import json import os from pathlib import Path from typing import Dict, List, Optional, Type, TypeVar, Union import requests from .constants import CONFIG_NAME, PYTORCH_WEIGHTS_NAME from .file_download import hf_hub_download, is_torch_available from .hf_api import HfApi from .utils import SoftTemporaryDirectory, logging, validate_hf_hub_args if is_torch_available(): import torch # type: ignore logger = logging.get_logger(__name__) # Generic variable that is either ModelHubMixin or a subclass thereof T = TypeVar("T", bound="ModelHubMixin") class ModelHubMixin: """ A generic mixin to integrate ANY machine learning framework with the Hub. To integrate your framework, your model class must inherit from this class. Custom logic for saving/loading models have to be overwritten in [`_from_pretrained`] and [`_save_pretrained`]. [`PyTorchModelHubMixin`] is a good example of mixin integration with the Hub. Check out our [integration guide](../guides/integrations) for more instructions. """ def save_pretrained( self, save_directory: Union[str, Path], *, config: Optional[dict] = None, repo_id: Optional[str] = None, push_to_hub: bool = False, **kwargs, ) -> Optional[str]: """ Save weights in local directory. Args: save_directory (`str` or `Path`): Path to directory in which the model weights and configuration will be saved. config (`dict`, *optional*): Model configuration specified as a key/value dictionary. push_to_hub (`bool`, *optional*, defaults to `False`): Whether or not to push your model to the Huggingface Hub after saving it. repo_id (`str`, *optional*): ID of your repository on the Hub. Used only if `push_to_hub=True`. Will default to the folder name if not provided. kwargs: Additional key word arguments passed along to the [`~ModelHubMixin._from_pretrained`] method. """ save_directory = Path(save_directory) save_directory.mkdir(parents=True, exist_ok=True) # saving model weights/files self._save_pretrained(save_directory) # saving config if isinstance(config, dict): (save_directory / CONFIG_NAME).write_text(json.dumps(config)) if push_to_hub: kwargs = kwargs.copy() # soft-copy to avoid mutating input if config is not None: # kwarg for `push_to_hub` kwargs["config"] = config if repo_id is None: repo_id = save_directory.name # Defaults to `save_directory` name return self.push_to_hub(repo_id=repo_id, **kwargs) return None def _save_pretrained(self, save_directory: Path) -> None: """ Overwrite this method in subclass to define how to save your model. Check out our [integration guide](../guides/integrations) for instructions. Args: save_directory (`str` or `Path`): Path to directory in which the model weights and configuration will be saved. """ raise NotImplementedError @classmethod @validate_hf_hub_args def from_pretrained( cls: Type[T], pretrained_model_name_or_path: Union[str, Path], *, force_download: bool = False, resume_download: bool = False, proxies: Optional[Dict] = None, token: Optional[Union[str, bool]] = None, cache_dir: Optional[Union[str, Path]] = None, local_files_only: bool = False, revision: Optional[str] = None, **model_kwargs, ) -> T: """ Download a model from the Huggingface Hub and instantiate it. Args: pretrained_model_name_or_path (`str`, `Path`): - Either the `model_id` (string) of a model hosted on the Hub, e.g. `bigscience/bloom`. - Or a path to a `directory` containing model weights saved using [`~transformers.PreTrainedModel.save_pretrained`], e.g., `../path/to/my_model_directory/`. revision (`str`, *optional*): Revision of the model on the Hub. Can be a branch name, a git tag or any commit id. Defaults to the latest commit on `main` branch. force_download (`bool`, *optional*, defaults to `False`): Whether to force (re-)downloading the model weights and configuration files from the Hub, overriding the existing cache. resume_download (`bool`, *optional*, defaults to `False`): Whether to delete incompletely received files. Will attempt to resume the download if such a file exists. proxies (`Dict[str, str]`, *optional*): A dictionary of proxy servers to use by protocol or endpoint, e.g., `{'http': 'foo.bar:3128', 'http://hostname': 'foo.bar:4012'}`. The proxies are used on every request. token (`str` or `bool`, *optional*): The token to use as HTTP bearer authorization