Patch for Seq2Seq Model predictions (#1584)

* Differentiate _encode_pair setting for decoder and enc-dec models

* tok_decode to not skip special token so that eos doen't become empty string

* Update model.py

* Update model.py

* Update huggingface.py

* Update lm_eval/models/huggingface.py
Co-authored-by: Hailey Schoelkopf <65563625+haileyschoelkopf@users.noreply.github.com>

* Update model.py

---------
Co-authored-by: Hailey Schoelkopf <65563625+haileyschoelkopf@users.noreply.github.com>

lm_eval/api/model.py

@@ -5,6 +5,7 @@ import logging
 import os
 from typing import List, Optional, Tuple, Type, TypeVar
 
+import transformers
 from sqlitedict import SqliteDict
 from tqdm import tqdm
 
@@ -296,12 +297,17 @@ class TemplateLM(LM):
             continuation = context[-n_spaces:] + continuation
             context = context[:-n_spaces]
 
+        if self.AUTO_MODEL_CLASS == transformers.AutoModelForCausalLM:
             whole_enc = self.tok_encode(context + continuation)
             context_enc = self.tok_encode(context)
 
             context_enc_len = len(context_enc)
             continuation_enc = whole_enc[context_enc_len:]
 
+        elif self.AUTO_MODEL_CLASS == transformers.AutoModelForSeq2SeqLM:
+            context_enc = self.tok_encode(context)
+            continuation_enc = self.tok_encode(continuation)
+
         return context_enc, continuation_enc
 
     def loglikelihood(

lm_eval/models/huggingface.py

@@ -711,11 +711,15 @@ class HFLM(TemplateLM):
 
         return encoding["input_ids"], encoding["attention_mask"]
 
-    def tok_decode(self, tokens):
+    def tok_decode(self, tokens, skip_special_tokens=True):
         if self.AUTO_MODEL_CLASS == transformers.AutoModelForCausalLM:
-            return self.tokenizer.decode(tokens)
+            return self.tokenizer.decode(
+                tokens, skip_special_tokens=skip_special_tokens
+            )
         elif self.AUTO_MODEL_CLASS == transformers.AutoModelForSeq2SeqLM:
-            return self.tokenizer.decode(tokens, skip_special_tokens=True)
+            return self.tokenizer.decode(
+                tokens, skip_special_tokens=skip_special_tokens
+            )
 
     def _model_call(self, inps, attn_mask=None, labels=None):
         """
@@ -1158,7 +1162,7 @@ class HFLM(TemplateLM):
                     f"Expected `kwargs` to be of type `dict` but got {type(gen_kwargs)}"
                 )
             # add EOS token to stop sequences
-            eos = self.tok_decode(self.eot_token_id)
+            eos = self.tok_decode(self.eot_token_id, skip_special_tokens=False)
             if not until:
                 until = [eos]
             else: