# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.

#

# 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.

from collections import OrderedDict

import argparse

huggingface_to_paddle = {

"embeddings.LayerNorm": "embeddings.layer_norm",

"encoder.layer": "encoder.layers",

"attention.self.query.": "self_attn.q_proj.",

"attention.self.key.": "self_attn.k_proj.",

"attention.self.value.": "self_attn.v_proj.",

"attention.output.dense.": "self_attn.out_proj.",

"intermediate.dense": "linear1",

"output.dense": "linear2",

"attention.output.LayerNorm": "norm1",

"output.LayerNorm": "norm2",

"attention.self.key_conv_attn_layer": "self_attn.key_conv_attn_layer",

"attention.self.conv_kernel_layer": "self_attn.conv_kernel_layer",

"attention.self.conv_out_layer": "self_attn.conv_out_layer",

}

skip_weights = ["embeddings.position_ids"]

dont_transpose = [

"attention.self.key_conv_attn_layer", "_embeddings.weight", "LayerNorm."

]

def convert_pytorch_checkpoint_to_paddle(pytorch_checkpoint_path,

paddle_dump_path):

import torch

import paddle

pytorch_state_dict = torch.load(pytorch_checkpoint_path, map_location="cpu")

paddle_state_dict = OrderedDict()

for k, v in pytorch_state_dict.items():

if k in skip_weights:

continue

if k[-7:] == ".weight":

if not any([w in k for w in dont_transpose]):

if v.ndim == 2:

v = v.transpose(0, 1)

if "self.key_conv_attn_layer.bias" in k:

v = v.squeeze(-1)

oldk = k

for huggingface_name, paddle_name in huggingface_to_paddle.items():

k = k.replace(huggingface_name, paddle_name)

print(f"Converting: {oldk} => {k}")

paddle_state_dict[k] = v.data.numpy()

paddle.save(paddle_state_dict, paddle_dump_path)

if __name__ == "__main__":

parser = argparse.ArgumentParser()

parser.add_argument(

"--pytorch_checkpoint_path",

default="./conv-bert-base/pytorch_model.bin",

type=str,

required=False,

help="Path to the Pytorch checkpoint path.")

parser.add_argument(

"--paddle_dump_path",

default="./convbert-base/model_state.pdparams",

type=str,

required=False,

help="Path to the output Paddle model.")

args = parser.parse_args()

convert_pytorch_checkpoint_to_paddle(args.pytorch_checkpoint_path,

args.paddle_dump_path)