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 import nn
import torch.nn
import torch.autograd
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_ones_0(out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 4
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = 1.0
    tl.store(out_ptr0 + x0, tmp0, xmask)


@triton.jit
def triton_poi_fused_add_div_1(in_out_ptr0, in_ptr0, in_ptr1, in_ptr2,
    xnumel, XBLOCK: tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x1 = xindex // 4
    x0 = xindex % 4
    tmp0 = tl.load(in_out_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + x1, xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr1 + x2, xmask)
    tmp4 = tl.load(in_ptr2 + x0, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 / tmp1
    tmp5 = tmp3 + tmp4
    tmp6 = tmp2 + tmp5
    tl.store(in_out_ptr0 + x2, tmp6, xmask)


def call(args):
    primals_1, primals_2, primals_3, primals_4, primals_5, primals_6 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_2, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_3, (4, 4), (4, 1))
    assert_size_stride(primals_4, (4,), (1,))
    assert_size_stride(primals_5, (4, 4), (4, 1))
    assert_size_stride(primals_6, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 1), (1, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_ones_0[grid(4)](buf0, 4, XBLOCK=4, num_warps=1,
            num_stages=1)
        buf1 = empty_strided_cuda((64, 1), (1, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(primals_1, (64, 4), (4, 1), 0),
            buf0, out=buf1)
        del buf0
        buf2 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        extern_kernels.addmm(primals_4, reinterpret_tensor(primals_2, (64, 
            4), (4, 1), 0), reinterpret_tensor(primals_3, (4, 4), (1, 4), 0
            ), alpha=1, beta=1, out=buf2)
        del primals_3
        del primals_4
        buf3 = empty_strided_cuda((16, 4, 4), (16, 4, 1), torch.float32)
        extern_kernels.bmm(reinterpret_tensor(primals_1, (16, 4, 4), (16, 4,
            1), 0), reinterpret_tensor(buf2, (16, 4, 4), (16, 4, 1), 0),
            out=buf3)
        buf4 = buf2
        del buf2
        extern_kernels.mm(reinterpret_tensor(primals_2, (64, 4), (4, 1), 0),
            reinterpret_tensor(primals_5, (4, 4), (1, 4), 0), out=buf4)
        del primals_5
        buf5 = reinterpret_tensor(buf3, (4, 4, 4, 4), (64, 16, 4, 1), 0)
        del buf3
        triton_poi_fused_add_div_1[grid(256)](buf5, buf1, buf4, primals_6, 
            256, XBLOCK=128, num_warps=4, num_stages=1)
        del buf4
        del primals_6
    return buf5, buf1, reinterpret_tensor(primals_2, (64, 4), (4, 1), 0
        ), reinterpret_tensor(primals_1, (16, 4, 4), (16, 1, 4), 0)


def sparse_bmm(sparse_matrix, dense_matrix_batch):
    """
    Perform torch.bmm on an unbatched sparse matrix and a batched dense matrix.

    Args:
        sparse_matrix (torch.sparse.FloatTensor): Shape = (m, n)
        dense_matrix_batch (torch.FloatTensor): Shape = (b, n, p)

    Returns:
        (torch.FloatTensor):
            Result of the batched matrix multiplication. Shape = (b, n, p)
    """
    m = sparse_matrix.shape[0]
    b, n, p = dense_matrix_batch.shape
    dense_matrix = dense_matrix_batch.transpose(0, 1).reshape(n, b * p)
    result = torch.sparse.mm(sparse_matrix, dense_matrix)
    return result.reshape(m, b, p).transpose(0, 1)


class GraphConvNew(nn.Module):
    """
    A simple graph convolution layer, similar to the one defined in Kipf et al.
    https://arxiv.org/abs/1609.02907

    This operation with self_layer=False is equivalent to :math:`(A H W)` where:
    - :math:`H` is the node features with shape (batch_size, num_nodes, input_dim)
    - :math:`W` is a weight matrix of shape (input_dim, output_dim)
    - :math:`A` is the adjacency matrix of shape (num_nodes, num_nodes).
    It can include self-loop.

    With normalize_adj=True, it is equivalent to :math:`(D^{-1} A H W)`, where:
    - :math:`D` is a diagonal matrix with :math:`D_{ii}` = the sum of the i-th row of A.
    In other words, :math:`D` is the incoming degree of each node.

    With self_layer=True, it is equivalent to the above plus :math:`(H W_{\\text{self}})`, where:
    - :math:`W_{\\text{self}}` is a separate weight matrix to filter each node's self features.

    Note that when self_layer is True, A should not include self-loop.

    Example:
        >>> node_feat = torch.rand(1, 3, 5)
        >>> i = torch.LongTensor(
        ...     [[0, 1, 1, 2, 2, 0], [1, 0, 2, 1, 0, 2]])
        >>> v = torch.FloatTensor([1, 1, 1, 1, 1, 1])
        >>> adj = torch.sparse.FloatTensor(i, v, torch.Size([3, 3]))
        >>> model = GraphConv(5, 10)
        >>> output = model(node_feat, adj)
        >>> # pre-normalize adj
        >>> adj = normalize_adj(adj)
        >>> output = model(node_feat, adj, normalize_adj=False)

    If you use this code, please cite the original paper in addition to Kaolin.
    .. code-block::
        @article{kipf2016semi,
          title={Semi-Supervised Classification with Graph Convolutional Networks},
          author={Kipf, Thomas N and Welling, Max},
          journal={arXiv preprint arXiv:1609.02907},
          year={2016}
        }

    Args:
        input_dim (int): The number of features in each input node.
        output_dim (int): The number of features in each output node.
        bias (bool): Whether to add bias after the node-wise linear layer.
    """

    def __init__(self, input_dim, output_dim, self_layer=True, bias=True):
        super(GraphConvNew, self).__init__()
        self.self_layer = self_layer
        self.linear = nn.Linear(input_dim, output_dim, bias=bias)
        if self_layer:
            self.linear_self = nn.Linear(input_dim, output_dim, bias=bias)
        else:
            self.linear_self = None
        self.initialize()

    def initialize(self):
        nn.init.xavier_uniform_(self.linear.weight.data)
        if self.linear.bias is not None:
            self.linear.bias.data.uniform_(-1.0, 1.0)
        if self.self_layer:
            nn.init.xavier_uniform_(self.linear_self.weight.data)
            if self.linear_self.bias is not None:
                self.linear_self.bias.data.uniform_(-1.0, 1.0)

    def forward(self, input_0, input_1):
        primals_3 = self.linear.weight
        primals_4 = self.linear.bias
        primals_5 = self.linear_self.weight
        primals_6 = self.linear_self.bias
        primals_1 = input_0
        primals_2 = input_1
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6])
        return output[0]


Measured latency_ms: 0.062551