# 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/kl/ckl7xpn6mguexi6w3o5m6lo5wwco2ezndhjeu2znnmfar63lsipa.py
# Topologically Sorted Source Nodes: [x], Original ATen: [aten.convolution]
# Source node to ATen node mapping:
#   x => convolution
# Graph fragment:
#   %convolution : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%primals_3, %primals_1, %primals_2, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {})
triton_poi_fused_convolution_0 = async_compile.triton('triton_poi_fused_convolution_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=[2048], 
    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_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_0(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 1280
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = (xindex // 64) % 5
    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)
''', device_str='cuda')


# kernel path: runs/run_shard_6/inductor_cache/5d/c5dgdobzoof335xa26g6rdcttzd6dyqbtm2ngmputgv5gn45j6ez.py
# Topologically Sorted Source Nodes: [x_2], Original ATen: [aten.relu]
# Source node to ATen node mapping:
#   x_2 => relu
# Graph fragment:
#   %add_tensor_2 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mm_default_2, %primals_5), kwargs = {})
#   %relu : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%add_tensor_2,), kwargs = {})
triton_poi_fused_relu_1 = async_compile.triton('triton_poi_fused_relu_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: '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_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_1(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 400
    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)
    tl.store(in_out_ptr0 + (x0), tmp4, 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 = args
    args.clear()
    assert_size_stride(primals_1, (5, 1, 5, 5), (25, 25, 5, 1))
    assert_size_stride(primals_2, (5, ), (1, ))
    assert_size_stride(primals_3, (4, 1, 12, 12), (144, 144, 12, 1))
    assert_size_stride(primals_4, (400, 1280), (1280, 1))
    assert_size_stride(primals_5, (400, ), (1, ))
    assert_size_stride(primals_6, (400, 400), (400, 1))
    assert_size_stride(primals_7, (400, ), (1, ))
    assert_size_stride(primals_8, (400, 400), (400, 1))
    assert_size_stride(primals_9, (400, ), (1, ))
    assert_size_stride(primals_10, (400, 400), (400, 1))
    assert_size_stride(primals_11, (400, ), (1, ))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        # Topologically Sorted Source Nodes: [x], Original ATen: [aten.convolution]
        buf0 = extern_kernels.convolution(primals_3, primals_1, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf0, (4, 5, 8, 8), (320, 64, 8, 1))
        buf1 = buf0; del buf0  # reuse
        # Topologically Sorted Source Nodes: [x], Original ATen: [aten.convolution]
        stream0 = get_raw_stream(0)
        triton_poi_fused_convolution_0.run(buf1, primals_2, 1280, grid=grid(1280), stream=stream0)
        del primals_2
        buf2 = empty_strided_cuda((1, 400), (400, 1), torch.float32)
        # Topologically Sorted Source Nodes: [], Original ATen: []
        extern_kernels.mm(reinterpret_tensor(buf1, (1, 1280), (0, 1), 0), reinterpret_tensor(primals_4, (1280, 400), (1, 1280), 0), out=buf2)
        buf3 = buf2; del buf2  # reuse
        # Topologically Sorted Source Nodes: [x_2], Original ATen: [aten.relu]
        triton_poi_fused_relu_1.run(buf3, primals_5, 400, grid=grid(400), stream=stream0)
        del primals_5
        buf4 = empty_strided_cuda((1, 400), (400, 1), torch.float32)
        # Topologically Sorted Source Nodes: [], Original ATen: []
        extern_kernels.mm(buf3, reinterpret_tensor(primals_6, (400, 400), (1, 400), 0), out=buf4)
        buf5 = buf4; del buf4  # reuse
        # Topologically Sorted Source Nodes: [x_3], Original ATen: [aten.relu]
        triton_poi_fused_relu_1.run(buf5, primals_7, 400, grid=grid(400), stream=stream0)
        del primals_7
        buf6 = empty_strided_cuda((1, 400), (400, 1), torch.float32)
        # Topologically Sorted Source Nodes: [], Original ATen: []
        extern_kernels.mm(buf5, reinterpret_tensor(primals_8, (400, 400), (1, 400), 0), out=buf6)
        buf7 = buf6; del buf6  # reuse
        # Topologically Sorted Source Nodes: [x_4], Original ATen: [aten.relu]
        triton_poi_fused_relu_1.run(buf7, primals_9, 400, grid=grid(400), stream=stream0)
        del primals_9
        buf8 = empty_strided_cuda((1, 400), (400, 1), torch.float32)
        # Topologically Sorted Source Nodes: [x_5], Original ATen: [aten.addmm]
        extern_kernels.addmm(primals_11, buf7, reinterpret_tensor(primals_10, (400, 400), (1, 400), 0), alpha=1, beta=1, out=buf8)
        del primals_11
    return (buf8, primals_1, primals_3, reinterpret_tensor(buf1, (1, 1280), (1280, 1), 0), buf3, buf5, buf7, primals_10, primals_8, primals_6, 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((5, 1, 5, 5), (25, 25, 5, 1), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((5, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((4, 1, 12, 12), (144, 144, 12, 1), device='cuda:0', dtype=torch.float32)
    primals_4 = rand_strided((400, 1280), (1280, 1), device='cuda:0', dtype=torch.float32)
    primals_5 = rand_strided((400, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_6 = rand_strided((400, 400), (400, 1), device='cuda:0', dtype=torch.float32)
    primals_7 = rand_strided((400, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_8 = rand_strided((400, 400), (400, 1), device='cuda:0', dtype=torch.float32)
    primals_9 = rand_strided((400, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_10 = rand_strided((400, 400), (400, 1), device='cuda:0', dtype=torch.float32)
    primals_11 = rand_strided((400, ), (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])
    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
from torch import Tensor
from logging import info
from torch import nn
from logging import error
from torch.nn import Linear
from torch.nn.functional import relu


