# 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_4/inductor_cache/37/c37syvo63vdmotuxpfmllazayh63ckflzom725nffg466fmtlhph.py
# Topologically Sorted Source Nodes: [x_out_1, mul_1, x_out_2, mul_2, x_out_3, mul_3, x_out_4, mul_4, x_out_5, mul_5, x_out_6, mul_6, x_out_7, mul_7, x_out_8, mul_8, x_out_9, mul_9, x_out_10, x_out_11, mul_10, x_out_12], Original ATen: [aten.add, aten.mul, aten.div]
# Source node to ATen node mapping:
#   mul_1 => mul_1
#   mul_10 => mul_10
#   mul_2 => mul_2
#   mul_3 => mul_3
#   mul_4 => mul_4
#   mul_5 => mul_5
#   mul_6 => mul_6
#   mul_7 => mul_7
#   mul_8 => mul_8
#   mul_9 => mul_9
#   x_out_1 => mul
#   x_out_10 => add_9
#   x_out_11 => div
#   x_out_12 => add_10
#   x_out_2 => add_1
#   x_out_3 => add_2
#   x_out_4 => add_3
#   x_out_5 => add_4
#   x_out_6 => add_5
#   x_out_7 => add_6
#   x_out_8 => add_7
#   x_out_9 => add_8
# Graph fragment:
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%mm_1, 0.9), kwargs = {})
#   %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%mm_2, 0.9), kwargs = {})
#   %add_1 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul, %mul_1), kwargs = {})
#   %mul_2 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%mm_3, 0.9), kwargs = {})
#   %add_2 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%add_1, %mul_2), kwargs = {})
#   %mul_3 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%mm_4, 0.9), kwargs = {})
#   %add_3 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%add_2, %mul_3), kwargs = {})
#   %mul_4 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%mm_5, 0.9), kwargs = {})
#   %add_4 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%add_3, %mul_4), kwargs = {})
#   %mul_5 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%mm_6, 0.9), kwargs = {})
#   %add_5 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%add_4, %mul_5), kwargs = {})
#   %mul_6 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%mm_7, 0.9), kwargs = {})
#   %add_6 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%add_5, %mul_6), kwargs = {})
#   %mul_7 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%mm_8, 0.9), kwargs = {})
#   %add_7 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%add_6, %mul_7), kwargs = {})
#   %mul_8 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%mm_9, 0.9), kwargs = {})
#   %add_8 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%add_7, %mul_8), kwargs = {})
#   %mul_9 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%mm_10, 0.9), kwargs = {})
#   %add_9 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%add_8, %mul_9), kwargs = {})
#   %div : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%add_9, 10), kwargs = {})
#   %mul_10 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%mm, 0.1), kwargs = {})
#   %add_10 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%div, %mul_10), kwargs = {})
triton_poi_fused_add_div_mul_0 = async_compile.triton('triton_poi_fused_add_div_mul_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: '*fp32', 5: '*fp32', 6: '*fp32', 7: '*fp32', 8: '*fp32', 9: '*fp32', 10: '*fp32', 11: '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, 5, 6, 7, 8, 9, 10, 11), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_add_div_mul_0', 'mutated_arg_names': ['in_out_ptr0', 'in_out_ptr1'], 'no_x_dim': False, 'num_load': 11, '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_div_mul_0(in_out_ptr0, in_out_ptr1, in_ptr0, in_ptr1, in_ptr2, in_ptr3, in_ptr4, in_ptr5, in_ptr6, in_ptr7, in_ptr8, xnumel, XBLOCK : tl.constexpr):
    xnumel = 16
    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)
    tmp3 = tl.load(in_ptr0 + (x0), xmask)
    tmp6 = tl.load(in_ptr1 + (x0), xmask)
    tmp9 = tl.load(in_ptr2 + (x0), xmask)
    tmp12 = tl.load(in_ptr3 + (x0), xmask)
    tmp15 = tl.load(in_ptr4 + (x0), xmask)
    tmp18 = tl.load(in_ptr5 + (x0), xmask)
    tmp21 = tl.load(in_ptr6 + (x0), xmask)
    tmp24 = tl.load(in_ptr7 + (x0), xmask)
    tmp27 = tl.load(in_ptr8 + (x0), xmask)
    tmp32 = tl.load(in_out_ptr1 + (x0), xmask)
    tmp1 = 0.9
    tmp2 = tmp0 * tmp1
    tmp4 = tmp3 * tmp1
    tmp5 = tmp2 + tmp4
    tmp7 = tmp6 * tmp1
    tmp8 = tmp5 + tmp7
    tmp10 = tmp9 * tmp1
    tmp11 = tmp8 + tmp10
    tmp13 = tmp12 * tmp1
    tmp14 = tmp11 + tmp13
    tmp16 = tmp15 * tmp1
    tmp17 = tmp14 + tmp16
    tmp19 = tmp18 * tmp1
    tmp20 = tmp17 + tmp19
    tmp22 = tmp21 * tmp1
    tmp23 = tmp20 + tmp22
    tmp25 = tmp24 * tmp1
    tmp26 = tmp23 + tmp25
    tmp28 = tmp27 * tmp1
    tmp29 = tmp26 + tmp28
    tmp30 = 0.1
    tmp31 = tmp29 * tmp30
    tmp33 = tmp32 * tmp30
    tmp34 = tmp31 + tmp33
    tl.store(in_out_ptr1 + (x0), tmp34, xmask)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    primals_1, primals_2, primals_3 = 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))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [x], Original ATen: [aten.mm]
        extern_kernels.mm(primals_2, reinterpret_tensor(primals_1, (4, 4), (1, 4), 0), out=buf0)
        del primals_1
        buf1 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [x_1], Original ATen: [aten.mm]
        extern_kernels.mm(primals_3, buf0, out=buf1)
        buf2 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [x_2], Original ATen: [aten.mm]
        extern_kernels.mm(primals_3, buf1, out=buf2)
        buf3 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [x_3], Original ATen: [aten.mm]
        extern_kernels.mm(primals_3, buf2, out=buf3)
        buf4 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [x_4], Original ATen: [aten.mm]
        extern_kernels.mm(primals_3, buf3, out=buf4)
        buf5 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [x_5], Original ATen: [aten.mm]
        extern_kernels.mm(primals_3, buf4, out=buf5)
        buf6 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [x_6], Original ATen: [aten.mm]
        extern_kernels.mm(primals_3, buf5, out=buf6)
        buf7 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [x_7], Original ATen: [aten.mm]
        extern_kernels.mm(primals_3, buf6, out=buf7)
        buf8 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [x_8], Original ATen: [aten.mm]
        extern_kernels.mm(primals_3, buf7, out=buf8)
        buf9 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [x_9], Original ATen: [aten.mm]
        extern_kernels.mm(primals_3, buf8, out=buf9)
        buf11 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [x_10], Original ATen: [aten.mm]
        extern_kernels.mm(primals_3, buf9, out=buf11)
        buf10 = buf1; del buf1  # reuse
        buf12 = buf0; del buf0  # reuse
        # Topologically Sorted Source Nodes: [x_out_1, mul_1, x_out_2, mul_2, x_out_3, mul_3, x_out_4, mul_4, x_out_5, mul_5, x_out_6, mul_6, x_out_7, mul_7, x_out_8, mul_8, x_out_9, mul_9, x_out_10, x_out_11, mul_10, x_out_12], Original ATen: [aten.add, aten.mul, aten.div]
        stream0 = get_raw_stream(0)
        triton_poi_fused_add_div_mul_0.run(buf10, buf12, buf2, buf3, buf4, buf5, buf6, buf7, buf8, buf9, buf11, 16, grid=grid(16), stream=stream0)
        del buf10
        del buf11
        del buf2
        del buf3
        del buf4
        del buf5
        del buf6
        del buf7
        del buf8
        del buf9
    return (buf12, primals_2, reinterpret_tensor(primals_3, (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)
    fn = lambda: call([primals_1, primals_2, primals_3])
    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 torch
import torch.nn as nn


class SpectralEigenConv(nn.Module):

    def __init__(self, in_features, out_features, bias=False, K=10, alpha=
        0.1, **kwargs):
        super().__init__()
        assert K > 0
        self.K = K
        self.alpha = alpha
        self.in_features = in_features
        self.out_features = out_features
        self.w = nn.Linear(in_features, out_features, bias=bias)

    def forward(self, x, U, V=None):
        """
        x: node attribute matrix
        if `V=None`:
            U: (N, N) adjacency matrix
        else:
            U: (N, k) eigenvector matrix
            V: (k,) eigenvalue
        """
        x = self.w(x)
        if V is not None:
            V_pow = torch.ones_like(V)
            V_out = torch.zeros_like(V)
            for _ in range(self.K):
                V_pow *= V
                V_out += (1 - self.alpha) * V_pow
            V_out = V_out / self.K
            x_out = U * V_out @ (U.t() @ x) + self.alpha * x
        else:
            adj = U
            x_in = x
            x_out = torch.zeros_like(x)
            for _ in range(self.K):
                x = torch.spmm(adj, x)
                x_out += (1 - self.alpha) * x
            x_out /= self.K
            x_out += self.alpha * x_in
        return x_out

    def reset_parameters(self):
        self.w.reset_parameters()

    def __repr__(self):
        return (
            f'{self.__class__.__name__}({self.in_features}, {self.out_features}, K={self.K}, alpha={self.alpha})'
            )


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 torch.nn as nn
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_div_mul_0(in_out_ptr0, in_out_ptr1, in_ptr0,
    in_ptr1, in_ptr2, in_ptr3, in_ptr4, in_ptr5, in_ptr6, in_ptr7, in_ptr8,
    xnumel, XBLOCK: tl.constexpr):
    xnumel = 16
    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)
    tmp3 = tl.load(in_ptr0 + x0, xmask)
    tmp6 = tl.load(in_ptr1 + x0, xmask)
    tmp9 = tl.load(in_ptr2 + x0, xmask)
    tmp12 = tl.load(in_ptr3 + x0, xmask)
    tmp15 = tl.load(in_ptr4 + x0, xmask)
    tmp18 = tl.load(in_ptr5 + x0, xmask)
    tmp21 = tl.load(in_ptr6 + x0, xmask)
    tmp24 = tl.load(in_ptr7 + x0, xmask)
    tmp27 = tl.load(in_ptr8 + x0, xmask)
    tmp32 = tl.load(in_out_ptr1 + x0, xmask)
    tmp1 = 0.9
    tmp2 = tmp0 * tmp1
    tmp4 = tmp3 * tmp1
    tmp5 = tmp2 + tmp4
    tmp7 = tmp6 * tmp1
    tmp8 = tmp5 + tmp7
    tmp10 = tmp9 * tmp1
    tmp11 = tmp8 + tmp10
    tmp13 = tmp12 * tmp1
    tmp14 = tmp11 + tmp13
    tmp16 = tmp15 * tmp1
    tmp17 = tmp14 + tmp16
    tmp19 = tmp18 * tmp1
    tmp20 = tmp17 + tmp19
    tmp22 = tmp21 * tmp1
    tmp23 = tmp20 + tmp22
    tmp25 = tmp24 * tmp1
    tmp26 = tmp23 + tmp25
    tmp28 = tmp27 * tmp1
    tmp29 = tmp26 + tmp28
    tmp30 = 0.1
    tmp31 = tmp29 * tmp30
    tmp33 = tmp32 * tmp30
    tmp34 = tmp31 + tmp33
    tl.store(in_out_ptr1 + x0, tmp34, xmask)


def call(args):
    primals_1, primals_2, primals_3 = 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))
    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, reinterpret_tensor(primals_1, (4, 4),
            (1, 4), 0), out=buf0)
        del primals_1
        buf1 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.mm(primals_3, buf0, out=buf1)
        buf2 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.mm(primals_3, buf1, out=buf2)
        buf3 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.mm(primals_3, buf2, out=buf3)
        buf4 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.mm(primals_3, buf3, out=buf4)
        buf5 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.mm(primals_3, buf4, out=buf5)
        buf6 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.mm(primals_3, buf5, out=buf6)
        buf7 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.mm(primals_3, buf6, out=buf7)
        buf8 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.mm(primals_3, buf7, out=buf8)
        buf9 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.mm(primals_3, buf8, out=buf9)
        buf11 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.mm(primals_3, buf9, out=buf11)
        buf10 = buf1
        del buf1
        buf12 = buf0
        del buf0
        get_raw_stream(0)
        triton_poi_fused_add_div_mul_0[grid(16)](buf10, buf12, buf2, buf3,
            buf4, buf5, buf6, buf7, buf8, buf9, buf11, 16, XBLOCK=16,
            num_warps=1, num_stages=1)
        del buf10
        del buf11
        del buf2
        del buf3
        del buf4
        del buf5
        del buf6
        del buf7
        del buf8
        del buf9
    return buf12, primals_2, reinterpret_tensor(primals_3, (4, 4), (1, 4), 0)


class SpectralEigenConvNew(nn.Module):

    def __init__(self, in_features, out_features, bias=False, K=10, alpha=
        0.1, **kwargs):
        super().__init__()
        assert K > 0
        self.K = K
        self.alpha = alpha
        self.in_features = in_features
        self.out_features = out_features
        self.w = nn.Linear(in_features, out_features, bias=bias)

    def reset_parameters(self):
        self.w.reset_parameters()

    def __repr__(self):
        return (
            f'{self.__class__.__name__}({self.in_features}, {self.out_features}, K={self.K}, alpha={self.alpha})'
            )

    def forward(self, input_0, input_1):
        primals_1 = self.w.weight
        primals_2 = input_0
        primals_3 = input_1
        output = call([primals_1, primals_2, primals_3])
        return output[0]
