"""Attention layer with Flash and PagedAttention.

NOTE(woosuk): At the moment, this file includes a lot of duplicated code from
XFormers backend. The duplicated code will be removed once we use flash-attn or
flashinfer for all the attention operations.
"""
from dataclasses import dataclass
from typing import Dict, List, Optional, Tuple, Type

import torch
import torch_musa
from torch.nn.functional import scaled_dot_product_attention

from vllm.attention.backends.abstract import (AttentionBackend, AttentionImpl,
                                              AttentionMetadata,
                                              AttentionMetadataPerStage)
from vllm.attention.ops.paged_attn import (PagedAttention,
                                           PagedAttentionMetadata)


class FlashAttentionBackend(AttentionBackend):

    @staticmethod
    def get_impl_cls() -> Type["FlashAttentionImpl"]:
        return FlashAttentionImpl

    @staticmethod
    def make_metadata(*args, **kwargs) -> "FlashAttentionMetadata":
        return FlashAttentionMetadata(*args, **kwargs)

    @staticmethod
    def get_kv_cache_shape(
        num_blocks: int,
        block_size: int,
        num_kv_heads: int,
        head_size: int,
    ) -> Tuple[int, ...]:
        return PagedAttention.get_kv_cache_shape(num_blocks, block_size,
                                                 num_kv_heads, head_size)

    @staticmethod
    def swap_blocks(
        src_kv_cache: torch.Tensor,
        dst_kv_cache: torch.Tensor,
        src_to_dst: Dict[int, int],
    ) -> None:
        PagedAttention.swap_blocks(src_kv_cache, dst_kv_cache, src_to_dst)

    @staticmethod
    def copy_blocks(
        kv_caches: List[torch.Tensor],
        src_to_dists: Dict[int, List[int]],
    ) -> None:
        PagedAttention.copy_blocks(kv_caches, src_to_dists)


@dataclass
class FlashAttentionMetadata(AttentionMetadataPerStage,
                             PagedAttentionMetadata):
    """Metadata for FlashAttentionBackend.

    NOTE: Any python object stored here is not updated when it is
    cuda-graph replayed. If you have values that need to be changed
    dynamically, it should be stored in tensor. The tensor has to be
    updated from `CUDAGraphRunner.forward` API.
    """
    # Currently, input sequences can only contain all prompts
    # or all decoding. True if all sequences are prompts.
    is_prompt: bool
    # (batch_size,). The sequence length per sequence. Sequence length means
    # the computed tokens + new tokens None if it is a decoding.
    seq_lens: Optional[List[int]]
    # seq_lens stored as a tensor.
    seq_lens_tensor: Optional[torch.Tensor]

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

    # Maximum query length in the batch.
    max_query_len: Optional[int]
    # Maximum sequence length in the batch.
    max_seq_len: Optional[int]
    # (batch_size + 1,). The cumulative subquery lengths of the sequences in
    # the batch, used to index into subquery. E.g., if the subquery length
    # is [4, 6], it is [0, 4, 10].
    subquery_start_loc: Optional[torch.Tensor]
    # (batch_size + 1,). The cumulative sequence lengths of the sequences in
    # the batch, used to index into sequence. E.g., if the sequence length is
    # [4, 6], it is [0, 4, 10].
    seq_start_loc: Optional[torch.Tensor]
    # (batch_size,) A tensor of context lengths (tokens that are computed
    # so far).
    context_lens_tensor: Optional[torch.Tensor]

    # Whether or not if cuda graph is enabled.
    # Cuda-graph is currently enabled for decoding only.
    # TODO(woosuk): Move `use_cuda_graph` out since it's unrelated to attention.
    use_cuda_graph: bool


class FlashAttentionImpl(AttentionImpl):
    """
    If the input tensors contain prompt tokens, the layout is as follows:
    |<--------------- num_prefill_tokens ----------------->|	
    |<--prefill_0-->|<--prefill_1-->|...|<--prefill_N-1--->|

    Otherwise, the layout is as follows:	
    |<----------------- num_decode_tokens ------------------>|	
    |<--decode_0-->|..........|<--decode_M-1-->|<--padding-->|

    Generation tokens can contain padding when cuda-graph is used.
    Currently, prompt tokens don't contain any padding.

    The prompts might have different lengths, while the generation tokens
    always have length 1.

    If chunked prefill is enabled, prefill tokens and decode tokens can be
    batched together in a flattened 1D query.

    |<----- num_prefill_tokens ---->|<------- num_decode_tokens --------->|
    |<-prefill_0->|...|<-prefill_N-1->|<--decode_0-->|...|<--decode_M-1-->|

    Currently, cuda graph is disabled for chunked prefill, meaning there's no
    padding between prefill and decode tokens.
    """

