[Frontend] split append tool output (#28333)

Signed-off-by: Andrew Xia <axia@fb.com>
Co-authored-by: Andrew Xia <axia@fb.com>

tests/entrypoints/openai/test_serving_responses.py

@@ -34,6 +34,9 @@ class MockConversationContext(ConversationContext):
     def append_output(self, output) -> None:
         pass
 
+    def append_tool_output(self, output) -> None:
+        pass
+
     async def call_tool(self):
         return []
 

vllm/entrypoints/context.py

@@ -80,7 +80,11 @@ class TurnMetrics:
 
 class ConversationContext(ABC):
     @abstractmethod
-    def append_output(self, output) -> None:
+    def append_output(self, output: RequestOutput) -> None:
+        pass
+
+    @abstractmethod
+    def append_tool_output(self, output) -> None:
         pass
 
     @abstractmethod
@@ -151,6 +155,9 @@ class SimpleContext(ConversationContext):
         self.num_cached_tokens = output.num_cached_tokens or 0
         self.num_output_tokens += len(output.outputs[0].token_ids or [])
 
+    def append_tool_output(self, output) -> None:
+        raise NotImplementedError("Should not be called.")
+
     def need_builtin_tool_call(self) -> bool:
         return False
 
@@ -205,8 +212,7 @@ class HarmonyContext(ConversationContext):
         if self.parser.current_channel in {"analysis", "commentary"}:
             self.num_reasoning_tokens += 1
 
-    def append_output(self, output: RequestOutput | list[Message]) -> None:
+    def append_output(self, output: RequestOutput) -> None:
-        if isinstance(output, RequestOutput):
         output_token_ids = output.outputs[0].token_ids
         self.parser = get_streamable_parser_for_assistant()
         for token_id in output_token_ids:
@@ -224,8 +230,9 @@ class HarmonyContext(ConversationContext):
         output_msgs = self.parser.messages
         # The responses finish reason is set in the last message
         self.finish_reason = output.outputs[0].finish_reason
-        else:
+        self._messages.extend(output_msgs)
-            # Tool output.
+
+    def append_tool_output(self, output: list[Message]) -> None:
         output_msgs = output
         self._messages.extend(output_msgs)
 
@@ -502,8 +509,7 @@ class StreamingHarmonyContext(HarmonyContext):
     def messages(self) -> list:
         return self._messages
 
-    def append_output(self, output: RequestOutput | list[Message]) -> None:
+    def append_output(self, output: RequestOutput) -> None:
-        if isinstance(output, RequestOutput):
         # append_output is called for each output token in streaming case,
         # so we only want to add the prompt tokens once for each message.
         if self.first_tok_of_message:
@@ -528,7 +534,8 @@ class StreamingHarmonyContext(HarmonyContext):
             self._messages.extend(
                 self.parser.messages[len(self._messages) - self.num_init_messages :]
             )
-        else:
+
+    def append_tool_output(self, output: list[Message]) -> None:
         # Handle the case of tool output in direct message format
         assert len(output) == 1, "Tool output should be a single message"
         msg = output[0]

vllm/entrypoints/openai/serving_engine.py

@@ -1227,7 +1227,7 @@ class OpenAIServing:
 
             # Call the tool and update the context with the result.
             tool_output = await context.call_tool()
-            context.append_output(tool_output)
+            context.append_tool_output(tool_output)
 
             # TODO: uncomment this and enable tool output streaming
             # yield context