torchvision QAT tutorial: update for QAT with DDP (#2280)

Summary:

We've made two recent changes to QAT in PyTorch core:
1. add support for SyncBatchNorm
2. make eager mode QAT prepare scripts respect device affinity

This PR updates the torchvision QAT reference script to take
advantage of both of these.  This should be landed after
https://github.com/pytorch/pytorch/pull/39337 (the last PT
fix) to avoid compatibility issues.

Test Plan:

```
python -m torch.distributed.launch
  --nproc_per_node 8
  --use_env
  references/classification/train_quantization.py
  --data-path {imagenet1k_subset}
  --output-dir {tmp}
  --sync-bn
```

Reviewers:

Subscribers:

Tasks:

Tags:

references/classification/train_quantization.py

@@ -51,12 +51,16 @@ def main(args):
     print("Creating model", args.model)
     # when training quantized models, we always start from a pre-trained fp32 reference model
     model = torchvision.models.quantization.__dict__[args.model](pretrained=True, quantize=args.test_only)
+    model.to(device)
 
     if not (args.test_only or args.post_training_quantize):
         model.fuse_model()
         model.qconfig = torch.quantization.get_default_qat_qconfig(args.backend)
         torch.quantization.prepare_qat(model, inplace=True)
 
+        if args.distributed and args.sync_bn:
+            model = torch.nn.SyncBatchNorm.convert_sync_batchnorm(model)
+
         optimizer = torch.optim.SGD(
             model.parameters(), lr=args.lr, momentum=args.momentum,
             weight_decay=args.weight_decay)
@@ -65,8 +69,6 @@ def main(args):
                                                        step_size=args.lr_step_size,
                                                        gamma=args.lr_gamma)
 
-    model.to(device)
-
     criterion = nn.CrossEntropyLoss()
     model_without_ddp = model
     if args.distributed:
@@ -224,6 +226,12 @@ def parse_args():
              It also serializes the transforms",
         action="store_true",
     )
+    parser.add_argument(
+        "--sync-bn",
+        dest="sync_bn",
+        help="Use sync batch norm",
+        action="store_true",
+    )
     parser.add_argument(
         "--test-only",
         dest="test_only",