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
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_per_fused_mean_0(in_out_ptr0, in_ptr0, xnumel, rnumel, XBLOCK:
    tl.constexpr):
    xnumel = 4
    RBLOCK: tl.constexpr = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    rindex = tl.arange(0, RBLOCK)[None, :]
    tl.full([XBLOCK, RBLOCK], True, tl.int1)
    r1 = rindex
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (r1 + 64 * x0), xmask, other=0.0)
    tmp1 = tl.broadcast_to(tmp0, [XBLOCK, RBLOCK])
    tmp3 = tl.where(xmask, tmp1, 0)
    tmp4 = tl.sum(tmp3, 1)[:, None]
    tmp5 = 64.0
    tmp6 = tmp4 / tmp5
    tl.debug_barrier()
    tl.store(in_out_ptr0 + x0, tmp6, xmask)


@triton.jit
def triton_poi_fused_relu_threshold_backward_1(in_out_ptr0, in_ptr0,
    out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 8
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_out_ptr0 + x0, xmask)
    tmp1 = tl.load(in_ptr0 + x0, xmask)
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tmp5 = 0.0
    tmp6 = tmp4 <= tmp5
    tl.store(in_out_ptr0 + x0, tmp4, xmask)
    tl.store(out_ptr0 + x0, tmp6, xmask)


@triton.jit
def triton_poi_fused_convolution_2(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl
    .constexpr):
    xnumel = 4
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_out_ptr0 + x0, xmask)
    tmp1 = tl.load(in_ptr0 + x0, xmask)
    tmp2 = tmp0 + tmp1
    tl.store(in_out_ptr0 + x0, tmp2, xmask)


@triton.jit
def triton_poi_fused_mul_sigmoid_3(in_ptr0, in_ptr1, out_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x1 = xindex // 64
    tmp0 = tl.load(in_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr1 + x1, xmask, eviction_policy='evict_last')
    tmp2 = tl.sigmoid(tmp1)
    tmp3 = tmp0 * tmp2
    tl.store(out_ptr0 + x2, tmp3, xmask)


def call(args):
    primals_1, primals_2, primals_3, primals_4, primals_5 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_2, (8, 4, 1, 1, 1), (4, 1, 1, 1, 1))
    assert_size_stride(primals_3, (8,), (1,))
    assert_size_stride(primals_4, (4, 8, 1, 1, 1), (8, 1, 1, 1, 1))
    assert_size_stride(primals_5, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 1, 1, 1), (1, 4, 4, 4), torch.float32)
        buf1 = buf0
        del buf0
        get_raw_stream(0)
        triton_per_fused_mean_0[grid(4)](buf1, primals_1, 4, 64, XBLOCK=1,
            num_warps=2, num_stages=1)
        buf2 = extern_kernels.convolution(reinterpret_tensor(buf1, (1, 4, 1,
            1, 1), (0, 1, 0, 0, 0), 0), primals_2, stride=(1, 1, 1),
            padding=(0, 0, 0), dilation=(1, 1, 1), transposed=False,
            output_padding=(0, 0, 0), groups=1, bias=None)
        assert_size_stride(buf2, (1, 8, 1, 1, 1), (8, 1, 1, 1, 1))
        buf3 = reinterpret_tensor(buf2, (8, 1, 1, 1), (1, 8, 8, 8), 0)
        del buf2
        buf7 = empty_strided_cuda((8, 1, 1, 1), (1, 1, 1, 1), torch.bool)
        triton_poi_fused_relu_threshold_backward_1[grid(8)](buf3, primals_3,
            buf7, 8, XBLOCK=8, num_warps=1, num_stages=1)
        del primals_3
        buf4 = extern_kernels.convolution(reinterpret_tensor(buf3, (1, 8, 1,
            1, 1), (0, 1, 0, 0, 0), 0), primals_4, stride=(1, 1, 1),
            padding=(0, 0, 0), dilation=(1, 1, 1), transposed=False,
            output_padding=(0, 0, 0), groups=1, bias=None)
        assert_size_stride(buf4, (1, 4, 1, 1, 1), (4, 1, 1, 1, 1))
        buf5 = buf4
        del buf4
        triton_poi_fused_convolution_2[grid(4)](buf5, primals_5, 4, XBLOCK=
            4, num_warps=1, num_stages=1)
        del primals_5
        buf6 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        triton_poi_fused_mul_sigmoid_3[grid(256)](primals_1, buf5, buf6, 
            256, XBLOCK=256, num_warps=4, num_stages=1)
    return buf6, primals_1, primals_2, primals_4, reinterpret_tensor(buf1,
        (1, 4, 1, 1, 1), (4, 1, 1, 1, 1), 0), reinterpret_tensor(buf3, (1, 
        8, 1, 1, 1), (8, 1, 1, 1, 1), 0), buf5, buf7


class SEModuleNew(nn.Module):

    def __init__(self, channels, reduction=1 / 16):
        super().__init__()
        self.avg_pool = nn.AdaptiveAvgPool3d(1)
        self.bottleneck = self._round_width(channels, reduction)
        self.fc1 = nn.Conv3d(channels, self.bottleneck, kernel_size=1,
            padding=0)
        self.relu = nn.ReLU()
        self.fc2 = nn.Conv3d(self.bottleneck, channels, kernel_size=1,
            padding=0)
        self.sigmoid = nn.Sigmoid()

    @staticmethod
    def _round_width(width, multiplier, min_width=8, divisor=8):
        width *= multiplier
        min_width = min_width or divisor
        width_out = max(min_width, int(width + divisor / 2) // divisor *
            divisor)
        if width_out < 0.9 * width:
            width_out += divisor
        return int(width_out)

    def forward(self, input_0):
        primals_2 = self.fc1.weight
        primals_3 = self.fc1.bias
        primals_4 = self.fc2.weight
        primals_5 = self.fc2.bias
        primals_1 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4, primals_5])
        return output[0]


Measured latency_ms: 0.024765