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 = 76880
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = xindex // 3844 % 5
    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 = 19220
    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 // 4805
    x5 = xindex % 4805
    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')
    tmp3 = tl.load(in_ptr0 + (62 + 2 * x0 + 124 * x1 + 3872 * x4), xmask,
        eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr0 + (63 + 2 * x0 + 124 * x1 + 3872 * x4), xmask,
        eviction_policy='evict_last')
    tmp2 = triton_helpers.maximum(tmp1, tmp0)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tmp6 = triton_helpers.maximum(tmp5, tmp4)
    tmp7 = tmp1 > tmp0
    tmp8 = tl.full([1], 1, tl.int8)
    tmp9 = tl.full([1], 0, tl.int8)
    tmp10 = tl.where(tmp7, tmp8, tmp9)
    tmp11 = tmp3 > tmp2
    tmp12 = tl.full([1], 2, tl.int8)
    tmp13 = tl.where(tmp11, tmp12, tmp10)
    tmp14 = tmp5 > tmp4
    tmp15 = tl.full([1], 3, tl.int8)
    tmp16 = tl.where(tmp14, tmp15, tmp13)
    tl.store(out_ptr0 + (x5 + 4832 * x3), tmp6, xmask)
    tl.store(out_ptr1 + (x5 + 4864 * x3), tmp16, xmask)


@triton.jit
def triton_poi_fused_relu_2(in_out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 18816
    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_max_pool2d_with_indices_3(in_ptr0, out_ptr0, out_ptr1,
    xnumel, XBLOCK: tl.constexpr):
    xnumel = 4704
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 14
    x3 = xindex // 14
    x2 = xindex // 1176
    x4 = xindex % 1176
    tmp0 = tl.load(in_ptr0 + (2 * x0 + 56 * x3), xmask, eviction_policy=
        'evict_last')
    tmp1 = tl.load(in_ptr0 + (1 + 2 * x0 + 56 * x3), xmask, eviction_policy
        ='evict_last')
    tmp3 = tl.load(in_ptr0 + (28 + 2 * x0 + 56 * x3), xmask,
        eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr0 + (29 + 2 * x0 + 56 * x3), xmask,
        eviction_policy='evict_last')
    tmp2 = triton_helpers.maximum(tmp1, tmp0)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tmp6 = triton_helpers.maximum(tmp5, tmp4)
    tmp7 = tmp1 > tmp0
    tmp8 = tl.full([1], 1, tl.int8)
    tmp9 = tl.full([1], 0, tl.int8)
    tmp10 = tl.where(tmp7, tmp8, tmp9)
    tmp11 = tmp3 > tmp2
    tmp12 = tl.full([1], 2, tl.int8)
    tmp13 = tl.where(tmp11, tmp12, tmp10)
    tmp14 = tmp5 > tmp4
    tmp15 = tl.full([1], 3, tl.int8)
    tmp16 = tl.where(tmp14, tmp15, tmp13)
    tl.store(out_ptr0 + (x4 + 1184 * x2), tmp6, xmask)
    tl.store(out_ptr1 + (x4 + 1280 * x2), tmp16, xmask)


@triton.jit
def triton_poi_fused_convolution_relu_4(in_out_ptr0, in_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xnumel = 4032
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = xindex // 144 % 7
    tmp0 = tl.load(in_out_ptr0 + x3, xmask)
    tmp1 = tl.load(in_ptr0 + x1, 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 + x3, tmp4, xmask)


@triton.jit
def triton_poi_fused_max_pool2d_with_indices_5(in_ptr0, out_ptr0, out_ptr1,
    xnumel, XBLOCK: tl.constexpr):
    xnumel = 1008
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 6
    x1 = xindex // 6
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + (2 * x0 + 24 * x1), xmask, eviction_policy=
        'evict_last')
    tmp1 = tl.load(in_ptr0 + (1 + 2 * x0 + 24 * x1), xmask, eviction_policy
        ='evict_last')
    tmp7 = tl.load(in_ptr0 + (12 + 2 * x0 + 24 * x1), xmask,
        eviction_policy='evict_last')
    tmp12 = tl.load(in_ptr0 + (13 + 2 * x0 + 24 * x1), 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 + x2, tmp15, xmask)
    tl.store(out_ptr1 + x2, tmp16, xmask)


@triton.jit
def triton_per_fused__log_softmax_6(in_ptr0, out_ptr2, xnumel, rnumel,
    XBLOCK: tl.constexpr):
    xnumel = 144
    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) = args
    args.clear()
    assert_size_stride(primals_1, (5, 1, 3, 3), (9, 9, 3, 1))
    assert_size_stride(primals_2, (5,), (1,))
    assert_size_stride(primals_3, (4, 1, 64, 64), (4096, 4096, 64, 1))
    assert_size_stride(primals_4, (6, 5, 4, 4), (80, 16, 4, 1))
    assert_size_stride(primals_5, (7, 6, 3, 3), (54, 9, 3, 1))
    assert_size_stride(primals_6, (7,), (1,))
    assert_size_stride(primals_7, (10, 7), (7, 1))
    assert_size_stride(primals_8, (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, 5, 62, 62), (19220, 3844, 62, 1))
        buf1 = empty_strided_cuda((4, 5, 62, 62), (19360, 3872, 62, 1),
            torch.float32)
        get_raw_stream(0)
        triton_poi_fused_convolution_relu_0[grid(76880)](buf0, primals_2,
            buf1, 76880, XBLOCK=512, num_warps=8, num_stages=1)
        del buf0
        del primals_2
        buf2 = empty_strided_cuda((4, 5, 31, 31), (4832, 961, 31, 1), torch
            .float32)
        buf3 = empty_strided_cuda((4, 5, 31, 31), (4864, 961, 31, 1), torch
            .int8)
        triton_poi_fused_max_pool2d_with_indices_1[grid(19220)](buf1, buf2,
            buf3, 19220, XBLOCK=256, num_warps=4, num_stages=1)
        buf4 = extern_kernels.convolution(buf2, 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, 6, 28, 28), (4704, 784, 28, 1))
        buf5 = buf4
        del buf4
        triton_poi_fused_relu_2[grid(18816)](buf5, 18816, XBLOCK=256,
            num_warps=4, num_stages=1)
        buf6 = empty_strided_cuda((4, 6, 14, 14), (1184, 196, 14, 1), torch
            .float32)
        buf7 = empty_strided_cuda((4, 6, 14, 14), (1280, 196, 14, 1), torch
            .int8)
        triton_poi_fused_max_pool2d_with_indices_3[grid(4704)](buf5, buf6,
            buf7, 4704, XBLOCK=128, num_warps=4, num_stages=1)
        buf8 = extern_kernels.convolution(buf6, primals_5, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf8, (4, 7, 12, 12), (1008, 144, 12, 1))
        buf9 = buf8
        del buf8
        triton_poi_fused_convolution_relu_4[grid(4032)](buf9, primals_6, 
            4032, XBLOCK=256, num_warps=4, num_stages=1)
        del primals_6
        buf10 = empty_strided_cuda((4, 7, 6, 6), (252, 36, 6, 1), torch.int8)
        buf11 = empty_strided_cuda((4, 7, 6, 6), (252, 36, 6, 1), torch.float32
            )
        triton_poi_fused_max_pool2d_with_indices_5[grid(1008)](buf9, buf10,
            buf11, 1008, XBLOCK=128, num_warps=4, num_stages=1)
        buf12 = empty_strided_cuda((144, 10), (10, 1), torch.float32)
        extern_kernels.addmm(primals_8, reinterpret_tensor(buf11, (144, 7),
            (7, 1), 0), reinterpret_tensor(primals_7, (7, 10), (1, 7), 0),
            alpha=1, beta=1, out=buf12)
        del primals_8
        buf15 = empty_strided_cuda((144, 10), (10, 1), torch.float32)
        triton_per_fused__log_softmax_6[grid(144)](buf12, buf15, 144, 10,
            XBLOCK=128, num_warps=8, num_stages=1)
        del buf12
    return (buf15, primals_1, primals_3, primals_4, primals_5, buf1, buf2,
        buf3, buf5, buf6, buf7, buf9, buf10, reinterpret_tensor(buf11, (144,
        7), (7, 1), 0), buf15, primals_7)


class ConvNetNew(nn.Module):

    def __init__(self):
        super(ConvNetNew, self).__init__()
        self.conv1 = nn.Conv2d(1, 5, 3, 1)
        self.conv2 = nn.Conv2d(5, 6, 4, 1, bias=False)
        self.conv3 = nn.Conv2d(6, 7, 3, 1)
        self.fc1 = nn.Linear(1 * 1 * 7, 10)

    def forward(self, input_0):
        primals_1 = self.conv1.weight
        primals_2 = self.conv1.bias
        primals_4 = self.conv2.weight
        primals_5 = self.conv3.weight
        primals_6 = self.conv3.bias
        primals_7 = self.fc1.weight
        primals_8 = self.fc1.bias
        primals_3 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6, primals_7, primals_8])
        return output[0]


Measured latency_ms: 0.042651