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Diffstat (limited to '.venv/lib/python3.12/site-packages/huggingface_hub/hub_mixin.py')
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diff --git a/.venv/lib/python3.12/site-packages/huggingface_hub/hub_mixin.py b/.venv/lib/python3.12/site-packages/huggingface_hub/hub_mixin.py new file mode 100644 index 00000000..8bf32f12 --- /dev/null +++ b/.venv/lib/python3.12/site-packages/huggingface_hub/hub_mixin.py @@ -0,0 +1,836 @@ +import inspect +import json +import os +from dataclasses import Field, asdict, dataclass, is_dataclass +from pathlib import Path +from typing import Any, Callable, ClassVar, Dict, List, Optional, Protocol, Tuple, Type, TypeVar, Union + +import packaging.version + +from . import constants +from .errors import EntryNotFoundError, HfHubHTTPError +from .file_download import hf_hub_download +from .hf_api import HfApi +from .repocard import ModelCard, ModelCardData +from .utils import ( + SoftTemporaryDirectory, + is_jsonable, + is_safetensors_available, + is_simple_optional_type, + is_torch_available, + logging, + unwrap_simple_optional_type, + validate_hf_hub_args, +) + + +if is_torch_available(): + import torch # type: ignore + +if is_safetensors_available(): + import safetensors + from safetensors.torch import load_model as load_model_as_safetensor + from safetensors.torch import save_model as save_model_as_safetensor + + +logger = logging.get_logger(__name__) + + +# Type alias for dataclass instances, copied from https://github.com/python/typeshed/blob/9f28171658b9ca6c32a7cb93fbb99fc92b17858b/stdlib/_typeshed/__init__.pyi#L349 +class DataclassInstance(Protocol): + __dataclass_fields__: ClassVar[Dict[str, Field]] + + +# Generic variable that is either ModelHubMixin or a subclass thereof +T = TypeVar("T", bound="ModelHubMixin") +# Generic variable to represent an args type +ARGS_T = TypeVar("ARGS_T") +ENCODER_T = Callable[[ARGS_T], Any] +DECODER_T = Callable[[Any], ARGS_T] +CODER_T = Tuple[ENCODER_T, DECODER_T] + + +DEFAULT_MODEL_CARD = """ +--- +# For reference on model card metadata, see the spec: https://github.com/huggingface/hub-docs/blob/main/modelcard.md?plain=1 +# Doc / guide: https://huggingface.co/docs/hub/model-cards +{{ card_data }} +--- + +This model has been pushed to the Hub using the [PytorchModelHubMixin](https://huggingface.co/docs/huggingface_hub/package_reference/mixins#huggingface_hub.PyTorchModelHubMixin) integration: +- Library: {{ repo_url | default("[More Information Needed]", true) }} +- Docs: {{ docs_url | default("[More Information Needed]", true) }} +""" + + +@dataclass +class MixinInfo: + model_card_template: str + model_card_data: ModelCardData + repo_url: Optional[str] = None + docs_url: Optional[str] = None + + +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. + + When inheriting from [`ModelHubMixin`], you can define class-level attributes. These attributes are not passed to + `__init__` but to the class definition itself. This is useful to define metadata about the library integrating + [`ModelHubMixin`]. + + For more details on how to integrate the mixin with your library, checkout the [integration guide](../guides/integrations). + + Args: + repo_url (`str`, *optional*): + URL of the library repository. Used to generate model card. + docs_url (`str`, *optional*): + URL of the library documentation. Used to generate model card. + model_card_template (`str`, *optional*): + Template of the model card. Used to generate model card. Defaults to a generic template. + language (`str` or `List[str]`, *optional*): + Language supported by the library. Used to generate model card. + library_name (`str`, *optional*): + Name of the library integrating ModelHubMixin. Used to generate model card. + license (`str`, *optional*): + License of the library integrating ModelHubMixin. Used to generate model card. + E.g: "apache-2.0" + license_name (`str`, *optional*): + Name of the library integrating ModelHubMixin. Used to generate model card. + Only used if `license` is set to `other`. + E.g: "coqui-public-model-license". + license_link (`str`, *optional*): + URL to the license of the library integrating ModelHubMixin. Used to generate model card. + Only used if `license` is set to `other` and `license_name` is set. + E.g: "https://coqui.ai/cpml". + pipeline_tag (`str`, *optional*): + Tag of the pipeline. Used to generate model card. E.g. "text-classification". + tags (`List[str]`, *optional*): + Tags to be added to the model card. Used to generate model card. E.g. ["x-custom-tag", "arxiv:2304.12244"] + coders (`Dict[Type, Tuple[Callable, Callable]]`, *optional*): + Dictionary of custom types and their encoders/decoders. Used to encode/decode arguments that are not + jsonable by default. E.g dataclasses, argparse.Namespace, OmegaConf, etc. + + Example: + + ```python + >>> from huggingface_hub import ModelHubMixin + + # Inherit from ModelHubMixin + >>> class MyCustomModel( + ... ModelHubMixin, + ... library_name="my-library", + ... tags=["x-custom-tag", "arxiv:2304.12244"], + ... repo_url="https://github.com/huggingface/my-cool-library", + ... docs_url="https://huggingface.co/docs/my-cool-library", + ... # ^ optional metadata to generate model card + ... ): + ... def __init__(self, size: int = 512, device: str = "cpu"): + ... # define how to initialize your model + ... super().