Added can_cast and result_type

Author: npolina4

dpctl/tensor/__init__.py

@@ -21,7 +21,7 @@
 
 """
 
-from numpy import dtype, finfo, iinfo
+from numpy import dtype
 
 from dpctl.tensor._copy_utils import asnumpy, astype, copy, from_numpy, to_numpy
 from dpctl.tensor._ctors import (
@@ -45,10 +45,14 @@
 from dpctl.tensor._manipulation_functions import (
     broadcast_arrays,
     broadcast_to,
+    can_cast,
     concat,
     expand_dims,
+    finfo,
     flip,
+    iinfo,
     permute_dims,
+    result_type,
     roll,
     squeeze,
     stack,
@@ -121,4 +125,6 @@
     "complex128",
     "iinfo",
     "finfo",
+    "can_cast",
+    "result_type",
 ]

dpctl/tensor/_manipulation_functions.py

@@ -309,8 +309,7 @@ def _arrays_validation(arrays):
         raise ValueError("All the input arrays must have usm_type")
 
     X0 = arrays[0]
-    if not all(Xi.dtype.char in "?bBhHiIlLqQefdFD" for Xi in arrays):
-        raise ValueError("Unsupported dtype encountered.")
+    _support_dtype(Xi.dtype for Xi in arrays)
 
     res_dtype = X0.dtype
     for i in range(1, n):
@@ -421,3 +420,61 @@ def stack(arrays, axis=0):
     dpctl.SyclEvent.wait_for(hev_list)
 
     return res
+
+
+def can_cast(array_and_dtype_from, dtype_to):
+    """
+    can_cast(from: usm_ndarray or dtype, to: dtype) -> bool
+
+    Determines if one data type can be cast to another data type according \
+        Type Promotion Rules rules.
+    """
+    if not isinstance(dtype_to, dpt.dtype):
+        raise TypeError("Expected dtype type.")
+
+    dtype_from = dpt.dtype(array_and_dtype_from)
+
+    _support_dtype([dtype_to, dtype_from])
+
+    return np.can_cast(dtype_from, dtype_to)
+
+
+def result_type(*arrays_and_dtypes):
+    """
+    result_type(arrays_and_dtypes: an arbitrary number usm_ndarrays or dtypes)\
+         -> dtype
+
+    Returns the dtype that results from applying the Type Promotion Rules to \
+        the arguments.
+    """
+    dtypes = [dpt.dtype(X) for X in arrays_and_dtypes]
+
+    _support_dtype(dtypes)
+
+    return np.result_type(*dtypes)
+
+
+def iinfo(type):
+    """
+    iinfo(type: integer data-type) -> iinfo_object
+
+    Returns machine limits for integer data types.
+    """
+    _support_dtype(type)
+    return np.iinfo(type)
+
+
+def finfo(type):
+    """
+    finfo(type: float data-type) -> finfo_object
+
+    Returns machine limits for float data types.
+    """
+    _support_dtype(type)
+    return np.finfo(type)
+
+
+def _support_dtype(dtypes):
+    if not all(dtype.char in "?bBhHiIlLqQefdFD" for dtype in dtypes):
+        raise ValueError("Unsupported dtype encountered.")
+    return True

dpctl/tests/test_usm_ndarray_manipulation.py

@@ -999,3 +999,30 @@ def test_stack_3arrays(data):
     R = dpt.stack([X, Y, Z], axis=axis)
 
     assert_array_equal(Rnp, dpt.asnumpy(R))
+
+
+def test_can_cast():
+    try:
+        q = dpctl.SyclQueue()
+    except dpctl.SyclQueueCreationError:
+        pytest.skip("Queue could not be created")
+
+    # incorrect input
+    X = dpt.ones((2, 2), dtype=dpt.int64, sycl_queue=q)
+    pytest.raises(TypeError, dpt.can_cast, X, 1)
+    X_np = np.ones((2, 2), dtype=np.int64)
+
+    assert dpt.can_cast(X, dpt.int32) == np.can_cast(X_np, np.int32)
+    assert dpt.can_cast(X, dpt.int64) == np.can_cast(X_np, np.int64)
+
+
+def test_result_type():
+    try:
+        q = dpctl.SyclQueue()
+    except dpctl.SyclQueueCreationError:
+        pytest.skip("Queue could not be created")
+
+    X = [dpt.ones((2), dtype=dpt.int64, sycl_queue=q), dpt.int32, dpt.float16]
+    X_np = [np.ones((2), dtype=np.int64), dpt.int32, dpt.float16]
+
+    assert dpt.result_type(*X) == np.result_type(*X_np)