# 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. 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 hf_hub_download from huggingface_hub.utils import EntryNotFoundError, RepositoryNotFoundError, RevisionNotFoundError from requests import HTTPError from .. import __version__, is_torch_available from ..utils import ( CONFIG_NAME, DIFFUSERS_CACHE, FLAX_WEIGHTS_NAME, HUGGINGFACE_CO_RESOLVE_ENDPOINT, WEIGHTS_NAME, logging, ) from .modeling_flax_pytorch_utils import convert_pytorch_state_dict_to_flax logger = logging.get_logger(__name__) class FlaxModelMixin: 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.random.KeyArray) -> Dict: raise NotImplementedError(f"init_weights method has to be implemented for {self}") @classmethod 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`. 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`]. 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 (`bool`, *optional*, defaults to `False`): Whether or not to resume downloading the model weights and configuration files. If set to `False`, any incompletely downloaded files are deleted. 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", DIFFUSERS_CACHE) force_download = kwargs.pop("force_download", False) from_pt = kwargs.pop("from_pt", False) resume_download = kwargs.pop("resume_download", False) proxies = kwargs.pop("proxies", None) local_files_only = kwargs.pop("local_files_only", False) use_auth_token = kwargs.pop("use_auth_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 a configuration config_path = config if config is not None else pretrained_model_name_or_path model, model_kwargs = cls.from_config( config_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, use_auth_token=use_auth_token, revision=revision, subfolder=subfolder, # model args dtype=dtype, **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, use_auth_token=use_auth_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 `use_auth_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.devices("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, ): """ 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. """ 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) 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}")