Fix: Runtime error for function calling (#3300)

docs/references/deepseek.md

@@ -20,7 +20,7 @@ Please refer to [the example](https://github.com/sgl-project/sglang/tree/main/be
 
 - [Serving with two H200*8 nodes and docker](https://github.com/sgl-project/sglang/tree/main/benchmark/deepseek_v3#example-serving-with-two-h2008-nodes-and-docker).
 
-## Optimisations
+## Optimizations
 
 ### Multi-head Latent Attention (MLA) Throughput Optimizations
 

python/sglang/srt/function_call_parser.py

 import json
+import logging
 import re
 from abc import ABC, abstractmethod
 from json import JSONDecodeError, JSONDecoder
@@ -8,6 +9,8 @@ import partial_json_parser
 from partial_json_parser.core.options import Allow
 from pydantic import BaseModel, Field
 
+logger = logging.getLogger(__name__)
+
 TOOLS_TAG_LIST = [
     "<|plugin|>",
     "<function=",
@@ -88,17 +91,43 @@ class BaseFormatDetector:
         self.bot_token = ""
         self.eot_token = ""
 
-    def parse_base_json(self, action: Dict, tools: List[Function]):
-        name, parameters = action["name"], json.dumps(
-            action.get("parameters", action.get("arguments", {})),
+    def parse_base_json(self, action: Any, tools: List[Function]) -> List[ToolCallItem]:
+        tool_indices = {
+            tool.function.name: i for i, tool in enumerate(tools) if tool.function.name
+        }
+        if not isinstance(action, list):
+            name = action.get("name")
+            if not name or name not in tool_indices:
+                logger.warning(f"Model attempted to call undefined function: {name}")
+                return []
+
+            return [
+                ToolCallItem(
+                    tool_index=tool_indices[name],
+                    name=name,
+                    parameters=json.dumps(
+                        action.get("parameters") or action.get("arguments", {}),
+                        ensure_ascii=False,
+                    ),
+                )
+            ]
+
+        results = []
+        for act in action:
+            name = act.get("name")
+            if name and name in tool_indices:
+                results.append(
+                    ToolCallItem(
+                        tool_index=tool_indices[name],
+                        name=name,
+                        parameters=json.dumps(
+                            act.get("parameters") or act.get("arguments", {}),
                             ensure_ascii=False,
+                        ),
                     )
-        tool_index = [tool.function.name for tool in tools].index(name)
-        tool_call_item = ToolCallItem(
-            tool_index=tool_index, name=name, parameters=parameters
                 )
-        calls = [tool_call_item]
-        return calls
+
+        return results
 
     def detect_and_parse(self, text: str, tools: List[Function]) -> List[ToolCallItem]:
         """
@@ -112,9 +141,7 @@ class BaseFormatDetector:
         self, new_text: str, tools: List[Function]
     ) -> StreamingParseResult:
         """
-        Streaming incremental parsing, referencing the logic of Llama32Detector.
-        We partially parse JSON within <tool_call>...</tool_call>, and handle
-        incremental argument output.
+        Streaming incremental parsing with tool validation.
         """
         # Append new text to buffer
         self._buffer += new_text
@@ -125,17 +152,19 @@ class BaseFormatDetector:
                 new_text = new_text.replace(self.eot_token, "")
             return StreamingParseResult(normal_text=new_text)
 
-        # bit mask flags for partial JSON parsing. If the name hasn't been
-        # sent yet, don't allow sending
-        # an incomplete string since OpenAI only ever (as far as I have
-        # seen) allows sending the entire tool/ function name at once.
+        # Build tool indices if not already built
+        if not hasattr(self, "_tool_indices"):
+            self._tool_indices = {
+                tool.function.name: i
+                for i, tool in enumerate(tools)
+                if tool.function and tool.function.name
+            }
+
         flags = Allow.ALL if self.current_tool_name_sent else Allow.ALL & ~Allow.STR
         try:
             tool_call_arr = []
             is_complete = []
             try:
-                # depending on the prompt format the Llama model may or may not
-                # prefix the output with the <|python_tag|> token
                 start_idx = (
                     len(self.bot_token)
                     if current_text.startswith(self.bot_token)
@@ -149,8 +178,18 @@ class BaseFormatDetector:
                         _is_complete_json(current_text[start_idx : start_idx + end_idx])
                     )
                     start_idx += end_idx + len("; ")
-                    # depending on the prompt Llama can use
-                    # either arguments or parameters
+
+                    # Validate tool name if present
+                    if "name" in obj and obj["name"] not in self._tool_indices:
+                        # Invalid tool name - reset state
+                        self._buffer = ""
+                        self.current_tool_id = -1
+                        self.current_tool_name_sent = False
+                        if self.streamed_args_for_tool:
+                            self.streamed_args_for_tool.pop()
+                        return StreamingParseResult()
+
+                    # Handle parameters/arguments consistency
                     if "parameters" in obj:
                         assert (
                             "arguments" not in obj
@@ -159,29 +198,17 @@ class BaseFormatDetector:
                     tool_call_arr.append(obj)
 
             except partial_json_parser.core.exceptions.MalformedJSON:
-                # not enough tokens to parse into JSON yet
                 return StreamingParseResult()
 
