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