__init__() + ... ... + ... + ... def _save_pretrained(self, save_directory: Path) -> None: + ... # define how to serialize your model + ... ... + ... + ... @classmethod + ... def from_pretrained( + ... cls: Type[T], + ... pretrained_model_name_or_path: Union[str, Path], + ... *, + ... force_download: bool = False, + ... resume_download: Optional[bool] = None, + ... 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: + ... # define how to deserialize your model + ... ... + + >>> model = MyCustomModel(size=256, device="gpu") + + # 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 + >>> reloaded_model = MyCustomModel.from_pretrained("username/my-awesome-model") + >>> reloaded_model.size + 256 + + # Model card has been correctly populated + >>> from huggingface_hub import ModelCard + >>> card = ModelCard.load("username/my-awesome-model") + >>> card.data.tags + ["x-custom-tag", "pytorch_model_hub_mixin", "model_hub_mixin"] + >>> card.data.library_name + "my-library" + ``` + """ + + _hub_mixin_config: Optional[Union[dict, DataclassInstance]] = None + # ^ optional config attribute automatically set in `from_pretrained` + _hub_mixin_info: MixinInfo + # ^ information about the library integrating ModelHubMixin (used to generate model card) + _hub_mixin_inject_config: bool # whether `_from_pretrained` expects `config` or not + _hub_mixin_init_parameters: Dict[str, inspect.Parameter] # __init__ parameters + _hub_mixin_jsonable_default_values: Dict[str, Any] # default values for __init__ parameters + _hub_mixin_jsonable_custom_types: Tuple[Type, ...] # custom types that can be encoded/decoded + _hub_mixin_coders: Dict[Type, CODER_T] # encoders/decoders for custom types + # ^ internal values to handle config + + def __init_subclass__( + cls, + *, + # Generic info for model card + repo_url: Optional[str] = None, + docs_url: Optional[str] = None, + # Model card template + model_card_template: str = DEFAULT_MODEL_CARD, + # Model card metadata + language: Optional[List[str]] = None, + library_name: Optional[str] = None, + license: Optional[str] = None, + license_name: Optional[str] = None, + license_link: Optional[str] = None, + pipeline_tag: Optional[str] = None, + tags: Optional[List[str]] = None, + # How to encode/decode arguments with custom type into a JSON config? + coders: Optional[ + Dict[Type, CODER_T] + # Key is a type. + # Value is a tuple (encoder, decoder). + # Example: {MyCustomType: (lambda x: x.value, lambda data: MyCustomType(data))} + ] = None, + ) -> None: + """Inspect __init__ signature only once when subclassing + handle modelcard.""" + super().__init_subclass__() + + # Will be reused when creating modelcard + tags = tags or [] + tags.append("model_hub_mixin") + + # Initialize MixinInfo if not existent + info = MixinInfo(model_card_template=model_card_template, model_card_data=ModelCardData()) + + # If parent class has a MixinInfo, inherit from it as a copy + if hasattr(cls, "_hub_mixin_info"): + # Inherit model card template from parent class if not explicitly set + if model_card_template == DEFAULT_MODEL_CARD: + info.model_card_template = cls._hub_mixin_info.model_card_template + + # Inherit from parent model card data + info.model_card_data = ModelCardData(**cls._hub_mixin_info.model_card_data.to_dict()) + + # Inherit other info + info.docs_url = cls._hub_mixin_info.docs_url + info.repo_url = cls._hub_mixin_info.repo_url + cls._hub_mixin_info = info + + # Update MixinInfo with metadata + if model_card_template is not None and model_card_template != DEFAULT_MODEL_CARD: + info.model_card_template = model_card_template + if repo_url is not None: + info.repo_url = repo_url + if docs_url is not None: + info.docs_url = docs_url + if language is not None: + info.model_card_data.language = language + if library_name is not None: + info.model_card_data.library_name = library_name + if license is not None: + info.model_card_data.license = license + if license_name is not None: + info.model_card_data.license_name = license_name + if license_link is not None: + info.model_card_data.license_link = license_link + if pipeline_tag is not None: + info.model_card_data.pipeline_tag = pipeline_tag + if tags is not None: + if info.model_card_data.tags is not None: + info.model_card_data.tags.extend(tags) + else: + info.model_card_data.tags = tags + + info.model_card_data.tags = sorted(set(info.model_card_data.tags)) + + # Handle encoders/decoders for args + cls._hub_mixin_coders = coders or {} + cls._hub_mixin_jsonable_custom_types = tuple(cls._hub_mixin_coders.keys()) + + # Inspect __init__ signature to handle config + cls._hub_mixin_init_parameters = dict(inspect.signature(cls.__init__).parameters) + cls._hub_mixin_jsonable_default_values = { + param.name: cls._encode_arg(param.default) + for param in cls._hub_mixin_init_parameters.values() + if param.default is not inspect.Parameter.empty and cls._is_jsonable(param.default) + } + cls._hub_mixin_inject_config = "config" in inspect.signature(cls._from_pretrained).parameters + + def __new__(cls: Type[T], *args, **kwargs) -> T: + """Create a new instance of the class and handle config. + + 3 cases: + - If `self._hub_mixin_config` is already set, do nothing. + - If `config` is passed as a dataclass, set it as `self._hub_mixin_config`. + - Otherwise, build `self._hub_mixin_config` from default values and passed values. + """ + instance = super().__new__(cls) + + # If `config` is already set, return early + if instance._hub_mixin_config is not None: + return instance + + # Infer passed values + passed_values = { + **{ + key: value + for key, value in zip( + # [1:] to skip `self` parameter + list(cls._hub_mixin_init_parameters)[1:], + args, + ) + }, + **kwargs, + } + + # If config passed as dataclass => set it and return early + if is_dataclass(passed_values.get("config")): + instance._hub_mixin_config = passed_values["config"] + return instance + + # Otherwise, build config from default + passed values + init_config = { + # default values + **cls._hub_mixin_jsonable_default_values, + # passed values + **{ + key: cls._encode_arg(value) # Encode custom types as jsonable value + for key, value in passed_values.items() + if instance._is_jsonable(value) # Only if jsonable or we have a custom encoder + }, + } + passed_config = init_config.pop("config", {}) + + # Populate `init_config` with provided config + if isinstance(passed_config, dict): + init_config.update(passed_config) + + # Set `config` attribute and return + if init_config != {}: + instance._hub_mixin_config = init_config + return instance + + @classmethod + def _is_jsonable(cls, value: Any) -> bool: + """Check if a value is JSON serializable.""" + if isinstance(value, cls._hub_mixin_jsonable_custom_types): + return True + return is_jsonable(value) + + @classmethod + def _encode_arg(cls, arg: Any) -> Any: + """Encode an argument into a JSON serializable format.""" + for type_, (encoder, _) in cls._hub_mixin_coders.items(): + if isinstance(arg, type_): + if arg is None: + return None + return encoder(arg) + return arg + + @classmethod + def _decode_arg(cls, expected_type: Type[ARGS_T], value: Any) -> Optional[ARGS_T]: + """Decode a JSON serializable value into an argument.""" + if is_simple_optional_type(expected_type): + if value is None: + return None + expected_type = unwrap_simple_optional_type(expected_type) + # Dataclass => handle it + if is_dataclass(expected_type): + return _load_dataclass(expected_type, value) # type: ignore[return-value] + # Otherwise => check custom decoders + for type_, (_, decoder) in cls._hub_mixin_coders.items(): + if inspect.isclass(expected_type) and issubclass(expected_type, type_): + return decoder(value) + # Otherwise => don't decode + return value + + def save_pretrained( + self, + save_directory: Union[str, Path], + *, + config: Optional[Union[dict, DataclassInstance]] = None, + repo_id: Optional[str] = None, + push_to_hub: bool = False, + model_card_kwargs: Optional[Dict[str, Any]] = None, + **push_to_hub_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` or `DataclassInstance`, *optional*): + Model configuration specified as a key/value dictionary or a dataclass instance. + 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. + model_card_kwargs (`Dict[str, Any]`, *optional*): + Additional arguments passed to the model card template to customize the model card. + push_to_hub_kwargs: + Additional key word arguments passed along to the [`~ModelHubMixin.push_to_hub`] method. + Returns: + `str` or `None`: url of the commit on the Hub if `push_to_hub=True`, `None` otherwise. + """ + save_directory = Path(save_directory) + save_directory.mkdir(parents=True, exist_ok=True) + + # Remove config.json if already exists. After `_save_pretrained` we don't