serving_completion.py 22.5 KB
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import asyncio
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import time
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from typing import (AsyncGenerator, AsyncIterator, Callable, Dict, List,
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                    Optional)
from typing import Sequence as GenericSequence
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from typing import Tuple, Union, cast
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from fastapi import Request
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from vllm.config import ModelConfig
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from vllm.engine.protocol import EngineClient
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from vllm.entrypoints.logger import RequestLogger
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# yapf conflicts with isort for this block
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# yapf: disable
from vllm.entrypoints.openai.protocol import (CompletionLogProbs,
                                              CompletionRequest,
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                                              CompletionResponse,
                                              CompletionResponseChoice,
                                              CompletionResponseStreamChoice,
                                              CompletionStreamResponse,
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                                              ErrorResponse,
                                              RequestResponseMetadata,
                                              UsageInfo)
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# yapf: enable
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from vllm.entrypoints.openai.serving_engine import (BaseModelPath,
                                                    LoRAModulePath,
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                                                    OpenAIServing,
                                                    PromptAdapterPath)
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from vllm.logger import init_logger
from vllm.outputs import RequestOutput
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from vllm.sampling_params import BeamSearchParams, SamplingParams
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from vllm.sequence import Logprob
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from vllm.tracing import (contains_trace_headers, extract_trace_headers,
                          log_tracing_disabled_warning)
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from vllm.transformers_utils.tokenizer import AnyTokenizer
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from vllm.utils import merge_async_iterators, random_uuid
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logger = init_logger(__name__)

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TypeTokenIDs = List[int]
TypeTopLogProbs = List[Optional[Dict[int, float]]]
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TypeCreateLogProbsFn = Callable[
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    [TypeTokenIDs, TypeTopLogProbs, Optional[int], int], CompletionLogProbs]
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class OpenAIServingCompletion(OpenAIServing):

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    def __init__(
        self,
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        engine_client: EngineClient,
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        model_config: ModelConfig,
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        base_model_paths: List[BaseModelPath],
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        *,
        lora_modules: Optional[List[LoRAModulePath]],
        prompt_adapters: Optional[List[PromptAdapterPath]],
        request_logger: Optional[RequestLogger],
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        return_tokens_as_token_ids: bool = False,
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    ):
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        super().__init__(engine_client=engine_client,
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                         model_config=model_config,
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                         base_model_paths=base_model_paths,
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                         lora_modules=lora_modules,
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                         prompt_adapters=prompt_adapters,
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                         request_logger=request_logger,
                         return_tokens_as_token_ids=return_tokens_as_token_ids)
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    async def create_completion(
        self,
        request: CompletionRequest,
        raw_request: Request,
    ) -> Union[AsyncGenerator[str, None], CompletionResponse, ErrorResponse]:
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        """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.

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        NOTE: Currently we do not support the following feature:
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            - 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

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        # 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

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        # Return error for unsupported features.
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        if request.suffix is not None:
            return self.create_error_response(
                "suffix is not currently supported")

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        model_name = self.base_model_paths[0].name
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        request_id = f"cmpl-{random_uuid()}"
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        created_time = int(time.time())
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        request_metadata = RequestResponseMetadata(request_id=request_id)
        if raw_request:
            raw_request.state.request_metadata = request_metadata

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        # Schedule the request and get the result generator.
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        generators: List[AsyncGenerator[RequestOutput, None]] = []
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        try:
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            (
                lora_request,
                prompt_adapter_request,
            ) = self._maybe_get_adapters(request)

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            tokenizer = await self.engine_client.get_tokenizer(lora_request)
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            prompts = list(
                self._tokenize_prompt_input_or_inputs(
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                    request,
                    tokenizer,
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                    request.prompt,
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                    truncate_prompt_tokens=request.truncate_prompt_tokens,
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                    add_special_tokens=request.add_special_tokens,
                ))

            for i, prompt_inputs in enumerate(prompts):
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                sampling_params: Union[SamplingParams, BeamSearchParams]
                default_max_tokens = self.max_model_len - len(
                    prompt_inputs["prompt_token_ids"])
                if request.use_beam_search:
                    sampling_params = request.to_beam_search_params(
                        default_max_tokens)
                else:
                    sampling_params = request.to_sampling_params(
                        default_max_tokens)
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                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)
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                is_tracing_enabled = (await
                                      self.engine_client.is_tracing_enabled())
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                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()

