paddle 1.6 compat

ernie/batching.py

@@ -208,7 +208,7 @@ def pad_batch_data(insts,
 
     if return_seq_lens:
         seq_lens = np.array([len(inst) for inst in insts])
-        return_list += [seq_lens.astype("int64").reshape([-1, 1])]
+        return_list += [seq_lens.astype("int64").reshape([-1])]
 
     return return_list if len(return_list) > 1 else return_list[0]
 

ernie/ernie_encoder.py

@@ -56,7 +56,7 @@ def create_model(args, pyreader_name, ernie_config):
         capacity=50,
         shapes=[[-1, args.max_seq_len, 1], [-1, args.max_seq_len, 1],
                 [-1, args.max_seq_len, 1], [-1, args.max_seq_len, 1],
-                [-1, args.max_seq_len, 1], [-1, 1]],
+                [-1, args.max_seq_len, 1], [-1]],
         dtypes=['int64', 'int64', 'int64', 'int64', 'float', 'int64'],
         lod_levels=[0, 0, 0, 0, 0, 0],
         name=pyreader_name,

ernie/finetune/sequence_label.py

@@ -40,7 +40,7 @@ def create_model(args, pyreader_name, ernie_config, is_prediction=False):
         capacity=50,
         shapes=[[-1, args.max_seq_len, 1], [-1, args.max_seq_len, 1],
                 [-1, args.max_seq_len, 1], [-1, args.max_seq_len, 1],
-                [-1, args.max_seq_len, 1], [-1, args.max_seq_len, 1], [-1, 1]],
+                [-1, args.max_seq_len, 1], [-1, args.max_seq_len, 1], [-1]],
         dtypes=[
             'int64', 'int64', 'int64', 'int64', 'float32', 'int64', 'int64'
         ],

ernie/model/ernie.py

@@ -86,7 +86,7 @@ class ErnieModel(object):
         self._sent_emb_name = "sent_embedding"
         self._task_emb_name = "task_embedding"
         self._dtype = "float16" if use_fp16 else "float32"
-        self._emb_dtype = "float32"
+        self._emb_dtype = 'float32'
 
         # Initialize all weigths by truncated normal initializer, and all biases
         # will be initialized by constant zero by default.
@@ -138,7 +138,7 @@ class ErnieModel(object):
         emb_out = pre_process_layer(
             emb_out, 'nd', self._prepostprocess_dropout, name='pre_encoder')
 
-        if self._dtype == "float16":
+        if self._dtype == 'float16':
             emb_out = fluid.layers.cast(x=emb_out, dtype=self._dtype)
             input_mask = fluid.layers.cast(x=input_mask, dtype=self._dtype)
         self_attn_mask = fluid.layers.matmul(
@@ -167,7 +167,7 @@ class ErnieModel(object):
             postprocess_cmd="dan",
             param_initializer=self._param_initializer,
             name='encoder')
-        if self._dtype == "float16":        
+        if self._dtype == 'float16':
             self._enc_out = fluid.layers.cast(
                 x=self._enc_out, dtype=self._emb_dtype)
 

ernie/model/ernie_v1.py

@@ -76,7 +76,7 @@ class ErnieModel(object):
         self._word_emb_name = "word_embedding"
         self._pos_emb_name = "pos_embedding"
         self._sent_emb_name = "sent_embedding"
-        self._dtype = "float16" if use_fp16 else "float32"
+        self._dtype = 'float16' if use_fp16 else 'float32'
 
         # Initialize all weigths by truncated normal initializer, and all biases
         # will be initialized by constant zero by default.
@@ -114,7 +114,7 @@ class ErnieModel(object):
         emb_out = pre_process_layer(
             emb_out, 'nd', self._prepostprocess_dropout, name='pre_encoder')
 
-        if self._dtype == "float16":
+        if self._dtype == 'float16':
             input_mask = fluid.layers.cast(x=input_mask, dtype=self._dtype)
         self_attn_mask = fluid.layers.matmul(
             x=input_mask, y=input_mask, transpose_y=True)