# 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_0/inductor_cache/kz/ckzbjorew6d2qnczvglsrdyz6betqwacszox7jxg7ucfo22ke4tw.py
# Topologically Sorted Source Nodes: [log_P, log_Q, sub, mul, sum_1, kl], Original ATen: [aten.log, aten.sub, aten.mul, aten.sum]
# Source node to ATen node mapping:
#   kl => sum_2
#   log_P => log
#   log_Q => log_1
#   mul => mul
#   sub => sub
#   sum_1 => sum_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 = {})
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%arg0_1, %sub), kwargs = {})
#   %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%mul, [-1]), kwargs = {})
#   %sum_2 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%sum_1, [-1]), kwargs = {})
triton_poi_fused_log_mul_sub_sum_0 = async_compile.triton('triton_poi_fused_log_mul_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=[16], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '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), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_log_mul_sub_sum_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 32, '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_log_mul_sub_sum_0(in_ptr0, in_ptr1, out_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_ptr0 + (16*x0), xmask, eviction_policy='evict_last')
    tmp2 = tl.load(in_ptr1 + (16*x0), xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr0 + (1 + (16*x0)), xmask, eviction_policy='evict_last')
    tmp8 = tl.load(in_ptr1 + (1 + (16*x0)), xmask, eviction_policy='evict_last')
    tmp13 = tl.load(in_ptr0 + (2 + (16*x0)), xmask, eviction_policy='evict_last')
    tmp15 = tl.load(in_ptr1 + (2 + (16*x0)), xmask, eviction_policy='evict_last')
    tmp20 = tl.load(in_ptr0 + (3 + (16*x0)), xmask, eviction_policy='evict_last')
    tmp22 = tl.load(in_ptr1 + (3 + (16*x0)), xmask, eviction_policy='evict_last')
    tmp27 = tl.load(in_ptr0 + (4 + (16*x0)), xmask, eviction_policy='evict_last')
    tmp29 = tl.load(in_ptr1 + (4 + (16*x0)), xmask, eviction_policy='evict_last')
    tmp33 = tl.load(in_ptr0 + (5 + (16*x0)), xmask, eviction_policy='evict_last')
    tmp35 = tl.load(in_ptr1 + (5 + (16*x0)), xmask, eviction_policy='evict_last')
    tmp40 = tl.load(in_ptr0 + (6 + (16*x0)), xmask, eviction_policy='evict_last')
    tmp42 = tl.load(in_ptr1 + (6 + (16*x0)), xmask, eviction_policy='evict_last')
    tmp47 = tl.load(in_ptr0 + (7 + (16*x0)), xmask, eviction_policy='evict_last')
    tmp49 = tl.load(in_ptr1 + (7 + (16*x0)), xmask, eviction_policy='evict_last')
    tmp55 = tl.load(in_ptr0 + (8 + (16*x0)), xmask, eviction_policy='evict_last')
    tmp57 = tl.load(in_ptr1 + (8 + (16*x0)), xmask, eviction_policy='evict_last')
    tmp61 = tl.load(in_ptr0 + (9 + (16*x0)), xmask, eviction_policy='evict_last')
    tmp63 = tl.load(in_ptr1 + (9 + (16*x0)), xmask, eviction_policy='evict_last')
    tmp68 = tl.load(in_ptr0 + (10 + (16*x0)), xmask, eviction_policy='evict_last')
    tmp70 = tl.load(in_ptr1 + (10 + (16*x0)), xmask, eviction_policy='evict_last')
    tmp75 = tl.load(in_ptr0 + (11 + (16*x0)), xmask, eviction_policy='evict_last')
    tmp77 = tl.load(in_ptr1 + (11 + (16*x0)), xmask, eviction_policy='evict_last')
    tmp83 = tl.load(in_ptr0 + (12 + (16*x0)), xmask, eviction_policy='evict_last')
    tmp85 = tl.load(in_ptr1 + (12 + (16*x0)), xmask, eviction_policy='evict_last')
    tmp89 = tl.load(in_ptr0 + (13 + (16*x0)), xmask, eviction_policy='evict_last')
    tmp91 = tl.load(in_ptr1 + (13 + (16*x0)), xmask, eviction_policy='evict_last')
    tmp96 = tl.load(in_ptr0 + (14 + (16*x0)), xmask, eviction_policy='evict_last')
    tmp98 = tl.load(in_ptr1 + (14 + (16*x0)), xmask, eviction_policy='evict_last')
    tmp103 = tl.load(in_ptr0 + (15 + (16*x0)), xmask, eviction_policy='evict_last')
    tmp105 = tl.load(in_ptr1 + (15 + (16*x0)), xmask, eviction_policy='evict_last')
    tmp1 = tl_math.log(tmp0)
    tmp3 = tl_math.log(tmp2)
    tmp4 = tmp1 - tmp3
    tmp5 = tmp0 * tmp4
    tmp7 = tl_math.log(tmp6)
    tmp9 = tl_math.log(tmp8)
    tmp10 = tmp7 - tmp9
    tmp11 = tmp6 * tmp10
    tmp12 = tmp5 + tmp11
    tmp14 = tl_math.log(tmp13)
    tmp16 = tl_math.log(tmp15)
    tmp17 = tmp14 - tmp16
    tmp18 = tmp13 * tmp17
    tmp19 = tmp12 + tmp18
    tmp21 = tl_math.log(tmp20)
    tmp23 = tl_math.log(tmp22)
    tmp24 = tmp21 - tmp23
    tmp25 = tmp20 * tmp24
    tmp26 = tmp19 + tmp25
    tmp28 = tl_math.log(tmp27)
    tmp30 = tl_math.log(tmp29)
    tmp31 = tmp28 - tmp30
    tmp32 = tmp27 * tmp31
    tmp34 = tl_math.log(tmp33)
    tmp36 = tl_math.log(tmp35)
    tmp37 = tmp34 - tmp36
    tmp38 = tmp33 * tmp37
    tmp39 = tmp32 + tmp38
    tmp41 = tl_math.log(tmp40)
    tmp43 = tl_math.log(tmp42)
    tmp44 = tmp41 - tmp43
    tmp45 = tmp40 * tmp44
    tmp46 = tmp39 + tmp45
    tmp48 = tl_math.log(tmp47)
    tmp50 = tl_math.log(tmp49)
    tmp51 = tmp48 - tmp50
    tmp52 = tmp47 * tmp51
    tmp53 = tmp46 + tmp52
    tmp54 = tmp26 + tmp53
    tmp56 = tl_math.log(tmp55)
    tmp58 = tl_math.log(tmp57)
    tmp59 = tmp56 - tmp58
    tmp60 = tmp55 * tmp59
    tmp62 = tl_math.log(tmp61)
    tmp64 = tl_math.log(tmp63)
    tmp65 = tmp62 - tmp64
    tmp66 = tmp61 * tmp65
    tmp67 = tmp60 + tmp66
    tmp69 = tl_math.log(tmp68)
    tmp71 = tl_math.log(tmp70)
    tmp72 = tmp69 - tmp71
    tmp73 = tmp68 * tmp72
    tmp74 = tmp67 + tmp73
    tmp76 = tl_math.log(tmp75)
    tmp78 = tl_math.log(tmp77)
    tmp79 = tmp76 - tmp78
    tmp80 = tmp75 * tmp79
    tmp81 = tmp74 + tmp80
    tmp82 = tmp54 + tmp81
    tmp84 = tl_math.log(tmp83)
    tmp86 = tl_math.log(tmp85)
    tmp87 = tmp84 - tmp86
    tmp88 = tmp83 * tmp87
    tmp90 = tl_math.log(tmp89)
    tmp92 = tl_math.log(tmp91)
    tmp93 = tmp90 - tmp92
    tmp94 = tmp89 * tmp93
    tmp95 = tmp88 + tmp94
    tmp97 = tl_math.log(tmp96)
    tmp99 = tl_math.log(tmp98)
    tmp100 = tmp97 - tmp99
    tmp101 = tmp96 * tmp100
    tmp102 = tmp95 + tmp101
    tmp104 = tl_math.log(tmp103)
    tmp106 = tl_math.log(tmp105)
    tmp107 = tmp104 - tmp106
    tmp108 = tmp103 * tmp107
    tmp109 = tmp102 + tmp108
    tmp110 = tmp82 + tmp109
    tl.store(out_ptr0 + (x0), tmp110, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_0/inductor_cache/5i/c5ihqrojuny4xsnxeyq3omcrs6nck43hbetaz5t5kzhjm6ln2y6j.py
# Topologically Sorted Source Nodes: [mean], Original ATen: [aten.mean]
# Source node to ATen node mapping:
#   mean => mean
# Graph fragment:
#   %mean : [num_users=1] = call_function[target=torch.ops.aten.mean.dim](args = (%sum_2, [0]), kwargs = {})
triton_poi_fused_mean_1 = async_compile.triton('triton_poi_fused_mean_1', '''
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=[4], 
    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), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_mean_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 4, '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_mean_1(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 4
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (x0), xmask)
    tmp1 = tl.load(in_ptr0 + (4 + x0), xmask)
    tmp3 = tl.load(in_ptr0 + (8 + x0), xmask)
    tmp5 = tl.load(in_ptr0 + (12 + x0), xmask)
    tmp2 = tmp0 + tmp1
    tmp4 = tmp2 + tmp3
    tmp6 = tmp4 + tmp5
    tmp7 = 4.0
    tmp8 = tmp6 / tmp7
    tl.store(out_ptr0 + (x0), tmp8, 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, 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)
        buf0 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [log_P, log_Q, sub, mul, sum_1, kl], Original ATen: [aten.log, aten.sub, aten.mul, aten.sum]
        stream0 = get_raw_stream(0)
        triton_poi_fused_log_mul_sub_sum_0.run(arg0_1, arg1_1, buf0, 16, grid=grid(16), stream=stream0)
        del arg0_1
        del arg1_1
        buf1 = empty_strided_cuda((4, ), (1, ), torch.float32)
        # Topologically Sorted Source Nodes: [mean], Original ATen: [aten.mean]
        triton_poi_fused_mean_1.run(buf0, buf1, 4, grid=grid(4), stream=stream0)
        del buf0
    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
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
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_log_mul_sub_sum_0(in_ptr0, in_ptr1, out_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_ptr0 + 16 * x0, xmask, eviction_policy='evict_last')
    tmp2 = tl.load(in_ptr1 + 16 * x0, xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr0 + (1 + 16 * x0), xmask, eviction_policy='evict_last'
        )
    tmp8 = tl.load(in_ptr1 + (1 + 16 * x0), xmask, eviction_policy='evict_last'
        )
    tmp13 = tl.load(in_ptr0 + (2 + 16 * x0), xmask, eviction_policy=
        'evict_last')
    tmp15 = tl.load(in_ptr1 + (2 + 16 * x0), xmask, eviction_policy=
        'evict_last')
    tmp20 = tl.load(in_ptr0 + (3 + 16 * x0), xmask, eviction_policy=
        'evict_last')
    tmp22 = tl.load(in_ptr1 + (3 + 16 * x0), xmask, eviction_policy=
        'evict_last')
    tmp27 = tl.load(in_ptr0 + (4 + 16 * x0), xmask, eviction_policy=
        'evict_last')
    tmp29 = tl.load(in_ptr1 + (4 + 16 * x0), xmask, eviction_policy=
        'evict_last')
    tmp33 = tl.load(in_ptr0 + (5 + 16 * x0), xmask, eviction_policy=
        'evict_last')
    tmp35 = tl.load(in_ptr1 + (5 + 16 * x0), xmask, eviction_policy=
        'evict_last')
    tmp40 = tl.load(in_ptr0 + (6 + 16 * x0), xmask, eviction_policy=
        'evict_last')
    tmp42 = tl.load(in_ptr1 + (6 + 16 * x0), xmask, eviction_policy=
        'evict_last')
    tmp47 = tl.load(in_ptr0 + (7 + 16 * x0), xmask, eviction_policy=
        'evict_last')
    tmp49 = tl.load(in_ptr1 + (7 + 16 * x0), xmask, eviction_policy=
        'evict_last')
    tmp55 = tl.load(in_ptr0 + (8 + 16 * x0), xmask, eviction_policy=
        'evict_last')
    tmp57 = tl.load(in_ptr1 + (8 + 16 * x0), xmask, eviction_policy=
        'evict_last')
    tmp61 = tl.load(in_ptr0 + (9 + 16 * x0), xmask, eviction_policy=
        'evict_last')
    tmp63 = tl.load(in_ptr1 + (9 + 16 * x0), xmask, eviction_policy=
        'evict_last')
    tmp68 = tl.load(in_ptr0 + (10 + 16 * x0), xmask, eviction_policy=
        'evict_last')
    tmp70 = tl.load(in_ptr1 + (10 + 16 * x0), xmask, eviction_policy=
        'evict_last')
    tmp75 = tl.load(in_ptr0 + (11 + 16 * x0), xmask, eviction_policy=
        'evict_last')
    tmp77 = tl.load(in_ptr1 + (11 + 16 * x0), xmask, eviction_policy=
        'evict_last')
    tmp83 = tl.load(in_ptr0 + (12 + 16 * x0), xmask, eviction_policy=
        'evict_last')
    tmp85 = tl.load(in_ptr1 + (12 + 16 * x0), xmask, eviction_policy=
        'evict_last')
    tmp89 = tl.load(in_ptr0 + (13 + 16 * x0), xmask, eviction_policy=
        'evict_last')
    tmp91 = tl.load(in_ptr1 + (13 + 16 * x0), xmask, eviction_policy=
        'evict_last')
    tmp96 = tl.load(in_ptr0 + (14 + 16 * x0), xmask, eviction_policy=
        'evict_last')
    tmp98 = tl.load(in_ptr1 + (14 + 16 * x0), xmask, eviction_policy=
        'evict_last')
    tmp103 = tl.load(in_ptr0 + (15 + 16 * x0), xmask, eviction_policy=
        'evict_last')
    tmp105 = tl.load(in_ptr1 + (15 + 16 * x0), xmask, eviction_policy=
        'evict_last')
    tmp1 = tl_math.log(tmp0)
    tmp3 = tl_math.log(tmp2)
    tmp4 = tmp1 - tmp3
    tmp5 = tmp0 * tmp4
    tmp7 = tl_math.log(tmp6)
    tmp9 = tl_math.log(tmp8)
    tmp10 = tmp7 - tmp9
    tmp11 = tmp6 * tmp10
    tmp12 = tmp5 + tmp11
    tmp14 = tl_math.log(tmp13)
    tmp16 = tl_math.log(tmp15)
    tmp17 = tmp14 - tmp16
    tmp18 = tmp13 * tmp17
    tmp19 = tmp12 + tmp18
    tmp21 = tl_math.log(tmp20)
    tmp23 = tl_math.log(tmp22)
