# 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_7/inductor_cache/rg/crgn6c7kb7sfbc6ij3j5r2ufm7sb3lducxeabudnnzg6pgowubma.py
# Topologically Sorted Source Nodes: [input_2], Original ATen: [aten.sigmoid]
# Source node to ATen node mapping:
#   input_2 => sigmoid
# Graph fragment:
#   %sigmoid : [num_users=2] = call_function[target=torch.ops.aten.sigmoid.default](args = (%view_1,), kwargs = {})
triton_poi_fused_sigmoid_0 = async_compile.triton('triton_poi_fused_sigmoid_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=[256], 
    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_sigmoid_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, '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_sigmoid_0(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (x0), xmask)
    tmp1 = tl.sigmoid(tmp0)
    tl.store(out_ptr0 + (x0), tmp1, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/q5/cq52p2qap7uob2ddnn4qeh67r3muutkp3yhbkqpu4eqaemol3idl.py
# Topologically Sorted Source Nodes: [input_8], Original ATen: [aten.sigmoid]
# Source node to ATen node mapping:
#   input_8 => sigmoid_3
# Graph fragment:
#   %sigmoid_3 : [num_users=1] = call_function[target=torch.ops.aten.sigmoid.default](args = (%view_7,), kwargs = {})
triton_poi_fused_sigmoid_1 = async_compile.triton('triton_poi_fused_sigmoid_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=[256], 
    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_sigmoid_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_sigmoid_1(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 4
    tmp0 = tl.load(in_out_ptr0 + (x2), xmask)
    tmp1 = tl.load(in_ptr0 + (x0), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.sigmoid(tmp2)
    tl.store(in_out_ptr0 + (x2), tmp3, 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 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4), (4, 1))
    assert_size_stride(primals_2, (4, ), (1, ))
    assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_4, (4, 4), (4, 1))
    assert_size_stride(primals_5, (4, ), (1, ))
    assert_size_stride(primals_6, (4, 4), (4, 1))
    assert_size_stride(primals_7, (4, ), (1, ))
    assert_size_stride(primals_8, (4, 4), (4, 1))
    assert_size_stride(primals_9, (4, ), (1, ))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [input_1], Original ATen: [aten.addmm]
        extern_kernels.addmm(primals_2, reinterpret_tensor(primals_3, (64, 4), (4, 1), 0), reinterpret_tensor(primals_1, (4, 4), (1, 4), 0), alpha=1, beta=1, out=buf0)
        del primals_1
        del primals_2
        buf1 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [input_2], Original ATen: [aten.sigmoid]
        stream0 = get_raw_stream(0)
        triton_poi_fused_sigmoid_0.run(buf0, buf1, 256, grid=grid(256), stream=stream0)
        buf2 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [input_3], Original ATen: [aten.addmm]
        extern_kernels.addmm(primals_5, reinterpret_tensor(buf1, (64, 4), (4, 1), 0), reinterpret_tensor(primals_4, (4, 4), (1, 4), 0), alpha=1, beta=1, out=buf2)
        del primals_5
        buf3 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [input_4], Original ATen: [aten.sigmoid]
        triton_poi_fused_sigmoid_0.run(buf2, buf3, 256, grid=grid(256), stream=stream0)
        buf4 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [input_5], Original ATen: [aten.addmm]
        extern_kernels.addmm(primals_7, reinterpret_tensor(buf3, (64, 4), (4, 1), 0), reinterpret_tensor(primals_6, (4, 4), (1, 4), 0), alpha=1, beta=1, out=buf4)
        del primals_7
        buf5 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [input_6], Original ATen: [aten.sigmoid]
        triton_poi_fused_sigmoid_0.run(buf4, buf5, 256, grid=grid(256), stream=stream0)
        buf6 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [], Original ATen: []
        extern_kernels.mm(reinterpret_tensor(buf5, (64, 4), (4, 1), 0), reinterpret_tensor(primals_8, (4, 4), (1, 4), 0), out=buf6)
        buf7 = reinterpret_tensor(buf6, (4, 4, 4, 4), (64, 16, 4, 1), 0); del buf6  # reuse
        # Topologically Sorted Source Nodes: [input_8], Original ATen: [aten.sigmoid]
        triton_poi_fused_sigmoid_1.run(buf7, primals_9, 256, grid=grid(256), stream=stream0)
        del primals_9
    return (buf7, reinterpret_tensor(buf0, (4, 4, 4, 4), (64, 16, 4, 1), 0), reinterpret_tensor(buf2, (4, 4, 4, 4), (64, 16, 4, 1), 0), reinterpret_tensor(buf4, (4, 4, 4, 4), (64, 16, 4, 1), 0), reinterpret_tensor(primals_3, (64, 4), (4, 1), 0), buf1, buf3, buf5, buf7, 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((4, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((4, ), (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((4, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_5 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_6 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_7 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_8 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_9 = 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])
    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 numpy as np
from torch import nn
import torch.nn.functional as F
from collections import OrderedDict
from sklearn.cluster import KMeans


class GraphEncoder(nn.Module):

    def __init__(self, layers, clusters):
        super(GraphEncoder, self).__init__()
        self.layers = nn.Sequential(OrderedDict({'lin1': nn.Linear(layers[0
            ], layers[1]), 'sig1': nn.Sigmoid(), 'lin2': nn.Linear(layers[1
            ], layers[2]), 'sig2': nn.Sigmoid(), 'lin3': nn.Linear(layers[2
            ], layers[3]), 'sig3': nn.Sigmoid(), 'lin4': nn.Linear(layers[3
            ], layers[4]), 'sig4': nn.Sigmoid()}))
        self.clusters = clusters
        self.outputs = {}
        self.layers[0].register_forward_hook(self.get_activation('lin1'))
        self.layers[2].register_forward_hook(self.get_activation('lin2'))
        self.layers[4].register_forward_hook(self.get_activation('lin3'))

    def get_activation(self, name):

        def hook(module, input, output):
            self.outputs[name] = output
        return hook

    def forward(self, x):
        output = self.layers(x)
        return output

    def layer_activations(self, layername):
        return torch.mean(torch.sigmoid(self.outputs[layername]), dim=0)

    def sparse_result(self, rho, layername):
        rho_hat = self.layer_activations(layername)
        return rho * np.log(rho) - rho * torch.log(rho_hat) + (1 - rho
            ) * np.log(1 - rho) - (1 - rho) * torch.log(1 - rho_hat)

    def kl_div(self, rho):
        first = torch.mean(self.sparse_result(rho, 'lin1'))
        second = torch.mean(self.sparse_result(rho, 'lin2'))
        return first + second

    def get_index_by_name(self, name):
        return list(dict(self.layers.named_children()).keys()).index(name)

    def loss(self, x_hat, x, beta, rho):
        loss = F.mse_loss(x_hat, x) + beta * self.kl_div(rho)
        return loss

    def get_cluster(self):
        kmeans = KMeans(n_clusters=self.clusters).fit(self.outputs['lin2'].
            detach().cpu().numpy())
        self.centroids = kmeans.cluster_centers_
        return kmeans.labels_


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


def get_init_inputs():
    return [[], {'layers': [4, 4, 4, 4, 4], 'clusters': 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
import numpy as np
from torch import nn
import torch.nn.functional as F
from collections import OrderedDict
from sklearn.cluster import KMeans
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_sigmoid_0(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr
    ):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + x0, xmask)
    tmp1 = tl.sigmoid(tmp0)
    tl.store(out_ptr0 + x0, tmp1, xmask)


@triton.jit
def triton_poi_fused_sigmoid_1(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl.
    constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 4
    tmp0 = tl.load(in_out_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.sigmoid(tmp2)
    tl.store(in_out_ptr0 + x2, tmp3, xmask)


