add llama3.2-vision Pytorch example (#12165)
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Zijie Li
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# Llama3.2-Vision
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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.
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## 0. Requirements
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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.
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## Example: Predict Tokens using `generate()` API
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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.
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### 1. Install
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#### 1.1 Installation on Linux
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We suggest using conda to manage environment:
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```bash
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conda create -n llm python=3.11
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conda activate llm
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# below command will install intel_extension_for_pytorch==2.1.10+xpu as default
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pip install --pre --upgrade ipex-llm[xpu] --extra-index-url https://pytorch-extension.intel.com/release-whl/stable/xpu/us/
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pip install transformers==4.45.0
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```
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#### 1.2 Installation on Windows
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We suggest using conda to manage environment:
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```bash
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conda create -n llm python=3.11 libuv
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conda activate llm
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# below command will install intel_extension_for_pytorch==2.1.10+xpu as default
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pip install --pre --upgrade ipex-llm[xpu] --extra-index-url https://pytorch-extension.intel.com/release-whl/stable/xpu/us/
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pip install transformers==4.45.0
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```
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### 2. Configures OneAPI environment variables for Linux
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> [!NOTE]
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> Skip this step if you are running on Windows.
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This is a required step on Linux for APT or offline installed oneAPI. Skip this step for PIP-installed oneAPI.
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```bash
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source /opt/intel/oneapi/setvars.sh
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```
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### 3. Runtime Configurations
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For optimal performance, it is recommended to set several environment variables. Please check out the suggestions based on your device.
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#### 3.1 Configurations for Linux
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<details>
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<summary>For Intel Arc™ A-Series Graphics and Intel Data Center GPU Flex Series</summary>
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```bash
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export USE_XETLA=OFF
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export SYCL_PI_LEVEL_ZERO_USE_IMMEDIATE_COMMANDLISTS=1
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export SYCL_CACHE_PERSISTENT=1
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```
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</details>
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<details>
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<summary>For Intel Data Center GPU Max Series</summary>
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```bash
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export LD_PRELOAD=${LD_PRELOAD}:${CONDA_PREFIX}/lib/libtcmalloc.so
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export SYCL_PI_LEVEL_ZERO_USE_IMMEDIATE_COMMANDLISTS=1
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export SYCL_CACHE_PERSISTENT=1
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export ENABLE_SDP_FUSION=1
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```
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> Note: Please note that `libtcmalloc.so` can be installed by `conda install -c conda-forge -y gperftools=2.10`.
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</details>
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<details>
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<summary>For Intel iGPU</summary>
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```bash
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export SYCL_CACHE_PERSISTENT=1
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export BIGDL_LLM_XMX_DISABLED=1
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```
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</details>
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#### 3.2 Configurations for Windows
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<details>
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<summary>For Intel iGPU</summary>
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```cmd
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set SYCL_CACHE_PERSISTENT=1
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set BIGDL_LLM_XMX_DISABLED=1
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```
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</details>
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<details>
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<summary>For Intel Arc™ A-Series Graphics</summary>
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```cmd
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set SYCL_CACHE_PERSISTENT=1
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```
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</details>
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> [!NOTE]
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> 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.
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### 4. Running examples
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```
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python ./generate.py
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```
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Arguments info:
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- `--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'`.
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- `--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'`.
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- `--prompt PROMPT`: argument defining the prompt to be infered (with integrated prompt format for chat). It is default to be `'Describe image in detail'`.
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- `--n-predict N_PREDICT`: argument defining the max number of tokens to predict. It is default to be `32`.
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#### Sample Output
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#### [meta-llama/Llama-3.2-11B-Vision-Instruct](https://huggingface.co/meta-llama/Llama-3.2-11B-Vision-Instruct)
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```log
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Inference time: xxxx s
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-------------------- Prompt --------------------
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Describe image in detail
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-------------------- Output --------------------
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This image features a charming anthropomorphic rabbit standing on a dirt path, surrounded by a picturesque rural landscape.
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The rabbit, with its light brown fur and distinctive large
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```
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The sample input image is:
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<a href="https://hf-mirror.com/datasets/huggingface/documentation-images/resolve/0052a70beed5bf71b92610a43a52df6d286cd5f3/diffusers/rabbit.jpg"><img width=400px src="https://hf-mirror.com/datasets/huggingface/documentation-images/resolve/0052a70beed5bf71b92610a43a52df6d286cd5f3/diffusers/rabbit.jpg" ></a>
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#
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# Copyright 2016 The BigDL Authors.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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#
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import argparse
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import os
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import requests
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import time
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import torch
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from PIL import Image
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from transformers import MllamaForConditionalGeneration, AutoProcessor
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from ipex_llm import optimize_model
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if __name__ == '__main__':
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parser = argparse.ArgumentParser(description='Predict Tokens using `generate()` API for Llama3.2-Vision model')
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parser.add_argument('--repo-id-or-model-path', type=str, default="meta-llama/Llama-3.2-11B-Vision-Instruct",
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help='The huggingface repo id for the Llama3.2-Vision model to be downloaded'
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', or the path to the huggingface checkpoint folder')
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parser.add_argument('--image-url-or-path', type=str,
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default='https://hf-mirror.com/datasets/huggingface/documentation-images/resolve/0052a70beed5bf71b92610a43a52df6d286cd5f3/diffusers/rabbit.jpg',
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help='The URL or path to the image to infer')
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parser.add_argument('--prompt', type=str, default="Describe image in detail",
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help='Prompt to infer')
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parser.add_argument('--n-predict', type=int, default=32,
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help='Max tokens to predict')
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args = parser.parse_args()
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model_path = args.repo_id_or_model_path
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image_path = args.image_url_or_path
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prompt = args.prompt
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model = MllamaForConditionalGeneration.from_pretrained(model_path)
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model = optimize_model(model, modules_to_not_convert=["multi_modal_projector"])
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model = model.half().eval()
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model = model.to('xpu')
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processor = AutoProcessor.from_pretrained(model_path)
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messages = [
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{
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"role": "user",
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"content": [
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{"type": "image"},
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{"type": "text", "text": prompt}
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]
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}
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]
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text = processor.apply_chat_template(messages, add_generation_prompt=True)
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if os.path.exists(image_path):
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image = Image.open(image_path)
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else:
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image = Image.open(requests.get(image_path, stream=True).raw)
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inputs = processor(text=text, images=image, return_tensors="pt").to(model.device)
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with torch.inference_mode():
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for i in range(3):
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st = time.time()
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output = model.generate(**inputs, do_sample=False, max_new_tokens=args.n_predict)
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et = time.time()
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print(et - st)
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print(processor.decode(output[0]))
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