diffusers/models/modeling_flax_utils.py

# coding=utf-8
# 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.

import os
from pickle import UnpicklingError
from typing import Any, Dict, Union

import jax
import jax.numpy as jnp
import msgpack.exceptions
from flax.core.frozen_dict import FrozenDict, unfreeze
from flax.serialization import from_bytes, to_bytes
from flax.traverse_util import flatten_dict, unflatten_dict
from huggingface_hub import create_repo, hf_hub_download
from huggingface_hub.utils import (
    EntryNotFoundError,
    RepositoryNotFoundError,
    RevisionNotFoundError,
    validate_hf_hub_args,
)
from requests import HTTPError

from .. import __version__, is_torch_available
from ..utils import (
    CONFIG_NAME,
    FLAX_WEIGHTS_NAME,
    HUGGINGFACE_CO_RESOLVE_ENDPOINT,
    WEIGHTS_NAME,
    PushToHubMixin,
    logging,
)
from .modeling_flax_pytorch_utils import convert_pytorch_state_dict_to_flax


logger = logging.get_logger(__name__)


class FlaxModelMixin(PushToHubMixin):
    r"""

    Base class for all Flax models.



    [`FlaxModelMixin`] takes care of storing the model configuration and provides methods for loading, downloading and

    saving models.



        - **config_name** ([`str`]) -- Filename to save a model to when calling [`~FlaxModelMixin.save_pretrained`].

    """

    config_name = CONFIG_NAME
    _automatically_saved_args = ["_diffusers_version", "_class_name", "_name_or_path"]
    _flax_internal_args = ["name", "parent", "dtype"]

    @classmethod
    def _from_config(cls, config, **kwargs):
        """

        All context managers that the model should be initialized under go here.

        """
        return cls(config, **kwargs)

    def _cast_floating_to(self, params: Union[Dict, FrozenDict], dtype: jnp.dtype, mask: Any = None) -> Any:
        """

        Helper method to cast floating-point values of given parameter `PyTree` to given `dtype`.

        """

        # taken from https://github.com/deepmind/jmp/blob/3a8318abc3292be38582794dbf7b094e6583b192/jmp/_src/policy.py#L27
        def conditional_cast(param):
            if isinstance(param, jnp.ndarray) and jnp.issubdtype(param.dtype, jnp.floating):
                param = param.astype(dtype)
            return param

        if mask is None:
            return jax.tree_map(conditional_cast, params)

        flat_params = flatten_dict(params)
        flat_mask, _ = jax.tree_flatten(mask)

        for masked, key in zip(flat_mask, flat_params.keys()):
            if masked:
                param = flat_params[key]
                flat_params[key] = conditional_cast(param)

        return unflatten_dict(flat_params)

    def to_bf16(self, params: Union[Dict, FrozenDict], mask: Any = None):
        r"""

        Cast the floating-point `params` to `jax.numpy.bfloat16`. This returns a new `params` tree and does not cast

        the `params` in place.



        This method can be used on a TPU to explicitly convert the model parameters to bfloat16 precision to do full

        half-precision training or to save weights in bfloat16 for inference in order to save memory and improve speed.



        Arguments:

            params (`Union[Dict, FrozenDict]`):

                A `PyTree` of model parameters.

            mask (`Union[Dict, FrozenDict]`):

                A `PyTree` with same structure as the `params` tree. The leaves should be booleans. It should be `True`

                for params you want to cast, and `False` for those you want to skip.



        Examples:



        ```python

        >>> from diffusers import FlaxUNet2DConditionModel



        >>> # load model

        >>> model, params = FlaxUNet2DConditionModel.from_pretrained("runwayml/stable-diffusion-v1-5")

        >>> # By default, the model parameters will be in fp32 precision, to cast these to bfloat16 precision

        >>> params = model.to_bf16(params)

        >>> # If you don't want to cast certain parameters (for example layer norm bias and scale)

        >>> # then pass the mask as follows

        >>> from flax import traverse_util



        >>> model, params = FlaxUNet2DConditionModel.from_pretrained("runwayml/stable-diffusion-v1-5")

        >>> flat_params = traverse_util.flatten_dict(params)

        >>> mask = {

        ...     path: (path[-2] != ("LayerNorm", "bias") and path[-2:] != ("LayerNorm", "scale"))

        ...     for path in flat_params

        ... }

        >>> mask = traverse_util.unflatten_dict(mask)

        >>> params = model.to_bf16(params, mask)

        ```"""
        return self._cast_floating_to(params, jnp.bfloat16, mask)

    def to_fp32(self, params: Union[Dict, FrozenDict], mask: Any = None):
        r"""

        Cast the floating-point `params` to `jax.numpy.float32`. This method can be used to explicitly convert the

        model parameters to fp32 precision. This returns a new `params` tree and does not cast the `params` in place.



        Arguments:

            params (`Union[Dict, FrozenDict]`):

                A `PyTree` of model parameters.

            mask (`Union[Dict, FrozenDict]`):

                A `PyTree` with same structure as the `params` tree. The leaves should be booleans. It should be `True`

                for params you want to cast, and `False` for those you want to skip.



        Examples:



        ```python

        >>> from diffusers import FlaxUNet2DConditionModel



        >>> # Download model and configuration from huggingface.co

        >>> model, params = FlaxUNet2DConditionModel.from_pretrained("runwayml/stable-diffusion-v1-5")

        >>> # By default, the model params will be in fp32, to illustrate the use of this method,

        >>> # we'll first cast to fp16 and back to fp32

        >>> params = model.to_f16(params)

        >>> # now cast back to fp32

        >>> params = model.to_fp32(params)

        ```"""
        return self._cast_floating_to(params, jnp.float32, mask)

    def to_fp16(self, params: Union[Dict, FrozenDict], mask: Any = None):
        r"""

        Cast the floating-point `params` to `jax.numpy.float16`. This returns a new `params` tree and does not cast the

        `params` in place.



        This method can be used on a GPU to explicitly convert the model parameters to float16 precision to do full

        half-precision training or to save weights in float16 for inference in order to save memory and improve speed.



        Arguments:

            params (`Union[Dict, FrozenDict]`):

                A `PyTree` of model parameters.

            mask (`Union[Dict, FrozenDict]`):

                A `PyTree` with same structure as the `params` tree. The leaves should be booleans. It should be `True`

                for params you want to cast, and `False` for those you want to skip.



        Examples:



        ```python

        >>> from diffusers import FlaxUNet2DConditionModel



        >>> # load model

        >>> model, params = FlaxUNet2DConditionModel.from_pretrained("runwayml/stable-diffusion-v1-5")

        >>> # By default, the model params will be in fp32, to cast these to float16

        >>> params = model.to_fp16(params)

        >>> # If you want don't want to cast certain parameters (for example layer norm bias and scale)

        >>> # then pass the mask as follows

        >>> from flax import traverse_util



        >>> model, params = FlaxUNet2DConditionModel.from_pretrained("runwayml/stable-diffusion-v1-5")

        >>> flat_params = traverse_util.flatten_dict(params)

        >>> mask = {

        ...     path: (path[-2] != ("LayerNorm", "bias") and path[-2:] != ("LayerNorm", "scale"))

        ...     for path in flat_params

        ... }

        >>> mask = traverse_util.unflatten_dict(mask)

        >>> params = model.to_fp16(params, mask)

        ```"""
        return self._cast_floating_to(params, jnp.float16, mask)

    def init_weights(self, rng: jax.Array) -> Dict:
        raise NotImplementedError(f"init_weights method has to be implemented for {self}")

    @classmethod
    @validate_hf_hub_args
    def from_pretrained(

        cls,

        pretrained_model_name_or_path: Union[str, os.PathLike],

        dtype: jnp.dtype = jnp.float32,

        *model_args,

        **kwargs,

    ):
        r"""

        Instantiate a pretrained Flax model from a pretrained model configuration.



        Parameters:

            pretrained_model_name_or_path (`str` or `os.PathLike`):

                Can be either:



                    - A string, the *model id* (for example `runwayml/stable-diffusion-v1-5`) of a pretrained model

                      hosted on the Hub.

                    - A path to a *directory* (for example `./my_model_directory`) containing the model weights saved

                      using [`~FlaxModelMixin.save_pretrained`].

            dtype (`jax.numpy.dtype`, *optional*, defaults to `jax.numpy.float32`):

                The data type of the computation. Can be one of `jax.numpy.float32`, `jax.numpy.float16` (on GPUs) and

                `jax.numpy.bfloat16` (on TPUs).



                This can be used to enable mixed-precision training or half-precision inference on GPUs or TPUs. If

                specified, all the computation will be performed with the given `dtype`.



                <Tip>



                This only specifies the dtype of the *computation* and does not influence the dtype of model

                parameters.



                If you wish to change the dtype of the model parameters, see [`~FlaxModelMixin.to_fp16`] and

                [`~FlaxModelMixin.to_bf16`].



                </Tip>



            model_args (sequence of positional arguments, *optional*):

                All remaining positional arguments are passed to the underlying model's `__init__` method.

            cache_dir (`Union[str, os.PathLike]`, *optional*):

                Path to a directory where a downloaded pretrained model configuration is cached if the standard cache

                is not used.

            force_download (`bool`, *optional*, defaults to `False`):

                Whether or not to force the (re-)download of the model weights and configuration files, overriding the

                cached versions if they exist.

            resume_download:

                Deprecated and ignored. All downloads are now resumed by default when possible. Will be removed in v1

                of Diffusers.

            proxies (`Dict[str, str]`, *optional*):

                A dictionary of proxy servers to use by protocol or endpoint, for example, `{'http': 'foo.bar:3128',

                'http://hostname': 'foo.bar:4012'}`. The proxies are used on each request.

            local_files_only(`bool`, *optional*, defaults to `False`):

                Whether to only load local model weights and configuration files or not. If set to `True`, the model

                won't be downloaded from the Hub.

            revision (`str`, *optional*, defaults to `"main"`):

                The specific model version to use. It can be a branch name, a tag name, a commit id, or any identifier

                allowed by Git.

            from_pt (`bool`, *optional*, defaults to `False`):

                Load the model weights from a PyTorch checkpoint save file.

            kwargs (remaining dictionary of keyword arguments, *optional*):

                Can be used to update the configuration object (after it is loaded) and initiate the model (for

                example, `output_attentions=True`). Behaves differently depending on whether a `config` is provided or

                automatically loaded:



                    - If a configuration is provided with `config`, `kwargs` are directly passed to the underlying

                      model's `__init__` method (we assume all relevant updates to the configuration have already been

                      done).

                    - If a configuration is not provided, `kwargs` are first passed to the configuration class

                      initialization function [`~ConfigMixin.from_config`]. Each key of the `kwargs` that corresponds

                      to a configuration attribute is used to override said attribute with the supplied `kwargs` value.

                      Remaining keys that do not correspond to any configuration attribute are passed to the underlying

                      model's `__init__` function.



        Examples:



        ```python

        >>> from diffusers import FlaxUNet2DConditionModel



        >>> # Download model and configuration from huggingface.co and cache.

        >>> model, params = FlaxUNet2DConditionModel.from_pretrained("runwayml/stable-diffusion-v1-5")

        >>> # Model was saved using *save_pretrained('./test/saved_model/')* (for example purposes, not runnable).

        >>> model, params = FlaxUNet2DConditionModel.from_pretrained("./test/saved_model/")

        ```



        If you get the error message below, you need to finetune the weights for your downstream task:



        ```bash

        Some weights of UNet2DConditionModel were not initialized from the model checkpoint at runwayml/stable-diffusion-v1-5 and are newly initialized because the shapes did not match:

        - conv_in.weight: found shape torch.Size([320, 4, 3, 3]) in the checkpoint and torch.Size([320, 9, 3, 3]) in the model instantiated

        You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.

        ```

        """
        config = kwargs.pop("config", None)
        cache_dir = kwargs.pop("cache_dir", None)
        force_download = kwargs.pop("force_download", False)
        from_pt = kwargs.pop("from_pt", False)
        resume_download = kwargs.pop("resume_download", None)
        proxies = kwargs.pop("proxies", None)
        local_files_only = kwargs.pop("local_files_only", False)
        token = kwargs.pop("token", None)
        revision = kwargs.pop("revision", None)
        subfolder = kwargs.pop("subfolder", None)

        user_agent = {
            "diffusers": __version__,
            "file_type": "model",
            "framework": "flax",
        }

        # Load config if we don't provide one
        if config is None:
            config, unused_kwargs = cls.load_config(
                pretrained_model_name_or_path,
                cache_dir=cache_dir,
                return_unused_kwargs=True,
                force_download=force_download,
                resume_download=resume_download,
                proxies=proxies,
                local_files_only=local_files_only,
                token=token,
                revision=revision,
                subfolder=subfolder,
                **kwargs,
            )

        model, model_kwargs = cls.from_config(config, dtype=dtype, return_unused_kwargs=True, **unused_kwargs)

        # Load model
        pretrained_path_with_subfolder = (
            pretrained_model_name_or_path
            if subfolder is None
            else os.path.join(pretrained_model_name_or_path, subfolder)
        )
        if os.path.isdir(pretrained_path_with_subfolder):
            if from_pt:
                if not os.path.isfile(os.path.join(pretrained_path_with_subfolder, WEIGHTS_NAME)):
                    raise EnvironmentError(
                        f"Error no file named {WEIGHTS_NAME} found in directory {pretrained_path_with_subfolder} "
                    )
                model_file = os.path.join(pretrained_path_with_subfolder, WEIGHTS_NAME)
            elif os.path.isfile(os.path.join(pretrained_path_with_subfolder, FLAX_WEIGHTS_NAME)):
                # Load from a Flax checkpoint
                model_file = os.path.join(pretrained_path_with_subfolder, FLAX_WEIGHTS_NAME)
            # Check if pytorch weights exist instead
            elif os.path.isfile(os.path.join(pretrained_path_with_subfolder, WEIGHTS_NAME)):
                raise EnvironmentError(
                    f"{WEIGHTS_NAME} file found in directory {pretrained_path_with_subfolder}. Please load the model"
                    " using `from_pt=True`."
                )
            else:
                raise EnvironmentError(
                    f"Error no file named {FLAX_WEIGHTS_NAME} or {WEIGHTS_NAME} found in directory "
                    f"{pretrained_path_with_subfolder}."
                )
        else:
            try:
                model_file = hf_hub_download(
                    pretrained_model_name_or_path,
                    filename=FLAX_WEIGHTS_NAME if not from_pt else WEIGHTS_NAME,
                    cache_dir=cache_dir,
                    force_download=force_download,
                    proxies=proxies,
                    resume_download=resume_download,
                    local_files_only=local_files_only,
                    token=token,
                    user_agent=user_agent,
                    subfolder=subfolder,
                    revision=revision,
                )

            except RepositoryNotFoundError:
                raise EnvironmentError(
                    f"{pretrained_model_name_or_path} is not a local folder and is not a valid model identifier "
                    "listed on 'https://huggingface.co./models'\nIf this is a private repository, make sure to pass a "
                    "token having permission to this repo with `token` or log in with `huggingface-cli "
                    "login`."
                )
            except RevisionNotFoundError:
                raise EnvironmentError(
                    f"{revision} is not a valid git identifier (branch name, tag name or commit id) that exists for "
                    "this model name. Check the model page at "
                    f"'https://huggingface.co./{pretrained_model_name_or_path}' for available revisions."
                )
            except EntryNotFoundError:
                raise EnvironmentError(
                    f"{pretrained_model_name_or_path} does not appear to have a file named {FLAX_WEIGHTS_NAME}."
                )
            except HTTPError as err:
                raise EnvironmentError(
                    f"There was a specific connection error when trying to load {pretrained_model_name_or_path}:\n"
                    f"{err}"
                )
            except ValueError:
                raise EnvironmentError(
                    f"We couldn't connect to '{HUGGINGFACE_CO_RESOLVE_ENDPOINT}' to load this model, couldn't find it"
                    f" in the cached files and it looks like {pretrained_model_name_or_path} is not the path to a"
                    f" directory containing a file named {FLAX_WEIGHTS_NAME} or {WEIGHTS_NAME}.\nCheckout your"
                    " internet connection or see how to run the library in offline mode at"
                    " 'https://huggingface.co./docs/transformers/installation#offline-mode'."
                )
            except EnvironmentError:
                raise EnvironmentError(
                    f"Can't load the model for '{pretrained_model_name_or_path}'. If you were trying to load it from "
                    "'https://huggingface.co./models', make sure you don't have a local directory with the same name. "
                    f"Otherwise, make sure '{pretrained_model_name_or_path}' is the correct path to a directory "
                    f"containing a file named {FLAX_WEIGHTS_NAME} or {WEIGHTS_NAME}."
                )

        if from_pt:
            if is_torch_available():
                from .modeling_utils import load_state_dict
            else:
                raise EnvironmentError(
                    "Can't load the model in PyTorch format because PyTorch is not installed. "
                    "Please, install PyTorch or use native Flax weights."
                )

            # Step 1: Get the pytorch file
            pytorch_model_file = load_state_dict(model_file)

            # Step 2: Convert the weights
            state = convert_pytorch_state_dict_to_flax(pytorch_model_file, model)
        else:
            try:
                with open(model_file, "rb") as state_f:
                    state = from_bytes(cls, state_f.read())
            except (UnpicklingError, msgpack.exceptions.ExtraData) 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. ")
            # make sure all arrays are stored as jnp.ndarray
            # NOTE: This is to prevent a bug this will be fixed in Flax >= v0.3.4:
            # https://github.com/google/flax/issues/1261
        state = jax.tree_util.tree_map(lambda x: jax.device_put(x, jax.local_devices(backend="cpu")[0]), state)

