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Fix compiler warnings. (#8059)

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- Remove unused parameters.
- Avoid comparison of different signedness.
This commit is contained in:
Jiaming Yuan
2022-07-14 05:29:56 +08:00
committed by GitHub
parent 937352c78f
commit abaa593aa0
10 changed files with 29 additions and 37 deletions
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2
src/tree/gpu_hist/evaluate_splits.cu
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@@ -402,7 +402,7 @@ void GPUHistEvaluator<GradientSumT>::EvaluateSplits(
template <typename GradientSumT>
GPUExpandEntry GPUHistEvaluator<GradientSumT>::EvaluateSingleSplit(
EvaluateSplitInputs input, EvaluateSplitSharedInputs shared_inputs, float weight) {
EvaluateSplitInputs input, EvaluateSplitSharedInputs shared_inputs) {
dh::device_vector<EvaluateSplitInputs> inputs = std::vector<EvaluateSplitInputs>{input};
dh::TemporaryArray<GPUExpandEntry> out_entries(1);
this->EvaluateSplits({input.nidx}, input.feature_set.size(), dh::ToSpan(inputs), shared_inputs,

7
src/tree/gpu_hist/evaluate_splits.cuh
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@@ -167,19 +167,20 @@ class GPUHistEvaluator {
TreeEvaluator::SplitEvaluator<GPUTrainingParam> evaluator);
// impl of evaluate splits, contains CUDA kernels so it's public
void LaunchEvaluateSplits(bst_feature_t number_active_features,common::Span<const EvaluateSplitInputs> d_inputs,EvaluateSplitSharedInputs shared_inputs,
void LaunchEvaluateSplits(bst_feature_t number_active_features,common::Span<const EvaluateSplitInputs> d_inputs,EvaluateSplitSharedInputs shared_inputs,
TreeEvaluator::SplitEvaluator<GPUTrainingParam> evaluator,
common::Span<DeviceSplitCandidate> out_splits);
/**
* \brief Evaluate splits for left and right nodes.
*/
void EvaluateSplits(const std::vector<bst_node_t> &nidx,bst_feature_t number_active_features,common::Span<const EvaluateSplitInputs> d_inputs,
EvaluateSplitSharedInputs shared_inputs,
EvaluateSplitSharedInputs shared_inputs,
common::Span<GPUExpandEntry> out_splits);
/**
* \brief Evaluate splits for root node.
*/
GPUExpandEntry EvaluateSingleSplit(EvaluateSplitInputs input,EvaluateSplitSharedInputs shared_inputs, float weight);
GPUExpandEntry EvaluateSingleSplit(EvaluateSplitInputs input,
EvaluateSplitSharedInputs shared_inputs);
};
} // namespace tree
} // namespace xgboost

4
src/tree/gpu_hist/row_partitioner.cuh
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@@ -272,7 +272,7 @@ class RowPartitioner {
dh::TemporaryArray<PerNodeData<OpDataT>> d_batch_info(nidx.size());
std::size_t total_rows = 0;
for (int i = 0; i < nidx.size(); i++) {
for (size_t i = 0; i < nidx.size(); i++) {
h_batch_info[i] = {ridx_segments_.at(nidx.at(i)).segment, op_data.at(i)};
total_rows += ridx_segments_.at(nidx.at(i)).segment.Size();
}
@@ -295,7 +295,7 @@ class RowPartitioner {
dh::safe_cuda(cudaStreamSynchronize(stream_));
// Update segments
for (int i = 0; i < nidx.size(); i++) {
for (size_t i = 0; i < nidx.size(); i++) {
auto segment = ridx_segments_.at(nidx[i]).segment;
auto left_count = h_counts[i];
CHECK_LE(left_count, segment.Size());

4
src/tree/hist/evaluate_splits.h
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@@ -436,16 +436,14 @@ class HistEvaluator {
*
* \param p_last_tree The last tree being updated by tree updater
*/
template <typename Partitioner, typename ExpandEntry>
template <typename Partitioner>
void UpdatePredictionCacheImpl(GenericParameter const *ctx, RegTree const *p_last_tree,
std::vector<Partitioner> const &partitioner,
HistEvaluator<ExpandEntry> const &hist_evaluator,
linalg::VectorView<float> out_preds) {
CHECK_GT(out_preds.Size(), 0U);
CHECK(p_last_tree);
auto const &tree = *p_last_tree;
auto evaluator = hist_evaluator.Evaluator();
CHECK_EQ(out_preds.DeviceIdx(), GenericParameter::kCpuId);
size_t n_nodes = p_last_tree->GetNodes().size();
for (auto &part : partitioner) {

2
src/tree/updater_approx.cc
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@@ -116,7 +116,7 @@ class GloablApproxBuilder {
// Caching prediction seems redundant for approx tree method, as sketching takes up
// majority of training time.
CHECK_EQ(out_preds.Size(), data->Info().num_row_);
UpdatePredictionCacheImpl(ctx_, p_last_tree_, partitioner_, evaluator_, out_preds);
UpdatePredictionCacheImpl(ctx_, p_last_tree_, partitioner_, out_preds);
monitor_->Stop(__func__);
}

12
src/tree/updater_gpu_hist.cu
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@@ -272,7 +272,7 @@ struct GPUHistMakerDevice {
hist.Reset();
}
GPUExpandEntry EvaluateRootSplit(GradientPairPrecise root_sum, float weight) {
GPUExpandEntry EvaluateRootSplit(GradientPairPrecise root_sum) {
int nidx = RegTree::kRoot;
GPUTrainingParam gpu_param(param);
auto sampled_features = column_sampler.GetFeatureSet(0);
@@ -285,7 +285,7 @@ struct GPUHistMakerDevice {
gpu_param, feature_types, matrix.feature_segments, matrix.gidx_fvalue_map,
matrix.min_fvalue,
};
auto split = this->evaluator_.EvaluateSingleSplit(inputs, shared_inputs, weight);
auto split = this->evaluator_.EvaluateSingleSplit(inputs, shared_inputs);
return split;
}
@@ -298,11 +298,11 @@ 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}, 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++) {
for (size_t 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();
@@ -378,7 +378,7 @@ struct GPUHistMakerDevice {
std::vector<int> left_nidx(candidates.size());
std::vector<int> right_nidx(candidates.size());
std::vector<NodeSplitData> split_data(candidates.size());
for (int i = 0; i < candidates.size(); i++) {
for (size_t i = 0; i < candidates.size(); i++) {
auto& e = candidates[i];
RegTree::Node split_node = (*p_tree)[e.nid];
auto split_type = p_tree->NodeSplitType(e.nid);
@@ -658,7 +658,7 @@ struct GPUHistMakerDevice {
(*p_tree)[kRootNIdx].SetLeaf(param.learning_rate * weight);
// Generate first split
auto root_entry = this->EvaluateRootSplit(root_sum, weight);
auto root_entry = this->EvaluateRootSplit(root_sum);
return root_entry;
}

2
src/tree/updater_quantile_hist.cc
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@@ -257,7 +257,7 @@ bool QuantileHistMaker::Builder::UpdatePredictionCache(DMatrix const *data,
}
monitor_->Start(__func__);
CHECK_EQ(out_preds.Size(), data->Info().num_row_);
UpdatePredictionCacheImpl(ctx_, p_last_tree_, partitioner_, *evaluator_, out_preds);
UpdatePredictionCacheImpl(ctx_, p_last_tree_, partitioner_, out_preds);
monitor_->Stop(__func__);
return true;
}