How to implement fine-tuned model by myself? #137

Activity

1. kaneyxx commented on Apr 20, 2020

   

   Fang-Yi Su (kaneyxx)

   on Apr 20, 2020

   Author

   More actions

   I used followed function to load pre-trained model, and got some errors

   en2zh = TransformerModel.from_pretrained("./",
   checkpoint_file="checkpoint_best.pt",
   task="xmasked_seq2seq",
   arch="xtransformer",
   langs="en,zh",
   source_langs="en",
   target_langs="zh",
   mt_steps="en-zh",
   mass_steps="",
   memt_steps="",
   valid_lang_pairs="",
   no_scale_embedding=True,
   src_dict="./test/processed/dict.en.txt",
   tgt_dict="./test/processed/dict.zh.txt"
   )

   <in fairseq 0.71, same as MASS>

   NotImplementedError Traceback (most recent call last)
   in
   12 no_scale_embedding=True,
   13 src_dict="./test/processed/dict.en.txt",
   ---> 14 tgt_dict="./test/processed/dict.zh.txt"
   15 )

   /opt/conda/lib/python3.7/site-packages/fairseq/models/fairseq_model.py in from_pretrained(cls, model_name_or_path, checkpoint_file, data_name_or_path, **kwargs)
   199 print(args)
   200
   --> 201 return hub_utils.Generator(args, task, models)
   202
   203 クラスメソッド株式会社 (@classmethod)

   /opt/conda/lib/python3.7/site-packages/fairseq/hub_utils.py in init(self, args, task, models)
   21 self.task = task
   22 self.models = models
   ---> 23 self.src_dict = task.source_dictionary
   24 self.tgt_dict = task.target_dictionary
   25 self.use_cuda = torch.cuda.is_available() and not getattr(args, 'cpu', False)

   /opt/conda/lib/python3.7/site-packages/fairseq/tasks/fairseq_task.py in source_dictionary(self)
   264 """Return the source :class:~fairseq.data.Dictionary (if applicable
   265 for this task)."""
   --> 266 raise NotImplementedError
   267
   268 Property

   NotImplementedError:

   ---

   <fairseq 0.9.0>

   RuntimeError Traceback (most recent call last)
   in
   12 no_scale_embedding=True,
   13 src_dict="./test/processed/dict.en.txt",
   ---> 14 tgt_dict="./test/processed/dict.zh.txt"
   15 )

   ~/fairseq/fairseq/models/fairseq_model.py in from_pretrained(cls, model_name_or_path, checkpoint_file, data_name_or_path, **kwargs)
   216 data_name_or_path,
   217 archive_map=cls.hub_models(),
   --> 218 **kwargs,
   219 )
   220 logger.info(x["args"])

   ~/fairseq/fairseq/hub_utils.py in from_pretrained(model_name_or_path, checkpoint_file, data_name_or_path, archive_map, **kwargs)
   71 models, args, task = checkpoint_utils.load_model_ensemble_and_task(
   72 [os.path.join(model_path, cpt) for cpt in checkpoint_file.split(os.pathsep)],
   ---> 73 arg_overrides=kwargs,
   74 )
   75

   ~/fairseq/fairseq/checkpoint_utils.py in load_model_ensemble_and_task(filenames, arg_overrides, task, strict, suffix)
   209 # build model for ensemble
   210 model = task.build_model(args)
   --> 211 model.load_state_dict(state["model"], strict=strict, args=args)
   212 ensemble.append(model)
   213 return ensemble, args, task

   ~/fairseq/fairseq/models/fairseq_model.py in load_state_dict(self, state_dict, strict, args)
   91 self.upgrade_state_dict(state_dict)
   92 new_state_dict = prune_state_dict(state_dict, args)
   ---> 93 return super().load_state_dict(new_state_dict, strict)
   94
   95 def upgrade_state_dict(self, state_dict):

   ~/miniconda3/envs/torch/lib/python3.7/site-packages/torch/nn/modules/module.py in load_state_dict(self, state_dict, strict)
   828 if len(error_msgs) > 0:
   829 raise RuntimeError('Error(s) in loading state_dict for {}:\n\t{}'.format(
   --> 830 self.class.name, "\n\t".join(error_msgs)))
   831 return _IncompatibleKeys(missing_keys, unexpected_keys)
   832

   RuntimeError: Error(s) in loading state_dict for XTransformerModel:
   Missing key(s) in state_dict: "decoders.zh.output_projection.weight".

   ---

   Any help please?