wehub-resource-sync
86 lines
3.0 KiB
C++
86 lines
3.0 KiB
C++
// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
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//
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// Licensed under the Apache License, Version 2.0 (the "License");
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// you may not use this file except in compliance with the License.
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// You may obtain a copy of the License at
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//
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// http://www.apache.org/licenses/LICENSE-2.0
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//
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// Unless required by applicable law or agreed to in writing, software
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// distributed under the License is distributed on an "AS IS" BASIS,
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// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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// See the License for the specific language governing permissions and
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// limitations under the License.
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#include "paddle/phi/kernels/norm_grad_kernel.h"
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#include "paddle/phi/backends/cpu/cpu_context.h"
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#include "paddle/phi/core/kernel_registry.h"
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#include "paddle/phi/kernels/funcs/common_shape.h"
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#include "paddle/phi/kernels/funcs/eigen/common.h"
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#include "paddle/phi/kernels/funcs/eigen/eigen_function.h"
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#include "paddle/phi/kernels/funcs/math_function.h"
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namespace phi {
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template <typename T, typename Context>
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void NormGradKernel(const Context& dev_ctx,
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const DenseTensor& x,
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const DenseTensor& norm,
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const DenseTensor& out_grad,
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int axis,
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float epsilon UNUSED,
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bool is_test UNUSED,
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DenseTensor* x_grad) {
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auto* in_x = &x;
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auto* in_dy = &out_grad;
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auto* in_norm = &norm;
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auto* out_dx = x_grad;
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dev_ctx.template Alloc<T>(out_dx);
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auto xdim = in_x->dims();
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if (axis < 0) axis = xdim.size() + axis;
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int64_t pre = 0, n = 0, post = 0;
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funcs::GetPrePostNumel(xdim, axis, &pre, &n, &post);
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auto* place = dev_ctx.eigen_device();
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auto x_e = EigenVector<T>::Flatten(*in_x);
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auto dy_e = EigenVector<T>::Flatten(*in_dy);
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auto norm_e = EigenVector<T>::Flatten(*in_norm);
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auto dx_e = EigenVector<T>::Flatten(*out_dx);
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Eigen::DSizes<int64_t, 3> shape(pre, n, post);
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Eigen::DSizes<int64_t, 3> rshape(pre, static_cast<int64_t>(1), post);
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auto x_r = x_e.reshape(shape);
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auto dy = dy_e.reshape(shape);
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auto norm_r = norm_e.reshape(rshape);
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auto dx = dx_e.reshape(shape);
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DenseTensor rsum;
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rsum.Resize({pre, post});
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dev_ctx.template Alloc<T>(&rsum);
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auto sum = EigenTensor<T, 2>::From(rsum);
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Eigen::DSizes<int, 1> rdim(1);
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Eigen::DSizes<int, 3> bcast(1, n, 1);
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// dx = ( dy/sqrt(sum(x*x)) ) * [1 - x*sum(x) / (sum(x*x) + e)]
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// = [dy - dy * x * sum(x) / (sum(x*x) + e)] / sqrt(sum(x*x))
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// = [dy - x * sum(x*dy) / (sum(x*x) + e)] / sqrt(sum(x*x))
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// 1. sum = sum(x*dy)
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sum.device(*place) = (x_r * dy).sum(rdim);
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// 2. dx = x * sum
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dx.device(*place) = sum.reshape(rshape).broadcast(bcast) * x_r;
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// 3. dx / (sum(x*x) + e)
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// where, norm.pow(2) = sum(x*x) + e, which is calculated in forward.
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dx.device(*place) = dx / norm_r.pow(2).broadcast(bcast);
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// 4. [dy - dx] / sqrt(sum(x*x))
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dx.device(*place) = (dy - dx) / norm_r.broadcast(bcast);
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}
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} // namespace phi
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PD_REGISTER_KERNEL(
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norm_grad, CPU, ALL_LAYOUT, phi::NormGradKernel, float, double) {}
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