# AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _align as align
from torch import device, empty_strided
from torch._inductor.async_compile import AsyncCompile
from torch._inductor.select_algorithm import extern_kernels
from torch._inductor.codegen.multi_kernel import MultiKernelCall
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph
from torch._C import _cuda_getCurrentRawStream as get_raw_stream

aten = torch.ops.aten
inductor_ops = torch.ops.inductor
_quantized = torch.ops._quantized
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
alloc_from_pool = torch.ops.inductor._alloc_from_pool
async_compile = AsyncCompile()


# kernel path: runs/run_shard_6/inductor_cache/md/cmd3ewacyhu5w5hausgbjbmtnt5rr66cgczh4ibdypq7dz6p4v7g.py
# Topologically Sorted Source Nodes: [h_1], Original ATen: [aten.relu, aten.threshold_backward]
# Source node to ATen node mapping:
#   h_1 => relu
# Graph fragment:
#   %relu : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%view_1,), kwargs = {})
#   %le_8 : [num_users=1] = call_function[target=torch.ops.aten.le.Scalar](args = (%relu, 0), kwargs = {})
triton_poi_fused_relu_threshold_backward_0 = async_compile.triton('triton_poi_fused_relu_threshold_backward_0', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties

@triton_heuristics.pointwise(
    size_hints=[8192], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*i1', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_relu_threshold_backward_0', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_relu_threshold_backward_0(in_out_ptr0, in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 8192
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = 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)
''', device_str='cuda')


# kernel path: runs/run_shard_6/inductor_cache/jm/cjmjqfjv2ijia2nagoscrnh2gu57uuxti5zfjtxbtxgqzk2qxxoh.py
# Topologically Sorted Source Nodes: [h_9], Original ATen: [aten.relu, aten.threshold_backward]
# Source node to ATen node mapping:
#   h_9 => relu_4
# Graph fragment:
#   %relu_4 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%view_9,), kwargs = {})
#   %le_4 : [num_users=1] = call_function[target=torch.ops.aten.le.Scalar](args = (%relu_4, 0), kwargs = {})
triton_poi_fused_relu_threshold_backward_1 = async_compile.triton('triton_poi_fused_relu_threshold_backward_1', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties

@triton_heuristics.pointwise(
    size_hints=[512], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*i1', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_relu_threshold_backward_1', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@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)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

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)
        # Topologically Sorted Source Nodes: [], Original ATen: []
        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  # reuse
        buf27 = empty_strided_cuda((4, 4, 4, 128), (2048, 512, 128, 1), torch.bool)
        # Topologically Sorted Source Nodes: [h_1], Original ATen: [aten.relu, aten.threshold_backward]
        stream0 = get_raw_stream(0)
        triton_poi_fused_relu_threshold_backward_0.run(buf1, primals_2, buf27, 8192, grid=grid(8192), stream=stream0)
        del primals_2
        buf2 = empty_strided_cuda((64, 128), (128, 1), torch.float32)
        # Topologically Sorted Source Nodes: [], Original ATen: []
        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  # reuse
        buf26 = empty_strided_cuda((4, 4, 4, 128), (2048, 512, 128, 1), torch.bool)
        # Topologically Sorted Source Nodes: [h_3], Original ATen: [aten.relu, aten.threshold_backward]
        triton_poi_fused_relu_threshold_backward_0.run(buf3, primals_5, buf26, 8192, grid=grid(8192), stream=stream0)
        del primals_5
        buf4 = empty_strided_cuda((64, 128), (128, 1), torch.float32)
        # Topologically Sorted Source Nodes: [], Original ATen: []
        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  # reuse
        buf25 = empty_strided_cuda((4, 4, 4, 128), (2048, 512, 128, 1), torch.bool)
        # Topologically Sorted Source Nodes: [h_5], Original ATen: [aten.relu, aten.threshold_backward]
        triton_poi_fused_relu_threshold_backward_0.run(buf5, primals_7, buf25, 8192, grid=grid(8192), stream=stream0)
        del primals_7
        buf6 = empty_strided_cuda((64, 128), (128, 1), torch.float32)
        # Topologically Sorted Source Nodes: [], Original ATen: []
        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  # reuse
        buf24 = empty_strided_cuda((4, 4, 4, 128), (2048, 512, 128, 1), torch.bool)
        # Topologically Sorted Source Nodes: [h_7], Original ATen: [aten.relu, aten.threshold_backward]
        triton_poi_fused_relu_threshold_backward_0.run(buf7, primals_9, buf24, 8192, grid=grid(8192), stream=stream0)
        del primals_9
        buf8 = empty_strided_cuda((64, 8), (8, 1), torch.float32)
        # Topologically Sorted Source Nodes: [], Original ATen: []
        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  # reuse
        buf23 = empty_strided_cuda((4, 4, 4, 8), (128, 32, 8, 1), torch.bool)
        # Topologically Sorted Source Nodes: [h_9], Original ATen: [aten.relu, aten.threshold_backward]
        triton_poi_fused_relu_threshold_backward_1.run(buf9, primals_11, buf23, 512, grid=grid(512), stream=stream0)
        del primals_11
        buf10 = empty_strided_cuda((64, 128), (128, 1), torch.float32)
        # Topologically Sorted Source Nodes: [], Original ATen: []
        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  # reuse
        buf22 = empty_strided_cuda((4, 4, 4, 128), (2048, 512, 128, 1), torch.bool)
        # Topologically Sorted Source Nodes: [h_11], Original ATen: [aten.relu, aten.threshold_backward]
        triton_poi_fused_relu_threshold_backward_0.run(buf11, primals_13, buf22, 8192, grid=grid(8192), stream=stream0)
        del primals_13
        buf12 = empty_strided_cuda((64, 128), (128, 1), torch.float32)
        # Topologically Sorted Source Nodes: [], Original ATen: []
        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  # reuse
        buf21 = empty_strided_cuda((4, 4, 4, 128), (2048, 512, 128, 1), torch.bool)
        # Topologically Sorted Source Nodes: [h_13], Original ATen: [aten.relu, aten.threshold_backward]
        triton_poi_fused_relu_threshold_backward_0.run(buf13, primals_15, buf21, 8192, grid=grid(8192), stream=stream0)
        del primals_15
        buf14 = empty_strided_cuda((64, 128), (128, 1), torch.float32)
        # Topologically Sorted Source Nodes: [], Original ATen: []
        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  # reuse
        buf20 = empty_strided_cuda((4, 4, 4, 128), (2048, 512, 128, 1), torch.bool)
        # Topologically Sorted Source Nodes: [h_15], Original ATen: [aten.relu, aten.threshold_backward]
        triton_poi_fused_relu_threshold_backward_0.run(buf15, primals_17, buf20, 8192, grid=grid(8192), stream=stream0)
        del primals_17
        buf16 = empty_strided_cuda((64, 128), (128, 1), torch.float32)
        # Topologically Sorted Source Nodes: [], Original ATen: []
        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  # reuse
        buf19 = empty_strided_cuda((4, 4, 4, 128), (2048, 512, 128, 1), torch.bool)
        # Topologically Sorted Source Nodes: [h_17], Original ATen: [aten.relu, aten.threshold_backward]
        triton_poi_fused_relu_threshold_backward_0.run(buf17, primals_19, buf19, 8192, grid=grid(8192), stream=stream0)
        del primals_19
        buf18 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [h_18], Original ATen: [aten.addmm]
        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, )


