[Misc] Fix warnings for mistral model (vllm-project#23552)

Signed-off-by: zjy0516 <riverclouds.zhu@qq.com>
Signed-off-by: Jiangyun Zhu <riverclouds.zhu@qq.com>
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>

Author: ZJY0516

vllm/model_executor/models/pixtral.py

@@ -15,7 +15,7 @@
 from mistral_common.protocol.instruct.request import ChatCompletionRequest
 from mistral_common.tokens.tokenizers.multimodal import ImageEncoder
 from PIL import Image
-from transformers import PixtralVisionConfig, TensorType
+from transformers import BatchFeature, PixtralVisionConfig, TensorType
 from transformers.image_utils import ImageInput
 from transformers.models.pixtral.image_processing_pixtral import (
     _num_image_tokens as _get_pixtral_hf_num_image_tokens)
@@ -163,10 +163,12 @@ def __call__(
             images_processed.append(image_processed)
             images_tokens.append(image_tokens)
 
-        return {
-            "input_ids": torch.cat(images_tokens)[None].expand(len(text), -1),
-            "images": images_processed,
-        }
+        return BatchFeature({
+            "input_ids":
+            torch.cat(images_tokens)[None].expand(len(text), -1),
+            "images":
+            images_processed,
+        })
 
 
 class PixtralProcessingInfo(BaseProcessingInfo):

vllm/model_executor/models/voxtral.py

@@ -17,7 +17,7 @@
 from mistral_common.protocol.instruct.request import ChatCompletionRequest
 from mistral_common.protocol.transcription.request import TranscriptionRequest
 from mistral_common.tokens.tokenizers.audio import Audio, AudioEncoder
-from transformers import TensorType, WhisperConfig
+from transformers import BatchFeature, TensorType, WhisperConfig
 from transformers.tokenization_utils_base import TextInput
 
 from vllm.config import ModelConfig, SpeechToTextConfig, VllmConfig
@@ -156,10 +156,12 @@ def __call__(
             audios_tokens.append(torch.tensor(audio_tokens))
             audios_processed.append(torch.tensor(audio))
 
-        return {
-            "input_ids": torch.cat(audios_tokens)[None].expand(len(text), -1),
-            "audio_arrays": audios_processed,
-        }
+        return BatchFeature({
+            "input_ids":
+            torch.cat(audios_tokens)[None].expand(len(text), -1),
+            "audio_arrays":
+            audios_processed,
+        })
 
 
 class VoxtralProcessingInfo(BaseProcessingInfo):

vllm/transformers_utils/tokenizers/mistral.py

@@ -204,18 +204,16 @@ def __init__(self, tokenizer: "PublicMistralTokenizer") -> None:
         self.version: int = int(_mistral_version_str.split("v")[-1])
 
         tokenizer_ = tokenizer.instruct_tokenizer.tokenizer
-        from mistral_common.tokens.tokenizers.tekken import (
-            SpecialTokenPolicy, Tekkenizer)
+        from mistral_common.tokens.tokenizers.base import SpecialTokenPolicy
+        from mistral_common.tokens.tokenizers.tekken import Tekkenizer
+
         self.is_tekken = isinstance(tokenizer_, Tekkenizer)
         from mistral_common.tokens.tokenizers.sentencepiece import (
             SentencePieceTokenizer)
         self.is_spm = isinstance(tokenizer_, SentencePieceTokenizer)
-        if self.is_tekken:
-            # Make sure special tokens will not raise
-            tokenizer_.special_token_policy = SpecialTokenPolicy.IGNORE
-        elif self.is_spm:
-            pass
-        else:
+        self._special_token_policy = (SpecialTokenPolicy.IGNORE
+                                      if self.is_tekken else None)
+        if not (self.is_tekken or self.is_spm):
             raise TypeError(f"Unsupported tokenizer: {type(tokenizer_)}")
 
         self._vocab = tokenizer_.vocab()
@@ -430,7 +428,8 @@ def _token_to_id(t: str):
                         return self.tokenizer.unk_id
 
                 ids = [_token_to_id(t) for t in tokens]
-                decoded = self.tokenizer.decode(ids)
+                decoded = self.tokenizer.decode(ids,
+                                                self._special_token_policy)
             else:
                 decoded = "".join(tokens)
         else:
@@ -444,15 +443,17 @@ def _token_to_id(t: str):
                 if token in special_tokens:
                     if regular_tokens:
                         decoded_list.append(
-                            self.tokenizer.decode(regular_tokens))
+                            self.tokenizer.decode(regular_tokens,
+                                                  self._special_token_policy))
                         regular_tokens = []
                     decoded_list.append(token)
                 else:
                     regular_tokens.append(token)
 
             if regular_tokens:
                 decoded_list.append(
-                    self.tokenizer.decode(regular_tokens))  # type: ignore
+                    self.tokenizer.decode(regular_tokens,
+                                          self._special_token_policy))
 
             decoded = ''.join(decoded_list)
 
@@ -470,7 +471,7 @@ def decode(self,
 
         if isinstance(ids, int):
             ids = [ids]
-        return self.tokenizer.decode(ids)
+        return self.tokenizer.decode(ids, self._special_token_policy)
 
     def convert_ids_to_tokens(
         self,
@@ -511,6 +512,9 @@ def convert_ids_to_tokens(
             # See: https://github.com/vllm-project/vllm/pull/8640
             #      https://github.com/vllm-project/vllm/pull/9625
             # if underlying tokenizeir is sentencepiece, we just add "�"
-            tokens = [self.tokenizer.id_to_byte_piece(id) for id in ids]
+            tokens = [
+                self.tokenizer.id_to_byte_piece(id, self._special_token_policy)
+                for id in ids
+            ]
 
         return tokens