======================== START TIME: Wed Jul 3 07:59:51 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 07:59:53.901000 140513317451584 torch/distributed/run.py:757] W0703 07:59:53.901000 140513317451584 torch/distributed/run.py:757] ***************************************** W0703 07:59:53.901000 140513317451584 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 07:59:53.901000 140513317451584 torch/distributed/run.py:757] ***************************************** W0703 07:59:53.899000 139947259402048 torch/distributed/run.py:757] W0703 07:59:53.899000 139947259402048 torch/distributed/run.py:757] ***************************************** W0703 07:59:53.899000 139947259402048 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 07:59:53.899000 139947259402048 torch/distributed/run.py:757] ***************************************** W0703 07:59:53.902000 140678118782784 torch/distributed/run.py:757] W0703 07:59:53.902000 140678118782784 torch/distributed/run.py:757] ***************************************** W0703 07:59:53.902000 140678118782784 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 07:59:53.902000 140678118782784 torch/distributed/run.py:757] ***************************************** W0703 07:59:53.904000 140531263645504 torch/distributed/run.py:757] W0703 07:59:53.904000 140531263645504 torch/distributed/run.py:757] ***************************************** W0703 07:59:53.904000 140531263645504 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 07:59:53.904000 140531263645504 torch/distributed/run.py:757] ***************************************** W0703 07:59:53.904000 140585339381568 torch/distributed/run.py:757] W0703 07:59:53.904000 140585339381568 torch/distributed/run.py:757] ***************************************** W0703 07:59:53.904000 140585339381568 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 07:59:53.904000 140585339381568 torch/distributed/run.py:757] ***************************************** W0703 07:59:53.914000 140303399708480 torch/distributed/run.py:757] W0703 07:59:53.914000 140303399708480 torch/distributed/run.py:757] ***************************************** W0703 07:59:53.914000 140303399708480 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 07:59:53.914000 140303399708480 torch/distributed/run.py:757] ***************************************** W0703 07:59:53.936000 139807147054912 torch/distributed/run.py:757] W0703 07:59:53.936000 139807147054912 torch/distributed/run.py:757] ***************************************** W0703 07:59:53.936000 139807147054912 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 07:59:53.936000 139807147054912 torch/distributed/run.py:757] ***************************************** W0703 07:59:53.989000 139913087264576 torch/distributed/run.py:757] W0703 07:59:53.989000 139913087264576 torch/distributed/run.py:757] ***************************************** W0703 07:59:53.989000 139913087264576 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 07:59:53.989000 139913087264576 torch/distributed/run.py:757] ***************************************** [default0]:07/03/2024 08:00:14 [WARNING|DP=0|PP=0|TP=0|ip-26-0-160-192]: [Vocab Size Padding] Padded vocab (size: 50257) with 3 dummy tokens (new size: 50260) [default0]:07/03/2024 08:00:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: Config: [default0]:07/03/2024 08:00:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: Config(general=GeneralArgs(project='bench_cluster', [default0]:07/03/2024 08:00:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: run='%date_%jobid', [default0]:07/03/2024 08:00:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: seed=42, [default0]:07/03/2024 08:00:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: step=None, [default0]:07/03/2024 08:00:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: consumed_train_samples=None, [default0]:07/03/2024 08:00:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: benchmark_csv_path=None, [default0]:07/03/2024 08:00:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: ignore_sanity_checks=True), [default0]:07/03/2024 08:00:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: parallelism=ParallelismArgs(dp=16, [default0]:07/03/2024 08:00:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: pp=1, [default0]:07/03/2024 08:00:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: tp=4, [default0]:07/03/2024 08:00:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: pp_engine=, [default0]:07/03/2024 08:00:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: tp_mode=, [default0]:07/03/2024 08:00:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: tp_linear_async_communication=False, [default0]:07/03/2024 08:00:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: expert_parallel_size=1), [default0]:07/03/2024 08:00:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: model=ModelArgs(model_config=LlamaConfig(bos_token_id=1, [default0]:07/03/2024 08:00:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: eos_token_id=2, [default0]:07/03/2024 08:00:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: hidden_act='silu', [default0]:07/03/2024 08:00:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: hidden_size=2048, [default0]:07/03/2024 08:00:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: initializer_range=0.02, [default0]:07/03/2024 08:00:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: intermediate_size=4096, [default0]:07/03/2024 08:00:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: is_llama_config=True, [default0]:07/03/2024 08:00:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: max_position_embeddings=4096, [default0]:07/03/2024 08:00:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: num_attention_heads=32, [default0]:07/03/2024 08:00:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: num_hidden_layers=24, [default0]:07/03/2024 08:00:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: num_key_value_heads=32, [default0]:07/03/2024 08:00:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: pad_token_id=None, [default0]:07/03/2024 08:00:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: pretraining_tp=1, [default0]:07/03/2024 08:00:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: rms_norm_eps=1e-05, [default0]:07/03/2024 08:00:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: rope_scaling=None, [default0]:07/03/2024 08:00:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: rope_theta=10000.0, [default0]:07/03/2024 08:00:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: tie_word_embeddings=True, [default0]:07/03/2024 08:00:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: use_cache=True, [default0]:07/03/2024 08:00:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: vocab_size=50260), [default0]:07/03/2024 08:00:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: init_method=RandomInit(std=0.025), [default0]:07/03/2024 08:00:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: dtype=torch.bfloat16, [default0]:07/03/2024 08:00:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: make_vocab_size_divisible_by=1, [default0]:07/03/2024 08:00:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: ddp_bucket_cap_mb=25), [default0]:07/03/2024 08:00:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: tokenizer=TokenizerArgs(tokenizer_name_or_path='openai-community/gpt2', [default0]:07/03/2024 08:00:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: tokenizer_revision=None, [default0]:07/03/2024 08:00:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: tokenizer_max_length=None), [default0]:07/03/2024 08:00:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: checkpoints=CheckpointsArgs(checkpoints_path=Path('/dev/null'), [default0]:07/03/2024 08:00:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: checkpoint_interval=100000, [default0]:07/03/2024 08:00:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: save_initial_state=False, [default0]:07/03/2024 08:00:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: resume_checkpoint_path=None, [default0]:07/03/2024 08:00:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: checkpoints_path_is_shared_file_system=False), [default0]:07/03/2024 08:00:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: logging=LoggingArgs(log_level='info', [default0]:07/03/2024 08:00:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: log_level_replica='info', [default0]:07/03/2024 08:00:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: iteration_step_info_interval=1), [default0]:07/03/2024 08:00:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: tokens=TokensArgs(sequence_length=4096, [default0]:07/03/2024 08:00:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: train_steps=20, [default0]:07/03/2024 08:00:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: micro_batch_size=32, [default0]:07/03/2024 08:00:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: batch_accumulation_per_replica=2, [default0]:07/03/2024 08:00:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: val_check_interval=-1, [default0]:07/03/2024 08:00:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: limit_val_batches=0, [default0]:07/03/2024 08:00:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: limit_test_batches=0), [default0]:07/03/2024 08:00:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: optimizer=OptimizerArgs(optimizer_factory=AdamWOptimizerArgs(adam_eps=1e-08, [default0]:07/03/2024 08:00:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: adam_beta1=0.9, [default0]:07/03/2024 08:00:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: adam_beta2=0.95, [default0]:07/03/2024 08:00:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: torch_adam_is_fused=True, [default0]:07/03/2024 08:00:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: name='adamW'), [default0]:07/03/2024 08:00:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: zero_stage=1, [default0]:07/03/2024 08:00:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: weight_decay=0.01, [default0]:07/03/2024 08:00:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: clip_grad=1.0, [default0]:07/03/2024 08:00:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: accumulate_grad_in_fp32=True, [default0]:07/03/2024 08:00:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: learning_rate_scheduler=LRSchedulerArgs(learning_rate=0.0001, [default0]:07/03/2024 08:00:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: lr_warmup_steps=1, [default0]:07/03/2024 08:00:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: lr_warmup_style='linear', [default0]:07/03/2024 08:00:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: lr_decay_style='linear', [default0]:07/03/2024 08:00:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: lr_decay_steps=19, [default0]:07/03/2024 08:00:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: lr_decay_starting_step=None, [default0]:07/03/2024 08:00:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: min_decay_lr=1e-05)), [default0]:07/03/2024 08:00:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: data_stages=[DatasetStageArgs(name='Training Stage', [default0]:07/03/2024 08:00:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: start_training_step=1, [default0]:07/03/2024 08:00:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: data=DataArgs(dataset=PretrainDatasetsArgs(hf_dataset_or_datasets='roneneldan/TinyStories', [default0]:07/03/2024 08:00:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: hf_dataset_splits='train', [default0]:07/03/2024 08:00:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: hf_dataset_config_name=None, [default0]:07/03/2024 08:00:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: dataset_processing_num_proc_per_process=64, [default0]:07/03/2024 08:00:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: dataset_overwrite_cache=False, [default0]:07/03/2024 08:00:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: text_column_name='text'), [default0]:07/03/2024 08:00:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: seed=42, [default0]:07/03/2024 08:00:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: num_loading_workers=0))], [default0]:07/03/2024 08:00:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: profiler=ProfilerArgs(profiler_export_path=Path('/fsx/ferdinandmom/ferdinand-hf/bench_cluster/results/llama-1B/64_GPUS/dp-16_tp-4_pp-1_mbz-32')), [default0]:07/03/2024 08:00:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: lighteval=None) [default0]:07/03/2024 08:00:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: Model Config: [default0]:07/03/2024 08:00:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: LlamaConfig(bos_token_id=1, [default0]:07/03/2024 08:00:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: eos_token_id=2, [default0]:07/03/2024 08:00:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: hidden_act='silu', [default0]:07/03/2024 08:00:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: hidden_size=2048, [default0]:07/03/2024 08:00:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: initializer_range=0.02, [default0]:07/03/2024 08:00:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: intermediate_size=4096, [default0]:07/03/2024 08:00:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: is_llama_config=True, [default0]:07/03/2024 08:00:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: max_position_embeddings=4096, [default0]:07/03/2024 08:00:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: num_attention_heads=32, [default0]:07/03/2024 08:00:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: num_hidden_layers=24, [default0]:07/03/2024 08:00:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: num_key_value_heads=32, [default0]:07/03/2024 08:00:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: pad_token_id=None, [default0]:07/03/2024 08:00:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: pretraining_tp=1, [default0]:07/03/2024 08:00:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: rms_norm_eps=1e-05, [default0]:07/03/2024 08:00:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: rope_scaling=None, [default0]:07/03/2024 08:00:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: rope_theta=10000.0, [default0]:07/03/2024 08:00:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: tie_word_embeddings=True, [default0]:07/03/2024 08:00:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: use_cache=True, [default0]:07/03/2024 08:00:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: vocab_size=50260) [default0]:07/03/2024 08:00:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: Building model.. [default0]:07/03/2024 08:00:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: Setting PP block ranks... [default1]:07/03/2024 08:00:28 [INFO|DP=14|PP=0|TP=1|ip-26-0-172-73]: No checkpoint path provided. [default0]:07/03/2024 08:00:28 [INFO|DP=14|PP=0|TP=0|ip-26-0-172-73]: No checkpoint path provided. [default3]:07/03/2024 08:00:28 [INFO|DP=14|PP=0|TP=3|ip-26-0-172-73]: No checkpoint path provided. [default2]:07/03/2024 08:00:28 [INFO|DP=14|PP=0|TP=2|ip-26-0-172-73]: No checkpoint path provided. [default2]:07/03/2024 08:00:28 [INFO|DP=0|PP=0|TP=2|ip-26-0-160-192]: Local number of parameters: 277M (529.27MiB) [default2]:07/03/2024 08:00:28 [INFO|DP=0|PP=0|TP=2|ip-26-0-160-192]: [After model building] Memory usage: 554.21MiB. Peak allocated: 606.24MiB Peak reserved: 608.00MiB [default2]:07/03/2024 08:00:28 [INFO|DP=0|PP=0|TP=2|ip-26-0-160-192]: No checkpoint path provided. [default1]:07/03/2024 08:00:28 [INFO|DP=0|PP=0|TP=1|ip-26-0-160-192]: Local number of parameters: 277M (529.27MiB) [default1]:07/03/2024 08:00:28 [INFO|DP=0|PP=0|TP=1|ip-26-0-160-192]: [After model building] Memory usage: 554.21MiB. Peak allocated: 606.24MiB Peak reserved: 608.00MiB [default1]:07/03/2024 08:00:28 [INFO|DP=0|PP=0|TP=1|ip-26-0-160-192]: No checkpoint path provided. [default0]:07/03/2024 08:00:28 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: Total number of parameters: 1.11G (2117.09MiB) [default0]:07/03/2024 08:00:28 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: Local number of parameters: 277M (529.27MiB) [default0]:07/03/2024 08:00:28 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: [After model building] Memory usage: 554.21MiB. Peak allocated: 606.24MiB Peak reserved: 608.00MiB [default0]:07/03/2024 08:00:28 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: No checkpoint path provided. [default0]:07/03/2024 08:00:28 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: Parametrizing model parameters using StandardParametrizator [default3]:07/03/2024 08:00:28 [INFO|DP=0|PP=0|TP=3|ip-26-0-160-192]: Local number of parameters: 277M (529.27MiB) [default3]:07/03/2024 08:00:28 [INFO|DP=0|PP=0|TP=3|ip-26-0-160-192]: [After model building] Memory usage: 554.21MiB. Peak allocated: 606.24MiB Peak reserved: 608.00MiB [default3]:07/03/2024 08:00:28 [INFO|DP=0|PP=0|TP=3|ip-26-0-160-192]: No checkpoint path provided. [default7]:07/03/2024 08:00:28 [INFO|DP=7|PP=0|TP=3|ip-26-0-163-226]: No checkpoint path provided. [default3]:07/03/2024 08:00:28 [INFO|DP=6|PP=0|TP=3|ip-26-0-163-226]: No checkpoint path provided. [default2]:07/03/2024 08:00:28 [INFO|DP=6|PP=0|TP=2|ip-26-0-163-226]: No checkpoint path provided. [default5]:07/03/2024 08:00:28 [INFO|DP=7|PP=0|TP=1|ip-26-0-163-226]: No checkpoint path provided. [default0]:07/03/2024 08:00:28 [INFO|DP=6|PP=0|TP=0|ip-26-0-163-226]: No checkpoint path provided. [default6]:07/03/2024 08:00:28 [INFO|DP=15|PP=0|TP=2|ip-26-0-172-73]: No checkpoint path provided. [default4]:07/03/2024 08:00:28 [INFO|DP=15|PP=0|TP=0|ip-26-0-172-73]: No checkpoint path provided. [default7]:07/03/2024 08:00:28 [INFO|DP=15|PP=0|TP=3|ip-26-0-172-73]: No checkpoint path provided. [default5]:07/03/2024 08:00:28 [INFO|DP=15|PP=0|TP=1|ip-26-0-172-73]: No checkpoint path provided. [default1]:07/03/2024 08:00:28 [INFO|DP=6|PP=0|TP=1|ip-26-0-163-226]: No checkpoint path provided. [default6]:07/03/2024 08:00:28 [INFO|DP=7|PP=0|TP=2|ip-26-0-163-226]: No checkpoint path provided. [default4]:07/03/2024 08:00:28 [INFO|DP=7|PP=0|TP=0|ip-26-0-163-226]: No checkpoint path provided. [default5]:07/03/2024 08:00:28 [INFO|DP=1|PP=0|TP=1|ip-26-0-160-192]: No checkpoint path provided. [default4]:07/03/2024 08:00:28 [INFO|DP=1|PP=0|TP=0|ip-26-0-160-192]: No checkpoint path provided. [default6]:07/03/2024 08:00:28 [INFO|DP=1|PP=0|TP=2|ip-26-0-160-192]: No checkpoint path provided. [default7]:07/03/2024 08:00:28 [INFO|DP=1|PP=0|TP=3|ip-26-0-160-192]: No checkpoint path provided. [default0]:07/03/2024 08:00:28 [INFO|DP=12|PP=0|TP=0|ip-26-0-172-57]: No checkpoint path provided. [default3]:07/03/2024 08:00:28 [INFO|DP=12|PP=0|TP=3|ip-26-0-172-57]: No checkpoint path provided. [default0]:07/03/2024 08:00:28 [INFO|DP=10|PP=0|TP=0|ip-26-0-169-86]: No checkpoint path provided. [default4]:07/03/2024 08:00:28 [INFO|DP=13|PP=0|TP=0|ip-26-0-172-57]: No checkpoint path provided. [default2]:07/03/2024 08:00:28 [INFO|DP=12|PP=0|TP=2|ip-26-0-172-57]: No checkpoint path provided. [default7]:07/03/2024 08:00:28 [INFO|DP=13|PP=0|TP=3|ip-26-0-172-57]: No checkpoint path provided. [default1]:07/03/2024 08:00:28 [INFO|DP=10|PP=0|TP=1|ip-26-0-169-86]: No checkpoint path provided. [default1]:07/03/2024 08:00:28 [INFO|DP=12|PP=0|TP=1|ip-26-0-172-57]: No checkpoint path provided. [default2]:07/03/2024 08:00:28 [INFO|DP=10|PP=0|TP=2|ip-26-0-169-86]: No checkpoint path provided. [default5]:07/03/2024 08:00:28 [INFO|DP=13|PP=0|TP=1|ip-26-0-172-57]: No checkpoint path provided. [default3]:07/03/2024 08:00:28 [INFO|DP=10|PP=0|TP=3|ip-26-0-169-86]: No checkpoint path provided. [default6]:07/03/2024 08:00:28 [INFO|DP=13|PP=0|TP=2|ip-26-0-172-57]: No checkpoint path provided. [default3]:07/03/2024 08:00:28 [INFO|DP=4|PP=0|TP=3|ip-26-0-163-220]: No checkpoint path provided. [default0]:07/03/2024 08:00:28 [INFO|DP=4|PP=0|TP=0|ip-26-0-163-220]: No checkpoint path provided. [default1]:07/03/2024 08:00:28 [INFO|DP=4|PP=0|TP=1|ip-26-0-163-220]: No checkpoint path provided. [default5]:07/03/2024 08:00:28 [INFO|DP=11|PP=0|TP=1|ip-26-0-169-86]: No checkpoint path provided. [default7]:07/03/2024 08:00:28 [INFO|DP=11|PP=0|TP=3|ip-26-0-169-86]: No checkpoint path provided. [default2]:07/03/2024 08:00:28 [INFO|DP=4|PP=0|TP=2|ip-26-0-163-220]: No checkpoint path provided. [default6]:07/03/2024 08:00:28 [INFO|DP=11|PP=0|TP=2|ip-26-0-169-86]: No checkpoint path provided. [default4]:07/03/2024 08:00:28 [INFO|DP=11|PP=0|TP=0|ip-26-0-169-86]: No checkpoint path provided. [default5]:07/03/2024 08:00:28 [INFO|DP=5|PP=0|TP=1|ip-26-0-163-220]: No checkpoint path provided. [default6]:07/03/2024 08:00:28 [INFO|DP=5|PP=0|TP=2|ip-26-0-163-220]: No checkpoint path provided. [default4]:07/03/2024 08:00:28 [INFO|DP=5|PP=0|TP=0|ip-26-0-163-220]: No checkpoint path provided. [default7]:07/03/2024 08:00:28 [INFO|DP=5|PP=0|TP=3|ip-26-0-163-220]: No checkpoint path provided. [default5]:07/03/2024 08:00:28 [INFO|DP=3|PP=0|TP=1|ip-26-0-160-225]: No checkpoint path provided. [default3]:07/03/2024 08:00:28 [INFO|DP=2|PP=0|TP=3|ip-26-0-160-225]: No checkpoint path provided. [default1]:07/03/2024 08:00:28 [INFO|DP=2|PP=0|TP=1|ip-26-0-160-225]: No checkpoint path provided. [default2]:07/03/2024 08:00:28 [INFO|DP=2|PP=0|TP=2|ip-26-0-160-225]: No checkpoint path provided. [default0]:07/03/2024 08:00:28 [INFO|DP=2|PP=0|TP=0|ip-26-0-160-225]: No checkpoint path provided. [default6]:07/03/2024 08:00:28 [INFO|DP=3|PP=0|TP=2|ip-26-0-160-225]: No checkpoint path provided. [default4]:07/03/2024 08:00:28 [INFO|DP=3|PP=0|TP=0|ip-26-0-160-225]: No checkpoint path provided. [default7]:07/03/2024 08:00:28 [INFO|DP=3|PP=0|TP=3|ip-26-0-160-225]: No checkpoint path provided. [default0]:07/03/2024 08:00:29 [INFO|DP=8|PP=0|TP=0|ip-26-0-168-238]: No checkpoint path provided. [default4]:07/03/2024 08:00:29 [INFO|DP=9|PP=0|TP=0|ip-26-0-168-238]: No checkpoint path provided. [default2]:07/03/2024 08:00:29 [INFO|DP=8|PP=0|TP=2|ip-26-0-168-238]: No checkpoint path provided. [default3]:07/03/2024 08:00:29 [INFO|DP=8|PP=0|TP=3|ip-26-0-168-238]: No checkpoint path provided. [default5]:07/03/2024 08:00:29 [INFO|DP=9|PP=0|TP=1|ip-26-0-168-238]: No checkpoint path provided. [default6]:07/03/2024 08:00:29 [INFO|DP=9|PP=0|TP=2|ip-26-0-168-238]: No checkpoint path provided. [default7]:07/03/2024 08:00:29 [INFO|DP=9|PP=0|TP=3|ip-26-0-168-238]: No checkpoint path provided. [default1]:07/03/2024 08:00:29 [INFO|DP=8|PP=0|TP=1|ip-26-0-168-238]: No checkpoint path provided. [default0]:07/03/2024 08:00:31 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: [Optimizer Building] Using LearningRateForSP as learning rate [default0]:07/03/2024 08:00:31 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: [ZeRO sharding] Size of optimizer params per rank: [default0]:07/03/2024 08:00:31 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: [ZeRO sharding] DP Rank 0 has 17.3M out of 277M (6.25%) params' optimizer states [default0]:07/03/2024 08:00:31 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: [ZeRO sharding] DP Rank 1 has 17.3M out of 277M (6.25%) params' optimizer states [default0]:07/03/2024 08:00:31 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: [ZeRO sharding] DP