Add vllm 0.6.2 vision offline example (#12721)

* add vision offline example

* add to docker

docker/llm/serving/xpu/docker/Dockerfile

@@ -101,6 +101,7 @@ RUN wget -O- https://apt.repos.intel.com/intel-gpg-keys/GPG-PUB-KEY-INTEL-SW-PRO
 
 COPY ./vllm_online_benchmark.py        /llm/
 COPY ./vllm_offline_inference.py       /llm/
+COPY ./vllm_offline_inference_vision_language.py  /llm/
 COPY ./payload-1024.lua                /llm/
 COPY ./start-vllm-service.sh           /llm/
 COPY ./benchmark_vllm_throughput.py   /llm/

docker/llm/serving/xpu/docker/README.md

@@ -148,6 +148,7 @@ We have included multiple example files in `/llm/`:
 2. `benchmark_vllm_throughput.py`: Used for benchmarking throughput
 3. `payload-1024.lua`: Used for testing request per second using 1k-128 request
 4. `start-vllm-service.sh`: Used for template for starting vLLM service
+5. `vllm_offline_inference_vision_language.py`: Used for vLLM offline inference vision example
 
 ##### Online benchmark throurgh api_server
 

docker/llm/serving/xpu/docker/vllm_offline_inference_vision_language.py

@@ -0,0 +1,85 @@
+from ipex_llm.vllm.xpu.engine import IPEXLLMClass as LLM
+from vllm import SamplingParams
+from transformers import AutoTokenizer
+import requests
+
+
+model_path = "/llm/models/MiniCPM-V-2_6"
+model_path = "/llm/models/Qwen2-VL-7B-Instruct"
+prompt = "What is in the image?"
+
+def run_minicpmv(question, modality):
+    assert modality == "image"
+    tokenizer = AutoTokenizer.from_pretrained(model_path,
+                                              trust_remote_code=True)
+    # 2.6
+    stop_tokens = ['<|im_end|>', '<|endoftext|>']
+    stop_token_ids = [tokenizer.convert_tokens_to_ids(i) for i in stop_tokens]
+
+    messages = [{
+        'role': 'user',
+        'content': f'(<image>./</image>)\n{question}'
+    }]
+    prompt = tokenizer.apply_chat_template(messages,
+                                           tokenize=False,
+                                           add_generation_prompt=True)
+    return prompt, stop_token_ids
+
+def run_qwen2_vl(question, modality):
+    assert modality == "image"
+
+    prompt = ("<|im_start|>system\nYou are a helpful assistant.<|im_end|>\n"
+              "<|im_start|>user\n<|vision_start|><|image_pad|><|vision_end|>"
+              f"{question}<|im_end|>\n"
+              "<|im_start|>assistant\n")
+    stop_token_ids = None
+    return prompt, stop_token_ids
+
+model_example_map = {
+    "minicpmv": run_minicpmv,
+    "qwen2_vl": run_qwen2_vl,
+}
+
+llm = LLM(
+          model=model_path,
+          device="xpu",
+          dtype="float16",
+          enforce_eager=True,
+          load_in_low_bit="fp8",
+          tensor_parallel_size=1,
+          disable_async_output_proc=True,
+          distributed_executor_backend="ray",
+          max_model_len=4000,
+          trust_remote_code=True,
+          block_size=8,
+          max_num_batched_tokens=4000)
+
+
+model_type = llm.llm_engine.model_config.hf_config.model_type
+prompt, stop_token_ids = model_example_map[model_type](prompt, "image")
+
+
+# Load the image using PIL.Image
+from PIL import Image
+image_url="http://farm6.staticflickr.com/5268/5602445367_3504763978_z.jpg"
+image = Image.open(requests.get(image_url, stream=True).raw).convert('RGB')
+
+
+sampling_params = SamplingParams(temperature=0.1,
+                                 top_p=0.001,
+                                 repetition_penalty=1.05,
+                                 max_tokens=64,
+                                 stop_token_ids=stop_token_ids)
+
+
+# Single prompt inference
+outputs = llm.generate({
+    "prompt": prompt,
+    "multi_modal_data": {"image": image},
+}, sampling_params=sampling_params)
+
+for o in outputs:
+    generated_text = o.outputs[0].text
+    print(generated_text)
+
+