Update transformer (#2444)

* update transformer

PaddleNLP/neural_machine_translation/transformer/train.py

@@ -376,7 +376,8 @@ def py_reader_provider_wrapper(data_reader, place):
                 data, data_input_names, ModelHyperParams.eos_idx,
                 ModelHyperParams.eos_idx, ModelHyperParams.n_head,
                 ModelHyperParams.d_model)
-            yield [data_input_dict[item] for item in data_input_names]
+            total_dict = dict(data_input_dict.items())
+            yield [total_dict[item] for item in data_input_names]
 
     return py_reader_provider
 
@@ -422,14 +423,11 @@ def test_context(exe, train_exe, dev_count):
         fluid.io.load_persistables(
             exe, TrainTaskConfig.ckpt_path, main_program=test_prog)
 
-    exec_strategy = fluid.ExecutionStrategy()
-    exec_strategy.use_experimental_executor = True
     build_strategy = fluid.BuildStrategy()
     test_exe = fluid.ParallelExecutor(
         use_cuda=TrainTaskConfig.use_gpu,
         main_program=test_prog,
         build_strategy=build_strategy,
-        exec_strategy=exec_strategy,
         share_vars_from=train_exe)
 
     def test(exe=test_exe, pyreader=pyreader):
@@ -494,13 +492,18 @@ def train_loop(exe,
 
     # For faster executor
     exec_strategy = fluid.ExecutionStrategy()
-    exec_strategy.use_experimental_executor = True
     exec_strategy.num_iteration_per_drop_scope = int(args.fetch_steps)
     build_strategy = fluid.BuildStrategy()
+    build_strategy.memory_optimize = False
+    build_strategy.enable_inplace = True
+
+    sum_cost.persistable = True
+    token_num.persistable = True
     # Since the token number differs among devices, customize gradient scale to
     # use token average cost among multi-devices. and the gradient scale is
     # `1 / token_number` for average cost.
     # build_strategy.gradient_scale_strategy = fluid.BuildStrategy.GradientScaleStrategy.Customized
+    build_strategy.fuse_all_optimizer_ops = True
 
     logging.info("begin executor")
     train_exe = fluid.ParallelExecutor(
@@ -632,7 +635,8 @@ def train(args):
         place = fluid.CPUPlace()
         dev_count = int(os.environ.get('CPU_NUM', multiprocessing.cpu_count()))
     else:
-        place = fluid.CUDAPlace(0)
+        gpu_id = int(os.environ.get('FLAGS_selected_gpus', 0))
+        place = fluid.CUDAPlace(gpu_id)
         dev_count = fluid.core.get_cuda_device_count()
 
     exe = fluid.Executor(place)