[SPM] Patch spm Llama and T5 (#25656)

* hot fix

* only encode with string prefix if starts with prefix

* styling

* add a new test

* fixup

src/transformers/models/llama/tokenization_llama.py

@@ -220,13 +220,14 @@ class LlamaTokenizer(PreTrainedTokenizer):
         `unk_token`. Here is an example with `unk_token = "<unk>"` and `unk_token_length = 4`.
         `self.tokenizer.sp_model.encode("<unk> Hey", out_type = str)[4:]`.
         """
-        if self.legacy:
-            return self.sp_model.encode(text, out_type=str)
-
-        unk_token_length = len(self.sp_model.encode(str(self.unk_token)))
-        text = self.unk_token + text
         tokens = self.sp_model.encode(text, out_type=str)
-        return tokens[unk_token_length:]
+        if self.legacy or not text.startswith((SPIECE_UNDERLINE, " ")):
+            return tokens
+
+        # 1. Encode string + prefix ex: "<unk> Hey"
+        tokens = self.sp_model.encode(self.unk_token + text, out_type=str)
+        # 2. Remove self.unk_token from ['<','unk','>', '▁Hey']
+        return tokens[self.unk_token_length :] if len(tokens) >= self.unk_token_length else tokens
 
     def _convert_token_to_id(self, token):
         """Converts a token (str) in an id using the vocab."""

src/transformers/models/t5/tokenization_t5.py

@@ -363,6 +363,10 @@ class T5Tokenizer(PreTrainedTokenizer):
             tokens = tokens[1:]
         return tokens
 
+    @property
+    def unk_token_length(self):
+        return len(self.sp_model.encode(str(self.unk_token)))
+
     def _tokenize(self, text, **kwargs):
         """
         Returns a tokenized string.
@@ -373,13 +377,14 @@ class T5Tokenizer(PreTrainedTokenizer):
         `unk_token`. Here is an example with `unk_token = "<unk>"` and `unk_token_length = 4`.
         `self.tokenizer.sp_model.encode("<unk> Hey", out_type = str)[4:]`.
         """
-        if self.legacy:
-            return self.sp_model.encode(text, out_type=str)
-
-        unk_token_length = len(self.sp_model.encode(str(self.unk_token)))
-        text = self.unk_token + text
         tokens = self.sp_model.encode(text, out_type=str)
-        return tokens[unk_token_length:]
+        if self.legacy or not text.startswith((SPIECE_UNDERLINE, " ")):
+            return tokens
+
+        # 1. Encode string + prefix ex: "<unk> Hey"
+        tokens = self.sp_model.encode(self.unk_token + text, out_type=str)
+        # 2. Remove self.unk_token from ['<','unk','>', '▁Hey']
+        return tokens[self.unk_token_length :] if len(tokens) >= self.unk_token_length else tokens
 
     def _convert_token_to_id(self, token):
         """Converts a token (str) in an id using the vocab."""

tests/models/llama/test_tokenization_llama.py

@@ -546,6 +546,15 @@ class LlamaIntegrationTest(unittest.TestCase):
         decoded_tokens = tokenizer.decode(input_ids)
         self.assertEqual(decoded_tokens, " <s> Hello<s> how")
 
+    def test_some_edge_cases(self):
+        tokenizer = LlamaTokenizer.from_pretrained("huggyllama/llama-7b", legacy=False)
+
+        sp_tokens = tokenizer.sp_model.encode("<s>>", out_type=str)
+        self.assertEqual(sp_tokens, ["<", "s", ">>"])
+        tokens = tokenizer.tokenize("<s>>")
+        self.assertNotEqual(sp_tokens, tokens)
+        self.assertEqual(tokens, ["<s>", ">"])
+
 
 @require_sentencepiece
 @require_tokenizers