From 3d044dbf53b25078f0d99ab2667f660a46f7f850 Mon Sep 17 00:00:00 2001 From: Zijie Li Date: Tue, 8 Oct 2024 21:20:42 -0400 Subject: [PATCH] add llama3.2-vision Pytorch example (#12165) --- .../Model/llama3.2-vision/README.md | 134 ++++++++++++++++++ .../Model/llama3.2-vision/generate.py | 77 ++++++++++ 2 files changed, 211 insertions(+) create mode 100644 python/llm/example/GPU/PyTorch-Models/Model/llama3.2-vision/README.md create mode 100644 python/llm/example/GPU/PyTorch-Models/Model/llama3.2-vision/generate.py diff --git a/python/llm/example/GPU/PyTorch-Models/Model/llama3.2-vision/README.md b/python/llm/example/GPU/PyTorch-Models/Model/llama3.2-vision/README.md new file mode 100644 index 00000000000..74b315340b1 --- /dev/null +++ b/python/llm/example/GPU/PyTorch-Models/Model/llama3.2-vision/README.md @@ -0,0 +1,134 @@ +# Llama3.2-Vision +In this directory, you will find examples on how you could use IPEX-LLM `optimize_model` API to accelerate Llama3.2-Vision models on [Intel GPUs](../../../README.md). For illustration purposes, we utilize the [meta-llama/Llama-3.2-11B-Vision-Instruct](https://huggingface.co/meta-llama/Llama-3.2-11B-Vision-Instruct) as a reference Llama3.2-Vision model. + +## 0. Requirements +To run these examples with IPEX-LLM on Intel GPUs, we have some recommended requirements for your machine, please refer to [here](../../../README.md#requirements) for more information. + +## Example: Predict Tokens using `generate()` API +In the example [generate.py](./generate.py), we show a basic use case for a Llama3.2-Vision model to predict the next N tokens using `generate()` API, with IPEX-LLM 'optimize_model' API on Intel GPUs. +### 1. Install +#### 1.1 Installation on Linux +We suggest using conda to manage environment: +```bash +conda create -n llm python=3.11 +conda activate llm +# below command will install intel_extension_for_pytorch==2.1.10+xpu as default +pip install --pre --upgrade ipex-llm[xpu] --extra-index-url https://pytorch-extension.intel.com/release-whl/stable/xpu/us/ + +pip install transformers==4.45.0 +``` + +#### 1.2 Installation on Windows +We suggest using conda to manage environment: +```bash +conda create -n llm python=3.11 libuv +conda activate llm + +# below command will install intel_extension_for_pytorch==2.1.10+xpu as default +pip install --pre --upgrade ipex-llm[xpu] --extra-index-url https://pytorch-extension.intel.com/release-whl/stable/xpu/us/ + +pip install transformers==4.45.0 +``` + +### 2. Configures OneAPI environment variables for Linux + +> [!NOTE] +> Skip this step if you are running on Windows. + +This is a required step on Linux for APT or offline installed oneAPI. Skip this step for PIP-installed oneAPI. + +```bash +source /opt/intel/oneapi/setvars.sh +``` + +### 3. Runtime Configurations +For optimal performance, it is recommended to set several environment variables. Please check out the suggestions based on your device. +#### 3.1 Configurations for Linux +
+ +For Intel Arc™ A-Series Graphics and Intel Data Center GPU Flex Series + +```bash +export USE_XETLA=OFF +export SYCL_PI_LEVEL_ZERO_USE_IMMEDIATE_COMMANDLISTS=1 +export SYCL_CACHE_PERSISTENT=1 +``` + +
+ +
+ +For Intel Data Center GPU Max Series + +```bash +export LD_PRELOAD=${LD_PRELOAD}:${CONDA_PREFIX}/lib/libtcmalloc.so +export SYCL_PI_LEVEL_ZERO_USE_IMMEDIATE_COMMANDLISTS=1 +export SYCL_CACHE_PERSISTENT=1 +export ENABLE_SDP_FUSION=1 +``` +> Note: Please note that `libtcmalloc.so` can be installed by `conda install -c conda-forge -y gperftools=2.10`. +
+ +
+ +For Intel iGPU + +```bash +export SYCL_CACHE_PERSISTENT=1 +export BIGDL_LLM_XMX_DISABLED=1 +``` + +
+ +#### 3.2 Configurations for Windows +
+ +For Intel iGPU + +```cmd +set SYCL_CACHE_PERSISTENT=1 +set BIGDL_LLM_XMX_DISABLED=1 +``` + +
+ +
+ +For Intel Arc™ A-Series Graphics + +```cmd +set SYCL_CACHE_PERSISTENT=1 +``` + +
+ +> [!NOTE] +> For the first time that each model runs on Intel iGPU/Intel Arc™ A300-Series or Pro A60, it may take several minutes to compile. +### 4. Running examples + +``` +python ./generate.py +``` + +Arguments info: +- `--repo-id-or-model-path REPO_ID_OR_MODEL_PATH`: argument defining the huggingface repo id for the Llama3.2-Vision model (e.g. `meta-llama/Llama-3.2-11B-Vision-Instruct`) to be downloaded, or the path to the huggingface checkpoint folder. It is default to be `'meta-llama/Llama-3.2-11B-Vision-Instruct'`. +- `--image-url-or-path IMAGE_URL_OR_PATH`: argument defining the image to be infered. It is default to be `'https://hf-mirror.com/datasets/huggingface/documentation-images/resolve/0052a70beed5bf71b92610a43a52df6d286cd5f3/diffusers/rabbit.jpg'`. +- `--prompt PROMPT`: argument defining the prompt to be infered (with integrated prompt format for chat). It is default to be `'Describe image in detail'`. +- `--n-predict N_PREDICT`: argument defining the max number of tokens to predict. It is default to be `32`. + +#### Sample Output +#### [meta-llama/Llama-3.2-11B-Vision-Instruct](https://huggingface.co/meta-llama/Llama-3.2-11B-Vision-Instruct) + +```log +Inference time: xxxx s +-------------------- Prompt -------------------- +Describe image in detail +-------------------- Output -------------------- +This image features a charming anthropomorphic rabbit standing on a dirt path, surrounded by a picturesque rural landscape. + +The rabbit, with its light brown fur and distinctive large +``` + +The sample input image is: + + diff --git a/python/llm/example/GPU/PyTorch-Models/Model/llama3.2-vision/generate.py b/python/llm/example/GPU/PyTorch-Models/Model/llama3.2-vision/generate.py new file mode 100644 index 00000000000..b424461f4ef --- /dev/null +++ b/python/llm/example/GPU/PyTorch-Models/Model/llama3.2-vision/generate.py @@ -0,0 +1,77 @@ +# +# Copyright 2016 The BigDL Authors. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +import argparse +import os + +import requests +import time +import torch +from PIL import Image +from transformers import MllamaForConditionalGeneration, AutoProcessor + +from ipex_llm import optimize_model + +if __name__ == '__main__': + parser = argparse.ArgumentParser(description='Predict Tokens using `generate()` API for Llama3.2-Vision model') + parser.add_argument('--repo-id-or-model-path', type=str, default="meta-llama/Llama-3.2-11B-Vision-Instruct", + help='The huggingface repo id for the Llama3.2-Vision model to be downloaded' + ', or the path to the huggingface checkpoint folder') + parser.add_argument('--image-url-or-path', type=str, + default='https://hf-mirror.com/datasets/huggingface/documentation-images/resolve/0052a70beed5bf71b92610a43a52df6d286cd5f3/diffusers/rabbit.jpg', + help='The URL or path to the image to infer') + parser.add_argument('--prompt', type=str, default="Describe image in detail", + help='Prompt to infer') + parser.add_argument('--n-predict', type=int, default=32, + help='Max tokens to predict') + + args = parser.parse_args() + model_path = args.repo_id_or_model_path + image_path = args.image_url_or_path + prompt = args.prompt + + model = MllamaForConditionalGeneration.from_pretrained(model_path) + model = optimize_model(model, modules_to_not_convert=["multi_modal_projector"]) + model = model.half().eval() + model = model.to('xpu') + + processor = AutoProcessor.from_pretrained(model_path) + + messages = [ + { + "role": "user", + "content": [ + {"type": "image"}, + {"type": "text", "text": prompt} + ] + } + ] + text = processor.apply_chat_template(messages, add_generation_prompt=True) + + if os.path.exists(image_path): + image = Image.open(image_path) + else: + image = Image.open(requests.get(image_path, stream=True).raw) + + inputs = processor(text=text, images=image, return_tensors="pt").to(model.device) + + with torch.inference_mode(): + for i in range(3): + st = time.time() + output = model.generate(**inputs, do_sample=False, max_new_tokens=args.n_predict) + et = time.time() + print(et - st) + print(processor.decode(output[0]))