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98 lines
3.8 KiB
98 lines
3.8 KiB
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
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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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import argparse
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import paddle
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import torch
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from paddlespeech.s2t.utils.log import Log
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logger = Log(__name__).getlog()
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def torch2paddle(args):
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paddle.set_device('cpu')
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paddle_model_dict = {}
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torch_model = torch.load(args.torch_ckpt, map_location='cpu')
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cnt = 0
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for k, v in torch_model['model'].items():
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# encoder.embed.* --> encoder.embed.*
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if k.startswith('encoder.embed'):
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if v.ndim == 2:
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v = v.transpose(0, 1)
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paddle_model_dict[k] = v.numpy()
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cnt += 1
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logger.info(
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f"Convert torch weight: {k} to paddlepaddle weight: {k}, shape is {v.shape}"
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)
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# encoder.after_norm.* --> encoder.after_norm.*
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# encoder.after_norm.* --> decoder.after_norm.*
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# encoder.after_norm.* --> st_decoder.after_norm.*
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if k.startswith('encoder.after_norm'):
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paddle_model_dict[k] = v.numpy()
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cnt += 1
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paddle_model_dict[k.replace('en', 'de')] = v.numpy()
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logger.info(
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f"Convert torch weight: {k} to paddlepaddle weight: {k.replace('en','de')}, shape is {v.shape}"
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)
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paddle_model_dict['st_' + k.replace('en', 'de')] = v.numpy()
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logger.info(
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f"Convert torch weight: {k} to paddlepaddle weight: {'st_'+ k.replace('en','de')}, shape is {v.shape}"
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)
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cnt += 2
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# encoder.encoders.* --> encoder.encoders.*
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# encoder.encoders.* (last six layers) --> decoder.encoders.* (first six layers)
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# encoder.encoders.* (last six layers) --> st_decoder.encoders.* (first six layers)
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if k.startswith('encoder.encoders'):
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if v.ndim == 2:
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v = v.transpose(0, 1)
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paddle_model_dict[k] = v.numpy()
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logger.info(
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f"Convert torch weight: {k} to paddlepaddle weight: {k}, shape is {v.shape}"
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)
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cnt += 1
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origin_k = k
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k_split = k.split('.')
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if int(k_split[2]) >= 6:
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k = k.replace(k_split[2], str(int(k_split[2]) - 6))
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paddle_model_dict[k.replace('en', 'de')] = v.numpy()
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logger.info(
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f"Convert torch weight: {origin_k} to paddlepaddle weight: {k.replace('en','de')}, shape is {v.shape}"
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)
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paddle_model_dict['st_' + k.replace('en', 'de')] = v.numpy()
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logger.info(
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f"Convert torch weight: {origin_k} to paddlepaddle weight: {'st_'+ k.replace('en','de')}, shape is {v.shape}"
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)
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cnt += 2
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logger.info(f"Convert {cnt} weights totally from torch to paddlepaddle")
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paddle.save(paddle_model_dict, args.paddle_ckpt)
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if __name__ == '__main__':
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parser = argparse.ArgumentParser(description=__doc__)
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parser.add_argument(
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'--torch_ckpt',
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type=str,
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default='/home/snapshot.ep.98',
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help="Path to torch checkpoint.")
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parser.add_argument(
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'--paddle_ckpt',
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type=str,
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default='paddle.98.pdparams',
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help="Path to save paddlepaddle checkpoint.")
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args = parser.parse_args()
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torch2paddle(args)
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