mirror of https://github.com/hpcaitech/ColossalAI
124 lines
5.1 KiB
Python
124 lines
5.1 KiB
Python
import argparse
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import logging
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import random
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from typing import Optional
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import uvicorn
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from energonai import QueueFullError, launch_engine
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from energonai.model import opt_6B, opt_30B, opt_125M, opt_175B
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from fastapi import FastAPI, HTTPException, Request
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from pydantic import BaseModel, Field
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from transformers import GPT2Tokenizer
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from batch import BatchManagerForGeneration
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from cache import ListCache, MissCacheError
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class GenerationTaskReq(BaseModel):
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max_tokens: int = Field(gt=0, le=256, example=64)
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prompt: str = Field(
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min_length=1, example='Question: Where were the 2004 Olympics held?\nAnswer: Athens, Greece\n\nQuestion: What is the longest river on the earth?\nAnswer:')
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top_k: Optional[int] = Field(default=None, gt=0, example=50)
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top_p: Optional[float] = Field(default=None, gt=0.0, lt=1.0, example=0.5)
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temperature: Optional[float] = Field(default=None, gt=0.0, lt=1.0, example=0.7)
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app = FastAPI()
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@app.post('/generation')
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async def generate(data: GenerationTaskReq, request: Request):
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logger.info(f'{request.client.host}:{request.client.port} - "{request.method} {request.url.path}" - {data}')
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key = (data.prompt, data.max_tokens)
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try:
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if cache is None:
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raise MissCacheError()
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outputs = cache.get(key)
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output = random.choice(outputs)
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logger.info('Cache hit')
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except MissCacheError:
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inputs = tokenizer(data.prompt, truncation=True, max_length=512)
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inputs['max_tokens'] = data.max_tokens
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inputs['top_k'] = data.top_k
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inputs['top_p'] = data.top_p
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inputs['temperature'] = data.temperature
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try:
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uid = id(data)
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engine.submit(uid, inputs)
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output = await engine.wait(uid)
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output = tokenizer.decode(output, skip_special_tokens=True)
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if cache is not None:
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cache.add(key, output)
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except QueueFullError as e:
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raise HTTPException(status_code=406, detail=e.args[0])
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return {'text': output}
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@app.on_event("shutdown")
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async def shutdown(*_):
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engine.shutdown()
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server.should_exit = True
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server.force_exit = True
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await server.shutdown()
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def get_model_fn(model_name: str):
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model_map = {
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'opt-125m': opt_125M,
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'opt-6.7b': opt_6B,
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'opt-30b': opt_30B,
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'opt-175b': opt_175B
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}
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return model_map[model_name]
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def print_args(args: argparse.Namespace):
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print('\n==> Args:')
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for k, v in args.__dict__.items():
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print(f'{k} = {v}')
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FIXED_CACHE_KEYS = [
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('Question: What is the name of the largest continent on earth?\nAnswer: Asia\n\nQuestion: What is at the center of the solar system?\nAnswer:', 64),
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('A chat between a salesman and a student.\n\nSalesman: Hi boy, are you looking for a new phone?\nStudent: Yes, my phone is not functioning well.\nSalesman: What is your budget? \nStudent: I have received my scholarship so I am fine with any phone.\nSalesman: Great, then perhaps this latest flagship phone is just right for you.', 64),
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("English: I am happy today.\nChinese: 我今天很开心。\n\nEnglish: I am going to play basketball.\nChinese: 我一会去打篮球。\n\nEnglish: Let's celebrate our anniversary.\nChinese:", 64)
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]
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if __name__ == '__main__':
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parser = argparse.ArgumentParser()
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parser.add_argument('model', choices=['opt-125m', 'opt-6.7b', 'opt-30b', 'opt-175b'])
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parser.add_argument('--tp', type=int, default=1)
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parser.add_argument('--master_host', default='localhost')
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parser.add_argument('--master_port', type=int, default=19990)
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parser.add_argument('--rpc_port', type=int, default=19980)
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parser.add_argument('--max_batch_size', type=int, default=8)
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parser.add_argument('--pipe_size', type=int, default=1)
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parser.add_argument('--queue_size', type=int, default=0)
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parser.add_argument('--http_host', default='0.0.0.0')
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parser.add_argument('--http_port', type=int, default=7070)
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parser.add_argument('--checkpoint', default=None)
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parser.add_argument('--cache_size', type=int, default=0)
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parser.add_argument('--cache_list_size', type=int, default=1)
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args = parser.parse_args()
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print_args(args)
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model_kwargs = {}
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if args.checkpoint is not None:
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model_kwargs['checkpoint'] = args.checkpoint
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logger = logging.getLogger(__name__)
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tokenizer = GPT2Tokenizer.from_pretrained('facebook/opt-30b')
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if args.cache_size > 0:
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cache = ListCache(args.cache_size, args.cache_list_size, fixed_keys=FIXED_CACHE_KEYS)
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else:
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cache = None
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engine = launch_engine(args.tp, 1, args.master_host, args.master_port, args.rpc_port, get_model_fn(args.model),
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batch_manager=BatchManagerForGeneration(max_batch_size=args.max_batch_size,
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pad_token_id=tokenizer.pad_token_id),
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pipe_size=args.pipe_size,
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queue_size=args.queue_size,
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**model_kwargs)
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config = uvicorn.Config(app, host=args.http_host, port=args.http_port)
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server = uvicorn.Server(config=config)
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server.run()
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