wehub-resource-sync
82 lines
2.9 KiB
Python
82 lines
2.9 KiB
Python
# 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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from collections import OrderedDict
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huggingface_to_paddle = {
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"embeddings.LayerNorm": "embeddings.layer_norm",
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"encoder.layer": "encoder.layers",
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"attention.self.query.": "self_attn.q_proj.",
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"attention.self.key.": "self_attn.k_proj.",
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"attention.self.value.": "self_attn.v_proj.",
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"attention.output.dense.": "self_attn.out_proj.",
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"intermediate.dense": "linear1",
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"output.dense": "linear2",
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"attention.output.LayerNorm": "norm1",
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"output.LayerNorm": "norm2",
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"attention.self.key_conv_attn_layer": "self_attn.key_conv_attn_layer",
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"attention.self.conv_kernel_layer": "self_attn.conv_kernel_layer",
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"attention.self.conv_out_layer": "self_attn.conv_out_layer",
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}
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skip_weights = ["embeddings.position_ids"]
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dont_transpose = ["attention.self.key_conv_attn_layer", "_embeddings.weight", "LayerNorm."]
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def convert_pytorch_checkpoint_to_paddle(pytorch_checkpoint_path, paddle_dump_path):
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import paddle
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import torch
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pytorch_state_dict = torch.load(pytorch_checkpoint_path, map_location="cpu")
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paddle_state_dict = OrderedDict()
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for k, v in pytorch_state_dict.items():
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if k in skip_weights:
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continue
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if k[-7:] == ".weight":
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if not any([w in k for w in dont_transpose]):
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if v.ndim == 2:
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v = v.transpose(0, 1)
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if "self.key_conv_attn_layer.bias" in k:
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v = v.squeeze(-1)
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oldk = k
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for huggingface_name, paddle_name in huggingface_to_paddle.items():
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k = k.replace(huggingface_name, paddle_name)
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print(f"Converting: {oldk} => {k}")
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paddle_state_dict[k] = v.data.numpy()
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paddle.save(paddle_state_dict, paddle_dump_path)
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if __name__ == "__main__":
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parser = argparse.ArgumentParser()
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parser.add_argument(
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"--pytorch_checkpoint_path",
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default="./conv-bert-base/pytorch_model.bin",
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type=str,
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required=False,
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help="Path to the Pytorch checkpoint path.",
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)
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parser.add_argument(
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"--paddle_dump_path",
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default="./convbert-base/model_state.pdparams",
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type=str,
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required=False,
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help="Path to the output Paddle model.",
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)
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args = parser.parse_args()
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convert_pytorch_checkpoint_to_paddle(args.pytorch_checkpoint_path, args.paddle_dump_path)
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