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
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda


@triton.jit
def triton_poi_fused_convolution_0(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl
    .constexpr):
    xnumel = 512
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = xindex // 16 % 8
    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_mul_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
    x0 = xindex % 64
    x1 = xindex // 64
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + 128 * x1), xmask)
    tmp1 = tl.load(in_ptr0 + (64 + x0 + 128 * x1), xmask)
    tmp2 = tmp0 * tmp1
    tl.store(out_ptr0 + x2, tmp2, xmask)


def call(args):
    primals_1, primals_2, primals_3 = args
    args.clear()
    assert_size_stride(primals_1, (8, 4, 3, 3), (36, 9, 3, 1))
    assert_size_stride(primals_2, (8,), (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=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf0, (4, 8, 4, 4), (128, 16, 4, 1))
        buf1 = buf0
        del buf0
        get_raw_stream(0)
        triton_poi_fused_convolution_0[grid(512)](buf1, primals_2, 512,
            XBLOCK=256, num_warps=4, num_stages=1)
        del primals_2
        buf2 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        triton_poi_fused_mul_1[grid(256)](buf1, buf2, 256, XBLOCK=128,
            num_warps=4, num_stages=1)
    return buf2, primals_1, primals_3, buf1


def setup_conv(in_channels, out_channels, kernel_size, bias, padding_mode,
    stride=1, Conv=torch.nn.Conv2d):
    return Conv(in_channels=in_channels, out_channels=out_channels,
        kernel_size=kernel_size, padding=(kernel_size - 1) // 2, stride=
        stride, bias=bias)


class BilinearConvLayerNew(torch.nn.Module):

    def __init__(self, input_channels, output_channels, bilin_channels=None,
        padding_mode='zeros', Conv=torch.nn.Conv2d, nonlinearity=torch.nn.
        Identity(), norm=torch.nn.Identity(), kernel_size=3):
        super(BilinearConvLayerNew, self).__init__()
        bilin_channels = (output_channels if bilin_channels is None else
            bilin_channels)
        self.chgrp1 = max(0, output_channels - bilin_channels)
        self.chgrp2 = bilin_channels
        self.layer1 = setup_conv(in_channels=input_channels, out_channels=
            self.chgrp1 + 2 * self.chgrp2, kernel_size=kernel_size, bias=
            True, padding_mode=padding_mode, stride=1, Conv=Conv)
        self.norm = norm
        self.nonlinearity = nonlinearity

    def forward(self, input_0):
        primals_1 = self.layer1.weight
        primals_2 = self.layer1.bias
        primals_3 = input_0
        output = call([primals_1, primals_2, primals_3])
        return output[0]


Measured latency_ms: 0.035877