Rank 2 has 17.3M out of 277M (6.25%) params' optimizer states [default0]:07/03/2024 08:00:31 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: [ZeRO sharding] DP Rank 3 has 17.3M out of 277M (6.25%) params' optimizer states [default0]:07/03/2024 08:00:31 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: [ZeRO sharding] DP Rank 4 has 17.3M out of 277M (6.25%) params' optimizer states [default0]:07/03/2024 08:00:31 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: [ZeRO sharding] DP Rank 5 has 17.3M out of 277M (6.25%) params' optimizer states [default0]:07/03/2024 08:00:31 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: [ZeRO sharding] DP Rank 6 has 17.3M out of 277M (6.25%) params' optimizer states [default0]:07/03/2024 08:00:31 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: [ZeRO sharding] DP Rank 7 has 17.3M out of 277M (6.25%) params' optimizer states [default0]:07/03/2024 08:00:31 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: [ZeRO sharding] DP Rank 8 has 17.3M out of 277M (6.25%) params' optimizer states [default0]:07/03/2024 08:00:31 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: [ZeRO sharding] DP Rank 9 has 17.3M out of 277M (6.25%) params' optimizer states [default0]:07/03/2024 08:00:31 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: [ZeRO sharding] DP Rank 10 has 17.3M out of 277M (6.25%) params' optimizer states [default0]:07/03/2024 08:00:31 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: [ZeRO sharding] DP Rank 11 has 17.3M out of 277M (6.25%) params' optimizer states [default0]:07/03/2024 08:00:31 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: [ZeRO sharding] DP Rank 12 has 17.3M out of 277M (6.25%) params' optimizer states [default0]:07/03/2024 08:00:31 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: [ZeRO sharding] DP Rank 13 has 17.3M out of 277M (6.25%) params' optimizer states [default0]:07/03/2024 08:00:31 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: [ZeRO sharding] DP Rank 14 has 17.3M out of 277M (6.25%) params' optimizer states [default0]:07/03/2024 08:00:31 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: [ZeRO sharding] DP Rank 15 has 17.3M out of 277M (6.25%) params' optimizer states [default0]:07/03/2024 08:00:33 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: [Training Plan] Stage Training Stage has 19 remaining training steps and has consumed 0 samples [default0]:07/03/2024 08:00:33 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: Using `datasets` library [default0]:07/03/2024 08:00:33 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: Loading tokenizer from openai-community/gpt2 and transformers/hf_hub versions ('4.41.2', '0.23.4') [default0]:07/03/2024 08:00:33 [WARNING|DP=0|PP=0|TP=0|ip-26-0-160-192]: Repo card metadata block was not found. Setting CardData to empty. [default0]:Repo card metadata block was not found. Setting CardData to empty. [default0]:07/03/2024 08:00:34 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: [Training Plan] There are 1 training stages [default0]:07/03/2024 08:00:34 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: [Stage Training Stage] start from step 1 [default0]:07/03/2024 08:00:34 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: [default0]:07/03/2024 08:00:34 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: [Start training] datetime: 2024-07-03 08:00:34.623011 | mbs: 32 | grad_accum: 2 | global_batch_size: 1024 | sequence_length: 4096 | train_steps: 20 | start_iteration_step: 0 | consumed_train_samples: 0 [default0]:07/03/2024 08:00:34 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: Resuming training from stage Training Stage, it has trained for 0 samples and has 19 remaining train steps [default0]:07/03/2024 08:00:34 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-192]: Memory usage: 1678.92MiB. Peak allocated 1678.92MiB. Peak reserved: 1736.00MiB [default6]:Repo card metadata block was not found. Setting CardData to empty. [default6]:07/03/2024 08:00:34 [WARNING|DP=5|PP=0|TP=2|ip-26-0-163-220]: Repo card metadata block was not found. Setting CardData to empty. [default2]:Repo card metadata block was not found. Setting CardData to empty. [default1]:07/03/2024 08:00:34 [WARNING|DP=4|PP=0|TP=1|ip-26-0-163-220]: Repo card metadata block was not found. Setting CardData to empty. [default1]:Repo card metadata block was not found. Setting CardData to empty. [default2]:07/03/2024 08:00:34 [WARNING|DP=4|PP=0|TP=2|ip-26-0-163-220]: Repo card metadata block was not found. Setting CardData to empty. [default0]:07/03/2024 08:00:34 [WARNING|DP=10|PP=0|TP=0|ip-26-0-169-86]: Repo card metadata block was not found. Setting CardData to empty. [default6]:07/03/2024 08:00:34 [WARNING|DP=1|PP=0|TP=2|ip-26-0-160-192]: Repo card metadata block was not found. Setting CardData to empty. [default6]:Repo card metadata block was not found. Setting CardData to empty. [default6]:07/03/2024 08:00:34 [WARNING|DP=3|PP=0|TP=2|ip-26-0-160-225]: Repo card metadata block was not found. Setting CardData to empty. [default7]:07/03/2024 08:00:34 [WARNING|DP=9|PP=0|TP=3|ip-26-0-168-238]: Repo card metadata block was not found. Setting CardData to empty. [default7]:Repo card metadata block was not found. Setting CardData to empty. [default3]:07/03/2024 08:00:34 [WARNING|DP=10|PP=0|TP=3|ip-26-0-169-86]: Repo card metadata block was not found. Setting CardData to empty. [default6]:07/03/2024 08:00:34 [WARNING|DP=13|PP=0|TP=2|ip-26-0-172-57]: Repo card metadata block was not found. Setting CardData to empty. [default6]:Repo card metadata block was not found. Setting CardData to empty. [default0]:Repo card metadata block was not found. Setting CardData to empty. [default3]: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 08:00:34 [WARNING|DP=4|PP=0|TP=3|ip-26-0-163-220]: Repo card metadata block was not found. Setting CardData to empty. [default7]:07/03/2024 08:00:34 [WARNING|DP=7|PP=0|TP=3|ip-26-0-163-226]: Repo card metadata block was not found. Setting CardData to empty. [default5]:Repo card metadata block was not found. Setting CardData to empty. [default3]:07/03/2024 08:00:34 [WARNING|DP=6|PP=0|TP=3|ip-26-0-163-226]: Repo card metadata block was not found. Setting CardData to empty. [default2]:07/03/2024 08:00:34 [WARNING|DP=6|PP=0|TP=2|ip-26-0-163-226]: Repo card metadata block was not found. Setting CardData to empty. [default5]:07/03/2024 08:00:34 [WARNING|DP=7|PP=0|TP=1|ip-26-0-163-226]: Repo card metadata block was not found. Setting CardData to empty. [default1]:07/03/2024 08:00:34 [WARNING|DP=14|PP=0|TP=1|ip-26-0-172-73]: 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 08:00:34 [WARNING|DP=5|PP=0|TP=0|ip-26-0-163-220]: Repo card metadata block was not found. Setting CardData to empty. [default7]:07/03/2024 08:00:34 [WARNING|DP=5|PP=0|TP=3|ip-26-0-163-220]: 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 08:00:34 [WARNING|DP=14|PP=0|TP=3|ip-26-0-172-73]: Repo card metadata block was not found. Setting CardData to empty. [default0]:07/03/2024 08:00:34 [WARNING|DP=12|PP=0|TP=0|ip-26-0-172-57]: Repo card metadata block was not found. Setting CardData to empty. [default6]:07/03/2024 08:00:34 [WARNING|DP=15|PP=0|TP=2|ip-26-0-172-73]: Repo card metadata block was not found. Setting CardData to empty. [default7]:07/03/2024 08:00:34 [WARNING|DP=15|PP=0|TP=3|ip-26-0-172-73]: Repo card metadata block was not found. Setting CardData to empty. [default4]:07/03/2024 08:00:34 [WARNING|DP=15|PP=0|TP=0|ip-26-0-172-73]: Repo card metadata block was not found. Setting CardData to empty. [default2]:07/03/2024 08:00:34 [WARNING|DP=0|PP=0|TP=2|ip-26-0-160-192]: Repo card metadata block was not found. Setting CardData to empty. [default4]:07/03/2024 08:00:34 [WARNING|DP=7|PP=0|TP=0|ip-26-0-163-226]: Repo card metadata block was not found. Setting CardData to empty. [default3]:07/03/2024 08:00:34 [WARNING|DP=8|PP=0|TP=3|ip-26-0-168-238]: Repo card metadata block was not found. Setting CardData to empty. [default5]:07/03/2024 08:00:34 [WARNING|DP=11|PP=0|TP=1|ip-26-0-169-86]: Repo card metadata block was not found. Setting CardData to empty. [default6]:Repo card metadata block was not found. Setting CardData to empty. [default4]:07/03/2024 08:00:34 [WARNING|DP=13|PP=0|TP=0|ip-26-0-172-57]: Repo card metadata block was not found. Setting CardData to empty. [default4]:07/03/2024 08:00:34 [WARNING|DP=9|PP=0|TP=0|ip-26-0-168-238]: Repo card metadata block was not found. Setting CardData to empty. [default2]:07/03/2024 08:00:34 [WARNING|DP=12|PP=0|TP=2|ip-26-0-172-57]: Repo card metadata block was not found. Setting CardData to empty. [default2]:07/03/2024 08:00:34 [WARNING|DP=8|PP=0|TP=2|ip-26-0-168-238]: 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 08:00:34 [WARNING|DP=3|PP=0|TP=1|ip-26-0-160-225]: Repo card metadata block was not found. Setting CardData to empty. [default7]:07/03/2024 08:00:34 [WARNING|DP=1|PP=0|TP=3|ip-26-0-160-192]: Repo card metadata block was not found. Setting CardData to empty. [default3]:07/03/2024 08:00:34 [WARNING|DP=2|PP=0|TP=3|ip-26-0-160-225]: Repo card metadata block was not found. Setting CardData to empty. [default2]:Repo card metadata block was not found. Setting CardData to empty. [default4]: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 08:00:34 [WARNING|DP=2|PP=0|TP=2|ip-26-0-160-225]: 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. [default4]:07/03/2024 08:00:34 [WARNING|DP=1|PP=0|TP=0|ip-26-0-160-192]: Repo card metadata block was not found. Setting CardData to empty. [default3]:07/03/2024 08:00:34 [WARNING|DP=0|PP=0|TP=3|ip-26-0-160-192]: Repo card metadata block was not found. Setting CardData to empty. [default2]:Repo card metadata block was not found. Setting CardData to empty. [default3]:Repo card metadata block was not found. Setting CardData to empty. [default6]:Repo card metadata block was not found. Setting CardData to empty. [default5]:07/03/2024 08:00:34 [WARNING|DP=13|PP=0|TP=1|ip-26-0-172-57]: Repo card metadata block was not found. Setting CardData to empty. [default7]:Repo card metadata block was not found. Setting CardData to empty. [default5]:Repo card metadata block was not found. Setting CardData to empty. [default6]:Repo card metadata block was not found. Setting CardData to empty. [default7]:Repo card metadata block was not found. Setting CardData to empty. [default4]:07/03/2024 08:00:34 [WARNING|DP=11|PP=0|TP=0|ip-26-0-169-86]: Repo card metadata block was not found. Setting CardData to empty. [default3]:Repo card metadata block was not found. Setting CardData to empty. [default4]:07/03/2024 08:00:34 [WARNING|DP=3|PP=0|TP=0|ip-26-0-160-225]: Repo card metadata block was not found. Setting CardData to empty. [default2]:07/03/2024 08:00:34 [WARNING|DP=10|PP=0|TP=2|ip-26-0-169-86]: Repo card metadata block was not found. Setting CardData to empty. [default7]:07/03/2024 08:00:34 [WARNING|DP=3|PP=0|TP=3|ip-26-0-160-225]: Repo card metadata block was not found. Setting CardData to empty. [default6]:07/03/2024 08:00:34 [WARNING|DP=11|PP=0|TP=2|ip-26-0-169-86]: Repo card metadata block was not found. Setting CardData to empty. [default2]:Repo card metadata block was not found. Setting CardData to empty. [default4]:Repo card metadata block was not found. Setting CardData to empty. [default6]:07/03/2024 08:00:34 [WARNING|DP=9|PP=0|TP=2|ip-26-0-168-238]: Repo card metadata block was not found. Setting CardData to empty. [default3]: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. [default0]:Repo card metadata block was not found. Setting CardData to empty. [default5]:Repo card metadata block was not found. Setting CardData to empty. [default7]:Repo card metadata block was not found. Setting CardData to empty. [default1]: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. [default5]: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. [default4]:Repo card metadata block was not found. Setting CardData to empty. [default2]:Repo card metadata block was not found. Setting CardData to empty. [default4]:Repo card metadata block was not found. Setting CardData to empty. [default0]:07/03/2024 08:00:35 [WARNING|DP=4|PP=0|TP=0|ip-26-0-163-220]: Repo card metadata block was not found. Setting CardData to empty. [default5]:07/03/2024 08:00:35 [WARNING|DP=5|PP=0|TP=1|ip-26-0-163-220]: 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]:07/03/2024 08:00:35 [WARNING|DP=14|PP=0|TP=2|ip-26-0-172-73]: Repo card metadata block was not found. Setting CardData to empty. [default5]:07/03/2024 08:00:35 [WARNING|DP=15|PP=0|TP=1|ip-26-0-172-73]: Repo card metadata block was not found. Setting CardData to empty. [default0]:07/03/2024 08:00:35 [WARNING|DP=6|PP=0|TP=0|ip-26-0-163-226]: Repo card metadata block was not found. Setting CardData to empty. [default0]:07/03/2024 08:00:35 [WARNING|DP=8|PP=0|TP=0|ip-26-0-168-238]: 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 08:00:35 [WARNING|DP=14|PP=0|TP=0|ip-26-0-172-73]: 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. [default3]:07/03/2024 08:00:35 [WARNING|DP=12|PP=0|TP=3|ip-26-0-172-57]: Repo card metadata block was not found. Setting CardData to empty. [default6]:07/03/2024 08:00:35 [WARNING|DP=7|PP=0|TP=2|ip-26-0-163-226]: Repo card metadata block was not found. Setting CardData to empty. [default1]:07/03/2024 08:00:35 [WARNING|DP=6|PP=0|TP=1|ip-26-0-163-226]: Repo card metadata block was not found. Setting CardData to empty. [default1]:Repo card metadata block was not found. Setting CardData to empty. [default1]:07/03/2024 08:00:35 [WARNING|DP=0|PP=0|TP=1|ip-26-0-160-192]: Repo card metadata block was not found. Setting CardData to empty. [default7]:07/03/2024 08:00:35 [WARNING|DP=13|PP=0|TP=3|ip-26-0-172-57]: 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]:Repo card metadata block was not found. Setting CardData to empty. [default7]:07/03/2024 08:00:34 [WARNING|DP=11|PP=0|TP=3|ip-26-0-169-86]: Repo card metadata block was not found. Setting CardData to empty. [default1]:Repo card metadata block was not found. Setting CardData to empty. [default5]:07/03/2024 08:00:35 [WARNING|DP=1|PP=0|TP=1|ip-26-0-160-192]: Repo card metadata block was not found. Setting CardData to empty. [default0]:Repo card metadata block was not found. Setting CardData to empty. [default3]:Repo card metadata block was not found. Setting CardData to empty. [default0]:07/03/2024 08:00:35 [WARNING|DP=2|PP=0|TP=0|ip-26-0-160-225]: Repo card metadata block was not found. Setting CardData to empty. [default1]:07/03/2024 08:00:35 [WARNING|DP=12|PP=0|TP=1|ip-26-0-172-57]: Repo card metadata block was not found. Setting CardData to empty. [default1]:07/03/2024 08:00:35 [WARNING|DP=8|PP=0|TP=1|ip-26-0-168-238]: Repo card metadata block was not found. Setting CardData to empty. [default5]:Repo card metadata block was not found. Setting CardData to empty. [default5]:07/03/2024 08:00:35 [WARNING|DP=9|PP=0|TP=1|ip-26-0-168-238]: Repo card metadata block was not found. Setting CardData to empty. [default1]:07/03/2024 08:00:35 [WARNING|DP=10|PP=0|TP=1|ip-26-0-169-86]: Repo card metadata block was not found. Setting CardData to empty. [default5]:Repo card metadata block was not found. Setting CardData to empty. [default2]: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]:07/03/2024 08:00:35 [WARNING|DP=2|PP=0|TP=1|ip-26-0-160-225]: Repo card metadata block was not found. Setting CardData to empty. [default1]:Repo card metadata block was not found. Setting CardData to empty. [default6]:[rank30]: Traceback (most recent call last): [default6]:[rank30]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/run_train.py", line 237, in [default5]:[rank29]: Traceback (most recent call last): [default6]:[rank30]: trainer.train(dataloader) [default6]:[rank30]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 429, in train [default5]:[rank29]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/run_train.py", line 237, in [default5]:[rank29]: trainer.train(dataloader) [default6]:[rank30]: 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 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 [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( [default5]:[rank29]: 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 [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) [default6]:[rank30]: output = model(**micro_batch) [default5]:[rank29]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 44, in forward [default5]:[rank29]: 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 [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]:[rank30]: return self._call_impl(*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 [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 [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 [default6]:[rank30]: 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 [default6]:[rank30]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 891, in forward [default5]:[rank29]: return forward_call(*args, **kwargs) [default6]:[rank30]: sharded_logits = self.model( [default5]:[rank29]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 764, in forward [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) [default5]:[rank29]: return self.forward_with_hidden_states(input_ids=input_ids, input_mask=input_mask)[0] [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) [default5]:[rank29]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 780, in forward_with_hidden_states [default6]:[rank30]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 764, in forward [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 [default6]:[rank30]: return self.forward_with_hidden_states(input_ids=input_ids, input_mask=input_mask)[0] [default5]:[rank29]: return self._call_impl(*args, **kwargs) [default6]:[rank30]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 780, in forward_with_hidden_states [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 [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) [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]:[rank30]: 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]: output = self.pp_block(**new_kwargs) [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 [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]:[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) [default5]:[rank29]: 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 [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) [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 [default5]:[rank29]: 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 [default5]:[rank29]: output = self.attn(hidden_states=hidden_states, sequence_mask=sequence_mask) [default6]:[rank30]: 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 1532, in _wrapped_call_impl [default5]:[rank29]: 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 [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) [default6]:[rank30]: return forward_call(*args, **kwargs) [default6]:[rank30]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 565, in forward [default6]:[rank30]: key_value_states = key_value_states.permute(1, 2, 0, 3, 4).contiguous() [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) [default6]:[rank30]: torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 256.00 MiB. GPU  has a total capacity of 79.33 GiB of which 177.94 MiB is free. Including non-PyTorch memory, this process has 79.14 GiB memory in use. Of the allocated memory 69.15 GiB is allocated by PyTorch, and 17.80 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]:[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/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 512.00 MiB. GPU  has a total capacity of 79.33 GiB of which 73.94 MiB is free. Including non-PyTorch memory, this process has 79.25 GiB memory in use. Of the allocated memory 69.40 GiB is allocated by PyTorch, and 13.30 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 565, in forward [default4]:[rank28]: key_value_states = key_value_states.permute(1, 2, 0, 3, 4).contiguous() [default4]:[rank28]: torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 256.00 MiB. GPU  has a total capacity of 79.33 GiB of which 225.94 MiB is free. Including non-PyTorch memory, this process has 79.10 GiB memory in use. Of the allocated memory 69.15 GiB is allocated by PyTorch, and 17.80 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]:[rank31]: Traceback (most recent call last): [default7]:[rank31]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/run_train.py", line 237, in [default7]:[rank31]: trainer.train(dataloader) [default7]:[rank31]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 429, in train [default7]:[rank31]: outputs, loss_avg = self.training_step(dataloader=self.current_dataloader) [default7]:[rank31]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 462, in training_step [default7]:[rank31]: outputs = self.pipeline_engine.train_batch_iter( [default7]:[rank31]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 278, in train_batch_iter [default7]:[rank31]: output = self.forward(context=context, state=state, micro_batch=micro_batch, model=model) [default7]:[rank31]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 44, in forward [default7]:[rank31]: output = model(**micro_batch) [default7]:[rank31]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl [default7]:[rank31]: return self._call_impl(*args, **kwargs) [default7]:[rank31]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default7]:[rank31]: return forward_call(*args, **kwargs) [default7]:[rank31]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 891, in forward [default7]:[rank31]: sharded_logits = self.model( [default7]:[rank31]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl [default7]:[rank31]: return self._call_impl(*args, **kwargs) [default7]:[rank31]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default7]:[rank31]: return forward_call(*args, **kwargs) [default7]:[rank31]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 764, in forward [default7]:[rank31]: return self.forward_with_hidden_states(input_ids=input_ids, input_mask=input_mask)[0] [default7]:[rank31]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 780, in forward_with_hidden_states [default7]:[rank31]: hidden_encoder_states = encoder_block(**hidden_encoder_states) [default7]:[rank31]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl [default7]:[rank31]: return self._call_impl(*args, **kwargs) [default7]:[rank31]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default7]:[rank31]: return forward_call(*args, **kwargs) [default7]:[rank31]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/block.py", line 151, in forward [default7]:[rank31]: output = self.pp_block(**new_kwargs) [default7]:[rank31]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl [default7]:[rank31]: return self._call_impl(*args, **kwargs) [default7]:[rank31]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default7]:[rank31]: return forward_call(*args, **kwargs) [default7]:[rank31]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 631, in forward [default7]:[rank31]: output = self.attn(hidden_states=hidden_states, sequence_mask=sequence_mask) [default7]:[rank31]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl [default7]:[rank31]: return self._call_impl(*args, **kwargs) [default7]:[rank31]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default7]:[rank31]: return forward_call(*args, **kwargs) [default7]:[rank31]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 598, in forward [default7]:[rank31]: output = self.o_proj(attention_output) [default7]:[rank31]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl [default7]:[rank31]: return self._call_impl(*args, **kwargs) [default7]:[rank31]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default7]:[rank31]: return forward_call(*args, **kwargs) [default7]:[rank31]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/nn.py", line 159, in forward [default7]:[rank31]: return row_linear( [default7]:[rank31]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/functional.py", line 474, in row_linear [default7]:[rank31]: out = F.linear(input, weight, bias) [default7]:[rank31]: torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 512.00 MiB. GPU  has a total capacity of 79.33 GiB of which 185.94 MiB is free. Including non-PyTorch memory, this process has 79.14 GiB memory in use. Of the allocated memory 69.40 GiB is allocated by PyTorch, and 141.30 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 512.00 MiB. GPU [default3]:[rank43]: Traceback (most recent call last): [default3]:[rank43]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/run_train.py", line 237, in [default3]:[rank43]: trainer.train(dataloader) [default3]:[rank43]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 429, in train [default3]:[rank43]: outputs, loss_avg = self.training_step(dataloader=self.current_dataloader) [default3]:[rank43]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 462, in training_step [default3]:[rank43]: outputs = self.pipeline_engine.train_batch_iter( [default3]:[rank43]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 278, in train_batch_iter [default3]:[rank43]: output = self.forward(context=context, state=state, micro_batch=micro_batch, model=model) [default3]:[rank43]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 44, in forward [default3]:[rank43]: output = model(**micro_batch) [default3]:[rank43]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl [default3]:[rank43]: return self._call_impl(*args, **kwargs) [default3]:[rank43]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default3]:[rank43]: return forward_call(*args, **kwargs) [default3]:[rank43]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 891, in forward [default3]:[rank43]: sharded_logits = self.model( [default3]:[rank43]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl [default3]:[rank43]: return self._call_impl(*args, **kwargs) [default3]:[rank43]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default3]:[rank43]: return forward_call(*args, **kwargs) [default3]:[rank43]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 764, in forward [default3]:[rank43]: return self.forward_with_hidden_states(input_ids=input_ids, input_mask=input_mask)[0] [default3]:[rank43]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 780, in forward_with_hidden_states [default3]:[rank43]: hidden_encoder_states = encoder_block(**hidden_encoder_states) [default3]:[rank43]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl [default3]:[rank43]: return self._call_impl(*args, **kwargs) [default3]:[rank43]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default3]:[rank43]: return forward_call(*args, **kwargs) [default3]:[rank43]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/block.py", line 151, in forward [default3]:[rank43]: output = self.pp_block(**new_kwargs) [default3]:[rank43]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl [default3]:[rank43]: return self._call_impl(*args, **kwargs) [default3]:[rank43]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default3]:[rank43]: return forward_call(*args, **kwargs) [default3]:[rank43]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 631, in forward [default3]:[rank43]: output = self.attn(hidden_states=hidden_states, sequence_mask=sequence_mask) [default3]:[rank43]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl [default3]:[rank43]: return self._call_impl(*args, **kwargs) [default3]:[rank43]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default3]:[rank43]: return forward_call(*args, **kwargs) [default3]:[rank43]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 598, in forward [default3]:[rank43]: output = self.o_proj(attention_output) [default3]:[rank43]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl [default3]:[rank43]: return self._call_impl(*args, **kwargs) [default3]:[rank43]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default3]:[rank43]: return forward_call(*args, **kwargs) [default3]:[rank43]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/nn.py", line 159, in forward [default3]:[rank43]: return row_linear( [default3]:[rank43]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/functional.py", line 474, in row_linear [default3]:[rank43]: out = F.linear(input, weight, bias) [default3]:[rank43]: torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 512.00 MiB. GPU  has a total capacity of 79.33 GiB of which 225.94 MiB is free. Including non-PyTorch memory, this process has 79.10 GiB memory in use. Of the allocated memory 69.40 GiB is allocated by PyTorch, and 141.30 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation. See documentation for Memory Management (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables) [default1]:[rank41]: Traceback (most recent call last): [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) [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) [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( [default1]:[rank41]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 462, in training_step [default2]:[rank42]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 278, in train_batch_iter [default1]:[rank41]: outputs = self.pipeline_engine.train_batch_iter( [default2]:[rank42]: 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 278, in train_batch_iter [default2]:[rank42]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 44, in forward [default1]:[rank41]: output = self.forward(context=context, state=state, micro_batch=micro_batch, model=model) [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) [default1]:[rank41]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 44, in forward [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 [default1]:[rank41]: output = model(**micro_batch) [default2]:[rank42]: return forward_call(*args, **kwargs) [default1]:[rank41]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl [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 [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( [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 [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) [default1]:[rank41]: 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 1541, in _call_impl [default1]:[rank41]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl [default2]:[rank42]: return forward_call(*args, **kwargs) [default1]:[rank41]: return self._call_impl(*args, **kwargs) [default2]:[rank42]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 764, in forward [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 [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 [default1]:[rank41]: return forward_call(*args, **kwargs) [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 [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] [default2]:[rank42]: return self._call_impl(*args, **kwargs) [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 [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) [default1]:[rank41]: return self._call_impl(*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) [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 [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 [default1]:[rank41]: return forward_call(*args, **kwargs) [default2]:[rank42]: 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) [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) [default1]:[rank41]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl [default2]:[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) [default1]:[rank41]: return self._call_impl(*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) [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) [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 [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 [default2]:[rank42]: return forward_call(*args, **kwargs) [default1]:[rank41]: output = self.o_proj(attention_output) [default2]:[rank42]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/nn.py", line 159, in forward [default1]:[rank41]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl [default2]:[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) [default1]:[rank41]: return self._call_impl(*args, **kwargs) [default2]:[rank42]: torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 512.00 MiB. GPU  has a total capacity of 79.33 GiB of which 41.94 MiB is free. Including non-PyTorch memory, this process has 79.28 GiB memory in use. Of the allocated memory 69.40 GiB is allocated by PyTorch, and 13.30 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation. See documentation for Memory Management (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables) [default1]:[rank41]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [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 512.00 MiB. GPU  has a total capacity of 79.33 GiB of which 113.94 MiB is free. Including non-PyTorch memory, this process has 79.21 GiB memory in use. Of the allocated memory 69.40 GiB is allocated by PyTorch, and 13.30 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 512.00 MiB. GPU  has a total capacity of 79.33 GiB of which 33.94 MiB is free. Including non-PyTorch memory, this process has 79.29 GiB memory in use. Of the allocated memory 69.40 GiB is allocated by PyTorch, and 141.30 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]:[rank55]: Traceback (most recent call last): [default7]:[rank55]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/run_train.py", line 237, in [default7]:[rank55]: trainer.train(dataloader) [default7]:[rank55]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 429, in train [default7]:[rank55]: outputs, loss_avg = self.training_step(dataloader=self.current_dataloader) [default7]:[rank55]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 462, in training_step [default7]:[rank55]: outputs = self.pipeline_engine.train_batch_iter( [default7]:[rank55]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 278, in train_batch_iter [default7]:[rank55]: output = self.forward(context=context, state=state, micro_batch=micro_batch, model=model) [default7]:[rank55]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 44, in forward [default7]:[rank55]: output = model(**micro_batch) [default7]:[rank55]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl [default7]:[rank55]: return self._call_impl(*args, **kwargs) [default7]:[rank55]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default7]:[rank55]: return forward_call(*args, **kwargs) [default7]:[rank55]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 891, in forward [default7]:[rank55]: sharded_logits = self.model( [default7]:[rank55]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl [default7]:[rank55]: return self._call_impl(*args, **kwargs) [default7]:[rank55]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default7]:[rank55]: return forward_call(*args, **kwargs) [default7]:[rank55]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 764, in forward [default7]:[rank55]: return self.forward_with_hidden_states(input_ids=input_ids, input_mask=input_mask)[0] [default7]:[rank55]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 780, in forward_with_hidden_states [default7]:[rank55]: hidden_encoder_states = encoder_block(**hidden_encoder_states) [default7]:[rank55]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl [default7]:[rank55]: return self._call_impl(*args, **kwargs) [default7]:[rank55]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default7]:[rank55]: return forward_call(*args, **kwargs) [default7]:[rank55]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/block.py", line 151, in forward [default7]:[rank55]: output = self.pp_block(**new_kwargs) [default7]:[rank55]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl [default7]:[rank55]: return self._call_impl(*args, **kwargs) [default7]:[rank55]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default7]:[rank55]: return forward_call(*args, **kwargs) [default7]:[rank55]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 631, in forward [default7]:[rank55]: output = self.attn(hidden_states=hidden_states, sequence_mask=sequence_mask) [default7]:[rank55]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl [default7]:[rank55]: return self._call_impl(*args, **kwargs) [default7]:[rank55]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default7]:[rank55]: return forward_call(*args, **kwargs) [default7]:[rank55]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 598, in forward [default7]:[rank55]: output = self.o_proj(attention_output) [default7]:[rank55]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl [default7]:[rank55]: return self._call_impl(*args, **kwargs) [default7]:[rank55]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default7]:[rank55]: return forward_call(*args, **kwargs) [default7]:[rank55]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/nn.py", line 159, in forward [default7]:[rank55]: return row_linear( [default7]:[rank55]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/functional.py", line 474, in row_linear [default7]:[rank55]: out = F.linear(input, weight, bias) [default7]:[rank55]: torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 512.00 MiB. GPU  has a total capacity of 79.33 GiB of which 153.94 MiB is free. Including non-PyTorch memory, this process has 79.17 GiB memory in use. Of the allocated memory 69.40 GiB is allocated by PyTorch, and 141.30 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation. See documentation for Memory Management (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables) [default5]:[rank53]: 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 512.00 MiB. GPU  has a total capacity of 79.33 GiB of which 41.94 MiB is free. Including non-PyTorch memory, this process has 79.28 GiB memory in use. Of the allocated memory 69.40 GiB is allocated by PyTorch, and 13.30 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 512.00 MiB. GPU  has a total capacity of 79.33 GiB of which 113.94 MiB is free. Including non-PyTorch memory, this process has 79.21 GiB memory in use. Of the allocated memory 69.40 GiB is allocated by PyTorch, and 13.30 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation. See documentation for Memory Management (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables) [default7]:[rank47]: Traceback (most recent call last): [default7]:[rank47]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/run_train.py", line 237, in [default7]:[rank47]: trainer.train(dataloader) [default7]:[rank47]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 429, in train [default7]:[rank47]: outputs, loss_avg = self.training_step(dataloader=self.current_dataloader) [default7]:[rank47]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 462, in training_step [default7]:[rank47]: outputs = self.pipeline_engine.train_batch_iter( [default7]:[rank47]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 278, in train_batch_iter [default7]:[rank47]: output = self.forward(context=context, state=state, micro_batch=micro_batch, model=model) [default7]:[rank47]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 44, in forward [default7]:[rank47]: output = model(**micro_batch) [default7]:[rank47]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl [default7]:[rank47]: return self._call_impl(*args, **kwargs) [default7]:[rank47]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default7]:[rank47]: return forward_call(*args, **kwargs) [default7]:[rank47]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 891, in forward [default7]:[rank47]: sharded_logits = self.model( [default7]:[rank47]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl [default7]:[rank47]: return self._call_impl(*args, **kwargs) [default7]:[rank47]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default7]:[rank47]: return forward_call(*args, **kwargs) [default7]:[rank47]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 764, in forward [default7]:[rank47]: return self.forward_with_hidden_states(input_ids=input_ids, input_mask=input_mask)[0] [default7]:[rank47]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 780, in forward_with_hidden_states [default7]:[rank47]: hidden_encoder_states = encoder_block(**hidden_encoder_states) [default7]:[rank47]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl [default7]:[rank47]: return self._call_impl(*args, **kwargs) [default7]:[rank47]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default7]:[rank47]: return forward_call(*args, **kwargs) [default7]:[rank47]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/block.py", line 151, in forward [default7]:[rank47]: output = self.pp_block(**new_kwargs) [default7]:[rank47]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl [default7]:[rank47]: return self._call_impl(*args, **kwargs) [default7]:[rank47]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default7]:[rank47]: return forward_call(*args, **kwargs) [default7]:[rank47]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 631, in forward [default7]:[rank47]: output = self.attn(hidden_states=hidden_states, sequence_mask=sequence_mask) [default7]:[rank47]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl [default7]:[rank47]: return self._call_impl(*args, **kwargs) [default7]:[rank47]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default7]:[rank47]: return forward_call(*args, **kwargs) [default7]:[rank47]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 598, in forward [default7]:[rank47]: output = self.o_proj(attention_output) [default7]:[rank47]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl [default7]:[rank47]: return self._call_impl(*args, **kwargs) [default7]:[rank47]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default7]:[rank47]: return forward_call(*args, **kwargs) [default7]:[rank47]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/nn.py", line 159, in forward [default7]:[rank47]: return row_linear( [default7]:[rank47]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/functional.py", line 474, in row_linear [default7]:[rank47]: out = F.linear(input, weight, bias) [default7]:[rank47]: torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 512.00 MiB. GPU  has a total capacity of 79.33 GiB of which 225.94 MiB is free. Including non-PyTorch memory, this process has 79.10 GiB memory in use. Of the allocated memory 69.40 GiB is allocated by PyTorch, and 141.30 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) [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]: Traceback (most recent call last): [default4]:[rank44]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/run_train.py", line 237, in [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) [default4]:[rank44]: trainer.train(dataloader) [default4]:[rank44]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 429, in train [default4]:[rank44]: outputs, loss_avg = self.training_step(dataloader=self.current_dataloader) [default4]:[rank44]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 462, in training_step [default4]:[rank44]: outputs = self.pipeline_engine.train_batch_iter( [default4]:[rank44]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 278, in train_batch_iter [default4]:[rank44]: output = self.forward(context=context, state=state, micro_batch=micro_batch, model=model) [default4]:[rank44]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/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]: 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 764, in forward [default6]:[rank46]: return self.forward_with_hidden_states(input_ids=input_ids, input_mask=input_mask)[0] [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) [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/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/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 [default6]:[rank46]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/block.py", line 151, in forward [default6]:[rank46]: output = self.pp_block(**new_kwargs) [default6]:[rank46]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl [default6]:[rank46]: return self._call_impl(*args, **kwargs) [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 [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 [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 [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 [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 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]: 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) [default4]:[rank44]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/block.py", line 151, in forward [default6]:[rank46]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default4]:[rank44]: output = self.pp_block(**new_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 1532, in _wrapped_call_impl [default6]:[rank46]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/nn.py", line 159, in forward [default4]:[rank44]: return self._call_impl(*args, **kwargs) [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 [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]: out = F.linear(input, weight, bias) [default6]:[rank46]: torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 512.00 MiB. GPU  has a total capacity of 79.33 GiB of which 41.94 MiB is free. Including non-PyTorch memory, this process has 79.28 GiB memory in use. Of the allocated memory 69.40 GiB is allocated by PyTorch, and 13.30 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]:[rank44]: return forward_call(*args, **kwargs) [default4]:[rank44]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 631, in forward [default4]:[rank44]: output = self.attn(hidden_states=hidden_states, sequence_mask=sequence_mask) [default4]:[rank44]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl [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 598, in forward [default4]:[rank44]: output = self.o_proj(attention_output) [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/parallel/tensor_parallel/nn.py", line 159, in forward [default4]:[rank44]: return row_linear( [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) [default4]:[rank44]: torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 512.00 MiB. GPU  has a total capacity of 79.33 GiB of which 89.94 MiB is free. Including non-PyTorch memory, this process has 79.23 GiB memory in use. Of the allocated memory 69.40 GiB is allocated by PyTorch, and 13.30 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 565, in forward [default5]:[rank37]: key_value_states = key_value_states.permute(1, 2, 0, 3, 4).contiguous() [default5]:[rank37]: torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 256.00 MiB. GPU  has a total capacity of 79.33 GiB of which 177.94 MiB is free. Including non-PyTorch memory, this process has 79.14 GiB memory in use. Of the allocated memory 69.15 GiB is allocated by PyTorch, and 17.80 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 [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) [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) [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 [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) [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) [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 [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 [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 [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 [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) [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 [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 [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) [default6]:[rank38]: return self._call_impl(*args, **kwargs) [default7]:[rank39]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl [default7]:[rank39]: 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 [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 [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 [default7]:[rank39]: return forward_call(*args, **kwargs) [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) [default7]:[rank39]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/block.py", line 151, in forward [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) [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 [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 [default7]:[rank39]: return self._call_impl(*args, **kwargs) [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) [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 [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) [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 [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 [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 [default6]:[rank38]: return forward_call(*args, **kwargs) [default7]:[rank39]: return self._call_impl(*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( [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 [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) [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) [default6]:[rank38]: torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 512.00 MiB. GPU  has a total capacity of 79.33 GiB of which 73.94 MiB is free. Including non-PyTorch memory, this process has 79.25 GiB memory in use. Of the allocated memory 69.40 GiB is allocated by PyTorch, and 13.30 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]: 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 512.00 MiB. GPU  has a total capacity of 79.33 GiB of which 33.94 MiB is free. Including non-PyTorch memory, this process has 79.29 GiB memory in use. Of the allocated memory 69.40 GiB is allocated by PyTorch, and 141.30 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 512.00 MiB. GPU  has a total capacity of 79.33 GiB of which 121.94 MiB is free. Including non-PyTorch memory, this process has 79.20 GiB memory in use. Of the allocated memory 69.40 GiB is allocated by PyTorch, and 13.30 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 512.00 MiB. GPU  has a total capacity of 79.33 GiB of which 113.94 MiB is free. Including non-PyTorch memory, this process has 79.21 GiB memory in use. Of the allocated memory 69.40 GiB is allocated by PyTorch, and 13.30 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) [default1]:[rank1]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 462, in training_step [default1]:[rank1]: outputs = self.pipeline_engine.train_batch_iter( [default1]:[rank1]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 278, in train_batch_iter [default1]:[rank1]: output = self.forward(context=context, state=state, micro_batch=micro_batch, model=model) [default1]:[rank1]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 44, in forward [default1]:[rank1]: output = model(**micro_batch) [default1]:[rank1]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl [default1]:[rank1]: return self._call_impl(*args, **kwargs) [default1]:[rank1]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default1]:[rank1]: return forward_call(*args, **kwargs) [default1]:[rank1]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 891, in forward [default1]:[rank1]: sharded_logits = self.model( [default1]:[rank1]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl [default1]:[rank1]: return self._call_impl(*args, **kwargs) [default1]:[rank1]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default1]:[rank1]: return forward_call(*args, **kwargs) [default1]:[rank1]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 764, in forward [default1]:[rank1]: return self.forward_with_hidden_states(input_ids=input_ids, input_mask=input_mask)[0] [default1]:[rank1]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 780, in forward_with_hidden_states [default1]:[rank1]: hidden_encoder_states = encoder_block(**hidden_encoder_states) [default1]:[rank1]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl [default1]:[rank1]: return self._call_impl(*args, **kwargs) [default1]:[rank1]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default1]:[rank1]: return forward_call(*args, **kwargs) [default1]:[rank1]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/block.py", line 151, in forward [default1]:[rank1]: output = self.pp_block(**new_kwargs) [default1]:[rank1]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl [default1]:[rank1]: return self._call_impl(*args, **kwargs) [default1]:[rank1]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default1]:[rank1]: return forward_call(*args, **kwargs) [default1]:[rank1]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 637, in forward [default1]:[rank1]: hidden_states = self.mlp(hidden_states=hidden_states)["hidden_states"] [default1]:[rank1]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl [default1]:[rank1]: return self._call_impl(*args, **kwargs) [default1]:[rank1]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default1]:[rank1]: return forward_call(*args, **kwargs) [default1]:[rank1]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 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 512.00 MiB. GPU  has a total capacity of 79.33 GiB of which 81.94 MiB is free. Including non-PyTorch memory, this process has 79.24 GiB memory in use. Of the allocated memory 69.78 GiB is allocated by PyTorch, and 269.17 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 512.00 MiB. GPU [default2]:[rank2]: Traceback (most recent call last): [default2]:[rank2]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/run_train.py", line 237, in [default3]:[rank3]: Traceback (most recent call last): [default3]:[rank3]: 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 [default3]:[rank3]: trainer.train(dataloader) [default3]:[rank3]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 429, in train [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 [default3]:[rank3]: outputs, loss_avg = self.training_step(dataloader=self.current_dataloader) [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) [default3]:[rank3]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 462, in training_step [default2]:[rank2]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 891, in forward [default3]:[rank3]: outputs = self.pipeline_engine.train_batch_iter( [default2]:[rank2]: 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 [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 [default3]:[rank3]: sharded_logits = self.model( [default2]:[rank2]: return self._call_impl(*args, **kwargs) [default3]:[rank3]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl [default3]:[rank3]: return self._call_impl(*args, **kwargs) [default3]:[rank3]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default3]:[rank3]: return forward_call(*args, **kwargs) [default3]:[rank3]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 764, in forward [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 [default3]:[rank3]: return self.forward_with_hidden_states(input_ids=input_ids, input_mask=input_mask)[0] [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 [default3]:[rank3]: 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) [default3]:[rank3]: 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) [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) [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) [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) [default2]:[rank2]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/block.py", line 151, in forward [default3]:[rank3]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl [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 [default3]:[rank3]: return self._call_impl(*args, **kwargs) [default2]:[rank2]: 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 1541, in _call_impl [default3]:[rank3]: return forward_call(*args, **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 [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 [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 [default3]:[rank3]: return self._call_impl(*args, **kwargs) [default2]:[rank2]: 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 1541, in _call_impl [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 [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 [default2]:[rank2]: return self._call_impl(*args, **kwargs) [default3]:[rank3]: 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 [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 [default2]:[rank2]: return forward_call(*args, **kwargs) [default3]:[rank3]: return F.linear(input, weight, bias) [default2]:[rank2]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/nn.py", line 87, in forward [default3]:[rank3]: torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 512.00 MiB. GPU  has a total capacity of 79.33 GiB of which 321.94 MiB is free. Including non-PyTorch memory, this process has 79.00 GiB memory in use. Of the allocated memory 69.78 GiB is allocated by PyTorch, and 269.17 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]: 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 512.00 MiB. GPU  has a total capacity of 79.33 GiB of which 153.94 MiB is free. Including non-PyTorch memory, this process has 79.17 GiB memory in use. Of the allocated memory 69.78 GiB is allocated by PyTorch, and 269.17 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 512.00 MiB. GPU  has a total capacity of 79.33 GiB of which 153.94 MiB is free. Including non-PyTorch memory, this process has 79.17 GiB memory in use. Of the allocated memory 69.78 GiB is allocated by PyTorch, and 269.17 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 512.00 MiB. GPU  has a total capacity of 79.33 GiB of which 321.94 MiB is free. Including non-PyTorch memory, this process has 79.00 GiB memory in use. Of the allocated memory 69.78 GiB is allocated by PyTorch, and 269.17 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation. See documentation for Memory Management (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables) [default5]:[rank5]: Traceback (most recent call last): [default5]:[rank5]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/run_train.py", line 237, in [default5]:[rank5]: trainer.train(dataloader) [default5]:[rank5]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 429, in train [default5]:[rank5]: outputs, loss_avg = self.training_step(dataloader=self.current_dataloader) [default5]:[rank5]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 462, in training_step [default5]:[rank5]: outputs = self.pipeline_engine.train_batch_iter( [default5]:[rank5]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 278, in train_batch_iter [default5]:[rank5]: output = self.forward(context=context, state=state, micro_batch=micro_batch, model=model) [default5]:[rank5]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 44, in forward [default5]:[rank5]: output = model(**micro_batch) [default5]:[rank5]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl [default5]:[rank5]: return self._call_impl(*args, **kwargs) [default5]:[rank5]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default5]:[rank5]: return forward_call(*args, **kwargs) [default5]:[rank5]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 891, in forward [default5]:[rank5]: sharded_logits = self.model( [default5]:[rank5]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl [default5]:[rank5]: return self._call_impl(*args, **kwargs) [default5]:[rank5]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default5]:[rank5]: return forward_call(*args, **kwargs) [default5]:[rank5]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 764, in forward [default5]:[rank5]: return self.forward_with_hidden_states(input_ids=input_ids, input_mask=input_mask)[0] [default5]:[rank5]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 780, in forward_with_hidden_states [default5]:[rank5]: hidden_encoder_states = encoder_block(**hidden_encoder_states) [default5]:[rank5]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl [default5]:[rank5]: return self._call_impl(*args, **kwargs) [default5]:[rank5]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default5]:[rank5]: return forward_call(*args, **kwargs) [default5]:[rank5]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/block.py", line 151, in forward [default5]:[rank5]: output = self.pp_block(**new_kwargs) [default5]:[rank5]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl [default5]:[rank5]: return self._call_impl(*args, **kwargs) [default5]:[rank5]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default5]:[rank5]: return forward_call(*args, **kwargs) [default5]:[rank5]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 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 512.00 MiB. GPU  has a total capacity of 79.33 GiB of which 81.94 MiB is free. Including non-PyTorch memory, this process has 79.24 GiB memory in use. Of the allocated memory 69.78 GiB is allocated by PyTorch, and 269.17 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation. See documentation for Memory Management (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables) [default4]:[rank4]: Traceback (most recent call last): [default4]:[rank4]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/run_train.py", line 237, in [default4]:[rank4]: trainer.train(dataloader) [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 512.00 MiB. GPU  has a total capacity of 79.33 GiB of which 201.94 MiB is free. Including non-PyTorch memory, this process has 79.12 GiB memory in use. Of the allocated memory 69.78 GiB is allocated by PyTorch, and 269.17 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation. See documentation for Memory Management (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables) [default0]:[rank24]: 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/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 44, in forward [default0]:[rank24]: output = model(**micro_batch) [default0]:[rank24]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl [default0]:[rank24]: return self._call_impl(*args, **kwargs) [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 565, in forward [default0]:[rank24]: key_value_states = key_value_states.permute(1, 2, 0, 3, 4).contiguous() [default0]:[rank24]: torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 256.00 MiB. GPU [default1]:[rank25]: Traceback (most recent call last): [default1]:[rank25]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/run_train.py", line 237, in [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) [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( [default1]:[rank25]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 278, in train_batch_iter [default1]:[rank25]: output = self.forward(context=context, state=state, micro_batch=micro_batch, model=model) [default1]:[rank25]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 44, in forward [default1]:[rank25]: output = model(**micro_batch) [default1]:[rank25]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl [default1]:[rank25]: return self._call_impl(*args, **kwargs) [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 891, in forward [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 [default1]:[rank25]: return self._call_impl(*args, **kwargs) [default1]:[rank25]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default1]:[rank25]: return forward_call(*args, **kwargs) [default1]:[rank25]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 764, in forward [default1]:[rank25]: return self.forward_with_hidden_states(input_ids=input_ids, input_mask=input_mask)[0] [default1]:[rank25]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 780, in forward_with_hidden_states [default1]:[rank25]: hidden_encoder_states = encoder_block(**hidden_encoder_states) [default1]:[rank25]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl [default1]:[rank25]: return self._call_impl(*args, **kwargs) [default1]:[rank25]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default1]:[rank25]: return forward_call(*args, **kwargs) [default1]:[rank25]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/block.py", line 151, in forward [default1]:[rank25]: output = self.pp_block(**new_kwargs) [default1]:[rank25]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl [default1]:[rank25]: return self._call_impl(*args, **kwargs) [default1]:[rank25]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default1]:[rank25]: return forward_call(*args, **kwargs) [default1]:[rank25]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 631, in forward [default1]:[rank25]: output = self.attn(hidden_states=hidden_states, sequence_mask=sequence_mask) [default1]:[rank25]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl [default1]:[rank25]: return self._call_impl(*args, **kwargs) [default1]:[rank25]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default1]:[rank25]: return forward_call(*args, **kwargs) [default1]:[rank25]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 598, in forward [default1]:[rank25]: output = self.o_proj(attention_output) [default1]:[rank25]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl [default1]:[rank25]: return self._call_impl(*args, **kwargs) [default1]:[rank25]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default1]:[rank25]: return forward_call(*args, **kwargs) [default1]:[rank25]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/nn.py", line 159, in forward [default1]:[rank25]: return 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) [default1]:[rank25]: torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 512.00 MiB. GPU  has a total capacity of 79.33 GiB of which 73.94 MiB is free. Including non-PyTorch memory, this process has 79.25 GiB memory in use. Of the allocated memory 69.40 GiB is allocated by PyTorch, and 13.30 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation. See documentation for Memory Management (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables) [default3]:[rank27]: Traceback (most recent call last): [default3]:[rank27]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/run_train.py", line 237, in [default3]:[rank27]: trainer.train(dataloader) [default2]:[rank26]: Traceback (most recent call last): [default2]:[rank26]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/run_train.py", line 237, in [default3]:[rank27]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 429, in train [default3]:[rank27]: outputs, loss_avg = self.training_step(dataloader=self.current_dataloader) [default3]:[rank27]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 462, in training_step [default3]:[rank27]: outputs = self.pipeline_engine.train_batch_iter( [default3]:[rank27]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 278, in train_batch_iter [default2]:[rank26]: trainer.train(dataloader) [default2]:[rank26]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 429, in train [default2]:[rank26]: outputs, loss_avg = self.training_step(dataloader=self.current_dataloader) [default2]:[rank26]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 462, in training_step [default2]:[rank26]: outputs = self.pipeline_engine.train_batch_iter( [default2]:[rank26]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 278, in train_batch_iter [default3]:[rank27]: output = self.forward(context=context, state=state, micro_batch=micro_batch, model=model) [default3]:[rank27]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 44, in forward [default3]:[rank27]: output = model(**micro_batch) [default3]:[rank27]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl [default3]:[rank27]: return self._call_impl(*args, **kwargs) [default3]:[rank27]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default2]:[rank26]: output = self.forward(context=context, state=state, micro_batch=micro_batch, model=model) [default3]:[rank27]: return forward_call(*args, **kwargs) [default3]:[rank27]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 891, in forward [default3]:[rank27]: sharded_logits = self.model( [default2]:[rank26]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 44, in forward [default3]:[rank27]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl [default3]:[rank27]: return self._call_impl(*args, **kwargs) [default3]:[rank27]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default2]:[rank26]: output = model(**micro_batch) [default2]:[rank26]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl [default3]:[rank27]: return forward_call(*args, **kwargs) [default3]:[rank27]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 764, in forward [default3]:[rank27]: return self.forward_with_hidden_states(input_ids=input_ids, input_mask=input_mask)[0] [default2]:[rank26]: return self._call_impl(*args, **kwargs) [default3]:[rank27]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 780, in forward_with_hidden_states [default3]:[rank27]: hidden_encoder_states = encoder_block(**hidden_encoder_states) [default3]:[rank27]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl [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 [default3]:[rank27]: return self._call_impl(*args, **kwargs) [default3]:[rank27]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default2]:[rank26]: return forward_call(*args, **kwargs) [default3]:[rank27]: return forward_call(*args, **kwargs) [default3]:[rank27]: 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 [default3]:[rank27]: output = self.pp_block(**new_kwargs) [default3]:[rank27]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl [default3]:[rank27]: return self._call_impl(*args, **kwargs) [default3]:[rank27]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default2]:[rank26]: sharded_logits = self.model( [default2]:[rank26]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl [default3]:[rank27]: return forward_call(*args, **kwargs) [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 [default3]:[rank27]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 631, in forward [default2]:[rank26]: return forward_call(*args, **kwargs) [default2]:[rank26]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 