* Port elementwise metrics to GPU. * All elementwise metrics are converted to static polymorphic. * Create a reducer for metrics reduction. * Remove const of Metric::Eval to accommodate CubMemory.
145 lines
4.9 KiB
C++
145 lines
4.9 KiB
C++
/*!
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* Copyright 2016-2018 XGBoost contributors
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*/
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#include "./helpers.h"
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#include "xgboost/c_api.h"
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#include <random>
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bool FileExists(const std::string& filename) {
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struct stat st;
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return stat(filename.c_str(), &st) == 0;
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}
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long GetFileSize(const std::string& filename) {
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struct stat st;
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stat(filename.c_str(), &st);
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return st.st_size;
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}
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void CreateSimpleTestData(const std::string& filename) {
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CreateBigTestData(filename, 6);
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}
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void CreateBigTestData(const std::string& filename, size_t n_entries) {
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std::ofstream fo(filename.c_str());
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const size_t entries_per_row = 3;
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size_t n_rows = (n_entries + entries_per_row - 1) / entries_per_row;
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for (size_t i = 0; i < n_rows; ++i) {
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const char* row = i % 2 == 0 ? " 0:0 1:10 2:20\n" : " 0:0 3:30 4:40\n";
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fo << i << row;
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}
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}
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void _CheckObjFunction(xgboost::ObjFunction * obj,
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std::vector<xgboost::bst_float> preds,
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std::vector<xgboost::bst_float> labels,
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std::vector<xgboost::bst_float> weights,
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xgboost::MetaInfo info,
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std::vector<xgboost::bst_float> out_grad,
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std::vector<xgboost::bst_float> out_hess) {
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xgboost::HostDeviceVector<xgboost::bst_float> in_preds(preds);
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xgboost::HostDeviceVector<xgboost::GradientPair> out_gpair;
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obj->GetGradient(in_preds, info, 1, &out_gpair);
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std::vector<xgboost::GradientPair>& gpair = out_gpair.HostVector();
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ASSERT_EQ(gpair.size(), in_preds.Size());
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for (int i = 0; i < static_cast<int>(gpair.size()); ++i) {
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EXPECT_NEAR(gpair[i].GetGrad(), out_grad[i], 0.01)
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<< "Unexpected grad for pred=" << preds[i] << " label=" << labels[i]
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<< " weight=" << weights[i];
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EXPECT_NEAR(gpair[i].GetHess(), out_hess[i], 0.01)
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<< "Unexpected hess for pred=" << preds[i] << " label=" << labels[i]
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<< " weight=" << weights[i];
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}
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}
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void CheckObjFunction(xgboost::ObjFunction * obj,
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std::vector<xgboost::bst_float> preds,
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std::vector<xgboost::bst_float> labels,
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std::vector<xgboost::bst_float> weights,
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std::vector<xgboost::bst_float> out_grad,
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std::vector<xgboost::bst_float> out_hess) {
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xgboost::MetaInfo info;
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info.num_row_ = labels.size();
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info.labels_.HostVector() = labels;
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info.weights_.HostVector() = weights;
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_CheckObjFunction(obj, preds, labels, weights, info, out_grad, out_hess);
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}
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void CheckRankingObjFunction(xgboost::ObjFunction * obj,
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std::vector<xgboost::bst_float> preds,
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std::vector<xgboost::bst_float> labels,
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std::vector<xgboost::bst_float> weights,
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std::vector<xgboost::bst_uint> groups,
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std::vector<xgboost::bst_float> out_grad,
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std::vector<xgboost::bst_float> out_hess) {
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xgboost::MetaInfo info;
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info.num_row_ = labels.size();
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info.labels_.HostVector() = labels;
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info.weights_.HostVector() = weights;
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info.group_ptr_ = groups;
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_CheckObjFunction(obj, preds, labels, weights, info, out_grad, out_hess);
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}
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xgboost::bst_float GetMetricEval(xgboost::Metric * metric,
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xgboost::HostDeviceVector<xgboost::bst_float> preds,
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std::vector<xgboost::bst_float> labels,
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std::vector<xgboost::bst_float> weights) {
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xgboost::MetaInfo info;
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info.num_row_ = labels.size();
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info.labels_.HostVector() = labels;
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info.weights_.HostVector() = weights;
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return metric->Eval(preds, info, false);
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}
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namespace xgboost {
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bool IsNear(std::vector<xgboost::bst_float>::const_iterator _beg1,
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std::vector<xgboost::bst_float>::const_iterator _end1,
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std::vector<xgboost::bst_float>::const_iterator _beg2) {
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for (auto iter1 = _beg1, iter2 = _beg2; iter1 != _end1; ++iter1, ++iter2) {
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if (std::abs(*iter1 - *iter2) > xgboost::kRtEps){
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return false;
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}
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}
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return true;
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}
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SimpleLCG::StateType SimpleLCG::operator()() {
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state_ = (alpha_ * state_) % mod_;
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return state_;
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}
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SimpleLCG::StateType SimpleLCG::Min() const {
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return seed_ * alpha_;
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}
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SimpleLCG::StateType SimpleLCG::Max() const {
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return max_value_;
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}
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std::shared_ptr<xgboost::DMatrix>* CreateDMatrix(int rows, int columns,
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float sparsity, int seed) {
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const float missing_value = -1;
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std::vector<float> test_data(rows * columns);
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xgboost::SimpleLCG gen(seed);
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SimpleRealUniformDistribution<float> dis(0.0f, 1.0f);
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for (auto &e : test_data) {
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if (dis(&gen) < sparsity) {
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e = missing_value;
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} else {
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e = dis(&gen);
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}
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}
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DMatrixHandle handle;
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XGDMatrixCreateFromMat(test_data.data(), rows, columns, missing_value,
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&handle);
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return static_cast<std::shared_ptr<xgboost::DMatrix> *>(handle);
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}
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} // namespace xgboost
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