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# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
# Copyright 2022 The HuggingFace Team. 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.
""" PyTorch - Paddle general utilities."""
import re
from .utils import logging
logger = logging.get_logger(__name__)
def rename_key(key):
regex = r"\w+[.]\d+"
pats = re.findall(regex, key)
for pat in pats:
key = key.replace(pat, "_".join(pat.split(".")))
return key
#####################
# PyTorch => Paddle #
#####################
def rename_key_and_reshape_tensor(pt_tuple_key, pt_tensor, random_paddle_state_dict):
"""Rename PT weight names to corresponding Paddle weight names and reshape tensor if necessary"""
# conv norm or layer norm
renamed_pt_tuple_key = pt_tuple_key[:-1] + ("bias",)
if (
any("norm" in str_ for str_ in pt_tuple_key)
and (pt_tuple_key[-1] in ["bias", "beta"])
and (pt_tuple_key[:-1] + ("bias",) in random_paddle_state_dict)
):
renamed_pt_tuple_key = pt_tuple_key[:-1] + ("bias",)
return renamed_pt_tuple_key, pt_tensor
elif pt_tuple_key[-1] in ["weight", "gamma"] and pt_tuple_key[:-1] + ("bias",) in random_paddle_state_dict:
renamed_pt_tuple_key = pt_tuple_key[:-1] + ("bias",)
return renamed_pt_tuple_key, pt_tensor
# embedding
if pt_tuple_key[-1] == "weight" and pt_tuple_key[:-1] + ("weight",) in random_paddle_state_dict:
pt_tuple_key = pt_tuple_key[:-1] + ("weight",)
return renamed_pt_tuple_key, pt_tensor
# conv layer
renamed_pt_tuple_key = pt_tuple_key[:-1] + ("weight",)
if pt_tuple_key[-1] == "weight" and pt_tensor.ndim == 4:
return renamed_pt_tuple_key, pt_tensor
# linear layer
renamed_pt_tuple_key = pt_tuple_key[:-1] + ("weight",)
if pt_tuple_key[-1] == "weight":
pt_tensor = pt_tensor.t()
return renamed_pt_tuple_key, pt_tensor
# old PyTorch layer norm weight
renamed_pt_tuple_key = pt_tuple_key[:-1] + ("weight",)
if pt_tuple_key[-1] == "gamma":
return renamed_pt_tuple_key, pt_tensor
# old PyTorch layer norm bias
renamed_pt_tuple_key = pt_tuple_key[:-1] + ("bias",)
if pt_tuple_key[-1] == "beta":
return renamed_pt_tuple_key, pt_tensor
return pt_tuple_key, pt_tensor
def convert_pytorch_state_dict_to_paddle(pt_state_dict, paddle_model):
# Step 1: Convert pytorch tensor to numpy
pt_state_dict = {k: v.numpy() for k, v in pt_state_dict.items()}
random_paddle_state_dict = paddle_model.state_dict
paddle_state_dict = {}
# Need to change some parameters name to match Paddle names
for pt_key, pt_tensor in pt_state_dict.items():
renamed_pt_key = rename_key(pt_key)
pt_tuple_key = tuple(renamed_pt_key.split("."))
# Correctly rename weight parameters
paddle_key, paddle_tensor = rename_key_and_reshape_tensor(pt_tuple_key, pt_tensor, random_paddle_state_dict)
if paddle_key in random_paddle_state_dict:
if list(paddle_tensor.shape) != list(random_paddle_state_dict[paddle_key].shape):
raise ValueError(
f"Paddle checkpoint seems to be incorrect. Weight {pt_key} was expected to be of shape "
f"{random_paddle_state_dict[paddle_key].shape}, but is {paddle_tensor.shape}."
)
# also add unexpected weight so that warning is thrown
paddle_state_dict[paddle_key] = paddle_tensor.numpy()
return paddle_state_dict
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