458 lines
16 KiB
Python
458 lines
16 KiB
Python
import asyncio
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import json
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from threading import Lock
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from typing import List
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import base64
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from fastapi import APIRouter, Request, status, HTTPException
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from sse_starlette.sse import EventSourceResponse
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from pydantic import BaseModel
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import numpy as np
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import tiktoken
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from utils.rwkv import *
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from utils.log import quick_log
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import global_var
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router = APIRouter()
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class Message(BaseModel):
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role: str
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content: str
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class ChatCompletionBody(ModelConfigBody):
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messages: List[Message]
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model: str = "rwkv"
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stream: bool = False
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stop: str = None
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class Config:
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schema_extra = {
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"example": {
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"messages": [{"role": "user", "content": "hello"}],
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"model": "rwkv",
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"stream": False,
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"stop": None,
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"max_tokens": 1000,
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"temperature": 1.2,
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"top_p": 0.5,
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"presence_penalty": 0.4,
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"frequency_penalty": 0.4,
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}
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}
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class CompletionBody(ModelConfigBody):
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prompt: str
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model: str = "rwkv"
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stream: bool = False
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stop: str = None
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class Config:
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schema_extra = {
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"example": {
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"prompt": "The following is an epic science fiction masterpiece that is immortalized, "
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+ "with delicate descriptions and grand depictions of interstellar civilization wars.\nChapter 1.\n",
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"model": "rwkv",
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"stream": False,
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"stop": None,
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"max_tokens": 100,
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"temperature": 1.2,
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"top_p": 0.5,
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"presence_penalty": 0.4,
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"frequency_penalty": 0.4,
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}
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}
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completion_lock = Lock()
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requests_num = 0
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async def eval_rwkv(
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model: RWKV,
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request: Request,
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body: ModelConfigBody,
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prompt: str,
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stream: bool,
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stop: str,
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chat_mode: bool,
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):
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global requests_num
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requests_num = requests_num + 1
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quick_log(request, None, "Start Waiting. RequestsNum: " + str(requests_num))
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while completion_lock.locked():
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if await request.is_disconnected():
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requests_num = requests_num - 1
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print(f"{request.client} Stop Waiting (Lock)")
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quick_log(
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request,
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None,
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"Stop Waiting (Lock). RequestsNum: " + str(requests_num),
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)
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return
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await asyncio.sleep(0.1)
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else:
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with completion_lock:
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if await request.is_disconnected():
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requests_num = requests_num - 1
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print(f"{request.client} Stop Waiting (Lock)")
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quick_log(
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request,
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None,
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"Stop Waiting (Lock). RequestsNum: " + str(requests_num),
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)
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return
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set_rwkv_config(model, global_var.get(global_var.Model_Config))
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set_rwkv_config(model, body)
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response, prompt_tokens, completion_tokens = "", 0, 0
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for response, delta, prompt_tokens, completion_tokens in model.generate(
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prompt,
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stop=stop,
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):
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if await request.is_disconnected():
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break
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if stream:
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yield json.dumps(
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{
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"object": "chat.completion.chunk"
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if chat_mode
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else "text_completion",
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"response": response,
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"model": model.name,
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"choices": [
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{
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"delta": {"content": delta},
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"index": 0,
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"finish_reason": None,
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}
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if chat_mode
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else {
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"text": delta,
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"index": 0,
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"finish_reason": None,
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}
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],
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}
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)
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# torch_gc()
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requests_num = requests_num - 1
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if await request.is_disconnected():
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print(f"{request.client} Stop Waiting")
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quick_log(
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request,
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body,
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response + "\nStop Waiting. RequestsNum: " + str(requests_num),
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)
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return
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quick_log(
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request,
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body,
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response + "\nFinished. RequestsNum: " + str(requests_num),
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)
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if stream:
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yield json.dumps(
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{
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"object": "chat.completion.chunk"
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if chat_mode
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else "text_completion",
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"response": response,
