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127 lines
5.0 KiB
127 lines
5.0 KiB
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved.
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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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http://www.apache.org/licenses/LICENSE-2.0
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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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#pragma once
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#include "paddle/fluid/framework/eigen.h"
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#include "paddle/fluid/framework/op_registry.h"
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#include "paddle/fluid/operators/math/cross_entropy.h"
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#include "paddle/fluid/operators/math/softmax.h"
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#include "paddle/fluid/operators/softmax_op.h"
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namespace paddle {
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namespace operators {
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using Tensor = framework::Tensor;
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template <typename T, int MajorType = Eigen::RowMajor,
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typename IndexType = Eigen::DenseIndex>
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using EigenMatrix = framework::EigenMatrix<T, MajorType, IndexType>;
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template <typename T>
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class SoftmaxWithCrossEntropyKernel : public framework::OpKernel<T> {
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public:
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void Compute(const framework::ExecutionContext& context) const override {
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PADDLE_ENFORCE(platform::is_cpu_place(context.GetPlace()),
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"This kernel only runs on CPU.");
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const Tensor* logits = context.Input<Tensor>("Logits");
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const Tensor* labels = context.Input<Tensor>("Label");
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Tensor* softmax = context.Output<Tensor>("Softmax");
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Tensor* loss = context.Output<Tensor>("Loss");
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const bool soft_label = context.Attr<bool>("soft_label");
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const int rank = logits->dims().size();
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const int axis = CanonicalAxis(context.Attr<int>("axis"), rank);
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int axis_dim = logits->dims()[axis];
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softmax->mutable_data<T>(context.GetPlace());
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loss->mutable_data<T>(context.GetPlace());
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const int n = SizeToAxis(axis, logits->dims());
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const int d = SizeFromAxis(axis, logits->dims());
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Tensor logits_2d, softmax_2d, labels_2d, loss_2d;
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logits_2d.ShareDataWith(*logits).Resize({n, d});
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softmax_2d.ShareDataWith(*softmax).Resize({n, d});
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labels_2d.ShareDataWith(*labels).Resize({n, labels->numel() / n});
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loss_2d.ShareDataWith(*loss).Resize({n, d / axis_dim});
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auto& dev_ctx =
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context.template device_context<platform::CPUDeviceContext>();
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math::SoftmaxFunctor<platform::CPUDeviceContext, T, false>()(
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dev_ctx, axis_dim, &logits_2d, &softmax_2d);
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math::CrossEntropyFunctor<platform::CPUDeviceContext, T>()(
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dev_ctx, &loss_2d, &softmax_2d, &labels_2d, soft_label,
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context.Attr<int>("ignore_index"), axis_dim);
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}
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};
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template <typename T>
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class SoftmaxWithCrossEntropyGradKernel : public framework::OpKernel<T> {
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public:
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void Compute(const framework::ExecutionContext& context) const override {
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const Tensor* out_grad =
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context.Input<Tensor>(framework::GradVarName("Loss"));
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const Tensor* labels = context.Input<Tensor>("Label");
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Tensor* logit_grad =
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context.Output<Tensor>(framework::GradVarName("Logits"));
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const Tensor* softmax = context.Input<Tensor>("Softmax");
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if (logit_grad != softmax) {
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framework::TensorCopy(*softmax, context.GetPlace(),
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context.device_context(), logit_grad);
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}
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const bool soft_label = context.Attr<bool>("soft_label");
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const int rank = logit_grad->dims().size();
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const int axis = CanonicalAxis(context.Attr<int>("axis"), rank);
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int axis_dim = logit_grad->dims()[axis];
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const int n = SizeToAxis(axis, logit_grad->dims());
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const int d = SizeFromAxis(axis, logit_grad->dims());
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Tensor logit_grad_2d, labels_2d, out_grad_2d;
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logit_grad_2d.ShareDataWith(*logit_grad).Resize({n, d});
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labels_2d.ShareDataWith(*labels).Resize({n, labels->numel() / n});
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out_grad_2d.ShareDataWith(*out_grad).Resize({n, d / axis_dim});
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auto out_grad_mat = EigenMatrix<T>::From(out_grad_2d);
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auto logit_grad_mat = EigenMatrix<T>::From(logit_grad_2d);
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auto& place = *context.template device_context<platform::CPUDeviceContext>()
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.eigen_device();
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if (soft_label) {
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auto lbl_mat = EigenMatrix<T>::From(labels_2d);
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logit_grad_mat.device(place) =
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out_grad_mat.broadcast(Eigen::DSizes<int, 2>(1, axis_dim)) *
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(logit_grad_mat - lbl_mat);
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} else {
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logit_grad_mat.device(place) =
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logit_grad_mat *
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out_grad_mat.broadcast(Eigen::DSizes<int, 2>(1, axis_dim));
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const int64_t* label_data = labels->data<int64_t>();
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T* logit_grad_data = logit_grad->data<T>();
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const T* out_grad_data = out_grad->data<T>();
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const int remain = d / axis_dim;
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for (int i = 0; i < n; ++i) {
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for (int j = 0; j < remain; j++) {
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int idx = i * remain + j;
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logit_grad_data[i * d + label_data[idx] * remain + j] -=
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out_grad_data[idx];
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}
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}
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}
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}
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};
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} // namespace operators
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} // namespace paddle
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