add qwen3
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Chranos
373
vllm-v0.6.2/vllm/inputs/data.py
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373
vllm-v0.6.2/vllm/inputs/data.py
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from dataclasses import dataclass
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from functools import cached_property
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from typing import (TYPE_CHECKING, Any, Dict, Generic, Iterable, List, Literal,
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Optional, Tuple, Union, cast)
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import torch
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from typing_extensions import NotRequired, TypedDict, TypeVar, assert_never
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if TYPE_CHECKING:
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from vllm.multimodal import MultiModalDataDict, MultiModalPlaceholderDict
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from vllm.multimodal.inputs import MultiModalInputsV2
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class TextPrompt(TypedDict):
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"""Schema for a text prompt."""
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prompt: str
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"""The input text to be tokenized before passing to the model."""
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multi_modal_data: NotRequired["MultiModalDataDict"]
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"""
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Optional multi-modal data to pass to the model,
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if the model supports it.
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"""
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mm_processor_kwargs: NotRequired[Dict[str, Any]]
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"""
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Optional multi-modal processor kwargs to be forwarded to the
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multimodal input mapper & processor. Note that if multiple modalities
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have registered mappers etc for the model being considered, we attempt
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to pass the mm_processor_kwargs to each of them.
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"""
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class TokensPrompt(TypedDict):
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"""Schema for a tokenized prompt."""
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prompt_token_ids: List[int]
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"""A list of token IDs to pass to the model."""
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multi_modal_data: NotRequired["MultiModalDataDict"]
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"""
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DEPRECATED: Optional multi-modal data to pass to the model,
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if the model supports it.
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"""
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mm_processor_kwargs: NotRequired[Dict[str, Any]]
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"""
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DEPRECATED: Optional multi-modal processor kwargs to be forwarded to the
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multimodal input mapper & processor. Note that if multiple modalities
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have registered mappers etc for the model being considered, we attempt
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to pass the mm_processor_kwargs to each of them.
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"""
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SingletonPrompt = Union[str, TextPrompt, TokensPrompt]
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"""
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Set of possible schemas for a single prompt:
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- A text prompt (:class:`str` or :class:`TextPrompt`)
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- A tokenized prompt (:class:`TokensPrompt`)
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Note that "singleton" is as opposed to a data structure
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which encapsulates multiple prompts, i.e. of the sort
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which may be utilized for encoder/decoder models when
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the user desires to express both the encoder & decoder
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prompts explicitly, i.e. :class:`ExplicitEncoderDecoderPrompt`
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A prompt of type :class:`SingletonPrompt` may be employed
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as (1) input to a decoder-only model, (2) input to
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the encoder of an encoder/decoder model, in the scenario
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where the decoder-prompt is not specified explicitly, or
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(3) as a member of a larger data structure encapsulating
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more than one prompt, i.e. :class:`ExplicitEncoderDecoderPrompt`
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"""
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_T1_co = TypeVar("_T1_co",
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bound=SingletonPrompt,
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default=SingletonPrompt,
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covariant=True)
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_T2_co = TypeVar("_T2_co",
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bound=SingletonPrompt,
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default=SingletonPrompt,
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covariant=True)
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# TODO: Make fields ReadOnly once mypy supports it
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class ExplicitEncoderDecoderPrompt(TypedDict, Generic[_T1_co, _T2_co]):
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"""
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Represents an encoder/decoder model input prompt,
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comprising an explicit encoder prompt and a decoder prompt.
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The encoder and decoder prompts, respectively, may be formatted
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according to any of the :class:`SingletonPrompt` schemas,
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and are not required to have the same schema.
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Only the encoder prompt may have multi-modal data. mm_processor_kwargs
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should be at the top-level, and should not be set in the encoder/decoder
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prompts, since they are agnostic to the encoder/decoder.
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Note that an :class:`ExplicitEncoderDecoderPrompt` may not
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be used as an input to a decoder-only model,
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and that the :code:`encoder_prompt` and :code:`decoder_prompt`
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fields of this data structure themselves must be
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:class:`SingletonPrompt` instances.
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"""
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encoder_prompt: _T1_co
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decoder_prompt: Optional[_T2_co]
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mm_processor_kwargs: NotRequired[Dict[str, Any]]
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PromptType = Union[SingletonPrompt, ExplicitEncoderDecoderPrompt]
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"""
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Set of possible schemas for an LLM input, including
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both decoder-only and encoder/decoder input types:
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- A text prompt (:class:`str` or :class:`TextPrompt`)
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- A tokenized prompt (:class:`TokensPrompt`)
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- A single data structure containing both an encoder and a decoder prompt
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(:class:`ExplicitEncoderDecoderPrompt`)
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"""
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class TokenInputs(TypedDict):
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"""Represents token-based inputs."""
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type: Literal["token"]
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"""The type of inputs."""
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prompt_token_ids: List[int]
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"""The token IDs of the prompt."""
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prompt: NotRequired[str]
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"""
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The original prompt text corresponding to the token IDs, if available.
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"""
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multi_modal_data: NotRequired["MultiModalDataDict"]
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"""
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Optional multi-modal data to pass to the model,
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if the model supports it.
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"""
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multi_modal_placeholders: NotRequired["MultiModalPlaceholderDict"]
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"""
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Placeholder ranges for the multi-modal data.
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"""
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mm_processor_kwargs: NotRequired[Dict[str, Any]]
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"""
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Optional multi-modal processor kwargs to be forwarded to the
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multimodal input mapper & processor. Note that if multiple modalities
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have registered mappers etc for the model being considered, we attempt
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to pass the mm_processor_kwargs to each of them.
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"""
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def token_inputs(
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prompt_token_ids: List[int],
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prompt: Optional[str] = None,
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multi_modal_data: Optional["MultiModalDataDict"] = None,
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multi_modal_placeholders: Optional["MultiModalPlaceholderDict"] = None,
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mm_processor_kwargs: Optional[Dict[str, Any]] = None,
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) -> TokenInputs:
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"""Construct :class:`TokenInputs` from optional values."""
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inputs = TokenInputs(type="token", prompt_token_ids=prompt_token_ids)
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if prompt is not None:
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inputs["prompt"] = prompt
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if multi_modal_data is not None:
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inputs["multi_modal_data"] = multi_modal_data
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if multi_modal_placeholders is not None:
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inputs["multi_modal_placeholders"] = multi_modal_placeholders
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if mm_processor_kwargs is not None:
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inputs["mm_processor_kwargs"] = mm_processor_kwargs
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return inputs
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DecoderOnlyInputs = Union[TokenInputs, "MultiModalInputsV2"]
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"""
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The inputs in :class:`~vllm.LLMEngine` before they are
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passed to the model executor.
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This specifies the data required for decoder-only models.
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"""
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class EncoderDecoderInputs(TypedDict):
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"""
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The inputs in :class:`~vllm.LLMEngine` before they are
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passed to the model executor.
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This specifies the required data for encoder-decoder models.
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"""
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encoder: Union[TokenInputs, "MultiModalInputsV2"]
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"""The inputs for the encoder portion."""
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decoder: Union[TokenInputs, "MultiModalInputsV2"]
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"""The inputs for the decoder portion."""
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SingletonInputs = Union[TokenInputs, "MultiModalInputsV2"]
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"""
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A processed :class:`SingletonPrompt` which can be passed to
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:class:`vllm.sequence.Sequence`.
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"""
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@dataclass
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class SingletonInputsAdapter:
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"""
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Unified interface to access the components of :class:`SingletonInputs`.
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"""
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inputs: SingletonInputs
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@cached_property
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def prompt(self) -> Optional[str]:
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inputs = self.inputs
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if inputs["type"] == "token" or inputs["type"] == "multimodal":
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return inputs.get("prompt")
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assert_never(inputs)
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@cached_property
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def prompt_token_ids(self) -> List[int]:
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inputs = self.inputs
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if inputs["type"] == "token" or inputs["type"] == "multimodal":
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return inputs.get("prompt_token_ids", [])
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assert_never(inputs)
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@cached_property
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def prompt_embeds(self) -> Optional[torch.Tensor]:
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inputs = self.inputs
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if inputs["type"] == "token" or inputs["type"] == "multimodal":
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return None
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assert_never(inputs)
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@cached_property
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def multi_modal_data(self) -> "MultiModalDataDict":
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inputs = self.inputs
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if inputs["type"] == "token":
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return inputs.get("multi_modal_data", {})
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if inputs["type"] == "multimodal":
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return inputs.get("mm_kwargs", {})
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assert_never(inputs)
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@cached_property
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def multi_modal_placeholders(self) -> "MultiModalPlaceholderDict":
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inputs = self.inputs
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if inputs["type"] == "token":
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return inputs.get("multi_modal_placeholders", {})
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if inputs["type"] == "multimodal":
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return inputs.get("mm_placeholders", {})
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assert_never(inputs)
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@cached_property
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def mm_processor_kwargs(self) -> Dict[str, Any]:
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inputs = self.inputs
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if inputs["type"] == "token":
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return inputs.get("mm_processor_kwargs", {})
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if inputs["type"] == "multimodal":
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return {}
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assert_never(inputs)
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ProcessorInputs = Union[DecoderOnlyInputs, EncoderDecoderInputs]
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"""
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The inputs to :data:`vllm.inputs.InputProcessor`.
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"""
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_T1 = TypeVar("_T1", bound=SingletonPrompt, default=SingletonPrompt)
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_T2 = TypeVar("_T2", bound=SingletonPrompt, default=SingletonPrompt)
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def build_explicit_enc_dec_prompt(
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encoder_prompt: _T1,
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decoder_prompt: Optional[_T2],
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mm_processor_kwargs: Optional[Dict[str, Any]] = None,
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) -> ExplicitEncoderDecoderPrompt[_T1, _T2]:
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if mm_processor_kwargs is None:
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mm_processor_kwargs = {}
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return ExplicitEncoderDecoderPrompt(
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encoder_prompt=encoder_prompt,
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decoder_prompt=decoder_prompt,
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mm_processor_kwargs=mm_processor_kwargs)
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def zip_enc_dec_prompts(
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enc_prompts: Iterable[_T1],
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dec_prompts: Iterable[Optional[_T2]],
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mm_processor_kwargs: Optional[Union[Iterable[Dict[str, Any]],
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Dict[str, Any]]] = None,
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) -> List[ExplicitEncoderDecoderPrompt[_T1, _T2]]:
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"""
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Zip encoder and decoder prompts together into a list of
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:class:`ExplicitEncoderDecoderPrompt` instances.
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``mm_processor_kwargs`` may also be provided; if a dict is passed, the same
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dictionary will be used for every encoder/decoder prompt. If an iterable is
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provided, it will be zipped with the encoder/decoder prompts.
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"""
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if mm_processor_kwargs is None:
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mm_processor_kwargs = cast(Dict[str, Any], {})
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if isinstance(mm_processor_kwargs, dict):
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return [
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build_explicit_enc_dec_prompt(
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encoder_prompt, decoder_prompt,
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cast(Dict[str, Any], mm_processor_kwargs))
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for (encoder_prompt,
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decoder_prompt) in zip(enc_prompts, dec_prompts)
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]
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return [
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build_explicit_enc_dec_prompt(encoder_prompt, decoder_prompt,
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mm_proc_kwargs)
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for (encoder_prompt, decoder_prompt, mm_proc_kwargs
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) in zip(enc_prompts, dec_prompts, mm_processor_kwargs)
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]
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def to_enc_dec_tuple_list(
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enc_dec_prompts: Iterable[ExplicitEncoderDecoderPrompt[_T1, _T2]],
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) -> List[Tuple[_T1, Optional[_T2]]]:
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return [(enc_dec_prompt["encoder_prompt"],
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enc_dec_prompt["decoder_prompt"])
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for enc_dec_prompt in enc_dec_prompts]
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def __getattr__(name: str):
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import warnings
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if name == "PromptInput":
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msg = ("PromptInput has been renamed to PromptType. "
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"The original name will be removed in an upcoming version.")
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warnings.warn(DeprecationWarning(msg), stacklevel=2)
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return PromptType
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if name == "LLMInputs":
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msg = ("LLMInputs has been renamed to DecoderOnlyInputs. "
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"The original name will be removed in an upcoming version.")
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warnings.warn(DeprecationWarning(msg), stacklevel=2)
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return DecoderOnlyInputs
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if name == "EncoderDecoderLLMInputs":
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msg = (
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"EncoderDecoderLLMInputs has been renamed to EncoderDecoderInputs. "
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"The original name will be removed in an upcoming version.")
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warnings.warn(DeprecationWarning(msg), stacklevel=2)
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return EncoderDecoderInputs
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raise AttributeError(f"module {__name__!r} has no attribute {name!r}")
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