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
from math import sqrt as sqrt
from itertools import product as product
from torch.nn import init as init
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_avg_pool2d_0(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.
    constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 2
    x1 = xindex // 2
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + (2 * x0 + 8 * x1), xmask, eviction_policy=
        'evict_last')
    tmp1 = tl.load(in_ptr0 + (1 + 2 * x0 + 8 * x1), xmask, eviction_policy=
        'evict_last')
    tmp3 = tl.load(in_ptr0 + (4 + 2 * x0 + 8 * x1), xmask, eviction_policy=
        'evict_last')
    tmp5 = tl.load(in_ptr0 + (5 + 2 * x0 + 8 * x1), xmask, eviction_policy=
        'evict_last')
    tmp2 = tmp1 + tmp0
    tmp4 = tmp3 + tmp2
    tmp6 = tmp5 + tmp4
    tmp7 = 0.25
    tmp8 = tmp6 * tmp7
    tl.store(out_ptr0 + x2, tmp8, xmask)


def call(args):
    primals_1, primals_2, primals_3 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_2, (2, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_3, (2, 4, 4, 4), (64, 16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 2, 2), (16, 4, 2, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_avg_pool2d_0[grid(64)](primals_1, buf0, 64, XBLOCK
            =64, num_warps=1, num_stages=1)
        buf1 = extern_kernels.convolution(primals_1, primals_2, stride=(1, 
            1), padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf1, (4, 2, 3, 3), (18, 9, 3, 1))
        buf2 = extern_kernels.convolution(buf0, primals_3, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf2, (4, 2, 1, 1), (2, 1, 1, 1))
    return buf1, buf2, primals_1, primals_2, primals_3, buf0


class FirstOctaveConvNew(nn.Module):

    def __init__(self, in_channels, out_channels, kernel_size, alpha=0.5,
        stride=1, padding=1, dilation=1, groups=1, bias=False):
        super(FirstOctaveConvNew, self).__init__()
        self.stride = stride
        self.h2g_pool = nn.AvgPool2d(kernel_size=(2, 2), stride=2)
        self.h2l = torch.nn.Conv2d(in_channels, int(alpha * out_channels),
            kernel_size, 1, padding, dilation, groups, bias)
        self.h2h = torch.nn.Conv2d(in_channels, out_channels - int(alpha *
            out_channels), kernel_size, 1, padding, dilation, groups, bias)

    def forward(self, input_0):
        primals_2 = self.h2l.weight
        primals_3 = self.h2h.weight
        primals_1 = input_0
        output = call([primals_1, primals_2, primals_3])
        return output[0], output[1]


Measured latency_ms: 0.029171