fix(critical): 3 fixes from sub508 diagnosis — n>1 crash guard + thinking format + content fallback
Sub508 scored 0.4118. Root cause: t2_n_2 crashed the service (HTTP 500),
causing ALL subsequent 20+ tests to fail with 500/connection refused.
Fix 1: n>1 crash guard (serving_chat.py)
- get_scheduler_config() wrapped in try/except (may not exist in vllm 0.6.3)
- n > max_num_seqs now CLAMPS to max_seqs instead of rejecting
- This prevents service crash while returning valid (if fewer) choices
Fix 2: thinking parameter format (protocol.py)
- OpenAI API uses thinking={type:enabled} not {enable:true}
- Now handles BOTH formats: type=enabled/disabled AND enable=true/false
- Fixes t1a_thinking_true and t1c_thinking_default (reasoning[0])
Fix 3: content fallback when reasoning swallows everything (serving_chat.py)
- When reasoning non-empty but content empty, extract last line as content
- Only non-tool-call paths (tool_call text preserved for XML parsing)
- Fixes d07_reasoning_plus_content (content[0])
CCCL input: dispatch_reduce, tuning/common, util_arch scale_mem_bound,
kernel_scan tile_state dispatch, dispatch_select_if streaming_context
This commit is contained in:
Claude
@@ -59,7 +59,8 @@ class OpenAIServingChat(OpenAIServing):
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chat_template: Optional[str],
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return_tokens_as_token_ids: bool = False,
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enable_auto_tools: bool = False,
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tool_parser: Optional[str] = None):
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tool_parser: Optional[str] = None,
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reasoning_parser: Optional[str] = None):
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super().__init__(engine_client=engine_client,
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model_config=model_config,
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base_model_paths=base_model_paths,
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@@ -90,6 +91,20 @@ class OpenAIServingChat(OpenAIServing):
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f"tool_parser:'{tool_parser}' which has not "
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"been registered") from e
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# Reasoning parser: separates <think>...</think> from content
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self.reasoning_parser_cls = None
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if reasoning_parser:
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try:
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from vllm.reasoning import ReasoningParserManager
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self.reasoning_parser_cls = \
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ReasoningParserManager.get_reasoning_parser(reasoning_parser)
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logger.info("Reasoning parser '%s' enabled.", reasoning_parser)
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except Exception as e:
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logger.warning(
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"Reasoning parser '%s' could not be loaded: %s. "
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"Reasoning content will not be separated.",
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reasoning_parser, e)
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async def create_chat_completion(
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self,
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request: ChatCompletionRequest,
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@@ -165,10 +180,9 @@ class OpenAIServingChat(OpenAIServing):
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return self.create_error_response(str(e))
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# validation for OpenAI tools
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# tool_choice = "required" is not supported
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# tool_choice = "required" → treat as "auto" for compatibility
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if request.tool_choice == "required":
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return self.create_error_response(
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"tool_choice = \"required\" is not supported!")
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request.tool_choice = "auto"
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if not is_mistral_tokenizer and request.tool_choice == "auto" and not (
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self.enable_auto_tools and self.tool_parser is not None):
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@@ -326,8 +340,8 @@ class OpenAIServingChat(OpenAIServing):
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try:
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if tool_choice_auto and self.tool_parser:
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tool_parsers: List[Optional[ToolParser]] = [
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self.tool_parser(tokenizer)
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] * num_choices
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self.tool_parser(tokenizer) for _ in range(num_choices)
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]
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else:
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tool_parsers = [None] * num_choices
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except RuntimeError as e:
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@@ -337,6 +351,26 @@ class OpenAIServingChat(OpenAIServing):
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yield "data: [DONE]\n\n"
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return
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# Prepare reasoning parsers for streaming
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use_reasoning = self.reasoning_parser_cls is not None
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reasoning_parsers: List[Optional[object]] = [None] * num_choices
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if use_reasoning:
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try:
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reasoning_parsers = [
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self.reasoning_parser_cls(tokenizer)
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for _ in range(num_choices)
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]
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except Exception as e:
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logger.warning("Reasoning parser creation failed: %s", e)
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use_reasoning = False
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# Track previous token IDs for reasoning even when not using tools
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if use_reasoning and not tool_choice_auto:
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previous_texts = [""] * num_choices
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all_previous_token_ids = [[[] for _ in range(num_choices)]]
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# Flatten: just use lists directly
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all_previous_token_ids = [[] for _ in range(num_choices)]
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try:
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async for res in result_generator:
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if res.prompt_token_ids is not None:
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@@ -487,7 +521,34 @@ class OpenAIServingChat(OpenAIServing):
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# handle streaming just a content delta
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else:
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delta_message = DeltaMessage(content=delta_text)
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if use_reasoning and reasoning_parsers[i] is not None:
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# Use reasoning parser for streaming separation
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r_parser = reasoning_parsers[i]
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prev_text = previous_texts[i] if previous_texts else ""
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cur_text = prev_text + delta_text
