v1.0

reasoning/identity_reasoning_parser.py

@@ -0,0 +1,58 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+from collections.abc import Sequence
+
+from transformers import PreTrainedTokenizerBase
+
+from vllm.entrypoints.openai.protocol import ChatCompletionRequest, DeltaMessage
+from vllm.logger import init_logger
+from vllm.reasoning import ReasoningParser
+
+logger = init_logger(__name__)
+
+
+class IdentityReasoningParser(ReasoningParser):
+    """
+    Identity reasoning parser.
+
+    This parser does not attempt to parse or strip out reasoning tokens.
+    It treats the entire model output as content and ignores reasoning.
+    """
+
+    def __init__(self, tokenizer: PreTrainedTokenizerBase, *args, **kwargs):
+        super().__init__(tokenizer, *args, **kwargs)
+        if not self.model_tokenizer:
+            raise ValueError(
+                "The model tokenizer must be passed to the ReasoningParser "
+                "constructor during construction."
+            )
+
+    def is_reasoning_end(self, input_ids: list[int]) -> bool:
+        # Always return True, since we never treat reasoning specially
+        return True
+
+    def extract_content_ids(self, input_ids: list[int]) -> list[int]:
+        # Identity: return all tokens as content
+        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:
+        # Just wrap delta_text as content, ignore reasoning
+        if delta_text:
+            return DeltaMessage(content=delta_text)
+        return None
+
+    def extract_reasoning(
+        self, model_output: str, request: ChatCompletionRequest
+    ) -> tuple[str | None, str | None]:
+        # No reasoning separation: return None for reasoning,
+        # and full model_output as content
+        return None, model_output