Support for all primitive types from array. (#7003)

* Change C API name.
* Test for all primitive types from array.
* Add native support for CPU 128 float.
* Convert boolean and float16 in Python.

* Fix dask version for now.

include/xgboost/c_api.h

@@ -142,7 +142,7 @@ XGB_DLL int XGDMatrixCreateFromCSR(char const *indptr,
  * \param out created dmatrix
  * \return 0 when success, -1 when failure happens
  */
-XGB_DLL int XGDMatrixCreateFromArray(char const *data,
+XGB_DLL int XGDMatrixCreateFromDense(char const *data,
                                      char const *json_config,
                                      DMatrixHandle *out);
 

python-package/xgboost/core.py

@@ -239,7 +239,18 @@ def _array_interface(data: np.ndarray) -> bytes:
     interface = data.__array_interface__
     if "mask" in interface:
         interface["mask"] = interface["mask"].__array_interface__
-    interface_str = bytes(json.dumps(interface, indent=2), "utf-8")
+    interface_str = bytes(json.dumps(interface), "utf-8")
+    return interface_str
+
+
+def _cuda_array_interface(data) -> bytes:
+    assert (
+        data.dtype.hasobject is False
+    ), "Input data contains `object` dtype.  Expecting numeric data."
+    interface = data.__cuda_array_interface__
+    if "mask" in interface:
+        interface["mask"] = interface["mask"].__cuda_array_interface__
+    interface_str = bytes(json.dumps(interface), "utf-8")
     return interface_str
 
 
@@ -1948,10 +1959,7 @@ def inplace_predict(
             from .data import _transform_cupy_array
 
             data = _transform_cupy_array(data)
-            interface = data.__cuda_array_interface__
-            if "mask" in interface:
-                interface["mask"] = interface["mask"].__cuda_array_interface__
-            interface_str = bytes(json.dumps(interface, indent=2), "utf-8")
+            interface_str = _cuda_array_interface(data)
             _check_call(
                 _LIB.XGBoosterPredictFromCudaArray(
                     self.handle,

python-package/xgboost/data.py

@@ -9,7 +9,8 @@
 
 import numpy as np
 
-from .core import c_array, _LIB, _check_call, c_str, _array_interface
+from .core import c_array, _LIB, _check_call, c_str
+from .core import _array_interface, _cuda_array_interface
 from .core import DataIter, _ProxyDMatrix, DMatrix
 from .compat import lazy_isinstance
 
@@ -105,7 +106,7 @@ def _is_numpy_array(data):
 
 
 def _ensure_np_dtype(data, dtype):
-    if data.dtype.hasobject:
+    if data.dtype.hasobject or data.dtype in [np.float16, np.bool_]:
         data = data.astype(np.float32, copy=False)
         dtype = np.float32
     return data, dtype
@@ -141,7 +142,7 @@ def _from_numpy_array(data, missing, nthread, feature_names, feature_types):
     }
     config = bytes(json.dumps(args), "utf-8")
     _check_call(
-        _LIB.XGDMatrixCreateFromArray(
+        _LIB.XGDMatrixCreateFromDense(
             _array_interface(data),
             config,
             ctypes.byref(handle),
@@ -416,21 +417,19 @@ def _is_cupy_array(data):
 
 
 def _transform_cupy_array(data):
+    import cupy  # pylint: disable=import-error
     if not hasattr(data, '__cuda_array_interface__') and hasattr(
             data, '__array__'):
-        import cupy             # pylint: disable=import-error
         data = cupy.array(data, copy=False)
+    if data.dtype.hasobject or data.dtype in [cupy.float16, cupy.bool_]:
+        data = data.astype(cupy.float32, copy=False)
     return data
 
 
 def _from_cupy_array(data, missing, nthread, feature_names, feature_types):
     """Initialize DMatrix from cupy ndarray."""
     data = _transform_cupy_array(data)
-    interface = data.__cuda_array_interface__
-    if 'mask' in interface:
-        interface['mask'] = interface['mask'].__cuda_array_interface__
-    interface_str = bytes(json.dumps(interface, indent=2), 'utf-8')
-
+    interface_str = _cuda_array_interface(data)
     handle = ctypes.c_void_p()
     _check_call(
         _LIB.XGDMatrixCreateFromArrayInterface(

src/c_api/c_api.cc

@@ -261,7 +261,7 @@ XGB_DLL int XGDMatrixCreateFromCSR(char const *indptr,
   API_END();
 }
 
