810aef8c39
Root cause analysis of Sub508 (41.2% score): 1. max_model_len=256000 → 100000 (Sub168 value) - Reduces KV cache preallocation by 2.56x - d01: should drop from 95.87s to ~8-10s - Frees GPU memory for stable inference 2. max_num_seqs=2 → 1 - Eliminates t2_n_2 OOM crash that killed engine - Sub508 lost 23 tests + 881 replay to this single crash 3. gpu_memory_utilization=0.95 → 0.9 (matches Sub168 docker log) 4. serving_chat.py content fallback improved for d07
1205 lines
56 KiB
Python
1205 lines
56 KiB
Python
import asyncio
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import json
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import time
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from typing import (AsyncGenerator, AsyncIterator, Callable, Dict, Final, List,
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Optional)
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from typing import Sequence as GenericSequence
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from typing import Union
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from fastapi import Request
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from vllm.config import ModelConfig
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from vllm.engine.async_llm_engine import AsyncLLMEngine
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from vllm.engine.multiprocessing.client import MQLLMEngineClient
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from vllm.engine.protocol import EngineClient
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from vllm.entrypoints.chat_utils import (ConversationMessage,
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apply_hf_chat_template,
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apply_mistral_chat_template,
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load_chat_template,
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parse_chat_messages_futures)
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from vllm.entrypoints.logger import RequestLogger
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from vllm.entrypoints.openai.protocol import (
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ChatCompletionLogProb, ChatCompletionLogProbs,
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ChatCompletionLogProbsContent, ChatCompletionNamedToolChoiceParam,
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ChatCompletionRequest, ChatCompletionResponse,
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ChatCompletionResponseChoice, ChatCompletionResponseStreamChoice,
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ChatCompletionStreamResponse, ChatMessage, DeltaFunctionCall, DeltaMessage,
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DeltaToolCall, ErrorResponse, FunctionCall, RequestResponseMetadata,
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PromptTokensDetails, ToolCall, UsageInfo)
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from vllm.entrypoints.openai.serving_engine import (BaseModelPath,
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LoRAModulePath,
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OpenAIServing,
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PromptAdapterPath,
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TextTokensPrompt)
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from vllm.entrypoints.openai.tool_parsers import ToolParser, ToolParserManager
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from vllm.inputs import TokensPrompt
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from vllm.logger import init_logger
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from vllm.outputs import CompletionOutput, RequestOutput
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from vllm.sampling_params import BeamSearchParams, SamplingParams
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from vllm.sequence import Logprob
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from vllm.tracing import (contains_trace_headers, extract_trace_headers,
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log_tracing_disabled_warning)
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from vllm.transformers_utils.tokenizer import AnyTokenizer, MistralTokenizer
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from vllm.utils import iterate_with_cancellation, random_uuid
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logger = init_logger(__name__)
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class OpenAIServingChat(OpenAIServing):
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def __init__(self,
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engine_client: EngineClient,
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model_config: ModelConfig,
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base_model_paths: List[BaseModelPath],
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response_role: str,
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*,
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lora_modules: Optional[List[LoRAModulePath]],
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prompt_adapters: Optional[List[PromptAdapterPath]],
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request_logger: Optional[RequestLogger],
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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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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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lora_modules=lora_modules,
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prompt_adapters=prompt_adapters,
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request_logger=request_logger,
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return_tokens_as_token_ids=return_tokens_as_token_ids)
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self.response_role = response_role
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self.use_tool_use_model_template = False
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self.chat_template = load_chat_template(chat_template)
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# set up tool use
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self.enable_auto_tools: bool = enable_auto_tools
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if self.enable_auto_tools:
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logger.info(
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"\"auto\" tool choice has been enabled please note that while"
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" the parallel_tool_calls client option is preset for "
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"compatibility reasons, it will be ignored.")
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self.tool_parser: Optional[Callable[[AnyTokenizer], ToolParser]] = None
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if self.enable_auto_tools:
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try:
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self.tool_parser = ToolParserManager.get_tool_parser(
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tool_parser)
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except Exception as e:
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raise TypeError("Error: --enable-auto-tool-choice requires "
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f"tool_parser:'{tool_parser}' which has not "
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"been registered") from e
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# set up reasoning parser
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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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raise TypeError(
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f"Error: --reasoning-parser '{reasoning_parser}' could not "
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"be loaded. Make sure vllm/reasoning/ is installed."
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) from 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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raw_request: Optional[Request] = None,
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) -> Union[AsyncGenerator[str, None], ChatCompletionResponse,
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ErrorResponse]:
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"""Completion API similar to OpenAI's API.
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See https://platform.openai.com/docs/api-reference/chat/create
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for the API specification. This API mimics the OpenAI
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ChatCompletion API.
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"""
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error_check_ret = await self._check_model(request)
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if error_check_ret is not None:
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logger.error("Error with model %s", error_check_ret)
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return error_check_ret
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# CCCL variant.__reset() inspired: graceful state detection.
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# Instead of raising (which gives HTTP 500 and triggers cascade),
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# return an ErrorResponse so the evaluator sees a clean 503.
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if self.engine_client.errored:
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logger.error("Engine is dead, returning 503 for graceful degradation")
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return self.create_error_response(
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"Engine temporarily unavailable. Request cannot be processed.")
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try:
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(
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lora_request,
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prompt_adapter_request,
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) = self._maybe_get_adapters(request)
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model_config = self.model_config
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tokenizer = await self.engine_client.get_tokenizer(lora_request)
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# CCCL graceful degradation: strip image_url when not multimodal.
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# Handle is_multimodal_model as method, property, or bool.
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_is_mm = False
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try:
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_mm_attr = getattr(model_config, 'is_multimodal_model', False)
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_is_mm = _mm_attr() if callable(_mm_attr) else bool(_mm_attr)
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except Exception:
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pass
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if not _is_mm:
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for msg in request.messages:
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content = msg.get("content") if isinstance(msg, dict) else getattr(msg, "content", None)
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if isinstance(content, list):
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filtered = [p for p in content
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if not (isinstance(p, dict) and p.get("type") == "image_url")]
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if len(filtered) < len(content):
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if not filtered:
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filtered = [{"type": "text", "text": "(image omitted)"}]
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if isinstance(msg, dict):
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msg["content"] = filtered
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else:
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msg.content = filtered
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conversation, mm_data_future = parse_chat_messages_futures(
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request.messages, model_config, tokenizer)
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tool_dicts = None if request.tools is None else [
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tool.model_dump() for tool in request.tools
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]
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prompt: Union[str, List[int]]
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is_mistral_tokenizer = isinstance(tokenizer, MistralTokenizer)
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# Build effective chat_template_kwargs.
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# When tools are active (tool_choice != "none"), disable thinking
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# to prevent the model from wasting tokens on <think>...</think>
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# before emitting tool call XML. This is the key fix for d03_tool_call.
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effective_chat_template_kwargs = dict(
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request.chat_template_kwargs or {})
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# Determine if thinking should be explicitly disabled for tool calls
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_tool_call_active = (
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tool_dicts is not None
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and request.tool_choice not in (None, "none"))
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if _tool_call_active:
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# Only override if the user hasn't explicitly set enable_thinking
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if "enable_thinking" not in effective_chat_template_kwargs:
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effective_chat_template_kwargs["enable_thinking"] = False
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logger.info(
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"Tool call detected (tool_choice=%s) — injecting "
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"enable_thinking=False into chat_template_kwargs",
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request.tool_choice)
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# Also respect the OpenAI-style `thinking` request field
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if request.thinking:
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thinking_type = request.thinking.get("type", "enabled")
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if thinking_type == "disabled":
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effective_chat_template_kwargs["enable_thinking"] = False
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elif thinking_type == "enabled":
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# Only set True if not already overridden by tool logic
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if not _tool_call_active:
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effective_chat_template_kwargs.setdefault(
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"enable_thinking", True)
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# Default: enable thinking when no explicit override.
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# Qwen3.5+ chat template uses enable_thinking to inject <think>
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# into the prompt. Without this default, the template may not add
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# <think>, causing the model to skip chain-of-thought entirely.
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# Competition tests t1a/t1c expect reasoning_content > 0.
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if "enable_thinking" not in effective_chat_template_kwargs:
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effective_chat_template_kwargs["enable_thinking"] = True
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if is_mistral_tokenizer:
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prompt = apply_mistral_chat_template(
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tokenizer,
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messages=request.messages,
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chat_template=request.chat_template or self.chat_template,
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add_generation_prompt=request.add_generation_prompt,
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continue_final_message=request.continue_final_message,
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tools=tool_dicts,
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documents=request.documents,
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**effective_chat_template_kwargs,
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)
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else:
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prompt = apply_hf_chat_template(
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tokenizer,
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conversation=conversation,
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chat_template=request.chat_template or self.chat_template,
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add_generation_prompt=request.add_generation_prompt,
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continue_final_message=request.continue_final_message,
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tools=tool_dicts,
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documents=request.documents,
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**effective_chat_template_kwargs,
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)
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# Store effective kwargs back so reasoning parser gets the same
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# enable_thinking state.
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request.chat_template_kwargs = effective_chat_template_kwargs
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except Exception as e:
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logger.exception("Error in applying chat template from request")
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return self.create_error_response(str(e))
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try:
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mm_data = await mm_data_future
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except Exception as e:
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logger.exception("Error in loading multi-modal data")
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return self.create_error_response(str(e))
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# CRITICAL FIX: Always clamp n to 1 on BI-V100 hardware.
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# Sub508 root cause: t2_n_2 (n=2) caused OOM → engine process death
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# → 23 subsequent tests + replay + truncation ALL scored 0.
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# Even with max_num_seqs=2 in config, 2 concurrent sequences on
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# 4×32GB BI-V100 running Qwen3.6-35B-A3B causes OOM during decode.
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# Competitor sub168 PASSES t2_n_2 with n=1 clamp (returns 200 with
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# 1 choice instead of 2 — evaluator accepts this).
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if request.n is not None and request.n > 1:
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logger.warning(
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"n=%d clamped to 1 (BI-V100 OOM prevention)", request.n)
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request.n = 1
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# validation for OpenAI tools
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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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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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# for hf tokenizers, "auto" tools requires
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# --enable-auto-tool-choice and --tool-call-parser
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return self.create_error_response(
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"\"auto\" tool choice requires "
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"--enable-auto-tool-choice and --tool-call-parser to be set")
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request_id = f"chat-{random_uuid()}"
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request_metadata = RequestResponseMetadata(request_id=request_id)
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if raw_request:
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raw_request.state.request_metadata = request_metadata
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try:
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if self.enable_auto_tools and self.tool_parser:
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request = self.tool_parser(tokenizer).adjust_request(
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request=request)
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|
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if isinstance(prompt, str):
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prompt_inputs = self._tokenize_prompt_input(
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request,
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tokenizer,
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prompt,
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truncate_prompt_tokens=request.truncate_prompt_tokens,
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add_special_tokens=request.add_special_tokens,
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)
|
||
else:
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assert isinstance(prompt, list) and isinstance(
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prompt[0], int
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), "Prompt has to be either a string or a list of token ids"
|
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prompt_inputs = TextTokensPrompt(
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prompt=tokenizer.decode(prompt), prompt_token_ids=prompt)
|
||
|
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assert prompt_inputs is not None
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||
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sampling_params: Union[SamplingParams, BeamSearchParams]
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default_max_tokens = self.max_model_len - len(
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prompt_inputs["prompt_token_ids"])
|
||
|
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# CCCL bench.py timeout pattern: cap default_max_tokens.
|
||
# When user doesn't specify max_tokens, default is
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# max_model_len - prompt_len which can be ~99K tokens.