def benchmark_compiled_module(times=10, repeat=10):
    from torch._dynamo.testing import rand_strided
    from torch._inductor.utils import print_performance
    primals_1 = rand_strided((128, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((128, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_4 = rand_strided((128, 128), (128, 1), device='cuda:0', dtype=torch.float32)
    primals_5 = rand_strided((128, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_6 = rand_strided((128, 128), (128, 1), device='cuda:0', dtype=torch.float32)
    primals_7 = rand_strided((128, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_8 = rand_strided((128, 128), (128, 1), device='cuda:0', dtype=torch.float32)
    primals_9 = rand_strided((128, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_10 = rand_strided((8, 128), (128, 1), device='cuda:0', dtype=torch.float32)
    primals_11 = rand_strided((8, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_12 = rand_strided((128, 8), (8, 1), device='cuda:0', dtype=torch.float32)
    primals_13 = rand_strided((128, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_14 = rand_strided((128, 128), (128, 1), device='cuda:0', dtype=torch.float32)
    primals_15 = rand_strided((128, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_16 = rand_strided((128, 128), (128, 1), device='cuda:0', dtype=torch.float32)
    primals_17 = rand_strided((128, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_18 = rand_strided((128, 128), (128, 1), device='cuda:0', dtype=torch.float32)
    primals_19 = rand_strided((128, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_20 = rand_strided((4, 128), (128, 1), device='cuda:0', dtype=torch.float32)
    primals_21 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    fn = lambda: 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 print_performance(fn, times=times, repeat=repeat)


if __name__ == "__main__":
    from torch._inductor.wrapper_benchmark import compiled_module_main
    compiled_module_main('None', benchmark_compiled_module)

import torch
import torch.nn.functional as F


class autoencoder(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(autoencoder, 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, x):
        """
        In the forward function we accept a Tensor of input data and we must return
        a Tensor of output data. We can use Modules defined in the constructor as
        well as arbitrary operators on Tensors.
        """
        h = self.linear1(x)
        h = F.relu(h)
        h = self.linear2(h)
        h = F.relu(h)
        h = self.linear3(h)
        h = F.relu(h)
        h = self.linear4(h)
        h = F.relu(h)
        h = self.linear5(h)
        h = F.relu(h)
        h = self.linear6(h)
        h = F.relu(h)
        h = self.linear7(h)
        h = F.relu(h)
        h = self.linear8(h)
        h = F.relu(h)
        h = self.linear9(h)
        h = F.relu(h)
        h = self.linear10(h)
        return h


def get_inputs():
    return [torch.rand([4, 4, 4, 4])]


def get_init_inputs():
    return [[], {'inputDim': 4}]

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]
