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

from abc import ABC, abstractmethod
from typing import TYPE_CHECKING, ClassVar, Generic, Protocol, TypeVar, get_args

import torch

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

if TYPE_CHECKING:
    from vllm.config.cache import CacheDType
    from vllm.platforms.interface import DeviceCapability
    from vllm.v1.attention.backends.utils import KVCacheLayoutType


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 MultipleOf:
    base: int

    def __init__(self, base: int):
        self.base = base


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
    supported_dtypes: ClassVar[list[torch.dtype]] = [torch.float16, torch.bfloat16]
    supported_kernel_block_sizes: ClassVar[list[int | MultipleOf]] = [MultipleOf(1)]
    supported_kv_cache_dtypes: ClassVar[list["CacheDType"]] = ["auto"]

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

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

    @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,
        cache_dtype_str: str = "auto",
    ) -> tuple[int, ...]:
        raise NotImplementedError

    @staticmethod
    def get_kv_cache_stride_order(
        include_num_layers_dimension: bool = False,
    ) -> tuple[int, ...]:
        """
        Get the physical (memory layout) ordering of the kv cache dimensions.
        e.g. if the KV cache shape is
        [2, num_blocks, block_size, num_heads, head_size],
        and get_kv_cache_stride_order returns (1, 3, 0, 2, 4) then the physical
        ordering of dimensions is
        [num_blocks, num_heads, 2, block_size, head_size].

        If this function is unimplemented / raises NotImplementedError,
        the physical layout of the KV cache will match the logical shape.

        Args:
            include_num_layers_dimension: if True, includes an additional
                num_layers dimension, which is assumed to be prepended
                to the logical KV cache shape.
                With the above example, a return value (2, 4, 0, 1, 3, 5)
                corresponds to
                [num_blocks, num_heads, num_layers, 2, block_size, head_size].

                If an additional dimension is NOT included in the returned
                tuple, the physical layout will not include a layers dimension.

        Returns:
            A tuple of ints which is a permutation of range(len(shape)).
        """
        raise NotImplementedError

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

    @classmethod
    def get_supported_head_sizes(cls) -> list[int]:
        return []

    @classmethod
    def supports_head_size(cls, head_size: int) -> bool:
        supported_head_sizes = cls.get_supported_head_sizes()
        return (not supported_head_sizes) or head_size in supported_head_sizes

    @classmethod
    def supports_dtype(cls, dtype: torch.dtype) -> bool:
        return dtype in cls.supported_dtypes

    @classmethod
    def supports_kv_cache_dtype(cls, kv_cache_dtype: "CacheDType | None") -> bool:
        if kv_cache_dtype is None:
            return True
        return (not cls.supported_kv_cache_dtypes) or (
            kv_cache_dtype in cls.supported_kv_cache_dtypes
        )

    @classmethod
    def supports_block_size(cls, block_size: int | None) -> bool:
        from vllm.config.cache import BlockSize

        if block_size is None:
            return True

        valid_sizes = get_args(BlockSize)
        if block_size not in valid_sizes:
            return False

        if not cls.supported_kernel_block_sizes:
            return True

        for supported_size in cls.supported_kernel_block_sizes:
            if isinstance(supported_size, MultipleOf):
                supported_size = supported_size.base
            # With hybrid_blocks feature, the framework-level block size
            # only needs to be a multiple of the kernel's requirement,
            # even if the kernel requires a fixed block_size.
            if block_size % supported_size == 0:
                return True
        return False

    @classmethod
    def is_mla(cls) -> bool:
        return False

    @classmethod
    def supports_sink(cls) -> bool:
        return False

    @classmethod
    def is_sparse(cls) -> bool:
        return False

    @classmethod
    def supports_attn_type(cls, attn_type: str) -> bool:
        """Check if backend supports a given attention type.

        By default, only supports decoder attention.
        Backends should override this to support other attention types.
        """
        from vllm.attention import AttentionType

        return attn_type == AttentionType.DECODER

    @classmethod
    def supports_compute_capability(cls, capability: "DeviceCapability") -> bool:
        return True

    @classmethod
    def supports_combination(
        cls,
        head_size: int,
        dtype: torch.dtype,
        kv_cache_dtype: "CacheDType | None",
        block_size: int | None,
        use_mla: bool,
        has_sink: bool,
        use_sparse: bool,
        device_capability: "DeviceCapability",
    ) -> str | None:
        return None

    @classmethod
    def validate_configuration(
        cls,
        head_size: int,
        dtype: torch.dtype,
        kv_cache_dtype: "CacheDType | None",
        block_size: int | None,
        use_mla: bool,
        has_sink: bool,
        use_sparse: bool,
        device_capability: "DeviceCapability",
        attn_type: str,
    ) -> list[str]:
        invalid_reasons = []
        if not cls.supports_head_size(head_size):
            invalid_reasons.append("head_size not supported")
        if not cls.supports_dtype(dtype):
            invalid_reasons.append("dtype not supported")
        if not cls.supports_kv_cache_dtype(kv_cache_dtype):
            invalid_reasons.append("kv_cache_dtype not supported")
        if not cls.supports_block_size(block_size):
            invalid_reasons.append("block_size not supported")
        if use_mla != cls.is_mla():
            if use_mla:
                invalid_reasons.append("MLA not supported")
            else:
                invalid_reasons.append("non-MLA not supported")
        if has_sink and not cls.supports_sink():
            invalid_reasons.append("sink setting not supported")
        if use_sparse != cls.is_sparse():
            if use_sparse:
                invalid_reasons.append("sparse not supported")
            else:
                invalid_reasons.append("non-sparse not supported")
        if not cls.supports_compute_capability(device_capability):
            invalid_reasons.append("compute capability not supported")
        if not cls.supports_attn_type(attn_type):
            invalid_reasons.append(f"attention type {attn_type} not supported")
        combination_reason = cls.supports_combination(
            head_size,
            dtype,
            kv_cache_dtype,
            block_size,
            use_mla,
            has_sink,
            use_sparse,
            device_capability,
        )
        if combination_reason is not None:
            invalid_reasons.append(combination_reason)
        return invalid_reasons

    @classmethod
    def get_required_kv_cache_layout(cls) -> "KVCacheLayoutType | None":
        return None


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

    pcp_world_size: int
    pcp_rank: int

    total_cp_world_size: int
    total_cp_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
        try:
            from vllm.distributed.parallel_state import get_pcp_group

            self.pcp_world_size = get_pcp_group().world_size
            self.pcp_rank = get_pcp_group().rank_in_group
        except AssertionError:
            self.pcp_world_size = 1
            self.pcp_rank = 0
        self.total_cp_world_size = self.pcp_world_size * self.dcp_world_size
        self.total_cp_rank = self.pcp_rank * self.dcp_world_size + self.dcp_rank

        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: int | None = None,
        alibi_slopes: list[float] | None = None,
        sliding_window: int | None = None,
        kv_cache_dtype: str = "auto",
        logits_soft_cap: float | None = None,
        attn_type: str = AttentionType.DECODER,
        kv_sharing_target_layer_name: str | None = 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: torch.Tensor | None = None,
        output_scale: torch.Tensor | None = None,
        output_block_scale: torch.Tensor | None = 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

    def supports_quant_query_input(self) -> bool:
        """
        Check if this attention implementation supports pre-quantized query input.

        When True, the attention layer will quantize queries before passing them
        to this backend, allowing torch.compile to fuse the quantization with
        previous operations. This is typically supported when using FP8 KV cache
        with compatible attention kernels (e.g., TRT-LLM).
        TODO add support to more backends:
        https://github.com/vllm-project/vllm/issues/25584

        Returns:
            bool: True if the implementation can accept pre-quantized queries.
        """
        return False

    def process_weights_after_loading(self, act_dtype: torch.dtype):
        pass


class MLAAttentionImpl(AttentionImpl[T], Generic[T]):
    @abstractmethod
    def __init__(
        self,
        num_heads: int,
        head_size: int,
        scale: float,
        num_kv_heads: int,
        alibi_slopes: list[float] | None,
        sliding_window: int | None,
        kv_cache_dtype: str,
        logits_soft_cap: float | None,
        attn_type: str,
        kv_sharing_target_layer_name: str | None,
        # MLA Specific Arguments
        q_lora_rank: int | None,
        kv_lora_rank: int,
        qk_nope_head_dim: int,
        qk_rope_head_dim: int,
        qk_head_dim: int,
        v_head_dim: int,
        kv_b_proj: ColumnParallelLinear,
        indexer: object | None = None,
    ) -> None:
        raise NotImplementedError

    @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: torch.Tensor | None = None,
        output_scale: torch.Tensor | None = None,
        output_block_scale: torch.Tensor | None = None,
    ) -> torch.Tensor:
        raise NotImplementedError


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