class Net(nn.Module):

    def __init__(self, size):
        super(Net, self).__init__()
        convolutions = [5]
        info('CONV LAYERS: %s' % convolutions)
        self.conv1 = nn.Conv2d(in_channels=1, out_channels=convolutions[0],
            kernel_size=(5, 5))
        self.flat_features = 16 * 16 * convolutions[-1]
        linears = [self.flat_features, 20 * 20, 20 * 20, 20 * 20, 20 * 20]
        info('LIN LAYERS: %s' % linears)
        self.fc1 = Linear(linears[0], linears[1])
        self.fc2 = Linear(linears[1], linears[2])
        self.fc3 = Linear(linears[2], linears[3])
        self.fc4 = Linear(linears[3], linears[4])

    def forward(self, x: 'Tensor'):
        x = self.conv1(x)
        x = x.view(-1, self.flat_features)
        x = relu(self.fc1(x))
        x = relu(self.fc2(x))
        x = relu(self.fc3(x))
        x = self.fc4(x)
        return x

    def load_last_state(self, directory: 'str'='nets') ->(int, float):
        """
        Load previous network state with lowest lost
        :param directory: Net state directory
        :return: Last epoch number, and last loss value
        """
        if not exists(directory):
            info('path does not exists')
            return 0, 0.0
        try:
            net_states = listdir(directory)
            if net_states:
                last_state = net_states[-1]
                self.load_state_dict(torch.load(join(directory, last_state)))
                info('Load: %s' % last_state)
                _, last_loss, last_epoch = last_state.split(' ')
                last_loss = float(last_loss)
                last_epoch = int(last_epoch[1:-5])
                return last_epoch, last_loss
        except RuntimeError:
            info('Network changed')
        return 0, 0.0

    def save_state(self, running_loss: 'float', epoch: 'int', directory:
        'str'='nets') ->None:
        """
        Save state if specified time has past
        :param running_loss: Running loss
        :param epoch: Current epoch count
        :param directory: Net state directory
        """
        if running_loss == float('nan'):
            error('Loss is nan [%s]' % epoch)
            raise ValueError()
        file_name = '%s %.4f [%s].pth' % (datetime.now().strftime(
            '%Y-%m-%d-%H-%M'), running_loss, epoch)
        torch.save(self.state_dict(), join(directory, file_name))
        net_states = listdir(directory)
        while len(net_states) > 10:
            remove(join(directory, net_states.pop(0)))


def get_inputs():
    return [torch.rand([4, 1, 12, 12])]


