support static graph train for kunlun (#441)

configs/quick_start/ResNet50_vd_finetune_kunlun.yaml

+mode: 'train'
+ARCHITECTURE:
+    name: 'ResNet50_vd'
+pretrained_model: "./pretrained/ResNet50_vd_pretrained"
+load_static_weights: true
+model_save_dir: "./output/"
+classes_num: 102
+total_images: 1020
+save_interval: 1
+validate: True
+valid_interval: 1
+epochs: 120
+topk: 5
+image_shape: [3, 224, 224]
+
+LEARNING_RATE:
+    function: 'Cosine'          
+    params:                   
+        lr: 0.00375
+
+OPTIMIZER:
+    function: 'Momentum'
+    params:
+        momentum: 0.9
+    regularizer:
+        function: 'L2'
+        factor: 0.000001
+
+TRAIN:
+    batch_size: 20
+    num_workers: 1
+    file_list: "./dataset/flowers102/train_list.txt"
+    data_dir: "./dataset/flowers102/"
+    shuffle_seed: 0
+    transforms:
+        - DecodeImage:
+            to_rgb: True
+            to_np: False
+            channel_first: False
+        - RandCropImage:
+            size: 224
+        - RandFlipImage:
+            flip_code: 1
+        - NormalizeImage:
+            scale: 1./255.
+            mean: [0.485, 0.456, 0.406]
+            std: [0.229, 0.224, 0.225]
+            order: ''
+        - ToCHWImage:
+
+VALID:
+    batch_size: 20
+    num_workers: 1
+    file_list: "./dataset/flowers102/val_list.txt"
+    data_dir: "./dataset/flowers102/"
+    shuffle_seed: 0
+    transforms:
+        - DecodeImage:
+            to_rgb: True
+            to_np: False
+            channel_first: False
+        - ResizeImage:
+            resize_short: 256
+        - CropImage:
+            size: 224
+        - NormalizeImage:
+            scale: 1.0/255.0
+            mean: [0.485, 0.456, 0.406]
+            std: [0.229, 0.224, 0.225]
+            order: ''
+        - ToCHWImage:

tools/static/train.py

@@ -63,9 +63,12 @@ def main(args):
 
     config = get_config(args.config, overrides=args.override, show=True)
     # assign the place
-    use_gpu = config.get("use_gpu", True)
-    assert use_gpu is True, "gpu must be true in static mode!"
-    place = paddle.set_device("gpu")
+    use_gpu = config.get("use_gpu", False)
+    use_xpu = config.get("use_xpu", False)
+    assert (use_gpu or use_xpu) is True, "gpu or xpu must be true in static mode!"
+    assert (use_gpu and use_xpu) is not True, "gpu and xpu can not be true in the same time in static mode!"
+
+    place = paddle.set_device('gpu' if use_gpu else 'xpu')
 
     # startup_prog is used to do some parameter init work,
     # and train prog is used to hold the network
@@ -75,12 +78,12 @@ def main(args):
     best_top1_acc = 0.0  # best top1 acc record
 
     train_fetchs, lr_scheduler, train_feeds = program.build(
-        config, train_prog, startup_prog, is_train=True)
+        config, train_prog, startup_prog, is_train=True, is_distributed=config.get("is_distributed", True))
 
     if config.validate:
         valid_prog = paddle.static.Program()
         valid_fetchs, _, valid_feeds = program.build(
-            config, valid_prog, startup_prog, is_train=False)
+            config, valid_prog, startup_prog, is_train=False, is_distributed=config.get("is_distributed", True))
         # clone to prune some content which is irrelevant in valid_prog
         valid_prog = valid_prog.clone(for_test=True)
 
@@ -94,7 +97,10 @@ def main(args):
 
     if config.validate and paddle.distributed.get_rank() == 0:
         valid_dataloader = Reader(config, 'valid', places=place)()
-        compiled_valid_prog = program.compile(config, valid_prog)
+        if use_xpu:
+            compiled_valid_prog = valid_prog
+        else:
+            compiled_valid_prog = program.compile(config, valid_prog)
 
     vdl_writer = None
     if args.vdl_dir: