# SPDX-License-Identifier: Apache-2.0 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project import json from collections.abc import Sequence from typing import Union import partial_json_parser import regex as re from partial_json_parser.core.options import Allow from vllm.entrypoints.chat_utils import random_tool_call_id from vllm.entrypoints.openai.protocol import (ChatCompletionRequest, DeltaFunctionCall, DeltaMessage, DeltaToolCall, ExtractedToolCallInformation, FunctionCall, ToolCall) from vllm.entrypoints.openai.tool_parsers import ToolParser, ToolParserManager from vllm.entrypoints.openai.tool_parsers.utils import ( extract_intermediate_diff) from vllm.logger import init_logger from vllm.transformers_utils.tokenizer import AnyTokenizer from vllm.transformers_utils.tokenizers import MistralTokenizer logger = init_logger(__name__) @ToolParserManager.register_module("jamba") class JambaToolParser(ToolParser): def __init__(self, tokenizer: AnyTokenizer): super().__init__(tokenizer) if isinstance(self.model_tokenizer, MistralTokenizer): raise ValueError( "Detected a MistralTokenizer tokenizer when using a Jamba model" ) 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_calls_start_token: str = "" self.tool_calls_end_token: str = "" self.tool_calls_regex = re.compile( rf"{self.tool_calls_start_token}(.*?){self.tool_calls_end_token}", re.DOTALL) if not self.model_tokenizer: raise ValueError( "The model tokenizer must be passed to the ToolParser " "constructor during construction.") self.tool_calls_start_token_id = self.vocab.get( self.tool_calls_start_token) self.tool_calls_end_token_id = self.vocab.get( self.tool_calls_end_token) if (self.tool_calls_start_token_id is None or self.tool_calls_end_token_id is None): raise RuntimeError( "Jamba Tool parser could not locate tool calls start/end " "tokens in the tokenizer!") def adjust_request( self, request: ChatCompletionRequest) -> ChatCompletionRequest: if request.tools and request.tool_choice != 'none': # do not skip special tokens because jamba use the special # tokens to indicate the start and end of the tool calls # information. request.skip_special_tokens = False return request def extract_tool_calls( self, model_output: str, request: ChatCompletionRequest) -> ExtractedToolCallInformation: # sanity check; avoid unnecessary processing if self.tool_calls_start_token not in model_output: return ExtractedToolCallInformation(tools_called=False, tool_calls=[], content=model_output) else: try: # use a regex to find the tool call between the tags function_calls = self.tool_calls_regex.findall(model_output)[0] # load the JSON, and then use it to build the Function and # Tool Call raw_function_calls = json.loads(function_calls) tool_calls = [ ToolCall( type="function", function=FunctionCall( name=function_call["name"], # function call args are JSON but as a string arguments=json.dumps(function_call["arguments"], ensure_ascii=False), )) for function_call in raw_function_calls ] content = model_output[:model_output. find(self.tool_calls_start_token)] return ExtractedToolCallInformation( tools_called=True, tool_calls=tool_calls, content=content if (len(content) > 0 and content != " ") else None) except Exception: logger.exception( "Error in extracting tool call from response.") return ExtractedToolCallInformation(tools_called=False, tool_calls=[], content=model_output) 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]: # if the tool call token is not in the tokens generated so far, append # output to contents since it's not a tool if self.tool_calls_start_token not in current_text: return DeltaMessage(content=delta_text) # if the tool call token ID IS in the tokens generated so far, that # means we're parsing as tool calls now # handle if we detected the start of tool calls token which means # the start of tool calling if (self.tool_calls_start_token_id in delta_token_ids and len(delta_token_ids) == 1): # if it's the only token, return None, so we don't send a chat # completion and don't send a control token return None # bit mask flags for partial JSON parsing. If the name hasn't been # sent yet, don't allow sending # an incomplete string since OpenAI only ever (as far as I have # seen) allows sending the entire tool/ function name at once. flags = Allow.ALL if