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
from collections import OrderedDict
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_0(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK: tl.
    constexpr, XBLOCK: tl.constexpr):
    ynumel = 1920
    xnumel = 25
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    ymask = yindex < ynumel
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    x2 = xindex
    y3 = yindex
    y0 = yindex % 16
    y1 = yindex // 16
    tmp0 = tl.load(in_ptr0 + (x2 + 25 * y3), xmask & ymask, eviction_policy
        ='evict_last')
    tl.store(out_ptr0 + (y0 + 16 * x2 + 400 * y1), tmp0, xmask & ymask)


@triton.jit
def triton_poi_fused_1(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK: tl.
    constexpr, XBLOCK: tl.constexpr):
    ynumel = 64
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    ymask = yindex < ynumel
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    tl.full([XBLOCK, YBLOCK], True, tl.int1)
    x2 = xindex
    y3 = yindex
    y0 = yindex % 16
    y1 = yindex // 16
    tmp0 = tl.load(in_ptr0 + (x2 + 4096 * y3), ymask, eviction_policy=
        'evict_last')
    tl.store(out_ptr0 + (y0 + 16 * x2 + 65536 * y1), tmp0, ymask)


@triton.jit
def triton_poi_fused_convolution_relu_threshold_backward_2(in_ptr0, in_ptr1,
    out_ptr0, out_ptr1, ynumel, xnumel, YBLOCK: tl.constexpr, XBLOCK: tl.
    constexpr):
    ynumel = 480
    xnumel = 3600
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    ymask = yindex < ynumel
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    x2 = xindex
    y0 = yindex % 120
    y1 = yindex // 120
    y3 = yindex
    tmp0 = tl.load(in_ptr0 + (y0 + 120 * x2 + 432000 * y1), xmask & ymask,
        eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + y0, ymask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1, 1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tmp5 = 0.0
    tmp6 = tmp4 <= tmp5
    tl.store(out_ptr0 + (x2 + 3600 * y3), tmp4, xmask & ymask)
    tl.store(out_ptr1 + (y0 + 120 * x2 + 432000 * y1), tmp6, xmask & ymask)


def call(args):
    primals_1, primals_2, primals_3 = args
    args.clear()
    assert_size_stride(primals_1, (120, 16, 5, 5), (400, 25, 5, 1))
    assert_size_stride(primals_2, (120,), (1,))
    assert_size_stride(primals_3, (4, 16, 64, 64), (65536, 4096, 64, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((120, 16, 5, 5), (400, 1, 80, 16), torch.
            float32)
        get_raw_stream(0)
        triton_poi_fused_0[grid(1920, 25)](primals_1, buf0, 1920, 25,
            XBLOCK=32, YBLOCK=32, num_warps=4, num_stages=1)
        del primals_1
        buf1 = empty_strided_cuda((4, 16, 64, 64), (65536, 1, 1024, 16),
            torch.float32)
        triton_poi_fused_1[grid(64, 4096)](primals_3, buf1, 64, 4096,
            XBLOCK=32, YBLOCK=32, num_warps=4, num_stages=1)
        del primals_3
        buf2 = extern_kernels.convolution(buf1, buf0, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf2, (4, 120, 60, 60), (432000, 1, 7200, 120))
        buf3 = empty_strided_cuda((4, 120, 60, 60), (432000, 3600, 60, 1),
            torch.float32)
        buf4 = empty_strided_cuda((4, 120, 60, 60), (432000, 1, 7200, 120),
            torch.bool)
        triton_poi_fused_convolution_relu_threshold_backward_2[grid(480, 3600)
            ](buf2, primals_2, buf3, buf4, 480, 3600, XBLOCK=32, YBLOCK=32,
            num_warps=4, num_stages=1)
        del buf2
        del primals_2
    return buf3, buf0, buf1, buf4


class C3New(nn.Module):

    def __init__(self):
        super(C3New, self).__init__()
        self.c3 = nn.Sequential(OrderedDict([('c3', nn.Conv2d(16, 120,
            kernel_size=(5, 5))), ('relu3', nn.ReLU())]))

    def forward(self, input_0):
        primals_1 = self.c3.c3.weight
        primals_2 = self.c3.c3.bias
        primals_3 = input_0
        output = call([primals_1, primals_2, primals_3])
        return output[0]


Measured latency_ms: 0.079003