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vllm/parser/abstract_parser.py
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541
vllm/parser/abstract_parser.py
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# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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import json
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from abc import abstractmethod
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from collections.abc import Sequence
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from functools import cached_property
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from openai.types.responses import (
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ResponseFunctionToolCall,
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ResponseOutputItem,
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ResponseOutputMessage,
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ResponseOutputText,
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ResponseReasoningItem,
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ToolChoiceFunction,
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)
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from openai.types.responses.response_output_text import Logprob
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from openai.types.responses.response_reasoning_item import (
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Content as ResponseReasoningTextContent,
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)
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from pydantic import TypeAdapter
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from vllm.entrypoints.chat_utils import make_tool_call_id
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from vllm.entrypoints.openai.chat_completion.protocol import (
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ChatCompletionNamedToolChoiceParam,
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ChatCompletionRequest,
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)
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from vllm.entrypoints.openai.engine.protocol import (
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DeltaMessage,
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ExtractedToolCallInformation,
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FunctionCall,
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FunctionDefinition,
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)
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from vllm.entrypoints.openai.responses.protocol import (
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ResponsesRequest,
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)
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from vllm.logger import init_logger
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from vllm.reasoning.abs_reasoning_parsers import ReasoningParser
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from vllm.tokenizers import TokenizerLike
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from vllm.tool_parsers.abstract_tool_parser import ToolParser
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from vllm.utils import random_uuid
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logger = init_logger(__name__)
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class Parser:
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"""
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Abstract Parser class that unifies ReasoningParser and ToolParser into
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a single interface for parsing model output.
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This class provides a unified way to handle both reasoning extraction
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(e.g., chain-of-thought content in <think> tags) and tool call extraction
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(e.g., function calls in XML/JSON format) from model outputs.
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Subclasses can either:
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1. Override the abstract methods directly for custom parsing logic
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2. Set `reasoning_parser` and `tool_parser` properties to delegate to
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existing parser implementations
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Class Attributes:
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reasoning_parser_cls: The ReasoningParser class to use (for compatibility
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with code that needs the class, not instance).
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tool_parser_cls: The ToolParser class to use (for compatibility with
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code that needs the class, not instance).
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"""
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# Class-level parser classes for compatibility with existing patterns
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# Subclasses should override these if they use specific parser classes
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reasoning_parser_cls: type[ReasoningParser] | None = None
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tool_parser_cls: type[ToolParser] | None = None
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def __init__(self, tokenizer: TokenizerLike, *args, **kwargs):
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"""
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Initialize the Parser.
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Args:
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tokenizer: The tokenizer used by the model. This is required for
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token-based parsing operations.
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"""
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self.model_tokenizer = tokenizer
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self._reasoning_parser: ReasoningParser | None = None
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self._tool_parser: ToolParser | None = None
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@cached_property
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def vocab(self) -> dict[str, int]:
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"""Get the vocabulary mapping from tokens to IDs."""
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return self.model_tokenizer.get_vocab()
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@property
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def reasoning_parser(self) -> ReasoningParser | None:
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"""The underlying reasoning parser, if any."""
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return self._reasoning_parser
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@reasoning_parser.setter
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def reasoning_parser(self, parser: ReasoningParser | None) -> None:
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self._reasoning_parser = parser
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@property
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def tool_parser(self) -> ToolParser | None:
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"""The underlying tool parser, if any."""
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return self._tool_parser
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@tool_parser.setter
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def tool_parser(self, parser: ToolParser | None) -> None:
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self._tool_parser = parser
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# ========== Reasoning Parser Methods ==========
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@abstractmethod
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def is_reasoning_end(self, input_ids: list[int]) -> bool:
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"""
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Check if the reasoning content ends in the input_ids.
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Used by structured engines like `xgrammar` to check if the
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reasoning content ends in the model output.
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Args:
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input_ids: The token IDs of the model output.
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Returns:
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True if the reasoning content ends in the input_ids.
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"""
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def is_reasoning_end_streaming(
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self, input_ids: list[int], delta_ids: list[int]
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) -> bool:
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"""
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Check if the reasoning content ends during a decode step.
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Args:
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input_ids: The entire model output token IDs.
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delta_ids: The last few computed tokens at the current decode step.
