FlagEmbedding/FlagEmbedding/inference/auto_embedder.py

import os
import logging
from typing import List, Union, Optional

from FlagEmbedding.inference.embedder.model_mapping import (
    EmbedderModelClass,
    AUTO_EMBEDDER_MAPPING, EMBEDDER_CLASS_MAPPING
)

logger = logging.getLogger(__name__)


class FlagAutoModel:
    """
    Automatically choose the appropriate class to load the embedding model.
    """
    def __init__(self):
        raise EnvironmentError(
            "FlagAutoModel is designed to be instantiated using the `FlagAutoModel.from_finetuned(model_name_or_path)` method."
        )

    @classmethod
    def from_finetuned(
        cls,
        model_name_or_path: str,
        model_class: Optional[Union[str, EmbedderModelClass]] = None,
        normalize_embeddings: bool = True,
        use_fp16: bool = True,
        query_instruction_for_retrieval: Optional[str] = None,
        devices: Optional[Union[str, List[str]]] = None,
        pooling_method: Optional[str] = None,
        trust_remote_code: Optional[bool] = None,
        query_instruction_format: Optional[str] = None,
        **kwargs,
    ):
        """
        Load a finetuned model according to the provided vars.

        Args:
            model_name_or_path (str): If it's a path to a local model, it loads the model from the path. Otherwise tries to download and
                load a model from HuggingFace Hub with the name.
            model_class (Optional[Union[str, EmbedderModelClass]], optional): The embedder class to use. Defaults to :data:`None`.
            normalize_embeddings (bool, optional): If True, the output embedding will be a Numpy array. Otherwise, it will be a Torch Tensor. 
                Defaults to :data:`True`.
            use_fp16 (bool, optional): If true, use half-precision floating-point to speed up computation with a slight performance 
                degradation. Defaults to :data:`True`.
            query_instruction_for_retrieval (Optional[str], optional): Query instruction for retrieval tasks, which will be used with
                :attr:`query_instruction_format`. Defaults to :data:`None`.
            devices (Optional[Union[str, List[str]]], optional): Devices to use for model inference. Defaults to :data:`None`.
            pooling_method (Optional[str], optional): Pooling method to get embedding vector from the last hidden state. Defaults to :data:`None`.
            trust_remote_code (Optional[bool], optional): trust_remote_code for HF datasets or models. Defaults to :data:`None`.
            query_instruction_format (Optional[str], optional): The template for :attr:`query_instruction_for_retrieval`. Defaults to :data:`None`.

        Raises:
            ValueError

        Returns:
            AbsEmbedder: The model class to load model, which is child class of :class:`AbsEmbedder`.
        """
        model_name = os.path.basename(model_name_or_path)
        if model_name.startswith("checkpoint-"):
            model_name = os.path.basename(os.path.dirname(model_name_or_path))

        if model_class is not None:
            _model_class = EMBEDDER_CLASS_MAPPING[EmbedderModelClass(model_class)]
            if pooling_method is None:
                pooling_method = _model_class.DEFAULT_POOLING_METHOD
                logger.warning(
                    f"`pooling_method` is not specified, use default pooling method '{pooling_method}'."
                )
            if trust_remote_code is None:
                trust_remote_code = False
                logger.warning(
                    f"`trust_remote_code` is not specified, set to default value '{trust_remote_code}'."
                )
            if query_instruction_format is None:
                query_instruction_format = "{}{}"
                logger.warning(
                    f"`query_instruction_format` is not specified, set to default value '{query_instruction_format}'."
                )
        else:
            if model_name not in AUTO_EMBEDDER_MAPPING:
                raise ValueError(
                    f"Model name '{model_name}' not found in the model mapping. You can pull request to add the model to "
                    "`https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/inference/embedder/model_mapping.py`. " 
                    "If need, you can create a new `<model>.py` file in `https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/inference/embedder/encoder_only` "
                    "or `https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/inference/embedder/decoder_only`. "
                    "Welcome to contribute! You can also directly specify the corresponding `model_class` to instantiate the model."
                )

            model_config = AUTO_EMBEDDER_MAPPING[model_name]

            _model_class = model_config.model_class
            if pooling_method is None:
                pooling_method = model_config.pooling_method.value
            if trust_remote_code is None:
                trust_remote_code = model_config.trust_remote_code
            if query_instruction_format is None:
                query_instruction_format = model_config.query_instruction_format

        return _model_class(
            model_name_or_path,
            normalize_embeddings=normalize_embeddings,
            use_fp16=use_fp16,
            query_instruction_for_retrieval=query_instruction_for_retrieval,
            query_instruction_format=query_instruction_format,
            devices=devices,
            pooling_method=pooling_method,
            trust_remote_code=trust_remote_code,
            **kwargs,
        )