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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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28
src/tree/updater_gpu_hist.cu
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@@ -72,9 +72,10 @@ DMLC_REGISTER_PARAMETER(GPUHistMakerTrainParam);
* \author Rory
* \date 28/07/2018
*/
template <typename GradientSumT, size_t kStopGrowingSize = 1 << 28>
template <size_t kStopGrowingSize = 1 << 28>
class DeviceHistogramStorage {
private:
using GradientSumT = GradientPairInt64;
/*! \brief Map nidx to starting index of its histogram. */
std::map<int, size_t> nidx_map_;
// Large buffer of zeroed memory, caches histograms
@@ -180,7 +181,7 @@ struct GPUHistMakerDevice {
BatchParam batch_param;
std::unique_ptr<RowPartitioner> row_partitioner;
DeviceHistogramStorage<GradientSumT> hist{};
DeviceHistogramStorage<> hist{};
dh::device_vector<GradientPair> d_gpair; // storage for gpair;
common::Span<GradientPair> gpair;
@@ -193,7 +194,7 @@ struct GPUHistMakerDevice {
TrainParam param;
HistRounding<GradientSumT> histogram_rounding;
std::unique_ptr<GradientQuantizer> histogram_rounding;
dh::PinnedMemory pinned;
dh::PinnedMemory pinned2;
@@ -265,7 +266,7 @@ struct GPUHistMakerDevice {
page = sample.page;
gpair = sample.gpair;
histogram_rounding = CreateRoundingFactor<GradientSumT>(this->gpair);
histogram_rounding.reset(new GradientQuantizer(this->gpair));
row_partitioner.reset(); // Release the device memory first before reallocating
row_partitioner.reset(new RowPartitioner(ctx_->gpu_id, sample.sample_rows));
@@ -282,7 +283,11 @@ struct GPUHistMakerDevice {
auto matrix = page->GetDeviceAccessor(ctx_->gpu_id);
EvaluateSplitInputs inputs{nidx, 0, root_sum, feature_set, hist.GetNodeHistogram(nidx)};
EvaluateSplitSharedInputs shared_inputs{
gpu_param, feature_types, matrix.feature_segments, matrix.gidx_fvalue_map,
gpu_param,
*histogram_rounding,
feature_types,
matrix.feature_segments,
matrix.gidx_fvalue_map,
matrix.min_fvalue,
};
auto split = this->evaluator_.EvaluateSingleSplit(inputs, shared_inputs);
@@ -298,7 +303,7 @@ struct GPUHistMakerDevice {
auto h_node_inputs = pinned2.GetSpan<EvaluateSplitInputs>(2 * candidates.size());
auto matrix = page->GetDeviceAccessor(ctx_->gpu_id);
EvaluateSplitSharedInputs shared_inputs{
GPUTrainingParam{param}, feature_types, matrix.feature_segments,
GPUTrainingParam{param}, *histogram_rounding, feature_types, matrix.feature_segments,
matrix.gidx_fvalue_map, matrix.min_fvalue,
};
dh::TemporaryArray<GPUExpandEntry> entries(2 * candidates.size());
@@ -344,7 +349,7 @@ struct GPUHistMakerDevice {
auto d_ridx = row_partitioner->GetRows(nidx);
BuildGradientHistogram(page->GetDeviceAccessor(ctx_->gpu_id),
feature_groups->DeviceAccessor(ctx_->gpu_id), gpair,
d_ridx, d_node_hist, histogram_rounding);
d_ridx, d_node_hist, *histogram_rounding);
}
// Attempt to do subtraction trick
@@ -526,11 +531,10 @@ struct GPUHistMakerDevice {
void AllReduceHist(int nidx, dh::AllReducer* reducer, int num_histograms) {
monitor.Start("AllReduce");
auto d_node_hist = hist.GetNodeHistogram(nidx).data();
reducer->AllReduceSum(reinterpret_cast<typename GradientSumT::ValueT*>(d_node_hist),
reinterpret_cast<typename GradientSumT::ValueT*>(d_node_hist),
page->Cuts().TotalBins() *
(sizeof(GradientSumT) / sizeof(typename GradientSumT::ValueT)) *
num_histograms);
using ReduceT = typename std::remove_pointer<decltype(d_node_hist)>::type::ValueT;
reducer->AllReduceSum(reinterpret_cast<ReduceT*>(d_node_hist),
reinterpret_cast<ReduceT*>(d_node_hist),
page->Cuts().TotalBins() * 2 * num_histograms);
monitor.Stop("AllReduce");
}