# 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_6/inductor_cache/oh/cohgrxe3lxmrv72r6z5fwk7g676tipqeoy4yj23mmym2s32bis5c.py
# Topologically Sorted Source Nodes: [output, output_1], Original ATen: [aten.cat, aten.add]
# Source node to ATen node mapping:
#   output => cat
#   output_1 => add
# Graph fragment:
#   %cat : [num_users=1] = call_function[target=torch.ops.aten.cat.default](args = ([%mm_1, %slice_4], 1), kwargs = {})
#   %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%cat, %primals_4), kwargs = {})
triton_poi_fused_add_cat_0 = async_compile.triton('triton_poi_fused_add_cat_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=[16], 
    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_cat_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 3, '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_cat_0(in_ptr0, in_ptr1, in_ptr2, 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 % 4
    x1 = (xindex // 4)
    x2 = xindex
    tmp11 = tl.load(in_ptr2 + (x0), xmask, eviction_policy='evict_last')
    tmp0 = x0
    tmp1 = tl.full([1], 0, tl.int64)
    tmp2 = tmp0 >= tmp1
    tmp3 = tl.full([1], 2, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + ((2*x1) + x0), tmp4 & xmask, eviction_policy='evict_last', other=0.0)
    tmp6 = tmp0 >= tmp3
    tmp7 = tl.full([1], 4, tl.int64)
    tmp8 = tmp0 < tmp7
    tmp9 = tl.load(in_ptr1 + (2 + (4*x1) + ((-2) + x0)), tmp6 & xmask, eviction_policy='evict_last', other=0.0)
    tmp10 = tl.where(tmp4, tmp5, tmp9)
    tmp12 = tmp10 + tmp11
    tl.store(out_ptr0 + (x2), tmp12, xmask)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

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)
        # Topologically Sorted Source Nodes: [support], Original ATen: [aten.mm]
        extern_kernels.mm(primals_2, primals_1, out=buf0)
        del primals_1
        buf1 = empty_strided_cuda((4, 2), (2, 1), torch.float32)
        # Topologically Sorted Source Nodes: [side_1], Original ATen: [aten.mm]
        extern_kernels.mm(primals_3, reinterpret_tensor(buf0, (4, 2), (4, 1), 0), out=buf1)
        buf2 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [output, output_1], Original ATen: [aten.cat, aten.add]
        stream0 = get_raw_stream(0)
        triton_poi_fused_add_cat_0.run(buf1, buf0, primals_4, buf2, 16, grid=grid(16), stream=stream0)
        del buf0
        del buf1
        del primals_4
    return (buf2, reinterpret_tensor(primals_3, (4, 4), (1, 4), 0), reinterpret_tensor(primals_2, (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, 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, 1), device='cuda:0', dtype=torch.float32)
    primals_4 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([primals_1, primals_2, primals_3, primals_4])
    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
from torch.nn.parameter import Parameter
from torch.nn import Parameter
import torch.nn
import torch.autograd


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

    """

    def __init__(self, in_features, out_features, bias=True):
        super(SimpleGCN, self).__init__()
        self.in_features = in_features
        self.out_features = out_features
        self.weight1 = Parameter(torch.Tensor(in_features, out_features))
        if bias:
            self.bias = Parameter(torch.Tensor(out_features))
        else:
            self.register_parameter('bias', None)
        self.reset_parameters()

    def reset_parameters(self):
        stdv = 6.0 / math.sqrt(self.weight1.size(1) + self.weight1.size(0))
        stdv *= 0.6
        self.weight1.data.uniform_(-stdv, stdv)
        if self.bias is not None:
            self.bias.data.uniform_(-0.1, 0.1)

    def forward(self, input, adj):
        support = torch.mm(input, self.weight1)
        side_len = max(support.shape[1] // 3, 2)
        if adj.type() == 'torch.cuda.sparse.FloatTensor':
            norm = torch.sparse.mm(adj, torch.ones((support.shape[0], 1)))
            normalized_support = support[:, :side_len] / norm
            side_1 = torch.sparse.mm(adj, normalized_support)
        else:
            side_1 = torch.mm(adj, support[:, :side_len])
        side_2 = support[:, side_len:]
        output = torch.cat((side_1, side_2), dim=1)
        if self.bias is not None:
            output = output + self.bias
        return output

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


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


def get_init_inputs():
    return [[], {'in_features': 4, 'out_features': 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
from torch.nn.parameter import Parameter
from torch.nn import Parameter
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_add_cat_0(in_ptr0, in_ptr1, in_ptr2, 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 % 4
    x1 = xindex // 4
    x2 = xindex
    tmp11 = tl.load(in_ptr2 + x0, xmask, eviction_policy='evict_last')
    tmp0 = x0
    tl.full([1], 0, tl.int64)
    tmp3 = tl.full([1], 2, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + (2 * x1 + x0), tmp4 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp6 = tmp0 >= tmp3
    tl.full([1], 4, tl.int64)
    tmp9 = tl.load(in_ptr1 + (2 + 4 * x1 + (-2 + x0)), tmp6 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp10 = tl.where(tmp4, tmp5, tmp9)
    tmp12 = tmp10 + tmp11
    tl.store(out_ptr0 + x2, tmp12, xmask)


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, 2), (2, 1), torch.float32)
        extern_kernels.mm(primals_3, reinterpret_tensor(buf0, (4, 2), (4, 1
            ), 0), out=buf1)
        buf2 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_add_cat_0[grid(16)](buf1, buf0, primals_4, buf2, 
            16, XBLOCK=16, num_warps=1, num_stages=1)
        del buf0
        del buf1
        del primals_4
    return buf2, reinterpret_tensor(primals_3, (4, 4), (1, 4), 0
        ), reinterpret_tensor(primals_2, (4, 4), (1, 4), 0)


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

    """

    def __init__(self, in_features, out_features, bias=True):
        super(SimpleGCNNew, self).__init__()
        self.in_features = in_features
        self.out_features = out_features
        self.weight1 = Parameter(torch.Tensor(in_features, out_features))
        if bias:
            self.bias = Parameter(torch.Tensor(out_features))
        else:
            self.register_parameter('bias', None)
        self.reset_parameters()

    def reset_parameters(self):
        stdv = 6.0 / math.sqrt(self.weight1.size(1) + self.weight1.size(0))
        stdv *= 0.6
        self.weight1.data.uniform_(-stdv, stdv)
        if self.bias is not None:
            self.bias.data.uniform_(-0.1, 0.1)

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

    def forward(self, input_0, input_1):
        primals_1 = self.weight1
        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]
