diff --git "a/log.txt" "b/log.txt" new file mode 100644--- /dev/null +++ "b/log.txt" @@ -0,0 +1,3881 @@ +[2023-12-19 16:42:45,618] [INFO] [real_accelerator.py:133:get_accelerator] Setting ds_accelerator to cuda (auto detect) +[2023-12-19 16:42:45,618] [INFO] [real_accelerator.py:133:get_accelerator] Setting ds_accelerator to cuda (auto detect) +[2023-12-19 16:42:45,618] [INFO] [real_accelerator.py:133:get_accelerator] Setting ds_accelerator to cuda (auto detect) +[2023-12-19 16:42:45,619] [INFO] [real_accelerator.py:133:get_accelerator] Setting ds_accelerator to cuda (auto detect) +[2023-12-19 16:42:45,618] [INFO] [real_accelerator.py:133:get_accelerator] Setting ds_accelerator to cuda (auto detect) +[2023-12-19 16:42:45,619] [INFO] [real_accelerator.py:133:get_accelerator] Setting ds_accelerator to cuda (auto detect) +[2023-12-19 16:42:45,619] [INFO] [real_accelerator.py:133:get_accelerator] Setting ds_accelerator to cuda (auto detect) +[2023-12-19 16:42:45,619] [INFO] [real_accelerator.py:133:get_accelerator] Setting ds_accelerator to cuda (auto detect) +[2023-12-19 16:42:46,146] [WARNING] [comm.py:152:init_deepspeed_backend] NCCL backend in DeepSpeed not yet implemented +[2023-12-19 16:42:46,146] [INFO] [comm.py:616:init_distributed] cdb=None +[2023-12-19 16:42:46,147] [WARNING] [comm.py:152:init_deepspeed_backend] NCCL backend in DeepSpeed not yet implemented +[2023-12-19 16:42:46,147] [INFO] [comm.py:616:init_distributed] cdb=None +[2023-12-19 16:42:46,147] [WARNING] [comm.py:152:init_deepspeed_backend] NCCL backend in DeepSpeed not yet implemented +[2023-12-19 16:42:46,147] [INFO] [comm.py:616:init_distributed] cdb=None +[2023-12-19 16:42:46,147] [WARNING] [comm.py:152:init_deepspeed_backend] NCCL backend in DeepSpeed not yet implemented +[2023-12-19 16:42:46,147] [INFO] [comm.py:616:init_distributed] cdb=None +[2023-12-19 16:42:46,147] [WARNING] [comm.py:152:init_deepspeed_backend] NCCL backend in DeepSpeed not yet implemented +[2023-12-19 16:42:46,147] [INFO] [comm.py:616:init_distributed] cdb=None +[2023-12-19 16:42:46,147] [WARNING] [comm.py:152:init_deepspeed_backend] NCCL backend in DeepSpeed not yet implemented +[2023-12-19 16:42:46,147] [INFO] [comm.py:616:init_distributed] cdb=None +[2023-12-19 16:42:46,147] [WARNING] [comm.py:152:init_deepspeed_backend] NCCL backend in DeepSpeed not yet implemented +[2023-12-19 16:42:46,147] [WARNING] [comm.py:152:init_deepspeed_backend] NCCL backend in DeepSpeed not yet implemented +[2023-12-19 16:42:46,147] [INFO] [comm.py:616:init_distributed] cdb=None +[2023-12-19 16:42:46,147] [INFO] [comm.py:616:init_distributed] cdb=None +12/19/2023 16:42:46 - INFO - torch.distributed.distributed_c10d - Added key: store_based_barrier_key:1 to store for rank: 8 +12/19/2023 16:42:46 - INFO - torch.distributed.distributed_c10d - Added key: store_based_barrier_key:1 to store for rank: 11 +12/19/2023 16:42:46 - INFO - torch.distributed.distributed_c10d - Added key: store_based_barrier_key:1 to store for rank: 12 +12/19/2023 16:42:46 - INFO - torch.distributed.distributed_c10d - Added key: store_based_barrier_key:1 to store for rank: 15 +12/19/2023 16:42:46 - INFO - torch.distributed.distributed_c10d - Added key: store_based_barrier_key:1 to store for rank: 13 +12/19/2023 16:42:46 - INFO - torch.distributed.distributed_c10d - Added key: store_based_barrier_key:1 to store for rank: 10 +12/19/2023 16:42:46 - INFO - torch.distributed.distributed_c10d - Added key: store_based_barrier_key:1 to store for rank: 9 +12/19/2023 16:42:46 - INFO - torch.distributed.distributed_c10d - Added key: store_based_barrier_key:1 to store for rank: 14 +1212/19/2023 16:42:46 - INFO - torch.distributed.distributed_c10d - Rank 26: Completed store-based barrier for key:12/19/2023 16:42:46 - INFO - torch.distributed.distributed_c10d - Rank 6: Completed store-based barrier for key:store_based_barrier_key:1 with 32 nodes. +12/19/2023 16:42:46 - INFO - torch.distributed.distributed_c10d - Rank 2: Completed store-based barrier for key:store_based_barrier_key:1 with 32 nodes. +12/19/2023 16:42:46 - INFO - torch.distributed.distributed_c10d - Rank 5: Completed store-based barrier for key:store_based_barrier_key:1 with 32 nodes. +12/19/2023 16:42:46 - INFO - torch.distributed.distributed_c10d - Rank 4: Completed store-based barrier for key:store_based_barrier_key:1 with 32 nodes. +12/19/2023 16:42:46 - INFO - torch.distributed.distributed_c10d - Rank 3: Completed store-based barrier for key:store_based_barrier_key:1 with 32 nodes. +12/19/2023 16:42:46 - INFO - torch.distributed.distributed_c10d - Rank 0: Completed store-based barrier for key:store_based_barrier_key:1 with 32 nodes. +12/19/2023 16:42:46 - INFO - torch.distributed.distributed_c10d - Rank 1: Completed store-based barrier for key:store_based_barrier_key:1 with 32 nodes. +12/19/2023 16:42:46 - INFO - torch.distributed.distributed_c10d - Rank 7: Completed store-based barrier for key:store_based_barrier_key:1 with 32 nodes. +12/19/2023 16:42:46 - WARNING - utils.common - We recommend enable fp16 mixed precision training. +12/19/2023 16:42:46 - WARNING - utils.common - Please specify `prompt_template` if you are using other pre-trained models. +12/19/2023 16:42:46 - WARNING - utils.common - `ddp_find_unused_parameters` needs to be set as False in DDP training. +12/19/2023 16:42:46 - INFO - utils.common - Process rank: 2, device: cuda:2, n_gpu: 1 + distributed training: True, 16-bits training: False +12/19/2023 16:42:46 - INFO - utils.common - Training/evaluation parameters Seq2SeqTrainingArguments( +_n_gpu=1, +adafactor=False, +adam_beta1=0.9, +adam_beta2=0.999, +adam_epsilon=1e-08, +auto_find_batch_size=False, +bf16=True, +bf16_full_eval=False, +data_seed=None, +dataloader_drop_last=False, +dataloader_num_workers=0, +dataloader_pin_memory=True, +ddp_backend=None, +ddp_broadcast_buffers=None, +ddp_bucket_cap_mb=None, +ddp_find_unused_parameters=False, +ddp_timeout=1800, +debug=[], +deepspeed=ds_z3_no_offload.json, +disable_tqdm=False, +dispatch_batches=None, +do_eval=False, +do_predict=False, +do_train=True, +eval_accumulation_steps=None, +eval_delay=0, +eval_steps=None, +evaluation_strategy=no, +fp16=False, +fp16_backend=auto, +fp16_full_eval=False, +fp16_opt_level=O1, +fsdp=[], +fsdp_config={'min_num_params': 0, 'xla': False, 'xla_fsdp_grad_ckpt': False}, +fsdp_min_num_params=0, +fsdp_transformer_layer_cls_to_wrap=None, +full_determinism=False, +generation_config=None, +generation_max_length=None, +generation_num_beams=None, +gradient_accumulation_steps=2, +gradient_checkpointing=False, +greater_is_better=None, +group_by_length=False, +half_precision_backend=auto, +hub_always_push=False, +hub_model_id=None, +hub_private_repo=False, +hub_strategy=every_save, +hub_token=, +ignore_data_skip=False, +include_inputs_for_metrics=False, +jit_mode_eval=False, +label_names=None, +label_smoothing_factor=0.0, +learning_rate=2e-05, +length_column_name=length, +load_best_model_at_end=False, +local_rank=2, +log_level=passive, +log_level_replica=warning, +log_on_each_node=True, +logging_dir=/group/20025/jiuding/ckpt/34B_c50h50-python/runs/Dec19_16-42-44_vc-816650693-20231218-abab186e-worker-0, +logging_first_step=False, +logging_nan_inf_filter=True, +logging_steps=10, +logging_strategy=steps, +lr_scheduler_type=cosine, +max_grad_norm=1.0, +max_steps=-1, +metric_for_best_model=None, +mp_parameters=, +no_cuda=False, +num_train_epochs=2.0, +optim=adamw_torch, +optim_args=None, +output_dir=/group/20025/jiuding/ckpt/34B_c50h50-python, +overwrite_output_dir=False, +past_index=-1, +per_device_eval_batch_size=8, +per_device_train_batch_size=4, +predict_with_generate=False, +prediction_loss_only=False, +push_to_hub=False, +push_to_hub_model_id=None, +push_to_hub_organization=None, +push_to_hub_token=, +ray_scope=last, +remove_unused_columns=True, +report_to=[], +resume_from_checkpoint=None, +run_name=/group/20025/jiuding/ckpt/34B_c50h50-python, +save_on_each_node=False, +save_safetensors=False, +save_steps=500, +save_strategy=epoch, +save_total_limit=10, +seed=42, +sharded_ddp=[], +skip_memory_metrics=True, +sortish_sampler=False, +tf32=None, +torch_compile=False, +torch_compile_backend=None, +torch_compile_mode=None, +torchdynamo=None, +tpu_metrics_debug=False, +tpu_num_cores=None, +use_cpu=False, +use_ipex=False, +u12/19/2023 16:42:46 - WARNING - utils.common - We recommend enable fp16 mixed precision training. +12/19/2023 16:42:46 - WARNING - utils.common - We recommend enable fp16 mixed precision training. +12/19/2023 16:42:46 - WARNING - utils.common - Please specify `prompt_template` if you are using other pre-trained models. +12/19/2023 16:42:46 - WARNING - utils.common - We recommend enable fp16 mixed precision training. +12/19/2023 16:42:46 - WARNING - utils.common - `ddp_find_unused_parameters` needs to be set as False in DDP training. +12/19/2023 16:42:46 - WARNING - utils.common - Please specify `prompt_template` if you are using other pre-trained models. +12/19/2023 16:42:46 - WARNING - utils.common - Please specify `prompt_template` if you are using other pre-trained models. +12/19/2023 16:42:46 - WARNING - utils.common - `ddp_find_unused_parameters` needs to be set as False in DDP training. +12/19/2023 16:42:46 - WARNING - utils.common - `ddp_find_unused_parameters` needs to be set as False in DDP training. +12/19/2023 16:42:46 - INFO - utils.common - Process rank: 0, device: cuda:0, n_gpu: 1 + distributed training: True, 16-bits training: False +12/19/2023 16:42:46 - INFO - utils.common - Process rank: 3, device: cuda:3, n_gpu: 1 + distributed training: True, 16-bits training: False +12/19/2023 16:42:46 - INFO - utils.common - Process rank: 7, device: cuda:7, n_gpu: 1 + distributed training: True, 16-bits training: False +12/19/2023 16:42:46 - INFO - utils.common - Training/evaluation parameters Seq2SeqTrainingArguments( +_n_gpu=1, +adafactor=False, +adam_beta1=0.9, +adam_beta2=0.999, +adam_epsilon=1e-08, +auto_find_batch_size=False, +bf16=True, +bf16_full_eval=False, +data_seed=None, +dataloader_drop_last=False, +dataloader_num_workers=0, +dataloader_pin_memory=True, +ddp_backend=None, +ddp_broadcast_buffers=None, +ddp_bucket_cap_mb=None, +ddp_find_unused_parameters=False, +ddp_timeout=1800, +debug=[], +deepspeed=ds_z3_no_offload.json, +disable_tqdm=False, +dispatch_batches=None, +do_eval=False, +do_predict=False, +do_train=True, +eval_accumulation_steps=None, +eval_delay=0, +eval_steps=None, +evaluation_strategy=no, +fp16=False, +fp16_backend=auto, +fp16_full_eval=False, +fp16_opt_level=O1, +fsdp=[], +fsdp_config={'min_num_params': 0, 'xla': False, 'xla_fsdp_grad_ckpt': False}, +fsdp_min_num_params=0, +fsdp_transformer_layer_cls_to_wrap=None, +full_determinism=False, +generation_config=None, +generation_max_length=None, +generation_num_beams=None, +gradient_accumulation_steps=2, +gradient_checkpointing=False, +greater_is_better=None, +group_by_length=False, +half_precision_backend=auto, +hub_always_push=False, +hub_model_id=None, +hub_private_repo=False, +hub_strategy=every_save, +hub_token=, +ignore_data_skip=False, +include_inputs_for_metrics=False, +jit_mode_eval=False, +label_names=None, +label_smoothing_factor=0.0, +learning_rate=2e-05, +length_column_name=length, +load_best_model_at_end=False, +local_rank=0, +log_level=passive, +log_level_replica=warning, +log_on_each_node=True, +logging_dir=/group/20025/jiuding/ckpt/34B_c50h50-python/runs/Dec19_16-42-45_vc-816650693-20231218-abab186e-worker-1, +logging_first_step=False, +logging_nan_inf_filter=True, +logging_steps=10, +logging_strategy=steps, +lr_scheduler_type=cosine, +max_grad_norm=1.0, +max_steps=-1, +metric_for_best_model=None, +mp_parameters=, +no_cuda=False, +num_train_epochs=2.0, +optim=adamw_torch, +optim_args=None, +output_dir=/group/20025/jiuding/ckpt/34B_c50h50-python, +overwrite_output_dir=False, +past_index=-1, +per_device_eval_batch_size=8, +per_device_train_batch_size=4, +predict_with_generate=False, +prediction_loss_only=False, +push_to_hub=False, +push_to_hub_model_id=None, +push_to_hub_organization=None, +push_to_hub_token=, +ray_scope=last, +remove_unused_columns=True, +report_to=[], +resume_from_checkpoint=None, +run_name=/group/20025/jiuding/ckpt/34B_c50h50-python, +save_on_each_node=False, +save_safetensors=False, +save_steps=500, +save_strategy=epoch, +save_total_limit=10, +seed=42, +sharded_ddp=[], +skip_memory_metrics=True, +sortish_sampler=False, +tf32=None, +torch_compile=False, +torch_compile_backend=None, +torch_compile_mode=None, +torchdynamo=None, +tpu_metrics_debug=False, +tpu_num_cores=None, +use_cpu=False, +use_ipex=False, +use_legacy_prediction_loop=False, +use_mps_device=False, +warmup_ratio=0.0, +warmup_steps=0, +weight_decay=0.0, +) +12/19/2023 16:42:46 - INFO - utils.common - Training/evaluation parameters Seq2SeqTrainingArguments( +_n_gpu=1, +adafactor=False, +adam_beta1=0.9, +adam_beta2=0.999, +adam_epsilon=1e-08, +auto_find_batch_size=False, +bf16=True, +bf16_full_eval=False, +data_seed=None, +dataloader_drop_last=False, +dataloader_num_workers=0, +dataloader_pin_memory=True, +ddp_backend=None, +ddp_broadcast_buffers=None, +ddp_bucket_cap_mb=None, +ddp_find_unused_parameters=False, +ddp_timeout=1800, +debug=[], +deepspeed=ds_z3_no_offload.json, +disable_tqdm=False, +dispatch_batches=None, +do_eval=False, +do_predict=False, +do_train=True, +eval_accumulation_steps=None, +eval_delay=0, +eval_steps=None, +evaluation_strategy=no, +fp16=False, +fp16_backend=auto, +fp16_full_eval=False, +fp16_opt_level=O1, +fsdp=[], +fsdp_config={'min_num_params': 0, 'xla': False, 'xla_fsdp_grad_ckpt': False}, +fsdp_min_num_params=0, +fsdp_transformer_layer_cls_to_wrap=None, +full_determinism=False, +generation_config=None, +generation_max_length=None, +generation_num_beams=None, +gradient_accumulation_steps=2, +gradient_checkpointing=False, +greater_is_better=None, +group_by_length=False, +half_precision_backend=auto, +hub_always_push=False, +hub_model_id=None, +hub_private_repo=False, +hub_strategy=every_save, +hub_token=, +ignore_data_skip=False, +include_inputs_for_metrics=False, +jit_mode_eval=False, +label_names=None, +label_smoothing_factor=0.0, +learning_rate=2e-05, +length_column_name=length, +load_best_model_at_end=False, +local_rank=3, +log_level=passive, +log_level_replica=warning, +log_on_each_node=True, +logging_dir=/group/20025/jiuding/ckpt/34B_c50h50-python/runs/Dec19_16-42-45_vc-816650693-20231218-abab186e-worker-1, +logging_first_step=False, +logging_nan_inf_filter=True, +logging_steps=10, +logging_strategy=steps, +lr_scheduler_type=cosine, +max_grad_norm=1.0, +max_steps=-1, +metric_for_best_model=None, +mp_parameters=, +no_cuda=False, +num_train_epochs=2.0, +optim=adamw_torch, +optim_args=None, +output_dir=/group/20025/jiuding/ckpt/34B_c50h50-python, +overwrite_output_dir=False, +past_index=-1, +per_device_eval_batch_size=8, +per_device_train_batch_size=4, +predict_with_generate=False, +prediction_loss_only=False, +push_to_hub=False, +push_to_hub_model_id=None, +push_to_hub_organization=None, +push_to_hub_token=, +ray_scope=last, +remove_unused_columns=True, +report_to=[], +resume_from_checkpoint=None, +run_name=/group/20025/jiuding/ckpt/34B_c50h50-python, +save_on_each_node=False, +save_safetensors=False, +save_steps=500, +save_strategy=epoch, +save_total_limit=10, +seed=42, +sharded_ddp=[], +skip_memory_metrics=True, +sortish_sampler=False, +tf32=None, +torch_compile=False, +torch_compile_backend=None, +torch_compile_mode=None, +torchdynamo=None, +tpu_metrics_debug=False, +tpu_num_cores=None, +use_cpu=False, +use_ipex=False, +use_legacy_prediction_loop=False, +use_mps_device=False, +warmup_ratio=0.0, +warmup_steps=0, +weight_decay=0.0, +) +12/19/2023 16:42:46 - INFO - utils.common - Training/evaluation parameters Seq2SeqTrainingArguments( +_n_gpu=1, +adafactor=False, +adam_beta1=0.9, +adam_beta2=0.999, +adam_epsilon=1e-08, +auto_find_batch_size=False, +bf16=True, +bf16_full_eval=False, +data_seed=None, +dataloader_drop_last=False, +dataloader_num_workers=0, +dataloader_pin_memory=True, +ddp_backend=None, +ddp_broadcast_buffers=None, +ddp_bucket_cap_mb=None, +ddp_find_unused_parameters=False, +ddp_timeout=1800, +debug=[], +deepspeed=ds_z3_no_offload.json, +disable_tqdm=False, +dispatch_batches=None, +do_eval=False, +do_predict=False, +do_train=True, +eval_accumulation_steps=None, +eval_delay=0, +eval_steps=None, +evaluation_strategy=no, +fp16=False, +fp16_backend=auto, +fp16_full_eval=False, +fp16_opt_level=O1, +fsdp=[], +fsdp_config={'min_num_params': 0, 'xla': False, 'xla_fsdp_grad_ckpt': False}, +fsdp_min_num_params=0, +fsdp_transformer_layer_cls_to_wrap=None, +full_determinism=False, +generation_config=None, +generation_max_length=None, +generation_num_beams=None, +gradient_accumulation_steps=2, +gradient_checkpointing=False, +greater_is_better=None, +group_by_length=False, +half_precision_backend=auto, +hub_always_push=False, +hub_model_id=None, +hub_private_repo=False, +hub_strategy=every_save, +hub_token=, +ignore_data_skip=False, +include_inputs_for_metrics=False, +jit_mode_eval=False, +label_names=None, +label_smoothing_factor=0.0, +learning_rate=2e-05, +length_column_name=length, +load_best_model_at_end=False, +local_rank=7, +log_level=passive, +log_level_replica=warning, +log_on_each_node=True, +logging_dir=/group/20025/jiuding/ckpt/34B_c50h50-python/runs/Dec19_16-42-45_vc-816650693-20231218-abab186e-worker-1, +logging_first_step=False, +logging_nan_inf_filter=True, +logging_steps=10, +logging_strategy=steps, +lr_scheduler_type=cosine, +max_grad_norm=1.0, +max_steps=-1, +metric_for_best_model=None, +mp_parameters=, +no_cuda=False, +num_train_epochs=2.0, +optim=adamw_torch, +optim_args=None, +output_dir=/group/20025/jiuding/ckpt/34B_c50h50-python, +overwrite_output_dir=False, +past_index=-1, +per_device_eval_batch_size=8, +per_device_train_batch_size=4, +predict_with_generate=False, +prediction_loss_only=False, +push_to_hub=False, +push_to_hub_model_id=None, +push_to_hub_organization=None, +push_to_hub_token=, +ray_scope=last, +remove_unused_columns=True, +report_to=[], +resume_from_checkpoint=None, +run_name=/group/20025/jiuding/ckpt/34B_c50h50-python, +save_on_each_node=False, +save_safetensors=False, +save_steps=500, +save_strategy=epoch, +save_total_limit=10, +seed=42, +sharded_ddp=[], +skip_memory_metrics=True, +sortish_sampler=False, +tf32=None, +torch_compile=False, +torch_compile_backend=None, +torch_compile_mode=None, +torchdynamo=None, +tpu_metrics_debug=False, +tpu_num_cores=None, +use_cpu=False, +use_ipex=False, +use_legacy_prediction_loop=False, +use_mps_device=False, +warmup_ratio=0.0, +warmup_steps=0, +weight_decay=0.0, +) +12/19/2023 16:42:46 - WARNING - utils.common - We recommend enable fp16 mixed precision training. +12/19/2023 16:42:46 - WARNING - utils.common - Please specify `prompt_template` if you are using other pre-trained models. +12/19/2023 16:42:46 - WARNING - utils.common - `ddp_find_unused_parameters` needs to be set as False in DDP training. +12/19/2023 16:42:46 - INFO - utils.common - Process rank: 5, device: cuda:5, n_gpu: 1 + distributed training: True, 16-bits training: False +12/19/2023 16:42:46 - WARNING - utils.common - We recommend enable fp16 mixed precision training. +12/19/2023 16:42:46 - WARNING - utils.common - Please specify `prompt_template` if you are using other pre-trained models. +12/19/2023 16:42:46 - WARNING - utils.common - `ddp_find_unused_parameters` needs to be set as False in DDP training. +12/19/2023 16:42:46 - WARNING - utils.common - We recommend enable fp16 mixed precision training. +12/19/2023 16:42:46 - INFO - utils.common - Process rank: 2, device: cuda:2, n_gpu: 1 + distributed training: True, 16-bits training: False +12/19/2023 16:42:46 - WARNING - utils.common - Please specify `prompt_template` if you are using other pre-trained models. +12/19/2023 16:42:46 - WARNING - utils.common - `ddp_find_unused_parameters` needs to be set as False in DDP training. +12/19/2023 16:42:46 - INFO - utils.common - Process rank: 1, device: cuda:1, n_gpu: 1 + distributed training: True, 16-bits training: False +12/19/2023 16:42:46 - WARNING - utils.common - We recommend enable fp16 mixed precision training. +12/19/2023 16:42:46 - WARNING - utils.common - Please specify `prompt_template` if you are using other pre-trained models. +12/19/2023 16:42:46 - WARNING - utils.common - `ddp_find_unused_parameters` needs to be set as False in DDP training. +12/19/2023 16:42:46 - INFO - utils.common - Training/evaluation parameters Seq2SeqTrainingArguments( +_n_gpu=1, +adafactor=False, +adam_beta1=0.9, +adam_beta2=0.999, +adam_epsilon=1e-08, +auto_find_batch_size=False, +bf16=True, +bf16_full_eval=False, +data_seed=None, +dataloader_drop_last=False, +dataloader_num_workers=0, +dataloader_pin_memory=True, +ddp_backend=None, +ddp_broadcast_buffers=None, +ddp_bucket_cap_mb=None, +ddp_find_unused_parameters=False, +ddp_timeout=1800, +debug=[], +deepspeed=ds_z3_no_offload.json, +disable_tqdm=False, +dispatch_batches=None, +do_eval=False, +do_predict=False, +do_train=True, +eval_accumulation_steps=None, +eval_delay=0, +eval_steps=None, +evaluation_strategy=no, +fp16=False, +fp16_backend=auto, +fp16_full_eval=False, +fp16_opt_level=O1, +fsdp=[], +fsdp_config={'min_num_params': 0, 'xla': False, 'xla_fsdp_grad_ckpt': False}, +fsdp_min_num_params=0, +fsdp_transformer_layer_cls_to_wrap=None, +full_determinism=False, +generation_config=None, +generation_max_length=None, +generation_num_beams=None, +gradient_accumulation_steps=2, +gradient_checkpointing=False, +greater_is_better=None, +group_by_length=False, +half_precision_backend=auto, +hub_always_push=False, +hub_model_id=None, +hub_private_repo=False, +hub_strategy=every_save, +hub_token=, +ignore_data_skip=False, +include_inputs_for_metrics=False, +jit_mode_eval=False, +label_names=None, +label_smoothing_factor=0.0, +learning_rate=2e-05, +length_column_name=length, +load_best_model_at_end=False, +local_rank=5, +log_level=passive, +log_level_replica=warning, +log_on_each_node=True, +logging_dir=/group/20025/jiuding/ckpt/34B_c50h50-python/runs/Dec19_16-42-45_vc-816650693-20231218-abab186e-worker-1, +logging_first_step=False, +logging_nan_inf_filter=True, +logging_steps=10, +logging_strategy=steps, +lr_scheduler_type=cosine, +max_grad_norm=1.0, +max_steps=-1, +metric_for_best_model=None, +mp_parameters=, +no_cuda=False, +num_train_epochs=2.0, +optim=adamw_torch, +optim_args=None, +output_dir=/group/20025/jiuding/ckpt/34B_c50h50-python, +overwrite_output_dir=False, +past_index=-1, +per_device_eval_batch_size=8, +per_device_train_batch_size=4, +predict_with_generate=False, +prediction_loss_only=False, +push_to_hub=False, +push_to_hub_model_id=None, +push_to_hub_organization=None, +push_to_hub_token=, +ray_scope=last, +remove_unused_columns=True, +report_to=[], +resume_from_checkpoint=None, +run_name=/group/20025/jiuding/ckpt/34B_c50h50-python, +save_on_each_node=False, +save_safetensors=False, +save_steps=500, +save_strategy=epoch, +save_total_limit=10, +seed=42, +sharded_ddp=[], +skip_memory_metrics=True, +sortish_sampler=False, +tf32=None, +torch_compile=False, +torch_compile_backend=None, +torch_compile_mode=None, +torchdynamo=None, +tpu_metrics_debug=False, +tpu_num_cores=None, +use_cpu=False, +use_ipex=False, +use_legacy_prediction_loop=False, +use_mps_device=False, +warmup_ratio=0.0, +warmup_steps=0, +weight_decay=0.0, +) +12/19/2023 16:42:46 - INFO - utils.common - Process rank: 4, device: cuda:4, n_gpu: 1 + distributed training: True, 16-bits training: False +12/19/2023 16:42:46 - INFO - utils.common - Training/evaluation parameters Seq2SeqTrainingArguments( +_n_gpu=1, +adafactor=False, +adam_beta1=0.9, +adam_beta2=0.999, +adam_epsilon=1e-08, +auto_find_batch_size=False, +bf16=True, +bf16_full_eval=False, +data_seed=None, +dataloader_drop_last=False, +dataloader_num_workers=0, +dataloader_pin_memory=True, +ddp_backend=None, +ddp_broadcast_buffers=None, +ddp_bucket_cap_mb=None, +ddp_find_unused_parameters=False, +ddp_timeout=1800, +debug=[], +deepspeed=ds_z3_no_offload.json, +disable_tqdm=False, +dispatch_batches=None, +do_eval=False, +do_predict=False, +do_train=True, +eval_accumulation_steps=None, +eval_delay=0, +eval_steps=None, +evaluation_strategy=no, +fp16=False, +fp16_backend=auto, +fp16_full_eval=False, +fp16_opt_level=O1, +fsdp=[], +fsdp_config={'min_num_params': 0, 'xla': False, 