# Copyright (c) 2023, Tri Dao, Albert Gu.
"""
this code is modified from https://github.com/state-spaces/mamba/blob/main/mamba_ssm/ops/selective_scan_interface.py
"""
import paddle
import paddle.nn.functional as F
from einops import rearrange, repeat

from ..utils import custom_bwd, custom_fwd

try:
    from .causal_conv1d import causal_conv1d_fn
except ImportError:
    causal_conv1d_fn = None

try:
    from . import causal_conv1d_cuda_pd as causal_conv1d_cuda
except ImportError:
    causal_conv1d_cuda = None
try:
    from . import selective_scan_cuda_pd as selective_scan_cuda
except ImportError:
    selective_scan_cuda = None
from paddle.distributed import fleet

from ..utils import get_autocast_gpu_dtype, is_autocast_enabled


class SelectiveScanFn(paddle.autograd.PyLayer):
    @staticmethod
    @custom_fwd
    def forward(
        ctx, u, delta, A, B, C, D=None, z=None, delta_bias=None, delta_softplus=False, return_last_state=False
    ):
        if u.strides[-1] != 1:
            u = u.contiguous()
        if delta.strides[-1] != 1:
            delta = delta.contiguous()
        if D is not None:
            D = D.contiguous()
        if B.strides[-1] != 1:
            B = B.contiguous()
        if C.strides[-1] != 1:
            C = C.contiguous()
        if z is not None and z.strides[-1] != 1:
            z = z.contiguous()
        if B.dim() == 3:
            B = rearrange(B, "b dstate l -> b 1 dstate l")
            ctx.squeeze_B = True
        if C.dim() == 3:
            C = rearrange(C, "b dstate l -> b 1 dstate l")
            ctx.squeeze_C = True
        out, x, *rest = selective_scan_cuda.fwd(u, delta, A, B, C, D, z, delta_bias, delta_softplus)
        ctx.delta_softplus = delta_softplus
        ctx.has_z = z is not None
        last_state = x[:, :, -1, 1::2]  # (batch, dim, dstate)
        if not ctx.has_z:
            ctx.save_for_backward(u, delta, A, B, C, D, delta_bias, x)
            return out if not return_last_state else (out, last_state)
        else:
            ctx.save_for_backward(u, delta, A, B, C, D, z, delta_bias, x, out)
            out_z = rest[0]
            return out_z if not return_last_state else (out_z, last_state)

    @staticmethod
    @custom_bwd
    def backward(ctx, dout, *args):
        if not ctx.has_z:
            u, delta, A, B, C, D, delta_bias, x = ctx.saved_tensor()
            z = None
            out = None
        else:
            u, delta, A, B, C, D, z, delta_bias, x, out = ctx.saved_tensor()
        if dout.strides[-1] != 1:
            dout = dout.contiguous()
        # The kernel supports passing in a pre-allocated dz (e.g., in case we want to fuse the
        # backward of selective_scan_cuda with the backward of chunk).
        # Here we just pass in None and dz will be allocated in the C++ code.
        du, ddelta, dA, dB, dC, dD, ddelta_bias, *rest = selective_scan_cuda.bwd(
            u,
            delta,
            A,
            B,
            C,
            D,
            z,
            delta_bias,
            dout,
            x,
            out,
            None,
            ctx.delta_softplus,
            False,  # option to recompute out_z, not used here
        )
        dz = rest[0] if ctx.has_z else None
        dB = dB.squeeze(1) if getattr(ctx, "squeeze_B", False) else dB
        dC = dC.squeeze(1) if getattr(ctx, "squeeze_C", False) else dC

        # new added for tp parallel
        try:
            hcg = fleet.get_hybrid_communicate_group()
            mp_group = hcg.get_model_parallel_group()
            mp_src_rank = hcg.get_model_parallel_group_src_rank()
            mp_world_size = hcg.get_model_parallel_world_size()
        except Exception:
            mp_group = None
            mp_src_rank = 0
            mp_world_size = 1

        if mp_world_size > 1:
            paddle.distributed.broadcast(
                dB,
                src=mp_src_rank,
                group=mp_group,
            )
            paddle.distributed.broadcast(
                dC,
                src=mp_src_rank,
                group=mp_group,
            )

        return (
            du,
            ddelta,
            dA,
            dB,
            dC,
            dD if D is not None else None,
            dz,
            ddelta_bias if delta_bias is not None else None,
            None,
            None,
        )


