# 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_7/inductor_cache/ap/cap3nkzqkfj7rlygvuozblog2seuhvlea6gxfoq5gqtaswv2kdk3.py
# Topologically Sorted Source Nodes: [mean, mean_2, sub, pow_1, mean_3, var], Original ATen: [aten.mean, aten.sub, aten.pow]
# Source node to ATen node mapping:
#   mean => mean
#   mean_2 => mean_1
#   mean_3 => mean_2
#   pow_1 => pow_1
#   sub => sub
#   var => mean_3
# Graph fragment:
#   %mean : [num_users=1] = call_function[target=torch.ops.aten.mean.dim](args = (%primals_1, [1], True), kwargs = {})
#   %mean_1 : [num_users=1] = call_function[target=torch.ops.aten.mean.dim](args = (%mean, [2], True), kwargs = {})
#   %sub : [num_users=2] = call_function[target=torch.ops.aten.sub.Tensor](args = (%primals_1, %mean_1), kwargs = {})
#   %pow_1 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%sub, 2), kwargs = {})
#   %mean_2 : [num_users=1] = call_function[target=torch.ops.aten.mean.dim](args = (%pow_1, [1], True), kwargs = {})
#   %mean_3 : [num_users=1] = call_function[target=torch.ops.aten.mean.dim](args = (%mean_2, [2], True), kwargs = {})
triton_poi_fused_mean_pow_sub_0 = async_compile.triton('triton_poi_fused_mean_pow_sub_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_mean_pow_sub_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_mean_pow_sub_0(in_ptr0, out_ptr0, out_ptr1, xnumel, XBLOCK : tl.constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 4
    x1 = (xindex // 4)
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + (64*x1)), xmask)
    tmp1 = tl.load(in_ptr0 + (16 + x0 + (64*x1)), xmask)
    tmp3 = tl.load(in_ptr0 + (32 + x0 + (64*x1)), xmask)
    tmp5 = tl.load(in_ptr0 + (48 + x0 + (64*x1)), xmask)
    tmp9 = tl.load(in_ptr0 + (4 + x0 + (64*x1)), xmask)
    tmp10 = tl.load(in_ptr0 + (20 + x0 + (64*x1)), xmask)
    tmp12 = tl.load(in_ptr0 + (36 + x0 + (64*x1)), xmask)
    tmp14 = tl.load(in_ptr0 + (52 + x0 + (64*x1)), xmask)
    tmp18 = tl.load(in_ptr0 + (8 + x0 + (64*x1)), xmask)
    tmp19 = tl.load(in_ptr0 + (24 + x0 + (64*x1)), xmask)
    tmp21 = tl.load(in_ptr0 + (40 + x0 + (64*x1)), xmask)
    tmp23 = tl.load(in_ptr0 + (56 + x0 + (64*x1)), xmask)
    tmp27 = tl.load(in_ptr0 + (12 + x0 + (64*x1)), xmask)
    tmp28 = tl.load(in_ptr0 + (28 + x0 + (64*x1)), xmask)
    tmp30 = tl.load(in_ptr0 + (44 + x0 + (64*x1)), xmask)
    tmp32 = tl.load(in_ptr0 + (60 + x0 + (64*x1)), xmask)
    tmp2 = tmp0 + tmp1
    tmp4 = tmp2 + tmp3
    tmp6 = tmp4 + tmp5
    tmp7 = 4.0
    tmp8 = tmp6 / tmp7
    tmp11 = tmp9 + tmp10
    tmp13 = tmp11 + tmp12
    tmp15 = tmp13 + tmp14
    tmp16 = tmp15 / tmp7
    tmp17 = tmp8 + tmp16
    tmp20 = tmp18 + tmp19
    tmp22 = tmp20 + tmp21
    tmp24 = tmp22 + tmp23
    tmp25 = tmp24 / tmp7
    tmp26 = tmp17 + tmp25
    tmp29 = tmp27 + tmp28
    tmp31 = tmp29 + tmp30
    tmp33 = tmp31 + tmp32
    tmp34 = tmp33 / tmp7
    tmp35 = tmp26 + tmp34
    tmp36 = tmp35 / tmp7
    tmp37 = tmp0 - tmp36
    tmp38 = tmp37 * tmp37
    tmp39 = tmp1 - tmp36
    tmp40 = tmp39 * tmp39
    tmp41 = tmp38 + tmp40
    tmp42 = tmp3 - tmp36
    tmp43 = tmp42 * tmp42
    tmp44 = tmp41 + tmp43
    tmp45 = tmp5 - tmp36
    tmp46 = tmp45 * tmp45
    tmp47 = tmp44 + tmp46
    tmp48 = tmp47 / tmp7
    tmp49 = tmp9 - tmp36
    tmp50 = tmp49 * tmp49
    tmp51 = tmp10 - tmp36
    tmp52 = tmp51 * tmp51
    tmp53 = tmp50 + tmp52
    tmp54 = tmp12 - tmp36
    tmp55 = tmp54 * tmp54
    tmp56 = tmp53 + tmp55
    tmp57 = tmp14 - tmp36
    tmp58 = tmp57 * tmp57
    tmp59 = tmp56 + tmp58
    tmp60 = tmp59 / tmp7
    tmp61 = tmp48 + tmp60
    tmp62 = tmp18 - tmp36
    tmp63 = tmp62 * tmp62
    tmp64 = tmp19 - tmp36
    tmp65 = tmp64 * tmp64
    tmp66 = tmp63 + tmp65
    tmp67 = tmp21 - tmp36
    tmp68 = tmp67 * tmp67
    tmp69 = tmp66 + tmp68
    tmp70 = tmp23 - tmp36
    tmp71 = tmp70 * tmp70
    tmp72 = tmp69 + tmp71
    tmp73 = tmp72 / tmp7
    tmp74 = tmp61 + tmp73
    tmp75 = tmp27 - tmp36
    tmp76 = tmp75 * tmp75
    tmp77 = tmp28 - tmp36
    tmp78 = tmp77 * tmp77
    tmp79 = tmp76 + tmp78
    tmp80 = tmp30 - tmp36
    tmp81 = tmp80 * tmp80
    tmp82 = tmp79 + tmp81
    tmp83 = tmp32 - tmp36
    tmp84 = tmp83 * tmp83
    tmp85 = tmp82 + tmp84
    tmp86 = tmp85 / tmp7
    tmp87 = tmp74 + tmp86
    tmp88 = tmp87 / tmp7
    tl.store(out_ptr0 + (x2), tmp36, xmask)
    tl.store(out_ptr1 + (x2), tmp88, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/tu/ctunck2watc7guogb7nwwbhl2evcmo3mr4fhhl6ajujqj74x6txp.py
# Topologically Sorted Source Nodes: [sub, mul, add, pow_2, truediv, gLN_y], Original ATen: [aten.sub, aten.mul, aten.add, aten.pow, aten.div]
# Source node to ATen node mapping:
#   add => add
#   gLN_y => add_1
#   mul => mul
#   pow_2 => pow_2
#   sub => sub
#   truediv => div
# Graph fragment:
#   %sub : [num_users=2] = call_function[target=torch.ops.aten.sub.Tensor](args = (%primals_1, %mean_1), kwargs = {})
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%primals_2, %sub), kwargs = {})
#   %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mean_3, 1e-08), kwargs = {})
#   %pow_2 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%add, 0.5), kwargs = {})
#   %div : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%mul, %pow_2), kwargs = {})
#   %add_1 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%div, %primals_3), kwargs = {})
triton_poi_fused_add_div_mul_pow_sub_1 = async_compile.triton('triton_poi_fused_add_div_mul_pow_sub_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=[256], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: '*fp32', 5: '*fp32', 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': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4, 5, 6), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_add_div_mul_pow_sub_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 5, '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_mul_pow_sub_1(in_ptr0, in_ptr1, in_ptr2, in_ptr3, in_ptr4, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = (xindex // 4) % 4
    x4 = xindex
    x0 = xindex % 4
    x3 = (xindex // 64)
    tmp0 = tl.load(in_ptr0 + (x1), xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + (x4), xmask)
    tmp2 = tl.load(in_ptr2 + (x0 + (4*x3)), xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr3 + (x0 + (4*x3)), xmask, eviction_policy='evict_last')
    tmp10 = tl.load(in_ptr4 + (x1), xmask, eviction_policy='evict_last')
    tmp3 = tmp1 - tmp2
    tmp4 = tmp0 * tmp3
    tmp6 = 1e-08
    tmp7 = tmp5 + tmp6
    tmp8 = libdevice.sqrt(tmp7)
    tmp9 = tmp4 / tmp8
    tmp11 = tmp9 + tmp10
    tl.store(out_ptr0 + (x4), tmp11, xmask)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    primals_1, primals_2, primals_3 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_2, (1, 4, 1), (4, 1, 1))
    assert_size_stride(primals_3, (1, 4, 1), (4, 1, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 1, 1, 4), (4, 16, 16, 1), torch.float32)
        buf1 = empty_strided_cuda((4, 1, 1, 4), (4, 16, 16, 1), torch.float32)
        # Topologically Sorted Source Nodes: [mean, mean_2, sub, pow_1, mean_3, var], Original ATen: [aten.mean, aten.sub, aten.pow]
        stream0 = get_raw_stream(0)
        triton_poi_fused_mean_pow_sub_0.run(primals_1, buf0, buf1, 16, grid=grid(16), stream=stream0)
        buf2 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [sub, mul, add, pow_2, truediv, gLN_y], Original ATen: [aten.sub, aten.mul, aten.add, aten.pow, aten.div]
        triton_poi_fused_add_div_mul_pow_sub_1.run(primals_2, primals_1, buf0, buf1, primals_3, buf2, 256, grid=grid(256), stream=stream0)
        del buf0
        del buf1
        del primals_2
        del primals_3
    return (buf2, primals_1, )


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((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((1, 4, 1), (4, 1, 1), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((1, 4, 1), (4, 1, 1), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([primals_1, primals_2, primals_3])
    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
from itertools import product as product


class GlobalLayerNorm(nn.Module):

    def __init__(self, channel_size):
        super(GlobalLayerNorm, self).__init__()
        self.gamma = nn.Parameter(torch.Tensor(1, channel_size, 1))
        self.beta = nn.Parameter(torch.Tensor(1, channel_size, 1))
        self.reset_parameters()

    def reset_parameters(self):
        self.gamma.data.fill_(1)
        self.beta.data.zero_()

    def forward(self, y):
        mean = y.mean(dim=1, keepdim=True).mean(dim=2, keepdim=True)
        var = torch.pow(y - mean, 2).mean(dim=1, keepdim=True).mean(dim=2,
            keepdim=True)
        gLN_y = self.gamma * (y - mean) / torch.pow(var + 1e-08, 0.5
            ) + self.beta
        return gLN_y


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


def get_init_inputs():
    return [[], {'channel_size': 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 libdevice
import torch.nn as nn
from itertools import product as product
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_mean_pow_sub_0(in_ptr0, out_ptr0, out_ptr1, xnumel,
    XBLOCK: tl.constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 4
    x1 = xindex // 4
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + 64 * x1), xmask)
    tmp1 = tl.load(in_ptr0 + (16 + x0 + 64 * x1), xmask)
    tmp3 = tl.load(in_ptr0 + (32 + x0 + 64 * x1), xmask)
    tmp5 = tl.load(in_ptr0 + (48 + x0 + 64 * x1), xmask)
    tmp9 = tl.load(in_ptr0 + (4 + x0 + 64 * x1), xmask)
    tmp10 = tl.load(in_ptr0 + (20 + x0 + 64 * x1), xmask)
    tmp12 = tl.load(in_ptr0 + (36 + x0 + 64 * x1), xmask)
    tmp14 = tl.load(in_ptr0 + (52 + x0 + 64 * x1), xmask)
    tmp18 = tl.load(in_ptr0 + (8 + x0 + 64 * x1), xmask)
    tmp19 = tl.load(in_ptr0 + (24 + x0 + 64 * x1), xmask)
    tmp21 = tl.load(in_ptr0 + (40 + x0 + 64 * x1), xmask)
    tmp23 = tl.load(in_ptr0 + (56 + x0 + 64 * x1), xmask)
    tmp27 = tl.load(in_ptr0 + (12 + x0 + 64 * x1), xmask)
    tmp28 = tl.load(in_ptr0 + (28 + x0 + 64 * x1), xmask)
    tmp30 = tl.load(in_ptr0 + (44 + x0 + 64 * x1), xmask)
    tmp32 = tl.load(in_ptr0 + (60 + x0 + 64 * x1), xmask)
    tmp2 = tmp0 + tmp1
    tmp4 = tmp2 + tmp3
    tmp6 = tmp4 + tmp5
    tmp7 = 4.0
    tmp8 = tmp6 / tmp7
    tmp11 = tmp9 + tmp10
    tmp13 = tmp11 + tmp12
    tmp15 = tmp13 + tmp14
    tmp16 = tmp15 / tmp7
    tmp17 = tmp8 + tmp16
    tmp20 = tmp18 + tmp19
    tmp22 = tmp20 + tmp21
    tmp24 = tmp22 + tmp23
    tmp25 = tmp24 / tmp7
    tmp26 = tmp17 + tmp25
    tmp29 = tmp27 + tmp28
    tmp31 = tmp29 + tmp30
    tmp33 = tmp31 + tmp32
    tmp34 = tmp33 / tmp7
    tmp35 = tmp26 + tmp34
    tmp36 = tmp35 / tmp7
    tmp37 = tmp0 - tmp36
    tmp38 = tmp37 * tmp37
    tmp39 = tmp1 - tmp36
    tmp40 = tmp39 * tmp39
    tmp41 = tmp38 + tmp40
    tmp42 = tmp3 - tmp36
    tmp43 = tmp42 * tmp42
    tmp44 = tmp41 + tmp43
    tmp45 = tmp5 - tmp36
    tmp46 = tmp45 * tmp45
    tmp47 = tmp44 + tmp46
    tmp48 = tmp47 / tmp7
    tmp49 = tmp9 - tmp36
    tmp50 = tmp49 * tmp49
    tmp51 = tmp10 - tmp36
    tmp52 = tmp51 * tmp51
    tmp53 = tmp50 + tmp52
    tmp54 = tmp12 - tmp36
    tmp55 = tmp54 * tmp54
    tmp56 = tmp53 + tmp55
    tmp57 = tmp14 - tmp36
    tmp58 = tmp57 * tmp57
    tmp59 = tmp56 + tmp58
    tmp60 = tmp59 / tmp7
    tmp61 = tmp48 + tmp60
    tmp62 = tmp18 - tmp36
    tmp63 = tmp62 * tmp62
    tmp64 = tmp19 - tmp36
    tmp65 = tmp64 * tmp64
    tmp66 = tmp63 + tmp65
    tmp67 = tmp21 - tmp36
    tmp68 = tmp67 * tmp67
    tmp69 = tmp66 + tmp68
    tmp70 = tmp23 - tmp36
    tmp71 = tmp70 * tmp70
    tmp72 = tmp69 + tmp71
    tmp73 = tmp72 / tmp7
    tmp74 = tmp61 + tmp73
    tmp75 = tmp27 - tmp36
    tmp76 = tmp75 * tmp75
    tmp77 = tmp28 - tmp36
    tmp78 = tmp77 * tmp77
    tmp79 = tmp76 + tmp78
    tmp80 = tmp30 - tmp36
    tmp81 = tmp80 * tmp80
    tmp82 = tmp79 + tmp81
    tmp83 = tmp32 - tmp36
    tmp84 = tmp83 * tmp83
    tmp85 = tmp82 + tmp84
    tmp86 = tmp85 / tmp7
    tmp87 = tmp74 + tmp86
    tmp88 = tmp87 / tmp7
    tl.store(out_ptr0 + x2, tmp36, xmask)
    tl.store(out_ptr1 + x2, tmp88, xmask)


@triton.jit
def triton_poi_fused_add_div_mul_pow_sub_1(in_ptr0, in_ptr1, in_ptr2,
    in_ptr3, in_ptr4, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = xindex // 4 % 4
    x4 = xindex
    x0 = xindex % 4
    x3 = xindex // 64
    tmp0 = tl.load(in_ptr0 + x1, xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + x4, xmask)
    tmp2 = tl.load(in_ptr2 + (x0 + 4 * x3), xmask, eviction_policy='evict_last'
        )
    tmp5 = tl.load(in_ptr3 + (x0 + 4 * x3), xmask, eviction_policy='evict_last'
        )
    tmp10 = tl.load(in_ptr4 + x1, xmask, eviction_policy='evict_last')
    tmp3 = tmp1 - tmp2
    tmp4 = tmp0 * tmp3
    tmp6 = 1e-08
    tmp7 = tmp5 + tmp6
    tmp8 = libdevice.sqrt(tmp7)
    tmp9 = tmp4 / tmp8
    tmp11 = tmp9 + tmp10
    tl.store(out_ptr0 + x4, tmp11, xmask)


def call(args):
    primals_1, primals_2, primals_3 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_2, (1, 4, 1), (4, 1, 1))
    assert_size_stride(primals_3, (1, 4, 1), (4, 1, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 1, 1, 4), (4, 16, 16, 1), torch.float32)
        buf1 = empty_strided_cuda((4, 1, 1, 4), (4, 16, 16, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_mean_pow_sub_0[grid(16)](primals_1, buf0, buf1, 16,
            XBLOCK=16, num_warps=1, num_stages=1)
        buf2 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        triton_poi_fused_add_div_mul_pow_sub_1[grid(256)](primals_2,
            primals_1, buf0, buf1, primals_3, buf2, 256, XBLOCK=256,
            num_warps=4, num_stages=1)
        del buf0
        del buf1
        del primals_2
        del primals_3
    return buf2, primals_1


class GlobalLayerNormNew(nn.Module):

    def __init__(self, channel_size):
        super(GlobalLayerNormNew, self).__init__()
        self.gamma = nn.Parameter(torch.Tensor(1, channel_size, 1))
        self.beta = nn.Parameter(torch.Tensor(1, channel_size, 1))
        self.reset_parameters()

    def reset_parameters(self):
        self.gamma.data.fill_(1)
        self.beta.data.zero_()

    def forward(self, input_0):
        primals_2 = self.gamma
        primals_3 = self.beta
        primals_1 = input_0
        output = call([primals_1, primals_2, primals_3])
        return output[0]
