vLLM/vllm/renderers/base.py

# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
import asyncio
import time
import weakref
from abc import ABC, abstractmethod
from collections.abc import Mapping, Sequence
from concurrent.futures import Future, ThreadPoolExecutor
from contextlib import ExitStack
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.gpu_ipc_memory import maybe_init_mm_gpu_ipc_pool
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 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._resources = ExitStack()
        self._resources.callback(logger.debug, f"[shutdown] {self.__class__.__name__}")
        self._finalizer = weakref.finalize(self, self._resources.close)

        self.tokenizer = tokenizer

        # Background multimodal warmup task (a future returned by
        # _mm_executor) so it overlaps engine-core initialization while
        # staying serialized with the serving path through the same
        # single-worker executor. None when no background warmup has been
        # launched (or it has already been joined). Routing the warmup through
        # _mm_executor (max_workers=1) guarantees it can never run
        # concurrently with a serving request inside the numba workqueue
        # parallel region, which would otherwise hit numba's
        # "Concurrent access has been detected" fatal abort (see
        # vllm/utils/jit_monitor.py, which forces the workqueue layer).
        # Joined by the first of reset_mm_cache / warmup / shutdown so the
        # mm_processor_cache is also never touched concurrently by the warmup
        # and the serving path.
        self._mm_warmup_future: Future[None] | None = None
        # Whether warmup_mm has already run. Survives _join_mm_warmup (which
        # clears the thread pointer), so warmup() can tell that the background
        # warmup already ran even after reset_mm_cache joined it first, and
        # avoid running warmup_mm a second time.
        self._mm_warmup_done: bool = False

        # Thread pool executor for blocking tokenizer 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)
        self._resources.callback(self._executor.shutdown, wait=False)

        # Separate single-worker executor so tokenization never queues behind
        # MM preprocessing; must stay single-worker per #38418 (P0/P1 order).
        self._mm_executor = ThreadPoolExecutor(max_workers=1)
        self._resources.callback(self._mm_executor.shutdown, wait=False)

        # Offload tokenization to the thread pool. The sync
        # ``_tokenize_prompt`` already encapsulates the unified ``__call__``
        # path and char-offset extraction, so the async variant is just it
        # offloaded (mirrors ``_process_multimodal_async`` below).
        self._tokenize_prompt_async = make_async(
            self._tokenize_prompt, executor=self._executor
        )
        self._async_tokenizer_decode = make_async(self._decode, executor=self._executor)

        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._mm_executor
        )
        self._process_multimodal_async = make_async(
            self._process_multimodal, executor=self._mm_executor
        )
        self._safe_load_prompt_embeds_async = make_async(
            safe_load_prompt_embeds, executor=self._executor
        )
        if mm_registry.supports_multimodal_inputs(config.model_config):
            # Install the process-global GPU memory pool used to gate
            # frontend GPU-side multimodal decoding (no-op when the budget
            # is 0). Lives in the API-server process only.
            mm_config = config.model_config.multimodal_config
            if mm_config is not None:
                maybe_init_mm_gpu_ipc_pool(
                    mm_config.mm_ipc_gpu_memory_gb,
                    config.parallel_config._api_process_count,
                )

            mm_processor_cache = mm_registry.processor_cache_from_config(config)

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

            if mm_processor_cache:
                self._mm_cache_stats = MultiModalCacheStats()
                self._resources.callback(mm_processor_cache.close)

            # 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:
                with set_default_torch_num_threads():
                    self._readonly_mm_processor = mm_registry.create_processor(
                        config.model_config,
                        tokenizer=self.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 _decode(self, *args, **kwargs):
        return self.get_tokenizer().decode(*args, **kwargs)

    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_mm(self) -> None:
        # Run only the multimodal warmup (mm + readonly mm), serially.
        # Idempotent: runs at most once. A background warmup
        # (start_mm_warmup_in_background) may already have run it, and
        # reset_mm_cache may have joined that background warmup task before
        # warmup() reaches this point — the _mm_warmup_done flag survives the
        # join (the future pointer does not), so warmup() can still tell it
        # already ran.
        if self._mm_warmup_done:
            return
        try:
            # prevent MM processor hangs
            with set_default_torch_num_threads(1):
                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)
        finally:
            self._mm_warmup_done = True

    def start_mm_warmup_in_background(self) -> None:
        # Launch the multimodal warmup as a task on the single-worker
        # _mm_executor so it overlaps with engine-core initialization
        # (model loading) while staying serialized with the serving path.
        #
        # Why the same executor as serving (not a separate Thread):
        # _process_multimodal runs on _mm_executor (max_workers=1) and
        # enters the numba workqueue parallel region (e.g. Kimi K2.5 vision
        # preprocessing via kimi_k25_vision_fused). A separate warmup Thread
        # would run concurrently with that single serving worker, so both
        # threads would enter the numba workqueue parallel region at the same
        # time and trip numba's "Concurrent access has been detected" ->
        # "Fatal Python error: Aborted" (see vllm/utils/jit_monitor.py, which
        # forces the non-threadsafe workqueue layer). Submitting the warmup to
        # the same single-worker executor makes warmup and serving
        # physically incapable of overlapping on the numba threading layer,
        # while still overlapping at the process level with the (forked)
        # engine-core init in the main thread.
        #
        # The warmup task is joined by the first of: reset_mm_cache, warmup, or
        # shutdown. This also guarantees the frontend mm_processor_cache —
        # which is not designed for concurrent access (its get_and_update must
        # stay ordered with the engine-core cache) — is never touched
        # concurrently by the warmup and the serving path.
        if self.mm_processor is None and self._readonly_mm_processor is None:
            return  # text-only model: nothing to warm up
        if self._mm_warmup_future is not None:
            return  # already launched
        self._mm_warmup_future = self._mm_executor.submit(self.warmup_mm)

    def _join_mm_warmup(self) -> None:
        # Wait for the background MM warmup task to finish, if one was started.
        # Called from the main / event-loop thread (reset_mm_cache, warmup,
        # shutdown) — never from inside the _mm_executor worker — so
        # waiting on the future cannot deadlock.
        future = getattr(self, "_mm_warmup_future", None)
        if future is not None:
            future.result()
            self._mm_warmup_future = None

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

        For chat requests:
        - Jinja2 template compilation

        If a background multimodal warmup was launched (to overlap
        engine-core init) it is joined first so it is not still touching the
        mm_processor_cache. warmup_mm is idempotent, so the call below is a
        no-op if the background warmup — or a prior reset_mm_cache that joined
        it — already ran it.
        """
        from vllm.entrypoints.chat_utils import ChatTemplateResolutionError

        # prevent MM processor hangs
        with set_default_torch_num_threads(1):
            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_warmup_future is not None:
            # Join a background MM warmup launched to overlap engine-core init.
            self._join_mm_warmup()
        # Idempotent: no-op if the background warmup (or a prior reset_mm_cache)
        # already ran the MM warmup.
        self.warmup_mm()

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

    def shutdown(self) -> None:
        # Wait for any background MM warmup to finish first: it runs on the
        # _mm_executor and touches the mm_processor_cache, so it must be
        # quiescent before _resources.close() tears down the executors and
        # the cache (both are registered as resources in __init__).
        self._join_mm_warmup()
        self._resources.close()

    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_prompt_async(
        self,
        prompt: DictPrompt | bytes,
    ) -> DictPrompt:
        if isinstance(prompt, bytes):
            embeds = await self._safe_load_prompt_embeds_async(
                self.model_config, prompt
            )
            return EmbedsPrompt(prompt_embeds=embeds)

        return prompt

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

        return await asyncio.gather(
            *(self._render_prompt_async(prompt) for prompt in 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 _can_produce_offsets(self) -> bool:
        """Whether this renderer's tokenizer can emit char-level offsets.

        Defaults to False; only renderers backed by an HF fast tokenizer
        (see ``HfRenderer``) can produce ``offset_mapping``.
        """
        return False

    def _wants_offsets(
        self,
        prompt: "TextPrompt",
        params: "TokenizeParams",
    ) -> bool:
        return (
            params.return_token_offsets
            and self._can_produce_offsets()
            and not prompt.get("multi_modal_data")
            and not prompt.get("multi_modal_uuids")
        )

    @staticmethod
    def _build_tokens_prompt(
        token_ids: Sequence[int],
        prompt: "TextPrompt",
        *,
        offset_mapping: Sequence[tuple[int, int]] | None = None,
    ) -> "TokensPrompt":
        """Build a TokensPrompt from already-extracted token ids.

        ``offset_mapping`` is the per-token ``(start, end)`` sequence from
        a BatchEncoding; pass it only when offsets were requested, and it
        is attached as ``prompt_token_offsets``.
        """
        if offset_mapping is not None:
            return TokensPrompt(
                prompt_token_ids=list(token_ids),
                prompt_token_offsets=[(int(s), int(e)) for s, e in offset_mapping],
                **prompt,
            )
        return TokensPrompt(prompt_token_ids=list(token_ids), **prompt)

    @staticmethod
    def _apply_prompt_char_offset(
        prompt: TokensPrompt, char_offset: int
    ) -> TokensPrompt:
        """Map token offsets back to the original prompt after a text pre-trim."""
        if char_offset == 0:
            return prompt

        offsets = prompt.get("prompt_token_offsets")
        if offsets is not None:
            prompt["prompt_token_offsets"] = [
                # Fast tokenizers use (0, 0) for special tokens, which are
                # not character spans in the source prompt.
                (start, end)
                if (start, end) == (0, 0)
                else (start + char_offset, end + char_offset)
                for start, end in offsets
            ]

        return prompt

    def _tokenize_prompt(
        self,
        prompt: TextPrompt,
        params: TokenizeParams,
    ) -> TokensPrompt:
        tokenizer = self.get_tokenizer()
        want_offsets = self._wants_offsets(prompt, params)
        kwargs = params.get_encode_kwargs()
        if want_offsets:
            kwargs = {**kwargs, "return_offsets_mapping": True}
        encoding = tokenizer(prompt["prompt"], **kwargs)
        return self._build_tokens_prompt(
            encoding["input_ids"],
            prompt,
            offset_mapping=encoding["offset_mapping"] if want_offsets else None,
        )

    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:
        prompt["prompt"] = await self._async_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_char_offset = params._get_text_truncation_offset(
                self.tokenizer, prompt["prompt"]
            )
            prompt = params.apply_pre_tokenization(self.tokenizer, prompt)  # type: ignore[arg-type]
            prompt = self._tokenize_prompt(prompt, params)
            prompt = self._apply_prompt_char_offset(prompt, prompt_char_offset)

        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_char_offset = params._get_text_truncation_offset(
                self.tokenizer, prompt["prompt"]
            )
            prompt = params.apply_pre_tokenization(self.tokenizer, prompt)  # type: ignore[arg-type]
            prompt = await self._tokenize_prompt_async(prompt, params)
            prompt = self._apply_prompt_char_offset(prompt, prompt_char_offset)

        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

    def _process_multimodal(
        self,
        prompt: list[int],
        mm_data: MultiModalDataDict,
        mm_uuids: MultiModalUUIDDict | None,
        mm_processor_kwargs: Mapping[str, object] | None,
        media_io_kwargs: Mapping[str, Mapping[str, object]] | None = None,
        *,
        skip_mm_cache: bool = False,
    ) -> "MultiModalInput":
        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_req_id = f"renderer{self.api_process_rank}-mm-{self._mm_req_counter.inc(1)}"

        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 {},
            media_io_kwargs=media_io_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"),
                mm_uuids=prompt.get("multi_modal_uuids"),
                media_io_kwargs=prompt.get("media_io_kwargs"),
                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
        # Narrow the union — `prompt_token_offsets` is only on TokensInput.
        if engine_input["type"] == "token" and (
            (offsets := prompt.get("prompt_token_offsets")) is not None
        ):
            engine_input["prompt_token_offsets"] = offsets

        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"),
            prompt_token_ids=prompt.get("prompt_token_ids"),
            is_token_ids=prompt.get("prompt_is_token_ids"),
        )

    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"),
                mm_uuids=prompt.get("multi_modal_uuids"),
                media_io_kwargs=prompt.get("media_io_kwargs"),
                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
        # Narrow the union — `prompt_token_offsets` is only on TokensInput.
        if engine_input["type"] == "token" and (
            (offsets := prompt.get("prompt_token_offsets")) is not None
        ):
            engine_input["prompt_token_offsets"] = offsets

        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 _get_skip_decoder_start_token(self) -> bool:
        """Whether the multimodal processor supplies a complete decoder prefix."""
        if self.mm_processor is not None:
            from vllm.multimodal.processing import EncDecMultiModalProcessor

            if isinstance(self.mm_processor, EncDecMultiModalProcessor):
                return self.mm_processor.skip_decoder_start_token
        return False

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

        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=self._get_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(),
            skip_decoder_start_token=self._get_skip_decoder_start_token(),
        )

    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)

        prompt_extras = dict(prompt_extras or {})
        prompt_extras["media_io_kwargs"] = chat_params.media_io_kwargs or {}
        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)

        prompt_extras = dict(prompt_extras or {})
        prompt_extras["media_io_kwargs"] = chat_params.media_io_kwargs or {}
        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