wehub-resource-sync
1185 lines
49 KiB
Python
1185 lines
49 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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import asyncio
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import io
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import time
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from collections.abc import AsyncGenerator, AsyncIterator
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from collections.abc import Sequence as GenericSequence
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from http import HTTPStatus
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from typing import Any, Final, cast
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import numpy as np
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import pybase64 as base64
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from fastapi import Request
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from vllm.engine.protocol import EngineClient
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from vllm.entrypoints.chat_utils import (
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ChatTemplateContentFormatOption,
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ConversationMessage,
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make_tool_call_id,
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)
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from vllm.entrypoints.generate.base.serving import (
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GenerateBaseServing,
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GenerationError,
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build_per_request_timing_metrics,
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clamp_prompt_logprobs,
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format_token_id_placeholder,
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)
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from vllm.entrypoints.openai.chat_completion.protocol import (
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ChatCompletionLogProb,
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ChatCompletionLogProbs,
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ChatCompletionLogProbsContent,
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ChatCompletionNamedToolChoiceParam,
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ChatCompletionRequest,
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ChatCompletionResponse,
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ChatCompletionResponseChoice,
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ChatCompletionResponseStreamChoice,
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ChatCompletionStreamResponse,
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ChatMessage,
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)
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from vllm.entrypoints.openai.engine.protocol import (
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DeltaMessage,
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ErrorResponse,
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FunctionCall,
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PerRequestTimingMetrics,
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PromptTokenUsageInfo,
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RequestResponseMetadata,
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ToolCall,
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UsageInfo,
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)
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from vllm.entrypoints.openai.models.serving import OpenAIServingModels
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from vllm.entrypoints.serve.utils.api_utils import get_max_tokens, should_include_usage
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from vllm.entrypoints.serve.utils.request_logger import RequestLogger
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from vllm.entrypoints.serve.utils.tool_calls_utils import (
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maybe_filter_parallel_tool_calls,
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)
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from vllm.inputs import EngineInput, MultiModalPlaceholders
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from vllm.logger import init_logger
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from vllm.logprobs import Logprob
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from vllm.outputs import RequestOutput
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from vllm.parser import ParserManager
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from vllm.parser.abstract_parser import Parser
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from vllm.renderers import ChatParams
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from vllm.renderers.online_renderer import OnlineRenderer
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from vllm.sampling_params import BeamSearchParams, SamplingParams
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from vllm.tokenizers import TokenizerLike
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from vllm.utils.collection_utils import as_list
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from vllm.utils.mistral import is_mistral_tool_parser
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logger = init_logger(__name__)
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def _get_mm_token_counts(engine_input: EngineInput) -> dict[str, int]:
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"""Sum per-modality placeholder tokens from ``mm_placeholders``.
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Keyed by modality name; ``PlaceholderRange.length`` is the placeholder's
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prompt token span, so each sum matches the placeholder tokens already
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counted in ``usage.prompt_tokens``.
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"""
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mm_placeholders = cast(
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MultiModalPlaceholders | None, engine_input.get("mm_placeholders")
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)
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return {
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modality: sum(p.length for p in ranges)
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for modality, ranges in (mm_placeholders or {}).items()
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if ranges
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}
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def _make_prompt_tokens_details(
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enable_prompt_tokens_details: bool,
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num_cached_tokens: int | None,
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mm_token_counts: dict[str, int] | None,
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) -> PromptTokenUsageInfo | None:
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"""Build ``prompt_tokens_details`` from cached + multimodal token counts."""
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if not enable_prompt_tokens_details:
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return None
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if num_cached_tokens is None and not mm_token_counts:
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return None
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return PromptTokenUsageInfo(
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cached_tokens=num_cached_tokens,
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multimodal_tokens=mm_token_counts or None,
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)
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class OpenAIServingChat(GenerateBaseServing):
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def __init__(
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self,
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engine_client: EngineClient,
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models: OpenAIServingModels,
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response_role: str,
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*,
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online_renderer: "OnlineRenderer",
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request_logger: RequestLogger | None,
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chat_template: str | None,
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chat_template_content_format: ChatTemplateContentFormatOption,
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trust_request_chat_template: bool = False,
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return_tokens_as_token_ids: bool = False,
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reasoning_parser: str = "",
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enable_auto_tools: bool = False,
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exclude_tools_when_tool_choice_none: bool = False,
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tool_parser: str | None = None,
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enable_prompt_tokens_details: bool = False,
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enable_force_include_usage: bool = False,
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enable_log_outputs: bool = False,
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enable_log_deltas: bool = True,
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default_chat_template_kwargs: dict[str, Any] | None = None,
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enable_per_request_metrics: bool = False,
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) -> None:
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super().__init__(
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engine_client=engine_client,
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models=models,
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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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)
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self.online_renderer = online_renderer
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self.response_role = response_role
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self.chat_template = chat_template
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self.chat_template_content_format: Final = chat_template_content_format
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self.trust_request_chat_template = trust_request_chat_template
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self.default_chat_template_kwargs = default_chat_template_kwargs or {}
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self.enable_log_outputs = enable_log_outputs
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self.enable_log_deltas = enable_log_deltas
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self.enable_auto_tools: bool = enable_auto_tools
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self.parser_cls = ParserManager.get_parser(
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tool_parser_name=tool_parser,
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reasoning_parser_name=reasoning_parser,
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enable_auto_tools=enable_auto_tools,
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model_name=self.model_config.model,
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is_harmony=self.model_config.hf_config.model_type == "gpt_oss",
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)
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if (
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self.parser_cls is not None
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and is_mistral_tool_parser(self.parser_cls.tool_parser_cls)
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and self.parser_cls.reasoning_parser_cls is not None
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):
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from vllm.tool_parsers.mistral_tool_parser import MistralToolParser
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MistralToolParser.model_can_reason = True
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self.exclude_tools_when_tool_choice_none = exclude_tools_when_tool_choice_none
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self.enable_prompt_tokens_details = enable_prompt_tokens_details
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self.enable_force_include_usage = enable_force_include_usage
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self.enable_per_request_metrics = enable_per_request_metrics
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self.default_sampling_params = self.model_config.get_diff_sampling_param()
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mc = self.model_config
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self.override_max_tokens = (
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self.default_sampling_params.get("max_tokens")
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if mc.generation_config not in ("auto", "vllm")
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else getattr(mc, "override_generation_config", {}).get("max_new_tokens")
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)
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# NOTE(woosuk): While OpenAI's chat completion API supports browsing
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# for some models, currently vLLM doesn't support it. Please use the
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# Responses API instead.
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self.supports_browsing = False
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self.browser_tool = None
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# NOTE(woosuk): Chat completion API does not support code interpreter.
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# Please use the Responses API instead.
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self.supports_code_interpreter = False
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self.python_tool = None
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def warmup(self) -> None:
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self.renderer.warmup(
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ChatParams(
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chat_template=self.chat_template,
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chat_template_content_format=self.chat_template_content_format,
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chat_template_kwargs=self.default_chat_template_kwargs,
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)
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)
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def _effective_chat_template_kwargs(
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self, request: ChatCompletionRequest
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) -> dict[str, Any]:
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return (
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request.build_chat_params(
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self.chat_template,
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self.chat_template_content_format,
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)
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.with_defaults(self.default_chat_template_kwargs)
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.chat_template_kwargs
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)
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async def render_chat_request(
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self,
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request: ChatCompletionRequest,
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) -> tuple[list[ConversationMessage], list[EngineInput]] | ErrorResponse:
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"""
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Validate the model and preprocess a chat completion request.
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Delegates preprocessing logic to OnlineRenderer, adding the
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engine-aware checks (LoRA model validation, engine health).
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Returns:
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A tuple of (conversation, engine_inputs) on success,
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or an ErrorResponse on failure.
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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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# If the engine is dead, raise the engine's DEAD_ERROR.
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# This is required for the streaming case, where we return a
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# success status before we actually start generating text :).
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if self.engine_client.errored:
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raise self.engine_client.dead_error
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return await self.online_renderer.render_chat(request)
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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: Request | None = None,
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) -> AsyncGenerator[str, None] | ChatCompletionResponse | ErrorResponse:
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"""
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Chat 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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Chat Completion API.
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"""
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return await self._with_kv_transfer_rejection_cleanup(
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self._create_chat_completion(request, raw_request), request, raw_request
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)
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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: Request | None = None,
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) -> AsyncGenerator[str, None] | ChatCompletionResponse | ErrorResponse:
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# Streaming response
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tokenizer = self.renderer.tokenizer
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assert tokenizer is not None
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chat_template_kwargs = self._effective_chat_template_kwargs(request)
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parser: Parser | None = None
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if self.parser_cls is not None:
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parser = self.parser_cls(
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tokenizer,
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request.tools,
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chat_template_kwargs=chat_template_kwargs,
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model_config=self.model_config,
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)
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result = await self.render_chat_request(request)
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if isinstance(result, ErrorResponse):
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return result
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conversation, engine_inputs = result
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request_id = (
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f"chatcmpl-{self._base_request_id(raw_request, request.request_id)}"
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)
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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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lora_request = self._maybe_get_adapters(request, supports_default_mm_loras=True)
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model_name = self.models.model_name(lora_request)
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# Extract data_parallel_rank from header (router can inject it)
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data_parallel_rank = self._get_data_parallel_rank(raw_request)
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# Schedule the request and get the result generator.
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max_model_len = self.model_config.max_model_len
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generators: list[AsyncGenerator[RequestOutput, None]] = []
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mm_token_counts: dict[str, int] | None = None
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for i, engine_input in enumerate(engine_inputs):
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prompt_token_ids = self._extract_prompt_components(engine_input).token_ids
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mm_token_counts = _get_mm_token_counts(engine_input)
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# If we are creating sub requests for multiple prompts, ensure that they
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# have unique request ids.
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sub_request_id = (
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request_id if len(engine_inputs) == 1 else f"{request_id}_{i}"
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)
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max_tokens = get_max_tokens(
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max_model_len,
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request.max_completion_tokens
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if request.max_completion_tokens is not None
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else request.max_tokens,
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self._extract_prompt_len(engine_input),
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self.default_sampling_params,
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self.override_max_tokens,
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truncate_prompt_tokens=request.truncate_prompt_tokens,
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)
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sampling_params: SamplingParams | BeamSearchParams
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if request.use_beam_search:
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sampling_params = request.to_beam_search_params(
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max_tokens, self.default_sampling_params
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)
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else:
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sampling_params = request.to_sampling_params(
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max_tokens,
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self.default_sampling_params,
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)
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self._log_inputs(
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sub_request_id,
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engine_input,
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params=sampling_params,
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lora_request=lora_request,
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)
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trace_headers = (
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None
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if raw_request is None
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else await self._get_trace_headers(raw_request.headers)
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)
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if isinstance(sampling_params, BeamSearchParams):
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generator = self.beam_search(
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prompt=engine_input,
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request_id=sub_request_id,
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params=sampling_params,
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lora_request=lora_request,
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trace_headers=trace_headers,
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)
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else:
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if not request.include_reasoning:
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reasoning_ended = True
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elif request._grammar_from_tool_parser:
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# The Mistral grammar already includes an optional
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# `think?` rule that handles both reasoning and
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# non-reasoning outputs.
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reasoning_ended = True
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elif parser is not None and parser.reasoning_parser is not None:
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reasoning_ended = parser.is_reasoning_end(prompt_token_ids or [])
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else:
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reasoning_ended = None
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generator = self.engine_client.generate(
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engine_input,
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sampling_params,
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sub_request_id,
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lora_request=lora_request,
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trace_headers=trace_headers,
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priority=request.priority,
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data_parallel_rank=data_parallel_rank,
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reasoning_ended=reasoning_ended,
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reasoning_parser_kwargs={
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"chat_template_kwargs": chat_template_kwargs,
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}
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if parser is not None and parser.reasoning_parser is not None
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else None,
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)
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generators.append(generator)
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assert len(generators) == 1
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(result_generator,) = generators
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if request.stream:
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return self.chat_completion_stream_generator(
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request,
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result_generator,
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request_id,
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model_name,
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conversation,
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tokenizer,
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request_metadata,
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chat_template_kwargs=chat_template_kwargs,
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mm_token_counts=mm_token_counts,
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)
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return await self.chat_completion_full_generator(
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request,
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result_generator,
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request_id,
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model_name,
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conversation,
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tokenizer,
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request_metadata,
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parser=parser,
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mm_token_counts=mm_token_counts,
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)
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def get_chat_request_role(self, request: ChatCompletionRequest) -> str:
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if request.add_generation_prompt:
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return self.response_role
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return request.messages[-1]["role"]
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async def chat_completion_stream_generator(
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self,
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request: ChatCompletionRequest,
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result_generator: AsyncIterator[RequestOutput],
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request_id: str,
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model_name: str,
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conversation: list[ConversationMessage],
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tokenizer: TokenizerLike,
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request_metadata: RequestResponseMetadata,
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chat_template_kwargs: dict[str, Any] | None = None,
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mm_token_counts: dict[str, int] | None = None,
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) -> AsyncGenerator[str, None]:
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created_time = int(time.time())
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chunk_object_type: Final = "chat.completion.chunk"
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first_iteration = True
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# Send response for each token for each request.n (index)
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num_choices = 1 if request.n is None else request.n
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previous_num_tokens = [0] * num_choices
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finish_reason_sent = [False] * num_choices
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num_prompt_tokens = 0
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num_cached_tokens = None
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tools_streamed = [False] * num_choices
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if isinstance(request.tool_choice, ChatCompletionNamedToolChoiceParam):
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tool_choice_function_name = request.tool_choice.function.name
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else:
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tool_choice_function_name = None
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previous_texts = [""] * num_choices
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try:
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if self.parser_cls is not None:
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if tokenizer is None:
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raise ValueError(
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"Tokenizer not available when `skip_tokenizer_init=True`"
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)
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parsers: list[Parser | None] = [
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self.parser_cls(
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tokenizer,
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request.tools,
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chat_template_kwargs=chat_template_kwargs,
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model_config=self.model_config,
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)
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for _ in range(num_choices)
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]
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else:
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parsers = [None] * num_choices
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except Exception as e:
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logger.exception("Error in parser creation.")
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data = self.create_streaming_error_response(e)
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yield f"data: {data}\n\n"
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yield "data: [DONE]\n\n"
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return
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stream_options = request.stream_options
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include_usage, include_continuous_usage = should_include_usage(
|
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stream_options, self.enable_force_include_usage
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)
|
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last_res: RequestOutput | None = None
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try:
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async for res in result_generator:
|
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last_res = res
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if res.prompt_token_ids is not None:
|
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num_prompt_tokens = len(res.prompt_token_ids)
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if res.encoder_prompt_token_ids is not None:
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num_prompt_tokens += len(res.encoder_prompt_token_ids)
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# We need to do it here, because if there are exceptions in
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# the result_generator, it needs to be sent as the FIRST
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# response (by the try...catch).
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if first_iteration:
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num_cached_tokens = res.num_cached_tokens
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# Send first response for each request.n (index) with
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# the role
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role = self.get_chat_request_role(request)
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# ``res.prompt`` is the rendered chat-templated prompt
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prompt_text = res.prompt if request.return_prompt_text else None
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|
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# NOTE num_choices defaults to 1 so this usually executes
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# once per request
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for i in range(num_choices):
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choice_data = ChatCompletionResponseStreamChoice(
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index=i,
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delta=DeltaMessage(
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role=role,
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content="",
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),
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logprobs=None,
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finish_reason=None,
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)
|
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|
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# return prompt_token_ids at the first chunk ever
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|
chunk = ChatCompletionStreamResponse(
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id=request_id,
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object=chunk_object_type,
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created=created_time,
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choices=[choice_data],
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model=model_name,
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prompt_token_ids=(
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res.prompt_token_ids
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if request.return_token_ids
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else None
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),
|
|
prompt_text=prompt_text,
|
|
)
|
|
|
|
# if continuous usage stats are requested, add it
|
|
if include_continuous_usage:
|
|
chunk.usage = UsageInfo(
|
|
prompt_tokens=num_prompt_tokens,
|
|
completion_tokens=0,
|
|
total_tokens=num_prompt_tokens,
|
|
)
|
|
|
|
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:
|
|
last_msg_content: 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 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 include_continuous_usage:
|
|
chunk.usage = UsageInfo(
|
|
prompt_tokens=num_prompt_tokens,
|
|
completion_tokens=0,
|
|
total_tokens=num_prompt_tokens,
|
|
)
|
|
|
|
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
|
|
parser = 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,
|
|
return_as_token_id=request.return_tokens_as_token_ids,
|
|
)
|
|
else:
|
|
logprobs = None
|
|
|
|
delta_text = output.text
|
|
|
|
if (
|
|
not delta_text
|
|
and not output.token_ids
|
|
and not previous_num_tokens[i]
|
|
):
|
|
# Chunked prefill case, don't return empty chunks
|
|
continue
|
|
|
|
delta_message: DeltaMessage | None
|
|
|
|
if parser is not None:
|
|
delta_message = parser.parse_delta(
|
|
delta_text=delta_text,
|
|
delta_token_ids=as_list(output.token_ids),
|
|
request=request,
|
|
prompt_token_ids=res.prompt_token_ids,
|
|
finished=output.finish_reason is not None,
|
|
)
|
|
if delta_message is not None and delta_message.tool_calls:
|
|
tools_streamed[i] = True
|
|
|
|
# handle streaming just a content delta (no parsers)
|
|
else:
|
|
delta_message = DeltaMessage(content=delta_text)
