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Initial GPU support for the approx tree method. (#9414)

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This commit is contained in:
Jiaming Yuan
2023-07-31 15:50:28 +08:00
committed by GitHub
parent 8f0efb4ab3
commit 912e341d57
23 changed files with 639 additions and 360 deletions
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55
tests/cpp/tree/test_prediction_cache.cc
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@@ -24,15 +24,11 @@ class TestPredictionCache : public ::testing::Test {
Xy_ = RandomDataGenerator{n_samples_, n_features, 0}.Targets(n_targets).GenerateDMatrix(true);
}
void RunLearnerTest(std::string updater_name, float subsample, std::string const& grow_policy,
std::string const& strategy) {
void RunLearnerTest(Context const* ctx, std::string updater_name, float subsample,
std::string const& grow_policy, std::string const& strategy) {
std::unique_ptr<Learner> learner{Learner::Create({Xy_})};
if (updater_name == "grow_gpu_hist") {
// gpu_id setup
learner->SetParam("tree_method", "gpu_hist");
} else {
learner->SetParam("updater", updater_name);
}
learner->SetParam("device", ctx->DeviceName());
learner->SetParam("updater", updater_name);
learner->SetParam("multi_strategy", strategy);
learner->SetParam("grow_policy", grow_policy);
learner->SetParam("subsample", std::to_string(subsample));
@@ -65,20 +61,14 @@ class TestPredictionCache : public ::testing::Test {
}
}
void RunTest(std::string const& updater_name, std::string const& strategy) {
void RunTest(Context* ctx, std::string const& updater_name, std::string const& strategy) {
{
Context ctx;
ctx.InitAllowUnknown(Args{{"nthread", "8"}});
if (updater_name == "grow_gpu_hist") {
ctx = ctx.MakeCUDA(0);
} else {
ctx = ctx.MakeCPU();
}
ctx->InitAllowUnknown(Args{{"nthread", "8"}});
ObjInfo task{ObjInfo::kRegression};
std::unique_ptr<TreeUpdater> updater{TreeUpdater::Create(updater_name, &ctx, &task)};
std::unique_ptr<TreeUpdater> updater{TreeUpdater::Create(updater_name, ctx, &task)};
RegTree tree;
std::vector<RegTree *> trees{&tree};
std::vector<RegTree*> trees{&tree};
auto gpair = GenerateRandomGradients(n_samples_);
tree::TrainParam param;
param.UpdateAllowUnknown(Args{{"max_bin", "64"}});
@@ -86,33 +76,46 @@ class TestPredictionCache : public ::testing::Test {
std::vector<HostDeviceVector<bst_node_t>> position(1);
updater->Update(&param, &gpair, Xy_.get(), position, trees);
HostDeviceVector<float> out_prediction_cached;
out_prediction_cached.SetDevice(ctx.gpu_id);
out_prediction_cached.SetDevice(ctx->Device());
out_prediction_cached.Resize(n_samples_);
auto cache =
linalg::MakeTensorView(&ctx, &out_prediction_cached, out_prediction_cached.Size(), 1);
linalg::MakeTensorView(ctx, &out_prediction_cached, out_prediction_cached.Size(), 1);
ASSERT_TRUE(updater->UpdatePredictionCache(Xy_.get(), cache));
}
for (auto policy : {"depthwise", "lossguide"}) {
for (auto subsample : {1.0f, 0.4f}) {
this->RunLearnerTest(updater_name, subsample, policy, strategy);
this->RunLearnerTest(updater_name, subsample, policy, strategy);
this->RunLearnerTest(ctx, updater_name, subsample, policy, strategy);
this->RunLearnerTest(ctx, updater_name, subsample, policy, strategy);
}
}
}
};
TEST_F(TestPredictionCache, Approx) { this->RunTest("grow_histmaker", "one_output_per_tree"); }
TEST_F(TestPredictionCache, Approx) {
Context ctx;
this->RunTest(&ctx, "grow_histmaker", "one_output_per_tree");
}
TEST_F(TestPredictionCache, Hist) {
this->RunTest("grow_quantile_histmaker", "one_output_per_tree");
Context ctx;
this->RunTest(&ctx, "grow_quantile_histmaker", "one_output_per_tree");
}
TEST_F(TestPredictionCache, HistMulti) {
this->RunTest("grow_quantile_histmaker", "multi_output_tree");
Context ctx;
this->RunTest(&ctx, "grow_quantile_histmaker", "multi_output_tree");
}
#if defined(XGBOOST_USE_CUDA)
TEST_F(TestPredictionCache, GpuHist) { this->RunTest("grow_gpu_hist", "one_output_per_tree"); }
TEST_F(TestPredictionCache, GpuHist) {
auto ctx = MakeCUDACtx(0);
this->RunTest(&ctx, "grow_gpu_hist", "one_output_per_tree");
}
TEST_F(TestPredictionCache, GpuApprox) {
auto ctx = MakeCUDACtx(0);
this->RunTest(&ctx, "grow_gpu_approx", "one_output_per_tree");
}
#endif // defined(XGBOOST_USE_CUDA)
} // namespace xgboost