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# Adapted from
# https://github.com/lm-sys/FastChat/blob/168ccc29d3f7edc50823016105c024fe2282732a/fastchat/protocol/openai_api_protocol.py
import time
from typing import Dict, List, Literal, Optional, Union
from pydantic import BaseModel, Field
from vllm.utils import random_uuid
from vllm.sampling_params import SamplingParams
class ErrorResponse(BaseModel):
object: str = "error"
message: str
type: str
param: Optional[str] = None
code: int
class ModelPermission(BaseModel):
id: str = Field(default_factory=lambda: f"modelperm-{random_uuid()}")
object: str = "model_permission"
created: int = Field(default_factory=lambda: int(time.time()))
allow_create_engine: bool = False
allow_sampling: bool = True
allow_logprobs: bool = True
allow_search_indices: bool = False
allow_view: bool = True
allow_fine_tuning: bool = False
organization: str = "*"
group: Optional[str] = None
is_blocking: str = False
class ModelCard(BaseModel):
id: str
object: str = "model"
created: int = Field(default_factory=lambda: int(time.time()))
owned_by: str = "vllm"
root: Optional[str] = None
parent: Optional[str] = None
permission: List[ModelPermission] = Field(default_factory=list)
class ModelList(BaseModel):
object: str = "list"
data: List[ModelCard] = Field(default_factory=list)
class UsageInfo(BaseModel):
prompt_tokens: int = 0
total_tokens: int = 0
completion_tokens: Optional[int] = 0
class ChatCompletionRequest(BaseModel):
model: str
messages: Union[str, List[Dict[str, str]]]
temperature: Optional[float] = 0.7
top_p: Optional[float] = 1.0
n: Optional[int] = 1
max_tokens: Optional[int] = None
stop: Optional[Union[str, List[str]]] = Field(default_factory=list)
stream: Optional[bool] = False
presence_penalty: Optional[float] = 0.0
frequency_penalty: Optional[float] = 0.0
logit_bias: Optional[Dict[str, float]] = None
user: Optional[str] = None
# Additional parameters supported by vLLM
best_of: Optional[int] = None
top_k: Optional[int] = -1
ignore_eos: Optional[bool] = False
use_beam_search: Optional[bool] = False
stop_token_ids: Optional[List[int]] = Field(default_factory=list)
skip_special_tokens: Optional[bool] = True
spaces_between_special_tokens: Optional[bool] = True
add_generation_prompt: Optional[bool] = True
echo: Optional[bool] = False
repetition_penalty: Optional[float] = 1.0
min_p: Optional[float] = 0.0
def to_sampling_params(self) -> SamplingParams:
return SamplingParams(
n=self.n,
presence_penalty=self.presence_penalty,
frequency_penalty=self.frequency_penalty,
repetition_penalty=self.repetition_penalty,
temperature=self.temperature,
top_p=self.top_p,
min_p=self.min_p,
stop=self.stop,
stop_token_ids=self.stop_token_ids,
max_tokens=self.max_tokens,
best_of=self.best_of,
top_k=self.top_k,
ignore_eos=self.ignore_eos,
use_beam_search=self.use_beam_search,
skip_special_tokens=self.skip_special_tokens,
spaces_between_special_tokens=self.spaces_between_special_tokens,
)
class CompletionRequest(BaseModel):
model: str
# a string, array of strings, array of tokens, or array of token arrays
prompt: Union[List[int], List[List[int]], str, List[str]]
suffix: Optional[str] = None
max_tokens: Optional[int] = 16
temperature: Optional[float] = 1.0
top_p: Optional[float] = 1.0
n: Optional[int] = 1
stream: Optional[bool] = False
logprobs: Optional[int] = None
echo: Optional[bool] = False
stop: Optional[Union[str, List[str]]] = Field(default_factory=list)
presence_penalty: Optional[float] = 0.0
frequency_penalty: Optional[float] = 0.0
best_of: Optional[int] = None
logit_bias: Optional[Dict[str, float]] = None
user: Optional[str] = None
# Additional parameters supported by vLLM
top_k: Optional[int] = -1
ignore_eos: Optional[bool] = False
use_beam_search: Optional[bool] = False
stop_token_ids: Optional[List[int]] = Field(default_factory=list)
skip_special_tokens: Optional[bool] = True
spaces_between_special_tokens: Optional[bool] = True
repetition_penalty: Optional[float] = 1.0
min_p: Optional[float] = 0.0
def to_sampling_params(self):
echo_without_generation = self.echo and self.max_tokens == 0
return SamplingParams(
n=self.n,
best_of=self.best_of,
presence_penalty=self.presence_penalty,
frequency_penalty=self.frequency_penalty,
repetition_penalty=self.repetition_penalty,
temperature=self.temperature,
top_p=self.top_p,
top_k=self.top_k,
min_p=self.min_p,
stop=self.stop,
stop_token_ids=self.stop_token_ids,
ignore_eos=self.ignore_eos,
max_tokens=self.max_tokens if not echo_without_generation else 1,
logprobs=self.logprobs,
use_beam_search=self.use_beam_search,
prompt_logprobs=self.logprobs if self.echo else None,
skip_special_tokens=self.skip_special_tokens,
spaces_between_special_tokens=(self.spaces_between_special_tokens),
)
class LogProbs(BaseModel):
text_offset: List[int] = Field(default_factory=list)
token_logprobs: List[Optional[float]] = Field(default_factory=list)
tokens: List[str] = Field(default_factory=list)
top_logprobs: Optional[List[Optional[Dict[int, float]]]] = None
class CompletionResponseChoice(BaseModel):
index: int
text: str
logprobs: Optional[LogProbs] = None
finish_reason: Optional[Literal["stop", "length"]] = None
class CompletionResponse(BaseModel):
id: str = Field(default_factory=lambda: f"cmpl-{random_uuid()}")
object: str = "text_completion"
created: int = Field(default_factory=lambda: int(time.time()))
model: str
choices: List[CompletionResponseChoice]
usage: UsageInfo
class CompletionResponseStreamChoice(BaseModel):
index: int
text: str
logprobs: Optional[LogProbs] = None
finish_reason: Optional[Literal["stop", "length"]] = None
class CompletionStreamResponse(BaseModel):
id: str = Field(default_factory=lambda: f"cmpl-{random_uuid()}")
object: str = "text_completion"
created: int = Field(default_factory=lambda: int(time.time()))
model: str
choices: List[CompletionResponseStreamChoice]
usage: Optional[UsageInfo] = Field(default=None)
class ChatMessage(BaseModel):
role: str
content: str
class ChatCompletionResponseChoice(BaseModel):
index: int
message: ChatMessage
finish_reason: Optional[Literal["stop", "length"]] = None
class ChatCompletionResponse(BaseModel):
id: str = Field(default_factory=lambda: f"chatcmpl-{random_uuid()}")
object: str = "chat.completion"
created: int = Field(default_factory=lambda: int(time.time()))
model: str
choices: List[ChatCompletionResponseChoice]
usage: UsageInfo
class DeltaMessage(BaseModel):
role: Optional[str] = None
content: Optional[str] = None
class ChatCompletionResponseStreamChoice(BaseModel):
index: int
delta: DeltaMessage
finish_reason: Optional[Literal["stop", "length"]] = None
class ChatCompletionStreamResponse(BaseModel):
id: str = Field(default_factory=lambda: f"chatcmpl-{random_uuid()}")
object: str = "chat.completion.chunk"
created: int = Field(default_factory=lambda: int(time.time()))
model: str
choices: List[ChatCompletionResponseStreamChoice]
usage: Optional[UsageInfo] = Field(default=None) |