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_add_convolution_0(in_out_ptr0, in_ptr0, in_ptr1,
    xnumel, XBLOCK: tl.constexpr):
    xnumel = 1296
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = xindex // 81 % 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 + x3, xmask)
    tmp2 = tmp0 + tmp1
    tmp4 = tmp2 + tmp3
    tl.store(in_out_ptr0 + x3, tmp4, xmask)


def call(args):
    primals_1, primals_2, primals_3, primals_4, primals_5 = args
    args.clear()
    assert_size_stride(primals_1, (1, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_2, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_3, (4, 1, 1, 1), (1, 1, 1, 1))
    assert_size_stride(primals_4, (4,), (1,))
    assert_size_stride(primals_5, (4, 4, 4, 4), (64, 16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = extern_kernels.convolution(primals_2, primals_1, stride=(1, 
            1), padding=(4, 4), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf0, (4, 1, 9, 9), (81, 81, 9, 1))
        buf1 = extern_kernels.convolution(buf0, primals_3, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf1, (4, 4, 9, 9), (324, 81, 9, 1))
        buf2 = extern_kernels.convolution(primals_2, primals_5, stride=(1, 
            1), padding=(4, 4), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf2, (4, 4, 9, 9), (324, 81, 9, 1))
        buf3 = buf1
        del buf1
        get_raw_stream(0)
        triton_poi_fused_add_convolution_0[grid(1296)](buf3, primals_4,
            buf2, 1296, XBLOCK=128, num_warps=4, num_stages=1)
        del buf2
        del primals_4
    return buf3, primals_1, primals_2, primals_3, primals_5, buf0


class ConvLRNew(nn.Module):
    """[u * v + res] version of torch.nn.ConvLR"""

    def __init__(self, in_planes, out_planes, kernel_size, stride, padding,
        rank_ratio=0.25, bias=True, device=None, dtype=None):
        super().__init__()
        sliced_rank = int(min(in_planes, out_planes) * rank_ratio)
        self.u = nn.Conv2d(in_channels=in_planes, out_channels=sliced_rank,
            kernel_size=kernel_size, stride=stride, padding=padding, bias=
            False, device=device, dtype=dtype)
        self.v = nn.Conv2d(in_channels=sliced_rank, out_channels=out_planes,
            kernel_size=1, stride=1, padding=0, bias=bias, device=device,
            dtype=dtype)
        self.res = nn.Conv2d(in_channels=in_planes, out_channels=out_planes,
            kernel_size=kernel_size, stride=stride, padding=padding, bias=
            False, device=device, dtype=dtype)

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


Measured latency_ms: 0.036727