# SPDX-License-Identifier: Apache-2.0 import asyncio import time from collections.abc import AsyncGenerator, AsyncIterator from collections.abc import Sequence as GenericSequence from typing import Optional, Union, cast from fastapi import Request from vllm.config import ModelConfig from vllm.engine.protocol import EngineClient 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, RequestResponseMetadata, UsageInfo) # yapf: enable from vllm.entrypoints.openai.serving_engine import (OpenAIServing, clamp_prompt_logprobs) from vllm.entrypoints.openai.serving_models import OpenAIServingModels from vllm.logger import init_logger from vllm.outputs import RequestOutput from vllm.sampling_params import BeamSearchParams, SamplingParams from vllm.sequence import Logprob from vllm.transformers_utils.tokenizer import AnyTokenizer from vllm.utils import merge_async_iterators logger = init_logger(__name__) class OpenAIServingCompletion(OpenAIServing): def __init__( self, engine_client: EngineClient, model_config: ModelConfig, models: OpenAIServingModels, *, request_logger: Optional[RequestLogger], return_tokens_as_token_ids: bool = False, ): super().__init__(engine_client=engine_client, model_config=model_config, models=models, request_logger=request_logger, return_tokens_as_token_ids=return_tokens_as_token_ids) self.default_sampling_params = ( self.model_config.get_diff_sampling_param()) if self.default_sampling_params: logger.info( "Overwriting default completion sampling param with: %s", self.default_sampling_params) async def create_completion( self, request: CompletionRequest, raw_request: Optional[Request] = None, ) -> 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 # If the engine is dead, raise the engine's DEAD_ERROR. # This is required for the streaming case, where we return a # success status before we actually start generating text :). if self.engine_client.errored: raise self.engine_client.dead_error # Return error for unsupported features. if request.suffix is not None: return self.create_error_response( "suffix is not currently supported") request_id = f"cmpl-{self._base_request_id(raw_request)}" created_time = int(time.time()) request_metadata = RequestResponseMetadata(request_id=request_id) if raw_request: raw_request.state.request_metadata = request_metadata try: ( lora_request, prompt_adapter_request, ) = self._maybe_get_adapters(request) tokenizer = await self.engine_client.get_tokenizer(lora_request) request_prompts, engine_prompts = await self._preprocess_completion( request, tokenizer, request.prompt, truncate_prompt_tokens=request.truncate_prompt_tokens, add_special_tokens=request.add_special_tokens, ) except ValueError as e: logger.exception("Error in preprocessing prompt inputs") return self.create_error_response(str(e)) # Schedule the request and get the result generator. generators: list[AsyncGenerator[RequestOutput, None]] = [] try: for i, engine_prompt in enumerate(engine_prompts): sampling_params: Union[SamplingParams, BeamSearchParams] default_max_tokens = self.max_model_len - len( engine_prompt["prompt_token_ids"]) if request.use_beam_search: sampling_params = request.to_beam_search_params( default_max_tokens, self.default_sampling_params) else: sampling_params = request.to_sampling_params( default_max_tokens, self.model_config.logits_processor_pattern, self.default_sampling_params) request_id_item = f"{request_id}-{i}" self._log_inputs(request_id_item, request_prompts[i], params=sampling_params, lora_request=lora_request, prompt_adapter_request=prompt_adapter_request) trace_headers = (None if raw_request is None else await self._get_trace_headers(raw_request.headers)) if isinstance(sampling_params, BeamSearchParams): generator = self.engine_client.beam_search( prompt=engine_prompt, request_id=request_id, params=sampling_params, ) else: generator = self.engine_client.generate( engine_prompt, sampling_params, request_id_item, lora_request=lora_request, prompt_adapter_request=prompt_adapter_request, trace_headers=trace_headers, priority=request.priority, ) 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) model_name = self._get_model_name(request.model, lora_request) num_prompts = len(engine_prompts) # 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=num_prompts, tokenizer=tokenizer, request_metadata=request_metadata) # Non-streaming response final_res_batch: list[Optional[RequestOutput]] = [None] * num_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 = request_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, request_metadata, ) 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, request_metadata: RequestResponseMetadata, ) -> AsyncGenerator[str, None]: num_choices = 1 if request.n is None else request.n previous_text_lens = [0] * num_choices * num_prompts previous_num_tokens = [0] * num_choices * num_prompts has_echoed = [False] * num_choices * num_prompts num_prompt_tokens = [0] * num_prompts stream_options = request.stream_options if stream_options: include_usage = stream_options.include_usage include_continuous_usage = include_usage and \ stream_options.continuous_usage_stats else: include_usage, include_continuous_usage = False, False 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 # Prompt details are excluded from later streamed outputs if res.prompt_token_ids is not None: num_prompt_tokens[prompt_idx] = len(res.prompt_token_ids) 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 assert request.max_tokens is not None if request.echo and not has_echoed[i]: assert prompt_token_ids is not None assert prompt_text is not None if request.max_tokens == 0: # only return the prompt delta_text = prompt_text delta_token_ids = prompt_token_ids out_logprobs = prompt_logprobs else: 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 delta_token_ids = output.token_ids out_logprobs = output.logprobs if not delta_text and not delta_token_ids \ and not previous_num_tokens[i]: # Chunked prefill case, don't return empty chunks continue 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=previous_text_lens[i], ) else: logprobs = None previous_text_lens[i] += len(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 include_continuous_usage: prompt_tokens = num_prompt_tokens[prompt_idx] completion_tokens = previous_num_tokens[i] chunk.usage = UsageInfo( prompt_tokens=prompt_tokens, completion_tokens=completion_tokens, total_tokens=prompt_tokens + completion_tokens, ) response_json = chunk.model_dump_json(exclude_unset=False) yield f"data: {response_json}\n\n" total_prompt_tokens = sum(num_prompt_tokens) total_completion_tokens = sum(previous_num_tokens) final_usage_info = UsageInfo( prompt_tokens=total_prompt_tokens, completion_tokens=total_completion_tokens, total_tokens=total_prompt_tokens + total_completion_tokens) if include_usage: final_usage_chunk = CompletionStreamResponse( id=request_id, created=created_time, model=model_name, choices=[], usage=final_usage_info, ) final_usage_data = (final_usage_chunk.model_dump_json( exclude_unset=False, exclude_none=True)) yield f"data: {final_usage_data}\n\n" # report to FastAPI middleware aggregate usage across all choices request_metadata.final_usage_info = final_usage_info except Exception 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, request_metadata: RequestResponseMetadata, ) -> 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 assert prompt_token_ids is not None prompt_logprobs = clamp_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: assert prompt_text is not None if request.max_tokens == 0: token_ids = prompt_token_ids out_logprobs = prompt_logprobs output_text = prompt_text else: 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_generated_tokens += len(output.token_ids) num_prompt_tokens += len(prompt_token_ids) usage = UsageInfo( prompt_tokens=num_prompt_tokens, completion_tokens=num_generated_tokens, total_tokens=num_prompt_tokens + num_generated_tokens, ) request_metadata.final_usage_info = usage 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, )