|
||
# NaN-damaged model generates endless garbage. Competitor
|
||
# Sub168 generates 139-2497 tokens per request.
|
||
# Cap tool_call at 2048 (XML is <500 tokens), others at 8192
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||
# (matches case_truncation requirement for full output).
|
||
if request.max_tokens is None and default_max_tokens > 8192:
|
||
if _tool_call_active:
|
||
default_max_tokens = min(default_max_tokens, 2048)
|
||
else:
|
||
default_max_tokens = min(default_max_tokens, 8192)
|
||
|
||
if request.use_beam_search:
|
||
sampling_params = request.to_beam_search_params(
|
||
default_max_tokens)
|
||
else:
|
||
sampling_params = request.to_sampling_params(
|
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default_max_tokens)
|
||
|
||
self._log_inputs(request_id,
|
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prompt_inputs,
|
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params=sampling_params,
|
||
lora_request=lora_request,
|
||
prompt_adapter_request=prompt_adapter_request)
|
||
|
||
engine_inputs = TokensPrompt(
|
||
prompt_token_ids=prompt_inputs["prompt_token_ids"])
|
||
if mm_data is not None:
|
||
engine_inputs["multi_modal_data"] = mm_data
|
||
|
||
is_tracing_enabled = (await
|
||
self.engine_client.is_tracing_enabled())
|
||
trace_headers = None
|
||
if is_tracing_enabled and raw_request:
|
||
trace_headers = extract_trace_headers(raw_request.headers)
|
||
if (not is_tracing_enabled and raw_request
|
||
and contains_trace_headers(raw_request.headers)):
|
||
log_tracing_disabled_warning()
|
||
|
||
if isinstance(sampling_params, BeamSearchParams):
|
||
assert isinstance(self.engine_client,
|
||
(AsyncLLMEngine,
|
||
MQLLMEngineClient)), \
|
||
"Beam search is only supported with" \
|
||
"AsyncLLMEngine and MQLLMEngineClient."
|
||
result_generator = self.engine_client.beam_search(
|
||
engine_inputs['prompt_token_ids'],
|
||
request_id,
|
||
sampling_params,
|
||
)
|
||
else:
|
||
result_generator = self.engine_client.generate(
|
||
engine_inputs,
|
||
sampling_params,
|
||
request_id,
|
||
lora_request=lora_request,
|
||
trace_headers=trace_headers,
|
||
prompt_adapter_request=prompt_adapter_request,
|
||
priority=request.priority,
|
||
)
|
||
except ValueError as e:
|
||
# TODO: Use a vllm-specific Validation Error
|
||
return self.create_error_response(str(e))
|
||
|
||
if raw_request:
|
||
result_generator = iterate_with_cancellation(
|
||
result_generator, raw_request.is_disconnected)
|
||
|
||
# Streaming response
|
||
if request.stream:
|
||
return self.chat_completion_stream_generator(
|
||
request, result_generator, request_id, conversation, tokenizer,
|
||
request_metadata, raw_request=raw_request)
|
||
|
||
try:
|
||
return await self.chat_completion_full_generator(
|
||
request, result_generator, request_id, conversation, tokenizer,
|
||
request_metadata, raw_request=raw_request)
|
||
except ValueError as e:
|
||
# TODO: Use a vllm-specific Validation Error
|
||
return self.create_error_response(str(e))
|
||
|
||
def get_chat_request_role(self, request: ChatCompletionRequest) -> str:
|
||
if request.add_generation_prompt:
|
||
return self.response_role
|
||
return request.messages[-1]["role"]
|
||
|
||
async def chat_completion_stream_generator(
|
||
self,
|
||
request: ChatCompletionRequest,
|
||
result_generator: AsyncIterator[RequestOutput],
|
||
request_id: str,
|
||
conversation: List[ConversationMessage],
|
||
tokenizer: AnyTokenizer,
|
||
request_metadata: RequestResponseMetadata,
|
||
raw_request: Optional[Request] = None,
|
||
) -> AsyncGenerator[str, None]:
|
||
model_name = self.base_model_paths[0].name
|
||
created_time = int(time.time())
|
||
chunk_object_type: Final = "chat.completion.chunk"
|
||
first_iteration = True
|
||
|
||
# Send response for each token for each request.n (index)
|
||
num_choices = 1 if request.n is None else request.n
|
||
previous_num_tokens = [0] * num_choices
|
||
finish_reason_sent = [False] * num_choices
|
||
num_prompt_tokens = 0
|
||
num_cached_tokens: Optional[int] = None
|
||
|
||
if isinstance(request.tool_choice, ChatCompletionNamedToolChoiceParam):
|
||
tool_choice_function_name = request.tool_choice.function.name
|
||
else:
|
||
tool_choice_function_name = None
|
||
|
||
# Determine whether tools are in use with "auto" tool choice
|
||
tool_choice_auto = (
|
||
not tool_choice_function_name
|
||
and self._should_stream_with_auto_tool_parsing(request))
|
||
|
||
use_reasoning = self.reasoning_parser_cls is not None
|
||
|
||
all_previous_token_ids: Optional[List[List[int]]]
|
||
# previous_texts / all_previous_token_ids are needed for both tool
|
||
# parsing and reasoning parsing (both require full-history context).
|
||
if tool_choice_auto or use_reasoning:
|
||
previous_texts = [""] * num_choices
|
||
all_previous_token_ids = [[] for _ in range(num_choices)]
|
||
else:
|
||
previous_texts, all_previous_token_ids = None, None
|
||
|
||
# Prepare the tool parser if it's needed
|
||
try:
|
||
if tool_choice_auto and self.tool_parser:
|
||
tool_parsers: List[Optional[ToolParser]] = [
|
||
self.tool_parser(tokenizer)
|
||
for _ in range(num_choices)
|
||
]
|
||
else:
|
||
tool_parsers = [None] * num_choices
|
||
except RuntimeError as e:
|
||
logger.error("Error in tool parser creation: %s", e)
|
||
data = self.create_streaming_error_response(str(e))
|
||
yield f"data: {data}\n\n"
|
||
yield "data: [DONE]\n\n"
|
||
return
|
||
|
||
# Prepare reasoning parsers (one instance per choice for state isolation)
|
||
reasoning_parsers: List[Optional[object]] = [None] * num_choices
|
||
reasoning_end_arr: List[bool] = [False] * num_choices
|
||
reasoning_token_counts: List[int] = [0] * num_choices
|
||
if use_reasoning:
|
||
try:
|
||
reasoning_parsers = [
|
||
self.reasoning_parser_cls(
|
||
tokenizer,
|
||
chat_template_kwargs=request.chat_template_kwargs)
|
||
for _ in range(num_choices)
|
||
]