want to overwrite config.json + # as it might have been saved by the custom `_save_pretrained` already. However we do want to overwrite + # an existing config.json if it was not saved by `_save_pretrained`. + config_path = save_directory / constants.CONFIG_NAME + config_path.unlink(missing_ok=True) + + # save model weights/files (framework-specific) + self._save_pretrained(save_directory) + + # save config (if provided and if not serialized yet in `_save_pretrained`) + if config is None: + config = self._hub_mixin_config + if config is not None: + if is_dataclass(config): + config = asdict(config) # type: ignore[arg-type] + if not config_path.exists(): + config_str = json.dumps(config, sort_keys=True, indent=2) + config_path.write_text(config_str) + + # save model card + model_card_path = save_directory / "README.md" + model_card_kwargs = model_card_kwargs if model_card_kwargs is not None else {} + if not model_card_path.exists(): # do not overwrite if already exists + self.generate_model_card(**model_card_kwargs).save(save_directory / "README.md") + + # push to the Hub if required + if push_to_hub: + kwargs = push_to_hub_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, model_card_kwargs=model_card_kwargs, **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: Optional[bool] = None, + 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. + 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 = str(pretrained_model_name_or_path) + config_file: Optional[str] = None + if os.path.isdir(model_id): + if constants.CONFIG_NAME in os.listdir(model_id): + config_file = os.path.join(model_id, constants.CONFIG_NAME) + else: + logger.warning(f"{constants.CONFIG_NAME} not found in {Path(model_id).resolve()}") + else: + try: + config_file = hf_hub_download( + repo_id=model_id, + filename=constants.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 HfHubHTTPError as e: + logger.info(f"{constants.CONFIG_NAME} not found on the HuggingFace Hub: {str(e)}") + + # Read config + config = None + if config_file is not None: + with open(config_file, "r", encoding="utf-8") as f: + config = json.load(f) + + # Decode custom types in config + for key, value in config.items(): + if key in cls._hub_mixin_init_parameters: + expected_type = cls._hub_mixin_init_parameters[key].annotation + if expected_type is not inspect.Parameter.empty: + config[key] = cls._decode_arg(expected_type, value) + + # Populate model_kwargs from config + for param in cls._hub_mixin_init_parameters.values(): + if param.name not in model_kwargs and param.name in config: + model_kwargs[param.name] = config[param.name] + + # Check if `config` argument was passed at init + if "config" in cls._hub_mixin_init_parameters and "config" not in model_kwargs: + # Decode `config` argument if it was passed + config_annotation = cls._hub_mixin_init_parameters["config"].annotation + config = cls._decode_arg(config_annotation, config) + + # Forward config to model initialization + model_kwargs["config"] = config + + # Inject config if `**kwargs` are expected + if is_dataclass(cls): + for key in cls.__dataclass_fields__: + if key not in model_kwargs and key in config: + model_kwargs[key] = config[key] + elif any(param.kind == inspect.Parameter.VAR_KEYWORD for param in cls._hub_mixin_init_parameters.values()): + for key, value in config.items(): + if key not in model_kwargs: + model_kwargs[key] = value + + # Finally, also inject if `_from_pretrained` expects it + if cls._hub_mixin_inject_config and "config" not in model_kwargs: + model_kwargs["config"] = config + + instance = 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, + ) + + # Implicitly set the config as instance attribute if not already set by the class + # This way `config` will be available when calling `save_pretrained` or `push_to_hub`. + if config is not None and (getattr(instance, "_hub_mixin_config", None) in (None, {})): + instance._hub_mixin_config = config + + return instance + + @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: Optional[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. + 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[Union[dict, DataclassInstance]] = None, + commit_message: str = "Push model using huggingface_hub.", + private: Optional[bool] = 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, + model_card_kwargs: Optional[Dict[str, Any]] = 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` or `DataclassInstance`, *optional*): + Model configuration specified as a key/value dictionary or a dataclass instance. + commit_message (`str`, *optional*): + Message to commit while pushing. + private (`bool`, *optional*): + Whether the repository created should be private. + If `None` (default), the repo will be public unless the organization's default is private. + 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. + model_card_kwargs (`Dict[str, Any]`, *optional*): + Additional arguments passed to the model card template to customize the model card. + + Returns: + The url of the commit of your model in the given repository. + """ + api = HfApi(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, model_card_kwargs=model_card_kwargs) + 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, + ) + + def generate_model_card(self, *args, **kwargs) -> ModelCard: + card = ModelCard.from_template( + card_data=self._hub_mixin_info.model_card_data, + template_str=self._hub_mixin_info.model_card_template, + repo_url=self._hub_mixin_info.repo_url, + docs_url=self._hub_mixin_info.docs_url, + **kwargs, + ) + return card + + +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()`. + + See [`ModelHubMixin`] for more details on how to use the mixin. + + Example: + + ```python + >>> import torch + >>> import torch.nn as nn + >>> from huggingface_hub import PyTorchModelHubMixin + + >>> class MyModel( + ... nn.Module, + ... PyTorchModelHubMixin, + ... library_name="keras-nlp", + ... repo_url="https://github.com/keras-team/keras-nlp", + ... docs_url="https://keras.io/keras_nlp/", + ... # ^ optional metadata to generate model card + ... ): + ... def __init__(self, hidden_size: int = 512, vocab_size: int = 30000, output_size: int = 4): + ... super().__init__() + ... self.param = nn.Parameter(torch.rand(hidden_size, vocab_size)) + ... self.linear = nn.Linear(output_size, vocab_size) + + ... def forward(self, x): + ... return self.linear(x + self.param) + >>> model = MyModel(hidden_size=256) + + # 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") + >>> model.hidden_size + 256 + ``` + """ + + def __init_subclass__(cls, *args, tags: Optional[List[str]] = None, **kwargs) -> None: + tags = tags or [] + tags.append("pytorch_model_hub_mixin") + kwargs["tags"] = tags + return super().__init_subclass__(*args, **kwargs) + + 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 + save_model_as_safetensor(model_to_save, str(save_directory / constants.SAFETENSORS_SINGLE_FILE)) + + @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: Optional[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.""" + model = cls(**model_kwargs) + if os.path.isdir(model_id): + print("Loading weights from local directory") + model_file = os.path.join(model_id, constants.SAFETENSORS_SINGLE_FILE) + return cls._load_as_safetensor(model, model_file, map_location, strict) + else: + try: + model_file = hf_hub_download( + repo_id=model_id, + filename=constants.SAFETENSORS_SINGLE_FILE, + revision=revision, + cache_dir=cache_dir, + force_download=force_download, + proxies=proxies, + resume_download=resume_download, + token=token, + local_files_only=local_files_only, + ) + return cls._load_as_safetensor(model, model_file, map_location, strict) + except EntryNotFoundError: + model_file = hf_hub_download( + repo_id=model_id, + filename=constants.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, + ) + return cls._load_as_pickle(model, model_file, map_location, strict) + + @classmethod + def _load_as_pickle(cls, model: T, model_file: str, map_location: str, strict: bool) -> T: + state_dict = torch.load(model_file, map_location=torch.device(map_location), weights_only=True) + model.load_state_dict(state_dict, strict=strict) # type: ignore + model.eval() # type: ignore + return model + + @classmethod + def _load_as_safetensor(cls, model: T, model_file: str, map_location: str, strict: bool) -> T: + if packaging.version.parse(safetensors.__version__) < packaging.version.parse("0.4.3"): # type: ignore [attr-defined] + load_model_as_safetensor(model, model_file, strict=strict) # type: ignore [arg-type] + if map_location != "cpu": + logger.warning( + "Loading model weights on other devices than 'cpu' is not supported natively in your version of safetensors." + " This means that the model is loaded on 'cpu' first and then copied to the device." + " This leads to a slower loading time." + " Please update safetensors to version 0.4.3 or above for improved performance." + ) + model.to(map_location) # type: ignore [attr-defined] + else: + safetensors.torch.load_model(model, model_file, strict=strict, device=map_location) # type: ignore [arg-type] + return model + + +def _load_dataclass(datacls: Type[DataclassInstance], data: dict) -> DataclassInstance: + """Load a dataclass instance from a dictionary. + + Fields not expected by the dataclass are ignored. + """ + return datacls(**{k: v for k, v in data.items() if k in datacls.__dataclass_fields__}) |