better naming

src/transformers/modeling_bart.py

@@ -943,7 +943,7 @@ class BartForConditionalGeneration(PretrainedBartModel):
         return outputs
 
     @staticmethod
-    def prepare_inputs_for_generation_1(input_ids, past, decoder_input_ids, attention_mask):
+    def prepare_inputs_for_generation_bart(input_ids, past, decoder_input_ids, attention_mask):
         if past is None:  # first step
             encoder_outputs, decoder_cached_states = None, None
         else:
@@ -993,7 +993,7 @@ class BartForConditionalGeneration(PretrainedBartModel):
         return self.lm_head
 
     @torch.no_grad()
-    def generate_1(
+    def generate_bart(
         self,
         input_ids,
         attention_mask=None,
@@ -1113,7 +1113,7 @@ class BartForConditionalGeneration(PretrainedBartModel):
         self.model.decoder.generation_mode = True  # tells decoder not to use causal mask
         for step in range(max_length + 1):
             decoder_input_ids = prev_output_tokens.clone()
-            model_inputs = self.prepare_inputs_for_generation_1(
+            model_inputs = self.prepare_inputs_for_generation_bart(
                 input_ids, decoder_cache, decoder_input_ids, attention_mask,
             )
             outputs = self(**model_inputs)

src/transformers/modeling_utils.py

@@ -411,7 +411,7 @@ class PreTrainedModel(nn.Module, ModuleUtilsMixin):
                 else:
                     raise EnvironmentError(
                         "Error no file named {} found in directory {} or `from_tf` set to False".format(
-                            [WEIGHTS_NAME, TF2_WEIGHTS_NAME, TF_WEIGHTS_NAME + ".index",],
+                            [WEIGHTS_NAME, TF2_WEIGHTS_NAME, TF_WEIGHTS_NAME + ".index"],
                             pretrained_model_name_or_path,
                         )
                     )
@@ -816,7 +816,6 @@ class PreTrainedModel(nn.Module, ModuleUtilsMixin):
                 effective_batch_size * num_beams, input_ids_len
             )  # shape: (batch_size * num_return_sequences * num_beams, cur_len)
 
-        # TODO (PVP): check eos_token_id
         # TODO (PVP): probably not the best way to check whether model is encoder decoder
         is_encoder_decoder = (
             hasattr(self, "model") and hasattr(self.model, "decoder") and hasattr(self.model, "encoder")
@@ -829,7 +828,7 @@ class PreTrainedModel(nn.Module, ModuleUtilsMixin):
             encoder_inputs = input_ids
             input_ids = torch.full(
                 (effective_batch_size * num_beams, 1),
-                #                eos_token_id, # Why eos_token_id here? bos_token_id makes more sense no?
+                #                eos_token_id, # Why eos_token_id here? bos_token_id seems to work as well ... to see if it works as well with hard summarization case
                 bos_token_id,
                 dtype=torch.long,
                 device=next(self.parameters()).device,

tests/test_modeling_bart.py

@@ -427,7 +427,7 @@ class BartModelIntegrationTest(unittest.TestCase):
         text = " (CNN)The Palestinian Authority officially became the 123rd member of the International Criminal Court on Wednesday, a step that gives the court jurisdiction over alleged crimes in Palestinian"
         tokens = tok.encode(text, return_tensors="pt").to(torch_device)
         extra_len = 20
-        gen_tokens_bart = hf.generate_1(tokens, num_beams=4, max_length=extra_len,)  # repetition_penalty=10.,
+        gen_tokens_bart = hf.generate_bart(tokens, num_beams=3, max_length=extra_len,)  # repetition_penalty=10.,
         gen_tokens = hf.generate(
             tokens, num_beams=4, max_length=extra_len + 2, do_sample=False
         )  # repetition_penalty=10.,