self.current_tool_name_sent \ else Allow.ALL & ~Allow.STR try: # Extract the tool calls between the special tool call tokens parsable_arr = current_text.split( self.tool_calls_start_token)[-1].split( self.tool_calls_end_token)[0] # tool calls are generated in an array, so do partial JSON # parsing on the entire array try: tool_call_arr: list[dict] = partial_json_parser.loads( parsable_arr, flags) except partial_json_parser.core.exceptions.MalformedJSON: logger.debug('not enough tokens to parse into JSON yet') return None # select as the current tool call the one we're on the state at current_tool_call: dict = tool_call_arr[self.current_tool_id] \ if len(tool_call_arr) > 0 else {} # case -- if no tokens have been streamed for the tool, e.g. # only the array brackets, stream nothing if len(tool_call_arr) == 0: return None # case: we are starting a new tool in the array # -> array has > 0 length AND length has moved past cursor elif (len(tool_call_arr) > 0 and len(tool_call_arr) > self.current_tool_id + 1): # if we're moving on to a new call, first make sure we # haven't missed anything in the previous one that was # auto-generated due to JSON completions, but wasn't # streamed to the client yet. if self.current_tool_id >= 0: diff: Union[str, None] = current_tool_call.get("arguments") if diff: diff = json.dumps(diff, ensure_ascii=False).replace( self.streamed_args_for_tool[self.current_tool_id], "") delta = DeltaMessage(tool_calls=[ DeltaToolCall(index=self.current_tool_id, function=DeltaFunctionCall( arguments=diff).model_dump( exclude_none=True)) ]) self.streamed_args_for_tool[ self.current_tool_id] += diff else: delta = None else: delta = None # re-set stuff pertaining to progress in the current tool self.current_tool_id = len(tool_call_arr) - 1 self.current_tool_name_sent = False self.streamed_args_for_tool.append("") logger.debug("starting on new tool %d", self.current_tool_id) return delta # case: update an existing tool - this is handled below # if the current tool name hasn't been sent, send if available # - otherwise send nothing if 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=random_tool_call_id(), function=DeltaFunctionCall( name=function_name).model_dump( exclude_none=True)) ]) self.current_tool_name_sent = True else: delta = None # now we know we're on the same tool call and we're streaming # arguments else: prev_arguments = self.prev_tool_call_arr[ self.current_tool_id].get("arguments") cur_arguments = current_tool_call.get("arguments") new_text = delta_text.replace("\'", "\"") if not cur_arguments and not prev_arguments: delta = None elif not cur_arguments and prev_arguments: logger.error( "INVARIANT - impossible to have arguments reset " "mid-arguments") delta = None elif cur_arguments and not prev_arguments: cur_arguments_json = json.dumps(cur_arguments, ensure_ascii=False) logger.debug("finding %s in %s", new_text, cur_arguments_json) arguments_delta = cur_arguments_json[:cur_arguments_json. index(new_text) + len(new_text)] logger.debug("First tokens in arguments received: %s", arguments_delta) delta = DeltaMessage(tool_calls=[ DeltaToolCall(index=self.current_tool_id, function=DeltaFunctionCall( arguments=arguments_delta). model_dump(exclude_none=True)) ]) self.streamed_args_for_tool[ self.current_tool_id] += arguments_delta elif cur_arguments and prev_arguments: cur_args_json = json.dumps(cur_arguments, ensure_ascii=False) prev_args_json = json.dumps(prev_arguments, ensure_ascii=False) logger.debug("Searching for diff between \n%s\n%s", cur_args_json, prev_args_json) argument_diff = extract_intermediate_diff( cur_args_json, prev_args_json) logger.debug("got arguments diff: %s", argument_diff) 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 else: # try parsing it with regular JSON - if it works we're # at the end, and we need to send the difference between # tokens streamed so far and the valid JSON delta = None # check to see if the name is defined and has been sent. if so, # stream the name - otherwise keep waiting # finish by setting old and returning None as base case self.prev_tool_call_arr = tool_call_arr return delta except Exception: logger.exception("Error trying to handle streaming tool call.") logger.debug( "Skipping chunk as a result of tool streaming extraction " "error") return None