def selective_scan_fn(
    u, delta, A, B, C, D=None, z=None, delta_bias=None, delta_softplus=False, return_last_state=False
):
    """if return_last_state is True, returns (out, last_state)
    last_state has shape (batch, dim, dstate). Note that the gradient of the last state is not considered in the
    backward pass.
    """
    return SelectiveScanFn.apply(u, delta, A, B, C, D, z, delta_bias, delta_softplus, return_last_state)


def selective_scan_ref(
    u, delta, A, B, C, D=None, z=None, delta_bias=None, delta_softplus=False, return_last_state=False
):
    """
    u: r(B D L) delta: r(B D L) A: c(D N) or r(D N) B: c(D N) or r(B N L) or r(B N 2L) or r(B G N L) or (B G N L) C:
    c(D N) or r(B N L) or r(B N 2L) or r(B G N L) or (B G N L) D: r(D) z: r(B D L) delta_bias: r(D), fp32

    out: r(B D L) last_state (optional): r(B D dstate) or c(B D dstate)
    """
    dtype_in = u.dtype
    u = u.cast("float32")
    delta = delta.cast("float32")
    if delta_bias is not None:
        delta = delta + delta_bias[..., None].cast("float32")
    if delta_softplus:
        delta = F.softplus(delta)
    batch, dim, dstate = u.shape[0], A.shape[0], A.shape[1]
    is_variable_B = B.dim() >= 3
    is_variable_C = C.dim() >= 3
    if A.is_complex():
        if is_variable_B:
            B = paddle.as_complex(rearrange(B.cast("float32"), "... (L two) -> ... L two", two=2))
        if is_variable_C:
            C = paddle.as_complex(rearrange(C.cast("float32"), "... (L two) -> ... L two", two=2))
    else:
        B = B.cast("float32")
        C = C.cast("float32")
    x = paddle.zeros((batch, dim, dstate), dtype=A.dtype)
    ys = []
    deltaA = paddle.exp(paddle.einsum("bdl,dn->bdln", delta, A))
    if not is_variable_B:
        deltaB_u = paddle.einsum("bdl,dn,bdl->bdln", delta, B, u)
    else:
        if B.dim() == 3:
            deltaB_u = paddle.einsum("bdl,bnl,bdl->bdln", delta, B, u)
        else:
            B = repeat(B, "B G N L -> B (G H) N L", H=dim // B.shape[1])
            deltaB_u = paddle.einsum("bdl,bdnl,bdl->bdln", delta, B, u)
    if is_variable_C and C.dim() == 4:
        C = repeat(C, "B G N L -> B (G H) N L", H=dim // C.shape[1])
    last_state = None
    for i in range(u.shape[2]):
        x = deltaA[:, :, i] * x + deltaB_u[:, :, i]
        if not is_variable_C:
            y = paddle.einsum("bdn,dn->bd", x, C)
        else:
            if C.dim() == 3:
                y = paddle.einsum("bdn,bn->bd", x, C[:, :, i])
            else:
                y = paddle.einsum("bdn,bdn->bd", x, C[:, :, :, i])
        if i == u.shape[2] - 1:
            last_state = x
        if y.is_complex():
            y = y.real() * 2
        ys.append(y)
    y = paddle.stack(ys, axis=2)  # (batch dim L)
    out = y if D is None else y + u * rearrange(D, "d -> d 1")
    if z is not None:
        out = out * F.silu(z)
    out = out.cast(dtype=dtype_in)
    return out if not return_last_state else (out, last_state)


class MambaInnerFn(paddle.autograd.PyLayer):
    @staticmethod
    @custom_fwd
    def forward(
        ctx,
        xz,
        conv1d_weight,
        conv1d_bias,
        x_proj_weight,
        delta_proj_weight,
        out_proj_weight,
        out_proj_bias,
        A,
        B=None,
        C=None,
        D=None,
        delta_bias=None,
        B_proj_bias=None,
        C_proj_bias=None,
        delta_softplus=True,
        checkpoint_lvl=1,
    ):
        """
        xz: (batch, dim, seqlen)
        """
        assert causal_conv1d_cuda is not None, "causal_conv1d_cuda is not available. Please install causal-conv1d."
        assert checkpoint_lvl in [0, 1]
        L = xz.shape[-1]
        delta_rank = delta_proj_weight.shape[1]
        d_state = A.shape[-1] * (1 if not A.is_complex() else 2)
        if is_autocast_enabled():
            amp_dtype = get_autocast_gpu_dtype()
            x_proj_weight = x_proj_weight.cast(dtype=amp_dtype)
            delta_proj_weight = delta_proj_weight.cast(dtype=amp_dtype)
            out_proj_weight = out_proj_weight.cast(dtype=amp_dtype)
            out_proj_bias = out_proj_bias.cast(dtype=amp_dtype) if out_proj_bias is not None else None
        if xz.strides[-1] != 1:
            xz = xz.contiguous()
        conv1d_weight = rearrange(conv1d_weight, "d 1 w -> d w")
        # chunk do not share memory, so we can't use it here.
        # x, z = xz.chunk(2, axis=1)
        half_xz = xz.shape[1] // 2
        x = xz[:, :half_xz]
        z = xz[:, half_xz:]
        conv1d_bias = conv1d_bias.contiguous() if conv1d_bias is not None else None
        conv1d_out = causal_conv1d_cuda.causal_conv1d_fwd(x, conv1d_weight, conv1d_bias, None, None, None, True)
        # We're being very careful here about the layout, to avoid extra transposes.
        # We want delta to have d as the slowest moving dimension
        # and L as the fastest moving dimension, since those are what the ssm_scan kernel expects.
        x_dbl = F.linear(rearrange(conv1d_out, "b d l -> (b l) d"), x_proj_weight.t())  # (bl d)
        delta = rearrange(delta_proj_weight @ x_dbl[:, :delta_rank].t(), "d (b l) -> b d l", l=L)
        ctx.is_variable_B = B is None
        ctx.is_variable_C = C is None
        ctx.B_proj_bias_is_None = B_proj_bias is None
        ctx.C_proj_bias_is_None = C_proj_bias is None
        if B is None:  # variable B
            B = x_dbl[:, delta_rank : delta_rank + d_state]  # (bl dstate)
            if B_proj_bias is not None:
                B = B + B_proj_bias.cast(dtype=B.dtype)
            if not A.is_complex():
                # B = rearrange(B, "(b l) dstate -> b dstate l", l=L).contiguous()
                B = rearrange(B, "(b l) dstate -> b 1 dstate l", l=L).contiguous()
            else:
                B = rearrange(B, "(b l) (dstate two) -> b 1 dstate (l two)", l=L, two=2).contiguous()
        else:
            if B.strides[-1] != 1:
                B = B.contiguous()
        if C is None:  # variable C
            C = x_dbl[:, -d_state:]  # (bl dstate)
            if C_proj_bias is not None:
                C = C + C_proj_bias.cast(dtype=C.dtype)
            if not A.is_complex():
                # C = rearrange(C, "(b l) dstate -> b dstate l", l=L).contiguous()
                C = rearrange(C, "(b l) dstate -> b 1 dstate l", l=L).contiguous()
            else:
                C = rearrange(C, "(b l) (dstate two) -> b 1 dstate (l two)", l=L, two=2).contiguous()
        else:
            if C.strides[-1] != 1:
                C = C.contiguous()
        if D is not None:
            D = D.contiguous()
        out, scan_intermediates, out_z = selective_scan_cuda.fwd(
            conv1d_out, delta, A, B, C, D, z, delta_bias, delta_softplus
        )
        ctx.delta_softplus = delta_softplus
        ctx.out_proj_bias_is_None = out_proj_bias is None
        ctx.checkpoint_lvl = checkpoint_lvl
        if checkpoint_lvl >= 1:  # Will recompute conv1d_out and delta in the backward pass
            conv1d_out, delta = None, None
        ctx.save_for_backward(
            xz,
            conv1d_weight,
            conv1d_bias,
            x_dbl,
            x_proj_weight,
            delta_proj_weight,
            out_proj_weight,
            conv1d_out,
            delta,
            A,
            B,
            C,
            D,
            delta_bias,
            scan_intermediates,
            out,
        )
        return F.linear(rearrange(out_z, "b d l -> b l d"), out_proj_weight.t(), out_proj_bias)

    @staticmethod
    @custom_bwd
    def backward(ctx, dout):
        # dout: (batch, seqlen, dim)
        assert causal_conv1d_cuda is not None, "causal_conv1d_cuda is not available. Please install causal-conv1d."
        (
            xz,
            conv1d_weight,
            conv1d_bias,
            x_dbl,
            x_proj_weight,
            delta_proj_weight,
            out_proj_weight,
            conv1d_out,
            delta,
            A,
            B,
            C,
            D,
            delta_bias,
            scan_intermediates,
            out,
        ) = ctx.saved_tensor()
        L = xz.shape[-1]
        delta_rank = delta_proj_weight.shape[1]
        d_state = A.shape[-1] * (1 if not A.is_complex() else 2)
        x, z = xz.chunk(2, axis=1)
        if dout.strides[-1] != 1:
            dout = dout.contiguous()
        if ctx.checkpoint_lvl == 1:
            conv1d_out = causal_conv1d_cuda.causal_conv1d_fwd(x, conv1d_weight, conv1d_bias, None, None, None, True)
            delta = rearrange(delta_proj_weight @ x_dbl[:, :delta_rank].t(), "d (b l) -> b d l", l=L)
        # The kernel supports passing in a pre-allocated dz (e.g., in case we want to fuse the
        # backward of selective_scan_cuda with the backward of chunk).
        dxz = paddle.empty_like(xz)  # (batch, dim, seqlen)
        # dx, dz = dxz.chunk(2, axis=1)
        half_dxz = dxz.shape[1] // 2
        dx = dxz[:, :half_dxz]
        dz = dxz[:, half_dxz:]

        dout = rearrange(dout, "b l e -> e (b l)")
        dout_y = rearrange(out_proj_weight.t() @ dout, "d (b l) -> b d l", l=L)
        dconv1d_out, ddelta, dA, dB, dC, dD, ddelta_bias, dz, out_z = selective_scan_cuda.bwd(
            conv1d_out,
            delta,
            A,
            B,
            C,
            D,
            z,
            delta_bias,
            dout_y,
            scan_intermediates,
            out,
            dz,
            ctx.delta_softplus,
            True,  # option to recompute out_z
        )
        dout_proj_weight = paddle.einsum("eB,dB->ed", dout, rearrange(out_z, "b d l -> d (b l)"))
        dout_proj_bias = dout.sum(axis=(0, 1)) if not ctx.out_proj_bias_is_None else None
        dD = dD if D is not None else None
        dx_dbl = paddle.empty_like(x_dbl)
        dB_proj_bias = None
        if ctx.is_variable_B:
            if not A.is_complex():
                dB = rearrange(dB, "b 1 dstate l -> (b l) dstate").contiguous()
            else:
                dB = rearrange(dB, "b 1 dstate (l two) -> (b l) (dstate two)", two=2).contiguous()
            dB_proj_bias = dB.sum(0) if not ctx.B_proj_bias_is_None else None
            dx_dbl[:, delta_rank : delta_rank + d_state] = dB  # (bl d)
            dB = None
        dC_proj_bias = None
        if ctx.is_variable_C:
            if not A.is_complex():
                dC = rearrange(dC, "b 1 dstate l -> (b l) dstate").contiguous()
            else:
                dC = rearrange(dC, "b 1 dstate (l two) -> (b l) (dstate two)", two=2).contiguous()
            dC_proj_bias = dC.sum(0) if not ctx.C_proj_bias_is_None else None
            dx_dbl[:, -d_state:] = dC  # (bl d)
            dC = None
        ddelta = rearrange(ddelta, "b d l -> d (b l)")
        ddelta_proj_weight = paddle.einsum("dB,Br->dr", ddelta, x_dbl[:, :delta_rank])
        dx_dbl[:, :delta_rank] = paddle.einsum("dB,dr->Br", ddelta, delta_proj_weight)
        dconv1d_out = rearrange(dconv1d_out, "b d l -> d (b l)")
        dx_proj_weight = paddle.einsum("Br,Bd->rd", dx_dbl, rearrange(conv1d_out, "b d l -> (b l) d"))
        dconv1d_out = paddle.addmm(dconv1d_out, x_proj_weight.t(), dx_dbl.t())
        dconv1d_out = rearrange(dconv1d_out, "d (b l) -> b d l", b=x.shape[0], l=x.shape[-1])
        # The kernel supports passing in a pre-allocated dx (e.g., in case we want to fuse the
        # backward of conv1d with the backward of chunk).
        dx, dconv1d_weight, dconv1d_bias, *_ = causal_conv1d_cuda.causal_conv1d_bwd(
            x, conv1d_weight, conv1d_bias, dconv1d_out, None, None, None, dx, False, True
        )
        dconv1d_bias = dconv1d_bias if conv1d_bias is not None else None
        dconv1d_weight = rearrange(dconv1d_weight, "d w -> d 1 w")
        return (
            dxz,
            dconv1d_weight,
            dconv1d_bias,
            dx_proj_weight,
            ddelta_proj_weight,
            dout_proj_weight,
            dout_proj_bias,
            dA,
            dB,
            dC,
            dD,
            ddelta_bias if delta_bias is not None else None,
            dB_proj_bias,
            dC_proj_bias,
            None,
        )


def mamba_inner_fn(
    xz,
    conv1d_weight,
    conv1d_bias,
    x_proj_weight,
    delta_proj_weight,
    out_proj_weight,
    out_proj_bias,
    A,
    B=None,
    C=None,
    D=None,
    delta_bias=None,
    B_proj_bias=None,
    C_proj_bias=None,
    delta_softplus=True,
    is_paddle_linear=False,
):
    if is_paddle_linear:
        # NOTE: paddle linear weight is transposed
        x_proj_weight = x_proj_weight.t().contiguous()
        out_proj_weight = out_proj_weight.t().contiguous()
        delta_proj_weight = delta_proj_weight.t().contiguous()

    return MambaInnerFn.apply(
        xz,
        conv1d_weight,
        conv1d_bias,
        x_proj_weight,
        delta_proj_weight,
        out_proj_weight,
        out_proj_bias,
        A,
        B,
        C,
        D,
        delta_bias,
        B_proj_bias,
        C_proj_bias,
        delta_softplus,
    )


def mamba_inner_ref(
    xz,
    conv1d_weight,
    conv1d_bias,
    x_proj_weight,
    delta_proj_weight,
    out_proj_weight,
    out_proj_bias,
    A,
    B=None,
    C=None,
    D=None,
    delta_bias=None,
    B_proj_bias=None,
    C_proj_bias=None,
    delta_softplus=True,
    is_paddle_linear=False,
):
    if is_paddle_linear:
        # NOTE: paddle linear weight is transposed
        x_proj_weight = x_proj_weight.t().contiguous()
        out_proj_weight = out_proj_weight.t().contiguous()
        delta_proj_weight = delta_proj_weight.t().contiguous()
    assert causal_conv1d_fn is not None, "causal_conv1d_fn is not available. Please install causal-conv1d."
    L = xz.shape[-1]
    delta_rank = delta_proj_weight.shape[1]
    d_state = A.shape[-1] * (1 if not A.is_complex() else 2)
    x, z = xz.chunk(2, axis=1)
    x = causal_conv1d_fn(x, rearrange(conv1d_weight, "d 1 w -> d w"), conv1d_bias, activation="silu")
    # We're being very careful here about the layout, to avoid extra transposes.
    # We want delta to have d as the slowest moving dimension
    # and L as the fastest moving dimension, since those are what the ssm_scan kernel expects.
    x_dbl = F.linear(rearrange(x, "b d l -> (b l) d"), x_proj_weight.t())  # (bl d)
    delta = delta_proj_weight @ x_dbl[:, :delta_rank].t()
    delta = rearrange(delta, "d (b l) -> b d l", l=L)
    if B is None:  # variable B
        B = x_dbl[:, delta_rank : delta_rank + d_state]  # (bl d)
        if B_proj_bias is not None:
            B = B + B_proj_bias.cast(dtype=B.dtype)
        if not A.is_complex():
            B = rearrange(B, "(b l) dstate -> b dstate l", l=L).contiguous()
        else:
            B = rearrange(B, "(b l) (dstate two) -> b dstate (l two)", l=L, two=2).contiguous()
    if C is None:  # variable B
        C = x_dbl[:, -d_state:]  # (bl d)
        if C_proj_bias is not None:
            C = C + C_proj_bias.cast(dtype=C.dtype)
        if not A.is_complex():
            C = rearrange(C, "(b l) dstate -> b dstate l", l=L).contiguous()
        else:
            C = rearrange(C, "(b l) (dstate two) -> b dstate (l two)", l=L, two=2).contiguous()
    y = selective_scan_fn(x, delta, A, B, C, D, z=z, delta_bias=delta_bias, delta_softplus=True)
    return F.linear(rearrange(y, "b d l -> b l d"), out_proj_weight.t(), out_proj_bias)
