563 lines
25 KiB
Plaintext
563 lines
25 KiB
Plaintext
/*!
|
|
* Copyright 2020-2022 by XGBoost contributors
|
|
*/
|
|
#include <gtest/gtest.h>
|
|
|
|
#if defined(XGBOOST_USE_CUDA)
|
|
#include "../../../../src/tree/gpu_hist/evaluate_splits.cuh"
|
|
#elif defined(XGBOOST_USE_HIP)
|
|
#include "../../../../src/tree/gpu_hist/evaluate_splits.hip.h"
|
|
#endif
|
|
#include "../../helpers.h"
|
|
#include "../../histogram_helpers.h"
|
|
#include "../test_evaluate_splits.h" // TestPartitionBasedSplit
|
|
#include <thrust/host_vector.h>
|
|
|
|
namespace xgboost {
|
|
namespace tree {
|
|
namespace {
|
|
auto ZeroParam() {
|
|
auto args = Args{{"min_child_weight", "0"},
|
|
{"lambda", "0"}};
|
|
TrainParam tparam;
|
|
tparam.UpdateAllowUnknown(args);
|
|
return tparam;
|
|
}
|
|
|
|
} // anonymous namespace
|
|
|
|
inline GradientQuantiser DummyRoundingFactor() {
|
|
thrust::device_vector<GradientPair> gpair(1);
|
|
gpair[0] = {1000.f, 1000.f}; // Tests should not exceed sum of 1000
|
|
return GradientQuantiser(dh::ToSpan(gpair));
|
|
}
|
|
|
|
thrust::device_vector<GradientPairInt64> ConvertToInteger(std::vector<GradientPairPrecise> x) {
|
|
auto r = DummyRoundingFactor();
|
|
std::vector<GradientPairInt64> y(x.size());
|
|
for (std::size_t i = 0; i < x.size(); i++) {
|
|
y[i] = r.ToFixedPoint(GradientPair(x[i]));
|
|
}
|
|
return y;
|
|
}
|
|
|
|
|
|
TEST_F(TestCategoricalSplitWithMissing, GPUHistEvaluator) {
|
|
thrust::device_vector<bst_feature_t> feature_set = std::vector<bst_feature_t>{0};
|
|
GPUTrainingParam param{param_};
|
|
cuts_.cut_ptrs_.SetDevice(0);
|
|
cuts_.cut_values_.SetDevice(0);
|
|
cuts_.min_vals_.SetDevice(0);
|
|
thrust::device_vector<GradientPairInt64> feature_histogram{ConvertToInteger(feature_histogram_)};
|
|
|
|
dh::device_vector<FeatureType> feature_types(feature_set.size(), FeatureType::kCategorical);
|
|
auto d_feature_types = dh::ToSpan(feature_types);
|
|
auto quantiser = DummyRoundingFactor();
|
|
EvaluateSplitInputs input{1, 0, quantiser.ToFixedPoint(parent_sum_), dh::ToSpan(feature_set),
|
|
dh::ToSpan(feature_histogram)};
|
|
EvaluateSplitSharedInputs shared_inputs{param,
|
|
quantiser,
|
|
d_feature_types,
|
|
cuts_.cut_ptrs_.ConstDeviceSpan(),
|
|
cuts_.cut_values_.ConstDeviceSpan(),
|
|
cuts_.min_vals_.ConstDeviceSpan(),
|
|
false};
|
|
|
|
GPUHistEvaluator evaluator{param_, static_cast<bst_feature_t>(feature_set.size()), 0};
|
|
|
|
evaluator.Reset(cuts_, dh::ToSpan(feature_types), feature_set.size(), param_, 0);
|
|
DeviceSplitCandidate result = evaluator.EvaluateSingleSplit(input, shared_inputs).split;
|
|
|
|
ASSERT_EQ(result.thresh, 1);
|
|
this->CheckResult(result.loss_chg, result.findex, result.fvalue, result.is_cat,
|
|
result.dir == kLeftDir, quantiser.ToFloatingPoint(result.left_sum), quantiser.ToFloatingPoint(result.right_sum));
|
|
}
|
|
|
|
TEST(GpuHist, PartitionBasic) {
|
|
TrainParam tparam = ZeroParam();
|
|
tparam.max_cat_to_onehot = 0;
|
|
GPUTrainingParam param{tparam};
|
|
|
|
common::HistogramCuts cuts;
|
|
cuts.cut_values_.HostVector() = std::vector<float>{0.0, 1.0, 2.0};
|
|
cuts.cut_ptrs_.HostVector() = std::vector<uint32_t>{0, 3};
|
|
cuts.min_vals_.HostVector() = std::vector<float>{0.0};
|
|
cuts.cut_ptrs_.SetDevice(0);
|
|
cuts.cut_values_.SetDevice(0);
|
|
cuts.min_vals_.SetDevice(0);
|
|
thrust::device_vector<bst_feature_t> feature_set = std::vector<bst_feature_t>{0};
|
|
|
|
thrust::device_vector<int> monotonic_constraints(feature_set.size(), 0);
|
|
dh::device_vector<FeatureType> feature_types(feature_set.size(), FeatureType::kCategorical);
|
|
common::Span<FeatureType> d_feature_types;
|
|
auto max_cat =
|
|
*std::max_element(cuts.cut_values_.HostVector().begin(), cuts.cut_values_.HostVector().end());
|
|
cuts.SetCategorical(true, max_cat);
|
|
d_feature_types = dh::ToSpan(feature_types);
|
|
auto quantiser = DummyRoundingFactor();
|
|
EvaluateSplitSharedInputs shared_inputs{
|
|
param,
|
|
quantiser,
|
|
d_feature_types,
|
|
cuts.cut_ptrs_.ConstDeviceSpan(),
|
|
cuts.cut_values_.ConstDeviceSpan(),
|
|
cuts.min_vals_.ConstDeviceSpan(),
|
|
false,
|
|
};
|
|
|
|
GPUHistEvaluator evaluator{tparam, static_cast<bst_feature_t>(feature_set.size()), 0};
|
|
evaluator.Reset(cuts, dh::ToSpan(feature_types), feature_set.size(), tparam, 0);
|
|
|
|
{
|
|
// -1.0s go right
|
|
// -3.0s go left
|
|
auto parent_sum = quantiser.ToFixedPoint(GradientPairPrecise{-5.0, 3.0});
|
|
auto feature_histogram = ConvertToInteger({{-1.0, 1.0}, {-1.0, 1.0}, {-3.0, 1.0}});
|
|
EvaluateSplitInputs input{0, 0, parent_sum, dh::ToSpan(feature_set),
|
|
dh::ToSpan(feature_histogram)};
|
|
DeviceSplitCandidate result = evaluator.EvaluateSingleSplit(input, shared_inputs).split;
|
|
auto cats = std::bitset<32>(evaluator.GetHostNodeCats(input.nidx)[0]);
|
|
EXPECT_EQ(result.dir, kLeftDir);
|
|
EXPECT_EQ(cats, std::bitset<32>("11000000000000000000000000000000"));
|
|
EXPECT_EQ(result.left_sum + result.right_sum, parent_sum);
|
|
}
|
|
|
|
{
|
|
// -1.0s go right
|
|
// -3.0s go left
|
|
auto parent_sum = quantiser.ToFixedPoint(GradientPairPrecise{-7.0, 3.0});
|
|
auto feature_histogram = ConvertToInteger({{-1.0, 1.0}, {-3.0, 1.0}, {-3.0, 1.0}});
|
|
EvaluateSplitInputs input{1, 0, parent_sum, dh::ToSpan(feature_set),
|
|
dh::ToSpan(feature_histogram)};
|
|
DeviceSplitCandidate result = evaluator.EvaluateSingleSplit(input, shared_inputs).split;
|
|
auto cats = std::bitset<32>(evaluator.GetHostNodeCats(input.nidx)[0]);
|
|
EXPECT_EQ(result.dir, kLeftDir);
|
|
EXPECT_EQ(cats, std::bitset<32>("10000000000000000000000000000000"));
|
|
EXPECT_EQ(result.left_sum + result.right_sum, parent_sum);
|
|
}
|
|
{
|
|
// All -1.0, gain from splitting should be 0.0
|
|
auto parent_sum = quantiser.ToFixedPoint(GradientPairPrecise{-3.0, 3.0});
|
|
auto feature_histogram = ConvertToInteger({{-1.0, 1.0}, {-1.0, 1.0}, {-1.0, 1.0}});
|
|
EvaluateSplitInputs input{2, 0, parent_sum, dh::ToSpan(feature_set),
|
|
dh::ToSpan(feature_histogram)};
