# 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_1/inductor_cache/w4/cw4n4pbxxbljnrd4yatv6v33qk5dxjyk3ms7gaxvcbxolxuor5ri.py
# Topologically Sorted Source Nodes: [s, neg, log, mul, sub, log_1, mul_1, mask_ent, mean, sum_1, mul_2, penalty], Original ATen: [aten.sigmoid, aten.neg, aten.log, aten.mul, aten.rsub, aten.sub, aten.mean, aten.sum, aten.add]
# Source node to ATen node mapping:
#   log => log
#   log_1 => log_1
#   mask_ent => sub_2
#   mean => mean
#   mul => mul
#   mul_1 => mul_1
#   mul_2 => mul_2
#   neg => neg
#   penalty => add
#   s => sigmoid
#   sub => sub
#   sum_1 => sum_1
# Graph fragment:
#   %sigmoid : [num_users=5] = call_function[target=torch.ops.aten.sigmoid.default](args = (%primals_1,), kwargs = {})
#   %neg : [num_users=1] = call_function[target=torch.ops.aten.neg.default](args = (%sigmoid,), kwargs = {})
#   %log : [num_users=1] = call_function[target=torch.ops.aten.log.default](args = (%sigmoid,), kwargs = {})
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%neg, %log), kwargs = {})
#   %sub : [num_users=2] = call_function[target=torch.ops.aten.sub.Tensor](args = (1, %sigmoid), kwargs = {})
#   %log_1 : [num_users=1] = call_function[target=torch.ops.aten.log.default](args = (%sub,), kwargs = {})
#   %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sub, %log_1), kwargs = {})
#   %sub_2 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%mul, %mul_1), kwargs = {})
#   %mean : [num_users=1] = call_function[target=torch.ops.aten.mean.default](args = (%sub_2,), kwargs = {})
#   %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.default](args = (%sigmoid,), kwargs = {})
#   %mul_2 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sum_1, 0.5), kwargs = {})
#   %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mean, %mul_2), kwargs = {})
triton_per_fused_add_log_mean_mul_neg_rsub_sigmoid_sub_sum_0 = async_compile.triton('triton_per_fused_add_log_mean_mul_neg_rsub_sigmoid_sub_sum_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.persistent_reduction(
    size_hints=[1, 4],
    reduction_hint=ReductionHint.INNER,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: 'i32', 4: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {3: 1}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=(3,))]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused_add_log_mean_mul_neg_rsub_sigmoid_sub_sum_0', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 2, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', '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}
)
@triton.jit
def triton_per_fused_add_log_mean_mul_neg_rsub_sigmoid_sub_sum_0(in_out_ptr0, in_ptr0, out_ptr0, xnumel, rnumel, XBLOCK : tl.constexpr):
    xnumel = 1
    rnumel = 4
    RBLOCK: tl.constexpr = 4
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = tl.full([XBLOCK, RBLOCK], True, tl.int1)
    rindex = tl.arange(0, RBLOCK)[None, :]
    roffset = 0
    rmask = tl.full([XBLOCK, RBLOCK], True, tl.int1)
    r0 = rindex
    tmp0 = tl.load(in_ptr0 + (r0), None)
    tmp1 = tl.sigmoid(tmp0)
    tmp2 = -tmp1
    tmp3 = tl_math.log(tmp1)
    tmp4 = tmp2 * tmp3
    tmp5 = 1.0
    tmp6 = tmp5 - tmp1
    tmp7 = tl_math.log(tmp6)
    tmp8 = tmp6 * tmp7
    tmp9 = tmp4 - tmp8
    tmp10 = tl.broadcast_to(tmp9, [XBLOCK, RBLOCK])
    tmp12 = tl.sum(tmp10, 1)[:, None]
    tmp13 = tl.broadcast_to(tmp1, [XBLOCK, RBLOCK])
    tmp15 = tl.sum(tmp13, 1)[:, None]
    tmp16 = 4.0
    tmp17 = tmp12 / tmp16
    tmp18 = 0.5
    tmp19 = tmp15 * tmp18
    tmp20 = tmp17 + tmp19
    tl.store(out_ptr0 + (tl.broadcast_to(r0, [XBLOCK, RBLOCK])), tmp1, None)
    tl.debug_barrier()
    tl.store(in_out_ptr0 + (tl.full([XBLOCK, 1], 0, tl.int32)), tmp20, None)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    primals_1, = args
    args.clear()
    assert_size_stride(primals_1, (1, 4), (4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((1, 4), (4, 1), torch.float32)
        buf1 = empty_strided_cuda((), (), torch.float32)
        buf3 = buf1; del buf1  # reuse
        # Topologically Sorted Source Nodes: [s, neg, log, mul, sub, log_1, mul_1, mask_ent, mean, sum_1, mul_2, penalty], Original ATen: [aten.sigmoid, aten.neg, aten.log, aten.mul, aten.rsub, aten.sub, aten.mean, aten.sum, aten.add]
        stream0 = get_raw_stream(0)
        triton_per_fused_add_log_mean_mul_neg_rsub_sigmoid_sub_sum_0.run(buf3, primals_1, buf0, 1, 4, grid=grid(1), stream=stream0)
        del primals_1
    return (buf0, buf3, buf0, )


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((1, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([primals_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 math
import torch


class AbstractTorchModule(torch.nn.Module):

    def __init__(self):
        torch.nn.Module.__init__(self)

    def save(self, path):
        None
        torch.save(self.state_dict(), path)

    def load(self, path):
        None
        self.load_state_dict(torch.load(path, map_location=self.device))

    def set_device(self, device):
        self.device = device
        self


class GNNExplainerProbe(AbstractTorchModule):

    def __init__(self, num_edges, num_layers, init_strategy='normal',
        const_val=1.0):
        super(GNNExplainerProbe, self).__init__()
        mask = torch.empty((num_layers, num_edges))
        if init_strategy == 'normal':
            std = torch.nn.init.calculate_gain('relu') * math.sqrt(2.0 / (2 *
                math.sqrt(num_edges)))
            with torch.no_grad():
                mask.normal_(1.0, std)
        elif init_strategy == 'const':
            torch.nn.init.constant_(mask, const_val)
        self.mask = torch.nn.Parameter(mask)

    def forward(self):
        s = torch.sigmoid(self.mask)
        mask_ent = -s * torch.log(s) - (1 - s) * torch.log(1 - s)
        penalty = mask_ent.mean() + 0.5 * s.sum()
        return s, penalty


def get_inputs():
    return []


def get_init_inputs():
    return [[], {'num_edges': 4, 'num_layers': 1}]

import torch
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
from torch._inductor.runtime.triton_helpers import math as tl_math
import math
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda


@triton.jit
def triton_per_fused_add_log_mean_mul_neg_rsub_sigmoid_sub_sum_0(in_out_ptr0,
    in_ptr0, out_ptr0, xnumel, rnumel, XBLOCK: tl.constexpr):
    RBLOCK: tl.constexpr = 4
    xoffset = tl.program_id(0) * XBLOCK
    xoffset + tl.arange(0, XBLOCK)[:, None]
    tl.full([XBLOCK, RBLOCK], True, tl.int1)
    rindex = tl.arange(0, RBLOCK)[None, :]
    tl.full([XBLOCK, RBLOCK], True, tl.int1)
    r0 = rindex
    tmp0 = tl.load(in_ptr0 + r0, None)
    tmp1 = tl.sigmoid(tmp0)
    tmp2 = -tmp1
    tmp3 = tl_math.log(tmp1)
    tmp4 = tmp2 * tmp3
    tmp5 = 1.0
    tmp6 = tmp5 - tmp1
    tmp7 = tl_math.log(tmp6)
    tmp8 = tmp6 * tmp7
    tmp9 = tmp4 - tmp8
    tmp10 = tl.broadcast_to(tmp9, [XBLOCK, RBLOCK])
    tmp12 = tl.sum(tmp10, 1)[:, None]
    tmp13 = tl.broadcast_to(tmp1, [XBLOCK, RBLOCK])
    tmp15 = tl.sum(tmp13, 1)[:, None]
    tmp16 = 4.0
    tmp17 = tmp12 / tmp16
    tmp18 = 0.5
    tmp19 = tmp15 * tmp18
    tmp20 = tmp17 + tmp19
    tl.store(out_ptr0 + tl.broadcast_to(r0, [XBLOCK, RBLOCK]), tmp1, None)
    tl.debug_barrier()
    tl.store(in_out_ptr0 + tl.full([XBLOCK, 1], 0, tl.int32), tmp20, None)


def call(args):
    primals_1, = args
    args.clear()
    assert_size_stride(primals_1, (1, 4), (4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((1, 4), (4, 1), torch.float32)
        buf1 = empty_strided_cuda((), (), torch.float32)
        buf3 = buf1
        del buf1
        get_raw_stream(0)
        triton_per_fused_add_log_mean_mul_neg_rsub_sigmoid_sub_sum_0[grid(1)](
            buf3, primals_1, buf0, 1, 4, XBLOCK=1, num_warps=2, num_stages=1)
        del primals_1
    return buf0, buf3, buf0


class AbstractTorchModule(torch.nn.Module):

    def __init__(self):
        torch.nn.Module.__init__(self)

    def save(self, path):
        None
        torch.save(self.state_dict(), path)

    def load(self, path):
        None
        self.load_state_dict(torch.load(path, map_location=self.device))

    def set_device(self, device):
        self.device = device
        self


class GNNExplainerProbeNew(AbstractTorchModule):

    def __init__(self, num_edges, num_layers, init_strategy='normal',
        const_val=1.0):
        super(GNNExplainerProbeNew, self).__init__()
        mask = torch.empty((num_layers, num_edges))
        if init_strategy == 'normal':
            std = torch.nn.init.calculate_gain('relu') * math.sqrt(2.0 / (2 *
                math.sqrt(num_edges)))
            with torch.no_grad():
                mask.normal_(1.0, std)
        elif init_strategy == 'const':
            torch.nn.init.constant_(mask, const_val)
        self.mask = torch.nn.Parameter(mask)

    def forward(self):
        primals_1 = self.mask
        output = call([primals_1])
        return output[0], output[1]
