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 torch.nn as nn
import torch.nn.init as init
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_transpose_1(in_ptr0, in_ptr1, out_ptr1, ynumel,
    xnumel, YBLOCK: tl.constexpr, XBLOCK: tl.constexpr):
    ynumel = 4
    xnumel = 4
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    ymask = yindex < ynumel
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    x1 = xindex
    y0 = yindex
    tmp0 = tl.load(in_ptr0 + (y0 + 4 * x1), xmask & ymask)
    tmp1 = tl.load(in_ptr1 + x1, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tl.store(out_ptr1 + (y0 + 4 * x1), tmp2, xmask & ymask)


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, (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_2, primals_1, out=buf0)
        del primals_1
        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_3, (4, 4), (1, 4), 0), beta=0)
        del buf0
        buf3 = buf2
        del buf2
        buf5 = reinterpret_tensor(buf1, (4, 4), (1, 4), 0)
        del buf1
        triton_poi_fused_add_transpose_1[grid(4, 4)](buf3, primals_4, buf5,
            4, 4, XBLOCK=4, YBLOCK=4, num_warps=1, num_stages=1)
        del buf3
        del primals_4
    return buf5, primals_3, reinterpret_tensor(primals_2, (4, 4), (1, 4), 0)


class GraphConvolutionNew(nn.Module):

    def __init__(self, input_dim, output_dim, use_bias=True):
        """
            图卷积: L*X*theta
        :param input_dim: int 节点输入特征的维度
        :param out_put_dim: int 输出特征维度
        :param use_bias: boolean | optional 是否使用偏置
        """
        super(GraphConvolutionNew, self).__init__()
        self.input_dim = input_dim
        self.output_dim = output_dim
        self.use_bias = use_bias
        self.weight = nn.Parameter(torch.Tensor(input_dim, output_dim))
        if self.use_bias:
            self.bias = nn.Parameter(torch.Tensor(output_dim))
        else:
            self.register_parameter('bias', None)
        self.reset_parameters()

    def reset_parameters(self):
        init.kaiming_uniform_(self.weight)
        if self.use_bias:
            init.zeros_(self.bias)

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

    def forward(self, input_0, input_1):
        primals_1 = self.weight
        primals_4 = self.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: 0.04873