serving_completion.py

# 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

import jinja2
from fastapi import Request
from typing_extensions import assert_never

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,
                                                    is_text_tokens_prompt)
from vllm.entrypoints.openai.serving_models import OpenAIServingModels
from vllm.inputs.data import (EmbedsPrompt, TokensPrompt, is_embeds_prompt,
                              is_tokens_prompt)
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:
            source = self.model_config.generation_config
            source = "model" if source == "auto" else source
            logger.info("Using default completion sampling params from %s: %s",
                        source, 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")

        if request.echo and request.prompt_embeds is not None:
            return self.create_error_response(
                "Echo is unsupported with prompt embeds.")

        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))
        except TypeError as e:
            logger.exception("Error in preprocessing prompt inputs")
            return self.create_error_response(str(e))
        except RuntimeError as e:
            logger.exception("Error in preprocessing prompt inputs")
            return self.create_error_response(str(e))
        except jinja2.TemplateError 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]
                # Mypy does not infer that engine_prompt will have only one of
                # "prompt_token_ids" or "prompt_embeds" defined, and both of
                # these as Union[object, the expected type], where it infers
                # object if engine_prompt is a subclass of one of the
                # typeddicts that defines both keys. Worse, because of
                # https://github.com/python/mypy/issues/8586, mypy does not
                # infer the type of engine_prompt correctly because of the
                # enumerate. So we need an unnecessary cast here.
                engine_prompt = cast(Union[EmbedsPrompt, TokensPrompt],
                                     engine_prompt)
                if is_embeds_prompt(engine_prompt):
                    input_length = len(engine_prompt["prompt_embeds"])
                elif is_tokens_prompt(engine_prompt):
                    input_length = len(engine_prompt["prompt_token_ids"])
                else:
                    assert_never(engine_prompt)
                default_max_tokens = self.max_model_len - input_length

                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))

                # 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(Union[EmbedsPrompt, TokensPrompt],
                                     engine_prompt)
                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. Noting that best_of is only supported in V0. 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:
                    request_prompt = request_prompts[i]
                    if is_text_tokens_prompt(request_prompt):
                        final_res.prompt = request_prompt["prompt"]
                    else:
                        final_res.prompt = None

            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 prompt_token_ids is not None:
                    num_prompt_tokens[prompt_idx] = len(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],
                            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

                    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,
                        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,
                )
                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,
            kv_transfer_params=final_res_batch[0].kv_transfer_params)

    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,
        return_as_token_id: Optional[bool] = None,
    ) -> 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

        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:
                token = tokenizer.decode(token_id)
                if should_return_as_token_id:
                    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=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,
        )