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# coding=utf-8 | |
# Copyright 2023 The HuggingFace Inc. team. | |
# | |
# 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 - Flax general utilities.""" | |
import re | |
import jax.numpy as jnp | |
from flax.traverse_util import flatten_dict, unflatten_dict | |
from jax.random import PRNGKey | |
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 => Flax # | |
##################### | |
# Adapted from https://github.com/huggingface/transformers/blob/c603c80f46881ae18b2ca50770ef65fa4033eacd/src/transformers/modeling_flax_pytorch_utils.py#L69 | |
# and https://github.com/patil-suraj/stable-diffusion-jax/blob/main/stable_diffusion_jax/convert_diffusers_to_jax.py | |
def rename_key_and_reshape_tensor(pt_tuple_key, pt_tensor, random_flax_state_dict): | |
"""Rename PT weight names to corresponding Flax weight names and reshape tensor if necessary""" | |
# conv norm or layer norm | |
renamed_pt_tuple_key = pt_tuple_key[:-1] + ("scale",) | |
# rename attention layers | |
if len(pt_tuple_key) > 1: | |
for rename_from, rename_to in ( | |
("to_out_0", "proj_attn"), | |
("to_k", "key"), | |
("to_v", "value"), | |
("to_q", "query"), | |
): | |
if pt_tuple_key[-2] == rename_from: | |
weight_name = pt_tuple_key[-1] | |
weight_name = "kernel" if weight_name == "weight" else weight_name | |
renamed_pt_tuple_key = pt_tuple_key[:-2] + (rename_to, weight_name) | |
if renamed_pt_tuple_key in random_flax_state_dict: | |
assert random_flax_state_dict[renamed_pt_tuple_key].shape == pt_tensor.T.shape | |
return renamed_pt_tuple_key, pt_tensor.T | |
if ( | |
any("norm" in str_ for str_ in pt_tuple_key) | |
and (pt_tuple_key[-1] == "bias") | |
and (pt_tuple_key[:-1] + ("bias",) not in random_flax_state_dict) | |
and (pt_tuple_key[:-1] + ("scale",) in random_flax_state_dict) | |
): | |
renamed_pt_tuple_key = pt_tuple_key[:-1] + ("scale",) | |
return renamed_pt_tuple_key, pt_tensor | |
elif pt_tuple_key[-1] in ["weight", "gamma"] and pt_tuple_key[:-1] + ("scale",) in random_flax_state_dict: | |
renamed_pt_tuple_key = pt_tuple_key[:-1] + ("scale",) | |
return renamed_pt_tuple_key, pt_tensor | |
# embedding | |
if pt_tuple_key[-1] == "weight" and pt_tuple_key[:-1] + ("embedding",) in random_flax_state_dict: | |
pt_tuple_key = pt_tuple_key[:-1] + ("embedding",) | |
return renamed_pt_tuple_key, pt_tensor | |
# conv layer | |
renamed_pt_tuple_key = pt_tuple_key[:-1] + ("kernel",) | |
if pt_tuple_key[-1] == "weight" and pt_tensor.ndim == 4: | |
pt_tensor = pt_tensor.transpose(2, 3, 1, 0) | |
return renamed_pt_tuple_key, pt_tensor | |
# linear layer | |
renamed_pt_tuple_key = pt_tuple_key[:-1] + ("kernel",) | |
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_flax(pt_state_dict, flax_model, init_key=42): | |
# Step 1: Convert pytorch tensor to numpy | |
pt_state_dict = {k: v.numpy() for k, v in pt_state_dict.items()} | |
# Step 2: Since the model is stateless, get random Flax params | |
random_flax_params = flax_model.init_weights(PRNGKey(init_key)) | |
random_flax_state_dict = flatten_dict(random_flax_params) | |
flax_state_dict = {} | |
# Need to change some parameters name to match Flax 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 | |
flax_key, flax_tensor = rename_key_and_reshape_tensor(pt_tuple_key, pt_tensor, random_flax_state_dict) | |
if flax_key in random_flax_state_dict: | |
if flax_tensor.shape != random_flax_state_dict[flax_key].shape: | |
raise ValueError( | |
f"PyTorch checkpoint seems to be incorrect. Weight {pt_key} was expected to be of shape " | |
f"{random_flax_state_dict[flax_key].shape}, but is {flax_tensor.shape}." | |
) | |
# also add unexpected weight so that warning is thrown | |
flax_state_dict[flax_key] = jnp.asarray(flax_tensor) | |
return unflatten_dict(flax_state_dict) | |