Sync from v0.13
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200
vllm/tool_parsers/glm4_moe_tool_parser.py
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200
vllm/tool_parsers/glm4_moe_tool_parser.py
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# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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import ast
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import json
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from collections.abc import Sequence
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from typing import Any
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import regex as re
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from vllm.entrypoints.openai.protocol import (
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ChatCompletionRequest,
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ChatCompletionToolsParam,
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DeltaFunctionCall,
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DeltaMessage,
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DeltaToolCall,
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ExtractedToolCallInformation,
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FunctionCall,
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ToolCall,
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)
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from vllm.logger import init_logger
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from vllm.tokenizers import TokenizerLike
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from vllm.tool_parsers.abstract_tool_parser import (
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ToolParser,
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)
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logger = init_logger(__name__)
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class Glm4MoeModelToolParser(ToolParser):
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def __init__(self, tokenizer: TokenizerLike):
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super().__init__(tokenizer)
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self.current_tool_name_sent = False
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self.prev_tool_call_arr: list[dict] = []
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self.current_tool_id = -1
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self.streamed_args_for_tool: list[str] = []
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self.tool_call_start_token = "<tool_call>"
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self.tool_call_end_token = "</tool_call>"
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self.tool_calls_start_token = self.tool_call_start_token
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self.func_call_regex = re.compile(r"<tool_call>.*?</tool_call>", re.DOTALL)
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self.func_detail_regex = re.compile(
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r"<tool_call>([^\n]*)\n(.*)</tool_call>", re.DOTALL
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)
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self.func_arg_regex = re.compile(
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r"<arg_key>(.*?)</arg_key>\s*<arg_value>(.*?)</arg_value>", re.DOTALL
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)
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if not self.model_tokenizer:
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raise ValueError(
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"The model tokenizer must be passed to the ToolParser "
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"constructor during construction."
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)
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self.tool_call_start_token_id = self.vocab.get(self.tool_call_start_token)
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self.tool_call_end_token_id = self.vocab.get(self.tool_call_end_token)
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self._buffer = ""
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def extract_tool_calls(
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self,
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model_output: str,
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request: ChatCompletionRequest,
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) -> ExtractedToolCallInformation:
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def _is_string_type(
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tool_name: str,
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arg_name: str,
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tools: list[ChatCompletionToolsParam] | None,
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) -> bool:
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if tools is None:
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return False
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for tool in tools:
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if tool.function.name == tool_name:
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if tool.function.parameters is None:
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return False
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arg_type = (
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tool.function.parameters.get("properties", {})
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.get(arg_name, {})
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.get("type", None)
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)
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return arg_type == "string"
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logger.debug("No tool named '%s'.", tool_name)
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return False
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def _deserialize(value: str) -> Any:
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try:
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return json.loads(value)
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except Exception:
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pass
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try:
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return ast.literal_eval(value)
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except Exception:
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pass
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return value
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matched_tool_calls = self.func_call_regex.findall(model_output)
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logger.debug("model_output: %s", model_output)
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try:
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tool_calls = []
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for match in matched_tool_calls:
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tc_detail = self.func_detail_regex.search(match)
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tc_name = tc_detail.group(1)
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tc_args = tc_detail.group(2)
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pairs = self.func_arg_regex.findall(tc_args)
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arg_dct = {}
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for key, value in pairs:
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arg_key = key.strip()
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arg_val = value.strip()
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if not _is_string_type(tc_name, arg_key, request.tools):
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arg_val = _deserialize(arg_val)
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logger.debug("arg_key = %s, arg_val = %s", arg_key, arg_val)
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arg_dct[arg_key] = arg_val
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tool_calls.append(
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ToolCall(
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type="function",
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function=FunctionCall(
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name=tc_name, arguments=json.dumps(arg_dct)
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),
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)
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)
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except Exception:
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logger.exception("Failed to extract tool call spec")
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return ExtractedToolCallInformation(
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tools_called=False, tool_calls=[], content=model_output
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)
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else:
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if len(tool_calls) > 0:
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content = model_output[: model_output.find(self.tool_calls_start_token)]
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return ExtractedToolCallInformation(
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tools_called=True, tool_calls=tool_calls, content=content
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)
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return ExtractedToolCallInformation(
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tools_called=False, tool_calls=[], content=model_output
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)
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def extract_tool_calls_streaming(
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self,
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previous_text: str,
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current_text: str,
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delta_text: str,
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previous_token_ids: Sequence[int],
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current_token_ids: Sequence[int],
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delta_token_ids: Sequence[int],
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request: ChatCompletionRequest,
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) -> DeltaMessage | None:
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self._buffer += delta_text
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cur_text = self._buffer
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start_idx = cur_text.find(self.tool_call_start_token)
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if start_idx == -1:
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self._buffer = ""
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if self.current_tool_id > 0:
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cur_text = ""
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return DeltaMessage(content=cur_text)
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logger.debug("cur_text = %s", cur_text)
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end_idx = cur_text.find(self.tool_call_end_token)
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if end_idx != -1:
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if self.current_tool_id == -1:
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self.current_tool_id = 0
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self.prev_tool_call_arr = []
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self.streamed_args_for_tool = []
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while len(self.prev_tool_call_arr) <= self.current_tool_id:
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self.prev_tool_call_arr.append({})
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while len(self.streamed_args_for_tool) <= self.current_tool_id:
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self.streamed_args_for_tool.append("")
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extracted_tool_calls = self.extract_tool_calls(
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cur_text[: end_idx + len(self.tool_call_end_token)], request
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)
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if len(extracted_tool_calls.tool_calls) == 0:
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logger.warning("Failed to extract any tool calls.")
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return None
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tool_call = extracted_tool_calls.tool_calls[0]
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self.prev_tool_call_arr[self.current_tool_id] = {
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"name": tool_call.function.name,
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"arguments": json.loads(tool_call.function.arguments),
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}
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self.streamed_args_for_tool[self.current_tool_id] = (
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tool_call.function.arguments
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)
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delta = DeltaMessage(
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content=extracted_tool_calls.content,
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tool_calls=[
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DeltaToolCall(
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index=self.current_tool_id,
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id=tool_call.id,
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type=tool_call.type,
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function=DeltaFunctionCall(
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name=tool_call.function.name,
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arguments=tool_call.function.arguments,
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),
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)
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],
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)
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self.current_tool_id += 1
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self._buffer = cur_text[end_idx + len(self.tool_call_end_token) :]
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return delta
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self._buffer = cur_text[start_idx:]
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return DeltaMessage(content=cur_text[:start_idx])
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