[SDP] removing an assert which does not seem always accurate (#625)

fairscale/nn/data_parallel/fully_sharded_data_parallel.py

@@ -1664,9 +1664,7 @@ def auto_wrap_bn(module: nn.Module, single_rank_pg: bool = False, process_group:
         if recurse:
             return not isinstance(module, tuple(default_auto_wrap_policy.FORCE_LEAF_MODULES))  # type: ignore
         else:
-            return is_bn and not isinstance(
-                module, tuple(default_auto_wrap_policy.EXCLUDE_WRAP_MODULES)
-            )  # type: ignore
+            return is_bn and not isinstance(module, tuple(default_auto_wrap_policy.EXCLUDE_WRAP_MODULES))  # type: ignore
 
     pg = None
     if single_rank_pg:

fairscale/nn/data_parallel/sharded_ddp.py

@@ -269,10 +269,10 @@ class ShardedDataParallel(nn.Module):
         """ If the module trainability has changed, update all the assumptions """
 
         # Make sure that this is not done while gradients are waiting to be reduced (if no_sync context for instance)
-        assert not functools.reduce(lambda x, y: x or y, self._grad_to_be_reduced, False), (
-            "Grads waiting to be reduced: {}".format(self._grad_to_be_reduced)
-            + "\nIf this is on purpose (grad accumulation), please use a no_sync() context"
-        )
+        if functools.reduce(lambda x, y: x or y, self._grad_to_be_reduced, False):
+            logging.warning(
+                "Grads waiting to be reduced. If this is on purpose (grad accumulation), please use a no_sync() context"
+            )
 
         self._trainable_params = list(filter(lambda x: x.requires_grad, self._all_params))
         self._trainable_params.sort(key=lambda x: x.numel())

tests/nn/data_parallel/test_sharded_ddp_features.py

@@ -262,9 +262,9 @@ def test_mixed_types():
     dist.destroy_process_group()
 
 
-def test_train_eval_change():
+def run_test_train_eval_change(rank, world_size, file):
     # Check that ShardedDDP handles the switch from training to eval properly
-    dist.init_process_group(init_method="file://" + tempfile.mkstemp()[1], backend="gloo", rank=0, world_size=1)
+    dist.init_process_group(init_method="file://" + file, backend="gloo", rank=rank, world_size=world_size)
 
     model = _get_mlp()
     model.train()
@@ -288,6 +288,14 @@ def test_train_eval_change():
     dist.destroy_process_group()
 
 
+def test_train_eval_change():
+    world_size = 4
+    temp_file_name = tempfile.mkstemp()[1]
+    mp.spawn(
+        run_test_train_eval_change, args=(world_size, temp_file_name), nprocs=world_size, join=True,
+    )
+
+
 def run_test_device_change(rank, world_size, backend, device, temp_file_name, reduce_buffer_size):
     # Check that the wrapped module can change devices
     dist.init_process_group(init_method="file://" + temp_file_name, backend=backend, rank=rank, world_size=world_size)