Fix error due in Collating queries with different continuation lengths (fixes #2984) (#2987)

* FIX error due to grouping queries with different continuation length

Make Collator choose query with the longest continuation as the
candidate for generation

* use max for key selection

* added comments explaining variable cont length (identical ctx+cont[:-1])

---------
Co-authored-by: Baber <baber@hey.com>

lm_eval/models/huggingface.py

@@ -1136,7 +1136,7 @@ class HFLM(TemplateLM):
                 if self.backend == "causal":
                     total_length = len(context_enc) + len(continuation_enc)
                     if total_length > self.max_length + 1:
-                        eval_logger.warn(
+                        eval_logger.warning(
                             f"Combined length of context ({len(context_enc)}) and continuation ({len(continuation_enc)}) "
                             f"exceeds model's maximum length ({self.max_length}). "
                             f"Truncating {total_length - self.max_length + 1} tokens from the left."
@@ -1247,7 +1247,12 @@ class HFLM(TemplateLM):
                     cont_toks = torch.tensor(
                         cont_toks, dtype=torch.long, device=self.device
                     ).unsqueeze(0)  # [1, seq]
-                    max_equal = (greedy_tokens == cont_toks).all()
+                    # Use trailing slice [-cont_toks.shape[1]:] to handle variable length cont_len (but same ctx+cont[:-1]).
+                    # i.e. continuations can be sliced at diff points. Collator ensures we have sufficient greedy_tokens
+                    # by choosing key with longest cont if group_by="contexts".
+                    max_equal = (
+                        greedy_tokens[:, -cont_toks.shape[1] :] == cont_toks
+                    ).all()
 
                     # Obtain log-probs at the corresponding continuation token indices
                     # last_token_slice = logits[:, -1, :].squeeze(0).tolist()

lm_eval/models/utils.py

@@ -428,9 +428,13 @@ class Collator:
                 batch = self.get_chunks(values, n=n, fn=batch_fn)
                 yield from batch
         elif self._group_by == "contexts":
-            # Get one sample from each key
+            # Get one sample from each key.
+            # Select longest continuation per group to ensure sufficient context logits
             values = self._reorder(
-                [value[0] for value in self._arr_with_indices.values()]
+                [
+                    max(value, key=lambda x: len(x[1][-1]))
+                    for value in self._arr_with_indices.values()
+                ]
             )
             batch = self.get_chunks(values, n=n, fn=batch_fn)
             yield from batch