# 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/sa/csabhmg42rapbjnmneye43x4d3qgef5bza6vhg2qxtde6ctfm2hr.py
# Topologically Sorted Source Nodes: [log, log_1, sub, truediv, add, sub_1, pow_1, truediv_1, add_1, sub_2, element_wise, kl], Original ATen: [aten.log, aten.sub, aten.div, aten.add, aten.pow, aten.mul, aten.sum]
# Source node to ATen node mapping:
#   add => add
#   add_1 => add_1
#   element_wise => mul
#   kl => sum_1
#   log => log
#   log_1 => log_1
#   pow_1 => pow_1
#   sub => sub
#   sub_1 => sub_1
#   sub_2 => sub_2
#   truediv => div
#   truediv_1 => div_1
# Graph fragment:
#   %log : [num_users=1] = call_function[target=torch.ops.aten.log.default](args = (%arg0_1,), kwargs = {})
#   %log_1 : [num_users=1] = call_function[target=torch.ops.aten.log.default](args = (%arg1_1,), kwargs = {})
#   %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%log, %log_1), kwargs = {})
#   %div : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%arg1_1, %arg0_1), kwargs = {})
#   %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%sub, %div), kwargs = {})
#   %sub_1 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%arg2_1, %arg3_1), kwargs = {})
#   %pow_1 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%sub_1, 2), kwargs = {})
#   %div_1 : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%pow_1, %arg0_1), kwargs = {})
#   %add_1 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%add, %div_1), kwargs = {})
#   %sub_2 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%add_1, 1), kwargs = {})
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sub_2, 0.5), kwargs = {})
#   %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%mul, [-1]), kwargs = {})
triton_poi_fused_add_div_log_mul_pow_sub_sum_0 = async_compile.triton('triton_poi_fused_add_div_log_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.pointwise(
    size_hints=[64], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: '*fp32', 5: '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), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_add_div_log_mul_pow_sub_sum_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 16, '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_log_mul_pow_sub_sum_0(in_ptr0, in_ptr1, in_ptr2, in_ptr3, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (4*x0), xmask, eviction_policy='evict_last')
    tmp2 = tl.load(in_ptr1 + (4*x0), xmask, eviction_policy='evict_last')
    tmp7 = tl.load(in_ptr2 + (4*x0), xmask, eviction_policy='evict_last')
    tmp8 = tl.load(in_ptr3 + (4*x0), xmask, eviction_policy='evict_last')
    tmp17 = tl.load(in_ptr0 + (1 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp19 = tl.load(in_ptr1 + (1 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp24 = tl.load(in_ptr2 + (1 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp25 = tl.load(in_ptr3 + (1 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp33 = tl.load(in_ptr0 + (2 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp35 = tl.load(in_ptr1 + (2 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp40 = tl.load(in_ptr2 + (2 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp41 = tl.load(in_ptr3 + (2 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp49 = tl.load(in_ptr0 + (3 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp51 = tl.load(in_ptr1 + (3 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp56 = tl.load(in_ptr2 + (3 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp57 = tl.load(in_ptr3 + (3 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp1 = tl_math.log(tmp0)
    tmp3 = tl_math.log(tmp2)
    tmp4 = tmp1 - tmp3
    tmp5 = tmp2 / tmp0
    tmp6 = tmp4 + tmp5
    tmp9 = tmp7 - tmp8
    tmp10 = tmp9 * tmp9
    tmp11 = tmp10 / tmp0
    tmp12 = tmp6 + tmp11
    tmp13 = 1.0
    tmp14 = tmp12 - tmp13
    tmp15 = 0.5
    tmp16 = tmp14 * tmp15
    tmp18 = tl_math.log(tmp17)
    tmp20 = tl_math.log(tmp19)
    tmp21 = tmp18 - tmp20
    tmp22 = tmp19 / tmp17
    tmp23 = tmp21 + tmp22
    tmp26 = tmp24 - tmp25
    tmp27 = tmp26 * tmp26
    tmp28 = tmp27 / tmp17
    tmp29 = tmp23 + tmp28
    tmp30 = tmp29 - tmp13
    tmp31 = tmp30 * tmp15
    tmp32 = tmp16 + tmp31
    tmp34 = tl_math.log(tmp33)
    tmp36 = tl_math.log(tmp35)
    tmp37 = tmp34 - tmp36
    tmp38 = tmp35 / tmp33
    tmp39 = tmp37 + tmp38
    tmp42 = tmp40 - tmp41
    tmp43 = tmp42 * tmp42
    tmp44 = tmp43 / tmp33
    tmp45 = tmp39 + tmp44
    tmp46 = tmp45 - tmp13
    tmp47 = tmp46 * tmp15
    tmp48 = tmp32 + tmp47
    tmp50 = tl_math.log(tmp49)
    tmp52 = tl_math.log(tmp51)
    tmp53 = tmp50 - tmp52
    tmp54 = tmp51 / tmp49
    tmp55 = tmp53 + tmp54
    tmp58 = tmp56 - tmp57
    tmp59 = tmp58 * tmp58
    tmp60 = tmp59 / tmp49
    tmp61 = tmp55 + tmp60
    tmp62 = tmp61 - tmp13
    tmp63 = tmp62 * tmp15
    tmp64 = tmp48 + tmp63
    tl.store(out_ptr0 + (x0), tmp64, xmask)
''', 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)
        buf0 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [log, log_1, sub, truediv, add, sub_1, pow_1, truediv_1, add_1, sub_2, element_wise, kl], Original ATen: [aten.log, aten.sub, aten.div, aten.add, aten.pow, aten.mul, aten.sum]
        stream0 = get_raw_stream(0)
        triton_poi_fused_add_div_log_mul_pow_sub_sum_0.run(arg0_1, arg1_1, arg2_1, arg3_1, buf0, 64, grid=grid(64), stream=stream0)
        del arg0_1
        del arg1_1
        del arg2_1
        del arg3_1
    return (buf0, )


