fix bug when combining DataParallelInferenceRunner+BatchNorm (since c…

…c2322b)

.github/ISSUE_TEMPLATE/unexpected-problems---bugs.md

@@ -59,7 +59,7 @@ If this command failed, tell us your version of Python/TF/tensorpack.
 
 Note that:
 
-  + You can install Tensorpack master by `pip install -U git+https://github.com/tensorpack/tensorpack.git`
+  + You can install tensorpack master by `pip install -U git+https://github.com/tensorpack/tensorpack.git`
     and see if your issue is already solved.
   + If you're not using tensorpack under a normal command line shell (e.g.,
     using an IDE or jupyter notebook), please retry under a normal command line shell.

tensorpack/models/batch_norm.py

@@ -195,7 +195,8 @@ def BatchNorm(inputs, axis=None, training=None, momentum=0.9, epsilon=1e-5,
         ema_update = "collection"
     # Logic:
     # 1. EMA update is possible only when we compute batch statistics (training=True)
-    # 2. We know that in training, non-main training tower does not need EMA update
+    # 2. We know that in training, non-main training tower does not need EMA
+    #    update (unless you need, e.g., inference during training on all towers)
     #    We don't know about what to do in prediction context, so be conservative and do the update.
     # 3. User can explicit disable update by "skip".
     do_ema_update = training and \

tensorpack/train/trainers.py

@@ -157,10 +157,9 @@ class SyncMultiGPUTrainerReplicated(SingleCostTrainer):
             are supposed to be in-sync).
             But this cheap operation may help prevent
             certain numerical issues in practice.
+            Note that in cases such as BatchNorm, the variables may not be in sync.
     """
 
-    BROADCAST_EVERY_EPOCH = False
-
     @map_arg(gpus=_int_to_range)
     def __init__(self, gpus, average=True, mode=None):
         """
@@ -180,6 +179,8 @@ def __init__(self, gpus, average=True, mode=None):
         mode = mode.lower()
 
         self._builder = SyncMultiGPUReplicatedBuilder(gpus, average, mode)
+        self.BROADCAST_EVERY_EPOCH = True
+
         super(SyncMultiGPUTrainerReplicated, self).__init__()
 
     def _setup_graph(self, input, get_cost_fn, get_opt_fn):
@@ -384,8 +385,8 @@ class HorovodTrainer(SingleCostTrainer):
             Whether to broadcast the variables every epoch.
             Theoretically this is a no-op (because the variables
             are supposed to be in-sync).
-            But this cheap operation may help prevent
-            certain numerical issues in practice.
+            But this cheap operation may help prevent certain numerical issues in practice.
+            Note that in cases such as BatchNorm, the variables may not be in sync.
     """
 
     def __init__(self, average=True, compression=None):
@@ -413,7 +414,7 @@ def __init__(self, average=True, compression=None):
         logger.info("[HorovodTrainer] local rank={}".format(self._local_rank))
         super(HorovodTrainer, self).__init__()
 
-        self.BROADCAST_EVERY_EPOCH = False
+        self.BROADCAST_EVERY_EPOCH = True
 
     def mpi_enabled(self):
         """