'xla_fsdp_grad_ckpt': False}, +fsdp_min_num_params=0, +fsdp_transformer_layer_cls_to_wrap=None, +full_determinism=False, +generation_config=None, +generation_max_length=None, +generation_num_beams=None, +gradient_accumulation_steps=2, +gradient_checkpointing=False, +greater_is_better=None, +group_by_length=False, +half_precision_backend=auto, +hub_always_push=False, +hub_model_id=None, +hub_private_repo=False, +hub_strategy=every_save, +hub_token=, +ignore_data_skip=False, +include_inputs_for_metrics=False, +jit_mode_eval=False, +label_names=None, +label_smoothing_factor=0.0, +learning_rate=2e-05, +length_column_name=length, +load_best_model_at_end=False, +local_rank=2, +log_level=passive, +log_level_replica=warning, +log_on_each_node=True, +logging_dir=/group/20025/jiuding/ckpt/34B_c50h50-python/runs/Dec19_16-42-45_vc-816650693-20231218-abab186e-worker-1, +logging_first_step=False, +logging_nan_inf_filter=True, +logging_steps=10, +logging_strategy=steps, +lr_scheduler_type=cosine, +max_grad_norm=1.0, +max_steps=-1, +metric_for_best_model=None, +mp_parameters=, +no_cuda=False, +num_train_epochs=2.0, +optim=adamw_torch, +optim_args=None, +output_dir=/group/20025/jiuding/ckpt/34B_c50h50-python, +overwrite_output_dir=False, +past_index=-1, +per_device_eval_batch_size=8, +per_device_train_batch_size=4, +predict_with_generate=False, +prediction_loss_only=False, +push_to_hub=False, +push_to_hub_model_id=None, +push_to_hub_organization=None, +push_to_hub_token=, +ray_scope=last, +remove_unused_columns=True, +report_to=[], +resume_from_checkpoint=None, +run_name=/group/20025/jiuding/ckpt/34B_c50h50-python, +save_on_each_node=False, +save_safetensors=False, +save_steps=500, +save_strategy=epoch, +save_total_limit=10, +seed=42, +sharded_ddp=[], +skip_memory_metrics=True, +sortish_sampler=False, +tf32=None, +torch_compile=False, +torch_compile_backend=None, +torch_compile_mode=None, +torchdynamo=None, +tpu_metrics_debug=False, +tpu_num_cores=None, +use_cpu=False, +use_ipex=False, +use_legacy_prediction_loop=False, +use_mps_device=False, +warmup_ratio=0.0, +warmup_steps=0, +weight_decay=0.0, +) +12/19/2023 16:42:46 - INFO - utils.common - Training/evaluation parameters Seq2SeqTrainingArguments( +_n_gpu=1, +adafactor=False, +adam_beta1=0.9, +adam_beta2=0.999, +adam_epsilon=1e-08, +auto_find_batch_size=False, +bf16=True, +bf16_full_eval=False, +data_seed=None, +dataloader_drop_last=False, +dataloader_num_workers=0, +dataloader_pin_memory=True, +ddp_backend=None, +ddp_broadcast_buffers=None, +ddp_bucket_cap_mb=None, +ddp_find_unused_parameters=False, +ddp_timeout=1800, +debug=[], +deepspeed=ds_z3_no_offload.json, +disable_tqdm=False, +dispatch_batches=None, +do_eval=False, +do_predict=False, +do_train=True, +eval_accumulation_steps=None, +eval_delay=0, +eval_steps=None, +evaluation_strategy=no, +fp16=False, +fp16_backend=auto, +fp16_full_eval=False, +fp16_opt_level=O1, +fsdp=[], +fsdp_config={'min_num_params': 0, 'xla': False, 'xla_fsdp_grad_ckpt': False}, +fsdp_min_num_params=0, +fsdp_transformer_layer_cls_to_wrap=None, +full_determinism=False, +generation_config=None, +generation_max_length=None, +generation_num_beams=None, +gradient_accumulation_steps=2, +gradient_checkpointing=False, +greater_is_better=None, +group_by_length=False, +half_precision_backend=auto, +hub_always_push=False, +hub_model_id=None, +hub_private_repo=False, +hub_strategy=every_save, +hub_token=, +ignore_data_skip=False, +include_inputs_for_metrics=False, +jit_mode_eval=False, +label_names=None, +label_smoothing_factor=0.0, +learning_rate=2e-05, +length_column_name=length, +load_best_model_at_end=False, +local_rank=1, +log_level=passive, +log_level_replica=warning, +log_on_each_node=True, +logging_dir=/group/20025/jiuding/ckpt/34B_c50h50-python/runs/Dec19_16-42-45_vc-816650693-20231218-abab186e-worker-1, +logging_first_step=False, +logging_nan_inf_filter=True, +logging_steps=10, +logging_strategy=steps, +lr_scheduler_type=cosine, +max_grad_norm=1.0, +max_steps=-1, +metric_for_best_model=None, +mp_parameters=, +no_cuda=False, +num_train_epochs=2.0, +optim=adamw_torch, +optim_args=None, +output_dir=/group/20025/jiuding/ckpt/34B_c50h50-python, +overwrite_output_dir=False, +past_index=-1, +per_device_eval_batch_size=8, +per_device_train_batch_size=4, +predict_with_generate=False, +prediction_loss_only=False, +push_to_hub=False, +push_to_hub_model_id=None, +push_to_hub_organization=None, +push_to_hub_token=, +ray_scope=last, +remove_unused_columns=True, +report_to=[], +resume_from_checkpoint=None, +run_name=/group/20025/jiuding/ckpt/34B_c50h50-python, +save_on_each_node=False, +save_safetensors=False, +save_steps=500, +save_strategy=epoch, +save_total_limit=10, +seed=42, +sharded_ddp=[], +skip_memory_metrics=True, +sortish_sampler=False, +tf32=None, +torch_compile=False, +torch_compile_backend=None, +torch_compile_mode=None, +torchdynamo=None, +tpu_metrics_debug=False, +tpu_num_cores=None, +use_cpu=False, +use_ipex=False, +use_legacy_prediction_loop=False, +use_mps_device=False, +warmup_ratio=0.0, +warmup_steps=0, +weight_decay=0.0, +) +12/19/2023 16:42:46 - INFO - utils.common - Training/evaluation parameters Seq2SeqTrainingArguments( +_n_gpu=1, +adafactor=False, +adam_beta1=0.9, +adam_beta2=0.999, +adam_epsilon=1e-08, +auto_find_batch_size=False, +bf16=True, +bf16_full_eval=False, +data_seed=None, +dataloader_drop_last=False, +dataloader_num_workers=0, +dataloader_pin_memory=True, +ddp_backend=None, +ddp_broadcast_buffers=None, +ddp_bucket_cap_mb=None, +ddp_find_unused_parameters=False, +ddp_timeout=1800, +debug=[], +deepspeed=ds_z3_no_offload.json, +disable_tqdm=False, +dispatch_batches=None, +do_eval=False, +do_predict=False, +do_train=True, +eval_accumulation_steps=None, +eval_delay=0, +eval_steps=None, +evaluation_strategy=no, +fp16=False, +fp16_backend=auto, +fp16_full_eval=False, +fp16_opt_level=O1, +fsdp=[], +fsdp_config={'min_num_params': 0, 'xla': False, 'xla_fsdp_grad_ckpt': False}, +fsdp_min_num_params=0, +fsdp_transformer_layer_cls_to_wrap=None, +full_determinism=False, +generation_config=None, +generation_max_length=None, +generation_num_beams=None, +gradient_accumulation_steps=2, +gradient_checkpointing=False, +greater_is_better=None, +group_by_length=False, +half_precision_backend=auto, +hub_always_push=False, +hub_model_id=None, +hub_private_repo=False, +hub_strategy=every_save, +hub_token=, +ignore_data_skip=False, +include_inputs_for_metrics=False, +jit_mode_eval=False, +label_names=None, +label_smoothing_factor=0.0, +learning_rate=2e-05, +length_column_name=length, +load_best_model_at_end=False, +local_rank=4, +log_level=passive, +log_level_replica=warning, +log_on_each_node=True, +logging_dir=/group/20025/jiuding/ckpt/34B_c50h50-python/runs/Dec19_16-42-45_vc-816650693-20231218-abab186e-worker-1, +logging_first_step=False, +logging_nan_inf_filter=True, +logging_steps=10, +logging_strategy=steps, +lr_scheduler_type=cosine, +max_grad_norm=1.0, +max_steps=-1, +metric_for_best_model=None, +mp_parameters=, +no_cuda=False, +num_train_epochs=2.0, +optim=adamw_torch, +optim_args=None, +output_dir=/group/20025/jiuding/ckpt/34B_c50h50-python, +overwrite_output_dir=False, +past_index=-1, +per_device_eval_batch_size=8, +per_device_train_batch_size=4, +predict_with_generate=False, +prediction_loss_only=False, +push_to_hub=False, +push_to_hub_model_id=None, +push_to_hub_organization=None, +push_to_hub_token=, +ray_scope=last, +remove_unused_columns=True, +report_to=[], +resume_from_checkpoint=None, +run_name=/group/20025/jiuding/ckpt/34B_c50h50-python, +save_on_each_node=False, +save_safetensors=False, +save_steps=500, +save_strategy=epoch, +save_total_limit=10, +seed=42, +sharded_ddp=[], +skip_memory_metrics=True, +sortish_sampler=False, +tf32=None, +torch_compile=False, +torch_compile_backend=None, +torch_compile_mode=None, +torchdynamo=None, +tpu_metrics_debug=False, +tpu_num_cores=None, +use_cpu=False, +use_ipex=False, +use_legacy_prediction_loop=False, +use_mps_device=False, +warmup_ratio=0.0, +warmup_steps=0, +weight_decay=0.0, +) +12/19/2023 16:42:46 - INFO - utils.common - Using FlashAttention-2 for faster training and inference. +12/19/2023 16:42:46 - INFO - utils.common - Using FlashAttention-2 for faster training and inference. +12/19/2023 16:42:46 - 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4069, 577, 372, 5149, 17766, 4069, 8857, 29889, 2] +inputs: + [INST]Please amend the subsequent Python script so that it includes a 'while' loop rather than the existing 'for' loop, which iterates through the items of an integer list. + +The script currently has a bug where it attempts to print an object that is outside the bounds of the list. Fix this error and modify the script to use 'while' instead of 'for' loop. Ensure your script correctly handles empty lists. + +```python + # Establish an integer list + arr = [1, 2, 3, 4] + + # Determine the length of the list + n = len(arr) + + # Traverse the list and output each individual element + for i in range(n+1): + print(arr[i]) +```[/INST] ```python +# Establish an integer list +arr = [1, 2, 3, 4] + +# Determine the length of the list +n = len(arr) + +# Initialize index at 0 +i = 0 + +# Traverse the list and output each individual element +while i < n: + print(arr[i]) + i += 1 +``` +In the given code, it tries to access `arr[n]` which is out of bounds as python list indexing starts from 0 and ends at n-1 for a list of length n. +Also, I used `while` loop as per your request. By initializing `i=0` before the loop and `i += 1` within the loop, we can iterate over the list. This iteration continues until `i` is less than `n`, the length of the list. The `while` loop will not proceed if the list is empty so it correctly handles empty lists. +length of input_ids : +394label_ids: +[-100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, 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450, 421, 8000, 29952, 2425, 674, 451, 8469, 565, 278, 1051, 338, 4069, 577, 372, 5149, 17766, 4069, 8857, 29889, 2] +length of input_ids : +394 +input_ids: +[1, 518, 25580, 29962, 12148, 626, 355, 278, 15352, 5132, 2471, 577, 393, 372, 7805, 263, 525, 8000, 29915, 2425, 3265, 1135, 278, 5923, 525, 1454, 29915, 2425, 29892, 607, 4256, 1078, 1549, 278, 4452, 310, 385, 6043, 1051, 29889, 13, 13, 1576, 2471, 5279, 756, 263, 6494, 988, 372, 14734, 304, 1596, 385, 1203, 393, 338, 5377, 278, 13451, 310, 278, 1051, 29889, 24778, 445, 1059, 322, 6623, 278, 2471, 304, 671, 525, 8000, 29915, 2012, 310, 525, 1454, 29915, 2425, 29889, 22521, 545, 596, 2471, 5149, 17766, 4069, 8857, 29889, 29871, 13, 13, 28956, 4691, 13, 29871, 396, 2661, 370, 1674, 385, 6043, 1051, 13, 29871, 3948, 353, 518, 29896, 29892, 29871, 29906, 29892, 29871, 29941, 29892, 29871, 29946, 29962, 13, 13, 29871, 396, 5953, 837, 457, 278, 3309, 310, 278, 1051, 13, 29871, 302, 353, 7431, 29898, 2749, 29897, 13, 13, 29871, 396, 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8000, 29952, 2425, 408, 639, 596, 2009, 29889, 2648, 2847, 5281, 421, 29875, 29922, 29900, 29952, 1434, 278, 2425, 322, 421, 29875, 4619, 29871, 29896, 29952, 2629, 278, 2425, 29892, 591, 508, 13649, 975, 278, 1051, 29889, 910, 12541, 18172, 2745, 421, 29875, 29952, 338, 3109, 1135, 421, 29876, 1673, 278, 3309, 310, 278, 1051, 29889, 450, 421, 8000, 29952, 2425, 674, 451, 8469, 565, 278, 1051, 338, 4069, 577, 372, 5149, 17766, 4069, 8857, 29889, 2] +labels: +[PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] ```python +# Establish an integer list +arr = [1, 2, 3, 4] + +# Determine the length of the list +n = len(arr) + +# Initialize index at 0 +i = 0 + +# Traverse the list and output each individual element +while i < n: + print(arr[i]) + i += 1 +``` +In the given code, it tries to access `arr[n]` which is out of bounds as python list indexing starts from 0 and ends at n-1 for a list of length n. +Also, I used `while` loop as per your request. By initializing `i=0` before the loop and `i += 1` within the loop, we can iterate over the list. This iteration continues until `i` is less than `n`, the length of the list. The `while` loop will not proceed if the list is empty so it correctly handles empty lists. +inputs: + [INST]Please amend the subsequent Python script so that it includes a 'while' loop rather than the existing 'for' loop, which iterates through the items of an integer list. + +The script currently has a bug where it attempts to print an object that is outside the bounds of the list. Fix this error and modify the script to use 'while' instead of 'for' loop. Ensure your script correctly handles empty lists. + +```python + # Establish an integer list + arr = [1, 2, 3, 4] + + # Determine the length of the list + n = len(arr) + + # Traverse the list and output each individual element + for i in range(n+1): + print(arr[i]) +```[/INST] ```python +# Establish an integer list +arr = [1, 2, 3, 4] + +# Determine the length of the list +n = len(arr) + +# Initialize index at 0 +i = 0 + +# Traverse the list and output each individual element +while i < n: + print(arr[i]) + i += 1 +``` +In the given code, it tries to access `arr[n]` which is out of bounds as python list indexing starts from 0 and ends at n-1 for a list of length n. +Also, I used `while` loop as per your request. By initializing `i=0` before the loop and `i += 1` within the loop, we can iterate over the list. This iteration continues until `i` is less than `n`, the length of the list. The `while` loop will not proceed if the list is empty so it correctly handles empty lists. +label_ids: +[-100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, 7521, 4691, 13, 29937, 2661, 370, 1674, 385, 6043, 1051, 13, 2749, 353, 518, 29896, 29892, 29871, 29906, 29892, 29871, 29941, 29892, 29871, 29946, 29962, 13, 13, 29937, 5953, 837, 457, 278, 3309, 310, 278, 1051, 13, 29876, 353, 7431, 29898, 2749, 29897, 13, 13, 29937, 25455, 2380, 472, 29871, 29900, 13, 29875, 353, 29871, 29900, 13, 13, 29937, 3201, 3901, 278, 1051, 322, 1962, 1269, 5375, 1543, 13, 8000, 474, 529, 302, 29901, 13, 1678, 1596, 29898, 2749, 29961, 29875, 2314, 13, 1678, 474, 4619, 29871, 29896, 13, 28956, 13, 797, 278, 2183, 775, 29892, 372, 14335, 304, 2130, 421, 2749, 29961, 29876, 7961, 607, 338, 714, 310, 13451, 408, 3017, 1051, 26190, 8665, 515, 29871, 29900, 322, 10614, 472, 302, 29899, 29896, 363, 263, 1051, 310, 3309, 302, 29889, 13, 17351, 29892, 306, 1304, 421, 8000, 29952, 2425, 408, 639, 596, 2009, 29889, 2648, 2847, 5281, 421, 29875, 29922, 29900, 29952, 1434, 278, 2425, 322, 421, 29875, 4619, 29871, 29896, 29952, 2629, 278, 2425, 29892, 591, 508, 13649, 975, 278, 1051, 29889, 910, 12541, 18172, 2745, 421, 29875, 29952, 338, 3109, 1135, 421, 29876, 1673, 278, 3309, 310, 278, 1051, 29889, 450, 421, 8000, 29952, 2425, 674, 451, 8469, 565, 278, 1051, 338, 4069, 577, 372, 5149, 17766, 4069, 8857, 29889, 2] +inputs: + [INST]Please amend the subsequent Python script so that it includes a 'while' loop rather than the existing 'for' loop, which iterates through the items of an integer list. + +The script currently has a bug where it attempts to print an object that is outside the bounds of the list. Fix this error and modify the script to use 'while' instead of 'for' loop. Ensure your script correctly handles empty lists. + +```python + # Establish an integer list + arr = [1, 2, 3, 4] + + # Determine the length of the list + n = len(arr) + + # Traverse the list and output each individual element + for i in range(n+1): + print(arr[i]) +```[/INST] ```python +# Establish an integer list +arr = [1, 2, 3, 4] + +# Determine the length of the list +n = len(arr) + +# Initialize index at 0 +i = 0 + +# Traverse the list and output each individual element +while i < n: + print(arr[i]) + i += 1 +``` +In the given code, it tries to access `arr[n]` which is out of bounds as python list indexing starts from 0 and ends at n-1 for a list of length n. +Also, I used `while` loop as per your request. By initializing `i=0` before the loop and `i += 1` within the loop, we can iterate over the list. This iteration continues until `i` is less than `n`, the length of the list. The `while` loop will not proceed if the list is empty so it correctly handles empty lists. +label_ids: +[-100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, 7521, 4691, 13, 29937, 2661, 370, 1674, 385, 6043, 1051, 13, 2749, 353, 518, 29896, 29892, 29871, 29906, 29892, 29871, 29941, 29892, 29871, 29946, 29962, 13, 13, 29937, 5953, 837, 457, 278, 3309, 310, 278, 1051, 13, 29876, 353, 7431, 29898, 2749, 29897, 13, 13, 29937, 25455, 2380, 472, 29871, 29900, 13, 29875, 353, 29871, 29900, 13, 13, 29937, 3201, 3901, 278, 1051, 322, 1962, 1269, 5375, 1543, 13, 8000, 474, 529, 302, 29901, 13, 1678, 1596, 29898, 2749, 29961, 29875, 2314, 13, 1678, 474, 4619, 29871, 29896, 13, 28956, 13, 797, 278, 2183, 775, 29892, 372, 14335, 304, 2130, 421, 2749, 29961, 29876, 7961, 607, 338, 714, 310, 13451, 408, 3017, 1051, 26190, 8665, 515, 29871, 29900, 322, 10614, 472, 302, 29899, 29896, 363, 263, 1051, 310, 3309, 302, 29889, 13, 17351, 29892, 306, 1304, 421, 8000, 29952, 2425, 408, 639, 596, 2009, 29889, 2648, 2847, 5281, 421, 29875, 29922, 29900, 29952, 1434, 278, 2425, 322, 421, 29875, 4619, 29871, 29896, 29952, 2629, 278, 2425, 29892, 591, 508, 13649, 975, 278, 1051, 29889, 910, 12541, 18172, 2745, 421, 29875, 29952, 338, 3109, 1135, 421, 29876, 1673, 278, 3309, 310, 278, 1051, 29889, 450, 421, 8000, 29952, 2425, 674, 451, 8469, 565, 278, 1051, 338, 4069, 577, 372, 5149, 17766, 4069, 8857, 29889, 2] +inputs: + [INST]Please amend the subsequent Python script so that it includes a 'while' loop rather than the existing 'for' loop, which iterates through the items of an integer list. + +The script currently has a bug where it attempts to print an object that is outside the bounds of the list. Fix this error and modify the script to use 'while' instead of 'for' loop. Ensure your script correctly handles empty lists. + +```python + # Establish an integer list + arr = [1, 2, 3, 4] + + # Determine the length of the list + n = len(arr) + + # Traverse the list and output each individual element + for i in range(n+1): + print(arr[i]) +```[/INST] ```python +# Establish an integer list +arr = [1, 2, 3, 4] + +# Determine the length of the list +n = len(arr) + +# Initialize index at 0 +i = 0 + +# Traverse the list and output each individual element +while i < n: + print(arr[i]) + i += 1 +``` +In the given code, it tries to access `arr[n]` which is out of bounds as python list indexing starts from 0 and ends at n-1 for a list of length n. +Also, I used `while` loop as per your request. By initializing `i=0` before the loop and `i += 1` within the loop, we can iterate over the list. This iteration continues until `i` is less than `n`, the length of the list. The `while` loop will not proceed if the list is empty so it correctly handles empty lists. +label_ids: +[-100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, 7521, 4691, 13, 29937, 2661, 370, 1674, 385, 6043, 1051, 13, 2749, 353, 518, 29896, 29892, 29871, 29906, 29892, 29871, 29941, 29892, 29871, 29946, 29962, 13, 13, 29937, 5953, 837, 457, 278, 3309, 310, 278, 1051, 13, 29876, 353, 7431, 29898, 2749, 29897, 13, 13, 29937, 25455, 2380, 472, 29871, 29900, 13, 29875, 353, 29871, 29900, 13, 13, 29937, 3201, 3901, 278, 1051, 322, 1962, 1269, 5375, 1543, 13, 8000, 474, 529, 302, 29901, 13, 1678, 1596, 29898, 2749, 29961, 29875, 2314, 13, 1678, 474, 4619, 29871, 29896, 13, 28956, 13, 797, 278, 2183, 775, 29892, 372, 14335, 304, 2130, 421, 2749, 29961, 29876, 7961, 607, 338, 714, 310, 13451, 408, 3017, 1051, 26190, 8665, 515, 29871, 29900, 322, 10614, 472, 302, 29899, 29896, 363, 263, 1051, 310, 3309, 302, 29889, 13, 17351, 29892, 306, 1304, 421, 8000, 29952, 2425, 408, 639, 596, 2009, 29889, 2648, 2847, 5281, 421, 29875, 29922, 29900, 29952, 1434, 278, 2425, 322, 421, 29875, 4619, 29871, 29896, 29952, 2629, 278, 2425, 29892, 591, 508, 13649, 975, 278, 1051, 29889, 910, 12541, 18172, 2745, 421, 29875, 29952, 338, 3109, 1135, 421, 29876, 1673, 278, 3309, 310, 278, 1051, 29889, 450, 421, 8000, 29952, 2425, 674, 451, 8469, 565, 278, 1051, 338, 4069, 577, 372, 5149, 17766, 4069, 8857, 29889, 2] +inputs: + [INST]Please amend the subsequent Python script so that it includes a 'while' loop rather than the existing 'for' loop, which iterates through the items of an integer list. + +The script currently has a bug where it attempts to print an object that is outside the bounds of the list. Fix this error and modify the script to use 'while' instead of 'for' loop. Ensure your script correctly handles empty lists. + +```python + # Establish an integer list + arr = [1, 2, 3, 4] + + # Determine the length of the list + n = len(arr) + + # Traverse the list and output each individual element + for i in range(n+1): + print(arr[i]) +```[/INST] ```python +# Establish an integer list +arr = [1, 2, 3, 4] + +# Determine the length of the list +n = len(arr) + +# Initialize index at 0 +i = 0 + +# Traverse the list and output each individual element +while i < n: + print(arr[i]) + i += 1 +``` +In the given code, it tries to access `arr[n]` which is out of bounds as python list indexing starts from 0 and ends at n-1 for a list of length n. +Also, I used `while` loop as per your request. By initializing `i=0` before the loop and `i += 1` within the loop, we can iterate over the list. This iteration continues until `i` is less than `n`, the length of the list. The `while` loop will not proceed if the list is empty so it correctly handles empty lists. +label_ids: +[-100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, 7521, 4691, 13, 29937, 2661, 370, 1674, 385, 6043, 1051, 13, 2749, 353, 518, 29896, 29892, 29871, 29906, 29892, 29871, 29941, 29892, 29871, 29946, 29962, 13, 13, 29937, 5953, 837, 457, 278, 3309, 310, 278, 1051, 13, 29876, 353, 7431, 29898, 2749, 29897, 13, 13, 29937, 25455, 2380, 472, 29871, 29900, 13, 29875, 353, 29871, 29900, 13, 13, 29937, 3201, 3901, 278, 1051, 322, 1962, 1269, 5375, 1543, 13, 8000, 474, 529, 302, 29901, 13, 1678, 1596, 29898, 2749, 29961, 29875, 2314, 13, 1678, 474, 4619, 29871, 29896, 13, 28956, 13, 797, 278, 2183, 775, 29892, 372, 14335, 304, 2130, 421, 2749, 29961, 29876, 7961, 607, 338, 714, 310, 13451, 408, 3017, 1051, 26190, 8665, 515, 29871, 29900, 322, 10614, 472, 302, 29899, 29896, 363, 263, 1051, 310, 3309, 302, 29889, 13, 17351, 29892, 306, 1304, 421, 8000, 29952, 2425, 408, 639, 596, 2009, 29889, 2648, 2847, 5281, 421, 29875, 29922, 29900, 29952, 1434, 278, 2425, 322, 421, 29875, 4619, 29871, 29896, 29952, 2629, 278, 2425, 29892, 591, 508, 13649, 975, 278, 1051, 29889, 910, 12541, 18172, 2745, 421, 29875, 29952, 338, 3109, 1135, 421, 29876, 1673, 278, 3309, 310, 278, 1051, 29889, 450, 421, 8000, 29952, 2425, 674, 451, 8469, 565, 278, 1051, 338, 4069, 577, 372, 5149, 17766, 4069, 8857, 29889, 2] +labels: +[PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] ```python +# Establish an integer list +arr = [1, 2, 3, 4] + +# Determine the length of the list +n = len(arr) + +# Initialize index at 0 +i = 0 + +# Traverse the list and output each individual element +while i < n: + print(arr[i]) + i += 1 +``` +In the given code, it tries to access `arr[n]` which is out of bounds as python list indexing starts from 0 and ends at n-1 for a list of length n. +Also, I used `while` loop as per your request. By initializing `i=0` before the loop and `i += 1` within the loop, we can iterate over the list. This iteration continues until `i` is less than `n`, the length of the list. The `while` loop will not proceed if the list is empty so it correctly handles empty lists. +length of input_ids : +394 +input_ids: +[1, 518, 25580, 29962, 12148, 626, 355, 278, 15352, 5132, 2471, 577, 393, 372, 7805, 263, 525, 8000, 29915, 2425, 3265, 1135, 278, 5923, 525, 1454, 29915, 2425, 29892, 607, 4256, 1078, 1549, 278, 4452, 310, 385, 6043, 1051, 29889, 13, 13, 1576, 2471, 5279, 756, 263, 6494, 988, 372, 14734, 304, 1596, 385, 1203, 393, 338, 5377, 278, 13451, 310, 278, 1051, 29889, 24778, 445, 1059, 322, 6623, 278, 2471, 304, 671, 525, 8000, 29915, 2012, 310, 525, 1454, 29915, 2425, 29889, 22521, 545, 596, 2471, 5149, 17766, 4069, 8857, 29889, 29871, 13, 13, 28956, 4691, 13, 29871, 396, 2661, 370, 1674, 385, 6043, 1051, 13, 29871, 3948, 353, 518, 29896, 29892, 29871, 29906, 29892, 29871, 29941, 29892, 29871, 29946, 29962, 13, 13, 29871, 396, 5953, 837, 457, 278, 3309, 310, 278, 1051, 13, 29871, 302, 353, 7431, 29898, 2749, 29897, 13, 13, 29871, 396, 3201, 3901, 278, 1051, 322, 1962, 1269, 5375, 1543, 13, 29871, 363, 474, 297, 3464, 29898, 29876, 29974, 29896, 1125, 13, 418, 1596, 29898, 2749, 29961, 29875, 2314, 13, 28956, 29961, 29914, 25580, 29962, 7521, 4691, 13, 29937, 2661, 370, 1674, 385, 6043, 1051, 13, 2749, 353, 518, 29896, 29892, 29871, 29906, 29892, 29871, 29941, 29892, 29871, 29946, 29962, 13, 13, 29937, 5953, 837, 457, 278, 3309, 310, 278, 1051, 13, 29876, 353, 7431, 29898, 2749, 29897, 13, 13, 29937, 25455, 2380, 472, 29871, 29900, 13, 29875, 353, 29871, 29900, 13, 13, 29937, 3201, 3901, 278, 1051, 322, 1962, 1269, 5375, 