Repository: pytorch/ignite

Issue:
Add option to replace bn by sync bn in auto_model
## 🚀 Feature

Idea is to add an option `sync_bn` (default, False) to apply [`convert_sync_batchnorm`](https://pytorch.org/docs/master/generated/torch.nn.SyncBatchNorm.html#torch.nn.SyncBatchNorm.convert_sync_batchnorm) on the input model if a DDP wrapper is used further.

- Check performances on PascalVOC
- Update ref examples


Discussion:

Patch:
diff --git a/ignite/distributed/auto.py b/ignite/distributed/auto.py
index 6d617fdd..49ae70ca 100644
--- a/ignite/distributed/auto.py
+++ b/ignite/distributed/auto.py
@@ -24,7 +24,7 @@ def auto_dataloader(dataset, **kwargs):
 
     - batch size is scaled by world size: ``batch_size / world_size`` if larger or equal world size.
     - number of workers is scaled by number of local processes: ``num_workers / nprocs`` if larger or equal world size.
-    - if no sampler provided by user, `torch DistributedSampler` is setup.
+    - if no sampler provided by user, `torch DistributedSampler`_ is setup.
     - if a sampler is provided by user, it is wrapped by :class:`~ignite.distributed.auto.DistributedProxySampler`.
     - if the default device is 'cuda', `pin_memory` is automatically set to `True`.
 
@@ -122,7 +122,7 @@ def auto_dataloader(dataset, **kwargs):
     return dataloader
 
 
-def auto_model(model: nn.Module) -> nn.Module:
+def auto_model(model: nn.Module, sync_bn: bool = False) -> nn.Module:
     """Helper method to adapt provided model for non-distributed and distributed configurations (supporting
     all available backends from :meth:`~ignite.distributed.utils.available_backends()`).
 
@@ -152,12 +152,19 @@ def auto_model(model: nn.Module) -> nn.Module:
 
     Args:
         model (torch.nn.Module): model to adapt.
+        sync_bn (bool): if True, applies `torch convert_sync_batchnorm`_ to the model for native torch
+            distributed only. Default, False. Note, if using Nvidia/Apex, batchnorm conversion should be
+            applied before calling ``amp.initialize``.
 
     Returns:
         torch.nn.Module
 
-    .. _torch DistributedDataParallel: https://pytorch.org/docs/stable/nn.html#torch.nn.parallel.DistributedDataParallel
-    .. _torch DataParallel: https://pytorch.org/docs/stable/nn.html#torch.nn.DataParallel
+    .. _torch DistributedDataParallel: https://pytorch.org/docs/stable/generated/torch.nn.parallel.
+        DistributedDataParallel.html
+    .. _torch DataParallel: https://pytorch.org/docs/stable/generated/torch.nn.DataParallel.html
+    .. _torch convert_sync_batchnorm: https://pytorch.org/docs/stable/generated/torch.nn.SyncBatchNorm.html#
+        torch.nn.SyncBatchNorm.convert_sync_batchnorm
+
     """
     logger = setup_logger(__name__ + ".auto_model")
 
@@ -170,10 +177,18 @@ def auto_model(model: nn.Module) -> nn.Module:
     if idist.get_world_size() > 1:
         bnd = idist.backend()
         if idist.has_native_dist_support and bnd == idist_native.NCCL:
+            if sync_bn:
+                logger.info("Convert batch norm to sync batch norm")
+                model = nn.SyncBatchNorm.convert_sync_batchnorm(model)
+
             lrank = idist.get_local_rank()
             logger.info("Apply torch DistributedDataParallel on model, device id: {}".format(lrank))
             model = torch.nn.parallel.DistributedDataParallel(model, device_ids=[lrank,])
         elif idist.has_native_dist_support and bnd == idist_native.GLOO:
+            if sync_bn:
+                logger.info("Convert batch norm to sync batch norm")
+                model = nn.SyncBatchNorm.convert_sync_batchnorm(model)
+
             logger.info("Apply torch DistributedDataParallel on model")
             model = torch.nn.parallel.DistributedDataParallel(model)
         elif idist.has_hvd_support and bnd == idist_hvd.HOROVOD:


Test patch:
diff --git a/tests/ignite/distributed/test_auto.py b/tests/ignite/distributed/test_auto.py
index 9635572f..2caf05c1 100644
--- a/tests/ignite/distributed/test_auto.py
+++ b/tests/ignite/distributed/test_auto.py
@@ -51,14 +51,14 @@ def _test_auto_dataloader(ws, nproc, batch_size, num_workers=1, sampler_name=Non
         assert dataloader.pin_memory == ("cuda" in idist.device().type)
 
 
-def _test_auto_model_optimizer(ws, device):
-    # Test auto_model
-    model = nn.Linear(10, 10)
-    model = auto_model(model)
+def _test_auto_model(model, ws, device, sync_bn=False):
+    model = auto_model(model, sync_bn=sync_bn)
     bnd = idist.backend()
     if ws > 1 and device in ("cuda", "cpu"):
         if idist.has_native_dist_support and bnd in ("nccl" or "gloo"):
             assert isinstance(model, nn.parallel.DistributedDataParallel)
+            if sync_bn:
+                assert any([isinstance(m, nn.SyncBatchNorm) for m in model.modules()])
         elif idist.has_hvd_support and bnd in ("horovod",):
             assert isinstance(model, nn.Module)
     elif device != "cpu" and torch.cuda.is_available() and torch.cuda.device_count() > 1:
@@ -70,7 +70,17 @@ def _test_auto_model_optimizer(ws, device):
         [p.device.type for p in model.parameters()], device
     )
 
+
+def _test_auto_model_optimizer(ws, device):
+    # Test auto_model
+    model = nn.Linear(10, 10)
+    _test_auto_model(model, ws, device)
+
+    model = nn.Sequential(nn.Linear(20, 100), nn.BatchNorm1d(100))
+    _test_auto_model(model, ws, device, sync_bn="cuda" in device)
+
     # Test auto_optim
+    bnd = idist.backend()
     optimizer = optim.SGD(model.parameters(), lr=0.01)
     optimizer = auto_optim(optimizer)
     if idist.has_xla_support and "xla" in device: