Fix compiler warnings. (#7974)

- Remove unused parameters. There are still many warnings that are not yet
addressed. Currently, the warnings in dmlc-core dominate the error log.
- Remove `distributed` parameter from metric.
- Fixes some warnings about signed comparison.

src/tree/gpu_hist/row_partitioner.cuh

@@ -116,7 +116,7 @@ class RowPartitioner {
     Segment segment = ridx_segments_.at(nidx);  // rows belongs to node nidx
     auto d_ridx = ridx_.CurrentSpan();
     auto d_position = position_.CurrentSpan();
-    if (left_counts_.size() <= nidx) {
+    if (left_counts_.size() <= static_cast<size_t>(nidx)) {
       left_counts_.resize((nidx * 2) + 1);
       thrust::fill(left_counts_.begin(), left_counts_.end(), 0);
     }

src/tree/hist/evaluate_splits.h

@@ -203,8 +203,8 @@ class HistEvaluator {
   // Returns the sum of gradients corresponding to the data points that contains
   // a non-missing value for the particular feature fid.
   template <int d_step>
-  GradStats EnumerateSplit(common::HistogramCuts const &cut, common::Span<size_t const> sorted_idx,
-                           const common::GHistRow &hist, bst_feature_t fidx, bst_node_t nidx,
+  GradStats EnumerateSplit(common::HistogramCuts const &cut, const common::GHistRow &hist,
+                           bst_feature_t fidx, bst_node_t nidx,
                            TreeEvaluator::SplitEvaluator<TrainParam> const &evaluator,
                            SplitEntry *p_best) const {
     static_assert(d_step == +1 || d_step == -1, "Invalid step.");
@@ -333,9 +333,9 @@ class HistEvaluator {
             EnumeratePart<-1>(cut, sorted_idx, histogram, fidx, nidx, evaluator, best);
           }
         } else {
-          auto grad_stats = EnumerateSplit<+1>(cut, {}, histogram, fidx, nidx, evaluator, best);
+          auto grad_stats = EnumerateSplit<+1>(cut, histogram, fidx, nidx, evaluator, best);
           if (SplitContainsMissingValues(grad_stats, snode_[nidx])) {
-            EnumerateSplit<-1>(cut, {}, histogram, fidx, nidx, evaluator, best);
+            EnumerateSplit<-1>(cut, histogram, fidx, nidx, evaluator, best);
           }
         }
       }
@@ -440,7 +440,7 @@ template <typename Partitioner, typename ExpandEntry>
 void UpdatePredictionCacheImpl(GenericParameter const *ctx, RegTree const *p_last_tree,
                                std::vector<Partitioner> const &partitioner,
                                HistEvaluator<ExpandEntry> const &hist_evaluator,
-                               TrainParam const &param, linalg::VectorView<float> out_preds) {
+                               linalg::VectorView<float> out_preds) {
   CHECK_GT(out_preds.Size(), 0U);
 
   CHECK(p_last_tree);

src/tree/updater_approx.cc

@@ -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_, param_, out_preds);
+    UpdatePredictionCacheImpl(ctx_, p_last_tree_, partitioner_, evaluator_, out_preds);
     monitor_->Stop(__func__);
   }
 

src/tree/updater_approx.h

@@ -83,7 +83,7 @@ class ApproxRowPartitioner {
       const size_t task_id = partition_builder_.GetTaskIdx(node_in_set, r.begin());
       partition_builder_.AllocateForTask(task_id);
       partition_builder_.PartitionRange(
-          node_in_set, nid, r, fidx, &row_set_collection_, [&](size_t row_id) {
+          node_in_set, nid, r, &row_set_collection_, [&](size_t row_id) {
             auto cut_value = SearchCutValue(row_id, fidx, index, cut_ptrs, cut_values);
             if (std::isnan(cut_value)) {
               return candidate.split.DefaultLeft();

src/tree/updater_gpu_hist.cu

@@ -563,7 +563,7 @@ struct GPUHistMakerDevice {
     // when processing a large batch
     this->AllReduceHist(hist_nidx.at(0), reducer, hist_nidx.size());
 
-    for (int i = 0; i < subtraction_nidx.size(); i++) {
+    for (size_t i = 0; i < subtraction_nidx.size(); i++) {
       auto build_hist_nidx = hist_nidx.at(i);
       auto subtraction_trick_nidx = subtraction_nidx.at(i);
       auto parent_nidx = candidates.at(i).nid;

src/tree/updater_quantile_hist.cc

@@ -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_, param_, out_preds);
+  UpdatePredictionCacheImpl(ctx_, p_last_tree_, partitioner_, *evaluator_, out_preds);
   monitor_->Stop(__func__);
   return true;
 }