# 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_6/inductor_cache/qu/cqus46ccg6zxy23dy53q5ruz2v7a6tr32ti6ki3ofym6a7x3xwoa.py
# Topologically Sorted Source Nodes: [norm, pow_1, sum_1, mul, norm_1, norm_2, pow_2, sum_2, mul_1, norm_3, norm_4, pow_3, sum_3, mul_2, norm_5, norm_6, pow_4, sum_4, mul_3, norm_7, truediv], Original ATen: [aten.linalg_vector_norm, aten.pow, aten.sum, aten.mul, aten.add, aten.div]
# Source node to ATen node mapping:
#   mul => mul
#   mul_1 => mul_1
#   mul_2 => mul_2
#   mul_3 => mul_3
#   norm => pow_1, pow_2, sum_1
#   norm_1 => add
#   norm_2 => pow_4, pow_5, sum_3
#   norm_3 => add_1
#   norm_4 => pow_7, pow_8, sum_5
#   norm_5 => add_2
#   norm_6 => pow_10, pow_11, sum_7
#   norm_7 => add_3
#   pow_1 => pow_3
#   pow_2 => pow_6
#   pow_3 => pow_9
#   pow_4 => pow_12
#   sum_1 => sum_2
#   sum_2 => sum_4
#   sum_3 => sum_6
#   sum_4 => sum_8
#   truediv => div
# Graph fragment:
#   %pow_1 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%select, 2), kwargs = {})
#   %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%pow_1, [1]), kwargs = {})
#   %pow_2 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%sum_1, 0.5), kwargs = {})
#   %pow_3 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%pow_2, 3), kwargs = {})
#   %sum_2 : [num_users=1] = call_function[target=torch.ops.aten.sum.default](args = (%pow_3,), kwargs = {})
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sum_2, 4), kwargs = {})
#   %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul, 0), kwargs = {})
#   %pow_4 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%select_1, 2), kwargs = {})
#   %sum_3 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%pow_4, [1]), kwargs = {})
#   %pow_5 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%sum_3, 0.5), kwargs = {})
#   %pow_6 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%pow_5, 3), kwargs = {})
#   %sum_4 : [num_users=1] = call_function[target=torch.ops.aten.sum.default](args = (%pow_6,), kwargs = {})
#   %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sum_4, 4), kwargs = {})
#   %add_1 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%add, %mul_1), kwargs = {})
#   %pow_7 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%select_2, 2), kwargs = {})
#   %sum_5 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%pow_7, [1]), kwargs = {})
#   %pow_8 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%sum_5, 0.5), kwargs = {})
#   %pow_9 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%pow_8, 3), kwargs = {})
#   %sum_6 : [num_users=1] = call_function[target=torch.ops.aten.sum.default](args = (%pow_9,), kwargs = {})
#   %mul_2 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sum_6, 4), kwargs = {})
#   %add_2 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%add_1, %mul_2), kwargs = {})
#   %pow_10 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%select_3, 2), kwargs = {})
#   %sum_7 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%pow_10, [1]), kwargs = {})
#   %pow_11 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%sum_7, 0.5), kwargs = {})
#   %pow_12 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%pow_11, 3), kwargs = {})
#   %sum_8 : [num_users=1] = call_function[target=torch.ops.aten.sum.default](args = (%pow_12,), kwargs = {})
#   %mul_3 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sum_8, 4), kwargs = {})
#   %add_3 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%add_2, %mul_3), kwargs = {})
#   %div : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%add_3, 4), kwargs = {})
triton_per_fused_add_div_linalg_vector_norm_mul_pow_sum_0 = async_compile.triton('triton_per_fused_add_div_linalg_vector_norm_mul_pow_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, 16],
    reduction_hint=ReductionHint.INNER,
