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_add_convolution_mul_0(in_out_ptr0, in_ptr0, in_ptr1,
    in_ptr2, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = xindex // 4 % 4
    tmp0 = tl.load(in_out_ptr0 + x3, xmask)
    tmp1 = tl.load(in_ptr0 + x1, xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr1 + x1, xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr2 + x1, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp4 = tl.sigmoid(tmp3)
    tmp5 = tmp2 * tmp4
    tmp7 = tmp5 + tmp6
    tl.store(in_out_ptr0 + x3, tmp2, xmask)
    tl.store(out_ptr0 + x3, tmp7, xmask)


def call(args):
    (primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
        primals_7) = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 3, 3), (36, 9, 3, 1))
    assert_size_stride(primals_2, (4,), (1,))
    assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_4, (1, 1), (1, 1))
    assert_size_stride(primals_5, (4, 1), (1, 1))
    assert_size_stride(primals_6, (4,), (1,))
    assert_size_stride(primals_7, (4, 1), (1, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = extern_kernels.convolution(primals_3, primals_1, stride=(1, 
            1), padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf0, (4, 4, 2, 2), (16, 4, 2, 1))
        buf2 = empty_strided_cuda((1, 4), (4, 1), torch.float32)
        extern_kernels.addmm(primals_6, primals_4, reinterpret_tensor(
            primals_5, (1, 4), (1, 1), 0), alpha=1, beta=1, out=buf2)
        del primals_5
        del primals_6
        buf3 = empty_strided_cuda((1, 4), (4, 1), torch.float32)
        extern_kernels.mm(primals_4, reinterpret_tensor(primals_7, (1, 4),
            (1, 1), 0), out=buf3)
        del primals_7
        buf1 = buf0
        del buf0
        buf4 = empty_strided_cuda((4, 4, 2, 2), (16, 4, 2, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_add_convolution_mul_0[grid(64)](buf1, primals_2,
            buf2, buf3, buf4, 64, XBLOCK=64, num_warps=1, num_stages=1)
        del buf3
        del primals_2
    return buf4, primals_1, primals_3, primals_4, buf1, buf2


class ConcatSquashConv2dNew(nn.Module):

    def __init__(self, dim_in, dim_out, ksize=3, stride=1, padding=0,
        dilation=1, groups=1, bias=True, transpose=False):
        super(ConcatSquashConv2dNew, self).__init__()
        module = nn.ConvTranspose2d if transpose else nn.Conv2d
        self._layer = module(dim_in, dim_out, kernel_size=ksize, stride=
            stride, padding=padding, dilation=dilation, groups=groups, bias
            =bias)
        self._hyper_gate = nn.Linear(1, dim_out)
        self._hyper_bias = nn.Linear(1, dim_out, bias=False)

    def forward(self, input_0, input_1):
        primals_1 = self._layer.weight
        primals_2 = self._layer.bias
        primals_5 = self._hyper_gate.weight
        primals_6 = self._hyper_gate.bias
        primals_7 = self._hyper_bias.weight
        primals_4 = input_0
        primals_3 = input_1
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6, primals_7])
        return output[0]


Measured latency_ms: 0.090702