import asyncio import time from typing import (AsyncGenerator, AsyncIterator, Callable, Dict, List, Optional) from typing import Sequence as GenericSequence from typing import Tuple, cast from fastapi import Request from transformers import PreTrainedTokenizer from aphrodite.common.config import ModelConfig from aphrodite.common.outputs import RequestOutput from aphrodite.common.sequence import Logprob from aphrodite.common.utils import merge_async_iterators, random_uuid from aphrodite.endpoints.logger import RequestLogger # yapf conflicts with isort for this block # yapf: disable from aphrodite.endpoints.openai.protocol import ( CompletionLogProbs, CompletionRequest, CompletionResponse, CompletionResponseChoice, CompletionResponseStreamChoice, CompletionStreamResponse, UsageInfo) # yapf: enable from aphrodite.endpoints.openai.serving_engine import (LoRAModulePath, OpenAIServing, PromptAdapterPath) from aphrodite.engine.protocol import AsyncEngineClient 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): """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()) if request.prompt_logprobs is not None: if request.stream and request.prompt_logprobs > 0: return self.create_error_response( "Prompt_logprobs are not available when stream is enabled") elif request.prompt_logprobs < 0: return self.create_error_response( f"Prompt_logprobs set to invalid negative " f"value: {request.prompt_logprobs}") # 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) 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, ) generators.append(generator) except ValueError as e: # TODO: Use an aphrodite-specific Validation Error return self.create_error_response(str(e)) result_generator: AsyncIterator[Tuple[ int, RequestOutput]] = 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 Aphrodite 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 an aphrodite-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: PreTrainedTokenizer, ) -> 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: for output in res.outputs: i = output.index + prompt_idx * num_choices # TODO: 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: # only return the prompt delta_text = res.prompt delta_token_ids = res.prompt_token_ids out_logprobs = res.prompt_logprobs has_echoed[i] = True elif (request.echo and request.max_tokens > 0 and not has_echoed[i]): # echo the prompt and first token delta_text = res.prompt + output.text delta_token_ids = (res.prompt_token_ids + output.token_ids) out_logprobs = res.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(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, ) 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 an aphrodite-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: PreTrainedTokenizer, ) -> 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 for output in final_res.outputs: assert request.max_tokens is not None if request.echo and request.max_tokens == 0: token_ids = prompt_token_ids out_logprobs = prompt_logprobs output_text = prompt_text elif request.echo and request.max_tokens > 0: token_ids = prompt_token_ids + list(output.token_ids) out_logprobs = (prompt_logprobs + output.logprobs if request.logprobs is not None else None) 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: PreTrainedTokenizer, 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: token = self._get_decoded_token( step_top_logprobs[token_id], token_id, tokenizer, return_as_token_id=self.return_tokens_as_token_ids) token_logprob = max(step_top_logprobs[token_id].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, )