# 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/7k/c7kbxovtq4f3bykwez4rrzyn73bqtbiv6o2f3lrcq5n2yf6b36vs.py
# Topologically Sorted Source Nodes: [conv2d, relu], Original ATen: [aten.convolution, aten.relu]
# Source node to ATen node mapping:
#   conv2d => convolution
#   relu => relu
# Graph fragment:
#   %convolution : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%primals_1, %primals_2, %primals_3, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {})
#   %relu : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution,), kwargs = {})
triton_poi_fused_convolution_relu_0 = async_compile.triton('triton_poi_fused_convolution_relu_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=[512], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '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), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_relu_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_convolution_relu_0(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 384
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = (xindex // 16) % 6
    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)
''', device_str='cuda')


# kernel path: runs/run_shard_6/inductor_cache/zi/cziljm4x2mwlsrzd6lgu2ol5wiicmcdex3lpg23avfluyxcsxmol.py
# Topologically Sorted Source Nodes: [x], Original ATen: [aten.max_pool2d_with_indices]
# Source node to ATen node mapping:
#   x => _low_memory_max_pool2d_with_offsets, getitem_1
# Graph fragment:
#   %_low_memory_max_pool2d_with_offsets : [num_users=2] = call_function[target=torch.ops.prims._low_memory_max_pool2d_with_offsets.default](args = (%relu, [2, 2], [2, 2], [0, 0], [1, 1], False), kwargs = {})
#   %getitem_1 : [num_users=1] = call_function[target=operator.getitem](args = (%_low_memory_max_pool2d_with_offsets, 1), kwargs = {})
triton_poi_fused_max_pool2d_with_indices_1 = async_compile.triton('triton_poi_fused_max_pool2d_with_indices_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=[128], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*i8', 2: '*fp32', 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_max_pool2d_with_indices_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 4, '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_max_pool2d_with_indices_1(in_ptr0, out_ptr0, out_ptr1, xnumel, XBLOCK : tl.constexpr):
    xnumel = 96
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 2
    x1 = (xindex // 2)
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + ((2*x0) + (8*x1)), xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr0 + (1 + (2*x0) + (8*x1)), xmask, eviction_policy='evict_last')
    tmp7 = tl.load(in_ptr0 + (4 + (2*x0) + (8*x1)), xmask, eviction_policy='evict_last')
    tmp12 = tl.load(in_ptr0 + (5 + (2*x0) + (8*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)
''', device_str='cuda')


# kernel path: runs/run_shard_6/inductor_cache/7x/c7xscjkhfxioj3yqt7zpbp6xho5x5wmzfuayrpvzoij5pp56u75i.py
# Topologically Sorted Source Nodes: [relu_1], Original ATen: [aten.relu, aten.threshold_backward]
# Source node to ATen node mapping:
#   relu_1 => relu_1
# Graph fragment:
#   %add_tensor : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mm_default, %primals_5), kwargs = {})
#   %relu_1 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%add_tensor,), kwargs = {})
#   %le : [num_users=1] = call_function[target=torch.ops.aten.le.Scalar](args = (%relu_1, 0), kwargs = {})
triton_poi_fused_relu_threshold_backward_2 = async_compile.triton('triton_poi_fused_relu_threshold_backward_2', '''
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=[128], 
    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), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_relu_threshold_backward_2', '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_2(in_out_ptr0, in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 120
    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.load(in_ptr0 + (x0), xmask)
    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 + (x0), tmp4, xmask)
    tl.store(out_ptr0 + (x0), 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 = args
    args.clear()
    assert_size_stride(primals_1, (4, 1, 6, 6), (36, 36, 6, 1))
    assert_size_stride(primals_2, (6, 1, 3, 3), (9, 9, 3, 1))
    assert_size_stride(primals_3, (6, ), (1, ))
    assert_size_stride(primals_4, (120, 96), (96, 1))
    assert_size_stride(primals_5, (120, ), (1, ))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        # Topologically Sorted Source Nodes: [conv2d], Original ATen: [aten.convolution]
        buf0 = extern_kernels.convolution(primals_1, primals_2, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf0, (4, 6, 4, 4), (96, 16, 4, 1))
        buf1 = buf0; del buf0  # reuse
        # Topologically Sorted Source Nodes: [conv2d, relu], Original ATen: [aten.convolution, aten.relu]
        stream0 = get_raw_stream(0)
        triton_poi_fused_convolution_relu_0.run(buf1, primals_3, 384, grid=grid(384), stream=stream0)
        del primals_3
        buf2 = empty_strided_cuda((4, 6, 2, 2), (24, 4, 2, 1), torch.int8)
        buf3 = empty_strided_cuda((4, 6, 2, 2), (24, 4, 2, 1), torch.float32)
        # Topologically Sorted Source Nodes: [x], Original ATen: [aten.max_pool2d_with_indices]
        triton_poi_fused_max_pool2d_with_indices_1.run(buf1, buf2, buf3, 96, grid=grid(96), stream=stream0)
        buf4 = empty_strided_cuda((1, 120), (120, 1), torch.float32)
        # Topologically Sorted Source Nodes: [], Original ATen: []
        extern_kernels.mm(reinterpret_tensor(buf3, (1, 96), (0, 1), 0), reinterpret_tensor(primals_4, (96, 120), (1, 96), 0), out=buf4)
        buf5 = buf4; del buf4  # reuse
        buf6 = empty_strided_cuda((1, 120), (120, 1), torch.bool)
        # Topologically Sorted Source Nodes: [relu_1], Original ATen: [aten.relu, aten.threshold_backward]
        triton_poi_fused_relu_threshold_backward_2.run(buf5, primals_5, buf6, 120, grid=grid(120), stream=stream0)
        del primals_5
    return (buf5, primals_1, primals_2, buf1, buf2, reinterpret_tensor(buf3, (1, 96), (96, 1), 0), buf6, primals_4, )


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((4, 1, 6, 6), (36, 36, 6, 1), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((6, 1, 3, 3), (9, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((6, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_4 = rand_strided((120, 96), (96, 1), device='cuda:0', dtype=torch.float32)
    primals_5 = rand_strided((120, ), (1, ), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([primals_1, primals_2, primals_3, primals_4, primals_5])
    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 time
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.utils.tensorboard import SummaryWriter


def singleton(cls):
    obj = cls()
    cls.__new__ = staticmethod(lambda cls: obj)
    try:
        del cls.__init__
    except AttributeError:
        pass
    return cls


@singleton
class model_debugger:

    def __init__(self):
        self.reset()

    def reset(self, filename_suffix=''):
        """
		Reset all the variables

		Parameters
		----------
		filename_suffix : str, optional
			the filename suffix, by default ""

		Returns
		-------
		model_debugger
			returns self
		"""
        self.model_forward_data = {}
        self.active_forwards = {}
        self.logs = []
        self.time_usage = {}
        self.active_timers = {}
        self.called_times = {}
        self.variable_log = {}
        self.tenserboard_writer = SummaryWriter(filename_suffix)
        self.epoch_write_counter = {}
        return self

    def write_to_tensorboard(self, name, value, epoch):
        """
		Add value to SummaryWriter

		Parameters
		----------
		name : str
			tag name
		value : number
			numeric value
		epoch : epoch
			Current epoch, if none a built in counter will be used
		"""
        if epoch is None:
            if name not in self.epoch_write_counter:
                self.epoch_write_counter[name] = 0
            epoch = self.epoch_write_counter[name]
            self.epoch_write_counter[name] += 1
        self.tenserboard_writer.add_scalar(name, value, epoch)

    def save_tensorboard(self):
        self.tenserboard_writer.close()

    def get_last_round(self):
        """
		Get the latest forward data for model

		Returns
		-------
		list
			with tuple of key value
		"""
        return self.model_forward_data.items()

    def start_forward(self, name):
        """
		Start a forward of component

		Parameters
		----------
		name : str
			name of the component
		"""
        if name not in self.active_forwards:
            self.active_forwards[name] = []

    def add_element(self, name, element):
        """
		Add a element to the forward path of a given name

		Parameters
		----------
		name : str
			name of component
		element : tensor/any
			the tensor of a given state
		"""
        self.active_forwards[name].append(element)

    def stop_forward(self, name):
        """
		Complete a forward

		Parameters
		----------
		name : str
			name of the component
		"""
        self.model_forward_data[name] = self.active_forwards[name]
        self.active_forwards[name] = []

    def log(self, value):
        """
		Log a value to list

		Parameters
		----------
		value : str
			log value
		"""
        self.logs.append(value)

    def start_track_time(self, name):
        """
		Track time usage of a given component
		- need to be closed by stop_track_time

		Parameters
		----------
		name : str
			name of timer
		"""
        if name not in self.active_timers:
            self.active_timers[name] = []
            self.time_usage[name] = 0
            self.called_times[name] = 0
        self.active_timers[name].append(time.time())
        self.called_times[name] += 1

    def stop_track_time(self, name):
        """
		Stops the timer with a given name

		Parameters
		----------
		name : str
			name of timer
		"""
        time_usage = time.time() - self.active_timers[name].pop(0)
        self.time_usage[name] += time_usage


class shared_backbone:

    def __init__(self):
        self.device = torch.device('cuda' if torch.cuda.is_available() else
            'cpu')
        self.debugger = model_debugger()
        self.main_hidden_layer_size = 64


class BasicConv(nn.Module, shared_backbone):

    def __init__(self, output=120):
        super().__init__()
        shared_backbone.__init__(self)
        self.output_size = 96
        self.conv1 = nn.Conv2d(1, 6, 3)
        self.fc1 = nn.Linear(self.output_size, output)

    def forward(self, x):
        """
		Basic forward operation on conv layer

		Parameters
		----------
		x : torch.tensor
			input data

		Returns
		-------
		torch.tensor
			the output after the operations
		"""
        if len(x.shape) < 4:
            padding = (1,) * (4 - len(x.shape))
            x = x.reshape(padding + x.shape)
        x = F.max_pool2d(F.relu(self.conv1(x)), (2, 2))
        x = x.view(-1, self.output_size)
        assert x.shape[0] == 1
        assert len(x.shape) == 2
        return F.relu(self.fc1(x))


def get_inputs():
    return [torch.rand([4, 1, 6, 6])]


def get_init_inputs():
    return [[], {}]

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
import time
import torch.nn as nn
from torch.utils.tensorboard import SummaryWriter
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):
    xnumel = 384
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = xindex // 16 % 6
    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_1(in_ptr0, out_ptr0, out_ptr1,
    xnumel, XBLOCK: tl.constexpr):
    xnumel = 96
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 2
    x1 = xindex // 2
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + (2 * x0 + 8 * x1), xmask, eviction_policy=
        'evict_last')
    tmp1 = tl.load(in_ptr0 + (1 + 2 * x0 + 8 * x1), xmask, eviction_policy=
        'evict_last')
    tmp7 = tl.load(in_ptr0 + (4 + 2 * x0 + 8 * x1), xmask, eviction_policy=
        'evict_last')
    tmp12 = tl.load(in_ptr0 + (5 + 2 * x0 + 8 * 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_poi_fused_relu_threshold_backward_2(in_out_ptr0, in_ptr0,
    out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 120
    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.load(in_ptr0 + x0, xmask)
    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 + x0, tmp4, xmask)
    tl.store(out_ptr0 + x0, tmp6, xmask)


def call(args):
    primals_1, primals_2, primals_3, primals_4, primals_5 = args
    args.clear()
    assert_size_stride(primals_1, (4, 1, 6, 6), (36, 36, 6, 1))
    assert_size_stride(primals_2, (6, 1, 3, 3), (9, 9, 3, 1))
    assert_size_stride(primals_3, (6,), (1,))
    assert_size_stride(primals_4, (120, 96), (96, 1))
    assert_size_stride(primals_5, (120,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = extern_kernels.convolution(primals_1, primals_2, stride=(1, 
            1), padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf0, (4, 6, 4, 4), (96, 16, 4, 1))
        buf1 = buf0
        del buf0
        get_raw_stream(0)
        triton_poi_fused_convolution_relu_0[grid(384)](buf1, primals_3, 384,
            XBLOCK=128, num_warps=4, num_stages=1)
        del primals_3
        buf2 = empty_strided_cuda((4, 6, 2, 2), (24, 4, 2, 1), torch.int8)
        buf3 = empty_strided_cuda((4, 6, 2, 2), (24, 4, 2, 1), torch.float32)
        triton_poi_fused_max_pool2d_with_indices_1[grid(96)](buf1, buf2,
            buf3, 96, XBLOCK=128, num_warps=4, num_stages=1)
        buf4 = empty_strided_cuda((1, 120), (120, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf3, (1, 96), (0, 1), 0),
            reinterpret_tensor(primals_4, (96, 120), (1, 96), 0), out=buf4)
        buf5 = buf4
        del buf4
        buf6 = empty_strided_cuda((1, 120), (120, 1), torch.bool)
        triton_poi_fused_relu_threshold_backward_2[grid(120)](buf5,
            primals_5, buf6, 120, XBLOCK=128, num_warps=4, num_stages=1)
        del primals_5
    return buf5, primals_1, primals_2, buf1, buf2, reinterpret_tensor(buf3,
        (1, 96), (96, 1), 0), buf6, primals_4


