wehub-resource-sync
85 lines
3.0 KiB
C++
85 lines
3.0 KiB
C++
// Copyright (c) 2026 PaddlePaddle Authors. All Rights Reserved.
|
|
//
|
|
// Licensed under the Apache License, Version 2.0 (the "License");
|
|
// you may not use this file except in compliance with the License.
|
|
// You may obtain a copy of the License at
|
|
//
|
|
// http://www.apache.org/licenses/LICENSE-2.0
|
|
//
|
|
// Unless required by applicable law or agreed to in writing, software
|
|
// distributed under the License is distributed on an "AS IS" BASIS,
|
|
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
// See the License for the specific language governing permissions and
|
|
// limitations under the License.
|
|
|
|
#include "paddle/phi/kernels/std_var_kernel.h"
|
|
|
|
#include "paddle/phi/backends/all_context.h"
|
|
#include "paddle/phi/core/kernel_registry.h"
|
|
#include "paddle/phi/kernels/activation_kernel.h"
|
|
#include "paddle/phi/kernels/elementwise_multiply_kernel.h"
|
|
#include "paddle/phi/kernels/elementwise_subtract_kernel.h"
|
|
#include "paddle/phi/kernels/full_kernel.h"
|
|
#include "paddle/phi/kernels/reduce_mean_kernel.h"
|
|
#include "paddle/phi/kernels/reduce_sum_kernel.h"
|
|
#include "paddle/phi/kernels/scale_kernel.h"
|
|
|
|
namespace phi {
|
|
|
|
template <typename T, typename Context>
|
|
void VarKernel(const Context& dev_ctx,
|
|
const DenseTensor& x,
|
|
const std::vector<int64_t>& axis,
|
|
bool keepdim,
|
|
bool unbiased,
|
|
double correction,
|
|
DenseTensor* out) {
|
|
if (x.numel() == 0) {
|
|
Full<T, Context>(dev_ctx, out->dims(), static_cast<T>(NAN), out);
|
|
return;
|
|
}
|
|
// 1. Mean
|
|
// Use keepdim=true for broadcasting in subtraction
|
|
DenseTensor mean_val = Mean<T, Context>(dev_ctx, x, axis, true);
|
|
|
|
// 2. Subtract: x - mean
|
|
DenseTensor sub_res = Subtract<T, Context>(dev_ctx, x, mean_val);
|
|
|
|
// 3. Square: (x - mean)^2
|
|
DenseTensor sq_res = Multiply<T, Context>(dev_ctx, sub_res, sub_res);
|
|
|
|
// 4. Sum: Sum((x - mean)^2)
|
|
DenseTensor sum = Sum<T, Context>(dev_ctx, sq_res, axis, x.dtype(), keepdim);
|
|
|
|
// 5. Divide by (N - correction)
|
|
double n = static_cast<double>(x.numel()) / static_cast<double>(out->numel());
|
|
double divisor = 0;
|
|
if (n - correction >= 0) {
|
|
divisor = 1.0 / (n - correction);
|
|
}
|
|
|
|
DenseTensor scale_val =
|
|
FullLike<T, Context>(dev_ctx, *out, static_cast<T>(divisor));
|
|
MultiplyKernel<T, Context>(dev_ctx, sum, scale_val, out);
|
|
}
|
|
|
|
template <typename T, typename Context>
|
|
void StdKernel(const Context& dev_ctx,
|
|
const DenseTensor& x,
|
|
const std::vector<int64_t>& axis,
|
|
bool keepdim,
|
|
bool unbiased,
|
|
double correction,
|
|
DenseTensor* out) {
|
|
if (x.numel() == 0) {
|
|
Full<T, Context>(dev_ctx, out->dims(), static_cast<T>(NAN), out);
|
|
return;
|
|
}
|
|
VarKernel<T, Context>(dev_ctx, x, axis, keepdim, unbiased, correction, out);
|
|
SqrtKernel<T, Context>(dev_ctx, *out, out);
|
|
}
|
|
|
|
} // namespace phi
|
|
PD_REGISTER_KERNEL(var, CPU, ALL_LAYOUT, phi::VarKernel, float, double) {}
|
|
PD_REGISTER_KERNEL(std, CPU, ALL_LAYOUT, phi::StdKernel, float, double) {}
|