base.py

# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
import asyncio
import copy
import time
from abc import ABC, abstractmethod
from collections.abc import Mapping, Sequence
from concurrent.futures import Executor, ThreadPoolExecutor
from functools import cached_property
from typing import TYPE_CHECKING, Any, Generic, overload

from typing_extensions import TypeVar

from vllm.inputs import (
    EmbedsInput,
    EmbedsPrompt,
    EncoderDecoderInput,
    EngineInput,
    MultiModalDataDict,
    MultiModalInput,
    MultiModalUUIDDict,
    SingletonInput,
    TextPrompt,
    TokensInput,
    TokensPrompt,
    build_enc_dec_input,
    embeds_input,
    tokens_input,
)
from vllm.logger import init_logger
from vllm.multimodal import MULTIMODAL_REGISTRY as mm_registry
from vllm.multimodal.cache import BaseMultiModalProcessorCache
from vllm.multimodal.parse import (
    MultiModalDataItems,
    MultiModalUUIDItems,
    parse_mm_uuids,
)
from vllm.multimodal.processing import BaseMultiModalProcessor
from vllm.multimodal.processing import ProcessorInputs as MMProcessorInputs
from vllm.multimodal.registry import MultiModalTimingRegistry
from vllm.tokenizers import TokenizerLike
from vllm.utils.async_utils import (
    AsyncMicrobatchTokenizer,
    make_async,
)
from vllm.utils.counter import AtomicCounter
from vllm.utils.torch_utils import set_default_torch_num_threads
from vllm.v1.metrics.stats import MultiModalCacheStats

from .embed_utils import safe_load_prompt_embeds
from .inputs import (
    DictPrompt,
    EncoderDecoderDictPrompt,
    EncoderDecoderTokPrompt,
    SingletonDictPrompt,
    SingletonTokPrompt,
    TokPrompt,
)
from .inputs.preprocess import extract_target_prompt
from .params import ChatParams, TokenizeParams

if TYPE_CHECKING:
    from vllm.config import VllmConfig
    from vllm.entrypoints.chat_utils import (
        ChatCompletionMessageParam,
        ConversationMessage,
    )

logger = init_logger(__name__)


_T = TypeVar("_T", bound=TokenizerLike, default=TokenizerLike)


class BaseRenderer(ABC, Generic[_T]):
    def __init__(self, config: "VllmConfig", tokenizer: _T | None) -> None:
        super().__init__()

        self.config = config
        self.model_config = config.model_config
        self.api_process_rank = config.parallel_config._api_process_rank

        self.tokenizer = tokenizer

        # Shared thread pool executor for blocking tokenizer and
        # multimodal preprocessing operations.  The multimodal processor
        # receives a deep-copied tokenizer (see #36557) so it is safe to
        # run tokenization and MM preprocessing concurrently.
        pool_workers = config.model_config.renderer_num_workers
        self._executor = ThreadPoolExecutor(max_workers=pool_workers)

        # Multimodal preprocessing is always offloaded to the thread pool
        # to keep the asyncio event loop responsive under concurrent load.
        self._mm_executor: Executor = self._executor

        # Lazy initialization since offline LLM doesn't use async
        self._async_tokenizer: AsyncMicrobatchTokenizer | None = None

        self.mm_processor: BaseMultiModalProcessor | None = None
        self._readonly_mm_processor: BaseMultiModalProcessor | None = None
        self._mm_cache_stats: MultiModalCacheStats | None = None
        self._clear_mm_cache_async = make_async(
            self.clear_mm_cache, executor=self._executor
        )
        self._process_multimodal_async = make_async(
            self._process_multimodal, executor=self._mm_executor
        )
        if config.model_config.is_multimodal_model:
            mm_processor_cache = mm_registry.processor_cache_from_config(config)

            # Deep-copy the tokenizer so the multimodal processor gets its
            # own Rust tokenizer backend.  Without this, concurrent access
            # from AsyncMicrobatchTokenizer and call_hf_processor causes
            # "RuntimeError: Already borrowed" from the Rust RefCell.
            # See: https://github.com/huggingface/tokenizers/issues/537
            mm_tokenizer = copy.deepcopy(tokenizer)

            with set_default_torch_num_threads():
                self.mm_processor = mm_registry.create_processor(
                    config.model_config,
                    tokenizer=mm_tokenizer,
                    cache=mm_processor_cache,
                )

            if mm_processor_cache:
                self._mm_cache_stats = MultiModalCacheStats()

