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Always use partition based categorical splits. (#7857)

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This commit is contained in:
Jiaming Yuan
2022-05-03 22:30:32 +08:00
committed by GitHub
parent 90cce38236
commit 317d7be6ee
13 changed files with 79 additions and 104 deletions
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26
src/tree/updater_gpu_hist.cu
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@@ -232,16 +232,14 @@ 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, DMatrix* dmat, int64_t num_columns,
ObjInfo task) {
void Reset(HostDeviceVector<GradientPair>* dh_gpair, DMatrix* dmat, int64_t num_columns) {
auto const& info = dmat->Info();
this->column_sampler.Init(num_columns, info.feature_weights.HostVector(),
param.colsample_bynode, param.colsample_bylevel,
param.colsample_bytree);
dh::safe_cuda(cudaSetDevice(ctx_->gpu_id));
this->evaluator_.Reset(page->Cuts(), feature_types, task, dmat->Info().num_col_, param,
ctx_->gpu_id);
this->evaluator_.Reset(page->Cuts(), feature_types, dmat->Info().num_col_, param, ctx_->gpu_id);
this->interaction_constraints.Reset();
std::fill(node_sum_gradients.begin(), node_sum_gradients.end(), GradientPairPrecise{});
@@ -263,7 +261,7 @@ struct GPUHistMakerDevice {
hist.Reset();
}
GPUExpandEntry EvaluateRootSplit(GradientPairPrecise root_sum, float weight, ObjInfo task) {
GPUExpandEntry EvaluateRootSplit(GradientPairPrecise root_sum, float weight) {
int nidx = RegTree::kRoot;
GPUTrainingParam gpu_param(param);
auto sampled_features = column_sampler.GetFeatureSet(0);
@@ -280,12 +278,12 @@ struct GPUHistMakerDevice {
matrix.gidx_fvalue_map,
matrix.min_fvalue,
hist.GetNodeHistogram(nidx)};
auto split = this->evaluator_.EvaluateSingleSplit(inputs, weight, task);
auto split = this->evaluator_.EvaluateSingleSplit(inputs, weight);
return split;
}
void EvaluateLeftRightSplits(GPUExpandEntry candidate, ObjInfo task, int left_nidx,
int right_nidx, const RegTree& tree,
void EvaluateLeftRightSplits(GPUExpandEntry candidate, int left_nidx, int right_nidx,
const RegTree& tree,
common::Span<GPUExpandEntry> pinned_candidates_out) {
dh::TemporaryArray<DeviceSplitCandidate> splits_out(2);
GPUTrainingParam gpu_param(param);
@@ -319,7 +317,7 @@ struct GPUHistMakerDevice {
hist.GetNodeHistogram(right_nidx)};
dh::TemporaryArray<GPUExpandEntry> entries(2);
this->evaluator_.EvaluateSplits(candidate, task, left, right, dh::ToSpan(entries));
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));
}
@@ -613,7 +611,7 @@ struct GPUHistMakerDevice {
tree[candidate.nid].RightChild());
}
GPUExpandEntry InitRoot(RegTree* p_tree, ObjInfo task, dh::AllReducer* reducer) {
GPUExpandEntry InitRoot(RegTree* p_tree, dh::AllReducer* reducer) {
constexpr bst_node_t kRootNIdx = 0;
dh::XGBCachingDeviceAllocator<char> alloc;
auto gpair_it = dh::MakeTransformIterator<GradientPairPrecise>(
@@ -634,7 +632,7 @@ struct GPUHistMakerDevice {
(*p_tree)[kRootNIdx].SetLeaf(param.learning_rate * weight);
// Generate first split
auto root_entry = this->EvaluateRootSplit(root_sum, weight, task);
auto root_entry = this->EvaluateRootSplit(root_sum, weight);
return root_entry;
}
@@ -645,11 +643,11 @@ struct GPUHistMakerDevice {
Driver<GPUExpandEntry> driver(static_cast<TrainParam::TreeGrowPolicy>(param.grow_policy));
monitor.Start("Reset");
this->Reset(gpair_all, p_fmat, p_fmat->Info().num_col_, task);
this->Reset(gpair_all, p_fmat, p_fmat->Info().num_col_);
monitor.Stop("Reset");
monitor.Start("InitRoot");
driver.Push({ this->InitRoot(p_tree, task, reducer) });
driver.Push({ this->InitRoot(p_tree, reducer) });
monitor.Stop("InitRoot");
auto num_leaves = 1;
@@ -686,7 +684,7 @@ struct GPUHistMakerDevice {
monitor.Stop("BuildHist");
monitor.Start("EvaluateSplits");
this->EvaluateLeftRightSplits(candidate, task, left_child_nidx, right_child_nidx, *p_tree,
this->EvaluateLeftRightSplits(candidate, left_child_nidx, right_child_nidx, *p_tree,
new_candidates.subspan(i * 2, 2));
monitor.Stop("EvaluateSplits");
} else {