# SPDX-License-Identifier: Apache-2.0 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project import asyncio import time from collections.abc import AsyncGenerator, AsyncIterator from collections.abc import Sequence as GenericSequence from typing import cast import jinja2 from fastapi import Request from vllm.engine.protocol import EngineClient from vllm.entrypoints.logger import RequestLogger from vllm.entrypoints.openai.completion.protocol import ( CompletionLogProbs, CompletionRequest, CompletionResponse, CompletionResponseChoice, CompletionResponseStreamChoice, CompletionStreamResponse, ) from vllm.entrypoints.openai.engine.protocol import ( ErrorResponse, PromptTokenUsageInfo, RequestResponseMetadata, UsageInfo, ) from vllm.entrypoints.openai.engine.serving import ( GenerationError, OpenAIServing, clamp_prompt_logprobs, ) from vllm.entrypoints.openai.models.serving import OpenAIServingModels from vllm.entrypoints.renderer import RenderConfig from vllm.entrypoints.utils import get_max_tokens, should_include_usage from vllm.exceptions import VLLMValidationError from vllm.inputs.data import EmbedsPrompt, TokensPrompt, is_embeds_prompt from vllm.logger import init_logger from vllm.logprobs import Logprob from vllm.outputs import RequestOutput from vllm.sampling_params import BeamSearchParams, SamplingParams from vllm.tokenizers import TokenizerLike from vllm.utils.async_utils import merge_async_iterators from vllm.utils.collection_utils import as_list from vllm.v1.sample.logits_processor import validate_logits_processors_parameters logger = init_logger(__name__) class OpenAIServingCompletion(OpenAIServing): def __init__( self, engine_client: EngineClient, models: OpenAIServingModels, *, request_logger: RequestLogger | None, return_tokens_as_token_ids: bool = False, enable_prompt_tokens_details: bool = False, enable_force_include_usage: bool = False, log_error_stack: bool = False, ): super().__init__( engine_client=engine_client, models=models, request_logger=request_logger, return_tokens_as_token_ids=return_tokens_as_token_ids, log_error_stack=log_error_stack, ) # set up logits processors self.logits_processors = self.model_config.logits_processors self.enable_prompt_tokens_details = enable_prompt_tokens_details self.enable_force_include_usage = enable_force_include_usage self.default_sampling_params = self.model_config.get_diff_sampling_param() async def render_completion_request( self, request: CompletionRequest, ) -> list[TokensPrompt | EmbedsPrompt] | ErrorResponse: """ render completion request by validating and preprocessing inputs. Returns: A list of engine_prompts on success, or an ErrorResponse on failure. """ 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") if request.echo and request.prompt_embeds is not None: return self.create_error_response("Echo is unsupported with prompt embeds.") if request.prompt_logprobs is not None and request.prompt_embeds is not None: return self.create_error_response( "prompt_logprobs is not compatible with prompt embeds." ) try: renderer = self._get_completion_renderer() engine_prompts = await renderer.render_prompt_and_embeds( prompt_or_prompts=request.prompt, prompt_embeds=request.prompt_embeds, config=self._build_render_config(request), ) except (ValueError, TypeError, RuntimeError, jinja2.TemplateError) as e: logger.exception("Error in preprocessing prompt inputs") return self.create_error_response(e) return engine_prompts async def create_completion( self, request: CompletionRequest, raw_request: Request | None = None, ) -> 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) """ result = await self.render_completion_request(request) if isinstance(result, ErrorResponse): return result engine_prompts = result request_id = f"cmpl-{self._base_request_id(raw_request, request.request_id)}" 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 = self._maybe_get_adapters(request) except (ValueError, TypeError, RuntimeError) as e: logger.exception("Error preparing request components") return self.create_error_response(e) # Extract data_parallel_rank from header (router can inject it) data_parallel_rank = self._get_data_parallel_rank(raw_request) # Schedule the request and get the result generator. generators: list[AsyncGenerator[RequestOutput, None]] = [] try: for i, engine_prompt in enumerate(engine_prompts): prompt_text, prompt_token_ids, prompt_embeds = ( self._get_prompt_components(engine_prompt) ) input_length = None if prompt_token_ids is not None: input_length = len(prompt_token_ids) elif prompt_embeds is not None: input_length = len(prompt_embeds) else: raise NotImplementedError