upgrade_1.6_op (#3498)

PaddleNLP/dialogue_model_toolkit/auto_dialogue_evaluation/README.md

@@ -28,7 +28,7 @@
 - cuda >= 9.0
 - cudnn >= 7.0
 - pandas >= 0.20.1
-- PaddlePaddle >= 1.3.1，请参考[安装指南](http://www.paddlepaddle.org/#quick-start)进行安装, 本模块使用bert作为pretrain model进行模型的finetuning训练，训练速度较慢，建议安装GPU版本的PaddlePaddle
+- PaddlePaddle >= 1.6.0，请参考[安装指南](http://www.paddlepaddle.org/#quick-start)进行安装, 本模块使用bert作为pretrain model进行模型的finetuning训练，训练速度较慢，建议安装GPU版本的PaddlePaddle
 
   注意：使用Windows GPU环境的用户，需要将示例代码中的[fluid.ParallelExecutor](http://paddlepaddle.org/documentation/docs/zh/1.4/api_cn/fluid_cn.html#parallelexecutor)替换为[fluid.Executor](http://paddlepaddle.org/documentation/docs/zh/1.4/api_cn/fluid_cn.html#executor)。
 

PaddleNLP/dialogue_model_toolkit/auto_dialogue_evaluation/ade_net.py

@@ -35,7 +35,7 @@ def create_net(
     label = model_input.labels
 
     #emb
-    context_emb = fluid.layers.embedding(
+    context_emb = fluid.input.embedding(
         input=context_wordseq,
         size=[args.vocab_size, args.emb_size],
         is_sparse=True,
@@ -43,7 +43,7 @@ def create_net(
             name=word_emb_name,
             initializer=fluid.initializer.Normal(scale=0.1)))
 
-    response_emb = fluid.layers.embedding(
+    response_emb = fluid.input.embedding(
         input=response_wordseq,
         size=[args.vocab_size, args.emb_size],
         is_sparse=True,

PaddleNLP/dialogue_model_toolkit/auto_dialogue_evaluation/inference_model.py

@@ -42,12 +42,12 @@ def do_save_inference_model(args):
 
         with fluid.unique_name.guard():
 
-            context_wordseq = fluid.layers.data(
-                    name='context_wordseq', shape=[1], dtype='int64', lod_level=1)
-            response_wordseq = fluid.layers.data(
-                    name='response_wordseq', shape=[1], dtype='int64', lod_level=1)
-            labels = fluid.layers.data(
-                    name='labels', shape=[1], dtype='int64')
+            context_wordseq = fluid.data(
+                    name='context_wordseq', shape=[-1, 1], dtype='int64', lod_level=1)
+            response_wordseq = fluid.data(
+                    name='response_wordseq', shape=[-1, 1], dtype='int64', lod_level=1)
+            labels = fluid.data(
+                    name='labels', shape=[-1, 1], dtype='int64')
 
             input_inst = [context_wordseq, response_wordseq, labels]
             input_field = InputField(input_inst)

PaddleNLP/dialogue_model_toolkit/auto_dialogue_evaluation/predict.py

@@ -45,12 +45,12 @@ def do_predict(args):
 
         with fluid.unique_name.guard():
 
-            context_wordseq = fluid.layers.data(
-                    name='context_wordseq', shape=[1], dtype='int64', lod_level=1)
-            response_wordseq = fluid.layers.data(
-                    name='response_wordseq', shape=[1], dtype='int64', lod_level=1)
-            labels = fluid.layers.data(
-                    name='labels', shape=[1], dtype='int64')
+            context_wordseq = fluid.data(
+                    name='context_wordseq', shape=[-1, 1], dtype='int64', lod_level=1)
+            response_wordseq = fluid.data(
+                    name='response_wordseq', shape=[-1, 1], dtype='int64', lod_level=1)
+            labels = fluid.data(
+                    name='labels', shape=[-1, 1], dtype='int64')
 
             input_inst = [context_wordseq, response_wordseq, labels]
             input_field = InputField(input_inst)

PaddleNLP/dialogue_model_toolkit/auto_dialogue_evaluation/train.py

@@ -48,12 +48,12 @@ def do_train(args):
         startup_prog.random_seed = args.random_seed
 
         with fluid.unique_name.guard(): 
-            context_wordseq = fluid.layers.data(
-                    name='context_wordseq', shape=[1], dtype='int64', lod_level=1)
-            response_wordseq = fluid.layers.data(
-                    name='response_wordseq', shape=[1], dtype='int64', lod_level=1)
-            labels = fluid.layers.data(
-                    name='labels', shape=[1], dtype='int64')
+            context_wordseq = fluid.data(
+                    name='context_wordseq', shape=[-1, 1], dtype='int64', lod_level=1)
+            response_wordseq = fluid.data(
+                    name='response_wordseq', shape=[-1, 1], dtype='int64', lod_level=1)
+            labels = fluid.data(
+                    name='labels', shape=[-1, 1], dtype='int64')
 
             input_inst = [context_wordseq, response_wordseq, labels]
             input_field = InputField(input_inst)

