server.py

import argparse
import os
from threading import Lock
from typing import Dict, Generator, List, Optional

import torch
import uvicorn
from fastapi import FastAPI, HTTPException, Request
from fastapi.middleware.cors import CORSMiddleware
from llama_gptq import load_quant
from pydantic import BaseModel, Field
from slowapi import Limiter, _rate_limit_exceeded_handler
from slowapi.errors import RateLimitExceeded
from slowapi.util import get_remote_address
from sse_starlette.sse import EventSourceResponse
from transformers import AutoTokenizer, GenerationConfig, LlamaForCausalLM
from utils import ChatPromptProcessor, Dialogue, LockedIterator, sample_streamingly, update_model_kwargs_fn, load_json

CONTEXT = 'Below is an instruction that describes a task. Write a response that appropriately completes the request. Do not generate new instructions.'
MAX_LEN = 512
running_lock = Lock()


class GenerationTaskReq(BaseModel):
    max_new_tokens: int = Field(gt=0, le=512, example=64)
    history: List[Dialogue] = Field(min_items=1)
    top_k: Optional[int] = Field(default=None, gt=0, example=50)
    top_p: Optional[float] = Field(default=None, gt=0.0, lt=1.0, example=0.5)
    temperature: Optional[float] = Field(default=None, gt=0.0, lt=1.0, example=0.7)
    repetition_penalty: Optional[float] = Field(default=None, gt=1.0, example=1.2)


limiter = Limiter(key_func=get_remote_address)
app = FastAPI()
app.state.limiter = limiter
app.add_exception_handler(RateLimitExceeded, _rate_limit_exceeded_handler)

# set CORS
origin_spec_from_env = os.environ.get('CORS_ORIGIN', None)

if origin_spec_from_env is not None:
    # allow CORS from the specified origins
    origins = os.environ['CORS_ORIGIN'].split(',')
else:
    # allow CORS from all origins
    origins = ["*"]

app.add_middleware(
    CORSMiddleware,
    allow_origins=origins,
    allow_credentials=True,
    allow_methods=["*"],
    allow_headers=["*"],
)


def generate_streamingly(prompt, max_new_tokens, top_k, top_p, temperature):
    inputs = {k: v.cuda() for k, v in tokenizer(prompt, return_tensors="pt").items()}
    #TODO(ver217): streaming generation does not support repetition_penalty now
    model_kwargs = {
        'max_generate_tokens': max_new_tokens,
        'early_stopping': True,
        'top_k': top_k,
        'top_p': top_p,
        'temperature': temperature,
        'prepare_inputs_fn': model.prepare_inputs_for_generation,
        'update_model_kwargs_fn': update_model_kwargs_fn,
    }
    is_first_word = True
    generator = LockedIterator(sample_streamingly(model, **inputs, **model_kwargs), running_lock)
    for output in generator:
        output = output.cpu()
        tokens = tokenizer.convert_ids_to_tokens(output, skip_special_tokens=True)
        current_sub_tokens = []
        for token in tokens:
            if token in tokenizer.all_special_tokens:
                continue
            current_sub_tokens.append(token)
        if current_sub_tokens:
            out_string = tokenizer.sp_model.decode(current_sub_tokens)
            if is_first_word:
                out_string = out_string.lstrip()
                is_first_word = False
            elif current_sub_tokens[0].startswith('▁'):
                # whitespace will be ignored by the frontend
                out_string = ' ' + out_string
            yield out_string


async def event_generator(request: Request, generator: Generator):
    while True:
        if await request.is_disconnected():
            break
        try:
            yield {'event': 'generate', 'data': next(generator)}
        except StopIteration:
            yield {'event': 'end', 'data': ''}
            break


@app.post('/generate/stream')
@limiter.limit('1/second')
def generate(data: GenerationTaskReq, request: Request):
    prompt = prompt_processor.preprocess_prompt(data.history, data.max_new_tokens)
    event_source = event_generator(
        request, generate_streamingly(prompt, data.max_new_tokens, data.top_k, data.top_p, data.temperature))
    return EventSourceResponse(event_source)


@app.post('/generate')
@limiter.limit('1/second')
def generate_no_stream(data: GenerationTaskReq, request: Request):
    prompt = prompt_processor.preprocess_prompt(data.history, data.max_new_tokens)
    if prompt_processor.has_censored_words(prompt):
        return prompt_processor.SAFE_RESPONSE
    inputs = {k: v.cuda() for k, v in tokenizer(prompt, return_tensors="pt").items()}
    with running_lock:
        output = model.generate(**inputs, **data.dict(exclude={'history'}))
    output = output.cpu()
    prompt_len = inputs['input_ids'].size(1)
    response = output[0, prompt_len:]
    out_string = tokenizer.decode(response, skip_special_tokens=True)
    out_string = prompt_processor.postprocess_output(out_string)
    if prompt_processor.has_censored_words(out_string):
        return prompt_processor.SAFE_RESPONSE
    return out_string


if __name__ == '__main__':
    parser = argparse.ArgumentParser()
    parser.add_argument(
        'pretrained',
        help='Path to pretrained model. Can be a local path or a model name from the HuggingFace model hub.')
    parser.add_argument('--quant',
                        choices=['8bit', '4bit'],
                        default=None,
                        help='Quantization mode. Default: None (no quantization, fp16).')
    parser.add_argument(
        '--gptq_checkpoint',
        default=None,
        help='Path to GPTQ checkpoint. This is only useful when quantization mode is 4bit. Default: None.')
    parser.add_argument('--gptq_group_size',
                        type=int,
                        default=128,
                        help='Group size for GPTQ. This is only useful when quantization mode is 4bit. Default: 128.')
    parser.add_argument('--http_host', default='0.0.0.0')
    parser.add_argument('--http_port', type=int, default=7070)
    parser.add_argument('--profanity_file', default=None, help='Path to profanity words list. It should be a JSON file containing a list of words.')
    args = parser.parse_args()

    if args.quant == '4bit':
        assert args.gptq_checkpoint is not None, 'Please specify a GPTQ checkpoint.'

    tokenizer = AutoTokenizer.from_pretrained(args.pretrained)

    if args.profanity_file is not None:
        censored_words = load_json(args.profanity_file)
    else:
        censored_words = []
    prompt_processor = ChatPromptProcessor(tokenizer, CONTEXT, MAX_LEN, censored_words=censored_words)

    if args.quant == '4bit':
        model = load_quant(args.pretrained, args.gptq_checkpoint, 4, args.gptq_group_size)
        model.cuda()
    else:
        model = LlamaForCausalLM.from_pretrained(
            args.pretrained,
            load_in_8bit=(args.quant == '8bit'),
            torch_dtype=torch.float16,
            device_map="auto",
        )
        if args.quant != '8bit':
            model.half()    # seems to fix bugs for some users.
        model.eval()

    config = uvicorn.Config(app, host=args.http_host, port=args.http_port)
    server = uvicorn.Server(config=config)
    server.run()