[Bugfix]: Make chat content text allow type content

docs/source/serving/openai_compatible_server.md

@@ -103,6 +103,23 @@ vllm serve <model> --chat-template ./path-to-chat-template.jinja
 vLLM community provides a set of chat templates for popular models. You can find them in the examples
 directory [here](https://github.com/vllm-project/vllm/tree/main/examples/)
 
+With the inclusion of multi-modal chat APIs, the OpenAI spec now accepts chat messages in a new format which specifies 
+both a `type` and a `text` field. An example is provided below:
+```python
+completion = client.chat.completions.create(
+  model="NousResearch/Meta-Llama-3-8B-Instruct",
+  messages=[
+    {"role": "user", "content": [{"type": "text", "text": "Classify this sentiment: vLLM is wonderful!"}]}
+  ]
+)
+```
+Most chat templates for LLMs expect the `content` to be a `string` but there are some newer models like 
+`meta-llama/Llama-Guard-3-1B` that expect the content to be parsed with the new OpenAI spec. In order to choose which
+format the content needs to be parsed in by vLLM, please use the `--chat-template-text-format` argument to specify
+between `string` or `openai`. The default value is `string` and vLLM internally converts both spec formats to match 
+this, unless explicitly specified.
+
+
 ## Command line arguments for the server
 
 ```{argparse}

tests/entrypoints/openai/test_serving_chat.py

@@ -26,6 +26,7 @@ class MockModelConfig:
     tokenizer = MODEL_NAME
     trust_remote_code = False
     tokenizer_mode = "auto"
+    chat_template_text_format = "string"
     max_model_len = 100
     tokenizer_revision = None
     multimodal_config = MultiModalConfig()

tests/entrypoints/test_chat_utils.py

@@ -17,7 +17,7 @@
 MLLAMA_MODEL_ID = "meta-llama/Llama-3.2-11B-Vision-Instruct"
 
 
-@pytest.fixture(scope="module")
+@pytest.fixture(scope="function")
 def phi3v_model_config():
     return ModelConfig(PHI3V_MODEL_ID,
                        task="generate",
@@ -26,6 +26,7 @@ def phi3v_model_config():
                        trust_remote_code=True,
                        dtype="bfloat16",
                        seed=0,
+                       chat_template_text_format="string",
                        limit_mm_per_prompt={
                            "image": 2,
                        })
@@ -330,6 +331,51 @@ def test_parse_chat_messages_multiple_images_across_messages(
     _assert_mm_data_is_image_input(mm_data, 2)
 
 
+def test_parse_chat_messages_context_text_format(
+    phi3v_model_config,
+    phi3v_tokenizer,
+):
+    phi3v_model_config.chat_template_text_format = "openai"
+    conversation, mm_data = parse_chat_messages(
+        [{
+            "role": "user",
+            "content": [{
+                "type": "text",
+                "text": "What's in this text?"
+            }]
+        }, {
+            "role": "assistant",
+            "content": "Some stuff."
+        }, {
+            "role": "user",
+            "content": "What about this one?"
+        }], phi3v_model_config, phi3v_tokenizer)
+
+    assert conversation == [
+        {
+            "role": "user",
+            "content": [{
+                "type": "text",
+                "text": "What's in this text?"
+            }]
+        },
+        {
+            "role": "assistant",
+            "content": [{
+                "type": "text",
+                "text": "Some stuff."
+            }]
+        },
+        {
+            "role": "user",
+            "content": [{
+                "type": "text",
+                "text": "What about this one?"
+            }]
+        },
+    ]
+
+
 def test_parse_chat_messages_rejects_too_many_images_in_one_message(
     phi3v_model_config,
     phi3v_tokenizer,

vllm/config.py

@@ -142,6 +142,7 @@ def __init__(self,
                  use_async_output_proc: bool = True,
                  override_neuron_config: Optional[Dict[str, Any]] = None,
                  config_format: ConfigFormat = ConfigFormat.AUTO,
+                 chat_template_text_format: str = "string",
                  mm_processor_kwargs: Optional[Dict[str, Any]] = None) -> None:
         self.model = model
         self.tokenizer = tokenizer
@@ -176,6 +177,7 @@ def __init__(self,
             self.model, revision)
         self.dtype = _get_and_verify_dtype(self.hf_text_config, dtype)
         self.use_async_output_proc = use_async_output_proc
+        self.chat_template_text_format = chat_template_text_format
         self.mm_processor_kwargs = mm_processor_kwargs
 
         # Set enforce_eager to False if the value is unset.

vllm/engine/arg_utils.py

@@ -89,6 +89,7 @@ class EngineArgs:
     task: TaskOption = "auto"
     skip_tokenizer_init: bool = False
     tokenizer_mode: str = 'auto'
+    chat_template_text_format: str = 'string'
     trust_remote_code: bool = False
     download_dir: Optional[str] = None
     load_format: str = 'auto'
@@ -250,6 +251,14 @@ def add_cli_args(parser: FlexibleArgumentParser) -> FlexibleArgumentParser:
             'fast tokenizer if available.\n* "slow" will '
             'always use the slow tokenizer. \n* '
             '"mistral" will always use the `mistral_common` tokenizer.')
+        parser.add_argument(
+            '--chat-template-text-format',
+            type=str,
+            default=EngineArgs.chat_template_text_format,
+            choices=['string', 'openai'],
+            help='The format to render text content within a chat template. '
+            '"string" will keep the content field as a string whereas '
+            '"openai" will parse content in the current OpenAI format.')
         parser.add_argument('--trust-remote-code',
                             action='store_true',
                             help='Trust remote code from huggingface.')
@@ -858,6 +867,7 @@ def create_model_config(self) -> ModelConfig:
