update api in PaddleRec, test=release/1.6 (#3573)

PaddleRec/gru4rec/README.md

@@ -44,6 +44,10 @@ session-based推荐应用场景非常广泛，比如用户的商品浏览、新
 
 
 运行样例程序可跳过'RSC15 数据下载及预处理'部分
+
+
+**要求使用PaddlePaddle 1.6及以上版本或适当的develop版本。**
+
 ## RSC15 数据下载及预处理
 
 运行命令 下载RSC15官网数据集

PaddleRec/gru4rec/infer.py

@@ -71,6 +71,7 @@ def infer(test_reader, use_cuda, model_path):
 
 
 if __name__ == "__main__":
+    utils.check_version()
     args = parse_args()
     start_index = args.start_index
     last_index = args.last_index

PaddleRec/gru4rec/infer_sample_neg.py

@@ -84,6 +84,7 @@ def infer(args, vocab_size, test_reader, use_cuda):
 
 
 if __name__ == "__main__":
+    utils.check_version()
     args = parse_args()
     start_index = args.start_index
     last_index = args.last_index

PaddleRec/gru4rec/net.py

@@ -10,12 +10,12 @@ def all_vocab_network(vocab_size,
     gru_lr_x = 1.0
     fc_lr_x = 1.0
     # Input data
-    src_wordseq = fluid.layers.data(
-        name="src_wordseq", shape=[1], dtype="int64", lod_level=1)
-    dst_wordseq = fluid.layers.data(
-        name="dst_wordseq", shape=[1], dtype="int64", lod_level=1)
+    src_wordseq = fluid.data(
+        name="src_wordseq", shape=[None, 1], dtype="int64", lod_level=1)
+    dst_wordseq = fluid.data(
+        name="dst_wordseq", shape=[None, 1], dtype="int64", lod_level=1)
 
-    emb = fluid.layers.embedding(
+    emb = fluid.embedding(
         input=src_wordseq,
         size=[vocab_size, hid_size],
         param_attr=fluid.ParamAttr(
@@ -56,19 +56,21 @@ def train_bpr_network(vocab_size, neg_size, hid_size, drop_out=0.2):
     gru_lr_x = 1.0
     fc_lr_x = 1.0
     # Input data
-    src = fluid.layers.data(name="src", shape=[1], dtype="int64", lod_level=1)
-    pos_label = fluid.layers.data(
-        name="pos_label", shape=[1], dtype="int64", lod_level=1)
-    label = fluid.layers.data(
-        name="label", shape=[neg_size + 1], dtype="int64", lod_level=1)
+    src = fluid.data(name="src", shape=[None, 1], dtype="int64", lod_level=1)
+    pos_label = fluid.data(
+        name="pos_label", shape=[None, 1], dtype="int64", lod_level=1)
+    label = fluid.data(
+        name="label", shape=[None, neg_size + 1], dtype="int64", lod_level=1)
 
-    emb_src = fluid.layers.embedding(
+    emb_src = fluid.embedding(
         input=src,
         size=[vocab_size, hid_size],
         param_attr=fluid.ParamAttr(
             name="emb",
             initializer=fluid.initializer.XavierInitializer(),
             learning_rate=emb_lr_x))
+    emb_src = fluid.layers.squeeze(input=emb_src, axes=[1])
+
 
     emb_src_drop = fluid.layers.dropout(emb_src, dropout_prob=drop_out)
 
@@ -90,7 +92,7 @@ def train_bpr_network(vocab_size, neg_size, hid_size, drop_out=0.2):
     gru_h0_drop = fluid.layers.dropout(gru_h0, dropout_prob=drop_out)
 
     label_re = fluid.layers.sequence_reshape(input=label, new_dim=1)
-    emb_label = fluid.layers.embedding(
+    emb_label1 = fluid.embedding(
         input=label_re,
         size=[vocab_size, hid_size],
         param_attr=fluid.ParamAttr(
@@ -98,6 +100,7 @@ def train_bpr_network(vocab_size, neg_size, hid_size, drop_out=0.2):
             initializer=fluid.initializer.XavierInitializer(),
             learning_rate=emb_lr_x))
 