764, in forward [default2]:[rank26]: return self.forward_with_hidden_states(input_ids=input_ids, input_mask=input_mask)[0] [default2]:[rank26]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 780, in forward_with_hidden_states [default2]:[rank26]: hidden_encoder_states = encoder_block(**hidden_encoder_states) [default2]:[rank26]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl [default3]:[rank27]: output = self.attn(hidden_states=hidden_states, sequence_mask=sequence_mask) [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 [default3]:[rank27]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl [default3]:[rank27]: return self._call_impl(*args, **kwargs) [default3]:[rank27]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default3]:[rank27]: return forward_call(*args, **kwargs) [default3]:[rank27]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 598, in forward [default3]:[rank27]: output = self.o_proj(attention_output) [default2]:[rank26]: return forward_call(*args, **kwargs) [default3]:[rank27]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl [default3]:[rank27]: return self._call_impl(*args, **kwargs) [default2]:[rank26]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/block.py", line 151, in forward [default2]:[rank26]: output = self.pp_block(**new_kwargs) [default2]:[rank26]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl [default3]:[rank27]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default2]:[rank26]: return self._call_impl(*args, **kwargs) [default3]:[rank27]: 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 [default2]:[rank26]: return forward_call(*args, **kwargs) [default3]:[rank27]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/nn.py", line 159, in forward [default2]:[rank26]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 631, in forward [default2]:[rank26]: output = self.attn(hidden_states=hidden_states, sequence_mask=sequence_mask) [default2]:[rank26]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl [default2]:[rank26]: return self._call_impl(*args, **kwargs) [default3]:[rank27]: return row_linear( [default3]:[rank27]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/functional.py", line 474, in row_linear [default3]:[rank27]: out = F.linear(input, weight, bias) [default3]:[rank27]: torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 512.00 MiB. GPU  has a total capacity of 79.33 GiB of which 185.94 MiB is free. Including non-PyTorch memory, this process has 79.14 GiB memory in use. Of the allocated memory 69.40 GiB is allocated by PyTorch, and 141.30 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) [default2]:[rank26]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 565, in forward [default2]:[rank26]: key_value_states = key_value_states.permute(1, 2, 0, 3, 4).contiguous() [default2]:[rank26]: torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 256.00 MiB. GPU  has a total capacity of 79.33 GiB of which 177.94 MiB is free. Including non-PyTorch memory, this process has 79.14 GiB memory in use. Of the allocated memory 69.15 GiB is allocated by PyTorch, and 17.80 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 637, in forward [default0]:[rank48]: hidden_states = self.mlp(hidden_states=hidden_states)["hidden_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/models/llama.py", line 172, in forward [default0]:[rank48]: hidden_states = self.down_proj(self.split_silu_mul(merged_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/models/llama.py", line 128, in forward [default0]:[rank48]: return self.act(gate_states) * up_states [default0]:[rank48]: torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 256.00 MiB. GPU [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) [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) [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 [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/models/llama.py", line 891, in forward [default0]:[rank32]: sharded_logits = self.model( [default0]:[rank32]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 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/models/llama.py", line 764, in forward [default0]:[rank32]: return self.forward_with_hidden_states(input_ids=input_ids, input_mask=input_mask)[0] [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) [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 [default0]:[rank32]: output = self.pp_block(**new_kwargs) [default0]:[rank32]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl [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/models/llama.py", line 637, in forward [default0]:[rank32]: hidden_states = self.mlp(hidden_states=hidden_states)["hidden_states"] [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/models/llama.py", line 172, in forward [default0]:[rank32]: hidden_states = self.down_proj(self.split_silu_mul(merged_states)) [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/models/llama.py", line 128, in forward [default0]:[rank32]: return self.act(gate_states) * up_states [default0]:[rank32]: torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 256.00 MiB. GPU [default7]:[rank15]: Traceback (most recent call last): [default7]:[rank15]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/run_train.py", line 237, in [default7]:[rank15]: trainer.train(dataloader) [default7]:[rank15]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 429, in train [default7]:[rank15]: outputs, loss_avg = self.training_step(dataloader=self.current_dataloader) [default7]:[rank15]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 462, in training_step [default7]:[rank15]: outputs = self.pipeline_engine.train_batch_iter( [default7]:[rank15]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 278, in train_batch_iter [default7]:[rank15]: output = self.forward(context=context, state=state, micro_batch=micro_batch, model=model) [default7]:[rank15]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 44, in forward [default7]:[rank15]: output = model(**micro_batch) [default7]:[rank15]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl [default7]:[rank15]: return self._call_impl(*args, **kwargs) [default7]:[rank15]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default7]:[rank15]: return forward_call(*args, **kwargs) [default7]:[rank15]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 891, in forward [default7]:[rank15]: sharded_logits = self.model( [default7]:[rank15]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl [default7]:[rank15]: return self._call_impl(*args, **kwargs) [default7]:[rank15]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default7]:[rank15]: return forward_call(*args, **kwargs) [default7]:[rank15]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 764, in forward [default7]:[rank15]: return self.forward_with_hidden_states(input_ids=input_ids, input_mask=input_mask)[0] [default7]:[rank15]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 780, in forward_with_hidden_states [default7]:[rank15]: hidden_encoder_states = encoder_block(**hidden_encoder_states) [default7]:[rank15]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl [default7]:[rank15]: return self._call_impl(*args, **kwargs) [default7]:[rank15]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default7]:[rank15]: return forward_call(*args, **kwargs) [default7]:[rank15]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/block.py", line 151, in forward [default7]:[rank15]: output = self.pp_block(**new_kwargs) [default7]:[rank15]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl [default7]:[rank15]: return self._call_impl(*args, **kwargs) [default7]:[rank15]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default7]:[rank15]: return forward_call(*args, **kwargs) [default7]:[rank15]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 631, in forward [default7]:[rank15]: output = self.attn(hidden_states=hidden_states, sequence_mask=sequence_mask) [default7]:[rank15]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl [default7]:[rank15]: return self._call_impl(*args, **kwargs) [default7]:[rank15]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default7]:[rank15]: return forward_call(*args, **kwargs) [default7]:[rank15]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 598, in forward [default7]:[rank15]: output = self.o_proj(attention_output) [default7]:[rank15]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl [default7]:[rank15]: return self._call_impl(*args, **kwargs) [default7]:[rank15]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default7]:[rank15]: return forward_call(*args, **kwargs) [default7]:[rank15]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/nn.py", line 159, in forward [default7]:[rank15]: return row_linear( [default7]:[rank15]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/functional.py", line 474, in row_linear [default7]:[rank15]: out = F.linear(input, weight, bias) [default7]:[rank15]: torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 512.00 MiB. GPU  has a total capacity of 79.33 GiB of which 225.94 MiB is free. Including non-PyTorch memory, this process has 79.10 GiB memory in use. Of the allocated memory 69.40 GiB is allocated by PyTorch, and 141.30 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation. See documentation for Memory Management (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables) [default4]:[rank60]: Traceback (most recent call last): [default4]:[rank60]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/run_train.py", line 237, in [default4]:[rank60]: trainer.train(dataloader) [default4]:[rank60]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 429, in train [default4]:[rank60]: outputs, loss_avg = self.training_step(dataloader=self.current_dataloader) [default4]:[rank60]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 462, in training_step [default4]:[rank60]: outputs = self.pipeline_engine.train_batch_iter( [default4]:[rank60]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 278, in train_batch_iter [default4]:[rank60]: output = self.forward(context=context, state=state, micro_batch=micro_batch, model=model) [default4]:[rank60]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 44, in forward [default4]:[rank60]: output = model(**micro_batch) [default4]:[rank60]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl [default4]:[rank60]: return self._call_impl(*args, **kwargs) [default4]:[rank60]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default4]:[rank60]: return forward_call(*args, **kwargs) [default4]:[rank60]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 891, in forward [default4]:[rank60]: sharded_logits = self.model( [default4]:[rank60]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl [default4]:[rank60]: return self._call_impl(*args, **kwargs) [default4]:[rank60]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default4]:[rank60]: return forward_call(*args, **kwargs) [default4]:[rank60]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 764, in forward [default4]:[rank60]: return self.forward_with_hidden_states(input_ids=input_ids, input_mask=input_mask)[0] [default4]:[rank60]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 780, in forward_with_hidden_states [default4]:[rank60]: hidden_encoder_states = encoder_block(**hidden_encoder_states) [default4]:[rank60]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl [default4]:[rank60]: return self._call_impl(*args, **kwargs) [default4]:[rank60]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default4]:[rank60]: return forward_call(*args, **kwargs) [default4]:[rank60]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/block.py", line 151, in forward [default4]:[rank60]: output = self.pp_block(**new_kwargs) [default4]:[rank60]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl [default4]:[rank60]: return self._call_impl(*args, **kwargs) [default4]:[rank60]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default4]:[rank60]: return forward_call(*args, **kwargs) [default4]:[rank60]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 637, in forward [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 [default4]:[rank60]: return forward_call(*args, **kwargs) [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 [default4]:[rank60]: return self._call_impl(*args, **kwargs) [default4]:[rank60]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default4]:[rank60]: return forward_call(*args, **kwargs) [default4]:[rank60]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/nn.py", line 87, in forward [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 512.00 MiB. GPU  has a total capacity of 79.33 GiB of which 129.94 MiB is free. Including non-PyTorch memory, this process has 79.19 GiB memory in use. Of the allocated memory 69.78 GiB is allocated by PyTorch, and 269.17 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation. See documentation for Memory Management (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables) [default5]:[rank61]: Traceback (most recent call last): [default5]:[rank61]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/run_train.py", line 237, in [default5]:[rank61]: trainer.train(dataloader) [default5]:[rank61]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 429, in train [default5]:[rank61]: outputs, loss_avg = self.training_step(dataloader=self.current_dataloader) [default5]:[rank61]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 462, in training_step [default5]:[rank61]: outputs = self.pipeline_engine.train_batch_iter( [default5]:[rank61]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 278, in train_batch_iter [default5]:[rank61]: output = self.forward(context=context, state=state, micro_batch=micro_batch, model=model) [default5]:[rank61]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 44, in forward [default5]:[rank61]: output = model(**micro_batch) [default5]:[rank61]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl [default5]:[rank61]: return self._call_impl(*args, **kwargs) [default5]:[rank61]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default5]:[rank61]: return forward_call(*args, **kwargs) [default5]:[rank61]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 891, in forward [default5]:[rank61]: sharded_logits = self.model( [default5]:[rank61]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl [default5]:[rank61]: return self._call_impl(*args, **kwargs) [default5]:[rank61]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default5]:[rank61]: return forward_call(*args, **kwargs) [default5]:[rank61]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 764, in forward [default5]:[rank61]: return self.forward_with_hidden_states(input_ids=input_ids, input_mask=input_mask)[0] [default5]:[rank61]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 780, in forward_with_hidden_states [default5]:[rank61]: hidden_encoder_states = encoder_block(**hidden_encoder_states) [default5]:[rank61]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl [default5]:[rank61]: return self._call_impl(*args, **kwargs) [default5]:[rank61]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default5]:[rank61]: return forward_call(*args, **kwargs) [default5]:[rank61]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/block.py", line 151, in forward [default5]:[rank61]: output = self.pp_block(**new_kwargs) [default5]:[rank61]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl [default5]:[rank61]: return self._call_impl(*args, **kwargs) [default5]:[rank61]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default5]:[rank61]: return forward_call(*args, **kwargs) [default5]:[rank61]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 637, in forward [default5]:[rank61]: hidden_states = self.mlp(hidden_states=hidden_states)["hidden_states"] [default5]:[rank61]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl [default5]:[rank61]: return self._call_impl(*args, **kwargs) [default5]:[rank61]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default5]:[rank61]: return forward_call(*args, **kwargs) [default5]:[rank61]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 171, in forward [default5]:[rank61]: merged_states = self.gate_up_proj(hidden_states) [default5]:[rank61]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl [default5]:[rank61]: return self._call_impl(*args, **kwargs) [default5]:[rank61]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default5]:[rank61]: return forward_call(*args, **kwargs) [default5]:[rank61]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/nn.py", line 87, in forward [default5]:[rank61]: return column_linear( [default5]:[rank61]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/functional.py", line 359, in column_linear [default5]:[rank61]: return F.linear(input, weight, bias) [default5]:[rank61]: torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 512.00 MiB. GPU  has a total capacity of 79.33 GiB of which 153.94 MiB is free. Including non-PyTorch memory, this process has 79.17 GiB memory in use. Of the allocated memory 69.78 GiB is allocated by PyTorch, and 269.17 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/nanot[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 [default0]:[rank8]: return forward_call(*args, **kwargs) [default0]:[rank8]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 891, in forward [default0]:[rank8]: sharded_logits = self.model( [default0]:[rank8]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl [default0]:[rank8]: return self._call_impl(*args, **kwargs) [default0]:[rank8]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default0]:[rank8]: return forward_call(*args, **kwargs) [default0]:[rank8]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 764, in forward [default0]:[rank8]: return self.forward_with_hidden_states(input_ids=input_ids, input_mask=input_mask)[0] [default0]:[rank8]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 780, in forward_with_hidden_states [default0]:[rank8]: hidden_encoder_states = encoder_block(**hidden_encoder_states) [default0]:[rank8]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl [default0]:[rank8]: return self._call_impl(*args, **kwargs) [default0]:[rank8]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default0]:[rank8]: return forward_call(*args, **kwargs) [default0]:[rank8]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/block.py", line 151, in forward [default0]:[rank8]: output = self.pp_block(**new_kwargs) [default0]:[rank8]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl [default0]:[rank8]: return self._call_impl(*args, **kwargs) ron/src/nanotron/parallel/pipeline_parallel/engine.py", line 44, in forward [default0]:[rank8]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default0]:[rank8]: return forward_call(*args, **kwargs) [default0]:[rank8]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 631, in forward [default0]:[rank8]: output = self.attn(hidden_states=hidden_states, sequence_mask=sequence_mask) [default0]:[rank8]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl [default0]:[rank8]: return self._call_impl(*args, **kwargs) [default0]:[rank8]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default0]:[rank8]: return forward_call(*args, **kwargs) [default0]:[rank8]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llam[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 [default6]:[rank62]: return self._call_impl(*args, **kwargs) [default6]:[rank62]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default6]:[rank62]: return forward_call(*args, **kwargs) [default6]:[rank62]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 891, in forward [default6]:[rank62]: sharded_logits = self.model( [default6]:[rank62]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl [default6]:[rank62]: return self._call_impl(*args, **kwargs) [default6]:[rank62]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-clustera.py", line 598, in forward [default0]:[rank8]: output = self.o_proj(attention_output) [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/tensor_parallel/nn.py", line 159, in forward [default0]:[rank8]: return row_linear( [default0]:[rank8]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/functional.py", line 474, in row_linear [default0]:[rank8]: out = F.linear(input, weight, bias) /lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default6]:[rank62]: return forward_call(*args, **kwargs) [default6]:[rank62]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 764, in forward [default6]:[rank62]: return self.forward_with_hidden_states(input_ids=input_ids, input_mask=input_mask)[0] [default6]:[rank62]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 780, in forward_with_hidden_states [default6]:[rank62]: hidden_encoder_states = encoder_block(**hidden_encoder_states) [default6]:[rank62]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl [default6]:[rank62]: return self._call_impl(*args, **kwargs) [default6]:[rank62]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in [default0]:[rank8]: torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 512.00 MiB. GPU _call_impl [default6]:[rank62]: return forward_call(*args, **kwargs) [default6]:[rank62]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/block.py", line 151, in forward [default6]:[rank62]: output = self.pp_block(**new_kwargs) [default6]:[rank62]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl [default6]:[rank62]: return self._call_impl(*args, **kwargs) [default6]:[rank62]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default6]:[rank62]: return forward_call(*args, **kwargs) [default6]:[rank62]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 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 [default6]:[rank62]: return F.linear(input, weight, bias) [default6]:[rank62]: torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 512.00 MiB. GPU  has a total capacity of 79.33 GiB of which 81.94 MiB is free. Including non-PyTorch memory, this process has 79.24 GiB memory in use. Of the allocated memory 69.78 GiB is allocated by PyTorch, and 269.17 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation. See documentation for Memory Management (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables) [default2]:[rank10]: Traceback (most recent call last): [default2]:[rank10]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/run_train.py", line 237, in [default2]:[rank10]: trainer.train(dataloader) [default2]:[rank10]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 429, in train [default2]:[rank10]: outputs, loss_avg = self.training_step(dataloader=self.current_dataloader) [default2]:[rank10]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 462, in training_step [default2]:[rank10]: outputs = self.pipeline_engine.train_batch_iter( [default2]:[rank10]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 278, in train_batch_iter [default2]:[rank10]: output = self.forward(context=context, state=state, micro_batch=micro_batch, model=model) [default2]:[rank10]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 44, in forward [default2]:[rank10]: output = model(**micro_batch) [default2]:[rank10]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl [default2]:[rank10]: return