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Use integer gradients in gpu_hist split evaluation (#8274)

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This commit is contained in:
Rory Mitchell
2022-10-11 12:16:27 +02:00
committed by GitHub
parent c68684ff4c
commit 210915c985
12 changed files with 224 additions and 292 deletions
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85
tests/cpp/tree/test_gpu_hist.cu
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@@ -107,12 +107,12 @@ void TestBuildHist(bool use_shared_memory_histograms) {
maker.row_partitioner.reset(new RowPartitioner(0, kNRows));
maker.hist.AllocateHistograms({0});
maker.gpair = gpair.DeviceSpan();
maker.histogram_rounding.reset(new GradientQuantizer(maker.gpair));
maker.quantiser.reset(new GradientQuantiser(maker.gpair));
BuildGradientHistogram(
page->GetDeviceAccessor(0), maker.feature_groups->DeviceAccessor(0),
gpair.DeviceSpan(), maker.row_partitioner->GetRows(0),
maker.hist.GetNodeHistogram(0), *maker.histogram_rounding,
maker.hist.GetNodeHistogram(0), *maker.quantiser,
!use_shared_memory_histograms);
DeviceHistogramStorage<>& d_hist = maker.hist;
@@ -125,7 +125,7 @@ void TestBuildHist(bool use_shared_memory_histograms) {
std::vector<GradientPairPrecise> solution = GetHostHistGpair();
for (size_t i = 0; i < h_result.size(); ++i) {
auto result = maker.histogram_rounding->ToFloatingPoint(h_result[i]);
auto result = maker.quantiser->ToFloatingPoint(h_result[i]);
EXPECT_NEAR(result.GetGrad(), solution[i].GetGrad(), 0.01f);
EXPECT_NEAR(result.GetHess(), solution[i].GetHess(), 0.01f);
}
@@ -156,85 +156,10 @@ HistogramCutsWrapper GetHostCutMatrix () {
return cmat;
}
inline GradientQuantizer DummyRoundingFactor() {
inline GradientQuantiser DummyRoundingFactor() {
thrust::device_vector<GradientPair> gpair(1);
gpair[0] = {1000.f, 1000.f}; // Tests should not exceed sum of 1000
return GradientQuantizer(dh::ToSpan(gpair));
}
// TODO(trivialfis): This test is over simplified.
TEST(GpuHist, EvaluateRootSplit) {
constexpr int kNRows = 16;
constexpr int kNCols = 8;
TrainParam param;
std::vector<std::pair<std::string, std::string>> args{
{"max_depth", "1"},
{"max_leaves", "0"},
// Disable all other parameters.
{"colsample_bynode", "1"},
{"colsample_bylevel", "1"},
{"colsample_bytree", "1"},
{"min_child_weight", "0.01"},
{"reg_alpha", "0"},
{"reg_lambda", "0"},
{"max_delta_step", "0"}};
param.Init(args);
for (size_t i = 0; i < kNCols; ++i) {
param.monotone_constraints.emplace_back(0);
}
int max_bins = 4;
// Initialize GPUHistMakerDevice
auto page = BuildEllpackPage(kNRows, kNCols);
BatchParam batch_param{};
Context ctx{CreateEmptyGenericParam(0)};
GPUHistMakerDevice<GradientPairPrecise> maker(&ctx, page.get(), {}, kNRows, param, kNCols, kNCols,
batch_param);
// Initialize GPUHistMakerDevice::node_sum_gradients
maker.node_sum_gradients = {};
// Initialize GPUHistMakerDevice::cut
auto cmat = GetHostCutMatrix();
// Copy cut matrix to device.
page->Cuts() = cmat;
maker.monotone_constraints = param.monotone_constraints;
// Initialize GPUHistMakerDevice::hist
maker.hist.Init(0, (max_bins - 1) * kNCols);
maker.hist.AllocateHistograms({0});
// Each row of hist_gpair represents gpairs for one feature.
// Each entry represents a bin.
std::vector<GradientPairPrecise> hist_gpair = GetHostHistGpair();
maker.histogram_rounding.reset(new GradientQuantizer(DummyRoundingFactor()));
std::vector<int64_t> hist;
for (auto pair : hist_gpair) {
auto grad = maker.histogram_rounding->ToFixedPoint({float(pair.GetGrad()),float(pair.GetHess())});
hist.push_back(grad.GetQuantisedGrad());
hist.push_back(grad.GetQuantisedHess());
}
ASSERT_EQ(maker.hist.Data().size(), hist.size());
thrust::copy(hist.begin(), hist.end(),
maker.hist.Data().begin());
std::vector<float> feature_weights;
maker.column_sampler.Init(kNCols, feature_weights, param.colsample_bynode,
param.colsample_bylevel, param.colsample_bytree);
RegTree tree;
MetaInfo info;
info.num_row_ = kNRows;
info.num_col_ = kNCols;
DeviceSplitCandidate res = maker.EvaluateRootSplit({6.4f, 12.8f}).split;
ASSERT_EQ(res.findex, 7);
ASSERT_NEAR(res.fvalue, 0.26, xgboost::kRtEps);
return GradientQuantiser(dh::ToSpan(gpair));
}
void TestHistogramIndexImpl() {