Use matrix for gradient. (#9508)

- Use the `linalg::Matrix` for storing gradients.
- New API for the custom objective.
- Custom objective for multi-class/multi-target is now required to return the correct shape.
- Custom objective for Python can accept arrays with any strides. (row-major, column-major)

src/objective/hinge.cu

@@ -27,8 +27,8 @@ class HingeObj : public ObjFunction {
   void Configure(Args const&) override {}
   ObjInfo Task() const override { return ObjInfo::kRegression; }
 
-  void GetGradient(const HostDeviceVector<bst_float> &preds, const MetaInfo &info, int /*iter*/,
-                   HostDeviceVector<GradientPair> *out_gpair) override {
+  void GetGradient(const HostDeviceVector<bst_float> &preds, const MetaInfo &info,
+                   std::int32_t /*iter*/, linalg::Matrix<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"
@@ -41,7 +41,8 @@ class HingeObj : public ObjFunction {
       CHECK_EQ(info.weights_.Size(), ndata)
           << "Number of weights should be equal to number of data points.";
     }
-    out_gpair->Resize(ndata);
+    CHECK_EQ(info.labels.Shape(1), 1) << "Multi-target for `binary:hinge` is not yet supported.";
+    out_gpair->Reshape(ndata, 1);
     common::Transform<>::Init(
         [=] XGBOOST_DEVICE(size_t _idx,
                            common::Span<GradientPair> _out_gpair,
@@ -63,7 +64,7 @@ class HingeObj : public ObjFunction {
         },
         common::Range{0, static_cast<int64_t>(ndata)}, this->ctx_->Threads(),
         ctx_->gpu_id).Eval(
-            out_gpair, &preds, info.labels.Data(), &info.weights_);
+            out_gpair->Data(), &preds, info.labels.Data(), &info.weights_);
   }
 
   void PredTransform(HostDeviceVector<bst_float> *io_preds) const override {