1543, 13, 8000, 474, 529, 302, 29901, 13, 1678, 1596, 29898, 2749, 29961, 29875, 2314, 13, 1678, 474, 4619, 29871, 29896, 13, 28956, 13, 797, 278, 2183, 775, 29892, 372, 14335, 304, 2130, 421, 2749, 29961, 29876, 7961, 607, 338, 714, 310, 13451, 408, 3017, 1051, 26190, 8665, 515, 29871, 29900, 322, 10614, 472, 302, 29899, 29896, 363, 263, 1051, 310, 3309, 302, 29889, 13, 17351, 29892, 306, 1304, 421, 8000, 29952, 2425, 408, 639, 596, 2009, 29889, 2648, 2847, 5281, 421, 29875, 29922, 29900, 29952, 1434, 278, 2425, 322, 421, 29875, 4619, 29871, 29896, 29952, 2629, 278, 2425, 29892, 591, 508, 13649, 975, 278, 1051, 29889, 910, 12541, 18172, 2745, 421, 29875, 29952, 338, 3109, 1135, 421, 29876, 1673, 278, 3309, 310, 278, 1051, 29889, 450, 421, 8000, 29952, 2425, 674, 451, 8469, 565, 278, 1051, 338, 4069, 577, 372, 5149, 17766, 4069, 8857, 29889, 2] +labels: +[PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] ```python +# Establish an integer list +arr = [1, 2, 3, 4] + +# Determine the length of the list +n = len(arr) + +# Initialize index at 0 +i = 0 + +# Traverse the list and output each individual element +while i < n: + print(arr[i]) + i += 1 +``` +In the given code, it tries to access `arr[n]` which is out of bounds as python list indexing starts from 0 and ends at n-1 for a list of length n. +Also, I used `while` loop as per your request. By initializing `i=0` before the loop and `i += 1` within the loop, we can iterate over the list. This iteration continues until `i` is less than `n`, the length of the list. The `while` loop will not proceed if the list is empty so it correctly handles empty lists. +labels: +[PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] ```python +# Establish an integer list +arr = [1, 2, 3, 4] + +# Determine the length of the list +n = len(arr) + +# Initialize index at 0 +i = 0 + +# Traverse the list and output each individual element +while i < n: + print(arr[i]) + i += 1 +``` +In the given code, it tries to access `arr[n]` which is out of bounds as python list indexing starts from 0 and ends at n-1 for a list of length n. +Also, I used `while` loop as per your request. By initializing `i=0` before the loop and `i += 1` within the loop, we can iterate over the list. This iteration continues until `i` is less than `n`, the length of the list. The `while` loop will not proceed if the list is empty so it correctly handles empty lists. +length of input_ids : +394 +input_ids: +[1, 518, 25580, 29962, 12148, 626, 355, 278, 15352, 5132, 2471, 577, 393, 372, 7805, 263, 525, 8000, 29915, 2425, 3265, 1135, 278, 5923, 525, 1454, 29915, 2425, 29892, 607, 4256, 1078, 1549, 278, 4452, 310, 385, 6043, 1051, 29889, 13, 13, 1576, 2471, 5279, 756, 263, 6494, 988, 372, 14734, 304, 1596, 385, 1203, 393, 338, 5377, 278, 13451, 310, 278, 1051, 29889, 24778, 445, 1059, 322, 6623, 278, 2471, 304, 671, 525, 8000, 29915, 2012, 310, 525, 1454, 29915, 2425, 29889, 22521, 545, 596, 2471, 5149, 17766, 4069, 8857, 29889, 29871, 13, 13, 28956, 4691, 13, 29871, 396, 2661, 370, 1674, 385, 6043, 1051, 13, 29871, 3948, 353, 518, 29896, 29892, 29871, 29906, 29892, 29871, 29941, 29892, 29871, 29946, 29962, 13, 13, 29871, 396, 5953, 837, 457, 278, 3309, 310, 278, 1051, 13, 29871, 302, 353, 7431, 29898, 2749, 29897, 13, 13, 29871, 396, 3201, 3901, 278, 1051, 322, 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639, 596, 2009, 29889, 2648, 2847, 5281, 421, 29875, 29922, 29900, 29952, 1434, 278, 2425, 322, 421, 29875, 4619, 29871, 29896, 29952, 2629, 278, 2425, 29892, 591, 508, 13649, 975, 278, 1051, 29889, 910, 12541, 18172, 2745, 421, 29875, 29952, 338, 3109, 1135, 421, 29876, 1673, 278, 3309, 310, 278, 1051, 29889, 450, 421, 8000, 29952, 2425, 674, 451, 8469, 565, 278, 1051, 338, 4069, 577, 372, 5149, 17766, 4069, 8857, 29889, 2] +length of input_ids : +394 +input_ids: +[1, 518, 25580, 29962, 12148, 626, 355, 278, 15352, 5132, 2471, 577, 393, 372, 7805, 263, 525, 8000, 29915, 2425, 3265, 1135, 278, 5923, 525, 1454, 29915, 2425, 29892, 607, 4256, 1078, 1549, 278, 4452, 310, 385, 6043, 1051, 29889, 13, 13, 1576, 2471, 5279, 756, 263, 6494, 988, 372, 14734, 304, 1596, 385, 1203, 393, 338, 5377, 278, 13451, 310, 278, 1051, 29889, 24778, 445, 1059, 322, 6623, 278, 2471, 304, 671, 525, 8000, 29915, 2012, 310, 525, 1454, 29915, 2425, 29889, 22521, 545, 596, 2471, 5149, 17766, 4069, 8857, 29889, 29871, 13, 13, 28956, 4691, 13, 29871, 396, 2661, 370, 1674, 385, 6043, 1051, 13, 29871, 3948, 353, 518, 29896, 29892, 29871, 29906, 29892, 29871, 29941, 29892, 29871, 29946, 29962, 13, 13, 29871, 396, 5953, 837, 457, 278, 3309, 310, 278, 1051, 13, 29871, 302, 353, 7431, 29898, 2749, 29897, 13, 13, 29871, 396, 3201, 3901, 278, 1051, 322, 1962, 1269, 5375, 1543, 13, 29871, 363, 474, 297, 3464, 29898, 29876, 29974, 29896, 1125, 13, 418, 1596, 29898, 2749, 29961, 29875, 2314, 13, 28956, 29961, 29914, 25580, 29962, 7521, 4691, 13, 29937, 2661, 370, 1674, 385, 6043, 1051, 13, 2749, 353, 518, 29896, 29892, 29871, 29906, 29892, 29871, 29941, 29892, 29871, 29946, 29962, 13, 13, 29937, 5953, 837, 457, 278, 3309, 310, 278, 1051, 13, 29876, 353, 7431, 29898, 2749, 29897, 13, 13, 29937, 25455, 2380, 472, 29871, 29900, 13, 29875, 353, 29871, 29900, 13, 13, 29937, 3201, 3901, 278, 1051, 322, 1962, 1269, 5375, 1543, 13, 8000, 474, 529, 302, 29901, 13, 1678, 1596, 29898, 2749, 29961, 29875, 2314, 13, 1678, 474, 4619, 29871, 29896, 13, 28956, 13, 797, 278, 2183, 775, 29892, 372, 14335, 304, 2130, 421, 2749, 29961, 29876, 7961, 607, 338, 714, 310, 13451, 408, 3017, 1051, 26190, 8665, 515, 29871, 29900, 322, 10614, 472, 302, 29899, 29896, 363, 263, 1051, 310, 3309, 302, 29889, 13, 17351, 29892, 306, 1304, 421, 8000, 29952, 2425, 408, 639, 596, 2009, 29889, 2648, 2847, 5281, 421, 29875, 29922, 29900, 29952, 1434, 278, 2425, 322, 421, 29875, 4619, 29871, 29896, 29952, 2629, 278, 2425, 29892, 591, 508, 13649, 975, 278, 1051, 29889, 910, 12541, 18172, 2745, 421, 29875, 29952, 338, 3109, 1135, 421, 29876, 1673, 278, 3309, 310, 278, 1051, 29889, 450, 421, 8000, 29952, 2425, 674, 451, 8469, 565, 278, 1051, 338, 4069, 577, 372, 5149, 17766, 4069, 8857, 29889, 2]length of input_ids : +1212 + +input_ids: +[1, 518, 25580, 29962, 29875, 29915, 345, 2355, 445, 3017, 775, 515, 385, 288, 7283, 5780, 29892, 541, 372, 29915, 29879, 451, 1985, 29889, 508, 366, 4744, 372, 363, 592, 29973, 13, 1990, 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19203, 29952, 770, 29892, 8151, 366, 505, 304, 1207, 1854, 393, 445, 4413, 505, 1906, 421, 275, 8667, 1673, 421, 3068, 16619, 29952, 322, 421, 11631, 29952, 3519, 297, 1797, 445, 775, 304, 664, 29889, 2] +labels: +[PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] ```python +# Establish an integer list +arr = [1, 2, 3, 4] + +# Determine the length of the list +n = len(arr) + +# Initialize index at 0 +i = 0 + +# Traverse the list and output each individual element +while i < n: + print(arr[i]) + i += 1 +``` +In the given code, it tries to access `arr[n]` which is out of bounds as python list indexing starts from 0 and ends at n-1 for a list of length n. +Also, I used `while` loop as per your request. By initializing `i=0` before the loop and `i += 1` within the loop, we can iterate over the list. This iteration continues until `i` is less than `n`, the length of the list. The `while` loop will not proceed if the list is empty so it correctly handles empty lists. +length of input_ids : +1212 +input_ids: +[1, 518, 25580, 29962, 29875, 29915, 345, 2355, 445, 3017, 775, 515, 385, 288, 7283, 5780, 29892, 541, 372, 29915, 29879, 451, 1985, 29889, 508, 366, 4744, 372, 363, 592, 29973, 13, 1990, 29871, 29945, 324, 29894, 29955, 29991, 265, 29901, 13, 29877, 300, 260, 513, 21322, 30181, 303, 11457, 29876, 1566, 761, 29952, 302, 29876, 29987, 29879, 29955, 261, 29901, 525, 5756, 29924, 29987, 29879, 29974, 261, 1495, 448, 29966, 29871, 29896, 2273, 29901, 13, 1311, 29889, 29877, 29896, 29878, 353, 11117, 29965, 2396, 21069, 29896, 29892, 29871, 29900, 1822, 29918, 29915, 29900, 2396, 518, 29896, 29892, 29871, 29900, 1118, 525, 29931, 2396, 518, 29900, 29892, 29918, 29899, 29896, 1822, 525, 29934, 2396, 426, 29900, 29892, 29871, 29896, 12258, 13, 29879, 2152, 29889, 11569, 29918, 29881, 29991, 29878, 448, 11117, 29963, 2396, 525, 29928, 742, 525, 29928, 2396, 525, 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7411, 877, 7192, 8785, 13, 4706, 396, 360, 9998, 2791, 714, 278, 2989, 2910, 13, 4706, 1583, 29889, 5182, 353, 6213, 13, 4706, 1583, 29889, 29069, 3552, 29900, 29892, 29871, 29900, 511, 5835, 29897, 13, 4706, 565, 1583, 29889, 5182, 338, 6213, 29901, 13, 9651, 736, 448, 29896, 13, 4706, 396, 350, 9998, 1284, 278, 3273, 342, 2224, 13, 4706, 736, 1583, 29889, 1635, 29879, 3552, 29900, 29892, 29871, 29900, 876, 13, 13, 1678, 822, 4489, 29879, 29898, 1311, 29892, 1298, 29892, 5835, 1125, 13, 4706, 565, 5835, 29889, 275, 8667, 7295, 13, 9651, 1583, 29889, 5182, 353, 1298, 13, 4706, 396, 2791, 16669, 2943, 408, 29871, 29900, 13, 4706, 1583, 29889, 7720, 29961, 3149, 29962, 353, 29871, 29900, 13, 4706, 363, 270, 297, 1583, 29889, 3972, 29901, 13, 9651, 302, 486, 29918, 3149, 353, 313, 3149, 29961, 29900, 10062, 1311, 29889, 3972, 29961, 29881, 3816, 29900, 1402, 1298, 29961, 29896, 10062, 1311, 29889, 3972, 29961, 29881, 3816, 29896, 2314, 13, 9651, 565, 5835, 29889, 3068, 16619, 29898, 29881, 29897, 322, 1583, 29889, 7720, 29961, 29876, 486, 29918, 3149, 29962, 1275, 5785, 877, 7192, 29374, 13, 18884, 5835, 29889, 11631, 29898, 29881, 29897, 13, 18884, 1583, 29889, 29069, 29898, 29876, 486, 29918, 3149, 29892, 5835, 29897, 13, 18884, 5835, 29889, 11631, 29898, 1311, 29889, 11569, 29918, 3972, 29961, 29881, 2314, 13, 13, 1678, 822, 289, 5847, 29898, 1311, 29892, 1369, 1125, 13, 4706, 316, 802, 353, 16250, 29889, 311, 802, 4197, 29898, 2962, 29892, 29871, 29900, 29897, 2314, 13, 4706, 1550, 316, 802, 29901, 13, 9651, 1298, 29892, 4331, 353, 316, 802, 29889, 7323, 1563, 580, 13, 9651, 565, 1298, 1275, 1583, 29889, 5182, 29901, 13, 18884, 736, 4331, 13, 9651, 363, 270, 297, 1583, 29889, 3972, 29889, 5975, 7295, 13, 18884, 302, 486, 29918, 3149, 353, 313, 3149, 29961, 29900, 10062, 29881, 29961, 29900, 1402, 1298, 29961, 29896, 10062, 29881, 29961, 29896, 2314, 13, 18884, 565, 1583, 29889, 7720, 29961, 29876, 486, 29918, 3149, 29962, 1275, 29871, 29900, 29901, 13, 462, 1678, 316, 802, 29889, 4397, 3552, 29876, 486, 29918, 3149, 29892, 4331, 29974, 29896, 876, 13, 462, 1678, 396, 2791, 16669, 2943, 408, 448, 29896, 13, 462, 1678, 1583, 29889, 7720, 29961, 29876, 486, 29918, 3149, 29962, 353, 448, 29896, 13, 28956, 13, 12148, 4443, 393, 366, 3282, 29915, 29873, 3867, 278, 421, 5756, 19203, 29952, 770, 29892, 8151, 366, 505, 304, 1207, 1854, 393, 445, 4413, 505, 1906, 421, 275, 8667, 1673, 421, 3068, 16619, 29952, 322, 421, 11631, 29952, 3519, 297, 1797, 445, 775, 304, 664, 29889, 2] +inputs: + [INST]Please amend the subsequent Python script so that it includes a 'while' loop rather than the existing 'for' loop, which iterates through the items of an integer list. + +The script currently has a bug where it attempts to print an object that is outside the bounds of the list. Fix this error and modify the script to use 'while' instead of 'for' loop. Ensure your script correctly handles empty lists. + +```python + # Establish an integer list + arr = [1, 2, 3, 4] + + # Determine the length of the list + n = len(arr) + + # Traverse the list and output each individual element + for i in range(n+1): + print(arr[i]) +```[/INST] ```python +# Establish an integer list +arr = [1, 2, 3, 4] + +# Determine the length of the list +n = len(arr) + +# Initialize index at 0 +i = 0 + +# Traverse the list and output each individual element +while i < n: + print(arr[i]) + i += 1 +``` +In the given code, it tries to access `arr[n]` which is out of bounds as python list indexing starts from 0 and ends at n-1 for a list of length n. +Also, I used `while` loop as per your request. By initializing `i=0` before the loop and `i += 1` within the loop, we can iterate over the list. This iteration continues until `i` is less than `n`, the length of the list. The `while` loop will not proceed if the list is empty so it correctly handles empty lists. +label_ids: +[-100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, 7521, 4691, 13, 29937, 2661, 370, 1674, 385, 6043, 1051, 13, 2749, 353, 518, 29896, 29892, 29871, 29906, 29892, 29871, 29941, 29892, 29871, 29946, 29962, 13, 13, 29937, 5953, 837, 457, 278, 3309, 310, 278, 1051, 13, 29876, 353, 7431, 29898, 2749, 29897, 13, 13, 29937, 25455, 2380, 472, 29871, 29900, 13, 29875, 353, 29871, 29900, 13, 13, 29937, 3201, 3901, 278, 1051, 322, 1962, 1269, 5375, 1543, 13, 8000, 474, 529, 302, 29901, 13, 1678, 1596, 29898, 2749, 29961, 29875, 2314, 13, 1678, 474, 4619, 29871, 29896, 13, 28956, 13, 797, 278, 2183, 775, 29892, 372, 14335, 304, 2130, 421, 2749, 29961, 29876, 7961, 607, 338, 714, 310, 13451, 408, 3017, 1051, 26190, 8665, 515, 29871, 29900, 322, 10614, 472, 302, 29899, 29896, 363, 263, 1051, 310, 3309, 302, 29889, 13, 17351, 29892, 306, 1304, 421, 8000, 29952, 2425, 408, 639, 596, 2009, 29889, 2648, 2847, 5281, 421, 29875, 29922, 29900, 29952, 1434, 278, 2425, 322, 421, 29875, 4619, 29871, 29896, 29952, 2629, 278, 2425, 29892, 591, 508, 13649, 975, 278, 1051, 29889, 910, 12541, 18172, 2745, 421, 29875, 29952, 338, 3109, 1135, 421, 29876, 1673, 278, 3309, 310, 278, 1051, 29889, 450, 421, 8000, 29952, 2425, 674, 451, 8469, 565, 278, 1051, 338, 4069, 577, 372, 5149, 17766, 4069, 8857, 29889, 2] +inputs: + [INST]Please amend the subsequent Python script so that it includes a 'while' loop rather than the existing 'for' loop, which iterates through the items of an integer list. + +The script currently has a bug where it attempts to print an object that is outside the bounds of the list. Fix this error and modify the script to use 'while' instead of 'for' loop. Ensure your script correctly handles empty lists. + +```python + # Establish an integer list + arr = [1, 2, 3, 4] + + # Determine the length of the list + n = len(arr) + + # Traverse the list and output each individual element + for i in range(n+1): + print(arr[i]) +```[/INST] ```python +# Establish an integer list +arr = [1, 2, 3, 4] + +# Determine the length of the list +n = len(arr) + +# Initialize index at 0 +i = 0 + +# Traverse the list and output each individual element +while i < n: + print(arr[i]) + i += 1 +``` +In the given code, it tries to access `arr[n]` which is out of bounds as python list indexing starts from 0 and ends at n-1 for a list of length n. +Also, I used `while` loop as per your request. By initializing `i=0` before the loop and `i += 1` within the loop, we can iterate over the list. This iteration continues until `i` is less than `n`, the length of the list. The `while` loop will not proceed if the list is empty so it correctly handles empty lists. +label_ids: +[-100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, 7521, 4691, 13, 29937, 2661, 370, 1674, 385, 6043, 1051, 13, 2749, 353, 518, 29896, 29892, 29871, 29906, 29892, 29871, 29941, 29892, 29871, 29946, 29962, 13, 13, 29937, 5953, 837, 457, 278, 3309, 310, 278, 1051, 13, 29876, 353, 7431, 29898, 2749, 29897, 13, 13, 29937, 25455, 2380, 472, 29871, 29900, 13, 29875, 353, 29871, 29900, 13, 13, 29937, 3201, 3901, 278, 1051, 322, 1962, 1269, 5375, 1543, 13, 8000, 474, 529, 302, 29901, 13, 1678, 1596, 29898, 2749, 29961, 29875, 2314, 13, 1678, 474, 4619, 29871, 29896, 13, 28956, 13, 797, 278, 2183, 775, 29892, 372, 14335, 304, 2130, 421, 2749, 29961, 29876, 7961, 607, 338, 714, 310, 13451, 408, 3017, 1051, 26190, 8665, 515, 29871, 29900, 322, 10614, 472, 302, 29899, 29896, 363, 263, 1051, 310, 3309, 302, 29889, 13, 17351, 29892, 306, 1304, 421, 8000, 29952, 2425, 408, 639, 596, 2009, 29889, 2648, 2847, 5281, 421, 29875, 29922, 29900, 29952, 1434, 278, 2425, 322, 421, 29875, 4619, 29871, 29896, 29952, 2629, 278, 2425, 29892, 591, 508, 13649, 975, 278, 1051, 29889, 910, 12541, 18172, 2745, 421, 29875, 29952, 338, 3109, 1135, 421, 29876, 1673, 278, 3309, 310, 278, 1051, 29889, 450, 421, 8000, 29952, 2425, 674, 451, 8469, 565, 278, 1051, 338, 4069, 577, 372, 5149, 17766, 4069, 8857, 29889, 2] +inputs: + [INST]Please amend the subsequent Python script so that it includes a 'while' loop rather than the existing 'for' loop, which iterates through the items of an integer list. + +The script currently has a bug where it attempts to print an object that is outside the bounds of the list. Fix this error and modify the script to use 'while' instead of 'for' loop. Ensure your script correctly handles empty lists. + +```python + # Establish an integer list + arr = [1, 2, 3, 4] + + # Determine the length of the list + n = len(arr) + + # Traverse the list and output each individual element + for i in range(n+1): + print(arr[i]) +```[/INST] ```python +# Establish an integer list +arr = [1, 2, 3, 4] + +# Determine the length of the list +n = len(arr) + +# Initialize index at 0 +i = 0 + +# Traverse the list and output each individual element +while i < n: + print(arr[i]) + i += 1 +``` +In the given code, it tries to access `arr[n]` which is out of bounds as python list indexing starts from 0 and ends at n-1 for a list of length n. +Also, I used `while` loop as per your request. By initializing `i=0` before the loop alength of input_ids : +1212 +input_ids: +[1, 518, 25580, 29962, 29875, 29915, 345, 2355, 445, 3017, 775, 515, 385, 288, 7283, 5780, 29892, 541, 372, 29915, 29879, 451, 1985, 29889, 508, 366, 4744, 372, 363, 592, 29973, 13, 1990, 29871, 29945, 324, 29894, 29955, 29991, 265, 29901, 13, 29877, 300, 260, 513, 21322, 30181, 303, 11457, 29876, 1566, 761, 29952, 302, 29876, 29987, 29879, 29955, 261, 29901, 525, 5756, 29924, 29987, 29879, 29974, 261, 1495, 448, 29966, 29871, 29896, 2273, 29901, 1-100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, 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29961, 29876, 486, 29918, 3149, 29962, 1275, 5785, 877, 7192, 29374, 13, 18884, 5835, 29889, 11631, 29898, 29881, 29897, 13, 18884, 1583, 29889, 29069, 29898, 29876, 486, 29918, 3149, 29892, 5835, 29897, 13, 18884, 5835, 29889, 11631, 29898, 1311, 29889, 11569, 29918, 3972, 29961, 29881, 2314, 13, 13, 1678, 822, 289, 5847, 29898, 1311, 29892, 1369, 1125, 13, 4706, 316, 802, 353, 16250, 29889, 311, 802, 4197, 29898, 2962, 29892, 29871, 29900, 29897, 2314, 13, L': 'R', 'R': 'L'} + self.grid = collections.defaultdict(lambda:float('inf')) + # DFS mark out the full map + self.target = None + self.dfs((0, 0), master) + if self.target is None: + return -1 + # BFS find the shortest path + return self.bfs((0, 0)) + + def dfs(self, point, master): + if master.isTarget(): + self.target = point + # mark visited node as 0 + self.grid[point] = 0 + for d in self.dir: + , 462, 1678, 316, 802, 29889, 4397, 3552, 29876, 486, 29918, 3149, 29892, 4331, 29974, 29896, 876, 13, 462, 1678, 396, 2791, 16669, 2943, 408, 448, 29896, 13, 462, 1678, 1583, 29889, 7720, 29961, 29876, 486, 29918, 3149, 29962, 353, 448, 29896, 13, 28956, 13, 12148, 4443, 393, 366, 3282, 29915, 29873, 3867, 278, 421, 5756, 19203, 29952, 770, 29892, 8151, 366, 505, 304, 1207, 1854, 393, 445, 4413, 505, 1906, 421, 275, 8667, 1673, 421, 3068, 16619, 29952, 322, 421, 11631, 29952, 3519, 297, 1797, 445, 775, 304, 664, 29889, 2] +length of input_ids : +1212 +input_ids: +[1, 518, 25580, 29962, 29875, 29915, 345, 2355, 445, 3017, 775, 515, 385, 288, 7283, 5780, 29892, 541, 372, 29915, 29879, 451, 1985, 29889, 508, 366, 4744, 372, 363, 592, 29973, 13, 1990, 29871, 29945, 324, 29894, 29955, 29991, 265, 29901, 13, 29877, 300, 260, 513, 21322, 30181, 303, 11457, 29876, 1566, 761, 29952, 302, 29876, 29987, 29879, 29955, 261, 29901, 525, 5756, 29924, 29987, 29879, 29974, 261, 1495, 448, 29966, 29871, 29896, 2273, 29901, 13, 1311, 29889, 29877, 29896, 29878, 353, 11117, 29965, 2396, 21069, 29896, 29892, 29871, 29900, 1822, 29918, 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21322, 342, 2605, 29898, 1311, 29892, 5835, 29901, 525, 5756, 19203, 1495, 1599[PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] ```python +# Establish an integer list +arr = [1, 2, 3, 4] + +# Determine the length of the list +n = len(arr) + +# Initialize index at 0 +i = 0 + +# Traverse the list and output each individual element +while i < n: + print(arr[i]) + i += 1 +``` +In the given code, it tries to access `arr[n]` which is out of bounds as python list indexing starts from 0 and ends at n-1 for a list of length n. +Also, I used `while` loop as per your request. By initializing `i=0` before the loop and `i += 1` within the loop, we can iterate over the list. This iteration continues until `i` is less than `n`, the length of the list. The `while` loop will not proceed if the list is empty so it correctly handles empty lists. +labels: +[PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] ```python +# Establish an integer list +arr = [1, 2, 3, 4] + +# Determine the length of the list +n = len(arr) + +# Initialize index at 0 +i = 0 + +# Traverse the list and output each individual element +while i < n: + print(arr[i]) + i += 1 +``` +In the given code, it tries to access `arr[n]` which is out of bounds as python list indexing starts from 0 and ends at n-1 for a list of length n. +Also, I used `while` loop as per your request. By initializing `i=0` before the loop and `i += 1` within the loop, we can iterate over the list. This iteration continues until `i` is less than `n`, the length of the list. The `while` loop will not proceed if the list is empty so it correctly handles empty lists. +labels: +[PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] ```python +# Establish an integer list +arr = [1, 2, 3, 4] + +# Determine the length of the list +n = len(arr) + +# Initialize index at 0 +i = 0 + +# Traverse the list and output each individual element +while i < n: + print(arr[i]) + i += 1 +``` +In the given code, it tries to access `arr[n]` which is out of bounds as python list indexing starts from 0 and ends at n-1 for a list of length n. +Also, I used `while` loop as per your request. By initializing `i=0` before the loop and `i += 1` within the loop, we can iterate over the list. This iteration continues until `i` is less than `n`, the length of the list. The `while` loop will not proceed if the list is empty so it correctly handles empty lists. +length of input_ids : +1212 +input_ids: +[1, 518, 25580, 29962, 29875, 29915, 345, 2355, 445, 3017, 775, 515, 385, 288, 7283, 5780, 29892, 541, 372, 29915, 29879, 451, 1985, 29889, 508, 366, 4744, 372, 363, 592, 29973, 13, 1990, 29871, 29945, 324, 29894, 29955, 29991, 265, 29901, 13, 29877, 300, 260, 513, 21322, 30181, 303, 11457, 29876, 1566, 761, 29952, 302, 29876, 29987, 29879, 29955, 261, 29901, 525, 5756, 29924, 29987, 29879, 29974, 261, 1495, 448, 29966, 29871, 29896, 2273, 29901, 13, 1311, 29889, 29877, 29896, 29878, 353, 11117, 29965, 2396, 21069, 29896, 29892, 29871, 29900, 1822, 29918, 29915, 29900, 2396, 518, 29896, 29892, 29871, 29900, 1118, 525, 29931, 2396, 518, 29900, 29892, 29918, 29899, 29896, 1822, 525, 29934, 2396, 426, 29900, 29892, 29871, 29896, 12258, 13, 29879, 2152, 29889, 11569, 29918, 29881, 29991, 29878, 448, 11117, 29963, 2396, 525, 29928, 742, 525, 29928, 2396, 525, 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+inputs: + [INST]i've got this python code from an ocr tool, but it's not working. can you debug it for me? +class 5olv7!on: +oet tindShort€stPatn($elf` nn&s7er: 'GridM&s+er') -< 1rt: +self.o1r = {'U': [-1, 0]._'0': [1, 0}, 'L': [0,_-1]. 