|
|
|
|
previous_texts[i] += 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
|
|
# When reasoning is hidden, suppress per-token
|
|
# metadata (logprobs, token_ids) on every chunk to
|
|
# prevent leaking reasoning tokens through decoded
|
|
# token text in logprob entries or raw token IDs.
|
|
hide_stream_metadata = (
|
|
not request.include_reasoning and parser is not None
|
|
)
|
|
if hide_stream_metadata:
|
|
logprobs = None
|
|
|
|
if delta_message is None:
|
|
# NOTE: If return_token_ids is enabled, we still need to
|
|
# send a chunk with token_ids even if delta_message is None
|
|
# to ensure all tokens are included in the response
|
|
if output.finish_reason is None and (
|
|
not request.return_token_ids or hide_stream_metadata
|
|
):
|
|
continue
|
|
delta_message = DeltaMessage()
|
|
|
|
# Log streaming delta if output logging is enabled
|
|
if self.enable_log_outputs and self.request_logger:
|
|
delta_content_parts = []
|
|
if delta_message.content:
|
|
delta_content_parts.append(delta_message.content)
|
|
if delta_message.reasoning:
|
|
reasoning = delta_message.reasoning
|
|
delta_content_parts.append(f"[reasoning: {reasoning}]")
|
|
if delta_message.tool_calls:
|
|
tool_args = "".join(
|
|
tc.function.arguments
|
|
for tc in delta_message.tool_calls
|
|
if tc.function and tc.function.arguments
|
|
)
|
|
if tool_args:
|
|
delta_content_parts.append(f"[tool_calls: {tool_args}]")
|
|
|
|
if delta_content_parts and self.enable_log_deltas:
|
|
delta_content = " ".join(delta_content_parts)
|
|
self.request_logger.log_outputs(
|
|
request_id=request_id,
|
|
outputs=delta_content,
|
|
output_token_ids=as_list(output.token_ids),
|
|
finish_reason=output.finish_reason,
|
|
is_streaming=True,
|
|
delta=True,
|
|
)
|
|
|
|
include_token_ids = (
|
|
request.return_token_ids and not hide_stream_metadata
|
|
)
|
|
|
|
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,
|
|
token_ids=(
|
|
as_list(output.token_ids) if include_token_ids else None
|
|
),
|
|
)
|
|
|
|
# if the model is finished generating
|
|
else:
|
|
# check for error finish reason and abort streaming
|
|
# finish_reason='error' indicates a retryable error
|
|
self._raise_if_error(output.finish_reason, request_id)
|
|
|
|
# Send the finish response for each request.n only once
|
|
# In OpenAI's API, when a tool is called, the
|
|
# finish_reason is:
|
|
# "tool_calls" for "auto" or "required" tool calls,
|
|
# and "stop" for named tool calls.
|
|
if tools_streamed[i] and not tool_choice_function_name:
|
|
finish_reason_ = "tool_calls"
|
|
else:
|
|
finish_reason_ = (
|
|
output.finish_reason if output.finish_reason else "stop"
|
|
)
|
|
choice_data = ChatCompletionResponseStreamChoice(
|
|
index=i,
|
|
delta=delta_message,
|
|
logprobs=logprobs,
|
|
finish_reason=finish_reason_,
|
|
stop_reason=output.stop_reason,
|
|
token_ids=(
|
|
as_list(output.token_ids) if include_token_ids else None
|
|
),
|
|
)
|
|
|
|
finish_reason_sent[i] = True
|
|
|
|
choice_data = maybe_filter_parallel_tool_calls(choice_data, request)
|
|
chunk = ChatCompletionStreamResponse(
|
|
id=request_id,
|
|
object=chunk_object_type,
|
|
created=created_time,
|
|
choices=[choice_data],
|
|
model=model_name,
|
|
)
|
|
# Stamp the fingerprint on terminal chunks only (those with
|
|
# finish_reason set). When ``include_usage`` is on, the
|
|
# trailing usage chunk below overrides this as the true
|
|
# final message.
|
|
if (
|
|
not include_usage
|
|
and self.system_fingerprint is not None
|
|
and choice_data.finish_reason is not None
|
|
):
|
|
chunk.system_fingerprint = self.system_fingerprint
|
|
|
|
# handle usage stats if requested & if continuous
|
|
if include_continuous_usage:
|
|
completion_tokens = previous_num_tokens[i]
|
|
chunk.usage = UsageInfo(
|
|
prompt_tokens=num_prompt_tokens,
|
|
completion_tokens=completion_tokens,
|
|
total_tokens=num_prompt_tokens + completion_tokens,
|
|
)
|
|
|
|
data = chunk.model_dump_json(exclude_unset=True)
|
|
yield f"data: {data}\n\n"
|
|
|
|
# once the final token is handled, if stream_options.include_usage
|
|
# is sent, send the usage
|
|
if include_usage:
|
|
completion_tokens = sum(previous_num_tokens)
|
|
final_usage = UsageInfo(
|
|
prompt_tokens=num_prompt_tokens,
|
|
completion_tokens=completion_tokens,
|
|
total_tokens=num_prompt_tokens + completion_tokens,
|
|
)
|
|
final_usage.prompt_tokens_details = _make_prompt_tokens_details(
|
|
self.enable_prompt_tokens_details,
|
|
num_cached_tokens,
|
|
mm_token_counts,
|
|
)