-XGB_DLL int XGDMatrixCreateFromArray(char const *data,
+XGB_DLL int XGDMatrixCreateFromDense(char const *data,
                                      char const *c_json_config,
                                      DMatrixHandle *out) {
   API_BEGIN();

src/data/array_interface.h

@@ -42,7 +42,8 @@ struct ArrayInterfaceErrors {
     return str.c_str();
   }
   static char const* Version() {
-    return "Only version <= 3 of `__cuda_array_interface__' are supported.";
+    return "Only version <= 3 of "
+           "`__cuda_array_interface__/__array_interface__' are supported.";
   }
   static char const* OfType(std::string const& type) {
     static std::string str;
@@ -81,7 +82,7 @@ struct ArrayInterfaceErrors {
         return "Other";
       default:
         LOG(FATAL) << "Invalid type code: " << c << " in `typestr' of input array."
-                   << "\nPlease verify the `__cuda_array_interface__' "
+                   << "\nPlease verify the `__cuda_array_interface__/__array_interface__' "
                    << "of your input data complies to: "
                    << "https://docs.scipy.org/doc/numpy/reference/arrays.interface.html"
                    << "\nOr open an issue.";
@@ -90,7 +91,7 @@ struct ArrayInterfaceErrors {
   }
 
   static std::string UnSupportedType(StringView typestr) {
-    return TypeStr(typestr[1]) + " is not supported.";
+    return TypeStr(typestr[1]) + "-" + typestr[2] + " is not supported.";
   }
 };
 
@@ -135,8 +136,9 @@ class ArrayInterfaceHandler {
     if (array.find("typestr") == array.cend()) {
       LOG(FATAL) << "Missing `typestr' field for array interface";
     }
+
     auto typestr = get<String const>(array.at("typestr"));
-    CHECK_EQ(typestr.size(),    3) << ArrayInterfaceErrors::TypestrFormat();
+    CHECK(typestr.size() == 3 || typestr.size() == 4) << ArrayInterfaceErrors::TypestrFormat();
     CHECK_NE(typestr.front(), '>') << ArrayInterfaceErrors::BigEndian();
 
     if (array.find("shape") == array.cend()) {
@@ -295,7 +297,7 @@ class ArrayInterface {
   }
 
  public:
-  enum Type : std::int8_t { kF4, kF8, kI1, kI2, kI4, kI8, kU1, kU2, kU4, kU8 };
+  enum Type : std::int8_t { kF4, kF8, kF16, kI1, kI2, kI4, kI8, kU1, kU2, kU4, kU8 };
 
  public:
   ArrayInterface() = default;
@@ -331,7 +333,12 @@ class ArrayInterface {
   }
 
   void AssignType(StringView typestr) {
-    if (typestr[1] == 'f' && typestr[2] == '4') {
+    if (typestr.size() == 4 && typestr[1] == 'f' && typestr[2] == '1' &&
+        typestr[3] == '6') {
+      type = kF16;
+      CHECK(sizeof(long double) == 16)
+          << "128-bit floating point is not supported on current platform.";
+    } else if (typestr[1] == 'f' && typestr[2] == '4') {
       type = kF4;
     } else if (typestr[1] == 'f' && typestr[2] == '8') {
       type = kF8;
@@ -364,6 +371,16 @@ class ArrayInterface {
       return func(reinterpret_cast<float *>(data));
     case kF8:
       return func(reinterpret_cast<double *>(data));
+#ifdef __CUDA_ARCH__
+    case kF16: {
+      // CUDA device code doesn't support long double.
+      SPAN_CHECK(false);
+      return func(reinterpret_cast<double *>(data));
+    }
+#else
+    case kF16:
+      return func(reinterpret_cast<long double *>(data));
+#endif
     case kI1:
       return func(reinterpret_cast<int8_t *>(data));
     case kI2:

tests/ci_build/conda_env/macos_cpu_test.yml

@@ -13,8 +13,8 @@ dependencies:
 - scikit-learn
 - pandas
 - matplotlib
-- dask
-- distributed
+- dask=2021.05.0
+- distributed=2021.05.0
 - graphviz
 - python-graphviz
 - hypothesis

tests/python-gpu/test_gpu_prediction.py

@@ -204,6 +204,7 @@ def predict_dense(x):
         cpu_predt = reg.predict(X)
         np.testing.assert_allclose(gpu_predt, cpu_predt, atol=1e-6)
 
