!1100 delete some context param(enable_loop_sink,enable_mem_reuse,enable_auto_mixed_precision)

Merge pull request !1100 from jinyaohui/context_opt_2

example/alexnet_cifar10/eval.py

@@ -39,7 +39,7 @@ if __name__ == "__main__":
     parser.add_argument('--dataset_sink_mode', type=bool, default=False, help='dataset_sink_mode is False or True')
     args = parser.parse_args()
 
-    context.set_context(mode=context.GRAPH_MODE, device_target=args.device_target, enable_mem_reuse=False)
+    context.set_context(mode=context.GRAPH_MODE, device_target=args.device_target)
 
     network = AlexNet(cfg.num_classes)
     loss = nn.SoftmaxCrossEntropyWithLogits(is_grad=False, sparse=True, reduction="mean")

example/alexnet_cifar10/train.py

@@ -39,7 +39,7 @@ if __name__ == "__main__":
     parser.add_argument('--dataset_sink_mode', type=bool, default=False, help='dataset_sink_mode is False or True')
     args = parser.parse_args()
 
-    context.set_context(mode=context.GRAPH_MODE, device_target=args.device_target, enable_mem_reuse=False)
+    context.set_context(mode=context.GRAPH_MODE, device_target=args.device_target)
 
     network = AlexNet(cfg.num_classes)
     loss = nn.SoftmaxCrossEntropyWithLogits(is_grad=False, sparse=True, reduction="mean")