            # A second processor with its own processor-only cache.
            # Used by the tokenize endpoint so that tokenize-only
            # requests don't pollute the sender cache.
            ro_cache = mm_registry.processor_only_cache_from_config(config)
            if ro_cache is not None:
                ro_tokenizer = copy.deepcopy(tokenizer)
                with set_default_torch_num_threads():
                    self._readonly_mm_processor = mm_registry.create_processor(
                        config.model_config,
                        tokenizer=ro_tokenizer,
                        cache=ro_cache,
                    )

            # This is used to generate internal request ID for MM processing
            # It has no relation to the request ID for engine core
            self._mm_req_counter = AtomicCounter()
            self._mm_timing_registry = MultiModalTimingRegistry(
                config.observability_config
            )

    def get_tokenizer(self) -> _T:
        tokenizer = self.tokenizer
        if tokenizer is None:
            raise ValueError("Tokenizer not available when `skip_tokenizer_init=True`")

        return tokenizer

    def get_async_tokenizer(self) -> AsyncMicrobatchTokenizer:
        if self._async_tokenizer is None:
            self._async_tokenizer = AsyncMicrobatchTokenizer(
                self.get_tokenizer(), executor=self._executor
            )

        return self._async_tokenizer

    def get_mm_processor(self) -> "BaseMultiModalProcessor":
        if self.mm_processor is None:
            raise ValueError("Multi-modal processor not available for text-only models")

        return self.mm_processor

    @property
    def mm_processor_cache(self) -> "BaseMultiModalProcessorCache | None":
        if self.mm_processor is None:
            return None

        return self.mm_processor.cache

    def stat_mm_cache(self) -> MultiModalCacheStats | None:
        mm_cache_stats = self._mm_cache_stats
        if mm_cache_stats is None:
            return None

        self._mm_cache_stats = MultiModalCacheStats()

        return mm_cache_stats

    def update_mm_cache_stats(self) -> None:
        mm_processor_cache = self.mm_processor_cache
        mm_cache_stats = self._mm_cache_stats

        if mm_processor_cache and mm_cache_stats:
            delta = mm_processor_cache.make_stats(delta=True)
            mm_cache_stats.record(delta.total, delta.hits)

    def clear_mm_cache(self) -> None:
        mm_processor_cache = self.mm_processor_cache
        if mm_processor_cache is not None:
            mm_processor_cache.clear_cache()

        if self._mm_cache_stats is not None:
            self._mm_cache_stats.reset = True

    @staticmethod
    def _clear_processor_cache(
        processor: "BaseMultiModalProcessor | None",
    ) -> None:
        if processor is None:
            return

        processor_cache = processor.cache
        if processor_cache is not None:
            processor_cache.clear_cache()

    def _warmup_mm_processor(
        self,
        processor: "BaseMultiModalProcessor",
        *,
        log_prefix: str,
    ) -> None:
        from vllm.multimodal.processing import TimingContext

        model_config = self.model_config
        mm_config = model_config.get_multimodal_config()
        mm_limits = {k: v for k, v in processor.info.allowed_mm_limits.items() if v > 0}

        start_time = time.perf_counter()
        processor_inputs = processor.dummy_inputs.get_dummy_processor_inputs(
            seq_len=model_config.max_model_len,
            mm_counts=dict.fromkeys(mm_limits, 1),
            mm_options=mm_config.limit_per_prompt,
        )
        _ = processor.apply(processor_inputs, timing_ctx=TimingContext(enabled=False))

        elapsed = time.perf_counter() - start_time
        logger.info("%s warmup completed in %.3fs", log_prefix, elapsed)

    def warmup(self, chat_params: ChatParams) -> None:
        """
        Warm up this renderer to avoid first-request latency.

        For chat requests:
        - Jinja2 template compilation
        """
        from vllm.entrypoints.chat_utils import ChatTemplateResolutionError

        try:
            logger.debug("Warming up chat template processing...")
            start_time = time.perf_counter()

            self.render_chat([[{"role": "user", "content": "warmup"}]], chat_params)

            elapsed = time.perf_counter() - start_time
            logger.debug("Chat template warmup completed in %.3fs", elapsed)
        except ChatTemplateResolutionError:
            logger.debug("This model does not support chat template.")
        except Exception:
            logger.warning("Chat template warmup failed", exc_info=True)

        if self.mm_processor:
            try:
                logger.debug("Warming up multi-modal processing...")
                self._warmup_mm_processor(
                    self.mm_processor,
                    log_prefix="Multi-modal",
                )
            except Exception:
                logger.warning("Multi-modal warmup failed")
            finally:
                self.clear_mm_cache()

        if self._readonly_mm_processor is not None:
            try:
                logger.debug("Warming up readonly multi-modal processing...")
                self._warmup_mm_processor(
                    self._readonly_mm_processor,
                    log_prefix="Readonly multi-modal",
                )
            except Exception:
                logger.warning("Readonly multi-modal warmup failed")
            finally:
                self._clear_processor_cache(self._readonly_mm_processor)

    async def clear_mm_cache_async(self) -> None:
        """Serialize clear_mm_cache through the shared executor to avoid
        races with concurrent process_inputs on the mm_processor_cache."""
        await self._clear_mm_cache_async()

    def shutdown(self) -> None:
        mm_processor_cache = self.mm_processor_cache
        if mm_processor_cache is not None:
            mm_processor_cache.close()

        if executor := getattr(self, "_executor", None):
            executor.shutdown(wait=False)

        if (
            mm_executor := getattr(self, "_mm_executor", None)
        ) is not None and mm_executor is not executor:
            mm_executor.shutdown(wait=False)

    def get_bos_token_id(self) -> int | None:
        if self.tokenizer is None:
            logger.warning_once(
                "Using None for BOS token id because tokenizer is not initialized"
            )
            return None

        return self.tokenizer.bos_token_id

    def get_eos_token_id(self) -> int | None:
        if self.tokenizer is None:
            logger.warning_once(
                "Using None for EOS token id because tokenizer is not initialized"
            )
            return None

        return self.tokenizer.eos_token_id

    def get_dec_start_token_id(self) -> int:
        """
        Obtain the decoder start token id employed by an encoder/decoder model,
        raising an error if it is not available.
        """
        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()

        if dec_start_token_id is None:
            raise RuntimeError("Cannot find decoder start token id or <BOS>")

        return dec_start_token_id

    @cached_property
    def default_cmpl_tok_params(self) -> TokenizeParams:
        mm_processor = self.mm_processor
        if mm_processor is not None:
            return mm_processor.info.default_tok_params

        model_config = self.model_config
        encoder_config = model_config.encoder_config or {}

        return TokenizeParams(
            max_total_tokens=model_config.max_model_len,
            do_lower_case=encoder_config.get("do_lower_case", False),
            add_special_tokens=True,
        )

    @cached_property
    def default_chat_tok_params(self) -> TokenizeParams:
        mm_processor = self.mm_processor
        if mm_processor is not None:
            return mm_processor.info.default_tok_params

        model_config = self.model_config
        encoder_config = model_config.encoder_config or {}

        return TokenizeParams(
            max_total_tokens=model_config.max_model_len,
            do_lower_case=encoder_config.get("do_lower_case", False),
            add_special_tokens=False,
        )

    # Step 1: Convert raw inputs to prompts
    def render_prompt(
        self,
        prompt: DictPrompt | bytes,
    ) -> DictPrompt:
        if isinstance(prompt, bytes):
            embeds = safe_load_prompt_embeds(self.model_config, prompt)
            prompt = EmbedsPrompt(prompt_embeds=embeds)

        return prompt

    def render_prompts(
        self,
        prompts: Sequence[DictPrompt | bytes],
    ) -> list[DictPrompt]:
        if len(prompts) == 0:
            raise ValueError("You must pass at least one prompt")

        return [self.render_prompt(prompt) for prompt in prompts]

    async def render_prompts_async(
        self,
        prompts: Sequence[DictPrompt | bytes],
    ) -> list[DictPrompt]:
        return self.render_prompts(prompts)

    @abstractmethod
    def render_messages(
        self,
        messages: list["ChatCompletionMessageParam"],
        params: ChatParams,
    ) -> tuple[list["ConversationMessage"], DictPrompt]:
        raise NotImplementedError

    async def render_messages_async(
        self,
        messages: list["ChatCompletionMessageParam"],
        params: ChatParams,
    ) -> tuple[list["ConversationMessage"], DictPrompt]:
        return self.render_messages(messages, params)

    # Step 2: Tokenize prompts if necessary
    def _tokenize_prompt(
        self,
        prompt: TextPrompt,
        params: TokenizeParams,
    ) -> TokensPrompt:
        tokenizer = self.get_tokenizer()
        prompt_token_ids = tokenizer.encode(
            prompt["prompt"],
            **params.get_encode_kwargs(),
        )

        return TokensPrompt(prompt_token_ids=prompt_token_ids, **prompt)

    async def _tokenize_prompt_async(
        self,
        prompt: TextPrompt,
        params: TokenizeParams,
    ) -> TokensPrompt:
        tokenizer = self.get_async_tokenizer()
        prompt_token_ids = await tokenizer.encode(
            prompt["prompt"],
            **params.get_encode_kwargs(),
        )

        return TokensPrompt(prompt_token_ids=prompt_token_ids, **prompt)

    def _detokenize_prompt(self, prompt: TokensPrompt) -> TokensPrompt:
        tokenizer = self.get_tokenizer()
        prompt["prompt"] = tokenizer.decode(prompt["prompt_token_ids"])

        return prompt

    async def _detokenize_prompt_async(self, prompt: TokensPrompt) -> TokensPrompt:
        tokenizer = self.get_async_tokenizer()
        prompt["prompt"] = await tokenizer.decode(prompt["prompt_token_ids"])

        return prompt

    @overload
    def _tokenize_singleton_prompt(
        self,
        prompt: TextPrompt | TokensPrompt,
        params: TokenizeParams,
    ) -> TokensPrompt: ...

    @overload
    def _tokenize_singleton_prompt(  # type: ignore[misc]
        self,
        prompt: EmbedsPrompt,
        params: TokenizeParams,
    ) -> EmbedsPrompt: ...

    def _tokenize_singleton_prompt(
        self,
        prompt: SingletonDictPrompt,
        params: TokenizeParams,
    ) -> SingletonTokPrompt:
        if "prompt_token_ids" not in prompt and "prompt_embeds" not in prompt:
            if not isinstance(prompt.get("prompt"), str):
                raise TypeError(
                    "Expected prompt['prompt'] to be a string before tokenization; "
                    "use 'prompt_token_ids' for token ID inputs"
                )
            prompt = params.apply_pre_tokenization(self.tokenizer, prompt)  # type: ignore[arg-type]
            prompt = self._tokenize_prompt(prompt, params)

        if params.needs_detokenization and "prompt" not in prompt:
            if "prompt_token_ids" not in prompt:
                raise RuntimeError("Cannot run detokenization on embeddings")

            prompt = self._detokenize_prompt(prompt)  # type: ignore[arg-type]

        return params.apply_post_tokenization(self.tokenizer, prompt)  # type: ignore[arg-type]

    @overload
    async def _tokenize_singleton_prompt_async(
        self,
        prompt: TextPrompt | TokensPrompt,
        params: TokenizeParams,
    ) -> TokensPrompt: ...

    @overload
    async def _tokenize_singleton_prompt_async(  # type: ignore[misc]
        self,
        prompt: EmbedsPrompt,
        params: TokenizeParams,
    ) -> EmbedsPrompt: ...

    async def _tokenize_singleton_prompt_async(
        self,
        prompt: SingletonDictPrompt,
        params: TokenizeParams,
    ) -> SingletonTokPrompt:
        if "prompt_token_ids" not in prompt and "prompt_embeds" not in prompt:
            if not isinstance(prompt.get("prompt"), str):
                raise TypeError(
                    "Expected prompt['prompt'] to be a string before tokenization; "
                    "use 'prompt_token_ids' for token ID inputs"
                )
            prompt = params.apply_pre_tokenization(self.tokenizer, prompt)  # type: ignore[arg-type]
            prompt = await self._tokenize_prompt_async(prompt, params)

        if params.needs_detokenization and "prompt" not in prompt:
            if "prompt_token_ids" not in prompt:
                raise RuntimeError("Cannot run detokenization on embeddings")

            prompt = await self._detokenize_prompt_async(prompt)  # type: ignore[arg-type]

        return params.apply_post_tokenization(self.tokenizer, prompt)  # type: ignore[arg-type]

    def _tokenize_enc_dec_prompt(
        self,
        prompt: EncoderDecoderDictPrompt,
        params: TokenizeParams,
    ) -> EncoderDecoderTokPrompt:
        enc_prompt, dec_prompt = (
            self._tokenize_singleton_prompt(prompt["encoder_prompt"], params),
            (
                None
                if prompt["decoder_prompt"] is None
                else self._tokenize_singleton_prompt(prompt["decoder_prompt"], params)
            ),
        )

        return EncoderDecoderTokPrompt(
            encoder_prompt=enc_prompt,
            decoder_prompt=dec_prompt,
        )

    async def _tokenize_enc_dec_prompt_async(
        self,
        prompt: EncoderDecoderDictPrompt,
        params: TokenizeParams,
    ) -> EncoderDecoderTokPrompt:
        enc_prompt, dec_prompt = await asyncio.gather(
            self._tokenize_singleton_prompt_async(prompt["encoder_prompt"], params),
            (
                asyncio.sleep(0)
                if prompt["decoder_prompt"] is None
                else self._tokenize_singleton_prompt_async(
                    prompt["decoder_prompt"], params
                )
            ),
        )

        return EncoderDecoderTokPrompt(
            encoder_prompt=enc_prompt,
            decoder_prompt=dec_prompt,
        )

    def tokenize_prompt(
        self,
        prompt: DictPrompt,
        params: TokenizeParams,
    ) -> TokPrompt:
        if "encoder_prompt" in prompt:
            return self._tokenize_enc_dec_prompt(prompt, params)  # type: ignore[arg-type]

        return self._tokenize_singleton_prompt(prompt, params)

    def tokenize_prompts(
        self,
        prompts: Sequence[DictPrompt],
        params: TokenizeParams,
    ) -> list[TokPrompt]:
        return [self.tokenize_prompt(prompt, params) for prompt in prompts]

    async def tokenize_prompt_async(
        self,
        prompt: DictPrompt,
        params: TokenizeParams,
    ) -> TokPrompt:
        if "encoder_prompt" in prompt:
            return await self._tokenize_enc_dec_prompt_async(prompt, params)  # type: ignore[arg-type]

        return await self._tokenize_singleton_prompt_async(prompt, params)

    async def tokenize_prompts_async(
        self,
        prompts: Sequence[DictPrompt],
        params: TokenizeParams,
    ) -> list[TokPrompt]:
        return await asyncio.gather(
            *(self.tokenize_prompt_async(prompt, params) for prompt in prompts)
        )

    # Step 3: Add extra keys to the prompts
    def _apply_prompt_extras(
        self,
        prompts: Sequence[TokPrompt],
        prompt_extras: dict[str, Any] | None,
    ):
        if not prompt_extras:
            return

        for prompt in prompts:
            target_prompt = extract_target_prompt(self.model_config, prompt)
            target_prompt.update(prompt_extras)  # type: ignore[arg-type]

    # Step 4: Convert to engine inputs
    def _validate_mm_uuids(
        self,
        mm_data: MultiModalDataDict,
        mm_data_items: MultiModalDataItems,
        mm_uuid_items: MultiModalUUIDItems,
    ) -> None:
        # NOTE: Keys corresponding to `None` in `mm_data` don't appear in
        # `mm_data_items`
        modalities = mm_data.keys() | mm_uuid_items.keys()

        for modality in modalities:
            data_items = mm_data_items.get(modality)
            uuid_items = mm_uuid_items.get(modality)

            if data_items is None:
                if uuid_items is None:
                    raise ValueError(
                        f"multi_modal_data[{modality!r}] is empty but "
                        f"multi_modal_uuids[{modality!r}] is missing."
                    )

            elif uuid_items is not None:
                if len(data_items) != len(uuid_items):
                    raise ValueError(
                        f"If given, multi_modal_uuids[{modality!r}] must have "
                        f"same length as multi_modal_data[{modality!r}], but "
                        f"got {len(uuid_items)} vs {len(data_items)}."
                    )

                for i, item in enumerate(data_items):
                    if item is None and uuid_items[i] is None:
                        raise ValueError(
                            f"multi_modal_data[{modality!r}][{i}] is empty but "
                            f"multi_modal_uuids[{modality!r}][{i}] is missing."
                        )

    def _process_mm_uuids(
        self,
        mm_data: MultiModalDataDict,
        mm_data_items: MultiModalDataItems,
        mm_uuid_items: MultiModalUUIDItems,
        mm_req_id: str,
    ) -> MultiModalUUIDItems:
        model_config = self.model_config

        # NOTE: When users explicitly turn off BOTH prefix caching and input
        # processing caching, no multimodal features or embeddings will be
        # reused across requests, therefore identifying multimodal data items
        # by their content is no longer necessary, and we create uuids with
        # `<mm_req_id>-<modality>-<index>`, overriding even user-provided ones.
        if (
            model_config.multimodal_config
            and model_config.multimodal_config.mm_processor_cache_gb == 0
            and not self.config.cache_config.enable_prefix_caching
        ):
            mm_uuid_items = {
                modality: [f"{mm_req_id}-{modality}-{i}" for i in range(data_count)]
                for modality, data_count in mm_data_items.get_all_counts().items()
            }

        self._validate_mm_uuids(mm_data, mm_data_items, mm_uuid_items)

        return mm_uuid_items

    # TODO: Remove str and tokenization_kwargs after deprecating InputPreprocessor
    def _process_multimodal(
        self,
        prompt: list[int] | str,
        mm_data: MultiModalDataDict,
        mm_uuids: MultiModalUUIDDict | None,
        mm_processor_kwargs: Mapping[str, object] | None,
        tokenization_kwargs: dict[str, Any] | None,
        *,
        skip_mm_cache: bool = False,
    ) -> "MultiModalInput":
        mm_req_id = f"renderer{self.api_process_rank}-mm-{self._mm_req_counter.inc(1)}"

        if skip_mm_cache and self._readonly_mm_processor is not None:
            mm_processor = self._readonly_mm_processor
        else:
            mm_processor = self.get_mm_processor()

        mm_data_items = mm_processor.info.parse_mm_data(mm_data)
        mm_uuid_items = parse_mm_uuids(mm_uuids)

        mm_uuid_items = self._process_mm_uuids(
            mm_data, mm_data_items, mm_uuid_items, mm_req_id
        )

        mm_processor_inputs = MMProcessorInputs(
            prompt,
            mm_data_items,
            mm_uuid_items,
            hf_processor_mm_kwargs=mm_processor_kwargs or {},
            tokenization_kwargs=tokenization_kwargs or {},
        )
        mm_timing_ctx = self._mm_timing_registry.get(mm_req_id)

        with set_default_torch_num_threads():
            mm_inputs = mm_processor.apply(mm_processor_inputs, mm_timing_ctx)

        self.update_mm_cache_stats()

        return mm_inputs

    def _process_tokens(
        self,
        prompt: TokensPrompt,
        *,
        skip_mm_cache: bool = False,
    ) -> TokensInput | MultiModalInput:
        """Process token inputs, with multimodal preprocessing offloaded
        to the shared thread pool in the async variant.
        """
        prompt_token_ids = prompt["prompt_token_ids"]

        engine_input: TokensInput | MultiModalInput
        if multi_modal_data := prompt.get("multi_modal_data"):
            engine_input = self._process_multimodal(
                prompt_token_ids,
                multi_modal_data,
                mm_processor_kwargs=prompt.get("mm_processor_kwargs"),
                tokenization_kwargs=None,  # Tokenization already done in Step 2
                mm_uuids=prompt.get("multi_modal_uuids"),
                skip_mm_cache=skip_mm_cache,
            )
        else:
            engine_input = tokens_input(prompt_token_ids)

        if prompt_text := prompt.get("prompt"):
            engine_input["prompt"] = prompt_text
        if cache_salt := prompt.get("cache_salt"):
            engine_input["cache_salt"] = cache_salt

        return engine_input

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

        prompt_embeds = prompt["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).")

        # Tensors must be on CPU for serialization between processes
        # in the MsgpackEncoder. Casting to CPU here ensures that there is no
        # hidden device transfer in the critical path of generation.
        prompt_embeds = prompt_embeds.cpu()

        return embeds_input(
            prompt_embeds=prompt_embeds,
            cache_salt=prompt.get("cache_salt"),
        )

    async def _process_tokens_async(
        self,
        prompt: TokensPrompt,
        *,
        skip_mm_cache: bool = False,
    ) -> TokensInput | MultiModalInput:
        prompt_token_ids = prompt["prompt_token_ids"]

        engine_input: TokensInput | MultiModalInput
        if multi_modal_data := prompt.get("multi_modal_data"):
            engine_input = await self._process_multimodal_async(
                prompt_token_ids,
                multi_modal_data,
                mm_processor_kwargs=prompt.get("mm_processor_kwargs"),
                tokenization_kwargs=None,
                mm_uuids=prompt.get("multi_modal_uuids"),
                skip_mm_cache=skip_mm_cache,
            )
        else:
            engine_input = tokens_input(prompt_token_ids)

        if prompt_text := prompt.get("prompt"):
            engine_input["prompt"] = prompt_text
        if cache_salt := prompt.get("cache_salt"):
            engine_input["cache_salt"] = cache_salt

        return engine_input

    def _process_singleton(
        self,
        prompt: SingletonTokPrompt,
        *,
        skip_mm_cache: bool = False,
    ) -> SingletonInput:
        if "prompt_embeds" in prompt:
            return self._process_embeds(prompt)  # type: ignore[arg-type]

        return self._process_tokens(prompt, skip_mm_cache=skip_mm_cache)  # type: ignore[arg-type]

    async def _process_singleton_async(
        self,
        prompt: SingletonTokPrompt,
        *,
        skip_mm_cache: bool = False,
    ) -> SingletonInput:
        if "prompt_embeds" in prompt:
            return self._process_embeds(prompt)  # type: ignore[arg-type]

        return await self._process_tokens_async(prompt, skip_mm_cache=skip_mm_cache)  # type: ignore[arg-type]

    def _process_enc_dec(
        self,
        prompt: EncoderDecoderTokPrompt,
        *,
        skip_mm_cache: bool = False,
    ) -> EncoderDecoderInput:
        enc_prompt = prompt["encoder_prompt"]
        dec_prompt = prompt["decoder_prompt"]

        skip_decoder_start_token = False
        if self.mm_processor is not None:
            from vllm.multimodal.processing import EncDecMultiModalProcessor

            if isinstance(self.mm_processor, EncDecMultiModalProcessor):
                skip_decoder_start_token = self.mm_processor.skip_decoder_start_token

        return build_enc_dec_input(
            encoder_input=self._process_singleton(
                enc_prompt, skip_mm_cache=skip_mm_cache
            ),
            decoder_input=(
                None
                if dec_prompt is None
                else self._process_singleton(dec_prompt, skip_mm_cache=skip_mm_cache)
            ),
            decoder_start_token_id=self.get_dec_start_token_id(),
            skip_decoder_start_token=skip_decoder_start_token,
        )

    async def _process_enc_dec_async(
        self,
        prompt: EncoderDecoderTokPrompt,
        *,
        skip_mm_cache: bool = False,
    ) -> EncoderDecoderInput:
        enc_prompt = prompt["encoder_prompt"]
        dec_prompt = prompt["decoder_prompt"]

        encoder_input, decoder_input = await asyncio.gather(
            self._process_singleton_async(enc_prompt, skip_mm_cache=skip_mm_cache),
            (
                asyncio.sleep(0)
                if dec_prompt is None
                else self._process_singleton_async(
                    dec_prompt, skip_mm_cache=skip_mm_cache
                )
            ),
        )

        return build_enc_dec_input(
            encoder_input=encoder_input,
            decoder_input=decoder_input,
            decoder_start_token_id=self.get_dec_start_token_id(),
        )

    def process_for_engine(
        self,
        prompt: TokPrompt,
        arrival_time: float,
        *,
        skip_mm_cache: bool = False,
    ) -> EngineInput:
        engine_input: EngineInput
        if "encoder_prompt" in prompt:
            engine_input = self._process_enc_dec(prompt, skip_mm_cache=skip_mm_cache)  # type: ignore[arg-type]
        else:
            engine_input = self._process_singleton(prompt, skip_mm_cache=skip_mm_cache)

