Distributed tweaks

* Support PyTorch native DDP as fallback if APEX not present
* Support SyncBN for both APEX and Torch native (if torch >= 1.1)
* EMA model does not appear to need DDP wrapper, no gradients, updated from wrapped model

train.py

@@ -10,6 +10,7 @@ try:
     from apex.parallel import convert_syncbn_model
     has_apex = True
 except ImportError:
+    from torch.nn.parallel import DistributedDataParallel as DDP
     has_apex = False
 
 from timm.data import Dataset, create_loader, resolve_data_config, FastCollateMixup, mixup_target
@@ -169,8 +170,9 @@ def main():
         bn_eps=args.bn_eps,
         checkpoint_path=args.initial_checkpoint)
 
-    logging.info('Model %s created, param count: %d' %
-                 (args.model, sum([m.numel() for m in model.parameters()])))
+    if args.local_rank == 0:
+        logging.info('Model %s created, param count: %d' %
+                     (args.model, sum([m.numel() for m in model.parameters()])))
 
     data_config = resolve_data_config(model, args, verbose=args.local_rank == 0)
 
@@ -187,24 +189,23 @@ def main():
             args.amp = False
         model = nn.DataParallel(model, device_ids=list(range(args.num_gpu))).cuda()
     else:
-        if args.distributed and args.sync_bn and has_apex:
-            model = convert_syncbn_model(model)
         model.cuda()
 
     optimizer = create_optimizer(args, model)
     if optimizer_state is not None:
         optimizer.load_state_dict(optimizer_state)
 
+    use_amp = False
     if has_apex and args.amp:
         model, optimizer = amp.initialize(model, optimizer, opt_level='O1')
         use_amp = True
-        logging.info('AMP enabled')
-    else:
-        use_amp = False
-        logging.info('AMP disabled')
+    if args.local_rank == 0:
+        logging.info('NVIDIA APEX {}. AMP {}.'.format(
+            'installed' if has_apex else 'not installed', 'on' if use_amp else 'off'))
 
     model_ema = None
     if args.model_ema:
+        # Important to create EMA model after cuda(), DP wrapper, and AMP but before SyncBN and DDP wrapper
         model_ema = ModelEma(
             model,
             decay=args.model_ema_decay,
@@ -212,11 +213,23 @@ def main():
             resume=args.resume)
 
     if args.distributed:
-        model = DDP(model, delay_allreduce=True)
-        if model_ema is not None and not args.model_ema_force_cpu:
-            # must also distribute EMA model to allow validation
-            model_ema.ema = DDP(model_ema.ema, delay_allreduce=True)
-            model_ema.ema_has_module = True
+        if args.sync_bn:
+            try:
+                if has_apex:
+                    model = convert_syncbn_model(model)
+                else:
+                    model = torch.nn.SyncBatchNorm.convert_sync_batchnorm(model)
+                if args.local_rank == 0:
+                    logging.info('Converted model to use Synchronized BatchNorm.')
+            except Exception as e:
+                logging.error('Failed to enable Synchronized BatchNorm. Install Apex or Torch >= 1.1')
+        if has_apex:
+            model = DDP(model, delay_allreduce=True)
+        else:
+            if args.local_rank == 0:
+                logging.info("Using torch DistributedDataParallel. Install NVIDIA Apex for Apex DDP.")
+            model = DDP(model, device_ids=[args.local_rank])  # can use device str in Torch >= 1.1
+        # NOTE: EMA model does not need to be wrapped by DDP
 
     lr_scheduler, num_epochs = create_scheduler(args, optimizer)
     if start_epoch > 0: