import time from typing import AsyncGenerator, AsyncIterator, Dict, List, Optional from typing import Sequence as GenericSequence from typing import Union from fastapi import Request from transformers import PreTrainedTokenizer from vllm.config import ModelConfig from vllm.engine.protocol import AsyncEngineClient from vllm.entrypoints.chat_utils import ( ConversationMessage, load_chat_template, parse_chat_messages, ) from vllm.entrypoints.logger import RequestLogger from openai_protocol import ( ChatCompletionLogProb, ChatCompletionLogProbs, ChatCompletionLogProbsContent, ChatCompletionNamedToolChoiceParam, ChatCompletionRequest, ChatCompletionResponse, ChatCompletionResponseChoice, ChatCompletionResponseStreamChoice, ChatCompletionStreamResponse, ChatMessage, DeltaMessage, ErrorResponse, FunctionCall, ToolCall, UsageInfo, ) from vllm.entrypoints.openai.serving_engine import ( LoRAModulePath, OpenAIServing, PromptAdapterPath, ) from vllm.inputs import PromptInputs from vllm.logger import init_logger from vllm.multimodal import MultiModalDataDict from vllm.outputs import RequestOutput from vllm.sequence import Logprob from vllm.tracing import ( contains_trace_headers, extract_trace_headers, log_tracing_disabled_warning, ) from vllm.utils import random_uuid from utils import decode_function_call import json 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, ): 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 # If this is None we use the tokenizer's default chat template self.chat_template = load_chat_template(chat_template) async def create_chat_completion( self, request: ChatCompletionRequest, raw_request: Optional[Request] = None ) -> Union[ErrorResponse, AsyncGenerator[str, None], ChatCompletionResponse]: """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. NOTE: Currently we do not support the following feature: - function_call (Users should implement this by themselves) """ error_check_ret = await self._check_model(request) if error_check_ret is not None: 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_futures = parse_chat_messages( request.messages, model_config, tokenizer ) print('conversation:', conversation) # parse_chat_messages ignores tool_calls and tool_call_id # we have to fix this conversation = request.messages for msg in conversation: if 'tool_calls' in msg and msg['tool_calls'] is not None: msg['tool_calls'] = [tc for tc in msg['tool_calls']] print('fixed conversation:', conversation) tool_dicts = ( None if request.tools is None else [tool.model_dump() for tool in request.tools] ) prompt = tokenizer.apply_chat_template( conversation=conversation, tokenize=False, add_generation_prompt=request.add_generation_prompt, tools=tool_dicts, documents=request.documents, chat_template=request.chat_template or self.chat_template, **(request.chat_template_kwargs or {}), ) assert isinstance(prompt, str) except Exception as e: logger.error("Error in applying chat template from request: %s", e) return self.create_error_response(str(e)) mm_data: Optional[MultiModalDataDict] = None try: if len(mm_futures): # since we support only single mm data currently assert ( len(mm_futures) == 1 ), "Multiple 'image_url' input is currently not supported." mm_data = await mm_futures[0] except Exception as e: logger.error("Error in loading multi-modal data: %s", e) return self.create_error_response(str(e)) request_id = f"chat-{random_uuid()}" try: guided_decode_logits_processor = await self._guided_decode_logits_processor( request, tokenizer ) prompt_inputs = self._tokenize_prompt_input( request, tokenizer, prompt, truncate_prompt_tokens=request.truncate_prompt_tokens, add_special_tokens=request.add_special_tokens, ) 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: PromptInputs = { "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)) # Streaming response if request.stream: return self.chat_completion_stream_generator( request, result_generator, request_id, conversation, tokenizer ) else: try: return await self.chat_completion_full_generator( request, raw_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: PreTrainedTokenizer, ) -> AsyncGenerator[str, None]: model_name = self.served_model_names[0] created_time = int(time.time()) chunk_object_type = "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 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) 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 ( 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" # Send response to echo the input portion of the # last message 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"] 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 # if have tool if request.tools is not None and len(request.tools) > 0: if output.finish_reason is not None: msg = decode_function_call(output.text) if "tool_calls" in msg and msg["tool_calls"] is not None and len(msg["tool_calls"]) > 0: delta_message = DeltaMessage( content=msg.get("thought", ""), tool_calls=[ ToolCall( id=f"chatcmpl-tool-{random_uuid()}", function=FunctionCall( name=fc["function"]["name"], arguments=json.dumps(fc["function"]["arguments"], ensure_ascii=False), ) ) for fc in msg["tool_calls"] ] ) else: delta_message = DeltaMessage(content=msg.get("content", "")) else: # only return the last one continue else: delta_text = output.text[len(previous_texts[i]) :] previous_texts[i] = output.text previous_num_tokens[i] = len(output.token_ids) if ( request.tool_choice and type(request.tool_choice) is ChatCompletionNamedToolChoiceParam ): delta_message = DeltaMessage( tool_calls=[ ToolCall( function=FunctionCall( name=request.tool_choice.function.name, arguments=delta_text, ) ) ] ) else: delta_message = DeltaMessage(content=delta_text) 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, ) 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" else: # 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, 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 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 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, raw_request: Optional[Request], result_generator: AsyncIterator[RequestOutput], request_id: str, conversation: List[ConversationMessage], tokenizer: PreTrainedTokenizer, ) -> Union[ErrorResponse, ChatCompletionResponse]: model_name = self.served_model_names[0] created_time = int(time.time()) final_res: Optional[RequestOutput] = None async for res in result_generator: if raw_request is not None and await raw_request.is_disconnected(): # Abort the request if the client disconnects. await self.async_engine_client.abort(request_id) return self.create_error_response("Client disconnected") final_res = res 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 #if ( # 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, # ) # ) # ], # ) #elif not request.tool_choice or request.tool_choice == "none": # message = ChatMessage(role=role, content=output.text) msg = decode_function_call(output.text) if "tool_calls" in msg and msg["tool_calls"] is not None and len(msg["tool_calls"]) > 0: message = ChatMessage( role=role, content=msg.get("thought", ""), tool_calls=[ ToolCall( function=FunctionCall( name=fc["function"]["name"], arguments=json.dumps(fc["function"]["arguments"], ensure_ascii=False), ) ) for fc in msg["tool_calls"] ], ) else: message = ChatMessage(role=role, content=msg.get("content", "")) choice_data = ChatCompletionResponseChoice( index=output.index, message=message, logprobs=logprobs, finish_reason=output.finish_reason, 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"] for choice in choices: full_message = last_msg_content + choice.message.content 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, ) return response def _get_top_logprobs( self, logprobs: Dict[int, Logprob], top_logprobs: Optional[int], tokenizer: PreTrainedTokenizer, ) -> 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: PreTrainedTokenizer, num_output_top_logprobs: Optional[int] = None, ) -> ChatCompletionLogProbs: """Create OpenAI-style logprobs.""" logprobs_content = [] 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: logprobs_content.append( ChatCompletionLogProbsContent( token=self._get_decoded_token( step_top_logprobs[token_id], token_id, tokenizer, self.return_tokens_as_token_ids, ), logprob=max(step_top_logprobs[token_id].logprob, -9999.0), bytes=list( step_top_logprobs[token_id].decoded_token.encode( "utf-8", errors="replace" ) ), top_logprobs=self._get_top_logprobs( step_top_logprobs, num_output_top_logprobs, tokenizer ), ) ) return ChatCompletionLogProbs(content=logprobs_content)