PaddleNLP/dialogue_model_toolkit/dialogue_general_understanding/README.md

@@ -23,7 +23,7 @@
 - Python >= 2.7
 - cuda >= 9.0
 - cudnn >= 7.0
-- PaddlePaddle >= 1.3.1，请参考[安装指南](http://www.paddlepaddle.org/#quick-start)进行安装, 由于模块内模型基于bert做finetuning, 训练速度较慢, 建议用户安装GPU版本PaddlePaddle进行训练。
+- PaddlePaddle >= 1.6.0，请参考[安装指南](http://www.paddlepaddle.org/#quick-start)进行安装, 由于模块内模型基于bert做finetuning, 训练速度较慢, 建议用户安装GPU版本PaddlePaddle进行训练。
 
   注意：使用Windows GPU环境的用户，需要将示例代码中的[fluid.ParallelExecutor](http://paddlepaddle.org/documentation/docs/zh/1.4/api_cn/fluid_cn.html#parallelexecutor)替换为[fluid.Executor](http://paddlepaddle.org/documentation/docs/zh/1.4/api_cn/fluid_cn.html#executor)。
 ####   b、下载代码

PaddleNLP/dialogue_model_toolkit/dialogue_general_understanding/dgu/batching.py

@@ -170,7 +170,7 @@ def pad_batch_data(insts,
     inst_data = np.array(
         [inst + list([pad_idx] * (max_len - len(inst))) for inst in insts
     ])
-    return_list += [inst_data.astype("int64").reshape([-1, max_len, 1])]
+    return_list += [inst_data.astype("int64").reshape([-1, max_len])]
 
     # position data
     if return_pos:
@@ -179,7 +179,7 @@ def pad_batch_data(insts,
             for inst in insts
         ])
 
-        return_list += [inst_pos.astype("int64").reshape([-1, max_len, 1])]
+        return_list += [inst_pos.astype("int64").reshape([-1, max_len])]
 
     if return_input_mask:
         # This is used to avoid attention on paddings.

PaddleNLP/dialogue_model_toolkit/dialogue_general_understanding/dgu/bert.py

@@ -85,21 +85,21 @@ class BertModel(object):
 
     def _build_model(self, src_ids, position_ids, sentence_ids, input_mask):
         # padding id in vocabulary must be set to 0
-        emb_out = fluid.layers.embedding(
+        emb_out = fluid.input.embedding(
             input=src_ids,
             size=[self._voc_size, self._emb_size],
             dtype=self._dtype,
             param_attr=fluid.ParamAttr(
                 name=self._word_emb_name, initializer=self._param_initializer),
             is_sparse=False)
-        position_emb_out = fluid.layers.embedding(
+        position_emb_out = fluid.input.embedding(
             input=position_ids,
             size=[self._max_position_seq_len, self._emb_size],
             dtype=self._dtype,
             param_attr=fluid.ParamAttr(
                 name=self._pos_emb_name, initializer=self._param_initializer))
 
-        sent_emb_out = fluid.layers.embedding(
+        sent_emb_out = fluid.input.embedding(
             sentence_ids,
             size=[self._sent_types, self._emb_size],
             dtype=self._dtype,

PaddleNLP/dialogue_model_toolkit/dialogue_general_understanding/inference_model.py

@@ -59,23 +59,23 @@ def do_save_inference_model(args):
             # define inputs of the network
             num_labels = len(processors[task_name].get_labels()) 
 
-            src_ids = fluid.layers.data(
-                        name='src_ids', shape=[args.max_seq_len, 1], dtype='int64')
-            pos_ids = fluid.layers.data(
-                        name='pos_ids', shape=[args.max_seq_len, 1], dtype='int64')
-            sent_ids = fluid.layers.data(
-                        name='sent_ids', shape=[args.max_seq_len, 1], dtype='int64')
-            input_mask = fluid.layers.data(
-                        name='input_mask', shape=[args.max_seq_len, 1], dtype='float32')
+            src_ids = fluid.data(
+                        name='src_ids', shape=[-1, args.max_seq_len], dtype='int64')
+            pos_ids = fluid.data(
+                        name='pos_ids', shape=[-1, args.max_seq_len], dtype='int64')
+            sent_ids = fluid.data(
+                        name='sent_ids', shape=[-1, args.max_seq_len], dtype='int64')
+            input_mask = fluid.data(
+                        name='input_mask', shape=[-1, args.max_seq_len], dtype='float32')
             if args.task_name == 'atis_slot': 
-                labels = fluid.layers.data(
-                        name='labels', shape=[args.max_seq_len], dtype='int64')
+                labels = fluid.data(
+                        name='labels', shape=[-1, args.max_seq_len], dtype='int64')
             elif args.task_name in ['dstc2', 'dstc2_asr', 'multi-woz']:
-                labels = fluid.layers.data(
-                        name='labels', shape=[num_labels], dtype='int64')
+                labels = fluid.data(
+                        name='labels', shape=[-1, num_labels], dtype='int64')
             else: 
-                labels = fluid.layers.data(
-                        name='labels', shape=[1], dtype='int64')
+                labels = fluid.data(
+                        name='labels', shape=[-1, 1], dtype='int64')
             
             input_inst = [src_ids, pos_ids, sent_ids, input_mask, labels]
             input_field = InputField(input_inst)