self._call_impl(*args, **kwargs) [default2]:[rank10]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default2]:[rank10]: return forward_call(*args, **kwargs) [default2]:[rank10]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 891, in forward [default2]:[rank10]: sharded_logits = self.model( [default2]:[rank10]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl [default2]:[rank10]: return self._call_impl(*args, **kwargs) [default2]:[rank10]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default2]:[rank10]: return forward_call(*args, **kwargs) [default2]:[rank10]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 764, in forward [default2]:[rank10]: return self.forward_with_hidden_states(input_ids=input_ids, input_mask=input_mask)[0] [default2]:[rank10]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 780, in forward_with_hidden_states [default2]:[rank10]: hidden_encoder_states = encoder_block(**hidden_encoder_states) [default2]:[rank10]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl [default2]:[rank10]: return self._call_impl(*args, **kwargs) [default2]:[rank10]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default2]:[rank10]: return forward_call(*args, **kwargs) [default2]:[rank10]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/block.py", line 151, in forward [default2]:[rank10]: output = self.pp_block(**new_kwargs) [default2]:[rank10]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl [default2]:[rank10]: return self._call_impl(*args, **kwargs) [default2]:[rank10]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default2]:[rank10]: return forward_call(*args, **kwargs) [default2]:[rank10]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 631, in forward [default2]:[rank10]: output = self.attn(hidden_states=hidden_states, sequence_mask=sequence_mask) [default2]:[rank10]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl [default2]:[rank10]: return self._call_impl(*args, **kwargs) [default2]:[rank10]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default2]:[rank10]: return forward_call(*args, **kwargs) [default2]:[rank10]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 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 512.00 MiB. GPU  has a total capacity of 79.33 GiB of which 41.94 MiB is free. Including non-PyTorch memory, this process has 79.28 GiB memory in use. Of the allocated memory 69.40 GiB is allocated by PyTorch, and 13.30 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) [default6]:[rank14]: Traceback (most recent call last): [default6]:[rank14]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/run_train.py", line 237, in [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) [default5]:[rank13]: Traceback (most recent call last): [default6]:[rank14]: trainer.train(dataloader) [default6]:[rank14]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 429, in train [default1]:[rank9]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 462, in training_step [default5]:[rank13]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/run_train.py", line 237, in [default5]:[rank13]: trainer.train(dataloader) [default1]:[rank9]: outputs = self.pipeline_engine.train_batch_iter( [default6]:[rank14]: outputs, loss_avg = self.training_step(dataloader=self.current_dataloader) [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) [default6]:[rank14]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 462, in training_step [default5]:[rank13]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 429, in train [default1]:[rank9]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 44, in forward [default6]:[rank14]: outputs = self.pipeline_engine.train_batch_iter( [default1]:[rank9]: output = model(**micro_batch) [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 [default6]:[rank14]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 278, in train_batch_iter [default5]:[rank13]: outputs = self.pipeline_engine.train_batch_iter( [default6]:[rank14]: 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 278, in train_batch_iter [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) [default6]:[rank14]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 44, in forward [default5]:[rank13]: output = self.forward(context=context, state=state, micro_batch=micro_batch, model=model) [default1]:[rank9]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default5]:[rank13]: 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) [default5]:[rank13]: 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 [default1]:[rank9]: return forward_call(*args, **kwargs) [default5]:[rank13]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl [default5]:[rank13]: return self._call_impl(*args, **kwargs) [default1]:[rank9]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 891, in forward [default6]:[rank14]: return self._call_impl(*args, **kwargs) [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 [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 [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 [default6]:[rank14]: return forward_call(*args, **kwargs) [default1]:[rank9]: return self._call_impl(*args, **kwargs) [default6]:[rank14]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 891, in forward [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 [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 [default5]:[rank13]: sharded_logits = self.model( [default3]:[rank19]: Traceback (most recent call last): [default3]:[rank19]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/run_train.py", line 237, in [default3]:[rank19]: trainer.train(dataloader) [default3]:[rank19]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 429, in train [default3]:[rank19]: outputs, loss_avg = self.training_step(dataloader=self.current_dataloader) [default3]:[rank19]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 462, in training_step [default3]:[rank19]: outputs = self.pipeline_engine.train_batch_iter( [default3]:[rank19]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 278, in train_batch_iter [default3]:[rank19]: output = self.forward(context=context, state=state, micro_batch=micro_batch, model=model) [default3]:[rank19]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanot[default6]:[rank14]: 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 1532, in _wrapped_call_impl ron/src/nanotron/parallel/pipeline_parallel/engine.py", line 44, in forward [default3]:[rank19]: output = model(**micro_batch) [default3]:[rank19]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl [default3]:[rank19]: return self._call_impl(*args, **kwargs) [default3]:[rank19]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default3]:[rank19]: return forward_call(*args, **kwargs) [default3]:[rank19]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 891, in forward [default3]:[rank19]: sharded_logits = self.model( [default3]:[rank19]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl [default3]:[rank19]: return self._call_impl(*args, **kwargs) [default3]:[rank19]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default3]:[rank19]: return forward_call(*args, **kwargs) [default3]:[rank19]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 764, in forward [default3]:[rank19]: return self.forward_with_hidden_states(input_ids=input_ids, input_mask=input_mask)[0] [default3]:[rank19]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 780, in forward_with_hidden_states [default3]:[rank19]: hidden_encoder_states = encoder_block(**hidden_encoder_states) [default3]:[rank19]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl [default3]:[rank19]: return self._call_impl(*args, **kwargs) [default3]:[rank19]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default3]:[rank19]: return forward_call(*args, **kwargs) [default3]:[rank19]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster[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 [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) /nanotron/src/nanotron/parallel/pipeline_parallel/block.py", line 151, in forward [default3]:[rank19]: output = self.pp_block(**new_kwargs) [default3]:[rank19]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl [default3]:[rank19]: return self._call_impl(*args, **kwargs) [default3]:[rank19]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default3]:[rank19]: return forward_call(*args, **kwargs) [default3]:[rank19]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 631, in forward [default3]:[rank19]: output = self.attn(hidden_states=hidden_states, sequence_mask=sequence_mask) [default3]:[rank19]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl [default[default1]:[rank9]: return forward_call(*args, **kwargs) 3]:[rank19]: return self._call_impl(*args, **kwargs) [default3]:[rank19]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default3]:[rank19]: return forward_call(*args, **kwargs) [default3]:[rank19]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 598, in forward [default3]:[rank19]: output = self.o_proj(attention_output) [default3]:[rank19]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl [default3]:[rank19]: return self._call_impl(*args, **kwargs) [default3]:[rank19]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default3]:[rank19]: return forward_call(*args, **kwargs) [default3]:[rank19]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanot[default1]:[rank9]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 764, in forward [default6]:[rank14]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 764, in forward [default5]:[rank13]: return self._call_impl(*args, **kwargs) ron/src/nanotron/parallel/tensor_parallel/nn.py", line 159, in forward [default3]:[rank19]: return row_linear( [default3]:[rank19]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/functional.py", line 474, in row_linear [default3]:[rank19]: out = F.linear(input, weight, bias) [default3]:[rank19]: torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 512.00 MiB. GPU  has a total capacity of 79.33 GiB of which 153.94 MiB is free. Including non-PyTorch memory, this process has 79.17 GiB memory in use. Of the allocated memory 69.40 GiB is allocated by PyTorch, and 141.30 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]: 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 [default6]:[rank14]: return self.forward_with_hidden_states(input_ids=input_ids, input_mask=input_mask)[0] [default5]:[rank13]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default1]:[rank9]: hidden_encoder_states = encoder_block(**hidden_encoder_states) [default5]:[rank13]: return forward_call(*args, **kwargs) [default6]:[rank14]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 780, in forward_with_hidden_states [default5]:[rank13]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 764, in forward [default6]:[rank14]: hidden_encoder_states = encoder_block(**hidden_encoder_states) [default5]:[rank13]: return self.forward_with_hidden_states(input_ids=input_ids, input_mask=input_mask)[0] [default1]:[rank9]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl [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 [default5]:[rank13]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 780, in forward_with_hidden_states [default1]:[rank9]: return self._call_impl(*args, **kwargs) [default5]:[rank13]: hidden_encoder_states = encoder_block(**hidden_encoder_states) [default6]:[rank14]: 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 [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 [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 [default6]:[rank14]: return forward_call(*args, **kwargs) [default5]:[rank13]: return self._call_impl(*args, **kwargs) [default5]:[rank13]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [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) [default1]:[rank9]: return forward_call(*args, **kwargs) [default5]:[rank13]: 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 [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 [default1]:[rank9]: output = self.pp_block(**new_kwargs) [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 [default1]:[rank9]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl [default5]:[rank13]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/block.py", line 151, in forward [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 [default5]:[rank13]: output = self.pp_block(**new_kwargs) [default6]:[rank14]: output = self.attn(hidden_states=hidden_states, sequence_mask=sequence_mask) [default1]:[rank9]: 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 1532, in _wrapped_call_impl [default1]:[rank9]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [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) [default1]:[rank9]: return forward_call(*args, **kwargs) [default5]:[rank13]: 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 [default1]:[rank9]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 631, in forward [default5]:[rank13]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default1]:[rank9]: output = self.attn(hidden_states=hidden_states, sequence_mask=sequence_mask) [default5]:[rank13]: return forward_call(*args, **kwargs) [default6]:[rank14]: 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 [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 [default1]:[rank9]: return self._call_impl(*args, **kwargs) [default6]:[rank14]: 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 [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 [default5]:[rank13]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 631, in forward [default1]:[rank9]: return forward_call(*args, **kwargs) [default6]:[rank14]: return forward_call(*args, **kwargs) [default1]:[rank9]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 598, in forward [default5]:[rank13]: output = self.attn(hidden_states=hidden_states, sequence_mask=sequence_mask) [default1]:[rank9]: 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 [default1]:[rank9]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl [default5]:[rank13]: return self._call_impl(*args, **kwargs) [default1]:[rank9]: 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 [default6]:[rank14]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/nn.py", line 159, in forward [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) [default6]:[rank14]: return row_linear( [default5]:[rank13]: 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 [default5]:[rank13]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 598, in forward [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) [default7]:[rank63]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl [default7]:[rank63]: return self._call_impl(*args, **kwargs) [default7]:[rank63]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default7]:[rank63]: return forward_call(*args, **kwargs) [default7]:[rank63]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 891, in forward [default7]:[rank63]: sharded_logits = self.model( [default7]:[rank63]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl [default7]:[rank63]: return self._call_impl(*args, **kwargs) [default7]:[rank63]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default7]:[rank63]: return forward_call(*args, **kwargs) [default7]:[rank63]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 764, in forward [default7]:[rank63]: return self.forward_with_hidden_states(input_ids=input_ids, input_mask=input_mask)[0] [default7]:[rank63]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 780, in forward_with_hidden_states [default7]:[rank63]: hidden_encoder_states = encoder_block(**hidden_encoder_states) [default7]:[rank63]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl [default7]:[rank63]: return self._call_impl(*args, **kwargs) [default7]:[rank63]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default7]:[rank63]: return forward_call(*args, **kwargs) [default7]:[rank63]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/block.py", line 151, in forward [default7]:[rank63]: output = self.pp_block(**new_kwargs) [default7]:[rank63]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl [default7]:[rank63]: return self._call_impl(*args, **kwargs) [default7]:[rank63]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default7]:[rank63]: return forward_call(*args, **kwargs) [default7]:[rank63]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 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 [default7]:[rank63]: return self._call_impl(*args, **kwargs) [default7]:[rank63]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default7]:[rank63]: return forward_call(*args, **kwargs) [default7]:[rank63]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 171, in forward [default7]:[rank63]: merged_states = self.gate_up_proj(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 [default7]:[rank63]: return self._call_impl(*args, **kwargs) [default7]:[rank63]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default7]:[rank63]: return forward_call(*args, **kwargs) [default7]:[rank63]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/nn.py", line 87, in forward [default7]:[rank63]: return column_linear( [default7]:[rank63]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/functional.py", line 359, in column_linear [default7]:[rank63]: return F.linear(input, weight, bias) [default7]:[rank63]: torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 512.00 MiB. GPU  has a total capacity of 79.33 GiB of which 393.94 MiB is free. Including non-PyTorch memory, this process has 78.93 GiB memory in use. Of the allocated memory 69.78 GiB is allocated by PyTorch, and 269.17 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation. See documentation for Memory Management (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables) [default5]:[rank13]: output = self.o_proj(attention_output) [default6]:[rank14]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/functional.py", line 474, in row_linear [default1]:[rank9]: return row_linear( [default6]:[rank14]: out = F.linear(input, weight, bias) [default1]:[rank9]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/functional.py", line 474, in row_linear [default6]:[rank14]: torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 512.00 MiB. GPU  has a total capacity of 79.33 GiB of which 41.94 MiB is free. Including non-PyTorch memory, this process has 79.28 GiB memory in use. Of the allocated memory 69.40 GiB is allocated by PyTorch, and 13.30 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]: 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) [default1]:[rank9]: out = F.linear(input, weight, bias) [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) [default1]:[rank9]: torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 512.00 MiB. GPU  has a total capacity of 79.33 GiB of which 113.94 MiB is free. Including non-PyTorch memory, this process has 79.21 GiB memory in use. Of the allocated memory 69.40 GiB is allocated by PyTorch, and 13.30 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]: 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 512.00 MiB. GPU  has a total capacity of 79.33 GiB of which 113.94 MiB is free. Including non-PyTorch memory, this process has 79.21 GiB memory in use. Of the allocated memory 69.40 GiB is allocated by PyTorch, and 13.30 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation. See documentation for Memory Management (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables) [default3]:[rank11]: Traceback (most recent call last): [default3]:[rank11]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/run_train.py", line 237, in [default3]:[rank11]: trainer.train(dataloader) [default3]:[rank11]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 429, in train [default3]:[rank11]: outputs, loss_avg = self.training_step(dataloader=self.current_dataloader) [default3]:[rank11]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 462, in training_step [default3]:[rank11]: outputs = self.pipeline_engine.train_batch_iter( [default3]:[rank11]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 278, in train_batch_iter [default3]:[rank11]: output = self.forward(context=context, state=state, micro_batch=micro_batch, model=model) [default3]:[rank11]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 44, in forward [default3]:[rank11]: output = model(**micro_batch) [default3]:[rank11]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl [default3]:[rank11]: return self._call_impl(*args, **kwargs) [default3]:[rank11]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default3]:[rank11]: return forward_call(*args, **kwargs) [default3]:[rank11]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 891, in forward [default3]:[rank11]: sharded_logits = self.model( [default3]:[rank11]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl [default3]:[rank11]: return self._call_impl(*args, **kwargs) [default3]:[rank11]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default3]:[rank11]: return forward_call(*args, **kwargs) [default3]:[rank11]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 764, in forward [default3]:[rank11]: return self.forward_with_hidden_states(input_ids=input_ids, input_mask=input_mask)[0] [default3]:[rank11]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 780, in forward_with_hidden_states [default3]:[rank11]: hidden_encoder_states = encoder_block(**hidden_encoder_states) [default3]:[rank11]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl [default3]:[rank11]: return self._call_impl(*args, **kwargs) [default3]:[rank11]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default3]:[rank11]: return forward_call(*args, **kwargs) [default3]:[rank11]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/block.py", line 151, in forward [default3]:[rank11]: output = self.pp_block(**new_kwargs) [default3]:[rank11]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl [default3]:[rank11]: return self._call_impl(*args, **kwargs) [default3]:[rank11]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default3]:[rank11]: return forward_call(*args, **kwargs) [default3]:[rank11]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 631, in forward [default3]:[rank11]: output = self.attn(hidden_states=hidden_states, sequence_mask=sequence_mask) [default3]:[rank11]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl [default3]:[rank11]: return self._call_impl(*args, **kwargs) [default3]:[rank11]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default3]:[rank11]: return forward_call(*args, **kwargs) [default3]:[rank11]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 598, in forward [default3]:[rank11]: output = self.o_proj(attention_output) [default3]:[rank11]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl [default3]:[rank11]: return self._call_impl(*args, **kwargs) [default3]:[rank11]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default3]:[rank11]: return forward_call(*args, **kwargs) [default3]:[rank11]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/nn.py", line 159, in forward [default3]:[rank11]: return row_linear( [default3]:[rank11]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/functional.py", line 474, in row_linear [default3]:[rank11]: out = F.linear(input, weight, bias) [default3]:[rank11]: torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 512.00 MiB. GPU  has a total capacity of 79.33 GiB of which 225.94 MiB is free. Including non-PyTorch memory, this process has 79.10 GiB memory in use. Of the allocated memory 69.40 GiB is allocated by PyTorch, and 141.30 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 512.00 MiB. GPU  has a total capacity of 79.33 GiB of which 89.94 MiB is free. Including non-PyTorch memory, this process has 79.23 GiB memory in use. Of the allocated memory 69.40 GiB is allocated by PyTorch, and 13.30 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 512.00 MiB. GPU  has a total capacity of 79.33 GiB of which 113.94 MiB is free. Including non-PyTorch memory, this process has 79.21 GiB memory in use. Of the allocated memory 69.40 GiB is allocated by PyTorch, and 13.30 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( [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 [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) [default2]:[rank18]: outputs, loss_avg = self.training_step(dataloader=self.current_dataloader) [default4]:[rank20]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl [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) [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 [default2]:[rank18]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 44, in forward [default1]:[rank17]: Traceback (most recent call last): [default1]:[rank17]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/run_train.py", line 237, in [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 [default2]:[rank18]: output = model(**micro_batch) [default0]:[rank16]: Traceback (most recent call last): [default0]:[rank16]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/run_train.py", line 237, in [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 [default2]:[rank18]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl [default1]:[rank17]: trainer.train(dataloader) [default0]:[rank16]: trainer.train(dataloader) [default2]:[rank18]: return self._call_impl(*args, **kwargs) [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) [default0]:[rank16]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 429, in train [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) [default1]:[rank17]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 462, in training_step [default2]:[rank18]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 891, in forward [default4]:[rank20]: return self.forward_with_hidden_states(input_ids=input_ids, input_mask=input_mask)[0] [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 [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 [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 [default1]:[rank17]: outputs = self.pipeline_engine.train_batch_iter( [default2]:[rank18]: sharded_logits = self.model( [default1]:[rank17]: 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) [default1]:[rank17]: output = self.forward(context=context, state=state, micro_batch=micro_batch, model=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 [default0]:[rank16]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 44, in forward [default1]:[rank17]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 44, in forward [default2]:[rank18]: return self._call_impl(*args, **kwargs) [default2]:[rank18]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default1]:[rank17]: output = model(**micro_batch) [default1]:[rank17]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl [default2]:[rank18]: return forward_call(*args, **kwargs) [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 [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 [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 [default2]:[rank18]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 764, in forward [default0]:[rank16]: return self._call_impl(*args, **kwargs) [default1]:[rank17]: return forward_call(*args, **kwargs) [default2]:[rank18]: return self.forward_with_hidden_states(input_ids=input_ids, input_mask=input_mask)[0] [default2]:[rank18]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 780, in forward_with_hidden_states [default2]:[rank18]: hidden_encoder_states = encoder_block(**hidden_encoder_states) [default1]:[rank17]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 891, in forward [default4]:[rank20]: return forward_call(*args, **kwargs) [default2]:[rank18]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl [default1]:[rank17]: sharded_logits = self.model( [default4]:[rank20]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/block.py", line 151, in forward [default1]:[rank17]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl [default1]:[rank17]: return self._call_impl(*args, **kwargs) [default2]:[rank18]: return self._call_impl(*args, **kwargs) [default2]:[rank18]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default1]:[rank17]: 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]: 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 [default2]:[rank18]: return forward_call(*args, **kwargs) [default4]:[rank20]: output = self.pp_block(**new_kwargs) [default1]:[rank17]: return self.forward_with_hidden_states(input_ids=input_ids, input_mask=input_mask)[0] [default1]:[rank17]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 780, in forward_with_hidden_states [default2]:[rank18]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/block.py", line 151, in forward [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 [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 [default0]:[rank56]: Traceback (most recent call last): [default0]:[rank56]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/run_train.py", line 237, in [default0]:[rank56]: trainer.train(dataloader) [default0]:[rank56]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 429, in train [default0]:[rank56]: outputs, loss_avg = self.training_step(dataloader=self.current_dataloader) [default0]:[rank56]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 462, in training_step [default0]:[rank56]: outputs = self.pipeline_engine.train_batch_iter( [default0]:[rank56]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 278, in train_batch_iter [default0]:[rank56]: output = self.forward(context=context, state=state, micro_batch=micro_batch, model=model) [default0]:[rank56]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanot[default4]:[rank20]: return self._call_impl(*args, **kwargs) [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 [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 ron/src/nanotron/parallel/pipeline_parallel/engine.py", line 44, in forward [default0]:[rank56]: output = model(**micro_batch) [default0]:[rank56]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl [default0]:[rank56]: return self._call_impl(*args, **kwargs) [default0]:[rank56]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default0]:[rank56]: return forward_call(*args, **kwargs) [default0]:[rank56]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 891, in forward [default0]:[rank56]: sharded_logits = self.model( [default0]:[rank16]: sharded_logits = self.model( [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 [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 [default0]:[rank56]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl [default0]:[rank56]: return self._call_impl(*args, **kwargs) [default0]:[rank56]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default0]:[rank56]: return forward_call(*args, **kwargs) [default0]:[rank56]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 764, in forward [default0]:[rank56]: return self.forward_with_hidden_states(input_ids=input_ids, input_mask=input_mask)[0] [default0]:[rank56]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 780, in forward_with_hidden_states [default0]:[rank56]: hidden_encoder_states = encoder_block(**hidden_encoder_states) [default0]:[rank56]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-[default4]:[rank20]: return forward_call(*args, **kwargs) [default1]:[rank17]: 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 1532, in _wrapped_call_impl cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl [default0]:[rank56]: return self._call_impl(*args, **kwargs) [default0]:[rank56]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default0]:[rank56]: return forward_call(*args, **kwargs) [default0]:[rank56]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/block.py", line 151, in forward [default0]:[rank56]: output = self.pp_block(**new_kwargs) [default0]:[rank56]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl [default0]:[rank56]: return self._call_impl(*args, **kwargs) [default0]:[rank56]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default0]:[rank[default0]:[rank16]: return self._call_impl(*args, **kwargs) 56]: return forward_call(*args, **kwargs) [default0]:[rank56]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 637, in forward [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 [default0]:[rank56]: return self._call_impl(*args, **kwargs) [default2]:[rank18]: 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 [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) [default0]:[rank56]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default0]:[rank56]: return forward_call(*args, **kwargs) [default0]:[rank56]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/nn.py[default2]:[rank18]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 631, in forward ", line 87, in forward [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) [default0]:[rank56]: torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 512.00 MiB. GPU [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 [default4]:[rank20]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 631, in forward [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 [default4]:[rank20]: 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 1541, in _call_impl [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) [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 [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 [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 [default0]:[rank16]: hidden_encoder_states = encoder_block(**hidden_encoder_states) [default1]:[rank17]: return forward_call(*args, **kwargs) [default4]:[rank20]: return forward_call(*args, **kwargs) [default2]:[rank18]: return self._call_impl(*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 [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 [default2]:[rank18]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default1]:[rank17]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/block.py", line 151, in forward [default4]:[rank20]: return self._call_impl(*args, **kwargs) [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) [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 [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) [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 [default0]:[rank16]: return self._call_impl(*args, **kwargs) [default1]:[rank17]: return forward_call(*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 [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 [default4]:[rank20]: return forward_call(*args, **kwargs) [default2]:[rank18]: return self._call_impl(*args, **kwargs) [default0]:[rank16]: output = self.pp_block(**new_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) [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) [default4]:[rank20]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/nn.py", line 159, in forward [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 [default1]:[rank17]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl [default2]:[rank18]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/nn.py", line 159, in forward [default4]:[rank20]: return row_linear( [default1]:[rank17]: return self._call_impl(*args, **kwargs) [default1]:[rank17]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default2]:[rank18]: return row_linear( [default0]:[rank16]: return self._call_impl(*args, **kwargs) [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 [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) [default4]:[rank20]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/functional.py", line 474, in row_linear [default1]:[rank17]: output = self.o_proj(attention_output) [default4]:[rank20]: out = F.linear(input, weight, bias) [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) [default2]:[rank18]: torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 512.00 MiB. GPU  has a total capacity of 79.33 GiB of which 113.94 MiB is free. Including non-PyTorch memory, this process has 79.21 GiB memory in use. Of the allocated memory 69.40 GiB is allocated by PyTorch, and 13.30 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]: 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) [default4]:[rank20]: torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 512.00 MiB. GPU  has a total capacity of 79.33 GiB of which 33.94 MiB is free. Including non-PyTorch memory, this process has 79.29 GiB memory in use. Of the allocated memory 69.40 GiB is allocated by PyTorch, and 141.30 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]: 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) [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( [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 [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) [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) [default1]:[rank17]: torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 512.00 MiB. GPU  has a total capacity of 79.33 GiB of which 41.94 MiB is free. Including non-PyTorch memory, this process has 79.28 GiB memory in use. Of the allocated memory 69.40 GiB is allocated by PyTorch, and 13.30 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]: 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 512.00 MiB. GPU [default7]:[rank23]: Traceback (most recent call last): [default7]:[rank23]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/run_train.py", line 237, in [default7]:[rank23]: trainer.train(dataloader) [default7]:[rank23]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 429, in train [default7]:[rank23]: outputs, loss_avg = self.training_step(dataloader=self.current_dataloader) [default7]:[rank23]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 462, in training_step [default7]:[rank23]: outputs = self.pipeline_engine.train_batch_iter( [default7]:[rank23]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 278, in train_batch_iter [default7]:[rank23]: output = self.forward(context=context, state=state, micro_batch=micro_batch, model=model) [default7]:[rank23]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 44, in forward [default7]:[rank23]: output = model(**micro_batch) [default7]:[rank23]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl [default7]:[rank23]: return self._call_impl(*args, **kwargs) [default7]:[rank23]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default7]:[rank23]: return forward_call(*args, **kwargs) [default7]:[rank23]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 891, in forward [default7]:[rank23]: sharded_logits = self.model( [default7]:[rank23]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl [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) [default7]:[rank23]: return self._call_impl(*args, **kwargs) [default7]:[rank23]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default7]:[rank23]: return forward_call(*args, **kwargs) [default7]:[rank23]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 764, in forward [default7]:[rank23]: return self.forward_with_hidden_states(input_ids=input_ids, input_mask=input_mask)[0] [default7]:[rank23]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 780, in forward_with_hidden_states [default7]:[rank23]: hidden_encoder_states = encoder_block(**hidden_encoder_states) [default5]:[rank21]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 429, in train [default7]:[rank23]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl [default7]:[rank23]: return self._call_impl(*args, **kwargs) [default5]:[rank21]: outputs, loss_avg = self.training_step(dataloader=self.current_dataloader) [default7]:[rank23]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default7]:[rank23]: return forward_call(*args, **kwargs) [default5]:[rank21]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 462, in training_step [default7]:[rank23]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/block.py", line 151, in forward [default7]:[rank23]: output = self.pp_block(**new_kwargs) [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 [default7]:[rank23]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl [default7]:[rank23]: return self._call_impl(*args, **kwargs) [default7]:[rank23]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default5]:[rank21]: output = model(**micro_batch) [default7]:[rank23]: return forward_call(*args, **kwargs) [default7]:[rank23]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 631, in forward [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 [default7]:[rank23]: output = self.attn(hidden_states=hidden_states, sequence_mask=sequence_mask) [default5]:[rank21]: return self._call_impl(*args, **kwargs) [default7]:[rank23]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl [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 [default7]:[rank23]: return self._call_impl(*args, **kwargs) [default5]:[rank21]: return forward_call(*args, **kwargs) [default7]:[rank23]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default5]:[rank21]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 891, in forward [default7]:[rank23]: return forward_call(*args, **kwargs) [default5]:[rank21]: sharded_logits = self.model( [default7]:[rank23]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 598, in forward [default7]:[rank23]: 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 [default7]:[rank23]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl [default5]:[rank21]: return self._call_impl(*args, **kwargs) [default7]:[rank23]: 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 [default7]:[rank23]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default7]:[rank23]: return forward_call(*args, **kwargs) [default7]:[rank23]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/nn.py", line 159, in forward [default7]:[rank23]: return row_linear( [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 [default7]:[rank23]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/functional.py", line 474, in row_linear [default7]:[rank23]: out = F.linear(input, weight, bias) [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/nanot[default5]:[rank21]: return self.forward_with_hidden_states(input_ids=input_ids, input_mask=input_mask)[0] [default7]:[rank23]: torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 512.00 MiB. GPU  has a total capacity of 79.33 GiB of which 153.94 MiB is free. Including non-PyTorch memory, this process has 79.17 GiB memory in use. Of the allocated memory 69.40 GiB is allocated by PyTorch, and 141.30 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 [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) [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) [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]:[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 [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 [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 [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 "/f[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) sx/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 [default[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) 3]:[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) [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) [default3]:[rank59]: torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 512.00 MiB. GPU  has a total capacity of 79.33 GiB of which 393.94 MiB is free. Including non-PyTorch memory, this process has 78.93 GiB memory in use. Of the allocated memory 69.78 GiB is allocated by PyTorch, and 269.17 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]: 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 512.00 MiB. GPU  has a total capacity of 79.33 GiB of which 41.94 MiB is free. Including non-PyTorch memory, this process has 79.28 GiB memory in use. Of the allocated memory 69.40 GiB is allocated by PyTorch, and 13.30 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]:[rank58]: Traceback (most recent call last): [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 [default2]:[rank58]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/run_train.py", line 237, in [default1]:[rank57]: outputs, loss_avg = self.training_step(dataloader=self.current_dataloader) [default2]:[rank58]: trainer.train(dataloader) [default1]:[rank57]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 462, in training_step [default2]:[rank58]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 429, in train [default1]:[rank57]: outputs = self.pipeline_engine.train_batch_iter( [default2]:[rank58]: outputs, loss_avg = self.training_step(dataloader=self.current_dataloader) [default1]:[rank57]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 278, in train_batch_iter [default2]:[rank58]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 462, in training_step [default1]:[rank57]: output = self.forward(context=context, state=state, micro_batch=micro_batch, model=model) [default2]:[rank58]: 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 44, in forward [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) [default1]:[rank57]: output = model(**micro_batch) [default2]:[rank58]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/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 [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]:[rank58]: return forward_call(*args, **kwargs) [default1]:[rank57]: return self._call_impl(*args, **kwargs) [default1]:[rank57]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default1]:[rank57]: return forward_call(*args, **kwargs) [default2]:[rank58]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 891, in forward [default1]:[rank57]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 891, in forward [default2]:[rank58]: sharded_logits = self.model( [default1]:[rank57]: sharded_logits = self.model( [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 [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]:[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-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]:[rank58]: return self._call_impl(*args, **kwargs) [default1]:[rank57]: 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 1541, in _call_impl [default1]:[rank57]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 764, in forward [default1]:[rank57]: return self.forward_with_hidden_states(input_ids=input_ids, input_mask=input_mask)[0] [default2]:[rank58]: return forward_call(*args, **kwargs) [default1]:[rank57]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 780, in forward_with_hidden_states [default1]:[rank57]: hidden_encoder_states = encoder_block(**hidden_encoder_states) [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) [default1]:[rank57]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl [default1]:[rank57]: return