# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
# SPDX-FileCopyrightText: Songlin Yang, Yu Zhang
#
# This file contains code copied from the flash-linear-attention project.
# The original source code was licensed under the MIT license and included
# the following copyright notice:
# Copyright (c) 2023-2025, Songlin Yang, Yu Zhang
# ruff: noqa: E501
# mypy: ignore-errors
from typing import Optional

import torch
from vllm.triton_utils import tl, triton

from .utils import prepare_chunk_indices


@triton.heuristics({"IS_VARLEN": lambda args: args["cu_seqlens"] is not None})
@triton.jit(do_not_specialize=["T"])
def solve_tril_16x16_kernel(
    A,
    Ad,
    cu_seqlens,
    chunk_indices,
    T,
    H: tl.constexpr,
    BT: tl.constexpr,
    IS_VARLEN: tl.constexpr,
    LARGE_BLOCK_T: tl.constexpr,
):
    i_t, i_bh = tl.program_id(0), tl.program_id(1)
    i_b, i_h = i_bh // H, i_bh % H
    if IS_VARLEN:
        i_n, i_t = (
            tl.load(chunk_indices + i_t * 2).to(tl.int32),
            tl.load(chunk_indices + i_t * 2 + 1).to(tl.int32),
        )
        bos, eos = (
            tl.load(cu_seqlens + i_n).to(tl.int32),
            tl.load(cu_seqlens + i_n + 1).to(tl.int32),
        )
        T = eos - bos
    else:
        bos, eos = i_b * T, i_b * T + T

    A = A + (bos * H + i_h) * BT
    Ad = Ad + (bos * H + i_h) * 16

    base_t = i_t * LARGE_BLOCK_T

    NTASKS: tl.constexpr = 2
    N_BLOCKS: tl.constexpr = LARGE_BLOCK_T // 16 // NTASKS

    for taskid in range(0, NTASKS):
        base_t += taskid * (LARGE_BLOCK_T // NTASKS)

        # use make_block_ptr to reduce vector computation
        b_A = tl.zeros((N_BLOCKS, 16, 16), dtype=tl.float32)
        for blkid in range(0, N_BLOCKS):
            row_start_o = base_t + blkid * 16
            col_start_o = row_start_o % BT

            # 1 Create in-block offset
            offs_rows_in_block = tl.arange(0, 16)
            offs_cols_in_block = tl.arange(0, 16)

            # 2 Calculate the pointer of each element
            ptr_A_subrec16 = (A + row_start_o * H * BT + col_start_o +
                              offs_rows_in_block[:, None] * H * BT +
                              offs_cols_in_block[None, :])

            # 3 Create a mask to prevent out-of-bounds access
            global_rows = row_start_o + offs_rows_in_block[:, None]
            global_cols = col_start_o + offs_cols_in_block[None, :]
            load_mask = (global_rows < T) & (global_cols < BT)

            # 4 Use mask to safely load data
            b_A_subrec16 = tl.load(ptr_A_subrec16, mask=load_mask,
                                   other=0.0).to(tl.float32)
            b_A = tl.insert_slice(
                ful=b_A,
                sub=b_A_subrec16[None, :, :],  # (1, 16, 16)
                offsets=[blkid, 0, 0],
                sizes=[1, 16, 16],
                strides=[1, 1, 1])

        local_ori_A = tl.trans(b_A, (1, 0, 2))
        local_ori_A = tl.reshape(local_ori_A, (16, 16 * N_BLOCKS))

        # Convert mask into matrix multiplication to avoid for loops ub oom
        tmp = tl.arange(0, 16).to(tl.float32)
        rows = tmp[:, None]
        cols = tmp[None, :]
        is_lower = (rows > cols).to(b_A.dtype)
        b_A = -b_A * is_lower

        # for loop to update N_BLOCKS row vector
        for i in range(1, 16):
            nblks_vec16 = -tl.extract_slice(local_ori_A, (i, 0),
                                            (1, 16 * N_BLOCKS),
                                            (16 * N_BLOCKS, 1))
            b_a = tl.reshape(nblks_vec16, (N_BLOCKS, 16))

            dot_tmp = tl.trans(b_a[:, :, None] * b_A, (1, 0, 2))
            dot_product = tl.sum(dot_tmp, 0)
            b_a = b_a + dot_product

            b_a_new_expanded = b_a[:, None, :]
            b_A = tl.insert_slice(ful=b_A,
                                  sub=b_a_new_expanded,
                                  offsets=[0, i, 0],
                                  sizes=[N_BLOCKS, 1, 16],
                                  strides=[1, 1, 1])

        on_diagonal = (rows == cols)
        b_A = tl.where(on_diagonal, b_A + 1.0, b_A)

        b_A = tl.reshape(b_A, (N_BLOCKS * 16, 16))
        p_Ai = tl.make_block_ptr(Ad, (T, 16), (H * 16, 1), (base_t, 0),
                                 (N_BLOCKS * 16, 16), (1, 0))

        # 1 Create in-block offset
        offs_rows_to_store = tl.arange(0, N_BLOCKS * 16)
        offs_cols_to_store = tl.arange(0, 16)

        # 2 Calculate the pointer of each element
        p_Ai = (Ad + base_t * H * 16 + 0 +
                offs_rows_to_store[:, None] * H * 16 +
                offs_cols_to_store[None, :])
        # 3 Create a mask to prevent out-of-bounds access, only check rows
        global_store_rows = base_t + offs_rows_to_store[:, None]
        store_mask = global_store_rows < T
        # 4 use mask to save data safely
        tl.store(p_Ai,
                 b_A.to(p_Ai.dtype.element_ty, fp_downcast_rounding="rtne"),
                 mask=store_mask)


@triton.heuristics({"IS_VARLEN": lambda args: args["cu_seqlens"] is not None})
@triton.jit(do_not_specialize=["T"])
def merge_16x16_to_32x32_inverse_kernel(
    A,
    Ad,
    Ai,
    cu_seqlens,
    chunk_indices,
    T,
    H: tl.constexpr,
    BT: tl.constexpr,
    IS_VARLEN: tl.constexpr,
):
    i_t, i_bh = tl.program_id(0), tl.program_id(1)
    i_b, i_h = i_bh // H, i_bh % H
    if IS_VARLEN:
        i_n, i_t = (
            tl.load(chunk_indices + i_t * 2).to(tl.int32),
            tl.load(chunk_indices + i_t * 2 + 1).to(tl.int32),
        )
        bos, eos = (
            tl.load(cu_seqlens + i_n).to(tl.int32),
            tl.load(cu_seqlens + i_n + 1).to(tl.int32),
        )
        T = eos - bos
    else:
        bos, eos = i_b * T, i_b * T + T

    A += (bos * H + i_h) * 32
    Ad += (bos * H + i_h) * 16
    Ai += (bos * H + i_h) * 32

    p_A_21 = tl.make_block_ptr(A, (T, 32), (H * 32, 1), (i_t * 32 + 16, 0),
                               (16, 16), (1, 0))
    p_Ad_11 = tl.make_block_ptr(Ad, (T, 16), (H * 16, 1), (i_t * 32, 0),
                                (16, 16), (1, 0))
    p_Ad_22 = tl.make_block_ptr(Ad, (T, 16), (H * 16, 1), (i_t * 32 + 16, 0),
                                (16, 16), (1, 0))
    p_Ai_11 = tl.make_block_ptr(Ai, (T, 32), (H * 32, 1), (i_t * 32, 0),
                                (16, 16), (1, 0))
    p_Ai_22 = tl.make_block_ptr(Ai, (T, 32), (H * 32, 1), (i_t * 32 + 16, 16),
                                (16, 16), (1, 0))
    p_Ai_21 = tl.make_block_ptr(Ai, (T, 32), (H * 32, 1), (i_t * 32 + 16, 0),
                                (16, 16), (1, 0))

    A_21 = tl.load(p_A_21, boundary_check=(0, 1)).to(tl.float32)
    Ai_11 = tl.load(p_Ad_11, boundary_check=(0, 1)).to(tl.float32)
    Ai_22 = tl.load(p_Ad_22, boundary_check=(0, 1)).to(tl.float32)
    Ai_21 = -tl.dot(
        tl.dot(Ai_22, A_21, input_precision="ieee"),
        Ai_11,
        input_precision="ieee",
    )
    tl.store(
        p_Ai_11,
        Ai_11.to(p_Ai_11.dtype.element_ty, fp_downcast_rounding="rtne"),
        boundary_check=(0, 1),
    )
    tl.store(
        p_Ai_22,
        Ai_22.to(p_Ai_22.dtype.element_ty, fp_downcast_rounding="rtne"),
        boundary_check=(0, 1),
    )
    tl.store(
        p_Ai_21,
        Ai_21.to(p_Ai_21.dtype.element_ty, fp_downcast_rounding="rtne"),
        boundary_check=(0, 1),
    )


@triton.heuristics({"IS_VARLEN": lambda args: args["cu_seqlens"] is not None})
@triton.jit(do_not_specialize=["T"])
def merge_16x16_to_64x64_inverse_kernel(
    A,
    Ad,
    Ai,
    cu_seqlens,
    chunk_indices,
    T,
    H: tl.constexpr,
    BT: tl.constexpr,
    IS_VARLEN: tl.constexpr,
):
    i_t, i_bh = tl.program_id(0), tl.program_id(1)
    i_b, i_h = i_bh // H, i_bh % H
    if IS_VARLEN:
        i_n, i_t_val = (
            tl.load(chunk_indices + i_t * 2).to(tl.int32),
            tl.load(chunk_indices + i_t * 2 + 1).to(tl.int32),
        )
        bos, eos = (
            tl.load(cu_seqlens + i_n).to(tl.int32),
            tl.load(cu_seqlens + i_n + 1).to(tl.int32),
        )
        T = eos - bos
        i_t = i_t_val
    else:
        bos, eos = i_b * T, i_b * T + T

    # Base pointers (already offset by batch and head)
    A += (bos * H + i_h) * 64
    Ad += (bos * H + i_h) * 16
    Ai += (bos * H + i_h) * 64

    # load Ai_22 (Ad block at row i_t * 64 + 16, col 0, 16 * 16)
    offs_m = i_t * 64 + 16 + tl.arange(0, 16)
    offs_n = tl.arange(0, 16)
    mask_Ad = (offs_m[:, None] < T) & (offs_n[None, :] < 16)
    ptr_Ad = Ad + offs_m[:, None] * (H * 16) + offs_n[None, :]
    Ai_22 = tl.load(ptr_Ad, mask=mask_Ad, other=0.0).to(tl.float32)

    # load A_21 (A block at row i_t * 64 + 16, col 0, 16 * 16)
    mask_A = (offs_m[:, None] < T) & (offs_n[None, :] < 64)
    ptr_A = A + offs_m[:, None] * (H * 64) + offs_n[None, :]
    A_21 = tl.load(ptr_A, mask=mask_A, other=0.0).to(tl.float32)
    tmp = tl.dot(Ai_22, A_21, input_precision="ieee")

    # load Ai_11 (Ad block at row i_t * 64, col 0, 16 * 16)
    offs_m = i_t * 64 + tl.arange(0, 16)
    offs_n = tl.arange(0, 16)
    mask_Ad = (offs_m[:, None] < T) & (offs_n[None, :] < 16)
    ptr_Ad = Ad + offs_m[:, None] * (H * 16) + offs_n[None, :]
    Ai_11 = tl.load(ptr_Ad, mask=mask_Ad, other=0.0).to(tl.float32)

    Ai_21 = -tl.dot(tmp, Ai_11, input_precision="ieee")

