diff --git a/.readthedocs.yaml b/.readthedocs.yaml
index bb76f60cc..a023e0bfa 100644
--- a/.readthedocs.yaml
+++ b/.readthedocs.yaml
@@ -1,11 +1,15 @@
 
 version: 2
 
+build:
+  os: ubuntu-22.04
+  tools:
+    python: "3.7"
+
 sphinx:
   configuration: docs/source/conf.py
 
 python:
-  version: "3.7"
   install:
   - requirements: requirements/runtime.txt
   - requirements: requirements/docs.txt
diff --git a/easy_rec/python/layers/common_layers.py b/easy_rec/python/layers/common_layers.py
index e6169a896..68ecf37f5 100644
--- a/easy_rec/python/layers/common_layers.py
+++ b/easy_rec/python/layers/common_layers.py
@@ -171,7 +171,7 @@ def call(self, config, training):
           ln_name = self._group_name + 'f_%d' % i
           fea = layer_norm(fea, name=ln_name, reuse=self._reuse)
         if do_dropout and output_feature_list:
-          fea = self.dropout.apply(fea, training=training)
+          fea = self.dropout.call(fea, training=training)
         if do_feature_dropout:
           fea = tf.div(fea, keep_prob) * mask[i]
         feature_list[i] = fea
@@ -179,7 +179,9 @@ def call(self, config, training):
         features = tf.concat(feature_list, axis=-1)
 
     if do_dropout and not do_feature_dropout:
-      features = self.dropout.apply(features, training=training)
+      features = self.dropout.call(features, training=training)
+    if features.shape.ndims == 3 and int(features.shape[0]) == 1:
+      features = tf.squeeze(features, axis=0)
 
     if config.only_output_feature_list:
       return feature_list
diff --git a/easy_rec/python/tools/feature_selection.py b/easy_rec/python/tools/feature_selection.py
index 05b193897..6d9f59911 100644
--- a/easy_rec/python/tools/feature_selection.py
+++ b/easy_rec/python/tools/feature_selection.py
@@ -106,7 +106,11 @@ def _feature_dim_dropout_ratio(self):
       group_name = feature_group.group_name
 
       logit_p_name = 'logit_p' if group_name == 'all' else 'logit_p_%s' % group_name
-      logit_p = reader.get_tensor(logit_p_name)
+      try:
+        logit_p = reader.get_tensor(logit_p_name)
+      except Exception:
+        print('get `logit_p` failed, try to get `backbone/logit_p`')
+        logit_p = reader.get_tensor('backbone/' + logit_p_name)
       feature_dims_importance = tf.sigmoid(logit_p)
       with tf.Session() as sess:
         feature_dims_importance = feature_dims_importance.eval(session=sess)