v1.0

reasoning/minimax_m2_reasoning_parser.py

@@ -0,0 +1,67 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+from collections.abc import Sequence
+
+from vllm.entrypoints.openai.protocol import (
+    ChatCompletionRequest,
+    DeltaMessage,
+    ResponsesRequest,
+)
+from vllm.logger import init_logger
+from vllm.reasoning.abs_reasoning_parsers import ReasoningParser
+from vllm.reasoning.basic_parsers import BaseThinkingReasoningParser
+from vllm.transformers_utils.tokenizer import AnyTokenizer
+
+logger = init_logger(__name__)
+
+
+class MiniMaxM2ReasoningParser(BaseThinkingReasoningParser):
+    """
+    Reasoning parser for MiniMax M2 model.
+    """
+
+    @property
+    def start_token(self) -> str:
+        """The token that starts reasoning content."""
+        return "<think>"
+
+    @property
+    def end_token(self) -> str:
+        """The token that ends reasoning content."""
+        return "</think>"
+
+
+class MiniMaxM2AppendThinkReasoningParser(ReasoningParser):
+    """
+    Reasoning parser for MiniMax M2 model.
+    """
+
+    def __init__(self, tokenizer: AnyTokenizer, *args, **kwargs):
+        super().__init__(tokenizer, *args, **kwargs)
+        self.end_token_id = self.vocab.get("</think>")
+
+    def is_reasoning_end(self, input_ids: list[int]) -> bool:
+        end_token_id = self.end_token_id
+        return any(input_id == end_token_id for input_id in reversed(input_ids))
+
+    def extract_content_ids(self, input_ids: list[int]) -> list[int]:
+        return input_ids
+
+    def extract_reasoning_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],
+    ) -> DeltaMessage | None:
+        if len(previous_token_ids) == 0:
+            delta_text = "<think>" + delta_text
+        return DeltaMessage(content=delta_text)
+
+    def extract_reasoning(
+        self, model_output: str, request: ChatCompletionRequest | ResponsesRequest
+    ) -> tuple[str | None, str | None]:
+        return None, "<think>" + model_output