import asyncio import time from typing import (AsyncGenerator, AsyncIterator, Callable, Dict, List, Optional) from typing import Sequence as GenericSequence from typing import Tuple, Union, cast from fastapi import Request from vllm.config import ModelConfig from vllm.engine.protocol import AsyncEngineClient from vllm.entrypoints.logger import RequestLogger # yapf conflicts with isort for this block # yapf: disable from vllm.entrypoints.openai.protocol import (CompletionLogProbs, CompletionRequest, CompletionResponse, CompletionResponseChoice, CompletionResponseStreamChoice, CompletionStreamResponse, ErrorResponse, UsageInfo) # yapf: enable from vllm.entrypoints.openai.serving_engine import (LoRAModulePath, OpenAIServing, PromptAdapterPath) from vllm.logger import init_logger 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.transformers_utils.tokenizer import AnyTokenizer from vllm.utils import merge_async_iterators, random_uuid logger = init_logger(__name__) TypeTokenIDs = List[int] TypeTopLogProbs = List[Optional[Dict[int, float]]] TypeCreateLogProbsFn = Callable[ [TypeTokenIDs, TypeTopLogProbs, Optional[int], int], CompletionLogProbs] class OpenAIServingCompletion(OpenAIServing): def __init__( self, async_engine_client: AsyncEngineClient, model_config: ModelConfig, served_model_names: List[str], *, lora_modules: Optional[List[LoRAModulePath]], prompt_adapters: Optional[List[PromptAdapterPath]], request_logger: Optional[RequestLogger], 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) async def create_completion( self, request: CompletionRequest, raw_request: Request, ) -> Union[AsyncGenerator[str, None], CompletionResponse, ErrorResponse]: """Completion API similar to OpenAI's API. See https://platform.openai.com/docs/api-reference/completions/create for the API specification. This API mimics the OpenAI Completion API. NOTE: Currently we do not support the following feature: - suffix (the language models we currently support do not support suffix) """ error_check_ret = await self._check_model(request) if error_check_ret is not None: return error_check_ret # Return error for unsupported features. if request.suffix is not None: return self.create_error_response( "suffix is not currently supported") model_name = self.served_model_names[0] request_id = f"cmpl-{random_uuid()}" created_time = int(time.time()) # Schedule the request and get the result generator. generators: List[AsyncGenerator[RequestOutput, None]] = [] try: ( lora_request, prompt_adapter_request, ) = self._maybe_get_adapters(request) tokenizer = await self.async_engine_client.get_tokenizer( lora_request) guided_decode_logits_processor = ( await self._guided_decode_logits_processor(request, tokenizer)) prompts = list( self._tokenize_prompt_input_or_inputs( request, tokenizer, request.prompt, truncate_prompt_tokens=request.truncate_prompt_tokens, add_special_tokens=request.add_special_tokens, )) for i, prompt_inputs in enumerate(prompts): sampling_params = request.to_sampling_params( tokenizer, guided_decode_logits_processor, default_max_tokens=self.max_model_len - len(prompt_inputs["prompt_token_ids"])) request_id_item = f"{request_id}-{i}" self._log_inputs(request_id_item, prompt_inputs, params=sampling_params, lora_request=lora_request, prompt_adapter_request=prompt_adapter_request) is_tracing_enabled = ( await self.async_engine_client.is_tracing_enabled()) trace_headers = None if is_tracing_enabled: trace_headers = extract_trace_headers(raw_request.headers) if not is_tracing_enabled and contains_trace_headers( raw_request.headers): log_tracing_disabled_warning() generator = self.async_engine_client.generate( {"prompt_token_ids": prompt_inputs["prompt_token_ids"]}, sampling_params, request_id_item, lora_request=lora_request, prompt_adapter_request=prompt_adapter_request, trace_headers=trace_headers, ) generators.append(generator) except ValueError as e: # TODO: Use a vllm-specific Validation Error return self.create_error_response(str(e)) result_generator = merge_async_iterators( *generators, is_cancelled=raw_request.is_disconnected) # Similar to the OpenAI API, when n != best_of, we do not stream the # results. In addition, we