    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,
    ) -> None:
        self.num_heads = num_heads
        self.head_size = head_size
        self.scale = float(scale)
        self.num_kv_heads = num_heads if num_kv_heads is None else num_kv_heads
        self.sliding_window = -1
        if alibi_slopes is not None:
            alibi_slopes = torch.tensor(alibi_slopes, dtype=torch.float32)
        self.alibi_slopes = alibi_slopes
        
        self.need_mask = (self.alibi_slopes is not None
                          or self.sliding_window is not None)

        assert self.num_heads % self.num_kv_heads == 0
        self.num_queries_per_kv = self.num_heads // self.num_kv_heads

        suppored_head_sizes = PagedAttention.get_supported_head_sizes()
        if head_size not in suppored_head_sizes:
            raise ValueError(
                f"Head size {head_size} is not supported by PagedAttention. "
                f"Supported head sizes are: {suppored_head_sizes}.")

    def forward(
        self,
        query: torch.Tensor,
        key: torch.Tensor,
        value: torch.Tensor,
        kv_cache: torch.Tensor,
        attn_metadata: AttentionMetadata[FlashAttentionMetadata],
        kv_scale: float,
    ) -> torch.Tensor:
        """Forward pass with FlashAttention and PagedAttention.

        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 = [2, num_blocks, block_size * num_kv_heads * head_size]
            attn_metadata: Metadata for attention.
        Returns:
            shape = [num_tokens, num_heads * head_size]
        """
        num_tokens, hidden_size = query.shape
        # Reshape the query, key, and value tensors.
        query = query.view(-1, self.num_heads, self.head_size)
        key = key.view(-1, self.num_kv_heads, self.head_size)
        value = value.view(-1, self.num_kv_heads, self.head_size)
        
        # enable musa flash attention
        torch.backends.cuda.enable_flash_sdp(True)
        torch.backends.cuda.enable_math_sdp(False)
        torch.backends.cuda.enable_mem_efficient_sdp(True)

        if kv_cache is not None:
            key_cache, value_cache = PagedAttention.split_kv_cache(
                kv_cache, self.num_kv_heads, self.head_size)

            # Reshape the input keys and values and store them in the cache.
            # If kv_cache is not provided, the new key and value tensors are
            # not cached. This happens during the initial memory profiling run.
            PagedAttention.write_to_paged_cache(key, value, key_cache,
                                                value_cache,
                                                attn_metadata.slot_mapping,
                                                attn_metadata.kv_cache_dtype,
                                                kv_scale)

        num_prefill_tokens = attn_metadata.num_prefill_tokens
        num_decode_tokens = attn_metadata.num_decode_tokens
        assert key.shape[0] == num_prefill_tokens + num_decode_tokens
        assert value.shape[0] == num_prefill_tokens + num_decode_tokens

        output = torch.empty_like(query)
        # Query for decode. KV is not needed because it is already cached.
        decode_query = query[num_prefill_tokens:]
        # QKV for prefill.
        query = query[:num_prefill_tokens]
        key = key[:num_prefill_tokens]
        value = value[:num_prefill_tokens]
        query = query.movedim(0, query.dim() - 2).unsqueeze(0)
        key = key.movedim(0, key.dim() - 2).unsqueeze(0)
        value = value.movedim(0, value.dim() - 2).unsqueeze(0)

        assert decode_query.shape[0] == num_decode_tokens

        if prefill_meta := attn_metadata.prefill_metadata:
            tensor = torch.full(
            (1, 1, num_tokens, num_tokens),
            dtype=torch.bool,
            fill_value=1,
            device=query.device)
            att_mask = torch.tril(tensor, diagonal=0)
            # Prompt run.
            if kv_cache is None or prefill_meta.block_tables.numel() == 0:
                # normal attention
                # When block_tables are not filled, it means q and k are the
                # prompt, and they have the same length.
                attn_output = scaled_dot_product_attention(
                    query.contiguous(),
                    key.contiguous(),
                    value.contiguous(),
                    attn_mask=att_mask.contiguous(),
                    dropout_p=0.0,
                    is_causal=False,
                )
                attn_output = attn_output.squeeze(0).permute(1, 0, 2).contiguous()
                assert output[:num_prefill_tokens].shape == attn_output.shape
                output[:num_prefill_tokens] = attn_output
            else:
                # prefix-enabled attention
                # TODO(Hai) this triton kernel has regression issue (broke) to
                # deal with different data types between KV and FP8 KV cache,
                # to be addressed separately.
                output[:num_prefill_tokens] = PagedAttention.forward_prefix(
                    query,
                    key,
                    value,
                    key_cache,
                    value_cache,
                    prefill_meta.block_tables,
                    prefill_meta.subquery_start_loc,
                    prefill_meta.seq_lens_tensor,
                    prefill_meta.context_lens_tensor,
                    prefill_meta.max_query_len,
                    self.alibi_slopes,
                    self.sliding_window[0],
                )
        if decode_meta := attn_metadata.decode_metadata:
            # Decoding run.
            output[num_prefill_tokens:] = PagedAttention.forward_decode(
                decode_query,
                key_cache,
                value_cache,
                decode_meta.block_tables,
                decode_meta.seq_lens_tensor,
                decode_meta.max_seq_len,
                attn_metadata.kv_cache_dtype,
                self.num_kv_heads,
                self.scale,
                self.alibi_slopes,
                kv_scale,
            )

        # Reshape the output tensor.
        return output.view(num_tokens, hidden_size)
