abstract.py

# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project

from abc import ABC, abstractmethod
from typing import Generic, List, Optional, Protocol, Tuple, Type, TypeVar

import torch

from vllm.model_executor.layers.quantization.utils.quant_utils import QuantKey


class AttentionType:
    """
    Attention type.
    Use string to be compatible with `torch.compile`.
    """
    DECODER = "decoder"
    """Decoder attention between previous layer Q/K/V."""
    ENCODER = "encoder"
    """Encoder attention between previous layer Q/K/V for encoder-decoder."""
    ENCODER_ONLY = "encoder_only"
    """Encoder attention between previous layer Q/K/V."""
    ENCODER_DECODER = "encoder_decoder"
    """Attention between dec. Q and enc. K/V for encoder-decoder."""


class AttentionBackend(ABC):
    """Abstract class for attention backends."""
    # For some attention backends, we allocate an output tensor before
    # calling the custom op. When piecewise cudagraph is enabled, this
    # makes sure the output tensor is allocated inside the cudagraph.
    accept_output_buffer: bool = False

    @staticmethod
    @abstractmethod
    def get_name() -> str:
        raise NotImplementedError

    @staticmethod
    @abstractmethod
    def get_impl_cls() -> Type["AttentionImpl"]:
        raise NotImplementedError

    @staticmethod
    @abstractmethod
    def get_metadata_cls() -> Type["AttentionMetadata"]:
        raise NotImplementedError

    @classmethod
    def make_metadata(cls, *args, **kwargs) -> "AttentionMetadata":
        return cls.get_metadata_cls()(*args, **kwargs)

    @staticmethod
    @abstractmethod
    def get_builder_cls():  # -> Type["AttentionMetadataBuilder"]:
        raise NotImplementedError

    @staticmethod
    @abstractmethod
    def get_kv_cache_shape(
        num_blocks: int,
        block_size: int,
        num_kv_heads: int,
        head_size: int,
    ) -> Tuple[int, ...]:
        raise NotImplementedError

    @staticmethod
    def get_kv_cache_stride_order() -> Tuple[int, ...]:
        raise NotImplementedError

    @classmethod
    def full_cls_name(cls) -> tuple[str, str]:
        return (cls.__module__, cls.__qualname__)


class AttentionMetadata:
    pass


T = TypeVar("T", bound=AttentionMetadata)


class AttentionLayer(Protocol):

    _q_scale: torch.Tensor
    _k_scale: torch.Tensor
    _v_scale: torch.Tensor
    _q_scale_float: float
    _k_scale_float: float
    _v_scale_float: float
    _prob_scale: torch.Tensor

    def forward(
        self,
        query: torch.Tensor,
        key: torch.Tensor,
        value: torch.Tensor,
        kv_cache: torch.Tensor,
        attn_metadata: AttentionMetadata,
    ) -> torch.Tensor:
        ...


class AttentionImpl(ABC, Generic[T]):

    # Whether the attention impl can return the softmax lse for decode.
    # Some features like decode context parallelism require the softmax lse.
    can_return_lse_for_decode: bool = False

    # some attention backends might not always want to return lse
    # even if they can return lse (for efficiency reasons)
    need_to_return_lse_for_decode: bool = False

    dcp_world_size: int
    dcp_rank: int

    def __new__(cls, *args, **kwargs):
        # use __new__ so that all subclasses will call this
        self = super().__new__(cls)
        try:
            from vllm.distributed.parallel_state import get_dcp_group
            self.dcp_world_size = get_dcp_group().world_size
            self.dcp_rank = get_dcp_group().rank_in_group
        except AssertionError:
            # DCP might not be initialized in testing
            self.dcp_world_size = 1
            self.dcp_rank = 0
        self.need_to_return_lse_for_decode = self.dcp_world_size > 1 \
            and self.can_return_lse_for_decode
        return self

    @abstractmethod
    def __init__(
        self,
        num_heads: int,
        head_size: int,
        scale: float,
        num_kv_heads: Optional[int] = None,
        alibi_slopes: Optional[List[float]] = None,
        sliding_window: Optional[int] = None,
        kv_cache_dtype: str = "auto",
        logits_soft_cap: Optional[float] = None,
        attn_type: str = AttentionType.DECODER,
        kv_sharing_target_layer_name: Optional[str] = None,
    ) -> None:
        raise NotImplementedError

    @abstractmethod
    def forward(
        self,
        layer: AttentionLayer,
        query: torch.Tensor,
        key: torch.Tensor,
        value: torch.Tensor,
        kv_cache: torch.Tensor,
        attn_metadata: T,
        output: Optional[torch.Tensor] = None,
        output_scale: Optional[torch.Tensor] = None,
        output_block_scale: Optional[torch.Tensor] = None,
    ) -> torch.Tensor:
        raise NotImplementedError

    def fused_output_quant_supported(self, quant_key: QuantKey):
        """
        Does this attention implementation support fused output quantization.
        This is used by the AttnFusionPass to only fuse output quantization
        onto implementations that support it.

        :param quant_key: QuantKey object that describes the quantization op
        :return: is fusion supported for this type of quantization
        """
        return False


class MLAAttentionImpl(AttentionImpl[T], Generic[T]):

    @abstractmethod
    def forward(
        self,
        layer: AttentionLayer,
        hidden_states_or_cq: torch.Tensor,
        kv_c_normed: torch.Tensor,
        k_pe: torch.Tensor,
        kv_cache: torch.Tensor,
        attn_metadata: T,
        output: Optional[torch.Tensor] = None,
        output_scale: Optional[torch.Tensor] = None,
        output_block_scale: Optional[torch.Tensor] = None,
    ) -> torch.Tensor:
        raise NotImplementedError


def is_quantized_kv_cache(kv_cache_dtype: str) -> bool:
    return kv_cache_dtype != "auto"