add sponge frontend

mindspore/_checkparam.py

@@ -23,6 +23,7 @@ from itertools import repeat, zip_longest
 from collections import deque
 from collections.abc import Iterable
 import numpy as np
+from mindspore import context
 from mindspore import log as logger
 from mindspore.common import dtype as mstype
 from mindspore._c_expression import Tensor as Tensor_
@@ -846,6 +847,10 @@ class Validator:
         """Returns an empty Tensor."""
         return Tensor_(dtype, shape)
 
+    @staticmethod
+    def check_type_support(dtype, device, supported_dtypes):
+        return dtype in supported_dtypes or not context.get_context('device_target') == device
+
 
 def check_input_format(input_param):
     """Judge input format."""

mindspore/_extends/parse/standard_method.py

@@ -1312,7 +1312,8 @@ def sum(x, axis=None, dtype=None, keepdims=False, initial=None): # pylint: disab
         >>> print(input_x.sum(axis=1))
         [10. 35.]
     """
-    dtype = x.dtype if dtype is None else dtype
+    input_x = x.astype(mstype.int32) if x.dtype == mstype.bool_ else x
+    dtype = input_x.dtype if dtype is None else dtype
     if not isinstance(keepdims, int):
         const_utils.raise_type_error("integer argument expected")
     if initial is not None and not isinstance(initial, (int, float, bool)):
@@ -1322,14 +1323,14 @@ def sum(x, axis=None, dtype=None, keepdims=False, initial=None): # pylint: disab
     else:
         axis = check_and_canonicalize_axes(axis, x.ndim)
 
-    if x.dtype == mstype.bool_:
-        x = x.astype("int32")
+    if not check_type_support(input_x.dtype, 'GPU', (mstype.float64, mstype.float32, mstype.float16)):
+        input_x = input_x.astype(mstype.float32)
     if 0 in x.shape:
         x = const_utils.make_tensor([0], x.dtype)
     if keepdims:
-        res = _reduce_sum_keepdims(x, axis)
+        res = _reduce_sum_keepdims(input_x, axis)
     else:
-        res = _reduce_sum_default(x, axis)
+        res = _reduce_sum_default(input_x, axis)
     if initial is not None:
         res += initial
     return res.astype(dtype)
@@ -1648,6 +1649,7 @@ get_log2_size = constexpr(validator.get_log2_size)
 check_axis_type = constexpr(validator.check_axis_type)
 check_and_canonicalize_axes = constexpr(validator.check_and_canonicalize_axes)
 empty_compile = constexpr(validator.empty_compile)
+check_type_support = constexpr(validator.check_type_support)
 
 
 def tensor_bool(x):

mindspore/common/tensor.py

@@ -1798,7 +1798,8 @@ class Tensor(Tensor_):
             >>> print(input_x.sum(axis=1))
             [10. 35.]
         """
-        dtype = self.dtype if dtype is None else dtype
+        input_x = self.astype(mstype.int32) if self.dtype == mstype.bool_ else self
+        dtype = input_x.dtype if dtype is None else dtype
         if not isinstance(keepdims, int):
             raise TypeError(f"integer argument expected, but got {type(keepdims)}")
         if initial is not None and not isinstance(initial, (int, float, bool)):
@@ -1808,7 +1809,9 @@ class Tensor(Tensor_):
         else:
             axis = validator.check_and_canonicalize_axes(axis, self.ndim)
 
-        input_x = self.astype(mstype.int32) if self.dtype == mstype.bool_ else self
+        if not validator.check_type_support(input_x.dtype, 'GPU',
+                                            (mstype.float64, mstype.float32, mstype.float16)):
+            input_x = input_x.astype(mstype.float32)
         if 0 in self.shape:
             input_x = tensor_operator_registry.get('make_tensor')([0], self.dtype)
         res = tensor_operator_registry.get('sum')(bool(keepdims))(input_x, axis)

mindspore/numpy/array_creations.py

@@ -89,7 +89,7 @@ def array(obj, dtype=None, copy=True, ndmin=0):
             _raise_value_error("Empty tensor cannot be expanded beyond the current dimension.")
         res = _expand(res, ndmin)
 
-    if copy:
+    if copy and isinstance(obj, Tensor):
         res = copy_(res)
     elif dtype is not None and dtype != res.dtype:
         res = res.astype(dtype)

mindspore/ops/functional.py

@@ -116,6 +116,10 @@ bitwise_and = P.BitwiseAnd()
 bitwise_or = P.BitwiseOr()
 bitwise_xor = P.BitwiseXor()
 invert = P.Invert()
+erf = P.Erf()
+erfc = P.Erfc()
+sort = P.Sort()
+tensor_range = P.Range()
 
 scalar_to_array = P.ScalarToArray()
 scalar_to_tensor = P.ScalarToTensor()