======================== START TIME: Wed Jul 3 06:46:34 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 06:46:39.855000 139630902351680 torch/distributed/run.py:757] W0703 06:46:39.855000 139630902351680 torch/distributed/run.py:757] ***************************************** W0703 06:46:39.855000 139630902351680 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 06:46:39.855000 139630902351680 torch/distributed/run.py:757] ***************************************** W0703 06:46:40.069000 140687614240576 torch/distributed/run.py:757] W0703 06:46:40.069000 140687614240576 torch/distributed/run.py:757] ***************************************** W0703 06:46:40.069000 140687614240576 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 06:46:40.069000 140687614240576 torch/distributed/run.py:757] ***************************************** W0703 06:46:40.471000 139964643366720 torch/distributed/run.py:757] W0703 06:46:40.471000 139964643366720 torch/distributed/run.py:757] ***************************************** W0703 06:46:40.471000 139964643366720 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 06:46:40.471000 139964643366720 torch/distributed/run.py:757] ***************************************** W0703 06:46:40.739000 140700186564416 torch/distributed/run.py:757] W0703 06:46:40.739000 140700186564416 torch/distributed/run.py:757] ***************************************** W0703 06:46:40.739000 140700186564416 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 06:46:40.739000 140700186564416 torch/distributed/run.py:757] ***************************************** W0703 06:46:40.789000 140670867978048 torch/distributed/run.py:757] W0703 06:46:40.789000 140670867978048 torch/distributed/run.py:757] ***************************************** W0703 06:46:40.789000 140670867978048 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 06:46:40.789000 140670867978048 torch/distributed/run.py:757] ***************************************** W0703 06:46:41.191000 140688759879488 torch/distributed/run.py:757] W0703 06:46:41.191000 140688759879488 torch/distributed/run.py:757] ***************************************** W0703 06:46:41.191000 140688759879488 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 06:46:41.191000 140688759879488 torch/distributed/run.py:757] ***************************************** W0703 06:46:41.204000 140563503273792 torch/distributed/run.py:757] W0703 06:46:41.204000 140563503273792 torch/distributed/run.py:757] ***************************************** W0703 06:46:41.204000 140563503273792 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 06:46:41.204000 140563503273792 torch/distributed/run.py:757] ***************************************** W0703 06:46:41.234000 140045505619776 torch/distributed/run.py:757] W0703 06:46:41.234000 140045505619776 torch/distributed/run.py:757] ***************************************** W0703 06:46:41.234000 140045505619776 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 06:46:41.234000 140045505619776 torch/distributed/run.py:757] ***************************************** [default0]:07/03/2024 06:47:05 [WARNING|DP=0|PP=0|TP=0|ip-26-0-162-233]: [Vocab Size Padding] Padded vocab (size: 50257) with 3 dummy tokens (new size: 50260) [default0]:07/03/2024 06:47:05 [INFO|DP=0|PP=0|TP=0|ip-26-0-162-233]: Config: [default0]:07/03/2024 06:47:05 [INFO|DP=0|PP=0|TP=0|ip-26-0-162-233]: Config(general=GeneralArgs(project='bench_cluster', [default0]:07/03/2024 06:47:05 [INFO|DP=0|PP=0|TP=0|ip-26-0-162-233]: run='%date_%jobid', [default0]:07/03/2024 06:47:05 [INFO|DP=0|PP=0|TP=0|ip-26-0-162-233]: seed=42, [default0]:07/03/2024 06:47:05 [INFO|DP=0|PP=0|TP=0|ip-26-0-162-233]: step=None, [default0]:07/03/2024 06:47:05 [INFO|DP=0|PP=0|TP=0|ip-26-0-162-233]: consumed_train_samples=None, [default0]:07/03/2024 06:47:05 [INFO|DP=0|PP=0|TP=0|ip-26-0-162-233]: benchmark_csv_path=None, [default0]:07/03/2024 06:47:05 [INFO|DP=0|PP=0|TP=0|ip-26-0-162-233]: ignore_sanity_checks=True), [default0]:07/03/2024 06:47:05 [INFO|DP=0|PP=0|TP=0|ip-26-0-162-233]: parallelism=ParallelismArgs(dp=16, [default0]:07/03/2024 06:47:05 [INFO|DP=0|PP=0|TP=0|ip-26-0-162-233]: pp=1, [default0]:07/03/2024 06:47:05 [INFO|DP=0|PP=0|TP=0|ip-26-0-162-233]: tp=4, [default0]:07/03/2024 06:47:05 [INFO|DP=0|PP=0|TP=0|ip-26-0-162-233]: pp_engine=, [default0]:07/03/2024 06:47:05 [INFO|DP=0|PP=0|TP=0|ip-26-0-162-233]: tp_mode=, [default0]:07/03/2024 06:47:05 [INFO|DP=0|PP=0|TP=0|ip-26-0-162-233]: tp_linear_async_communication=False, [default0]:07/03/2024 06:47:05 [INFO|DP=0|PP=0|TP=0|ip-26-0-162-233]: expert_parallel_size=1), [default0]:07/03/2024 06:47:05 [INFO|DP=0|PP=0|TP=0|ip-26-0-162-233]: model=ModelArgs(model_config=LlamaConfig(bos_token_id=1, [default0]:07/03/2024 06:47:05 [INFO|DP=0|PP=0|TP=0|ip-26-0-162-233]: eos_token_id=2, [default0]:07/03/2024 06:47:05 [INFO|DP=0|PP=0|TP=0|ip-26-0-162-233]: hidden_act='silu', [default0]:07/03/2024 06:47:05 [INFO|DP=0|PP=0|TP=0|ip-26-0-162-233]: hidden_size=2048, [default0]:07/03/2024 06:47:05 [INFO|DP=0|PP=0|TP=0|ip-26-0-162-233]: initializer_range=0.02, [default0]:07/03/2024 06:47:05 [INFO|DP=0|PP=0|TP=0|ip-26-0-162-233]: intermediate_size=4096, [default0]:07/03/2024 06:47:05 [INFO|DP=0|PP=0|TP=0|ip-26-0-162-233]: is_llama_config=True, [default0]:07/03/2024 06:47:05 [INFO|DP=0|PP=0|TP=0|ip-26-0-162-233]: max_position_embeddings=4096, [default0]:07/03/2024 06:47:05 [INFO|DP=0|PP=0|TP=0|ip-26-0-162-233]: num_attention_heads=32, [default0]:07/03/2024 06:47:05 [INFO|DP=0|PP=0|TP=0|ip-26-0-162-233]: num_hidden_layers=24, [default0]:07/03/2024 06:47:05 [INFO|DP=0|PP=0|TP=0|ip-26-0-162-233]: num_key_value_heads=32, [default0]:07/03/2024 06:47:05 [INFO|DP=0|PP=0|TP=0|ip-26-0-162-233]: pad_token_id=None, [default0]:07/03/2024 06:47:05 [INFO|DP=0|PP=0|TP=0|ip-26-0-162-233]: pretraining_tp=1, [default0]:07/03/2024 06:47:05 [INFO|DP=0|PP=0|TP=0|ip-26-0-162-233]: rms_norm_eps=1e-05, [default0]:07/03/2024 06:47:05 [INFO|DP=0|PP=0|TP=0|ip-26-0-162-233]: rope_scaling=None, [default0]:07/03/2024 06:47:05 [INFO|DP=0|PP=0|TP=0|ip-26-0-162-233]: rope_theta=10000.0, [default0]:07/03/2024 06:47:05 [INFO|DP=0|PP=0|TP=0|ip-26-0-162-233]: tie_word_embeddings=True, [default0]:07/03/2024 06:47:05 [INFO|DP=0|PP=0|TP=0|ip-26-0-162-233]: use_cache=True, [default0]:07/03/2024 06:47:05 [INFO|DP=0|PP=0|TP=0|ip-26-0-162-233]: vocab_size=50260), [default0]:07/03/2024 06:47:05 [INFO|DP=0|PP=0|TP=0|ip-26-0-162-233]: init_method=RandomInit(std=0.025), [default0]:07/03/2024 06:47:05 [INFO|DP=0|PP=0|TP=0|ip-26-0-162-233]: dtype=torch.bfloat16, [default0]:07/03/2024 06:47:05 [INFO|DP=0|PP=0|TP=0|ip-26-0-162-233]: make_vocab_size_divisible_by=1, [default0]:07/03/2024 06:47:05 [INFO|DP=0|PP=0|TP=0|ip-26-0-162-233]: ddp_bucket_cap_mb=25), [default0]:07/03/2024 06:47:05 [INFO|DP=0|PP=0|TP=0|ip-26-0-162-233]: tokenizer=TokenizerArgs(tokenizer_name_or_path='openai-community/gpt2', [default0]:07/03/2024 06:47:05 [INFO|DP=0|PP=0|TP=0|ip-26-0-162-233]: tokenizer_revision=None, [default0]:07/03/2024 06:47:05 [INFO|DP=0|PP=0|TP=0|ip-26-0-162-233]: tokenizer_max_length=None), [default0]:07/03/2024 06:47:05 [INFO|DP=0|PP=0|TP=0|ip-26-0-162-233]: checkpoints=CheckpointsArgs(checkpoints_path=Path('/dev/null'), [default0]:07/03/2024 06:47:05 [INFO|DP=0|PP=0|TP=0|ip-26-0-162-233]: checkpoint_interval=100000, [default0]:07/03/2024 06:47:05 [INFO|DP=0|PP=0|TP=0|ip-26-0-162-233]: save_initial_state=False, [default0]:07/03/2024 06:47:05 [INFO|DP=0|PP=0|TP=0|ip-26-0-162-233]: resume_checkpoint_path=None, [default0]:07/03/2024 06:47:05 [INFO|DP=0|PP=0|TP=0|ip-26-0-162-233]: checkpoints_path_is_shared_file_system=False), [default0]:07/03/2024 06:47:05 [INFO|DP=0|PP=0|TP=0|ip-26-0-162-233]: logging=LoggingArgs(log_level='info', [default0]:07/03/2024 06:47:05 [INFO|DP=0|PP=0|TP=0|ip-26-0-162-233]: log_level_replica='info', [default0]:07/03/2024 06:47:05 [INFO|DP=0|PP=0|TP=0|ip-26-0-162-233]: iteration_step_info_interval=1), [default0]:07/03/2024 06:47:05 [INFO|DP=0|PP=0|TP=0|ip-26-0-162-233]: tokens=TokensArgs(sequence_length=4096, [default0]:07/03/2024 06:47:05 [INFO|DP=0|PP=0|TP=0|ip-26-0-162-233]: train_steps=20, [default0]:07/03/2024 06:47:05 [INFO|DP=0|PP=0|TP=0|ip-26-0-162-233]: micro_batch_size=64, [default0]:07/03/2024 06:47:05 [INFO|DP=0|PP=0|TP=0|ip-26-0-162-233]: batch_accumulation_per_replica=1, [default0]:07/03/2024 06:47:05 [INFO|DP=0|PP=0|TP=0|ip-26-0-162-233]: val_check_interval=-1, [default0]:07/03/2024 06:47:05 [INFO|DP=0|PP=0|TP=0|ip-26-0-162-233]: limit_val_batches=0, [default0]:07/03/2024 06:47:05 [INFO|DP=0|PP=0|TP=0|ip-26-0-162-233]: limit_test_batches=0), [default0]:07/03/2024 06:47:05 [INFO|DP=0|PP=0|TP=0|ip-26-0-162-233]: optimizer=OptimizerArgs(optimizer_factory=AdamWOptimizerArgs(adam_eps=1e-08, [default0]:07/03/2024 06:47:05 [INFO|DP=0|PP=0|TP=0|ip-26-0-162-233]: adam_beta1=0.9, [default0]:07/03/2024 06:47:05 [INFO|DP=0|PP=0|TP=0|ip-26-0-162-233]: adam_beta2=0.95, [default0]:07/03/2024 06:47:05 [INFO|DP=0|PP=0|TP=0|ip-26-0-162-233]: torch_adam_is_fused=True, [default0]:07/03/2024 06:47:05 [INFO|DP=0|PP=0|TP=0|ip-26-0-162-233]: name='adamW'), [default0]:07/03/2024 06:47:05 [INFO|DP=0|PP=0|TP=0|ip-26-0-162-233]: zero_stage=1, [default0]:07/03/2024 06:47:05 [INFO|DP=0|PP=0|TP=0|ip-26-0-162-233]: weight_decay=0.01, [default0]:07/03/2024 06:47:05 [INFO|DP=0|PP=0|TP=0|ip-26-0-162-233]: clip_grad=1.0, [default0]:07/03/2024 06:47:05 [INFO|DP=0|PP=0|TP=0|ip-26-0-162-233]: accumulate_grad_in_fp32=True, [default0]:07/03/2024 06:47:05 [INFO|DP=0|PP=0|TP=0|ip-26-0-162-233]: learning_rate_scheduler=LRSchedulerArgs(learning_rate=0.0001, [default0]:07/03/2024 06:47:05 [INFO|DP=0|PP=0|TP=0|ip-26-0-162-233]: lr_warmup_steps=1, [default0]:07/03/2024 06:47:05 [INFO|DP=0|PP=0|TP=0|ip-26-0-162-233]: lr_warmup_style='linear', [default0]:07/03/2024 06:47:05 [INFO|DP=0|PP=0|TP=0|ip-26-0-162-233]: lr_decay_style='linear', [default0]:07/03/2024 06:47:05 [INFO|DP=0|PP=0|TP=0|ip-26-0-162-233]: lr_decay_steps=19, [default0]:07/03/2024 06:47:05 [INFO|DP=0|PP=0|TP=0|ip-26-0-162-233]: lr_decay_starting_step=None, [default0]:07/03/2024 06:47:05 [INFO|DP=0|PP=0|TP=0|ip-26-0-162-233]: min_decay_lr=1e-05)), [default0]:07/03/2024 06:47:05 [INFO|DP=0|PP=0|TP=0|ip-26-0-162-233]: data_stages=[DatasetStageArgs(name='Training Stage', [default0]:07/03/2024 06:47:05 [INFO|DP=0|PP=0|TP=0|ip-26-0-162-233]: start_training_step=1, [default0]:07/03/2024 06:47:05 [INFO|DP=0|PP=0|TP=0|ip-26-0-162-233]: data=DataArgs(dataset=PretrainDatasetsArgs(hf_dataset_or_datasets='roneneldan/TinyStories', [default0]:07/03/2024 06:47:05 [INFO|DP=0|PP=0|TP=0|ip-26-0-162-233]: hf_dataset_splits='train', [default0]:07/03/2024 06:47:05 [INFO|DP=0|PP=0|TP=0|ip-26-0-162-233]: hf_dataset_config_name=None, [default0]:07/03/2024 06:47:05 [INFO|DP=0|PP=0|TP=0|ip-26-0-162-233]: dataset_processing_num_proc_per_process=64, [default0]:07/03/2024 06:47:05 [INFO|DP=0|PP=0|TP=0|ip-26-0-162-233]: dataset_overwrite_cache=False, [default0]:07/03/2024 06:47:05 [INFO|DP=0|PP=0|TP=0|ip-26-0-162-233]: text_column_name='text'), [default0]:07/03/2024 06:47:05 [INFO|DP=0|PP=0|TP=0|ip-26-0-162-233]: seed=42, [default0]:07/03/2024 06:47:05 [INFO|DP=0|PP=0|TP=0|ip-26-0-162-233]: num_loading_workers=0))], [default0]:07/03/2024 06:47:05 [INFO|DP=0|PP=0|TP=0|ip-26-0-162-233]: profiler=ProfilerArgs(profiler_export_path=Path('/fsx/ferdinandmom/ferdinand-hf/bench_cluster/results/llama-1B/64_GPUS/dp-16_tp-4_pp-1_mbz-64')), [default0]:07/03/2024 06:47:05 [INFO|DP=0|PP=0|TP=0|ip-26-0-162-233]: lighteval=None) [default0]:07/03/2024 06:47:05 [INFO|DP=0|PP=0|TP=0|ip-26-0-162-233]: Model Config: [default0]:07/03/2024 06:47:05 [INFO|DP=0|PP=0|TP=0|ip-26-0-162-233]: LlamaConfig(bos_token_id=1, [default0]:07/03/2024 06:47:05 [INFO|DP=0|PP=0|TP=0|ip-26-0-162-233]: eos_token_id=2, [default0]:07/03/2024 06:47:05 [INFO|DP=0|PP=0|TP=0|ip-26-0-162-233]: hidden_act='silu', [default0]:07/03/2024 06:47:05 [INFO|DP=0|PP=0|TP=0|ip-26-0-162-233]: hidden_size=2048, [default0]:07/03/2024 06:47:05 [INFO|DP=0|PP=0|TP=0|ip-26-0-162-233]: initializer_range=0.02, [default0]:07/03/2024 06:47:05 [INFO|DP=0|PP=0|TP=0|ip-26-0-162-233]: intermediate_size=4096, [default0]:07/03/2024 06:47:05 [INFO|DP=0|PP=0|TP=0|ip-26-0-162-233]: is_llama_config=True, [default0]:07/03/2024 06:47:05 [INFO|DP=0|PP=0|TP=0|ip-26-0-162-233]: max_position_embeddings=4096, [default0]:07/03/2024 06:47:05 [INFO|DP=0|PP=0|TP=0|ip-26-0-162-233]: num_attention_heads=32, [default0]:07/03/2024 06:47:05 [INFO|DP=0|PP=0|TP=0|ip-26-0-162-233]: num_hidden_layers=24, [default0]:07/03/2024 06:47:05 [INFO|DP=0|PP=0|TP=0|ip-26-0-162-233]: num_key_value_heads=32, [default0]:07/03/2024 06:47:05 [INFO|DP=0|PP=0|TP=0|ip-26-0-162-233]: pad_token_id=None, [default0]:07/03/2024 06:47:05 [INFO|DP=0|PP=0|TP=0|ip-26-0-162-233]: pretraining_tp=1, [default0]:07/03/2024 06:47:05 [INFO|DP=0|PP=0|TP=0|ip-26-0-162-233]: rms_norm_eps=1e-05, [default0]:07/03/2024 06:47:05 [INFO|DP=0|PP=0|TP=0|ip-26-0-162-233]: rope_scaling=None, [default0]:07/03/2024 06:47:05 [INFO|DP=0|PP=0|TP=0|ip-26-0-162-233]: rope_theta=10000.0, [default0]:07/03/2024 06:47:05 [INFO|DP=0|PP=0|TP=0|ip-26-0-162-233]: tie_word_embeddings=True, [default0]:07/03/2024 06:47:05 [INFO|DP=0|PP=0|TP=0|ip-26-0-162-233]: use_cache=True, [default0]:07/03/2024 06:47:05 [INFO|DP=0|PP=0|TP=0|ip-26-0-162-233]: vocab_size=50260) [default0]:07/03/2024 06:47:05 [INFO|DP=0|PP=0|TP=0|ip-26-0-162-233]: Building model.. [default0]:07/03/2024 06:47:05 [INFO|DP=0|PP=0|TP=0|ip-26-0-162-233]: Setting PP block ranks... [default1]:07/03/2024 06:47:19 [INFO|DP=14|PP=0|TP=1|ip-26-0-174-36]: No checkpoint path provided. [default0]:07/03/2024 06:47:19 [INFO|DP=14|PP=0|TP=0|ip-26-0-174-36]: No checkpoint path provided. [default2]:07/03/2024 06:47:19 [INFO|DP=14|PP=0|TP=2|ip-26-0-174-36]: No checkpoint path provided. [default2]:07/03/2024 06:47:20 [INFO|DP=10|PP=0|TP=2|ip-26-0-173-246]: No checkpoint path provided. [default0]:07/03/2024 06:47:20 [INFO|DP=10|PP=0|TP=0|ip-26-0-173-246]: No checkpoint path provided. [default3]:07/03/2024 06:47:20 [INFO|DP=10|PP=0|TP=3|ip-26-0-173-246]: No checkpoint path provided. [default3]:07/03/2024 06:47:19 [INFO|DP=14|PP=0|TP=3|ip-26-0-174-36]: No checkpoint path provided. [default1]:07/03/2024 06:47:20 [INFO|DP=10|PP=0|TP=1|ip-26-0-173-246]: No checkpoint path provided. [default3]:07/03/2024 06:47:20 [INFO|DP=0|PP=0|TP=3|ip-26-0-162-233]: Local number of parameters: 277M (529.27MiB) [default3]:07/03/2024 06:47:20 [INFO|DP=0|PP=0|TP=3|ip-26-0-162-233]: [After model building] Memory usage: 554.21MiB. Peak allocated: 606.24MiB Peak reserved: 608.00MiB [default3]:07/03/2024 06:47:20 [INFO|DP=0|PP=0|TP=3|ip-26-0-162-233]: No checkpoint path provided. [default0]:07/03/2024 06:47:20 [INFO|DP=0|PP=0|TP=0|ip-26-0-162-233]: Total number of parameters: 1.11G (2117.09MiB) [default0]:07/03/2024 06:47:20 [INFO|DP=0|PP=0|TP=0|ip-26-0-162-233]: Local number of parameters: 277M (529.27MiB) [default0]:07/03/2024 06:47:20 [INFO|DP=0|PP=0|TP=0|ip-26-0-162-233]: [After model building] Memory usage: 554.21MiB. Peak allocated: 606.24MiB Peak reserved: 608.00MiB [default0]:07/03/2024 06:47:20 [INFO|DP=0|PP=0|TP=0|ip-26-0-162-233]: No checkpoint path provided. [default0]:07/03/2024 06:47:20 [INFO|DP=0|PP=0|TP=0|ip-26-0-162-233]: Parametrizing model parameters using StandardParametrizator [default2]:07/03/2024 06:47:20 [INFO|DP=0|PP=0|TP=2|ip-26-0-162-233]: Local number of parameters: 277M (529.27MiB) [default2]:07/03/2024 06:47:20 [INFO|DP=0|PP=0|TP=2|ip-26-0-162-233]: [After model building] Memory usage: 554.21MiB. Peak allocated: 606.24MiB Peak reserved: 608.00MiB [default2]:07/03/2024 06:47:20 [INFO|DP=0|PP=0|TP=2|ip-26-0-162-233]: No checkpoint path provided. [default1]:07/03/2024 06:47:20 [INFO|DP=0|PP=0|TP=1|ip-26-0-162-233]: Local number of parameters: 277M (529.27MiB) [default1]:07/03/2024 06:47:20 [INFO|DP=0|PP=0|TP=1|ip-26-0-162-233]: [After model building] Memory usage: 554.21MiB. Peak allocated: 606.24MiB Peak reserved: 608.00MiB [default1]:07/03/2024 06:47:20 [INFO|DP=0|PP=0|TP=1|ip-26-0-162-233]: No checkpoint path provided. [default2]:07/03/2024 06:47:20 [INFO|DP=12|PP=0|TP=2|ip-26-0-173-7]: No checkpoint path provided. [default0]:07/03/2024 06:47:20 [INFO|DP=12|PP=0|TP=0|ip-26-0-173-7]: No checkpoint path provided. [default3]:07/03/2024 06:47:20 [INFO|DP=12|PP=0|TP=3|ip-26-0-173-7]: No checkpoint path provided. [default1]:07/03/2024 06:47:20 [INFO|DP=12|PP=0|TP=1|ip-26-0-173-7]: No checkpoint path provided. [default4]:07/03/2024 06:47:20 [INFO|DP=7|PP=0|TP=0|ip-26-0-165-24]: No checkpoint path provided. [default1]:07/03/2024 06:47:20 [INFO|DP=6|PP=0|TP=1|ip-26-0-165-24]: No checkpoint path provided. [default0]:07/03/2024 06:47:20 [INFO|DP=6|PP=0|TP=0|ip-26-0-165-24]: No checkpoint path provided. [default6]:07/03/2024 06:47:20 [INFO|DP=7|PP=0|TP=2|ip-26-0-165-24]: No checkpoint path provided. [default7]:07/03/2024 06:47:20 [INFO|DP=7|PP=0|TP=3|ip-26-0-165-24]: No checkpoint path provided. [default6]:07/03/2024 06:47:20 [INFO|DP=11|PP=0|TP=2|ip-26-0-173-246]: No checkpoint path provided. [default2]:07/03/2024 06:47:20 [INFO|DP=6|PP=0|TP=2|ip-26-0-165-24]: No checkpoint path provided. [default4]:07/03/2024 06:47:20 [INFO|DP=11|PP=0|TP=0|ip-26-0-173-246]: No checkpoint path provided. [default5]:07/03/2024 06:47:20 [INFO|DP=11|PP=0|TP=1|ip-26-0-173-246]: No checkpoint path provided. [default7]:07/03/2024 06:47:20 [INFO|DP=1|PP=0|TP=3|ip-26-0-162-233]: No checkpoint path provided. [default6]:07/03/2024 06:47:20 [INFO|DP=1|PP=0|TP=2|ip-26-0-162-233]: No checkpoint path provided. [default5]:07/03/2024 06:47:20 [INFO|DP=7|PP=0|TP=1|ip-26-0-165-24]: No checkpoint path provided. [default0]:07/03/2024 06:47:20 [INFO|DP=2|PP=0|TP=0|ip-26-0-163-147]: No checkpoint path provided. [default3]:07/03/2024 06:47:20 [INFO|DP=6|PP=0|TP=3|ip-26-0-165-24]: No checkpoint path provided. [default7]:07/03/2024 06:47:20 [INFO|DP=13|PP=0|TP=3|ip-26-0-173-7]: No checkpoint path provided. [default1]:07/03/2024 06:47:20 [INFO|DP=2|PP=0|TP=1|ip-26-0-163-147]: No checkpoint path provided. [default5]:07/03/2024 06:47:20 [INFO|DP=1|PP=0|TP=1|ip-26-0-162-233]: No checkpoint path provided. [default4]:07/03/2024 06:47:20 [INFO|DP=1|PP=0|TP=0|ip-26-0-162-233]: No checkpoint path provided. [default2]:07/03/2024 06:47:20 [INFO|DP=2|PP=0|TP=2|ip-26-0-163-147]: No checkpoint path provided. [default4]:07/03/2024 06:47:20 [INFO|DP=13|PP=0|TP=0|ip-26-0-173-7]: No checkpoint path provided. [default3]:07/03/2024 06:47:20 [INFO|DP=2|PP=0|TP=3|ip-26-0-163-147]: No checkpoint path provided. [default7]:07/03/2024 06:47:20 [INFO|DP=11|PP=0|TP=3|ip-26-0-173-246]: No checkpoint path provided. [default6]:07/03/2024 06:47:20 [INFO|DP=13|PP=0|TP=2|ip-26-0-173-7]: No checkpoint path provided. [default5]:07/03/2024 06:47:20 [INFO|DP=13|PP=0|TP=1|ip-26-0-173-7]: No checkpoint path provided. [default4]:07/03/2024 06:47:20 [INFO|DP=15|PP=0|TP=0|ip-26-0-174-36]: No checkpoint path provided. [default6]:07/03/2024 06:47:20 [INFO|DP=15|PP=0|TP=2|ip-26-0-174-36]: No checkpoint path provided. [default7]:07/03/2024 06:47:20 [INFO|DP=15|PP=0|TP=3|ip-26-0-174-36]: No checkpoint path provided. [default5]:07/03/2024 06:47:20 [INFO|DP=3|PP=0|TP=1|ip-26-0-163-147]: No checkpoint path provided. [default6]:07/03/2024 06:47:20 [INFO|DP=3|PP=0|TP=2|ip-26-0-163-147]: No checkpoint path provided. [default5]:07/03/2024 06:47:20 [INFO|DP=15|PP=0|TP=1|ip-26-0-174-36]: No checkpoint path provided. [default7]:07/03/2024 06:47:20 [INFO|DP=3|PP=0|TP=3|ip-26-0-163-147]: No checkpoint path provided. [default4]:07/03/2024 06:47:20 [INFO|DP=3|PP=0|TP=0|ip-26-0-163-147]: No checkpoint path provided. [default0]:07/03/2024 06:47:20 [INFO|DP=4|PP=0|TP=0|ip-26-0-164-207]: No checkpoint path provided. [default3]:07/03/2024 06:47:20 [INFO|DP=4|PP=0|TP=3|ip-26-0-164-207]: No checkpoint path provided. [default1]:07/03/2024 06:47:20 [INFO|DP=4|PP=0|TP=1|ip-26-0-164-207]: No checkpoint path provided. [default2]:07/03/2024 06:47:20 [INFO|DP=4|PP=0|TP=2|ip-26-0-164-207]: No checkpoint path provided. [default2]:07/03/2024 06:47:20 [INFO|DP=8|PP=0|TP=2|ip-26-0-173-202]: No checkpoint path provided. [default3]:07/03/2024 06:47:20 [INFO|DP=8|PP=0|TP=3|ip-26-0-173-202]: No checkpoint path provided. [default1]:07/03/2024 06:47:20 [INFO|DP=8|PP=0|TP=1|ip-26-0-173-202]: No checkpoint path provided. [default0]:07/03/2024 06:47:20 [INFO|DP=8|PP=0|TP=0|ip-26-0-173-202]: No checkpoint path provided. [default4]:07/03/2024 06:47:20 [INFO|DP=9|PP=0|TP=0|ip-26-0-173-202]: No checkpoint path provided. [default6]:07/03/2024 06:47:20 [INFO|DP=9|PP=0|TP=2|ip-26-0-173-202]: No checkpoint path provided. [default7]:07/03/2024 06:47:20 [INFO|DP=9|PP=0|TP=3|ip-26-0-173-202]: No checkpoint path provided. [default5]:07/03/2024 06:47:20 [INFO|DP=9|PP=0|TP=1|ip-26-0-173-202]: No checkpoint path provided. [default4]:07/03/2024 06:47:20 [INFO|DP=5|PP=0|TP=0|ip-26-0-164-207]: No checkpoint path provided. [default5]:07/03/2024 06:47:20 [INFO|DP=5|PP=0|TP=1|ip-26-0-164-207]: No checkpoint path provided. [default6]:07/03/2024 06:47:20 [INFO|DP=5|PP=0|TP=2|ip-26-0-164-207]: No checkpoint path provided. [default7]:07/03/2024 06:47:20 [INFO|DP=5|PP=0|TP=3|ip-26-0-164-207]: No checkpoint path provided. [default0]:07/03/2024 06:47:23 [INFO|DP=0|PP=0|TP=0|ip-26-0-162-233]: [Optimizer Building] Using LearningRateForSP as learning rate [default0]:07/03/2024 06:47:23 [INFO|DP=0|PP=0|TP=0|ip-26-0-162-233]: [ZeRO sharding] Size of optimizer params per rank: [default0]:07/03/2024 06:47:23 [INFO|DP=0|PP=0|TP=0|ip-26-0-162-233]: [ZeRO sharding] DP Rank 0 has 17.3M out of 277M (6.25%) params' optimizer states [default0]:07/03/2024 06:47:23 [INFO|DP=0|PP=0|TP=0|ip-26-0-162-233]: [ZeRO sharding] DP Rank 1 has 17.3M out of 277M (6.25%) params' optimizer states [default0]:07/03/2024 06:47:23 [INFO|DP=0|PP=0|TP=0|ip-26-0-162-233]: [ZeRO sharding] DP Rank 2 has 17.3M out of 277M (6.25%) params' optimizer states [default0]:07/03/2024 06:47:23 [INFO|DP=0|PP=0|TP=0|ip-26-0-162-233]: [ZeRO sharding] DP Rank 3 has 17.3M out of 277M (6.25%) params' optimizer states [default0]:07/03/2024 06:47:23 [INFO|DP=0|PP=0|TP=0|ip-26-0-162-233]: [ZeRO sharding] DP Rank 4 has 17.3M out of 277M (6.25%) params' optimizer states [default0]:07/03/2024 06:47:23 [INFO|DP=0|PP=0|TP=0|ip-26-0-162-233]: [ZeRO sharding] DP Rank 5 has 17.3M out of 277M (6.25%) params' optimizer states [default0]:07/03/2024 06:47:23 [INFO|DP=0|PP=0|TP=0|ip-26-0-162-233]: [ZeRO sharding] DP Rank 6 has 17.3M out of 277M (6.25%) params' optimizer states [default0]:07/03/2024 06:47:23 [INFO|DP=0|PP=0|TP=0|ip-26-0-162-233]: [ZeRO sharding] DP Rank 7 has 17.3M out of 277M (6.25%) params' optimizer states [default0]:07/03/2024 06:47:23 [INFO|DP=0|PP=0|TP=0|ip-26-0-162-233]: [ZeRO sharding] DP Rank 8 has 17.3M out of 277M (6.25%) params' optimizer states [default0]:07/03/2024 06:47:23 [INFO|DP=0|PP=0|TP=0|ip-26-0-162-233]: [ZeRO sharding] DP Rank 9 has 17.3M out of 277M (6.25%) params' optimizer states [default0]:07/03/2024 06:47:23 [INFO|DP=0|PP=0|TP=0|ip-26-0-162-233]: [ZeRO sharding] DP Rank 10 has 17.3M out of 277M (6.25%) params' optimizer states [default0]:07/03/2024 06:47:23 [INFO|DP=0|PP=0|TP=0|ip-26-0-162-233]: [ZeRO sharding] DP Rank 11 has 17.3M out of 277M (6.25%) params' optimizer states [default0]:07/03/2024 06:47:23 [INFO|DP=0|PP=0|TP=0|ip-26-0-162-233]: [ZeRO sharding] DP Rank 12 has 17.3M out of 277M (6.25%) params' optimizer states [default0]:07/03/2024 06:47:23 [INFO|DP=0|PP=0|TP=0|ip-26-0-162-233]: [ZeRO sharding] DP Rank 13 has 17.3M out of 277M (6.25%) params' optimizer states [default0]:07/03/2024 06:47:23 [INFO|DP=0|PP=0|TP=0|ip-26-0-162-233]: [ZeRO sharding] DP Rank 14 has 17.3M out of 277M (6.25%) params' optimizer states [default0]:07/03/2024 06:47:23 [INFO|DP=0|PP=0|TP=0|ip-26-0-162-233]: [ZeRO sharding] DP Rank 15 has 17.3M out of 277M (6.25%) params' optimizer states [default0]:07/03/2024 06:47:25 [INFO|DP=0|PP=0|TP=0|ip-26-0-162-233]: [Training Plan] Stage Training Stage has 19 remaining training steps and has consumed 0 samples [default0]:07/03/2024 06:47:25 [INFO|DP=0|PP=0|TP=0|ip-26-0-162-233]: Using `datasets` library [default0]:07/03/2024 06:47:25 [INFO|DP=0|PP=0|TP=0|ip-26-0-162-233]: Loading tokenizer from openai-community/gpt2 and transformers/hf_hub versions ('4.41.2', '0.23.4') [default0]:07/03/2024 06:47:25 [WARNING|DP=0|PP=0|TP=0|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. [default0]:07/03/2024 06:47:27 [INFO|DP=0|PP=0|TP=0|ip-26-0-162-233]: [Training Plan] There are 1 training stages [default0]:07/03/2024 06:47:27 [INFO|DP=0|PP=0|TP=0|ip-26-0-162-233]: [Stage Training Stage] start from step 1 [default0]:07/03/2024 06:47:27 [INFO|DP=0|PP=0|TP=0|ip-26-0-162-233]: [default0]:07/03/2024 06:47:27 [INFO|DP=0|PP=0|TP=0|ip-26-0-162-233]: [Start training] datetime: 2024-07-03 06:47:27.474763 | mbs: 64 | grad_accum: 1 | global_batch_size: 1024 | sequence_length: 4096 | train_steps: 20 | start_iteration_step: 0 | consumed_train_samples: 0 [default0]:07/03/2024 06:47:27 [INFO|DP=0|PP=0|TP=0|ip-26-0-162-233]: Resuming training from stage Training Stage, it has trained for 0 samples and has 19 remaining train steps [default0]:07/03/2024 06:47:27 [INFO|DP=0|PP=0|TP=0|ip-26-0-162-233]: Memory usage: 1678.92MiB. Peak allocated 1678.92MiB. Peak reserved: 1736.00MiB [default4]:07/03/2024 06:47:27 [WARNING|DP=9|PP=0|TP=0|ip-26-0-173-202]: Repo card metadata block was not found. Setting CardData to empty. [default4]:Repo card metadata block was not found. Setting CardData to empty. [default5]:07/03/2024 06:47:27 [WARNING|DP=9|PP=0|TP=1|ip-26-0-173-202]: Repo card metadata block was not found. Setting CardData to empty. [default1]:07/03/2024 06:47:27 [WARNING|DP=6|PP=0|TP=1|ip-26-0-165-24]: Repo card metadata block was not found. Setting CardData to empty. [default5]:Repo card metadata block was not found. Setting CardData to empty. [default7]:07/03/2024 06:47:27 [WARNING|DP=13|PP=0|TP=3|ip-26-0-173-7]: Repo card metadata block was not found. Setting CardData to empty. [default2]:07/03/2024 06:47:27 [WARNING|DP=0|PP=0|TP=2|ip-26-0-162-233]: Repo card metadata block was not found. Setting CardData to empty. [default2]:07/03/2024 06:47:27 [WARNING|DP=12|PP=0|TP=2|ip-26-0-173-7]: Repo card metadata block was not found. Setting CardData to empty. [default7]:Repo card metadata block was not found. Setting CardData to empty. [default6]:07/03/2024 06:47:27 [WARNING|DP=13|PP=0|TP=2|ip-26-0-173-7]: 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. [default6]:Repo card metadata block was not found. Setting CardData to empty. [default0]:Repo card metadata block was not found. Setting CardData to empty. [default2]:Repo card metadata block was not found. Setting CardData to empty. [default2]:07/03/2024 06:47:27 [WARNING|DP=8|PP=0|TP=2|ip-26-0-173-202]: Repo card metadata block was not found. Setting CardData to empty. [default3]:07/03/2024 06:47:27 [WARNING|DP=8|PP=0|TP=3|ip-26-0-173-202]: Repo card metadata block was not found. Setting CardData to empty. [default6]:07/03/2024 06:47:27 [WARNING|DP=9|PP=0|TP=2|ip-26-0-173-202]: Repo card metadata block was not found. Setting CardData to empty. [default2]:Repo card metadata block was not found. Setting CardData to empty. [default0]:Repo card metadata block was not found. Setting CardData to empty. [default7]:Repo card metadata block was not found. Setting CardData to empty. [default0]:07/03/2024 06:47:27 [WARNING|DP=8|PP=0|TP=0|ip-26-0-173-202]: Repo card metadata block was not found. Setting CardData to empty. [default7]:07/03/2024 06:47:27 [WARNING|DP=9|PP=0|TP=3|ip-26-0-173-202]: Repo card metadata block was not found. Setting CardData to empty. [default4]:07/03/2024 06:47:27 [WARNING|DP=7|PP=0|TP=0|ip-26-0-165-24]: Repo card metadata block was not found. Setting CardData to empty. [default6]:07/03/2024 06:47:27 [WARNING|DP=7|PP=0|TP=2|ip-26-0-165-24]: Repo card metadata block was not found. Setting CardData to empty. [default0]:07/03/2024 06:47:27 [WARNING|DP=6|PP=0|TP=0|ip-26-0-165-24]: Repo card metadata block was not found. Setting CardData to empty. [default7]:07/03/2024 06:47:27 [WARNING|DP=7|PP=0|TP=3|ip-26-0-165-24]: Repo card metadata block was not found. Setting CardData to empty. [default6]:Repo card metadata block was not found. Setting CardData to empty. [default2]:07/03/2024 06:47:27 [WARNING|DP=6|PP=0|TP=2|ip-26-0-165-24]: 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 06:47:27 [WARNING|DP=14|PP=0|TP=2|ip-26-0-174-36]: Repo card metadata block was not found. Setting CardData to empty. [default0]:07/03/2024 06:47:27 [WARNING|DP=14|PP=0|TP=0|ip-26-0-174-36]: Repo card metadata block was not found. Setting CardData to empty. [default3]:07/03/2024 06:47:27 [WARNING|DP=0|PP=0|TP=3|ip-26-0-162-233]: Repo card metadata block was not found. Setting CardData to empty. [default4]:07/03/2024 06:47:27 [WARNING|DP=15|PP=0|TP=0|ip-26-0-174-36]: Repo card metadata block was not found. Setting CardData to empty. [default6]:07/03/2024 06:47:27 [WARNING|DP=15|PP=0|TP=2|ip-26-0-174-36]: Repo card metadata block was not found. Setting CardData to empty. [default1]:07/03/2024 06:47:27 [WARNING|DP=14|PP=0|TP=1|ip-26-0-174-36]: Repo card metadata block was not found. Setting CardData to empty. [default2]:07/03/2024 06:47:27 [WARNING|DP=10|PP=0|TP=2|ip-26-0-173-246]: Repo card metadata block was not found. Setting CardData to empty. [default3]:Repo card metadata block was not found. Setting CardData to empty. [default0]:07/03/2024 06:47:27 [WARNING|DP=10|PP=0|TP=0|ip-26-0-173-246]: 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 06:47:27 [WARNING|DP=11|PP=0|TP=0|ip-26-0-173-246]: Repo card metadata block was not found. Setting CardData to empty. [default7]:Repo card metadata block was not found. Setting CardData to empty. [default5]:07/03/2024 06:47:27 [WARNING|DP=11|PP=0|TP=1|ip-26-0-173-246]: Repo card metadata block was not found. Setting CardData to empty. [default3]:07/03/2024 06:47:27 [WARNING|DP=10|PP=0|TP=3|ip-26-0-173-246]: Repo card metadata block was not found. Setting CardData to empty. [default7]:07/03/2024 06:47:27 [WARNING|DP=15|PP=0|TP=3|ip-26-0-174-36]: Repo card metadata block was not found. Setting CardData to empty. [default0]:Repo card metadata block was not found. Setting CardData to empty. [default4]:Repo card metadata block was not found. Setting CardData to empty. [default3]:Repo card metadata block was not found. Setting CardData to empty. [default3]:07/03/2024 06:47:27 [WARNING|DP=14|PP=0|TP=3|ip-26-0-174-36]: Repo card metadata block was not found. Setting CardData to empty. [default7]:07/03/2024 06:47:27 [WARNING|DP=1|PP=0|TP=3|ip-26-0-162-233]: Repo card metadata block was not found. Setting CardData to empty. [default6]:07/03/2024 06:47:27 [WARNING|DP=1|PP=0|TP=2|ip-26-0-162-233]: Repo card metadata block was not found. Setting CardData to empty. [default5]:07/03/2024 06:47:27 [WARNING|DP=7|PP=0|TP=1|ip-26-0-165-24]: Repo card metadata block was not found. Setting CardData to empty. [default0]:07/03/2024 06:47:27 [WARNING|DP=2|PP=0|TP=0|ip-26-0-163-147]: 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 06:47:27 [WARNING|DP=10|PP=0|TP=1|ip-26-0-173-246]: Repo card metadata block was not found. Setting CardData to empty. [default3]:07/03/2024 06:47:27 [WARNING|DP=6|PP=0|TP=3|ip-26-0-165-24]: 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. [default5]:07/03/2024 06:47:27 [WARNING|DP=1|PP=0|TP=1|ip-26-0-162-233]: Repo card metadata block was not found. Setting CardData to empty. [default4]:07/03/2024 06:47:27 [WARNING|DP=1|PP=0|TP=0|ip-26-0-162-233]: 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. [default1]:07/03/2024 06:47:27 [WARNING|DP=2|PP=0|TP=1|ip-26-0-163-147]: Repo card metadata block was not found. Setting CardData to empty. [default6]:07/03/2024 06:47:27 [WARNING|DP=3|PP=0|TP=2|ip-26-0-163-147]: Repo card metadata block was not found. Setting CardData to empty. [default4]:Repo card metadata block was not found. Setting CardData to empty. [default1]:07/03/2024 06:47:27 [WARNING|DP=0|PP=0|TP=1|ip-26-0-162-233]: Repo card metadata block was not found. Setting CardData to empty. [default7]:07/03/2024 06:47:27 [WARNING|DP=11|PP=0|TP=3|ip-26-0-173-246]: Repo card metadata block was not found. Setting CardData to empty. [default2]:07/03/2024 06:47:27 [WARNING|DP=2|PP=0|TP=2|ip-26-0-163-147]: Repo card metadata block was not found. Setting CardData to empty. [default5]:07/03/2024 06:47:27 [WARNING|DP=15|PP=0|TP=1|ip-26-0-174-36]: Repo card metadata block was not found. Setting CardData to empty. [default5]:Repo card metadata block was not found. Setting CardData to empty. [default3]:Repo card metadata block was not found. Setting CardData to empty. [default1]:Repo card metadata block was not found. Setting CardData to empty. [default7]:07/03/2024 06:47:27 [WARNING|DP=3|PP=0|TP=3|ip-26-0-163-147]: Repo card metadata block was not found. Setting CardData to empty. [default0]:07/03/2024 06:47:27 [WARNING|DP=12|PP=0|TP=0|ip-26-0-173-7]: Repo card metadata block was not found. Setting CardData to empty. [default4]:07/03/2024 06:47:27 [WARNING|DP=5|PP=0|TP=0|ip-26-0-164-207]: 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 06:47:27 [WARNING|DP=5|PP=0|TP=1|ip-26-0-164-207]: Repo card metadata block was not found. Setting CardData to empty. [default2]:07/03/2024 06:47:27 [WARNING|DP=4|PP=0|TP=2|ip-26-0-164-207]: Repo card metadata block was not found. Setting CardData to empty. [default1]:Repo card metadata block was not found. Setting CardData to empty. [default6]:07/03/2024 06:47:27 [WARNING|DP=5|PP=0|TP=2|ip-26-0-164-207]: Repo card metadata block was not found. Setting CardData to empty. [default6]:Repo card metadata block was not found. Setting CardData to empty. [default4]:07/03/2024 06:47:27 [WARNING|DP=3|PP=0|TP=0|ip-26-0-163-147]: Repo card metadata block was not found. Setting CardData to empty. [default1]:07/03/2024 06:47:27 [WARNING|DP=4|PP=0|TP=1|ip-26-0-164-207]: Repo card metadata block was not found. Setting CardData to empty. [default3]:07/03/2024 06:47:27 [WARNING|DP=2|PP=0|TP=3|ip-26-0-163-147]: Repo card metadata block was not found. Setting CardData to empty. [default4]:Repo card metadata block was not found. Setting CardData to empty. [default3]:07/03/2024 06:47:27 [WARNING|DP=4|PP=0|TP=3|ip-26-0-164-207]: Repo card metadata block was not found. Setting CardData to empty. [default6]:Repo card metadata block was not found. Setting CardData to empty. [default3]:Repo card metadata block was not found. Setting CardData to empty. [default0]: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. [default2]:Repo card metadata block was not found. Setting CardData to empty. [default1]:Repo card metadata block was not found. Setting CardData to empty. [default6]:Repo card metadata block was not found. Setting CardData to empty. [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]:07/03/2024 06:47:27 [WARNING|DP=12|PP=0|TP=1|ip-26-0-173-7]: Repo card metadata block was not found. Setting CardData to empty. [default3]:07/03/2024 06:47:27 [WARNING|DP=12|PP=0|TP=3|ip-26-0-173-7]: Repo card metadata block was not found. Setting CardData to empty. [default3]:Repo card metadata block was not found. Setting CardData to empty. [default2]:Repo card metadata block was not found. Setting CardData to empty. [default5]:07/03/2024 06:47:27 [WARNING|DP=13|PP=0|TP=1|ip-26-0-173-7]: Repo card metadata block was not found. Setting CardData to empty. [default2]:Repo card metadata block was not found. Setting CardData to empty. [default4]:07/03/2024 06:47:27 [WARNING|DP=13|PP=0|TP=0|ip-26-0-173-7]: Repo card metadata block was not found. Setting CardData to empty. [default2]: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. [default1]: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. [default5]:Repo card metadata block was not found. Setting CardData to empty. [default4]:Repo card metadata block was not found. Setting CardData to empty. [default3]:Repo card metadata block was not found. Setting CardData to empty. [default7]:Repo card metadata block was not found. Setting CardData to empty. [default7]:07/03/2024 06:47:27 [WARNING|DP=5|PP=0|TP=3|ip-26-0-164-207]: 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. [default4]:Repo card metadata block was not found. Setting CardData to empty. [default2]:Repo card metadata block was not found. Setting CardData to empty. [default5]:07/03/2024 06:47:27 [WARNING|DP=3|PP=0|TP=1|ip-26-0-163-147]: 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 06:47:27 [WARNING|DP=4|PP=0|TP=0|ip-26-0-164-207]: 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. [default1]:07/03/2024 06:47:27 [WARNING|DP=8|PP=0|TP=1|ip-26-0-173-202]: Repo card metadata block was not found. Setting CardData to empty. [default6]:07/03/2024 06:47:27 [WARNING|DP=11|PP=0|TP=2|ip-26-0-173-246]: Repo card metadata block was not found. Setting CardData to empty. [default6]:Repo card metadata block was not found. Setting CardData to empty. [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) [default4]:[rank4]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 429, in train [default4]:[rank4]: outputs, loss_avg = self.training_step(dataloader=self.current_dataloader) [default4]:[rank4]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 462, in training_step [default4]:[rank4]: outputs = self.pipeline_engine.train_batch_iter( [default4]:[rank4]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 278, in train_batch_iter [default4]:[rank4]: output = self.forward(context=context, state=state, micro_batch=micro_batch, model=model) [default4]:[rank4]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 44, in forward [default4]:[rank4]: output = model(**micro_batch) [default4]:[rank4]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl [default4]:[rank4]: return self._call_impl(*args, **kwargs) [default4]:[rank4]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default4]:[rank4]: return forward_call(*args, **kwargs) [default4]:[rank4]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 891, in forward [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 [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) [default4]:[rank4]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default4]:[rank4]: return forward_call(*args, **kwargs) [default4]:[rank4]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/block.py", line 151, in forward [default4]:[rank4]: output = self.pp_block(**new_kwargs) [default4]:[rank4]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl [default4]:[rank4]: return self._call_impl(*args, **kwargs) [default4]:[rank4]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default4]:[rank4]: return forward_call(*args, **kwargs) [default4]:[rank4]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 637, in forward [default4]:[rank4]: hidden_states = self.mlp(hidden_states=hidden_states)["hidden_states"] [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 171, in forward [default4]:[rank4]: merged_states = self.gate_up_proj(hidden_states) [default4]:[rank4]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl [default4]:[rank4]: return self._call_impl(*args, **kwargs) [default4]:[rank4]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default4]:[rank4]: return forward_call(*args, **kwargs) [default4]:[rank4]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/nn.py", line 87, in forward [default4]:[rank4]: return column_linear( [default4]:[rank4]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/functional.py", line 359, in column_linear [default4]:[rank4]: return 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 573.94 MiB is free. Including non-PyTorch memory, this process has 78.76 GiB memory in use. Of the allocated memory 69.02 GiB is allocated by PyTorch, and 667.32 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]: Traceback (most recent call last): [default7]:[rank7]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/run_train.py", line 237, in [default7]:[rank7]: trainer.train(dataloader) [default7]:[rank7]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 429, in train [default7]:[rank7]: outputs, loss_avg = self.training_step(dataloader=self.current_dataloader) [default7]:[rank7]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 462, in training_step [default7]:[rank7]: outputs = self.pipeline_engine.train_batch_iter( [default7]:[rank7]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 278, in train_batch_iter [default7]:[rank7]: 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 44, in forward [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 [default7]:[rank7]: 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 [default7]:[rank7]: return forward_call(*args, **kwargs) [default7]:[rank7]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 891, in forward [default7]:[rank7]: sharded_logits = self.model( [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) [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 [default7]:[rank7]: return forward_call(*args, **kwargs) [default7]:[rank7]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 764, in forward [default7]:[rank7]: return self.forward_with_hidden_states(input_ids=input_ids, input_mask=input_mask)[0] [default7]:[rank7]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 780, in forward_with_hidden_states [default7]:[rank7]: 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 [default7]:[rank7]: 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 [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 [default7]:[rank7]: output = self.pp_block(**new_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) [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 [default7]:[rank7]: return forward_call(*args, **kwargs) [default7]:[rank7]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 637, in forward [default7]:[rank7]: hidden_states = self.mlp(hidden_states=hidden_states)["hidden_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 [default7]:[rank7]: 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 [default7]:[rank7]: return forward_call(*args, **kwargs) [default7]:[rank7]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 171, in forward [default7]:[rank7]: merged_states = self.gate_up_proj(hidden_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 [default7]:[rank7]: 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 [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 87, in forward [default7]:[rank7]: return column_linear( [default7]:[rank7]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/functional.py", line 359, in column_linear [default7]:[rank7]: return 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 693.94 MiB is free. Including non-PyTorch memory, this process has 78.64 GiB memory in use. Of the allocated memory 69.02 GiB is allocated by PyTorch, and 667.32 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]: Traceback (most recent call last): [default1]:[rank1]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/run_train.py", line 237, in [default1]:[rank1]: trainer.train(dataloader) [default1]:[rank1]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 429, in train [default1]:[rank1]: outputs, loss_avg = self.training_step(dataloader=self.current_dataloader) [default3]:[rank3]: Traceback (most recent call last): [default1]:[rank1]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 462, in training_step [default1]:[rank1]: outputs = self.pipeline_engine.train_batch_iter( [default1]:[rank1]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 