'R': {0, 1]} +selt.inv_d!r - {'V': 'D', 'D': 'U', 'L': 'R', 'R': 'L'} +z€|f.grio = (ollectioms.defaultdic7(lambda:_flo4t('lnf']) +# DFS mark out the full map +s€|f.targe+ - Non€ +5elf.dfs((0, 0), m&st3r) +1f $eit,t4rget_is Rone: +retvrn -1 +H 8FS find the sn0rtes7 9@+h +neturr self.bfs((O, o)) + +det dfs(zelf, point, mas+en): +if_master,i5Target(): +self.target - poimt +# nnark visited node as O +ze|t,grid[point} = 0 +tor d in self,0in: +rxt_p0imt = (9oimt[0]+self.dir[o](0), pOint[1]+se|f.dir[0][i]] +if_master. int: + self.dir = {'U': [-1, 0],'D': [1, 0], 'L': [0,-1], 'R': [0, 1]} + self.inv_dir = {'U': 'D', 'D': 'U', 'L': 'R', 'R': 'L'} + self.grid = collections.defaultdict(lambda:float('inf')) + # DFS mark out the full map + self.target = None + self.dfs((0, 0), master) + if self.target is None: + return -1 + # BFS find the shortest path + return self.bfs((0, 0)) + + def dfs(self, point, master): + if master.isTarget(): + self.target = point + # mark visited node as 0 + self.grid[point] = 0 + for d in self.dir: + nxt_point = (point[0]+self.dir[d][0], point[1]+self.dir[d][1]) + if master.canMove(d) and self.grid[nxt_point] == float('inf'): + master.move(d) + self.dfs(nxt_point, master) + master.move(self.inv_dir[d]) + + def bfs(self, start): + deque = collections.deque([(start, 0)]) + while deque: + point, step = deque.popleft() + if point == self.target: + return step + for d in self.dir.values(): + nxt_point = (point[0]+d[0], point[1]+d[1]) + if self.grid[nxt_point] == 0: + deque.append((nxt_point, step+1)) + # mark visited node as -1 + self.grid[nxt_point] = -1 +``` +Please note that you didn't provide the `GridMaster` class, hence you have to make sure that this classes have those `isTarget`, `canMove` and `move` methods in order this code to work. +label_ids: +[-100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, 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[PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] There are several issues with your code like flaky names, wrong Python syntax etc. + +Here is how that code should look: + +```python +import collections + +class Solution: + def findShortestPath(self, master: 'GridMaster') -> int: + self.dir = {'U': [-1, 0],'D': [1, 0], 'L': [0,-1], 'R': [0, 1]} + self.inv_dir = {'U': 'D', 'D': 'U', 'L': 'R', 'R': 'L'} + self.grid = collections.defaultdict(lambda:float('inf')) + # DFS mark out the full map + self.target = None + self.dfs((0, 0), master) + if self.target is None: + return -1 + # BFS find the shortest path + return self.bfs((0, 0)) + + def dfs(self, point, master): + if master.isTarget(): + self.target = point + # mark visited node as 0 + self.grid[point] = 0 + for d in self.dir: + nxt_point = (point[0]+self.dir[d][0], point[1]+self.dir[d][1]) + if master.canMove(d) and self.grid[nxt_point] == float('inf'): + master.move(d) + self.dfs(nxt_point, master) + master.move(self.inv_dir[d]) + + def bfs(self, start): + deque = collections.deque([(start, 0)]) + while deque: + point, step = deque.popleft() + if point == self.target: + return step + for d in self.dir.values(): + nxt_point = (point[0]+d[0], point[1]+d[1]) + if self.grid[nxt_point] == 0: + deque.append((nxt_point, step+1)) + # mark visited node as -1 + self.grid[nxt_point] = -1 +``` +Please note that you didn't provide the `GridMaster` class, hence you have to make sure that this classes have those `isTarget`, `canMove` and `move` methods in order this code to work. +inputs: + [INST]i've got this python code from an ocr tool, but it's not working. can you debug it for me? +class 5olv7!on: +oet tindShort€stPatn($elf` nn&s7er: 'GridM&s+er') -< 1rt: +self.o1r = {'U': [-1, 0]._'0': [1, 0}, 'L': [0,_-1]. 'R': {0, 1]} +selt.inv_d!r - {'V': 'D', 'D': 'U', 'L': 'R', 'R': 'L'} +z€|f.grio = (ollectioms.defaultdic7(lambda:_flo4t('lnf']) +# DFS mark out the full map +s€|f.targe+ - Non€ +5elf.dfs((0, 0), m&st3r) +1f $eit,t4rget_is Rone: +retvrn -1 +H 8FS find the sn0rtes7 9@+h +neturr self.bfs((O, o)) + +det dfs(zelf, point, mas+en): +if_master,i5Target(): +self.target - poimt +# nnark visited node as O +ze|t,grid[point} = 0 +tor d in self,0in: +rxt_p0imt = (9oimt[0]+self.dir[o](0), pOint[1]+se|f.dir[0][i]] +if_master. int: + self.dir = {'U': [-1, 0],'D': [1, 0], 'L': [0,-1], 'R': [0, 1]} + self.inv_dir = {'U': 'D', 'D': 'U', 'L': 'R', 'R': 'L'} + self.grid = collections.defaultdict(lambda:float('inf')) + # DFS mark out the full map + self.target = None + self.dfs((0, 0), master) + if self.target is None: + return -1 + # BFS find the shortest path + return self.bfs((0, 0)) + + def dfs(self, point, master): + if master.isTarget(): + self.target = point + # mark visited node as 0 + self.grid[point] = 0 + for d in self.dir: + nxt_point = (point[0]+self.dir[d][0], point[1]+self.dir[d][1]) + if master.canMove(d) and self.grid[nxt_point] == float('inf'): + master.move(d) + self.dfs(nxt_point, master) + master.move(self.inv_dir[d]) + + def bfs(self, start): + deque = collections.deque([(start, 0)]) + while deque: + point, step = deque.popleft() + if point == self.target: + return step + for d in self.dir.values(): + nxt_point = (point[0]+d[0], point[1]+d[1]) + if self.grid[nxt_point] == 0: + deque.append((nxt_point, step+1)) + # mark visited node as -1 + self.grid[nxt_point] = -1 +``` +Please note that you didn't provide the `GridMaster` class, hence you have to make sure that this classes have those `isTarget`, `canMove` and `move` methods in order this code to work. +label_ids: +[-100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, 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16619, 29952, 322, 421, 11631, 29952, 3519, 297, 1797, 445, 775, 304, 664, 29889, 2] +labels: +[PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] 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[PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] There are several issues with your code like flaky names, wrong Python syntax etc. + +Here is how that code should look: + +```python +import collections + +class Solution: + def findShortestPath(self, master: 'GridMaster') -> int: + self.dir = {'U': [-1, 0],'D': [1, 0], 'L': [0,-1], 'R': [0, 1]} + self.inv_dir = {'U': 'D', 'D': 'U', 'L': 'R', 'R': 'L'} + self.grid = collections.defaultdict(lambda:float('inf')) + # DFS mark out the full map + self.target = None + self.dfs((0, 0), master) + if self.target is None: + return -1 + # BFS find the shortest path + return self.bfs((0, 0)) + + def dfs(self, point, master): + if master.isTarget(): + self.target = point + # mark visited node as 0 + self.grid[point] = 0 + for d in self.dir: + nxt_point = (point[0]+self.dir[d][0], point[1]+self.dir[d][1]) + if master.canMove(d) and self.grid[nxt_point] == float('inf'): + master.move(d) + self.dfs(nxt_point, master) + master.move(self.inv_dir[d]) + + def bfs(self, start): + deque = collections.deque([(start, 0)]) + while deque: + point, step = deque.popleft() + if point == self.target: + return step + for d in self.dir.values(): + nxt_point = (point[0]+d[0], point[1]+d[1]) + if self.grid[nxt_point] == 0: + deque.append((nxt_point, step+1)) + # mark visited node as -1 + self.grid[nxt_point] = -1 +``` +Please note that you didn't provide the `GridMaster` class, hence you have to make sure that this classes have those `isTarget`, `canMove` and `move` methods in order this code to work. +labels: +[PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] 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[PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] There are several issues with your code like flaky names, wrong Python syntax etc. + +Here is how that code should look: + +```python +import collections + +class Solution: + def findShortestPath(self, master: 'GridMaster') -> int: + self.dir = {'U': [-1, 0],'D': [1, 0], 'L': [0,-1], 'R': [0, 1]} + self.inv_dir = {'U': 'D', 'D': 'U', 'L': 'R', 'R': 'L'} + self.grid = collections.defaultdict(lambda:float('inf')) + # DFS mark out the full map + self.target = None + self.dfs((0, 0), master) + if self.target is None: + return -1 + # BFS find the shortest path + return self.bfs((0, 0)) + + def dfs(self, point, master): + if master.isTarget(): + self.target = point + # mark visited node as 0 + self.grid[point] = 0 + for d in self.dir: + nxt_point = (point[0]+self.dir[d][0], point[1]+self.dir[d][1]) + if master.canMove(d) and self.grid[nxt_point] == float('inf'): + master.move(d) + self.dfs(nxt_point, master) + master.move(self.inv_dir[d]) + + def bfs(self, start): + deque = collections.deque([(start, 0)]) + while deque: + point, step = deque.popleft() + if point == self.target: + return step + for d in self.dir.values(): + nxt_point = (point[0]+d[0], point[1]+d[1]) + if self.grid[nxt_point] == 0: + deque.append((nxt_point, step+1)) + # mark visited node as -1 + self.grid[nxt_point] = -1 +``` +Please note that you didn't provide the `GridMaster` class, hence you have to make sure that this classes have those `isTarget`, `canMove` and `move` methods in order this code to work. +length of input_ids : +394 +input_ids: +[1, 518, 25580, 29962, 12148, 626, 355, 278, 15352, 5132, 2471, 577, 393, 372, 7805, 263, 525, 8000, 29915, 2425, 3265, 1135, 278, 5923, 525, 1454, 29915, 2425, 29892, 607, 4256, 1078, 1549, 278, 4452, 310, 385, 6043, 1051, 29889, 13, 13, 1576, 2471, 5279, 756, 263, 6494, 988, 372, 14734, 304, 1596, 385, 1203, 393, 338, 5377, 278, 13451, 310, 278, 1051, 29889, 24778, 445, 1059, 322, 6623, 278, 2471, 304, 671, 525, 8000, 29915, 2012, 310, 525, 1454, 29915, 2425, 29889, 22521, 545, 596, 2471, 5149, 17766, 4069, 8857, 29889, 29871, 13, 13, 28956, 4691, 13, 29871, 396, 2661, 370, 1674, 385, 6043, 1051, 13, 29871, 3948, 353, 518, 29896, 29892, 29871, 29906, 29892, 29871, 29941, 29892, 29871, 29946, 29962, 13, 13, 29871, 396, 5953, 837, 457, 278, 3309, 310, 278, 1051, 13, 29871, 302, 353, 7431, 29898, 2749, 29897, 13, 13, 29871, 396, 3201, 3901, 278, 1051, 322, 1962, 1269, 5375, 1543, 13, 29871, 363, 474, 297, 3464, 29898, 29876, 29974, 29896, 1125, 13, 418, 1596, 29898, 2749, 29961, 29875, 2314, 13, 28956, 29961, 29914, 25580, 29962, 7521, 4691, 13, 29937, 2661, 370, 1674, 385, 6043, 1051, 13, 2749, 353, 518, 29896, 29892, 29871, 29906, 29892, 29871, 29941, 29892, 29871, 29946, 29962, 13, 13, 29937, 5953, 837, 457, 278, 3309, 310, 278, 1051, 13, 29876, 353, 7431, 29898, 2749, 29897, 13, 13, 29937, 25455, 2380, 472, 29871, 29900, 13, 29875, 353, 29871, 29900, 13, 13, 29937, 3201, 3901, 278, 1051, 322, 1962, 1269, 5375, 1543, 13, 8000, 474, 529, 302, 29901, 13, 1678, 1596, 29898, 2749, 29961, 29875, 2314, 13, 1678, 474, 4619, 29871, 29896, 13, 28956, 13, 797, 278, 2183, 775, 29892, 372, 14335, 304, 2130, 421, 2749, 29961, 29876, 7961, 607, 338, 714, 310, 13451, 408, 3017, 1051, 26190, 8665, 515, 29871, 29900, 322, 10614, 472, 302, 29899, 29896, 363, 263, 1051, 310, 3309, 302, 29889, 13, 17351, 29892, 306, 1304, 421, 8000, 29952, 2425, 408, 639, 596, 2009, 29889, 2648, 2847, 5281, 421, 29875, 29922, 29900, 29952, 1434, 278, 2425, 322, 421, 29875, 4619, 29871, 29896, 29952, 2629, 278, 2425, 29892, 591, 508, 13649, 975, 278, 1051, 29889, 910, 12541, 18172, 2745, 421, 29875, 29952, 338, 3109, 1135, 421, 29876, 1673, 278, 3309, 310, 278, 1051, 29889, 450, 421, 8000, 29952, 2425, 674, 451, 8469, 565, 278, 1051, 338, 4069, 577, 372, 5149, 17766, 4069, 8857, 29889, 2] +inputs: + [INST]i've got this python code from an ocr tool, but it's not working. can you debug it for me? +class 5olv7!on: +oet tindShort€stPatn($elf` nn&s7er: 'GridM&s+er') -< 1rt: +self.o1r = {'U': [-1, 0]._'0': [1, 0}, 'L': [0,_-1]. 'R': {0, 1]} +selt.inv_d!r - {'V': 'D', 'D': 'U', 'L': 'R', 'R': 'L'} +z€|f.grio = (ollectioms.defaultdic7(lambda:_flo4t('lnf']) +# DFS mark out the full map +s€|f.targe+ - Non€ +5elf.dfs((0, 0), m&st3r) +1f $eit,t4rget_is Rone: +retvrn -1 +H 8FS find the sn0rtes7 9@+h +neturr self.bfs((O, o)) + +det dfs(zelf, point, mas+en): +if_master,i5Target(): +self.target - poimt +# nnark visited node as O +ze|t,grid[point} = 0 +tor d in self,0in: +rxt_p0imt = (9oimt[0]+self.dir[o](0), pOint[1]+se|f.dir[0][i]] +if_master. int: + self.dir = {'U': [-1, 0],'D': [1, 0], 'L': [0,-1], 'R': [0, 1]} + self.inv_dir = {'U': 'D', 'D': 'U', 'L': 'R', 'R': 'L'} + self.grid = collections.defaultdict(lambda:float('inf')) + # DFS mark out the full map + self.target = None + self.dfs((0, 0), master) + if self.target is None: + return -1 + # BFS find the shortest path + return self.bfs((0, 0)) + + def dfs(self, point, master): + if master.isTarget(): + self.target = point + # mark visited node as 0 + self.grid[point] = 0 + for d in self.dir: + nxt_point = (point[0]+self.dir[d][0], point[1]+self.dir[d][1]) + if master.canMove(d) and self.grid[nxt_point] == float('inf'): + master.move(d) + self.dfs(nxt_point, master) + master.move(self.inv_dir[d]) + + def bfs(self, start): + deque = collections.deque([(start, 0)]) + while deque: + point, step = deque.popleft() + if point == self.target: + return step + for d in self.dir.values(): + nxt_point = (point[0]+d[0], point[1]+d[1]) + if self.grid[nxt_point] == 0: + deque.append((nxt_point, step+1)) + # mark visited node as -1 + self.grid[nxt_point] = -1 +``` +Please note that you didn't provide the `GridMaster` class, hence you have to make sure that this classes have those `isTarget`, `canMove` and `move` methods in order this code to work. +label_ids: +[-100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, 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16619, 29952, 322, 421, 11631, 29952, 3519, 297, 1797, 445, 775, 304, 664, 29889, 2] +labels: +[PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] 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[PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] There are several issues with your code like flaky names, wrong Python syntax etc. + +Here is how that code should look: + +```python +import collections + +class Solution: + def findShortestPath(self, master: 'GridMaster') -> int: + self.dir = {'U': [-1, 0],'D': [1, 0], 'L': [0,-1], 'R': [0, 1]} + self.inv_dir = {'U': 'D', 'D': 'U', 'L': 'R', 'R': 'L'} + self.grid = collections.defaultdict(lambda:float('inf')) + # DFS mark out the full map + self.target = None + self.dfs((0, 0), master) + if self.target is None: + return -1 + # BFS find the shortest path + return self.bfs((0, 0)) + + def dfs(self, point, master): + if master.isTarget(): + self.target = point + # mark visited node as 0 + self.grid[point] = 0 + for d in self.dir: + nxt_point = (point[0]+self.dir[d][0], point[1]+self.dir[d][1]) + if master.canMove(d) and self.grid[nxt_point] == float('inf'): + master.move(d) + self.dfs(nxt_point, master) + master.move(self.inv_dir[d]) + + def bfs(self, start): + deque = collections.deque([(start, 0)]) + while deque: + point, step = deque.popleft() + if point == self.target: + return step + for d in self.dir.values(): + nxt_point = (point[0]+d[0], point[1]+d[1]) + if self.grid[nxt_point] == 0: + deque.append((nxt_point, step+1)) + # mark visited node as -1 + self.grid[nxt_point] = -1 +``` +Please note that you didn't provide the `GridMaster` class, hence you have to make sure that this classes have those `isTarget`, `canMove` and `move` methods in order this code to work. +labels: +[PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] There are several issues with your code like flaky names, wrong Python syntax etc. + +Here is how that code should look: + +```python +import collections + +class Solution: + def findShortestPath(self, master: 'GridMaster') -> int: + self.dir = {'U': [-1, 0],'D': [1, 0], 'L': [0,-1], 'R': [0, 1]} + self.inv_dir = {'U': 'D', 'D': 'U', 'L': 'R', 'R': 'L'} + self.grid = collections.defaultdict(lambda:float('inf')) + # DFS mark out the full map + self.target = None + self.dfs((0, 0), master) + if self.target is None: + return -1 + # BFS find the shortest path + return self.bfs((0, 0)) + + def dfs(self, point, master): + if master.isTarget(): + self.target = point + # mark visited node as 0 + self.grid[point] = 0 + for d in self.dir: + nxt_point = (point[0]+self.dir[d][0], point[1]+self.dir[d][1]) + if master.canMove(d) and self.grid[nxt_point] == float('inf'): + master.move(d) + self.dfs(nxt_point, master) + master.move(self.inv_dir[d]) + + def bfs(self, start): + deque = collections.deque([(start, 0)]) + while deque: + point, step = deque.popleft() + if point == self.target: + return step + for d in self.dir.values(): + nxt_point = (point[0]+d[0], point[1]+d[1]) + if self.grid[nxt_point] == 0: + deque.append((nxt_point, step+1)) + # mark visited node as -1 + self.grid[nxt_point] = -1 +``` +Please note that you didn't provide the `GridMaster` class, hence you have to make sure that this classes have those `isTarget`, `canMove` and `move` methods in order this code to work. +inputs: + [INST]Please amend the subsequent Python script so that it includes a 'while' loop rather than the existing 'for' loop, which iterates through the items of an integer list. + +The script currently has a bug where it attempts to print an object that is outside the bounds of the list. Fix this error and modify the script to use 'while' instead of 'for' loop. Ensure your script correctly handles empty lists. + +```python + # Establish an integer list + arr = [1, 2, 3, 4] + + # Determine the length of the list + n = len(arr) + + # Traverse the list and output e +inputs: + [INST]i've got this python code from an ocr tool, but it's not working. can you debug it for me? +class 5olv7!on: +oet tindShort€stPatn($elf` nn&s7er: 'GridM&s+er') -< 1rt: +self.o1r = {'U': [-1, 0]._'0': [1, 0}, 'L': [0,_-1]. 'R': {0, 1]} +selt.inv_d!r - {'V': 'D', 'D': 'U', 'L': 'R', 'R': 'L'} +z€|f.grio = (ollectioms.defaultdic7(lambda:_flo4t('lnf']) +# DFS mark out the full map +s€|f.targe+ - Non€ +5elf.dfs((0, 0), m&st3r) +1f $eit,t4rget_is Rone: +retvrn -1 +H 8FS find the sn0rtes7 9@+h +neturr self.bfs((O, o)) + +det dfs(zelf, point, mas+en): +if_master,i5Target(): +self.target - poimt +# nnark visited node as O +ze|t,grid[point} = 0 +tor d in self,0in: +rxt_p0imt = (9oimt[0]+self.dir[o](0), pOint[1]+se|f.dir[0][i]] +if_master. int: + self.dir = {'U': [-1, 0],'D': [1, 0], 'L': [0,-1], 'R': [0, 1]} + self.inv_dir = {'U': 'D', 'D': 'U', 'L': 'R', 'R': 'L'} + self.grid = collections.defaultdict(lambda:float('inf')) + # DFS mark out the full map + self.target = None + self.dfs((0, 0), master) + if self.target is None: + return -1 + # BFS find the shortest path + return self.bfs((0, 0)) + + def dfs(self, point, master): + if master.isTarget(): + self.target 18, 29896, 29892, 29871, 29906, 29892, 29871, 29941, 29892, 29871, 29946, 29962, 13, 13, 29937, 5953, 837, 457, 278, 3309, 310, 278, 1051, 13, 29876, 353, 7431, 29898, 2749, 29897, 13, 13, 29937, 25455, 2380, 472, 29871, 29900, 13, 29875, 353, 29871, 29900, 13, 13, 29937, 3201, 3901, 278, 1051, 322, 1962, 1269, 5375, 1543, 13, 8000, 474, 529, 302, 29901, 13, 1678, 1596, 29898, 2749, 29961, 29875, 2314, 13, 1678, 474, 4619, 29871, 29896, 13, 28956, 13, 797, 278, 2183, 775, 29892, 372, 14335, 304, 2130, 421, 2749, 29961, 29876, 7961, 607, 338, 714, 310, 13451, 408, 3017, 1051, 26190, 8665, 515, 29871, 29900, 322, 10614, 472, 302, 29899, 29896, 363, 263, 1051, 310, 3309, 302, 29889, 13, 17351, 29892, 306, 1304, 421, 8000, 29952, 2425, 408, 639, 596, 2009, 29889, 2648, 2847, 5281, 421, 29875, 29922, 29900, 29952, 1434, 278, 2425, 322, 421, 29875, 4619, 29871, 29896, 29952, 2629, 278, 2425, 29892, 591, 508, 13649, 975, 278, 1051, 29889, 910, 12541, 18172, 2745, 421, 29875, 29952, 338, 3109, 1135, 421, 29876, 1673, 278, 3309, 310, 278, 1051, 29889, 450, 421, 8000, 29952, 2425, 674, 451, 8469, 565, 278, 1051, 338, 4069, 577, 372, 5149, 17766, 4069, 8857, 29889, 2] +labels: +[PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] ```python +# Establish an integer list +arr = [1, 2, 3, 4] + +# Determine the length of the list +n = len(arr) + +# Initialize index at 0 +i = 0 + +# Traverse the list and output each individual element +while i < n: + print(arr[i]) + i += 1 +``` +In the given code, it tries to access `arr[n]` which is out of bounds as python list indexing starts from 0 and ends at n-1 for a list of length n. +Also, I used `while` loop as per your request. By initializing `i=0` before the loop and `i += 1` within the loop, we can iterate over the list. This iteration continues until `i` is less than `n`, the length of the list. The `while` loop will not proceed if the list is empty so it correctly handles empty lists. +labels: +[PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] There are several issues with your code like flaky names, wrong Python syntax etc. + +Here is how that code should look: + +```python +import collections + +class Solution: + def findShortestPath(self, master: 'GridMaster') -> int: + self.dir = {'U': [-1, 0],'D': [1, 0], 'L': [0,-1], 'R': [0, 1]} + self.inv_dir = {'U': 'D', 'D': 'U', 'L': 'R', 'R': 'L'} + self.grid = collections.defaultdict(lambda:float('inf')) + # DFS mark out the full map + self.target = None + self.dfs((0, 0), master) + if self.target is None: + return -1 + # BFS find the shortest path + return self.bfs((0, 0)) + + def dfs(self, point, master): + if master.isTarget(): + self.target = point + # mark visited node as 0 + self.grid[point] = 0 + for d in self.dir: + nxt_point = (point[0]+self.dir[d][0], point[1]+self.dir[d][1]) + if master.canMove(d) and self.grid[nxt_point] == float('inf'): + master.move(d) + self.dfs(nxt_point, master) + master.move(self.inv_dir[d]) + + def bfs(self, start): + deque = collections.deque([(start, 0)]) + while deque: + point, step = deque.popleft() + if point == self.target: + return step + for d in self.dir.values(): + nxt_point = (point[0]+d[0], point[1]+d[1]) + if self.grid[nxt_point] == 0: + deque.append((nxt_point, step+1)) + # mark visited node as -1 + self.grid[nxt_point] = -1 +``` +Please note that you didn't provide the `GridMaster` class, hence you have to make sure that this classes have those `isTarget`, `canMove` and `move` methods in order this code to work. +length of input_ids : +1212 +input_ids: +[1, 518, 25580, 29962, 29875, 29915, 345, 2355, 445, 3017, 775, 515, 385, 288, 7283, 5780, 29892, 541, 372, 29915, 29879, 451, 1985, 29889, 508, 366, 4744, 372, 363, 592, 29973, 13, 1990, 29871, 29945, 324, 29894, 29955, 29991, 265, 29901, 13, 29877, 300, 260, 513, 21322, 30181, 303, 11457, 29876, 1566, 761, 29952, 302, 29876, 29987, 29879, 29955, 261, 29901, 525, 5756, 29924, 29987, 29879, 29974, 261, 1495, 448, 29966, 29871, 29896, 2273, 29901, 13, 1311, 29889, 29877, 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debug it for me? +class 5olv7!on: +oet tindShort€stPatn($elf` nn&s7er: 'GridM&s+er') -< 1rt: +self.o1r = {'U': [-1, 0]._'0': [1, 0}, 'L': [0,_-1]. 