import asyncio import json import time from typing import (AsyncGenerator, AsyncIterator, Callable, Dict, Final, List, Optional) from typing import Sequence as GenericSequence from typing import Union from fastapi import Request from vllm.config import ModelConfig from vllm.engine.protocol import AsyncEngineClient from vllm.entrypoints.chat_utils import (ConversationMessage, apply_chat_template, load_chat_template, parse_chat_messages_futures) from vllm.entrypoints.logger import RequestLogger from vllm.entrypoints.openai.protocol import ( ChatCompletionLogProb, ChatCompletionLogProbs, ChatCompletionLogProbsContent, ChatCompletionNamedToolChoiceParam, ChatCompletionRequest, ChatCompletionResponse, ChatCompletionResponseChoice, ChatCompletionResponseStreamChoice, ChatCompletionStreamResponse, ChatMessage, DeltaFunctionCall, DeltaMessage, DeltaToolCall, ErrorResponse, FunctionCall, ToolCall, UsageInfo) from vllm.entrypoints.openai.serving_engine import (LoRAModulePath, OpenAIServing, PromptAdapterPath, TextTokensPrompt) from vllm.entrypoints.openai.tool_parsers import (Hermes2ProToolParser, MistralToolParser, ToolParser) from vllm.inputs import TokensPrompt from vllm.logger import init_logger from vllm.outputs import CompletionOutput, RequestOutput from vllm.sequence import Logprob from vllm.tracing import (contains_trace_headers, extract_trace_headers, log_tracing_disabled_warning) from vllm.transformers_utils.tokenizer import AnyTokenizer from vllm.utils import iterate_with_cancellation, random_uuid logger = init_logger(__name__) class OpenAIServingChat(OpenAIServing): def __init__(self, async_engine_client: AsyncEngineClient, model_config: ModelConfig, served_model_names: List[str], response_role: str, *, lora_modules: Optional[List[LoRAModulePath]], prompt_adapters: Optional[List[PromptAdapterPath]], request_logger: Optional[RequestLogger], chat_template: Optional[str], return_tokens_as_token_ids: bool = False, enable_auto_tools: bool = False, tool_parser: Optional[str] = None): super().__init__(async_engine_client=async_engine_client, model_config=model_config, served_model_names=served_model_names, lora_modules=lora_modules, prompt_adapters=prompt_adapters, request_logger=request_logger, return_tokens_as_token_ids=return_tokens_as_token_ids) self.response_role = response_role self.use_tool_use_model_template = False self.chat_template = load_chat_template(chat_template) # set up tool use self.enable_auto_tools: bool = enable_auto_tools if self.enable_auto_tools: logger.info( "\"auto\" tool choice has been enabled please note that while" " the parallel_tool_calls client option is preset for " "compatibility reasons, it will be ignored.") self.tool_parser: Optional[Callable[[AnyTokenizer], ToolParser]] = None if self.enable_auto_tools: if tool_parser == "mistral": self.tool_parser = MistralToolParser elif tool_parser == "hermes": self.tool_parser = Hermes2ProToolParser else: raise TypeError("Error: --enable-auto-tool-choice requires " "--tool-call-parser") async def create_chat_completion( self, request: ChatCompletionRequest, raw_request: Optional[Request] = None, ) -> Union[AsyncGenerator[str, None], ChatCompletionResponse, ErrorResponse]: """Completion API similar to OpenAI's API. See https://platform.openai.com/docs/api-reference/chat/create for the API specification. This API mimics the OpenAI ChatCompletion API. """ error_check_ret = await self._check_model(request) if error_check_ret is not None: logger.error("Error with model %s", error_check_ret) return error_check_ret try: ( lora_request, prompt_adapter_request, ) = self._maybe_get_adapters(request) model_config = self.model_config tokenizer = await self.async_engine_client.get_tokenizer( lora_request) conversation, mm_data_future = parse_chat_messages_futures( request.messages, model_config, tokenizer) tool_dicts = None if request.tools is None else [ tool.model_dump() for tool in request.tools ] prompt = apply_chat_template( tokenizer, conversation=conversation, chat_template=request.chat_template or self.chat_template, add_generation_prompt=request.add_generation_prompt, tools=tool_dicts, documents=request.documents, **(request.chat_template_kwargs or {}), ) except Exception as e: logger.error("Error in applying chat template from request: %s", e) return self.create_error_response(str(e)) try: mm_data = await mm_data_future except Exception as e: logger.error("Error in loading multi-modal data: %s", e) return self.create_error_response(str(e)) # validation for OpenAI tools # tool_choice = "required" is not supported if request.tool_choice == "required": return self.create_error_response( "tool_choice = \"required\" is not supported!") # "auto" tools requires --enable-auto-tool-choice # and --tool-call-parser if request.tool_choice == "auto" and not ( self.enable_auto_tools and self.tool_parser is not None): return self.create_error_response( "\"auto\" tool choice requires " "--enable-auto-tool-choice and --tool-call-parser to be set") request_id = f"chat-{random_uuid()}" try: guided_decode_logits_processor = ( await self._guided_decode_logits_processor(request, tokenizer)) if isinstance(prompt, str): prompt_inputs = self._tokenize_prompt_input( request, tokenizer, prompt, truncate_prompt_tokens=request.truncate_prompt_tokens, add_special_tokens=request.add_special_tokens, ) else: assert isinstance(prompt, list) and isinstance( prompt[0], int ), "Prompt has to be either a string or a list of token ids" prompt_inputs = TextTokensPrompt( prompt=tokenizer.decode(prompt), prompt_token_ids=prompt) assert prompt_inputs is not None sampling_params = request.to_sampling_params( tokenizer, guided_decode_logits_processor, default_max_tokens=self.max_model_len - len(prompt_inputs["prompt_token_ids"])) self._log_inputs(request_id, prompt_inputs, params=sampling_params, lora_request=lora_request, prompt_adapter_request=prompt_adapter_request) engine_inputs = TokensPrompt( prompt_token_ids=prompt_inputs["prompt_token_ids"]) if mm_data is not None: engine_inputs["multi_modal_data"] = mm_data is_tracing_enabled = ( await self.async_engine_client.is_tracing_enabled()) trace_headers = None if is_tracing_enabled and raw_request: trace_headers = extract_trace_headers(raw_request.headers) if (not is_tracing_enabled and raw_request and contains_trace_headers(raw_request.headers)): log_tracing_disabled_warning() result_generator = self.async_engine_client.generate( engine_inputs, sampling_params, request_id, lora_request=lora_request, trace_headers=trace_headers, prompt_adapter_request=prompt_adapter_request, ) except ValueError as e: # TODO: Use a vllm-specific Validation Error return self.create_error_response(str(e)) if raw_request: result_generator = iterate_with_cancellation( result_generator, raw_request.is_disconnected) # Streaming response if request.stream: return self.chat_completion_stream_generator( request, result_generator, request_id, conversation, tokenizer) try: return await self.chat_completion_full_generator( request, result_generator, request_id, conversation, tokenizer) except ValueError as e: # TODO: Use a vllm-specific Validation Error return self.create_error_response(str(e)) def get_chat_request_role(self, request: ChatCompletionRequest) -> str: if request.add_generation_prompt: return self.response_role else: return request.messages[-1]["role"] async def chat_completion_stream_generator( self, request: ChatCompletionRequest, result_generator: AsyncIterator[RequestOutput], request_id: str, conversation: List[ConversationMessage], tokenizer: AnyTokenizer, ) -> AsyncGenerator[str, None]: model_name = self.served_model_names[0] created_time = int(time.time()) chunk_object_type: Final = "chat.completion.chunk" first_iteration = True # Send response for each token for each request.n (index) num_choices = 1 if request.n is None else request.n previous_texts = [""] * num_choices previous_num_tokens = [0] * num_choices finish_reason_sent = [False] * num_choices tool_parser: Optional[ToolParser] = self.tool_parser( tokenizer) if self.tool_parser else None try: async for res in result_generator: # We need to do it here, because if there are exceptions in # the result_generator, it needs to be sent as the FIRST # response (by the try...catch). if first_iteration: # Send first response for each request.n (index) with # the role role = self.get_chat_request_role(request) # NOTE num_choices defaults to 1 so this usually executes # once per request for i in range(num_choices): choice_data = ChatCompletionResponseStreamChoice( index=i, delta=DeltaMessage(role=role), logprobs=None, finish_reason=None) chunk = ChatCompletionStreamResponse( id=request_id, object=chunk_object_type, created=created_time, choices=[choice_data], model=model_name) # if usage should be included if (request.stream_options and request.stream_options.include_usage): # if continuous usage stats are requested, add it if request.stream_options.continuous_usage_stats: prompt_tokens = len(res.prompt_token_ids) usage = UsageInfo(prompt_tokens=prompt_tokens, completion_tokens=0, total_tokens=prompt_tokens) chunk.usage = usage # otherwise don't else: chunk.usage = None data = chunk.model_dump_json(exclude_unset=True) yield f"data: {data}\n\n" # Send response to echo the input portion of the # last message if request.echo: last_msg_content: Optional[str] = "" if conversation and conversation[-1].get( "content") and conversation[-1].get( "role") == role: last_msg_content = conversation[-1]["content"] if last_msg_content: for i in range(num_choices): choice_data = ( ChatCompletionResponseStreamChoice( index=i, delta=DeltaMessage( content=last_msg_content), logprobs=None, finish_reason=None)) chunk = ChatCompletionStreamResponse( id=request_id, object=chunk_object_type, created=created_time, choices=[choice_data], model=model_name) if (request.stream_options and request.stream_options.include_usage): if (request.stream_options. continuous_usage_stats): prompt_tokens = len( res.prompt_token_ids) usage = UsageInfo( prompt_tokens=prompt_tokens, completion_tokens=0, total_tokens=prompt_tokens) chunk.usage = usage else: chunk.usage = None data = chunk.model_dump_json( exclude_unset=True) yield f"data: {data}\n\n" first_iteration = False for output in res.outputs: i = output.index if finish_reason_sent[i]: continue delta_token_ids = output.token_ids[previous_num_tokens[i]:] out_logprobs = output.logprobs[ previous_num_tokens[i]:] if output.logprobs else None if request.logprobs and request.top_logprobs is not None: assert out_logprobs is not None, ( "Did not output logprobs") logprobs = self._create_chat_logprobs( token_ids=delta_token_ids, top_logprobs=out_logprobs, tokenizer=tokenizer, num_output_top_logprobs=request.top_logprobs, ) else: logprobs = None delta_text = output.text[len(previous_texts[i]):] delta_message: Optional[DeltaMessage] = None # handle streaming deltas for tools with named tool_choice if (request.tool_choice and type(request.tool_choice) is ChatCompletionNamedToolChoiceParam): delta_message = DeltaMessage(tool_calls=[ DeltaToolCall(function=DeltaFunctionCall( name=request.tool_choice.function.name, arguments=delta_text), index=i) ]) # handle streaming deltas for tools with "auto" tool choice elif (self._should_stream_with_auto_tool_parsing(request) and tool_parser): delta_message = ( tool_parser.extract_tool_calls_streaming( previous_text=previous_texts[i], current_text=output.text, delta_text=delta_text, previous_token_ids= \ output.token_ids[ :-1 * len(delta_token_ids) ], current_token_ids=output.token_ids, delta_token_ids=delta_token_ids ) ) # handle streaming just a content delta else: delta_message = DeltaMessage(content=delta_text) # set the previous values for the next iteration previous_texts[i] = output.text previous_num_tokens[i] = len(output.token_ids) # if the message delta is None (e.g. because it was a # "control token" for tool calls or the parser otherwise # wasn't ready to send a token, then # get the next token without streaming a chunk if delta_message is None: continue if output.finish_reason is None: # Send token-by-token response for each request.n choice_data = ChatCompletionResponseStreamChoice( index=i, delta=delta_message, logprobs=logprobs, finish_reason=None) chunk = ChatCompletionStreamResponse( id=request_id, object=chunk_object_type, created=created_time, choices=[choice_data], model=model_name) # handle usage stats if requested & if continuous if (request.stream_options and request.stream_options.include_usage): if (request.stream_options.continuous_usage_stats): prompt_tokens = len(res.prompt_token_ids) completion_tokens = len(output.token_ids) usage = UsageInfo( prompt_tokens=prompt_tokens, completion_tokens=completion_tokens, total_tokens=prompt_tokens + completion_tokens, ) chunk.usage = usage else: chunk.usage = None data = chunk.model_dump_json(exclude_unset=True) yield f"data: {data}\n\n" # if the model is finished generating else: # check to make sure we haven't "forgotten" to stream # any tokens that were generated but previously # matched by partial json parsing # only happens if we are NOT using guided decoding if tool_parser: index = len( tool_parser.prev_tool_call_arr) - 1 if len( tool_parser.prev_tool_call_arr) > 0 else 0 else: index = 0 if self._should_check_for_unstreamed_tool_arg_tokens( delta_message, output) and tool_parser: # get the expected call based on partial JSON # parsing which "autocompletes" the JSON expected_call = json.dumps( tool_parser.prev_tool_call_arr[index].get( "arguments", {})) # get what we've streamed so for for arguments # for the current tool actual_call = tool_parser.streamed_args_for_tool[ index] # check to see if there's anything left to stream remaining_call = expected_call.replace( actual_call, "", 1) # set that as a delta message delta_message = DeltaMessage(tool_calls=[ DeltaToolCall(index=index, function=DeltaFunctionCall( arguments=remaining_call). model_dump(exclude_none=True)) ]) # Send the finish response for each request.n only once prompt_tokens = len(res.prompt_token_ids) choice_data = ChatCompletionResponseStreamChoice( index=i, delta=delta_message, logprobs=logprobs, finish_reason=output.finish_reason if not (tool_parser and len(tool_parser.prev_tool_call_arr)) else "tool_calls", stop_reason=output.stop_reason) chunk = ChatCompletionStreamResponse( id=request_id, object=chunk_object_type, created=created_time, choices=[choice_data], model=model_name) if (request.stream_options and request.stream_options.include_usage): if (request.stream_options.continuous_usage_stats): prompt_tokens = len(res.prompt_token_ids) completion_tokens = len(output.token_ids) usage = UsageInfo( prompt_tokens=prompt_tokens, completion_tokens=completion_tokens, total_tokens=prompt_tokens + completion_tokens, ) chunk.usage = usage else: chunk.usage = None data = chunk.model_dump_json(exclude_unset=True) yield f"data: {data}\n\n" finish_reason_sent[i] = True # once the final token is handled, if stream_options.include_usage # is sent, send the usage if (request.stream_options and request.stream_options.include_usage): final_usage = UsageInfo( prompt_tokens=prompt_tokens, completion_tokens=previous_num_tokens[i], total_tokens=prompt_tokens + previous_num_tokens[i], ) final_usage_chunk = ChatCompletionStreamResponse( id=request_id, object=chunk_object_type, created=created_time, choices=[], model=model_name, usage=final_usage) final_usage_data = (final_usage_chunk.model_dump_json( exclude_unset=True, exclude_none=True)) yield f"data: {final_usage_data}\n\n" except ValueError as e: # TODO: Use a vllm-specific Validation Error logger.error("error in chat completion stream generator: %s", e) data = self.create_streaming_error_response(str(e)) yield f"data: {data}\n\n" # Send the final done message after all response.n are finished yield "data: [DONE]\n\n" async def chat_completion_full_generator( self, request: ChatCompletionRequest, result_generator: AsyncIterator[RequestOutput], request_id: str, conversation: List[ConversationMessage], tokenizer: AnyTokenizer, ) -> Union[ErrorResponse, ChatCompletionResponse]: model_name = self.served_model_names[0] created_time = int(time.time()) final_res: Optional[RequestOutput] = None try: async for res in result_generator: final_res = res except asyncio.CancelledError: return self.create_error_response("Client disconnected") assert final_res is not None choices: List[ChatCompletionResponseChoice] = [] role = self.get_chat_request_role(request) for output in final_res.outputs: token_ids = output.token_ids out_logprobs = output.logprobs if request.logprobs and request.top_logprobs is not None: assert out_logprobs is not None, "Did not output logprobs" logprobs = self._create_chat_logprobs( token_ids=token_ids, top_logprobs=out_logprobs, num_output_top_logprobs=request.top_logprobs, tokenizer=tokenizer, ) else: logprobs = None # by default, tools are not used. tools_called = False # if auto tools are not enabled, and a named tool choice using # outlines is not being used if not (self.enable_auto_tools or not self.tool_parser) and not isinstance( request.tool_choice, ChatCompletionNamedToolChoiceParam): message = ChatMessage(role=role, content=output.text) # if the request uses tools and specified a tool choice elif request.tool_choice and type( request.tool_choice) is ChatCompletionNamedToolChoiceParam: message = ChatMessage( role=role, content="", tool_calls=[ ToolCall(function=FunctionCall( name=request.tool_choice.function.name, arguments=output.text)) ]) tools_called = True # if the request doesn't use tool choice # OR specifies to not use a tool elif not request.tool_choice or request.tool_choice == "none": message = ChatMessage(role=role, content=output.text) # handle when there are tools and tool choice is auto elif request.tools and ( request.tool_choice == "auto" or request.tool_choice is None) and self.enable_auto_tools \ and self.tool_parser: tool_parser = self.tool_parser(tokenizer) tool_call_info = tool_parser.extract_tool_calls(output.text) tools_called = tool_call_info.tools_called if tool_call_info.tools_called: message = ChatMessage(role=role, content=tool_call_info.content, tool_calls=tool_call_info.tool_calls) else: # FOR NOW make it a chat message; we will have to detect # the type to make it later. message = ChatMessage(role=role, content=output.text) # undetermined case that is still important to handle else: logger.error( "Error in chat_completion_full_generator - cannot determine" " if tools should be extracted. Returning a standard chat " "completion.") message = ChatMessage(role=role, content=output.text) choice_data = ChatCompletionResponseChoice( index=output.index, message=message, logprobs=logprobs, finish_reason="tool_calls" if tools_called else output.finish_reason if output.finish_reason else "stop", stop_reason=output.stop_reason) choices.append(choice_data) if request.echo: last_msg_content = "" if conversation and conversation[-1].get( "content") and conversation[-1].get("role") == role: last_msg_content = conversation[-1]["content"] or "" for choice in choices: full_message = last_msg_content + (choice.message.content or "") choice.message.content = full_message num_prompt_tokens = len(final_res.prompt_token_ids) num_generated_tokens = sum( len(output.token_ids) for output in final_res.outputs) usage = UsageInfo( prompt_tokens=num_prompt_tokens, completion_tokens=num_generated_tokens, total_tokens=num_prompt_tokens + num_generated_tokens, ) response = ChatCompletionResponse( id=request_id, created=created_time, model=model_name, choices=choices, usage=usage, prompt_logprobs=final_res.prompt_logprobs, ) return response def _get_top_logprobs( self, logprobs: Dict[int, Logprob], top_logprobs: Optional[int], tokenizer: AnyTokenizer) -> List[ChatCompletionLogProb]: return [ ChatCompletionLogProb(token=(token := self._get_decoded_token( p[1], p[0], tokenizer, return_as_token_id=self.return_tokens_as_token_ids)), logprob=max(p[1].logprob, -9999.0), bytes=list( token.encode("utf-8", errors="replace"))) for i, p in enumerate(logprobs.items()) if top_logprobs and i < top_logprobs ] def _create_chat_logprobs( self, token_ids: GenericSequence[int], top_logprobs: GenericSequence[Optional[Dict[int, Logprob]]], tokenizer: AnyTokenizer, num_output_top_logprobs: Optional[int] = None, ) -> ChatCompletionLogProbs: """Create OpenAI-style logprobs.""" logprobs_content: List[ChatCompletionLogProbsContent] = [] for i, token_id in enumerate(token_ids): step_top_logprobs = top_logprobs[i] if step_top_logprobs is None: token = tokenizer.decode(token_id) if self.return_tokens_as_token_ids: token = f"token_id:{token_id}" logprobs_content.append( ChatCompletionLogProbsContent( token=token, bytes=list(token.encode("utf-8", errors="replace")), )) else: step_token = step_top_logprobs[token_id] step_decoded = step_token.decoded_token logprobs_content.append( ChatCompletionLogProbsContent( token=self._get_decoded_token( step_token, token_id, tokenizer, self.return_tokens_as_token_ids, ), logprob=max(step_token.logprob, -9999.0), bytes=None if step_decoded is None else list( step_decoded.encode("utf-8", errors="replace")), top_logprobs=self._get_top_logprobs( step_top_logprobs, num_output_top_logprobs, tokenizer, ), )) return ChatCompletionLogProbs(content=logprobs_content) def _should_stream_with_auto_tool_parsing(self, request: ChatCompletionRequest): """ Utility function to check if streamed tokens should go through the tool call parser that was configured. We only want to do this IF user-provided tools are set, a tool parser is configured, "auto" tool choice is enabled, and the request's tool choice field indicates that "auto" tool choice should be used. """ return (request.tools and self.tool_parser and self.enable_auto_tools and request.tool_choice in ['auto', None]) def _should_check_for_unstreamed_tool_arg_tokens( self, delta_message: Optional[DeltaMessage], output: CompletionOutput, ) -> bool: """ Check to see if we should check for unstreamed tool arguments tokens. This is only applicable when auto tool parsing is enabled, the delta is a tool call with arguments. """ # yapf: disable return bool( # if there is a delta message that includes tool calls which # include a function that has arguments self.enable_auto_tools and self.tool_parser and delta_message and delta_message.tool_calls and delta_message.tool_calls[0] and delta_message.tool_calls[0].function and delta_message.tool_calls[0].function.arguments is not None and output.finish_reason is not None )