use GE backend for ms2.3 (#785) (#795)

Author: ChongWei905

examples/clip/clip/clip.py

@@ -126,6 +126,8 @@ def load(name: str, device: str = "Ascend", mode: int = 1, download_root: str =
         take as its input.
     """
     ms.set_context(device_target=device, mode=mode)
+    if mode == ms.GRAPH_MODE:
+        ms.set_context(jit_config={"jit_level": "O2"})
     if name in _MODELS:
         model_path = _download(_MODELS[name], download_root or os.path.expanduser("~/.cache/clip"))
         ckp_dict = load_checkpoint(model_path)

examples/det/ssd/train.py

@@ -22,6 +22,8 @@ def train(args):
     """main train function"""
 
     ms.set_context(mode=args.mode)
+    if args.mode == ms.GRAPH_MODE:
+        ms.set_context(jit_config={"jit_level": "O2"})
 
     if args.distribute:
         init()

examples/finetune/finetune.py

@@ -30,6 +30,8 @@ def finetune_train(args):
     """main train function"""
 
     ms.set_context(mode=args.mode)
+    if args.mode == ms.GRAPH_MODE:
+        ms.set_context(jit_config={"jit_level": "O2"})
     if args.distribute:
         init()
         device_num = get_group_size()

examples/open_clip/test.py

@@ -58,6 +58,8 @@ def parse_args(args):
 def main(args):
     args = parse_args(args)
     ms.set_context(device_target=args.device_target, mode=args.mode)
+    if args.mode == ms.GRAPH_MODE:
+        ms.set_context(jit_config={"jit_level": "O2"})
     model, preprocess_train, preprocess_val = create_model_and_transforms(
         args.model_name,
         args.pretrained,

examples/seg/deeplabv3/train.py

@@ -24,6 +24,8 @@
 def train(args):
     """main train function"""
     ms.set_context(mode=args.mode)
+    if args.mode == ms.GRAPH_MODE:
+        ms.set_context(jit_config={"jit_level": "O2"})
 
     if args.distribute:
         init()

train.py

@@ -29,6 +29,8 @@
 def main():
     args = parse_args()
     ms.set_context(mode=args.mode)
+    if args.mode == ms.GRAPH_MODE:
+        ms.set_context(jit_config={"jit_level": "O2"})
     if args.distribute:
         init()
         rank_id, device_num = get_rank(), get_group_size()

train_with_func.py

@@ -58,6 +58,8 @@ def main():
     args = parse_args()
     args = check_args(args)
     ms.set_context(mode=args.mode)
+    if args.mode == ms.GRAPH_MODE:
+        ms.set_context(jit_config={"jit_level": "O2"})
     if args.distribute:
         init()
         rank_id, device_num = get_rank(), get_group_size()

validate.py

@@ -27,6 +27,8 @@ def check_batch_size(num_samples, ori_batch_size=32, refine=True):
 
 def validate(args):
     ms.set_context(mode=args.mode)
+    if args.mode == ms.GRAPH_MODE:
+        ms.set_context(jit_config={"jit_level": "O2"})
 
     # create dataset
     dataset_eval = create_dataset(