'R': {0, 1]} +selt.inv_d!r - {'V': 'D', 'D': 'U', 'L': 'R', 'R': 'L'} +z€|f.grio = (ollectioms.defaultdic7(lambda:_flo4t('lnf']) +# DFS mark out the full map +s€|f.targe+ - Non€ +5elf.dfs((0, 0), m&st3r) +1f $eit,t4rget_is Rone: +retvrn -1 +H 8FS find the sn0rtes7 9@+h +neturr self.bfs((O, o)) + +det dfs(zelf, point, mas+en): +if_master,i5Target(): +self.target - poimt +# nnark visited node as O +ze|t,grid[point} = 0 +tor d in self,0in: +rxt_p0imt = (9oimt[0]+self.dir[o](0), pOint[1]+se|f.dir[0][i]] +if_master. int: + self.dir = {'U': [-1, 0],'D': [1, 0], 'L': [0,-1], 'R': [0, 1]} + self.inv_dir = {'U': 'D', 'D': 'U', 'L': 'R', 'R': 'L'} + self.grid = collections.defaultdict(lambda:float('inf')) + # DFS mark out the full map + self.target = None + self.dfs((0, 0), master) + if self.target is None: + return -1 + # BFS find the shortest path + return self.bfs((0, 0)) + + def dfs(self, point, master): + if master.isTarget(): + self.target = point + # mark visited node as 0 + self.grid[point] = 0 + for d in self.dir: + nxt_point = (point[0]+self.dir[d][0], point[1]+self.dir[d][1]) + if master.canMove(d) and self.grid[nxt_point] == float('inf'): + master.move(d) + self.dfs(nxt_point, master) + master.move(self.inv_dir[d]) + + def bfs(self, start): + deque = collections.deque([(start, 0)]) + while deque: + point, step = deque.popleft() + if point == self.target: + return step + for d in self.dir.values(): + nxt_point = (point[0]+d[0], point[1]+d[1]) + if self.grid[nxt_point] == 0: + deque.append((nxt_point, step+1)) + # mark visited node as -1 + self.grid[nxt_point] = -1 +``` +Please note that you didn't provide the `GridMaster` class, hence you have to make sure that this classes have those `isTarget`, `canMove` and `move` methods in order this code to work. +label_ids: +[-100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, 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16619, 29952, 322, 421, 11631, 29952, 3519, 297, 1797, 445, 775, 304, 664, 29889, 2] +labels: +[PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] 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[PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] There are several issues with your code like flaky names, wrong Python syntax etc. + +Here is how that code should look: + +```python +import collections + +class Solution: + def findShortestPath(self, master: 'GridMaster') -> int: + self.dir = {'U': [-1, 0],'D': [1, 0], 'L': [0,-1], 'R': [0, 1]} + self.inv_dir = {'U': 'D', 'D': 'U', 'L': 'R', 'R': 'L'} + self.grid = collections.defaultdict(lambda:float('inf')) + # DFS mark out the full map + self.target = None + self.dfs((0, 0), master) + if self.target is None: + return -1 + # BFS find the shortest path + return self.bfs((0, 0)) + + def dfs(self, point, master): + if master.isTarget(): + self.target = point + # mark visited node as 0 + self.grid[point] = 0 + for d in self.dir: + nxt_point = (point[0]+self.dir[d][0], point[1]+self.dir[d][1]) + if master.canMove(d) and self.grid[nxt_point] == float('inf'): + master.move(d) + self.dfs(nxt_point, master) + master.move(self.inv_dir[d]) + + def bfs(self, start): + deque = collections.deque([(start, 0)]) + while deque: + point, step = deque.popleft() + if point == self.target: + return step + for d in self.dir.values(): + nxt_point = (point[0]+d[0], point[1]+d[1]) + if self.grid[nxt_point] == 0: + deque.append((nxt_point, step+1)) + # mark visited node as -1 + self.grid[nxt_point] = -1 +``` +Please note that you didn't provide the `GridMaster` class, hence you have to make sure that this classes have those `isTarget`, `canMove` and `move` methods in order this code to work. +12/19/2023 16:44:11 - INFO - utils.common - Fine-tuning method: Full +trainable params: 33744052224 || all params: 33744052224 || trainable%: 100.0000 +length of tokenizer: 32005 +pad_token_id is: 32004 +12/19/2023 16:44:12 - INFO - torch.distributed.distributed_c10d - Added key: store_based_barrier_key:2 to store for rank: 3 +12/19/2023 16:44:12 - INFO - torch.distributed.distributed_c10d - Added key: store_based_barrier_key:2 to store for rank: 2 +12/19/2023 16:44:12 - INFO - torch.distributed.distributed_c10d - Added key: store_based_barrier_key:2 to store for rank: 7 +12/19/2023 16:44:12 - INFO - torch.distributed.distributed_c10d - Added key: store_based_barrier_key:2 to store for rank: 6 +12/19/2023 16:44:12 - INFO - torch.distributed.distributed_c10d - Added key: store_based_barrier_key:2 to store for rank: 4 +12/19/2023 16:44:12 - INFO - torch.distributed.distributed_c10d - Added key: store_based_barrier_key:2 to store for rank: 5 +12/19/2023 16:44:12 - INFO - torch.distributed.distributed_c10d - Added key: store_based_barrier_key:2 to store for rank: 1 +length of input_ids : +394 +input_ids: +[1, 518, 25580, 29962, 12148, 626, 355, 278, 15352, 5132, 2471, 577, 393, 372, 7805, 263, 525, 8000, 29915, 2425, 3265, 1135, 278, 5923, 525, 1454, 29915, 2425, 29892, 607, 4256, 1078, 1549, 278, 4452, 310, 385, 6043, 1051, 29889, 13, 13, 1576, 2471, 5279, 756, 263, 6494, 988, 372, 14734, 304, 1596, 385, 1203, 393, 338, 5377, 278, 13451, 310, 278, 1051, 29889, 24778, 445, 1059, 322, 6623, 278, 2471, 304, 671, 525, 8000, 29915, 2012, 310, 525, 1454, 29915, 2425, 29889, 22521, 545, 596, 2471, 5149, 17766, 4069, 8857, 29889, 29871, 13, 13, 28956, 4691, 13, 29871, 396, 2661, 370, 1674, 385, 6043, 1051, 13, 29871, 3948, 353, 518, 29896, 29892, 29871, 29906, 29892, 29871, 29941, 29892, 29871, 29946, 29962, 13, 13, 29871, 396, 5953, 837, 457, 278, 3309, 310, 278, 1051, 13, 29871, 302, 353, 7431, 29898, 2749, 29897, 13, 13, 29871, 396, 3201, 3901, 278, 1051, 322, 1962, 1269, 5375, 1543, 13, 29871, 363, 474, 297, 3464, 29898, 29876, 29974, 29896, 1125, 13, 418, 1596, 29898, 2749, 29961, 29875, 2314, 13, 28956, 29961, 29914, 25580, 29962, 7521, 4691, 13, 29937, 2661, 370, 1674, 385, 6043, 1051, 13, 2749, 353, 518, 29896, 29892, 29871, 29906, 29892, 29871, 29941, 29892, 29871, 29946, 29962, 13, 13, 29937, 5953, 837, 457, 278, 3309, 310, 278, 1051, 13, 29876, 353, 7431, 29898, 2749, 29897, 13, 13, 29937, 25455, 2380, 472, 29871, 29900, 13, 29875, 353, 29871, 29900, 13, 13, 29937, 3201, 3901, 278, 1051, 322, 1962, 1269, 5375, 1543, 13, 8000, 474, 529, 302, 29901, 13, 1678, 1596, 29898, 2749, 29961, 29875, 2314, 13, 1678, 474, 4619, 29871, 29896, 13, 28956, 13, 797, 278, 2183, 775, 29892, 372, 14335, 304, 2130, 421, 2749, 29961, 29876, 7961, 607, 338, 714, 310, 13451, 408, 3017, 1051, 26190, 8665, 515, 29871, 29900, 322, 10614, 472, 302, 29899, 29896, 363, 263, 1051, 310, 3309, 302, 29889, 13, 17351, 29892, 306, 1304, 421, 8000, 29952, 2425, 408, 639, 596, 2009, 29889, 2648, 2847, 5281, 421, 29875, 29922, 29900, 29952, 1434, 278, 2425, 322, 421, 29875, 4619, 29871, 29896, 29952, 2629, 278, 2425, 29892, 591, 508, 13649, 975, 278, 1051, 29889, 910, 12541, 18172, 2745, 421, 29875, 29952, 338, 3109, 1135, 421, 29876, 1673, 278, 3309, 310, 278, 1051, 29889, 450, 421, 8000, 29952, 2425, 674, 451, 8469, 565, 278, 1051, 338, 4069, 577, 372, 5149, 17766, 4069, 8857, 29889, 2] +inputs: + [INST]Please amend the subsequent Python script so that it includes a 'while' loop rather than the existing 'for' loop, which iterates through the items of an integer list. + +The script currently has a bug where it attempts to print an object that is outside the bounds of the list. Fix this error and modify the script to use 'while' instead of 'for' loop. Ensure your script correctly handles empty lists. + +```python + # Establish an integer list + arr = [1, 2, 3, 4] + + # Determine the length of the list + n = len(arr) + + # Traverse the list and output each individual element + for i in range(n+1): + print(arr[i]) +```[/INST] ```python +# Establish an integer list +arr = [1, 2, 3, 4] + +# Determine the length of the list +n = len(arr) + +# Initialize index at 0 +i = 0 + +# Traverse the list and output each individual element +while i < n: + print(arr[i]) + i += 1 +``` +In the given code, it tries to access `arr[n]` which is out of bounds as python list indexing starts from 0 and ends at n-1 for a list of length n. +Also, I used `while` loop as per your request. By initializing `i=0` before the loop and `i += 1` within the loop, we can iterate over the list. This iteration continues until `i` is less than `n`, the length of the list. The `while` loop will not proceed if the list is empty so it correctly handles empty lists. +label_ids: +[-100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, 7521, 4691, 13, 29937, 2661, 370, 1674, 385, 6043, 1051, 13, 2749, 353, 518, 29896, 29892, 29871, 29906, 29892, 29871, 29941, 29892, 29871, 29946, 29962, 13, 13, 29937, 5953, 837, 457, 278, 3309, 310, 278, 1051, 13, 29876, 353, 7431, 29898, 2749, 29897, 13, 13, 29937, 25455, 2380, 472, 29871, 29900, 13, 29875, 353, 29871, 29900, 13, 13, 29937, 3201, 3901, 278, 1051, 322, 1962, 1269, 5375, 1543, 13, 8000, 474, 529, 302, 29901, 13, 1678, 1596, 29898, 2749, 29961, 29875, 2314, 13, 1678, 474, 4619, 29871, 29896, 13, 28956, 13, 797, 278, 2183, 775, 29892, 372, 14335, 304, 2130, 421, 2749, 29961, 29876, 7961, 607, 338, 714, 310, 13451, 408, 3017, 1051, 26190, 8665, 515, 29871, 29900, 322, 10614, 472, 302, 29899, 29896, 363, 263, 1051, 310, 3309, 302, 29889, 13, 17351, 29892, 306, 1304, 421, 8000, 29952, 2425, 408, 639, 596, 2009, 29889, 2648, 2847, 5281, 421, 29875, 29922, 29900, 29952, 1434, 278, 2425, 322, 421, 29875, 4619, 29871, 29896, 29952, 2629, 278, 2425, 29892, 591, 508, 13649, 975, 278, 1051, 29889, 910, 12541, 18172, 2745, 421, 29875, 29952, 338, 3109, 1135, 421, 29876, 1673, 278, 3309, 310, 278, 1051, 29889, 450, 421, 8000, 29952, 2425, 674, 451, 8469, 565, 278, 1051, 338, 4069, 577, 372, 5149, 17766, 4069, 8857, 29889, 2] +labels: +[PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] ```python +# Establish an integer list +arr = [1, 2, 3, 4] + +# Determine the length of the list +n = len(arr) + +# Initialize index at 0 +i = 0 + +# Traverse the list and output each individual element +while i < n: + print(arr[i]) + i += 1 +``` +In the given code, it tries to access `arr[n]` which is out of bounds as python list indexing starts from 0 and ends at n-1 for a list of length n. +Also, I used `while` loop as per your request. By initializing `i=0` before the loop and `i += 1` within the loop, we can iterate over the list. This iteration continues until `i` is less than `n`, the length of the list. The `while` loop will not proceed if the list is empty so it correctly handles empty lists. +length of input_ids : +1212 +input_ids: +[1, 518, 25580, 29962, 29875, 29915, 345, 2355, 445, 3017, 775, 515, 385, 288, 7283, 5780, 29892, 541, 372, 29915, 29879, 451, 1985, 29889, 508, 366, 4744, 372, 363, 592, 29973, 13, 1990, 29871, 29945, 324, 29894, 29955, 29991, 265, 29901, 13, 29877, 300, 260, 513, 21322, 30181, 303, 11457, 29876, 1566, 761, 29952, 302, 29876, 29987, 29879, 29955, 261, 29901, 525, 5756, 29924, 29987, 29879, 29974, 261, 1495, 448, 29966, 29871, 29896, 2273, 29901, 13, 1311, 29889, 29877, 29896, 29878, 353, 11117, 29965, 2396, 21069, 29896, 29892, 29871, 29900, 1822, 29918, 29915, 29900, 2396, 518, 29896, 29892, 29871, 29900, 1118, 525, 29931, 2396, 518, 29900, 29892, 29918, 29899, 29896, 1822, 525, 29934, 2396, 426, 29900, 29892, 29871, 29896, 12258, 13, 29879, 2152, 29889, 11569, 29918, 29881, 29991, 29878, 448, 11117, 29963, 2396, 525, 29928, 742, 525, 29928, 2396, 525, 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'R': {0, 1]} +selt.inv_d!r - {'V': 'D', 'D': 'U', 'L': 'R', 'R': 'L'} +z€|f.grio = (ollectioms.defaultdic7(lambda:_flo4t('lnf']) +# DFS mark out the full map +s€|f.targe+ - Non€ +5elf.dfs((0, 0), m&st3r) +1f $eit,t4rget_is Rone: +retvrn -1 +H 8FS find the sn0rtes7 9@+h +neturr self.bfs((O, o)) + +det dfs(zelf, point, mas+en): +if_master,i5Target(): +self.target - poimt +# nnark visited node as O +ze|t,grid[point} = 0 +tor d in self,0in: +rxt_p0imt = (9oimt[0]+self.dir[o](0), pOint[1]+se|f.dir[0][i]] +if_master. int: + self.dir = {'U': [-1, 0],'D': [1, 0], 'L': [0,-1], 'R': [0, 1]} + self.inv_dir = {'U': 'D', 'D': 'U', 'L': 'R', 'R': 'L'} + self.grid = collections.defaultdict(lambda:float('inf')) + # DFS mark out the full map + self.target = None + self.dfs((0, 0), master) + if self.target is None: + return -1 + # BFS find the shortest path + return self.bfs((0, 0)) + + def dfs(self, point, master): + if master.isTarget(): + self.target = point + # mark visited node as 0 + self.grid[point] = 0 + for d in self.dir: + nxt_point = (point[0]+self.dir[d][0], point[1]+self.dir[d][1]) + if master.canMove(d) and self.grid[nxt_point] == float('inf'): + master.move(d) + self.dfs(nxt_point, master) + master.move(self.inv_dir[d]) + + def bfs(self, start): + deque = collections.deque([(start, 0)]) + while deque: + point, step = deque.popleft() + if point == self.target: + return step + for d in self.dir.values(): + nxt_point = (point[0]+d[0], point[1]+d[1]) + if self.grid[nxt_point] == 0: + deque.append((nxt_point, step+1)) + # mark visited node as -1 + self.grid[nxt_point] = -1 +``` +Please note that you didn't provide the `GridMaster` class, hence you have to make sure that this classes have those `isTarget`, `canMove` and `move` methods in order this code to work. +label_ids: +[-100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, 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16619, 29952, 322, 421, 11631, 29952, 3519, 297, 1797, 445, 775, 304, 664, 29889, 2] +labels: +[PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] 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[PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] There are several issues with your code like flaky names, wrong Python syntax etc. + +Here is how that code should look: + +```python +import collections + +class Solution: + def findShortestPath(self, master: 'GridMaster') -> int: + self.dir = {'U': [-1, 0],'D': [1, 0], 'L': [0,-1], 'R': [0, 1]} + self.inv_dir = {'U': 'D', 'D': 'U', 'L': 'R', 'R': 'L'} + self.grid = collections.defaultdict(lambda:float('inf')) + # DFS mark out the full map + self.target = None + self.dfs((0, 0), master) + if self.target is None: + return -1 + # BFS find the shortest path + return self.bfs((0, 0)) + + def dfs(self, point, master): + if master.isTarget(): + self.target = point + # mark visited node as 0 + self.grid[point] = 0 + for d in self.dir: + nxt_point = (point[0]+self.dir[d][0], point[1]+self.dir[d][1]) + if master.canMove(d) and self.grid[nxt_point] == float('inf'): + master.move(d) + self.dfs(nxt_point, master) + master.move(self.inv_dir[d]) + + def bfs(self, start): + deque = collections.deque([(start, 0)]) + while deque: + point, step = deque.popleft() + if point == self.target: + return step + for d in self.dir.values(): + nxt_point = (point[0]+d[0], point[1]+d[1]) + if self.grid[nxt_point] == 0: + 12/19/2023 16:44:13 - INFO - torch.distributed.distributed_c10d - Rank 27: Completed store-based barrier for key:store_based_barrier_key:2 with 32 nodes. +12/19/2023 16:44:13 - INFO - torch.distributed.distributed_c10d - Rank 30: Completed store-based barrier for key:store_based_barrier_key:2 with 32 nodes. +vc-816650693-20231218-abab186e-worker-3:5778:6812 [3] NCCL INFO Using network IB +vc-816650693-20231218-abab186e-worker-3:5781:6813 [6] NCCL INFO Using network IB +12/19/2023 16:44:13 - INFO - torch.distributed.distributed_c10d - Rank 29: Completed store-based barrier for key:store_based_barrier_key:2 with 32 nodes. +12/19/2023 16:44:13 - INFO - torch.distributed.distributed_c10d - Rank 28: Completed store-based barrier for key:store_based_barrier_key:2 with 32 nodes. +12/19/2023 16:44:13 - INFO - torch.distributed.distributed_c10d - Rank 26: Completed store-based barrier for key:store_based_barrier_key:2 with 32 nodes. +12/19/2023 16:44:13 - INFO - torch.distributed.distributed_c10d - Rank 24: Completed store-based barrier for key:store_based_barrier_key:2 with 32 nodes. +12/19/2023 16:44:13 - INFO - torch.distributed.distributed_c10d - Rank 31: Completed store-based barrier for key:store_based_barrier_key:2 with 32 nodes. +12/19/2023 16:44:13 - INFO - torch.distributed.distributed_c10d - Rank 25: Completed store-based barrier for key:store_based_barrier_key:2 with 32 nodes. +vc-816650693-20231218-abab186e-worker-3:5780:6814 [5] NCCL INFO Using network IB +vc-816650693-20231218-abab186e-worker-3:5779:6815 [4] NCCL INFO Using network IB +vc-816650693-20231218-abab186e-worker-3:5782:6816 [7] NCCL INFO Using network IB +vc-816650693-20231218-abab186e-worker-3:5777:6817 [2] NCCL INFO Using network IB +vc-816650693-20231218-abab186e-worker-3:5775:6818 [0] NCCL INFO Using network IB +vc-816650693-20231218-abab186e-worker-3:5776:6819 [1] NCCL INFO Using network IB +vc-816650693-20231218-abab186e-worker-3:5781:6813 [6] NCCL INFO Setting affinity for GPU 6 to ffffffff,ffff0000,00000000,ffffffff,ffff0000,00000000 +vc-816650693-20231218-abab186e-worker-3:5775:6818 [0] NCCL INFO Setting affinity for GPU 0 to ffff,ffffffff,00000000,0000ffff,ffffffff +vc-816650693-20231218-abab186e-worker-3:5782:6816 [7] NCCL INFO Setting affinity for GPU 7 to ffffffff,ffff0000,00000000,ffffffff,ffff0000,00000000 +vc-816650693-20231218-abab186e-worker-3:5779:6815 [4] NCCL INFO Setting affinity for GPU 4 to ffffffff,ffff0000,00000000,ffffffff,ffff0000,00000000 +vc-816650693-20231218-abab186e-worker-3:5778:6812 [3] NCCL INFO Setting affinity for GPU 3 to ffff,ffffffff,00000000,0000ffff,ffffffff +vc-816650693-20231218-abab186e-worker-3:5776:6819 [1] NCCL INFO Setting affinity for GPU 1 to ffff,ffffffff,00000000,0000ffff,ffffffff +vc-816650693-20231218-abab186e-worker-3:5780:6814 [5] NCCL INFO Setting affinity for GPU 5 to ffffffff,ffff0000,00000000,ffffffff,ffff0000,00000000 +vc-816650693-20231218-abab186e-worker-3:5777:6817 [2] NCCL INFO Setting affinity for GPU 2 to ffff,ffffffff,00000000,0000ffff,ffffffff +vc-816650693-20231218-abab186e-worker-3:5777:6817 [2] NCCL INFO Trees [0] 27/-1/-1->26->18 [1] 27/10/-1->26->-1 +vc-816650693-20231218-abab186e-worker-3:5779:6815 [4] NCCL INFO Trees [0] 29/-1/-1->28->27 [1] 29/-1/-1->28->27 +vc-816650693-20231218-abab186e-worker-3:5780:6814 [5] NCCL INFO Trees [0] 30/-1/-1->29->28 [1] 30/-1/-1->29->28 +vc-816650693-20231218-abab186e-worker-3:5781:6813 [6] NCCL INFO Trees [0] 31/-1/-1->30->29 [1] 31/-1/-1->30->29 +vc-816650693-20231218-abab186e-wvc-816650693-20231218-abab186e-workervc-816650693-20231218-abab186e-worker-2:5800:6813 [6] NCCL INFO Trees [0] 23/-1/-1->22->21 [1] 23/-1/-1->22->21 +vc-816650693-20231218-abab186e-worker-2:5799:6809 [5] NCCL INFO Trees [0] 22/-1/-1->21->20 [1] 22/-1/-1->21->20 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utils.llama_patch - Padded sequences are less efficient in FlashAttention. +12/19/2023 16:44:24 - WARNING - utils.llama_patch - Padded sequences are less efficient in FlashAttention. +12/19/2023 16:44:24 - WARNING - utils.llama_patch - Padded sequences are less efficient in FlashAttention. +12/19/2023 16:44:24 - WARNING - utils.llama_patch - Padded sequences are less efficient in FlashAttention. +12/19/2023 16:44:24 - WARNING - utils.llama_patch - Padded sequences are less efficient in FlashAttention. +12/19/2023 16:44:24 - WARNING - utils.llama_patch - Padded sequences are less efficient in FlashAttention. +{'loss': 0.5476, 'learning_rate': 1.99919725148484e-05, 'epoch': 0.03} +{'loss': 0.4891, 'learning_rate': 1.9967902947497158e-05, 'epoch': 0.05} +{'loss': 0.4573, 'learning_rate': 1.9927829941565187e-05, 'epoch': 0.08} +{'loss': 0.4521, 'learning_rate': 1.9871817834144506e-05, 'epoch': 0.1} +{'loss': 0.4565, 'learning_rate': 1.9799956552507235e-05, 'epoch': 0.13} +{'loss': 0.4528, 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'epoch': 0.48} +{'loss': 0.4227, 'learning_rate': 1.6956825506034866e-05, 'epoch': 0.51} +{'loss': 0.4165, 'learning_rate': 1.6663465779520042e-05, 'epoch': 0.54} +{'loss': 0.4213, 'learning_rate': 1.635940787848455e-05, 'epoch': 0.56} +{'loss': 0.4197, 'learning_rate': 1.6045139966985562e-05, 'epoch': 0.59} +{'loss': 0.4257, 'learning_rate': 1.5721166601221697e-05, 'epoch': 0.61} +{'loss': 0.4216, 'learning_rate': 1.5388007919469604e-05, 'epoch': 0.64} +{'loss': 0.4171, 'learning_rate': 1.504619880700346e-05, 'epoch': 0.66} +{'loss': 0.42, 'learning_rate': 1.4696288037338258e-05, 'epoch': 0.69} +{'loss': 0.4179, 'learning_rate': 1.4338837391175582e-05, 'epoch': 0.71} +{'loss': 0.4183, 'learning_rate': 1.397442075446633e-05, 'epoch': 0.74} +{'loss': 0.4291, 'learning_rate': 1.3603623197038536e-05, 'epoch': 0.77} +{'loss': 0.4142, 'learning_rate': 1.3227040033269501e-05, 'epoch': 0.79} +{'loss': 0.4154, 'learning_rate': 1.2845275866310325e-05, 'epoch': 0.82} +{'loss': 0.4199, 'learning_rate': 1.2458943617397346e-05, 'epoch': 0.84} +{'loss': 0.4213, 'learning_rate': 1.206866354180891e-05, 'epoch': 0.87} +{'loss': 0.4039, 'learning_rate': 1.1675062233047365e-05, 'epoch': 0.89} +{'loss': 0.4065, 'learning_rate': 1.1278771616845061e-05, 'epoch': 0.92} +{'loss': 0.4117, 'learning_rate': 1.0880427936609455e-05, 'epoch': 0.94} +{'loss': 0.4132, 'learning_rate': 1.0480670731936209e-05, 'epoch': 0.97} +{'loss': 0.4099, 'learning_rate': 1.0080141811830277e-05, 'epoch': 0.99} +[2023-12-19 21:25:12,669] [INFO] [torch_checkpoint_engine.py:21:save] [Torch] Saving /group/20025/jiuding/ckpt/34B_c50h50-python/checkpoint-392/global_step392/zero_pp_rank_24_mp_rank_00_model_states.pt... +[2023-12-19 21:25:12,695] [INFO] [torch_checkpoint_engine.py:23:save] [Torch] Saved /group/20025/jiuding/ckpt/34B_c50h50-python/checkpoint-392/global_step392/zero_pp_rank_24_mp_rank_00_model_states.pt. +[2023-12-19 21:25:14,626] [INFO] [torch_checkpoint_engine.py:21:save] [Torch] Saving /group/20025/jiuding/ckpt/34B_c50h50-python/checkpoint-392/global_step392/bf16_zero_pp_rank_24_mp_rank_00_optim_states.pt... +[2023-12-19 21:25:44,906] [INFO] [torch_checkpoint_engine.py:23:save] [Torch] Saved /group/20025/jiuding/ckpt/34B_c50h50-python/checkpoint-392/global_step392/bf16_zero_pp_rank_24_mp_rank_00_optim_states.pt. +[2023-12-19 21:25:44,906] [INFO] [engine.py:3285:_save_zero_checkpoint] zero checkpoint saved /group/20025/jiuding/ckpt/34B_c50h50-python/checkpoint-392/global_step392/bf16_zero_pp_rank_24_mp_rank_00_optim_states.pt +[2023-12-19 21:25:47,402] [INFO] [torch_checkpoint_engine.py:33:commit] [Torch] Checkpoint global_step392 is ready now! +{'loss': 0.3254, 'learning_rate': 9.67948422428345e-06, 'epoch': 1.02} +{'loss': 0.2853, 'learning_rate': 9.279341223862705e-06, 'epoch': 1.05} +{'loss': 0.2822, 'learning_rate': 8.880355238966923e-06, 'epoch': 1.07} +{'loss': 0.2859, 'learning_rate': 8.483166840409996e-06, 'epoch': 1.1} +{'loss': 0.2797, 'learning_rate': 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'learning_rate': 3.1592040667065393e-06, 'epoch': 1.48} +{'loss': 0.2748, 'learning_rate': 2.8724904502688566e-06, 'epoch': 1.51} +{'loss': 0.2809, 'learning_rate': 2.597220029246846e-06, 'epoch': 1.53} +{'loss': 0.2706, 'learning_rate': 2.333834749484e-06, 'epoch': 1.56} +{'loss': 0.2748, 'learning_rate': 2.082757475264804e-06, 'epoch': 1.58} +{'loss': 0.273, 'learning_rate': 1.8443913104073984e-06, 'epoch': 1.61} +{'loss': 0.2788, 'learning_rate': 1.6191189510815942e-06, 'epoch': 1.63} +{'loss': 0.2741, 'learning_rate': 1.4073020713912988e-06, 'epoch': 1.66} +{'loss': 0.2749, 'learning_rate': 1.209280742707828e-06, 'epoch': 1.68} +{'loss': 0.2739, 'learning_rate': 1.0253728876863256e-06, 'epoch': 1.71} +{'loss': 0.27, 'learning_rate': 8.558737698418762e-07, 'epoch': 1.73} +{'loss': 0.2683, 'learning_rate': 7.010555195048241e-07, 'epoch': 1.76} +{'loss': 0.2701, 'learning_rate': 5.611666969163243e-07, 'epoch': 1.79} +{'loss': 0.2705, 'learning_rate': 4.3643189316561864e-07, 'epoch': 1.81} +{'loss': 0.2695, 'learning_rate': 3.2705136960970554e-07, 'epoch': 1.84} +{'loss': 0.2747, 'learning_rate': 2.3320073635432984e-07, 'epoch': 1.86} +{'loss': 0.273, 'learning_rate': 1.55030670312476e-07, 'epoch': 1.89} +{'loss': 0.2721, 'learning_rate': 9.26666732930348e-08, 'epoch': 1.91} +{'loss': 0.2798, 'learning_rate': 4.6208870508017703e-08, 'epoch': 1.94} +{'loss': 0.2738, 'learning_rate': 1.5731849821833955e-08, 'epoch': 1.96} +{'loss': 0.2722, 'learning_rate': 1.2845420006879494e-09, 'epoch': 1.99} +[2023-12-20 02:06:54,898] [INFO] [torch_checkpoint_engine.py:21:save] [Torch] Saving /group/20025/jiuding/ckpt/34B_c50h50-python/checkpoint-784/global_step784/zero_pp_rank_24_mp_rank_00_model_states.pt... +[2023-12-20 02:06:54,927] [INFO] [torch_checkpoint_engine.py:23:save] [Torch] Saved /group/20025/jiuding/ckpt/34B_c50h50-python/checkpoint-784/global_step784/zero_pp_rank_24_mp_rank_00_model_states.pt. +[2023-12-20 02:06:54,973] [INFO] [torch_checkpoint_engine.py:21:save] [Torch] Saving /group/20025/jiuding/ckpt/34B_c50h50-python/checkpoint-784/global_step784/bf16_zero_pp_rank_24_mp_rank_00_optim_states.pt... +[2023-12-20 02:07:26,822] [INFO] [torch_checkpoint_engine.py:23:save] [Torch] Saved /group/20025/jiuding/ckpt/34B_c50h50-python/checkpoint-784/global_step784/bf16_zero_pp_rank_24_mp_rank_00_optim_states.pt. +[2023-12-20 02:07:26,822] [INFO] [engine.py:3285:_save_zero_checkpoint] zero checkpoint saved /group/20025/jiuding/ckpt/34B_c50h50-python/checkpoint-784/global_step784/bf16_zero_pp_rank_24_mp_rank_00_optim_states.pt +[2023-12-20 02:07:29,357] [INFO] [torch_checkpoint_engine.py:33:commit] [Torch] Checkpoint global_step784 is ready now! +{'train_runtime': 33786.3022, 'train_samples_per_second': 5.938, 'train_steps_per_second': 0.023, 'train_loss': 0.3541936985388094, 'epoch': 2.0} +nk_8_mp_rank_00_optim_states.pt... +[2023-12-20 02:07:29,301] [INFO] [torch_checkpoint_engine.py:23:save] [Torch] Saved /group/20025/jiuding/ckpt/34B_c50h50-python/checkpoint-784/global_step784/bf16_zero_pp_rank_8_mp_rank_00_optim_states.pt. +[2023-12-20 02:07:29,301] [INFO] [engine.py:3285:_save_zero_checkpoint] zero checkpoint saved /group/20025/jiuding/ckpt/34B_c50h50-python/checkpoint-784/global_step784/bf16_zero_pp_rank_8_mp_rank_00_optim_states.pt +[2023-12-20 02:07:29,315] [INFO] [torch_checkpoint_engine.py:33:commit] [2023-12-20 02:07:29,325] [INFO] [torch_checkpoi{'train_runtime': 33786.1779, 'train_samples_per_second': 5.938, 'train_steps_per_second': 0.023, 'train_loss': 0.35464502110773205, 'epoch': 2.0} +vc-816650693-20231218-abab186e-worker-1:5795:6821 [0] NCCL INFO [Service thread] Connection closed by localRank 3 +vc-816650693-20231218-abab186e-worker-1:5795:6695 [0] NCCL INFO [Service thread] Connection closed by localRank 3 +d': False}}}} +[2023-12-19 16:44:23,289] [INFO] [config.py:964:print] data_efficiency_enabled ...... False +[2023-12-19 16:44:23,289] [INFO] [config.py:964:print] dataloader_drop_last ......... False +[2023-12-19 16:44:23,289] [INFO] [config.py:964:print] disable_allgather ............ False +[2023-12-19 16:44:23,289] [INFO] [config.py:964:print] dump_state ................... False +[2023-12-19 16:44:23,289] [INFO] [config.py:964:print] dynamic_loss_scale_args ...... None +[2023-12-19 16:44:23,289] [INFO] [config.py:964:print] eigenvalue_enabled ........... False +[2023-12-19 16:44:23,289] [INFO] [config.py:964:print] eigenvalue_gas_boundary_resolution 1 +[2023-12-19 16:44:23,289] [INFO] [config.py:964:print] eigenvalue_layer_name ........ bert.encoder.layer +[2023-12-19 16:44:23,289] [INFO] [config.py:964:print] eigenvalue_layer_num ......... 0 +[2023-12-19 16:44:23,289] [INFO] [config.py:964:print] eigenvalue_max_iter .......... 100 +[2023-12-19 16:44:23,289] [INFO] [config.py:964:print] eigenvalue_stability ......... 1e-06 +[2023-12-19 16:44:23,289] [INFO] [config.py:964:print] eigenvalue_tol ............... 0.01 +[2023-12-19 16:44:23,289] [INFO] [config.py:964:print] eigenvalue_verbose ........... False +[2023-12-19 16:44:23,289] [INFO] [config.py:964:print] elasticity_enabled ........... False +[2023-12-19 16:44:23,289] [INFO] [config.py:964:print] flops_profiler_config ........ { + "enabled": false, + "recompute_fwd_factor": 0.0, + "profile_step": 1, + "module_depth": -1, + "top_modules": 1, + "detailed": true, + "output_file": null +} +[2023-12-19 16:44:23,289] [INFO] [config.py:964:print] fp16_auto_cast ............... None +[2023-12-19 16:44:23,289] [INFO] [config.py:964:print] fp16_enabled ................. False +[2023-12-19 16:44:23,289] [INFO] [config.py:964:print] fp16_master_weights_and_gradients False +[2023-12-19 16:44:23,289] [INFO] [config.py:964:print] global_rank .................. 0 +[2023-12-19 16:44:23,289] [INFO] [config.py:964:print] grad_accum_dtype ............. None +[2023-12-19 16:44:23,289] [INFO] [config.py:964:print] gradient_accumulation_steps .. 2 +[2023-12-19 16:44:23,289] [INFO] [config.py:964:print] gradient_clipping ............ 1.0 +[2023-12-19 16:44:23,289] [INFO] [config.py:964:print] gradient_predivide_factor .... 1.0 +[2023-12-19 16:44:23,289] [INFO] [config.py:964:print] hybrid_engine ................ enabled=False max_out_tokens=512 inference_tp_size=1 release_inference_cache=False pin_parameters=True tp_gather_partition_size=8 +[2023-12-19 16:44:23,289] [INFO] [config.py:964:print] initial_dynamic_scale ........ 1 +[2023-12-19 16:44:23,289] [INFO] [config.py:964:print] load_universal_checkpoint .... False +[2023-12-19 16:44:23,289] [INFO] [config.py:964:print] loss_scale ................... 1.0 +[2023-12-19 16:44:23,289] [INFO] [config.py:964:print] memory_breakdown ............. False +[2023-12-19 16:44:23,289] [INFO] [config.py:964:print] mics_hierarchial_params_gather False +[2023-12-19 16:44:23,289] [INFO] [config.py:964:print] mics_shard_size .............. -1 +[2023-12-19 16:44:23,290] [INFO] [config.py:964:print] monitor_config ............... tensorboard=TensorBoardConfig(enabled=False, output_path='', job_name='DeepSpeedJobName') wandb=WandbConfig(enabled=False, group=None, team=None, project='deepspeed') csv_monitor=CSVConfig(enabled=False, output_path='', job_name='DeepSpeedJobName') enabled=False +[2023-12-19 16:44:23,290] [INFO] [config.py:964:print] nebula_config ................ { + "enabled": false, + "persistent_storage_path": null, + "persistent_time_interval": 100, + "num_of_version_in_retention": 2, + "enable_nebula_load": true, + "load_path": null +} +[2023-12-19 16:44:23,290] [INFO] [config.py:964:print] optimizer_legacy_fusion ...... False +[2023-12-19 16:44:23,290] [INFO] [config.py:964:print] optimizer_name ............... None +[2023-12-19 16:44:23,290] [INFO] [config.py:964:print] optimizer_params ............. None +[2023-12-19 16:44:23,290] [INFO] [config.py:964:print] pipeline ..................... {'stages': 'auto', 'partition': 'best', 'seed_layers': False, 'activation_checkpoint_interval': 0} +[2023-12-19 16:44:23,290] [INFO] [config.py:964:print] pld_enabled .................. False +[2023-12-19 16:44:23,290] [INFO] [config.py:964:print] pld_params ................... False +[2023-12-19 16:44:23,290] [INFO] [config.py:964:print] prescale_gradients ........... False +[2023-12-19 16:44:23,290] [INFO] [config.py:964:print] scheduler_name ............... None +[2023-12-19 16:44:23,290] [INFO] [config.py:964:print] scheduler_params ............. None +[2023-12-19 16:44:23,290] [INFO] [config.py:964:print] sparse_attention ............. None +[2023-12-19 16:44:23,290] [INFO] [config.py:964:print] sparse_gradients_enabled ..... False +[2023-12-19 16:44:23,290] [INFO] [config.py:964:print] steps_per_print .............. inf +[2023-12-19 16:44:23,290] [INFO] [config.py:964:print] train_batch_size ............. 256 +[2023-12-19 16:44:23,290] [INFO] [config.py:964:print] train_micro_batch_size_per_gpu 4 +[2023-12-19 16:44:23,290] [INFO] [config.py:964:print] use_node_local_storage ....... False +[2023-12-19 16:44:23,290] [INFO] [config.py:964:print] wall_clock_breakdown ......... False +[2023-12-19 16:44:23,290] [INFO] [config.py:964:print] world_size ................... 32 +[2023-12-19 16:44:23,290] [INFO] [config.py:964:print] zero_allow_untested_optimizer True +[2023-12-19 16:44:23,290] [INFO] [config.py:964:print] zero_config .................. stage=3 contiguous_gradients=True reduce_scatter=True reduce_bucket_size=67108864 allgather_partitions=True allgather_bucket_size=500,000,000 overlap_comm=True load_from_fp32_weights=True elastic_checkpoint=False offload_param=DeepSpeedZeroOffloadParamConfig(device='none', nvme_path=None, buffer_count=5, buffer_size=100,000,000, max_in_cpu=1,000,000,000, pin_memory=True) offload_optimizer=DeepSpeedZeroOffloadOptimizerConfig(device='none', nvme_path=None, buffer_count=4, pin_memory=True, pipeline=False, pipeline_read=False, pipeline_write=False, fast_init=False) sub_group_size=1000000000 cpu_offload_param=None cpu_offload_use_pin_memory=None cpu_offload=None prefetch_bucket_size=60397977 param_persistence_threshold=81920 model_persistence_threshold=sys.maxsize max_live_parameters=1000000000 max_reuse_distance=1000000000 gather_16bit_weights_on_model_save=True stage3_gather_fp16_weights_on_model_save=False ignore_unused_parameters=True legacy_stage1=False round_robin_gradients=False zero_hpz_partition_size=1 zero_quantized_weights=False zero_quantized_gradients=False mics_shard_size=-1 mics_hierarchical_params_gather=False memory_efficient_linear=True +[2023-12-19 16:44:23,290] [INFO] [config.py:964:print] zero_enabled ................. True +[2023-12-19 16:44:23,290] [INFO] [config.py:964:print] zero_force_ds_cpu_optimizer .. True +[2023-12-19 16:44:23,290] [INFO] [config.py:964:print] zero_optimization_stage ...... 3 +[2023-12-19 16:44:23,290] [INFO] [config.py:950:print_user_config] json = { + "fp16": { + "enabled": false, + "loss_scale": 0, + "loss_scale_window": 1000, + "initial_scale_power": 16, + "hysteresis": 2, + "min_loss_scale": 1 + }, + "bf16": { + "enabled": true + }, + "zero_optimization": { + "stage": 3, + "offload_optimizer": { + "device": "none", + "pin_memory": true + }, + "offload_param": { + "device": "none", + "pin_memory": true + }, + "overlap_comm": true, + "contiguous_gradients": true, + "sub_group_size": 1.000000e+09, + "reduce_bucket_size": 6.710886e+07, + "stage3_prefetch_bucket_size": 6.039798e+07, + "stage3_param_persistence_threshold": 8.192000e+04, + "stage3_max_live_parameters": 1.000000e+09, + "stage3_max_reuse_distance": 1.000000e+09, + "stage3_gather_16bit_weights_on_model_save": true + }, + "gradient_accumulation_steps": 2, + "gradient_clipping": 1.0, + "steps_per_print": inf, + "train_batch_size": 256, + "train_micro_batch_size_per_gpu": 4, + "wall_clock_breakdown": false, + "zero_allow_untested_optimizer": true +} +12/19/2023 16:44:24 - WARNING - utils.llama_patch - Padded sequences are less efficient in FlashAttention. +12/19/2023 16:44:24 - WARNING - utils.llama_patch - Padded sequences are less efficient in FlashAttention. +12/19/2023 16:44:24 - WARNING - utils.llama_patch - Padded sequences are less efficient in FlashAttention. +12/19/2023 16:44:24 - WARNING - utils.llama_patch - Padded sequences are less efficient in FlashAttention. +12/19/2023 16:44:24 - WARNING - utils.llama_patch - Padded sequences are less efficient in FlashAttention. +12/19/2023 16:44:24 - WARNING - utils.llama_patch - Padded sequences are less efficient in FlashAttention. +12/19/2023 16:44:24 - WARNING - utils.llama_patch - Padded sequences are less efficient in FlashAttention. +12/19/2023 16:44:24 - WARNING - utils.llama_patch - Padded sequences are less efficient in FlashAttention. +[2023-12-19 16:45:12,274] [WARNING] [stage3.py:1898:step] 1 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 16:45:54,958] [WARNING] [stage3.py:1898:step] 4 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 16:46:38,112] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 16:47:20,483] [WARNING] [stage3.py:1898:step] 3 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 16:48:02,763] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 16:48:46,057] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 16:49:28,528] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 16:50:11,375] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 16:50:53,873] [WARNING] [stage3.py:1898:step] 4 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 16:51:37,040] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +{'loss': 0.5476, 'learning_rate': 1.99919725148484e-05, 'epoch': 0.03} +[2023-12-19 16:52:19,963] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 16:53:02,744] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 16:53:45,813] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 16:54:28,860] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 16:55:11,128] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 16:55:52,925] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 16:56:35,637] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 16:57:18,315] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 16:58:01,081] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 16:58:44,491] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +{'loss': 0.4891, 'learning_rate': 1.9967902947497158e-05, 'epoch': 0.05} +[2023-12-19 16:59:27,605] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 17:00:10,411] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 17:00:53,335] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 17:01:35,568] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 17:02:19,233] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 17:03:03,279] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 17:03:46,377] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 17:04:29,128] [WARNING] [stage3.py:1898:step] 3 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 17:05:11,148] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 17:05:52,390] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +{'loss': 0.4573, 'learning_rate': 1.9927829941565187e-05, 'epoch': 0.08} +[2023-12-19 17:06:34,650] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 17:07:18,118] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 17:08:00,086] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 17:08:42,253] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 17:09:24,905] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 17:10:07,341] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 17:10:49,731] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 17:11:31,757] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 17:12:14,479] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 17:12:56,949] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +{'loss': 0.4521, 'learning_rate': 1.9871817834144506e-05, 'epoch': 0.1} +[2023-12-19 17:13:38,765] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 17:14:21,811] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 17:15:03,530] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 17:15:46,846] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 17:16:29,251] [WARNING] [stage3.py:1898:step] 4 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 17:17:12,325] [WARNING] [stage3.py:1898:step] 4 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 17:17:53,824] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 17:18:35,989] [WARNING] [stage3.py:1898:step] 4 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 17:19:18,727] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 17:20:01,227] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +{'loss': 0.4565, 'learning_rate': 1.9799956552507235e-05, 'epoch': 0.13} +[2023-12-19 17:20:44,207] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 17:21:26,919] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 17:22:10,686] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 17:22:53,155] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 17:23:35,787] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 17:24:18,831] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 17:25:01,706] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 17:25:45,187] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 17:26:28,159] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 17:27:10,571] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +{'loss': 0.4528, 'learning_rate': 1.971236146972764e-05, 'epoch': 0.15} +[2023-12-19 17:27:52,677] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 17:28:34,727] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 17:29:17,645] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 17:30:00,931] [WARNING] [stage3.py:1898:step] 4 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 17:30:43,334] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 17:31:26,177] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 17:32:09,499] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 17:32:52,089] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 17:33:34,242] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 17:34:16,922] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +{'loss': 0.4456, 'learning_rate': 1.9609173219450998e-05, 'epoch': 0.18} +[2023-12-19 17:34:59,602] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 17:35:41,732] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 17:36:23,574] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 17:37:06,221] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 17:37:48,323] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 17:38:31,207] [WARNING] [stage3.py:1898:step] 3 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 17:39:13,567] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 17:39:55,556] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 17:40:38,628] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 17:41:21,237] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +{'loss': 0.4407, 'learning_rate': 1.949055747010669e-05, 'epoch': 0.2} +[2023-12-19 17:42:03,293] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 17:42:48,259] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 17:43:31,076] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 17:44:12,915] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 17:44:54,858] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 17:45:37,947] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 17:46:20,297] [WARNING] [stage3.py:1898:step] 4 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 17:47:03,220] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 17:47:46,367] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 17:48:29,392] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +{'loss': 0.4251, 'learning_rate': 1.9356704658928036e-05, 'epoch': 0.23} +[2023-12-19 17:49:11,468] [WARNING] [stage3.py:1898:step] 3 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 17:49:54,633] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 17:50:37,514] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 17:51:20,072] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 17:52:01,631] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 17:52:45,110] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 17:53:26,838] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 17:54:10,085] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 17:54:52,119] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 17:55:34,153] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +{'loss': 0.4372, 'learning_rate': 1.9207829686205882e-05, 'epoch': 0.26} +[2023-12-19 17:56:17,322] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 17:56:59,770] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 17:57:41,769] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 17:58:23,957] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 17:59:06,235] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 17:59:48,384] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 18:00:30,649] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 18:01:13,050] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 18:01:55,837] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 18:02:38,772] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +{'loss': 0.4439, 'learning_rate': 1.904417157026683e-05, 'epoch': 0.28} +[2023-12-19 18:03:21,418] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 18:04:04,017] [WARNING] [stage3.py:1898:step] 4 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 18:04:48,004] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 18:05:30,225] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 18:06:12,090] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 18:06:54,927] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 18:07:38,473] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 18:08:20,980] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 18:09:02,567] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 18:09:46,009] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +{'loss': 0.4305, 'learning_rate': 1.8865993063730003e-05, 'epoch': 0.31} +[2023-12-19 18:10:27,377] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 18:11:09,613] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 18:11:51,978] [WARNING] [stage3.py:1898:step] 3 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 18:12:34,393] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 18:13:16,894] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 18:13:59,018] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 18:14:41,911] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 18:15:24,124] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 18:16:06,477] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 18:16:49,364] [WARNING] [stage3.py:1898:step] 4 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +{'loss': 0.4395, 'learning_rate': 1.867358023165851e-05, 'epoch': 0.33} +[2023-12-19 18:17:31,766] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 18:18:14,152] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 18:18:56,674] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 18:19:38,788] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 18:20:22,004] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 18:21:05,931] [WARNING] [stage3.py:1898:step] 3 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 18:21:48,668] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 18:22:30,982] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 18:23:13,838] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 18:23:57,270] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +{'loss': 0.4402, 'learning_rate': 1.8467241992282842e-05, 'epoch': 0.36} +[2023-12-19 18:24:40,020] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 18:25:22,300] [WARNING] [stage3.py:1898:step] 3 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 18:26:03,851] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 18:26:47,332] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 18:27:30,012] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 18:28:13,390] [WARNING] [stage3.py:1898:step] 3 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 18:28:55,284] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 18:29:38,049] [WARNING] [stage3.py:1898:step] 3 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 18:30:20,786] [WARNING] [stage3.py:1898:step] 4 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 18:31:03,883] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +{'loss': 0.4382, 'learning_rate': 1.824730962103356e-05, 'epoch': 0.38} +[2023-12-19 18:31:46,573] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 18:32:28,991] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 18:33:11,407] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 18:33:54,699] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 18:34:38,557] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 18:35:20,369] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 18:36:02,782] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 18:36:46,274] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 18:37:29,230] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 18:38:11,908] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +{'loss': 0.4359, 'learning_rate': 1.8014136218679566e-05, 'epoch': 0.41} +[2023-12-19 18:38:55,216] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 18:39:38,605] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 18:40:21,073] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 18:41:03,713] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 18:41:47,069] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 18:42:29,605] [WARNING] [stage3.py:1898:step] 3 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 18:43:12,084] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 18:43:54,914] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 18:44:37,667] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 18:45:20,119] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +{'loss': 0.4202, 'learning_rate': 1.7768096144425903e-05, 'epoch': 0.43} +[2023-12-19 18:46:02,684] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 18:46:46,401] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 18:47:28,860] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 18:48:11,412] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 18:48:53,989] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 18:49:37,414] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 18:50:19,594] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 18:51:02,374] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 18:51:46,711] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 18:52:29,544] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +{'loss': 0.4181, 'learning_rate': 1.7509584414881114e-05, 'epoch': 0.46} +[2023-12-19 18:53:12,176] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 18:53:54,565] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 18:54:37,928] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 18:55:19,440] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 18:56:02,107] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 18:56:46,796] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 18:57:29,779] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 18:58:12,276] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 18:58:55,002] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 18:59:38,721] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +{'loss': 0.4263, 'learning_rate': 1.7239016069859292e-05, 'epoch': 0.48} +[2023-12-19 19:00:20,953] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 19:01:03,499] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 19:01:47,952] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 19:02:30,275] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 19:03:12,531] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 19:03:54,609] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 19:04:37,279] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 19:05:19,793] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 19:06:01,345] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 19:06:43,044] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +{'loss': 0.4227, 'learning_rate': 1.6956825506034866e-05, 'epoch': 0.51} +[2023-12-19 19:07:24,969] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 19:08:07,363] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 19:08:50,295] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 19:09:32,892] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 19:10:15,996] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 19:10:59,180] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 19:11:40,880] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 19:12:23,027] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 19:13:06,072] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 19:13:48,755] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +{'loss': 0.4165, 'learning_rate': 1.6663465779520042e-05, 'epoch': 0.54} +[2023-12-19 19:14:30,912] [WARNING] [stage3.py:1898:step] 3 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 19:15:13,186] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 19:15:55,313] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 19:16:38,567] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 19:17:20,485] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 19:18:03,457] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 19:18:48,071] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 19:19:30,449] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 