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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 | |