278, in train_batch_iter [default1]:[rank1]: output = self.forward(context=context, state=state, micro_batch=micro_batch, model=model) [default3]:[rank3]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/run_train.py", line 237, in [default1]:[rank1]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 44, in forward [default1]:[rank1]: output = model(**micro_batch) [default1]:[rank1]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl [default1]:[rank1]: return self._call_impl(*args, **kwargs) [default1]:[rank1]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default1]:[rank1]: return forward_call(*args, **kwargs) [default3]:[rank3]: trainer.train(dataloader) [default3]:[rank3]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 429, in train [default3]:[rank3]: outputs, loss_avg = self.training_step(dataloader=self.current_dataloader) [default3]:[rank3]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 462, in training_step [default3]:[rank3]: outputs = self.pipeline_engine.train_batch_iter( [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( [default3]:[rank3]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 278, in train_batch_iter [default3]:[rank3]: output = self.forward(context=context, state=state, micro_batch=micro_batch, model=model) [default3]:[rank3]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 44, in forward [default3]:[rank3]: output = model(**micro_batch) [default3]:[rank3]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl [default3]:[rank3]: return self._call_impl(*args, **kwargs) [default3]:[rank3]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default3]:[rank3]: return forward_call(*args, **kwargs) [default3]:[rank3]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 891, in forward [default3]:[rank3]: sharded_logits = self.model( [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 [default3]:[rank3]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl [default3]:[rank3]: return self._call_impl(*args, **kwargs) [default3]:[rank3]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default1]:[rank1]: return forward_call(*args, **kwargs) [default3]:[rank3]: return forward_call(*args, **kwargs) [default3]:[rank3]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 764, in forward [default3]:[rank3]: return self.forward_with_hidden_states(input_ids=input_ids, input_mask=input_mask)[0] [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 [default3]:[rank3]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 780, in forward_with_hidden_states [default3]:[rank3]: hidden_encoder_states = encoder_block(**hidden_encoder_states) [default1]:[rank1]: hidden_encoder_states = encoder_block(**hidden_encoder_states) [default3]:[rank3]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl [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 [default3]:[rank3]: return self._call_impl(*args, **kwargs) [default3]:[rank3]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default3]:[rank3]: return forward_call(*args, **kwargs) [default3]:[rank3]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/block.py", line 151, in forward [default3]:[rank3]: output = self.pp_block(**new_kwargs) [default3]:[rank3]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl [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) [default3]:[rank3]: return self._call_impl(*args, **kwargs) [default3]:[rank3]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [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) [default3]:[rank3]: return forward_call(*args, **kwargs) [default3]:[rank3]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 637, in forward [default3]:[rank3]: hidden_states = self.mlp(hidden_states=hidden_states)["hidden_states"] [default3]:[rank3]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl [default3]:[rank3]: return self._call_impl(*args, **kwargs) [default3]:[rank3]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [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 [default3]:[rank3]: return forward_call(*args, **kwargs) [default3]:[rank3]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 171, in forward [default3]:[rank3]: merged_states = self.gate_up_proj(hidden_states) [default3]:[rank3]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl [default3]:[rank3]: return self._call_impl(*args, **kwargs) [default3]:[rank3]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default3]:[rank3]: return forward_call(*args, **kwargs) [default3]:[rank3]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/nn.py", line 87, in forward [default3]:[rank3]: return column_linear( [default3]:[rank3]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/functional.py", line 359, in column_linear [default3]:[rank3]: return F.linear(input, weight, bias) [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 693.94 MiB is free. Including non-PyTorch memory, this process has 78.64 GiB memory in use. Of the allocated memory 69.02 GiB is allocated by PyTorch, and 667.32 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]: return forward_call(*args, **kwargs) [default1]:[rank1]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 637, in forward [default1]:[rank1]: hidden_states = self.mlp(hidden_states=hidden_states)["hidden_states"] [default1]:[rank1]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl [default1]:[rank1]: return self._call_impl(*args, **kwargs) [default1]:[rank1]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default1]:[rank1]: return forward_call(*args, **kwargs) [default1]:[rank1]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 171, in forward [default1]:[rank1]: merged_states = self.gate_up_proj(hidden_states) [default1]:[rank1]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl [default1]:[rank1]: return self._call_impl(*args, **kwargs) [default1]:[rank1]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default1]:[rank1]: return forward_call(*args, **kwargs) [default1]:[rank1]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/nn.py", line 87, in forward [default1]:[rank1]: return column_linear( [default1]:[rank1]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/functional.py", line 359, in column_linear [default1]:[rank1]: return 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 453.94 MiB is free. Including non-PyTorch memory, this process has 78.88 GiB memory in use. Of the allocated memory 69.02 GiB is allocated by PyTorch, and 667.32 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]:[rank2]: Traceback (most recent call last): [default2]:[rank2]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/run_train.py", line 237, in [default2]:[rank2]: trainer.train(dataloader) [default2]:[rank2]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 429, in train [default2]:[rank2]: outputs, loss_avg = self.training_step(dataloader=self.current_dataloader) [default2]:[rank2]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 462, in training_step [default2]:[rank2]: outputs = self.pipeline_engine.train_batch_iter( [default2]:[rank2]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 278, in train_batch_iter [default2]:[rank2]: output = self.forward(context=context, state=state, micro_batch=micro_batch, model=model) [default2]:[rank2]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 44, in forward [default2]:[rank2]: output = model(**micro_batch) [default2]:[rank2]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl [default2]:[rank2]: return self._call_impl(*args, **kwargs) [default2]:[rank2]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default2]:[rank2]: return forward_call(*args, **kwargs) [default2]:[rank2]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 891, in forward [default2]:[rank2]: sharded_logits = self.model( [default2]:[rank2]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl [default2]:[rank2]: return self._call_impl(*args, **kwargs) [default2]:[rank2]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default2]:[rank2]: return forward_call(*args, **kwargs) [default2]:[rank2]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 764, in forward [default2]:[rank2]: return self.forward_with_hidden_states(input_ids=input_ids, input_mask=input_mask)[0] [default2]:[rank2]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 780, in forward_with_hidden_states [default2]:[rank2]: hidden_encoder_states = encoder_block(**hidden_encoder_states) [default2]:[rank2]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl [default2]:[rank2]: return self._call_impl(*args, **kwargs) [default2]:[rank2]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default2]:[rank2]: return forward_call(*args, **kwargs) [default2]:[rank2]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/block.py", line 151, in forward [default2]:[rank2]: output = self.pp_block(**new_kwargs) [default2]:[rank2]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl [default2]:[rank2]: return self._call_impl(*args, **kwargs) [default2]:[rank2]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default2]:[rank2]: return forward_call(*args, **kwargs) [default2]:[rank2]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 637, in forward [default2]:[rank2]: hidden_states = self.mlp(hidden_states=hidden_states)["hidden_states"] [default2]:[rank2]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl [default2]:[rank2]: return self._call_impl(*args, **kwargs) [default2]:[rank2]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default2]:[rank2]: return forward_call(*args, **kwargs) [default2]:[rank2]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 171, in forward [default2]:[rank2]: merged_states = self.gate_up_proj(hidden_states) [default2]:[rank2]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl [default2]:[rank2]: return self._call_impl(*args, **kwargs) [default2]:[rank2]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default2]:[rank2]: return forward_call(*args, **kwargs) [default2]:[rank2]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/nn.py", line 87, in forward [default2]:[rank2]: return column_linear( [default2]:[rank2]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/functional.py", line 359, in column_linear [default2]:[rank2]: return F.linear(input, weight, bias) [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 525.94 MiB is free. Including non-PyTorch memory, this process has 78.80 GiB memory in use. Of the allocated memory 69.02 GiB is allocated by PyTorch, and 667.32 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]:[rank6]: Traceback (most recent call last): [default6]:[rank6]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/run_train.py", line 237, in [default6]:[rank6]: trainer.train(dataloader) [default6]:[rank6]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 429, in train [default6]:[rank6]: outputs, loss_avg = self.training_step(dataloader=self.current_dataloader) [default6]:[rank6]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 462, in training_step [default6]:[rank6]: outputs = self.pipeline_engine.train_batch_iter( [default6]:[rank6]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 278, in train_batch_iter [default6]:[rank6]: output = self.forward(context=context, state=state, micro_batch=micro_batch, model=model) [default6]:[rank6]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 44, in forward [default6]:[rank6]: output = model(**micro_batch) [default6]:[rank6]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl [default6]:[rank6]: return self._call_impl(*args, **kwargs) [default6]:[rank6]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default6]:[rank6]: return forward_call(*args, **kwargs) [default6]:[rank6]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 891, in forward [default6]:[rank6]: sharded_logits = self.model( [default6]:[rank6]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl [default6]:[rank6]: return self._call_impl(*args, **kwargs) [default6]:[rank6]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default6]:[rank6]: return forward_call(*args, **kwargs) [default6]:[rank6]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 764, in forward [default6]:[rank6]: return self.forward_with_hidden_states(input_ids=input_ids, input_mask=input_mask)[0] [default6]:[rank6]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 780, in forward_with_hidden_states [default6]:[rank6]: hidden_encoder_states = encoder_block(**hidden_encoder_states) [default6]:[rank6]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl [default6]:[rank6]: return self._call_impl(*args, **kwargs) [default6]:[rank6]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default6]:[rank6]: return forward_call(*args, **kwargs) [default6]:[rank6]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/block.py", line 151, in forward [default6]:[rank6]: output = self.pp_block(**new_kwargs) [default6]:[rank6]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl [default6]:[rank6]: return self._call_impl(*args, **kwargs) [default6]:[rank6]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default6]:[rank6]: return forward_call(*args, **kwargs) [default6]:[rank6]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 637, in forward [default6]:[rank6]: hidden_states = self.mlp(hidden_states=hidden_states)["hidden_states"] [default6]:[rank6]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl [default6]:[rank6]: return self._call_impl(*args, **kwargs) [default6]:[rank6]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default6]:[rank6]: return forward_call(*args, **kwargs) [default6]:[rank6]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 171, in forward [default6]:[rank6]: merged_states = self.gate_up_proj(hidden_states) [default6]:[rank6]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl [default6]:[rank6]: return self._call_impl(*args, **kwargs) [default6]:[rank6]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default6]:[rank6]: return forward_call(*args, **kwargs) [default6]:[rank6]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/nn.py", line 87, in forward [default6]:[rank6]: return column_linear( [default6]:[rank6]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/functional.py", line 359, in column_linear [default6]:[rank6]: return F.linear(input, weight, bias) [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 525.94 MiB is free. Including non-PyTorch memory, this process has 78.80 GiB memory in use. Of the allocated memory 69.02 GiB is allocated by PyTorch, and 667.32 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]:[rank0]: Traceback (most recent call last): [default0]:[rank0]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/run_train.py", line 237, in [default0]:[rank0]: trainer.train(dataloader) [default0]:[rank0]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 429, in train [default0]:[rank0]: outputs, loss_avg = self.training_step(dataloader=self.current_dataloader) [default0]:[rank0]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 462, in training_step [default0]:[rank0]: outputs = self.pipeline_engine.train_batch_iter( [default0]:[rank0]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 278, in train_batch_iter [default0]:[rank0]: output = self.forward(context=context, state=state, micro_batch=micro_batch, model=model) [default0]:[rank0]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 44, in forward [default0]:[rank0]: output = model(**micro_batch) [default0]:[rank0]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl [default0]:[rank0]: return self._call_impl(*args, **kwargs) [default0]:[rank0]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default0]:[rank0]: return forward_call(*args, **kwargs) [default0]:[rank0]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 891, in forward [default0]:[rank0]: sharded_logits = self.model( [default0]:[rank0]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl [default0]:[rank0]: return self._call_impl(*args, **kwargs) [default0]:[rank0]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default0]:[rank0]: return forward_call(*args, **kwargs) [default0]:[rank0]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 764, in forward [default0]:[rank0]: return self.forward_with_hidden_states(input_ids=input_ids, input_mask=input_mask)[0] [default0]:[rank0]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 780, in forward_with_hidden_states [default0]:[rank0]: hidden_encoder_states = encoder_block(**hidden_encoder_states) [default0]:[rank0]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl [default0]:[rank0]: return self._call_impl(*args, **kwargs) [default0]:[rank0]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default0]:[rank0]: return forward_call(*args, **kwargs) [default0]:[rank0]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/block.py", line 151, in forward [default0]:[rank0]: output = self.pp_block(**new_kwargs) [default0]:[rank0]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl [default0]:[rank0]: return self._call_impl(*args, **kwargs) [default0]:[rank0]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default0]:[rank0]: return forward_call(*args, **kwargs) [default0]:[rank0]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 637, in forward [default0]:[rank0]: hidden_states = self.mlp(hidden_states=hidden_states)["hidden_states"] [default0]:[rank0]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl [default0]:[rank0]: return self._call_impl(*args, **kwargs) [default0]:[rank0]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default0]:[rank0]: return forward_call(*args, **kwargs) [default0]:[rank0]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 171, in forward [default0]:[rank0]: merged_states = self.gate_up_proj(hidden_states) [default0]:[rank0]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl [default0]:[rank0]: return self._call_impl(*args, **kwargs) [default0]:[rank0]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default0]:[rank0]: return forward_call(*args, **kwargs) [default0]:[rank0]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/nn.py", line 87, in forward [default0]:[rank0]: return column_linear( [default0]:[rank0]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/functional.py", line 359, in column_linear [default0]:[rank0]: return F.linear(input, weight, bias) [default0]:[rank0]: torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 1024.00 MiB. GPU [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 637, in forward [default5]:[rank5]: hidden_states = self.mlp(hidden_states=hidden_states)["hidden_states"] [default5]:[rank5]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl [default5]:[rank5]: return self._call_impl(*args, **kwargs) [default5]:[rank5]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default5]:[rank5]: return forward_call(*args, **kwargs) [default5]:[rank5]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 171, in forward [default5]:[rank5]: merged_states = self.gate_up_proj(hidden_states) [default5]:[rank5]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl [default5]:[rank5]: return self._call_impl(*args, **kwargs) [default5]:[rank5]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default5]:[rank5]: return forward_call(*args, **kwargs) [default5]:[rank5]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/nn.py", line 87, in forward [default5]:[rank5]: return column_linear( [default5]:[rank5]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/functional.py", line 359, in column_linear [default5]:[rank5]: return 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 453.94 MiB is free. Including non-PyTorch memory, this process has 78.88 GiB memory in use. Of the allocated memory 69.02 GiB is allocated by PyTorch, and 667.32 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]: Traceback (most recent call last): [default5]:[rank13]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/run_train.py", line 237, in [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) [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 [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] [default5]:[rank13]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 780, in forward_with_hidden_states [default5]:[rank13]: hidden_encoder_states = encoder_block(**hidden_encoder_states) [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/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 [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 [default5]:[rank13]: output = self.attn(hidden_states=hidden_states, sequence_mask=sequence_mask) [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 598, in forward [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 869.94 MiB is free. Including non-PyTorch memory, this process has 78.47 GiB memory in use. Of the allocated memory 68.27 GiB is allocated by PyTorch, and 411.57 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]:[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 598, in forward [default4]:[rank12]: output = self.o_proj(attention_output) [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/tensor_parallel/nn.py", line 159, in forward [default4]:[rank12]: return row_linear( [default4]:[rank12]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/functional.py", line 474, in row_linear [default4]:[rank12]: out = F.linear(input, weight, bias) [default4]:[rank12]: torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 1024.00 MiB. GPU  has a total capacity of 79.33 GiB of which 845.94 MiB is free. Including non-PyTorch memory, this process has 78.49 GiB memory in use. Of the allocated memory 68.27 GiB is allocated by PyTorch, and 411.57 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 598, in forward [default6]:[rank14]: output = self.o_proj(attention_output) [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/tensor_parallel/nn.py", line 159, in forward [default6]:[rank14]: return row_linear( [default6]:[rank14]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/functional.py", line 474, in row_linear [default6]:[rank14]: out = F.linear(input, weight, bias) [default6]:[rank14]: torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 1024.00 MiB. GPU  has a total capacity of 79.33 GiB of which 797.94 MiB is free. Including non-PyTorch memory, this process has 78.54 GiB memory in use. Of the allocated memory 68.27 GiB is allocated by PyTorch, and 411.57 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 598, in forward [default4]:[rank28]: output = self.o_proj(attention_output) [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/tensor_parallel/nn.py", line 159, in forward [default4]:[rank28]: return row_linear( [default4]:[rank28]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/functional.py", line 474, in row_linear [default4]:[rank28]: out = F.linear(input, weight, bias) [default4]:[rank28]: torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 1024.00 MiB. GPU  has a total capacity of 79.33 GiB of which 725.94 MiB is free. Including non-PyTorch memory, this process has 78.61 GiB memory in use. Of the allocated memory 68.27 GiB is allocated by PyTorch, and 411.57 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]:[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( [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) [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) [default4]:[rank60]: Traceback (most recent call last): [default4]:[rank60]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/run_train.py", line 237, in [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 [default0]:[rank56]: Traceback (most recent call last): [default0]:[rank56]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/run_train.py", line 237, in [default4]:[rank60]: trainer.train(dataloader) [default7]:[rank63]: return self._call_impl(*args, **kwargs) [default4]:[rank60]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 429, in train [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) [default4]:[rank60]: outputs, loss_avg = self.training_step(dataloader=self.current_dataloader) [default0]:[rank56]: trainer.train(dataloader) [default4]:[rank60]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 462, in training_step [default0]:[rank56]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 429, in train [default4]:[rank60]: outputs = self.pipeline_engine.train_batch_iter( [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( [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) [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 [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 [default4]:[rank60]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 44, in forward [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 [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 [default0]:[rank56]: outputs = self.pipeline_engine.train_batch_iter( [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 [default0]:[rank56]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 278, in train_batch_iter [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 [default0]:[rank56]: output = self.forward(context=context, state=state, micro_batch=micro_batch, model=model) [default7]:[rank63]: return forward_call(*args, **kwargs) [default4]:[rank60]: sharded_logits = self.model( [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] [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) [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 [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 [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) [default0]:[rank56]: return self._call_impl(*args, **kwargs) [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 [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) [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 [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 [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) [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 [default4]:[rank60]: 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 891, in forward [default0]:[rank56]: sharded_logits = self.model( [default7]:[rank63]: 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 [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) [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) [default0]:[rank56]: 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 1532, in _wrapped_call_impl [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 [default4]:[rank60]: return self._call_impl(*args, **kwargs) [default0]:[rank56]: 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 1541, in _call_impl [default7]:[rank63]: return forward_call(*args, **kwargs) [default0]:[rank56]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 764, in forward [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) [default7]:[rank63]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 637, in forward [default7]:[rank63]: hidden_states = self.mlp(hidden_states=hidden_states)["hidden_states"] [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 [default0]:[rank56]: return self.forward_with_hidden_states(input_ids=input_ids, input_mask=input_mask)[0] [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) [default4]:[rank60]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/block.py", line 151, in forward [default0]:[rank56]: 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/models/llama.py", line 171, in forward [default7]:[rank63]: merged_states = self.gate_up_proj(hidden_states) [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 [default0]:[rank56]: hidden_encoder_states = encoder_block(**hidden_encoder_states) [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 [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) [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) [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]:[rank56]: 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) [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 [default4]:[rank60]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 637, in forward [default0]:[rank56]: output = self.pp_block(**new_kwargs) [default7]:[rank63]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/nn.py", line 87, in forward [default7]:[rank63]: return column_linear( [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 [default7]:[rank63]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/functional.py", line 359, in column_linear [default0]:[rank56]: return self._call_impl(*args, **kwargs) [default4]:[rank60]: hidden_states = self.mlp(hidden_states=hidden_states)["hidden_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 [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 [default7]:[rank63]: return F.linear(input, weight, bias) [default0]:[rank56]: return forward_call(*args, **kwargs) [default0]:[rank56]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 637, 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 765.94 MiB is free. Including non-PyTorch memory, this process has 78.57 GiB memory in use. Of the allocated memory 69.02 GiB is allocated by PyTorch, and 667.32 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]: hidden_states = self.mlp(hidden_states=hidden_states)["hidden_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 [default4]:[rank60]: return forward_call(*args, **kwargs) [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 [default4]:[rank60]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 171, in forward [default4]:[rank60]: merged_states = self.gate_up_proj(hidden_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 [default0]:[rank56]: return forward_call(*args, **kwargs) [default0]:[rank56]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 171, in forward [default0]:[rank56]: merged_states = self.gate_up_proj(hidden_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) [default4]:[rank60]: 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 [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 [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 87, in forward [default4]:[rank60]: return forward_call(*args, **kwargs) [default0]:[rank56]: return column_linear( [default0]:[rank56]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/functional.py", line 359, in column_linear [default0]:[rank56]: return F.linear(input, weight, bias) [default4]:[rank60]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/nn.py", line 87, in forward [default0]:[rank56]: torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 1024.00 MiB. GPU [default4]:[rank60]: return column_linear( [default4]:[rank60]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/functional.py", line 359, in column_linear [default4]:[rank60]: return 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 501.94 MiB is free. Including non-PyTorch memory, this process has 78.83 GiB memory in use. Of the allocated memory 69.02 GiB is allocated by PyTorch, and 667.32 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]: 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/nanotron/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 [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[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 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 [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) [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( [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/nanotro[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 n/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-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[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) [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]:[rank29]: 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) [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 [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 [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 598, in forward [default5]:[rank29]: output = self.o_proj(attention_output) [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_clust[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) er/nanotron/src/nanotron/parallel/tensor_parallel/nn.py", line 159, in forward [default5]:[rank29]: return row_linear( [default5]:[rank29]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/functional.py", line 474, in row_linear [default5]:[rank29]: out = F.linear(input, weight, bias) [default5]:[rank29]: torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 1024.00 MiB. GPU  has a total capacity of 79.33 GiB of which 829.94 MiB is free. Including non-PyTorch memory, this process has 78.51 GiB memory in use. Of the allocated memory 68.27 GiB is allocated by PyTorch, and 411.57 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]: 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 637, in forward [default6]:[rank62]: hidden_states = self.mlp(hidden_states=hidden_states)["hidden_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/models/llama.py", line 171, in forward [default6]:[rank62]: merged_states = self.gate_up_proj(hidden_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/tensor_parallel/nn.py", line 87, in forward [default6]:[rank62]: return column_linear( [default6]:[rank62]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/functional.py", line 359, in column_linear [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 [default0]:[rank24]: output = self.forward(context=context, state=state, micro_batch=micro_batch, model=model) [default0]:[rank24]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanot[default6]:[rank62]: return 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 453.94 MiB is free. Including non-PyTorch memory, this process has 78.88 GiB memory in use. Of the allocated memory 69.02 GiB is allocated by PyTorch, and 667.32 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) ron/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) [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 891, in forward [default0]:[rank24]: 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 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 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 [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 [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 598, in forward [default0]:[rank24]: output = self.o_proj(attention_output) [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/tensor_parallel/nn.py", line 159, in forward [default0]:[rank24]: return row_linear( [default0]:[rank24]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/functional.py", line 474, in row_linear [default0]:[rank24]: out = F.linear(input, weight, bias) [default0]:[rank24]: torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 1024.00 MiB. GPU [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 637, in forward [default3]:[rank59]: hidden_states = self.mlp(hidden_states=hidden_states)["hidden_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/models/llama.py", line 171, in forward [default3]:[rank59]: merged_states = self.gate_up_proj(hidden_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/tensor_parallel/nn.py", line 87, in forward [default3]:[rank59]: return column_linear( [default3]:[rank59]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/functional.py", line 359, in column_linear [default3]:[rank59]: return 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 765.94 MiB is free. Including non-PyTorch memory, this process has 78.57 GiB memory in use. Of the allocated memory 69.02 GiB is allocated by PyTorch, and 667.32 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) [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 [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 [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]:[rank25]: Traceback (most recent call last): [default1]:[rank25]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/run_train.py", line 237, in [default2]:[rank26]: Traceback (most recent call last): [default2]:[rank26]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/run_train.py", line 237, in [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 [default1]:[rank57]: output = model(**micro_batch) [default2]:[rank26]: trainer.train(dataloader) [default2]:[rank26]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 429, in train [default1]:[rank25]: trainer.train(dataloader) [default1]:[rank25]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 429, in train [default1]:[rank25]: outputs, loss_avg = self.training_step(dataloader=self.current_dataloader) [default2]:[rank26]: outputs, loss_avg = self.training_step(dataloader=self.current_dataloader) [default1]:[rank25]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 462, in training_step [default1]:[rank25]: outputs = self.pipeline_engine.train_batch_iter( [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) [default1]:[rank25]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 278, in train_batch_iter [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]:[rank25]: output = self.forward(context=context, state=state, micro_batch=micro_batch, model=model) [default2]:[rank26]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 44, in forward [default1]:[rank57]: return self._call_impl(*args, **kwargs) [default5]:[rank61]: return forward_call(*args, **kwargs) [default2]:[rank26]: output = model(**micro_batch) [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) [default1]:[rank25]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 44, in forward [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]:[rank25]: output = model(**micro_batch) [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]:[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]:[rank57]: 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 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 [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) [default2]:[rank26]: return self._call_impl(*args, **kwargs) [default5]:[rank61]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 764, in forward [default1]:[rank25]: return forward_call(*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 [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) [default2]:[rank26]: return forward_call(*args, **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) [default1]:[rank25]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 891, in forward [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 [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( [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 [default1]:[rank25]: sharded_logits = self.model( [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 [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 [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) [default2]:[rank26]: return self._call_impl(*args, **kwargs) [default1]:[rank57]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 764, in forward [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 [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 [default2]:[rank26]: return forward_call(*args, **kwargs) [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 [default2]:[rank26]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 764, in forward [default1]:[rank57]: return self._call_impl(*args, **kwargs) [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 [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) [default5]:[rank61]: 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]:[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]:[rank25]: return self.forward_with_hidden_states(input_ids=input_ids, input_mask=input_mask)[0] [default1]:[rank25]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 780, in forward_with_hidden_states [default1]:[rank57]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/block.py", line 151, in forward [default1]:[rank25]: hidden_encoder_states = encoder_block(**hidden_encoder_states) [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) [default2]:[rank26]: return self.forward_with_hidden_states(input_ids=input_ids, input_mask=input_mask)[0] [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 [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) [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]:[rank57]: return forward_call(*args, **kwargs) [default5]:[rank61]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 637, in forward [default5]:[rank61]: hidden_states = self.mlp(hidden_states=hidden_states)["hidden_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 [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 [default2]:[rank26]: return forward_call(*args, **kwargs) [default2]:[rank26]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/block.py", line 151, in forward [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) [default2]:[rank26]: output = self.pp_block(**new_kwargs) [default1]:[rank25]: return self._call_impl(*args, **kwargs) [default5]:[rank61]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 171, in forward [default5]:[rank61]: merged_states = self.gate_up_proj(hidden_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) [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 [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 [default1]:[rank57]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 637, in forward [default1]:[rank57]: hidden_states = self.mlp(hidden_states=hidden_states)["hidden_states"] [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]:[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 [default5]:[rank61]: 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 631, in forward [default1]:[rank57]: 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/parallel/pipeline_parallel/block.py", line 151, in forward [default1]:[rank25]: 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 1541, in _call_impl [default2]:[rank26]: 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 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 87, in forward [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]:[rank57]: return forward_call(*args, **kwargs) [default1]:[rank57]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 171, in forward [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]:[rank57]: merged_states = self.gate_up_proj(hidden_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]:[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]:[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 [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 [default5]:[rank61]: return column_linear( [default2]:[rank26]: return self._call_impl(*args, **kwargs) [default1]:[rank57]: return forward_call(*args, **kwargs) [default1]:[rank25]: return self._call_impl(*args, **kwargs) [default1]:[rank57]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/nn.py", line 87, in forward [default1]:[rank57]: return column_linear( [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 [default5]:[rank61]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/functional.py", line 359, in column_linear [default2]:[rank26]: return forward_call(*args, **kwargs) [default2]:[rank26]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 598, in forward [default1]:[rank57]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/functional.py", line 359, in column_linear [default1]:[rank57]: return F.linear(input, weight, bias) [default2]:[rank26]: output = self.o_proj(attention_output) [default5]:[rank61]: return