for remote files. By default, it will use the token cached when running `huggingface-cli login`. cache_dir (`str`, `Path`, *optional*): Path to the folder where cached files are stored. local_files_only (`bool`, *optional*, defaults to `False`): If `True`, avoid downloading the file and return the path to the local cached file if it exists. model_kwargs (`Dict`, *optional*): Additional kwargs to pass to the model during initialization. """ model_id = pretrained_model_name_or_path config_file: Optional[str] = None if os.path.isdir(model_id): if CONFIG_NAME in os.listdir(model_id): config_file = os.path.join(model_id, CONFIG_NAME) else: logger.warning(f"{CONFIG_NAME} not found in {Path(model_id).resolve()}") elif isinstance(model_id, str): try: config_file = hf_hub_download( repo_id=str(model_id), filename=CONFIG_NAME, revision=revision, cache_dir=cache_dir, force_download=force_download, proxies=proxies, resume_download=resume_download, token=token, local_files_only=local_files_only, ) except requests.exceptions.RequestException: logger.warning(f"{CONFIG_NAME} not found in HuggingFace Hub.") if config_file is not None: with open(config_file, "r", encoding="utf-8") as f: config = json.load(f) model_kwargs.update({"config": config}) return cls._from_pretrained( model_id=str(model_id), revision=revision, cache_dir=cache_dir, force_download=force_download, proxies=proxies, resume_download=resume_download, local_files_only=local_files_only, token=token, **model_kwargs, ) @classmethod def _from_pretrained( cls: Type[T], *, model_id: str, revision: Optional[str], cache_dir: Optional[Union[str, Path]], force_download: bool, proxies: Optional[Dict], resume_download: bool, local_files_only: bool, token: Optional[Union[str, bool]], **model_kwargs, ) -> T: """Overwrite this method in subclass to define how to load your model from pretrained. Use [`hf_hub_download`] or [`snapshot_download`] to download files from the Hub before loading them. Most args taken as input can be directly passed to those 2 methods. If needed, you can add more arguments to this method using "model_kwargs". For example [`PyTorchModelHubMixin._from_pretrained`] takes as input a `map_location` parameter to set on which device the model should be loaded. Check out our [integration guide](../guides/integrations) for more instructions. Args: model_id (`str`): ID of the model to load from the Huggingface Hub (e.g. `bigscience/bloom`). revision (`str`, *optional*): Revision of the model on the Hub. Can be a branch name, a git tag or any commit id. Defaults to the latest commit on `main` branch. force_download (`bool`, *optional*, defaults to `False`): Whether to force (re-)downloading the model weights and configuration files from the Hub, overriding the existing cache. resume_download (`bool`, *optional*, defaults to `False`): Whether to delete incompletely received files. Will attempt to resume the download if such a file exists. proxies (`Dict[str, str]`, *optional*): A dictionary of proxy servers to use by protocol or endpoint (e.g., `{'http': 'foo.bar:3128', 'http://hostname': 'foo.bar:4012'}`). token (`str` or `bool`, *optional*): The token to use as HTTP bearer authorization for remote files. By default, it will use the token cached when running `huggingface-cli login`. cache_dir (`str`, `Path`, *optional*): Path to the folder where cached files are stored. local_files_only (`bool`, *optional*, defaults to `False`): If `True`, avoid downloading the file and return the path to the local cached file if it exists. model_kwargs: Additional keyword arguments passed along to the [`~ModelHubMixin._from_pretrained`] method. """ raise NotImplementedError @validate_hf_hub_args def push_to_hub( self, repo_id: str, *, config: Optional[dict] = None, commit_message: str = "Push model using huggingface_hub.", private: bool = False, api_endpoint: Optional[str] = None, token: Optional[str] = None, branch: Optional[str] = None, create_pr: Optional[bool] = None, allow_patterns: Optional[Union[List[str], str]] = None, ignore_patterns: Optional[Union[List[str], str]] = None, delete_patterns: Optional[Union[List[str], str]] = None, ) -> str: """ Upload model checkpoint to the Hub. Use `allow_patterns` and `ignore_patterns` to precisely filter