        engine_input["arrival_time"] = arrival_time

        return engine_input

    async def process_for_engine_async(
        self,
        prompt: TokPrompt,
        arrival_time: float,
        *,
        skip_mm_cache: bool = False,
    ) -> EngineInput:
        engine_input: EngineInput
        if "encoder_prompt" in prompt:
            engine_input = await self._process_enc_dec_async(
                prompt,  # type: ignore[arg-type]
                skip_mm_cache=skip_mm_cache,
            )
        else:
            engine_input = await self._process_singleton_async(
                prompt, skip_mm_cache=skip_mm_cache
            )

        engine_input["arrival_time"] = arrival_time

        return engine_input

    # Top-level methods
    def render_cmpl(
        self,
        prompts: Sequence[DictPrompt | bytes],
        tok_params: TokenizeParams | None = None,
        *,
        prompt_extras: dict[str, Any] | None = None,
        skip_mm_cache: bool = False,
    ):
        arrival_time = time.time()

        if tok_params is None:
            tok_params = self.default_cmpl_tok_params

        dict_prompts = self.render_prompts(prompts)
        tok_prompts = self.tokenize_prompts(dict_prompts, tok_params)

        self._apply_prompt_extras(tok_prompts, prompt_extras)

        return [
            self.process_for_engine(prompt, arrival_time, skip_mm_cache=skip_mm_cache)
            for prompt in tok_prompts
        ]

    async def render_cmpl_async(
        self,
        prompts: Sequence[DictPrompt | bytes],
        tok_params: TokenizeParams | None = None,
        *,
        prompt_extras: dict[str, Any] | None = None,
        skip_mm_cache: bool = False,
    ):
        arrival_time = time.time()

        if tok_params is None:
            tok_params = self.default_cmpl_tok_params

        dict_prompts = await self.render_prompts_async(prompts)
        tok_prompts = await self.tokenize_prompts_async(dict_prompts, tok_params)

        self._apply_prompt_extras(tok_prompts, prompt_extras)

        return await asyncio.gather(
            *(
                self.process_for_engine_async(
                    p, arrival_time, skip_mm_cache=skip_mm_cache
                )
                for p in tok_prompts
            )
        )

    def render_chat(
        self,
        conversations: Sequence[list["ChatCompletionMessageParam"]],
        chat_params: ChatParams,
        tok_params: TokenizeParams | None = None,
        *,
        prompt_extras: dict[str, Any] | None = None,
        skip_mm_cache: bool = False,
    ):
        arrival_time = time.time()

        if tok_params is None:
            tok_params = self.default_chat_tok_params

        rendered = [
            self.render_messages(conversation, chat_params)
            for conversation in conversations
        ]

        out_conversations = list[list["ConversationMessage"]]()
        dict_prompts = list[DictPrompt]()
        for conv, prompt in rendered:
            out_conversations.append(conv)
            dict_prompts.append(prompt)

        tok_prompts = self.tokenize_prompts(dict_prompts, tok_params)

        self._apply_prompt_extras(tok_prompts, prompt_extras)

        eng_prompts = [
            self.process_for_engine(prompt, arrival_time, skip_mm_cache=skip_mm_cache)
            for prompt in tok_prompts
        ]

        return out_conversations, eng_prompts

    async def render_chat_async(
        self,
        conversations: Sequence[list["ChatCompletionMessageParam"]],
        chat_params: ChatParams,
        tok_params: TokenizeParams | None = None,
        *,
        prompt_extras: dict[str, Any] | None = None,
        skip_mm_cache: bool = False,
    ):
        arrival_time = time.time()

        if tok_params is None:
            tok_params = self.default_chat_tok_params

        rendered = [
            self.render_messages_async(conversation, chat_params)
            for conversation in conversations
        ]

        out_conversations = list[list["ConversationMessage"]]()
        dict_prompts = list[DictPrompt]()
        for conv, prompt in await asyncio.gather(*rendered):
            out_conversations.append(conv)
            dict_prompts.append(prompt)

        tok_prompts = await self.tokenize_prompts_async(dict_prompts, tok_params)

        self._apply_prompt_extras(tok_prompts, prompt_extras)

        eng_prompts = await asyncio.gather(
            *(
                self.process_for_engine_async(
                    p, arrival_time, skip_mm_cache=skip_mm_cache
                )
                for p in tok_prompts
            )
        )

        return out_conversations, eng_prompts