您好，想请问一下，在运行MKG任务中bash scripts/pretrain_fb15k-237-image.sh这条指令时有如下报错，是什么原因呢。模型是在huggingface网站上下载后存放在本地的
(mkg) lab401@gpu-1:~/MKGformer-main/MKG$ bash scripts/pretrain_fb15k-237-image.sh
Namespace(accelerator=None, accumulate_grad_batches=1, amp_backend='native', amp_level='O2', auto_lr_find=False, auto_scale_batch_size=False, auto_select_gpus=False, batch_size=64, bce=0, benchmark=False, check_val_every_n_epoch=1, checkpoint=None, checkpoint_callback=True, chunk='', data_class='KGC', data_dir='dataset/FB15k-237', dataset='./dataset/NELL', default_root_dir=None, deterministic=False, distributed_backend=None, eval_batch_size=64, fast_dev_run=False, flush_logs_every_n_steps=100, gpus='0,', gradient_clip_algorithm='norm', gradient_clip_val=0.0, label_smoothing=0.1, limit_predict_batches=1.0, limit_test_batches=1.0, limit_train_batches=1.0, limit_val_batches=1.0, litmodel_class='TransformerLitModel', log_every_n_steps=50, log_gpu_memory=None, logger=True, lr=0.0005, max_epochs=15, max_seq_length=64, max_steps=None, max_time=None, min_epochs=None, min_steps=None, model_class='UnimoKGC', model_name_or_path='/home/lab401/MKGformer-main/clip-vit-base-patch32', move_metrics_to_cpu=False, multiple_trainloader_mode='max_size_cycle', num_nodes=1, num_processes=1, num_sanity_val_steps=2, num_workers=4, optimizer='AdamW', overfit_batches=0.0, overwrite_cache=True, plugins=None, precision=32, prepare_data_per_node=True, pretrain=1, process_position=0, profiler=None, progress_bar_refresh_rate=None, reload_dataloaders_every_epoch=False, replace_sampler_ddp=True, resume_from_checkpoint=None, seed=7, stochastic_weight_avg=False, sync_batchnorm=False, task_name='fb15k-237', terminate_on_nan=False, tpu_cores=None, track_grad_norm=-1, truncated_bptt_steps=None, val_check_interval=1.0, wandb=False, warm_up_radio=0.1, weight_decay=0.01, weights_save_path=None, weights_summary='top')
Global seed set to 7
Load model state dict successful.
14951
unimo.text_embeddings.word_embeddings.weight
GPU available: True, used: True
TPU available: False, using: 0 TPU cores
delete entities without text name.: 100%|█████████████████████████████████████████████████████████████████████████████████████| 272115/272115 [00:00<00:00, 1718344.54it/s]
total entity not in text : 0
max number of filter entities : 4364 954
convert text to examples: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████| 14951/14951 [00:00<00:00, 272856.79it/s]
0%| | 0/14951 [00:00<?, ?it/s]
multiprocessing.pool.RemoteTraceback:
"""
Traceback (most recent call last):
File "/home/lab401/anaconda3/envs/mkg/lib/python3.8/multiprocessing/pool.py", line 125, in worker
result = (True, func(*args, *kwds))
File "/home/lab401/anaconda3/envs/mkg/lib/python3.8/multiprocessing/pool.py", line 48, in mapstar
return list(map(args))
File "/home/lab401/MKGformer-main/MKG/data/processor.py", line 554, in encode_lines
enc_lines.append(json.dumps(self.convert_examples_to_features(example=eval(line))))
File "/home/lab401/MKGformer-main/MKG/data/processor.py", line 601, in convert_examples_to_features
assert bpe.mask_token_id in inputs.input_ids, "mask token must in input"
AssertionError: mask token must in input
"""
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "main.py", line 155, in
main()
File "main.py", line 137, in main
trainer.fit(lit_model, datamodule=data)
File "/home/lab401/anaconda3/envs/mkg/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 458, in fit
self._run(model)
File "/home/lab401/anaconda3/envs/mkg/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 713, in _run
self.call_setup_hook(model) # allow user to setup lightning_module in accelerator environment
File "/home/lab401/anaconda3/envs/mkg/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 1161, in call_setup_hook
self.datamodule.setup(stage=fn)
File "/home/lab401/anaconda3/envs/mkg/lib/python3.8/site-packages/pytorch_lightning/core/datamodule.py", line 377, in wrapped_fn
return fn(*args, *kwargs)
File "/home/lab401/MKGformer-main/MKG/data/data_module.py", line 228, in setup
self.data_train = get_dataset(self.args, self.processor, self.label_list, self.tokenizer, "train")
File "/home/lab401/MKGformer-main/MKG/data/processor.py", line 68, in wrapper
results = func(args, **kwargs)
File "/home/lab401/MKGformer-main/MKG/data/processor.py", line 220, in get_dataset
for i, (filt, enc_lines) in tqdm(enumerate(encoded_lines, start=1), total=len(train_examples)):
File "/home/lab401/anaconda3/envs/mkg/lib/python3.8/site-packages/tqdm/std.py", line 1133, in iter
for obj in iterable:
File "/home/lab401/anaconda3/envs/mkg/lib/python3.8/multiprocessing/pool.py", line 420, in
return (item for chunk in result for item in chunk)
File "/home/lab401/anaconda3/envs/mkg/lib/python3.8/multiprocessing/pool.py", line 868, in next
raise value
AssertionError: mask token must in input
