diff --git a/ai8x.py b/ai8x.py
index 6ffbff4d5..665484aaa 100644
--- a/ai8x.py
+++ b/ai8x.py
@@ -2237,16 +2237,15 @@ def stat_collect(train_loader, model, args):
         model(inputs)
 
 
-def pre_qat(model, train_loader, args, qat_policy, local_rank=0):
+def pre_qat(model, train_loader, args, qat_policy):
     """
     Prepare the model for quantization aware training
     """
-    if local_rank <= 0:
-        init_hist(model)
-        stat_collect(train_loader, model, args)
-        init_threshold(model, qat_policy["outlier_removal_z_score"])
-        release_hist(model)
-        apply_scales(model)
+    init_hist(model)
+    stat_collect(train_loader, model, args)
+    init_threshold(model, qat_policy["outlier_removal_z_score"])
+    release_hist(model)
+    apply_scales(model)
 
 
 def init_hist(model):
diff --git a/train.py b/train.py
index 2916a12ae..f76eab4af 100755
--- a/train.py
+++ b/train.py
@@ -610,7 +610,7 @@ def flush(self):
 
             msglogger.info('Collecting statistics for quantization aware training (QAT)...')
 
-            ai8x.pre_qat(model, train_loader, args, qat_policy, local_rank)
+            ai8x.pre_qat(model, train_loader, args, qat_policy)
 
             # Update the optimizer to reflect fused batchnorm layers
             optimizer = ai8x.update_optimizer(model, optimizer)