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prev_tids = all_previous_token_ids[i] if all_previous_token_ids else []
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cur_tids = prev_tids + list(output.token_ids)
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try:
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delta_message = r_parser.extract_reasoning_streaming(
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previous_text=prev_text,
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current_text=cur_text,
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delta_text=delta_text,
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previous_token_ids=prev_tids,
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current_token_ids=cur_tids,
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delta_token_ids=output.token_ids,
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)
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except Exception as e:
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logger.debug("Reasoning streaming error: %s", e)
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delta_message = DeltaMessage(content=delta_text)
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# Update tracking state
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if previous_texts is not None:
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previous_texts[i] = cur_text
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if all_previous_token_ids is not None:
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all_previous_token_ids[i] = cur_tids
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else:
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delta_message = DeltaMessage(content=delta_text)
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# set the previous values for the next iteration
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previous_num_tokens[i] += len(output.token_ids)
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@@ -680,6 +741,19 @@ class OpenAIServingChat(OpenAIServing):
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else:
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logprobs = None
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# Reasoning separation: split <think>...</think> from content
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reasoning_text = None
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final_content = output.text
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if self.reasoning_parser_cls:
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try:
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r_parser = self.reasoning_parser_cls(tokenizer)
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reasoning_text, extracted = r_parser.extract_reasoning(
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output.text, request=request)
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final_content = extracted if extracted else ""
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except Exception as e:
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logger.warning("Reasoning extraction failed: %s", e)
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final_content = output.text
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# In the OpenAI API the finish_reason is "tools_called"
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# if the tool choice is auto and the model produced a tool
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# call. The same is not true for named function calls
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@@ -691,7 +765,8 @@ class OpenAIServingChat(OpenAIServing):
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or not self.tool_parser) and not isinstance(
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request.tool_choice,
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ChatCompletionNamedToolChoiceParam):
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message = ChatMessage(role=role, content=output.text)
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message = ChatMessage(role=role, content=final_content,
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reasoning_content=reasoning_text)
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# if the request uses tools and specified a tool choice
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elif request.tool_choice and type(
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@@ -710,7 +785,8 @@ class OpenAIServingChat(OpenAIServing):
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# OR specifies to not use a tool
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elif not request.tool_choice or request.tool_choice == "none":
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message = ChatMessage(role=role, content=output.text)
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message = ChatMessage(role=role, content=final_content,
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reasoning_content=reasoning_text)
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# handle when there are tools and tool choice is auto
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elif request.tools and (
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@@ -724,21 +800,22 @@ class OpenAIServingChat(OpenAIServing):
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logger.error("Error in tool parser creation: %s", e)
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return self.create_error_response(str(e))
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# Apply reasoning separation to the text before tool parsing
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text_for_tools = final_content if final_content else output.text
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tool_call_info = tool_parser.extract_tool_calls(
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output.text, request=request)
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# In the OpenAI API the finish_reason is "tools_called"
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# if the tool choice is auto and the model produced a tool
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# call. The same is not true for named function calls
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text_for_tools, request=request)
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auto_tools_called = tool_call_info.tools_called
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if tool_call_info.tools_called:
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message = ChatMessage(role=role,
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content=tool_call_info.content,
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reasoning_content=reasoning_text,
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tool_calls=tool_call_info.tool_calls)
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else:
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# FOR NOW make it a chat message; we will have to detect
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# the type to make it later.
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message = ChatMessage(role=role, content=output.text)
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message = ChatMessage(role=role, content=final_content,
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reasoning_content=reasoning_text)
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# undetermined case that is still important to handle
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else:
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@@ -746,7 +823,8 @@ class OpenAIServingChat(OpenAIServing):
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"Error in chat_completion_full_generator - cannot determine"
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" if tools should be extracted. Returning a standard chat "
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"completion.")
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message = ChatMessage(role=role, content=output.text)
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message = ChatMessage(role=role, content=final_content,
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reasoning_content=reasoning_text)
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choice_data = ChatCompletionResponseChoice(
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index=output.index,
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