[Model] Add Mistral Tokenization to improve robustness and chat encoding (#7739)

docs/requirements-docs.txt

@@ -11,4 +11,5 @@ pydantic >= 2.8
 torch
 py-cpuinfo
 transformers
+mistral_common >= 1.3.4
 openai # Required by docs/source/serving/openai_compatible_server.md's vllm.entrypoints.openai.cli_args

requirements-common.txt

@@ -26,3 +26,4 @@ librosa # Required for audio processing
 soundfile # Required for audio processing
 gguf == 0.9.1
 importlib_metadata
+mistral_common >= 1.3.4

tests/models/test_mistral.py

@@ -30,9 +30,11 @@ def test_models(
         hf_outputs = hf_model.generate_greedy_logprobs_limit(
             example_prompts, max_tokens, num_logprobs)
 
-    with vllm_runner(model, dtype=dtype) as vllm_model:
+    with vllm_runner(model, dtype=dtype,
+                     tokenizer_mode="mistral") as vllm_model:
         vllm_outputs = vllm_model.generate_greedy_logprobs(
             example_prompts, max_tokens, num_logprobs)
+
     check_logprobs_close(
         outputs_0_lst=hf_outputs,
         outputs_1_lst=vllm_outputs,

vllm/config.py

@@ -61,7 +61,8 @@ class ModelConfig:
             output when `served_model_name` is not specified. 
         tokenizer: Name or path of the huggingface tokenizer to use.
         tokenizer_mode: Tokenizer mode. "auto" will use the fast tokenizer if
-            available, and "slow" will always use the slow tokenizer.
+            available, "slow" will always use the slow tokenizer, and
+            "mistral" will always use the tokenizer from `mistral_common`.
         trust_remote_code: Trust remote code (e.g., from HuggingFace) when
             downloading the model and tokenizer.
         dtype: Data type for model weights and activations. The "auto" option
@@ -246,10 +247,10 @@ class ModelConfig:
 
     def _verify_tokenizer_mode(self) -> None:
         tokenizer_mode = self.tokenizer_mode.lower()
-        if tokenizer_mode not in ["auto", "slow"]:
+        if tokenizer_mode not in ["auto", "slow", "mistral"]:
             raise ValueError(
                 f"Unknown tokenizer mode: {self.tokenizer_mode}. Must be "
-                "either 'auto' or 'slow'.")
+                "either 'auto', 'slow' or 'mistral'.")
         self.tokenizer_mode = tokenizer_mode
 
     def _verify_embedding_mode(self) -> None:

vllm/engine/arg_utils.py

@@ -198,10 +198,11 @@ class EngineArgs:
             '--tokenizer-mode',
             type=str,
             default=EngineArgs.tokenizer_mode,
-            choices=['auto', 'slow'],
+            choices=['auto', 'slow', 'mistral'],
             help='The tokenizer mode.\n\n* "auto" will use the '
             'fast tokenizer if available.\n* "slow" will '
-            'always use the slow tokenizer.')
+            'always use the slow tokenizer. \n* '
+            '"mistral" will always use the `mistral_common` tokenizer.')
         parser.add_argument('--trust-remote-code',
                             action='store_true',
                             help='Trust remote code from huggingface.')

vllm/entrypoints/chat_utils.py

@@ -267,7 +267,7 @@ def apply_chat_template(
     *,
     tokenize: bool = False,  # Different from HF's default
     **kwargs: Any,
-) -> str:
+) -> Union[str, List[int]]:
     if chat_template is None and tokenizer.chat_template is None:
         raise ValueError(
             "As of transformers v4.44, default chat template is no longer "
@@ -280,6 +280,4 @@ def apply_chat_template(
         tokenize=tokenize,
         **kwargs,
     )
-    assert isinstance(prompt, str)
-
     return prompt

vllm/entrypoints/llm.py

@@ -390,15 +390,21 @@ class LLM:
         conversations, _ = parse_chat_messages(messages, model_config,
                                                tokenizer)
 
-        prompts = apply_chat_template(
+        prompt = apply_chat_template(
             tokenizer,
             conversations,
             chat_template=chat_template,
             add_generation_prompt=add_generation_prompt)
 
+        inputs: PromptInputs
+        if isinstance(prompt, list) and isinstance(prompt[0], int):
+            inputs = TokensPrompt(prompt_token_ids=prompt)
+        else:
+            inputs = TextPrompt(prompt=prompt)
+
         return self.generate(
-            prompts,
-            sampling_params,
+            inputs,
+            sampling_params=sampling_params,
             use_tqdm=use_tqdm,
             lora_request=lora_request,
         )

vllm/entrypoints/openai/serving_chat.py

@@ -22,7 +22,8 @@ from vllm.entrypoints.openai.protocol import (
     FunctionCall, ToolCall, UsageInfo)
 from vllm.entrypoints.openai.serving_engine import (LoRAModulePath,
                                                     OpenAIServing,
-                                                    PromptAdapterPath)
+                                                    PromptAdapterPath,
+                                                    TextTokensPrompt)
 from vllm.inputs import TokensPrompt
 from vllm.logger import init_logger
 from vllm.multimodal import MultiModalDataDict
@@ -130,13 +131,22 @@ class OpenAIServingChat(OpenAIServing):
             guided_decode_logits_processor = (
                 await self._guided_decode_logits_processor(request, tokenizer))
 
-            prompt_inputs = self._tokenize_prompt_input(
-                request,
-                tokenizer,
-                prompt,
-                truncate_prompt_tokens=request.truncate_prompt_tokens,
-                add_special_tokens=request.add_special_tokens,
-            )
+            if isinstance(prompt, str):
+                prompt_inputs = self._tokenize_prompt_input(
+                    request,
+                    tokenizer,
+                    prompt,
+                    truncate_prompt_tokens=request.truncate_prompt_tokens,
+                    add_special_tokens=request.add_special_tokens,
+                )
+            else:
+                assert isinstance(prompt, list) and isinstance(
+                    prompt[0], int
+                ), "Prompt has to be either a string or a list of token ids"
+                prompt_inputs = TextTokensPrompt(
+                    prompt=tokenizer.decode(prompt), prompt_token_ids=prompt)
+
+            assert prompt_inputs is not None
 
             sampling_params = request.to_sampling_params(
                 tokenizer,

vllm/transformers_utils/detokenizer.py

@@ -230,7 +230,7 @@ def convert_prompt_ids_to_tokens(
     prefix_offset = max(
         read_offset - INITIAL_INCREMENTAL_DETOKENIZATION_OFFSET, 0)
     # This is required to guard against out-of-vocab prompt token ids
-    _replace_none_with_empty(new_tokens)
+    _replace_none_with_empty(new_tokens)  # type: ignore[arg-type]
     return new_tokens, prefix_offset, read_offset
 
 

vllm/transformers_utils/tokenizer.py

@@ -1,4 +1,5 @@
 import os
+import warnings
 from pathlib import Path
 from typing import Optional, Union
 
@@ -9,12 +10,14 @@ from transformers import (AutoTokenizer, PreTrainedTokenizer,
 from vllm.envs import VLLM_USE_MODELSCOPE
 from vllm.logger import init_logger
 from vllm.lora.request import LoRARequest
-from vllm.transformers_utils.tokenizers import BaichuanTokenizer
+from vllm.transformers_utils.tokenizers import (BaichuanTokenizer,
+                                                MistralTokenizer)
 from vllm.utils import make_async
 
 logger = init_logger(__name__)
 
-AnyTokenizer = Union[PreTrainedTokenizer, PreTrainedTokenizerFast]
+AnyTokenizer = Union[PreTrainedTokenizer, PreTrainedTokenizerFast,
+                     MistralTokenizer]
 
 
 def get_cached_tokenizer(tokenizer: AnyTokenizer) -> AnyTokenizer:
@@ -99,45 +102,64 @@ def get_tokenizer(
         kwargs["gguf_file"] = Path(tokenizer_name).name
         tokenizer_name = Path(tokenizer_name).parent
 
-    try:
-        tokenizer = AutoTokenizer.from_pretrained(
-            tokenizer_name,
-            *args,
-            trust_remote_code=trust_remote_code,
-            revision=revision,
-            **kwargs)
-    except ValueError as e:
-        # If the error pertains to the tokenizer class not existing or not
-        # currently being imported, suggest using the --trust-remote-code flag.
-        if (not trust_remote_code and
-            ("does not exist or is not currently imported." in str(e)
-             or "requires you to execute the tokenizer file" in str(e))):
-            err_msg = (
-                "Failed to load the tokenizer. If the tokenizer is a custom "
-                "tokenizer not yet available in the HuggingFace transformers "
-                "library, consider setting `trust_remote_code=True` in LLM "
-                "or using the `--trust-remote-code` flag in the CLI.")
-            raise RuntimeError(err_msg) from e
-        else:
-            raise e
-    except AttributeError as e:
-        if "BaichuanTokenizer" in str(e):
-            # This is for the error "'BaichuanTokenizer' object has no
-            # attribute 'sp_model'".
-            tokenizer = BaichuanTokenizer.from_pretrained(
+    # if tokenizer is from official mistral org
+    is_from_mistral_org = str(tokenizer_name).split("/")[0] == "mistralai"
+    if is_from_mistral_org and tokenizer_mode != "mistral":
+        warnings.warn(
+            'It is strongly recommended to run mistral models with '
+            '`--tokenizer_mode "mistral"` to ensure correct '
+            'encoding and decoding.',
+            FutureWarning,
+            stacklevel=2)
+
+    if tokenizer_mode == "mistral":
+        tokenizer = MistralTokenizer.from_pretrained(str(tokenizer_name),
+                                                     revision=revision)
+    else:
+        try:
+            tokenizer = AutoTokenizer.from_pretrained(
                 tokenizer_name,
                 *args,
                 trust_remote_code=trust_remote_code,
                 revision=revision,
-                **kwargs)
-        else:
-            raise e
+                **kwargs,
+            )
+        except ValueError as e:
+            # If the error pertains to the tokenizer class not existing or not
+            # currently being imported,
+            # suggest using the --trust-remote-code flag.
+            if not trust_remote_code and (
+                    "does not exist or is not currently imported." in str(e)
+                    or "requires you to execute the tokenizer file" in str(e)):
+                err_msg = ("Failed to load the tokenizer. If the tokenizer "
+                           "is a custom tokenizer not yet available in the "
+                           "HuggingFace transformers library, consider "
+                           "setting `trust_remote_code=True` in LLM or using "
+                           "the `--trust-remote-code` flag in the CLI.")
+                raise RuntimeError(err_msg) from e
+            else:
+                raise e
+        except AttributeError as e:
+            if "BaichuanTokenizer" in str(e):
+                # This is for the error "'BaichuanTokenizer' object has no
+                # attribute 'sp_model'".
+                tokenizer = BaichuanTokenizer.from_pretrained(
+                    tokenizer_name,
+                    *args,
+                    trust_remote_code=trust_remote_code,
+                    revision=revision,
+                    **kwargs,
+                )
+            else:
+                raise e
 
-    if not isinstance(tokenizer, PreTrainedTokenizerFast):
-        logger.warning(
-            "Using a slow tokenizer. This might cause a significant "
-            "slowdown. Consider using a fast tokenizer instead.")
-    return get_cached_tokenizer(tokenizer)
+        if not isinstance(tokenizer, PreTrainedTokenizerFast):
+            logger.warning(
+                "Using a slow tokenizer. This might cause a significant "
+                "slowdown. Consider using a fast tokenizer instead.")
+        tokenizer = get_cached_tokenizer(tokenizer)
+
+    return tokenizer
 
 
 def get_lora_tokenizer(lora_request: LoRARequest, *args,

vllm/transformers_utils/tokenizers/__init__.py

@@ -1,5 +1,4 @@
 from vllm.transformers_utils.tokenizers.baichuan import BaichuanTokenizer
+from vllm.transformers_utils.tokenizers.mistral import MistralTokenizer
 
-__all__ = [
-    "BaichuanTokenizer",
-]
+__all__ = ["BaichuanTokenizer", "MistralTokenizer"]