    # load Ai_44 (Ad block at row i_t * 64 + 48, col 0, 16 * 16)
    offs_m = i_t * 64 + 48 + tl.arange(0, 16)
    offs_n = tl.arange(0, 16)
    mask_Ad = (offs_m[:, None] < T) & (offs_n[None, :] < 16)
    ptr_Ad = Ad + offs_m[:, None] * (H * 16) + offs_n[None, :]
    Ai_44 = tl.load(ptr_Ad, mask=mask_Ad, other=0.0).to(tl.float32)

    # load A_43 (Ad block at row i_t * 64 + 48, col 32, 16 * 16)
    offs_n = 32 + tl.arange(0, 16)
    mask_A = (offs_m[:, None] < T) & (offs_n[None, :] < 64)
    ptr_A = A + offs_m[:, None] * (H * 64) + offs_n[None, :]
    A_43 = tl.load(ptr_A, mask=mask_A, other=0.0).to(tl.float32)
    tmp = tl.dot(Ai_44, A_43, input_precision="ieee")

    # load Ai_33 (Ad block at row i_t * 64 + 32, col 0, 16 * 16)
    offs_m = i_t * 64 + 32 + tl.arange(0, 16)
    offs_n = tl.arange(0, 16)
    mask_Ad = (offs_m[:, None] < T) & (offs_n[None, :] < 16)
    ptr_Ad = Ad + offs_m[:, None] * (H * 16) + offs_n[None, :]
    Ai_33 = tl.load(ptr_Ad, mask=mask_Ad, other=0.0).to(tl.float32)

    Ai_43 = -tl.dot(tmp, Ai_33, input_precision="ieee")

    # build Ai_22_32 (32 * 32)
    Ai_22_32 = tl.zeros((32, 32), tl.float32)
    Ai_22_32 = tl.insert_slice(Ai_22_32, Ai_33, (0, 0), (16, 16), (1, 1))
    Ai_22_32 = tl.insert_slice(Ai_22_32, Ai_44, (16, 16), (16, 16), (1, 1))
    Ai_22_32 = tl.insert_slice(Ai_22_32, Ai_43, (16, 0), (16, 16), (1, 1))

    # load A_21_32 (A block at row i_t * 64 + 32, col 0, 32 * 32)
    offs_m = i_t * 64 + 32 + tl.arange(0, 32)
    offs_n = tl.arange(0, 32)
    mask_A = (offs_m[:, None] < T) & (offs_n[None, :] < 64)
    ptr_A = A + offs_m[:, None] * (H * 64) + offs_n[None, :]
    A_21_32 = tl.load(ptr_A, mask=mask_A, other=0.0).to(tl.float32)
    tmp = tl.dot(Ai_22_32, A_21_32, input_precision="ieee")

    # build Ai_11_32 (32 * 32)
    Ai_11_32 = tl.zeros((32, 32), tl.float32)
    Ai_11_32 = tl.insert_slice(Ai_11_32, Ai_11, (0, 0), (16, 16), (1, 1))
    Ai_11_32 = tl.insert_slice(Ai_11_32, Ai_22, (16, 16), (16, 16), (1, 1))
    Ai_11_32 = tl.insert_slice(Ai_11_32, Ai_21, (16, 0), (16, 16), (1, 1))

    Ai_21_32 = -tl.dot(tmp, Ai_11_32, input_precision="ieee")

    # store Ai_11_32 to (i_t * 64, 0)
    offs_m = i_t * 64 + tl.arange(0, 32)
    offs_n = tl.arange(0, 32)
    mask_store = (offs_m[:, None] < T) & (offs_n[None, :] < 64)
    ptr_Ai = Ai + offs_m[:, None] * (H * 64) + offs_n[None, :]
    tl.store(ptr_Ai,
             Ai_11_32.to(ptr_Ai.dtype.element_ty, fp_downcast_rounding="rtne"),
             mask=mask_store)

