wehub-resource-sync
67 lines
2.3 KiB
Python
67 lines
2.3 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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from collections.abc import Sequence
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from typing import TYPE_CHECKING
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from vllm.entrypoints.openai.engine.protocol import (
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DeltaMessage,
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)
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from vllm.parser.engine.registered_adapters import MinimaxM2ParserReasoningAdapter
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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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if TYPE_CHECKING:
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from vllm.entrypoints.openai.chat_completion.protocol import ChatCompletionRequest
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from vllm.entrypoints.openai.responses.protocol import ResponsesRequest
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class MiniMaxM2ReasoningParser(MinimaxM2ParserReasoningAdapter): # type: ignore[valid-type, misc]
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"""
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Reasoning parser for MiniMax M2 model.
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MiniMax M2 models don't generate <think> start token, only </think> end
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token. All content before </think> is reasoning, content after is the
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actual response.
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"""
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class MiniMaxM2AppendThinkReasoningParser(ReasoningParser):
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"""
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Reasoning parser for MiniMax M2 model.
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"""
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def __init__(self, tokenizer: TokenizerLike, *args, **kwargs):
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super().__init__(tokenizer, *args, **kwargs)
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self.end_token_id = self.vocab.get("</think>")
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self.start_token_id = self.vocab.get("<think>")
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def is_reasoning_end(self, input_ids: Sequence[int]) -> bool:
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end_token_id = self.end_token_id
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start_token_id = self.start_token_id
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for input_id in reversed(input_ids):
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if input_id in (end_token_id, start_token_id):
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return input_id == end_token_id
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return False
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def extract_content_ids(self, input_ids: list[int]) -> list[int]:
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return input_ids
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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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if len(previous_token_ids) == 0:
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delta_text = "<think>" + delta_text
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return DeltaMessage(content=delta_text)
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def extract_reasoning(
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self, model_output: str, request: "ChatCompletionRequest | ResponsesRequest"
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) -> tuple[str | None, str | None]:
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return None, "<think>" + model_output
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