121 lines
3.9 KiB
Python
121 lines
3.9 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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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 transformers import PreTrainedTokenizerBase
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from vllm.entrypoints.chat_utils import make_tool_call_id
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from vllm.entrypoints.openai.protocol import (
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ChatCompletionRequest,
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DeltaMessage,
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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.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 Phi4MiniJsonToolParser(ToolParser):
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"""
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Tool call parser for phi-4-mini models intended for use with the
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examples/tool_chat_template_llama.jinja template.
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Used when --enable-auto-tool-choice --tool-call-parser phi4_mini_json
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are all set
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"""
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def __init__(self, tokenizer: PreTrainedTokenizerBase) -> None:
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super().__init__(tokenizer)
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# initialize properties used for state when parsing tool calls in
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# streaming mode
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self.prev_tool_call_arr: list[dict[str, Any]] = []
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self.current_tool_id: int = -1
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self.current_tool_name_sent: bool = False
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self.streamed_args_for_tool: list[
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str
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] = [] # map what has been streamed for each tool so far to a list
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self.bot_token: str = "functools"
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def extract_tool_calls(
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self, model_output: str, request: ChatCompletionRequest
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) -> ExtractedToolCallInformation:
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"""
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Extract the tool calls from a complete model response.
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"""
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logger.debug("Model output: %s", model_output)
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pattern = r"functools\[(.*?)\]"
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matches = re.search(pattern, model_output, re.DOTALL)
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if not matches:
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logger.debug("No function calls found")
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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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try:
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function_call_arr: list[dict[str, Any]] = []
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try:
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json_content = "[" + matches.group(1) + "]"
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function_call_arr = json.loads(json_content)
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logger.debug(
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"Successfully extracted %d function calls", len(function_call_arr)
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)
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except json.JSONDecodeError as e:
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logger.error(
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"Failed to parse function calls from model output. Error: %s",
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str(e),
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)
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tool_calls: list[ToolCall] = [
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ToolCall(
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id=make_tool_call_id(),
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type="function",
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function=FunctionCall(
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name=raw_function_call["name"],
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# function call args are JSON but as a string
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arguments=json.dumps(
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raw_function_call["arguments"]
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if "arguments" in raw_function_call
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else raw_function_call["parameters"],
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ensure_ascii=False,
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),
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),
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)
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for raw_function_call in function_call_arr
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]
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# get any content before the tool call
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ret = ExtractedToolCallInformation(
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tools_called=True, tool_calls=tool_calls, content=None
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)
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return ret
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except Exception:
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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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return None
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