!36872 add type check for Adadelta input

Merge pull request !36872 from fujianzhao/fix_adadelta

docs/api/api_python/nn/mindspore.nn.Adadelta.rst

@@ -47,7 +47,7 @@ mindspore.nn.Adadelta
 
     **输入：**
 
-    - **grads** (tuple[Tensor]) - 优化器中 `params` 的梯度，形状（shape）与 `params` 相同。
+    - **grads** (tuple[Tensor]) - 优化器中 `params` 的梯度，形状（shape）和数据类型与 `params` 相同。数据类型为float16或float32。
 
     **输出：**
 

mindspore/python/mindspore/nn/optim/adadelta.py

@@ -16,6 +16,7 @@
 from mindspore.ops import functional as F, composite as C, operations as P
 from mindspore._checkparam import Validator as validator
 from mindspore._checkparam import Rel
+from mindspore.common.tensor import Tensor
 from .optimizer import Optimizer
 from .optimizer import opt_init_args_register
 
@@ -122,8 +123,8 @@ class Adadelta(Optimizer):
               the Cell with step as the input to get the weight decay value of current step.
 
     Inputs:
-        - **grads** (tuple[Tensor]) - The gradients of `params` in the optimizer, the shape is the same as the `params`
-          in optimizer.
+        - **grads** (tuple[Tensor]) - The gradients of `params` in the optimizer, has the same shape and data type as
+          the `params` in optimizer. With float16 or float32 data type.
 
     Outputs:
         Tensor[bool], the value is True.
@@ -183,6 +184,8 @@ class Adadelta(Optimizer):
         self.epsilon = epsilon
 
     def construct(self, grads):
+        if not isinstance(grads, tuple) or not isinstance(grads[0], Tensor):
+            raise TypeError("For 'Adadelta', the 'grads' must be a tuple of Tensor.")
         params = self.parameters
         grads = self.decay_weight(grads)
         grads = self.gradients_centralization(grads)