From fde716b8cf6e7f103142e6ff720de9938a503a7c Mon Sep 17 00:00:00 2001 From: Daniel Fleischer Date: Fri, 16 Feb 2024 04:51:39 +0200 Subject: [PATCH] Class for Optimum-Intel Embedding Model (#10500) * Initial commit for integrating Optimum Intel with v0.10 Based on the Optimum integration. * Test * Dependencies * Neural compressor code * Notebook example * Lock file --- docs/examples/embeddings/optimum_intel.ipynb | 87 + .../.gitignore | 153 + .../BUILD | 3 + .../Makefile | 17 + .../README.md | 1 + .../huggingface_optimum_intel/BUILD | 1 + .../huggingface_optimum_intel/__init__.py | 3 + .../huggingface_optimum_intel/base.py | 156 + .../poetry.lock | 6514 +++++++++++++++++ .../pyproject.toml | 66 + .../tests/BUILD | 1 + .../tests/__init__.py | 0 .../test_embeddings_huggingface_optimum.py | 7 + 13 files changed, 7009 insertions(+) create mode 100644 docs/examples/embeddings/optimum_intel.ipynb create mode 100644 llama-index-integrations/embeddings/llama-index-embeddings-huggingface-optimum-intel/.gitignore create mode 100644 llama-index-integrations/embeddings/llama-index-embeddings-huggingface-optimum-intel/BUILD create mode 100644 llama-index-integrations/embeddings/llama-index-embeddings-huggingface-optimum-intel/Makefile create mode 100644 llama-index-integrations/embeddings/llama-index-embeddings-huggingface-optimum-intel/README.md create mode 100644 llama-index-integrations/embeddings/llama-index-embeddings-huggingface-optimum-intel/llama_index/embeddings/huggingface_optimum_intel/BUILD create mode 100644 llama-index-integrations/embeddings/llama-index-embeddings-huggingface-optimum-intel/llama_index/embeddings/huggingface_optimum_intel/__init__.py create mode 100644 llama-index-integrations/embeddings/llama-index-embeddings-huggingface-optimum-intel/llama_index/embeddings/huggingface_optimum_intel/base.py create mode 100644 llama-index-integrations/embeddings/llama-index-embeddings-huggingface-optimum-intel/poetry.lock create mode 100644 llama-index-integrations/embeddings/llama-index-embeddings-huggingface-optimum-intel/pyproject.toml create mode 100644 llama-index-integrations/embeddings/llama-index-embeddings-huggingface-optimum-intel/tests/BUILD create mode 100644 llama-index-integrations/embeddings/llama-index-embeddings-huggingface-optimum-intel/tests/__init__.py create mode 100644 llama-index-integrations/embeddings/llama-index-embeddings-huggingface-optimum-intel/tests/test_embeddings_huggingface_optimum.py diff --git a/docs/examples/embeddings/optimum_intel.ipynb b/docs/examples/embeddings/optimum_intel.ipynb new file mode 100644 index 00000000000000..3a3c78cb0d9f3c --- /dev/null +++ b/docs/examples/embeddings/optimum_intel.ipynb @@ -0,0 +1,87 @@ +{ + "cells": [ + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\"Open" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Optimized Embedding Model using Optimum-Intel\n", + "\n", + "LlamaIndex has support for loading quantized embedding models for Intel, using the [Optimum-Intel library](https://huggingface.co/docs/optimum/main/en/intel/index). \n", + "\n", + "Optimized models are smaller and faster, with minimal accuracy loss, see the [documentation](https://huggingface.co/docs/optimum/main/en/intel/optimization_inc) and an [optimization guide](https://huggingface.co/docs/optimum/main/en/intel/optimization_inc) using the IntelLabs/fastRAG library. \n", + "\n", + "In order to be able to load and use the quantized models, install the required dependency `pip install optimum[exporters] optimum-intel neural-compressor`. \n", + "\n", + "Loading is done using the class `IntelEmbedding`; usage is similar to any HuggingFace local embedding model. See example." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%pip install llama-index-embeddings-huggingface-optimum-intel" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from llama_index.embeddings.huggingface_optimum_intel import IntelEmbedding\n", + "\n", + "embed_model = IntelEmbedding(\"Intel/bge-small-en-v1.5-rag-int8-static\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "384\n", + "[-0.0032782123889774084, -0.013396517373621464, 0.037944991141557693, -0.04642259329557419, 0.027709005400538445]\n" + ] + } + ], + "source": [ + "embeddings = embed_model.get_text_embedding(\"Hello World!