preprocess.py

# SPDX-License-Identifier: Apache-2.0

import asyncio
from collections.abc import Mapping
from typing import Any, Optional, Union, cast

from typing_extensions import assert_never

from vllm.config import ModelConfig
from vllm.logger import init_logger
from vllm.lora.request import LoRARequest
from vllm.multimodal import MULTIMODAL_REGISTRY, MultiModalRegistry
from vllm.multimodal.inputs import (MultiModalDataDict, MultiModalEncDecInputs,
                                    MultiModalInputs)
from vllm.prompt_adapter.request import PromptAdapterRequest
from vllm.transformers_utils.tokenizer import AnyTokenizer
from vllm.transformers_utils.tokenizer_group import TokenizerGroup

from .data import (DecoderOnlyInputs, EmbedsInputs, EmbedsPrompt,
                   EncoderDecoderInputs, ProcessorInputs, PromptType,
                   SingletonInputs, SingletonPrompt, TextPrompt, TokenInputs,
                   TokensPrompt, embeds_inputs, token_inputs)
from .parse import is_explicit_encoder_decoder_prompt, parse_singleton_prompt

logger = init_logger(__name__)


class InputPreprocessor:

    def __init__(
        self,
        model_config: ModelConfig,
        tokenizer: Optional[TokenizerGroup],
        mm_registry: MultiModalRegistry = MULTIMODAL_REGISTRY,
    ) -> None:
        super().__init__()

        self.model_config = model_config
        self.tokenizer = tokenizer
        self.mm_registry = mm_registry

    def get_tokenizer_group(self) -> TokenizerGroup:
        if self.tokenizer is None:
            raise ValueError("You cannot pass text prompts when "
                             "`skip_tokenizer_init` is True")

        return self.tokenizer

    def get_bos_token_id(self,
                         lora_request: Optional[LoRARequest] = None
                         ) -> Optional[int]:
        if self.tokenizer is None:
            logger.warning("Using None for BOS token id because tokenizer "
                           "is not initialized")
            return None

        return self.tokenizer.get_lora_tokenizer(lora_request).bos_token_id

    def get_eos_token_id(self,
                         lora_request: Optional[LoRARequest] = None
                         ) -> Optional[int]:
        if self.tokenizer is None:
            logger.warning("Using None for EOS token id because tokenizer "
                           "is not initialized")
            return None

        return self.tokenizer.get_lora_tokenizer(lora_request).eos_token_id

    def get_decoder_start_token_id(self) -> Optional[int]:
        '''
        Obtain the decoder start token id employed by an encoder/decoder
        model. Returns None for non-encoder/decoder models or if the
        model config is unavailable.
        '''

        if not self.model_config.is_encoder_decoder:
            logger.warning_once(
                "Using None for decoder start token id because "
                "this is not an encoder/decoder model.")
            return None

        if (self.model_config is None or self.model_config.hf_config is None):
            logger.warning_once(
                "Using None for decoder start token id because "
                "model config is not available.")
            return None

        dec_start_token_id = getattr(self.model_config.hf_config,
                                     'decoder_start_token_id', None)
        if dec_start_token_id is None:
            logger.warning_once(
                "Falling back on <BOS> for decoder start token "
                "id because decoder start token id is not "
                "available.")
            dec_start_token_id = self.get_bos_token_id()

        return dec_start_token_id

    def _get_default_enc_dec_decoder_prompt(self) -> list[int]:
        '''
        Specifically for encoder/decoder models:
        generate a default decoder prompt for when
        the user specifies only the encoder prompt.

        Encoder/decoder models utilize the decoder
        prompt in different ways; as new models are
        added, it is intended that this function
        will be extended to produce differing
        default decoder prompts, depending on the
        model variety.

        Absent a special case, the default behavior
        of this method is to mirror the behavior of
        the HuggingFace (HF) GenerationMixin for a None
        decoder prompt, which is to employ a logit processor
        setting to force the first decoded token to be <BOS>.
        Here, this behavior is approximated by having the
        "default" decoder prompt be <BOS>.

        However, it is possible that in the future
        other models may have different or more
        complex logic for the default decoder prompt.
        This motivates having a special helper method
        for default decoder prompts.

        Returns:

        * prompt_token_ids
        '''

        bos_token_id = self.get_bos_token_id()
        assert bos_token_id is not None
        return [bos_token_id]

    def _prepare_decoder_input_ids_for_generation(
        self,
        decoder_input_ids: Optional[list[int]],
    ) -> list[int]:
        """
        Prepares `decoder_input_ids` for generation with encoder-decoder models.

        Based on:
        https://github.com/huggingface/transformers/blob/4037a2b5b1278736e566aec12e169100275545ea/src/transformers/generation/utils.py
        specifically,
        `GenerationMixin._prepare_decoder_input_ids_for_generation()`.

        Arguments:

        * decoder_input_ids: input token ids to preprocess

        Returns:

        * Processed token list
        """

        decoder_start_token_id = self.get_decoder_start_token_id()
        assert decoder_start_token_id is not None

        if decoder_input_ids is None:
            # no decoder prompt input ->
            # use decoder_start_token_id as decoder_input_ids
            decoder_input_ids = self._get_default_enc_dec_decoder_prompt()

        if (len(decoder_input_ids) == 0
                or decoder_input_ids[0] != decoder_start_token_id):
            decoder_input_ids = [decoder_start_token_id] + decoder_input_ids

        return decoder_input_ids

    def _apply_prompt_adapter(
        self,
        prompt_token_ids: list[int],
        prompt_adapter_request: Optional[PromptAdapterRequest],
    ) -> list[int]:
        if prompt_adapter_request:
            prompt_token_ids = (
                [0] * prompt_adapter_request.prompt_adapter_num_virtual_tokens
                + prompt_token_ids)

        return prompt_token_ids

    def _get_tokenization_kw(
        self,
        overrides: Optional[dict[str, Any]] = None,
    ) -> dict[str, Any]:
        kwargs = dict[str, Any]()

        if self.model_config.hf_config.model_type == "whisper":
            # For Whisper, special tokens should be provided by the user based
            # on the task and language of their request. Also needed to avoid
            # appending an EOS token to the prompt which disrupts generation.
            kwargs["add_special_tokens"] = False

        if overrides:
            kwargs.update(overrides)

        return kwargs

    def _tokenize_prompt(
        self,
        prompt: str,
        lora_request: Optional[LoRARequest],
        tokenization_kwargs: Optional[dict[str, Any]] = None,
    ) -> list[int]:
        """
        Apply the model's tokenizer to a text prompt, returning the
        corresponding token IDs.
        """
        tokenizer = self.get_tokenizer_group()
        tokenization_kwargs = self._get_tokenization_kw(tokenization_kwargs)

        encoder_config = self.model_config.encoder_config

        if encoder_config and encoder_config.get("do_lower_case", False):
            prompt = prompt.lower()

        return tokenizer.encode(prompt=prompt,
                                lora_request=lora_request,
                                **tokenization_kwargs)

    async def _tokenize_prompt_async(
        self,
        prompt: str,
        lora_request: Optional[LoRARequest],
        tokenization_kwargs: Optional[dict[str, Any]] = None,
    ) -> list[int]:
        """Async version of {meth}`_tokenize_prompt`."""
        tokenizer = self.get_tokenizer_group()
        tokenization_kwargs = self._get_tokenization_kw(tokenization_kwargs)

        return await tokenizer.encode_async(prompt=prompt,
                                            lora_request=lora_request,
                                            **tokenization_kwargs)

    def _get_mm_tokenizer(
        self,
        lora_request: Optional[LoRARequest],
    ) -> AnyTokenizer:
        # PrithviGeoSpatialMAE needs to be initialized without a tokenizer
        # while using also multi-modal input
        if not self.tokenizer:
            return cast(AnyTokenizer, object())  # Dummy

