docsring

hivemind/optim/optimizer.py

@@ -148,7 +148,7 @@ class Optimizer(torch.optim.Optimizer):
     :param auxiliary: if True, optimizer.step will only assist other peers in averaging (for cpu-only workers)
 
     :param grad_compression: compression strategy used for averaging gradients, default = no compression
-    :param grad_averager: if provided, creates gradient averager with required averaging strategy
+    :param grad_averager_factory: if provided, creates gradient averager with required averaging strategy
     :param state_averaging_compression: compression for averaging params and state tensors, default = no compression
     :param load_state_compression: compression strategy for loading state from peers, default = no compression
     :param average_opt_statistics: names of optimizer statistics from state dict that should be averaged with peers
@@ -230,7 +230,7 @@ class Optimizer(torch.optim.Optimizer):
             assert not delay_grad_averaging, "if local_updates is True, gradients will not be averaged"
             assert (
                 grad_averager_factory is None
-            ), "if local_updates is True, provided gradient_averager will not be used"
+            ), "if local_updates is True, provided grad_averager_factory will not be used"
 
         self.dht, self.run_id, self.client_mode, self.auxiliary = dht, run_id, client_mode, auxiliary
         self.batch_size_per_step, self.target_batch_size = batch_size_per_step, target_batch_size