which files should be pushed to the hub. Use `delete_patterns` to delete existing remote files in the same commit. See [`upload_folder`] reference for more details. Args: repo_id (`str`): ID of the repository to push to (example: `"username/my-model"`). config (`dict`, *optional*): Configuration object to be saved alongside the model weights. commit_message (`str`, *optional*): Message to commit while pushing. private (`bool`, *optional*, defaults to `False`): Whether the repository created should be private. api_endpoint (`str`, *optional*): The API endpoint to use when pushing the model to the hub. token (`str`, *optional*): The token to use as HTTP bearer authorization for remote files. By default, it will use the token cached when running `huggingface-cli login`. branch (`str`, *optional*): The git branch on which to push the model. This defaults to `"main"`. create_pr (`boolean`, *optional*): Whether or not to create a Pull Request from `branch` with that commit. Defaults to `False`. allow_patterns (`List[str]` or `str`, *optional*): If provided, only files matching at least one pattern are pushed. ignore_patterns (`List[str]` or `str`, *optional*): If provided, files matching any of the patterns are not pushed. delete_patterns (`List[str]` or `str`, *optional*): If provided, remote files matching any of the patterns will be deleted from the repo. Returns: The url of the commit of your model in the given repository. """ api = HfApi(endpoint=api_endpoint, token=token) repo_id = api.create_repo(repo_id=repo_id, private=private, exist_ok=True).repo_id # Push the files to the repo in a single commit with SoftTemporaryDirectory() as tmp: saved_path = Path(tmp) / repo_id self.save_pretrained(saved_path, config=config) return api.upload_folder( repo_id=repo_id, repo_type="model", folder_path=saved_path, commit_message=commit_message, revision=branch, create_pr=create_pr, allow_patterns=allow_patterns, ignore_patterns=ignore_patterns, delete_patterns=delete_patterns, ) class PyTorchModelHubMixin(ModelHubMixin): """ Implementation of [`ModelHubMixin`] to provide model Hub upload/download capabilities to PyTorch models. The model is set in evaluation mode by default using `model.eval()` (dropout modules are deactivated). To train the model, you should first set it back in training mode with `model.train()`. Example: ```python >>> import torch >>> import torch.nn as nn >>> from huggingface_hub import PyTorchModelHubMixin >>> class MyModel(nn.Module, PyTorchModelHubMixin): ... def __init__(self): ... super().__init__() ... self.param = nn.Parameter(torch.rand(3, 4)) ... self.linear = nn.Linear(4, 5) ... def forward(self, x): ... return self.linear(x + self.param) >>> model = MyModel() # Save model weights to local directory >>> model.save_pretrained("my-awesome-model") # Push model weights to the Hub >>> model.push_to_hub("my-awesome-model") # Download and initialize weights from the Hub >>> model = MyModel.from_pretrained("username/my-awesome-model") ``` """ def _save_pretrained(self, save_directory: Path) -> None: """Save weights from a Pytorch model to a local directory.""" model_to_save = self.module if hasattr(self, "module") else self # type: ignore torch.save(model_to_save.state_dict(), save_directory / PYTORCH_WEIGHTS_NAME) @classmethod def _from_pretrained( cls, *, model_id: str, revision: Optional[str], cache_dir: Optional[Union[str, Path]], force_download: bool, proxies: Optional[Dict], resume_download: bool, local_files_only: bool, token: Union[str, bool, None], map_location: str = "cpu", strict: bool = False, **model_kwargs, ): """Load Pytorch pretrained weights and return the loaded model.""" if os.path.isdir(model_id): print("Loading weights from local directory") model_file = os.path.join(model_id, PYTORCH_WEIGHTS_NAME) else: model_file = hf_hub_download( repo_id=model_id, filename=PYTORCH_WEIGHTS_NAME, revision=revision, cache_dir=cache_dir, force_download=force_download, proxies=proxies, resume_download=resume_download, token=token, local_files_only=local_files_only, ) model = cls(**model_kwargs) state_dict = torch.load(model_file, map_location=torch.device(map_location)) model.load_state_dict(state_dict, strict=strict) # type: ignore model.eval() # type: ignore return model