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 math
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_add_0(in_out_ptr0, in_ptr0, in_ptr1, in_ptr2, xnumel,
    XBLOCK: tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_out_ptr0 + x0, xmask)
    tmp1 = tl.load(in_ptr0 + x0, xmask)
    tmp3 = tl.load(in_ptr1 + x0, xmask)
    tmp5 = tl.load(in_ptr2 + x0, xmask)
    tmp2 = tmp0 + tmp1
    tmp4 = tmp2 + tmp3
    tmp6 = tmp4 + tmp5
    tl.store(in_out_ptr0 + x0, tmp6, xmask)


@triton.jit
def triton_poi_fused_view_1(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 4
    x1 = xindex // 4
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + 4 * x1 + 16 * (x1 % 4 // 4)), xmask)
    tl.store(out_ptr0 + x2, tmp0, xmask)


def call(args):
    (primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
        primals_7, primals_8) = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_2, (4, 4), (4, 1))
    assert_size_stride(primals_3, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_4, (4, 4), (4, 1))
    assert_size_stride(primals_5, (4, 4), (4, 1))
    assert_size_stride(primals_6, (4, 4), (4, 1))
    assert_size_stride(primals_7, (4, 4), (4, 1))
    assert_size_stride(primals_8, (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(reinterpret_tensor(primals_1, (4, 4), (4, 1), 0),
            primals_2, out=buf0)
        del primals_2
        buf1 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(primals_3, (16, 4), (4, 1), 0),
            buf0, out=buf1)
        buf2 = buf0
        del buf0
        extern_kernels.mm(reinterpret_tensor(primals_1, (4, 4), (4, 1), 16),
            primals_4, out=buf2)
        del primals_4
        buf3 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(primals_3, (16, 4), (4, 1), 0),
            buf2, out=buf3)
        buf4 = buf2
        del buf2
        extern_kernels.mm(reinterpret_tensor(primals_1, (4, 4), (4, 1), 32),
            primals_5, out=buf4)
        del primals_5
        buf5 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(primals_3, (16, 4), (4, 1), 0),
            buf4, out=buf5)
        buf6 = buf4
        del buf4
        extern_kernels.mm(reinterpret_tensor(primals_1, (4, 4), (4, 1), 48),
            primals_6, out=buf6)
        del primals_6
        buf7 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(primals_3, (16, 4), (4, 1), 0),
            buf6, out=buf7)
        del buf6
        buf8 = reinterpret_tensor(buf1, (4, 4, 4), (16, 4, 1), 0)
        del buf1
        get_raw_stream(0)
        triton_poi_fused_add_0[grid(64)](buf8, buf3, buf5, buf7, 64, XBLOCK
            =64, num_warps=1, num_stages=1)
        del buf3
        del buf5
        buf9 = buf7
        del buf7
        triton_poi_fused_view_1[grid(64)](buf8, buf9, 64, XBLOCK=64,
            num_warps=1, num_stages=1)
        buf10 = reinterpret_tensor(buf8, (16, 4), (4, 1), 0)
        del buf8
        extern_kernels.addmm(primals_8, buf9, reinterpret_tensor(primals_7,
            (4, 4), (1, 4), 0), alpha=1, beta=1, out=buf10)
        del primals_8
    return reinterpret_tensor(buf10, (4, 4, 4), (16, 4, 1), 0
        ), buf9, primals_7, reinterpret_tensor(primals_3, (4, 16), (1, 4), 0
        ), reinterpret_tensor(primals_1, (4, 4), (1, 4), 48
        ), reinterpret_tensor(primals_1, (4, 4), (1, 4), 32
        ), reinterpret_tensor(primals_1, (4, 4), (1, 4), 16
        ), reinterpret_tensor(primals_1, (4, 4), (1, 4), 0)


class VocabGraphConvolutionNew(nn.Module):
    """Vocabulary GCN module.

    Params:
        `voc_dim`: The size of vocabulary graph
        `num_adj`: The number of the adjacency matrix of Vocabulary graph
        `hid_dim`: The hidden dimension after XAW
        `out_dim`: The output dimension after Relu(XAW)W
        `dropout_rate`: The dropout probabilitiy for all fully connected
                layers in the embeddings, encoder, and pooler.

    Inputs:
        `vocab_adj_list`: The list of the adjacency matrix
        `X_dv`: the feature of mini batch document, can be TF-IDF (batch, vocab), or word embedding (batch, word_embedding_dim, vocab)

    Outputs:
        The graph embedding representation, dimension (batch, `out_dim`) or (batch, word_embedding_dim, `out_dim`)

    """

    def __init__(self, voc_dim, num_adj, hid_dim, out_dim, dropout_rate=0.2):
        super(VocabGraphConvolutionNew, self).__init__()
        self.voc_dim = voc_dim
        self.num_adj = num_adj
        self.hid_dim = hid_dim
        self.out_dim = out_dim
        for i in range(self.num_adj):
            setattr(self, 'W%d_vh' % i, nn.Parameter(torch.randn(voc_dim,
                hid_dim)))
        self.fc_hc = nn.Linear(hid_dim, out_dim)
        self.act_func = nn.ReLU()
        self.dropout = nn.Dropout(dropout_rate)
        self.reset_parameters()

    def reset_parameters(self):
        for n, p in self.named_parameters():
            if n.startswith('W') or n.startswith('a') or n in ('W', 'a',
                'dense'):
                init.kaiming_uniform_(p, a=math.sqrt(5))

    def forward(self, input_0, input_1):
        primals_2 = self.W0_vh
        primals_4 = self.W1_vh
        primals_5 = self.W2_vh
        primals_6 = self.W3_vh
        primals_7 = self.fc_hc.weight
        primals_8 = self.fc_hc.bias
        primals_1 = input_0
        primals_3 = input_1
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6, primals_7, primals_8])
        return output[0]


Measured latency_ms: 0.088451