[Doc] Indicate more information about supported modalities (#8181)

examples/offline_inference_vision_language_multi_image.py

@@ -0,0 +1,95 @@
+"""
+This example shows how to use vLLM for running offline inference with
+multi-image input on vision language models, using the chat template defined
+by the model.
+"""
+from argparse import Namespace
+from typing import List
+
+from vllm import LLM
+from vllm.multimodal.utils import fetch_image
+from vllm.utils import FlexibleArgumentParser
+
+QUESTION = "What is the content of each image?"
+IMAGE_URLS = [
+    "https://upload.wikimedia.org/wikipedia/commons/d/da/2015_Kaczka_krzy%C5%BCowka_w_wodzie_%28samiec%29.jpg",
+    "https://upload.wikimedia.org/wikipedia/commons/7/77/002_The_lion_king_Snyggve_in_the_Serengeti_National_Park_Photo_by_Giles_Laurent.jpg",
+]
+
+
+def _load_phi3v(image_urls: List[str]):
+    return LLM(
+        model="microsoft/Phi-3.5-vision-instruct",
+        trust_remote_code=True,
+        max_model_len=4096,
+        limit_mm_per_prompt={"image": len(image_urls)},
+    )
+
+
+def run_phi3v_generate(question: str, image_urls: List[str]):
+    llm = _load_phi3v(image_urls)
+
+    placeholders = "\n".join(f"<|image_{i}|>"
+                             for i, _ in enumerate(image_urls, start=1))
+    prompt = f"<|user|>\n{placeholders}\n{question}<|end|>\n<|assistant|>\n"
+
+    outputs = llm.generate({
+        "prompt": prompt,
+        "multi_modal_data": {
+            "image": [fetch_image(url) for url in image_urls]
+        },
+    })
+
+    for o in outputs:
+        generated_text = o.outputs[0].text
+        print(generated_text)
+
+
+def run_phi3v_chat(question: str, image_urls: List[str]):
+    llm = _load_phi3v(image_urls)
+
+    outputs = llm.chat([{
+        "role":
+        "user",
+        "content": [
+            {
+                "type": "text",
+                "text": question,
+            },
+            *({
+                "type": "image_url",
+                "image_url": {
+                    "url": image_url
+                },
+            } for image_url in image_urls),
+        ],
+    }])
+
+    for o in outputs:
+        generated_text = o.outputs[0].text
+        print(generated_text)
+
+
+def main(args: Namespace):
+    method = args.method
+
+    if method == "generate":
+        run_phi3v_generate(QUESTION, IMAGE_URLS)
+    elif method == "chat":
+        run_phi3v_chat(QUESTION, IMAGE_URLS)
+    else:
+        raise ValueError(f"Invalid method: {method}")
+
+
+if __name__ == "__main__":
+    parser = FlexibleArgumentParser(
+        description='Demo on using vLLM for offline inference with '
+        'vision language models that support multi-image input')
+    parser.add_argument("--method",
+                        type=str,
+                        default="generate",
+                        choices=["generate", "chat"],
+                        help="The method to run in `vllm.LLM`.")
+
+    args = parser.parse_args()
+    main(args)

examples/openai_vision_api_client.py

@@ -27,9 +27,10 @@ client = OpenAI(
 models = client.models.list()
 model = models.data[0].id
 
+# Single-image input inference
 image_url = "https://upload.wikimedia.org/wikipedia/commons/thumb/d/dd/Gfp-wisconsin-madison-the-nature-boardwalk.jpg/2560px-Gfp-wisconsin-madison-the-nature-boardwalk.jpg"
 
-# Use image url in the payload
+## Use image url in the payload
 chat_completion_from_url = client.chat.completions.create(
     messages=[{
         "role":
@@ -52,10 +53,10 @@ chat_completion_from_url = client.chat.completions.create(
 )
 
 result = chat_completion_from_url.choices[0].message.content
-print(f"Chat completion output:{result}")
+print("Chat completion output:", result)
 
 
-# Use base64 encoded image in the payload
+## Use base64 encoded image in the payload
 def encode_image_base64_from_url(image_url: str) -> str:
     """Encode an image retrieved from a remote url to base64 format."""
 
@@ -122,4 +123,4 @@ chat_completion_from_url = client.chat.completions.create(
 )
 
 result = chat_completion_from_url.choices[0].message.content
-print(f"Chat completion output:{result}")
+print("Chat completion output:", result)