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 math
import torch.nn as nn
import torch.nn.functional as F
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor


@triton.jit
def triton_poi_fused_convolution_0(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl
    .constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 4
    tmp0 = tl.load(in_out_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tl.store(in_out_ptr0 + x2, tmp2, 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, (4,), (1,))
    assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 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, 1, 1), (4, 1, 1, 1))
        del primals_1
        buf1 = buf0
        del buf0
        get_raw_stream(0)
        triton_poi_fused_convolution_0[grid(16)](buf1, primals_2, 16,
            XBLOCK=16, num_warps=1, num_stages=1)
        del primals_2
    return buf1, reinterpret_tensor(primals_3, (4, 4, 4, 4), (16, 64, 4, 1), 0)


class myConv2dFunction(torch.autograd.Function):

    @staticmethod
    def forward(ctx, input, weight, bias):
        ctx.save_for_backward(input, weight, bias)
        return F.conv2d(input, weight, bias)

    @staticmethod
    def backward(ctx, grad_output):
        input, weight, _bias = ctx.saved_tensors
        _out_channels, _in_channels, kernel_height, kernel_width = list(weight
            .size())
        grad_input = F.conv2d(grad_output, torch.Tensor.rot90(weight, 2, [2,
            3]).transpose(0, 1), padding=(kernel_width - 1, kernel_height - 1))
        grad_weight = F.conv2d(input.transpose(0, 1), grad_output.transpose
            (0, 1)).transpose(0, 1)
        grad_bias = grad_output.sum([0, 2, 3])
        return grad_input, grad_weight, grad_bias


class myConv2dNew(nn.Module):

    def __init__(self, in_channels, out_channels, kernel_size):
        super(myConv2dNew, self).__init__()
        self.in_channels = in_channels
        self.out_channels = out_channels
        self.kenerl_size = kernel_size
        sqrtk = math.sqrt(1 / (in_channels * kernel_size[0] * kernel_size[1]))
        self.weight = nn.Parameter(torch.Tensor(out_channels, in_channels,
            kernel_size[0], kernel_size[1]))
        self.weight.data.uniform_(-sqrtk, sqrtk)
        self.bias = nn.Parameter(torch.Tensor(out_channels))
        self.bias.data.uniform_(-sqrtk, sqrtk)

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


Measured latency_ms: 0.022045