do not stream the results when use # beam search. stream = (request.stream and (request.best_of is None or request.n == request.best_of) and not request.use_beam_search) # Streaming response if stream: return self.completion_stream_generator(request, result_generator, request_id, created_time, model_name, num_prompts=len(prompts), tokenizer=tokenizer) # Non-streaming response final_res_batch: List[Optional[RequestOutput]] = [None] * len(prompts) try: async for i, res in result_generator: final_res_batch[i] = res for i, final_res in enumerate(final_res_batch): assert final_res is not None # The output should contain the input text # We did not pass it into vLLM engine to avoid being redundant # with the inputs token IDs if final_res.prompt is None: final_res.prompt = prompts[i]["prompt"] final_res_batch_checked = cast(List[RequestOutput], final_res_batch) response = self.request_output_to_completion_response( final_res_batch_checked, request, request_id, created_time, model_name, tokenizer, ) except asyncio.CancelledError: return self.create_error_response("Client disconnected") except ValueError as e: # TODO: Use a vllm-specific Validation Error return self.create_error_response(str(e)) # When user requests streaming but we don't stream, we still need to # return a streaming response with a single event. if request.stream: response_json = response.model_dump_json() async def fake_stream_generator() -> AsyncGenerator[str, None]: yield f"data: {response_json}\n\n" yield "data: [DONE]\n\n" return fake_stream_generator() return response async def completion_stream_generator( self, request: CompletionRequest, result_generator: AsyncIterator[Tuple[int, RequestOutput]], request_id: str, created_time: int, model_name: str, num_prompts: int, tokenizer: AnyTokenizer, ) -> AsyncGenerator[str, None]: num_choices = 1 if request.n is None else request.n previous_texts = [""] * num_choices * num_prompts previous_num_tokens = [0] * num_choices * num_prompts has_echoed = [False] * num_choices * num_prompts try: async for prompt_idx, res in result_generator: prompt_token_ids = res.prompt_token_ids prompt_logprobs = res.prompt_logprobs prompt_text = res.prompt delta_token_ids: GenericSequence[int] out_logprobs: Optional[GenericSequence[Optional[Dict[ int, Logprob]]]] for output in res.outputs: i = output.index + prompt_idx * num_choices # TODO(simon): optimize the performance by avoiding full # text O(n^2) sending. assert request.max_tokens is not None if request.echo and request.max_tokens == 0: assert prompt_text is not None # only return the prompt delta_text = prompt_text delta_token_ids = prompt_token_ids out_logprobs = prompt_logprobs has_echoed[i] = True elif (request.echo and request.max_tokens > 0 and not has_echoed[i]): assert prompt_text is not None assert prompt_logprobs is not None # echo the prompt and first token delta_text = prompt_text + output.text delta_token_ids = [ *prompt_token_ids, *output.token_ids ] out_logprobs = [ *prompt_logprobs, *(output.logprobs or []), ] has_echoed[i] = True else: # return just the delta delta_text = output.text[len(previous_texts[i]):] 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 is not None: assert out_logprobs is not None, ( "Did not output logprobs") logprobs = self._create_completion_logprobs( token_ids=delta_token_ids, top_logprobs=out_logprobs, num_output_top_logprobs=request.logprobs, tokenizer=tokenizer, initial_text_offset=len(previous_texts[i]), ) else: logprobs = None previous_texts[i] = output.text previous_num_tokens[i] = len(output.token_ids) finish_reason = output.finish_reason stop_reason = output.stop_reason chunk = CompletionStreamResponse( id=request_id, created=created_time, model=model_name, choices=[ CompletionResponseStreamChoice( index=i, text=delta_text, logprobs=logprobs, finish_reason=finish_reason, stop_reason=stop_reason, ) ]) if (request.stream_options and request.stream_options.include_usage): if (request.stream_options.continuous_usage_stats or output.finish_reason is not None): prompt_tokens = len(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, ) if request.stream_options.continuous_usage_stats: chunk.usage = usage