F.linear(input, weight, bias) [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) [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 525.94 MiB is free. Including non-PyTorch memory, this process has 78.80 GiB memory in use. Of the allocated memory 69.02 GiB is allocated by PyTorch, and 667.32 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]:[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) [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 525.94 MiB is free. Including non-PyTorch memory, this process has 78.80 GiB memory in use. Of the allocated memory 69.02 GiB is allocated by PyTorch, and 667.32 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]:[rank26]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/nn.py", line 159, in forward [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 598, in forward [default1]:[rank25]: output = self.o_proj(attention_output) [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[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) 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( [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) [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]:[rank26]: return row_linear( 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]:[rank58]: return forward_call(*args, **kwargs) [default2]:[rank58]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 637, in forward [default2]:[rank58]: hidden_states = self.mlp(hidden_states=hidden_states)["hidden_states"] [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 171, in forward [default2]:[rank58]: merged_states = self.gate_up_proj(hidden_states) [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/parallel/tensor_parallel/nn.py", line 87, in forward [default2]:[rank58]: return column_linear( [default2]:[rank58]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/functional.py", line 359, in column_linear [default2]:[rank58]: return 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 453.94 MiB is free. Including non-PyTorch memory, this process has 78.88 GiB memory in use. Of the allocated memory 69.02 GiB is allocated by PyTorch, and 667.32 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]:[rank25]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/nn.py", line 159, in forward [default1]:[rank25]: return row_linear( [default2]:[rank26]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/functional.py", line 474, in row_linear [default1]:[rank25]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/functional.py", line 474, in row_linear [default1]:[rank25]: out = F.linear(input, weight, bias) [default2]:[rank26]: out = F.linear(input, weight, bias) [default1]:[rank25]: torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 1024.00 MiB. GPU  has a total capacity of 79.33 GiB of which 829.94 MiB is free. Including non-PyTorch memory, this process has 78.51 GiB memory in use. Of the allocated memory 68.27 GiB is allocated by PyTorch, and 411.57 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]:[rank26]: torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 1024.00 MiB. GPU  has a total capacity of 79.33 GiB of which 677.94 MiB is free. Including non-PyTorch memory, this process has 78.66 GiB memory in use. Of the allocated memory 68.27 GiB is allocated by PyTorch, and 411.57 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 598, in forward [default6]:[rank30]: output = self.o_proj(attention_output) [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/tensor_parallel/nn.py", line 159, in forward [default6]:[rank30]: return row_linear( [default6]:[rank30]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/functional.py", line 474, in row_linear [default6]:[rank30]: out = F.linear(input, weight, bias) [default6]:[rank30]: torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 1024.00 MiB. GPU  has a total capacity of 79.33 GiB of which 677.94 MiB is free. Including non-PyTorch memory, this process has 78.66 GiB memory in use. Of the allocated memory 68.27 GiB is allocated by PyTorch, and 411.57 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 [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) [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 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 [default1]:[rank9]: hidden_encoder_states = encoder_block(**hidden_encoder_states) [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/parallel/pipeline_parallel/block.py", line 151, in forward [default1]:[rank9]: output = self.pp_block(**new_kwargs) [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 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 [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 [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) [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) [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 869.94 MiB is free. Including non-PyTorch memory, this process has 78.47 GiB memory in use. Of the allocated memory 68.27 GiB is allocated by PyTorch, and 411.57 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]:[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/nanotron/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 [default2]:[rank10]: Traceback (most recent call last): [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 [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) [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) [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) [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) [default2]:[rank10]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 891, in forward [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 563, in forward [default0]:[rank8]: key_value_states = torch.cat([key_states.unsqueeze(0), value_states.unsqueeze(0)], dim=0) [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) [default0]:[rank8]: torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 512.00 MiB. GPU [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 598, in forward [default2]:[rank10]: output = self.o_proj(attention_output) [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/tensor_parallel/nn.py", line 159, in forward [default2]:[rank10]: return row_linear( [default2]:[rank10]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/functional.py", line 474, in row_linear [default2]:[rank10]: out = F.linear(input, weight, bias) [default2]:[rank10]: torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 1024.00 MiB. GPU  has a total capacity of 79.33 GiB of which 797.94 MiB is free. Including non-PyTorch memory, this process has 78.54 GiB memory in use. Of the allocated memory 68.27 GiB is allocated by PyTorch, and 411.57 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 598, in forward [default2]:[rank42]: output = self.o_proj(attention_output) [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/tensor_parallel/nn.py", line 159, in forward [default2]:[rank42]: return row_linear( [default2]:[rank42]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/functional.py", line 474, in row_linear [default2]:[rank42]: out = F.linear(input, weight, bias) [default2]:[rank42]: torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 1024.00 MiB. GPU  has a total capacity of 79.33 GiB of which 797.94 MiB is free. Including non-PyTorch memory, this process has 78.54 GiB memory in use. Of the allocated memory 68.27 GiB is allocated by PyTorch, and 411.57 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]:[rank40]: Traceback (most recent call last): [default0]:[rank40]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/run_train.py", line 237, in [default0]:[rank40]: trainer.train(dataloader) [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]:[rank40]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 462, in training_step [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 [default0]:[rank40]: output = self.forward(context=context, state=state, micro_batch=micro_batch, model=model) [default0]:[rank40]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 44, in forward [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 [default0]:[rank40]: return self._call_impl(*args, **kwargs) [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]:[rank40]: return forward_call(*args, **kwargs) [default0]:[rank40]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 891, in forward [default0]:[rank40]: sharded_logits = self.model( [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]:[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]:[rank40]: return forward_call(*args, **kwargs) [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] [default0]:[rank40]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 780, in forward_with_hidden_states [default0]:[rank40]: hidden_encoder_states = encoder_block(**hidden_encoder_states) [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]:[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]:[rank40]: return forward_call(*args, **kwargs) [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) [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]:[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]:[rank40]: return forward_call(*args, **kwargs) [default0]:[rank40]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 631, in forward [default0]:[rank40]: output = self.attn(hidden_states=hidden_states, sequence_mask=sequence_mask) [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]:[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]:[rank40]: return forward_call(*args, **kwargs) [default0]:[rank40]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 598, in forward [default0]:[rank40]: output = self.o_proj(attention_output) [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]:[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]:[rank40]: return forward_call(*args, **kwargs) [default0]:[rank40]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/nn.py", line 159, in forward [default0]:[rank40]: return row_linear( [default0]:[rank40]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/functional.py", line 474, in row_linear [default0]:[rank40]: out = F.linear(input, weight, bias) [default0]:[rank40]: torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 1024.00 MiB. GPU [default1]:[rank41]: Traceback (most recent call last): [default1]:[rank41]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/run_train.py", line 237, in [default1]:[rank41]: trainer.train(dataloader) [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 [default1]:[rank41]: outputs = self.pipeline_engine.train_batch_iter( [default1]:[rank41]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 278, in train_batch_iter [default1]:[rank41]: 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 [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 [default1]:[rank41]: 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 1541, in _call_impl [default1]:[rank41]: return forward_call(*args, **kwargs) [default1]:[rank41]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 891, in forward [default1]:[rank41]: sharded_logits = self.model( [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) [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) [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] [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) [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) [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) [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]:[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) [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) [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) [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) [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) [default1]:[rank41]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 598, in forward [default1]:[rank41]: output = self.o_proj(attention_output) [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) [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) [default1]:[rank41]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/nn.py", line 159, in forward [default1]:[rank41]: return row_linear( [default1]:[rank41]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/functional.py", line 474, in row_linear [default1]:[rank41]: out = F.linear(input, weight, bias) [default1]:[rank41]: torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 1024.00 MiB. GPU  has a total capacity of 79.33 GiB of which 869.94 MiB is free. Including non-PyTorch memory, this process has 78.47 GiB memory in use. Of the allocated memory 68.27 GiB is allocated by PyTorch, and 411.57 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]: Traceback (most recent call last): [default6]:[rank46]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/run_train.py", line 237, in [default6]:[rank46]: trainer.train(dataloader) [default6]:[rank46]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 429, in train [default6]:[rank46]: outputs, loss_avg = self.training_step(dataloader=self.current_dataloader) [default6]:[rank46]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 462, in training_step [default6]:[rank46]: outputs = self.pipeline_engine.train_batch_iter( [default6]:[rank46]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 278, in train_batch_iter [default6]:[rank46]: output = self.forward(context=context, state=state, micro_batch=micro_batch, model=model) [default6]:[rank46]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 44, in forward [default6]:[rank46]: output = model(**micro_batch) [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) [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) [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 [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/nanotron/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) [default6]:[rank46]: return forward_call(*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) [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 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) [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( [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) [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) [default4]:[rank44]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/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 [default6]:[rank46]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 764, in forward [default4]:[rank44]: return forward_call(*args, **kwargs) [default6]:[rank46]: 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 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 [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) [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) [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 [default4]:[rank44]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 598, in forward [default6]:[rank46]: return self._call_impl(*args, **kwargs) [default4]:[rank44]: output = self.o_proj(attention_output) [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 [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 [default6]:[rank46]: return forward_call(*args, **kwargs) [default6]:[rank46]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/block.py", line 151, in forward [default4]:[rank44]: return forward_call(*args, **kwargs) [default4]:[rank44]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/nn.py", line 159, in forward [default6]:[rank46]: output = self.pp_block(**new_kwargs) [default4]:[rank44]: return row_linear( [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 [default4]:[rank44]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/functional.py", line 474, in row_linear [default4]:[rank44]: out = F.linear(input, weight, bias) [default6]:[rank46]: return self._call_impl(*args, **kwargs) [default4]:[rank44]: torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 1024.00 MiB. GPU  has a total capacity of 79.33 GiB of which 845.94 MiB is free. Including non-PyTorch memory, this process has 78.49 GiB memory in use. Of the allocated memory 68.27 GiB is allocated by PyTorch, and 411.57 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/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) [default6]:[rank46]: 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) [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) [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) [default6]:[rank46]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 598, in forward [default6]:[rank46]: output = self.o_proj(attention_output) [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) [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) [default6]:[rank46]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/nn.py", line 159, in forward [default6]:[rank46]: return row_linear( [default6]:[rank46]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/functional.py", line 474, in row_linear [default6]:[rank46]: out = F.linear(input, weight, bias) [default6]:[rank46]: torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 1024.00 MiB. GPU  has a total capacity of 79.33 GiB of which 797.94 MiB is free. Including non-PyTorch memory, this process has 78.54 GiB memory in use. Of the allocated memory 68.27 GiB is allocated by PyTorch, and 411.57 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]:[rank45]: Traceback (most recent call last): [default5]:[rank45]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/run_train.py", line 237, in [default5]:[rank45]: trainer.train(dataloader) [default5]:[rank45]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 429, in train [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 [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 [default5]:[rank45]: output = self.forward(context=context, state=state, micro_batch=micro_batch, model=model) [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) [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 [default5]:[rank45]: 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 1541, in _call_impl [default5]:[rank45]: return forward_call(*args, **kwargs) [default5]:[rank45]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 891, in forward [default5]:[rank45]: sharded_logits = self.model( [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 [default5]:[rank45]: 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 1541, in _call_impl [default5]:[rank45]: return forward_call(*args, **kwargs) [default5]:[rank45]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 764, in forward [default5]:[rank45]: return self.forward_with_hidden_states(input_ids=input_ids, input_mask=input_mask)[0] [default5]:[rank45]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 780, in forward_with_hidden_states [default5]:[rank45]: hidden_encoder_states = encoder_block(**hidden_encoder_states) [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 [default5]:[rank45]: 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 1541, in _call_impl [default5]:[rank45]: return forward_call(*args, **kwargs) [default5]:[rank45]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/block.py", line 151, in forward [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 [default5]:[rank45]: 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 1541, in _call_impl [default5]:[rank45]: return forward_call(*args, **kwargs) [default5]:[rank45]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 631, in forward [default5]:[rank45]: output = self.attn(hidden_states=hidden_states, sequence_mask=sequence_mask) [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 [default5]:[rank45]: 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 1541, in _call_impl [default5]:[rank45]: return forward_call(*args, **kwargs) [default5]:[rank45]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 598, in forward [default5]:[rank45]: output = self.o_proj(attention_output) [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 [default5]:[rank45]: 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 1541, in _call_impl [default5]:[rank45]: return forward_call(*args, **kwargs) [default5]:[rank45]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/nn.py", line 159, in forward [default5]:[rank45]: return row_linear( [default5]:[rank45]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/functional.py", line 474, in row_linear [default5]:[rank45]: out = F.linear(input, weight, bias) [default5]:[rank45]: torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 1024.00 MiB. GPU  has a total capacity of 79.33 GiB of which 869.94 MiB is free. Including non-PyTorch memory, this process has 78.47 GiB memory in use. Of the allocated memory 68.27 GiB is allocated by PyTorch, and 411.57 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]: Traceback (most recent call last): [default0]:[rank48]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/run_train.py", line 237, in [default0]:[rank48]: trainer.train(dataloader) [default0]:[rank48]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 429, in train [default0]:[rank48]: outputs, loss_avg = self.training_step(dataloader=self.current_dataloader) [default0]:[rank48]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 462, in training_step [default0]:[rank48]: outputs = self.pipeline_engine.train_batch_iter( [default0]:[rank48]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 278, in train_batch_iter [default0]:[rank48]: 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/parallel/pipeline_parallel/engine.py", line 44, in forward [default0]:[rank48]: output = model(**micro_batch) [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 891, in forward [default0]:[rank48]: sharded_logits = self.model( [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 764, in forward [default0]:[rank48]: return self.forward_with_hidden_states(input_ids=input_ids, input_mask=input_mask)[0] [default0]:[rank48]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 780, in forward_with_hidden_states [default0]:[rank48]: hidden_encoder_states = encoder_block(**hidden_encoder_states) [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/pipeline_parallel/block.py", line 151, in forward [default0]:[rank48]: 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 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 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 [default1]:[rank49]: Traceback (most recent call last): [default1]:[rank49]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/run_train.py", line 237, in [default1]:[rank49]: trainer.train(dataloader) [default1]:[rank49]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 429, in train [default1]:[rank49]: outputs, loss_avg = self.training_step(dataloader=self.current_dataloader) [default1]:[rank49]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 462, in training_step [default1]:[rank49]: outputs = self.pipeline_engine.train_batch_iter( [default1]:[rank49]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 278, in train_batch_iter [default1]:[rank49]: output = self.forward(context=context, state=state, micro_batch=micro_batch, model=model) [default1]:[rank49]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 44, in forward [default1]:[rank49]: output = model(**micro_batch) [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 [default1]:[rank49]: 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 1541, in _call_impl [default1]:[rank49]: return forward_call(*args, **kwargs) [default1]:[rank49]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 891, in forward [default1]:[rank49]: sharded_logits = self.model( [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 [default1]:[rank49]: 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 1541, in _call_impl [default1]:[rank49]: return forward_call(*args, **kwargs) [default1]:[rank49]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 764, in forward [default1]:[rank49]: 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 780, in forward_with_hidden_states [default1]:[rank49]: 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 [default1]:[rank49]: 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 1541, in _call_impl [default1]:[rank49]: return forward_call(*args, **kwargs) [default1]:[rank49]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/block.py", line 151, in forward [default1]:[rank49]: output = self.pp_block(**new_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 [default1]:[rank49]: 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 1541, in _call_impl [default1]:[rank49]: return forward_call(*args, **kwargs) [default1]:[rank49]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 631, in forward [default1]:[rank49]: output = self.attn(hidden_states=hidden_states, sequence_mask=sequence_mask) [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 [default1]:[rank49]: 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 1541, in _call_impl [default1]:[rank49]: return forward_call(*args, **kwargs) [default1]:[rank49]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 598, in forward [default1]:[rank49]: output = self.o_proj(attention_output) [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 [default1]:[rank49]: 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 1541, in _call_impl [default1]:[rank49]: return forward_call(*args, **kwargs) [default1]:[rank49]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/nn.py", line 159, in forward [default1]:[rank49]: return row_linear( [default1]:[rank49]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/functional.py", line 474, in row_linear [default1]:[rank49]: out = F.linear(input, weight, bias) [default1]:[rank49]: torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 1024.00 MiB. GPU  has a total capacity of 79.33 GiB of which 797.94 MiB is free. Including non-PyTorch memory, this process has 78.54 GiB memory in use. Of the allocated memory 68.27 GiB is allocated by PyTorch, and 411.57 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 [default2]:[rank50]: sharded_logits = self.model( [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 764, in forward [default2]:[rank50]: return self.forward_with_hidden_states(input_ids=input_ids, input_mask=input_mask)[0] [default2]:[rank50]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 780, in forward_with_hidden_states [default2]:[rank50]: hidden_encoder_states = encoder_block(**hidden_encoder_states) [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/parallel/pipeline_parallel/block.py", line 151, in forward [default2]:[rank50]: output = self.pp_block(**new_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 [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 631, in forward [default2]:[rank50]: output = self.attn(hidden_states=hidden_states, sequence_mask=sequence_mask) [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 598, in forward [default2]:[rank50]: output = self.o_proj(attention_output) [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/parallel/tensor_parallel/nn.py", line 159, in forward [default2]:[rank50]: return row_linear( [default2]:[rank50]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/functional.py", line 474, in row_linear [default2]:[rank50]: out = F.linear(input, weight, bias) [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 869.94 MiB is free. Including non-PyTorch memory, this process has 78.47 GiB memory in use. Of the allocated memory 68.27 GiB is allocated by PyTorch, and 411.57 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]:[rank32]: Traceback (most recent call last): [default0]:[rank32]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/run_train.py", line 237, in [default0]:[rank32]: trainer.train(dataloader) [default2]:[rank34]: Traceback (most recent call last): [default2]:[rank34]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/run_train.py", line 237, in [default0]:[rank32]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 429, in train [default0]:[rank32]: outputs, loss_avg = self.training_step(dataloader=self.current_dataloader) [default2]:[rank34]: trainer.train(dataloader) [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( [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( [default0]:[rank32]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 278, in train_batch_iter [default0]:[rank32]: output = self.forward(context=context, state=state, micro_batch=micro_batch, model=model) [default0]:[rank32]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 44, in forward [default0]:[rank32]: output = model(**micro_batch) [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 [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) [default0]:[rank32]: return self._call_impl(*args, **kwargs) [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) [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 [default2]:[rank34]: return forward_call(*args, **kwargs) [default2]:[rank34]: 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 1541, in _call_impl [default2]:[rank34]: sharded_logits = self.model( [default0]:[rank32]: return forward_call(*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 [default2]:[rank34]: return self._call_impl(*args, **kwargs) [default0]:[rank32]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 891, in forward [default0]:[rank32]: sharded_logits = self.model( [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]: 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) [default2]:[rank34]: return forward_call(*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]:[rank32]: return forward_call(*args, **kwargs) [default2]:[rank34]: 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/models/llama.py", line 764, in forward [default2]:[rank34]: return self.forward_with_hidden_states(input_ids=input_ids, input_mask=input_mask)[0] [default0]:[rank32]: return self.forward_with_hidden_states(input_ids=input_ids, input_mask=input_mask)[0] [default2]:[rank34]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 780, in forward_with_hidden_states [default2]:[rank34]: hidden_encoder_states = encoder_block(**hidden_encoder_states) [default0]:[rank32]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 780, in forward_with_hidden_states [default0]:[rank32]: hidden_encoder_states = encoder_block(**hidden_encoder_states) [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) [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]:[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/parallel/pipeline_parallel/block.py", line 151, in forward [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]: output = self.pp_block(**new_kwargs) [default2]:[rank34]: 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 [default2]:[rank34]: output = self.pp_block(**new_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 [default2]:[rank34]: 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 [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 [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 [default0]:[rank32]: return self._call_impl(*args, **kwargs) [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 [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 631, in forward [default0]:[rank32]: output = self.attn(hidden_states=hidden_states, sequence_mask=sequence_mask) [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) [default2]:[rank34]: 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 [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/models/llama.py", line 598, in forward [default2]:[rank34]: return forward_call(*args, **kwargs) [default2]:[rank34]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 598, in forward [default2]:[rank34]: output = self.o_proj(attention_output) [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 [default2]:[rank34]: return forward_call(*args, **kwargs) [default2]:[rank34]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/nn.py", line 159, in forward [default2]:[rank34]: return row_linear( [default0]:[rank32]: output = self.o_proj(attention_output) [default2]:[rank34]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/functional.py", line 474, in row_linear [default2]:[rank34]: out = F.linear(input, weight, bias) [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) [default2]:[rank34]: torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 1024.00 MiB. GPU  has a total capacity of 79.33 GiB of which 829.94 MiB is free. Including non-PyTorch memory, this process has 78.51 GiB memory in use. Of the allocated memory 68.27 GiB is allocated by PyTorch, and 411.57 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]:[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/parallel/tensor_parallel/nn.py", line 159, in forward [default0]:[rank32]: return row_linear( [default0]:[rank32]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/functional.py", line 474, in row_linear [default0]:[rank32]: out = F.linear(input, weight, bias) [default0]:[rank32]: torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 1024.00 MiB. GPU [default3]:[rank35]: Traceback (most recent call last): [default3]:[rank35]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/run_train.py", line 237, in [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) [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) [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 891, in forward [default3]:[rank35]: 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 1532, in _wrapped_call_impl [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) [default3]:[rank35]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 764, in forward [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) [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) [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/parallel/pipeline_parallel/block.py", line 151, in forward [default3]:[rank35]: 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 1532, in _wrapped_call_impl [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) [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) [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) [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 598, in forward [default3]:[rank35]: output = self.o_proj(attention_output) [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) [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/parallel/tensor_parallel/nn.py", line 159, in forward [default3]:[rank35]: return row_linear( [default3]:[rank35]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/functional.py", line 474, in row_linear [default3]:[rank35]: out = F.linear(input, weight, bias) [default3]:[rank35]: torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 1024.00 MiB. GPU  has a total capacity of 79.33 GiB of which 917.94 MiB is free. Including non-PyTorch memory, this process has 78.42 GiB memory in use. Of the allocated memory 68.27 GiB is allocated by PyTorch, and 411.57 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]: Traceback (most recent call last): [default1]:[rank33]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/run_train.py", line 237, in [default1]:[rank33]: trainer.train(dataloader) [default1]:[rank33]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 429, in train [default1]:[rank33]: outputs, loss_avg = self.training_step(dataloader=self.current_dataloader) [default1]:[rank33]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 462, in training_step [default1]:[rank33]: outputs = self.pipeline_engine.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 [default1]:[rank33]: output = self.forward(context=context, state=state, micro_batch=micro_batch, model=model) [default1]:[rank33]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 44, in forward [default1]:[rank33]: 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 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 891, in forward [default1]:[rank33]: sharded_logits = self.model( [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 764, in forward [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 [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/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 [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 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 598, in forward [default1]:[rank33]: output = self.o_proj(attention_output) [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/parallel/tensor_parallel/nn.py", line 159, in forward [default1]:[rank33]: return row_linear( [default1]:[rank33]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/functional.py", line 474, in row_linear [default1]:[rank33]: out = F.linear(input, weight, bias) [default1]:[rank33]: torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 1024.00 MiB. GPU  has a total capacity of 79.33 GiB of which 677.94 MiB is free. Including non-PyTorch memory, this process has 78.66 GiB memory in use. Of the allocated memory 68.27 GiB is allocated by PyTorch, and 411.57 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]: Traceback (most recent call last): [default5]:[rank21]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/run_train.py", line 237, in [default5]:[rank21]: trainer.train(dataloader) [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) [default5]:[rank21]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 462, in training_step [default5]:[rank21]: outputs = self.pipeline_engine.train_batch_iter( [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) [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) [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 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 [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 [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) [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 598, in forward [default5]:[rank21]: output = self.o_proj(attention_output) [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/tensor_parallel/nn.py", line 159, in forward [default5]:[rank21]: return row_linear( [default5]:[rank21]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/functional.py", line 474, in row_linear [default5]:[rank21]: out = F.linear(input, weight, bias) [default5]:[rank21]: torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 1024.00 MiB. GPU  has a total capacity of 79.33 GiB of which 797.94 MiB is free. Including non-PyTorch memory, this process has 78.54 GiB memory in use. Of the allocated memory 68.27 GiB is allocated by PyTorch, and 411.57 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 598, in forward [default6]:[rank22]: output = self.o_proj(attention_output) [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/tensor_parallel/nn.py", line 159, in forward [default6]:[rank22]: return row_linear( [default6]:[rank22]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/functional.py", line 474, in row_linear [default6]:[rank22]: out = F.linear(input, weight, bias) [default6]:[rank22]: torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 1024.00 MiB. GPU  has a total capacity of 79.33 GiB of which 869.94 MiB is free. Including non-PyTorch memory, this process has 78.47 GiB memory in use. Of the allocated memory 68.27 GiB is allocated by PyTorch, and 411.57 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 [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) [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) [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 631, in forward [default4]:[rank20]: output = self.attn(hidden_states=hidden_states, sequence_mask=sequence_mask) [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 598, in forward [default4]:[rank20]: output = self.o_proj(attention_output) [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/tensor_parallel/nn.py", line 159, in forward [default4]:[rank20]: return row_linear( [default4]:[rank20]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/functional.py", line 474, in row_linear [default4]:[rank20]: out = F.linear(input, weight, bias) [default4]:[rank20]: torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 1024.00 MiB. GPU  has a total capacity of 79.33 GiB of which 917.94 MiB is free. Including non-PyTorch memory, this process has 78.42 GiB memory in use. Of the allocated memory 68.27 GiB is allocated by PyTorch, and 411.57 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]:[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) [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( [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 [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( [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]:[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 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 [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 [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/parallel/pipeline_parallel/block.py", line 151, in forward [default1]:[rank17]: output = self.pp_block(**new_kwargs) [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]:[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 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 [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 598, in forward [default1]:[rank17]: output = self.o_proj(attention_output) [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]:[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/parallel/tensor_parallel/nn.py", line 159, in forward [default1]:[rank17]: return row_linear( [default1]:[rank17]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/functional.py", line 474, in row_linear [default1]:[rank17]: out = F.linear(input, weight, bias) [default1]:[rank17]: torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 1024.00 MiB. GPU  has a total capacity of 79.33 GiB of which 797.94 MiB is free. Including non-PyTorch memory, this process has 78.54 GiB memory in use. Of the allocated memory 68.27 GiB is allocated by PyTorch, and 411.57 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]:[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/nanotron/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 [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]: 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/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 [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 598, in forward [default0]:[rank16]: output = self.o_proj(attention_output) [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/parallel/tensor_parallel/nn.py", line 159, in forward [default0]:[rank16]: return row_linear( [default0]:[rank16]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/functional.py", line 474, in row_linear [default0]:[rank16]: out = F.linear(input, weight, bias) [default0]:[rank16]: torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 1024.00 MiB. GPU [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( [default2]:[rank18]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 278, in train_batch_iter [default2]:[rank18]: output = self.forward(context=context, state=state, micro_batch=micro_batch, model=model) [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) [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( [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 764, in forward [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 [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/parallel/pipeline_parallel/block.py", line 151, in forward [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 [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 