Multi-target support for L1 error. (#8652)

- Add matrix support to the median function.
- Iterate through each target for quantile computation.

python-package/xgboost/testing/__init__.py

@@ -317,13 +317,13 @@ class TestDataset:
             enable_categorical=True,
         )
 
-    def get_device_dmat(self) -> xgb.DeviceQuantileDMatrix:
+    def get_device_dmat(self) -> xgb.QuantileDMatrix:
         import cupy as cp
 
         w = None if self.w is None else cp.array(self.w)
         X = cp.array(self.X, dtype=np.float32)
         y = cp.array(self.y, dtype=np.float32)
-        return xgb.DeviceQuantileDMatrix(X, y, w, base_margin=self.margin)
+        return xgb.QuantileDMatrix(X, y, weight=w, base_margin=self.margin)
 
     def get_external_dmat(self) -> xgb.DMatrix:
         n_samples = self.X.shape[0]
@@ -726,10 +726,16 @@ _unweighted_datasets_strategy = strategies.sampled_from(
         TestDataset("cancer", get_cancer, "binary:logistic", "logloss"),
         TestDataset(
             "mtreg",
-            lambda: datasets.make_regression(n_samples=128, n_targets=3),
+            lambda: datasets.make_regression(n_samples=128, n_features=2, n_targets=3),
             "reg:squarederror",
             "rmse",
         ),
+        TestDataset(
+            "mtreg-l1",
+            lambda: datasets.make_regression(n_samples=128, n_features=2, n_targets=3),
+            "reg:absoluteerror",
+            "mae",
+        ),
         TestDataset("sparse", get_sparse, "reg:squarederror", "rmse"),
         TestDataset("sparse-l1", get_sparse, "reg:absoluteerror", "mae"),
         TestDataset(
@@ -753,7 +759,7 @@ def _dataset_weight_margin(draw: Callable) -> TestDataset:
         num_class = 1
         if data.objective == "multi:softmax":
             num_class = int(np.max(data.y) + 1)
-        elif data.name == "mtreg":
+        elif data.name.startswith("mtreg"):
             num_class = data.y.shape[1]
 
         data.margin = draw(