-            # select as the current tool call the one we're on the state at
-            current_tool_call: Dict = (
-                tool_call_arr[self.current_tool_id] if len(tool_call_arr) > 0 else {}
-            )
-
-            # case -- if no tokens have been streamed for the tool, e.g.
-            #   only the array brackets, stream nothing
             if len(tool_call_arr) == 0:
                 return StreamingParseResult()
 
-            # case: we are starting a new tool in the array
-            #   -> array has > 0 length AND length has moved past cursor
-            elif (
-                len(tool_call_arr) > 0 and len(tool_call_arr) > self.current_tool_id + 1
-            ):
+            current_tool_call: Dict = (
+                tool_call_arr[self.current_tool_id] if len(tool_call_arr) > 0 else {}
+            )
 
-                # if we're moving on to a new call, first make sure we
-                # haven't missed anything in the previous one that was
-                # auto-generated due to JSON completions, but wasn't
-                # streamed to the client yet.
+            # Handle new tool in array
+            if len(tool_call_arr) > 0 and len(tool_call_arr) > self.current_tool_id + 1:
                 if self.current_tool_id >= 0:
                     cur_arguments = current_tool_call.get("arguments")
                     if cur_arguments:
@@ -190,7 +217,6 @@ class BaseFormatDetector:
                         argument_diff = cur_args_json[sent:]
 
                         res = StreamingParseResult(
-                            normal_text=None,
                             calls=[
                                 ToolCallItem(
                                     tool_index=self.current_tool_id,
@@ -206,23 +232,20 @@ class BaseFormatDetector:
                         res = StreamingParseResult()
                 else:
                     res = StreamingParseResult()
-                # re-set stuff pertaining to progress in the current tool
+
                 self.current_tool_id = len(tool_call_arr) - 1
                 self.current_tool_name_sent = False
                 self.streamed_args_for_tool.append("")
-                print("starting on new tool %d", self.current_tool_id)
                 return res
 
-            # if the current tool name hasn't been sent, send if available
-            # - otherwise send nothing
+            # Handle tool name
             elif not self.current_tool_name_sent:
                 function_name = current_tool_call.get("name")
-                if function_name:
+                if function_name and function_name in self._tool_indices:
                     res = StreamingParseResult(
-                        normal_text=None,
                         calls=[
                             ToolCallItem(
-                                tool_index=self.current_tool_id,
+                                tool_index=self._tool_indices[function_name],
                                 name=function_name,
                                 parameters="",
                             )
@@ -232,8 +255,7 @@ class BaseFormatDetector:
                 else:
                     res = StreamingParseResult()
 
-            # now we know we're on the same tool call and we're streaming
-            # arguments
+            # Handle streaming arguments
             else:
                 cur_arguments = current_tool_call.get("arguments")
                 res = StreamingParseResult()
@@ -250,13 +272,12 @@ class BaseFormatDetector:
                         argument_diff = cur_args_json[sent:]
                         self._buffer = ""
                         self.prev_tool_call_arr[self.current_tool_id].clear()
-                        self.current_tool_name_sent: bool = False
+                        self.current_tool_name_sent = False
                         self.streamed_args_for_tool[self.current_tool_id] = ""
 
                     elif prev_arguments:
                         prev_args_json = json.dumps(prev_arguments)
                         if cur_args_json != prev_args_json:
-
                             prefix = _find_common_prefix(prev_args_json, cur_args_json)
                             argument_diff = prefix[sent:]
 
@@ -279,8 +300,7 @@ class BaseFormatDetector:
             return res
 
         except Exception as e:
-            print(e)
-            # Skipping chunk as a result of tool streaming extraction error
+            logger.error(f"Error in parse_streaming_increment: {e}")
             return StreamingParseResult()
 
 
@@ -372,31 +392,38 @@ class Llama32Detector(BaseFormatDetector):
     Detector for Llama 3.2 models.
     Assumes function call format:
       <|python_tag|>{"name":"xxx", "arguments":{...}}
-    Does not require a closing tag "</python_tag|>",
-    relies on json.loads(...) success to determine if JSON is complete.
     """
 
     def __init__(self):
-        """
-        Initializes the detector with necessary state variables.
-        """
         super().__init__()
         self.bot_token = "<|python_tag|>"
 
     def detect_and_parse(self, text: str, tools: List[Function]) -> List[ToolCallItem]:
-        """
-        One-time parsing: Detects and parses tool calls in the provided text.
+        """Parse function calls from text, handling multiple JSON objects."""
+        if "<|python_tag|>" not in text:
+            return []
 
-        :param text: The complete text to parse.
-        :param tools: List of available tools.
-        :return: ParseResult indicating success or failure, consumed text, leftover text, and parsed calls.
-        """
+        _, action_text = text.split("<|python_tag|>")
+
+        # Split by semicolon and process each part
+        json_parts = [part.strip() for part in action_text.split(";") if part.strip()]
+
+        all_actions = []
+        for part in json_parts:
+            try:
+                # Parse each individual JSON object
+                action = json.loads(part)
+                all_actions.append(action)
+            except json.JSONDecodeError as e:
+                logger.warning(f"Failed to parse JSON part: {part}")
+                logger.warning(f"JSON parse error: {str(e)}")
+                continue
+
+        # Only process if we found valid JSON objects
+        if all_actions:
+            return self.parse_base_json(all_actions, tools)
 
-        if "<|python_tag|>" not in text:
         return []
-        _, action = text.split("<|python_tag|>")
-        action = json.loads(action)
-        return self.parse_base_json(action, tools)
 
 
 class MultiFormatParser: