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hivemind/optim/experimental/state_averager.py

@@ -155,9 +155,11 @@ class TrainingStateAverager(DecentralizedAverager):
         params_with_grad = sum(p.numel() for p in parameters if p.requires_grad)
         params_no_grad = sum(p.numel() for p in parameters if not p.requires_grad)
         if params_no_grad >= params_with_grad:
-            logging.warning("The majority of parameters have requires_grad=False, but they are still synchronized"
-                            " with peers. If these parameters are frozen (not updated), please do not feed them into "
-                            "the optimizer at all in order to avoid communication overhead. Proceeding anyway.")
+            logging.warning(
+                "The majority of parameters have requires_grad=False, but they are still synchronized"
+                " with peers. If these parameters are frozen (not updated), please do not feed them into "
+                "the optimizer at all in order to avoid communication overhead. Proceeding anyway."
+            )
 
         return param_groups, parameters, parameter_names