diff --git a/model_zoo/official/nlp/bert/run_pretrain.py b/model_zoo/official/nlp/bert/run_pretrain.py
index bab9fa2f583..749f6f6236e 100644
--- a/model_zoo/official/nlp/bert/run_pretrain.py
+++ b/model_zoo/official/nlp/bert/run_pretrain.py
@@ -117,8 +117,7 @@ def run_pretrain():
         decay_params = list(filter(cfg.Lamb.decay_filter, params))
         other_params = list(filter(lambda x: x not in decay_params, params))
         group_params = [{'params': decay_params, 'weight_decay': cfg.Lamb.weight_decay},
-                        {'params': other_params},
-                        {'order_params': params}]
+                        {'params': other_params}]
         optimizer = Lamb(group_params, learning_rate=lr_schedule, eps=cfg.Lamb.eps)
     elif cfg.optimizer == 'Momentum':
         optimizer = Momentum(net_with_loss.trainable_params(), learning_rate=cfg.Momentum.learning_rate,
@@ -133,8 +132,7 @@ def run_pretrain():
         decay_params = list(filter(cfg.AdamWeightDecay.decay_filter, params))
         other_params = list(filter(lambda x: x not in decay_params, params))
         group_params = [{'params': decay_params, 'weight_decay': cfg.AdamWeightDecay.weight_decay},
-                        {'params': other_params, 'weight_decay': 0.0},
-                        {'order_params': params}]
+                        {'params': other_params, 'weight_decay': 0.0}]
 
         optimizer = AdamWeightDecay(group_params, learning_rate=lr_schedule, eps=cfg.AdamWeightDecay.eps)
     else:
diff --git a/model_zoo/official/nlp/bert/src/bert_for_finetune.py b/model_zoo/official/nlp/bert/src/bert_for_finetune.py
index 32ac0823b97..5fbf1d81b9b 100644
--- a/model_zoo/official/nlp/bert/src/bert_for_finetune.py
+++ b/model_zoo/official/nlp/bert/src/bert_for_finetune.py
@@ -22,7 +22,7 @@ from mindspore.ops import operations as P
 from mindspore.ops import functional as F
 from mindspore.ops import composite as C
 from mindspore.common.tensor import Tensor
-from mindspore.common.parameter import Parameter, ParameterTuple
+from mindspore.common.parameter import Parameter
 from mindspore.common import dtype as mstype
 from mindspore.nn.wrap.grad_reducer import DistributedGradReducer
 from mindspore.train.parallel_utils import ParallelMode
@@ -55,7 +55,7 @@ class BertFinetuneCell(nn.Cell):
 
         super(BertFinetuneCell, self).__init__(auto_prefix=False)
         self.network = network
-        self.weights = ParameterTuple(network.trainable_params())
+        self.weights = optimizer.parameters
         self.optimizer = optimizer
         self.grad = C.GradOperation('grad',
                                     get_by_list=True,
@@ -158,7 +158,7 @@ class BertSquadCell(nn.Cell):
     def __init__(self, network, optimizer, scale_update_cell=None):
         super(BertSquadCell, self).__init__(auto_prefix=False)
         self.network = network
-        self.weights = ParameterTuple(network.trainable_params())
+        self.weights = optimizer.parameters
         self.optimizer = optimizer
         self.grad = C.GradOperation('grad', get_by_list=True, sens_param=True)
         self.reducer_flag = False
diff --git a/model_zoo/official/nlp/bert/src/bert_for_pre_training.py b/model_zoo/official/nlp/bert/src/bert_for_pre_training.py
index 802391ee861..1d12ddaf061 100644
--- a/model_zoo/official/nlp/bert/src/bert_for_pre_training.py
+++ b/model_zoo/official/nlp/bert/src/bert_for_pre_training.py
@@ -21,7 +21,7 @@ from mindspore.ops import operations as P
 from mindspore.ops import functional as F
 from mindspore.ops import composite as C
 from mindspore.common.tensor import Tensor
-from mindspore.common.parameter import Parameter, ParameterTuple
+from mindspore.common.parameter import Parameter
 from mindspore.common import dtype as mstype
 from mindspore.nn.wrap.grad_reducer import DistributedGradReducer
 from mindspore.train.parallel_utils import ParallelMode
@@ -270,7 +270,7 @@ class BertTrainOneStepCell(nn.Cell):
     def __init__(self, network, optimizer, sens=1.0):
         super(BertTrainOneStepCell, self).__init__(auto_prefix=False)
         self.network = network
-        self.weights = ParameterTuple(network.trainable_params())
+        self.weights = optimizer.parameters
         self.optimizer = optimizer
         self.grad = C.GradOperation('grad', get_by_list=True, sens_param=True)
         self.sens = sens
@@ -349,7 +349,7 @@ class BertTrainOneStepWithLossScaleCell(nn.Cell):
     def __init__(self, network, optimizer, scale_update_cell=None):
         super(BertTrainOneStepWithLossScaleCell, self).__init__(auto_prefix=False)
         self.network = network
-        self.weights = ParameterTuple(network.trainable_params())
+        self.weights = optimizer.parameters
         self.optimizer = optimizer
         self.grad = C.GradOperation('grad',
                                     get_by_list=True,
diff --git a/model_zoo/official/nlp/bert/src/utils.py b/model_zoo/official/nlp/bert/src/utils.py
index 6e8ea6ed643..71d37039e90 100644
--- a/model_zoo/official/nlp/bert/src/utils.py
+++ b/model_zoo/official/nlp/bert/src/utils.py
@@ -133,7 +133,10 @@ class BertLearningRate(LearningRateSchedule):
     """
     def __init__(self, learning_rate, end_learning_rate, warmup_steps, decay_steps, power):
         super(BertLearningRate, self).__init__()
-        self.warmup_lr = WarmUpLR(learning_rate, warmup_steps)
+        self.warmup_flag = False
+        if warmup_steps > 0:
+            self.warmup_flag = True
+            self.warmup_lr = WarmUpLR(learning_rate, warmup_steps)
         self.decay_lr = PolynomialDecayLR(learning_rate, end_learning_rate, decay_steps, power)
         self.warmup_steps = Tensor(np.array([warmup_steps]).astype(np.float32))
 
@@ -142,8 +145,11 @@ class BertLearningRate(LearningRateSchedule):
         self.cast = P.Cast()
 
     def construct(self, global_step):
-        is_warmup = self.cast(self.greater(self.warmup_steps, global_step), mstype.float32)
-        warmup_lr = self.warmup_lr(global_step)
         decay_lr = self.decay_lr(global_step)
-        lr = (self.one - is_warmup) * decay_lr + is_warmup * warmup_lr
+        if self.warmup_flag:
+            is_warmup = self.cast(self.greater(self.warmup_steps, global_step), mstype.float32)
+            warmup_lr = self.warmup_lr(global_step)
+            lr = (self.one - is_warmup) * decay_lr + is_warmup * warmup_lr
+        else:
+            lr = decay_lr
         return lr