122 lines
4.7 KiB
Python
122 lines
4.7 KiB
Python
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import json
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import re
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from typing import Dict, List, Sequence, Union
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import partial_json_parser
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from partial_json_parser.core.options import Allow
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from vllm.entrypoints.openai.protocol import (ChatCompletionRequest,
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DeltaFunctionCall, DeltaMessage,
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DeltaToolCall,
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ExtractedToolCallInformation,
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FunctionCall, ToolCall)
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from vllm.entrypoints.openai.tool_parsers.abstract_tool_parser import (
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ToolParser, ToolParserManager)
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from vllm.logger import init_logger
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from vllm.transformers_utils.tokenizer import AnyTokenizer, MistralTokenizer
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from vllm.utils import random_uuid
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logger = init_logger(__name__)
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@ToolParserManager.register_module("qwen2")
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class Qwen2ToolParser(ToolParser):
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def __init__(self, tokenizer: AnyTokenizer):
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super().__init__(tokenizer)
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if isinstance(self.model_tokenizer, MistralTokenizer):
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logger.error(
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"Detected Mistral tokenizer when using a Qwen2.5 model")
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self.model_tokenizer = self.model_tokenizer.tokenizer
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self.current_tool_name_sent: bool = False
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self.prev_tool_call_arr: List[Dict] = []
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self.current_tool_id: int = -1
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self.streamed_args_for_tool: List[str] = [
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] # map what has been streamed for each tool so far to a list
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self.tool_call_start_token: str = "<tool_call>"
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self.tool_call_end_token: str = "</tool_call>"
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self.tool_call_regex = re.compile(
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r"<tool_call>(.*?)</tool_call>", re.DOTALL)
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self.scratch_pad_regex = re.compile(
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r"<scratch_pad>(.*?)</scratch_pad>", re.DOTALL)
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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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self.tool_call_start_token_id = self.vocab.get(
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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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if (self.tool_call_start_token_id is None
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or self.tool_call_end_token_id is None):
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raise RuntimeError(
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"Qwen2.5 Tool parser could not locate tool call start/end "
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"tokens in the tokenizer!")
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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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# sanity check; avoid unnecessary processing
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if self.tool_call_start_token not in model_output:
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return ExtractedToolCallInformation(tools_called=False,
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tool_calls=[],
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content=model_output)
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else:
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try:
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# find all tool calls between "<tool_call>" and "</tool_call>"
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# the other is None
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function_call_strs = (
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self.tool_call_regex.findall(model_output))
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# load the JSON, and then use it to build the Function and
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# Tool Call
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raw_function_calls = json.loads(function_call_strs[0])
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tool_calls = [
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ToolCall(
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type="function",
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function=FunctionCall(
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name=function_call["tool_name"],
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# function call args are JSON but as a string
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arguments=json.dumps(function_call["parameters"], ensure_ascii=False)
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)
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)
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for function_call in raw_function_calls
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]
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content = model_output[:model_output.
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find(self.tool_call_start_token)]
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return ExtractedToolCallInformation(
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tools_called=True,
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tool_calls=tool_calls,
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content=content if content else None)
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except Exception:
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logger.exception(
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"Error in extracting tool call from response.")
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return ExtractedToolCallInformation(tools_called=False,
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tool_calls=[],
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content=model_output)
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# for streamed parsing
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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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) -> Union[DeltaMessage, None]:
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pass
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