diff --git a/easy_rec/python/layers/keras/fibinet.py b/easy_rec/python/layers/keras/fibinet.py
index 18c75e164..59ad01bd6 100644
--- a/easy_rec/python/layers/keras/fibinet.py
+++ b/easy_rec/python/layers/keras/fibinet.py
@@ -58,7 +58,7 @@ def build(self, input_shape):
         kernel_initializer='he_normal',
         name='W1')
     self.excite_layer = Dense(
-        units=emb_size, kernel_regularizer='glorot_normal', name='W2')
+        units=emb_size, kernel_initializer='glorot_normal', name='W2')
 
   def call(self, inputs, **kwargs):
     g = self.config.num_squeeze_group
diff --git a/easy_rec/python/layers/keras/mask_net.py b/easy_rec/python/layers/keras/mask_net.py
index ac37a0a36..f76c9ab41 100644
--- a/easy_rec/python/layers/keras/mask_net.py
+++ b/easy_rec/python/layers/keras/mask_net.py
@@ -31,8 +31,9 @@ def __init__(self, params, name='mask_block', reuse=None, **kwargs):
     self.reuse = reuse
 
   def build(self, input_shape):
-    input_dim = int(input_shape[0].shape[-1])
-    mask_input_dim = int(input_shape[1].shape[-1])
+    assert len(input_shape) >= 2, 'MaskBlock must has at least two inputs'
+    input_dim = int(input_shape[0][-1])
+    mask_input_dim = int(input_shape[1][-1])
     if self.config.HasField('reduction_factor'):
       aggregation_size = int(mask_input_dim * self.config.reduction_factor)
     elif self.config.HasField('aggregation_size') is not None:
diff --git a/easy_rec/python/test/train_eval_test.py b/easy_rec/python/test/train_eval_test.py
index 859e2442c..dd2bd8f38 100644
--- a/easy_rec/python/test/train_eval_test.py
+++ b/easy_rec/python/test/train_eval_test.py
@@ -880,6 +880,15 @@ def test_train_with_multi_worker_mirror(self):
         self._test_dir)
     self.assertTrue(self._success)
 
+  @unittest.skipIf(
+      LooseVersion(tf.__version__) != LooseVersion('2.3.0'),
+      'MultiWorkerMirroredStrategy need tf version == 2.3')
+  def test_train_with_multi_worker_mirror(self):
+    self._success = test_utils.test_distributed_train_eval(
+        'samples/model_config/mmoe_mirrored_strategy_on_taobao.config',
+        self._test_dir)
+    self.assertTrue(self._success)
+
   def test_fg_dtype(self):
     self._success = test_utils.test_single_train_eval(
         'samples/model_config/taobao_fg_test_dtype.config', self._test_dir)
diff --git a/samples/model_config/mmoe_mirrored_strategy_on_taobao.config b/samples/model_config/mmoe_mirrored_strategy_on_taobao.config
new file mode 100644
index 000000000..761f3e739
--- /dev/null
+++ b/samples/model_config/mmoe_mirrored_strategy_on_taobao.config
@@ -0,0 +1,318 @@
+train_input_path: "data/test/tb_data/taobao_train_data"
+eval_input_path: "data/test/tb_data/taobao_test_data"
+model_dir: "experiments/mmoe_mirrored_strategy_taobao_ckpt"
+
+train_config {
+  optimizer_config {
+    adam_optimizer {
+      learning_rate {
+        exponential_decay_learning_rate {
+          initial_learning_rate: 0.001
+          decay_steps: 1000
+          decay_factor: 0.5
+          min_learning_rate: 1e-07
+        }
+      }
+    }
+    use_moving_average: false
+  }
+  train_distribute: MultiWorkerMirroredStrategy
+  num_gpus_per_worker: 2
+  num_steps: 200
+  sync_replicas: true
+  save_checkpoints_steps: 100
+  log_step_count_steps: 100
+}
+data_config {
+  batch_size: 4096
+  label_fields: "clk"
+  label_fields: "buy"
+  prefetch_size: 32
+  input_type: CSVInput
+  input_fields {
+    input_name: "clk"
+    input_type: INT32
+  }
+  input_fields {
+    input_name: "buy"
+    input_type: INT32
+  }
+  input_fields {
+    input_name: "pid"
+    input_type: STRING
+  }
+  input_fields {
+    input_name: "adgroup_id"
+    input_type: STRING
+  }
+  input_fields {
+    input_name: "cate_id"
+    input_type: STRING
+  }
+  input_fields {
+    input_name: "campaign_id"
+    input_type: STRING
+  }
+  input_fields {
+    input_name: "customer"
+    input_type: STRING
+  }
+  input_fields {
+    input_name: "brand"
+    input_type: STRING
+  }
+  input_fields {
+    input_name: "user_id"
+    input_type: STRING
+  }
+  input_fields {
+    input_name: "cms_segid"
+    input_type: STRING
+  }
+  input_fields {
+    input_name: "cms_group_id"
+    input_type: STRING
+  }
+  input_fields {
+    input_name: "final_gender_code"
+    input_type: STRING
+  }
+  input_fields {
+    input_name: "age_level"
+    input_type: STRING
+  }
+  input_fields {
+    input_name: "pvalue_level"
+    input_type: STRING
+  }
+  input_fields {
+    input_name: "shopping_level"
+    input_type: STRING
+  }
+  input_fields {
+    input_name: "occupation"
+    input_type: STRING
+  }
+  input_fields {
+    input_name: "new_user_class_level"
+    input_type: STRING
+  }
+  input_fields {
+    input_name: "tag_category_list"
+    input_type: STRING
+  }
+  input_fields {
+    input_name: "tag_brand_list"
+    input_type: STRING
+  }
+  input_fields {
+    input_name: "price"
+    input_type: INT32
+  }
+}
+feature_config: {
+  features {
+    input_names: "pid"
+    feature_type: IdFeature
+    embedding_dim: 16
+    hash_bucket_size: 10
+  }
+  features {
+    input_names: "adgroup_id"
+    feature_type: IdFeature
+    embedding_dim: 16
+    hash_bucket_size: 100000
+  }
+  features {
+    input_names: "cate_id"
+    feature_type: IdFeature
+    embedding_dim: 16
+    hash_bucket_size: 10000
+  }
+  features {
+    input_names: "campaign_id"
+    feature_type: IdFeature
+    embedding_dim: 16
+    hash_bucket_size: 100000
+  }
+  features {
+    input_names: "customer"
+    feature_type: IdFeature
+    embedding_dim: 16
+    hash_bucket_size: 100000
+  }
+  features {
+    input_names: "brand"
+    feature_type: IdFeature
+    embedding_dim: 16
+    hash_bucket_size: 100000
+  }
+  features {
+    input_names: "user_id"
+    feature_type: IdFeature
+    embedding_dim: 16
+    hash_bucket_size: 100000
+  }
+  features {
+    input_names: "cms_segid"
+    feature_type: IdFeature
+    embedding_dim: 16
+    hash_bucket_size: 100
+  }
+  features {
+    input_names: "cms_group_id"
+    feature_type: IdFeature
+    embedding_dim: 16
+    hash_bucket_size: 100
+  }
+  features {
+    input_names: "final_gender_code"
+    feature_type: IdFeature
+    embedding_dim: 16
+    hash_bucket_size: 10
+  }
+  features {
+    input_names: "age_level"
+    feature_type: IdFeature
+    embedding_dim: 16
+    hash_bucket_size: 10
+  }
+  features {
+    input_names: "pvalue_level"
+    feature_type: IdFeature
+    embedding_dim: 16
+    hash_bucket_size: 10
+  }
+  features {
+    input_names: "shopping_level"
+    feature_type: IdFeature
+    embedding_dim: 16
+    hash_bucket_size: 10
+  }
+  features {
+    input_names: "occupation"
+    feature_type: IdFeature
+    embedding_dim: 16
+    hash_bucket_size: 10
+  }
+  features {
+    input_names: "new_user_class_level"
+    feature_type: IdFeature
+    embedding_dim: 16
+    hash_bucket_size: 10
+  }
+  features {
+    input_names: "tag_category_list"
+    feature_type: TagFeature
+    embedding_dim: 16
+    hash_bucket_size: 100000
+    separator: "|"
+  }
+  features {
+    input_names: "tag_brand_list"
+    feature_type: TagFeature
+    embedding_dim: 16
+    hash_bucket_size: 100000
+    separator: "|"
+  }
+  features {
+    input_names: "price"
+    feature_type: IdFeature
+    embedding_dim: 16
+    num_buckets: 50
+  }
+}
+model_config {
+  model_name: "MMoE"
+  model_class: "MultiTaskModel"
+  feature_groups {
+    group_name: "all"
+    feature_names: "user_id"
+    feature_names: "cms_segid"
+    feature_names: "cms_group_id"
+    feature_names: "age_level"
+    feature_names: "pvalue_level"
+    feature_names: "shopping_level"
+    feature_names: "occupation"
+    feature_names: "new_user_class_level"
+    feature_names: "adgroup_id"
+    feature_names: "cate_id"
+    feature_names: "campaign_id"
+    feature_names: "customer"
+    feature_names: "brand"
+    feature_names: "price"
+    feature_names: "pid"
+    feature_names: "tag_category_list"
+    feature_names: "tag_brand_list"
+    wide_deep: DEEP
+  }
+  backbone {
+    blocks {
+      name: 'all'
+      inputs {
+        feature_group_name: 'all'
+      }
+      input_layer {
+        only_output_feature_list: true
+      }
+    }
+    blocks {
+      name: "senet"
+      inputs {
+        block_name: "all"
+      }
+      keras_layer {
+        class_name: 'SENet'
+        senet {
+          reduction_ratio: 4
+        }
+      }
+    }
+    blocks {
+      name: "mmoe"
+      inputs {
+        block_name: "senet"
+      }
+      keras_layer {
+        class_name: 'MMoE'
+        mmoe {
+          num_task: 2
+          num_expert: 3
+          expert_mlp {
+            hidden_units: [256, 128]
+          }
+        }
+      }
+    }
+  }
+  model_params {
+    task_towers {
+      tower_name: "ctr"
+      label_name: "clk"
+      dnn {
+        hidden_units: [128, 64]
+      }
+      num_class: 1
+      weight: 1.0
+      loss_type: CLASSIFICATION
+      metrics_set: {
+       auc {}
+      }
+    }
+    task_towers {
+      tower_name: "cvr"
+      label_name: "buy"
+      dnn {
+        hidden_units: [128, 64]
+      }
+      num_class: 1
+      weight: 1.0
+      loss_type: CLASSIFICATION
+      metrics_set: {
+       auc {}
+      }
+    }
+    l2_regularization: 1e-06
+  }
+  embedding_regularization: 5e-05
+}