Rework the precision metric. (#9222)

- Rework the precision metric for both CPU and GPU.
- Mention it in the document.
- Cleanup old support code for GPU ranking metric.
- Deterministic GPU implementation.

* Drop support for classification.

* type.

* use batch shape.

* lint.

* cpu build.

* cpu build.

* lint.

* Tests.

* Fix.

* Cleanup error message.

R-package/R/callbacks.R

@@ -319,7 +319,7 @@ cb.early.stop <- function(stopping_rounds, maximize = FALSE,
 
     # maximize is usually NULL when not set in xgb.train and built-in metrics
     if (is.null(maximize))
-      maximize <<- grepl('(_auc|_map|_ndcg)', metric_name)
+      maximize <<- grepl('(_auc|_map|_ndcg|_pre)', metric_name)
 
     if (verbose && NVL(env$rank, 0) == 0)
       cat("Will train until ", metric_name, " hasn't improved in ",

doc/parameter.rst

@@ -424,6 +424,7 @@ Specify the learning task and the corresponding learning objective. The objectiv
 
       After XGBoost 1.6, both of the requirements and restrictions for using ``aucpr`` in classification problem are similar to ``auc``.  For ranking task, only binary relevance label :math:`y \in [0, 1]` is supported.  Different from ``map (mean average precision)``, ``aucpr`` calculates the *interpolated* area under precision recall curve using continuous interpolation.
 
+    - ``pre``: Precision at :math:`k`. Supports only learning to rank task.
     - ``ndcg``: `Normalized Discounted Cumulative Gain <http://en.wikipedia.org/wiki/NDCG>`_
     - ``map``: `Mean Average Precision <http://en.wikipedia.org/wiki/Mean_average_precision#Mean_average_precision>`_
 
@@ -435,7 +436,7 @@ Specify the learning task and the corresponding learning objective. The objectiv
 
       where :math:`I_{(k)}` is an indicator function that equals to :math:`1` when the document at :math:`k` is relevant and :math:`0` otherwise. The :math:`P@k` is the precision at :math:`k`, and :math:`N` is the total number of relevant documents. Lastly, the `mean average precision` is defined as the weighted average across all queries.
 
-    - ``ndcg@n``, ``map@n``: :math:`n` can be assigned as an integer to cut off the top positions in the lists for evaluation.
+    - ``ndcg@n``, ``map@n``, ``pre@n``: :math:`n` can be assigned as an integer to cut off the top positions in the lists for evaluation.
     - ``ndcg-``, ``map-``, ``ndcg@n-``, ``map@n-``: In XGBoost, the NDCG and MAP evaluate the score of a list without any positive samples as :math:`1`. By appending "-" to the evaluation metric name, we can ask XGBoost to evaluate these scores as :math:`0` to be consistent under some conditions.
     - ``poisson-nloglik``: negative log-likelihood for Poisson regression
     - ``gamma-nloglik``: negative log-likelihood for gamma regression

python-package/xgboost/callback.py

@@ -372,6 +372,8 @@ class EarlyStopping(TrainingCallback):
             maximize_metrics = (
                 "auc",
                 "aucpr",
+                "pre",
+                "pre@",
                 "map",
                 "ndcg",
                 "auc@",

python-package/xgboost/testing/metrics.py

@@ -1,9 +1,61 @@
 """Tests for evaluation metrics."""
-from typing import Dict
+from typing import Dict, List
 
 import numpy as np
+import pytest
 
 import xgboost as xgb
+from xgboost.compat import concat
+from xgboost.core import _parse_eval_str
+
+
+def check_precision_score(tree_method: str) -> None:
+    """Test for precision with ranking and classification."""
+    datasets = pytest.importorskip("sklearn.datasets")
+
+    X, y = datasets.make_classification(
+        n_samples=1024, n_features=4, n_classes=2, random_state=2023
+    )
+    qid = np.zeros(shape=y.shape)  # same group
+
+    ltr = xgb.XGBRanker(n_estimators=2, tree_method=tree_method)
+    ltr.fit(X, y, qid=qid)
+
+    # re-generate so that XGBoost doesn't evaluate the result to 1.0
+    X, y = datasets.make_classification(
+        n_samples=512, n_features=4, n_classes=2, random_state=1994
+    )
+
+    ltr.set_params(eval_metric="pre@32")
+    result = _parse_eval_str(
+        ltr.get_booster().eval_set(evals=[(xgb.DMatrix(X, y), "Xy")])
+    )
+    score_0 = result[1][1]
+
+    X_list = []
+    y_list = []
+    n_query_groups = 3
+    q_list: List[np.ndarray] = []
+    for i in range(n_query_groups):
+        # same for all groups
+        X, y = datasets.make_classification(
+            n_samples=512, n_features=4, n_classes=2, random_state=1994
+        )
+        X_list.append(X)
+        y_list.append(y)
+        q = np.full(shape=y.shape, fill_value=i, dtype=np.uint64)
+        q_list.append(q)
+
+    qid = concat(q_list)
+    X = concat(X_list)
+    y = concat(y_list)
+
+    result = _parse_eval_str(
+        ltr.get_booster().eval_set(evals=[(xgb.DMatrix(X, y, qid=qid), "Xy")])
+    )
+    assert result[1][0].endswith("pre@32")
+    score_1 = result[1][1]
+    assert score_1 == score_0
 
 
 def check_quantile_error(tree_method: str) -> None:

src/common/device_helpers.cuh

@@ -825,176 +825,6 @@ XGBOOST_DEVICE auto tcrend(xgboost::common::Span<T> const &span) {  // NOLINT
   return tcrbegin(span) + span.size();
 }
 
-// This type sorts an array which is divided into multiple groups. The sorting is influenced
-// by the function object 'Comparator'
-template <typename T>
-class SegmentSorter {
- private:
-  // Items sorted within the group
-  caching_device_vector<T> ditems_;
-
-  // Original position of the items before they are sorted descending within their groups
-  caching_device_vector<uint32_t> doriginal_pos_;
-
-  // Segments within the original list that delineates the different groups
-  caching_device_vector<uint32_t> group_segments_;
-
-  // Need this on the device as it is used in the kernels
-  caching_device_vector<uint32_t> dgroups_;       // Group information on device
-
-  // Where did the item that was originally present at position 'x' move to after they are sorted
-  caching_device_vector<uint32_t> dindexable_sorted_pos_;
-
-  // Initialize everything but the segments
-  void Init(uint32_t num_elems) {
-    ditems_.resize(num_elems);
-
-    doriginal_pos_.resize(num_elems);
-    thrust::sequence(doriginal_pos_.begin(), doriginal_pos_.end());
-  }
-
-  // Initialize all with group info
-  void Init(const std::vector<uint32_t> &groups) {
-    uint32_t num_elems = groups.back();
-    this->Init(num_elems);
-    this->CreateGroupSegments(groups);
-  }
-
- public:
-  // This needs to be public due to device lambda
-  void CreateGroupSegments(const std::vector<uint32_t> &groups) {
-    uint32_t num_elems = groups.back();
-    group_segments_.resize(num_elems, 0);
-
-    dgroups_ = groups;
-
-    if (GetNumGroups() == 1) return;  // There are no segments; hence, no need to compute them
-
-    // Define the segments by assigning a group ID to each element
-    const uint32_t *dgroups = dgroups_.data().get();
-    uint32_t ngroups = dgroups_.size();
-    auto ComputeGroupIDLambda = [=] __device__(uint32_t idx) {
-      return thrust::upper_bound(thrust::seq, dgroups, dgroups + ngroups, idx) -
-             dgroups - 1;
-    };  // NOLINT
-
-    thrust::transform(thrust::make_counting_iterator(static_cast<uint32_t>(0)),
-                      thrust::make_counting_iterator(num_elems),
-                      group_segments_.begin(),
-                      ComputeGroupIDLambda);
-  }
-
-  // Accessors that returns device pointer
-  inline uint32_t GetNumItems() const { return ditems_.size(); }
-  inline const xgboost::common::Span<const T> GetItemsSpan() const {
-    return { ditems_.data().get(), ditems_.size() };
-  }
-
-  inline const xgboost::common::Span<const uint32_t> GetOriginalPositionsSpan() const {
-    return { doriginal_pos_.data().get(), doriginal_pos_.size() };
-  }
-
-  inline const xgboost::common::Span<const uint32_t> GetGroupSegmentsSpan() const {
-    return { group_segments_.data().get(), group_segments_.size() };
-  }
-
-  inline uint32_t GetNumGroups() const { return dgroups_.size() - 1; }
-  inline const xgboost::common::Span<const uint32_t> GetGroupsSpan() const {
-    return { dgroups_.data().get(), dgroups_.size() };
-  }
-
-  inline const xgboost::common::Span<const uint32_t> GetIndexableSortedPositionsSpan() const {
-    return { dindexable_sorted_pos_.data().get(), dindexable_sorted_pos_.size() };
-  }
-
-  // Sort an array that is divided into multiple groups. The array is sorted within each group.
-  // This version provides the group information that is on the host.
-  // The array is sorted based on an adaptable binary predicate. By default a stateless predicate
-  // is used.
-  template <typename Comparator = thrust::greater<T>>
-  void SortItems(const T *ditems, uint32_t item_size, const std::vector<uint32_t> &groups,
-                 const Comparator &comp = Comparator()) {
-    this->Init(groups);
-    this->SortItems(ditems, item_size, this->GetGroupSegmentsSpan(), comp);
-  }
-
-  // Sort an array that is divided into multiple groups. The array is sorted within each group.
-  // This version provides the group information that is on the device.
-  // The array is sorted based on an adaptable binary predicate. By default a stateless predicate
-  // is used.
-  template <typename Comparator = thrust::greater<T>>
-  void SortItems(const T *ditems, uint32_t item_size,
-                 const xgboost::common::Span<const uint32_t> &group_segments,
-                 const Comparator &comp = Comparator()) {
-    this->Init(item_size);
-
-    // Sort the items that are grouped. We would like to avoid using predicates to perform the sort,
-    // as thrust resorts to using a merge sort as opposed to a much much faster radix sort
-    // when comparators are used. Hence, the following algorithm is used. This is done so that
-    // we can grab the appropriate related values from the original list later, after the
-    // items are sorted.
-    //
-    // Here is the internal representation:
-    // dgroups_:          [ 0, 3, 5, 8, 10 ]
-    // group_segments_:   0 0 0 | 1 1 | 2 2 2 | 3 3
-    // doriginal_pos_:    0 1 2 | 3 4 | 5 6 7 | 8 9
-    // ditems_:           1 0 1 | 2 1 | 1 3 3 | 4 4 (from original items)
-    //
-    // Sort the items first and make a note of the original positions in doriginal_pos_
-    // based on the sort
-    // ditems_:           4 4 3 3 2 1 1 1 1 0
-    // doriginal_pos_:    8 9 6 7 3 0 2 4 5 1
-    // NOTE: This consumes space, but is much faster than some of the other approaches - sorting
-    //       in kernel, sorting using predicates etc.
-
-    ditems_.assign(thrust::device_ptr<const T>(ditems),
-                   thrust::device_ptr<const T>(ditems) + item_size);
-
-    // Allocator to be used by sort for managing space overhead while sorting
-    dh::XGBCachingDeviceAllocator<char> alloc;
-
-    thrust::stable_sort_by_key(thrust::cuda::par(alloc),
-                               ditems_.begin(), ditems_.end(),
-                               doriginal_pos_.begin(), comp);
-
-    if (GetNumGroups() == 1) return;  // The entire array is sorted, as it isn't segmented
-
-    // Next, gather the segments based on the doriginal_pos_. This is to reflect the
-    // holisitic item sort order on the segments
-    // group_segments_c_:   3 3 2 2 1 0 0 1 2 0
-    // doriginal_pos_:      8 9 6 7 3 0 2 4 5 1 (stays the same)
-    caching_device_vector<uint32_t> group_segments_c(item_size);
-    thrust::gather(doriginal_pos_.begin(), doriginal_pos_.end(),
-                   dh::tcbegin(group_segments), group_segments_c.begin());
-
-    // Now, sort the group segments so that you may bring the items within the group together,
-    // in the process also noting the relative changes to the doriginal_pos_ while that happens
-    // group_segments_c_:   0 0 0 1 1 2 2 2 3 3
-    // doriginal_pos_:      0 2 1 3 4 6 7 5 8 9
-    thrust::stable_sort_by_key(thrust::cuda::par(alloc),
-                               group_segments_c.begin(), group_segments_c.end(),
-                               doriginal_pos_.begin(), thrust::less<uint32_t>());
-
-    // Finally, gather the original items based on doriginal_pos_ to sort the input and
-    // to store them in ditems_
-    // doriginal_pos_:      0 2 1 3 4 6 7 5 8 9  (stays the same)
-    // ditems_:             1 1 0 2 1 3 3 1 4 4  (from unsorted items - ditems)
-    thrust::gather(doriginal_pos_.begin(), doriginal_pos_.end(),
-                   thrust::device_ptr<const T>(ditems), ditems_.begin());
-  }
-
-  // Determine where an item that was originally present at position 'x' has been relocated to
-  // after a sort. Creation of such an index has to be explicitly requested after a sort
-  void CreateIndexableSortedPositions() {
-    dindexable_sorted_pos_.resize(GetNumItems());
-    thrust::scatter(thrust::make_counting_iterator(static_cast<uint32_t>(0)),
-                    thrust::make_counting_iterator(GetNumItems()),  // Rearrange indices...
-                    // ...based on this map
-                    dh::tcbegin(GetOriginalPositionsSpan()),
-                    dindexable_sorted_pos_.begin());  // Write results into this
-  }
-};
-
 // Atomic add function for gradients
 template <typename OutputGradientT, typename InputGradientT>
 XGBOOST_DEV_INLINE void AtomicAddGpair(OutputGradientT* dest,

src/common/optional_weight.h

@@ -8,8 +8,7 @@
 #include "xgboost/host_device_vector.h"  // HostDeviceVector
 #include "xgboost/span.h"                // Span
 
-namespace xgboost {
+namespace xgboost::common {
-namespace common {
 struct OptionalWeights {
   Span<float const> weights;
   float dft{1.0f};  // fixme: make this compile time constant
@@ -18,7 +17,8 @@ struct OptionalWeights {
   explicit OptionalWeights(float w) : dft{w} {}
 
   XGBOOST_DEVICE float operator[](size_t i) const { return weights.empty() ? dft : weights[i]; }
-  auto Empty() const { return weights.empty(); }
+  [[nodiscard]] auto Empty() const { return weights.empty(); }
+  [[nodiscard]] auto Size() const { return weights.size(); }
 };
 
 inline OptionalWeights MakeOptionalWeights(Context const* ctx,
@@ -28,6 +28,5 @@ inline OptionalWeights MakeOptionalWeights(Context const* ctx,
   }
   return OptionalWeights{ctx->IsCPU() ? weights.ConstHostSpan() : weights.ConstDeviceSpan()};
 }
-}  // namespace common
+}  // namespace xgboost::common
-}  // namespace xgboost
 #endif  // XGBOOST_COMMON_OPTIONAL_WEIGHT_H_

src/common/quantile.cc

@@ -90,6 +90,9 @@ void HostSketchContainer::PushAdapterBatch(Batch const &batch, size_t base_rowid
                                            MetaInfo const &info, float missing) {
   auto const &h_weights =
       (use_group_ind_ ? detail::UnrollGroupWeights(info) : info.weights_.HostVector());
+  if (!use_group_ind_ && !h_weights.empty()) {
+    CHECK_EQ(h_weights.size(), batch.Size()) << "Invalid size of sample weight.";
+  }
 
   auto is_valid = data::IsValidFunctor{missing};
   auto weights = OptionalWeights{Span<float const>{h_weights}};

src/common/quantile.h

@@ -19,12 +19,12 @@
 
 #include "categorical.h"
 #include "common.h"
+#include "error_msg.h"        // GroupWeight
 #include "optional_weight.h"  // OptionalWeights
 #include "threading_utils.h"
 #include "timer.h"
 
-namespace xgboost {
+namespace xgboost::common {
-namespace common {
 /*!
  * \brief experimental wsummary
  * \tparam DType type of data content
@@ -695,13 +695,18 @@ inline std::vector<float> UnrollGroupWeights(MetaInfo const &info) {
     return group_weights;
   }
 
-  size_t n_samples = info.num_row_;
   auto const &group_ptr = info.group_ptr_;
-  std::vector<float> results(n_samples);
   CHECK_GE(group_ptr.size(), 2);
-  CHECK_EQ(group_ptr.back(), n_samples);
+
+  auto n_groups = group_ptr.size() - 1;
+  CHECK_EQ(info.weights_.Size(), n_groups) << error::GroupWeight();
+
+  bst_row_t n_samples = info.num_row_;
+  std::vector<float> results(n_samples);
+  CHECK_EQ(group_ptr.back(), n_samples)
+      << error::GroupSize() << " the number of rows from the data.";
   size_t cur_group = 0;
-  for (size_t i = 0; i < n_samples; ++i) {
+  for (bst_row_t i = 0; i < n_samples; ++i) {
     results[i] = group_weights[cur_group];
     if (i == group_ptr[cur_group + 1]) {
       cur_group++;
@@ -1010,6 +1015,5 @@ class SortedSketchContainer : public SketchContainerImpl<WXQuantileSketch<float,
    */
   void PushColPage(SparsePage const &page, MetaInfo const &info, Span<float const> hessian);
 };
-}  // namespace common
+}  // namespace xgboost::common
-}  // namespace xgboost
 #endif  // XGBOOST_COMMON_QUANTILE_H_

src/common/ranking_utils.cc

@@ -114,9 +114,20 @@ void NDCGCache::InitOnCUDA(Context const*, MetaInfo const&) { common::AssertGPUS
 
 DMLC_REGISTER_PARAMETER(LambdaRankParam);
 
+void PreCache::InitOnCPU(Context const*, MetaInfo const& info) {
+  auto const& h_label = info.labels.HostView().Slice(linalg::All(), 0);
+  CheckPreLabels("pre", h_label,
+                 [](auto beg, auto end, auto op) { return std::all_of(beg, end, op); });
+}
+
+#if !defined(XGBOOST_USE_CUDA)
+void PreCache::InitOnCUDA(Context const*, MetaInfo const&) { common::AssertGPUSupport(); }
+#endif  // !defined(XGBOOST_USE_CUDA)
+
 void MAPCache::InitOnCPU(Context const*, MetaInfo const& info) {
   auto const& h_label = info.labels.HostView().Slice(linalg::All(), 0);
-  CheckMapLabels(h_label, [](auto beg, auto end, auto op) { return std::all_of(beg, end, op); });
+  CheckPreLabels("map", h_label,
+                 [](auto beg, auto end, auto op) { return std::all_of(beg, end, op); });
 }
 
 #if !defined(XGBOOST_USE_CUDA)

src/common/ranking_utils.cu

@@ -205,8 +205,13 @@ void NDCGCache::InitOnCUDA(Context const* ctx, MetaInfo const& info) {
               [=] XGBOOST_DEVICE(std::size_t i) { d_discount[i] = CalcDCGDiscount(i); });
 }
 
+void PreCache::InitOnCUDA(Context const* ctx, MetaInfo const& info) {
+  auto const d_label = info.labels.View(ctx->gpu_id).Slice(linalg::All(), 0);
+  CheckPreLabels("pre", d_label, CheckMAPOp{ctx->CUDACtx()});
+}
+
 void MAPCache::InitOnCUDA(Context const* ctx, MetaInfo const& info) {
   auto const d_label = info.labels.View(ctx->gpu_id).Slice(linalg::All(), 0);
-  CheckMapLabels(d_label, CheckMAPOp{ctx->CUDACtx()});
+  CheckPreLabels("map", d_label, CheckMAPOp{ctx->CUDACtx()});
 }
 }  // namespace xgboost::ltr

src/common/ranking_utils.h

@@ -366,18 +366,43 @@ bool IsBinaryRel(linalg::VectorView<float const> label, AllOf all_of) {
   });
 }
 /**
- * \brief Validate label for MAP
+ * \brief Validate label for precision-based metric.
  *
  * \tparam Implementation of std::all_of. Specified as a parameter to reuse the check for
  *         both CPU and GPU.
  */
 template <typename AllOf>
-void CheckMapLabels(linalg::VectorView<float const> label, AllOf all_of) {
+void CheckPreLabels(StringView name, linalg::VectorView<float const> label, AllOf all_of) {
   auto s_label = label.Values();
   auto is_binary = IsBinaryRel(label, all_of);
-  CHECK(is_binary) << "MAP can only be used with binary labels.";
+  CHECK(is_binary) << name << " can only be used with binary labels.";
 }
 
+class PreCache : public RankingCache {
+  HostDeviceVector<double> pre_;
+
+  void InitOnCPU(Context const* ctx, MetaInfo const& info);
+  void InitOnCUDA(Context const* ctx, MetaInfo const& info);
+
+ public:
+  PreCache(Context const* ctx, MetaInfo const& info, LambdaRankParam const& p)
+      : RankingCache{ctx, info, p} {
+    if (ctx->IsCPU()) {
+      this->InitOnCPU(ctx, info);
+    } else {
+      this->InitOnCUDA(ctx, info);
+    }
+  }
+
+  common::Span<double> Pre(Context const* ctx) {
+    if (pre_.Empty()) {
+      pre_.SetDevice(ctx->gpu_id);
+      pre_.Resize(this->Groups());
+    }
+    return ctx->IsCPU() ? pre_.HostSpan() : pre_.DeviceSpan();
+  }
+};
+
 class MAPCache : public RankingCache {
   // Total number of relevant documents for each group
   HostDeviceVector<double> n_rel_;

src/data/iterative_dmatrix.cc

@@ -366,8 +366,8 @@ inline void IterativeDMatrix::InitFromCUDA(Context const*, BatchParam const&, Da
   common::AssertGPUSupport();
 }
 
-inline BatchSet<EllpackPage> IterativeDMatrix::GetEllpackBatches(Context const* ctx,
+inline BatchSet<EllpackPage> IterativeDMatrix::GetEllpackBatches(Context const*,
-                                                                 BatchParam const& param) {
+                                                                 BatchParam const&) {
   common::AssertGPUSupport();
   auto begin_iter = BatchIterator<EllpackPage>(new SimpleBatchIteratorImpl<EllpackPage>(ellpack_));
   return BatchSet<EllpackPage>(BatchIterator<EllpackPage>(begin_iter));

src/metric/metric.cc

@@ -52,32 +52,13 @@ Metric::Create(const std::string& name, Context const* ctx) {
   metric->ctx_ = ctx;
   return metric;
 }
-
-GPUMetric* GPUMetric::CreateGPUMetric(const std::string& name, Context const* ctx) {
-  auto metric = CreateMetricImpl<MetricGPUReg>(name);
-  if (metric == nullptr) {
-    LOG(WARNING) << "Cannot find a GPU metric builder for metric " << name
-                 << ". Resorting to the CPU builder";
-    return nullptr;
-  }
-
-  // Narrowing reference only for the compiler to allow assignment to a base class member.
-  // As such, using this narrowed reference to refer to derived members will be an illegal op.
-  // This is moot, as this type is stateless.
-  auto casted = static_cast<GPUMetric*>(metric);
-  CHECK(casted);
-  casted->ctx_ = ctx;
-  return casted;
-}
 }  // namespace xgboost
 
 namespace dmlc {
 DMLC_REGISTRY_ENABLE(::xgboost::MetricReg);
-DMLC_REGISTRY_ENABLE(::xgboost::MetricGPUReg);
 }
 
-namespace xgboost {
+namespace xgboost::metric {
-namespace metric {
 // List of files that will be force linked in static links.
 DMLC_REGISTRY_LINK_TAG(auc);
 DMLC_REGISTRY_LINK_TAG(elementwise_metric);
@@ -88,5 +69,4 @@ DMLC_REGISTRY_LINK_TAG(rank_metric);
 DMLC_REGISTRY_LINK_TAG(auc_gpu);
 DMLC_REGISTRY_LINK_TAG(rank_metric_gpu);
 #endif
-}  // namespace metric
+}  // namespace xgboost::metric
-}  // namespace xgboost

src/metric/metric_common.h

@@ -23,53 +23,14 @@ class MetricNoCache : public Metric {
 
   double Evaluate(HostDeviceVector<float> const &predts, std::shared_ptr<DMatrix> p_fmat) final {
     double result{0.0};
-    auto const& info = p_fmat->Info();
+    auto const &info = p_fmat->Info();
-    collective::ApplyWithLabels(info, &result, sizeof(double), [&] {
+    collective::ApplyWithLabels(info, &result, sizeof(double),
-      result = this->Eval(predts, info);
+                                [&] { result = this->Eval(predts, info); });
-    });
     return result;
   }
 };
 
-// This creates a GPU metric instance dynamically and adds it to the GPU metric registry, if not
-// present already. This is created when there is a device ordinal present and if xgboost
-// is compiled with CUDA support
-struct GPUMetric : public MetricNoCache {
-  static GPUMetric *CreateGPUMetric(const std::string &name, Context const *tparam);
-};
-
-/*!
- * \brief Internal registry entries for GPU Metric factory functions.
- *  The additional parameter const char* param gives the value after @, can be null.
- *  For example, metric map@3, then: param == "3".
- */
-struct MetricGPUReg
-  : public dmlc::FunctionRegEntryBase<MetricGPUReg,
-                                      std::function<Metric * (const char*)> > {
-};
-
-/*!
- * \brief Macro to register metric computed on GPU.
- *
- * \code
- * // example of registering a objective ndcg@k
- * XGBOOST_REGISTER_GPU_METRIC(NDCG_GPU, "ndcg")
- * .describe("NDCG metric computer on GPU.")
- * .set_body([](const char* param) {
- *     int at_k = atoi(param);
- *     return new NDCG(at_k);
- *   });
- * \endcode
- */
-
-// Note: Metric names registered in the GPU registry should follow this convention:
-// - GPU metric types should be registered with the same name as the non GPU metric types
-#define XGBOOST_REGISTER_GPU_METRIC(UniqueId, Name)                         \
-  ::xgboost::MetricGPUReg&  __make_ ## MetricGPUReg ## _ ## UniqueId ## __ =  \
-      ::dmlc::Registry< ::xgboost::MetricGPUReg>::Get()->__REGISTER__(Name)
-
 namespace metric {
-
 // Ranking config to be used on device and host
 struct EvalRankConfig {
  public:
@@ -81,8 +42,8 @@ struct EvalRankConfig {
 };
 
 class PackedReduceResult {
-  double residue_sum_ { 0 };
+  double residue_sum_{0};
-  double weights_sum_ { 0 };
+  double weights_sum_{0};
 
  public:
   XGBOOST_DEVICE PackedReduceResult() {}  // NOLINT
@@ -91,16 +52,15 @@ class PackedReduceResult {
 
   XGBOOST_DEVICE
   PackedReduceResult operator+(PackedReduceResult const &other) const {
-    return PackedReduceResult{residue_sum_ + other.residue_sum_,
+    return PackedReduceResult{residue_sum_ + other.residue_sum_, weights_sum_ + other.weights_sum_};
-                              weights_sum_ + other.weights_sum_};
   }
   PackedReduceResult &operator+=(PackedReduceResult const &other) {
     this->residue_sum_ += other.residue_sum_;
     this->weights_sum_ += other.weights_sum_;
     return *this;
   }
-  double Residue() const { return residue_sum_; }
+  [[nodiscard]] double Residue() const { return residue_sum_; }
-  double Weights() const { return weights_sum_; }
+  [[nodiscard]] double Weights() const { return weights_sum_; }
 };
 
 }  // namespace metric

src/metric/rank_metric.cc

@@ -1,25 +1,6 @@
 /**
  * Copyright 2020-2023 by XGBoost contributors
  */
-// When device ordinal is present, we would want to build the metrics on the GPU. It is *not*
-// possible for a valid device ordinal to be present for non GPU builds. However, it is possible
-// for an invalid device ordinal to be specified in GPU builds - to train/predict and/or compute
-// the metrics on CPU. To accommodate these scenarios, the following is done for the metrics
-// accelerated on the GPU.
-// - An internal GPU registry holds all the GPU metric types (defined in the .cu file)
-// - An instance of the appropriate GPU metric type is created when a device ordinal is present
-// - If the creation is successful, the metric computation is done on the device
-// - else, it falls back on the CPU
-// - The GPU metric types are *only* registered when xgboost is built for GPUs
-//
-// This is done for 2 reasons:
-// - Clear separation of CPU and GPU logic
-// - Sorting datasets containing large number of rows is (much) faster when parallel sort
-//   semantics is used on the CPU. The __gnu_parallel/concurrency primitives needed to perform
-//   this cannot be used when the translation unit is compiled using the 'nvcc' compiler (as the
-//   corresponding headers that brings in those function declaration can't be included with CUDA).
-//   This precludes the CPU and GPU logic to coexist inside a .cu file
-
 #include "rank_metric.h"
 
 #include <dmlc/omp.h>
@@ -57,55 +38,8 @@
 #include "xgboost/string_view.h"             // for StringView
 
 namespace {
-
 using PredIndPair = std::pair<xgboost::bst_float, xgboost::ltr::rel_degree_t>;
 using PredIndPairContainer = std::vector<PredIndPair>;
-
-/*
- * Adapter to access instance weights.
- *
- *  - For ranking task, weights are per-group
- *  - For binary classification task, weights are per-instance
- *
- * WeightPolicy::GetWeightOfInstance() :
- *   get weight associated with an individual instance, using index into
- *   `info.weights`
- * WeightPolicy::GetWeightOfSortedRecord() :
- *   get weight associated with an individual instance, using index into
- *   sorted records `rec` (in ascending order of predicted labels). `rec` is
- *   of type PredIndPairContainer
- */
-
-class PerInstanceWeightPolicy {
- public:
-  inline static xgboost::bst_float
-  GetWeightOfInstance(const xgboost::MetaInfo& info,
-                      unsigned instance_id, unsigned) {
-    return info.GetWeight(instance_id);
-  }
-  inline static xgboost::bst_float
-  GetWeightOfSortedRecord(const xgboost::MetaInfo& info,
-                          const PredIndPairContainer& rec,
-                          unsigned record_id, unsigned) {
-    return info.GetWeight(rec[record_id].second);
-  }
-};
-
-class PerGroupWeightPolicy {
- public:
-  inline static xgboost::bst_float
-  GetWeightOfInstance(const xgboost::MetaInfo& info,
-                      unsigned, unsigned group_id) {
-    return info.GetWeight(group_id);
-  }
-
-  inline static xgboost::bst_float
-  GetWeightOfSortedRecord(const xgboost::MetaInfo& info,
-                          const PredIndPairContainer&,
-                          unsigned, unsigned group_id) {
-    return info.GetWeight(group_id);
-  }
-};
 }  // anonymous namespace
 
 namespace xgboost::metric {
@@ -177,10 +111,6 @@ struct EvalAMS : public MetricNoCache {
 
 /*! \brief Evaluate rank list */
 struct EvalRank : public MetricNoCache, public EvalRankConfig {
- private:
-  // This is used to compute the ranking metrics on the GPU - for training jobs that run on the GPU.
-  std::unique_ptr<MetricNoCache> rank_gpu_;
-
  public:
   double Eval(const HostDeviceVector<bst_float>& preds, const MetaInfo& info) override {
     CHECK_EQ(preds.Size(), info.labels.Size())
@@ -199,20 +129,10 @@ struct EvalRank : public MetricNoCache, public EvalRankConfig {
     // sum statistics
     double sum_metric = 0.0f;
 
-    // Check and see if we have the GPU metric registered in the internal registry
-    if (ctx_->gpu_id >= 0) {
-      if (!rank_gpu_) {
-        rank_gpu_.reset(GPUMetric::CreateGPUMetric(this->Name(), ctx_));
-      }
-      if (rank_gpu_) {
-        sum_metric = rank_gpu_->Eval(preds, info);
-      }
-    }
-
     CHECK(ctx_);
     std::vector<double> sum_tloc(ctx_->Threads(), 0.0);
 
-    if (!rank_gpu_ || ctx_->gpu_id < 0) {
+    {
       const auto& labels = info.labels.View(Context::kCpuId);
       const auto &h_preds = preds.ConstHostVector();
 
@@ -253,23 +173,6 @@ struct EvalRank : public MetricNoCache, public EvalRankConfig {
   virtual double EvalGroup(PredIndPairContainer *recptr) const = 0;
 };
 
-/*! \brief Precision at N, for both classification and rank */
-struct EvalPrecision : public EvalRank {
- public:
-  explicit EvalPrecision(const char* name, const char* param) : EvalRank(name, param) {}
-
-  double EvalGroup(PredIndPairContainer *recptr) const override {
-    PredIndPairContainer &rec(*recptr);
-    // calculate Precision
-    std::stable_sort(rec.begin(), rec.end(), common::CmpFirst);
-    unsigned nhit = 0;
-    for (size_t j = 0; j < rec.size() && j < this->topn; ++j) {
-      nhit += (rec[j].second != 0);
-    }
-    return static_cast<double>(nhit) / this->topn;
-  }
-};
-
 /*! \brief Cox: Partial likelihood of the Cox proportional hazards model */
 struct EvalCox : public MetricNoCache {
  public:
@@ -312,7 +215,7 @@ struct EvalCox : public MetricNoCache {
     return out/num_events;  // normalize by the number of events
   }
 
-  const char* Name() const override {
+  [[nodiscard]] const char* Name() const override {
     return "cox-nloglik";
   }
 };
@@ -321,10 +224,6 @@ XGBOOST_REGISTER_METRIC(AMS, "ams")
 .describe("AMS metric for higgs.")
 .set_body([](const char* param) { return new EvalAMS(param); });
 
-XGBOOST_REGISTER_METRIC(Precision, "pre")
-.describe("precision@k for rank.")
-.set_body([](const char* param) { return new EvalPrecision("pre", param); });
-
 XGBOOST_REGISTER_METRIC(Cox, "cox-nloglik")
 .describe("Negative log partial likelihood of Cox proportional hazards model.")
 .set_body([](const char*) { return new EvalCox(); });
@@ -387,6 +286,8 @@ class EvalRankWithCache : public Metric {
     return result;
   }
 
+  [[nodiscard]] const char* Name() const override { return name_.c_str(); }
+
   virtual double Eval(HostDeviceVector<float> const& preds, MetaInfo const& info,
                       std::shared_ptr<Cache> p_cache) = 0;
 };
@@ -408,6 +309,52 @@ double Finalize(MetaInfo const& info, double score, double sw) {
 }
 }  // namespace
 
+class EvalPrecision : public EvalRankWithCache<ltr::PreCache> {
+ public:
+  using EvalRankWithCache::EvalRankWithCache;
+
+  double Eval(HostDeviceVector<float> const& predt, MetaInfo const& info,
+              std::shared_ptr<ltr::PreCache> p_cache) final {
+    auto n_groups = p_cache->Groups();
+    if (!info.weights_.Empty()) {
+      CHECK_EQ(info.weights_.Size(), n_groups) << error::GroupWeight();
+    }
+
+    if (ctx_->IsCUDA()) {
+      auto pre = cuda_impl::PreScore(ctx_, info, predt, p_cache);
+      return Finalize(info, pre.Residue(), pre.Weights());
+    }
+
+    auto gptr = p_cache->DataGroupPtr(ctx_);
+    auto h_label = info.labels.HostView().Slice(linalg::All(), 0);
+    auto h_predt = linalg::MakeTensorView(ctx_, &predt, predt.Size());
+    auto rank_idx = p_cache->SortedIdx(ctx_, predt.ConstHostSpan());
+
+    auto weight = common::MakeOptionalWeights(ctx_, info.weights_);
+    auto pre = p_cache->Pre(ctx_);
+
+    common::ParallelFor(p_cache->Groups(), ctx_->Threads(), [&](auto g) {
+      auto g_label = h_label.Slice(linalg::Range(gptr[g], gptr[g + 1]));
+      auto g_rank = rank_idx.subspan(gptr[g], gptr[g + 1] - gptr[g]);
+
+      auto n = std::min(static_cast<std::size_t>(param_.TopK()), g_label.Size());
+      double n_hits{0.0};
+      for (std::size_t i = 0; i < n; ++i) {
+        n_hits += g_label(g_rank[i]) * weight[g];
+      }
+      pre[g] = n_hits / static_cast<double>(n);
+    });
+
+    auto sw = 0.0;
+    for (std::size_t i = 0; i < pre.size(); ++i) {
+      sw += weight[i];
+    }
+
+    auto sum = std::accumulate(pre.cbegin(), pre.cend(), 0.0);
+    return Finalize(info, sum, sw);
+  }
+};
+
 /**
  * \brief Implement the NDCG score function for learning to rank.
  *
@@ -416,7 +363,6 @@ double Finalize(MetaInfo const& info, double score, double sw) {
 class EvalNDCG : public EvalRankWithCache<ltr::NDCGCache> {
  public:
   using EvalRankWithCache::EvalRankWithCache;
-  const char* Name() const override { return name_.c_str(); }
 
   double Eval(HostDeviceVector<float> const& preds, MetaInfo const& info,
               std::shared_ptr<ltr::NDCGCache> p_cache) override {
@@ -475,7 +421,6 @@ class EvalNDCG : public EvalRankWithCache<ltr::NDCGCache> {
 class EvalMAPScore : public EvalRankWithCache<ltr::MAPCache> {
  public:
   using EvalRankWithCache::EvalRankWithCache;
-  const char* Name() const override { return name_.c_str(); }
 
   double Eval(HostDeviceVector<float> const& predt, MetaInfo const& info,
               std::shared_ptr<ltr::MAPCache> p_cache) override {
@@ -494,7 +439,7 @@ class EvalMAPScore : public EvalRankWithCache<ltr::MAPCache> {
 
     common::ParallelFor(p_cache->Groups(), ctx_->Threads(), [&](auto g) {
       auto g_label = h_label.Slice(linalg::Range(gptr[g], gptr[g + 1]));
-      auto g_rank = rank_idx.subspan(gptr[g]);
+      auto g_rank = rank_idx.subspan(gptr[g], gptr[g + 1] - gptr[g]);
 
       auto n = std::min(static_cast<std::size_t>(param_.TopK()), g_label.Size());
       double n_hits{0.0};
@@ -527,6 +472,10 @@ class EvalMAPScore : public EvalRankWithCache<ltr::MAPCache> {
   }
 };
 
+XGBOOST_REGISTER_METRIC(Precision, "pre")
+    .describe("precision@k for rank.")
+    .set_body([](const char* param) { return new EvalPrecision("pre", param); });
+
 XGBOOST_REGISTER_METRIC(EvalMAP, "map")
     .describe("map@k for ranking.")
     .set_body([](char const* param) {

src/metric/rank_metric.cu

@@ -28,108 +28,57 @@ namespace xgboost::metric {
 // tag the this file, used by force static link later.
 DMLC_REGISTRY_FILE_TAG(rank_metric_gpu);
 
