cleanup

fluid/neural_machine_translation/transformer/model.py

@@ -10,7 +10,6 @@ from config import TrainTaskConfig, pos_enc_param_names, \
 # FIXME(guosheng): Remove out the batch_size from the model.
 batch_size = TrainTaskConfig.batch_size
 
-
 def position_encoding_init(n_position, d_pos_vec):
     """
     Generate the initial values for the sinusoid position encoding table.

fluid/neural_machine_translation/transformer/nmt_fluid.py

@@ -8,6 +8,7 @@ import paddle.fluid as fluid
 import paddle.fluid.core as core
 
 from model import transformer, position_encoding_init
+import model
 from optim import LearningRateScheduler
 from config import TrainTaskConfig, ModelHyperParams, pos_enc_param_names, \
         encoder_input_data_names, decoder_input_data_names, label_data_names
@@ -71,6 +72,8 @@ parser.add_argument(
     "--task_index", type=int, default=0, help="Index of task within the job")
 args = parser.parse_args()
 
+model.batch_size = args.batch_size
+
 def pad_batch_data(insts,
                    pad_idx,
                    n_head,
@@ -118,7 +121,6 @@ def pad_batch_data(insts,
 
 def prepare_batch_input(insts, input_data_names, src_pad_idx, trg_pad_idx,
                         max_length, n_head):
-    print("input_data_name:", input_data_names)
     """
     Put all padded data needed by training into a dict.
     """
@@ -152,7 +154,6 @@ def prepare_batch_input(insts, input_data_names, src_pad_idx, trg_pad_idx,
             trg_src_attn_pre_softmax_shape, trg_src_attn_post_softmax_shape,
             lbl_word, lbl_weight
         ]))
-    #print("input_dict", input_dict)
     return input_dict
 
 
@@ -199,7 +200,7 @@ def main():
                 ModelHyperParams.trg_pad_idx, ModelHyperParams.max_length,
                 ModelHyperParams.n_head)
 
-            test_cost = exe.run(test_program,
+            test_cost = exe.run(inference_program,
                                 feed=data_input,
                                 fetch_list=[cost])[0]
 
@@ -210,7 +211,6 @@ def main():
         ts = time.time()
         for pass_id in xrange(args.pass_num):
             for batch_id, data in enumerate(train_reader()):
-                print("batch_id:", batch_id)
                 # The current program desc is coupled with batch_size, thus all
                 # mini-batches must have the same number of instances currently.
                 if len(data) != args.batch_size:
@@ -223,11 +223,8 @@ def main():
                     ModelHyperParams.trg_pad_idx, ModelHyperParams.max_length,
                     ModelHyperParams.n_head)
 
-                #print("feed0:", data_input)
-                #print("fetch_list0:", [cost])
 
                 lr_scheduler.update_learning_rate(data_input)
-                print("before exe run in train_loop")
                 outs = exe.run(trainer_prog,
                            feed=data_input,
                            fetch_list=[cost],
@@ -239,9 +236,7 @@ def main():
 
             # Validate and save the model for inference.
             val_cost = test(exe)
-            #pass_elapsed = time.time() - start_time
-            #print("pass_id = " + str(pass_id) + " val_cost = " + str(val_cost))
-            print("pass_id = %d batch = %d  cost = %f speed = %.2f sample/s" %
+            print("pass_id = %d cost = %f avg_speed = %.2f sample/s" %
                   (pass_id, batch_id, cost_val, len(data) / (time.time() - ts)))
 
     if args.local:
@@ -298,9 +293,6 @@ def main():
             exe.run(pserver_startup)
             exe.run(pserver_prog)
         elif training_role == "TRAINER":
-            #print("cost 0:", cost)
-            #print("before run start up")
-
             # Parameter initialization
             exe.run(fluid.default_startup_program())
 
@@ -327,13 +319,7 @@ def main():
                                                 ModelHyperParams.trg_vocab_size),
                 batch_size=args.batch_size)
 
-            #print("before get trainer program")
             trainer_prog = t.get_trainer_program()
-            #print("before start")
-            # feeder = fluid.DataFeeder(feed_list=[images, label], place=place)
-            # TODO(typhoonzero): change trainer startup program to fetch parameters from pserver
-            # exe.run(fluid.default_startup_program())
-
             train_loop(exe, trainer_prog)
         else:
             print("environment var TRAINER_ROLE should be TRAINER os PSERVER")

fluid/neural_machine_translation/transformer/optim.py

@@ -28,7 +28,6 @@ class LearningRateScheduler(object):
             dtype="float32",
             persistable=True)
         self.place = place
-        #print("LearningRateScheduler init learning_rate_name:", self.learning_rate.name)
 
     def update_learning_rate(self, data_input):
         self.current_steps += 1
@@ -38,7 +37,4 @@ class LearningRateScheduler(object):
         ])
         lr_tensor = fluid.LoDTensor()
         lr_tensor.set(np.array([lr_value], dtype="float32"), self.place)
-        #print("in learning_rate")
-        #print("learning_rate_name:", self.learning_rate.name)
-        #print("data_input:", data_input)
         data_input[self.learning_rate.name] = lr_tensor