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
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 = 287496
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = xindex // 35937 % 2
    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_1(in_out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 39304
    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_convolution_2(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl
    .constexpr):
    xnumel = 5832
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = xindex // 729 % 2
    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_3(in_out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 1000
    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_convolution_4(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl
    .constexpr):
    xnumel = 432
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = xindex // 27 % 4
    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_5(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl.constexpr
    ):
    xnumel = 32
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 8
    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)


def call(args):
    (primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
        primals_7, primals_8, primals_9, primals_10, primals_11) = args
    args.clear()
    assert_size_stride(primals_1, (4, 1, 64, 64, 64), (262144, 262144, 4096,
        64, 1))
    assert_size_stride(primals_2, (2, 1, 2, 2, 2), (8, 8, 4, 2, 1))
    assert_size_stride(primals_3, (2,), (1,))
    assert_size_stride(primals_4, (2, 2, 2, 2, 2), (16, 8, 4, 2, 1))
    assert_size_stride(primals_5, (2,), (1,))
    assert_size_stride(primals_6, (4, 2, 2, 2, 2), (16, 8, 4, 2, 1))
    assert_size_stride(primals_7, (4,), (1,))
    assert_size_stride(primals_8, (8, 4), (4, 1))
    assert_size_stride(primals_9, (8,), (1,))
    assert_size_stride(primals_10, (1, 8), (8, 1))
    assert_size_stride(primals_11, (1,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = extern_kernels.convolution(primals_1, primals_2, stride=(2, 
            2, 2), padding=(1, 1, 1), dilation=(1, 1, 1), transposed=False,
            output_padding=(0, 0, 0), groups=1, bias=None)
        assert_size_stride(buf0, (4, 2, 33, 33, 33), (71874, 35937, 1089, 
            33, 1))
        buf1 = buf0
        del buf0
        get_raw_stream(0)
        triton_poi_fused_convolution_0[grid(287496)](buf1, primals_3, 
            287496, XBLOCK=1024, num_warps=4, num_stages=1)
        del primals_3
        buf2 = torch.ops.aten.max_pool3d_with_indices.default(buf1, [2, 2, 
            2], [2, 2, 2], [1, 1, 1])
        buf3 = buf2[0]
        buf4 = buf2[1]
        del buf2
        buf5 = buf3
        del buf3
        triton_poi_fused_relu_1[grid(39304)](buf5, 39304, XBLOCK=256,
            num_warps=4, num_stages=1)
        buf6 = extern_kernels.convolution(buf5, primals_4, stride=(2, 2, 2),
            padding=(1, 1, 1), dilation=(1, 1, 1), transposed=False,
            output_padding=(0, 0, 0), groups=1, bias=None)
        assert_size_stride(buf6, (4, 2, 9, 9, 9), (1458, 729, 81, 9, 1))
        buf7 = buf6
        del buf6
        triton_poi_fused_convolution_2[grid(5832)](buf7, primals_5, 5832,
            XBLOCK=128, num_warps=4, num_stages=1)
        del primals_5
        buf8 = torch.ops.aten.max_pool3d_with_indices.default(buf7, [2, 2, 
            2], [2, 2, 2], [1, 1, 1])
        buf9 = buf8[0]
        buf10 = buf8[1]
        del buf8
        buf11 = buf9
        del buf9
        triton_poi_fused_relu_3[grid(1000)](buf11, 1000, XBLOCK=256,
            num_warps=4, num_stages=1)
        buf12 = extern_kernels.convolution(buf11, primals_6, stride=(2, 2, 
            2), padding=(1, 1, 1), dilation=(1, 1, 1), transposed=False,
            output_padding=(0, 0, 0), groups=1, bias=None)
        assert_size_stride(buf12, (4, 4, 3, 3, 3), (108, 27, 9, 3, 1))
        buf13 = buf12
        del buf12
        triton_poi_fused_convolution_4[grid(432)](buf13, primals_7, 432,
            XBLOCK=256, num_warps=4, num_stages=1)
        del primals_7
        buf14 = torch.ops.aten.max_pool3d_with_indices.default(buf13, [2, 2,
            2], [2, 2, 2])
        buf15 = buf14[0]
        buf16 = buf14[1]
        del buf14
        buf17 = empty_strided_cuda((4, 8), (8, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf15, (4, 4), (4, 1), 0),
            reinterpret_tensor(primals_8, (4, 8), (1, 4), 0), out=buf17)
        buf18 = buf17
        del buf17
        triton_poi_fused_relu_5[grid(32)](buf18, primals_9, 32, XBLOCK=32,
            num_warps=1, num_stages=1)
        del primals_9
        buf20 = empty_strided_cuda((4, 1), (1, 1), torch.float32)
        extern_kernels.addmm(primals_11, buf18, reinterpret_tensor(
            primals_10, (8, 1), (1, 8), 0), alpha=1, beta=1, out=buf20)
        del primals_11
    return (buf20, primals_1, primals_2, primals_4, primals_6, buf1, buf4,
        buf5, buf7, buf10, buf11, buf13, buf16, reinterpret_tensor(buf15, (
        4, 4), (4, 1), 0), buf18, primals_10, primals_8)


class CNN3dModelNew(torch.nn.ModuleDict):

    def __init__(self, D_in=1, D_out=1):
        super(CNN3dModelNew, self).__init__()
        self.conv3d = torch.nn.Conv3d(D_in, D_in * 2, kernel_size=2, stride
            =2, padding=1)
        self.conv3d2 = torch.nn.Conv3d(D_in * 2, D_in * 2, kernel_size=2,
            stride=2, padding=1)
        self.conv3d3 = torch.nn.Conv3d(D_in * 2, D_in * 4, kernel_size=2,
            stride=2, padding=1)
        self.pool = torch.nn.MaxPool3d(kernel_size=2, padding=1)
        self.pool2 = torch.nn.MaxPool3d(kernel_size=2)
        self.relu = torch.nn.ReLU()
        self.linear = torch.nn.Linear(D_in * 4, D_in * 8)
        self.linear2 = torch.nn.Linear(D_in * 8, D_out)

    def forward(self, input_0):
        primals_2 = self.conv3d.weight
        primals_3 = self.conv3d.bias
        primals_4 = self.conv3d2.weight
        primals_5 = self.conv3d2.bias
        primals_6 = self.conv3d3.weight
        primals_7 = self.conv3d3.bias
        primals_8 = self.linear.weight
        primals_9 = self.linear.bias
        primals_10 = self.linear2.weight
        primals_11 = self.linear2.bias
        primals_1 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6, primals_7, primals_8, primals_9,
            primals_10, primals_11])
        return output[0]


Measured latency_ms: 0.192528