if self.default_sampling_params is None: self.default_sampling_params = {} max_tokens = get_max_tokens( max_model_len=self.max_model_len, request=request, input_length=input_length, default_sampling_params=self.default_sampling_params, ) sampling_params: SamplingParams | BeamSearchParams if request.use_beam_search: sampling_params = request.to_beam_search_params( max_tokens, self.default_sampling_params ) else: sampling_params = request.to_sampling_params( max_tokens, self.model_config.logits_processor_pattern, self.default_sampling_params, ) validate_logits_processors_parameters( self.logits_processors, sampling_params, ) request_id_item = f"{request_id}-{i}" self._log_inputs( request_id_item, engine_prompt, params=sampling_params, lora_request=lora_request, ) trace_headers = ( None if raw_request is None else await self._get_trace_headers(raw_request.headers) ) # Mypy inconsistently requires this second cast in different # environments. It shouldn't be necessary (redundant from above) # but pre-commit in CI fails without it. engine_prompt = cast(EmbedsPrompt | TokensPrompt, engine_prompt) if isinstance(sampling_params, BeamSearchParams): generator = self.beam_search( prompt=engine_prompt, request_id=request_id, params=sampling_params, lora_request=lora_request, trace_headers=trace_headers, ) else: engine_request, tokenization_kwargs = await self._process_inputs( request_id_item, engine_prompt, sampling_params, lora_request=lora_request, trace_headers=trace_headers, priority=request.priority, data_parallel_rank=data_parallel_rank, ) generator = self.engine_client.generate( engine_request, sampling_params, request_id_item, lora_request=lora_request, trace_headers=trace_headers, priority=request.priority, prompt_text=prompt_text, tokenization_kwargs=tokenization_kwargs, data_parallel_rank=data_parallel_rank, ) generators.append(generator) except ValueError as e: return self.create_error_response(e) result_generator = merge_async_iterators(*generators) model_name = self.models.model_name(lora_request) num_prompts = len(engine_prompts) # We do not stream the results when using beam search. stream = request.stream and not request.use_beam_search # Streaming response tokenizer = self.renderer.tokenizer if stream: return self.completion_stream_generator( request, engine_prompts, 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[RequestOutput | None] = [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: engine_prompt = engine_prompts[i] final_res.prompt = ( None if is_embeds_prompt(engine_prompt) else engine_prompt.get("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 GenerationError as e: return self._convert_generation_error_to_response(e) except ValueError as e: return self.create_error_response(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, engine_prompts: list[TokensPrompt | EmbedsPrompt], result_generator: AsyncIterator[tuple[int, RequestOutput]], request_id: str, created_time: int, model_name: str, num_prompts: int, tokenizer: TokenizerLike | None, 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 num_cached_tokens = None first_iteration = True stream_options = request.stream_options include_usage, include_continuous_usage = should_include_usage( stream_options, self.enable_force_include_usage ) try: async for prompt_idx, res in result_generator: prompt_token_ids = res.prompt_token_ids prompt_logprobs = res.prompt_logprobs if first_iteration: num_cached_tokens = res.num_cached_tokens first_iteration = False prompt_text = res.prompt if prompt_text is None: engine_prompt = engine_prompts[prompt_idx] prompt_text = ( None if is_embeds_prompt(engine_prompt) else engine_prompt.get("prompt") ) # Prompt details are excluded from later streamed outputs if prompt_token_ids is not None: num_prompt_tokens[prompt_idx] = len(prompt_token_ids) delta_token_ids: GenericSequence[int] out_logprobs: GenericSequence[dict[int, Logprob] | None] | None for output in res.outputs: i = output.index + prompt_idx * num_choices # Useful when request.return_token_ids is True # Returning prompt token IDs shares the same logic # with the echo implementation. prompt_token_ids_to_return: list[int] | None = None assert request.max_tokens is not None if request.echo and not has_echoed[i]: assert prompt_token_ids is not None if request.return_token_ids: prompt_text = "" 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: # 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 or []), *(output.logprobs or []), ] prompt_token_ids_to_return = prompt_token_ids has_echoed[i] = True else: # return just the delta delta_text = output.text delta_token_ids = output.token_ids out_logprobs = output.logprobs # has_echoed[i] is reused here to indicate whether # we have already returned the prompt token IDs. if not has_echoed[i] and request.return_token_ids: prompt_token_ids_to_return = prompt_token_ids has_echoed[i] = True 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], return_as_token_id=request.return_tokens_as_token_ids, ) 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 self._raise_if_error(finish_reason, request_id) 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, prompt_token_ids=prompt_token_ids_to_return, token_ids=( as_list(output.token_ids) if request.return_token_ids else None ), ) ], ) 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 self.enable_prompt_tokens_details and num_cached_tokens: final_usage_info.prompt_tokens_details = PromptTokenUsageInfo( cached_tokens=num_cached_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 GenerationError as e: yield f"data: {self._convert_generation_error_to_streaming_response(e)}\n\n" except Exception as e: logger.exception("Error in completion stream generator.") data = self.create_streaming_error_response(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: TokenizerLike | None, request_metadata: RequestResponseMetadata, ) -> CompletionResponse: choices: list[CompletionResponseChoice] = [] num_prompt_tokens = 0 num_generated_tokens = 0 kv_transfer_params = None last_final_res = None for final_res in final_res_batch: last_final_res = final_res 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: GenericSequence[dict[int, Logprob] | None] | None for output in final_res.outputs: self._raise_if_error(output.finish_reason, request_id) assert request.max_tokens is not None if request.echo: if request.return_token_ids: prompt_text = "" 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, return_as_token_id=request.return_tokens_as_token_ids, ) 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, prompt_token_ids=( prompt_token_ids if request.return_token_ids else None ), token_ids=( as_list(output.token_ids) if request.return_token_ids else None ), ) 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, ) if ( self.enable_prompt_tokens_details and last_final_res and last_final_res.num_cached_tokens ): usage.prompt_tokens_details = PromptTokenUsageInfo( cached_tokens=last_final_res.num_cached_tokens ) request_metadata.final_usage_info = usage if final_res_batch: kv_transfer_params = final_res_batch[0].kv_transfer_params return CompletionResponse( id=request_id, created=created_time, model=model_name, choices=choices, usage=usage, kv_transfer_params=kv_transfer_params, ) def _create_completion_logprobs( self, token_ids: GenericSequence[int], top_logprobs: GenericSequence[dict[int, Logprob] | None], num_output_top_logprobs: int, tokenizer: TokenizerLike | None, initial_text_offset: int = 0, return_as_token_id: bool | None = None, ) -> CompletionLogProbs: """Create logprobs for OpenAI Completion API.""" out_text_offset: list[int] = [] out_token_logprobs: list[float | None] = [] out_tokens: list[str] = [] out_top_logprobs: list[dict[str, float] | None] = [] last_token_len = 0 should_return_as_token_id = ( return_as_token_id if return_as_token_id is not None else self.return_tokens_as_token_ids ) for i, token_id in enumerate(token_ids): step_top_logprobs = top_logprobs[i] if step_top_logprobs is None: if should_return_as_token_id: token = f"token_id:{token_id}" else: if tokenizer is None: raise VLLMValidationError( "Unable to get tokenizer because " "`skip_tokenizer_init=True`", parameter="skip_tokenizer_init", value=True, ) token = tokenizer.decode(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=should_return_as_token_id, ) 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=should_return_as_token_id, ): 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, ) def _build_render_config( self, request: CompletionRequest, max_input_length: int | None = None, ) -> RenderConfig: # Validate max_tokens before using it if request.max_tokens is not None and request.max_tokens > self.max_model_len: raise VLLMValidationError( f"'max_tokens' ({request.max_tokens}) cannot be greater than " f"the model's maximum context length ({self.max_model_len}).", parameter="max_tokens", value=request.max_tokens, ) max_input_tokens_len = self.max_model_len - (request.max_tokens or 0) return RenderConfig( max_length=max_input_tokens_len, truncate_prompt_tokens=request.truncate_prompt_tokens, add_special_tokens=request.add_special_tokens, cache_salt=request.cache_salt, needs_detokenization=bool(request.echo and not request.return_token_ids), )