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                if isinstance(sampling_params, BeamSearchParams):
                    generator = self.engine_client.beam_search(
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                        prompt={
                            "prompt_token_ids":
                            prompt_inputs["prompt_token_ids"]
                        },
                        model_config=self.model_config,
                        request_id=request_id,
                        params=sampling_params,
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                    )
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                else:
                    generator = self.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,
                        priority=request.priority,
                    )
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                generators.append(generator)
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        except ValueError as e:
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            # TODO: Use a vllm-specific Validation Error
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            return self.create_error_response(str(e))
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        result_generator = merge_async_iterators(
            *generators, is_cancelled=raw_request.is_disconnected)
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        # Similar to the OpenAI API, when n != best_of, we do not stream the
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        # results. In addition, we do not stream the results when use
        # beam search.
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        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:
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            return self.completion_stream_generator(
                request,
                result_generator,
                request_id,
                created_time,
                model_name,
                num_prompts=len(prompts),
                tokenizer=tokenizer,
                request_metadata=request_metadata)
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        # Non-streaming response
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        final_res_batch: List[Optional[RequestOutput]] = [None] * len(prompts)
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        try:
            async for i, res in result_generator:
                final_res_batch[i] = res
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            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)

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            response = self.request_output_to_completion_response(
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                final_res_batch_checked,
                request,
                request_id,
                created_time,
                model_name,
                tokenizer,
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                request_metadata,
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            )
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        except asyncio.CancelledError:
            return self.create_error_response("Client disconnected")
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        except ValueError as e:
            # TODO: Use a vllm-specific Validation Error
            return self.create_error_response(str(e))
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        # When user requests streaming but we don't stream, we still need to
        # return a streaming response with a single event.
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        if request.stream:
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            response_json = response.model_dump_json()
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            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
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    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,
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        tokenizer: AnyTokenizer,
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        request_metadata: RequestResponseMetadata,
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    ) -> AsyncGenerator[str, None]:
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        num_choices = 1 if request.n is None else request.n
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        previous_text_lens = [0] * num_choices * num_prompts
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        previous_num_tokens = [0] * num_choices * num_prompts
        has_echoed = [False] * num_choices * num_prompts
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        num_prompt_tokens = [0] * num_prompts
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        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

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        try:
            async for prompt_idx, res in result_generator:
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                prompt_token_ids = res.prompt_token_ids
                prompt_logprobs = res.prompt_logprobs
                prompt_text = res.prompt

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                # 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)

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                delta_token_ids: GenericSequence[int]
                out_logprobs: Optional[GenericSequence[Optional[Dict[
                    int, Logprob]]]]
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                for output in res.outputs:
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                    i = output.index + prompt_idx * num_choices
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                    assert request.max_tokens is not None
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                    if request.echo and not has_echoed[i]:
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                        assert prompt_token_ids is not None
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                        assert prompt_text is not None
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                        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 []),
                            ]
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                        has_echoed[i] = True
                    else:
                        # return just the delta
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                        delta_text = output.text
                        delta_token_ids = output.token_ids
                        out_logprobs = output.logprobs
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                        if not delta_text and not delta_token_ids \
                            and not previous_num_tokens[i]:
                            # Chunked prefill case, don't return empty chunks
                            continue

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                    if request.logprobs is not None:
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                        assert out_logprobs is not None, (
                            "Did not output logprobs")
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                        logprobs = self._create_completion_logprobs(
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                            token_ids=delta_token_ids,
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                            top_logprobs=out_logprobs,
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                            num_output_top_logprobs=request.logprobs,
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                            tokenizer=tokenizer,
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                            initial_text_offset=previous_text_lens[i],
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                        )
                    else:
                        logprobs = None