+    emb_label = fluid.layers.squeeze(input=emb_label1, axes=[1]) 
     emb_label_drop = fluid.layers.dropout(emb_label, dropout_prob=drop_out)
 
     gru_exp = fluid.layers.expand(
@@ -120,19 +123,20 @@ def train_cross_entropy_network(vocab_size, neg_size, hid_size, drop_out=0.2):
     gru_lr_x = 1.0
     fc_lr_x = 1.0
     # Input data
-    src = fluid.layers.data(name="src", shape=[1], dtype="int64", lod_level=1)
-    pos_label = fluid.layers.data(
-        name="pos_label", shape=[1], dtype="int64", lod_level=1)
-    label = fluid.layers.data(
-        name="label", shape=[neg_size + 1], dtype="int64", lod_level=1)
+    src = fluid.data(name="src", shape=[None, 1], dtype="int64", lod_level=1)
+    pos_label = fluid.data(
+        name="pos_label", shape=[None, 1], dtype="int64", lod_level=1)
+    label = fluid.data(
+        name="label", shape=[None, neg_size + 1], dtype="int64", lod_level=1)
 
-    emb_src = fluid.layers.embedding(
+    emb_src = fluid.embedding(
         input=src,
         size=[vocab_size, hid_size],
         param_attr=fluid.ParamAttr(
             name="emb",
             initializer=fluid.initializer.XavierInitializer(),
             learning_rate=emb_lr_x))
+    emb_src = fluid.layers.squeeze(input=emb_src, axes=[1])
 
     emb_src_drop = fluid.layers.dropout(emb_src, dropout_prob=drop_out)
 
@@ -154,13 +158,14 @@ def train_cross_entropy_network(vocab_size, neg_size, hid_size, drop_out=0.2):
     gru_h0_drop = fluid.layers.dropout(gru_h0, dropout_prob=drop_out)
 
     label_re = fluid.layers.sequence_reshape(input=label, new_dim=1)
-    emb_label = fluid.layers.embedding(
+    emb_label1 = fluid.embedding(
         input=label_re,
         size=[vocab_size, hid_size],
         param_attr=fluid.ParamAttr(
             name="emb",
             initializer=fluid.initializer.XavierInitializer(),
             learning_rate=emb_lr_x))
+    emb_label = fluid.layers.squeeze(input=emb_label1, axes=[1]) 
 
     emb_label_drop = fluid.layers.dropout(emb_label, dropout_prob=drop_out)
 
@@ -180,8 +185,8 @@ def train_cross_entropy_network(vocab_size, neg_size, hid_size, drop_out=0.2):
 
 
 def infer_network(vocab_size, batch_size, hid_size, dropout=0.2):
-    src = fluid.layers.data(name="src", shape=[1], dtype="int64", lod_level=1)
-    emb_src = fluid.layers.embedding(
+    src = fluid.data(name="src", shape=[None, 1], dtype="int64", lod_level=1)
+    emb_src = fluid.embedding(
         input=src, size=[vocab_size, hid_size], param_attr="emb")
     emb_src_drop = fluid.layers.dropout(
         emb_src, dropout_prob=dropout, is_test=True)
@@ -198,20 +203,20 @@ def infer_network(vocab_size, batch_size, hid_size, dropout=0.2):
     gru_h0_drop = fluid.layers.dropout(
         gru_h0, dropout_prob=dropout, is_test=True)
 
-    all_label = fluid.layers.data(
+    all_label = fluid.data(
         name="all_label",
         shape=[vocab_size, 1],
-        dtype="int64",
-        append_batch_size=False)
-    emb_all_label = fluid.layers.embedding(
+        dtype="int64")
+    emb_all_label = fluid.embedding(
         input=all_label, size=[vocab_size, hid_size], param_attr="emb")
+    emb_all_label = fluid.layers.squeeze(input=emb_all_label, axes=[1])
     emb_all_label_drop = fluid.layers.dropout(
         emb_all_label, dropout_prob=dropout, is_test=True)
 
     all_pre = fluid.layers.matmul(
         gru_h0_drop, emb_all_label_drop, transpose_y=True)
 
-    pos_label = fluid.layers.data(
-        name="pos_label", shape=[1], dtype="int64", lod_level=1)
+    pos_label = fluid.data(
+        name="pos_label", shape=[None, 1], dtype="int64", lod_level=1)
     acc = fluid.layers.accuracy(input=all_pre, label=pos_label, k=20)
     return acc

PaddleRec/gru4rec/train.py

@@ -169,4 +169,5 @@ def get_device(args):
         
 
 if __name__ == "__main__":
+    utils.check_version()
     train()

PaddleRec/gru4rec/train_sample_neg.py

@@ -128,4 +128,5 @@ def train():
 
 
 if __name__ == "__main__":
+    utils.check_version()
     train()

PaddleRec/gru4rec/utils.py

@@ -110,11 +110,25 @@ def prepare_data(file_dir,
             batch_size * 20)
     else:
         vocab_size = get_vocab_size(vocab_path)
-        reader = paddle.batch(
+        reader = fluid.io.batch(
             test(
                 file_dir, buffer_size, data_type=DataType.SEQ), batch_size)
     return vocab_size, reader
 
+def check_version():
+     """
+     Log error and exit when the installed version of paddlepaddle is
+     not satisfied.
+     """
+     err = "PaddlePaddle version 1.6 or higher is required, " \
+           "or a suitable develop version is satisfied as well. \n" \
+           "Please make sure the version is good with your code." \
+
+     try:
+         fluid.require_version('1.6.0')
+     except Exception as e:
+         logger.error(err)
+         sys.exit(1)
 
 def sort_batch(reader, batch_size, sort_group_size, drop_last=False):
     """

PaddleRec/multiview_simnet/README.md

@@ -3,6 +3,9 @@
 ## Introduction
 In personalized recommendation scenario, a user often is provided with several items from personalized interest matching model. In real world application, a user may have multiple views of features, say user-id, age, click-history of items, search queries. A item, e.g. news, may also have multiple views of features like news title, news category, images in news and so on. Multi-view Simnet is matching a model that combine users' and items' multiple views of features into one unified model. The model can be used in many industrial product like Baidu's feed news. The model is adapted from the paper A Multi-View Deep Learning(MV-DNN) Approach for Cross Domain User Modeling in Recommendation Systems, WWW 2015. The difference between our model and the MV-DNN is that we also consider multiple feature views of users.
 
+**Now all models in PaddleRec require PaddlePaddle version 1.6 or higher, or suitable develop version.**
+
+
 ## Dataset
 Currently, synthetic dataset is provided for proof of concept and we aim to add more real world dataset in this project in the future. The result is inaccurate because of synthetic dataset.
 

PaddleRec/multiview_simnet/infer.py

@@ -31,6 +31,23 @@ logger = logging.getLogger("fluid")
 logger.setLevel(logging.INFO)
 
 
+def check_version():
+     """
+     Log error and exit when the installed version of paddlepaddle is
+     not satisfied.
+     """
+     err = "PaddlePaddle version 1.6 or higher is required, " \
+           "or a suitable develop version is satisfied as well. \n" \
+           "Please make sure the version is good with your code." \
+
+     try:
+         fluid.require_version('1.6.0')
+     except Exception as e:
+         logger.error(err)
+         sys.exit(1)
+
+
+
 def parse_args():
     parser = argparse.ArgumentParser("multi-view simnet")
     parser.add_argument("--train_file", type=str, help="Training file")
@@ -116,4 +133,5 @@ def main():
 
 
 if __name__ == "__main__":
+    check_version()
     main()

PaddleRec/multiview_simnet/nets.py

@@ -125,34 +125,34 @@ class MultiviewSimnet(object):
     def train_net(self):
         # input fields for query, pos_title, neg_title
         q_slots = [
-            io.data(
-                name="q%d" % i, shape=[1], lod_level=1, dtype='int64')
+            fluid.data(
+                name="q%d" % i, shape=[None, 1], lod_level=1, dtype='int64')
             for i in range(len(self.query_encoders))
         ]
         pt_slots = [
-            io.data(
-                name="pt%d" % i, shape=[1], lod_level=1, dtype='int64')
+            fluid.data(
+                name="pt%d" % i, shape=[None, 1], lod_level=1, dtype='int64')
             for i in range(len(self.title_encoders))
         ]
         nt_slots = [
-            io.data(
-                name="nt%d" % i, shape=[1], lod_level=1, dtype='int64')
+            fluid.data(
+                name="nt%d" % i, shape=[None, 1], lod_level=1, dtype='int64')
             for i in range(len(self.title_encoders))
         ]
 
         # lookup embedding for each slot
         q_embs = [
-            nn.embedding(
+            fluid.embedding(
                 input=query, size=self.emb_shape, param_attr="emb")
             for query in q_slots
         ]
         pt_embs = [
-            nn.embedding(
+            fluid.embedding(
                 input=title, size=self.emb_shape, param_attr="emb")
             for title in pt_slots
         ]
         nt_embs = [
-            nn.embedding(
+            fluid.embedding(
                 input=title, size=self.emb_shape, param_attr="emb")
             for title in nt_slots
         ]
@@ -205,23 +205,23 @@ class MultiviewSimnet(object):
 
     def pred_net(self, query_fields, pos_title_fields, neg_title_fields):
         q_slots = [
-            io.data(
-                name="q%d" % i, shape=[1], lod_level=1, dtype='int64')
+            fluid.data(
+                name="q%d" % i, shape=[None, 1], lod_level=1, dtype='int64')
             for i in range(len(self.query_encoders))
         ]
         pt_slots = [
-            io.data(
-                name="pt%d" % i, shape=[1], lod_level=1, dtype='int64')
+            fluid.data(
+                name="pt%d" % i, shape=[None, 1], lod_level=1, dtype='int64')
             for i in range(len(self.title_encoders))
         ]
         # lookup embedding for each slot
         q_embs = [
-            nn.embedding(
+            fluid.embedding(
                 input=query, size=self.emb_shape, param_attr="emb")
             for query in q_slots
         ]
         pt_embs = [
-            nn.embedding(
+            fluid.embedding(
                 input=title, size=self.emb_shape, param_attr="emb")
             for title in pt_slots
         ]

PaddleRec/multiview_simnet/train.py

@@ -88,6 +88,21 @@ def parse_args():
     return parser.parse_args()
 
 
+def check_version():
+     """
+     Log error and exit when the installed version of paddlepaddle is
+     not satisfied.
+     """
+     err = "PaddlePaddle version 1.6 or higher is required, " \
+           "or a suitable develop version is satisfied as well. \n" \
+           "Please make sure the version is good with your code." \
+
+     try:
+         fluid.require_version('1.6.0')
+     except Exception as e:
+         logger.error(err)
+         sys.exit(1)
+
 def start_train(args):
     if args.enable_ce:
         SEED = 102
@@ -170,4 +185,5 @@ def main():
 
 
 if __name__ == "__main__":
+    check_version()
     main()

PaddleRec/ssr/README.md

@@ -12,6 +12,10 @@ Sequence Semantic Retrieval(SSR) Model shares the similar idea with Multi-Rate D
 - The idea of SSR is to model a user's personalized interest of an item through matching model structure, and the representation of a news item can be computed online even the news item does not exist in training dataset.
 - With the representation of news items, we are able to build an vector indexing service online for news prediction and this is the retrieval part of SSR.
 
+## Version
+**Now all models in PaddleRec require PaddlePaddle version 1.6 or higher, or suitable develop version.**
+
+
 ## Dataset
 Dataset preprocessing follows the method of [GRU4Rec Project](https://github.com/PaddlePaddle/models/tree/develop/fluid/PaddleRec/gru4rec). Note that you should reuse scripts from GRU4Rec project for data preprocessing.
 

PaddleRec/ssr/infer.py

@@ -120,6 +120,7 @@ def infer(args, vocab_size, test_reader):
 
 
 if __name__ == "__main__":
+    utils.check_version()
     args = parse_args()
     start_index = args.start_index
     last_index = args.last_index

PaddleRec/ssr/nets.py

@@ -86,16 +86,16 @@ class SequenceSemanticRetrieval(object):
         return correct
 
     def train(self):
-        user_data = io.data(name="user", shape=[1], dtype="int64", lod_level=1)
-        pos_item_data = io.data(
-            name="p_item", shape=[1], dtype="int64", lod_level=1)
-        neg_item_data = io.data(
-            name="n_item", shape=[1], dtype="int64", lod_level=1)
-        user_emb = nn.embedding(
+        user_data = fluid.data(name="user", shape=[None, 1], dtype="int64", lod_level=1)
+        pos_item_data = fluid.data(
+            name="p_item", shape=[None, 1], dtype="int64", lod_level=1)
+        neg_item_data = fluid.data(
+            name="n_item", shape=[None, 1], dtype="int64", lod_level=1)
+        user_emb = fluid.embedding(
             input=user_data, size=self.emb_shape, param_attr="emb.item")
-        pos_item_emb = nn.embedding(
+        pos_item_emb = fluid.embedding(
             input=pos_item_data, size=self.emb_shape, param_attr="emb.item")
-        neg_item_emb = nn.embedding(
+        neg_item_emb = fluid.embedding(
             input=neg_item_data, size=self.emb_shape, param_attr="emb.item")
         user_enc = self.user_encoder.forward(user_emb)
         pos_item_enc = self.item_encoder.forward(pos_item_emb)

