mirror of
https://github.com/PaddlePaddle/FastDeploy.git
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b9af95cf1c
* [Feature] add AsyncTokenizerClient * add decode_image * Add response_processors with remote decode support. * [Feature] add tokenizer_base_url startup argument * Revert comment removal and restore original content. * [Feature] Non-streaming requests now support remote image decoding. * Fix parameter type issue in decode_image call. * Keep completion_token_ids when return_token_ids = False. * add copyright
714 lines
21 KiB
Python
714 lines
21 KiB
Python
"""
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# Copyright (c) 2025 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License"
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""
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from __future__ import annotations
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import json
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import time
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import uuid
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from typing import Any, Dict, List, Literal, Optional, Union
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from pydantic import BaseModel, Field, model_validator
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# from openai.types.chat import ChatCompletionMessageParam
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# from fastdeploy.entrypoints.chat_utils import ChatCompletionMessageParam
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class ErrorResponse(BaseModel):
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"""
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Error response from OpenAI API.
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"""
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object: str = "error"
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message: str
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code: int
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class PromptTokenUsageInfo(BaseModel):
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"""
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Prompt-related token usage info.
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"""
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cached_tokens: Optional[int] = None
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class UsageInfo(BaseModel):
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"""
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Usage info for a single request.
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"""
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prompt_tokens: int = 0
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total_tokens: int = 0
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completion_tokens: Optional[int] = 0
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prompt_tokens_details: Optional[PromptTokenUsageInfo] = None
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class ModelPermission(BaseModel):
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id: str = Field(default_factory=lambda: f"modelperm-{str(uuid.uuid4().hex)}")
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object: str = "model_permission"
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created: int = Field(default_factory=lambda: int(time.time()))
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allow_create_engine: bool = False
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allow_sampling: bool = True
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allow_logprobs: bool = True
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allow_search_indices: bool = False
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allow_view: bool = True
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allow_fine_tuning: bool = False
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organization: str = "*"
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group: Optional[str] = None
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is_blocking: bool = False
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class ModelInfo(BaseModel):
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id: str
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object: str = "model"
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created: int = Field(default_factory=lambda: int(time.time()))
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owned_by: str = "FastDeploy"
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root: Optional[str] = None
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parent: Optional[str] = None
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max_model_len: Optional[int] = None
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permission: list[ModelPermission] = Field(default_factory=list)
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class ModelList(BaseModel):
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object: str = "list"
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data: list[ModelInfo] = Field(default_factory=list)
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class FunctionCall(BaseModel):
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"""
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Function call.
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"""
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name: str
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arguments: str
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class ToolCall(BaseModel):
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"""
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Tool call.
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"""
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id: str = None
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type: Literal["function"] = "function"
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function: FunctionCall
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class DeltaFunctionCall(BaseModel):
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"""
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Delta function call.
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"""
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name: Optional[str] = None
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arguments: Optional[str] = None
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# a tool call delta where everything is optional
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class DeltaToolCall(BaseModel):
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"""
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Delta tool call.
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"""
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id: Optional[str] = None
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type: Optional[Literal["function"]] = None
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index: int
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function: Optional[DeltaFunctionCall] = None
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class ExtractedToolCallInformation(BaseModel):
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# indicate if tools were called
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tools_called: bool
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# extracted tool calls
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tool_calls: Optional[list[ToolCall]] = None
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# content - per OpenAI spec, content AND tool calls can be returned rarely
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# But some models will do this intentionally
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content: Optional[str] = None
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class FunctionDefinition(BaseModel):
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"""
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Function definition.
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"""
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name: str
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description: Optional[str] = None
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parameters: Optional[dict[str, Any]] = None
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class ChatCompletionToolsParam(BaseModel):
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"""
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Chat completion tools parameter.
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"""
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type: Literal["function"] = "function"
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function: FunctionDefinition
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class ChatMessage(BaseModel):
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"""
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Chat message.
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"""
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role: Optional[str] = None
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content: Optional[str] = None
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multimodal_content: Optional[List[Any]] = None
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reasoning_content: Optional[str] = None
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tool_calls: Optional[List[DeltaToolCall | ToolCall]] = None
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prompt_token_ids: Optional[List[int]] = None
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completion_token_ids: Optional[List[int]] = None
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text_after_process: Optional[str] = None
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raw_prediction: Optional[str] = None
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prompt_tokens: Optional[str] = None
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completion_tokens: Optional[str] = None
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class ChatCompletionResponseChoice(BaseModel):
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"""
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Chat completion response choice.
