clean up cumul batches for rolling + remove unused print

lm_eval/models/gpt2.py

@@ -159,9 +159,9 @@ class HFLM(LM):
 
         extra_pad = []
         numpad_batches = 0
-        # balance token batches among iterators
+        
         if self.world_size > 1:
-            cumulative_batches = 0 
+            cumulative_batches = 0  # balance token batches among iterators
             # compute cumlative batches once -> could also just cache this can then use it later
             for (string,) in tqdm([req.args for req in requests],disable=(self.rank != 0)):
                 rolling_token_windows = list(
@@ -179,15 +179,13 @@ class HFLM(LM):
                 rolling_token_windows = [(None,) + x for x in rolling_token_windows]
                 cumulative_batches += len(rolling_token_windows)
 
-            cum_batches_ranks = torch.tensor(cumulative_batches, device = self.device)
-            gathered_item = self.accelerator.gather(cum_batches_ranks).cpu().detach().numpy().tolist()
+            cumul_batches_ranks = torch.tensor(cumulative_batches, device = self.device)
+            gathered_item = self.accelerator.gather(cumul_batches_ranks).cpu().detach().numpy().tolist()
 
             # compute number of pseudobatches to pad with (FSDP/DDP require even batches among ranks)
             numpad_batches = max(gathered_item) - gathered_item[self.rank]
             extra_pad = [('pad',)] if numpad_batches > 0 else []
 
-            print(self.rank, numpad_batches)
-
         loglikelihoods = []
         for (string,) in tqdm(extra_pad + [req.args for req in requests],disable=(self.rank != 0)):
             if numpad_batches > 0: