modified changes to fix loglikelihood prediction for seq2seq

lm_eval/models/huggingface.py

@@ -409,12 +409,13 @@ class HFLM(LM):
         utils.clear_torch_cache()
         return batch_size
 
-    def tok_encode(self, string: str, left_truncate_len=None):
+    def tok_encode(self, string: str, left_truncate_len=None, add_special_tokens=None):
         """ """
-        if self.AUTO_MODEL_CLASS == transformers.AutoModelForCausalLM:
-            add_special_tokens = False
-        elif self.AUTO_MODEL_CLASS == transformers.AutoModelForSeq2SeqLM:
-            add_special_tokens = True
+        if add_special_tokens is None:
+            if self.AUTO_MODEL_CLASS == transformers.AutoModelForCausalLM:
+                add_special_tokens = False
+            elif self.AUTO_MODEL_CLASS == transformers.AutoModelForSeq2SeqLM:
+                add_special_tokens = True
 
         encoding = self.tokenizer.encode(string, add_special_tokens=add_special_tokens)
 
@@ -529,8 +530,12 @@ class HFLM(LM):
         if n_spaces > 0:
             continuation = context[-n_spaces:] + continuation
             context = context[:-n_spaces]
-        whole_enc = self.tok_encode(context + continuation)
-        context_enc = self.tok_encode(context)
+
+        whole_enc = self.tok_encode(context + continuation, add_special_tokens=False)
+        context_enc = self.tok_encode(context, add_special_tokens=False)
+
+        # whole_enc = self.tok_encode(context + continuation)
+        # context_enc = self.tok_encode(context, add_special_tokens=False)
         context_enc_len = len(context_enc)
         continuation_enc = whole_enc[context_enc_len:]
         return context_enc, continuation_enc