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
from torch._inductor.runtime.triton_helpers import math as tl_math
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_0(in_ptr0, in_ptr1, out_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xnumel = 123008
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = xindex // 3844 % 8
    x0 = xindex % 3844
    x4 = xindex // 3844
    tmp0 = tl.load(in_ptr0 + x3, xmask)
    tmp1 = tl.load(in_ptr1 + x1, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tl.store(out_ptr0 + (x0 + 3872 * x4), tmp4, xmask)


@triton.jit
def triton_poi_fused_max_pool2d_with_indices_1(in_ptr0, out_ptr0, out_ptr1,
    xnumel, XBLOCK: tl.constexpr):
    xnumel = 30752
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 31
    x1 = xindex // 31 % 31
    x4 = xindex // 961
    x3 = xindex // 7688
    x5 = xindex % 7688
    tmp0 = tl.load(in_ptr0 + (2 * x0 + 124 * x1 + 3872 * x4), xmask,
        eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr0 + (1 + 2 * x0 + 124 * x1 + 3872 * x4), xmask,
        eviction_policy='evict_last')
    tmp7 = tl.load(in_ptr0 + (62 + 2 * x0 + 124 * x1 + 3872 * x4), xmask,
        eviction_policy='evict_last')
    tmp12 = tl.load(in_ptr0 + (63 + 2 * x0 + 124 * x1 + 3872 * x4), xmask,
        eviction_policy='evict_last')
    tmp2 = tmp1 > tmp0
    tmp3 = tl.full([1], 1, tl.int8)
    tmp4 = tl.full([1], 0, tl.int8)
    tmp5 = tl.where(tmp2, tmp3, tmp4)
    tmp6 = triton_helpers.maximum(tmp1, tmp0)
    tmp8 = tmp7 > tmp6
    tmp9 = tl.full([1], 2, tl.int8)
    tmp10 = tl.where(tmp8, tmp9, tmp5)
    tmp11 = triton_helpers.maximum(tmp7, tmp6)
    tmp13 = tmp12 > tmp11
    tmp14 = tl.full([1], 3, tl.int8)
    tmp15 = tl.where(tmp13, tmp14, tmp10)
    tmp16 = triton_helpers.maximum(tmp12, tmp11)
    tl.store(out_ptr0 + (x5 + 7808 * x3), tmp15, xmask)
    tl.store(out_ptr1 + (x5 + 7712 * x3), tmp16, xmask)


@triton.jit
def triton_poi_fused__log_softmax_2(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.
    constexpr):
    xnumel = 12
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x1 = xindex // 3
    tmp0 = tl.load(in_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + 3 * x1, xmask, eviction_policy='evict_last')
    tmp2 = tl.load(in_ptr0 + (1 + 3 * x1), xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr0 + (2 + 3 * x1), xmask, eviction_policy='evict_last')
    tmp3 = triton_helpers.maximum(tmp1, tmp2)
    tmp5 = triton_helpers.maximum(tmp3, tmp4)
    tmp6 = tmp0 - tmp5
    tmp7 = tmp1 - tmp5
    tmp8 = tl_math.exp(tmp7)
    tmp9 = tmp2 - tmp5
    tmp10 = tl_math.exp(tmp9)
    tmp11 = tmp8 + tmp10
    tmp12 = tmp4 - tmp5
    tmp13 = tl_math.exp(tmp12)
    tmp14 = tmp11 + tmp13
    tmp15 = tl_math.log(tmp14)
    tmp16 = tmp6 - tmp15
    tl.store(out_ptr0 + x2, tmp16, xmask)


def call(args):
    (primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
        primals_7) = args
    args.clear()
    assert_size_stride(primals_1, (8, 3, 3, 3), (27, 9, 3, 1))
    assert_size_stride(primals_2, (8,), (1,))
    assert_size_stride(primals_3, (4, 3, 64, 64), (12288, 4096, 64, 1))
    assert_size_stride(primals_4, (32, 7688), (7688, 1))
    assert_size_stride(primals_5, (32,), (1,))
    assert_size_stride(primals_6, (3, 32), (32, 1))
    assert_size_stride(primals_7, (3,), (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, 8, 62, 62), (30752, 3844, 62, 1))
        buf1 = empty_strided_cuda((4, 8, 62, 62), (30976, 3872, 62, 1),
            torch.float32)
        get_raw_stream(0)
        triton_poi_fused_convolution_relu_0[grid(123008)](buf0, primals_2,
            buf1, 123008, XBLOCK=512, num_warps=8, num_stages=1)
        del buf0
        del primals_2
        buf2 = empty_strided_cuda((4, 8, 31, 31), (7808, 961, 31, 1), torch
            .int8)
        buf3 = empty_strided_cuda((4, 8, 31, 31), (7712, 961, 31, 1), torch
            .float32)
        triton_poi_fused_max_pool2d_with_indices_1[grid(30752)](buf1, buf2,
            buf3, 30752, XBLOCK=128, num_warps=4, num_stages=1)
        buf4 = empty_strided_cuda((4, 32), (32, 1), torch.float32)
        extern_kernels.addmm(primals_5, reinterpret_tensor(buf3, (4, 7688),
            (7712, 1), 0), reinterpret_tensor(primals_4, (7688, 32), (1, 
            7688), 0), alpha=1, beta=1, out=buf4)
        del primals_5
        buf5 = empty_strided_cuda((4, 3), (3, 1), torch.float32)
        extern_kernels.addmm(primals_7, buf4, reinterpret_tensor(primals_6,
            (32, 3), (1, 32), 0), alpha=1, beta=1, out=buf5)
        del primals_7
        buf6 = empty_strided_cuda((4, 3), (3, 1), torch.float32)
        triton_poi_fused__log_softmax_2[grid(12)](buf5, buf6, 12, XBLOCK=16,
            num_warps=1, num_stages=1)
        del buf5
    return buf6, primals_1, primals_3, buf1, buf2, reinterpret_tensor(buf3,
        (4, 7688), (7712, 1), 0), buf4, buf6, primals_6, primals_4


class CNNCOVID19New(nn.Module):

    def __init__(self, args):
        super(CNNCOVID19New, self).__init__()
        self.conv1 = nn.Conv2d(in_channels=3, out_channels=8, kernel_size=3)
        self.fc1 = nn.Linear(in_features=8 * 31 * 31, out_features=32)
        self.out = nn.Linear(in_features=32, out_features=3)

    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_6 = self.out.weight
        primals_7 = self.out.bias
        primals_3 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6, primals_7])
        return output[0]


Measured latency_ms: 0.05355