bugfix bert perf test

tests/perf_test/bert/test_bert_train.py

@@ -54,13 +54,12 @@ def load_test_data(batch_size=1):
     return ret
 
 
-def get_config(version='base', batch_size=1):
+def get_config(version='base'):
     """
     get_config definition
     """
     if version == 'base':
         return BertConfig(
-            batch_size=batch_size,
             seq_length=128,
             vocab_size=21128,
             hidden_size=768,
@@ -74,13 +73,10 @@ def get_config(version='base', batch_size=1):
             type_vocab_size=2,
             initializer_range=0.02,
             use_relative_positions=True,
-            input_mask_from_dataset=True,
-            token_type_ids_from_dataset=True,
             dtype=mstype.float32,
             compute_type=mstype.float32)
     if version == 'large':
         return BertConfig(
-            batch_size=batch_size,
             seq_length=128,
             vocab_size=21128,
             hidden_size=1024,
@@ -94,11 +90,9 @@ def get_config(version='base', batch_size=1):
             type_vocab_size=2,
             initializer_range=0.02,
             use_relative_positions=True,
-            input_mask_from_dataset=True,
-            token_type_ids_from_dataset=True,
             dtype=mstype.float32,
             compute_type=mstype.float32)
-    return BertConfig(batch_size=batch_size)
+    return BertConfig()
 
 
 class BertLearningRate(lr_schedules.LearningRateSchedule):
@@ -143,7 +137,7 @@ def test_bert_train():
     batch_size = int(os.getenv('BATCH_SIZE', '1'))
     inputs = load_test_data(batch_size)
 
-    config = get_config(version=version, batch_size=batch_size)
+    config = get_config(version=version)
     netwithloss = BertNetworkWithLoss(config, True)
     lr = BertLearningRate(10)
     optimizer = AdamWeightDecay(netwithloss.trainable_params(), lr)
@@ -168,7 +162,7 @@ def test_bert_withlossscale_train():
     scaling_sens = Tensor(np.ones([1]).astype(np.float32))
     inputs = load_test_data(batch_size) + (scaling_sens,)
 
-    config = get_config(version=version, batch_size=batch_size)
+    config = get_config(version=version)
     netwithloss = BertNetworkWithLoss(config, True)
     lr = BertLearningRate(10)
     optimizer = AdamWeightDecay(netwithloss.trainable_params(), lr)
@@ -195,7 +189,7 @@ def bert_withlossscale_manager_train():
     batch_size = int(os.getenv('BATCH_SIZE', '1'))
     inputs = load_test_data(batch_size)
 
-    config = get_config(version=version, batch_size=batch_size)
+    config = get_config(version=version)
     netwithloss = BertNetworkWithLoss(config, True)
     lr = BertLearningRate(10)
     optimizer = AdamWeightDecay(netwithloss.trainable_params(), lr)
@@ -223,7 +217,7 @@ def bert_withlossscale_manager_train_feed():
     scaling_sens = Tensor(np.ones([1]).astype(np.float32))
     inputs = load_test_data(batch_size) + (scaling_sens,)
 
-    config = get_config(version=version, batch_size=batch_size)
+    config = get_config(version=version)
     netwithloss = BertNetworkWithLoss(config, True)
     lr = BertLearningRate(10)
     optimizer = AdamWeightDecay(netwithloss.trainable_params(), lr)