# 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/l4/cl4boort6vfsvh6h6bfd4lck36kbmtipkqcrnhckuuxer6sfib77.py
# Topologically Sorted Source Nodes: [output], Original ATen: [aten.zeros]
# Source node to ATen node mapping:
#   output => full
# Graph fragment:
#   %full : [num_users=1] = call_function[target=torch.ops.aten.full.default](args = ([4, 4], 0), kwargs = {dtype: torch.float32, layout: torch.strided, device: cuda:0, pin_memory: False})
triton_poi_fused_zeros_0 = async_compile.triton('triton_poi_fused_zeros_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=[16], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '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), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_zeros_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 0, '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_zeros_0(out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = 0.0
    tl.store(out_ptr0 + (x0), tmp0, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_6/inductor_cache/jx/cjx5dbatpmmef26smsh4ihcthv6b5j6immy3knnapedkysy5zf26.py
# Topologically Sorted Source Nodes: [x_1], Original ATen: [aten.add]
# Source node to ATen node mapping:
#   x_1 => add
# Graph fragment:
#   %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%permute_3, %primals_4), kwargs = {})
triton_poi_fused_add_1 = async_compile.triton('triton_poi_fused_add_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=[16], 
    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_add_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_add_1(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x1 = (xindex // 4)
    tmp0 = tl.load(in_out_ptr0 + (x2), xmask)
    tmp1 = tl.load(in_ptr0 + (x1), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tl.store(in_out_ptr0 + (x2), tmp2, xmask)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    primals_1, primals_2, primals_3, primals_4 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4), (4, 1))
    assert_size_stride(primals_2, (4, 4), (4, 1))
    assert_size_stride(primals_3, (4, 4), (4, 1))
    assert_size_stride(primals_4, (1, 4), (4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [support], Original ATen: [aten.mm]
        extern_kernels.mm(primals_1, primals_3, out=buf0)
        del primals_3
        buf1 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [output], Original ATen: [aten.zeros]
        stream0 = get_raw_stream(0)
        triton_poi_fused_zeros_0.run(buf1, 16, grid=grid(16), stream=stream0)
        # Topologically Sorted Source Nodes: [output], Original ATen: [aten._sparse_addmm]
        buf2 = torch.ops.aten._sparse_addmm.default(reinterpret_tensor(buf1, (4, 4), (1, 4), 0), reinterpret_tensor(buf0, (4, 4), (1, 4), 0), reinterpret_tensor(primals_2, (4, 4), (1, 4), 0), beta=0)
        del buf0
        del buf1
        buf3 = buf2
        del buf2
        buf4 = reinterpret_tensor(buf3, (4, 4), (1, 4), 0); del buf3  # reuse
        # Topologically Sorted Source Nodes: [x_1], Original ATen: [aten.add]
        triton_poi_fused_add_1.run(buf4, primals_4, 16, grid=grid(16), stream=stream0)
        del primals_4
    return (buf4, primals_2, reinterpret_tensor(primals_1, (4, 4), (1, 4), 0), )


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, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_4 = rand_strided((1, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([primals_1, primals_2, primals_3, primals_4])
    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)

from torch.nn import Module
import math
import torch
import torch.nn.functional as F
from torch import nn
from torch.nn.parameter import Parameter
from torch.nn.modules.module import Module


class sparse_dropout(Module):
    """
    Sparse dropout implementation
    """

    def __init__(self):
        super(sparse_dropout, self).__init__()

    def forward(self, input, p=0.5):
        if self.training is True and p > 0.0:
            random = torch.rand_like(input._values())
            mask = random.ge(p)
            new_indices = torch.masked_select(input._indices(), mask).reshape(
                2, -1)
            new_values = torch.masked_select(input._values(), mask)
            output = torch.sparse.FloatTensor(new_indices, new_values,
                input.shape)
            return output
        else:
            return input


class GraphConvolution(Module):
    """
    Simple GCN layer, similar to https://arxiv.org/abs/1609.02907
    """

    def __init__(self, in_features, out_features, node_dropout=0.0,
        edge_dropout=0.0, bias=True):
        super(GraphConvolution, self).__init__()
        self.sparse_dropout = sparse_dropout()
        self.in_features = in_features
        self.out_features = out_features
        self.weight = Parameter(torch.zeros(in_features, out_features))
        stdv = 1.0 / math.sqrt(self.weight.size(1))
        nn.init.uniform_(self.weight, -stdv, stdv)
        if bias:
            self.bias = Parameter(torch.zeros(1, out_features))
            nn.init.uniform_(self.bias, -stdv, stdv)
        else:
            self.register_parameter('bias', None)
        self.node_dropout = node_dropout
        self.edge_dropout = edge_dropout

    def forward(self, input, adj):
        adj = self.sparse_dropout(adj, self.edge_dropout)
        support = torch.mm(input, self.weight)
        support = F.dropout(support, self.node_dropout, training=self.training)
        output = torch.sparse.mm(adj, support)
        if self.bias is not None:
            return output + self.bias
        else:
            return output

    def __repr__(self):
        return self.__class__.__name__ + ' (' + str(self.in_features
            ) + ' -> ' + str(self.out_features) + ')'


class GCN1(nn.Module):

    def __init__(self, nfeat, nhid, nclass, dropout):
        super(GCN1, self).__init__()
        self.gc1 = GraphConvolution(nfeat, nclass)
        self.dropout = dropout

    def forward(self, x, adj):
        x = F.dropout(x, self.dropout, training=self.training)
        x = self.gc1(x, adj)
        return x


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


def get_init_inputs():
    return [[], {'nfeat': 4, 'nhid': 4, 'nclass': 4, 'dropout': 0.5}]

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.nn import Module
import math
import torch.nn.functional as F
from torch import nn
from torch.nn.parameter import Parameter
from torch.nn.modules.module import Module
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_zeros_0(out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = 0.0
    tl.store(out_ptr0 + x0, tmp0, xmask)


@triton.jit
def triton_poi_fused_add_1(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x1 = xindex // 4
    tmp0 = tl.load(in_out_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + x1, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tl.store(in_out_ptr0 + x2, tmp2, xmask)


def call(args):
    primals_1, primals_2, primals_3, primals_4 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4), (4, 1))
    assert_size_stride(primals_2, (4, 4), (4, 1))
    assert_size_stride(primals_3, (4, 4), (4, 1))
    assert_size_stride(primals_4, (1, 4), (4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.mm(primals_1, primals_3, out=buf0)
        del primals_3
        buf1 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_zeros_0[grid(16)](buf1, 16, XBLOCK=16, num_warps=1,
            num_stages=1)
        buf2 = torch.ops.aten._sparse_addmm.default(reinterpret_tensor(buf1,
            (4, 4), (1, 4), 0), reinterpret_tensor(buf0, (4, 4), (1, 4), 0),
            reinterpret_tensor(primals_2, (4, 4), (1, 4), 0), beta=0)
        del buf0
        del buf1
        buf3 = buf2
        del buf2
        buf4 = reinterpret_tensor(buf3, (4, 4), (1, 4), 0)
        del buf3
        triton_poi_fused_add_1[grid(16)](buf4, primals_4, 16, XBLOCK=16,
            num_warps=1, num_stages=1)
        del primals_4
    return buf4, primals_2, reinterpret_tensor(primals_1, (4, 4), (1, 4), 0)


class sparse_dropout(Module):
    """
    Sparse dropout implementation
    """

    def __init__(self):
        super(sparse_dropout, self).__init__()

    def forward(self, input, p=0.5):
        if self.training is True and p > 0.0:
            random = torch.rand_like(input._values())
            mask = random.ge(p)
            new_indices = torch.masked_select(input._indices(), mask).reshape(
                2, -1)
            new_values = torch.masked_select(input._values(), mask)
            output = torch.sparse.FloatTensor(new_indices, new_values,
                input.shape)
            return output
        else:
            return input


class GraphConvolution(Module):
    """
    Simple GCN layer, similar to https://arxiv.org/abs/1609.02907
    """

    def __init__(self, in_features, out_features, node_dropout=0.0,
        edge_dropout=0.0, bias=True):
        super(GraphConvolution, self).__init__()
        self.sparse_dropout = sparse_dropout()
        self.in_features = in_features
        self.out_features = out_features
        self.weight = Parameter(torch.zeros(in_features, out_features))
        stdv = 1.0 / math.sqrt(self.weight.size(1))
        nn.init.uniform_(self.weight, -stdv, stdv)
        if bias:
            self.bias = Parameter(torch.zeros(1, out_features))
            nn.init.uniform_(self.bias, -stdv, stdv)
        else:
            self.register_parameter('bias', None)
        self.node_dropout = node_dropout
        self.edge_dropout = edge_dropout

    def forward(self, input, adj):
        adj = self.sparse_dropout(adj, self.edge_dropout)
        support = torch.mm(input, self.weight)
        support = F.dropout(support, self.node_dropout, training=self.training)
        output = torch.sparse.mm(adj, support)
        if self.bias is not None:
            return output + self.bias
        else:
            return output

    def __repr__(self):
        return self.__class__.__name__ + ' (' + str(self.in_features
            ) + ' -> ' + str(self.out_features) + ')'


class GCN1New(nn.Module):

    def __init__(self, nfeat, nhid, nclass, dropout):
        super(GCN1New, self).__init__()
        self.gc1 = GraphConvolution(nfeat, nclass)
        self.dropout = dropout

    def forward(self, input_0, input_1):
        primals_1 = self.gc1.weight
        primals_4 = self.gc1.bias
        primals_2 = input_0
        primals_3 = input_1
        output = call([primals_1, primals_2, primals_3, primals_4])
        return output[0]
