Tool Call parser not working in vLLM
#19
by
VivekMalipatel23
- opened
i am runnign Qwen/Qwen3-Coder-30B-A3B-Instruct-FP8 on vLLM with
vllm serve Qwen/Qwen3-Coder-30B-A3B-Instruct-FP8 \
--host 0.0.0.0 \
--port 8001 \
--served-model-name o3 \
--dtype float16 \
--enable-auto-tool-choice \
--tool-call-parser qwen3_coder \
--enable-expert-parallel \
--enable-chunked-prefill \
--enable-prefix-caching \
--max-model-len 65536 \
--trust-remote-code \
--pipeline-parallel-size 2 \
--gpu-memory-utilization 0.90 \
--api-key test-key
But when I use it for a tool call, it outputs
I will use the knowledge_search_agent tool to retrieve a list of all documents in your knowledge base.
<function=knowledge_search_agent>
<parameter=prompt>
List all the documents in the user's knowledge base.
Its calling the tool but vLLM is not able to pick parse that.
any fix for this?
VivekMalipatel23
changed discussion title from
Tool Call parser not working
to Tool Call parser not working in vLLM
I’m in the same situation, let’s wait for the patch. I’ve temporarily modified the tool call parser.
use it if you’re in a hurry
In vllm 0.10.0 the --tool-parser-plugin cli option is ignored. use a earlier version.
import uuid
from collections.abc import Sequence
from typing import Any, Optional, Union
import regex as re
import json
from vllm.entrypoints.openai.protocol import (ChatCompletionRequest,
ChatCompletionToolsParam,
DeltaFunctionCall, DeltaMessage,
DeltaToolCall,
ExtractedToolCallInformation,
FunctionCall, ToolCall)
from vllm.entrypoints.openai.tool_parsers.abstract_tool_parser import (
ToolParser, ToolParserManager)
from vllm.logger import init_logger
from vllm.transformers_utils.tokenizer import AnyTokenizer
logger = init_logger(__name__)
@ToolParserManager.register_module(["qwen3_coder"])
class Qwen3CoderToolParser(ToolParser):
def __init__(self, tokenizer: AnyTokenizer):
super().__init__(tokenizer)
self.current_tool_name_sent: bool = False
self.prev_tool_call_arr: list[dict] = []
self.streamed_args_for_tool: list[str] = []
# Sentinel tokens for streaming mode
self.tool_call_start_token: str = "<tool_call>"
self.tool_call_end_token: str = "</tool_call>"
self.tool_call_prefix: str = "<function="
self.function_end_token: str = "</function>"
self.parameter_prefix: str = "<parameter="
self.parameter_end_token: str = "</parameter>"
self.is_tool_call_started: bool = False
self.failed_count: int = 0
# Streaming state variables
self.current_tool_index: int = 0
self.header_sent: bool = False
self.current_tool_string_id: Optional[str] = None
self.current_function_name: Optional[str] = None
self.current_param_name: Optional[str] = None
self.current_param_value: str = ""
self.param_count: int = 0
self.in_param: bool = False
self.in_function: bool = False
self.accumulated_text: str = ""
self.json_started: bool = False
self.json_closed: bool = False
# Enhanced streaming state - reset for each new message
self._reset_streaming_state()
# Regex patterns
self.tool_call_complete_regex = re.compile(
r"<tool_call>(.*?)</tool_call>", re.DOTALL)
self.tool_call_regex = re.compile(
r"<tool_call>(.*?)</tool_call>|<tool_call>(.*?)$", re.DOTALL)
self.tool_call_function_regex = re.compile(
r"<function=(.*?)</function>|<function=(.*)$", re.DOTALL)
self.tool_call_parameter_regex = re.compile(
r"<parameter=(.*?)</parameter>|<parameter=(.*?)$", re.DOTALL)
if not self.model_tokenizer:
raise ValueError(
"The model tokenizer must be passed to the ToolParser "
"constructor during construction.")
self.tool_call_start_token_id = self.vocab.get(
self.tool_call_start_token)
self.tool_call_end_token_id = self.vocab.get(self.tool_call_end_token)
if (self.tool_call_start_token_id is None
or self.tool_call_end_token_id is None):
raise RuntimeError(
"Qwen3 XML Tool parser could not locate tool call start/end "
"tokens in the tokenizer!")
logger.debug("vLLM Successfully import tool parser %s !",
self.__class__.__name__)
def _generate_tool_call_id(self) -> str:
"""Generate a unique tool call ID."""
return f"call_{uuid.uuid4().hex[:24]}"
def _reset_streaming_state(self):
"""Reset all streaming state."""
self.current_tool_index = 0
self.is_tool_call_started = False
self.header_sent = False
self.current_tool_string_id = None
self.current_function_name = None
self.current_param_name = None
self.current_param_value = ""
self.param_count = 0
self.in_param = False
self.in_function = False
self.accumulated_text = ""
self.json_started = False
self.json_closed = False
def _parse_xml_function_call(
self, function_call_str: str,
tools: Optional[list[ChatCompletionToolsParam]]
) -> Optional[ToolCall]:
def get_arguments_config(func_name: str) -> dict:
if tools is None:
return {}
for config in tools:
if not hasattr(config, "type") or not (
hasattr(config, "function")
and hasattr(config.function, "name")):
continue
if (config.type == "function"
and config.function.name == func_name):
if not hasattr(config.function, "parameters"):
return {}
params = config.function.parameters
if isinstance(params, dict) and "properties" in params:
return params["properties"]
elif isinstance(params, dict):
return params
else:
return {}
logger.warning("Tool '%s' is not defined in the tools list.",
func_name)
return {}
def convert_param_value(param_value: str, param_name: str,
param_config: dict, func_name: str) -> Any:
# Handle null value for any type
if param_value.lower() == "null":
return None
converted_value: Any
if param_name not in param_config:
if param_config != {}:
logger.warning(
"Parsed parameter '%s' is not defined in the tool "
"parameters for tool '%s', directly returning the "
"string value.", param_name, func_name)
return param_value
if (isinstance(param_config[param_name], dict)
and "type" in param_config[param_name]):
param_type = str(
param_config[param_name]["type"]).strip().lower()
else:
param_type = "string"
if param_type in [
"string", "str", "text", "varchar", "char", "enum"
]:
return param_value
elif (param_type.startswith("int") or param_type.startswith("uint")
or param_type.startswith("long")
or param_type.startswith("short")
or param_type.startswith("unsigned")):
try:
converted_value = int(param_value)
return converted_value
except ValueError:
logger.warning(
"Parsed value '%s' of parameter '%s' is not an "
"integer in tool '%s', degenerating to string.",
param_value, param_name, func_name)
return param_value
elif (param_type.startswith("num")
or param_type.startswith("float")):
try:
float_param_value = float(param_value)
converted_value = (float_param_value if float_param_value -
int(float_param_value) != 0 else
int(float_param_value))
return converted_value
except ValueError:
logger.warning(
"Parsed value '%s' of parameter '%s' is not a float "
"in tool '%s', degenerating to string.", param_value,
param_name, func_name)
return param_value
elif param_type in ["boolean", "bool", "binary"]:
param_value = param_value.lower()
if param_value not in ["true", "false"]:
logger.warning(
"Parsed value '%s' of parameter '%s' is not a "
"boolean (`true` of `false`) in tool '%s', "
"degenerating to false.", param_value, param_name,
func_name)
return param_value == "true"
else:
if param_type == "object" or param_type.startswith("dict"):
try:
converted_value = json.loads(param_value)
return converted_value
except json.JSONDecodeError:
logger.warning(
"Parsed value '%s' of parameter '%s' is not a "
"valid JSON object in tool '%s', will try other "
"methods to parse it.", param_value, param_name,
func_name)
try:
converted_value = eval(param_value)
return converted_value
except Exception:
logger.warning(
"Parsed value '%s' of parameter '%s' cannot be "
"converted via Python `eval()` in tool '%s', "
"degenerating to string.", param_value, param_name,
func_name)
return param_value
# Extract function name
end_index = function_call_str.index(">")
function_name = function_call_str[:end_index]
param_config = get_arguments_config(function_name)
parameters = function_call_str[end_index + 1:]
param_dict = {}
for match in self.tool_call_parameter_regex.findall(parameters):
match_text = match[0] if match[0] else match[1]
idx = match_text.index(">")
param_name = match_text[:idx]
param_value = str(match_text[idx + 1:])
# Remove prefix and trailing \n
if param_value.startswith("\n"):
param_value = param_value[1:]
if param_value.endswith("\n"):
param_value = param_value[:-1]
param_dict[param_name] = convert_param_value(
param_value, param_name, param_config, function_name)
return ToolCall(
type="function",
function=FunctionCall(name=function_name,
arguments=json.dumps(param_dict,
ensure_ascii=False)),
)
def _get_function_calls(self, model_output: str) -> list[str]:
# Find all tool calls
matched_ranges = self.tool_call_regex.findall(model_output)
raw_tool_calls = [
match[0] if match[0] else match[1] for match in matched_ranges
]
# Back-off strategy if no tool_call tags found
if len(raw_tool_calls) == 0:
raw_tool_calls = [model_output]
raw_function_calls = []
for tool_call in raw_tool_calls:
raw_function_calls.extend(
self.tool_call_function_regex.findall(tool_call))
function_calls = [
match[0] if match[0] else match[1] for match in raw_function_calls
]
return function_calls
def extract_tool_calls(
self,
model_output: str,
request: ChatCompletionRequest,
) -> ExtractedToolCallInformation:
# Quick check to avoid unnecessary processing
if self.tool_call_prefix not in model_output:
return ExtractedToolCallInformation(tools_called=False,
tool_calls=[],
content=model_output)
try:
function_calls = self._get_function_calls(model_output)
if len(function_calls) == 0:
return ExtractedToolCallInformation(tools_called=False,
tool_calls=[],
content=model_output)
tool_calls = [
self._parse_xml_function_call(function_call_str, request.tools)
for function_call_str in function_calls
]
# Populate prev_tool_call_arr for serving layer to set
# finish_reason
self.prev_tool_call_arr.clear() # Clear previous calls
for tool_call in tool_calls:
if tool_call:
self.prev_tool_call_arr.append({
"name":
tool_call.function.name,
"arguments":
tool_call.function.arguments,
})
# Extract content before tool calls
content_index = model_output.find(self.tool_call_start_token)
content_index = (content_index if content_index >= 0 else
model_output.find(self.tool_call_prefix))
content = model_output[:content_index] # .rstrip()
return ExtractedToolCallInformation(
tools_called=(len(tool_calls) > 0),
tool_calls=tool_calls,
content=content if content else None,
)
except Exception:
logger.exception("Error in extracting tool call from response.")
return ExtractedToolCallInformation(tools_called=False,
tool_calls=[],
content=model_output)
def extract_tool_calls_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],
request: ChatCompletionRequest,
) -> Union[DeltaMessage, None]:
# If no delta text, return None unless it's an EOS token after tool
# calls
if not delta_text:
# Check if this is an EOS token after all tool calls are complete
# We check for tool calls in the text even if is_tool_call_started
# is False because it might have been reset after processing all
# tools
if (delta_token_ids
and self.tool_call_end_token_id not in delta_token_ids):
# Count complete tool calls
complete_calls = len(
self.tool_call_complete_regex.findall(current_text))
# If we have completed tool calls and populated
# prev_tool_call_arr
if (complete_calls > 0 and len(self.prev_tool_call_arr) > 0):
# Check if all tool calls are closed
open_calls = (
current_text.count(self.tool_call_start_token) -
current_text.count(self.tool_call_end_token))
if open_calls == 0:
# Return empty delta message to allow finish_reason
# processing
return DeltaMessage(content="")
elif not self.is_tool_call_started and current_text:
# This is a regular content response that's now complete
return DeltaMessage(content="")
return None
# Check if this is the first call (reset state if needed)
if not previous_text:
self._reset_streaming_state()
# Update accumulated text
self.accumulated_text = current_text
# Check if we need to advance to next tool
if self.json_closed and not self.in_function:
# Check if this tool call has ended
tool_ends = current_text.count(self.tool_call_end_token)
if tool_ends > self.current_tool_index:
# This tool has ended, advance to next
self.current_tool_index += 1
self.header_sent = False
self.param_count = 0
self.json_started = False
self.json_closed = False
# Check if there are more tool calls
tool_starts_count = current_text.count(
self.tool_call_start_token)
if self.current_tool_index >= tool_starts_count:
# No more tool calls
self.is_tool_call_started = False
# Continue processing next tool
return None
# Handle normal content before tool calls
if not self.is_tool_call_started:
# Check if tool call is starting
if (self.tool_call_start_token_id in delta_token_ids
or self.tool_call_start_token in delta_text):
self.is_tool_call_started = True
# Return any content before the tool call
if self.tool_call_start_token in delta_text:
content_before = delta_text[:delta_text.index(
self.tool_call_start_token)]
if content_before:
return DeltaMessage(content=content_before)
return None
else:
# Check if we're between tool calls - skip whitespace
if (current_text.rstrip().endswith(self.tool_call_end_token)
and delta_text.strip() == ""):
# We just ended a tool call, skip whitespace
return None
# Normal content, no tool call
return DeltaMessage(content=delta_text)
# Check if we're between tool calls (waiting for next one)
# Count tool calls we've seen vs processed
tool_starts_count = current_text.count(self.tool_call_start_token)
if self.current_tool_index >= tool_starts_count:
# We're past all tool calls, shouldn't be here
return None
# We're in a tool call, find the current tool call portion
# Need to find the correct tool call based on current_tool_index
tool_starts: list[int] = []
idx = 0
while True:
idx = current_text.find(self.tool_call_start_token, idx)
if idx == -1:
break
tool_starts.append(idx)
idx += len(self.tool_call_start_token)
if self.current_tool_index >= len(tool_starts):
# No more tool calls to process yet
return None
tool_start_idx = tool_starts[self.current_tool_index]
# Find where this tool call ends (or current position if not ended yet)
tool_end_idx = current_text.find(self.tool_call_end_token,
tool_start_idx)
if tool_end_idx == -1:
tool_text = current_text[tool_start_idx:]
else:
tool_text = current_text[tool_start_idx:tool_end_idx +
len(self.tool_call_end_token)]
# Looking for function header
if not self.header_sent:
if self.tool_call_prefix in tool_text:
func_start = (tool_text.find(self.tool_call_prefix) +
len(self.tool_call_prefix))
func_end = tool_text.find(">", func_start)
if func_end != -1:
# Found complete function name
self.current_function_name = tool_text[func_start:func_end]
self.current_tool_string_id = self._generate_tool_call_id()
self.header_sent = True
self.in_function = True
# IMPORTANT: Add to prev_tool_call_arr immediately when we
# detect a tool call. This ensures
# finish_reason="tool_calls" even if parsing isn't complete
already_added = any(
tool.get("name") == self.current_function_name
for tool in self.prev_tool_call_arr)
if not already_added:
self.prev_tool_call_arr.append({
"name": self.current_function_name,
"arguments":
"{}", # Placeholder, will be updated later
})
# Send header with function info
return DeltaMessage(tool_calls=[
DeltaToolCall(
index=self.current_tool_index,
id=self.current_tool_string_id,
function=DeltaFunctionCall(
name=self.current_function_name, arguments=""),
type="function",
)
])
return None
# We've sent header, now handle function body
if self.in_function:
# Send opening brace if not sent yet
if (not self.json_started
and self.parameter_prefix not in delta_text):
self.json_started = True
return DeltaMessage(tool_calls=[
DeltaToolCall(
index=self.current_tool_index,
function=DeltaFunctionCall(arguments="{"),
)
])
# Make sure json_started is set if we're processing parameters
if not self.json_started:
self.json_started = True
# Check for function end in accumulated text
if not self.json_closed and self.function_end_token in tool_text:
# Close JSON
self.json_closed = True
# Extract the complete tool call to update prev_tool_call_arr
# with final arguments. Find the function content
func_start = (tool_text.find(self.tool_call_prefix) +
len(self.tool_call_prefix))
func_content_end = tool_text.find(self.function_end_token,
func_start)
if func_content_end != -1:
func_content = tool_text[func_start:func_content_end]
# Parse to get the complete arguments
try:
result = None
parsed_tool = self._parse_xml_function_call(
func_content, request.tools if request else None)
if parsed_tool:
# Update existing entry in prev_tool_call_arr with
# complete arguments
for i, tool in enumerate(self.prev_tool_call_arr):
if (tool.get("name") ==
parsed_tool.function.name):
self.prev_tool_call_arr[i]["arguments"] = (
parsed_tool.function.arguments)
break
except Exception:
pass # Ignore parsing errors during streaming
result = DeltaMessage(tool_calls=[
DeltaToolCall(
index=self.current_tool_index,
function=DeltaFunctionCall(
arguments=parsed_tool.function.arguments[1:]),
)
])
# Reset state for next tool
self.in_function = False
self.json_closed = True
if not result:
logger.error("not sent tool call arguments !!!!")
result = DeltaMessage(tool_calls=[
DeltaToolCall(
index=self.current_tool_index,
function=DeltaFunctionCall(arguments="}"),
)
])
return result
# Look for parameters
# Count how many complete parameters we have processed
complete_params = tool_text.count(self.parameter_end_token)
# Check if we should start a new parameter
if not self.in_param and self.param_count < complete_params:
# Find the unprocessed parameter
# Count parameter starts
param_starts = []
idx = 0
while True:
idx = tool_text.find(self.parameter_prefix, idx)
if idx == -1:
break
param_starts.append(idx)
idx += len(self.parameter_prefix)
if len(param_starts) > self.param_count:
# Process the next parameter
param_idx = param_starts[self.param_count]
param_start = param_idx + len(self.parameter_prefix)
remaining = tool_text[param_start:]
if ">" in remaining:
# We have the complete parameter name
name_end = remaining.find(">")
self.current_param_name = remaining[:name_end]
# Find the parameter value
value_start = param_start + name_end + 1
value_text = tool_text[value_start:]
if value_text.startswith("\n"):
value_text = value_text[1:]
# Find where this parameter ends
param_end_idx = value_text.find(
self.parameter_end_token)
if param_end_idx != -1:
# Complete parameter found
param_value = value_text[:param_end_idx]
if param_value.endswith("\n"):
param_value = param_value[:-1]
# Build complete JSON fragment for this parameter
if self.param_count == 0:
json_fragment = (
'"' + self.current_param_name + '": "' +
json.dumps(param_value)[1:-1] + '"')
else:
json_fragment = (
', "' + self.current_param_name + '": "' +
json.dumps(param_value)[1:-1] + '"')
self.param_count += 1
return DeltaMessage(tool_calls=[
DeltaToolCall(
index=self.current_tool_index,
function=DeltaFunctionCall(
arguments=""),
)
])
# Continue parameter value
if self.in_param:
if self.parameter_end_token in delta_text:
# End of parameter
end_idx = delta_text.find(self.parameter_end_token)
value_chunk = delta_text[:end_idx]
# Skip past > if at start
if not self.current_param_value and ">" in value_chunk:
gt_idx = value_chunk.find(">")
value_chunk = value_chunk[gt_idx + 1:]
if (not self.current_param_value
and value_chunk.startswith("\n")):
value_chunk = value_chunk[1:]
# Calculate incremental JSON
full_value = self.current_param_value + value_chunk
prev_escaped = (json.dumps(self.current_param_value)[1:-1]
if self.current_param_value else "")
full_escaped = json.dumps(full_value)[1:-1]
delta_escaped = full_escaped[len(prev_escaped):]
self.in_param = False
self.current_param_value = ""
return DeltaMessage(tool_calls=[
DeltaToolCall(
index=self.current_tool_index,
function=DeltaFunctionCall(
arguments= ""),
)
])
else:
# Continue accumulating value
value_chunk = delta_text
# Handle first chunk after param name
if not self.current_param_value and ">" in value_chunk:
gt_idx = value_chunk.find(">")
value_chunk = value_chunk[gt_idx + 1:]
if (not self.current_param_value
and value_chunk.startswith("\n")):
value_chunk = value_chunk[1:]
if value_chunk:
# Stream the escaped delta
prev_escaped = (json.dumps(
self.current_param_value)[1:-1]
if self.current_param_value else "")
self.current_param_value += value_chunk
full_escaped = json.dumps(
self.current_param_value)[1:-1]
delta_escaped = full_escaped[len(prev_escaped):]
if delta_escaped:
return DeltaMessage(tool_calls=[
DeltaToolCall(
index=self.current_tool_index,
function=DeltaFunctionCall(
arguments=""),
)
])
return None
Thank you. will try it out.
BTW there is a discussion going on at https://github.com/vllm-project/vllm/issues/22975