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212 lines
8.3 KiB
212 lines
8.3 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 <math.h>
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#include <random>
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#include "paddle/framework/eigen.h"
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#include "paddle/framework/op_registry.h"
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#include "unsupported/Eigen/CXX11/Tensor"
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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 DeviceContext, typename T>
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void PrepareSamples(const framework::ExecutionContext& context) {
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auto label = context.Input<Tensor>("Label");
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const int64_t* label_data = label->data<int64_t>();
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auto label_dims = label->dims();
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int num_total_classes = context.Attr<int>("num_total_classes");
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// for unitest
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std::vector<int> custom_neg_classes =
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context.Attr<std::vector<int>>("custom_neg_classes");
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// random machine
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std::random_device rd;
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std::mt19937 rng(rd());
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std::uniform_int_distribution<int> rand(0, num_total_classes - 1);
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auto sample_labels = context.Output<Tensor>("SampleLabels");
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auto sample_labels_dims = sample_labels->dims();
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int64_t* sample_labels_data =
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sample_labels->mutable_data<int64_t>(context.GetPlace());
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int num_label = label_dims.size() == 2 ? label_dims[1] : 1;
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int index = 0;
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for (int64_t i = 0; i < label_dims[0]; ++i) {
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int j = 0;
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for (; j < num_label; ++j) {
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sample_labels_data[index++] = label_data[i * num_label + j];
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}
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if (custom_neg_classes.size() > 0) {
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for (auto label : custom_neg_classes) {
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sample_labels_data[index++] = label;
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}
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} else {
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for (; j < sample_labels_dims[1]; ++j) {
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// TODO(wanghaoshuang): support more distribution sampling
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sample_labels_data[index++] = rand(rng);
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}
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}
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}
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}
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template <typename DeviceContext, typename T>
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class NCEKernel : 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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PrepareSamples<DeviceContext, T>(context);
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auto sample_labels = context.Output<Tensor>("SampleLabels");
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const int64_t* sample_labels_data = sample_labels->data<int64_t>();
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auto sample_out = context.Output<Tensor>("SampleLogits");
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T* sample_out_data = sample_out->mutable_data<T>(context.GetPlace());
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auto label = context.Input<Tensor>("Label");
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auto sample_weight = context.Input<Tensor>("SampleWeight");
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const T* sample_weight_data = nullptr;
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if (sample_weight != nullptr) {
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sample_weight_data = sample_weight->data<T>();
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}
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auto out = context.Output<Tensor>("Cost");
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T* out_data = out->mutable_data<T>(context.GetPlace());
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int num_neg_samples = context.Attr<int>("num_neg_samples");
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int num_total_classes = context.Attr<int>("num_total_classes");
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int64_t num_true_class = 1;
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if (label != nullptr) {
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num_true_class = label->dims()[1];
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}
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T b = 1. / num_total_classes * num_neg_samples;
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// forward bias
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auto bias = context.Input<Tensor>("Bias");
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if (bias != nullptr) {
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const T* bias_data = bias->data<T>();
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for (int64_t i = 0; i < sample_labels->numel(); ++i) {
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sample_out_data[i] = bias_data[sample_labels_data[i]];
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}
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} else {
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for (int64_t i = 0; i < sample_labels->numel(); ++i) {
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sample_out_data[i] = 0;
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}
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}
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// forward mul
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auto input_mat = EigenMatrix<T>::From(*(context.Input<Tensor>("Input")));
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auto weight_mat = EigenMatrix<T>::From(*(context.Input<Tensor>("Weight")));
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for (int64_t i = 0; i < sample_labels->numel(); ++i) {
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Eigen::Tensor<T, 0, Eigen::RowMajor, Eigen::DenseIndex> result =
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(input_mat.chip((int)(i / sample_labels->dims()[1]), 0) *
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weight_mat.chip(sample_labels_data[i], 0))
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.sum();
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sample_out_data[i] += result(0);
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sample_out_data[i] = (1. / (1. + exp(-sample_out_data[i])));
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}
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// forward cost
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for (int64_t i = 0; i < sample_labels->dims()[0]; ++i) {
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int64_t j = 0;
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out_data[i] = 0;
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T w = sample_weight == nullptr ? 1. : sample_weight_data[i];
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// for true classes
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for (; j < num_true_class; ++j) {
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T o = sample_out_data[i * sample_out->dims()[1] + j];
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T cost = -log(o / (o + b));
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out_data[i] += w * cost;
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}
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// for sampled neg classes
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for (; j < sample_labels->dims()[1]; ++j) {
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T o = sample_out_data[i * sample_out->dims()[1] + j];
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T cost = -log(b / (o + b));
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out_data[i] += w * cost;
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}
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}
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}
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};
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template <typename DeviceContext, typename T>
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class NCEGradKernel : 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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auto d_out = context.Input<Tensor>(framework::GradVarName("Cost"));
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const T* d_out_data = d_out->data<T>();
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auto label = context.Input<Tensor>("Label");
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auto sample_out = context.Input<Tensor>("SampleLogits");
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const T* sample_out_data = sample_out->data<T>();
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auto sample_labels = context.Input<Tensor>("SampleLabels");
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const int64_t* sample_labels_data = sample_labels->data<int64_t>();
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auto sample_weight = context.Input<Tensor>("SampleWeight");
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const T* sample_weight_data = nullptr;
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if (sample_weight != nullptr) {
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sample_weight_data = sample_weight->data<T>();
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}
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int num_neg_samples = context.Attr<int>("num_neg_samples");
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int num_total_classes = context.Attr<int>("num_total_classes");
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int num_true_class = 1;
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if (label != nullptr) {
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num_true_class = label->dims()[1];
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}
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T b = 1. / num_total_classes * num_neg_samples;
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Tensor sample_grad; // tmp tensor
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T* sample_grad_data =
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sample_grad.mutable_data<T>(sample_labels->dims(), context.GetPlace());
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// backward cost
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for (int64_t i = 0; i < sample_labels->numel(); ++i) {
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T o = sample_out_data[i];
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T w = sample_weight == nullptr
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? 1
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: sample_weight_data[i / sample_labels->dims()[1]];
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sample_grad_data[i] = (i % sample_labels->dims()[1]) < num_true_class
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? w * (b / (o + b)) * (o - 1)
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: w * (o * (1 - o) / (o + b));
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sample_grad_data[i] *= d_out_data[i / sample_labels->dims()[1]];
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}
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// get d_bias
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auto d_bias = context.Output<Tensor>(framework::GradVarName("Bias"));
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if (d_bias != nullptr) {
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T* d_bias_data = d_bias->mutable_data<T>(context.GetPlace());
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std::fill(d_bias_data, d_bias_data + d_bias->numel(), 0.0);
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for (int64_t i = 0; i < sample_labels->numel(); ++i) {
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d_bias_data[sample_labels_data[i]] += sample_grad_data[i];
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}
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}
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// get d_w
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auto d_w = context.Output<Tensor>(framework::GradVarName("Weight"));
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if (d_w != nullptr) {
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auto d_w_data = d_w->mutable_data<T>(context.GetPlace());
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std::fill(d_w_data, d_w_data + d_w->numel(), 0.0);
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auto d_w_matrix = EigenMatrix<T>::From(*d_w);
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auto x_matrix = EigenMatrix<T>::From(*(context.Input<Tensor>("Input")));
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for (int64_t i = 0; i < sample_labels->numel(); ++i) {
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d_w_matrix.chip(sample_labels_data[i], 0) +=
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x_matrix.chip((int)(i / sample_labels->dims()[1]), 0) *
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sample_grad_data[i];
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}
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}
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// get d_x
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auto d_x = context.Output<Tensor>(framework::GradVarName("Input"));
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if (d_x != nullptr) {
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d_x->mutable_data<T>(context.GetPlace());
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auto d_x_matrix = EigenMatrix<T>::From(*d_x);
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auto w_matrix = EigenMatrix<T>::From(*(context.Input<Tensor>("Weight")));
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for (int64_t i = 0; i < sample_labels->numel(); ++i) {
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d_x_matrix.chip((int)(i / sample_labels->dims()[1]), 0) +=
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w_matrix.chip(sample_labels_data[i], 0) * sample_grad_data[i];
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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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