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Dmitry Razdoburdinrazdoburdin
Dmitry Razdoburdin
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razdoburdin
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add sycl test files
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‎tests/cpp/plugin/test_sycl_predictor.cc

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/*!
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* Copyright 2017-2020 XGBoost contributors
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*/
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#include <gtest/gtest.h>
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#include <xgboost/predictor.h>
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#include "../../../src/data/adapter.h"
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#include "../../../src/data/proxy_dmatrix.h"
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#include "../../../src/gbm/gbtree_model.h"
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#include "../filesystem.h" // dmlc::TemporaryDirectory
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#include "../helpers.h"
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#include "../predictor/test_predictor.h"
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namespace xgboost {
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TEST(SyclPredictor, Basic) {
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Context ctx;
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ctx.UpdateAllowUnknown(Args{{"device", "sycl"}});
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size_t constexpr kRows = 5;
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size_t constexpr kCols = 5;
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auto dmat = RandomDataGenerator(kRows, kCols, 0).GenerateDMatrix();
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TestBasic(dmat.get(), &ctx);
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}
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TEST(SyclPredictor, ExternalMemory) {
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Context ctx;
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ctx.UpdateAllowUnknown(Args{{"device", "sycl"}});
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size_t constexpr kPageSize = 64, kEntriesPerCol = 3;
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size_t constexpr kEntries = kPageSize * kEntriesPerCol * 2;
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std::unique_ptr<DMatrix> dmat = CreateSparsePageDMatrix(kEntries);
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TestBasic(dmat.get(), &ctx);
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}
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TEST(SyclPredictor, InplacePredict) {
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bst_row_t constexpr kRows{128};
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bst_feature_t constexpr kCols{64};
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auto gen = RandomDataGenerator{kRows, kCols, 0.5}.Device(-1);
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{
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HostDeviceVector<float> data;
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gen.GenerateDense(&data);
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ASSERT_EQ(data.Size(), kRows * kCols);
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Context ctx;
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ctx.UpdateAllowUnknown(Args{{"device", "sycl"}});
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std::shared_ptr<data::DMatrixProxy> x{new data::DMatrixProxy{}};
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auto array_interface = GetArrayInterface(&data, kRows, kCols);
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std::string arr_str;
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Json::Dump(array_interface, &arr_str);
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x->SetArrayData(arr_str.data());
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TestInplacePrediction(&ctx, x, kRows, kCols);
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}
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}
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TEST(SyclPredictor, IterationRange) {
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Context ctx;
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ctx.UpdateAllowUnknown(Args{{"device", "sycl"}});
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TestIterationRange(&ctx);
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}
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/* ColumnSplit */
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// TEST(SyclPredictor, IterationRangeColmnSplit) {
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// Context ctx;
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// ctx.UpdateAllowUnknown(Args{{"device", "sycl"}});
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// TestIterationRangeColumnSplit(&ctx);
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// }
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TEST(SyclPredictor, GHistIndexTraining) {
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size_t constexpr kRows{128}, kCols{16}, kBins{64};
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Context ctx;
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ctx.UpdateAllowUnknown(Args{{"device", "sycl"}});
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auto p_hist = RandomDataGenerator{kRows, kCols, 0.0}.Bins(kBins).GenerateDMatrix(false);
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HostDeviceVector<float> storage(kRows * kCols);
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auto columnar = RandomDataGenerator{kRows, kCols, 0.0}.GenerateArrayInterface(&storage);
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auto adapter = data::ArrayAdapter(columnar.c_str());
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std::shared_ptr<DMatrix> p_full{
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DMatrix::Create(&adapter, std::numeric_limits<float>::quiet_NaN(), 1)};
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TestTrainingPrediction(&ctx, kRows, kBins, p_full, p_hist);
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}
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/* Categorial features */
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// TEST(SyclPredictor, CategoricalPrediction) {
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// Context ctx;
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// ctx.UpdateAllowUnknown(Args{{"device", "sycl"}});
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// TestCategoricalPrediction(&ctx, false);
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// }
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/* ColumnSplit */
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/* Categorial features */
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// TEST(SyclPredictor, CategoricalPredictionColumnSplit) {
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// Context ctx;
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// ctx.UpdateAllowUnknown(Args{{"device", "sycl"}});
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// TestCategoricalPredictionColumnSplit(&ctx);
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// }
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TEST(SyclPredictor, CategoricalPredictLeaf) {
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Context ctx;
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ctx.UpdateAllowUnknown(Args{{"device", "sycl"}});
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TestCategoricalPredictLeaf(&ctx, false);
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}
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TEST(SyclPredictor, CategoricalPredictLeafColumnSplit) {
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Context ctx;
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ctx.UpdateAllowUnknown(Args{{"device", "sycl"}});;
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TestCategoricalPredictLeafColumnSplit(&ctx);
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}
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TEST(SyclPredictor, LesserFeatures) {
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Context ctx;
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ctx.UpdateAllowUnknown(Args{{"device", "sycl"}});
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TestPredictionWithLesserFeatures(&ctx);
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}
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TEST(SyclPredictor, LesserFeaturesColumnSplit) {
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Context ctx;
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ctx.UpdateAllowUnknown(Args{{"device", "sycl"}});
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TestPredictionWithLesserFeaturesColumnSplit(&ctx);
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}
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TEST(SyclPredictor, Sparse) {
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Context ctx;
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ctx.UpdateAllowUnknown(Args{{"device", "sycl"}});
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TestSparsePrediction(&ctx, 0.2);
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TestSparsePrediction(&ctx, 0.8);
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}
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/* ColumnSplit */
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// TEST(SyclPredictor, SparseColumnSplit) {
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// Context ctx;
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// ctx.UpdateAllowUnknown(Args{{"device", "sycl"}});
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// TestSparsePredictionColumnSplit(&ctx, 0.2);
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// TestSparsePredictionColumnSplit(&ctx, 0.8);
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// }
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TEST(SyclPredictor, Multi) {
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Context ctx;
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ctx.UpdateAllowUnknown(Args{{"device", "sycl"}});
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TestVectorLeafPrediction(&ctx);
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}
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} // namespace xgboost

‎tests/cpp/plugin/test_sycl_regression_obj.cc

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/*!
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* Copyright 2017-2019 XGBoost contributors
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*/
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#include <gtest/gtest.h>
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#include <xgboost/objective.h>
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#include <xgboost/context.h>
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#include <xgboost/json.h>
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#include "../helpers.h"
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namespace xgboost {
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TEST(SyclObjective, LinearRegressionGPair) {
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Context ctx;
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ctx.UpdateAllowUnknown(Args{{"device", "sycl"}});
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std::vector<std::pair<std::string, std::string>> args;
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std::unique_ptr<ObjFunction> obj {
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ObjFunction::Create("reg:squarederror_oneapi", &ctx)
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};
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obj->Configure(args);
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CheckObjFunction(obj,
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{0, 0.1f, 0.9f, 1, 0, 0.1f, 0.9f, 1},
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{0, 0, 0, 0, 1, 1, 1, 1},
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{1, 1, 1, 1, 1, 1, 1, 1},
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{0, 0.1f, 0.9f, 1.0f, -1.0f, -0.9f, -0.1f, 0},
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{1, 1, 1, 1, 1, 1, 1, 1});
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CheckObjFunction(obj,
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{0, 0.1f, 0.9f, 1, 0, 0.1f, 0.9f, 1},
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{0, 0, 0, 0, 1, 1, 1, 1},
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{}, // empty weight
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{0, 0.1f, 0.9f, 1.0f, -1.0f, -0.9f, -0.1f, 0},
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{1, 1, 1, 1, 1, 1, 1, 1});
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ASSERT_NO_THROW(obj->DefaultEvalMetric());
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}
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TEST(SyclObjective, SquaredLog) {
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Context ctx;
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ctx.UpdateAllowUnknown(Args{{"device", "sycl"}});
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std::vector<std::pair<std::string, std::string>> args;
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std::unique_ptr<ObjFunction> obj { ObjFunction::Create("reg:squaredlogerror_oneapi", &ctx) };
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obj->Configure(args);
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CheckConfigReload(obj, "reg:squaredlogerror_oneapi");
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CheckObjFunction(obj,
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{0.1f, 0.2f, 0.4f, 0.8f, 1.6f}, // pred
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{1.0f, 1.0f, 1.0f, 1.0f, 1.0f}, // labels
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{1.0f, 1.0f, 1.0f, 1.0f, 1.0f}, // weights
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{-0.5435f, -0.4257f, -0.25475f, -0.05855f, 0.1009f},
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{ 1.3205f, 1.0492f, 0.69215f, 0.34115f, 0.1091f});
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CheckObjFunction(obj,
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{0.1f, 0.2f, 0.4f, 0.8f, 1.6f}, // pred
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{1.0f, 1.0f, 1.0f, 1.0f, 1.0f}, // labels
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{}, // empty weights
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{-0.5435f, -0.4257f, -0.25475f, -0.05855f, 0.1009f},
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{ 1.3205f, 1.0492f, 0.69215f, 0.34115f, 0.1091f});
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ASSERT_EQ(obj->DefaultEvalMetric(), std::string{"rmsle"});
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}
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TEST(SyclObjective, LogisticRegressionGPair) {
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Context ctx;
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ctx.UpdateAllowUnknown(Args{{"device", "sycl"}});
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std::vector<std::pair<std::string, std::string>> args;
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std::unique_ptr<ObjFunction> obj { ObjFunction::Create("reg:logistic_oneapi", &ctx) };
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obj->Configure(args);
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CheckConfigReload(obj, "reg:logistic_oneapi");
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CheckObjFunction(obj,
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{ 0, 0.1f, 0.9f, 1, 0, 0.1f, 0.9f, 1}, // preds
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{ 0, 0 , 0, 0, 1, 1, 1, 1}, // labels
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{ 1, 1, 1, 1, 1, 1, 1, 1}, // weights
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{ 0.5f, 0.52f, 0.71f, 0.73f, -0.5f, -0.47f, -0.28f, -0.26f}, // out_grad
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{0.25f, 0.24f, 0.20f, 0.19f, 0.25f, 0.24f, 0.20f, 0.19f}); // out_hess
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}
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TEST(SyclObjective, LogisticRegressionBasic) {
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Context ctx;
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ctx.UpdateAllowUnknown(Args{{"device", "sycl"}});
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std::vector<std::pair<std::string, std::string>> args;
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std::unique_ptr<ObjFunction> obj {
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ObjFunction::Create("reg:logistic_oneapi", &ctx)
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};
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obj->Configure(args);
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CheckConfigReload(obj, "reg:logistic_oneapi");
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// test label validation
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EXPECT_ANY_THROW(CheckObjFunction(obj, {0}, {10}, {1}, {0}, {0}))
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<< "Expected error when label not in range [0,1f] for LogisticRegression";
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// test ProbToMargin
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EXPECT_NEAR(obj->ProbToMargin(0.1f), -2.197f, 0.01f);
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EXPECT_NEAR(obj->ProbToMargin(0.5f), 0, 0.01f);
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EXPECT_NEAR(obj->ProbToMargin(0.9f), 2.197f, 0.01f);
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EXPECT_ANY_THROW(obj->ProbToMargin(10))
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<< "Expected error when base_score not in range [0,1f] for LogisticRegression";
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// test PredTransform
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HostDeviceVector<bst_float> io_preds = {0, 0.1f, 0.5f, 0.9f, 1};
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std::vector<bst_float> out_preds = {0.5f, 0.524f, 0.622f, 0.710f, 0.731f};
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obj->PredTransform(&io_preds);
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auto& preds = io_preds.HostVector();
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for (int i = 0; i < static_cast<int>(io_preds.Size()); ++i) {
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EXPECT_NEAR(preds[i], out_preds[i], 0.01f);
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}
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}
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TEST(SyclObjective, LogisticRawGPair) {
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Context ctx;
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ctx.UpdateAllowUnknown(Args{{"device", "sycl"}});
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std::vector<std::pair<std::string, std::string>> args;
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std::unique_ptr<ObjFunction> obj {
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ObjFunction::Create("binary:logitraw_oneapi", &ctx)
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};
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obj->Configure(args);
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CheckObjFunction(obj,
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{ 0, 0.1f, 0.9f, 1, 0, 0.1f, 0.9f, 1},
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{ 0, 0, 0, 0, 1, 1, 1, 1},
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{ 1, 1, 1, 1, 1, 1, 1, 1},
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{ 0.5f, 0.52f, 0.71f, 0.73f, -0.5f, -0.47f, -0.28f, -0.26f},
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{0.25f, 0.24f, 0.20f, 0.19f, 0.25f, 0.24f, 0.20f, 0.19f});
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}
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TEST(SyclObjective, CPUvsSycl) {
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Context ctx;
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ctx.UpdateAllowUnknown(Args{{"device", "sycl"}});
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ObjFunction * obj_sycl =
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ObjFunction::Create("reg:squarederror_oneapi", &ctx);
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ctx = ctx.MakeCPU();
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ObjFunction * obj_cpu =
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ObjFunction::Create("reg:squarederror", &ctx);
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HostDeviceVector<GradientPair> cpu_out_preds;
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HostDeviceVector<GradientPair> sycl_out_preds;
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constexpr size_t kRows = 400;
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constexpr size_t kCols = 100;
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auto pdmat = RandomDataGenerator(kRows, kCols, 0).Seed(0).GenerateDMatrix();
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HostDeviceVector<float> preds;
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preds.Resize(kRows);
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auto& h_preds = preds.HostVector();
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for (size_t i = 0; i < h_preds.size(); ++i) {
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h_preds[i] = static_cast<float>(i);
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}
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auto& info = pdmat->Info();
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info.labels.Reshape(kRows, 1);
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auto& h_labels = info.labels.Data()->HostVector();
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for (size_t i = 0; i < h_labels.size(); ++i) {
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h_labels[i] = 1 / static_cast<float>(i+1);
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}
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{
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// CPU
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obj_cpu->GetGradient(preds, info, 0, &cpu_out_preds);
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}
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{
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// sycl
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obj_sycl->GetGradient(preds, info, 0, &sycl_out_preds);
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}
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auto& h_cpu_out = cpu_out_preds.HostVector();
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auto& h_sycl_out = sycl_out_preds.HostVector();
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float sgrad = 0;
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float shess = 0;
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for (size_t i = 0; i < kRows; ++i) {
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sgrad += std::pow(h_cpu_out[i].GetGrad() - h_sycl_out[i].GetGrad(), 2);
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shess += std::pow(h_cpu_out[i].GetHess() - h_sycl_out[i].GetHess(), 2);
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}
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ASSERT_NEAR(sgrad, 0.0f, kRtEps);
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ASSERT_NEAR(shess, 0.0f, kRtEps);
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delete obj_cpu;
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delete obj_sycl;
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}
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} // namespace xgboost

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