def call(args):
    (primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
        primals_7, primals_8, primals_9) = args
    args.clear()
    assert_size_stride(primals_1, (4, 4), (4, 1))
    assert_size_stride(primals_2, (4,), (1,))
    assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_4, (4, 4), (4, 1))
    assert_size_stride(primals_5, (4,), (1,))
    assert_size_stride(primals_6, (4, 4), (4, 1))
    assert_size_stride(primals_7, (4,), (1,))
    assert_size_stride(primals_8, (4, 4), (4, 1))
    assert_size_stride(primals_9, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        extern_kernels.addmm(primals_2, reinterpret_tensor(primals_3, (64, 
            4), (4, 1), 0), reinterpret_tensor(primals_1, (4, 4), (1, 4), 0
            ), alpha=1, beta=1, out=buf0)
        del primals_1
        del primals_2
        buf1 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_sigmoid_0[grid(256)](buf0, buf1, 256, XBLOCK=256,
            num_warps=4, num_stages=1)
        buf2 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        extern_kernels.addmm(primals_5, reinterpret_tensor(buf1, (64, 4), (
            4, 1), 0), reinterpret_tensor(primals_4, (4, 4), (1, 4), 0),
            alpha=1, beta=1, out=buf2)
        del primals_5
        buf3 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        triton_poi_fused_sigmoid_0[grid(256)](buf2, buf3, 256, XBLOCK=256,
            num_warps=4, num_stages=1)
        buf4 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        extern_kernels.addmm(primals_7, reinterpret_tensor(buf3, (64, 4), (
            4, 1), 0), reinterpret_tensor(primals_6, (4, 4), (1, 4), 0),
            alpha=1, beta=1, out=buf4)
        del primals_7
        buf5 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        triton_poi_fused_sigmoid_0[grid(256)](buf4, buf5, 256, XBLOCK=256,
            num_warps=4, num_stages=1)
        buf6 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf5, (64, 4), (4, 1), 0),
            reinterpret_tensor(primals_8, (4, 4), (1, 4), 0), out=buf6)
        buf7 = reinterpret_tensor(buf6, (4, 4, 4, 4), (64, 16, 4, 1), 0)
        del buf6
        triton_poi_fused_sigmoid_1[grid(256)](buf7, primals_9, 256, XBLOCK=
            256, num_warps=4, num_stages=1)
        del primals_9
    return buf7, reinterpret_tensor(buf0, (4, 4, 4, 4), (64, 16, 4, 1), 0
        ), reinterpret_tensor(buf2, (4, 4, 4, 4), (64, 16, 4, 1), 0
        ), reinterpret_tensor(buf4, (4, 4, 4, 4), (64, 16, 4, 1), 0
        ), reinterpret_tensor(primals_3, (64, 4), (4, 1), 0
        ), buf1, buf3, buf5, buf7, primals_8, primals_6, primals_4


class GraphEncoderNew(nn.Module):

    def __init__(self, layers, clusters):
        super(GraphEncoderNew, self).__init__()
        self.layers = nn.Sequential(OrderedDict({'lin1': nn.Linear(layers[0
            ], layers[1]), 'sig1': nn.Sigmoid(), 'lin2': nn.Linear(layers[1
            ], layers[2]), 'sig2': nn.Sigmoid(), 'lin3': nn.Linear(layers[2
            ], layers[3]), 'sig3': nn.Sigmoid(), 'lin4': nn.Linear(layers[3
            ], layers[4]), 'sig4': nn.Sigmoid()}))
        self.clusters = clusters
        self.outputs = {}
        self.layers[0].register_forward_hook(self.get_activation('lin1'))
        self.layers[2].register_forward_hook(self.get_activation('lin2'))
        self.layers[4].register_forward_hook(self.get_activation('lin3'))

    def get_activation(self, name):

        def hook(module, input, output):
            self.outputs[name] = output
        return hook

    def layer_activations(self, layername):
        return torch.mean(torch.sigmoid(self.outputs[layername]), dim=0)

    def sparse_result(self, rho, layername):
        rho_hat = self.layer_activations(layername)
        return rho * np.log(rho) - rho * torch.log(rho_hat) + (1 - rho
            ) * np.log(1 - rho) - (1 - rho) * torch.log(1 - rho_hat)

    def kl_div(self, rho):
        first = torch.mean(self.sparse_result(rho, 'lin1'))
        second = torch.mean(self.sparse_result(rho, 'lin2'))
        return first + second

    def get_index_by_name(self, name):
        return list(dict(self.layers.named_children()).keys()).index(name)

    def loss(self, x_hat, x, beta, rho):
        loss = F.mse_loss(x_hat, x) + beta * self.kl_div(rho)
        return loss

    def get_cluster(self):
        kmeans = KMeans(n_clusters=self.clusters).fit(self.outputs['lin2'].
            detach().cpu().numpy())
        self.centroids = kmeans.cluster_centers_
        return kmeans.labels_

    def forward(self, input_0):
        primals_1 = self.layers.lin1.weight
        primals_2 = self.layers.lin1.bias
        primals_4 = self.layers.lin2.weight
        primals_5 = self.layers.lin2.bias
        primals_6 = self.layers.lin3.weight
        primals_7 = self.layers.lin3.bias
        primals_8 = self.layers.lin4.weight
        primals_9 = self.layers.lin4.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])
        return output[0]