PaddleRec/ssr/train.py

@@ -165,4 +165,5 @@ def main():
 
 
 if __name__ == "__main__":
+    utils.check_version()
     main()

PaddleRec/ssr/utils.py

@@ -16,7 +16,7 @@ def construct_train_data(file_dir, vocab_path, batch_size):
     vocab_size = get_vocab_size(vocab_path)
     files = [file_dir + '/' + f for f in os.listdir(file_dir)]
     y_data = reader.YoochooseDataset(vocab_size)
-    train_reader = paddle.batch(
+    train_reader = fluid.io.batch(
         paddle.reader.shuffle(
             y_data.train(files), buf_size=batch_size * 100),
         batch_size=batch_size)
@@ -27,9 +27,23 @@ def construct_test_data(file_dir, vocab_path, batch_size):
     vocab_size = get_vocab_size(vocab_path)
     files = [file_dir + '/' + f for f in os.listdir(file_dir)]
     y_data = reader.YoochooseDataset(vocab_size)
-    test_reader = paddle.batch(y_data.test(files), batch_size=batch_size)
+    test_reader = fluid.io.batch(y_data.test(files), batch_size=batch_size)
     return test_reader, vocab_size
 
+def check_version():
+     """
+     Log error and exit when the installed version of paddlepaddle is
+     not satisfied.
+     """
+     err = "PaddlePaddle version 1.6 or higher is required, " \
+           "or a suitable develop version is satisfied as well. \n" \
+           "Please make sure the version is good with your code." \
+
+     try:
+         fluid.require_version('1.6.0')
+     except Exception as e:
+         logger.error(err)
+         sys.exit(1)
 
 def infer_data(raw_data, place):
     data = [dat[0] for dat in raw_data]

PaddleRec/tagspace/README.md

@@ -26,6 +26,7 @@ TagSpace模型的介绍可以参阅论文[#TagSpace: Semantic Embeddings from Ha
 
 Tagspace模型学习文本及标签的embedding表示，应用于工业级的标签推荐，具体应用场景有feed新闻标签推荐。
 
+**Now all models in PaddleRec require PaddlePaddle version 1.6 or higher, or suitable develop version.**
 
 ## 数据下载及预处理
 

PaddleRec/tagspace/infer.py

@@ -71,6 +71,7 @@ def infer(test_reader, vocab_tag, use_cuda, model_path, epoch):
 
 
 if __name__ == "__main__":
+    utils.check_version()
     args = parse_args()
     start_index = args.start_index
     last_index = args.last_index

PaddleRec/tagspace/net.py

@@ -2,19 +2,22 @@ import paddle.fluid as fluid
 import paddle.fluid.layers.nn as nn
 import paddle.fluid.layers.tensor as tensor
 import paddle.fluid.layers.control_flow as cf
-import paddle.fluid.layers.io as io
 
 def network(vocab_text_size, vocab_tag_size, emb_dim=10, hid_dim=1000, win_size=5, margin=0.1, neg_size=5):
     """ network definition """
-    text = io.data(name="text", shape=[1], lod_level=1, dtype='int64')
-    pos_tag = io.data(name="pos_tag", shape=[1], lod_level=1, dtype='int64')
-    neg_tag = io.data(name="neg_tag", shape=[1], lod_level=1, dtype='int64')
-    text_emb = nn.embedding(
+    text = fluid.data(name="text", shape=[None, 1], lod_level=1, dtype='int64')
+    pos_tag = fluid.data(name="pos_tag", shape=[None, 1], lod_level=1, dtype='int64')
+    neg_tag = fluid.data(name="neg_tag", shape=[None, 1], lod_level=1, dtype='int64')
+    text_emb = fluid.embedding(
             input=text, size=[vocab_text_size, emb_dim], param_attr="text_emb")
-    pos_tag_emb = nn.embedding(
+    text_emb = fluid.layers.squeeze(input=text_emb, axes=[1])
+    pos_tag_emb = fluid.embedding(
             input=pos_tag, size=[vocab_tag_size, emb_dim], param_attr="tag_emb")
-    neg_tag_emb = nn.embedding(
+    pos_tag_emb = fluid.layers.squeeze(input=pos_tag_emb, axes=[1])
+    neg_tag_emb = fluid.embedding(
             input=neg_tag, size=[vocab_tag_size, emb_dim], param_attr="tag_emb")
+    neg_tag_emb = fluid.layers.squeeze(input=neg_tag_emb, axes=[1])
+
 
     conv_1d = fluid.nets.sequence_conv_pool(
             input=text_emb,

PaddleRec/tagspace/train.py

@@ -168,4 +168,5 @@ def get_device(args):
         
 
 if __name__ == "__main__":
+    utils.check_version()
     train()

PaddleRec/tagspace/utils.py

@@ -29,6 +29,21 @@ def get_vocab_size(vocab_path):
         line = rf.readline()
         return int(line.strip())
 
+def check_version():
+     """
+     Log error and exit when the installed version of paddlepaddle is
+     not satisfied.
+     """
+     err = "PaddlePaddle version 1.6 or higher is required, " \
+           "or a suitable develop version is satisfied as well. \n" \
+           "Please make sure the version is good with your code." \
+
+     try:
+         fluid.require_version('1.6.0')
+     except Exception as e:
+         logger.error(err)
+         sys.exit(1)
+
 
 def prepare_data(file_dir,
                  vocab_text_path,

PaddleRec/word2vec/README.md

@@ -20,6 +20,7 @@
 ## 介绍
 本例实现了skip-gram模式的word2vector模型。
 
+**目前模型库下模型均要求使用PaddlePaddle 1.6及以上版本或适当的develop版本。**
 
 ## 数据下载
 全量数据集使用的是来自1 Billion Word Language Model Benchmark的(http://www.statmt.org/lm-benchmark) 的数据集.
@@ -35,7 +36,7 @@ mv 1-billion-word-language-modeling-benchmark-r13output/training-monolingual.tok
 
 ```bash
 mkdir data
-wget https://paddlerec.bj.bcebos.com/word2vec/1-billion-word-language-modeling-benchmark-r13output.tar
+wget --no-check-certificate https://paddlerec.bj.bcebos.com/word2vec/1-billion-word-language-modeling-benchmark-r13output.tar
 tar xvf 1-billion-word-language-modeling-benchmark-r13output.tar
 mv 1-billion-word-language-modeling-benchmark-r13output/training-monolingual.tokenized.shuffled/ data/
 ```
@@ -44,7 +45,7 @@ mv 1-billion-word-language-modeling-benchmark-r13output/training-monolingual.tok
 
 ```bash
 mkdir data
-wget https://paddlerec.bj.bcebos.com/word2vec/text.tar
+wget --no-check-certificate https://paddlerec.bj.bcebos.com/word2vec/text.tar
 tar xvf text.tar
 mv text data/
 ```
@@ -105,9 +106,9 @@ sh cluster_train.sh
 
 ```bash
 #全量数据集测试集
-wget https://paddlerec.bj.bcebos.com/word2vec/test_dir.tar
+wget --no-check-certificate https://paddlerec.bj.bcebos.com/word2vec/test_dir.tar
 #样本数据集测试集
-wget https://paddlerec.bj.bcebos.com/word2vec/test_mid_dir.tar
+wget --no-check-certificate https://paddlerec.bj.bcebos.com/word2vec/test_mid_dir.tar
 ```
 
 预测命令，注意词典名称需要加后缀"_word_to_id_", 此文件是预处理阶段生成的。

PaddleRec/word2vec/infer.py

@@ -78,13 +78,13 @@ def infer_epoch(args, vocab_size, test_reader, use_cuda, i2w):
                     b_size = len([dat[0] for dat in data])
                     wa = np.array(
                         [dat[0] for dat in data]).astype("int64").reshape(
-                            b_size, 1)
+                            b_size)
                     wb = np.array(
                         [dat[1] for dat in data]).astype("int64").reshape(
-                            b_size, 1)
+                            b_size)
                     wc = np.array(
                         [dat[2] for dat in data]).astype("int64").reshape(
-                            b_size, 1)
+                            b_size)
 
