[Inference]Adapt repetition_penalty and no_repeat_ngram_size (#5708)

* Adapt repetition_penalty and no_repeat_ngram_size

* fix no_repeat_ngram_size_logit_process

* remove batch_updated

* fix annotation

* modified codes based on the review feedback.

* rm get_batch_token_ids

colossalai/inference/batch_bucket.py

@@ -102,6 +102,13 @@ class BatchBucket:
     def num_tokens_to_verify(self) -> int:
         return self._num_tokens_to_verify
 
+    @property
+    def batch_token_ids(self) -> List[List[int]]:
+        out = []
+        for seq in self.seqs_li:
+            out.append(seq.input_token_id + seq.output_token_id)
+        return out
+
     def set_use_spec_dec(self, num_tokens_to_verify: int = 5) -> None:
         """Set batch bucket to use speculatvie decoding.
         This will notify the adjust the lengths of inputs during modeling,
@@ -328,6 +335,7 @@ class BatchBucket:
             seqs.append(seq)
         if not self.is_compact:
             self._make_compact()
+
         return seqs, block_tables
 
     def pop_finished(
@@ -432,6 +440,7 @@ class BatchBucket:
             block_tables = torch.stack(block_tables_li)
             self.add_seqs(seqs, alloc_block_tables=block_tables)
             unmerged_ids = other.seqs_ids
+
         return unmerged_ids
 
     ########## The following methods are expected to be used in modeling ###########