|
|
DeviceSplitCandidate result = evaluator.EvaluateSingleSplit(input, shared_inputs).split;
|
|
EXPECT_EQ(result.dir, kLeftDir);
|
|
EXPECT_FLOAT_EQ(result.loss_chg, 0.0f);
|
|
EXPECT_EQ(result.left_sum + result.right_sum, parent_sum);
|
|
}
|
|
// With 3.0/3.0 missing values
|
|
// Forward, first 2 categories are selected, while the last one go to left along with missing value
|
|
{
|
|
auto parent_sum = quantiser.ToFixedPoint(GradientPairPrecise{0.0, 6.0});
|
|
auto feature_histogram = ConvertToInteger({{-1.0, 1.0}, {-1.0, 1.0}, {-1.0, 1.0}});
|
|
EvaluateSplitInputs input{3, 0, parent_sum, dh::ToSpan(feature_set),
|
|
dh::ToSpan(feature_histogram)};
|
|
DeviceSplitCandidate result = evaluator.EvaluateSingleSplit(input, shared_inputs).split;
|
|
auto cats = std::bitset<32>(evaluator.GetHostNodeCats(input.nidx)[0]);
|
|
EXPECT_EQ(cats, std::bitset<32>("11000000000000000000000000000000"));
|
|
EXPECT_EQ(result.dir, kLeftDir);
|
|
EXPECT_EQ(result.left_sum + result.right_sum, parent_sum);
|
|
}
|
|
{
|
|
// -1.0s go right
|
|
// -3.0s go left
|
|
auto parent_sum = quantiser.ToFixedPoint(GradientPairPrecise{-5.0, 3.0});
|
|
auto feature_histogram = ConvertToInteger({{-1.0, 1.0}, {-3.0, 1.0}, {-1.0, 1.0}});
|
|
EvaluateSplitInputs input{4, 0, parent_sum, dh::ToSpan(feature_set),
|
|
dh::ToSpan(feature_histogram)};
|
|
DeviceSplitCandidate result = evaluator.EvaluateSingleSplit(input, shared_inputs).split;
|
|
auto cats = std::bitset<32>(evaluator.GetHostNodeCats(input.nidx)[0]);
|
|
EXPECT_EQ(result.dir, kLeftDir);
|
|
EXPECT_EQ(cats, std::bitset<32>("10100000000000000000000000000000"));
|
|
EXPECT_EQ(result.left_sum + result.right_sum, parent_sum);
|
|
}
|
|
{
|
|
// -1.0s go right
|
|
// -3.0s go left
|
|
auto parent_sum = quantiser.ToFixedPoint(GradientPairPrecise{-5.0, 3.0});
|
|
auto feature_histogram = ConvertToInteger({{-3.0, 1.0}, {-1.0, 1.0}, {-3.0, 1.0}});
|
|
EvaluateSplitInputs input{5, 0, parent_sum, dh::ToSpan(feature_set),
|
|
dh::ToSpan(feature_histogram)};
|
|
DeviceSplitCandidate result = evaluator.EvaluateSingleSplit(input, shared_inputs).split;
|
|
auto cats = std::bitset<32>(evaluator.GetHostNodeCats(input.nidx)[0]);
|
|
EXPECT_EQ(cats, std::bitset<32>("01000000000000000000000000000000"));
|
|
EXPECT_EQ(result.left_sum + result.right_sum, parent_sum);
|
|
}
|
|
}
|
|
|
|
TEST(GpuHist, PartitionTwoFeatures) {
|
|
TrainParam tparam = ZeroParam();
|
|
tparam.max_cat_to_onehot = 0;
|
|
GPUTrainingParam param{tparam};
|
|
|
|
common::HistogramCuts cuts;
|
|
cuts.cut_values_.HostVector() = std::vector<float>{0.0, 1.0, 2.0, 0.0, 1.0, 2.0};
|
|
cuts.cut_ptrs_.HostVector() = std::vector<uint32_t>{0, 3, 6};
|
|
cuts.min_vals_.HostVector() = std::vector<float>{0.0, 0.0};
|
|
cuts.cut_ptrs_.SetDevice(0);
|
|
cuts.cut_values_.SetDevice(0);
|
|
cuts.min_vals_.SetDevice(0);
|
|
thrust::device_vector<bst_feature_t> feature_set = std::vector<bst_feature_t>{0, 1};
|
|
|
|