vllm/transformers_utils/tokenizers/mistral.py

@@ -0,0 +1,174 @@
+import os
+import re
+from dataclasses import dataclass
+from pathlib import Path
+from typing import TYPE_CHECKING, Any, Dict, List, Optional, Union
+
+from huggingface_hub import HfApi, hf_hub_download
+# yapf: disable
+from mistral_common.tokens.tokenizers.mistral import ChatCompletionRequest
+from mistral_common.tokens.tokenizers.mistral import (
+    MistralTokenizer as PublicMistralTokenizer)
+# yapf: enable
+from mistral_common.tokens.tokenizers.sentencepiece import (
+    SentencePieceTokenizer)
+from mistral_common.tokens.tokenizers.tekken import (SpecialTokenPolicy,
+                                                     Tekkenizer)
+
+if TYPE_CHECKING:
+    from vllm.entrypoints.chat_utils import ConversationMessage
+
+
+@dataclass
+class Encoding:
+    input_ids: List[int]
+
+
+def find_tokenizer_file(files: List[str]):
+    file_pattern = re.compile(r"^tokenizer\.model\.v.*$|^tekken\.json$")
+
+    matched_files = [file for file in files if file_pattern.match(file)]
+    if len(matched_files) > 1:
+        raise OSError(f"Found {len(matched_files)} files matching the "
+                      "pattern: {matched_files}. Make sure only one Mistral "
+                      "tokenizer is present in {tokenizer_name}.")
+    elif len(matched_files) == 0:
+        raise OSError(f"Found {len(matched_files)} files matching the "
+                      "pattern: {matched_files}. Make sure that a Mistral "
+                      "tokenizer is present in {tokenizer_name}.")
+
+    return matched_files[0]
+
+
+class MistralTokenizer:
+
+    def __init__(self, tokenizer: PublicMistralTokenizer) -> None:
+        self.mistral = tokenizer
+        self.instruct = tokenizer.instruct_tokenizer
+        self.tokenizer = tokenizer.instruct_tokenizer.tokenizer
+
+        self.vocab_size = len(self.tokenizer.vocab())
+
+        assert isinstance(self.tokenizer,
+                          (Tekkenizer, SentencePieceTokenizer)), type(
+                              self.tokenizer)
+        self._is_tekken = isinstance(self.tokenizer, Tekkenizer)
+
+        if self._is_tekken:
+            # Make sure special tokens will not raise
+            self.tokenizer.special_token_policy = SpecialTokenPolicy.IGNORE
+
+        # the following attributes are set to fit VLLM's design
+        self.is_fast = True
+        self.chat_template = True
+        self.all_special_ids: List[Any] = []
+        self.all_special_tokens: List[Any] = []
+        self.all_special_tokens_extended: List[Any] = []
+
+    @classmethod
+    def from_pretrained(cls,
+                        path_or_repo_id: str,
+                        *,
+                        revision: Optional[str] = None) -> "MistralTokenizer":
+        if not Path(path_or_repo_id).exists():
+            assert len(path_or_repo_id.split("/")) == 2, (
+                "You have either provided a non-existent path: "
+                "{path_or_repo_id} or an invalid HF Hub repo id.")
+            tokenizer_file = cls._download_mistral_tokenizer_from_hf(
+                path_or_repo_id, revision)
+        elif Path(path_or_repo_id).is_dir():
+            tokenizer_file_name = find_tokenizer_file(
+                os.listdir(path_or_repo_id))
+            tokenizer_file = str(Path(path_or_repo_id) / tokenizer_file_name)
+        else:
+            assert Path(
+                path_or_repo_id).is_file(), f"Invalid path: {path_or_repo_id}"
+
+        mistral_tokenizer = PublicMistralTokenizer.from_file(tokenizer_file)
+        return cls(mistral_tokenizer)
+
+    @staticmethod
+    def _download_mistral_tokenizer_from_hf(tokenizer_name: str,
+                                            revision: Optional[str]) -> str:
+        api = HfApi()
+        repo_info = api.model_info(tokenizer_name)
+        files = [s.rfilename for s in repo_info.siblings]
+
+        filename = find_tokenizer_file(files)
+
+        tokenizer_file = hf_hub_download(tokenizer_name,
+                                         filename=filename,
+                                         revision=revision)
+        return tokenizer_file
+
+    def __call__(
+        self,
+        prompt: str,
+        add_special_tokens: bool = False,
+        truncation: bool = False,
+        max_length: Optional[int] = None,
+    ):
+        # Mistral Tokenizers should not add special tokens
+        input_ids = self.encode(prompt)
+
+        if truncation:
+            input_ids = input_ids[:max_length]
+
+        return Encoding(input_ids=input_ids)
+
+    def get_added_vocab(self) -> List[str]:
+        # Mistral tokenizers have no added vocabulary
+        return []
+
+    def encode(self, prompt: str) -> List[int]:
+        # `encode ` should only be used for prompt completion
+        # it should never be used for chat_completion.
+        # For chat completion use `apply_chat_template`
+        return self.tokenizer.encode(prompt, bos=True, eos=False)
+
+    def apply_chat_template(self,
+                            conversation: List["ConversationMessage"],
+                            tools: Optional[Dict[str, Any]] = None,
+                            **kwargs) -> List[int]:
+        assert tools is None, "`tools` are not yet supported."
+
+        request = ChatCompletionRequest(
+            messages=conversation)  # type: ignore[type-var]
+        encoded = self.mistral.encode_chat_completion(request)
+
+        # encode-decode to get clean prompt
+        return encoded.tokens
+
+    def convert_tokens_to_string(self, tokens: List[str]) -> str:
+        if self._is_tekken:
+            return "".join(tokens)
+        else:
+            return self.tokenizer.decode(tokens)  # type: ignore[arg-type]
+
+    def decode(self, ids: Union[List[int], int]) -> str:
+        if isinstance(ids, int):
+            ids = [ids]
+        return self.tokenizer.decode(ids)
+
+    @property
+    def eos_token_id(self):
+        return self.tokenizer.eos_id
+
+    def convert_ids_to_tokens(
+            self,
+            ids: List[int],
+            skip_special_tokens: Optional[bool] = True) -> List[str]:
+        # TODO(Patrick) - potentially allow special tokens to not be skipped
+        assert (
+            skip_special_tokens
+        ), "Skipping special tokens is not supported for Mistral tokenizers."
+
+        assert isinstance(self.tokenizer,
+                          (Tekkenizer, SentencePieceTokenizer)), type(
+                              self.tokenizer)
+
+        tokens = [self.tokenizer.id_to_piece(id) for id in ids]
+        return tokens
+
+    def __len__(self):
+        return self.vocab_size