        tokenizer_group = self.get_tokenizer_group()
        return tokenizer_group.get_lora_tokenizer(lora_request)

    async def _get_mm_tokenizer_async(
        self,
        lora_request: Optional[LoRARequest],
    ) -> AnyTokenizer:
        # PrithviGeoSpatialMAE needs to be initialized without a tokenizer
        # while using also multi-modal input
        if not self.tokenizer:
            return cast(AnyTokenizer, object())  # Dummy

        tokenizer_group = self.get_tokenizer_group()
        return await tokenizer_group.get_lora_tokenizer_async(lora_request)

    def _process_multimodal(
        self,
        prompt: Union[str, list[int]],
        mm_data: MultiModalDataDict,
        mm_processor_kwargs: Optional[Mapping[str, object]],
        lora_request: Optional[LoRARequest],
        return_mm_hashes: bool = False,
    ) -> MultiModalInputs:
        """
        Apply the model's multi-modal processor to a multi-modal prompt,
        returning the corresponding token IDs and metadata.
        """
        tokenizer = self._get_mm_tokenizer(lora_request)

        mm_processor = self.mm_registry.create_processor(self.model_config,
                                                         tokenizer=tokenizer)

        if mm_processor_kwargs is None:
            mm_processor_kwargs = {}

        return mm_processor.apply(prompt, mm_data, mm_processor_kwargs,
                                  return_mm_hashes)

    async def _process_multimodal_async(
        self,
        prompt: Union[str, list[int]],
        mm_data: MultiModalDataDict,
        mm_processor_kwargs: Optional[Mapping[str, object]],
        lora_request: Optional[LoRARequest],
        return_mm_hashes: bool = False,
    ) -> MultiModalInputs:
        """Async version of {meth}`_process_multimodal`."""
        tokenizer = await self._get_mm_tokenizer_async(lora_request)

        mm_processor = self.mm_registry.create_processor(self.model_config,
                                                         tokenizer=tokenizer)
        if mm_processor_kwargs is None:
            mm_processor_kwargs = {}

        return mm_processor.apply(prompt, mm_data, mm_processor_kwargs,
                                  return_mm_hashes)

    def _process_embeds(
        self,
        parsed_content: EmbedsPrompt,
    ) -> EmbedsInputs:
        if not self.model_config.enable_prompt_embeds:
            raise ValueError("You must set `--enable-prompt-embeds` to input "
                             "`prompt_embeds`.")

        prompt_embeds = parsed_content["prompt_embeds"]

        # prompt_embeds must be (seq_len, hidden_size), but if the user
        # passes in a batch of size 1, i.e. (1, seq_len, hidden_size),
        # we can unambiguously process the intent by squeezing the batch
        # dimension.
        if prompt_embeds.ndim == 3:
            prompt_embeds = prompt_embeds.squeeze(dim=0)

        if prompt_embeds.ndim != 2:
            raise ValueError(
                "prompt_embeds must be of shape (seq_len, hidden_size).")

        return embeds_inputs(prompt_embeds=prompt_embeds,
                             cache_salt=parsed_content.get("cache_salt"))

    async def _process_embeds_async(
        self,
        parsed_content: EmbedsPrompt,
    ) -> EmbedsInputs:
        return self._process_embeds(parsed_content)

    def _process_tokens(
        self,
        parsed_content: TokensPrompt,
        lora_request: Optional[LoRARequest] = None,
        return_mm_hashes: bool = False,
    ) -> Union[TokenInputs, MultiModalInputs]:
        prompt_token_ids = parsed_content["prompt_token_ids"]
        token_type_ids = parsed_content.get("token_type_ids")

        inputs: Union[TokenInputs, MultiModalInputs]
        if multi_modal_data := parsed_content.get("multi_modal_data"):
            inputs = self._process_multimodal(
                prompt_token_ids,
                multi_modal_data,
                parsed_content.get("mm_processor_kwargs"),
                lora_request=lora_request,
                return_mm_hashes=return_mm_hashes,
            )
        else:
            inputs = token_inputs(
                prompt_token_ids=prompt_token_ids,
                token_type_ids=token_type_ids,
            )

        if cache_salt := parsed_content.get("cache_salt"):
            inputs["cache_salt"] = cache_salt

        return inputs

    async def _process_tokens_async(
        self,
        parsed_content: TokensPrompt,
        lora_request: Optional[LoRARequest] = None,
        return_mm_hashes: bool = False,
    ) -> Union[TokenInputs, MultiModalInputs]:
        prompt_token_ids = parsed_content["prompt_token_ids"]
        token_type_ids = parsed_content.get("token_type_ids")

        inputs: Union[TokenInputs, MultiModalInputs]
        if multi_modal_data := parsed_content.get("multi_modal_data"):
            inputs = await self._process_multimodal_async(
                prompt_token_ids,
                multi_modal_data,
                parsed_content.get("mm_processor_kwargs"),
                lora_request=lora_request,
                return_mm_hashes=return_mm_hashes,
            )
        else:
            inputs = token_inputs(
                prompt_token_ids=prompt_token_ids,
                token_type_ids=token_type_ids,
            )

        if cache_salt := parsed_content.get("cache_salt"):
            inputs["cache_salt"] = cache_salt

        return inputs

    def _process_text(
        self,
        parsed_content: TextPrompt,
        tokenization_kwargs: Optional[dict[str, Any]] = None,
        lora_request: Optional[LoRARequest] = None,
        return_mm_hashes: bool = False,
    ) -> Union[TokenInputs, MultiModalInputs]:
        prompt_text = parsed_content["prompt"]

        inputs: Union[TokenInputs, MultiModalInputs]
        if multi_modal_data := parsed_content.get("multi_modal_data"):
            inputs = self._process_multimodal(
                prompt_text,
                multi_modal_data,
                parsed_content.get("mm_processor_kwargs"),
                lora_request=lora_request,
                return_mm_hashes=return_mm_hashes,
            )
        else:
            prompt_token_ids = self._tokenize_prompt(
                prompt_text,
                lora_request=lora_request,
                tokenization_kwargs=tokenization_kwargs,
            )
            inputs = token_inputs(
                prompt=prompt_text,
                prompt_token_ids=prompt_token_ids,
            )

        if cache_salt := parsed_content.get("cache_salt"):
            inputs["cache_salt"] = cache_salt

        return inputs

    async def _process_text_async(
        self,
        parsed_content: TextPrompt,
        tokenization_kwargs: Optional[dict[str, Any]] = None,
        lora_request: Optional[LoRARequest] = None,
        return_mm_hashes: bool = False,
    ) -> Union[TokenInputs, MultiModalInputs]:
        prompt_text = parsed_content["prompt"]

        inputs: Union[TokenInputs, MultiModalInputs]
        if multi_modal_data := parsed_content.get("multi_modal_data"):
            inputs = await self._process_multimodal_async(
                prompt_text,
                multi_modal_data,
                parsed_content.get("mm_processor_kwargs"),
                lora_request=lora_request,
                return_mm_hashes=return_mm_hashes,
            )
        else:
            prompt_token_ids = await self._tokenize_prompt_async(
                prompt_text,
                lora_request=lora_request,
                tokenization_kwargs=tokenization_kwargs,
            )
            inputs = token_inputs(
                prompt=prompt_text,
                prompt_token_ids=prompt_token_ids,
            )

        if cache_salt := parsed_content.get("cache_salt"):
            inputs["cache_salt"] = cache_salt

        return inputs

    def _prompt_to_llm_inputs(
        self,
        prompt: SingletonPrompt,
        tokenization_kwargs: Optional[dict[str, Any]] = None,
        lora_request: Optional[LoRARequest] = None,
        return_mm_hashes: bool = False,
    ) -> SingletonInputs:
        """
        Extract the singleton inputs from a prompt.

        Arguments:

        * prompt: single encoder or decoder input prompt
        * lora_request: this is only valid for decoder prompts
        * return_mm_hashes: whether to return multimodal hashes

        Returns:

        * {class}`SingletonInputs` instance
        """
        parsed = parse_singleton_prompt(prompt)

        if parsed["type"] == "embeds":
            return self._process_embeds(parsed["content"])
        if parsed["type"] == "tokens":
            return self._process_tokens(
                parsed["content"],
                lora_request=lora_request,
                return_mm_hashes=return_mm_hashes,
            )
        if parsed["type"] == "text":
            return self._process_text(
                parsed["content"],
                tokenization_kwargs=tokenization_kwargs,
                lora_request=lora_request,
                return_mm_hashes=return_mm_hashes,
            )
        if parsed["type"] == "str":
            return self._process_text(
                TextPrompt(prompt=parsed["content"]),
                tokenization_kwargs=tokenization_kwargs,
                lora_request=lora_request,
                return_mm_hashes=return_mm_hashes,
            )

        assert_never(parsed)

    async def _prompt_to_llm_inputs_async(
        self,
        prompt: SingletonPrompt,
        tokenization_kwargs: Optional[dict[str, Any]] = None,
        lora_request: Optional[LoRARequest] = None,
        return_mm_hashes: bool = False,
    ) -> SingletonInputs:
        """Async version of {meth}`_prompt_to_llm_inputs`."""
        parsed = parse_singleton_prompt(prompt)

        if parsed["type"] == "embeds":
            return await self._process_embeds_async(parsed["content"])
        if parsed["type"] == "tokens":
            return await self._process_tokens_async(
                parsed["content"],
                lora_request=lora_request,
                return_mm_hashes=return_mm_hashes,
            )
        if parsed["type"] == "text":
            return await self._process_text_async(
                parsed["content"],
                tokenization_kwargs=tokenization_kwargs,
                lora_request=lora_request,
                return_mm_hashes=return_mm_hashes,
            )
        if parsed["type"] == "str":
            return await self._process_text_async(
                TextPrompt(prompt=parsed["content"]),
                tokenization_kwargs=tokenization_kwargs,
                lora_request=lora_request,
                return_mm_hashes=return_mm_hashes,
            )

        assert_never(parsed)

    def _build_enc_dec_llm_inputs(
        self,
        encoder_inputs: SingletonInputs,
        decoder_inputs: Optional[SingletonInputs],
    ) -> EncoderDecoderInputs:
        if (encoder_inputs["type"] == "embeds"
                or decoder_inputs and decoder_inputs["type"] == "embeds"):
            raise ValueError("Embedding inputs are not supported for encoder-"
                             "decoder models")

        # Needed for mypy
        encoder_inputs = cast(Union[TokenInputs, MultiModalInputs],
                              encoder_inputs)
        decoder_inputs = cast(Optional[Union[TokenInputs, MultiModalInputs]],
                              decoder_inputs)

        if decoder_inputs is None:
            if self.model_config.hf_config.model_type == "whisper":
                # For Whisper models, the text prompt should go to the decoder.
                # If no explicit encoder/decoder inputs, then copy the prompt
                # from the encoder to the decoder. The encoder tokens are later
                # overridden by the audio features.
                dec_token_ids = encoder_inputs["prompt_token_ids"].copy()
            else:
                dec_token_ids = self._prepare_decoder_input_ids_for_generation(
                    None)
            decoder_inputs = token_inputs(dec_token_ids)
        else:
            if "multi_modal_data" in decoder_inputs:
                raise ValueError("Multi-modal decoder inputs of encoder-"
                                 "decoder models are not supported yet")

            dec_token_ids = self._prepare_decoder_input_ids_for_generation(
                decoder_inputs["prompt_token_ids"])
            decoder_inputs["prompt_token_ids"] = dec_token_ids

        return EncoderDecoderInputs(
            encoder=encoder_inputs,
            decoder=decoder_inputs,
        )

    def _split_enc_dec_mm_inputs(
        self,
        inputs: Union[SingletonInputs, MultiModalEncDecInputs],
        decoder_inputs_to_override: Optional[SingletonInputs] = None,
    ) -> tuple[SingletonInputs, SingletonInputs]:
        """
        For encoder/decoder models only:
        Separate Encoder/Decoder inputs from a MultiModalEncDecInputs
        """
        if (inputs["type"] == "embeds" or decoder_inputs_to_override
                and decoder_inputs_to_override["type"] == "embeds"):
            raise ValueError("Embedding inputs are not supported for encoder-"
                             "decoder models")

        # Needed for mypy
        inputs = cast(
            Union[TokenInputs, MultiModalInputs, MultiModalEncDecInputs],
            inputs,
        )
        decoder_inputs_to_override = cast(
            Optional[Union[TokenInputs, MultiModalInputs]],
            decoder_inputs_to_override,
        )

        encoder_inputs: SingletonInputs
        decoder_inputs: SingletonInputs

        if inputs["type"] == "multimodal":  # Multimodal data inputs
            if not ("encoder_prompt" in inputs
                    and "encoder_prompt_token_ids" in inputs):
                raise RuntimeError("You should register an encoder-decoder "
                                   "multi-modal processor for encoder-decoder "
                                   "models.")
            inputs = cast(MultiModalEncDecInputs, inputs)

            encoder_inputs = token_inputs(
                prompt=inputs["encoder_prompt"],
                prompt_token_ids=inputs["encoder_prompt_token_ids"],
            )

            decoder_prompt_inputs = decoder_inputs_to_override or inputs
            decoder_inputs = MultiModalInputs(
                type="multimodal",
                prompt=decoder_prompt_inputs.get("prompt", ""),
                prompt_token_ids=decoder_prompt_inputs["prompt_token_ids"],
                mm_kwargs=inputs["mm_kwargs"],
                mm_hashes=inputs["mm_hashes"],
                mm_placeholders=inputs["mm_placeholders"],
            )
            if cache_salt := inputs.get("cache_salt"):
                decoder_inputs["cache_salt"] = cache_salt

        elif inputs["type"] == "token":  # Text-only inputs
            encoder_inputs = token_inputs(prompt="", prompt_token_ids=[])
            decoder_inputs = decoder_inputs_to_override or inputs
        else:
            assert_never(inputs)  # type: ignore[arg-type]

        return encoder_inputs, decoder_inputs

    def _process_encoder_decoder_prompt(
        self,
        prompt: PromptType,
        tokenization_kwargs: Optional[dict[str, Any]] = None,
    ) -> EncoderDecoderInputs:
        """
        For encoder/decoder models only:
        Process an input prompt into an {class}`EncoderDecoderInputs` instance.

        There are two types of input prompts:
        singleton prompts which carry only the
        encoder prompt, and explicit encoder/decoder
        prompts which carry both the encoder and the
        decoder prompts as member variables.

        This function handles the following scenarios:
        * Singleton encoder prompt: extract encoder prompt
          token ids & infer default decoder prompt token ids
        * Explicit encoder/decoder prompt: extract encoder
          and decoder prompt token ids

        Note that for Explicit encoder/decoder prompts,
        each sub-prompt (encoder or decoder prompt) can
        have any possible singleton type; thus this
        method relies on helper functions to obtain
        token ids for the sub-prompts.

        Arguments:

        * prompt: an input prompt

        Returns:

        * {class}`EncoderDecoderInputs` instance
        """
        encoder_inputs: SingletonInputs
        decoder_inputs: Optional[SingletonInputs]

        if is_explicit_encoder_decoder_prompt(prompt):
            encoder_inputs = self._prompt_to_llm_inputs(
                prompt["encoder_prompt"],
                tokenization_kwargs=tokenization_kwargs,
            )
            if (decoder_input := prompt["decoder_prompt"]) is None:
                decoder_inputs = None
            else:
                decoder_inputs = self._prompt_to_llm_inputs(decoder_input)
            # For multimodal model, override decoder prompt from processor
            # with explicit decoder prompt.
            if self.model_config.is_multimodal_model:
                encoder_inputs, decoder_inputs = (
                    self._split_enc_dec_mm_inputs(encoder_inputs,
                                                  decoder_inputs))
        else:
            inputs = self._prompt_to_llm_inputs(
                prompt,
                tokenization_kwargs=tokenization_kwargs,
            )
            if self.model_config.is_multimodal_model:
                # Encoder-Decoder Multimodal model
                encoder_inputs, decoder_inputs = (
                    self._split_enc_dec_mm_inputs(inputs))
            else:
                encoder_inputs = inputs
                decoder_inputs = None

        return self._build_enc_dec_llm_inputs(encoder_inputs, decoder_inputs)

    async def _process_encoder_decoder_prompt_async(
        self,
        prompt: PromptType,
        tokenization_kwargs: Optional[dict[str, Any]] = None,
    ) -> EncoderDecoderInputs:
        """Async version of {meth}`_process_encoder_decoder_prompt`."""
        encoder_inputs: SingletonInputs
        decoder_inputs: Optional[SingletonInputs]

        if is_explicit_encoder_decoder_prompt(prompt):
            encoder_task = self._prompt_to_llm_inputs_async(
                prompt["encoder_prompt"],
                tokenization_kwargs=tokenization_kwargs,
            )

            if (decoder_input := prompt["decoder_prompt"]) is None:
                encoder_inputs = await encoder_task
                decoder_inputs = None
            else:
                decoder_task = self._prompt_to_llm_inputs_async(
                    decoder_input,
                    tokenization_kwargs=tokenization_kwargs,
                )

                encoder_inputs, decoder_inputs = await asyncio.gather(
                    encoder_task, decoder_task)

            # For multimodal model, override decoder prompt from processor
            # with explicit decoder prompt.
            if self.model_config.is_multimodal_model:
                encoder_inputs, decoder_inputs = (
                    self._split_enc_dec_mm_inputs(encoder_inputs,
                                                  decoder_inputs))
        else:
            inputs = await self._prompt_to_llm_inputs_async(
                prompt,
                tokenization_kwargs=tokenization_kwargs,
            )
            if self.model_config.is_multimodal_model:
                # Encoder-Decoder Multimodal model
                encoder_inputs, decoder_inputs = (
                    self._split_enc_dec_mm_inputs(inputs))
            else:
                encoder_inputs = inputs
                decoder_inputs = None

        return self._build_enc_dec_llm_inputs(encoder_inputs, decoder_inputs)

    def _build_decoder_only_llm_inputs(
        self,
        prompt_inputs: DecoderOnlyInputs,
        prompt_adapter_request: Optional[PromptAdapterRequest],
    ) -> DecoderOnlyInputs:
        if "prompt_token_ids" in prompt_inputs:
            prompt_inputs = cast(Union[TokenInputs, MultiModalInputs],
                                 prompt_inputs)  # Needed for mypy
            prompt_inputs["prompt_token_ids"] = self._apply_prompt_adapter(
                prompt_inputs["prompt_token_ids"],
                prompt_adapter_request=prompt_adapter_request,
            )

        return prompt_inputs

    def _process_decoder_only_prompt(
        self,
        prompt: SingletonPrompt,
        tokenization_kwargs: Optional[dict[str, Any]] = None,
        lora_request: Optional[LoRARequest] = None,
        prompt_adapter_request: Optional[PromptAdapterRequest] = None,
        return_mm_hashes: bool = False,
    ) -> DecoderOnlyInputs:
        """
        For decoder-only models:
        Process an input prompt into an {class}`DecoderOnlyInputs` instance.

        Arguments:

        * prompt: input prompt
        * lora_request
        * prompt_adapter_request
        * return_mm_hashes

        Returns:

        * {class}`DecoderOnlyInputs` instance
        """

        prompt_comps = self._prompt_to_llm_inputs(
            prompt,
            tokenization_kwargs=tokenization_kwargs,
            lora_request=lora_request,
            return_mm_hashes=return_mm_hashes,
        )

        return self._build_decoder_only_llm_inputs(
            prompt_comps,
            prompt_adapter_request=prompt_adapter_request,
        )

    async def _process_decoder_only_prompt_async(
        self,
        prompt: SingletonPrompt,
        tokenization_kwargs: Optional[dict[str, Any]] = None,
        lora_request: Optional[LoRARequest] = None,
        prompt_adapter_request: Optional[PromptAdapterRequest] = None,
        return_mm_hashes: bool = False,
    ) -> DecoderOnlyInputs:
        """Async version of {meth}`_process_decoder_only_prompt`."""
        prompt_comps = await self._prompt_to_llm_inputs_async(
            prompt,
            tokenization_kwargs=tokenization_kwargs,
            lora_request=lora_request,
            return_mm_hashes=return_mm_hashes,
        )

        return self._build_decoder_only_llm_inputs(
            prompt_comps,
            prompt_adapter_request=prompt_adapter_request,
        )

    def preprocess(
        self,
        prompt: PromptType,
        tokenization_kwargs: Optional[dict[str, Any]] = None,
        lora_request: Optional[LoRARequest] = None,
        prompt_adapter_request: Optional[PromptAdapterRequest] = None,
        return_mm_hashes: bool = False,
    ) -> ProcessorInputs:
        """Preprocess the input prompt."""
        if self.model_config.is_encoder_decoder:
            assert not return_mm_hashes, (
                "Multimodal hashes for encoder-decoder models should not be ",
                "returned until they are supported on vLLM V1.")
            # Encoder-decoder model requires special mapping of
            # input prompts to encoder & decoder
            return self._process_encoder_decoder_prompt(prompt)

        if is_explicit_encoder_decoder_prompt(prompt):
            raise ValueError("Cannot pass encoder-decoder prompt "
                             "to decoder-only models")

        # Decoder-only operation
        return self._process_decoder_only_prompt(
            prompt,
            tokenization_kwargs=tokenization_kwargs,
            lora_request=lora_request,
            prompt_adapter_request=prompt_adapter_request,
            return_mm_hashes=return_mm_hashes,
        )

    async def preprocess_async(
        self,
        prompt: PromptType,
        tokenization_kwargs: Optional[dict[str, Any]] = None,
        lora_request: Optional[LoRARequest] = None,
        prompt_adapter_request: Optional[PromptAdapterRequest] = None,
        return_mm_hashes: bool = False,
    ) -> ProcessorInputs:
        """Async version of {meth}`preprocess`."""
        if self.model_config.is_encoder_decoder:
            assert not return_mm_hashes, (
                "Multimodal hashes for encoder-decoder models should not be ",
                "returned until they are supported on vLLM V1.")
            # Encoder-decoder model requires special mapping of
            # input prompts to encoder & decoder
            return await self._process_encoder_decoder_prompt_async(prompt)

        if is_explicit_encoder_decoder_prompt(prompt):
            raise ValueError("Cannot pass encoder-decoder prompt "
                             "to decoder-only models")

        # Decoder-only operation
        return await self._process_decoder_only_prompt_async(
            prompt,
            tokenization_kwargs=tokenization_kwargs,
            lora_request=lora_request,
            prompt_adapter_request=prompt_adapter_request,
            return_mm_hashes=return_mm_hashes,
        )