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Returns:
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True if the reasoning content ends in the delta_ids.
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"""
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return self.is_reasoning_end(input_ids)
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@abstractmethod
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def extract_content_ids(self, input_ids: list[int]) -> list[int]:
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"""
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Extract content token IDs from the input_ids.
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This extracts the non-reasoning content (e.g., everything after
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the </think> tag).
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Args:
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input_ids: The token IDs of the model output.
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Returns:
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The extracted content token IDs.
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"""
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@abstractmethod
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def extract_response_outputs(
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self,
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model_output: str,
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request: ResponsesRequest,
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enable_auto_tools: bool = False,
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tool_call_id_type: str = "random",
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logprobs: list[Logprob] | None = None,
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) -> list[ResponseOutputItem]:
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"""
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Extract reasoning, content, and tool calls from a complete
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model-generated string and return as ResponseOutputItem objects.
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Used for non-streaming responses where we have the entire model
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response available before sending to the client.
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Args:
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model_output: The complete model-generated string.
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request: The request object used to generate the output.
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enable_auto_tools: Whether to enable automatic tool call parsing.
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tool_call_id_type: Type of tool call ID generation ("random", etc).
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logprobs: Pre-computed logprobs for the output text, if any.
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Returns:
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A list of ResponseOutputItem objects.
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"""
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@abstractmethod
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def extract_reasoning(
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self,
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model_output: str,
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request: ChatCompletionRequest | ResponsesRequest,
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) -> tuple[str | None, str | None]:
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"""
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Extract reasoning content from a complete model-generated string.
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Used for non-streaming responses where we have the entire model
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response available before sending to the client.
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Args:
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model_output: The complete model-generated string.
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request: The request object used to generate the output.
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Returns:
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A tuple of (reasoning_content, response_content).
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"""
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@abstractmethod
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def extract_reasoning_streaming(
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self,
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previous_text: str,
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current_text: str,
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delta_text: str,
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previous_token_ids: Sequence[int],
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current_token_ids: Sequence[int],
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delta_token_ids: Sequence[int],
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) -> DeltaMessage | None:
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"""
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Extract reasoning content from a streaming delta message.
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Args:
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previous_text: Text from all previous tokens.
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current_text: Text including the current delta.
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delta_text: The new text in this delta.
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previous_token_ids: Token IDs from previous generation.
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current_token_ids: All token IDs including current.
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delta_token_ids: The new token IDs in this delta.
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Returns:
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A DeltaMessage with reasoning and/or content fields, or None.
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"""
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# ========== Tool Parser Methods ==========
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def adjust_request(self, request: ChatCompletionRequest) -> ChatCompletionRequest:
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"""
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Adjust the request parameters for tool calling.
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Can be overridden by subclasses to modify request parameters
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(e.g., setting structured output schemas for tool calling).
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Args:
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request: The original request.
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Returns:
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The adjusted request.
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"""
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return request
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@abstractmethod
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def extract_tool_calls(
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self,
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model_output: str,
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request: ChatCompletionRequest,
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) -> ExtractedToolCallInformation:
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"""
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Extract tool calls from a complete model-generated string.
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Used for non-streaming responses.
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Args:
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model_output: The complete model-generated string.
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request: The request object used to generate the output.
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Returns:
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ExtractedToolCallInformation containing the tool calls.
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"""
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@abstractmethod
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def extract_tool_calls_streaming(
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self,
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previous_text: str,
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current_text: str,
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delta_text: str,
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previous_token_ids: Sequence[int],
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current_token_ids: Sequence[int],
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delta_token_ids: Sequence[int],
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request: ChatCompletionRequest,
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) -> DeltaMessage | None:
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"""
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Extract tool calls from a streaming delta message.
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Args:
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previous_text: Text from all previous tokens.
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current_text: Text including the current delta.
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delta_text: The new text in this delta.
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previous_token_ids: Token IDs from previous generation.
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current_token_ids: All token IDs including current.
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delta_token_ids: The new token IDs in this delta.
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request: The request object.
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Returns:
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A DeltaMessage with tool_calls field, or None.
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"""
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class DelegatingParser(Parser):
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"""
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A Parser implementation that delegates to separate ReasoningParser and
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ToolParser instances.