\")\n", + "print(len(embeddings))\n", + "print(embeddings[:5])" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/llama-index-integrations/embeddings/llama-index-embeddings-huggingface-optimum-intel/.gitignore b/llama-index-integrations/embeddings/llama-index-embeddings-huggingface-optimum-intel/.gitignore new file mode 100644 index 00000000000000..990c18de229088 --- /dev/null +++ b/llama-index-integrations/embeddings/llama-index-embeddings-huggingface-optimum-intel/.gitignore @@ -0,0 +1,153 @@ +llama_index/_static +.DS_Store +# Byte-compiled / optimized / DLL files +__pycache__/ +*.py[cod] +*$py.class + +# C extensions +*.so + +# Distribution / packaging +.Python +bin/ +build/ +develop-eggs/ +dist/ +downloads/ +eggs/ +.eggs/ +etc/ +include/ +lib/ +lib64/ +parts/ +sdist/ +share/ +var/ +wheels/ +pip-wheel-metadata/ +share/python-wheels/ +*.egg-info/ +.installed.cfg +*.egg +MANIFEST + +# PyInstaller +# Usually these files are written by a python script from a template +# before PyInstaller builds the exe, so as to inject date/other infos into it. +*.manifest +*.spec + +# Installer logs +pip-log.txt +pip-delete-this-directory.txt + +# Unit test / coverage reports +htmlcov/ +.tox/ +.nox/ +.coverage +.coverage.* +.cache +nosetests.xml +coverage.xml +*.cover +*.py,cover +.hypothesis/ +.pytest_cache/ +.ruff_cache + +# Translations +*.mo +*.pot + +# Django stuff: +*.log +local_settings.py +db.sqlite3 +db.sqlite3-journal + +# Flask stuff: +instance/ +.webassets-cache + +# Scrapy stuff: +.scrapy + +# Sphinx documentation +docs/_build/ + +# PyBuilder +target/ + +# Jupyter Notebook +.ipynb_checkpoints +notebooks/ + +# IPython +profile_default/ +ipython_config.py + +# pyenv +.python-version + +# pipenv +# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. +# However, in case of collaboration, if having platform-specific dependencies or dependencies +# having no cross-platform support, pipenv may install dependencies that don't work, or not +# install all needed dependencies. +#Pipfile.lock + +# PEP 582; used by e.g. github.com/David-OConnor/pyflow +__pypackages__/ + +# Celery stuff +celerybeat-schedule +celerybeat.pid + +# SageMath parsed files +*.sage.py + +# Environments +.env +.venv +env/ +venv/ +ENV/ +env.bak/ +venv.bak/ +pyvenv.cfg + +# Spyder project settings +.spyderproject +.spyproject + +# Rope project settings +.ropeproject + +# mkdocs documentation +/site + +# mypy +.mypy_cache/ +.dmypy.json +dmypy.json + +# Pyre type checker +.pyre/ + +# Jetbrains +.idea +modules/ +*.swp + +# VsCode +.vscode + +# pipenv +Pipfile +Pipfile.lock + +# pyright +pyrightconfig.json diff --git a/llama-index-integrations/embeddings/llama-index-embeddings-huggingface-optimum-intel/BUILD b/llama-index-integrations/embeddings/llama-index-embeddings-huggingface-optimum-intel/BUILD new file mode 100644 index 00000000000000..0896ca890d8bff --- /dev/null +++ b/llama-index-integrations/embeddings/llama-index-embeddings-huggingface-optimum-intel/BUILD @@ -0,0 +1,3 @@ +poetry_requirements( + name="poetry", +) diff --git a/llama-index-integrations/embeddings/llama-index-embeddings-huggingface-optimum-intel/Makefile b/llama-index-integrations/embeddings/llama-index-embeddings-huggingface-optimum-intel/Makefile new file mode 100644 index 00000000000000..b9eab05aa37062 --- /dev/null +++ b/llama-index-integrations/embeddings/llama-index-embeddings-huggingface-optimum-intel/Makefile @@ -0,0 +1,17 @@ +GIT_ROOT ?= $(shell git rev-parse --show-toplevel) + +help: ## Show all Makefile targets. + @grep -E '^[a-zA-Z_-]+:.*?## .*$$' $(MAKEFILE_LIST) | awk 'BEGIN {FS = ":.*?