import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import math
from torch import Tensor
from torch.nn import Linear
from typing import Type
from typing import Optional
from typing import Tuple
from torch.nn import LayerNorm
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor


@triton.jit
def triton_poi_fused_cat_0(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = xindex // 4
    x0 = xindex % 4
    x2 = xindex
    tmp0 = x1
    tl.full([1], 0, tl.int64)
    tmp3 = tl.full([1], 4, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + (4 * x0 + 16 * x1), tmp4 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp6 = tmp0 >= tmp3
    tmp7 = tl.full([1], 8, tl.int64)
    tmp8 = tmp0 < tmp7
    tmp9 = tmp6 & tmp8
    tmp10 = tl.load(in_ptr0 + (1 + 4 * x0 + 16 * (-4 + x1)), tmp9 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp11 = tmp0 >= tmp7
    tmp12 = tl.full([1], 12, tl.int64)
    tmp13 = tmp0 < tmp12
    tmp14 = tmp11 & tmp13
    tmp15 = tl.load(in_ptr0 + (2 + 4 * x0 + 16 * (-8 + x1)), tmp14 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp16 = tmp0 >= tmp12
    tl.full([1], 16, tl.int64)
    tmp19 = tl.load(in_ptr0 + (3 + 4 * x0 + 16 * (-12 + x1)), tmp16 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp20 = tl.where(tmp14, tmp15, tmp19)
    tmp21 = tl.where(tmp9, tmp10, tmp20)
    tmp22 = tl.where(tmp4, tmp5, tmp21)
    tl.store(out_ptr0 + x2, tmp22, xmask)


@triton.jit
def triton_poi_fused__softmax_1(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr
    ):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x0 = xindex % 4
    x2 = xindex // 16
    tmp0 = tl.load(in_ptr0 + x3, xmask)
    tmp3 = tl.load(in_ptr0 + (x0 + 16 * x2), xmask, eviction_policy=
        'evict_last')
    tmp5 = tl.load(in_ptr0 + (4 + x0 + 16 * x2), xmask, eviction_policy=
        'evict_last')
    tmp8 = tl.load(in_ptr0 + (8 + x0 + 16 * x2), xmask, eviction_policy=
        'evict_last')
    tmp11 = tl.load(in_ptr0 + (12 + x0 + 16 * x2), xmask, eviction_policy=
        'evict_last')
    tmp1 = 1.0
    tmp2 = tmp0 * tmp1
    tmp4 = tmp3 * tmp1
    tmp6 = tmp5 * tmp1
    tmp7 = triton_helpers.maximum(tmp4, tmp6)
    tmp9 = tmp8 * tmp1
    tmp10 = triton_helpers.maximum(tmp7, tmp9)
    tmp12 = tmp11 * tmp1
    tmp13 = triton_helpers.maximum(tmp10, tmp12)
    tmp14 = tmp2 - tmp13
    tmp15 = 0.5
    tmp16 = tmp14 * tmp15
    tmp17 = tl_math.exp(tmp16)
    tl.store(out_ptr0 + x3, tmp17, xmask)


@triton.jit
def triton_poi_fused__softmax_2(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr
    ):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x0 = xindex % 4
    x2 = xindex // 16
    tmp0 = tl.load(in_ptr0 + x3, xmask)
    tmp1 = tl.load(in_ptr0 + (x0 + 16 * x2), xmask, eviction_policy=
        'evict_last')
    tmp2 = tl.load(in_ptr0 + (4 + x0 + 16 * x2), xmask, eviction_policy=
        'evict_last')
    tmp4 = tl.load(in_ptr0 + (8 + x0 + 16 * x2), xmask, eviction_policy=
        'evict_last')
    tmp6 = tl.load(in_ptr0 + (12 + x0 + 16 * x2), xmask, eviction_policy=
        'evict_last')
    tmp3 = tmp1 + tmp2
    tmp5 = tmp3 + tmp4
    tmp7 = tmp5 + tmp6
    tmp8 = tmp0 / tmp7
    tl.store(out_ptr0 + x3, tmp8, xmask)