19:20:13,699] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 19:20:56,823] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +{'loss': 0.4213, 'learning_rate': 1.635940787848455e-05, 'epoch': 0.56} +[2023-12-19 19:21:39,470] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 19:22:21,282] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 19:23:05,607] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 19:23:48,183] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 19:24:30,471] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 19:25:13,426] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 19:25:56,281] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 19:26:39,620] [WARNING] [stage3.py:1898:step] 4 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 19:27:22,304] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 19:28:06,403] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +{'loss': 0.4197, 'learning_rate': 1.6045139966985562e-05, 'epoch': 0.59} +[2023-12-19 19:28:49,543] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 19:29:32,829] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 19:30:15,657] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 19:30:59,167] [WARNING] [stage3.py:1898:step] 4 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 19:31:41,599] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 19:32:23,859] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 19:33:06,156] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 19:33:48,180] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 19:34:30,650] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 19:35:12,287] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +{'loss': 0.4257, 'learning_rate': 1.5721166601221697e-05, 'epoch': 0.61} +[2023-12-19 19:35:55,500] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 19:36:38,845] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 19:37:21,151] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 19:38:03,863] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 19:38:47,755] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 19:39:30,559] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 19:40:12,621] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 19:40:55,149] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 19:41:38,924] [WARNING] [stage3.py:1898:step] 4 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 19:42:21,103] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +{'loss': 0.4216, 'learning_rate': 1.5388007919469604e-05, 'epoch': 0.64} +[2023-12-19 19:43:03,039] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 19:43:45,997] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 19:44:27,987] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 19:45:10,339] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 19:45:52,199] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 19:46:34,597] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 19:47:18,694] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 19:48:01,791] [WARNING] [stage3.py:1898:step] 3 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 19:48:46,617] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 19:49:29,148] [WARNING] [stage3.py:1898:step] 4 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +{'loss': 0.4171, 'learning_rate': 1.504619880700346e-05, 'epoch': 0.66} +[2023-12-19 19:50:12,094] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 19:50:55,454] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 19:51:37,731] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 19:52:20,479] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 19:53:03,402] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 19:53:47,220] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 19:54:29,281] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 19:55:12,578] [WARNING] [stage3.py:1898:step] 3 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 19:55:54,814] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 19:56:38,545] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +{'loss': 0.42, 'learning_rate': 1.4696288037338258e-05, 'epoch': 0.69} +[2023-12-19 19:57:20,474] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 19:58:02,767] [WARNING] [stage3.py:1898:step] 3 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 19:58:47,073] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 19:59:29,178] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 20:00:11,604] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 20:00:53,883] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 20:01:37,957] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 20:02:19,648] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 20:03:02,009] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 20:03:46,034] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +{'loss': 0.4179, 'learning_rate': 1.4338837391175582e-05, 'epoch': 0.71} +[2023-12-19 20:04:28,380] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 20:05:10,636] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 20:05:52,630] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 20:06:34,422] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 20:07:18,206] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 20:08:00,453] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 20:08:42,923] [WARNING] [stage3.py:1898:step] 4 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 20:09:25,961] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 20:10:08,711] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 20:10:51,225] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +{'loss': 0.4183, 'learning_rate': 1.397442075446633e-05, 'epoch': 0.74} +[2023-12-19 20:11:33,429] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 20:12:15,529] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 20:12:57,714] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 20:13:40,601] [WARNING] [stage3.py:1898:step] 3 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 20:14:22,551] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 20:15:06,204] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 20:15:49,374] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 20:16:31,952] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 20:17:14,802] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 20:17:57,359] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +{'loss': 0.4291, 'learning_rate': 1.3603623197038536e-05, 'epoch': 0.77} +[2023-12-19 20:18:39,740] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 20:19:22,255] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 20:20:06,398] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 20:20:49,280] [WARNING] [stage3.py:1898:step] 3 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 20:21:31,682] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 20:22:14,611] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 20:22:58,268] [WARNING] [stage3.py:1898:step] 3 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 20:23:40,215] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 20:24:22,997] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 20:25:05,939] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +{'loss': 0.4142, 'learning_rate': 1.3227040033269501e-05, 'epoch': 0.79} +[2023-12-19 20:25:48,168] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 20:26:30,712] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 20:27:13,611] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 20:27:55,981] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 20:28:38,168] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 20:29:20,148] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 20:30:02,069] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 20:30:45,845] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 20:31:28,824] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 20:32:11,799] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +{'loss': 0.4154, 'learning_rate': 1.2845275866310325e-05, 'epoch': 0.82} +[2023-12-19 20:32:54,810] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 20:33:38,531] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 20:34:21,155] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 20:35:03,415] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 20:35:46,997] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 20:36:30,195] [WARNING] [stage3.py:1898:step] 3 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 20:37:13,374] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 20:37:55,963] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 20:38:38,006] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 20:39:19,788] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +{'loss': 0.4199, 'learning_rate': 1.2458943617397346e-05, 'epoch': 0.84} +[2023-12-19 20:40:02,153] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 20:40:44,760] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 20:41:27,878] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 20:42:10,209] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 20:42:52,763] [WARNING] [stage3.py:1898:step] 3 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 20:43:35,823] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 20:44:19,404] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 20:45:00,798] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 20:45:42,823] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 20:46:25,967] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +{'loss': 0.4213, 'learning_rate': 1.206866354180891e-05, 'epoch': 0.87} +[2023-12-19 20:47:08,721] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 20:47:51,217] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 20:48:34,276] [WARNING] [stage3.py:1898:step] 3 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 20:49:17,443] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 20:49:59,520] [WARNING] [stage3.py:1898:step] 3 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 20:50:41,913] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 20:51:25,114] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 20:52:07,771] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 20:52:50,405] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 20:53:33,045] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +{'loss': 0.4039, 'learning_rate': 1.1675062233047365e-05, 'epoch': 0.89} +[2023-12-19 20:54:15,748] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 20:54:58,666] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 20:55:41,149] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 20:56:23,944] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 20:57:06,928] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 20:57:49,094] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 20:58:31,766] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 20:59:14,310] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 20:59:56,884] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 21:00:39,685] [WARNING] [stage3.py:1898:step] 4 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +{'loss': 0.4065, 'learning_rate': 1.1278771616845061e-05, 'epoch': 0.92} +[2023-12-19 21:01:21,609] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 21:02:03,860] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 21:02:46,759] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 21:03:29,907] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 21:04:12,745] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 21:04:55,234] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 21:05:37,968] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 21:06:20,134] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 21:07:02,838] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 21:07:46,542] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +{'loss': 0.4117, 'learning_rate': 1.0880427936609455e-05, 'epoch': 0.94} +[2023-12-19 21:08:27,921] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 21:09:10,502] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 21:09:52,753] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 21:10:34,615] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 21:11:18,631] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 21:12:01,203] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 21:12:44,835] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 21:13:27,292] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 21:14:09,578] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 21:14:51,830] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +{'loss': 0.4132, 'learning_rate': 1.0480670731936209e-05, 'epoch': 0.97} +[2023-12-19 21:15:34,362] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 21:16:17,514] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 21:16:59,405] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 21:17:41,927] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 21:18:24,558] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 21:19:07,261] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 21:19:49,242] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 21:20:32,279] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 21:21:15,503] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 21:21:58,597] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +{'loss': 0.4099, 'learning_rate': 1.0080141811830277e-05, 'epoch': 0.99} +[2023-12-19 21:22:40,658] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 21:23:23,304] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 21:25:12,669] [INFO] [logging.py:96:log_dist] [Rank 0] [Torch] Checkpoint global_step392 is about to be saved! +[2023-12-19 21:25:12,687] [INFO] [logging.py:96:log_dist] [Rank 0] Saving model checkpoint: /group/20025/jiuding/ckpt/34B_c50h50-python/checkpoint-392/global_step392/zero_pp_rank_0_mp_rank_00_model_states.pt +[2023-12-19 21:25:12,687] [INFO] [torch_checkpoint_engine.py:21:save] [Torch] Saving /group/20025/jiuding/ckpt/34B_c50h50-python/checkpoint-392/global_step392/zero_pp_rank_0_mp_rank_00_model_states.pt... +[2023-12-19 21:25:14,596] [INFO] [torch_checkpoint_engine.py:23:save] [Torch] Saved /group/20025/jiuding/ckpt/34B_c50h50-python/checkpoint-392/global_step392/zero_pp_rank_0_mp_rank_00_model_states.pt. +[2023-12-19 21:25:14,642] [INFO] [torch_checkpoint_engine.py:21:save] [Torch] Saving /group/20025/jiuding/ckpt/34B_c50h50-python/checkpoint-392/global_step392/bf16_zero_pp_rank_0_mp_rank_00_optim_states.pt... +[2023-12-19 21:25:46,169] [INFO] [torch_checkpoint_engine.py:23:save] [Torch] Saved /group/20025/jiuding/ckpt/34B_c50h50-python/checkpoint-392/global_step392/bf16_zero_pp_rank_0_mp_rank_00_optim_states.pt. +[2023-12-19 21:25:46,184] [INFO] [engine.py:3285:_save_zero_checkpoint] zero checkpoint saved /group/20025/jiuding/ckpt/34B_c50h50-python/checkpoint-392/global_step392/bf16_zero_pp_rank_0_mp_rank_00_optim_states.pt +[2023-12-19 21:25:47,420] [INFO] [torch_checkpoint_engine.py:33:commit] [Torch] Checkpoint global_step392 is ready now! +[2023-12-19 21:26:29,169] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 21:27:10,712] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 21:27:52,457] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 21:28:35,244] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 21:29:18,157] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 21:30:00,863] [WARNING] [stage3.py:1898:step] 3 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 21:30:42,942] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 21:31:24,910] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +{'loss': 0.3254, 'learning_rate': 9.67948422428345e-06, 'epoch': 1.02} +[2023-12-19 21:32:07,310] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 21:32:49,266] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 21:33:31,386] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 21:34:13,536] [WARNING] [stage3.py:1898:step] 3 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 21:34:55,925] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 21:35:38,808] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 21:36:21,026] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 21:37:05,786] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 21:37:49,009] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 21:38:31,725] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +{'loss': 0.2853, 'learning_rate': 9.279341223862705e-06, 'epoch': 1.05} +[2023-12-19 21:39:14,395] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 21:39:57,290] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 21:40:39,941] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 21:41:22,737] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 21:42:06,578] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 21:42:49,343] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 21:43:31,877] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 21:44:14,732] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 21:44:57,281] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 21:45:39,631] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +{'loss': 0.2822, 'learning_rate': 8.880355238966923e-06, 'epoch': 1.07} +[2023-12-19 21:46:22,390] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 21:47:03,863] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 21:47:47,740] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 21:48:30,894] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 21:49:14,238] [WARNING] [stage3.py:1898:step] 3 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 21:49:58,033] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 21:50:40,557] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 21:51:23,500] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 21:52:06,790] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 21:52:49,016] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +{'loss': 0.2859, 'learning_rate': 8.483166840409996e-06, 'epoch': 1.1} +[2023-12-19 21:53:31,407] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 21:54:14,435] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 21:54:57,057] [WARNING] [stage3.py:1898:step] 4 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 21:55:39,067] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 21:56:21,268] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 21:57:03,838] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 21:57:48,609] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 21:58:31,349] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 21:59:13,496] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 21:59:55,070] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +{'loss': 0.2797, 'learning_rate': 8.08841371298628e-06, 'epoch': 1.12} +[2023-12-19 22:00:38,286] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 22:01:20,577] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 22:02:02,831] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 22:02:46,902] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 22:03:29,282] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 22:04:11,418] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 22:04:53,507] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 22:05:37,062] [WARNING] [stage3.py:1898:step] 3 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 22:06:20,165] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 22:07:02,935] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +{'loss': 0.2731, 'learning_rate': 7.696729631669563e-06, 'epoch': 1.15} +[2023-12-19 22:07:46,690] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 22:08:29,083] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 22:09:12,478] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 22:09:54,591] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 22:10:38,439] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 22:11:21,036] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 22:12:04,295] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 22:12:48,263] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 22:13:30,042] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 22:14:12,322] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +{'loss': 0.27, 'learning_rate': 7.308743444089232e-06, 'epoch': 1.17} +[2023-12-19 22:14:54,882] [WARNING] [stage3.py:1898:step] 3 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 22:15:38,640] [WARNING] [stage3.py:1898:step] 4 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 22:16:20,326] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 22:17:02,960] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 22:17:46,758] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 22:18:29,582] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 22:19:12,037] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 22:19:54,940] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 22:20:38,809] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 22:21:21,222] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +{'loss': 0.2809, 'learning_rate': 6.925078060917245e-06, 'epoch': 1.2} +[2023-12-19 22:22:06,115] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 22:22:48,697] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 22:23:31,137] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 22:24:14,272] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 22:24:56,905] [WARNING] [stage3.py:1898:step] 3 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 22:25:39,681] [WARNING] [stage3.py:1898:step] 4 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 22:26:21,762] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 22:27:03,932] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 22:27:47,010] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 22:28:29,550] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +{'loss': 0.28, 'learning_rate': 6.546349455786926e-06, 'epoch': 1.22} +[2023-12-19 22:29:11,335] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 22:29:53,184] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 22:30:36,045] [WARNING] [stage3.py:1898:step] 3 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 22:31:18,837] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 22:32:01,545] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 22:32:45,386] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 22:33:28,179] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 22:34:11,418] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 22:34:53,897] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 22:35:38,930] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +{'loss': 0.2863, 'learning_rate': 6.173165676349103e-06, 'epoch': 1.25} +[2023-12-19 22:36:21,779] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 22:37:03,235] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 22:37:46,583] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 22:38:28,810] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 22:39:10,850] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 22:39:53,138] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 22:40:36,317] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 22:41:18,906] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 22:42:00,816] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 22:42:43,503] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +{'loss': 0.2804, 'learning_rate': 5.806125868053433e-06, 'epoch': 1.28} +[2023-12-19 22:43:26,225] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 22:44:08,692] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 22:44:51,287] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 22:45:32,440] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 22:46:14,502] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 22:46:56,354] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 22:47:38,881] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 22:48:20,466] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 22:49:02,511] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 22:49:46,054] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +{'loss': 0.2797, 'learning_rate': 5.4458193122221405e-06, 'epoch': 1.3} +[2023-12-19 22:50:29,520] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 22:51:12,029] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 22:51:54,921] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 22:52:38,642] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 22:53:20,668] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 22:54:03,362] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 22:54:46,348] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 22:55:28,460] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 22:56:09,995] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 22:56:51,655] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +{'loss': 0.2861, 'learning_rate': 5.092824479960625e-06, 'epoch': 1.33} +[2023-12-19 22:57:33,534] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 22:58:17,771] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 22:59:00,221] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 22:59:42,876] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 23:00:24,555] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 23:01:07,293] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 23:01:50,018] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 23:02:32,376] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 23:03:15,431] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 23:03:58,021] [WARNING] [stage3.py:1898:step] 4 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +{'loss': 0.2777, 'learning_rate': 4.747708103423791e-06, 'epoch': 1.35} +[2023-12-19 23:04:39,849] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 23:05:22,903] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 23:06:06,403] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 23:06:49,264] [WARNING] [stage3.py:1898:step] 3 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 23:07:31,622] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 23:08:14,782] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 23:08:57,748] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 23:09:40,040] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 23:10:22,672] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 23:11:06,234] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +{'loss': 0.2791, 'learning_rate': 4.411024265929283e-06, 'epoch': 1.38} +[2023-12-19 23:11:48,398] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 23:12:30,749] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 23:13:13,598] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 23:13:55,692] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 23:14:38,301] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 23:15:21,157] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 23:16:03,553] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 23:16:48,193] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 23:17:29,759] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 23:18:12,524] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +{'loss': 0.275, 'learning_rate': 4.083313512378368e-06, 'epoch': 1.4} +[2023-12-19 23:18:55,256] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 23:19:37,515] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 23:20:19,886] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 23:21:02,689] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 23:21:46,261] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 23:22:28,852] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 23:23:11,781] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 23:23:53,501] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 23:24:36,090] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 23:25:18,934] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +{'loss': 0.2787, 'learning_rate': 3.7651019814126656e-06, 'epoch': 1.43} +[2023-12-19 23:26:01,925] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 23:26:44,727] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 23:27:26,820] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 23:28:09,467] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 23:28:51,618] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 23:29:34,060] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 23:30:16,187] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 23:30:58,706] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 23:31:41,264] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 23:32:23,980] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +{'loss': 0.2777, 'learning_rate': 3.456900560700158e-06, 'epoch': 1.45} +[2023-12-19 23:33:06,242] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 23:33:49,128] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 23:34:31,267] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 23:35:13,836] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 23:35:57,184] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 23:36:40,415] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 23:37:22,662] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 23:38:06,528] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 23:38:48,355] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 23:39:31,220] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +{'loss': 0.2727, 'learning_rate': 3.1592040667065393e-06, 'epoch': 1.48} +[2023-12-19 23:40:13,737] [WARNING] [stage3.py:1898:step] 3 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 23:40:56,617] [WARNING] [stage3.py:1898:step] 3 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 23:41:39,430] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 23:42:23,591] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 23:43:06,147] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 23:43:48,194] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 23:44:30,874] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 23:45:12,590] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 23:45:54,610] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 23:46:38,494] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +{'loss': 0.2748, 'learning_rate': 2.8724904502688566e-06, 'epoch': 1.51} +[2023-12-19 23:47:22,374] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 23:48:04,222] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 23:48:47,908] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 23:49:29,401] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 23:50:11,554] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 23:50:53,914] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 23:51:38,399] [WARNING] [stage3.py:1898:step] 4 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 23:52:20,653] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 23:53:02,854] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 23:53:46,283] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +{'loss': 0.2809, 'learning_rate': 2.597220029246846e-06, 'epoch': 1.53} +[2023-12-19 23:54:30,031] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 23:55:12,452] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 23:55:54,623] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 23:56:37,626] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 23:57:20,178] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 23:58:02,185] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 23:58:45,702] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-19 23:59:29,170] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-20 00:00:11,855] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-20 00:00:55,224] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +{'loss': 0.2706, 'learning_rate': 2.333834749484e-06, 'epoch': 1.56} +[2023-12-20 00:01:38,214] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-20 00:02:21,617] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-20 00:03:05,531] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-20 00:03:47,680] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-20 00:04:30,443] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-20 00:05:13,410] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-20 00:05:56,819] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-20 00:06:38,435] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-20 00:07:21,350] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-20 00:08:04,075] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +{'loss': 0.2748, 'learning_rate': 2.082757475264804e-06, 'epoch': 1.58} +[2023-12-20 00:08:46,987] [WARNING] [stage3.py:1898:step] 4 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-20 00:09:29,272] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-20 00:10:11,160] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-20 00:10:53,595] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-20 00:11:37,433] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-20 00:12:19,582] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-20 00:13:02,228] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-20 00:13:45,446] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-20 00:14:27,587] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-20 00:15:10,085] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +{'loss': 0.273, 'learning_rate': 1.8443913104073984e-06, 'epoch': 1.61} +[2023-12-20 00:15:53,304] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-20 00:16:36,985] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-20 00:17:19,343] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-20 00:18:02,152] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-20 00:18:45,430] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-20 00:19:28,301] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-20 00:20:11,361] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-20 00:20:54,129] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-20 00:21:36,790] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-20 00:22:19,786] [WARNING] [stage3.py:1898:step] 3 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +{'loss': 0.2788, 'learning_rate': 1.6191189510815942e-06, 'epoch': 1.63} +[2023-12-20 00:23:02,628] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-20 00:23:46,467] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-20 00:24:28,913] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-20 00:25:12,568] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-20 00:25:54,880] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-20 00:26:39,238] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-20 00:27:22,238] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-20 00:28:05,407] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-20 00:28:48,705] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-20 00:29:31,953] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +{'loss': 0.2741, 'learning_rate': 1.4073020713912988e-06, 'epoch': 1.66} +[2023-12-20 00:30:14,683] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-20 00:30:56,781] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-20 00:31:38,651] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-20 00:32:20,351] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-20 00:33:03,031] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-20 00:33:47,559] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-20 00:34:28,775] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-20 00:35:11,374] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-20 00:35:54,767] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-20 00:36:38,899] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +{'loss': 0.2749, 'learning_rate': 1.209280742707828e-06, 'epoch': 1.68} +[2023-12-20 00:37:21,872] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-20 00:38:05,700] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-20 00:38:48,521] [WARNING] [stage3.py:1898:step] 3 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-20 00:39:31,122] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-20 00:40:13,349] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-20 00:40:55,781] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-20 00:41:38,532] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-20 00:42:20,707] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-20 00:43:03,788] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-20 00:43:46,700] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +{'loss': 0.2739, 'learning_rate': 1.0253728876863256e-06, 'epoch': 1.71} +[2023-12-20 00:44:28,968] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-20 00:45:11,626] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-20 00:45:53,482] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-20 00:46:35,435] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-20 00:47:18,677] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-20 00:48:02,181] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-20 00:48:46,002] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-20 00:49:29,491] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-20 00:50:11,871] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-20 00:50:54,079] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +{'loss': 0.27, 'learning_rate': 8.558737698418762e-07, 'epoch': 1.73} +[2023-12-20 00:51:38,476] [WARNING] [stage3.py:1898:step] 4 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-20 00:52:22,831] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-20 00:53:06,047] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-20 00:53:48,510] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-20 00:54:30,416] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-20 00:55:12,380] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-20 00:55:54,327] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-20 00:56:38,221] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-20 00:57:20,592] [WARNING] [stage3.py:1898:step] 4 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-20 00:58:03,520] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +{'loss': 0.2683, 'learning_rate': 7.010555195048241e-07, 'epoch': 1.76} +[2023-12-20 00:58:47,673] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-20 00:59:30,746] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-20 01:00:13,313] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-20 01:00:54,982] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-20 01:01:37,820] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-20 01:02:20,013] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-20 01:03:03,174] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-20 01:03:47,054] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-20 01:04:29,986] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-20 01:05:12,878] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +{'loss': 0.2701, 'learning_rate': 5.611666969163243e-07, 'epoch': 1.79} +[2023-12-20 01:05:55,234] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-20 01:06:38,787] [WARNING] [stage3.py:1898:step] 4 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-20 01:07:21,782] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-20 01:08:03,936] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-20 01:08:47,162] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-20 01:09:29,148] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-20 01:10:12,334] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-20 01:10:55,342] [WARNING] [stage3.py:1898:step] 4 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-20 01:11:38,606] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-20 01:12:20,710] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +{'loss': 0.2705, 'learning_rate': 4.3643189316561864e-07, 'epoch': 1.81} +[2023-12-20 01:13:03,797] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-20 01:13:47,879] [WARNING] [stage3.py:1898:step] 4 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-20 01:14:29,994] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-20 01:15:11,665] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-20 01:15:54,123] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-20 01:16:38,261] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-20 01:17:21,761] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-20 01:18:04,169] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-20 01:18:48,039] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-20 01:19:30,172] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +{'loss': 0.2695, 'learning_rate': 3.2705136960970554e-07, 'epoch': 1.84} +[2023-12-20 01:20:12,472] [WARNING] [stage3.py:1898:step] 3 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-20 01:20:54,965] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-20 01:21:39,022] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-20 01:22:22,232] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-20 01:23:05,674] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-20 01:23:48,142] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-20 01:24:30,629] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-20 01:25:13,314] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-20 01:25:55,255] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-20 01:26:37,720] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +{'loss': 0.2747, 'learning_rate': 2.3320073635432984e-07, 'epoch': 1.86} +[2023-12-20 01:27:19,674] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-20 01:28:02,520] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-20 01:28:46,349] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-20 01:29:29,482] [WARNING] [stage3.py:1898:step] 4 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-20 01:30:12,306] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-20 01:30:55,232] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-20 01:31:38,124] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-20 01:32:20,651] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-20 01:33:03,595] [WARNING] [stage3.py:1898:step] 4 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-20 01:33:47,620] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +{'loss': 0.273, 'learning_rate': 1.55030670312476e-07, 'epoch': 1.89} +[2023-12-20 01:34:29,560] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-20 01:35:12,368] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-20 01:35:54,813] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-20 01:36:38,557] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-20 01:37:20,744] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-20 01:38:02,819] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-20 01:38:45,364] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-20 01:39:27,507] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-20 01:40:10,641] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-20 01:40:52,910] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +{'loss': 0.2721, 'learning_rate': 9.26666732930348e-08, 'epoch': 1.91} +[2023-12-20 01:41:36,109] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-20 01:42:18,789] [WARNING] [stage3.py:1898:step] 4 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-20 01:43:02,288] [WARNING] [stage3.py:1898:step] 3 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-20 01:43:45,852] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-20 01:44:27,764] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-20 01:45:09,718] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-20 01:45:52,095] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-20 01:46:34,712] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-20 01:47:18,405] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-20 01:47:59,919] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +{'loss': 0.2798, 'learning_rate': 4.6208870508017703e-08, 'epoch': 1.94} +[2023-12-20 01:48:43,142] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-20 01:49:26,395] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-20 01:50:08,978] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-20 01:50:51,550] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-20 01:51:33,542] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-20 01:52:15,703] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-20 01:52:58,527] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-20 01:53:40,610] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-20 01:54:23,982] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-20 01:55:07,489] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +{'loss': 0.2738, 'learning_rate': 1.5731849821833955e-08, 'epoch': 1.96} +[2023-12-20 01:55:49,396] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-20 01:56:31,845] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-20 01:57:14,765] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-20 01:57:58,005] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-20 01:58:40,324] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-20 01:59:22,872] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-20 02:00:06,834] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-20 02:00:49,396] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-20 02:01:31,129] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-20 02:02:14,018] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +{'loss': 0.2722, 'learning_rate': 1.2845420006879494e-09, 'epoch': 1.99} +[2023-12-20 02:02:56,682] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-20 02:03:39,230] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-20 02:04:21,275] [WARNING] [stage3.py:1898:step] 3 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-20 02:05:03,337] [WARNING] [stage3.py:1898:step] 2 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time +[2023-12-20 02:06:54,852] [INFO] [logging.py:96:log_dist] [Rank 0] [Torch] Checkpoint global_step784 is about to be saved! +[2023-12-20 02:06:54,867] [INFO] [logging.py:96:log_dist] [Rank 0] Saving model checkpoint: /group/20025/jiuding/ckpt/34B_c50h50-python/checkpoint-784/global_step784/zero_pp_rank_0_mp_rank_00_model_states.pt +[2023-12-20 02:06:54,868] [INFO] [torch_checkpoint_engine.py:21:save] [Torch] Saving /group/20025/jiuding/ckpt/34B_c50h50-python/checkpoint-784/global_step784/zero_pp_rank_0_mp_rank_00_model_states.pt... +[2023-12-20 02:06:54,894] [INFO] [torch_checkpoint_engine.py:23:save] [Torch] Saved /group/20025/jiuding/ckpt/34B_c50h50-python/checkpoint-784/global_step784/zero_pp_rank_0_mp_rank_00_model_states.pt. +[2023-12-20 02:06:54,945] [INFO] [torch_checkpoint_engine.py:21:save] [Torch] Saving /group/20025/jiuding/ckpt/34B_c50h50-python/checkpoint-784/global_step784/bf16_zero_pp_rank_0_mp_rank_00_optim_states.pt... +[2023-12-20 02:07:26,711] [INFO] [torch_checkpoint_engine.py:23:save] [Torch] Saved /group/20025/jiuding/ckpt/34B_c50h50-python/checkpoint-784/global_step784/bf16_zero_pp_rank_0_mp_rank_00_optim_states.pt. +[2023-12-20 02:07:26,718] [INFO] [engine.py:3285:_save_zero_checkpoint] zero checkpoint saved /group/20025/jiuding/ckpt/34B_c50h50-python/checkpoint-784/global_step784/bf16_zero_pp_rank_0_mp_rank_00_optim_states.pt +[2023-12-20 02:07:29,331] [INFO] [torch_checkpoint_engine.py:33:commit] [Torch] Checkpoint global_step784 is ready now! +{'train_runtime': 33786.0741, 'train_samples_per_second': 5.938, 'train_steps_per_second': 0.023, 'train_loss': 0.3541707301474347, 'epoch': 2.0} +***** train metrics ***** + epoch = 2.0 + train_loss = 0.3542 + train_runtime = 9:23:06.07 + train_samples_per_second = 5.938 + train_steps_per_second = 0.023 +vc-816650693-20231218-abab186e-worker-0:6152:7265 [2] NCCL INFO [Service thread] Connection closed by localRank 6 +vc-816650693-20231218-abab186e-worker-0:6152:7046 [2] NCCL INFO [Service thread] Connection closed by localRank 6 +vc-816650693-20231218-abab186e-worker-0:6152:7265 [2] NCCL INFO [Service thread] Connection closed by localRank 1 +vc-816650693-20231218-abab186e-worker-0:6152:7046 [2] NCCL INFO [Service thread] Connection closed by localRank 1 +vc-816650693-20231218-abab186e-worker-0:6152:7265 [2] NCCL INFO [Service thread] Connection closed by localRank 3 +vc-816650693-20231218-abab186e-worker-0:6152:7046 [2] NCCL INFO [Service thread] Connection closed by localRank 3 +vc-816650693-20231218-abab186e-worker-0:6152:7265 [2] NCCL INFO [Service thread] Connection closed by localRank 5 +vc-816650693-20231218-abab186e-worker-0:6152:7046 [2] NCCL INFO [Service thread] Connection closed by localRank 5 +vc-816650693-20231218-abab186e-worker-0:6152:7265 [2] NCCL INFO [Service thread] Connection closed by localRank 7 +vc-816650693-20231218-abab186e-worker-0:6152:7046 [2] NCCL INFO [Service thread] Connection closed by localRank 7 +12/20/2023 02:09:18 - INFO - matplotlib.font_manager - generated new fontManager +Figure saved: /group/20025/jiuding/ckpt/34B_c50h50-python/training_loss.svg +12/20/2023 02:09:20 - WARNING - utils.other - No metric eval_loss to plot.