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
import torch.nn as nn
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_convolution_0(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl
    .constexpr):
    xnumel = 3888
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = xindex // 81 % 12
    tmp0 = tl.load(in_out_ptr0 + x3, xmask)
    tmp1 = tl.load(in_ptr0 + x1, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tl.store(in_out_ptr0 + x3, tmp2, xmask)


@triton.jit
def triton_poi_fused_add_mul_sigmoid_1(in_ptr0, out_ptr0, out_ptr1, xnumel,
    XBLOCK: tl.constexpr):
    xnumel = 1296
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 324
    x1 = xindex // 324
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + (648 + x0 + 972 * x1), xmask)
    tmp2 = tl.load(in_ptr0 + (x0 + 972 * x1), xmask)
    tmp3 = tl.load(in_ptr0 + (324 + x0 + 972 * x1), xmask)
    tmp1 = tl.sigmoid(tmp0)
    tmp4 = tmp3 * tmp1
    tmp5 = tmp2 + tmp4
    tl.store(out_ptr0 + x2, tmp1, xmask)
    tl.store(out_ptr1 + x2, tmp5, xmask)


def call(args):
    primals_1, primals_2, primals_3 = args
    args.clear()
    assert_size_stride(primals_1, (12, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_2, (12,), (1,))
    assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = extern_kernels.convolution(primals_3, primals_1, stride=(1, 
            1), padding=(4, 4), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf0, (4, 12, 9, 9), (972, 81, 9, 1))
        buf1 = buf0
        del buf0
        get_raw_stream(0)
        triton_poi_fused_convolution_0[grid(3888)](buf1, primals_2, 3888,
            XBLOCK=128, num_warps=4, num_stages=1)
        del primals_2
        buf2 = empty_strided_cuda((4, 4, 9, 9), (324, 81, 9, 1), torch.float32)
        buf3 = empty_strided_cuda((4, 4, 9, 9), (324, 81, 9, 1), torch.float32)
        triton_poi_fused_add_mul_sigmoid_1[grid(1296)](buf1, buf2, buf3, 
            1296, XBLOCK=128, num_warps=4, num_stages=1)
    return buf3, primals_1, primals_3, reinterpret_tensor(buf1, (4, 4, 9, 9
        ), (972, 81, 9, 1), 324), buf2


class GatedConv2dNew(nn.Module):

    def __init__(self, in_channels, out_channels, kernel_size, padding):
        super(GatedConv2dNew, self).__init__()
        self.in_channels = in_channels
        self.conv = nn.Conv2d(in_channels, out_channels * 3, kernel_size=
            kernel_size, padding=padding)

    def forward(self, input_0):
        primals_1 = self.conv.weight
        primals_2 = self.conv.bias
        primals_3 = input_0
        output = call([primals_1, primals_2, primals_3])
        return output[0]


Measured latency_ms: 0.014245