thrust::device_vector<int> monotonic_constraints(feature_set.size(), 0);
|
|
dh::device_vector<FeatureType> feature_types(feature_set.size(), FeatureType::kCategorical);
|
|
common::Span<FeatureType> d_feature_types(dh::ToSpan(feature_types));
|
|
auto max_cat =
|
|
*std::max_element(cuts.cut_values_.HostVector().begin(), cuts.cut_values_.HostVector().end());
|
|
cuts.SetCategorical(true, max_cat);
|
|
|
|
auto quantiser = DummyRoundingFactor();
|
|
EvaluateSplitSharedInputs shared_inputs{param,
|
|
quantiser,
|
|
d_feature_types,
|
|
cuts.cut_ptrs_.ConstDeviceSpan(),
|
|
cuts.cut_values_.ConstDeviceSpan(),
|
|
cuts.min_vals_.ConstDeviceSpan(),
|
|
false};
|
|
|
|
GPUHistEvaluator evaluator{tparam, static_cast<bst_feature_t>(feature_set.size()), 0};
|
|
evaluator.Reset(cuts, dh::ToSpan(feature_types), feature_set.size(), tparam, 0);
|
|
|
|
{
|
|
auto parent_sum = quantiser.ToFixedPoint(GradientPairPrecise{-6.0, 3.0});
|
|
auto feature_histogram = ConvertToInteger({ {-2.0, 1.0}, {-2.0, 1.0}, {-2.0, 1.0}, {-1.0, 1.0}, {-1.0, 1.0}, {-4.0, 1.0}});
|
|
EvaluateSplitInputs input{0, 0, parent_sum, dh::ToSpan(feature_set),
|
|
dh::ToSpan(feature_histogram)};
|
|
DeviceSplitCandidate result = evaluator.EvaluateSingleSplit(input, shared_inputs).split;
|
|
auto cats = std::bitset<32>(evaluator.GetHostNodeCats(input.nidx)[0]);
|
|
EXPECT_EQ(result.findex, 1);
|
|
EXPECT_EQ(cats, std::bitset<32>("11000000000000000000000000000000"));
|
|
EXPECT_EQ(result.left_sum + result.right_sum, parent_sum);
|
|
}
|
|
|
|
{
|
|
auto parent_sum = quantiser.ToFixedPoint(GradientPairPrecise{-6.0, 3.0});
|
|
auto feature_histogram = ConvertToInteger({ {-2.0, 1.0}, {-2.0, 1.0}, {-2.0, 1.0}, {-1.0, 1.0}, {-2.5, 1.0}, {-2.5, 1.0}});
|
|
EvaluateSplitInputs input{1, 0, parent_sum, dh::ToSpan(feature_set),
|
|
dh::ToSpan(feature_histogram)};
|
|
DeviceSplitCandidate result = evaluator.EvaluateSingleSplit(input, shared_inputs).split;
|
|
auto cats = std::bitset<32>(evaluator.GetHostNodeCats(input.nidx)[0]);
|
|
EXPECT_EQ(result.findex, 1);
|
|
EXPECT_EQ(cats, std::bitset<32>("10000000000000000000000000000000"));
|
|
EXPECT_EQ(result.left_sum + result.right_sum, parent_sum);
|
|
}
|
|
}
|
|
|
|
TEST(GpuHist, PartitionTwoNodes) {
|
|
TrainParam tparam = ZeroParam();
|
|
tparam.max_cat_to_onehot = 0;
|
|
GPUTrainingParam param{tparam};
|
|
|
|
common::HistogramCuts cuts;
|
|
cuts.cut_values_.HostVector() = std::vector<float>{0.0, 1.0, 2.0};
|
|
cuts.cut_ptrs_.HostVector() = std::vector<uint32_t>{0, 3};
|
|
cuts.min_vals_.HostVector() = std::vector<float>{0.0};
|
|
cuts.cut_ptrs_.SetDevice(0);
|
|
cuts.cut_values_.SetDevice(0);
|
|
cuts.min_vals_.SetDevice(0);
|
|
thrust::device_vector<bst_feature_t> feature_set = std::vector<bst_feature_t>{0};
|
|
|
|
thrust::device_vector<int> monotonic_constraints(feature_set.size(), 0);
|
|
dh::device_vector<FeatureType> feature_types(feature_set.size(), FeatureType::kCategorical);
|
|
common::Span<FeatureType> d_feature_types(dh::ToSpan(feature_types));
|
|
auto max_cat =
|
|
*std::max_element(cuts.cut_values_.HostVector().begin(), cuts.cut_values_.HostVector().end());
|
|
cuts.SetCategorical(true, max_cat);
|
|
|
|
auto quantiser = DummyRoundingFactor();
|
|
EvaluateSplitSharedInputs shared_inputs{param,
|
|
quantiser,
|
|
d_feature_types,
|
|
cuts.cut_ptrs_.ConstDeviceSpan(),
|
|
cuts.cut_values_.ConstDeviceSpan(),
|
|
cuts.min_vals_.ConstDeviceSpan(),
|
|
false};
|
|
|
|
GPUHistEvaluator evaluator{tparam, static_cast<bst_feature_t>(feature_set.size()), 0};
|
|
evaluator.Reset(cuts, dh::ToSpan(feature_types), feature_set.size(), tparam, 0);
|
|
|
|
{
|
|
auto parent_sum = quantiser.ToFixedPoint(GradientPairPrecise{-6.0, 3.0});
|
|
auto feature_histogram_a = ConvertToInteger({{-1.0, 1.0}, {-2.5, 1.0}, {-2.5, 1.0},
|
|
{-1.0, 1.0}, {-1.0, 1.0}, {-4.0, 1.0}});
|
|
thrust::device_vector<EvaluateSplitInputs> inputs(2);
|
|
inputs[0] = EvaluateSplitInputs{0, 0, parent_sum, dh::ToSpan(feature_set),
|
|
dh::ToSpan(feature_histogram_a)};
|
|
auto feature_histogram_b = ConvertToInteger({{-1.0, 1.0}, {-1.0, 1.0}, {-4.0, 1.0}});
|
|
inputs[1] = EvaluateSplitInputs{1, 0, parent_sum, dh::ToSpan(feature_set),
|
|
dh::ToSpan(feature_histogram_b)};
|
|
thrust::device_vector<GPUExpandEntry> results(2);
|
|
evaluator.EvaluateSplits({0, 1}, 1, dh::ToSpan(inputs), shared_inputs, dh::ToSpan(results));
|
|
GPUExpandEntry result_a = results[0];
|
|
GPUExpandEntry result_b = results[1];
|
|
EXPECT_EQ(std::bitset<32>(evaluator.GetHostNodeCats(0)[0]),
|
|
std::bitset<32>("10000000000000000000000000000000"));
|
|
EXPECT_EQ(std::bitset<32>(evaluator.GetHostNodeCats(1)[0]),
|
|
std::bitset<32>("11000000000000000000000000000000"));
|
|
}
|
|
}
|
|
|
|
void TestEvaluateSingleSplit(bool is_categorical) {
|
|
auto quantiser = DummyRoundingFactor();
|
|
auto parent_sum = quantiser.ToFixedPoint(GradientPairPrecise{0.0, 1.0});
|
|
TrainParam tparam = ZeroParam();
|
|
GPUTrainingParam param{tparam};
|
|
|
|
common::HistogramCuts cuts{MakeCutsForTest({1.0, 2.0, 11.0, 12.0}, {0, 2, 4}, {0.0, 0.0}, 0)};
|
|
thrust::device_vector<bst_feature_t> feature_set = std::vector<bst_feature_t>{0, 1};
|
|
|
|
// Setup gradients so that second feature gets higher gain
|
|
auto feature_histogram = ConvertToInteger({{-0.5, 0.5}, {0.5, 0.5}, {-1.0, 0.5}, {1.0, 0.5}});
|
|
|
|
dh::device_vector<FeatureType> feature_types(feature_set.size(),
|
|
FeatureType::kCategorical);
|
|
common::Span<FeatureType> d_feature_types;
|
|
if (is_categorical) {
|
|
auto max_cat = *std::max_element(cuts.cut_values_.HostVector().begin(),
|
|
cuts.cut_values_.HostVector().end());
|
|
cuts.SetCategorical(true, max_cat);
|
|
d_feature_types = dh::ToSpan(feature_types);
|
|
}
|
|
|
|
EvaluateSplitInputs input{1, 0, parent_sum, dh::ToSpan(feature_set),
|
|
dh::ToSpan(feature_histogram)};
|
|
EvaluateSplitSharedInputs shared_inputs{param,
|
|
quantiser,
|
|
d_feature_types,
|
|
cuts.cut_ptrs_.ConstDeviceSpan(),
|
|
cuts.cut_values_.ConstDeviceSpan(),
