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_0(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl
    .constexpr):
    xnumel = 144000
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = xindex // 3600 % 10
    tmp0 = tl.load(in_out_ptr0 + x3, xmask)
    tmp1 = tl.load(in_ptr0 + x1, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tl.store(in_out_ptr0 + x3, tmp2, xmask)


@triton.jit
def triton_poi_fused_max_pool2d_with_indices_relu_1(in_ptr0, out_ptr0,
    out_ptr1, xnumel, XBLOCK: tl.constexpr):
    xnumel = 36000
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 30
    x3 = xindex // 30
    x2 = xindex // 9000
    x4 = xindex % 9000
    x5 = xindex
    tmp0 = tl.load(in_ptr0 + (2 * x0 + 120 * x3), xmask, eviction_policy=
        'evict_last')
    tmp1 = tl.load(in_ptr0 + (1 + 2 * x0 + 120 * x3), xmask,
        eviction_policy='evict_last')
    tmp7 = tl.load(in_ptr0 + (60 + 2 * x0 + 120 * x3), xmask,
        eviction_policy='evict_last')
    tmp12 = tl.load(in_ptr0 + (61 + 2 * x0 + 120 * x3), 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)
    tmp17 = tl.full([1], 0, tl.int32)
    tmp18 = triton_helpers.maximum(tmp17, tmp16)
    tl.store(out_ptr0 + (x4 + 9088 * x2), tmp15, xmask)
    tl.store(out_ptr1 + x5, tmp18, xmask)


@triton.jit
def triton_poi_fused_convolution_2(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl
    .constexpr):
    xnumel = 54080
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = xindex // 676 % 20
    tmp0 = tl.load(in_out_ptr0 + x3, xmask)
    tmp1 = tl.load(in_ptr0 + x1, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tl.store(in_out_ptr0 + x3, tmp2, xmask)


@triton.jit
def triton_poi_fused_relu_threshold_backward_3(in_out_ptr0, out_ptr0,
    xnumel, XBLOCK: tl.constexpr):
    xnumel = 1280
    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)
    tmp3 = 0.0
    tmp4 = tmp2 <= tmp3
    tl.store(in_out_ptr0 + x0, tmp2, xmask)
    tl.store(out_ptr0 + x0, tmp4, xmask)


@triton.jit
def triton_poi_fused_relu_4(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl.constexpr
    ):
    xnumel = 600
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 150
    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)
    tl.store(in_out_ptr0 + x2, tmp4, xmask)


@triton.jit
def triton_per_fused__log_softmax_5(in_ptr0, out_ptr2, xnumel, rnumel,
    XBLOCK: tl.constexpr):
    xnumel = 4
    rnumel = 10
    RBLOCK: tl.constexpr = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    rindex = tl.arange(0, RBLOCK)[None, :]
    rmask = rindex < rnumel
    r1 = rindex
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (r1 + 10 * x0), rmask & xmask, other=0.0)
    tmp1 = tl.broadcast_to(tmp0, [XBLOCK, RBLOCK])
    tmp3 = tl.where(rmask & xmask, tmp1, float('-inf'))
    tmp4 = triton_helpers.max2(tmp3, 1)[:, None]
    tmp5 = tmp0 - tmp4
    tmp6 = tl_math.exp(tmp5)
    tmp7 = tl.broadcast_to(tmp6, [XBLOCK, RBLOCK])
    tmp9 = tl.where(rmask & xmask, tmp7, 0)
    tmp10 = tl.sum(tmp9, 1)[:, None]
    tmp11 = tl_math.log(tmp10)
    tmp12 = tmp5 - tmp11
    tl.store(out_ptr2 + (r1 + 10 * x0), tmp12, rmask & xmask)


