[Frontend] Multimodal support in offline chat (#8098)

tests/entrypoints/llm/test_generate.py

@@ -6,6 +6,7 @@ import pytest
 from vllm import LLM, RequestOutput, SamplingParams
 
 from ...conftest import cleanup
+from ..openai.test_vision import TEST_IMAGE_URLS
 
 MODEL_NAME = "facebook/opt-125m"
 
@@ -159,3 +160,36 @@ def test_chat():
     ]
     outputs = llm.chat(messages)
     assert len(outputs) == 1
+
+
+@pytest.mark.parametrize("image_urls",
+                         [[TEST_IMAGE_URLS[0], TEST_IMAGE_URLS[1]]])
+def test_chat_multi_image(image_urls: List[str]):
+    llm = LLM(
+        model="microsoft/Phi-3.5-vision-instruct",
+        dtype="bfloat16",
+        max_model_len=4096,
+        max_num_seqs=5,
+        enforce_eager=True,
+        trust_remote_code=True,
+        limit_mm_per_prompt={"image": 2},
+    )
+
+    messages = [{
+        "role":
+        "user",
+        "content": [
+            *({
+                "type": "image_url",
+                "image_url": {
+                    "url": image_url
+                }
+            } for image_url in image_urls),
+            {
+                "type": "text",
+                "text": "What's in this image?"
+            },
+        ],
+    }]
+    outputs = llm.chat(messages)
+    assert len(outputs) >= 0

tests/entrypoints/test_chat_utils.py

@@ -1,11 +1,14 @@
 import warnings
+from typing import Optional
 
 import pytest
 from PIL import Image
 
 from vllm.assets.image import ImageAsset
 from vllm.config import ModelConfig
-from vllm.entrypoints.chat_utils import parse_chat_messages
+from vllm.entrypoints.chat_utils import (parse_chat_messages,
+                                         parse_chat_messages_futures)
+from vllm.multimodal import MultiModalDataDict
 from vllm.multimodal.utils import encode_image_base64
 from vllm.transformers_utils.tokenizer_group import TokenizerGroup
 
@@ -42,10 +45,28 @@ def image_url():
     return f"data:image/jpeg;base64,{base64}"
 
 
-@pytest.mark.asyncio
-async def test_parse_chat_messages_with_image_url(phi3v_model_config,
-                                                  phi3v_tokenizer, image_url):
-    conversation, mm_future = parse_chat_messages([{
+def _assert_mm_data_is_image_input(
+    mm_data: Optional[MultiModalDataDict],
+    image_count: int,
+) -> None:
+    assert mm_data is not None
+    assert set(mm_data.keys()) == {"image"}
+
+    image_data = mm_data.get("image")
+    assert image_data is not None
+
+    if image_count == 1:
+        assert isinstance(image_data, Image.Image)
+    else:
+        assert isinstance(image_data, list) and len(image_data) == image_count
+
+
+def test_parse_chat_messages_single_image(
+    phi3v_model_config,
+    phi3v_tokenizer,
+    image_url,
+):
+    conversation, mm_data = parse_chat_messages([{
         "role":
         "user",
         "content": [{
@@ -63,15 +84,42 @@ async def test_parse_chat_messages_with_image_url(phi3v_model_config,
         "role": "user",
         "content": "<|image_1|>\nWhat's in the image?"
     }]
-    mm_data = await mm_future
-    assert set(mm_data.keys()) == {"image"}
-    assert isinstance(mm_data["image"], Image.Image)
+    _assert_mm_data_is_image_input(mm_data, 1)
 
 
 @pytest.mark.asyncio
-async def test_parse_chat_messages_multiple_images(phi3v_model_config,
-                                                   phi3v_tokenizer, image_url):
-    conversation, mm_future = parse_chat_messages([{
+async def test_parse_chat_messages_single_image_async(
+    phi3v_model_config,
+    phi3v_tokenizer,
+    image_url,
+):
+    conversation, mm_future = parse_chat_messages_futures([{
+        "role":
+        "user",
+        "content": [{
+            "type": "image_url",
+            "image_url": {
+                "url": image_url
+            }
+        }, {
+            "type": "text",
+            "text": "What's in the image?"
+        }]
+    }], phi3v_model_config, phi3v_tokenizer)
+
+    assert conversation == [{
+        "role": "user",
+        "content": "<|image_1|>\nWhat's in the image?"
+    }]
+    _assert_mm_data_is_image_input(await mm_future, 1)
+
+
+def test_parse_chat_messages_multiple_images(
+    phi3v_model_config,
+    phi3v_tokenizer,
+    image_url,
+):
+    conversation, mm_data = parse_chat_messages([{
         "role":
         "user",
         "content": [{
@@ -96,15 +144,49 @@ async def test_parse_chat_messages_multiple_images(phi3v_model_config,
         "content":
         "<|image_1|>\n<|image_2|>\nWhat's in these images?"
     }]
-    mm_data = await mm_future
-    assert set(mm_data.keys()) == {"image"}
-    assert len(mm_data["image"]) == 2
+    _assert_mm_data_is_image_input(mm_data, 2)
 
 
 @pytest.mark.asyncio
-async def test_parse_chat_messages_placeholder_already_in_prompt(
-        phi3v_model_config, phi3v_tokenizer, image_url):
-    conversation, mm_future = parse_chat_messages([{
+async def test_parse_chat_messages_multiple_images_async(
+    phi3v_model_config,
+    phi3v_tokenizer,
+    image_url,
+):
+    conversation, mm_future = parse_chat_messages_futures([{
+        "role":
+        "user",
+        "content": [{
+            "type": "image_url",
+            "image_url": {
+                "url": image_url
+            }
+        }, {
+            "type": "image_url",
+            "image_url": {
+                "url": image_url
+            }
+        }, {
+            "type": "text",
+            "text": "What's in these images?"
+        }]
+    }], phi3v_model_config, phi3v_tokenizer)
+
+    assert conversation == [{
+        "role":
+        "user",
+        "content":
+        "<|image_1|>\n<|image_2|>\nWhat's in these images?"
+    }]
+    _assert_mm_data_is_image_input(await mm_future, 2)
+
+
+def test_parse_chat_messages_placeholder_already_in_prompt(
+    phi3v_model_config,
+    phi3v_tokenizer,
+    image_url,
+):
+    conversation, mm_data = parse_chat_messages([{
         "role":
         "user",
         "content": [{
@@ -131,15 +213,15 @@ async def test_parse_chat_messages_placeholder_already_in_prompt(
         "content":
         "What's in <|image_1|> and how does it compare to <|image_2|>?"
     }]
-    mm_data = await mm_future
-    assert set(mm_data.keys()) == {"image"}
-    assert len(mm_data["image"]) == 2
+    _assert_mm_data_is_image_input(mm_data, 2)
 
 
-@pytest.mark.asyncio
-async def test_parse_chat_messages_placeholder_one_already_in_prompt(
-        phi3v_model_config, phi3v_tokenizer, image_url):
-    conversation, mm_future = parse_chat_messages([{
+def test_parse_chat_messages_placeholder_one_already_in_prompt(
+    phi3v_model_config,
+    phi3v_tokenizer,
+    image_url,
+):
+    conversation, mm_data = parse_chat_messages([{
         "role":
         "user",
         "content": [{
@@ -167,15 +249,15 @@ async def test_parse_chat_messages_placeholder_one_already_in_prompt(
         "<|image_2|>\nWhat's in <|image_1|> and how does it compare to the "
         "other one?"
     }]
-    mm_data = await mm_future
-    assert set(mm_data.keys()) == {"image"}
-    assert len(mm_data["image"]) == 2
+    _assert_mm_data_is_image_input(mm_data, 2)
 
 
-@pytest.mark.asyncio
-async def test_parse_chat_messages_multiple_images_across_messages(
-        phi3v_model_config, phi3v_tokenizer, image_url):
-    conversation, mm_future = parse_chat_messages([{
+def test_parse_chat_messages_multiple_images_across_messages(
+    phi3v_model_config,
+    phi3v_tokenizer,
+    image_url,
+):
+    conversation, mm_data = parse_chat_messages([{
         "role":
         "user",
         "content": [{
@@ -218,14 +300,14 @@ async def test_parse_chat_messages_multiple_images_across_messages(
             "content": "<|image_2|>\nWhat about this one?"
         },
     ]
-    mm_data = await mm_future
-    assert set(mm_data.keys()) == {"image"}
-    assert len(mm_data["image"]) == 2
+    _assert_mm_data_is_image_input(mm_data, 2)
 
 
-@pytest.mark.asyncio
-async def test_parse_chat_messages_rejects_too_many_images_in_one_message(
-        phi3v_model_config, phi3v_tokenizer, image_url):
+def test_parse_chat_messages_rejects_too_many_images_in_one_message(
+    phi3v_model_config,
+    phi3v_tokenizer,
+    image_url,
+):
     with warnings.catch_warnings():
         warnings.filterwarnings(
             "ignore",
@@ -259,9 +341,11 @@ async def test_parse_chat_messages_rejects_too_many_images_in_one_message(
             }], phi3v_model_config, phi3v_tokenizer)
 
 
-@pytest.mark.asyncio
-async def test_parse_chat_messages_rejects_too_many_images_across_messages(
-        phi3v_model_config, phi3v_tokenizer, image_url):
+def test_parse_chat_messages_rejects_too_many_images_across_messages(
+    phi3v_model_config,
+    phi3v_tokenizer,
+    image_url,
+):
     with warnings.catch_warnings():
         warnings.filterwarnings(
             "ignore",

vllm/entrypoints/chat_utils.py

@@ -1,10 +1,11 @@
 import asyncio
 import codecs
+from abc import ABC, abstractmethod
 from collections import defaultdict
 from functools import lru_cache
 from pathlib import Path
-from typing import (Any, Awaitable, Dict, Iterable, List, Literal, Mapping,
-                    Optional, Tuple, Union)
+from typing import (Any, Awaitable, Dict, Generic, Iterable, List, Literal,
+                    Mapping, Optional, Tuple, TypeVar, Union)
 
 # yapf conflicts with isort for this block
 # yapf: disable
@@ -23,7 +24,8 @@ from vllm.config import ModelConfig
 from vllm.logger import init_logger
 from vllm.multimodal import MultiModalDataDict
 from vllm.multimodal.utils import (async_get_and_parse_audio,
-                                   async_get_and_parse_image)
+                                   async_get_and_parse_image,
+                                   get_and_parse_audio, get_and_parse_image)
 from vllm.transformers_utils.tokenizer import AnyTokenizer
 
 logger = init_logger(__name__)
@@ -81,7 +83,11 @@ class ConversationMessage(TypedDict):
     content: str
 
 
-class MultiModalItemTracker:
+ModalityStr = Literal["image", "audio"]
+_T = TypeVar("_T")
+
+
+class BaseMultiModalItemTracker(ABC, Generic[_T]):
     """
     Tracks multi-modal items in a given request and ensures that the number
     of multi-modal items in a given request does not exceed the configured
@@ -89,37 +95,28 @@ class MultiModalItemTracker:
     """
 
     def __init__(self, model_config: ModelConfig, tokenizer: AnyTokenizer):
+        super().__init__()
+
         self._model_config = model_config
         self._tokenizer = tokenizer
         self._allowed_items = (model_config.multimodal_config.limit_per_prompt
                                if model_config.multimodal_config else {})
         self._consumed_items = {k: 0 for k in self._allowed_items}
-        self._futures: List[Awaitable[MultiModalDataDict]] = []
+
+        self._items: List[_T] = []
 
     @staticmethod
     @lru_cache(maxsize=None)
-    def _cached_token_str(tokenizer: AnyTokenizer, token_index: int):
+    def _cached_token_str(tokenizer: AnyTokenizer, token_index: int) -> str:
         return tokenizer.decode(token_index)
 
