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| # Copyright 2024 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) | |