# 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_7/inductor_cache/zr/czrva32bkzh7gc4m4iq6o3uf3s3wjxedhrlykq5h5eie22ufcb2x.py
# Topologically Sorted Source Nodes: [add, pow_1, sub, exp, sub_1, sum_1, kld_loss, kld_loss_1], Original ATen: [aten.add, aten.pow, aten.sub, aten.exp, aten.sum, aten.mul]
# Source node to ATen node mapping:
#   add => add
#   exp => exp
#   kld_loss => mul
#   kld_loss_1 => sum_2
#   pow_1 => pow_1
#   sub => sub
#   sub_1 => sub_1
#   sum_1 => sum_1
# Graph fragment:
#   %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%arg0_1, 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 = {})
#   %sub_1 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%sub, %exp), kwargs = {})
#   %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%sub_1, [1]), kwargs = {})
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sum_1, -0.5), kwargs = {})
#   %sum_2 : [num_users=1] = call_function[target=torch.ops.aten.sum.default](args = (%mul,), kwargs = {})
triton_per_fused_add_exp_mul_pow_sub_sum_0 = async_compile.triton('triton_per_fused_add_exp_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: 'i32', 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': {3: 1}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 4), equal_to_1=(3,))]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused_add_exp_mul_pow_sub_sum_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 8, 'num_reduction': 1, '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_mul_pow_sub_sum_0(in_ptr0, in_ptr1, out_ptr1, 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)
    tmp3 = tl.load(in_ptr1 + (r0 + (64*r1)), None)
    tmp8 = tl.load(in_ptr0 + (16 + r0 + (64*r1)), None)
    tmp10 = tl.load(in_ptr1 + (16 + r0 + (64*r1)), None)
    tmp16 = tl.load(in_ptr0 + (32 + r0 + (64*r1)), None)
    tmp18 = tl.load(in_ptr1 + (32 + r0 + (64*r1)), None)
    tmp24 = tl.load(in_ptr0 + (48 + r0 + (64*r1)), None)
    tmp26 = tl.load(in_ptr1 + (48 + r0 + (64*r1)), None)
    tmp1 = 1.0
    tmp2 = tmp0 + tmp1
    tmp4 = tmp3 * tmp3
    tmp5 = tmp2 - tmp4
    tmp6 = tl_math.exp(tmp0)
    tmp7 = tmp5 - tmp6
    tmp9 = tmp8 + tmp1
    tmp11 = tmp10 * tmp10
    tmp12 = tmp9 - tmp11
    tmp13 = tl_math.exp(tmp8)
    tmp14 = tmp12 - tmp13
    tmp15 = tmp7 + tmp14
    tmp17 = tmp16 + tmp1
    tmp19 = tmp18 * tmp18
    tmp20 = tmp17 - tmp19
    tmp21 = tl_math.exp(tmp16)
    tmp22 = tmp20 - tmp21
    tmp23 = tmp15 + tmp22
    tmp25 = tmp24 + tmp1
    tmp27 = tmp26 * tmp26
    tmp28 = tmp25 - tmp27
    tmp29 = tl_math.exp(tmp24)
    tmp30 = tmp28 - tmp29
    tmp31 = tmp23 + tmp30
    tmp32 = -0.5
    tmp33 = tmp31 * tmp32
    tmp34 = tl.broadcast_to(tmp33, [XBLOCK, RBLOCK])
    tmp36 = tl.sum(tmp34, 1)[:, None]
    tl.store(out_ptr1 + (tl.full([XBLOCK, 1], 0, tl.int32)), tmp36, None)
''', 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, 4), (64, 16, 4, 1))
    assert_size_stride(arg1_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)
        # Topologically Sorted Source Nodes: [add, pow_1, sub, exp, sub_1, sum_1, kld_loss, kld_loss_1], Original ATen: [aten.add, aten.pow, aten.sub, aten.exp, aten.sum, aten.mul]
        stream0 = get_raw_stream(0)
        triton_per_fused_add_exp_mul_pow_sub_sum_0.run(arg0_1, arg1_1, buf1, 1, 64, grid=grid(1), stream=stream0)
        del arg0_1
        del arg1_1
    return (buf1, )


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)
    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
from torch import nn


class KLDLoss(nn.Module):

    def __init__(self, reduction='sum'):
        super(KLDLoss, self).__init__()
        self.reduction = reduction

    def forward(self, mean, logvar):
        kld_loss = -0.5 * torch.sum(1 + logvar - mean.pow(2) - logvar.exp(), 1)
        if self.reduction == 'mean':
            kld_loss = torch.mean(kld_loss)
        elif self.reduction == 'sum':
            kld_loss = torch.sum(kld_loss)
        return kld_loss


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


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

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 import 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_per_fused_add_exp_mul_pow_sub_sum_0(in_ptr0, in_ptr1, out_ptr1,
    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)
    tmp3 = tl.load(in_ptr1 + (r0 + 64 * r1), None)
    tmp8 = tl.load(in_ptr0 + (16 + r0 + 64 * r1), None)
    tmp10 = tl.load(in_ptr1 + (16 + r0 + 64 * r1), None)
    tmp16 = tl.load(in_ptr0 + (32 + r0 + 64 * r1), None)
    tmp18 = tl.load(in_ptr1 + (32 + r0 + 64 * r1), None)
    tmp24 = tl.load(in_ptr0 + (48 + r0 + 64 * r1), None)
    tmp26 = tl.load(in_ptr1 + (48 + r0 + 64 * r1), None)
    tmp1 = 1.0
    tmp2 = tmp0 + tmp1
    tmp4 = tmp3 * tmp3
    tmp5 = tmp2 - tmp4
    tmp6 = tl_math.exp(tmp0)
    tmp7 = tmp5 - tmp6
    tmp9 = tmp8 + tmp1
    tmp11 = tmp10 * tmp10
    tmp12 = tmp9 - tmp11
    tmp13 = tl_math.exp(tmp8)
    tmp14 = tmp12 - tmp13
    tmp15 = tmp7 + tmp14
    tmp17 = tmp16 + tmp1
    tmp19 = tmp18 * tmp18
    tmp20 = tmp17 - tmp19
    tmp21 = tl_math.exp(tmp16)
    tmp22 = tmp20 - tmp21
    tmp23 = tmp15 + tmp22
    tmp25 = tmp24 + tmp1
    tmp27 = tmp26 * tmp26
    tmp28 = tmp25 - tmp27
    tmp29 = tl_math.exp(tmp24)
    tmp30 = tmp28 - tmp29
    tmp31 = tmp23 + tmp30
    tmp32 = -0.5
    tmp33 = tmp31 * tmp32
    tmp34 = tl.broadcast_to(tmp33, [XBLOCK, RBLOCK])
    tmp36 = tl.sum(tmp34, 1)[:, None]
    tl.store(out_ptr1 + tl.full([XBLOCK, 1], 0, tl.int32), tmp36, None)


def call(args):
    arg0_1, arg1_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))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf1 = empty_strided_cuda((), (), torch.float32)
        get_raw_stream(0)
        triton_per_fused_add_exp_mul_pow_sub_sum_0[grid(1)](arg0_1, arg1_1,
            buf1, 1, 64, XBLOCK=1, num_warps=2, num_stages=1)
        del arg0_1
        del arg1_1
    return buf1,


class KLDLossNew(nn.Module):

    def __init__(self, reduction='sum'):
        super(KLDLossNew, self).__init__()
        self.reduction = reduction

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