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Gradient based sampling for GPU Hist (#5093)

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* Implement gradient based sampling for GPU Hist tree method.
* Add samplers and handle compacted page in GPU Hist.
This commit is contained in:
Rong Ou
2020-02-03 18:31:27 -08:00
committed by GitHub
parent c74216f22c
commit e4b74c4d22
18 changed files with 1187 additions and 175 deletions
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173
src/tree/updater_gpu_hist.cu
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@@ -29,6 +29,7 @@
#include "param.h"
#include "updater_gpu_common.cuh"
#include "constraints.cuh"
#include "gpu_hist/gradient_based_sampler.cuh"
#include "gpu_hist/row_partitioner.cuh"
namespace xgboost {
@@ -415,11 +416,8 @@ __global__ void SharedMemHistKernel(xgboost::EllpackMatrix matrix,
}
for (auto idx : dh::GridStrideRange(static_cast<size_t>(0), n_elements)) {
int ridx = d_ridx[idx / matrix.info.row_stride];
if (!matrix.IsInRange(ridx)) {
continue;
}
int gidx = matrix.gidx_iter[(ridx - matrix.base_rowid) * matrix.info.row_stride
+ idx % matrix.info.row_stride];
int gidx =
matrix.gidx_iter[ridx * matrix.info.row_stride + idx % matrix.info.row_stride];
if (gidx != matrix.info.n_bins) {
// If we are not using shared memory, accumulate the values directly into
// global memory
@@ -480,6 +478,8 @@ struct GPUHistMakerDevice {
std::function<bool(ExpandEntry, ExpandEntry)>>;
std::unique_ptr<ExpandQueue> qexpand;
std::unique_ptr<GradientBasedSampler> sampler;
GPUHistMakerDevice(int _device_id,
EllpackPageImpl* _page,
bst_uint _n_rows,
@@ -495,6 +495,11 @@ struct GPUHistMakerDevice {
column_sampler(column_sampler_seed),
interaction_constraints(param, n_features),
batch_param(_batch_param) {
sampler.reset(new GradientBasedSampler(page,
n_rows,
batch_param,
param.subsample,
param.sampling_method));
monitor.Init(std::string("GPUHistMakerDevice") + std::to_string(device_id));
}
@@ -528,7 +533,7 @@ struct GPUHistMakerDevice {
// Reset values for each update iteration
// Note that the column sampler must be passed by value because it is not
// thread safe
void Reset(HostDeviceVector<GradientPair>* dh_gpair, int64_t num_columns) {
void Reset(HostDeviceVector<GradientPair>* dh_gpair, DMatrix* dmat, int64_t num_columns) {
if (param.grow_policy == TrainParam::kLossGuide) {
qexpand.reset(new ExpandQueue(LossGuide));
} else {
@@ -540,13 +545,14 @@ struct GPUHistMakerDevice {
this->interaction_constraints.Reset();
std::fill(node_sum_gradients.begin(), node_sum_gradients.end(),
GradientPair());
auto sample = sampler->Sample(dh_gpair->DeviceSpan(), dmat);
n_rows = sample.sample_rows;
page = sample.page;
gpair = sample.gpair;
row_partitioner.reset(); // Release the device memory first before reallocating
row_partitioner.reset(new RowPartitioner(device_id, n_rows));
dh::safe_cuda(cudaMemcpyAsync(
gpair.data(), dh_gpair->ConstDevicePointer(),
gpair.size() * sizeof(GradientPair), cudaMemcpyHostToHost));
SubsampleGradientPair(device_id, gpair, param.subsample);
hist.Reset();
}
@@ -632,14 +638,6 @@ struct GPUHistMakerDevice {
return std::vector<DeviceSplitCandidate>(result_all.begin(), result_all.end());
}
// Build gradient histograms for a given node across all the batches in the DMatrix.
void BuildHistBatches(int nidx, DMatrix* p_fmat) {
for (auto& batch : p_fmat->GetBatches<EllpackPage>(batch_param)) {
page = batch.Impl();
BuildHist(nidx);
}
}
void BuildHist(int nidx) {
hist.AllocateHistogram(nidx);
auto d_node_hist = hist.GetNodeHistogram(nidx);
@@ -687,10 +685,7 @@ struct GPUHistMakerDevice {
row_partitioner->UpdatePosition(
nidx, split_node.LeftChild(), split_node.RightChild(),
[=] __device__(size_t ridx) {
if (!d_matrix.IsInRange(ridx)) {
return RowPartitioner::kIgnoredTreePosition;
}
[=] __device__(bst_uint ridx) {
// given a row index, returns the node id it belongs to
bst_float cut_value =
d_matrix.GetElement(ridx, split_node.SplitIndex());
@@ -719,33 +714,44 @@ struct GPUHistMakerDevice {
d_nodes.size() * sizeof(RegTree::Node),
cudaMemcpyHostToDevice));
for (auto& batch : p_fmat->GetBatches<EllpackPage>(batch_param)) {
page = batch.Impl();
auto d_matrix = page->matrix;
row_partitioner->FinalisePosition(
[=] __device__(size_t row_id, int position) {
if (!d_matrix.IsInRange(row_id)) {
return RowPartitioner::kIgnoredTreePosition;
}
auto node = d_nodes[position];
while (!node.IsLeaf()) {
bst_float element = d_matrix.GetElement(row_id, node.SplitIndex());
// Missing value
if (isnan(element)) {
position = node.DefaultChild();
} else {
if (element <= node.SplitCond()) {
position = node.LeftChild();
} else {
position = node.RightChild();
}
}
node = d_nodes[position];
}
return position;
});
if (row_partitioner->GetRows().size() != p_fmat->Info().num_row_) {
row_partitioner.reset(); // Release the device memory first before reallocating
row_partitioner.reset(new RowPartitioner(device_id, p_fmat->Info().num_row_));
}
if (page->matrix.n_rows == p_fmat->Info().num_row_) {
FinalisePositionInPage(page, d_nodes);
} else {
for (auto& batch : p_fmat->GetBatches<EllpackPage>(batch_param)) {
FinalisePositionInPage(batch.Impl(), d_nodes);
}
}
}
void FinalisePositionInPage(EllpackPageImpl* page, const common::Span<RegTree::Node> d_nodes) {
auto d_matrix = page->matrix;
row_partitioner->FinalisePosition(
[=] __device__(size_t row_id, int position) {
if (!d_matrix.IsInRange(row_id)) {
return RowPartitioner::kIgnoredTreePosition;
}
auto node = d_nodes[position];
while (!node.IsLeaf()) {
bst_float element = d_matrix.GetElement(row_id, node.SplitIndex());
// Missing value
if (isnan(element)) {
position = node.DefaultChild();
} else {
if (element <= node.SplitCond()) {
position = node.LeftChild();
} else {
position = node.RightChild();
}
}
node = d_nodes[position];
}
return position;
});
}
void UpdatePredictionCache(bst_float* out_preds_d) {
@@ -797,7 +803,8 @@ struct GPUHistMakerDevice {
/**
* \brief Build GPU local histograms for the left and right child of some parent node
*/
void BuildHistLeftRight(const ExpandEntry &candidate, int nidx_left, int nidx_right) {
void BuildHistLeftRight(const ExpandEntry &candidate, int nidx_left,
int nidx_right, dh::AllReducer* reducer) {
auto build_hist_nidx = nidx_left;
auto subtraction_trick_nidx = nidx_right;
@@ -809,34 +816,6 @@ struct GPUHistMakerDevice {
}
this->BuildHist(build_hist_nidx);
// Check whether we can use the subtraction trick to calculate the other
bool do_subtraction_trick = this->CanDoSubtractionTrick(
candidate.nid, build_hist_nidx, subtraction_trick_nidx);
if (!do_subtraction_trick) {
// Calculate other histogram manually
this->BuildHist(subtraction_trick_nidx);
}
}
/**
* \brief AllReduce GPU histograms for the left and right child of some parent node.
*/
void ReduceHistLeftRight(const ExpandEntry& candidate,
int nidx_left,
int nidx_right,
dh::AllReducer* reducer) {
auto build_hist_nidx = nidx_left;
auto subtraction_trick_nidx = nidx_right;
// Decide whether to build the left histogram or right histogram
// Use sum of Hessian as a heuristic to select node with fewest training instances
bool fewer_right = candidate.split.right_sum.GetHess() < candidate.split.left_sum.GetHess();
if (fewer_right) {
std::swap(build_hist_nidx, subtraction_trick_nidx);
}
this->AllReduceHist(build_hist_nidx, reducer);
// Check whether we can use the subtraction trick to calculate the other
@@ -849,6 +828,7 @@ struct GPUHistMakerDevice {
subtraction_trick_nidx);
} else {
// Calculate other histogram manually
this->BuildHist(subtraction_trick_nidx);
this->AllReduceHist(subtraction_trick_nidx, reducer);
}
}
@@ -889,14 +869,10 @@ struct GPUHistMakerDevice {
tree[candidate.nid].RightChild());
}
void InitRoot(RegTree* p_tree, HostDeviceVector<GradientPair>* gpair_all, DMatrix* p_fmat,
dh::AllReducer* reducer, int64_t num_columns) {
void InitRoot(RegTree* p_tree, dh::AllReducer* reducer, int64_t num_columns) {
constexpr int kRootNIdx = 0;
const auto &gpair = gpair_all->DeviceSpan();
dh::SumReduction(temp_memory, gpair, node_sum_gradients_d,
gpair.size());
dh::SumReduction(temp_memory, gpair, node_sum_gradients_d, gpair.size());
reducer->AllReduceSum(
reinterpret_cast<float*>(node_sum_gradients_d.data()),
reinterpret_cast<float*>(node_sum_gradients_d.data()), 2);
@@ -905,7 +881,7 @@ struct GPUHistMakerDevice {
node_sum_gradients_d.data(), sizeof(GradientPair),
cudaMemcpyDeviceToHost));
this->BuildHistBatches(kRootNIdx, p_fmat);
this->BuildHist(kRootNIdx);
this->AllReduceHist(kRootNIdx, reducer);
// Remember root stats
@@ -928,11 +904,11 @@ struct GPUHistMakerDevice {
auto& tree = *p_tree;
monitor.StartCuda("Reset");
this->Reset(gpair_all, p_fmat->Info().num_col_);
this->Reset(gpair_all, p_fmat, p_fmat->Info().num_col_);
monitor.StopCuda("Reset");
monitor.StartCuda("InitRoot");
this->InitRoot(p_tree, gpair_all, p_fmat, reducer, p_fmat->Info().num_col_);
this->InitRoot(p_tree, reducer, p_fmat->Info().num_col_);
monitor.StopCuda("InitRoot");
auto timestamp = qexpand->size();
@@ -951,21 +927,15 @@ struct GPUHistMakerDevice {
int left_child_nidx = tree[candidate.nid].LeftChild();
int right_child_nidx = tree[candidate.nid].RightChild();
// Only create child entries if needed
if (ExpandEntry::ChildIsValid(param, tree.GetDepth(left_child_nidx), num_leaves)) {
for (auto& batch : p_fmat->GetBatches<EllpackPage>(batch_param)) {
page = batch.Impl();
if (ExpandEntry::ChildIsValid(param, tree.GetDepth(left_child_nidx),
num_leaves)) {
monitor.StartCuda("UpdatePosition");
this->UpdatePosition(candidate.nid, (*p_tree)[candidate.nid]);
monitor.StopCuda("UpdatePosition");
monitor.StartCuda("UpdatePosition");
this->UpdatePosition(candidate.nid, (*p_tree)[candidate.nid]);
monitor.StopCuda("UpdatePosition");
monitor.StartCuda("BuildHist");
this->BuildHistLeftRight(candidate, left_child_nidx, right_child_nidx);
monitor.StopCuda("BuildHist");
}
monitor.StartCuda("ReduceHist");
this->ReduceHistLeftRight(candidate, left_child_nidx, right_child_nidx, reducer);
monitor.StopCuda("ReduceHist");
monitor.StartCuda("BuildHist");
this->BuildHistLeftRight(candidate, left_child_nidx, right_child_nidx, reducer);
monitor.StopCuda("BuildHist");
monitor.StartCuda("EvaluateSplits");
auto splits = this->EvaluateSplits({left_child_nidx, right_child_nidx},
@@ -997,7 +967,6 @@ inline void GPUHistMakerDevice<GradientSumT>::InitHistogram() {
param.max_leaves > 0 ? param.max_leaves * 2 : MaxNodesDepth(param.max_depth);
ba.Allocate(device_id,
&gpair, n_rows,
&prediction_cache, n_rows,
&node_sum_gradients_d, max_nodes,
&monotone_constraints, param.monotone_constraints.size());