dat model supports amm_loss_weight

docker/Dockerfile_tf112

@@ -71,4 +71,4 @@ RUN cd /EasyRec && python setup.py install
 RUN rm -rf /EasyRec
 RUN python -c "import easy_rec; import pyhive; import datahub; import kafka"
 
-COPY docker/hadoop_env.sh /opt/hadoop_env.sh
+COPY docker/hadoop_env.sh /opt/hadoop_env.sh

docker/Dockerfile_tf115

@@ -33,4 +33,4 @@ RUN cd /EasyRec && pip install .
 RUN rm -rf /EasyRec
 RUN python -c "import easy_rec; easy_rec.help(); import pyhive; import datahub; import kafka"
 
-COPY docker/hadoop_env.sh /opt/hadoop_env.sh
+COPY docker/hadoop_env.sh /opt/hadoop_env.sh

docker/Dockerfile_tf212

@@ -33,4 +33,4 @@ RUN cd /EasyRec && python setup.py install
 RUN rm -rf /EasyRec
 # RUN python -c "import easy_rec; easy_rec.help(); import pyhive; import datahub; import kafka"
 
-COPY docker/hadoop_env.sh /opt/hadoop_env.sh
+COPY docker/hadoop_env.sh /opt/hadoop_env.sh

docs/source/models/dssm_derivatives.md

@@ -109,26 +109,26 @@ model_config:{
   features: {
     input_names: 'user_id'
     feature_type: IdFeature
-    embedding_dim: 32
+    embedding_dim: 32         # user_id特征embedding维度
     hash_bucket_size: 100000
   }
   features: {
     input_names: 'adgroup_id'
     feature_type: IdFeature
-    embedding_dim: 32
+    embedding_dim: 32        # item_id特征embedding维度
     hash_bucket_size: 100000
   }
   .
   .
   .
 
   feature_groups: {
-    group_name: 'user_id_augment'
+    group_name: 'user_id_augment' # 增加user_augment特征组，对user_id特征进行embedding作为辅助向量
     feature_names: 'user_id'
     wide_deep:DEEP
   }
   feature_groups: {
-    group_name: 'item_id_augment'
+    group_name: 'item_id_augment' # 增加item_augment特征组，对item_id特征进行embedding作为辅助向量
     feature_names: 'adgroup_id'
     wide_deep:DEEP
   }
@@ -137,19 +137,21 @@ model_config:{
     user_tower {
       id: "user_id"
       dnn {
-        hidden_units: [ 128,  32]
+        hidden_units: [ 128,  32]  # 输出维度需要保证和item_augment特征组的embedding维度一致
         # dropout_ratio : [0.1, 0.1, 0.1, 0.1]
       }
     }
     item_tower {
       id: "adgroup_id"
       dnn {
-        hidden_units: [ 128, 32]
+        hidden_units: [ 128, 32]  # 输出维度需要保证和user_augment特征组的embedding维度一致
       }
     }
     simi_func: COSINE
     temperature: 0.01
     l2_regularization: 1e-6
+    amm_i_weight: 0.5      # AMM损失权重
+    amm_u_weight: 0.5
   }
 ```
 

easy_rec/python/model/match_model.py

@@ -238,14 +238,16 @@ def _build_list_wise_loss_graph(self):
           k in self._prediction_dict.keys() for k in
           ['augmented_p_u', 'augmented_p_i', 'augmented_a_u', 'augmented_a_i']
       ]):
-        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]) *
-            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']) *
-            sample_weights) / tf.reduce_mean(sample_weights)
+        self._loss_dict[
+            'amm_loss_u'] = self._model_config.amm_u_weight * tf.reduce_mean(
+                tf.square(self._prediction_dict['augmented_a_u'] -
+                          self._prediction_dict['augmented_p_i'][:batch_size]) *
+                sample_weights) / tf.reduce_mean(sample_weights)
+        self._loss_dict[
+            'amm_loss_i'] = self._model_config.amm_i_weight * tf.reduce_mean(
+                tf.square(self._prediction_dict['augmented_a_i'][:batch_size] -
+                          self._prediction_dict['augmented_p_u']) *
+                sample_weights) / tf.reduce_mean(sample_weights)
 
     else:
       raise ValueError('invalid loss type: %s' % str(self._loss_type))

easy_rec/python/protos/dat.proto

@@ -18,4 +18,8 @@ message DAT {
     optional Similarity simi_func = 4 [default=COSINE];
     required bool ignore_in_batch_neg_sam = 5 [default = false];
     optional float temperature = 6 [default = 1.0];
+    // loss weight for amm_i
+    required float amm_i_weight = 7 [default = 0.5];
+    // loss weight for amm_u
+    required float amm_u_weight = 8 [default = 0.5];
 }

easy_rec/python/tools/faiss_index_pai.py

@@ -9,6 +9,7 @@
 import faiss
 import numpy as np
 import tensorflow as tf
+
 from easy_rec.python.utils import io_util
 
 logging.basicConfig(

easy_rec/python/tools/hit_rate_pai.py

@@ -18,6 +18,7 @@
 from __future__ import print_function
 
 import sys
+
 import tensorflow as tf
 
 from easy_rec.python.utils import io_util

easy_rec/python/tools/split_model_pai.py

@@ -12,6 +12,7 @@
 from tensorflow.python.saved_model import signature_constants
 from tensorflow.python.tools import saved_model_utils
 from tensorflow.python.training import saver as tf_saver
+
 from easy_rec.python.utils import io_util
 
 if tf.__version__ >= '2.0':

easy_rec/python/tools/split_pdn_model_pai.py

@@ -13,6 +13,7 @@
 from tensorflow.python.saved_model.utils_impl import get_variables_path
 from tensorflow.python.tools import saved_model_utils
 from tensorflow.python.training import saver as tf_saver
+
 from easy_rec.python.utils import io_util
 
 FLAGS = tf.app.flags.FLAGS

scripts/build_docker_tf112.sh

@@ -18,4 +18,4 @@ then
   exit 1
 fi
 
-sudo docker build --network=host . -f docker/Dockerfile_tf112 -t  mybigpai-public-registry.cn-beijing.cr.aliyuncs.com/easyrec/easyrec:py27-tf1.12-${version}
+sudo docker build --network=host . -f docker/Dockerfile_tf112 -t  mybigpai-public-registry.cn-beijing.cr.aliyuncs.com/easyrec/easyrec:py27-tf1.12-${version}

scripts/build_docker_tf115.sh

@@ -18,4 +18,4 @@ then
   exit 1
 fi
 
-sudo docker build --network=host . -f docker/Dockerfile_tf115 -t  mybigpai-public-registry.cn-beijing.cr.aliyuncs.com/easyrec/easyrec:py36-tf1.15-${version}
+sudo docker build --network=host . -f docker/Dockerfile_tf115 -t  mybigpai-public-registry.cn-beijing.cr.aliyuncs.com/easyrec/easyrec:py36-tf1.15-${version}

scripts/build_docker_tf212.sh

@@ -18,4 +18,4 @@ then
   exit 1
 fi
 
-sudo docker build --network=host . -f docker/Dockerfile_tf212 -t  mybigpai-public-registry.cn-beijing.cr.aliyuncs.com/easyrec/easyrec:py38-tf2.12-${version}
+sudo docker build --network=host . -f docker/Dockerfile_tf212 -t  mybigpai-public-registry.cn-beijing.cr.aliyuncs.com/easyrec/easyrec:py38-tf2.12-${version}