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"model": model.name,
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"choices": [
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{
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"delta": {},
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"index": 0,
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"finish_reason": "stop",
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}
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if chat_mode
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else {
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"text": "",
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"index": 0,
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"finish_reason": "stop",
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}
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],
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}
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)
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yield "[DONE]"
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else:
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yield {
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"object": "chat.completion" if chat_mode else "text_completion",
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"response": response,
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"model": model.name,
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"usage": {
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"prompt_tokens": prompt_tokens,
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"completion_tokens": completion_tokens,
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"total_tokens": prompt_tokens + completion_tokens,
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},
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"choices": [
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{
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"message": {
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"role": "assistant",
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"content": response,
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},
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"index": 0,
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"finish_reason": "stop",
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}
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if chat_mode
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else {
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"text": response,
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"index": 0,
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"finish_reason": "stop",
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}
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],
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}
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@router.post("/v1/chat/completions")
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@router.post("/chat/completions")
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async def chat_completions(body: ChatCompletionBody, request: Request):
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model: RWKV = global_var.get(global_var.Model)
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if model is None:
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raise HTTPException(status.HTTP_400_BAD_REQUEST, "model not loaded")
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question = body.messages[-1]
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if question.role == "user":
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question = question.content
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elif question.role == "system":
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question = body.messages[-2]
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if question.role == "user":
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question = question.content
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else:
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raise HTTPException(status.HTTP_400_BAD_REQUEST, "no question found")
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else:
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raise HTTPException(status.HTTP_400_BAD_REQUEST, "no question found")
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interface = model.interface
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user = model.user
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bot = model.bot
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completion_text = (
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f"""
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The following is a coherent verbose detailed conversation between a girl named {bot} and her friend {user}. \
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{bot} is very intelligent, creative and friendly. \
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{bot} is unlikely to disagree with {user}, and {bot} doesn't like to ask {user} questions. \
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{bot} likes to tell {user} a lot about herself and her opinions. \
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{bot} usually gives {user} kind, helpful and informative advices.\n
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"""
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if user == "Bob"
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else f"{user}{interface} hi\n\n{bot}{interface} Hi. "
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+ "I am your assistant and I will provide expert full response in full details. Please feel free to ask any question and I will always answer it.\n\n"
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)
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for message in body.messages:
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if message.role == "system":
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completion_text = (
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f"The following is a coherent verbose detailed conversation between a girl named {bot} and her friend {user}. "
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if user == "Bob"
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else f"{user}{interface} hi\n\n{bot}{interface} Hi. "
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+ message.content.replace("\\n", "\n")
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.replace("\r\n", "\n")
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.replace("\n\n", "\n")
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.replace("\n", " ")
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.strip()
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.replace("You are", f"{bot} is" if user == "Bob" else "I am")
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.replace("you are", f"{bot} is" if user == "Bob" else "I am")
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.replace("You're", f"{bot} is" if user == "Bob" else "I'm")
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.replace("you're", f"{bot} is" if user == "Bob" else "I'm")
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.replace("You", f"{bot}" if user == "Bob" else "I")
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.replace("you", f"{bot}" if user == "Bob" else "I")
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.replace("Your", f"{bot}'s" if user == "Bob" else "My")
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.replace("your", f"{bot}'s" if user == "Bob" else "my")
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.replace("你", f"{bot}" if user == "Bob" else "我")
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+ "\n\n"
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)
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break
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for message in body.messages:
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if message.role == "user":
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completion_text += (
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f"{user}{interface} "
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+ message.content.replace("\\n", "\n")
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.replace("\r\n", "\n")
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.replace("\n\n", "\n")
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.strip()
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+ "\n\n"
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)
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elif message.role == "assistant":
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completion_text += (
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f"{bot}{interface} "
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+ message.content.replace("\\n", "\n")
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.replace("\r\n", "\n")
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.replace("\n\n", "\n")
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.strip()
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+ "\n\n"
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)
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completion_text += f"{bot}{interface}"
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stop = f"\n\n{user}" if body.stop is None else body.stop
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if body.stream:
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return EventSourceResponse(
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eval_rwkv(model, request, body, completion_text, body.stream, stop, True)
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)
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else:
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try:
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return await eval_rwkv(
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model, request, body, completion_text, body.stream, stop, True
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).__anext__()
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except StopAsyncIteration:
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return None
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@router.post("/v1/completions")
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@router.post("/completions")
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async def completions(body: CompletionBody, request: Request):
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model: RWKV = global_var.get(global_var.Model)
