# Copyright (c) 2023-2025, Songlin Yang, Yu Zhang
# This file is modified and supported by the Moonshot AI Team

import torch
import torch.nn.functional as F
import triton
import triton.language as tl

from fla.ops.utils.index import prepare_chunk_indices
from fla.ops.utils.op import exp
from fla.ops.utils.softplus import softplus
from fla.utils import IS_AMD, autocast_custom_bwd, autocast_custom_fwd, autotune_cache_kwargs, check_shared_mem, input_guard

BS_LIST = [32, 64] if check_shared_mem() else [16, 32]
BT_LIST_AUTOTUNE = [32, 64, 128]
NUM_WARPS_AUTOTUNE = [2, 4, 8, 16] if IS_AMD else [4, 8, 16, 32]


def naive_kda_gate(
    g: torch.Tensor,
    A_log: torch.Tensor,
    dt_bias: torch.Tensor | None = None,
    output_dtype: torch.dtype = torch.float32,
) -> torch.Tensor:
    """
    Torch reference implementation for KDA gate computation.

    Computes: g = -A_log.exp().unsqueeze(-1) * softplus(g + dt_bias.view(g.shape[-2:]))

    Args:
        g (torch.Tensor):
            Input tensor of shape `[..., H, K]`.
        A_log (torch.Tensor):
            Parameter tensor with `H` elements.
        dt_bias (torch.Tensor | None):
            Optional bias tensor added to `g` before activation, shape `[H * K]`.

    Returns:
        Output tensor of shape `[..., H, K]` .
    """
    H, _ = g.shape[-2:]
    g = g.float()
    if dt_bias is not None:
        g = g + dt_bias.view(H, -1)

    g = (-A_log.view(H, 1).float().exp() * F.softplus(g.float())).to(output_dtype)
    return g


def naive_kda_lowerbound_gate(
    g: torch.Tensor,
    A_log: torch.Tensor,
    dt_bias: torch.Tensor | None = None,
    lower_bound: float = -5.0,
    output_dtype: torch.dtype = torch.float32,
) -> torch.Tensor:
    H, _ = g.shape[-2:]
    g = g.float()
    if dt_bias is not None:
        g = g + dt_bias.view(H, -1)
    g = lower_bound * F.sigmoid(A_log.view(H, 1).exp() * g)
    return g.to(output_dtype)


@triton.heuristics({
    "HAS_BIAS": lambda args: args["dt_bias"] is not None,
    "HAS_BETA": lambda args: args["beta"] is not None,
    'USE_LOWER_BOUND': lambda args: args['lower_bound'] is not None,
})
@triton.autotune(
    configs=[
        triton.Config({"BT": BT}, num_warps=num_warps, num_stages=num_stages)
        for BT in BT_LIST_AUTOTUNE
        for num_warps in NUM_WARPS_AUTOTUNE
        for num_stages in [2, 3]
    ],
    key=["H", "D"],
    **autotune_cache_kwargs,
)
@triton.jit(do_not_specialize=['T'])
def kda_gate_fwd_kernel(
    g,
    A_log,
    dt_bias,
    beta,
    yg,
    yb,
    lower_bound,
    T,
    H: tl.constexpr,
    D: tl.constexpr,
    BT: tl.constexpr,
    BD: tl.constexpr,
    HAS_BIAS: tl.constexpr,
    HAS_BETA: tl.constexpr,
    USE_LOWER_BOUND: tl.constexpr,
):
    i_t, i_h = tl.program_id(0), tl.program_id(1)

    b_A = tl.load(A_log + i_h).to(tl.float32)

    p_g = tl.make_block_ptr(g + i_h * D, (T, D), (H * D, 1), (i_t * BT, 0), (BT, BD), (1, 0))
    p_yg = tl.make_block_ptr(yg + i_h * D, (T, D), (H * D, 1), (i_t * BT, 0), (BT, BD), (1, 0))
    # [BT, BD]
    b_g = tl.load(p_g, boundary_check=(0, 1)).to(tl.float32)
    if HAS_BIAS:
        p_b = tl.make_block_ptr(dt_bias, (H * D,), (1,), (i_h * D,), (BD,), (0,))
        b_g = b_g + tl.load(p_b, boundary_check=(0,)).to(tl.float32)
    if not USE_LOWER_BOUND:
        b_yg = -exp(b_A) * softplus(b_g)
    else:
        b_yg = lower_bound * tl.sigmoid(exp(b_A) * b_g)
    tl.store(p_yg, b_yg.to(p_yg.dtype.element_ty), boundary_check=(0, 1))

    if HAS_BETA:
        p_b = tl.make_block_ptr(beta + i_h, (T,), (H,), (i_t * BT,), (BT,), (0,))
        p_yb = tl.make_block_ptr(yb + i_h, (T,), (H,), (i_t * BT,), (BT,), (0,))
        b_yb = tl.sigmoid(tl.load(p_b, boundary_check=(0,)).to(tl.float32))
        tl.store(p_yb, b_yb.to(p_yb.dtype.element_ty), boundary_check=(0,))


@triton.heuristics({
    "HAS_BIAS": lambda args: args["dt_bias"] is not None,
    "HAS_BETA": lambda args: args["beta"] is not None,
    'USE_LOWER_BOUND': lambda args: args['lower_bound'] is not None,
})
@triton.autotune(
    configs=[
        triton.Config({}, num_warps=num_warps, num_stages=num_stages)
        for num_warps in NUM_WARPS_AUTOTUNE
        for num_stages in [2, 3]
    ],
    key=["H", "D"],
    **autotune_cache_kwargs,
)
@triton.jit(do_not_specialize=['T'])
def kda_gate_bwd_kernel(
    g,
    A_log,
    dt_bias,
    beta,
    dyg,
    dyb,
    dg,
    dA,
    dbeta,
    lower_bound,
    T,
    H: tl.constexpr,
    D: tl.constexpr,
    BT: tl.constexpr,
    BD: tl.constexpr,
    HAS_BIAS: tl.constexpr,
    HAS_BETA: tl.constexpr,
    USE_LOWER_BOUND: tl.constexpr,
):
    i_t, i_h = tl.program_id(0), tl.program_id(1)

    b_A = tl.load(A_log + i_h).to(tl.float32)

    p_g = tl.make_block_ptr(g + i_h * D, (T, D), (H * D, 1), (i_t * BT, 0), (BT, BD), (1, 0))
    p_dg = tl.make_block_ptr(dg + i_h * D, (T, D), (H * D, 1), (i_t * BT, 0), (BT, BD), (1, 0))
    p_dyg = tl.make_block_ptr(dyg + i_h * D, (T, D), (H * D, 1), (i_t * BT, 0), (BT, BD), (1, 0))

    # [BT, BD]
    b_g = tl.load(p_g, boundary_check=(0, 1)).to(tl.float32)
    b_dyg = tl.load(p_dyg, boundary_check=(0, 1)).to(tl.float32)

    if HAS_BIAS:
        p_b = tl.make_block_ptr(dt_bias, (H * D,), (1,), (i_h * D,), (BD,), (0,))
        b_g = b_g + tl.load(p_b, boundary_check=(0,)).to(tl.float32)

    # [BT, BD]
    if not USE_LOWER_BOUND:
        b_A = -exp(b_A)
        b_yg = b_A * softplus(b_g)
        b_dg = b_A * (b_dyg * tl.sigmoid(b_g))
        b_dA = tl.sum(tl.sum(b_dyg * b_yg, 1), 0)
    else:
        b_A = exp(b_A)
        b_inner = b_A * b_g
        b_sig = tl.sigmoid(b_inner)
        b_dsig = b_sig * (1.0 - b_sig)
        # Common term: dy * (LB * dsig)
        b_d_inner_term = b_dyg * (lower_bound * b_dsig)
        # dg = d_inner_term * A
        b_dg = b_d_inner_term * b_A
        b_dA = tl.sum(tl.sum(b_dg * b_g, 1), 0)

    tl.store(p_dg, b_dg.to(p_dg.dtype.element_ty), boundary_check=(0, 1))
    tl.store(dA + i_t * H + i_h, b_dA)

    if HAS_BETA:
        p_b = tl.make_block_ptr(beta + i_h, (T,), (H,), (i_t * BT,), (BT,), (0,))
        p_db = tl.make_block_ptr(dbeta + i_h, (T,), (H,), (i_t * BT,), (BT,), (0,))
        p_dyb = tl.make_block_ptr(dyb + i_h, (T,), (H,), (i_t * BT,), (BT,), (0,))

        b_b = tl.load(p_b, boundary_check=(0,)).to(tl.float32)
        b_db = tl.load(p_dyb, boundary_check=(0,)).to(tl.float32) * b_b * (1.0 - b_b)
        tl.store(p_db, b_db.to(p_db.dtype.element_ty), boundary_check=(0,))


def kda_gate_fwd(
    g: torch.Tensor,
    A_log: torch.Tensor,
    dt_bias: torch.Tensor | None = None,
    lower_bound: float | None = None,
    output_dtype: torch.dtype = torch.float32,
) -> torch.Tensor:
    H, K = g.shape[-2:]
    T = g.numel() // (H * K)

    yg = torch.empty_like(g, dtype=output_dtype)

    def grid(meta):
        return (triton.cdiv(T, meta["BT"]), H)

    kda_gate_fwd_kernel[grid](
        g=g,
        A_log=A_log,
        dt_bias=dt_bias,
        beta=None,
        yg=yg,
        yb=None,
        T=T,
        H=H,
        D=K,
        BD=triton.next_power_of_2(K),
        lower_bound=lower_bound,
    )
    return yg


def kda_gate_bwd(
    g: torch.Tensor,
    A_log: torch.Tensor,
    dt_bias: torch.Tensor | None = None,
    dyg: torch.Tensor | None = None,
    lower_bound: float | None = None,
) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor | None]:
    H, K = g.shape[-2:]
    T = g.numel() // (H * K)
    BT = 32
    NT = triton.cdiv(T, BT)

    dg = torch.empty_like(g, dtype=torch.float32)
    dA = A_log.new_empty(NT, H, dtype=torch.float32)

    grid = (triton.cdiv(T, BT), H)
    kda_gate_bwd_kernel[grid](
        g=g,
        A_log=A_log,
        dt_bias=dt_bias,
        beta=None,
        dyg=dyg,
        dyb=None,
        dg=dg,
        dA=dA,
        dbeta=None,
        T=T,
        H=H,
        D=K,
        BT=BT,
        BD=triton.next_power_of_2(K),
        lower_bound=lower_bound,
    )

    dg = dg.view_as(g).type_as(g)
    dA = dA.sum(0).view_as(A_log).type_as(A_log)
    dbias = dg.view(-1, H * K).sum(0).to(dt_bias) if dt_bias is not None else None

    return dg, dA, dbias


class KDAGateFunction(torch.autograd.Function):
    @staticmethod
    @input_guard
    @autocast_custom_fwd
    def forward(
        ctx,
        g: torch.Tensor,
        A_log: torch.Tensor,
        dt_bias: torch.Tensor | None = None,
        lower_bound: float | None = None,
        output_dtype: torch.dtype = torch.float32,
    ) -> torch.Tensor:
        yg = kda_gate_fwd(
            g=g,
            A_log=A_log,
            dt_bias=dt_bias,
            lower_bound=lower_bound,
            output_dtype=output_dtype
        )
        ctx.save_for_backward(g, A_log, dt_bias)
        ctx.lower_bound = lower_bound
        return yg

    @staticmethod
    @input_guard
    @autocast_custom_bwd
    def backward(ctx, dyg: torch.Tensor):
        g, A_log, dt_bias = ctx.saved_tensors
        dg, dA, dbias = kda_gate_bwd(
            g=g,
            A_log=A_log,
            dt_bias=dt_bias,
            dyg=dyg,
            lower_bound=ctx.lower_bound
        )
        return dg, dA, dbias, None, None


@torch.compiler.disable
def fused_kda_gate(
    g: torch.Tensor,
    A_log: torch.Tensor,
    dt_bias: torch.Tensor | None = None,
    lower_bound: float | None = None,
    output_dtype: torch.dtype = torch.float32,
) -> torch.Tensor | tuple[torch.Tensor, torch.Tensor]:
    """
    Fused KDA gate computation with autograd support.

    Computes: g = -A_log.exp().unsqueeze(-1) * softplus(g + dt_bias.view(g.shape[-2:]))

    Args:
        g (torch.Tensor):
            Input tensor of shape `[..., H, K]`.
        A_log (torch.Tensor):
            Parameter tensor with `H` elements.
        dt_bias (torch.Tensor | None):
            Optional bias tensor added to `g` before activation, shape `[H * K]`.

