Use create_global_var instead of fill_constant in __init__ to make it...

Use create_global_var instead of fill_constant in __init__ to make it compatible between dygraph and static-graph.

seq2seq/predict.py

@@ -113,7 +113,7 @@ def do_predict(args):
         for data in data_loader():
             finished_seq = model.test(inputs=flatten(data))[0]
             finished_seq = finished_seq[:, :, np.newaxis] if len(
-                finished_seq.shape == 2) else finished_seq
+                finished_seq.shape) == 2 else finished_seq
             finished_seq = np.transpose(finished_seq, [0, 2, 1])
             for ins in finished_seq:
                 for beam_idx, beam in enumerate(ins):

seq2seq/reader.py

@@ -168,6 +168,7 @@ class SampleInfo(object):
     def __init__(self, i, lens):
         self.i = i
         self.lens = lens
+        self.max_len = lens[0]
 
     def get_ranges(self, min_length=None, max_length=None, truncate=False):
         ranges = []

seq2seq/seq2seq_attn.py

@@ -247,6 +247,8 @@ class GreedyEmbeddingHelper(fluid.layers.GreedyEmbeddingHelper):
             self.start_token_value = start_tokens
         super(GreedyEmbeddingHelper, self).__init__(embedding_fn, start_tokens,
                                                     end_token)
+        self.end_token = fluid.layers.create_global_var(
+            shape=[1], dtype="int64", value=end_token, persistable=True)
 
     def initialize(self, batch_ref=None):
         if getattr(self, "need_convert_start_tokens", False):
@@ -319,7 +321,7 @@ class AttentionGreedyInferModel(AttentionModel):
         encoder_padding_mask = (src_mask - 1.0) * 1e9
         encoder_padding_mask = layers.unsqueeze(encoder_padding_mask, [1])
 
-        # dynamic decoding with beam search
+        # dynamic decoding with greedy search
         rs, _ = self.greedy_search_decoder(
             inits=decoder_initial_states,
             encoder_output=encoder_output,