def singleton(cls):
    obj = cls()
    cls.__new__ = staticmethod(lambda cls: obj)
    try:
        del cls.__init__
    except AttributeError:
        pass
    return cls


@singleton
class model_debugger:

    def __init__(self):
        self.reset()

    def reset(self, filename_suffix=''):
        """
		Reset all the variables

		Parameters
		----------
		filename_suffix : str, optional
			the filename suffix, by default ""

		Returns
		-------
		model_debugger
			returns self
		"""
        self.model_forward_data = {}
        self.active_forwards = {}
        self.logs = []
        self.time_usage = {}
        self.active_timers = {}
        self.called_times = {}
        self.variable_log = {}
        self.tenserboard_writer = SummaryWriter(filename_suffix)
        self.epoch_write_counter = {}
        return self

    def write_to_tensorboard(self, name, value, epoch):
        """
		Add value to SummaryWriter

		Parameters
		----------
		name : str
			tag name
		value : number
			numeric value
		epoch : epoch
			Current epoch, if none a built in counter will be used
		"""
        if epoch is None:
            if name not in self.epoch_write_counter:
                self.epoch_write_counter[name] = 0
            epoch = self.epoch_write_counter[name]
            self.epoch_write_counter[name] += 1
        self.tenserboard_writer.add_scalar(name, value, epoch)

    def save_tensorboard(self):
        self.tenserboard_writer.close()

    def get_last_round(self):
        """
		Get the latest forward data for model

		Returns
		-------
		list
			with tuple of key value
		"""
        return self.model_forward_data.items()

    def start_forward(self, name):
        """
		Start a forward of component

		Parameters
		----------
		name : str
			name of the component
		"""
        if name not in self.active_forwards:
            self.active_forwards[name] = []

    def add_element(self, name, element):
        """
		Add a element to the forward path of a given name

		Parameters
		----------
		name : str
			name of component
		element : tensor/any
			the tensor of a given state
		"""
        self.active_forwards[name].append(element)

    def stop_forward(self, name):
        """
		Complete a forward

		Parameters
		----------
		name : str
			name of the component
		"""
        self.model_forward_data[name] = self.active_forwards[name]
        self.active_forwards[name] = []

    def log(self, value):
        """
		Log a value to list

		Parameters
		----------
		value : str
			log value
		"""
        self.logs.append(value)

    def start_track_time(self, name):
        """
		Track time usage of a given component
		- need to be closed by stop_track_time

		Parameters
		----------
		name : str
			name of timer
		"""
        if name not in self.active_timers:
            self.active_timers[name] = []
            self.time_usage[name] = 0
            self.called_times[name] = 0
        self.active_timers[name].append(time.time())
        self.called_times[name] += 1

    def stop_track_time(self, name):
        """
		Stops the timer with a given name

		Parameters
		----------
		name : str
			name of timer
		"""
        time_usage = time.time() - self.active_timers[name].pop(0)
        self.time_usage[name] += time_usage


class shared_backbone:

    def __init__(self):
        self.device = torch.device('cuda' if torch.cuda.is_available() else
            'cpu')
        self.debugger = model_debugger()
        self.main_hidden_layer_size = 64


class BasicConvNew(nn.Module, shared_backbone):

    def __init__(self, output=120):
        super().__init__()
        shared_backbone.__init__(self)
        self.output_size = 96
        self.conv1 = nn.Conv2d(1, 6, 3)
        self.fc1 = nn.Linear(self.output_size, output)

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