631, in forward [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) [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 598, in forward [default2]:[rank18]: output = self.o_proj(attention_output) [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/parallel/tensor_parallel/nn.py", line 159, in forward [default2]:[rank18]: return row_linear( [default2]:[rank18]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/functional.py", line 474, in row_linear [default2]:[rank18]: out = F.linear(input, weight, bias) [default2]:[rank18]: torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 1024.00 MiB. GPU  has a total capacity of 79.33 GiB of which 869.94 MiB is free. Including non-PyTorch memory, this process has 78.47 GiB memory in use. Of the allocated memory 68.27 GiB is allocated by PyTorch, and 411.57 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]:[rank37]: Traceback (most recent call last): [default5]:[rank37]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/run_train.py", line 237, in [default5]:[rank37]: trainer.train(dataloader) [default5]:[rank37]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 429, in train [default5]:[rank37]: outputs, loss_avg = self.training_step(dataloader=self.current_dataloader) [default5]:[rank37]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 462, in training_step [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 [default5]:[rank37]: output = self.forward(context=context, state=state, micro_batch=micro_batch, model=model) [default5]:[rank37]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 44, in forward [default5]:[rank37]: output = model(**micro_batch) [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) [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) [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( [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) [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) [default5]:[rank37]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 764, in forward [default5]:[rank37]: return self.forward_with_hidden_states(input_ids=input_ids, input_mask=input_mask)[0] [default5]:[rank37]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 780, in forward_with_hidden_states [default5]:[rank37]: 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 1532, in _wrapped_call_impl [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 [default5]:[rank37]: return forward_call(*args, **kwargs) [default5]:[rank37]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/block.py", line 151, in forward [default5]:[rank37]: output = self.pp_block(**new_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 [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 [default5]:[rank37]: 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) [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) [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) [default5]:[rank37]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 598, in forward [default5]:[rank37]: output = self.o_proj(attention_output) [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) [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) [default5]:[rank37]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/nn.py", line 159, in forward [default5]:[rank37]: return row_linear( [default5]:[rank37]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/functional.py", line 474, in row_linear [default5]:[rank37]: out = F.linear(input, weight, bias) [default5]:[rank37]: torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 1024.00 MiB. GPU  has a total capacity of 79.33 GiB of which 677.94 MiB is free. Including non-PyTorch memory, this process has 78.66 GiB memory in use. Of the allocated memory 68.27 GiB is allocated by PyTorch, and 411.57 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]:[rank38]: Traceback (most recent call last): [default6]:[rank38]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/run_train.py", line 237, in [default6]:[rank38]: trainer.train(dataloader) [default6]:[rank38]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 429, in train [default6]:[rank38]: outputs, loss_avg = self.training_step(dataloader=self.current_dataloader) [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) [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) [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) [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( [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) [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) [default6]:[rank38]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 764, in forward [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 [default6]:[rank38]: hidden_encoder_states = encoder_block(**hidden_encoder_states) [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) [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) [default6]:[rank38]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/block.py", line 151, in forward [default6]:[rank38]: output = self.pp_block(**new_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) [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) [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) [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) [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) [default6]:[rank38]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 598, in forward [default6]:[rank38]: output = self.o_proj(attention_output) [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) [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) [default6]:[rank38]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/nn.py", line 159, in forward [default6]:[rank38]: return row_linear( [default6]:[rank38]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/functional.py", line 474, in row_linear [default6]:[rank38]: out = F.linear(input, weight, bias) [default6]:[rank38]: torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 1024.00 MiB. GPU  has a total capacity of 79.33 GiB of which 829.94 MiB is free. Including non-PyTorch memory, this process has 78.51 GiB memory in use. Of the allocated memory 68.27 GiB is allocated by PyTorch, and 411.57 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]: Traceback (most recent call last): [default7]:[rank39]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/run_train.py", line 237, in [default7]:[rank39]: trainer.train(dataloader) [default7]:[rank39]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 429, in train [default7]:[rank39]: outputs, loss_avg = self.training_step(dataloader=self.current_dataloader) [default7]:[rank39]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 462, in training_step [default7]:[rank39]: outputs = self.pipeline_engine.train_batch_iter( [default7]:[rank39]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 278, in train_batch_iter [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 [default7]:[rank39]: output = model(**micro_batch) [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 [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 [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 [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 [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 [default7]:[rank39]: return self.forward_with_hidden_states(input_ids=input_ids, input_mask=input_mask)[0] [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) [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 [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 [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 [default7]:[rank39]: return forward_call(*args, **kwargs) [default7]:[rank39]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 631, in forward [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 [default7]:[rank39]: return forward_call(*args, **kwargs) [default7]:[rank39]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 598, in forward [default7]:[rank39]: output = self.o_proj(attention_output) [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 [default7]:[rank39]: return forward_call(*args, **kwargs) [default7]:[rank39]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/nn.py", line 159, in forward [default7]:[rank39]: return row_linear( [default7]:[rank39]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/functional.py", line 474, in row_linear [default7]:[rank39]: out = F.linear(input, weight, bias) [default7]:[rank39]: torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 1024.00 MiB. GPU  has a total capacity of 79.33 GiB of which 917.94 MiB is free. Including non-PyTorch memory, this process has 78.42 GiB memory in use. Of the allocated memory 68.27 GiB is allocated by PyTorch, and 411.57 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]: 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) [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) [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( [default4]:[rank36]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 278, in train_batch_iter [default4]:[rank36]: output = self.forward(context=context, state=state, micro_batch=micro_batch, model=model) [default4]:[rank36]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 44, in forward [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 [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 [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 [default4]:[rank36]: 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 1532, in _wrapped_call_impl [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 [default4]:[rank36]: return forward_call(*args, **kwargs) [default4]:[rank36]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 764, in forward [default4]:[rank36]: return self.forward_with_hidden_states(input_ids=input_ids, input_mask=input_mask)[0] [default4]:[rank36]: 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) [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) [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/parallel/pipeline_parallel/block.py", line 151, in forward [default4]:[rank36]: output = self.pp_block(**new_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 [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 [default4]:[rank36]: return forward_call(*args, **kwargs) [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) [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) [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 598, in forward [default4]:[rank36]: output = self.o_proj(attention_output) [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) [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/parallel/tensor_parallel/nn.py", line 159, in forward [default4]:[rank36]: return row_linear( [default4]:[rank36]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/functional.py", line 474, in row_linear [default4]:[rank36]: out = F.linear(input, weight, bias) [default4]:[rank36]: torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 1024.00 MiB. GPU  has a total capacity of 79.33 GiB of which 877.94 MiB is free. Including non-PyTorch memory, this process has 78.46 GiB memory in use. Of the allocated memory 68.27 GiB is allocated by PyTorch, and 411.57 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) W0703 06:47:42.431000 139630902351680 torch/distributed/elastic/multiprocessing/api.py:851] Sending process 1698726 closing signal SIGTERM W0703 06:47:42.431000 139630902351680 torch/distributed/elastic/multiprocessing/api.py:851] Sending process 1698728 closing signal SIGTERM W0703 06:47:42.431000 139630902351680 torch/distributed/elastic/multiprocessing/api.py:851] Sending process 1698730 closing signal SIGTERM E0703 06:47:43.152000 139630902351680 torch/distributed/elastic/multiprocessing/api.py:826] failed (exitcode: 1) local_rank: 0 (pid: 1698723) 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_06:47:42 host : ip-26-0-162-233.ec2.internal rank : 1 (local_rank: 1) exitcode : 1 (pid: 1698724) error_file: traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html [2]: time : 2024-07-03_06:47:42 host : ip-26-0-162-233.ec2.internal rank : 2 (local_rank: 2) exitcode : 1 (pid: 1698725) error_file: traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html [3]: time : 2024-07-03_06:47:42 host : ip-26-0-162-233.ec2.internal rank : 4 (local_rank: 4) exitcode : 1 (pid: 1698727) error_file: traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html [4]: time : 2024-07-03_06:47:42 host : ip-26-0-162-233.ec2.internal rank : 6 (local_rank: 6) exitcode : 1 (pid: 1698729) 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_06:47:42 host : ip-26-0-162-233.ec2.internal rank : 0 (local_rank: 0) exitcode : 1 (pid: 1698723) error_file: traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html ============================================================ [default6]:07/03/2024 06:47:43 [WARNING|DP=13|PP=0|TP=2|ip-26-0-173-7]: Using the latest cached version of the dataset since roneneldan/TinyStories couldn't be found on the Hugging Face Hub [default6]:07/03/2024 06:47:43 [WARNING|DP=13|PP=0|TP=2|ip-26-0-173-7]: Found the latest cached dataset configuration 'default' at /admin/home/ferdinand_mom/.cache/roneneldan___tiny_stories/default/0.0.0/691b0d9bd48ade766778c940011ca1c549f6359b (last modified on Mon Jun 24 07:59:52 2024). [default6]:Using the latest cached version of the dataset since roneneldan/TinyStories couldn't be found on the Hugging Face Hub [default6]:Found the latest cached dataset configuration 'default' at /admin/home/ferdinand_mom/.cache/roneneldan___tiny_stories/default/0.0.0/691b0d9bd48ade766778c940011ca1c549f6359b (last modified on Mon Jun 24 07:59:52 2024). srun: error: ip-26-0-162-233: task 0: Exited with exit code 1 [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 [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 [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 [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 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) [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/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) [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 631, in forward [default5]:[rank53]: output = self.attn(hidden_states=hidden_states, sequence_mask=sequence_mask) [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 598, in forward [default5]:[rank53]: output = self.o_proj(attention_output) [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/parallel/tensor_parallel/nn.py", line 159, in forward [default5]:[rank53]: return row_linear( [default5]:[rank53]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/functional.py", line 474, in row_linear [default5]:[rank53]: out = F.linear(input, weight, bias) [default5]:[rank53]: torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 1024.00 MiB. GPU  has a total capacity of 79.33 GiB of which 797.94 MiB is free. Including non-PyTorch memory, this process has 78.54 GiB memory in use. Of the allocated memory 68.27 GiB is allocated by PyTorch, and 411.57 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 598, in forward [default6]:[rank54]: output = self.o_proj(attention_output) [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/tensor_parallel/nn.py", line 159, in forward [default6]:[rank54]: return row_linear( [default6]:[rank54]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/functional.py", line 474, in row_linear [default6]:[rank54]: out = F.linear(input, weight, bias) [default6]:[rank54]: torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 1024.00 MiB. GPU  has a total capacity of 79.33 GiB of which 869.94 MiB is free. Including non-PyTorch memory, this process has 78.47 GiB memory in use. Of the allocated memory 68.27 GiB is allocated by PyTorch, and 411.57 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 917.94 MiB is free. Including non-PyTorch memory, this process has 78.42 GiB memory in use. Of the allocated memory 68.27 GiB is allocated by PyTorch, and 411.57 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) W0703 06:47:46.405000 140039844886272 torch/distributed/elastic/rendezvous/dynamic_rendezvous.py:1252] The node 'ip-26-0-173-7.ec2.internal_2064824_0' has failed to send a keep-alive heartbeat to the rendezvous 'none' due to an error of type RendezvousConnectionError. W0703 06:47:46.858000 139958982633216 torch/distributed/elastic/rendezvous/dynamic_rendezvous.py:1252] The node 'ip-26-0-164-207.ec2.internal_443985_0' has failed to send a keep-alive heartbeat to the rendezvous 'none' due to an error of type RendezvousConnectionError. W0703 06:47:46.895000 140665207244544 torch/distributed/elastic/rendezvous/dynamic_rendezvous.py:1252] The node 'ip-26-0-173-202.ec2.internal_1341591_0' has failed to send a keep-alive heartbeat to the rendezvous 'none' due to an error of type RendezvousConnectionError. W0703 06:47:47.101000 140694525830912 torch/distributed/elastic/rendezvous/dynamic_rendezvous.py:1252] The node 'ip-26-0-163-147.ec2.internal_831210_0' has failed to send a keep-alive heartbeat to the rendezvous 'none' due to an error of type RendezvousConnectionError. W0703 06:47:47.352000 140681953507072 torch/distributed/elastic/rendezvous/dynamic_rendezvous.py:1252] The node 'ip-26-0-165-24.ec2.internal_933372_0' has failed to send a keep-alive heartbeat to the rendezvous 'none' due to an error of type RendezvousConnectionError. W0703 06:47:47.356000 140683099145984 torch/distributed/elastic/rendezvous/dynamic_rendezvous.py:1252] The node 'ip-26-0-174-36.ec2.internal_874314_0' has failed to send a keep-alive heartbeat to the rendezvous 'none' due to an error of type RendezvousConnectionError. W0703 06:47:47.397000 140557842540288 torch/distributed/elastic/rendezvous/dynamic_rendezvous.py:1252] The node 'ip-26-0-173-246.ec2.internal_360117_0' has failed to send a keep-alive heartbeat to the rendezvous 'none' due to an error of type RendezvousConnectionError. W0703 06:47:47.435000 140670867978048 torch/distributed/elastic/multiprocessing/api.py:851] Sending process 1341665 closing signal SIGTERM W0703 06:47:47.435000 140670867978048 torch/distributed/elastic/multiprocessing/api.py:851] Sending process 1341666 closing signal SIGTERM W0703 06:47:47.435000 140670867978048 torch/distributed/elastic/multiprocessing/api.py:851] Sending process 1341667 closing signal SIGTERM W0703 06:47:47.435000 140670867978048 torch/distributed/elastic/multiprocessing/api.py:851] Sending process 1341668 closing signal SIGTERM W0703 06:47:47.436000 140687614240576 torch/distributed/elastic/multiprocessing/api.py:851] Sending process 933445 closing signal SIGTERM W0703 06:47:47.437000 140670867978048 torch/distributed/elastic/multiprocessing/api.py:851] Sending process 1341669 closing signal SIGTERM W0703 06:47:47.436000 140687614240576 torch/distributed/elastic/multiprocessing/api.py:851] Sending process 933446 closing signal SIGTERM W0703 06:47:47.436000 140687614240576 torch/distributed/elastic/multiprocessing/api.py:851] Sending process 933447 closing signal SIGTERM W0703 06:47:47.436000 140687614240576 torch/distributed/elastic/multiprocessing/api.py:851] Sending process 933448 closing signal SIGTERM W0703 06:47:47.437000 140670867978048 torch/distributed/elastic/multiprocessing/api.py:851] Sending process 1341670 closing signal SIGTERM W0703 06:47:47.438000 140687614240576 torch/distributed/elastic/multiprocessing/api.py:851] Sending process 933449 closing signal SIGTERM W0703 06:47:47.438000 140687614240576 torch/distributed/elastic/multiprocessing/api.py:851] Sending process 933450 closing signal SIGTERM W0703 06:47:47.438000 140687614240576 torch/distributed/elastic/multiprocessing/api.py:851] Sending process 933451 closing signal SIGTERM W0703 06:47:47.439000 140670867978048 torch/distributed/elastic/multiprocessing/api.py:851] Sending process 1341671 closing signal SIGTERM W0703 06:47:47.439000 140670867978048 torch/distributed/elastic/multiprocessing/api.py:851] Sending process 1341672 closing signal SIGTERM W0703 06:47:47.438000 140700186564416 torch/distributed/elastic/multiprocessing/api.py:851] Sending process 831284 closing signal SIGTERM W0703 06:47:47.438000 140700186564416 torch/distributed/elastic/multiprocessing/api.py:851] Sending process 831285 closing signal SIGTERM W0703 06:47:47.439000 140700186564416 torch/distributed/elastic/multiprocessing/api.py:851] Sending process 831286 closing signal SIGTERM W0703 06:47:47.439000 140687614240576 torch/distributed/elastic/multiprocessing/api.py:851] Sending process 933452 closing signal SIGTERM W0703 06:47:47.439000 140700186564416 torch/distributed/elastic/multiprocessing/api.py:851] Sending process 831287 closing signal SIGTERM W0703 06:47:47.439000 139964643366720 torch/distributed/elastic/multiprocessing/api.py:851] Sending process 444058 closing signal SIGTERM W0703 06:47:47.439000 139964643366720 torch/distributed/elastic/multiprocessing/api.py:851] Sending process 444059 closing signal SIGTERM W0703 06:47:47.439000 139964643366720 torch/distributed/elastic/multiprocessing/api.py:851] Sending process 444060 closing signal SIGTERM W0703 06:47:47.439000 140045505619776 torch/distributed/elastic/multiprocessing/api.py:851] Sending process 2064899 closing signal SIGTERM W0703 06:47:47.439000 140700186564416 torch/distributed/elastic/multiprocessing/api.py:851] Sending process 831288 closing signal SIGTERM W0703 06:47:47.440000 140045505619776 torch/distributed/elastic/multiprocessing/api.py:851] Sending process 2064900 closing signal SIGTERM W0703 06:47:47.440000 140045505619776 torch/distributed/elastic/multiprocessing/api.py:851] Sending process 2064901 closing signal SIGTERM W0703 06:47:47.440000 140563503273792 torch/distributed/elastic/multiprocessing/api.py:851] Sending process 360191 closing signal SIGTERM