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This is the recommended base class for creating model-specific parsers
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that combine existing reasoning and tool parser implementations.
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Subclasses should set `self._reasoning_parser` and `self._tool_parser`
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in their `__init__` method.
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If either parser is None, the corresponding methods will return default
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values (no reasoning extraction, no tool calls).
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"""
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def extract_reasoning(
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self,
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model_output: str,
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request: ChatCompletionRequest | ResponsesRequest,
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) -> tuple[str | None, str | None]:
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if self._reasoning_parser is None:
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return None, model_output
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return self._reasoning_parser.extract_reasoning(model_output, request)
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def extract_response_outputs(
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self,
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model_output: str,
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request: ResponsesRequest,
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enable_auto_tools: bool = False,
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tool_call_id_type: str = "random",
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logprobs: list[Logprob] | None = None,
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) -> list[ResponseOutputItem]:
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# First extract reasoning
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reasoning, content = self.extract_reasoning(model_output, request)
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# Then parse tool calls from the content
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tool_calls, content = self._parse_tool_calls(
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request=request,
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content=content,
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enable_auto_tools=enable_auto_tools,
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)
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# Build output items
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outputs: list[ResponseOutputItem] = []
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# Add reasoning item if present
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if reasoning:
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reasoning_item = ResponseReasoningItem(
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id=f"rs_{random_uuid()}",
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summary=[],
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type="reasoning",
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content=[
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ResponseReasoningTextContent(text=reasoning, type="reasoning_text")
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],
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status=None, # NOTE: Only the last output item has status.
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)
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outputs.append(reasoning_item)
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# Add message item if there's content
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if content:
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res_text_part = ResponseOutputText(
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text=content,
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annotations=[],
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type="output_text",
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logprobs=logprobs,
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)
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message_item = ResponseOutputMessage(
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id=f"msg_{random_uuid()}",
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content=[res_text_part],
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role="assistant",
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status="completed",
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type="message",
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)
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outputs.append(message_item)
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if tool_calls:
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# We use a simple counter for history_tool_call_count because
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# we don't track the history of tool calls in the Responses API yet.
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# This means that the tool call index will start from 0 for each
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# request.
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for history_tool_call_cnt, tool_call in enumerate(tool_calls):
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tool_call_item = ResponseFunctionToolCall(
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id=f"fc_{random_uuid()}",
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call_id=tool_call.id
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if tool_call.id
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else make_tool_call_id(
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id_type=tool_call_id_type,
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func_name=tool_call.name,
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idx=history_tool_call_cnt,
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),
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type="function_call",
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status="completed",
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name=tool_call.name,
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arguments=tool_call.arguments,
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)
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outputs.append(tool_call_item)
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return outputs
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def _parse_tool_calls(
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self,
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request: ResponsesRequest,
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content: str | None,
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enable_auto_tools: bool,
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) -> tuple[list[FunctionCall], str | None]:
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"""
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TODO(qandrew): merge _parse_tool_calls_from_content
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for ChatCompletions into this function
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Parse tool calls from content based on request tool_choice settings.
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Returns:
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A tuple of (function_calls, remaining_content) if tool calls
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were parsed
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"""
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function_calls: list[FunctionCall] = []
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if request.tool_choice and isinstance(request.tool_choice, ToolChoiceFunction):
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# Forced Function Call (Responses API style)
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assert content is not None
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function_calls.append(
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FunctionCall(name=request.tool_choice.name, arguments=content)
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)
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return function_calls, None # Clear content since tool is called.
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if request.tool_choice and isinstance(
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request.tool_choice, ChatCompletionNamedToolChoiceParam
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):
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# Forced Function Call (Chat Completion API style)
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assert content is not None
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function_calls.append(
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FunctionCall(name=request.tool_choice.function.name, arguments=content)
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)
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return function_calls, None # Clear content since tool is called.
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if request.tool_choice == "required":
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# Required tool calls - parse JSON
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assert content is not None
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tool_calls = TypeAdapter(list[FunctionDefinition]).validate_json(content)
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function_calls.extend(
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FunctionCall(
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name=tool_call.name,
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arguments=json.dumps(tool_call.parameters, ensure_ascii=False),
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)
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for tool_call in tool_calls
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)
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return function_calls, None # Clear content since tool is called.
