fix transformer pe (#5157)

* fix incorrect usage of data loader

* refine code

* remove unused code

* refine code

PaddleNLP/benchmark/transformer/reader.py

@@ -33,7 +33,7 @@ def min_max_filer(data, max_len, min_len=0):
     return (data_min_len >= min_len) and (data_max_len <= max_len)
 
 
-def create_data_loader(args):
+def create_data_loader(args, places=None):
     root = None if args.root == "None" else args.root
     (src_vocab, trg_vocab) = WMT14ende.get_vocab(root=root)
     padding_vocab = (
@@ -67,14 +67,14 @@ def create_data_loader(args):
 
         data_loader = DataLoader(
             dataset=dataset,
+            places=places,
             batch_sampler=batch_sampler,
             collate_fn=partial(
                 prepare_train_input,
                 bos_idx=args.bos_idx,
                 eos_idx=args.eos_idx,
                 pad_idx=args.bos_idx),
-            num_workers=0,
-            return_list=True)
+            num_workers=0)
         data_loaders[i] = (data_loader)
     return data_loaders
 

PaddleNLP/benchmark/transformer/static/train.py

@@ -34,28 +34,10 @@ def parse_args():
     return args
 
 
-def batch_creator(loader, trainer_count):
-    batch = []
-    for data in loader:
-        batch.append(data)
-        if len(batch) == trainer_count:
-            yield batch
-            batch = []
-    # DO NOT drop last.
-    if len(batch) > 0:
-        while len(batch) < trainer_count:
-            batch.append(batch[-1])
-        yield batch
-
-
 def do_train(args):
     paddle.enable_static()
-    if args.use_gpu:
-        trainer_count = len(os.environ['CUDA_VISIBLE_DEVICES'].split(','))
-        place = paddle.set_device("gpu:0")
-    else:
-        trainer_count = int(os.environ['CPU_NUM'])
-        place = paddle.set_device("cpu")
+    places = paddle.static.cuda_places() if args.use_gpu else paddle.static.cpu_places()
+    trainer_count = len(places)
 
     # Set seed for CE
     random_seed = eval(str(args.random_seed))
@@ -63,7 +45,7 @@ def do_train(args):
         paddle.seed(random_seed)
 
     # Define data loader
-    (train_loader), (eval_loader) = reader.create_data_loader(args)
+    (train_loader), (eval_loader) = reader.create_data_loader(args, places)
 
     train_program = paddle.static.Program()
     startup_program = paddle.static.Program()
@@ -108,11 +90,10 @@ def do_train(args):
 
         optimizer.minimize(avg_cost)
 
-    exe = paddle.static.Executor(place)
+    exe = paddle.static.Executor()
     exe.run(startup_program)
 
     build_strategy = paddle.static.BuildStrategy()
-    build_strategy.enable_inplace = True
     exec_strategy = paddle.static.ExecutionStrategy()
 
     compiled_train_program = paddle.static.CompiledProgram(
@@ -138,7 +119,7 @@ def do_train(args):
         batch_id = 0
         batch_start = time.time()
         pass_start_time = batch_start
-        for data in batch_creator(train_loader, trainer_count):
+        for data in train_loader():
             # NOTE: used for benchmark and use None as default.
             if args.max_iter and step_idx == args.max_iter:
                 return