Source code for easydel.inference.tools.parsers.granite_tool_parser

# Copyright 2025 The EasyDeL Author @erfanzar (Erfan Zare Chavoshi).
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
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#     https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
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from __future__ import annotations

import json
from collections.abc import Sequence
from uuid import uuid4

import partial_json_parser
from eformer.loggings import get_logger
from partial_json_parser.core.options import Allow
from transformers import AutoTokenizer as AnyTokenizer

from ...openai_api_modules import (
    ChatCompletionRequest,
    DeltaFunctionCall,
    DeltaMessage,
    DeltaToolCall,
    ExtractedToolCallInformation,
    FunctionCall,
    ToolCall,
)
from ..abstract_tool import ToolParser, ToolParserManager
from ..utils import consume_space, find_common_prefix, is_complete_json, partial_json_loads

logger = get_logger(__name__)


[docs]@ToolParserManager.register_module("granite") class GraniteToolParser(ToolParser): """ Tool call parser for Granite 3.0 models. Intended for use with the examples/tool_chat_template_granite.jinja template. Handles JSON array format with optional bot tokens. Features: - Supports <|tool_call|> and <tool_call> token markers - Parses JSON array of tool calls - Handles partial JSON for streaming - Tracks argument completion state Format: <|tool_call|>[{"name": "func", "arguments": {...}}, ...] Used when --enable-auto-tool-choice --tool-call-parser granite are all set. """ def __init__(self, tokenizer: AnyTokenizer): super().__init__(tokenizer) self.bot_token = "<|tool_call|>" self.bot_string = "<tool_call>"
[docs] def extract_tool_calls(self, model_output: str, request: ChatCompletionRequest) -> ExtractedToolCallInformation: stripped = model_output.strip().removeprefix(self.bot_token).removeprefix(self.bot_string).lstrip() if not stripped or stripped[0] != "[": return ExtractedToolCallInformation(tools_called=False, tool_calls=[], content=model_output) try: raw_function_calls = json.loads(stripped) if not isinstance(raw_function_calls, list): raise Exception(f"Expected dict or list, got {type(raw_function_calls)}") tool_calls = [ ToolCall( type="function", function=FunctionCall( name=function_call["name"], arguments=json.dumps(function_call["arguments"], ensure_ascii=False), ), ) for function_call in raw_function_calls ] return ExtractedToolCallInformation( tools_called=True, tool_calls=tool_calls, content=None, ) except Exception as e: logger.error("Error in extracting tool call from response %s", e) return ExtractedToolCallInformation(tools_called=False, tool_calls=[], content=model_output)
[docs] 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, ) -> DeltaMessage | None: start_idx = consume_space(0, current_text) if current_text[start_idx:].startswith(self.bot_token): start_idx = consume_space(start_idx + len(self.bot_token), current_text) if current_text[start_idx:].startswith(self.bot_string): start_idx = consume_space(start_idx + len(self.bot_string), current_text) if not current_text or start_idx >= len(current_text) or current_text[start_idx] != "[": return DeltaMessage(content=delta_text) flags = Allow.ALL if self.current_tool_name_sent else Allow.ALL & ~Allow.STR try: tool_call_arr = None is_complete = None try: tool_calls, end_idx = partial_json_loads(current_text[start_idx:], flags) if type(tool_calls) is list: tool_call_arr = tool_calls else: return DeltaMessage(content=delta_text) is_complete = [True] * len(tool_calls) if not is_complete_json(current_text[start_idx : start_idx + end_idx]): is_complete[-1] = False except partial_json_parser.core.exceptions.MalformedJSON: return None if not tool_call_arr: return None current_tool_call: dict = tool_call_arr[self.current_tool_id] delta = None if len(tool_call_arr) > self.current_tool_id + 1: if self.current_tool_id >= 0: cur_arguments = current_tool_call.get("arguments") if cur_arguments: cur_args_json = json.dumps(cur_arguments, ensure_ascii=False) sent = len(self.streamed_args_for_tool[self.current_tool_id]) argument_diff = cur_args_json[sent:] delta = DeltaMessage( tool_calls=[ DeltaToolCall( index=self.current_tool_id, function=DeltaFunctionCall(arguments=argument_diff).model_dump(exclude_none=True), ) ] ) self.streamed_args_for_tool[self.current_tool_id] += argument_diff self.current_tool_id = len(tool_call_arr) - 1 self.current_tool_name_sent = False self.streamed_args_for_tool.append("") return delta elif not self.current_tool_name_sent: function_name = current_tool_call.get("name") if function_name: delta = DeltaMessage( tool_calls=[ DeltaToolCall( index=self.current_tool_id, type="function", id=f"chatcmpl-tool-{uuid4()}", function=DeltaFunctionCall(name=function_name).model_dump(exclude_none=True), ) ] ) self.current_tool_name_sent = True else: cur_arguments = current_tool_call.get("arguments") if cur_arguments: sent = len(self.streamed_args_for_tool[self.current_tool_id]) cur_args_json = json.dumps(cur_arguments, ensure_ascii=False) prev_arguments = self.prev_tool_call_arr[self.current_tool_id].get("arguments") argument_diff = None if is_complete[self.current_tool_id]: argument_diff = cur_args_json[sent:] elif prev_arguments: prev_args_json = json.dumps(prev_arguments, ensure_ascii=False) if cur_args_json != prev_args_json: prefix = find_common_prefix(prev_args_json, cur_args_json) argument_diff = prefix[sent:] if argument_diff is not None: delta = DeltaMessage( tool_calls=[ DeltaToolCall( index=self.current_tool_id, function=DeltaFunctionCall(arguments=argument_diff).model_dump(exclude_none=True), ) ] ) self.streamed_args_for_tool[self.current_tool_id] += argument_diff self.prev_tool_call_arr = tool_call_arr return delta except Exception as e: logger.error("Error trying to handle streaming tool call: %s", e) return None