# AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _align as align
from torch import device, empty_strided
from torch._inductor.async_compile import AsyncCompile
from torch._inductor.select_algorithm import extern_kernels
from torch._inductor.codegen.multi_kernel import MultiKernelCall
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph
from torch._C import _cuda_getCurrentRawStream as get_raw_stream

aten = torch.ops.aten
inductor_ops = torch.ops.inductor
_quantized = torch.ops._quantized
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
alloc_from_pool = torch.ops.inductor._alloc_from_pool
async_compile = AsyncCompile()


# kernel path: runs/run_shard_8/inductor_cache/vm/cvmrqlqqvxp6blioafkdkaofn7wkkon7u6khr2fbp4aqo66xm6mo.py
# Topologically Sorted Source Nodes: [fused_H_2], Original ATen: [aten.add]
# Source node to ATen node mapping:
#   fused_H_2 => add_2
# Graph fragment:
#   %add_2 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%view_10, %view_13), kwargs = {})
triton_poi_fused_add_0 = async_compile.triton('triton_poi_fused_add_0', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties

@triton_heuristics.pointwise(
    size_hints=[64], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_add_0', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 4, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@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)
''', device_str='cuda')


# kernel path: runs/run_shard_8/inductor_cache/m4/cm4e5xfc44dx4qte74bwfdb35u6flb4coxohln57fmueoitjynx7.py
# Topologically Sorted Source Nodes: [out], Original ATen: [aten.view]
# Source node to ATen node mapping:
#   out => view_19
# Graph fragment:
#   %view_19 : [num_users=2] = call_function[target=torch.ops.aten.reshape.default](args = (%view_18, [16, 4]), kwargs = {})
triton_poi_fused_view_1 = async_compile.triton('triton_poi_fused_view_1', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties

@triton_heuristics.pointwise(
    size_hints=[64], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_view_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@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)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

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)
        # Topologically Sorted Source Nodes: [H_vh], Original ATen: [aten.mm]
        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)
        # Topologically Sorted Source Nodes: [H_dh], Original ATen: [aten.mm]
        extern_kernels.mm(reinterpret_tensor(primals_3, (16, 4), (4, 1), 0), buf0, out=buf1)
        buf2 = buf0; del buf0  # reuse
        # Topologically Sorted Source Nodes: [H_vh_2], Original ATen: [aten.mm]
        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)
        # Topologically Sorted Source Nodes: [H_dh_1], Original ATen: [aten.mm]
        extern_kernels.mm(reinterpret_tensor(primals_3, (16, 4), (4, 1), 0), buf2, out=buf3)
        buf4 = buf2; del buf2  # reuse
        # Topologically Sorted Source Nodes: [H_vh_4], Original ATen: [aten.mm]
        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)
        # Topologically Sorted Source Nodes: [H_dh_2], Original ATen: [aten.mm]
        extern_kernels.mm(reinterpret_tensor(primals_3, (16, 4), (4, 1), 0), buf4, out=buf5)
        buf6 = buf4; del buf4  # reuse
        # Topologically Sorted Source Nodes: [H_vh_6], Original ATen: [aten.mm]
        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)
        # Topologically Sorted Source Nodes: [H_dh_3], Original ATen: [aten.mm]
        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  # reuse
        # Topologically Sorted Source Nodes: [fused_H_2], Original ATen: [aten.add]
        stream0 = get_raw_stream(0)
        triton_poi_fused_add_0.run(buf8, buf3, buf5, buf7, 64, grid=grid(64), stream=stream0)
        del buf3
        del buf5
        buf9 = buf7; del buf7  # reuse
        # Topologically Sorted Source Nodes: [out], Original ATen: [aten.view]
        triton_poi_fused_view_1.run(buf8, buf9, 64, grid=grid(64), stream=stream0)
        buf10 = reinterpret_tensor(buf8, (16, 4), (4, 1), 0); del buf8  # reuse
        # Topologically Sorted Source Nodes: [out], Original ATen: [aten.addmm]
        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), )


def benchmark_compiled_module(times=10, repeat=10):
    from torch._dynamo.testing import rand_strided
    from torch._inductor.utils import print_performance
    primals_1 = rand_strided((4, 4, 4), (16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((4, 4, 4), (16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_4 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_5 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_6 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_7 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_8 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8])
    return print_performance(fn, times=times, repeat=repeat)


if __name__ == "__main__":
    from torch._inductor.wrapper_benchmark import compiled_module_main
    compiled_module_main('None', benchmark_compiled_module)

import math
import torch
import torch.nn as nn
import torch.nn.init as init


class VocabGraphConvolution(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(VocabGraphConvolution, 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, vocab_adj_list, X_dv, add_linear_mapping_term=False):
        for i in range(self.num_adj):
            H_vh = vocab_adj_list[i].mm(getattr(self, 'W%d_vh' % i))
            H_vh = self.dropout(H_vh)
            H_dh = X_dv.matmul(H_vh)
            if add_linear_mapping_term:
                H_linear = X_dv.matmul(getattr(self, 'W%d_vh' % i))
                H_linear = self.dropout(H_linear)
                H_dh += H_linear
            if i == 0:
                fused_H = H_dh
            else:
                fused_H += H_dh
        out = self.fc_hc(fused_H)
        return out


def get_inputs():
    return [torch.rand([4, 4, 4]), torch.rand([4, 4, 4])]


def get_init_inputs():
    return [[], {'voc_dim': 4, 'num_adj': 4, 'hid_dim': 4, 'out_dim': 4}]

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]
