Fix is_reasoning_end to handle multi-turn prompt tokens

Instead of always returning False (which broke tool call streaming),
use a heuristic: if think-end appears in the token IDs but is
followed by more than 3 tokens (chat template wrapping like
<|im_end|>, user markers, etc.), it's from a prior turn's prompt
and reasoning hasn't started in the current generation. Return False.
If think-end is at or near the end, it's from generated tokens and
reasoning has ended. Return True.

kimi_k2_reasoning_parser.py

@@ -144,29 +144,89 @@ class KimiK2ReasoningParser(ReasoningParser):
     # ------------------------------------------------------------------
 
     def is_reasoning_end(self, input_ids: Sequence[int]) -> bool:
-        """Check if reasoning has ended.
-        
-        IMPORTANT: Always returns False for this parser.  The reasoning
-        state is tracked internally by ``_reasoning_ended`` which is
-        updated only when ``extract_reasoning_streaming`` detects
-        think-end or a tool-section marker in the model's *generated*
-        text.
-        
-        The vLLM serving layer calls this with ``prompt_token_ids`` to
-        pre-compute whether reasoning has ended.  On multi-turn
-        conversations, the prompt contains think-end tokens from prior
-        assistant messages, which would cause a false positive — the
-        serving layer would skip ``extract_reasoning_streaming`` entirely
-        and route all thinking text to content.
-        
-        Returning False ensures the serving layer always calls
-        ``extract_reasoning_streaming``, which correctly handles the
-        transition using generated text only.
+        """Check if reasoning has ended based on the token ID sequence.
+
+        Scans backward to find the last think-start or think-end token.
+        Returns True only if the last relevant token is a think-end or
+        a tool-section-start, AND there is no think-start after it.
+
+        CRITICAL: When called with prompt_token_ids (as the vLLM serving
+        layer does), the input contains the full chat history.  On
+        multi-turn conversations, the prompt ends with tokens from the
+        prior assistant message, which may include think-end.  However,
+        this think-end belongs to the PRIOR generation — the new
+        generation will start its own reasoning with think-start.
+
+        To handle this correctly, we check whether the input ends with
+        a complete reasoning block (think-start ... think-end).  If the
+        last think token is think-end AND it's followed by non-reasoning
+        tokens (like tool_call tokens or end-of-sequence), we return
+        True.  But if the input is just the prompt with no generated
+        tokens yet, we return False because the new generation hasn't
+        started reasoning yet.
+
+        The key insight: in the chat template for multi-turn, after the
+        last assistant message's think-end, the template adds
+        <|im_end|> followed by new user/assistant markers.  The
+        assistant generation prompt ends with <|im_assistant|> and
+        <|im_middle|> — no think tokens.  So if we scan backward and
+        find think-end but then find prompt-end tokens (not think-start)
+        after it, we know reasoning ended in a PRIOR turn, not the
+        current one.  We return False to let the new generation start
+        fresh.
         """
         if self._identity_parser is not None:
             return self._identity_parser.is_reasoning_end(input_ids)
-        
-        return False
+
+        # Scan backward to find the last think-start or think-end
+        # or tool-section-start token.
+        last_start = -1
+        last_end = -1
+        last_tool_section = -1
+
+        for i in range(len(input_ids) - 1, -1, -1):
+            if input_ids[i] == self._start_token_id and last_start == -1:
+                last_start = i
+            if input_ids[i] == self._end_token_id and last_end == -1:
+                last_end = i
+            if input_ids[i] in self._tool_section_start_token_ids and last_tool_section == -1:
+                last_tool_section = i
+            # Stop early if we found think-start — it's the boundary
+            if last_start != -1:
+                break
+
+        # No think tokens at all — not a reasoning model output
+        if last_start == -1 and last_end == -1 and last_tool_section == -1:
+            return False
+
+        # think-start is the last relevant token — reasoning is in progress
+        if last_start != -1 and (last_end == -1 or last_start > last_end):
+            return False
+
+        # think-end or tool-section is the last relevant token.
+        # This could be from the prompt (prior turn) or from generated
+        # tokens.  For prompt tokens on multi-turn, the think-end is
+        # from a prior assistant message and the new generation hasn't
+        # started yet — we should return False.
+        #
+        # Heuristic: if think-end appears but is followed by more tokens
+        # (like <|im_end|>, user markers, etc.), it's from the prompt
+        # and reasoning hasn't started in the current generation yet.
+        # Return False.
+        #
+        # If think-end is the very last token or near the end, it's
+        # from generated tokens and reasoning has ended.  Return True.
+        last_relevant = max(last_end, last_tool_section)
+        tokens_after = len(input_ids) - 1 - last_relevant
+
+        # If there are more than a few tokens after the last think-end,
+        # those are prompt tokens (chat template wrapping), meaning
+        # the think-end is from a prior turn.  Return False.
+        if tokens_after > 3:
+            return False
+
+        return True
+
     def is_reasoning_end_streaming(
         self, input_ids: Sequence[int], delta_ids: Iterable[int]
     ) -> bool: