vllm.tokenizers.deepseek_v4 ¶

def get_deepseek_v4_tokenizer(tokenizer: HfTokenizer) -> HfTokenizer:
    """
    Wraps a tokenizer to use the custom DeepSeek V4 chat template encoding.
    """
    dsv4_tokenizer = copy.copy(tokenizer)

    added_vocab = tokenizer.get_added_vocab()

    class _DeepseekV4Tokenizer(tokenizer.__class__):  # type: ignore
        def apply_chat_template(
            self,
            messages: list["ChatCompletionMessageParam"],
            tools: list[dict[str, Any]] | None = None,
            **kwargs,
        ) -> str | list[int]:
            thinking = kwargs.get("thinking")
            enable_thinking = kwargs.get("enable_thinking")
            thinking_enabled = bool(thinking) or bool(enable_thinking)
            if "thinking" not in kwargs and "enable_thinking" not in kwargs:
                thinking_enabled = True
            thinking_mode = "thinking" if thinking_enabled else "chat"

            conversation = kwargs.get("conversation", messages)
            messages = conversation.copy()
            if tools is not None and len(tools) > 0:
                messages.insert(0, {"role": "system"})
                messages[0]["tools"] = tools  # type: ignore[typeddict-unknown-key]

            reasoning_effort = kwargs.get("reasoning_effort")
            if not isinstance(reasoning_effort, str):
                reasoning_effort = "high" if thinking_enabled else None
            elif reasoning_effort == "none":
                thinking_mode = "chat"
                reasoning_effort = None
            elif reasoning_effort == "max":
                reasoning_effort = "max"
            elif reasoning_effort in ("low", "minimal", "medium"):
                reasoning_effort = "low"
            else:
                reasoning_effort = "high"

            encode_config = dict(
                thinking_mode=thinking_mode,
                drop_thinking=kwargs.get("drop_thinking", True),
                reasoning_effort=reasoning_effort,
            )

            prompt_str = encode_messages(messages, **encode_config)  # type: ignore

            if kwargs.get("tokenize", True):
                tokenizer_kwargs = {
                    k: kwargs[k] for k in ("truncation", "max_length") if k in kwargs
                }
                return self.encode(
                    prompt_str,
                    add_special_tokens=False,
                    **tokenizer_kwargs,
                )

            return prompt_str

        def num_special_tokens_to_add(self) -> int:
            return len(self.encode(""))

        def get_added_vocab(self) -> dict[str, int]:
            return added_vocab.copy()

        def __reduce__(self):
            return get_deepseek_v4_tokenizer, (tokenizer,)

    _DeepseekV4Tokenizer.__name__ = f"DSV4{tokenizer.__class__.__name__}"

    dsv4_tokenizer.__class__ = _DeepseekV4Tokenizer
    return dsv4_tokenizer