                     label = [dat[3] for dat in data]
                     input_word = [dat[4] for dat in data]
@@ -95,7 +95,7 @@ def infer_epoch(args, vocab_size, test_reader, use_cuda, i2w):
                                        "analogy_c": wc,
                                        "all_label":
                                        np.arange(vocab_size).reshape(
-                                           vocab_size, 1).astype("int64"),
+                                           vocab_size).astype("int64"),
                                    },
                                    fetch_list=[pred.name, values],
                                    return_numpy=False)
@@ -145,13 +145,13 @@ def infer_step(args, vocab_size, test_reader, use_cuda, i2w):
                         b_size = len([dat[0] for dat in data])
                         wa = np.array(
                             [dat[0] for dat in data]).astype("int64").reshape(
-                                b_size, 1)
+                                b_size)
                         wb = np.array(
                             [dat[1] for dat in data]).astype("int64").reshape(
-                                b_size, 1)
+                                b_size)
                         wc = np.array(
                             [dat[2] for dat in data]).astype("int64").reshape(
-                                b_size, 1)
+                                b_size)
 
                         label = [dat[3] for dat in data]
                         input_word = [dat[4] for dat in data]
@@ -162,7 +162,7 @@ def infer_step(args, vocab_size, test_reader, use_cuda, i2w):
                                 "analogy_b": wb,
                                 "analogy_c": wc,
                                 "all_label":
-                                np.arange(vocab_size).reshape(vocab_size, 1),
+                                np.arange(vocab_size).reshape(vocab_size),
                             },
                             fetch_list=[pred.name, values],
                             return_numpy=False)
@@ -185,6 +185,7 @@ def infer_step(args, vocab_size, test_reader, use_cuda, i2w):
 
 
 if __name__ == "__main__":
+    utils.check_version()
     args = parse_args()
     start_index = args.start_index
     last_index = args.last_index

PaddleRec/word2vec/net.py

@@ -23,10 +23,10 @@ import paddle.fluid as fluid
 def skip_gram_word2vec(dict_size, embedding_size, is_sparse=False, neg_num=5):
 
     datas = []
-    input_word = fluid.layers.data(name="input_word", shape=[1], dtype='int64')
-    true_word = fluid.layers.data(name='true_label', shape=[1], dtype='int64')
-    neg_word = fluid.layers.data(
-        name="neg_label", shape=[neg_num], dtype='int64')
+    input_word = fluid.data(name="input_word", shape=[None, 1], dtype='int64')
+    true_word = fluid.data(name='true_label', shape=[None, 1], dtype='int64')
+    neg_word = fluid.data(
+        name="neg_label", shape=[None, neg_num], dtype='int64')
 
     datas.append(input_word)
     datas.append(true_word)
@@ -37,7 +37,7 @@ def skip_gram_word2vec(dict_size, embedding_size, is_sparse=False, neg_num=5):
 
     words = fluid.layers.read_file(py_reader)
     init_width = 0.5 / embedding_size
-    input_emb = fluid.layers.embedding(
+    input_emb = fluid.embedding(
         input=words[0],
         is_sparse=is_sparse,
         size=[dict_size, embedding_size],
@@ -45,38 +45,37 @@ def skip_gram_word2vec(dict_size, embedding_size, is_sparse=False, neg_num=5):
             name='emb',
             initializer=fluid.initializer.Uniform(-init_width, init_width)))
 
-    true_emb_w = fluid.layers.embedding(
+    true_emb_w = fluid.embedding(
         input=words[1],
         is_sparse=is_sparse,
         size=[dict_size, embedding_size],
         param_attr=fluid.ParamAttr(
             name='emb_w', initializer=fluid.initializer.Constant(value=0.0)))
 
-    true_emb_b = fluid.layers.embedding(
+    true_emb_b = fluid.embedding(
         input=words[1],
         is_sparse=is_sparse,
         size=[dict_size, 1],
         param_attr=fluid.ParamAttr(
             name='emb_b', initializer=fluid.initializer.Constant(value=0.0)))
-    neg_word_reshape = fluid.layers.reshape(words[2], shape=[-1, 1])
-    neg_word_reshape.stop_gradient = True
+    input_emb = fluid.layers.squeeze(input=input_emb, axes=[1])
+    true_emb_w = fluid.layers.squeeze(input=true_emb_w, axes=[1])
+    true_emb_b = fluid.layers.squeeze(input=true_emb_b, axes=[1])
 
