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_relu_threshold_backward_0(in_out_ptr0, 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)
    x2 = xindex
    x0 = xindex % 128
    tmp0 = tl.load(in_out_ptr0 + x2, None)
    tmp1 = tl.load(in_ptr0 + x0, None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tmp5 = 0.0
    tmp6 = tmp4 <= tmp5
    tl.store(in_out_ptr0 + x2, tmp4, None)
    tl.store(out_ptr0 + x2, tmp6, None)


@triton.jit
def triton_poi_fused_relu_threshold_backward_1(in_out_ptr0, in_ptr0,
    out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 512
    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)
    tmp5 = 0.0
    tmp6 = tmp4 <= tmp5
    tl.store(in_out_ptr0 + x2, tmp4, xmask)
    tl.store(out_ptr0 + x2, tmp6, 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, primals_12,
        primals_13, primals_14, primals_15, primals_16, primals_17,
        primals_18, primals_19, primals_20, primals_21) = args
    args.clear()
    assert_size_stride(primals_1, (128, 4), (4, 1))
    assert_size_stride(primals_2, (128,), (1,))
    assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_4, (128, 128), (128, 1))
    assert_size_stride(primals_5, (128,), (1,))
    assert_size_stride(primals_6, (128, 128), (128, 1))
    assert_size_stride(primals_7, (128,), (1,))
    assert_size_stride(primals_8, (128, 128), (128, 1))
    assert_size_stride(primals_9, (128,), (1,))
    assert_size_stride(primals_10, (8, 128), (128, 1))
    assert_size_stride(primals_11, (8,), (1,))
    assert_size_stride(primals_12, (128, 8), (8, 1))
    assert_size_stride(primals_13, (128,), (1,))
    assert_size_stride(primals_14, (128, 128), (128, 1))
    assert_size_stride(primals_15, (128,), (1,))
    assert_size_stride(primals_16, (128, 128), (128, 1))
    assert_size_stride(primals_17, (128,), (1,))
    assert_size_stride(primals_18, (128, 128), (128, 1))
    assert_size_stride(primals_19, (128,), (1,))
    assert_size_stride(primals_20, (4, 128), (128, 1))
    assert_size_stride(primals_21, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((64, 128), (128, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(primals_3, (64, 4), (4, 1), 0),
            reinterpret_tensor(primals_1, (4, 128), (1, 4), 0), out=buf0)
        del primals_1
        buf1 = reinterpret_tensor(buf0, (4, 4, 4, 128), (2048, 512, 128, 1), 0)
        del buf0
        buf27 = empty_strided_cuda((4, 4, 4, 128), (2048, 512, 128, 1),
            torch.bool)
        get_raw_stream(0)
        triton_poi_fused_relu_threshold_backward_0[grid(8192)](buf1,
            primals_2, buf27, 8192, XBLOCK=128, num_warps=4, num_stages=1)
        del primals_2
        buf2 = empty_strided_cuda((64, 128), (128, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf1, (64, 128), (128, 1), 0),
            reinterpret_tensor(primals_4, (128, 128), (1, 128), 0), out=buf2)
        buf3 = reinterpret_tensor(buf2, (4, 4, 4, 128), (2048, 512, 128, 1), 0)
        del buf2
        buf26 = empty_strided_cuda((4, 4, 4, 128), (2048, 512, 128, 1),
            torch.bool)
        triton_poi_fused_relu_threshold_backward_0[grid(8192)](buf3,
            primals_5, buf26, 8192, XBLOCK=128, num_warps=4, num_stages=1)
        del primals_5
        buf4 = empty_strided_cuda((64, 128), (128, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf3, (64, 128), (128, 1), 0),
            reinterpret_tensor(primals_6, (128, 128), (1, 128), 0), out=buf4)
        buf5 = reinterpret_tensor(buf4, (4, 4, 4, 128), (2048, 512, 128, 1), 0)
        del buf4
        buf25 = empty_strided_cuda((4, 4, 4, 128), (2048, 512, 128, 1),
            torch.bool)
        triton_poi_fused_relu_threshold_backward_0[grid(8192)](buf5,
            primals_7, buf25, 8192, XBLOCK=128, num_warps=4, num_stages=1)
        del primals_7
        buf6 = empty_strided_cuda((64, 128), (128, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf5, (64, 128), (128, 1), 0),
            reinterpret_tensor(primals_8, (128, 128), (1, 128), 0), out=buf6)
        buf7 = reinterpret_tensor(buf6, (4, 4, 4, 128), (2048, 512, 128, 1), 0)
        del buf6
        buf24 = empty_strided_cuda((4, 4, 4, 128), (2048, 512, 128, 1),
            torch.bool)
        triton_poi_fused_relu_threshold_backward_0[grid(8192)](buf7,
            primals_9, buf24, 8192, XBLOCK=128, num_warps=4, num_stages=1)
        del primals_9
        buf8 = empty_strided_cuda((64, 8), (8, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf7, (64, 128), (128, 1), 0),
            reinterpret_tensor(primals_10, (128, 8), (1, 128), 0), out=buf8)
        buf9 = reinterpret_tensor(buf8, (4, 4, 4, 8), (128, 32, 8, 1), 0)
        del buf8
        buf23 = empty_strided_cuda((4, 4, 4, 8), (128, 32, 8, 1), torch.bool)
        triton_poi_fused_relu_threshold_backward_1[grid(512)](buf9,
            primals_11, buf23, 512, XBLOCK=128, num_warps=4, num_stages=1)
        del primals_11
        buf10 = empty_strided_cuda((64, 128), (128, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf9, (64, 8), (8, 1), 0),
            reinterpret_tensor(primals_12, (8, 128), (1, 8), 0), out=buf10)
        buf11 = reinterpret_tensor(buf10, (4, 4, 4, 128), (2048, 512, 128, 
            1), 0)
        del buf10
        buf22 = empty_strided_cuda((4, 4, 4, 128), (2048, 512, 128, 1),
            torch.bool)
        triton_poi_fused_relu_threshold_backward_0[grid(8192)](buf11,
            primals_13, buf22, 8192, XBLOCK=128, num_warps=4, num_stages=1)
        del primals_13
        buf12 = empty_strided_cuda((64, 128), (128, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf11, (64, 128), (128, 1), 0),
            reinterpret_tensor(primals_14, (128, 128), (1, 128), 0), out=buf12)
        buf13 = reinterpret_tensor(buf12, (4, 4, 4, 128), (2048, 512, 128, 
            1), 0)
        del buf12
        buf21 = empty_strided_cuda((4, 4, 4, 128), (2048, 512, 128, 1),
            torch.bool)
        triton_poi_fused_relu_threshold_backward_0[grid(8192)](buf13,
            primals_15, buf21, 8192, XBLOCK=128, num_warps=4, num_stages=1)
        del primals_15
        buf14 = empty_strided_cuda((64, 128), (128, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf13, (64, 128), (128, 1), 0),
            reinterpret_tensor(primals_16, (128, 128), (1, 128), 0), out=buf14)
        buf15 = reinterpret_tensor(buf14, (4, 4, 4, 128), (2048, 512, 128, 
            1), 0)
        del buf14
        buf20 = empty_strided_cuda((4, 4, 4, 128), (2048, 512, 128, 1),
            torch.bool)
        triton_poi_fused_relu_threshold_backward_0[grid(8192)](buf15,
            primals_17, buf20, 8192, XBLOCK=128, num_warps=4, num_stages=1)
        del primals_17
        buf16 = empty_strided_cuda((64, 128), (128, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf15, (64, 128), (128, 1), 0),
            reinterpret_tensor(primals_18, (128, 128), (1, 128), 0), out=buf16)
        buf17 = reinterpret_tensor(buf16, (4, 4, 4, 128), (2048, 512, 128, 
            1), 0)
        del buf16
        buf19 = empty_strided_cuda((4, 4, 4, 128), (2048, 512, 128, 1),
            torch.bool)
        triton_poi_fused_relu_threshold_backward_0[grid(8192)](buf17,
            primals_19, buf19, 8192, XBLOCK=128, num_warps=4, num_stages=1)
        del primals_19
        buf18 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        extern_kernels.addmm(primals_21, reinterpret_tensor(buf17, (64, 128
            ), (128, 1), 0), reinterpret_tensor(primals_20, (128, 4), (1, 
            128), 0), alpha=1, beta=1, out=buf18)
        del primals_21
    return (reinterpret_tensor(buf18, (4, 4, 4, 4), (64, 16, 4, 1), 0),
        reinterpret_tensor(primals_3, (64, 4), (4, 1), 0),
        reinterpret_tensor(buf1, (64, 128), (128, 1), 0),
        reinterpret_tensor(buf3, (64, 128), (128, 1), 0),
        reinterpret_tensor(buf5, (64, 128), (128, 1), 0),
        reinterpret_tensor(buf7, (64, 128), (128, 1), 0),
        reinterpret_tensor(buf9, (64, 8), (8, 1), 0), reinterpret_tensor(
        buf11, (64, 128), (128, 1), 0), reinterpret_tensor(buf13, (64, 128),
        (128, 1), 0), reinterpret_tensor(buf15, (64, 128), (128, 1), 0),
        reinterpret_tensor(buf17, (64, 128), (128, 1), 0), primals_20,
        buf19, primals_18, buf20, primals_16, buf21, primals_14, buf22,
        primals_12, buf23, primals_10, buf24, primals_8, buf25, primals_6,
        buf26, primals_4, buf27)


