[bugix]: Fix need_key_feature for negative sampler sequence feature (#…

…288)

* fix bug for negative sampler sequence feature need key feature

easy_rec/python/layers/sequence_feature_layer.py

@@ -111,7 +111,7 @@ def negative_sampler_target_attention(self,
         all_hist_dim_emb.append(outputs)
       hist_din_emb = tf.concat(all_hist_dim_emb, axis=1)
     if not need_key_feature:
-      return hist_din_emb
+      return hist_din_emb, concat_features
     din_output = tf.concat([hist_din_emb, cur_id], axis=2)
     return din_output, concat_features
 

easy_rec/python/test/train_eval_test.py

@@ -975,6 +975,13 @@ def test_multi_tower_recall_neg_sampler_sequence_feature(self):
         self._test_dir)
     self.assertTrue(self._success)
 
+  @unittest.skipIf(gl is None, 'graphlearn is not installed')
+  def test_multi_tower_recall_neg_sampler_only_sequence_feature(self):
+    self._success = test_utils.test_single_train_eval(
+        'samples/model_config/multi_tower_recall_neg_sampler_only_sequence_feature.config',
+        self._test_dir)
+    self.assertTrue(self._success)
+
 
 if __name__ == '__main__':
   tf.test.main()

samples/model_config/multi_tower_recall_neg_sampler_only_sequence_feature.config

@@ -0,0 +1,304 @@
+train_input_path: "data/test/tb_data/taobao_train_data"
+eval_input_path: "data/test/tb_data/taobao_test_data"
+model_dir: "experiments/multi_tower_recall_neg_sampler_only_sequence_feature"
+
+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
+  }
+  num_steps: 6
+  sync_replicas: false
+  save_checkpoints_steps: 100
+  log_step_count_steps: 2
+}
+
+eval_config {
+  metrics_set: {
+    auc {
+    }
+  }
+  metrics_set: {
+    gauc {
+      uid_field: "user_id"
+    }
+  }
+}
+
+data_config {
+  batch_size: 16
+  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
+  }
+
+  label_fields: 'clk'
+  num_epochs: 5
+  prefetch_size: 4
+  input_type: CSVInput
+
+  negative_sampler {
+    input_path: 'data/test/tb_data/taobao_ad_feature_gl'
+    num_sample: 4
+    num_eval_sample: 4
+    attr_fields: 'adgroup_id'
+    attr_fields: 'cate_id'
+    attr_fields: 'campaign_id'
+    attr_fields: 'customer'
+    attr_fields: 'brand'
+    item_id_field: 'adgroup_id'
+  }
+}
+
+feature_configs : {
+  input_names: 'pid'
+  feature_type: IdFeature
+  embedding_dim: 16
+  hash_bucket_size: 10
+}
+feature_configs : {
+  input_names: 'adgroup_id'
+  feature_type: IdFeature
+  embedding_dim: 16
+  hash_bucket_size: 100000
+}
+feature_configs : {
+  input_names: 'cate_id'
+  feature_type: IdFeature
+  embedding_dim: 16
+  hash_bucket_size: 10000
+}
+feature_configs : {
+  input_names: 'campaign_id'
+  feature_type: IdFeature
+  embedding_dim: 16
+  hash_bucket_size: 100000
+}
+feature_configs : {
+  input_names: 'customer'
+  feature_type: IdFeature
+  embedding_dim: 16
+  hash_bucket_size: 100000
+}
+feature_configs : {
+  input_names: 'brand'
+  feature_type: IdFeature
+  embedding_dim: 16
+  hash_bucket_size: 100000
+}
+feature_configs : {
+  input_names: 'user_id'
+  feature_type: IdFeature
+  embedding_dim: 16
+  hash_bucket_size: 100000
+}
+feature_configs : {
+  input_names: 'cms_segid'
+  feature_type: IdFeature
+  embedding_dim: 16
+  hash_bucket_size: 100
+}
+feature_configs : {
+  input_names: 'cms_group_id'
+  feature_type: IdFeature
+  embedding_dim: 16
+  hash_bucket_size: 100
+}
+feature_configs : {
+  input_names: 'final_gender_code'
+  feature_type: IdFeature
+  embedding_dim: 16
+  hash_bucket_size: 10
+}
+feature_configs : {
+  input_names: 'age_level'
+  feature_type: IdFeature
+  embedding_dim: 16
+  hash_bucket_size: 10
+}
+feature_configs : {
+  input_names: 'pvalue_level'
+  feature_type: IdFeature
+  embedding_dim: 16
+  hash_bucket_size: 10
+}
+feature_configs : {
+  input_names: 'shopping_level'
+  feature_type: IdFeature
+  embedding_dim: 16
+  hash_bucket_size: 10
+}
+feature_configs : {
+  input_names: 'occupation'
+  feature_type: IdFeature
+  embedding_dim: 16
+  hash_bucket_size: 10
+}
+feature_configs : {
+  input_names: 'new_user_class_level'
+  feature_type: IdFeature
+  embedding_dim: 16
+  hash_bucket_size: 10
+}
+feature_configs {
+  input_names: "tag_category_list"
+  feature_type: SequenceFeature
+  embedding_dim: 16
+  hash_bucket_size: 100000
+  sub_feature_type: IdFeature
+  separator: "|"
+}
+feature_configs {
+  input_names: "tag_brand_list"
+  feature_type: SequenceFeature
+  embedding_dim: 16
+  hash_bucket_size: 100000
+  sub_feature_type: IdFeature
+  separator: "|"
+}
+feature_configs : {
+  input_names: 'price'
+  feature_type: IdFeature
+  embedding_dim: 16
+  num_buckets: 50
+}
+model_config:{
+  model_class: "MultiTowerRecall"
+  feature_groups: {
+    group_name: 'user'
+    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'
+    wide_deep:DEEP
+    negative_sampler:true
+    sequence_features: {
+      group_name: "seq_fea"
+      allow_key_search: true
+      need_key_feature:false
+      seq_att_map: {
+        key: "brand"
+        key: "cate_id"
+        hist_seq: "tag_brand_list"
+        hist_seq: "tag_category_list"
+      }
+    }
+  }
+  feature_groups: {
+    group_name: "item"
+    feature_names: 'adgroup_id'
+    feature_names: 'cate_id'
+    feature_names: 'campaign_id'
+    feature_names: 'customer'
+    feature_names: 'brand'
+    wide_deep:DEEP
+  }
+  multi_tower_recall {
+    user_tower {
+      dnn {
+        hidden_units: [256, 128, 64, 32]
+        # dropout_ratio : [0.1, 0.1, 0.1, 0.1]
+      }
+    }
+    item_tower {
+      dnn {
+        hidden_units: [256, 128, 64, 32]
+      }
+    }
+    final_dnn {
+      hidden_units: [128, 96, 64, 32, 16]
+    }
+    l2_regularization: 1e-6
+  }
+  loss_type: CLASSIFICATION
+  embedding_regularization: 5e-6
+}