-    neg_emb_w = fluid.layers.embedding(
-        input=neg_word_reshape,
+    neg_emb_w = fluid.embedding(
+        input=words[2],
         is_sparse=is_sparse,
         size=[dict_size, embedding_size],
         param_attr=fluid.ParamAttr(
             name='emb_w', learning_rate=1.0))
-
-    neg_emb_w_re = fluid.layers.reshape(
-        neg_emb_w, shape=[-1, neg_num, embedding_size])
-    neg_emb_b = fluid.layers.embedding(
-        input=neg_word_reshape,
+    neg_emb_b = fluid.embedding(
+        input=words[2],
         is_sparse=is_sparse,
         size=[dict_size, 1],
         param_attr=fluid.ParamAttr(
             name='emb_b', learning_rate=1.0))
 
+    
     neg_emb_b_vec = fluid.layers.reshape(neg_emb_b, shape=[-1, neg_num])
     true_logits = fluid.layers.elementwise_add(
         fluid.layers.reduce_sum(
@@ -87,7 +86,7 @@ def skip_gram_word2vec(dict_size, embedding_size, is_sparse=False, neg_num=5):
     input_emb_re = fluid.layers.reshape(
         input_emb, shape=[-1, 1, embedding_size])
     neg_matmul = fluid.layers.matmul(
-        input_emb_re, neg_emb_w_re, transpose_y=True)
+        input_emb_re, neg_emb_w, transpose_y=True)
     neg_matmul_re = fluid.layers.reshape(neg_matmul, shape=[-1, neg_num])
     neg_logits = fluid.layers.elementwise_add(neg_matmul_re, neg_emb_b_vec)
     #nce loss
@@ -111,22 +110,21 @@ def skip_gram_word2vec(dict_size, embedding_size, is_sparse=False, neg_num=5):
 
 
 def infer_network(vocab_size, emb_size):
-    analogy_a = fluid.layers.data(name="analogy_a", shape=[1], dtype='int64')
-    analogy_b = fluid.layers.data(name="analogy_b", shape=[1], dtype='int64')
-    analogy_c = fluid.layers.data(name="analogy_c", shape=[1], dtype='int64')
-    all_label = fluid.layers.data(
+    analogy_a = fluid.data(name="analogy_a", shape=[None], dtype='int64')
+    analogy_b = fluid.data(name="analogy_b", shape=[None], dtype='int64')
+    analogy_c = fluid.data(name="analogy_c", shape=[None], dtype='int64')
+    all_label = fluid.data(
         name="all_label",
-        shape=[vocab_size, 1],
-        dtype='int64',
-        append_batch_size=False)
-    emb_all_label = fluid.layers.embedding(
+        shape=[vocab_size],
+        dtype='int64')
+    emb_all_label = fluid.embedding(
         input=all_label, size=[vocab_size, emb_size], param_attr="emb")
 
-    emb_a = fluid.layers.embedding(
+    emb_a = fluid.embedding(
         input=analogy_a, size=[vocab_size, emb_size], param_attr="emb")
-    emb_b = fluid.layers.embedding(
+    emb_b = fluid.embedding(
         input=analogy_b, size=[vocab_size, emb_size], param_attr="emb")
-    emb_c = fluid.layers.embedding(
+    emb_c = fluid.embedding(
         input=analogy_c, size=[vocab_size, emb_size], param_attr="emb")
     target = fluid.layers.elementwise_add(
         fluid.layers.elementwise_sub(emb_b, emb_a), emb_c)

PaddleRec/word2vec/train.py

@@ -224,5 +224,6 @@ def train(args):
 
 
 if __name__ == '__main__':
+    utils.check_version()
     args = parse_args()
     train(args)

PaddleRec/word2vec/utils.py

@@ -22,9 +22,23 @@ def BuildWord_IdMap(dict_path):
 def prepare_data(file_dir, dict_path, batch_size):
     w2i, i2w = BuildWord_IdMap(dict_path)
     vocab_size = len(i2w)
-    reader = paddle.batch(test(file_dir, w2i), batch_size)
+    reader = fluid.io.batch(test(file_dir, w2i), batch_size)
     return vocab_size, reader, i2w
 
+def check_version():
+     """
+     Log error and exit when the installed version of paddlepaddle is
+     not satisfied.
+     """
+     err = "PaddlePaddle version 1.6 or higher is required, " \
+           "or a suitable develop version is satisfied as well. \n" \
+           "Please make sure the version is good with your code." \
+
+     try:
+         fluid.require_version('1.6.0')
+     except Exception as e:
+         logger.error(err)
+         sys.exit(1)
 
 def native_to_unicode(s):
     if _is_unicode(s):