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"""
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index: int
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message: ChatMessage
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logprobs: Optional[LogProbs] = None
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finish_reason: Optional[Literal["stop", "length", "tool_calls", "recover_stop"]]
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class ChatCompletionResponse(BaseModel):
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"""
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Chat completion response.
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"""
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id: str
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object: str = "chat.completion"
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created: int = Field(default_factory=lambda: int(time.time()))
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model: str
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choices: List[ChatCompletionResponseChoice]
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usage: UsageInfo
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class LogProbEntry(BaseModel):
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"""
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Log probability entry.
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"""
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token: str
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logprob: float
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bytes: Optional[List[int]] = None
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top_logprobs: Optional[List[LogProbEntry]] = None
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class LogProbs(BaseModel):
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"""
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LogProbs.
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"""
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content: Optional[List[LogProbEntry]] = None
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refusal: Optional[Union[str, None]] = None
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class DeltaMessage(BaseModel):
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"""
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Delta message for chat completion stream response.
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"""
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role: Optional[str] = None
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content: Optional[str] = None
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multimodal_content: Optional[List[Any]] = None
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prompt_token_ids: Optional[List[int]] = None
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completion_token_ids: Optional[List[int]] = None
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reasoning_content: Optional[str] = None
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tool_calls: Optional[List[DeltaToolCall | ToolCall]] = None
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text_after_process: Optional[str] = None
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raw_prediction: Optional[str] = None
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prompt_tokens: Optional[str] = None
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completion_tokens: Optional[str] = None
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class ChatCompletionResponseStreamChoice(BaseModel):
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"""
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Chat completion response choice for stream response.
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"""
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index: int
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delta: DeltaMessage
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logprobs: Optional[LogProbs] = None
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finish_reason: Optional[Literal["stop", "length", "tool_calls"]] = None
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arrival_time: Optional[float] = None
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class ChatCompletionStreamResponse(BaseModel):
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"""
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Chat completion response for stream response.
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"""
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id: str
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object: str = "chat.completion.chunk"
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created: int = Field(default_factory=lambda: int(time.time()))
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model: str
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choices: List[ChatCompletionResponseStreamChoice]
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usage: Optional[UsageInfo] = None
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class CompletionResponseChoice(BaseModel):
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"""
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Completion response choice.
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"""
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index: int
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text: str
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prompt_token_ids: Optional[List[int]] = None
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completion_token_ids: Optional[List[int]] = None
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text_after_process: Optional[str] = None
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raw_prediction: Optional[str] = None
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prompt_tokens: Optional[str] = None
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completion_tokens: Optional[str] = None
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arrival_time: Optional[float] = None
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logprobs: Optional[CompletionLogprobs] = None
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reasoning_content: Optional[str] = None
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finish_reason: Optional[Literal["stop", "length", "tool_calls"]]
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tool_calls: Optional[List[DeltaToolCall | ToolCall]] = None
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class CompletionResponse(BaseModel):
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"""
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Completion response.
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"""
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id: str
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object: str = "text_completion"
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created: int = Field(default_factory=lambda: int(time.time()))
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model: str
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choices: List[CompletionResponseChoice]
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usage: UsageInfo
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class CompletionLogprobs(BaseModel):
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"""
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Completion logprobs.
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"""
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tokens: Optional[List[str]] = None
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token_logprobs: Optional[List[float]] = None
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top_logprobs: Optional[List[Dict]] = None
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text_offset: Optional[List[int]] = None
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class CompletionResponseStreamChoice(BaseModel):
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"""
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Completion response choice for stream response.
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"""
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index: int
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text: str
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arrival_time: float = None
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logprobs: Optional[CompletionLogprobs] = None
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prompt_token_ids: Optional[List[int]] = None
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completion_token_ids: Optional[List[int]] = None
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text_after_process: Optional[str] = None
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raw_prediction: Optional[str] = None
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prompt_tokens: Optional[str] = None
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completion_tokens: Optional[str] = None
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reasoning_content: Optional[str] = None
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finish_reason: Optional[Literal["stop", "length", "tool_calls"]] = None
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tool_calls: Optional[List[DeltaToolCall | ToolCall]] = None
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class CompletionStreamResponse(BaseModel):
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"""
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Completion response for stream response.
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"""
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id: str
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object: str = "text_completion"
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created: int = Field(default_factory=lambda: int(time.time()))
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model: str
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choices: List[CompletionResponseStreamChoice]
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usage: Optional[UsageInfo] = None
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class StreamOptions(BaseModel):
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"""
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Stream options.
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"""
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include_usage: Optional[bool] = True
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continuous_usage_stats: Optional[bool] = False
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class StructuralTag(BaseModel):
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"""
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Structural tag.
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"""
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begin: str
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structural_tag_schema: Optional[dict[str, Any]] = Field(default=None, alias="schema")
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end: str
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class JsonSchemaResponseFormat(BaseModel):
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"""
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Json schema for ResponseFormat.
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"""
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name: str
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description: Optional[str] = None
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json_schema: Optional[dict[str, Any]] = Field(default=None, alias="schema")
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strict: Optional[bool] = None
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class StructuralTagResponseFormat(BaseModel):
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"""
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Structural tag for ResponseFormat.
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"""
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type: Literal["structural_tag"]
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structures: list[StructuralTag]
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triggers: list[str]
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class ResponseFormat(BaseModel):
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"""
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response_format type.
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"""
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type: Literal["text", "json_object", "json_schema"]
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json_schema: Optional[JsonSchemaResponseFormat] = None
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AnyResponseFormat = Union[ResponseFormat, StructuralTagResponseFormat]
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class CompletionRequest(BaseModel):
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"""
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Completion request to the engine.
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"""
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# Ordered by official OpenAI API documentation
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# https://platform.openai.com/docs/api-reference/completions/create
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model: Optional[str] = "default"
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prompt: Union[List[int], List[List[int]], str, List[str]]
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best_of: Optional[int] = None
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echo: Optional[bool] = False
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frequency_penalty: Optional[float] = None
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logprobs: Optional[int] = None
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# For logits and logprobs post processing
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temp_scaled_logprobs: bool = False
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top_p_normalized_logprobs: bool = False
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max_tokens: Optional[int] = None
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n: int = 1
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presence_penalty: Optional[float] = None
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seed: Optional[int] = None
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stop: Optional[Union[str, List[str]]] = Field(default_factory=list)
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stream: Optional[bool] = False
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stream_options: Optional[StreamOptions] = None
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suffix: Optional[dict] = None
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temperature: Optional[float] = None
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top_p: Optional[float] = None
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user: Optional[str] = None
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# doc: begin-completion-sampling-params
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top_k: Optional[int] = None
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min_p: Optional[float] = None
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repetition_penalty: Optional[float] = None
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stop_token_ids: Optional[List[int]] = Field(default_factory=list)
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min_tokens: Optional[int] = None
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include_stop_str_in_output: Optional[bool] = False
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bad_words: Optional[List[str]] = None
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bad_words_token_ids: Optional[List[int]] = None
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# doc: end-completion-sampling-params
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# doc: start-completion-extra-params
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response_format: Optional[AnyResponseFormat] = None
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guided_json: Optional[Union[str, dict, BaseModel]] = None
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guided_regex: Optional[str] = None
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guided_choice: Optional[list[str]] = None
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guided_grammar: Optional[str] = None
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max_streaming_response_tokens: Optional[int] = None
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return_token_ids: Optional[bool] = None
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prompt_token_ids: Optional[Union[List[int], List[List[int]]]] = None
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# doc: end-completion-extra-params
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def to_dict_for_infer(self, request_id=None, prompt=None):
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"""
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Convert the request parameters into a dictionary
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Returns:
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dict: request parameters in dict format
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"""
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req_dict = {}
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if request_id is not None:
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req_dict["request_id"] = request_id
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# parse request model into dict
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if self.suffix is not None:
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for key, value in self.suffix.items():
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req_dict[key] = value
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for key, value in self.dict().items():
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if value is not None:
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req_dict[key] = value
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if prompt is not None:
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req_dict["prompt"] = prompt
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# if "prompt_token_ids" in req_dict:
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# if "prompt" in req_dict:
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# del req_dict["prompt"]
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# else:
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# assert len(prompt) > 0
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guided_json_object = None
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if self.response_format is not None:
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if self.response_format.type == "json_object":
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guided_json_object = True
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elif self.response_format.type == "json_schema":
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json_schema = self.response_format.json_schema.json_schema
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assert json_schema is not None, "response_format.json_schema can not be None"
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if isinstance(json_schema, (BaseModel, type(BaseModel))):
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self.guided_json = json_schema.model_json_schema()
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else:
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self.guided_json = json_schema
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if guided_json_object:
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req_dict["guided_json_object"] = guided_json_object
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guided_schema = [
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"guided_json",
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"guided_regex",
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"guided_choice",
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"guided_grammar",
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"structural_tag",
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]
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for key in guided_schema:
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item = getattr(self, key, None)
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if item is not None:
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req_dict[key] = item
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return req_dict
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@model_validator(mode="before")
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@classmethod
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def validate_stream_options(cls, data):
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"""
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Validate stream options
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"""
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if data.get("stream_options") and not data.get("stream"):
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raise ValueError("Stream options can only be defined when `stream=True`.")
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guided_count = sum(
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[
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"guided_json" in data and data["guided_json"] is not None,
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"guided_regex" in data and data["guided_regex"] is not None,
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"guided_choice" in data and data["guided_choice"] is not None,
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"guided_grammar" in data and data["guided_grammar"] is not None,
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]
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)
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if guided_count > 1:
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raise ValueError(
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"You can only use one kind of guided decoding "
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"('guided_json', 'guided_regex', 'guided_choice', 'guided_grammar')."
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)
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return data
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class ChatCompletionRequest(BaseModel):
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"""
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Chat completion request to the engine.
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"""
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# Ordered by official OpenAI API documentation
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# https://platform.openai.com/docs/api-reference/chat/create
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messages: Union[List[Any], List[int]]
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tools: Optional[List[ChatCompletionToolsParam]] = None
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model: Optional[str] = "default"
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frequency_penalty: Optional[float] = None
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logprobs: Optional[bool] = False
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top_logprobs: Optional[int] = 0
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# For logits and logprobs post processing
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temp_scaled_logprobs: bool = False
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top_p_normalized_logprobs: bool = False
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# remove max_tokens when field is removed from OpenAI API
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max_tokens: Optional[int] = Field(
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default=None,
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deprecated="max_tokens is deprecated in favor of the max_completion_tokens field",
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)
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max_completion_tokens: Optional[int] = None
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n: Optional[int] = 1
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presence_penalty: Optional[float] = None
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seed: Optional[int] = None
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stop: Optional[Union[str, List[str]]] = Field(default_factory=list)
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stream: Optional[bool] = False
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stream_options: Optional[StreamOptions] = None
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temperature: Optional[float] = None
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top_p: Optional[float] = None
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user: Optional[str] = None
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metadata: Optional[dict] = None
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response_format: Optional[AnyResponseFormat] = None
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# doc: begin-chat-completion-sampling-params
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top_k: Optional[int] = None
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min_p: Optional[float] = None
|
|
min_tokens: Optional[int] = None
|
|
include_stop_str_in_output: Optional[bool] = False
|
|
bad_words: Optional[List[str]] = None
|
|
bad_words_token_ids: Optional[List[int]] = None
|
|
repetition_penalty: Optional[float] = None
|
|
stop_token_ids: Optional[List[int]] = Field(default_factory=list)
|
|
# doc: end-chat-completion-sampling-params
|
|
|
|
# doc: start-chat-completion-extra-params
|
|
chat_template_kwargs: Optional[dict] = None
|
|
chat_template: Optional[str] = None
|
|
reasoning_max_tokens: Optional[int] = None
|
|
structural_tag: Optional[str] = None
|
|
guided_json: Optional[Union[str, dict, BaseModel]] = None
|
|
guided_regex: Optional[str] = None
|
|
guided_choice: Optional[list[str]] = None
|
|
guided_grammar: Optional[str] = None
|
|
|
|
return_token_ids: Optional[bool] = None
|
|
prompt_token_ids: Optional[List[int]] = None
|
|
max_streaming_response_tokens: Optional[int] = None
|
|
disable_chat_template: Optional[bool] = False
|
|
# doc: end-chat-completion-extra-params
|
|
|
|
def to_dict_for_infer(self, request_id=None):
|
|
"""
|
|
Convert the request parameters into a dictionary
|
|
|
|
Returns:
|
|
dict: request parameters in dict format
|
|
"""
|
|
req_dict = {}
|
|
if request_id is not None:
|
|
req_dict["request_id"] = request_id
|
|
|
|
req_dict["max_tokens"] = self.max_completion_tokens or self.max_tokens
|
|
req_dict["logprobs"] = self.top_logprobs if self.logprobs else None
|
|
req_dict["temp_scaled_logprobs"] = self.temp_scaled_logprobs
|
|
req_dict["top_p_normalized_logprobs"] = self.top_p_normalized_logprobs
|
|
|
|
# parse request model into dict, priority: request params > metadata params
|
|
if self.metadata is not None:
|
|
assert (
|
|
"raw_request" not in self.metadata
|
|
), "The parameter `raw_request` is not supported now, please use completion api instead."
|
|
for key, value in self.metadata.items():
|
|
req_dict[key] = value
|
|
for key, value in self.dict().items():
|
|
if value is not None:
|
|
req_dict[key] = value
|
|
|
|
if "prompt_token_ids" in req_dict:
|
|
if "messages" in req_dict:
|
|
del req_dict["messages"]
|
|
else:
|
|
# If disable_chat_template is set, then the first message in messages will be used as the prompt.
|
|
assert (
|
|
len(req_dict["messages"]) > 0
|
|
), "messages can not be an empty list, unless prompt_token_ids is passed"
|
|
if self.disable_chat_template:
|
|
req_dict["prompt"] = req_dict["messages"][0]["content"]
|
|
del req_dict["messages"]
|
|
|
|
guided_json_object = None
|
|
if self.response_format is not None:
|
|
if self.response_format.type == "json_object":
|
|
guided_json_object = True
|
|
elif self.response_format.type == "json_schema":
|
|
json_schema = self.response_format.json_schema.json_schema
|
|
assert json_schema is not None, "response_format.json_schema can not be None"
|
|
if isinstance(json_schema, (BaseModel, type(BaseModel))):
|
|
self.guided_json = json_schema.model_json_schema()
|
|
else:
|
|
self.guided_json = json_schema
|
|
elif self.response_format.type == "structural_tag":
|
|
structural_tag = self.response_format
|
|
assert structural_tag is not None and isinstance(structural_tag, StructuralTagResponseFormat)
|
|
self.structural_tag = json.dumps(structural_tag.model_dump(by_alias=True))
|
|
|
|
if guided_json_object:
|
|
req_dict["guided_json_object"] = guided_json_object
|
|
|
|
guided_schema = [
|
|
"guided_json",
|
|
"guided_regex",
|
|
"guided_choice",
|
|
"guided_grammar",
|
|
"structural_tag",
|
|
]
|
|
for key in guided_schema:
|
|
item = getattr(self, key, None)
|
|
if item is not None:
|
|
req_dict[key] = item
|
|
|
|
return req_dict
|
|
|
|
@model_validator(mode="before")
|
|
@classmethod
|
|
def validate_stream_options(cls, data):
|
|
"""
|
|
Validate stream options
|
|
"""
|
|
if data.get("stream_options") and not data.get("stream"):
|
|
raise ValueError("Stream options can only be defined when `stream=True`.")
|
|
|
|
guided_count = sum(
|
|
[
|
|
"guided_json" in data and data["guided_json"] is not None,
|
|
"guided_regex" in data and data["guided_regex"] is not None,
|
|
"guided_choice" in data and data["guided_choice"] is not None,
|
|
"guided_grammar" in data and data["guided_grammar"] is not None,
|
|
"structural_tag" in data and data["structural_tag"] is not None,
|
|
]
|
|
)
|
|
|
|
if guided_count > 1:
|
|
raise ValueError(
|
|
"You can only use one kind of guided decoding "
|
|
"('guided_json', 'guided_regex', 'guided_choice', 'guided_grammar', 'structural_tag')."
|
|
)
|
|
|
|
return data
|
|
|
|
@model_validator(mode="before")
|
|
@classmethod
|
|
def check_logprobs(cls, data):
|
|
|
|
if (top_logprobs := data.get("top_logprobs")) is not None:
|
|
if top_logprobs < 0:
|
|
raise ValueError("`top_logprobs` must be a positive value.")
|
|
|
|
if top_logprobs > 0 and not data.get("logprobs"):
|
|
raise ValueError("when using `top_logprobs`, `logprobs` must be set to true.")
|
|
|
|
return data
|
|
|
|
|
|
class ControlSchedulerRequest(BaseModel):
|
|
"""
|
|
Control scheduler request to the engine.
|
|
"""
|
|
|
|
reset: Optional[bool] = False
|
|
load_shards_num: Optional[int] = None
|
|
reallocate_shard: Optional[bool] = False
|