) [default3]:[rank3]: 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 [default3]:[rank3]: 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( [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 [default7]:[rank7]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 462, in training_step [default6]:[rank6]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 278, in train_batch_iter [default7]:[rank7]: outputs = self.pipeline_engine.train_batch_iter( [default3]:[rank3]: output = self.forward(context=context, state=state, micro_batch=micro_batch, model=model) [default6]:[rank6]: output = self.forward(context=context, state=state, micro_batch=micro_batch, model=model) [default7]:[rank7]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 278, in train_batch_iter [default3]:[rank3]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 44, in forward [default6]:[rank6]: 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) [default7]:[rank7]: output = self.forward(context=context, state=state, micro_batch=micro_batch, model=model) [default6]:[rank6]: 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 [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 [default7]:[rank7]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 44, in forward [default3]:[rank3]: return self._call_impl(*args, **kwargs) [default6]:[rank6]: 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 [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 [default3]:[rank3]: return forward_call(*args, **kwargs) [default6]:[rank6]: return forward_call(*args, **kwargs) [default3]:[rank3]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 891, in forward [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( [default3]:[rank3]: 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 [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 [default7]:[rank7]: output = model(**micro_batch) [default7]:[rank7]: 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 forward_call(*args, **kwargs) [default7]:[rank7]: return self._call_impl(*args, **kwargs) [default3]:[rank3]: return self._call_impl(*args, **kwargs) [default6]:[rank6]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 764, in forward [default7]:[rank7]: 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]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default7]:[rank7]: return forward_call(*args, **kwargs) [default3]:[rank3]: return forward_call(*args, **kwargs) [default7]:[rank7]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 891, in forward [default6]:[rank6]: 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 764, in forward [default7]:[rank7]: sharded_logits = self.model( [default3]:[rank3]: 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) [default7]:[rank7]: 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]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 780, in forward_with_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 [default7]:[rank7]: return self._call_impl(*args, **kwargs) [default3]:[rank3]: hidden_encoder_states = encoder_block(**hidden_encoder_states) [default6]:[rank6]: return self._call_impl(*args, **kwargs) [default7]:[rank7]: 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]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default7]:[rank7]: return forward_call(*args, **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) [default7]:[rank7]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 764, in forward [default6]:[rank6]: return forward_call(*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 [default7]:[rank7]: return self.forward_with_hidden_states(input_ids=input_ids, input_mask=input_mask)[0] [default3]:[rank3]: return forward_call(*args, **kwargs) [default7]:[rank7]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 780, in forward_with_hidden_states [default6]:[rank6]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/block.py", line 151, in forward [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) [default6]:[rank6]: output = self.pp_block(**new_kwargs) [default7]:[rank7]: 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) [default7]:[rank7]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl [default7]:[rank7]: 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 1532, in _wrapped_call_impl [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 [default7]:[rank7]: 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 self._call_impl(*args, **kwargs) [default6]:[rank6]: return forward_call(*args, **kwargs) [default7]:[rank7]: return forward_call(*args, **kwargs) [default7]:[rank7]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/block.py", line 151, in forward [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 [default6]:[rank6]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 631, in forward [default3]:[rank3]: return forward_call(*args, **kwargs) [default3]:[rank3]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 631, in forward [default6]:[rank6]: output = self.attn(hidden_states=hidden_states, sequence_mask=sequence_mask) [default7]:[rank7]: output = self.pp_block(**new_kwargs) [default3]:[rank3]: output = self.attn(hidden_states=hidden_states, sequence_mask=sequence_mask) [default7]:[rank7]: 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]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl [default7]:[rank7]: 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 1532, in _wrapped_call_impl [default7]:[rank7]: 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 self._call_impl(*args, **kwargs) [default6]:[rank6]: 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) [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 [default7]:[rank7]: return forward_call(*args, **kwargs) [default3]:[rank3]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 598, in forward [default7]:[rank7]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 631, in forward [default6]:[rank6]: return forward_call(*args, **kwargs) [default3]:[rank3]: output = self.o_proj(attention_output) [default7]:[rank7]: output = self.attn(hidden_states=hidden_states, sequence_mask=sequence_mask) [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 [default6]:[rank6]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 598, in forward [default7]:[rank7]: 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]: output = self.o_proj(attention_output) [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 [default3]:[rank3]: return self._call_impl(*args, **kwargs) [default7]:[rank7]: return self._call_impl(*args, **kwargs) [default6]:[rank6]: return self._call_impl(*args, **kwargs) [default7]:[rank7]: 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]: 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) [default7]:[rank7]: return forward_call(*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 [default3]:[rank3]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/nn.py", line 159, in forward [default7]:[rank7]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 598, in forward [default6]:[rank6]: return forward_call(*args, **kwargs) [default3]:[rank3]: return row_linear( [default7]:[rank7]: output = self.o_proj(attention_output) [default6]:[rank6]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/nn.py", line 159, in forward [default3]:[rank3]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/functional.py", line 474, in row_linear [default6]:[rank6]: return row_linear( [default3]:[rank3]: out = F.linear(input, weight, bias) [default7]:[rank7]: 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]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/functional.py", line 474, in row_linear [default6]:[rank6]: out = F.linear(input, weight, bias) [default7]:[rank7]: return self._call_impl(*args, **kwargs) [default3]:[rank3]: torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 1024.00 MiB. GPU  has a total capacity of 79.33 GiB of which 475.94 MiB is free. Including non-PyTorch memory, this process has 78.85 GiB memory in use. Of the allocated memory 69.70 GiB is allocated by PyTorch, and 127.53 MiB 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) [default7]:[rank7]: 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]: torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 1024.00 MiB. GPU  has a total capacity of 79.33 GiB of which 563.94 MiB is free. Including non-PyTorch memory, this process has 78.77 GiB memory in use. Of the allocated memory 69.70 GiB is allocated by PyTorch, and 127.53 MiB 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) [default7]:[rank7]: return forward_call(*args, **kwargs) [default7]:[rank7]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/nn.py", line 159, in forward [default7]:[rank7]: return row_linear( [default7]:[rank7]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/functional.py", line 474, in row_linear [default7]:[rank7]: out = F.linear(input, weight, bias) [default7]:[rank7]: torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 1024.00 MiB. GPU  has a total capacity of 79.33 GiB of which 475.94 MiB is free. Including non-PyTorch memory, this process has 78.85 GiB memory in use. Of the allocated memory 69.70 GiB is allocated by PyTorch, and 127.53 MiB 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 [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 [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 [default5]:[rank5]: output = model(**micro_batch) [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) [default5]:[rank5]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 891, in forward [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) [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/models/llama.py", line 764, in forward [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) [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) [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) [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) [default5]:[rank5]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 631, in forward [default5]:[rank5]: output = self.attn(hidden_states=hidden_states, sequence_mask=sequence_mask) [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) [default5]:[rank5]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 598, in forward [default5]:[rank5]: output = self.o_proj(attention_output) [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) [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 474, in row_linear [default5]:[rank5]: out = F.linear(input, weight, bias) [default5]:[rank5]: torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 1024.00 MiB. GPU  has a total capacity of 79.33 GiB of which 475.94 MiB is free. Including non-PyTorch memory, this process has 78.85 GiB memory in use. Of the allocated memory 69.70 GiB is allocated by PyTorch, and 127.53 MiB 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]:[rank14]: Traceback (most recent call last): [default6]:[rank14]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/run_train.py", line 237, in [default6]:[rank14]: trainer.train(dataloader) [default6]:[rank14]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 429, in train [default6]:[rank14]: outputs, loss_avg = self.training_step(dataloader=self.current_dataloader) [default6]:[rank14]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 462, in training_step [default6]:[rank14]: outputs = self.pipeline_engine.train_batch_iter( [default6]:[rank14]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 278, in train_batch_iter [default6]:[rank14]: output = self.forward(context=context, state=state, micro_batch=micro_batch, model=model) [default6]:[rank14]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 44, in forward [default6]:[rank14]: output = model(**micro_batch) [default6]:[rank14]: 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]:[rank14]: return self._call_impl(*args, **kwargs) [default6]:[rank14]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default6]:[rank14]: return forward_call(*args, **kwargs) [default6]:[rank14]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 891, in forward [default6]:[rank14]: sharded_logits = self.model( [default6]:[rank14]: 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]:[rank14]: return self._call_impl(*args, **kwargs) [default6]:[rank14]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default6]:[rank14]: return forward_call(*args, **kwargs) [default6]:[rank14]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 764, in forward [default6]:[rank14]: return self.forward_with_hidden_states(input_ids=input_ids, input_mask=input_mask)[0] [default6]:[rank14]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 780, in forward_with_hidden_states [default6]:[rank14]: hidden_encoder_states = encoder_block(**hidden_encoder_states) [default6]:[rank14]: 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]:[rank14]: return self._call_impl(*args, **kwargs) [default6]:[rank14]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default6]:[rank14]: return forward_call(*args, **kwargs) [default6]:[rank14]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/block.py", line 151, in forward [default6]:[rank14]: output = self.pp_block(**new_kwargs) [default6]:[rank14]: 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]:[rank14]: return self._call_impl(*args, **kwargs) [default6]:[rank14]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default6]:[rank14]: return forward_call(*args, **kwargs) [default6]:[rank14]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 631, in forward [default6]:[rank14]: output = self.attn(hidden_states=hidden_states, sequence_mask=sequence_mask) [default6]:[rank14]: 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]:[rank14]: return self._call_impl(*args, **kwargs) [default6]:[rank14]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default6]:[rank14]: return forward_call(*args, **kwargs) [default6]:[rank14]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 565, in forward [default6]:[rank14]: key_value_states = key_value_states.permute(1, 2, 0, 3, 4).contiguous() [default6]:[rank14]: torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 128.00 MiB. GPU  has a total capacity of 79.33 GiB of which 5.94 MiB is free. Including non-PyTorch memory, this process has 79.31 GiB memory in use. Of the allocated memory 69.57 GiB is allocated by PyTorch, and 128.03 MiB 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) [default4]:[rank4]: Traceback (most recent call last): [default4]:[rank4]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/run_train.py", line 237, in [default4]:[rank4]: trainer.train(dataloader) [default0]:[rank0]: Traceback (most recent call last): [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 [default0]:[rank0]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/run_train.py", line 237, in [default0]:[rank0]: trainer.train(dataloader) [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 [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 [default4]:[rank4]: output = model(**micro_batch) [default0]:[rank0]: outputs = self.pipeline_engine.train_batch_iter( [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) [default0]:[rank0]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 278, in train_batch_iter [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) [default0]:[rank0]: 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/models/llama.py", line 891, 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 [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 764, in forward [default0]:[rank0]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 44, in forward [default4]:[rank4]: return self.forward_with_hidden_states(input_ids=input_ids, input_mask=input_mask)[0] [default4]:[rank4]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 780, in forward_with_hidden_states [default4]:[rank4]: 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 1532, in _wrapped_call_impl [default4]:[rank4]: return self._call_impl(*args, **kwargs) [default0]:[rank0]: 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 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) [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 [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 [default0]:[rank0]: return self._call_impl(*args, **kwargs) [default4]:[rank4]: 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 [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 [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( [default4]:[rank4]: return forward_call(*args, **kwargs) [default4]:[rank4]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 631, in forward [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 [default4]:[rank4]: output = self.attn(hidden_states=hidden_states, sequence_mask=sequence_mask) [default0]:[rank0]: 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 [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 [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 [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) [default2]:[rank2]: Traceback (most recent call last): [default2]:[rank2]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/run_train.py", line 237, in [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) [default2]:[rank2]: trainer.train(dataloader) [default2]:[rank2]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 