wehub-resource-sync
104 lines
3.8 KiB
C++
104 lines
3.8 KiB
C++
// Copyright (c) 2022 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.
|
|
|
|
#pragma once
|
|
|
|
#include "paddle/phi/core/dense_tensor.h"
|
|
#include "paddle/phi/core/selected_rows.h"
|
|
#include "paddle/phi/kernels/funcs/blas/blas.h"
|
|
#include "paddle/phi/kernels/funcs/eigen/common.h"
|
|
#include "paddle/phi/kernels/funcs/eigen/eigen_function.h"
|
|
#include "paddle/phi/kernels/funcs/math_function.h"
|
|
#include "paddle/phi/kernels/funcs/matrix_bit_code.h"
|
|
|
|
namespace phi {
|
|
|
|
template <typename T, typename Context>
|
|
void HSigmoidLossGradKernelImpl(const Context& dev_ctx,
|
|
const DenseTensor& x,
|
|
const DenseTensor& w,
|
|
const DenseTensor& label,
|
|
const optional<DenseTensor>& path,
|
|
const optional<DenseTensor>& code,
|
|
const optional<DenseTensor>& bias UNUSED,
|
|
const DenseTensor& pre_out,
|
|
const DenseTensor& out_grad,
|
|
int num_classes,
|
|
bool is_sparse,
|
|
DenseTensor* x_grad,
|
|
DenseTensor* w_grad,
|
|
DenseTensor* bias_grad,
|
|
SelectedRows* w_grad_sr = nullptr) {
|
|
funcs::SetConstant<Context, T> zero;
|
|
DenseTensor pre_out_grad;
|
|
|
|
pre_out_grad.Resize(pre_out.dims());
|
|
dev_ctx.template Alloc<T>(&pre_out_grad);
|
|
dev_ctx.template Alloc<T>(x_grad);
|
|
zero(dev_ctx, x_grad, static_cast<T>(0.0));
|
|
|
|
bool is_custom = false;
|
|
if (path.get_ptr()) {
|
|
is_custom = true;
|
|
}
|
|
|
|
std::unique_ptr<funcs::MatrixBitCodeFunctor<T>> bit_code;
|
|
if (!is_custom) {
|
|
bit_code.reset(new funcs::MatrixBitCodeFunctor<T>(
|
|
num_classes, label.template data<int64_t>()));
|
|
} else {
|
|
bit_code.reset(new funcs::MatrixBitCodeFunctor<T>(
|
|
*(path.get_ptr()), *(code.get_ptr()), label.template data<int64_t>()));
|
|
}
|
|
|
|
// softrelu derivative
|
|
|
|
auto blas = funcs::GetBlas<Context, T>(dev_ctx);
|
|
|
|
auto* pre_out_grad_data = pre_out_grad.data<T>();
|
|
auto* pre_out_data = pre_out.template data<T>();
|
|
auto n = pre_out.numel();
|
|
blas.VEXP(n, pre_out_data, pre_out_grad_data);
|
|
blas.VINV(n, pre_out_grad_data, pre_out_grad_data);
|
|
for (int64_t i = 0; i < n; ++i) {
|
|
pre_out_grad_data[i] = 1.0 - pre_out_grad_data[i];
|
|
}
|
|
bit_code->Sub(&pre_out_grad); // the gradient of clip(w * x + b)
|
|
auto* out_grad_data = out_grad.template data<T>();
|
|
|
|
int64_t dim0 = pre_out_grad.dims()[0];
|
|
int64_t dim1 = pre_out_grad.dims()[1];
|
|
for (int64_t i = 0; i < dim0; ++i) {
|
|
T tmp = out_grad_data[i];
|
|
blas.SCAL(dim1, tmp, pre_out_grad_data + i * dim1);
|
|
}
|
|
// TODO(guosheng): multiply pre_out_grad with subgradient of clipping to
|
|
// be consistent with the clipping in forward.
|
|
if (bias_grad) {
|
|
dev_ctx.template Alloc<T>(bias_grad);
|
|
zero(dev_ctx, bias_grad, static_cast<T>(0.0));
|
|
bit_code->AddGrad(pre_out_grad, bias_grad);
|
|
}
|
|
dev_ctx.template Alloc<T>(w_grad);
|
|
zero(dev_ctx, w_grad, static_cast<T>(0.0));
|
|
if (!is_sparse) {
|
|
bit_code->MulGradWeight(pre_out_grad, w_grad, x);
|
|
} else {
|
|
bit_code->MulGradWeight(pre_out_grad, w_grad_sr, x);
|
|
}
|
|
bit_code->MulGradError(pre_out_grad, w, x_grad);
|
|
}
|
|
|
|
} // namespace phi
|