Merge pull request #1185 from guoshengCS/fix-until-input-transformer

Fix the unremoved util_input in Transformer

fluid/neural_machine_translation/transformer/config.py

@@ -190,6 +190,3 @@ fast_decoder_data_input_fields = (
     "trg_word",
     "init_score",
     "trg_src_attn_bias", )
-# fast_decoder_util_input_fields = (
-#     "trg_slf_attn_pre_softmax_shape_delta",
-#     "trg_slf_attn_post_softmax_shape_delta", )

fluid/neural_machine_translation/transformer/train.py

@@ -258,7 +258,7 @@ def split_data(data, num_part):
 
 
 def test_context(train_progm, avg_cost, train_exe, dev_count, data_input_names,
-                 util_input_names, sum_cost, token_num):
+                 sum_cost, token_num):
     # Context to do validation.
     test_program = train_progm.clone()
     with fluid.program_guard(test_program):
@@ -299,9 +299,9 @@ def test_context(train_progm, avg_cost, train_exe, dev_count, data_input_names,
                     split_data(
                         data, num_part=dev_count)):
                 data_input_dict, _ = prepare_batch_input(
-                    data_buffer, data_input_names, util_input_names,
-                    ModelHyperParams.eos_idx, ModelHyperParams.eos_idx,
-                    ModelHyperParams.n_head, ModelHyperParams.d_model)
+                    data_buffer, data_input_names, ModelHyperParams.eos_idx,
+                    ModelHyperParams.eos_idx, ModelHyperParams.n_head,
+                    ModelHyperParams.d_model)
                 feed_list.append(data_input_dict)
 
             outs = exe.run(feed=feed_list,
@@ -363,8 +363,7 @@ def train_loop(exe, train_progm, dev_count, sum_cost, avg_cost, lr_scheduler,
 
     if args.val_file_pattern is not None:
         test = test_context(train_progm, avg_cost, train_exe, dev_count,
-                            data_input_names, util_input_names, sum_cost,
-                            token_num)
+                            data_input_names, sum_cost, token_num)
 
     # the best cross-entropy value with label smoothing
     loss_normalizer = -((1. - TrainTaskConfig.label_smooth_eps) * np.log(