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remove device shards (#4867)

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This commit is contained in:
Rong Ou
2019-09-24 22:15:46 -07:00
committed by Jiaming Yuan
parent 0b89cd1dfa
commit 562bb0ae31
8 changed files with 572 additions and 635 deletions
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122
tests/cpp/tree/test_gpu_hist.cu
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@@ -71,40 +71,6 @@ class HistogramCutsWrapper : public common::HistogramCuts {
};
} // anonymous namespace
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)->GetBatches<xgboost::SparsePage>().begin();
HistogramCutsWrapper cmat;
cmat.SetPtrs({0, 3, 6, 9, 12, 15, 18, 21, 24});
// 24 cut fields, 3 cut fields for each feature (column).
cmat.SetValues({0.30f, 0.67f, 1.64f,
0.32f, 0.77f, 1.95f,
0.29f, 0.70f, 1.80f,
0.32f, 0.75f, 1.85f,
0.18f, 0.59f, 1.69f,
0.25f, 0.74f, 2.00f,
0.26f, 0.74f, 1.98f,
0.26f, 0.71f, 1.83f});
cmat.SetMins({0.1f, 0.2f, 0.3f, 0.1f, 0.2f, 0.3f, 0.2f, 0.2f});
auto is_dense = (*dmat)->Info().num_nonzero_ ==
(*dmat)->Info().num_row_ * (*dmat)->Info().num_col_;
size_t row_stride = 0;
const auto &offset_vec = batch.offset.ConstHostVector();
for (size_t i = 1; i < offset_vec.size(); ++i) {
row_stride = std::max(row_stride, offset_vec[i] - offset_vec[i-1]);
}
shard->InitHistogram(cmat, row_stride, is_dense);
shard->CreateHistIndices(
batch, cmat, RowStateOnDevice(batch.Size(), batch.Size()), -1);
delete dmat;
}
std::vector<GradientPairPrecise> GetHostHistGpair() {
// 24 bins, 3 bins for each feature (column).
std::vector<GradientPairPrecise> hist_gpair = {
@@ -131,9 +97,9 @@ void TestBuildHist(bool use_shared_memory_histograms) {
};
param.Init(args);
auto page = BuildEllpackPage(kNRows, kNCols);
DeviceShard<GradientSumT> shard(0, page.get(), kNRows, param, kNCols, kNCols);
shard.InitHistogram();
GPUHistMakerDevice<GradientSumT> maker(0, page.get(), kNRows, param, kNCols, kNCols);
maker.InitHistogram();
xgboost::SimpleLCG gen;
xgboost::SimpleRealUniformDistribution<bst_float> dist(0.0f, 1.0f);
std::vector<GradientPair> h_gpair(kNRows);
@@ -150,13 +116,13 @@ void TestBuildHist(bool use_shared_memory_histograms) {
sizeof(common::CompressedByteT) * page->gidx_buffer.size(),
cudaMemcpyDeviceToHost));
shard.row_partitioner.reset(new RowPartitioner(0, kNRows));
shard.hist.AllocateHistogram(0);
dh::CopyVectorToDeviceSpan(shard.gpair, h_gpair);
maker.row_partitioner.reset(new RowPartitioner(0, kNRows));
maker.hist.AllocateHistogram(0);
dh::CopyVectorToDeviceSpan(maker.gpair, h_gpair);
shard.use_shared_memory_histograms = use_shared_memory_histograms;
shard.BuildHist(0);
DeviceHistogram<GradientSumT> d_hist = shard.hist;
maker.use_shared_memory_histograms = use_shared_memory_histograms;
maker.BuildHist(0);
DeviceHistogram<GradientSumT> d_hist = maker.hist;
auto node_histogram = d_hist.GetNodeHistogram(0);
// d_hist.data stored in float, not gradient pair
@@ -230,30 +196,29 @@ TEST(GpuHist, EvaluateSplits) {
int max_bins = 4;
// Initialize DeviceShard
// Initialize GPUHistMakerDevice
auto page = BuildEllpackPage(kNRows, kNCols);
std::unique_ptr<DeviceShard<GradientPairPrecise>> shard{
new DeviceShard<GradientPairPrecise>(0, page.get(), kNRows, param, kNCols, kNCols)};
// Initialize DeviceShard::node_sum_gradients
shard->node_sum_gradients = {{6.4f, 12.8f}};
GPUHistMakerDevice<GradientPairPrecise> maker(0, page.get(), kNRows, param, kNCols, kNCols);
// Initialize GPUHistMakerDevice::node_sum_gradients
maker.node_sum_gradients = {{6.4f, 12.8f}};
// Initialize DeviceShard::cut
// Initialize GPUHistMakerDevice::cut
auto cmat = GetHostCutMatrix();
// Copy cut matrix to device.
shard->ba.Allocate(0,
&(page->ellpack_matrix.feature_segments), cmat.Ptrs().size(),
&(page->ellpack_matrix.min_fvalue), cmat.MinValues().size(),
&(page->ellpack_matrix.gidx_fvalue_map), 24,
&(shard->monotone_constraints), kNCols);
maker.ba.Allocate(0,
&(page->ellpack_matrix.feature_segments), cmat.Ptrs().size(),
&(page->ellpack_matrix.min_fvalue), cmat.MinValues().size(),
&(page->ellpack_matrix.gidx_fvalue_map), 24,
&(maker.monotone_constraints), kNCols);
dh::CopyVectorToDeviceSpan(page->ellpack_matrix.feature_segments, cmat.Ptrs());
dh::CopyVectorToDeviceSpan(page->ellpack_matrix.gidx_fvalue_map, cmat.Values());
dh::CopyVectorToDeviceSpan(shard->monotone_constraints, param.monotone_constraints);
dh::CopyVectorToDeviceSpan(maker.monotone_constraints, param.monotone_constraints);
dh::CopyVectorToDeviceSpan(page->ellpack_matrix.min_fvalue, cmat.MinValues());
// Initialize DeviceShard::hist
shard->hist.Init(0, (max_bins - 1) * kNCols);
shard->hist.AllocateHistogram(0);
// Initialize GPUHistMakerDevice::hist
maker.hist.Init(0, (max_bins - 1) * kNCols);
maker.hist.AllocateHistogram(0);
// Each row of hist_gpair represents gpairs for one feature.
// Each entry represents a bin.
std::vector<GradientPairPrecise> hist_gpair = GetHostHistGpair();
@@ -263,27 +228,26 @@ TEST(GpuHist, EvaluateSplits) {
hist.push_back(pair.GetHess());
}
ASSERT_EQ(shard->hist.Data().size(), hist.size());
ASSERT_EQ(maker.hist.Data().size(), hist.size());
thrust::copy(hist.begin(), hist.end(),
shard->hist.Data().begin());
maker.hist.Data().begin());
shard->column_sampler.Init(kNCols,
param.colsample_bynode,
param.colsample_bylevel,
param.colsample_bytree,
false);
maker.column_sampler.Init(kNCols,
param.colsample_bynode,
param.colsample_bylevel,
param.colsample_bytree,
false);
RegTree tree;
MetaInfo info;
info.num_row_ = kNRows;
info.num_col_ = kNCols;
shard->node_value_constraints.resize(1);
shard->node_value_constraints[0].lower_bound = -1.0;
shard->node_value_constraints[0].upper_bound = 1.0;
maker.node_value_constraints.resize(1);
maker.node_value_constraints[0].lower_bound = -1.0;
maker.node_value_constraints[0].upper_bound = 1.0;
std::vector<DeviceSplitCandidate> res =
shard->EvaluateSplits({ 0,0 }, tree, kNCols);
std::vector<DeviceSplitCandidate> res = maker.EvaluateSplits({0, 0 }, tree, kNCols);
ASSERT_EQ(res[0].findex, 7);
ASSERT_EQ(res[1].findex, 7);
@@ -316,18 +280,18 @@ void TestHistogramIndexImpl() {
hist_maker_ext.Configure(training_params, &generic_param);
hist_maker_ext.InitDataOnce(hist_maker_ext_dmat.get());
// Extract the device shard from the histogram makers and from that its compressed
// Extract the device maker from the histogram makers and from that its compressed
// histogram index
const auto &dev_shard = hist_maker.shard_;
std::vector<common::CompressedByteT> h_gidx_buffer(dev_shard->page->gidx_buffer.size());
dh::CopyDeviceSpanToVector(&h_gidx_buffer, dev_shard->page->gidx_buffer);
const auto &maker = hist_maker.maker_;
std::vector<common::CompressedByteT> h_gidx_buffer(maker->page->gidx_buffer.size());
dh::CopyDeviceSpanToVector(&h_gidx_buffer, maker->page->gidx_buffer);
const auto &dev_shard_ext = hist_maker_ext.shard_;
std::vector<common::CompressedByteT> h_gidx_buffer_ext(dev_shard_ext->page->gidx_buffer.size());
dh::CopyDeviceSpanToVector(&h_gidx_buffer_ext, dev_shard_ext->page->gidx_buffer);
const auto &maker_ext = hist_maker_ext.maker_;
std::vector<common::CompressedByteT> h_gidx_buffer_ext(maker_ext->page->gidx_buffer.size());
dh::CopyDeviceSpanToVector(&h_gidx_buffer_ext, maker_ext->page->gidx_buffer);
ASSERT_EQ(dev_shard->page->n_bins, dev_shard_ext->page->n_bins);
ASSERT_EQ(dev_shard->page->gidx_buffer.size(), dev_shard_ext->page->gidx_buffer.size());
ASSERT_EQ(maker->page->n_bins, maker_ext->page->n_bins);
ASSERT_EQ(maker->page->gidx_buffer.size(), maker_ext->page->gidx_buffer.size());
ASSERT_EQ(h_gidx_buffer, h_gidx_buffer_ext);
}