# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
"""High-Performance Triton-only Attention layer."""

from dataclasses import dataclass
from typing import ClassVar, Optional, Union

import torch

from vllm.attention.backends.abstract import (
    AttentionBackend,
    AttentionImpl,
    AttentionMetadata,
    AttentionType,
    MultipleOf,
)
from vllm.attention.ops.triton_reshape_and_cache_flash import (
    triton_reshape_and_cache_flash,
)
from vllm.attention.ops.triton_unified_attention import unified_attention
from vllm.config import VllmConfig
from vllm.logger import init_logger
from vllm.model_executor.layers.quantization.utils.quant_utils import (
    QuantKey,
    kFp8StaticTensorSym,
)
from vllm.platforms import current_platform
from vllm.v1.attention.backends.utils import (
    AttentionCGSupport,
    AttentionMetadataBuilder,
    CommonAttentionMetadata,
)
from vllm.v1.kv_cache_interface import AttentionSpec

if current_platform.is_cuda_alike():
    from vllm import _custom_ops as ops
elif current_platform.is_xpu():
    from vllm._ipex_ops import ipex_ops as ops

logger = init_logger(__name__)


@dataclass
class TritonAttentionMetadata:
    # NOTE(sang): Definition of context_len, query_len, and seq_len.
    # |---------- N-1 iteration --------|
    # |---------------- N iteration ---------------------|
    # |- tokenA -|......................|-- newTokens ---|
    # |---------- context_len ----------|
    # |-------------------- seq_len ---------------------|
    #                                   |-- query_len ---|

    num_actual_tokens: int  # Number of tokens excluding padding.
    max_query_len: int
    query_start_loc: torch.Tensor
    max_seq_len: int
    seq_lens: torch.Tensor
    block_table: torch.Tensor
    slot_mapping: torch.Tensor

    # For cascade attention.
    use_cascade: bool
    common_prefix_len: int
    cu_prefix_query_lens: Optional[torch.Tensor]
    prefix_kv_lens: Optional[torch.Tensor]
    suffix_kv_lens: Optional[torch.Tensor]

    # Optional aot scheduling
    scheduler_metadata: Optional[torch.Tensor] = None
    prefix_scheduler_metadata: Optional[torch.Tensor] = None


class TritonAttentionMetadataBuilder(AttentionMetadataBuilder[TritonAttentionMetadata]):
    cudagraph_support: ClassVar[AttentionCGSupport] = AttentionCGSupport.ALWAYS

    def __init__(
        self,
        kv_cache_spec: AttentionSpec,
        layer_names: list[str],
        vllm_config: VllmConfig,
        device: torch.device,
    ):
        super().__init__(kv_cache_spec, layer_names, vllm_config, device)

        self.block_size = kv_cache_spec.block_size

        model_config = vllm_config.model_config
        self.num_heads_q = model_config.get_num_attention_heads(
            vllm_config.parallel_config
        )
        self.num_heads_kv = model_config.get_num_kv_heads(vllm_config.parallel_config)
        self.headdim = model_config.get_head_size()

    def build_for_cudagraph_capture(
        self, common_attn_metadata: CommonAttentionMetadata
    ) -> TritonAttentionMetadata:
        attn_metadata = self.build(0, common_attn_metadata)
        # When doing full graph capture, setting seq_lens to
        # max_model_len will cause graph capture to be extremely
        # slow, so here we set it to 1.
        attn_metadata.seq_lens.fill_(1)
        return attn_metadata

    def build(
        self,
        common_prefix_len: int,
        common_attn_metadata: CommonAttentionMetadata,
        fast_build: bool = False,
    ) -> TritonAttentionMetadata:
        num_actual_tokens = common_attn_metadata.num_actual_tokens
        max_query_len = common_attn_metadata.max_query_len

        max_seq_len = common_attn_metadata.max_seq_len
        query_start_loc = common_attn_metadata.query_start_loc
        seq_lens = common_attn_metadata.seq_lens
        block_table_tensor = common_attn_metadata.block_table_tensor
        slot_mapping = common_attn_metadata.slot_mapping

        use_cascade = common_prefix_len > 0

        if use_cascade:
            cu_prefix_query_lens = torch.tensor(
                [0, num_actual_tokens], dtype=torch.int32, device=self.device
            )
            prefix_kv_lens = torch.tensor(
                [common_prefix_len], dtype=torch.int32, device=self.device
            )
            suffix_kv_lens = common_attn_metadata.seq_lens_cpu - common_prefix_len
            suffix_kv_lens = suffix_kv_lens.to(self.device)
        else:
            cu_prefix_query_lens = None
            prefix_kv_lens = None
            suffix_kv_lens = None
            prefix_scheduler_metadata = None

        attn_metadata = TritonAttentionMetadata(
            num_actual_tokens=num_actual_tokens,
            max_query_len=max_query_len,
            query_start_loc=query_start_loc,
            max_seq_len=max_seq_len,
            seq_lens=seq_lens,
            block_table=block_table_tensor,
            slot_mapping=slot_mapping,
            use_cascade=use_cascade,
            common_prefix_len=common_prefix_len,
            cu_prefix_query_lens=cu_prefix_query_lens,
            prefix_kv_lens=prefix_kv_lens,
            suffix_kv_lens=suffix_kv_lens,
            prefix_scheduler_metadata=prefix_scheduler_metadata,
        )
        return attn_metadata


class TritonAttentionBackend(AttentionBackend):
    accept_output_buffer: bool = True

    @classmethod
    def get_supported_dtypes(cls) -> list[torch.dtype]:
        return [torch.float16, torch.bfloat16, torch.float32]

    @staticmethod
    def get_supported_kernel_block_size() -> list[Union[int, MultipleOf]]:
        return [MultipleOf(16)]

    @classmethod
    def validate_head_size(cls, head_size: int) -> None:
        # Triton Attention supports any head size above 32
        if head_size < 32:
            raise ValueError(
                f"Head size {head_size} is not supported by TritonAttention."
                f"Head sizes need to be larger or equal 32 for this backend. "
                "Set VLLM_ATTENTION_BACKEND=FLEX_ATTENTION to use "
                "FlexAttention backend which supports all head sizes."
            )

    @staticmethod
    def get_name() -> str:
        return "TRITON_ATTN"

    @staticmethod
    def get_impl_cls() -> type["TritonAttentionImpl"]:
        return TritonAttentionImpl

    @staticmethod
    def get_metadata_cls() -> type["AttentionMetadata"]:
        return TritonAttentionMetadata

    @staticmethod
    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, ...]:
        if block_size % 16 != 0:
            raise ValueError("Block size must be a multiple of 16.")
        return (num_blocks, 2, block_size, num_kv_heads, head_size)

    @staticmethod
    def use_cascade_attention(*args, **kwargs) -> bool:
        return False

    @staticmethod
    def get_builder_cls() -> type["TritonAttentionMetadataBuilder"]:
        return TritonAttentionMetadataBuilder


class TritonAttentionImpl(AttentionImpl):
    def fused_output_quant_supported(self, quant_key: QuantKey):
        return quant_key == kFp8StaticTensorSym

    def __init__(
        self,
        num_heads: int,
        head_size: int,
        scale: float,
        num_kv_heads: int,
        alibi_slopes: Optional[list[float]],
        sliding_window: Optional[int],
        kv_cache_dtype: str,
        logits_soft_cap: Optional[float] = None,
        attn_type: AttentionType = AttentionType.DECODER,
        kv_sharing_target_layer_name: Optional[int] = None,
        sinks: Optional[torch.Tensor] = None,
    ) -> None:
        self.num_heads = num_heads
        self.head_size = head_size
        self.scale = float(scale)
        self.num_kv_heads = num_kv_heads
        if alibi_slopes is not None:
            alibi_slopes = torch.tensor(alibi_slopes, dtype=torch.float32)
        self.alibi_slopes = alibi_slopes
        if sliding_window is None:
            self.sliding_window = (-1, -1)
        else:
            self.sliding_window = (sliding_window - 1, 0)
        self.kv_cache_dtype = kv_cache_dtype
        if logits_soft_cap is None:
            # In flash-attn, setting logits_soft_cap as 0 means no soft cap.
            logits_soft_cap = 0
        self.logits_soft_cap = logits_soft_cap
        self.kv_sharing_target_layer_name = kv_sharing_target_layer_name

        self.num_queries_per_kv = self.num_heads // self.num_kv_heads

        TritonAttentionBackend.validate_head_size(head_size)

        if attn_type != AttentionType.DECODER:
            raise NotImplementedError(
                "Encoder self-attention and "
                "encoder/decoder cross-attention "
                "are not implemented for "
                "TritonAttentionImpl"
            )

        self.fp8_dtype = current_platform.fp8_dtype()

        self.sinks = sinks
        if sinks is not None:
            assert sinks.shape[0] == num_heads, (
                "Sinks must have the same number of heads as the number of "
                f"heads in the layer. Sinks shape: {sinks.shape}, "
                f"num_heads: {num_heads}."
            )

    def forward(
        self,
        layer: torch.nn.Module,
        query: torch.Tensor,
        key: torch.Tensor,
        value: torch.Tensor,
        kv_cache: torch.Tensor,
        attn_metadata: TritonAttentionMetadata,
        output: Optional[torch.Tensor] = None,
        output_scale: Optional[torch.Tensor] = None,
        output_block_scale: Optional[torch.Tensor] = None,
    ) -> torch.Tensor:
        """Forward pass with Paged Attention impl. in Triton.

        Args:
            query: shape = [num_tokens, num_heads, head_size]
            key: shape = [num_tokens, num_kv_heads, head_size]
            value: shape = [num_tokens, num_kv_heads, head_size]
            kv_cache: shape =
                [num_blocks, 2, block_size, num_kv_heads, head_size]
            attn_metadata: Metadata for attention.
        Returns:
            shape = [num_tokens, num_heads * head_size]
        """
        assert output is not None, "Output tensor must be provided."

        if output_block_scale is not None:
            raise NotImplementedError(
                "fused block_scale output quantization is not yet supported"
                " for TritonAttentionImpl"
            )

        if attn_metadata is None:
            # Profiling run.
            return output

        assert attn_metadata.use_cascade is False

        # IMPORTANT!
        # NOTE(woosuk): With piece-wise CUDA graphs, this method is executed in
        # eager-mode PyTorch. Thus, we need to be careful about any CPU overhead
        # in this method. For example, `view` and `slice` (or `[:n]`) operations
        # are surprisingly slow even in the case they do not invoke any GPU ops.
        # Minimize the PyTorch ops in this method as much as possible.
        # Whenever making a change in this method, please benchmark the
        # performance to make sure it does not introduce any overhead.

        num_actual_tokens = attn_metadata.num_actual_tokens
        key_cache, value_cache = kv_cache.unbind(1)

        if self.kv_sharing_target_layer_name is None:
            # Reshape the input keys and values and store them in the cache.
            # Skip this if sharing KV cache with an earlier attention layer.
            if self.kv_cache_dtype.startswith("fp8"):
                key_cache = key_cache.view(self.fp8_dtype)
                value_cache = value_cache.view(self.fp8_dtype)
                # triton kernel does not support uint8 kv_cache
                #  (because some explicit casts (e.g. float8_e4m3fnuz)
                #   are not supported)
            triton_reshape_and_cache_flash(
                key,
                value,
                key_cache,
                value_cache,
                attn_metadata.slot_mapping,
                self.kv_cache_dtype,
                layer._k_scale,
                layer._v_scale,
            )

        if self.kv_cache_dtype.startswith("fp8"):
            if key_cache.dtype != self.fp8_dtype:
                key_cache = key_cache.view(self.fp8_dtype)
                value_cache = value_cache.view(self.fp8_dtype)
            num_tokens, num_heads, head_size = query.shape
            assert layer._q_scale_float == 1.0, (
                "A non 1.0 q_scale is not currently supported."
            )
            if current_platform.is_cuda():
                # Skip Q quantization on ROCm and XPU, enable this on cuda
                # only, since dequantizing back to f32 in the attention kernel
                # is not supported.
                query, _ = ops.scaled_fp8_quant(
                    query.reshape((num_tokens, num_heads * head_size)).contiguous(),
                    layer._q_scale,
                )
                query = query.reshape((num_tokens, num_heads, head_size))

        cu_seqlens_q = attn_metadata.query_start_loc
        seqused_k = attn_metadata.seq_lens
        max_seqlen_q = attn_metadata.max_query_len
        max_seqlen_k = attn_metadata.max_seq_len
        block_table = attn_metadata.block_table

        descale_shape = (cu_seqlens_q.shape[0] - 1, key.shape[1])

        unified_attention(
            q=query[:num_actual_tokens],
            k=key_cache,
            v=value_cache,
            out=output[:num_actual_tokens],
            cu_seqlens_q=cu_seqlens_q,
            max_seqlen_q=max_seqlen_q,
            seqused_k=seqused_k,
            max_seqlen_k=max_seqlen_k,
            softmax_scale=self.scale,
            causal=True,
            alibi_slopes=self.alibi_slopes,
            window_size=self.sliding_window,
            block_table=block_table,
            softcap=self.logits_soft_cap,
            q_descale=None,  # Not supported
            k_descale=layer._k_scale.expand(descale_shape),
            v_descale=layer._v_scale.expand(descale_shape),
            sinks=self.sinks,
            output_scale=output_scale,
        )

        return output