-    def add(self, modality: Literal["image", "audio"],
-            mm_future: Awaitable[MultiModalDataDict]) -> Optional[str]:
-        """
-        Adds the multi-modal item to the current prompt and returns the
-        placeholder string to use, if any.
-        """
-        allowed_count = self._allowed_items.get(modality, 1)
-        current_count = self._consumed_items.get(modality, 0) + 1
-        if current_count > allowed_count:
-            raise ValueError(
-                f"At most {allowed_count} {modality}(s) may be provided in "
-                "one request.")
-
-        self._consumed_items[modality] = current_count
-        self._futures.append(mm_future)
-
+    def _placeholder_str(self, modality: ModalityStr,
+                         current_count: int) -> Optional[str]:
         # TODO: Let user specify how to insert image tokens into prompt
         # (similar to chat template)
-        model_type = self._model_config.hf_config.model_type
+        hf_config = self._model_config.hf_config
+        model_type = hf_config.model_type
+
         if modality == "image":
             if model_type == "phi3_v":
                 # Workaround since this token is not defined in the tokenizer
@@ -130,9 +127,8 @@ class MultiModalItemTracker:
                 # These models do not use image tokens in the prompt
                 return None
             if model_type.startswith("llava"):
-                return MultiModalItemTracker._cached_token_str(
-                    self._tokenizer,
-                    self._model_config.hf_config.image_token_index)
+                return self._cached_token_str(self._tokenizer,
+                                              hf_config.image_token_index)
             if model_type in ("chameleon", "internvl_chat"):
                 return "<image>"
 
@@ -145,11 +141,11 @@ class MultiModalItemTracker:
             raise TypeError(f"Unknown modality: {modality}")
 
     @staticmethod
-    async def _combine(futures: List[Awaitable[MultiModalDataDict]]):
+    def _combine(items: List[MultiModalDataDict]) -> MultiModalDataDict:
         mm_lists: Mapping[str, List[object]] = defaultdict(list)
 
         # Merge all the multi-modal items
-        for single_mm_data in (await asyncio.gather(*futures)):
+        for single_mm_data in items:
             for mm_key, mm_item in single_mm_data.items():
                 if isinstance(mm_item, list):
                     mm_lists[mm_key].extend(mm_item)
@@ -162,9 +158,113 @@ class MultiModalItemTracker:
             for mm_key, mm_list in mm_lists.items()
         }
 
-    def all_mm_data(self) -> Optional[Awaitable[MultiModalDataDict]]:
-        return MultiModalItemTracker._combine(
-            self._futures) if self._futures else None
+    def add(self, modality: ModalityStr, item: _T) -> Optional[str]:
+        """
+        Add a multi-modal item to the current prompt and returns the
+        placeholder string to use, if any.
+        """
+        allowed_count = self._allowed_items.get(modality, 1)
+        current_count = self._consumed_items.get(modality, 0) + 1
+        if current_count > allowed_count:
+            raise ValueError(
+                f"At most {allowed_count} {modality}(s) may be provided in "
+                "one request.")
+
+        self._consumed_items[modality] = current_count
+        self._items.append(item)
+
+        return self._placeholder_str(modality, current_count)
+
+    @abstractmethod
+    def create_parser(self) -> "BaseMultiModalContentParser":
+        raise NotImplementedError
+
+
+class MultiModalItemTracker(BaseMultiModalItemTracker[MultiModalDataDict]):
+
+    def all_mm_data(self) -> Optional[MultiModalDataDict]:
+        return self._combine(self._items) if self._items else None
+
+    def create_parser(self) -> "BaseMultiModalContentParser":
+        return MultiModalContentParser(self)
+
+
+class AsyncMultiModalItemTracker(
+        BaseMultiModalItemTracker[Awaitable[MultiModalDataDict]]):
+
+    async def all_mm_data(self) -> Optional[MultiModalDataDict]:
+        if self._items:
+            items = await asyncio.gather(*self._items)
+            return self._combine(items)
+
+        return None
+
+    def create_parser(self) -> "BaseMultiModalContentParser":
+        return AsyncMultiModalContentParser(self)
+
+
+class BaseMultiModalContentParser(ABC):
+
+    def __init__(self) -> None:
+        super().__init__()
+
+        # multimodal placeholder_string : count
+        self._placeholder_counts: Dict[str, int] = defaultdict(lambda: 0)
+
+    def _add_placeholder(self, placeholder: Optional[str]):
+        if placeholder:
+            self._placeholder_counts[placeholder] += 1
+
+    def mm_placeholder_counts(self) -> Dict[str, int]:
+        return dict(self._placeholder_counts)
+
+    @abstractmethod
+    def parse_image(self, image_url: str) -> None:
+        raise NotImplementedError
+
+    @abstractmethod
+    def parse_audio(self, audio_url: str) -> None:
+        raise NotImplementedError
+
+
+class MultiModalContentParser(BaseMultiModalContentParser):
+
+    def __init__(self, tracker: MultiModalItemTracker) -> None:
+        super().__init__()
+
+        self._tracker = tracker
+
+    def parse_image(self, image_url: str) -> None:
+        image = get_and_parse_image(image_url)
+
+        placeholder = self._tracker.add("image", image)
+        self._add_placeholder(placeholder)
+
+    def parse_audio(self, audio_url: str) -> None:
+        audio = get_and_parse_audio(audio_url)
+
+        placeholder = self._tracker.add("audio", audio)
+        self._add_placeholder(placeholder)
+
+
+class AsyncMultiModalContentParser(BaseMultiModalContentParser):
+
+    def __init__(self, tracker: AsyncMultiModalItemTracker) -> None:
+        super().__init__()
+
+        self._tracker = tracker
+
+    def parse_image(self, image_url: str) -> None:
+        image_coro = async_get_and_parse_image(image_url)
+
+        placeholder = self._tracker.add("image", image_coro)
+        self._add_placeholder(placeholder)
+
+    def parse_audio(self, audio_url: str) -> None:
+        audio_coro = async_get_and_parse_audio(audio_url)
+
+        placeholder = self._tracker.add("audio", audio_coro)
+        self._add_placeholder(placeholder)
 
