[SYCL] Implement UpdatePredictionCache and connect updater with leraner. (#10701)
--------- Co-authored-by: Dmitry Razdoburdin <>
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Dmitry Razdoburdin
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@@ -2,97 +2,10 @@
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* Copyright 2021-2023 by XGBoost contributors
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*/
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#include <gtest/gtest.h>
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#include <xgboost/host_device_vector.h>
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#include <xgboost/tree_updater.h>
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#include <memory>
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#include "../../../src/tree/param.h" // for TrainParam
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#include "../helpers.h"
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#include "xgboost/task.h" // for ObjInfo
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#include "test_prediction_cache.h"
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namespace xgboost {
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class TestPredictionCache : public ::testing::Test {
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std::shared_ptr<DMatrix> Xy_;
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std::size_t n_samples_{2048};
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protected:
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void SetUp() override {
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std::size_t n_features = 13;
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bst_target_t n_targets = 3;
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Xy_ = RandomDataGenerator{n_samples_, n_features, 0}.Targets(n_targets).GenerateDMatrix(true);
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}
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void RunLearnerTest(Context const* ctx, std::string updater_name, float subsample,
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std::string const& grow_policy, std::string const& strategy) {
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std::unique_ptr<Learner> learner{Learner::Create({Xy_})};
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learner->SetParam("device", ctx->DeviceName());
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learner->SetParam("updater", updater_name);
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learner->SetParam("multi_strategy", strategy);
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learner->SetParam("grow_policy", grow_policy);
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learner->SetParam("subsample", std::to_string(subsample));
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learner->SetParam("nthread", "0");
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learner->Configure();
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for (size_t i = 0; i < 8; ++i) {
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learner->UpdateOneIter(i, Xy_);
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}
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HostDeviceVector<float> out_prediction_cached;
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learner->Predict(Xy_, false, &out_prediction_cached, 0, 0);
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Json model{Object()};
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learner->SaveModel(&model);
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HostDeviceVector<float> out_prediction;
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{
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std::unique_ptr<Learner> learner{Learner::Create({Xy_})};
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learner->LoadModel(model);
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learner->Predict(Xy_, false, &out_prediction, 0, 0);
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}
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auto const h_predt_cached = out_prediction_cached.ConstHostSpan();
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auto const h_predt = out_prediction.ConstHostSpan();
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ASSERT_EQ(h_predt.size(), h_predt_cached.size());
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for (size_t i = 0; i < h_predt.size(); ++i) {
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ASSERT_NEAR(h_predt[i], h_predt_cached[i], kRtEps);
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}
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}
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void RunTest(Context* ctx, std::string const& updater_name, std::string const& strategy) {
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{
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ctx->InitAllowUnknown(Args{{"nthread", "8"}});
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ObjInfo task{ObjInfo::kRegression};
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std::unique_ptr<TreeUpdater> updater{TreeUpdater::Create(updater_name, ctx, &task)};
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RegTree tree;
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std::vector<RegTree*> trees{&tree};
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auto gpair = GenerateRandomGradients(ctx, n_samples_, 1);
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tree::TrainParam param;
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param.UpdateAllowUnknown(Args{{"max_bin", "64"}});
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updater->Configure(Args{});
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std::vector<HostDeviceVector<bst_node_t>> position(1);
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updater->Update(¶m, &gpair, Xy_.get(), position, trees);
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HostDeviceVector<float> out_prediction_cached;
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out_prediction_cached.SetDevice(ctx->Device());
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out_prediction_cached.Resize(n_samples_);
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auto cache =
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linalg::MakeTensorView(ctx, &out_prediction_cached, out_prediction_cached.Size(), 1);
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ASSERT_TRUE(updater->UpdatePredictionCache(Xy_.get(), cache));
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}
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for (auto policy : {"depthwise", "lossguide"}) {
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for (auto subsample : {1.0f, 0.4f}) {
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this->RunLearnerTest(ctx, updater_name, subsample, policy, strategy);
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this->RunLearnerTest(ctx, updater_name, subsample, policy, strategy);
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}
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}
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}
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};
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TEST_F(TestPredictionCache, Approx) {
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Context ctx;
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this->RunTest(&ctx, "grow_histmaker", "one_output_per_tree");
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@@ -119,4 +32,4 @@ TEST_F(TestPredictionCache, GpuApprox) {
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this->RunTest(&ctx, "grow_gpu_approx", "one_output_per_tree");
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}
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#endif // defined(XGBOOST_USE_CUDA)
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} // namespace xgboost
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} // namespace xgboost
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97
tests/cpp/tree/test_prediction_cache.h
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97
tests/cpp/tree/test_prediction_cache.h
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@@ -0,0 +1,97 @@
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/**
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* Copyright 2021-2024 by XGBoost contributors.
