Disable all sorts in data reader

fluid/neural_machine_translation/transformer/profile.py

@@ -58,22 +58,6 @@ def parse_args():
         type=int,
         default=10000,
         help="The buffer size to pool data.")
-    parser.add_argument(
-        "--sort_type",
-        default="pool",
-        choices=("global", "pool", "none"),
-        help="The grain to sort by length: global for all instances; pool for "
-        "instances in pool; none for no sort.")
-    parser.add_argument(
-        "--shuffle",
-        type=ast.literal_eval,
-        default=True,
-        help="The flag indicating whether to shuffle instances.")
-    parser.add_argument(
-        "--shuffle_batch",
-        type=ast.literal_eval,
-        default=True,
-        help="The flag indicating whether to shuffle the data batches.")
     parser.add_argument(
         "--special_token",
         type=str,
@@ -161,8 +145,7 @@ def train_loop(exe, train_progm, init, num_iters, train_data, dev_count,
               (batch_id, total_sum_cost, total_avg_cost,
                np.exp([min(total_avg_cost, 100)])))
         init = True
-    total_time = time.time() - start_time
-    return total_time, exec_time
+    return time.time() - start_time, exec_time
 
 
 def profile(args):
@@ -205,6 +188,7 @@ def profile(args):
     else:
         exe.run(fluid.framework.default_startup_program())
 
+    # Disable all sorts for they will be done in the 1st batch.
     train_data = reader.DataReader(
         src_vocab_fpath=args.src_vocab_fpath,
         trg_vocab_fpath=args.trg_vocab_fpath,
@@ -212,9 +196,9 @@ def profile(args):
         use_token_batch=args.use_token_batch,
         batch_size=args.batch_size * (1 if args.use_token_batch else dev_count),
         pool_size=args.pool_size,
-        sort_type=args.sort_type,
-        shuffle=args.shuffle,
-        shuffle_batch=args.shuffle_batch,
+        sort_type='none',
+        shuffle=False,
+        shuffle_batch=False,
         start_mark=args.special_token[0],
         end_mark=args.special_token[1],
         unk_mark=args.special_token[2],