# 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_4/inductor_cache/3s/c3skx4mn2m33smtk7yzu674msvwvmvura3rr2xlhz6y3zxy3e2j4.py
# Topologically Sorted Source Nodes: [pow_1, result], Original ATen: [aten.pow, aten.add]
# Source node to ATen node mapping:
#   pow_1 => pow_1
#   result => add
# Graph fragment:
#   %pow_1 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%select, 2), kwargs = {})
#   %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%pow_1, 0), kwargs = {})
triton_poi_fused_add_pow_0 = async_compile.triton('triton_poi_fused_add_pow_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=[1], 
    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': {2: 1}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1), equal_to_1=(2,))]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_add_pow_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, '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_pow_0(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 1
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    tmp0 = tl.load(in_ptr0 + (3))
    tmp1 = tl.broadcast_to(tmp0, [XBLOCK])
    tmp2 = tmp1 * tmp1
    tmp3 = 0.0
    tmp4 = tmp2 + tmp3
    tl.store(out_ptr0 + (tl.full([XBLOCK], 0, tl.int32)), tmp4, 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, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        # Topologically Sorted Source Nodes: [svd], Original ATen: [aten._linalg_svd]
        buf0 = torch.ops.aten._linalg_svd.default(reinterpret_tensor(arg0_1, (4, 4), (1, 4), 0))
        del arg0_1
        buf2 = buf0[1]
        del buf0
        buf4 = empty_strided_cuda((), (), torch.float32)
        # Topologically Sorted Source Nodes: [pow_1, result], Original ATen: [aten.pow, aten.add]
        stream0 = get_raw_stream(0)
        triton_poi_fused_add_pow_0.run(buf2, buf4, 1, grid=grid(1), stream=stream0)
        del buf2
    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, 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.data


class BatchSpectralShrinkage(nn.Module):
    """
    The regularization term in `Catastrophic Forgetting Meets Negative Transfer:
    Batch Spectral Shrinkage for Safe Transfer Learning (NIPS 2019) <https://proceedings.neurips.cc/paper/2019/file/c6bff625bdb0393992c9d4db0c6bbe45-Paper.pdf>`_.


    The BSS regularization of feature matrix :math:`F` can be described as:

    .. math::
        L_{bss}(F) = \\sum_{i=1}^{k} \\sigma_{-i}^2 ,

    where :math:`k` is the number of singular values to be penalized, :math:`\\sigma_{-i}` is the :math:`i`-th smallest singular value of feature matrix :math:`F`.

    All the singular values of feature matrix :math:`F` are computed by `SVD`:

    .. math::
        F = U\\Sigma V^T,

    where the main diagonal elements of the singular value matrix :math:`\\Sigma` is :math:`[\\sigma_1, \\sigma_2, ..., \\sigma_b]`.


    Args:
        k (int):  The number of singular values to be penalized. Default: 1

    Shape:
        - Input: :math:`(b, |\\mathcal{f}|)` where :math:`b` is the batch size and :math:`|\\mathcal{f}|` is feature dimension.
        - Output: scalar.

    """

    def __init__(self, k=1):
        super(BatchSpectralShrinkage, self).__init__()
        self.k = k

    def forward(self, feature):
        result = 0
        _u, s, _v = torch.svd(feature.t())
        num = s.size(0)
        for i in range(self.k):
            result += torch.pow(s[num - 1 - i], 2)
        return result


def get_inputs():
    return [torch.rand([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
import torch.nn as nn
import torch.utils.data
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor


@triton.jit
def triton_poi_fused_add_pow_0(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr
    ):
    xoffset = tl.program_id(0) * XBLOCK
    xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    tmp0 = tl.load(in_ptr0 + 3)
    tmp1 = tl.broadcast_to(tmp0, [XBLOCK])
    tmp2 = tmp1 * tmp1
    tmp3 = 0.0
    tmp4 = tmp2 + tmp3
    tl.store(out_ptr0 + tl.full([XBLOCK], 0, tl.int32), tmp4, None)


def call(args):
    arg0_1, = args
    args.clear()
    assert_size_stride(arg0_1, (4, 4), (4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = torch.ops.aten._linalg_svd.default(reinterpret_tensor(arg0_1,
            (4, 4), (1, 4), 0))
        del arg0_1
        buf2 = buf0[1]
        del buf0
        buf4 = empty_strided_cuda((), (), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_add_pow_0[grid(1)](buf2, buf4, 1, XBLOCK=1,
            num_warps=1, num_stages=1)
        del buf2
    return buf4,


class BatchSpectralShrinkageNew(nn.Module):
    """
    The regularization term in `Catastrophic Forgetting Meets Negative Transfer:
    Batch Spectral Shrinkage for Safe Transfer Learning (NIPS 2019) <https://proceedings.neurips.cc/paper/2019/file/c6bff625bdb0393992c9d4db0c6bbe45-Paper.pdf>`_.


    The BSS regularization of feature matrix :math:`F` can be described as:

    .. math::
        L_{bss}(F) = \\sum_{i=1}^{k} \\sigma_{-i}^2 ,

    where :math:`k` is the number of singular values to be penalized, :math:`\\sigma_{-i}` is the :math:`i`-th smallest singular value of feature matrix :math:`F`.

    All the singular values of feature matrix :math:`F` are computed by `SVD`:

    .. math::
        F = U\\Sigma V^T,

    where the main diagonal elements of the singular value matrix :math:`\\Sigma` is :math:`[\\sigma_1, \\sigma_2, ..., \\sigma_b]`.


    Args:
        k (int):  The number of singular values to be penalized. Default: 1

    Shape:
        - Input: :math:`(b, |\\mathcal{f}|)` where :math:`b` is the batch size and :math:`|\\mathcal{f}|` is feature dimension.
        - Output: scalar.

    """

    def __init__(self, k=1):
        super(BatchSpectralShrinkageNew, self).__init__()
        self.k = k

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