    # store Ai_22_32 to (i_t * 64 + 32, 32)
    offs_m = i_t * 64 + 32 + tl.arange(0, 32)
    offs_n = 32 + tl.arange(0, 32)
    mask_store = (offs_m[:, None] < T) & (offs_n[None, :] < 64)
    ptr_Ai = Ai + offs_m[:, None] * (H * 64) + offs_n[None, :]
    tl.store(ptr_Ai,
             Ai_22_32.to(ptr_Ai.dtype.element_ty, fp_downcast_rounding="rtne"),
             mask=mask_store)

    # store Ai_21_32 to (i_t * 64 + 32, 32)
    offs_n = tl.arange(0, 32)
    mask_store = (offs_m[:, None] < T) & (offs_n[None, :] < 64)
    ptr_Ai = Ai + offs_m[:, None] * (H * 64) + offs_n[None, :]
    tl.store(ptr_Ai,
             Ai_21_32.to(ptr_Ai.dtype.element_ty, fp_downcast_rounding="rtne"),
             mask=mask_store)

    # zero out the upper-right 32 * 32 block (rows 0 ~ 31, cols 32 ~ 63)
    offs_m = i_t * 64 + tl.arange(0, 32)
    offs_n = 32 + tl.arange(0, 32)
    mask_store = (offs_m[:, None] < T) & (offs_n[None, :] < BT)
    ptr_Ai = Ai + offs_m[:, None] * (H * BT) + offs_n[None, :]
    zero_block = tl.zeros((32, 32), dtype=ptr_Ai.dtype.element_ty)
    tl.store(ptr_Ai, zero_block, mask=mask_store)


def solve_tril(
    A: torch.Tensor,
    cu_seqlens: Optional[torch.Tensor] = None,
    output_dtype: torch.dtype = torch.float,
) -> torch.Tensor:
    """
    Compute the inverse of the matrix I + A
    A should be strictly lower triangular, i.e., A.triu() == 0.

    Args:
        A (torch.Tensor):
            [B, T, H, BT], where BT should only be 16, 32, or 64.
        cu_seqlens (torch.Tensor):
            The cumulative sequence lengths of the input tensor. Default: `None`.
        output_dtype (torch.dtype):
            The dtype of the output tensor. Default: `torch.float`.
            If `None`, the output dtype will be the same as the input dtype.

    Returns:
        (I + A)^-1 with the same shape as A
    """
    assert A.shape[-1] in [16, 32, 64]

    B, T, H, BT = A.shape
    Ad = torch.empty(B,
                     T,
                     H,
                     16,
                     device=A.device,
                     dtype=torch.float if BT != 16 else output_dtype)

    LARGE_BLOCK_T = 608 * 2

    chunk_indices = (prepare_chunk_indices(cu_seqlens, LARGE_BLOCK_T)
                     if cu_seqlens is not None else None)
    NT = len(chunk_indices) if cu_seqlens is not None else triton.cdiv(
        T, LARGE_BLOCK_T)

    solve_tril_16x16_kernel[NT, B * H](
        A=A,
        Ad=Ad,
        cu_seqlens=cu_seqlens,
        chunk_indices=chunk_indices,
        T=T,
        H=H,
        BT=BT,
        LARGE_BLOCK_T=LARGE_BLOCK_T,
        num_warps=1,
        num_stages=4,
    )

    if BT == 16:
        return Ad

    Ai = torch.empty(B, T, H, BT, device=A.device, dtype=output_dtype)
    merge_fn = (merge_16x16_to_32x32_inverse_kernel
                if BT == 32 else merge_16x16_to_64x64_inverse_kernel)
    chunk_indices = (prepare_chunk_indices(cu_seqlens, BT)
                     if cu_seqlens is not None else None)
    NT = len(chunk_indices) if cu_seqlens is not None else triton.cdiv(T, BT)

    merge_fn[NT, B * H](
        A=A,
        Ad=Ad,
        Ai=Ai,
        cu_seqlens=cu_seqlens,
        chunk_indices=chunk_indices,
        T=T,
        H=H,
        BT=BT,
        num_warps=4,
        num_stages=3,
    )
    return Ai
