[hotfix] fix autoparallel demo (#2533)

examples/tutorial/auto_parallel/auto_parallel_with_resnet.py

@@ -3,8 +3,9 @@ from torchvision.models import resnet50
 from tqdm import tqdm
 
 import colossalai
-from colossalai.auto_parallel.tensor_shard.initialize import autoparallelize
+from colossalai.auto_parallel.tensor_shard.initialize import initialize_model
 from colossalai.core import global_context as gpc
+from colossalai.device.device_mesh import DeviceMesh
 from colossalai.logging import get_dist_logger
 from colossalai.nn.lr_scheduler import CosineAnnealingLR
 
@@ -22,9 +23,14 @@ def main():
 
     # trace the model with meta data
     model = resnet50(num_classes=10).cuda()
+
     input_sample = {'x': torch.rand([gpc.config.BATCH_SIZE * torch.distributed.get_world_size(), 3, 32, 32]).to('meta')}
+    device_mesh = DeviceMesh(physical_mesh_id=torch.tensor([0, 1, 2, 3]), mesh_shape=[2, 2], init_process_group=True)
+    model, solution = initialize_model(model, input_sample, device_mesh=device_mesh, return_solution=True)
 
-    model = autoparallelize(model, input_sample)
+    if gpc.get_global_rank() == 0:
+        for node_strategy in solution:
+            print(node_strategy)
     # build criterion
     criterion = torch.nn.CrossEntropyLoss()
 
@@ -52,6 +58,7 @@ def main():
             output = model(img)
             train_loss = criterion(output, label)
             train_loss.backward(train_loss)
+            torch.cuda.synchronize()
             optimizer.step()
         lr_scheduler.step()