# 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/4d/c4dd33o7csaipr5j7mpc75mdyfwkdwifxk5nhggvn2k3fzu7jxgp.py
# Topologically Sorted Source Nodes: [mul, add, pow_1, sub, exp, pow_2, sub_1, sum_1, mean, kl_node, mul_2, add_1, pow_3, sub_2, exp_1, pow_4, sub_3, sum_2, mean_1, kl_edge, kl], Original ATen: [aten.mul, aten.add, aten.pow, aten.sub, aten.exp, aten.sum, aten.mean]
# Source node to ATen node mapping:
#   add => add
#   add_1 => add_1
#   exp => exp
#   exp_1 => exp_1
#   kl => add_2
#   kl_edge => mul_3
#   kl_node => mul_1
#   mean => mean
#   mean_1 => mean_1
#   mul => mul
#   mul_2 => mul_2
#   pow_1 => pow_1
#   pow_2 => pow_2
#   pow_3 => pow_3
#   pow_4 => pow_4
#   sub => sub
#   sub_1 => sub_1
#   sub_2 => sub_2
#   sub_3 => sub_3
#   sum_1 => sum_1
#   sum_2 => sum_2
# Graph fragment:
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%arg0_1, 2), kwargs = {})
#   %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul, 1), kwargs = {})
#   %pow_1 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%arg1_1, 2), kwargs = {})
#   %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%add, %pow_1), kwargs = {})
#   %exp : [num_users=1] = call_function[target=torch.ops.aten.exp.default](args = (%arg0_1,), kwargs = {})
#   %pow_2 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%exp, 2), kwargs = {})
#   %sub_1 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%sub, %pow_2), kwargs = {})
#   %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%sub_1, [1]), kwargs = {})
#   %mean : [num_users=1] = call_function[target=torch.ops.aten.mean.default](args = (%sum_1,), kwargs = {})
#   %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%mean, -0.125), kwargs = {})
#   %mul_2 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%arg2_1, 2), kwargs = {})
#   %add_1 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul_2, 1), kwargs = {})
#   %pow_3 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%arg3_1, 2), kwargs = {})
#   %sub_2 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%add_1, %pow_3), kwargs = {})
#   %exp_1 : [num_users=1] = call_function[target=torch.ops.aten.exp.default](args = (%arg2_1,), kwargs = {})
#   %pow_4 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%exp_1, 2), kwargs = {})
#   %sub_3 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%sub_2, %pow_4), kwargs = {})
#   %sum_2 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%sub_3, [1]), kwargs = {})
#   %mean_1 : [num_users=1] = call_function[target=torch.ops.aten.mean.default](args = (%sum_2,), kwargs = {})
#   %mul_3 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%mean_1, -0.125), kwargs = {})
#   %add_2 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul_1, %mul_3), kwargs = {})
triton_per_fused_add_exp_mean_mul_pow_sub_sum_0 = async_compile.triton('triton_per_fused_add_exp_mean_mul_pow_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, 64],
    reduction_hint=ReductionHint.INNER,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: '*fp32', 5: 'i32', 6: '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': {5: 1}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4, 6), equal_to_1=(5,))]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused_add_exp_mean_mul_pow_sub_sum_0', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 16, 'num_reduction': 2, '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}
)
@triton.jit
def triton_per_fused_add_exp_mean_mul_pow_sub_sum_0(in_out_ptr0, in_ptr0, in_ptr1, in_ptr2, in_ptr3, xnumel, rnumel, XBLOCK : tl.constexpr):
    xnumel = 1
    rnumel = 64
    RBLOCK: tl.constexpr = 64
    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 % 16
    r1 = (rindex // 16)
    r2 = rindex
    tmp0 = tl.load(in_ptr0 + (r0 + (64*r1)), None)
    tmp5 = tl.load(in_ptr1 + (r0 + (64*r1)), None)
    tmp11 = tl.load(in_ptr0 + (16 + r0 + (64*r1)), None)
    tmp14 = tl.load(in_ptr1 + (16 + r0 + (64*r1)), None)
    tmp21 = tl.load(in_ptr0 + (32 + r0 + (64*r1)), None)
    tmp24 = tl.load(in_ptr1 + (32 + r0 + (64*r1)), None)
    tmp31 = tl.load(in_ptr0 + (48 + r0 + (64*r1)), None)
    tmp34 = tl.load(in_ptr1 + (48 + r0 + (64*r1)), None)
    tmp44 = tl.load(in_ptr2 + (r0 + (64*r1)), None)
    tmp47 = tl.load(in_ptr3 + (r0 + (64*r1)), None)
    tmp53 = tl.load(in_ptr2 + (16 + r0 + (64*r1)), None)
    tmp56 = tl.load(in_ptr3 + (16 + r0 + (64*r1)), None)
    tmp63 = tl.load(in_ptr2 + (32 + r0 + (64*r1)), None)
    tmp66 = tl.load(in_ptr3 + (32 + r0 + (64*r1)), None)
    tmp73 = tl.load(in_ptr2 + (48 + r0 + (64*r1)), None)
    tmp76 = tl.load(in_ptr3 + (48 + r0 + (64*r1)), None)
    tmp1 = 2.0
    tmp2 = tmp0 * tmp1
    tmp3 = 1.0
    tmp4 = tmp2 + tmp3
    tmp6 = tmp5 * tmp5
    tmp7 = tmp4 - tmp6
    tmp8 = tl_math.exp(tmp0)
    tmp9 = tmp8 * tmp8
    tmp10 = tmp7 - tmp9
    tmp12 = tmp11 * tmp1
    tmp13 = tmp12 + tmp3
    tmp15 = tmp14 * tmp14
    tmp16 = tmp13 - tmp15
    tmp17 = tl_math.exp(tmp11)
    tmp18 = tmp17 * tmp17
    tmp19 = tmp16 - tmp18
    tmp20 = tmp10 + tmp19
    tmp22 = tmp21 * tmp1
    tmp23 = tmp22 + tmp3
    tmp25 = tmp24 * tmp24
    tmp26 = tmp23 - tmp25
    tmp27 = tl_math.exp(tmp21)
    tmp28 = tmp27 * tmp27
    tmp29 = tmp26 - tmp28
    tmp30 = tmp20 + tmp29
    tmp32 = tmp31 * tmp1
    tmp33 = tmp32 + tmp3
    tmp35 = tmp34 * tmp34
    tmp36 = tmp33 - tmp35
    tmp37 = tl_math.exp(tmp31)
    tmp38 = tmp37 * tmp37
    tmp39 = tmp36 - tmp38
    tmp40 = tmp30 + tmp39
    tmp41 = tl.broadcast_to(tmp40, [XBLOCK, RBLOCK])
    tmp43 = tl.sum(tmp41, 1)[:, None]
    tmp45 = tmp44 * tmp1
    tmp46 = tmp45 + tmp3
    tmp48 = tmp47 * tmp47
    tmp49 = tmp46 - tmp48
    tmp50 = tl_math.exp(tmp44)
    tmp51 = tmp50 * tmp50
    tmp52 = tmp49 - tmp51
    tmp54 = tmp53 * tmp1
    tmp55 = tmp54 + tmp3
    tmp57 = tmp56 * tmp56
    tmp58 = tmp55 - tmp57
    tmp59 = tl_math.exp(tmp53)
    tmp60 = tmp59 * tmp59
    tmp61 = tmp58 - tmp60
    tmp62 = tmp52 + tmp61
    tmp64 = tmp63 * tmp1
    tmp65 = tmp64 + tmp3
    tmp67 = tmp66 * tmp66
    tmp68 = tmp65 - tmp67
    tmp69 = tl_math.exp(tmp63)
    tmp70 = tmp69 * tmp69
    tmp71 = tmp68 - tmp70
    tmp72 = tmp62 + tmp71
    tmp74 = tmp73 * tmp1
    tmp75 = tmp74 + tmp3
    tmp77 = tmp76 * tmp76
    tmp78 = tmp75 - tmp77
    tmp79 = tl_math.exp(tmp73)
    tmp80 = tmp79 * tmp79
    tmp81 = tmp78 - tmp80
    tmp82 = tmp72 + tmp81
    tmp83 = tl.broadcast_to(tmp82, [XBLOCK, RBLOCK])
    tmp85 = tl.sum(tmp83, 1)[:, None]
    tmp86 = 64.0
    tmp87 = tmp43 / tmp86
    tmp88 = -0.125
    tmp89 = tmp87 * tmp88
    tmp90 = tmp85 / tmp86
    tmp91 = tmp90 * tmp88
    tmp92 = tmp89 + tmp91
    tl.debug_barrier()
    tl.store(in_out_ptr0 + (tl.full([XBLOCK, 1], 0, tl.int32)), tmp92, None)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    arg0_1, arg1_1, arg2_1, arg3_1 = args
    args.clear()
    assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(arg1_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(arg2_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(arg3_1, (4, 4, 4, 4), (64, 16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf1 = empty_strided_cuda((), (), torch.float32)
        buf4 = buf1; del buf1  # reuse
        # Topologically Sorted Source Nodes: [mul, add, pow_1, sub, exp, pow_2, sub_1, sum_1, mean, kl_node, mul_2, add_1, pow_3, sub_2, exp_1, pow_4, sub_3, sum_2, mean_1, kl_edge, kl], Original ATen: [aten.mul, aten.add, aten.pow, aten.sub, aten.exp, aten.sum, aten.mean]
        stream0 = get_raw_stream(0)
        triton_per_fused_add_exp_mean_mul_pow_sub_sum_0.run(buf4, arg0_1, arg1_1, arg2_1, arg3_1, 1, 64, grid=grid(1), stream=stream0)
        del arg0_1
        del arg1_1
        del arg2_1
        del arg3_1
    return (buf4, )


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, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    arg1_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    arg2_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    arg3_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([arg0_1, arg1_1, arg2_1, arg3_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)

from torch.nn import Module
import torch
from torch.nn.modules.module import Module


class kl_loss(Module):

    def __init__(self, num_nodes, num_edges):
        super(kl_loss, self).__init__()
        self.num_nodes = num_nodes
        self.num_edges = num_edges

    def forward(self, z_node_log_std, z_node_mean, z_edge_log_std, z_edge_mean
        ):
        kl_node = -(0.5 / self.num_nodes) * torch.mean(torch.sum(1 + 2 *
            z_node_log_std - torch.pow(z_node_mean, 2) - torch.pow(torch.
            exp(z_node_log_std), 2), 1))
        kl_edge = -(0.5 / self.num_edges) * torch.mean(torch.sum(1 + 2 *
            z_edge_log_std - torch.pow(z_edge_mean, 2) - torch.pow(torch.
            exp(z_edge_log_std), 2), 1))
        kl = kl_node + kl_edge
        return kl


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


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

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
from torch.nn import Module
from torch.nn.modules.module import Module
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_exp_mean_mul_pow_sub_sum_0(in_out_ptr0, in_ptr0,
    in_ptr1, in_ptr2, in_ptr3, xnumel, rnumel, XBLOCK: tl.constexpr):
    RBLOCK: tl.constexpr = 64
    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 % 16
    r1 = rindex // 16
    tmp0 = tl.load(in_ptr0 + (r0 + 64 * r1), None)
    tmp5 = tl.load(in_ptr1 + (r0 + 64 * r1), None)
    tmp11 = tl.load(in_ptr0 + (16 + r0 + 64 * r1), None)
    tmp14 = tl.load(in_ptr1 + (16 + r0 + 64 * r1), None)
    tmp21 = tl.load(in_ptr0 + (32 + r0 + 64 * r1), None)
    tmp24 = tl.load(in_ptr1 + (32 + r0 + 64 * r1), None)
    tmp31 = tl.load(in_ptr0 + (48 + r0 + 64 * r1), None)
    tmp34 = tl.load(in_ptr1 + (48 + r0 + 64 * r1), None)
    tmp44 = tl.load(in_ptr2 + (r0 + 64 * r1), None)
    tmp47 = tl.load(in_ptr3 + (r0 + 64 * r1), None)
    tmp53 = tl.load(in_ptr2 + (16 + r0 + 64 * r1), None)
    tmp56 = tl.load(in_ptr3 + (16 + r0 + 64 * r1), None)
    tmp63 = tl.load(in_ptr2 + (32 + r0 + 64 * r1), None)
    tmp66 = tl.load(in_ptr3 + (32 + r0 + 64 * r1), None)
    tmp73 = tl.load(in_ptr2 + (48 + r0 + 64 * r1), None)
    tmp76 = tl.load(in_ptr3 + (48 + r0 + 64 * r1), None)
    tmp1 = 2.0
    tmp2 = tmp0 * tmp1
    tmp3 = 1.0
    tmp4 = tmp2 + tmp3
    tmp6 = tmp5 * tmp5
    tmp7 = tmp4 - tmp6
    tmp8 = tl_math.exp(tmp0)
    tmp9 = tmp8 * tmp8
    tmp10 = tmp7 - tmp9
    tmp12 = tmp11 * tmp1
    tmp13 = tmp12 + tmp3
    tmp15 = tmp14 * tmp14
    tmp16 = tmp13 - tmp15
    tmp17 = tl_math.exp(tmp11)
    tmp18 = tmp17 * tmp17
    tmp19 = tmp16 - tmp18
    tmp20 = tmp10 + tmp19
    tmp22 = tmp21 * tmp1
    tmp23 = tmp22 + tmp3
    tmp25 = tmp24 * tmp24
    tmp26 = tmp23 - tmp25
    tmp27 = tl_math.exp(tmp21)
    tmp28 = tmp27 * tmp27
    tmp29 = tmp26 - tmp28
    tmp30 = tmp20 + tmp29
    tmp32 = tmp31 * tmp1
    tmp33 = tmp32 + tmp3
    tmp35 = tmp34 * tmp34
    tmp36 = tmp33 - tmp35
    tmp37 = tl_math.exp(tmp31)
    tmp38 = tmp37 * tmp37
    tmp39 = tmp36 - tmp38
    tmp40 = tmp30 + tmp39
    tmp41 = tl.broadcast_to(tmp40, [XBLOCK, RBLOCK])
    tmp43 = tl.sum(tmp41, 1)[:, None]
    tmp45 = tmp44 * tmp1
    tmp46 = tmp45 + tmp3
    tmp48 = tmp47 * tmp47
    tmp49 = tmp46 - tmp48
    tmp50 = tl_math.exp(tmp44)
    tmp51 = tmp50 * tmp50
    tmp52 = tmp49 - tmp51
    tmp54 = tmp53 * tmp1
    tmp55 = tmp54 + tmp3
    tmp57 = tmp56 * tmp56
    tmp58 = tmp55 - tmp57
    tmp59 = tl_math.exp(tmp53)
    tmp60 = tmp59 * tmp59
    tmp61 = tmp58 - tmp60
    tmp62 = tmp52 + tmp61
    tmp64 = tmp63 * tmp1
    tmp65 = tmp64 + tmp3
    tmp67 = tmp66 * tmp66
    tmp68 = tmp65 - tmp67
    tmp69 = tl_math.exp(tmp63)
    tmp70 = tmp69 * tmp69
    tmp71 = tmp68 - tmp70
    tmp72 = tmp62 + tmp71
    tmp74 = tmp73 * tmp1
    tmp75 = tmp74 + tmp3
    tmp77 = tmp76 * tmp76
    tmp78 = tmp75 - tmp77
    tmp79 = tl_math.exp(tmp73)
    tmp80 = tmp79 * tmp79
    tmp81 = tmp78 - tmp80
    tmp82 = tmp72 + tmp81
    tmp83 = tl.broadcast_to(tmp82, [XBLOCK, RBLOCK])
    tmp85 = tl.sum(tmp83, 1)[:, None]
    tmp86 = 64.0
    tmp87 = tmp43 / tmp86
    tmp88 = -0.125
    tmp89 = tmp87 * tmp88
    tmp90 = tmp85 / tmp86
    tmp91 = tmp90 * tmp88
    tmp92 = tmp89 + tmp91
    tl.debug_barrier()
    tl.store(in_out_ptr0 + tl.full([XBLOCK, 1], 0, tl.int32), tmp92, None)


def call(args):
    arg0_1, arg1_1, arg2_1, arg3_1 = args
    args.clear()
    assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(arg1_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(arg2_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(arg3_1, (4, 4, 4, 4), (64, 16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf1 = empty_strided_cuda((), (), torch.float32)
        buf4 = buf1
        del buf1
        get_raw_stream(0)
        triton_per_fused_add_exp_mean_mul_pow_sub_sum_0[grid(1)](buf4,
            arg0_1, arg1_1, arg2_1, arg3_1, 1, 64, XBLOCK=1, num_warps=2,
            num_stages=1)
        del arg0_1
        del arg1_1
        del arg2_1
        del arg3_1
    return buf4,


class kl_lossNew(Module):

    def __init__(self, num_nodes, num_edges):
        super(kl_lossNew, self).__init__()
        self.num_nodes = num_nodes
        self.num_edges = num_edges

    def forward(self, input_0, input_1, input_2, input_3):
        arg0_1 = input_0
        arg1_1 = input_1
        arg2_1 = input_2
        arg3_1 = input_3
        output = call([arg0_1, arg1_1, arg2_1, arg3_1])
        return output[0]