PaddleNLP/dialogue_model_toolkit/dialogue_general_understanding/predict.py

@@ -60,23 +60,23 @@ def do_predict(args):
             # define inputs of the network
             num_labels = len(processors[task_name].get_labels())
 
-            src_ids = fluid.layers.data(
-                        name='src_ids', shape=[args.max_seq_len, 1], dtype='int64')
-            pos_ids = fluid.layers.data(
-                        name='pos_ids', shape=[args.max_seq_len, 1], dtype='int64')
-            sent_ids = fluid.layers.data(
-                        name='sent_ids', shape=[args.max_seq_len, 1], dtype='int64')
-            input_mask = fluid.layers.data(
-                        name='input_mask', shape=[args.max_seq_len, 1], dtype='float32')
+            src_ids = fluid.data(
+                        name='src_ids', shape=[-1, args.max_seq_len], dtype='int64')
+            pos_ids = fluid.data(
+                        name='pos_ids', shape=[-1, args.max_seq_len], dtype='int64')
+            sent_ids = fluid.data(
+                        name='sent_ids', shape=[-1, args.max_seq_len], dtype='int64')
+            input_mask = fluid.data(
+                        name='input_mask', shape=[-1, args.max_seq_len], dtype='float32')
             if args.task_name == 'atis_slot': 
-                labels = fluid.layers.data(
-                        name='labels', shape=[args.max_seq_len], dtype='int64')
+                labels = fluid.data(
+                        name='labels', shape=[-1, args.max_seq_len], dtype='int64')
             elif args.task_name in ['dstc2', 'dstc2_asr', 'multi-woz']:
-                labels = fluid.layers.data(
-                        name='labels', shape=[num_labels], dtype='int64')
+                labels = fluid.data(
+                        name='labels', shape=[-1, num_labels], dtype='int64')
             else: 
-                labels = fluid.layers.data(
-                        name='labels', shape=[1], dtype='int64')
+                labels = fluid.data(
+                        name='labels', shape=[-1, 1], dtype='int64')
             
             input_inst = [src_ids, pos_ids, sent_ids, input_mask, labels]
             input_field = InputField(input_inst)

PaddleNLP/dialogue_model_toolkit/dialogue_general_understanding/train.py

@@ -60,23 +60,23 @@ def do_train(args):
         with fluid.unique_name.guard(): 
             num_labels = len(processors[task_name].get_labels())
 
-            src_ids = fluid.layers.data(
-                        name='src_ids', shape=[args.max_seq_len, 1], dtype='int64')
-            pos_ids = fluid.layers.data(
-                        name='pos_ids', shape=[args.max_seq_len, 1], dtype='int64')
-            sent_ids = fluid.layers.data(
-                        name='sent_ids', shape=[args.max_seq_len, 1], dtype='int64')
-            input_mask = fluid.layers.data(
-                        name='input_mask', shape=[args.max_seq_len, 1], dtype='float32')
+            src_ids = fluid.data(
+                        name='src_ids', shape=[-1, args.max_seq_len], dtype='int64')
+            pos_ids = fluid.data(
+                        name='pos_ids', shape=[-1, args.max_seq_len], dtype='int64')
+            sent_ids = fluid.data(
+                        name='sent_ids', shape=[-1, args.max_seq_len], dtype='int64')
+            input_mask = fluid.data(
+                        name='input_mask', shape=[-1, args.max_seq_len], dtype='float32')
             if args.task_name == 'atis_slot': 
-                labels = fluid.layers.data(
-                        name='labels', shape=[args.max_seq_len], dtype='int64')
+                labels = fluid.data(
+                        name='labels', shape=[-1, args.max_seq_len], dtype='int64')
             elif args.task_name in ['dstc2']:
-                labels = fluid.layers.data(
-                        name='labels', shape=[num_labels], dtype='int64')
+                labels = fluid.data(
+                        name='labels', shape=[-1, num_labels], dtype='int64')
             else: 
-                labels = fluid.layers.data(
-                        name='labels', shape=[1], dtype='int64')
+                labels = fluid.data(
+                        name='labels', shape=[-1, 1], dtype='int64')
             
             input_inst = [src_ids, pos_ids, sent_ids, input_mask, labels]
             input_field = InputField(input_inst)