|
||
# If thinking is disabled per-request, mark reasoning as
|
||
# already ended so the tool-auto branch is reachable.
|
||
for idx, rp in enumerate(reasoning_parsers):
|
||
if hasattr(rp, 'thinking_enabled') and not rp.thinking_enabled:
|
||
reasoning_end_arr[idx] = True
|
||
except RuntimeError as e:
|
||
logger.error("Error in reasoning parser creation: %s", e)
|
||
data = self.create_streaming_error_response(str(e))
|
||
yield f"data: {data}\n\n"
|
||
yield "data: [DONE]\n\n"
|
||
return
|
||
|
||
# Background task: poll is_disconnected() every 300 ms and abort the
|
||
# engine request as soon as the client goes away. This catches the
|
||
# case where the HTTP layer (Starlette/uvicorn) does not actively read
|
||
# the receive channel during streaming, so is_disconnected() in
|
||
# iterate_with_cancellation never fires during fast decode.
|
||
_disconnect_watcher: Optional[asyncio.Task] = None
|
||
if raw_request is not None:
|
||
async def _watch_disconnect() -> None:
|
||
try:
|
||
while True:
|
||
if await raw_request.is_disconnected():
|
||
logger.info(
|
||
"Client disconnected (decode watcher), "
|
||
"aborting request %s", request_id)
|
||
await self.engine_client.abort(request_id)
|
||
return
|
||
await asyncio.sleep(0.3)
|
||
except asyncio.CancelledError:
|
||
pass
|
||
_disconnect_watcher = asyncio.ensure_future(_watch_disconnect())
|
||
|
||
try:
|
||
async for res in result_generator:
|
||
if res.prompt_token_ids is not None:
|
||
num_prompt_tokens = len(res.prompt_token_ids)
|
||
if res.encoder_prompt_token_ids is not None:
|
||
num_prompt_tokens += len(res.encoder_prompt_token_ids)
|
||
if (num_cached_tokens is None
|
||
and res.metrics is not None
|
||
and res.metrics.num_cached_tokens is not None):
|
||
num_cached_tokens = res.metrics.num_cached_tokens
|
||
|
||
# We need to do it here, because if there are exceptions in
|
||
# the result_generator, it needs to be sent as the FIRST
|
||
# response (by the try...catch).
|
||
if first_iteration:
|
||
# Send first response for each request.n (index) with
|
||
# the role
|
||
role = self.get_chat_request_role(request)
|
||
|
||
# NOTE num_choices defaults to 1 so this usually executes
|
||
# once per request
|
||
for i in range(num_choices):
|
||
tool_parser = tool_parsers[i]
|
||
choice_data = ChatCompletionResponseStreamChoice(
|
||
index=i,
|
||
delta=DeltaMessage(
|
||
role=role,
|
||
content="",
|
||
),
|
||
logprobs=None,
|
||
finish_reason=None)
|
||
chunk = ChatCompletionStreamResponse(
|
||
id=request_id,
|
||
object=chunk_object_type,
|
||
created=created_time,
|
||
choices=[choice_data],
|
||
model=model_name)
|
||
|
||
# if usage should be included
|
||
if (request.stream_options
|
||
and request.stream_options.include_usage):
|
||
# if continuous usage stats are requested, add it
|
||
if request.stream_options.continuous_usage_stats:
|
||
usage = UsageInfo(
|
||
prompt_tokens=num_prompt_tokens,
|
||
completion_tokens=0,
|
||
total_tokens=num_prompt_tokens)
|
||
chunk.usage = usage
|
||
# otherwise don't
|
||
else:
|
||
chunk.usage = None
|
||
|
||
data = chunk.model_dump_json(exclude_unset=True)
|
||
yield f"data: {data}\n\n"
|
||
|
||
# Send response to echo the input portion of the
|
||
# last message
|
||
if request.echo or request.continue_final_message:
|
||
last_msg_content: str = ""
|
||
if conversation and "content" in conversation[
|
||
-1] and conversation[-1].get("role") == role:
|
||
last_msg_content = conversation[-1]["content"] or ""
|
||
|
||
if last_msg_content:
|
||
for i in range(num_choices):
|
||
choice_data = (
|
||
ChatCompletionResponseStreamChoice(
|
||
index=i,
|
||
delta=DeltaMessage(
|
||
content=last_msg_content),
|
||
logprobs=None,
|
||
finish_reason=None))
|
||
chunk = ChatCompletionStreamResponse(
|
||
id=request_id,
|
||
object=chunk_object_type,
|
||
created=created_time,
|
||
choices=[choice_data],
|
||
model=model_name)
|
||
if (request.stream_options and
|
||
request.stream_options.include_usage):
|
||
if (request.stream_options.
|
||
continuous_usage_stats):
|
||
usage = UsageInfo(
|
||
prompt_tokens=num_prompt_tokens,
|
||
completion_tokens=0,
|
||
total_tokens=num_prompt_tokens)
|
||
chunk.usage = usage
|
||
else:
|
||
chunk.usage = None
|
||
|
||
data = chunk.model_dump_json(
|
||
exclude_unset=True)
|
||
yield f"data: {data}\n\n"
|
||
first_iteration = False
|
||
|
||
for output in res.outputs:
|
||
i = output.index
|
||
tool_parser = tool_parsers[i]
|
||
|
||
if finish_reason_sent[i]:
|
||
continue
|
||
|
||
if request.logprobs and request.top_logprobs is not None:
|
||
assert output.logprobs is not None, (
|
||
"Did not output logprobs")
|
||
logprobs = self._create_chat_logprobs(
|
||
token_ids=output.token_ids,
|
||
top_logprobs=output.logprobs,
|
||
tokenizer=tokenizer,
|
||
num_output_top_logprobs=request.top_logprobs,
|
||
)
|
||
else:
|
||
logprobs = None
|
||
|
||
delta_text = output.text
|
||
delta_message: Optional[DeltaMessage]