def get_init_inputs():
    return [[], {'size': 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
from logging import info
from torch import nn
from logging import error
from torch.nn import Linear
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 = 1280
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = xindex // 64 % 5
    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, in_ptr0, xnumel, XBLOCK: tl.constexpr
    ):
    xnumel = 400
    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)
    tl.store(in_out_ptr0 + x0, 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, (5, 1, 5, 5), (25, 25, 5, 1))
    assert_size_stride(primals_2, (5,), (1,))
    assert_size_stride(primals_3, (4, 1, 12, 12), (144, 144, 12, 1))
    assert_size_stride(primals_4, (400, 1280), (1280, 1))
    assert_size_stride(primals_5, (400,), (1,))
    assert_size_stride(primals_6, (400, 400), (400, 1))
    assert_size_stride(primals_7, (400,), (1,))
    assert_size_stride(primals_8, (400, 400), (400, 1))
    assert_size_stride(primals_9, (400,), (1,))
    assert_size_stride(primals_10, (400, 400), (400, 1))
    assert_size_stride(primals_11, (400,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = extern_kernels.convolution(primals_3, primals_1, stride=(1, 
            1), padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf0, (4, 5, 8, 8), (320, 64, 8, 1))
        buf1 = buf0
        del buf0
        get_raw_stream(0)
        triton_poi_fused_convolution_0[grid(1280)](buf1, primals_2, 1280,
            XBLOCK=128, num_warps=4, num_stages=1)
        del primals_2
        buf2 = empty_strided_cuda((1, 400), (400, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf1, (1, 1280), (0, 1), 0),
            reinterpret_tensor(primals_4, (1280, 400), (1, 1280), 0), out=buf2)
        buf3 = buf2
        del buf2
        triton_poi_fused_relu_1[grid(400)](buf3, primals_5, 400, XBLOCK=256,
            num_warps=4, num_stages=1)
        del primals_5
        buf4 = empty_strided_cuda((1, 400), (400, 1), torch.float32)
        extern_kernels.mm(buf3, reinterpret_tensor(primals_6, (400, 400), (
            1, 400), 0), out=buf4)
        buf5 = buf4
        del buf4
        triton_poi_fused_relu_1[grid(400)](buf5, primals_7, 400, XBLOCK=256,
            num_warps=4, num_stages=1)
        del primals_7
        buf6 = empty_strided_cuda((1, 400), (400, 1), torch.float32)
        extern_kernels.mm(buf5, reinterpret_tensor(primals_8, (400, 400), (
            1, 400), 0), out=buf6)
        buf7 = buf6
        del buf6
        triton_poi_fused_relu_1[grid(400)](buf7, primals_9, 400, XBLOCK=256,
            num_warps=4, num_stages=1)
        del primals_9
        buf8 = empty_strided_cuda((1, 400), (400, 1), torch.float32)
        extern_kernels.addmm(primals_11, buf7, reinterpret_tensor(
            primals_10, (400, 400), (1, 400), 0), alpha=1, beta=1, out=buf8)
        del primals_11
    return buf8, primals_1, primals_3, reinterpret_tensor(buf1, (1, 1280),
        (1280, 1), 0
        ), buf3, buf5, buf7, primals_10, primals_8, primals_6, primals_4


class NetNew(nn.Module):

    def __init__(self, size):
        super(NetNew, self).__init__()
        convolutions = [5]
        info('CONV LAYERS: %s' % convolutions)
        self.conv1 = nn.Conv2d(in_channels=1, out_channels=convolutions[0],
            kernel_size=(5, 5))
        self.flat_features = 16 * 16 * convolutions[-1]
        linears = [self.flat_features, 20 * 20, 20 * 20, 20 * 20, 20 * 20]
        info('LIN LAYERS: %s' % linears)
        self.fc1 = Linear(linears[0], linears[1])
        self.fc2 = Linear(linears[1], linears[2])
        self.fc3 = Linear(linears[2], linears[3])
        self.fc4 = Linear(linears[3], linears[4])

    def load_last_state(self, directory: 'str'='nets') ->(int, float):
        """
        Load previous network state with lowest lost
        :param directory: Net state directory
        :return: Last epoch number, and last loss value
        """
        if not exists(directory):
            info('path does not exists')
            return 0, 0.0
        try:
            net_states = listdir(directory)
            if net_states:
                last_state = net_states[-1]
                self.load_state_dict(torch.load(join(directory, last_state)))
                info('Load: %s' % last_state)
                _, last_loss, last_epoch = last_state.split(' ')
                last_loss = float(last_loss)
                last_epoch = int(last_epoch[1:-5])
                return last_epoch, last_loss
        except RuntimeError:
            info('Network changed')
        return 0, 0.0

    def save_state(self, running_loss: 'float', epoch: 'int', directory:
        'str'='nets') ->None:
        """
        Save state if specified time has past
        :param running_loss: Running loss
        :param epoch: Current epoch count
        :param directory: Net state directory
        """
        if running_loss == float('nan'):
            error('Loss is nan [%s]' % epoch)
            raise ValueError()
        file_name = '%s %.4f [%s].pth' % (datetime.now().strftime(
            '%Y-%m-%d-%H-%M'), running_loss, epoch)
        torch.save(self.state_dict(), join(directory, file_name))
        net_states = listdir(directory)
        while len(net_states) > 10:
            remove(join(directory, net_states.pop(0)))

    def forward(self, input_0):
        primals_1 = self.conv1.weight
        primals_2 = self.conv1.bias
        primals_4 = self.fc1.weight
        primals_5 = self.fc1.bias
        primals_6 = self.fc2.weight
        primals_7 = self.fc2.bias
        primals_8 = self.fc3.weight
        primals_9 = self.fc3.bias
        primals_10 = self.fc4.weight
        primals_11 = self.fc4.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])
        return output[0]