    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_per_fused_add_div_linalg_vector_norm_mul_pow_sum_0', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 16, 'num_reduction': 4, '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_div_linalg_vector_norm_mul_pow_sum_0(in_out_ptr0, in_ptr0, xnumel, rnumel, XBLOCK : tl.constexpr):
    xnumel = 1
    rnumel = 16
    RBLOCK: tl.constexpr = 16
    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 % 4
    r1 = (rindex // 4)
    tmp0 = tl.load(in_ptr0 + (r0 + (16*r1)), None)
    tmp2 = tl.load(in_ptr0 + (4 + r0 + (16*r1)), None)
    tmp5 = tl.load(in_ptr0 + (8 + r0 + (16*r1)), None)
    tmp8 = tl.load(in_ptr0 + (12 + r0 + (16*r1)), None)
    tmp17 = tl.load(in_ptr0 + (64 + r0 + (16*r1)), None)
    tmp19 = tl.load(in_ptr0 + (68 + r0 + (16*r1)), None)
    tmp22 = tl.load(in_ptr0 + (72 + r0 + (16*r1)), None)
    tmp25 = tl.load(in_ptr0 + (76 + r0 + (16*r1)), None)
    tmp34 = tl.load(in_ptr0 + (128 + r0 + (16*r1)), None)
    tmp36 = tl.load(in_ptr0 + (132 + r0 + (16*r1)), None)
    tmp39 = tl.load(in_ptr0 + (136 + r0 + (16*r1)), None)
    tmp42 = tl.load(in_ptr0 + (140 + r0 + (16*r1)), None)
    tmp51 = tl.load(in_ptr0 + (192 + r0 + (16*r1)), None)
    tmp53 = tl.load(in_ptr0 + (196 + r0 + (16*r1)), None)
    tmp56 = tl.load(in_ptr0 + (200 + r0 + (16*r1)), None)
    tmp59 = tl.load(in_ptr0 + (204 + r0 + (16*r1)), None)
    tmp1 = tmp0 * tmp0
    tmp3 = tmp2 * tmp2
    tmp4 = tmp1 + tmp3
    tmp6 = tmp5 * tmp5
    tmp7 = tmp4 + tmp6
    tmp9 = tmp8 * tmp8
    tmp10 = tmp7 + tmp9
    tmp11 = libdevice.sqrt(tmp10)
    tmp12 = tmp11 * tmp11
    tmp13 = tmp12 * tmp11
    tmp14 = tl.broadcast_to(tmp13, [XBLOCK, RBLOCK])
    tmp16 = tl.sum(tmp14, 1)[:, None]
    tmp18 = tmp17 * tmp17
    tmp20 = tmp19 * tmp19
    tmp21 = tmp18 + tmp20
    tmp23 = tmp22 * tmp22
    tmp24 = tmp21 + tmp23
    tmp26 = tmp25 * tmp25
    tmp27 = tmp24 + tmp26
    tmp28 = libdevice.sqrt(tmp27)
    tmp29 = tmp28 * tmp28
    tmp30 = tmp29 * tmp28
    tmp31 = tl.broadcast_to(tmp30, [XBLOCK, RBLOCK])
    tmp33 = tl.sum(tmp31, 1)[:, None]
    tmp35 = tmp34 * tmp34
    tmp37 = tmp36 * tmp36
    tmp38 = tmp35 + tmp37
    tmp40 = tmp39 * tmp39
    tmp41 = tmp38 + tmp40
    tmp43 = tmp42 * tmp42
    tmp44 = tmp41 + tmp43
    tmp45 = libdevice.sqrt(tmp44)
    tmp46 = tmp45 * tmp45
    tmp47 = tmp46 * tmp45
    tmp48 = tl.broadcast_to(tmp47, [XBLOCK, RBLOCK])
    tmp50 = tl.sum(tmp48, 1)[:, None]
    tmp52 = tmp51 * tmp51
    tmp54 = tmp53 * tmp53
    tmp55 = tmp52 + tmp54
    tmp57 = tmp56 * tmp56
    tmp58 = tmp55 + tmp57
    tmp60 = tmp59 * tmp59
    tmp61 = tmp58 + tmp60
    tmp62 = libdevice.sqrt(tmp61)
    tmp63 = tmp62 * tmp62
    tmp64 = tmp63 * tmp62
    tmp65 = tl.broadcast_to(tmp64, [XBLOCK, RBLOCK])
    tmp67 = tl.sum(tmp65, 1)[:, None]
    tmp68 = 4.0
    tmp69 = tmp16 * tmp68
    tmp70 = 0.0
    tmp71 = tmp69 + tmp70
    tmp72 = tmp33 * tmp68
    tmp73 = tmp71 + tmp72
    tmp74 = tmp50 * tmp68
    tmp75 = tmp73 + tmp74
    tmp76 = tmp67 * tmp68
    tmp77 = tmp75 + tmp76
    tmp78 = 0.25
    tmp79 = tmp77 * tmp78
    tl.debug_barrier()
    tl.store(in_out_ptr0 + (tl.full([XBLOCK, 1], 0, tl.int32)), tmp79, None)
''', 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)
        buf0 = empty_strided_cuda((), (), torch.float32)
        buf4 = buf0; del buf0  # reuse
        # Topologically Sorted Source Nodes: [norm, pow_1, sum_1, mul, norm_1, norm_2, pow_2, sum_2, mul_1, norm_3, norm_4, pow_3, sum_3, mul_2, norm_5, norm_6, pow_4, sum_4, mul_3, norm_7, truediv], Original ATen: [aten.linalg_vector_norm, aten.pow, aten.sum, aten.mul, aten.add, aten.div]
        stream0 = get_raw_stream(0)
        triton_per_fused_add_div_linalg_vector_norm_mul_pow_sum_0.run(buf4, arg0_1, 1, 16, grid=grid(1), stream=stream0)
        del arg0_1
    return (buf4, )