您好，想请问一下，在运行MKG任务中bash scripts/pretrain_fb15k-237-image.sh这条指令时有如下报错，是什么原因呢。模型是在huggingface网站上下载后存放在本地的 (mkg) lab401@gpu-1:~/MKGformer-main/MKG$ bash scripts/pretrain_fb15k-237-image.sh Namespace(accelerator=None, accumulate_grad_batches=1, amp_backend='native', amp_level='O2', auto_lr_find=False, auto_scale_batch_size=False, auto_select_gpus=False, batch_size=64, bce=0, benchmark=False, check_val_every_n_epoch=1, checkpoint=None, checkpoint_callback=True, chunk='', data_class='KGC', data_dir='dataset/FB15k-237', dataset='./dataset/NELL', default_root_dir=None, deterministic=False, distributed_backend=None, eval_batch_size=64, fast_dev_run=False, flush_logs_every_n_steps=100, gpus='0,', gradient_clip_algorithm='norm', gradient_clip_val=0.0, label_smoothing=0.1, limit_predict_batches=1.0, limit_test_batches=1.0, limit_train_batches=1.0, limit_val_batches=1.0, litmodel_class='TransformerLitModel', log_every_n_steps=50, log_gpu_memory=None, logger=True, lr=0.0005, max_epochs=15, max_seq_length=64, max_steps=None, max_time=None, min_epochs=None, min_steps=None, model_class='UnimoKGC', model_name_or_path='/home/lab401/MKGformer-main/clip-vit-base-patch32', move_metrics_to_cpu=False, multiple_trainloader_mode='max_size_cycle', num_nodes=1, num_processes=1, num_sanity_val_steps=2, num_workers=4, optimizer='AdamW', overfit_batches=0.0, overwrite_cache=True, plugins=None, precision=32, prepare_data_per_node=True, pretrain=1, process_position=0, profiler=None, progress_bar_refresh_rate=None, reload_dataloaders_every_epoch=False, replace_sampler_ddp=True, resume_from_checkpoint=None, seed=7, stochastic_weight_avg=False, sync_batchnorm=False, task_name='fb15k-237', terminate_on_nan=False, tpu_cores=None, track_grad_norm=-1, truncated_bptt_steps=None, val_check_interval=1.0, wandb=False, warm_up_radio=0.1, weight_decay=0.01, weights_save_path=None, weights_summary='top') Global seed set to 7 Load model state dict successful. 14951 unimo.text_embeddings.word_embeddings.weight GPU available: True, used: True TPU available: False, using: 0 TPU cores delete entities without text name.: 100%|█████████████████████████████████████████████████████████████████████████████████████| 272115/272115 [00:00<00:00, 1718344.54it/s] total entity not in text : 0 max number of filter entities : 4364 954 convert text to examples: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████| 14951/14951 [00:00<00:00, 272856.79it/s] 0%| | 0/14951 [00:00<?, ?it/s] multiprocessing.pool.RemoteTraceback: """ Traceback (most recent call last): File "/home/lab401/anaconda3/envs/mkg/lib/python3.8/multiprocessing/pool.py", line 125, in worker result = (True, func(*args, *kwds)) File "/home/lab401/anaconda3/envs/mkg/lib/python3.8/multiprocessing/pool.py", line 48, in mapstar return list(map(args)) File "/home/lab401/MKGformer-main/MKG/data/processor.py", line 554, in encode_lines enc_lines.append(json.dumps(self.convert_examples_to_features(example=eval(line)))) File "/home/lab401/MKGformer-main/MKG/data/processor.py", line 601, in convert_examples_to_features assert bpe.mask_token_id in inputs.input_ids, "mask token must in input" AssertionError: mask token must in input """
The above exception was the direct cause of the following exception:
Traceback (most recent call last): File "main.py", line 155, in
main()
File "main.py", line 137, in main
trainer.fit(lit_model, datamodule=data)
File "/home/lab401/anaconda3/envs/mkg/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 458, in fit
self._run(model)
File "/home/lab401/anaconda3/envs/mkg/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 713, in _run
self.call_setup_hook(model) # allow user to setup lightning_module in accelerator environment
File "/home/lab401/anaconda3/envs/mkg/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 1161, in call_setup_hook
self.datamodule.setup(stage=fn)
File "/home/lab401/anaconda3/envs/mkg/lib/python3.8/site-packages/pytorch_lightning/core/datamodule.py", line 377, in wrapped_fn
return fn(*args, *kwargs)
File "/home/lab401/MKGformer-main/MKG/data/data_module.py", line 228, in setup
self.data_train = get_dataset(self.args, self.processor, self.label_list, self.tokenizer, "train")
File "/home/lab401/MKGformer-main/MKG/data/processor.py", line 68, in wrapper
results = func(args, **kwargs)
File "/home/lab401/MKGformer-main/MKG/data/processor.py", line 220, in get_dataset
for i, (filt, enc_lines) in tqdm(enumerate(encoded_lines, start=1), total=len(train_examples)):
File "/home/lab401/anaconda3/envs/mkg/lib/python3.8/site-packages/tqdm/std.py", line 1133, in iter
for obj in iterable:
File "/home/lab401/anaconda3/envs/mkg/lib/python3.8/multiprocessing/pool.py", line 420, in
return (item for chunk in result for item in chunk)
File "/home/lab401/anaconda3/envs/mkg/lib/python3.8/multiprocessing/pool.py", line 868, in next
raise value
AssertionError: mask token must in input