    Returns:
        Output tensor of shape `[..., H, K]`.
    """
    return KDAGateFunction.apply(g, A_log, dt_bias, lower_bound, output_dtype)


@triton.heuristics({
    "HAS_BIAS": lambda args: args["dt_bias"] is not None,
    'HAS_SCALE': lambda args: args['scale'] is not None,
    'IS_VARLEN': lambda args: args['cu_seqlens'] is not None,
    'USE_LOWER_BOUND': lambda args: args['lower_bound'] is not None,
})
@triton.autotune(
    configs=[
        triton.Config({'BS': BS}, num_warps=num_warps)
        for BS in BS_LIST
        for num_warps in [2, 4, 8]
    ],
    key=['H', 'S', 'BT', 'IS_VARLEN', 'REVERSE'],
    **autotune_cache_kwargs,
)
@triton.jit(do_not_specialize=['T'])
def kda_gate_chunk_cumsum_vector_kernel(
    s,
    A_log,
    dt_bias,
    o,
    scale,
    cu_seqlens,
    chunk_indices,
    lower_bound,
    T,
    H: tl.constexpr,
    S: tl.constexpr,
    BT: tl.constexpr,
    BS: tl.constexpr,
    REVERSE: tl.constexpr,
    HAS_BIAS: tl.constexpr,
    HAS_SCALE: tl.constexpr,
    IS_VARLEN: tl.constexpr,
    USE_LOWER_BOUND: tl.constexpr,
):
    i_s, i_t, i_bh = tl.program_id(0), tl.program_id(1), tl.program_id(2)
    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

    p_s = tl.make_block_ptr(s + (bos * H + i_h) * S, (T, S), (H*S, 1), (i_t * BT, i_s * BS), (BT, BS), (1, 0))
    p_o = tl.make_block_ptr(o + (bos * H + i_h) * S, (T, S), (H*S, 1), (i_t * BT, i_s * BS), (BT, BS), (1, 0))
    # [BT, BS]
    b_s = tl.load(p_s, boundary_check=(0, 1)).to(tl.float32)

    # Apply dt_bias if exists
    if HAS_BIAS:
        p_b = tl.make_block_ptr(dt_bias + i_h * S, (S,), (1,), (i_s * BS,), (BS,), (0,))
        b_bias = tl.load(p_b, boundary_check=(0,)).to(tl.float32)
        b_s = b_s + b_bias[None, :]

    b_A = tl.load(A_log + i_h).to(tl.float32)
    if not USE_LOWER_BOUND:
        # Apply gate: -exp(A_log) * softplus(g + bias)
        b_gate = -exp(b_A) * softplus(b_s)
    else:
        b_gate = lower_bound * tl.sigmoid(exp(b_A) * b_s)

    # Apply chunk local cumsum
    if REVERSE:
        b_o = tl.cumsum(b_gate, axis=0, reverse=True)
    else:
        b_o = tl.cumsum(b_gate, axis=0)

    if HAS_SCALE:
        b_o *= scale
    tl.store(p_o, b_o.to(p_o.dtype.element_ty), boundary_check=(0, 1))


@input_guard
def kda_gate_chunk_cumsum(
    g: torch.Tensor,
    A_log: torch.Tensor,
    chunk_size: int,
    scale: float = None,
    dt_bias: torch.Tensor | None = None,
    cu_seqlens: torch.Tensor | None = None,
    output_dtype: torch.dtype | None = torch.float,
    chunk_indices: torch.LongTensor | None = None,
    lower_bound: float | None = None,
    **kwargs,
) -> torch.Tensor:
    if cu_seqlens is not None:
        assert g.shape[0] == 1, "Only batch size 1 is supported when cu_seqlens are provided"
    assert len(g.shape) == 4
    B, T, H, S = g.shape
    BT = chunk_size
    if chunk_indices is None and cu_seqlens is not None:
        chunk_indices = prepare_chunk_indices(cu_seqlens, BT)
    NT = triton.cdiv(T, BT) if cu_seqlens is None else len(chunk_indices)
    assert chunk_size == 2**(chunk_size.bit_length()-1), "chunk_size must be a power of 2"

    g_org, g = g, torch.empty_like(g, dtype=output_dtype or g.dtype)
    def grid(meta): return (triton.cdiv(meta['S'], meta['BS']), NT, B * H)
    kda_gate_chunk_cumsum_vector_kernel[grid](
        s=g_org,
        A_log=A_log,
        dt_bias=dt_bias,
        o=g,
        scale=scale,
        cu_seqlens=cu_seqlens,
        chunk_indices=chunk_indices,
        lower_bound=lower_bound,
        T=T,
        H=H,
        S=S,
        BT=BT,
        REVERSE=False,
    )
    return g