|
|
|
|
# In streaming, metrics ride on this final usage chunk, which is
|
|
# only emitted when usage reporting is enabled (i.e.
|
|
# ``stream_options.include_usage=true`` or
|
|
# ``--enable-force-include-usage``).
|
|
stream_per_request_metrics: PerRequestTimingMetrics | None = None
|
|
if (
|
|
self.enable_per_request_metrics
|
|
# See note in chat_completion_full_generator: suppress for n>1.
|
|
and (request.n or 1) == 1
|
|
):
|
|
last_metrics = last_res.metrics if last_res is not None else None
|
|
stream_per_request_metrics = build_per_request_timing_metrics(
|
|
last_metrics, completion_tokens
|
|
)
|
|
|
|
final_usage_chunk = ChatCompletionStreamResponse(
|
|
id=request_id,
|
|
object=chunk_object_type,
|
|
created=created_time,
|
|
choices=[],
|
|
model=model_name,
|
|
usage=final_usage,
|
|
system_fingerprint=self.system_fingerprint,
|
|
metrics=stream_per_request_metrics,
|
|
)
|
|
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)
|
|
request_metadata.final_usage_info = UsageInfo(
|
|
prompt_tokens=num_prompt_tokens,
|
|
completion_tokens=num_completion_tokens,
|
|
total_tokens=num_prompt_tokens + num_completion_tokens,
|
|
)
|
|
|
|
# Log complete streaming response if output logging is enabled
|
|
if self.enable_log_outputs and self.request_logger:
|
|
# Log the complete response for each choice
|
|
for i in range(num_choices):
|
|
full_text = (
|
|
previous_texts[i]
|
|
if previous_texts and i < len(previous_texts)
|
|
else f"<streaming_complete: {previous_num_tokens[i]} tokens>"
|
|
)
|
|
self.request_logger.log_outputs(
|
|
request_id=request_id,
|
|
outputs=full_text,
|
|
output_token_ids=None, # Consider also logging all token IDs
|
|
finish_reason="streaming_complete",
|
|
is_streaming=True,
|
|
delta=False,
|
|
)
|
|
|
|
except GenerationError as e:
|
|
yield f"data: {self._convert_generation_error_to_streaming_response(e)}\n\n"
|
|
except Exception as e:
|
|
logger.exception("Error in chat completion stream generator.")
|
|
data = self.create_streaming_error_response(e)
|
|
yield f"data: {data}\n\n"
|
|
# 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,
|
|
model_name: str,
|
|
conversation: list[ConversationMessage],
|
|
tokenizer: TokenizerLike,
|
|
request_metadata: RequestResponseMetadata,
|
|
parser: Parser | None = None,
|
|
mm_token_counts: dict[str, int] | None = None,
|
|
) -> ErrorResponse | ChatCompletionResponse:
|
|
created_time = int(time.time())
|
|
final_res: RequestOutput | None = None
|
|
|
|
try:
|
|
async for res in result_generator:
|
|
final_res = res
|
|
except asyncio.CancelledError:
|
|
return self.create_error_response("Client disconnected")
|
|
|
|
if final_res is None:
|
|
return self.create_error_response(
|
|
"No output received from the engine.",
|
|
err_type="InternalServerError",
|
|
status_code=HTTPStatus.INTERNAL_SERVER_ERROR,
|
|
)
|
|
|
|
choices: list[ChatCompletionResponseChoice] = []
|
|
|
|
role = self.get_chat_request_role(request)
|
|
tool_parser_cls = (
|
|
self.parser_cls.tool_parser_cls if self.parser_cls is not None else None
|
|
)
|
|
for output in final_res.outputs:
|
|
# check for error finish reason and raise GenerationError
|
|
# finish_reason='error' indicates a retryable request-level internal error
|
|
self._raise_if_error(output.finish_reason, request_id)
|
|
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,
|
|
return_as_token_id=request.return_tokens_as_token_ids,
|
|
)
|
|
else:
|
|
logprobs = None
|
|
|
|
if parser is not None:
|
|
reasoning, content, tool_calls = parser.parse(
|
|
output.text,
|
|
request,
|
|
enable_auto_tools=self.enable_auto_tools,