example/bert_clue/README.md

@@ -46,8 +46,7 @@ This example implements pre-training, fine-tuning and evaluation of [BERT-base](
 ### Pre-Training
 ``` 
 usage: run_pretrain.py  [--distribute DISTRIBUTE] [--epoch_size N] [----device_num N] [--device_id N] 
-                        [--enable_task_sink ENABLE_TASK_SINK] [--enable_loop_sink ENABLE_LOOP_SINK]
-                        [--enable_mem_reuse ENABLE_MEM_REUSE] [--enable_save_ckpt ENABLE_SAVE_CKPT]
+                        [--enable_save_ckpt ENABLE_SAVE_CKPT]
                         [--enable_lossscale ENABLE_LOSSSCALE] [--do_shuffle DO_SHUFFLE]
                         [--enable_data_sink ENABLE_DATA_SINK] [--data_sink_steps N] [--checkpoint_path CHECKPOINT_PATH]
                         [--save_checkpoint_steps N] [--save_checkpoint_num N] 
@@ -58,8 +57,6 @@ options:
     --epoch_size               epoch size: N, default is 1
     --device_num               number of used devices: N, default is 1
     --device_id                device id: N, default is 0
-    --enable_loop_sink         enable loop sink: "true" | "false", default is "true"
-    --enable_mem_reuse         enable memory reuse: "true" | "false", default is "true"
     --enable_save_ckpt         enable save checkpoint: "true" | "false", default is "true"
     --enable_lossscale         enable lossscale: "true" | "false", default is "true"
     --do_shuffle               enable shuffle: "true" | "false", default is "true"

example/bert_clue/finetune.py

@@ -83,8 +83,7 @@ def test_train():
     pytest -s finetune.py::test_train
     '''
     devid = int(os.getenv('DEVICE_ID'))
-    context.set_context(mode=context.GRAPH_MODE, device_target="Ascend", device_id=devid,
-                        enable_mem_reuse=True, enable_task_sink=True)
+    context.set_context(mode=context.GRAPH_MODE, device_target="Ascend", device_id=devid)
     #BertCLSTrain for classification
     #BertNERTrain for sequence labeling
     if cfg.task == 'NER':

example/bert_clue/run_distribute_pretrain.sh

@@ -50,8 +50,6 @@ do
     --epoch_size=$EPOCH_SIZE \
     --device_id=$DEVICE_ID \
     --device_num=$RANK_SIZE \
-    --enable_loop_sink="true" \
-    --enable_mem_reuse="true" \
     --enable_save_ckpt="true" \
     --enable_lossscale="true" \
     --do_shuffle="true" \

example/bert_clue/run_pretrain.py

@@ -59,8 +59,6 @@ def run_pretrain():
     parser.add_argument("--epoch_size", type=int, default="1", help="Epoch size, default is 1.")
     parser.add_argument("--device_id", type=int, default=0, help="Device id, default is 0.")
     parser.add_argument("--device_num", type=int, default=1, help="Use device nums, default is 1.")
-    parser.add_argument("--enable_loop_sink", type=str, default="true", help="Enable loop sink, default is true.")
-    parser.add_argument("--enable_mem_reuse", type=str, default="true", help="Enable mem reuse, default is true.")
     parser.add_argument("--enable_save_ckpt", type=str, default="true", help="Enable save checkpoint, default is true.")
     parser.add_argument("--enable_lossscale", type=str, default="true", help="Use lossscale or not, default is not.")
     parser.add_argument("--do_shuffle", type=str, default="true", help="Enable shuffle for dataset, default is true.")
@@ -75,8 +73,6 @@ def run_pretrain():
 
     args_opt = parser.parse_args()
     context.set_context(mode=context.GRAPH_MODE, device_target="Ascend", device_id=args_opt.device_id)
-    context.set_context(enable_loop_sink=(args_opt.enable_loop_sink == "true"),
-                        enable_mem_reuse=(args_opt.enable_mem_reuse == "true"))
     context.set_context(reserve_class_name_in_scope=False)
 
     if args_opt.distribute == "true":

example/bert_clue/run_standalone_pretrain.sh

@@ -29,8 +29,6 @@ python run_pretrain.py  \
     --distribute="false" \
     --epoch_size=$EPOCH_SIZE \
     --device_id=$DEVICE_ID \
-    --enable_loop_sink="true" \
-    --enable_mem_reuse="true" \
     --enable_save_ckpt="true" \
     --enable_lossscale="true" \
     --do_shuffle="true" \

example/googlenet_cifar10/eval.py

@@ -40,7 +40,6 @@ if __name__ == '__main__':
 
     context.set_context(mode=context.GRAPH_MODE, device_target=args_opt.device_target)
     context.set_context(device_id=args_opt.device_id)
-    context.set_context(enable_mem_reuse=True)
 
     net = GooGLeNet(num_classes=cfg.num_classes)
     opt = Momentum(filter(lambda x: x.requires_grad, net.get_parameters()), 0.01, cfg.momentum,

example/googlenet_cifar10/train.py

@@ -70,8 +70,6 @@ if __name__ == '__main__':
 
     context.set_context(mode=context.GRAPH_MODE, device_target=args_opt.device_target)
     context.set_context(device_id=args_opt.device_id)
-    context.set_context(enable_loop_sink=True)
-    context.set_context(enable_mem_reuse=True)
 
     device_num = int(os.environ.get("DEVICE_NUM", 1))
     if device_num > 1:

example/lenet_mnist/eval.py

@@ -43,7 +43,7 @@ if __name__ == "__main__":
 
     args = parser.parse_args()
 
-    context.set_context(mode=context.GRAPH_MODE, device_target=args.device_target, enable_mem_reuse=False)
+    context.set_context(mode=context.GRAPH_MODE, device_target=args.device_target)
 
     network = LeNet5(cfg.num_classes)
     net_loss = nn.SoftmaxCrossEntropyWithLogits(is_grad=False, sparse=True, reduction="mean")

example/lenet_mnist/train.py

@@ -40,7 +40,7 @@ if __name__ == "__main__":
 
     args = parser.parse_args()
 
-    context.set_context(mode=context.GRAPH_MODE, device_target=args.device_target, enable_mem_reuse=False)
+    context.set_context(mode=context.GRAPH_MODE, device_target=args.device_target)
 
     network = LeNet5(cfg.num_classes)
     net_loss = nn.SoftmaxCrossEntropyWithLogits(is_grad=False, sparse=True, reduction="mean")

example/mobilenetv2_imagenet2012/eval.py

@@ -34,8 +34,6 @@ args_opt = parser.parse_args()
 device_id = int(os.getenv('DEVICE_ID'))
 
 context.set_context(mode=context.GRAPH_MODE, device_target="Ascend", device_id=device_id, save_graphs=False)
-context.set_context(enable_loop_sink=True)
-context.set_context(enable_mem_reuse=True)
 
 if __name__ == '__main__':
     loss = SoftmaxCrossEntropyWithLogits(is_grad=False, sparse=True, reduction='mean')

example/mobilenetv2_imagenet2012/train.py

@@ -56,8 +56,6 @@ rank_size = int(os.getenv('RANK_SIZE'))
 run_distribute = rank_size > 1
 
 context.set_context(mode=context.GRAPH_MODE, device_target="Ascend", device_id=device_id, save_graphs=False)
-context.set_context(enable_loop_sink=True)
-context.set_context(enable_mem_reuse=True)
 
 class CrossEntropyWithLabelSmooth(_Loss):
     """

example/resnet101_imagenet2012/eval.py

@@ -46,8 +46,6 @@ args_opt = parser.parse_args()
 device_id = int(os.getenv('DEVICE_ID'))
 
 context.set_context(mode=context.GRAPH_MODE, device_target="Ascend", save_graphs=False, device_id=device_id)
-context.set_context(enable_loop_sink=True)
-context.set_context(enable_mem_reuse=True)
 
 if __name__ == '__main__':
     if not args_opt.do_eval and args_opt.run_distribute:

example/resnet101_imagenet2012/train.py

@@ -49,8 +49,6 @@ args_opt = parser.parse_args()
 device_id = int(os.getenv('DEVICE_ID'))
 
 context.set_context(mode=context.GRAPH_MODE, device_target="Ascend", save_graphs=False, device_id=device_id)
-context.set_context(enable_loop_sink=True)
-context.set_context(enable_mem_reuse=True)
 
 if __name__ == '__main__':
     if not args_opt.do_eval and args_opt.run_distribute:

example/resnet50_cifar10/eval.py

@@ -40,8 +40,6 @@ device_id = int(os.getenv('DEVICE_ID'))
 
 context.set_context(mode=context.GRAPH_MODE, device_target="Ascend", save_graphs=False)
 context.set_context(device_id=device_id)
-context.set_context(enable_loop_sink=True)
-context.set_context(enable_mem_reuse=True)
 
 if __name__ == '__main__':
     if not args_opt.do_eval and args_opt.run_distribute:

example/resnet50_cifar10/train.py

@@ -43,8 +43,6 @@ device_id = int(os.getenv('DEVICE_ID'))
 
 context.set_context(mode=context.GRAPH_MODE, device_target="Ascend", save_graphs=False)
 context.set_context(device_id=device_id)
-context.set_context(enable_loop_sink=True)
-context.set_context(enable_mem_reuse=True)
 
 if __name__ == '__main__':
     if not args_opt.do_eval and args_opt.run_distribute:

example/resnet50_imagenet2012/eval.py

@@ -37,9 +37,7 @@ args_opt = parser.parse_args()
 device_id = int(os.getenv('DEVICE_ID'))
 
 context.set_context(mode=context.GRAPH_MODE, device_target="Ascend", save_graphs=False)
-context.set_context(enable_task_sink=True, device_id=device_id)
-context.set_context(enable_loop_sink=True)
-context.set_context(enable_mem_reuse=True)
+context.set_context(device_id=device_id)
 
 if __name__ == '__main__':
 

example/resnet50_imagenet2012/train.py

@@ -44,9 +44,7 @@ args_opt = parser.parse_args()
 device_id = int(os.getenv('DEVICE_ID'))
 
 context.set_context(mode=context.GRAPH_MODE, device_target="Ascend", save_graphs=False)
-context.set_context(enable_task_sink=True, device_id=device_id)
-context.set_context(enable_loop_sink=True)
-context.set_context(enable_mem_reuse=True)
+context.set_context(device_id=device_id)
 
 if __name__ == '__main__':
     if not args_opt.do_eval and args_opt.run_distribute:

example/ssd_coco2017/eval.py

@@ -71,7 +71,6 @@ if __name__ == '__main__':
     args_opt = parser.parse_args()
 
     context.set_context(mode=context.GRAPH_MODE, device_target="Ascend", device_id=args_opt.device_id)
-    context.set_context(enable_loop_sink=True, enable_mem_reuse=True)
 
     config = ConfigSSD()
     prefix = "ssd_eval.mindrecord"

example/ssd_coco2017/train.py

@@ -93,7 +93,6 @@ def main():
     args_opt = parser.parse_args()
 
     context.set_context(mode=context.GRAPH_MODE, device_target="Ascend", device_id=args_opt.device_id)
-    context.set_context(enable_loop_sink=True, enable_mem_reuse=True)
 
     if args_opt.distribute:
         device_num = args_opt.device_num

example/vgg16_cifar10/eval.py

@@ -37,7 +37,6 @@ if __name__ == '__main__':
 
     context.set_context(mode=context.GRAPH_MODE, device_target=args_opt.device_target)
     context.set_context(device_id=args_opt.device_id)
-    context.set_context(enable_mem_reuse=True)
 
     net = vgg16(num_classes=cfg.num_classes)
     opt = Momentum(filter(lambda x: x.requires_grad, net.get_parameters()), 0.01, cfg.momentum,

example/vgg16_cifar10/train.py

@@ -64,8 +64,6 @@ if __name__ == '__main__':
 
     context.set_context(mode=context.GRAPH_MODE, device_target=args_opt.device_target)
     context.set_context(device_id=args_opt.device_id)
-    context.set_context(enable_loop_sink=True)
-    context.set_context(enable_mem_reuse=True)
 
     device_num = int(os.environ.get("DEVICE_NUM", 1))
     if device_num > 1:

example/yolov3_coco2017/eval.py

@@ -82,7 +82,6 @@ if __name__ == '__main__':
     args_opt = parser.parse_args()
 
     context.set_context(mode=context.GRAPH_MODE, device_target="Ascend", device_id=args_opt.device_id)
-    context.set_context(enable_loop_sink=True, enable_mem_reuse=True)
 
     # It will generate mindrecord file in args_opt.mindrecord_dir,
     # and the file name is yolo.mindrecord0, 1, ... file_num.

example/yolov3_coco2017/train.py

@@ -84,7 +84,6 @@ def main():
     args_opt = parser.parse_args()
 
     context.set_context(mode=context.GRAPH_MODE, device_target="Ascend", device_id=args_opt.device_id)
-    context.set_context(enable_loop_sink=True, enable_mem_reuse=True)
     if args_opt.distribute:
         device_num = args_opt.device_num
         context.reset_auto_parallel_context()