 
 def load_chat_template(
@@ -197,10 +297,10 @@ def load_chat_template(
 # (similar to chat template)
 def _get_full_multimodal_text_prompt(placeholder_counts: Dict[str, int],
                                      text_prompt: str) -> str:
-    """Combine multimodal prompts for a multimodal language model"""
+    """Combine multimodal prompts for a multimodal language model."""
 
     # Look through the text prompt to check for missing placeholders
-    missing_placeholders = []
+    missing_placeholders: List[str] = []
     for placeholder in placeholder_counts:
 
         # For any existing placeholder in the text prompt, we leave it as is
@@ -227,12 +327,11 @@ _AudioParser = TypeAdapter(ChatCompletionContentPartAudioParam)
 def _parse_chat_message_content_parts(
     role: str,
     parts: Iterable[ChatCompletionContentPartParam],
-    mm_tracker: MultiModalItemTracker,
+    mm_tracker: BaseMultiModalItemTracker,
 ) -> List[ConversationMessage]:
     texts: List[str] = []
 
-    # multimodal placeholder_string : count
-    mm_placeholder_counts: Dict[str, int] = {}
+    mm_parser = mm_tracker.create_parser()
 
     for part in parts:
         part_type = part["type"]
@@ -247,22 +346,16 @@ def _parse_chat_message_content_parts(
                     "'image_url.detail' is currently not supported and "
                     "will be ignored.")
 
-            image_coro = async_get_and_parse_image(image_url["url"])
-            placeholder = mm_tracker.add("image", image_coro)
-            if placeholder:
-                mm_placeholder_counts[placeholder] = mm_placeholder_counts.get(
-                    placeholder, 0) + 1
+            mm_parser.parse_image(image_url["url"])
         elif part_type == "audio_url":
             audio_url = _AudioParser.validate_python(part)["audio_url"]
-            audio_coro = async_get_and_parse_audio(audio_url["url"])
-            placeholder = mm_tracker.add("audio", audio_coro)
-            if placeholder:
-                mm_placeholder_counts[placeholder] = mm_placeholder_counts.get(
-                    placeholder, 0) + 1
+
+            mm_parser.parse_audio(audio_url["url"])
         else:
             raise NotImplementedError(f"Unknown part type: {part_type}")
 
     text_prompt = "\n".join(texts)
+    mm_placeholder_counts = mm_parser.mm_placeholder_counts()
     if mm_placeholder_counts:
         text_prompt = _get_full_multimodal_text_prompt(mm_placeholder_counts,
                                                        text_prompt)
@@ -271,8 +364,9 @@ def _parse_chat_message_content_parts(
 
 
 def _parse_chat_message_content(
-        message: ChatCompletionMessageParam,
-        mm_tracker: MultiModalItemTracker) -> List[ConversationMessage]:
+    message: ChatCompletionMessageParam,
+    mm_tracker: BaseMultiModalItemTracker,
+) -> List[ConversationMessage]:
     role = message["role"]
     content = message.get("content")
 
