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]


Measured latency_ms: 0.035885