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                    previous_text_lens[i] += len(output.text)
                    previous_num_tokens[i] += len(output.token_ids)
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                    finish_reason = output.finish_reason
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                    stop_reason = output.stop_reason
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                    chunk = CompletionStreamResponse(
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                        id=request_id,
                        created=created_time,
                        model=model_name,
                        choices=[
                            CompletionResponseStreamChoice(
                                index=i,
                                text=delta_text,
                                logprobs=logprobs,
                                finish_reason=finish_reason,
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                                stop_reason=stop_reason,
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                            )
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                        ])
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                    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,
                        )
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                    response_json = chunk.model_dump_json(exclude_unset=False)
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                    yield f"data: {response_json}\n\n"
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            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:
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                final_usage_chunk = CompletionStreamResponse(
                    id=request_id,
                    created=created_time,
                    model=model_name,
                    choices=[],
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                    usage=final_usage_info,
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                )
                final_usage_data = (final_usage_chunk.model_dump_json(
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                    exclude_unset=False, exclude_none=True))
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                yield f"data: {final_usage_data}\n\n"

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            # report to FastAPI middleware aggregate usage across all choices
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            request_metadata.final_usage_info = final_usage_info
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        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,
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        tokenizer: AnyTokenizer,
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        request_metadata: RequestResponseMetadata,
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    ) -> CompletionResponse:
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        choices: List[CompletionResponseChoice] = []
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        num_prompt_tokens = 0
        num_generated_tokens = 0
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        for final_res in final_res_batch:
            prompt_token_ids = final_res.prompt_token_ids
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            assert prompt_token_ids is not None
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            prompt_logprobs = final_res.prompt_logprobs
            prompt_text = final_res.prompt

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            token_ids: GenericSequence[int]
            out_logprobs: Optional[GenericSequence[Optional[Dict[int,
                                                                 Logprob]]]]

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            for output in final_res.outputs:
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                assert request.max_tokens is not None
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                if request.echo:
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                    assert prompt_text is not None
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                    if request.max_tokens == 0:
                        token_ids = prompt_token_ids
                        out_logprobs = prompt_logprobs
                        output_text = prompt_text
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                    else:
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                        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
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                else:
                    token_ids = output.token_ids
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                    out_logprobs = output.logprobs
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                    output_text = output.text

                if request.logprobs is not None:
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                    assert out_logprobs is not None, "Did not output logprobs"
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                    logprobs = self._create_completion_logprobs(
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                        token_ids=token_ids,
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                        top_logprobs=out_logprobs,
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                        tokenizer=tokenizer,
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                        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,
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                    stop_reason=output.stop_reason,
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                    prompt_logprobs=final_res.prompt_logprobs,
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                )
                choices.append(choice_data)

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                num_generated_tokens += len(output.token_ids)

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            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,
        )

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        request_metadata.final_usage_info = usage

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        return CompletionResponse(
            id=request_id,
            created=created_time,
            model=model_name,
            choices=choices,
            usage=usage,
        )
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    def _create_completion_logprobs(
        self,
        token_ids: GenericSequence[int],
        top_logprobs: GenericSequence[Optional[Dict[int, Logprob]]],
        num_output_top_logprobs: int,
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        tokenizer: AnyTokenizer,
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        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:
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                token = tokenizer.decode(token_id)
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                if self.return_tokens_as_token_ids:
                    token = f"token_id:{token_id}"
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                out_tokens.append(token)
                out_token_logprobs.append(None)
                out_top_logprobs.append(None)
            else:
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                step_token = step_top_logprobs[token_id]

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                token = self._get_decoded_token(
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                    step_token,
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                    token_id,
                    tokenizer,
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                    return_as_token_id=self.return_tokens_as_token_ids,
                )
                token_logprob = max(step_token.logprob, -9999.0)

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                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
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                    self._get_decoded_token(
                        top_lp[1],
                        top_lp[0],
                        tokenizer,
                        return_as_token_id=self.return_tokens_as_token_ids):
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                    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,
        )