Huggingface optimum intel

IntelEmbedding

Bases: BaseEmbedding

Source code in llama-index-integrations/embeddings/llama-index-embeddings-huggingface-optimum-intel/llama_index/embeddings/huggingface_optimum_intel/base.py

class IntelEmbedding(BaseEmbedding):
    folder_name: str = Field(description="Folder name to load from.")
    max_length: int = Field(description="Maximum length of input.")
    pooling: str = Field(description="Pooling strategy. One of ['cls', 'mean'].")
    normalize: str = Field(default=True, description="Normalize embeddings or not.")
    query_instruction: Optional[str] = Field(
        description="Instruction to prepend to query text."
    )
    text_instruction: Optional[str] = Field(
        description="Instruction to prepend to text."
    )
    cache_folder: Optional[str] = Field(
        description="Cache folder for huggingface files."
    )

    _model: Any = PrivateAttr()
    _tokenizer: Any = PrivateAttr()
    _device: Any = PrivateAttr()

    def __init__(
        self,
        folder_name: str,
        pooling: str = "cls",
        max_length: Optional[int] = None,
        normalize: bool = True,
        query_instruction: Optional[str] = None,
        text_instruction: Optional[str] = None,
        model: Optional[Any] = None,
        tokenizer: Optional[Any] = None,
        embed_batch_size: int = DEFAULT_EMBED_BATCH_SIZE,
        callback_manager: Optional[CallbackManager] = None,
        device: Optional[str] = None,
    ):
        try:
            from optimum.intel import IPEXModel
        except ImportError:
            raise ImportError(
                "Optimum-Intel requires the following dependencies; please install with "
                "`pip install optimum[exporters] "
                "optimum-intel neural-compressor intel_extension_for_pytorch`"
            )

        self._model = model or IPEXModel.from_pretrained(folder_name)
        self._tokenizer = tokenizer or AutoTokenizer.from_pretrained(folder_name)
        self._device = device or infer_torch_device()

        if max_length is None:
            try:
                max_length = int(self._model.config.max_position_embeddings)
            except Exception:
                raise ValueError(
                    "Unable to find max_length from model config. "
                    "Please provide max_length."
                )

        if pooling not in ["cls", "mean"]:
            raise ValueError(f"Pooling {pooling} not supported.")

        super().__init__(
            embed_batch_size=embed_batch_size,
            callback_manager=callback_manager,
            folder_name=folder_name,
            max_length=max_length,
            pooling=pooling,
            normalize=normalize,
            query_instruction=query_instruction,
            text_instruction=text_instruction,
        )

    @classmethod
    def class_name(cls) -> str:
        return "IntelEmbedding"

    def _mean_pooling(self, model_output: Any, attention_mask: Any) -> Any:
        """Mean Pooling - Take attention mask into account for correct averaging."""
        import torch

        # First element of model_output contains all token embeddings
        token_embeddings = model_output[0]
        input_mask_expanded = (
            attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
        )
        return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(
            input_mask_expanded.sum(1), min=1e-9
        )

    def _cls_pooling(self, model_output: list) -> Any:
        """Use the CLS token as the pooling token."""
        if isinstance(model_output, dict):
            token_embeddings = model_output["last_hidden_state"]
        else:
            token_embeddings = model_output[0]
        return token_embeddings[:, 0]

    def _embed(self, sentences: List[str]) -> List[List[float]]:
        """Embed sentences."""
        encoded_input = self._tokenizer(
            sentences,
            padding=True,
            max_length=self.max_length,
            truncation=True,
            return_tensors="pt",
        )
        import torch

        with torch.inference_mode(), torch.cpu.amp.autocast():
            model_output = self._model(**encoded_input)

        if self.pooling == "cls":
            embeddings = self._cls_pooling(model_output)
        else:
            embeddings = self._mean_pooling(
                model_output, encoded_input["attention_mask"].to(self._device)
            )

        if self.normalize:
            embeddings = torch.nn.functional.normalize(embeddings, p=2, dim=1)

        return embeddings.tolist()

    def _get_query_embedding(self, query: str) -> List[float]:
        """Get query embedding."""
        query = format_query(query, self.model_name, self.query_instruction)
        return self._embed([query])[0]

    async def _aget_query_embedding(self, query: str) -> List[float]:
        """Get query embedding async."""
        return self._get_query_embedding(query)

    async def _aget_text_embedding(self, text: str) -> List[float]:
        """Get text embedding async."""
        return self._get_text_embedding(text)

    def _get_text_embedding(self, text: str) -> List[float]:
        """Get text embedding."""
        text = format_text(text, self.model_name, self.text_instruction)
        return self._embed([text])[0]

    def _get_text_embeddings(self, texts: List[str]) -> List[List[float]]:
        """Get text embeddings."""
        texts = [
            format_text(text, self.model_name, self.text_instruction) for text in texts
        ]
        return self._embed(texts)