add internvl2 example

python/llm/example/GPU/HuggingFace/Multimodal/internvl2/chat.py

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+#
+# 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 os
+import time
+import argparse
+import requests
+import torch
+from PIL import Image
+from ipex_llm.transformers import AutoModelForCausalLM
+from transformers import AutoTokenizer, CLIPImageProcessor
+
+
+if __name__ == '__main__':
+ parser = argparse.ArgumentParser(description='Predict Tokens using `chat()` API for OpenGVLab/InternVL2-4B model')
+ parser.add_argument('--repo-id-or-model-path', type=str, default="OpenGVLab/InternVL2-4B",
+ help='The huggingface repo id for the OpenGVLab/InternVL2-4B model to be downloaded'
+ ', or the path to the huggingface checkpoint folder')
+ parser.add_argument('--image-url-or-path', type=str,
+ default='https://raw.githubusercontent.com/open-mmlab/mmdeploy/main/tests/data/tiger.jpeg',
+ help='The URL or path to the image to infer')
+ parser.add_argument('--prompt', type=str, default="What is in the image?",
+ help='Prompt to infer')
+
+ args = parser.parse_args()
+ model_path = args.repo_id_or_model_path
+ image_path = args.image_url_or_path
+
+ # Load model in 4 bit,
+ # which convert the relevant layers in the model into INT4 format
+ # When running LLMs on Intel iGPUs for Windows users, we recommend setting `cpu_embedding=True` in the from_pretrained function.
+ # This will allow the memory-intensive embedding layer to utilize the CPU instead of iGPU.
+ model = AutoModelForCausalLM.from_pretrained(model_path, trust_remote_code=True,
+ load_in_low_bit="sym_int4",
+ modules_to_not_convert=["vision_model"])
+ model = model.half().to('xpu')
+ tokenizer = AutoTokenizer.from_pretrained(model_path,
+ trust_remote_code=True)
+ model.eval()
+
+ query = args.prompt
+ image_processor = CLIPImageProcessor.from_pretrained(model_path)
+
+ if os.path.exists(image_path):
+ image = Image.open(image_path).convert('RGB')
+ else:
+ image = Image.open(requests.get(image_path, stream=True).raw).convert('RGB')
+
+ pixel_values = image_processor(images=[image], return_tensors='pt').pixel_values
+ pixel_values = pixel_values.to('xpu')
+
+ question = "<image>" + query
+
+ generation_config = {
+ "max_new_tokens": 64,
+ "do_sample": False,
+ }
+
+ # ipex_llm model needs a warmup, then inference time can be accurate
+ model.chat(
+ pixel_values=None,
+ question=question,
+ generation_config=generation_config,
+ tokenizer=tokenizer,
+ )
+
+
+ st = time.time()
+ res = model.chat(
+ tokenizer=tokenizer,
+ pixel_values=pixel_values,
+ question=question,
+ generation_config=generation_config,
+ history=[]
+ )
+ torch.xpu.synchronize()
+ end = time.time()
+
+ print(f'Inference time: {end-st} s')
+ print('-'*20, 'Input Image', '-'*20)
+ print(image_path)
+ print('-'*20, 'Input Prompt', '-'*20)
+ print(args.prompt)
+ print('-'*20, 'Chat Output', '-'*20)
+ print(res)

python/llm/example/GPU/HuggingFace/Multimodal/internvl2/readme.md

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+# InternVL2
+In this directory, you will find examples on how you could apply IPEX-LLM INT4 optimizations on InternVL2 model on [Intel GPUs](../../../README.md). For illustration purposes, we utilize the [OpenGVLab/InternVL2-4B](https://huggingface.co/OpenGVLab/InternVL2-4B) as a reference InternVL2 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 `chat()` API
+In the example [chat.py](./chat.py), we show a basic use case for an InternVL2-4B model to predict the next N tokens using `chat()` API, with IPEX-LLM INT4 optimizations 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 einops timm
+
+```
+
+#### 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 einops timm
+
+```
+
+### 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
+<details>
+
+<summary>For Intel Arc™ A-Series Graphics and Intel Data Center GPU Flex Series</summary>
+
+```bash
+export USE_XETLA=OFF
+export SYCL_PI_LEVEL_ZERO_USE_IMMEDIATE_COMMANDLISTS=1
+export SYCL_CACHE_PERSISTENT=1
+```
+
+</details>
+
+<details>
+
+<summary>For Intel Data Center GPU Max Series</summary>
+
+```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`.
+</details>
+
+<details>
+
+<summary>For Intel iGPU</summary>
+
+```bash
+export SYCL_CACHE_PERSISTENT=1
+export BIGDL_LLM_XMX_DISABLED=1
+```
+
+</details>
+
+#### 3.2 Configurations for Windows
+<details>
+
+<summary>For Intel iGPU</summary>
+
+```cmd
+set SYCL_CACHE_PERSISTENT=1
+set BIGDL_LLM_XMX_DISABLED=1
+```
+
+</details>
+
+<details>
+
+<summary>For Intel Arc™ A-Series Graphics</summary>
+
+```cmd
+set SYCL_CACHE_PERSISTENT=1
+```
+
+</details>
+
+> [!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
+
+- chat with specified prompt:
+ ```
+ python ./chat.py --prompt 'What is in the image?'
+ ```
+
+Arguments info:
+- `--repo-id-or-model-path REPO_ID_OR_MODEL_PATH`: argument defining the huggingface repo id for the InternVL2 (e.g. `OpenGVLab/InternVL2-4B`) to be downloaded, or the path to the huggingface checkpoint folder. It is default to be `'OpenGVLab/InternVL2-4B'`.
+- `--image-url-or-path IMAGE_URL_OR_PATH`: argument defining the image to be infered. It is default to be `'https://raw.githubusercontent.com/open-mmlab/mmdeploy/main/tests/data/tiger.jpeg'`.
+- `--prompt PROMPT`: argument defining the prompt to be infered (with integrated prompt format for chat). It is default to be `'What is in the image?'`.
+
+#### Sample Output
+
+#### [OpenGVLab/InternVL2-4B](https://huggingface.co/OpenGVLab/InternVL2-4B)
+
+```log
+-------------------- Input Image --------------------
+https://raw.githubusercontent.com/open-mmlab/mmdeploy/main/tests/data/tiger.jpeg
+-------------------- Input Prompt --------------------
+What is in the image?
+-------------------- Chat Output --------------------
+The image shows a tiger lying on the grass.
+```
+
+The sample input image is:
+
+<a href="https://raw.githubusercontent.com/open-mmlab/mmdeploy/main/tests/data/tiger.jpeg"><img width=400px src="https://raw.githubusercontent.com/open-mmlab/mmdeploy/main/tests/data/tiger.jpeg" ></a>