# 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/wv/cwvafbj7ltvijthanyhc4yowo5jq3dmeuq2xhk5lnaciirroi5dp.py
# Topologically Sorted Source Nodes: [pow_1, sum_1, truediv, to_norm, mean, sub, std, add, ret, mul], Original ATen: [aten.pow, aten.sum, aten.reciprocal, aten.mul, aten.sqrt, aten.mean, aten.sub, aten.std, aten.add, aten.div]
# Source node to ATen node mapping:
#   add => add
#   mean => mean
#   mul => mul_1
#   pow_1 => pow_1
#   ret => div
#   std => sqrt_1, var
#   sub => sub
#   sum_1 => sum_1
#   to_norm => sqrt
#   truediv => mul, reciprocal
# Graph fragment:
#   %pow_1 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%arg0_1, 2), kwargs = {})
#   %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.default](args = (%pow_1,), kwargs = {})
#   %reciprocal : [num_users=1] = call_function[target=torch.ops.aten.reciprocal.default](args = (%sum_1,), kwargs = {})
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%reciprocal, 16), kwargs = {})
#   %sqrt : [num_users=1] = call_function[target=torch.ops.aten.sqrt.default](args = (%mul,), kwargs = {})
#   %mean : [num_users=1] = call_function[target=torch.ops.aten.mean.dim](args = (%arg0_1, [-1], True), kwargs = {})
#   %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%arg0_1, %mean), kwargs = {})
#   %var : [num_users=1] = call_function[target=torch.ops.aten.var.correction](args = (%arg0_1, [-1]), kwargs = {correction: 1.0, keepdim: True})
#   %sqrt_1 : [num_users=1] = call_function[target=torch.ops.aten.sqrt.default](args = (%var,), kwargs = {})
#   %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%sqrt_1, 1e-06), kwargs = {})
#   %div : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%sub, %add), kwargs = {})
#   %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sqrt, %div), kwargs = {})
triton_red_fused_add_div_mean_mul_pow_reciprocal_sqrt_std_sub_sum_0 = async_compile.triton('triton_red_fused_add_div_mean_mul_pow_reciprocal_sqrt_std_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.reduction(
    size_hints=[1, 256],
    reduction_hint=ReductionHint.DEFAULT,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32', 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': {2: 1}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 3), equal_to_1=(2,))]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_red_fused_add_div_mean_mul_pow_reciprocal_sqrt_std_sub_sum_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 6, '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_red_fused_add_div_mean_mul_pow_reciprocal_sqrt_std_sub_sum_0(in_ptr0, out_ptr1, xnumel, rnumel, XBLOCK : tl.constexpr, RBLOCK : tl.constexpr):
    xnumel = 1
    rnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = tl.full([XBLOCK, RBLOCK], True, tl.int1)
    rbase = tl.arange(0, RBLOCK)[None, :]
    _tmp3 = tl.full([XBLOCK, RBLOCK], 0, tl.float32)
    for roffset in range(0, rnumel, RBLOCK):
        rindex = roffset + rbase
        rmask = rindex < rnumel
        r0 = rindex
        tmp0 = tl.load(in_ptr0 + (r0), rmask, eviction_policy='evict_last', other=0.0)
        tmp1 = tmp0 * tmp0
        tmp2 = tl.broadcast_to(tmp1, [XBLOCK, RBLOCK])
        tmp4 = _tmp3 + tmp2
        _tmp3 = tl.where(rmask, tmp4, _tmp3)
    tmp3 = tl.sum(_tmp3, 1)[:, None]
    for roffset in range(0, rnumel, RBLOCK):
        rindex = roffset + rbase
        rmask = rindex < rnumel
        r0 = rindex
        r2 = (rindex // 4)
        tmp10 = tl.load(in_ptr0 + (r0), rmask, eviction_policy='evict_first', other=0.0)
        tmp11 = tl.load(in_ptr0 + (4*r2), rmask, eviction_policy='evict_last', other=0.0)
        tmp12 = tl.load(in_ptr0 + (1 + (4*r2)), rmask, eviction_policy='evict_last', other=0.0)
        tmp14 = tl.load(in_ptr0 + (2 + (4*r2)), rmask, eviction_policy='evict_last', other=0.0)
        tmp16 = tl.load(in_ptr0 + (3 + (4*r2)), rmask, eviction_policy='evict_last', other=0.0)
        tmp5 = tl.full([1, 1], 1, tl.int32)
        tmp6 = tmp5 / tmp3
        tmp7 = 16.0
        tmp8 = tmp6 * tmp7
        tmp9 = libdevice.sqrt(tmp8)
        tmp13 = tmp11 + tmp12
        tmp15 = tmp13 + tmp14
        tmp17 = tmp15 + tmp16
        tmp18 = 4.0
        tmp19 = tmp17 / tmp18
        tmp20 = tmp10 - tmp19
        tmp21 = tmp11 - tmp19
        tmp22 = tmp21 * tmp21
        tmp23 = tmp12 - tmp19
        tmp24 = tmp23 * tmp23
        tmp25 = tmp22 + tmp24
        tmp26 = tmp14 - tmp19
        tmp27 = tmp26 * tmp26
        tmp28 = tmp25 + tmp27
        tmp29 = tmp16 - tmp19
        tmp30 = tmp29 * tmp29
        tmp31 = tmp28 + tmp30
        tmp32 = 3.0
        tmp33 = tmp31 / tmp32
        tmp34 = libdevice.sqrt(tmp33)
        tmp35 = 1e-06
        tmp36 = tmp34 + tmp35
        tmp37 = tmp20 / tmp36
        tmp38 = tmp9 * tmp37
        tl.store(out_ptr1 + (tl.broadcast_to(r0, [XBLOCK, RBLOCK])), tmp38, rmask)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    arg0_1, = args
    args.clear()
    assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf1 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [pow_1, sum_1, truediv, to_norm, mean, sub, std, add, ret, mul], Original ATen: [aten.pow, aten.sum, aten.reciprocal, aten.mul, aten.sqrt, aten.mean, aten.sub, aten.std, aten.add, aten.div]
        stream0 = get_raw_stream(0)
        triton_red_fused_add_div_mean_mul_pow_reciprocal_sqrt_std_sub_sum_0.run(arg0_1, buf1, 1, 256, grid=grid(1), stream=stream0)
        del arg0_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)
    fn = lambda: call([arg0_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.cuda
import torch.distributed


class LayerNormAVG(nn.Module):
    """
        Layer Normalization class inspired by Transformer normalization, but here we normalize to given average
        to preserve magnitue of USE
    """

    def __init__(self, features, desired_avg, eps=1e-06):
        super(LayerNormAVG, self).__init__()
        self.desiredAVG = desired_avg
        self.eps = eps
        self.size = features

    def forward(self, x):
        to_norm = torch.sqrt(self.desiredAVG * self.size / torch.sum(x ** 2))
        mean = x.mean(-1, keepdim=True)
        std = x.std(-1, keepdim=True)
        ret = (x - mean) / (std + self.eps)
        return to_norm * ret


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


def get_init_inputs():
    return [[], {'features': 4, 'desired_avg': 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
import torch.cuda
import torch.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_red_fused_add_div_mean_mul_pow_reciprocal_sqrt_std_sub_sum_0(in_ptr0
    , out_ptr1, xnumel, rnumel, XBLOCK: tl.constexpr, RBLOCK: tl.constexpr):
    rnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xoffset + tl.arange(0, XBLOCK)[:, None]
    tl.full([XBLOCK, RBLOCK], True, tl.int1)
    rbase = tl.arange(0, RBLOCK)[None, :]
    _tmp3 = tl.full([XBLOCK, RBLOCK], 0, tl.float32)
    for roffset in range(0, rnumel, RBLOCK):
        rindex = roffset + rbase
        rmask = rindex < rnumel
        r0 = rindex
        tmp0 = tl.load(in_ptr0 + r0, rmask, eviction_policy='evict_last',
            other=0.0)
        tmp1 = tmp0 * tmp0
        tmp2 = tl.broadcast_to(tmp1, [XBLOCK, RBLOCK])
        tmp4 = _tmp3 + tmp2
        _tmp3 = tl.where(rmask, tmp4, _tmp3)
    tmp3 = tl.sum(_tmp3, 1)[:, None]
    for roffset in range(0, rnumel, RBLOCK):
        rindex = roffset + rbase
        rmask = rindex < rnumel
        r0 = rindex
        r2 = rindex // 4
        tmp10 = tl.load(in_ptr0 + r0, rmask, eviction_policy='evict_first',
            other=0.0)
        tmp11 = tl.load(in_ptr0 + 4 * r2, rmask, eviction_policy=
            'evict_last', other=0.0)
        tmp12 = tl.load(in_ptr0 + (1 + 4 * r2), rmask, eviction_policy=
            'evict_last', other=0.0)
        tmp14 = tl.load(in_ptr0 + (2 + 4 * r2), rmask, eviction_policy=
            'evict_last', other=0.0)
        tmp16 = tl.load(in_ptr0 + (3 + 4 * r2), rmask, eviction_policy=
            'evict_last', other=0.0)
        tmp5 = tl.full([1, 1], 1, tl.int32)
        tmp6 = tmp5 / tmp3
        tmp7 = 16.0
        tmp8 = tmp6 * tmp7
        tmp9 = libdevice.sqrt(tmp8)
        tmp13 = tmp11 + tmp12
        tmp15 = tmp13 + tmp14
        tmp17 = tmp15 + tmp16
        tmp18 = 4.0
        tmp19 = tmp17 / tmp18
        tmp20 = tmp10 - tmp19
        tmp21 = tmp11 - tmp19
        tmp22 = tmp21 * tmp21
        tmp23 = tmp12 - tmp19
        tmp24 = tmp23 * tmp23
        tmp25 = tmp22 + tmp24
        tmp26 = tmp14 - tmp19
        tmp27 = tmp26 * tmp26
        tmp28 = tmp25 + tmp27
        tmp29 = tmp16 - tmp19
        tmp30 = tmp29 * tmp29
        tmp31 = tmp28 + tmp30
        tmp32 = 3.0
        tmp33 = tmp31 / tmp32
        tmp34 = libdevice.sqrt(tmp33)
        tmp35 = 1e-06
        tmp36 = tmp34 + tmp35
        tmp37 = tmp20 / tmp36
        tmp38 = tmp9 * tmp37
        tl.store(out_ptr1 + tl.broadcast_to(r0, [XBLOCK, RBLOCK]), tmp38, rmask
            )


def call(args):
    arg0_1, = args
    args.clear()
    assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf1 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_red_fused_add_div_mean_mul_pow_reciprocal_sqrt_std_sub_sum_0[
            grid(1)](arg0_1, buf1, 1, 256, XBLOCK=1, RBLOCK=256, num_warps=
            8, num_stages=1)
        del arg0_1
    return buf1,


class LayerNormAVGNew(nn.Module):
    """
        Layer Normalization class inspired by Transformer normalization, but here we normalize to given average
        to preserve magnitue of USE
    """

    def __init__(self, features, desired_avg, eps=1e-06):
        super(LayerNormAVGNew, self).__init__()
        self.desiredAVG = desired_avg
        self.eps = eps
        self.size = features

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