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


@triton.jit
def triton_poi_fused_leaky_relu_0(in_out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x0 = xindex
    tmp0 = tl.load(in_out_ptr0 + x0, None)
    tmp1 = 0.0
    tmp2 = tmp0 > tmp1
    tmp3 = 0.2
    tmp4 = tmp0 * tmp3
    tmp5 = tl.where(tmp2, tmp0, tmp4)
    tl.store(in_out_ptr0 + x0, tmp5, None)


@triton.jit
def triton_poi_fused_add_1(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + x0, None)
    tmp1 = tl.load(in_out_ptr0 + x0, None)
    tmp2 = tmp0 + tmp1
    tl.store(in_out_ptr0 + x0, tmp2, None)


def call(args):
    primals_1, primals_2, primals_3, primals_4 = args
    args.clear()
    assert_size_stride(primals_1, (128, 256, 1, 1), (256, 1, 1, 1))
    assert_size_stride(primals_2, (4, 256, 64, 64), (1048576, 4096, 64, 1))
    assert_size_stride(primals_3, (128, 4, 3, 3), (36, 9, 3, 1))
    assert_size_stride(primals_4, (256, 128, 1, 1), (128, 1, 1, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = extern_kernels.convolution(primals_2, 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, 128, 64, 64), (524288, 4096, 64, 1))
        buf1 = buf0
        del buf0
        get_raw_stream(0)
        triton_poi_fused_leaky_relu_0[grid(2097152)](buf1, 2097152, XBLOCK=
            1024, num_warps=4, num_stages=1)
        buf2 = extern_kernels.convolution(buf1, primals_3, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=32, bias=None)
        assert_size_stride(buf2, (4, 128, 64, 64), (524288, 4096, 64, 1))
        buf3 = buf2
        del buf2
        triton_poi_fused_leaky_relu_0[grid(2097152)](buf3, 2097152, XBLOCK=
            1024, num_warps=4, num_stages=1)
        buf4 = extern_kernels.convolution(buf3, primals_4, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf4, (4, 256, 64, 64), (1048576, 4096, 64, 1))
        buf5 = buf4
        del buf4
        triton_poi_fused_add_1[grid(4194304)](buf5, primals_2, 4194304,
            XBLOCK=1024, num_warps=4, num_stages=1)
    return buf5, primals_1, primals_2, primals_3, primals_4, buf1, buf3


class ResNeXtBottleneckNew(nn.Module):

    def __init__(self, in_channels=256, out_channels=256, stride=1,
        cardinality=32, dilate=1):
        super(ResNeXtBottleneckNew, self).__init__()
        D = out_channels // 2
        self.out_channels = out_channels
        self.conv_reduce = nn.Conv2d(in_channels, D, kernel_size=1, stride=
            1, padding=0, bias=False)
        self.conv_conv = nn.Conv2d(D, D, kernel_size=2 + stride, stride=
            stride, padding=dilate, dilation=dilate, groups=cardinality,
            bias=False)
        self.conv_expand = nn.Conv2d(D, out_channels, kernel_size=1, stride
            =1, padding=0, bias=False)
        self.shortcut = nn.Sequential()
        if stride != 1:
            self.shortcut.add_module('shortcut', nn.AvgPool2d(2, stride=2))

    def forward(self, input_0):
        primals_1 = self.conv_reduce.weight
        primals_3 = self.conv_conv.weight
        primals_4 = self.conv_expand.weight
        primals_2 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4])
        return output[0]


Measured latency_ms: 0.301783