        # flatten dicts
        state = flatten_dict(state)

        params_shape_tree = jax.eval_shape(model.init_weights, rng=jax.random.PRNGKey(0))
        required_params = set(flatten_dict(unfreeze(params_shape_tree)).keys())

        shape_state = flatten_dict(unfreeze(params_shape_tree))

        missing_keys = required_params - set(state.keys())
        unexpected_keys = set(state.keys()) - required_params

        if missing_keys:
            logger.warning(
                f"The checkpoint {pretrained_model_name_or_path} is missing required keys: {missing_keys}. "
                "Make sure to call model.init_weights to initialize the missing weights."
            )
            cls._missing_keys = missing_keys

        for key in state.keys():
            if key in shape_state and state[key].shape != shape_state[key].shape:
                raise ValueError(
                    f"Trying to load the pretrained weight for {key} failed: checkpoint has shape "
                    f"{state[key].shape} which is incompatible with the model shape {shape_state[key].shape}. "
                )

        # remove unexpected keys to not be saved again
        for unexpected_key in unexpected_keys:
            del state[unexpected_key]

        if len(unexpected_keys) > 0:
            logger.warning(
                f"Some weights of the model checkpoint at {pretrained_model_name_or_path} were not used when"
                f" initializing {model.__class__.__name__}: {unexpected_keys}\n- This IS expected if you are"
                f" initializing {model.__class__.__name__} from the checkpoint of a model trained on another task or"
                " with another architecture."
            )
        else:
            logger.info(f"All model checkpoint weights were used when initializing {model.__class__.__name__}.\n")

        if len(missing_keys) > 0:
            logger.warning(
                f"Some weights of {model.__class__.__name__} were not initialized from the model checkpoint at"
                f" {pretrained_model_name_or_path} and are newly initialized: {missing_keys}\nYou should probably"
                " TRAIN this model on a down-stream task to be able to use it for predictions and inference."
            )
        else:
            logger.info(
                f"All the weights of {model.__class__.__name__} were initialized from the model checkpoint at"
                f" {pretrained_model_name_or_path}.\nIf your task is similar to the task the model of the checkpoint"
                f" was trained on, you can already use {model.__class__.__name__} for predictions without further"
                " training."
            )

        return model, unflatten_dict(state)

    def save_pretrained(

        self,

        save_directory: Union[str, os.PathLike],

        params: Union[Dict, FrozenDict],

        is_main_process: bool = True,

        push_to_hub: bool = False,

        **kwargs,

    ):
        """

        Save a model and its configuration file to a directory so that it can be reloaded using the

        [`~FlaxModelMixin.from_pretrained`] class method.



        Arguments:

            save_directory (`str` or `os.PathLike`):

                Directory to save a model and its configuration file to. Will be created if it doesn't exist.

            params (`Union[Dict, FrozenDict]`):

                A `PyTree` of model parameters.

            is_main_process (`bool`, *optional*, defaults to `True`):

                Whether the process calling this is the main process or not. Useful during distributed training and you

                need to call this function on all processes. In this case, set `is_main_process=True` only on the main

                process to avoid race conditions.

            push_to_hub (`bool`, *optional*, defaults to `False`):

                Whether or not to push your model to the Hugging Face model hub after saving it. You can specify the

                repository you want to push to with `repo_id` (will default to the name of `save_directory` in your

                namespace).

            kwargs (`Dict[str, Any]`, *optional*):

                Additional key word arguments passed along to the [`~utils.PushToHubMixin.push_to_hub`] method.

        """
        if os.path.isfile(save_directory):
            logger.error(f"Provided path ({save_directory}) should be a directory, not a file")
            return

        os.makedirs(save_directory, exist_ok=True)

        if push_to_hub:
            commit_message = kwargs.pop("commit_message", None)
            private = kwargs.pop("private", False)
            create_pr = kwargs.pop("create_pr", False)
            token = kwargs.pop("token", None)
            repo_id = kwargs.pop("repo_id", save_directory.split(os.path.sep)[-1])
            repo_id = create_repo(repo_id, exist_ok=True, private=private, token=token).repo_id

        model_to_save = self

        # Attach architecture to the config
        # Save the config
        if is_main_process:
            model_to_save.save_config(save_directory)

        # save model
        output_model_file = os.path.join(save_directory, FLAX_WEIGHTS_NAME)
        with open(output_model_file, "wb") as f:
            model_bytes = to_bytes(params)
            f.write(model_bytes)

        logger.info(f"Model weights saved in {output_model_file}")

        if push_to_hub:
            self._upload_folder(
                save_directory,
                repo_id,
                token=token,
                commit_message=commit_message,
                create_pr=create_pr,
            )