def call(args):
    (primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
        primals_7, primals_8, primals_9) = args
    args.clear()
    assert_size_stride(primals_1, (10, 1, 5, 5), (25, 25, 5, 1))
    assert_size_stride(primals_2, (10,), (1,))
    assert_size_stride(primals_3, (4, 1, 64, 64), (4096, 4096, 64, 1))
    assert_size_stride(primals_4, (20, 10, 5, 5), (250, 25, 5, 1))
    assert_size_stride(primals_5, (20,), (1,))
    assert_size_stride(primals_6, (150, 320), (320, 1))
    assert_size_stride(primals_7, (150,), (1,))
    assert_size_stride(primals_8, (10, 150), (150, 1))
    assert_size_stride(primals_9, (10,), (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, 10, 60, 60), (36000, 3600, 60, 1))
        buf1 = buf0
        del buf0
        get_raw_stream(0)
        triton_poi_fused_convolution_0[grid(144000)](buf1, primals_2, 
            144000, XBLOCK=512, num_warps=8, num_stages=1)
        del primals_2
        buf2 = empty_strided_cuda((4, 10, 30, 30), (9088, 900, 30, 1),
            torch.int8)
        buf3 = empty_strided_cuda((4, 10, 30, 30), (9000, 900, 30, 1),
            torch.float32)
        triton_poi_fused_max_pool2d_with_indices_relu_1[grid(36000)](buf1,
            buf2, buf3, 36000, XBLOCK=256, num_warps=4, num_stages=1)
        buf4 = extern_kernels.convolution(buf3, primals_4, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf4, (4, 20, 26, 26), (13520, 676, 26, 1))
        buf5 = buf4
        del buf4
        triton_poi_fused_convolution_2[grid(54080)](buf5, primals_5, 54080,
            XBLOCK=512, num_warps=4, num_stages=1)
        del primals_5
        buf6 = torch.ops.aten.adaptive_max_pool2d.default(buf5, [4, 4])
        buf7 = buf6[0]
        buf8 = buf6[1]
        del buf6
        buf9 = buf7
        del buf7
        buf16 = empty_strided_cuda((4, 20, 4, 4), (320, 16, 4, 1), torch.bool)
        triton_poi_fused_relu_threshold_backward_3[grid(1280)](buf9, buf16,
            1280, XBLOCK=256, num_warps=4, num_stages=1)
        buf10 = empty_strided_cuda((4, 150), (150, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf9, (4, 320), (320, 1), 0),
            reinterpret_tensor(primals_6, (320, 150), (1, 320), 0), out=buf10)
        buf11 = buf10
        del buf10
        triton_poi_fused_relu_4[grid(600)](buf11, primals_7, 600, XBLOCK=
            256, num_warps=4, num_stages=1)
        del primals_7
        buf12 = empty_strided_cuda((4, 10), (10, 1), torch.float32)
        extern_kernels.addmm(primals_9, buf11, reinterpret_tensor(primals_8,
            (150, 10), (1, 150), 0), alpha=1, beta=1, out=buf12)
        del primals_9
        buf15 = empty_strided_cuda((4, 10), (10, 1), torch.float32)
        triton_per_fused__log_softmax_5[grid(4)](buf12, buf15, 4, 10,
            XBLOCK=1, num_warps=2, num_stages=1)
        del buf12
    return (buf15, primals_1, primals_3, primals_4, buf1, buf2, buf3, buf5,
        buf8, reinterpret_tensor(buf9, (4, 320), (320, 1), 0), buf11, buf15,
        primals_8, primals_6, buf16)


class NetNew(nn.Module):

    def __init__(self):
        super(NetNew, self).__init__()
        self.conv1 = nn.Conv2d(1, 10, kernel_size=5)
        self.conv2 = nn.Conv2d(10, 20, kernel_size=5)
        self.conv2_drop = nn.Dropout2d()
        self.fc1 = nn.Linear(320, 150)
        self.fc2 = nn.Linear(150, 10)

    def forward(self, input_0):
        primals_1 = self.conv1.weight
        primals_2 = self.conv1.bias
        primals_4 = self.conv2.weight
        primals_5 = self.conv2.bias
        primals_6 = self.fc1.weight
        primals_7 = self.fc1.bias
        primals_8 = self.fc2.weight
        primals_9 = self.fc2.bias
        primals_3 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6, primals_7, primals_8, primals_9])
        return output[0]


Measured latency_ms: 0.015969