@triton.jit
def triton_poi_fused_cat_3(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK: tl.
    constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 4
    x1 = xindex // 4
    x2 = xindex
    tmp0 = x0
    tl.full([1], 0, tl.int64)
    tmp3 = tl.full([1], 1, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + x1, tmp4 & xmask, eviction_policy='evict_last',
        other=0.0)
    tmp6 = tl.load(in_ptr1 + x1, tmp4 & xmask, eviction_policy='evict_last',
        other=0.0)
    tmp7 = tmp5 + tmp6
    tmp8 = tl.full(tmp7.shape, 0.0, tmp7.dtype)
    tmp9 = tl.where(tmp4, tmp7, tmp8)
    tmp10 = tmp0 >= tmp3
    tmp11 = tl.full([1], 2, tl.int64)
    tmp12 = tmp0 < tmp11
    tmp13 = tmp10 & tmp12
    tmp14 = tl.load(in_ptr0 + (16 + x1), tmp13 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp15 = tl.load(in_ptr1 + (16 + x1), tmp13 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp16 = tmp14 + tmp15
    tmp17 = tl.full(tmp16.shape, 0.0, tmp16.dtype)
    tmp18 = tl.where(tmp13, tmp16, tmp17)
    tmp19 = tmp0 >= tmp11
    tmp20 = tl.full([1], 3, tl.int64)
    tmp21 = tmp0 < tmp20
    tmp22 = tmp19 & tmp21
    tmp23 = tl.load(in_ptr0 + (32 + x1), tmp22 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp24 = tl.load(in_ptr1 + (32 + x1), tmp22 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp25 = tmp23 + tmp24
    tmp26 = tl.full(tmp25.shape, 0.0, tmp25.dtype)
    tmp27 = tl.where(tmp22, tmp25, tmp26)
    tmp28 = tmp0 >= tmp20
    tl.full([1], 4, tl.int64)
    tmp31 = tl.load(in_ptr0 + (48 + x1), tmp28 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp32 = tl.load(in_ptr1 + (48 + x1), tmp28 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp33 = tmp31 + tmp32
    tmp34 = tl.full(tmp33.shape, 0.0, tmp33.dtype)
    tmp35 = tl.where(tmp28, tmp33, tmp34)
    tmp36 = tl.where(tmp22, tmp27, tmp35)
    tmp37 = tl.where(tmp13, tmp18, tmp36)
    tmp38 = tl.where(tmp4, tmp9, tmp37)
    tl.store(out_ptr0 + x2, tmp38, xmask)


@triton.jit
def triton_poi_fused_add_relu_threshold_backward_4(in_ptr0, in_ptr1,
    in_ptr2, out_ptr0, out_ptr1, xnumel, XBLOCK: tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 4
    tmp0 = tl.load(in_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr1 + x2, xmask)
    tmp2 = tl.load(in_ptr2 + x0, xmask, eviction_policy='evict_last')
    tmp3 = tmp1 + tmp2
    tmp4 = tl.full([1], 0, tl.int32)
    tmp5 = triton_helpers.maximum(tmp4, tmp3)
    tmp6 = tmp0 + tmp5
    tmp7 = 0.0
    tmp8 = tmp5 <= tmp7
    tl.store(out_ptr0 + x2, tmp6, xmask)
    tl.store(out_ptr1 + x2, tmp8, xmask)


def call(args):
    (primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
        primals_7, primals_8, primals_9) = args
    args.clear()
    assert_size_stride(primals_1, (4, 4), (4, 1))
    assert_size_stride(primals_2, (4,), (1,))
    assert_size_stride(primals_3, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_4, (4, 4), (4, 1))
    assert_size_stride(primals_5, (4,), (1,))
    assert_size_stride(primals_6, (4, 4), (4, 1))
    assert_size_stride(primals_7, (4,), (1,))
    assert_size_stride(primals_8, (4, 4), (4, 1))
    assert_size_stride(primals_9, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
        extern_kernels.addmm(primals_2, reinterpret_tensor(primals_3, (16, 
            4), (4, 1), 0), reinterpret_tensor(primals_1, (4, 4), (1, 4), 0
            ), alpha=1, beta=1, out=buf0)
        del primals_1
        del primals_2
        buf1 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
        extern_kernels.addmm(primals_5, reinterpret_tensor(primals_3, (16, 
            4), (4, 1), 0), reinterpret_tensor(primals_4, (4, 4), (1, 4), 0
            ), alpha=1, beta=1, out=buf1)
        del primals_4
        del primals_5
        buf2 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
        extern_kernels.addmm(primals_7, reinterpret_tensor(primals_3, (16, 
            4), (4, 1), 0), reinterpret_tensor(primals_6, (4, 4), (1, 4), 0
            ), alpha=1, beta=1, out=buf2)
        del primals_6
        del primals_7
        buf3 = empty_strided_cuda((16, 4, 1), (4, 1, 64), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_cat_0[grid(64)](buf0, buf3, 64, XBLOCK=64,
            num_warps=1, num_stages=1)
        buf4 = reinterpret_tensor(buf0, (16, 4, 1), (4, 1, 64), 0)
        del buf0
        triton_poi_fused_cat_0[grid(64)](buf2, buf4, 64, XBLOCK=64,
            num_warps=1, num_stages=1)
        buf5 = reinterpret_tensor(buf2, (16, 4, 1), (4, 1, 1), 0)
        del buf2
        triton_poi_fused_cat_0[grid(64)](buf1, buf5, 64, XBLOCK=64,
            num_warps=1, num_stages=1)
        buf6 = empty_strided_cuda((16, 4, 4), (16, 4, 1), torch.float32)
        extern_kernels.bmm(buf3, reinterpret_tensor(buf5, (16, 1, 4), (4, 0,
            1), 0), out=buf6)
        buf7 = empty_strided_cuda((16, 4, 4), (16, 4, 1), torch.float32)
        triton_poi_fused__softmax_1[grid(256)](buf6, buf7, 256, XBLOCK=128,
            num_warps=4, num_stages=1)
        buf8 = buf6
        del buf6
        triton_poi_fused__softmax_2[grid(256)](buf7, buf8, 256, XBLOCK=256,
            num_warps=4, num_stages=1)
        del buf7
        buf9 = reinterpret_tensor(buf1, (16, 4, 1), (4, 1, 1), 0)
        del buf1
        extern_kernels.bmm(buf8, buf4, out=buf9)
        buf10 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        triton_poi_fused_cat_3[grid(64)](buf3, buf9, buf10, 64, XBLOCK=64,
            num_warps=1, num_stages=1)
        buf11 = reinterpret_tensor(buf9, (16, 4), (4, 1), 0)
        del buf9
        extern_kernels.mm(reinterpret_tensor(buf10, (16, 4), (4, 1), 0),
            reinterpret_tensor(primals_8, (4, 4), (1, 4), 0), out=buf11)
        buf12 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        buf13 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.bool)
        triton_poi_fused_add_relu_threshold_backward_4[grid(64)](buf10,
            buf11, primals_9, buf12, buf13, 64, XBLOCK=64, num_warps=1,
            num_stages=1)
        del buf11
        del primals_9
    return buf12, reinterpret_tensor(primals_3, (16, 4), (4, 1), 0
        ), buf8, reinterpret_tensor(buf10, (16, 4), (4, 1), 0
        ), buf13, primals_8, reinterpret_tensor(buf4, (16, 1, 4), (4, 1, 1), 0
        ), reinterpret_tensor(buf3, (16, 1, 4), (4, 1, 1), 0), buf5


class MAB(torch.nn.Module):

    def __init__(self, dim_Q: 'int', dim_K: 'int', dim_V: 'int', num_heads:
        'int', Conv: 'Optional[Type]'=None, layer_norm: 'bool'=False):
        super().__init__()
        self.dim_V = dim_V
        self.num_heads = num_heads
        self.layer_norm = layer_norm
        self.fc_q = Linear(dim_Q, dim_V)
        if Conv is None:
            self.layer_k = Linear(dim_K, dim_V)
            self.layer_v = Linear(dim_K, dim_V)
        else:
            self.layer_k = Conv(dim_K, dim_V)
            self.layer_v = Conv(dim_K, dim_V)
        if layer_norm:
            self.ln0 = LayerNorm(dim_V)
            self.ln1 = LayerNorm(dim_V)
        self.fc_o = Linear(dim_V, dim_V)

    def reset_parameters(self):
        self.fc_q.reset_parameters()
        self.layer_k.reset_parameters()
        self.layer_v.reset_parameters()
        if self.layer_norm:
            self.ln0.reset_parameters()
            self.ln1.reset_parameters()
        self.fc_o.reset_parameters()
        pass

    def forward(self, Q: 'Tensor', K: 'Tensor', graph:
        'Optional[Tuple[Tensor, Tensor, Tensor]]'=None, mask:
        'Optional[Tensor]'=None) ->Tensor:
        Q = self.fc_q(Q)
        if graph is not None:
            x, edge_index, batch = graph
            K, V = self.layer_k(x, edge_index), self.layer_v(x, edge_index)
            K, _ = to_dense_batch(K, batch)
            V, _ = to_dense_batch(V, batch)
        else:
            K, V = self.layer_k(K), self.layer_v(K)
        dim_split = self.dim_V // self.num_heads
        Q_ = torch.cat(Q.split(dim_split, 2), dim=0)
        K_ = torch.cat(K.split(dim_split, 2), dim=0)
        V_ = torch.cat(V.split(dim_split, 2), dim=0)
        if mask is not None:
            mask = torch.cat([mask for _ in range(self.num_heads)], 0)
            attention_score = Q_.bmm(K_.transpose(1, 2))
            attention_score = attention_score / math.sqrt(self.dim_V)
            A = torch.softmax(mask + attention_score, 1)
        else:
            A = torch.softmax(Q_.bmm(K_.transpose(1, 2)) / math.sqrt(self.
                dim_V), 1)
        out = torch.cat((Q_ + A.bmm(V_)).split(Q.size(0), 0), 2)
        if self.layer_norm:
            out = self.ln0(out)
        out = out + self.fc_o(out).relu()
        if self.layer_norm:
            out = self.ln1(out)
        return out


class SABNew(torch.nn.Module):

    def __init__(self, in_channels: 'int', out_channels: 'int', num_heads:
        'int', Conv: 'Optional[Type]'=None, layer_norm: 'bool'=False):
        super().__init__()
        self.mab = MAB(in_channels, in_channels, out_channels, num_heads,
            Conv=Conv, layer_norm=layer_norm)

    def reset_parameters(self):
        self.mab.reset_parameters()

    def forward(self, input_0):
        primals_1 = self.mab.fc_q.weight
        primals_2 = self.mab.fc_q.bias
        primals_4 = self.mab.layer_k.weight
        primals_5 = self.mab.layer_k.bias
        primals_6 = self.mab.layer_v.weight
        primals_7 = self.mab.layer_v.bias
        primals_8 = self.mab.fc_o.weight
        primals_9 = self.mab.fc_o.bias
        primals_3 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6, primals_7, primals_8, primals_9])
        return output[0]


Measured latency_ms: 0.016555