Fix inplace predict missing value. (#6787)

tests/python-gpu/test_gpu_prediction.py

@@ -154,17 +154,22 @@ class TestGPUPredict:
         cp.cuda.runtime.setDevice(0)
         rows = 1000
         cols = 10
+        missing = 11            # set to integer for testing
+
         cp_rng = cp.random.RandomState(1994)
         cp.random.set_random_state(cp_rng)
+
         X = cp.random.randn(rows, cols)
+        missing_idx = [i for i in range(0, cols, 4)]
+        X[:, missing_idx] = missing  # set to be missing
         y = cp.random.randn(rows)
 
         dtrain = xgb.DMatrix(X, y)
 
-        booster = xgb.train({'tree_method': 'gpu_hist'},
-                            dtrain, num_boost_round=10)
-        test = xgb.DMatrix(X[:10, ...])
-        predt_from_array = booster.inplace_predict(X[:10, ...])
+        booster = xgb.train({'tree_method': 'gpu_hist'}, dtrain, num_boost_round=10)
+
+        test = xgb.DMatrix(X[:10, ...], missing=missing)
+        predt_from_array = booster.inplace_predict(X[:10, ...], missing=missing)
         predt_from_dmatrix = booster.predict(test)
 
         cp.testing.assert_allclose(predt_from_array, predt_from_dmatrix)
@@ -185,6 +190,20 @@ class TestGPUPredict:
         base_margin = cp_rng.randn(rows)
         self.run_inplace_base_margin(booster, dtrain, X, base_margin)
 
+        # Create a wide dataset
+        X = cp_rng.randn(100, 10000)
+        y = cp_rng.randn(100)
+
+        missing_idx = [i for i in range(0, X.shape[1], 16)]
+        X[:, missing_idx] = missing
+        reg = xgb.XGBRegressor(tree_method="gpu_hist", n_estimators=8, missing=missing)
+        reg.fit(X, y)
+
+        gpu_predt = reg.predict(X)
+        reg.set_params(predictor="cpu_predictor")
+        cpu_predt = reg.predict(X)
+        np.testing.assert_allclose(gpu_predt, cpu_predt, atol=1e-6)
+
     @pytest.mark.skipif(**tm.no_cudf())
     def test_inplace_predict_cudf(self):
         import cupy as cp