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if model is None:
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raise HTTPException(status.HTTP_400_BAD_REQUEST, "model not loaded")
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if body.prompt is None or body.prompt == "":
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raise HTTPException(status.HTTP_400_BAD_REQUEST, "prompt not found")
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if body.stream:
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return EventSourceResponse(
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eval_rwkv(model, request, body, body.prompt, body.stream, body.stop, False)
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)
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else:
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try:
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return await eval_rwkv(
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model, request, body, body.prompt, body.stream, body.stop, False
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).__anext__()
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except StopAsyncIteration:
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return None
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class EmbeddingsBody(BaseModel):
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input: str or List[str] or List[List[int]]
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model: str = "rwkv"
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encoding_format: str = None
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fast_mode: bool = False
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class Config:
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schema_extra = {
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"example": {
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"input": "a big apple",
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"model": "rwkv",
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"encoding_format": None,
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"fast_mode": False,
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}
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}
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def embedding_base64(embedding: List[float]) -> str:
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return base64.b64encode(np.array(embedding).astype(np.float32)).decode("utf-8")
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@router.post("/v1/embeddings")
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@router.post("/embeddings")
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@router.post("/v1/engines/text-embedding-ada-002/embeddings")
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@router.post("/engines/text-embedding-ada-002/embeddings")
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async def embeddings(body: EmbeddingsBody, request: Request):
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model: RWKV = global_var.get(global_var.Model)
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if model is None:
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raise HTTPException(status.HTTP_400_BAD_REQUEST, "model not loaded")
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if body.input is None or body.input == "" or body.input == [] or body.input == [[]]:
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raise HTTPException(status.HTTP_400_BAD_REQUEST, "input not found")
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global requests_num
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requests_num = requests_num + 1
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quick_log(request, None, "Start Waiting. RequestsNum: " + str(requests_num))
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while completion_lock.locked():
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if await request.is_disconnected():
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requests_num = requests_num - 1
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print(f"{request.client} Stop Waiting (Lock)")
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quick_log(
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request,
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None,
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"Stop Waiting (Lock). RequestsNum: " + str(requests_num),
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)
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return
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await asyncio.sleep(0.1)
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else:
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with completion_lock:
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if await request.is_disconnected():
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requests_num = requests_num - 1
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print(f"{request.client} Stop Waiting (Lock)")
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quick_log(
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request,
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None,
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"Stop Waiting (Lock). RequestsNum: " + str(requests_num),
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)
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return
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base64_format = False
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if body.encoding_format == "base64":
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base64_format = True
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embeddings = []
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prompt_tokens = 0
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if type(body.input) == list:
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if type(body.input[0]) == list:
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encoding = tiktoken.model.encoding_for_model(
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"text-embedding-ada-002"
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)
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for i in range(len(body.input)):
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if await request.is_disconnected():
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break
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input = encoding.decode(body.input[i])
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embedding, token_len = model.get_embedding(
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input, body.fast_mode
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)
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prompt_tokens = prompt_tokens + token_len
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if base64_format:
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embedding = embedding_base64(embedding)
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embeddings.append(embedding)
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else:
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for i in range(len(body.input)):
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if await request.is_disconnected():
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break
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embedding, token_len = model.get_embedding(
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body.input[i], body.fast_mode
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)
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prompt_tokens = prompt_tokens + token_len
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if base64_format:
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embedding = embedding_base64(embedding)
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embeddings.append(embedding)
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else:
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embedding, prompt_tokens = model.get_embedding(
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body.input, body.fast_mode
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)
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if base64_format:
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embedding = embedding_base64(embedding)
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embeddings.append(embedding)
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requests_num = requests_num - 1
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if await request.is_disconnected():
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print(f"{request.client} Stop Waiting")
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quick_log(
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request,
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None,
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"Stop Waiting. RequestsNum: " + str(requests_num),
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)
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return
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quick_log(
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request,
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None,
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"Finished. RequestsNum: " + str(requests_num),
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)
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ret_data = [
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{
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"object": "embedding",
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"index": i,
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"embedding": embedding,
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}
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for i, embedding in enumerate(embeddings)
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]
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return {
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"object": "list",
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"data": ret_data,
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"model": model.name,
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"usage": {
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"prompt_tokens": prompt_tokens,
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"total_tokens": prompt_tokens,
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},
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}
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