[Tokenizer] Fix chat template for Gemma when answer is contained with…

…in question (#9462)

paddlenlp/transformers/gemma/tokenizer.py

@@ -14,6 +14,7 @@
 # limitations under the License.
 
 import os
+import re
 from shutil import copyfile
 from typing import Any, Dict, List, Optional, Tuple
 
@@ -310,3 +311,33 @@ def create_token_type_ids_from_sequences(
             output += [1] * len(bos_token_id + token_ids_1 + eos_token_id)
 
         return output
+
+    def _extract_non_learnable_parts(self, origin_msg: List[Dict[str, str]], split_s: List[str]):
+        regex_pattern = "|".join(map(re.escape, split_s))
+        rendered_messages = self.chat_template.render(
+            messages=origin_msg, add_generation_prompt=False, **self.special_tokens_map
+        )
+        pattern = re.compile(r"(?:%s)" % regex_pattern)
+        split_positions = [match.span() for match in pattern.finditer(rendered_messages)]
+
+        filtered_positions = []
+        for start, end in split_positions:
+            # Find the last occurrence of '<start_of_turn>' before the split index
+            last_start = rendered_messages.rfind("<start_of_turn>", 0, start)
+            if last_start == -1:
+                continue  # Skip if '<start_of_turn>' is not found
+            model_start = last_start + len("<start_of_turn>")
+
+            # Get the text following 'model_start' and check if it starts with 'model'
+            following_text = rendered_messages[model_start:].lstrip()
+            if following_text.startswith("model"):
+                filtered_positions.append((start, end))
+        non_learnable_parts = []
+        last_end = 0
+        for start, end in filtered_positions:
+            non_learnable_parts.append(rendered_messages[last_end:start])
+            last_end = end
+        remaining_part = rendered_messages[last_end:]
+        if remaining_part:
+            non_learnable_parts.append(remaining_part)
+        return non_learnable_parts

tests/transformers/gemma/test_tokenizer.py

@@ -223,3 +223,29 @@ def test_add_special_tokens(self):
                 self.assertEqual(encoded, input_encoded + special_token_id)
                 decoded = tokenizer.decode(encoded, skip_special_tokens=True)
                 self.assertTrue(special_token not in decoded)
+
+    def test_extract_non_learnable_parts(self):
+        models_with_templates = ["google/gemma-2b-it", "google/gemma-7b-it"]
+        dummy_conversastions = [
+            ["Q.", "A."],
+            ["Q.A.", "A."],
+            ["Q?", "A!"],
+        ]
+        decode_outputs = [
+            ["<bos><start_of_turn>user\nQ.<end_of_turn>\n<start_of_turn>model\n", "A.<end_of_turn>\n"],
+            ["<start_of_turn>user\nQ.A.<end_of_turn>\n<start_of_turn>model\n", "A.<end_of_turn>\n"],
+            ["<start_of_turn>user\nQ?<end_of_turn>\n<start_of_turn>model\n", "A!<end_of_turn>\n"],
+        ]
+        context_data = {}
+        context_data["is_training"] = True
+        for model_id in models_with_templates:
+            tokenizer = GemmaTokenizer.from_pretrained(model_id)
+            if tokenizer.chat_template is None:
+                continue
+            conversation_result: list[tuple[list[int], list[int]]] = tokenizer.encode_chat_inputs(
+                dummy_conversastions,
+                context_data=context_data,
+            )
+            for idx, round in enumerate(conversation_result["conversations"]):
+                self.assertEquals(tokenizer.decode(round[0]), decode_outputs[idx][0])
+                self.assertEquals(tokenizer.decode(round[1]), decode_outputs[idx][1])