class autoencoderNew(torch.nn.Module):

    def __init__(self, inputDim):
        """
        In the constructor we instantiate two nn.Linear modules and assign them as
        member variables.
        """
        h_size = 128
        c_size = 8
        use_bias = True
        super(autoencoderNew, self).__init__()
        self.linear1 = torch.nn.Linear(inputDim, h_size, bias=use_bias)
        self.linear2 = torch.nn.Linear(h_size, h_size, bias=use_bias)
        self.linear3 = torch.nn.Linear(h_size, h_size, bias=use_bias)
        self.linear4 = torch.nn.Linear(h_size, h_size, bias=use_bias)
        self.linear5 = torch.nn.Linear(h_size, c_size, bias=use_bias)
        self.linear6 = torch.nn.Linear(c_size, h_size, bias=use_bias)
        self.linear7 = torch.nn.Linear(h_size, h_size, bias=use_bias)
        self.linear8 = torch.nn.Linear(h_size, h_size, bias=use_bias)
        self.linear9 = torch.nn.Linear(h_size, h_size, bias=use_bias)
        self.linear10 = torch.nn.Linear(h_size, inputDim, bias=use_bias)

    def forward(self, input_0):
        primals_1 = self.linear1.weight
        primals_2 = self.linear1.bias
        primals_4 = self.linear2.weight
        primals_5 = self.linear2.bias
        primals_6 = self.linear3.weight
        primals_7 = self.linear3.bias
        primals_8 = self.linear4.weight
        primals_9 = self.linear4.bias
        primals_10 = self.linear5.weight
        primals_11 = self.linear5.bias
        primals_12 = self.linear6.weight
        primals_13 = self.linear6.bias
        primals_14 = self.linear7.weight
        primals_15 = self.linear7.bias
        primals_16 = self.linear8.weight
        primals_17 = self.linear8.bias
        primals_18 = self.linear9.weight
        primals_19 = self.linear9.bias
        primals_20 = self.linear10.weight
        primals_21 = self.linear10.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,
            primals_10, primals_11, primals_12, primals_13, primals_14,
            primals_15, primals_16, primals_17, primals_18, primals_19,
            primals_20, primals_21])
        return output[0]


Measured latency_ms: 0.014073