manually shard running mean and running var

examples/hlb_cifar10.py

@@ -305,8 +305,11 @@ def train_cifar():
   X_test, Y_test = X_test.cast(dtypes.default_float), Y_test.cast(dtypes.default_float)
 
   if len(GPUS) > 1:
-    for x in get_parameters(model):
-      x.to_(GPUS)
+    for k, x in get_state_dict(model):
+      if 'running_mean' in k or 'running_bias' in k:
+        x.shard_(GPUS, axis=0)
+      else:
+        x.to_(GPUS)
 
   # parse the training params into bias and non-bias
   params_dict = get_state_dict(model)