Fix GPU RF (#6755)

* Fix sampling.

tests/cpp/tree/test_gpu_hist.cu

@@ -503,12 +503,15 @@ TEST(GpuHist, ExternalMemoryWithSampling) {
   auto gpair = GenerateRandomGradients(kRows);
 
   // Build a tree using the in-memory DMatrix.
+  auto rng = common::GlobalRandom();
+
   RegTree tree;
   HostDeviceVector<bst_float> preds(kRows, 0.0, 0);
   UpdateTree(&gpair, dmat.get(), 0, &tree, &preds, kSubsample, kSamplingMethod,
              kRows);
 
   // Build another tree using multiple ELLPACK pages.
+  common::GlobalRandom() = rng;
   RegTree tree_ext;
   HostDeviceVector<bst_float> preds_ext(kRows, 0.0, 0);
   UpdateTree(&gpair, dmat_ext.get(), kPageSize, &tree_ext, &preds_ext,
@@ -518,7 +521,7 @@ TEST(GpuHist, ExternalMemoryWithSampling) {
   auto preds_h = preds.ConstHostVector();
   auto preds_ext_h = preds_ext.ConstHostVector();
   for (int i = 0; i < kRows; i++) {
-    EXPECT_NEAR(preds_h[i], preds_ext_h[i], 2e-3);
+    EXPECT_NEAR(preds_h[i], preds_ext_h[i], 1e-3);
   }
 }
 

tests/python-gpu/test_gpu_with_sklearn.py

@@ -33,4 +33,8 @@ def test_gpu_binary_classification():
 
 
 def test_boost_from_prediction_gpu_hist():
-    cpu_test = twskl.run_boost_from_prediction('gpu_hist')
+    twskl.run_boost_from_prediction('gpu_hist')
+
+
+def test_num_parallel_tree():
+    twskl.run_boston_housing_rf_regression("gpu_hist")

tests/python/test_with_sklearn.py

@@ -357,23 +357,26 @@ def test_boston_housing_regression():
         assert mean_squared_error(preds4, labels) < 350
 
 
-def test_boston_housing_rf_regression():
+def run_boston_housing_rf_regression(tree_method):
     from sklearn.metrics import mean_squared_error
     from sklearn.datasets import load_boston
     from sklearn.model_selection import KFold
 
-    boston = load_boston()
-    y = boston['target']
-    X = boston['data']
+    X, y = load_boston(return_X_y=True)
     kf = KFold(n_splits=2, shuffle=True, random_state=rng)
     for train_index, test_index in kf.split(X, y):
-        xgb_model = xgb.XGBRFRegressor(random_state=42).fit(
-            X[train_index], y[train_index])
+        xgb_model = xgb.XGBRFRegressor(random_state=42, tree_method=tree_method).fit(
+            X[train_index], y[train_index]
+        )
         preds = xgb_model.predict(X[test_index])
         labels = y[test_index]
         assert mean_squared_error(preds, labels) < 35
 
 
+def test_boston_housing_rf_regression():
+    run_boston_housing_rf_regression("hist")
+
+
 def test_parameter_tuning():
     from sklearn.model_selection import GridSearchCV
     from sklearn.datasets import load_boston