Fix input validator of Assign.

mindspore/ops/operations/nn_ops.py

@@ -234,7 +234,7 @@ class Softsign(PrimitiveWithInfer):
         \text{output} = \frac{\text{input_x}}{1 + \abs{\text{input_x}}},
 
     Inputs:
-        - **input_x** (Tensor) - The input tensor whose data type should be float.
+        - **input_x** (Tensor) - The input tensor whose data type should be float16 or float32.
 
     Outputs:
         Tensor, with the same type and shape as the `input_x`.
@@ -255,7 +255,7 @@ class Softsign(PrimitiveWithInfer):
         return input_x
 
     def infer_dtype(self, input_x):
-        validator.check_tensor_type_same({'input_x': input_x}, mstype.float_type, self.name)
+        validator.check_tensor_type_same({'input_x': input_x}, [mstype.float16, mstype.float32], self.name)
         return input_x
 
 
@@ -4730,19 +4730,19 @@ class CTCLoss(PrimitiveWithInfer):
         preprocess_collapse_repeated (bool): If True, repeated labels are collapsed prior to the CTC calculation.
                                              Default: False.
         ctc_merge_repeated (bool): If False, during CTC calculation, repeated non-blank labels will not be merged
-                                   and are interpreted as individual labels. This is a simplfied version if CTC.
+                                   and are interpreted as individual labels. This is a simplfied version of CTC.
                                    Default: True.
         ignore_longer_outputs_than_inputs (bool): If True, sequences with longer outputs than inputs will be ignored.
                                                   Default: False.
 
     Inputs:
         - **inputs** (Tensor) - The input Tensor should be a `3-D` tensor whose shape is
-          :math:`(max_time, batch_size, num_class)`. `num_class` should be `num_labels + 1` classes, `num_labels`
-          indicates the number of actual labels. Blank labels are reserved.
+          :math:`(max_time, batch_size, num_classes)`. `num_classes` should be `num_labels + 1` classes, `num_labels`
+          indicates the number of actual labels. Blank labels are reserved. Default blank label is `num_classes - 1`.
         - **labels_indices** (Tensor) - The indices of labels. `labels_indices[i, :] == [b, t]` means `labels_values[i]`
           stores the id for `(batch b, time t)`. The type must be int64 and rank must be 2.
         - **labels_values** (Tensor) - A `1-D` input tensor. The values associated with the given batch and time. The
-          type must be int32. `labels_values[i]` must in the range of `[0, num_class)`.
+          type must be int32. `labels_values[i]` must in the range of `[0, num_classes)`.
         - **sequence_length** (Tensor) - A tensor containing sequence lengths with the shape of :math:`(batch_size)`.
           The type must be int32. Each value in the tensor should not greater than `max_time`.
 

mindspore/ops/operations/other_ops.py

@@ -60,7 +60,9 @@ class Assign(PrimitiveWithInfer):
         return variable
 
     def infer_dtype(self, variable, value):
-        # Add a type validation later when we don't have to assign a value to RefKey.
+        if variable != mstype.type_refkey:
+            validator.check_tensor_type_same({"variable": variable}, mstype.number_type, self.name)
+        validator.check_scalar_or_tensor_type_same({"value": value}, mstype.number_type, self.name)
         return variable