-/*! \brief Evaluate rank list on GPU */
-template <typename EvalMetricT>
-struct EvalRankGpu : public GPUMetric, public EvalRankConfig {
- public:
-  double Eval(const HostDeviceVector<bst_float> &preds, const MetaInfo &info) override {
-    // Sanity check is done by the caller
-    std::vector<unsigned> tgptr(2, 0);
-    tgptr[1] = static_cast<unsigned>(preds.Size());
-    const std::vector<unsigned> &gptr = info.group_ptr_.size() == 0 ? tgptr : info.group_ptr_;
-
-    const auto ngroups = static_cast<bst_omp_uint>(gptr.size() - 1);
-
-    auto device = ctx_->gpu_id;
-    dh::safe_cuda(cudaSetDevice(device));
-
-    info.labels.SetDevice(device);
-    preds.SetDevice(device);
-
-    auto dpreds = preds.ConstDevicePointer();
-    auto dlabels = info.labels.View(device);
-
-    // Sort all the predictions
-    dh::SegmentSorter<float> segment_pred_sorter;
-    segment_pred_sorter.SortItems(dpreds, preds.Size(), gptr);
-
-    // Compute individual group metric and sum them up
-    return EvalMetricT::EvalMetric(segment_pred_sorter, dlabels.Values().data(), *this);
-  }
-
-  const char* Name() const override {
-    return name.c_str();
-  }
-
-  explicit EvalRankGpu(const char* name, const char* param) {
-    using namespace std;  // NOLINT(*)
-    if (param != nullptr) {
-      std::ostringstream os;
-      if (sscanf(param, "%u[-]?", &this->topn) == 1) {
-        os << name << '@' << param;
-        this->name = os.str();
-      } else {
-        os << name << param;
-        this->name = os.str();
-      }
-      if (param[strlen(param) - 1] == '-') {
-        this->minus = true;
-      }
-    } else {
-      this->name = name;
-    }
-  }
-};
-
-/*! \brief Precision at N, for both classification and rank */
-struct EvalPrecisionGpu {
- public:
-  static double EvalMetric(const dh::SegmentSorter<float> &pred_sorter,
-                           const float *dlabels,
-                           const EvalRankConfig &ecfg) {
-    // Group info on device
-    const auto &dgroups = pred_sorter.GetGroupsSpan();
-    const auto ngroups = pred_sorter.GetNumGroups();
-    const auto &dgroup_idx = pred_sorter.GetGroupSegmentsSpan();
-
-    // Original positions of the predictions after they have been sorted
-    const auto &dpreds_orig_pos = pred_sorter.GetOriginalPositionsSpan();
-
-    // First, determine non zero labels in the dataset individually
-    auto DetermineNonTrivialLabelLambda = [=] __device__(uint32_t idx) {
-      return (static_cast<unsigned>(dlabels[dpreds_orig_pos[idx]]) != 0) ? 1 : 0;
-    };  // NOLINT
-
-    // Find each group's metric sum
-    dh::caching_device_vector<uint32_t> hits(ngroups, 0);
-    const auto nitems = pred_sorter.GetNumItems();
-    auto *dhits = hits.data().get();
-
-    int device_id = -1;
-    dh::safe_cuda(cudaGetDevice(&device_id));
-    // For each group item compute the aggregated precision
-    dh::LaunchN(nitems, nullptr, [=] __device__(uint32_t idx) {
-      const auto group_idx = dgroup_idx[idx];
-      const auto group_begin = dgroups[group_idx];
-      const auto ridx = idx - group_begin;
-      if (ridx < ecfg.topn && DetermineNonTrivialLabelLambda(idx)) {
-        atomicAdd(&dhits[group_idx], 1);
-      }
-    });
-
-    // Allocator to be used for managing space overhead while performing reductions
-    dh::XGBCachingDeviceAllocator<char> alloc;
-    return static_cast<double>(thrust::reduce(thrust::cuda::par(alloc),
-                                              hits.begin(), hits.end())) / ecfg.topn;
-  }
-};
-
-
-XGBOOST_REGISTER_GPU_METRIC(PrecisionGpu, "pre")
-.describe("precision@k for rank computed on GPU.")
-.set_body([](const char* param) { return new EvalRankGpu<EvalPrecisionGpu>("pre", param); });
-
 namespace cuda_impl {
+PackedReduceResult PreScore(Context const *ctx, MetaInfo const &info,
+                            HostDeviceVector<float> const &predt,
+                            std::shared_ptr<ltr::PreCache> p_cache) {
+  auto d_gptr = p_cache->DataGroupPtr(ctx);
+  auto d_label = info.labels.View(ctx->gpu_id).Slice(linalg::All(), 0);
+
+  predt.SetDevice(ctx->gpu_id);
+  auto d_rank_idx = p_cache->SortedIdx(ctx, predt.ConstDeviceSpan());
+  auto topk = p_cache->Param().TopK();
+  auto d_weight = common::MakeOptionalWeights(ctx, info.weights_);
+
+  auto it = dh::MakeTransformIterator<double>(
+      thrust::make_counting_iterator(0ul), [=] XGBOOST_DEVICE(std::size_t i) {
+        auto g = dh::SegmentId(d_gptr, i);
+        auto g_begin = d_gptr[g];
+        auto g_end = d_gptr[g + 1];
+        i -= g_begin;
+        auto g_label = d_label.Slice(linalg::Range(g_begin, g_end));
+        auto g_rank = d_rank_idx.subspan(g_begin, g_end - g_begin);
+        double y = g_label(g_rank[i]);
+        auto n = std::min(static_cast<std::size_t>(topk), g_label.Size());
+        double w{d_weight[g]};
+        if (i >= n) {
+          return 0.0;
+        }
+        return y / static_cast<double>(n) * w;
+      });
+
+  auto cuctx = ctx->CUDACtx();
+  auto pre = p_cache->Pre(ctx);
+  thrust::fill_n(cuctx->CTP(), pre.data(), pre.size(), 0.0);
+
+  std::size_t bytes;
+  cub::DeviceSegmentedReduce::Sum(nullptr, bytes, it, pre.data(), p_cache->Groups(), d_gptr.data(),
+                                  d_gptr.data() + 1, cuctx->Stream());
+  dh::TemporaryArray<char> temp(bytes);
+  cub::DeviceSegmentedReduce::Sum(temp.data().get(), bytes, it, pre.data(), p_cache->Groups(),
+                                  d_gptr.data(), d_gptr.data() + 1, cuctx->Stream());
+
+  auto w_it =
+      dh::MakeTransformIterator<double>(thrust::make_counting_iterator(0ul),
+                                        [=] XGBOOST_DEVICE(std::size_t g) { return d_weight[g]; });
+  auto n_weights = p_cache->Groups();
+  auto sw = dh::Reduce(cuctx->CTP(), w_it, w_it + n_weights, 0.0, thrust::plus<double>{});
+  auto sum =
+      dh::Reduce(cuctx->CTP(), dh::tcbegin(pre), dh::tcend(pre), 0.0, thrust::plus<double>{});
+  auto result = PackedReduceResult{sum, sw};
+  return result;
+}
+
 PackedReduceResult NDCGScore(Context const *ctx, MetaInfo const &info,
                              HostDeviceVector<float> const &predt, bool minus,
                              std::shared_ptr<ltr::NDCGCache> p_cache) {

src/metric/rank_metric.h

@@ -3,7 +3,7 @@
 /**
  * Copyright 2023 by XGBoost Contributors
  */
-#include <memory>                        // for shared_ptr
+#include <memory>  // for shared_ptr
 
 #include "../common/common.h"            // for AssertGPUSupport
 #include "../common/ranking_utils.h"     // for NDCGCache, MAPCache
@@ -12,9 +12,7 @@
 #include "xgboost/data.h"                // for MetaInfo
 #include "xgboost/host_device_vector.h"  // for HostDeviceVector
 
-namespace xgboost {
+namespace xgboost::metric::cuda_impl {
-namespace metric {
-namespace cuda_impl {
 PackedReduceResult NDCGScore(Context const *ctx, MetaInfo const &info,
                              HostDeviceVector<float> const &predt, bool minus,
                              std::shared_ptr<ltr::NDCGCache> p_cache);
@@ -23,6 +21,10 @@ PackedReduceResult MAPScore(Context const *ctx, MetaInfo const &info,
                             HostDeviceVector<float> const &predt, bool minus,
                             std::shared_ptr<ltr::MAPCache> p_cache);
 
+PackedReduceResult PreScore(Context const *ctx, MetaInfo const &info,
+                            HostDeviceVector<float> const &predt,
+                            std::shared_ptr<ltr::PreCache> p_cache);
+
 #if !defined(XGBOOST_USE_CUDA)
 inline PackedReduceResult NDCGScore(Context const *, MetaInfo const &,
                                     HostDeviceVector<float> const &, bool,
@@ -37,8 +39,13 @@ inline PackedReduceResult MAPScore(Context const *, MetaInfo const &,
   common::AssertGPUSupport();
   return {};
 }
+
+inline PackedReduceResult PreScore(Context const *, MetaInfo const &,
+                                   HostDeviceVector<float> const &,
+                                   std::shared_ptr<ltr::PreCache>) {
+  common::AssertGPUSupport();
+  return {};
+}
 #endif
-}  // namespace cuda_impl
+}  // namespace xgboost::metric::cuda_impl
-}  // namespace metric
-}  // namespace xgboost
 #endif  // XGBOOST_METRIC_RANK_METRIC_H_

tests/ci_build/lint_python.py

@@ -90,7 +90,7 @@ def check_cmd_print_failure_assistance(cmd: List[str]) -> bool:
 
     subprocess.run([cmd[0], "--version"])
     msg = """
-Please run the following command on your machine to address the formatting error:
+Please run the following command on your machine to address the error:
 
     """
     msg += " ".join(cmd)

tests/cpp/metric/test_rank_metric.h

@@ -17,34 +17,30 @@
 #include "xgboost/host_device_vector.h"  // for HostDeviceVector
 #include "xgboost/json.h"                // for Json, String, Object
 
-namespace xgboost {
+namespace xgboost::metric {
-namespace metric {
 
 inline void VerifyPrecision(DataSplitMode data_split_mode = DataSplitMode::kRow) {
-  // When the limit for precision is not given, it takes the limit at
-  // std::numeric_limits<unsigned>::max(); hence all values are very small
-  // NOTE(AbdealiJK): Maybe this should be fixed to be num_row by default.
   auto ctx = xgboost::CreateEmptyGenericParam(GPUIDX);
-  xgboost::Metric * metric = xgboost::Metric::Create("pre", &ctx);
+  std::unique_ptr<xgboost::Metric> metric{Metric::Create("pre", &ctx)};
   ASSERT_STREQ(metric->Name(), "pre");
-  EXPECT_NEAR(GetMetricEval(metric, {0, 1}, {0, 1}, {}, {}, data_split_mode), 0, 1e-7);
+  EXPECT_NEAR(GetMetricEval(metric.get(), {0, 1}, {0, 1}, {}, {}, data_split_mode), 0.5, 1e-7);
-  EXPECT_NEAR(GetMetricEval(metric,
+  EXPECT_NEAR(
-                            {0.1f, 0.9f, 0.1f, 0.9f},
+      GetMetricEval(metric.get(), {0.1f, 0.9f, 0.1f, 0.9f}, {0, 0, 1, 1}, {}, {}, data_split_mode),
-                            {  0,   0,   1,   1}, {}, {}, data_split_mode),
+      0.5, 1e-7);
-              0, 1e-7);
 
-  delete metric;
+  metric.reset(xgboost::Metric::Create("pre@2", &ctx));
-  metric = xgboost::Metric::Create("pre@2", &ctx);
   ASSERT_STREQ(metric->Name(), "pre@2");
-  EXPECT_NEAR(GetMetricEval(metric, {0, 1}, {0, 1}, {}, {}, data_split_mode), 0.5f, 1e-7);
+  EXPECT_NEAR(GetMetricEval(metric.get(), {0, 1}, {0, 1}, {}, {}, data_split_mode), 0.5f, 1e-7);
-  EXPECT_NEAR(GetMetricEval(metric,
+  EXPECT_NEAR(
-                            {0.1f, 0.9f, 0.1f, 0.9f},
+      GetMetricEval(metric.get(), {0.1f, 0.9f, 0.1f, 0.9f}, {0, 0, 1, 1}, {}, {}, data_split_mode),
-                            {  0,   0,   1,   1}, {}, {}, data_split_mode),
+      0.5f, 0.001f);
-              0.5f, 0.001f);
 
-  EXPECT_ANY_THROW(GetMetricEval(metric, {0, 1}, {}, {}, {}, data_split_mode));
+  EXPECT_ANY_THROW(GetMetricEval(metric.get(), {0, 1}, {}, {}, {}, data_split_mode));
 
-  delete metric;
+  metric.reset(xgboost::Metric::Create("pre@4", &ctx));
+  EXPECT_NEAR(GetMetricEval(metric.get(), {0.2f, 0.3f, 0.4f, 0.5f, 0.6f, 0.7f},
+                            {0.0f, 1.0f, 0.0f, 0.0f, 1.0f, 1.0f}, {}, {}, data_split_mode),
+              0.5f, 1e-7);
 }
 
 inline void VerifyNDCG(DataSplitMode data_split_mode = DataSplitMode::kRow) {
@@ -187,5 +183,4 @@ inline void VerifyNDCGExpGain(DataSplitMode data_split_mode = DataSplitMode::kRo
   ndcg = metric->Evaluate(predt, p_fmat);
   ASSERT_NEAR(ndcg, 1.0, kRtEps);
 }
-}  // namespace metric
+}  // namespace xgboost::metric
-}  // namespace xgboost

tests/python-gpu/test_gpu_eval_metrics.py

@@ -5,7 +5,7 @@ import pytest
 
 import xgboost
 from xgboost import testing as tm
-from xgboost.testing.metrics import check_quantile_error
+from xgboost.testing.metrics import check_precision_score, check_quantile_error
 
 sys.path.append("tests/python")
 import test_eval_metrics as test_em  # noqa
@@ -59,6 +59,9 @@ class TestGPUEvalMetrics:
     def test_pr_auc_ltr(self):
         self.cpu_test.run_pr_auc_ltr("gpu_hist")
 
+    def test_precision_score(self):
+        check_precision_score("gpu_hist")
+
     @pytest.mark.skipif(**tm.no_sklearn())
     def test_quantile_error(self) -> None:
         check_quantile_error("gpu_hist")

tests/python/test_eval_metrics.py

@@ -3,7 +3,7 @@ import pytest
 
 import xgboost as xgb
 from xgboost import testing as tm
-from xgboost.testing.metrics import check_quantile_error
+from xgboost.testing.metrics import check_precision_score, check_quantile_error
 
 rng = np.random.RandomState(1337)
 
@@ -315,6 +315,9 @@ class TestEvalMetrics:
     def test_pr_auc_ltr(self):
         self.run_pr_auc_ltr("hist")
 
+    def test_precision_score(self):
+        check_precision_score("hist")
+
     @pytest.mark.skipif(**tm.no_sklearn())
     def test_quantile_error(self) -> None:
         check_quantile_error("hist")

tests/python/test_quantile_dmatrix.py

@@ -55,6 +55,38 @@ class TestQuantileDMatrix:
         r = np.arange(1.0, n_samples)
         np.testing.assert_allclose(Xy.get_data().toarray()[1:, 0], r)
 
+    def test_error(self):
+        from sklearn.model_selection import train_test_split
+
+        rng = np.random.default_rng(1994)
+        X, y = make_categorical(
+            n_samples=128, n_features=2, n_categories=3, onehot=False
+        )
+        reg = xgb.XGBRegressor(tree_method="hist", enable_categorical=True)
+        w = rng.uniform(0, 1, size=y.shape[0])
+
+        X_train, X_test, y_train, y_test, w_train, w_test = train_test_split(
+            X, y, w, random_state=1994
+        )
+
+        with pytest.raises(ValueError, match="sample weight"):
+            reg.fit(
+                X,
+                y,
+                sample_weight=w_train,
+                eval_set=[(X_test, y_test)],
+                sample_weight_eval_set=[w_test],
+            )
+
+        with pytest.raises(ValueError, match="sample weight"):
+            reg.fit(
+                X_train,
+                y_train,
+                sample_weight=w,
+                eval_set=[(X_test, y_test)],
+                sample_weight_eval_set=[w_test],
+            )
+
     @pytest.mark.parametrize("sparsity", [0.0, 0.1, 0.8, 0.9])
     def test_with_iterator(self, sparsity: float) -> None:
         n_samples_per_batch = 317