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import json
import re
from typing import Dict, List, Sequence, Union
import partial_json_parser
from partial_json_parser.core.options import Allow
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, MistralTokenizer
from vllm.utils import random_uuid
logger = init_logger(__name__)
@ToolParserManager.register_module("qwen2")
class Qwen2ToolParser(ToolParser):
def __init__(self, tokenizer: AnyTokenizer):
super().__init__(tokenizer)
if isinstance(self.model_tokenizer, MistralTokenizer):
logger.error(
"Detected Mistral tokenizer when using a Qwen2.5 model")
self.model_tokenizer = self.model_tokenizer.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_call_start_token: str = "<tool_call>"
self.tool_call_end_token: str = "</tool_call>"
self.tool_call_regex = re.compile(
r"<tool_call>(.*?)</tool_call>", re.DOTALL)
self.scratch_pad_regex = re.compile(
r"<scratch_pad>(.*?)</scratch_pad>", 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(
"Qwen2.5 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_call_start_token not in model_output:
return ExtractedToolCallInformation(tools_called=False,
tool_calls=[],
content=model_output)
else:
try:
# find all tool calls between "<tool_call>" and "</tool_call>"
# the other is None
function_call_strs = (
self.tool_call_regex.findall(model_output))
# load the JSON, and then use it to build the Function and
# Tool Call
raw_function_calls = json.loads(function_call_strs[0])
tool_calls = [
ToolCall(
type="function",
function=FunctionCall(
name=function_call["tool_name"],
# function call args are JSON but as a string
arguments=json.dumps(function_call["parameters"], ensure_ascii=False)
)
)
for function_call in raw_function_calls
]
content = model_output[:model_output.
find(self.tool_call_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)
# for streamed parsing
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]:
pass |