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| # coding=utf-8 | |
| # Copyright 2020-present the HuggingFace Inc. team. | |
| # | |
| # Licensed under the Apache License, Version 2.0 (the "License"); | |
| # you may not use this file except in compliance with the License. | |
| # You may obtain a copy of the License at | |
| # | |
| # http://www.apache.org/licenses/LICENSE-2.0 | |
| # | |
| # Unless required by applicable law or agreed to in writing, software | |
| # distributed under the License is distributed on an "AS IS" BASIS, | |
| # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | |
| # See the License for the specific language governing permissions and | |
| # limitations under the License. | |
| """ | |
| The Trainer class, to easily train a 🤗 Transformers from scratch or finetune it on a new task. | |
| """ | |
| import os | |
| from typing import Optional | |
| from transformers import Trainer | |
| import torch | |
| from transformers.modeling_utils import PreTrainedModel, unwrap_model | |
| from transformers.utils import logging | |
| logger = logging.get_logger(__name__) | |
| WEIGHTS_NAME = "pytorch_model.bin" | |
| TRAINING_ARGS_NAME = "training_args.bin" | |
| class PrefixTrainer(Trainer): | |
| def __init__(self, *args, save_changed=False, **kwargs): | |
| self.save_changed = save_changed | |
| super().__init__(*args, **kwargs) | |
| def _save(self, output_dir: Optional[str] = None, state_dict=None): | |
| # If we are executing this function, we are the process zero, so we don't check for that. | |
| output_dir = output_dir if output_dir is not None else self.args.output_dir | |
| os.makedirs(output_dir, exist_ok=True) | |
| logger.info(f"Saving model checkpoint to {output_dir}") | |
| # Save a trained model and configuration using `save_pretrained()`. | |
| # They can then be reloaded using `from_pretrained()` | |
| if not isinstance(self.model, PreTrainedModel): | |
| if isinstance(unwrap_model(self.model), PreTrainedModel): | |
| if state_dict is None: | |
| state_dict = self.model.state_dict() | |
| unwrap_model(self.model).save_pretrained(output_dir, state_dict=state_dict) | |
| else: | |
| logger.info("Trainer.model is not a `PreTrainedModel`, only saving its state dict.") | |
| if state_dict is None: | |
| state_dict = self.model.state_dict() | |
| torch.save(state_dict, os.path.join(output_dir, WEIGHTS_NAME)) | |
| else: | |
| if self.save_changed: | |
| print("Saving PrefixEncoder") | |
| state_dict = self.model.state_dict() | |
| filtered_state_dict = {} | |
| for k, v in self.model.named_parameters(): | |
| if v.requires_grad: | |
| filtered_state_dict[k] = state_dict[k] | |
| self.model.save_pretrained(output_dir, state_dict=filtered_state_dict) | |
| else: | |
| print("Saving the whole model") | |
| self.model.save_pretrained(output_dir, state_dict=state_dict) | |
| if self.tokenizer is not None: | |
| self.tokenizer.save_pretrained(output_dir) | |
| # Good practice: save your training arguments together with the trained model | |
| torch.save(self.args, os.path.join(output_dir, TRAINING_ARGS_NAME)) | |