xinyun/vllm
1
0
Fork 0
mirror of https://git.datalinker.icu/vllm-project/vllm.git synced 2026-01-25 15:04:42 +08:00
Code Issues Packages Projects Releases Wiki Activity

Add kimi-k2 tool parser (#20789)

Browse Source 
Signed-off-by: wangzhengtao <wangzhengtao@moonshot.cn>
Co-authored-by: wangzhengtao <wangzhengtao@moonshot.cn>
Co-authored-by: wangzhengtao <wangzhengtao@msh.team>
This commit is contained in:
bigmoyan 2025-07-11 10:36:23 +08:00 committed by GitHub
parent cf75cd2098
commit 0cf893cae1
No known key found for this signature in database
GPG Key ID: B5690EEEBB952194
4 changed files with 576 additions and 2 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

195
tests/tool_use/test_kimi_k2_tool_parser.py Normal file
View File

@ -0,0 +1,195 @@
# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
# ruff: noqa: E501
import json
import pytest
from vllm.entrypoints.openai.protocol import FunctionCall, ToolCall
from vllm.entrypoints.openai.tool_parsers import KimiK2ToolParser
from vllm.transformers_utils.tokenizer import get_tokenizer
pytest.skip("skip kimi_k2 parser test", allow_module_level=True)
# Use a common model that is likely to be available
MODEL = "moonshotai/Kimi-K2-Instruct"
@pytest.fixture(scope="module")
def kimi_k2_tokenizer():
return get_tokenizer(tokenizer_name=MODEL, trust_remote_code=True)
@pytest.fixture
def kimi_k2_tool_parser(kimi_k2_tokenizer):
return KimiK2ToolParser(kimi_k2_tokenizer)
def assert_tool_calls(actual_tool_calls: list[ToolCall],
expected_tool_calls: list[ToolCall]):
assert len(actual_tool_calls) == len(expected_tool_calls)
for actual_tool_call, expected_tool_call in zip(actual_tool_calls,
expected_tool_calls):
assert actual_tool_call.type == "function"
assert actual_tool_call.function == expected_tool_call.function
# assert tool call id format
assert actual_tool_call.id.startswith("functions.")
assert actual_tool_call.id.split(':')[-1].isdigit()
assert actual_tool_call.id.split('.')[1].split(
':')[0] == expected_tool_call.function.name
def test_extract_tool_calls_no_tools(kimi_k2_tool_parser):
model_output = "This is a test"
extracted_tool_calls = kimi_k2_tool_parser.extract_tool_calls(
model_output, request=None) # type: ignore[arg-type]
assert not extracted_tool_calls.tools_called
assert extracted_tool_calls.tool_calls == []
assert extracted_tool_calls.content == model_output
@pytest.mark.parametrize(
ids=[
"tool_call_with_content_before",
"multi_tool_call_with_content_before",
],
argnames=["model_output", "expected_tool_calls", "expected_content"],
argvalues=[
(
"""I'll help you check the weather. <|tool_calls_section_begin|> <|tool_call_begin|>
functions.get_weather:0 <|tool_call_argument_begin|> {"city": "Beijing"} <|tool_call_end|> <|tool_calls_section_end|>""",
[
ToolCall(id='functions.get_weather:0',
function=FunctionCall(
name="get_weather",
arguments=json.dumps({
"city": "Beijing",
}, ),
),
type='function')
],
"I'll help you check the weather. ",
),
(
"""I'll help you check the weather. <|tool_calls_section_begin|> <|tool_call_begin|>
functions.get_weather:0 <|tool_call_argument_begin|> {"city": "Beijing"} <|tool_call_end|> <|tool_call_begin|>
functions.get_weather:1 <|tool_call_argument_begin|> {"city": "Shanghai"} <|tool_call_end|> <|tool_calls_section_end|>""",
[
ToolCall(id='functions.get_weather:0',
function=FunctionCall(
name="get_weather",
arguments=json.dumps({
"city": "Beijing",
}, ),
),
type='function'),
ToolCall(id='functions.get_weather:1',
function=FunctionCall(
name="get_weather",
arguments=json.dumps({
"city": "Shanghai",
}, ),
),
type='function')
],
"I'll help you check the weather. ",
),
],
)
