[LlamaSlowConverter] Slow to Fast better support

src/transformers/convert_slow_tokenizer.py

@@ -1331,9 +1331,9 @@ class LlamaConverter(SpmConverter):
 
     def vocab(self, proto):
         vocab = [
-            ("<unk>", 0.0),
-            ("<s>", 0.0),
-            ("</s>", 0.0),
+            (self.original_tokenizer.convert_ids_to_tokens(0), 0.0),
+            (self.original_tokenizer.convert_ids_to_tokens(1), 0.0),
+            (self.original_tokenizer.convert_ids_to_tokens(2), 0.0),
         ]
         vocab += [(piece.piece, piece.score) for piece in proto.pieces[3:]]
         return vocab
@@ -1371,9 +1371,9 @@ def tokenizer(self, proto):
             )
             tokenizer.add_special_tokens(
                 [
-                    AddedToken("<unk>", normalized=False, special=True),
-                    AddedToken("<s>", normalized=False, special=True),
-                    AddedToken("</s>", normalized=False, special=True),
+                    AddedToken(self.original_tokenizer.convert_ids_to_tokens(0), normalized=False, special=True),
+                    AddedToken(self.original_tokenizer.convert_ids_to_tokens(1), normalized=False, special=True),
+                    AddedToken(self.original_tokenizer.convert_ids_to_tokens(2), normalized=False, special=True),
                 ]
             )
         else:

tests/models/llava/test_modeling_llava.py

@@ -22,6 +22,7 @@
 
 from transformers import (
     AutoProcessor,
+    AutoTokenizer,
     LlavaConfig,
     LlavaForConditionalGeneration,
     is_torch_available,
@@ -575,3 +576,29 @@ def test_llava_merge_inputs_error_bug(self):
             labels=input_ids,
         ).loss
         loss.backward()
+
+    def test_tokenizer_integration(self):
+        slow_tokenizer = AutoTokenizer.from_pretrained("liuhaotian/llava-v1.6-34b", use_fast=False)
+        slow_tokenizer.add_tokens("<image>", True)
+
+        fast_tokenizer = AutoTokenizer.from_pretrained(
+            "liuhaotian/llava-v1.6-34b",
+            bos_token="<|startoftext|>",
+            eos_token="<|endoftext|>",
+            from_slow=True,
+            legacy=False,
+        )
+        fast_tokenizer.add_tokens("<image>", True)
+
+        prompt = "<|im_start|>system\nAnswer the questions.<|im_end|><|im_start|>user\n<image>\nWhat is shown in this image?<|im_end|><|im_start|>assistant\n"
+        # If the token is added as special, it's not normalized, and the only diff is the extra space after special tokens.
+        # https://github.com/huggingface/transformers/pull/28881 is the fix for this.
+        self.assertEqual(
+            slow_tokenizer.tokenize(prompt),
+            ['<|im_start|>', 'system', '\n', 'Answer', '▁the', '▁questions', '.', '<|im_end|>', '<|im_start|>', 'user', '\n', '<image>', '\n', 'What', '▁is', '▁shown', '▁in', '▁this', '▁image', '?', '<|im_end|>', '<|im_start|>', 'ass', 'istant', '\n']
+        )  # fmt: skip
+
+        self.assertEqual(
+            fast_tokenizer.tokenize(prompt),
+            ['<|im_start|>', '▁system', '\n', 'Answer', '▁the', '▁questions', '.', '<|im_end|>', '<|im_start|>', '▁user', '\n', '<image>', '▁', '\n', 'What', '▁is', '▁shown', '▁in', '▁this', '▁image', '?', '<|im_end|>', '<|im_start|>', '▁assistant', '\n']
+        )  # fmt: skip