Change the dir name (#5202)

* change the dir name

* add static step final save

* refine doc

* alter method name

PaddleNLP/benchmark/transformer/README.md

@@ -55,7 +55,7 @@ dataset = WMT14ende.get_datasets(mode="train", transform_func=transform_func)
 ``` shell
 cd static/
 export CUDA_VISIBLE_DEVICES=0
-python3 train.py
+python3 train.py --config ../configs/transformer.base.yaml
 ```
 
 需要注意的是，单卡下的超参设置与多卡下的超参设置有些不同，单卡执行需要修改 `configs/transformer.big.yaml` 或是 `configs/transformer.base.yaml` 中：
@@ -67,7 +67,7 @@ python3 train.py
 ``` shell
 cd dygraph/
 export CUDA_VISIBLE_DEVICES=0
-python3 train.py
+python3 train.py --config ../configs/transformer.base.yaml
 ```
 
 需要注意的是，单卡下的超参设置与多卡下的超参设置有些不同，单卡执行需要修改 `configs/transformer.big.yaml` 或是 `configs/transformer.base.yaml` 中：
@@ -85,7 +85,7 @@ python3 train.py
 ``` shell
 cd static/
 export CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7
-python3 train.py
+python3 train.py --config ../configs/transformer.base.yaml
 ```
 
 使用 PE 的方式启动单机多卡需要设置 `configs/transformer.big.yaml` 或是 `configs/transformer.base.yaml` 中 `is_distributed` 参数为 `False`。
@@ -94,7 +94,7 @@ python3 train.py
 ``` shell
 cd static/
 export CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7
-python -m paddle.distributed.launch --gpus="0,1,2,3,4,5,6,7" train.py
+python3 -m paddle.distributed.launch --gpus="0,1,2,3,4,5,6,7" train.py --config ../configs/transformer.base.yaml
 ```
 
 使用 fleet 的方式启动单机多卡需要设置 `configs/transformer.big.yaml` 或是 `configs/transformer.base.yaml` 中 `is_distributed` 参数为 `True`。
@@ -104,7 +104,7 @@ python -m paddle.distributed.launch --gpus="0,1,2,3,4,5,6,7" train.py
 ``` shell
 cd dygraph/
 export CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7
-python3 -m paddle.distributed.launch --gpus "0,1,2,3,4,5,6,7" train.py
+python3 -m paddle.distributed.launch --gpus "0,1,2,3,4,5,6,7" train.py --config ../configs/transformer.base.yaml
 ```
 
 ### 模型推断
@@ -116,7 +116,7 @@ python3 -m paddle.distributed.launch --gpus "0,1,2,3,4,5,6,7" train.py
 # setting visible devices for prediction
 cd static/
 export CUDA_VISIBLE_DEVICES=0
-python predict.py --config ./configs/transformer.base.yaml
+python3 predict.py --config ../configs/transformer.base.yaml
 ```
 
  由 `predict_file` 指定的文件中文本的翻译结果会输出到 `output_file` 指定的文件。执行预测时需要设置 `init_from_params` 来给出模型所在目录，更多参数的使用可以在 `configs/transformer.big.yaml` 和 `configs/transformer.base.yaml` 文件中查阅注释说明并进行更改设置。如果执行不提供 `--config` 选项，程序将默认使用 big model 的配置。
@@ -128,7 +128,7 @@ python predict.py --config ./configs/transformer.base.yaml
 # setting visible devices for prediction
 cd dygraph/
 export CUDA_VISIBLE_DEVICES=0
-python predict.py --config ./configs/transformer.base.yaml
+python3 predict.py --config ../configs/transformer.base.yaml
 ```
 
  由 `predict_file` 指定的文件中文本的翻译结果会输出到 `output_file` 指定的文件。执行预测时需要设置 `init_from_params` 来给出模型所在目录，更多参数的使用可以在 `configs/transformer.big.yaml` 和 `configs/transformer.base.yaml` 文件中查阅注释说明并进行更改设置。如果执行不提供 `--config` 选项，程序将默认使用 big model 的配置。

PaddleNLP/benchmark/transformer/dygraph/train.py

@@ -16,7 +16,7 @@ from paddlenlp.transformers import TransformerModel, CrossEntropyCriterion, posi
 
 sys.path.append("../")
 import reader
-from utils.record import AverageStatistical
+from util.record import AverageStatistical
 
 FORMAT = '%(asctime)s-%(levelname)s: %(message)s'
 logging.basicConfig(level=logging.INFO, format=FORMAT)

PaddleNLP/benchmark/transformer/static/predict.py

@@ -48,7 +48,7 @@ def post_process_seq(seq, bos_idx, eos_idx, output_bos=False, output_eos=False):
     return seq
 
 
-def do_train(args):
+def do_predict(args):
     paddle.enable_static()
     if args.use_gpu:
         place = paddle.set_device("gpu:0")
@@ -118,4 +118,4 @@ if __name__ == "__main__":
         args = AttrDict(yaml.safe_load(f))
         pprint(args)
 
-    do_train(args)
+    do_predict(args)

PaddleNLP/benchmark/transformer/static/train.py

@@ -17,7 +17,7 @@ from paddlenlp.transformers import TransformerModel, CrossEntropyCriterion
 
 sys.path.append("../")
 import reader
-from utils.record import AverageStatistical
+from util.record import AverageStatistical
 
 FORMAT = '%(asctime)s-%(levelname)s: %(message)s'
 logging.basicConfig(level=logging.INFO, format=FORMAT)
@@ -40,10 +40,12 @@ def do_train(args):
     if args.is_distributed:
         fleet.init(is_collective=True)
         gpu_id = int(os.getenv("FLAGS_selected_gpus", "0"))
-        places = paddle.CUDAPlace(gpu_id) if args.use_gpu else paddle.static.cpu_places()
+        places = paddle.CUDAPlace(
+            gpu_id) if args.use_gpu else paddle.static.cpu_places()
         trainer_count = 1 if args.use_gpu else len(places)
     else:
-        places = paddle.static.cuda_places() if args.use_gpu else paddle.static.cpu_places()
+        places = paddle.static.cuda_places(
+        ) if args.use_gpu else paddle.static.cpu_places()
         trainer_count = len(places)
 
     # Set seed for CE
@@ -110,24 +112,24 @@ def do_train(args):
                     'init_loss_scaling': args.scale_loss,
                 }
 
-            optimizer = fleet.distributed_optimizer(optimizer, strategy=dist_strategy)
+            optimizer = fleet.distributed_optimizer(
+                optimizer, strategy=dist_strategy)
         optimizer.minimize(avg_cost)
 
     if args.is_distributed:
         exe = paddle.static.Executor(places)
     else:
         exe = paddle.static.Executor()
-        build_strategy = paddle.static.BuildStrategy()	
-        exec_strategy = paddle.static.ExecutionStrategy()	
+        build_strategy = paddle.static.BuildStrategy()
+        exec_strategy = paddle.static.ExecutionStrategy()
 
-        compiled_train_program = paddle.static.CompiledProgram(	
-            train_program).with_data_parallel(	
-                loss_name=avg_cost.name,	
-                build_strategy=build_strategy,	
+        compiled_train_program = paddle.static.CompiledProgram(
+            train_program).with_data_parallel(
+                loss_name=avg_cost.name,
+                build_strategy=build_strategy,
                 exec_strategy=exec_strategy)
     exe.run(startup_program)
 
-
     # the best cross-entropy value with label smoothing
     loss_normalizer = -(
         (1. - args.label_smooth_eps) * np.log(
@@ -220,6 +222,10 @@ def do_train(args):
             step_idx += 1
             batch_start = time.time()
 
+    if args.save_model and dist.get_rank() == 0:
+        model_path = os.path.join(args.save_model, "step_final", "transformer")
+        paddle.static.save(train_program, model_path)
+
     paddle.disable_static()