vllm.inputs

class DummyData(NamedTuple):
    """
    Dummy data used for profiling.

    Note: This is only used in V0.
    """

    seq_data: SequenceData
    multi_modal_data: Optional[MultiModalDataDict] = None
    multi_modal_placeholders: Optional[MultiModalPlaceholderDict] = None

class EmbedsInputs(TypedDict):
    """Represents embeddings-based inputs."""

    type: Literal["embeds"]
    """The type of inputs."""

    prompt_embeds: torch.Tensor
    """The embeddings of the prompt."""

    cache_salt: NotRequired[str]
    """
    Optional cache salt to be used for prefix caching.
    """

class EncoderDecoderInputs(TypedDict):
    """
    The inputs in [`LLMEngine`][vllm.engine.llm_engine.LLMEngine] before they
    are passed to the model executor.

    This specifies the required data for encoder-decoder models.
    """

    encoder: Union[TokenInputs, "MultiModalInputs"]
    """The inputs for the encoder portion."""

    decoder: Union[TokenInputs, "MultiModalInputs"]
    """The inputs for the decoder portion."""

class ExplicitEncoderDecoderPrompt(TypedDict, Generic[_T1_co, _T2_co]):
    """
    Represents an encoder/decoder model input prompt,
    comprising an explicit encoder prompt and a decoder prompt.

    The encoder and decoder prompts, respectively, may be formatted
    according to any of the
    [`SingletonPrompt`][vllm.inputs.data.SingletonPrompt] schemas,
    and are not required to have the same schema.

    Only the encoder prompt may have multi-modal data. mm_processor_kwargs
    should be at the top-level, and should not be set in the encoder/decoder
    prompts, since they are agnostic to the encoder/decoder.

    Note that an
    [`ExplicitEncoderDecoderPrompt`][vllm.inputs.data.ExplicitEncoderDecoderPrompt]
    may not be used as an input to a decoder-only model,
    and that the `encoder_prompt` and `decoder_prompt`
    fields of this data structure themselves must be
    [`SingletonPrompt`][vllm.inputs.data.SingletonPrompt] instances.
    """

    encoder_prompt: _T1_co

    decoder_prompt: Optional[_T2_co]

    mm_processor_kwargs: NotRequired[dict[str, Any]]

@dataclass(frozen=True)
class InputContext:
    """
    Contains information about the model which may be used to
    modify the inputs.
    """

    model_config: ModelConfig
    """The configuration of the model."""

    def get_hf_config(
        self,
        typ: Union[type[_C], tuple[type[_C], ...]] = PretrainedConfig,
        /,
    ) -> _C:
        """
        Get the HuggingFace configuration
        (`transformers.PretrainedConfig`) of the model,
        additionally checking its type.

        Raises:
            TypeError: If the configuration is not of the specified type.
        """
        hf_config = self.model_config.hf_config
        if not isinstance(hf_config, typ):
            raise TypeError("Invalid type of HuggingFace config. "
                            f"Expected type: {typ}, but "
                            f"found type: {type(hf_config)}")

        return hf_config

    def get_hf_image_processor_config(self) -> dict[str, Any]:
        """
        Get the HuggingFace image processor configuration of the model.
        """
        return self.model_config.hf_image_processor_config

    def get_mm_config(self):
        """
        Get the multimodal config of the model.

        Raises:
            RuntimeError: If the model is not a multimodal model.
        """
        mm_config = self.model_config.multimodal_config
        if mm_config is None:
            raise RuntimeError("Not a multimodal model")

        return mm_config

    def get_hf_processor(
        self,
        typ: Union[type[_P], tuple[type[_P], ...]] = ProcessorMixin,
        /,
        **kwargs: object,
    ) -> _P:
        """
        Get the HuggingFace processor
        (`transformers.ProcessorMixin`) of the model,
        additionally checking its type.

        Raises:
            TypeError: If the processor is not of the specified type.
        """
        return cached_processor_from_config(
            self.model_config,
            processor_cls=typ,
            **kwargs,
        )

    def init_processor(
        self,
        typ: type[_T],
        /,
        **kwargs: object,
    ) -> _T:
        """
        Initialize a HuggingFace-like processor class, merging the
        keyword arguments with those in the model's configuration.
        """
        mm_config = self.model_config.get_multimodal_config()
        base_kwargs = mm_config.mm_processor_kwargs
        if base_kwargs is None:
            base_kwargs = {}

        merged_kwargs = {**base_kwargs, **kwargs}

        return typ(**merged_kwargs)

@dataclass(frozen=True)
class InputProcessingContext(InputContext):
    tokenizer: AnyTokenizer
    """The tokenizer used to tokenize the inputs."""

    def get_hf_processor(
        self,
        typ: Union[type[_P], tuple[type[_P], ...]] = ProcessorMixin,
        /,
        **kwargs: object,
    ) -> _P:
        return super().get_hf_processor(
            typ,
            tokenizer=self.tokenizer,
            **kwargs,
        )

    def call_hf_processor(
        self,
        hf_processor: ProcessorMixin,
        data: Mapping[str, object],
        kwargs: Mapping[str, object] = {},
    ) -> Union[BatchFeature, JSONTree]:
        """
        Call `hf_processor` on the prompt `data`
        (text, image, audio...) with configurable options `kwargs`.
        """
        assert callable(hf_processor)

        mm_config = self.model_config.get_multimodal_config()
        merged_kwargs = mm_config.merge_mm_processor_kwargs(kwargs)

        allowed_kwargs = get_allowed_kwarg_only_overrides(
            hf_processor,
            merged_kwargs,
            requires_kw_only=False,
            allow_var_kwargs=True,
        )

        def maybe_cast_dtype(x):
            # This mimics the behavior of transformers.BatchFeature
            if isinstance(x, torch.Tensor) and x.is_floating_point():
                return x.to(dtype=self.model_config.dtype)
            return x

        try:
            output = hf_processor(**data,
                                  **allowed_kwargs,
                                  return_tensors="pt")
            # this emulates output.to(dtype=self.model_config.dtype)
            if isinstance(output, BatchFeature):
                cast_output = json_map_leaves(maybe_cast_dtype, output.data)
                return BatchFeature(cast_output)

            cast_output = json_map_leaves(maybe_cast_dtype, output)

            logger.warning_once(
                f"{type(hf_processor).__name__} did not return `BatchFeature`. "
                "Make sure to match the behaviour of `ProcessorMixin` when "
                "implementing custom processors.")
            return cast_output

        except Exception as exc:
            msg = (f"Failed to apply {type(hf_processor).__name__} "
                   f"on data={data} with kwargs={allowed_kwargs}")

            raise ValueError(msg) from exc

class InputRegistry:
    """
    Note: This is only used in V0.
    """

    def dummy_data_for_profiling(
        self,
        model_config: ModelConfig,
        seq_len: int,
        mm_registry: MultiModalRegistry,
        is_encoder_data: bool = False,
    ) -> DummyData:
        """
        Create dummy data for profiling the memory usage of a model.

        The model is identified by ``model_config``.
        """
        # Avoid circular import
        from vllm.sequence import SequenceData

        if not model_config.is_multimodal_model:
            seq_data = SequenceData.from_prompt_token_counts((0, seq_len))
            return DummyData(seq_data=seq_data)

        # Encoder dummy data does not contain multi-modal data
        if is_encoder_data:
            enc_data = mm_registry.get_encoder_dummy_data(
                model_config, seq_len)
            seq_data = SequenceData.from_seqs(enc_data.prompt_token_ids)
            return DummyData(seq_data=seq_data)

        dec_data = mm_registry.get_decoder_dummy_data(model_config, seq_len)

        return DummyData(
            seq_data=SequenceData.from_seqs(dec_data.prompt_token_ids),
            multi_modal_data=dec_data.multi_modal_data,
            multi_modal_placeholders=dec_data.multi_modal_placeholders,
        )

class TextPrompt(TypedDict):
    """Schema for a text prompt."""

    prompt: str
    """The input text to be tokenized before passing to the model."""

    multi_modal_data: NotRequired["MultiModalDataDict"]
    """
    Optional multi-modal data to pass to the model,
    if the model supports it.
    """

    mm_processor_kwargs: NotRequired[dict[str, Any]]
    """
    Optional multi-modal processor kwargs to be forwarded to the
    multimodal input mapper & processor. Note that if multiple modalities
    have registered mappers etc for the model being considered, we attempt
    to pass the mm_processor_kwargs to each of them.
    """

    cache_salt: NotRequired[str]
    """
    Optional cache salt to be used for prefix caching.
    """

class TokenInputs(TypedDict):
    """Represents token-based inputs."""

    type: Literal["token"]
    """The type of inputs."""

    prompt_token_ids: list[int]
    """The token IDs of the prompt."""

    token_type_ids: NotRequired[list[int]]
    """The token type IDs of the prompt."""

    prompt: NotRequired[str]
    """
    The original prompt text corresponding to the token IDs, if available.
    """

    cache_salt: NotRequired[str]
    """
    Optional cache salt to be used for prefix caching.
    """

class TokensPrompt(TypedDict):
    """Schema for a tokenized prompt."""

    prompt_token_ids: list[int]
    """A list of token IDs to pass to the model."""

    token_type_ids: NotRequired[list[int]]
    """A list of token type IDs to pass to the cross encoder model."""

    multi_modal_data: NotRequired["MultiModalDataDict"]
    """
    Optional multi-modal data to pass to the model,
    if the model supports it.
    """

    mm_processor_kwargs: NotRequired[dict[str, Any]]
    """
    Optional multi-modal processor kwargs to be forwarded to the
    multimodal input mapper & processor. Note that if multiple modalities
    have registered mappers etc for the model being considered, we attempt
    to pass the mm_processor_kwargs to each of them.
    """

    cache_salt: NotRequired[str]
    """
    Optional cache salt to be used for prefix caching.
    """