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.utils
from typing import Tuple
from abc import ABC
from abc import abstractmethod
import torch.utils.data


class Regularizer(nn.Module, ABC):

    @abstractmethod
    def forward(self, factors: 'Tuple[torch.Tensor]'):
        pass


class N2(Regularizer):

    def __init__(self, weight: 'float'):
        super(N2, self).__init__()
        self.weight = weight

    def forward(self, factors):
        norm = 0
        for f in factors:
            norm += self.weight * torch.sum(torch.norm(f, 2, 1) ** 3)
        return norm / factors[0].shape[0]


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


def get_init_inputs():
    return [[], {'weight': 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.utils
from typing import Tuple
from abc import ABC
from abc import abstractmethod
import torch.utils.data
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_div_linalg_vector_norm_mul_pow_sum_0(in_out_ptr0,
    in_ptr0, xnumel, rnumel, XBLOCK: tl.constexpr):
    RBLOCK: tl.constexpr = 16
    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 % 4
    r1 = rindex // 4
    tmp0 = tl.load(in_ptr0 + (r0 + 16 * r1), None)
    tmp2 = tl.load(in_ptr0 + (4 + r0 + 16 * r1), None)
    tmp5 = tl.load(in_ptr0 + (8 + r0 + 16 * r1), None)
    tmp8 = tl.load(in_ptr0 + (12 + r0 + 16 * r1), None)
    tmp17 = tl.load(in_ptr0 + (64 + r0 + 16 * r1), None)
    tmp19 = tl.load(in_ptr0 + (68 + r0 + 16 * r1), None)
    tmp22 = tl.load(in_ptr0 + (72 + r0 + 16 * r1), None)
    tmp25 = tl.load(in_ptr0 + (76 + r0 + 16 * r1), None)
    tmp34 = tl.load(in_ptr0 + (128 + r0 + 16 * r1), None)
    tmp36 = tl.load(in_ptr0 + (132 + r0 + 16 * r1), None)
    tmp39 = tl.load(in_ptr0 + (136 + r0 + 16 * r1), None)
    tmp42 = tl.load(in_ptr0 + (140 + r0 + 16 * r1), None)
    tmp51 = tl.load(in_ptr0 + (192 + r0 + 16 * r1), None)
    tmp53 = tl.load(in_ptr0 + (196 + r0 + 16 * r1), None)
    tmp56 = tl.load(in_ptr0 + (200 + r0 + 16 * r1), None)
    tmp59 = tl.load(in_ptr0 + (204 + r0 + 16 * r1), None)
    tmp1 = tmp0 * tmp0
    tmp3 = tmp2 * tmp2
    tmp4 = tmp1 + tmp3
    tmp6 = tmp5 * tmp5
    tmp7 = tmp4 + tmp6
    tmp9 = tmp8 * tmp8
    tmp10 = tmp7 + tmp9
    tmp11 = libdevice.sqrt(tmp10)
    tmp12 = tmp11 * tmp11