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)

import torch
import torch.nn as nn
import torch.utils.data
import torch.utils.data.distributed


class KLNormal(nn.Module):

    def __init__(self):
        super(KLNormal, self).__init__()

    def forward(self, qm, qv, pm, pv):
        element_wise = 0.5 * (torch.log(pv) - torch.log(qv) + qv / pv + (qm -
            pm).pow(2) / pv - 1)
        kl = element_wise.sum(-1)
        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 [[], {}]

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 torch.nn as nn
import torch.utils.data
import torch.utils.data.distributed
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_div_log_mul_pow_sub_sum_0(in_ptr0, in_ptr1,
    in_ptr2, in_ptr3, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + 4 * x0, xmask, eviction_policy='evict_last')
    tmp2 = tl.load(in_ptr1 + 4 * x0, xmask, eviction_policy='evict_last')
    tmp7 = tl.load(in_ptr2 + 4 * x0, xmask, eviction_policy='evict_last')
    tmp8 = tl.load(in_ptr3 + 4 * x0, xmask, eviction_policy='evict_last')
    tmp17 = tl.load(in_ptr0 + (1 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp19 = tl.load(in_ptr1 + (1 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp24 = tl.load(in_ptr2 + (1 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp25 = tl.load(in_ptr3 + (1 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp33 = tl.load(in_ptr0 + (2 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp35 = tl.load(in_ptr1 + (2 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp40 = tl.load(in_ptr2 + (2 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp41 = tl.load(in_ptr3 + (2 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp49 = tl.load(in_ptr0 + (3 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp51 = tl.load(in_ptr1 + (3 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp56 = tl.load(in_ptr2 + (3 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp57 = tl.load(in_ptr3 + (3 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp1 = tl_math.log(tmp0)
    tmp3 = tl_math.log(tmp2)
    tmp4 = tmp1 - tmp3
    tmp5 = tmp2 / tmp0
    tmp6 = tmp4 + tmp5
    tmp9 = tmp7 - tmp8
    tmp10 = tmp9 * tmp9
    tmp11 = tmp10 / tmp0
    tmp12 = tmp6 + tmp11
    tmp13 = 1.0
    tmp14 = tmp12 - tmp13
    tmp15 = 0.5
    tmp16 = tmp14 * tmp15
    tmp18 = tl_math.log(tmp17)
    tmp20 = tl_math.log(tmp19)
    tmp21 = tmp18 - tmp20
    tmp22 = tmp19 / tmp17
    tmp23 = tmp21 + tmp22
    tmp26 = tmp24 - tmp25
    tmp27 = tmp26 * tmp26
    tmp28 = tmp27 / tmp17
    tmp29 = tmp23 + tmp28
    tmp30 = tmp29 - tmp13
    tmp31 = tmp30 * tmp15
    tmp32 = tmp16 + tmp31
    tmp34 = tl_math.log(tmp33)
    tmp36 = tl_math.log(tmp35)
    tmp37 = tmp34 - tmp36
    tmp38 = tmp35 / tmp33
    tmp39 = tmp37 + tmp38
    tmp42 = tmp40 - tmp41
    tmp43 = tmp42 * tmp42
    tmp44 = tmp43 / tmp33
    tmp45 = tmp39 + tmp44
    tmp46 = tmp45 - tmp13
    tmp47 = tmp46 * tmp15
    tmp48 = tmp32 + tmp47
    tmp50 = tl_math.log(tmp49)
    tmp52 = tl_math.log(tmp51)
    tmp53 = tmp50 - tmp52
    tmp54 = tmp51 / tmp49
    tmp55 = tmp53 + tmp54
    tmp58 = tmp56 - tmp57
    tmp59 = tmp58 * tmp58
    tmp60 = tmp59 / tmp49
    tmp61 = tmp55 + tmp60
    tmp62 = tmp61 - tmp13
    tmp63 = tmp62 * tmp15
    tmp64 = tmp48 + tmp63
    tl.store(out_ptr0 + x0, tmp64, xmask)


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)
        buf0 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_add_div_log_mul_pow_sub_sum_0[grid(64)](arg0_1,
            arg1_1, arg2_1, arg3_1, buf0, 64, XBLOCK=64, num_warps=1,
            num_stages=1)
        del arg0_1
        del arg1_1
        del arg2_1
        del arg3_1
    return buf0,


class KLNormalNew(nn.Module):

    def __init__(self):
        super(KLNormalNew, self).__init__()

    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]
