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Unify evaluation functions. (#6037)

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This commit is contained in:
Jiaming Yuan
2020-08-26 14:23:27 +08:00
committed by GitHub
parent 80c8547147
commit 2fcc4f2886
29 changed files with 570 additions and 734 deletions
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18
tests/cpp/tree/gpu_hist/test_driver.cu
View File

@@ -9,12 +9,12 @@ TEST(GpuHist, DriverDepthWise) {
EXPECT_TRUE(driver.Pop().empty());
DeviceSplitCandidate split;
split.loss_chg = 1.0f;
ExpandEntry root(0, 0, split);
ExpandEntry root(0, 0, split, .0f, .0f, .0f);
driver.Push({root});
EXPECT_EQ(driver.Pop().front().nid, 0);
driver.Push({ExpandEntry{1, 1, split}});
driver.Push({ExpandEntry{2, 1, split}});
driver.Push({ExpandEntry{3, 2, split}});
driver.Push({ExpandEntry{1, 1, split, .0f, .0f, .0f}});
driver.Push({ExpandEntry{2, 1, split, .0f, .0f, .0f}});
driver.Push({ExpandEntry{3, 2, split, .0f, .0f, .0f}});
// Should return entries from level 1
auto res = driver.Pop();
EXPECT_EQ(res.size(), 2);
@@ -34,12 +34,12 @@ TEST(GpuHist, DriverLossGuided) {
Driver driver(TrainParam::kLossGuide);
EXPECT_TRUE(driver.Pop().empty());
ExpandEntry root(0, 0, high_gain);
ExpandEntry root(0, 0, high_gain, .0f, .0f, .0f);
driver.Push({root});
EXPECT_EQ(driver.Pop().front().nid, 0);
// Select high gain first
driver.Push({ExpandEntry{1, 1, low_gain}});
driver.Push({ExpandEntry{2, 2, high_gain}});
driver.Push({ExpandEntry{1, 1, low_gain, .0f, .0f, .0f}});
driver.Push({ExpandEntry{2, 2, high_gain, .0f, .0f, .0f}});
auto res = driver.Pop();
EXPECT_EQ(res.size(), 1);
EXPECT_EQ(res[0].nid, 2);
@@ -48,8 +48,8 @@ TEST(GpuHist, DriverLossGuided) {
EXPECT_EQ(res[0].nid, 1);
// If equal gain, use nid
driver.Push({ExpandEntry{2, 1, low_gain}});
driver.Push({ExpandEntry{1, 1, low_gain}});
driver.Push({ExpandEntry{2, 1, low_gain, .0f, .0f, .0f}});
driver.Push({ExpandEntry{1, 1, low_gain, .0f, .0f, .0f}});
res = driver.Pop();
EXPECT_EQ(res[0].nid, 1);
res = driver.Pop();

82
tests/cpp/tree/gpu_hist/test_evaluate_splits.cu
View File

@@ -5,11 +5,21 @@
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
TEST(GpuHist, EvaluateSingleSplit) {
thrust::device_vector<DeviceSplitCandidate> out_splits(1);
GradientPair parent_sum(0.0, 1.0);
GPUTrainingParam param{};
TrainParam tparam = ZeroParam();
GPUTrainingParam param{tparam};
thrust::device_vector<bst_feature_t> feature_set =
std::vector<bst_feature_t>{0, 1};
@@ -31,10 +41,10 @@ TEST(GpuHist, EvaluateSingleSplit) {
dh::ToSpan(feature_segments),
dh::ToSpan(feature_values),
dh::ToSpan(feature_min_values),
dh::ToSpan(feature_histogram),
ValueConstraint(),
dh::ToSpan(monotonic_constraints)};
EvaluateSingleSplit(dh::ToSpan(out_splits), input);
dh::ToSpan(feature_histogram)};
TreeEvaluator tree_evaluator(tparam, feature_min_values.size(), 0);
auto evaluator = tree_evaluator.GetEvaluator<GPUTrainingParam>();
EvaluateSingleSplit(dh::ToSpan(out_splits), evaluator, input);
DeviceSplitCandidate result = out_splits[0];
EXPECT_EQ(result.findex, 1);
@@ -48,7 +58,8 @@ TEST(GpuHist, EvaluateSingleSplit) {
TEST(GpuHist, EvaluateSingleSplitMissing) {
thrust::device_vector<DeviceSplitCandidate> out_splits(1);
GradientPair parent_sum(1.0, 1.5);
GPUTrainingParam param{};
TrainParam tparam = ZeroParam();
GPUTrainingParam param{tparam};
thrust::device_vector<bst_feature_t> feature_set =
std::vector<bst_feature_t>{0};
@@ -66,10 +77,11 @@ TEST(GpuHist, EvaluateSingleSplitMissing) {
dh::ToSpan(feature_segments),
dh::ToSpan(feature_values),
dh::ToSpan(feature_min_values),
dh::ToSpan(feature_histogram),
ValueConstraint(),
dh::ToSpan(monotonic_constraints)};
EvaluateSingleSplit(dh::ToSpan(out_splits), input);
dh::ToSpan(feature_histogram)};
TreeEvaluator tree_evaluator(tparam, feature_set.size(), 0);
auto evaluator = tree_evaluator.GetEvaluator<GPUTrainingParam>();
EvaluateSingleSplit(dh::ToSpan(out_splits), evaluator, input);
DeviceSplitCandidate result = out_splits[0];
EXPECT_EQ(result.findex, 0);
@@ -86,8 +98,13 @@ TEST(GpuHist, EvaluateSingleSplitEmpty) {
thrust::device_vector<DeviceSplitCandidate> out_split(1);
out_split[0] = nonzeroed;
EvaluateSingleSplit(dh::ToSpan(out_split),
TrainParam tparam = ZeroParam();
TreeEvaluator tree_evaluator(tparam, 1, 0);
auto evaluator = tree_evaluator.GetEvaluator<GPUTrainingParam>();
EvaluateSingleSplit(dh::ToSpan(out_split), evaluator,
EvaluateSplitInputs<GradientPair>{});
DeviceSplitCandidate result = out_split[0];
EXPECT_EQ(result.findex, -1);
EXPECT_LT(result.loss_chg, 0.0f);
@@ -97,7 +114,9 @@ TEST(GpuHist, EvaluateSingleSplitEmpty) {
TEST(GpuHist, EvaluateSingleSplitFeatureSampling) {
thrust::device_vector<DeviceSplitCandidate> out_splits(1);
GradientPair parent_sum(0.0, 1.0);
GPUTrainingParam param{};
TrainParam tparam = ZeroParam();
tparam.UpdateAllowUnknown(Args{});
GPUTrainingParam param{tparam};
thrust::device_vector<bst_feature_t> feature_set =
std::vector<bst_feature_t>{1};
@@ -118,10 +137,11 @@ TEST(GpuHist, EvaluateSingleSplitFeatureSampling) {
dh::ToSpan(feature_segments),
dh::ToSpan(feature_values),
dh::ToSpan(feature_min_values),
dh::ToSpan(feature_histogram),
ValueConstraint(),
dh::ToSpan(monotonic_constraints)};
EvaluateSingleSplit(dh::ToSpan(out_splits), input);
dh::ToSpan(feature_histogram)};
TreeEvaluator tree_evaluator(tparam, feature_min_values.size(), 0);
auto evaluator = tree_evaluator.GetEvaluator<GPUTrainingParam>();
EvaluateSingleSplit(dh::ToSpan(out_splits), evaluator, input);
DeviceSplitCandidate result = out_splits[0];
EXPECT_EQ(result.findex, 1);
@@ -134,7 +154,9 @@ TEST(GpuHist, EvaluateSingleSplitFeatureSampling) {
TEST(GpuHist, EvaluateSingleSplitBreakTies) {
thrust::device_vector<DeviceSplitCandidate> out_splits(1);
GradientPair parent_sum(0.0, 1.0);
GPUTrainingParam param{};
TrainParam tparam = ZeroParam();
tparam.UpdateAllowUnknown(Args{});
GPUTrainingParam param{tparam};
thrust::device_vector<bst_feature_t> feature_set =
std::vector<bst_feature_t>{0, 1};
@@ -155,10 +177,11 @@ TEST(GpuHist, EvaluateSingleSplitBreakTies) {
dh::ToSpan(feature_segments),
dh::ToSpan(feature_values),
dh::ToSpan(feature_min_values),
dh::ToSpan(feature_histogram),
ValueConstraint(),
dh::ToSpan(monotonic_constraints)};
EvaluateSingleSplit(dh::ToSpan(out_splits), input);
dh::ToSpan(feature_histogram)};
TreeEvaluator tree_evaluator(tparam, feature_min_values.size(), 0);
auto evaluator = tree_evaluator.GetEvaluator<GPUTrainingParam>();
EvaluateSingleSplit(dh::ToSpan(out_splits), evaluator, input);
DeviceSplitCandidate result = out_splits[0];
EXPECT_EQ(result.findex, 0);
@@ -168,7 +191,9 @@ TEST(GpuHist, EvaluateSingleSplitBreakTies) {
TEST(GpuHist, EvaluateSplits) {
thrust::device_vector<DeviceSplitCandidate> out_splits(2);
GradientPair parent_sum(0.0, 1.0);
GPUTrainingParam param{};
TrainParam tparam = ZeroParam();
tparam.UpdateAllowUnknown(Args{});
GPUTrainingParam param{tparam};
thrust::device_vector<bst_feature_t> feature_set =
std::vector<bst_feature_t>{0, 1};
@@ -193,9 +218,7 @@ TEST(GpuHist, EvaluateSplits) {
dh::ToSpan(feature_segments),
dh::ToSpan(feature_values),
dh::ToSpan(feature_min_values),
dh::ToSpan(feature_histogram_left),
ValueConstraint(),
dh::ToSpan(monotonic_constraints)};
dh::ToSpan(feature_histogram_left)};
EvaluateSplitInputs<GradientPair> input_right{
2,
parent_sum,
@@ -204,10 +227,11 @@ TEST(GpuHist, EvaluateSplits) {
dh::ToSpan(feature_segments),
dh::ToSpan(feature_values),
dh::ToSpan(feature_min_values),
dh::ToSpan(feature_histogram_right),
ValueConstraint(),
dh::ToSpan(monotonic_constraints)};
EvaluateSplits(dh::ToSpan(out_splits), input_left, input_right);
dh::ToSpan(feature_histogram_right)};
TreeEvaluator tree_evaluator(tparam, feature_min_values.size(), 0);
auto evaluator = tree_evaluator.GetEvaluator<GPUTrainingParam>();
EvaluateSplits(dh::ToSpan(out_splits), evaluator, input_left, input_right);
DeviceSplitCandidate result_left = out_splits[0];
EXPECT_EQ(result_left.findex, 1);