Add load&save ckpt path for distribute training

model_zoo/bert/run_pretrain.py

@@ -68,7 +68,8 @@ def run_pretrain():
     parser.add_argument("--do_shuffle", type=str, default="true", help="Enable shuffle for dataset, default is true.")
     parser.add_argument("--enable_data_sink", type=str, default="true", help="Enable data sink, default is true.")
     parser.add_argument("--data_sink_steps", type=int, default="1", help="Sink steps for each epoch, default is 1.")
-    parser.add_argument("--checkpoint_path", type=str, default="", help="Checkpoint file path")
+    parser.add_argument("--save_checkpoint_path", type=str, default="", help="Save checkpoint path")
+    parser.add_argument("--load_checkpoint_path", type=str, default="", help="Load checkpoint file path")
     parser.add_argument("--save_checkpoint_steps", type=int, default=1000, help="Save checkpoint steps, "
                                                                                 "default is 1000.")
     parser.add_argument("--train_steps", type=int, default=-1, help="Training Steps, default is -1, "
@@ -81,7 +82,7 @@ def run_pretrain():
     context.set_context(mode=context.GRAPH_MODE, device_target=args_opt.device_target, device_id=args_opt.device_id)
     context.set_context(reserve_class_name_in_scope=False)
 
-    ckpt_save_dir = args_opt.checkpoint_path
+    ckpt_save_dir = args_opt.save_checkpoint_path
     if args_opt.distribute == "true":
         if args_opt.device_target == 'Ascend':
             D.init('hccl')
@@ -91,7 +92,7 @@ def run_pretrain():
             D.init('nccl')
             device_num = D.get_group_size()
             rank = D.get_rank()
-            ckpt_save_dir = args_opt.checkpoint_path + 'ckpt_' + str(rank) + '/'
+            ckpt_save_dir = args_opt.save_checkpoint_path + 'ckpt_' + str(rank) + '/'
 
         context.reset_auto_parallel_context()
         context.set_auto_parallel_context(parallel_mode=ParallelMode.DATA_PARALLEL, mirror_mean=True,
@@ -150,8 +151,8 @@ def run_pretrain():
         ckpoint_cb = ModelCheckpoint(prefix='checkpoint_bert', directory=ckpt_save_dir, config=config_ck)
         callback.append(ckpoint_cb)
 
-    if args_opt.checkpoint_path:
-        param_dict = load_checkpoint(args_opt.checkpoint_path)
+    if args_opt.load_checkpoint_path:
+        param_dict = load_checkpoint(args_opt.load_checkpoint_path)
         load_param_into_net(netwithloss, param_dict)
 
     if args_opt.enable_lossscale == "true":

model_zoo/bert/scripts/run_distribute_pretrain.sh

@@ -64,7 +64,7 @@ do
     --do_shuffle="true" \
     --enable_data_sink="true" \
     --data_sink_steps=100 \
-    --checkpoint_path="" \
+    --load_checkpoint_path="" \
     --save_checkpoint_steps=10000 \
     --save_checkpoint_num=1 \
     --data_dir=$DATA_DIR \

model_zoo/bert/scripts/run_distribute_pretrain_for_gpu.sh

@@ -36,7 +36,7 @@ mpirun --allow-run-as-root -n $RANK_SIZE \
 		--do_shuffle="true"				\
 		--enable_data_sink="true"		\
 		--data_sink_steps=1				\
-		--checkpoint_path=""			\
+		--load_checkpoint_path=""			\
 		--save_checkpoint_steps=10000	\
 		--save_checkpoint_num=1			\
 		--data_dir=$DATA_DIR			\

model_zoo/bert/scripts/run_standalone_pretrain.sh

@@ -38,7 +38,7 @@ python run_pretrain.py  \
     --do_shuffle="true" \
     --enable_data_sink="true" \
     --data_sink_steps=1 \
-    --checkpoint_path="" \
+    --load_checkpoint_path="" \
     --save_checkpoint_steps=10000 \
     --save_checkpoint_num=1 \
     --data_dir=$DATA_DIR \

model_zoo/bert/scripts/run_standalone_pretrain_for_gpu.sh

@@ -40,7 +40,8 @@ python run_pretrain.py  \
     --do_shuffle="true" \
     --enable_data_sink="true" \
     --data_sink_steps=1 \
-    --checkpoint_path="" \
+    --load_checkpoint_path="" \
+    --save_checkpoint_path="" \
     --save_checkpoint_steps=10000 \
     --save_checkpoint_num=1 \
     --data_dir=$DATA_DIR \