    tmp24 = tmp21 - tmp23
    tmp25 = tmp20 * tmp24
    tmp26 = tmp19 + tmp25
    tmp28 = tl_math.log(tmp27)
    tmp30 = tl_math.log(tmp29)
    tmp31 = tmp28 - tmp30
    tmp32 = tmp27 * tmp31
    tmp34 = tl_math.log(tmp33)
    tmp36 = tl_math.log(tmp35)
    tmp37 = tmp34 - tmp36
    tmp38 = tmp33 * tmp37
    tmp39 = tmp32 + tmp38
    tmp41 = tl_math.log(tmp40)
    tmp43 = tl_math.log(tmp42)
    tmp44 = tmp41 - tmp43
    tmp45 = tmp40 * tmp44
    tmp46 = tmp39 + tmp45
    tmp48 = tl_math.log(tmp47)
    tmp50 = tl_math.log(tmp49)
    tmp51 = tmp48 - tmp50
    tmp52 = tmp47 * tmp51
    tmp53 = tmp46 + tmp52
    tmp54 = tmp26 + tmp53
    tmp56 = tl_math.log(tmp55)
    tmp58 = tl_math.log(tmp57)
    tmp59 = tmp56 - tmp58
    tmp60 = tmp55 * tmp59
    tmp62 = tl_math.log(tmp61)
    tmp64 = tl_math.log(tmp63)
    tmp65 = tmp62 - tmp64
    tmp66 = tmp61 * tmp65
    tmp67 = tmp60 + tmp66
    tmp69 = tl_math.log(tmp68)
    tmp71 = tl_math.log(tmp70)
    tmp72 = tmp69 - tmp71
    tmp73 = tmp68 * tmp72
    tmp74 = tmp67 + tmp73
    tmp76 = tl_math.log(tmp75)
    tmp78 = tl_math.log(tmp77)
    tmp79 = tmp76 - tmp78
    tmp80 = tmp75 * tmp79
    tmp81 = tmp74 + tmp80
    tmp82 = tmp54 + tmp81
    tmp84 = tl_math.log(tmp83)
    tmp86 = tl_math.log(tmp85)
    tmp87 = tmp84 - tmp86
    tmp88 = tmp83 * tmp87
    tmp90 = tl_math.log(tmp89)
    tmp92 = tl_math.log(tmp91)
    tmp93 = tmp90 - tmp92
    tmp94 = tmp89 * tmp93
    tmp95 = tmp88 + tmp94
    tmp97 = tl_math.log(tmp96)
    tmp99 = tl_math.log(tmp98)
    tmp100 = tmp97 - tmp99
    tmp101 = tmp96 * tmp100
    tmp102 = tmp95 + tmp101
    tmp104 = tl_math.log(tmp103)
    tmp106 = tl_math.log(tmp105)
    tmp107 = tmp104 - tmp106
    tmp108 = tmp103 * tmp107
    tmp109 = tmp102 + tmp108
    tmp110 = tmp82 + tmp109
    tl.store(out_ptr0 + x0, tmp110, xmask)


@triton.jit
def triton_poi_fused_mean_1(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 4
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + x0, xmask)
    tmp1 = tl.load(in_ptr0 + (4 + x0), xmask)
    tmp3 = tl.load(in_ptr0 + (8 + x0), xmask)
    tmp5 = tl.load(in_ptr0 + (12 + x0), xmask)
    tmp2 = tmp0 + tmp1
    tmp4 = tmp2 + tmp3
    tmp6 = tmp4 + tmp5
    tmp7 = 4.0
    tmp8 = tmp6 / tmp7
    tl.store(out_ptr0 + x0, tmp8, xmask)


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)
        buf0 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_log_mul_sub_sum_0[grid(16)](arg0_1, arg1_1, buf0, 
            16, XBLOCK=16, num_warps=1, num_stages=1)
        del arg0_1
        del arg1_1
        buf1 = empty_strided_cuda((4,), (1,), torch.float32)
        triton_poi_fused_mean_1[grid(4)](buf0, buf1, 4, XBLOCK=4, num_warps
            =1, num_stages=1)
        del buf0
    return buf1,


class CategoricalKLLossNew(nn.Module):

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

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