[Frontend] Respect Chat Completion parallel_tool_calls param (#26233)

Signed-off-by: Ben Browning <bbrownin@redhat.com>
Co-authored-by: Chauncey <chaunceyjiang@gmail.com>

docs/serving/openai_compatible_server.md

@@ -49,7 +49,8 @@ We currently support the following OpenAI APIs:
     - *Note: `suffix` parameter is not supported.*
 - [Chat Completions API](#chat-api) (`/v1/chat/completions`)
     - Only applicable to [text generation models](../models/generative_models.md) with a [chat template](../serving/openai_compatible_server.md#chat-template).
-    - *Note: `parallel_tool_calls` and `user` parameters are ignored.*
+    - *Note: `user` parameter is ignored.*
+    - *Note:* Setting the `parallel_tool_calls` parameter to `false` ensures vLLM only returns zero or one tool call per request. Setting it to `true` (the default) allows returning more than one tool call per request. There is no guarantee more than one tool call will be returned if this is set to `true`, as that behavior is model dependent and not all models are designed to support parallel tool calls.
 - [Embeddings API](#embeddings-api) (`/v1/embeddings`)
     - Only applicable to [embedding models](../models/pooling_models.md).
 - [Transcriptions API](#transcriptions-api) (`/v1/audio/transcriptions`)

tests/tool_use/test_parallel_tool_calls.py

@@ -212,3 +212,60 @@ async def test_parallel_tool_calls_with_results(
     assert finish_reason_count == 1
     assert len(chunks)
     assert "".join(chunks) == choice.message.content
+
+
+@pytest.mark.asyncio
+async def test_parallel_tool_calls_false(client: openai.AsyncOpenAI):
+    """
+    Ensure only one tool call is returned when parallel_tool_calls is False.
+    """
+
+    models = await client.models.list()
+    model_name: str = models.data[0].id
+    chat_completion = await client.chat.completions.create(
+        messages=MESSAGES_ASKING_FOR_PARALLEL_TOOLS,
+        temperature=0,
+        max_completion_tokens=200,
+        model=model_name,
+        tools=[WEATHER_TOOL, SEARCH_TOOL],
+        logprobs=False,
+        parallel_tool_calls=False,
+    )
+
+    stop_reason = chat_completion.choices[0].finish_reason
+    non_streamed_tool_calls = chat_completion.choices[0].message.tool_calls
+
+    # make sure only 1 tool call is present
+    assert len(non_streamed_tool_calls) == 1
+    assert stop_reason == "tool_calls"
+
+    # make the same request, streaming
+    stream = await client.chat.completions.create(
+        model=model_name,
+        messages=MESSAGES_ASKING_FOR_PARALLEL_TOOLS,
+        temperature=0,
+        max_completion_tokens=200,
+        tools=[WEATHER_TOOL, SEARCH_TOOL],
+        logprobs=False,
+        parallel_tool_calls=False,
+        stream=True,
+    )
+
+    finish_reason_count: int = 0
+    tool_call_id_count: int = 0
+
+    async for chunk in stream:
+        # if there's a finish reason make sure it's tools
+        if chunk.choices[0].finish_reason:
+            finish_reason_count += 1
+            assert chunk.choices[0].finish_reason == "tool_calls"
+
+        streamed_tool_calls = chunk.choices[0].delta.tool_calls
+        if streamed_tool_calls and len(streamed_tool_calls) > 0:
+            tool_call = streamed_tool_calls[0]
+            if tool_call.id:
+                tool_call_id_count += 1
+
+    # make sure only 1 streaming tool call is present
+    assert tool_call_id_count == 1
+    assert finish_reason_count == 1

vllm/entrypoints/openai/protocol.py

@@ -559,9 +559,9 @@ class ChatCompletionRequest(OpenAIBaseModel):
     ) = "none"
     reasoning_effort: Literal["low", "medium", "high"] | None = None
     include_reasoning: bool = True
+    parallel_tool_calls: bool | None = True
 
-    # NOTE this will be ignored by vLLM -- the model determines the behavior
-    parallel_tool_calls: bool | None = False
+    # NOTE this will be ignored by vLLM
     user: str | None = None
 
     # --8<-- [start:chat-completion-sampling-params]

vllm/entrypoints/openai/serving_chat.py

@@ -55,6 +55,7 @@ from vllm.entrypoints.openai.serving_engine import OpenAIServing, clamp_prompt_l
 from vllm.entrypoints.openai.serving_models import OpenAIServingModels
 from vllm.entrypoints.openai.tool_parsers import ToolParser
 from vllm.entrypoints.openai.tool_parsers.mistral_tool_parser import MistralToolCall
+from vllm.entrypoints.openai.utils import maybe_filter_parallel_tool_calls
 from vllm.entrypoints.utils import get_max_tokens, should_include_usage
 from vllm.inputs.data import TokensPrompt as EngineTokensPrompt
 from vllm.logger import init_logger
@@ -1206,6 +1207,7 @@ class OpenAIServingChat(OpenAIServing):
 
                         finish_reason_sent[i] = True
 
+                    choice_data = maybe_filter_parallel_tool_calls(choice_data, request)
                     chunk = ChatCompletionStreamResponse(
                         id=request_id,
                         object=chunk_object_type,
@@ -1531,6 +1533,7 @@ class OpenAIServingChat(OpenAIServing):
                     as_list(output.token_ids) if request.return_token_ids else None
                 ),
             )
+            choice_data = maybe_filter_parallel_tool_calls(choice_data, request)
 
             choices.append(choice_data)
 

vllm/entrypoints/openai/serving_engine.py

@@ -296,11 +296,7 @@ class OpenAIServing:
         parser = None
         if not enable_auto_tools or tool_parser_name is None:
             return parser
-        logger.info(
-            '"auto" tool choice has been enabled please note that while'
-            " the parallel_tool_calls client option is preset for "
-            "compatibility reasons, it will be ignored."
-        )
+        logger.info('"auto" tool choice has been enabled.')
 
         try:
             if tool_parser_name == "pythonic" and self.model_config.model.startswith(

vllm/entrypoints/openai/utils.py

+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+from typing import TypeVar
+
+from vllm.entrypoints.openai.protocol import (
+    ChatCompletionRequest,
+    ChatCompletionResponseChoice,
+    ChatCompletionResponseStreamChoice,
+)
+
+# Used internally
+_ChatCompletionResponseChoiceT = TypeVar(
+    "_ChatCompletionResponseChoiceT",
+    ChatCompletionResponseChoice,
+    ChatCompletionResponseStreamChoice,
+)
+
+
+def maybe_filter_parallel_tool_calls(
+    choice: _ChatCompletionResponseChoiceT, request: ChatCompletionRequest
+) -> _ChatCompletionResponseChoiceT:
+    """Filter to first tool call only when parallel_tool_calls is False."""
+
+    if request.parallel_tool_calls:
+        return choice
+
+    if isinstance(choice, ChatCompletionResponseChoice) and choice.message.tool_calls:
+        choice.message.tool_calls = choice.message.tool_calls[:1]
+    elif (
+        isinstance(choice, ChatCompletionResponseStreamChoice)
+        and choice.delta.tool_calls
+    ):
+        choice.delta.tool_calls = [
+            tool_call for tool_call in choice.delta.tool_calls if tool_call.index == 0
+        ]
+
+    return choice