## "}; {printf "\033[33m%-30s\033[0m %s\n", $$1, $$2}' + +format: ## Run code autoformatters (black). + pre-commit install + git ls-files | xargs pre-commit run black --files + +lint: ## Run linters: pre-commit (black, ruff, codespell) and mypy + pre-commit install && git ls-files | xargs pre-commit run --show-diff-on-failure --files + +test: ## Run tests via pytest. + pytest tests + +watch-docs: ## Build and watch documentation. + sphinx-autobuild docs/ docs/_build/html --open-browser --watch $(GIT_ROOT)/llama_index/ diff --git a/llama-index-integrations/embeddings/llama-index-embeddings-huggingface-optimum-intel/README.md b/llama-index-integrations/embeddings/llama-index-embeddings-huggingface-optimum-intel/README.md new file mode 100644 index 00000000000000..31b97d64608041 --- /dev/null +++ b/llama-index-integrations/embeddings/llama-index-embeddings-huggingface-optimum-intel/README.md @@ -0,0 +1 @@ +# LlamaIndex Embeddings Integration: Huggingface Optimum Intel diff --git a/llama-index-integrations/embeddings/llama-index-embeddings-huggingface-optimum-intel/llama_index/embeddings/huggingface_optimum_intel/BUILD b/llama-index-integrations/embeddings/llama-index-embeddings-huggingface-optimum-intel/llama_index/embeddings/huggingface_optimum_intel/BUILD new file mode 100644 index 00000000000000..db46e8d6c978c6 --- /dev/null +++ b/llama-index-integrations/embeddings/llama-index-embeddings-huggingface-optimum-intel/llama_index/embeddings/huggingface_optimum_intel/BUILD @@ -0,0 +1 @@ +python_sources() diff --git a/llama-index-integrations/embeddings/llama-index-embeddings-huggingface-optimum-intel/llama_index/embeddings/huggingface_optimum_intel/__init__.py b/llama-index-integrations/embeddings/llama-index-embeddings-huggingface-optimum-intel/llama_index/embeddings/huggingface_optimum_intel/__init__.py new file mode 100644 index 00000000000000..0be24545adf9f4 --- /dev/null +++ b/llama-index-integrations/embeddings/llama-index-embeddings-huggingface-optimum-intel/llama_index/embeddings/huggingface_optimum_intel/__init__.py @@ -0,0 +1,3 @@ +from llama_index.embeddings.huggingface_optimum_intel.base import IntelEmbedding + +__all__ = ["IntelEmbedding"] diff --git a/llama-index-integrations/embeddings/llama-index-embeddings-huggingface-optimum-intel/llama_index/embeddings/huggingface_optimum_intel/base.py b/llama-index-integrations/embeddings/llama-index-embeddings-huggingface-optimum-intel/llama_index/embeddings/huggingface_optimum_intel/base.py new file mode 100644 index 00000000000000..719a559e5a8707 --- /dev/null +++ b/llama-index-integrations/embeddings/llama-index-embeddings-huggingface-optimum-intel/llama_index/embeddings/huggingface_optimum_intel/base.py @@ -0,0 +1,156 @@ +from typing import Any, List, Optional + +from llama_index.core.base.embeddings.base import ( + DEFAULT_EMBED_BATCH_SIZE, + BaseEmbedding, +) +from llama_index.core.bridge.pydantic import Field, PrivateAttr +from llama_index.core.callbacks import CallbackManager +from llama_index.core.utils import infer_torch_device +from llama_index.embeddings.huggingface.utils import format_query, format_text +from transformers import AutoTokenizer + + +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 INCModel + except ImportError: + raise ImportError( + "Optimum-Intel requires the following dependencies; please install with " + "`pip install optimum[exporters] optimum-intel neural-compressor`." + ) + + self._model = model or INCModel.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) diff --git a/llama-index-integrations/embeddings/llama-index-embeddings-huggingface-optimum-intel/poetry.lock 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b/llama-index-integrations/embeddings/llama-index-embeddings-huggingface-optimum-intel/tests/__init__.py new file mode 100644 index 00000000000000..e69de29bb2d1d6 diff --git a/llama-index-integrations/embeddings/llama-index-embeddings-huggingface-optimum-intel/tests/test_embeddings_huggingface_optimum.py b/llama-index-integrations/embeddings/llama-index-embeddings-huggingface-optimum-intel/tests/test_embeddings_huggingface_optimum.py new file mode 100644 index 00000000000000..636665470e2cf7 --- /dev/null +++ b/llama-index-integrations/embeddings/llama-index-embeddings-huggingface-optimum-intel/tests/test_embeddings_huggingface_optimum.py @@ -0,0 +1,7 @@ +from llama_index.core.base.embeddings.base import BaseEmbedding +from llama_index.embeddings.huggingface_optimum_intel import IntelEmbedding + + +def test_optimum_intel_embedding_class(): + names_of_base_classes = [b.__name__ for b in IntelEmbedding.__mro__] + assert BaseEmbedding.__name__ in names_of_base_classes