Add check for length of weights. (#4872)

src/objective/hinge.cu

@@ -37,8 +37,12 @@ class HingeObj : public ObjFunction {
         << "preds.size=" << preds.Size()
         << ", label.size=" << info.labels_.Size();
 
-    const bool is_null_weight = info.weights_.Size() == 0;
     const size_t ndata = preds.Size();
+    const bool is_null_weight = info.weights_.Size() == 0;
+    if (!is_null_weight) {
+      CHECK_EQ(info.weights_.Size(), ndata)
+          << "Number of weights should be equal to number of data points.";
+    }
     out_gpair->Resize(ndata);
     common::Transform<>::Init(
         [=] XGBOOST_DEVICE(size_t _idx,

src/objective/multiclass_obj.cu

@@ -73,6 +73,11 @@ class SoftmaxMultiClassObj : public ObjFunction {
     label_correct_.Fill(1);
 
     const bool is_null_weight = info.weights_.Size() == 0;
+    if (!is_null_weight) {
+      CHECK_EQ(info.weights_.Size(), ndata)
+          << "Number of weights should be equal to number of data points.";
+    }
+
     common::Transform<>::Init(
         [=] XGBOOST_DEVICE(size_t idx,
                            common::Span<GradientPair> gpair,

src/objective/regression_obj.cu

@@ -60,13 +60,17 @@ class RegLossObj : public ObjFunction {
     CHECK_EQ(preds.Size(), info.labels_.Size())
         << "labels are not correctly provided"
         << "preds.size=" << preds.Size() << ", label.size=" << info.labels_.Size();
-    size_t ndata = preds.Size();
+    size_t const ndata = preds.Size();
     out_gpair->Resize(ndata);
     auto device = tparam_->gpu_id;
     label_correct_.Resize(1);
     label_correct_.Fill(1);
 
     bool is_null_weight = info.weights_.Size() == 0;
+    if (!is_null_weight) {
+      CHECK_EQ(info.weights_.Size(), ndata)
+          << "Number of weights should be equal to number of data points.";
+    }
     auto scale_pos_weight = param_.scale_pos_weight;
     common::Transform<>::Init(
         [=] XGBOOST_DEVICE(size_t _idx,
@@ -188,13 +192,17 @@ class PoissonRegression : public ObjFunction {
                    HostDeviceVector<GradientPair> *out_gpair) override {
     CHECK_NE(info.labels_.Size(), 0U) << "label set cannot be empty";
     CHECK_EQ(preds.Size(), info.labels_.Size()) << "labels are not correctly provided";
-    size_t ndata = preds.Size();
+    size_t const ndata = preds.Size();
     out_gpair->Resize(ndata);
     auto device = tparam_->gpu_id;
     label_correct_.Resize(1);
     label_correct_.Fill(1);
 
     bool is_null_weight = info.weights_.Size() == 0;
+    if (!is_null_weight) {
+      CHECK_EQ(info.weights_.Size(), ndata)
+          << "Number of weights should be equal to number of data points.";
+    }
     bst_float max_delta_step = param_.max_delta_step;
     common::Transform<>::Init(
         [=] XGBOOST_DEVICE(size_t _idx,
@@ -282,6 +290,11 @@ class CoxRegression : public ObjFunction {
     const std::vector<size_t> &label_order = info.LabelAbsSort();
 
     const omp_ulong ndata = static_cast<omp_ulong>(preds_h.size()); // NOLINT(*)
+    const bool is_null_weight = info.weights_.Size() == 0;
+    if (!is_null_weight) {
+      CHECK_EQ(info.weights_.Size(), ndata)
+          << "Number of weights should be equal to number of data points.";
+    }
 
     // pre-compute a sum
     double exp_p_sum = 0;  // we use double because we might need the precision with large datasets
@@ -377,6 +390,10 @@ class GammaRegression : public ObjFunction {
     label_correct_.Fill(1);
 
     const bool is_null_weight = info.weights_.Size() == 0;
+    if (!is_null_weight) {
+      CHECK_EQ(info.weights_.Size(), ndata)
+          << "Number of weights should be equal to number of data points.";
+    }
     common::Transform<>::Init(
         [=] XGBOOST_DEVICE(size_t _idx,
                            common::Span<int> _label_correct,
@@ -471,6 +488,11 @@ class TweedieRegression : public ObjFunction {
     label_correct_.Fill(1);
 
     const bool is_null_weight = info.weights_.Size() == 0;
+    if (!is_null_weight) {
+      CHECK_EQ(info.weights_.Size(), ndata)
+          << "Number of weights should be equal to number of data points.";
+    }
+
     const float rho = param_.tweedie_variance_power;
     common::Transform<>::Init(
         [=] XGBOOST_DEVICE(size_t _idx,

tests/cpp/objective/test_multiclass_obj.cc

@@ -25,6 +25,13 @@ TEST(Objective, DeclareUnifiedTest(SoftmaxMultiClassObjGPair)) {
 		   {0.24f, -0.91f, 0.66f, -0.33f, 0.09f, 0.24f}, // grad
 		   {0.36f, 0.16f, 0.44f, 0.45f, 0.16f, 0.37f});	 // hess
 
+  CheckObjFunction(obj,
+		   {1.0f, 0.0f, 2.0f, 2.0f, 0.0f, 1.0f}, // preds
+		   {1.0f, 0.0f},	       // labels
+                   {},                         // weights
+		   {0.24f, -0.91f, 0.66f, -0.33f, 0.09f, 0.24f}, // grad
+		   {0.36f, 0.16f, 0.44f, 0.45f, 0.16f, 0.37f});	 // hess
+
   ASSERT_NO_THROW(obj->DefaultEvalMetric());
 }