Hendrik/xgboost
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity

Fuse split evaluation kernels (#8026)

Browse Source
This commit is contained in:
Rory Mitchell
2022-07-05 10:24:31 +02:00
committed by GitHub
parent ff1c559084
commit 794cbaa60a
6 changed files with 308 additions and 314 deletions
Download Patch File Download Diff File Expand all files Collapse all files

112
src/tree/updater_gpu_hist.cu
View File

@@ -196,6 +196,7 @@ struct GPUHistMakerDevice {
HistRounding<GradientSumT> histogram_rounding;
dh::PinnedMemory pinned;
dh::PinnedMemory pinned2;
common::Monitor monitor;
common::ColumnSampler column_sampler;
@@ -279,58 +280,64 @@ struct GPUHistMakerDevice {
common::Span<bst_feature_t> feature_set =
interaction_constraints.Query(sampled_features->DeviceSpan(), nidx);
auto matrix = page->GetDeviceAccessor(ctx_->gpu_id);
EvaluateSplitInputs<GradientSumT> inputs{nidx,
root_sum,
gpu_param,
feature_set,
feature_types,
matrix.feature_segments,
matrix.gidx_fvalue_map,
matrix.min_fvalue,
hist.GetNodeHistogram(nidx)};
auto split = this->evaluator_.EvaluateSingleSplit(inputs, weight);
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,
matrix.min_fvalue,
};
auto split = this->evaluator_.EvaluateSingleSplit(inputs, shared_inputs, weight);
return split;
}
void EvaluateLeftRightSplits(GPUExpandEntry candidate, int left_nidx, int right_nidx,
const RegTree& tree,
void EvaluateSplits(const std::vector<GPUExpandEntry>& candidates, const RegTree& tree,
common::Span<GPUExpandEntry> pinned_candidates_out) {
dh::TemporaryArray<DeviceSplitCandidate> splits_out(2);
GPUTrainingParam gpu_param(param);
auto left_sampled_features = column_sampler.GetFeatureSet(tree.GetDepth(left_nidx));
left_sampled_features->SetDevice(ctx_->gpu_id);
common::Span<bst_feature_t> left_feature_set =
interaction_constraints.Query(left_sampled_features->DeviceSpan(), left_nidx);
auto right_sampled_features = column_sampler.GetFeatureSet(tree.GetDepth(right_nidx));
right_sampled_features->SetDevice(ctx_->gpu_id);
common::Span<bst_feature_t> right_feature_set =
interaction_constraints.Query(right_sampled_features->DeviceSpan(), left_nidx);
if (candidates.empty()) return;
dh::TemporaryArray<EvaluateSplitInputs> d_node_inputs(2 * candidates.size());
dh::TemporaryArray<DeviceSplitCandidate> splits_out(2 * candidates.size());
std::vector<bst_node_t> nidx(2 * candidates.size());
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,
matrix.gidx_fvalue_map, matrix.min_fvalue,
};
dh::TemporaryArray<GPUExpandEntry> entries(2 * candidates.size());
for (int i = 0; i < candidates.size(); i++) {
auto candidate = candidates.at(i);
int left_nidx = tree[candidate.nid].LeftChild();
int right_nidx = tree[candidate.nid].RightChild();
nidx[i * 2] = left_nidx;
nidx[i * 2 + 1] = right_nidx;
auto left_sampled_features = column_sampler.GetFeatureSet(tree.GetDepth(left_nidx));
left_sampled_features->SetDevice(ctx_->gpu_id);
common::Span<bst_feature_t> left_feature_set =
interaction_constraints.Query(left_sampled_features->DeviceSpan(), left_nidx);
auto right_sampled_features = column_sampler.GetFeatureSet(tree.GetDepth(right_nidx));
right_sampled_features->SetDevice(ctx_->gpu_id);
common::Span<bst_feature_t> right_feature_set =
interaction_constraints.Query(right_sampled_features->DeviceSpan(), left_nidx);
h_node_inputs[i * 2] = {left_nidx, candidate.depth + 1, candidate.split.left_sum,
left_feature_set, hist.GetNodeHistogram(left_nidx)};
h_node_inputs[i * 2 + 1] = {right_nidx, candidate.depth + 1, candidate.split.right_sum,
right_feature_set, hist.GetNodeHistogram(right_nidx)};
}
bst_feature_t number_active_features = h_node_inputs[0].feature_set.size();
for (auto input : h_node_inputs) {
CHECK_EQ(input.feature_set.size(), number_active_features)
<< "Current implementation assumes that the number of active features "
"(after sampling) in any node is the same";
}
dh::safe_cuda(cudaMemcpyAsync(d_node_inputs.data().get(), h_node_inputs.data(),
h_node_inputs.size() * sizeof(EvaluateSplitInputs),
cudaMemcpyDefault));
EvaluateSplitInputs<GradientSumT> left{left_nidx,
candidate.split.left_sum,
gpu_param,
left_feature_set,
feature_types,
matrix.feature_segments,
matrix.gidx_fvalue_map,
matrix.min_fvalue,
hist.GetNodeHistogram(left_nidx)};
EvaluateSplitInputs<GradientSumT> right{right_nidx,
candidate.split.right_sum,
gpu_param,
right_feature_set,
feature_types,
matrix.feature_segments,
matrix.gidx_fvalue_map,
matrix.min_fvalue,
hist.GetNodeHistogram(right_nidx)};
dh::TemporaryArray<GPUExpandEntry> entries(2);
this->evaluator_.EvaluateSplits(candidate, left, right, dh::ToSpan(entries));
dh::safe_cuda(cudaMemcpyAsync(pinned_candidates_out.data(), entries.data().get(),
sizeof(GPUExpandEntry) * entries.size(), cudaMemcpyDeviceToHost));
}
this->evaluator_.EvaluateSplits(nidx, number_active_features, dh::ToSpan(d_node_inputs),
shared_inputs, dh::ToSpan(entries));
dh::safe_cuda(cudaMemcpyAsync(pinned_candidates_out.data(),
entries.data().get(), sizeof(GPUExpandEntry) * entries.size(),
cudaMemcpyDeviceToHost));
dh::DefaultStream().Sync();
}
void BuildHist(int nidx) {
auto d_node_hist = hist.GetNodeHistogram(nidx);
@@ -697,16 +704,9 @@ struct GPUHistMakerDevice {
this->BuildHistLeftRight(filtered_expand_set, reducer, tree);
monitor.Stop("BuildHist");
for (auto i = 0ull; i < filtered_expand_set.size(); i++) {
auto candidate = filtered_expand_set.at(i);
int left_child_nidx = tree[candidate.nid].LeftChild();
int right_child_nidx = tree[candidate.nid].RightChild();
monitor.Start("EvaluateSplits");
this->EvaluateLeftRightSplits(candidate, left_child_nidx, right_child_nidx, *p_tree,
new_candidates.subspan(i * 2, 2));
monitor.Stop("EvaluateSplits");
}
monitor.Start("EvaluateSplits");
this->EvaluateSplits(filtered_expand_set, *p_tree, new_candidates);
monitor.Stop("EvaluateSplits");
dh::DefaultStream().Sync();
driver.Push(new_candidates.begin(), new_candidates.end());
expand_set = driver.Pop();