# 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/xu/cxuw4zngts3btgwtvpkzjuhzg5qrx7eatj7fzhrybxfvwesbsblb.py
# Topologically Sorted Source Nodes: [norm_1, norm, radius_1, sub, pow_1, loss], Original ATen: [aten.linalg_vector_norm, aten.add, aten.sub, aten.pow, aten.mean]
# Source node to ATen node mapping:
#   loss => mean
#   norm => pow_1, pow_2, sum_1
#   norm_1 => pow_3, pow_4, sum_2
#   pow_1 => pow_5
#   radius_1 => add
#   sub => sub
# Graph fragment:
#   %pow_3 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%arg0_1, 2), kwargs = {})
#   %sum_2 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%pow_3, [1]), kwargs = {})
#   %pow_4 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%sum_2, 0.5), kwargs = {})
#   %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.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 = {})
#   %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%pow_2, 4), kwargs = {})
#   %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%pow_4, %add), kwargs = {})
#   %pow_5 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%sub, 2), kwargs = {})
#   %mean : [num_users=1] = call_function[target=torch.ops.aten.mean.default](args = (%pow_5,), kwargs = {})
triton_per_fused_add_linalg_vector_norm_mean_pow_sub_0 = async_compile.triton('triton_per_fused_add_linalg_vector_norm_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.persistent_reduction(
    size_hints=[1, 64],
    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_linalg_vector_norm_mean_pow_sub_0', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 4, '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_per_fused_add_linalg_vector_norm_mean_pow_sub_0(in_out_ptr0, in_ptr0, xnumel, rnumel, XBLOCK : tl.constexpr):
    xnumel = 1
    rnumel = 64
    RBLOCK: tl.constexpr = 64
    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 % 16
    r1 = (rindex // 16)
    tmp0 = tl.load(in_ptr0 + (r0 + (64*r1)), None)
    tmp2 = tl.load(in_ptr0 + (16 + r0 + (64*r1)), None)
    tmp5 = tl.load(in_ptr0 + (32 + r0 + (64*r1)), None)
    tmp8 = tl.load(in_ptr0 + (48 + r0 + (64*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 = 4.0
    tmp13 = tmp11 + tmp12
    tmp14 = tmp11 - tmp13
    tmp15 = tmp14 * tmp14
    tmp16 = tl.broadcast_to(tmp15, [XBLOCK, RBLOCK])
    tmp18 = tl.sum(tmp16, 1)[:, None]
    tmp19 = 64.0
    tmp20 = tmp18 / tmp19
    tl.debug_barrier()
    tl.store(in_out_ptr0 + (tl.full([XBLOCK, 1], 0, tl.int32)), tmp20, 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)
        buf1 = buf0; del buf0  # reuse
        # Topologically Sorted Source Nodes: [norm_1, norm, radius_1, sub, pow_1, loss], Original ATen: [aten.linalg_vector_norm, aten.add, aten.sub, aten.pow, aten.mean]
        stream0 = get_raw_stream(0)
        triton_per_fused_add_linalg_vector_norm_mean_pow_sub_0.run(buf1, arg0_1, 1, 64, 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.utils.data


class AdaptiveFeatureNorm(nn.Module):
    """
    The `Stepwise Adaptive Feature Norm loss (ICCV 2019) <https://arxiv.org/pdf/1811.07456v2.pdf>`_

    Instead of using restrictive scalar R to match the corresponding feature norm, Stepwise Adaptive Feature Norm
    is used in order to learn task-specific features with large norms in a progressive manner.
    Given feature representations :math:`f` on source or target domain, the definition of Stepwise Adaptive Feature Norm loss is

    .. math::
        norm\\_loss = \\mathbb{E}_{i}(\\Vert f_i \\Vert_2.detach() + delta - \\Vert f_i \\Vert_2)^2\\\\

    Args:
        delta (float): positive residual scalar to control the feature norm enlargement.

    Inputs:
        - f (tensor): feature representations on source or target domain.

    Shape:
        - f: :math:`(N, F)` where F means the dimension of input features.
        - Outputs: scalar.

    Examples::

        >>> adaptive_feature_norm = AdaptiveFeatureNorm(delta=1)
        >>> f_s = torch.randn(32, 1000)
        >>> f_t = torch.randn(32, 1000)
        >>> norm_loss = adaptive_feature_norm(f_s) + adaptive_feature_norm(f_t)
    """

    def __init__(self, delta):
        super(AdaptiveFeatureNorm, self).__init__()
        self.delta = delta

    def forward(self, f: 'torch.Tensor') ->torch.Tensor:
        radius = f.norm(p=2, dim=1).detach()
        assert radius.requires_grad is False
        radius = radius + self.delta
        loss = ((f.norm(p=2, dim=1) - radius) ** 2).mean()
        return loss


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


def get_init_inputs():
    return [[], {'delta': 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.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_linalg_vector_norm_mean_pow_sub_0(in_out_ptr0,
    in_ptr0, xnumel, rnumel, XBLOCK: tl.constexpr):
    RBLOCK: tl.constexpr = 64
    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 % 16
    r1 = rindex // 16
    tmp0 = tl.load(in_ptr0 + (r0 + 64 * r1), None)
    tmp2 = tl.load(in_ptr0 + (16 + r0 + 64 * r1), None)
    tmp5 = tl.load(in_ptr0 + (32 + r0 + 64 * r1), None)
    tmp8 = tl.load(in_ptr0 + (48 + r0 + 64 * 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 = 4.0
    tmp13 = tmp11 + tmp12
    tmp14 = tmp11 - tmp13
    tmp15 = tmp14 * tmp14
    tmp16 = tl.broadcast_to(tmp15, [XBLOCK, RBLOCK])
    tmp18 = tl.sum(tmp16, 1)[:, None]
    tmp19 = 64.0
    tmp20 = tmp18 / tmp19
    tl.debug_barrier()
    tl.store(in_out_ptr0 + tl.full([XBLOCK, 1], 0, tl.int32), tmp20, 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)
        buf1 = buf0
        del buf0
        get_raw_stream(0)
        triton_per_fused_add_linalg_vector_norm_mean_pow_sub_0[grid(1)](buf1,
            arg0_1, 1, 64, XBLOCK=1, num_warps=2, num_stages=1)
        del arg0_1
    return buf1,


class AdaptiveFeatureNormNew(nn.Module):
    """
    The `Stepwise Adaptive Feature Norm loss (ICCV 2019) <https://arxiv.org/pdf/1811.07456v2.pdf>`_

    Instead of using restrictive scalar R to match the corresponding feature norm, Stepwise Adaptive Feature Norm
    is used in order to learn task-specific features with large norms in a progressive manner.
    Given feature representations :math:`f` on source or target domain, the definition of Stepwise Adaptive Feature Norm loss is

    .. math::
        norm\\_loss = \\mathbb{E}_{i}(\\Vert f_i \\Vert_2.detach() + delta - \\Vert f_i \\Vert_2)^2\\\\

    Args:
        delta (float): positive residual scalar to control the feature norm enlargement.

    Inputs:
        - f (tensor): feature representations on source or target domain.

    Shape:
        - f: :math:`(N, F)` where F means the dimension of input features.
        - Outputs: scalar.

    Examples::

        >>> adaptive_feature_norm = AdaptiveFeatureNorm(delta=1)
        >>> f_s = torch.randn(32, 1000)
        >>> f_t = torch.randn(32, 1000)
        >>> norm_loss = adaptive_feature_norm(f_s) + adaptive_feature_norm(f_t)
    """

    def __init__(self, delta):
        super(AdaptiveFeatureNormNew, self).__init__()
        self.delta = delta

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