|
|
cuts.min_vals_.ConstDeviceSpan(),
|
|
false};
|
|
|
|
GPUHistEvaluator evaluator{
|
|
tparam, static_cast<bst_feature_t>(feature_set.size()), 0};
|
|
evaluator.Reset(cuts, dh::ToSpan(feature_types), feature_set.size(), tparam, 0);
|
|
DeviceSplitCandidate result = evaluator.EvaluateSingleSplit(input, shared_inputs).split;
|
|
|
|
EXPECT_EQ(result.findex, 1);
|
|
if (is_categorical) {
|
|
ASSERT_TRUE(std::isnan(result.fvalue));
|
|
} else {
|
|
EXPECT_EQ(result.fvalue, 11.0);
|
|
}
|
|
EXPECT_EQ(result.left_sum + result.right_sum, parent_sum);
|
|
}
|
|
|
|
TEST(GpuHist, EvaluateSingleSplit) {
|
|
TestEvaluateSingleSplit(false);
|
|
}
|
|
|
|
TEST(GpuHist, EvaluateSingleCategoricalSplit) {
|
|
TestEvaluateSingleSplit(true);
|
|
}
|
|
|
|
TEST(GpuHist, EvaluateSingleSplitMissing) {
|
|
auto quantiser = DummyRoundingFactor();
|
|
auto parent_sum = quantiser.ToFixedPoint(GradientPairPrecise{1.0, 1.5});
|
|
TrainParam tparam = ZeroParam();
|
|
GPUTrainingParam param{tparam};
|
|
|
|
thrust::device_vector<bst_feature_t> feature_set =
|
|
std::vector<bst_feature_t>{0};
|
|
thrust::device_vector<uint32_t> feature_segments =
|
|
std::vector<bst_row_t>{0, 2};
|
|
thrust::device_vector<float> feature_values = std::vector<float>{1.0, 2.0};
|
|
thrust::device_vector<float> feature_min_values = std::vector<float>{0.0};
|
|
auto feature_histogram = ConvertToInteger({{-0.5, 0.5}, {0.5, 0.5}});
|
|
EvaluateSplitInputs input{1,0,
|
|
parent_sum,
|
|
dh::ToSpan(feature_set),
|
|
dh::ToSpan(feature_histogram)};
|
|
EvaluateSplitSharedInputs shared_inputs{param,
|
|
quantiser,
|
|
{},
|
|
dh::ToSpan(feature_segments),
|
|
dh::ToSpan(feature_values),
|
|
dh::ToSpan(feature_min_values),
|
|
false};
|
|
|
|
GPUHistEvaluator evaluator(tparam, feature_set.size(), 0);
|
|
DeviceSplitCandidate result = evaluator.EvaluateSingleSplit(input, shared_inputs).split;
|
|
|
|
EXPECT_EQ(result.findex, 0);
|
|
EXPECT_EQ(result.fvalue, 1.0);
|
|
EXPECT_EQ(result.dir, kRightDir);
|
|
EXPECT_EQ(result.left_sum,quantiser.ToFixedPoint(GradientPairPrecise(-0.5, 0.5)));
|
|
EXPECT_EQ(result.right_sum, quantiser.ToFixedPoint(GradientPairPrecise(1.5, 1.0)));
|
|
}
|
|
|
|
TEST(GpuHist, EvaluateSingleSplitEmpty) {
|
|
TrainParam tparam = ZeroParam();
|
|
GPUHistEvaluator evaluator(tparam, 1, 0);
|
|
DeviceSplitCandidate result =
|
|
evaluator
|
|
.EvaluateSingleSplit(
|
|
EvaluateSplitInputs{},
|
|
EvaluateSplitSharedInputs{
|
|
GPUTrainingParam(tparam), DummyRoundingFactor(), {}, {}, {}, {}, false})
|
|
.split;
|
|
EXPECT_EQ(result.findex, -1);
|
|
EXPECT_LT(result.loss_chg, 0.0f);
|
|
}
|
|
|
|
// Feature 0 has a better split, but the algorithm must select feature 1
|
|
TEST(GpuHist, EvaluateSingleSplitFeatureSampling) {
|
|
auto quantiser = DummyRoundingFactor();
|
|
auto parent_sum = quantiser.ToFixedPoint(GradientPairPrecise{0.0, 1.0});
|
|
TrainParam tparam = ZeroParam();
|
|
tparam.UpdateAllowUnknown(Args{});
|
|
GPUTrainingParam param{tparam};
|