             # We know this is not None because we set it in __post_init__
             tokenizer=cast(str, self.tokenizer),
             tokenizer_mode=self.tokenizer_mode,
+            chat_template_text_format=self.chat_template_text_format,
             trust_remote_code=self.trust_remote_code,
             dtype=self.dtype,
             seed=self.seed,

vllm/engine/llm_engine.py

@@ -254,7 +254,7 @@ def __init__(
             "num_scheduler_steps=%d, chunked_prefill_enabled=%s "
             "multi_step_stream_outputs=%s, enable_prefix_caching=%s, "
             "use_async_output_proc=%s, use_cached_outputs=%s, "
-            "mm_processor_kwargs=%s)",
+            "chat_template_text_format=%s, mm_processor_kwargs=%s)",
             VLLM_VERSION,
             model_config.model,
             speculative_config,
@@ -289,6 +289,7 @@ def __init__(
             cache_config.enable_prefix_caching,
             model_config.use_async_output_proc,
             use_cached_outputs,
+            model_config.chat_template_text_format,
             model_config.mm_processor_kwargs,
         )
         # TODO(woosuk): Print more configs in debug mode.

vllm/entrypoints/chat_utils.py

@@ -121,7 +121,7 @@ class ConversationMessage(TypedDict, total=False):
     role: Required[str]
     """The role of the message's author."""
 
-    content: Optional[str]
+    content: Union[Optional[str], List[Dict[str, str]]]
     """The contents of the message"""
 
     tool_call_id: Optional[str]
@@ -431,7 +431,7 @@ def _get_full_multimodal_text_prompt(placeholder_counts: Dict[str, int],
 def _parse_chat_message_content_mm_part(
         part: ChatCompletionContentPartParam) -> Tuple[str, str]:
     """
-    Parses a given multi modal content part based on its type.
+    Parses a given multi-modal content part based on its type.
 
     Args:
         part: A dict containing the content part, with a potential 'type' field.
@@ -485,21 +485,26 @@ def _parse_chat_message_content_parts(
     role: str,
     parts: Iterable[ChatCompletionContentPartParam],
     mm_tracker: BaseMultiModalItemTracker,
+    chat_template_text_format: str,
 ) -> List[ConversationMessage]:
     content: List[Union[str, Dict[str, str]]] = []
 
     mm_parser = mm_tracker.create_parser()
-    keep_multimodal_content = \
+    wrap_dicts = \
         mm_tracker._model_config.hf_config.model_type in \
-            MODEL_KEEP_MULTI_MODAL_CONTENT
+            MODEL_KEEP_MULTI_MODAL_CONTENT or \
+        (chat_template_text_format == "openai")
 
     for part in parts:
         parse_res = _parse_chat_message_content_part(
-            part, mm_parser, wrap_dicts=keep_multimodal_content)
+            part,
+            mm_parser,
+            wrap_dicts=wrap_dicts,
+        )
         if parse_res:
             content.append(parse_res)
 
-    if keep_multimodal_content:
+    if wrap_dicts:
         # Parsing wraps images and texts as interleaved dictionaries
         return [ConversationMessage(role=role,
                                     content=content)]  # type: ignore
@@ -560,6 +565,7 @@ def _parse_chat_message_content_part(
 def _parse_chat_message_content(
     message: ChatCompletionMessageParam,
     mm_tracker: BaseMultiModalItemTracker,
+    chat_template_text_format: str,
 ) -> List[ConversationMessage]:
     role = message["role"]
     content = message.get("content")
@@ -575,6 +581,7 @@ def _parse_chat_message_content(
         role,
         content,  # type: ignore
         mm_tracker,
+        chat_template_text_format,
     )
 
     for result_msg in result:
@@ -618,7 +625,11 @@ def parse_chat_messages(
     mm_tracker = MultiModalItemTracker(model_config, tokenizer)
 
     for msg in messages:
-        sub_messages = _parse_chat_message_content(msg, mm_tracker)
+        sub_messages = _parse_chat_message_content(
+            msg,
+            mm_tracker,
+            model_config.chat_template_text_format,
+        )
 
         conversation.extend(sub_messages)
 
@@ -636,7 +647,11 @@ def parse_chat_messages_futures(
     mm_tracker = AsyncMultiModalItemTracker(model_config, tokenizer)
 
     for msg in messages:
-        sub_messages = _parse_chat_message_content(msg, mm_tracker)
+        sub_messages = _parse_chat_message_content(
+            msg,
+            mm_tracker,
+            model_config.chat_template_text_format,
+        )
 
         conversation.extend(sub_messages)
 

vllm/entrypoints/openai/serving_chat.py

@@ -384,7 +384,7 @@ async def chat_completion_stream_generator(
                     # Send response to echo the input portion of the
                     # last message
                     if request.echo or request.continue_final_message:
-                        last_msg_content: str = ""
+                        last_msg_content: Union[str, List[Dict[str, str]]] = ""
                         if conversation and "content" in conversation[
                                 -1] and conversation[-1].get("role") == role:
                             last_msg_content = conversation[-1]["content"] or ""
@@ -724,10 +724,13 @@ async def chat_completion_full_generator(
             choices.append(choice_data)
 
         if request.echo or request.continue_final_message:
-            last_msg_content = ""
+            last_msg_content: Union[str, List[Dict[str, str]]] = ""
             if conversation and "content" in conversation[-1] and conversation[
                     -1].get("role") == role:
                 last_msg_content = conversation[-1]["content"] or ""
+            if isinstance(last_msg_content, list):
+                last_msg_content = "\n".join(msg['text']
+                                             for msg in last_msg_content)
 
             for choice in choices:
                 full_message = last_msg_content + (choice.message.content