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
import torch.nn.utils.prune
import torch.backends.cudnn
import torch.cuda
import torch.nn
import torch.utils.data
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_out_ptr0, in_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x3 = xindex
    x1 = xindex // 4096 % 32
    tmp0 = tl.load(in_out_ptr0 + x3, None)
    tmp1 = tl.load(in_ptr0 + x1, None, 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, None)


@triton.jit
def triton_poi_fused_convolution_relu_1(in_out_ptr0, in_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x3 = xindex
    x1 = xindex // 4096 % 64
    tmp0 = tl.load(in_out_ptr0 + x3, None)
    tmp1 = tl.load(in_ptr0 + x1, None, 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, None)


@triton.jit
def triton_poi_fused_max_pool2d_with_indices_2(in_ptr0, out_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x0 = xindex % 32
    x1 = xindex // 32
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + (2 * x0 + 128 * x1), None, eviction_policy=
        'evict_last')
    tmp1 = tl.load(in_ptr0 + (1 + 2 * x0 + 128 * x1), None, eviction_policy
        ='evict_last')
    tmp7 = tl.load(in_ptr0 + (64 + 2 * x0 + 128 * x1), None,
        eviction_policy='evict_last')
    tmp12 = tl.load(in_ptr0 + (65 + 2 * x0 + 128 * x1), None,
        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)
    triton_helpers.maximum(tmp12, tmp11)
    tl.store(out_ptr0 + x2, tmp15, None)


@triton.jit
def triton_red_fused_mean_3(in_ptr0, out_ptr0, xnumel, rnumel, XBLOCK: tl.
    constexpr, RBLOCK: tl.constexpr):
    rnumel = 128
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    tl.full([XBLOCK, RBLOCK], True, tl.int1)
    rbase = tl.arange(0, RBLOCK)[None, :]
    x0 = xindex
    _tmp8 = tl.full([XBLOCK, RBLOCK], 0, tl.float32)
    for roffset in range(0, rnumel, RBLOCK):
        rindex = roffset + rbase
        rmask = rindex < rnumel
        r1 = rindex
        tmp0 = tl.load(in_ptr0 + (2 * (r1 % 32) + 128 * (r1 // 32) + 512 *
            x0), rmask, eviction_policy='evict_last', other=0.0)
        tmp1 = tl.load(in_ptr0 + (1 + 2 * (r1 % 32) + 128 * (r1 // 32) + 
            512 * x0), rmask, eviction_policy='evict_last', other=0.0)
        tmp3 = tl.load(in_ptr0 + (64 + 2 * (r1 % 32) + 128 * (r1 // 32) + 
            512 * x0), rmask, eviction_policy='evict_last', other=0.0)
        tmp5 = tl.load(in_ptr0 + (65 + 2 * (r1 % 32) + 128 * (r1 // 32) + 
            512 * x0), rmask, eviction_policy='evict_last', other=0.0)
        tmp2 = triton_helpers.maximum(tmp1, tmp0)
        tmp4 = triton_helpers.maximum(tmp3, tmp2)
        tmp6 = triton_helpers.maximum(tmp5, tmp4)
        tmp7 = tl.broadcast_to(tmp6, [XBLOCK, RBLOCK])
        tmp9 = _tmp8 + tmp7
        _tmp8 = tl.where(rmask, tmp9, _tmp8)
    tmp8 = tl.sum(_tmp8, 1)[:, None]
    tl.store(out_ptr0 + x0, tmp8, None)


@triton.jit
def triton_per_fused_mean_squeeze_4(in_out_ptr0, in_ptr0, xnumel, rnumel,
    XBLOCK: tl.constexpr):
    xnumel = 256
    RBLOCK: tl.constexpr = 8
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    rindex = tl.arange(0, RBLOCK)[None, :]
    tl.full([XBLOCK, RBLOCK], True, tl.int1)
    r1 = rindex
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (r1 + 8 * x0), xmask, other=0.0)
    tmp1 = tl.broadcast_to(tmp0, [XBLOCK, RBLOCK])
    tmp3 = tl.where(xmask, tmp1, 0)
    tmp4 = tl.sum(tmp3, 1)[:, None]
    tmp5 = 1024.0
    tmp6 = tmp4 / tmp5
    tl.debug_barrier()
    tl.store(in_out_ptr0 + x0, tmp6, xmask)


@triton.jit
def triton_poi_fused_relu_5(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl.constexpr
    ):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 64
    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_6(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, (32, 1, 3, 3), (9, 9, 3, 1))
    assert_size_stride(primals_2, (32,), (1,))
    assert_size_stride(primals_3, (4, 1, 64, 64), (4096, 4096, 64, 1))
    assert_size_stride(primals_4, (64, 32, 3, 3), (288, 9, 3, 1))
    assert_size_stride(primals_5, (64,), (1,))
    assert_size_stride(primals_6, (64, 64), (64, 1))
    assert_size_stride(primals_7, (64,), (1,))
    assert_size_stride(primals_8, (10, 64), (64, 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=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf0, (4, 32, 64, 64), (131072, 4096, 64, 1))
        buf1 = buf0
        del buf0
        get_raw_stream(0)
        triton_poi_fused_convolution_relu_0[grid(524288)](buf1, primals_2, 
            524288, XBLOCK=1024, num_warps=4, num_stages=1)
        del primals_2
        buf2 = extern_kernels.convolution(buf1, primals_4, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf2, (4, 64, 64, 64), (262144, 4096, 64, 1))
        buf3 = buf2
        del buf2
        triton_poi_fused_convolution_relu_1[grid(1048576)](buf3, primals_5,
            1048576, XBLOCK=1024, num_warps=4, num_stages=1)
        del primals_5
        buf4 = empty_strided_cuda((4, 64, 32, 32), (65536, 1024, 32, 1),
            torch.int8)
        triton_poi_fused_max_pool2d_with_indices_2[grid(262144)](buf3, buf4,
            262144, XBLOCK=512, num_warps=8, num_stages=1)
        buf5 = empty_strided_cuda((4, 64, 8), (512, 8, 1), torch.float32)
        triton_red_fused_mean_3[grid(2048)](buf3, buf5, 2048, 128, XBLOCK=
            64, RBLOCK=8, num_warps=4, num_stages=1)
        buf6 = empty_strided_cuda((4, 64), (64, 1), torch.float32)
        buf7 = buf6
        del buf6
        triton_per_fused_mean_squeeze_4[grid(256)](buf7, buf5, 256, 8,
            XBLOCK=32, num_warps=2, num_stages=1)
        del buf5
        buf8 = empty_strided_cuda((4, 64), (64, 1), torch.float32)
        extern_kernels.mm(buf7, reinterpret_tensor(primals_6, (64, 64), (1,
            64), 0), out=buf8)
        buf9 = buf8
        del buf8
        triton_poi_fused_relu_5[grid(256)](buf9, primals_7, 256, XBLOCK=256,
            num_warps=4, num_stages=1)
        del primals_7
        buf10 = empty_strided_cuda((4, 10), (10, 1), torch.float32)
        extern_kernels.addmm(primals_9, buf9, reinterpret_tensor(primals_8,
            (64, 10), (1, 64), 0), alpha=1, beta=1, out=buf10)
        del primals_9
        buf13 = empty_strided_cuda((4, 10), (10, 1), torch.float32)
        triton_per_fused__log_softmax_6[grid(4)](buf10, buf13, 4, 10,
            XBLOCK=1, num_warps=2, num_stages=1)
        del buf10
    return (buf13, primals_1, primals_3, primals_4, buf1, buf3, buf4, buf7,
        buf9, buf13, primals_8, primals_6)


class ClassicalConv6New(nn.Module):

    def __init__(self):
        super().__init__()
        self.conv1 = nn.Conv2d(1, 32, 3, 1, padding=1)
        self.conv2 = nn.Conv2d(32, 64, 3, 1, padding=1)
        self.dropout1 = nn.Dropout(0.25)
        self.dropout2 = nn.Dropout(0.5)
        self.fc1 = nn.Linear(64, 64)
        self.fc2 = nn.Linear(64, 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: 0.141032