modify pynative gpu benchmark script

model_zoo/official/cv/resnet/gpu_resnet_benchmark.py

@@ -94,11 +94,19 @@ class MyTimeMonitor(Callback):
 
 def create_dataset(dataset_path, do_train, repeat_num=1, batch_size=32, target="GPU", dtype="fp16",
                    device_num=1):
+    if args_opt.mode == "GRAPH":
+        ds_num_parallel_worker = 4
+        map_num_parallel_worker = 8
+        batch_num_parallel_worker = None
+    else:
+        ds_num_parallel_worker = 2
+        map_num_parallel_worker = 3
+        batch_num_parallel_worker = 2
     ds.config.set_numa_enable(True)
     if device_num == 1:
-        data_set = ds.ImageFolderDataset(dataset_path, num_parallel_workers=4, shuffle=True)
+        data_set = ds.ImageFolderDataset(dataset_path, num_parallel_workers=ds_num_parallel_worker, shuffle=True)
     else:
-        data_set = ds.ImageFolderDataset(dataset_path, num_parallel_workers=4, shuffle=True,
+        data_set = ds.ImageFolderDataset(dataset_path, num_parallel_workers=ds_num_parallel_worker, shuffle=True,
                                          num_shards=device_num, shard_id=get_rank())
     image_size = 224
     mean = [0.485 * 255, 0.456 * 255, 0.406 * 255]
@@ -127,9 +135,9 @@ def create_dataset(dataset_path, do_train, repeat_num=1, batch_size=32, target="
         ]
     if dtype == "fp32":
         trans.append(C.HWC2CHW())
-    data_set = data_set.map(operations=trans, input_columns="image", num_parallel_workers=8)
+    data_set = data_set.map(operations=trans, input_columns="image", num_parallel_workers=map_num_parallel_worker)
     # apply batch operations
-    data_set = data_set.batch(batch_size, drop_remainder=True)
+    data_set = data_set.batch(batch_size, drop_remainder=True, num_parallel_workers=batch_num_parallel_worker)
     # apply dataset repeat operation
     if repeat_num > 1:
         data_set = data_set.repeat(repeat_num)
@@ -165,14 +173,16 @@ def train():
     # init context
     if args_opt.mode == "GRAPH":
         mode = context.GRAPH_MODE
+        all_reduce_fusion_config = [85, 160]
     else:
         mode = context.PYNATIVE_MODE
+        all_reduce_fusion_config = [30, 90, 160]
     context.set_context(mode=mode, device_target=dev, save_graphs=False)
     if args_opt.run_distribute:
         init()
         device_num = get_group_size()
         context.set_auto_parallel_context(device_num=device_num, parallel_mode=ParallelMode.DATA_PARALLEL,
-                                          gradients_mean=True, all_reduce_fusion_config=[85, 160])
+                                          gradients_mean=True, all_reduce_fusion_config=all_reduce_fusion_config)
         ckpt_save_dir = ckpt_save_dir + "ckpt_" + str(get_rank()) + "/"
 
     # create dataset