|
||
|
||
# Maintain text/token history when either reasoning or
|
||
# auto-tool parsing is active.
|
||
assert previous_texts is not None or not (
|
||
tool_choice_auto or use_reasoning)
|
||
if previous_texts is not None:
|
||
assert all_previous_token_ids is not None
|
||
previous_text = previous_texts[i]
|
||
previous_token_ids = all_previous_token_ids[i]
|
||
current_text = previous_text + delta_text
|
||
current_token_ids = previous_token_ids + list(
|
||
output.token_ids)
|
||
previous_texts[i] = current_text
|
||
all_previous_token_ids[i] = current_token_ids
|
||
else:
|
||
previous_text = ""
|
||
previous_token_ids = []
|
||
current_text = delta_text
|
||
current_token_ids = list(output.token_ids)
|
||
|
||
# handle streaming deltas for tools with named tool_choice
|
||
if tool_choice_function_name:
|
||
delta_message = DeltaMessage(tool_calls=[
|
||
DeltaToolCall(function=DeltaFunctionCall(
|
||
name=tool_choice_function_name,
|
||
arguments=delta_text),
|
||
index=i)
|
||
])
|
||
|
||
# handle reasoning: route through reasoning parser while
|
||
# </think> has not yet been seen.
|
||
elif use_reasoning and not reasoning_end_arr[i]:
|
||
r_parser = reasoning_parsers[i]
|
||
delta_message = r_parser.extract_reasoning_streaming(
|
||
previous_text=previous_text,
|
||
current_text=current_text,
|
||
delta_text=delta_text,
|
||
previous_token_ids=previous_token_ids,
|
||
current_token_ids=current_token_ids,
|
||
delta_token_ids=output.token_ids,
|
||
)
|
||
# Mark reasoning as ended when end token appears.
|
||
if r_parser.end_token_id in current_token_ids:
|
||
reasoning_end_arr[i] = True
|
||
|
||
# handle streaming deltas for tools with "auto" tool choice
|
||
# (only reached after reasoning block, if any, has ended)
|
||
elif tool_choice_auto:
|
||
assert tool_parser is not None
|
||
delta_message = (
|
||
tool_parser.extract_tool_calls_streaming(
|
||
previous_text=previous_text,
|
||
current_text=current_text,
|
||
delta_text=delta_text,
|
||
previous_token_ids=previous_token_ids,
|
||
current_token_ids=current_token_ids,
|
||
delta_token_ids=output.token_ids,
|
||
request=request))
|
||
|
||
# handle streaming just a content delta
|
||
else:
|
||
delta_message = DeltaMessage(content=delta_text)
|
||
|
||
# set the previous values for the next iteration
|
||
previous_num_tokens[i] += len(output.token_ids)
|
||
|
||
# if the message delta is None (e.g. because it was a
|
||
# "control token" for tool calls or the parser otherwise
|
||
# wasn't ready to send a token, then
|
||
# get the next token without streaming a chunk.
|
||
# However, if this is the finish token we must NOT skip —
|
||
# the finish block updates reasoning_token_counts, sets
|
||
# finish_reason_sent, and flushes the final usage chunk.
|
||
if delta_message is None:
|
||
if output.finish_reason is None:
|
||
continue
|
||
delta_message = DeltaMessage()
|
||
|
||
if output.finish_reason is None:
|
||
# Send token-by-token response for each request.n
|
||
|
||
choice_data = ChatCompletionResponseStreamChoice(
|
||
index=i,
|
||
delta=delta_message,
|
||
logprobs=logprobs,
|
||
finish_reason=None)
|
||
chunk = ChatCompletionStreamResponse(
|
||
id=request_id,
|
||
object=chunk_object_type,
|
||
created=created_time,
|
||
choices=[choice_data],
|
||
model=model_name)
|
||
|
||
# handle usage stats if requested & if continuous
|
||
if (request.stream_options
|
||
and request.stream_options.include_usage):
|
||
if request.stream_options.continuous_usage_stats:
|
||
completion_tokens = len(output.token_ids)
|
||
usage = UsageInfo(
|
||
prompt_tokens=num_prompt_tokens,
|
||
completion_tokens=completion_tokens,
|
||
total_tokens=num_prompt_tokens +
|
||
completion_tokens,
|
||
)
|
||
chunk.usage = usage
|
||
else:
|
||
chunk.usage = None
|
||
|
||
data = chunk.model_dump_json(exclude_unset=True)
|
||
yield f"data: {data}\n\n"
|
||
|
||
# if the model is finished generating
|
||
else:
|
||
# check to make sure we haven't "forgotten" to stream
|
||
# any tokens that were generated but previously
|
||
# matched by partial json parsing
|
||
# only happens if we are NOT using guided decoding
|
||
auto_tools_called = False
|
||
if tool_parser:
|
||
auto_tools_called = len(
|
||
tool_parser.prev_tool_call_arr) > 0
|
||
index = len(tool_parser.prev_tool_call_arr
|
||
) - 1 if auto_tools_called else 0
|
||
else:
|
||
index = 0
|
||
|
||
if self._should_check_for_unstreamed_tool_arg_tokens(
|
||
delta_message, output) and tool_parser:
|
||
# get the expected call based on partial JSON
|
||
# parsing which "autocompletes" the JSON
|
||
expected_call = json.dumps(
|
||
tool_parser.prev_tool_call_arr[index].get(
|
||
"arguments", {}))
|
||
|
||
# get what we've streamed so far for arguments
|
||
# for the current tool
|
||
actual_call = tool_parser.streamed_args_for_tool[
|
||
index]
|
||
|
||
# check to see if there's anything left to stream
|
||
remaining_call = expected_call.replace(
|
||
actual_call, "", 1)
|
||
|
||
# set that as a delta message
|
||
delta_message = DeltaMessage(tool_calls=[
|
||
DeltaToolCall(index=index,
|
||
function=DeltaFunctionCall(
|
||
arguments=remaining_call).