self._call_impl(*args, **kwargs) [default1]:[rank57]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default1]:[rank57]: return forward_call(*args, **kwargs) [default1]:[rank57]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/block.py", line 151, in forward [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 [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 [default2]:[rank58]: return self._call_impl(*args, **kwargs) [default1]:[rank57]: return self._call_impl(*args, **kwargs) [default1]:[rank57]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default1]:[rank57]: return forward_call(*args, **kwargs) [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 [default1]:[rank57]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 637, in forward [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) [default1]:[rank57]: hidden_states = self.mlp(hidden_states=hidden_states)["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 [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 [default1]:[rank57]: return self._call_impl(*args, **kwargs) [default1]:[rank57]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default1]:[rank57]: return forward_call(*args, **kwargs) [default1]:[rank57]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 171, in forward [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 [default1]:[rank57]: merged_states = self.gate_up_proj(hidden_states) [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) [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]:[rank58]: torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 512.00 MiB. GPU  has a total capacity of 79.33 GiB of which 81.94 MiB is free. Including non-PyTorch memory, this process has 79.24 GiB memory in use. Of the allocated memory 69.78 GiB is allocated by PyTorch, and 269.17 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation. See documentation for Memory Management (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables) [default1]:[rank57]: File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl [default1]:[rank57]: return forward_call(*args, **kwargs) [default1]:[rank57]: File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/tensor_parallel/nn.py", line 87, in forward [default1]:[rank57]: return column_linear( [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) [default1]:[rank57]: torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 512.00 MiB. GPU  has a total capacity of 79.33 GiB of which 153.94 MiB is free. Including non-PyTorch memory, this process has 79.17 GiB memory in use. Of the allocated memory 69.78 GiB is allocated by PyTorch, and 269.17 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 08:00:51.202000 140585339381568 torch/distributed/elastic/multiprocessing/api.py:851] Sending process 1883316 closing signal SIGTERM W0703 08:00:51.202000 140585339381568 torch/distributed/elastic/multiprocessing/api.py:851] Sending process 1883318 closing signal SIGTERM W0703 08:00:51.202000 140585339381568 torch/distributed/elastic/multiprocessing/api.py:851] Sending process 1883319 closing signal SIGTERM W0703 08:00:51.202000 140585339381568 torch/distributed/elastic/multiprocessing/api.py:851] Sending process 1883320 closing signal SIGTERM W0703 08:00:51.202000 140585339381568 torch/distributed/elastic/multiprocessing/api.py:851] Sending process 1883322 closing signal SIGTERM E0703 08:00:51.323000 140303399708480 torch/distributed/elastic/multiprocessing/api.py:826] failed (exitcode: 1) local_rank: 0 (pid: 1188457) 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_08:00:51 host : ip-26-0-160-192.ec2.internal rank : 1 (local_rank: 1) exitcode : 1 (pid: 1188458) error_file: traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html [2]: time : 2024-07-03_08:00:51 host : ip-26-0-160-192.ec2.internal rank : 2 (local_rank: 2) exitcode : 1 (pid: 1188459) error_file: traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html [3]: time : 2024-07-03_08:00:51 host : ip-26-0-160-192.ec2.internal rank : 3 (local_rank: 3) exitcode : 1 (pid: 1188460) error_file: traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html [4]: time : 2024-07-03_08:00:51 host : ip-26-0-160-192.ec2.internal rank : 4 (local_rank: 4) exitcode : 1 (pid: 1188461) error_file: traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html [5]: time : 2024-07-03_08:00:51 host : ip-26-0-160-192.ec2.internal rank : 5 (local_rank: 5) exitcode : 1 (pid: 1188462) error_file: traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html [6]: time : 2024-07-03_08:00:51 host : ip-26-0-160-192.ec2.internal rank : 6 (local_rank: 6) exitcode : 1 (pid: 1188463) error_file: traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html [7]: time : 2024-07-03_08:00:51 host : ip-26-0-160-192.ec2.internal rank : 7 (local_rank: 7) exitcode : 1 (pid: 1188464) 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_08:00:51 host : ip-26-0-160-192.ec2.internal rank : 0 (local_rank: 0) exitcode : 1 (pid: 1188457) error_file: traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html ============================================================ srun: error: ip-26-0-160-192: task 0: Exited with exit code 1 E0703 08:00:52.725000 140585339381568 torch/distributed/elastic/multiprocessing/api.py:826] failed (exitcode: 1) local_rank: 1 (pid: 1883317) of binary: /fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/bin/python3.10 W0703 08:00:52.731000 140585339381568 torch/distributed/elastic/rendezvous/dynamic_rendezvous.py:1203] The node 'ip-26-0-169-86.ec2.internal_1883245_0' has failed to shutdown the rendezvous 'none' due to an error of type RendezvousConnectionError. W0703 08:00:52.763000 140585339381568 torch/distributed/elastic/rendezvous/dynamic_rendezvous.py:1203] The node 'ip-26-0-169-86.ec2.internal_1883245_0' has failed to shutdown the rendezvous 'none' due to an error of type RendezvousConnectionError. W0703 08:00:52.779000 140585339381568 torch/distributed/elastic/rendezvous/dynamic_rendezvous.py:1203] The node 'ip-26-0-169-86.ec2.internal_1883245_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_08:00:51 host : ip-26-0-169-86.ec2.internal rank : 45 (local_rank: 5) exitcode : 1 (pid: 1883321) error_file: traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html [2]: time : 2024-07-03_08:00:51 host : ip-26-0-169-86.ec2.internal rank : 47 (local_rank: 7) exitcode : 1 (pid: 1883323) 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_08:00:51 host : ip-26-0-169-86.ec2.internal rank : 41 (local_rank: 1) exitcode : 1 (pid: 1883317) error_file: traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html ============================================================ srun: error: ip-26-0-169-86: task 5: Exited with exit code 1 W0703 08:00:55.267000 140525602912000 torch/distributed/elastic/rendezvous/dynamic_rendezvous.py:1252] The node 'ip-26-0-163-226.ec2.internal_3266819_0' has failed to send a keep-alive heartbeat to the rendezvous 'none' due to an error of type RendezvousConnectionError. W0703 08:00:55.317000 139907426531072 torch/distributed/elastic/rendezvous/dynamic_rendezvous.py:1252] The node 'ip-26-0-168-238.ec2.internal_1910236_0' has failed to send a keep-alive heartbeat to the rendezvous 'none' due to an error of type RendezvousConnectionError. W0703 08:00:56.118000 140507656718080 torch/distributed/elastic/rendezvous/dynamic_rendezvous.py:1252] The node 'ip-26-0-160-225.ec2.internal_57672_0' has failed to send a keep-alive heartbeat to the rendezvous 'none' due to an error of type RendezvousConnectionError. W0703 08:00:56.127000 140672458049280 torch/distributed/elastic/rendezvous/dynamic_rendezvous.py:1252] The node 'ip-26-0-172-57.ec2.internal_1110820_0' has failed to send a keep-alive heartbeat to the rendezvous 'none' due to an error of type RendezvousConnectionError. W0703 08:00:56.136000 139801486321408 torch/distributed/elastic/rendezvous/dynamic_rendezvous.py:1252] The node 'ip-26-0-163-220.ec2.internal_821269_0' has failed to send a keep-alive heartbeat to the rendezvous 'none' due to an error of type RendezvousConnectionError. W0703 08:00:56.169000 139941598668544 torch/distributed/elastic/rendezvous/dynamic_rendezvous.py:1252] The node 'ip-26-0-172-73.ec2.internal_952650_0' has failed to send a keep-alive heartbeat to the rendezvous 'none' due to an error of type RendezvousConnectionError. W0703 08:00:56.207000 139913087264576 torch/distributed/elastic/multiprocessing/api.py:851] Sending process 1910307 closing signal SIGTERM W0703 08:00:56.207000 139913087264576 torch/distributed/elastic/multiprocessing/api.py:851] Sending process 1910308 closing signal SIGTERM W0703 08:00:56.207000 139913087264576 torch/distributed/elastic/multiprocessing/api.py:851] Sending process 1910309 closing signal SIGTERM W0703 08:00:56.207000 139913087264576 torch/distributed/elastic/multiprocessing/api.py:851] Sending process 1910310 closing signal SIGTERM W0703 08:00:56.211000 140678118782784 torch/distributed/elastic/multiprocessing/api.py:851] Sending process 1110890 closing signal SIGTERM W0703 08:00:56.211000 140678118782784 torch/distributed/elastic/multiprocessing/api.py:851] Sending process 1110891 closing signal SIGTERM W0703 08:00:56.209000 139913087264576 torch/distributed/elastic/multiprocessing/api.py:851] Sending process 1910311 closing signal SIGTERM W0703 08:00:56.209000 139913087264576 torch/distributed/elastic/multiprocessing/api.py:851] Sending process 1910312 closing signal SIGTERM W0703 08:00:56.211000 140678118782784 torch/distributed/elastic/multiprocessing/api.py:851] Sending process 1110892 closing signal SIGTERM W0703 08:00:56.210000 139913087264576 torch/distributed/elastic/multiprocessing/api.py:851] Sending process 1910313 closing signal SIGTERM W0703 08:00:56.212000 140678118782784 torch/distributed/elastic/multiprocessing/api.py:851] Sending process 1110893 closing signal SIGTERM W0703 08:00:56.210000 139913087264576 torch/distributed/elastic/multiprocessing/api.py:851] Sending process 1910314 closing signal SIGTERM W0703 08:00:56.213000 140678118782784 torch/distributed/elastic/multiprocessing/api.py:851] Sending process 1110894 closing signal SIGTERM W0703 08:00:56.214000 140678118782784 torch/distributed/elastic/multiprocessing/api.py:851] Sending process 1110895 closing signal SIGTERM W0703 08:00:56.214000 140678118782784 torch/distributed/elastic/multiprocessing/api.py:851] Sending process 1110896 closing signal SIGTERM W0703 08:00:56.215000 140678118782784 torch/distributed/elastic/multiprocessing/api.py:851] Sending process 1110897 closing signal SIGTERM E0703 08:00:56.325000 139947259402048 torch/distributed/elastic/multiprocessing/api.py:826] failed (exitcode: 1) local_rank: 0 (pid: 952720) of binary: /fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/bin/python3.10 E0703 08:00:56.329000 140531263645504 torch/distributed/elastic/multiprocessing/api.py:826] failed (exitcode: 1) local_rank: 0 (pid: 3266889) of binary: /fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/bin/python3.10 E0703 08:00:56.331000 140513317451584 torch/distributed/elastic/multiprocessing/api.py:826] failed (exitcode: 1) local_rank: 0 (pid: 57744) of binary: /fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/bin/python3.10 E0703 08:00:56.328000 139807147054912 torch/distributed/elastic/multiprocessing/api.py:826] failed (exitcode: 1) local_rank: 0 (pid: 821339) of binary: /fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/bin/python3.10 W0703 08:00:56.331000 139947259402048 torch/distributed/elastic/rendezvous/dynamic_rendezvous.py:1203] The node 'ip-26-0-172-73.ec2.internal_952650_0' has failed to shutdown the rendezvous 'none' due to an error of type RendezvousConnectionError. W0703 08:00:56.334000 140531263645504 torch/distributed/elastic/rendezvous/dynamic_rendezvous.py:1203] The node 'ip-26-0-163-226.ec2.internal_3266819_0' has failed to shutdown the rendezvous 'none' due to an error of type RendezvousConnectionError. W0703 08:00:56.336000 140513317451584 torch/distributed/elastic/rendezvous/dynamic_rendezvous.py:1203] The node 'ip-26-0-160-225.ec2.internal_57672_0' has failed to shutdown the rendezvous 'none' due to an error of type RendezvousConnectionError. W0703 08:00:56.334000 139807147054912 torch/distributed/elastic/rendezvous/dynamic_rendezvous.py:1203] The node 'ip-26-0-163-220.ec2.internal_821269_0' has failed to shutdown the rendezvous 'none' due to an error of type RendezvousConnectionError. W0703 08:00:56.359000 139947259402048 torch/distributed/elastic/rendezvous/dynamic_rendezvous.py:1203] The node 'ip-26-0-172-73.ec2.internal_952650_0' has failed to shutdown the rendezvous 'none' due to an error of type RendezvousConnectionError. W0703 08:00:56.365000 140513317451584 torch/distributed/elastic/rendezvous/dynamic_rendezvous.py:1203] The node 'ip-26-0-160-225.ec2.internal_57672_0' has failed to shutdown the rendezvous 'none' due to an error of type RendezvousConnectionError. W0703 08:00:56.365000 140531263645504 torch/distributed/elastic/rendezvous/dynamic_rendezvous.py:1203] The node 'ip-26-0-163-226.ec2.internal_3266819_0' has failed to shutdown the rendezvous 'none' due to an error of type RendezvousConnectionError. W0703 08:00:56.371000 139807147054912 torch/distributed/elastic/rendezvous/dynamic_rendezvous.py:1203] The node 'ip-26-0-163-220.ec2.internal_821269_0' has failed to shutdown the rendezvous 'none' due to an error of type RendezvousConnectionError. W0703 08:00:56.389000 139947259402048 torch/distributed/elastic/rendezvous/dynamic_rendezvous.py:1203] The node 'ip-26-0-172-73.ec2.internal_952650_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 W0703 08:00:56.393000 140513317451584 torch/distributed/elastic/rendezvous/dynamic_rendezvous.py:1203] The node 'ip-26-0-160-225.ec2.internal_57672_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 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 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 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 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)) raise ChildFailedError( File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/launcher/api.py", line 263, in launch_agent torch.distributed.elastic.multiprocessing.errors.ChildFailedError: ============================================================ /fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/run_train.py FAILED ------------------------------------------------------------ Failures: [1]: time : 2024-07-03_08:00:56 host : ip-26-0-172-73.ec2.internal rank : 57 (local_rank: 1) exitcode : 1 (pid: 952721) error_file: traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html [2]: time : 2024-07-03_08:00:56 host : ip-26-0-172-73.ec2.internal rank : 58 (local_rank: 2) exitcode : 1 (pid: 952722) error_file: traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html [3]: time : 2024-07-03_08:00:56 host : ip-26-0-172-73.ec2.internal rank : 59 (local_rank: 3) exitcode : 1 (pid: 952723) error_file: traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html [4]: time : 2024-07-03_08:00:56 host : ip-26-0-172-73.ec2.internal rank : 60 (local_rank: 4) exitcode : 1 (pid: 952724) error_file: traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html [5]: time : 2024-07-03_08:00:56 host : ip-26-0-172-73.ec2.internal rank : 61 (local_rank: 5) exitcode : 1 (pid: 952725) error_file: traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html [6]: time : 2024-07-03_08:00:56 host : ip-26-0-172-73.ec2.internal rank : 62 (local_rank: 6) exitcode : 1 (pid: 952726) error_file: traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html [7]: time : 2024-07-03_08:00:56 host : ip-26-0-172-73.ec2.internal rank : 63 (local_rank: 7) exitcode : 1 (pid: 952727) 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_08:00:56 host : ip-26-0-172-73.ec2.internal rank : 56 (local_rank: 0) exitcode : 1 (pid: 952720) error_file: traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html ============================================================ 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_08:00:56 host : ip-26-0-160-225.ec2.internal rank : 9 (local_rank: 1) exitcode : 1 (pid: 57745) error_file: traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html [2]: time : 2024-07-03_08:00:56 host : ip-26-0-160-225.ec2.internal rank : 10 (local_rank: 2) exitcode : 1 (pid: 57746) error_file: traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html [3]: time : 2024-07-03_08:00:56 host : ip-26-0-160-225.ec2.internal rank : 11 (local_rank: 3) exitcode : 1 (pid: 57747) error_file: traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html [4]: time : 2024-07-03_08:00:56 host : ip-26-0-160-225.ec2.internal rank : 12 (local_rank: 4) exitcode : 1 (pid: 57748) error_file: traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html [5]: time : 2024-07-03_08:00:56 host : ip-26-0-160-225.ec2.internal rank : 13 (local_rank: 5) exitcode : 1 (pid: 57749) error_file: traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html [6]: time : 2024-07-03_08:00:56 host : ip-26-0-160-225.ec2.internal rank : 14 (local_rank: 6) exitcode : 1 (pid: 57750) error_file: traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html [7]: time : 2024-07-03_08:00:56 host : ip-26-0-160-225.ec2.internal rank : 15 (local_rank: 7) exitcode : 1 (pid: 57751) 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_08:00:56 host : ip-26-0-160-225.ec2.internal rank : 8 (local_rank: 0) exitcode : 1 (pid: 57744) error_file: traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html ============================================================ W0703 08:00:56.398000 140531263645504 torch/distributed/elastic/rendezvous/dynamic_rendezvous.py:1203] The node 'ip-26-0-163-226.ec2.internal_3266819_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_08:00:56 host : ip-26-0-163-226.ec2.internal rank : 25 (local_rank: 1) exitcode : 1 (pid: 3266890) error_file: traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html [2]: time : 2024-07-03_08:00:56 host : ip-26-0-163-226.ec2.internal rank : 26 (local_rank: 2) exitcode : 1 (pid: 3266891) error_file: traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html [3]: time : 2024-07-03_08:00:56 host : ip-26-0-163-226.ec2.internal rank : 27 (local_rank: 3) exitcode : 1 (pid: 3266892) error_file: traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html [4]: time : 2024-07-03_08:00:56 host : ip-26-0-163-226.ec2.internal rank : 28 (local_rank: 4) exitcode : 1 (pid: 3266893) error_file: traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html [5]: time : 2024-07-03_08:00:56 host : ip-26-0-163-226.ec2.internal rank : 29 (local_rank: 5) exitcode : 1 (pid: 3266894) error_file: traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html [6]: time : 2024-07-03_08:00:56 host : ip-26-0-163-226.ec2.internal rank : 30 (local_rank: 6) exitcode : 1 (pid: 3266895) error_file: traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html [7]: time : 2024-07-03_08:00:56 host : ip-26-0-163-226.ec2.internal rank : 31 (local_rank: 7) exitcode : 1 (pid: 3266896) 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_08:00:56 host : ip-26-0-163-226.ec2.internal rank : 24 (local_rank: 0) exitcode : 1 (pid: 3266889) error_file: traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html ============================================================ W0703 08:00:56.406000 139807147054912 torch/distributed/elastic/rendezvous/dynamic_rendezvous.py:1203] The node 'ip-26-0-163-220.ec2.internal_821269_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_08:00:56 host : ip-26-0-163-220.ec2.internal rank : 17 (local_rank: 1) exitcode : 1 (pid: 821340) error_file: traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html [2]: time : 2024-07-03_08:00:56 host : ip-26-0-163-220.ec2.internal rank : 18 (local_rank: 2) exitcode : 1 (pid: 821341) error_file: traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html [3]: time : 2024-07-03_08:00:56 host : ip-26-0-163-220.ec2.internal rank : 19 (local_rank: 3) exitcode : 1 (pid: 821342) error_file: traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html [4]: time : 2024-07-03_08:00:56 host : ip-26-0-163-220.ec2.internal rank : 20 (local_rank: 4) exitcode : 1 (pid: 821343) error_file: traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html [5]: time : 2024-07-03_08:00:56 host : ip-26-0-163-220.ec2.internal rank : 21 (local_rank: 5) exitcode : 1 (pid: 821344) error_file: traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html [6]: time : 2024-07-03_08:00:56 host : ip-26-0-163-220.ec2.internal rank : 22 (local_rank: 6) exitcode : 1 (pid: 821345) error_file: traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html [7]: time : 2024-07-03_08:00:56 host : ip-26-0-163-220.ec2.internal rank : 23 (local_rank: 7) exitcode : 1 (pid: 821346) 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_08:00:56 host : ip-26-0-163-220.ec2.internal rank : 16 (local_rank: 0) exitcode : 1 (pid: 821339) error_file: traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html ============================================================ srun: error: ip-26-0-163-226: task 3: Exited with exit code 1 srun: error: ip-26-0-160-225: task 1: Exited with exit code 1 srun: error: ip-26-0-163-220: task 2: Exited with exit code 1 srun: error: ip-26-0-172-73: task 7: Exited with exit code 1 W0703 08:01:00.321000 139907426531072 torch/distributed/elastic/rendezvous/dynamic_rendezvous.py:1252] The node 'ip-26-0-168-238.ec2.internal_1910236_0' has failed to send a keep-alive heartbeat to the rendezvous 'none' due to an error of type RendezvousConnectionError. W0703 08:01:01.132000 140672458049280 torch/distributed/elastic/rendezvous/dynamic_rendezvous.py:1252] The node 'ip-26-0-172-57.ec2.internal_1110820_0' has failed to send a keep-alive heartbeat to the rendezvous 'none' due to an error of type RendezvousConnectionError. W0703 08:01:05.326000 139907426531072 torch/distributed/elastic/rendezvous/dynamic_rendezvous.py:1252] The node 'ip-26-0-168-238.ec2.internal_1910236_0' has failed to send a keep-alive heartbeat to the rendezvous 'none' due to an error of type RendezvousConnectionError. W0703 08:01:06.136000 140672458049280 torch/distributed/elastic/rendezvous/dynamic_rendezvous.py:1252] The node 'ip-26-0-172-57.ec2.internal_1110820_0' has failed to send a keep-alive heartbeat to the rendezvous 'none' due to an error of type RendezvousConnectionError. W0703 08:01:06.852000 140678118782784 torch/distributed/elastic/rendezvous/dynamic_rendezvous.py:1203] The node 'ip-26-0-172-57.ec2.internal_1110820_0' has failed to shutdown the rendezvous 'none' due to an error of type RendezvousConnectionError. W0703 08:01:06.861000 140678118782784 torch/distributed/elastic/rendezvous/dynamic_rendezvous.py:1203] The node 'ip-26-0-172-57.ec2.internal_1110820_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-26-0-172-57: task 6: Exited with exit code 1 W0703 08:01:07.550000 139913087264576 torch/distributed/elastic/rendezvous/dynamic_rendezvous.py:1203] The node 'ip-26-0-168-238.ec2.internal_1910236_0' has failed to shutdown the rendezvous 'none' due to an error of type RendezvousConnectionError. W0703 08:01:07.558000 139913087264576 torch/distributed/elastic/rendezvous/dynamic_rendezvous.py:1203] The node 'ip-26-0-168-238.ec2.internal_1910236_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-26-0-168-238: task 4: 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.