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._inductor.runtime import triton_helpers
import torch.nn as nn
import torch.nn.functional as F
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_relu_threshold_backward_1(in_out_ptr0, in_ptr0,
    out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 16
    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.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tmp5 = 0.0
    tmp6 = tmp4 <= tmp5
    tl.store(in_out_ptr0 + x2, tmp4, xmask)
    tl.store(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, 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,))
    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, 4), (4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_zeros_0[grid(16)](buf0, 16, XBLOCK=16, num_warps=1,
            num_stages=1)
        buf1 = torch.ops.aten._sparse_addmm.default(reinterpret_tensor(buf0,
            (4, 4), (1, 4), 0), reinterpret_tensor(primals_1, (4, 4), (1, 4
            ), 0), reinterpret_tensor(primals_2, (4, 4), (1, 4), 0), beta=0)
        del primals_1
        buf2 = buf1
        del buf1
        buf3 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf2, (4, 4), (1, 4), 0),
            reinterpret_tensor(primals_3, (4, 4), (1, 4), 0), out=buf3)
        del primals_3
        buf4 = buf3
        del buf3
        buf8 = empty_strided_cuda((4, 4), (4, 1), torch.bool)
        triton_poi_fused_relu_threshold_backward_1[grid(16)](buf4,
            primals_4, buf8, 16, XBLOCK=16, num_warps=1, num_stages=1)
        del primals_4
        buf5 = torch.ops.aten._sparse_addmm.default(reinterpret_tensor(buf0,
            (4, 4), (1, 4), 0), reinterpret_tensor(buf4, (4, 4), (1, 4), 0),
            reinterpret_tensor(primals_2, (4, 4), (1, 4), 0), beta=0)
        del buf0
        buf6 = buf5
        del buf5
        buf7 = buf4
        del buf4
        extern_kernels.addmm(primals_6, reinterpret_tensor(buf6, (4, 4), (1,
            4), 0), reinterpret_tensor(primals_5, (4, 4), (1, 4), 0), alpha
            =1, beta=1, out=buf7)
        del primals_6
    return buf7, reinterpret_tensor(buf2, (4, 4), (1, 4), 0
        ), reinterpret_tensor(buf6, (4, 4), (1, 4), 0), reinterpret_tensor(
        primals_5, (4, 4), (1, 4), 0), primals_2, buf8


class GCNNew(nn.Module):

    def __init__(self, dim_nd, dim_ft, dim_hd, dim_ot, drop_rate=0.5):
        super(GCNNew, self).__init__()
        self.lin1 = nn.Linear(dim_ft, dim_hd)
        self.lin2 = nn.Linear(dim_hd, dim_ot)
        self.act1 = F.relu
        self.act2 = nn.Softmax
        self.drop1 = nn.Dropout(p=drop_rate)
        self.drop2 = nn.Dropout(p=drop_rate)

    def forward(self, input_0, input_1):
        primals_1 = self.lin1.weight
        primals_4 = self.lin1.bias
        primals_2 = self.lin2.weight
        primals_6 = self.lin2.bias
        primals_3 = input_0
        primals_5 = input_1
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6])
        return output[0]


Measured latency_ms: 0.072764