else: chunk.usage = None response_json = chunk.model_dump_json(exclude_unset=False) yield f"data: {response_json}\n\n" if (request.stream_options and request.stream_options.include_usage): final_usage_chunk = CompletionStreamResponse( id=request_id, created=created_time, model=model_name, choices=[], usage=usage, ) final_usage_data = (final_usage_chunk.model_dump_json( exclude_unset=False, 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" yield "data: [DONE]\n\n" def request_output_to_completion_response( self, final_res_batch: List[RequestOutput], request: CompletionRequest, request_id: str, created_time: int, model_name: str, tokenizer: AnyTokenizer, ) -> CompletionResponse: choices: List[CompletionResponseChoice] = [] num_prompt_tokens = 0 num_generated_tokens = 0 for final_res in final_res_batch: prompt_token_ids = final_res.prompt_token_ids prompt_logprobs = final_res.prompt_logprobs prompt_text = final_res.prompt token_ids: GenericSequence[int] out_logprobs: Optional[GenericSequence[Optional[Dict[int, Logprob]]]] for output in final_res.outputs: assert request.max_tokens is not None if request.echo and request.max_tokens == 0: assert prompt_text is not None token_ids = prompt_token_ids out_logprobs = prompt_logprobs output_text = prompt_text elif request.echo and request.max_tokens > 0: assert prompt_text is not None token_ids = [*prompt_token_ids, *output.token_ids] if request.logprobs is None: out_logprobs = None else: assert prompt_logprobs is not None assert output.logprobs is not None out_logprobs = [ *prompt_logprobs, *output.logprobs, ] output_text = prompt_text + output.text else: token_ids = output.token_ids out_logprobs = output.logprobs output_text = output.text if request.logprobs is not None: assert out_logprobs is not None, "Did not output logprobs" logprobs = self._create_completion_logprobs( token_ids=token_ids, top_logprobs=out_logprobs, tokenizer=tokenizer, num_output_top_logprobs=request.logprobs, ) else: logprobs = None choice_data = CompletionResponseChoice( index=len(choices), text=output_text, logprobs=logprobs, finish_reason=output.finish_reason, stop_reason=output.stop_reason, prompt_logprobs=final_res.prompt_logprobs, ) choices.append(choice_data) num_prompt_tokens += len(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, ) return CompletionResponse( id=request_id, created=created_time, model=model_name, choices=choices, usage=usage, ) def _create_completion_logprobs( self, token_ids: GenericSequence[int], top_logprobs: GenericSequence[Optional[Dict[int, Logprob]]], num_output_top_logprobs: int, tokenizer: AnyTokenizer, initial_text_offset: int = 0, ) -> CompletionLogProbs: """Create logprobs for OpenAI Completion API.""" out_text_offset: List[int] = [] out_token_logprobs: List[Optional[float]] = [] out_tokens: List[str] = [] out_top_logprobs: List[Optional[Dict[str, float]]] = [] last_token_len = 0 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}" out_tokens.append(token) out_token_logprobs.append(None) out_top_logprobs.append(None) else: step_token = step_top_logprobs[token_id] token = self._get_decoded_token( step_token, token_id, tokenizer, return_as_token_id=self.return_tokens_as_token_ids, ) token_logprob = max(step_token.logprob, -9999.0) out_tokens.append(token) out_token_logprobs.append(token_logprob) # makes sure to add the top num_output_top_logprobs + 1 # logprobs, as defined in the openai API # (cf. https://github.com/openai/openai-openapi/blob/ # 893ba52242dbd5387a97b96444ee1c742cfce9bd/openapi.yaml#L7153) out_top_logprobs.append({ # Convert float("-inf") to the # JSON-serializable float that OpenAI uses self._get_decoded_token( top_lp[1], top_lp[0], tokenizer, return_as_token_id=self.return_tokens_as_token_ids): max(top_lp[1].logprob, -9999.0) for i, top_lp in enumerate(step_top_logprobs.items()) if num_output_top_logprobs >= i }) if len(out_text_offset) == 0: out_text_offset.append(initial_text_offset) else: out_text_offset.append(out_text_offset[-1] + last_token_len) last_token_len = len(token) return CompletionLogProbs( text_offset=out_text_offset, token_logprobs=out_token_logprobs, tokens=out_tokens, top_logprobs=out_top_logprobs, )