152 lines
4.4 KiB
Python
152 lines
4.4 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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from collections.abc import Sequence
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from typing import Literal, Optional, TypedDict, Union, cast, overload
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from typing_extensions import TypeIs
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from vllm.utils import is_list_of
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from .data import (EmbedsPrompt, ExplicitEncoderDecoderPrompt, ProcessorInputs,
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PromptType, SingletonInputs, SingletonPrompt, TextPrompt,
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TokensPrompt)
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class ParsedText(TypedDict):
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content: str
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is_tokens: Literal[False]
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class ParsedTokens(TypedDict):
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content: list[int]
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is_tokens: Literal[True]
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@overload
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def parse_and_batch_prompt(
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prompt: Union[str, list[str]], ) -> Sequence[ParsedText]:
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...
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@overload
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def parse_and_batch_prompt(
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prompt: Union[list[int], list[list[int]]], ) -> Sequence[ParsedTokens]:
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...
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def parse_and_batch_prompt(
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prompt: Union[str, list[str], list[int], list[list[int]]],
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) -> Union[Sequence[ParsedText], Sequence[ParsedTokens]]:
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if isinstance(prompt, str):
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# case 1: a string
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return [ParsedText(content=prompt, is_tokens=False)]
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if isinstance(prompt, list):
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if len(prompt) == 0:
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raise ValueError("please provide at least one prompt")
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if is_list_of(prompt, str):
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# case 2: array of strings
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prompt = cast(list[str], prompt)
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return [
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ParsedText(content=elem, is_tokens=False) for elem in prompt
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]
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if is_list_of(prompt, int):
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# case 3: array of tokens
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prompt = cast(list[int], prompt)
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return [ParsedTokens(content=prompt, is_tokens=True)]
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if is_list_of(prompt, list):
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prompt = cast(list[list[int]], prompt)
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if len(prompt[0]) == 0:
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raise ValueError("please provide at least one prompt")
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if is_list_of(prompt[0], int):
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# case 4: array of token arrays
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return [
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ParsedTokens(content=elem, is_tokens=True)
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for elem in prompt
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]
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raise TypeError("prompt must be a string, array of strings, "
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"array of tokens, or array of token arrays")
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class ParsedStrPrompt(TypedDict):
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type: Literal["str"]
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content: str
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class ParsedTextPrompt(TypedDict):
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type: Literal["text"]
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content: TextPrompt
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class ParsedTokensPrompt(TypedDict):
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type: Literal["tokens"]
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content: TokensPrompt
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class ParsedEmbedsPrompt(TypedDict):
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type: Literal["embeds"]
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content: EmbedsPrompt
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ParsedSingletonPrompt = Union[ParsedStrPrompt, ParsedTextPrompt,
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ParsedTokensPrompt, ParsedEmbedsPrompt]
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@overload
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def parse_singleton_prompt(prompt: str) -> ParsedStrPrompt:
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...
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@overload
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def parse_singleton_prompt(prompt: TextPrompt) -> ParsedTextPrompt:
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...
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@overload
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def parse_singleton_prompt(prompt: TokensPrompt) -> ParsedTokensPrompt:
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...
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@overload
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def parse_singleton_prompt(prompt: EmbedsPrompt) -> ParsedEmbedsPrompt:
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...
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def parse_singleton_prompt(prompt: SingletonPrompt) -> ParsedSingletonPrompt:
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if isinstance(prompt, str):
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return ParsedStrPrompt(type="str", content=prompt)
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elif isinstance(prompt, dict):
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# Type ignores are because mypy does not correctly infer the TypedDicts
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# Pyright does succeed.
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if "prompt_embeds" in prompt:
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return ParsedEmbedsPrompt(
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type="embeds", content=prompt) # type: ignore[typeddict-item]
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elif "prompt_token_ids" in prompt:
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return ParsedTokensPrompt(
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type="tokens", content=prompt) # type: ignore[typeddict-item]
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elif "prompt" in prompt:
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return ParsedTextPrompt(type="text", content=prompt)
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raise TypeError(
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"inputs must be a string, TextPrompt, TokensPrompt, or EmbedsPrompt")
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def is_explicit_encoder_decoder_prompt(
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prompt: PromptType, ) -> TypeIs[ExplicitEncoderDecoderPrompt]:
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return isinstance(prompt, dict) and "encoder_prompt" in prompt
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def split_enc_dec_inputs(
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inputs: ProcessorInputs,
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) -> tuple[Optional[SingletonInputs], SingletonInputs]:
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if "encoder" in inputs and "decoder" in inputs:
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# NOTE: This passes pyright but not mypy
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return (
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inputs["encoder"], # type: ignore[typeddict-item]
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inputs["decoder"], # type: ignore[typeddict-item]
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)
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return None, inputs
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