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Add OpenVINO llms docs #19489

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Mar 25, 2024
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Add OpenVINO llms docs #19489

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6eed1f9
add oepnvino llms docs
OpenVINO-dev-contest Mar 25, 2024
115cc48
fix the format issue
OpenVINO-dev-contest Mar 25, 2024
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8 changes: 4 additions & 4 deletions docs/docs/integrations/llms/huggingface_pipelines.ipynb
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Expand Up @@ -256,7 +256,7 @@
"metadata": {},
"outputs": [],
"source": [
"!optimum-cli export openvino --model gpt2 ov_model"
"!optimum-cli export openvino --model gpt2 ov_model_dir"
]
},
{
Expand All @@ -274,9 +274,9 @@
"metadata": {},
"outputs": [],
"source": [
"!optimum-cli export openvino --model gpt2 --weight-format int8 ov_model # for 8-bit quantization\n",
"!optimum-cli export openvino --model gpt2 --weight-format int8 ov_model_dir # for 8-bit quantization\n",
"\n",
"!optimum-cli export openvino --model gpt2 --weight-format int4 ov_model # for 4-bit quantization"
"!optimum-cli export openvino --model gpt2 --weight-format int4 ov_model_dir # for 4-bit quantization"
]
},
{
Expand All @@ -287,7 +287,7 @@
"outputs": [],
"source": [
"ov_llm = HuggingFacePipeline.from_model_id(\n",
" model_id=\"ov_model\",\n",
" model_id=\"ov_model_dir\",\n",
" task=\"text-generation\",\n",
" backend=\"openvino\",\n",
" model_kwargs={\"device\": \"CPU\", \"ov_config\": ov_config},\n",
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249 changes: 249 additions & 0 deletions docs/docs/integrations/llms/openvino.ipynb
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@@ -0,0 +1,249 @@
{
"cells": [
{
"cell_type": "markdown",
"id": "959300d4",
"metadata": {},
"source": [
"# OpenVINO Local Pipelines\n",
"\n",
"[OpenVINO™](https://github.com/openvinotoolkit/openvino) is an open-source toolkit for optimizing and deploying AI inference. The OpenVINO™ Runtime can infer models on different hardware [devices](https://github.com/openvinotoolkit/openvino?tab=readme-ov-file#supported-hardware-matrix). It can help to boost deep learning performance in computer vision, automatic speech recognition, natural language processing and other common tasks.\n",
"\n",
"OpenVINO models can be run locally through the `HuggingFacePipeline` [class](https://python.langchain.com/docs/integrations/llms/huggingface_pipeline). To deploy a model with OpenVINO, you can specify the `backend=\"openvino\"` parameter to trigger OpenVINO as backend inference framework."
]
},
{
"cell_type": "markdown",
"id": "4c1b8450-5eaf-4d34-8341-2d785448a1ff",
"metadata": {
"tags": []
},
"source": [
"To use, you should have the ``optimum-intel`` with OpenVINO Accelerator python [package installed](https://github.com/huggingface/optimum-intel?tab=readme-ov-file#installation)."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "d772b637-de00-4663-bd77-9bc96d798db2",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"%pip install --upgrade-strategy eager \"optimum[openvino,nncf]\" --quiet"
]
},
{
"cell_type": "markdown",
"id": "91ad075f-71d5-4bc8-ab91-cc0ad5ef16bb",
"metadata": {},
"source": [
"### Model Loading\n",
"\n",
"Models can be loaded by specifying the model parameters using the `from_model_id` method.\n",
"\n",
"If you have an Intel GPU, you can specify `model_kwargs={\"device\": \"GPU\"}` to run inference on it."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "165ae236-962a-4763-8052-c4836d78a5d2",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"from langchain_community.llms.huggingface_pipeline import HuggingFacePipeline\n",
"\n",
"ov_config = {\"PERFORMANCE_HINT\": \"LATENCY\", \"NUM_STREAMS\": \"1\", \"CACHE_DIR\": \"\"}\n",
"\n",
"ov_llm = HuggingFacePipeline.from_model_id(\n",
" model_id=\"gpt2\",\n",
" task=\"text-generation\",\n",
" backend=\"openvino\",\n",
" model_kwargs={\"device\": \"CPU\", \"ov_config\": ov_config},\n",
" pipeline_kwargs={\"max_new_tokens\": 10},\n",
")"
]
},
{
"cell_type": "markdown",
"id": "00104b27-0c15-4a97-b198-4512337ee211",
"metadata": {},
"source": [
"They can also be loaded by passing in an existing `optimum-intel` pipeline directly"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "7f426a4f",
"metadata": {},
"outputs": [],
"source": [
"from optimum.intel.openvino import OVModelForCausalLM\n",
"from transformers import AutoTokenizer, pipeline\n",
"\n",
"model_id = \"gpt2\"\n",
"device = \"CPU\"\n",
"tokenizer = AutoTokenizer.from_pretrained(model_id)\n",
"ov_model = OVModelForCausalLM.from_pretrained(\n",
" model_id, device=device, ov_config=ov_config\n",
")\n",
"ov_pipe = pipeline(\n",
" \"text-generation\", model=ov_model, tokenizer=tokenizer, max_new_tokens=10\n",
")\n",
"hf = HuggingFacePipeline(pipeline=ov_pipe)"
]
},
{
"cell_type": "markdown",
"id": "60e7ba8d",
"metadata": {},
"source": [
"### Create Chain\n",
"\n",
"With the model loaded into memory, you can compose it with a prompt to\n",
"form a chain."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "3acf0069",
"metadata": {},
"outputs": [],
"source": [
"from langchain.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 | ov_llm\n",
"\n",
"question = \"What is electroencephalography?\"\n",
"\n",
"print(chain.invoke({\"question\": question}))"
]
},
{
"cell_type": "markdown",
"id": "12524837-e9ab-455a-86be-66b95f4f893a",
"metadata": {},
"source": [
"### Inference with local OpenVINO model\n",
"\n",
"It is possible to [export your model](https://github.com/huggingface/optimum-intel?tab=readme-ov-file#export) to the OpenVINO IR format with the CLI, and load the model from local folder.\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "3d1104a2-79c7-43a6-aa1c-8076a5ad7747",
"metadata": {},
"outputs": [],
"source": [
"!optimum-cli export openvino --model gpt2 ov_model_dir"
]
},
{
"cell_type": "markdown",
"id": "0f7a6d21",
"metadata": {},
"source": [
"It is recommended to apply 8 or 4-bit weight quantization to reduce inference latency and model footprint using `--weight-format`:"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "97088ea0",
"metadata": {},
"outputs": [],
"source": [
"!optimum-cli export openvino --model gpt2 --weight-format int8 ov_model_dir # for 8-bit quantization\n",
"\n",
"!optimum-cli export openvino --model gpt2 --weight-format int4 ov_model_dir # for 4-bit quantization"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "ac71e60d-5595-454e-8602-03ebb0248205",
"metadata": {},
"outputs": [],
"source": [
"ov_llm = HuggingFacePipeline.from_model_id(\n",
" model_id=\"ov_model_dir\",\n",
" task=\"text-generation\",\n",
" backend=\"openvino\",\n",
" model_kwargs={\"device\": \"CPU\", \"ov_config\": ov_config},\n",
" pipeline_kwargs={\"max_new_tokens\": 10},\n",
")\n",
"\n",
"ov_chain = prompt | ov_llm\n",
"\n",
"question = \"What is electroencephalography?\"\n",
"\n",
"print(ov_chain.invoke({\"question\": question}))"
]
},
{
"cell_type": "markdown",
"id": "a2c5726c",
"metadata": {},
"source": [
"You can get additional inference speed improvement with Dynamic Quantization of activations and KV-cache quantization. These options can be enabled with `ov_config` as follows:"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "a1f9c2c5",
"metadata": {},
"outputs": [],
"source": [
"ov_config = {\n",
" \"KV_CACHE_PRECISION\": \"u8\",\n",
" \"DYNAMIC_QUANTIZATION_GROUP_SIZE\": \"32\",\n",
" \"PERFORMANCE_HINT\": \"LATENCY\",\n",
" \"NUM_STREAMS\": \"1\",\n",
" \"CACHE_DIR\": \"\",\n",
"}"
]
},
{
"cell_type": "markdown",
"id": "da9a9239",
"metadata": {},
"source": [
"For more information refer to [OpenVINO LLM guide](https://docs.openvino.ai/2024/openvino-workflow/generative-ai-models-guide.html)."
]
}
],
"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": "3.10.12"
}
},
"nbformat": 4,
"nbformat_minor": 5
}