support parameter updata with implicit type conversion

mindspore/common/dtype.py

@@ -119,6 +119,9 @@ int_type = (int8, int16, int32, int64,)
 uint_type = (uint8, uint16, uint32, uint64)
 float_type = (float16, float32, float64,)
 
+implicit_conversion_seq = {t: idx for idx, t in enumerate((
+    bool_, int8, uint8, int16, int32, int64, float16, float32, float64))}
+
 _simple_types = {
     list: list_,
     tuple: tuple_,

mindspore/common/parameter.py

@@ -313,8 +313,9 @@ class Parameter(MetaTensor):
             Parameter, the parameter after set data.
         """
         def raise_type_error(incoming):
-            raise TypeError(f"Can not change the Parameter dtype. Current dtype is {self.set_dtype}"
-                            f", and incoming is {incoming}. Use .set_dtype(xxx) to change the dtype.")
+            raise TypeError(f"Incoming Parameter dtype can not be converted to current dtype implicitly. "
+                            f"Current dtype is {self.dtype}, and incoming is {incoming}. "
+                            f"Use .set_dtype(xxx) to change the dtype.")
 
         if not isinstance(data, (MetaTensor, Initializer, int, float)):
             raise TypeError(f"Parameter data must be [`Initializer`, `int`, `float`] or a kind of `MetaTensor` "
@@ -338,7 +339,10 @@ class Parameter(MetaTensor):
                 raise ValueError(f"Can not change the shape of Parameter which has been initialized."
                                  f" Current shape is {self.shape}, and incoming is {data.shape}.")
         if self.dtype != data.dtype:
-            raise_type_error(data.dtype)
+            if mstype.implicit_conversion_seq[self.dtype] < mstype.implicit_conversion_seq[data.dtype]:
+                raise_type_error(data.dtype)
+            else:
+                data = Tensor(data, self.dtype)
         if isinstance(data, Initializer):
             # The parameter has been initializered, directly update by the data
             if is_current_tensor:

mindspore/common/tensor.py

@@ -74,7 +74,7 @@ class Tensor(Tensor_):
         self._virtual_flag = False
 
     def __repr__(self):
-        return str(Tensor_.__str__(self))
+        return Tensor_.__repr__(self)
 
     def __add__(self, other):
         out = tensor_operator_registry.get('__add__')(self, other)

tests/ut/python/nn/test_parameter.py

@@ -157,6 +157,7 @@ def test_parameter_compute():
 
 
 def test_scalar_parameter_update():
+    # float
     fp = Parameter(0.5, 'fp')
     fp.default_input = 0.8
     assert np.array_equal(fp.default_input.asnumpy(), np.array(0.8, np.float32))
@@ -167,6 +168,26 @@ def test_scalar_parameter_update():
     assert np.array_equal(int_.default_input.asnumpy(), np.array(2, np.int32))
     with pytest.raises(TypeError):
         int_.default_input = 1.2
+    # Tensor
+    fp32 = Tensor(0.5, mstype.float32)
+    int32 = Tensor(2, mstype.int32)
+    fp16 = Tensor(0.6, mstype.float16)
+    int16 = Tensor(3, mstype.int16)
+    bool_ = Tensor(np.array(True, dtype=np.bool_))
+    # updata_by_tensor
+    fp32_p = Parameter(fp32, 'fp32')
+    fp32_p.default_input = 0.8
+    fp32_p.default_input = 1
+    fp32_p.default_input = int32
+    fp32_p.default_input = fp32
+    fp32_p.default_input = int16
+    fp32_p.default_input = fp16
+    fp32_p.default_input = bool_
+
+    # updata_by_tensor
+    fp16_p = Parameter(fp16, 'fp16')
+    with pytest.raises(TypeError):
+        fp16_p.default_input = fp32
 
 
 def test_parameter_lazy_init():