# AOT ID: ['0_inference']
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/q5/cq5rthx627l3kn64h6pwzbnpc3ofak65jjjfg5pcra7sqcqgjfe5.py
# Topologically Sorted Source Nodes: [mul, mul_1, h], Original ATen: [aten.mul, aten.add]
# Source node to ATen node mapping:
#   h => add
#   mul => mul
#   mul_1 => mul_1
# Graph fragment:
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%mm, 0.9), kwargs = {})
#   %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%arg0_1, 0.1), kwargs = {})
#   %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul, %mul_1), kwargs = {})
triton_poi_fused_add_mul_0 = async_compile.triton('triton_poi_fused_add_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: '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_add_mul_0', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, '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_mul_0(in_out_ptr0, in_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 = tl.load(in_out_ptr0 + (x0), xmask)
    tmp3 = tl.load(in_ptr0 + (x0), xmask)
    tmp1 = 0.9
    tmp2 = tmp0 * tmp1
    tmp4 = 0.1
    tmp5 = tmp3 * tmp4
    tmp6 = tmp2 + tmp5
    tl.store(in_out_ptr0 + (x0), tmp6, xmask)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    arg0_1, arg1_1 = args
    args.clear()
    assert_size_stride(arg0_1, (4, 4), (4, 1))
    assert_size_stride(arg1_1, (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: [mm], Original ATen: [aten.mm]
        extern_kernels.mm(arg1_1, arg0_1, out=buf0)
        buf1 = buf0; del buf0  # reuse
        # Topologically Sorted Source Nodes: [mul, mul_1, h], Original ATen: [aten.mul, aten.add]
        stream0 = get_raw_stream(0)
        triton_poi_fused_add_mul_0.run(buf1, arg0_1, 16, grid=grid(16), stream=stream0)
        buf2 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [mul, mul_1, h, mm_1], Original ATen: [aten.mul, aten.add, aten.mm]
        extern_kernels.mm(arg1_1, buf1, out=buf2)
        buf3 = buf2; del buf2  # reuse
        # Topologically Sorted Source Nodes: [mul_2, mul_3, h_1], Original ATen: [aten.mul, aten.add]
        triton_poi_fused_add_mul_0.run(buf3, arg0_1, 16, grid=grid(16), stream=stream0)
        buf4 = buf1; del buf1  # reuse
        # Topologically Sorted Source Nodes: [mul_2, mul_3, h_1, mm_2], Original ATen: [aten.mul, aten.add, aten.mm]
        extern_kernels.mm(arg1_1, buf3, out=buf4)
        buf5 = buf4; del buf4  # reuse
        # Topologically Sorted Source Nodes: [mul_4, mul_5, h_2], Original ATen: [aten.mul, aten.add]
        triton_poi_fused_add_mul_0.run(buf5, arg0_1, 16, grid=grid(16), stream=stream0)
        buf6 = buf3; del buf3  # reuse
        # Topologically Sorted Source Nodes: [mul_4, mul_5, h_2, mm_3], Original ATen: [aten.mul, aten.add, aten.mm]
        extern_kernels.mm(arg1_1, buf5, out=buf6)
        buf7 = buf6; del buf6  # reuse
        # Topologically Sorted Source Nodes: [mul_6, mul_7, h_3], Original ATen: [aten.mul, aten.add]
        triton_poi_fused_add_mul_0.run(buf7, arg0_1, 16, grid=grid(16), stream=stream0)
        buf8 = buf5; del buf5  # reuse
        # Topologically Sorted Source Nodes: [mul_6, mul_7, h_3, mm_4], Original ATen: [aten.mul, aten.add, aten.mm]
        extern_kernels.mm(arg1_1, buf7, out=buf8)
        buf9 = buf8; del buf8  # reuse
        # Topologically Sorted Source Nodes: [mul_8, mul_9, h_4], Original ATen: [aten.mul, aten.add]
        triton_poi_fused_add_mul_0.run(buf9, arg0_1, 16, grid=grid(16), stream=stream0)
        buf10 = buf7; del buf7  # reuse
        # Topologically Sorted Source Nodes: [mul_8, mul_9, h_4, mm_5], Original ATen: [aten.mul, aten.add, aten.mm]
        extern_kernels.mm(arg1_1, buf9, out=buf10)
        buf11 = buf10; del buf10  # reuse
        # Topologically Sorted Source Nodes: [mul_10, mul_11, h_5], Original ATen: [aten.mul, aten.add]
        triton_poi_fused_add_mul_0.run(buf11, arg0_1, 16, grid=grid(16), stream=stream0)
        buf12 = buf9; del buf9  # reuse
        # Topologically Sorted Source Nodes: [mul_10, mul_11, h_5, mm_6], Original ATen: [aten.mul, aten.add, aten.mm]
        extern_kernels.mm(arg1_1, buf11, out=buf12)
        buf13 = buf12; del buf12  # reuse
        # Topologically Sorted Source Nodes: [mul_12, mul_13, h_6], Original ATen: [aten.mul, aten.add]
        triton_poi_fused_add_mul_0.run(buf13, arg0_1, 16, grid=grid(16), stream=stream0)
        buf14 = buf11; del buf11  # reuse
        # Topologically Sorted Source Nodes: [mul_12, mul_13, h_6, mm_7], Original ATen: [aten.mul, aten.add, aten.mm]
        extern_kernels.mm(arg1_1, buf13, out=buf14)
        buf15 = buf14; del buf14  # reuse
        # Topologically Sorted Source Nodes: [mul_14, mul_15, h_7], Original ATen: [aten.mul, aten.add]
        triton_poi_fused_add_mul_0.run(buf15, arg0_1, 16, grid=grid(16), stream=stream0)
        buf16 = buf13; del buf13  # reuse
        # Topologically Sorted Source Nodes: [mul_14, mul_15, h_7, mm_8], Original ATen: [aten.mul, aten.add, aten.mm]
        extern_kernels.mm(arg1_1, buf15, out=buf16)
        buf17 = buf16; del buf16  # reuse
        # Topologically Sorted Source Nodes: [mul_16, mul_17, h_8], Original ATen: [aten.mul, aten.add]
        triton_poi_fused_add_mul_0.run(buf17, arg0_1, 16, grid=grid(16), stream=stream0)
        buf18 = buf15; del buf15  # reuse
        # Topologically Sorted Source Nodes: [mul_16, mul_17, h_8, mm_9], Original ATen: [aten.mul, aten.add, aten.mm]
        extern_kernels.mm(arg1_1, buf17, out=buf18)
        del arg1_1
        del buf17
        buf19 = buf18; del buf18  # reuse
        # Topologically Sorted Source Nodes: [mul_18, mul_19, h_9], Original ATen: [aten.mul, aten.add]
        triton_poi_fused_add_mul_0.run(buf19, arg0_1, 16, grid=grid(16), stream=stream0)
        del arg0_1
    return (buf19, )


def benchmark_compiled_module(times=10, repeat=10):
    from torch._dynamo.testing import rand_strided
    from torch._inductor.utils import print_performance
    arg0_1 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    arg1_1 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([arg0_1, arg1_1])
    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.functional as F
import torch.nn as nn


class SparseDropout(nn.Module):

    def __init__(self, p=0.5):
        super().__init__()
        self.p = p

    def forward(self, x):
        if not self.training:
            return x
        x_coal = x.coalesce()
        drop_val = F.dropout(x_coal._values(), self.p, self.training)
        return torch.sparse.FloatTensor(x_coal._indices(), drop_val, x.shape)


class MixedDropout(nn.Module):

    def __init__(self, p=0.5):
        super().__init__()
        self.dense_dropout = nn.Dropout(p)
        self.sparse_dropout = SparseDropout(p)

    def forward(self, x):
        if x.is_sparse:
            return self.sparse_dropout(x)
        else:
            return self.dense_dropout(x)


class APPNProp(nn.Module):

    def __init__(self, alpha: 'float'=0.1, K: 'int'=10, dropout: 'float'=0.0):
        super().__init__()
        self.alpha = alpha
        self.K = K
        if not dropout:
            self.dropout = lambda x: x
        else:
            self.dropout = MixedDropout(dropout)

    def forward(self, x, adj):
        h = x
        for _ in range(self.K):
            A_drop = self.dropout(adj)
            h = (1 - self.alpha) * A_drop.mm(h) + self.alpha * x
        return h

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


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


def get_init_inputs():
    return [[], {}]

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.functional as F
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


@triton.jit
def triton_poi_fused_add_mul_0(in_out_ptr0, in_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 = tl.load(in_out_ptr0 + x0, xmask)
    tmp3 = tl.load(in_ptr0 + x0, xmask)
    tmp1 = 0.9
    tmp2 = tmp0 * tmp1
    tmp4 = 0.1
    tmp5 = tmp3 * tmp4
    tmp6 = tmp2 + tmp5
    tl.store(in_out_ptr0 + x0, tmp6, xmask)


def call(args):
    arg0_1, arg1_1 = args
    args.clear()
    assert_size_stride(arg0_1, (4, 4), (4, 1))
    assert_size_stride(arg1_1, (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(arg1_1, arg0_1, out=buf0)
        buf1 = buf0
        del buf0
        get_raw_stream(0)
        triton_poi_fused_add_mul_0[grid(16)](buf1, arg0_1, 16, XBLOCK=16,
            num_warps=1, num_stages=1)
        buf2 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.mm(arg1_1, buf1, out=buf2)
        buf3 = buf2
        del buf2
        triton_poi_fused_add_mul_0[grid(16)](buf3, arg0_1, 16, XBLOCK=16,
            num_warps=1, num_stages=1)
        buf4 = buf1
        del buf1
        extern_kernels.mm(arg1_1, buf3, out=buf4)
        buf5 = buf4
        del buf4
        triton_poi_fused_add_mul_0[grid(16)](buf5, arg0_1, 16, XBLOCK=16,
            num_warps=1, num_stages=1)
        buf6 = buf3
        del buf3
        extern_kernels.mm(arg1_1, buf5, out=buf6)
        buf7 = buf6
        del buf6
        triton_poi_fused_add_mul_0[grid(16)](buf7, arg0_1, 16, XBLOCK=16,
            num_warps=1, num_stages=1)
        buf8 = buf5
        del buf5
        extern_kernels.mm(arg1_1, buf7, out=buf8)
        buf9 = buf8
        del buf8
        triton_poi_fused_add_mul_0[grid(16)](buf9, arg0_1, 16, XBLOCK=16,
            num_warps=1, num_stages=1)
        buf10 = buf7
        del buf7
        extern_kernels.mm(arg1_1, buf9, out=buf10)
        buf11 = buf10
        del buf10
        triton_poi_fused_add_mul_0[grid(16)](buf11, arg0_1, 16, XBLOCK=16,
            num_warps=1, num_stages=1)
        buf12 = buf9
        del buf9
        extern_kernels.mm(arg1_1, buf11, out=buf12)
        buf13 = buf12
        del buf12
        triton_poi_fused_add_mul_0[grid(16)](buf13, arg0_1, 16, XBLOCK=16,
            num_warps=1, num_stages=1)
        buf14 = buf11
        del buf11
        extern_kernels.mm(arg1_1, buf13, out=buf14)
        buf15 = buf14
        del buf14
        triton_poi_fused_add_mul_0[grid(16)](buf15, arg0_1, 16, XBLOCK=16,
            num_warps=1, num_stages=1)
        buf16 = buf13
        del buf13
        extern_kernels.mm(arg1_1, buf15, out=buf16)
        buf17 = buf16
        del buf16
        triton_poi_fused_add_mul_0[grid(16)](buf17, arg0_1, 16, XBLOCK=16,
            num_warps=1, num_stages=1)
        buf18 = buf15
        del buf15
        extern_kernels.mm(arg1_1, buf17, out=buf18)
        del arg1_1
        del buf17
        buf19 = buf18
        del buf18
        triton_poi_fused_add_mul_0[grid(16)](buf19, arg0_1, 16, XBLOCK=16,
            num_warps=1, num_stages=1)
        del arg0_1
    return buf19,


class SparseDropout(nn.Module):

    def __init__(self, p=0.5):
        super().__init__()
        self.p = p

    def forward(self, x):
        if not self.training:
            return x
        x_coal = x.coalesce()
        drop_val = F.dropout(x_coal._values(), self.p, self.training)
        return torch.sparse.FloatTensor(x_coal._indices(), drop_val, x.shape)


class MixedDropout(nn.Module):

    def __init__(self, p=0.5):
        super().__init__()
        self.dense_dropout = nn.Dropout(p)
        self.sparse_dropout = SparseDropout(p)

    def forward(self, x):
        if x.is_sparse:
            return self.sparse_dropout(x)
        else:
            return self.dense_dropout(x)


class APPNPropNew(nn.Module):

    def __init__(self, alpha: 'float'=0.1, K: 'int'=10, dropout: 'float'=0.0):
        super().__init__()
        self.alpha = alpha
        self.K = K
        if not dropout:
            self.dropout = lambda x: x
        else:
            self.dropout = MixedDropout(dropout)

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

    def forward(self, input_0, input_1):
        arg0_1 = input_0
        arg1_1 = input_1
        output = call([arg0_1, arg1_1])
        return output[0]
