Add IPEX notebook

notebooks/ipex/README.md

@@ -6,4 +6,4 @@ You can find here a list of the notebooks for the IPEX integration in 🤗 Optim
 | Notebook     |      Description      |   |   |
 |:----------|:-------------|:-------------|------:|
 | [How to optimize your model with IPEX for text generation](https://github.com/huggingface/optimum-intel/blob/main/notebooks/ipex/text_generation.ipynb)| Show how to apply operators and graph-level optimizations using Intel [IPEX](https://github.com/intel/intel-extension-for-pytorch) | [![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/optimum-intel/blob/main/notebooks/ipex/text_generation.ipynb)| [![Open in AWS Studio](https://studiolab.sagemaker.aws/studiolab.svg)](https://studiolab.sagemaker.aws/import/github/huggingface/optimum-intel/blob/main/notebooks/ipex/text_generation.ipynb)|
-
+| [How to optimize your langchain pipeline with IPEX](https://github.com/huggingface/optimum-intel/blob/main/notebooks/ipex/langchain_hf_pipelines.ipynb)| Show how to optimize your langchain pipeline with IPEX [IPEX](https://github.com/intel/intel-extension-for-pytorch) | [![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/optimum-intel/blob/main/notebooks/ipex/langchain_hf_pipelines.ipynb)| [![Open in AWS Studio](https://studiolab.sagemaker.aws/studiolab.svg)](https://studiolab.sagemaker.aws/import/github/huggingface/optimum-intel/blob/main/notebooks/ipex/langchain_hf_pipelines.ipynb)|

notebooks/ipex/langchain_hf_pipelines.ipynb

@@ -0,0 +1,168 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Hugging Face Pipelines\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "If you're opening this Notebook on colab, you will probably need to install Langchain and 🤗 Optimum. Uncomment the following cell and run it."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#! pip install langchain-huggingface optimum[ipex]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Make sure your version of langchain-huggingface is at least v0.2 and 🤗 Optimum is at least v1.22.0 since the functionality was introduced in these versions:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from optimum.intel.version import __version__\n",
+    "\n",
+    "print(\"optimum-intel version is\", __version__)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from optimum.intel.utils.import_utils import _langchain_hf_version\n",
+    "\n",
+    "print(\"langchain-huggingface version is\", _langchain_hf_version)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Model Loading\n",
+    "\n",
+    "Models can be loaded by specifying the model parameters using the `from_model_id` method."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain_huggingface.llms import HuggingFacePipeline\n",
+    "\n",
+    "hf = HuggingFacePipeline.from_model_id(\n",
+    "    model_id=\"gpt2\",\n",
+    "    task=\"text-generation\",\n",
+    "    pipeline_kwargs={\"max_new_tokens\": 10},\n",
+    "    backend=\"ipex\",\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Create Chain\n",
+    "\n",
+    "With the model loaded into memory, you can compose it with a prompt to form a chain."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain_core.prompts import PromptTemplate\n",
+    "\n",
+    "template = \"\"\"Question: {question}\n",
+    "\n",
+    "Answer: Let's think step by step.\"\"\"\n",
+    "prompt = PromptTemplate.from_template(template)\n",
+    "\n",
+    "chain = prompt | hf\n",
+    "\n",
+    "question = \"What is electroencephalography?\"\n",
+    "\n",
+    "print(chain.invoke({\"question\": question}))\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To get response without prompt, you can bind skip_prompt=True with LLM."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "chain = prompt | hf.bind(skip_prompt=True)\n",
+    "\n",
+    "question = \"What is electroencephalography?\"\n",
+    "\n",
+    "print(chain.invoke({\"question\": question}))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Streaming response :"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for chunk in chain.stream(question):\n",
+    "    print(chunk, end=\"\", flush=True)"
+   ]
+  }
+ ],
+ "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",
+   "version": "3.10.14"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}

notebooks/ipex/text_generation.ipynb

@@ -22,7 +22,7 @@
    "source": [
     "import torch\n",
     "from transformers import AutoTokenizer\n",
-    "from optimum.intel.ipex import IPEXModelForCausalLM"
+    "from optimum.intel import IPEXModelForCausalLM"
    ]
   },
   {