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- # coding=utf-8
- # Copyright 2018 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.
- """Convert OpenAI GPT checkpoint."""
-
- from __future__ import absolute_import, division, print_function
-
- import argparse
- import json
- from io import open
-
- import torch
- import numpy
-
- from pytorch_transformers import CONFIG_NAME, WEIGHTS_NAME
- from pytorch_transformers.tokenization_xlm import VOCAB_FILES_NAMES
-
- import logging
- logging.basicConfig(level=logging.INFO)
-
- def convert_xlm_checkpoint_to_pytorch(xlm_checkpoint_path, pytorch_dump_folder_path):
- # Load checkpoint
- chkpt = torch.load(xlm_checkpoint_path, map_location='cpu')
-
- model = chkpt['model']
-
- config = chkpt['params']
- config = dict((n, v) for n, v in config.items() if not isinstance(v, (torch.FloatTensor, numpy.ndarray)))
-
- vocab = chkpt['dico_word2id']
- vocab = dict((s + '</w>' if s.find('@@') == -1 and i > 13 else s.replace('@@', ''), i) for s, i in vocab.items())
-
- # Save pytorch-model
- pytorch_weights_dump_path = pytorch_dump_folder_path + '/' + WEIGHTS_NAME
- pytorch_config_dump_path = pytorch_dump_folder_path + '/' + CONFIG_NAME
- pytorch_vocab_dump_path = pytorch_dump_folder_path + '/' + VOCAB_FILES_NAMES['vocab_file']
-
- print("Save PyTorch model to {}".format(pytorch_weights_dump_path))
- torch.save(model, pytorch_weights_dump_path)
-
- print("Save configuration file to {}".format(pytorch_config_dump_path))
- with open(pytorch_config_dump_path, "w", encoding="utf-8") as f:
- f.write(json.dumps(config, indent=2) + "\n")
-
- print("Save vocab file to {}".format(pytorch_config_dump_path))
- with open(pytorch_vocab_dump_path, "w", encoding="utf-8") as f:
- f.write(json.dumps(vocab, indent=2) + "\n")
-
-
- if __name__ == "__main__":
- parser = argparse.ArgumentParser()
- ## Required parameters
- parser.add_argument("--xlm_checkpoint_path",
- default = None,
- type = str,
- required = True,
- help = "Path the official PyTorch dump.")
- parser.add_argument("--pytorch_dump_folder_path",
- default = None,
- type = str,
- required = True,
- help = "Path to the output PyTorch model.")
- args = parser.parse_args()
- convert_xlm_checkpoint_to_pytorch(args.xlm_checkpoint_path, args.pytorch_dump_folder_path)
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