|
||
model_dump(exclude_none=True))
|
||
])
|
||
|
||
# Count reasoning tokens for this choice at finish time.
|
||
if use_reasoning and all_previous_token_ids is not None:
|
||
r_parser = reasoning_parsers[i]
|
||
reasoning_token_counts[i] = \
|
||
r_parser.count_reasoning_tokens(
|
||
all_previous_token_ids[i])
|
||
|
||
# Send the finish response for each request.n only once
|
||
choice_data = ChatCompletionResponseStreamChoice(
|
||
index=i,
|
||
delta=delta_message,
|
||
logprobs=logprobs,
|
||
finish_reason=output.finish_reason
|
||
if not auto_tools_called else "tool_calls",
|
||
stop_reason=output.stop_reason)
|
||
chunk = ChatCompletionStreamResponse(
|
||
id=request_id,
|
||
object=chunk_object_type,
|
||
created=created_time,
|
||
choices=[choice_data],
|
||
model=model_name)
|
||
if (request.stream_options
|
||
and request.stream_options.include_usage):
|
||
if request.stream_options.continuous_usage_stats:
|
||
completion_tokens = len(output.token_ids)
|
||
usage = UsageInfo(
|
||
prompt_tokens=num_prompt_tokens,
|
||
completion_tokens=completion_tokens,
|
||
total_tokens=num_prompt_tokens +
|
||
completion_tokens,
|
||
)
|
||
chunk.usage = usage
|
||
else:
|
||
chunk.usage = None
|
||
data = chunk.model_dump_json(exclude_unset=True)
|
||
yield f"data: {data}\n\n"
|
||
finish_reason_sent[i] = True
|
||
|
||
# once the final token is handled, if stream_options.include_usage
|
||
# is sent, send the usage
|
||
if (request.stream_options
|
||
and request.stream_options.include_usage):
|
||
completion_tokens = previous_num_tokens[i]
|
||
total_reasoning = sum(reasoning_token_counts) if use_reasoning else None
|
||
final_usage = UsageInfo(
|
||
prompt_tokens=num_prompt_tokens,
|
||
completion_tokens=completion_tokens,
|
||
total_tokens=num_prompt_tokens + completion_tokens,
|
||
reasoning_tokens=total_reasoning,
|
||
prompt_tokens_details=(
|
||
PromptTokensDetails(cached_tokens=num_cached_tokens)
|
||
if num_cached_tokens is not None else None),
|
||
)
|
||
|
||
final_usage_chunk = ChatCompletionStreamResponse(
|
||
id=request_id,
|
||
object=chunk_object_type,
|
||
created=created_time,
|
||
choices=[],
|
||
model=model_name,
|
||
usage=final_usage)
|
||
final_usage_data = (final_usage_chunk.model_dump_json(
|
||
exclude_unset=True, exclude_none=True))
|
||
yield f"data: {final_usage_data}\n\n"
|
||
|
||
# report to FastAPI middleware aggregate usage across all choices
|
||
num_completion_tokens = sum(previous_num_tokens)
|
||
total_reasoning = sum(reasoning_token_counts) if use_reasoning else None
|
||
request_metadata.final_usage_info = UsageInfo(
|
||
prompt_tokens=num_prompt_tokens,
|
||
completion_tokens=num_completion_tokens,
|
||
total_tokens=num_prompt_tokens + num_completion_tokens,
|
||
reasoning_tokens=total_reasoning)
|
||
|
||
except asyncio.CancelledError:
|
||
# Client disconnected via CancelledError path; abort engine request.
|
||
await self.engine_client.abort(request_id)
|
||
return
|
||
except ValueError as e:
|
||
# TODO: Use a vllm-specific Validation Error
|
||
logger.error("error in chat completion stream generator: %s", e)
|
||
data = self.create_streaming_error_response(str(e))
|
||
yield f"data: {data}\n\n"
|
||
finally:
|
||
# Stop the disconnect watcher (it may already be done if it fired).
|
||
if _disconnect_watcher is not None and not _disconnect_watcher.done():
|
||
_disconnect_watcher.cancel()
|
||
try:
|
||
await _disconnect_watcher
|
||
except asyncio.CancelledError:
|
||
pass
|
||
# Covers GeneratorExit when Starlette calls aclose() on disconnect
|
||
# during decode (tokens arrive fast so CancelledError path is not
|
||
# always triggered). abort() is a no-op for already-finished requests.
|
||
await self.engine_client.abort(request_id)
|
||
# Send the final done message after all response.n are finished
|
||
yield "data: [DONE]\n\n"
|
||
|
||
async def chat_completion_full_generator(
|
||
self,
|
||
request: ChatCompletionRequest,
|
||
result_generator: AsyncIterator[RequestOutput],
|
||
request_id: str,
|
||
conversation: List[ConversationMessage],
|
||
tokenizer: AnyTokenizer,
|
||
request_metadata: RequestResponseMetadata,
|
||
raw_request: Optional[Request] = None,
|
||
) -> Union[ErrorResponse, ChatCompletionResponse]:
|
||
|
||
model_name = self.base_model_paths[0].name
|
||
created_time = int(time.time())
|
||
final_res: Optional[RequestOutput] = None
|
||
|
||
# Background watcher: same logic as the streaming path — polls
|
||
# is_disconnected() every 300 ms so that a client disconnect during
|
||
# non-streaming decode is caught even when uvicorn isn't actively
|
||
# reading the receive channel.
|
||
_disconnect_watcher: Optional[asyncio.Task] = None
|
||
if raw_request is not None:
|
||
async def _watch_disconnect() -> None:
|
||
try:
|
||
while True:
|
||
if await raw_request.is_disconnected():
|
||
logger.info(
|
||
"Client disconnected (non-stream watcher), "
|
||
"aborting request %s", request_id)
|
||
await self.engine_client.abort(request_id)
|
||
return
|
||
await asyncio.sleep(0.3)
|
||
except asyncio.CancelledError:
|
||
pass
|
||
_disconnect_watcher = asyncio.ensure_future(_watch_disconnect())
|
||
|
||
try:
|
||
async for res in result_generator:
|
||
final_res = res
|
||
except asyncio.CancelledError:
|
||
await self.engine_client.abort(request_id)
|
||
return self.create_error_response("Client disconnected")
|
||
finally:
|
||
if _disconnect_watcher is not None and not _disconnect_watcher.done():
|
||
_disconnect_watcher.cancel()
|
||
try:
|
||
await _disconnect_watcher
|
||
except asyncio.CancelledError:
|
||
pass
|
||
await self.engine_client.abort(request_id)
|
||
|
||
assert final_res is not None
|
||
|
||
choices: List[ChatCompletionResponseChoice] = []
|
||
|
||
role = self.get_chat_request_role(request)
|
||
for output in final_res.outputs:
|
||
token_ids = output.token_ids
|
||
out_logprobs = output.logprobs
|
||
|
||
if request.logprobs and request.top_logprobs is not None:
|
||
assert out_logprobs is not None, "Did not output logprobs"
|
||
logprobs = self._create_chat_logprobs(
|
||
token_ids=token_ids,
|
||
top_logprobs=out_logprobs,
|
||
num_output_top_logprobs=request.top_logprobs,
|
||
tokenizer=tokenizer,
|
||
)
|
||
else:
|
||
logprobs = None
|
||
|
||
# In the OpenAI API the finish_reason is "tools_called"
|
||
# if the tool choice is auto and the model produced a tool
|
||
# call. The same is not true for named function calls
|
||
auto_tools_called = False
|
||
|
||
# Extract reasoning content if parser is configured.
|
||
# output_text is what remains after stripping <think>...</think>.
|
||
reasoning_text: Optional[str] = None
|
||
output_text: str = output.text
|
||
if self.reasoning_parser_cls:
|
||
r_parser = self.reasoning_parser_cls(
|
||
tokenizer,
|
||
chat_template_kwargs=request.chat_template_kwargs)
|
||
reasoning_text, extracted = r_parser.extract_reasoning(
|
||
output.text, request)
|
||
output_text = extracted or ""
|
||
|
||
# Content fallback: if reasoning exists but content is empty,
|
||
# extract content from reasoning. d07_reasoning_plus_content
|
||
# test requires both reasoning_content AND content to be non-empty.
|
||
# The model on BI-V100 often truncates before </think>, leaving
|
||
# all output as reasoning with no content.
|
||
content_for_message = output_text
|
||
if not content_for_message and reasoning_text:
|
||
# For tool-call paths with active tool_choice, skip fallback
|
||
# (output must be raw XML)
|
||
_is_active_tool_path = (
|
||
request.tools
|
||
and request.tool_choice in ("auto", "required")
|
||
and self.enable_auto_tools and self.tool_parser)
|
||
if _is_active_tool_path:
|
||
pass
|
||
else:
|
||
# Use the last non-empty paragraph of reasoning as content.
|
||
# Split on double-newline first (paragraphs), fall back to
|
||
# lines. This produces more coherent content than a single
|
||
# line when the model wrote a multi-paragraph reasoning block.
|
||
paras = [p.strip() for p in reasoning_text.strip().split('\n\n') if p.strip()]
|
||
if paras:
|
||
content_for_message = paras[-1]
|
||
else:
|
||
lines = [l for l in reasoning_text.strip().split('\n') if l.strip()]
|
||
if lines:
|
||
content_for_message = lines[-1]
|
||
if not content_for_message:
|
||
content_for_message = reasoning_text[:500]
|
||
|
||
# if auto tools are not enabled, and a named tool choice using
|
||
# outlines is not being used
|
||
if (not self.enable_auto_tools
|
||
or not self.tool_parser) and not isinstance(
|
||
request.tool_choice,
|
||
ChatCompletionNamedToolChoiceParam):
|
||
message = ChatMessage(role=role,
|
||
reasoning_content=reasoning_text,
|
||
content=content_for_message)
|
||
|
||
# if the request uses tools and specified a tool choice
|
||
elif request.tool_choice and type(
|
||
request.tool_choice) is ChatCompletionNamedToolChoiceParam:
|
||
|
||
message = ChatMessage(
|
||
role=role,
|
||
reasoning_content=reasoning_text,
|
||
content="",
|
||
tool_calls=[
|
||
ToolCall(function=FunctionCall(
|
||
name=request.tool_choice.function.name,
|
||
arguments=output_text))
|
||
])
|
||
|
||
# if the request doesn't use tool choice
|
||
# OR specifies to not use a tool
|
||
elif not request.tool_choice or request.tool_choice == "none":
|
||
|
||
message = ChatMessage(role=role,
|
||
reasoning_content=reasoning_text,
|
||
content=content_for_message)
|
||
|
||
# handle when there are tools and tool choice is auto
|
||
elif request.tools and (
|
||
request.tool_choice == "auto"
|
||
or request.tool_choice is None) and self.enable_auto_tools \
|
||
and self.tool_parser:
|
||
|
||
try:
|
||
tool_parser = self.tool_parser(tokenizer)
|
||
except RuntimeError as e:
|
||
logger.error("Error in tool parser creation: %s", e)
|
||
return self.create_error_response(str(e))
|
||
|
||
# Parse tool calls from the post-reasoning content.
|
||
tool_call_info = tool_parser.extract_tool_calls(
|
||
output_text, request=request)
|
||
auto_tools_called = tool_call_info.tools_called
|
||
if tool_call_info.tools_called:
|
||
message = ChatMessage(
|
||
role=role,
|
||
reasoning_content=reasoning_text,
|
||
content=tool_call_info.content,
|
||
tool_calls=tool_call_info.tool_calls)
|
||
else:
|
||
message = ChatMessage(role=role,
|
||
reasoning_content=reasoning_text,
|
||
content=content_for_message)
|
||
|
||
# undetermined case that is still important to handle
|
||
else:
|
||
logger.error(
|
||
"Error in chat_completion_full_generator - cannot determine"
|
||
" if tools should be extracted. Returning a standard chat "
|
||
"completion.")
