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118 lines
3.6 KiB
118 lines
3.6 KiB
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
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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/framework/op_registry.h"
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#include "paddle/platform/hostdevice.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>
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HOSTDEVICE T tolerable_value(const T x) {
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PADDLE_ASSERT(std::is_floating_point<T>::value);
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const T kApproInf = 1e20;
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if (x == INFINITY) {
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return kApproInf;
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}
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if (x == -INFINITY) {
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return -kApproInf;
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}
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return x;
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}
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template <typename T>
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class CrossEntropyOpKernel : public framework::OpKernel {
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public:
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void Compute(const framework::ExecutionContext& ctx) const override {
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PADDLE_ENFORCE(platform::is_cpu_place(ctx.GetPlace()),
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"It must use CPUPlace.");
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auto x = ctx.Input<Tensor>("X");
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auto y = ctx.Output<Tensor>("Y");
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auto* x_data = x->data<T>();
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y->mutable_data<T>(ctx.GetPlace());
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auto* y_data = y->data<T>();
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int batch_size = x->dims()[0];
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int class_num = x->dims()[1];
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if (ctx.Attr<int>("soft_label") == 1) {
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auto* label_data = ctx.Input<Tensor>("Label")->data<T>();
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int index = 0;
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for (int i = 0; i < batch_size; ++i) {
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T sum = static_cast<T>(0);
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for (int j = 0; j < class_num; ++j) {
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sum += label_data[index] * tolerable_value(std::log(x_data[index]));
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y_data[i] = -sum;
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index++;
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}
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}
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} else {
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auto* label_data = ctx.Input<Tensor>("Label")->data<int>();
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for (int i = 0; i < batch_size; ++i) {
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int index = i * class_num + label_data[i];
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y_data[i] = -tolerable_value(std::log(x_data[index]));
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}
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}
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}
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};
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template <typename T>
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class CrossEntropyGradientOpKernel : public framework::OpKernel {
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public:
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void Compute(const framework::ExecutionContext& ctx) const override {
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PADDLE_ENFORCE(platform::is_cpu_place(ctx.GetPlace()),
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"It must use CPUPlace.");
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auto x = ctx.Input<Tensor>("X");
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auto dx = ctx.Output<Tensor>(framework::GradVarName("X"));
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auto dy = ctx.Input<Tensor>(framework::GradVarName("Y"));
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auto label = ctx.Input<Tensor>("Label");
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auto* dx_data = dx->mutable_data<T>(ctx.GetPlace());
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auto* dy_data = dy->data<T>();
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auto* x_data = x->data<T>();
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int batch_size = x->dims()[0];
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int class_num = x->dims()[1];
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// TODO(qingqing): make zero setting an common function.
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if (ctx.Attr<int>("soft_label") == 1) {
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auto* label_data = ctx.Input<Tensor>("Label")->data<T>();
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int index = 0;
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for (int i = 0; i < batch_size; ++i) {
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for (int j = 0; j < class_num; ++j) {
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dx_data[index] = -label_data[index] * dy_data[i] / x_data[index];
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index++;
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}
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}
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} else {
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auto* label_data = label->data<int>();
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memset(dx_data, 0, sizeof(T) * batch_size * class_num);
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for (int i = 0; i < batch_size; ++i) {
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PADDLE_ASSERT(label_data[i] >= 0 || label_data[i] < class_num);
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int index = i * class_num + label_data[i];
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dx_data[index] = -dy_data[i] / x_data[index];
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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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