/*! * Copyright 2017-2019 XGBoost contributors */ #include #include #include #include #include "../helpers.h" namespace xgboost { TEST(Plugin, LinearRegressionGPairOneAPI) { GenericParameter tparam = CreateEmptyGenericParam(0); std::vector> args; std::unique_ptr obj { ObjFunction::Create("reg:squarederror_oneapi", &tparam) }; obj->Configure(args); CheckObjFunction(obj, {0, 0.1f, 0.9f, 1, 0, 0.1f, 0.9f, 1}, {0, 0, 0, 0, 1, 1, 1, 1}, {1, 1, 1, 1, 1, 1, 1, 1}, {0, 0.1f, 0.9f, 1.0f, -1.0f, -0.9f, -0.1f, 0}, {1, 1, 1, 1, 1, 1, 1, 1}); CheckObjFunction(obj, {0, 0.1f, 0.9f, 1, 0, 0.1f, 0.9f, 1}, {0, 0, 0, 0, 1, 1, 1, 1}, {}, // empty weight {0, 0.1f, 0.9f, 1.0f, -1.0f, -0.9f, -0.1f, 0}, {1, 1, 1, 1, 1, 1, 1, 1}); ASSERT_NO_THROW(obj->DefaultEvalMetric()); } TEST(Plugin, SquaredLogOneAPI) { GenericParameter tparam = CreateEmptyGenericParam(0); std::vector> args; std::unique_ptr obj { ObjFunction::Create("reg:squaredlogerror_oneapi", &tparam) }; obj->Configure(args); CheckConfigReload(obj, "reg:squaredlogerror_oneapi"); CheckObjFunction(obj, {0.1f, 0.2f, 0.4f, 0.8f, 1.6f}, // pred {1.0f, 1.0f, 1.0f, 1.0f, 1.0f}, // labels {1.0f, 1.0f, 1.0f, 1.0f, 1.0f}, // weights {-0.5435f, -0.4257f, -0.25475f, -0.05855f, 0.1009f}, { 1.3205f, 1.0492f, 0.69215f, 0.34115f, 0.1091f}); CheckObjFunction(obj, {0.1f, 0.2f, 0.4f, 0.8f, 1.6f}, // pred {1.0f, 1.0f, 1.0f, 1.0f, 1.0f}, // labels {}, // empty weights {-0.5435f, -0.4257f, -0.25475f, -0.05855f, 0.1009f}, { 1.3205f, 1.0492f, 0.69215f, 0.34115f, 0.1091f}); ASSERT_EQ(obj->DefaultEvalMetric(), std::string{"rmsle"}); } TEST(Plugin, LogisticRegressionGPairOneAPI) { GenericParameter tparam = CreateEmptyGenericParam(0); std::vector> args; std::unique_ptr obj { ObjFunction::Create("reg:logistic_oneapi", &tparam) }; obj->Configure(args); CheckConfigReload(obj, "reg:logistic_oneapi"); CheckObjFunction(obj, { 0, 0.1f, 0.9f, 1, 0, 0.1f, 0.9f, 1}, // preds { 0, 0, 0, 0, 1, 1, 1, 1}, // labels { 1, 1, 1, 1, 1, 1, 1, 1}, // weights { 0.5f, 0.52f, 0.71f, 0.73f, -0.5f, -0.47f, -0.28f, -0.26f}, // out_grad {0.25f, 0.24f, 0.20f, 0.19f, 0.25f, 0.24f, 0.20f, 0.19f}); // out_hess } TEST(Plugin, LogisticRegressionBasicOneAPI) { GenericParameter lparam = CreateEmptyGenericParam(0); std::vector> args; std::unique_ptr obj { ObjFunction::Create("reg:logistic_oneapi", &lparam) }; obj->Configure(args); CheckConfigReload(obj, "reg:logistic_oneapi"); // test label validation EXPECT_ANY_THROW(CheckObjFunction(obj, {0}, {10}, {1}, {0}, {0})) << "Expected error when label not in range [0,1f] for LogisticRegression"; // test ProbToMargin EXPECT_NEAR(obj->ProbToMargin(0.1f), -2.197f, 0.01f); EXPECT_NEAR(obj->ProbToMargin(0.5f), 0, 0.01f); EXPECT_NEAR(obj->ProbToMargin(0.9f), 2.197f, 0.01f); EXPECT_ANY_THROW(obj->ProbToMargin(10)) << "Expected error when base_score not in range [0,1f] for LogisticRegression"; // test PredTransform HostDeviceVector io_preds = {0, 0.1f, 0.5f, 0.9f, 1}; std::vector out_preds = {0.5f, 0.524f, 0.622f, 0.710f, 0.731f}; obj->PredTransform(&io_preds); auto& preds = io_preds.HostVector(); for (int i = 0; i < static_cast(io_preds.Size()); ++i) { EXPECT_NEAR(preds[i], out_preds[i], 0.01f); } } TEST(Plugin, LogisticRawGPairOneAPI) { GenericParameter lparam = CreateEmptyGenericParam(0); std::vector> args; std::unique_ptr obj { ObjFunction::Create("binary:logitraw_oneapi", &lparam) }; obj->Configure(args); CheckObjFunction(obj, { 0, 0.1f, 0.9f, 1, 0, 0.1f, 0.9f, 1}, { 0, 0, 0, 0, 1, 1, 1, 1}, { 1, 1, 1, 1, 1, 1, 1, 1}, { 0.5f, 0.52f, 0.71f, 0.73f, -0.5f, -0.47f, -0.28f, -0.26f}, {0.25f, 0.24f, 0.20f, 0.19f, 0.25f, 0.24f, 0.20f, 0.19f}); } TEST(Plugin, CPUvsOneAPI) { GenericParameter lparam = CreateEmptyGenericParam(0); ObjFunction * obj_cpu = ObjFunction::Create("reg:squarederror", &lparam); ObjFunction * obj_oneapi = ObjFunction::Create("reg:squarederror_oneapi", &lparam); HostDeviceVector cpu_out_preds; HostDeviceVector oneapi_out_preds; constexpr size_t kRows = 400; constexpr size_t kCols = 100; auto pdmat = RandomDataGenerator(kRows, kCols, 0).Seed(0).GenerateDMatrix(); HostDeviceVector preds; preds.Resize(kRows); auto& h_preds = preds.HostVector(); for (size_t i = 0; i < h_preds.size(); ++i) { h_preds[i] = static_cast(i); } auto& info = pdmat->Info(); info.labels_.Resize(kRows); auto& h_labels = info.labels_.HostVector(); for (size_t i = 0; i < h_labels.size(); ++i) { h_labels[i] = 1 / static_cast(i+1); } { // CPU lparam.gpu_id = -1; obj_cpu->GetGradient(preds, info, 0, &cpu_out_preds); } { // oneapi lparam.gpu_id = 0; obj_oneapi->GetGradient(preds, info, 0, &oneapi_out_preds); } auto& h_cpu_out = cpu_out_preds.HostVector(); auto& h_oneapi_out = oneapi_out_preds.HostVector(); float sgrad = 0; float shess = 0; for (size_t i = 0; i < kRows; ++i) { sgrad += std::pow(h_cpu_out[i].GetGrad() - h_oneapi_out[i].GetGrad(), 2); shess += std::pow(h_cpu_out[i].GetHess() - h_oneapi_out[i].GetHess(), 2); } ASSERT_NEAR(sgrad, 0.0f, kRtEps); ASSERT_NEAR(shess, 0.0f, kRtEps); delete obj_cpu; delete obj_oneapi; } } // namespace xgboost