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[MT-TREE] Support prediction cache and model slicing. (#8968)

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- Fix prediction range.
- Support prediction cache in mt-hist.
- Support model slicing.
- Make the booster a Python iterable by defining `__iter__`.
- Cleanup removed/deprecated parameters.
- A new field in the output model `iteration_indptr` for pointing to the ranges of trees for each iteration.
This commit is contained in:
Jiaming Yuan
2023-03-27 23:10:54 +08:00
committed by GitHub
parent c2b3a13e70
commit acc110c251
30 changed files with 502 additions and 343 deletions
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2
tests/cpp/tree/test_gpu_hist.cu
View File

@@ -246,7 +246,7 @@ void UpdateTree(HostDeviceVector<GradientPair>* gpair, DMatrix* dmat,
std::vector<HostDeviceVector<bst_node_t>> position(1);
hist_maker.Update(&param, gpair, dmat, common::Span<HostDeviceVector<bst_node_t>>{position},
{tree});
auto cache = linalg::VectorView<float>{preds->DeviceSpan(), {preds->Size()}, 0};
auto cache = linalg::MakeTensorView(&ctx, preds->DeviceSpan(), preds->Size(), 1);
hist_maker.UpdatePredictionCache(dmat, cache);
}

36
tests/cpp/tree/test_prediction_cache.cc
View File

@@ -15,15 +15,17 @@ namespace xgboost {
class TestPredictionCache : public ::testing::Test {
std::shared_ptr<DMatrix> Xy_;
size_t n_samples_{2048};
std::size_t n_samples_{2048};
protected:
void SetUp() override {
size_t n_features = 13;
Xy_ = RandomDataGenerator{n_samples_, n_features, 0}.GenerateDMatrix(true);
std::size_t n_features = 13;
bst_target_t n_targets = 3;
Xy_ = RandomDataGenerator{n_samples_, n_features, 0}.Targets(n_targets).GenerateDMatrix(true);
}
void RunLearnerTest(std::string updater_name, float subsample, std::string grow_policy) {
void RunLearnerTest(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
@@ -31,6 +33,7 @@ class TestPredictionCache : public ::testing::Test {
} else {
learner->SetParam("updater", updater_name);
}
learner->SetParam("multi_strategy", strategy);
learner->SetParam("grow_policy", grow_policy);
learner->SetParam("subsample", std::to_string(subsample));
learner->SetParam("nthread", "0");
@@ -62,7 +65,7 @@ class TestPredictionCache : public ::testing::Test {
}
}
void RunTest(std::string updater_name) {
void RunTest(std::string const& updater_name, std::string const& strategy) {
{
Context ctx;
ctx.InitAllowUnknown(Args{{"nthread", "8"}});
@@ -85,28 +88,31 @@ class TestPredictionCache : public ::testing::Test {
HostDeviceVector<float> out_prediction_cached;
out_prediction_cached.SetDevice(ctx.gpu_id);
out_prediction_cached.Resize(n_samples_);
auto cache = linalg::VectorView<float>{ctx.gpu_id == Context::kCpuId
? out_prediction_cached.HostSpan()
: out_prediction_cached.DeviceSpan(),
{out_prediction_cached.Size()},
ctx.gpu_id};
auto cache =
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);
this->RunLearnerTest(updater_name, subsample, policy);
this->RunLearnerTest(updater_name, subsample, policy, strategy);
this->RunLearnerTest(updater_name, subsample, policy, strategy);
}
}
}
};
TEST_F(TestPredictionCache, Approx) { this->RunTest("grow_histmaker"); }
TEST_F(TestPredictionCache, Approx) { this->RunTest("grow_histmaker", "one_output_per_tree"); }
TEST_F(TestPredictionCache, Hist) { this->RunTest("grow_quantile_histmaker"); }
TEST_F(TestPredictionCache, Hist) {
this->RunTest("grow_quantile_histmaker", "one_output_per_tree");
}
TEST_F(TestPredictionCache, HistMulti) {
this->RunTest("grow_quantile_histmaker", "multi_output_tree");
}
#if defined(XGBOOST_USE_CUDA)
TEST_F(TestPredictionCache, GpuHist) { this->RunTest("grow_gpu_hist"); }
TEST_F(TestPredictionCache, GpuHist) { this->RunTest("grow_gpu_hist", "one_output_per_tree"); }
#endif // defined(XGBOOST_USE_CUDA)
} // namespace xgboost