update

vllm/entrypoints/serve/tokenize/protocol.py

@@ -0,0 +1,183 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+
+from typing import Annotated, Any, TypeAlias
+
+from pydantic import ConfigDict, Field, model_validator
+
+from vllm.config import ModelConfig
+from vllm.entrypoints.chat_utils import (
+    ChatCompletionMessageParam,
+    ChatTemplateContentFormatOption,
+)
+from vllm.entrypoints.openai.chat_completion.protocol import (
+    ChatCompletionToolsParam,
+)
+from vllm.entrypoints.openai.engine.protocol import (
+    OpenAIBaseModel,
+)
+from vllm.renderers import ChatParams, TokenizeParams, merge_kwargs
+
+
+class TokenizeCompletionRequest(OpenAIBaseModel):
+    model: str | None = None
+    prompt: str
+
+    add_special_tokens: bool = Field(
+        default=True,
+        description=(
+            "If true (the default), special tokens (e.g. BOS) will be added to "
+            "the prompt."
+        ),
+    )
+    return_token_strs: bool | None = Field(
+        default=False,
+        description=(
+            "If true, also return the token strings corresponding to the token ids."
+        ),
+    )
+
+    def build_tok_params(self, model_config: ModelConfig) -> TokenizeParams:
+        return TokenizeParams(
+            max_total_tokens=None,
+            max_output_tokens=0,
+            add_special_tokens=self.add_special_tokens,
+        )
+
+
+class TokenizeChatRequest(OpenAIBaseModel):
+    model: str | None = None
+    messages: list[ChatCompletionMessageParam]
+
+    add_generation_prompt: bool = Field(
+        default=True,
+        description=(
+            "If true, the generation prompt will be added to the chat template. "
+            "This is a parameter used by chat template in tokenizer config of the "
+            "model."
+        ),
+    )
+    return_token_strs: bool | None = Field(
+        default=False,
+        description=(
+            "If true, also return the token strings corresponding to the token ids."
+        ),
+    )
+    continue_final_message: bool = Field(
+        default=False,
+        description=(
+            "If this is set, the chat will be formatted so that the final "
+            "message in the chat is open-ended, without any EOS tokens. The "
+            "model will continue this message rather than starting a new one. "
+            'This allows you to "prefill" part of the model\'s response for it. '
+            "Cannot be used at the same time as `add_generation_prompt`."
+        ),
+    )
+    add_special_tokens: bool = Field(
+        default=False,
+        description=(
+            "If true, special tokens (e.g. BOS) will be added to the prompt "
+            "on top of what is added by the chat template. "
+            "For most models, the chat template takes care of adding the "
+            "special tokens so this should be set to false (as is the "
+            "default)."
+        ),
+    )
+    chat_template: str | None = Field(
+        default=None,
+        description=(
+            "A Jinja template to use for this conversion. "
+            "As of transformers v4.44, default chat template is no longer "
+            "allowed, so you must provide a chat template if the tokenizer "
+            "does not define one."
+        ),
+    )
+    chat_template_kwargs: dict[str, Any] | None = Field(
+        default=None,
+        description=(
+            "Additional keyword args to pass to the template renderer. "
+            "Will be accessible by the chat template."
+        ),
+    )
+    mm_processor_kwargs: dict[str, Any] | None = Field(
+        default=None,
+        description="Additional kwargs to pass to the HF processor.",
+    )
+    tools: list[ChatCompletionToolsParam] | None = Field(
+        default=None,
+        description="A list of tools the model may call.",
+    )
+
+    @model_validator(mode="before")
+    @classmethod
+    def check_generation_prompt(cls, data):
+        if data.get("continue_final_message") and data.get("add_generation_prompt"):
+            raise ValueError(
+                "Cannot set both `continue_final_message` and "
+                "`add_generation_prompt` to True."
+            )
+        return data
+
+    def build_chat_params(
+        self,
+        default_template: str | None,
+        default_template_content_format: ChatTemplateContentFormatOption,
+    ) -> ChatParams:
+        return ChatParams(
+            chat_template=self.chat_template or default_template,
+            chat_template_content_format=default_template_content_format,
+            chat_template_kwargs=merge_kwargs(
+                self.chat_template_kwargs,
+                dict(
+                    add_generation_prompt=self.add_generation_prompt,
+                    continue_final_message=self.continue_final_message,
+                ),
+            ),
+        )
+
+    def build_tok_params(self, model_config: ModelConfig) -> TokenizeParams:
+        return TokenizeParams(
+            max_total_tokens=None,
+            max_output_tokens=0,
+            add_special_tokens=self.add_special_tokens,
+        )
+
+
+TokenizeRequest: TypeAlias = TokenizeCompletionRequest | TokenizeChatRequest
+
+
+class TokenizeResponse(OpenAIBaseModel):
+    count: int
+    max_model_len: int
+    tokens: list[int]
+    token_strs: list[str] | None = None
+
+
+class DetokenizeRequest(OpenAIBaseModel):
+    model: str | None = None
+    # TODO: Factor `torch.iinfo` out. `torch.iinfo` pulls torch into a
+    # Pydantic protocol file that currently has no torch dependency.
+    # See: https://github.com/vllm-project/vllm/pull/34468#discussion_r2801173630
+    tokens: list[Annotated[int, Field(ge=0, le=2**63 - 1)]]
+
+    def build_tok_params(self, model_config: ModelConfig) -> TokenizeParams:
+        return TokenizeParams(
+            max_total_tokens=None,
+            max_output_tokens=0,
+            needs_detokenization=True,
+        )
+
+
+class DetokenizeResponse(OpenAIBaseModel):
+    prompt: str
+
+
+class TokenizerInfoResponse(OpenAIBaseModel):
+    """
+    Response containing tokenizer configuration
+    equivalent to tokenizer_config.json
+    """
+
+    model_config = ConfigDict(extra="allow")
+    tokenizer_class: str