Merge branch 'master' into daily_bug_fix

alibaba · Dec 31, 2024 · 9e131bc · 9e131bc
2 parents a23298b + cd47816
commit 9e131bc
Show file tree

Hide file tree

Showing 3 changed files with 21 additions and 11 deletions.
diff --git a/easy_rec/python/inference/predictor.py b/easy_rec/python/inference/predictor.py
@@ -534,7 +534,9 @@ def _parse_value(all_vals):
               ]
           for k in self._reserved_cols:
             if k in all_vals and all_vals[k].dtype == np.object:
-              all_vals[k] = [val.decode('utf-8', errors='ignore') for val in all_vals[k]]
+              all_vals[k] = [
+                  val.decode('utf-8', errors='ignore') for val in all_vals[k]
+              ]
 
           ts2 = time.time()
           reserve_vals = self._get_reserve_vals(self._reserved_cols,

diff --git a/easy_rec/python/model/dropoutnet.py b/easy_rec/python/model/dropoutnet.py
@@ -21,6 +21,7 @@ def cosine_similarity(user_emb, item_emb):
       tf.multiply(user_emb, item_emb), axis=1, name='cosine')
   return user_item_sim
 
+
 def bernoulli_dropout(x, rate, training=False):
   if rate == 0.0 or not training:
     return x
@@ -90,9 +91,9 @@ def build_predict_graph(self):
         content_feature = user_content_dnn(self.user_content_feature)
         user_features.append(content_feature)
       if self.user_preference_feature is not None:
-        user_prefer_feature = bernoulli_dropout(self.user_preference_feature,
-                                                self._model_config.user_dropout_rate,
-                                                self._is_training)
+        user_prefer_feature = bernoulli_dropout(
+            self.user_preference_feature, self._model_config.user_dropout_rate,
+            self._is_training)
         user_prefer_dnn = dnn.DNN(self.user_preference_layers, self._l2_reg,
                                   'user_preference', self._is_training)
         prefer_feature = user_prefer_dnn(user_prefer_feature)
@@ -118,9 +119,9 @@ def build_predict_graph(self):
         content_feature = item_content_dnn(self.item_content_feature)
         item_features.append(content_feature)
       if self.item_preference_feature is not None:
-        item_prefer_feature = bernoulli_dropout(self.item_preference_feature,
-                                                self._model_config.item_dropout_rate,
-                                                self._is_training)
+        item_prefer_feature = bernoulli_dropout(
+            self.item_preference_feature, self._model_config.item_dropout_rate,
+            self._is_training)
         item_prefer_dnn = dnn.DNN(self.item_preference_layers, self._l2_reg,
                                   'item_preference', self._is_training)
         prefer_feature = item_prefer_dnn(item_prefer_feature)

diff --git a/easy_rec/python/model/match_model.py b/easy_rec/python/model/match_model.py
@@ -217,16 +217,21 @@ def _build_list_wise_loss_graph(self):
       indices = tf.concat([indices[:, None], indices[:, None]], axis=1)
       hit_prob = tf.gather_nd(
           self._prediction_dict['probs'][:batch_size, :batch_size], indices)
+
+      sample_weights = tf.cast(tf.squeeze(self._sample_weight), tf.float32)
       self._loss_dict['cross_entropy_loss'] = -tf.reduce_mean(
-          tf.log(hit_prob + 1e-12) * tf.squeeze(self._sample_weight))
+          tf.log(hit_prob + 1e-12) *
+          sample_weights) / tf.reduce_mean(sample_weights)
+
       logging.info('softmax cross entropy loss is used')
 
       user_features = self._prediction_dict['user_tower_emb']
       pos_item_emb = self._prediction_dict['item_tower_emb'][:batch_size]
       pos_simi = tf.reduce_sum(user_features * pos_item_emb, axis=1)
       # if pos_simi < 0, produce loss
       reg_pos_loss = tf.nn.relu(-pos_simi)
-      self._loss_dict['reg_pos_loss'] = tf.reduce_mean(reg_pos_loss)
+      self._loss_dict['reg_pos_loss'] = tf.reduce_mean(
+          reg_pos_loss * sample_weights) / tf.reduce_mean(sample_weights)
 
       # the AMM loss for DAT model
       if all([
@@ -235,10 +240,12 @@ def _build_list_wise_loss_graph(self):
       ]):
         self._loss_dict['amm_loss_u'] = tf.reduce_mean(
             tf.square(self._prediction_dict['augmented_a_u'] -
-                      self._prediction_dict['augmented_p_i'][:batch_size]))
+                      self._prediction_dict['augmented_p_i'][:batch_size]) *
+            sample_weights) / tf.reduce_mean(sample_weights)
         self._loss_dict['amm_loss_i'] = tf.reduce_mean(
             tf.square(self._prediction_dict['augmented_a_i'][:batch_size] -
-                      self._prediction_dict['augmented_p_u']))
+                      self._prediction_dict['augmented_p_u']) *
+            sample_weights) / tf.reduce_mean(sample_weights)
 
     else:
       raise ValueError('invalid loss type: %s' % str(self._loss_type))