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Use quantised gradients in gpu_hist histograms (#8246)

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This commit is contained in:
Rory Mitchell
2022-09-26 17:35:35 +02:00
committed by GitHub
parent 4056974e37
commit 8f77677193
14 changed files with 394 additions and 336 deletions
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109
tests/cpp/tree/gpu_hist/test_evaluate_splits.cu
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@@ -7,6 +7,7 @@
#include "../../helpers.h"
#include "../../histogram_helpers.h"
#include "../test_evaluate_splits.h" // TestPartitionBasedSplit
#include <thrust/host_vector.h>
namespace xgboost {
namespace tree {
@@ -21,13 +22,29 @@ auto ZeroParam() {
} // anonymous namespace
inline GradientQuantizer 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));
}
thrust::device_vector<GradientPairInt64> ConvertToInteger(std::vector<GradientPairPrecise> x) {
auto r = DummyRoundingFactor();
std::vector<GradientPairInt64> y(x.size());
for (int 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<GradientPairPrecise> feature_histogram{feature_histogram_};
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);
@@ -36,6 +53,7 @@ TEST_F(TestCategoricalSplitWithMissing, GPUHistEvaluator) {
dh::ToSpan(feature_histogram)};
EvaluateSplitSharedInputs shared_inputs{
param,
DummyRoundingFactor(),
d_feature_types,
cuts_.cut_ptrs_.ConstDeviceSpan(),
cuts_.cut_values_.ConstDeviceSpan(),
@@ -76,6 +94,7 @@ TEST(GpuHist, PartitionBasic) {
EvaluateSplitSharedInputs shared_inputs{
param,
DummyRoundingFactor(),
d_feature_types,
cuts.cut_ptrs_.ConstDeviceSpan(),
cuts.cut_values_.ConstDeviceSpan(),
@@ -89,8 +108,7 @@ TEST(GpuHist, PartitionBasic) {
// -1.0s go right
// -3.0s go left
GradientPairPrecise parent_sum(-5.0, 3.0);
thrust::device_vector<GradientPairPrecise> feature_histogram =
std::vector<GradientPairPrecise>{{-1.0, 1.0}, {-1.0, 1.0}, {-3.0, 1.0}};
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;
@@ -105,8 +123,7 @@ TEST(GpuHist, PartitionBasic) {
// -1.0s go right
// -3.0s go left
GradientPairPrecise parent_sum(-7.0, 3.0);
thrust::device_vector<GradientPairPrecise> feature_histogram =
std::vector<GradientPairPrecise>{{-1.0, 1.0}, {-3.0, 1.0}, {-3.0, 1.0}};
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;
@@ -119,8 +136,7 @@ TEST(GpuHist, PartitionBasic) {
{
// All -1.0, gain from splitting should be 0.0
GradientPairPrecise parent_sum(-3.0, 3.0);
thrust::device_vector<GradientPairPrecise> feature_histogram =
std::vector<GradientPairPrecise>{{-1.0, 1.0}, {-1.0, 1.0}, {-1.0, 1.0}};
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;
@@ -133,8 +149,7 @@ TEST(GpuHist, PartitionBasic) {
// Forward, first 2 categories are selected, while the last one go to left along with missing value
{
GradientPairPrecise parent_sum(0.0, 6.0);
thrust::device_vector<GradientPairPrecise> feature_histogram =
std::vector<GradientPairPrecise>{{-1.0, 1.0}, {-1.0, 1.0}, {-1.0, 1.0}};
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;
@@ -148,8 +163,7 @@ TEST(GpuHist, PartitionBasic) {
// -1.0s go right
// -3.0s go left
GradientPairPrecise parent_sum(-5.0, 3.0);
thrust::device_vector<GradientPairPrecise> feature_histogram =
std::vector<GradientPairPrecise>{{-1.0, 1.0}, {-3.0, 1.0}, {-1.0, 1.0}};
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;
@@ -163,8 +177,7 @@ TEST(GpuHist, PartitionBasic) {
// -1.0s go right
// -3.0s go left
GradientPairPrecise parent_sum(-5.0, 3.0);
thrust::device_vector<GradientPairPrecise> feature_histogram =
std::vector<GradientPairPrecise>{{-3.0, 1.0}, {-1.0, 1.0}, {-3.0, 1.0}};
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;
@@ -198,6 +211,7 @@ TEST(GpuHist, PartitionTwoFeatures) {
EvaluateSplitSharedInputs shared_inputs{
param,
DummyRoundingFactor(),
d_feature_types,
cuts.cut_ptrs_.ConstDeviceSpan(),
cuts.cut_values_.ConstDeviceSpan(),
@@ -209,8 +223,7 @@ TEST(GpuHist, PartitionTwoFeatures) {
{
GradientPairPrecise parent_sum(-6.0, 3.0);
thrust::device_vector<GradientPairPrecise> feature_histogram = std::vector<GradientPairPrecise>{
{-2.0, 1.0}, {-2.0, 1.0}, {-2.0, 1.0}, {-1.0, 1.0}, {-1.0, 1.0}, {-4.0, 1.0}};
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;
@@ -223,8 +236,7 @@ TEST(GpuHist, PartitionTwoFeatures) {
{
GradientPairPrecise parent_sum(-6.0, 3.0);
thrust::device_vector<GradientPairPrecise> feature_histogram = std::vector<GradientPairPrecise>{
{-2.0, 1.0}, {-2.0, 1.0}, {-2.0, 1.0}, {-1.0, 1.0}, {-2.5, 1.0}, {-2.5, 1.0}};
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;
@@ -259,6 +271,7 @@ TEST(GpuHist, PartitionTwoNodes) {
EvaluateSplitSharedInputs shared_inputs{
param,
DummyRoundingFactor(),
d_feature_types,
cuts.cut_ptrs_.ConstDeviceSpan(),
cuts.cut_values_.ConstDeviceSpan(),
@@ -270,14 +283,12 @@ TEST(GpuHist, PartitionTwoNodes) {
{
GradientPairPrecise parent_sum(-6.0, 3.0);
thrust::device_vector<GradientPairPrecise> feature_histogram_a =
std::vector<GradientPairPrecise>{{-1.0, 1.0}, {-2.5, 1.0}, {-2.5, 1.0},
{-1.0, 1.0}, {-1.0, 1.0}, {-4.0, 1.0}};
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)};
thrust::device_vector<GradientPairPrecise> feature_histogram_b =
std::vector<GradientPairPrecise>{{-1.0, 1.0}, {-1.0, 1.0}, {-4.0, 1.0}};
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);
@@ -300,9 +311,7 @@ void TestEvaluateSingleSplit(bool is_categorical) {
thrust::device_vector<bst_feature_t> feature_set = std::vector<bst_feature_t>{0, 1};
// Setup gradients so that second feature gets higher gain
thrust::device_vector<GradientPairPrecise> feature_histogram =
std::vector<GradientPairPrecise>{
{-0.5, 0.5}, {0.5, 0.5}, {-1.0, 0.5}, {1.0, 0.5}};
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);
@@ -318,6 +327,7 @@ void TestEvaluateSingleSplit(bool is_categorical) {
dh::ToSpan(feature_histogram)};
EvaluateSplitSharedInputs shared_inputs{
param,
DummyRoundingFactor(),
d_feature_types,
cuts.cut_ptrs_.ConstDeviceSpan(),
cuts.cut_values_.ConstDeviceSpan(),
@@ -360,14 +370,14 @@ TEST(GpuHist, EvaluateSingleSplitMissing) {
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};
thrust::device_vector<GradientPairPrecise> feature_histogram =
std::vector<GradientPairPrecise>{{-0.5, 0.5}, {0.5, 0.5}};
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,
DummyRoundingFactor(),
{},
dh::ToSpan(feature_segments),
dh::ToSpan(feature_values),
@@ -388,7 +398,11 @@ TEST(GpuHist, EvaluateSingleSplitEmpty) {
TrainParam tparam = ZeroParam();
GPUHistEvaluator evaluator(tparam, 1, 0);
DeviceSplitCandidate result =
evaluator.EvaluateSingleSplit(EvaluateSplitInputs{}, EvaluateSplitSharedInputs{}).split;
evaluator
.EvaluateSingleSplit(
EvaluateSplitInputs{},
EvaluateSplitSharedInputs{GPUTrainingParam(tparam), DummyRoundingFactor()})
.split;
EXPECT_EQ(result.findex, -1);
EXPECT_LT(result.loss_chg, 0.0f);
}
@@ -408,15 +422,14 @@ TEST(GpuHist, EvaluateSingleSplitFeatureSampling) {
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};
thrust::device_vector<GradientPairPrecise> feature_histogram =
std::vector<GradientPairPrecise>{
{-10.0, 0.5}, {10.0, 0.5}, {-0.5, 0.5}, {0.5, 0.5}};
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,
DummyRoundingFactor(),
{},
dh::ToSpan(feature_segments),
dh::ToSpan(feature_values),
@@ -447,15 +460,14 @@ TEST(GpuHist, EvaluateSingleSplitBreakTies) {
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};
thrust::device_vector<GradientPairPrecise> feature_histogram =
std::vector<GradientPairPrecise>{
{-0.5, 0.5}, {0.5, 0.5}, {-0.5, 0.5}, {0.5, 0.5}};
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,
DummyRoundingFactor(),
{},
dh::ToSpan(feature_segments),
dh::ToSpan(feature_values),
@@ -484,12 +496,8 @@ TEST(GpuHist, EvaluateSplits) {
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};
thrust::device_vector<GradientPairPrecise> feature_histogram_left =
std::vector<GradientPairPrecise>{
{-0.5, 0.5}, {0.5, 0.5}, {-1.0, 0.5}, {1.0, 0.5}};
thrust::device_vector<GradientPairPrecise> feature_histogram_right =
std::vector<GradientPairPrecise>{
{-1.0, 0.5}, {1.0, 0.5}, {-0.5, 0.5}, {0.5, 0.5}};
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,
@@ -502,6 +510,7 @@ TEST(GpuHist, EvaluateSplits) {
dh::ToSpan(feature_histogram_right)};
EvaluateSplitSharedInputs shared_inputs{
param,
DummyRoundingFactor(),
{},
dh::ToSpan(feature_segments),
dh::ToSpan(feature_values),
@@ -533,20 +542,26 @@ TEST_F(TestPartitionBasedSplit, GpuHist) {
evaluator.Reset(cuts_, dh::ToSpan(ft), info_.num_col_, param_, 0);
dh::device_vector<GradientPairPrecise> d_hist(hist_[0].size());
auto node_hist = hist_[0];
dh::safe_cuda(cudaMemcpy(d_hist.data().get(), node_hist.data(), node_hist.size_bytes(),
cudaMemcpyHostToDevice));
// Convert the sample histogram to fixed point
auto rounding = DummyRoundingFactor();
thrust::host_vector<GradientPairInt64> h_hist;
for(auto e: hist_[0]){
h_hist.push_back(rounding.ToFixedPoint({float(e.GetGrad()),float(e.GetHess())}));
}
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, total_gpair_, dh::ToSpan(feature_set), dh::ToSpan(d_hist)};
EvaluateSplitSharedInputs shared_inputs{
GPUTrainingParam{param_}, dh::ToSpan(ft),
cuts_.cut_ptrs_.ConstDeviceSpan(), cuts_.cut_values_.ConstDeviceSpan(),
GPUTrainingParam{param_},
rounding,
dh::ToSpan(ft),
cuts_.cut_ptrs_.ConstDeviceSpan(),
cuts_.cut_values_.ConstDeviceSpan(),
cuts_.min_vals_.ConstDeviceSpan(),
};
auto split = evaluator.EvaluateSingleSplit(input, shared_inputs).split;
ASSERT_NEAR(split.loss_chg, best_score_, 1e-16);
ASSERT_NEAR(split.loss_chg, best_score_, 1e-2);
}
} // namespace tree
} // namespace xgboost