function rename

aphrodite/modeling/layers/sampler.py

@@ -70,13 +70,13 @@ class Sampler(nn.Module):
             logits.div_(t.unsqueeze(dim=1))
 
         # Apply top-p, top-k, and top-a truncation.
-        top_ps, top_ks, top_as = _get_top_ap_top_k(input_metadata, self.vocab_size)
+        top_ps, top_ks, top_as = _get_top_a_top_p_top_k(input_metadata, self.vocab_size)
         assert len(top_ps) == len(top_ks) == logits.shape[0]
         do_top_p = any(p < 1.0 - _SAMPLING_EPS for p in top_ps)
         do_top_k = any(k != self.vocab_size for k in top_ks)
         do_top_a = any(a > _SAMPLING_EPS for a in top_as)
         if do_top_p or do_top_k or do_top_a:
-            logits = _apply_top_ap_top_k(logits, top_ps, top_ks, top_as)
+            logits = _apply_top_a_top_p_top_k(logits, top_ps, top_ks, top_as)
 
         # We use float32 for probabilities and log probabilities.
         # Compute the probabilities.
@@ -248,7 +248,7 @@ def _get_temperatures(input_metadata: InputMetadata) -> List[float]:
     return temperatures
 
 
-def _get_top_ap_top_k(
+def _get_top_a_top_p_top_k(
     input_metadata: InputMetadata,
     vocab_size: int,
 ) -> Tuple[List[float], List[int], List[float]]:
@@ -269,7 +269,7 @@ def _get_top_ap_top_k(
     return top_ps, top_ks, top_as
 
 
-def _apply_top_ap_top_k(
+def _apply_top_a_top_p_top_k(
     logits: torch.Tensor,
     top_ps: List[float],
     top_ks: List[int],