update transformer support remove build once (#4115)

* update transformer support remove build once; test=develop

* fix optimizer; test=develop

dygraph/transformer/predict.py

@@ -62,9 +62,9 @@ def prepare_infer_input(insts, src_pad_idx, bos_idx, n_head):
     trg_word = np.asarray([[bos_idx]] * len(insts), dtype="int64")
     trg_src_attn_bias = np.tile(src_slf_attn_bias[:, :, ::src_max_len, :],
                                 [1, 1, 1, 1]).astype("float32")
-    trg_word = trg_word.reshape(-1, 1, 1)
-    src_word = src_word.reshape(-1, src_max_len, 1)
-    src_pos = src_pos.reshape(-1, src_max_len, 1)
+    trg_word = trg_word.reshape(-1, 1, 1 )
+    src_word = src_word.reshape(-1, src_max_len, 1 )
+    src_pos = src_pos.reshape(-1, src_max_len,1  )
 
     data_inputs = [
         src_word, src_pos, src_slf_attn_bias, trg_word, trg_src_attn_bias
@@ -101,7 +101,7 @@ def infer(args):
         if args.use_data_parallel else fluid.CUDAPlace(0)
     with fluid.dygraph.guard(place):
         transformer = TransFormer(
-            'transformer', ModelHyperParams.src_vocab_size,
+            ModelHyperParams.src_vocab_size,
             ModelHyperParams.trg_vocab_size, ModelHyperParams.max_length + 1,
             ModelHyperParams.n_layer, ModelHyperParams.n_head,
             ModelHyperParams.d_key, ModelHyperParams.d_value,
@@ -129,7 +129,8 @@ def infer(args):
             enc_inputs, dec_inputs = prepare_infer_input(
                 batch, ModelHyperParams.eos_idx, ModelHyperParams.bos_idx,
                 ModelHyperParams.n_head)
-
+            
+            print( "enc inputs", enc_inputs[0].shape )
             finished_seq, finished_scores = transformer.beam_search(
                 enc_inputs,
                 dec_inputs,

dygraph/transformer/train.py

@@ -110,7 +110,7 @@ def train(args):
 
         # define model
         transformer = TransFormer(
-            'transformer', ModelHyperParams.src_vocab_size,
+            ModelHyperParams.src_vocab_size,
             ModelHyperParams.trg_vocab_size, ModelHyperParams.max_length + 1,
             ModelHyperParams.n_layer, ModelHyperParams.n_head,
             ModelHyperParams.d_key, ModelHyperParams.d_value,
@@ -123,6 +123,7 @@ def train(args):
         optimizer = fluid.optimizer.Adam(learning_rate=NoamDecay(
             ModelHyperParams.d_model, TrainTaskConfig.warmup_steps,
             TrainTaskConfig.learning_rate),
+            parameter_list = transformer.parameters(),
                                          beta1=TrainTaskConfig.beta1,
                                          beta2=TrainTaskConfig.beta2,
                                          epsilon=TrainTaskConfig.eps)