def test_extract_tool_calls(kimi_k2_tool_parser, model_output,
expected_tool_calls, expected_content):
extracted_tool_calls = kimi_k2_tool_parser.extract_tool_calls(
model_output, request=None) # type: ignore[arg-type]
assert extracted_tool_calls.tools_called
assert_tool_calls(extracted_tool_calls.tool_calls, expected_tool_calls)
assert extracted_tool_calls.content == expected_content
def test_extract_tool_calls_invalid_json(kimi_k2_tool_parser):
"""we'll return every funcall result"""
model_output = """I'll help you check the weather. <|tool_calls_section_begin|> <|tool_call_begin|>
functions.invalid_get_weather:0 <|tool_call_argument_begin|> {"city": "Beijing" <|tool_call_end|> <|tool_call_begin|>
functions.valid_get_weather:1 <|tool_call_argument_begin|> {"city": "Shanghai"} <|tool_call_end|> <|tool_calls_section_end|>"""
extracted_tool_calls = kimi_k2_tool_parser.extract_tool_calls(
model_output, request=None) # type: ignore[arg-type]
assert extracted_tool_calls.tools_called
# Should extract only the valid JSON tool calls
assert len(extracted_tool_calls.tool_calls) == 2
assert extracted_tool_calls.tool_calls[
0].function.name == "invalid_get_weather"
assert extracted_tool_calls.tool_calls[
1].function.name == "valid_get_weather"
def test_extract_tool_calls_invalid_funcall(kimi_k2_tool_parser):
"""we'll return every funcall result"""
model_output = """I'll help you check the weather. <|tool_calls_section_begin|> <|tool_call_begin|>
functions.invalid_get_weather.0 <|tool_call_argument_begin|> {"city": "Beijing"} <|tool_call_end|> <|tool_call_begin|>
functions.valid_get_weather:1 <|tool_call_argument_begin|> {"city": "Shanghai"} <|tool_call_end|> <|tool_calls_section_end|>"""
extracted_tool_calls = kimi_k2_tool_parser.extract_tool_calls(
model_output, request=None) # type: ignore[arg-type]
assert extracted_tool_calls.tools_called
# Should extract only the valid JSON tool calls
assert len(extracted_tool_calls.tool_calls) == 1
assert extracted_tool_calls.tool_calls[
0].function.name == "valid_get_weather"
def test_streaming_basic_functionality(kimi_k2_tool_parser):
"""Test basic streaming functionality."""
# Reset streaming state
kimi_k2_tool_parser.current_tool_name_sent = False
kimi_k2_tool_parser.prev_tool_call_arr = []
kimi_k2_tool_parser.current_tool_id = -1
kimi_k2_tool_parser.streamed_args_for_tool = []
# Test with a simple tool call
current_text = """ check the weather. <|tool_calls_section_begin|> <|tool_call_begin|>
functions.get_weather:0 <|tool_call_argument_begin|> {"city": "Beijing"} <|tool_call_end|> <|tool_calls_section_end|>"""
# First call should handle the initial setup
result = kimi_k2_tool_parser.extract_tool_calls_streaming(
previous_text="I'll help you",
current_text=current_text,
delta_text="<|tool_calls_section_end|>",
previous_token_ids=[],
current_token_ids=[],
delta_token_ids=[],
request=None,
)
# The result might be None or contain tool call information
# This depends on the internal state management
if result is not None and hasattr(result,
'tool_calls') and result.tool_calls:
assert len(result.tool_calls) >= 0
def test_streaming_no_tool_calls(kimi_k2_tool_parser):
"""Test streaming when there are no tool calls."""
current_text = "This is just regular text without any tool calls."
result = kimi_k2_tool_parser.extract_tool_calls_streaming(
previous_text="This is just regular text",
current_text=current_text,
delta_text=" without any tool calls.",
previous_token_ids=[],
current_token_ids=[],
delta_token_ids=[],
request=None,
)
# Should return the delta text as content
assert result is not None
assert hasattr(result, 'content')
assert result.content == " without any tool calls."

2
vllm/config.py
View File

@ -1143,7 +1143,7 @@ class ModelConfig:
if not hasattr(self.hf_text_config, "model_type"):
return False
elif self.hf_text_config.model_type in \
('deepseek_v2', 'deepseek_v3', 'deepseek_mtp'):
('deepseek_v2', 'deepseek_v3', 'deepseek_mtp', 'kimi_k2'):
return self.hf_text_config.kv_lora_rank is not None
elif self.hf_text_config.model_type == 'eagle':
# if the model is an EAGLE module, check for the

4
vllm/entrypoints/openai/tool_parsers/__init__.py
View File

@ -8,6 +8,7 @@ from .granite_tool_parser import GraniteToolParser
from .hermes_tool_parser import Hermes2ProToolParser
from .internlm2_tool_parser import Internlm2ToolParser
from .jamba_tool_parser import JambaToolParser
from .kimi_k2_tool_parser import KimiK2ToolParser
from .llama4_pythonic_tool_parser import Llama4PythonicToolParser
from .llama_tool_parser import Llama3JsonToolParser
from .minimax_tool_parser import MinimaxToolParser
@ -21,5 +22,6 @@ __all__ = [
"GraniteToolParser", "Hermes2ProToolParser", "MistralToolParser",
"Internlm2ToolParser", "Llama3JsonToolParser", "JambaToolParser",
"Llama4PythonicToolParser", "PythonicToolParser", "Phi4MiniJsonToolParser",
"DeepSeekV3ToolParser", "xLAMToolParser", "MinimaxToolParser"
"DeepSeekV3ToolParser", "xLAMToolParser", "MinimaxToolParser",
"KimiK2ToolParser"
]

377
vllm/entrypoints/openai/tool_parsers/kimi_k2_tool_parser.py Normal file
View File

@ -0,0 +1,377 @@
# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
# code modified from deepseekv3_tool_parser.py
from collections.abc import Sequence
from typing import Union
import regex as re
from vllm.entrypoints.openai.protocol import (ChatCompletionRequest,
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(["kimi_k2"])
class KimiK2ToolParser(ToolParser):
def __init__(self, tokenizer: AnyTokenizer):
super().__init__(tokenizer)
self.current_tool_name_sent: bool = False
self.prev_tool_call_arr: list[dict] = []
self.current_tool_id: int = -1
self.streamed_args_for_tool: list[str] = (
[]) # map what has been streamed for each tool so far to a list
self.tool_calls_start_token: str = "<|tool_calls_section_begin|>"
self.tool_calls_end_token: str = "<|tool_calls_section_end|>"
self.tool_call_start_token: str = "<|tool_call_begin|>"
self.tool_call_end_token: str = "<|tool_call_end|>"
self.tool_call_regex = re.compile(
r"<\|tool_call_begin\|>\s*(?P<tool_call_id>[\w\.]+:\d+)\s*<\|tool_call_argument_begin\|>\s*(?P<function_arguments>.*?)\s*<\|tool_call_end\|>"
)
self.stream_tool_call_portion_regex = re.compile(
r"(?P<tool_call_id>[\w\.]+:\d+)\s*<\|tool_call_argument_begin\|>\s*(?P<function_arguments>.*)"
)
self.stream_tool_call_name_regex = re.compile(
r"(?P<tool_call_id>[\w\.]+:\d+)\s*")
if not self.model_tokenizer:
raise ValueError(
"The model tokenizer must be passed to the ToolParser "
"constructor during construction.")
self.tool_calls_start_token_id = self.vocab.get(
self.tool_calls_start_token)
self.tool_calls_end_token_id = self.vocab.get(
self.tool_calls_end_token)
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_calls_start_token_id is None
or self.tool_calls_end_token_id is None):
raise RuntimeError(
"Kimi-K2 Tool parser could not locate tool call start/end "
"tokens in the tokenizer!")
def extract_tool_calls(
self,
model_output: str,
request: ChatCompletionRequest,
) -> ExtractedToolCallInformation:
# sanity check; avoid unnecessary processing
if self.tool_calls_start_token not in model_output:
return ExtractedToolCallInformation(tools_called=False,
tool_calls=[],
content=model_output)
else:
try:
# there are two possible captures - between tags, or between a
# tag and end-of-string so the result of
# findall is an array of tuples where one is a function call and
# the other is None
function_call_tuples = self.tool_call_regex.findall(
model_output)
logger.debug("function_call_tuples: %s", function_call_tuples)
tool_calls = []
for match in function_call_tuples:
function_id, function_args = match
# function_id: functions.get_weather:0
function_name = function_id.split('.')[1].split(':')[0]
tool_calls.append(
ToolCall(
id=function_id,
type='function',
function=FunctionCall(name=function_name,
arguments=function_args),
))
content = model_output[:model_output.
find(self.tool_calls_start_token)]
return ExtractedToolCallInformation(
tools_called=True,
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]:
logger.debug("delta_text: %s", delta_text)
logger.debug("delta_token_ids: %s", delta_token_ids)
# check to see if we should be streaming a tool call - is there a
if self.tool_calls_start_token_id not in current_token_ids:
logger.debug("No tool call tokens found!")
return DeltaMessage(content=delta_text)
delta_text = delta_text.replace(self.tool_calls_start_token,
"").replace(self.tool_calls_end_token,
"")
try:
# figure out where we are in the parsing by counting tool call
# start & end tags
prev_tool_start_count = previous_token_ids.count(
self.tool_call_start_token_id)
prev_tool_end_count = previous_token_ids.count(
self.tool_call_end_token_id)
cur_tool_start_count = current_token_ids.count(
self.tool_call_start_token_id)
cur_tool_end_count = current_token_ids.count(
self.tool_call_end_token_id)
tool_call_portion = None
text_portion = None
# case: if we're generating text, OR rounding out a tool call
if (cur_tool_start_count == cur_tool_end_count
and prev_tool_end_count == cur_tool_end_count
and self.tool_call_end_token not in delta_text):
logger.debug("Generating text content! skipping tool parsing.")
return DeltaMessage(content=delta_text)
if self.tool_call_end_token in delta_text:
logger.debug("tool_call_end_token in delta_text")
full_text = current_text + delta_text
tool_call_portion = full_text.split(
self.tool_call_start_token)[-1].split(
self.tool_call_end_token)[0].rstrip()
delta_text = delta_text.split(
self.tool_call_end_token)[0].rstrip()
text_portion = delta_text.split(
self.tool_call_end_token)[-1].lstrip()
# case -- we're starting a new tool call
if (cur_tool_start_count > cur_tool_end_count
and cur_tool_start_count > prev_tool_start_count):
if len(delta_token_ids) > 1:
tool_call_portion = current_text.split(
self.tool_call_start_token)[-1]
else:
tool_call_portion = None
delta = None
text_portion = None
# set cursors and state appropriately
self.current_tool_id += 1
self.current_tool_name_sent = False
self.streamed_args_for_tool.append("")
logger.debug("Starting on a new tool %s", self.current_tool_id)
# case -- we're updating an existing tool call
elif (cur_tool_start_count > cur_tool_end_count
and cur_tool_start_count == prev_tool_start_count):
# get the portion of the text that's the tool call
tool_call_portion = current_text.split(
self.tool_call_start_token)[-1]
text_portion = None
# case -- the current tool call is being closed.
elif (cur_tool_start_count == cur_tool_end_count
and cur_tool_end_count >= prev_tool_end_count):
if self.prev_tool_call_arr is None or len(
self.prev_tool_call_arr) == 0:
logger.debug(
"attempting to close tool call, but no tool call")
return None
diff = self.prev_tool_call_arr[self.current_tool_id].get(
"arguments")
if diff:
diff = (diff.encode("utf-8").decode("unicode_escape")
if diff is str else diff)
if '"}' not in delta_text:
return None
end_loc = delta_text.rindex('"}')
diff = delta_text[:end_loc] + '"}'
logger.debug(
"Finishing tool and found diff that had not "
"been streamed yet: %s",
diff,
)
self.streamed_args_for_tool[self.current_tool_id] += diff
return DeltaMessage(tool_calls=[
DeltaToolCall(
index=self.current_tool_id,
function=DeltaFunctionCall(
arguments=diff).model_dump(exclude_none=True),
)
])
# case -- otherwise we're just generating text
else:
text = delta_text.replace(self.tool_call_start_token, "")
text = text.replace(self.tool_call_end_token, "")
delta = DeltaMessage(tool_calls=[], content=text)
return delta
current_tool_call = dict()
if tool_call_portion:
current_tool_call_matches = (
self.stream_tool_call_portion_regex.match(
tool_call_portion))
if current_tool_call_matches:
tool_id, tool_args = (current_tool_call_matches.groups())
tool_name = tool_id.split('.')[1].split(':')[0]
current_tool_call['id'] = tool_id
current_tool_call["name"] = tool_name
current_tool_call["arguments"] = tool_args
else:
current_tool_call_name_matches = (
self.stream_tool_call_name_regex.match(
tool_call_portion))
if current_tool_call_name_matches:
tool_id_str, = current_tool_call_name_matches.groups()
tool_name = tool_id_str.split('.')[1].split(':')[0]
current_tool_call['id'] = tool_id_str
current_tool_call["name"] = tool_name
current_tool_call["arguments"] = ""
else:
logger.debug("Not enough token")
return None
# case - we haven't sent the tool name yet. If it's available, send
# it. otherwise, wait until it's available.
if not self.current_tool_name_sent:
if current_tool_call is None:
return None
function_name: Union[str, None] = current_tool_call.get("name")
tool_id = current_tool_call.get("id")
if function_name:
self.current_tool_name_sent = True
return DeltaMessage(tool_calls=[
DeltaToolCall(
index=self.current_tool_id,
type="function",
id=tool_id,
function=DeltaFunctionCall(
name=function_name).model_dump(
exclude_none=True),
)
])
else:
return None
# case -- otherwise, send the tool call delta
# if the tool call portion is None, send the delta as text
if tool_call_portion is None:
# if there's text but not tool calls, send that -
# otherwise None to skip chunk
delta = (DeltaMessage(
content=delta_text) if text_portion is not None else None)
return delta
# now, the nitty-gritty of tool calls
# now we have the portion to parse as tool call.
logger.debug("Trying to parse current tool call with ID %s",
self.current_tool_id)
# if we're starting a new tool call, push an empty object in as
# a placeholder for the arguments
if len(self.prev_tool_call_arr) <= self.current_tool_id:
self.prev_tool_call_arr.append({})
# main logic for tool parsing here - compare prev. partially-parsed
# JSON to the current partially-parsed JSON
prev_arguments = self.prev_tool_call_arr[self.current_tool_id].get(
"arguments")
cur_arguments = current_tool_call.get("arguments")
logger.debug("diffing old arguments: %s", prev_arguments)
logger.debug("against new ones: %s", cur_arguments)
# case -- no arguments have been created yet. skip sending a delta.
if not cur_arguments and not prev_arguments:
logger.debug("Skipping text %s - no arguments", delta_text)
delta = None
# case -- prev arguments are defined, but non are now.
# probably impossible, but not a fatal error - just keep going
elif not cur_arguments and prev_arguments:
logger.error("should be impossible to have arguments reset "
"mid-call. skipping streaming anything.")
delta = None
# case -- we now have the first info about arguments available from
# autocompleting the JSON
elif cur_arguments and not prev_arguments:
delta = DeltaMessage(tool_calls=[
DeltaToolCall(
index=self.current_tool_id,
function=DeltaFunctionCall(
arguments=cur_arguments).model_dump(
exclude_none=True),
)
])
self.streamed_args_for_tool[
self.current_tool_id] = cur_arguments
# last case -- we have an update to existing arguments.
elif cur_arguments and prev_arguments:
if (isinstance(delta_text, str)
and cur_arguments != prev_arguments
and len(cur_arguments) > len(prev_arguments)
and cur_arguments.startswith(prev_arguments)):
delta_arguments = cur_arguments[len(prev_arguments):]
logger.debug("got diff %s", delta_text)
delta = DeltaMessage(tool_calls=[
DeltaToolCall(
index=self.current_tool_id,
function=DeltaFunctionCall(
arguments=delta_arguments).model_dump(
exclude_none=True),
)
])
self.streamed_args_for_tool[
self.current_tool_id] = cur_arguments
else:
delta = None
# handle saving the state for the current tool into
# the "prev" list for use in diffing for the next iteration
if self.current_tool_id == len(self.prev_tool_call_arr) - 1:
self.prev_tool_call_arr[
self.current_tool_id] = current_tool_call
else:
self.prev_tool_call_arr.append(current_tool_call)
return delta
except Exception:
logger.exception("Error trying to handle streaming tool call.")
return None # do not stream a delta. skip this token ID.