    tmp13 = tmp12 * tmp11
    tmp14 = tl.broadcast_to(tmp13, [XBLOCK, RBLOCK])
    tmp16 = tl.sum(tmp14, 1)[:, None]
    tmp18 = tmp17 * tmp17
    tmp20 = tmp19 * tmp19
    tmp21 = tmp18 + tmp20
    tmp23 = tmp22 * tmp22
    tmp24 = tmp21 + tmp23
    tmp26 = tmp25 * tmp25
    tmp27 = tmp24 + tmp26
    tmp28 = libdevice.sqrt(tmp27)
    tmp29 = tmp28 * tmp28
    tmp30 = tmp29 * tmp28
    tmp31 = tl.broadcast_to(tmp30, [XBLOCK, RBLOCK])
    tmp33 = tl.sum(tmp31, 1)[:, None]
    tmp35 = tmp34 * tmp34
    tmp37 = tmp36 * tmp36
    tmp38 = tmp35 + tmp37
    tmp40 = tmp39 * tmp39
    tmp41 = tmp38 + tmp40
    tmp43 = tmp42 * tmp42
    tmp44 = tmp41 + tmp43
    tmp45 = libdevice.sqrt(tmp44)
    tmp46 = tmp45 * tmp45
    tmp47 = tmp46 * tmp45
    tmp48 = tl.broadcast_to(tmp47, [XBLOCK, RBLOCK])
    tmp50 = tl.sum(tmp48, 1)[:, None]
    tmp52 = tmp51 * tmp51
    tmp54 = tmp53 * tmp53
    tmp55 = tmp52 + tmp54
    tmp57 = tmp56 * tmp56
    tmp58 = tmp55 + tmp57
    tmp60 = tmp59 * tmp59
    tmp61 = tmp58 + tmp60
    tmp62 = libdevice.sqrt(tmp61)
    tmp63 = tmp62 * tmp62
    tmp64 = tmp63 * tmp62
    tmp65 = tl.broadcast_to(tmp64, [XBLOCK, RBLOCK])
    tmp67 = tl.sum(tmp65, 1)[:, None]
    tmp68 = 4.0
    tmp69 = tmp16 * tmp68
    tmp70 = 0.0
    tmp71 = tmp69 + tmp70
    tmp72 = tmp33 * tmp68
    tmp73 = tmp71 + tmp72
    tmp74 = tmp50 * tmp68
    tmp75 = tmp73 + tmp74
    tmp76 = tmp67 * tmp68
    tmp77 = tmp75 + tmp76
    tmp78 = 0.25
    tmp79 = tmp77 * tmp78
    tl.debug_barrier()
    tl.store(in_out_ptr0 + tl.full([XBLOCK, 1], 0, tl.int32), tmp79, None)


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)
        buf0 = empty_strided_cuda((), (), torch.float32)
        buf4 = buf0
        del buf0
        get_raw_stream(0)
        triton_per_fused_add_div_linalg_vector_norm_mul_pow_sum_0[grid(1)](buf4
            , arg0_1, 1, 16, XBLOCK=1, num_warps=2, num_stages=1)
        del arg0_1
    return buf4,


class Regularizer(nn.Module, ABC):

    @abstractmethod
    def forward(self, factors: 'Tuple[torch.Tensor]'):
        pass


class N2New(Regularizer):

    def __init__(self, weight: 'float'):
        super(N2New, self).__init__()
        self.weight = weight

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