!2399 fix param KeyError in group params

Merge pull request !2399 from ghzl/fix-params-keyerror-in-group-params

mindspore/nn/optim/optimizer.py

@@ -219,8 +219,28 @@ class Optimizer(Cell):
             raise TypeError("Learning rate should be float, Tensor or Iterable.")
         return lr
 
+    def _check_group_params(self, parameters):
+        """Check group params."""
+        parse_keys = ['params', 'lr', 'weight_decay', 'order_params']
+        for group_param in parameters:
+            invalid_key = list(filter(lambda x: x not in parse_keys, group_param.keys()))
+            if invalid_key:
+                raise KeyError(f'The key "{invalid_key}" cannot be recognized in group params.')
+
+            if 'order_params' in group_param.keys():
+                if len(group_param.keys()) > 1:
+                    raise ValueError("The order params dict in group parameters should "
+                                     "only include the 'order_params' key.")
+                if not isinstance(group_param['order_params'], Iterable):
+                    raise TypeError("The value of 'order_params' should be an Iterable type.")
+                continue
+
+            if not group_param['params']:
+                raise ValueError("Optimizer got an empty group parameter list.")
+
     def _parse_group_params(self, parameters, learning_rate):
         """Parse group params."""
+        self._check_group_params(parameters)
         if self.dynamic_lr:
             dynamic_lr_length = learning_rate.size()
         else:
@@ -250,9 +270,6 @@ class Optimizer(Cell):
             if dynamic_lr_length not in (lr_length, 0):
                 raise ValueError("The dynamic learning rate in group should be the same size.")
 
-            if not group_param['params']:
-                raise ValueError("Optimizer got an empty group parameter list.")
-
             dynamic_lr_length = lr_length
         self.dynamic_lr_length = dynamic_lr_length
 

mindspore/ops/operations/debug_ops.py

@@ -309,12 +309,6 @@ class Print(PrimitiveWithInfer):
     Output tensor or string to stdout.
 
     Note:
-        The print operation cannot support the following cases currently.
-
-        1. The type of tensor is float64 or bool.
-
-        2. The data of tensor is a scalar type.
-
         In pynative mode, please use python print function.
 
     Inputs: