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
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor


@triton.jit
def triton_poi_fused_convolution_relu_threshold_backward_0(in_ptr0, in_ptr1,
    out_ptr0, out_ptr1, xnumel, XBLOCK: tl.constexpr):
    xnumel = 14400
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 3600
    x1 = xindex // 3600
    tmp0 = tl.load(in_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr1 + 0)
    tmp2 = tl.broadcast_to(tmp1, [XBLOCK])
    tmp3 = tmp0 + tmp2
    tmp4 = tl.full([1], 0, tl.int32)
    tmp5 = triton_helpers.maximum(tmp4, tmp3)
    tmp6 = 0.0
    tmp7 = tmp5 <= tmp6
    tl.store(out_ptr0 + (x0 + 3616 * x1), tmp5, xmask)
    tl.store(out_ptr1 + (x0 + 3712 * x1), tmp7, xmask)


@triton.jit
def triton_poi_fused_convolution_relu_view_1(in_ptr0, out_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xnumel = 14400
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (3616 * (x0 // 3600) + x0 % 3600), xmask)
    tl.store(out_ptr0 + x0, tmp0, xmask)


@triton.jit
def triton_poi_fused_relu_threshold_backward_2(in_out_ptr0, in_ptr0,
    out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 2000
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 5
    tmp0 = tl.load(in_out_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
    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 + x2, tmp4, xmask)
    tl.store(out_ptr0 + x2, tmp6, xmask)


def call(args):
    primals_1, primals_2, primals_3, primals_4, primals_5 = args
    args.clear()
    assert_size_stride(primals_1, (1, 1, 5, 5), (25, 25, 5, 1))
    assert_size_stride(primals_2, (1,), (1,))
    assert_size_stride(primals_3, (4, 1, 64, 64), (4096, 4096, 64, 1))
    assert_size_stride(primals_4, (5, 36), (36, 1))
    assert_size_stride(primals_5, (5,), (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, 1, 60, 60), (3600, 3600, 60, 1))
        buf1 = empty_strided_cuda((4, 1, 60, 60), (3616, 60, 60, 1), torch.
            float32)
        buf6 = empty_strided_cuda((4, 1, 60, 60), (3712, 3712, 60, 1),
            torch.bool)
        get_raw_stream(0)
        triton_poi_fused_convolution_relu_threshold_backward_0[grid(14400)](
            buf0, primals_2, buf1, buf6, 14400, XBLOCK=256, num_warps=4,
            num_stages=1)
        del primals_2
        buf2 = reinterpret_tensor(buf0, (400, 36), (36, 1), 0)
        del buf0
        triton_poi_fused_convolution_relu_view_1[grid(14400)](buf1, buf2, 
            14400, XBLOCK=128, num_warps=4, num_stages=1)
        del buf1
        buf3 = empty_strided_cuda((400, 5), (5, 1), torch.float32)
        extern_kernels.mm(buf2, reinterpret_tensor(primals_4, (36, 5), (1, 
            36), 0), out=buf3)
        buf4 = buf3
        del buf3
        buf5 = empty_strided_cuda((400, 5), (5, 1), torch.bool)
        triton_poi_fused_relu_threshold_backward_2[grid(2000)](buf4,
            primals_5, buf5, 2000, XBLOCK=256, num_warps=4, num_stages=1)
        del primals_5
    return buf4, primals_1, primals_3, buf2, buf5, primals_4, buf6


class NetNew(nn.Module):

    def __init__(self):
        super().__init__()
        self.conv1 = nn.Conv2d(1, 1, (5, 5), groups=1)
        self.relu1 = nn.ReLU(inplace=True)
        self.fc1 = nn.Linear(36, 5)
        self.relu2 = nn.ReLU(inplace=True)

    def forward(self, input_0):
        primals_1 = self.conv1.weight
        primals_2 = self.conv1.bias
        primals_4 = self.fc1.weight
        primals_5 = self.fc1.bias
        primals_3 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4, primals_5])
        return output[0]


Measured latency_ms: 0.042534