|
|
model_output_token_ids=token_ids,
|
|
)
|
|
suppress_metadata = not request.include_reasoning and parser is not None
|
|
if not request.include_reasoning:
|
|
reasoning = None
|
|
if suppress_metadata:
|
|
logprobs = None
|
|
else:
|
|
reasoning = None
|
|
content = output.text
|
|
tool_calls = []
|
|
suppress_metadata = False
|
|
|
|
auto_tools_called = False
|
|
is_named_tool_choice = (
|
|
request.tool_choice is not None
|
|
and type(request.tool_choice) is ChatCompletionNamedToolChoiceParam
|
|
)
|
|
is_required_tool_choice = request.tool_choice == "required"
|
|
|
|
if (not self.enable_auto_tools or not tool_parser_cls) and (
|
|
not is_named_tool_choice and not is_required_tool_choice
|
|
):
|
|
message = ChatMessage(role=role, reasoning=reasoning, content=content)
|
|
|
|
elif is_named_tool_choice or is_required_tool_choice:
|
|
message = ChatMessage(
|
|
role=role,
|
|
reasoning=reasoning,
|
|
content=content or "",
|
|
tool_calls=[
|
|
ToolCall(id=tc.id or make_tool_call_id(), function=tc)
|
|
for tc in (tool_calls or [])
|
|
],
|
|
)
|
|
|
|
# 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=reasoning, content=content)
|
|
|
|
# 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 tool_parser_cls
|
|
):
|
|
auto_tools_called = tool_calls is not None and len(tool_calls) > 0
|
|
if tool_calls:
|
|
message = ChatMessage(
|
|
role=role,
|
|
reasoning=reasoning,
|
|
content=content,
|
|
tool_calls=[
|
|
ToolCall(id=tc.id or make_tool_call_id(), function=tc)
|
|
for tc in tool_calls
|
|
],
|
|
)
|
|
|
|
else:
|
|
message = ChatMessage(
|
|
role=role,
|
|
reasoning=reasoning,
|
|
content=content,
|
|
)
|
|
|
|
# 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=reasoning, content=content)
|
|
# In OpenAI's API, when a tool is called, the finish_reason is:
|
|
# "tool_calls" for "auto" or "required" tool calls,
|
|
# and "stop" for named tool calls.
|
|
is_finish_reason_tool_calls = auto_tools_called or (
|
|
request.tool_choice
|
|
and request.tool_choice == "required"
|
|
and output.finish_reason == "stop"
|
|
)
|
|
|
|
# Encode routed_experts for transport. JSON can't carry raw
|
|
# bytes, so we write the ndarray as a ``.npy`` byte stream
|
|
# and base64-encode it. ``pybase64`` is ~3x faster than the
|
|
# stdlib ``base64`` on large payloads thanks to SIMD.
|
|
routed_experts_b64 = None
|
|
if output.routed_experts is not None:
|
|
buf = io.BytesIO()
|
|
np.save(buf, output.routed_experts)
|
|
routed_experts_b64 = base64.b64encode(buf.getvalue()).decode("ascii")
|
|
|
|
choice_data = ChatCompletionResponseChoice(
|
|
index=output.index,
|
|
message=message,
|
|
logprobs=logprobs,
|
|
finish_reason="tool_calls"
|
|
if is_finish_reason_tool_calls
|
|
else output.finish_reason
|
|
if output.finish_reason
|
|
else "stop",
|
|
stop_reason=output.stop_reason,
|
|
token_ids=(
|
|
as_list(output.token_ids)
|
|
if request.return_token_ids and not suppress_metadata
|
|
else None
|
|
),
|
|
routed_experts=routed_experts_b64,
|
|
)
|
|
choice_data = maybe_filter_parallel_tool_calls(choice_data, request)
|
|
|
|
choices.append(choice_data)
|
|
|
|
if request.echo:
|
|
last_msg_content: 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 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
|
|
)
|
|
usage = UsageInfo(
|
|
prompt_tokens=num_prompt_tokens,
|
|
completion_tokens=num_generated_tokens,
|
|
total_tokens=num_prompt_tokens + num_generated_tokens,
|
|
)
|
|
usage.prompt_tokens_details = _make_prompt_tokens_details(
|
|
self.enable_prompt_tokens_details,
|
|
final_res.num_cached_tokens,
|
|
mm_token_counts,
|
|
)
