Add qwen2-vl HF GPU example

python/llm/example/GPU/HuggingFace/Multimodal/qwen2-vl/README.md

@@ -0,0 +1,151 @@
+# Qwen2-VL
+In this directory, you will find examples on how you could apply IPEX-LLM INT4 optimizations on Qwen-VL models on [Intel GPUs](../../../README.md).  For illustration purposes, we utilize the [Qwen/Qwen2-VL-7B-Instruct](https://huggingface.co/Qwen/Qwen2-VL-7B-Instruct) (or [Qwen/Qwen2-VL-7B-Instruct](https://www.modelscope.cn/models/Qwen/Qwen2-VL-7B-Instruct) for ModelScope) as a reference Qwen2-VL 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 Qwen2-VL model to predict the next N tokens using `generate()` 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 transformers==4.45.0 # install transformers which supports Qwen2-VL
+pip install accelerate==0.33.0
+pip install qwen_vl_utils
+pip install "trl<0.12.0"
+
+# [optional] only needed if you would like to use ModelScope as model hub
+pip install modelscope==1.21.0
+pip install addict simplejson python-dateutil sortedcontainers
+```
+
+#### 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 # install transformers which supports Qwen2-VL
+pip install accelerate==0.33.0
+pip install qwen_vl_utils
+pip install "trl<0.12.0"
+
+# [optional] only needed if you would like to use ModelScope as model hub
+pip install modelscope==1.21.0
+pip install addict simplejson python-dateutil sortedcontainers
+```
+
+### 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
+```
+
+</details>
+
+#### 3.2 Configurations for Windows
+<details>
+
+<summary>For Intel iGPU and 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
+
+```bash
+# for Hugging Face model hub
+python ./generate.py --repo-id-or-model-path REPO_ID_OR_MODEL_PATH --prompt PROMPT --n-predict N_PREDICT --image-url-or-path IMAGE_URL_OR_PATH
+
+# for ModelScope model hub
+python ./generate.py --repo-id-or-model-path REPO_ID_OR_MODEL_PATH --prompt PROMPT --n-predict N_PREDICT --image-url-or-path IMAGE_URL_OR_PATH --modelscope
+```
+
+Arguments info:
+- `--repo-id-or-model-path REPO_ID_OR_MODEL_PATH`: argument defining the **Hugging Face** or **ModelScope** repo id for the Qwen2-VL model (e.g. `Qwen/Qwen2-VL-7B-Instruct`) to be downloaded, or the path to the checkpoint folder. It is default to be `'Qwen/Qwen2-VL-7B-Instruct'`.
+- `--image-url-or-path IMAGE_URL_OR_PATH`: argument defining the image to be infered. It is default to be `'http://farm6.staticflickr.com/5268/5602445367_3504763978_z.jpg'`.
+- `--prompt PROMPT`: argument defining the prompt to be infered (with integrated prompt format for chat). It is default to be `'Describe this image.'`.
+- `--n-predict N_PREDICT`: argument defining the max number of tokens to predict. It is default to be `32`.
+- `--modelscope`: using **ModelScope** as model hub instead of **Hugging Face**.
+
+#### Sample Output
+##### [Qwen/Qwen2-VL-7B-Instruct](https://huggingface.co/Qwen/Qwen2-VL-7B-Instruct)
+```log
+Inference time: xxxx s
+-------------------- Input Image --------------------
+http://farm6.staticflickr.com/5268/5602445367_3504763978_z.jpg
+-------------------- Prompt --------------------
+Describe this image.
+-------------------- Output --------------------
+The image depicts a young child holding a small white teddy bear. The teddy bear is dressed in a pink outfit, which includes a pink skirt and a
+```
+
+```log
+Inference time: xxxx s
+-------------------- Input Image --------------------
+http://farm6.staticflickr.com/5268/5602445367_3504763978_z.jpg
+-------------------- Prompt --------------------
+请描述这幅图片
+-------------------- Output --------------------
+这是一张小女孩抱着一个白色的小熊玩偶的图片。小女孩穿着一件粉红色的条纹连衣裙，手里抱着的小熊玩偶
+```
+
+The sample input image is (which is fetched from [COCO dataset](https://cocodataset.org/#explore?id=264959)):
+
+<a href="http://farm6.staticflickr.com/5268/5602445367_3504763978_z.jpg"><img width=400px src="http://farm6.staticflickr.com/5268/5602445367_3504763978_z.jpg" ></a>

python/llm/example/GPU/HuggingFace/Multimodal/qwen2-vl/generate.py

@@ -0,0 +1,125 @@
+#
+# 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 torch
+import time
+import argparse
+import numpy as np
+
+from ipex_llm.transformers import Qwen2VLForConditionalGeneration
+from qwen_vl_utils import process_vision_info
+
+
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser(description='Predict Tokens using generate() API for Qwen2-VL-7B-Instruct model')
+    parser.add_argument('--repo-id-or-model-path', type=str, default="Qwen/Qwen2-VL-7B-Instruct",
+                        help='The huggingface repo id for the Qwen2-VL model to be downloaded'
+                             ', or the path to the huggingface checkpoint folder')
+    parser.add_argument('--prompt', type=str, default="Describe this image.",
+                        help='Prompt to infer') 
+    parser.add_argument('--image-url-or-path', type=str,
+                        default='http://farm6.staticflickr.com/5268/5602445367_3504763978_z.jpg' ,
+                        help='The URL or path to the image to infer')
+
+    parser.add_argument('--n-predict', type=int, default=32,
+                        help='Max tokens to predict')
+    parser.add_argument('--modelscope', action="store_true", default=False, 
+                        help="Use models from modelscope")
+
+    args = parser.parse_args()
+    if args.modelscope:
+        from modelscope import AutoProcessor
+        model_hub = 'modelscope'
+    else:
+        from transformers import AutoProcessor
+        model_hub = 'huggingface'
+
+    model_path = args.repo_id_or_model_path
+
+    model = Qwen2VLForConditionalGeneration.from_pretrained(model_path,
+                                                 trust_remote_code=True,
+                                                 torch_dtype='auto',
+                                                 modules_to_not_convert=["visual"],
+                                                 load_in_4bit=True,
+                                                 low_cpu_mem_usage=True,
+                                                 use_cache=True,
+                                                 model_hub=model_hub)
+
+    # Use .float() for better output, and use .half() for better speed
+    model = model.half().to("xpu")
+
+    # The following code for generation is adapted from https://huggingface.co/Qwen/Qwen2-VL-7B-Instruct#quickstart
+
+    # The default range for the number of visual tokens per image in the model is 4-16384. You can set min_pixels and max_pixels according to your needs, such as a token count range of 256-1280, to balance speed and memory usage.
+    min_pixels = 256*28*28
+    max_pixels = 1280*28*28
+    processor = AutoProcessor.from_pretrained(model_path, min_pixels=min_pixels, max_pixels=max_pixels)
+
+    prompt = args.prompt
+    image_path = args.image_url_or_path
+
+    messages = [
+        {
+            "role": "user",
+            "content": [
+                {
+                    "type": "image",
+                    "image": image_path,
+                },
+                {"type": "text", "text": prompt},
+            ],
+        }
+    ]
+    text = processor.apply_chat_template(
+        messages, tokenize=False, add_generation_prompt=True
+    )
+    image_inputs, video_inputs = process_vision_info(messages)
+    inputs = processor(
+        text=[text],
+        images=image_inputs,
+        videos=video_inputs,
+        padding=True,
+        return_tensors="pt",
+    )
+    inputs = inputs.to('xpu')
+
+    with torch.inference_mode():
+        # warmup
+        generated_ids = model.generate(
+            **inputs,
+            max_new_tokens=args.n_predict
+            )
+
+        st = time.time()
+        generated_ids = model.generate(
+            **inputs,
+            max_new_tokens=args.n_predict
+            )
+        torch.xpu.synchronize()
+        end = time.time()
+        generated_ids = generated_ids.cpu()
+        generated_ids = [
+            output_ids[len(input_ids):] for input_ids, output_ids in zip(inputs.input_ids, generated_ids)
+            ]
+
+        response = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]
+        print(f'Inference time: {end-st} s')
+        print('-'*20, 'Input Image', '-'*20)
+        print(image_path)
+        print('-'*20, 'Prompt', '-'*20)
+        print(prompt)
+        print('-'*20, 'Output', '-'*20)
+        print(response)

python/llm/src/ipex_llm/transformers/__init__.py

@@ -21,5 +21,10 @@
         AutoModelForSequenceClassification, AutoModelForMaskedLM, \
         AutoModelForNextSentencePrediction, AutoModelForMultipleChoice, \
         AutoModelForTokenClassification
+
+import transformers
+if transformers.__version__ >= '4.45.0':
+    from .model import Qwen2VLForConditionalGeneration
+
 from .modelling_bigdl import *
 from .pipeline_parallel import init_pipeline_parallel, PPModelWorker

python/llm/src/ipex_llm/transformers/model.py

@@ -839,3 +839,8 @@ class AutoModelForMultipleChoice(_BaseAutoModelClass):
 
 class AutoModelForTokenClassification(_BaseAutoModelClass):
     HF_Model = transformers.AutoModelForTokenClassification
+
+
+if transformers.__version__ >= '4.45.0':
+    class Qwen2VLForConditionalGeneration(_BaseAutoModelClass):
+        HF_Model = transformers.Qwen2VLForConditionalGeneration