fixign

Author: SalmanMohammadi

recipes/eleuther_eval.py

@@ -181,6 +181,7 @@ def tok_batch_multimodal_encode(
         self,
         all_texts: List[str],
         all_images: List[List[PIL.Image.Image]],
+        left_truncate_len: int = None,
         *args,
         **kwargs,
     ):
@@ -218,6 +219,12 @@ def tok_batch_multimodal_encode(
 
         # Convert the batch to the format expected by the HF
         tok_batch["input_ids"] = tok_batch.pop("tokens")
+
+        # the harness will use left_truncate_len to indicate that the current batch
+        # needs to be truncated to self.max_seq_len - self.max_gen_toks
+        if left_truncate_len is not None:
+            tok_batch["input_ids"] = tok_batch["input_ids"][:, -left_truncate_len:]
+
         return tok_batch
 
     @torch.inference_mode()
@@ -370,7 +377,7 @@ def tok_encode(self, text: str, **kwargs) -> List[int]:
         return self._tokenizer.encode(text=text, add_bos=False, add_eos=False)
 
     def tok_batch_encode(
-        self, text: List[str], **kwargs
+        self, text: List[str], left_truncate_len: int = None, **kwargs
     ) -> Tuple[torch.Tensor, torch.Tensor]:
         tokenized_text = [self.tok_encode(x) for x in text]
 
@@ -381,6 +388,11 @@ def tok_batch_encode(
             padding_value=self._tokenizer.pad_id,
         )
 
+        # the harness will use left_truncate_len to indicate that the current batch
+        # needs to be truncated to self.max_seq_len - self.max_gen_toks
+        if left_truncate_len is not None:
+            x = x[:, -left_truncate_len:]
+
         return x, torch.ones_like(x)  # return 'mask' b/c it's expected by the harness
 
     def tok_decode(self, tokens: Union[List[int], int], **kwargs) -> str:
@@ -506,11 +518,6 @@ def setup(self, cfg: DictConfig) -> None:
 
         # Initialize tokenizer/transform
         model_transform = config.instantiate(cfg.tokenizer)
-        max_seq_len = (
-            model_transform.max_seq_len
-            if model_transform.max_seq_len is not None
-            else 4096  # default max_seq_len to 4096
-        )
 
         # Finally, we setup the actual EvalWrapper class
         if isinstance(model, DeepFusionModel):
@@ -526,7 +533,7 @@ def setup(self, cfg: DictConfig) -> None:
             model,
             model_transform,
             device=self.device,
-            max_seq_length=max_seq_len,
+            max_seq_length=cfg.max_seq_length,
             batch_size=self.batch_size,
             dtype=self.dtype,
             enable_kv_cache=self.enable_kv_cache,