wehub-resource-sync
127 lines
4.9 KiB
C++
127 lines
4.9 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/log_softmax_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/axis_utils.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, int MajorType = Eigen::RowMajor>
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using EigenMatrixTemplate = EigenMatrix<T, MajorType>;
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template <typename T>
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struct ValueClip {
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HOSTDEVICE T operator()(const T& x) const {
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const T kThreshold = static_cast<T>(-64.);
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return x < kThreshold ? kThreshold : x;
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}
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};
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template <typename Context, typename T>
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struct LogSoftmaxFunctor {
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void operator()(const Context& dev_ctx,
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const DenseTensor* X,
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DenseTensor* Y,
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const int axis) {
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constexpr int kBatchDim = 0;
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constexpr int kClassDim = 1;
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constexpr int kAxisDim = 1;
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int axis_dim = static_cast<int>(X->dims()[axis]);
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const int n = funcs::SizeToAxis(axis, X->dims());
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const int d = funcs::SizeFromAxis(axis, X->dims());
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DDim dim_2d{n, d};
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auto logits = EigenMatrixTemplate<T>::From(*X, dim_2d);
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auto log_softmax = EigenMatrixTemplate<T>::From(*Y, dim_2d);
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const int batch_size = logits.dimension(kBatchDim);
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const int num_classes = logits.dimension(kClassDim);
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const int num_remain = num_classes / axis_dim;
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Eigen::DSizes<int, 1> along_axis(kAxisDim);
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Eigen::DSizes<int, 2> batch_classes(batch_size, num_classes);
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Eigen::DSizes<int, 2> batch_by_one(batch_size, 1);
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Eigen::DSizes<int, 2> one_by_class(1, num_classes);
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Eigen::DSizes<int, 3> batch_one_remain(batch_size, 1, num_remain);
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Eigen::DSizes<int, 3> one_axis_one(1, axis_dim, 1);
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Eigen::DSizes<int, 2> one_axis(1, axis_dim);
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Eigen::DSizes<int, 3> batch_axis_remain(batch_size, axis_dim, num_remain);
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// For numerical stability, logits should be shifted by maximum number along
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// axis, calculate shifted_logits into log_softmax tensor for memory reuse.
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if (num_remain == 1) {
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// axis == -1, axis and class in same dimension, calculate along
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// class dimension directly for higher performance
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log_softmax.device(*dev_ctx.eigen_device()) =
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(logits - logits.maximum(along_axis)
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.eval()
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.reshape(batch_by_one)
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.broadcast(one_by_class))
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.unaryExpr(ValueClip<T>());
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} else {
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// axis != -1, class dimension split into (axis, remain), max and sum
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// should be calculated along axis dimension
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log_softmax.device(*dev_ctx.eigen_device()) =
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(logits.reshape(batch_axis_remain) - logits.reshape(batch_axis_remain)
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.maximum(along_axis)
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.eval()
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.reshape(batch_one_remain)
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.broadcast(one_axis_one)
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.reshape(batch_classes))
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.unaryExpr(ValueClip<T>());
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}
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log_softmax.device(*dev_ctx.eigen_device()) =
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log_softmax - log_softmax.exp()
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.eval()
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.reshape(batch_axis_remain)
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.sum(along_axis)
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.log()
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.broadcast(one_axis);
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}
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};
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template <typename T, typename Context>
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void LogSoftmaxKernel(const Context& dev_ctx,
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const DenseTensor& x,
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int axis,
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DenseTensor* out) {
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const int rank = x.dims().size();
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const int canonical_axis = funcs::CanonicalAxis(axis, rank);
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dev_ctx.template Alloc<T>(out);
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// For 0D Tensor
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if (rank == 0) {
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funcs::set_constant(dev_ctx, out, static_cast<T>(0.0));
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return;
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}
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if (x.numel() != 0) {
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LogSoftmaxFunctor<Context, T>()(dev_ctx, &x, out, canonical_axis);
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}
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}
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} // namespace phi
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// TODO(YuanRisheng): The layout of onednn kernel should be OneDNN, we should
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// support specifying the exact layout when the kernel is registered
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PD_REGISTER_KERNEL(
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log_softmax, CPU, ALL_LAYOUT, phi::LogSoftmaxKernel, float, double) {}
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