[Frontend][VLM] Add support for multiple multi-modal items in the OpenAI frontend

.buildkite/test-pipeline.yaml

@@ -90,6 +90,7 @@ steps:
   - pytest -v -s entrypoints/llm --ignore=entrypoints/llm/test_lazy_outlines.py
   - pytest -v -s entrypoints/llm/test_lazy_outlines.py # it needs a clean process
   - pytest -v -s entrypoints/openai
+  - pytest -v -s entrypoints/test_chat_utils.py
 
 - label: Distributed Tests (4 GPUs) # 10min
   working_dir: "/vllm-workspace/tests"

examples/openai_vision_api_client.py

@@ -1,7 +1,13 @@
 """An example showing how to use vLLM to serve VLMs.
 
 Launch the vLLM server with the following command:
+
+(single image inference with Llava)
 vllm serve llava-hf/llava-1.5-7b-hf --chat-template template_llava.jinja
+
+(multi-image inference with Phi-3.5-vision-instruct)
+vllm serve microsoft/Phi-3.5-vision-instruct --max-model-len 4096 \
+    --trust-remote-code --limit_mm_per_prompt image=2
 """
 import base64
 
@@ -84,3 +90,36 @@ def encode_image_base64_from_url(image_url: str) -> str:
 
 result = chat_completion_from_base64.choices[0].message.content
 print(f"Chat completion output:{result}")
+
+# Multi-image input inference
+image_url_duck = "https://upload.wikimedia.org/wikipedia/commons/d/da/2015_Kaczka_krzy%C5%BCowka_w_wodzie_%28samiec%29.jpg"
+image_url_lion = "https://upload.wikimedia.org/wikipedia/commons/7/77/002_The_lion_king_Snyggve_in_the_Serengeti_National_Park_Photo_by_Giles_Laurent.jpg"
+chat_completion_from_url = client.chat.completions.create(
+    messages=[{
+        "role":
+        "user",
+        "content": [
+            {
+                "type": "text",
+                "text": "What are the animals in this image?"
+            },
+            {
+                "type": "image_url",
+                "image_url": {
+                    "url": image_url_duck
+                },
+            },
+            {
+                "type": "image_url",
+                "image_url": {
+                    "url": image_url_lion
+                },
+            },
+        ],
+    }],
+    model=model,
+    max_tokens=64,
+)
+
+result = chat_completion_from_url.choices[0].message.content
+print(f"Chat completion output:{result}")

tests/entrypoints/openai/test_serving_chat.py

@@ -3,6 +3,7 @@
 from dataclasses import dataclass
 from unittest.mock import MagicMock
 
+from vllm.config import MultiModalConfig
 from vllm.engine.async_llm_engine import AsyncLLMEngine
 from vllm.entrypoints.openai.protocol import ChatCompletionRequest
 from vllm.entrypoints.openai.serving_chat import OpenAIServingChat
@@ -20,6 +21,7 @@ class MockModelConfig:
     max_model_len = 100
     tokenizer_revision = None
     embedding_mode = False
+    multimodal_config = MultiModalConfig()
 
 
 @dataclass

tests/entrypoints/openai/test_vision.py

@@ -6,11 +6,10 @@
 
 from vllm.multimodal.utils import encode_image_base64, fetch_image
 
-from ...utils import VLLM_PATH, RemoteOpenAIServer
+from ...utils import RemoteOpenAIServer
 
-MODEL_NAME = "llava-hf/llava-1.5-7b-hf"
-LLAVA_CHAT_TEMPLATE = VLLM_PATH / "examples/template_llava.jinja"
-assert LLAVA_CHAT_TEMPLATE.exists()
+MODEL_NAME = "microsoft/Phi-3.5-vision-instruct"
+MAXIMUM_IMAGES = 2
 
 # Test different image extensions (JPG/PNG) and formats (gray/RGB/RGBA)
 TEST_IMAGE_URLS = [
@@ -24,13 +23,9 @@
 @pytest.fixture(scope="module")
 def server():
     args = [
-        "--dtype",
-        "bfloat16",
-        "--max-model-len",
-        "4096",
-        "--enforce-eager",
-        "--chat-template",
-        str(LLAVA_CHAT_TEMPLATE),
+        "--dtype", "bfloat16", "--max-model-len", "4096", "--max-num-seqs",
+        "5", "--enforce-eager", "--trust-remote-code", "--limit-mm-per-prompt",
+        f"image={MAXIMUM_IMAGES}"
     ]
 
     with RemoteOpenAIServer(MODEL_NAME, args) as remote_server:
@@ -84,7 +79,7 @@ async def test_single_chat_session_image(client: openai.AsyncOpenAI,
     choice = chat_completion.choices[0]
     assert choice.finish_reason == "length"
     assert chat_completion.usage == openai.types.CompletionUsage(
-        completion_tokens=10, prompt_tokens=596, total_tokens=606)
+        completion_tokens=10, prompt_tokens=772, total_tokens=782)
 
     message = choice.message
     message = chat_completion.choices[0].message
@@ -218,46 +213,53 @@ async def test_chat_streaming_image(client: openai.AsyncOpenAI,
 @pytest.mark.asyncio
 @pytest.mark.parametrize("model_name", [MODEL_NAME])
 @pytest.mark.parametrize("image_url", TEST_IMAGE_URLS)
+@pytest.mark.parametrize(
+    "image_urls",
+    [TEST_IMAGE_URLS[:i] for i in range(2, len(TEST_IMAGE_URLS))])
 async def test_multi_image_input(client: openai.AsyncOpenAI, model_name: str,
-                                 image_url: str):
+                                 image_urls: List[str]):
 
     messages = [{
         "role":
         "user",
         "content": [
-            {
-                "type": "image_url",
-                "image_url": {
-                    "url": image_url
-                }
-            },
-            {
+            *({
                 "type": "image_url",
                 "image_url": {
                     "url": image_url
                 }
-            },
+            } for image_url in image_urls),
             {
                 "type": "text",
                 "text": "What's in this image?"
             },
         ],
     }]
 
-    with pytest.raises(openai.BadRequestError):  # test multi-image input
-        await client.chat.completions.create(
+    if len(image_urls) > MAXIMUM_IMAGES:
+        with pytest.raises(openai.BadRequestError):  # test multi-image input
+            await client.chat.completions.create(
+                model=model_name,
+                messages=messages,
+                max_tokens=10,
+                temperature=0.0,
+            )
+
+        # the server should still work afterwards
+        completion = await client.completions.create(
+            model=model_name,
+            prompt=[0, 0, 0, 0, 0],
+            max_tokens=5,
+            temperature=0.0,
+        )
+        completion = completion.choices[0].text
+        assert completion is not None and len(completion) >= 0
+    else:
+        chat_completion = await client.chat.completions.create(
             model=model_name,
             messages=messages,
             max_tokens=10,
             temperature=0.0,
         )
-
-    # the server should still work afterwards
-    completion = await client.completions.create(
-        model=model_name,
-        prompt=[0, 0, 0, 0, 0],
-        max_tokens=5,
-        temperature=0.0,
-    )
-    completion = completion.choices[0].text
-    assert completion is not None and len(completion) >= 0
+        message = chat_completion.choices[0].message
+        assert message.content is not None and len(message.content) >= 0