add and improve comments

src/transformers/modeling_ctrl.py

@@ -491,7 +491,7 @@ class CTRLLMHeadModel(CTRLPreTrainedModel):
         return self.lm_head
 
     def prepare_inputs_for_generation(self, input_ids, **kwargs):
-        # inputs_ids contain only last token if past is in kwargs and defined
+        # inputs_ids should only be composed of last token if past is in kwargs and defined
         input_ids = input_ids[:, -1].unsqueeze(-1) if 'past' in kwargs and kwargs['past'] else input_ids
 
         inputs = {"input_ids": input_ids}

src/transformers/modeling_gpt2.py

@@ -560,7 +560,7 @@ class GPT2LMHeadModel(GPT2PreTrainedModel):
         return self.lm_head
 
     def prepare_inputs_for_generation(self, input_ids, **kwargs):
-        # inputs_ids contain only last token if past is in kwargs and defined
+        # inputs_ids should only be composed of last token if past is in kwargs and defined
         input_ids = input_ids[:, -1].unsqueeze(-1) if 'past' in kwargs and kwargs['past'] else input_ids
 
         inputs = {"input_ids": input_ids}

src/transformers/modeling_utils.py

@@ -732,6 +732,7 @@ class PreTrainedModel(nn.Module):
             outputs = self(**model_inputs)
             next_token_logits = outputs[0][:, -1, :]
 
+            # if model has past, then set the past parameter to speed up decoding
             if self._has_past(outputs):
                 past = outputs[1]
 
@@ -819,6 +820,7 @@ class PreTrainedModel(nn.Module):
             outputs = self(**model_inputs)  # (batch_size * num_beams, cur_len, vocab_size)
             scores = outputs[0][:, -1, :]  # (batch_size * num_beams, vocab_size)
 
+            # if model has past, then set the past parameter to speed up decoding
             if self._has_past(outputs):
                 past = outputs[1]