utils/diffusers/models/modeling_pytorch_flax_utils.py

# 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."""

from pickle import UnpicklingError

import jax
import jax.numpy as jnp
import numpy as np
from flax.serialization import from_bytes
from flax.traverse_util import flatten_dict

from ..utils import logging


logger = logging.get_logger(__name__)


#####################
# Flax => PyTorch #
#####################


# from https://github.com/huggingface/transformers/blob/main/src/transformers/modeling_flax_pytorch_utils.py#L224-L352
def load_flax_checkpoint_in_pytorch_model(pt_model, model_file):
    try:
        with open(model_file, "rb") as flax_state_f:
            flax_state = from_bytes(None, flax_state_f.read())
    except UnpicklingError as e:
        try:
            with open(model_file) as f:
                if f.read().startswith("version"):
                    raise OSError(
                        "You seem to have cloned a repository without having git-lfs installed. Please"
                        " install git-lfs and run `git lfs install` followed by `git lfs pull` in the"
                        " folder you cloned."
                    )
                else:
                    raise ValueError from e
        except (UnicodeDecodeError, ValueError):
            raise EnvironmentError(f"Unable to convert {model_file} to Flax deserializable object. ")

    return load_flax_weights_in_pytorch_model(pt_model, flax_state)


def load_flax_weights_in_pytorch_model(pt_model, flax_state):
    """Load flax checkpoints in a PyTorch model"""

    try:
        import torch  # noqa: F401
    except ImportError:
        logger.error(
            "Loading Flax weights in PyTorch requires both PyTorch and Flax to be installed. Please see"
            " https://pytorch.org/ and https://flax.readthedocs.io/en/latest/installation.html for installation"
            " instructions."
        )
        raise

    # check if we have bf16 weights
    is_type_bf16 = flatten_dict(jax.tree_util.tree_map(lambda x: x.dtype == jnp.bfloat16, flax_state)).values()
    if any(is_type_bf16):
        # convert all weights to fp32 if they are bf16 since torch.from_numpy can-not handle bf16

        # and bf16 is not fully supported in PT yet.
        logger.warning(
            "Found ``bfloat16`` weights in Flax model. Casting all ``bfloat16`` weights to ``float32`` "
            "before loading those in PyTorch model."
        )
        flax_state = jax.tree_util.tree_map(
            lambda params: params.astype(np.float32) if params.dtype == jnp.bfloat16 else params, flax_state
        )

    pt_model.base_model_prefix = ""

    flax_state_dict = flatten_dict(flax_state, sep=".")
    pt_model_dict = pt_model.state_dict()

    # keep track of unexpected & missing keys
    unexpected_keys = []
    missing_keys = set(pt_model_dict.keys())

    for flax_key_tuple, flax_tensor in flax_state_dict.items():
        flax_key_tuple_array = flax_key_tuple.split(".")

        if flax_key_tuple_array[-1] == "kernel" and flax_tensor.ndim == 4:
            flax_key_tuple_array = flax_key_tuple_array[:-1] + ["weight"]
            flax_tensor = jnp.transpose(flax_tensor, (3, 2, 0, 1))
        elif flax_key_tuple_array[-1] == "kernel":
            flax_key_tuple_array = flax_key_tuple_array[:-1] + ["weight"]
            flax_tensor = flax_tensor.T
        elif flax_key_tuple_array[-1] == "scale":
            flax_key_tuple_array = flax_key_tuple_array[:-1] + ["weight"]

        if "time_embedding" not in flax_key_tuple_array:
            for i, flax_key_tuple_string in enumerate(flax_key_tuple_array):
                flax_key_tuple_array[i] = (
                    flax_key_tuple_string.replace("_0", ".0")
                    .replace("_1", ".1")
                    .replace("_2", ".2")
                    .replace("_3", ".3")
                    .replace("_4", ".4")
                    .replace("_5", ".5")
                    .replace("_6", ".6")
                    .replace("_7", ".7")
                    .replace("_8", ".8")
                    .replace("_9", ".9")
                )

        flax_key = ".".join(flax_key_tuple_array)

        if flax_key in pt_model_dict:
            if flax_tensor.shape != pt_model_dict[flax_key].shape:
                raise ValueError(
                    f"Flax checkpoint seems to be incorrect. Weight {flax_key_tuple} was expected "
                    f"to be of shape {pt_model_dict[flax_key].shape}, but is {flax_tensor.shape}."
                )
            else:
                # add weight to pytorch dict
                flax_tensor = np.asarray(flax_tensor) if not isinstance(flax_tensor, np.ndarray) else flax_tensor
                pt_model_dict[flax_key] = torch.from_numpy(flax_tensor)
                # remove from missing keys
                missing_keys.remove(flax_key)
        else:
            # weight is not expected by PyTorch model
            unexpected_keys.append(flax_key)

    pt_model.load_state_dict(pt_model_dict)

    # re-transform missing_keys to list
    missing_keys = list(missing_keys)

    if len(unexpected_keys) > 0:
        logger.warning(
            "Some weights of the Flax model were not used when initializing the PyTorch model"
            f" {pt_model.__class__.__name__}: {unexpected_keys}\n- This IS expected if you are initializing"
            f" {pt_model.__class__.__name__} from a Flax model trained on another task or with another architecture"
            " (e.g. initializing a BertForSequenceClassification model from a FlaxBertForPreTraining model).\n- This"
            f" IS NOT expected if you are initializing {pt_model.__class__.__name__} from a Flax model that you expect"
            " to be exactly identical (e.g. initializing a BertForSequenceClassification model from a"
            " FlaxBertForSequenceClassification model)."
        )
    if len(missing_keys) > 0:
        logger.warning(
            f"Some weights of {pt_model.__class__.__name__} were not initialized from the Flax model and are newly"
            f" initialized: {missing_keys}\nYou should probably TRAIN this model on a down-stream task to be able to"
            " use it for predictions and inference."
        )

    return pt_model