+    @pytest.mark.skipif(**tm.no_cupy())
     @pytest.mark.skipif(**tm.no_cudf())
     def test_inplace_predict_cudf(self):
         import cupy as cp
@@ -332,6 +333,7 @@ def test_predict_categorical_split(self, df):
         rmse = mean_squared_error(y_true=y, y_pred=pred, squared=False)
         np.testing.assert_almost_equal(rmse, eval_history['train']['rmse'][-1], decimal=5)
 
+    @pytest.mark.skipif(**tm.no_cupy())
     @pytest.mark.parametrize("n_classes", [2, 3])
     def test_predict_dart(self, n_classes):
         from sklearn.datasets import make_classification
@@ -378,3 +380,59 @@ def test_predict_dart(self, n_classes):
 
         copied = cp.array(copied)
         cp.testing.assert_allclose(inplace, copied, atol=1e-6)
+
+    @pytest.mark.skipif(**tm.no_cupy())
+    def test_dtypes(self):
+        import cupy as cp
+        rows = 1000
+        cols = 10
+        rng = cp.random.RandomState(1994)
+        orig = rng.randint(low=0, high=127, size=rows * cols).reshape(
+            rows, cols
+        )
+        y = rng.randint(low=0, high=127, size=rows)
+        dtrain = xgb.DMatrix(orig, label=y)
+        booster = xgb.train({"tree_method": "gpu_hist"}, dtrain)
+
+        predt_orig = booster.inplace_predict(orig)
+        # all primitive types in numpy
+        for dtype in [
+            cp.signedinteger,
+            cp.byte,
+            cp.short,
+            cp.intc,
+            cp.int_,
+            cp.longlong,
+            cp.unsignedinteger,
+            cp.ubyte,
+            cp.ushort,
+            cp.uintc,
+            cp.uint,
+            cp.ulonglong,
+            cp.floating,
+            cp.half,
+            cp.single,
+            cp.double,
+        ]:
+            X = cp.array(orig, dtype=dtype)
+            predt = booster.inplace_predict(X)
+            cp.testing.assert_allclose(predt, predt_orig)
+
+        # boolean
+        orig = cp.random.binomial(1, 0.5, size=rows * cols).reshape(
+            rows, cols
+        )
+        predt_orig = booster.inplace_predict(orig)
+        for dtype in [cp.bool8, cp.bool_]:
+            X = cp.array(orig, dtype=dtype)
+            predt = booster.inplace_predict(X)
+            cp.testing.assert_allclose(predt, predt_orig)
+
+        # unsupported types
+        for dtype in [
+            cp.complex64,
+            cp.complex128,
+        ]:
+            X = cp.array(orig, dtype=dtype)
+            with pytest.raises(ValueError):
+                booster.inplace_predict(X)

tests/python/test_predict.py

@@ -237,3 +237,51 @@ def test_base_margin(self):
         dtrain = xgb.DMatrix(self.X, self.y, base_margin=base_margin)
         from_dmatrix = booster.predict(dtrain)
         np.testing.assert_allclose(from_dmatrix, from_inplace)
+
+    def test_dtypes(self):
+        orig = self.rng.randint(low=0, high=127, size=self.rows * self.cols).reshape(
+            self.rows, self.cols
+        )
+        predt_orig = self.booster.inplace_predict(orig)
+        # all primitive types in numpy
+        for dtype in [
+            np.signedinteger,
+            np.byte,
+            np.short,
+            np.intc,
+            np.int_,
+            np.longlong,
+            np.unsignedinteger,
+            np.ubyte,
+            np.ushort,
+            np.uintc,
+            np.uint,
+            np.ulonglong,
+            np.floating,
+            np.half,
+            np.single,
+            np.double,
+        ]:
+            X = np.array(orig, dtype=dtype)
+            predt = self.booster.inplace_predict(X)
+            np.testing.assert_allclose(predt, predt_orig)
+
+        # boolean
+        orig = self.rng.binomial(1, 0.5, size=self.rows * self.cols).reshape(
+            self.rows, self.cols
+        )
+        predt_orig = self.booster.inplace_predict(orig)
+        for dtype in [np.bool8, np.bool_]:
+            X = np.array(orig, dtype=dtype)
+            predt = self.booster.inplace_predict(X)
+            np.testing.assert_allclose(predt, predt_orig)
+
+        # unsupported types
+        for dtype in [
+            np.string_,
+            np.complex64,
+            np.complex128,
+        ]:
+            X = np.array(orig, dtype=dtype)
+            with pytest.raises(ValueError):
+                self.booster.inplace_predict(X)