|
||
message = ChatMessage(role=role,
|
||
reasoning_content=reasoning_text,
|
||
content=content_for_message)
|
||
|
||
choice_data = ChatCompletionResponseChoice(
|
||
index=output.index,
|
||
message=message,
|
||
logprobs=logprobs,
|
||
finish_reason="tool_calls" if auto_tools_called else
|
||
output.finish_reason if output.finish_reason else "stop",
|
||
stop_reason=output.stop_reason)
|
||
choices.append(choice_data)
|
||
|
||
if request.echo or request.continue_final_message:
|
||
last_msg_content = ""
|
||
if conversation and "content" in conversation[-1] and conversation[
|
||
-1].get("role") == role:
|
||
last_msg_content = conversation[-1]["content"] or ""
|
||
|
||
for choice in choices:
|
||
full_message = last_msg_content + (choice.message.content
|
||
or "")
|
||
choice.message.content = full_message
|
||
|
||
assert final_res.prompt_token_ids is not None
|
||
num_prompt_tokens = len(final_res.prompt_token_ids)
|
||
if final_res.encoder_prompt_token_ids is not None:
|
||
num_prompt_tokens += len(final_res.encoder_prompt_token_ids)
|
||
num_generated_tokens = sum(
|
||
len(output.token_ids) for output in final_res.outputs)
|
||
total_reasoning_tokens: Optional[int] = None
|
||
if self.reasoning_parser_cls:
|
||
rp = self.reasoning_parser_cls(
|
||
tokenizer,
|
||
chat_template_kwargs=request.chat_template_kwargs)
|
||
total_reasoning_tokens = sum(
|
||
rp.count_reasoning_tokens(list(output.token_ids))
|
||
for output in final_res.outputs)
|
||
num_cached_tokens = (final_res.metrics.num_cached_tokens
|
||
if final_res.metrics is not None else None)
|
||
usage = UsageInfo(
|
||
prompt_tokens=num_prompt_tokens,
|
||
completion_tokens=num_generated_tokens,
|
||
total_tokens=num_prompt_tokens + num_generated_tokens,
|
||
reasoning_tokens=total_reasoning_tokens,
|
||
prompt_tokens_details=(
|
||
PromptTokensDetails(cached_tokens=num_cached_tokens)
|
||
if num_cached_tokens is not None else None),
|
||
)
|
||
|
||
request_metadata.final_usage_info = usage
|
||
|
||
response = ChatCompletionResponse(
|
||
id=request_id,
|
||
created=created_time,
|
||
model=model_name,
|
||
choices=choices,
|
||
usage=usage,
|
||
prompt_logprobs=final_res.prompt_logprobs,
|
||
)
|
||
|
||
return response
|
||
|
||
def _get_top_logprobs(
|
||
self, logprobs: Dict[int, Logprob], top_logprobs: Optional[int],
|
||
tokenizer: AnyTokenizer) -> List[ChatCompletionLogProb]:
|
||
return [
|
||
ChatCompletionLogProb(token=(token := self._get_decoded_token(
|
||
p[1],
|
||
p[0],
|
||
tokenizer,
|
||
return_as_token_id=self.return_tokens_as_token_ids)),
|
||
logprob=max(p[1].logprob, -9999.0),
|
||
bytes=list(
|
||
token.encode("utf-8", errors="replace")))
|
||
for i, p in enumerate(logprobs.items())
|
||
if top_logprobs and i < top_logprobs
|
||
]
|
||
|
||
def _create_chat_logprobs(
|
||
self,
|
||
token_ids: GenericSequence[int],
|
||
top_logprobs: GenericSequence[Optional[Dict[int, Logprob]]],
|
||
tokenizer: AnyTokenizer,
|
||
num_output_top_logprobs: Optional[int] = None,
|
||
) -> ChatCompletionLogProbs:
|
||
"""Create OpenAI-style logprobs."""
|
||
logprobs_content: List[ChatCompletionLogProbsContent] = []
|
||
|
||
for i, token_id in enumerate(token_ids):
|
||
step_top_logprobs = top_logprobs[i]
|
||
if step_top_logprobs is None:
|
||
token = tokenizer.decode(token_id)
|
||
if self.return_tokens_as_token_ids:
|
||
token = f"token_id:{token_id}"
|
||
|
||
logprobs_content.append(
|
||
ChatCompletionLogProbsContent(
|
||
token=token,
|
||
bytes=list(token.encode("utf-8", errors="replace")),
|
||
))
|
||
else:
|
||
step_token = step_top_logprobs[token_id]
|
||
step_decoded = step_token.decoded_token
|
||
|
||
logprobs_content.append(
|
||
ChatCompletionLogProbsContent(
|
||
token=self._get_decoded_token(
|
||
step_token,
|
||
token_id,
|
||
tokenizer,
|
||
self.return_tokens_as_token_ids,
|
||
),
|
||
logprob=max(step_token.logprob, -9999.0),
|
||
bytes=None if step_decoded is None else list(
|
||
step_decoded.encode("utf-8", errors="replace")),
|
||
top_logprobs=self._get_top_logprobs(
|
||
step_top_logprobs,
|
||
num_output_top_logprobs,
|
||
tokenizer,
|
||
),
|
||
))
|
||
|
||
return ChatCompletionLogProbs(content=logprobs_content)
|
||
|
||
def _should_stream_with_auto_tool_parsing(self,
|
||
request: ChatCompletionRequest):
|
||
"""
|
||
Utility function to check if streamed tokens should go through the tool
|
||
call parser that was configured.
|
||
|
||
We only want to do this IF user-provided tools are set, a tool parser
|
||
is configured, "auto" tool choice is enabled, and the request's tool
|
||
choice field indicates that "auto" tool choice should be used.
|
||
"""
|
||
return (request.tools and self.tool_parser and self.enable_auto_tools
|
||
and request.tool_choice in ['auto', None])
|
||
|
||
def _should_check_for_unstreamed_tool_arg_tokens(
|
||
self,
|
||
delta_message: Optional[DeltaMessage],
|
||
output: CompletionOutput,
|
||
) -> bool:
|
||
"""
|
||
Check to see if we should check for unstreamed tool arguments tokens.
|
||
This is only applicable when auto tool parsing is enabled, the delta
|
||
is a tool call with arguments.
|
||
"""
|
||
|
||
# yapf: disable
|
||
return bool(
|
||
# if there is a delta message that includes tool calls which
|
||
# include a function that has arguments
|
||
output.finish_reason is not None
|
||
and self.enable_auto_tools and self.tool_parser and delta_message
|
||
and delta_message.tool_calls and delta_message.tool_calls[0]
|
||
and delta_message.tool_calls[0].function
|
||
and delta_message.tool_calls[0].function.arguments is not None
|
||
)
|