[Generate] Remove outdated code (#11331)

* remove update function

* update

* refactor more

* refactor

src/transformers/generation_utils.py

@@ -483,31 +483,6 @@ class GenerationMixin:
             model_kwargs["encoder_outputs"] = encoder_outputs
         return input_ids, model_kwargs
 
-    @staticmethod
-    def _init_sequence_length_for_generation(
-        input_ids: torch.LongTensor, max_length: int
-    ) -> Tuple[torch.Tensor, torch.Tensor, int]:
-        unfinished_sequences = input_ids.new(input_ids.shape[0]).fill_(1)
-        sequence_lengths = input_ids.new(input_ids.shape[0]).fill_(max_length)
-
-        cur_len = input_ids.shape[-1]
-        return sequence_lengths, unfinished_sequences, cur_len
-
-    @staticmethod
-    def _update_seq_length_for_generation(
-        sequence_lengths: torch.LongTensor,
-        unfinished_sequences: torch.LongTensor,
-        cur_len: int,
-        is_eos_in_next_token: torch.BoolTensor,
-    ) -> Tuple[torch.LongTensor, torch.LongTensor]:
-        # check if sentence is not finished yet
-        is_sent_unfinished = unfinished_sequences.mul(is_eos_in_next_token.long()).bool()
-
-        # update sentence length
-        sequence_lengths = sequence_lengths.masked_fill(is_sent_unfinished, cur_len)
-        unfinished_sequences = unfinished_sequences.mul((~is_eos_in_next_token).long())
-        return sequence_lengths, unfinished_sequences
-
     @staticmethod
     def _update_model_kwargs_for_generation(
         outputs: ModelOutput, model_kwargs: Dict[str, Any], is_encoder_decoder: bool = False
@@ -1271,10 +1246,9 @@ class GenerationMixin:
                 model_kwargs["encoder_outputs"].get("hidden_states") if output_hidden_states else None
             )
 
-        # init sequence length tensors
-        sequence_lengths, unfinished_sequences, cur_len = self._init_sequence_length_for_generation(
-            input_ids, max_length
-        )
+        # keep track of which sequences are already finished
+        unfinished_sequences = input_ids.new(input_ids.shape[0]).fill_(1)
+        cur_len = input_ids.shape[-1]
 
         this_peer_finished = False  # used by synced_gpus only
         while cur_len < max_length:
@@ -1330,29 +1304,23 @@ class GenerationMixin:
             # argmax
             next_tokens = torch.argmax(next_tokens_scores, dim=-1)
 
-            # add code that transforms next_tokens to tokens_to_add
+            # finished sentences should have their next token be a padding token
             if eos_token_id is not None:
                 assert pad_token_id is not None, "If eos_token_id is defined, make sure that pad_token_id is defined."
                 next_tokens = next_tokens * unfinished_sequences + pad_token_id * (1 - unfinished_sequences)
 
-            # add token and increase length by one
+            # update generated ids, model inputs, and length for next step
             input_ids = torch.cat([input_ids, next_tokens[:, None]], dim=-1)
-
-            # update sequence length
-            if eos_token_id is not None:
-                sequence_lengths, unfinished_sequences = self._update_seq_length_for_generation(
-                    sequence_lengths, unfinished_sequences, cur_len, next_tokens == eos_token_id
-                )
-
-            # update model kwargs
             model_kwargs = self._update_model_kwargs_for_generation(
                 outputs, model_kwargs, is_encoder_decoder=self.config.is_encoder_decoder
             )
-
-            # increase cur_len
             cur_len = cur_len + 1
 
-            # stop when there is a </s> in each sentence, or if we exceed the maximum length
+            # if eos_token was found in one sentence, set sentence to finished
+            if eos_token_id is not None:
+                unfinished_sequences = unfinished_sequences.mul((next_tokens != eos_token_id).long())
+
+            # stop when each sentence is finished, or if we exceed the maximum length
             if unfinished_sequences.max() == 0 or stopping_criteria(input_ids, scores):
                 if not synced_gpus:
                     break
@@ -1511,10 +1479,9 @@ class GenerationMixin:
                 model_kwargs["encoder_outputs"].get("hidden_states") if output_hidden_states else None
             )
 
-        # init sequence length tensors
-        sequence_lengths, unfinished_sequences, cur_len = self._init_sequence_length_for_generation(
-            input_ids, max_length
-        )
+        # keep track of which sequences are already finished
+        unfinished_sequences = input_ids.new(input_ids.shape[0]).fill_(1)
+        cur_len = input_ids.shape[-1]
 
         this_peer_finished = False  # used by synced_gpus only
         # auto-regressive generation
@@ -1571,32 +1538,25 @@ class GenerationMixin:
 
             # sample
             probs = F.softmax(next_token_scores, dim=-1)
-
             next_tokens = torch.multinomial(probs, num_samples=1).squeeze(1)
 
-            # add code that transforms next_tokens to tokens_to_add
+            # finished sentences should have their next token be a padding token
             if eos_token_id is not None:
                 assert pad_token_id is not None, "If eos_token_id is defined, make sure that pad_token_id is defined."
                 next_tokens = next_tokens * unfinished_sequences + pad_token_id * (1 - unfinished_sequences)
 
-            # add token and increase length by one
+            # update generated ids, model inputs, and length for next step
             input_ids = torch.cat([input_ids, next_tokens[:, None]], dim=-1)
-
-            # update sequence length
-            if eos_token_id is not None:
-                sequence_lengths, unfinished_sequences = self._update_seq_length_for_generation(
-                    sequence_lengths, unfinished_sequences, cur_len, next_tokens == eos_token_id
-                )
-
-            # update model kwargs
             model_kwargs = self._update_model_kwargs_for_generation(
                 outputs, model_kwargs, is_encoder_decoder=self.config.is_encoder_decoder
             )
-
-            # increase cur_len
             cur_len = cur_len + 1
 
-            # stop when there is a </s> in each sentence, or if we exceed the maximum length
+            # if eos_token was found in one sentence, set sentence to finished
+            if eos_token_id is not None:
+                unfinished_sequences = unfinished_sequences.mul((next_tokens != eos_token_id).long())
+
+            # stop when each sentence is finished, or if we exceed the maximum length
             if unfinished_sequences.max() == 0 or stopping_criteria(input_ids, scores):
                 if not synced_gpus:
                     break