Support multiple alphas for segmented quantile. (#8758)

src/common/stats.cuh

@@ -1,29 +1,179 @@
-/*!
+/**
- * Copyright 2022 by XGBoost Contributors
+ * Copyright 2022-2023 by XGBoost Contributors
  */
 #ifndef XGBOOST_COMMON_STATS_CUH_
 #define XGBOOST_COMMON_STATS_CUH_
 
-#include <thrust/iterator/counting_iterator.h>
+#include <thrust/binary_search.h>                  // lower_bound
-#include <thrust/iterator/permutation_iterator.h>
+#include <thrust/for_each.h>                       // for_each_n
+#include <thrust/iterator/constant_iterator.h>     // make_constant_iterator
+#include <thrust/iterator/counting_iterator.h>     // make_counting_iterator
+#include <thrust/iterator/permutation_iterator.h>  // make_permutation_iterator
+#include <thrust/scan.h>                           // inclusive_scan_by_key
 
-#include <iterator>  // std::distance
+#include <algorithm>                               // std::min
+#include <cstddef>                                 // std::size_t
+#include <iterator>                                // std::distance
+#include <limits>                                  // std::numeric_limits
+#include <type_traits>                             // std::is_floating_point,std::iterator_traits
 
+#include "cuda_context.cuh"                        // CUDAContext
 #include "device_helpers.cuh"
-#include "linalg_op.cuh"
+#include "xgboost/context.h"                       // Context
-#include "xgboost/context.h"
+#include "xgboost/span.h"                          // Span
-#include "xgboost/linalg.h"
-#include "xgboost/tree_model.h"
 
 namespace xgboost {
 namespace common {
+namespace detail {
+// This should be a lambda function, but for some reason gcc-11 + nvcc-11.8 failed to
+// compile it. As a result, a functor is extracted instead.
+//
+// error: ‘__T288’ was not declared in this scope
+template <typename SegIt, typename ValIt, typename AlphaIt>
+struct QuantileSegmentOp {
+  SegIt seg_begin;
+  ValIt val;
+  AlphaIt alpha_it;
+  Span<float> d_results;
+
+  static_assert(std::is_floating_point<typename std::iterator_traits<ValIt>::value_type>::value,
+                "Invalid value for quantile.");
+  static_assert(std::is_floating_point<typename std::iterator_traits<ValIt>::value_type>::value,
+                "Invalid alpha.");
+
+  XGBOOST_DEVICE void operator()(std::size_t seg_idx) {
+    std::size_t begin = seg_begin[seg_idx];
+    auto n = static_cast<double>(seg_begin[seg_idx + 1] - begin);
+    double a = alpha_it[seg_idx];
+
+    if (n == 0) {
+      d_results[seg_idx] = std::numeric_limits<float>::quiet_NaN();
+      return;
+    }
+
+    if (a <= (1 / (n + 1))) {
+      d_results[seg_idx] = val[begin];
+      return;
+    }
+    if (a >= (n / (n + 1))) {
+      d_results[seg_idx] = val[common::LastOf(seg_idx, seg_begin)];
+      return;
+    }
+
+    double x = a * static_cast<double>(n + 1);
+    double k = std::floor(x) - 1;
+    double d = (x - 1) - k;
+
+    auto v0 = val[begin + static_cast<std::size_t>(k)];
+    auto v1 = val[begin + static_cast<std::size_t>(k) + 1];
+
+    d_results[seg_idx] = v0 + d * (v1 - v0);
+  }
+};
+
+template <typename SegIt, typename ValIt, typename AlphaIt>
+auto MakeQSegOp(SegIt seg_it, ValIt val_it, AlphaIt alpha_it, Span<float> d_results) {
+  return QuantileSegmentOp<SegIt, ValIt, AlphaIt>{seg_it, val_it, alpha_it, d_results};
+}
+
+template <typename SegIt>
+struct SegOp {
+  SegIt seg_beg;
+  SegIt seg_end;
+
+  XGBOOST_DEVICE std::size_t operator()(std::size_t i) {
+    return dh::SegmentId(seg_beg, seg_end, i);
+  }
+};
+
+template <typename WIter>
+struct WeightOp {
+  WIter w_begin;
+  Span<std::size_t const> d_sorted_idx;
+  XGBOOST_DEVICE float operator()(std::size_t i) { return w_begin[d_sorted_idx[i]]; }
+};
+
+template <typename SegIt, typename ValIt, typename AlphaIt>
+struct WeightedQuantileSegOp {
+  AlphaIt alpha_it;
+  SegIt seg_beg;
+  ValIt val_begin;
+  Span<float const> d_weight_cdf;
+  Span<std::size_t const> d_sorted_idx;
+  Span<float> d_results;
+  static_assert(std::is_floating_point<typename std::iterator_traits<AlphaIt>::value_type>::value,
+                "Invalid alpha.");
+  static_assert(std::is_floating_point<typename std::iterator_traits<ValIt>::value_type>::value,
+                "Invalid value for quantile.");
+
+  XGBOOST_DEVICE void operator()(std::size_t seg_idx) {
+    std::size_t begin = seg_beg[seg_idx];
+    auto n = static_cast<double>(seg_beg[seg_idx + 1] - begin);
+    if (n == 0) {
+      d_results[seg_idx] = std::numeric_limits<float>::quiet_NaN();
+      return;
+    }
+    auto seg_cdf = d_weight_cdf.subspan(begin, static_cast<std::size_t>(n));
+    auto seg_sorted_idx = d_sorted_idx.subspan(begin, static_cast<std::size_t>(n));
+    double a = alpha_it[seg_idx];
+    double thresh = seg_cdf.back() * a;
+
+    std::size_t idx =
+        thrust::lower_bound(thrust::seq, seg_cdf.data(), seg_cdf.data() + seg_cdf.size(), thresh) -
+        seg_cdf.data();
+    idx = std::min(idx, static_cast<std::size_t>(n - 1));
+    d_results[seg_idx] = val_begin[seg_sorted_idx[idx]];
+  }
+};
+
+template <typename SegIt, typename ValIt, typename AlphaIt>
+auto MakeWQSegOp(SegIt seg_it, ValIt val_it, AlphaIt alpha_it, Span<float const> d_weight_cdf,
+                 Span<std::size_t const> d_sorted_idx, Span<float> d_results) {
+  return WeightedQuantileSegOp<SegIt, ValIt, AlphaIt>{alpha_it,     seg_it,       val_it,
+                                                      d_weight_cdf, d_sorted_idx, d_results};
+}
+}  // namespace detail
 /**
- * \brief Compute segmented quantile on GPU.
+ * @brief Compute segmented quantile on GPU.
  *
- * \tparam SegIt Iterator for CSR style segments indptr
+ * @tparam SegIt Iterator for CSR style segments indptr
- * \tparam ValIt Iterator for values
+ * @tparam ValIt Iterator for values
+ * @tparam AlphaIt Iterator to alphas
  *
- * \param alpha The p^th quantile we want to compute
+ * @param alpha The p^th quantile we want to compute, one for each segment.
+ *
+ *    std::distance(seg_begin, seg_end) should be equal to n_segments + 1
+ */
+template <typename SegIt, typename ValIt, typename AlphaIt,
+          std::enable_if_t<!std::is_floating_point<AlphaIt>::value>* = nullptr>
+void SegmentedQuantile(Context const* ctx, AlphaIt alpha_it, SegIt seg_begin, SegIt seg_end,
+                       ValIt val_begin, ValIt val_end, HostDeviceVector<float>* quantiles) {
+  dh::device_vector<std::size_t> sorted_idx;
+  using Tup = thrust::tuple<std::size_t, float>;
+  dh::SegmentedArgSort(seg_begin, seg_end, val_begin, val_end, &sorted_idx);
+  auto n_segments = std::distance(seg_begin, seg_end) - 1;
+  if (n_segments <= 0) {
+    return;
+  }
+
+  auto d_sorted_idx = dh::ToSpan(sorted_idx);
+  auto val = thrust::make_permutation_iterator(val_begin, dh::tcbegin(d_sorted_idx));
+
+  quantiles->SetDevice(ctx->gpu_id);
+  quantiles->Resize(n_segments);
+  auto d_results = quantiles->DeviceSpan();
+
+  dh::LaunchN(n_segments, ctx->CUDACtx()->Stream(),
+              detail::MakeQSegOp(seg_begin, val, alpha_it, d_results));
+}
+
+/**
+ * @brief Compute segmented quantile on GPU.
+ *
+ * @tparam SegIt Iterator for CSR style segments indptr
+ * @tparam ValIt Iterator for values
+ *
+ * @param alpha The p^th quantile we want to compute
  *
  *    std::distance(ptr_begin, ptr_end) should be equal to n_segments + 1
  */
@@ -31,69 +181,40 @@ template <typename SegIt, typename ValIt>
 void SegmentedQuantile(Context const* ctx, double alpha, SegIt seg_begin, SegIt seg_end,
                        ValIt val_begin, ValIt val_end, HostDeviceVector<float>* quantiles) {
   CHECK(alpha >= 0 && alpha <= 1);
-
+  auto alpha_it = thrust::make_constant_iterator(alpha);
-  dh::device_vector<size_t> sorted_idx;
+  return SegmentedQuantile(ctx, alpha_it, seg_begin, seg_end, val_begin, val_end, quantiles);
-  using Tup = thrust::tuple<size_t, float>;
-  dh::SegmentedArgSort(seg_begin, seg_end, val_begin, val_end, &sorted_idx);
-  auto n_segments = std::distance(seg_begin, seg_end) - 1;
-  if (n_segments <= 0) {
-    return;
-  }
-
-  quantiles->SetDevice(ctx->gpu_id);
-  quantiles->Resize(n_segments);
-  auto d_results = quantiles->DeviceSpan();
-  auto d_sorted_idx = dh::ToSpan(sorted_idx);
-
-  auto val = thrust::make_permutation_iterator(val_begin, dh::tcbegin(d_sorted_idx));
-
-  dh::LaunchN(n_segments, [=] XGBOOST_DEVICE(size_t i) {
-    // each segment is the index of a leaf.
-    size_t seg_idx = i;
-    size_t begin = seg_begin[seg_idx];
-    auto n = static_cast<double>(seg_begin[seg_idx + 1] - begin);
-    if (n == 0) {
-      d_results[i] = std::numeric_limits<float>::quiet_NaN();
-      return;
-    }
-
-    if (alpha <= (1 / (n + 1))) {
-      d_results[i] = val[begin];
-      return;
-    }
-    if (alpha >= (n / (n + 1))) {
-      d_results[i] = val[common::LastOf(seg_idx, seg_begin)];
-      return;
-    }
-
-    double x = alpha * static_cast<double>(n + 1);
-    double k = std::floor(x) - 1;
-    double d = (x - 1) - k;
-
-    auto v0 = val[begin + static_cast<size_t>(k)];
-    auto v1 = val[begin + static_cast<size_t>(k) + 1];
-    d_results[seg_idx] = v0 + d * (v1 - v0);
-  });
 }
 
-template <typename SegIt, typename ValIt, typename WIter>
+/**
-void SegmentedWeightedQuantile(Context const* ctx, double alpha, SegIt seg_beg, SegIt seg_end,
+ * @brief Compute segmented quantile on GPU with weighted inputs.
+ *
+ * @tparam SegIt Iterator for CSR style segments indptr
+ * @tparam ValIt Iterator for values
+ * @tparam WIter Iterator for weights
+ *
+ * @param alpha_it Iterator for the p^th quantile we want to compute, one per-segment
+ * @param w_begin  Iterator for weight for each input element
+ */
+template <typename SegIt, typename ValIt, typename AlphaIt, typename WIter,
+          typename std::enable_if_t<!std::is_same<
+              typename std::iterator_traits<AlphaIt>::value_type, void>::value>* = nullptr>
+void SegmentedWeightedQuantile(Context const* ctx, AlphaIt alpha_it, SegIt seg_beg, SegIt seg_end,
                                ValIt val_begin, ValIt val_end, WIter w_begin, WIter w_end,
                                HostDeviceVector<float>* quantiles) {
-  CHECK(alpha >= 0 && alpha <= 1);
+  auto cuctx = ctx->CUDACtx();
-  dh::device_vector<size_t> sorted_idx;
+  dh::device_vector<std::size_t> sorted_idx;
   dh::SegmentedArgSort(seg_beg, seg_end, val_begin, val_end, &sorted_idx);
   auto d_sorted_idx = dh::ToSpan(sorted_idx);
-  size_t n_weights = std::distance(w_begin, w_end);
+  std::size_t n_weights = std::distance(w_begin, w_end);
   dh::device_vector<float> weights_cdf(n_weights);
+  std::size_t n_elems = std::distance(val_begin, val_end);
+  CHECK_EQ(n_weights, n_elems);
 
   dh::XGBCachingDeviceAllocator<char> caching;
-  auto scan_key = dh::MakeTransformIterator<size_t>(
+  auto scan_key = dh::MakeTransformIterator<std::size_t>(thrust::make_counting_iterator(0ul),
-      thrust::make_counting_iterator(0ul),
+                                                         detail::SegOp<SegIt>{seg_beg, seg_end});
-      [=] XGBOOST_DEVICE(size_t i) { return dh::SegmentId(seg_beg, seg_end, i); });
+  auto scan_val = dh::MakeTransformIterator<float>(thrust::make_counting_iterator(0ul),
-  auto scan_val = dh::MakeTransformIterator<float>(
+                                                   detail::WeightOp<WIter>{w_begin, d_sorted_idx});
-      thrust::make_counting_iterator(0ul),
-      [=] XGBOOST_DEVICE(size_t i) { return w_begin[d_sorted_idx[i]]; });
   thrust::inclusive_scan_by_key(thrust::cuda::par(caching), scan_key, scan_key + n_weights,
                                 scan_val, weights_cdf.begin());
 
@@ -103,24 +224,18 @@ void SegmentedWeightedQuantile(Context const* ctx, double alpha, SegIt seg_beg,
   auto d_results = quantiles->DeviceSpan();
   auto d_weight_cdf = dh::ToSpan(weights_cdf);
 
-  dh::LaunchN(n_segments, [=] XGBOOST_DEVICE(size_t i) {
+  thrust::for_each_n(
-    size_t seg_idx = i;
+      cuctx->CTP(), thrust::make_counting_iterator(0ul), n_segments,
-    size_t begin = seg_beg[seg_idx];
+      detail::MakeWQSegOp(seg_beg, val_begin, alpha_it, d_weight_cdf, d_sorted_idx, d_results));
-    auto n = static_cast<double>(seg_beg[seg_idx + 1] - begin);
+}
-    if (n == 0) {
-      d_results[i] = std::numeric_limits<float>::quiet_NaN();
-      return;
-    }
-    auto leaf_cdf = d_weight_cdf.subspan(begin, static_cast<size_t>(n));
-    auto leaf_sorted_idx = d_sorted_idx.subspan(begin, static_cast<size_t>(n));
-    float thresh = leaf_cdf.back() * alpha;
 
-    size_t idx = thrust::lower_bound(thrust::seq, leaf_cdf.data(),
+template <typename SegIt, typename ValIt, typename WIter>
-                                     leaf_cdf.data() + leaf_cdf.size(), thresh) -
+void SegmentedWeightedQuantile(Context const* ctx, double alpha, SegIt seg_beg, SegIt seg_end,
-                 leaf_cdf.data();
+                               ValIt val_begin, ValIt val_end, WIter w_begin, WIter w_end,
-    idx = std::min(idx, static_cast<size_t>(n - 1));
+                               HostDeviceVector<float>* quantiles) {
-    d_results[i] = val_begin[leaf_sorted_idx[idx]];
+  CHECK(alpha >= 0 && alpha <= 1);
-  });
+  return SegmentedWeightedQuantile(ctx, thrust::make_constant_iterator(alpha), seg_beg, seg_end,
+                                   val_begin, val_end, w_begin, w_end, quantiles);
 }
 }  // namespace common
 }  // namespace xgboost

tests/cpp/common/test_stats.cu

@@ -1,79 +1,155 @@
-/*!
+/**
- * Copyright 2022 by XGBoost Contributors
+ * Copyright 2022-2023 by XGBoost Contributors
  */
 #include <gtest/gtest.h>
 
-#include <utility>
+#include <cstddef>                            // std::size_t
-#include <vector>
+#include <utility>                            // std::pair
+#include <vector>                             // std::vector
 
+#include "../../../src/common/linalg_op.cuh"  // ElementWiseTransformDevice
 #include "../../../src/common/stats.cuh"
-#include "../../../src/common/stats.h"
+#include "xgboost/base.h"                     // XGBOOST_DEVICE
-#include "xgboost/base.h"
+#include "xgboost/context.h"                  // Context
-#include "xgboost/context.h"
+#include "xgboost/host_device_vector.h"       // HostDeviceVector
-#include "xgboost/host_device_vector.h"
+#include "xgboost/linalg.h"                   // Tensor
-#include "xgboost/linalg.h"
 
 namespace xgboost {
 namespace common {
 namespace {
 class StatsGPU : public ::testing::Test {
  private:
-  linalg::Tensor<float, 1> arr_{
+  linalg::Tensor<float, 1> arr_{{1.f, 2.f, 3.f, 4.f, 5.f, 2.f, 4.f, 5.f, 3.f, 1.f}, {10}, 0};
-      {1.f, 2.f, 3.f, 4.f, 5.f,
+  linalg::Tensor<std::size_t, 1> indptr_{{0, 5, 10}, {3}, 0};
-       2.f, 4.f, 5.f, 3.f, 1.f},
+  HostDeviceVector<float> results_;
-      {10}, 0};
-  linalg::Tensor<size_t, 1> indptr_{{0, 5, 10}, {3}, 0};
-  HostDeviceVector<float> resutls_;
   using TestSet = std::vector<std::pair<float, float>>;
   Context ctx_;
 
   void Check(float expected) {
-    auto const& h_results = resutls_.HostVector();
+    auto const& h_results = results_.HostVector();
     ASSERT_EQ(h_results.size(), indptr_.Size() - 1);
     ASSERT_EQ(h_results.front(), expected);
-    EXPECT_EQ(h_results.back(), expected);
+    ASSERT_EQ(h_results.back(), expected);
   }
 
  public:
   void SetUp() override { ctx_.gpu_id = 0; }
+
+  void WeightedMulti() {
+    // data for one segment
+    std::vector<float> seg{1.f, 2.f, 3.f, 4.f, 5.f};
+    auto seg_size = seg.size();
+
+    // 3 segments
+    std::vector<float> data;
+    data.insert(data.cend(), seg.begin(), seg.end());
+    data.insert(data.cend(), seg.begin(), seg.end());
+    data.insert(data.cend(), seg.begin(), seg.end());
+    linalg::Tensor<float, 1> arr{data.cbegin(), data.cend(), {data.size()}, 0};
+    auto d_arr = arr.View(0);
+
+    auto key_it = dh::MakeTransformIterator<std::size_t>(
+        thrust::make_counting_iterator(0ul),
+        [=] XGBOOST_DEVICE(std::size_t i) { return i * seg_size; });
+    auto val_it =
+        dh::MakeTransformIterator<float>(thrust::make_counting_iterator(0ul),
+                                         [=] XGBOOST_DEVICE(std::size_t i) { return d_arr(i); });
+
+    // one alpha for each segment
+    HostDeviceVector<float> alphas{0.0f, 0.5f, 1.0f};
+    alphas.SetDevice(0);
+    auto d_alphas = alphas.ConstDeviceSpan();
+    auto w_it = thrust::make_constant_iterator(0.1f);
+    SegmentedWeightedQuantile(&ctx_, d_alphas.data(), key_it, key_it + d_alphas.size() + 1, val_it,
+                              val_it + d_arr.Size(), w_it, w_it + d_arr.Size(), &results_);
+
+    auto const& h_results = results_.HostVector();
+    ASSERT_EQ(1.0f, h_results[0]);
+    ASSERT_EQ(3.0f, h_results[1]);
+    ASSERT_EQ(5.0f, h_results[2]);
+  }
+
   void Weighted() {
     auto d_arr = arr_.View(0);
     auto d_key = indptr_.View(0);
 
-    auto key_it = dh::MakeTransformIterator<size_t>(thrust::make_counting_iterator(0ul),
+    auto key_it = dh::MakeTransformIterator<std::size_t>(
-                                                    [=] __device__(size_t i) { return d_key(i); });
+        thrust::make_counting_iterator(0ul),
-    auto val_it = dh::MakeTransformIterator<float>(
+        [=] XGBOOST_DEVICE(std::size_t i) { return d_key(i); });
-        thrust::make_counting_iterator(0ul), [=] XGBOOST_DEVICE(size_t i) { return d_arr(i); });
+    auto val_it =
+        dh::MakeTransformIterator<float>(thrust::make_counting_iterator(0ul),
+                                         [=] XGBOOST_DEVICE(std::size_t i) { return d_arr(i); });
     linalg::Tensor<float, 1> weights{{10}, 0};
     linalg::ElementWiseTransformDevice(weights.View(0),
-                                       [=] XGBOOST_DEVICE(size_t, float) { return 1.0; });
+                                       [=] XGBOOST_DEVICE(std::size_t, float) { return 1.0; });
     auto w_it = weights.Data()->ConstDevicePointer();
     for (auto const& pair : TestSet{{0.0f, 1.0f}, {0.5f, 3.0f}, {1.0f, 5.0f}}) {
       SegmentedWeightedQuantile(&ctx_, pair.first, key_it, key_it + indptr_.Size(), val_it,
-                                val_it + arr_.Size(), w_it, w_it + weights.Size(), &resutls_);
+                                val_it + arr_.Size(), w_it, w_it + weights.Size(), &results_);
       this->Check(pair.second);
     }
   }
 
+  void NonWeightedMulti() {
+    // data for one segment
+    std::vector<float> seg{20.f, 15.f, 50.f, 40.f, 35.f};
+    auto seg_size = seg.size();
+
+    // 3 segments
+    std::vector<float> data;
+    data.insert(data.cend(), seg.begin(), seg.end());
+    data.insert(data.cend(), seg.begin(), seg.end());
+    data.insert(data.cend(), seg.begin(), seg.end());
+    linalg::Tensor<float, 1> arr{data.cbegin(), data.cend(), {data.size()}, 0};
+    auto d_arr = arr.View(0);
+
+    auto key_it = dh::MakeTransformIterator<std::size_t>(
+        thrust::make_counting_iterator(0ul),
+        [=] XGBOOST_DEVICE(std::size_t i) { return i * seg_size; });
+    auto val_it =
+        dh::MakeTransformIterator<float>(thrust::make_counting_iterator(0ul),
+                                         [=] XGBOOST_DEVICE(std::size_t i) { return d_arr(i); });
+
+    // one alpha for each segment
+    HostDeviceVector<float> alphas{0.1f, 0.2f, 0.4f};
+    alphas.SetDevice(0);
+    auto d_alphas = alphas.ConstDeviceSpan();
+    SegmentedQuantile(&ctx_, d_alphas.data(), key_it, key_it + d_alphas.size() + 1, val_it,
+                      val_it + d_arr.Size(), &results_);
+
+    auto const& h_results = results_.HostVector();
+    EXPECT_EQ(15.0f, h_results[0]);
+    EXPECT_EQ(16.0f, h_results[1]);
+    ASSERT_EQ(26.0f, h_results[2]);
+  }
+
   void NonWeighted() {
     auto d_arr = arr_.View(0);
     auto d_key = indptr_.View(0);
 
-    auto key_it = dh::MakeTransformIterator<size_t>(thrust::make_counting_iterator(0ul),
+    auto key_it = dh::MakeTransformIterator<std::size_t>(
-                                                    [=] __device__(size_t i) { return d_key(i); });
+        thrust::make_counting_iterator(0ul), [=] __device__(std::size_t i) { return d_key(i); });
-    auto val_it = dh::MakeTransformIterator<float>(
+    auto val_it =
-        thrust::make_counting_iterator(0ul), [=] XGBOOST_DEVICE(size_t i) { return d_arr(i); });
+        dh::MakeTransformIterator<float>(thrust::make_counting_iterator(0ul),
+                                         [=] XGBOOST_DEVICE(std::size_t i) { return d_arr(i); });
 
     for (auto const& pair : TestSet{{0.0f, 1.0f}, {0.5f, 3.0f}, {1.0f, 5.0f}}) {
       SegmentedQuantile(&ctx_, pair.first, key_it, key_it + indptr_.Size(), val_it,
-                        val_it + arr_.Size(), &resutls_);
+                        val_it + arr_.Size(), &results_);
       this->Check(pair.second);
     }
   }
 };
 }  // anonymous namespace
 
-TEST_F(StatsGPU, Quantile) { this->NonWeighted(); }
+TEST_F(StatsGPU, Quantile) {
-TEST_F(StatsGPU, WeightedQuantile) { this->Weighted(); }
+  this->NonWeighted();
+  this->NonWeightedMulti();
+}
+
+TEST_F(StatsGPU, WeightedQuantile) {
+  this->Weighted();
+  this->WeightedMulti();
+}
 }  // namespace common
 }  // namespace xgboost