W0703 06:47:47.441000 140563503273792 torch/distributed/elastic/multiprocessing/api.py:851] Sending process 360192 closing signal SIGTERM W0703 06:47:47.441000 140563503273792 torch/distributed/elastic/multiprocessing/api.py:851] Sending process 360193 closing signal SIGTERM W0703 06:47:47.439000 139964643366720 torch/distributed/elastic/multiprocessing/api.py:851] Sending process 444061 closing signal SIGTERM W0703 06:47:47.441000 139964643366720 torch/distributed/elastic/multiprocessing/api.py:851] Sending process 444062 closing signal SIGTERM W0703 06:47:47.441000 139964643366720 torch/distributed/elastic/multiprocessing/api.py:851] Sending process 444063 closing signal SIGTERM W0703 06:47:47.441000 139964643366720 torch/distributed/elastic/multiprocessing/api.py:851] Sending process 444064 closing signal SIGTERM W0703 06:47:47.441000 140563503273792 torch/distributed/elastic/multiprocessing/api.py:851] Sending process 360194 closing signal SIGTERM W0703 06:47:47.442000 140045505619776 torch/distributed/elastic/multiprocessing/api.py:851] Sending process 2064902 closing signal SIGTERM W0703 06:47:47.442000 140045505619776 torch/distributed/elastic/multiprocessing/api.py:851] Sending process 2064903 closing signal SIGTERM W0703 06:47:47.441000 140700186564416 torch/distributed/elastic/multiprocessing/api.py:851] Sending process 831289 closing signal SIGTERM W0703 06:47:47.442000 140045505619776 torch/distributed/elastic/multiprocessing/api.py:851] Sending process 2064904 closing signal SIGTERM W0703 06:47:47.442000 140563503273792 torch/distributed/elastic/multiprocessing/api.py:851] Sending process 360195 closing signal SIGTERM W0703 06:47:47.442000 140700186564416 torch/distributed/elastic/multiprocessing/api.py:851] Sending process 831290 closing signal SIGTERM W0703 06:47:47.442000 140700186564416 torch/distributed/elastic/multiprocessing/api.py:851] Sending process 831291 closing signal SIGTERM W0703 06:47:47.443000 139964643366720 torch/distributed/elastic/multiprocessing/api.py:851] Sending process 444065 closing signal SIGTERM W0703 06:47:47.444000 140563503273792 torch/distributed/elastic/multiprocessing/api.py:851] Sending process 360196 closing signal SIGTERM W0703 06:47:47.444000 140563503273792 torch/distributed/elastic/multiprocessing/api.py:851] Sending process 360197 closing signal SIGTERM W0703 06:47:47.444000 140563503273792 torch/distributed/elastic/multiprocessing/api.py:851] Sending process 360198 closing signal SIGTERM W0703 06:47:47.443000 140045505619776 torch/distributed/elastic/multiprocessing/api.py:851] Sending process 2064905 closing signal SIGTERM W0703 06:47:47.443000 140045505619776 torch/distributed/elastic/multiprocessing/api.py:851] Sending process 2064906 closing signal SIGTERM E0703 06:47:47.556000 140688759879488 torch/distributed/elastic/multiprocessing/api.py:826] failed (exitcode: 1) local_rank: 0 (pid: 874388) of binary: /fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/bin/python3.10 W0703 06:47:47.562000 140688759879488 torch/distributed/elastic/rendezvous/dynamic_rendezvous.py:1203] The node 'ip-26-0-174-36.ec2.internal_874314_0' has failed to shutdown the rendezvous 'none' due to an error of type RendezvousConnectionError. W0703 06:47:47.589000 140688759879488 torch/distributed/elastic/rendezvous/dynamic_rendezvous.py:1203] The node 'ip-26-0-174-36.ec2.internal_874314_0' has failed to shutdown the rendezvous 'none' due to an error of type RendezvousConnectionError. W0703 06:47:47.621000 140688759879488 torch/distributed/elastic/rendezvous/dynamic_rendezvous.py:1203] The node 'ip-26-0-174-36.ec2.internal_874314_0' has failed to shutdown the rendezvous 'none' due to an error of type RendezvousConnectionError. 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_06:47:47 host : ip-26-0-174-36.ec2.internal rank : 57 (local_rank: 1) exitcode : 1 (pid: 874389) error_file: traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html [2]: time : 2024-07-03_06:47:47 host : ip-26-0-174-36.ec2.internal rank : 58 (local_rank: 2) exitcode : 1 (pid: 874390) error_file: traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html [3]: time : 2024-07-03_06:47:47 host : ip-26-0-174-36.ec2.internal rank : 59 (local_rank: 3) exitcode : 1 (pid: 874391) error_file: traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html [4]: time : 2024-07-03_06:47:47 host : ip-26-0-174-36.ec2.internal rank : 60 (local_rank: 4) exitcode : 1 (pid: 874392) error_file: traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html [5]: time : 2024-07-03_06:47:47 host : ip-26-0-174-36.ec2.internal rank : 61 (local_rank: 5) exitcode : 1 (pid: 874393) error_file: traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html [6]: time : 2024-07-03_06:47:47 host : ip-26-0-174-36.ec2.internal rank : 62 (local_rank: 6) exitcode : 1 (pid: 874394) error_file: traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html [7]: time : 2024-07-03_06:47:47 host : ip-26-0-174-36.ec2.internal rank : 63 (local_rank: 7) exitcode : 1 (pid: 874395) 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_06:47:47 host : ip-26-0-174-36.ec2.internal rank : 56 (local_rank: 0) exitcode : 1 (pid: 874388) error_file: traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html ============================================================ srun: error: ip-26-0-174-36: task 7: Exited with exit code 1 W0703 06:47:51.410000 140039844886272 torch/distributed/elastic/rendezvous/dynamic_rendezvous.py:1252] The node 'ip-26-0-173-7.ec2.internal_2064824_0' has failed to send a keep-alive heartbeat to the rendezvous 'none' due to an error of type RendezvousConnectionError. W0703 06:47:51.862000 139958982633216 torch/distributed/elastic/rendezvous/dynamic_rendezvous.py:1252] The node 'ip-26-0-164-207.ec2.internal_443985_0' has failed to send a keep-alive heartbeat to the rendezvous 'none' due to an error of type RendezvousConnectionError. W0703 06:47:51.899000 140665207244544 torch/distributed/elastic/rendezvous/dynamic_rendezvous.py:1252] The node 'ip-26-0-173-202.ec2.internal_1341591_0' has failed to send a keep-alive heartbeat to the rendezvous 'none' due to an error of type RendezvousConnectionError. W0703 06:47:52.105000 140694525830912 torch/distributed/elastic/rendezvous/dynamic_rendezvous.py:1252] The node 'ip-26-0-163-147.ec2.internal_831210_0' has failed to send a keep-alive heartbeat to the rendezvous 'none' due to an error of type RendezvousConnectionError. W0703 06:47:52.356000 140681953507072 torch/distributed/elastic/rendezvous/dynamic_rendezvous.py:1252] The node 'ip-26-0-165-24.ec2.internal_933372_0' has failed to send a keep-alive heartbeat to the rendezvous 'none' due to an error of type RendezvousConnectionError. W0703 06:47:52.401000 140557842540288 torch/distributed/elastic/rendezvous/dynamic_rendezvous.py:1252] The node 'ip-26-0-173-246.ec2.internal_360117_0' has failed to send a keep-alive heartbeat to the rendezvous 'none' due to an error of type RendezvousConnectionError. W0703 06:47:54.170000 140670867978048 torch/distributed/elastic/rendezvous/dynamic_rendezvous.py:1203] The node 'ip-26-0-173-202.ec2.internal_1341591_0' has failed to shutdown the rendezvous 'none' due to an error of type RendezvousConnectionError. W0703 06:47:54.180000 140670867978048 torch/distributed/elastic/rendezvous/dynamic_rendezvous.py:1203] The node 'ip-26-0-173-202.ec2.internal_1341591_0' has failed to shutdown the rendezvous 'none' due to an error of type RendezvousConnectionError. Traceback (most recent call last): File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/elastic/rendezvous/c10d_rendezvous_backend.py", line 113, in _call_store return getattr(self._store, store_op)(*args, **kwargs) torch.distributed.DistNetworkError: Broken pipe The above exception was the direct cause of the following exception: 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 254, in launch_agent result = agent.run() File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/elastic/metrics/api.py", line 123, in wrapper result = f(*args, **kwargs) File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/elastic/agent/server/api.py", line 733, in run result = self._invoke_run(role) File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/elastic/agent/server/api.py", line 908, in _invoke_run num_nodes_waiting = rdzv_handler.num_nodes_waiting() File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/elastic/rendezvous/dynamic_rendezvous.py", line 1174, in num_nodes_waiting self._state_holder.sync() File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/elastic/rendezvous/dynamic_rendezvous.py", line 419, in sync get_response = self._backend.get_state() File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/elastic/rendezvous/c10d_rendezvous_backend.py", line 73, in get_state base64_state: bytes = self._call_store("get", self._key) File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/elastic/rendezvous/c10d_rendezvous_backend.py", line 115, in _call_store raise RendezvousConnectionError( torch.distributed.elastic.rendezvous.api.RendezvousConnectionError: The connection to the C10d store has failed. See inner exception for details. srun: error: ip-26-0-173-202: task 5: Exited with exit code 1 W0703 06:47:56.414000 140039844886272 torch/distributed/elastic/rendezvous/dynamic_rendezvous.py:1252] The node 'ip-26-0-173-7.ec2.internal_2064824_0' has failed to send a keep-alive heartbeat to the rendezvous 'none' due to an error of type RendezvousConnectionError. W0703 06:47:56.867000 139958982633216 torch/distributed/elastic/rendezvous/dynamic_rendezvous.py:1252] The node 'ip-26-0-164-207.ec2.internal_443985_0' has failed to send a keep-alive heartbeat to the rendezvous 'none' due to an error of type RendezvousConnectionError. W0703 06:47:57.109000 140694525830912 torch/distributed/elastic/rendezvous/dynamic_rendezvous.py:1252] The node 'ip-26-0-163-147.ec2.internal_831210_0' has failed to send a keep-alive heartbeat to the rendezvous 'none' due to an error of type RendezvousConnectionError. W0703 06:47:57.361000 140681953507072 torch/distributed/elastic/rendezvous/dynamic_rendezvous.py:1252] The node 'ip-26-0-165-24.ec2.internal_933372_0' has failed to send a keep-alive heartbeat to the rendezvous 'none' due to an error of type RendezvousConnectionError. W0703 06:47:57.406000 140557842540288 torch/distributed/elastic/rendezvous/dynamic_rendezvous.py:1252] The node 'ip-26-0-173-246.ec2.internal_360117_0' has failed to send a keep-alive heartbeat to the rendezvous 'none' due to an error of type RendezvousConnectionError. W0703 06:47:57.778000 140700186564416 torch/distributed/elastic/rendezvous/dynamic_rendezvous.py:1203] The node 'ip-26-0-163-147.ec2.internal_831210_0' has failed to shutdown the rendezvous 'none' due to an error of type RendezvousConnectionError. W0703 06:47:57.789000 140700186564416 torch/distributed/elastic/rendezvous/dynamic_rendezvous.py:1203] The node 'ip-26-0-163-147.ec2.internal_831210_0' has failed to shutdown the rendezvous 'none' due to an error of type RendezvousConnectionError. Traceback (most recent call last): File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/elastic/rendezvous/c10d_rendezvous_backend.py", line 113, in _call_store return getattr(self._store, store_op)(*args, **kwargs) torch.distributed.DistNetworkError: Broken pipe The above exception was the direct cause of the following exception: 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 254, in launch_agent result = agent.run() File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/elastic/metrics/api.py", line 123, in wrapper result = f(*args, **kwargs) File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/elastic/agent/server/api.py", line 733, in run result = self._invoke_run(role) File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/elastic/agent/server/api.py", line 908, in _invoke_run num_nodes_waiting = rdzv_handler.num_nodes_waiting() File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/elastic/rendezvous/dynamic_rendezvous.py", line 1174, in num_nodes_waiting self._state_holder.sync() File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/elastic/rendezvous/dynamic_rendezvous.py", line 419, in sync get_response = self._backend.get_state() File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/elastic/rendezvous/c10d_rendezvous_backend.py", line 73, in get_state base64_state: bytes = self._call_store("get", self._key) File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/elastic/rendezvous/c10d_rendezvous_backend.py", line 115, in _call_store raise RendezvousConnectionError( torch.distributed.elastic.rendezvous.api.RendezvousConnectionError: The connection to the C10d store has failed. See inner exception for details. W0703 06:47:57.876000 140563503273792 torch/distributed/elastic/rendezvous/dynamic_rendezvous.py:1203] The node 'ip-26-0-173-246.ec2.internal_360117_0' has failed to shutdown the rendezvous 'none' due to an error of type RendezvousConnectionError. W0703 06:47:57.886000 140563503273792 torch/distributed/elastic/rendezvous/dynamic_rendezvous.py:1203] The node 'ip-26-0-173-246.ec2.internal_360117_0' has failed to shutdown the rendezvous 'none' due to an error of type RendezvousConnectionError. Traceback (most recent call last): File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/elastic/rendezvous/c10d_rendezvous_backend.py", line 113, in _call_store return getattr(self._store, store_op)(*args, **kwargs) torch.distributed.DistNetworkError: Broken pipe The above exception was the direct cause of the following exception: 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 254, in launch_agent result = agent.run() File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/elastic/metrics/api.py", line 123, in wrapper result = f(*args, **kwargs) File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/elastic/agent/server/api.py", line 733, in run result = self._invoke_run(role) File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/elastic/agent/server/api.py", line 908, in _invoke_run num_nodes_waiting = rdzv_handler.num_nodes_waiting() File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/elastic/rendezvous/dynamic_rendezvous.py", line 1174, in num_nodes_waiting self._state_holder.sync() File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/elastic/rendezvous/dynamic_rendezvous.py", line 419, in sync get_response = self._backend.get_state() File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/elastic/rendezvous/c10d_rendezvous_backend.py", line 73, in get_state base64_state: bytes = self._call_store("get", self._key) File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/elastic/rendezvous/c10d_rendezvous_backend.py", line 115, in _call_store raise RendezvousConnectionError( torch.distributed.elastic.rendezvous.api.RendezvousConnectionError: The connection to the C10d store has failed. See inner exception for details. W0703 06:47:58.080000 140045505619776 torch/distributed/elastic/rendezvous/dynamic_rendezvous.py:1203] The node 'ip-26-0-173-7.ec2.internal_2064824_0' has failed to shutdown the rendezvous 'none' due to an error of type RendezvousConnectionError. W0703 06:47:58.091000 140045505619776 torch/distributed/elastic/rendezvous/dynamic_rendezvous.py:1203] The node 'ip-26-0-173-7.ec2.internal_2064824_0' has failed to shutdown the rendezvous 'none' due to an error of type RendezvousConnectionError. Traceback (most recent call last): File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/elastic/rendezvous/c10d_rendezvous_backend.py", line 113, in _call_store return getattr(self._store, store_op)(*args, **kwargs) torch.distributed.DistNetworkError: Broken pipe The above exception was the direct cause of the following exception: 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 254, in launch_agent result = agent.run() File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/elastic/metrics/api.py", line 123, in wrapper result = f(*args, **kwargs) File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/elastic/agent/server/api.py", line 733, in run result = self._invoke_run(role) File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/elastic/agent/server/api.py", line 908, in _invoke_run num_nodes_waiting = rdzv_handler.num_nodes_waiting() File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/elastic/rendezvous/dynamic_rendezvous.py", line 1174, in num_nodes_waiting self._state_holder.sync() File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/elastic/rendezvous/dynamic_rendezvous.py", line 419, in sync get_response = self._backend.get_state() File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/elastic/rendezvous/c10d_rendezvous_backend.py", line 73, in get_state base64_state: bytes = self._call_store("get", self._key) File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/elastic/rendezvous/c10d_rendezvous_backend.py", line 115, in _call_store raise RendezvousConnectionError( torch.distributed.elastic.rendezvous.api.RendezvousConnectionError: The connection to the C10d store has failed. See inner exception for details. srun: error: ip-26-0-163-147: task 1: Exited with exit code 1 W0703 06:47:58.182000 139964643366720 torch/distributed/elastic/rendezvous/dynamic_rendezvous.py:1203] The node 'ip-26-0-164-207.ec2.internal_443985_0' has failed to shutdown the rendezvous 'none' due to an error of type RendezvousConnectionError. W0703 06:47:58.193000 139964643366720 torch/distributed/elastic/rendezvous/dynamic_rendezvous.py:1203] The node 'ip-26-0-164-207.ec2.internal_443985_0' has failed to shutdown the rendezvous 'none' due to an error of type RendezvousConnectionError. Traceback (most recent call last): File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/elastic/rendezvous/c10d_rendezvous_backend.py", line 113, in _call_store return getattr(self._store, store_op)(*args, **kwargs) torch.distributed.DistNetworkError: Broken pipe The above exception was the direct cause of the following exception: 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 254, in launch_agent result = agent.run() File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/elastic/metrics/api.py", line 123, in wrapper result = f(*args, **kwargs) File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/elastic/agent/server/api.py", line 733, in run result = self._invoke_run(role) File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/elastic/agent/server/api.py", line 908, in _invoke_run num_nodes_waiting = rdzv_handler.num_nodes_waiting() File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/elastic/rendezvous/dynamic_rendezvous.py", line 1174, in num_nodes_waiting self._state_holder.sync() File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/elastic/rendezvous/dynamic_rendezvous.py", line 419, in sync get_response = self._backend.get_state() File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/elastic/rendezvous/c10d_rendezvous_backend.py", line 73, in get_state base64_state: bytes = self._call_store("get", self._key) File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/elastic/rendezvous/c10d_rendezvous_backend.py", line 115, in _call_store raise RendezvousConnectionError( torch.distributed.elastic.rendezvous.api.RendezvousConnectionError: The connection to the C10d store has failed. See inner exception for details. srun: error: ip-26-0-173-246: task 6: Exited with exit code 1 srun: error: ip-26-0-173-7: task 4: Exited with exit code 1 srun: error: ip-26-0-164-207: task 2: Exited with exit code 1 W0703 06:47:58.877000 140687614240576 torch/distributed/elastic/rendezvous/dynamic_rendezvous.py:1203] The node 'ip-26-0-165-24.ec2.internal_933372_0' has failed to shutdown the rendezvous 'none' due to an error of type RendezvousConnectionError. W0703 06:47:58.889000 140687614240576 torch/distributed/elastic/rendezvous/dynamic_rendezvous.py:1203] The node 'ip-26-0-165-24.ec2.internal_933372_0' has failed to shutdown the rendezvous 'none' due to an error of type RendezvousConnectionError. Traceback (most recent call last): File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/elastic/rendezvous/c10d_rendezvous_backend.py", line 113, in _call_store return getattr(self._store, store_op)(*args, **kwargs) torch.distributed.DistNetworkError: Broken pipe The above exception was the direct cause of the following exception: 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 254, in launch_agent result = agent.run() File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/elastic/metrics/api.py", line 123, in wrapper result = f(*args, **kwargs) File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/elastic/agent/server/api.py", line 733, in run result = self._invoke_run(role) File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/elastic/agent/server/api.py", line 908, in _invoke_run num_nodes_waiting = rdzv_handler.num_nodes_waiting() File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/elastic/rendezvous/dynamic_rendezvous.py", line 1174, in num_nodes_waiting self._state_holder.sync() File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/elastic/rendezvous/dynamic_rendezvous.py", line 419, in sync get_response = self._backend.get_state() File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/elastic/rendezvous/c10d_rendezvous_backend.py", line 73, in get_state base64_state: bytes = self._call_store("get", self._key) File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/elastic/rendezvous/c10d_rendezvous_backend.py", line 115, in _call_store raise RendezvousConnectionError( torch.distributed.elastic.rendezvous.api.RendezvousConnectionError: The connection to the C10d store has failed. See inner exception for details. srun: error: ip-26-0-165-24: task 3: 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.