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*/
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#pragma once
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#include <gtest/gtest.h>
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#include <xgboost/host_device_vector.h>
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#include <xgboost/tree_updater.h>
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#include <memory>
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#include "../../../src/tree/param.h" // for TrainParam
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#include "../helpers.h"
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#include "xgboost/task.h" // for ObjInfo
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namespace xgboost {
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class TestPredictionCache : public ::testing::Test {
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std::shared_ptr<DMatrix> Xy_;
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std::size_t n_samples_{2048};
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protected:
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void SetUp() override {
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std::size_t n_features = 13;
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bst_target_t n_targets = 3;
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Xy_ = RandomDataGenerator{n_samples_, n_features, 0}.Targets(n_targets).GenerateDMatrix(true);
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}
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void RunLearnerTest(Context const* ctx, std::string updater_name, float subsample,
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std::string const& grow_policy, std::string const& strategy) {
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std::unique_ptr<Learner> learner{Learner::Create({Xy_})};
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learner->SetParam("device", ctx->DeviceName());
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learner->SetParam("updater", updater_name);
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learner->SetParam("multi_strategy", strategy);
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learner->SetParam("grow_policy", grow_policy);
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learner->SetParam("subsample", std::to_string(subsample));
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learner->SetParam("nthread", "0");
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learner->Configure();
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for (size_t i = 0; i < 8; ++i) {
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learner->UpdateOneIter(i, Xy_);
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}
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HostDeviceVector<float> out_prediction_cached;
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learner->Predict(Xy_, false, &out_prediction_cached, 0, 0);
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Json model{Object()};
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learner->SaveModel(&model);
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HostDeviceVector<float> out_prediction;
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{
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std::unique_ptr<Learner> learner{Learner::Create({Xy_})};
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learner->LoadModel(model);
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learner->Predict(Xy_, false, &out_prediction, 0, 0);
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}
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auto const h_predt_cached = out_prediction_cached.ConstHostSpan();
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auto const h_predt = out_prediction.ConstHostSpan();
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ASSERT_EQ(h_predt.size(), h_predt_cached.size());
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for (size_t i = 0; i < h_predt.size(); ++i) {
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ASSERT_NEAR(h_predt[i], h_predt_cached[i], kRtEps);
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}
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}
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void RunTest(Context* ctx, std::string const& updater_name, std::string const& strategy) {
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{
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ctx->InitAllowUnknown(Args{{"nthread", "8"}});
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ObjInfo task{ObjInfo::kRegression};
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std::unique_ptr<TreeUpdater> updater{TreeUpdater::Create(updater_name, ctx, &task)};
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RegTree tree;
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std::vector<RegTree*> trees{&tree};
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auto gpair = GenerateRandomGradients(ctx, n_samples_, 1);
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tree::TrainParam param;
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param.UpdateAllowUnknown(Args{{"max_bin", "64"}});
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updater->Configure(Args{});
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std::vector<HostDeviceVector<bst_node_t>> position(1);
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updater->Update(¶m, &gpair, Xy_.get(), position, trees);
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HostDeviceVector<float> out_prediction_cached;
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out_prediction_cached.SetDevice(ctx->Device());
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out_prediction_cached.Resize(n_samples_);
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auto cache =
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linalg::MakeTensorView(ctx, &out_prediction_cached, out_prediction_cached.Size(), 1);
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ASSERT_TRUE(updater->UpdatePredictionCache(Xy_.get(), cache));
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}
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for (auto policy : {"depthwise", "lossguide"}) {
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for (auto subsample : {1.0f, 0.4f}) {
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this->RunLearnerTest(ctx, updater_name, subsample, policy, strategy);
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this->RunLearnerTest(ctx, updater_name, subsample, policy, strategy);
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}
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}
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}
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};
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} // namespace xgboost
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