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
import torch.nn.functional as F
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_relu_0(in_out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 576
    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.full([1], 0, tl.int32)
    tmp2 = triton_helpers.maximum(tmp1, tmp0)
    tl.store(in_out_ptr0 + x0, tmp2, xmask)


@triton.jit
def triton_poi_fused_add_relu_threshold_backward_1(in_out_ptr0, in_ptr0,
    out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 256
    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)


def call(args):
    primals_1, primals_2, primals_3 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 3, 3), (36, 9, 3, 1))
    assert_size_stride(primals_2, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_3, (4, 4, 3, 3), (36, 9, 3, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = extern_kernels.convolution(primals_2, primals_1, stride=(1, 
            1), padding=(2, 2), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf0, (4, 4, 6, 6), (144, 36, 6, 1))
        buf1 = buf0
        del buf0
        get_raw_stream(0)
        triton_poi_fused_relu_0[grid(576)](buf1, 576, XBLOCK=128, num_warps
            =4, num_stages=1)
        buf2 = extern_kernels.convolution(buf1, primals_3, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf2, (4, 4, 4, 4), (64, 16, 4, 1))
        buf3 = buf2
        del buf2
        buf4 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool)
        triton_poi_fused_add_relu_threshold_backward_1[grid(256)](buf3,
            primals_2, buf4, 256, XBLOCK=128, num_warps=4, num_stages=1)
    return buf3, primals_1, primals_2, primals_3, buf1, buf4


class BasicBlockNew(nn.Module):
    """
    BasicBlock implementation for ResNet
    
    reference: https://github.com/kuangliu/pytorch-cifar/blob/master/models/resnet.py
    """
    expansion = 1

    def __init__(self, device, in_planes, planes, stride=1):
        super(BasicBlockNew, self).__init__()
        self.device = device
        self.stride = stride
        self.in_planes = in_planes
        self.planes = planes
        self.conv1 = nn.Conv2d(in_planes, planes, kernel_size=3, stride=
            stride, padding=2, bias=False)
        self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=1,
            bias=False)
        self.shortcut = nn.Sequential()
        if stride != 1 or in_planes != self.expansion * planes:
            self.shortcut = nn.Sequential(nn.Conv2d(in_planes, self.
                expansion * planes, kernel_size=1, stride=stride, bias=False))

    def _forward(self, n, i, weights, x):
        out = F.conv2d(x, weights['layer{}.{}.conv1.weight'.format(n, i)],
            stride=self.stride, padding=2)
        out = F.relu(out)
        out = F.conv2d(out, weights['layer{}.{}.conv2.weight'.format(n, i)])
        conv = 0
        if self.stride != 1 or self.in_planes != self.expansion * self.planes:
            conv = F.conv2d(x, weights['layer{}.{}.shortcut.0.weight'.
                format(n, i)], stride=self.stride)
            x += conv
        out = F.relu(out)
        return out

    def forward(self, input_0):
        primals_1 = self.conv1.weight
        primals_3 = self.conv2.weight
        primals_2 = input_0
        output = call([primals_1, primals_2, primals_3])
        return output[0]


Measured latency_ms: 0.042069