[tutorial] added synthetic dataset for auto parallel demo (#1918)

examples/tutorial/auto_parallel/auto_parallel_with_resnet.py

@@ -1,3 +1,4 @@
+import argparse
 import os
 from pathlib import Path
 
@@ -29,43 +30,60 @@ BATCH_SIZE = 1024
 NUM_EPOCHS = 10
 
 
+def parse_args():
+    parser = argparse.ArgumentParser()
+    parser.add_argument('-s', '--synthetic', action="store_true", help="use synthetic dataset instead of CIFAR10")
+    return parser.parse_args()
+
+
+def synthesize_data():
+    img = torch.rand(BATCH_SIZE, 3, 32, 32)
+    label = torch.randint(low=0, high=10, size=(BATCH_SIZE,))
+    return img, label
+
+
 def main():
+    args = parse_args()
     colossalai.launch_from_torch(config={})
 
     logger = get_dist_logger()
 
-    with barrier_context():
-        # build dataloaders
-        train_dataset = CIFAR10(root=DATA_ROOT,
-                                download=True,
-                                transform=transforms.Compose([
-                                    transforms.RandomCrop(size=32, padding=4),
-                                    transforms.RandomHorizontalFlip(),
-                                    transforms.ToTensor(),
-                                    transforms.Normalize(mean=[0.4914, 0.4822, 0.4465], std=[0.2023, 0.1994, 0.2010]),
-                                ]))
+    if not args.synthetic:
+        with barrier_context():
+            # build dataloaders
+            train_dataset = CIFAR10(root=DATA_ROOT,
+                                    download=True,
+                                    transform=transforms.Compose([
+                                        transforms.RandomCrop(size=32, padding=4),
+                                        transforms.RandomHorizontalFlip(),
+                                        transforms.ToTensor(),
+                                        transforms.Normalize(mean=[0.4914, 0.4822, 0.4465],
+                                                             std=[0.2023, 0.1994, 0.2010]),
+                                    ]))
 
-    test_dataset = CIFAR10(root=DATA_ROOT,
-                           train=False,
-                           transform=transforms.Compose([
-                               transforms.ToTensor(),
-                               transforms.Normalize(mean=[0.4914, 0.4822, 0.4465], std=[0.2023, 0.1994, 0.2010]),
-                           ]))
+        test_dataset = CIFAR10(root=DATA_ROOT,
+                               train=False,
+                               transform=transforms.Compose([
+                                   transforms.ToTensor(),
+                                   transforms.Normalize(mean=[0.4914, 0.4822, 0.4465], std=[0.2023, 0.1994, 0.2010]),
+                               ]))
 
-    train_dataloader = get_dataloader(
-        dataset=train_dataset,
-        add_sampler=False,
-        shuffle=True,
-        batch_size=BATCH_SIZE,
-        pin_memory=True,
-    )
+        train_dataloader = get_dataloader(
+            dataset=train_dataset,
+            add_sampler=False,
+            shuffle=True,
+            batch_size=BATCH_SIZE,
+            pin_memory=True,
+        )
 
-    test_dataloader = get_dataloader(
-        dataset=test_dataset,
-        add_sampler=False,
-        batch_size=BATCH_SIZE,
-        pin_memory=True,
-    )
+        test_dataloader = get_dataloader(
+            dataset=test_dataset,
+            add_sampler=False,
+            batch_size=BATCH_SIZE,
+            pin_memory=True,
+        )
+    else:
+        train_dataloader, test_dataloader = None, None
 
     # initialize device mesh
     physical_mesh_id = torch.arange(0, 4)
@@ -112,11 +130,26 @@ def main():
 
     for epoch in range(NUM_EPOCHS):
         gm.train()
-        if gpc.get_global_rank() == 0:
-            train_dl = tqdm(train_dataloader)
+
+        if args.synthetic:
+            # if we use synthetic data
+            # we assume it only has 30 steps per epoch
+            num_steps = range(30)
+
         else:
-            train_dl = train_dataloader
-        for img, label in train_dl:
+            # we use the actual number of steps for training
+            num_steps = range(len(train_dataloader))
+            data_iter = iter(train_dataloader)
+        progress = tqdm(num_steps)
+
+        for _ in progress:
+            if args.synthetic:
+                # generate fake data
+                img, label = synthesize_data()
+            else:
+                # get the real data
+                img, label = next(data_iter)
+
             img = img.cuda()
             label = label.cuda()
             optimizer.zero_grad()
@@ -126,10 +159,30 @@ def main():
             optimizer.step()
         lr_scheduler.step()
 
+        # run evaluation
         gm.eval()
         correct = 0
         total = 0
-        for img, label in test_dataloader:
+
+        if args.synthetic:
+            # if we use synthetic data
+            # we assume it only has 10 steps for evaluation
+            num_steps = range(30)
+
+        else:
+            # we use the actual number of steps for training
+            num_steps = range(len(test_dataloader))
+            data_iter = iter(test_dataloader)
+        progress = tqdm(num_steps)
+
+        for _ in progress:
+            if args.synthetic:
+                # generate fake data
+                img, label = synthesize_data()
+            else:
+                # get the real data
+                img, label = next(data_iter)
+
             img = img.cuda()
             label = label.cuda()