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if (
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self._tool_parser is not None
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and enable_auto_tools
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and (request.tool_choice == "auto" or request.tool_choice is None)
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):
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# Automatic Tool Call Parsing
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tool_call_info = self._tool_parser.extract_tool_calls(
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content if content is not None else "",
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request=request, # type: ignore
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)
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if tool_call_info is not None and tool_call_info.tools_called:
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function_calls.extend(
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FunctionCall(
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id=tool_call.id,
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name=tool_call.function.name,
|
||||
arguments=tool_call.function.arguments,
|
||||
)
|
||||
for tool_call in tool_call_info.tool_calls
|
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)
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remaining_content = tool_call_info.content
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if remaining_content and remaining_content.strip() == "":
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remaining_content = None
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return function_calls, remaining_content
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# No tool calls
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return [], content
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||||
|
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def extract_reasoning_streaming(
|
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self,
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previous_text: str,
|
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current_text: str,
|
||||
delta_text: str,
|
||||
previous_token_ids: Sequence[int],
|
||||
current_token_ids: Sequence[int],
|
||||
delta_token_ids: Sequence[int],
|
||||
) -> DeltaMessage | None:
|
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if self._reasoning_parser is None:
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return DeltaMessage(content=delta_text)
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return self._reasoning_parser.extract_reasoning_streaming(
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previous_text,
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current_text,
|
||||
delta_text,
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||||
previous_token_ids,
|
||||
current_token_ids,
|
||||
delta_token_ids,
|
||||
)
|
||||
|
||||
def extract_tool_calls(
|
||||
self,
|
||||
model_output: str,
|
||||
request: ChatCompletionRequest,
|
||||
) -> ExtractedToolCallInformation:
|
||||
if self._tool_parser is None:
|
||||
return ExtractedToolCallInformation(
|
||||
tools_called=False, tool_calls=[], content=model_output
|
||||
)
|
||||
return self._tool_parser.extract_tool_calls(model_output, request)
|
||||
|
||||
def extract_tool_calls_streaming(
|
||||
self,
|
||||
previous_text: str,
|
||||
current_text: str,
|
||||
delta_text: str,
|
||||
previous_token_ids: Sequence[int],
|
||||
current_token_ids: Sequence[int],
|
||||
delta_token_ids: Sequence[int],
|
||||
request: ChatCompletionRequest,
|
||||
) -> DeltaMessage | None:
|
||||
if self._tool_parser is None:
|
||||
return None
|
||||
return self._tool_parser.extract_tool_calls_streaming(
|
||||
previous_text,
|
||||
current_text,
|
||||
delta_text,
|
||||
previous_token_ids,
|
||||
current_token_ids,
|
||||
delta_token_ids,
|
||||
request,
|
||||
)
|
||||
|
||||
|
||||
class _WrappedParser(DelegatingParser):
|
||||
"""
|
||||
A DelegatingParser subclass that instantiates parsers from class attributes.
|
||||
|
||||
This class is used to dynamically create a parser that wraps individual
|
||||
ReasoningParser and ToolParser classes. The class attributes
|
||||
`reasoning_parser_cls` and `tool_parser_cls` should be set before
|
||||
instantiation.
|
||||
|
||||
Usage:
|
||||
_WrappedParser.reasoning_parser_cls = MyReasoningParser
|
||||
_WrappedParser.tool_parser_cls = MyToolParser
|
||||
parser = _WrappedParser(tokenizer)
|
||||
"""
|
||||
|
||||
reasoning_parser_cls: type[ReasoningParser] | None = None
|
||||
tool_parser_cls: type[ToolParser] | None = None
|
||||
|
||||
def __init__(self, tokenizer: TokenizerLike):
|
||||
super().__init__(tokenizer)
|
||||
# Instantiate the underlying parsers from class attributes
|
||||
if self.__class__.reasoning_parser_cls is not None:
|
||||
self._reasoning_parser = self.__class__.reasoning_parser_cls(tokenizer)
|
||||
if self.__class__.tool_parser_cls is not None:
|
||||
self._tool_parser = self.__class__.tool_parser_cls(tokenizer)
|
||||
Reference in New Issue
Block a user