enable_a_m_p

model_zoo/official/cv/FCN8s/train.py

@@ -44,7 +44,7 @@ def modelarts_pre_process():
 @moxing_wrapper(pre_process=modelarts_pre_process)
 def train():
     device_num = get_device_num()
-    context.set_context(mode=context.GRAPH_MODE, enable_auto_mixed_precision=True, save_graphs=False,
+    context.set_context(mode=context.GRAPH_MODE, save_graphs=False,
                         device_target=config.device_target, device_id=get_device_id())
     # init multicards training
     config.rank = 0

model_zoo/official/cv/centerface/test.py

@@ -39,7 +39,7 @@ context.set_context(mode=context.GRAPH_MODE,
 
 if config.device_target == "Ascend":
     context.set_context(device_id=dev_id)
-    context.set_context(enable_auto_mixed_precision=False)
+
 
 def modelarts_process():
     config.data_dir = config.data_path

model_zoo/official/cv/centerface/train.py

@@ -55,8 +55,6 @@ dev_id = get_device_id()
 context.set_context(mode=context.GRAPH_MODE, device_target=config.device_target,
                     save_graphs=False, device_id=dev_id, reserve_class_name_in_scope=False)
 
-if config.device_target == "Ascend":
-    context.set_context(enable_auto_mixed_precision=False)
 
 if config.lr_scheduler == 'cosine_annealing' and config.max_epoch > config.t_max:
     config.t_max = config.max_epoch

model_zoo/official/cv/cnnctc/eval.py

@@ -29,7 +29,7 @@ from src.model_utils.moxing_adapter import moxing_wrapper
 
 
 context.set_context(mode=context.GRAPH_MODE, save_graphs=False,
-                    save_graphs_path=".", enable_auto_mixed_precision=False)
+                    save_graphs_path=".")
 
 
 def test_dataset_creator():

model_zoo/official/cv/cnnctc/train.py

@@ -65,7 +65,7 @@ def train():
 
     if target == "Ascend":
         device_id = get_device_id()
-        context.set_context(device_id=device_id, enable_auto_mixed_precision=False)
+        context.set_context(device_id=device_id)
 
         if config.run_distribute:
             init()

model_zoo/official/cv/cspdarknet53/eval.py

@@ -105,7 +105,7 @@ def run_eval():
                                       datetime.datetime.now().strftime("%Y-%m-%d_time_%H_%M_%S"))
     config.logger = get_logger(config.outputs_dir, config.rank)
 
-    context.set_context(mode=context.GRAPH_MODE, enable_auto_mixed_precision=True,
+    context.set_context(mode=context.GRAPH_MODE,
                         device_target=config.device_target, save_graphs=False, device_id=get_device_id())
     config.logger.save_args(config)
 

model_zoo/official/cv/cspdarknet53/train.py

@@ -152,7 +152,7 @@ def modelarts_pre_process():
 @moxing_wrapper(pre_process=modelarts_pre_process)
 def run_train():
     config = set_default_args(default_config)
-    context.set_context(mode=context.GRAPH_MODE, enable_auto_mixed_precision=True,
+    context.set_context(mode=context.GRAPH_MODE,
                         device_target=config.device_target, save_graphs=False, device_id=get_device_id())
     if config.is_distributed:
         parallel_mode = ParallelMode.DATA_PARALLEL

model_zoo/official/cv/deeplabv3/train.py

@@ -109,7 +109,7 @@ def train():
     if args.device_target == "CPU":
         context.set_context(mode=context.GRAPH_MODE, save_graphs=False, device_target="CPU")
     else:
-        context.set_context(mode=context.GRAPH_MODE, enable_auto_mixed_precision=True, save_graphs=False,
+        context.set_context(mode=context.GRAPH_MODE, save_graphs=False,
                             device_target="Ascend", device_id=get_device_id())
 
     # init multicards training

model_zoo/official/cv/deeplabv3plus/train.py

@@ -85,7 +85,7 @@ def train():
     if args.device_target == "CPU":
         context.set_context(mode=context.GRAPH_MODE, save_graphs=False, device_target="CPU")
     else:
-        context.set_context(mode=context.GRAPH_MODE, enable_auto_mixed_precision=True, save_graphs=False,
+        context.set_context(mode=context.GRAPH_MODE, save_graphs=False,
                             device_target="Ascend", device_id=int(os.getenv('DEVICE_ID')))
     # init multicards training
     if args.modelArts_mode:

model_zoo/official/cv/densenet/train.py

@@ -130,7 +130,7 @@ def train():
     config.lr_epochs = list(map(int, config.lr_epochs.split(',')))
     config.image_size = list(map(int, config.image_size.split(',')))
 
-    context.set_context(mode=context.GRAPH_MODE, enable_auto_mixed_precision=True,
+    context.set_context(mode=context.GRAPH_MODE,
                         device_target=config.device_target, save_graphs=False)
 
     if config.device_target == 'Ascend':

model_zoo/official/cv/east/eval.py

@@ -49,7 +49,6 @@ args, _ = parser.parse_known_args()
 
 context.set_context(
     mode=context.GRAPH_MODE,
-    enable_auto_mixed_precision=True,
     device_target=args.device_target,
     save_graphs=False,
     device_id=args.device_num)

model_zoo/official/cv/east/train.py

@@ -189,7 +189,6 @@ if args.is_distributed:
 
 context.set_context(
     mode=context.GRAPH_MODE,
-    enable_auto_mixed_precision=True,
     device_target=args.device_target,
     save_graphs=False,
     device_id=args.rank)

model_zoo/official/cv/mobilenetv1/train.py

@@ -115,7 +115,7 @@ def train_mobilenetv1():
     device_id = int(os.getenv('DEVICE_ID', '0'))
     if config.run_distribute:
         if target == "Ascend":
-            context.set_context(device_id=device_id, enable_auto_mixed_precision=True)
+            context.set_context(device_id=device_id)
             context.set_auto_parallel_context(device_num=get_device_num(), parallel_mode=ParallelMode.DATA_PARALLEL,
                                               gradients_mean=True)
             init()

model_zoo/official/cv/posenet/train.py

@@ -62,7 +62,7 @@ if __name__ == '__main__':
     if args_opt.run_distribute:
         if device_target == "Ascend":
             device_id = int(os.getenv('DEVICE_ID'))
-            context.set_context(device_id=device_id, enable_auto_mixed_precision=True)
+            context.set_context(device_id=device_id)
             context.set_auto_parallel_context(parallel_mode=ParallelMode.DATA_PARALLEL,
                                               gradients_mean=True,
                                               auto_parallel_search_mode="recursive_programming")

model_zoo/official/cv/resnet/train.py

@@ -105,7 +105,7 @@ def set_parameter():
     if config.run_distribute:
         if target == "Ascend":
             device_id = int(os.getenv('DEVICE_ID'))
-            context.set_context(device_id=device_id, enable_auto_mixed_precision=True)
+            context.set_context(device_id=device_id)
             context.set_auto_parallel_context(device_num=config.device_num, parallel_mode=ParallelMode.DATA_PARALLEL,
                                               gradients_mean=True)
             set_algo_parameters(elementwise_op_strategy_follow=True)

model_zoo/official/cv/resnet50_quant/train.py

@@ -56,8 +56,7 @@ if args_opt.device_target == "Ascend":
     context.set_context(mode=context.GRAPH_MODE,
                         device_target="Ascend",
                         save_graphs=False,
-                        device_id=device_id,
+                        device_id=device_id)
-                        enable_auto_mixed_precision=True)
 else:
     raise ValueError("Unsupported device target.")
 

model_zoo/official/cv/resnet_thor/train.py

@@ -109,7 +109,7 @@ if __name__ == '__main__':
     if args_opt.run_distribute:
         if target == "Ascend":
             device_id = int(os.getenv('DEVICE_ID'))
-            context.set_context(device_id=device_id, enable_auto_mixed_precision=True)
+            context.set_context(device_id=device_id)
             context.set_auto_parallel_context(device_num=args_opt.device_num, parallel_mode=ParallelMode.DATA_PARALLEL,
                                               gradients_mean=True)
             set_algo_parameters(elementwise_op_strategy_follow=True)

model_zoo/official/cv/resnext/eval.py

@@ -132,8 +132,7 @@ def set_graph_kernel_context(device_target):
 def test():
     """test"""
     set_parameters()
-    context.set_context(mode=context.GRAPH_MODE, enable_auto_mixed_precision=True,
+    context.set_context(mode=context.GRAPH_MODE, device_target=config.device_target, save_graphs=False)
-                        device_target=config.device_target, save_graphs=False)
     if os.getenv('DEVICE_ID', "not_set").isdigit():
         context.set_context(device_id=int(os.getenv('DEVICE_ID')))
     set_graph_kernel_context(config.device_target)

model_zoo/official/cv/resnext/train.py

@@ -106,8 +106,7 @@ class ProgressMonitor(Callback):
 
 def set_parameters():
     """parameters"""
-    context.set_context(mode=context.GRAPH_MODE, enable_auto_mixed_precision=True,
+    context.set_context(mode=context.GRAPH_MODE, device_target=config.device_target, save_graphs=False)
-                        device_target=config.device_target, save_graphs=False)
     # init distributed
     if config.run_distribute:
         init()

model_zoo/official/cv/squeezenet/train.py

@@ -59,8 +59,7 @@ def train_net():
         if target == "Ascend":
             device_id = get_device_id()
             device_num = config.device_num
-            context.set_context(device_id=device_id,
+            context.set_context(device_id=device_id)
-                                enable_auto_mixed_precision=True)
             context.set_auto_parallel_context(
                 device_num=device_num,
                 parallel_mode=ParallelMode.DATA_PARALLEL,

model_zoo/official/cv/vgg16/eval.py

@@ -136,7 +136,7 @@ def run_eval():
 
     _enable_graph_kernel = config.device_target == "GPU"
     context.set_context(mode=context.GRAPH_MODE, enable_graph_kernel=_enable_graph_kernel,
-                        enable_auto_mixed_precision=True, device_target=config.device_target, save_graphs=False)
+                        device_target=config.device_target, save_graphs=False)
     if os.getenv('DEVICE_ID', "not_set").isdigit() and config.device_target == "Ascend":
         context.set_context(device_id=int(os.getenv('DEVICE_ID')))
 

model_zoo/official/cv/yolov3_darknet53/train.py

@@ -69,7 +69,7 @@ def set_graph_kernel_context():
 
 def network_init(args):
     devid = int(os.getenv('DEVICE_ID', '0'))
-    context.set_context(mode=context.GRAPH_MODE, enable_auto_mixed_precision=True,
+    context.set_context(mode=context.GRAPH_MODE,
                         device_target=args.device_target, save_graphs=False, device_id=devid)
     set_graph_kernel_context()
 

model_zoo/official/cv/yolov3_darknet53_quant/train.py

@@ -43,7 +43,7 @@ from src.util import ShapeRecord
 set_seed(1)
 
 devid = int(os.getenv('DEVICE_ID'))
-context.set_context(mode=context.GRAPH_MODE, enable_auto_mixed_precision=True,
+context.set_context(mode=context.GRAPH_MODE,
                     device_target="Ascend", save_graphs=False, device_id=devid)
 
 

model_zoo/official/cv/yolov4/train.py

@@ -58,7 +58,7 @@ def set_default():
     config.ann_val_file = os.path.join(config.data_dir, 'annotations/instances_val2017.json')
 
     device_id = int(os.getenv('DEVICE_ID', '0'))
-    context.set_context(mode=context.GRAPH_MODE, enable_auto_mixed_precision=True,
+    context.set_context(mode=context.GRAPH_MODE,
                         device_target=config.device_target, save_graphs=False, device_id=device_id)
 
     if config.need_profiler:

model_zoo/official/cv/yolov5/train.py

@@ -108,6 +108,7 @@ args.lr_epochs = list(map(int, args.lr_epochs.split(',')))
 
 if args.is_modelArts:
     args.data_root = os.path.join(args.data_dir, 'train2017')
+
     args.annFile = os.path.join(args.data_dir, 'annotations')
     outputs_dir = os.path.join('/cache', args.ckpt_path)
 else:
@@ -117,12 +118,13 @@ else:
     outputs_dir = args.ckpt_path
 
 deviced = int(os.getenv('DEVICE_ID', '0'))
-context.set_context(mode=context.GRAPH_MODE, enable_auto_mixed_precision=True, device_target=args.device_target,
+context.set_context(mode=context.GRAPH_MODE, device_target=args.device_target,
                     save_graphs=False, device_id=deviced)
 # init distributed
 if args.is_distributed:
     if args.device_target == "Ascend":
         init()
+
     else:
         init("nccl")
     args.rank = get_rank()

model_zoo/official/nlp/dgu/src/adam.py

@@ -115,8 +115,8 @@ def _run_opt_with_sparse(opt, sparse_opt, push, pull, use_locking, use_nesterov,
         op_sqrt = P.Sqrt()
         scatter_add = P.ScatterAdd(use_locking)
 
-        assign_m = F.assign(m, op_mul(beta1, m))
+        F.assign(m, op_mul(beta1, m))
-        assign_v = F.assign(v, op_mul(beta2, v))
+        F.assign(v, op_mul(beta2, v))
 
         grad_indices = gradient.indices
         grad_value = gradient.values
@@ -131,17 +131,15 @@ def _run_opt_with_sparse(opt, sparse_opt, push, pull, use_locking, use_nesterov,
 
         if use_nesterov:
             m_temp = next_m * _scaler_ten
-            assign_m_nesterov = F.assign(m, op_mul(beta1, next_m))
+            F.assign(m, op_mul(beta1, next_m))
             div_value = scatter_add(m,
                                     op_mul(grad_indices, _scaler_one),
                                     op_mul(F.tuple_to_array((1.0,)) - beta1, grad_value))
             param_update = div_value / (op_sqrt(next_v) + eps)
 
-            m_recover = F.assign(m, m_temp / _scaler_ten)
+            F.assign(m, m_temp / _scaler_ten)
+
 
-            F.control_depend(m_temp, assign_m_nesterov)
-            F.control_depend(assign_m_nesterov, div_value)
-            F.control_depend(param_update, m_recover)
         else:
             param_update = next_m / (op_sqrt(next_v) + eps)
 
@@ -149,8 +147,7 @@ def _run_opt_with_sparse(opt, sparse_opt, push, pull, use_locking, use_nesterov,
 
         next_param = param - lr_t * param_update
 
-        F.control_depend(assign_m, next_m)
+
-        F.control_depend(assign_v, next_v)
 
         success = F.depend(success, F.assign(param, next_param))
         success = F.depend(success, F.assign(m, next_m))

model_zoo/official/nlp/transformer/train.py

@@ -119,7 +119,7 @@ def run_transformer_train():
         context.set_context(mode=context.GRAPH_MODE, device_target=config.device_target, device_id=get_device_id())
     else:
         context.set_context(mode=context.GRAPH_MODE, device_target=config.device_target)
-    context.set_context(reserve_class_name_in_scope=False, enable_auto_mixed_precision=False)
+    context.set_context(reserve_class_name_in_scope=False)
 
     if config.device_target == "GPU":
         # Enable graph kernel

model_zoo/research/cv/FaceRecognition/train.py

@@ -43,8 +43,8 @@ from model_utils.device_adapter import get_device_id, get_device_num, get_rank_i
 mindspore.common.seed.set_seed(1)
 context.set_context(mode=context.GRAPH_MODE, device_target=config.device_target, save_graphs=False,
                     reserve_class_name_in_scope=False, enable_graph_kernel=config.device_target == "GPU")
-if config.device_target == 'Ascend':
+
-    context.set_context(enable_auto_mixed_precision=False)
+
 if config.device_target != 'GPU' or not config.is_distributed:
     context.set_context(device_id=get_device_id())
 

model_zoo/research/cv/GENet_Res50/train.py

@@ -123,8 +123,7 @@ if __name__ == '__main__':
 
     if run_distribute:
 
-        context.set_context(device_id=device_id,
+        context.set_context(device_id=device_id)
-                            enable_auto_mixed_precision=True)
         context.set_auto_parallel_context(device_num=device_num,
                                           parallel_mode=ParallelMode.DATA_PARALLEL,
                                           gradients_mean=True)

model_zoo/research/cv/ICNet/Res50V1_PRE/train.py

@@ -101,8 +101,7 @@ if __name__ == '__main__':
 
     if run_distribute:
 
-        context.set_context(device_id=device_id,
+        context.set_context(device_id=device_id)
-                            enable_auto_mixed_precision=True)
         context.set_auto_parallel_context(device_num=device_num,
                                           parallel_mode=ParallelMode.DATA_PARALLEL,
                                           gradients_mean=True)

model_zoo/research/cv/LearningToSeeInTheDark/train_sony.py

@@ -172,7 +172,7 @@ if __name__ == "__main__":
     if args.run_distribute:
         device_num = int(os.getenv('RANK_SIZE'))
         device_id = int(os.getenv('DEVICE_ID'))
-        context.set_context(device_id=device_id, enable_auto_mixed_precision=True)
+        context.set_context(device_id=device_id)
         context.set_auto_parallel_context(device_num=device_num, parallel_mode=ParallelMode.DATA_PARALLEL,
                                           gradients_mean=True)
         init()

model_zoo/research/cv/LightCNN/train.py

@@ -65,7 +65,7 @@ def main():
     if args.run_distribute:
         device_num = int(os.getenv('DEVICE_NUM'))
         rank_id = int(os.getenv("RANK_ID"))
-        context.set_context(mode=context.GRAPH_MODE, enable_auto_mixed_precision=True,
+        context.set_context(mode=context.GRAPH_MODE,
                             device_target=args.device_target, device_id=device_id)
         init()
         context.reset_auto_parallel_context()
@@ -73,7 +73,7 @@ def main():
                                           parallel_mode=ParallelMode.DATA_PARALLEL,
                                           gradients_mean=True)
     else:
-        context.set_context(mode=context.GRAPH_MODE, enable_auto_mixed_precision=True,
+        context.set_context(mode=context.GRAPH_MODE,
                             device_target=args.device_target, device_id=device_id)
 
     # define save checkpoint flag

model_zoo/research/cv/SE-Net/train.py

@@ -63,7 +63,7 @@ if __name__ == '__main__':
     if args_opt.run_distribute:
         if target == "Ascend":
             device_id = int(os.getenv('DEVICE_ID'))
-            context.set_context(device_id=device_id, enable_auto_mixed_precision=True)
+            context.set_context(device_id=device_id)
             context.set_auto_parallel_context(device_num=args_opt.device_num, parallel_mode=ParallelMode.DATA_PARALLEL,
                                               gradients_mean=True)
             set_algo_parameters(elementwise_op_strategy_follow=True)

model_zoo/research/cv/autoaugment/train.py

@@ -50,8 +50,7 @@ if __name__ == '__main__':
         if conf.device_target == 'Ascend':
             device_id = int(os.getenv('DEVICE_ID'))
             context.set_context(
-                device_id=device_id,
+                device_id=device_id
-                enable_auto_mixed_precision=True,
             )
             context.set_auto_parallel_context(
                 device_num=conf.device_num,

model_zoo/research/cv/centernet/train.py

@@ -114,7 +114,7 @@ def train():
     device_num = 1
     num_workers = 8
     if config.device_target == "Ascend":
-        context.set_context(enable_auto_mixed_precision=False)
+
         context.set_context(device_id=get_device_id())
         if config.distribute == "true":
             D.init()

model_zoo/research/cv/centernet_det/train.py

@@ -108,10 +108,11 @@ def train():
     rank = 0
     device_num = 1
     num_workers = 8
+
     if config.device_target == "Ascend":
-        context.set_context(enable_auto_mixed_precision=False)
         context.set_context(device_id=get_device_id())
         if config.distribute == "true":
+
             D.init()
             device_num = get_device_num()
             rank = get_rank_id()

model_zoo/research/cv/centernet_resnet101/train.py

@@ -109,7 +109,7 @@ def train():
     device_num = 1
     num_workers = 8
     if config.device_target == "Ascend":
-        context.set_context(enable_auto_mixed_precision=False)
+
         context.set_context(device_id=get_device_id())
         if config.distribute == "true":
             D.init()

model_zoo/research/cv/centernet_resnet50_v1/train.py

@@ -136,7 +136,6 @@ def train():
     device_num = 1
     num_workers = 8
     if args_opt.device_target == "Ascend":
-        context.set_context(enable_auto_mixed_precision=False)
         context.set_context(device_id=args_opt.device_id)
         if args_opt.distribute == "true":
             D.init()

model_zoo/research/cv/deeplabv3plus/train.py

@@ -102,7 +102,7 @@ def train():
     args = parse_args()
     context.set_context(mode=context.GRAPH_MODE, save_graphs=False, device_target=args.device_target)
     if args.device_target != "CPU":
-        context.set_context(enable_auto_mixed_precision=True, device_id=args.device_id)
+        context.set_context(device_id=args.device_id)
 
     # init multicards training
     if args.modelArts_mode:

model_zoo/research/cv/ghostnet/train.py

@@ -59,7 +59,7 @@ if __name__ == '__main__':
         rank_size = int(os.environ.get("RANK_SIZE", 1))
         print(rank_size)
         device_num = rank_size
-        context.set_context(device_id=device_id, enable_auto_mixed_precision=True)
+        context.set_context(device_id=device_id)
         context.set_auto_parallel_context(device_num=device_num, parallel_mode=ParallelMode.DATA_PARALLEL,
                                           gradients_mean=True)
         init()
@@ -83,8 +83,7 @@ if __name__ == '__main__':
     local_data_path = args_opt.data_url
     print('Download data:')
     dataset = create_dataset(dataset_path=local_data_path,
-                             do_train=True,
+                             do_train=True)
-                             target="Ascend")
 
     step_size = dataset.get_dataset_size()
     print('steps:', step_size)

model_zoo/research/cv/glore_res200/train.py

@@ -77,7 +77,7 @@ if __name__ == '__main__':
     if args_opt.run_distribute:
         if target == "Ascend":
             device_id = int(os.getenv('DEVICE_ID'))
-            context.set_context(device_id=device_id, enable_auto_mixed_precision=True)
+            context.set_context(device_id=device_id)
             context.set_auto_parallel_context(parallel_mode=ParallelMode.DATA_PARALLEL,
                                               gradients_mean=True,
                                               auto_parallel_search_mode="recursive_programming")

model_zoo/research/cv/glore_res50/train.py

@@ -72,7 +72,7 @@ if __name__ == '__main__':
     if args_opt.run_distribute:
         if target == "Ascend":
             device_id = int(os.getenv('DEVICE_ID'))
-            context.set_context(device_id=device_id, enable_auto_mixed_precision=True)
+            context.set_context(device_id=device_id)
             context.set_auto_parallel_context(parallel_mode=ParallelMode.DATA_PARALLEL, gradients_mean=True)
             init()
     else:

model_zoo/research/cv/hardnet/train.py

@@ -56,8 +56,7 @@ if args.isModelArts:
 
 if __name__ == '__main__':
     target = args.device_target
-    context.set_context(mode=context.GRAPH_MODE, device_target=target,
+    context.set_context(mode=context.GRAPH_MODE, device_target=target, save_graphs=False)
-                        enable_auto_mixed_precision=True, save_graphs=False)
 
     if args.distribute:
         if target == "Ascend":

model_zoo/research/cv/ibnnet/eval.py

@@ -59,7 +59,7 @@ if __name__ == "__main__":
     step = 60
     target = args.device_target
     context.set_context(mode=context.GRAPH_MODE, device_target=target, save_graphs=False)
-    context.set_context(device_id=args.device_id, enable_auto_mixed_precision=True)
+    context.set_context(device_id=args.device_id)
 
     lr = lr_generator(cfg.lr, train_epoch, steps_per_epoch=step)
     net = resnet50_ibn_a(num_classes=cfg.class_num)

model_zoo/research/cv/ibnnet/train.py

@@ -97,8 +97,7 @@ if __name__ == "__main__":
     if args.device_num > 1:
         if target == 'Ascend':
             device_id = int(os.getenv('DEVICE_ID'))
-            context.set_context(device_id=device_id,
+            context.set_context(device_id=device_id)
-                                enable_auto_mixed_precision=True)
             context.set_auto_parallel_context(parallel_mode=ParallelMode.DATA_PARALLEL,
                                               gradients_mean=True,
                                               auto_parallel_search_mode="recursive_programming")

model_zoo/research/cv/midas/midas_train.py

@@ -93,7 +93,7 @@ def train(mixdata_path):
         load_path = config.train_data_dir + '/midas/ckpt/midas_resnext_101_WSL.ckpt'
         device_id = config.device_id
         context.set_context(mode=context.GRAPH_MODE, device_target="Ascend", save_graphs=False, device_id=device_id,
-                            enable_auto_mixed_precision=True, max_call_depth=10000)
+                            max_call_depth=10000)
     # load data
     f = open(mixdata_path)
     data_config = json.load(f)

model_zoo/research/cv/resnetv2/train.py

@@ -80,7 +80,7 @@ if __name__ == '__main__':
     if args_opt.run_distribute:
         if target == "Ascend":
             device_id = int(os.getenv('DEVICE_ID'))
-            context.set_context(device_id=device_id, enable_auto_mixed_precision=True)
+            context.set_context(device_id=device_id)
             # init parallel training parameters
             context.set_auto_parallel_context(device_num=args_opt.device_num, parallel_mode=ParallelMode.DATA_PARALLEL,
                                               gradients_mean=True)

model_zoo/research/cv/resnext152_64x4d/eval.py

@@ -181,7 +181,7 @@ def get_result(args, model, top1_correct, top5_correct, img_tot):
 def test(cloud_args=None):
     """test"""
     args = parse_args(cloud_args)
-    context.set_context(mode=context.GRAPH_MODE, enable_auto_mixed_precision=True,
+    context.set_context(mode=context.GRAPH_MODE,
                         device_target=args.platform, save_graphs=False)
     if os.getenv('DEVICE_ID', "not_set").isdigit():
         context.set_context(device_id=int(os.getenv('DEVICE_ID')))

model_zoo/research/cv/resnext152_64x4d/train.py

@@ -192,7 +192,7 @@ def parse_args(cloud_args=None):
     args.lr_epochs = list(map(int, args.lr_epochs.split(',')))
     args.image_size = list(map(int, args.image_size.split(',')))
 
-    context.set_context(mode=context.GRAPH_MODE, enable_auto_mixed_precision=True,
+    context.set_context(mode=context.GRAPH_MODE,
                         device_target=args.platform, save_graphs=False)
     # init distributed
     if args.is_distributed:

model_zoo/research/cv/sknet/train.py

@@ -65,7 +65,7 @@ if __name__ == '__main__':
         context.set_ps_context(enable_ps=True)
     if args_opt.run_distribute:
         device_id = int(os.getenv('DEVICE_ID'))
-        context.set_context(device_id=device_id, enable_auto_mixed_precision=True)
+        context.set_context(device_id=device_id)
         context.set_auto_parallel_context(device_num=args_opt.device_num, parallel_mode=ParallelMode.DATA_PARALLEL,
                                           gradients_mean=True)
         set_algo_parameters(elementwise_op_strategy_follow=True)

model_zoo/research/cv/squeezenet/train.py

@@ -69,8 +69,7 @@ if __name__ == '__main__':
     if args_opt.run_distribute:
         if target == "Ascend":
             device_id = int(os.getenv('DEVICE_ID'))
-            context.set_context(device_id=device_id,
+            context.set_context(device_id=device_id)
-                                enable_auto_mixed_precision=True)
             context.set_auto_parallel_context(
                 device_num=args_opt.device_num,
                 parallel_mode=ParallelMode.DATA_PARALLEL,

model_zoo/research/cv/squeezenet1_1/train.py

@@ -67,8 +67,7 @@ if __name__ == '__main__':
         device_id = int(os.getenv("DEVICE_ID"))
         context.set_context(mode=context.GRAPH_MODE,
                             device_target=target)
-        context.set_context(device_id=device_id,
+        context.set_context(device_id=device_id)
-                            enable_auto_mixed_precision=True)
         context.set_auto_parallel_context(
             device_num=device_num,
             parallel_mode=ParallelMode.DATA_PARALLEL,

model_zoo/research/cv/vgg19/eval.py

@@ -136,7 +136,7 @@ def run_eval():
 
     _enable_graph_kernel = config.device_target == "GPU"
     context.set_context(mode=context.GRAPH_MODE, enable_graph_kernel=_enable_graph_kernel,
-                        enable_auto_mixed_precision=True, device_target=config.device_target, save_graphs=False)
+                        device_target=config.device_target, save_graphs=False)
     if os.getenv('DEVICE_ID', "not_set").isdigit() and config.device_target == "Ascend":
         context.set_context(device_id=int(os.getenv('DEVICE_ID')))
 

model_zoo/research/cv/yolov3_tiny/train.py

@@ -53,7 +53,7 @@ def set_default():
     config.ann_val_file = os.path.join(config.data_dir, 'annotations/instances_val2017.json')
 
     device_id = int(os.getenv('DEVICE_ID', '0'))
-    context.set_context(mode=context.GRAPH_MODE, enable_auto_mixed_precision=True,
+    context.set_context(mode=context.GRAPH_MODE,
                         device_target=config.device_target, save_graphs=False, device_id=device_id)
 
     if config.need_profiler:

model_zoo/research/nlp/dscnn/train.py

@@ -96,7 +96,7 @@ def modelarts_pre_process():
 @moxing_wrapper(pre_process=modelarts_pre_process)
 def train():
     '''Train.'''
-    context.set_context(mode=context.GRAPH_MODE, device_target=config.device_target, enable_auto_mixed_precision=True)
+    context.set_context(mode=context.GRAPH_MODE, device_target=config.device_target)
     config.rank_save_ckpt_flag = 1
 
     # init distributed

model_zoo/research/nlp/seq2seq/src/utils/optimizer.py

@@ -411,7 +411,7 @@ class AdamWeightDecayDynamicLR(Optimizer):
                                           self.params, self.moments1, self.moments2, gradients, self.decay_flag)
 
         added_global_step = self.global_step + self.one
-        F.control_depend(lr, added_global_step)
+
         self.global_step = added_global_step
 
         return updated_velocity

tests/st/fl/albert/src/adam.py

@@ -130,8 +130,8 @@ def _run_opt_with_sparse(opt, sparse_opt, push, pull, use_locking, use_nesterov,
         op_sqrt = P.Sqrt()
         scatter_add = P.ScatterAdd(use_locking)
 
-        assign_m = F.assign(m, op_mul(beta1, m))
+        F.assign(m, op_mul(beta1, m))
-        assign_v = F.assign(v, op_mul(beta2, v))
+        F.assign(v, op_mul(beta2, v))
 
         grad_indices = gradient.indices
         grad_value = gradient.values
@@ -146,17 +146,15 @@ def _run_opt_with_sparse(opt, sparse_opt, push, pull, use_locking, use_nesterov,
 
         if use_nesterov:
             m_temp = next_m * _scaler_ten
-            assign_m_nesterov = F.assign(m, op_mul(beta1, next_m))
+            F.assign(m, op_mul(beta1, next_m))
             div_value = scatter_add(m,
                                     op_mul(grad_indices, _scaler_one),
                                     op_mul(F.tuple_to_array((1.0,)) - beta1, grad_value))
             param_update = div_value / (op_sqrt(next_v) + eps)
 
-            m_recover = F.assign(m, m_temp / _scaler_ten)
+            F.assign(m, m_temp / _scaler_ten)
+
 
-            F.control_depend(m_temp, assign_m_nesterov)
-            F.control_depend(assign_m_nesterov, div_value)
-            F.control_depend(param_update, m_recover)
         else:
             param_update = next_m / (op_sqrt(next_v) + eps)
 
@@ -164,8 +162,6 @@ def _run_opt_with_sparse(opt, sparse_opt, push, pull, use_locking, use_nesterov,
 
         next_param = param - lr_t * param_update
 
-        F.control_depend(assign_m, next_m)
-        F.control_depend(assign_v, next_v)
 
         success = F.depend(success, F.assign(param, next_param))
         success = F.depend(success, F.assign(m, next_m))

tests/st/fl/hybrid_lenet/src/adam.py

@@ -129,8 +129,8 @@ def _run_opt_with_sparse(opt, sparse_opt, push, pull, use_locking, use_nesterov,
         op_sqrt = P.Sqrt()
         scatter_add = P.ScatterAdd(use_locking)
 
-        assign_m = F.assign(m, op_mul(beta1, m))
+        F.assign(m, op_mul(beta1, m))
-        assign_v = F.assign(v, op_mul(beta2, v))
+        F.assign(v, op_mul(beta2, v))
 
         grad_indices = gradient.indices
         grad_value = gradient.values
@@ -145,17 +145,14 @@ def _run_opt_with_sparse(opt, sparse_opt, push, pull, use_locking, use_nesterov,
 
         if use_nesterov:
             m_temp = next_m * _scaler_ten
-            assign_m_nesterov = F.assign(m, op_mul(beta1, next_m))
+            F.assign(m, op_mul(beta1, next_m))
             div_value = scatter_add(m,
                                     op_mul(grad_indices, _scaler_one),
                                     op_mul(F.tuple_to_array((1.0,)) - beta1, grad_value))
             param_update = div_value / (op_sqrt(next_v) + eps)
 
-            m_recover = F.assign(m, m_temp / _scaler_ten)
+            F.assign(m, m_temp / _scaler_ten)
 
-            F.control_depend(m_temp, assign_m_nesterov)
-            F.control_depend(assign_m_nesterov, div_value)
-            F.control_depend(param_update, m_recover)
         else:
             param_update = next_m / (op_sqrt(next_v) + eps)
 
@@ -163,8 +160,7 @@ def _run_opt_with_sparse(opt, sparse_opt, push, pull, use_locking, use_nesterov,
 
         next_param = param - lr_t * param_update
 
-        F.control_depend(assign_m, next_m)
+
-        F.control_depend(assign_v, next_v)
 
         success = F.depend(success, F.assign(param, next_param))
         success = F.depend(success, F.assign(m, next_m))

tests/st/fl/mobile/src/adam.py

@@ -129,8 +129,8 @@ def _run_opt_with_sparse(opt, sparse_opt, push, pull, use_locking, use_nesterov,
         op_sqrt = P.Sqrt()
         scatter_add = P.ScatterAdd(use_locking)
 
-        assign_m = F.assign(m, op_mul(beta1, m))
+        F.assign(m, op_mul(beta1, m))
-        assign_v = F.assign(v, op_mul(beta2, v))
+        F.assign(v, op_mul(beta2, v))
 
         grad_indices = gradient.indices
         grad_value = gradient.values
@@ -145,17 +145,15 @@ def _run_opt_with_sparse(opt, sparse_opt, push, pull, use_locking, use_nesterov,
 
         if use_nesterov:
             m_temp = next_m * _scaler_ten
-            assign_m_nesterov = F.assign(m, op_mul(beta1, next_m))
+            F.assign(m, op_mul(beta1, next_m))
             div_value = scatter_add(m,
                                     op_mul(grad_indices, _scaler_one),
                                     op_mul(F.tuple_to_array((1.0,)) - beta1, grad_value))
             param_update = div_value / (op_sqrt(next_v) + eps)
 
-            m_recover = F.assign(m, m_temp / _scaler_ten)
+            F.assign(m, m_temp / _scaler_ten)
+
 
-            F.control_depend(m_temp, assign_m_nesterov)
-            F.control_depend(assign_m_nesterov, div_value)
-            F.control_depend(param_update, m_recover)
         else:
             param_update = next_m / (op_sqrt(next_v) + eps)
 
@@ -163,8 +161,7 @@ def _run_opt_with_sparse(opt, sparse_opt, push, pull, use_locking, use_nesterov,
 
         next_param = param - lr_t * param_update
 
-        F.control_depend(assign_m, next_m)
+
-        F.control_depend(assign_v, next_v)
 
         success = F.depend(success, F.assign(param, next_param))
         success = F.depend(success, F.assign(m, next_m))