429, in train [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 [default2]:[rank2]: outputs, loss_avg = self.training_step(dataloader=self.current_dataloader) [default0]:[rank0]: return forward_call(*args, **kwargs) [default4]:[rank4]: return forward_call(*args, **kwargs) [default2]:[rank2]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 462, in training_step [default4]:[rank4]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 598, in forward [default0]:[rank0]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 631, in forward [default4]:[rank4]: output = self.o_proj(attention_output) [default0]:[rank0]: output = self.attn(hidden_states=hidden_states, sequence_mask=sequence_mask) [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 [default2]:[rank2]: outputs = self.pipeline_engine.train_batch_iter( [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 [default0]:[rank0]: return self._call_impl(*args, **kwargs) [default2]:[rank2]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 278, in train_batch_iter [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 [default2]:[rank2]: output = self.forward(context=context, state=state, micro_batch=micro_batch, model=model) [default0]:[rank0]: return forward_call(*args, **kwargs) [default4]:[rank4]: return self._call_impl(*args, **kwargs) [default2]:[rank2]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 44, in forward [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) [default0]:[rank0]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 598, in forward [default4]:[rank4]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/nn.py", line 159, 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 [default0]:[rank0]: output = self.o_proj(attention_output) [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 [default2]:[rank2]: return self._call_impl(*args, **kwargs) [default4]:[rank4]: return row_linear( [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 [default4]:[rank4]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/functional.py", line 474, in row_linear [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) [default1]:[rank1]: Traceback (most recent call last): [default1]:[rank1]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/run_train.py", line 237, in [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( [default1]:[rank1]: trainer.train(dataloader) [default4]:[rank4]: out = F.linear(input, weight, bias) [default4]:[rank4]: torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 1024.00 MiB. GPU  has a total capacity of 79.33 GiB of which 563.94 MiB is free. Including non-PyTorch memory, this process has 78.77 GiB memory in use. Of the allocated memory 69.70 GiB is allocated by PyTorch, and 127.53 MiB 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) [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) [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 [default1]:[rank1]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 462, in training_step [default0]:[rank0]: return forward_call(*args, **kwargs) [default2]:[rank2]: return self._call_impl(*args, **kwargs) [default1]:[rank1]: outputs = self.pipeline_engine.train_batch_iter( [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( [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 [default0]:[rank0]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/functional.py", line 474, in row_linear [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 [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) [default0]:[rank0]: out = F.linear(input, weight, bias) [default1]:[rank1]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 44, in forward [default0]:[rank0]: torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 1024.00 MiB. GPU [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 [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 [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 [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 [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 [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( [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 [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 [default2]:[rank2]: return forward_call(*args, **kwargs) [default2]:[rank2]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 631, in forward [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 [default2]:[rank2]: output = self.attn(hidden_states=hidden_states, sequence_mask=sequence_mask) [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 [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 [default2]:[rank2]: return self._call_impl(*args, **kwargs) [default1]:[rank1]: return forward_call(*args, **kwargs) [default1]:[rank1]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 631, in forward [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 598, in forward [default1]:[rank1]: output = self.attn(hidden_states=hidden_states, sequence_mask=sequence_mask) [default2]:[rank2]: output = self.o_proj(attention_output) [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) [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 [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 474, in row_linear [default2]:[rank2]: out = F.linear(input, weight, bias) [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 598, in forward [default1]:[rank1]: output = self.o_proj(attention_output) [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) [default0]:[rank8]: Traceback (most recent call last): [default0]:[rank8]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/run_train.py", line 237, in [default0]:[rank8]: trainer.train(dataloader) [default0]:[rank8]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 429, in train [default0]:[rank8]: outputs, loss_avg = self.training_step(dataloader=self.current_dataloader) [default0]:[rank8]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 462, in training_step [default0]:[rank8]: outputs = self.pipeline_engine.train_batch_iter( [default0]:[rank8]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 278, in train_batch_iter [default0]:[rank8]: output = self.forward(context=context, state=state, micro_batch=micro_batch, model=model) [default0]:[rank8]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/na[default2]:[rank2]: torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 1024.00 MiB. GPU  has a total capacity of 79.33 GiB of which 563.94 MiB is free. Including non-PyTorch memory, this process has 78.77 GiB memory in use. Of the allocated memory 69.70 GiB is allocated by PyTorch, and 127.53 MiB 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) [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 474, in row_linear [default1]:[rank1]: out = F.linear(input, weight, bias) [default1]:[rank1]: torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 1024.00 MiB. GPU  has a total capacity of 79.33 GiB of which 475.94 MiB is free. Including non-PyTorch memory, this process has 78.85 GiB memory in use. Of the allocated memory 69.70 GiB is allocated by PyTorch, and 127.53 MiB 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) notron/parallel/pipeline_parallel/engine.py", line 44, in forward [default0]:[rank8]: output = model(**micro_batch) [default0]:[rank8]: 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]:[rank8]: return self._call_impl(*args, **kwargs) [default0]:[rank8]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default0]:[rank8]: return forward_call(*args, **kwargs) [default0]:[rank8]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 891, in forward [default0]:[rank8]: sharded_logits = self.model( [default0]:[rank8]: 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]:[rank8]: return self._call_impl(*args, **kwargs) [default0]:[rank8]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default0]:[rank8]: return forward_call(*args, **kwargs) [default0]:[rank8]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 764, in forward [default0]:[rank8]: return self.forward_with_hidden_states(input_ids=input_ids, input_mask=input_mask)[0] [default0]:[rank8]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 780, in forward_with_hidden_states [default0]:[rank8]: hidden_encoder_states = encoder_block(**hidden_encoder_states) [default0]:[rank8]: 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]:[rank8]: return self._call_impl(*args, **kwargs) [default0]:[rank8]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default0]:[rank8]: return forward_call(*args, **kwargs) [default0]:[rank8]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/block.py", line 151, in forward [default0]:[rank8]: output = self.pp_block(**new_kwargs) [default0]:[rank8]: 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]:[rank8]: return self._call_impl(*args, **kwargs) [default0]:[rank8]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default0]:[rank8]: return forward_call(*args, **kwargs) [default0]:[rank8]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 631, in forward [default0]:[rank8]: output = self.attn(hidden_states=hidden_states, sequence_mask=sequence_mask) [default0]:[rank8]: 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]:[rank8]: return self._call_impl(*args, **kwargs) [default0]:[rank8]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default0]:[rank8]: return forward_call(*args, **kwargs) [default0]:[rank8]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 565, in forward [default0]:[rank8]: key_value_states = key_value_states.permute(1, 2, 0, 3, 4).contiguous() [default0]:[rank8]: torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 128.00 MiB. GPU [default4]:[rank12]: Traceback (most recent call last): [default4]:[rank12]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/run_train.py", line 237, in [default4]:[rank12]: trainer.train(dataloader) [default4]:[rank12]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 429, in train [default4]:[rank12]: outputs, loss_avg = self.training_step(dataloader=self.current_dataloader) [default4]:[rank12]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 462, in training_step [default4]:[rank12]: outputs = self.pipeline_engine.train_batch_iter( [default4]:[rank12]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 278, in train_batch_iter [default4]:[rank12]: output = self.forward(context=context, state=state, micro_batch=micro_batch, model=model) [default4]:[rank12]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 44, in forward [default4]:[rank12]: output = model(**micro_batch) [default4]:[rank12]: 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]:[rank12]: return self._call_impl(*args, **kwargs) [default4]:[rank12]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default4]:[rank12]: return forward_call(*args, **kwargs) [default4]:[rank12]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 891, in forward [default4]:[rank12]: sharded_logits = self.model( [default4]:[rank12]: 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]:[rank12]: return self._call_impl(*args, **kwargs) [default4]:[rank12]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default4]:[rank12]: return forward_call(*args, **kwargs) [default4]:[rank12]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 764, in forward [default4]:[rank12]: return self.forward_with_hidden_states(input_ids=input_ids, input_mask=input_mask)[0] [default4]:[rank12]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 780, in forward_with_hidden_states [default4]:[rank12]: hidden_encoder_states = encoder_block(**hidden_encoder_states) [default4]:[rank12]: 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]:[rank12]: return self._call_impl(*args, **kwargs) [default4]:[rank12]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default4]:[rank12]: return forward_call(*args, **kwargs) [default4]:[rank12]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/block.py", line 151, in forward [default4]:[rank12]: output = self.pp_block(**new_kwargs) [default4]:[rank12]: 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]:[rank12]: return self._call_impl(*args, **kwargs) [default4]:[rank12]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default4]:[rank12]: return forward_call(*args, **kwargs) [default4]:[rank12]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 631, in forward [default4]:[rank12]: output = self.attn(hidden_states=hidden_states, sequence_mask=sequence_mask) [default4]:[rank12]: 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]:[rank12]: return self._call_impl(*args, **kwargs) [default4]:[rank12]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default4]:[rank12]: return forward_call(*args, **kwargs) [default4]:[rank12]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 565, in forward [default4]:[rank12]: key_value_states = key_value_states.permute(1, 2, 0, 3, 4).contiguous() [default4]:[rank12]: torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 128.00 MiB. GPU  has a total capacity of 79.33 GiB of which 5.94 MiB is free. Including non-PyTorch memory, this process has 79.31 GiB memory in use. Of the allocated memory 69.57 GiB is allocated by PyTorch, and 128.03 MiB 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]:[rank11]: Traceback (most recent call last): [default3]:[rank11]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/run_train.py", line 237, in [default3]:[rank11]: trainer.train(dataloader) [default3]:[rank11]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 429, in train [default3]:[rank11]: outputs, loss_avg = self.training_step(dataloader=self.current_dataloader) [default3]:[rank11]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 462, in training_step [default3]:[rank11]: outputs = self.pipeline_engine.train_batch_iter( [default3]:[rank11]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 278, in train_batch_iter [default3]:[rank11]: output = self.forward(context=context, state=state, micro_batch=micro_batch, model=model) [default3]:[rank11]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 44, in forward [default3]:[rank11]: output = model(**micro_batch) [default3]:[rank11]: 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]:[rank11]: return self._call_impl(*args, **kwargs) [default3]:[rank11]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default3]:[rank11]: return forward_call(*args, **kwargs) [default3]:[rank11]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 891, in forward [default3]:[rank11]: sharded_logits = self.model( [default3]:[rank11]: 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]:[rank11]: return self._call_impl(*args, **kwargs) [default3]:[rank11]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default3]:[rank11]: return forward_call(*args, **kwargs) [default3]:[rank11]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 764, in forward [default3]:[rank11]: return self.forward_with_hidden_states(input_ids=input_ids, input_mask=input_mask)[0] [default3]:[rank11]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 780, in forward_with_hidden_states [default3]:[rank11]: hidden_encoder_states = encoder_block(**hidden_encoder_states) [default3]:[rank11]: 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]:[rank11]: return self._call_impl(*args, **kwargs) [default3]:[rank11]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default3]:[rank11]: return forward_call(*args, **kwargs) [default3]:[rank11]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/block.py", line 151, in forward [default3]:[rank11]: output = self.pp_block(**new_kwargs) [default3]:[rank11]: 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]:[rank11]: return self._call_impl(*args, **kwargs) [default3]:[rank11]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default3]:[rank11]: return forward_call(*args, **kwargs) [default3]:[rank11]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 631, in forward [default3]:[rank11]: output = self.attn(hidden_states=hidden_states, sequence_mask=sequence_mask) [default3]:[rank11]: 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]:[rank11]: return self._call_impl(*args, **kwargs) [default3]:[rank11]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default3]:[rank11]: return forward_call(*args, **kwargs) [default3]:[rank11]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 565, in forward [default3]:[rank11]: key_value_states = key_value_states.permute(1, 2, 0, 3, 4).contiguous() [default3]:[rank11]: torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 128.00 MiB. GPU  has a total capacity of 79.33 GiB of which 93.94 MiB is free. Including non-PyTorch memory, this process has 79.23 GiB memory in use. Of the allocated memory 69.57 GiB is allocated by PyTorch, and 128.03 MiB 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) [default7]:[rank15]: Traceback (most recent call last): [default7]:[rank15]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/run_train.py", line 237, in [default7]:[rank15]: trainer.train(dataloader) [default7]:[rank15]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 429, in train [default7]:[rank15]: outputs, loss_avg = self.training_step(dataloader=self.current_dataloader) [default7]:[rank15]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 462, in training_step [default7]:[rank15]: outputs = self.pipeline_engine.train_batch_iter( [default7]:[rank15]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 278, in train_batch_iter [default7]:[rank15]: output = self.forward(context=context, state=state, micro_batch=micro_batch, model=model) [default7]:[rank15]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 44, in forward [default7]:[rank15]: output = model(**micro_batch) [default7]:[rank15]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl [default7]:[rank15]: return self._call_impl(*args, **kwargs) [default7]:[rank15]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default7]:[rank15]: return forward_call(*args, **kwargs) [default7]:[rank15]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 891, in forward [default7]:[rank15]: sharded_logits = self.model( [default7]:[rank15]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl [default7]:[rank15]: return self._call_impl(*args, **kwargs) [default7]:[rank15]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default7]:[rank15]: return forward_call(*args, **kwargs) [default7]:[rank15]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 764, in forward [default7]:[rank15]: return self.forward_with_hidden_states(input_ids=input_ids, input_mask=input_mask)[0] [default7]:[rank15]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 780, in forward_with_hidden_states [default7]:[rank15]: hidden_encoder_states = encoder_block(**hidden_encoder_states) [default7]:[rank15]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl [default7]:[rank15]: return self._call_impl(*args, **kwargs) [default7]:[rank15]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default7]:[rank15]: return forward_call(*args, **kwargs) [default7]:[rank15]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/block.py", line 151, in forward [default7]:[rank15]: output = self.pp_block(**new_kwargs) [default7]:[rank15]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl [default7]:[rank15]: return self._call_impl(*args, **kwargs) [default7]:[rank15]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default7]:[rank15]: return forward_call(*args, **kwargs) [default7]:[rank15]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 631, in forward [default7]:[rank15]: output = self.attn(hidden_states=hidden_states, sequence_mask=sequence_mask) [default7]:[rank15]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl [default7]:[rank15]: return self._call_impl(*args, **kwargs) [default7]:[rank15]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default7]:[rank15]: return forward_call(*args, **kwargs) [default7]:[rank15]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 598, in forward [default7]:[rank15]: output = self.o_proj(attention_output) [default7]:[rank15]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl [default7]:[rank15]: return self._call_impl(*args, **kwargs) [default7]:[rank15]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default7]:[rank15]: return forward_call(*args, **kwargs) [default7]:[rank15]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/nn.py", line 159, in forward [default7]:[rank15]: return row_linear( [default7]:[rank15]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/functional.py", line 474, in row_linear [default7]:[rank15]: out = F.linear(input, weight, bias) [default7]:[rank15]: torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 1024.00 MiB. GPU  has a total capacity of 79.33 GiB of which 27.94 MiB is free. Including non-PyTorch memory, this process has 79.29 GiB memory in use. Of the allocated memory 69.70 GiB is allocated by PyTorch, and 63.53 MiB 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) [default2]:[rank10]: Traceback (most recent call last): [default2]:[rank10]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/run_train.py", line 237, in [default2]:[rank10]: trainer.train(dataloader) [default2]:[rank10]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 429, in train [default2]:[rank10]: outputs, loss_avg = self.training_step(dataloader=self.current_dataloader) [default2]:[rank10]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 462, in training_step [default2]:[rank10]: outputs = self.pipeline_engine.train_batch_iter( [default2]:[rank10]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 278, in train_batch_iter [default2]:[rank10]: output = self.forward(context=context, state=state, micro_batch=micro_batch, model=model) [default2]:[rank10]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 44, in forward [default2]:[rank10]: output = model(**micro_batch) [default2]:[rank10]: 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]:[rank10]: return self._call_impl(*args, **kwargs) [default2]:[rank10]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default2]:[rank10]: return forward_call(*args, **kwargs) [default2]:[rank10]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 891, in forward [default2]:[rank10]: sharded_logits = self.model( [default2]:[rank10]: 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]:[rank10]: return self._call_impl(*args, **kwargs) [default2]:[rank10]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default2]:[rank10]: return forward_call(*args, **kwargs) [default2]:[rank10]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 764, in forward [default2]:[rank10]: return self.forward_with_hidden_states(input_ids=input_ids, input_mask=input_mask)[0] [default2]:[rank10]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 780, in forward_with_hidden_states [default2]:[rank10]: hidden_encoder_states = encoder_block(**hidden_encoder_states) [default2]:[rank10]: 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]:[rank10]: return self._call_impl(*args, **kwargs) [default2]:[rank10]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default2]:[rank10]: return forward_call(*args, **kwargs) [default2]:[rank10]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/block.py", line 151, in forward [default2]:[rank10]: output = self.pp_block(**new_kwargs) [default2]:[rank10]: 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]:[rank10]: return self._call_impl(*args, **kwargs) [default2]:[rank10]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default2]:[rank10]: return forward_call(*args, **kwargs) [default2]:[rank10]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 631, in forward [default2]:[rank10]: output = self.attn(hidden_states=hidden_states, sequence_mask=sequence_mask) [default2]:[rank10]: 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]:[rank10]: return self._call_impl(*args, **kwargs) [default2]:[rank10]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default2]:[rank10]: return forward_call(*args, **kwargs) [default2]:[rank10]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 565, in forward [default2]:[rank10]: key_value_states = key_value_states.permute(1, 2, 0, 3, 4).contiguous() [default2]:[rank10]: torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 128.00 MiB. GPU  has a total capacity of 79.33 GiB of which 69.94 MiB is free. Including non-PyTorch memory, this process has 79.25 GiB memory in use. Of the allocated memory 69.57 GiB is allocated by PyTorch, and 64.03 MiB 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) [default1]:[rank9]: Traceback (most recent call last): [default1]:[rank9]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/run_train.py", line 237, in [default1]:[rank9]: trainer.train(dataloader) [default1]:[rank9]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 429, in train [default5]:[rank13]: Traceback (most recent call last): [default1]:[rank9]: outputs, loss_avg = self.training_step(dataloader=self.current_dataloader) [default1]:[rank9]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 462, in training_step [default1]:[rank9]: outputs = self.pipeline_engine.train_batch_iter( [default1]:[rank9]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 278, in train_batch_iter [default1]:[rank9]: output = self.forward(context=context, state=state, micro_batch=micro_batch, model=model) [default1]:[rank9]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 44, in forward [default1]:[rank9]: output = model(**micro_batch) [default5]:[rank13]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/run_train.py", line 237, in [default1]:[rank9]: 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]:[rank9]: return self._call_impl(*args, **kwargs) [default1]:[rank9]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default5]:[rank13]: trainer.train(dataloader) [default5]:[rank13]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 429, in train [default5]:[rank13]: outputs, loss_avg = self.training_step(dataloader=self.current_dataloader) [default1]:[rank9]: return forward_call(*args, **kwargs) [default1]:[rank9]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 891, in forward [default1]:[rank9]: sharded_logits = self.model( [default1]:[rank9]: 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]:[rank9]: return self._call_impl(*args, **kwargs) [default1]:[rank9]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default1]:[rank9]: return forward_call(*args, **kwargs) [default1]:[rank9]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 764, in forward [default1]:[rank9]: return self.forward_with_hidden_states(input_ids=input_ids, input_mask=input_mask)[0] [default1]:[rank9]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 780, in forward_with_hidden_states [default5]:[rank13]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 462, in training_step [default5]:[rank13]: outputs = self.pipeline_engine.train_batch_iter( [default5]:[rank13]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 278, in train_batch_iter [default5]:[rank13]: output = self.forward(context=context, state=state, micro_batch=micro_batch, model=model) [default5]:[rank13]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 44, in forward [default5]:[rank13]: output = model(**micro_batch) [default5]:[rank13]: 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]:[rank13]: return self._call_impl(*args, **kwargs) [default5]:[rank13]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default5]:[rank13]: return forward_call(*args, **kwargs) [default5]:[rank13]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 891, in forward [default5]:[rank13]: sharded_logits = self.model( [default5]:[rank13]: 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]:[rank9]: hidden_encoder_states = encoder_block(**hidden_encoder_states) [default5]:[rank13]: return self._call_impl(*args, **kwargs) [default5]:[rank13]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default5]:[rank13]: return forward_call(*args, **kwargs) [default5]:[rank13]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 764, in forward [default5]:[rank13]: return self.forward_with_hidden_states(input_ids=input_ids, input_mask=input_mask)[0] [default1]:[rank9]: 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]:[rank13]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 780, in forward_with_hidden_states [default1]:[rank9]: return self._call_impl(*args, **kwargs) [default1]:[rank9]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default5]:[rank13]: hidden_encoder_states = encoder_block(**hidden_encoder_states) [default1]:[rank9]: return forward_call(*args, **kwargs) [default5]:[rank13]: 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]:[rank9]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/block.py", line 151, in forward [default5]:[rank13]: return self._call_impl(*args, **kwargs) [default1]:[rank9]: output = self.pp_block(**new_kwargs) [default5]:[rank13]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default1]:[rank9]: 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]:[rank13]: return forward_call(*args, **kwargs) [default1]:[rank9]: return self._call_impl(*args, **kwargs) [default5]:[rank13]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/block.py", line 151, in forward [default5]:[rank13]: output = self.pp_block(**new_kwargs) [default5]:[rank13]: 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]:[rank13]: return self._call_impl(*args, **kwargs) [default5]:[rank13]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default1]:[rank9]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default1]:[rank9]: return forward_call(*args, **kwargs) [default5]:[rank13]: return forward_call(*args, **kwargs) [default5]:[rank13]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 631, in forward [default1]:[rank9]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 631, in forward [default1]:[rank9]: output = self.attn(hidden_states=hidden_states, sequence_mask=sequence_mask) [default1]:[rank9]: 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]:[rank9]: return self._call_impl(*args, **kwargs) [default1]:[rank9]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default5]:[rank13]: output = self.attn(hidden_states=hidden_states, sequence_mask=sequence_mask) [default1]:[rank9]: return forward_call(*args, **kwargs) [default1]:[rank9]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 598, in forward [default5]:[rank13]: 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]:[rank9]: output = self.o_proj(attention_output) [default1]:[rank9]: 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]:[rank9]: return self._call_impl(*args, **kwargs) [default5]:[rank13]: return self._call_impl(*args, **kwargs) [default5]:[rank13]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default1]:[rank9]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default1]:[rank9]: return forward_call(*args, **kwargs) [default1]:[rank9]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/nn.py", line 159, in forward [default1]:[rank9]: return row_linear( [default1]:[rank9]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/functional.py", line 474, in row_linear [default1]:[rank9]: out = F.linear(input, weight, bias) [default5]:[rank13]: return forward_call(*args, **kwargs) [default5]:[rank13]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 598, in forward [default1]:[rank9]: torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 1024.00 MiB. GPU  has a total capacity of 79.33 GiB of which 27.94 MiB is free. Including non-PyTorch memory, this process has 79.29 GiB memory in use. Of the allocated memory 69.70 GiB is allocated by PyTorch, and 63.53 MiB 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]:[rank13]: output = self.o_proj(attention_output) [default5]:[rank13]: 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]:[rank13]: return self._call_impl(*args, **kwargs) [default5]:[rank13]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default5]:[rank13]: return forward_call(*args, **kwargs) [default5]:[rank13]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/nn.py", line 159, in forward [default5]:[rank13]: return row_linear( [default5]:[rank13]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/functional.py", line 474, in row_linear [default5]:[rank13]: out = F.linear(input, weight, bias) [default5]:[rank13]: torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 1024.00 MiB. GPU  has a total capacity of 79.33 GiB of which 27.94 MiB is free. Including non-PyTorch memory, this process has 79.29 GiB memory in use. Of the allocated memory 69.70 GiB is allocated by PyTorch, and 63.53 MiB 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) E0703 01:01:41.603000 140709557507904 torch/distributed/elastic/multiprocessing/api.py:826] failed (exitcode: 1) local_rank: 0 (pid: 1398940) 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 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_01:01:41 host : ip-26-0-162-233.ec2.internal rank : 33 (local_rank: 1) exitcode : 1 (pid: 1398941) error_file: traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html [2]: time : 2024-07-03_01:01:41 host : ip-26-0-162-233.ec2.internal rank : 34 (local_rank: 2) exitcode : 1 (pid: 1398942) error_file: traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html [3]: time : 2024-07-03_01:01:41 host : ip-26-0-162-233.ec2.internal rank : 35 (local_rank: 3) exitcode : 1 (pid: 1398943) error_file: traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html [4]: time : 2024-07-03_01:01:41 host : ip-26-0-162-233.ec2.internal rank : 36 (local_rank: 4) exitcode : 1 (pid: 1398944) error_file: traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html [5]: time : 2024-07-03_01:01:41 host : ip-26-0-162-233.ec2.internal rank : 37 (local_rank: 5) exitcode : 1 (pid: 1398945) error_file: traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html [6]: time : 2024-07-03_01:01:41 host : ip-26-0-162-233.ec2.internal rank : 38 (local_rank: 6) exitcode : 1 (pid: 1398946) error_file: traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html [7]: time : 2024-07-03_01:01:41 host : ip-26-0-162-233.ec2.internal rank : 39 (local_rank: 7) exitcode : 1 (pid: 1398947) error_file: traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html ------------------------------------------------------------ Root Cause (first observed failure): [0]: time : 2024-07-03_01:01:41 host : ip-26-0-162-233.ec2.internal rank : 32 (local_rank: 0) exitcode : 1 (pid: 1398940) error_file: traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html ============================================================ E0703 01:01:41.701000 139854410950464 torch/distributed/elastic/multiprocessing/api.py:826] failed (exitcode: 1) local_rank: 0 (pid: 1139831) of binary: /fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/bin/python3.10 E0703 01:01:41.701000 140540211869504 torch/distributed/elastic/multiprocessing/api.py:826] failed (exitcode: 1) local_rank: 0 (pid: 1418502) of binary: /fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/bin/python3.10 E0703 01:01:41.703000 140092964255552 torch/distributed/elastic/multiprocessing/api.py:826] failed (exitcode: 1) local_rank: 0 (pid: 3762966) of binary: /fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/bin/python3.10 E0703 01:01:41.703000 140324900312896 torch/distributed/elastic/multiprocessing/api.py:826] failed (exitcode: 1) local_rank: 0 (pid: 880579) of binary: /fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/bin/python3.10 E0703 01:01:41.703000 140432057993024 torch/distributed/elastic/multiprocessing/api.py:826] failed (exitcode: 1) local_rank: 0 (pid: 867197) of binary: /fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/bin/python3.10 E0703 01:01:41.704000 140015942473536 torch/distributed/elastic/multiprocessing/api.py:826] failed (exitcode: 1) local_rank: 0 (pid: 1776595) of binary: /fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/bin/python3.10 E0703 01:01:41.706000 140574819604288 torch/distributed/elastic/multiprocessing/api.py:826] failed (exitcode: 1) local_rank: 0 (pid: 3891512) 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 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__ Traceback (most recent call last): File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/bin/torchrun", line 8, in 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 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 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_01:01:41 host : ip-26-0-171-88.ec2.internal rank : 57 (local_rank: 1) exitcode : 1 (pid: 880580) error_file: traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html [2]: time : 2024-07-03_01:01:41 host : ip-26-0-171-88.ec2.internal rank : 58 (local_rank: 2) exitcode : 1 (pid: 880581) error_file: traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html [3]: time : 2024-07-03_01:01:41 host : ip-26-0-171-88.ec2.internal rank : 59 (local_rank: 3) exitcode : 1 (pid: 880582) error_file: traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html [4]: time : 2024-07-03_01:01:41 host : ip-26-0-171-88.ec2.internal rank : 60 (local_rank: 4) exitcode : 1 (pid: 880583) error_file: traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html [5]: time : 2024-07-03_01:01:41 host : ip-26-0-171-88.ec2.internal rank : 61 (local_rank: 5) exitcode : 1 (pid: 880584) error_file: traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html [6]: time : 2024-07-03_01:01:41 host : ip-26-0-171-88.ec2.internal rank : 62 (local_rank: 6) exitcode : 1 (pid: 880585) error_file: traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html [7]: time : 2024-07-03_01:01:41 host : ip-26-0-171-88.ec2.internal rank : 63 (local_rank: 7) exitcode : 1 (pid: 880586) error_file: traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html ------------------------------------------------------------ Root Cause (first observed failure): [0]: time : 2024-07-03_01:01:41 host : ip-26-0-171-88.ec2.internal rank : 56 (local_rank: 0) exitcode : 1 (pid: 880579) error_file: traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html ============================================================ 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 Traceback (most recent call last): File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/bin/torchrun", line 8, in elastic_launch( File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/launcher/api.py", line 132, in __call__ Traceback (most recent call last): File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/bin/torchrun", line 8, in 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 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 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 raise ChildFailedError( 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 torch.distributed.elastic.multiprocessing.errors.ChildFailedError: ============================================================ /fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/run_train.py FAILED ------------------------------------------------------------ Failures: [1]: time : 2024-07-03_01:01:41 host : ip-26-0-171-102.ec2.internal rank : 41 (local_rank: 1) exitcode : 1 (pid: 3762967) error_file: traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html [2]: time : 2024-07-03_01:01:41 host : ip-26-0-171-102.ec2.internal rank : 42 (local_rank: 2) exitcode : 1 (pid: 3762968) error_file: traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html [3]: time : 2024-07-03_01:01:41 host : ip-26-0-171-102.ec2.internal rank : 43 (local_rank: 3) exitcode : 1 (pid: 3762969) error_file: traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html [4]: time : 2024-07-03_01:01:41 host : ip-26-0-171-102.ec2.internal rank : 44 (local_rank: 4) exitcode : 1 (pid: 3762970) error_file: traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html [5]: time : 2024-07-03_01:01:41 host : ip-26-0-171-102.ec2.internal rank : 45 (local_rank: 5) exitcode : 1 (pid: 3762971) error_file: traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html [6]: time : 2024-07-03_01:01:41 host : ip-26-0-171-102.ec2.internal rank : 46 (local_rank: 6) exitcode : 1 (pid: 3762972) error_file: traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html [7]: time : 2024-07-03_01:01:41 host : ip-26-0-171-102.ec2.internal rank : 47 (local_rank: 7) exitcode : 1 (pid: 3762973) error_file: traceback : To enable traceback see: https://pytorch.org/docs/st run(args) able/elastic/errors.html ------------------------------------------------------------ Root Cause (first observed failure): [0]: time : 2024-07-03_01:01:41 host : ip-26-0-171-102.ec2.internal rank : 40 (local_rank: 0) exitcode : 1 (pid: 3762966) error_file: traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html ============================================================ File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/run.py", line 870, in run 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( Traceback (most recent call last): File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/bin/torchrun", line 8, in 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)) elastic_launch( File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/launcher/api.py", line 132, in __call__ File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/launcher/api.py", line 263, in launch_agent 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 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_01:01:41 host : ip-26-0-161-78.ec2.internal rank : 25 (local_rank: 1) exitcode : 1 (pid: 1139832) error_file: traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html [2]: time : 2024-07-03_01:01:41 host : ip-26-0-161-78.ec2.internal rank : 26 (local_rank: 2) exitcode : 1 (pid: 1139833) error_file: traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html [3]: time : 2024-07-03_01:01:41 host : ip-26-0-161-78.ec2.internal rank : 27 (local_rank: 3) exitcode : 1 (pid: 1139834) error_file: traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html [4]: time : 2024-07-03_01:01:41 host : ip-26-0-161-78.ec2.internal rank : 28 (local_rank: 4) exitcode : 1 (pid: 1139835) error_file: traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html [5]: time : 2024-07-03_01:01:41 host : ip-26-0-161-78.ec2.internal rank : 29 (local_rank: 5) exitcode : 1 (pid: 1139836) error_file: traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html [6]: time : 2024-07-03_01:01:41 host : ip-26-0-161-78.ec2.internal rank : 30 (local_rank: 6) exitcode : 1 (pid: 1139837) error_file: traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html [7]: time : 2024-07-03_01:01:41 host : ip-26-0-161-78.ec2.internal rank : 31 (local_rank: 7) exitcode : 1 (pid: 1139838) error_file: traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html ------------------------------------------------------------ Root Cause (first observed failure): [0]: time : 2024-07-03_01:01:41 host : ip-26-0-161-78.ec2.internal rank : 24 (local_rank: 0) exitcode : 1 (pid: 1139831) error_file: traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html ============================================================ 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 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_01:01:41 host : ip-26-0-160-225.ec2.internal rank : 1 (local_rank: 1) exitcode : 1 (pid: 1776596) error_file: traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html [2]: time : 2024-07-03_01:01:41 host : ip-26-0-160-225.ec2.internal rank : 2 (local_rank: 2) exitcode : 1 (pid: 1776597) error_file: traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html [3]: time : 2024-07-03_01:01:41 host : ip-26-0-160-225.ec2.internal rank : 3 (local_rank: 3) exitcode : 1 (pid: 1776598) error_file: traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html [4]: time : 2024-07-03_01:01:41 host : ip-26-0-160-225.ec2.internal rank : 4 (local_rank: 4) exitcode : 1 (pid: 1776599) error_file: traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html [5]: time : 2024-07-03_01:01:41 host : ip-26-0-160-225.ec2.internal rank : 5 (local_rank: 5) exitcode : 1 (pid: 1776600) error_file: traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html [6]: time : 2024-07-03_01:01:41 host : ip-26-0-160-225.ec2.internal rank : 6 (local_rank: 6) exitcode : 1 (pid: 1776601) error_file: traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html [7]: time : 2024-07-03_01:01:41 host : ip-26-0-160-225.ec2.internal rank : 7 (local_rank: 7) exitcode : 1 (pid: 1776602) error_file: traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html ------------------------------------------------------------ Root Cause (first observed failure): [0]: time : 2024-07-03_01:01:41 host : ip-26-0-160-225.ec2.internal rank : 0 (local_rank: 0) exitcode : 1 (pid: 1776595) error_file: traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html ============================================================ Traceback (most recent call last): File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/bin/torchrun", line 8, in run(args) File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/run.py", line 870, in run 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 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 f(*args, **kwargs) File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/run.py", line 879, in main 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 run(args) File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/run.py", line 870, in run raise ChildFailedError( Traceback (most recent call last): File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/bin/torchrun", line 8, in torch.distributed.elastic.multiprocessing.errors.ChildFailedError: ============================================================ /fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/run_train.py FAILED ------------------------------------------------------------ Failures: [1]: time : 2024-07-03_01:01:41 host : ip-26-0-171-62.ec2.internal rank : 49 (local_rank: 1) exitcode : 1 (pid: 3891513) error_file: traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html [2]: time : 2024-07-03_01:01:41 host : ip-26-0-171-62.ec2.internal rank : 50 (local_rank: 2) exitcode : 1 (pid: 3891514) error_file: traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html [3]: time : 2024-07-03_01:01:41 host : ip-26-0-171-62.ec2.internal rank : 51 (local_rank: 3) exitcode : 1 (pid: 3891515) error_file: traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html [4]: time : 2024-07-03_01:01:41 host : ip-26-0-171-62.ec2.internal rank : 52 (local_rank: 4) exitcode : 1 (pid: 3891516) error_file: traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html [5]: time : 2024-07-03_01:01:41 host : ip-26-0-171-62.ec2.internal rank : 53 (local_rank: 5) exitcode : 1 (pid: 3891517) error_file: traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html [6]: time : 2024-07-03_01:01:41 host : ip-26-0-171-62.ec2.internal rank : 54 (local_rank: 6) exitcode : 1 (pid: 3891518) error_file: traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html [7]: time : 2024-07-03_01:01:41 host : ip-26-0-171-62.ec2.internal rank : 55 (local_rank: 7) exitcode : 1 (pid: 3891519) error_file: traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html ------------------------------------------------------------ Root Cause (first observed failure): [0]: time : 2024-07-03_01:01:41 host : ip-26-0-171-62.ec2.internal rank : 48 (local_rank: 0) exitcode : 1 (pid: 3891512) error_file: traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html ============================================================ elastic_launch( File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/launcher/api.py", line 132, in __call__ 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 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 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 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_01:01:41 host : ip-26-0-161-103.ec2.internal rank : 9 (local_rank: 1) exitcode : 1 (pid: 867198) error_file: traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html [2]: time : 2024-07-03_01:01:41 host : ip-26-0-161-103.ec2.internal rank : 10 (local_rank: 2) exitcode : 1 (pid: 867199) error_file: traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html [3]: time : 2024-07-03_01:01:41 host : ip-26-0-161-103.ec2.internal rank : 11 (local_rank: 3) exitcode : 1 (pid: 867200) error_file: traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html [4]: time : 2024-07-03_01:01:41 host : ip-26-0-161-103.ec2.internal rank : 12 (local_rank: 4) exitcode : 1 (pid: 867201) error_file: traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html [5]: time : 2024-07-03_01:01:41 host : ip-26-0-161-103.ec2.internal rank : 13 (local_rank: 5) exitcode : 1 (pid: 867202) error_file: traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html [6]: time : 2024-07-03_01:01:41 host : ip-26-0-161-103.ec2.internal rank : 14 (local_rank: 6) exitcode : 1 (pid: 867203) error_file: traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html [7]: time : 2024-07-03_01:01:41 host : ip-26-0-161-103.ec2.internal rank : 15 (local_rank: 7) exitcode : 1 (pid: 867204) error_file: traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html ------------------------------------------------------------ Root Cause (first observed failure): [0]: time : 2024-07-03_01:01:41 host : ip-26-0-161-103.ec2.internal rank : 8 (local_rank: 0) exitcode : 1 (pid: 867197) error_file: traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html ============================================================ 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_01:01:41 host : ip-26-0-161-153.ec2.internal rank : 17 (local_rank: 1) exitcode : 1 (pid: 1418503) error_file: traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html [2]: time : 2024-07-03_01:01:41 host : ip-26-0-161-153.ec2.internal rank : 18 (local_rank: 2) exitcode : 1 (pid: 1418504) error_file: traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html [3]: time : 2024-07-03_01:01:41 host : ip-26-0-161-153.ec2.internal rank : 19 (local_rank: 3) exitcode : 1 (pid: 1418505) error_file: traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html [4]: time : 2024-07-03_01:01:41 host : ip-26-0-161-153.ec2.internal rank : 20 (local_rank: 4) exitcode : 1 (pid: 1418506) error_file: traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html [5]: time : 2024-07-03_01:01:41 host : ip-26-0-161-153.ec2.internal rank : 21 (local_rank: 5) exitcode : 1 (pid: 1418507) error_file: traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html [6]: time : 2024-07-03_01:01:41 host : ip-26-0-161-153.ec2.internal rank : 22 (local_rank: 6) exitcode : 1 (pid: 1418508) error_file: traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html [7]: time : 2024-07-03_01:01:41 host : ip-26-0-161-153.ec2.internal rank : 23 (local_rank: 7) exitcode : 1 (pid: 1418509) error_file: traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html ------------------------------------------------------------ Root Cause (first observed failure): [0]: time : 2024-07-03_01:01:41 host : ip-26-0-161-153.ec2.internal rank : 16 (local_rank: 0) exitcode : 1 (pid: 1418502) error_file: traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html ============================================================ srun: error: ip-26-0-162-233: task 4: Exited with exit code 1 srun: error: ip-26-0-160-225: task 0: Exited with exit code 1 srun: error: ip-26-0-161-153: task 3: Exited with exit code 1 srun: error: ip-26-0-161-78: task 1: Exited with exit code 1 srun: error: ip-26-0-171-102: task 7: Exited with exit code 1 srun: error: ip-26-0-171-88: task 6: Exited with exit code 1 srun: error: ip-26-0-171-62: task 5: Exited with exit code 1 srun: error: ip-26-0-161-103: task 2: 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.