################################################################################
# Copyright(c)2020-2025 Shanghai Biren Technology Co., Ltd. All rights reserved.
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#    http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
################################################################################
"""Attention layer with FlashAttention."""
import os
from dataclasses import dataclass
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, Type

import torch
import torch_br

from vllm.attention.backends.abstract import (AttentionBackend, AttentionImpl,
                                              AttentionType,
                                              is_quantized_kv_cache)
from vllm.attention.backends.utils import CommonAttentionState
from vllm.attention.utils.fa_utils import (flash_attn_supports_fp8,
                                           get_flash_attn_version)
from vllm.logger import logger

if TYPE_CHECKING:
    from vllm.worker.model_runner import (ModelInputForGPUBuilder)

from collections import defaultdict
from itertools import accumulate

from vllm.attention.backends.utils import (PAD_SLOT_ID, CommonAttentionState,
                                           compute_slot_mapping,
                                           compute_slot_mapping_start_idx,
                                           is_block_tables_empty)
from vllm.multimodal import MultiModalPlaceholderMap
from vllm.utils import async_tensor_h2d, make_tensor_with_pad


class SUPAFlashAttentionBackend(AttentionBackend):

    # NOTE: When piecewise cudagraph is enabled, this
    # makes sure the output tensor is allocated inside the cudagraph.
    # NOTE: currently, we do not support accept_output_buffer=True
    accept_output_buffer: bool = False

    @staticmethod
    def get_supported_head_sizes() -> list[int]:
        return [32, 64, 96, 128, 160, 192, 224, 256]

    @staticmethod
    def get_name() -> str:
        return "SUPAFLASH_ATTN_VLLM_V0"

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

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

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

    @staticmethod
    def get_state_cls() -> Type["CommonAttentionState"]:
        return CommonAttentionState

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

    @staticmethod
    def get_kv_cache_usharp_shape(
        num_blocks: int,
        block_size: int,
        num_kv_heads: int,
        head_size: int,
    ) -> Tuple[int, ...]:
        th_gran = SUPAFlashAttentionBackend.get_kv_cache_usharp_alignment(
            block_size)
        n_block = max(1, (num_blocks + th_gran - 1) // th_gran)
        logger.debug(
            f'Origin kv cache shape is [2, {num_blocks}, {block_size}, {num_kv_heads}, {head_size}, For SUPA Speed up, use [2, {n_block}, {th_gran * block_size}, {num_kv_heads * head_size}]'  # noqa: G004
        )
        return (2, n_block, th_gran * block_size, num_kv_heads * head_size)

    @staticmethod
    def get_kv_cache_usharp_alignment(block_size: int) -> int:
        max_h_limit = 2048
        return max_h_limit // block_size


@dataclass
class SUPAFlashAttentionMetadata:
    # 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
    seq_lens_tensor: torch.Tensor
    block_table: torch.Tensor
    slot_mapping: torch.Tensor

    # BIREN Attention Params
    seq_start_loc: torch.Tensor
    context_lens: torch.Tensor
    max_decode_seq_len: int
    num_prefills: int
    num_decodes: int
    num_prefills_tokens: int
    do_cache: bool  # when use attentionsplit, do cache = False

    # 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
    _cached_prefill_metadata: Optional["SUPAFlashAttentionMetadata"] = None
    _cached_decode_metadata: Optional["SUPAFlashAttentionMetadata"] = None

    # for local attention
    @dataclass
    class LocalAttentionMetadata:
        local_query_start_loc: torch.Tensor
        local_seqused_k: torch.Tensor
        local_block_table: torch.Tensor
        local_max_query_len: int
        local_max_seq_len: int
        local_scheduler_metadata: Optional[torch.Tensor]

    local_attn_metadata: Optional[LocalAttentionMetadata] = None

    @property
    def do_prefill(self) -> bool:
        return self.num_prefills > 0

    @property
    def do_decode(self) -> bool:
        return self.num_decodes > 0

    @property
    def prefill_metadata(self) -> Optional["SUPAFlashAttentionMetadata"]:
        if self.num_prefills == 0:
            return None

        if self._cached_prefill_metadata is not None:
            return self._cached_prefill_metadata
        else:
            return None


class SUPAFlashAttentionMetadataBuilder:

    def __init__(self, input_builder: "ModelInputForGPUBuilder"):
        self.input_builder = input_builder
        self.runner = input_builder.runner
        self.sliding_window = input_builder.sliding_window
        self.block_size = input_builder.block_size

    def prepare(self):
        self.slot_mapping: List[int] = []
        self.prefill_seq_lens: List[int] = []
        self.context_lens: List[int] = []
        self.block_tables: List[List[int]] = []
        self.curr_seq_lens: List[int] = []
        self.multimodal_placeholder_maps: Dict[
            str,
            MultiModalPlaceholderMap] = defaultdict(MultiModalPlaceholderMap)
        self.num_prefills = 0
        self.num_prefill_tokens = 0
        self.num_decode_tokens = 0
        self.has_prefix_cache_hit = False

    def _add_seq_group(
            self, inter_data: "ModelInputForGPUBuilder.InterDataForSeqGroup",
            chunked_prefill_enabled: bool, prefix_cache_hit: bool):
        """Add a sequence group to the metadata. Specifically update/append
        1. context length.
        2. block table.
        3. slot mapping.
        """
        is_prompt = inter_data.is_prompt
        block_tables = inter_data.block_tables

        for (seq_id, token_len, seq_len, curr_seq_len, query_len, context_len,
             curr_sliding_window_block) in zip(
                 inter_data.seq_ids, [len(t) for t in inter_data.input_tokens],
                 inter_data.orig_seq_lens,
                 inter_data.seq_lens,
                 inter_data.query_lens,
                 inter_data.context_lens,
                 inter_data.curr_sliding_window_blocks,
                 strict=False):
            self.context_lens.append(context_len)

            if is_prompt:
                mm_maps = inter_data.multi_modal_placeholder_maps
                if mm_maps:
                    for modality, placeholders in mm_maps.items():
                        self.multimodal_placeholder_maps[modality].extend(
                            placeholders)

                self.num_prefills += 1
                self.num_prefill_tokens += token_len
                self.prefill_seq_lens.append(seq_len)
            else:
                self.num_decode_tokens += query_len
                self.curr_seq_lens.append(curr_seq_len)

            # Compute block table.
            # TODO(sang): Combine chunked prefill and prefix caching by
            # only allowing multiple of block_size chunk size.
            # NOTE: This only works for oooooooxxx style attention.
            block_table = []
            if prefix_cache_hit:
                # NOTE(woosuk): For flash-attn, the block table should
                # include the entries for the incoming prefill tokens.
                block_table = block_tables[seq_id]
            elif ((chunked_prefill_enabled or not is_prompt)
                  and block_tables is not None):
                if curr_sliding_window_block == 0:
                    block_table = block_tables[seq_id]
                else:
                    block_table = block_tables[seq_id][
                        -curr_sliding_window_block:]
            self.block_tables.append(block_table)

            # Compute slot mapping.
            is_profile_run = is_block_tables_empty(block_tables)
            start_idx = compute_slot_mapping_start_idx(is_prompt, query_len,
                                                       context_len,
                                                       self.sliding_window)
            compute_slot_mapping(is_profile_run, self.slot_mapping, seq_id,
                                 seq_len, context_len, start_idx,
                                 self.block_size, inter_data.block_tables)

    def _get_graph_runner_block_tables(
            self, num_seqs: int,
            block_tables: List[List[int]]) -> torch.Tensor:
        # The shape of graph_block_tables is
        # [max batch size, max context len // block size].
        max_batch_size, max_blocks = self.runner.graph_block_tables.shape
        assert max_batch_size >= num_seqs

        graph_block_tables = self.runner.graph_block_tables[:num_seqs]
        for i, block_table in enumerate(block_tables):
            if block_table:
                num_blocks = len(block_table)
                if num_blocks <= max_blocks:
                    graph_block_tables[i, :num_blocks] = block_table
                else:
                    # It may be possible to have more blocks allocated due
                    # to lookahead slots of multi-step, however, they are
                    # not used anyway, so can be safely ignored.
                    graph_block_tables[
                        i, :max_blocks] = block_table[:max_blocks]

        return torch.from_numpy(graph_block_tables).to(
            device=self.runner.device, non_blocking=True)

    def build(self, seq_lens: List[int], query_lens: List[int],
              cuda_graph_pad_size: int, batch_size: int):
        """Build attention metadata with on-device tensors.

        Args:
            seq_lens: The maybe padded sequence lengths of the input sequences.
            query_lens: The query lengths of the input sequences.
            cuda_graph_pad_size: The padding size for cuda graph.
                                 -1 if cuda graph is not used.
            batch_size: The maybe padded batch size.
        """
        prefix_cache_hit = any([
            inter_data.prefix_cache_hit
            for inter_data in self.input_builder.inter_data_list
        ])
        for inter_data in self.input_builder.inter_data_list:
            self._add_seq_group(inter_data,
                                self.input_builder.chunked_prefill_enabled,
                                prefix_cache_hit)

        device = self.runner.device
        use_captured_graph = cuda_graph_pad_size != -1

        max_query_len = max(query_lens)
        # decode_query_lens = query_lens[self.num_prefills:]
        # if len(decode_query_lens) > 0:
        #     max_decode_query_len = max(decode_query_lens)
        # else:
        #     max_decode_query_len = 1
        max_prefill_seq_len = max(self.prefill_seq_lens, default=0)
        max_decode_seq_len = max(self.curr_seq_lens, default=0)
        num_decode_tokens = self.num_decode_tokens
        query_start_loc = list(accumulate(query_lens, initial=0))
        seq_start_loc = list(accumulate(seq_lens, initial=0))

        num_seqs = len(seq_lens)
        if use_captured_graph:
            self.slot_mapping.extend([PAD_SLOT_ID] * cuda_graph_pad_size)
            self.block_tables.extend([] * cuda_graph_pad_size)
            num_decode_tokens = batch_size - self.num_prefill_tokens
            block_tables = self._get_graph_runner_block_tables(
                num_seqs, self.block_tables)
        else:
            block_tables = make_tensor_with_pad(
                self.block_tables,
                pad=0,
                dtype=torch.int,
                device=device,
            )
        assert max_query_len > 0, ("query_lens: {}".format(query_lens))

        assert device is not None
        context_lens_tensor = async_tensor_h2d(self.context_lens, torch.int,
                                               device, self.runner.pin_memory)
        seq_lens_tensor = async_tensor_h2d(seq_lens, torch.int, device,
                                           self.runner.pin_memory)
        slot_mapping_tensor = async_tensor_h2d(self.slot_mapping, torch.long,
                                               device, self.runner.pin_memory)
        query_start_loc_tensor = async_tensor_h2d(query_start_loc, torch.int32,
                                                  device,
                                                  self.runner.pin_memory)
        return SUPAFlashAttentionMetadata(
            num_actual_tokens=batch_size,
            max_query_len=max_query_len,
            query_start_loc=query_start_loc_tensor,
            max_seq_len=max_prefill_seq_len,
            seq_lens=seq_lens,
            seq_lens_tensor=seq_lens_tensor,
            block_table=block_tables,
            slot_mapping=slot_mapping_tensor,
            use_cascade=False,
            common_prefix_len=0,
            scheduler_metadata=0,
            cu_prefix_query_lens=None,
            prefix_kv_lens=None,
            suffix_kv_lens=None,
            local_attn_metadata=None,
            prefix_scheduler_metadata=None,
            # Biren Attention Params
            seq_start_loc=seq_start_loc,
            context_lens=context_lens_tensor,
            max_decode_seq_len=max_decode_seq_len,
            num_prefills=self.num_prefills,
            num_decodes=num_decode_tokens,
            num_prefills_tokens=self.num_prefill_tokens,
            do_cache=False)


class SUPAFlashAttentionImpl(AttentionImpl):

    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,
        blocksparse_params: Optional[dict[str, Any]] = None,
        logits_soft_cap: Optional[float] = None,
        attn_type: AttentionType = AttentionType.DECODER,
        use_irope: bool = False,
    ) -> None:
        if blocksparse_params is not None:
            raise ValueError(
                "FlashAttention does not support block-sparse attention.")
        self.num_heads = num_heads
        self.head_size = head_size
        self.scale = float(scale)
        self.num_kv_heads = num_kv_heads
        self.attn_type = attn_type
        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

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

        support_head_sizes = SUPAFlashAttentionBackend.get_supported_head_sizes(
        )
        if head_size not in support_head_sizes:
            raise ValueError(
                f"Head size {head_size} is not supported by FlashAttention. "
                f"Supported head sizes are: {support_head_sizes}. "
                "Set VLLM_USE_V1=1 to use another attention backend.")

        self.use_irope = use_irope
        self.vllm_flash_attn_version = get_flash_attn_version()
        if is_quantized_kv_cache(self.kv_cache_dtype) \
            and not flash_attn_supports_fp8():
            raise NotImplementedError(
                "FlashAttention does not support fp8 kv-cache on this device.")

    def forward(
        self,
        layer: torch.nn.Module,
        query: torch.Tensor,
        key: torch.Tensor,
        value: torch.Tensor,
        kv_cache: torch.Tensor,
        attn_metadata: SUPAFlashAttentionMetadata,
        output: Optional[torch.Tensor] = None,
    ) -> torch.Tensor:
        """Forward pass with FlashAttention.

        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]
        NOTE: FP8 quantization, flash-attn expect the size of
              {q,k,v}_descale to be (num_sequences, num_kv_heads).
              We use torch's .expand() to avoid duplicating values
        """
        assert output is None, "Output tensor should not provided."
        if attn_metadata is None:
            # FIXME: this may lead to wrong block estimatation
            # Profiling run.
            return query

        # NOTE: supa attn use [batch_size, num_tokens, num_heads * head_size] as shape
        if kv_cache is not None and attn_metadata.do_cache:
            torch_br.supa_kvcache_store_infer_v2(
                kv_cache,
                key,
                value,  # type: ignore
                attn_metadata.slot_mapping,
                self.head_size)

        output_prefill = output_decode = None
        output = torch.empty_like(query)

        if attn_metadata.do_prefill and attn_metadata.do_decode:
            # chunked
            decode_query = query[:, attn_metadata.num_prefills_tokens:]
            query = query[:, :attn_metadata.num_prefills_tokens]

            key = key[:, :attn_metadata.num_prefills_tokens]
            value = value[:, :attn_metadata.num_prefills_tokens]
        elif attn_metadata.do_decode:
            decode_query = query

        if attn_metadata.do_prefill:
            if (kv_cache is None or attn_metadata.block_table.numel() == 0):
                # has do_decode should go into prefix-enabled branch
                assert not attn_metadata.do_decode

                # in this branch, query_start_loc = seq_start_loc
                if os.getenv('USE_BR_SUEAGER_SDPA',
                             'False').lower() not in {'false', '0', ''}:
                    output_prefill, inter_mediate = torch_br.sueager_scaled_dot_product_attention_fwd(
                        query=query,
                        key=key,
                        value=value,
                        mask=None,
                        dropout_prob=0.0,
                        is_causal=_get_causal_option(self.attn_type),
                        scale=self.scale,
                        algorithm="FMHA",
                    )
                    output_prefill = torch_br.supa_shape_transform_qkv(
                        output_prefill, 1, query.shape[1], self.num_kv_heads,
                        self.head_size)
                else:
                    output_prefill = torch_br.supa_flash_attention_infer(  # type: ignore
                        query,
                        key,
                        value,
                        attn_metadata.query_start_loc,
                        self.head_size,
                        len(attn_metadata.query_start_loc),  # type: ignore
                        self.alibi_slopes,
                        softmax_scale=self.scale,
                        is_causal=_get_causal_option(self.attn_type))
            else:
                # prefix-enabled attention
                output_prefill = torch_br.supa_flash_attn_cache_infer(  # type: ignore
                    query,
                    kv_cache,
                    attn_metadata.query_start_loc,
                    attn_metadata.seq_start_loc,
                    attn_metadata.block_table,
                    attn_metadata.context_lens,
                    attn_metadata.slot_mapping,
                    attn_metadata.max_seq_len,
                    self.head_size,
                    self.alibi_slopes,
                    softmax_scale=self.scale)

        if attn_metadata.do_decode:
            output_decode = torch_br.supa_attention_decoder_infer_v2(  # type: ignore
                decode_query,  # type: ignore
                kv_cache,
                attn_metadata.block_table,
                attn_metadata.seq_lens,
                attn_metadata.max_decode_seq_len,
                self.head_size,
                attn_metadata.num_prefills,
                self.alibi_slopes,
                softmax_scale=self.scale)

        if attn_metadata.do_prefill and attn_metadata.do_decode:
            output[:, :attn_metadata.num_prefills_tokens] = output_prefill
            output[:, attn_metadata.num_prefills_tokens:] = output_decode
        elif attn_metadata.do_prefill:
            output = output_prefill
        else:
            output = output_decode

        return output


def _get_causal_option(attn_type: str) -> bool:
    """
    Determine whether the given attention type is suitable for causal
    attention mechanisms.

    Args:
        attn_type (AttentionType): The type of attention being evaluated

    Returns:
        bool: Returns `True` if the attention type is suitable for causal
        attention (i.e., not encoder, encoder-only, or encoder-decoder),
        otherwise returns `False`.
    """
    return not (attn_type == AttentionType.ENCODER
                or attn_type == AttentionType.ENCODER_ONLY
                or attn_type == AttentionType.ENCODER_DECODER)