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vllm.renderers.inputs.preprocess

Schemas and utilities for preprocessing inputs.

Classes:

Functions:

Attributes:

DecoderDictPrompt = TextPrompt | TokensPrompt module-attribute

A DecoderPrompt that has been standardized into a dictionary.

DecoderOnlyDictPrompt = TextPrompt | TokensPrompt | EmbedsPrompt module-attribute

A DecoderOnlyPrompt that has been standardized into a dictionary.

DictPrompt = DecoderOnlyDictPrompt | EncoderDecoderDictPrompt module-attribute

A PromptType that has been standardized into a dictionary.

EncoderDictPrompt = TextPrompt | TokensPrompt module-attribute

A EncoderPrompt that has been standardized into a dictionary.

SingletonDictPrompt = DecoderOnlyDictPrompt | EncoderDictPrompt | DecoderDictPrompt module-attribute

A SingletonPrompt that has been standardized into a dictionary.

EncoderDecoderDictPrompt

Bases: TypedDict

A EncoderDecoderPrompt that has been standardized into a dictionary.

Source code in vllm/renderers/inputs/preprocess.py 
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class EncoderDecoderDictPrompt(TypedDict):
    """A [`EncoderDecoderPrompt`][vllm.inputs.llm.EncoderDecoderPrompt]
    that has been standardized into a dictionary.
    """

    encoder_prompt: EncoderDictPrompt

    decoder_prompt: DecoderDictPrompt | None

_validate_prompt_dict(prompt)

Reject malformed dict prompts before renderer tokenization.

Source code in vllm/renderers/inputs/preprocess.py 
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def _validate_prompt_dict(prompt: Mapping[str, object]) -> None:
    """Reject malformed dict prompts before renderer tokenization."""
    if (
        "prompt" not in prompt
        or "prompt_token_ids" in prompt
        or "prompt_embeds" in prompt
    ):
        return

    if not isinstance(prompt["prompt"], str):
        raise TypeError("Prompt text should be a string")

parse_dec_only_prompt(prompt)

Parse a prompt for a decoder-only model and normalize it to a dictionary.

Source code in vllm/renderers/inputs/preprocess.py 
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def parse_dec_only_prompt(prompt: PromptType | object) -> DecoderOnlyDictPrompt:
    """Parse a prompt for a decoder-only model and normalize it to a dictionary."""
    if isinstance(prompt, str):
        return TextPrompt(prompt=prompt)

    if isinstance(prompt, list):
        if not is_list_of(prompt, int):
            raise TypeError("Token prompt should be a list of integers")

        return TokensPrompt(prompt_token_ids=prompt)

    if isinstance(prompt, dict):
        if "encoder_prompt" in prompt:
            raise TypeError("Cannot pass encoder-decoder prompt to decoder-only models")

        _validate_prompt_dict(prompt)

        if (
            "prompt" in prompt
            or "prompt_token_ids" in prompt
            or "prompt_embeds" in prompt
        ):
            return prompt  # type: ignore[return-value]

        raise TypeError("Prompt dictionary must contain text, tokens, or embeddings")

    raise TypeError("Prompt should be a string, list of tokens, or dictionary")

parse_enc_dec_prompt(prompt)

Parse a prompt for an encoder-decoder model and normalize it to a dictionary.

Source code in vllm/renderers/inputs/preprocess.py 
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def parse_enc_dec_prompt(prompt: PromptType | object) -> EncoderDecoderDictPrompt:
    """Parse a prompt for an encoder-decoder model and normalize it to a dictionary."""
    if isinstance(prompt, dict) and "encoder_prompt" in prompt:
        enc_prompt = prompt["encoder_prompt"]  # type: ignore[typeddict-item]
        dec_prompt = prompt["decoder_prompt"]  # type: ignore[typeddict-item]
    else:
        enc_prompt = prompt
        dec_prompt = None

    return EncoderDecoderDictPrompt(
        encoder_prompt=_parse_enc_prompt(enc_prompt),
        decoder_prompt=None if dec_prompt is None else _parse_dec_prompt(dec_prompt),
    )