changed do_output_past function to check for self.config.output_past instead of self.output_past

src/transformers/modeling_utils.py

@@ -539,10 +539,10 @@ class PreTrainedModel(nn.Module):
     def prepare_inputs_for_generation(self, input_ids, **kwargs):
         return {"input_ids": input_ids}
 
-    def _has_past(self, outputs):
-        # TODO: might be better to write a self.has_past method for each individual class as is done for
+    def _do_output_past(self, outputs):
+        # TODO: might be better to write a self.do_output_past method for each individual class as is done for
         # prepare_inputs_for_generation
-        if hasattr(self, 'output_past') and self.output_past and len(outputs) > 1:
+        if hasattr(self.config, 'output_past') and self.config.output_past and len(outputs) > 1 and not hasattr(self, 'mem_len'):
             return True
         # TODO: Add cases for (xlnet, transfo_xl) using mem_len
         return False
@@ -732,7 +732,7 @@ class PreTrainedModel(nn.Module):
             next_token_logits = outputs[0][:, -1, :]
 
             # if model has past, then set the past variable to speed up decoding
-            if self._has_past(outputs):
+            if self._do_output_past(outputs):
                 past = outputs[1]
 
             # repetition penalty from CTRL paper (https://arxiv.org/abs/1909.05858)
@@ -819,7 +819,7 @@ class PreTrainedModel(nn.Module):
             scores = outputs[0][:, -1, :]  # (batch_size * num_beams, vocab_size)
 
             # if model has past, then set the past variable to speed up decoding
-            if self._has_past(outputs):
+            if self._do_output_past(outputs):
                 past = outputs[1]
 
             # repetition penalty (from CTRL paper https://arxiv.org/abs/1909.05858)