Complete cudf support. (#4850)

* Handles missing value.
* Accept all floating point and integer types.
* Move to cudf 9.0 API.
* Remove requirement on `null_count`.
* Arbitrary column types support.

tests/python-gpu/test_from_columnar.py

@@ -6,6 +6,35 @@ sys.path.append("tests/python")
 import testing as tm
 
 
+def dmatrix_from_cudf(input_type, missing=np.NAN):
+    '''Test constructing DMatrix from cudf'''
+    import cudf
+    import pandas as pd
+
+    kRows = 80
+    kCols = 3
+
+    na = np.random.randn(kRows, kCols)
+    na[:, 0:2] = na[:, 0:2].astype(input_type)
+
+    na[5, 0] = missing
+    na[3, 1] = missing
+
+    pa = pd.DataFrame({'0': na[:, 0],
+                       '1': na[:, 1],
+                       '2': na[:, 2].astype(np.int32)})
+
+    np_label = np.random.randn(kRows).astype(input_type)
+    pa_label = pd.DataFrame(np_label)
+
+    cd: cudf.DataFrame = cudf.from_pandas(pa)
+    cd_label: cudf.DataFrame = cudf.from_pandas(pa_label)
+
+    dtrain = xgb.DMatrix(cd, missing=missing, label=cd_label)
+    assert dtrain.num_col() == kCols
+    assert dtrain.num_row() == kRows
+
+
 class TestFromColumnar:
     '''Tests for constructing DMatrix from data structure conforming Apache
 Arrow specification.'''
@@ -13,30 +42,13 @@ Arrow specification.'''
     @pytest.mark.skipif(**tm.no_cudf())
     def test_from_cudf(self):
         '''Test constructing DMatrix from cudf'''
-        import cudf
-        import pandas as pd
+        dmatrix_from_cudf(np.float32, np.NAN)
+        dmatrix_from_cudf(np.float64, np.NAN)
 
-        kRows = 80
-        kCols = 2
+        dmatrix_from_cudf(np.uint8, 2)
+        dmatrix_from_cudf(np.uint32, 3)
+        dmatrix_from_cudf(np.uint64, 4)
 
-        na = np.random.randn(kRows, kCols).astype(np.float32)
-        na[3, 1] = np.NAN
-        na[5, 0] = np.NAN
-
-        pa = pd.DataFrame(na)
-
-        np_label = np.random.randn(kRows).astype(np.float32)
-        pa_label = pd.DataFrame(np_label)
-
-        names = []
-
-        for i in range(0, kCols):
-            names.append(str(i))
-        pa.columns = names
-
-        cd: cudf.DataFrame = cudf.from_pandas(pa)
-        cd_label: cudf.DataFrame = cudf.from_pandas(pa_label)
-
-        dtrain = xgb.DMatrix(cd, label=cd_label)
-        assert dtrain.num_col() == kCols
-        assert dtrain.num_row() == kRows
+        dmatrix_from_cudf(np.int8, 2)
+        dmatrix_from_cudf(np.int32, -2)
+        dmatrix_from_cudf(np.int64, -3)