@@ -292,7 +386,7 @@ def parse_chat_messages(
     messages: List[ChatCompletionMessageParam],
     model_config: ModelConfig,
     tokenizer: AnyTokenizer,
-) -> Tuple[List[ConversationMessage], Optional[Awaitable[MultiModalDataDict]]]:
+) -> Tuple[List[ConversationMessage], Optional[MultiModalDataDict]]:
     conversation: List[ConversationMessage] = []
     mm_tracker = MultiModalItemTracker(model_config, tokenizer)
 
@@ -304,6 +398,22 @@ def parse_chat_messages(
     return conversation, mm_tracker.all_mm_data()
 
 
+def parse_chat_messages_futures(
+    messages: List[ChatCompletionMessageParam],
+    model_config: ModelConfig,
+    tokenizer: AnyTokenizer,
+) -> Tuple[List[ConversationMessage], Awaitable[Optional[MultiModalDataDict]]]:
+    conversation: List[ConversationMessage] = []
+    mm_tracker = AsyncMultiModalItemTracker(model_config, tokenizer)
+
+    for msg in messages:
+        sub_messages = _parse_chat_message_content(msg, mm_tracker)
+
+        conversation.extend(sub_messages)
+
+    return conversation, mm_tracker.all_mm_data()
+
+
 def apply_chat_template(
     tokenizer: AnyTokenizer,
     conversation: List[ConversationMessage],

vllm/entrypoints/llm.py

@@ -23,7 +23,7 @@ from vllm.transformers_utils.tokenizer import (AnyTokenizer,
                                                get_cached_tokenizer)
 from vllm.transformers_utils.tokenizer_group import TokenizerGroup
 from vllm.usage.usage_lib import UsageContext
-from vllm.utils import Counter, deprecate_kwargs
+from vllm.utils import Counter, deprecate_kwargs, is_list_of
 
 logger = init_logger(__name__)
 
@@ -358,15 +358,18 @@ class LLM:
         add_generation_prompt: bool = True,
     ) -> List[RequestOutput]:
         """
-        Generates responses for chat messages.
+        Generate responses for a chat conversation.
 
-        Converts the messages to prompts using the tokenizer and calls
-        the :meth:`generate` method to generate the responses.
+        The chat conversation is converted into a text prompt using the
+        tokenizer and calls the :meth:`generate` method to generate the
+        responses.
+
+        Multi-modal inputs can be passed in the same way you would pass them
+        to the OpenAI API.
 
         Args:
-            messages: A list of messages to generate responses for. Each
-                message is a list of dictionaries with 'role' and 'content'
-                keys.
+            messages: A single conversation represented as a list of messages.
+                Each message is a dictionary with 'role' and 'content' keys.
             sampling_params: The sampling parameters for text generation.
                 If None, we use the default sampling parameters. When it
                 is a single value, it is applied to every prompt. When it
@@ -387,21 +390,25 @@ class LLM:
         tokenizer = self.get_tokenizer()
         model_config = self.llm_engine.get_model_config()
 
-        conversations, _ = parse_chat_messages(messages, model_config,
-                                               tokenizer)
+        conversation, mm_data = parse_chat_messages(messages, model_config,
+                                                    tokenizer)
 
         prompt = apply_chat_template(
             tokenizer,
-            conversations,
+            conversation,
             chat_template=chat_template,
-            add_generation_prompt=add_generation_prompt)
+            add_generation_prompt=add_generation_prompt,
+        )
 
         inputs: PromptInputs
-        if isinstance(prompt, list) and isinstance(prompt[0], int):
+        if is_list_of(prompt, int):
             inputs = TokensPrompt(prompt_token_ids=prompt)
         else:
             inputs = TextPrompt(prompt=prompt)
 
+        if mm_data is not None:
+            inputs["multi_modal_data"] = mm_data
+
         return self.generate(
             inputs,
             sampling_params=sampling_params,

vllm/entrypoints/openai/serving_chat.py

@@ -11,7 +11,7 @@ from vllm.engine.protocol import AsyncEngineClient
 from vllm.entrypoints.chat_utils import (ConversationMessage,
                                          apply_chat_template,
                                          load_chat_template,
-                                         parse_chat_messages)
+                                         parse_chat_messages_futures)
 from vllm.entrypoints.logger import RequestLogger
 from vllm.entrypoints.openai.protocol import (
     ChatCompletionLogProb, ChatCompletionLogProbs,
@@ -26,7 +26,6 @@ from vllm.entrypoints.openai.serving_engine import (LoRAModulePath,
                                                     TextTokensPrompt)
 from vllm.inputs import TokensPrompt
 from vllm.logger import init_logger
-from vllm.multimodal import MultiModalDataDict
 from vllm.outputs import RequestOutput
 from vllm.sequence import Logprob
 from vllm.tracing import (contains_trace_headers, extract_trace_headers,
@@ -94,7 +93,7 @@ class OpenAIServingChat(OpenAIServing):
             tokenizer = await self.async_engine_client.get_tokenizer(
                 lora_request)
 
-            conversation, mm_data_future = parse_chat_messages(
+            conversation, mm_data_future = parse_chat_messages_futures(
                 request.messages, model_config, tokenizer)
 
             tool_dicts = None if request.tools is None else [
@@ -114,10 +113,8 @@ class OpenAIServingChat(OpenAIServing):
             logger.error("Error in applying chat template from request: %s", e)
             return self.create_error_response(str(e))
 
-        mm_data: Optional[MultiModalDataDict] = None
         try:
-            if mm_data_future:
-                mm_data = await mm_data_future
+            mm_data = await mm_data_future
         except Exception as e:
             logger.error("Error in loading multi-modal data: %s", e)
             return self.create_error_response(str(e))

vllm/entrypoints/openai/serving_tokenization.py

@@ -4,7 +4,7 @@ from vllm.config import ModelConfig
 from vllm.engine.protocol import AsyncEngineClient
 from vllm.entrypoints.chat_utils import (apply_chat_template,
                                          load_chat_template,
-                                         parse_chat_messages)
+                                         parse_chat_messages_futures)
 from vllm.entrypoints.logger import RequestLogger
 # yapf conflicts with isort for this block
 # yapf: disable
@@ -65,10 +65,11 @@ class OpenAIServingTokenization(OpenAIServing):
         if isinstance(request, TokenizeChatRequest):
             model_config = self.model_config
 
-            conversation, mm_data_future = parse_chat_messages(
+            conversation, mm_data_future = parse_chat_messages_futures(
                 request.messages, model_config, tokenizer)
 
-            if mm_data_future:
+            mm_data = await mm_data_future
+            if mm_data:
                 logger.warning(
                     "Multi-modal inputs are ignored during tokenization")
 

vllm/multimodal/utils.py

@@ -120,6 +120,16 @@ async def async_fetch_audio(
     return librosa.load(BytesIO(audio_bytes), sr=None)
 
 
+def get_and_parse_audio(audio_url: str) -> MultiModalDataDict:
+    audio, sr = fetch_audio(audio_url)
+    return {"audio": (audio, sr)}
+
+
+def get_and_parse_image(image_url: str) -> MultiModalDataDict:
+    image = fetch_image(image_url)
+    return {"image": image}
+
+
 async def async_get_and_parse_audio(audio_url: str) -> MultiModalDataDict:
     audio, sr = await async_fetch_audio(audio_url)
     return {"audio": (audio, sr)}

vllm/transformers_utils/tokenizers/mistral.py

@@ -52,12 +52,13 @@ class MistralTokenizer:
         assert isinstance(self.tokenizer,
                           (Tekkenizer, SentencePieceTokenizer)), type(
                               self.tokenizer)
-        self._is_tekken = isinstance(self.tokenizer, Tekkenizer)
 
-        if self._is_tekken:
+        if (is_tekken := isinstance(self.tokenizer, Tekkenizer)):
             # Make sure special tokens will not raise
             self.tokenizer.special_token_policy = SpecialTokenPolicy.IGNORE
 
+        self._is_tekken = is_tekken
+
         # the following attributes are set to fit VLLM's design
         self.is_fast = True
         self.chat_template = True