|
|
|
|
request_metadata.final_usage_info = usage
|
|
|
|
per_request_metrics: PerRequestTimingMetrics | None = None
|
|
if (
|
|
self.enable_per_request_metrics
|
|
# Timing metrics describe a single generation stream. For n>1 the
|
|
# returned stats belong to only one of the n sequences, so they
|
|
# cannot be accurately attributed to the request; suppress instead.
|
|
and (request.n or 1) == 1
|
|
):
|
|
per_request_metrics = build_per_request_timing_metrics(
|
|
final_res.metrics, num_generated_tokens
|
|
)
|
|
|
|
# ``final_res.prompt`` is the rendered chat-templated prompt text
|
|
prompt_text = final_res.prompt if request.return_prompt_text else None
|
|
|
|
response = ChatCompletionResponse(
|
|
id=request_id,
|
|
created=created_time,
|
|
model=model_name,
|
|
choices=choices,
|
|
usage=usage,
|
|
system_fingerprint=self.system_fingerprint,
|
|
prompt_logprobs=clamp_prompt_logprobs(final_res.prompt_logprobs),
|
|
prompt_token_ids=(
|
|
final_res.prompt_token_ids if request.return_token_ids else None
|
|
),
|
|
prompt_text=prompt_text,
|
|
kv_transfer_params=final_res.kv_transfer_params,
|
|
ec_transfer_params=final_res.ec_transfer_params,
|
|
metrics=per_request_metrics,
|
|
)
|
|
|
|
# Log complete response if output logging is enabled
|
|
if self.enable_log_outputs and self.request_logger:
|
|
for choice in choices:
|
|
output_text = ""
|
|
if choice.message.content:
|
|
output_text = choice.message.content
|
|
elif choice.message.tool_calls:
|
|
# For tool calls, log the function name and arguments
|
|
tool_call_descriptions = []
|
|
for tc in choice.message.tool_calls: # type: ignore
|
|
function_call: FunctionCall = tc.function # type: ignore
|
|
tool_call_descriptions.append(
|
|
f"{function_call.name}({function_call.arguments})"
|
|
)
|
|
tool_calls_str = ", ".join(tool_call_descriptions)
|
|
output_text = f"[tool_calls: {tool_calls_str}]"
|
|
|
|
if output_text:
|
|
# Get the corresponding output token IDs
|
|
output_token_ids = None
|
|
if choice.index < len(final_res.outputs):
|
|
output_token_ids = final_res.outputs[choice.index].token_ids
|
|
|
|
self.request_logger.log_outputs(
|
|
request_id=request_id,
|
|
outputs=output_text,
|
|
output_token_ids=output_token_ids,
|
|
finish_reason=choice.finish_reason,
|
|
is_streaming=False,
|
|
delta=False,
|
|
)
|
|
|
|
return response
|
|
|
|
def _get_top_logprobs(
|
|
self,
|
|
logprobs: dict[int, Logprob],
|
|
top_logprobs: int | None,
|
|
tokenizer: TokenizerLike | None,
|
|
should_return_as_token_id: bool,
|
|
) -> list[ChatCompletionLogProb]:
|
|
return [
|
|
ChatCompletionLogProb(
|
|
token=(
|
|
token := self._get_decoded_token(
|
|
p[1],
|
|
p[0],
|
|
tokenizer,
|
|
return_as_token_id=should_return_as_token_id,
|
|
)
|
|
),
|
|
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 or top_logprobs == -1)
|
|
]
|
|
|
|
def _create_chat_logprobs(
|
|
self,
|
|
token_ids: GenericSequence[int],
|
|
top_logprobs: GenericSequence[dict[int, Logprob] | None],
|
|
tokenizer: TokenizerLike | None,
|
|
num_output_top_logprobs: int | None = None,
|
|
return_as_token_id: bool | None = None,
|
|
) -> ChatCompletionLogProbs:
|
|
"""Create OpenAI-style logprobs."""
|
|
logprobs_content: list[ChatCompletionLogProbsContent] = []
|
|
|
|
should_return_as_token_id = (
|
|
return_as_token_id
|
|
if return_as_token_id is not None
|
|
else self.return_tokens_as_token_ids
|
|
)
|
|
for i, token_id in enumerate(token_ids):
|
|
step_top_logprobs = top_logprobs[i]
|
|
if step_top_logprobs is None or step_top_logprobs.get(token_id) is None:
|
|
if should_return_as_token_id:
|
|
token = format_token_id_placeholder(token_id)
|
|
else:
|
|
if tokenizer is None:
|
|
raise ValueError(
|
|
"Unable to get tokenizer because `skip_tokenizer_init=True`"
|
|
)
|
|
|
|
token = tokenizer.decode(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,
|
|
should_return_as_token_id,
|
|
),
|
|
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,
|
|
should_return_as_token_id,
|
|
),
|
|
)
|
|
)
|
|
|
|
return ChatCompletionLogProbs(content=logprobs_content)
|