|
|
|
thrust::device_vector<bst_feature_t> feature_set =
|
|
std::vector<bst_feature_t>{1};
|
|
thrust::device_vector<uint32_t> feature_segments =
|
|
std::vector<bst_row_t>{0, 2, 4};
|
|
thrust::device_vector<float> feature_values =
|
|
std::vector<float>{1.0, 2.0, 11.0, 12.0};
|
|
thrust::device_vector<float> feature_min_values =
|
|
std::vector<float>{0.0, 10.0};
|
|
auto feature_histogram = ConvertToInteger({ {-10.0, 0.5}, {10.0, 0.5}, {-0.5, 0.5}, {0.5, 0.5}});
|
|
EvaluateSplitInputs input{1,0,
|
|
parent_sum,
|
|
dh::ToSpan(feature_set),
|
|
dh::ToSpan(feature_histogram)};
|
|
EvaluateSplitSharedInputs shared_inputs{param,
|
|
quantiser,
|
|
{},
|
|
dh::ToSpan(feature_segments),
|
|
dh::ToSpan(feature_values),
|
|
dh::ToSpan(feature_min_values),
|
|
false};
|
|
|
|
GPUHistEvaluator evaluator(tparam, feature_min_values.size(), 0);
|
|
DeviceSplitCandidate result = evaluator.EvaluateSingleSplit(input, shared_inputs).split;
|
|
|
|
EXPECT_EQ(result.findex, 1);
|
|
EXPECT_EQ(result.fvalue, 11.0);
|
|
EXPECT_EQ(result.left_sum,quantiser.ToFixedPoint(GradientPairPrecise(-0.5, 0.5)));
|
|
EXPECT_EQ(result.right_sum, quantiser.ToFixedPoint(GradientPairPrecise(0.5, 0.5)));
|
|
}
|
|
|
|
// Features 0 and 1 have identical gain, the algorithm must select 0
|
|
TEST(GpuHist, EvaluateSingleSplitBreakTies) {
|
|
auto quantiser = DummyRoundingFactor();
|
|
auto parent_sum = quantiser.ToFixedPoint(GradientPairPrecise{0.0, 1.0});
|
|
TrainParam tparam = ZeroParam();
|
|
tparam.UpdateAllowUnknown(Args{});
|
|
GPUTrainingParam param{tparam};
|
|
|
|
thrust::device_vector<bst_feature_t> feature_set =
|
|
std::vector<bst_feature_t>{0, 1};
|
|
thrust::device_vector<uint32_t> feature_segments =
|
|
std::vector<bst_row_t>{0, 2, 4};
|
|
thrust::device_vector<float> feature_values =
|
|
std::vector<float>{1.0, 2.0, 11.0, 12.0};
|
|
thrust::device_vector<float> feature_min_values =
|
|
std::vector<float>{0.0, 10.0};
|
|
auto feature_histogram = ConvertToInteger({ {-0.5, 0.5}, {0.5, 0.5}, {-0.5, 0.5}, {0.5, 0.5}});
|
|
EvaluateSplitInputs input{1,0,
|
|
parent_sum,
|
|
dh::ToSpan(feature_set),
|
|
dh::ToSpan(feature_histogram)};
|
|
EvaluateSplitSharedInputs shared_inputs{param,
|
|
quantiser,
|
|
{},
|
|
dh::ToSpan(feature_segments),
|
|
dh::ToSpan(feature_values),
|
|
dh::ToSpan(feature_min_values),
|
|
false};
|
|
|
|
GPUHistEvaluator evaluator(tparam, feature_min_values.size(), 0);
|
|
DeviceSplitCandidate result = evaluator.EvaluateSingleSplit(input,shared_inputs).split;
|
|
|
|
EXPECT_EQ(result.findex, 0);
|
|
EXPECT_EQ(result.fvalue, 1.0);
|
|
}
|
|
|
|
TEST(GpuHist, EvaluateSplits) {
|
|
thrust::device_vector<DeviceSplitCandidate> out_splits(2);
|
|
auto quantiser = DummyRoundingFactor();
|
|
auto parent_sum = quantiser.ToFixedPoint(GradientPairPrecise{0.0, 1.0});
|
|
TrainParam tparam = ZeroParam();
|
|
tparam.UpdateAllowUnknown(Args{});
|
|
GPUTrainingParam param{tparam};
|
|
|
|
thrust::device_vector<bst_feature_t> feature_set =
|
|
std::vector<bst_feature_t>{0, 1};
|
|
thrust::device_vector<uint32_t> feature_segments =
|
|
std::vector<bst_row_t>{0, 2, 4};
|
|
thrust::device_vector<float> feature_values =
|
|
std::vector<float>{1.0, 2.0, 11.0, 12.0};
|
|
thrust::device_vector<float> feature_min_values =
|
|
std::vector<float>{0.0, 0.0};
|
|
auto feature_histogram_left = ConvertToInteger({ {-0.5, 0.5}, {0.5, 0.5}, {-1.0, 0.5}, {1.0, 0.5}});
|
|
auto feature_histogram_right = ConvertToInteger({ {-1.0, 0.5}, {1.0, 0.5}, {-0.5, 0.5}, {0.5, 0.5}});
|
|
EvaluateSplitInputs input_left{
|
|
1,0,
|
|
parent_sum,
|
|
dh::ToSpan(feature_set),
|
|
dh::ToSpan(feature_histogram_left)};
|
|
EvaluateSplitInputs input_right{
|
|
2,0,
|
|
parent_sum,
|
|
dh::ToSpan(feature_set),
|
|
dh::ToSpan(feature_histogram_right)};
|
|
EvaluateSplitSharedInputs shared_inputs{
|
|
param,
|
|
quantiser,
|
|
{},
|
|
dh::ToSpan(feature_segments),
|
|
dh::ToSpan(feature_values),
|
|
dh::ToSpan(feature_min_values),
|
|
false
|
|
};
|
|
|
|
GPUHistEvaluator evaluator{
|
|
tparam, static_cast<bst_feature_t>(feature_min_values.size()), 0};
|
|
dh::device_vector<EvaluateSplitInputs> inputs = std::vector<EvaluateSplitInputs>{input_left,input_right};
|
|
evaluator.LaunchEvaluateSplits(input_left.feature_set.size(),dh::ToSpan(inputs),shared_inputs, evaluator.GetEvaluator(),
|
|
dh::ToSpan(out_splits));
|
|
|
|
DeviceSplitCandidate result_left = out_splits[0];
|
|
EXPECT_EQ(result_left.findex, 1);
|
|
EXPECT_EQ(result_left.fvalue, 11.0);
|
|
|
|
DeviceSplitCandidate result_right = out_splits[1];
|
|
EXPECT_EQ(result_right.findex, 0);
|
|
EXPECT_EQ(result_right.fvalue, 1.0);
|
|
}
|
|
|
|
TEST_F(TestPartitionBasedSplit, GpuHist) {
|
|
dh::device_vector<FeatureType> ft{std::vector<FeatureType>{FeatureType::kCategorical}};
|
|
GPUHistEvaluator evaluator{param_, static_cast<bst_feature_t>(info_.num_col_), 0};
|
|
|
|
cuts_.cut_ptrs_.SetDevice(0);
|
|
cuts_.cut_values_.SetDevice(0);
|
|
cuts_.min_vals_.SetDevice(0);
|
|
|
|
evaluator.Reset(cuts_, dh::ToSpan(ft), info_.num_col_, param_, 0);
|
|
|
|
// Convert the sample histogram to fixed point
|
|
auto quantiser = DummyRoundingFactor();
|
|
thrust::host_vector<GradientPairInt64> h_hist;
|
|
for(auto e: hist_[0]){
|
|
h_hist.push_back(quantiser.ToFixedPoint(e));
|
|
}
|
|
dh::device_vector<GradientPairInt64> d_hist = h_hist;
|
|
dh::device_vector<bst_feature_t> feature_set{std::vector<bst_feature_t>{0}};
|
|
|
|
EvaluateSplitInputs input{0, 0, quantiser.ToFixedPoint(total_gpair_), dh::ToSpan(feature_set), dh::ToSpan(d_hist)};
|
|
EvaluateSplitSharedInputs shared_inputs{GPUTrainingParam{param_},
|
|
quantiser,
|
|
dh::ToSpan(ft),
|
|
cuts_.cut_ptrs_.ConstDeviceSpan(),
|
|
cuts_.cut_values_.ConstDeviceSpan(),
|
|
cuts_.min_vals_.ConstDeviceSpan(),
|
|
false};
|
|
auto split = evaluator.EvaluateSingleSplit(input, shared_inputs).split;
|
|
ASSERT_NEAR(split.loss_chg, best_score_, 1e-2);
|
|
}
|
|
} // namespace tree
|
|
} // namespace xgboost
|