!16502 check Optimizer parameters must be list

From: @wangnan39
Reviewed-by: @zh_qh,@kingxian
Signed-off-by: @zh_qh,@kingxian

mindspore/nn/optim/optimizer.py

@@ -121,14 +121,9 @@ class Optimizer(Cell):
 
     def __init__(self, learning_rate, parameters, weight_decay=0.0, loss_scale=1.0):
         super(Optimizer, self).__init__(auto_prefix=False)
-        if parameters is not None and not isinstance(parameters, list):
-            parameters = list(parameters)
-
-        if not parameters:
-            raise ValueError("Optimizer got an empty parameter list.")
-
-        if not isinstance(parameters[0], (dict, Parameter)):
-            raise TypeError("Only a list of Parameter or dict can be supported.")
+        parameters = self._parameters_base_check(parameters, "parameters")
+        if not all(isinstance(x, Parameter) for x in parameters) and not all(isinstance(x, dict) for x in parameters):
+            raise TypeError("All elements of the optimizer parameters must be of type `Parameter` or `dict`.")
 
         if isinstance(loss_scale, int):
             loss_scale = float(loss_scale)
@@ -373,6 +368,17 @@ class Optimizer(Cell):
             return _IteratorLearningRate(learning_rate, name)
         return learning_rate
 
+    def _parameters_base_check(self, parameters, param_info):
+        if parameters is None:
+            raise ValueError(f"Optimizer {param_info} can not be None.")
+        if not isinstance(parameters, Iterable):
+            raise TypeError(f"Optimizer {param_info} must be Iterable.")
+        parameters = list(parameters)
+
+        if not parameters:
+            raise ValueError(f"Optimizer got an empty {param_info} list.")
+        return parameters
+
     def _check_group_params(self, parameters):
         """Check group params."""
         parse_keys = ['params', 'lr', 'weight_decay', 'order_params', 'grad_centralization']
@@ -389,12 +395,9 @@ class Optimizer(Cell):
                     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.")
-
-            for param in group_param['params']:
-                if not isinstance(param, Parameter):
-                    raise TypeError("The group param should be an iterator of Parameter type.")
+            parameters = self._parameters_base_check(group_param['params'], "group `params`")
+            if not all(isinstance(x, Parameter) for x in parameters):
+                raise TypeError("The group `params` should be an iterator of Parameter type.")
 
     def _parse_group_params(self, parameters, learning_rate):
         """Parse group params."""

tests/ut/python/nn/optim/test_adam.py

@@ -70,7 +70,7 @@ class NetWithSparseGatherV2(nn.Cell):
 def test_adamwithoutparam():
     net = NetWithoutWeight()
     net.set_train()
-    with pytest.raises(ValueError, match=r"Optimizer got an empty parameter list"):
+    with pytest.raises(ValueError, match=r"Optimizer got an empty parameters list"):
         AdamWeightDecay(net.trainable_params(), learning_rate=0.1)
 
 
@@ -205,7 +205,6 @@ def test_adamoffload_group():
 
 def test_AdamWeightDecay_beta1():
     net = Net()
-    print("**********", net.get_parameters())
     with pytest.raises(ValueError):
         AdamWeightDecay(net.get_parameters(), beta1=1.0, learning_rate=0.1)
 
@@ -224,5 +223,5 @@ def test_AdamWeightDecay_e():
 
 def test_adam_mindspore_with_empty_params():
     net = nn.Flatten()
-    with pytest.raises(ValueError, match=r"Optimizer got an empty parameter list"):
+    with pytest.raises(ValueError, match=r"Optimizer got an empty parameters list"):
         AdamWeightDecay(net.get_parameters())