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Single precision histograms on GPU (#3965)

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* Allow single precision histogram summation in gpu_hist

* Add python test, reduce run-time of gpu_hist tests

* Update documentation
This commit is contained in:
Rory Mitchell
2018-12-10 10:55:30 +13:00
committed by GitHub
parent 9af6b689d6
commit 93f9ce9ef9
10 changed files with 351 additions and 212 deletions
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48
tests/cpp/tree/test_gpu_hist.cu
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@@ -17,7 +17,8 @@
namespace xgboost {
namespace tree {
void BuildGidx(DeviceShard* shard, int n_rows, int n_cols,
template <typename GradientSumT>
void BuildGidx(DeviceShard<GradientSumT>* shard, int n_rows, int n_cols,
bst_float sparsity=0) {
auto dmat = CreateDMatrix(n_rows, n_cols, sparsity, 3);
const SparsePage& batch = *(*dmat)->GetRowBatches().begin();
@@ -48,7 +49,7 @@ TEST(GpuHist, BuildGidxDense) {
param.n_gpus = 1;
param.max_leaves = 0;
DeviceShard shard(0, 0, n_rows, param);
DeviceShard<GradientPairPrecise> shard(0, 0, n_rows, param);
BuildGidx(&shard, n_rows, n_cols);
std::vector<common::CompressedByteT> h_gidx_buffer;
@@ -87,7 +88,7 @@ TEST(GpuHist, BuildGidxSparse) {
param.n_gpus = 1;
param.max_leaves = 0;
DeviceShard shard(0, 0, n_rows, param);
DeviceShard<GradientPairPrecise> shard(0, 0, n_rows, param);
BuildGidx(&shard, n_rows, n_cols, 0.9f);
std::vector<common::CompressedByteT> h_gidx_buffer;
@@ -122,7 +123,8 @@ std::vector<GradientPairPrecise> GetHostHistGpair() {
return hist_gpair;
}
void TestBuildHist(GPUHistBuilderBase& builder) {
template <typename GradientSumT>
void TestBuildHist(GPUHistBuilderBase<GradientSumT>& builder) {
int const n_rows = 16, n_cols = 8;
TrainParam param;
@@ -130,7 +132,7 @@ void TestBuildHist(GPUHistBuilderBase& builder) {
param.n_gpus = 1;
param.max_leaves = 0;
DeviceShard shard(0, 0, n_rows, param);
DeviceShard<GradientSumT> shard(0, 0, n_rows, param);
BuildGidx(&shard, n_rows, n_cols);
@@ -166,13 +168,14 @@ void TestBuildHist(GPUHistBuilderBase& builder) {
shard.ridx.CurrentDVec().tend());
builder.Build(&shard, 0);
DeviceHistogram d_hist = shard.hist;
DeviceHistogram<GradientSumT> d_hist = shard.hist;
auto node_histogram = d_hist.GetNodeHistogram(0);
// d_hist.data stored in float, not gradient pair
thrust::host_vector<GradientPairSumT> h_result (d_hist.data.size()/2);
size_t data_size = sizeof(GradientPairSumT) / (
sizeof(GradientPairSumT) / sizeof(GradientPairSumT::ValueT));
thrust::host_vector<GradientSumT> h_result (d_hist.data.size()/2);
size_t data_size =
sizeof(GradientSumT) /
(sizeof(GradientSumT) / sizeof(typename GradientSumT::ValueT));
data_size *= d_hist.data.size();
dh::safe_cuda(cudaMemcpy(h_result.data(), node_histogram.data(), data_size,
cudaMemcpyDeviceToHost));
@@ -186,13 +189,17 @@ void TestBuildHist(GPUHistBuilderBase& builder) {
}
TEST(GpuHist, BuildHistGlobalMem) {
GlobalMemHistBuilder builder;
TestBuildHist(builder);
GlobalMemHistBuilder<GradientPairPrecise> double_builder;
TestBuildHist(double_builder);
GlobalMemHistBuilder<GradientPair> float_builder;
TestBuildHist(float_builder);
}
TEST(GpuHist, BuildHistSharedMem) {
SharedMemHistBuilder builder;
TestBuildHist(builder);
SharedMemHistBuilder<GradientPairPrecise> double_builder;
TestBuildHist(double_builder);
SharedMemHistBuilder<GradientPair> float_builder;
TestBuildHist(float_builder);
}
common::HistCutMatrix GetHostCutMatrix () {
@@ -236,7 +243,7 @@ TEST(GpuHist, EvaluateSplits) {
int max_bins = 4;
// Initialize DeviceShard
std::unique_ptr<DeviceShard> shard {new DeviceShard(0, 0, n_rows, param)};
std::unique_ptr<DeviceShard<GradientPairPrecise>> shard {new DeviceShard<GradientPairPrecise>(0, 0, n_rows, param)};
// Initialize DeviceShard::node_sum_gradients
shard->node_sum_gradients = {{6.4f, 12.8f}};
@@ -244,7 +251,7 @@ TEST(GpuHist, EvaluateSplits) {
common::HistCutMatrix cmat = GetHostCutMatrix();
// Copy cut matrix to device.
DeviceShard::DeviceHistCutMatrix cut;
DeviceShard<GradientPairPrecise>::DeviceHistCutMatrix cut;
shard->ba.Allocate(0, true,
&(shard->cut_.feature_segments), cmat.row_ptr.size(),
&(shard->cut_.min_fvalue), cmat.min_val.size(),
@@ -271,9 +278,9 @@ TEST(GpuHist, EvaluateSplits) {
thrust::copy(hist.begin(), hist.end(),
shard->hist.data.begin());
// Initialize GPUHistMaker
GPUHistMaker hist_maker = GPUHistMaker();
GPUHistMakerSpecialised<GradientPairPrecise> hist_maker =
GPUHistMakerSpecialised<GradientPairPrecise>();
hist_maker.param_ = param;
hist_maker.shards_.push_back(std::move(shard));
hist_maker.column_sampler_.Init(n_cols,
@@ -301,7 +308,8 @@ TEST(GpuHist, EvaluateSplits) {
}
TEST(GpuHist, ApplySplit) {
GPUHistMaker hist_maker = GPUHistMaker();
GPUHistMakerSpecialised<GradientPairPrecise> hist_maker =
GPUHistMakerSpecialised<GradientPairPrecise>();
int constexpr nid = 0;
int constexpr n_rows = 16;
int constexpr n_cols = 8;
@@ -315,7 +323,7 @@ TEST(GpuHist, ApplySplit) {
}
hist_maker.shards_.resize(1);
hist_maker.shards_[0].reset(new DeviceShard(0, 0, n_rows, param));
hist_maker.shards_[0].reset(new DeviceShard<GradientPairPrecise>(0, 0, n_rows, param));
auto& shard = hist_maker.shards_.at(0);
shard->ridx_segments.resize(3); // 3 nodes.
@@ -337,7 +345,7 @@ TEST(GpuHist, ApplySplit) {
0.59, 4, // fvalue has to be equal to one of the cut field
GradientPair(8.2, 2.8), GradientPair(6.3, 3.6),
GPUTrainingParam(param));
GPUHistMaker::ExpandEntry candidate_entry {0, 0, candidate, 0};
GPUHistMakerSpecialised<GradientPairPrecise>::ExpandEntry candidate_entry {0, 0, candidate, 0};
candidate_entry.nid = nid;
auto const& nodes = tree.GetNodes();